error:\\\" % (path, \\\", \\\".join(args))\\n print \\\"%s\\\" % e\",\n \"def step(self, action):\\n pass\",\n \"def step(self, action):\\n pass\",\n \"def dispatch(self):\\n try:\\n # dispatch\\n options = self._parser.parse_args()\\n\\n # only dispatch args if available\\n if len(vars(options)) > 1:\\n options.func(self.app, options)\\n else:\\n options.func(self.app)\\n except AttributeError:\\n # no command selected\\n print_failure('Please specify a command')\\n self._parser.print_help(sys.stderr)\\n sys.exit(2)\",\n \"def do_run(self, args):\\n if self.exploit is None:\\n eprint(colorize('No exploit set; nothing to do. Select an exploit with the \\\\'use\\\\' command', 'cyan'))\\n else:\\n eprint(colorize('Running %s...' % self.exploit_name, 'cyan'))\\n start = time.perf_counter()\\n if self.input is None:\\n if self.output is None:\\n self.exploit.run()\\n else:\\n self.exploit.run(self.output)\\n else:\\n # prepare is used to update internal state of input generators prior to running\\n if hasattr(self.input, 'prepare'):\\n self.input.prepare()\\n if self.output is None:\\n self.exploit.run(self.input)\\n else:\\n self.exploit.run(self.input, self.output)\\n end = time.perf_counter()\\n eprint(colorize('Finished running %s (%.2f seconds)' % (self.exploit_name, end - start), 'cyan'))\",\n \"def run_main_parsed(self, args: dict) -> int:\\n if args['firrtl'] is not None and len(args['firrtl']) > 0:\\n print(\\\"firrtl convenience argument not yet implemented\\\", file=sys.stderr)\\n return 1\\n\\n driver, errors = self.args_to_driver(args)\\n\\n # Check for action after creating the driver (e.g. for custom actions like hierarchical actions).\\n action = str(args['action']) # type: str\\n if action not in self.valid_actions():\\n print(\\\"Invalid action {action}\\\".format(action=action), file=sys.stderr)\\n print(\\\"Valid actions are: {actions}\\\".format(actions=\\\", \\\".join(self.valid_actions())), file=sys.stderr)\\n return 1\\n\\n action_func = self.action_map()[action]\\n output_str = None # type: Optional[str]\\n if is_config_action(action_func):\\n action_func = cast(CLIActionConfigType, action_func)\\n output_config = action_func(driver, errors.append) # type: Optional[dict]\\n if output_config is not None:\\n output_str = json.dumps(output_config, indent=4)\\n elif is_string_action(action_func):\\n action_func = cast(CLIActionStringType, action_func)\\n output_str = action_func(driver, errors.append)\\n else:\\n raise NotImplementedError(\\\"Invalid action function\\\")\\n if output_str is None:\\n print(\\\"Action {action} failed with errors\\\".format(action=action), file=sys.stderr)\\n for err in errors:\\n print(err, file=sys.stderr)\\n return 1\\n else:\\n with open(args[\\\"output\\\"], \\\"w\\\") as f:\\n f.write(output_str)\\n print(output_str)\\n return 0\",\n \"def execute_action(self, a):\\n return self.emulator.next(a)\",\n \"def do_exec(self, arg):\\n self.run_file(arg['path'])\",\n \"def execute_actions(self, actions):\\n execute_actions(self.board, self.agent_locs, actions)\",\n \"def step(self, action, agent_index=0):\\n return self.env.step(action)\",\n \"def perform(self, action, render=False, delay=0):\\n self.current_reward = 0\\n\\n # Take four steps\\n for step in range(4):\\n image, reward, done, info = self.env.step(action)\\n if render:\\n sleep(delay)\\n self.env.render()\\n if info[\\\"ale.lives\\\"] < 6:\\n self.joe.dead = True\\n if reward > 0:\\n self.current_reward = reward\\n\\n # Update objects\\n self.joe.update(image)\\n self.skull.update(image)\",\n \"def __call__( self, *args, **kw ):\\n return self.run( *args, **kw )\",\n \"def runSequence(self, **kwargs):\\n\\n answer = 'n' \\n action_number = 0\\n\\n while action_number < len(self.actionSequence):\\n# pdb.set_trace()\\n\\n (colour,pose_offset) = self.actionSequence[action_number]\\n rospy.loginfo('Running action %s for %s block' % (str(pose_offset), colour))\\n# pose_offset = self.bl.baxter_actions[action]['joint_position']\\n# goal_pose = tuple(map(operator.add, self.locator.pose, pose_offset))\\n success = self.locator.locate(colour, pose_offset)\\n\\n rospy.sleep(0.2)\\n if success:\\n rospy.loginfo('Action %s executed successfully, moving to next action.' % str(pose_offset))\\n else:\\n answer = raw_input('Failed to execute action. Try again? (y/n): ')\\n if answer in ('y'):\\n action_number -= 1\\n continue\\n \\n action_number += 1 \\n\\n self.baxter.mm.changeMenuTitle(\\\"Action sequence completed successfully!\\\")\",\n \"def stage_run(args):\\n\\n print \\\"stage_run args:\\\", args\\n if args.phase == \\\"split\\\":\\n adapters.split(args.stage, args)\\n if args.phase == \\\"main\\\":\\n adapters.main(args.stage, args)\\n if args.phase == \\\"join\\\":\\n\\n #for key in args.__dict__:\\n # if isinstance(getattr(args, key), list) and len(getattr(args, key)) == 1:\\n # setattr(args, key, getattr(args, key)[0])\\n\\n adapters.join(args.stage, args)\",\n \"def begin(self, pipeline: osbuild.Pipeline):\",\n \"def run(self):\\n self.assign_inputs()\\n self.execute()\\n self.collect_outputs()\",\n \"def do_action(self, action, **kwargs):\\r\\n print(action)\\r\\n action_method = getattr(self, action._method.__name__)\\r\\n if action_method:\\r\\n action_method(**kwargs)\",\n \"def execute_action(self, action_name, *args, **kw):\\n action_dict = {}\\n for robot_id in self._robot_group._robots_id_in_group_list:\\n robot_module = self._robot_group.all_robots_dict[robot_id].get_module(self._module_name)\\n action_dict[robot_id] = getattr(robot_module, action_name)(*args, **kw)\\n logger.info(\\\"Multi Module robot id {0}: begin to execute the action\\\".format(robot_id))\\n multi_action = MultiAction(action_dict)\\n return multi_action\",\n \"def run_one_step(self):\\n pass\",\n \"def run(_):\\n pass\",\n \"def _RunHookAction(self, hook_dict, matching_file_list):\\n command = hook_dict['action'][:]\\n if command[0] == 'python':\\n # If the hook specified \\\"python\\\" as the first item, the action is a\\n # Python script. Run it by starting a new copy of the same\\n # interpreter.\\n command[0] = sys.executable\\n\\n if '$matching_files' in command:\\n splice_index = command.index('$matching_files')\\n command[splice_index:splice_index + 1] = matching_file_list\\n\\n # Use a discrete exit status code of 2 to indicate that a hook action\\n # failed. Users of this script may wish to treat hook action failures\\n # differently from VC failures.\\n gclient_utils.SubprocessCall(command, self._root_dir, fail_status=2)\",\n \"def act(self) -> None:\\n pass\",\n \"def run(self):\\n # Get the dictionary of changes.\\n changes = self.context.get_changes()\\n\\n # Compare the changes to the regular expressions.\\n for change in changes:\\n logger.debug('path = \\\"{0}\\\"'.format(change.path))\\n logger.debug('chgtype = \\\"{0}\\\"'.format(change.type))\\n\\n # Check for a change path mismatch.\\n if self.pathregex \\\\\\n and not self.pathregex.search(change.path):\\n continue\\n\\n # Check for a change type mismatch.\\n if self.typeregex \\\\\\n and not self.typeregex.match(change.type):\\n continue\\n\\n # Save the triggering change details.\\n self.context.tokens['Path'] = change.path\\n self.context.tokens['ChgType'] = change.type\\n\\n # Execute the child actions. If they produce a non-zero\\n # exit code, or if only looking for the first match, stop\\n # checking.\\n exitcode = super(FilterCommitList, self).run()\\n if exitcode or self.matchfirst: return exitcode\\n\\n # Either nothing matched, or the child actions were\\n # successful.\\n return 0\",\n \"def octopus_predefined_run(self, msg, args):\\r\\n return self.predefined.run(msg, args)\",\n \"def run(self):\\n self.fn(*self.args, **self.kwargs)\",\n \"def run(self, reuse=False):\\n build = True\\n if 'shub://' in self.action['uses']:\\n image = self.action['uses']\\n build = False\\n elif './' in self.action['uses']:\\n image = 'action/' + os.path.basename(self.action['uses'])\\n singularityfile_path = os.path.join(\\n os.getcwd(), self.action['uses'])\\n else:\\n image = '/'.join(self.action['uses'].split('/')[:2])\\n singularityfile_path = os.path.join(self.action['repo_dir'],\\n self.action['action_dir'])\\n\\n if not reuse:\\n if self.singularity_exists():\\n self.singularity_rm()\\n if build:\\n self.singularity_build(singularityfile_path, image)\\n else:\\n self.singularity_pull(image)\\n else:\\n if not self.singularity_exists():\\n if build:\\n self.singularity_build(singularityfile_path, image)\\n else:\\n self.singularity_pull(image)\\n\\n e = self.singularity_start(image)\\n\\n if e != 0:\\n pu.fail('Action {} failed!\\\\n'.format(self.action['name']))\",\n \"def run(self, player=None, **kwargs):\\n self._pre_process_hook(player, **kwargs)\\n self.process(player, **kwargs)\\n self._post_process_hook(player, **kwargs)\",\n \"def perform_action(self, action_name, *action_parameters_):\\n result = Being.perform_action(self, action_name, *action_parameters_)\\n # return original result\\n return result\",\n \"def Execute(self):\\n self.FSM.Execute()\",\n \"def do_run(self, line: str):\\n if self._real_module is None:\\n print(\\\"'run' command depends on using a module. See 'use' for help.\\\")\\n return\\n\\n self._real_module.run()\",\n \"def run(self):\\r\\n self.log(texto=f\\\"Executando {self._name}\\\")\",\n \"def __call__(self, state, action):\\n pass\",\n \"def run_cmd(self):\\r\\n self.run = True\",\n \"def run(self,transient = None):\\n if self.flowComponent == None:\\n #\\n # Is it possible ? No\\n #\\n raise Exception(\\\" the flowcomponent solver has to be defined prior to the call of the run method\\\")\\n if not transient:\\n #raw_input(\\\" running elmer \\\")\\n self.flowComponent.run()\\n pass\\n else:\\n self.flowComponent.launch()\\n #dir(self.flowComponent)\\n self.flowComponent.run()\\n pass\",\n \"def p_action(option,opt_str,value,parser):\\n global_params.exec_in_context(value)\\n global something_executed\\n something_executed=True\",\n \"def act(self):\\n pass\",\n \"def run(self):\\n\\n if (self.action == 'read'):\\n self.read()\\n else:\\n self.write()\",\n \"def action(self):\\n current_action = self.get_script_entry()\\n if current_action[\\\"type\\\"] == \\\"request\\\":\\n self._handle_request(current_action)\\n elif current_action[\\\"type\\\"] == \\\"event\\\":\\n self._handle_event(current_action)\\n elif current_action[\\\"type\\\"] == \\\"response\\\":\\n self._handle_response(current_action)\\n else:\\n raise AttributeError(\\\"Wrong action type!\\\" +\\n \\\" Scenario: \\\" + str(self._loaded_sc[\\\"name\\\"]) +\\n \\\" Action: \\\" + str(self._scenario_script_cur))\",\n \"def main():\\n parent, actions = get_parsers(main.__doc__)\\n\\n options = parent.parse_args()\\n\\n sys.argv.pop(1)\\n\\n # Get sub-parser options\\n action_options = actions.choices.get(options.action[0]).parse_args()\\n\\n # Make the action call\\n try:\\n with OXDB('SNOWFLAKE') as dbh:\\n getattr(\\n importlib.import_module(\\n '.actions.%s' % options.action[0],\\n package='ox_dw_snowflake_odfi_etl'),\\n options.action[0])(action_options, dbh.connection)\\n except pid.PidFileAlreadyLockedError as exception:\\n sys.stderr.write(\\\"PidLock found! %s; Exiting...\\\" % str(exception))\\n except JobNotFoundException as exc:\\n sys.stderr.write(\\\"%s Please run bootstrap\\\\n\\\" % exc)\\n actions.choices.get('bootstrap').print_help()\\n sys.exit(1)\",\n \"def execute(self):\\r\\n for pilot in self._game.pilots_by_skill():\\r\\n pilot.active = True\\r\\n\\r\\n # Apply this pilot's maneuver\\r\\n pilot.chosen_maneuver.apply(pilot)\\r\\n\\r\\n # Choose an action to perform\\r\\n if pilot.can_perform_action():\\r\\n chosen_action = self._game.player(pilot.faction).choose_action(pilot)\\r\\n\\r\\n # TODO: Do something with this\\r\\n\\r\\n pilot.active = False\",\n \"def active_run(*args, **kwargs):\\n return fluent.active_run(*args, **kwargs)\",\n \"def execute(self) -> None:\\n self.command(self.target)\",\n \"def call_method(self, action):\\n\\n\\t\\tif action[0] in self.methods:\\n\\t\\t\\tself.methods[action[0]](action[0:])\\n\\t\\telse:\\n\\t\\t\\tself.no_such_method()\",\n \"def run_code(self, test):\\n for action in test:\\n self.assertEquals(1, len(action))\\n action_type, action = list(action.items())[0]\\n\\n if hasattr(self, \\\"run_\\\" + action_type):\\n getattr(self, \\\"run_\\\" + action_type)(action)\\n else:\\n raise InvalidActionType(action_type)\",\n \"def perform_user_action(action_index):\\r\\n if action_index is not None:\\r\\n print()\\r\\n action = available_actions[action_index]\\r\\n if current_state in action[\\\"valid_states\\\"]:\\r\\n function_to_run = available_actions[action_index][\\\"function\\\"]\\r\\n if function_to_run is not None:\\r\\n function_to_run()\\r\\n else:\\r\\n print(\\\"Internal error: NOT IMPLEMENTED (no function assigned for the action)!\\\")\\r\\n else:\\r\\n print(\\\"This function is not allowed in the current system state (%s)\\\" % current_state)\\r\\n else:\\r\\n print(\\\"Invalid input, please choose a valid action\\\")\\r\\n print()\\r\\n return None\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6953901","0.6952061","0.6602551","0.6561766","0.6464926","0.64080197","0.6405319","0.63914955","0.63781947","0.63781947","0.6359134","0.63469976","0.63318545","0.63070595","0.62973326","0.6241613","0.6240744","0.6202429","0.6176801","0.6176801","0.6174359","0.6146353","0.61218935","0.6107614","0.61045605","0.60971063","0.60971063","0.6094879","0.60900986","0.6073935","0.6043748","0.6034217","0.60328543","0.6030767","0.60259104","0.59758407","0.59682536","0.5950701","0.5947935","0.5931235","0.59290326","0.59223443","0.59145516","0.59110326","0.590262","0.59009403","0.58959526","0.5894106","0.588797","0.5870676","0.58518076","0.5829743","0.58215976","0.5815649","0.5762836","0.5760766","0.57504106","0.57504106","0.5739859","0.57347697","0.571705","0.571063","0.57084346","0.5700009","0.56947166","0.5690115","0.5680896","0.5679294","0.56729823","0.56723475","0.56714684","0.5669933","0.56685466","0.56624347","0.5654905","0.5643331","0.56392914","0.56286514","0.56285137","0.56264895","0.56254447","0.56240815","0.56211483","0.56060046","0.56031483","0.55987513","0.5595415","0.55936384","0.5593114","0.5587566","0.55861133","0.55851173","0.5578961","0.5570509","0.5568586","0.55644935","0.556164","0.5558973","0.55577445","0.5548052"],"string":"[\n \"0.6953901\",\n \"0.6952061\",\n \"0.6602551\",\n \"0.6561766\",\n \"0.6464926\",\n \"0.64080197\",\n \"0.6405319\",\n \"0.63914955\",\n \"0.63781947\",\n \"0.63781947\",\n \"0.6359134\",\n \"0.63469976\",\n \"0.63318545\",\n \"0.63070595\",\n \"0.62973326\",\n \"0.6241613\",\n \"0.6240744\",\n \"0.6202429\",\n \"0.6176801\",\n \"0.6176801\",\n \"0.6174359\",\n \"0.6146353\",\n \"0.61218935\",\n \"0.6107614\",\n \"0.61045605\",\n \"0.60971063\",\n \"0.60971063\",\n \"0.6094879\",\n \"0.60900986\",\n \"0.6073935\",\n \"0.6043748\",\n \"0.6034217\",\n \"0.60328543\",\n \"0.6030767\",\n \"0.60259104\",\n \"0.59758407\",\n \"0.59682536\",\n \"0.5950701\",\n \"0.5947935\",\n \"0.5931235\",\n \"0.59290326\",\n \"0.59223443\",\n \"0.59145516\",\n \"0.59110326\",\n \"0.590262\",\n \"0.59009403\",\n \"0.58959526\",\n \"0.5894106\",\n \"0.588797\",\n \"0.5870676\",\n \"0.58518076\",\n \"0.5829743\",\n \"0.58215976\",\n \"0.5815649\",\n \"0.5762836\",\n \"0.5760766\",\n \"0.57504106\",\n \"0.57504106\",\n \"0.5739859\",\n \"0.57347697\",\n \"0.571705\",\n \"0.571063\",\n \"0.57084346\",\n \"0.5700009\",\n \"0.56947166\",\n \"0.5690115\",\n \"0.5680896\",\n \"0.5679294\",\n \"0.56729823\",\n \"0.56723475\",\n \"0.56714684\",\n \"0.5669933\",\n \"0.56685466\",\n \"0.56624347\",\n \"0.5654905\",\n \"0.5643331\",\n \"0.56392914\",\n \"0.56286514\",\n \"0.56285137\",\n \"0.56264895\",\n \"0.56254447\",\n \"0.56240815\",\n \"0.56211483\",\n \"0.56060046\",\n \"0.56031483\",\n \"0.55987513\",\n \"0.5595415\",\n \"0.55936384\",\n \"0.5593114\",\n \"0.5587566\",\n \"0.55861133\",\n \"0.55851173\",\n \"0.5578961\",\n \"0.5570509\",\n \"0.5568586\",\n \"0.55644935\",\n \"0.556164\",\n \"0.5558973\",\n \"0.55577445\",\n \"0.5548052\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6402181"},"document_rank":{"kind":"string","value":"7"}}},{"rowIdx":17874,"cells":{"query":{"kind":"string","value":"Generator of stages. A stages is a list of actions that can be executed in parallel."},"document":{"kind":"string","value":"def get_stages(self):\n current_stage = self.wf['root']\n\n while current_stage:\n yield current_stage\n next_stage = set()\n for n in current_stage:\n next_stage.update(self.wf['action'][n].get('next', set()))\n current_stage = next_stage"},"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 stages(self):\n return StageManager(session=self._session)","def stages(self):\r\n return pipelines.Stages(self)","def run(stages, maxsize=0):\n\n if isinstance(stages, list) and len(stages) == 0:\n raise ValueError(\"Expected at least 1 stage to run\")\n\n elif isinstance(stages, list):\n stage = concat(stages, maxsize=maxsize)\n\n else:\n stage = stages\n\n stage = to_iterable(stage, maxsize=maxsize)\n\n for _ in stages:\n pass","def gen_stage_loop(cls, _opts, tests, put_next_stage, _put_result_stage):\n for test in tests:\n put_next_stage(test)","def list_stages():\n for name in Manager.STAGES:\n click.echo('{}'.format(name))","def run(stages: typing.List[Stage], maxsize: int = 0) -> None:\n\n if isinstance(stages, list) and len(stages) == 0:\n raise ValueError(\"Expected at least 1 stage to run\")\n\n elif isinstance(stages, list):\n stage = concat(stages, maxsize=maxsize)\n\n else:\n stage = stages\n\n stage = to_iterable(stage, maxsize=maxsize)\n\n for _ in stages:\n pass","def stages(self):\n return self._stages","def stages(self):\n return self._stages","def stage_list(args):\n\n for stage in args.stages:\n print stage","def process_stages(self) -> List[str]:\n return self._process_stages","def stages() -> List[Tuple[str, str]]:\n return [TestStatus.preparation, TestStatus.testing, TestStatus.completed]","def expand_gbk(stages, pipeline_context):\n # type: (Iterable[Stage], TransformContext) -> Iterator[Stage]\n for stage in stages:\n transform = only_transform(stage.transforms)\n if transform.spec.urn == common_urns.primitives.GROUP_BY_KEY.urn:\n for pcoll_id in transform.inputs.values():\n pipeline_context.length_prefix_pcoll_coders(pcoll_id)\n for pcoll_id in transform.outputs.values():\n if pipeline_context.use_state_iterables:\n pipeline_context.components.pcollections[\n pcoll_id].coder_id = pipeline_context.with_state_iterables(\n pipeline_context.components.pcollections[pcoll_id].coder_id)\n pipeline_context.length_prefix_pcoll_coders(pcoll_id)\n\n # This is used later to correlate the read and write.\n transform_id = stage.name\n if transform != pipeline_context.components.transforms.get(transform_id):\n transform_id = unique_name(\n pipeline_context.components.transforms, stage.name)\n pipeline_context.components.transforms[transform_id].CopyFrom(transform)\n grouping_buffer = create_buffer_id(transform_id, kind='group')\n gbk_write = Stage(\n transform.unique_name + '/Write',\n [\n beam_runner_api_pb2.PTransform(\n unique_name=transform.unique_name + '/Write',\n inputs=transform.inputs,\n spec=beam_runner_api_pb2.FunctionSpec(\n urn=bundle_processor.DATA_OUTPUT_URN,\n payload=grouping_buffer))\n ],\n downstream_side_inputs=frozenset(),\n must_follow=stage.must_follow)\n yield gbk_write\n\n yield Stage(\n transform.unique_name + '/Read',\n [\n beam_runner_api_pb2.PTransform(\n unique_name=transform.unique_name + '/Read',\n outputs=transform.outputs,\n spec=beam_runner_api_pb2.FunctionSpec(\n urn=bundle_processor.DATA_INPUT_URN,\n payload=grouping_buffer))\n ],\n downstream_side_inputs=stage.downstream_side_inputs,\n must_follow=union(frozenset([gbk_write]), stage.must_follow))\n else:\n yield stage","def job_stages(self) -> Sequence['outputs.JobStagesResponse']:\n return pulumi.get(self, \"job_stages\")","def job_stages(self) -> Sequence['outputs.JobStagesResponse']:\n return pulumi.get(self, \"job_stages\")","def job_stages(self) -> Sequence['outputs.JobStagesResponse']:\n return pulumi.get(self, \"job_stages\")","def stages(self, stages):\n if stages is None:\n self._stages = None\n else:\n self._stages = stages if isinstance(stages, list) else [stages] * len(self.pidevice.allaxes)\n debug('ControllerStartup.stages = %s', itemstostr(self._stages))","def set_stages(self, stages):\n\n if not isinstance(stages, list) or not all([issubclass(type(x), Stage) for x in stages]):\n raise ValueError(\"stages must be a list of Stages's only!\")\n\n self.stages = stages\n\n return self","def extract_impulse_stages(stages, pipeline_context):\n # type: (Iterable[Stage], TransformContext) -> Iterator[Stage]\n for stage in stages:\n for transform in list(stage.transforms):\n if transform.spec.urn == common_urns.primitives.IMPULSE.urn:\n stage.transforms.remove(transform)\n yield Stage(\n transform.unique_name,\n transforms=[transform],\n downstream_side_inputs=stage.downstream_side_inputs,\n must_follow=stage.must_follow,\n parent=stage.parent)\n\n if stage.transforms:\n yield stage","def sort(self):\n for _ in self.stage1():\n yield\n for _ in self.stage2():\n yield","def sort_stages(stages, pipeline_context):\n # type: (Iterable[Stage], TransformContext) -> List[Stage]\n all_stages = set(stages)\n seen = set() # type: Set[Stage]\n ordered = []\n\n producers = {\n pcoll: stage\n for stage in all_stages for t in stage.transforms\n for pcoll in t.outputs.values()\n }\n\n def process(stage):\n if stage not in seen:\n seen.add(stage)\n if stage not in all_stages:\n return\n for prev in stage.must_follow:\n process(prev)\n stage_outputs = set(\n pcoll for transform in stage.transforms\n for pcoll in transform.outputs.values())\n for transform in stage.transforms:\n for pcoll in transform.inputs.values():\n if pcoll not in stage_outputs:\n process(producers[pcoll])\n ordered.append(stage)\n\n for stage in stages:\n process(stage)\n return ordered","def expand_sdf(stages, context):\n # type: (Iterable[Stage], TransformContext) -> Iterator[Stage]\n for stage in stages:\n transform = only_transform(stage.transforms)\n if transform.spec.urn == common_urns.primitives.PAR_DO.urn:\n\n pardo_payload = proto_utils.parse_Bytes(\n transform.spec.payload, beam_runner_api_pb2.ParDoPayload)\n\n if pardo_payload.restriction_coder_id:\n\n def copy_like(protos, original, suffix='_copy', **kwargs):\n if isinstance(original, str):\n key = original\n original = protos[original]\n else:\n key = 'component'\n new_id = unique_name(protos, key + suffix)\n protos[new_id].CopyFrom(original)\n proto = protos[new_id]\n for name, value in kwargs.items():\n if isinstance(value, dict):\n getattr(proto, name).clear()\n getattr(proto, name).update(value)\n elif isinstance(value, list):\n del getattr(proto, name)[:]\n getattr(proto, name).extend(value)\n elif name == 'urn':\n proto.spec.urn = value\n elif name == 'payload':\n proto.spec.payload = value\n else:\n setattr(proto, name, value)\n if 'unique_name' not in kwargs and hasattr(proto, 'unique_name'):\n proto.unique_name = unique_name(\n {p.unique_name\n for p in protos.values()},\n original.unique_name + suffix)\n return new_id\n\n def make_stage(base_stage, transform_id, extra_must_follow=()):\n # type: (Stage, str, Iterable[Stage]) -> Stage\n transform = context.components.transforms[transform_id]\n return Stage(\n transform.unique_name, [transform],\n base_stage.downstream_side_inputs,\n union(base_stage.must_follow, frozenset(extra_must_follow)),\n parent=base_stage.name,\n environment=base_stage.environment)\n\n main_input_tag = only_element(\n tag for tag in transform.inputs.keys()\n if tag not in pardo_payload.side_inputs)\n main_input_id = transform.inputs[main_input_tag]\n element_coder_id = context.components.pcollections[\n main_input_id].coder_id\n # Tuple[element, restriction]\n paired_coder_id = context.add_or_get_coder_id(\n beam_runner_api_pb2.Coder(\n spec=beam_runner_api_pb2.FunctionSpec(\n urn=common_urns.coders.KV.urn),\n component_coder_ids=[\n element_coder_id, pardo_payload.restriction_coder_id\n ]))\n # Tuple[Tuple[element, restriction], double]\n sized_coder_id = context.add_or_get_coder_id(\n beam_runner_api_pb2.Coder(\n spec=beam_runner_api_pb2.FunctionSpec(\n urn=common_urns.coders.KV.urn),\n component_coder_ids=[\n paired_coder_id,\n context.add_or_get_coder_id(\n # context can be None here only because FloatCoder does\n # not have components\n coders.FloatCoder().to_runner_api(None), # type: ignore\n 'doubles_coder')\n ]))\n\n paired_pcoll_id = copy_like(\n context.components.pcollections,\n main_input_id,\n '_paired',\n coder_id=paired_coder_id)\n pair_transform_id = copy_like(\n context.components.transforms,\n transform,\n unique_name=transform.unique_name + '/PairWithRestriction',\n urn=common_urns.sdf_components.PAIR_WITH_RESTRICTION.urn,\n outputs={'out': paired_pcoll_id})\n\n split_pcoll_id = copy_like(\n context.components.pcollections,\n main_input_id,\n '_split',\n coder_id=sized_coder_id)\n split_transform_id = copy_like(\n context.components.transforms,\n transform,\n unique_name=transform.unique_name + '/SplitAndSizeRestriction',\n urn=common_urns.sdf_components.SPLIT_AND_SIZE_RESTRICTIONS.urn,\n inputs=dict(transform.inputs, **{main_input_tag: paired_pcoll_id}),\n outputs={'out': split_pcoll_id})\n\n reshuffle_stage = None\n if common_urns.composites.RESHUFFLE.urn in context.known_runner_urns:\n reshuffle_pcoll_id = copy_like(\n context.components.pcollections,\n main_input_id,\n '_reshuffle',\n coder_id=sized_coder_id)\n reshuffle_transform_id = copy_like(\n context.components.transforms,\n transform,\n unique_name=transform.unique_name + '/Reshuffle',\n urn=common_urns.composites.RESHUFFLE.urn,\n payload=b'',\n inputs=dict(transform.inputs, **{main_input_tag: split_pcoll_id}),\n outputs={'out': reshuffle_pcoll_id})\n reshuffle_stage = make_stage(stage, reshuffle_transform_id)\n else:\n reshuffle_pcoll_id = split_pcoll_id\n reshuffle_transform_id = None\n\n if context.is_drain:\n truncate_pcoll_id = copy_like(\n context.components.pcollections,\n main_input_id,\n '_truncate_restriction',\n coder_id=sized_coder_id)\n # Lengthprefix the truncate output.\n context.length_prefix_pcoll_coders(truncate_pcoll_id)\n truncate_transform_id = copy_like(\n context.components.transforms,\n transform,\n unique_name=transform.unique_name + '/TruncateAndSizeRestriction',\n urn=common_urns.sdf_components.TRUNCATE_SIZED_RESTRICTION.urn,\n inputs=dict(\n transform.inputs, **{main_input_tag: reshuffle_pcoll_id}),\n outputs={'out': truncate_pcoll_id})\n process_transform_id = copy_like(\n context.components.transforms,\n transform,\n unique_name=transform.unique_name + '/Process',\n urn=common_urns.sdf_components.\n PROCESS_SIZED_ELEMENTS_AND_RESTRICTIONS.urn,\n inputs=dict(\n transform.inputs, **{main_input_tag: truncate_pcoll_id}))\n else:\n process_transform_id = copy_like(\n context.components.transforms,\n transform,\n unique_name=transform.unique_name + '/Process',\n urn=common_urns.sdf_components.\n PROCESS_SIZED_ELEMENTS_AND_RESTRICTIONS.urn,\n inputs=dict(\n transform.inputs, **{main_input_tag: reshuffle_pcoll_id}))\n\n yield make_stage(stage, pair_transform_id)\n split_stage = make_stage(stage, split_transform_id)\n yield split_stage\n if reshuffle_stage:\n yield reshuffle_stage\n if context.is_drain:\n yield make_stage(\n stage, truncate_transform_id, extra_must_follow=[split_stage])\n yield make_stage(stage, process_transform_id)\n else:\n yield make_stage(\n stage, process_transform_id, extra_must_follow=[split_stage])\n\n else:\n yield stage\n\n else:\n yield stage","def create_and_optimize_stages(\n pipeline_proto, # type: beam_runner_api_pb2.Pipeline\n phases,\n known_runner_urns, # type: FrozenSet[str]\n use_state_iterables=False,\n is_drain=False):\n # type: (...) -> Tuple[TransformContext, List[Stage]]\n pipeline_context = TransformContext(\n pipeline_proto.components,\n known_runner_urns,\n use_state_iterables=use_state_iterables,\n is_drain=is_drain)\n\n # Initial set of stages are singleton leaf transforms.\n stages = list(\n leaf_transform_stages(\n pipeline_proto.root_transform_ids,\n pipeline_proto.components,\n known_composites=union(known_runner_urns, KNOWN_COMPOSITES)))\n\n # Apply each phase in order.\n for phase in phases:\n _LOGGER.info('%s %s %s', '=' * 20, phase, '=' * 20)\n stages = list(phase(stages, pipeline_context))\n _LOGGER.debug('%s %s' % (len(stages), [len(s.transforms) for s in stages]))\n _LOGGER.debug('Stages: %s', [str(s) for s in stages])\n\n # Return the (possibly mutated) context and ordered set of stages.\n return pipeline_context, stages","def make_pipeline(steps):\n def compose2(f, g):\n return lambda x: g(f(x))\n return functools.reduce(compose2, steps)","def steps(self):\n for step in self._steps:\n yield step","def stage_states(self) -> pulumi.Output[Sequence['outputs.ExecutionStageStateResponse']]:\n return pulumi.get(self, \"stage_states\")","def _add_stage(self, name):\n def stage_func(self, *args, **kwargs):\n \"\"\" Stage function.\n\n :param args: Positional arguments.\n :param kwargs: Keyword arguments.\n :return: Pipeline (for method chaining).\n \"\"\"\n self._pipe.append(Stage(name, args, kwargs))\n return self\n\n setattr(Pipeline, name, stage_func)","def sweep_stages(self) -> List[str]:\n return self._sweep_stages","def load_stages():\n\n def load_stages_from_dir(mro_dir):\n \"\"\"Iterate over MRO file in a directory, parse them, and accumulate\n their stages.\n \"\"\"\n stages = {}\n for file_name in os.listdir(mro_dir):\n if file_name.endswith(\".mro\"):\n stages.update(mro_parser.get_stages(\n os.path.join(mro_dir, file_name)))\n return stages\n\n stages = {}\n for mro_dir in os.environ[\"MROPATH\"].split(':'):\n stages.update(load_stages_from_dir(mro_dir))\n return stages","def sink_flattens(stages, pipeline_context):\n # type: (Iterable[Stage], TransformContext) -> Iterator[Stage]\n # TODO(robertwb): Actually attempt to sink rather than always materialize.\n # TODO(robertwb): Possibly fuse multi-input flattens into one of the stages.\n for stage in fix_flatten_coders(stages,\n pipeline_context,\n common_urns.primitives.FLATTEN.urn):\n transform = only_element(stage.transforms)\n if (transform.spec.urn == common_urns.primitives.FLATTEN.urn and\n len(transform.inputs) > 1):\n # This is used later to correlate the read and writes.\n buffer_id = create_buffer_id(transform.unique_name)\n flatten_writes = [] # type: List[Stage]\n for local_in, pcoll_in in transform.inputs.items():\n flatten_write = Stage(\n transform.unique_name + '/Write/' + local_in,\n [\n beam_runner_api_pb2.PTransform(\n unique_name=transform.unique_name + '/Write/' + local_in,\n inputs={local_in: pcoll_in},\n spec=beam_runner_api_pb2.FunctionSpec(\n urn=bundle_processor.DATA_OUTPUT_URN,\n payload=buffer_id),\n environment_id=transform.environment_id)\n ],\n downstream_side_inputs=frozenset(),\n must_follow=stage.must_follow)\n flatten_writes.append(flatten_write)\n yield flatten_write\n\n yield Stage(\n transform.unique_name + '/Read',\n [\n beam_runner_api_pb2.PTransform(\n unique_name=transform.unique_name + '/Read',\n outputs=transform.outputs,\n spec=beam_runner_api_pb2.FunctionSpec(\n urn=bundle_processor.DATA_INPUT_URN, payload=buffer_id),\n environment_id=transform.environment_id)\n ],\n downstream_side_inputs=stage.downstream_side_inputs,\n must_follow=union(frozenset(flatten_writes), stage.must_follow))\n\n else:\n yield stage","def concat(stages, maxsize=0):\n\n stages = [_to_stage(s) for s in stages]\n\n return _Stage(\n worker_constructor=WORKER,\n workers=1,\n maxsize=maxsize,\n on_start=None,\n on_done=None,\n target=_concat,\n args=tuple(),\n dependencies=stages,\n )","def main():\n init()\n separator_len = 40\n for s in stage_instances:\n print('='*separator_len)\n print(s.name)\n print('-'*separator_len)\n\n s.add_tasks() # Add tasks from previous stage\n s.revive_or_archive() # Revive killed tasks or move them to failed\n s.schedule_jobs() # Schedule new jobs if needed\n s.print_status()\n print('='*separator_len + '\\n')\n render(stage_instances)","def stage_states(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['ExecutionStageStateArgs']]]]:\n return pulumi.get(self, \"stage_states\")","def get_pipelines() -> Iterable[DataPipeline]:\n for pipeline_name in get_pipeline_names():\n yield DataPipeline.load(pipeline_name)","def get_parser(stages):\n\n # martian\n parser = argparse.ArgumentParser(prog=\"martian\")\n subparsers = parser.add_subparsers()\n\n # martian stage\n stage_parser = subparsers.add_parser(\n \"stage\", help=\"Work with Martian stages.\")\n\n stage_subparsers = stage_parser.add_subparsers(\n title=\"Stage subcommands\",\n help=\"Actions than can be performed on Martian stages.\")\n\n # martian stage list\n stage_list_parser = stage_subparsers.add_parser(\n \"list\",\n help=\"List all available stages.\")\n stage_list_parser.set_defaults(func=stage_list)\n stage_list_parser.set_defaults(stages=stages)\n\n # martian stage describe <stage_name>\n stage_describe_parser = stage_subparsers.add_parser(\n \"describe\",\n help=\"Describe the inputs, outputs, and source location of a stage\")\n stage_describe_parser.add_argument(\n \"stage_name\",\n help=\"Name of the stage to describe\")\n stage_describe_parser.set_defaults(func=stage_describe)\n stage_describe_parser.set_defaults(stages=stages)\n\n # martian stage run <stage_name> <stage_phase> <stage_args...>\n stage_run_parser = stage_subparsers.add_parser(\n \"run\",\n help=\"Run a stage\")\n\n stage_run_subparsers = stage_run_parser.add_subparsers(\n title=\"Stages available to be run\",\n help=\"Names of available stages.\")\n\n for stage in stages.values():\n\n individual_stage_parser = stage_run_subparsers.add_parser(\n stage.name,\n help=\"Execute stage \" + stage.name)\n individual_stage_subparsers = individual_stage_parser.add_subparsers()\n\n # Some stages don't have a split or join\n available_stage_phases = ['split', 'join', 'main'] if (stage.splits or stage.force_split) else ['main']\n\n for phase in available_stage_phases:\n phase_parser = individual_stage_subparsers.add_parser(\n phase,\n help='Run the ' + phase + ' of ' + stage.name)\n\n phase_parser.set_defaults(func=stage_run)\n phase_parser.set_defaults(phase=phase)\n phase_parser.set_defaults(stage=stage)\n\n for input_ in _stage_inputs(stage, phase):\n help_message = \"Type: \" + input_.type\n if input_.help:\n help_message += \" Help: \" + input_.help\n phase_parser.add_argument(\n \"--\" + input_.name,\n type=martian_type_to_python_type(input_.type),\n nargs=martian_type_to_nargs(input_.type),\n default=None,\n help=help_message)\n\n # Handle the \"split_file\" for mains that come after a split\n if phase == 'main' and 'split' in available_stage_phases:\n phase_parser.add_argument(\n \"--split_file\",\n type=martian_type_to_python_type(\"File\"),\n nargs=martian_type_to_nargs(\"File\"),\n default=None,\n help=\"File with split arguments.\")\n\n return parser","def merge_action_stages(env):\n stage_draft = env.ref('mgmtsystem_action.stage_draft')\n stage_open = env.ref('mgmtsystem_action.stage_open')\n stage_close = env.ref('mgmtsystem_action.stage_close')\n\n old_stage_draft_id = env.ref('crm_claim.stage_claim1').id\n old_stage_open_id = env.ref('crm_claim.stage_claim5').id\n old_stage_close_id = env.ref('crm_claim.stage_claim2').id\n\n env['mgmtsystem.action'].search([\n ('stage_id', '=', old_stage_draft_id)\n ]).write({'stage_id': stage_draft.id})\n\n env['mgmtsystem.action'].search([\n ('stage_id', '=', old_stage_open_id)\n ]).write({'stage_id': stage_open.id})\n\n env['mgmtsystem.action'].search([\n ('stage_id', '=', old_stage_close_id)\n ]).write({'stage_id': stage_close.id})\n\n env['mgmtsystem.action.stage'].browse([\n old_stage_draft_id, old_stage_open_id, old_stage_close_id\n ]).unlink()","def run_presub(stages):\n if is_presub(stages[0]):\n # Pop the first stage\n stage, stages = stages[0], stages[1:]\n # Execute it only if Python was invoked normally, not in a queue job\n if STAGE_ID is None:\n msg = \"Only the first stage can execute on the login node\"\n assert all([not is_presub(i) for i in stages]), msg\n alog.info(\"Pre-submit stage starting: %s\", stage)\n stage()\n alog.info(\"Pre-submit stage done: %s\", stage)\n return stages","def _collect_stages(self) -> Set[str]:\n # Get the stage name associated with the previous deployment and update stage\n # Stage needs to be flushed so that new changes will be visible immediately\n api_resource = get_resource_by_id(self._stacks, ResourceIdentifier(self._api_identifier))\n stage_resources = get_resource_ids_by_type(self._stacks, AWS_APIGATEWAY_STAGE)\n deployment_resources = get_resource_ids_by_type(self._stacks, AWS_APIGATEWAY_DEPLOYMENT)\n\n stages = set()\n # If it is a SAM resource, get the StageName property\n if api_resource:\n if api_resource.get(\"Type\") == AWS_SERVERLESS_API:\n # The customer defined stage name\n stage_name = api_resource.get(\"Properties\", {}).get(\"StageName\")\n if stage_name:\n stages.add(cast(str, stage_name))\n\n # The stage called \"Stage\"\n if stage_name != \"Stage\":\n response_sta = cast(Dict, self._api_client.get_stages(restApiId=self._api_physical_id))\n for item in response_sta.get(\"item\"): # type: ignore\n if item.get(\"stageName\") == \"Stage\":\n stages.add(\"Stage\")\n\n # For both SAM and ApiGateway resource, check if any refs from stage resources\n for stage_resource in stage_resources:\n # RestApiId is a required field in stage\n stage_dict = get_resource_by_id(self._stacks, stage_resource)\n if not stage_dict:\n continue\n rest_api_id = stage_dict.get(\"Properties\", {}).get(\"RestApiId\")\n dep_id = stage_dict.get(\"Properties\", {}).get(\"DeploymentId\")\n # If the stage doesn't have a deployment associated then no need to update\n if dep_id is None:\n continue\n # If the stage's deployment ID is not static and the rest API ID matchs, then update\n for deployment_resource in deployment_resources:\n if deployment_resource.resource_iac_id == dep_id and rest_api_id == self._api_identifier:\n stages.add(cast(str, stage_dict.get(\"Properties\", {}).get(\"StageName\")))\n break\n\n return stages","def iter_jobs(self):\n for param in self._parameters:\n yield param","def stages(self, pipeline_name):\n\n pipeline_name = self._require_param('pipeline_name', locals())\n\n response = self._session.get(\n path=self._session.urljoin(self.PIPELINES_RESOURCE_PATH, pipeline_name, 'stages.xml').format(\n base_api=self.base_api\n ),\n headers={'Accept': 'application/xml'},\n )\n\n return response.text","def init_process_group_by_stages(self, stages: List[int]) -> ProcessGroup:\n return self.pg_mesh.get_group_along_axis(self.pipeline_axis, stages)","def run_stage_loop(cls, _opts, tests_results, put_next_stage):\n for _, result in tests_results:\n put_next_stage(result)","def annotate_downstream_side_inputs(stages, pipeline_context):\n # type: (Iterable[Stage], TransformContext) -> Iterable[Stage]\n consumers = collections.defaultdict(\n list) # type: DefaultDict[str, List[Stage]]\n\n def get_all_side_inputs():\n # type: () -> Set[str]\n all_side_inputs = set() # type: Set[str]\n for stage in stages:\n for transform in stage.transforms:\n for input in transform.inputs.values():\n consumers[input].append(stage)\n for si in stage.side_inputs():\n all_side_inputs.add(si)\n return all_side_inputs\n\n all_side_inputs = frozenset(get_all_side_inputs())\n\n downstream_side_inputs_by_stage = {} # type: Dict[Stage, FrozenSet[str]]\n\n def compute_downstream_side_inputs(stage):\n # type: (Stage) -> FrozenSet[str]\n if stage not in downstream_side_inputs_by_stage:\n downstream_side_inputs = frozenset() # type: FrozenSet[str]\n for transform in stage.transforms:\n for output in transform.outputs.values():\n if output in all_side_inputs:\n downstream_side_inputs = union(\n downstream_side_inputs, frozenset([output]))\n for consumer in consumers[output]:\n downstream_side_inputs = union(\n downstream_side_inputs,\n compute_downstream_side_inputs(consumer))\n downstream_side_inputs_by_stage[stage] = downstream_side_inputs\n return downstream_side_inputs_by_stage[stage]\n\n for stage in stages:\n stage.downstream_side_inputs = compute_downstream_side_inputs(stage)\n return stages","def _execute_stage(self, index, stage, stop):\n if stop.is_set():\n _LOGGER.info(\"Stopped pipeline on group %s\", self._group)\n return\n _LOGGER.info(\" -> Running stage '%s' on group %s\", stage, self._group)\n if stage.name == 'on':\n self._group.on = True\n elif stage.name == 'off':\n self._group.on = False\n elif stage.name == 'hue':\n self._group.hue = stage.args[0]\n elif stage.name == 'saturation':\n self._group.saturation = stage.args[0]\n elif stage.name == 'color':\n self._group.color = Color(*stage.args)\n elif stage.name == 'brightness':\n self._group.brightness = stage.args[0]\n elif stage.name == 'temperature':\n self._group.temperature = stage.args[0]\n elif stage.name == 'transition':\n self._group.transition(*stage.args, **stage.kwargs)\n elif stage.name == 'white':\n self._group.white()\n elif stage.name == 'white_up':\n self._group.white_up()\n elif stage.name == 'white_down':\n self._group.white_down()\n elif stage.name == 'red_up':\n self._group.red_up()\n elif stage.name == 'red_down':\n self._group.red_down()\n elif stage.name == 'green_up':\n self._group.green_up()\n elif stage.name == 'green_down':\n self._group.green_down()\n elif stage.name == 'blue_up':\n self._group.blue_up()\n elif stage.name == 'blue_down':\n self._group.blue_down()\n elif stage.name == 'night_light':\n self._group.night_light()\n elif stage.name == 'link':\n self._group.link()\n elif stage.name == 'unlink':\n self._group.unlink()\n elif stage.name == 'flash':\n self._group.flash(**stage.kwargs)\n elif stage.name == 'repeat':\n self._repeat(index, stage, stop)\n elif stage.name == 'wait':\n time.sleep(*stage.args)\n elif stage.name == 'callback':\n stage.args[0](*stage.args[1:], **stage.kwargs)","def stage(self, pipeline_name, pipeline_counter, stage_name, stage_counter):\n\n func_args = locals()\n pipeline_name = self._require_param('pipeline_name', func_args)\n pipeline_counter = self._require_param('pipeline_counter', func_args)\n stage_name = self._require_param('stage_name', func_args)\n stage_counter = self._require_param('stage_counter', func_args)\n\n response = self._session.get(\n path=self._session.urljoin(\n self.PIPELINES_RESOURCE_PATH,\n pipeline_name,\n pipeline_counter,\n stage_name,\n '{}.xml'.format(stage_counter)\n ).format(\n base_api=self._session.base_api(api_path=''), # WTF?!!\n ),\n headers={'Accept': 'application/xml'},\n )\n\n return response.text","def flat_map(\n f, stage=pypeln_utils.UNDEFINED, workers=1, maxsize=0, on_start=None, on_done=None\n):\n\n if utils.is_undefined(stage):\n return utils.Partial(\n lambda stage: flat_map(\n f,\n stage,\n workers=workers,\n maxsize=maxsize,\n on_start=on_start,\n on_done=on_done,\n )\n )\n\n stage = _to_stage(stage)\n\n if isinstance(f, types.GeneratorType):\n _f = f\n f = lambda *args, **kwargs: _f(*args, **kwargs)\n\n return _Stage(\n worker_constructor=WORKER,\n workers=workers,\n maxsize=maxsize,\n on_start=on_start,\n on_done=on_done,\n target=_flat_map,\n args=(f,),\n dependencies=[stage],\n )","def concat(stages: typing.List[Stage], maxsize: int = 0) -> Stage:\n\n stages = [to_stage(stage) for stage in stages]\n\n return Concat(f=None, on_start=None, on_done=None, timeout=0, dependencies=stages,)","def _patch_update_stages(\n self,\n stage_changes_list: list[dict[str, Any]],\n changed_fields: CHANGED_FIELDS_LIST_TYPE\n ) -> bool:\n stages: list[Stage] = []\n for change_info in stage_changes_list:\n stage_was_updated = False\n # Check if valid ID is provided and fetch stage if it exists.\n if 'id' not in change_info:\n self.abort(400, msg='Missing stage ID in stage updates')\n id = change_info['id']\n stage = Stage.get_by_id(id)\n if not stage:\n self.abort(400, msg=f'Stage not found for ID {id}')\n\n # Update stage fields.\n for field, field_type in api_specs.STAGE_FIELD_DATA_TYPES:\n if field not in change_info:\n continue\n form_field_name = change_info[field]['form_field_name']\n old_value = getattr(stage, field)\n new_value = change_info[field]['value']\n self._update_field_value(stage, field, field_type, new_value)\n changed_fields.append((form_field_name, old_value, new_value))\n stage_was_updated = True\n\n # Update milestone fields.\n milestones = stage.milestones\n for field, field_type in api_specs.MILESTONESET_FIELD_DATA_TYPES:\n if field not in change_info:\n continue\n if milestones is None:\n milestones = MilestoneSet()\n form_field_name = change_info[field]['form_field_name']\n old_value = getattr(milestones, field)\n new_value = change_info[field]['value']\n self._update_field_value(milestones, field, field_type, new_value)\n changed_fields.append((form_field_name, old_value, new_value))\n stage_was_updated = True\n stage.milestones = milestones\n\n if stage_was_updated:\n stages.append(stage)\n\n # Save all of the updates made.\n # Return a boolean representing if any changes were made to any stages.\n if stages:\n ndb.put_multi(stages)\n return True\n return False","def steps(self):\n for step in self._get_paged(\"steps\", trailing=True):\n yield self.__get_object(step)\n\n return","def lift_combiners(stages, context):\n # type: (List[Stage], TransformContext) -> Iterator[Stage]\n def is_compatible_with_combiner_lifting(trigger):\n '''Returns whether this trigger is compatible with combiner lifting.\n\n Certain triggers, such as those that fire after a certain number of\n elements, need to observe every element, and as such are incompatible\n with combiner lifting (which may aggregate several elements into one\n before they reach the triggering code after shuffle).\n '''\n if trigger is None:\n return True\n elif trigger.WhichOneof('trigger') in (\n 'default',\n 'always',\n 'never',\n 'after_processing_time',\n 'after_synchronized_processing_time'):\n return True\n elif trigger.HasField('element_count'):\n return trigger.element_count.element_count == 1\n elif trigger.HasField('after_end_of_window'):\n return is_compatible_with_combiner_lifting(\n trigger.after_end_of_window.early_firings\n ) and is_compatible_with_combiner_lifting(\n trigger.after_end_of_window.late_firings)\n elif trigger.HasField('after_any'):\n return all(\n is_compatible_with_combiner_lifting(t)\n for t in trigger.after_any.subtriggers)\n elif trigger.HasField('repeat'):\n return is_compatible_with_combiner_lifting(trigger.repeat.subtrigger)\n else:\n return False\n\n def can_lift(combine_per_key_transform):\n windowing = context.components.windowing_strategies[\n context.components.pcollections[only_element(\n list(combine_per_key_transform.inputs.values())\n )].windowing_strategy_id]\n return is_compatible_with_combiner_lifting(windowing.trigger)\n\n def make_stage(base_stage, transform):\n # type: (Stage, beam_runner_api_pb2.PTransform) -> Stage\n return Stage(\n transform.unique_name, [transform],\n downstream_side_inputs=base_stage.downstream_side_inputs,\n must_follow=base_stage.must_follow,\n parent=base_stage.name,\n environment=base_stage.environment)\n\n def lifted_stages(stage):\n transform = stage.transforms[0]\n combine_payload = proto_utils.parse_Bytes(\n transform.spec.payload, beam_runner_api_pb2.CombinePayload)\n\n input_pcoll = context.components.pcollections[only_element(\n list(transform.inputs.values()))]\n output_pcoll = context.components.pcollections[only_element(\n list(transform.outputs.values()))]\n\n element_coder_id = input_pcoll.coder_id\n element_coder = context.components.coders[element_coder_id]\n key_coder_id, _ = element_coder.component_coder_ids\n accumulator_coder_id = combine_payload.accumulator_coder_id\n\n key_accumulator_coder = beam_runner_api_pb2.Coder(\n spec=beam_runner_api_pb2.FunctionSpec(urn=common_urns.coders.KV.urn),\n component_coder_ids=[key_coder_id, accumulator_coder_id])\n key_accumulator_coder_id = context.add_or_get_coder_id(\n key_accumulator_coder)\n\n accumulator_iter_coder = beam_runner_api_pb2.Coder(\n spec=beam_runner_api_pb2.FunctionSpec(\n urn=common_urns.coders.ITERABLE.urn),\n component_coder_ids=[accumulator_coder_id])\n accumulator_iter_coder_id = context.add_or_get_coder_id(\n accumulator_iter_coder)\n\n key_accumulator_iter_coder = beam_runner_api_pb2.Coder(\n spec=beam_runner_api_pb2.FunctionSpec(urn=common_urns.coders.KV.urn),\n component_coder_ids=[key_coder_id, accumulator_iter_coder_id])\n key_accumulator_iter_coder_id = context.add_or_get_coder_id(\n key_accumulator_iter_coder)\n\n precombined_pcoll_id = unique_name(\n context.components.pcollections, 'pcollection')\n context.components.pcollections[precombined_pcoll_id].CopyFrom(\n beam_runner_api_pb2.PCollection(\n unique_name=transform.unique_name + '/Precombine.out',\n coder_id=key_accumulator_coder_id,\n windowing_strategy_id=input_pcoll.windowing_strategy_id,\n is_bounded=input_pcoll.is_bounded))\n\n grouped_pcoll_id = unique_name(\n context.components.pcollections, 'pcollection')\n context.components.pcollections[grouped_pcoll_id].CopyFrom(\n beam_runner_api_pb2.PCollection(\n unique_name=transform.unique_name + '/Group.out',\n coder_id=key_accumulator_iter_coder_id,\n windowing_strategy_id=output_pcoll.windowing_strategy_id,\n is_bounded=output_pcoll.is_bounded))\n\n merged_pcoll_id = unique_name(\n context.components.pcollections, 'pcollection')\n context.components.pcollections[merged_pcoll_id].CopyFrom(\n beam_runner_api_pb2.PCollection(\n unique_name=transform.unique_name + '/Merge.out',\n coder_id=key_accumulator_coder_id,\n windowing_strategy_id=output_pcoll.windowing_strategy_id,\n is_bounded=output_pcoll.is_bounded))\n\n yield make_stage(\n stage,\n beam_runner_api_pb2.PTransform(\n unique_name=transform.unique_name + '/Precombine',\n spec=beam_runner_api_pb2.FunctionSpec(\n urn=common_urns.combine_components.COMBINE_PER_KEY_PRECOMBINE.\n urn,\n payload=transform.spec.payload),\n inputs=transform.inputs,\n outputs={'out': precombined_pcoll_id},\n environment_id=transform.environment_id))\n\n yield make_stage(\n stage,\n beam_runner_api_pb2.PTransform(\n unique_name=transform.unique_name + '/Group',\n spec=beam_runner_api_pb2.FunctionSpec(\n urn=common_urns.primitives.GROUP_BY_KEY.urn),\n inputs={'in': precombined_pcoll_id},\n outputs={'out': grouped_pcoll_id}))\n\n yield make_stage(\n stage,\n beam_runner_api_pb2.PTransform(\n unique_name=transform.unique_name + '/Merge',\n spec=beam_runner_api_pb2.FunctionSpec(\n urn=common_urns.combine_components.\n COMBINE_PER_KEY_MERGE_ACCUMULATORS.urn,\n payload=transform.spec.payload),\n inputs={'in': grouped_pcoll_id},\n outputs={'out': merged_pcoll_id},\n environment_id=transform.environment_id))\n\n yield make_stage(\n stage,\n beam_runner_api_pb2.PTransform(\n unique_name=transform.unique_name + '/ExtractOutputs',\n spec=beam_runner_api_pb2.FunctionSpec(\n urn=common_urns.combine_components.\n COMBINE_PER_KEY_EXTRACT_OUTPUTS.urn,\n payload=transform.spec.payload),\n inputs={'in': merged_pcoll_id},\n outputs=transform.outputs,\n environment_id=transform.environment_id))\n\n def unlifted_stages(stage):\n transform = stage.transforms[0]\n for sub in transform.subtransforms:\n yield make_stage(stage, context.components.transforms[sub])\n\n for stage in stages:\n transform = only_transform(stage.transforms)\n if transform.spec.urn == common_urns.composites.COMBINE_PER_KEY.urn:\n expansion = lifted_stages if can_lift(transform) else unlifted_stages\n for substage in expansion(stage):\n yield substage\n else:\n yield stage","def RunStages(self):\n self._RunStage(build_stages.InitSDKStage)\n self.RunSetupBoard()\n self._RunStage(report_stages.RefreshPackageStatusStage)","def _update_stages(self, stages: Set[str], deployment_id: Optional[str]) -> Set[str]:\n prev_dep_ids = set()\n for stage in stages:\n # Collects previous deployment IDs to clean up\n response_get = cast(Dict, self._api_client.get_stage(restApiId=self._api_physical_id, stageName=stage))\n prev_dep_id = response_get.get(\"deploymentId\")\n if prev_dep_id:\n prev_dep_ids.add(cast(str, prev_dep_id))\n\n # Updates the stage with newest deployment\n LOG.debug(\"%sTrying to update the stage %s through client\", self.log_prefix, stage)\n response_upd = cast(\n Dict,\n self._api_client.update_stage(\n restApiId=self._api_physical_id,\n stageName=stage,\n patchOperations=[{\"op\": \"replace\", \"path\": \"/deploymentId\", \"value\": deployment_id}],\n ),\n )\n LOG.debug(\"%sUpdate Stage Result: %s\", self.log_prefix, response_upd)\n\n # Flushes the cache so that the changes are calleable\n self._api_client.flush_stage_cache(restApiId=self._api_physical_id, stageName=stage)\n self._api_client.flush_stage_authorizers_cache(restApiId=self._api_physical_id, stageName=stage)\n return prev_dep_ids","def fix_flatten_coders(\n stages, pipeline_context, identity_urn=bundle_processor.IDENTITY_DOFN_URN):\n # type: (Iterable[Stage], TransformContext, str) -> Iterator[Stage]\n pcollections = pipeline_context.components.pcollections\n for stage in stages:\n transform = only_element(stage.transforms)\n if transform.spec.urn == common_urns.primitives.FLATTEN.urn:\n output_pcoll_id = only_element(transform.outputs.values())\n output_coder_id = pcollections[output_pcoll_id].coder_id\n for local_in, pcoll_in in list(transform.inputs.items()):\n if pcollections[pcoll_in].coder_id != output_coder_id:\n # Flatten requires that all its inputs be materialized with the\n # same coder as its output. Add stages to transcode flatten\n # inputs that use different coders.\n transcoded_pcollection = unique_name(\n pcollections,\n transform.unique_name + '/Transcode/' + local_in + '/out')\n transcode_name = unique_name(\n pipeline_context.components.transforms,\n transform.unique_name + '/Transcode/' + local_in)\n yield Stage(\n transcode_name,\n [\n beam_runner_api_pb2.PTransform(\n unique_name=transcode_name,\n inputs={local_in: pcoll_in},\n outputs={'out': transcoded_pcollection},\n spec=beam_runner_api_pb2.FunctionSpec(urn=identity_urn),\n environment_id=transform.environment_id)\n ],\n downstream_side_inputs=frozenset(),\n must_follow=stage.must_follow)\n pcollections[transcoded_pcollection].CopyFrom(pcollections[pcoll_in])\n pcollections[transcoded_pcollection].unique_name = (\n transcoded_pcollection)\n pcollections[transcoded_pcollection].coder_id = output_coder_id\n transform.inputs[local_in] = transcoded_pcollection\n\n yield stage","def do_actions(self, stage):\n actions = itertools.chain(*[self.get_control_actions(control)\n for control in self.controls.get(stage, [])])\n messages = []\n\n for action, args in actions:\n if action == 'done':\n # end execution of actions\n break\n elif action == 'gameover':\n # end execution of actions and end the game\n self.game_over = True\n break\n elif action == 'replace':\n # replace the input command and restart execution\n self.state.current_turn.replace_command(' '.join(args))\n return self.do_actions(stage)\n\n # treat name=value args as keyword args\n pargs, kwargs = [], {}\n for arg in args:\n if '=' in arg:\n kwargs.update([arg.split('=', 1)])\n else:\n pargs.append(arg)\n\n msgs = getattr(self.actions, action)(*pargs, **kwargs)\n if isinstance(msgs, str):\n messages.append(msgs)\n elif msgs is not None:\n messages.extend(msgs)\n\n return messages","def _stage_inputs(stage, phase):\n\n def arrayify(martian_io_field):\n \"\"\"Convert the type of a Martian input field to an array of that type.\n\n This is necessary for the join phase.\n \"\"\"\n return mro_parser.MartianIOField(\n martian_io_field.modifier,\n martian_io_field.type + '[]',\n martian_io_field.name,\n martian_io_field.help)\n\n def add_tag_to_name(martian_io_field, tag):\n return mro_parser.MartianIOField(\n martian_io_field.modifier,\n martian_io_field.type,\n martian_io_field.name + '_' + tag,\n martian_io_field.help)\n\n if phase == 'split':\n return stage.inputs\n elif phase == 'main':\n return stage.inputs + stage.splits\n elif phase == 'join':\n # The inputs to join are arrays of the split and output fields since it's pulling\n # together outputs of multiple main steps.\n # Also, \"split\" and \"output\" need to be added to the field names or there are collisions\n return stage.inputs + \\\n [add_tag_to_name(arrayify(s), \"split\") for s in stage.splits] + \\\n [add_tag_to_name(arrayify(s), \"output\") for s in stage.outputs]","def __iter__( self ):\n assert isinstance( self._env, Env )\n assert isinstance( self._steps, list )\n\n return iter( self._steps )","def get_step_actions(self):\n return self.actor(tf.numpy_function(self.get_states, [], self.states[0].dtype))","def map(\n f: typing.Callable,\n stage: Stage = pypeln_utils.UNDEFINED,\n workers: int = None,\n maxsize: int = None,\n timeout: float = 0,\n on_start: typing.Callable = None,\n on_done: typing.Callable = None,\n) -> Stage:\n\n if pypeln_utils.is_undefined(stage):\n return pypeln_utils.Partial(\n lambda stage: map(\n f,\n stage=stage,\n workers=workers,\n maxsize=maxsize,\n timeout=timeout,\n on_start=on_start,\n on_done=on_done,\n )\n )\n\n stage = to_stage(stage)\n\n return Map(\n f=f, on_start=on_start, on_done=on_done, timeout=timeout, dependencies=[stage],\n )","def middleware(store: Store, action: Action) -> Iterable[Action]:\n kind = action[\"kind\"]\n if kind == ON_BUTTON_GROUP:\n payload = action[\"payload\"]\n yield set_active(payload[\"row_index\"], payload[\"active\"])\n elif kind == ON_SAVE:\n if get_mode(store.state) == \"edit\":\n index = edit_index(store.state)\n else:\n index = next_index(store.state)\n yield save_layer(index, action[\"payload\"])\n else:\n yield action","def N_stages(self):\n return 5","def stage(self, stage_id):\r\n return pipelines.Stage(self, stage_id)","def move_stages(self,positions):\n for ind_move,pos in enumerate(positions): #move all activated modules to specified positions\n #self.move_modules[ind_move].move_Abs(pos)\n self.move_modules_commands[ind_move].emit(utils.ThreadCommand(command=\"move_Abs\", attributes=[pos]))","def main() -> co.Parallel:\n actors = [\"Oprah Winfrey\", \"Kate Mara\", \"Don Cheadle\", \"Dwayne Johnson\"]\n root = co.Parallel(image=_get_image())\n for actor in actors:\n root[actor] = co.Lazy(\n f\"python pipeline.py all_by_actor '{actor}'\"\n )\n return root","def solve_all_stages(stages, objects_dic, predicates_rules, gstate, actionlist, problem_dic):\n\n result = {}\n result[\"visualStages\"] = []\n for stage in stages:\n\n stage_dic = {}\n object_dic_copy = copy.deepcopy(objects_dic)\n predicates = stage[\"items\"]\n sorted_predicates = priority(predicates, predicates_rules)\n\n # For hanoi problem, reset each stage\n # For logistics problem, reset each stage\n for fname in gstate[\"reset_function\"]:\n gstate[fname] = {}\n solvepredicates(sorted_predicates, object_dic_copy, predicates_rules, gstate)\n stage_dic[\"visualSprites\"] = object_dic_copy\n if \"stageName\" not in stage:\n stage_dic[\"stageName\"] = \"Inital Stage\"\n stage_dic[\"stageInfo\"] = \"No step information\"\n\n else:\n stage_dic[\"stageName\"] = stage[\"stageName\"]\n stage_dic[\"stageInfo\"] = stage[\"stageInfo\"]\n\n result[\"visualStages\"].append(stage_dic)\n\n result[\"subgoals\"] = Subgoal.get_subgoal(stages, problem_dic[1]['goal'].copy(), actionlist.copy())\n\n return result","def act(self, states: np.ndarray, eps: float = 0.0) -> List[np.ndarray]:\n actions = [\n agent.act(state.reshape(-1, 1).T, eps)\n for agent, state in zip(self.agents, states)\n ]\n return actions","def transduce(self,inputs):\n self.start()\n return [self.step(inp) for inp in inputs]","def flat_map(\n f: typing.Callable,\n stage: Stage = pypeln_utils.UNDEFINED,\n workers: int = 1,\n maxsize: int = 0,\n timeout: float = 0,\n on_start: typing.Callable = None,\n on_done: typing.Callable = None,\n) -> Stage:\n\n if pypeln_utils.is_undefined(stage):\n return pypeln_utils.Partial(\n lambda stage: flat_map(\n f,\n stage=stage,\n workers=workers,\n maxsize=maxsize,\n timeout=timeout,\n on_start=on_start,\n on_done=on_done,\n )\n )\n\n stage = to_stage(stage)\n\n return FlatMap(\n f=f, on_start=on_start, on_done=on_done, timeout=timeout, dependencies=[stage],\n )","def impulse_to_input(stages, pipeline_context):\n # type: (Iterable[Stage], TransformContext) -> Iterator[Stage]\n for stage in stages:\n for transform in list(stage.transforms):\n if transform.spec.urn == common_urns.primitives.IMPULSE.urn:\n stage.transforms.remove(transform)\n stage.transforms.append(\n beam_runner_api_pb2.PTransform(\n unique_name=transform.unique_name,\n spec=beam_runner_api_pb2.FunctionSpec(\n urn=bundle_processor.DATA_INPUT_URN,\n payload=IMPULSE_BUFFER),\n outputs=transform.outputs))\n yield stage","def make_stage(num_blocks, input_channels, output_channels, stride, expand_ratio, norm, activation):\n blocks = []\n blocks.append(\n InvertedResBlock(input_channels, output_channels, stride=stride, expand_ratio=expand_ratio,\n norm=norm, activation=activation, use_shortcut=False)\n )\n for i in range(num_blocks - 1):\n blocks.append(\n InvertedResBlock(output_channels, output_channels, stride=1, expand_ratio=expand_ratio,\n norm=norm, activation=activation)\n )\n\n return blocks","def map(\n f, stage=pypeln_utils.UNDEFINED, workers=1, maxsize=0, on_start=None, on_done=None\n):\n\n if utils.is_undefined(stage):\n return utils.Partial(\n lambda stage: map(\n f,\n stage,\n workers=workers,\n maxsize=maxsize,\n on_start=on_start,\n on_done=on_done,\n )\n )\n\n stage = _to_stage(stage)\n\n return _Stage(\n worker_constructor=WORKER,\n workers=workers,\n maxsize=maxsize,\n on_start=on_start,\n on_done=on_done,\n target=_map,\n args=(f,),\n dependencies=[stage],\n )","def reset_stages(self) -> List[str]:\n return self._reset_stages","def _read_group_stage_ids(self, stages, domain, order):\n stage_ids = self.env['salon.stage'].search([])\n return stage_ids","def pipeline(self, *funcs) -> \"fn\":\n return self._mod.pipeline(self, *funcs)","def greedily_fuse(stages, pipeline_context):\n # type: (Iterable[Stage], TransformContext) -> FrozenSet[Stage]\n producers_by_pcoll = {} # type: Dict[str, Stage]\n consumers_by_pcoll = collections.defaultdict(\n list) # type: DefaultDict[str, List[Stage]]\n\n # Used to always reference the correct stage as the producer and\n # consumer maps are not updated when stages are fused away.\n replacements = {} # type: Dict[Stage, Stage]\n\n def replacement(s):\n old_ss = []\n while s in replacements:\n old_ss.append(s)\n s = replacements[s]\n for old_s in old_ss[:-1]:\n replacements[old_s] = s\n return s\n\n def fuse(producer, consumer):\n fused = producer.fuse(consumer, pipeline_context)\n replacements[producer] = fused\n replacements[consumer] = fused\n\n # First record the producers and consumers of each PCollection.\n for stage in stages:\n for transform in stage.transforms:\n for input in transform.inputs.values():\n consumers_by_pcoll[input].append(stage)\n for output in transform.outputs.values():\n producers_by_pcoll[output] = stage\n\n # Now try to fuse away all pcollections.\n for pcoll, producer in producers_by_pcoll.items():\n write_pcoll = None\n for consumer in consumers_by_pcoll[pcoll]:\n producer = replacement(producer)\n consumer = replacement(consumer)\n # Update consumer.must_follow set, as it's used in can_fuse.\n consumer.must_follow = frozenset(\n replacement(s) for s in consumer.must_follow)\n if producer.can_fuse(consumer, pipeline_context):\n fuse(producer, consumer)\n else:\n # If we can't fuse, do a read + write.\n pipeline_context.length_prefix_pcoll_coders(pcoll)\n buffer_id = create_buffer_id(pcoll)\n if write_pcoll is None:\n write_pcoll = Stage(\n pcoll + '/Write',\n [\n beam_runner_api_pb2.PTransform(\n unique_name=pcoll + '/Write',\n inputs={'in': pcoll},\n spec=beam_runner_api_pb2.FunctionSpec(\n urn=bundle_processor.DATA_OUTPUT_URN,\n payload=buffer_id))\n ],\n downstream_side_inputs=producer.downstream_side_inputs)\n fuse(producer, write_pcoll)\n if consumer.has_as_main_input(pcoll):\n read_pcoll = Stage(\n pcoll + '/Read',\n [\n beam_runner_api_pb2.PTransform(\n unique_name=pcoll + '/Read',\n outputs={'out': pcoll},\n spec=beam_runner_api_pb2.FunctionSpec(\n urn=bundle_processor.DATA_INPUT_URN,\n payload=buffer_id))\n ],\n downstream_side_inputs=consumer.downstream_side_inputs,\n must_follow=frozenset([write_pcoll]))\n fuse(read_pcoll, consumer)\n else:\n consumer.must_follow = union(\n consumer.must_follow, frozenset([write_pcoll]))\n\n # Everything that was originally a stage or a replacement, but wasn't\n # replaced, should be in the final graph.\n final_stages = frozenset(stages).union(list(replacements.values()))\\\n .difference(list(replacements))\n\n for stage in final_stages:\n # Update all references to their final values before throwing\n # the replacement data away.\n stage.must_follow = frozenset(replacement(s) for s in stage.must_follow)\n # Two reads of the same stage may have been fused. This is unneeded.\n stage.deduplicate_read()\n return final_stages","def stepregistry():\n registry = StepRegistry()\n yield registry","def _stage(self):\n\n pass","def rollout_algorithm(self):\n states, actions = [], []\n done = False\n state = self.env._reset()\n while not done:\n supervisor_label = self.supervisor.eval_policy(self.env)\n action_packed = [supervisor_label, 1]\n next_state, _, done, _ = self.env._step(action_packed)\n\n states.append(state)\n actions.append(supervisor_label)\n state = next_state\n\n return states, actions","def process(self, pipelines: List[Pipeline]) -> Iterator[cat.Catalog]:\n merged: Optional[cat.Catalog] = None\n logger.debug(f'merge entering process with {len(pipelines)} pipelines')\n for pipeline in pipelines:\n catalog = next(pipeline.process(None))\n merged = self._merge_catalog(merged, catalog)\n yield merged","def increment_stage_in_forms(forms):\n for index, form in enumerate(forms.all(), 1):\n form.stage = index\n form.save(update_fields=['stage'])","def Move_Stage(self):\n for i in range(3):\n if self.set_pos[i] == 0:\n continue\n print \"Moving stage %s by %s steps\\n\"%(self.POS_NAME[i], self.set_pos[i])\n self.ser.write('F,C'+self.STEPPER_NAME[i]+str(self.set_pos[i])+',R')\n time.sleep(0.5)\n time.sleep(0.5)\n return","def to_iterable(\n stage: Stage = pypeln_utils.UNDEFINED, maxsize: int = 0\n) -> typing.Iterable:\n\n if pypeln_utils.is_undefined(stage):\n return pypeln_utils.Partial(lambda stage: to_iterable(stage, maxsize=maxsize))\n\n if isinstance(stage, Stage):\n iterable = stage.to_iterable(maxsize=maxsize)\n else:\n iterable = stage\n\n return iterable","def __call__(self, states, agent_states=None):\n if agent_states is None:\n agent_states = [None] * len(states)\n if self.preprocessor is not None:\n states = self.preprocessor(states)\n probs_v = self.model(states)\n if self.apply_softmax:\n probs_v = tf.nn.softmax(probs_v, axis=1)\n probs = probs_v.numpy()\n actions = self.action_selector(probs)\n return np.array(actions), agent_states","def arun(*stages, **kwargs):\n loglevel = kwargs.pop(\"loglevel\", \"INFO\")\n alog.setLevel(getattr(logging, loglevel))\n STAGE_ID = os.environ.get(\"STAGE_ID\")\n \n def run_presub(stages):\n \"\"\"Execute any pre-submission stages.\"\"\"\n if is_presub(stages[0]):\n # Pop the first stage\n stage, stages = stages[0], stages[1:]\n # Execute it only if Python was invoked normally, not in a queue job\n if STAGE_ID is None:\n msg = \"Only the first stage can execute on the login node\"\n assert all([not is_presub(i) for i in stages]), msg\n alog.info(\"Pre-submit stage starting: %s\", stage)\n stage()\n alog.info(\"Pre-submit stage done: %s\", stage)\n return stages\n \n global ID # pylint:disable=W0603\n testID = kwargs.pop(\"testID\", None)\n if testID is not None:\n alog.info(\"Calling arun() with testID=%s\", testID)\n stages = run_presub(stages)\n ID = testID\n for STAGE_ID in range(len(stages)):\n os.environ[\"STAGE_ID\"] = str(STAGE_ID)\n stages[STAGE_ID]()\n # arun(*stages, loglevel=loglevel, testID=testID)\n return\n \n alog.debug(\"ID=%s, STAGE_ID=%s\", ID, STAGE_ID)\n assert not kwargs, \"Undefined keyword arguments: %s\" % kwargs\n assert NID is not None, \"Need to call set_NID() before arun()\"\n stages = run_presub(stages)\n # Return early if there are no stages to submit jobs for\n if not stages:\n return\n if STAGE_ID is None: # not invoked as queue job, so submit jobs\n for this, next_ in zip(stages, stages[1:]):\n if is_par(this) and is_par(next_):\n msg = \"Consecutive stages %s and %s are both parallel.\"\n msg += \" qsub dependencies cannot handle this case.\"\n msg += \" Workaround: insert a serial arrayjob.wait.\"\n raise AssertionError(msg % (this, next_))\n on_opt = \"-W depend=on:%s\" % (NID / ppn)\n jobid = {} # storing receipts from qsub\n jobdep = defaultdict(list) # accumulating dependencies\n # boolean vector: whether each stage follows a parallel one\n afterpar = np.array([(STAGE_ID > 0) and is_par(stages[STAGE_ID - 1]) \n for STAGE_ID, stage in enumerate(stages)])\n # array of (STAGE_ID, stage) tuples\n istage = np.array(list(enumerate(stages)))\n for STAGE_ID, stage in istage[afterpar]:\n # submit each stage that follows a parallel one\n jobid[STAGE_ID] = submit(STAGE_ID, on_opt, *opt[\"qopt\"][stage])\n # prepare for the parallel one to depend on the one just submitted\n jobdep[STAGE_ID - 1].append(\"beforeok:%s\" % jobid[STAGE_ID])\n for STAGE_ID, stage in istage[~afterpar]:\n # submit each stage that does not follow a parallel one\n if STAGE_ID > 0:\n # prepare for this stage to depend on the previous one\n jobdep[STAGE_ID].append(\"afterok:%s\" % jobid[STAGE_ID - 1])\n dep = jobdep[STAGE_ID]\n dep_opt = (\"-W depend=\" + \",\".join(dep)) if dep else \"\"\n arr_opt = array_opt(NID / ppn) if is_par(stage) else \"\"\n jobid[STAGE_ID] = submit(STAGE_ID, dep_opt, arr_opt, \n *opt[\"qopt\"][stage])\n else: # invoked as queue job\n stage = stages[int(STAGE_ID)]\n alog.info(\"Stage starting: %s\", stage)\n stage()\n alog.info(\"Stage done: %s\", stage)","def job_gen(self):\n for job in self.task_manager.jobs(self.time_frame):\n if job.status == TASK.SCHEDULED:\n yield job","def populate_data_channel_coders(stages, pipeline_context):\n # type: (Iterable[Stage], TransformContext) -> Iterable[Stage]\n for stage in stages:\n for transform in stage.transforms:\n if transform.spec.urn in (bundle_processor.DATA_INPUT_URN,\n bundle_processor.DATA_OUTPUT_URN):\n if transform.spec.urn == bundle_processor.DATA_INPUT_URN:\n sdk_pcoll_id = only_element(transform.outputs.values())\n else:\n sdk_pcoll_id = only_element(transform.inputs.values())\n pipeline_context.add_data_channel_coder(sdk_pcoll_id)\n\n return stages","def get_processes():\n yield from psutil.process_iter()","def _create_jobs(self, actions, lockable_objects, proc_uuid=None):\n procedures = set()\n jobs = []\n for number in range(0, len(actions)):\n job = self._create_job(\n actions[number], lockable_objects, proc_uuid\n )\n jobs.append(job)\n procedures.add(job.procedure)\n return list(procedures), jobs","def stage(self, stage: osbuild.Stage):","def f_chains(self) -> List[Callable[[], Chain]]:\n return [delayed_run_chain() for _ in range(self.n_chains)]","def each_step(graph):\n\n steps = graph.topological_sort()\n steps.reverse()\n\n for step in steps:\n deps = graph.downstream(step.name)\n yield (step, deps)","def __init__(self):\n self._pipe = []\n self._group = None\n stages = ['on', 'off', 'color', 'transition', 'flash', 'callback',\n 'repeat', 'brightness', 'wait', 'temperature', 'white',\n 'white_up', 'white_down', 'red_up', 'red_down',\n 'green_up', 'green_down', 'blue_up', 'blue_down',\n 'night_light', 'link', 'unlink']\n for name in stages:\n self._add_stage(name)","def execution_order(phases):\r\n\r\n # Its key that we process phase dependencies depth first to maintain initial phase ordering as\r\n # passed in when phase graphs are dependency disjoint. A breadth first sort could mix next\r\n # order executions and violate the implied intent of the passed in phase ordering.\r\n\r\n processed = set()\r\n\r\n def order(_phases):\r\n for phase in _phases:\r\n if phase not in processed:\r\n processed.add(phase)\r\n for goal in phase.goals():\r\n for dep in order(goal.dependencies):\r\n yield dep\r\n yield phase\r\n\r\n for ordered in order(phases):\r\n yield ordered","def evaluate_batch(self, pipelines):","def _iter_transitions_all_(self):\n for state in self.iter_states():\n for t in state.transitions:\n yield t","def sequence_macros(macros):\n\n def foo():\n for m in macros:\n m()\n\n return foo","def iterate(input,\n actions={},\n default_action=None,\n input_compression='ext',\n depth=1,\n parameter_puns=True,\n **more_actions):\n\n actions = dict(actions, **more_actions)\n if parameter_puns:\n actions = {k: __parameter_puns_decorator(v)\n for k, v in six.iteritems(actions)}\n if default_action:\n default_action = __parameter_puns_decorator(default_action)\n\n def has_action(tag):\n return tag in actions or default_action\n\n stks = []\n trail = []\n with __compressed_open(input, 'r', input_compression) as f:\n context = iter(ET.iterparse(f, events=(\"start\", \"end\")))\n for evt, elem in context:\n if evt == 'start':\n trail.append(Element(elem, trail[::-1]))\n if len(trail) > depth and has_action(elem.tag):\n stks.append([])\n elif evt == 'end':\n element = trail.pop()\n if len(trail) >= depth and has_action(elem.tag):\n element._finalize(elem.text, elem.tail, stks.pop())\n res = actions.get(elem.tag, default_action)(element)\n if not inspect.isgenerator(res):\n res = (res,)\n for x in res:\n if x is not None:\n if len(stks) > 0:\n stks[-1].append(x)\n else:\n yield x\n elem.clear()","def __call__(self, *pipeline_factories, exceptions=None, wait=True):\n return self.run(*pipeline_factories, exceptions=exceptions, wait=wait)","def steps(self):\n\n if not os.path.exists(self.build_path):\n raise exceptions.ProjectNotBuildError()\n\n steps = []\n for filename in os.listdir(self.build_path):\n match = re.match(r'(\\d{4})_(.*)\\.json', filename)\n if not match:\n continue\n\n with open(os.path.join(self.build_path, filename), 'r') as f:\n template = json.loads(f.read())\n\n template_type = 'custom' if '_type' in template else 'cloudformation'\n steps.append((int(match.groups()[0]), match.groups()[1], filename, template_type))\n steps = sorted(steps, key=lambda x: x[0])\n\n return steps","def sequential(*layers: tp.Callable[..., tp.Any]) -> tp.Callable[..., tp.Any]:\n\n def call(inputs, *args, **kwargs):\n\n out = inputs\n for i, layer in enumerate(layers):\n if i == 0:\n out = layer(out, *args, **kwargs)\n else:\n out = layer(out)\n\n if not isinstance(layer, module.Module):\n if hooks.summaries_active():\n name = utils.get_name(layer)\n\n path = module.get_module_path()\n path = path if path is not None else ()\n\n hooks.add_summary(path + (name,), layer, out)\n return out\n\n return call","def get_callbacks(self, stage=None):\n if stage:\n return self._registry.get(stage, [])\n else:\n callbacks = []\n for stage_callbacks in self._registry.values():\n callbacks += stage_callbacks\n return callbacks","def step(self, closure=None):\n loss = None\n if closure is not None:\n loss = closure()\n\n for group in self.param_groups:\n lr = group[\"lr\"]\n weight_decay = group[\"weight_decay\"]\n momentum = group['momentum']\n dampening = group[\"dampening\"]\n nesterov = group[\"nesterov\"]\n maximize = group[\"maximize\"]\n\n for p in group[\"params\"]:\n if p.grad is None:\n continue\n\n grad = p.grad\n\n if grad.is_sparse:\n raise RuntimeError(\"SGD does not support sparse gradients.\")\n\n grad = grad if not maximize else -grad\n\n if weight_decay != 0:\n grad = grad.add(p, alpha=weight_decay)\n\n if momentum != 0:\n buf = self.state[p][\"momentum_buffer\"]\n\n buf.mul_(momentum).add_(grad, alpha=1.0 - dampening)\n\n if nesterov:\n grad.add_(buf, alpha=momentum)\n else:\n grad = buf\n\n p.add_(-lr * grad)\n\n return loss"],"string":"[\n \"def stages(self):\\n return StageManager(session=self._session)\",\n \"def stages(self):\\r\\n return pipelines.Stages(self)\",\n \"def run(stages, maxsize=0):\\n\\n if isinstance(stages, list) and len(stages) == 0:\\n raise ValueError(\\\"Expected at least 1 stage to run\\\")\\n\\n elif isinstance(stages, list):\\n stage = concat(stages, maxsize=maxsize)\\n\\n else:\\n stage = stages\\n\\n stage = to_iterable(stage, maxsize=maxsize)\\n\\n for _ in stages:\\n pass\",\n \"def gen_stage_loop(cls, _opts, tests, put_next_stage, _put_result_stage):\\n for test in tests:\\n put_next_stage(test)\",\n \"def list_stages():\\n for name in Manager.STAGES:\\n click.echo('{}'.format(name))\",\n \"def run(stages: typing.List[Stage], maxsize: int = 0) -> None:\\n\\n if isinstance(stages, list) and len(stages) == 0:\\n raise ValueError(\\\"Expected at least 1 stage to run\\\")\\n\\n elif isinstance(stages, list):\\n stage = concat(stages, maxsize=maxsize)\\n\\n else:\\n stage = stages\\n\\n stage = to_iterable(stage, maxsize=maxsize)\\n\\n for _ in stages:\\n pass\",\n \"def stages(self):\\n return self._stages\",\n \"def stages(self):\\n return self._stages\",\n \"def stage_list(args):\\n\\n for stage in args.stages:\\n print stage\",\n \"def process_stages(self) -> List[str]:\\n return self._process_stages\",\n \"def stages() -> List[Tuple[str, str]]:\\n return [TestStatus.preparation, TestStatus.testing, TestStatus.completed]\",\n \"def expand_gbk(stages, pipeline_context):\\n # type: (Iterable[Stage], TransformContext) -> Iterator[Stage]\\n for stage in stages:\\n transform = only_transform(stage.transforms)\\n if transform.spec.urn == common_urns.primitives.GROUP_BY_KEY.urn:\\n for pcoll_id in transform.inputs.values():\\n pipeline_context.length_prefix_pcoll_coders(pcoll_id)\\n for pcoll_id in transform.outputs.values():\\n if pipeline_context.use_state_iterables:\\n pipeline_context.components.pcollections[\\n pcoll_id].coder_id = pipeline_context.with_state_iterables(\\n pipeline_context.components.pcollections[pcoll_id].coder_id)\\n pipeline_context.length_prefix_pcoll_coders(pcoll_id)\\n\\n # This is used later to correlate the read and write.\\n transform_id = stage.name\\n if transform != pipeline_context.components.transforms.get(transform_id):\\n transform_id = unique_name(\\n pipeline_context.components.transforms, stage.name)\\n pipeline_context.components.transforms[transform_id].CopyFrom(transform)\\n grouping_buffer = create_buffer_id(transform_id, kind='group')\\n gbk_write = Stage(\\n transform.unique_name + '/Write',\\n [\\n beam_runner_api_pb2.PTransform(\\n unique_name=transform.unique_name + '/Write',\\n inputs=transform.inputs,\\n spec=beam_runner_api_pb2.FunctionSpec(\\n urn=bundle_processor.DATA_OUTPUT_URN,\\n payload=grouping_buffer))\\n ],\\n downstream_side_inputs=frozenset(),\\n must_follow=stage.must_follow)\\n yield gbk_write\\n\\n yield Stage(\\n transform.unique_name + '/Read',\\n [\\n beam_runner_api_pb2.PTransform(\\n unique_name=transform.unique_name + '/Read',\\n outputs=transform.outputs,\\n spec=beam_runner_api_pb2.FunctionSpec(\\n urn=bundle_processor.DATA_INPUT_URN,\\n payload=grouping_buffer))\\n ],\\n downstream_side_inputs=stage.downstream_side_inputs,\\n must_follow=union(frozenset([gbk_write]), stage.must_follow))\\n else:\\n yield stage\",\n \"def job_stages(self) -> Sequence['outputs.JobStagesResponse']:\\n return pulumi.get(self, \\\"job_stages\\\")\",\n \"def job_stages(self) -> Sequence['outputs.JobStagesResponse']:\\n return pulumi.get(self, \\\"job_stages\\\")\",\n \"def job_stages(self) -> Sequence['outputs.JobStagesResponse']:\\n return pulumi.get(self, \\\"job_stages\\\")\",\n \"def stages(self, stages):\\n if stages is None:\\n self._stages = None\\n else:\\n self._stages = stages if isinstance(stages, list) else [stages] * len(self.pidevice.allaxes)\\n debug('ControllerStartup.stages = %s', itemstostr(self._stages))\",\n \"def set_stages(self, stages):\\n\\n if not isinstance(stages, list) or not all([issubclass(type(x), Stage) for x in stages]):\\n raise ValueError(\\\"stages must be a list of Stages's only!\\\")\\n\\n self.stages = stages\\n\\n return self\",\n \"def extract_impulse_stages(stages, pipeline_context):\\n # type: (Iterable[Stage], TransformContext) -> Iterator[Stage]\\n for stage in stages:\\n for transform in list(stage.transforms):\\n if transform.spec.urn == common_urns.primitives.IMPULSE.urn:\\n stage.transforms.remove(transform)\\n yield Stage(\\n transform.unique_name,\\n transforms=[transform],\\n downstream_side_inputs=stage.downstream_side_inputs,\\n must_follow=stage.must_follow,\\n parent=stage.parent)\\n\\n if stage.transforms:\\n yield stage\",\n \"def sort(self):\\n for _ in self.stage1():\\n yield\\n for _ in self.stage2():\\n yield\",\n \"def sort_stages(stages, pipeline_context):\\n # type: (Iterable[Stage], TransformContext) -> List[Stage]\\n all_stages = set(stages)\\n seen = set() # type: Set[Stage]\\n ordered = []\\n\\n producers = {\\n pcoll: stage\\n for stage in all_stages for t in stage.transforms\\n for pcoll in t.outputs.values()\\n }\\n\\n def process(stage):\\n if stage not in seen:\\n seen.add(stage)\\n if stage not in all_stages:\\n return\\n for prev in stage.must_follow:\\n process(prev)\\n stage_outputs = set(\\n pcoll for transform in stage.transforms\\n for pcoll in transform.outputs.values())\\n for transform in stage.transforms:\\n for pcoll in transform.inputs.values():\\n if pcoll not in stage_outputs:\\n process(producers[pcoll])\\n ordered.append(stage)\\n\\n for stage in stages:\\n process(stage)\\n return ordered\",\n \"def expand_sdf(stages, context):\\n # type: (Iterable[Stage], TransformContext) -> Iterator[Stage]\\n for stage in stages:\\n transform = only_transform(stage.transforms)\\n if transform.spec.urn == common_urns.primitives.PAR_DO.urn:\\n\\n pardo_payload = proto_utils.parse_Bytes(\\n transform.spec.payload, beam_runner_api_pb2.ParDoPayload)\\n\\n if pardo_payload.restriction_coder_id:\\n\\n def copy_like(protos, original, suffix='_copy', **kwargs):\\n if isinstance(original, str):\\n key = original\\n original = protos[original]\\n else:\\n key = 'component'\\n new_id = unique_name(protos, key + suffix)\\n protos[new_id].CopyFrom(original)\\n proto = protos[new_id]\\n for name, value in kwargs.items():\\n if isinstance(value, dict):\\n getattr(proto, name).clear()\\n getattr(proto, name).update(value)\\n elif isinstance(value, list):\\n del getattr(proto, name)[:]\\n getattr(proto, name).extend(value)\\n elif name == 'urn':\\n proto.spec.urn = value\\n elif name == 'payload':\\n proto.spec.payload = value\\n else:\\n setattr(proto, name, value)\\n if 'unique_name' not in kwargs and hasattr(proto, 'unique_name'):\\n proto.unique_name = unique_name(\\n {p.unique_name\\n for p in protos.values()},\\n original.unique_name + suffix)\\n return new_id\\n\\n def make_stage(base_stage, transform_id, extra_must_follow=()):\\n # type: (Stage, str, Iterable[Stage]) -> Stage\\n transform = context.components.transforms[transform_id]\\n return Stage(\\n transform.unique_name, [transform],\\n base_stage.downstream_side_inputs,\\n union(base_stage.must_follow, frozenset(extra_must_follow)),\\n parent=base_stage.name,\\n environment=base_stage.environment)\\n\\n main_input_tag = only_element(\\n tag for tag in transform.inputs.keys()\\n if tag not in pardo_payload.side_inputs)\\n main_input_id = transform.inputs[main_input_tag]\\n element_coder_id = context.components.pcollections[\\n main_input_id].coder_id\\n # Tuple[element, restriction]\\n paired_coder_id = context.add_or_get_coder_id(\\n beam_runner_api_pb2.Coder(\\n spec=beam_runner_api_pb2.FunctionSpec(\\n urn=common_urns.coders.KV.urn),\\n component_coder_ids=[\\n element_coder_id, pardo_payload.restriction_coder_id\\n ]))\\n # Tuple[Tuple[element, restriction], double]\\n sized_coder_id = context.add_or_get_coder_id(\\n beam_runner_api_pb2.Coder(\\n spec=beam_runner_api_pb2.FunctionSpec(\\n urn=common_urns.coders.KV.urn),\\n component_coder_ids=[\\n paired_coder_id,\\n context.add_or_get_coder_id(\\n # context can be None here only because FloatCoder does\\n # not have components\\n coders.FloatCoder().to_runner_api(None), # type: ignore\\n 'doubles_coder')\\n ]))\\n\\n paired_pcoll_id = copy_like(\\n context.components.pcollections,\\n main_input_id,\\n '_paired',\\n coder_id=paired_coder_id)\\n pair_transform_id = copy_like(\\n context.components.transforms,\\n transform,\\n unique_name=transform.unique_name + '/PairWithRestriction',\\n urn=common_urns.sdf_components.PAIR_WITH_RESTRICTION.urn,\\n outputs={'out': paired_pcoll_id})\\n\\n split_pcoll_id = copy_like(\\n context.components.pcollections,\\n main_input_id,\\n '_split',\\n coder_id=sized_coder_id)\\n split_transform_id = copy_like(\\n context.components.transforms,\\n transform,\\n unique_name=transform.unique_name + '/SplitAndSizeRestriction',\\n urn=common_urns.sdf_components.SPLIT_AND_SIZE_RESTRICTIONS.urn,\\n inputs=dict(transform.inputs, **{main_input_tag: paired_pcoll_id}),\\n outputs={'out': split_pcoll_id})\\n\\n reshuffle_stage = None\\n if common_urns.composites.RESHUFFLE.urn in context.known_runner_urns:\\n reshuffle_pcoll_id = copy_like(\\n context.components.pcollections,\\n main_input_id,\\n '_reshuffle',\\n coder_id=sized_coder_id)\\n reshuffle_transform_id = copy_like(\\n context.components.transforms,\\n transform,\\n unique_name=transform.unique_name + '/Reshuffle',\\n urn=common_urns.composites.RESHUFFLE.urn,\\n payload=b'',\\n inputs=dict(transform.inputs, **{main_input_tag: split_pcoll_id}),\\n outputs={'out': reshuffle_pcoll_id})\\n reshuffle_stage = make_stage(stage, reshuffle_transform_id)\\n else:\\n reshuffle_pcoll_id = split_pcoll_id\\n reshuffle_transform_id = None\\n\\n if context.is_drain:\\n truncate_pcoll_id = copy_like(\\n context.components.pcollections,\\n main_input_id,\\n '_truncate_restriction',\\n coder_id=sized_coder_id)\\n # Lengthprefix the truncate output.\\n context.length_prefix_pcoll_coders(truncate_pcoll_id)\\n truncate_transform_id = copy_like(\\n context.components.transforms,\\n transform,\\n unique_name=transform.unique_name + '/TruncateAndSizeRestriction',\\n urn=common_urns.sdf_components.TRUNCATE_SIZED_RESTRICTION.urn,\\n inputs=dict(\\n transform.inputs, **{main_input_tag: reshuffle_pcoll_id}),\\n outputs={'out': truncate_pcoll_id})\\n process_transform_id = copy_like(\\n context.components.transforms,\\n transform,\\n unique_name=transform.unique_name + '/Process',\\n urn=common_urns.sdf_components.\\n PROCESS_SIZED_ELEMENTS_AND_RESTRICTIONS.urn,\\n inputs=dict(\\n transform.inputs, **{main_input_tag: truncate_pcoll_id}))\\n else:\\n process_transform_id = copy_like(\\n context.components.transforms,\\n transform,\\n unique_name=transform.unique_name + '/Process',\\n urn=common_urns.sdf_components.\\n PROCESS_SIZED_ELEMENTS_AND_RESTRICTIONS.urn,\\n inputs=dict(\\n transform.inputs, **{main_input_tag: reshuffle_pcoll_id}))\\n\\n yield make_stage(stage, pair_transform_id)\\n split_stage = make_stage(stage, split_transform_id)\\n yield split_stage\\n if reshuffle_stage:\\n yield reshuffle_stage\\n if context.is_drain:\\n yield make_stage(\\n stage, truncate_transform_id, extra_must_follow=[split_stage])\\n yield make_stage(stage, process_transform_id)\\n else:\\n yield make_stage(\\n stage, process_transform_id, extra_must_follow=[split_stage])\\n\\n else:\\n yield stage\\n\\n else:\\n yield stage\",\n \"def create_and_optimize_stages(\\n pipeline_proto, # type: beam_runner_api_pb2.Pipeline\\n phases,\\n known_runner_urns, # type: FrozenSet[str]\\n use_state_iterables=False,\\n is_drain=False):\\n # type: (...) -> Tuple[TransformContext, List[Stage]]\\n pipeline_context = TransformContext(\\n pipeline_proto.components,\\n known_runner_urns,\\n use_state_iterables=use_state_iterables,\\n is_drain=is_drain)\\n\\n # Initial set of stages are singleton leaf transforms.\\n stages = list(\\n leaf_transform_stages(\\n pipeline_proto.root_transform_ids,\\n pipeline_proto.components,\\n known_composites=union(known_runner_urns, KNOWN_COMPOSITES)))\\n\\n # Apply each phase in order.\\n for phase in phases:\\n _LOGGER.info('%s %s %s', '=' * 20, phase, '=' * 20)\\n stages = list(phase(stages, pipeline_context))\\n _LOGGER.debug('%s %s' % (len(stages), [len(s.transforms) for s in stages]))\\n _LOGGER.debug('Stages: %s', [str(s) for s in stages])\\n\\n # Return the (possibly mutated) context and ordered set of stages.\\n return pipeline_context, stages\",\n \"def make_pipeline(steps):\\n def compose2(f, g):\\n return lambda x: g(f(x))\\n return functools.reduce(compose2, steps)\",\n \"def steps(self):\\n for step in self._steps:\\n yield step\",\n \"def stage_states(self) -> pulumi.Output[Sequence['outputs.ExecutionStageStateResponse']]:\\n return pulumi.get(self, \\\"stage_states\\\")\",\n \"def _add_stage(self, name):\\n def stage_func(self, *args, **kwargs):\\n \\\"\\\"\\\" Stage function.\\n\\n :param args: Positional arguments.\\n :param kwargs: Keyword arguments.\\n :return: Pipeline (for method chaining).\\n \\\"\\\"\\\"\\n self._pipe.append(Stage(name, args, kwargs))\\n return self\\n\\n setattr(Pipeline, name, stage_func)\",\n \"def sweep_stages(self) -> List[str]:\\n return self._sweep_stages\",\n \"def load_stages():\\n\\n def load_stages_from_dir(mro_dir):\\n \\\"\\\"\\\"Iterate over MRO file in a directory, parse them, and accumulate\\n their stages.\\n \\\"\\\"\\\"\\n stages = {}\\n for file_name in os.listdir(mro_dir):\\n if file_name.endswith(\\\".mro\\\"):\\n stages.update(mro_parser.get_stages(\\n os.path.join(mro_dir, file_name)))\\n return stages\\n\\n stages = {}\\n for mro_dir in os.environ[\\\"MROPATH\\\"].split(':'):\\n stages.update(load_stages_from_dir(mro_dir))\\n return stages\",\n \"def sink_flattens(stages, pipeline_context):\\n # type: (Iterable[Stage], TransformContext) -> Iterator[Stage]\\n # TODO(robertwb): Actually attempt to sink rather than always materialize.\\n # TODO(robertwb): Possibly fuse multi-input flattens into one of the stages.\\n for stage in fix_flatten_coders(stages,\\n pipeline_context,\\n common_urns.primitives.FLATTEN.urn):\\n transform = only_element(stage.transforms)\\n if (transform.spec.urn == common_urns.primitives.FLATTEN.urn and\\n len(transform.inputs) > 1):\\n # This is used later to correlate the read and writes.\\n buffer_id = create_buffer_id(transform.unique_name)\\n flatten_writes = [] # type: List[Stage]\\n for local_in, pcoll_in in transform.inputs.items():\\n flatten_write = Stage(\\n transform.unique_name + '/Write/' + local_in,\\n [\\n beam_runner_api_pb2.PTransform(\\n unique_name=transform.unique_name + '/Write/' + local_in,\\n inputs={local_in: pcoll_in},\\n spec=beam_runner_api_pb2.FunctionSpec(\\n urn=bundle_processor.DATA_OUTPUT_URN,\\n payload=buffer_id),\\n environment_id=transform.environment_id)\\n ],\\n downstream_side_inputs=frozenset(),\\n must_follow=stage.must_follow)\\n flatten_writes.append(flatten_write)\\n yield flatten_write\\n\\n yield Stage(\\n transform.unique_name + '/Read',\\n [\\n beam_runner_api_pb2.PTransform(\\n unique_name=transform.unique_name + '/Read',\\n outputs=transform.outputs,\\n spec=beam_runner_api_pb2.FunctionSpec(\\n urn=bundle_processor.DATA_INPUT_URN, payload=buffer_id),\\n environment_id=transform.environment_id)\\n ],\\n downstream_side_inputs=stage.downstream_side_inputs,\\n must_follow=union(frozenset(flatten_writes), stage.must_follow))\\n\\n else:\\n yield stage\",\n \"def concat(stages, maxsize=0):\\n\\n stages = [_to_stage(s) for s in stages]\\n\\n return _Stage(\\n worker_constructor=WORKER,\\n workers=1,\\n maxsize=maxsize,\\n on_start=None,\\n on_done=None,\\n target=_concat,\\n args=tuple(),\\n dependencies=stages,\\n )\",\n \"def main():\\n init()\\n separator_len = 40\\n for s in stage_instances:\\n print('='*separator_len)\\n print(s.name)\\n print('-'*separator_len)\\n\\n s.add_tasks() # Add tasks from previous stage\\n s.revive_or_archive() # Revive killed tasks or move them to failed\\n s.schedule_jobs() # Schedule new jobs if needed\\n s.print_status()\\n print('='*separator_len + '\\\\n')\\n render(stage_instances)\",\n \"def stage_states(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['ExecutionStageStateArgs']]]]:\\n return pulumi.get(self, \\\"stage_states\\\")\",\n \"def get_pipelines() -> Iterable[DataPipeline]:\\n for pipeline_name in get_pipeline_names():\\n yield DataPipeline.load(pipeline_name)\",\n \"def get_parser(stages):\\n\\n # martian\\n parser = argparse.ArgumentParser(prog=\\\"martian\\\")\\n subparsers = parser.add_subparsers()\\n\\n # martian stage\\n stage_parser = subparsers.add_parser(\\n \\\"stage\\\", help=\\\"Work with Martian stages.\\\")\\n\\n stage_subparsers = stage_parser.add_subparsers(\\n title=\\\"Stage subcommands\\\",\\n help=\\\"Actions than can be performed on Martian stages.\\\")\\n\\n # martian stage list\\n stage_list_parser = stage_subparsers.add_parser(\\n \\\"list\\\",\\n help=\\\"List all available stages.\\\")\\n stage_list_parser.set_defaults(func=stage_list)\\n stage_list_parser.set_defaults(stages=stages)\\n\\n # martian stage describe <stage_name>\\n stage_describe_parser = stage_subparsers.add_parser(\\n \\\"describe\\\",\\n help=\\\"Describe the inputs, outputs, and source location of a stage\\\")\\n stage_describe_parser.add_argument(\\n \\\"stage_name\\\",\\n help=\\\"Name of the stage to describe\\\")\\n stage_describe_parser.set_defaults(func=stage_describe)\\n stage_describe_parser.set_defaults(stages=stages)\\n\\n # martian stage run <stage_name> <stage_phase> <stage_args...>\\n stage_run_parser = stage_subparsers.add_parser(\\n \\\"run\\\",\\n help=\\\"Run a stage\\\")\\n\\n stage_run_subparsers = stage_run_parser.add_subparsers(\\n title=\\\"Stages available to be run\\\",\\n help=\\\"Names of available stages.\\\")\\n\\n for stage in stages.values():\\n\\n individual_stage_parser = stage_run_subparsers.add_parser(\\n stage.name,\\n help=\\\"Execute stage \\\" + stage.name)\\n individual_stage_subparsers = individual_stage_parser.add_subparsers()\\n\\n # Some stages don't have a split or join\\n available_stage_phases = ['split', 'join', 'main'] if (stage.splits or stage.force_split) else ['main']\\n\\n for phase in available_stage_phases:\\n phase_parser = individual_stage_subparsers.add_parser(\\n phase,\\n help='Run the ' + phase + ' of ' + stage.name)\\n\\n phase_parser.set_defaults(func=stage_run)\\n phase_parser.set_defaults(phase=phase)\\n phase_parser.set_defaults(stage=stage)\\n\\n for input_ in _stage_inputs(stage, phase):\\n help_message = \\\"Type: \\\" + input_.type\\n if input_.help:\\n help_message += \\\" Help: \\\" + input_.help\\n phase_parser.add_argument(\\n \\\"--\\\" + input_.name,\\n type=martian_type_to_python_type(input_.type),\\n nargs=martian_type_to_nargs(input_.type),\\n default=None,\\n help=help_message)\\n\\n # Handle the \\\"split_file\\\" for mains that come after a split\\n if phase == 'main' and 'split' in available_stage_phases:\\n phase_parser.add_argument(\\n \\\"--split_file\\\",\\n type=martian_type_to_python_type(\\\"File\\\"),\\n nargs=martian_type_to_nargs(\\\"File\\\"),\\n default=None,\\n help=\\\"File with split arguments.\\\")\\n\\n return parser\",\n \"def merge_action_stages(env):\\n stage_draft = env.ref('mgmtsystem_action.stage_draft')\\n stage_open = env.ref('mgmtsystem_action.stage_open')\\n stage_close = env.ref('mgmtsystem_action.stage_close')\\n\\n old_stage_draft_id = env.ref('crm_claim.stage_claim1').id\\n old_stage_open_id = env.ref('crm_claim.stage_claim5').id\\n old_stage_close_id = env.ref('crm_claim.stage_claim2').id\\n\\n env['mgmtsystem.action'].search([\\n ('stage_id', '=', old_stage_draft_id)\\n ]).write({'stage_id': stage_draft.id})\\n\\n env['mgmtsystem.action'].search([\\n ('stage_id', '=', old_stage_open_id)\\n ]).write({'stage_id': stage_open.id})\\n\\n env['mgmtsystem.action'].search([\\n ('stage_id', '=', old_stage_close_id)\\n ]).write({'stage_id': stage_close.id})\\n\\n env['mgmtsystem.action.stage'].browse([\\n old_stage_draft_id, old_stage_open_id, old_stage_close_id\\n ]).unlink()\",\n \"def run_presub(stages):\\n if is_presub(stages[0]):\\n # Pop the first stage\\n stage, stages = stages[0], stages[1:]\\n # Execute it only if Python was invoked normally, not in a queue job\\n if STAGE_ID is None:\\n msg = \\\"Only the first stage can execute on the login node\\\"\\n assert all([not is_presub(i) for i in stages]), msg\\n alog.info(\\\"Pre-submit stage starting: %s\\\", stage)\\n stage()\\n alog.info(\\\"Pre-submit stage done: %s\\\", stage)\\n return stages\",\n \"def _collect_stages(self) -> Set[str]:\\n # Get the stage name associated with the previous deployment and update stage\\n # Stage needs to be flushed so that new changes will be visible immediately\\n api_resource = get_resource_by_id(self._stacks, ResourceIdentifier(self._api_identifier))\\n stage_resources = get_resource_ids_by_type(self._stacks, AWS_APIGATEWAY_STAGE)\\n deployment_resources = get_resource_ids_by_type(self._stacks, AWS_APIGATEWAY_DEPLOYMENT)\\n\\n stages = set()\\n # If it is a SAM resource, get the StageName property\\n if api_resource:\\n if api_resource.get(\\\"Type\\\") == AWS_SERVERLESS_API:\\n # The customer defined stage name\\n stage_name = api_resource.get(\\\"Properties\\\", {}).get(\\\"StageName\\\")\\n if stage_name:\\n stages.add(cast(str, stage_name))\\n\\n # The stage called \\\"Stage\\\"\\n if stage_name != \\\"Stage\\\":\\n response_sta = cast(Dict, self._api_client.get_stages(restApiId=self._api_physical_id))\\n for item in response_sta.get(\\\"item\\\"): # type: ignore\\n if item.get(\\\"stageName\\\") == \\\"Stage\\\":\\n stages.add(\\\"Stage\\\")\\n\\n # For both SAM and ApiGateway resource, check if any refs from stage resources\\n for stage_resource in stage_resources:\\n # RestApiId is a required field in stage\\n stage_dict = get_resource_by_id(self._stacks, stage_resource)\\n if not stage_dict:\\n continue\\n rest_api_id = stage_dict.get(\\\"Properties\\\", {}).get(\\\"RestApiId\\\")\\n dep_id = stage_dict.get(\\\"Properties\\\", {}).get(\\\"DeploymentId\\\")\\n # If the stage doesn't have a deployment associated then no need to update\\n if dep_id is None:\\n continue\\n # If the stage's deployment ID is not static and the rest API ID matchs, then update\\n for deployment_resource in deployment_resources:\\n if deployment_resource.resource_iac_id == dep_id and rest_api_id == self._api_identifier:\\n stages.add(cast(str, stage_dict.get(\\\"Properties\\\", {}).get(\\\"StageName\\\")))\\n break\\n\\n return stages\",\n \"def iter_jobs(self):\\n for param in self._parameters:\\n yield param\",\n \"def stages(self, pipeline_name):\\n\\n pipeline_name = self._require_param('pipeline_name', locals())\\n\\n response = self._session.get(\\n path=self._session.urljoin(self.PIPELINES_RESOURCE_PATH, pipeline_name, 'stages.xml').format(\\n base_api=self.base_api\\n ),\\n headers={'Accept': 'application/xml'},\\n )\\n\\n return response.text\",\n \"def init_process_group_by_stages(self, stages: List[int]) -> ProcessGroup:\\n return self.pg_mesh.get_group_along_axis(self.pipeline_axis, stages)\",\n \"def run_stage_loop(cls, _opts, tests_results, put_next_stage):\\n for _, result in tests_results:\\n put_next_stage(result)\",\n \"def annotate_downstream_side_inputs(stages, pipeline_context):\\n # type: (Iterable[Stage], TransformContext) -> Iterable[Stage]\\n consumers = collections.defaultdict(\\n list) # type: DefaultDict[str, List[Stage]]\\n\\n def get_all_side_inputs():\\n # type: () -> Set[str]\\n all_side_inputs = set() # type: Set[str]\\n for stage in stages:\\n for transform in stage.transforms:\\n for input in transform.inputs.values():\\n consumers[input].append(stage)\\n for si in stage.side_inputs():\\n all_side_inputs.add(si)\\n return all_side_inputs\\n\\n all_side_inputs = frozenset(get_all_side_inputs())\\n\\n downstream_side_inputs_by_stage = {} # type: Dict[Stage, FrozenSet[str]]\\n\\n def compute_downstream_side_inputs(stage):\\n # type: (Stage) -> FrozenSet[str]\\n if stage not in downstream_side_inputs_by_stage:\\n downstream_side_inputs = frozenset() # type: FrozenSet[str]\\n for transform in stage.transforms:\\n for output in transform.outputs.values():\\n if output in all_side_inputs:\\n downstream_side_inputs = union(\\n downstream_side_inputs, frozenset([output]))\\n for consumer in consumers[output]:\\n downstream_side_inputs = union(\\n downstream_side_inputs,\\n compute_downstream_side_inputs(consumer))\\n downstream_side_inputs_by_stage[stage] = downstream_side_inputs\\n return downstream_side_inputs_by_stage[stage]\\n\\n for stage in stages:\\n stage.downstream_side_inputs = compute_downstream_side_inputs(stage)\\n return stages\",\n \"def _execute_stage(self, index, stage, stop):\\n if stop.is_set():\\n _LOGGER.info(\\\"Stopped pipeline on group %s\\\", self._group)\\n return\\n _LOGGER.info(\\\" -> Running stage '%s' on group %s\\\", stage, self._group)\\n if stage.name == 'on':\\n self._group.on = True\\n elif stage.name == 'off':\\n self._group.on = False\\n elif stage.name == 'hue':\\n self._group.hue = stage.args[0]\\n elif stage.name == 'saturation':\\n self._group.saturation = stage.args[0]\\n elif stage.name == 'color':\\n self._group.color = Color(*stage.args)\\n elif stage.name == 'brightness':\\n self._group.brightness = stage.args[0]\\n elif stage.name == 'temperature':\\n self._group.temperature = stage.args[0]\\n elif stage.name == 'transition':\\n self._group.transition(*stage.args, **stage.kwargs)\\n elif stage.name == 'white':\\n self._group.white()\\n elif stage.name == 'white_up':\\n self._group.white_up()\\n elif stage.name == 'white_down':\\n self._group.white_down()\\n elif stage.name == 'red_up':\\n self._group.red_up()\\n elif stage.name == 'red_down':\\n self._group.red_down()\\n elif stage.name == 'green_up':\\n self._group.green_up()\\n elif stage.name == 'green_down':\\n self._group.green_down()\\n elif stage.name == 'blue_up':\\n self._group.blue_up()\\n elif stage.name == 'blue_down':\\n self._group.blue_down()\\n elif stage.name == 'night_light':\\n self._group.night_light()\\n elif stage.name == 'link':\\n self._group.link()\\n elif stage.name == 'unlink':\\n self._group.unlink()\\n elif stage.name == 'flash':\\n self._group.flash(**stage.kwargs)\\n elif stage.name == 'repeat':\\n self._repeat(index, stage, stop)\\n elif stage.name == 'wait':\\n time.sleep(*stage.args)\\n elif stage.name == 'callback':\\n stage.args[0](*stage.args[1:], **stage.kwargs)\",\n \"def stage(self, pipeline_name, pipeline_counter, stage_name, stage_counter):\\n\\n func_args = locals()\\n pipeline_name = self._require_param('pipeline_name', func_args)\\n pipeline_counter = self._require_param('pipeline_counter', func_args)\\n stage_name = self._require_param('stage_name', func_args)\\n stage_counter = self._require_param('stage_counter', func_args)\\n\\n response = self._session.get(\\n path=self._session.urljoin(\\n self.PIPELINES_RESOURCE_PATH,\\n pipeline_name,\\n pipeline_counter,\\n stage_name,\\n '{}.xml'.format(stage_counter)\\n ).format(\\n base_api=self._session.base_api(api_path=''), # WTF?!!\\n ),\\n headers={'Accept': 'application/xml'},\\n )\\n\\n return response.text\",\n \"def flat_map(\\n f, stage=pypeln_utils.UNDEFINED, workers=1, maxsize=0, on_start=None, on_done=None\\n):\\n\\n if utils.is_undefined(stage):\\n return utils.Partial(\\n lambda stage: flat_map(\\n f,\\n stage,\\n workers=workers,\\n maxsize=maxsize,\\n on_start=on_start,\\n on_done=on_done,\\n )\\n )\\n\\n stage = _to_stage(stage)\\n\\n if isinstance(f, types.GeneratorType):\\n _f = f\\n f = lambda *args, **kwargs: _f(*args, **kwargs)\\n\\n return _Stage(\\n worker_constructor=WORKER,\\n workers=workers,\\n maxsize=maxsize,\\n on_start=on_start,\\n on_done=on_done,\\n target=_flat_map,\\n args=(f,),\\n dependencies=[stage],\\n )\",\n \"def concat(stages: typing.List[Stage], maxsize: int = 0) -> Stage:\\n\\n stages = [to_stage(stage) for stage in stages]\\n\\n return Concat(f=None, on_start=None, on_done=None, timeout=0, dependencies=stages,)\",\n \"def _patch_update_stages(\\n self,\\n stage_changes_list: list[dict[str, Any]],\\n changed_fields: CHANGED_FIELDS_LIST_TYPE\\n ) -> bool:\\n stages: list[Stage] = []\\n for change_info in stage_changes_list:\\n stage_was_updated = False\\n # Check if valid ID is provided and fetch stage if it exists.\\n if 'id' not in change_info:\\n self.abort(400, msg='Missing stage ID in stage updates')\\n id = change_info['id']\\n stage = Stage.get_by_id(id)\\n if not stage:\\n self.abort(400, msg=f'Stage not found for ID {id}')\\n\\n # Update stage fields.\\n for field, field_type in api_specs.STAGE_FIELD_DATA_TYPES:\\n if field not in change_info:\\n continue\\n form_field_name = change_info[field]['form_field_name']\\n old_value = getattr(stage, field)\\n new_value = change_info[field]['value']\\n self._update_field_value(stage, field, field_type, new_value)\\n changed_fields.append((form_field_name, old_value, new_value))\\n stage_was_updated = True\\n\\n # Update milestone fields.\\n milestones = stage.milestones\\n for field, field_type in api_specs.MILESTONESET_FIELD_DATA_TYPES:\\n if field not in change_info:\\n continue\\n if milestones is None:\\n milestones = MilestoneSet()\\n form_field_name = change_info[field]['form_field_name']\\n old_value = getattr(milestones, field)\\n new_value = change_info[field]['value']\\n self._update_field_value(milestones, field, field_type, new_value)\\n changed_fields.append((form_field_name, old_value, new_value))\\n stage_was_updated = True\\n stage.milestones = milestones\\n\\n if stage_was_updated:\\n stages.append(stage)\\n\\n # Save all of the updates made.\\n # Return a boolean representing if any changes were made to any stages.\\n if stages:\\n ndb.put_multi(stages)\\n return True\\n return False\",\n \"def steps(self):\\n for step in self._get_paged(\\\"steps\\\", trailing=True):\\n yield self.__get_object(step)\\n\\n return\",\n \"def lift_combiners(stages, context):\\n # type: (List[Stage], TransformContext) -> Iterator[Stage]\\n def is_compatible_with_combiner_lifting(trigger):\\n '''Returns whether this trigger is compatible with combiner lifting.\\n\\n Certain triggers, such as those that fire after a certain number of\\n elements, need to observe every element, and as such are incompatible\\n with combiner lifting (which may aggregate several elements into one\\n before they reach the triggering code after shuffle).\\n '''\\n if trigger is None:\\n return True\\n elif trigger.WhichOneof('trigger') in (\\n 'default',\\n 'always',\\n 'never',\\n 'after_processing_time',\\n 'after_synchronized_processing_time'):\\n return True\\n elif trigger.HasField('element_count'):\\n return trigger.element_count.element_count == 1\\n elif trigger.HasField('after_end_of_window'):\\n return is_compatible_with_combiner_lifting(\\n trigger.after_end_of_window.early_firings\\n ) and is_compatible_with_combiner_lifting(\\n trigger.after_end_of_window.late_firings)\\n elif trigger.HasField('after_any'):\\n return all(\\n is_compatible_with_combiner_lifting(t)\\n for t in trigger.after_any.subtriggers)\\n elif trigger.HasField('repeat'):\\n return is_compatible_with_combiner_lifting(trigger.repeat.subtrigger)\\n else:\\n return False\\n\\n def can_lift(combine_per_key_transform):\\n windowing = context.components.windowing_strategies[\\n context.components.pcollections[only_element(\\n list(combine_per_key_transform.inputs.values())\\n )].windowing_strategy_id]\\n return is_compatible_with_combiner_lifting(windowing.trigger)\\n\\n def make_stage(base_stage, transform):\\n # type: (Stage, beam_runner_api_pb2.PTransform) -> Stage\\n return Stage(\\n transform.unique_name, [transform],\\n downstream_side_inputs=base_stage.downstream_side_inputs,\\n must_follow=base_stage.must_follow,\\n parent=base_stage.name,\\n environment=base_stage.environment)\\n\\n def lifted_stages(stage):\\n transform = stage.transforms[0]\\n combine_payload = proto_utils.parse_Bytes(\\n transform.spec.payload, beam_runner_api_pb2.CombinePayload)\\n\\n input_pcoll = context.components.pcollections[only_element(\\n list(transform.inputs.values()))]\\n output_pcoll = context.components.pcollections[only_element(\\n list(transform.outputs.values()))]\\n\\n element_coder_id = input_pcoll.coder_id\\n element_coder = context.components.coders[element_coder_id]\\n key_coder_id, _ = element_coder.component_coder_ids\\n accumulator_coder_id = combine_payload.accumulator_coder_id\\n\\n key_accumulator_coder = beam_runner_api_pb2.Coder(\\n spec=beam_runner_api_pb2.FunctionSpec(urn=common_urns.coders.KV.urn),\\n component_coder_ids=[key_coder_id, accumulator_coder_id])\\n key_accumulator_coder_id = context.add_or_get_coder_id(\\n key_accumulator_coder)\\n\\n accumulator_iter_coder = beam_runner_api_pb2.Coder(\\n spec=beam_runner_api_pb2.FunctionSpec(\\n urn=common_urns.coders.ITERABLE.urn),\\n component_coder_ids=[accumulator_coder_id])\\n accumulator_iter_coder_id = context.add_or_get_coder_id(\\n accumulator_iter_coder)\\n\\n key_accumulator_iter_coder = beam_runner_api_pb2.Coder(\\n spec=beam_runner_api_pb2.FunctionSpec(urn=common_urns.coders.KV.urn),\\n component_coder_ids=[key_coder_id, accumulator_iter_coder_id])\\n key_accumulator_iter_coder_id = context.add_or_get_coder_id(\\n key_accumulator_iter_coder)\\n\\n precombined_pcoll_id = unique_name(\\n context.components.pcollections, 'pcollection')\\n context.components.pcollections[precombined_pcoll_id].CopyFrom(\\n beam_runner_api_pb2.PCollection(\\n unique_name=transform.unique_name + '/Precombine.out',\\n coder_id=key_accumulator_coder_id,\\n windowing_strategy_id=input_pcoll.windowing_strategy_id,\\n is_bounded=input_pcoll.is_bounded))\\n\\n grouped_pcoll_id = unique_name(\\n context.components.pcollections, 'pcollection')\\n context.components.pcollections[grouped_pcoll_id].CopyFrom(\\n beam_runner_api_pb2.PCollection(\\n unique_name=transform.unique_name + '/Group.out',\\n coder_id=key_accumulator_iter_coder_id,\\n windowing_strategy_id=output_pcoll.windowing_strategy_id,\\n is_bounded=output_pcoll.is_bounded))\\n\\n merged_pcoll_id = unique_name(\\n context.components.pcollections, 'pcollection')\\n context.components.pcollections[merged_pcoll_id].CopyFrom(\\n beam_runner_api_pb2.PCollection(\\n unique_name=transform.unique_name + '/Merge.out',\\n coder_id=key_accumulator_coder_id,\\n windowing_strategy_id=output_pcoll.windowing_strategy_id,\\n is_bounded=output_pcoll.is_bounded))\\n\\n yield make_stage(\\n stage,\\n beam_runner_api_pb2.PTransform(\\n unique_name=transform.unique_name + '/Precombine',\\n spec=beam_runner_api_pb2.FunctionSpec(\\n urn=common_urns.combine_components.COMBINE_PER_KEY_PRECOMBINE.\\n urn,\\n payload=transform.spec.payload),\\n inputs=transform.inputs,\\n outputs={'out': precombined_pcoll_id},\\n environment_id=transform.environment_id))\\n\\n yield make_stage(\\n stage,\\n beam_runner_api_pb2.PTransform(\\n unique_name=transform.unique_name + '/Group',\\n spec=beam_runner_api_pb2.FunctionSpec(\\n urn=common_urns.primitives.GROUP_BY_KEY.urn),\\n inputs={'in': precombined_pcoll_id},\\n outputs={'out': grouped_pcoll_id}))\\n\\n yield make_stage(\\n stage,\\n beam_runner_api_pb2.PTransform(\\n unique_name=transform.unique_name + '/Merge',\\n spec=beam_runner_api_pb2.FunctionSpec(\\n urn=common_urns.combine_components.\\n COMBINE_PER_KEY_MERGE_ACCUMULATORS.urn,\\n payload=transform.spec.payload),\\n inputs={'in': grouped_pcoll_id},\\n outputs={'out': merged_pcoll_id},\\n environment_id=transform.environment_id))\\n\\n yield make_stage(\\n stage,\\n beam_runner_api_pb2.PTransform(\\n unique_name=transform.unique_name + '/ExtractOutputs',\\n spec=beam_runner_api_pb2.FunctionSpec(\\n urn=common_urns.combine_components.\\n COMBINE_PER_KEY_EXTRACT_OUTPUTS.urn,\\n payload=transform.spec.payload),\\n inputs={'in': merged_pcoll_id},\\n outputs=transform.outputs,\\n environment_id=transform.environment_id))\\n\\n def unlifted_stages(stage):\\n transform = stage.transforms[0]\\n for sub in transform.subtransforms:\\n yield make_stage(stage, context.components.transforms[sub])\\n\\n for stage in stages:\\n transform = only_transform(stage.transforms)\\n if transform.spec.urn == common_urns.composites.COMBINE_PER_KEY.urn:\\n expansion = lifted_stages if can_lift(transform) else unlifted_stages\\n for substage in expansion(stage):\\n yield substage\\n else:\\n yield stage\",\n \"def RunStages(self):\\n self._RunStage(build_stages.InitSDKStage)\\n self.RunSetupBoard()\\n self._RunStage(report_stages.RefreshPackageStatusStage)\",\n \"def _update_stages(self, stages: Set[str], deployment_id: Optional[str]) -> Set[str]:\\n prev_dep_ids = set()\\n for stage in stages:\\n # Collects previous deployment IDs to clean up\\n response_get = cast(Dict, self._api_client.get_stage(restApiId=self._api_physical_id, stageName=stage))\\n prev_dep_id = response_get.get(\\\"deploymentId\\\")\\n if prev_dep_id:\\n prev_dep_ids.add(cast(str, prev_dep_id))\\n\\n # Updates the stage with newest deployment\\n LOG.debug(\\\"%sTrying to update the stage %s through client\\\", self.log_prefix, stage)\\n response_upd = cast(\\n Dict,\\n self._api_client.update_stage(\\n restApiId=self._api_physical_id,\\n stageName=stage,\\n patchOperations=[{\\\"op\\\": \\\"replace\\\", \\\"path\\\": \\\"/deploymentId\\\", \\\"value\\\": deployment_id}],\\n ),\\n )\\n LOG.debug(\\\"%sUpdate Stage Result: %s\\\", self.log_prefix, response_upd)\\n\\n # Flushes the cache so that the changes are calleable\\n self._api_client.flush_stage_cache(restApiId=self._api_physical_id, stageName=stage)\\n self._api_client.flush_stage_authorizers_cache(restApiId=self._api_physical_id, stageName=stage)\\n return prev_dep_ids\",\n \"def fix_flatten_coders(\\n stages, pipeline_context, identity_urn=bundle_processor.IDENTITY_DOFN_URN):\\n # type: (Iterable[Stage], TransformContext, str) -> Iterator[Stage]\\n pcollections = pipeline_context.components.pcollections\\n for stage in stages:\\n transform = only_element(stage.transforms)\\n if transform.spec.urn == common_urns.primitives.FLATTEN.urn:\\n output_pcoll_id = only_element(transform.outputs.values())\\n output_coder_id = pcollections[output_pcoll_id].coder_id\\n for local_in, pcoll_in in list(transform.inputs.items()):\\n if pcollections[pcoll_in].coder_id != output_coder_id:\\n # Flatten requires that all its inputs be materialized with the\\n # same coder as its output. Add stages to transcode flatten\\n # inputs that use different coders.\\n transcoded_pcollection = unique_name(\\n pcollections,\\n transform.unique_name + '/Transcode/' + local_in + '/out')\\n transcode_name = unique_name(\\n pipeline_context.components.transforms,\\n transform.unique_name + '/Transcode/' + local_in)\\n yield Stage(\\n transcode_name,\\n [\\n beam_runner_api_pb2.PTransform(\\n unique_name=transcode_name,\\n inputs={local_in: pcoll_in},\\n outputs={'out': transcoded_pcollection},\\n spec=beam_runner_api_pb2.FunctionSpec(urn=identity_urn),\\n environment_id=transform.environment_id)\\n ],\\n downstream_side_inputs=frozenset(),\\n must_follow=stage.must_follow)\\n pcollections[transcoded_pcollection].CopyFrom(pcollections[pcoll_in])\\n pcollections[transcoded_pcollection].unique_name = (\\n transcoded_pcollection)\\n pcollections[transcoded_pcollection].coder_id = output_coder_id\\n transform.inputs[local_in] = transcoded_pcollection\\n\\n yield stage\",\n \"def do_actions(self, stage):\\n actions = itertools.chain(*[self.get_control_actions(control)\\n for control in self.controls.get(stage, [])])\\n messages = []\\n\\n for action, args in actions:\\n if action == 'done':\\n # end execution of actions\\n break\\n elif action == 'gameover':\\n # end execution of actions and end the game\\n self.game_over = True\\n break\\n elif action == 'replace':\\n # replace the input command and restart execution\\n self.state.current_turn.replace_command(' '.join(args))\\n return self.do_actions(stage)\\n\\n # treat name=value args as keyword args\\n pargs, kwargs = [], {}\\n for arg in args:\\n if '=' in arg:\\n kwargs.update([arg.split('=', 1)])\\n else:\\n pargs.append(arg)\\n\\n msgs = getattr(self.actions, action)(*pargs, **kwargs)\\n if isinstance(msgs, str):\\n messages.append(msgs)\\n elif msgs is not None:\\n messages.extend(msgs)\\n\\n return messages\",\n \"def _stage_inputs(stage, phase):\\n\\n def arrayify(martian_io_field):\\n \\\"\\\"\\\"Convert the type of a Martian input field to an array of that type.\\n\\n This is necessary for the join phase.\\n \\\"\\\"\\\"\\n return mro_parser.MartianIOField(\\n martian_io_field.modifier,\\n martian_io_field.type + '[]',\\n martian_io_field.name,\\n martian_io_field.help)\\n\\n def add_tag_to_name(martian_io_field, tag):\\n return mro_parser.MartianIOField(\\n martian_io_field.modifier,\\n martian_io_field.type,\\n martian_io_field.name + '_' + tag,\\n martian_io_field.help)\\n\\n if phase == 'split':\\n return stage.inputs\\n elif phase == 'main':\\n return stage.inputs + stage.splits\\n elif phase == 'join':\\n # The inputs to join are arrays of the split and output fields since it's pulling\\n # together outputs of multiple main steps.\\n # Also, \\\"split\\\" and \\\"output\\\" need to be added to the field names or there are collisions\\n return stage.inputs + \\\\\\n [add_tag_to_name(arrayify(s), \\\"split\\\") for s in stage.splits] + \\\\\\n [add_tag_to_name(arrayify(s), \\\"output\\\") for s in stage.outputs]\",\n \"def __iter__( self ):\\n assert isinstance( self._env, Env )\\n assert isinstance( self._steps, list )\\n\\n return iter( self._steps )\",\n \"def get_step_actions(self):\\n return self.actor(tf.numpy_function(self.get_states, [], self.states[0].dtype))\",\n \"def map(\\n f: typing.Callable,\\n stage: Stage = pypeln_utils.UNDEFINED,\\n workers: int = None,\\n maxsize: int = None,\\n timeout: float = 0,\\n on_start: typing.Callable = None,\\n on_done: typing.Callable = None,\\n) -> Stage:\\n\\n if pypeln_utils.is_undefined(stage):\\n return pypeln_utils.Partial(\\n lambda stage: map(\\n f,\\n stage=stage,\\n workers=workers,\\n maxsize=maxsize,\\n timeout=timeout,\\n on_start=on_start,\\n on_done=on_done,\\n )\\n )\\n\\n stage = to_stage(stage)\\n\\n return Map(\\n f=f, on_start=on_start, on_done=on_done, timeout=timeout, dependencies=[stage],\\n )\",\n \"def middleware(store: Store, action: Action) -> Iterable[Action]:\\n kind = action[\\\"kind\\\"]\\n if kind == ON_BUTTON_GROUP:\\n payload = action[\\\"payload\\\"]\\n yield set_active(payload[\\\"row_index\\\"], payload[\\\"active\\\"])\\n elif kind == ON_SAVE:\\n if get_mode(store.state) == \\\"edit\\\":\\n index = edit_index(store.state)\\n else:\\n index = next_index(store.state)\\n yield save_layer(index, action[\\\"payload\\\"])\\n else:\\n yield action\",\n \"def N_stages(self):\\n return 5\",\n \"def stage(self, stage_id):\\r\\n return pipelines.Stage(self, stage_id)\",\n \"def move_stages(self,positions):\\n for ind_move,pos in enumerate(positions): #move all activated modules to specified positions\\n #self.move_modules[ind_move].move_Abs(pos)\\n self.move_modules_commands[ind_move].emit(utils.ThreadCommand(command=\\\"move_Abs\\\", attributes=[pos]))\",\n \"def main() -> co.Parallel:\\n actors = [\\\"Oprah Winfrey\\\", \\\"Kate Mara\\\", \\\"Don Cheadle\\\", \\\"Dwayne Johnson\\\"]\\n root = co.Parallel(image=_get_image())\\n for actor in actors:\\n root[actor] = co.Lazy(\\n f\\\"python pipeline.py all_by_actor '{actor}'\\\"\\n )\\n return root\",\n \"def solve_all_stages(stages, objects_dic, predicates_rules, gstate, actionlist, problem_dic):\\n\\n result = {}\\n result[\\\"visualStages\\\"] = []\\n for stage in stages:\\n\\n stage_dic = {}\\n object_dic_copy = copy.deepcopy(objects_dic)\\n predicates = stage[\\\"items\\\"]\\n sorted_predicates = priority(predicates, predicates_rules)\\n\\n # For hanoi problem, reset each stage\\n # For logistics problem, reset each stage\\n for fname in gstate[\\\"reset_function\\\"]:\\n gstate[fname] = {}\\n solvepredicates(sorted_predicates, object_dic_copy, predicates_rules, gstate)\\n stage_dic[\\\"visualSprites\\\"] = object_dic_copy\\n if \\\"stageName\\\" not in stage:\\n stage_dic[\\\"stageName\\\"] = \\\"Inital Stage\\\"\\n stage_dic[\\\"stageInfo\\\"] = \\\"No step information\\\"\\n\\n else:\\n stage_dic[\\\"stageName\\\"] = stage[\\\"stageName\\\"]\\n stage_dic[\\\"stageInfo\\\"] = stage[\\\"stageInfo\\\"]\\n\\n result[\\\"visualStages\\\"].append(stage_dic)\\n\\n result[\\\"subgoals\\\"] = Subgoal.get_subgoal(stages, problem_dic[1]['goal'].copy(), actionlist.copy())\\n\\n return result\",\n \"def act(self, states: np.ndarray, eps: float = 0.0) -> List[np.ndarray]:\\n actions = [\\n agent.act(state.reshape(-1, 1).T, eps)\\n for agent, state in zip(self.agents, states)\\n ]\\n return actions\",\n \"def transduce(self,inputs):\\n self.start()\\n return [self.step(inp) for inp in inputs]\",\n \"def flat_map(\\n f: typing.Callable,\\n stage: Stage = pypeln_utils.UNDEFINED,\\n workers: int = 1,\\n maxsize: int = 0,\\n timeout: float = 0,\\n on_start: typing.Callable = None,\\n on_done: typing.Callable = None,\\n) -> Stage:\\n\\n if pypeln_utils.is_undefined(stage):\\n return pypeln_utils.Partial(\\n lambda stage: flat_map(\\n f,\\n stage=stage,\\n workers=workers,\\n maxsize=maxsize,\\n timeout=timeout,\\n on_start=on_start,\\n on_done=on_done,\\n )\\n )\\n\\n stage = to_stage(stage)\\n\\n return FlatMap(\\n f=f, on_start=on_start, on_done=on_done, timeout=timeout, dependencies=[stage],\\n )\",\n \"def impulse_to_input(stages, pipeline_context):\\n # type: (Iterable[Stage], TransformContext) -> Iterator[Stage]\\n for stage in stages:\\n for transform in list(stage.transforms):\\n if transform.spec.urn == common_urns.primitives.IMPULSE.urn:\\n stage.transforms.remove(transform)\\n stage.transforms.append(\\n beam_runner_api_pb2.PTransform(\\n unique_name=transform.unique_name,\\n spec=beam_runner_api_pb2.FunctionSpec(\\n urn=bundle_processor.DATA_INPUT_URN,\\n payload=IMPULSE_BUFFER),\\n outputs=transform.outputs))\\n yield stage\",\n \"def make_stage(num_blocks, input_channels, output_channels, stride, expand_ratio, norm, activation):\\n blocks = []\\n blocks.append(\\n InvertedResBlock(input_channels, output_channels, stride=stride, expand_ratio=expand_ratio,\\n norm=norm, activation=activation, use_shortcut=False)\\n )\\n for i in range(num_blocks - 1):\\n blocks.append(\\n InvertedResBlock(output_channels, output_channels, stride=1, expand_ratio=expand_ratio,\\n norm=norm, activation=activation)\\n )\\n\\n return blocks\",\n \"def map(\\n f, stage=pypeln_utils.UNDEFINED, workers=1, maxsize=0, on_start=None, on_done=None\\n):\\n\\n if utils.is_undefined(stage):\\n return utils.Partial(\\n lambda stage: map(\\n f,\\n stage,\\n workers=workers,\\n maxsize=maxsize,\\n on_start=on_start,\\n on_done=on_done,\\n )\\n )\\n\\n stage = _to_stage(stage)\\n\\n return _Stage(\\n worker_constructor=WORKER,\\n workers=workers,\\n maxsize=maxsize,\\n on_start=on_start,\\n on_done=on_done,\\n target=_map,\\n args=(f,),\\n dependencies=[stage],\\n )\",\n \"def reset_stages(self) -> List[str]:\\n return self._reset_stages\",\n \"def _read_group_stage_ids(self, stages, domain, order):\\n stage_ids = self.env['salon.stage'].search([])\\n return stage_ids\",\n \"def pipeline(self, *funcs) -> \\\"fn\\\":\\n return self._mod.pipeline(self, *funcs)\",\n \"def greedily_fuse(stages, pipeline_context):\\n # type: (Iterable[Stage], TransformContext) -> FrozenSet[Stage]\\n producers_by_pcoll = {} # type: Dict[str, Stage]\\n consumers_by_pcoll = collections.defaultdict(\\n list) # type: DefaultDict[str, List[Stage]]\\n\\n # Used to always reference the correct stage as the producer and\\n # consumer maps are not updated when stages are fused away.\\n replacements = {} # type: Dict[Stage, Stage]\\n\\n def replacement(s):\\n old_ss = []\\n while s in replacements:\\n old_ss.append(s)\\n s = replacements[s]\\n for old_s in old_ss[:-1]:\\n replacements[old_s] = s\\n return s\\n\\n def fuse(producer, consumer):\\n fused = producer.fuse(consumer, pipeline_context)\\n replacements[producer] = fused\\n replacements[consumer] = fused\\n\\n # First record the producers and consumers of each PCollection.\\n for stage in stages:\\n for transform in stage.transforms:\\n for input in transform.inputs.values():\\n consumers_by_pcoll[input].append(stage)\\n for output in transform.outputs.values():\\n producers_by_pcoll[output] = stage\\n\\n # Now try to fuse away all pcollections.\\n for pcoll, producer in producers_by_pcoll.items():\\n write_pcoll = None\\n for consumer in consumers_by_pcoll[pcoll]:\\n producer = replacement(producer)\\n consumer = replacement(consumer)\\n # Update consumer.must_follow set, as it's used in can_fuse.\\n consumer.must_follow = frozenset(\\n replacement(s) for s in consumer.must_follow)\\n if producer.can_fuse(consumer, pipeline_context):\\n fuse(producer, consumer)\\n else:\\n # If we can't fuse, do a read + write.\\n pipeline_context.length_prefix_pcoll_coders(pcoll)\\n buffer_id = create_buffer_id(pcoll)\\n if write_pcoll is None:\\n write_pcoll = Stage(\\n pcoll + '/Write',\\n [\\n beam_runner_api_pb2.PTransform(\\n unique_name=pcoll + '/Write',\\n inputs={'in': pcoll},\\n spec=beam_runner_api_pb2.FunctionSpec(\\n urn=bundle_processor.DATA_OUTPUT_URN,\\n payload=buffer_id))\\n ],\\n downstream_side_inputs=producer.downstream_side_inputs)\\n fuse(producer, write_pcoll)\\n if consumer.has_as_main_input(pcoll):\\n read_pcoll = Stage(\\n pcoll + '/Read',\\n [\\n beam_runner_api_pb2.PTransform(\\n unique_name=pcoll + '/Read',\\n outputs={'out': pcoll},\\n spec=beam_runner_api_pb2.FunctionSpec(\\n urn=bundle_processor.DATA_INPUT_URN,\\n payload=buffer_id))\\n ],\\n downstream_side_inputs=consumer.downstream_side_inputs,\\n must_follow=frozenset([write_pcoll]))\\n fuse(read_pcoll, consumer)\\n else:\\n consumer.must_follow = union(\\n consumer.must_follow, frozenset([write_pcoll]))\\n\\n # Everything that was originally a stage or a replacement, but wasn't\\n # replaced, should be in the final graph.\\n final_stages = frozenset(stages).union(list(replacements.values()))\\\\\\n .difference(list(replacements))\\n\\n for stage in final_stages:\\n # Update all references to their final values before throwing\\n # the replacement data away.\\n stage.must_follow = frozenset(replacement(s) for s in stage.must_follow)\\n # Two reads of the same stage may have been fused. This is unneeded.\\n stage.deduplicate_read()\\n return final_stages\",\n \"def stepregistry():\\n registry = StepRegistry()\\n yield registry\",\n \"def _stage(self):\\n\\n pass\",\n \"def rollout_algorithm(self):\\n states, actions = [], []\\n done = False\\n state = self.env._reset()\\n while not done:\\n supervisor_label = self.supervisor.eval_policy(self.env)\\n action_packed = [supervisor_label, 1]\\n next_state, _, done, _ = self.env._step(action_packed)\\n\\n states.append(state)\\n actions.append(supervisor_label)\\n state = next_state\\n\\n return states, actions\",\n \"def process(self, pipelines: List[Pipeline]) -> Iterator[cat.Catalog]:\\n merged: Optional[cat.Catalog] = None\\n logger.debug(f'merge entering process with {len(pipelines)} pipelines')\\n for pipeline in pipelines:\\n catalog = next(pipeline.process(None))\\n merged = self._merge_catalog(merged, catalog)\\n yield merged\",\n \"def increment_stage_in_forms(forms):\\n for index, form in enumerate(forms.all(), 1):\\n form.stage = index\\n form.save(update_fields=['stage'])\",\n \"def Move_Stage(self):\\n for i in range(3):\\n if self.set_pos[i] == 0:\\n continue\\n print \\\"Moving stage %s by %s steps\\\\n\\\"%(self.POS_NAME[i], self.set_pos[i])\\n self.ser.write('F,C'+self.STEPPER_NAME[i]+str(self.set_pos[i])+',R')\\n time.sleep(0.5)\\n time.sleep(0.5)\\n return\",\n \"def to_iterable(\\n stage: Stage = pypeln_utils.UNDEFINED, maxsize: int = 0\\n) -> typing.Iterable:\\n\\n if pypeln_utils.is_undefined(stage):\\n return pypeln_utils.Partial(lambda stage: to_iterable(stage, maxsize=maxsize))\\n\\n if isinstance(stage, Stage):\\n iterable = stage.to_iterable(maxsize=maxsize)\\n else:\\n iterable = stage\\n\\n return iterable\",\n \"def __call__(self, states, agent_states=None):\\n if agent_states is None:\\n agent_states = [None] * len(states)\\n if self.preprocessor is not None:\\n states = self.preprocessor(states)\\n probs_v = self.model(states)\\n if self.apply_softmax:\\n probs_v = tf.nn.softmax(probs_v, axis=1)\\n probs = probs_v.numpy()\\n actions = self.action_selector(probs)\\n return np.array(actions), agent_states\",\n \"def arun(*stages, **kwargs):\\n loglevel = kwargs.pop(\\\"loglevel\\\", \\\"INFO\\\")\\n alog.setLevel(getattr(logging, loglevel))\\n STAGE_ID = os.environ.get(\\\"STAGE_ID\\\")\\n \\n def run_presub(stages):\\n \\\"\\\"\\\"Execute any pre-submission stages.\\\"\\\"\\\"\\n if is_presub(stages[0]):\\n # Pop the first stage\\n stage, stages = stages[0], stages[1:]\\n # Execute it only if Python was invoked normally, not in a queue job\\n if STAGE_ID is None:\\n msg = \\\"Only the first stage can execute on the login node\\\"\\n assert all([not is_presub(i) for i in stages]), msg\\n alog.info(\\\"Pre-submit stage starting: %s\\\", stage)\\n stage()\\n alog.info(\\\"Pre-submit stage done: %s\\\", stage)\\n return stages\\n \\n global ID # pylint:disable=W0603\\n testID = kwargs.pop(\\\"testID\\\", None)\\n if testID is not None:\\n alog.info(\\\"Calling arun() with testID=%s\\\", testID)\\n stages = run_presub(stages)\\n ID = testID\\n for STAGE_ID in range(len(stages)):\\n os.environ[\\\"STAGE_ID\\\"] = str(STAGE_ID)\\n stages[STAGE_ID]()\\n # arun(*stages, loglevel=loglevel, testID=testID)\\n return\\n \\n alog.debug(\\\"ID=%s, STAGE_ID=%s\\\", ID, STAGE_ID)\\n assert not kwargs, \\\"Undefined keyword arguments: %s\\\" % kwargs\\n assert NID is not None, \\\"Need to call set_NID() before arun()\\\"\\n stages = run_presub(stages)\\n # Return early if there are no stages to submit jobs for\\n if not stages:\\n return\\n if STAGE_ID is None: # not invoked as queue job, so submit jobs\\n for this, next_ in zip(stages, stages[1:]):\\n if is_par(this) and is_par(next_):\\n msg = \\\"Consecutive stages %s and %s are both parallel.\\\"\\n msg += \\\" qsub dependencies cannot handle this case.\\\"\\n msg += \\\" Workaround: insert a serial arrayjob.wait.\\\"\\n raise AssertionError(msg % (this, next_))\\n on_opt = \\\"-W depend=on:%s\\\" % (NID / ppn)\\n jobid = {} # storing receipts from qsub\\n jobdep = defaultdict(list) # accumulating dependencies\\n # boolean vector: whether each stage follows a parallel one\\n afterpar = np.array([(STAGE_ID > 0) and is_par(stages[STAGE_ID - 1]) \\n for STAGE_ID, stage in enumerate(stages)])\\n # array of (STAGE_ID, stage) tuples\\n istage = np.array(list(enumerate(stages)))\\n for STAGE_ID, stage in istage[afterpar]:\\n # submit each stage that follows a parallel one\\n jobid[STAGE_ID] = submit(STAGE_ID, on_opt, *opt[\\\"qopt\\\"][stage])\\n # prepare for the parallel one to depend on the one just submitted\\n jobdep[STAGE_ID - 1].append(\\\"beforeok:%s\\\" % jobid[STAGE_ID])\\n for STAGE_ID, stage in istage[~afterpar]:\\n # submit each stage that does not follow a parallel one\\n if STAGE_ID > 0:\\n # prepare for this stage to depend on the previous one\\n jobdep[STAGE_ID].append(\\\"afterok:%s\\\" % jobid[STAGE_ID - 1])\\n dep = jobdep[STAGE_ID]\\n dep_opt = (\\\"-W depend=\\\" + \\\",\\\".join(dep)) if dep else \\\"\\\"\\n arr_opt = array_opt(NID / ppn) if is_par(stage) else \\\"\\\"\\n jobid[STAGE_ID] = submit(STAGE_ID, dep_opt, arr_opt, \\n *opt[\\\"qopt\\\"][stage])\\n else: # invoked as queue job\\n stage = stages[int(STAGE_ID)]\\n alog.info(\\\"Stage starting: %s\\\", stage)\\n stage()\\n alog.info(\\\"Stage done: %s\\\", stage)\",\n \"def job_gen(self):\\n for job in self.task_manager.jobs(self.time_frame):\\n if job.status == TASK.SCHEDULED:\\n yield job\",\n \"def populate_data_channel_coders(stages, pipeline_context):\\n # type: (Iterable[Stage], TransformContext) -> Iterable[Stage]\\n for stage in stages:\\n for transform in stage.transforms:\\n if transform.spec.urn in (bundle_processor.DATA_INPUT_URN,\\n bundle_processor.DATA_OUTPUT_URN):\\n if transform.spec.urn == bundle_processor.DATA_INPUT_URN:\\n sdk_pcoll_id = only_element(transform.outputs.values())\\n else:\\n sdk_pcoll_id = only_element(transform.inputs.values())\\n pipeline_context.add_data_channel_coder(sdk_pcoll_id)\\n\\n return stages\",\n \"def get_processes():\\n yield from psutil.process_iter()\",\n \"def _create_jobs(self, actions, lockable_objects, proc_uuid=None):\\n procedures = set()\\n jobs = []\\n for number in range(0, len(actions)):\\n job = self._create_job(\\n actions[number], lockable_objects, proc_uuid\\n )\\n jobs.append(job)\\n procedures.add(job.procedure)\\n return list(procedures), jobs\",\n \"def stage(self, stage: osbuild.Stage):\",\n \"def f_chains(self) -> List[Callable[[], Chain]]:\\n return [delayed_run_chain() for _ in range(self.n_chains)]\",\n \"def each_step(graph):\\n\\n steps = graph.topological_sort()\\n steps.reverse()\\n\\n for step in steps:\\n deps = graph.downstream(step.name)\\n yield (step, deps)\",\n \"def __init__(self):\\n self._pipe = []\\n self._group = None\\n stages = ['on', 'off', 'color', 'transition', 'flash', 'callback',\\n 'repeat', 'brightness', 'wait', 'temperature', 'white',\\n 'white_up', 'white_down', 'red_up', 'red_down',\\n 'green_up', 'green_down', 'blue_up', 'blue_down',\\n 'night_light', 'link', 'unlink']\\n for name in stages:\\n self._add_stage(name)\",\n \"def execution_order(phases):\\r\\n\\r\\n # Its key that we process phase dependencies depth first to maintain initial phase ordering as\\r\\n # passed in when phase graphs are dependency disjoint. A breadth first sort could mix next\\r\\n # order executions and violate the implied intent of the passed in phase ordering.\\r\\n\\r\\n processed = set()\\r\\n\\r\\n def order(_phases):\\r\\n for phase in _phases:\\r\\n if phase not in processed:\\r\\n processed.add(phase)\\r\\n for goal in phase.goals():\\r\\n for dep in order(goal.dependencies):\\r\\n yield dep\\r\\n yield phase\\r\\n\\r\\n for ordered in order(phases):\\r\\n yield ordered\",\n \"def evaluate_batch(self, pipelines):\",\n \"def _iter_transitions_all_(self):\\n for state in self.iter_states():\\n for t in state.transitions:\\n yield t\",\n \"def sequence_macros(macros):\\n\\n def foo():\\n for m in macros:\\n m()\\n\\n return foo\",\n \"def iterate(input,\\n actions={},\\n default_action=None,\\n input_compression='ext',\\n depth=1,\\n parameter_puns=True,\\n **more_actions):\\n\\n actions = dict(actions, **more_actions)\\n if parameter_puns:\\n actions = {k: __parameter_puns_decorator(v)\\n for k, v in six.iteritems(actions)}\\n if default_action:\\n default_action = __parameter_puns_decorator(default_action)\\n\\n def has_action(tag):\\n return tag in actions or default_action\\n\\n stks = []\\n trail = []\\n with __compressed_open(input, 'r', input_compression) as f:\\n context = iter(ET.iterparse(f, events=(\\\"start\\\", \\\"end\\\")))\\n for evt, elem in context:\\n if evt == 'start':\\n trail.append(Element(elem, trail[::-1]))\\n if len(trail) > depth and has_action(elem.tag):\\n stks.append([])\\n elif evt == 'end':\\n element = trail.pop()\\n if len(trail) >= depth and has_action(elem.tag):\\n element._finalize(elem.text, elem.tail, stks.pop())\\n res = actions.get(elem.tag, default_action)(element)\\n if not inspect.isgenerator(res):\\n res = (res,)\\n for x in res:\\n if x is not None:\\n if len(stks) > 0:\\n stks[-1].append(x)\\n else:\\n yield x\\n elem.clear()\",\n \"def __call__(self, *pipeline_factories, exceptions=None, wait=True):\\n return self.run(*pipeline_factories, exceptions=exceptions, wait=wait)\",\n \"def steps(self):\\n\\n if not os.path.exists(self.build_path):\\n raise exceptions.ProjectNotBuildError()\\n\\n steps = []\\n for filename in os.listdir(self.build_path):\\n match = re.match(r'(\\\\d{4})_(.*)\\\\.json', filename)\\n if not match:\\n continue\\n\\n with open(os.path.join(self.build_path, filename), 'r') as f:\\n template = json.loads(f.read())\\n\\n template_type = 'custom' if '_type' in template else 'cloudformation'\\n steps.append((int(match.groups()[0]), match.groups()[1], filename, template_type))\\n steps = sorted(steps, key=lambda x: x[0])\\n\\n return steps\",\n \"def sequential(*layers: tp.Callable[..., tp.Any]) -> tp.Callable[..., tp.Any]:\\n\\n def call(inputs, *args, **kwargs):\\n\\n out = inputs\\n for i, layer in enumerate(layers):\\n if i == 0:\\n out = layer(out, *args, **kwargs)\\n else:\\n out = layer(out)\\n\\n if not isinstance(layer, module.Module):\\n if hooks.summaries_active():\\n name = utils.get_name(layer)\\n\\n path = module.get_module_path()\\n path = path if path is not None else ()\\n\\n hooks.add_summary(path + (name,), layer, out)\\n return out\\n\\n return call\",\n \"def get_callbacks(self, stage=None):\\n if stage:\\n return self._registry.get(stage, [])\\n else:\\n callbacks = []\\n for stage_callbacks in self._registry.values():\\n callbacks += stage_callbacks\\n return callbacks\",\n \"def step(self, closure=None):\\n loss = None\\n if closure is not None:\\n loss = closure()\\n\\n for group in self.param_groups:\\n lr = group[\\\"lr\\\"]\\n weight_decay = group[\\\"weight_decay\\\"]\\n momentum = group['momentum']\\n dampening = group[\\\"dampening\\\"]\\n nesterov = group[\\\"nesterov\\\"]\\n maximize = group[\\\"maximize\\\"]\\n\\n for p in group[\\\"params\\\"]:\\n if p.grad is None:\\n continue\\n\\n grad = p.grad\\n\\n if grad.is_sparse:\\n raise RuntimeError(\\\"SGD does not support sparse gradients.\\\")\\n\\n grad = grad if not maximize else -grad\\n\\n if weight_decay != 0:\\n grad = grad.add(p, alpha=weight_decay)\\n\\n if momentum != 0:\\n buf = self.state[p][\\\"momentum_buffer\\\"]\\n\\n buf.mul_(momentum).add_(grad, alpha=1.0 - dampening)\\n\\n if nesterov:\\n grad.add_(buf, alpha=momentum)\\n else:\\n grad = buf\\n\\n p.add_(-lr * grad)\\n\\n return loss\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6442683","0.63786435","0.6202054","0.6113744","0.6091911","0.60464627","0.60097075","0.60097075","0.5929696","0.5866913","0.5734935","0.56417054","0.56399095","0.56399095","0.56399095","0.56128675","0.55290216","0.5513827","0.54971427","0.5468624","0.5463165","0.5462","0.543548","0.5422332","0.5402847","0.5354267","0.5299588","0.5281375","0.5265026","0.52311677","0.52114105","0.51801133","0.51740646","0.516168","0.515625","0.5117049","0.5105237","0.5081273","0.5060321","0.50423616","0.5029331","0.50139207","0.5001858","0.49644563","0.4958839","0.49577764","0.4947958","0.49045882","0.489485","0.48926154","0.48883626","0.48878872","0.48856997","0.48705092","0.4846291","0.4827836","0.4825191","0.4794447","0.47895655","0.47784805","0.4777508","0.47735786","0.4769741","0.47672564","0.472778","0.47273675","0.47250143","0.47228658","0.4722272","0.47172654","0.47170812","0.4702943","0.47006622","0.46970785","0.4662145","0.46586004","0.46533346","0.46340093","0.46171346","0.4592315","0.45918944","0.45825437","0.4580956","0.45758358","0.45705736","0.45677146","0.45656905","0.45631903","0.4561391","0.4557807","0.45529586","0.45501944","0.4542613","0.45343605","0.45263734","0.45179114","0.45093378","0.4508219","0.4499451","0.44969305"],"string":"[\n \"0.6442683\",\n \"0.63786435\",\n \"0.6202054\",\n \"0.6113744\",\n \"0.6091911\",\n \"0.60464627\",\n \"0.60097075\",\n \"0.60097075\",\n \"0.5929696\",\n \"0.5866913\",\n \"0.5734935\",\n \"0.56417054\",\n \"0.56399095\",\n \"0.56399095\",\n \"0.56399095\",\n \"0.56128675\",\n \"0.55290216\",\n \"0.5513827\",\n \"0.54971427\",\n \"0.5468624\",\n \"0.5463165\",\n \"0.5462\",\n \"0.543548\",\n \"0.5422332\",\n \"0.5402847\",\n \"0.5354267\",\n \"0.5299588\",\n \"0.5281375\",\n \"0.5265026\",\n \"0.52311677\",\n \"0.52114105\",\n \"0.51801133\",\n \"0.51740646\",\n \"0.516168\",\n \"0.515625\",\n \"0.5117049\",\n \"0.5105237\",\n \"0.5081273\",\n \"0.5060321\",\n \"0.50423616\",\n \"0.5029331\",\n \"0.50139207\",\n \"0.5001858\",\n \"0.49644563\",\n \"0.4958839\",\n \"0.49577764\",\n \"0.4947958\",\n \"0.49045882\",\n \"0.489485\",\n \"0.48926154\",\n \"0.48883626\",\n \"0.48878872\",\n \"0.48856997\",\n \"0.48705092\",\n \"0.4846291\",\n \"0.4827836\",\n \"0.4825191\",\n \"0.4794447\",\n \"0.47895655\",\n \"0.47784805\",\n \"0.4777508\",\n \"0.47735786\",\n \"0.4769741\",\n \"0.47672564\",\n \"0.472778\",\n \"0.47273675\",\n \"0.47250143\",\n \"0.47228658\",\n \"0.4722272\",\n \"0.47172654\",\n \"0.47170812\",\n \"0.4702943\",\n \"0.47006622\",\n \"0.46970785\",\n \"0.4662145\",\n \"0.46586004\",\n \"0.46533346\",\n \"0.46340093\",\n \"0.46171346\",\n \"0.4592315\",\n \"0.45918944\",\n \"0.45825437\",\n \"0.4580956\",\n \"0.45758358\",\n \"0.45705736\",\n \"0.45677146\",\n \"0.45656905\",\n \"0.45631903\",\n \"0.4561391\",\n \"0.4557807\",\n \"0.45529586\",\n \"0.45501944\",\n \"0.4542613\",\n \"0.45343605\",\n \"0.45263734\",\n \"0.45179114\",\n \"0.45093378\",\n \"0.4508219\",\n \"0.4499451\",\n \"0.44969305\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.71270746"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":17875,"cells":{"query":{"kind":"string","value":"Runs the singularity action"},"document":{"kind":"string","value":"def run(self, reuse=False):\n build = True\n if 'shub://' in self.action['uses']:\n image = self.action['uses']\n build = False\n elif './' in self.action['uses']:\n image = 'action/' + os.path.basename(self.action['uses'])\n singularityfile_path = os.path.join(\n os.getcwd(), self.action['uses'])\n else:\n image = '/'.join(self.action['uses'].split('/')[:2])\n singularityfile_path = os.path.join(self.action['repo_dir'],\n self.action['action_dir'])\n\n if not reuse:\n if self.singularity_exists():\n self.singularity_rm()\n if build:\n self.singularity_build(singularityfile_path, image)\n else:\n self.singularity_pull(image)\n else:\n if not self.singularity_exists():\n if build:\n self.singularity_build(singularityfile_path, image)\n else:\n self.singularity_pull(image)\n\n e = self.singularity_start(image)\n\n if e != 0:\n pu.fail('Action {} failed!\\n'.format(self.action['name']))"},"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 test_smoke(self):\n\t\tinit_state = torch.tensor(0.0)\n\t\ttotal_time = torch.tensor(4.0)\n\t\tprint('Agent state trajectory and actions:')\n\t\tAgent().play(init_state, total_time)\n\t\tpyro.clear_param_store()","def singularity_start(self, image):\n env_vars = self.action.get('env', {})\n\n for s in self.action.get('secrets', []):\n env_vars.update({s: os.environ[s]})\n\n for e, v in self.env.items():\n env_vars.update({e: v})\n\n env_vars.update({'HOME': os.environ['HOME']})\n\n # sets the env variables\n for k, v in env_vars.items():\n Client.setenv(k, v)\n\n e = Client.run(image=self.generate_image_name(image),\n args=' '.join(self.action.get('args', '')),\n return_result=True)\n return e['return_code']","def handleSingularity(singularity_list, singularity_type, trajectory, robot):\n \n multiStepInter = False\n\n target_trajectory = np.copy(trajectory)\n \n if multiStepInter is False:\n for i in range(0,np.size(singularity_list)):\n \n index = singularity_list[i]\n \n if(singularity_type[i] == 0):\n \n target_trajectory[index][0] = (trajectory[index-1][0] + trajectory[index+1][0]) / 2\n target_trajectory[index][1] = (trajectory[index-1][1] + trajectory[index+1][1]) / 2\n target_trajectory[index][2] = (trajectory[index-1][2] + trajectory[index+1][2]) / 2\n target_trajectory[index][3] = (trajectory[index-1][3] + trajectory[index+1][3]) / 2\n target_trajectory[index][4] = (trajectory[index-1][4] + trajectory[index+1][4]) / 2\n target_trajectory[index][5] = (trajectory[index-1][5] + trajectory[index+1][5]) / 2\n \n for j in range(0,6):\n for k in range(0,2): \n \n delta_angle_joint = np.abs(target_trajectory[index-1+k][j] - trajectory[index+k][j])\n \n if delta_angle_joint > max_velocity[j]*time_step: \n print \"No possible path found because of overhead singularity.\"\n return np.array([robot.GetDOFValues()])\n \n if(singularity_type[i] == 1):\n \n target_trajectory[index][0] = (trajectory[index-1][0] + trajectory[index+1][0]) / 2\n target_trajectory[index][1] = (trajectory[index-1][1] + trajectory[index+1][1]) / 2\n target_trajectory[index][2] = (trajectory[index-1][2] + trajectory[index+1][2]) / 2\n target_trajectory[index][3] = (trajectory[index-1][3] + trajectory[index+1][3]) / 2\n target_trajectory[index][4] = (trajectory[index-1][4] + trajectory[index+1][4]) / 2\n target_trajectory[index][5] = (trajectory[index-1][5] + trajectory[index+1][5]) / 2\n \n for j in range(0,6):\n for k in range(0,2): \n \n delta_angle_joint = np.abs(target_trajectory[index-1+k][j] - trajectory[index+k][j])\n \n if delta_angle_joint > max_velocity[j]*time_step: \n print \"No possible path found because of wrist singularity.\"\n print \n return np.array([robot.GetDOFValues()]) \n else:\n # Multi step interpolation dont function yet\n interStep = 2\n InterSuccess = False\n \n while InterSuccess is False:\n \n for i in range(0,np.size(singularity_list)):\n \n index = singularity_list[i]\n \n #Todo: check if interpolation width hurts trajectory range or other singularity interpolations \n \n target_trajectory[index][0] = (target_trajectory[index-(interStep/2)][0] + target_trajectory[index+(interStep/2)][0]) / 2\n target_trajectory[index][1] = (target_trajectory[index-(interStep/2)][1] + target_trajectory[index+(interStep/2)][1]) / 2\n target_trajectory[index][2] = (target_trajectory[index-(interStep/2)][2] + target_trajectory[index+(interStep/2)][2]) / 2\n target_trajectory[index][3] = (target_trajectory[index-(interStep/2)][3] + target_trajectory[index+(interStep/2)][3]) / 2\n target_trajectory[index][4] = (target_trajectory[index-(interStep/2)][4] + target_trajectory[index+(interStep/2)][4]) / 2\n target_trajectory[index][5] = (target_trajectory[index-(interStep/2)][5] + target_trajectory[index+(interStep/2)][5]) / 2\n \n delta_traj = target_trajectory[index]\n delta_traj[0] = (target_trajectory[index+(interStep/2)][0] - target_trajectory[index-(interStep/2)][0]) / interStep\n delta_traj[1] = (target_trajectory[index+(interStep/2)][1] - target_trajectory[index-(interStep/2)][1]) / interStep\n delta_traj[2] = (target_trajectory[index+(interStep/2)][2] - target_trajectory[index-(interStep/2)][2]) / interStep\n delta_traj[3] = (target_trajectory[index+(interStep/2)][3] - target_trajectory[index-(interStep/2)][3]) / interStep\n delta_traj[4] = (target_trajectory[index+(interStep/2)][4] - target_trajectory[index-(interStep/2)][4]) / interStep\n delta_traj[5] = (target_trajectory[index+(interStep/2)][5] - target_trajectory[index-(interStep/2)][5]) / interStep\n \n for s in range(1,interStep/2): \n target_trajectory[index-s][0] = target_trajectory[index][0] - (s * delta_traj[0]) \n target_trajectory[index-s][1] = target_trajectory[index][1] - (s * delta_traj[1]) \n target_trajectory[index-s][2] = target_trajectory[index][2] - (s * delta_traj[2])\n target_trajectory[index-s][3] = target_trajectory[index][3] - (s * delta_traj[3])\n target_trajectory[index-s][4] = target_trajectory[index][4] - (s * delta_traj[4])\n target_trajectory[index-s][5] = target_trajectory[index][5] - (s * delta_traj[5])\n \n target_trajectory[index+s][0] = target_trajectory[index][0] + (s * delta_traj[0]) \n target_trajectory[index+s][1] = target_trajectory[index][1] + (s * delta_traj[1]) \n target_trajectory[index+s][2] = target_trajectory[index][2] + (s * delta_traj[2])\n target_trajectory[index+s][3] = target_trajectory[index][3] + (s * delta_traj[3])\n target_trajectory[index+s][4] = target_trajectory[index][4] + (s * delta_traj[4])\n target_trajectory[index+s][5] = target_trajectory[index][5] + (s * delta_traj[5])\n \n maxVelHurt = False\n \n for j in range(0,6):\n for k in range(0,interStep): \n \n delta_angle_joint = np.abs(target_trajectory[index-(interStep/2)+k][j] - target_trajectory[index-((interStep/2)-1)+k][j])\n \n if delta_angle_joint > max_velocity[j]*time_step and maxVelHurt is False:\n \n maxVelHurt = True \n print \"Interpolation step = \" \n print interStep \n print \"No possible path found because of singularity.\"\n print \"joint:\"\n print j\n print \"step:\"\n print k\n \n print delta_angle_joint\n print \">\"\n print (max_velocity[j]*time_step)\n print \"______________________________________________\"\n #return np.array([robot.GetDOFValues()])\n interStep = interStep + 2\n if interStep == 30:\n return np.array([robot.GetDOFValues()])\n \n \n \n if maxVelHurt is False:\n InterSuccess = True \n \n \n \n return target_trajectory","def act(self, env: FakeEnv, s: ActorStrategy):\n action = env.action_space.sample()\n print(f\"Sampled action shape : {action.shape}\")\n env.step(action)","def run_single(self):\n self.run_sim_time(1)","def act(self, state, epsilon, env):\n if random.random() > epsilon:\n state = Variable(torch.FloatTensor(state)).unsqueeze(0) # adds extra dim when single input\n state = self.vari_gpu(state)\n _, u_opt = self.forward(state)\n action = (u_opt.cpu().detach().numpy()) # compute the u*[0] \n #print('act:q_value ',q_value)\n #print('act:model action ',action)\n else:\n rand = np.random.rand(int(np.array(env.action_space.shape)))\n high = env.action_space.high\n low = env.action_space.low\n action = low + rand*(high-low)\n #print('act: ',action)\n return action","def Main():\n EnigmaSim = simulation() #Creates the simulation object\n EnigmaSim.Run() #Runs the simulation","def execute(self, agent: Agent, state: SimState) -> None:\n if agent.state() == AgentState.SUSCEPTIBLE and self.days == state.vaccine_time() \\\n and np.random.random() < state.vaccine_share():\n agent.set_state(AgentState.IMMUNE)","async def handle(self):\n local_controller = self.controller\n local_controller.add_action(local_controller.larvae.random.train(HYDRALISK))\n return True","def _run(self):\n logging.warning('-> perform EMPTY experiment...')","def test_sparsity(config):\n total_zeros = 0\n total_nonzeros = 0\n\n print ('<===sparsity type is {}'.format(config.sparsity_type))\n print ('<===layers to be pruned are {}'.format(config._prune_ratios))\n if config.masked_progressive and (config.sparsity_type == 'filter' or config.sparsity_type =='column'or config.sparsity_type == \"bn_filter\" ):\n ### test both column and row sparsity\n print (\"***********checking column sparsity*************\")\n for name,W in config.model.named_parameters():\n if name not in config.prune_ratios:\n continue\n W = W.cpu().detach().numpy()\n shape = W.shape\n W2d = W.reshape(shape[0],-1)\n column_l2_norm = LA.norm(W2d,2,axis=0)\n zero_column = np.sum(column_l2_norm == 0)\n nonzero_column = np.sum(column_l2_norm !=0)\n\n print (\"column sparsity of layer {} is {}\".format(name,zero_column/(zero_column+nonzero_column)))\n print (\"***********checking filter sparsity*************\") \n for name,W in config.model.named_parameters():\n if name not in config.prune_ratios:\n continue\n W = W.cpu().detach().numpy()\n shape = W.shape\n W2d = W.reshape(shape[0],-1)\n row_l2_norm = LA.norm(W2d,2,axis=1)\n zero_row = np.sum(row_l2_norm == 0)\n nonzero_row = np.sum(row_l2_norm !=0)\n print (\"filter sparsity of layer {} is {}\".format(name,zero_row/(zero_row+nonzero_row)))\n print (\"************checking overall sparsity in conv layers*************\")\n for name,W in config.model.named_parameters():\n if name not in config.prune_ratios:\n continue\n W = W.cpu().detach().numpy() \n total_zeros +=np.sum(W==0)\n total_nonzeros +=np.sum(W!=0)\n print ('only consider conv layers, compression rate is {}'.format((total_zeros+total_nonzeros)/total_nonzeros))\n return\n \n if config.sparsity_type == \"irregular\":\n for name,W in config.model.named_parameters():\n if 'bias' in name:\n continue\n W = W.cpu().detach().numpy()\n zeros = np.sum(W==0)\n total_zeros+=zeros\n nonzeros = np.sum(W!=0)\n total_nonzeros+=nonzeros\n print (\"sparsity at layer {} is {}\".format(name,zeros/(zeros+nonzeros)))\n total_weight_number = total_zeros+total_nonzeros\n print ('overal compression rate is {}'.format(total_weight_number/total_nonzeros))\n elif config.sparsity_type == \"column\":\n for name,W in config.model.named_parameters():\n if name not in config.prune_ratios:\n continue\n W = W.cpu().detach().numpy()\n shape = W.shape\n W2d = W.reshape(shape[0],-1)\n column_l2_norm = LA.norm(W2d,2,axis=0)\n zero_column = np.sum(column_l2_norm == 0)\n nonzero_column = np.sum(column_l2_norm !=0)\n total_zeros +=np.sum(W==0)\n total_nonzeros +=np.sum(W!=0)\n print (\"column sparsity of layer {} is {}\".format(name,zero_column/(zero_column+nonzero_column)))\n print ('only consider conv layers, compression rate is {}'.format((total_zeros+total_nonzeros)/total_nonzeros)) \n elif config.sparsity_type == \"filter\":\n print ('inside if')\n print (config.prune_ratios)\n for name,W in config.model.named_parameters():\n if name not in config.prune_ratios:\n continue\n W = W.cpu().detach().numpy()\n shape = W.shape\n W2d = W.reshape(shape[0],-1)\n row_l2_norm = LA.norm(W2d,2,axis=1)\n zero_row = np.sum(row_l2_norm == 0)\n nonzero_row = np.sum(row_l2_norm !=0)\n total_zeros +=np.sum(W==0)\n total_nonzeros +=np.sum(W!=0)\n print (\"filter sparsity of layer {} is {}\".format(name,zero_row/(zero_row+nonzero_row)))\n print ('only consider conv layers, compression rate is {}'.format((total_zeros+total_nonzeros)/total_nonzeros))\n elif config.sparsity_type == \"bn_filter\":\n print ('inside bn_filter')\n print (config.prune_ratios)\n for i,(name,W) in enumerate(config.model.named_parameters()):\n if name not in config.prune_ratios:\n continue\n W = W.cpu().detach().numpy()\n zeros = np.sum(W==0)\n nonzeros = np.sum(W!=0)\n print (\"sparsity at layer {} is {}\".format(name,zeros/(zeros+nonzeros)))","def sing(self):\n if self._energy < self._sing_cost:\n return\n\n self._energy = self._energy - self._sing_cost\n self._env.simulate()","def execute(self, agent: Agent, state: SimState) -> None:\n if agent.state() is not AgentState.INFECTIVE:\n return\n\n if np.random.random() < state.remove_prob():\n if np.random.random() < state.lethality():\n agent.set_state(AgentState.DEAD)\n else:\n agent.set_state(AgentState.IMMUNE)\n else:\n agent.update_sick_days()","async def handle(self):\n local_controller = self.controller\n local_controller.add_action(local_controller.larvae.random.train(DRONE))\n return True","def solveOneStep(self):\n ### Student code goes here\n return True","def RUN(self):","def start(self, world):\n self.sense(world)","def run(self):\n sites = [x for x in self.cell.all_sections['axonnode']]\n ptype = 'pulses'\n self.default_durs=[10., 100., 25.]\n stimdict = { # set up the stimulus parameters\n 'NP': 20,\n 'Sfreq': 500.0,\n 'delay': self.default_durs[0],\n 'dur': 0.5,\n 'amp': 2.0,\n 'PT': 0.0,\n 'dt': self.dt,\n }\n istim = h.iStim(0.5, sec=self.cell.soma)\n istim.delay = 0.\n istim.dur = 1e9 # these actually do not matter...\n istim.iMax = 0.0\n self.run_one(istim, stimdict, sites=sites) #do one simulation","def act(self, s, exploration, game):\n agent_p = self.compute_marginal_pi(s, one_hot=False)\n if self.exploration and random.random() < self.episilon:\n agent_action = random.randint(0, self.action_num - 1)\n else:\n if self.verbose:\n for s in self.Q.keys():\n print('{}--------------'.format(self.id_))\n print('Q of agent {}: state {}: {}'.format(self.id_, s, str(self.Q[s])))\n # print('QAof agent {}: state {}: {}'.format(self.id_, s, str(self.Q_A[s])))\n # self.Q_A\n print('pi of agent {}: state {}: {}'.format(self.id_, s, self.pi[s]))\n # print('pi of opponent agent {}: state{}: {}'.format(self.id_, s, self.opponent_best_pi[s]))\n print('{}--------------'.format(self.id_))\n agent_action = np.argmax(agent_p)\n return agent_action","def simulateOneTimeStep(self):\n\n self.susceptibleToInfected()\n self.infectedToRecovered()\n\n # add the new values of healthy/infected/recovered to the arrays keeping track\n SIR_t = np.array([self.getSusceptible(), self.getInfected(), self.getRecovered()])\n #update SIR time series\n self.SIR = np.concatenate([self.SIR, SIR_t[:,np.newaxis]], axis=1)\n\n # add the new snapshot of the simulation\n self.snapshots.append(self.getSpace().copy())","def step(self, action):\n\n # handling error\n \n assert (action < self.NumofBandits)#, \"[ERROR] un-identified arm\"\n\n # return reward via sampling from normal distribution\n return np.random.normal(self.MeanList[action], self.sigma, 1)[0]","def test_simulation(self):\n\t\tprint \"Simulation is being tested\"\n\n\t\tif toggles.DEBUG_FLAG:\n\t\t\tprint \"Debug Flag Set!\"\n\t\t\tprint self.getConfig()\n\n\t\tif toggles.PACKING:\n\t\t\ttoggles.OUTPUT_PATH = toggles.OUTPUT_PATH+toggles.RUN_NAME+'/'\n\t\t\tpackageMaker(toggles.OUTPUT_PATH,self.getConfig())\n\t\tif toggles.IDEAL_GRID:\n\t\t\tself.consensusGrid()\n\n\t\tif toggles.REAL_DATA:\n\t\t\tsampleData = self.load_data()\n\t\t\tif toggles.RUN_DATA_STATS:\n\t\t\t\tself.output_data_stats(sampleData)\n\t\t\t\tself.reset_database()\n\t\t\tif toggles.RUN_AVERAGE_COST:\n\t\t\t\tself.sim_average_cost(sampleData)\n\t\t\t\tself.reset_database()\n\t\t\tif toggles.RUN_SINGLE_PAIR:\n\t\t\t\tself.sim_single_pair_cost(sampleData, pending_eddy(self.pick_worker([0], [0])))\n\t\t\t\tself.reset_database()\n\t\telse:\n\t\t\tsampleData = {}\n\t\t\tsyn_load_data()\n\n\t\tif toggles.RUN_ITEM_ROUTING and not (toggles.RUN_TASKS_COUNT or toggles.RUN_MULTI_ROUTING):\n\t\t\tif toggles.DEBUG_FLAG:\n\t\t\t\tprint \"Running: item Routing\"\n\t\t\tself.run_sim(deepcopy(sampleData))\n\t\t\tself.reset_database()\n\n\t\tif PRED_SCORE_COUNT and not (RUN_TASKS_COUNT or RUN_MULTI_ROUTING):\n\t\t\tif DEBUG_FLAG:\n\t\t\t\tprint \"Running: Pred Score count\"\n\t\t\tself.run_sim(sampleData)\n\t\t\tself.reset_database()\n\n\n\n\t\tif toggles.COUNT_TICKETS and not (toggles.RUN_TASKS_COUNT or toggles.RUN_MULTI_ROUTING):\n\t\t\tif toggles.DEBUG_FLAG:\n\t\t\t\tprint \"Running: ticket counting\"\n\t\t\tself.run_sim(deepcopy(sampleData))\n\t\t\tself.reset_database()\n\n\t\tif toggles.SELECTIVITY_GRAPH and not (toggles.RUN_TASKS_COUNT or toggles.RUN_MULTI_ROUTING):\n\t\t\tif toggles.DEBUG_FLAG:\n\t\t\t\tprint \"Running: selectivity amounts over time\"\n\t\t\tself.run_sim(sampleData)\n\t\t\tself.reset_database()\n\n\t\t#____FOR LOOKING AT ACCURACY OF RUNS___#\n\t\tif toggles.TEST_ACCURACY and toggles.REAL_DATA:\n\t\t\tcorrectAnswers = self.get_correct_answers(toggles.INPUT_PATH + toggles.ITEM_TYPE + '_correct_answers.csv')\n\t\t\tpassedItems = self.get_passed_items(correctAnswers)\n\n\n\t\tif toggles.RUN_OPTIMAL_SIM:\n\t\t\tcountingArr=[]\n\t\t\tself.reset_database()\n\t\t\tfor i in range(toggles.NUM_SIM):\n\t\t\t\tprint \"running optimal_sim \" +str(i)\n\t\t\t\tself.num_tasks = self.optimal_sim(sampleData)\n\t\t\t\tcountingArr.append(self.num_tasks)\n\t\t\t\tself.reset_database()\n\t\t\tdest = toggles.OUTPUT_PATH+toggles.RUN_NAME+'_optimal_tasks'\n\t\t\tgeneric_csv_write(dest+'.csv',[countingArr])\n\t\t\tif toggles.DEBUG_FLAG:\n\t\t\t\tprint \"Wrote File: \" + dest+'.csv'\n\n\n\n\t\tif toggles.RUN_TASKS_COUNT or toggles.RUN_MULTI_ROUTING or toggles.RUN_CONSENSUS_COUNT:\n\t\t\tif toggles.RUN_TASKS_COUNT:\n\t\t\t\t#print \"Running: task_count\"\n\t\t\t\t#f = open(toggles.OUTPUT_PATH + toggles.RUN_NAME + '_tasks_count.csv', 'a')\n\t\t\t\t#f1 = open(toggles.OUTPUT_PATH + toggles.RUN_NAME + '_incorrect_count.csv', 'a')\n\n\t\t\t\tif toggles.GEN_GRAPHS:\n\t\t\t\t\toutputArray = []\n\n\t\t\trunTasksArray = []\n\t\t\tgoodArray, badArray = [], []\n\t\t\tgoodPoints, badPoints = [], []\n\t\t\taccCount = []\n\t\t\tlocArray = [[],[],[],[]]\n\n\t\t\tfor i in range(toggles.NUM_SIM):\n\t\t\t\tprint \"running simulation \" + str(i+1)\n\t\t\t\tself.run_sim(deepcopy(sampleData))\n\t\t\t\trunTasksArray.append(self.num_tasks)\n\n\t\t\t\t#____FOR LOOKING AT ACCURACY OF RUNS___#\n\t\t\t\tif toggles.TEST_ACCURACY and toggles.REAL_DATA:\n\t\t\t\t\tnum_incorrect = self.final_item_mismatch(passedItems)\n\t\t\t\t\taccCount.append(num_incorrect)\n\t\t\t\tif toggles.RUN_CONSENSUS_COUNT or toggles.VOTE_GRID:\n\t\t\t\t\tdonePairs = IP_Pair.objects.filter(Q(num_no__gt=0)|Q(num_yes__gt=0))\n\t\t\t\t\tif toggles.TEST_ACCURACY:\n\t\t\t\t\t\tgoodPairs, badPairs = [], []\n\t\t\t\t\t\tfor pair in donePairs:\n\t\t\t\t\t\t\tval = bool((pair.num_yes-pair.num_no)>0)\n\t\t\t\t\t\t\tif toggles.REAL_DATA:\n\t\t\t\t\t\t\t\tcorrect = ((correctAnswers[(pair.item,pair.predicate)]) == val)\n\t\t\t\t\t\t\telse:\n\t\t\t\t\t\t\t\tcorrect = (pair.true_answer == val)\n\t\t\t\t\t\t\tif correct:\n\t\t\t\t\t\t\t\tgoodArray.append(pair.num_no+pair.num_yes)\n\t\t\t\t\t\t\t\tgoodPoints.append((pair.num_no,pair.num_yes))\n\t\t\t\t\t\t\telse:\n\t\t\t\t\t\t\t\tbadArray.append(pair.num_no+pair.num_yes)\n\t\t\t\t\t\t\t\tbadPoints.append((pair.num_no,pair.num_yes))\n\t\t\t\t\telse:\n\t\t\t\t\t\tfor pair in donePairs:\n\t\t\t\t\t\t\tgoodArray.append(pair.num_no + pair.num_yes)\n\t\t\t\t\t\t\tgoodPoints.append((pair.num_no,pair.num_yes))\n\n\t\t\t\t\t#print \"This is number of incorrect items: \", num_incorrect\n\n\t\t\t\tself.reset_database()\n\n\t\t\tif toggles.RUN_TASKS_COUNT:\n\t\t\t\tgeneric_csv_write(toggles.OUTPUT_PATH+toggles.RUN_NAME+'_tasks_count.csv',[runTasksArray])\n\t\t\t\tif toggles.DEBUG_FLAG:\n\t\t\t\t\tprint \"Wrote File: \" + toggles.OUTPUT_PATH + toggles.RUN_NAME + '_tasks_count.csv'\n\t\t\t\tif toggles.GEN_GRAPHS:\n\t\t\t\t\tif len(runTasksArray)>1:\n\t\t\t\t\t\tdest = toggles.OUTPUT_PATH + toggles.RUN_NAME + '_tasks_count.png'\n\t\t\t\t\t\ttitle = toggles.RUN_NAME + ' Cost distribution'\n\t\t\t\t\t\thist_gen(runTasksArray, dest, labels = ('Cost','Frequency'), title = title)\n\t\t\t\t\t\tif toggles.DEBUG_FLAG:\n\t\t\t\t\t\t\tprint \"Wrote File: \" + dest\n\t\t\t\t\telif toggles.DEBUG_FLAG:\n\t\t\t\t\t\tprint \"only ran one sim, not running hist_gen\"\n\n\t\t\tif toggles.RUN_MULTI_ROUTING:\n\t\t\t\t\tdest = toggles.OUTPUT_PATH + toggles.RUN_NAME + '_Eddy_sys_' + str(toggles.EDDY_SYS) + '_multi_routing.png'\n\t\t\t\t\ttitle = toggles.RUN_NAME + ' Average Predicate Routing'\n\t\t\t\t\tquestions = toggles.CHOSEN_PREDS\n\t\t\t\t\tarrayData = []\n\t\t\t\t\tfor i in range(len(questions)):\n\t\t\t\t\t\tarrayData.append([])\n\t\t\t\t\tfor routingL in ROUTING_ARRAY:\n\t\t\t\t\t\tfor i in range(len(questions)):\n\t\t\t\t\t\t\tarrayData[i].append(routingL[i])\n\t\t\t\t\tmrsavefile = open(toggles.OUTPUT_PATH+toggles.RUN_NAME+'_multi_routing.csv','w')\n\t\t\t\t\tmrwriter = csv.writer(mrsavefile)\n\t\t\t\t\tmrwriter.writerow(questions)\n\t\t\t\t\tfor row in arrayData:\n\t\t\t\t\t\tmrwriter.writerow(row)\n\t\t\t\t\tmrsavefile.close()\n\t\t\t\t\tif toggles.DEBUG_FLAG:\n\t\t\t\t\t\tprint \"Wrote File: \"+toggles.OUTPUT_PATH+toggles.RUN_NAME+'_multi_routing.csv'\n\t\t\t\t\tif toggles.GEN_GRAPHS:\n\t\t\t\t\t\tstats_bar_graph_gen(arrayData, questions, dest, labels = ('Predicate','# of Items Routed'), title = title)\n\t\t\t\t\t\tif toggles.DEBUG_FLAG:\n\t\t\t\t\t\t\tprint \"Wrote File: \" + toggles.OUTPUT_PATH+toggles.RUN_NAME+'_multi_routing.png'\n\t\t\tif toggles.ACCURACY_COUNT:\n\t\t\t\tdest = toggles.OUTPUT_PATH+toggles.RUN_NAME+'_acc_count'\n\t\t\t\tgeneric_csv_write(dest+'.csv',[accCount])\n\t\t\t\tif toggles.GEN_GRAPHS:\n\t\t\t\t\thist_gen(accCount, dest+'.png')\n\n\t\t\tif toggles.RUN_CONSENSUS_COUNT:\n\t\t\t\tdest = toggles.OUTPUT_PATH + toggles.RUN_NAME+'_consensus_count'\n\t\t\t\tif len(goodArray)>1:\n\t\t\t\t\tif len(badArray) == 0:\n\t\t\t\t\t\tgeneric_csv_write(dest+'.csv',[goodArray])\n\t\t\t\t\t\t#print goodArray\n\t\t\t\t\telse:\n\t\t\t\t\t\tgeneric_csv_write(dest+'.csv',[goodArray,badArray])\n\t\t\t\t\t\t#print goodArray,badArray\n\t\t\t\t\tif toggles.DEBUG_FLAG:\n\t\t\t\t\t\tprint \"Wrote File: \" + dest + '.csv'\n\t\t\t\t\tif toggles.GEN_GRAPHS:\n\t\t\t\t\t\ttitle = 'Normalized Distribution of Tasks before Consensus'\n\t\t\t\t\t\tlabels = ('Number of Tasks', 'Frequency')\n\t\t\t\t\t\tif len(badArray) < 2:\n\t\t\t\t\t\t\thist_gen(goodArray, dest+'.png',labels=labels,title=title)\n\t\t\t\t\t\telse:\n\t\t\t\t\t\t\tleg = ('Correctly Evaluated IP pairs','Incorrectly Evaluated IP pairs')\n\t\t\t\t\t\t\tmulti_hist_gen([goodArray,badArray],leg,dest+'.png',labels=labels,title=title)\n\t\t\t\telif toggles.DEBUG_FLAG:\n\t\t\t\t\tprint \"only ran one sim, ignoring results\"\n\t\t\tif toggles.VOTE_GRID:\n\t\t\t\tdest = toggles.OUTPUT_PATH + toggles.RUN_NAME+'_vote_grid'\n\t\t\t\tif len(goodPoints)>1:\n\t\t\t\t\tif len(badPoints)==0:\n\t\t\t\t\t\tgeneric_csv_write(dest+'.csv',goodPoints)\n\t\t\t\t\telse:\n\t\t\t\t\t\tgeneric_csv_write(dest+'_good.csv',goodPoints)\n\t\t\t\t\t\tgeneric_csv_write(dest+'_bad.csv',badPoints)\n\t\t\t\t\tif toggles.GEN_GRAPHS:\n\t\t\t\t\t\ttitle = \"Vote Grid Graph\"\n\t\t\t\t\t\tlabels = (\"Number of No Votes\",\"Number of Yes Votes\")\n\t\t\t\t\t\tif len(badPoints)==0:\n\t\t\t\t\t\t\txL,yL=zip(*goodPoints)\n\t\t\t\t\t\t\tline_graph_gen(xL,yL,dest+'.png',title=title,labels=labels,scatter=True,square=True)\n\t\t\t\t\t\telse:\n\t\t\t\t\t\t\tgX,gY = zip(*goodPoints)\n\t\t\t\t\t\t\tbX,bY = zip(*badPoints)\n\t\t\t\t\t\t\tmulti_line_graph_gen((gX,bX),(gY,bY),('Correct','Incorrect'),dest+'_both.png',title=title,labels=labels,scatter=True,square=True)\n\t\t\t\t\t\t\tline_graph_gen(gX,gY,dest+'_good.png',title=title+\" goodPoints\",labels=labels,scatter=True,square=True)\n\t\t\t\t\t\t\tline_graph_gen(bX,bY,dest+'_bad.png',title=title+\" badPoints\",labels=labels,scatter=True,square=True)\n\t\tif toggles.TIME_SIMS:\n\t\t\tself.timeRun(sampleData)\n\n\t\tif toggles.RUN_ABSTRACT_SIM:\n\t\t\tself.abstract_sim(sampleData, toggles.ABSTRACT_VARIABLE, toggles.ABSTRACT_VALUES)","def _simulate(self, action=None):\n for k in range(int(self.SIMULATION_FREQUENCY // self.config[\"policy_frequency\"])):\n if action is not None and \\\n self.time % int(self.SIMULATION_FREQUENCY // self.config[\"policy_frequency\"]) == 0:\n # Forward action to the spacecraft\n self.spacecraft.act(self.ACTIONS[action])\n\n self.space.act()\n self.space.step(1 / self.SIMULATION_FREQUENCY)\n self.time += 1\n\n # Automatically render intermediate simulation steps if a viewer has been launched\n # Ignored if the rendering is done offscreen\n self._automatic_rendering()\n\n # Stop at terminal states\n if self.done or self._is_terminal():\n break\n self.enable_auto_render = False","def run(self):\n self._display_sims(self._compute_sims())","def run_game_logic(self):\n pass","def test_singular_func(self):\n\n m = Mothur(**self.init_vars)\n m.help()\n\n return","def run_sim(self):\n self.operator, var_form, opt = self.generate_VQE_args()\n\n backend = Aer.get_backend('statevector_simulator')\n quantum_instance = QuantumInstance(backend=backend)\n vqe = VQE(self.operator, var_form, opt) \n\n self.result = vqe.run(quantum_instance)\n solution = self.extract_solution(self.result, False)\n return solution","def testSimCompletes(self):\n sim = Simulation()\n self.assertEqual(25, sim.run_simple(1, 11, \"output\", 0.1, 2, 10))","def run(self) -> None:\n self._hass.turn_on('scene.{0}'.format(self._args['scene']))","def action_run(self):\n pass","def Execute(self,settings,IsStatusBar=False): \n if settings.IsSeed:\n np.random.seed(5) \n \n self._IsInitial = True\n self.settings = settings\n self.sim_t = copy.copy(settings.starttime) # does not have to start at zero if we perform sequential simulations \n self.X_matrix = copy.deepcopy(settings.X_matrix) \n self.fixed_species_amount = copy.deepcopy(self.parse.fixed_species_amount) \n \n try:\n self.volume_code = settings.volume_code\n except AttributeError: # No volume_code present in settings\n self.volume_code = \"self._current_volume = 1\" \n \n #self.species_to_update = [s for s in range(self.n_species)] # ensure that the first run updates all species \n self.Propensities() \n \n if not self.sim_t: \n self.timestep = 1 \n self.sim_output = []\n self.propensities_output = [] \n self.V_output = []\n self._IsTrackPropensities = copy.copy(settings.IsTrackPropensities)\n self.SpeciesSelection() \n self.RateSelection() \n self.SetEvents() # April 15, moved into here, because otherwise each new cell division cycle starts with a time event, if specified \n if not settings.IsOnlyLastTimepoint:\n self.Initial_Conditions() \n \n nstep_counter = 1\n t1 = time.time()\n while (self.sim_t < settings.endtime) and (self.timestep < settings.timesteps): \n if self.sim_a_0 <= 0: # All reactants got exhausted\n settings.endtime = 10**50\n break\n \n self.RunExactTimestep() # Run direct SSA \n self.HandleEvents()\n \n # Update Propensities selectively \n if self.sim_t < settings.endtime: \n if not self._IsPerformEvent:\n self.species_to_update = self.parse.reaction_affects[self.reaction_index] # Determine vars to update \n else:\n self.species_to_update = [s for s in range(self.n_species)] \n \n self.Propensities()\n \n if not settings.IsOnlyLastTimepoint: # Store Output\n self.GenerateOutput()\n \n self._IsPerformEvent = False # set to false (or just to make sure).\n t2 = time.time() \n if IsStatusBar and t2-t1> 1:\n t1 = time.time()\n sys.stdout.write('\\rsimulating {0:s}\\r'.format('.'*nstep_counter) ) \n sys.stdout.flush() \n nstep_counter+=1\n if nstep_counter > 10:\n nstep_counter = 1 \n sys.stdout.write('\\rsimulating {0:s} '.format('.'*nstep_counter))\n sys.stdout.flush()\n if settings.IsOnlyLastTimepoint or settings.endtime != 10**50: \n self.GenerateOutput() \n if IsStatusBar and t1 and not settings.quiet:\n sys.stdout.write('\\rsimulation done! \\n')","def test_run(env, model_type, hidden_size,action_type, seeds, noise_std, render = False):\r\n\r\n net = build_net(env, seeds, model_type, hidden_size, noise_std,action_type)\r\n hidden_out = (torch.zeros((1, 1, hidden_size), dtype=torch.float),torch.zeros((1, 1, hidden_size), dtype=torch.float))\r\n state = env.reset()\r\n \r\n for step in range(2000):\r\n hidden_in = hidden_out\r\n if step % 5 == 0:\r\n if render:\r\n env.render()\r\n \r\n state = torch.from_numpy(state).float()\r\n action, hidden_out = net(state, hidden_in)\r\n state, reward, done, info = env.step(action[0])\r\n if done:\r\n plt.close()\r\n break","def run(self):\n\n self._action.execute()","def one_step(self):\r\n assert (self.uv_vol is not None)\r\n assert (self.guv_vol is not None)\r\n assert (self.uv_bound is not None)\r\n assert (self.vf_vect_bound is not None)\r\n assert (self.vF_vect_vol is not None)\r\n # Shape checks\r\n assert (self.vF_vect_vol.size == self.vF_vect_vol.shape[0])\r\n assert (self.vf_vect_bound.size == self.vf_vect_bound.shape[0])\r\n assert (self.vF_vect_vol.shape == self.vf_vect_bound.shape)\r\n assert (self.uv_vol.shape[0] == self.uv_vol.shape[1])\r\n assert (self.uv_vol.shape == self.guv_vol.shape)\r\n assert (self.uv_vol.shape == self.uv_bound.shape)\r\n assert (self.uv_vol.shape[0] == self.vF_vect_vol.shape[0])\r\n \r\n if self.step == 0:\r\n self.check_k_matrix_stability()\r\n # print(\"Epsilon is :\"+str(self.Epsilon))\r\n # print(\"Beta is :\"+str(self.Beta))\r\n\r\n # Form \"Stiffness\" matrix:\r\n K = self.make_k_matrix()\r\n # Form \"Force\" vector: \r\n f = self.vF_vect_vol + (self.Epsilon / self.Beta) * self.vf_vect_bound\r\n\r\n # print(\"FORCE VECTOR:\")\r\n # print(f)\r\n # print(\"STIFFNESS MATRIX\")\r\n # print(K)\r\n # print(\"UV_VOL\")\r\n # print(self.uv_vol)\r\n # print(\"EPSILON * GUV_VOL\")\r\n # print(self.Epsilon * self.guv_vol)\r\n # print(\"UV_BOUND * COEFF\")\r\n # print((self.Epsilon / self.Beta) * self.uv_bound)\r\n sol = scipy_sparse_linsolve(K, f)\r\n # print(\"SOLUTION\")\r\n # print(sol)\r\n return sol","def run_one_step(self):\n pass","def test_svd_sharpness2(self):\n \t\t\n\t\tprint(\"----test_svd_sharpness-----\")\n\n\t\t#model = models.vgg11(pretrained=True)\n\t\tmodel = models.vgg11(weights='VGG11_Weights.IMAGENET1K_V1')\n\n\t\tself.watcher = ww.WeightWatcher(model=model, log_level=logging.WARNING)\n\t\t\n\t\t\n\t\tesd_before = self.watcher.get_ESD(layer=self.third_layer) \n\t\t\n\t\tself.watcher.SVDSharpness(layers=[self.third_layer])\n\t\tesd_after = self.watcher.get_ESD(layer=self.third_layer) \n\t\t\n\t\tprint(\"max esd before {}\".format(np.max(esd_before)))\n\t\tprint(\"max esd after {}\".format(np.max(esd_after)))\n\n\t\tself.assertGreater(np.max(esd_before),np.max(esd_after))","def test_run_simulation_returns_nothing(self):\n sim = ss.Simulation()\n assert sim.run_simulation(10) is None","def step(self):\n self.world.slosh_oceans()\n self.world.transfer_energy_vertically()\n self.world.transfer_energy_horizontally()\n self.world.absorb_energy_from_core()\n self.world.absorb_energy_from_sun(self.sun)","def step(self):\n self.smoke = iterate_jacobi(*self.sparse_disc_matrix, self.source + self.smoke, self.smoke, self.obstacles)\n self.smoke_field.update(np.reshape(self.smoke, self.obstacles.shape)[1:-1, 1:-1])","def singularity_handling(self):\n return self._sing_handling","def run():\n\n # Set up environment and agent\n e = Environment() # create environment (also adds some dummy traffic)\n a = e.create_agent(LearningAgent) # create agent\n e.set_primary_agent(a, enforce_deadline= True ) # set agent to track\n\n # Now simulate it\n sim = Simulator(e, update_delay=0.5) # reduce update_delay to speed up simulation\n sim.run(n_trials=100) # press Esc or close pygame window to quit","def run():\n\n # Set up environment and agent\n e = Environment() # create environment (also adds some dummy traffic)\n a = e.create_agent(LearningAgent) # create agent\n e.set_primary_agent(a, enforce_deadline=True) # set agent to track\n\n # Now simulate it\n sim = Simulator(e, update_delay=0.5) # reduce update_delay to speed up simulation\n sim.run(n_trials=100) # press Esc or close pygame window to quit","def _run(self):\n result = self._consensus()\n if self._decision.split_group and result:\n self._set_decision(result)","def Run():\r\n pass","def run(self, GameState):\n pass","def step(self): \n self.reset_parameters()\n\n if np.random.uniform(0, 1) < self.model.churn_prob: self.exit_triggered = True \n if self.exit_triggered:\n self.exit()\n else:\n self.register_deposit(self.deposit_intent)\n self.register_contribution(self.contribution_intent)\n self.register_sponsorship(self.sponsor_intent)\n self.register_euro_exchange(self.euro_exchange_intent)\n self.register_teo_exchange(self.teo_exchange_intent)\n self.register_withdraw(self.withdraw_intent)","def test_sugiyama(self):\n # import the experiment variable from the example\n exp = sugiyama_example(render=False)\n\n # run the experiment for a few time steps to ensure it doesn't fail\n exp.run(1, 5)","def _exe_(self):\n print(\"\\n Start simulation (using Pharlap) ...\")\n dic = \"data/sim/{dn}/{rad}/\".format(dn=self.event.strftime(\"%Y.%m.%d.%H.%M\"), rad=self.rad)\n self._copy_ne_()\n [self._compute_(case) for case in [\"bgc\", \"flare\"]]\n plotlib.plot_exp_rays(dic, self.event, self.bmnum, \"bgc\")\n plotlib.plot_exp_rays(dic, self.event, self.bmnum, \"flare\")\n self._compute_doppler_()\n rec = self._compute_velocity_()\n return rec","def initialise_sim(self):\n pass","def _step(self, action):\n\n\n assert action.ndim == 2, 'Action must be a two dimensional array with shape (num_models, num_stocks + 1)'\n assert action.shape[1] == len(self.sim[0].asset_names) + 1\n assert action.shape[0] == len(self.model_names)\n\n print(\"before normalisation\",action)\n\n #Let's ALLOW THE SHORT!!!\n # normalise just in case\n #action = np.clip(action, 0, 1)\n action = np.clip(action, -1, 1)\n\n weights = action # np.array([cash_bias] + list(action)) # [w0, w1...]\n #weights /= (np.sum(weights, axis=1, keepdims=True) + eps)\n\n # so if weights are all zeros we normalise to [1,0...]\n weights[:, 0] += np.clip(1 - np.sum(weights, axis=1), 0, 1)\n #assert ((action >= 0) * (action <= 1)).all(), 'all action values should be between 0 and 1. Not %s' % action\n\n #assert ((action >= 0) * (action <= 1)).all(), 'all action values should be between 0 and 1. Not %s' % action\n\n\n pos_w = [abs(number) for number in weights]\n\n\n np.testing.assert_almost_equal(np.sum(pos_w, axis=1), np.ones(shape=(weights.shape[0])), 3,\n err_msg='weights should sum to 1. action=\"%s\"' % weights)\n\n observation, done1, ground_truth_obs = self.src._step()\n\n # concatenate observation with ones\n cash_observation = np.ones((1, self.window_length, observation.shape[2]))\n observation = np.concatenate((cash_observation, observation), axis=0)\n\n cash_ground_truth = np.ones((1, 1, ground_truth_obs.shape[2]))\n ground_truth_obs = np.concatenate((cash_ground_truth, ground_truth_obs), axis=0)\n\n\n # relative price vector of last observation day (close/open)\n\n\n close_price_vector = observation[:, -1, 3]\n open_price_vector = observation[:, -1, 0]\n\n\n y1_long = close_price_vector / open_price_vector\n\n y1_short= open_price_vector/close_price_vector\n #for i in len(range(observation.shape[0])):\n # if\n\n #print(\"THIS IS Y1\",y1)\n #print(\"y1 shape\",y1.shape)\n #print(\"wieh\",weights.shape)\n\n rewards = np.empty(shape=(weights.shape[0]))\n info = {}\n dones = np.empty(shape=(weights.shape[0]), dtype=bool)\n for i in range(weights.shape[0]):\n reward, current_info, done2 = self.sim[i]._step(weights[i], y1_long,y1_short)\n rewards[i] = reward\n info[self.model_names[i]] = current_info['portfolio_value']\n info['return'] = current_info['return']\n dones[i] = done2\n\n # calculate return for buy and hold a bit of each asset\n info['market_value'] = np.cumprod([inf[\"return\"] for inf in self.infos + [info]])[-1]\n # add dates\n info['date'] = index_to_date(self.start_idx + self.src.idx + self.src.step)\n info['steps'] = self.src.step\n info['next_obs'] = ground_truth_obs\n\n self.infos.append(info)\n\n #print (info)\n\n #print(\"My action is :\",action)\n\n\n\n return observation, rewards, np.all(dones) or done1, info","def test_do_boss_science(self):\n sopTester.updateModel('mcp', TestHelper.mcpState['boss_science'])\n nExp = 2\n self._do_boss_science(nExp, 35, 0, 0, nExp=nExp)","def run(self):\n cpt = 1\n while cpt < (self.BOUND):\n cpt += 1\n self.incBound()\n\n res = self.s.check()\n if res == sat:\n m = self.s.model()\n self.checkModel(m)\n self.printModel(m)\n return\n else:\n print(\"The problem is UNSAT\")","def execute(self, agent: Agent, state: SimState) -> None:\n pass","def main():\n # Goal is to model the OSSOS resonance detections given a file with parameters for those resonances.\n # e.g. from Crompvoets et al. (2021)\n\n # now run a survey simulation.\n params = sys.argv[1]\n H_max = float(sys.argv[2])\n outfile=f\"{os.path.splitext(params)[0]}_Model.dat\"\n print(f\"Saving results to {outfile}\")\n if not os.access(outfile, os.R_OK):\n run(outfile, params, 123456789, H_max=H_max)\n\n # confirm this looks like the OSSOS detections using rose plot.\n face_down_plot(outfile)","def run(self):\n self.cancelled = False\n # get version from IDF object or by parsing the IDF file for it\n\n # Move files into place\n self.epw = self.idf.epw.copy(self.run_dir / \"in.epw\").expand()\n self.idfname = Path(self.idf.savecopy(self.run_dir / \"in.idf\")).expand()\n self.idd = self.idf.iddname.copy(self.run_dir).expand()\n\n # Get executable using shutil.which (determines the extension based on\n # the platform, eg: .exe. And copy the executable to tmp\n slab_exe = shutil.which(\"Slab\", path=self.eplus_home)\n if slab_exe is None:\n log(\n f\"The Slab program could not be found at \" f\"'{self.eplus_home}'\",\n lg.WARNING,\n )\n return\n self.slabexe = Path(slab_exe).copy(self.run_dir)\n self.slabidd = (self.eplus_home / \"SlabGHT.idd\").copy(self.run_dir)\n\n # The GHTin.idf file is copied from the self.include list (added by\n # ExpandObjects. If self.include is empty, no need to run Slab.\n self.include = [Path(file).copy(self.run_dir) for file in self.idf.include]\n if not self.include:\n self.cleanup_callback()\n return\n\n # Run Slab Program\n with logging_redirect_tqdm(loggers=[lg.getLogger(self.idf.name)]):\n with tqdm(\n unit_scale=True,\n miniters=1,\n desc=f\"RunSlab #{self.idf.position}-{self.idf.name}\",\n position=self.idf.position,\n ) as progress:\n\n self.p = subprocess.Popen(\n self.cmd,\n stdout=subprocess.PIPE,\n stderr=subprocess.PIPE,\n shell=True, # can use shell\n cwd=self.run_dir.abspath(),\n )\n start_time = time.time()\n self.msg_callback(\"Begin Slab Temperature Calculation processing . . .\")\n for line in self.p.stdout:\n self.msg_callback(line.decode(\"utf-8\").strip(\"\\n\"))\n progress.update()\n\n # We explicitly close stdout\n self.p.stdout.close()\n\n # Wait for process to complete\n self.p.wait()\n\n # Communicate callbacks\n if self.cancelled:\n self.msg_callback(\"RunSlab cancelled\")\n # self.cancelled_callback(self.std_out, self.std_err)\n else:\n if self.p.returncode == 0:\n self.msg_callback(\n \"RunSlab completed in {:,.2f} seconds\".format(\n time.time() - start_time\n )\n )\n self.success_callback()\n for line in self.p.stderr:\n self.msg_callback(line.decode(\"utf-8\"))\n else:\n self.msg_callback(\"RunSlab failed\")\n self.failure_callback()","def __ExecuteActionForRandomAgentWithNoisyTransitionModel__(self, idx_request_action, noise_level):\n n = len(self.AGENT_ALLOWED_ACTIONS)\n pp = noise_level/(n-1) * np.ones((n,1))\n pp[idx_request_action] = 1.0 - noise_level\n idx_actual = np.random.choice(n, 1, p=pp.flatten()) # sample from the distrbution of actions\n actual_action = self.AGENT_ALLOWED_ACTIONS[int(idx_actual)]\n self.agent_host.sendCommand(actual_action)\n self.solution_report.addAction()\n return actual_action","def main():\n\n GAME = \"Assignment1-Taxi-v2\"\n env = gym.make(GAME)\n n_state = env.observation_space.n\n n_action = env.action_space.n\n env = Monitor(env, \"taxi_simple\", force=True)\n\n s = env.reset()\n steps = 100\n for step in range(steps):\n env.render()\n action = int(input(\"Please type in the next action:\"))\n s, r, done, info = env.step(action)\n print(s)\n print(r)\n print(done)\n print(info)\n\n # close environment and monitor\n env.close()","def test_svd_sharpness_no_model(self):\n \t\t\n\t\n\t\tesd_before = self.watcher.get_ESD(model=self.model, layer=self.fc2_layer) \n\t\t\n\t\tself.watcher.SVDSharpness(model=self.model, layers=[self.fc2_layer])\n\t\tesd_after = self.watcher.get_ESD(layer=self.fc2_layer) \n\t\t\n\t\tprint(\"max esd before {}\".format(np.max(esd_before)))\n\t\tprint(\"max esd after {}\".format(np.max(esd_after)))\n\n\t\tself.assertGreater(np.max(esd_before)-2.0,np.max(esd_after))","def sample(self,noise,s):\n rhs=self.prior.sqrtM*noise\n self.prior.Msolver.solve(s,rhs)","def run():\n import hmmmAssembler ; reload(hmmmAssembler) # import helpers\n hmmmAssembler.main(Random) # this runs the code!","def run(): \n learning_rate = 0.42\n discount_rate = 0.15\n initial_q_hat = 4\n \n # Set up environment and agent\n e = Environment() # create environment (also adds some dummy traffic)\n a = e.create_agent(LearningAgent, learning_rate, discount_rate, initial_q_hat) # create agent\n e.set_primary_agent(a, enforce_deadline=True) # specify agent to track\n # NOTE: You can set enforce_deadline=False while debugging to allow longer trials\n\n # Now simulate it\n sim = Simulator(e, update_delay=0, display=False) # create simulator (uses pygame when display=True, if available)\n # NOTE: To speed up simulation, reduce update_delay and/or set display=False\n\n sim.run(n_trials=100) # run for a specified number of trials\n print \"Failed trials: \"\n print a.get_failed_trials()\n # NOTE: To quit midway, press Esc or close pygame window, or hit Ctrl+C on the command-line","def run_step(self):\n \n # Ensure model has been initialized at least once\n self._model_has_been_initialized(\"run_step\")\n\n # Check if sim is steady-state (doesn't contain \"doStep\" method)\n if \"doStep\" not in dir(self.fmu):\n error_log = \"[run_step] FMU model cannot be run one step-forward, since it is a steady-state sim. \"\n error_log += \"No step advance will be applied.\"\n print(error_log)\n return\n\n self.fmu.doStep(currentCommunicationPoint=self.sim_time, communicationStepSize=self.step_size)\n self.sim_time += self.step_size\n return","def test_full(args, model, device): \n test_path = '../data/full/original/'\n generate_path = '../data/full/generate/'\n test_image_num = len([name for name in os.listdir(test_path)\n if os.path.isfile(os.path.join(test_path, name))])\n\n score_psnr, score_ssim_skimage, score_ssim_minstar, score_msssim_minstar = 0.0, 0.0, 0.0, 0.0\n ind = 0\n for name in os.listdir(test_path):\n if os.path.isfile(os.path.join(test_path, name)):\n ind += 1\n test_original, test_style, image_height, image_width = load_test_dataset(name)\n x = torch.from_numpy(test_original).float()\n y_real = torch.from_numpy(test_style).float()\n x = x.view(image_height, image_width, config.channels).permute(2, 0, 1).to(device)\n y_real = y_real.view(image_height, image_width, config.channels).permute(2, 0, 1).to(device)\n \n y_fake = model.gen_g(x.view(-1, config.channels, image_height, image_width))\n y_fake = y_fake.view(config.channels, image_height, image_width)\n \n # Calculate PSNR & SSIM scores\n score_psnr += psnr_full(y_fake, y_real)\n \n y_fake_np = y_fake.detach().cpu().numpy().transpose(1, 2, 0)\n y_real_np = y_real.cpu().numpy().transpose(1, 2, 0)\n temp_ssim, _ = compare_ssim(y_fake_np, y_real_np, multichannel=True, gaussian_weights=True, full=True)\n score_ssim_skimage += temp_ssim\n \n temp_ssim, _ = ssim(y_fake, y_real, kernel_size=11, kernel_sigma=1.5)\n score_ssim_minstar += temp_ssim\n \n score_msssim_minstar += multi_scale_ssim(y_fake, y_real, kernel_size=11, kernel_sigma=1.5)\n print('PSNR & SSIM scores of {} images are calculated.'.format(ind))\n \n utils.save_image(y_fake, os.path.join(generate_path, '{}-x.jpg'.format(name[:5] + args.model_type)))\n\n score_psnr /= test_image_num\n score_ssim_skimage /= test_image_num\n score_ssim_minstar /= test_image_num\n score_msssim_minstar /= test_image_num\n print('PSNR : {:.4f}, SSIM_skimage : {:.4f}, SSIM_minstar : {:.4f}, SSIM_msssim: {:.4f}'.format(\n score_psnr, score_ssim_skimage, score_ssim_minstar, score_msssim_minstar))","def main():\n # combineSloEff(makePlots=True, writeDB=False, runMC=False, seedNum=1)\n GPXSloEff(makePlots=True, writeDB=False)","def startSimulation(self):\n self.saveParameters()\n self.simulation.main()","def __call__(self,sensation,reward=None):\n raise NYI","def action(self, args):\n mean, stddev = args\n dist = tfp.distributions.Normal(loc=mean, scale=stddev)\n action = dist.sample(1)\n action = K.clip(action,\n self.env.action_space.low[0],\n self.env.action_space.high[0])\n return action","def simulate(self):\r\n\t\tprint(\"##################################\")\r\n\t\tprint(\"SIMULATING GAME - SpaceInvaders..\")\r\n\t\tprint(\"##################################\")\r\n\t\t\r\n\t\t# Play 3 episodes:\r\n\t\tfor i in range(3):\r\n\t\t\tprint(\"Playing Episode %d\" % i)\r\n\t\t\tstate = self.env.reset()\r\n\t\t\t#self.env.render()\r\n\t\t\tdone = False\r\n\t\t\ttot_reward = 0\r\n\t\t\tstate,_ = stack_frames(self.stack_size,self.stacked_frames, \r\n\t\t\t\t\t\t\t\t\t\tstate, True)\r\n\t\t\t# play until dead.\t\t\t\r\n\t\t\twhile not done:\r\n\t\t\t\t# get the value predicted by the model and perform that action.\r\n\t\t\t\t# keras conv2d expects a 4D input. So add an empty axis. \r\n\t\t\t\tstate = np.expand_dims(state, axis=0)\r\n\t\t\t\t# predict action directly from the saved neural network.\r\n\t\t\t\taction = np.argmax(self.dqn.getModel().predict(state)[0])\r\n\t\t\t\t# perform that action.\r\n\t\t\t\tstate, reward, done, _ = self.env.step(action)\r\n\t\t\t\tself.env.render()\r\n\t\t\t\tstate,_ = stack_frames(self.stack_size,self.stacked_frames, \r\n\t\t\t\t\t\t\t\t\t\tstate, False)\r\n\t\t\t\ttot_reward+=reward\r\n\t\t\tprint(\"Reward: \", tot_reward)\r\n\t\tself.env.close() # to avoid sys.meta_path error\r","def run(self) -> None:\n\n num_trials = 3\n num_successes = 0\n\n # Initialize the scene.\n self.init_scene()\n for i in range(num_trials):\n status = self.lift_container()\n if status != TaskStatus.success:\n continue\n status = self.lift_target_object()\n if status != TaskStatus.success:\n continue\n status = self.try_put_in_container()\n print(f\"Tried to put object in container: {status}\")\n if status == TaskStatus.success:\n num_successes += 1\n accuracy = float(num_successes) / num_trials\n print(f\"Accuracy: {accuracy}\")","def run_sim(self):\n \n OS = self.OpticalSystem\n TL = self.TargetList\n SU = self.SimulatedUniverse\n Obs = self.Observatory\n TK = self.TimeKeeping\n \n # TODO: start using this self.currentSep\n # set occulter separation if haveOcculter\n if OS.haveOcculter == True:\n self.currentSep = Obs.occulterSep\n \n # choose observing modes selected for detection (default marked with a flag)\n allModes = OS.observingModes\n det_modes = list(filter(lambda mode: 'imag' in mode['inst']['name'], allModes))\n # and for characterization (default is first spectro/IFS mode)\n spectroModes = list(filter(lambda mode: 'spec' in mode['inst']['name'], allModes))\n if np.any(spectroModes):\n char_modes = spectroModes\n # if no spectro mode, default char mode is first observing mode\n else:\n char_modes = [allModes[0]]\n \n # begin Survey, and loop until mission is finished\n log_begin = 'OB%s: survey beginning.'%(TK.OBnumber + 1)\n self.logger.info(log_begin)\n self.vprint(log_begin)\n t0 = time.time()\n sInd = None\n ObsNum = 0\n while not TK.mission_is_over(OS, Obs, det_modes[0]):\n \n # acquire the NEXT TARGET star index and create DRM\n old_sInd = sInd #used to save sInd if returned sInd is None\n DRM, sInd, det_intTime, waitTime, det_mode = self.next_target(sInd, det_modes)\n \n if sInd is not None:\n ObsNum += 1\n\n if OS.haveOcculter == True:\n # advance to start of observation (add slew time for selected target)\n success = TK.advanceToAbsTime(TK.currentTimeAbs.copy() + waitTime)\n \n # beginning of observation, start to populate DRM\n DRM['star_ind'] = sInd\n DRM['star_name'] = TL.Name[sInd]\n DRM['arrival_time'] = TK.currentTimeNorm.copy().to('day')\n DRM['OB_nb'] = TK.OBnumber\n DRM['ObsNum'] = ObsNum\n pInds = np.where(SU.plan2star == sInd)[0]\n DRM['plan_inds'] = pInds.astype(int)\n log_obs = (' Observation #%s, star ind %s (of %s) with %s planet(s), ' \\\n + 'mission time at Obs start: %s')%(ObsNum, sInd, TL.nStars, len(pInds), \n TK.currentTimeNorm.to('day').copy().round(2))\n self.logger.info(log_obs)\n self.vprint(log_obs)\n\n # PERFORM DETECTION and populate revisit list attribute\n DRM['det_info'] = []\n detected, det_fZ, det_systemParams, det_SNR, FA = \\\n self.observation_detection(sInd, det_intTime, det_mode)\n # update the occulter wet mass\n if OS.haveOcculter == True:\n DRM = self.update_occulter_mass(DRM, sInd, det_intTime, 'det')\n det_data = {}\n det_data['det_status'] = detected\n det_data['det_SNR'] = det_SNR\n det_data['det_fZ'] = det_fZ.to('1/arcsec2')\n det_data['det_params'] = det_systemParams\n det_data['det_mode'] = dict(det_mode)\n det_data['det_time'] = det_intTime.to('day')\n del det_data['det_mode']['inst'], det_data['det_mode']['syst']\n DRM['det_info'].append(det_data)\n\n # PERFORM CHARACTERIZATION and populate spectra list attribute\n DRM['char_info'] = []\n if char_modes[0]['SNR'] not in [0, np.inf]:\n characterized, char_fZ, char_systemParams, char_SNR, char_intTime = \\\n self.observation_characterization(sInd, char_modes)\n else:\n char_intTime = None\n lenChar = len(pInds) + 1 if True in FA else len(pInds)\n characterized = np.zeros((lenChar,len(char_modes)), dtype=float)\n char_SNR = np.zeros((lenChar,len(char_modes)), dtype=float)\n char_fZ = np.array([0./u.arcsec**2, 0./u.arcsec**2])\n char_systemParams = SU.dump_system_params(sInd)\n\n for mode_index, char_mode in enumerate(char_modes):\n char_data = {}\n assert char_intTime != 0, \"Integration time can't be 0.\"\n # update the occulter wet mass\n if OS.haveOcculter == True and char_intTime is not None:\n char_data = self.update_occulter_mass(char_data, sInd, char_intTime, 'char')\n if np.any(characterized):\n vprint(' Char. results are: {}'.format(characterized[:-1, mode_index]))\n # populate the DRM with characterization results\n char_data['char_time'] = char_intTime.to('day') if char_intTime else 0.*u.day\n char_data['char_status'] = characterized[:-1, mode_index] if FA else characterized[:,mode_index]\n char_data['char_SNR'] = char_SNR[:-1, mode_index] if FA else char_SNR[:, mode_index]\n char_data['char_fZ'] = char_fZ[mode_index].to('1/arcsec2')\n char_data['char_params'] = char_systemParams\n # populate the DRM with FA results\n char_data['FA_det_status'] = int(FA)\n char_data['FA_char_status'] = characterized[-1, mode_index] if FA else 0\n char_data['FA_char_SNR'] = char_SNR[-1] if FA else 0.\n char_data['FA_char_fEZ'] = self.lastDetected[sInd,1][-1]/u.arcsec**2 \\\n if FA else 0./u.arcsec**2\n char_data['FA_char_dMag'] = self.lastDetected[sInd,2][-1] if FA else 0.\n char_data['FA_char_WA'] = self.lastDetected[sInd,3][-1]*u.arcsec \\\n if FA else 0.*u.arcsec\n \n # populate the DRM with observation modes\n char_data['char_mode'] = dict(char_mode)\n del char_data['char_mode']['inst'], char_data['char_mode']['syst']\n DRM['char_info'].append(char_data)\n \n DRM['exoplanetObsTime'] = TK.exoplanetObsTime.copy()\n\n # append result values to self.DRM\n self.DRM.append(DRM)\n \n else:#sInd == None\n sInd = old_sInd#Retain the last observed star\n if(TK.currentTimeNorm.copy() >= TK.OBendTimes[TK.OBnumber]): # currentTime is at end of OB\n #Conditional Advance To Start of Next OB\n if not TK.mission_is_over(OS, Obs, det_mode):#as long as the mission is not over\n TK.advancetToStartOfNextOB()#Advance To Start of Next OB\n elif(waitTime is not None):\n #CASE 1: Advance specific wait time\n success = TK.advanceToAbsTime(TK.currentTimeAbs.copy() + waitTime)\n self.vprint('waitTime is not None')\n else:\n startTimes = TK.currentTimeAbs.copy() + np.zeros(TL.nStars)*u.d # Start Times of Observations\n observableTimes = Obs.calculate_observableTimes(TL,np.arange(TL.nStars),startTimes,self.koMap,self.koTimes,self.mode)[0]\n #CASE 2 If There are no observable targets for the rest of the mission\n if((observableTimes[(TK.missionFinishAbs.copy().value*u.d > observableTimes.value*u.d)*(observableTimes.value*u.d >= TK.currentTimeAbs.copy().value*u.d)].shape[0]) == 0):#Are there any stars coming out of keepout before end of mission\n self.vprint('No Observable Targets for Remainder of mission at currentTimeNorm= ' + str(TK.currentTimeNorm.copy()))\n #Manually advancing time to mission end\n TK.currentTimeNorm = TK.missionLife\n TK.currentTimeAbs = TK.missionFinishAbs\n else:#CASE 3 nominal wait time if at least 1 target is still in list and observable\n #TODO: ADD ADVANCE TO WHEN FZMIN OCURS\n inds1 = np.arange(TL.nStars)[observableTimes.value*u.d > TK.currentTimeAbs.copy().value*u.d]\n inds2 = np.intersect1d(self.intTimeFilterInds, inds1) #apply intTime filter\n inds3 = self.revisitFilter(inds2, TK.currentTimeNorm.copy() + self.dt_max.to(u.d)) #apply revisit Filter #NOTE this means stars you added to the revisit list \n self.vprint(\"Filtering %d stars from advanceToAbsTime\"%(TL.nStars - len(inds3)))\n oTnowToEnd = observableTimes[inds3]\n if not oTnowToEnd.value.shape[0] == 0: #there is at least one observableTime between now and the end of the mission\n tAbs = np.min(oTnowToEnd)#advance to that observable time\n else:\n tAbs = TK.missionStart + TK.missionLife#advance to end of mission\n tmpcurrentTimeNorm = TK.currentTimeNorm.copy()\n success = TK.advanceToAbsTime(tAbs)#Advance Time to this time OR start of next OB following this time\n self.vprint('No Observable Targets a currentTimeNorm= %.2f Advanced To currentTimeNorm= %.2f'%(tmpcurrentTimeNorm.to('day').value, TK.currentTimeNorm.to('day').value))\n else:#TK.mission_is_over()\n dtsim = (time.time() - t0)*u.s\n log_end = \"Mission complete: no more time available.\\n\" \\\n + \"Simulation duration: %s.\\n\"%dtsim.astype('int') \\\n + \"Results stored in SurveySimulation.DRM (Design Reference Mission).\"\n self.logger.info(log_end)\n print(log_end)","def run(self):\n print('\\n Početak simulacije Petrijeve mreže\\n')\n print('Definicija:\\n{}\\n'.format(self.__str__()))\n print('Početno stanje:\\n{}\\n---------\\n'.format(self.get_M_as_string()))\n\n transitions_available = True\n\n while transitions_available: \n transitions_available = False\n for transition in random.sample(self.T,len(self.T)): \n if transition.is_enabled():\n transition.fire()\n transitions_available = True\n break\n \n\n print('\\nViše nije moguće izvesti niti jedan prijelaz.')\n print('Trenutno stanje mreže jest:\\n{}\\n'.format(self.get_M_as_string()))\n\n \n if self.check_net_finality():\n print('Mreža je završila u finalnom stanju u kojemu su sve značke' +\n ' na mjestima koja nemaju izlaznih lukova.')\n else:\n print('Protok mreže je zaglavio u stanju u kojemu su značke' +\n ' raspoređene i na mjestima koja imaju izlazne' +\n ' lukove.')","def act(self, state, episode_num, add_noise=True):\n state = torch.from_numpy(state).float().to(device)\n if episode_num>WARM_UP:\n self.actor_local.eval() \n with torch.no_grad():\n action = self.actor_local(state).cpu().data.numpy()\n self.actor_local.train()\n if add_noise:\n #print(\"action_before_noise\",action)\n action += self.epsilon * self.noise.sample()\n #print(\"action_after_noise\",action)\n else:\n #generate random action between -1 and 1\n action=np.random.uniform(-1,1,self.action_size)\n return np.clip(action,-1,1)","def step(self, actions=None):\n \n if actions is not None:\n # implement the actions\n# print(actions)\n for asset, valve_position in zip(self.config[\"action_space\"], actions):\n# print(asset, valve_position)\n self._setValvePosition(asset, valve_position)\n\n # take the step !\n time = self.sim._model.swmm_step()\n done = False if time > 0 else True\n return done","def run_scenario(self):\n self.initialize_random_map()\n self.visualize_environment('initial')\n self.get_tower_target_coverages()\n self.solve_environment()\n self.visualize_environment('solved')","def step(self, action):","def _sense_and_act(self):\n pass","def main():\n # output filename\n param = read_parameters('Kanki01_input.yaml')\n s = seal(param)\n s.solve_zeroth()\n s.plot_res()","def runall():\n sclogic.runall()","def test_qqlnu_sm(self):\n R = flavio.sm_prediction('R_13(pp->munu)', 1e3, 2e3)\n self.assertEqual(R, 1,msg=f'SM prediction for R ratio: {R} (has to be 1)')","def act(self, state):\n # Random\n if np.random.rand() <= self.epsilon:\n return self.environment.action_space.sample()\n\n # Model prediction\n return np.argmax(self.model.predict(state)[0])","def run(mu_v, Sigma_w, Sigma_z, a_mu, l_sensor):\n N = 1000\n # Init tracking\n mu_x = np.zeros(N) # Belief or estimation of hidden state \n F = np.zeros(N) # Free Energy of AI neuron\n mu_y = np.zeros(N) # Belief or prediction of sensory signal \n x = np.zeros(N) # True hidden state\n y = np.zeros(N) # Sensory signal as input to AI neuron\n\n robot_brain = pp_unit(dt, mu_v, Sigma_w, Sigma_z, a_mu) #make pp object\n \n \n\n start_time = time.time()\n for i in np.arange(1, N):\n #Active inference\n y[i] = l_sensor.ambient_light_intensity #take sensor reading\n print('light reading', y[i])\n F[i], mu_x[i], mu_y[i] = robot_brain.inference_step(i, mu_v, y[i])\n\n\n t_elapsed = time.time() - start_time\n\n print(\"Elapsed Time\", t_elapsed, \"sec\")\n return F, mu_x, mu_y, x, y","def Main():\n numberOfPopulation = 350\n numberOfDays = 60\n \n simulation = Simulation(Covid19(), numberOfPopulation, numberOfDays, \"Covid 19 Simulation\")\n simulation.run() \n simulation = Simulation(Ebola(), numberOfPopulation, numberOfDays, \"Ebola Simulation\")\n simulation.run()","def runSurvey():\n fieldFile = globals()['settings']['fieldFile']\n # Number of particles to launch\n numParticles = globals()['settings']['numParticles']\n # Radius of spherical simulation boundary used for launching and exiting\n rLim = globals()['settings']['rLim']\n # Particle stepping method\n steppingMethod = globals()['settings']['steppingMethod']\n # Coarseness of output grid that counts particle fluxes in simulation volume\n fluxGridCoarseness = globals()['settings']['fluxGridCoarseness']\n \n # B field in R and Z\n r, z, BR = fieldGrid('fields/Brs_' + fieldFile)\n _, _, BZ = fieldGrid('fields/Bzs_' + fieldFile)\n _, _, habitatBR = fieldGrid('fields/Brs_habitat_' + fieldFile)\n _, _, habitatBZ = fieldGrid('fields/Bzs_habitat_' + fieldFile)\n r = r[:-1]\n z = z[:-1]\n BR = BR[:-1,:-1] # I MAY CAUSE A BUG IN THE FUTURE\n BZ = BZ[:-1,:-1]\n habitatMax = np.max((habitatBR**2+habitatBZ**2)**.5)\n habitatPrescription = 30\n BR += habitatBR*habitatPrescription/habitatMax\n BZ += habitatBZ*habitatPrescription/habitatMax\n print('Habitat prescription (T):', habitatPrescription)\n Bmagnitude = (BR**2+BZ**2)**.5\n\n qms, vs = qmAndVelocitySpectrum(numParticles)\n if globals()['settings']['qmPrescribed']:\n qms = np.ones(numParticles)*globals()['settings']['qmPrescribed']\n if globals()['settings']['v0Prescribed']:\n vs = np.ones(numParticles)*globals()['settings']['v0Prescribed']\n\n startingPoints = [randomPointOnSphere(rLim) for _ in range(numParticles)]\n directions = [randomDirectionCos(-sp) for sp in startingPoints]\n\n # Simulate without magnetic field\n start = time.time()\n rReduced, zReduced, gridOff, _, habitatCrossingsOff, GDTcrossingsOff, gridOffUnscaled, _ = monteCarloRun(startingPoints, qms, vs, directions, BR, BZ, r, z, rLim, fluxGridCoarseness, 0)\n print('Time elapsed (s):', int(time.time()-start))\n \n # Simulate with magnetic field\n start = time.time()\n _, _, gridOn, trappedOn, habitatCrossingsOn, GDTcrossingsOn, gridOnUnscaled, trappedOnUnscaled = monteCarloRun(startingPoints, qms, vs, directions, BR, BZ, r, z, rLim, fluxGridCoarseness, steppingMethod)\n print('Time elapsed (s):', int(time.time()-start))\n # np.save('cache/{}particles_accel.npy'.format(numParticles), [rReduced, zReduced, gridOn])\n try:\n print('---\\nGDT crossing change: {}%'.format(round(100*(GDTcrossingsOn-GDTcrossingsOff)/GDTcrossingsOff, 3)))\n print('Habitat crossing change: {}%\\n---'.format(round(100*(habitatCrossingsOn-habitatCrossingsOff)/habitatCrossingsOff, 3)))\n except Exception as e:\n print(e)\n \n # plotDiff(r, z, Bmagnitude, gridOn, gridOff)\n plot6panel(r, z, rReduced, zReduced, Bmagnitude, gridOn, gridOff, trappedOn)","async def stan(self, ctx, *args):\n if args:\n if args[0] == 'update':\n amount = len(self.artists)\n self.artists = []\n urls_to_scrape = ['https://kprofiles.com/k-pop-girl-groups/',\n 'https://kprofiles.com/k-pop-boy-groups/',\n 'https://kprofiles.com/co-ed-groups-profiles/',\n 'https://kprofiles.com/kpop-duets-profiles/',\n 'https://kprofiles.com/kpop-solo-singers/']\n for url in urls_to_scrape:\n self.artists += scrape_kprofiles(url)\n\n database.set_attr(\"data\", \"artists\", self.artists)\n\n await ctx.send(f\"Artist list succesfully updated, {len(self.artists) - amount} new entries, \"\n f\"{len(self.artists)} total entries\")\n self.logger.info(misolog.format_log(ctx, f\"artist list updated; {len(self.artists) - amount} new, \"\n f\"{len(self.artists)} total\"))\n return\n\n elif args[0] == 'clear':\n self.artists = []\n database.set_attr(\"data\", \"artists\", self.artists)\n await ctx.send(\"Artist list cleared\")\n self.logger.info(misolog.format_log(ctx, f\"artist list cleared\"))\n return\n\n if self.artists:\n artist = str(rd.choice(self.artists))\n await ctx.send('stan ' + artist)\n self.logger.info(misolog.format_log(ctx, f\"artist={artist}\"))\n else:\n await ctx.send(\"Error: artist list is empty, please use >stan update\")\n self.logger.warning(misolog.format_log(ctx, f\"artist list empty\"))","def run(self, steps):\n self.sim.run(steps)","def perform_actual_action(self, action):\n self.game.perform_action(action)","def main():\n\n # test()\n\n DIM = 10\n SHIPS = [2,3,3,4,5]\n\n device = 'cuda' if torch.cuda.is_available() else 'cpu'\n\n model = train_Convnet(DIM, SHIPS, device)\n torch.save(model.state_dict(),'./saved_models/convenet.torch' )\n\n conv_model = ModelConvnet(\"Vikram\", DIM, len(SHIPS), device).to(device)\n conv_model.load_state_dict(torch.load('./saved_models/convenet.torch'))\n\n g = Game(conv_model, ModelRandom(\"Betal\", DIM, len(SHIPS), device), Environment(DIM, SHIPS, \"Vikram\"), Environment(DIM, SHIPS, \"Betal\"))\n g.play()","def Execute(self):\n self.FSM.Execute()","def run_one_step_simvodis(self):\n self.model_lr_scheduler.step()\n self.set_train()\n\n inputs = next(iter(self.train_loader))\n\n before_op_time = time.time()\n\n outputs, losses = self.process_batch(inputs)\n\n self.model_optimizer.zero_grad()\n losses[\"loss\"].backward()\n self.model_optimizer.step()\n\n duration = time.time() - before_op_time\n\n if self.arguments[\"iteration\"] % (self.opt.save_frequency // 2) == 0:\n self.log_time(self.arguments[\"iteration\"], duration, losses[\"loss\"].cpu().data)\n\n if \"depth_gt\" in inputs:\n self.compute_depth_losses(inputs, outputs, losses)\n\n self.log(\"train\", inputs, outputs, losses)\n self.val()\n\n self.step += 1","def update_model(X, U, S, k, n, mu,\n svdmethod='full',\n missingmethod='zero'):\n\n if len(X) == 0:\n printt(\"Error: No data in X.\")\n return None, None, None, -1, None\n #print('%d items in X' % X.shape[1])\n #print('init U:', U)\n\n # If there is no previous U, and we just got a single item in X,\n # set U to all 0's (degenerate SVD),\n # and return it with mu.\n # (PR #22 sets first value to 1; see decals implementation)\n if len(U) == 0 and X.shape[1] == 1:\n mu = X\n # Do this no matter what. Let mu get NaNs in it as needed.\n U = np.zeros_like(mu)\n U[0] = 1\n S = np.array([0])\n n = 1\n pcts = [1.0]\n return U, S, mu, n, pcts\n\n ###########################################################################\n # Do full SVD of X if this is requested, regardless of what is in U \n # Also, if n = 0 or U is empty, start from scratch\n output_k = False\n if svdmethod == 'full' or len(U) == 0 or n == 0:\n if n == 0:\n if len(U) == 0:\n printt(\"----- initial SVD -----\")\n output_k = True\n else:\n # Reshape so we don't have an empty dimension (yay python)\n U = U.reshape(-1, 1)\n elif len(U) == 0:\n printt(\"WARNING: N (number of items modeled by U) is %d, not zero, but U is empty!\" % n)\n\n # Bootstrap\n if missingmethod == 'ignore':\n printt(\"ERROR: ignore with full is not possible under ordinary circumstances.\")\n printt(\"Use --increm-brand to impute for NaNs.\")\n printt(\"For now, we are filling NaNs with 0.\")\n X = copy.deepcopy(X)\n z = np.where(np.isnan(X))\n X[z] = 0\n\n mu = np.mean(X, axis=1).reshape(-1,1)\n X = X - mu\n U, S, V = linalg.svd(X, full_matrices=False)\n printt('Just did full SVD on %d items.' % X.shape[1])\n #print('X:', X)\n #print('U:', U)\n # Reset U to all 0's if we only have one item in X (degenerate SVD)\n if X.shape[1] == 1:\n U = np.zeros_like(U)\n \n # Keep only the first k components\n S_full = S\n S = S[0:k]\n U = U[:,0:k]\n\n # Update n to number of new items in X\n n = X.shape[1]\n \n ###########################################################################\n # Incremental SVD from Ross\n elif svdmethod == 'increm-ross':\n # Incremental SVD from Ross et al. 2008\n # \"Incremental Learning for Robust Visual Tracking\"\n # based on Lim and Ross's sklm.m implementation in MATLAB.\n\n # This method DOES NOT handle missing values.\n if missingmethod == 'ignore':\n print('ERROR: increm-ross cannot handle missing values.')\n print('If they are present, try svdmethod=increm-brand')\n print(' or use missingmethod=zero to zero-fill.')\n print('If there are no missing values, specify missingmethod=none.')\n sys.exit(1)\n\n n_new = X.shape[1]\n \n # Compute mean\n # Weirdly, the later 'X-mu_new' is MUCH faster if you reshape as shown.\n # This is because of differences in the way numpy treats a 1d array versus a 2d column.\n mu_new = np.mean(X, axis=1).reshape(-1,1)\n\n # Subtract the mean, append it as a column vector, and update mu\n # X - mu_new will be zero if X has only 1 item\n mu_old = mu\n # New mu is a weighted sum of old and new mus\n mu = (n * mu_old + n_new * mu_new) / (n + n_new)\n B = np.hstack((X - mu,\n math.sqrt(n_new * n/float(n_new+n)) * \\\n (mu_old - mu_new)))\n printt(\"Now tracking mean for %d -> %d items; mu.min %f, mu.max %f \" % \\\n (n, n+n_new, np.nanmin(mu), np.nanmax(mu)))\n n = n + n_new\n\n if S.all() == 0:\n npcs = U.shape[1]\n diagS = np.zeros((npcs, npcs))\n else:\n diagS = np.diag(S)\n\n # I don't think this is right. At this point B is the augmented\n # matrix rather than the single observation.\n proj = np.dot(U.T, B)\n reproj_err = B - np.dot(U, proj)\n\n # to get orthogonal form of reproj_err\n # This should return q with dimensions [d(X) by n_new+1], square\n q, dummy = linalg.qr(reproj_err, mode='full')\n # print('q.shape should be 7x2: ', q.shape)\n Q = np.hstack((U, q))\n\n # From Ross and Lim, 2008\n # R = [ [ Sigma, U.T * X ] [ 0, orthog. component of reproj error ] ]\n k_now = diagS.shape[0]\n new_dim = k_now + n_new + 1\n R = np.zeros((new_dim, new_dim))\n R[0:k_now,0:k_now] = diagS\n R[0:k_now,k_now:] = proj\n orthog_reproj_err = np.dot(q.T, reproj_err)\n R[k_now:, k_now:] = orthog_reproj_err\n \n # Perform SVD of R. Then finally update U.\n U, S, V = linalg.svd(R, full_matrices=False)\n printt('Just did increm-ross SVD on %d items.' % n)\n\n U = np.dot(Q, U)\n \n # Keep only the first k components\n U = U[:,0:min([n,k])]\n S_full = S\n S = S[0:min([n,k])]\n\n ###########################################################################\n # Incremental SVD from Brand\n elif svdmethod == 'increm-brand':\n # Pulled out James's attempt to handle NaNs into\n # increm-brand-james.py. Starting over from scratch here.\n n_new = X.shape[1]\n\n if n_new != 1:\n print(\"WARNING: increm-brand will probably only work by adding one item at a time.\")\n input('\\nPress enter to continue or ^C/EOF to exit. ')\n\n if missingmethod == 'ignore':\n # 1. Update mu\n mu_old = mu\n mu_new = X\n\n # Be careful! For any pre-existing NaNs in mu,\n # let mu_new fill them in. Can't get any worse!\n naninds = np.where(np.isnan(mu_old))[0]\n if naninds.size > 0:\n mu_old[naninds,0] = mu_new[naninds,0]\n # And likewise for mu_new -- fill with good values from mu_old.\n naninds = np.where(np.isnan(mu_new))[0]\n if naninds.size > 0:\n mu_new[naninds,0] = mu_old[naninds,0]\n # At this point, the only NaNs that should appear are\n # values that were NaN for both mu and X to start with.\n # They will stay NaN and that's okay.\n \n # New mu is a weighted sum of old and new mus\n mu = (n * mu_old + n_new * mu_new) / (n + n_new)\n printt(\"Now tracking mean for %d -> %d items; mu.min %f, mu.max %f \" % \\\n (n, n+n_new, np.nanmin(mu), np.nanmax(mu)))\n n = n + n_new\n\n # 2. Subtract off the mean\n X = X - mu\n\n # 3. Compute L, the projection of X onto U\n # Note: this will only work for a single item in X\n goodinds = np.where(~np.isnan(X))[0]\n #print('X: %d of %d are good.' % (len(goodinds), X.shape[0]))\n\n diagS = np.diag(S)\n # This is Brand's method, which involves S:\n L = np.dot(diagS,\n np.dot(np.linalg.pinv(np.dot(U[goodinds,:],\n diagS)),\n X[goodinds,:]))\n # Simplified version that does not use S (but is probably wrong):\n #L = np.dot(U[goodinds,:].T,\n # X[goodinds,:])\n # Top row of the Q matrix (eqn 12, Brand 2002)\n Q1 = np.hstack([diagS, L])\n\n # 4. Compute J, the orthogonal basis of H, which is\n # the component of X orthog to U (i.e., unrepresentable direction)\n # 5. Compute K, the projection of X onto J (i.e., unrep. content)\n K = linalg.norm(X[goodinds,:] - np.dot(U[goodinds,:],\n np.dot(U[goodinds,:].T,\n X[goodinds,:])))\n # H = X - UL\n J = np.zeros((U.shape[0], 1))\n J[goodinds] = np.dot(K,\n np.linalg.pinv(X[goodinds,:] -\n np.dot(U[goodinds,:],\n L))).T\n \n # Bottom row of Q matrix (eqn 12, Brand 2002)\n Q2 = np.hstack([np.zeros([1, len(S)]), np.array(K).reshape(1,1)])\n Q = np.vstack([Q1, Q2])\n\n # 6. Take the SVD of Q\n Uq, Sq, Vq = linalg.svd(Q, full_matrices=False)\n\n # 7. Update U and S (eqn 4, Brand 2002)\n # Note: Since J is zero-filled for badinds, now U is too.\n # Alternatively, we give J NaNs and let them get into U as well.\n # I think that is a worse idea though.\n U = np.dot(np.hstack([U, J]), Uq)\n S = Sq\n # Updating V requires knowing old V,\n # but we don't need the new one either so it's okay to skip.\n\n printt('Just did increm-brand SVD on %d items.' % n)\n \n ############# end ###########\n \n else: # No missing values (or not 'ignore')\n # 1. Update mu\n mu_old = mu\n mu_new = X\n # New mu is a weighted sum of old and new mus\n mu = (n * mu_old + n_new * mu_new) / (n + n_new)\n n = n + n_new\n\n # 2. Subtract off the mean\n X = X - mu\n\n # 3. Compute L, the projection of X onto U\n L = np.dot(U.T, X)\n Q1 = np.hstack([np.diag(S), L])\n\n # 4. Compute J, the orthogonal basis of H, which is\n # the component of X orthog to U (i.e., unrepresentable direction)\n # 5. Compute K, the projection of X onto J (i.e., unrep. content)\n JK = X - np.dot(U, L)\n (J, K) = linalg.qr(JK)\n\n Q2 = np.hstack([np.zeros([1, len(S)]), np.array(K).reshape(1,1)])\n Q = np.vstack([Q1, Q2])\n\n # 6. Take the SVD of Q\n Uq, Sq, Vq = linalg.svd(Q, full_matrices=False)\n\n # 7. Update U and S (eqn 4, Brand 2002)\n U = np.dot(np.hstack([U, J]), Uq)\n S = Sq\n # V requires knowing old V,\n # but we don't need the new one either so it's okay.\n \n printt('Just did regular increm SVD on %d items.' % n)\n\n # Keep only the first k components\n U = U[:,0:min([n,k])]\n S = S[0:min([n,k])]\n\n Usum = U.sum(1)\n\n\n ###########################################################################\n # We have a bad svdmethod, but somehow didn't catch it earlier.\n else:\n printt(\"504: Bad Gateway in protocol <Skynet_authentication.exe>\")\n return None, None, None, None, None\n\n indivpcts = None\n\n # This only works if a full SVD was done\n if (svdmethod == 'full' and output_k and opts['k_var'] == -773038.0):\n # Calculate percent variance captured by each \n cumsum = np.cumsum(S_full)\n #print(cumsum.shape)\n if cumsum[-1] != 0:\n indivpcts = S / cumsum[-1]\n indivpcts = indivpcts[0:k] # truncate to first k\n cumpercents = cumsum / cumsum[-1]\n else:\n indivpcts = []\n\n # Calculate percent variance captured\n if k >= cumsum.shape[0]:\n printt('Cannot estimate data variance; specified k (%d) exceeds the number of SVs (%d).' % (k, cumsum.shape[0]))\n else:\n printt(\"Selected value of k=%d captures %5.2f%% of the data variance\" % \\\n (k, cumpercents[k-1] * 100))\n if opts['pause']: input(\"Press enter to continue\\n\")\n\n #print('U:', U)\n #print('mu:', mu)\n return U, S, mu, n, indivpcts","def _exe_(self):\n print(\"\\n Start simulation (using Pharlap) ...\")\n dic = \"data/sim/{dn}/{rad}/\".format(dn=self.event.strftime(\"%Y.%m.%d.%H.%M\"), rad=self.rad)\n self._estimate_edens_()\n self._compute_()\n plotlib.plot_exp_rays(dic, self.event, self.bmnum, \"bgc\")\n plotlib.plot_exp_rays(dic, self.event, self.bmnum, \"flare\")\n if self.verbose: print(\"\\n Processing Doppler.\")\n self._compute_doppler_()\n rec = self._compute_velocity_()\n return rec","def singularity_build(self, path, image):\n Client.build(os.path.join(\n path, 'singularity.def'\n ), self.generate_image_name(image))","def process_stimulus(self, stimulus, epoch, stimNum):\n diverge = False\n self.init_for_run(stimulus)\n harmony, state, stateC = self.calc_max_Harmony()\n # FIXME: Should I update the states here ?\n # I think so, since state and stateC take the input into account, whereas at this point state is just randomly\n \"\"\"\n print(stateC)\n print(f\"After harmony calc: {self.find_TPname(self.find_winner()[1])}\")\n \"\"\"\n self.state = state\n #assert torch.allclose(stateC, self.toConceptual(self.state))\n self.stateC = self.toConceptual(self.state)\n # self.visualizeC(self.stateC)\n # Update bowl parameters (They depend on the stimulus)\n self._bowl_params()\n\n # Update after each step\n self.updateAfterStep(harmony)\n self.consoleLog_step(stim=stimNum, epoch=epoch+1, stimNum=stimNum+1)\n\n # Loop over steps ~30 000 steps\n for step in range(1, self.settings['maxSteps']):\n self.vars['step'] = step\n\n # Compute step components\n self.Hg = self.calc_harmony_dynamics() # Harmony gradient\n self.LV_c, self.LV_s = self.LotkaVolterra_Dynamics() # Lotka gradient\n self.add_noise() # add noise\n\n # Copy the actual tensor to prev_S (RNN)\n self.stateC = self.toConceptual(self.state)\n self.state_prev = self.state.clone()\n self.stateC_prev = self.stateC.clone()\n\n # Now update the actual state\n state_increment = (\n float(self.vars['lambda']) * self.Hg) + (1-float(self.vars['lambda']) * self.LV_s)\n\n state_increment = float(\n self.vars['dt']) * state_increment + self.noise\n # increment_check = torch.sqrt(\n # torch.tensordot(state_increment, state_increment))\n # if increment_check > 0:\n # self.vars['dt'] = min(\n # self.vars['max_dt'], self.vars['min_dt'] / increment_check)\n # self.vars['dt'] = max(self.vars['min_dt'], self.vars['dt'])\n # if self.vars['step'] % self.settings[\"printInterval\"] == 0:\n # print(self.vars['dt'])\n\n self.state += state_increment\n self.stateC = self.toConceptual(self.state)\n \"\"\"DEBUG\n if self.vars['step'] % 50 == 0:\n # self.visualizeC(self.stateC)\n pass\n print(\n f\"after Increment: {self.find_TPname(self.find_winner()[1])}\")\n print(self.stateC)\n \"\"\"\n\n # Clamping --> Check if I should clamp here or before the increment\n if self.settings['clamp']:\n # Dummy debug variant\n #self.stateC += stimulus\n #self.state = self.toNeural(self.stateC)\n self.calc_clamping(stimulus)\n\n if self.check_overflow():\n diverge = True\n print(f\"Net overflowed at step: {step}\")\n break\n\n harmony = self.calc_harmony()\n\n self.updateAfterStep(harmony=harmony)\n # Update Animation\n # Print information if step % print_every == 0\n self.consoleLog_step(\n stim=stimNum, epoch=epoch+1, stimNum=stimNum+1)\n\n # Update Lambda and Temperature!\n self.update_lambda_T()\n\n if self.check_convergency():\n print(\n f\"The net has converged at step {self.vars['step']}, nearest State: {self.vars['TP_trace'][-1]}\")\n break\n\n if diverge:\n # Don't use this step\n self.vars['step'] -= 1\n if self.vars['step'] == self.settings['maxSteps']:\n print(\"FINAL STEP\")\n print(\n f\"The net has reached the maximum number of steps {self.settings['maxSteps']}\")\n self.consoleLog_step(stim=stimNum)\n\n return diverge, harmony","def test_svd_sharpness_with_model(self):\n \t\t\n\t\tesd_before = self.watcher.get_ESD(model=self.model, layer=self.fc2_layer) \n\t\t\n\t\tself.watcher.SVDSharpness(layers=[self.fc2_layer])\n\t\tesd_after = self.watcher.get_ESD(layer=self.fc2_layer) \n\t\t\n\t\tprint(\"max esd before {}\".format(np.max(esd_before)))\n\t\tprint(\"max esd after {}\".format(np.max(esd_after)))\n\n\t\tself.assertGreater(np.max(esd_before),np.max(esd_after))","def do_start(self, arg):\n args = arg.split(\" \")\n self.model.initialise(args[0])\n self.model.run()","def event11310201():\n header(11310201)\n if_event_flag_on(1, EVENT.SkeletonsDisturbed)\n if_not_in_world_area(1, 13, 1)\n if_condition_true(0, 1)\n flag.disable(EVENT.SkeletonsDisturbed)","def main(args):\n\n\t##############################################################################\n\t######## Pass user command line arguments to setup.py which will #############\n\t############# initialise some parameters for the analysis ###################\n\t##############################################################################\n\tinit_ = setup.initialise_user_input(args)\n\n\t##############################################################################\n\t######## Define system_ which is the object, of class nanoCISC, ##############\n\t######## which contains all relevant information about your nanoparticle ####\n\t##############################################################################\n\tsystem_ = nano_cisc.nanoCISC(init_.nano_particle, init_.anchors, init_.beta, init_.calcrange, \n init_.curves, init_.targetinc, init_.density) \n\t# initialise system_ as nanoCISC class here ^^^\n\n\t# If density is being calculated, define grid from grid class\n\tif args['density']:\n\t\tgrid=grids.grid(system_)\n\n\n\t##############################################################################\n\t################ Process trajectory, frame by frame ##########################\n\t##############################################################################\n\n\tfor ts in init_.u.trajectory: # loop through trajectory frames here \n\t\tprint \"Processing snapshot %d \" % (ts.frame)\n\n\t\t# Array for calculating intrinsic density is initialised to {0}\n\t\tintrinsic_count=np.zeros( ( np.ceil( 3 * system_.calculation_range).astype(np.int) ,len(system_.density) ), dtype = np.float32) \n\n\t\t# Array that stores the instantaneous volume of each spatial interval is initialised to {0}\n\t\tvolume_at_dist=np.zeros( ( np.ceil( 3 * system_.calculation_range).astype(np.int) ,len(system_.density) ), dtype = np.float32) \n\n\t\t# Centre of mass position is updated\n\t\tsystem_.update_com()\n\n\t\t# Vectors describing the anchor points are updated \n\t\tsystem_.update_anchors() \n\n\t\t# Nanoparticle depth values are updated\n\t\tsystem_.update_surface() \t\n\n\t\tif args['XYZsurface']:\n\t\t\tsystem_.write_surface(init_.f_visualise_surface) # write micelle surface to xyz file\n \n \t\tif args['density']: \n \t\t\tgrid.update_volume_estimate(volume_at_dist, system_) # volume estimate is updated for snapshot\n\t\t\tsystem_.calculate_density(intrinsic_count, volume_at_dist) # calculate density here\n\n\t\tsystem_.frames_processed += 1\n\n\t##################################\n\t##### Print results to files #####\n\t##################################\n\tif args['density']:\n\t\tsystem_.print_intrinsic_density(init_.f_intrinsic_density_out)\n\t\tsystem_.print_radial_density()\n\n\n\tprint \"Program finished successfully!!!\\n\"","def run():\n\n # Set up environment and agent\n e = Environment() # create environment (also adds some dummy traffic)\n a = e.create_agent(LearningAgent) # create agent\n e.set_primary_agent(a, enforce_deadline=True) # specify agent to track\n # NOTE: You can set enforce_deadline=False while debugging to allow longer trials\n\n # Now simulate it\n sim = Simulator(e, update_delay=0.001, display=True) # create simulator (uses pygame when display=True, if available)\n # NOTE: To speed up simulation, reduce update_delay and/or set display=False\n\n sim.run(n_trials=100) # run for a specified number of trials\n # NOTE: To quit midway, press Esc or close pygame window, or hit Ctrl+C on the command-line","def step(self, action):\n actions = action.reshape(2, 2)\n action = {'GoalieBrain': actions[0], 'StrikerBrain': actions[1]}\n #action = {'GoalieBrain': [5, 5], 'StrikerBrain': [2, 2]}\n #print(action)\n info = self.env.step(action)\n\n state0 = info[self.brain_names[0]].vector_observations\n state1 = info[self.brain_names[1]].vector_observations\n state = np.vstack((state0, state1))\n reward0 = info[self.brain_names[0]].rewards\n reward1 = info[self.brain_names[1]].rewards\n reward = reward0 + reward1\n done0 = info[self.brain_names[0]].local_done\n done1 = info[self.brain_names[1]].local_done\n done = done0 + done1\n return state, reward, done","def run():\n\n # Set up environment and agent\n e = Environment() # create environment (also adds some dummy traffic)\n a = e.create_agent(LearningAgent) # create agent\n e.set_primary_agent(a, enforce_deadline=True) # specify agent to track\n # NOTE: You can set enforce_deadline=False while debugging to allow longer trials\n\n # Now simulate it\n sim = Simulator(e, update_delay=0.00000001, display=False) # create simulator (uses pygame when display=True, if available)\n # NOTE: To speed up simulation, reduce update_delay and/or set display=False\n\n sim.run(n_trials=100) # run for a specified number of trials\n # NOTE: To quit midway, press Esc or close pygame window, or hit Ctrl+C on the command-line"],"string":"[\n \"def test_smoke(self):\\n\\t\\tinit_state = torch.tensor(0.0)\\n\\t\\ttotal_time = torch.tensor(4.0)\\n\\t\\tprint('Agent state trajectory and actions:')\\n\\t\\tAgent().play(init_state, total_time)\\n\\t\\tpyro.clear_param_store()\",\n \"def singularity_start(self, image):\\n env_vars = self.action.get('env', {})\\n\\n for s in self.action.get('secrets', []):\\n env_vars.update({s: os.environ[s]})\\n\\n for e, v in self.env.items():\\n env_vars.update({e: v})\\n\\n env_vars.update({'HOME': os.environ['HOME']})\\n\\n # sets the env variables\\n for k, v in env_vars.items():\\n Client.setenv(k, v)\\n\\n e = Client.run(image=self.generate_image_name(image),\\n args=' '.join(self.action.get('args', '')),\\n return_result=True)\\n return e['return_code']\",\n \"def handleSingularity(singularity_list, singularity_type, trajectory, robot):\\n \\n multiStepInter = False\\n\\n target_trajectory = np.copy(trajectory)\\n \\n if multiStepInter is False:\\n for i in range(0,np.size(singularity_list)):\\n \\n index = singularity_list[i]\\n \\n if(singularity_type[i] == 0):\\n \\n target_trajectory[index][0] = (trajectory[index-1][0] + trajectory[index+1][0]) / 2\\n target_trajectory[index][1] = (trajectory[index-1][1] + trajectory[index+1][1]) / 2\\n target_trajectory[index][2] = (trajectory[index-1][2] + trajectory[index+1][2]) / 2\\n target_trajectory[index][3] = (trajectory[index-1][3] + trajectory[index+1][3]) / 2\\n target_trajectory[index][4] = (trajectory[index-1][4] + trajectory[index+1][4]) / 2\\n target_trajectory[index][5] = (trajectory[index-1][5] + trajectory[index+1][5]) / 2\\n \\n for j in range(0,6):\\n for k in range(0,2): \\n \\n delta_angle_joint = np.abs(target_trajectory[index-1+k][j] - trajectory[index+k][j])\\n \\n if delta_angle_joint > max_velocity[j]*time_step: \\n print \\\"No possible path found because of overhead singularity.\\\"\\n return np.array([robot.GetDOFValues()])\\n \\n if(singularity_type[i] == 1):\\n \\n target_trajectory[index][0] = (trajectory[index-1][0] + trajectory[index+1][0]) / 2\\n target_trajectory[index][1] = (trajectory[index-1][1] + trajectory[index+1][1]) / 2\\n target_trajectory[index][2] = (trajectory[index-1][2] + trajectory[index+1][2]) / 2\\n target_trajectory[index][3] = (trajectory[index-1][3] + trajectory[index+1][3]) / 2\\n target_trajectory[index][4] = (trajectory[index-1][4] + trajectory[index+1][4]) / 2\\n target_trajectory[index][5] = (trajectory[index-1][5] + trajectory[index+1][5]) / 2\\n \\n for j in range(0,6):\\n for k in range(0,2): \\n \\n delta_angle_joint = np.abs(target_trajectory[index-1+k][j] - trajectory[index+k][j])\\n \\n if delta_angle_joint > max_velocity[j]*time_step: \\n print \\\"No possible path found because of wrist singularity.\\\"\\n print \\n return np.array([robot.GetDOFValues()]) \\n else:\\n # Multi step interpolation dont function yet\\n interStep = 2\\n InterSuccess = False\\n \\n while InterSuccess is False:\\n \\n for i in range(0,np.size(singularity_list)):\\n \\n index = singularity_list[i]\\n \\n #Todo: check if interpolation width hurts trajectory range or other singularity interpolations \\n \\n target_trajectory[index][0] = (target_trajectory[index-(interStep/2)][0] + target_trajectory[index+(interStep/2)][0]) / 2\\n target_trajectory[index][1] = (target_trajectory[index-(interStep/2)][1] + target_trajectory[index+(interStep/2)][1]) / 2\\n target_trajectory[index][2] = (target_trajectory[index-(interStep/2)][2] + target_trajectory[index+(interStep/2)][2]) / 2\\n target_trajectory[index][3] = (target_trajectory[index-(interStep/2)][3] + target_trajectory[index+(interStep/2)][3]) / 2\\n target_trajectory[index][4] = (target_trajectory[index-(interStep/2)][4] + target_trajectory[index+(interStep/2)][4]) / 2\\n target_trajectory[index][5] = (target_trajectory[index-(interStep/2)][5] + target_trajectory[index+(interStep/2)][5]) / 2\\n \\n delta_traj = target_trajectory[index]\\n delta_traj[0] = (target_trajectory[index+(interStep/2)][0] - target_trajectory[index-(interStep/2)][0]) / interStep\\n delta_traj[1] = (target_trajectory[index+(interStep/2)][1] - target_trajectory[index-(interStep/2)][1]) / interStep\\n delta_traj[2] = (target_trajectory[index+(interStep/2)][2] - target_trajectory[index-(interStep/2)][2]) / interStep\\n delta_traj[3] = (target_trajectory[index+(interStep/2)][3] - target_trajectory[index-(interStep/2)][3]) / interStep\\n delta_traj[4] = (target_trajectory[index+(interStep/2)][4] - target_trajectory[index-(interStep/2)][4]) / interStep\\n delta_traj[5] = (target_trajectory[index+(interStep/2)][5] - target_trajectory[index-(interStep/2)][5]) / interStep\\n \\n for s in range(1,interStep/2): \\n target_trajectory[index-s][0] = target_trajectory[index][0] - (s * delta_traj[0]) \\n target_trajectory[index-s][1] = target_trajectory[index][1] - (s * delta_traj[1]) \\n target_trajectory[index-s][2] = target_trajectory[index][2] - (s * delta_traj[2])\\n target_trajectory[index-s][3] = target_trajectory[index][3] - (s * delta_traj[3])\\n target_trajectory[index-s][4] = target_trajectory[index][4] - (s * delta_traj[4])\\n target_trajectory[index-s][5] = target_trajectory[index][5] - (s * delta_traj[5])\\n \\n target_trajectory[index+s][0] = target_trajectory[index][0] + (s * delta_traj[0]) \\n target_trajectory[index+s][1] = target_trajectory[index][1] + (s * delta_traj[1]) \\n target_trajectory[index+s][2] = target_trajectory[index][2] + (s * delta_traj[2])\\n target_trajectory[index+s][3] = target_trajectory[index][3] + (s * delta_traj[3])\\n target_trajectory[index+s][4] = target_trajectory[index][4] + (s * delta_traj[4])\\n target_trajectory[index+s][5] = target_trajectory[index][5] + (s * delta_traj[5])\\n \\n maxVelHurt = False\\n \\n for j in range(0,6):\\n for k in range(0,interStep): \\n \\n delta_angle_joint = np.abs(target_trajectory[index-(interStep/2)+k][j] - target_trajectory[index-((interStep/2)-1)+k][j])\\n \\n if delta_angle_joint > max_velocity[j]*time_step and maxVelHurt is False:\\n \\n maxVelHurt = True \\n print \\\"Interpolation step = \\\" \\n print interStep \\n print \\\"No possible path found because of singularity.\\\"\\n print \\\"joint:\\\"\\n print j\\n print \\\"step:\\\"\\n print k\\n \\n print delta_angle_joint\\n print \\\">\\\"\\n print (max_velocity[j]*time_step)\\n print \\\"______________________________________________\\\"\\n #return np.array([robot.GetDOFValues()])\\n interStep = interStep + 2\\n if interStep == 30:\\n return np.array([robot.GetDOFValues()])\\n \\n \\n \\n if maxVelHurt is False:\\n InterSuccess = True \\n \\n \\n \\n return target_trajectory\",\n \"def act(self, env: FakeEnv, s: ActorStrategy):\\n action = env.action_space.sample()\\n print(f\\\"Sampled action shape : {action.shape}\\\")\\n env.step(action)\",\n \"def run_single(self):\\n self.run_sim_time(1)\",\n \"def act(self, state, epsilon, env):\\n if random.random() > epsilon:\\n state = Variable(torch.FloatTensor(state)).unsqueeze(0) # adds extra dim when single input\\n state = self.vari_gpu(state)\\n _, u_opt = self.forward(state)\\n action = (u_opt.cpu().detach().numpy()) # compute the u*[0] \\n #print('act:q_value ',q_value)\\n #print('act:model action ',action)\\n else:\\n rand = np.random.rand(int(np.array(env.action_space.shape)))\\n high = env.action_space.high\\n low = env.action_space.low\\n action = low + rand*(high-low)\\n #print('act: ',action)\\n return action\",\n \"def Main():\\n EnigmaSim = simulation() #Creates the simulation object\\n EnigmaSim.Run() #Runs the simulation\",\n \"def execute(self, agent: Agent, state: SimState) -> None:\\n if agent.state() == AgentState.SUSCEPTIBLE and self.days == state.vaccine_time() \\\\\\n and np.random.random() < state.vaccine_share():\\n agent.set_state(AgentState.IMMUNE)\",\n \"async def handle(self):\\n local_controller = self.controller\\n local_controller.add_action(local_controller.larvae.random.train(HYDRALISK))\\n return True\",\n \"def _run(self):\\n logging.warning('-> perform EMPTY experiment...')\",\n \"def test_sparsity(config):\\n total_zeros = 0\\n total_nonzeros = 0\\n\\n print ('<===sparsity type is {}'.format(config.sparsity_type))\\n print ('<===layers to be pruned are {}'.format(config._prune_ratios))\\n if config.masked_progressive and (config.sparsity_type == 'filter' or config.sparsity_type =='column'or config.sparsity_type == \\\"bn_filter\\\" ):\\n ### test both column and row sparsity\\n print (\\\"***********checking column sparsity*************\\\")\\n for name,W in config.model.named_parameters():\\n if name not in config.prune_ratios:\\n continue\\n W = W.cpu().detach().numpy()\\n shape = W.shape\\n W2d = W.reshape(shape[0],-1)\\n column_l2_norm = LA.norm(W2d,2,axis=0)\\n zero_column = np.sum(column_l2_norm == 0)\\n nonzero_column = np.sum(column_l2_norm !=0)\\n\\n print (\\\"column sparsity of layer {} is {}\\\".format(name,zero_column/(zero_column+nonzero_column)))\\n print (\\\"***********checking filter sparsity*************\\\") \\n for name,W in config.model.named_parameters():\\n if name not in config.prune_ratios:\\n continue\\n W = W.cpu().detach().numpy()\\n shape = W.shape\\n W2d = W.reshape(shape[0],-1)\\n row_l2_norm = LA.norm(W2d,2,axis=1)\\n zero_row = np.sum(row_l2_norm == 0)\\n nonzero_row = np.sum(row_l2_norm !=0)\\n print (\\\"filter sparsity of layer {} is {}\\\".format(name,zero_row/(zero_row+nonzero_row)))\\n print (\\\"************checking overall sparsity in conv layers*************\\\")\\n for name,W in config.model.named_parameters():\\n if name not in config.prune_ratios:\\n continue\\n W = W.cpu().detach().numpy() \\n total_zeros +=np.sum(W==0)\\n total_nonzeros +=np.sum(W!=0)\\n print ('only consider conv layers, compression rate is {}'.format((total_zeros+total_nonzeros)/total_nonzeros))\\n return\\n \\n if config.sparsity_type == \\\"irregular\\\":\\n for name,W in config.model.named_parameters():\\n if 'bias' in name:\\n continue\\n W = W.cpu().detach().numpy()\\n zeros = np.sum(W==0)\\n total_zeros+=zeros\\n nonzeros = np.sum(W!=0)\\n total_nonzeros+=nonzeros\\n print (\\\"sparsity at layer {} is {}\\\".format(name,zeros/(zeros+nonzeros)))\\n total_weight_number = total_zeros+total_nonzeros\\n print ('overal compression rate is {}'.format(total_weight_number/total_nonzeros))\\n elif config.sparsity_type == \\\"column\\\":\\n for name,W in config.model.named_parameters():\\n if name not in config.prune_ratios:\\n continue\\n W = W.cpu().detach().numpy()\\n shape = W.shape\\n W2d = W.reshape(shape[0],-1)\\n column_l2_norm = LA.norm(W2d,2,axis=0)\\n zero_column = np.sum(column_l2_norm == 0)\\n nonzero_column = np.sum(column_l2_norm !=0)\\n total_zeros +=np.sum(W==0)\\n total_nonzeros +=np.sum(W!=0)\\n print (\\\"column sparsity of layer {} is {}\\\".format(name,zero_column/(zero_column+nonzero_column)))\\n print ('only consider conv layers, compression rate is {}'.format((total_zeros+total_nonzeros)/total_nonzeros)) \\n elif config.sparsity_type == \\\"filter\\\":\\n print ('inside if')\\n print (config.prune_ratios)\\n for name,W in config.model.named_parameters():\\n if name not in config.prune_ratios:\\n continue\\n W = W.cpu().detach().numpy()\\n shape = W.shape\\n W2d = W.reshape(shape[0],-1)\\n row_l2_norm = LA.norm(W2d,2,axis=1)\\n zero_row = np.sum(row_l2_norm == 0)\\n nonzero_row = np.sum(row_l2_norm !=0)\\n total_zeros +=np.sum(W==0)\\n total_nonzeros +=np.sum(W!=0)\\n print (\\\"filter sparsity of layer {} is {}\\\".format(name,zero_row/(zero_row+nonzero_row)))\\n print ('only consider conv layers, compression rate is {}'.format((total_zeros+total_nonzeros)/total_nonzeros))\\n elif config.sparsity_type == \\\"bn_filter\\\":\\n print ('inside bn_filter')\\n print (config.prune_ratios)\\n for i,(name,W) in enumerate(config.model.named_parameters()):\\n if name not in config.prune_ratios:\\n continue\\n W = W.cpu().detach().numpy()\\n zeros = np.sum(W==0)\\n nonzeros = np.sum(W!=0)\\n print (\\\"sparsity at layer {} is {}\\\".format(name,zeros/(zeros+nonzeros)))\",\n \"def sing(self):\\n if self._energy < self._sing_cost:\\n return\\n\\n self._energy = self._energy - self._sing_cost\\n self._env.simulate()\",\n \"def execute(self, agent: Agent, state: SimState) -> None:\\n if agent.state() is not AgentState.INFECTIVE:\\n return\\n\\n if np.random.random() < state.remove_prob():\\n if np.random.random() < state.lethality():\\n agent.set_state(AgentState.DEAD)\\n else:\\n agent.set_state(AgentState.IMMUNE)\\n else:\\n agent.update_sick_days()\",\n \"async def handle(self):\\n local_controller = self.controller\\n local_controller.add_action(local_controller.larvae.random.train(DRONE))\\n return True\",\n \"def solveOneStep(self):\\n ### Student code goes here\\n return True\",\n \"def RUN(self):\",\n \"def start(self, world):\\n self.sense(world)\",\n \"def run(self):\\n sites = [x for x in self.cell.all_sections['axonnode']]\\n ptype = 'pulses'\\n self.default_durs=[10., 100., 25.]\\n stimdict = { # set up the stimulus parameters\\n 'NP': 20,\\n 'Sfreq': 500.0,\\n 'delay': self.default_durs[0],\\n 'dur': 0.5,\\n 'amp': 2.0,\\n 'PT': 0.0,\\n 'dt': self.dt,\\n }\\n istim = h.iStim(0.5, sec=self.cell.soma)\\n istim.delay = 0.\\n istim.dur = 1e9 # these actually do not matter...\\n istim.iMax = 0.0\\n self.run_one(istim, stimdict, sites=sites) #do one simulation\",\n \"def act(self, s, exploration, game):\\n agent_p = self.compute_marginal_pi(s, one_hot=False)\\n if self.exploration and random.random() < self.episilon:\\n agent_action = random.randint(0, self.action_num - 1)\\n else:\\n if self.verbose:\\n for s in self.Q.keys():\\n print('{}--------------'.format(self.id_))\\n print('Q of agent {}: state {}: {}'.format(self.id_, s, str(self.Q[s])))\\n # print('QAof agent {}: state {}: {}'.format(self.id_, s, str(self.Q_A[s])))\\n # self.Q_A\\n print('pi of agent {}: state {}: {}'.format(self.id_, s, self.pi[s]))\\n # print('pi of opponent agent {}: state{}: {}'.format(self.id_, s, self.opponent_best_pi[s]))\\n print('{}--------------'.format(self.id_))\\n agent_action = np.argmax(agent_p)\\n return agent_action\",\n \"def simulateOneTimeStep(self):\\n\\n self.susceptibleToInfected()\\n self.infectedToRecovered()\\n\\n # add the new values of healthy/infected/recovered to the arrays keeping track\\n SIR_t = np.array([self.getSusceptible(), self.getInfected(), self.getRecovered()])\\n #update SIR time series\\n self.SIR = np.concatenate([self.SIR, SIR_t[:,np.newaxis]], axis=1)\\n\\n # add the new snapshot of the simulation\\n self.snapshots.append(self.getSpace().copy())\",\n \"def step(self, action):\\n\\n # handling error\\n \\n assert (action < self.NumofBandits)#, \\\"[ERROR] un-identified arm\\\"\\n\\n # return reward via sampling from normal distribution\\n return np.random.normal(self.MeanList[action], self.sigma, 1)[0]\",\n \"def test_simulation(self):\\n\\t\\tprint \\\"Simulation is being tested\\\"\\n\\n\\t\\tif toggles.DEBUG_FLAG:\\n\\t\\t\\tprint \\\"Debug Flag Set!\\\"\\n\\t\\t\\tprint self.getConfig()\\n\\n\\t\\tif toggles.PACKING:\\n\\t\\t\\ttoggles.OUTPUT_PATH = toggles.OUTPUT_PATH+toggles.RUN_NAME+'/'\\n\\t\\t\\tpackageMaker(toggles.OUTPUT_PATH,self.getConfig())\\n\\t\\tif toggles.IDEAL_GRID:\\n\\t\\t\\tself.consensusGrid()\\n\\n\\t\\tif toggles.REAL_DATA:\\n\\t\\t\\tsampleData = self.load_data()\\n\\t\\t\\tif toggles.RUN_DATA_STATS:\\n\\t\\t\\t\\tself.output_data_stats(sampleData)\\n\\t\\t\\t\\tself.reset_database()\\n\\t\\t\\tif toggles.RUN_AVERAGE_COST:\\n\\t\\t\\t\\tself.sim_average_cost(sampleData)\\n\\t\\t\\t\\tself.reset_database()\\n\\t\\t\\tif toggles.RUN_SINGLE_PAIR:\\n\\t\\t\\t\\tself.sim_single_pair_cost(sampleData, pending_eddy(self.pick_worker([0], [0])))\\n\\t\\t\\t\\tself.reset_database()\\n\\t\\telse:\\n\\t\\t\\tsampleData = {}\\n\\t\\t\\tsyn_load_data()\\n\\n\\t\\tif toggles.RUN_ITEM_ROUTING and not (toggles.RUN_TASKS_COUNT or toggles.RUN_MULTI_ROUTING):\\n\\t\\t\\tif toggles.DEBUG_FLAG:\\n\\t\\t\\t\\tprint \\\"Running: item Routing\\\"\\n\\t\\t\\tself.run_sim(deepcopy(sampleData))\\n\\t\\t\\tself.reset_database()\\n\\n\\t\\tif PRED_SCORE_COUNT and not (RUN_TASKS_COUNT or RUN_MULTI_ROUTING):\\n\\t\\t\\tif DEBUG_FLAG:\\n\\t\\t\\t\\tprint \\\"Running: Pred Score count\\\"\\n\\t\\t\\tself.run_sim(sampleData)\\n\\t\\t\\tself.reset_database()\\n\\n\\n\\n\\t\\tif toggles.COUNT_TICKETS and not (toggles.RUN_TASKS_COUNT or toggles.RUN_MULTI_ROUTING):\\n\\t\\t\\tif toggles.DEBUG_FLAG:\\n\\t\\t\\t\\tprint \\\"Running: ticket counting\\\"\\n\\t\\t\\tself.run_sim(deepcopy(sampleData))\\n\\t\\t\\tself.reset_database()\\n\\n\\t\\tif toggles.SELECTIVITY_GRAPH and not (toggles.RUN_TASKS_COUNT or toggles.RUN_MULTI_ROUTING):\\n\\t\\t\\tif toggles.DEBUG_FLAG:\\n\\t\\t\\t\\tprint \\\"Running: selectivity amounts over time\\\"\\n\\t\\t\\tself.run_sim(sampleData)\\n\\t\\t\\tself.reset_database()\\n\\n\\t\\t#____FOR LOOKING AT ACCURACY OF RUNS___#\\n\\t\\tif toggles.TEST_ACCURACY and toggles.REAL_DATA:\\n\\t\\t\\tcorrectAnswers = self.get_correct_answers(toggles.INPUT_PATH + toggles.ITEM_TYPE + '_correct_answers.csv')\\n\\t\\t\\tpassedItems = self.get_passed_items(correctAnswers)\\n\\n\\n\\t\\tif toggles.RUN_OPTIMAL_SIM:\\n\\t\\t\\tcountingArr=[]\\n\\t\\t\\tself.reset_database()\\n\\t\\t\\tfor i in range(toggles.NUM_SIM):\\n\\t\\t\\t\\tprint \\\"running optimal_sim \\\" +str(i)\\n\\t\\t\\t\\tself.num_tasks = self.optimal_sim(sampleData)\\n\\t\\t\\t\\tcountingArr.append(self.num_tasks)\\n\\t\\t\\t\\tself.reset_database()\\n\\t\\t\\tdest = toggles.OUTPUT_PATH+toggles.RUN_NAME+'_optimal_tasks'\\n\\t\\t\\tgeneric_csv_write(dest+'.csv',[countingArr])\\n\\t\\t\\tif toggles.DEBUG_FLAG:\\n\\t\\t\\t\\tprint \\\"Wrote File: \\\" + dest+'.csv'\\n\\n\\n\\n\\t\\tif toggles.RUN_TASKS_COUNT or toggles.RUN_MULTI_ROUTING or toggles.RUN_CONSENSUS_COUNT:\\n\\t\\t\\tif toggles.RUN_TASKS_COUNT:\\n\\t\\t\\t\\t#print \\\"Running: task_count\\\"\\n\\t\\t\\t\\t#f = open(toggles.OUTPUT_PATH + toggles.RUN_NAME + '_tasks_count.csv', 'a')\\n\\t\\t\\t\\t#f1 = open(toggles.OUTPUT_PATH + toggles.RUN_NAME + '_incorrect_count.csv', 'a')\\n\\n\\t\\t\\t\\tif toggles.GEN_GRAPHS:\\n\\t\\t\\t\\t\\toutputArray = []\\n\\n\\t\\t\\trunTasksArray = []\\n\\t\\t\\tgoodArray, badArray = [], []\\n\\t\\t\\tgoodPoints, badPoints = [], []\\n\\t\\t\\taccCount = []\\n\\t\\t\\tlocArray = [[],[],[],[]]\\n\\n\\t\\t\\tfor i in range(toggles.NUM_SIM):\\n\\t\\t\\t\\tprint \\\"running simulation \\\" + str(i+1)\\n\\t\\t\\t\\tself.run_sim(deepcopy(sampleData))\\n\\t\\t\\t\\trunTasksArray.append(self.num_tasks)\\n\\n\\t\\t\\t\\t#____FOR LOOKING AT ACCURACY OF RUNS___#\\n\\t\\t\\t\\tif toggles.TEST_ACCURACY and toggles.REAL_DATA:\\n\\t\\t\\t\\t\\tnum_incorrect = self.final_item_mismatch(passedItems)\\n\\t\\t\\t\\t\\taccCount.append(num_incorrect)\\n\\t\\t\\t\\tif toggles.RUN_CONSENSUS_COUNT or toggles.VOTE_GRID:\\n\\t\\t\\t\\t\\tdonePairs = IP_Pair.objects.filter(Q(num_no__gt=0)|Q(num_yes__gt=0))\\n\\t\\t\\t\\t\\tif toggles.TEST_ACCURACY:\\n\\t\\t\\t\\t\\t\\tgoodPairs, badPairs = [], []\\n\\t\\t\\t\\t\\t\\tfor pair in donePairs:\\n\\t\\t\\t\\t\\t\\t\\tval = bool((pair.num_yes-pair.num_no)>0)\\n\\t\\t\\t\\t\\t\\t\\tif toggles.REAL_DATA:\\n\\t\\t\\t\\t\\t\\t\\t\\tcorrect = ((correctAnswers[(pair.item,pair.predicate)]) == val)\\n\\t\\t\\t\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\t\\t\\t\\tcorrect = (pair.true_answer == val)\\n\\t\\t\\t\\t\\t\\t\\tif correct:\\n\\t\\t\\t\\t\\t\\t\\t\\tgoodArray.append(pair.num_no+pair.num_yes)\\n\\t\\t\\t\\t\\t\\t\\t\\tgoodPoints.append((pair.num_no,pair.num_yes))\\n\\t\\t\\t\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\t\\t\\t\\tbadArray.append(pair.num_no+pair.num_yes)\\n\\t\\t\\t\\t\\t\\t\\t\\tbadPoints.append((pair.num_no,pair.num_yes))\\n\\t\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\t\\tfor pair in donePairs:\\n\\t\\t\\t\\t\\t\\t\\tgoodArray.append(pair.num_no + pair.num_yes)\\n\\t\\t\\t\\t\\t\\t\\tgoodPoints.append((pair.num_no,pair.num_yes))\\n\\n\\t\\t\\t\\t\\t#print \\\"This is number of incorrect items: \\\", num_incorrect\\n\\n\\t\\t\\t\\tself.reset_database()\\n\\n\\t\\t\\tif toggles.RUN_TASKS_COUNT:\\n\\t\\t\\t\\tgeneric_csv_write(toggles.OUTPUT_PATH+toggles.RUN_NAME+'_tasks_count.csv',[runTasksArray])\\n\\t\\t\\t\\tif toggles.DEBUG_FLAG:\\n\\t\\t\\t\\t\\tprint \\\"Wrote File: \\\" + toggles.OUTPUT_PATH + toggles.RUN_NAME + '_tasks_count.csv'\\n\\t\\t\\t\\tif toggles.GEN_GRAPHS:\\n\\t\\t\\t\\t\\tif len(runTasksArray)>1:\\n\\t\\t\\t\\t\\t\\tdest = toggles.OUTPUT_PATH + toggles.RUN_NAME + '_tasks_count.png'\\n\\t\\t\\t\\t\\t\\ttitle = toggles.RUN_NAME + ' Cost distribution'\\n\\t\\t\\t\\t\\t\\thist_gen(runTasksArray, dest, labels = ('Cost','Frequency'), title = title)\\n\\t\\t\\t\\t\\t\\tif toggles.DEBUG_FLAG:\\n\\t\\t\\t\\t\\t\\t\\tprint \\\"Wrote File: \\\" + dest\\n\\t\\t\\t\\t\\telif toggles.DEBUG_FLAG:\\n\\t\\t\\t\\t\\t\\tprint \\\"only ran one sim, not running hist_gen\\\"\\n\\n\\t\\t\\tif toggles.RUN_MULTI_ROUTING:\\n\\t\\t\\t\\t\\tdest = toggles.OUTPUT_PATH + toggles.RUN_NAME + '_Eddy_sys_' + str(toggles.EDDY_SYS) + '_multi_routing.png'\\n\\t\\t\\t\\t\\ttitle = toggles.RUN_NAME + ' Average Predicate Routing'\\n\\t\\t\\t\\t\\tquestions = toggles.CHOSEN_PREDS\\n\\t\\t\\t\\t\\tarrayData = []\\n\\t\\t\\t\\t\\tfor i in range(len(questions)):\\n\\t\\t\\t\\t\\t\\tarrayData.append([])\\n\\t\\t\\t\\t\\tfor routingL in ROUTING_ARRAY:\\n\\t\\t\\t\\t\\t\\tfor i in range(len(questions)):\\n\\t\\t\\t\\t\\t\\t\\tarrayData[i].append(routingL[i])\\n\\t\\t\\t\\t\\tmrsavefile = open(toggles.OUTPUT_PATH+toggles.RUN_NAME+'_multi_routing.csv','w')\\n\\t\\t\\t\\t\\tmrwriter = csv.writer(mrsavefile)\\n\\t\\t\\t\\t\\tmrwriter.writerow(questions)\\n\\t\\t\\t\\t\\tfor row in arrayData:\\n\\t\\t\\t\\t\\t\\tmrwriter.writerow(row)\\n\\t\\t\\t\\t\\tmrsavefile.close()\\n\\t\\t\\t\\t\\tif toggles.DEBUG_FLAG:\\n\\t\\t\\t\\t\\t\\tprint \\\"Wrote File: \\\"+toggles.OUTPUT_PATH+toggles.RUN_NAME+'_multi_routing.csv'\\n\\t\\t\\t\\t\\tif toggles.GEN_GRAPHS:\\n\\t\\t\\t\\t\\t\\tstats_bar_graph_gen(arrayData, questions, dest, labels = ('Predicate','# of Items Routed'), title = title)\\n\\t\\t\\t\\t\\t\\tif toggles.DEBUG_FLAG:\\n\\t\\t\\t\\t\\t\\t\\tprint \\\"Wrote File: \\\" + toggles.OUTPUT_PATH+toggles.RUN_NAME+'_multi_routing.png'\\n\\t\\t\\tif toggles.ACCURACY_COUNT:\\n\\t\\t\\t\\tdest = toggles.OUTPUT_PATH+toggles.RUN_NAME+'_acc_count'\\n\\t\\t\\t\\tgeneric_csv_write(dest+'.csv',[accCount])\\n\\t\\t\\t\\tif toggles.GEN_GRAPHS:\\n\\t\\t\\t\\t\\thist_gen(accCount, dest+'.png')\\n\\n\\t\\t\\tif toggles.RUN_CONSENSUS_COUNT:\\n\\t\\t\\t\\tdest = toggles.OUTPUT_PATH + toggles.RUN_NAME+'_consensus_count'\\n\\t\\t\\t\\tif len(goodArray)>1:\\n\\t\\t\\t\\t\\tif len(badArray) == 0:\\n\\t\\t\\t\\t\\t\\tgeneric_csv_write(dest+'.csv',[goodArray])\\n\\t\\t\\t\\t\\t\\t#print goodArray\\n\\t\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\t\\tgeneric_csv_write(dest+'.csv',[goodArray,badArray])\\n\\t\\t\\t\\t\\t\\t#print goodArray,badArray\\n\\t\\t\\t\\t\\tif toggles.DEBUG_FLAG:\\n\\t\\t\\t\\t\\t\\tprint \\\"Wrote File: \\\" + dest + '.csv'\\n\\t\\t\\t\\t\\tif toggles.GEN_GRAPHS:\\n\\t\\t\\t\\t\\t\\ttitle = 'Normalized Distribution of Tasks before Consensus'\\n\\t\\t\\t\\t\\t\\tlabels = ('Number of Tasks', 'Frequency')\\n\\t\\t\\t\\t\\t\\tif len(badArray) < 2:\\n\\t\\t\\t\\t\\t\\t\\thist_gen(goodArray, dest+'.png',labels=labels,title=title)\\n\\t\\t\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\t\\t\\tleg = ('Correctly Evaluated IP pairs','Incorrectly Evaluated IP pairs')\\n\\t\\t\\t\\t\\t\\t\\tmulti_hist_gen([goodArray,badArray],leg,dest+'.png',labels=labels,title=title)\\n\\t\\t\\t\\telif toggles.DEBUG_FLAG:\\n\\t\\t\\t\\t\\tprint \\\"only ran one sim, ignoring results\\\"\\n\\t\\t\\tif toggles.VOTE_GRID:\\n\\t\\t\\t\\tdest = toggles.OUTPUT_PATH + toggles.RUN_NAME+'_vote_grid'\\n\\t\\t\\t\\tif len(goodPoints)>1:\\n\\t\\t\\t\\t\\tif len(badPoints)==0:\\n\\t\\t\\t\\t\\t\\tgeneric_csv_write(dest+'.csv',goodPoints)\\n\\t\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\t\\tgeneric_csv_write(dest+'_good.csv',goodPoints)\\n\\t\\t\\t\\t\\t\\tgeneric_csv_write(dest+'_bad.csv',badPoints)\\n\\t\\t\\t\\t\\tif toggles.GEN_GRAPHS:\\n\\t\\t\\t\\t\\t\\ttitle = \\\"Vote Grid Graph\\\"\\n\\t\\t\\t\\t\\t\\tlabels = (\\\"Number of No Votes\\\",\\\"Number of Yes Votes\\\")\\n\\t\\t\\t\\t\\t\\tif len(badPoints)==0:\\n\\t\\t\\t\\t\\t\\t\\txL,yL=zip(*goodPoints)\\n\\t\\t\\t\\t\\t\\t\\tline_graph_gen(xL,yL,dest+'.png',title=title,labels=labels,scatter=True,square=True)\\n\\t\\t\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\t\\t\\tgX,gY = zip(*goodPoints)\\n\\t\\t\\t\\t\\t\\t\\tbX,bY = zip(*badPoints)\\n\\t\\t\\t\\t\\t\\t\\tmulti_line_graph_gen((gX,bX),(gY,bY),('Correct','Incorrect'),dest+'_both.png',title=title,labels=labels,scatter=True,square=True)\\n\\t\\t\\t\\t\\t\\t\\tline_graph_gen(gX,gY,dest+'_good.png',title=title+\\\" goodPoints\\\",labels=labels,scatter=True,square=True)\\n\\t\\t\\t\\t\\t\\t\\tline_graph_gen(bX,bY,dest+'_bad.png',title=title+\\\" badPoints\\\",labels=labels,scatter=True,square=True)\\n\\t\\tif toggles.TIME_SIMS:\\n\\t\\t\\tself.timeRun(sampleData)\\n\\n\\t\\tif toggles.RUN_ABSTRACT_SIM:\\n\\t\\t\\tself.abstract_sim(sampleData, toggles.ABSTRACT_VARIABLE, toggles.ABSTRACT_VALUES)\",\n \"def _simulate(self, action=None):\\n for k in range(int(self.SIMULATION_FREQUENCY // self.config[\\\"policy_frequency\\\"])):\\n if action is not None and \\\\\\n self.time % int(self.SIMULATION_FREQUENCY // self.config[\\\"policy_frequency\\\"]) == 0:\\n # Forward action to the spacecraft\\n self.spacecraft.act(self.ACTIONS[action])\\n\\n self.space.act()\\n self.space.step(1 / self.SIMULATION_FREQUENCY)\\n self.time += 1\\n\\n # Automatically render intermediate simulation steps if a viewer has been launched\\n # Ignored if the rendering is done offscreen\\n self._automatic_rendering()\\n\\n # Stop at terminal states\\n if self.done or self._is_terminal():\\n break\\n self.enable_auto_render = False\",\n \"def run(self):\\n self._display_sims(self._compute_sims())\",\n \"def run_game_logic(self):\\n pass\",\n \"def test_singular_func(self):\\n\\n m = Mothur(**self.init_vars)\\n m.help()\\n\\n return\",\n \"def run_sim(self):\\n self.operator, var_form, opt = self.generate_VQE_args()\\n\\n backend = Aer.get_backend('statevector_simulator')\\n quantum_instance = QuantumInstance(backend=backend)\\n vqe = VQE(self.operator, var_form, opt) \\n\\n self.result = vqe.run(quantum_instance)\\n solution = self.extract_solution(self.result, False)\\n return solution\",\n \"def testSimCompletes(self):\\n sim = Simulation()\\n self.assertEqual(25, sim.run_simple(1, 11, \\\"output\\\", 0.1, 2, 10))\",\n \"def run(self) -> None:\\n self._hass.turn_on('scene.{0}'.format(self._args['scene']))\",\n \"def action_run(self):\\n pass\",\n \"def Execute(self,settings,IsStatusBar=False): \\n if settings.IsSeed:\\n np.random.seed(5) \\n \\n self._IsInitial = True\\n self.settings = settings\\n self.sim_t = copy.copy(settings.starttime) # does not have to start at zero if we perform sequential simulations \\n self.X_matrix = copy.deepcopy(settings.X_matrix) \\n self.fixed_species_amount = copy.deepcopy(self.parse.fixed_species_amount) \\n \\n try:\\n self.volume_code = settings.volume_code\\n except AttributeError: # No volume_code present in settings\\n self.volume_code = \\\"self._current_volume = 1\\\" \\n \\n #self.species_to_update = [s for s in range(self.n_species)] # ensure that the first run updates all species \\n self.Propensities() \\n \\n if not self.sim_t: \\n self.timestep = 1 \\n self.sim_output = []\\n self.propensities_output = [] \\n self.V_output = []\\n self._IsTrackPropensities = copy.copy(settings.IsTrackPropensities)\\n self.SpeciesSelection() \\n self.RateSelection() \\n self.SetEvents() # April 15, moved into here, because otherwise each new cell division cycle starts with a time event, if specified \\n if not settings.IsOnlyLastTimepoint:\\n self.Initial_Conditions() \\n \\n nstep_counter = 1\\n t1 = time.time()\\n while (self.sim_t < settings.endtime) and (self.timestep < settings.timesteps): \\n if self.sim_a_0 <= 0: # All reactants got exhausted\\n settings.endtime = 10**50\\n break\\n \\n self.RunExactTimestep() # Run direct SSA \\n self.HandleEvents()\\n \\n # Update Propensities selectively \\n if self.sim_t < settings.endtime: \\n if not self._IsPerformEvent:\\n self.species_to_update = self.parse.reaction_affects[self.reaction_index] # Determine vars to update \\n else:\\n self.species_to_update = [s for s in range(self.n_species)] \\n \\n self.Propensities()\\n \\n if not settings.IsOnlyLastTimepoint: # Store Output\\n self.GenerateOutput()\\n \\n self._IsPerformEvent = False # set to false (or just to make sure).\\n t2 = time.time() \\n if IsStatusBar and t2-t1> 1:\\n t1 = time.time()\\n sys.stdout.write('\\\\rsimulating {0:s}\\\\r'.format('.'*nstep_counter) ) \\n sys.stdout.flush() \\n nstep_counter+=1\\n if nstep_counter > 10:\\n nstep_counter = 1 \\n sys.stdout.write('\\\\rsimulating {0:s} '.format('.'*nstep_counter))\\n sys.stdout.flush()\\n if settings.IsOnlyLastTimepoint or settings.endtime != 10**50: \\n self.GenerateOutput() \\n if IsStatusBar and t1 and not settings.quiet:\\n sys.stdout.write('\\\\rsimulation done! \\\\n')\",\n \"def test_run(env, model_type, hidden_size,action_type, seeds, noise_std, render = False):\\r\\n\\r\\n net = build_net(env, seeds, model_type, hidden_size, noise_std,action_type)\\r\\n hidden_out = (torch.zeros((1, 1, hidden_size), dtype=torch.float),torch.zeros((1, 1, hidden_size), dtype=torch.float))\\r\\n state = env.reset()\\r\\n \\r\\n for step in range(2000):\\r\\n hidden_in = hidden_out\\r\\n if step % 5 == 0:\\r\\n if render:\\r\\n env.render()\\r\\n \\r\\n state = torch.from_numpy(state).float()\\r\\n action, hidden_out = net(state, hidden_in)\\r\\n state, reward, done, info = env.step(action[0])\\r\\n if done:\\r\\n plt.close()\\r\\n break\",\n \"def run(self):\\n\\n self._action.execute()\",\n \"def one_step(self):\\r\\n assert (self.uv_vol is not None)\\r\\n assert (self.guv_vol is not None)\\r\\n assert (self.uv_bound is not None)\\r\\n assert (self.vf_vect_bound is not None)\\r\\n assert (self.vF_vect_vol is not None)\\r\\n # Shape checks\\r\\n assert (self.vF_vect_vol.size == self.vF_vect_vol.shape[0])\\r\\n assert (self.vf_vect_bound.size == self.vf_vect_bound.shape[0])\\r\\n assert (self.vF_vect_vol.shape == self.vf_vect_bound.shape)\\r\\n assert (self.uv_vol.shape[0] == self.uv_vol.shape[1])\\r\\n assert (self.uv_vol.shape == self.guv_vol.shape)\\r\\n assert (self.uv_vol.shape == self.uv_bound.shape)\\r\\n assert (self.uv_vol.shape[0] == self.vF_vect_vol.shape[0])\\r\\n \\r\\n if self.step == 0:\\r\\n self.check_k_matrix_stability()\\r\\n # print(\\\"Epsilon is :\\\"+str(self.Epsilon))\\r\\n # print(\\\"Beta is :\\\"+str(self.Beta))\\r\\n\\r\\n # Form \\\"Stiffness\\\" matrix:\\r\\n K = self.make_k_matrix()\\r\\n # Form \\\"Force\\\" vector: \\r\\n f = self.vF_vect_vol + (self.Epsilon / self.Beta) * self.vf_vect_bound\\r\\n\\r\\n # print(\\\"FORCE VECTOR:\\\")\\r\\n # print(f)\\r\\n # print(\\\"STIFFNESS MATRIX\\\")\\r\\n # print(K)\\r\\n # print(\\\"UV_VOL\\\")\\r\\n # print(self.uv_vol)\\r\\n # print(\\\"EPSILON * GUV_VOL\\\")\\r\\n # print(self.Epsilon * self.guv_vol)\\r\\n # print(\\\"UV_BOUND * COEFF\\\")\\r\\n # print((self.Epsilon / self.Beta) * self.uv_bound)\\r\\n sol = scipy_sparse_linsolve(K, f)\\r\\n # print(\\\"SOLUTION\\\")\\r\\n # print(sol)\\r\\n return sol\",\n \"def run_one_step(self):\\n pass\",\n \"def test_svd_sharpness2(self):\\n \\t\\t\\n\\t\\tprint(\\\"----test_svd_sharpness-----\\\")\\n\\n\\t\\t#model = models.vgg11(pretrained=True)\\n\\t\\tmodel = models.vgg11(weights='VGG11_Weights.IMAGENET1K_V1')\\n\\n\\t\\tself.watcher = ww.WeightWatcher(model=model, log_level=logging.WARNING)\\n\\t\\t\\n\\t\\t\\n\\t\\tesd_before = self.watcher.get_ESD(layer=self.third_layer) \\n\\t\\t\\n\\t\\tself.watcher.SVDSharpness(layers=[self.third_layer])\\n\\t\\tesd_after = self.watcher.get_ESD(layer=self.third_layer) \\n\\t\\t\\n\\t\\tprint(\\\"max esd before {}\\\".format(np.max(esd_before)))\\n\\t\\tprint(\\\"max esd after {}\\\".format(np.max(esd_after)))\\n\\n\\t\\tself.assertGreater(np.max(esd_before),np.max(esd_after))\",\n \"def test_run_simulation_returns_nothing(self):\\n sim = ss.Simulation()\\n assert sim.run_simulation(10) is None\",\n \"def step(self):\\n self.world.slosh_oceans()\\n self.world.transfer_energy_vertically()\\n self.world.transfer_energy_horizontally()\\n self.world.absorb_energy_from_core()\\n self.world.absorb_energy_from_sun(self.sun)\",\n \"def step(self):\\n self.smoke = iterate_jacobi(*self.sparse_disc_matrix, self.source + self.smoke, self.smoke, self.obstacles)\\n self.smoke_field.update(np.reshape(self.smoke, self.obstacles.shape)[1:-1, 1:-1])\",\n \"def singularity_handling(self):\\n return self._sing_handling\",\n \"def run():\\n\\n # Set up environment and agent\\n e = Environment() # create environment (also adds some dummy traffic)\\n a = e.create_agent(LearningAgent) # create agent\\n e.set_primary_agent(a, enforce_deadline= True ) # set agent to track\\n\\n # Now simulate it\\n sim = Simulator(e, update_delay=0.5) # reduce update_delay to speed up simulation\\n sim.run(n_trials=100) # press Esc or close pygame window to quit\",\n \"def run():\\n\\n # Set up environment and agent\\n e = Environment() # create environment (also adds some dummy traffic)\\n a = e.create_agent(LearningAgent) # create agent\\n e.set_primary_agent(a, enforce_deadline=True) # set agent to track\\n\\n # Now simulate it\\n sim = Simulator(e, update_delay=0.5) # reduce update_delay to speed up simulation\\n sim.run(n_trials=100) # press Esc or close pygame window to quit\",\n \"def _run(self):\\n result = self._consensus()\\n if self._decision.split_group and result:\\n self._set_decision(result)\",\n \"def Run():\\r\\n pass\",\n \"def run(self, GameState):\\n pass\",\n \"def step(self): \\n self.reset_parameters()\\n\\n if np.random.uniform(0, 1) < self.model.churn_prob: self.exit_triggered = True \\n if self.exit_triggered:\\n self.exit()\\n else:\\n self.register_deposit(self.deposit_intent)\\n self.register_contribution(self.contribution_intent)\\n self.register_sponsorship(self.sponsor_intent)\\n self.register_euro_exchange(self.euro_exchange_intent)\\n self.register_teo_exchange(self.teo_exchange_intent)\\n self.register_withdraw(self.withdraw_intent)\",\n \"def test_sugiyama(self):\\n # import the experiment variable from the example\\n exp = sugiyama_example(render=False)\\n\\n # run the experiment for a few time steps to ensure it doesn't fail\\n exp.run(1, 5)\",\n \"def _exe_(self):\\n print(\\\"\\\\n Start simulation (using Pharlap) ...\\\")\\n dic = \\\"data/sim/{dn}/{rad}/\\\".format(dn=self.event.strftime(\\\"%Y.%m.%d.%H.%M\\\"), rad=self.rad)\\n self._copy_ne_()\\n [self._compute_(case) for case in [\\\"bgc\\\", \\\"flare\\\"]]\\n plotlib.plot_exp_rays(dic, self.event, self.bmnum, \\\"bgc\\\")\\n plotlib.plot_exp_rays(dic, self.event, self.bmnum, \\\"flare\\\")\\n self._compute_doppler_()\\n rec = self._compute_velocity_()\\n return rec\",\n \"def initialise_sim(self):\\n pass\",\n \"def _step(self, action):\\n\\n\\n assert action.ndim == 2, 'Action must be a two dimensional array with shape (num_models, num_stocks + 1)'\\n assert action.shape[1] == len(self.sim[0].asset_names) + 1\\n assert action.shape[0] == len(self.model_names)\\n\\n print(\\\"before normalisation\\\",action)\\n\\n #Let's ALLOW THE SHORT!!!\\n # normalise just in case\\n #action = np.clip(action, 0, 1)\\n action = np.clip(action, -1, 1)\\n\\n weights = action # np.array([cash_bias] + list(action)) # [w0, w1...]\\n #weights /= (np.sum(weights, axis=1, keepdims=True) + eps)\\n\\n # so if weights are all zeros we normalise to [1,0...]\\n weights[:, 0] += np.clip(1 - np.sum(weights, axis=1), 0, 1)\\n #assert ((action >= 0) * (action <= 1)).all(), 'all action values should be between 0 and 1. Not %s' % action\\n\\n #assert ((action >= 0) * (action <= 1)).all(), 'all action values should be between 0 and 1. Not %s' % action\\n\\n\\n pos_w = [abs(number) for number in weights]\\n\\n\\n np.testing.assert_almost_equal(np.sum(pos_w, axis=1), np.ones(shape=(weights.shape[0])), 3,\\n err_msg='weights should sum to 1. action=\\\"%s\\\"' % weights)\\n\\n observation, done1, ground_truth_obs = self.src._step()\\n\\n # concatenate observation with ones\\n cash_observation = np.ones((1, self.window_length, observation.shape[2]))\\n observation = np.concatenate((cash_observation, observation), axis=0)\\n\\n cash_ground_truth = np.ones((1, 1, ground_truth_obs.shape[2]))\\n ground_truth_obs = np.concatenate((cash_ground_truth, ground_truth_obs), axis=0)\\n\\n\\n # relative price vector of last observation day (close/open)\\n\\n\\n close_price_vector = observation[:, -1, 3]\\n open_price_vector = observation[:, -1, 0]\\n\\n\\n y1_long = close_price_vector / open_price_vector\\n\\n y1_short= open_price_vector/close_price_vector\\n #for i in len(range(observation.shape[0])):\\n # if\\n\\n #print(\\\"THIS IS Y1\\\",y1)\\n #print(\\\"y1 shape\\\",y1.shape)\\n #print(\\\"wieh\\\",weights.shape)\\n\\n rewards = np.empty(shape=(weights.shape[0]))\\n info = {}\\n dones = np.empty(shape=(weights.shape[0]), dtype=bool)\\n for i in range(weights.shape[0]):\\n reward, current_info, done2 = self.sim[i]._step(weights[i], y1_long,y1_short)\\n rewards[i] = reward\\n info[self.model_names[i]] = current_info['portfolio_value']\\n info['return'] = current_info['return']\\n dones[i] = done2\\n\\n # calculate return for buy and hold a bit of each asset\\n info['market_value'] = np.cumprod([inf[\\\"return\\\"] for inf in self.infos + [info]])[-1]\\n # add dates\\n info['date'] = index_to_date(self.start_idx + self.src.idx + self.src.step)\\n info['steps'] = self.src.step\\n info['next_obs'] = ground_truth_obs\\n\\n self.infos.append(info)\\n\\n #print (info)\\n\\n #print(\\\"My action is :\\\",action)\\n\\n\\n\\n return observation, rewards, np.all(dones) or done1, info\",\n \"def test_do_boss_science(self):\\n sopTester.updateModel('mcp', TestHelper.mcpState['boss_science'])\\n nExp = 2\\n self._do_boss_science(nExp, 35, 0, 0, nExp=nExp)\",\n \"def run(self):\\n cpt = 1\\n while cpt < (self.BOUND):\\n cpt += 1\\n self.incBound()\\n\\n res = self.s.check()\\n if res == sat:\\n m = self.s.model()\\n self.checkModel(m)\\n self.printModel(m)\\n return\\n else:\\n print(\\\"The problem is UNSAT\\\")\",\n \"def execute(self, agent: Agent, state: SimState) -> None:\\n pass\",\n \"def main():\\n # Goal is to model the OSSOS resonance detections given a file with parameters for those resonances.\\n # e.g. from Crompvoets et al. (2021)\\n\\n # now run a survey simulation.\\n params = sys.argv[1]\\n H_max = float(sys.argv[2])\\n outfile=f\\\"{os.path.splitext(params)[0]}_Model.dat\\\"\\n print(f\\\"Saving results to {outfile}\\\")\\n if not os.access(outfile, os.R_OK):\\n run(outfile, params, 123456789, H_max=H_max)\\n\\n # confirm this looks like the OSSOS detections using rose plot.\\n face_down_plot(outfile)\",\n \"def run(self):\\n self.cancelled = False\\n # get version from IDF object or by parsing the IDF file for it\\n\\n # Move files into place\\n self.epw = self.idf.epw.copy(self.run_dir / \\\"in.epw\\\").expand()\\n self.idfname = Path(self.idf.savecopy(self.run_dir / \\\"in.idf\\\")).expand()\\n self.idd = self.idf.iddname.copy(self.run_dir).expand()\\n\\n # Get executable using shutil.which (determines the extension based on\\n # the platform, eg: .exe. And copy the executable to tmp\\n slab_exe = shutil.which(\\\"Slab\\\", path=self.eplus_home)\\n if slab_exe is None:\\n log(\\n f\\\"The Slab program could not be found at \\\" f\\\"'{self.eplus_home}'\\\",\\n lg.WARNING,\\n )\\n return\\n self.slabexe = Path(slab_exe).copy(self.run_dir)\\n self.slabidd = (self.eplus_home / \\\"SlabGHT.idd\\\").copy(self.run_dir)\\n\\n # The GHTin.idf file is copied from the self.include list (added by\\n # ExpandObjects. If self.include is empty, no need to run Slab.\\n self.include = [Path(file).copy(self.run_dir) for file in self.idf.include]\\n if not self.include:\\n self.cleanup_callback()\\n return\\n\\n # Run Slab Program\\n with logging_redirect_tqdm(loggers=[lg.getLogger(self.idf.name)]):\\n with tqdm(\\n unit_scale=True,\\n miniters=1,\\n desc=f\\\"RunSlab #{self.idf.position}-{self.idf.name}\\\",\\n position=self.idf.position,\\n ) as progress:\\n\\n self.p = subprocess.Popen(\\n self.cmd,\\n stdout=subprocess.PIPE,\\n stderr=subprocess.PIPE,\\n shell=True, # can use shell\\n cwd=self.run_dir.abspath(),\\n )\\n start_time = time.time()\\n self.msg_callback(\\\"Begin Slab Temperature Calculation processing . . .\\\")\\n for line in self.p.stdout:\\n self.msg_callback(line.decode(\\\"utf-8\\\").strip(\\\"\\\\n\\\"))\\n progress.update()\\n\\n # We explicitly close stdout\\n self.p.stdout.close()\\n\\n # Wait for process to complete\\n self.p.wait()\\n\\n # Communicate callbacks\\n if self.cancelled:\\n self.msg_callback(\\\"RunSlab cancelled\\\")\\n # self.cancelled_callback(self.std_out, self.std_err)\\n else:\\n if self.p.returncode == 0:\\n self.msg_callback(\\n \\\"RunSlab completed in {:,.2f} seconds\\\".format(\\n time.time() - start_time\\n )\\n )\\n self.success_callback()\\n for line in self.p.stderr:\\n self.msg_callback(line.decode(\\\"utf-8\\\"))\\n else:\\n self.msg_callback(\\\"RunSlab failed\\\")\\n self.failure_callback()\",\n \"def __ExecuteActionForRandomAgentWithNoisyTransitionModel__(self, idx_request_action, noise_level):\\n n = len(self.AGENT_ALLOWED_ACTIONS)\\n pp = noise_level/(n-1) * np.ones((n,1))\\n pp[idx_request_action] = 1.0 - noise_level\\n idx_actual = np.random.choice(n, 1, p=pp.flatten()) # sample from the distrbution of actions\\n actual_action = self.AGENT_ALLOWED_ACTIONS[int(idx_actual)]\\n self.agent_host.sendCommand(actual_action)\\n self.solution_report.addAction()\\n return actual_action\",\n \"def main():\\n\\n GAME = \\\"Assignment1-Taxi-v2\\\"\\n env = gym.make(GAME)\\n n_state = env.observation_space.n\\n n_action = env.action_space.n\\n env = Monitor(env, \\\"taxi_simple\\\", force=True)\\n\\n s = env.reset()\\n steps = 100\\n for step in range(steps):\\n env.render()\\n action = int(input(\\\"Please type in the next action:\\\"))\\n s, r, done, info = env.step(action)\\n print(s)\\n print(r)\\n print(done)\\n print(info)\\n\\n # close environment and monitor\\n env.close()\",\n \"def test_svd_sharpness_no_model(self):\\n \\t\\t\\n\\t\\n\\t\\tesd_before = self.watcher.get_ESD(model=self.model, layer=self.fc2_layer) \\n\\t\\t\\n\\t\\tself.watcher.SVDSharpness(model=self.model, layers=[self.fc2_layer])\\n\\t\\tesd_after = self.watcher.get_ESD(layer=self.fc2_layer) \\n\\t\\t\\n\\t\\tprint(\\\"max esd before {}\\\".format(np.max(esd_before)))\\n\\t\\tprint(\\\"max esd after {}\\\".format(np.max(esd_after)))\\n\\n\\t\\tself.assertGreater(np.max(esd_before)-2.0,np.max(esd_after))\",\n \"def sample(self,noise,s):\\n rhs=self.prior.sqrtM*noise\\n self.prior.Msolver.solve(s,rhs)\",\n \"def run():\\n import hmmmAssembler ; reload(hmmmAssembler) # import helpers\\n hmmmAssembler.main(Random) # this runs the code!\",\n \"def run(): \\n learning_rate = 0.42\\n discount_rate = 0.15\\n initial_q_hat = 4\\n \\n # Set up environment and agent\\n e = Environment() # create environment (also adds some dummy traffic)\\n a = e.create_agent(LearningAgent, learning_rate, discount_rate, initial_q_hat) # create agent\\n e.set_primary_agent(a, enforce_deadline=True) # specify agent to track\\n # NOTE: You can set enforce_deadline=False while debugging to allow longer trials\\n\\n # Now simulate it\\n sim = Simulator(e, update_delay=0, display=False) # create simulator (uses pygame when display=True, if available)\\n # NOTE: To speed up simulation, reduce update_delay and/or set display=False\\n\\n sim.run(n_trials=100) # run for a specified number of trials\\n print \\\"Failed trials: \\\"\\n print a.get_failed_trials()\\n # NOTE: To quit midway, press Esc or close pygame window, or hit Ctrl+C on the command-line\",\n \"def run_step(self):\\n \\n # Ensure model has been initialized at least once\\n self._model_has_been_initialized(\\\"run_step\\\")\\n\\n # Check if sim is steady-state (doesn't contain \\\"doStep\\\" method)\\n if \\\"doStep\\\" not in dir(self.fmu):\\n error_log = \\\"[run_step] FMU model cannot be run one step-forward, since it is a steady-state sim. \\\"\\n error_log += \\\"No step advance will be applied.\\\"\\n print(error_log)\\n return\\n\\n self.fmu.doStep(currentCommunicationPoint=self.sim_time, communicationStepSize=self.step_size)\\n self.sim_time += self.step_size\\n return\",\n \"def test_full(args, model, device): \\n test_path = '../data/full/original/'\\n generate_path = '../data/full/generate/'\\n test_image_num = len([name for name in os.listdir(test_path)\\n if os.path.isfile(os.path.join(test_path, name))])\\n\\n score_psnr, score_ssim_skimage, score_ssim_minstar, score_msssim_minstar = 0.0, 0.0, 0.0, 0.0\\n ind = 0\\n for name in os.listdir(test_path):\\n if os.path.isfile(os.path.join(test_path, name)):\\n ind += 1\\n test_original, test_style, image_height, image_width = load_test_dataset(name)\\n x = torch.from_numpy(test_original).float()\\n y_real = torch.from_numpy(test_style).float()\\n x = x.view(image_height, image_width, config.channels).permute(2, 0, 1).to(device)\\n y_real = y_real.view(image_height, image_width, config.channels).permute(2, 0, 1).to(device)\\n \\n y_fake = model.gen_g(x.view(-1, config.channels, image_height, image_width))\\n y_fake = y_fake.view(config.channels, image_height, image_width)\\n \\n # Calculate PSNR & SSIM scores\\n score_psnr += psnr_full(y_fake, y_real)\\n \\n y_fake_np = y_fake.detach().cpu().numpy().transpose(1, 2, 0)\\n y_real_np = y_real.cpu().numpy().transpose(1, 2, 0)\\n temp_ssim, _ = compare_ssim(y_fake_np, y_real_np, multichannel=True, gaussian_weights=True, full=True)\\n score_ssim_skimage += temp_ssim\\n \\n temp_ssim, _ = ssim(y_fake, y_real, kernel_size=11, kernel_sigma=1.5)\\n score_ssim_minstar += temp_ssim\\n \\n score_msssim_minstar += multi_scale_ssim(y_fake, y_real, kernel_size=11, kernel_sigma=1.5)\\n print('PSNR & SSIM scores of {} images are calculated.'.format(ind))\\n \\n utils.save_image(y_fake, os.path.join(generate_path, '{}-x.jpg'.format(name[:5] + args.model_type)))\\n\\n score_psnr /= test_image_num\\n score_ssim_skimage /= test_image_num\\n score_ssim_minstar /= test_image_num\\n score_msssim_minstar /= test_image_num\\n print('PSNR : {:.4f}, SSIM_skimage : {:.4f}, SSIM_minstar : {:.4f}, SSIM_msssim: {:.4f}'.format(\\n score_psnr, score_ssim_skimage, score_ssim_minstar, score_msssim_minstar))\",\n \"def main():\\n # combineSloEff(makePlots=True, writeDB=False, runMC=False, seedNum=1)\\n GPXSloEff(makePlots=True, writeDB=False)\",\n \"def startSimulation(self):\\n self.saveParameters()\\n self.simulation.main()\",\n \"def __call__(self,sensation,reward=None):\\n raise NYI\",\n \"def action(self, args):\\n mean, stddev = args\\n dist = tfp.distributions.Normal(loc=mean, scale=stddev)\\n action = dist.sample(1)\\n action = K.clip(action,\\n self.env.action_space.low[0],\\n self.env.action_space.high[0])\\n return action\",\n \"def simulate(self):\\r\\n\\t\\tprint(\\\"##################################\\\")\\r\\n\\t\\tprint(\\\"SIMULATING GAME - SpaceInvaders..\\\")\\r\\n\\t\\tprint(\\\"##################################\\\")\\r\\n\\t\\t\\r\\n\\t\\t# Play 3 episodes:\\r\\n\\t\\tfor i in range(3):\\r\\n\\t\\t\\tprint(\\\"Playing Episode %d\\\" % i)\\r\\n\\t\\t\\tstate = self.env.reset()\\r\\n\\t\\t\\t#self.env.render()\\r\\n\\t\\t\\tdone = False\\r\\n\\t\\t\\ttot_reward = 0\\r\\n\\t\\t\\tstate,_ = stack_frames(self.stack_size,self.stacked_frames, \\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tstate, True)\\r\\n\\t\\t\\t# play until dead.\\t\\t\\t\\r\\n\\t\\t\\twhile not done:\\r\\n\\t\\t\\t\\t# get the value predicted by the model and perform that action.\\r\\n\\t\\t\\t\\t# keras conv2d expects a 4D input. So add an empty axis. \\r\\n\\t\\t\\t\\tstate = np.expand_dims(state, axis=0)\\r\\n\\t\\t\\t\\t# predict action directly from the saved neural network.\\r\\n\\t\\t\\t\\taction = np.argmax(self.dqn.getModel().predict(state)[0])\\r\\n\\t\\t\\t\\t# perform that action.\\r\\n\\t\\t\\t\\tstate, reward, done, _ = self.env.step(action)\\r\\n\\t\\t\\t\\tself.env.render()\\r\\n\\t\\t\\t\\tstate,_ = stack_frames(self.stack_size,self.stacked_frames, \\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tstate, False)\\r\\n\\t\\t\\t\\ttot_reward+=reward\\r\\n\\t\\t\\tprint(\\\"Reward: \\\", tot_reward)\\r\\n\\t\\tself.env.close() # to avoid sys.meta_path error\\r\",\n \"def run(self) -> None:\\n\\n num_trials = 3\\n num_successes = 0\\n\\n # Initialize the scene.\\n self.init_scene()\\n for i in range(num_trials):\\n status = self.lift_container()\\n if status != TaskStatus.success:\\n continue\\n status = self.lift_target_object()\\n if status != TaskStatus.success:\\n continue\\n status = self.try_put_in_container()\\n print(f\\\"Tried to put object in container: {status}\\\")\\n if status == TaskStatus.success:\\n num_successes += 1\\n accuracy = float(num_successes) / num_trials\\n print(f\\\"Accuracy: {accuracy}\\\")\",\n \"def run_sim(self):\\n \\n OS = self.OpticalSystem\\n TL = self.TargetList\\n SU = self.SimulatedUniverse\\n Obs = self.Observatory\\n TK = self.TimeKeeping\\n \\n # TODO: start using this self.currentSep\\n # set occulter separation if haveOcculter\\n if OS.haveOcculter == True:\\n self.currentSep = Obs.occulterSep\\n \\n # choose observing modes selected for detection (default marked with a flag)\\n allModes = OS.observingModes\\n det_modes = list(filter(lambda mode: 'imag' in mode['inst']['name'], allModes))\\n # and for characterization (default is first spectro/IFS mode)\\n spectroModes = list(filter(lambda mode: 'spec' in mode['inst']['name'], allModes))\\n if np.any(spectroModes):\\n char_modes = spectroModes\\n # if no spectro mode, default char mode is first observing mode\\n else:\\n char_modes = [allModes[0]]\\n \\n # begin Survey, and loop until mission is finished\\n log_begin = 'OB%s: survey beginning.'%(TK.OBnumber + 1)\\n self.logger.info(log_begin)\\n self.vprint(log_begin)\\n t0 = time.time()\\n sInd = None\\n ObsNum = 0\\n while not TK.mission_is_over(OS, Obs, det_modes[0]):\\n \\n # acquire the NEXT TARGET star index and create DRM\\n old_sInd = sInd #used to save sInd if returned sInd is None\\n DRM, sInd, det_intTime, waitTime, det_mode = self.next_target(sInd, det_modes)\\n \\n if sInd is not None:\\n ObsNum += 1\\n\\n if OS.haveOcculter == True:\\n # advance to start of observation (add slew time for selected target)\\n success = TK.advanceToAbsTime(TK.currentTimeAbs.copy() + waitTime)\\n \\n # beginning of observation, start to populate DRM\\n DRM['star_ind'] = sInd\\n DRM['star_name'] = TL.Name[sInd]\\n DRM['arrival_time'] = TK.currentTimeNorm.copy().to('day')\\n DRM['OB_nb'] = TK.OBnumber\\n DRM['ObsNum'] = ObsNum\\n pInds = np.where(SU.plan2star == sInd)[0]\\n DRM['plan_inds'] = pInds.astype(int)\\n log_obs = (' Observation #%s, star ind %s (of %s) with %s planet(s), ' \\\\\\n + 'mission time at Obs start: %s')%(ObsNum, sInd, TL.nStars, len(pInds), \\n TK.currentTimeNorm.to('day').copy().round(2))\\n self.logger.info(log_obs)\\n self.vprint(log_obs)\\n\\n # PERFORM DETECTION and populate revisit list attribute\\n DRM['det_info'] = []\\n detected, det_fZ, det_systemParams, det_SNR, FA = \\\\\\n self.observation_detection(sInd, det_intTime, det_mode)\\n # update the occulter wet mass\\n if OS.haveOcculter == True:\\n DRM = self.update_occulter_mass(DRM, sInd, det_intTime, 'det')\\n det_data = {}\\n det_data['det_status'] = detected\\n det_data['det_SNR'] = det_SNR\\n det_data['det_fZ'] = det_fZ.to('1/arcsec2')\\n det_data['det_params'] = det_systemParams\\n det_data['det_mode'] = dict(det_mode)\\n det_data['det_time'] = det_intTime.to('day')\\n del det_data['det_mode']['inst'], det_data['det_mode']['syst']\\n DRM['det_info'].append(det_data)\\n\\n # PERFORM CHARACTERIZATION and populate spectra list attribute\\n DRM['char_info'] = []\\n if char_modes[0]['SNR'] not in [0, np.inf]:\\n characterized, char_fZ, char_systemParams, char_SNR, char_intTime = \\\\\\n self.observation_characterization(sInd, char_modes)\\n else:\\n char_intTime = None\\n lenChar = len(pInds) + 1 if True in FA else len(pInds)\\n characterized = np.zeros((lenChar,len(char_modes)), dtype=float)\\n char_SNR = np.zeros((lenChar,len(char_modes)), dtype=float)\\n char_fZ = np.array([0./u.arcsec**2, 0./u.arcsec**2])\\n char_systemParams = SU.dump_system_params(sInd)\\n\\n for mode_index, char_mode in enumerate(char_modes):\\n char_data = {}\\n assert char_intTime != 0, \\\"Integration time can't be 0.\\\"\\n # update the occulter wet mass\\n if OS.haveOcculter == True and char_intTime is not None:\\n char_data = self.update_occulter_mass(char_data, sInd, char_intTime, 'char')\\n if np.any(characterized):\\n vprint(' Char. results are: {}'.format(characterized[:-1, mode_index]))\\n # populate the DRM with characterization results\\n char_data['char_time'] = char_intTime.to('day') if char_intTime else 0.*u.day\\n char_data['char_status'] = characterized[:-1, mode_index] if FA else characterized[:,mode_index]\\n char_data['char_SNR'] = char_SNR[:-1, mode_index] if FA else char_SNR[:, mode_index]\\n char_data['char_fZ'] = char_fZ[mode_index].to('1/arcsec2')\\n char_data['char_params'] = char_systemParams\\n # populate the DRM with FA results\\n char_data['FA_det_status'] = int(FA)\\n char_data['FA_char_status'] = characterized[-1, mode_index] if FA else 0\\n char_data['FA_char_SNR'] = char_SNR[-1] if FA else 0.\\n char_data['FA_char_fEZ'] = self.lastDetected[sInd,1][-1]/u.arcsec**2 \\\\\\n if FA else 0./u.arcsec**2\\n char_data['FA_char_dMag'] = self.lastDetected[sInd,2][-1] if FA else 0.\\n char_data['FA_char_WA'] = self.lastDetected[sInd,3][-1]*u.arcsec \\\\\\n if FA else 0.*u.arcsec\\n \\n # populate the DRM with observation modes\\n char_data['char_mode'] = dict(char_mode)\\n del char_data['char_mode']['inst'], char_data['char_mode']['syst']\\n DRM['char_info'].append(char_data)\\n \\n DRM['exoplanetObsTime'] = TK.exoplanetObsTime.copy()\\n\\n # append result values to self.DRM\\n self.DRM.append(DRM)\\n \\n else:#sInd == None\\n sInd = old_sInd#Retain the last observed star\\n if(TK.currentTimeNorm.copy() >= TK.OBendTimes[TK.OBnumber]): # currentTime is at end of OB\\n #Conditional Advance To Start of Next OB\\n if not TK.mission_is_over(OS, Obs, det_mode):#as long as the mission is not over\\n TK.advancetToStartOfNextOB()#Advance To Start of Next OB\\n elif(waitTime is not None):\\n #CASE 1: Advance specific wait time\\n success = TK.advanceToAbsTime(TK.currentTimeAbs.copy() + waitTime)\\n self.vprint('waitTime is not None')\\n else:\\n startTimes = TK.currentTimeAbs.copy() + np.zeros(TL.nStars)*u.d # Start Times of Observations\\n observableTimes = Obs.calculate_observableTimes(TL,np.arange(TL.nStars),startTimes,self.koMap,self.koTimes,self.mode)[0]\\n #CASE 2 If There are no observable targets for the rest of the mission\\n if((observableTimes[(TK.missionFinishAbs.copy().value*u.d > observableTimes.value*u.d)*(observableTimes.value*u.d >= TK.currentTimeAbs.copy().value*u.d)].shape[0]) == 0):#Are there any stars coming out of keepout before end of mission\\n self.vprint('No Observable Targets for Remainder of mission at currentTimeNorm= ' + str(TK.currentTimeNorm.copy()))\\n #Manually advancing time to mission end\\n TK.currentTimeNorm = TK.missionLife\\n TK.currentTimeAbs = TK.missionFinishAbs\\n else:#CASE 3 nominal wait time if at least 1 target is still in list and observable\\n #TODO: ADD ADVANCE TO WHEN FZMIN OCURS\\n inds1 = np.arange(TL.nStars)[observableTimes.value*u.d > TK.currentTimeAbs.copy().value*u.d]\\n inds2 = np.intersect1d(self.intTimeFilterInds, inds1) #apply intTime filter\\n inds3 = self.revisitFilter(inds2, TK.currentTimeNorm.copy() + self.dt_max.to(u.d)) #apply revisit Filter #NOTE this means stars you added to the revisit list \\n self.vprint(\\\"Filtering %d stars from advanceToAbsTime\\\"%(TL.nStars - len(inds3)))\\n oTnowToEnd = observableTimes[inds3]\\n if not oTnowToEnd.value.shape[0] == 0: #there is at least one observableTime between now and the end of the mission\\n tAbs = np.min(oTnowToEnd)#advance to that observable time\\n else:\\n tAbs = TK.missionStart + TK.missionLife#advance to end of mission\\n tmpcurrentTimeNorm = TK.currentTimeNorm.copy()\\n success = TK.advanceToAbsTime(tAbs)#Advance Time to this time OR start of next OB following this time\\n self.vprint('No Observable Targets a currentTimeNorm= %.2f Advanced To currentTimeNorm= %.2f'%(tmpcurrentTimeNorm.to('day').value, TK.currentTimeNorm.to('day').value))\\n else:#TK.mission_is_over()\\n dtsim = (time.time() - t0)*u.s\\n log_end = \\\"Mission complete: no more time available.\\\\n\\\" \\\\\\n + \\\"Simulation duration: %s.\\\\n\\\"%dtsim.astype('int') \\\\\\n + \\\"Results stored in SurveySimulation.DRM (Design Reference Mission).\\\"\\n self.logger.info(log_end)\\n print(log_end)\",\n \"def run(self):\\n print('\\\\n Početak simulacije Petrijeve mreže\\\\n')\\n print('Definicija:\\\\n{}\\\\n'.format(self.__str__()))\\n print('Početno stanje:\\\\n{}\\\\n---------\\\\n'.format(self.get_M_as_string()))\\n\\n transitions_available = True\\n\\n while transitions_available: \\n transitions_available = False\\n for transition in random.sample(self.T,len(self.T)): \\n if transition.is_enabled():\\n transition.fire()\\n transitions_available = True\\n break\\n \\n\\n print('\\\\nViše nije moguće izvesti niti jedan prijelaz.')\\n print('Trenutno stanje mreže jest:\\\\n{}\\\\n'.format(self.get_M_as_string()))\\n\\n \\n if self.check_net_finality():\\n print('Mreža je završila u finalnom stanju u kojemu su sve značke' +\\n ' na mjestima koja nemaju izlaznih lukova.')\\n else:\\n print('Protok mreže je zaglavio u stanju u kojemu su značke' +\\n ' raspoređene i na mjestima koja imaju izlazne' +\\n ' lukove.')\",\n \"def act(self, state, episode_num, add_noise=True):\\n state = torch.from_numpy(state).float().to(device)\\n if episode_num>WARM_UP:\\n self.actor_local.eval() \\n with torch.no_grad():\\n action = self.actor_local(state).cpu().data.numpy()\\n self.actor_local.train()\\n if add_noise:\\n #print(\\\"action_before_noise\\\",action)\\n action += self.epsilon * self.noise.sample()\\n #print(\\\"action_after_noise\\\",action)\\n else:\\n #generate random action between -1 and 1\\n action=np.random.uniform(-1,1,self.action_size)\\n return np.clip(action,-1,1)\",\n \"def step(self, actions=None):\\n \\n if actions is not None:\\n # implement the actions\\n# print(actions)\\n for asset, valve_position in zip(self.config[\\\"action_space\\\"], actions):\\n# print(asset, valve_position)\\n self._setValvePosition(asset, valve_position)\\n\\n # take the step !\\n time = self.sim._model.swmm_step()\\n done = False if time > 0 else True\\n return done\",\n \"def run_scenario(self):\\n self.initialize_random_map()\\n self.visualize_environment('initial')\\n self.get_tower_target_coverages()\\n self.solve_environment()\\n self.visualize_environment('solved')\",\n \"def step(self, action):\",\n \"def _sense_and_act(self):\\n pass\",\n \"def main():\\n # output filename\\n param = read_parameters('Kanki01_input.yaml')\\n s = seal(param)\\n s.solve_zeroth()\\n s.plot_res()\",\n \"def runall():\\n sclogic.runall()\",\n \"def test_qqlnu_sm(self):\\n R = flavio.sm_prediction('R_13(pp->munu)', 1e3, 2e3)\\n self.assertEqual(R, 1,msg=f'SM prediction for R ratio: {R} (has to be 1)')\",\n \"def act(self, state):\\n # Random\\n if np.random.rand() <= self.epsilon:\\n return self.environment.action_space.sample()\\n\\n # Model prediction\\n return np.argmax(self.model.predict(state)[0])\",\n \"def run(mu_v, Sigma_w, Sigma_z, a_mu, l_sensor):\\n N = 1000\\n # Init tracking\\n mu_x = np.zeros(N) # Belief or estimation of hidden state \\n F = np.zeros(N) # Free Energy of AI neuron\\n mu_y = np.zeros(N) # Belief or prediction of sensory signal \\n x = np.zeros(N) # True hidden state\\n y = np.zeros(N) # Sensory signal as input to AI neuron\\n\\n robot_brain = pp_unit(dt, mu_v, Sigma_w, Sigma_z, a_mu) #make pp object\\n \\n \\n\\n start_time = time.time()\\n for i in np.arange(1, N):\\n #Active inference\\n y[i] = l_sensor.ambient_light_intensity #take sensor reading\\n print('light reading', y[i])\\n F[i], mu_x[i], mu_y[i] = robot_brain.inference_step(i, mu_v, y[i])\\n\\n\\n t_elapsed = time.time() - start_time\\n\\n print(\\\"Elapsed Time\\\", t_elapsed, \\\"sec\\\")\\n return F, mu_x, mu_y, x, y\",\n \"def Main():\\n numberOfPopulation = 350\\n numberOfDays = 60\\n \\n simulation = Simulation(Covid19(), numberOfPopulation, numberOfDays, \\\"Covid 19 Simulation\\\")\\n simulation.run() \\n simulation = Simulation(Ebola(), numberOfPopulation, numberOfDays, \\\"Ebola Simulation\\\")\\n simulation.run()\",\n \"def runSurvey():\\n fieldFile = globals()['settings']['fieldFile']\\n # Number of particles to launch\\n numParticles = globals()['settings']['numParticles']\\n # Radius of spherical simulation boundary used for launching and exiting\\n rLim = globals()['settings']['rLim']\\n # Particle stepping method\\n steppingMethod = globals()['settings']['steppingMethod']\\n # Coarseness of output grid that counts particle fluxes in simulation volume\\n fluxGridCoarseness = globals()['settings']['fluxGridCoarseness']\\n \\n # B field in R and Z\\n r, z, BR = fieldGrid('fields/Brs_' + fieldFile)\\n _, _, BZ = fieldGrid('fields/Bzs_' + fieldFile)\\n _, _, habitatBR = fieldGrid('fields/Brs_habitat_' + fieldFile)\\n _, _, habitatBZ = fieldGrid('fields/Bzs_habitat_' + fieldFile)\\n r = r[:-1]\\n z = z[:-1]\\n BR = BR[:-1,:-1] # I MAY CAUSE A BUG IN THE FUTURE\\n BZ = BZ[:-1,:-1]\\n habitatMax = np.max((habitatBR**2+habitatBZ**2)**.5)\\n habitatPrescription = 30\\n BR += habitatBR*habitatPrescription/habitatMax\\n BZ += habitatBZ*habitatPrescription/habitatMax\\n print('Habitat prescription (T):', habitatPrescription)\\n Bmagnitude = (BR**2+BZ**2)**.5\\n\\n qms, vs = qmAndVelocitySpectrum(numParticles)\\n if globals()['settings']['qmPrescribed']:\\n qms = np.ones(numParticles)*globals()['settings']['qmPrescribed']\\n if globals()['settings']['v0Prescribed']:\\n vs = np.ones(numParticles)*globals()['settings']['v0Prescribed']\\n\\n startingPoints = [randomPointOnSphere(rLim) for _ in range(numParticles)]\\n directions = [randomDirectionCos(-sp) for sp in startingPoints]\\n\\n # Simulate without magnetic field\\n start = time.time()\\n rReduced, zReduced, gridOff, _, habitatCrossingsOff, GDTcrossingsOff, gridOffUnscaled, _ = monteCarloRun(startingPoints, qms, vs, directions, BR, BZ, r, z, rLim, fluxGridCoarseness, 0)\\n print('Time elapsed (s):', int(time.time()-start))\\n \\n # Simulate with magnetic field\\n start = time.time()\\n _, _, gridOn, trappedOn, habitatCrossingsOn, GDTcrossingsOn, gridOnUnscaled, trappedOnUnscaled = monteCarloRun(startingPoints, qms, vs, directions, BR, BZ, r, z, rLim, fluxGridCoarseness, steppingMethod)\\n print('Time elapsed (s):', int(time.time()-start))\\n # np.save('cache/{}particles_accel.npy'.format(numParticles), [rReduced, zReduced, gridOn])\\n try:\\n print('---\\\\nGDT crossing change: {}%'.format(round(100*(GDTcrossingsOn-GDTcrossingsOff)/GDTcrossingsOff, 3)))\\n print('Habitat crossing change: {}%\\\\n---'.format(round(100*(habitatCrossingsOn-habitatCrossingsOff)/habitatCrossingsOff, 3)))\\n except Exception as e:\\n print(e)\\n \\n # plotDiff(r, z, Bmagnitude, gridOn, gridOff)\\n plot6panel(r, z, rReduced, zReduced, Bmagnitude, gridOn, gridOff, trappedOn)\",\n \"async def stan(self, ctx, *args):\\n if args:\\n if args[0] == 'update':\\n amount = len(self.artists)\\n self.artists = []\\n urls_to_scrape = ['https://kprofiles.com/k-pop-girl-groups/',\\n 'https://kprofiles.com/k-pop-boy-groups/',\\n 'https://kprofiles.com/co-ed-groups-profiles/',\\n 'https://kprofiles.com/kpop-duets-profiles/',\\n 'https://kprofiles.com/kpop-solo-singers/']\\n for url in urls_to_scrape:\\n self.artists += scrape_kprofiles(url)\\n\\n database.set_attr(\\\"data\\\", \\\"artists\\\", self.artists)\\n\\n await ctx.send(f\\\"Artist list succesfully updated, {len(self.artists) - amount} new entries, \\\"\\n f\\\"{len(self.artists)} total entries\\\")\\n self.logger.info(misolog.format_log(ctx, f\\\"artist list updated; {len(self.artists) - amount} new, \\\"\\n f\\\"{len(self.artists)} total\\\"))\\n return\\n\\n elif args[0] == 'clear':\\n self.artists = []\\n database.set_attr(\\\"data\\\", \\\"artists\\\", self.artists)\\n await ctx.send(\\\"Artist list cleared\\\")\\n self.logger.info(misolog.format_log(ctx, f\\\"artist list cleared\\\"))\\n return\\n\\n if self.artists:\\n artist = str(rd.choice(self.artists))\\n await ctx.send('stan ' + artist)\\n self.logger.info(misolog.format_log(ctx, f\\\"artist={artist}\\\"))\\n else:\\n await ctx.send(\\\"Error: artist list is empty, please use >stan update\\\")\\n self.logger.warning(misolog.format_log(ctx, f\\\"artist list empty\\\"))\",\n \"def run(self, steps):\\n self.sim.run(steps)\",\n \"def perform_actual_action(self, action):\\n self.game.perform_action(action)\",\n \"def main():\\n\\n # test()\\n\\n DIM = 10\\n SHIPS = [2,3,3,4,5]\\n\\n device = 'cuda' if torch.cuda.is_available() else 'cpu'\\n\\n model = train_Convnet(DIM, SHIPS, device)\\n torch.save(model.state_dict(),'./saved_models/convenet.torch' )\\n\\n conv_model = ModelConvnet(\\\"Vikram\\\", DIM, len(SHIPS), device).to(device)\\n conv_model.load_state_dict(torch.load('./saved_models/convenet.torch'))\\n\\n g = Game(conv_model, ModelRandom(\\\"Betal\\\", DIM, len(SHIPS), device), Environment(DIM, SHIPS, \\\"Vikram\\\"), Environment(DIM, SHIPS, \\\"Betal\\\"))\\n g.play()\",\n \"def Execute(self):\\n self.FSM.Execute()\",\n \"def run_one_step_simvodis(self):\\n self.model_lr_scheduler.step()\\n self.set_train()\\n\\n inputs = next(iter(self.train_loader))\\n\\n before_op_time = time.time()\\n\\n outputs, losses = self.process_batch(inputs)\\n\\n self.model_optimizer.zero_grad()\\n losses[\\\"loss\\\"].backward()\\n self.model_optimizer.step()\\n\\n duration = time.time() - before_op_time\\n\\n if self.arguments[\\\"iteration\\\"] % (self.opt.save_frequency // 2) == 0:\\n self.log_time(self.arguments[\\\"iteration\\\"], duration, losses[\\\"loss\\\"].cpu().data)\\n\\n if \\\"depth_gt\\\" in inputs:\\n self.compute_depth_losses(inputs, outputs, losses)\\n\\n self.log(\\\"train\\\", inputs, outputs, losses)\\n self.val()\\n\\n self.step += 1\",\n \"def update_model(X, U, S, k, n, mu,\\n svdmethod='full',\\n missingmethod='zero'):\\n\\n if len(X) == 0:\\n printt(\\\"Error: No data in X.\\\")\\n return None, None, None, -1, None\\n #print('%d items in X' % X.shape[1])\\n #print('init U:', U)\\n\\n # If there is no previous U, and we just got a single item in X,\\n # set U to all 0's (degenerate SVD),\\n # and return it with mu.\\n # (PR #22 sets first value to 1; see decals implementation)\\n if len(U) == 0 and X.shape[1] == 1:\\n mu = X\\n # Do this no matter what. Let mu get NaNs in it as needed.\\n U = np.zeros_like(mu)\\n U[0] = 1\\n S = np.array([0])\\n n = 1\\n pcts = [1.0]\\n return U, S, mu, n, pcts\\n\\n ###########################################################################\\n # Do full SVD of X if this is requested, regardless of what is in U \\n # Also, if n = 0 or U is empty, start from scratch\\n output_k = False\\n if svdmethod == 'full' or len(U) == 0 or n == 0:\\n if n == 0:\\n if len(U) == 0:\\n printt(\\\"----- initial SVD -----\\\")\\n output_k = True\\n else:\\n # Reshape so we don't have an empty dimension (yay python)\\n U = U.reshape(-1, 1)\\n elif len(U) == 0:\\n printt(\\\"WARNING: N (number of items modeled by U) is %d, not zero, but U is empty!\\\" % n)\\n\\n # Bootstrap\\n if missingmethod == 'ignore':\\n printt(\\\"ERROR: ignore with full is not possible under ordinary circumstances.\\\")\\n printt(\\\"Use --increm-brand to impute for NaNs.\\\")\\n printt(\\\"For now, we are filling NaNs with 0.\\\")\\n X = copy.deepcopy(X)\\n z = np.where(np.isnan(X))\\n X[z] = 0\\n\\n mu = np.mean(X, axis=1).reshape(-1,1)\\n X = X - mu\\n U, S, V = linalg.svd(X, full_matrices=False)\\n printt('Just did full SVD on %d items.' % X.shape[1])\\n #print('X:', X)\\n #print('U:', U)\\n # Reset U to all 0's if we only have one item in X (degenerate SVD)\\n if X.shape[1] == 1:\\n U = np.zeros_like(U)\\n \\n # Keep only the first k components\\n S_full = S\\n S = S[0:k]\\n U = U[:,0:k]\\n\\n # Update n to number of new items in X\\n n = X.shape[1]\\n \\n ###########################################################################\\n # Incremental SVD from Ross\\n elif svdmethod == 'increm-ross':\\n # Incremental SVD from Ross et al. 2008\\n # \\\"Incremental Learning for Robust Visual Tracking\\\"\\n # based on Lim and Ross's sklm.m implementation in MATLAB.\\n\\n # This method DOES NOT handle missing values.\\n if missingmethod == 'ignore':\\n print('ERROR: increm-ross cannot handle missing values.')\\n print('If they are present, try svdmethod=increm-brand')\\n print(' or use missingmethod=zero to zero-fill.')\\n print('If there are no missing values, specify missingmethod=none.')\\n sys.exit(1)\\n\\n n_new = X.shape[1]\\n \\n # Compute mean\\n # Weirdly, the later 'X-mu_new' is MUCH faster if you reshape as shown.\\n # This is because of differences in the way numpy treats a 1d array versus a 2d column.\\n mu_new = np.mean(X, axis=1).reshape(-1,1)\\n\\n # Subtract the mean, append it as a column vector, and update mu\\n # X - mu_new will be zero if X has only 1 item\\n mu_old = mu\\n # New mu is a weighted sum of old and new mus\\n mu = (n * mu_old + n_new * mu_new) / (n + n_new)\\n B = np.hstack((X - mu,\\n math.sqrt(n_new * n/float(n_new+n)) * \\\\\\n (mu_old - mu_new)))\\n printt(\\\"Now tracking mean for %d -> %d items; mu.min %f, mu.max %f \\\" % \\\\\\n (n, n+n_new, np.nanmin(mu), np.nanmax(mu)))\\n n = n + n_new\\n\\n if S.all() == 0:\\n npcs = U.shape[1]\\n diagS = np.zeros((npcs, npcs))\\n else:\\n diagS = np.diag(S)\\n\\n # I don't think this is right. At this point B is the augmented\\n # matrix rather than the single observation.\\n proj = np.dot(U.T, B)\\n reproj_err = B - np.dot(U, proj)\\n\\n # to get orthogonal form of reproj_err\\n # This should return q with dimensions [d(X) by n_new+1], square\\n q, dummy = linalg.qr(reproj_err, mode='full')\\n # print('q.shape should be 7x2: ', q.shape)\\n Q = np.hstack((U, q))\\n\\n # From Ross and Lim, 2008\\n # R = [ [ Sigma, U.T * X ] [ 0, orthog. component of reproj error ] ]\\n k_now = diagS.shape[0]\\n new_dim = k_now + n_new + 1\\n R = np.zeros((new_dim, new_dim))\\n R[0:k_now,0:k_now] = diagS\\n R[0:k_now,k_now:] = proj\\n orthog_reproj_err = np.dot(q.T, reproj_err)\\n R[k_now:, k_now:] = orthog_reproj_err\\n \\n # Perform SVD of R. Then finally update U.\\n U, S, V = linalg.svd(R, full_matrices=False)\\n printt('Just did increm-ross SVD on %d items.' % n)\\n\\n U = np.dot(Q, U)\\n \\n # Keep only the first k components\\n U = U[:,0:min([n,k])]\\n S_full = S\\n S = S[0:min([n,k])]\\n\\n ###########################################################################\\n # Incremental SVD from Brand\\n elif svdmethod == 'increm-brand':\\n # Pulled out James's attempt to handle NaNs into\\n # increm-brand-james.py. Starting over from scratch here.\\n n_new = X.shape[1]\\n\\n if n_new != 1:\\n print(\\\"WARNING: increm-brand will probably only work by adding one item at a time.\\\")\\n input('\\\\nPress enter to continue or ^C/EOF to exit. ')\\n\\n if missingmethod == 'ignore':\\n # 1. Update mu\\n mu_old = mu\\n mu_new = X\\n\\n # Be careful! For any pre-existing NaNs in mu,\\n # let mu_new fill them in. Can't get any worse!\\n naninds = np.where(np.isnan(mu_old))[0]\\n if naninds.size > 0:\\n mu_old[naninds,0] = mu_new[naninds,0]\\n # And likewise for mu_new -- fill with good values from mu_old.\\n naninds = np.where(np.isnan(mu_new))[0]\\n if naninds.size > 0:\\n mu_new[naninds,0] = mu_old[naninds,0]\\n # At this point, the only NaNs that should appear are\\n # values that were NaN for both mu and X to start with.\\n # They will stay NaN and that's okay.\\n \\n # New mu is a weighted sum of old and new mus\\n mu = (n * mu_old + n_new * mu_new) / (n + n_new)\\n printt(\\\"Now tracking mean for %d -> %d items; mu.min %f, mu.max %f \\\" % \\\\\\n (n, n+n_new, np.nanmin(mu), np.nanmax(mu)))\\n n = n + n_new\\n\\n # 2. Subtract off the mean\\n X = X - mu\\n\\n # 3. Compute L, the projection of X onto U\\n # Note: this will only work for a single item in X\\n goodinds = np.where(~np.isnan(X))[0]\\n #print('X: %d of %d are good.' % (len(goodinds), X.shape[0]))\\n\\n diagS = np.diag(S)\\n # This is Brand's method, which involves S:\\n L = np.dot(diagS,\\n np.dot(np.linalg.pinv(np.dot(U[goodinds,:],\\n diagS)),\\n X[goodinds,:]))\\n # Simplified version that does not use S (but is probably wrong):\\n #L = np.dot(U[goodinds,:].T,\\n # X[goodinds,:])\\n # Top row of the Q matrix (eqn 12, Brand 2002)\\n Q1 = np.hstack([diagS, L])\\n\\n # 4. Compute J, the orthogonal basis of H, which is\\n # the component of X orthog to U (i.e., unrepresentable direction)\\n # 5. Compute K, the projection of X onto J (i.e., unrep. content)\\n K = linalg.norm(X[goodinds,:] - np.dot(U[goodinds,:],\\n np.dot(U[goodinds,:].T,\\n X[goodinds,:])))\\n # H = X - UL\\n J = np.zeros((U.shape[0], 1))\\n J[goodinds] = np.dot(K,\\n np.linalg.pinv(X[goodinds,:] -\\n np.dot(U[goodinds,:],\\n L))).T\\n \\n # Bottom row of Q matrix (eqn 12, Brand 2002)\\n Q2 = np.hstack([np.zeros([1, len(S)]), np.array(K).reshape(1,1)])\\n Q = np.vstack([Q1, Q2])\\n\\n # 6. Take the SVD of Q\\n Uq, Sq, Vq = linalg.svd(Q, full_matrices=False)\\n\\n # 7. Update U and S (eqn 4, Brand 2002)\\n # Note: Since J is zero-filled for badinds, now U is too.\\n # Alternatively, we give J NaNs and let them get into U as well.\\n # I think that is a worse idea though.\\n U = np.dot(np.hstack([U, J]), Uq)\\n S = Sq\\n # Updating V requires knowing old V,\\n # but we don't need the new one either so it's okay to skip.\\n\\n printt('Just did increm-brand SVD on %d items.' % n)\\n \\n ############# end ###########\\n \\n else: # No missing values (or not 'ignore')\\n # 1. Update mu\\n mu_old = mu\\n mu_new = X\\n # New mu is a weighted sum of old and new mus\\n mu = (n * mu_old + n_new * mu_new) / (n + n_new)\\n n = n + n_new\\n\\n # 2. Subtract off the mean\\n X = X - mu\\n\\n # 3. Compute L, the projection of X onto U\\n L = np.dot(U.T, X)\\n Q1 = np.hstack([np.diag(S), L])\\n\\n # 4. Compute J, the orthogonal basis of H, which is\\n # the component of X orthog to U (i.e., unrepresentable direction)\\n # 5. Compute K, the projection of X onto J (i.e., unrep. content)\\n JK = X - np.dot(U, L)\\n (J, K) = linalg.qr(JK)\\n\\n Q2 = np.hstack([np.zeros([1, len(S)]), np.array(K).reshape(1,1)])\\n Q = np.vstack([Q1, Q2])\\n\\n # 6. Take the SVD of Q\\n Uq, Sq, Vq = linalg.svd(Q, full_matrices=False)\\n\\n # 7. Update U and S (eqn 4, Brand 2002)\\n U = np.dot(np.hstack([U, J]), Uq)\\n S = Sq\\n # V requires knowing old V,\\n # but we don't need the new one either so it's okay.\\n \\n printt('Just did regular increm SVD on %d items.' % n)\\n\\n # Keep only the first k components\\n U = U[:,0:min([n,k])]\\n S = S[0:min([n,k])]\\n\\n Usum = U.sum(1)\\n\\n\\n ###########################################################################\\n # We have a bad svdmethod, but somehow didn't catch it earlier.\\n else:\\n printt(\\\"504: Bad Gateway in protocol <Skynet_authentication.exe>\\\")\\n return None, None, None, None, None\\n\\n indivpcts = None\\n\\n # This only works if a full SVD was done\\n if (svdmethod == 'full' and output_k and opts['k_var'] == -773038.0):\\n # Calculate percent variance captured by each \\n cumsum = np.cumsum(S_full)\\n #print(cumsum.shape)\\n if cumsum[-1] != 0:\\n indivpcts = S / cumsum[-1]\\n indivpcts = indivpcts[0:k] # truncate to first k\\n cumpercents = cumsum / cumsum[-1]\\n else:\\n indivpcts = []\\n\\n # Calculate percent variance captured\\n if k >= cumsum.shape[0]:\\n printt('Cannot estimate data variance; specified k (%d) exceeds the number of SVs (%d).' % (k, cumsum.shape[0]))\\n else:\\n printt(\\\"Selected value of k=%d captures %5.2f%% of the data variance\\\" % \\\\\\n (k, cumpercents[k-1] * 100))\\n if opts['pause']: input(\\\"Press enter to continue\\\\n\\\")\\n\\n #print('U:', U)\\n #print('mu:', mu)\\n return U, S, mu, n, indivpcts\",\n \"def _exe_(self):\\n print(\\\"\\\\n Start simulation (using Pharlap) ...\\\")\\n dic = \\\"data/sim/{dn}/{rad}/\\\".format(dn=self.event.strftime(\\\"%Y.%m.%d.%H.%M\\\"), rad=self.rad)\\n self._estimate_edens_()\\n self._compute_()\\n plotlib.plot_exp_rays(dic, self.event, self.bmnum, \\\"bgc\\\")\\n plotlib.plot_exp_rays(dic, self.event, self.bmnum, \\\"flare\\\")\\n if self.verbose: print(\\\"\\\\n Processing Doppler.\\\")\\n self._compute_doppler_()\\n rec = self._compute_velocity_()\\n return rec\",\n \"def singularity_build(self, path, image):\\n Client.build(os.path.join(\\n path, 'singularity.def'\\n ), self.generate_image_name(image))\",\n \"def process_stimulus(self, stimulus, epoch, stimNum):\\n diverge = False\\n self.init_for_run(stimulus)\\n harmony, state, stateC = self.calc_max_Harmony()\\n # FIXME: Should I update the states here ?\\n # I think so, since state and stateC take the input into account, whereas at this point state is just randomly\\n \\\"\\\"\\\"\\n print(stateC)\\n print(f\\\"After harmony calc: {self.find_TPname(self.find_winner()[1])}\\\")\\n \\\"\\\"\\\"\\n self.state = state\\n #assert torch.allclose(stateC, self.toConceptual(self.state))\\n self.stateC = self.toConceptual(self.state)\\n # self.visualizeC(self.stateC)\\n # Update bowl parameters (They depend on the stimulus)\\n self._bowl_params()\\n\\n # Update after each step\\n self.updateAfterStep(harmony)\\n self.consoleLog_step(stim=stimNum, epoch=epoch+1, stimNum=stimNum+1)\\n\\n # Loop over steps ~30 000 steps\\n for step in range(1, self.settings['maxSteps']):\\n self.vars['step'] = step\\n\\n # Compute step components\\n self.Hg = self.calc_harmony_dynamics() # Harmony gradient\\n self.LV_c, self.LV_s = self.LotkaVolterra_Dynamics() # Lotka gradient\\n self.add_noise() # add noise\\n\\n # Copy the actual tensor to prev_S (RNN)\\n self.stateC = self.toConceptual(self.state)\\n self.state_prev = self.state.clone()\\n self.stateC_prev = self.stateC.clone()\\n\\n # Now update the actual state\\n state_increment = (\\n float(self.vars['lambda']) * self.Hg) + (1-float(self.vars['lambda']) * self.LV_s)\\n\\n state_increment = float(\\n self.vars['dt']) * state_increment + self.noise\\n # increment_check = torch.sqrt(\\n # torch.tensordot(state_increment, state_increment))\\n # if increment_check > 0:\\n # self.vars['dt'] = min(\\n # self.vars['max_dt'], self.vars['min_dt'] / increment_check)\\n # self.vars['dt'] = max(self.vars['min_dt'], self.vars['dt'])\\n # if self.vars['step'] % self.settings[\\\"printInterval\\\"] == 0:\\n # print(self.vars['dt'])\\n\\n self.state += state_increment\\n self.stateC = self.toConceptual(self.state)\\n \\\"\\\"\\\"DEBUG\\n if self.vars['step'] % 50 == 0:\\n # self.visualizeC(self.stateC)\\n pass\\n print(\\n f\\\"after Increment: {self.find_TPname(self.find_winner()[1])}\\\")\\n print(self.stateC)\\n \\\"\\\"\\\"\\n\\n # Clamping --> Check if I should clamp here or before the increment\\n if self.settings['clamp']:\\n # Dummy debug variant\\n #self.stateC += stimulus\\n #self.state = self.toNeural(self.stateC)\\n self.calc_clamping(stimulus)\\n\\n if self.check_overflow():\\n diverge = True\\n print(f\\\"Net overflowed at step: {step}\\\")\\n break\\n\\n harmony = self.calc_harmony()\\n\\n self.updateAfterStep(harmony=harmony)\\n # Update Animation\\n # Print information if step % print_every == 0\\n self.consoleLog_step(\\n stim=stimNum, epoch=epoch+1, stimNum=stimNum+1)\\n\\n # Update Lambda and Temperature!\\n self.update_lambda_T()\\n\\n if self.check_convergency():\\n print(\\n f\\\"The net has converged at step {self.vars['step']}, nearest State: {self.vars['TP_trace'][-1]}\\\")\\n break\\n\\n if diverge:\\n # Don't use this step\\n self.vars['step'] -= 1\\n if self.vars['step'] == self.settings['maxSteps']:\\n print(\\\"FINAL STEP\\\")\\n print(\\n f\\\"The net has reached the maximum number of steps {self.settings['maxSteps']}\\\")\\n self.consoleLog_step(stim=stimNum)\\n\\n return diverge, harmony\",\n \"def test_svd_sharpness_with_model(self):\\n \\t\\t\\n\\t\\tesd_before = self.watcher.get_ESD(model=self.model, layer=self.fc2_layer) \\n\\t\\t\\n\\t\\tself.watcher.SVDSharpness(layers=[self.fc2_layer])\\n\\t\\tesd_after = self.watcher.get_ESD(layer=self.fc2_layer) \\n\\t\\t\\n\\t\\tprint(\\\"max esd before {}\\\".format(np.max(esd_before)))\\n\\t\\tprint(\\\"max esd after {}\\\".format(np.max(esd_after)))\\n\\n\\t\\tself.assertGreater(np.max(esd_before),np.max(esd_after))\",\n \"def do_start(self, arg):\\n args = arg.split(\\\" \\\")\\n self.model.initialise(args[0])\\n self.model.run()\",\n \"def event11310201():\\n header(11310201)\\n if_event_flag_on(1, EVENT.SkeletonsDisturbed)\\n if_not_in_world_area(1, 13, 1)\\n if_condition_true(0, 1)\\n flag.disable(EVENT.SkeletonsDisturbed)\",\n \"def main(args):\\n\\n\\t##############################################################################\\n\\t######## Pass user command line arguments to setup.py which will #############\\n\\t############# initialise some parameters for the analysis ###################\\n\\t##############################################################################\\n\\tinit_ = setup.initialise_user_input(args)\\n\\n\\t##############################################################################\\n\\t######## Define system_ which is the object, of class nanoCISC, ##############\\n\\t######## which contains all relevant information about your nanoparticle ####\\n\\t##############################################################################\\n\\tsystem_ = nano_cisc.nanoCISC(init_.nano_particle, init_.anchors, init_.beta, init_.calcrange, \\n init_.curves, init_.targetinc, init_.density) \\n\\t# initialise system_ as nanoCISC class here ^^^\\n\\n\\t# If density is being calculated, define grid from grid class\\n\\tif args['density']:\\n\\t\\tgrid=grids.grid(system_)\\n\\n\\n\\t##############################################################################\\n\\t################ Process trajectory, frame by frame ##########################\\n\\t##############################################################################\\n\\n\\tfor ts in init_.u.trajectory: # loop through trajectory frames here \\n\\t\\tprint \\\"Processing snapshot %d \\\" % (ts.frame)\\n\\n\\t\\t# Array for calculating intrinsic density is initialised to {0}\\n\\t\\tintrinsic_count=np.zeros( ( np.ceil( 3 * system_.calculation_range).astype(np.int) ,len(system_.density) ), dtype = np.float32) \\n\\n\\t\\t# Array that stores the instantaneous volume of each spatial interval is initialised to {0}\\n\\t\\tvolume_at_dist=np.zeros( ( np.ceil( 3 * system_.calculation_range).astype(np.int) ,len(system_.density) ), dtype = np.float32) \\n\\n\\t\\t# Centre of mass position is updated\\n\\t\\tsystem_.update_com()\\n\\n\\t\\t# Vectors describing the anchor points are updated \\n\\t\\tsystem_.update_anchors() \\n\\n\\t\\t# Nanoparticle depth values are updated\\n\\t\\tsystem_.update_surface() \\t\\n\\n\\t\\tif args['XYZsurface']:\\n\\t\\t\\tsystem_.write_surface(init_.f_visualise_surface) # write micelle surface to xyz file\\n \\n \\t\\tif args['density']: \\n \\t\\t\\tgrid.update_volume_estimate(volume_at_dist, system_) # volume estimate is updated for snapshot\\n\\t\\t\\tsystem_.calculate_density(intrinsic_count, volume_at_dist) # calculate density here\\n\\n\\t\\tsystem_.frames_processed += 1\\n\\n\\t##################################\\n\\t##### Print results to files #####\\n\\t##################################\\n\\tif args['density']:\\n\\t\\tsystem_.print_intrinsic_density(init_.f_intrinsic_density_out)\\n\\t\\tsystem_.print_radial_density()\\n\\n\\n\\tprint \\\"Program finished successfully!!!\\\\n\\\"\",\n \"def run():\\n\\n # Set up environment and agent\\n e = Environment() # create environment (also adds some dummy traffic)\\n a = e.create_agent(LearningAgent) # create agent\\n e.set_primary_agent(a, enforce_deadline=True) # specify agent to track\\n # NOTE: You can set enforce_deadline=False while debugging to allow longer trials\\n\\n # Now simulate it\\n sim = Simulator(e, update_delay=0.001, display=True) # create simulator (uses pygame when display=True, if available)\\n # NOTE: To speed up simulation, reduce update_delay and/or set display=False\\n\\n sim.run(n_trials=100) # run for a specified number of trials\\n # NOTE: To quit midway, press Esc or close pygame window, or hit Ctrl+C on the command-line\",\n \"def step(self, action):\\n actions = action.reshape(2, 2)\\n action = {'GoalieBrain': actions[0], 'StrikerBrain': actions[1]}\\n #action = {'GoalieBrain': [5, 5], 'StrikerBrain': [2, 2]}\\n #print(action)\\n info = self.env.step(action)\\n\\n state0 = info[self.brain_names[0]].vector_observations\\n state1 = info[self.brain_names[1]].vector_observations\\n state = np.vstack((state0, state1))\\n reward0 = info[self.brain_names[0]].rewards\\n reward1 = info[self.brain_names[1]].rewards\\n reward = reward0 + reward1\\n done0 = info[self.brain_names[0]].local_done\\n done1 = info[self.brain_names[1]].local_done\\n done = done0 + done1\\n return state, reward, done\",\n \"def run():\\n\\n # Set up environment and agent\\n e = Environment() # create environment (also adds some dummy traffic)\\n a = e.create_agent(LearningAgent) # create agent\\n e.set_primary_agent(a, enforce_deadline=True) # specify agent to track\\n # NOTE: You can set enforce_deadline=False while debugging to allow longer trials\\n\\n # Now simulate it\\n sim = Simulator(e, update_delay=0.00000001, display=False) # create simulator (uses pygame when display=True, if available)\\n # NOTE: To speed up simulation, reduce update_delay and/or set display=False\\n\\n sim.run(n_trials=100) # run for a specified number of trials\\n # NOTE: To quit midway, press Esc or close pygame window, or hit Ctrl+C on the command-line\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.5975073","0.5935403","0.59130096","0.57898796","0.5710506","0.56999266","0.56843907","0.5681977","0.55953836","0.55517167","0.5515142","0.55127794","0.5501622","0.5477187","0.5396195","0.53956157","0.5391933","0.53681904","0.53640187","0.53324854","0.53042185","0.52998745","0.5297435","0.52967316","0.5284339","0.5277941","0.52753276","0.52611053","0.5259433","0.5232182","0.52297217","0.5226952","0.5217142","0.521539","0.5211066","0.52086693","0.5202922","0.5192463","0.5190207","0.51866907","0.5170844","0.5168607","0.5158222","0.5157182","0.51525265","0.51453286","0.5141214","0.5135978","0.51346","0.5126811","0.51112217","0.5104391","0.50985307","0.50924426","0.50891376","0.5084865","0.5077587","0.5060403","0.5055857","0.5049948","0.5047201","0.50463635","0.5038826","0.5037029","0.5032756","0.503232","0.50306535","0.5026631","0.50259405","0.5025502","0.5025501","0.5023207","0.5020693","0.50192565","0.50175714","0.5009499","0.5001345","0.49999347","0.49952266","0.4982556","0.49799302","0.497568","0.49679396","0.49674228","0.49643558","0.49630457","0.49606913","0.4959738","0.49585217","0.49532086","0.4952556","0.49477974","0.4947791","0.49467576","0.49463084","0.4944488","0.49430254","0.49426138","0.4942164","0.49416885"],"string":"[\n \"0.5975073\",\n \"0.5935403\",\n \"0.59130096\",\n \"0.57898796\",\n \"0.5710506\",\n \"0.56999266\",\n \"0.56843907\",\n \"0.5681977\",\n \"0.55953836\",\n \"0.55517167\",\n \"0.5515142\",\n \"0.55127794\",\n \"0.5501622\",\n \"0.5477187\",\n \"0.5396195\",\n \"0.53956157\",\n \"0.5391933\",\n \"0.53681904\",\n \"0.53640187\",\n \"0.53324854\",\n \"0.53042185\",\n \"0.52998745\",\n \"0.5297435\",\n \"0.52967316\",\n \"0.5284339\",\n \"0.5277941\",\n \"0.52753276\",\n \"0.52611053\",\n \"0.5259433\",\n \"0.5232182\",\n \"0.52297217\",\n \"0.5226952\",\n \"0.5217142\",\n \"0.521539\",\n \"0.5211066\",\n \"0.52086693\",\n \"0.5202922\",\n \"0.5192463\",\n \"0.5190207\",\n \"0.51866907\",\n \"0.5170844\",\n \"0.5168607\",\n \"0.5158222\",\n \"0.5157182\",\n \"0.51525265\",\n \"0.51453286\",\n \"0.5141214\",\n \"0.5135978\",\n \"0.51346\",\n \"0.5126811\",\n \"0.51112217\",\n \"0.5104391\",\n \"0.50985307\",\n \"0.50924426\",\n \"0.50891376\",\n \"0.5084865\",\n \"0.5077587\",\n \"0.5060403\",\n \"0.5055857\",\n \"0.5049948\",\n \"0.5047201\",\n \"0.50463635\",\n \"0.5038826\",\n \"0.5037029\",\n \"0.5032756\",\n \"0.503232\",\n \"0.50306535\",\n \"0.5026631\",\n \"0.50259405\",\n \"0.5025502\",\n \"0.5025501\",\n \"0.5023207\",\n \"0.5020693\",\n \"0.50192565\",\n \"0.50175714\",\n \"0.5009499\",\n \"0.5001345\",\n \"0.49999347\",\n \"0.49952266\",\n \"0.4982556\",\n \"0.49799302\",\n \"0.497568\",\n \"0.49679396\",\n \"0.49674228\",\n \"0.49643558\",\n \"0.49630457\",\n \"0.49606913\",\n \"0.4959738\",\n \"0.49585217\",\n \"0.49532086\",\n \"0.4952556\",\n \"0.49477974\",\n \"0.4947791\",\n \"0.49467576\",\n \"0.49463084\",\n \"0.4944488\",\n \"0.49430254\",\n \"0.49426138\",\n \"0.4942164\",\n \"0.49416885\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6079829"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":17876,"cells":{"query":{"kind":"string","value":"Generates the image name from the image url."},"document":{"kind":"string","value":"def generate_image_name(self, image):\n return image.replace('shub://', '').replace('/', '-') + '.simg'"},"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_generated_image_name(full_image_url):\r\n\r\n logging.debug('get_generated_image_name({})'.format(full_image_url))\r\n\r\n image_name = datetime.datetime.now().strftime(\"%Y%m%d%H%M%S\")\r\n image_extension = full_image_url.split(\".\")[-1]\r\n image_name = image_name + \".\" + image_extension\r\n logging.debug('get_generated_image_name - image_name = {}'.format(image_name))\r\n return image_name","def _ImageName(self, image):\n\n image_without_protocol = image.split('/')[-1]\n if '@' in image_without_protocol:\n return image_without_protocol.split('@')[0]\n elif ':' in image:\n return image_without_protocol.split(':')[0]\n else:\n return image_without_protocol","def image_name(name):\n \n # Gets the '.' position\n dot = name.find('.')\n # Slice the name from beginning and before '.'\n img = name[:dot]\n # return string with jpg format\n return \"{}.jpg\".format(img)","def GenerateImageName(cls, build_target=None, build_id=None):\n if not build_target and not build_id:\n return \"image-\" + uuid.uuid4().hex\n name = cls.IMAGE_NAME_FMT.format(\n build_target=build_target,\n build_id=build_id,\n uuid=uuid.uuid4().hex[:8])\n return cls._FormalizeName(name)","def filename(self,imgurl):\n if imgurl.find('/'):\n return imgurl.rsplit('/', 1)[1]","def _image_filename(image_name):\n return '{}.tar'.format(image_name.replace(':', '_').replace('/', '_'))","def get_image_filename(self, filename):\n path = 'images/{folder}/{filename}'.format(\n folder=self.folder,\n filename=filename\n )\n return path","def _get_image_name(image_meta, max_len=pvm_const.MaxLen.FILENAME_DEFAULT):\n return pvm_util.sanitize_file_name_for_api(\n image_meta.name, prefix=DiskType.IMAGE + '_',\n suffix='_' + image_meta.checksum, max_len=max_len)","def build_image_name(self, tag):\n return self.repository_name + ':' + tag","def namer(self, image_url, page_url):\n title = page_url.rsplit('/', 2)[1]\n image_ext = image_url.rsplit('.', 1)[1]\n return '%s.%s' % (title, image_ext)","def image_name(self) -> str:\n return self._image_name","def image_name(self) -> pulumi.Output[Optional[str]]:\n return pulumi.get(self, \"image_name\")","def path_to_name(img):\n\n return os.path.dirname(img) + '_' + os.path.basename(img)","def image_file_name(instance, filename):\n\text = filename[-4:]\n\tnew_filename = os.path.join('images',str(instance.image_folder),str(instance.user).replace(\" \",\"\").lower()+ext)\n\treturn new_filename","def imageNamed(imageName):\n\t\treturn os.path.join(AppVars.imagePath(), imageName)","def get_image_url():","def get_image_name(resource_id):\n match_images = re.match(r\".*images/(?P<image_name>[^/]*).*\", resource_id, flags=re.IGNORECASE)\n if match_images:\n return match_images.group(\"image_name\")\n return \"\"","def generate_image_filename():\n now = datetime.now().strftime('%a-%w-%b-%H:%M:%S')\n return 'CCTV_{0}.jpg'.format(now)","def _get_image_name(self) -> str:\n dirname = os.path.basename(os.getcwd())\n default_image_name = f\"{dirname}_{self.config_name}\"\n image_name = self.config_options.get(\"image\", default_image_name)\n return image_name","def generate_image_name(self, schedule, server_name):\n\n max_name_length = 255\n prefix = self._get_image_prefix(schedule)\n now = str(calendar.timegm(self._get_utcnow().utctimetuple()))\n\n # NOTE(ameade): Truncate the server name so the image name is within\n # 255 characters total\n server_name_len = max_name_length - len(now) - len(prefix) - len('--')\n server_name = server_name[:server_name_len]\n\n return (\"%s-%s-%s\" % (prefix, server_name, str(now)))","def name(self, strippath=False):\n return _image.image_name(self, strippath)","def _gen_image_filename(instance, filename):\n # First, store the original filename in the model\n instance.original_filename = filename\n\n return _unique_path(instance.owner.pk, filename)","def imId2name(self, im_id):\n \n if isinstance(im_id, int):\n name = str(im_id).zfill(self.STR_ID_LEN) + '.jpg'\n elif isinstance(im_id, str):\n name = im_id + '.jpg'\n else:\n raise AssertionError('Image ID should be of type string or int')\n return name","def _make_name(self, name=None):\n\n if name:\n new_name = name.split(\"/\")[-1].split(\".png\")[0]\n if new_name.startswith((\"AWS-\", \"Amazon-\")):\n new_name = new_name.split(\"-\", 1)[1]\n # Replace non-alphanumeric with underscores (1:1 mapping)\n new_name = re.sub(r'\\W+', '_', new_name)\n return new_name","def image_name(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"image_name\")","def image_name(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"image_name\")","def image_url(self):\n context = aq_inner(self.context)\n obj_url = context.absolute_url()\n if hasattr(context, 'getField'):\n field = self.context.getField('image')\n if not field:\n field = context.getField(IMAGE_FIELD_NAME)\n\n if field and field.get_size(context) > 0:\n return u'%s/%s_%s' % (obj_url, field.getName(), 'thumb')\n\n return u\"%s/isaw_logo.png\" % self.portal.absolute_url()","def l10n_img_file_name(ctx, url):\n url = url.lstrip('/')\n locale = ctx.get('LANG', None)\n if not locale:\n locale = settings.LANGUAGE_CODE\n\n # We use the same localized screenshots for all Spanishes\n if locale.startswith('es') and not _l10n_media_exists('img', locale, url):\n locale = 'es-ES'\n\n if locale != settings.LANGUAGE_CODE:\n if not _l10n_media_exists('img', locale, url):\n locale = settings.LANGUAGE_CODE\n\n return path.join('img', 'l10n', locale, url)","def get_country_image_name(country):\n\n country = country.replace(\" \", \"-\").replace(\".\", \"\").lower()\n return \"%s.png\" % (country)","def image_url(self, name):\r\n s3_key = self._generate_s3_key(name)\r\n return s3_key.generate_url(self.IMAGE_LINK_DURATION)","def acquire_image_filename(self):\n\t\titem_key = self.contents_data[ITEM_KEY]\n\t\tif not item_key: return None\n\t\treturn \"portrait_\" + item_key + \".bmp\"","def picture_name(self, filename):\n return '%s%s'%(self.username, splitext(filename)[1])","def getImageName(self):\n return [os.path.basename(name) for name in self.meta['sources']]","def test_get_image_name(self):\n ssp = self._get_ssp_stor()\n\n def verify_image_name(name, checksum, expected):\n img_meta = image_meta.ImageMeta(name=name, checksum=checksum)\n self.assertEqual(expected, ssp._get_image_name(img_meta))\n self.assertTrue(len(expected) <= const.MaxLen.FILENAME_DEFAULT)\n\n verify_image_name('foo', 'bar', 'image_foo_bar')\n # Ensure a really long name gets truncated properly. Note also '-'\n # chars are sanitized.\n verify_image_name(\n 'Template_zw82enbix_PowerVM-CI-18y2385y9123785192364',\n 'b518a8ba2b152b5607aceb5703fac072',\n 'image_Template_zw82enbix_PowerVM_CI_18y2385y91'\n '_b518a8ba2b152b5607aceb5703fac072')","def l10n_img(ctx, url):\n return static(l10n_img_file_name(ctx, url))","def make_img_name(file_ext='.png'):\r\n fn = []\r\n # format seqs and write out to temp file\r\n for i in range(0, 30):\r\n fn.append(choice(ALPHABET))\r\n return ''.join(fn) + file_ext","def get_base_image_name(image_name):\n # Index where suffix sequence starts\n suffix_start = image_name.find(IMAGE_IDENTIFIER_SEQUENCE)\n\n base_image_name = image_name\n if suffix_start != -1:\n # Index where extension sequence starts (.jpg, .jpeg, or .png)\n extension_start = image_name.lower().find(\".jp\")\n if extension_start == -1:\n extension_start = image_name.lower().find(\".png\")\n if extension_start == -1:\n raise Exception(\"Invalid extension in image name: \" + image_name)\n\n # imagename(--===--)(4).jpg becomes imagename.jpg\n base_image_name = image_name[:suffix_start] # imagename\n extension = image_name[extension_start:] # .jpg\n base_image_name += extension # imagename.jpg\n\n return base_image_name","def generate_envoy_image_name_from_tag(image_tag: str) -> str:\n image_prefix = get_envoy_image_prefix(image_tag)\n envoy_image = \"{prefix}:{hash}\".format(prefix=image_prefix, hash=image_tag)\n return envoy_image","def get_image_url(img):\n # element['data-src'] and element.get('data-src') doesn't work\n for k, v in img.items():\n if k == 'data-src':\n # https://t.nhentai.net/galleries/<gallerycode>/<page#>t.<extension>\n # https://i.nhentai.net/galleries/<gallerycode>/<page#>.<extension>\n return v[:8] + 'i' + v[9:32] + v[32:].replace('t.', '.', 1)","def getImageName(self):\r\n return self.imageName","def get_image_name(name: str, tag: str, image_prefix: str = \"\") -> str:\n versioned_tag = name.strip() + \":\" + tag.strip()\n if image_prefix:\n versioned_tag = image_prefix.strip().rstrip(\"/\") + \"/\" + versioned_tag\n return versioned_tag","def create_file_name(self):\n # create a unique id for the file name\n index = self.helpers.alpha_uuid()\n\n filename = self.form['FieldStorage'][self.image_cid].filename\n extension = guess_extension(guess_type(filename)[0])\n return ( # concatenates the following data\n self.articleData.get('directory') + # directory\n '/' + # slash\n self.articleData.get('article_name') + # the article name\n '-' + # hyphen character\n index + # the id of the image\n extension\n )","def get_image(self, img):\n if img == \"rss\":\n img = \"feed-icon-16x16.png\"\n loc = os.path.abspath(os.path.dirname(__file__))\n img = os.path.join(loc, img)\n if not os.path.exists(img):\n raise FileNotFoundError( # pragma: no cover\n f\"Unable to find {img!r}.\")\n return img\n else:\n raise FileNotFoundError( # pragma: no cover\n f\"Unable to get image name: {img!r}.\")","def image_upload_filename(instance, filename):\n prefix = 'photos'\n uhash = abs(hash(u'%s%s' % (datetime.now(), filename)))\n user = instance.album.user.username\n return u'%s/%s/%s_%s' % (prefix, user, uhash, filename)","def _get_file_name(self) -> str:\n headers = self._get_url_http_headers()\n file_type = self._check_url_file_type(headers)\n file_name = self._get_filename_from_headers(headers)\n\n if not file_name:\n file_name = self._get_filename_from_url()\n\n if file_name is None:\n raise FileNameCannotBeEvaluatedException\n\n if file_type:\n file_name = self._add_file_extension(file_name, file_type)\n\n return file_name","def get_image_url(self, image_name: str, thumbnail: bool = False) -> str:\n pass","def get_file_name(instance, filename):\n filename = make_unique_filename(filename)\n return os.path.join('uploads/profile_pics', filename)","def generate_file_filename(instance, filename):\n return _generate_filename(instance, filename, 'photos')","def image_url(self) -> str:\n return pulumi.get(self, \"image_url\")","def add_string_to_image_url(url, addition):\n filename, ext = splitext(url)\n return ''.join([filename, '-', addition, ext])","def _file_name(size):\n timestamp = str(int(time.time()))\n return '%s_%dx%d.%s' % (timestamp, size, size, 'jpg')","def remove_suffix(image_url):\n image_url = image_url.removeprefix('../../')\n real_image_url = \"https://books.toscrape.com/\" + image_url\n return real_image_url","def filename_generate(image_class, size=12, chars=string.ascii_uppercase + string.ascii_lowercase + string.digits):\n\tnew_filename = time.strftime(\"%d-%m-%Y_\")\n\tnew_filename = new_filename + ''.join(random.choice(chars) for _ in range(size))\n\tnew_filename = new_filename + \"_P\" + str(image_class)\n\treturn new_filename","def _filename_from_url(url):\n file_name = url.split(\"/\")[-1]\n return file_name","def propose_image_path():\n image_name = \"image_{}.png\".format(''.join(random.choice('abcdefghijklmnopqrstuvwxyz') for _ in range(8)))\n image_path = os.path.join(_IMAGE_BASE_PATH, image_name)\n image_url = '/images/' + image_name\n return image_path, image_url","def update_destination_file_name (file_name):\n\tglobal COUNTER \n\tCOUNTER += 1\n\tsplitted = file_name.split('/')\n\treturn file_name[:len(file_name)-len(splitted[-1])] + 'Image%05d' % COUNTER +'_'+splitted[-1]","def get_nickname(image):\r\n extension = len(image.split('/')[-1:][0].split('.')[-1:][0])\r\n return image.split('/')[-1:][0][:-extension-1]","def generate_avatar_name(email, ext):\n hashed_email = hashlib.sha256(f'{email}{settings.SECRET_KEY}'.encode('utf-8')).hexdigest()\n return hashed_email[:10] + ext","def content_file_name(self, filename):\n ext = filename.split('.')[-1]\n filename = \"%s_%s.%s\" % (filename, self.id, ext)\n return os.path.join('pictures/static/pictures/', filename)","def get_filename(self) -> str:\n fname = self.url.split(\"/\")[-1]\n if \",\" in fname:\n _fname, _i = fname.split(\",\")\n _split_fname = _fname.split(\".\")\n _name = _split_fname[0]\n _extension = _split_fname[-1]\n return _name + _i + \".\" + _extension\n else:\n return fname","def get_image_name_for_hook(module):\n os.makedirs(INSTANCE_FOLDER, exist_ok=True)\n base_name = str(module).split('(')[0]\n index = 0\n image_name = '.' # '.' is surely exist, to make first loop condition True\n while os.path.exists(image_name):\n index += 1\n image_name = os.path.join(\n INSTANCE_FOLDER, '%s_%d.png' % (base_name, index))\n return image_name","def url_file_name(url):\r\n return url[url.rfind('/') + 1:]","def get_thumbnail_name(self, thumbnail_name, with_size=None):","def url_for(**options):\n\n url_parts = get_url_parts(**options)\n image_hash = hashlib.md5(b(options[\"image_url\"])).hexdigest()\n url_parts.append(image_hash)\n\n return \"/\".join(url_parts)","def GetImageName(event):\n spec = event.get(u'pathspec', {})\n if isinstance(spec, basestring):\n # This is needed in case data come from elasticsearch. event['pathspec']\n # is naturally a nested dictionary but elastic search returns it as a\n # string.\n spec = eval(spec) # pylint: disable=eval-used\n\n while u'parent' in spec:\n spec = spec[u'parent']\n location = spec.get(u'location', u'')\n location_tokens = location.split(u'/')\n reversed_location_tokens = location_tokens[::-1]\n t_location = u'/'.join(reversed_location_tokens)\n return t_location","def _UrlBaseName(url):\n return url.rstrip('/').rpartition('/')[-1]","def docker_image_name(self):\n raise NotImplementedError","def format_image_filename(device_image):\n return \"{}-{}-{}-{}.bit\".format(device_image.bitstream_type,\n device_image.pci_vendor,\n device_image.pci_device,\n device_image.uuid)","def downloadicon_name(self):\n return 'platform_%s.gif' % \\\n re.sub(r'\\W', '_', self.context.getPlatform()).lower()","def _get_image_url(self, image_filepath):\n return self.IMAGE_URL + image_filepath","def image(self) -> str:\n return pulumi.get(self, \"image\")","def get_filename(img_path):\n filename = os.path.splitext(img_path)\n return os.path.basename(filename[0])","def get_image_url(self, image, hostname):\n\n if image.image_type == 'local':\n return \"http://\" + hostname + \"/\" + settings.IMAGES_URL + \"/\" + image.deployment.campaign.short_name + \"/\" + image.deployment.short_name + \"/\" + image.image_name\n elif image.image_type == 'envirocoms':\n return \"http://\" + hostname + \"/ecoms_proxy?image=\" + image.image_path","def build_image_path(self, src):\r\n o = urlparse(src)\r\n # we have a full url\r\n if o.hostname:\r\n return o.geturl()\r\n # we have a relative url\r\n return urljoin(self.target_url, src)","def getImagePath(self)->str:\n\n returnStr = '../../../../assets/image/{}.png'.format(randint(1,15))\n return returnStr","def get_image_link(self):\n table = self.soup.find('table')\n image_tag = table.find('img')\n image_name = self.soup.find_all(\"b\")[1].text\n return image_tag['src'], image_name\n\n # image = td.find_all('img')\n # print(image)\n # if image is not None:\n # return urljoin(self.base_url, image['src'])","def get_name(self, index):\n return self.image_files[index]","def name_to_path(img, origin):\n\n orig_file_parts = img.split('_')[1:]\n\n category = orig_file_parts[-3]\n filename = orig_file_parts[-2]+'_'+orig_file_parts[-1]\n\n orig_file = os.path.sep.join([origin, category])\n orig_file = os.path.sep.join([orig_file, filename])\n\n return orig_file","def get_image(result):\n article_id = result['id']\n id_ = article_id[14:]\n href = article_id[:14]\n\n #FIXME: not working\n image_url = \"http://www.jpress.nli.org.il/Olive/APA/NLI_heb/get/GetImage.ashx?kind=block&href=%s&id=%s&ext=.png\" %(href, id_)\n \n return image_url","def img_url_display(self):\n url = '%s=s%s' % (self.img_url, self.DISPLAY_SIZE_PX)\n if self.img_rot in Plaque.ALLOWED_ROTATIONS:\n url = \"%s-r%s\" % (url, self.img_rot)\n return url","def thumbgen_filename(filename):\n name, ext = os.path.splitext(filename)\n return '%s_thumb%s' % (name, ext)","def thumbgen_filename(filename):\n name, ext = os.path.splitext(filename)\n return '%s_thumb%s' % (name, ext)","def get_image_path(instance, filename):\n filename, file_extension = path.splitext(filename)\n return path.join(str(uuid4()) + file_extension)","def get_content_name(self, content_url):\n endpoint = content_url.split('/')[-1]\n return re.match(r'(.+\\.(?:jpg|mp4))', endpoint).group(0)","def get_file_name(image_dir, image_name_prefix, current_count):\n if imageNumOn:\n # you could also use os.path.join to construct image path file_path\n file_path = image_dir+ \"/\"+image_name_prefix+str(current_count)+\".jpg\"\n else:\n right_now = datetime.datetime.now()\n file_path = (\"%s/%s%04d%02d%02d-%02d%02d%02d.jpg\"\n % (image_dir, image_name_prefix,\n right_now.year, right_now.month, right_now.day,\n right_now.hour, right_now.minute, right_now.second))\n return file_path","def make_frame_name(frame_num):\n return str(frame_num).zfill(3) + '.jpg'","def getReferenceImageUrl(self, name):\n bucket = self.productSearch.bucket\n blobName = self._getReferenceImageBlobName(name)\n return bucket.blob(blobName).public_url","def container_image_name(registry, component_name, version):\n if version is None:\n image = component_name + ':dev'\n else:\n image = '%s/%s:%s' % (registry, component_name, version)\n\n return image","def get_image_url(project_id, image):\n assert is_valid_project_id(project_id), project_id\n assert is_valid_image(image), image\n return (\n 'https://www.googleapis.com/compute/v1/projects/%s/global/images/%s' % (\n project_id, image))","def modify_image_url(image_url, type=''):\n parsed_uri = urlparse(image_url)\n if parsed_uri.scheme == 'https' or parsed_uri.scheme == 'http':\n pass\n elif image_url == '':\n image_url = '/media/default_' + type + '.jpg'\n else:\n image_url = '/media/' + image_url\n return image_url","def get_url(self, image_id):\n key = image_id if image_id else self.default_image\n if key:\n return u'{bucket_url}{key}'.format(\n bucket_url=self.connection.bucket_url,\n key=self.id_to_key(key))\n else:\n return None","def image_url(self) -> str:\n return self._image_url","def image_registry_name(image_name):\n name = image_name\n\n if name.startswith('docker.io/'):\n name = name.replace('docker.io/', '')\n\n if name.startswith('library/'):\n name = name.replace('library/', '')\n\n if ':' not in name.rsplit('/')[-1]:\n name = '{}:latest'.format(name)\n\n return name","def _build_image_names(self, num_sequence, image_count):\n for i in self._get_image_sequence_list(image_count):\n self._image_names.append(self._get_image_name(i))","def save_image(url):\n ext = url.split('.')[-1]\n filename = IMAGEDIR+os.sep+hashlib.md5(url.encode('utf-8')).hexdigest()+'.'+ext\n if os.path.exists(filename):\n return filename\n try:\n content = urlopen(url).read()\n f = open(filename,'wb') \n f.write(content)\n f.close()\n except:\n return None\n return filename","def get_direct_image_url(image_url):\n imgur_id = re.findall('http[s]?://imgur.com/((?:[a-zA-Z]|[0-9])+)', image_url)\n if imgur_id:\n image_url = 'http://i.imgur.com/{0}.png'.format(imgur_id[0])\n return image_url","def get_url_image(self, obj):\n return settings.IMAGE_HOST + obj.image.url","def get_url_image(self, obj):\n return settings.IMAGE_HOST + obj.image.url","def get_url_image(self, obj):\n return settings.IMAGE_HOST + obj.image.url","def rename(img):\n ext = splitext(img)[1].lower()\n name = get_date(open(img))\n if name is not None:\n name = name + ext\n return copy(img, name)"],"string":"[\n \"def get_generated_image_name(full_image_url):\\r\\n\\r\\n logging.debug('get_generated_image_name({})'.format(full_image_url))\\r\\n\\r\\n image_name = datetime.datetime.now().strftime(\\\"%Y%m%d%H%M%S\\\")\\r\\n image_extension = full_image_url.split(\\\".\\\")[-1]\\r\\n image_name = image_name + \\\".\\\" + image_extension\\r\\n logging.debug('get_generated_image_name - image_name = {}'.format(image_name))\\r\\n return image_name\",\n \"def _ImageName(self, image):\\n\\n image_without_protocol = image.split('/')[-1]\\n if '@' in image_without_protocol:\\n return image_without_protocol.split('@')[0]\\n elif ':' in image:\\n return image_without_protocol.split(':')[0]\\n else:\\n return image_without_protocol\",\n \"def image_name(name):\\n \\n # Gets the '.' position\\n dot = name.find('.')\\n # Slice the name from beginning and before '.'\\n img = name[:dot]\\n # return string with jpg format\\n return \\\"{}.jpg\\\".format(img)\",\n \"def GenerateImageName(cls, build_target=None, build_id=None):\\n if not build_target and not build_id:\\n return \\\"image-\\\" + uuid.uuid4().hex\\n name = cls.IMAGE_NAME_FMT.format(\\n build_target=build_target,\\n build_id=build_id,\\n uuid=uuid.uuid4().hex[:8])\\n return cls._FormalizeName(name)\",\n \"def filename(self,imgurl):\\n if imgurl.find('/'):\\n return imgurl.rsplit('/', 1)[1]\",\n \"def _image_filename(image_name):\\n return '{}.tar'.format(image_name.replace(':', '_').replace('/', '_'))\",\n \"def get_image_filename(self, filename):\\n path = 'images/{folder}/{filename}'.format(\\n folder=self.folder,\\n filename=filename\\n )\\n return path\",\n \"def _get_image_name(image_meta, max_len=pvm_const.MaxLen.FILENAME_DEFAULT):\\n return pvm_util.sanitize_file_name_for_api(\\n image_meta.name, prefix=DiskType.IMAGE + '_',\\n suffix='_' + image_meta.checksum, max_len=max_len)\",\n \"def build_image_name(self, tag):\\n return self.repository_name + ':' + tag\",\n \"def namer(self, image_url, page_url):\\n title = page_url.rsplit('/', 2)[1]\\n image_ext = image_url.rsplit('.', 1)[1]\\n return '%s.%s' % (title, image_ext)\",\n \"def image_name(self) -> str:\\n return self._image_name\",\n \"def image_name(self) -> pulumi.Output[Optional[str]]:\\n return pulumi.get(self, \\\"image_name\\\")\",\n \"def path_to_name(img):\\n\\n return os.path.dirname(img) + '_' + os.path.basename(img)\",\n \"def image_file_name(instance, filename):\\n\\text = filename[-4:]\\n\\tnew_filename = os.path.join('images',str(instance.image_folder),str(instance.user).replace(\\\" \\\",\\\"\\\").lower()+ext)\\n\\treturn new_filename\",\n \"def imageNamed(imageName):\\n\\t\\treturn os.path.join(AppVars.imagePath(), imageName)\",\n \"def get_image_url():\",\n \"def get_image_name(resource_id):\\n match_images = re.match(r\\\".*images/(?P<image_name>[^/]*).*\\\", resource_id, flags=re.IGNORECASE)\\n if match_images:\\n return match_images.group(\\\"image_name\\\")\\n return \\\"\\\"\",\n \"def generate_image_filename():\\n now = datetime.now().strftime('%a-%w-%b-%H:%M:%S')\\n return 'CCTV_{0}.jpg'.format(now)\",\n \"def _get_image_name(self) -> str:\\n dirname = os.path.basename(os.getcwd())\\n default_image_name = f\\\"{dirname}_{self.config_name}\\\"\\n image_name = self.config_options.get(\\\"image\\\", default_image_name)\\n return image_name\",\n \"def generate_image_name(self, schedule, server_name):\\n\\n max_name_length = 255\\n prefix = self._get_image_prefix(schedule)\\n now = str(calendar.timegm(self._get_utcnow().utctimetuple()))\\n\\n # NOTE(ameade): Truncate the server name so the image name is within\\n # 255 characters total\\n server_name_len = max_name_length - len(now) - len(prefix) - len('--')\\n server_name = server_name[:server_name_len]\\n\\n return (\\\"%s-%s-%s\\\" % (prefix, server_name, str(now)))\",\n \"def name(self, strippath=False):\\n return _image.image_name(self, strippath)\",\n \"def _gen_image_filename(instance, filename):\\n # First, store the original filename in the model\\n instance.original_filename = filename\\n\\n return _unique_path(instance.owner.pk, filename)\",\n \"def imId2name(self, im_id):\\n \\n if isinstance(im_id, int):\\n name = str(im_id).zfill(self.STR_ID_LEN) + '.jpg'\\n elif isinstance(im_id, str):\\n name = im_id + '.jpg'\\n else:\\n raise AssertionError('Image ID should be of type string or int')\\n return name\",\n \"def _make_name(self, name=None):\\n\\n if name:\\n new_name = name.split(\\\"/\\\")[-1].split(\\\".png\\\")[0]\\n if new_name.startswith((\\\"AWS-\\\", \\\"Amazon-\\\")):\\n new_name = new_name.split(\\\"-\\\", 1)[1]\\n # Replace non-alphanumeric with underscores (1:1 mapping)\\n new_name = re.sub(r'\\\\W+', '_', new_name)\\n return new_name\",\n \"def image_name(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"image_name\\\")\",\n \"def image_name(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"image_name\\\")\",\n \"def image_url(self):\\n context = aq_inner(self.context)\\n obj_url = context.absolute_url()\\n if hasattr(context, 'getField'):\\n field = self.context.getField('image')\\n if not field:\\n field = context.getField(IMAGE_FIELD_NAME)\\n\\n if field and field.get_size(context) > 0:\\n return u'%s/%s_%s' % (obj_url, field.getName(), 'thumb')\\n\\n return u\\\"%s/isaw_logo.png\\\" % self.portal.absolute_url()\",\n \"def l10n_img_file_name(ctx, url):\\n url = url.lstrip('/')\\n locale = ctx.get('LANG', None)\\n if not locale:\\n locale = settings.LANGUAGE_CODE\\n\\n # We use the same localized screenshots for all Spanishes\\n if locale.startswith('es') and not _l10n_media_exists('img', locale, url):\\n locale = 'es-ES'\\n\\n if locale != settings.LANGUAGE_CODE:\\n if not _l10n_media_exists('img', locale, url):\\n locale = settings.LANGUAGE_CODE\\n\\n return path.join('img', 'l10n', locale, url)\",\n \"def get_country_image_name(country):\\n\\n country = country.replace(\\\" \\\", \\\"-\\\").replace(\\\".\\\", \\\"\\\").lower()\\n return \\\"%s.png\\\" % (country)\",\n \"def image_url(self, name):\\r\\n s3_key = self._generate_s3_key(name)\\r\\n return s3_key.generate_url(self.IMAGE_LINK_DURATION)\",\n \"def acquire_image_filename(self):\\n\\t\\titem_key = self.contents_data[ITEM_KEY]\\n\\t\\tif not item_key: return None\\n\\t\\treturn \\\"portrait_\\\" + item_key + \\\".bmp\\\"\",\n \"def picture_name(self, filename):\\n return '%s%s'%(self.username, splitext(filename)[1])\",\n \"def getImageName(self):\\n return [os.path.basename(name) for name in self.meta['sources']]\",\n \"def test_get_image_name(self):\\n ssp = self._get_ssp_stor()\\n\\n def verify_image_name(name, checksum, expected):\\n img_meta = image_meta.ImageMeta(name=name, checksum=checksum)\\n self.assertEqual(expected, ssp._get_image_name(img_meta))\\n self.assertTrue(len(expected) <= const.MaxLen.FILENAME_DEFAULT)\\n\\n verify_image_name('foo', 'bar', 'image_foo_bar')\\n # Ensure a really long name gets truncated properly. Note also '-'\\n # chars are sanitized.\\n verify_image_name(\\n 'Template_zw82enbix_PowerVM-CI-18y2385y9123785192364',\\n 'b518a8ba2b152b5607aceb5703fac072',\\n 'image_Template_zw82enbix_PowerVM_CI_18y2385y91'\\n '_b518a8ba2b152b5607aceb5703fac072')\",\n \"def l10n_img(ctx, url):\\n return static(l10n_img_file_name(ctx, url))\",\n \"def make_img_name(file_ext='.png'):\\r\\n fn = []\\r\\n # format seqs and write out to temp file\\r\\n for i in range(0, 30):\\r\\n fn.append(choice(ALPHABET))\\r\\n return ''.join(fn) + file_ext\",\n \"def get_base_image_name(image_name):\\n # Index where suffix sequence starts\\n suffix_start = image_name.find(IMAGE_IDENTIFIER_SEQUENCE)\\n\\n base_image_name = image_name\\n if suffix_start != -1:\\n # Index where extension sequence starts (.jpg, .jpeg, or .png)\\n extension_start = image_name.lower().find(\\\".jp\\\")\\n if extension_start == -1:\\n extension_start = image_name.lower().find(\\\".png\\\")\\n if extension_start == -1:\\n raise Exception(\\\"Invalid extension in image name: \\\" + image_name)\\n\\n # imagename(--===--)(4).jpg becomes imagename.jpg\\n base_image_name = image_name[:suffix_start] # imagename\\n extension = image_name[extension_start:] # .jpg\\n base_image_name += extension # imagename.jpg\\n\\n return base_image_name\",\n \"def generate_envoy_image_name_from_tag(image_tag: str) -> str:\\n image_prefix = get_envoy_image_prefix(image_tag)\\n envoy_image = \\\"{prefix}:{hash}\\\".format(prefix=image_prefix, hash=image_tag)\\n return envoy_image\",\n \"def get_image_url(img):\\n # element['data-src'] and element.get('data-src') doesn't work\\n for k, v in img.items():\\n if k == 'data-src':\\n # https://t.nhentai.net/galleries/<gallerycode>/<page#>t.<extension>\\n # https://i.nhentai.net/galleries/<gallerycode>/<page#>.<extension>\\n return v[:8] + 'i' + v[9:32] + v[32:].replace('t.', '.', 1)\",\n \"def getImageName(self):\\r\\n return self.imageName\",\n \"def get_image_name(name: str, tag: str, image_prefix: str = \\\"\\\") -> str:\\n versioned_tag = name.strip() + \\\":\\\" + tag.strip()\\n if image_prefix:\\n versioned_tag = image_prefix.strip().rstrip(\\\"/\\\") + \\\"/\\\" + versioned_tag\\n return versioned_tag\",\n \"def create_file_name(self):\\n # create a unique id for the file name\\n index = self.helpers.alpha_uuid()\\n\\n filename = self.form['FieldStorage'][self.image_cid].filename\\n extension = guess_extension(guess_type(filename)[0])\\n return ( # concatenates the following data\\n self.articleData.get('directory') + # directory\\n '/' + # slash\\n self.articleData.get('article_name') + # the article name\\n '-' + # hyphen character\\n index + # the id of the image\\n extension\\n )\",\n \"def get_image(self, img):\\n if img == \\\"rss\\\":\\n img = \\\"feed-icon-16x16.png\\\"\\n loc = os.path.abspath(os.path.dirname(__file__))\\n img = os.path.join(loc, img)\\n if not os.path.exists(img):\\n raise FileNotFoundError( # pragma: no cover\\n f\\\"Unable to find {img!r}.\\\")\\n return img\\n else:\\n raise FileNotFoundError( # pragma: no cover\\n f\\\"Unable to get image name: {img!r}.\\\")\",\n \"def image_upload_filename(instance, filename):\\n prefix = 'photos'\\n uhash = abs(hash(u'%s%s' % (datetime.now(), filename)))\\n user = instance.album.user.username\\n return u'%s/%s/%s_%s' % (prefix, user, uhash, filename)\",\n \"def _get_file_name(self) -> str:\\n headers = self._get_url_http_headers()\\n file_type = self._check_url_file_type(headers)\\n file_name = self._get_filename_from_headers(headers)\\n\\n if not file_name:\\n file_name = self._get_filename_from_url()\\n\\n if file_name is None:\\n raise FileNameCannotBeEvaluatedException\\n\\n if file_type:\\n file_name = self._add_file_extension(file_name, file_type)\\n\\n return file_name\",\n \"def get_image_url(self, image_name: str, thumbnail: bool = False) -> str:\\n pass\",\n \"def get_file_name(instance, filename):\\n filename = make_unique_filename(filename)\\n return os.path.join('uploads/profile_pics', filename)\",\n \"def generate_file_filename(instance, filename):\\n return _generate_filename(instance, filename, 'photos')\",\n \"def image_url(self) -> str:\\n return pulumi.get(self, \\\"image_url\\\")\",\n \"def add_string_to_image_url(url, addition):\\n filename, ext = splitext(url)\\n return ''.join([filename, '-', addition, ext])\",\n \"def _file_name(size):\\n timestamp = str(int(time.time()))\\n return '%s_%dx%d.%s' % (timestamp, size, size, 'jpg')\",\n \"def remove_suffix(image_url):\\n image_url = image_url.removeprefix('../../')\\n real_image_url = \\\"https://books.toscrape.com/\\\" + image_url\\n return real_image_url\",\n \"def filename_generate(image_class, size=12, chars=string.ascii_uppercase + string.ascii_lowercase + string.digits):\\n\\tnew_filename = time.strftime(\\\"%d-%m-%Y_\\\")\\n\\tnew_filename = new_filename + ''.join(random.choice(chars) for _ in range(size))\\n\\tnew_filename = new_filename + \\\"_P\\\" + str(image_class)\\n\\treturn new_filename\",\n \"def _filename_from_url(url):\\n file_name = url.split(\\\"/\\\")[-1]\\n return file_name\",\n \"def propose_image_path():\\n image_name = \\\"image_{}.png\\\".format(''.join(random.choice('abcdefghijklmnopqrstuvwxyz') for _ in range(8)))\\n image_path = os.path.join(_IMAGE_BASE_PATH, image_name)\\n image_url = '/images/' + image_name\\n return image_path, image_url\",\n \"def update_destination_file_name (file_name):\\n\\tglobal COUNTER \\n\\tCOUNTER += 1\\n\\tsplitted = file_name.split('/')\\n\\treturn file_name[:len(file_name)-len(splitted[-1])] + 'Image%05d' % COUNTER +'_'+splitted[-1]\",\n \"def get_nickname(image):\\r\\n extension = len(image.split('/')[-1:][0].split('.')[-1:][0])\\r\\n return image.split('/')[-1:][0][:-extension-1]\",\n \"def generate_avatar_name(email, ext):\\n hashed_email = hashlib.sha256(f'{email}{settings.SECRET_KEY}'.encode('utf-8')).hexdigest()\\n return hashed_email[:10] + ext\",\n \"def content_file_name(self, filename):\\n ext = filename.split('.')[-1]\\n filename = \\\"%s_%s.%s\\\" % (filename, self.id, ext)\\n return os.path.join('pictures/static/pictures/', filename)\",\n \"def get_filename(self) -> str:\\n fname = self.url.split(\\\"/\\\")[-1]\\n if \\\",\\\" in fname:\\n _fname, _i = fname.split(\\\",\\\")\\n _split_fname = _fname.split(\\\".\\\")\\n _name = _split_fname[0]\\n _extension = _split_fname[-1]\\n return _name + _i + \\\".\\\" + _extension\\n else:\\n return fname\",\n \"def get_image_name_for_hook(module):\\n os.makedirs(INSTANCE_FOLDER, exist_ok=True)\\n base_name = str(module).split('(')[0]\\n index = 0\\n image_name = '.' # '.' is surely exist, to make first loop condition True\\n while os.path.exists(image_name):\\n index += 1\\n image_name = os.path.join(\\n INSTANCE_FOLDER, '%s_%d.png' % (base_name, index))\\n return image_name\",\n \"def url_file_name(url):\\r\\n return url[url.rfind('/') + 1:]\",\n \"def get_thumbnail_name(self, thumbnail_name, with_size=None):\",\n \"def url_for(**options):\\n\\n url_parts = get_url_parts(**options)\\n image_hash = hashlib.md5(b(options[\\\"image_url\\\"])).hexdigest()\\n url_parts.append(image_hash)\\n\\n return \\\"/\\\".join(url_parts)\",\n \"def GetImageName(event):\\n spec = event.get(u'pathspec', {})\\n if isinstance(spec, basestring):\\n # This is needed in case data come from elasticsearch. event['pathspec']\\n # is naturally a nested dictionary but elastic search returns it as a\\n # string.\\n spec = eval(spec) # pylint: disable=eval-used\\n\\n while u'parent' in spec:\\n spec = spec[u'parent']\\n location = spec.get(u'location', u'')\\n location_tokens = location.split(u'/')\\n reversed_location_tokens = location_tokens[::-1]\\n t_location = u'/'.join(reversed_location_tokens)\\n return t_location\",\n \"def _UrlBaseName(url):\\n return url.rstrip('/').rpartition('/')[-1]\",\n \"def docker_image_name(self):\\n raise NotImplementedError\",\n \"def format_image_filename(device_image):\\n return \\\"{}-{}-{}-{}.bit\\\".format(device_image.bitstream_type,\\n device_image.pci_vendor,\\n device_image.pci_device,\\n device_image.uuid)\",\n \"def downloadicon_name(self):\\n return 'platform_%s.gif' % \\\\\\n re.sub(r'\\\\W', '_', self.context.getPlatform()).lower()\",\n \"def _get_image_url(self, image_filepath):\\n return self.IMAGE_URL + image_filepath\",\n \"def image(self) -> str:\\n return pulumi.get(self, \\\"image\\\")\",\n \"def get_filename(img_path):\\n filename = os.path.splitext(img_path)\\n return os.path.basename(filename[0])\",\n \"def get_image_url(self, image, hostname):\\n\\n if image.image_type == 'local':\\n return \\\"http://\\\" + hostname + \\\"/\\\" + settings.IMAGES_URL + \\\"/\\\" + image.deployment.campaign.short_name + \\\"/\\\" + image.deployment.short_name + \\\"/\\\" + image.image_name\\n elif image.image_type == 'envirocoms':\\n return \\\"http://\\\" + hostname + \\\"/ecoms_proxy?image=\\\" + image.image_path\",\n \"def build_image_path(self, src):\\r\\n o = urlparse(src)\\r\\n # we have a full url\\r\\n if o.hostname:\\r\\n return o.geturl()\\r\\n # we have a relative url\\r\\n return urljoin(self.target_url, src)\",\n \"def getImagePath(self)->str:\\n\\n returnStr = '../../../../assets/image/{}.png'.format(randint(1,15))\\n return returnStr\",\n \"def get_image_link(self):\\n table = self.soup.find('table')\\n image_tag = table.find('img')\\n image_name = self.soup.find_all(\\\"b\\\")[1].text\\n return image_tag['src'], image_name\\n\\n # image = td.find_all('img')\\n # print(image)\\n # if image is not None:\\n # return urljoin(self.base_url, image['src'])\",\n \"def get_name(self, index):\\n return self.image_files[index]\",\n \"def name_to_path(img, origin):\\n\\n orig_file_parts = img.split('_')[1:]\\n\\n category = orig_file_parts[-3]\\n filename = orig_file_parts[-2]+'_'+orig_file_parts[-1]\\n\\n orig_file = os.path.sep.join([origin, category])\\n orig_file = os.path.sep.join([orig_file, filename])\\n\\n return orig_file\",\n \"def get_image(result):\\n article_id = result['id']\\n id_ = article_id[14:]\\n href = article_id[:14]\\n\\n #FIXME: not working\\n image_url = \\\"http://www.jpress.nli.org.il/Olive/APA/NLI_heb/get/GetImage.ashx?kind=block&href=%s&id=%s&ext=.png\\\" %(href, id_)\\n \\n return image_url\",\n \"def img_url_display(self):\\n url = '%s=s%s' % (self.img_url, self.DISPLAY_SIZE_PX)\\n if self.img_rot in Plaque.ALLOWED_ROTATIONS:\\n url = \\\"%s-r%s\\\" % (url, self.img_rot)\\n return url\",\n \"def thumbgen_filename(filename):\\n name, ext = os.path.splitext(filename)\\n return '%s_thumb%s' % (name, ext)\",\n \"def thumbgen_filename(filename):\\n name, ext = os.path.splitext(filename)\\n return '%s_thumb%s' % (name, ext)\",\n \"def get_image_path(instance, filename):\\n filename, file_extension = path.splitext(filename)\\n return path.join(str(uuid4()) + file_extension)\",\n \"def get_content_name(self, content_url):\\n endpoint = content_url.split('/')[-1]\\n return re.match(r'(.+\\\\.(?:jpg|mp4))', endpoint).group(0)\",\n \"def get_file_name(image_dir, image_name_prefix, current_count):\\n if imageNumOn:\\n # you could also use os.path.join to construct image path file_path\\n file_path = image_dir+ \\\"/\\\"+image_name_prefix+str(current_count)+\\\".jpg\\\"\\n else:\\n right_now = datetime.datetime.now()\\n file_path = (\\\"%s/%s%04d%02d%02d-%02d%02d%02d.jpg\\\"\\n % (image_dir, image_name_prefix,\\n right_now.year, right_now.month, right_now.day,\\n right_now.hour, right_now.minute, right_now.second))\\n return file_path\",\n \"def make_frame_name(frame_num):\\n return str(frame_num).zfill(3) + '.jpg'\",\n \"def getReferenceImageUrl(self, name):\\n bucket = self.productSearch.bucket\\n blobName = self._getReferenceImageBlobName(name)\\n return bucket.blob(blobName).public_url\",\n \"def container_image_name(registry, component_name, version):\\n if version is None:\\n image = component_name + ':dev'\\n else:\\n image = '%s/%s:%s' % (registry, component_name, version)\\n\\n return image\",\n \"def get_image_url(project_id, image):\\n assert is_valid_project_id(project_id), project_id\\n assert is_valid_image(image), image\\n return (\\n 'https://www.googleapis.com/compute/v1/projects/%s/global/images/%s' % (\\n project_id, image))\",\n \"def modify_image_url(image_url, type=''):\\n parsed_uri = urlparse(image_url)\\n if parsed_uri.scheme == 'https' or parsed_uri.scheme == 'http':\\n pass\\n elif image_url == '':\\n image_url = '/media/default_' + type + '.jpg'\\n else:\\n image_url = '/media/' + image_url\\n return image_url\",\n \"def get_url(self, image_id):\\n key = image_id if image_id else self.default_image\\n if key:\\n return u'{bucket_url}{key}'.format(\\n bucket_url=self.connection.bucket_url,\\n key=self.id_to_key(key))\\n else:\\n return None\",\n \"def image_url(self) -> str:\\n return self._image_url\",\n \"def image_registry_name(image_name):\\n name = image_name\\n\\n if name.startswith('docker.io/'):\\n name = name.replace('docker.io/', '')\\n\\n if name.startswith('library/'):\\n name = name.replace('library/', '')\\n\\n if ':' not in name.rsplit('/')[-1]:\\n name = '{}:latest'.format(name)\\n\\n return name\",\n \"def _build_image_names(self, num_sequence, image_count):\\n for i in self._get_image_sequence_list(image_count):\\n self._image_names.append(self._get_image_name(i))\",\n \"def save_image(url):\\n ext = url.split('.')[-1]\\n filename = IMAGEDIR+os.sep+hashlib.md5(url.encode('utf-8')).hexdigest()+'.'+ext\\n if os.path.exists(filename):\\n return filename\\n try:\\n content = urlopen(url).read()\\n f = open(filename,'wb') \\n f.write(content)\\n f.close()\\n except:\\n return None\\n return filename\",\n \"def get_direct_image_url(image_url):\\n imgur_id = re.findall('http[s]?://imgur.com/((?:[a-zA-Z]|[0-9])+)', image_url)\\n if imgur_id:\\n image_url = 'http://i.imgur.com/{0}.png'.format(imgur_id[0])\\n return image_url\",\n \"def get_url_image(self, obj):\\n return settings.IMAGE_HOST + obj.image.url\",\n \"def get_url_image(self, obj):\\n return settings.IMAGE_HOST + obj.image.url\",\n \"def get_url_image(self, obj):\\n return settings.IMAGE_HOST + obj.image.url\",\n \"def rename(img):\\n ext = splitext(img)[1].lower()\\n name = get_date(open(img))\\n if name is not None:\\n name = name + ext\\n return copy(img, name)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.8253151","0.7566465","0.73151815","0.7306501","0.72865564","0.72370976","0.7237034","0.7213149","0.7112175","0.71032536","0.7042305","0.70135736","0.7007883","0.6985449","0.6945476","0.6920966","0.68803626","0.68720126","0.6842178","0.68296844","0.681122","0.67095834","0.66644984","0.664952","0.6646352","0.6646352","0.6579825","0.6519138","0.65161616","0.65113056","0.6499144","0.6499137","0.64956784","0.64152414","0.6405373","0.6370732","0.6355908","0.63553256","0.63501287","0.63401616","0.6327546","0.62881285","0.6277965","0.6271219","0.62711024","0.6262796","0.62500405","0.62468404","0.6246213","0.62346977","0.6232134","0.62306917","0.62199384","0.6208104","0.61906916","0.618673","0.6166486","0.6144007","0.6134135","0.6117707","0.61108595","0.6084831","0.60826045","0.607863","0.6073422","0.6071123","0.606813","0.6067872","0.60658205","0.6051232","0.60505193","0.604526","0.6042129","0.60382485","0.60165507","0.6015817","0.6015346","0.6013908","0.5995298","0.599098","0.5963597","0.5963597","0.5961395","0.5957618","0.59560806","0.59474784","0.5937881","0.59341747","0.5924845","0.5919782","0.59134835","0.5909555","0.59061134","0.59032404","0.5883316","0.5875485","0.5872924","0.5872924","0.5872924","0.5858327"],"string":"[\n \"0.8253151\",\n \"0.7566465\",\n \"0.73151815\",\n \"0.7306501\",\n \"0.72865564\",\n \"0.72370976\",\n \"0.7237034\",\n \"0.7213149\",\n \"0.7112175\",\n \"0.71032536\",\n \"0.7042305\",\n \"0.70135736\",\n \"0.7007883\",\n \"0.6985449\",\n \"0.6945476\",\n \"0.6920966\",\n \"0.68803626\",\n \"0.68720126\",\n \"0.6842178\",\n \"0.68296844\",\n \"0.681122\",\n \"0.67095834\",\n \"0.66644984\",\n \"0.664952\",\n \"0.6646352\",\n \"0.6646352\",\n \"0.6579825\",\n \"0.6519138\",\n \"0.65161616\",\n \"0.65113056\",\n \"0.6499144\",\n \"0.6499137\",\n \"0.64956784\",\n \"0.64152414\",\n \"0.6405373\",\n \"0.6370732\",\n \"0.6355908\",\n \"0.63553256\",\n \"0.63501287\",\n \"0.63401616\",\n \"0.6327546\",\n \"0.62881285\",\n \"0.6277965\",\n \"0.6271219\",\n \"0.62711024\",\n \"0.6262796\",\n \"0.62500405\",\n \"0.62468404\",\n \"0.6246213\",\n \"0.62346977\",\n \"0.6232134\",\n \"0.62306917\",\n \"0.62199384\",\n \"0.6208104\",\n \"0.61906916\",\n \"0.618673\",\n \"0.6166486\",\n \"0.6144007\",\n \"0.6134135\",\n \"0.6117707\",\n \"0.61108595\",\n \"0.6084831\",\n \"0.60826045\",\n \"0.607863\",\n \"0.6073422\",\n \"0.6071123\",\n \"0.606813\",\n \"0.6067872\",\n \"0.60658205\",\n \"0.6051232\",\n \"0.60505193\",\n \"0.604526\",\n \"0.6042129\",\n \"0.60382485\",\n \"0.60165507\",\n \"0.6015817\",\n \"0.6015346\",\n \"0.6013908\",\n \"0.5995298\",\n \"0.599098\",\n \"0.5963597\",\n \"0.5963597\",\n \"0.5961395\",\n \"0.5957618\",\n \"0.59560806\",\n \"0.59474784\",\n \"0.5937881\",\n \"0.59341747\",\n \"0.5924845\",\n \"0.5919782\",\n \"0.59134835\",\n \"0.5909555\",\n \"0.59061134\",\n \"0.59032404\",\n \"0.5883316\",\n \"0.5875485\",\n \"0.5872924\",\n \"0.5872924\",\n \"0.5872924\",\n \"0.5858327\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.8002894"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":17877,"cells":{"query":{"kind":"string","value":"Check whether an instance exists or not."},"document":{"kind":"string","value":"def singularity_exists(self):\n instances = Client.instances(quiet=self.quiet)\n for instance in instances:\n if self.pid in instance.name:\n return True\n return False"},"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 _Exists(self, instance_only: bool = False) -> bool:\n cmd = util.GcloudCommand(self, 'spanner', 'instances', 'describe',\n self.name)\n\n # Do not log error or warning when checking existence.\n _, _, retcode = cmd.Issue(suppress_warning=True, raise_on_failure=False)\n if retcode != 0:\n logging.info('Could not find GCP Spanner instance %s.', self.name)\n return False\n\n if instance_only:\n return True\n\n cmd = util.GcloudCommand(self, 'spanner', 'databases', 'describe',\n self.database)\n cmd.flags['instance'] = self.name\n\n # Do not log error or warning when checking existence.\n _, _, retcode = cmd.Issue(suppress_warning=True, raise_on_failure=False)\n if retcode != 0:\n logging.info('Could not find GCP Spanner database %s.', self.database)\n return False\n\n return True","def exists(\n instance_id=None,\n name=None,\n tags=None,\n region=None,\n key=None,\n keyid=None,\n profile=None,\n in_states=None,\n filters=None,\n):\n instances = find_instances(\n instance_id=instance_id,\n name=name,\n tags=tags,\n region=region,\n key=key,\n keyid=keyid,\n profile=profile,\n in_states=in_states,\n filters=filters,\n )\n if instances:\n log.info(\"Instance exists.\")\n return True\n else:\n log.warning(\"Instance does not exist.\")\n return False","def exists(self):\n\n if self:\n pass","def have_this_instance(self, instance):\n for i in self.all_instances:\n if i == instance:\n print(\"YES ITS ME!\")\n return True\n print(\"NO S.B. ELSE\")\n return False","def exists(self):\n return True","def exists(self):\n return True","def _Exists(self):\n cmd = util.GcloudCommand(self, 'beta', 'bigtable', 'instances', 'list')\n cmd.flags['format'] = 'json'\n cmd.flags['project'] = self.project\n # The zone flag makes this command fail.\n cmd.flags['zone'] = []\n stdout, stderr, retcode = cmd.Issue(\n suppress_warning=True, raise_on_failure=False)\n if retcode != 0:\n # This is not ideal, as we're returning false not because we know\n # the table isn't there, but because we can't figure out whether\n # it is there. This behavior is consistent without other\n # _Exists methods.\n logging.error('Unable to list GCP Bigtable instances. Return code %s '\n 'STDOUT: %s\\nSTDERR: %s', retcode, stdout, stderr)\n return False\n result = json.loads(stdout)\n instances = {instance['name'] for instance in result}\n full_name = 'projects/{}/instances/{}'.format(self.project, self.name)\n return full_name in instances","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 exist(self):","def checkDBImportInstance(self, instance):\n\n\t\tsession = self.configDBSession()\n\t\tdbimportInstances = aliased(configSchema.dbimportInstances)\n\n\t\tresult = (session.query(\n\t\t\t\tdbimportInstances.name\n\t\t\t)\n\t\t\t.select_from(dbimportInstances)\n\t\t\t.filter(dbimportInstances.name == instance)\n\t\t\t.count())\n\n\t\tif result == 0:\n\t\t\tlogging.error(\"No DBImport Instance with that name can be found in table 'dbimport_instances'\")\n\t\t\tself.remove_temporary_files()\n\t\t\tsys.exit(1)","def exists(self) -> bool:\n try:\n result = self.get()\n except KeyError:\n return False\n return True","def check_that_instance_is_alive(self):\n if not self.instance.is_running():\n raise Exception(f\"Starter instance is not running. Base directory: {str(self.basedir)}\")\n if self.instance.status() == psutil.STATUS_ZOMBIE:\n raise Exception(f\"Starter instance is a zombie. Base directory: {str(self.basedir)}\")","def exists(path):\n return get_instance(path).exists(path)","def objExists(*args, **kwargs)->bool:\n pass","def exists(self):\n return self.obj is not None","def object_exists(self, fname):\n return True","def exists(self):\r\n try:\r\n self.refresh()\r\n except:\r\n return False\r\n return True","def object_exists(self, fname):\n return False","def object_exists(self, fname):\n return self.object_exists","def exists(self, name):\n try:\n self.container.get_object(name)\n return True\n except NoSuchObject:\n return False","def exists(self, obj):\n return False","def _verify_unique_instance_name(self, name):\n existing = self.instances.find_one({'name': name, 'deleted': False})\n if existing:\n raise AXApiInvalidParam(\"Fixture instance with name '{}' already exists\".format(name))","def is_existing(self):\n return self.backend.is_existing","def __contains__(self, instance: object) -> bool:\n try:\n state = attributes.instance_state(instance)\n except exc.NO_STATE as err:\n raise exc.UnmappedInstanceError(instance) from err\n return self._contains_state(state)","def _do_check(self):\n try:\n #breakpoint()\n ApplicationsItem.objects.exists()\n #print (\"Checking\")\n return True\n\n except Exception:\n client.captureException()\n return False","def check(key):\n instance = key.get()\n if not instance:\n logging.warning('Instance does not exist: %s', key)\n return\n\n if not instance.active_metadata_update:\n logging.warning('Instance active metadata operation unspecified: %s', key)\n return\n\n if not instance.active_metadata_update.url:\n logging.warning(\n 'Instance active metadata operation URL unspecified: %s', key)\n return\n\n now = utils.utcnow()\n result = net.json_request(\n instance.active_metadata_update.url, scopes=gce.AUTH_SCOPES)\n if result['status'] != 'DONE':\n return\n\n if result.get('error'):\n logging.warning(\n 'Instance metadata operation failed: %s\\n%s',\n key,\n json.dumps(result, indent=2),\n )\n metrics.instance_set_metadata_time.add(\n (now - instance.active_metadata_update.operation_ts).total_seconds(),\n fields={\n 'success': False,\n 'zone': instance.instance_group_manager.id(),\n },\n )\n metrics.send_machine_event('METADATA_UPDATE_FAILED', instance.hostname)\n reschedule_active_metadata_update(key, instance.active_metadata_update.url)\n metrics.send_machine_event('METADATA_UPDATE_READY', instance.hostname)\n else:\n metrics.instance_set_metadata_time.add(\n (now - instance.active_metadata_update.operation_ts).total_seconds(),\n fields={\n 'success': True,\n 'zone': instance.instance_group_manager.id(),\n },\n )\n metrics.send_machine_event('METADATA_UPDATE_SUCCEEDED', instance.hostname)\n clear_active_metadata_update(key, instance.active_metadata_update.url)","def is_exist(self, class_reference, **key_value):\n return True if service.select_from(class_reference, **key_value).get() else False","def check_instance(self, class_name, inst_id, stored_objects):\n '''get '<class_name>.id' to FileStorage.__objects key format'''\n instance = \"{}.{}\".format(class_name, inst_id)\n if instance not in stored_objects:\n \"\"\"given id does not exist\"\"\"\n print(\"** no instance found **\")\n instance = False\n return instance","def exists(self):\n return _os.path.exists(self.__str__())","def exists(self):\n return bool(self.get())","def instance_valid(instance):\n return zope.interface.verify.verifyObject(IKeyValueDB, instance)","def test_instance_running(self) -> None:\n if self.prod_env:\n ec2_name = 'saints-xctf-server-prod-asg'\n else:\n ec2_name = 'saints-xctf-server-dev-asg'\n\n instances = self.get_ec2(ec2_name)\n self.assertTrue(len(instances) > 0)","def exists (self, db):\n return hasattr(self, db) and isinstance(getattr(self, db), Database)","def exists(self, name):\n return self.endpoint.exists(name)","def check_exists(cls, **kwargs):\n return bool(cls.query.filter_by(**kwargs).first())","def test_exists_true(self):\n self.assertTrue(Sample.exists(self.sample_id, self.sample_template))","def exists(self, name):\n return self.backend.exists(name)","def _check_row_exists(self, pk):\n session = self.session_factory()\n exists = session.query(PipelineRun).filter_by(id=pk).first()\n session.close()\n if exists:\n return True\n return False","def test_exists(self):\n with self.assertRaises(IncompetentQiitaDeveloperError):\n BaseSample.exists('SKM7.640188', SampleTemplate(1))","def exists(self):\n return self._repository is not None","def _is_instance_running(settings, instance_id_ip,\n ip_given=False):\n instance = get_this_instance(settings,\n instance_id_ip, ip_given)\n if instance:\n if ip_given:\n ip_address = instance_id_ip\n else:\n ip_address = get_instance_ip(instance)\n state = instance.state\n print 'Current status of Instance'\\\n ' with IP [%s]: %s' %(ip_address, state)\n if state == \"running\" and ip_address:\n return True\n return False","def object_exists(self, name: str):\n file_path = self.__get_file_path(name)\n return os.path.exists(file_path)","def check_status():\n js = _get_jetstream_conn()\n i = js.compute.instances.get(session.attributes.get('instance_id'))\n if not i:\n return question(\"There was a problem. Please retry your command.\")\n\n status = i.state\n if session.attributes['status'] != status:\n msg = \"New instance status is {0}.\".format(status)\n if not session.attributes['public_ip'] and status == 'running':\n # Attach a floating IP to the instance\n fip = None\n fips = js.network.floating_ips()\n for ip in fips:\n if not ip.in_use():\n fip = ip\n if fip:\n i.add_floating_ip(fip.public_ip)\n session.attributes['public_ip'] = fip.public_ip\n else:\n msg = \"Instance status is {0}\".format(status)\n\n session.attributes['status'] = status\n\n if session.attributes['status'] != 'running':\n q = \"Would you like to check the status again?\"\n return question(msg + q).reprompt(q)\n else:\n card_content = 'Access your instance at http://{0}'.format(\n session.attributes.get('public_ip'))\n return statement(msg).simple_card(\n title=\"Instance {0} was launched.\".format(i.name),\n content=msg + card_content)","def test_exists_true(self):\n self.assertTrue(SampleTemplate.exists(self.test_study.id))","def test_exists_false(self):\n self.assertFalse(Sample.exists('Not_a_Sample', self.sample_template))","def test_exists_true(self):\n self.assertTrue(PrepTemplate.exists(1))","def is_instance_up(self):\n logging.debug(\"checking if starter instance booted: \" + str(self.basedir))\n if not self.instance.is_running():\n message = \"Starter Instance {0.name} is gone!\".format(self)\n logging.error(message)\n raise Exception(message)\n\n # if the logfile contains up and running we are fine\n lfs = self.get_log_file()\n regx = re.compile(r\"(\\w*) up and running \")\n for line in lfs.splitlines():\n match = regx.search(line)\n if match:\n groups = match.groups()\n if len(groups) == 1 and groups[0] == \"agent\":\n continue\n return True\n\n return False","def exists(self, name):\n raise NotImplementedError()","def test_live_migration_src_check_instance_not_running(self):\n\n instance_id = self._create_instance(power_state=power_state.NOSTATE)\n i_ref = db.instance_get(self.context, instance_id)\n\n try:\n self.scheduler.driver._live_migration_src_check(self.context,\n i_ref)\n except exception.Invalid, e:\n c = (e.message.find('is not running') > 0)\n\n self.assertTrue(c)\n db.instance_destroy(self.context, instance_id)","def exists(self):\n try:\n key = self.key\n except DoesNotExist:\n \"\"\"\n If the object doesn't exists anymore, its PK is deleted, so the\n \"self.key\" call will raise a DoesnotExist exception. We catch it\n to return False, as the field doesn't exists too.\n \"\"\"\n return False\n else:\n return self.connection.exists(key)","async def _exists(self, key):\n with await self._connect() as redis:\n exists = await redis.exists(key)\n return True if exists > 0 else False","def exists(self):\r\n return bool(self.bucket.lookup(self.name))","def __is_type_instance( self, instance_type ):\n for index, instance in enumerate(INSTANCE_TYPES):\n if instance == instance_type:\n return True\n return False","def node_exists(self):\n return self.oid is not None","def needs_unique_instance(type_):\n return type_ in unique_instance_types","def test_instance_profile_exists(self) -> None:\n self.assertTrue(self.validate_instance_profile('s3-access-role', is_prod=self.prod_env))","def private_instance(self) -> bool:\n return pulumi.get(self, \"private_instance\")","def isExist(data):\n return True/False","def __instancecheck__(self, instance):\n\n if isinstance(instance, ObjCInstance):\n return bool(instance.isKindOfClass(self))\n else:\n return False","async def exists(self, payload: TPayload) -> bool:","def test_exists_true(self):\n self.assertTrue(PrepSample.exists(self.sample_id, self.prep_template))","def object_exists(self, request):\n HttpRequest = request.to_http_info(self.api_client.configuration)\n return self.__make_request(HttpRequest, 'GET', 'ObjectExist')","def exists(self):\n return self.path.exists()","def exists1(self, cls, **attr):\n nodes = getattr(self.graph, getattr(models, cls).element_plural).query(**attr).all()\n return len(nodes) > 0","def does_resource_exist(resource):\n try:\n resource.load()\n return True\n except botocore.exceptions.ClientError as e:\n if e.response['Error']['Code'] == 'ValidationError':\n return False\n else:\n raise e","def check_model_exists(class_name):\n if path.exists(settings.get('FALAFEL_DIR') + settings.get('MODELS_DIR') + '/' + class_name + '.py'):\n return True\n else:\n return False","def test_exists_false(self):\n self.assertFalse(PrepSample.exists('Not_a_Sample', self.prep_template))","def check_availability(self):\n pass","def exists(self):\r\n return os.path.exists(self.full_path)","def ResourceExists(self, name):\n pass","def exists(self):\n try:\n self.world.find(self.ehandle)\n except KeyError:\n return False\n else:\n return True","def exists(self, arg):\n raise NotImplementedError","def exists(self):\n return self.properties.get(\"Exists\", None)","def health_check(self):\n with self.session as session:\n try:\n query = session.execute('SELECT 1')\n except Exception as e:\n raise UnhealthyCheck()\n\n return True","def exists(self, Search_ID):\n if self.get_id(Search_ID) is None:\n return False\n else:\n return True","def _exists (self):\n cursor = self._exec (self.select)\n return bool (cursor.fetchall ())","def entity_exists(entity):\n return entity.get_by_id(entity.key.id())","def _status_exists(self, cls=MySQLStatus):","def check(self):\r\n self._check_object(self._config.name)","def exists (self, uuid):\n return self.read (uuid) is not None","def checkIfExists(self, key):\n\t\t\t\n\t\tif self.db.get(key):\n\t\t\treturn self.db.get(key)\n\t\telse:\n\t\t\treturn False","def _exist(self, name):\r\n cmd = 'exist(\"%s\")' % name\r\n if not self._engine:\r\n msg = \"Session is not open\"\r\n raise Oct2PyError(msg)\r\n resp = self._engine.eval(cmd, silent=True).strip()\r\n exist = int(resp.split()[-1])\r\n if exist == 0:\r\n cmd = \"class(%s)\" % name\r\n resp = self._engine.eval(cmd, silent=True).strip()\r\n if \"error:\" in resp:\r\n msg = 'Value \"%s\" does not exist in Octave workspace'\r\n raise Oct2PyError(msg % name)\r\n else:\r\n exist = 2\r\n return exist","def find_instance(self, name_or_id, ignore_missing=True):\n return self._find(\n _instance.Instance, name_or_id, ignore_missing=ignore_missing\n )","def _user_exists(self, username):\n return self.db.query(User).filter_by(name=username).first() is not None","async def exists(self, tag_name):\n try:\n if await self.get_id(tag_name):\n return True\n except RtbDoesntExists:\n return False","def check(cls, control_instance):\n pass","def object_exists_async(self, request):\n HttpRequest = request.to_http_info(self.api_client.configuration)\n return self.__make_request_async(HttpRequest, 'GET', 'ObjectExist')","def test_class_exists(self):\n\n self.assertTrue(hasattr(Account, self.klass_name))","def _id_exists(self):\n return self.app_id in self.engine.app_list","def _check_host_existence(self, hostname: str) -> bool:\n with self.lock:\n hosts = self.hosts.all()\n for host in hosts:\n if host['hostname'] == hostname:\n return True\n return False","def test_rebuild_on_host_instance_exists(self):\n db.instance_update(self.context, self.inst.uuid,\n {\"task_state\": task_states.SCHEDULING})\n self.compute.build_and_run_instance(self.context,\n self.inst, {}, {}, {}, [], block_device_mapping=[])\n\n self.stub_out('nova.virt.fake.FakeDriver.instance_on_disk',\n lambda *a, **kw: True)\n patch_remove_allocs = mock.patch(\n 'nova.scheduler.client.report.SchedulerReportClient.'\n 'remove_provider_tree_from_instance_allocation')\n with patch_remove_allocs:\n self.assertRaises(exception.InstanceExists,\n lambda: self._rebuild(on_shared_storage=True))","def check_exist(self):\n helper.RbdImageOperator._check_rbd_image(self.real_path)","def exists(self):\n\n return os.path.exists(self.path)","def is_existing_object(did):\n if not d1_gmn.app.did.is_existing_object(did):\n raise d1_common.types.exceptions.NotFound(\n 0,\n \"Identifier is {}. Expected a Persistent ID (PID) for an existing \"\n 'object. id=\"{}\"'.format(d1_gmn.app.did.classify_identifier(did), did),\n identifier=did,\n )","def test_exists_false(self):\n self.assertFalse(PrepTemplate.exists(2))","def db_exists(self):\n \n with self.connection:\n c = self.connection.cursor()\n c.execute(\"SELECT EXISTS(SELECT 1 FROM sqlite_master WHERE name=?)\", [PUBLICATIONS_TABLE])\n exists = c.fetchone()\n if(exists[0] == 1):\n return True\n else:\n return False","def __verify__(cls):\n\n try:\n UpstartSystem()\n return True\n except Exception as e:\n try:\n UpstartSystem(bus=DirectUpstartBus())\n return True\n except Exception as e:\n return False","def db_exists(self, db):\n raise NotImplementedError()","def check_if_exists(self, query=None):\n if query is not None:\n self.execute_query(query)\n if len(self.recordset.fetchall()):\n with allure.step(\"The record exists after executing the query\"):\n assert True\n else:\n with allure.step(\"The records does not exist after executing the query\"):\n assert False\n\n return self","def exists(self):\n try:\n select_template(self.get_paths())\n return True\n except TemplateDoesNotExist:\n return False"],"string":"[\n \"def _Exists(self, instance_only: bool = False) -> bool:\\n cmd = util.GcloudCommand(self, 'spanner', 'instances', 'describe',\\n self.name)\\n\\n # Do not log error or warning when checking existence.\\n _, _, retcode = cmd.Issue(suppress_warning=True, raise_on_failure=False)\\n if retcode != 0:\\n logging.info('Could not find GCP Spanner instance %s.', self.name)\\n return False\\n\\n if instance_only:\\n return True\\n\\n cmd = util.GcloudCommand(self, 'spanner', 'databases', 'describe',\\n self.database)\\n cmd.flags['instance'] = self.name\\n\\n # Do not log error or warning when checking existence.\\n _, _, retcode = cmd.Issue(suppress_warning=True, raise_on_failure=False)\\n if retcode != 0:\\n logging.info('Could not find GCP Spanner database %s.', self.database)\\n return False\\n\\n return True\",\n \"def exists(\\n instance_id=None,\\n name=None,\\n tags=None,\\n region=None,\\n key=None,\\n keyid=None,\\n profile=None,\\n in_states=None,\\n filters=None,\\n):\\n instances = find_instances(\\n instance_id=instance_id,\\n name=name,\\n tags=tags,\\n region=region,\\n key=key,\\n keyid=keyid,\\n profile=profile,\\n in_states=in_states,\\n filters=filters,\\n )\\n if instances:\\n log.info(\\\"Instance exists.\\\")\\n return True\\n else:\\n log.warning(\\\"Instance does not exist.\\\")\\n return False\",\n \"def exists(self):\\n\\n if self:\\n pass\",\n \"def have_this_instance(self, instance):\\n for i in self.all_instances:\\n if i == instance:\\n print(\\\"YES ITS ME!\\\")\\n return True\\n print(\\\"NO S.B. ELSE\\\")\\n return False\",\n \"def exists(self):\\n return True\",\n \"def exists(self):\\n return True\",\n \"def _Exists(self):\\n cmd = util.GcloudCommand(self, 'beta', 'bigtable', 'instances', 'list')\\n cmd.flags['format'] = 'json'\\n cmd.flags['project'] = self.project\\n # The zone flag makes this command fail.\\n cmd.flags['zone'] = []\\n stdout, stderr, retcode = cmd.Issue(\\n suppress_warning=True, raise_on_failure=False)\\n if retcode != 0:\\n # This is not ideal, as we're returning false not because we know\\n # the table isn't there, but because we can't figure out whether\\n # it is there. This behavior is consistent without other\\n # _Exists methods.\\n logging.error('Unable to list GCP Bigtable instances. Return code %s '\\n 'STDOUT: %s\\\\nSTDERR: %s', retcode, stdout, stderr)\\n return False\\n result = json.loads(stdout)\\n instances = {instance['name'] for instance in result}\\n full_name = 'projects/{}/instances/{}'.format(self.project, self.name)\\n return full_name in instances\",\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 exist(self):\",\n \"def checkDBImportInstance(self, instance):\\n\\n\\t\\tsession = self.configDBSession()\\n\\t\\tdbimportInstances = aliased(configSchema.dbimportInstances)\\n\\n\\t\\tresult = (session.query(\\n\\t\\t\\t\\tdbimportInstances.name\\n\\t\\t\\t)\\n\\t\\t\\t.select_from(dbimportInstances)\\n\\t\\t\\t.filter(dbimportInstances.name == instance)\\n\\t\\t\\t.count())\\n\\n\\t\\tif result == 0:\\n\\t\\t\\tlogging.error(\\\"No DBImport Instance with that name can be found in table 'dbimport_instances'\\\")\\n\\t\\t\\tself.remove_temporary_files()\\n\\t\\t\\tsys.exit(1)\",\n \"def exists(self) -> bool:\\n try:\\n result = self.get()\\n except KeyError:\\n return False\\n return True\",\n \"def check_that_instance_is_alive(self):\\n if not self.instance.is_running():\\n raise Exception(f\\\"Starter instance is not running. Base directory: {str(self.basedir)}\\\")\\n if self.instance.status() == psutil.STATUS_ZOMBIE:\\n raise Exception(f\\\"Starter instance is a zombie. Base directory: {str(self.basedir)}\\\")\",\n \"def exists(path):\\n return get_instance(path).exists(path)\",\n \"def objExists(*args, **kwargs)->bool:\\n pass\",\n \"def exists(self):\\n return self.obj is not None\",\n \"def object_exists(self, fname):\\n return True\",\n \"def exists(self):\\r\\n try:\\r\\n self.refresh()\\r\\n except:\\r\\n return False\\r\\n return True\",\n \"def object_exists(self, fname):\\n return False\",\n \"def object_exists(self, fname):\\n return self.object_exists\",\n \"def exists(self, name):\\n try:\\n self.container.get_object(name)\\n return True\\n except NoSuchObject:\\n return False\",\n \"def exists(self, obj):\\n return False\",\n \"def _verify_unique_instance_name(self, name):\\n existing = self.instances.find_one({'name': name, 'deleted': False})\\n if existing:\\n raise AXApiInvalidParam(\\\"Fixture instance with name '{}' already exists\\\".format(name))\",\n \"def is_existing(self):\\n return self.backend.is_existing\",\n \"def __contains__(self, instance: object) -> bool:\\n try:\\n state = attributes.instance_state(instance)\\n except exc.NO_STATE as err:\\n raise exc.UnmappedInstanceError(instance) from err\\n return self._contains_state(state)\",\n \"def _do_check(self):\\n try:\\n #breakpoint()\\n ApplicationsItem.objects.exists()\\n #print (\\\"Checking\\\")\\n return True\\n\\n except Exception:\\n client.captureException()\\n return False\",\n \"def check(key):\\n instance = key.get()\\n if not instance:\\n logging.warning('Instance does not exist: %s', key)\\n return\\n\\n if not instance.active_metadata_update:\\n logging.warning('Instance active metadata operation unspecified: %s', key)\\n return\\n\\n if not instance.active_metadata_update.url:\\n logging.warning(\\n 'Instance active metadata operation URL unspecified: %s', key)\\n return\\n\\n now = utils.utcnow()\\n result = net.json_request(\\n instance.active_metadata_update.url, scopes=gce.AUTH_SCOPES)\\n if result['status'] != 'DONE':\\n return\\n\\n if result.get('error'):\\n logging.warning(\\n 'Instance metadata operation failed: %s\\\\n%s',\\n key,\\n json.dumps(result, indent=2),\\n )\\n metrics.instance_set_metadata_time.add(\\n (now - instance.active_metadata_update.operation_ts).total_seconds(),\\n fields={\\n 'success': False,\\n 'zone': instance.instance_group_manager.id(),\\n },\\n )\\n metrics.send_machine_event('METADATA_UPDATE_FAILED', instance.hostname)\\n reschedule_active_metadata_update(key, instance.active_metadata_update.url)\\n metrics.send_machine_event('METADATA_UPDATE_READY', instance.hostname)\\n else:\\n metrics.instance_set_metadata_time.add(\\n (now - instance.active_metadata_update.operation_ts).total_seconds(),\\n fields={\\n 'success': True,\\n 'zone': instance.instance_group_manager.id(),\\n },\\n )\\n metrics.send_machine_event('METADATA_UPDATE_SUCCEEDED', instance.hostname)\\n clear_active_metadata_update(key, instance.active_metadata_update.url)\",\n \"def is_exist(self, class_reference, **key_value):\\n return True if service.select_from(class_reference, **key_value).get() else False\",\n \"def check_instance(self, class_name, inst_id, stored_objects):\\n '''get '<class_name>.id' to FileStorage.__objects key format'''\\n instance = \\\"{}.{}\\\".format(class_name, inst_id)\\n if instance not in stored_objects:\\n \\\"\\\"\\\"given id does not exist\\\"\\\"\\\"\\n print(\\\"** no instance found **\\\")\\n instance = False\\n return instance\",\n \"def exists(self):\\n return _os.path.exists(self.__str__())\",\n \"def exists(self):\\n return bool(self.get())\",\n \"def instance_valid(instance):\\n return zope.interface.verify.verifyObject(IKeyValueDB, instance)\",\n \"def test_instance_running(self) -> None:\\n if self.prod_env:\\n ec2_name = 'saints-xctf-server-prod-asg'\\n else:\\n ec2_name = 'saints-xctf-server-dev-asg'\\n\\n instances = self.get_ec2(ec2_name)\\n self.assertTrue(len(instances) > 0)\",\n \"def exists (self, db):\\n return hasattr(self, db) and isinstance(getattr(self, db), Database)\",\n \"def exists(self, name):\\n return self.endpoint.exists(name)\",\n \"def check_exists(cls, **kwargs):\\n return bool(cls.query.filter_by(**kwargs).first())\",\n \"def test_exists_true(self):\\n self.assertTrue(Sample.exists(self.sample_id, self.sample_template))\",\n \"def exists(self, name):\\n return self.backend.exists(name)\",\n \"def _check_row_exists(self, pk):\\n session = self.session_factory()\\n exists = session.query(PipelineRun).filter_by(id=pk).first()\\n session.close()\\n if exists:\\n return True\\n return False\",\n \"def test_exists(self):\\n with self.assertRaises(IncompetentQiitaDeveloperError):\\n BaseSample.exists('SKM7.640188', SampleTemplate(1))\",\n \"def exists(self):\\n return self._repository is not None\",\n \"def _is_instance_running(settings, instance_id_ip,\\n ip_given=False):\\n instance = get_this_instance(settings,\\n instance_id_ip, ip_given)\\n if instance:\\n if ip_given:\\n ip_address = instance_id_ip\\n else:\\n ip_address = get_instance_ip(instance)\\n state = instance.state\\n print 'Current status of Instance'\\\\\\n ' with IP [%s]: %s' %(ip_address, state)\\n if state == \\\"running\\\" and ip_address:\\n return True\\n return False\",\n \"def object_exists(self, name: str):\\n file_path = self.__get_file_path(name)\\n return os.path.exists(file_path)\",\n \"def check_status():\\n js = _get_jetstream_conn()\\n i = js.compute.instances.get(session.attributes.get('instance_id'))\\n if not i:\\n return question(\\\"There was a problem. Please retry your command.\\\")\\n\\n status = i.state\\n if session.attributes['status'] != status:\\n msg = \\\"New instance status is {0}.\\\".format(status)\\n if not session.attributes['public_ip'] and status == 'running':\\n # Attach a floating IP to the instance\\n fip = None\\n fips = js.network.floating_ips()\\n for ip in fips:\\n if not ip.in_use():\\n fip = ip\\n if fip:\\n i.add_floating_ip(fip.public_ip)\\n session.attributes['public_ip'] = fip.public_ip\\n else:\\n msg = \\\"Instance status is {0}\\\".format(status)\\n\\n session.attributes['status'] = status\\n\\n if session.attributes['status'] != 'running':\\n q = \\\"Would you like to check the status again?\\\"\\n return question(msg + q).reprompt(q)\\n else:\\n card_content = 'Access your instance at http://{0}'.format(\\n session.attributes.get('public_ip'))\\n return statement(msg).simple_card(\\n title=\\\"Instance {0} was launched.\\\".format(i.name),\\n content=msg + card_content)\",\n \"def test_exists_true(self):\\n self.assertTrue(SampleTemplate.exists(self.test_study.id))\",\n \"def test_exists_false(self):\\n self.assertFalse(Sample.exists('Not_a_Sample', self.sample_template))\",\n \"def test_exists_true(self):\\n self.assertTrue(PrepTemplate.exists(1))\",\n \"def is_instance_up(self):\\n logging.debug(\\\"checking if starter instance booted: \\\" + str(self.basedir))\\n if not self.instance.is_running():\\n message = \\\"Starter Instance {0.name} is gone!\\\".format(self)\\n logging.error(message)\\n raise Exception(message)\\n\\n # if the logfile contains up and running we are fine\\n lfs = self.get_log_file()\\n regx = re.compile(r\\\"(\\\\w*) up and running \\\")\\n for line in lfs.splitlines():\\n match = regx.search(line)\\n if match:\\n groups = match.groups()\\n if len(groups) == 1 and groups[0] == \\\"agent\\\":\\n continue\\n return True\\n\\n return False\",\n \"def exists(self, name):\\n raise NotImplementedError()\",\n \"def test_live_migration_src_check_instance_not_running(self):\\n\\n instance_id = self._create_instance(power_state=power_state.NOSTATE)\\n i_ref = db.instance_get(self.context, instance_id)\\n\\n try:\\n self.scheduler.driver._live_migration_src_check(self.context,\\n i_ref)\\n except exception.Invalid, e:\\n c = (e.message.find('is not running') > 0)\\n\\n self.assertTrue(c)\\n db.instance_destroy(self.context, instance_id)\",\n \"def exists(self):\\n try:\\n key = self.key\\n except DoesNotExist:\\n \\\"\\\"\\\"\\n If the object doesn't exists anymore, its PK is deleted, so the\\n \\\"self.key\\\" call will raise a DoesnotExist exception. We catch it\\n to return False, as the field doesn't exists too.\\n \\\"\\\"\\\"\\n return False\\n else:\\n return self.connection.exists(key)\",\n \"async def _exists(self, key):\\n with await self._connect() as redis:\\n exists = await redis.exists(key)\\n return True if exists > 0 else False\",\n \"def exists(self):\\r\\n return bool(self.bucket.lookup(self.name))\",\n \"def __is_type_instance( self, instance_type ):\\n for index, instance in enumerate(INSTANCE_TYPES):\\n if instance == instance_type:\\n return True\\n return False\",\n \"def node_exists(self):\\n return self.oid is not None\",\n \"def needs_unique_instance(type_):\\n return type_ in unique_instance_types\",\n \"def test_instance_profile_exists(self) -> None:\\n self.assertTrue(self.validate_instance_profile('s3-access-role', is_prod=self.prod_env))\",\n \"def private_instance(self) -> bool:\\n return pulumi.get(self, \\\"private_instance\\\")\",\n \"def isExist(data):\\n return True/False\",\n \"def __instancecheck__(self, instance):\\n\\n if isinstance(instance, ObjCInstance):\\n return bool(instance.isKindOfClass(self))\\n else:\\n return False\",\n \"async def exists(self, payload: TPayload) -> bool:\",\n \"def test_exists_true(self):\\n self.assertTrue(PrepSample.exists(self.sample_id, self.prep_template))\",\n \"def object_exists(self, request):\\n HttpRequest = request.to_http_info(self.api_client.configuration)\\n return self.__make_request(HttpRequest, 'GET', 'ObjectExist')\",\n \"def exists(self):\\n return self.path.exists()\",\n \"def exists1(self, cls, **attr):\\n nodes = getattr(self.graph, getattr(models, cls).element_plural).query(**attr).all()\\n return len(nodes) > 0\",\n \"def does_resource_exist(resource):\\n try:\\n resource.load()\\n return True\\n except botocore.exceptions.ClientError as e:\\n if e.response['Error']['Code'] == 'ValidationError':\\n return False\\n else:\\n raise e\",\n \"def check_model_exists(class_name):\\n if path.exists(settings.get('FALAFEL_DIR') + settings.get('MODELS_DIR') + '/' + class_name + '.py'):\\n return True\\n else:\\n return False\",\n \"def test_exists_false(self):\\n self.assertFalse(PrepSample.exists('Not_a_Sample', self.prep_template))\",\n \"def check_availability(self):\\n pass\",\n \"def exists(self):\\r\\n return os.path.exists(self.full_path)\",\n \"def ResourceExists(self, name):\\n pass\",\n \"def exists(self):\\n try:\\n self.world.find(self.ehandle)\\n except KeyError:\\n return False\\n else:\\n return True\",\n \"def exists(self, arg):\\n raise NotImplementedError\",\n \"def exists(self):\\n return self.properties.get(\\\"Exists\\\", None)\",\n \"def health_check(self):\\n with self.session as session:\\n try:\\n query = session.execute('SELECT 1')\\n except Exception as e:\\n raise UnhealthyCheck()\\n\\n return True\",\n \"def exists(self, Search_ID):\\n if self.get_id(Search_ID) is None:\\n return False\\n else:\\n return True\",\n \"def _exists (self):\\n cursor = self._exec (self.select)\\n return bool (cursor.fetchall ())\",\n \"def entity_exists(entity):\\n return entity.get_by_id(entity.key.id())\",\n \"def _status_exists(self, cls=MySQLStatus):\",\n \"def check(self):\\r\\n self._check_object(self._config.name)\",\n \"def exists (self, uuid):\\n return self.read (uuid) is not None\",\n \"def checkIfExists(self, key):\\n\\t\\t\\t\\n\\t\\tif self.db.get(key):\\n\\t\\t\\treturn self.db.get(key)\\n\\t\\telse:\\n\\t\\t\\treturn False\",\n \"def _exist(self, name):\\r\\n cmd = 'exist(\\\"%s\\\")' % name\\r\\n if not self._engine:\\r\\n msg = \\\"Session is not open\\\"\\r\\n raise Oct2PyError(msg)\\r\\n resp = self._engine.eval(cmd, silent=True).strip()\\r\\n exist = int(resp.split()[-1])\\r\\n if exist == 0:\\r\\n cmd = \\\"class(%s)\\\" % name\\r\\n resp = self._engine.eval(cmd, silent=True).strip()\\r\\n if \\\"error:\\\" in resp:\\r\\n msg = 'Value \\\"%s\\\" does not exist in Octave workspace'\\r\\n raise Oct2PyError(msg % name)\\r\\n else:\\r\\n exist = 2\\r\\n return exist\",\n \"def find_instance(self, name_or_id, ignore_missing=True):\\n return self._find(\\n _instance.Instance, name_or_id, ignore_missing=ignore_missing\\n )\",\n \"def _user_exists(self, username):\\n return self.db.query(User).filter_by(name=username).first() is not None\",\n \"async def exists(self, tag_name):\\n try:\\n if await self.get_id(tag_name):\\n return True\\n except RtbDoesntExists:\\n return False\",\n \"def check(cls, control_instance):\\n pass\",\n \"def object_exists_async(self, request):\\n HttpRequest = request.to_http_info(self.api_client.configuration)\\n return self.__make_request_async(HttpRequest, 'GET', 'ObjectExist')\",\n \"def test_class_exists(self):\\n\\n self.assertTrue(hasattr(Account, self.klass_name))\",\n \"def _id_exists(self):\\n return self.app_id in self.engine.app_list\",\n \"def _check_host_existence(self, hostname: str) -> bool:\\n with self.lock:\\n hosts = self.hosts.all()\\n for host in hosts:\\n if host['hostname'] == hostname:\\n return True\\n return False\",\n \"def test_rebuild_on_host_instance_exists(self):\\n db.instance_update(self.context, self.inst.uuid,\\n {\\\"task_state\\\": task_states.SCHEDULING})\\n self.compute.build_and_run_instance(self.context,\\n self.inst, {}, {}, {}, [], block_device_mapping=[])\\n\\n self.stub_out('nova.virt.fake.FakeDriver.instance_on_disk',\\n lambda *a, **kw: True)\\n patch_remove_allocs = mock.patch(\\n 'nova.scheduler.client.report.SchedulerReportClient.'\\n 'remove_provider_tree_from_instance_allocation')\\n with patch_remove_allocs:\\n self.assertRaises(exception.InstanceExists,\\n lambda: self._rebuild(on_shared_storage=True))\",\n \"def check_exist(self):\\n helper.RbdImageOperator._check_rbd_image(self.real_path)\",\n \"def exists(self):\\n\\n return os.path.exists(self.path)\",\n \"def is_existing_object(did):\\n if not d1_gmn.app.did.is_existing_object(did):\\n raise d1_common.types.exceptions.NotFound(\\n 0,\\n \\\"Identifier is {}. Expected a Persistent ID (PID) for an existing \\\"\\n 'object. id=\\\"{}\\\"'.format(d1_gmn.app.did.classify_identifier(did), did),\\n identifier=did,\\n )\",\n \"def test_exists_false(self):\\n self.assertFalse(PrepTemplate.exists(2))\",\n \"def db_exists(self):\\n \\n with self.connection:\\n c = self.connection.cursor()\\n c.execute(\\\"SELECT EXISTS(SELECT 1 FROM sqlite_master WHERE name=?)\\\", [PUBLICATIONS_TABLE])\\n exists = c.fetchone()\\n if(exists[0] == 1):\\n return True\\n else:\\n return False\",\n \"def __verify__(cls):\\n\\n try:\\n UpstartSystem()\\n return True\\n except Exception as e:\\n try:\\n UpstartSystem(bus=DirectUpstartBus())\\n return True\\n except Exception as e:\\n return False\",\n \"def db_exists(self, db):\\n raise NotImplementedError()\",\n \"def check_if_exists(self, query=None):\\n if query is not None:\\n self.execute_query(query)\\n if len(self.recordset.fetchall()):\\n with allure.step(\\\"The record exists after executing the query\\\"):\\n assert True\\n else:\\n with allure.step(\\\"The records does not exist after executing the query\\\"):\\n assert False\\n\\n return self\",\n \"def exists(self):\\n try:\\n select_template(self.get_paths())\\n return True\\n except TemplateDoesNotExist:\\n return False\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7803968","0.76079255","0.70579004","0.7034308","0.7027742","0.7027742","0.6946009","0.689386","0.67451686","0.6722067","0.6699531","0.66554385","0.6641805","0.66386664","0.66077375","0.6606492","0.6554289","0.65404296","0.6523959","0.651226","0.6500812","0.64558464","0.64506847","0.6441972","0.6436156","0.6418387","0.6367281","0.6344695","0.62431663","0.62420857","0.62333775","0.6228978","0.62250626","0.62123054","0.61950886","0.6187653","0.6175358","0.6147314","0.61357886","0.6121876","0.6115074","0.6107569","0.610003","0.6086873","0.60853505","0.6084571","0.6077383","0.6066391","0.6035221","0.6034998","0.6016372","0.6013513","0.600562","0.60026795","0.6000669","0.5999771","0.5992521","0.5992454","0.5990131","0.5980974","0.5977372","0.59662855","0.59624285","0.5955128","0.59517056","0.5945446","0.5945186","0.59107554","0.589677","0.5883471","0.5879388","0.5877536","0.58615464","0.58579826","0.5849007","0.58486426","0.5843605","0.583891","0.58257645","0.58237267","0.5816429","0.5809835","0.58068645","0.58000964","0.5797685","0.57937247","0.5789591","0.5785443","0.5774939","0.57741225","0.5771598","0.5771545","0.5769111","0.5760891","0.57563037","0.5755106","0.5750927","0.57474494","0.574466","0.57376343"],"string":"[\n \"0.7803968\",\n \"0.76079255\",\n \"0.70579004\",\n \"0.7034308\",\n \"0.7027742\",\n \"0.7027742\",\n \"0.6946009\",\n \"0.689386\",\n \"0.67451686\",\n \"0.6722067\",\n \"0.6699531\",\n \"0.66554385\",\n \"0.6641805\",\n \"0.66386664\",\n \"0.66077375\",\n \"0.6606492\",\n \"0.6554289\",\n \"0.65404296\",\n \"0.6523959\",\n \"0.651226\",\n \"0.6500812\",\n \"0.64558464\",\n \"0.64506847\",\n \"0.6441972\",\n \"0.6436156\",\n \"0.6418387\",\n \"0.6367281\",\n \"0.6344695\",\n \"0.62431663\",\n \"0.62420857\",\n \"0.62333775\",\n \"0.6228978\",\n \"0.62250626\",\n \"0.62123054\",\n \"0.61950886\",\n \"0.6187653\",\n \"0.6175358\",\n \"0.6147314\",\n \"0.61357886\",\n \"0.6121876\",\n \"0.6115074\",\n \"0.6107569\",\n \"0.610003\",\n \"0.6086873\",\n \"0.60853505\",\n \"0.6084571\",\n \"0.6077383\",\n \"0.6066391\",\n \"0.6035221\",\n \"0.6034998\",\n \"0.6016372\",\n \"0.6013513\",\n \"0.600562\",\n \"0.60026795\",\n \"0.6000669\",\n \"0.5999771\",\n \"0.5992521\",\n \"0.5992454\",\n \"0.5990131\",\n \"0.5980974\",\n \"0.5977372\",\n \"0.59662855\",\n \"0.59624285\",\n \"0.5955128\",\n \"0.59517056\",\n \"0.5945446\",\n \"0.5945186\",\n \"0.59107554\",\n \"0.589677\",\n \"0.5883471\",\n \"0.5879388\",\n \"0.5877536\",\n \"0.58615464\",\n \"0.58579826\",\n \"0.5849007\",\n \"0.58486426\",\n \"0.5843605\",\n \"0.583891\",\n \"0.58257645\",\n \"0.58237267\",\n \"0.5816429\",\n \"0.5809835\",\n \"0.58068645\",\n \"0.58000964\",\n \"0.5797685\",\n \"0.57937247\",\n \"0.5789591\",\n \"0.5785443\",\n \"0.5774939\",\n \"0.57741225\",\n \"0.5771598\",\n \"0.5771545\",\n \"0.5769111\",\n \"0.5760891\",\n \"0.57563037\",\n \"0.5755106\",\n \"0.5750927\",\n \"0.57474494\",\n \"0.574466\",\n \"0.57376343\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.77773875"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":17878,"cells":{"query":{"kind":"string","value":"Stops and removes an instance."},"document":{"kind":"string","value":"def singularity_rm(self):\n Client.instances(self.pid, quiet=self.quiet).stop()"},"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_instance(self):\n instance_id = self._choose_among_running_instances()\n\n # Cancel\n if not instance_id:\n print 'Operation cancelled'\n return\n\n print '# Stopping the instance \"%s\"' % instance_id\n self.compute.stop_instance(instance_id)\n print 'The instance has been stopped'","def destroy(self, instance):\n # TODO(imsplitbit): This needs to check the state of the VE\n # and if it isn't stopped it needs to stop it first. This is\n # an openvz limitation that needs to be worked around.\n # For now we will assume it needs to be stopped prior to destroying it.\n self._stop(instance)\n\n try:\n _, err = utils.execute('sudo', 'vzctl', 'destroy', instance['id'])\n if err:\n LOG.error(err)\n except ProcessExecutionError:\n raise exception.Error('Error destroying %d' % instance['id'])","def stop(instance):\n if instance.state == STOPPED:\n return\n\n Queue.objects.add(function=\"terminate\", instance=instance)","def perform_destroy(self, instance):\n pass","async def stop(self, now=False):\n alive = await self.remote_signal(15)\n\n try:\n self.stop_ec2_instance(self.ec2_instance_id) # function that uses boto3 to stop an instance based on instance_id\n except Exception as e:\n self.log.error(\"Error in terminating instance\") # easy to save the instance id when you start the instance\n self.log.error(str(e)) # this will print the error on our JupyterHub process' output\n\n self.clear_state()","def stop_instance():\n send_line('stop instance')\n os.system(f'gcloud compute instances stop {os.uname()[1]} --zone us-east1-b')","def _stop(self, instance):\n try:\n _, err = utils.execute('sudo', 'vzctl', 'stop', instance['id'])\n if err:\n LOG.error(err)\n except ProcessExecutionError:\n raise exception.Error('Failed to stop %s' % instance['id'])\n\n # Update instance state\n try:\n db.instance_set_state(context.get_admin_context(),\n instance['id'],\n power_state.SHUTDOWN)\n except exception.DBError as err:\n LOG.error(err)\n raise exception.Error('Failed to update db for %s' % instance['id'])\n \n return True","def stop_instance(InstanceId=None, Force=None):\n pass","def delete_instance(cls, args, config):\n if len(args) == 0:\n print \"Usage: molns instance delete INSTANCE_ID\"\n return\n try:\n instance_id = int(args[0])\n except ValueError:\n print \"instance ID must be a integer\"\n return\n instance = config.get_instance_by_id(instance_id)\n if instance is None:\n print \"instance not found\"\n else:\n config.delete_instance(instance)\n print \"instance {0} deleted\".format(instance_id)","def terminate_instance(self):\n # connect to ec2\n try:\n ec2_region = [r for r in boto.ec2.regions() if r.name == self._region][0]\n except indexerror:\n print >> sys.stderr, 'unknown region: %s' % self._region\n exit(2)\n ec2_connection = ec2_region.connect()\n\n #import code; code.interact(local=locals())\n instances = reduce(list.__add__, [reservation.instances for reservation in ec2_connection.get_all_instances()])\n name_matches = [i for i in instances\n if i.tags.get('Name', None) == self._instance_name and i.state == 'running']\n\n if (not name_matches):\n raise ValueError('No instance found with name %s' % self._instance_name)\n elif len(name_matches) > 1:\n raise ValueError('Multiple instances found with name %s' % self._instance_name)\n\n instance = name_matches[0]\n\n ec2_connection.terminate_instances(instance_ids=[instance.id])","def test_remove_vm(self, instance_name):\n self.instances.pop(instance_name)","def do_destroy(self, *args):\n args = [ele for ele in args[0].split(' ')]\n if args[0] == '':\n print(\"** class name missing **\")\n return\n if args[0] not in self.list_classes:\n print(\"** class doesn't exist **\")\n return\n if len(args) != 2:\n print(\"** instance id missing **\")\n return\n\n storage.reload()\n dict_objs = storage.all()\n if dict_objs is None or dict_objs == []:\n print(\"** no instance found **\")\n return\n\n key = \"{}.{}\".format(args[0], args[1])\n if key in dict_objs.keys():\n del dict_objs[key]\n storage.save()\n else:\n print(\"** no instance found **\")","def remove_instance(instance_class, instance, verbose = False):\n if instance in instance_class.instances.values():\n if verbose:\n print 'removing: {0}'.format(instance)\n print ' by id: {0}'.format(instance.id)\n del instance_class.instances[instance.id]\n if verbose:\n print ' {0}.instances: {1}'.format(instance_class.__class__.__name__, \n instance_class.instances)\n print ' by name: {0}'.format(instance.name)\n del instance_class.instances_by_name[instance.name]\n if verbose:\n print ' {0}.instances_by_name: {1}'.format(instance_class.__class__.__name__, \n instance_class.instances_by_name)\n\n # if no more remaining instances, remove instance_class from Base_Instance.instance_classes set\n if not instance_class.instances.values():\n Base_Instance.instance_classes.remove(instance_class)","def stop_instance(self, args):\n message = MessageClass()\n region = args[\"Region\"]\n instanceid = args[\"Instance-ID\"].replace(\",\", \" \").split()\n\n # Boto3 resource creation by providing the access_id and access_secret\n ec2 = boto3.resource(service_name='ec2', region_name=region, api_version=None, use_ssl=True,\n verify=None, endpoint_url=None, aws_access_key_id=self.aws_access_key,\n aws_secret_access_key=self.aws_secret_token, aws_session_token=None,\n config=None)\n ec2.instances.filter(InstanceIds=instanceid).stop()\n\n message.message_text = \"Instance Stopped\"\n return message.to_json()","def cancel_instance(self, instance_id):\r\n return self.guest.deleteObject(id=instance_id)","def delete(self):\n request_pb = messages_v2_pb2.DeleteInstanceRequest(name=self.name)\n # We expect a `google.protobuf.empty_pb2.Empty`\n self._client._instance_stub.DeleteInstance(\n request_pb, self._client.timeout_seconds)","def endInstance(self):\n if self.currentInstance is None:\n return\n allInstances = self.root.findall('.instances')[0].append(self.currentInstance)\n self.currentInstance = None","def stop(self):\n with self._lock:\n if not self.stopped():\n self._started = None\n getattr(self.factory, 'stop_' + self.class_name())(self)","def do_destroy(self, arg):\n arg_list = arg.split(\" \") if type(arg) == str else arg\n if not arg:\n print(\"** class name missing **\")\n return\n if arg_list[0] not in HBNBCommand.class_list:\n print(\"** class doesn't exist **\")\n return\n if len(arg_list) < 2:\n print(\"** instance id missing **\")\n return\n key = arg_list[0] + \".\" + arg_list[1]\n if key in storage.all():\n del storage.all()[key]\n storage.save()\n return\n print(\"** no instance found **\")","def remove(self):\n instance = self.get_object() \n instance.delete() \n return self.response(status='Successfully Delete')","def do_destroy(self, arg):\n args = shlex.split(arg)\n if len(args) == 0:\n print(\"** class name missing **\")\n elif args[0] in class_type:\n if len(args) > 1:\n key = args[0] + \".\" + args[1]\n if key in models.storage.all():\n models.storage.all().pop(key)\n models.storage.save()\n else:\n print(\"** no instance found **\")\n else:\n print(\"** instance id missing **\")\n else:\n print(\"** class doesn't exist **\")","def kill_instance(self):\n logging.info(\"StarterManager: Killing: %s\", str(self.default_starter_args + self.arguments))\n self.instance.kill()\n try:\n logging.info(str(self.instance.wait(timeout=45)))\n self.add_logfile_to_report()\n except Exception as ex:\n raise Exception(\"Failed to KILL the starter instance? \" + repr(self)) from ex\n\n logging.info(\"StarterManager: Instance now dead.\")\n self.instance = None","def do_destroy(self, arg):\n if len(arg) == 0:\n print(\"** class name missing **\")\n return\n coms = tuple(arg.split())\n if coms[0] not in self.cls:\n print(\"** class doesn't exist **\")\n elif len(coms) < 2:\n print(\"** instance id missing **\")\n else:\n obj = coms[0] + \".\" + coms[1]\n if obj not in storage.all().keys():\n print(\"** no instance found **\")\n else:\n del storage.all()[obj]\n storage.save()","def __del__(self):\n if self.running:\n self.stop()","def delete_instance(InstanceId=None, DeleteElasticIp=None, DeleteVolumes=None):\n pass","def help_destroy(self):\n print(\"delete an instance based on the class name and id\")","def remove_instance(self, instance_selfLink):\n remove_instance_operation = self.compute.targetPools().removeInstance(\n project=self.project,\n region=self.region,\n targetPool=self.target_pool_name,\n body={\n 'instances': [{\n 'instance': instance_selfLink}]\n }).execute()\n self.operations.wait_for_region_operation(\n remove_instance_operation['name'])\n return remove_instance_operation","def do_destroy(self, line):\n args = line.split()\n\n if not args:\n print(\"** class name missing **\")\n elif args[0] not in HBNBCommand.class_list:\n print(\"** class doesn't exist **\")\n elif len(args) < 2:\n print(\"** instance id missing **\")\n else:\n key = args[0] + \".\" + args[1]\n dict_objects = storage.all()\n obj = dict_objects.get(key)\n if obj:\n dict_objects.pop(key)\n storage.save()\n else:\n print(\"** no instance found **\")","def disassociate(self, endpoint_name=None, instance_id=None):\n if instance_id is None:\n raise Exception(\"Instance required!\")\n if endpoint_name is None:\n self.request('/v1.1/endpoint/instances/%s' % instance_id, 'DELETE')\n else:\n self.request('/v1.1/endpoints/%s/instances/%s' %\n (endpoint_name, instance_id), 'DELETE')","def destroy(self, class_name, inst_id, stored_objects):\n instance = \"{}.{}\".format(class_name, inst_id)\n if instance not in stored_objects:\n print(\"** no instance found **\")\n else:\n del stored_objects[instance]","def remove_instance(self, instance_id, load_balancer_name): # type: (str, str) -> None\n elb = self._get_client()\n elb.deregister_instances_from_load_balancer(\n LoadBalancerName=load_balancer_name,\n Instances=[{'InstanceId': instance_id}]\n )\n self._logger.info('Removed instance %s from load balancer %s', instance_id, load_balancer_name)","def destroy(self):\n if not self.status:\n self.class_logger.info(\"Skip iperf tg id:{0}({1}) destroying because \"\n \"it's has already Off status.\".format(self.id, self.name))\n return\n self.stop()\n\n self.sanitize()","def disconnect(self):\r\n # TODO: Should destroy all instances which have been started dynamically\r\n raise NotImplementedError","def destroy(self, request, *args, **kwargs):\n instance = self.get_object()\n\n count_deleted = self.perform_destroy(instance)[0]\n if count_deleted == 0:\n _logger.warning('failed to delete instance id: %s of type: %s',\n instance.id, instance.__class__)\n\n return delete_response(request, count_deleted)","def deregister_instance(InstanceId=None):\n pass","def stop(self):\n\n self.keep_running = False","def do_destroy(self, args):\n args = args.split()\n if len(args) == 0:\n print(\"** class name missing **\")\n return\n if len(args) == 1:\n print(\"** instance id missing **\")\n return\n if args[0] not in HBNBCommand.class_check:\n print(\"** class doesn't exist **\")\n return\n\n all_objs = storage.all()\n key = args[0] + '.' + args[1]\n if key in all_objs:\n all_objs.pop(key)\n storage.save()\n else:\n print(\"** no instance found **\")","def stop_instance(tcserver_dir, instance_name=\"instance1\"):\n print(\"Stopping a tcServer instance...\")\n\n pushdir(tcserver_dir)\n subprocess.call([\"./tcruntime-ctl.sh\", instance_name, \"stop\"])\n popdir()","async def stop(self):\n await self.node._send(op='stop', guildId=self.guild_id)\n self.current = None","def delete(self):\n self.stop()\n try:\n self.skil.api.delete_model(self.deployment.id, self.model.id)\n except api_exception as e:\n self.skil.printer.pprint(\n \">>> Exception when calling delete_model_instance: %s\\n\" % e)","def delete_app_instance(self, instance_id):\n self.stop_app_instance(instance_id)\n aic = self.get_app_instances_configs(instance_id=instance_id)\n # invoking on_uninstall callback , so app can run cleanup routines .\n ai_obj = self.get_app_instance_obj(instance_id)\n try:\n if hasattr(ai_obj,\"on_uninstall\"):\n ai_obj.on_uninstall()\n except Exception as ex:\n log.exception(ex)\n if aic:\n self.app_instances_configs.remove(aic[0])\n self.serialize_instances_config()","def remove_object(self, instance, using=None, **kwargs):\n backend = self.get_backend(using)\n\n if backend is not None:\n backend.remove(instance, **kwargs)","def do_destroy(self, args):\n args = shlex.split(args)\n dicti = storage.all()\n if not args:\n print(\"** class name missing **\")\n elif not args[0] in name_of_class:\n print(\"** class doesn't exist **\")\n elif len(args) == 1:\n print(\"** instance id missing **\")\n elif \"{}.{}\".format(args[0], args[1]) in dicti:\n dicti.pop(\"{}.{}\".format(args[0], args[1]))\n storage.save()\n else:\n print(\"** no instance found **\")","def unassign_instance(InstanceId=None):\n pass","def stop(self):\n self.__running = False","def stop_instance(instance_id):\n # Instantiate the service resource object\n ec2_resource = session.resource('ec2', region_name=region)\n try:\n # Stop an instance\n response = ec2_resource.Instance(instance_id).stop(DryRun=False)\n print(response)\n print(\"\\nSuccessfully stopping instance: \", instance_id)\n except botocore.exceptions.ClientError as e:\n if e.response['Error']['Code'] == \"InvalidInstanceID.Malformed\":\n print(\"Error: Invalid instance id!!\")\n else:\n raise\n return","def delete(self, instance: object) -> None:\n if self._warn_on_events:\n self._flush_warning(\"Session.delete()\")\n\n try:\n state = attributes.instance_state(instance)\n except exc.NO_STATE as err:\n raise exc.UnmappedInstanceError(instance) from err\n\n self._delete_impl(state, instance, head=True)","def do_destroy(self, arg):\n arg = arg.split()\n try:\n args = arg[0] + \".\" + arg[1]\n except:\n pass\n objects = storage.all()\n if len(arg) is 0:\n print(\"** class name missing **\")\n elif len(arg) == 1 and arg[0] in self.dict.keys():\n print(\"** instance id missing **\")\n elif arg[0] not in self.dict.keys():\n print(\"** class doesn't exist **\")\n elif args not in objects:\n print(\"** no instance found **\")\n else:\n del objects[args]\n storage.save()","def stop(self):\n self.alive = False","def stop(self):\n self.alive = False","def remove(self):\n return self.delete_instance()","def stop(self):\n self.api.stop()","def stop(self, **kwargs):\n return self.client.api.stop(self.id, **kwargs)","def destroy():\n # instance first\n old_vm = _existing.vm\n _destroy_resource('vm')\n if not dry and old_vm is not None:\n # Wait for instance to be fully terminated before carrying on or we will have\n # dependency issues.\n print('Waiting for instance to be terminated before deleting other resources...')\n old_vm.wait_until_terminated()\n time.sleep(1) # One would think that wait for terminated should be enough...\n\n _destroy_resource('disk')\n\n # detach before destroy\n _detach_vpc_igw(vpc=_existing.vpc, igw=_existing.igw)\n _destroy_resource('igw')\n\n # sg and sub before vpc\n _destroy_resource('sg')\n _destroy_resource('sub')\n\n _destroy_resource('vpc')","def ec2_stop(resource, metadata):\n instances = resource.instances.filter(\n Filters=[{'Name': 'instance-state-name', 'Values': ['running']},\n {'Name': 'tag:Name', 'Values': [metadata['fqdn']]}, ])\n\n for instance in instances:\n print(\"Terminating vm id {0} name {1}\".format(instance.id, instance.tags[0]['Value']))\n # resource.instances.filter(InstanceIds=[instance.id]).stop()\n resource.instances.filter(InstanceIds=[instance.id]).terminate()","def destory_instance(InstanceIds):\n client = boto3.client('ec2')\n # TODO: delete an EC2 instance\n # Wait for the instance to enter the running state\n # Reload the instance attributes\n\n try:\n response = client.terminate_instances(\n InstanceIds=InstanceIds,\n DryRun=False\n )\n except ClientError as e:\n print(e)\n\n return","def stop(self):\n self._alive = False","def do_destroy(self, arg):\n args = arg.split()\n if len(args) == 0:\n print(\"** class name missing **\")\n return\n elif len(args) < 2 and args[0] in self.class_dict:\n print(\"** instance id missing **\")\n return\n elif len(args) < 2:\n print(\"** class name missing **\")\n return\n\n object_dict = storage.all()\n if args[0] in self.class_dict:\n for full_key in object_dict:\n key = full_key.split(\".\")\n if key[1] == args[1]:\n del object_dict[full_key]\n storage.save()\n return\n print(\"** no instance found **\")\n else:\n print(\"** class doesn't exist **\")","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 terminate_ow_instance(ow, ow_instance_id):\n log.info(\"terminate_ow_instance( %s )\", ow_instance_id)\n try:\n ow.stop_instance(InstanceId=ow_instance_id)\n except Exception, e:\n print(e)\n log.info(e)\n sys.exit()\n while True:\n data = ow.describe_instances(InstanceIds=[ow_instance_id])['Instances']\n raw = json.dumps(data)\n ow_instance_json = json.loads(raw)\n print(ow_instance_json[0]['InstanceId'], ow_instance_json[0]['Status'])\n log.info(\"%s %s\", ow_instance_json[0]['InstanceId'],\n ow_instance_json[0]['Status'])\n if ow_instance_json[0]['Status'] == \"stopped\":\n print(ow_instance_json[0]['InstanceId'],\n ow_instance_json[0]['Status'])\n log.info(\"%s %s\", ow_instance_json[0]['InstanceId'],\n ow_instance_json[0]['Status'])\n response = ow.delete_instance(InstanceId=ow_instance_id)\n print(response)\n log.info(\"Delete instance = %s\", response)\n break\n else:\n time.sleep(60)\n continue","def stop(self):\n self._running = False","def stop(self):\n self._running = False","def stop(self, remove=True):\n if not remove:\n self.container.stop()\n else:\n self.container.remove(v=True, force=True)","def stop(self):\n\n self.active = False","def __delete__(self, obj):\n self._instances.pop(obj, None)","def stop(self) -> None:\n for instance in self.instances:\n instance.listener = None\n instance.stop()","def __del__(self):\n self.stop()","def __del__(self):\n self.stop()","def __del__(self):\n self.stop()","def __del__(self):\n self.stop()","def __delete__(self, instance):\n self.session.close()","def terminate_instances(self, props):\n return self._vm_async_apply(props, 'delete')","def stop(self):\n self._schedule(0, 0)\n self._started = False","def destroy_VM_instance(settings, ip_address):\n print(\"Terminating VM instance %s\" % ip_address)\n if _is_instance_running(settings, ip_address, ip_given=True):\n try:\n if not confirm_teardown():\n return\n instance = get_this_instance(settings, ip_address,\n ip_given=True)\n instance_id = instance.id\n ip_address = get_instance_ip(instance)\n delete_chef_node_client(settings, instance_id, ip_address)\n connection = create_cloud_connection(settings)\n connection.terminate_instances([instance_id])\n _wait_for_instance_to_terminate(settings, ip_address)\n except Exception:\n traceback.print_exc(file=sys.stdout)\n raise\n else:\n print \"VM instance with IP %s doesn't exist\" % ip_address","def perform_destroy(self, instance):\n instance.is_active = False\n instance.save()","def stop(self):\n self._log.info(\"Stopping\")\n self._running.clear()","def teardown(self):\n # Only terminate if instance is running\n if self.instance:\n instance_status = aws.check_instance_status(self.config, self.instance.id)\n if instance_status == \"running\":\n aws.terminate_ec2_instance(self.config, self.instance.id)\n super().teardown()","def delete_foundation_instance(instance_id):\n locked_instance = get_instance_or_404(instance_id)\n with locked_instance.lock:\n try:\n locked_instance.instance.stop_notifications()\n except AttributeError:\n pass\n STORE.pop(instance_id)\n return '', 204","def stop_instance(ec2_client, instances):\n # get a list of instance ids\n instances_ids = [i.instance_id for i in instances]\n \n # start the instances\n ec2_client.stop_instances(InstanceIds=instances_ids)\n \n # wait till instance is stopped\n waiter = ec2_client.get_waiter(\"instance_stopped\")\n waiter.wait(InstanceIds=instances_ids)\n print(\"\\n===EC2 instance has stopped!\")","def stop(self):\n self.scion_sh('stop')","def do_destroy(self, line):\n try:\n tokens = split(line)\n except ValueError:\n return None\n if len(tokens) < 1:\n print(\"** class name missing **\")\n else:\n objects = models.storage.all()\n cls = models.getmodel(tokens[0])\n if cls is None:\n print(\"** class doesn't exist **\")\n elif len(tokens) < 2:\n print(\"** instance id missing **\")\n elif \".\".join(tokens[:2]) not in objects:\n print(\"** no instance found **\")\n else:\n del objects[\".\".join(tokens[:2])]\n models.storage.save()","def kill_vrouter_instance(self):\n # Stop vrouter\n if (self.vr_args['vtest_only']):\n self.logger.info(\"Stopping vrouter pid=\" + str(self.pid))\n if (self.pid > 0):\n try:\n os.kill(self.pid, signal.SIGTERM)\n time.sleep(1)\n except OSError as e:\n self.logger.error(e)","def stop(self):\n self.stopped = True","def stop(self):\n self.stopped = True","def stop(self):\r\n self.running = False","def stop(self):\r\n self.running = False","def stop_ec2_instance(client, instance_id, hibernate=False):\n\n response = client.stop_instances(\n InstanceIds=[instance_id],\n )\n return response","def stop(self):\n self.killed = True","def stop(self) -> None:\n self._running = False","def delete(self):\n self.id = uuid4()\n DataStore.remove_instance(self)","async def stop(self):\n await self._bot.lavalink.ws.send(op='stop', guildId=self.guild_id)\n self.current = None","def do_destroy(self, args):\n args = shlex.split(args)\n if len(args) == 0:\n print(\"** class name missing **\")\n return False\n if args[0] in classes:\n if len(args) > 1:\n key = args[0] + \".\" + args[1]\n if key in models.storage.all():\n models.storage.all().pop(key)\n models.storage.save()\n else:\n print(\"** no instance found **\")\n else:\n print(\"** instance id missing **\")\n else:\n print(\"** class doesn't exist **\")","def do_destroy(self, line):\n list_line = line.split(' ')\n if line == \"\":\n print(\"** class name missing **\")\n elif list_line[0] not in HBNBCommand.classes.keys():\n print(\"** class doesn't exist **\")\n elif len(list_line) < 2:\n print(\"** instance id missing **\")\n elif list_line[0] + '.' + list_line[1] not in \\\n models.storage.all().keys():\n print(\"** no instance found **\")\n else:\n models.storage.all().pop(list_line[0] + '.' + list_line[1], None)\n models.storage.save()","def delete(self):\n self._instance.delete()\n self._instance = None\n self._data_defs = []","def soft_delete(self, instance):\n self.destroy(instance)","def stop(self, name=None, **kwargs):\n result = None\n compute_service = self._get_compute_service()\n _operation = None\n if name is None:\n return\n try:\n\n project_id = kwargs.pop('project_id', self.auth[\"project_id\"])\n zone = kwargs.pop('zone', self.default[\"zone\"])\n\n _operation = compute_service.instances().stop(\n project=project_id,\n zone=zone,\n instance=name).execute()\n\n self._wait_for_operation(compute_service,\n _operation,\n project_id,\n zone,\n name)\n\n # Get the instance details to update DB.\n result = self.__info(name, displayType=\"vm\")\n\n except Exception as se:\n print(se)\n if type(se) == HttpError:\n Console.error(\n f'Unable to stop instance {name}. Reason: {se._get_reason()}')\n else:\n Console.error(f'Unable to stop instance {name}.')\n\n return result"],"string":"[\n \"def stop_instance(self):\\n instance_id = self._choose_among_running_instances()\\n\\n # Cancel\\n if not instance_id:\\n print 'Operation cancelled'\\n return\\n\\n print '# Stopping the instance \\\"%s\\\"' % instance_id\\n self.compute.stop_instance(instance_id)\\n print 'The instance has been stopped'\",\n \"def destroy(self, instance):\\n # TODO(imsplitbit): This needs to check the state of the VE\\n # and if it isn't stopped it needs to stop it first. This is\\n # an openvz limitation that needs to be worked around.\\n # For now we will assume it needs to be stopped prior to destroying it.\\n self._stop(instance)\\n\\n try:\\n _, err = utils.execute('sudo', 'vzctl', 'destroy', instance['id'])\\n if err:\\n LOG.error(err)\\n except ProcessExecutionError:\\n raise exception.Error('Error destroying %d' % instance['id'])\",\n \"def stop(instance):\\n if instance.state == STOPPED:\\n return\\n\\n Queue.objects.add(function=\\\"terminate\\\", instance=instance)\",\n \"def perform_destroy(self, instance):\\n pass\",\n \"async def stop(self, now=False):\\n alive = await self.remote_signal(15)\\n\\n try:\\n self.stop_ec2_instance(self.ec2_instance_id) # function that uses boto3 to stop an instance based on instance_id\\n except Exception as e:\\n self.log.error(\\\"Error in terminating instance\\\") # easy to save the instance id when you start the instance\\n self.log.error(str(e)) # this will print the error on our JupyterHub process' output\\n\\n self.clear_state()\",\n \"def stop_instance():\\n send_line('stop instance')\\n os.system(f'gcloud compute instances stop {os.uname()[1]} --zone us-east1-b')\",\n \"def _stop(self, instance):\\n try:\\n _, err = utils.execute('sudo', 'vzctl', 'stop', instance['id'])\\n if err:\\n LOG.error(err)\\n except ProcessExecutionError:\\n raise exception.Error('Failed to stop %s' % instance['id'])\\n\\n # Update instance state\\n try:\\n db.instance_set_state(context.get_admin_context(),\\n instance['id'],\\n power_state.SHUTDOWN)\\n except exception.DBError as err:\\n LOG.error(err)\\n raise exception.Error('Failed to update db for %s' % instance['id'])\\n \\n return True\",\n \"def stop_instance(InstanceId=None, Force=None):\\n pass\",\n \"def delete_instance(cls, args, config):\\n if len(args) == 0:\\n print \\\"Usage: molns instance delete INSTANCE_ID\\\"\\n return\\n try:\\n instance_id = int(args[0])\\n except ValueError:\\n print \\\"instance ID must be a integer\\\"\\n return\\n instance = config.get_instance_by_id(instance_id)\\n if instance is None:\\n print \\\"instance not found\\\"\\n else:\\n config.delete_instance(instance)\\n print \\\"instance {0} deleted\\\".format(instance_id)\",\n \"def terminate_instance(self):\\n # connect to ec2\\n try:\\n ec2_region = [r for r in boto.ec2.regions() if r.name == self._region][0]\\n except indexerror:\\n print >> sys.stderr, 'unknown region: %s' % self._region\\n exit(2)\\n ec2_connection = ec2_region.connect()\\n\\n #import code; code.interact(local=locals())\\n instances = reduce(list.__add__, [reservation.instances for reservation in ec2_connection.get_all_instances()])\\n name_matches = [i for i in instances\\n if i.tags.get('Name', None) == self._instance_name and i.state == 'running']\\n\\n if (not name_matches):\\n raise ValueError('No instance found with name %s' % self._instance_name)\\n elif len(name_matches) > 1:\\n raise ValueError('Multiple instances found with name %s' % self._instance_name)\\n\\n instance = name_matches[0]\\n\\n ec2_connection.terminate_instances(instance_ids=[instance.id])\",\n \"def test_remove_vm(self, instance_name):\\n self.instances.pop(instance_name)\",\n \"def do_destroy(self, *args):\\n args = [ele for ele in args[0].split(' ')]\\n if args[0] == '':\\n print(\\\"** class name missing **\\\")\\n return\\n if args[0] not in self.list_classes:\\n print(\\\"** class doesn't exist **\\\")\\n return\\n if len(args) != 2:\\n print(\\\"** instance id missing **\\\")\\n return\\n\\n storage.reload()\\n dict_objs = storage.all()\\n if dict_objs is None or dict_objs == []:\\n print(\\\"** no instance found **\\\")\\n return\\n\\n key = \\\"{}.{}\\\".format(args[0], args[1])\\n if key in dict_objs.keys():\\n del dict_objs[key]\\n storage.save()\\n else:\\n print(\\\"** no instance found **\\\")\",\n \"def remove_instance(instance_class, instance, verbose = False):\\n if instance in instance_class.instances.values():\\n if verbose:\\n print 'removing: {0}'.format(instance)\\n print ' by id: {0}'.format(instance.id)\\n del instance_class.instances[instance.id]\\n if verbose:\\n print ' {0}.instances: {1}'.format(instance_class.__class__.__name__, \\n instance_class.instances)\\n print ' by name: {0}'.format(instance.name)\\n del instance_class.instances_by_name[instance.name]\\n if verbose:\\n print ' {0}.instances_by_name: {1}'.format(instance_class.__class__.__name__, \\n instance_class.instances_by_name)\\n\\n # if no more remaining instances, remove instance_class from Base_Instance.instance_classes set\\n if not instance_class.instances.values():\\n Base_Instance.instance_classes.remove(instance_class)\",\n \"def stop_instance(self, args):\\n message = MessageClass()\\n region = args[\\\"Region\\\"]\\n instanceid = args[\\\"Instance-ID\\\"].replace(\\\",\\\", \\\" \\\").split()\\n\\n # Boto3 resource creation by providing the access_id and access_secret\\n ec2 = boto3.resource(service_name='ec2', region_name=region, api_version=None, use_ssl=True,\\n verify=None, endpoint_url=None, aws_access_key_id=self.aws_access_key,\\n aws_secret_access_key=self.aws_secret_token, aws_session_token=None,\\n config=None)\\n ec2.instances.filter(InstanceIds=instanceid).stop()\\n\\n message.message_text = \\\"Instance Stopped\\\"\\n return message.to_json()\",\n \"def cancel_instance(self, instance_id):\\r\\n return self.guest.deleteObject(id=instance_id)\",\n \"def delete(self):\\n request_pb = messages_v2_pb2.DeleteInstanceRequest(name=self.name)\\n # We expect a `google.protobuf.empty_pb2.Empty`\\n self._client._instance_stub.DeleteInstance(\\n request_pb, self._client.timeout_seconds)\",\n \"def endInstance(self):\\n if self.currentInstance is None:\\n return\\n allInstances = self.root.findall('.instances')[0].append(self.currentInstance)\\n self.currentInstance = None\",\n \"def stop(self):\\n with self._lock:\\n if not self.stopped():\\n self._started = None\\n getattr(self.factory, 'stop_' + self.class_name())(self)\",\n \"def do_destroy(self, arg):\\n arg_list = arg.split(\\\" \\\") if type(arg) == str else arg\\n if not arg:\\n print(\\\"** class name missing **\\\")\\n return\\n if arg_list[0] not in HBNBCommand.class_list:\\n print(\\\"** class doesn't exist **\\\")\\n return\\n if len(arg_list) < 2:\\n print(\\\"** instance id missing **\\\")\\n return\\n key = arg_list[0] + \\\".\\\" + arg_list[1]\\n if key in storage.all():\\n del storage.all()[key]\\n storage.save()\\n return\\n print(\\\"** no instance found **\\\")\",\n \"def remove(self):\\n instance = self.get_object() \\n instance.delete() \\n return self.response(status='Successfully Delete')\",\n \"def do_destroy(self, arg):\\n args = shlex.split(arg)\\n if len(args) == 0:\\n print(\\\"** class name missing **\\\")\\n elif args[0] in class_type:\\n if len(args) > 1:\\n key = args[0] + \\\".\\\" + args[1]\\n if key in models.storage.all():\\n models.storage.all().pop(key)\\n models.storage.save()\\n else:\\n print(\\\"** no instance found **\\\")\\n else:\\n print(\\\"** instance id missing **\\\")\\n else:\\n print(\\\"** class doesn't exist **\\\")\",\n \"def kill_instance(self):\\n logging.info(\\\"StarterManager: Killing: %s\\\", str(self.default_starter_args + self.arguments))\\n self.instance.kill()\\n try:\\n logging.info(str(self.instance.wait(timeout=45)))\\n self.add_logfile_to_report()\\n except Exception as ex:\\n raise Exception(\\\"Failed to KILL the starter instance? \\\" + repr(self)) from ex\\n\\n logging.info(\\\"StarterManager: Instance now dead.\\\")\\n self.instance = None\",\n \"def do_destroy(self, arg):\\n if len(arg) == 0:\\n print(\\\"** class name missing **\\\")\\n return\\n coms = tuple(arg.split())\\n if coms[0] not in self.cls:\\n print(\\\"** class doesn't exist **\\\")\\n elif len(coms) < 2:\\n print(\\\"** instance id missing **\\\")\\n else:\\n obj = coms[0] + \\\".\\\" + coms[1]\\n if obj not in storage.all().keys():\\n print(\\\"** no instance found **\\\")\\n else:\\n del storage.all()[obj]\\n storage.save()\",\n \"def __del__(self):\\n if self.running:\\n self.stop()\",\n \"def delete_instance(InstanceId=None, DeleteElasticIp=None, DeleteVolumes=None):\\n pass\",\n \"def help_destroy(self):\\n print(\\\"delete an instance based on the class name and id\\\")\",\n \"def remove_instance(self, instance_selfLink):\\n remove_instance_operation = self.compute.targetPools().removeInstance(\\n project=self.project,\\n region=self.region,\\n targetPool=self.target_pool_name,\\n body={\\n 'instances': [{\\n 'instance': instance_selfLink}]\\n }).execute()\\n self.operations.wait_for_region_operation(\\n remove_instance_operation['name'])\\n return remove_instance_operation\",\n \"def do_destroy(self, line):\\n args = line.split()\\n\\n if not args:\\n print(\\\"** class name missing **\\\")\\n elif args[0] not in HBNBCommand.class_list:\\n print(\\\"** class doesn't exist **\\\")\\n elif len(args) < 2:\\n print(\\\"** instance id missing **\\\")\\n else:\\n key = args[0] + \\\".\\\" + args[1]\\n dict_objects = storage.all()\\n obj = dict_objects.get(key)\\n if obj:\\n dict_objects.pop(key)\\n storage.save()\\n else:\\n print(\\\"** no instance found **\\\")\",\n \"def disassociate(self, endpoint_name=None, instance_id=None):\\n if instance_id is None:\\n raise Exception(\\\"Instance required!\\\")\\n if endpoint_name is None:\\n self.request('/v1.1/endpoint/instances/%s' % instance_id, 'DELETE')\\n else:\\n self.request('/v1.1/endpoints/%s/instances/%s' %\\n (endpoint_name, instance_id), 'DELETE')\",\n \"def destroy(self, class_name, inst_id, stored_objects):\\n instance = \\\"{}.{}\\\".format(class_name, inst_id)\\n if instance not in stored_objects:\\n print(\\\"** no instance found **\\\")\\n else:\\n del stored_objects[instance]\",\n \"def remove_instance(self, instance_id, load_balancer_name): # type: (str, str) -> None\\n elb = self._get_client()\\n elb.deregister_instances_from_load_balancer(\\n LoadBalancerName=load_balancer_name,\\n Instances=[{'InstanceId': instance_id}]\\n )\\n self._logger.info('Removed instance %s from load balancer %s', instance_id, load_balancer_name)\",\n \"def destroy(self):\\n if not self.status:\\n self.class_logger.info(\\\"Skip iperf tg id:{0}({1}) destroying because \\\"\\n \\\"it's has already Off status.\\\".format(self.id, self.name))\\n return\\n self.stop()\\n\\n self.sanitize()\",\n \"def disconnect(self):\\r\\n # TODO: Should destroy all instances which have been started dynamically\\r\\n raise NotImplementedError\",\n \"def destroy(self, request, *args, **kwargs):\\n instance = self.get_object()\\n\\n count_deleted = self.perform_destroy(instance)[0]\\n if count_deleted == 0:\\n _logger.warning('failed to delete instance id: %s of type: %s',\\n instance.id, instance.__class__)\\n\\n return delete_response(request, count_deleted)\",\n \"def deregister_instance(InstanceId=None):\\n pass\",\n \"def stop(self):\\n\\n self.keep_running = False\",\n \"def do_destroy(self, args):\\n args = args.split()\\n if len(args) == 0:\\n print(\\\"** class name missing **\\\")\\n return\\n if len(args) == 1:\\n print(\\\"** instance id missing **\\\")\\n return\\n if args[0] not in HBNBCommand.class_check:\\n print(\\\"** class doesn't exist **\\\")\\n return\\n\\n all_objs = storage.all()\\n key = args[0] + '.' + args[1]\\n if key in all_objs:\\n all_objs.pop(key)\\n storage.save()\\n else:\\n print(\\\"** no instance found **\\\")\",\n \"def stop_instance(tcserver_dir, instance_name=\\\"instance1\\\"):\\n print(\\\"Stopping a tcServer instance...\\\")\\n\\n pushdir(tcserver_dir)\\n subprocess.call([\\\"./tcruntime-ctl.sh\\\", instance_name, \\\"stop\\\"])\\n popdir()\",\n \"async def stop(self):\\n await self.node._send(op='stop', guildId=self.guild_id)\\n self.current = None\",\n \"def delete(self):\\n self.stop()\\n try:\\n self.skil.api.delete_model(self.deployment.id, self.model.id)\\n except api_exception as e:\\n self.skil.printer.pprint(\\n \\\">>> Exception when calling delete_model_instance: %s\\\\n\\\" % e)\",\n \"def delete_app_instance(self, instance_id):\\n self.stop_app_instance(instance_id)\\n aic = self.get_app_instances_configs(instance_id=instance_id)\\n # invoking on_uninstall callback , so app can run cleanup routines .\\n ai_obj = self.get_app_instance_obj(instance_id)\\n try:\\n if hasattr(ai_obj,\\\"on_uninstall\\\"):\\n ai_obj.on_uninstall()\\n except Exception as ex:\\n log.exception(ex)\\n if aic:\\n self.app_instances_configs.remove(aic[0])\\n self.serialize_instances_config()\",\n \"def remove_object(self, instance, using=None, **kwargs):\\n backend = self.get_backend(using)\\n\\n if backend is not None:\\n backend.remove(instance, **kwargs)\",\n \"def do_destroy(self, args):\\n args = shlex.split(args)\\n dicti = storage.all()\\n if not args:\\n print(\\\"** class name missing **\\\")\\n elif not args[0] in name_of_class:\\n print(\\\"** class doesn't exist **\\\")\\n elif len(args) == 1:\\n print(\\\"** instance id missing **\\\")\\n elif \\\"{}.{}\\\".format(args[0], args[1]) in dicti:\\n dicti.pop(\\\"{}.{}\\\".format(args[0], args[1]))\\n storage.save()\\n else:\\n print(\\\"** no instance found **\\\")\",\n \"def unassign_instance(InstanceId=None):\\n pass\",\n \"def stop(self):\\n self.__running = False\",\n \"def stop_instance(instance_id):\\n # Instantiate the service resource object\\n ec2_resource = session.resource('ec2', region_name=region)\\n try:\\n # Stop an instance\\n response = ec2_resource.Instance(instance_id).stop(DryRun=False)\\n print(response)\\n print(\\\"\\\\nSuccessfully stopping instance: \\\", instance_id)\\n except botocore.exceptions.ClientError as e:\\n if e.response['Error']['Code'] == \\\"InvalidInstanceID.Malformed\\\":\\n print(\\\"Error: Invalid instance id!!\\\")\\n else:\\n raise\\n return\",\n \"def delete(self, instance: object) -> None:\\n if self._warn_on_events:\\n self._flush_warning(\\\"Session.delete()\\\")\\n\\n try:\\n state = attributes.instance_state(instance)\\n except exc.NO_STATE as err:\\n raise exc.UnmappedInstanceError(instance) from err\\n\\n self._delete_impl(state, instance, head=True)\",\n \"def do_destroy(self, arg):\\n arg = arg.split()\\n try:\\n args = arg[0] + \\\".\\\" + arg[1]\\n except:\\n pass\\n objects = storage.all()\\n if len(arg) is 0:\\n print(\\\"** class name missing **\\\")\\n elif len(arg) == 1 and arg[0] in self.dict.keys():\\n print(\\\"** instance id missing **\\\")\\n elif arg[0] not in self.dict.keys():\\n print(\\\"** class doesn't exist **\\\")\\n elif args not in objects:\\n print(\\\"** no instance found **\\\")\\n else:\\n del objects[args]\\n storage.save()\",\n \"def stop(self):\\n self.alive = False\",\n \"def stop(self):\\n self.alive = False\",\n \"def remove(self):\\n return self.delete_instance()\",\n \"def stop(self):\\n self.api.stop()\",\n \"def stop(self, **kwargs):\\n return self.client.api.stop(self.id, **kwargs)\",\n \"def destroy():\\n # instance first\\n old_vm = _existing.vm\\n _destroy_resource('vm')\\n if not dry and old_vm is not None:\\n # Wait for instance to be fully terminated before carrying on or we will have\\n # dependency issues.\\n print('Waiting for instance to be terminated before deleting other resources...')\\n old_vm.wait_until_terminated()\\n time.sleep(1) # One would think that wait for terminated should be enough...\\n\\n _destroy_resource('disk')\\n\\n # detach before destroy\\n _detach_vpc_igw(vpc=_existing.vpc, igw=_existing.igw)\\n _destroy_resource('igw')\\n\\n # sg and sub before vpc\\n _destroy_resource('sg')\\n _destroy_resource('sub')\\n\\n _destroy_resource('vpc')\",\n \"def ec2_stop(resource, metadata):\\n instances = resource.instances.filter(\\n Filters=[{'Name': 'instance-state-name', 'Values': ['running']},\\n {'Name': 'tag:Name', 'Values': [metadata['fqdn']]}, ])\\n\\n for instance in instances:\\n print(\\\"Terminating vm id {0} name {1}\\\".format(instance.id, instance.tags[0]['Value']))\\n # resource.instances.filter(InstanceIds=[instance.id]).stop()\\n resource.instances.filter(InstanceIds=[instance.id]).terminate()\",\n \"def destory_instance(InstanceIds):\\n client = boto3.client('ec2')\\n # TODO: delete an EC2 instance\\n # Wait for the instance to enter the running state\\n # Reload the instance attributes\\n\\n try:\\n response = client.terminate_instances(\\n InstanceIds=InstanceIds,\\n DryRun=False\\n )\\n except ClientError as e:\\n print(e)\\n\\n return\",\n \"def stop(self):\\n self._alive = False\",\n \"def do_destroy(self, arg):\\n args = arg.split()\\n if len(args) == 0:\\n print(\\\"** class name missing **\\\")\\n return\\n elif len(args) < 2 and args[0] in self.class_dict:\\n print(\\\"** instance id missing **\\\")\\n return\\n elif len(args) < 2:\\n print(\\\"** class name missing **\\\")\\n return\\n\\n object_dict = storage.all()\\n if args[0] in self.class_dict:\\n for full_key in object_dict:\\n key = full_key.split(\\\".\\\")\\n if key[1] == args[1]:\\n del object_dict[full_key]\\n storage.save()\\n return\\n print(\\\"** no instance found **\\\")\\n else:\\n print(\\\"** class doesn't exist **\\\")\",\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 terminate_ow_instance(ow, ow_instance_id):\\n log.info(\\\"terminate_ow_instance( %s )\\\", ow_instance_id)\\n try:\\n ow.stop_instance(InstanceId=ow_instance_id)\\n except Exception, e:\\n print(e)\\n log.info(e)\\n sys.exit()\\n while True:\\n data = ow.describe_instances(InstanceIds=[ow_instance_id])['Instances']\\n raw = json.dumps(data)\\n ow_instance_json = json.loads(raw)\\n print(ow_instance_json[0]['InstanceId'], ow_instance_json[0]['Status'])\\n log.info(\\\"%s %s\\\", ow_instance_json[0]['InstanceId'],\\n ow_instance_json[0]['Status'])\\n if ow_instance_json[0]['Status'] == \\\"stopped\\\":\\n print(ow_instance_json[0]['InstanceId'],\\n ow_instance_json[0]['Status'])\\n log.info(\\\"%s %s\\\", ow_instance_json[0]['InstanceId'],\\n ow_instance_json[0]['Status'])\\n response = ow.delete_instance(InstanceId=ow_instance_id)\\n print(response)\\n log.info(\\\"Delete instance = %s\\\", response)\\n break\\n else:\\n time.sleep(60)\\n continue\",\n \"def stop(self):\\n self._running = False\",\n \"def stop(self):\\n self._running = False\",\n \"def stop(self, remove=True):\\n if not remove:\\n self.container.stop()\\n else:\\n self.container.remove(v=True, force=True)\",\n \"def stop(self):\\n\\n self.active = False\",\n \"def __delete__(self, obj):\\n self._instances.pop(obj, None)\",\n \"def stop(self) -> None:\\n for instance in self.instances:\\n instance.listener = None\\n instance.stop()\",\n \"def __del__(self):\\n self.stop()\",\n \"def __del__(self):\\n self.stop()\",\n \"def __del__(self):\\n self.stop()\",\n \"def __del__(self):\\n self.stop()\",\n \"def __delete__(self, instance):\\n self.session.close()\",\n \"def terminate_instances(self, props):\\n return self._vm_async_apply(props, 'delete')\",\n \"def stop(self):\\n self._schedule(0, 0)\\n self._started = False\",\n \"def destroy_VM_instance(settings, ip_address):\\n print(\\\"Terminating VM instance %s\\\" % ip_address)\\n if _is_instance_running(settings, ip_address, ip_given=True):\\n try:\\n if not confirm_teardown():\\n return\\n instance = get_this_instance(settings, ip_address,\\n ip_given=True)\\n instance_id = instance.id\\n ip_address = get_instance_ip(instance)\\n delete_chef_node_client(settings, instance_id, ip_address)\\n connection = create_cloud_connection(settings)\\n connection.terminate_instances([instance_id])\\n _wait_for_instance_to_terminate(settings, ip_address)\\n except Exception:\\n traceback.print_exc(file=sys.stdout)\\n raise\\n else:\\n print \\\"VM instance with IP %s doesn't exist\\\" % ip_address\",\n \"def perform_destroy(self, instance):\\n instance.is_active = False\\n instance.save()\",\n \"def stop(self):\\n self._log.info(\\\"Stopping\\\")\\n self._running.clear()\",\n \"def teardown(self):\\n # Only terminate if instance is running\\n if self.instance:\\n instance_status = aws.check_instance_status(self.config, self.instance.id)\\n if instance_status == \\\"running\\\":\\n aws.terminate_ec2_instance(self.config, self.instance.id)\\n super().teardown()\",\n \"def delete_foundation_instance(instance_id):\\n locked_instance = get_instance_or_404(instance_id)\\n with locked_instance.lock:\\n try:\\n locked_instance.instance.stop_notifications()\\n except AttributeError:\\n pass\\n STORE.pop(instance_id)\\n return '', 204\",\n \"def stop_instance(ec2_client, instances):\\n # get a list of instance ids\\n instances_ids = [i.instance_id for i in instances]\\n \\n # start the instances\\n ec2_client.stop_instances(InstanceIds=instances_ids)\\n \\n # wait till instance is stopped\\n waiter = ec2_client.get_waiter(\\\"instance_stopped\\\")\\n waiter.wait(InstanceIds=instances_ids)\\n print(\\\"\\\\n===EC2 instance has stopped!\\\")\",\n \"def stop(self):\\n self.scion_sh('stop')\",\n \"def do_destroy(self, line):\\n try:\\n tokens = split(line)\\n except ValueError:\\n return None\\n if len(tokens) < 1:\\n print(\\\"** class name missing **\\\")\\n else:\\n objects = models.storage.all()\\n cls = models.getmodel(tokens[0])\\n if cls is None:\\n print(\\\"** class doesn't exist **\\\")\\n elif len(tokens) < 2:\\n print(\\\"** instance id missing **\\\")\\n elif \\\".\\\".join(tokens[:2]) not in objects:\\n print(\\\"** no instance found **\\\")\\n else:\\n del objects[\\\".\\\".join(tokens[:2])]\\n models.storage.save()\",\n \"def kill_vrouter_instance(self):\\n # Stop vrouter\\n if (self.vr_args['vtest_only']):\\n self.logger.info(\\\"Stopping vrouter pid=\\\" + str(self.pid))\\n if (self.pid > 0):\\n try:\\n os.kill(self.pid, signal.SIGTERM)\\n time.sleep(1)\\n except OSError as e:\\n self.logger.error(e)\",\n \"def stop(self):\\n self.stopped = True\",\n \"def stop(self):\\n self.stopped = True\",\n \"def stop(self):\\r\\n self.running = False\",\n \"def stop(self):\\r\\n self.running = False\",\n \"def stop_ec2_instance(client, instance_id, hibernate=False):\\n\\n response = client.stop_instances(\\n InstanceIds=[instance_id],\\n )\\n return response\",\n \"def stop(self):\\n self.killed = True\",\n \"def stop(self) -> None:\\n self._running = False\",\n \"def delete(self):\\n self.id = uuid4()\\n DataStore.remove_instance(self)\",\n \"async def stop(self):\\n await self._bot.lavalink.ws.send(op='stop', guildId=self.guild_id)\\n self.current = None\",\n \"def do_destroy(self, args):\\n args = shlex.split(args)\\n if len(args) == 0:\\n print(\\\"** class name missing **\\\")\\n return False\\n if args[0] in classes:\\n if len(args) > 1:\\n key = args[0] + \\\".\\\" + args[1]\\n if key in models.storage.all():\\n models.storage.all().pop(key)\\n models.storage.save()\\n else:\\n print(\\\"** no instance found **\\\")\\n else:\\n print(\\\"** instance id missing **\\\")\\n else:\\n print(\\\"** class doesn't exist **\\\")\",\n \"def do_destroy(self, line):\\n list_line = line.split(' ')\\n if line == \\\"\\\":\\n print(\\\"** class name missing **\\\")\\n elif list_line[0] not in HBNBCommand.classes.keys():\\n print(\\\"** class doesn't exist **\\\")\\n elif len(list_line) < 2:\\n print(\\\"** instance id missing **\\\")\\n elif list_line[0] + '.' + list_line[1] not in \\\\\\n models.storage.all().keys():\\n print(\\\"** no instance found **\\\")\\n else:\\n models.storage.all().pop(list_line[0] + '.' + list_line[1], None)\\n models.storage.save()\",\n \"def delete(self):\\n self._instance.delete()\\n self._instance = None\\n self._data_defs = []\",\n \"def soft_delete(self, instance):\\n self.destroy(instance)\",\n \"def stop(self, name=None, **kwargs):\\n result = None\\n compute_service = self._get_compute_service()\\n _operation = None\\n if name is None:\\n return\\n try:\\n\\n project_id = kwargs.pop('project_id', self.auth[\\\"project_id\\\"])\\n zone = kwargs.pop('zone', self.default[\\\"zone\\\"])\\n\\n _operation = compute_service.instances().stop(\\n project=project_id,\\n zone=zone,\\n instance=name).execute()\\n\\n self._wait_for_operation(compute_service,\\n _operation,\\n project_id,\\n zone,\\n name)\\n\\n # Get the instance details to update DB.\\n result = self.__info(name, displayType=\\\"vm\\\")\\n\\n except Exception as se:\\n print(se)\\n if type(se) == HttpError:\\n Console.error(\\n f'Unable to stop instance {name}. Reason: {se._get_reason()}')\\n else:\\n Console.error(f'Unable to stop instance {name}.')\\n\\n return result\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.74867284","0.7001102","0.68385786","0.6705385","0.6695328","0.66951686","0.667478","0.6628798","0.66176504","0.660671","0.6593816","0.65497553","0.6535843","0.6532505","0.64770555","0.64421254","0.6439605","0.6426153","0.6410027","0.6365153","0.635097","0.6345597","0.63014543","0.627757","0.6273419","0.62605906","0.6258612","0.62173647","0.6216874","0.61921227","0.6190481","0.6170182","0.6164644","0.6158767","0.61574507","0.61554295","0.6150228","0.61499566","0.6138506","0.6129913","0.6123972","0.61220026","0.61184293","0.6117377","0.61057526","0.61049813","0.6100236","0.6096643","0.60941","0.60941","0.60903907","0.60865694","0.60831326","0.6078803","0.60762554","0.60645837","0.6063814","0.6058224","0.605634","0.605634","0.605634","0.605634","0.605634","0.60546094","0.60412043","0.60412043","0.6037123","0.6005466","0.5996384","0.5995671","0.5992565","0.5992565","0.5992565","0.5992565","0.5989051","0.5988371","0.59864706","0.5983621","0.597535","0.595535","0.5954741","0.59537095","0.59359854","0.5933235","0.59297305","0.59249824","0.59194106","0.59194106","0.5916758","0.5916758","0.5915675","0.59153545","0.5904887","0.5896815","0.5894313","0.58884823","0.5883703","0.5882633","0.5863275","0.5860533"],"string":"[\n \"0.74867284\",\n \"0.7001102\",\n \"0.68385786\",\n \"0.6705385\",\n \"0.6695328\",\n \"0.66951686\",\n \"0.667478\",\n \"0.6628798\",\n \"0.66176504\",\n \"0.660671\",\n \"0.6593816\",\n \"0.65497553\",\n \"0.6535843\",\n \"0.6532505\",\n \"0.64770555\",\n \"0.64421254\",\n \"0.6439605\",\n \"0.6426153\",\n \"0.6410027\",\n \"0.6365153\",\n \"0.635097\",\n \"0.6345597\",\n \"0.63014543\",\n \"0.627757\",\n \"0.6273419\",\n \"0.62605906\",\n \"0.6258612\",\n \"0.62173647\",\n \"0.6216874\",\n \"0.61921227\",\n \"0.6190481\",\n \"0.6170182\",\n \"0.6164644\",\n \"0.6158767\",\n \"0.61574507\",\n \"0.61554295\",\n \"0.6150228\",\n \"0.61499566\",\n \"0.6138506\",\n \"0.6129913\",\n \"0.6123972\",\n \"0.61220026\",\n \"0.61184293\",\n \"0.6117377\",\n \"0.61057526\",\n \"0.61049813\",\n \"0.6100236\",\n \"0.6096643\",\n \"0.60941\",\n \"0.60941\",\n \"0.60903907\",\n \"0.60865694\",\n \"0.60831326\",\n \"0.6078803\",\n \"0.60762554\",\n \"0.60645837\",\n \"0.6063814\",\n \"0.6058224\",\n \"0.605634\",\n \"0.605634\",\n \"0.605634\",\n \"0.605634\",\n \"0.605634\",\n \"0.60546094\",\n \"0.60412043\",\n \"0.60412043\",\n \"0.6037123\",\n \"0.6005466\",\n \"0.5996384\",\n \"0.5995671\",\n \"0.5992565\",\n \"0.5992565\",\n \"0.5992565\",\n \"0.5992565\",\n \"0.5989051\",\n \"0.5988371\",\n \"0.59864706\",\n \"0.5983621\",\n \"0.597535\",\n \"0.595535\",\n \"0.5954741\",\n \"0.59537095\",\n \"0.59359854\",\n \"0.5933235\",\n \"0.59297305\",\n \"0.59249824\",\n \"0.59194106\",\n \"0.59194106\",\n \"0.5916758\",\n \"0.5916758\",\n \"0.5915675\",\n \"0.59153545\",\n \"0.5904887\",\n \"0.5896815\",\n \"0.5894313\",\n \"0.58884823\",\n \"0.5883703\",\n \"0.5882633\",\n \"0.5863275\",\n \"0.5860533\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.63447744"},"document_rank":{"kind":"string","value":"22"}}},{"rowIdx":17879,"cells":{"query":{"kind":"string","value":"Pulls an docker or singularity images from hub."},"document":{"kind":"string","value":"def singularity_pull(self, image):\n Client.pull(image)"},"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 pull(self) -> None:\n cached_zip_filepath = None\n try:\n scheme, name, tag, secret = parse_hub_uri(self.args.uri)\n\n executor = HubIO.fetch(name, tag=tag, secret=secret)\n\n if not tag:\n tag = executor.tag\n\n uuid = executor.uuid\n image_name = executor.image_name\n archive_url = executor.archive_url\n md5sum = executor.md5sum\n\n if scheme == 'jinahub+docker':\n # pull the Docker image\n with TimeContext(f'pulling {image_name}', self.logger):\n image = self._client.images.pull(image_name)\n if isinstance(image, list):\n image = image[0]\n image_tag = image.tags[0] if image.tags else ''\n self.logger.success(\n f'🎉 pulled {image_tag} ({image.short_id}) uncompressed size: {get_readable_size(image.attrs[\"Size\"])}'\n )\n return\n if exist_local(uuid, tag):\n self.logger.debug(\n f'The executor `{self.args.uri}` has already been downloaded.'\n )\n return\n # download the package\n with TimeContext(f'downloading {self.args.uri}', self.logger):\n cache_dir = Path(\n os.environ.get(\n 'JINA_HUB_CACHE_DIR', Path.home().joinpath('.cache', 'jina')\n )\n )\n cache_dir.mkdir(parents=True, exist_ok=True)\n cached_zip_filename = f'{uuid}-{md5sum}.zip'\n cached_zip_filepath = download_with_resume(\n archive_url,\n cache_dir,\n cached_zip_filename,\n md5sum=md5sum,\n )\n\n with TimeContext(f'unpacking {self.args.uri}', self.logger):\n try:\n install_local(\n cached_zip_filepath,\n uuid,\n tag,\n install_deps=self.args.install_requirements,\n )\n except Exception as ex:\n raise HubDownloadError(str(ex))\n\n except Exception as e:\n self.logger.error(\n f'Error when pulling the executor `{self.args.uri}`: {e!r}'\n )\n finally:\n # delete downloaded zip package if existed\n if cached_zip_filepath is not None:\n cached_zip_filepath.unlink()","def pullall():\n\tprint(red('\\t\\tStarting download of QNIBTerminal images\\t\\t'))\n\t# pull all the needed images\n\tdocker_images={'fd20','terminal','helixdns','elk','slurm','compute'}\n\tfor image in docker_images:\n\t\tprint 'docker pull qnib/'+image\n\t\t# run('docker pull qnib/'+image)","def pull_image(image):\n\n subprocess.check_call(['docker', 'pull', image])","def _pull_image(self, docker_client: \"DockerClient\"):\n image, tag = self._get_image_and_tag()\n\n return docker_client.images.pull(image, tag)","def pull_image(self):\n status = []\n for key, container in self.containers.items():\n result = container.pull()\n status.append(result)\n return status","def pull_dlc_images(images):\n for image in images:\n run(f\"docker pull {image}\", hide=\"out\")","def pull_base_images(ctx, dockerfile, signed_pull=True):\n images = set()\n stages = set()\n\n with open(dockerfile, \"r\") as f:\n for line in f:\n words = line.split()\n # Get source images\n if len(words) < 2 or words[0].lower() != \"from\":\n continue\n images.add(words[1])\n # Get stage names to remove them from pull\n if len(words) < 4 or words[2].lower() != \"as\":\n continue\n stages.add(words[3])\n\n if stages:\n print(\"Ignoring intermediate stage names: {}\".format(\", \".join(stages)))\n images -= stages\n\n print(\"Pulling following base images: {}\".format(\", \".join(images)))\n\n pull_env = {}\n if signed_pull:\n pull_env[\"DOCKER_CONTENT_TRUST\"] = \"1\"\n\n for i in images:\n ctx.run(\"docker pull {}\".format(i), env=pull_env)","def cli(ctx, image_file):\n if not image_file:\n return\n for pull_image in image_file.readline():\n pull_image = pull_image.rstrip('\\n')\n if len(pull_image) == 0:\n continue\n docker.pull(pull_image)\n push_image = '%s/%s/%s' % (DEFAULT_REGISTRY,\n DEFAULR_NAMESPACE,\n pull_image.split('/')[-1])\n docker.tag(pull_image, push_image)\n docker.push(push_image)","def pullDockerImage(client, names):\n imgNotExistList = []\n for name in names:\n try:\n client.images.pull(name)\n # some invalid image names will not be pulled but the pull method\n # will not throw an exception so the only way to confirm if a pull\n # succeeded is to attempt a docker inspect on the image\n client.images.get(name)\n except Exception:\n imgNotExistList.append(name)\n if len(imgNotExistList) != 0:\n raise DockerImageNotFoundError('Could not find multiple images ',\n image_name=imgNotExistList)","def pull_image(self, tag):\n image_name = self.build_image_name(tag)\n image = self.client.images.pull(image_name)\n return image","def pull(self, repo, tag):\n check_blacklist(repo)\n logger.info(\"Pulling Docker image {}:{}\".format(repo, tag))\n with SimpleFlock(self.FLOCKFILE, timeout=1200):\n stream = self.client.pull(repo, tag=tag, stream=True, insecure_registry=True)\n log_output(stream)","def get_images(name):\n url = \"/\".join([REGISTRY_BASE, name, \"/tags/list\"])\n response = req(url)\n image_list = []\n if response is not None:\n headers = {\"Accept\": \"application/vnd.docker.distribution.manifest.v2+json\"}\n tags = response[\"tags\"]\n for tag in tags:\n url = \"/\".join([REGISTRY_BASE, name, \"/manifests\", tag])\n response = req(url, headers)\n if response is not None:\n image = {}\n image[\"size\"] = response[\"config\"][\"size\"]\n for i in response[\"layers\"]:\n image[\"size\"] += i[\"size\"]\n image[\"size\"] = round(float(image[\"size\"]) / 1024 / 1024, 2)\n image[\"id\"] = response[\"config\"][\"digest\"][7:19]\n image[\"tag\"] = tag\n image[\"cmd\"] = \"docker pull uk8s.com/\" + name + \":\" + tag\n image_list.append(image)\n return sorted(image_list, reverse=True)","def fetch_image(client, name):\n try:\n image = client.images.get(name)\n except docker.errors.ImageNotFound:\n name, tag = _parse_image_tag(name)\n tag = 'latest' if tag is None else tag\n\n log.info(\"Pulling tag '{}' for image '{}'...\".format(tag, name))\n image = client.images.pull(name, tag=tag)\n\n log.debug(\"Found image '{}' for tag '{}'\".format(image.id, name))\n return image","def pull(self, data):\n required = {'token', 'source'}\n api.validate(data, required)\n token = data['token']\n repo = data['source']\n self.credentials_module.authorize(token)\n result = self.docker_module.pull_image(repo)\n # credentials_module.add_image(token, result['image_id'])\n return result","def fetch_image(self, image: Image):\n self.log.info(\"Fetching image %s\", image)\n for line in image.repository.registry.client.pull(str(image.repository), image.tag, stream=True, decode=True):\n self.log.debug(line)","def dockerhub_pull(services):\n docker_services = get_list_of_services(services)\n service_list = construct_docker_compose_file(docker_services)\n\n procs = []\n for service in service_list:\n process = Process(target=run_docker_compose_command, args=(['pull', service],))\n process.start()\n procs.append(process)\n\n for p in procs:\n p.join()\n\n remove_docker_compose_file()","def pull(release):\n image = f\"breqwatr/rsyslog:{release}\"\n ecr.pull(image)","def run(docker_hub_client, args):\n resp = docker_hub_client.get_tags(args.orgname, args.reponame, args.page)\n if resp['code'] == 200:\n if resp['content']['count'] > 0:\n rows = []\n for repo in resp['content']['results']:\n formatted_date = ''\n if repo['last_updated']:\n formatted_date = dateutil.parser \\\n .parse(repo['last_updated'])\n formatted_date = formatted_date.strftime(\"%Y-%m-%d %H:%M\")\n # Convert full_size in bytes to KB\n size_in_kb = repo['full_size'] / 1024\n formatted_size = readableMemoryFormat(size_in_kb)\n rows.append([repo['name'], formatted_size, formatted_date])\n header = ['Name', 'Size', 'Last updated']\n print_result(args.format, rows, header, resp['content']['count'],\n args.page)\n else:\n print('Error fetching tags for: {0}/{1}'.\n format(args.orgname, args.reponame))","def pull_image(image):\n docker_client = _docker_client()\n response = docker_client.pull(image)\n lines = [line for line in response.splitlines() if line]\n\n # The last line of the response contains the overall result of the pull\n # operation.\n pull_result = json.loads(lines[-1])\n if \"error\" in pull_result:\n raise Exception(\"Could not pull {}: {}\".format(\n image, pull_result[\"error\"]))","def docker_pull(args, image): # type: (EnvironmentConfig, str) -> None\n if '@' not in image and ':' not in image:\n display.info('Skipping pull of image without tag or digest: %s' % image, verbosity=2)\n return\n\n if docker_image_exists(args, image):\n display.info('Skipping pull of existing image: %s' % image, verbosity=2)\n return\n\n for _iteration in range(1, 10):\n try:\n docker_command(args, ['pull', image], capture=False)\n return\n except SubprocessError:\n display.warning('Failed to pull docker image \"%s\". Waiting a few seconds before trying again.' % image)\n time.sleep(3)\n\n raise ApplicationError('Failed to pull docker image \"%s\".' % image)","def fetch(\n name: str,\n tag: Optional[str] = None,\n secret: Optional[str] = None,\n ) -> HubExecutor:\n\n with ImportExtensions(required=True):\n import requests\n\n pull_url = get_hubble_url() + f'/{name}/?'\n path_params = {}\n if secret:\n path_params['secret'] = secret\n if tag:\n path_params['tag'] = tag\n\n request_headers = HubIO._get_request_header()\n\n pull_url += urlencode(path_params)\n resp = requests.get(pull_url, headers=request_headers)\n if resp.status_code != 200:\n if resp.text:\n raise Exception(resp.text)\n resp.raise_for_status()\n\n resp = resp.json()\n\n result = HubExecutor(\n resp['id'],\n resp.get('alias', None),\n resp['tag'],\n resp['visibility'],\n resp['image'],\n resp['package']['download'],\n resp['package']['md5'],\n )\n\n return result","def test_pull_multiple_tags(self):\n pr = {\n 'system': self.system,\n 'itype': self.itype,\n 'tag': self.public,\n 'remotetype': 'dockerv2'\n }\n # Do the pull\n session = self.m.new_session(self.auth, self.system)\n rec = self.m.pull(session, pr) # ,delay=False)\n id = rec['_id']\n self.assertIsNotNone(rec)\n # Confirm record\n q = {'system': self.system, 'itype': self.itype,\n 'pulltag': self.public}\n mrec = self.images.find_one(q)\n self.assertIn('_id', mrec)\n # Track through transistions\n state = self.time_wait(id)\n self.assertEqual(state, 'READY')\n\n # Now reppull with a different tag for the same image\n newtag = self.public.replace('latest', '1')\n pr = {\n 'system': self.system,\n 'itype': self.itype,\n 'tag': newtag,\n 'remotetype': 'dockerv2'\n }\n rec = self.m.pull(session, pr) # ,delay=False)\n id = rec['_id']\n self.assertIsNotNone(rec)\n # Track through transistions\n state = self.time_wait(id)\n # Requery the original record\n mrec = self.images.find_one(q)\n self.assertIn(self.public, mrec['tag'])\n self.assertIn(newtag, mrec['tag'])","def pull(self, action, image_name, **kwargs):\n config_id = action.config_id\n registry, __, image = config_id.config_name.rpartition('/')\n login_kwargs = {}\n if _check_insecure_registry(kwargs):\n login_kwargs['insecure_registry'] = True\n if registry and '.' in registry and registry not in self._login_registries:\n self.login(action, registry, **login_kwargs)\n log.info(\"Pulling image %s:%s.\", config_id.config_name, config_id.instance_name)\n res = action.client.pull(repository=config_id.config_name, tag=config_id.instance_name, **kwargs)\n log.debug(\"Done pulling image %s:%s.\", config_id.config_name, config_id.instance_name)\n self._policy.images[action.client_name].refresh_repo(config_id.config_name)\n log.debug(\"Refreshed image cache for repo %s.\", config_id.config_name)\n return res","def _load(self, resource_handler):\n log.info('[%s] Loading Docker image origin=%r image=%r tag=%r',\n resource_handler.name, self.origin, self.image, self.tag)\n if self.origin == 'dockerhub':\n resource_handler.cli.pull(repository=self.image, tag=self.tag)\n elif self.origin == 'local':\n pass\n else:\n resource_handler.cli.import_image_from_url(\n url=self.origin,\n repository=self.image,\n tag=self.tag\n )","def images_refresh(self, kwargs=None):\n scode, hosts = Rest.get('Host')\n filter = {}\n n = 1\n e = {}\n data = []\n for host in hosts:\n os.environ[\"DOCKER_HOST\"] = host['Ip'] + \":\" + str(host['Port'])\n filter['Ip'] = os.environ[\"DOCKER_HOST\"].split(':')[0]\n self.client = docker.from_env()\n try:\n images = self.client.images.list(**kwargs)\n except docker.errors.APIError as e:\n Console.error(e.explanation)\n return\n\n if len(images) == 0:\n Console.info(\"No images exist\")\n continue\n\n for imagem in images:\n image = imagem.__dict__['attrs']\n image['Ip'] = os.environ[\"DOCKER_HOST\"].split(':')[0]\n data.append(image)\n d = {}\n d['Ip'] = os.environ[\"DOCKER_HOST\"].split(':')[0]\n d['Id'] = image['Id']\n if image['RepoTags'] == None:\n d['Repository'] = image['RepoDigests'][0]\n else:\n d['Repository'] = image['RepoTags'][0]\n # d['Size'] = image['Size']\n d['Size(GB)'] = round(image['Size'] / float(1 << 30), 2)\n e[n] = d\n n = n + 1\n Rest.delete('Image', filter)\n Rest.post('Image', data)\n Console.ok(str(Printer.dict_table(e, order=['Ip', 'Id', 'Repository', 'Size(GB)'])))","def push(images, tag, registry):\n manager = Manager('push', tag, images=images, registry_url=registry)\n manager.run()","def all(tag=None, env=None):\n build_images(tag=tag)\n push_images(tag=tag)\n deploy(env=env, tag=tag)","def test_image_exists_registry_no_pull(self, mock_docker_environment):\n mock_docker_environment.images.get.side_effect = DockerNotFound(\"testing\")\n build_image_if_needed(TEST_IMAGE_NAME, pull=False)\n mock_docker_environment.images.build.assert_called_with(**self.default_call_kwargs)","def test_image_exists_registry_pull_not_found(self, mock_docker_environment):\n mock_docker_environment.images.get.side_effect = DockerNotFound(\"testing\")\n mock_docker_environment.api.pull.side_effect = DockerNotFound(\"testing\")\n\n build_image_if_needed(TEST_IMAGE_NAME)\n mock_docker_environment.api.pull.assert_called_with(\n TEST_IMAGE_NAME, \"latest\", decode=True, stream=True\n )\n mock_docker_environment.images.build.assert_called_with(**self.default_call_kwargs)","def run_docker_push(image_list, docker_client):\n logger.info(\"--- push image to registry ---\")\n if runtime == Runtime.local:\n logger.info(\"Detected local runtime, stop here.\")\n return\n\n if not len(success_image_list):\n logger.info(\"No success image to push, stop here.\")\n return\n\n if GIT_BRANCH != \"master\":\n logger.info(\"Not master branch, stop here\")\n return\n\n docker_client.login(username=DOCKER_HUB_USERNAME, password=DOCKER_HUB_PASSWORD)\n for image in image_list:\n image.run_docker_push(docker_client)\n print(\"Finished.\")","def pull_image_if_not_exists(self, tag):\n image_name = self.build_image_name(tag)\n try:\n # Check if exists\n image = self.client.images.get(image_name)\n except Exception as ex:\n print('Image does not exist: {}'.format(str(ex)))\n # Pull if doesn't exist\n print('Pulling image {}'.format(image_name))\n image = self.client.images.pull(image_name)\n\n return image","def pull1(repo, **kwargs):\n ret = do_pull(repo, \"topology.virl\")\n if not ret:\n exit(1)","async def _pull(self) -> None:\n raise NotImplementedError()","def pull(path, service_names=None):\n\n project = __load_project(path)\n if isinstance(project, dict):\n return project\n else:\n try:\n project.pull(service_names)\n except Exception as inst: # pylint: disable=broad-except\n return __handle_except(inst)\n return __standardize_result(\n True, \"Pulling containers images via docker-compose succeeded\", None, None\n )","def pull_image_version(self, app, image_name, image_tag, parent_task, fail_silently=False):\n start_time = datetime.datetime.now().replace(microsecond=0)\n\n assert isinstance(image_name, str)\n assert isinstance(image_tag, str)\n\n # The string \"local\" has a special meaning which means the most recent\n # local image of that name, so we skip the remote call/check.\n if image_tag == \"local\":\n if fail_silently:\n return None\n else:\n raise ImagePullFailure(\n \"Cannot pull a local image\",\n remote_name=None,\n image_tag=image_tag\n )\n\n # Check if the image already exists locally\n # This is an optimization to save a trip to the registry: 1-2 sec per image\n if image_tag != \"latest\":\n try:\n self.host.images.image_version(image_name, image_tag)\n return None\n except ImageNotFoundException:\n # The image will be pulled from the registry\n pass\n\n registry = self.get_registry(app)\n\n # See if the registry is willing to give us a URL (it's logged in)\n if registry:\n registry_url = registry.url(self.host)\n else:\n registry_url = None\n if registry_url is None:\n if fail_silently:\n return None\n else:\n raise ImagePullFailure(\n \"No registry configured\",\n remote_name=None,\n image_tag=image_tag\n )\n\n task = Task(\n \"Pulling remote image {}:{}\".format(image_name, image_tag),\n parent=parent_task,\n progress_formatter=lambda x: \"{} MB\".format(x // (1024 ** 2)),\n )\n\n remote_name = \"{registry_url}/{image_name}\".format(\n registry_url=registry_url,\n image_name=image_name,\n )\n\n stream = self._pull(app, task, remote_name, image_tag)\n\n layer_status = {}\n current = None\n total = None\n for json_line in convert_to_json_stream(stream):\n if 'error' in json_line:\n task.finish(status=\"Failed\", status_flavor=Task.FLAVOR_WARNING)\n if fail_silently:\n return\n else:\n raise ImagePullFailure(\n json_line['error'],\n remote_name=remote_name,\n image_tag=image_tag\n )\n elif 'id' in json_line:\n if json_line['status'].lower() == \"downloading\":\n layer_status[json_line['id']] = json_line['progressDetail']\n\n elif \"complete\" in json_line['status'].lower() and json_line['id'] in layer_status:\n layer_status[json_line['id']]['current'] = layer_status[json_line['id']]['total']\n\n if layer_status:\n statuses = [x for x in layer_status.values()\n if \"current\" in x and \"total\" in x]\n current = sum(x['current'] for x in statuses)\n total = sum(x['total'] for x in statuses)\n\n if total is not None:\n task.update(progress=(current, total))\n\n end_time = datetime.datetime.now().replace(microsecond=0)\n time_delta_str = str(end_time - start_time)\n if time_delta_str.startswith('0:'):\n time_delta_str = time_delta_str[2:]\n task.finish(status='Done [{}]'.format(time_delta_str), status_flavor=Task.FLAVOR_GOOD)\n\n # Tag the remote image as the right name\n self._tag_image(remote_name, image_tag, image_name, image_tag, fail_silently)\n self._tag_image(remote_name, image_tag, image_name, \"latest\", fail_silently)","def tests_pull_workflow(self):\n daemon_client = {}\n client = self.get_client(daemon_client)\n\n for container in client.containers(all=True):\n if 'test-container' in \\\n ''.join([name for name in container.get('Names')]):\n client.remove_container('test-container')\n\n if ['{0}:latest'.format(TEST_IMAGE)] in \\\n [i.get('RepoTags') for i in client.images()]:\n client.remove_image(TEST_IMAGE, force=True)\n\n # execute install workflow\n self.env.execute('install', task_retries=0)\n\n container_instance = {}\n\n for instance in self.env.storage.get_node_instances():\n if 'container_id' in instance.runtime_properties.keys():\n container_instance = instance\n\n self.assertTrue(container_instance is not None,\n 'Failed getting container.')\n\n container_id = container_instance.runtime_properties.get(\n 'container_id')\n containers = client.containers(all=True)\n self.assertTrue(container_id in [c.get('Id') for c in containers])\n\n self.env.execute('uninstall', task_retries=3)\n repotags = []\n for i in client.images():\n repotags.append(i.get('RepoTags'))\n\n self.assertFalse(TEST_IMAGE in [tag for tag in repotags])\n if ['{0}:latest'.format(TEST_IMAGE)] in \\\n [i.get('RepoTags') for i in client.images()]:\n client.remove_image(TEST_IMAGE, force=True)","def fetch(self, tag):\n return fetch_image(self.collection.client, tag)","def push_base():\n docker('login')\n docker('push %s' % env.base_image_name)","def remote_pull(*keys):","def pull_snapshot(edition, force):\n from docker import DockerClient\n from docker.errors import ImageNotFound\n docker = DockerClient.from_env(version=\"auto\")\n artifact = resolve_artifact_name(edition)\n if force:\n return download_snapshot_artifact(artifact)\n else:\n derived = derive_image_tag(artifact)\n try:\n docker.images.get(derived)\n except ImageNotFound:\n return download_snapshot_artifact(artifact)\n else:\n return derived","def pull(self, repository, tag=None, stream=False, raise_on_error=False, **kwargs):\n response = super(DockerClientWrapper, self).pull(repository, tag=tag, stream=stream, **kwargs)\n if stream:\n result = self._docker_status_stream(response, raise_on_error)\n else:\n result = self._docker_status_stream(response.split('\\r\\n') if response else (), raise_on_error)\n return result and not result.get('error')","def test_image_exists_registry(self, mock_docker_environment):\n mock_docker_environment.images.get.side_effect = DockerNotFound(\"testing\")\n build_image_if_needed(TEST_IMAGE_NAME)\n mock_docker_environment.api.pull.assert_called_with(\n TEST_IMAGE_NAME, \"latest\", decode=True, stream=True\n )\n mock_docker_environment.images.build.assert_not_called()","def pull_image_then_retag(docker_client=None, repository=None, tag='latest', retag_repository=None):\n try:\n Logger.debug(f\"ready to pull the image {repository}:{tag}\")\n image = docker_client.images.pull(repository, tag)\n Logger.info(f\"pull the image {image.attrs['RepoTags'][0]} completed,ready to re-tag.\")\n return image.tag(retag_repository, tag)\n except docker.errors.APIError as error:\n Logger.error(error)\n return False","def handle_api_list_images(self, http_context):\n\n command = self.docker + ['images', '--format', '\\'{{json .}}\\'', '--no-trunc', '-a']\n images = []\n for line in subprocess.check_output(command).decode().splitlines():\n image = json.loads(line)\n image['hash'] = image['ID'].split(':')[1][:12]\n images.append(image)\n return images","def fetch_images(client, images):\n return [fetch_image(client, image) for image in images]","def push_image(self, tag_list, push_to_defaults, additional_registries=[], version_release_tuple=None,\n push_late=False, dry_run=False):\n\n # Late pushes allow certain images to be the last of a group to be\n # pushed to mirrors. CI/CD systems may initiate operations based on the\n # update a given image and all other images need to be in place\n # when that special image is updated. The special images are there\n # pushed \"late\"\n # Actions that need to push all images need to push all images\n # need to make two passes/invocations of this method: one\n # with push_late=False and one with push_late=True.\n\n is_late_push = False\n if self.config.push.late is not Missing:\n is_late_push = self.config.push.late\n\n if push_late != is_late_push:\n return True\n\n push_names = []\n\n if push_to_defaults:\n push_names.extend(self.metadata.get_default_push_names())\n\n push_names.extend(self.metadata.get_additional_push_names(additional_registries))\n\n # Nothing to push to? We are done.\n if not push_names:\n return True\n\n with Dir(self.distgit_dir):\n\n if version_release_tuple:\n version = version_release_tuple[0]\n release = version_release_tuple[1]\n else:\n\n # History\n # We used to rely on the \"release\" label being set in the Dockerfile, but this is problematic for several reasons.\n # (1) If 'release' is not set, OSBS will determine one automatically that does not conflict\n # with a pre-existing image build. This is extremely helpful since we don't have to\n # worry about bumping the release during refresh images. This means we generally DON'T\n # want the release label in the file and can't, therefore, rely on it being there.\n # (2) People have logged into distgit before in order to bump the release field. This happening\n # at the wrong time breaks the build.\n\n # If the version & release information was not specified,\n # try to detect latest build from brew.\n # Read in version information from the Distgit dockerfile\n _, version, release = self.metadata.get_latest_build_info()\n\n try:\n record = {\n \"distgit_key\": self.metadata.distgit_key,\n \"distgit\": '{}/{}'.format(self.metadata.namespace, self.metadata.name),\n \"image\": self.config.name,\n \"version\": version,\n \"release\": release,\n \"message\": \"Unknown failure\",\n \"status\": -1,\n # Status defaults to failure until explicitly set by success. This handles raised exceptions.\n }\n\n # pull just the main image name first\n image_name_and_version = \"%s:%s-%s\" % (self.config.name, version, release)\n brew_image_url = \"/\".join((constants.BREW_IMAGE_HOST, image_name_and_version))\n pull_image(brew_image_url)\n record['message'] = \"Successfully pulled image\"\n record['status'] = 0\n except Exception as err:\n record[\"message\"] = \"Exception occurred: %s\" % str(err)\n self.logger.info(\"Error pulling %s: %s\" % (self.metadata.name, err))\n raise\n finally:\n self.runtime.add_record('pull', **record)\n\n push_tags = list(tag_list)\n\n # If no tags were specified, build defaults\n if not push_tags:\n push_tags = self.metadata.get_default_push_tags(version, release)\n\n for image_name in push_names:\n try:\n\n repo = image_name.split('/', 1)\n\n action = \"push\"\n record = {\n \"distgit_key\": self.metadata.distgit_key,\n \"distgit\": '{}/{}'.format(self.metadata.namespace, self.metadata.name),\n \"repo\": repo, # ns/repo\n \"name\": image_name, # full registry/ns/repo\n \"version\": version,\n \"release\": release,\n \"message\": \"Unknown failure\",\n \"tags\": \", \".join(push_tags),\n \"status\": -1,\n # Status defaults to failure until explicitly set by success. This handles raised exceptions.\n }\n\n for push_tag in push_tags:\n push_url = '{}:{}'.format(image_name, push_tag)\n\n if dry_run:\n rc = 0\n self.logger.info('Would have tagged {} as {}'.format(brew_image_url, push_url))\n self.logger.info('Would have pushed {}'.format(push_url))\n else:\n rc, out, err = exectools.cmd_gather([\"docker\", \"tag\", brew_image_url, push_url])\n\n if rc != 0:\n # Unable to tag the image\n raise IOError(\"Error tagging image as: %s\" % push_url)\n\n for r in range(10):\n self.logger.info(\"Pushing image to mirror [retry=%d]: %s\" % (r, push_url))\n rc, out, err = exectools.cmd_gather([\"docker\", \"push\", push_url])\n if rc == 0:\n break\n self.logger.info(\"Error pushing image -- retrying in 60 seconds\")\n time.sleep(60)\n\n if rc != 0:\n # Unable to push to registry\n raise IOError(\"Error pushing image: %s\" % push_url)\n\n record[\"message\"] = \"Successfully pushed all tags\"\n record[\"status\"] = 0\n\n except Exception as err:\n record[\"message\"] = \"Exception occurred: %s\" % str(err)\n self.logger.info(\"Error pushing %s: %s\" % (self.metadata.name, err))\n raise\n\n finally:\n self.runtime.add_record(action, **record)\n\n return True","async def fetch_all_images(sess: Session = Depends(get_db)):\n image_list = utils_com.get_com_image_list(sess)\n return image_list","def get_images(stage=0):\n return get_files(stage)[0]","def image(self, name=None):\n return self.find(self.images(), name=name)","def pull(args):\n do_all_projects(args + [\"pull\"])","def test_pull(self, mock_docker_environment, snapshot, capsys):\n mock_client = mock_docker_environment\n pull_image(TEST_IMAGE_NAME)\n mock_client.api.pull.assert_called_with(\n TEST_IMAGE_NAME, \"latest\", stream=True, decode=True\n )\n out, err = capsys.readouterr()\n snapshot.assert_match(out)","def push_dockerhub(c, app, version, latest=False):\n if app.lower() == 'core':\n c.run('sudo docker push kinecosystem/stellar-core:{version}'.format(version=version))\n if latest:\n c.run('sudo docker push kinecosystem/stellar-core:latest')\n elif app.lower() == 'horizon':\n c.run('sudo docker push kinecosystem/horizon:{version}'.format(version=version))\n if latest:\n c.run('sudo docker push kinecosystem/horizon:latest')\n elif app.lower() == 'friendbot':\n c.run('sudo docker push kinecosystem/friendbot:{version}'.format(version=version))\n if latest:\n c.run('sudo docker push kinecosystem/friendbot:latest')\n else:\n Exit('Unknown application {}'.format(app))","def get_latest_bing_wallpaper_remote():\r\n\r\n logging.debug('get_latest_bing_wallpaper_remote()')\r\n\r\n # get image url\r\n if use_proxy:\r\n response = requests.get(\"https://www.bing.com/HPImageArchive.aspx?format=js&idx=0&n=1&mkt=en-US\", proxies=proxies, timeout=5, verify=False)\r\n else:\r\n response = requests.get(\"https://www.bing.com/HPImageArchive.aspx?format=js&idx=0&n=1&mkt=en-US\")\r\n image_data = json.loads(response.text)\r\n\r\n image_url = image_data[\"images\"][0][\"url\"]\r\n image_url = image_url.split(\"&\")[0]\r\n full_image_url = \"https://www.bing.com\" + image_url\r\n logging.debug('get_latest_bing_wallpaper_remote - full_image_url = {}'.format(full_image_url))\r\n\r\n # image's name\r\n image_name = get_generated_image_name(full_image_url)\r\n\r\n # Check and maintain DB\r\n if not exists_image_in_database(full_image_url):\r\n add_image_to_database(full_image_url, image_name, \"bing\")\r\n # download and save image\r\n full_image_path = download_image(full_image_url, image_name)\r\n update_image_in_database(full_image_url, full_image_path)\r\n else:\r\n full_image_path = get_image_path_from_database(full_image_url)\r\n\r\n # Return full path to image\r\n logging.debug('get_latest_bing_wallpaper_remote - full_image_path = {}'.format(full_image_path))\r\n return full_image_path","def pull(repo, **kwargs):\n ret = do_pull(repo, \"topology.yaml\")\n if not ret:\n ret = do_pull(repo, \"topology.virl\")\n if not ret:\n exit(1)","def list_docker_images():\n raw_result = subprocess.getstatusoutput('docker images')\n return result_handler(raw_result)","def _list(logger, image_name: str = None, image_kind: str = None,\n image_type: str = None, image_keywords: Sequence = ()) -> Optional[Dict[str, Any]]:\n # TODO: Shouldn't pass a default argument for keywords. Need to handle after lambda function gets fixed\n with resource_stream('jina', '/'.join(('resources', 'hubapi.yml'))) as fp:\n hubapi_yml = yaml.load(fp)\n hubapi_url = hubapi_yml['hubapi']['url'] + hubapi_yml['hubapi']['list']\n\n params = {\n 'name': image_name,\n 'kind': image_kind,\n 'type': image_type,\n 'keywords': ','.join(image_keywords) if image_keywords else None\n }\n params = {k: v for k, v in params.items() if v}\n if params:\n data = urlencode(params)\n request = Request(f'{hubapi_url}?{data}')\n with TimeContext('searching', logger):\n try:\n with urlopen(request) as resp:\n response = json.load(resp)\n except HTTPError as err:\n if err.code == 400:\n logger.warning('no matched executors found. please use different filters and retry.')\n elif err.code == 500:\n logger.error(f'server is down: {err.reason}')\n else:\n logger.error(f'unknown error: {err.reason}')\n return\n\n manifests = response['manifest']\n local_manifest = _load_local_hub_manifest()\n if local_manifest:\n tb = _make_hub_table_with_local(manifests, local_manifest)\n else:\n tb = _make_hub_table(manifests)\n logger.info('\\n'.join(tb))\n return manifests","def pull(self, remote, branch, *args):\n return self.cmd('pull', remote, branch, *args)","def main():\n images = Images()\n #print images.create_image_urls()\n print images.get_image_random()\n print images.get_image(12)","def _update_image(name):\n\n if not name or name == 'None':\n return None, None\n\n old = docker.image_digest(name)\n for _ in range(3):\n new = docker.pull(name)\n if new:\n break\n time.sleep(5)\n\n return name, old != new","def build_container(client):\n client.images.build(path=os.path.join(os.path.abspath(\"\"), \"docker\"), tag=\"scrape_light\")","def _register_to_mongodb(logger, summary: Dict = None):\n logger.info('registering image to Jina Hub database...')\n\n with resource_stream('jina', '/'.join(('resources', 'hubapi.yml'))) as fp:\n hubapi_yml = yaml.load(fp)\n\n hubapi_url = hubapi_yml['hubapi']['url'] + hubapi_yml['hubapi']['push']\n\n if not credentials_file().is_file():\n logger.error(f'user hasnot logged in. please login using command: {colored(\"jina hub login\", attrs=[\"bold\"])}')\n return\n\n with open(credentials_file(), 'r') as cf:\n cred_yml = yaml.load(cf)\n access_token = cred_yml['access_token']\n\n if not access_token:\n logger.error(f'user has not logged in. please login using command: {colored(\"jina hub login\", attrs=[\"bold\"])}')\n return\n\n headers = {\n 'Accept': 'application/json',\n 'authorizationToken': access_token\n }\n try:\n import requests\n response = requests.post(url=f'{hubapi_url}',\n headers=headers,\n data=json.dumps(summary))\n if response.status_code == requests.codes.ok:\n logger.info(response.text)\n elif response.status_code == requests.codes.unauthorized:\n logger.error(f'user is unauthorized to perform push operation. '\n f'please login using command: {colored(\"jina hub login\", attrs=[\"bold\"])}')\n elif response.status_code == requests.codes.internal_server_error:\n if 'auth' in response.text.lower():\n logger.error(f'authentication issues!'\n f'please login using command: {colored(\"jina hub login\", attrs=[\"bold\"])}')\n logger.error(f'got an error from the API: {response.text}')\n except Exception as exp:\n logger.error(f'got an exception while invoking hubapi for push {repr(exp)}')","def test_pull2(self):\n\n # Use defaults for format, arch, os, ostcount, replication\n pr = self.pull\n # Do the pull\n session = self.mtm.new_session(self.auth, self.system)\n rec1 = self.mtm.pull(session, pr) # ,delay=False)\n pr['tag'] = self.tag2\n rec2 = self.mtm.pull(session, pr) # ,delay=False)\n self.assertIsNotNone(rec1)\n id1 = rec1['_id']\n self.assertIsNotNone(rec2)\n id2 = rec2['_id']\n # Confirm record\n q = {'system': self.system, 'itype': self.itype, 'pulltag': self.tag}\n mrec = self.images.find_one(q)\n self.assertIn('_id', mrec)\n state = self.time_wait(id1)\n self.assertEqual(state, 'READY')\n state = self.time_wait(id2)\n self.assertEqual(state, 'READY')\n mrec = self.images.find_one(q)\n self.images.drop()","def setup(self):\n\n exists = [i for i in self.client.images() if self.image in i['RepoTags']]\n\n # Only pull the image if we don't have it\n if not exists or self.pull:\n self.client.pull(self.image)\n self.logger.debug(\"Pulled {}\".format(self.image))\n\n self.container = self.client.create_container(\n image=self.image,\n host_config=self.host_config,\n name=self.name,\n command=self.command,\n environment=self.environment\n )\n self.logger.debug(\"Created container {}\".format(self.container['Id']))","def test_pull_mocked(self):\n # Use defaults for format, arch, os, ostcount, replication\n pr = self.pull\n # Do the pull\n session = self.mtm.new_session(self.auth, self.system)\n rec = self.mtm.pull(session, pr) # ,delay=False)\n self.assertIsNotNone(rec)\n id = rec['_id']\n # Confirm record\n q = {'system': self.system, 'itype': self.itype, 'pulltag': self.tag}\n mrec = self.images.find_one(q)\n self.assertIn('_id', mrec)\n # Track through transistions\n state = self.time_wait(id)\n self.assertEqual(state, 'READY')\n imagerec = self.mtm.lookup(session, self.pull)\n self.assertIn('ENTRY', imagerec)\n self.assertIn('ENV', imagerec)\n # Cause a failure\n self.images.drop()\n self.mtm.workers.set_mode(2)\n rec = self.mtm.pull(session, pr)\n time.sleep(2)\n self.assertIsNotNone(rec)\n id = rec['_id']\n state = self.mtm.get_state(id)\n self.assertEqual(state, 'FAILURE')\n self.mtm.workers.set_mode(1)","def build_all_images_for_lang(lang):\n if not args.git_checkout:\n if args.release != \"master\":\n print(\n \"Cannot use --release without also enabling --git_checkout.\\n\"\n )\n sys.exit(1)\n releases = [args.release]\n else:\n if args.release == \"all\":\n releases = client_matrix.get_release_tags(lang)\n else:\n # Build a particular release.\n if args.release not in [\"master\"] + client_matrix.get_release_tags(\n lang\n ):\n jobset.message(\n \"SKIPPED\",\n \"%s for %s is not defined\" % (args.release, lang),\n do_newline=True,\n )\n return []\n releases = [args.release]\n\n images = []\n for release in releases:\n images += build_all_images_for_release(lang, release)\n jobset.message(\n \"SUCCESS\",\n \"All docker images built for %s at %s.\" % (lang, releases),\n do_newline=True,\n )\n return images","def pull(connection, rid=None, repo=None):\n\n if repo is None:\n repo = Repository(connection, rid)\n\n return repo.pull()","def build_and_push_3p_images():\n\n log.info(\n \"Building Lambda Stack docker images (these are not on DockerHub). \"\n \"FMI see https://github.com/lambdal/lambda-stack-dockerfiles\")\n run_cmd(\"\"\"\n cd /tmp &&\n (rm -rf lambda-stack-dockerfiles || true) &&\n git clone https://github.com/lambdal/lambda-stack-dockerfiles &&\n cd lambda-stack-dockerfiles &&\n git checkout d762400d61636c074533416674426a84cc4d8992 &&\n docker build -t oarphpy/lambda-stack:22.04 -f Dockerfile.jammy . &&\n docker push oarphpy/lambda-stack:22.04\n \"\"\")","async def pull(self) -> None:\n pull_fun = getattr(self, '_pull')\n if inspect.iscoroutinefunction(pull_fun):\n await pull_fun()\n return\n await run_sync(pull_fun)","def build(path, tag):\n print (\"*** BUILDING IS INITIATED\")\n try:\n response = client.images.build(path = path, tag=tag, rm=True)\n except Exception as ex:\n template = \"An exception of type {0} occured. Arguments:\\n{1!r}\"\n message = template.format(type(ex).__name__, ex.args)\n click.echo(message)\n print(\"*** BUILD OUTPUT:\")\n for object in response[1]:\n click.echo(object)\n click.echo('*** BUILDING FINISHED')","def _pull(self) -> None:\n raise NotImplementedError() # pragma: no cover","def get_build_images(self) -> List[Image]:\n images = []\n image_names = []\n conflicting_names = []\n for registry_name, registry in self.registries.items():\n # if the registry is not marked as source, skip it\n if not registry.source:\n continue\n\n images += self.get_images_from_registry(registry)\n\n if conflicting_names:\n raise RuntimeError(\"Images found in multiple 'source' repositories: %s\", conflicting_names)\n\n return images","def load_remote_image(image_url):\n response = requests.get(image_url, stream=True)\n img = Image.open(BytesIO(response.content))\n image = cv2.cvtColor(np.asarray(img), cv2.COLOR_RGB2BGR)\n return image","def container_pull(ctx, token, source):\n try:\n out = ctx.obj.container_pull(token, source)\n print_message(out)\n except BaseException as e:\n print_error(e.message)","def mixin_hub_pull_parser(parser):\n\n def hub_uri(uri: str) -> str:\n from ...hubble.helper import parse_hub_uri\n\n parse_hub_uri(uri)\n return uri\n\n parser.add_argument(\n 'uri',\n type=hub_uri,\n help='The URI of the executor to pull (e.g., jinahub[+docker]://UUID8)',\n )\n mixin_hub_pull_options_parser(parser)","def test_aws_service_api_public_images_get(self):\n pass","def get_images(url):\n \n # =============================================================================\n # Selenium.\n # =============================================================================\n\n chrome_options = Options()\n #chrome_options.add_argument('--incognito')\n #chrome_options.add_argument('--headless')\n #chrome_options.add_argument('--no-sandbox')\n \n driver = webdriver.Chrome(options=chrome_options,executable_path='/usr/local/bin/chromedriver') # Optional argument, if not specified will search path.\n driver.get('https://' + url)\n \n #scrolling to bottom to load all images on the page\n driver.execute_script(\"window.scrollTo(0, document.body.scrollHeight);\")\n #sleep to make sure everything loads\n time.sleep(5)\n \n \n html_source = driver.page_source\n \n img_alt_src(html_source)\n \n driver.close()\n driver.quit()","def pull(self):","async def fetch_image_by_id(\n image_uid: str\n):\n image_uid = int(image_uid)\n image = utils_com.get_com_image_by_uid(image_uid)\n return image","def get_a_bing_archive_wallpaper_remote():\r\n \r\n logging.debug('get_a_bing_archive_wallpaper_remote()')\r\n\r\n now = datetime.datetime.now()\r\n url = \"https://bingwallpaper.anerg.com/de/{}\".format(now.strftime('%Y%m'))\r\n\r\n # get image url\r\n if use_proxy:\r\n \r\n response = requests.get(url, proxies=proxies, timeout=15, verify=False)\r\n else:\r\n response = requests.get(url)\r\n match = re.findall('.*src=\\\"([^\\\"]*\\.jpg)\\\".*', response.text)\r\n for i in range(0, len(match)):\r\n full_image_url = \"https:{}\".format(match[i])\r\n \r\n # image's name\r\n image_name = get_generated_image_name(full_image_url)\r\n \r\n # Check and maintain DB\r\n if not exists_image_in_database(full_image_url) and i+1 < len(match):\r\n add_image_to_database(full_image_url, image_name, \"bingarchive\")\r\n # download and save image\r\n full_image_path = download_image(full_image_url, image_name)\r\n update_image_in_database(full_image_url, full_image_path)\r\n\r\n # Return full path to image\r\n logging.debug('get_a_bing_archive_wallpaper_remote - full_image_path = {}'.format(full_image_path))\r\n return full_image_path\r\n elif i+1 == len(match):\r\n full_image_path = get_image_path_from_database(full_image_url)\r\n\r\n # Return full path to image\r\n logging.debug('get_a_bing_archive_wallpaper_remote - full_image_path = {}'.format(full_image_path))\r\n return full_image_path","def local_images(self, images):\n registries = self.known_docker_registries()\n found_images = []\n for image in images:\n # docker could have the image name as-is or prefixed with any registry\n imglist = [image] + [reg + \"/\" + image for reg in registries]\n if self.is_image_local(imglist):\n found_images.append(image)\n return found_images","def get_image(name):\r\n return nova.images.find(name=name)","def get_pulls(self):\n url = self.base_url + 'pulls'\n\n req = requests.get(headers=self.headers, url=url)\n\n return req.json()","def test_pull_silent(self, mock_docker_environment, snapshot, capsys):\n mock_client = mock_docker_environment\n pull_image(TEST_IMAGE_NAME, silent=True)\n mock_client.api.pull.assert_called_with(\n TEST_IMAGE_NAME, \"latest\", stream=False, decode=False\n )\n out, err = capsys.readouterr()\n snapshot.assert_match(out)","def test_unknown_registry(self, mock_docker_environment):\n mock_docker_environment.images.get.side_effect = DockerNotFound(\"testing\")\n build_image_if_needed(\"unknown.registry.com/foo/bar\")\n mock_docker_environment.images.build.assert_called_with(\n dockerfile=\"Dockerfile\",\n tag=\"unknown.registry.com/foo/bar:latest\",\n path=\"/opt/ixian_docker\",\n )","def _docker_search(term):\n http_conn = httplib.HTTPConnection('localhost', 4243)\n http_conn.request('GET',\n '/images/search?term={0}'.format(urllib.quote(term)))\n resp = http_conn.getresponse()\n data = resp.read()\n if resp.status != 200:\n return []\n return [repos['Name'] for repos in json.loads(data)]","async def get_url_images(session, url):\n content = await get_page(session, url)\n if not content:\n return []\n soup = BeautifulSoup(content, features=\"html.parser\")\n image_sources = [img['src'] for img in soup.find_all('img')]\n image_sources_fixed = [f'https:{source}' if 'https:' not in source else source for source in image_sources]\n images = []\n for source in image_sources_fixed:\n image = await get_image(session, source)\n if image:\n images.append((source, image))\n\n return images","def cli(ctx, wheels_config, image, output, uid, gid, fetch_srcs, wheel):\n wheel_config_manager = WheelConfigManager.open(ctx.config, wheels_config)\n cachemgr = CacheManager(ctx.config.cache_path)\n wheel_config = wheel_config_manager.get_wheel_config(wheel)\n # `image` is an image_name until here\n try:\n image = wheel_config.get_image(image)\n except KeyError:\n warn(\"Warning: Image '%s' is not in '%s' imageset\", image, wheel_config.imageset.name)\n # we could do imageset autodetection here but that seems like a waste of time, this command isn't supposed to be\n # user facing like `wheel`. Just use whatever image is instructed.\n wheel_config.set_imageset(imageset=ctx.config.make_imageset('_ephemeral_', [image]), force=True)\n image = wheel_config.get_image(image)\n ectx = LocalExecutionContext(image)\n forge = ForgeWheel(wheel_config, cachemgr, ectx.run_context, image=image)\n if fetch_srcs:\n forge.cache_sources()\n forge.bdist_wheel(output=output, uid=uid, gid=gid)","def article_images(id):\n try:\n query_string = '&artile_id='+str(id)\n logger.info('Calling the api' + APIURL + '/images/?format=json'+query_string)\n response = requests.get(APIURL + '/images/?format=json'+query_string)\n parser = json.loads(response.content)\n return parser\n except:\n logger.error('Calling the api error in article_images')\n raise Http404(\"Article image does not exist\")","def images_list(self, kwargs=None):\n\n try:\n scode, images = Rest.get('Image')\n except docker.errors.APIError as e:\n Console.error(e.explanation)\n return\n\n if len(images) == 0:\n Console.info(\"No images exist\")\n return\n\n n = 1\n e = {}\n for image in images:\n d = {}\n d['Ip'] = image['Ip']\n d['Id'] = image['Id']\n if image['RepoTags'] == None:\n d['Repository'] = image['RepoDigests'][0]\n else:\n d['Repository'] = image['RepoTags'][0]\n # d['Size'] = image['Size']\n d['Size(GB)'] = round(image['Size'] / float(1 << 30), 2) # Converting the size to GB\n e[n] = d\n n = n + 1\n Console.ok(str(Printer.dict_table(e, order=['Ip', 'Id', 'Repository', 'Size(GB)'])))","def pull(self, **kwargs):\n return _taskpipeoperation(self,'pull', **kwargs)","def build_all_images_for_release(lang, release):\n docker_images = []\n build_jobs = []\n\n env = {}\n # If we not using current tree or the sibling for grpc stack, do checkout.\n stack_base = \"\"\n if args.git_checkout:\n stack_base = checkout_grpc_stack(lang, release)\n var = {\n \"go\": \"GRPC_GO_ROOT\",\n \"java\": \"GRPC_JAVA_ROOT\",\n \"node\": \"GRPC_NODE_ROOT\",\n }.get(lang, \"GRPC_ROOT\")\n env[var] = stack_base\n\n for runtime in client_matrix.get_runtimes_for_lang_release(lang, release):\n job = build_image_jobspec(runtime, env, release, stack_base)\n docker_images.append(job.tag)\n build_jobs.append(job)\n\n jobset.message(\"START\", \"Building interop docker images.\", do_newline=True)\n print(\"Jobs to run: \\n%s\\n\" % \"\\n\".join(str(j) for j in build_jobs))\n\n num_failures, _ = jobset.run(\n build_jobs, newline_on_success=True, maxjobs=multiprocessing.cpu_count()\n )\n if num_failures:\n jobset.message(\n \"FAILED\", \"Failed to build interop docker images.\", do_newline=True\n )\n docker_images_cleanup.extend(docker_images)\n sys.exit(1)\n\n jobset.message(\n \"SUCCESS\",\n \"All docker images built for %s at %s.\" % (lang, release),\n do_newline=True,\n )\n\n if release != \"master\":\n commit_log = os.path.join(stack_base, \"commit_log\")\n if os.path.exists(commit_log):\n for image in docker_images:\n add_files_to_image(image, [commit_log], \"release=%s\" % release)\n return docker_images","def list_images(self, repository_name, registry_id=None):\n repository = None\n found = False\n if repository_name in self.repositories:\n repository = self.repositories[repository_name]\n if registry_id:\n if repository.registry_id == registry_id:\n found = True\n else:\n found = True\n\n if not found:\n raise RepositoryNotFoundException(\n repository_name, registry_id or DEFAULT_REGISTRY_ID\n )\n\n images = []\n for image in repository.images:\n images.append(image)\n return images","def get_images(self):\n return self._get_brains(\"Image\")","def _get_local_repodigest(self, context):\n images_list = context.docker_client.images.list(self._name())\n if len(images_list) == 0:\n return None\n sorted_by_created = sorted(images_list, key=lambda x: x.attrs.get('Created'), reverse=True)\n image = sorted_by_created[0]\n repo_digests = image.attrs.get('RepoDigests')\n if len(repo_digests) != 1:\n raise Exception(\"Ambiguous RepoDigests, do not know how to handle this: %s\" % str(repo_digests))\n repo_digest = repo_digests[0]\n return repo_digest.split(\"@\")[-1]","def push_image(image):\n\n subprocess.check_call(['docker', 'push', image])","def test_pull_tag(self, mock_docker_environment, snapshot, capsys):\n mock_client = mock_docker_environment\n pull_image(TEST_IMAGE_NAME, \"custom_tag\")\n mock_client.api.pull.assert_called_with(\n TEST_IMAGE_NAME, \"custom_tag\", stream=True, decode=True\n )\n out, err = capsys.readouterr()\n snapshot.assert_match(out)","def get_image(camera_name: str):\n img_bytes = requests.post(f\"http://{IP}:{PORT}/get_image\", data=pickle.dumps(camera_name)).content\n img = pickle.loads(img_bytes)","def main():\n for city in CITIES:\n fetchNewseumImage(city)","async def images(self, ctx, *, query: str=None):\n # Handle empty query\n if query is None:\n return await ctx.error('Please provide a query!')\n\n # Using these specific headers and \"lnms\" as source, will provide divs with \"rg_meta\" classes,\n # The modern image search page being JS rendered, data in these divs are jsons with raw image URLs\n # Old image search pages, only have thumbnails and a direct link to websites\n params = {'q': quote_plus(query), 'source': 'lmns', 'tbm': 'isch'}\n async with self.aiohttp_session.get(self.url, params=params, headers=self.image_headers) as r:\n html = await r.text()\n\n # Healthy\n soup = BeautifulSoup(html, 'lxml')\n\n # Go over 4 items, json.loads the item text, and grab \"ou\" probably stands for \"original url\"\n images = []\n for i, item in enumerate(soup.select('div.rg_meta')[:4]):\n js = json.loads(item.text)\n images.append((f\"{i+1}. {js['st']} - {js['s']}\", js[\"ou\"]))\n newl = '\\n'\n await ctx.message.edit(content=f\"```py\\n{newl.join([x[0] for x in images])}\"\n f\"\\n# Choose the appropriate number or type 0 to leave\\n```\")\n\n def check(m):\n return m.author == ctx.author and m.content.isdigit() and m.channel == ctx.channel\n message = await self.bot.wait_for('message', check=check)\n if message.content == \"0\":\n await message.delete()\n return await ctx.message.delete()\n choice = int(message.content) - 1\n await message.delete()\n await ctx.message.edit(content=images[choice][1])","def image(image_id):\n\n found = False\n img = None\n \n try:\n for img in api.get_all_images():\n if img.id == image_id:\n found = True\n break\n except Exception:\n logging.error(\"Cannot make API connection to retrieve image info!\")\n\n if not found:\n return None\n\n return img"],"string":"[\n \"def pull(self) -> None:\\n cached_zip_filepath = None\\n try:\\n scheme, name, tag, secret = parse_hub_uri(self.args.uri)\\n\\n executor = HubIO.fetch(name, tag=tag, secret=secret)\\n\\n if not tag:\\n tag = executor.tag\\n\\n uuid = executor.uuid\\n image_name = executor.image_name\\n archive_url = executor.archive_url\\n md5sum = executor.md5sum\\n\\n if scheme == 'jinahub+docker':\\n # pull the Docker image\\n with TimeContext(f'pulling {image_name}', self.logger):\\n image = self._client.images.pull(image_name)\\n if isinstance(image, list):\\n image = image[0]\\n image_tag = image.tags[0] if image.tags else ''\\n self.logger.success(\\n f'🎉 pulled {image_tag} ({image.short_id}) uncompressed size: {get_readable_size(image.attrs[\\\"Size\\\"])}'\\n )\\n return\\n if exist_local(uuid, tag):\\n self.logger.debug(\\n f'The executor `{self.args.uri}` has already been downloaded.'\\n )\\n return\\n # download the package\\n with TimeContext(f'downloading {self.args.uri}', self.logger):\\n cache_dir = Path(\\n os.environ.get(\\n 'JINA_HUB_CACHE_DIR', Path.home().joinpath('.cache', 'jina')\\n )\\n )\\n cache_dir.mkdir(parents=True, exist_ok=True)\\n cached_zip_filename = f'{uuid}-{md5sum}.zip'\\n cached_zip_filepath = download_with_resume(\\n archive_url,\\n cache_dir,\\n cached_zip_filename,\\n md5sum=md5sum,\\n )\\n\\n with TimeContext(f'unpacking {self.args.uri}', self.logger):\\n try:\\n install_local(\\n cached_zip_filepath,\\n uuid,\\n tag,\\n install_deps=self.args.install_requirements,\\n )\\n except Exception as ex:\\n raise HubDownloadError(str(ex))\\n\\n except Exception as e:\\n self.logger.error(\\n f'Error when pulling the executor `{self.args.uri}`: {e!r}'\\n )\\n finally:\\n # delete downloaded zip package if existed\\n if cached_zip_filepath is not None:\\n cached_zip_filepath.unlink()\",\n \"def pullall():\\n\\tprint(red('\\\\t\\\\tStarting download of QNIBTerminal images\\\\t\\\\t'))\\n\\t# pull all the needed images\\n\\tdocker_images={'fd20','terminal','helixdns','elk','slurm','compute'}\\n\\tfor image in docker_images:\\n\\t\\tprint 'docker pull qnib/'+image\\n\\t\\t# run('docker pull qnib/'+image)\",\n \"def pull_image(image):\\n\\n subprocess.check_call(['docker', 'pull', image])\",\n \"def _pull_image(self, docker_client: \\\"DockerClient\\\"):\\n image, tag = self._get_image_and_tag()\\n\\n return docker_client.images.pull(image, tag)\",\n \"def pull_image(self):\\n status = []\\n for key, container in self.containers.items():\\n result = container.pull()\\n status.append(result)\\n return status\",\n \"def pull_dlc_images(images):\\n for image in images:\\n run(f\\\"docker pull {image}\\\", hide=\\\"out\\\")\",\n \"def pull_base_images(ctx, dockerfile, signed_pull=True):\\n images = set()\\n stages = set()\\n\\n with open(dockerfile, \\\"r\\\") as f:\\n for line in f:\\n words = line.split()\\n # Get source images\\n if len(words) < 2 or words[0].lower() != \\\"from\\\":\\n continue\\n images.add(words[1])\\n # Get stage names to remove them from pull\\n if len(words) < 4 or words[2].lower() != \\\"as\\\":\\n continue\\n stages.add(words[3])\\n\\n if stages:\\n print(\\\"Ignoring intermediate stage names: {}\\\".format(\\\", \\\".join(stages)))\\n images -= stages\\n\\n print(\\\"Pulling following base images: {}\\\".format(\\\", \\\".join(images)))\\n\\n pull_env = {}\\n if signed_pull:\\n pull_env[\\\"DOCKER_CONTENT_TRUST\\\"] = \\\"1\\\"\\n\\n for i in images:\\n ctx.run(\\\"docker pull {}\\\".format(i), env=pull_env)\",\n \"def cli(ctx, image_file):\\n if not image_file:\\n return\\n for pull_image in image_file.readline():\\n pull_image = pull_image.rstrip('\\\\n')\\n if len(pull_image) == 0:\\n continue\\n docker.pull(pull_image)\\n push_image = '%s/%s/%s' % (DEFAULT_REGISTRY,\\n DEFAULR_NAMESPACE,\\n pull_image.split('/')[-1])\\n docker.tag(pull_image, push_image)\\n docker.push(push_image)\",\n \"def pullDockerImage(client, names):\\n imgNotExistList = []\\n for name in names:\\n try:\\n client.images.pull(name)\\n # some invalid image names will not be pulled but the pull method\\n # will not throw an exception so the only way to confirm if a pull\\n # succeeded is to attempt a docker inspect on the image\\n client.images.get(name)\\n except Exception:\\n imgNotExistList.append(name)\\n if len(imgNotExistList) != 0:\\n raise DockerImageNotFoundError('Could not find multiple images ',\\n image_name=imgNotExistList)\",\n \"def pull_image(self, tag):\\n image_name = self.build_image_name(tag)\\n image = self.client.images.pull(image_name)\\n return image\",\n \"def pull(self, repo, tag):\\n check_blacklist(repo)\\n logger.info(\\\"Pulling Docker image {}:{}\\\".format(repo, tag))\\n with SimpleFlock(self.FLOCKFILE, timeout=1200):\\n stream = self.client.pull(repo, tag=tag, stream=True, insecure_registry=True)\\n log_output(stream)\",\n \"def get_images(name):\\n url = \\\"/\\\".join([REGISTRY_BASE, name, \\\"/tags/list\\\"])\\n response = req(url)\\n image_list = []\\n if response is not None:\\n headers = {\\\"Accept\\\": \\\"application/vnd.docker.distribution.manifest.v2+json\\\"}\\n tags = response[\\\"tags\\\"]\\n for tag in tags:\\n url = \\\"/\\\".join([REGISTRY_BASE, name, \\\"/manifests\\\", tag])\\n response = req(url, headers)\\n if response is not None:\\n image = {}\\n image[\\\"size\\\"] = response[\\\"config\\\"][\\\"size\\\"]\\n for i in response[\\\"layers\\\"]:\\n image[\\\"size\\\"] += i[\\\"size\\\"]\\n image[\\\"size\\\"] = round(float(image[\\\"size\\\"]) / 1024 / 1024, 2)\\n image[\\\"id\\\"] = response[\\\"config\\\"][\\\"digest\\\"][7:19]\\n image[\\\"tag\\\"] = tag\\n image[\\\"cmd\\\"] = \\\"docker pull uk8s.com/\\\" + name + \\\":\\\" + tag\\n image_list.append(image)\\n return sorted(image_list, reverse=True)\",\n \"def fetch_image(client, name):\\n try:\\n image = client.images.get(name)\\n except docker.errors.ImageNotFound:\\n name, tag = _parse_image_tag(name)\\n tag = 'latest' if tag is None else tag\\n\\n log.info(\\\"Pulling tag '{}' for image '{}'...\\\".format(tag, name))\\n image = client.images.pull(name, tag=tag)\\n\\n log.debug(\\\"Found image '{}' for tag '{}'\\\".format(image.id, name))\\n return image\",\n \"def pull(self, data):\\n required = {'token', 'source'}\\n api.validate(data, required)\\n token = data['token']\\n repo = data['source']\\n self.credentials_module.authorize(token)\\n result = self.docker_module.pull_image(repo)\\n # credentials_module.add_image(token, result['image_id'])\\n return result\",\n \"def fetch_image(self, image: Image):\\n self.log.info(\\\"Fetching image %s\\\", image)\\n for line in image.repository.registry.client.pull(str(image.repository), image.tag, stream=True, decode=True):\\n self.log.debug(line)\",\n \"def dockerhub_pull(services):\\n docker_services = get_list_of_services(services)\\n service_list = construct_docker_compose_file(docker_services)\\n\\n procs = []\\n for service in service_list:\\n process = Process(target=run_docker_compose_command, args=(['pull', service],))\\n process.start()\\n procs.append(process)\\n\\n for p in procs:\\n p.join()\\n\\n remove_docker_compose_file()\",\n \"def pull(release):\\n image = f\\\"breqwatr/rsyslog:{release}\\\"\\n ecr.pull(image)\",\n \"def run(docker_hub_client, args):\\n resp = docker_hub_client.get_tags(args.orgname, args.reponame, args.page)\\n if resp['code'] == 200:\\n if resp['content']['count'] > 0:\\n rows = []\\n for repo in resp['content']['results']:\\n formatted_date = ''\\n if repo['last_updated']:\\n formatted_date = dateutil.parser \\\\\\n .parse(repo['last_updated'])\\n formatted_date = formatted_date.strftime(\\\"%Y-%m-%d %H:%M\\\")\\n # Convert full_size in bytes to KB\\n size_in_kb = repo['full_size'] / 1024\\n formatted_size = readableMemoryFormat(size_in_kb)\\n rows.append([repo['name'], formatted_size, formatted_date])\\n header = ['Name', 'Size', 'Last updated']\\n print_result(args.format, rows, header, resp['content']['count'],\\n args.page)\\n else:\\n print('Error fetching tags for: {0}/{1}'.\\n format(args.orgname, args.reponame))\",\n \"def pull_image(image):\\n docker_client = _docker_client()\\n response = docker_client.pull(image)\\n lines = [line for line in response.splitlines() if line]\\n\\n # The last line of the response contains the overall result of the pull\\n # operation.\\n pull_result = json.loads(lines[-1])\\n if \\\"error\\\" in pull_result:\\n raise Exception(\\\"Could not pull {}: {}\\\".format(\\n image, pull_result[\\\"error\\\"]))\",\n \"def docker_pull(args, image): # type: (EnvironmentConfig, str) -> None\\n if '@' not in image and ':' not in image:\\n display.info('Skipping pull of image without tag or digest: %s' % image, verbosity=2)\\n return\\n\\n if docker_image_exists(args, image):\\n display.info('Skipping pull of existing image: %s' % image, verbosity=2)\\n return\\n\\n for _iteration in range(1, 10):\\n try:\\n docker_command(args, ['pull', image], capture=False)\\n return\\n except SubprocessError:\\n display.warning('Failed to pull docker image \\\"%s\\\". Waiting a few seconds before trying again.' % image)\\n time.sleep(3)\\n\\n raise ApplicationError('Failed to pull docker image \\\"%s\\\".' % image)\",\n \"def fetch(\\n name: str,\\n tag: Optional[str] = None,\\n secret: Optional[str] = None,\\n ) -> HubExecutor:\\n\\n with ImportExtensions(required=True):\\n import requests\\n\\n pull_url = get_hubble_url() + f'/{name}/?'\\n path_params = {}\\n if secret:\\n path_params['secret'] = secret\\n if tag:\\n path_params['tag'] = tag\\n\\n request_headers = HubIO._get_request_header()\\n\\n pull_url += urlencode(path_params)\\n resp = requests.get(pull_url, headers=request_headers)\\n if resp.status_code != 200:\\n if resp.text:\\n raise Exception(resp.text)\\n resp.raise_for_status()\\n\\n resp = resp.json()\\n\\n result = HubExecutor(\\n resp['id'],\\n resp.get('alias', None),\\n resp['tag'],\\n resp['visibility'],\\n resp['image'],\\n resp['package']['download'],\\n resp['package']['md5'],\\n )\\n\\n return result\",\n \"def test_pull_multiple_tags(self):\\n pr = {\\n 'system': self.system,\\n 'itype': self.itype,\\n 'tag': self.public,\\n 'remotetype': 'dockerv2'\\n }\\n # Do the pull\\n session = self.m.new_session(self.auth, self.system)\\n rec = self.m.pull(session, pr) # ,delay=False)\\n id = rec['_id']\\n self.assertIsNotNone(rec)\\n # Confirm record\\n q = {'system': self.system, 'itype': self.itype,\\n 'pulltag': self.public}\\n mrec = self.images.find_one(q)\\n self.assertIn('_id', mrec)\\n # Track through transistions\\n state = self.time_wait(id)\\n self.assertEqual(state, 'READY')\\n\\n # Now reppull with a different tag for the same image\\n newtag = self.public.replace('latest', '1')\\n pr = {\\n 'system': self.system,\\n 'itype': self.itype,\\n 'tag': newtag,\\n 'remotetype': 'dockerv2'\\n }\\n rec = self.m.pull(session, pr) # ,delay=False)\\n id = rec['_id']\\n self.assertIsNotNone(rec)\\n # Track through transistions\\n state = self.time_wait(id)\\n # Requery the original record\\n mrec = self.images.find_one(q)\\n self.assertIn(self.public, mrec['tag'])\\n self.assertIn(newtag, mrec['tag'])\",\n \"def pull(self, action, image_name, **kwargs):\\n config_id = action.config_id\\n registry, __, image = config_id.config_name.rpartition('/')\\n login_kwargs = {}\\n if _check_insecure_registry(kwargs):\\n login_kwargs['insecure_registry'] = True\\n if registry and '.' in registry and registry not in self._login_registries:\\n self.login(action, registry, **login_kwargs)\\n log.info(\\\"Pulling image %s:%s.\\\", config_id.config_name, config_id.instance_name)\\n res = action.client.pull(repository=config_id.config_name, tag=config_id.instance_name, **kwargs)\\n log.debug(\\\"Done pulling image %s:%s.\\\", config_id.config_name, config_id.instance_name)\\n self._policy.images[action.client_name].refresh_repo(config_id.config_name)\\n log.debug(\\\"Refreshed image cache for repo %s.\\\", config_id.config_name)\\n return res\",\n \"def _load(self, resource_handler):\\n log.info('[%s] Loading Docker image origin=%r image=%r tag=%r',\\n resource_handler.name, self.origin, self.image, self.tag)\\n if self.origin == 'dockerhub':\\n resource_handler.cli.pull(repository=self.image, tag=self.tag)\\n elif self.origin == 'local':\\n pass\\n else:\\n resource_handler.cli.import_image_from_url(\\n url=self.origin,\\n repository=self.image,\\n tag=self.tag\\n )\",\n \"def images_refresh(self, kwargs=None):\\n scode, hosts = Rest.get('Host')\\n filter = {}\\n n = 1\\n e = {}\\n data = []\\n for host in hosts:\\n os.environ[\\\"DOCKER_HOST\\\"] = host['Ip'] + \\\":\\\" + str(host['Port'])\\n filter['Ip'] = os.environ[\\\"DOCKER_HOST\\\"].split(':')[0]\\n self.client = docker.from_env()\\n try:\\n images = self.client.images.list(**kwargs)\\n except docker.errors.APIError as e:\\n Console.error(e.explanation)\\n return\\n\\n if len(images) == 0:\\n Console.info(\\\"No images exist\\\")\\n continue\\n\\n for imagem in images:\\n image = imagem.__dict__['attrs']\\n image['Ip'] = os.environ[\\\"DOCKER_HOST\\\"].split(':')[0]\\n data.append(image)\\n d = {}\\n d['Ip'] = os.environ[\\\"DOCKER_HOST\\\"].split(':')[0]\\n d['Id'] = image['Id']\\n if image['RepoTags'] == None:\\n d['Repository'] = image['RepoDigests'][0]\\n else:\\n d['Repository'] = image['RepoTags'][0]\\n # d['Size'] = image['Size']\\n d['Size(GB)'] = round(image['Size'] / float(1 << 30), 2)\\n e[n] = d\\n n = n + 1\\n Rest.delete('Image', filter)\\n Rest.post('Image', data)\\n Console.ok(str(Printer.dict_table(e, order=['Ip', 'Id', 'Repository', 'Size(GB)'])))\",\n \"def push(images, tag, registry):\\n manager = Manager('push', tag, images=images, registry_url=registry)\\n manager.run()\",\n \"def all(tag=None, env=None):\\n build_images(tag=tag)\\n push_images(tag=tag)\\n deploy(env=env, tag=tag)\",\n \"def test_image_exists_registry_no_pull(self, mock_docker_environment):\\n mock_docker_environment.images.get.side_effect = DockerNotFound(\\\"testing\\\")\\n build_image_if_needed(TEST_IMAGE_NAME, pull=False)\\n mock_docker_environment.images.build.assert_called_with(**self.default_call_kwargs)\",\n \"def test_image_exists_registry_pull_not_found(self, mock_docker_environment):\\n mock_docker_environment.images.get.side_effect = DockerNotFound(\\\"testing\\\")\\n mock_docker_environment.api.pull.side_effect = DockerNotFound(\\\"testing\\\")\\n\\n build_image_if_needed(TEST_IMAGE_NAME)\\n mock_docker_environment.api.pull.assert_called_with(\\n TEST_IMAGE_NAME, \\\"latest\\\", decode=True, stream=True\\n )\\n mock_docker_environment.images.build.assert_called_with(**self.default_call_kwargs)\",\n \"def run_docker_push(image_list, docker_client):\\n logger.info(\\\"--- push image to registry ---\\\")\\n if runtime == Runtime.local:\\n logger.info(\\\"Detected local runtime, stop here.\\\")\\n return\\n\\n if not len(success_image_list):\\n logger.info(\\\"No success image to push, stop here.\\\")\\n return\\n\\n if GIT_BRANCH != \\\"master\\\":\\n logger.info(\\\"Not master branch, stop here\\\")\\n return\\n\\n docker_client.login(username=DOCKER_HUB_USERNAME, password=DOCKER_HUB_PASSWORD)\\n for image in image_list:\\n image.run_docker_push(docker_client)\\n print(\\\"Finished.\\\")\",\n \"def pull_image_if_not_exists(self, tag):\\n image_name = self.build_image_name(tag)\\n try:\\n # Check if exists\\n image = self.client.images.get(image_name)\\n except Exception as ex:\\n print('Image does not exist: {}'.format(str(ex)))\\n # Pull if doesn't exist\\n print('Pulling image {}'.format(image_name))\\n image = self.client.images.pull(image_name)\\n\\n return image\",\n \"def pull1(repo, **kwargs):\\n ret = do_pull(repo, \\\"topology.virl\\\")\\n if not ret:\\n exit(1)\",\n \"async def _pull(self) -> None:\\n raise NotImplementedError()\",\n \"def pull(path, service_names=None):\\n\\n project = __load_project(path)\\n if isinstance(project, dict):\\n return project\\n else:\\n try:\\n project.pull(service_names)\\n except Exception as inst: # pylint: disable=broad-except\\n return __handle_except(inst)\\n return __standardize_result(\\n True, \\\"Pulling containers images via docker-compose succeeded\\\", None, None\\n )\",\n \"def pull_image_version(self, app, image_name, image_tag, parent_task, fail_silently=False):\\n start_time = datetime.datetime.now().replace(microsecond=0)\\n\\n assert isinstance(image_name, str)\\n assert isinstance(image_tag, str)\\n\\n # The string \\\"local\\\" has a special meaning which means the most recent\\n # local image of that name, so we skip the remote call/check.\\n if image_tag == \\\"local\\\":\\n if fail_silently:\\n return None\\n else:\\n raise ImagePullFailure(\\n \\\"Cannot pull a local image\\\",\\n remote_name=None,\\n image_tag=image_tag\\n )\\n\\n # Check if the image already exists locally\\n # This is an optimization to save a trip to the registry: 1-2 sec per image\\n if image_tag != \\\"latest\\\":\\n try:\\n self.host.images.image_version(image_name, image_tag)\\n return None\\n except ImageNotFoundException:\\n # The image will be pulled from the registry\\n pass\\n\\n registry = self.get_registry(app)\\n\\n # See if the registry is willing to give us a URL (it's logged in)\\n if registry:\\n registry_url = registry.url(self.host)\\n else:\\n registry_url = None\\n if registry_url is None:\\n if fail_silently:\\n return None\\n else:\\n raise ImagePullFailure(\\n \\\"No registry configured\\\",\\n remote_name=None,\\n image_tag=image_tag\\n )\\n\\n task = Task(\\n \\\"Pulling remote image {}:{}\\\".format(image_name, image_tag),\\n parent=parent_task,\\n progress_formatter=lambda x: \\\"{} MB\\\".format(x // (1024 ** 2)),\\n )\\n\\n remote_name = \\\"{registry_url}/{image_name}\\\".format(\\n registry_url=registry_url,\\n image_name=image_name,\\n )\\n\\n stream = self._pull(app, task, remote_name, image_tag)\\n\\n layer_status = {}\\n current = None\\n total = None\\n for json_line in convert_to_json_stream(stream):\\n if 'error' in json_line:\\n task.finish(status=\\\"Failed\\\", status_flavor=Task.FLAVOR_WARNING)\\n if fail_silently:\\n return\\n else:\\n raise ImagePullFailure(\\n json_line['error'],\\n remote_name=remote_name,\\n image_tag=image_tag\\n )\\n elif 'id' in json_line:\\n if json_line['status'].lower() == \\\"downloading\\\":\\n layer_status[json_line['id']] = json_line['progressDetail']\\n\\n elif \\\"complete\\\" in json_line['status'].lower() and json_line['id'] in layer_status:\\n layer_status[json_line['id']]['current'] = layer_status[json_line['id']]['total']\\n\\n if layer_status:\\n statuses = [x for x in layer_status.values()\\n if \\\"current\\\" in x and \\\"total\\\" in x]\\n current = sum(x['current'] for x in statuses)\\n total = sum(x['total'] for x in statuses)\\n\\n if total is not None:\\n task.update(progress=(current, total))\\n\\n end_time = datetime.datetime.now().replace(microsecond=0)\\n time_delta_str = str(end_time - start_time)\\n if time_delta_str.startswith('0:'):\\n time_delta_str = time_delta_str[2:]\\n task.finish(status='Done [{}]'.format(time_delta_str), status_flavor=Task.FLAVOR_GOOD)\\n\\n # Tag the remote image as the right name\\n self._tag_image(remote_name, image_tag, image_name, image_tag, fail_silently)\\n self._tag_image(remote_name, image_tag, image_name, \\\"latest\\\", fail_silently)\",\n \"def tests_pull_workflow(self):\\n daemon_client = {}\\n client = self.get_client(daemon_client)\\n\\n for container in client.containers(all=True):\\n if 'test-container' in \\\\\\n ''.join([name for name in container.get('Names')]):\\n client.remove_container('test-container')\\n\\n if ['{0}:latest'.format(TEST_IMAGE)] in \\\\\\n [i.get('RepoTags') for i in client.images()]:\\n client.remove_image(TEST_IMAGE, force=True)\\n\\n # execute install workflow\\n self.env.execute('install', task_retries=0)\\n\\n container_instance = {}\\n\\n for instance in self.env.storage.get_node_instances():\\n if 'container_id' in instance.runtime_properties.keys():\\n container_instance = instance\\n\\n self.assertTrue(container_instance is not None,\\n 'Failed getting container.')\\n\\n container_id = container_instance.runtime_properties.get(\\n 'container_id')\\n containers = client.containers(all=True)\\n self.assertTrue(container_id in [c.get('Id') for c in containers])\\n\\n self.env.execute('uninstall', task_retries=3)\\n repotags = []\\n for i in client.images():\\n repotags.append(i.get('RepoTags'))\\n\\n self.assertFalse(TEST_IMAGE in [tag for tag in repotags])\\n if ['{0}:latest'.format(TEST_IMAGE)] in \\\\\\n [i.get('RepoTags') for i in client.images()]:\\n client.remove_image(TEST_IMAGE, force=True)\",\n \"def fetch(self, tag):\\n return fetch_image(self.collection.client, tag)\",\n \"def push_base():\\n docker('login')\\n docker('push %s' % env.base_image_name)\",\n \"def remote_pull(*keys):\",\n \"def pull_snapshot(edition, force):\\n from docker import DockerClient\\n from docker.errors import ImageNotFound\\n docker = DockerClient.from_env(version=\\\"auto\\\")\\n artifact = resolve_artifact_name(edition)\\n if force:\\n return download_snapshot_artifact(artifact)\\n else:\\n derived = derive_image_tag(artifact)\\n try:\\n docker.images.get(derived)\\n except ImageNotFound:\\n return download_snapshot_artifact(artifact)\\n else:\\n return derived\",\n \"def pull(self, repository, tag=None, stream=False, raise_on_error=False, **kwargs):\\n response = super(DockerClientWrapper, self).pull(repository, tag=tag, stream=stream, **kwargs)\\n if stream:\\n result = self._docker_status_stream(response, raise_on_error)\\n else:\\n result = self._docker_status_stream(response.split('\\\\r\\\\n') if response else (), raise_on_error)\\n return result and not result.get('error')\",\n \"def test_image_exists_registry(self, mock_docker_environment):\\n mock_docker_environment.images.get.side_effect = DockerNotFound(\\\"testing\\\")\\n build_image_if_needed(TEST_IMAGE_NAME)\\n mock_docker_environment.api.pull.assert_called_with(\\n TEST_IMAGE_NAME, \\\"latest\\\", decode=True, stream=True\\n )\\n mock_docker_environment.images.build.assert_not_called()\",\n \"def pull_image_then_retag(docker_client=None, repository=None, tag='latest', retag_repository=None):\\n try:\\n Logger.debug(f\\\"ready to pull the image {repository}:{tag}\\\")\\n image = docker_client.images.pull(repository, tag)\\n Logger.info(f\\\"pull the image {image.attrs['RepoTags'][0]} completed,ready to re-tag.\\\")\\n return image.tag(retag_repository, tag)\\n except docker.errors.APIError as error:\\n Logger.error(error)\\n return False\",\n \"def handle_api_list_images(self, http_context):\\n\\n command = self.docker + ['images', '--format', '\\\\'{{json .}}\\\\'', '--no-trunc', '-a']\\n images = []\\n for line in subprocess.check_output(command).decode().splitlines():\\n image = json.loads(line)\\n image['hash'] = image['ID'].split(':')[1][:12]\\n images.append(image)\\n return images\",\n \"def fetch_images(client, images):\\n return [fetch_image(client, image) for image in images]\",\n \"def push_image(self, tag_list, push_to_defaults, additional_registries=[], version_release_tuple=None,\\n push_late=False, dry_run=False):\\n\\n # Late pushes allow certain images to be the last of a group to be\\n # pushed to mirrors. CI/CD systems may initiate operations based on the\\n # update a given image and all other images need to be in place\\n # when that special image is updated. The special images are there\\n # pushed \\\"late\\\"\\n # Actions that need to push all images need to push all images\\n # need to make two passes/invocations of this method: one\\n # with push_late=False and one with push_late=True.\\n\\n is_late_push = False\\n if self.config.push.late is not Missing:\\n is_late_push = self.config.push.late\\n\\n if push_late != is_late_push:\\n return True\\n\\n push_names = []\\n\\n if push_to_defaults:\\n push_names.extend(self.metadata.get_default_push_names())\\n\\n push_names.extend(self.metadata.get_additional_push_names(additional_registries))\\n\\n # Nothing to push to? We are done.\\n if not push_names:\\n return True\\n\\n with Dir(self.distgit_dir):\\n\\n if version_release_tuple:\\n version = version_release_tuple[0]\\n release = version_release_tuple[1]\\n else:\\n\\n # History\\n # We used to rely on the \\\"release\\\" label being set in the Dockerfile, but this is problematic for several reasons.\\n # (1) If 'release' is not set, OSBS will determine one automatically that does not conflict\\n # with a pre-existing image build. This is extremely helpful since we don't have to\\n # worry about bumping the release during refresh images. This means we generally DON'T\\n # want the release label in the file and can't, therefore, rely on it being there.\\n # (2) People have logged into distgit before in order to bump the release field. This happening\\n # at the wrong time breaks the build.\\n\\n # If the version & release information was not specified,\\n # try to detect latest build from brew.\\n # Read in version information from the Distgit dockerfile\\n _, version, release = self.metadata.get_latest_build_info()\\n\\n try:\\n record = {\\n \\\"distgit_key\\\": self.metadata.distgit_key,\\n \\\"distgit\\\": '{}/{}'.format(self.metadata.namespace, self.metadata.name),\\n \\\"image\\\": self.config.name,\\n \\\"version\\\": version,\\n \\\"release\\\": release,\\n \\\"message\\\": \\\"Unknown failure\\\",\\n \\\"status\\\": -1,\\n # Status defaults to failure until explicitly set by success. This handles raised exceptions.\\n }\\n\\n # pull just the main image name first\\n image_name_and_version = \\\"%s:%s-%s\\\" % (self.config.name, version, release)\\n brew_image_url = \\\"/\\\".join((constants.BREW_IMAGE_HOST, image_name_and_version))\\n pull_image(brew_image_url)\\n record['message'] = \\\"Successfully pulled image\\\"\\n record['status'] = 0\\n except Exception as err:\\n record[\\\"message\\\"] = \\\"Exception occurred: %s\\\" % str(err)\\n self.logger.info(\\\"Error pulling %s: %s\\\" % (self.metadata.name, err))\\n raise\\n finally:\\n self.runtime.add_record('pull', **record)\\n\\n push_tags = list(tag_list)\\n\\n # If no tags were specified, build defaults\\n if not push_tags:\\n push_tags = self.metadata.get_default_push_tags(version, release)\\n\\n for image_name in push_names:\\n try:\\n\\n repo = image_name.split('/', 1)\\n\\n action = \\\"push\\\"\\n record = {\\n \\\"distgit_key\\\": self.metadata.distgit_key,\\n \\\"distgit\\\": '{}/{}'.format(self.metadata.namespace, self.metadata.name),\\n \\\"repo\\\": repo, # ns/repo\\n \\\"name\\\": image_name, # full registry/ns/repo\\n \\\"version\\\": version,\\n \\\"release\\\": release,\\n \\\"message\\\": \\\"Unknown failure\\\",\\n \\\"tags\\\": \\\", \\\".join(push_tags),\\n \\\"status\\\": -1,\\n # Status defaults to failure until explicitly set by success. This handles raised exceptions.\\n }\\n\\n for push_tag in push_tags:\\n push_url = '{}:{}'.format(image_name, push_tag)\\n\\n if dry_run:\\n rc = 0\\n self.logger.info('Would have tagged {} as {}'.format(brew_image_url, push_url))\\n self.logger.info('Would have pushed {}'.format(push_url))\\n else:\\n rc, out, err = exectools.cmd_gather([\\\"docker\\\", \\\"tag\\\", brew_image_url, push_url])\\n\\n if rc != 0:\\n # Unable to tag the image\\n raise IOError(\\\"Error tagging image as: %s\\\" % push_url)\\n\\n for r in range(10):\\n self.logger.info(\\\"Pushing image to mirror [retry=%d]: %s\\\" % (r, push_url))\\n rc, out, err = exectools.cmd_gather([\\\"docker\\\", \\\"push\\\", push_url])\\n if rc == 0:\\n break\\n self.logger.info(\\\"Error pushing image -- retrying in 60 seconds\\\")\\n time.sleep(60)\\n\\n if rc != 0:\\n # Unable to push to registry\\n raise IOError(\\\"Error pushing image: %s\\\" % push_url)\\n\\n record[\\\"message\\\"] = \\\"Successfully pushed all tags\\\"\\n record[\\\"status\\\"] = 0\\n\\n except Exception as err:\\n record[\\\"message\\\"] = \\\"Exception occurred: %s\\\" % str(err)\\n self.logger.info(\\\"Error pushing %s: %s\\\" % (self.metadata.name, err))\\n raise\\n\\n finally:\\n self.runtime.add_record(action, **record)\\n\\n return True\",\n \"async def fetch_all_images(sess: Session = Depends(get_db)):\\n image_list = utils_com.get_com_image_list(sess)\\n return image_list\",\n \"def get_images(stage=0):\\n return get_files(stage)[0]\",\n \"def image(self, name=None):\\n return self.find(self.images(), name=name)\",\n \"def pull(args):\\n do_all_projects(args + [\\\"pull\\\"])\",\n \"def test_pull(self, mock_docker_environment, snapshot, capsys):\\n mock_client = mock_docker_environment\\n pull_image(TEST_IMAGE_NAME)\\n mock_client.api.pull.assert_called_with(\\n TEST_IMAGE_NAME, \\\"latest\\\", stream=True, decode=True\\n )\\n out, err = capsys.readouterr()\\n snapshot.assert_match(out)\",\n \"def push_dockerhub(c, app, version, latest=False):\\n if app.lower() == 'core':\\n c.run('sudo docker push kinecosystem/stellar-core:{version}'.format(version=version))\\n if latest:\\n c.run('sudo docker push kinecosystem/stellar-core:latest')\\n elif app.lower() == 'horizon':\\n c.run('sudo docker push kinecosystem/horizon:{version}'.format(version=version))\\n if latest:\\n c.run('sudo docker push kinecosystem/horizon:latest')\\n elif app.lower() == 'friendbot':\\n c.run('sudo docker push kinecosystem/friendbot:{version}'.format(version=version))\\n if latest:\\n c.run('sudo docker push kinecosystem/friendbot:latest')\\n else:\\n Exit('Unknown application {}'.format(app))\",\n \"def get_latest_bing_wallpaper_remote():\\r\\n\\r\\n logging.debug('get_latest_bing_wallpaper_remote()')\\r\\n\\r\\n # get image url\\r\\n if use_proxy:\\r\\n response = requests.get(\\\"https://www.bing.com/HPImageArchive.aspx?format=js&idx=0&n=1&mkt=en-US\\\", proxies=proxies, timeout=5, verify=False)\\r\\n else:\\r\\n response = requests.get(\\\"https://www.bing.com/HPImageArchive.aspx?format=js&idx=0&n=1&mkt=en-US\\\")\\r\\n image_data = json.loads(response.text)\\r\\n\\r\\n image_url = image_data[\\\"images\\\"][0][\\\"url\\\"]\\r\\n image_url = image_url.split(\\\"&\\\")[0]\\r\\n full_image_url = \\\"https://www.bing.com\\\" + image_url\\r\\n logging.debug('get_latest_bing_wallpaper_remote - full_image_url = {}'.format(full_image_url))\\r\\n\\r\\n # image's name\\r\\n image_name = get_generated_image_name(full_image_url)\\r\\n\\r\\n # Check and maintain DB\\r\\n if not exists_image_in_database(full_image_url):\\r\\n add_image_to_database(full_image_url, image_name, \\\"bing\\\")\\r\\n # download and save image\\r\\n full_image_path = download_image(full_image_url, image_name)\\r\\n update_image_in_database(full_image_url, full_image_path)\\r\\n else:\\r\\n full_image_path = get_image_path_from_database(full_image_url)\\r\\n\\r\\n # Return full path to image\\r\\n logging.debug('get_latest_bing_wallpaper_remote - full_image_path = {}'.format(full_image_path))\\r\\n return full_image_path\",\n \"def pull(repo, **kwargs):\\n ret = do_pull(repo, \\\"topology.yaml\\\")\\n if not ret:\\n ret = do_pull(repo, \\\"topology.virl\\\")\\n if not ret:\\n exit(1)\",\n \"def list_docker_images():\\n raw_result = subprocess.getstatusoutput('docker images')\\n return result_handler(raw_result)\",\n \"def _list(logger, image_name: str = None, image_kind: str = None,\\n image_type: str = None, image_keywords: Sequence = ()) -> Optional[Dict[str, Any]]:\\n # TODO: Shouldn't pass a default argument for keywords. Need to handle after lambda function gets fixed\\n with resource_stream('jina', '/'.join(('resources', 'hubapi.yml'))) as fp:\\n hubapi_yml = yaml.load(fp)\\n hubapi_url = hubapi_yml['hubapi']['url'] + hubapi_yml['hubapi']['list']\\n\\n params = {\\n 'name': image_name,\\n 'kind': image_kind,\\n 'type': image_type,\\n 'keywords': ','.join(image_keywords) if image_keywords else None\\n }\\n params = {k: v for k, v in params.items() if v}\\n if params:\\n data = urlencode(params)\\n request = Request(f'{hubapi_url}?{data}')\\n with TimeContext('searching', logger):\\n try:\\n with urlopen(request) as resp:\\n response = json.load(resp)\\n except HTTPError as err:\\n if err.code == 400:\\n logger.warning('no matched executors found. please use different filters and retry.')\\n elif err.code == 500:\\n logger.error(f'server is down: {err.reason}')\\n else:\\n logger.error(f'unknown error: {err.reason}')\\n return\\n\\n manifests = response['manifest']\\n local_manifest = _load_local_hub_manifest()\\n if local_manifest:\\n tb = _make_hub_table_with_local(manifests, local_manifest)\\n else:\\n tb = _make_hub_table(manifests)\\n logger.info('\\\\n'.join(tb))\\n return manifests\",\n \"def pull(self, remote, branch, *args):\\n return self.cmd('pull', remote, branch, *args)\",\n \"def main():\\n images = Images()\\n #print images.create_image_urls()\\n print images.get_image_random()\\n print images.get_image(12)\",\n \"def _update_image(name):\\n\\n if not name or name == 'None':\\n return None, None\\n\\n old = docker.image_digest(name)\\n for _ in range(3):\\n new = docker.pull(name)\\n if new:\\n break\\n time.sleep(5)\\n\\n return name, old != new\",\n \"def build_container(client):\\n client.images.build(path=os.path.join(os.path.abspath(\\\"\\\"), \\\"docker\\\"), tag=\\\"scrape_light\\\")\",\n \"def _register_to_mongodb(logger, summary: Dict = None):\\n logger.info('registering image to Jina Hub database...')\\n\\n with resource_stream('jina', '/'.join(('resources', 'hubapi.yml'))) as fp:\\n hubapi_yml = yaml.load(fp)\\n\\n hubapi_url = hubapi_yml['hubapi']['url'] + hubapi_yml['hubapi']['push']\\n\\n if not credentials_file().is_file():\\n logger.error(f'user hasnot logged in. please login using command: {colored(\\\"jina hub login\\\", attrs=[\\\"bold\\\"])}')\\n return\\n\\n with open(credentials_file(), 'r') as cf:\\n cred_yml = yaml.load(cf)\\n access_token = cred_yml['access_token']\\n\\n if not access_token:\\n logger.error(f'user has not logged in. please login using command: {colored(\\\"jina hub login\\\", attrs=[\\\"bold\\\"])}')\\n return\\n\\n headers = {\\n 'Accept': 'application/json',\\n 'authorizationToken': access_token\\n }\\n try:\\n import requests\\n response = requests.post(url=f'{hubapi_url}',\\n headers=headers,\\n data=json.dumps(summary))\\n if response.status_code == requests.codes.ok:\\n logger.info(response.text)\\n elif response.status_code == requests.codes.unauthorized:\\n logger.error(f'user is unauthorized to perform push operation. '\\n f'please login using command: {colored(\\\"jina hub login\\\", attrs=[\\\"bold\\\"])}')\\n elif response.status_code == requests.codes.internal_server_error:\\n if 'auth' in response.text.lower():\\n logger.error(f'authentication issues!'\\n f'please login using command: {colored(\\\"jina hub login\\\", attrs=[\\\"bold\\\"])}')\\n logger.error(f'got an error from the API: {response.text}')\\n except Exception as exp:\\n logger.error(f'got an exception while invoking hubapi for push {repr(exp)}')\",\n \"def test_pull2(self):\\n\\n # Use defaults for format, arch, os, ostcount, replication\\n pr = self.pull\\n # Do the pull\\n session = self.mtm.new_session(self.auth, self.system)\\n rec1 = self.mtm.pull(session, pr) # ,delay=False)\\n pr['tag'] = self.tag2\\n rec2 = self.mtm.pull(session, pr) # ,delay=False)\\n self.assertIsNotNone(rec1)\\n id1 = rec1['_id']\\n self.assertIsNotNone(rec2)\\n id2 = rec2['_id']\\n # Confirm record\\n q = {'system': self.system, 'itype': self.itype, 'pulltag': self.tag}\\n mrec = self.images.find_one(q)\\n self.assertIn('_id', mrec)\\n state = self.time_wait(id1)\\n self.assertEqual(state, 'READY')\\n state = self.time_wait(id2)\\n self.assertEqual(state, 'READY')\\n mrec = self.images.find_one(q)\\n self.images.drop()\",\n \"def setup(self):\\n\\n exists = [i for i in self.client.images() if self.image in i['RepoTags']]\\n\\n # Only pull the image if we don't have it\\n if not exists or self.pull:\\n self.client.pull(self.image)\\n self.logger.debug(\\\"Pulled {}\\\".format(self.image))\\n\\n self.container = self.client.create_container(\\n image=self.image,\\n host_config=self.host_config,\\n name=self.name,\\n command=self.command,\\n environment=self.environment\\n )\\n self.logger.debug(\\\"Created container {}\\\".format(self.container['Id']))\",\n \"def test_pull_mocked(self):\\n # Use defaults for format, arch, os, ostcount, replication\\n pr = self.pull\\n # Do the pull\\n session = self.mtm.new_session(self.auth, self.system)\\n rec = self.mtm.pull(session, pr) # ,delay=False)\\n self.assertIsNotNone(rec)\\n id = rec['_id']\\n # Confirm record\\n q = {'system': self.system, 'itype': self.itype, 'pulltag': self.tag}\\n mrec = self.images.find_one(q)\\n self.assertIn('_id', mrec)\\n # Track through transistions\\n state = self.time_wait(id)\\n self.assertEqual(state, 'READY')\\n imagerec = self.mtm.lookup(session, self.pull)\\n self.assertIn('ENTRY', imagerec)\\n self.assertIn('ENV', imagerec)\\n # Cause a failure\\n self.images.drop()\\n self.mtm.workers.set_mode(2)\\n rec = self.mtm.pull(session, pr)\\n time.sleep(2)\\n self.assertIsNotNone(rec)\\n id = rec['_id']\\n state = self.mtm.get_state(id)\\n self.assertEqual(state, 'FAILURE')\\n self.mtm.workers.set_mode(1)\",\n \"def build_all_images_for_lang(lang):\\n if not args.git_checkout:\\n if args.release != \\\"master\\\":\\n print(\\n \\\"Cannot use --release without also enabling --git_checkout.\\\\n\\\"\\n )\\n sys.exit(1)\\n releases = [args.release]\\n else:\\n if args.release == \\\"all\\\":\\n releases = client_matrix.get_release_tags(lang)\\n else:\\n # Build a particular release.\\n if args.release not in [\\\"master\\\"] + client_matrix.get_release_tags(\\n lang\\n ):\\n jobset.message(\\n \\\"SKIPPED\\\",\\n \\\"%s for %s is not defined\\\" % (args.release, lang),\\n do_newline=True,\\n )\\n return []\\n releases = [args.release]\\n\\n images = []\\n for release in releases:\\n images += build_all_images_for_release(lang, release)\\n jobset.message(\\n \\\"SUCCESS\\\",\\n \\\"All docker images built for %s at %s.\\\" % (lang, releases),\\n do_newline=True,\\n )\\n return images\",\n \"def pull(connection, rid=None, repo=None):\\n\\n if repo is None:\\n repo = Repository(connection, rid)\\n\\n return repo.pull()\",\n \"def build_and_push_3p_images():\\n\\n log.info(\\n \\\"Building Lambda Stack docker images (these are not on DockerHub). \\\"\\n \\\"FMI see https://github.com/lambdal/lambda-stack-dockerfiles\\\")\\n run_cmd(\\\"\\\"\\\"\\n cd /tmp &&\\n (rm -rf lambda-stack-dockerfiles || true) &&\\n git clone https://github.com/lambdal/lambda-stack-dockerfiles &&\\n cd lambda-stack-dockerfiles &&\\n git checkout d762400d61636c074533416674426a84cc4d8992 &&\\n docker build -t oarphpy/lambda-stack:22.04 -f Dockerfile.jammy . &&\\n docker push oarphpy/lambda-stack:22.04\\n \\\"\\\"\\\")\",\n \"async def pull(self) -> None:\\n pull_fun = getattr(self, '_pull')\\n if inspect.iscoroutinefunction(pull_fun):\\n await pull_fun()\\n return\\n await run_sync(pull_fun)\",\n \"def build(path, tag):\\n print (\\\"*** BUILDING IS INITIATED\\\")\\n try:\\n response = client.images.build(path = path, tag=tag, rm=True)\\n except Exception as ex:\\n template = \\\"An exception of type {0} occured. Arguments:\\\\n{1!r}\\\"\\n message = template.format(type(ex).__name__, ex.args)\\n click.echo(message)\\n print(\\\"*** BUILD OUTPUT:\\\")\\n for object in response[1]:\\n click.echo(object)\\n click.echo('*** BUILDING FINISHED')\",\n \"def _pull(self) -> None:\\n raise NotImplementedError() # pragma: no cover\",\n \"def get_build_images(self) -> List[Image]:\\n images = []\\n image_names = []\\n conflicting_names = []\\n for registry_name, registry in self.registries.items():\\n # if the registry is not marked as source, skip it\\n if not registry.source:\\n continue\\n\\n images += self.get_images_from_registry(registry)\\n\\n if conflicting_names:\\n raise RuntimeError(\\\"Images found in multiple 'source' repositories: %s\\\", conflicting_names)\\n\\n return images\",\n \"def load_remote_image(image_url):\\n response = requests.get(image_url, stream=True)\\n img = Image.open(BytesIO(response.content))\\n image = cv2.cvtColor(np.asarray(img), cv2.COLOR_RGB2BGR)\\n return image\",\n \"def container_pull(ctx, token, source):\\n try:\\n out = ctx.obj.container_pull(token, source)\\n print_message(out)\\n except BaseException as e:\\n print_error(e.message)\",\n \"def mixin_hub_pull_parser(parser):\\n\\n def hub_uri(uri: str) -> str:\\n from ...hubble.helper import parse_hub_uri\\n\\n parse_hub_uri(uri)\\n return uri\\n\\n parser.add_argument(\\n 'uri',\\n type=hub_uri,\\n help='The URI of the executor to pull (e.g., jinahub[+docker]://UUID8)',\\n )\\n mixin_hub_pull_options_parser(parser)\",\n \"def test_aws_service_api_public_images_get(self):\\n pass\",\n \"def get_images(url):\\n \\n # =============================================================================\\n # Selenium.\\n # =============================================================================\\n\\n chrome_options = Options()\\n #chrome_options.add_argument('--incognito')\\n #chrome_options.add_argument('--headless')\\n #chrome_options.add_argument('--no-sandbox')\\n \\n driver = webdriver.Chrome(options=chrome_options,executable_path='/usr/local/bin/chromedriver') # Optional argument, if not specified will search path.\\n driver.get('https://' + url)\\n \\n #scrolling to bottom to load all images on the page\\n driver.execute_script(\\\"window.scrollTo(0, document.body.scrollHeight);\\\")\\n #sleep to make sure everything loads\\n time.sleep(5)\\n \\n \\n html_source = driver.page_source\\n \\n img_alt_src(html_source)\\n \\n driver.close()\\n driver.quit()\",\n \"def pull(self):\",\n \"async def fetch_image_by_id(\\n image_uid: str\\n):\\n image_uid = int(image_uid)\\n image = utils_com.get_com_image_by_uid(image_uid)\\n return image\",\n \"def get_a_bing_archive_wallpaper_remote():\\r\\n \\r\\n logging.debug('get_a_bing_archive_wallpaper_remote()')\\r\\n\\r\\n now = datetime.datetime.now()\\r\\n url = \\\"https://bingwallpaper.anerg.com/de/{}\\\".format(now.strftime('%Y%m'))\\r\\n\\r\\n # get image url\\r\\n if use_proxy:\\r\\n \\r\\n response = requests.get(url, proxies=proxies, timeout=15, verify=False)\\r\\n else:\\r\\n response = requests.get(url)\\r\\n match = re.findall('.*src=\\\\\\\"([^\\\\\\\"]*\\\\.jpg)\\\\\\\".*', response.text)\\r\\n for i in range(0, len(match)):\\r\\n full_image_url = \\\"https:{}\\\".format(match[i])\\r\\n \\r\\n # image's name\\r\\n image_name = get_generated_image_name(full_image_url)\\r\\n \\r\\n # Check and maintain DB\\r\\n if not exists_image_in_database(full_image_url) and i+1 < len(match):\\r\\n add_image_to_database(full_image_url, image_name, \\\"bingarchive\\\")\\r\\n # download and save image\\r\\n full_image_path = download_image(full_image_url, image_name)\\r\\n update_image_in_database(full_image_url, full_image_path)\\r\\n\\r\\n # Return full path to image\\r\\n logging.debug('get_a_bing_archive_wallpaper_remote - full_image_path = {}'.format(full_image_path))\\r\\n return full_image_path\\r\\n elif i+1 == len(match):\\r\\n full_image_path = get_image_path_from_database(full_image_url)\\r\\n\\r\\n # Return full path to image\\r\\n logging.debug('get_a_bing_archive_wallpaper_remote - full_image_path = {}'.format(full_image_path))\\r\\n return full_image_path\",\n \"def local_images(self, images):\\n registries = self.known_docker_registries()\\n found_images = []\\n for image in images:\\n # docker could have the image name as-is or prefixed with any registry\\n imglist = [image] + [reg + \\\"/\\\" + image for reg in registries]\\n if self.is_image_local(imglist):\\n found_images.append(image)\\n return found_images\",\n \"def get_image(name):\\r\\n return nova.images.find(name=name)\",\n \"def get_pulls(self):\\n url = self.base_url + 'pulls'\\n\\n req = requests.get(headers=self.headers, url=url)\\n\\n return req.json()\",\n \"def test_pull_silent(self, mock_docker_environment, snapshot, capsys):\\n mock_client = mock_docker_environment\\n pull_image(TEST_IMAGE_NAME, silent=True)\\n mock_client.api.pull.assert_called_with(\\n TEST_IMAGE_NAME, \\\"latest\\\", stream=False, decode=False\\n )\\n out, err = capsys.readouterr()\\n snapshot.assert_match(out)\",\n \"def test_unknown_registry(self, mock_docker_environment):\\n mock_docker_environment.images.get.side_effect = DockerNotFound(\\\"testing\\\")\\n build_image_if_needed(\\\"unknown.registry.com/foo/bar\\\")\\n mock_docker_environment.images.build.assert_called_with(\\n dockerfile=\\\"Dockerfile\\\",\\n tag=\\\"unknown.registry.com/foo/bar:latest\\\",\\n path=\\\"/opt/ixian_docker\\\",\\n )\",\n \"def _docker_search(term):\\n http_conn = httplib.HTTPConnection('localhost', 4243)\\n http_conn.request('GET',\\n '/images/search?term={0}'.format(urllib.quote(term)))\\n resp = http_conn.getresponse()\\n data = resp.read()\\n if resp.status != 200:\\n return []\\n return [repos['Name'] for repos in json.loads(data)]\",\n \"async def get_url_images(session, url):\\n content = await get_page(session, url)\\n if not content:\\n return []\\n soup = BeautifulSoup(content, features=\\\"html.parser\\\")\\n image_sources = [img['src'] for img in soup.find_all('img')]\\n image_sources_fixed = [f'https:{source}' if 'https:' not in source else source for source in image_sources]\\n images = []\\n for source in image_sources_fixed:\\n image = await get_image(session, source)\\n if image:\\n images.append((source, image))\\n\\n return images\",\n \"def cli(ctx, wheels_config, image, output, uid, gid, fetch_srcs, wheel):\\n wheel_config_manager = WheelConfigManager.open(ctx.config, wheels_config)\\n cachemgr = CacheManager(ctx.config.cache_path)\\n wheel_config = wheel_config_manager.get_wheel_config(wheel)\\n # `image` is an image_name until here\\n try:\\n image = wheel_config.get_image(image)\\n except KeyError:\\n warn(\\\"Warning: Image '%s' is not in '%s' imageset\\\", image, wheel_config.imageset.name)\\n # we could do imageset autodetection here but that seems like a waste of time, this command isn't supposed to be\\n # user facing like `wheel`. Just use whatever image is instructed.\\n wheel_config.set_imageset(imageset=ctx.config.make_imageset('_ephemeral_', [image]), force=True)\\n image = wheel_config.get_image(image)\\n ectx = LocalExecutionContext(image)\\n forge = ForgeWheel(wheel_config, cachemgr, ectx.run_context, image=image)\\n if fetch_srcs:\\n forge.cache_sources()\\n forge.bdist_wheel(output=output, uid=uid, gid=gid)\",\n \"def article_images(id):\\n try:\\n query_string = '&artile_id='+str(id)\\n logger.info('Calling the api' + APIURL + '/images/?format=json'+query_string)\\n response = requests.get(APIURL + '/images/?format=json'+query_string)\\n parser = json.loads(response.content)\\n return parser\\n except:\\n logger.error('Calling the api error in article_images')\\n raise Http404(\\\"Article image does not exist\\\")\",\n \"def images_list(self, kwargs=None):\\n\\n try:\\n scode, images = Rest.get('Image')\\n except docker.errors.APIError as e:\\n Console.error(e.explanation)\\n return\\n\\n if len(images) == 0:\\n Console.info(\\\"No images exist\\\")\\n return\\n\\n n = 1\\n e = {}\\n for image in images:\\n d = {}\\n d['Ip'] = image['Ip']\\n d['Id'] = image['Id']\\n if image['RepoTags'] == None:\\n d['Repository'] = image['RepoDigests'][0]\\n else:\\n d['Repository'] = image['RepoTags'][0]\\n # d['Size'] = image['Size']\\n d['Size(GB)'] = round(image['Size'] / float(1 << 30), 2) # Converting the size to GB\\n e[n] = d\\n n = n + 1\\n Console.ok(str(Printer.dict_table(e, order=['Ip', 'Id', 'Repository', 'Size(GB)'])))\",\n \"def pull(self, **kwargs):\\n return _taskpipeoperation(self,'pull', **kwargs)\",\n \"def build_all_images_for_release(lang, release):\\n docker_images = []\\n build_jobs = []\\n\\n env = {}\\n # If we not using current tree or the sibling for grpc stack, do checkout.\\n stack_base = \\\"\\\"\\n if args.git_checkout:\\n stack_base = checkout_grpc_stack(lang, release)\\n var = {\\n \\\"go\\\": \\\"GRPC_GO_ROOT\\\",\\n \\\"java\\\": \\\"GRPC_JAVA_ROOT\\\",\\n \\\"node\\\": \\\"GRPC_NODE_ROOT\\\",\\n }.get(lang, \\\"GRPC_ROOT\\\")\\n env[var] = stack_base\\n\\n for runtime in client_matrix.get_runtimes_for_lang_release(lang, release):\\n job = build_image_jobspec(runtime, env, release, stack_base)\\n docker_images.append(job.tag)\\n build_jobs.append(job)\\n\\n jobset.message(\\\"START\\\", \\\"Building interop docker images.\\\", do_newline=True)\\n print(\\\"Jobs to run: \\\\n%s\\\\n\\\" % \\\"\\\\n\\\".join(str(j) for j in build_jobs))\\n\\n num_failures, _ = jobset.run(\\n build_jobs, newline_on_success=True, maxjobs=multiprocessing.cpu_count()\\n )\\n if num_failures:\\n jobset.message(\\n \\\"FAILED\\\", \\\"Failed to build interop docker images.\\\", do_newline=True\\n )\\n docker_images_cleanup.extend(docker_images)\\n sys.exit(1)\\n\\n jobset.message(\\n \\\"SUCCESS\\\",\\n \\\"All docker images built for %s at %s.\\\" % (lang, release),\\n do_newline=True,\\n )\\n\\n if release != \\\"master\\\":\\n commit_log = os.path.join(stack_base, \\\"commit_log\\\")\\n if os.path.exists(commit_log):\\n for image in docker_images:\\n add_files_to_image(image, [commit_log], \\\"release=%s\\\" % release)\\n return docker_images\",\n \"def list_images(self, repository_name, registry_id=None):\\n repository = None\\n found = False\\n if repository_name in self.repositories:\\n repository = self.repositories[repository_name]\\n if registry_id:\\n if repository.registry_id == registry_id:\\n found = True\\n else:\\n found = True\\n\\n if not found:\\n raise RepositoryNotFoundException(\\n repository_name, registry_id or DEFAULT_REGISTRY_ID\\n )\\n\\n images = []\\n for image in repository.images:\\n images.append(image)\\n return images\",\n \"def get_images(self):\\n return self._get_brains(\\\"Image\\\")\",\n \"def _get_local_repodigest(self, context):\\n images_list = context.docker_client.images.list(self._name())\\n if len(images_list) == 0:\\n return None\\n sorted_by_created = sorted(images_list, key=lambda x: x.attrs.get('Created'), reverse=True)\\n image = sorted_by_created[0]\\n repo_digests = image.attrs.get('RepoDigests')\\n if len(repo_digests) != 1:\\n raise Exception(\\\"Ambiguous RepoDigests, do not know how to handle this: %s\\\" % str(repo_digests))\\n repo_digest = repo_digests[0]\\n return repo_digest.split(\\\"@\\\")[-1]\",\n \"def push_image(image):\\n\\n subprocess.check_call(['docker', 'push', image])\",\n \"def test_pull_tag(self, mock_docker_environment, snapshot, capsys):\\n mock_client = mock_docker_environment\\n pull_image(TEST_IMAGE_NAME, \\\"custom_tag\\\")\\n mock_client.api.pull.assert_called_with(\\n TEST_IMAGE_NAME, \\\"custom_tag\\\", stream=True, decode=True\\n )\\n out, err = capsys.readouterr()\\n snapshot.assert_match(out)\",\n \"def get_image(camera_name: str):\\n img_bytes = requests.post(f\\\"http://{IP}:{PORT}/get_image\\\", data=pickle.dumps(camera_name)).content\\n img = pickle.loads(img_bytes)\",\n \"def main():\\n for city in CITIES:\\n fetchNewseumImage(city)\",\n \"async def images(self, ctx, *, query: str=None):\\n # Handle empty query\\n if query is None:\\n return await ctx.error('Please provide a query!')\\n\\n # Using these specific headers and \\\"lnms\\\" as source, will provide divs with \\\"rg_meta\\\" classes,\\n # The modern image search page being JS rendered, data in these divs are jsons with raw image URLs\\n # Old image search pages, only have thumbnails and a direct link to websites\\n params = {'q': quote_plus(query), 'source': 'lmns', 'tbm': 'isch'}\\n async with self.aiohttp_session.get(self.url, params=params, headers=self.image_headers) as r:\\n html = await r.text()\\n\\n # Healthy\\n soup = BeautifulSoup(html, 'lxml')\\n\\n # Go over 4 items, json.loads the item text, and grab \\\"ou\\\" probably stands for \\\"original url\\\"\\n images = []\\n for i, item in enumerate(soup.select('div.rg_meta')[:4]):\\n js = json.loads(item.text)\\n images.append((f\\\"{i+1}. {js['st']} - {js['s']}\\\", js[\\\"ou\\\"]))\\n newl = '\\\\n'\\n await ctx.message.edit(content=f\\\"```py\\\\n{newl.join([x[0] for x in images])}\\\"\\n f\\\"\\\\n# Choose the appropriate number or type 0 to leave\\\\n```\\\")\\n\\n def check(m):\\n return m.author == ctx.author and m.content.isdigit() and m.channel == ctx.channel\\n message = await self.bot.wait_for('message', check=check)\\n if message.content == \\\"0\\\":\\n await message.delete()\\n return await ctx.message.delete()\\n choice = int(message.content) - 1\\n await message.delete()\\n await ctx.message.edit(content=images[choice][1])\",\n \"def image(image_id):\\n\\n found = False\\n img = None\\n \\n try:\\n for img in api.get_all_images():\\n if img.id == image_id:\\n found = True\\n break\\n except Exception:\\n logging.error(\\\"Cannot make API connection to retrieve image info!\\\")\\n\\n if not found:\\n return None\\n\\n return img\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6917054","0.66231585","0.6467475","0.64548194","0.64449644","0.6421621","0.63432056","0.631559","0.63136727","0.631053","0.630868","0.62965673","0.6283755","0.6199952","0.619788","0.61943036","0.61171836","0.6069643","0.6063285","0.6025357","0.59519005","0.58871335","0.5868561","0.580386","0.5755364","0.5746389","0.5672375","0.5663332","0.56624484","0.5611057","0.559632","0.5572502","0.5557081","0.5540134","0.5528036","0.5525324","0.54949343","0.5493454","0.5470989","0.5354741","0.5343562","0.53342605","0.5324503","0.528916","0.52880234","0.5285695","0.52832156","0.52770174","0.5265938","0.5262984","0.5240543","0.5238768","0.52187055","0.52174777","0.5205431","0.519472","0.51899135","0.5141804","0.51338995","0.5116286","0.51101655","0.5108003","0.5107323","0.510032","0.5098995","0.50969183","0.5094617","0.5087075","0.508613","0.50665975","0.50633216","0.5061592","0.5053791","0.50493395","0.50487924","0.5042252","0.5037127","0.5036593","0.503523","0.5030538","0.50192434","0.50094634","0.4989177","0.49879906","0.49877733","0.49869353","0.4986856","0.49743974","0.49626145","0.49546635","0.4951471","0.49491045","0.49481952","0.49423605","0.4939465","0.49310505","0.49269387","0.49229038","0.49197942","0.49163207"],"string":"[\n \"0.6917054\",\n \"0.66231585\",\n \"0.6467475\",\n \"0.64548194\",\n \"0.64449644\",\n \"0.6421621\",\n \"0.63432056\",\n \"0.631559\",\n \"0.63136727\",\n \"0.631053\",\n \"0.630868\",\n \"0.62965673\",\n \"0.6283755\",\n \"0.6199952\",\n \"0.619788\",\n \"0.61943036\",\n \"0.61171836\",\n \"0.6069643\",\n \"0.6063285\",\n \"0.6025357\",\n \"0.59519005\",\n \"0.58871335\",\n \"0.5868561\",\n \"0.580386\",\n \"0.5755364\",\n \"0.5746389\",\n \"0.5672375\",\n \"0.5663332\",\n \"0.56624484\",\n \"0.5611057\",\n \"0.559632\",\n \"0.5572502\",\n \"0.5557081\",\n \"0.5540134\",\n \"0.5528036\",\n \"0.5525324\",\n \"0.54949343\",\n \"0.5493454\",\n \"0.5470989\",\n \"0.5354741\",\n \"0.5343562\",\n \"0.53342605\",\n \"0.5324503\",\n \"0.528916\",\n \"0.52880234\",\n \"0.5285695\",\n \"0.52832156\",\n \"0.52770174\",\n \"0.5265938\",\n \"0.5262984\",\n \"0.5240543\",\n \"0.5238768\",\n \"0.52187055\",\n \"0.52174777\",\n \"0.5205431\",\n \"0.519472\",\n \"0.51899135\",\n \"0.5141804\",\n \"0.51338995\",\n \"0.5116286\",\n \"0.51101655\",\n \"0.5108003\",\n \"0.5107323\",\n \"0.510032\",\n \"0.5098995\",\n \"0.50969183\",\n \"0.5094617\",\n \"0.5087075\",\n \"0.508613\",\n \"0.50665975\",\n \"0.50633216\",\n \"0.5061592\",\n \"0.5053791\",\n \"0.50493395\",\n \"0.50487924\",\n \"0.5042252\",\n \"0.5037127\",\n \"0.5036593\",\n \"0.503523\",\n \"0.5030538\",\n \"0.50192434\",\n \"0.50094634\",\n \"0.4989177\",\n \"0.49879906\",\n \"0.49877733\",\n \"0.49869353\",\n \"0.4986856\",\n \"0.49743974\",\n \"0.49626145\",\n \"0.49546635\",\n \"0.4951471\",\n \"0.49491045\",\n \"0.49481952\",\n \"0.49423605\",\n \"0.4939465\",\n \"0.49310505\",\n \"0.49269387\",\n \"0.49229038\",\n \"0.49197942\",\n \"0.49163207\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6308105"},"document_rank":{"kind":"string","value":"11"}}},{"rowIdx":17880,"cells":{"query":{"kind":"string","value":"Builds an image from a recipefile."},"document":{"kind":"string","value":"def singularity_build(self, path, image):\n Client.build(os.path.join(\n path, 'singularity.def'\n ), self.generate_image_name(image))"},"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 build(image_name, path='.'):\n try:\n doxy.images.build(path=path, tag=image_name)\n message = '[*] Image {} built.'\n print message.format(image_name)\n except Exception as err:\n print err\n raise","def parseRecipe (self,filename):\r\n return RecipeFileObject(os.path.join(main.settings.RECIPE_DIR,filename)).getParsed(self.actualTopology, self.anlage)","def build_container(client):\n client.images.build(path=os.path.join(os.path.abspath(\"\"), \"docker\"), tag=\"scrape_light\")","def create_image(name: str, distribution: str, apt_repo: str, release_track: str, release_label: str, flavour: str,\n organization: str, docker_registry: str, rosdistro_path: pathlib.Path, timestamp:str,\n publish: bool = False):\n\n # Read configuration files\n common_config = yaml.safe_load((rosdistro_path / 'config/recipes.yaml').open())['common']\n recipe = yaml.safe_load((rosdistro_path / 'config/images.yaml').open())['images']\n distro = recipe[name]['distro']\n build_type = recipe[name]['build_type']\n env = source_file(f'{os.environ[\"BUNDLE_ROOT\"]}/{distro}/setup.bash')\n today = timestamp\n extra_vars: List[Any] = []\n\n try:\n package = recipe[name]['package']\n provision_file = recipe[name]['provision_file']\n except KeyError:\n package = '/tailor-image'\n provision_file = f'{build_type}.yaml'\n\n env['ANSIBLE_CONFIG'] = find_package(package, 'ansible.cfg', env)\n template_path = f'/tailor-image/environment/image_recipes/{build_type}/{build_type}.json'\n provision_file_path = find_package(package, 'playbooks/' + provision_file, env)\n\n optional_vars = []\n optional_var_names = ['username', 'password', 'extra_arguments_ansible',\n 'ansible_command', 'description', 'disk_size', 'group']\n\n for var in optional_var_names:\n if var in recipe[name]:\n optional_vars.extend(['-var', f'{var}={recipe[name][var]}'])\n\n if build_type == 'docker':\n image_name = f'tailor-image-{name}-{distribution}-{release_label}'\n docker_registry_data = docker_registry.replace('https://', '').split('/')\n ecr_server = docker_registry_data[0]\n ecr_repository = docker_registry_data[1]\n extra_vars = [\n '-var', f'type={build_type}',\n '-var', f'bundle_flavour={flavour}',\n '-var', f'image_name={image_name}',\n '-var', f'ecr_server={ecr_server}',\n '-var', f'os_version={distribution}',\n '-var', f'ecr_repository={ecr_repository}',\n '-var', f'aws_access_key={os.environ[\"AWS_ACCESS_KEY_ID\"]}',\n '-var', f'aws_secret_key={os.environ[\"AWS_SECRET_ACCESS_KEY\"]}'\n ]\n\n if not publish:\n extra_vars += ['-except', 'publish']\n\n # Make sure we remove old containers before creting new ones\n run_command(['docker', 'rm', '-f', 'default'], check=False)\n\n elif build_type in ['bare_metal', 'lxd'] and publish:\n # Get information about base image\n base_image = recipe[name]['base_image'].replace('$distribution', distribution)\n\n # Get disk size to use\n disk_size = recipe[name].get('disk_size', 9) # In GB\n\n # Get base image\n base_image_local_path = '/tmp/' + base_image\n base_image_key = release_label + '/images/' + base_image\n click.echo(f'Downloading image from {base_image_key}')\n try:\n boto3.resource('s3').Bucket(apt_repo).download_file(base_image_key, base_image_local_path)\n except botocore.exceptions.ClientError:\n click.echo(f'Unable to download base image from {base_image_key}, creating a new one')\n run_command(['bash',\n '/tailor-image/environment/create_base_image.bash',\n f'{base_image_local_path}',\n f'{distribution}'])\n boto3.resource('s3').Bucket(apt_repo).upload_file(base_image_local_path, base_image_key)\n\n # Enable nbd kernel module, necesary for qemu's packer chroot builder\n run_command(['modprobe', 'nbd'])\n\n # Resize image\n run_command(['qemu-img', 'resize', base_image_local_path, '30G'])\n\n # Copy image\n tmp_image = base_image_local_path.replace('disk1', 'disk1-resized')\n run_command(['cp', base_image_local_path, tmp_image])\n\n # Resize partition inside qcow image\n run_command(['virt-resize', '--expand', '/dev/sda1', base_image_local_path, tmp_image])\n run_command(['mv', tmp_image, base_image_local_path])\n\n # Generate image name\n image_name = f'{organization}_{name}_{distribution}_{release_label}_{today}'\n\n extra_vars = [\n '-var', f'image_name={image_name}',\n '-var', f's3_bucket={apt_repo}',\n '-var', f'iso_image={base_image_local_path}',\n '-var', f'distribution={distribution}',\n '-var', f'disk_size={disk_size}'\n ]\n\n # Make sure to clean old image builds\n run_command(['rm', '-rf', '/tmp/images'])\n\n elif build_type == 'ami':\n image_name = f'{organization}_{name}_{distribution}_ami_{release_label}'\n # Get ami-id for base image\n source_ami_id = recipe[name]['source_ami'].get(distribution)\n\n if not source_ami_id:\n click.echo(f'You need to specify a bas AMI for the desired distribution {distribution}')\n sys.exit(1)\n\n # Increase fow how long we wait for image to be ready. Default is 30 minutes, sometime it might take longer\n env['AWS_MAX_ATTEMPTS'] = '90' # minutes\n env['AWS_POLL_DELAY_SECONDS'] = '60' # Poll for status every minute\n\n extra_vars = [\n '-var', f'build_date={today}',\n '-var', f'image_name={image_name}',\n '-var', f'name={name}',\n '-var', f'source_ami_id={source_ami_id}',\n '-var', f'distribution={distribution}',\n '-var', f'release_label={release_label}',\n '-var', f'aws_access_key={os.environ[\"AWS_ACCESS_KEY_ID\"]}',\n '-var', f'aws_secret_key={os.environ[\"AWS_SECRET_ACCESS_KEY\"]}'\n ]\n else:\n return 0\n\n extra_vars.extend(optional_vars)\n\n click.echo(f'Building {build_type} image with: {provision_file}', err=True)\n\n command = ['packer', 'build',\n '-var', f'playbook_file={provision_file_path}',\n '-var', f'organization={organization}',\n '-var', f'bundle_track={release_track}',\n '-var', f'bundle_version={release_label}'] + extra_vars + ['-timestamp-ui', template_path]\n\n run_command(command, env=env, cwd='/tmp')\n\n if build_type in ['bare_metal', 'lxd'] and publish:\n update_image_index(release_label, apt_repo, common_config, image_name)","def _ReadRecipeFromFileObject(\n self, file_object: Union[StringIO, TextIOWrapper, TextIO]) -> Recipe:\n json_dict = json.load(file_object)\n\n description = json_dict['description']\n del json_dict['description']\n\n args = []\n for arg_list in json_dict['args']:\n args.append(RecipeArgs(*arg_list))\n del json_dict['args']\n\n return resources.Recipe(description, json_dict, args)","def build_runtime(self, docker_image_name, dockerfile):\n logger.debug('Building new docker image from Dockerfile')\n logger.debug('Docker image name: {}'.format(docker_image_name))\n\n entry_point = os.path.join(os.path.dirname(__file__), 'entry_point.py')\n create_handler_zip(k8s_config.FH_ZIP_LOCATION, entry_point, 'lithopsentry.py')\n\n if dockerfile:\n cmd = '{} build -t {} -f {} .'.format(k8s_config.DOCKER_PATH,\n docker_image_name,\n dockerfile)\n else:\n cmd = '{} build -t {} .'.format(k8s_config.DOCKER_PATH, docker_image_name)\n\n if logger.getEffectiveLevel() != logging.DEBUG:\n cmd = cmd + \" >{} 2>&1\".format(os.devnull)\n\n logger.info('Building runtime')\n res = os.system(cmd)\n if res != 0:\n raise Exception('There was an error building the runtime')\n\n self._delete_function_handler_zip()\n\n cmd = '{} push {}'.format(k8s_config.DOCKER_PATH, docker_image_name)\n if logger.getEffectiveLevel() != logging.DEBUG:\n cmd = cmd + \" >{} 2>&1\".format(os.devnull)\n res = os.system(cmd)\n if res != 0:\n raise Exception('There was an error pushing the runtime to the container registry')\n logger.debug('Building done!')","def build(config, version_tag):\n config_dict = get_config(config)\n image_name = config_dict['IMAGE'].split(':')[0]\n image = '{}:{}'.format(image_name, version_tag)\n base_image = config_dict['BASE_IMAGE']\n\n cmd = 'docker build -t {image} --build-arg base_image={base_image} .'.format(image=image,\n base_image=base_image)\n with cd(env.project_dir):\n run(cmd)\n return image","def build(self, source, config, repo, tag):\n check_blacklist(repo)\n env = ' '.join(\"{}='{}'\".format(\n k, v.encode('unicode-escape').replace(\"'\", \"\\\\'\")) for k, v in config.viewitems())\n dockerfile = \"FROM {}\\nENV {}\".format(source, env)\n f = io.BytesIO(dockerfile.encode('utf-8'))\n target_repo = \"{}/{}:{}\".format(self.registry, repo, tag)\n logger.info(\"Building Docker image {}\".format(target_repo))\n with SimpleFlock(self.FLOCKFILE, timeout=1200):\n stream = self.client.build(fileobj=f, tag=target_repo, stream=True, rm=True)\n log_output(stream)","def _build(build_context,\n image_tag,\n image_name,\n nocache,\n credstore_env=None,\n registries=None):\n _logger.info('Starting build ...')\n\n # Build the image\n docker_builder = DockerBuilder(\n build_context=build_context,\n image_name=image_name,\n image_tag=image_tag,\n credstore_env=credstore_env,\n registries=registries,\n )\n docker_builder.login_private_registries()\n if docker_builder.check_image():\n # Image already built\n docker_builder.clean()\n return docker_builder\n if not docker_builder.build(nocache=nocache):\n docker_builder.clean()\n raise BuildException('The docker image could not be built.')\n return docker_builder","def make_image(self):\n\n if self.type == 'passthrough':\n return\n render_template(\n os.path.dirname(self.main_module),\n os.path.basename(self.main_module_path),\n self.language,\n self.requirements,\n self.whitelist,\n self.type,\n into=self.code_dir)\n self.build()","def build_docker(c):\n tag = c.run('git describe', hide=True)\n docker_img = f'{docker_repo}:{tag.stdout.strip()}'\n c.run(f'docker build -t {docker_img} .')","def build_image(self, df_path):\n cmd = \"{} build -f {} -t {} .\".format(\n self.binary, df_path, self.vars['image'])\n LOG.info(\"Running: {}\".format(cmd))\n res = subprocess.run(cmd, shell=True)\n if res.returncode != 0:\n sys.exit(2)\n return res","def _build(registry, image_name, git_sha):\n\n image = f\"{registry}/{image_name}\"\n\n # If this image is not in our registry, nothing we can do.\n if registry != config.DOCKER_REGISTRY:\n return \"\"\n\n # If image:{git_sha} already exists, then return.\n logger.info(\n f\"Pulling git_sha tag {image}:{git_sha} to check if it already exists\"\n )\n if (\n not args.dry_run\n and docker_silent.pull(f\"{image}:{git_sha}\").returncode == 0\n ):\n # better to find the hash-{hash} of this image, and return hash\n # but currently, it is not easy to find all tags of the same image digest through registry API.\n # so we return image digest instead.\n digest = get_image_digest(f\"{image}:{git_sha}\")\n logger.info(\n f\"git_sha tag {image}:{git_sha} already exists, digest: %s\", digest\n )\n if not digest:\n raise Exception(\"Failed to get digest for existing image\")\n _tag_to_extra_tags(args, image, git_sha)\n return digest\n\n # Enter build context directory if it is specified\n build_context = enter_build_context(image_name)\n\n # Parse .dockerignore in build context\n dockerignore_files_set = parse_dockerignore(build_context)\n\n # Check if the dockerfile exists\n dockerfile_path = locate_dockerfile(image_name)\n if not os.path.isfile(dockerfile_path):\n logger.error(\n \"%s not exists or is not a file, so %s cannot get build\",\n dockerfile_path,\n image_name,\n )\n raise Exception(\"Building cannot continue\")\n\n dockerfile = Dockerfile(dockerfile_path, build_arg=build_arg)\n\n hasher = sha256()\n\n # Build parents, and calc parents hash\n for parent_image in dockerfile.parent_images:\n (\n parent_image_registry,\n parent_image_name,\n parent_image_tag,\n ) = parse_docker_image_identity(parent_image)\n parent_digest = _build(\n parent_image_registry, parent_image_name, parent_image_tag\n )\n if parent_digest is None:\n raise Exception(f\"Failed to get parent_digest for {image}\")\n hasher.update(parent_digest.encode())\n hash_logger.info(\n \"parent: %s, digest: (%s, %s), hash: %s\",\n parent_image,\n parent_digest,\n parent_digest.encode(),\n hasher.hexdigest(),\n )\n\n # Calc current image files hash\n\n def update_file_hash(f):\n if not os.path.isfile(f):\n return\n if f in dockerignore_files_set:\n hash_logger.debug(\"ignore: %s\", f)\n return\n with open(f, \"rb\") as open_file:\n buf = open_file.read(config.READ_FILE_BLOCKSIZE)\n while len(buf) > 0:\n hasher.update(buf)\n buf = open_file.read(config.READ_FILE_BLOCKSIZE)\n hash_logger.info(\"update: %s, hash: %s\", f, hasher.hexdigest())\n\n srcs = [dockerfile_path] + dockerfile.copied_srcs + dockerfile.added_srcs\n # TODO: if the src is a url, download it and hash it (even crane didn't do that)\n for src in srcs:\n for f in sorted(glob.glob(src)):\n # We match every file in a directory recursively\n if os.path.isdir(f):\n for sub_f in sorted(glob.glob(f\"{f}/**\", recursive=True)):\n update_file_hash(sub_f)\n else:\n update_file_hash(f)\n\n files_hash = hasher.hexdigest()\n hash_logger.info(\"image: %s, hash: %s\", image, files_hash)\n\n hash_tag = config.FILES_HASH_TAG_PATTERN.format(files_hash=files_hash)\n # FIXME(harry): hack, remove this\n old_hash_image = f\"docker-registry.example.com:5000/{image_name}:{hash_tag}\"\n\n logger.info(\n f\"Pulling files_hash tag {image}:{hash_tag} to check if it already exists\"\n )\n # If image:hash-{hash} already exists,\n # then content didn't change, return.\n # We just need to tag it to latest code version.\n if (\n not args.dry_run\n and docker_silent.pull(f\"{image}:{hash_tag}\").returncode == 0\n ):\n logger.info(\n f\"files_hash tag {image}:{hash_tag} already exists, \"\n \"it means content didn't change, we can just tag the old image to new git_sha version tag\"\n )\n # FIXME(harry): hack, remove this\n elif not args.dry_run and docker_silent.pull(old_hash_image).returncode == 0:\n logger.info(f\"NOTE: files_hash tag {old_hash_image} already exists!\")\n # tag and push this hash image\n if docker.tag(old_hash_image, f\"{image}:{hash_tag}\").returncode != 0:\n logger.error(\"Failed to tag old hash image\")\n return\n if docker.push(f\"{image}:{hash_tag}\").returncode != 0:\n logger.error(\"Failed to push hash_tag image\")\n return\n # If image:hash-{hash} not exists, then build it from Dockerfile.\n else:\n logger.info(\n f\"files_hash tag {image}:{hash_tag} dosen't exists, \"\n \"it means content may changed, gonna build it from Dockerfile\"\n )\n if build_with_raw_command(args, image, dockerfile_path, hash_tag) != 0:\n logger.error(f\"Failed to build {image}:{hash_tag}\")\n return\n if docker.push(f\"{image}:{hash_tag}\").returncode != 0:\n logger.error(f\"Failed to push image\")\n return\n logger.info(f\"image files_hash tag {image}:{hash_tag} is pushed\")\n\n # tag and push this final image\n if docker.tag(f\"{image}:{hash_tag}\", f\"{image}:{git_sha}\").returncode != 0:\n logger.error(\"Failed to tag image\")\n return\n _tag_to_extra_tags(args, image, git_sha)\n if docker.push(f\"{image}:{git_sha}\").returncode != 0:\n logger.error(\"Failed to push image\")\n return\n digest = get_image_digest(f\"{image}:{git_sha}\")\n if not digest:\n logger.error(\"Failed to get digest for image\")\n return\n logger.info(f\"image {image}:{git_sha} is pushed, digest: {digest}\")\n\n return digest","def newimagefromfile(self, infile):\n return _image.image_newimagefromfile(self, infile)","def build(path, tag):\n print (\"*** BUILDING IS INITIATED\")\n try:\n response = client.images.build(path = path, tag=tag, rm=True)\n except Exception as ex:\n template = \"An exception of type {0} occured. Arguments:\\n{1!r}\"\n message = template.format(type(ex).__name__, ex.args)\n click.echo(message)\n print(\"*** BUILD OUTPUT:\")\n for object in response[1]:\n click.echo(object)\n click.echo('*** BUILDING FINISHED')","def make_image(self, path):\n\t\treturn None","def _build_container(\n self, target_image, odcs, repo_type, repo_list, terminate_event,\n scratch, record):\n self.logger.info(\"Building image: %s\" % target_image)\n cmd_list = [\"rhpkg\", \"--path=%s\" % self.distgit_dir]\n\n if self.runtime.user is not None:\n cmd_list.append(\"--user=%s\" % self.runtime.user)\n\n cmd_list += (\n \"container-build\",\n \"--nowait\",\n )\n\n if odcs:\n if odcs == 'signed':\n odcs = 'release' # convenience option for those used to the old types\n cmd_list.append('--signing-intent')\n cmd_list.append(odcs)\n else:\n if repo_type:\n repo_list = list(repo_list) # In case we get a tuple\n repo_list.append(self.metadata.cgit_url(\".oit/\" + repo_type + \".repo\"))\n\n if repo_list:\n # rhpkg supports --repo-url [URL [URL ...]]\n cmd_list.append(\"--repo-url\")\n cmd_list.extend(repo_list)\n\n if scratch:\n cmd_list.append(\"--scratch\")\n\n # Run the build with --nowait so that we can immediately get information about the brew task\n rc, out, err = exectools.cmd_gather(cmd_list)\n\n if rc != 0:\n # Probably no point in continuing.. can't contact brew?\n self.logger.info(\"Unable to create brew task: out={} ; err={}\".format(out, err))\n return False\n\n # Otherwise, we should have a brew task we can monitor listed in the stdout.\n out_lines = out.splitlines()\n\n # Look for a line like: \"Created task: 13949050\" . Extract the identifier.\n task_id = next((created_line.split(\":\")[1]).strip() for created_line in out_lines if\n created_line.startswith(\"Created task:\"))\n\n record[\"task_id\"] = task_id\n\n # Look for a line like: \"Task info: https://brewweb.engineering.redhat.com/brew/taskinfo?taskID=13948942\"\n task_url = next((info_line.split(\":\", 1)[1]).strip() for info_line in out_lines if\n info_line.startswith(\"Task info:\"))\n\n self.logger.info(\"Build running: {}\".format(task_url))\n\n record[\"task_url\"] = task_url\n\n # Now that we have the basics about the task, wait for it to complete\n error = watch_task(self.logger.info, task_id, terminate_event)\n\n # Looking for something like the following to conclude the image has already been built:\n # BuildError: Build for openshift-enterprise-base-v3.7.0-0.117.0.0 already exists, id 588961\n if error is not None and \"already exists\" in error:\n self.logger.info(\"Image already built against this dist-git commit (or version-release tag): {}\".format(target_image))\n error = None\n\n # Gather brew-logs\n logs_dir = \"%s/%s\" % (self.runtime.brew_logs_dir, self.metadata.name)\n logs_rc, _, logs_err = exectools.cmd_gather([\"brew\", \"download-logs\", \"-d\", logs_dir, task_id])\n\n if logs_rc != 0:\n self.logger.info(\"Error downloading build logs from brew for task %s: %s\" % (task_id, logs_err))\n\n if error is not None:\n # An error occurred. We don't have a viable build.\n self.logger.info(\"Error building image: {}, {}\".format(task_url, error))\n return False\n\n self.logger.info(\"Successfully built image: {} ; {}\".format(target_image, task_url))\n return True","def render_dockerfile(self):\n logger.info(\"Rendering Dockerfile...\")\n\n if self._params.get('redhat'):\n self._inject_redhat_defaults()\n\n self.image['pkg_manager'] = self._params.get('package_manager', 'yum')\n self.image.process_defaults()\n\n template_file = os.path.join(os.path.dirname(__file__),\n '..',\n 'templates',\n 'template.jinja')\n loader = FileSystemLoader(os.path.dirname(template_file))\n env = Environment(loader=loader, trim_blocks=True, lstrip_blocks=True)\n env.globals['helper'] = TemplateHelper()\n env.globals['addhelp'] = self._params.get('addhelp')\n\n template = env.get_template(os.path.basename(template_file))\n\n dockerfile = os.path.join(self.target,\n 'image',\n 'Dockerfile')\n if not os.path.exists(os.path.dirname(dockerfile)):\n os.makedirs(os.path.dirname(dockerfile))\n\n with open(dockerfile, 'wb') as f:\n f.write(template.render(\n self.image).encode('utf-8'))\n logger.debug(\"Dockerfile rendered\")\n\n if self.image.get('help', {}).get('template', \"\"):\n help_template_path = self.image['help']['template']\n elif self._params.get('help_template'):\n help_template_path = self._params['help_template']\n else:\n help_template_path = os.path.join(os.path.dirname(__file__),\n '..',\n 'templates',\n 'help.jinja')\n\n help_dirname, help_basename = os.path.split(help_template_path)\n loader = FileSystemLoader(help_dirname)\n env = Environment(loader=loader, trim_blocks=True, lstrip_blocks=True)\n env.globals['helper'] = TemplateHelper()\n help_template = env.get_template(help_basename)\n\n helpfile = os.path.join(self.target, 'image', 'help.md')\n with open(helpfile, 'wb') as f:\n f.write(help_template.render(\n self.image).encode('utf-8'))\n logger.debug(\"help.md rendered\")","def test_build_image(self):\n labels = {\"apple\": \"red\", \"grape\": \"green\"}\n self.docker.images.build(\n path=\"test/python/docker/build_labels\",\n labels=labels,\n tag=\"labels\",\n isolation=\"default\",\n )\n image = self.docker.images.get(\"labels\")\n self.assertEqual(image.labels[\"apple\"], labels[\"apple\"])\n self.assertEqual(image.labels[\"grape\"], labels[\"grape\"])","def create_target_image(self, builder, target, base_image, parameters):","def build(obj):\n logger.info(ASCII_LOGO)\n logger.info(\"Started building SageMaker Docker image. It will take some minutes...\\n\")\n\n try:\n config_file_path = os.path.join('.sagify.json')\n if not os.path.isfile(config_file_path):\n raise ValueError()\n\n config = ConfigManager(config_file_path).get_config()\n api_build.build(\n source_dir=config.sagify_module_dir,\n requirements_dir=config.requirements_dir,\n docker_tag=obj['docker_tag'],\n image_name=config.image_name,\n python_version=config.python_version)\n\n logger.info(\"Docker image built successfully!\")\n except ValueError:\n logger.info(\"This is not a sagify directory: {}\".format(dir))\n sys.exit(-1)\n except subprocess.CalledProcessError as e:\n logger.debug(e.output)\n raise\n except Exception as e:\n logger.info(\"{}\".format(e))\n sys.exit(-1)","def _build_final_image(self, image):\n raise NotImplementedError","def load_recipes_from_file(cls, args):\n with open(args.recipes_file, 'r') as f:\n reader = csv.DictReader(f)\n for row in reader:\n cls._recipes.append(row)\n cls._add_indices_to_recipes()\n cls._initialize_recipes_status()\n logging.info(\"Recipes loaded.\")","def build(project_name, compose_file, container, container_dir_to_copy, env):\n compose_build(project_name, compose_file, container, container_dir_to_copy, env=env)","def main():\n parser = argparse.ArgumentParser(\n epilog=main.__doc__, formatter_class=argparse.RawDescriptionHelpFormatter\n )\n parser.add_argument(\n \"-d\", \"--dry-run\", action=\"store_true\", default=0, help=\"Dry run mode.\"\n )\n parser.add_argument(\n \"-v\",\n \"--verbose\",\n action=\"count\",\n default=0,\n help=\"Verbosity. Default is WARNING level.\",\n )\n\n subparsers = parser.add_subparsers(help=\"Sub commands\", dest=\"subparser\")\n subparsers.required = True\n\n build_parser = subparsers.add_parser(\n \"build\",\n description=\"Build an image from Dockerfile, caching image hierarchy\",\n help=\"Build an image from a Dockerfile\",\n )\n build_parser.add_argument(\n \"path\", metavar=\"PATH\", help=\"The build context directory\"\n )\n build_parser.add_argument(\n \"-f\",\n \"--file\",\n help=\"Name of the Dockerfile. If not provided, \"\n \"will use config.DOCKERFILE_PATH_PATTERN to compute. \",\n )\n build_parser.add_argument(\n \"-v\",\n \"--git-sha\",\n required=True,\n help=\"The version of code to build against, \" \"will pass as GIT_SHA variable\",\n )\n build_parser.add_argument(\n \"-n\", \"--name\", required=True, help=\"The name of the image to build\"\n )\n build_parser.add_argument(\n \"--build-arg\",\n metavar=\"ARG=VALUE\",\n nargs=\"*\",\n default=[],\n help=\"Set extra build-time variables. GIT_SHA, TIMESTAMP will be passed by default.\",\n )\n build_parser.add_argument(\n \"-r\",\n \"--raw\",\n action=\"store_true\",\n help=\"Whether to use raw docker build command to build, skipping caching logic\",\n )\n build_parser.add_argument(\n \"--registry\",\n default=config.DOCKER_REGISTRY,\n help=\"Docker registry use to determine the image identity, \"\n \"can be set via IMAGE_BUILDER_DOCKER_REGISTRY environment variable, \"\n 'or set DOCKER_REGISTRY in config.py. Default is \"%(default)s\"',\n )\n build_parser.add_argument(\n \"-t\",\n \"--tag-pattern\",\n default=config.GIT_SHA_TAG_PATTERN,\n help=\"Tag pattern, can only include one `{git_sha}` placeholder, \"\n 'such as \"{git_sha}-new\". If the tag exists, we won\\'t rebuild it. '\n 'Default is \"%(default)s\"',\n )\n build_parser.add_argument(\n \"-e\",\n \"--extra-tag\",\n nargs=\"*\",\n default=[],\n help=\"Extra tags to tag to the final images\",\n )\n build_parser.add_argument(\n \"--extra-name\",\n nargs=\"*\",\n default=[],\n help=\"Extra name and optionally with a tag in the 'name:tag' format\",\n )\n build_parser.add_argument(\n \"-o\", \"--output-hash\", help=\"The output filename of the files hash log.\"\n )\n build_parser.set_defaults(func=build)\n\n args = parser.parse_args()\n if args.dry_run:\n # DRY_RUN env will be read in image_builder.libs.process\n os.environ[\"DRY_RUN\"] = \"1\"\n\n if args.func == build:\n args.path = expand_path(args.path)\n if args.output_hash:\n args.output_hash = expand_path(args.output_hash)\n\n args.file = args.file or locate_dockerfile(args.name)\n args.file = expand_path(args.file)\n # set environ for main dockerfile for possibly retrieving later\n os.environ[\n config.DOCKERFILE_ENV_PATTERN.format(image_name=args.name)\n ] = args.file\n\n # change CWD to PATH\n os.chdir(args.path)\n\n if not args.registry:\n parser.error(\n \"--registry should be provied \"\n \"or specified by IMAGE_BUILDER_DOCKER_REGISTRY environment variable or set DOCKER_REGISTRY in config.py\"\n )\n if not all(\"=\" in kv for kv in args.build_arg):\n parser.error(\"--build_arg must be in ARG=VALUE format\")\n\n # set git_sha_tag\n try:\n args.git_sha_tag = args.tag_pattern.format(git_sha=args.git_sha)\n except KeyError:\n parser.error(\n 'Wrong --tag-pattern provided. Can only include one `{git_sha}` placeholder, such as \"{git_sha}-new\"'\n )\n\n # setup logging\n level = logging.WARNING - args.verbose * 10\n logging.basicConfig(\n level=level, format=\"%(asctime)s %(name)s %(levelname)s %(message)s\"\n )\n\n if args.output_hash:\n h = logging.FileHandler(args.output_hash)\n h.setLevel(logging.DEBUG)\n h.setFormatter(logging.Formatter(\"%(message)s\"))\n hash_logger.addHandler(h)\n\n # Suppress warning when we don't verify ssl\n import urllib3\n\n urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)\n\n return args.func(args)","def _extract_docker_image_file(*, instance, filename: str):\n try:\n with instance.image.open(mode=\"rb\") as im, tarfile.open(\n fileobj=im, mode=\"r\"\n ) as t:\n member = dict(zip(t.getnames(), t.getmembers()))[filename]\n file = t.extractfile(member).read()\n return file\n except (KeyError, tarfile.ReadError):\n raise ValidationError(\n f\"{filename} not found at the root of the container image \"\n f\"file. Was this created with docker save?\"\n )","def build(self):\n docker = Client(version='auto')\n status = docker.build(\n fileobj=self.tarfile,\n custom_context=True,\n tag=self.tag,\n pull=True,\n nocache=True,\n rm=True,\n )\n\n for line in status: # This effectively blocks on `docker build`\n try:\n current_app.logger.debug(line)\n except RuntimeError: # Outside of application context\n print line\n if \"successfully built\" not in line.lower():\n raise BuildError(\"Failed to build {}: {}\".format(self.tag, line))\n\n self.built = True","def build_image(image, build_args):\n\n subprocess.check_call(['docker', 'build', '--no-cache', '--force-rm', '-t',\n image] + build_args)","def build_impl(**kwargs: Any) -> None:\n try:\n config = configuration.create_transient_build_config(kwargs)\n except configuration.CLIArgumentError as e:\n print(e, file=sys.stderr)\n sys.exit(1)\n store = image.ImageStore(backend_dir=config.image_backend, frontend_dir=None)\n builder = build.ImageBuilder(config, store)\n builder.build()\n sys.exit(0)","def generate_dockerfile_extension(base_image, template_name, config_path):\n template_path = get_template_path(template_name, config_path)\n template_file = os.path.join(template_path, \"Dockerfile\")\n dockerfile = \".Dockerfile.luda\"\n\n def remove():\n if os.path.exists(dockerfile):\n os.remove(dockerfile)\n\n with cd(template_path, remove):\n with open(dockerfile, \"w\") as output:\n docker_str = j2docker.render(base_image, template_file).decode().strip()\n output.write(docker_str)\n client = docker.from_env()\n if base_image.startswith(\"luda/\"):\n _, _, image_name = base_image.partition(\"luda/\")\n image_name, _, tag = image_name.partition(\":\")\n image_name = \"luda/{0}:{1}-{2}\".format(image_name, tag, template_name)\n else:\n image_name = \"luda/{0}:{1}\".format(base_image.replace('/', '-').replace(':', '-'), template_name)\n click.echo(\"Building image: {0} ...\".format(image_name))\n client.images.build(path=os.getcwd(), tag=image_name, dockerfile=dockerfile) # This line doesn't work with Python 3...\n return image_name","def create_base_image(self, builder, template, parameters):","def read_dockerfile_for_args(target):\n import colorama\n build_args = {}\n missing_args = {}\n empty_string = \"\"\n\n # read dockerfile for args that have no value\n try:\n with open(target + '/Dockerfile') as dockerfile:\n for line in dockerfile:\n if line.startswith(\"ARG \"):\n dockerfile_args = line.replace(\n \"ARG \", \"\").strip(\"\\n\").split(\"=\")\n\n arg_name = dockerfile_args[0]\n arg_value = \"\"\n\n if len(dockerfile_args) > 1:\n arg_value = dockerfile_args[1].strip(\"\\n\")\n\n env_value = os.environ.get(arg_name)\n\n build_args[arg_name] = arg_value\n if not env_value is None:\n build_args[arg_name] = env_value\n\n if build_args[arg_name] is empty_string:\n missing_args[arg_name] = arg_name\n except FileNotFoundError:\n exit(f\"Dockerfile not found: {target}/Dockerfile\")\n\n if len(missing_args) > 1:\n message = \"WARNING: Arguments found with no defined value \" \\\n \"found in Dockerfile or environment [{}]\"\n print(colorama.Fore.YELLOW + colorama.Style.BRIGHT +\n message.format(\", \".join(missing_args)))\n\n return build_args","def build(self, conanfile):\n app = ConanApp(self._conan_api.cache_folder)\n conanfile.folders.set_base_package(conanfile.folders.base_build)\n conanfile.folders.set_base_pkg_metadata(os.path.join(conanfile.build_folder, \"metadata\"))\n run_build_method(conanfile, app.hook_manager)","def build_command(self, args):\n self._validate_common(args)\n self._set_manifests(args)\n\n manifest = self._manager._recipes\n\n records = self._get_matching_records(args, manifest)\n\n if not len(records):\n sys.exit(\"No matching items found.\")\n\n for record in records:\n result = self._manager.build(record['_type'], **record)\n print('The requested asset has been built: {}'.format(result['_path']))\n\n if len(records) > 1:\n print('All files have been successfully built. Thank you.')","def image(self, label, fname):\n if not os.path.exists(fname):\n raise OptionError(\"%s - no such file or directory\" % label)\n try:\n return Image(fname)\n except:\n raise OptionError(\"%s - invalid image file\" % label)","def generate_build_file(startpath, outfilename='build.yml'):\n buildfiles = {}\n buildtables = {}\n\n def file_node(ext, fullpath):\n return fullpath\n\n def table_node(ext, fullpath):\n return [ext.lower(), fullpath]\n\n def add_to_contents(contents, nodefunc, path, files):\n try:\n safepath = [_pythonize_name(d) if d != '.' else '.' for d in path]\n except BuildException:\n warning = \"Warning: could not determine a Python-legal name for {path}; skipping.\"\n print(warning.format(path=os.sep.join(path)))\n return\n\n ptr = contents\n for folder in safepath:\n ptr = ptr.setdefault(folder, {})\n\n for file in files:\n fullpath = os.path.join(os.path.join(*path), file)\n name, ext = splitext_no_dot(file)\n ptr[_pythonize_name(name)] = nodefunc(ext, fullpath)\n\n for root, dirs, files in os.walk(startpath):\n # skip hidden directories\n for d in dirs:\n if d.startswith('.') or d == PACKAGE_DIR_NAME:\n dirs.remove(d)\n\n rel_path = os.path.relpath(root, startpath)\n path = rel_path.split(os.sep)\n\n tablefiles = []\n rawfiles = []\n for file in files:\n # skip hidden files\n if file.startswith('.'):\n continue\n\n name, ext = splitext_no_dot(file)\n # separate files into tables and raw\n if ext.lower() in TARGET['pandas']:\n tablefiles.append(file)\n else:\n rawfiles.append(file)\n\n if rawfiles:\n add_to_contents(buildfiles, file_node, path, rawfiles)\n\n if tablefiles:\n add_to_contents(buildtables, table_node, path, tablefiles)\n\n for contents in [buildfiles, buildtables]:\n for node in ['.', '..']:\n if node in contents:\n for key in contents[node]:\n contents[key] = contents[node][key]\n del contents[node]\n\n contents = dict(files=buildfiles, tables=buildtables)\n buildfilepath = os.path.join(startpath, outfilename)\n with open(buildfilepath, 'w') as outfile:\n yaml.dump(contents, outfile)\n return buildfilepath","def build_image_jobspec(runtime, env, gcr_tag, stack_base):\n basename = \"grpc_interop_%s\" % runtime\n tag = \"%s/%s:%s\" % (args.gcr_path, basename, gcr_tag)\n build_env = {\"INTEROP_IMAGE\": tag, \"BASE_NAME\": basename}\n build_env.update(env)\n image_builder_path = _IMAGE_BUILDER\n if client_matrix.should_build_docker_interop_image_from_release_tag(lang):\n image_builder_path = os.path.join(stack_base, _IMAGE_BUILDER)\n build_job = jobset.JobSpec(\n cmdline=[image_builder_path],\n environ=build_env,\n shortname=\"build_docker_%s\" % runtime,\n timeout_seconds=30 * 60,\n )\n build_job.tag = tag\n return build_job","def create_molns_image(self):\n file_to_remove = None\n try:\n dockerfile, file_to_remove = self._create_dockerfile(installSoftware.InstallSW.get_command_list())\n image_id = self.docker.build_image(dockerfile)\n return image_id\n except Exception as e:\n logging.exception(e)\n raise ProviderException(\"Failed to create molns image: {0}\".format(e))\n finally:\n if file_to_remove is not None:\n os.remove(file_to_remove)","def set_image_from_file(self, file):\n if isinstance(file, str):\n try:\n file = self.files.filter(current=True, path=file)[0]\n except IndexError:\n return\n\n content = file.get_content()\n format = file.get_format()\n ext = format.default_extension if format else \"\"\n\n # The file name needs to be unique to bust any caches.\n file = ContentFile(content)\n file.name = f\"{self.id}-{shortuuid.uuid()}{ext}\"\n\n self.image_file = file\n self.image_updated = timezone.now()\n self.save()","def create_recipe(*, recipe_in: RecipeCreate) -> dict:\n new_entry_id = len(RECIPES) + 1\n recipe_entry = Recipe(\n id=new_entry_id,\n label=recipe_in.label,\n source=recipe_in.source,\n url=recipe_in.url,\n )\n RECIPES.append(recipe_entry.dict())\n\n return recipe_entry","def generate_from_file(self, filename):\n lines = open(filename, 'r').readlines()\n chip_name = lines[0].strip()\n input_part = re.sub('\\s+', ', ', lines[1].strip())\n output_part = re.sub('\\s+', ', ', lines[2].strip())\n\n def name_to_names(name):\n m = re.match('(.*)\\[(\\d+)\\]', name)\n if m is None:\n return [name, ]\n else:\n return ['%s[%d]' % (m.group(1), i) for i in range(int(m.group(2)))]\n\n def part_to_names(part):\n names = []\n for name in part.split(', '):\n names.extend(name_to_names(name))\n return names\n\n input_names = part_to_names(input_part)\n output_names = part_to_names(output_part)\n line_expr = ''.join(lines[3:]).replace('\\\\\\n', '').replace('{', '').replace('}', '')\n exprs = line_expr.split(', ')\n exprs = map(lambda expr: expr.strip(), exprs)\n\n parts_statement = self.generate(exprs, input_names, output_names)\n\n return self.src_code_format % (chip_name, input_part, output_part, parts_statement)","def create_from_file(cls, file_path: str) -> \"ImageClassifier\":\n base_options = _BaseOptions(file_name=file_path)\n options = ImageClassifierOptions(base_options=base_options)\n return cls.create_from_options(options)","def build_runtime(self, runtime_name, file):\n self.compute_handler.build_runtime(runtime_name, file)","def load_recipe(root: Path) -> None:\n module_name = 'dotops.recipes.' + root.name\n fpath = find_recipe(root)\n\n try:\n _ast = import_file_to_ast(fpath, module_name)\n mod = imp.new_module(module_name)\n\n mod.__file__ = fpath\n eval(ast_compile(_ast, fpath, \"exec\"), _globals(root, mod))\n except (HyTypeError, LexException) as e:\n if e.source is None:\n with open(fpath, 'rt') as fp:\n e.source = fp.read()\n e.filename = fpath\n raise\n except Exception:\n sys.modules.pop(module_name, None)\n raise","def get_image(filename=None, astrotarget=None,\n **kwargs):\n return Image(filename,astrotarget=astrotarget,\n **kwargs)","def prep_image_data(arg_dict):\n cat_df = pd.read_csv(arg_dict['category_file'],\n skiprows=1,\n sep='\\s+')\n bbox_df = pd.read_csv(arg_dict['bbox_file'],\n skiprows=1,\n sep='\\s+')\n img_dir = arg_dict['image_dir']\n\n combo_df = pd.merge(cat_df, bbox_df, how='outer', on='image_name')\n combo_df['image_name'] = combo_df['image_name'].apply(\n lambda x: x[len('img'):-len('.jpg')])\n labels = Labels(combo_df, img_dir, n_images_loaded=-1)\n labels.set_data_target('raw_image', chunksize=3000)\n return labels","def pyfunc_build_image(model_uri, extra_args=None):\n name = uuid.uuid4().hex\n cmd = [\"mlflow\", \"models\", \"build-docker\", \"-m\", model_uri, \"-n\", name]\n if extra_args:\n cmd += extra_args\n p = subprocess.Popen(cmd,)\n assert p.wait() == 0, \"Failed to build docker image to serve model from %s\" % model_uri\n return name","def construct_list_of_recipes():\n sub_list = []\n with open(args.input_file, 'r') as f:\n lines = [line if line == '\\n' else line.rstrip('\\n') for line in f]\n\n recipes_list = []\n for element in lines:\n if element == '\\n':\n recipes_list.append(sub_list)\n sub_list = []\n else:\n sub_list.append(element)\n recipes_list.append(sub_list)\n return recipes_list","def build(config_file, install=False):\n return exec_fn(lambda: _build(config_file, install))","def build_docker_image(\n name: str,\n version: str,\n build_args: str = \"\",\n docker_image_prefix: str = \"\",\n dockerfile: Optional[str] = None,\n additional_build_args: str = \"\",\n exit_on_error: bool = True,\n) -> subprocess.CompletedProcess:\n # Check if docker exists on the system\n build_utils.command_exists(\"docker\", exit_on_error=exit_on_error)\n\n versioned_tag = get_image_name(name=name, tag=version)\n latest_tag = get_image_name(name=name, tag=\"latest\")\n\n dockerfile_command = \"\"\n if dockerfile:\n dockerfile_command = \" -f \" + dockerfile\n\n completed_process = build_utils.run(\n \"docker build \"\n + dockerfile_command\n + \"-t \"\n + versioned_tag\n + \" -t \"\n + latest_tag\n + \" \"\n + build_args\n + \" ./\",\n exit_on_error=exit_on_error,\n )\n\n if completed_process.returncode > 0:\n build_utils.log(f\"Failed to build Docker image {versioned_tag}\")\n return completed_process\n\n if docker_image_prefix:\n remote_versioned_tag = get_image_name(\n name=name, tag=version, image_prefix=docker_image_prefix\n )\n build_utils.run(\n \"docker tag \" + versioned_tag + \" \" + remote_versioned_tag,\n exit_on_error=exit_on_error,\n )\n\n return completed_process","def docker_build(c):\n cli_tasks.docker_build.run(c)","def from_baked_recipe(\n cls,\n recipe: BakedRecipe,\n arguments: Arguments = Arguments(),\n labels: Dict = {}\n ):\n input_dict = {\n 'recipe': recipe.to_dict(),\n 'arguments': arguments.to_dict(),\n 'labels': labels,\n }\n\n return cls.parse_obj(input_dict)","def get_base_docker_image(docker_file):\n with open(docker_file) as f:\n from_line = next(\n line for line in f.read().split(\"\\n\") if line.startswith(\"FROM\")\n )\n _from, base_image = from_line.split()\n return base_image","def build(images, tag, quiet):\n # images to build\n build_images = None\n\n # list of available images\n image_list = build_image_map().keys()\n\n # image specified: check if it exists\n if images:\n build_images = []\n for check_name in images:\n if check_name in image_list:\n build_images.append(check_name)\n continue\n raise click.ClickException(\n \"invalid image name '{}'\".format(check_name))\n click.echo('building images {}'.format(', '.join(build_images)))\n # no image specified: build all\n else:\n click.echo('building images {}'.format(', '.join(image_list)))\n\n manager = Manager('build', tag, images=build_images, verbose=not quiet)\n manager.run()","def newimage(self, infile):\n return _image.image_newimage(self, infile)","def build (self, buildtype):\n layout = self.xlayout()\n\n # NuGet removes the prefixing v from the version.\n vless_version = self.kv['version']\n if vless_version[0] == 'v':\n vless_version = vless_version[1:]\n\n\n self.stpath = tempfile.mkdtemp(prefix=\"out-\", suffix=\"-%s\" % buildtype,\n dir=\".\")\n\n self.render('librdkafka.redist.nuspec')\n self.copy_template('librdkafka.redist.targets',\n destpath=os.path.join('build', 'native'))\n self.copy_template('librdkafka.redist.props',\n destpath=os.path.join('build', 'native'))\n self.copy_template('librdkafka.redist.props',\n destpath=os.path.join('build', 'net'))\n for f in default_doc:\n shutil.copy(f, self.stpath)\n\n self.extract_artifacts(layout)\n\n print('Tree extracted to %s' % self.stpath)\n\n # After creating a bare-bone nupkg layout containing the artifacts\n # and some spec and props files, call the 'nuget' utility to\n # make a proper nupkg of it (with all the metadata files).\n subprocess.check_call(\"./nuget.sh pack %s -BasePath '%s' -NonInteractive\" % \\\n (os.path.join(self.stpath, 'librdkafka.redist.nuspec'),\n self.stpath), shell=True)\n return ['librdkafka.redist.%s.nupkg' % vless_version]","def Build(self, docker_client):\n log.info('Building docker image %s from %s/Dockerfile:',\n self.tag, self._dockerfile_dir)\n\n width, _ = console_attr_os.GetTermSize()\n log.status.Print(DOCKER_OUTPUT_BEGIN.center(width, DOCKER_OUTPUT_LINE_CHAR))\n\n build_res = docker_client.build(\n path=self._dockerfile_dir, tag=self.tag, quiet=False, fileobj=None,\n nocache=self._nocache, rm=self._rm, pull=False)\n\n info = None\n error = None\n error_detail = None\n log_records = []\n try:\n for line in build_res:\n line = line.strip()\n if not line:\n continue\n log_record = json.loads(line)\n log_records.append(log_record)\n if 'stream' in log_record:\n info = log_record['stream'].strip()\n log.status.Print(info)\n if 'error' in log_record:\n error = log_record['error'].strip()\n # will be logged to log.error in the thrown exception\n log.status.Print(error)\n if 'errorDetail' in log_record:\n error_detail = log_record['errorDetail']['message'].strip()\n log.status.Print(error_detail)\n except docker.errors.APIError as e:\n log.error(e.explanation)\n error = e.explanation\n error_detail = ''\n finally:\n log.status.Print(DOCKER_OUTPUT_LINE_CHAR * width + '\\n')\n\n if not log_records:\n raise ImageBuildError(\n 'Error building docker image {0} [with no output]'.format(self.tag))\n\n success_message = log_records[-1].get(_STREAM)\n if success_message:\n m = _SUCCESSFUL_BUILD_PATTERN.match(success_message)\n if m:\n # The build was successful.\n self._id = m.group(1)\n log.info('Image %s built, id = %s', self.tag, self.id)\n return\n\n raise ImageBuildError('Docker build aborted: ' + error)","def _read_image_from_file(file_name):\n image_file = open(file_name, 'rb')\n image = image_file.read()\n image_file.close()\n return image","def builder_will_create_target_image(self, builder, target, image_id, template, parameters):","def build_sonar_img_from_log_file(filename, start_index=49, end_index=149):\n assert filename.endswith('.bin'), 'filename must be a .bin file'\n\n parser = get_bin_file_parser(filename)\n\n data_list = []\n for index, (timestamp, decoded_message) in enumerate(parser):\n if index >= start_index and index <= end_index:\n data_list.append(decoded_message.data)\n\n jpeg_save_path = os.path.join(os.path.dirname(__file__),\n 'sampleData', 'Sonar_Image.jpeg')\n\n sonar_img = build_sonar_image(data_list, display_results=True,\n jpeg_save_path=jpeg_save_path)\n return sonar_img","def create_image_builder(Name=None, ImageName=None, ImageArn=None, InstanceType=None, Description=None, DisplayName=None, VpcConfig=None, EnableDefaultInternetAccess=None, DomainJoinInfo=None, AppstreamAgentVersion=None):\n pass","def read_image_with_label(dir, file):\n assert type(file) == str, \"File name is not string.\"\n f = os.path.join(dir, file)\n info = file.split(\"_\")\n try:\n label = [int(info[x]) for x in range(1, 3)]\n except:\n print(\"The format of file name is not correct.\")\n else:\n return Image.open(f), label","def _load_image(file: str) -> pyglet.image.AbstractImage:\n\n return pyglet.image.load(Config.RES_DIR + \"img\" + Config.FILE_SEPARATOR + file)","def create_from_file(cls, file_name: str) -> \"TensorImage\":\n image_data = image_utils.decode_image_from_file(file_name)\n return cls(image_data, is_from_numpy_array=False)","def get_image_and_prep(self,file_path):\r\n img = np.array(Image.open(file_path).convert('1'))\r\n img = img.reshape(28,28,1)\r\n return img","def build_images(prefix, images, tag=None, commit_range=None, push=False, chart_version=None, skip_build=False):\n value_modifications = {}\n for name, options in images.items():\n image_path = options.get('contextPath', os.path.join('images', name))\n image_tag = tag\n # include chartpress.yaml itself as it can contain build args and\n # similar that influence the image that would be built\n paths = list(options.get('paths', [])) + [image_path, 'chartpress.yaml']\n last_commit = last_modified_commit(*paths)\n if tag is None:\n if chart_version:\n image_tag = \"{}-{}\".format(chart_version, last_commit)\n else:\n image_tag = last_commit\n image_name = prefix + name\n image_spec = '{}:{}'.format(image_name, image_tag)\n\n value_modifications[options['valuesPath']] = {\n 'repository': image_name,\n 'tag': SingleQuotedScalarString(image_tag),\n }\n\n if skip_build:\n continue\n\n template_namespace = {\n 'LAST_COMMIT': last_commit,\n 'TAG': image_tag,\n }\n\n if tag or image_needs_building(image_spec):\n build_args = render_build_args(options, template_namespace)\n build_image(image_path, image_spec, build_args, options.get('dockerfilePath'))\n else:\n print(f\"Skipping build for {image_spec}, it already exists\")\n\n if push:\n if tag or image_needs_pushing(image_spec):\n check_call([\n 'docker', 'push', image_spec\n ])\n else:\n print(f\"Skipping push for {image_spec}, already on registry\")\n return value_modifications","def read_image(self, item):\n assert item['image_dtype'] == 'uint16'\n\n filename = os.path.join(self.home(item['basename']))\n s = open(filename, 'rb').read()\n assert hashlib.md5(s).hexdigest() == item['md5']\n img = np.fromstring(s, dtype=item['image_dtype']).byteswap()\n img = img.reshape(item['image_shape'])\n return img","def test_recipe_valid(recipe_file, config_user, monkeypatch):\n # Mock input files\n find_files = create_autospec(esmvalcore._data_finder.find_files,\n spec_set=True)\n find_files.side_effect = lambda *_, **__: [\n 'test_0000-1849.nc',\n 'test_1850-9999.nc',\n ]\n monkeypatch.setattr(esmvalcore._data_finder, 'find_files', find_files)\n\n # Mock vertical levels\n levels = create_autospec(esmvalcore._recipe.get_reference_levels,\n spec_set=True)\n levels.side_effect = lambda *_, **__: [1, 2]\n monkeypatch.setattr(esmvalcore._recipe, 'get_reference_levels', levels)\n\n # Mock valid NCL version\n ncl_version = create_autospec(esmvalcore._recipe_checks.ncl_version,\n spec_set=True)\n monkeypatch.setattr(esmvalcore._recipe_checks, 'ncl_version', ncl_version)\n\n # Mock interpreters installed\n def which(executable):\n if executable in ('julia', 'ncl', 'python', 'Rscript'):\n path = '/path/to/' + executable\n else:\n path = None\n return path\n\n monkeypatch.setattr(esmvalcore._task, 'which', which)\n\n # Create a shapefile for extract_shape preprocessor if needed\n recipe = yaml.safe_load(recipe_file.read_text())\n for preproc in recipe.get('preprocessors', {}).values():\n extract_shape = preproc.get('extract_shape')\n if extract_shape and 'shapefile' in extract_shape:\n filename = Path(\n config_user['auxiliary_data_dir']) / extract_shape['shapefile']\n filename.parent.mkdir(parents=True, exist_ok=True)\n filename.touch()\n\n esmvalcore._recipe.read_recipe_file(recipe_file, config_user)","def _ensure_image(testkit_path, branch_name, artifacts_path):\n # Construct Docker image name from branch name\n image_name = \"runner:%s\" % branch_name\n image_path = os.path.join(testkit_path, \"runner_image\")\n docker.build_and_tag(image_name, image_path, log_path=artifacts_path)\n\n return image_name","def buildModelFromFile(fname):\n directory = os.path.dirname(fname)\n\n f = open(fname, \"r\")\n in_map = yaml.safe_load(f)\n f.close()\n\n expression = \"\"\n\n return build_model_from_dict(in_map)","def getImage(file):\n image = imread(file)\n image = crop(image, 64, 64)\n image = color.rgb2gray(image)\n\n return image","def build_base():\n with lcd(env.local_path):\n put('./requirements.txt', '/srv/build/requirements.txt')\n\n with cd('/srv/build'):\n run('docker build -t {base_image_name} .'.format(\n base_image_name=env.base_image_name,\n ))","def build():","def build(self, file_number, data):\n pass","def create(self):\n self._finish_creation_settings()\n return self.project.create_recipe(self.recipe_proto, self.creation_settings)","def build_container_image(self) -> None:\n print_version_of_tools()\n try:\n self.fs_watcher.start()\n runner = PluginsRunner(self,\n self.plugins_conf,\n self.plugin_files,\n self.keep_plugins_running,\n plugins_results=self.data.plugins_results)\n runner.run()\n finally:\n self.fs_watcher.finish()","def startRecipeFromFilename(self, filename):\r\n recipe = RecipeFileObject(os.path.join(main.settings.RECIPE_DIR, filename)).getParsed(self.actualTopology, self.anlage)\r\n if recipe.isValid:\r\n if self.actualRecipeThread == None:\r\n thread = RecipeRootThread(sys.stdout, recipe)\r\n self.actualRecipeThread = thread\r\n thread.start()\r\n else:\r\n self.message = 'Rezept läuft noch.'\r\n return False\r\n else:\r\n self.message = recipe.message\r\n return False\r\n return True","def load_image(file_path):\r\n return Image.open(file_path)","def image_generation(formula: str) -> Image:\n\n expr = \"$\\displaystyle \" + formula + \"$\"\n print(f\"Generating image for {formula}\")\n # This creates a ByteIO stream and saves there the output of sympy.preview\n f = BytesIO()\n sympy.preview(expr, euler=False,\n preamble=r\"\\documentclass[varwidth,convert]{standalone}\"\n r\"\\usepackage{amsmath,amssymb,amsthm,mathtools}\"\n r\"\\begin{document}\",\n viewer=\"BytesIO\", output=\"ps\", outputbuffer=f)\n f.seek(0)\n\n # Open the image as if it were a file.\n img = Image.open(f)\n\n # Resize the image to look sharper\n img.load(scale=50)\n print(f\"Generated image for {formula}\")\n return img","def load_autopkg_recipe(path):\n recipe = None\n\n if path.endswith(\".yaml\"):\n try:\n # try to read it as yaml\n with open(path, \"rb\") as f:\n recipe = yaml.safe_load(f)\n except Exception as err:\n print(\"{}: yaml parsing error: {}\".format(path, err))\n elif path.endswith(\".json\"):\n try:\n # try to read it as json\n with open(path, \"rb\") as f:\n recipe = json.load(f)\n except Exception as err:\n print(\"{}: json parsing error: {}\".format(path, err))\n else:\n try:\n # try to read it as a plist\n with open(path, \"rb\") as f:\n recipe = plistlib.load(f)\n except Exception as err:\n print(\"{}: plist parsing error: {}\".format(path, err))\n\n return recipe","def make_image(self, **kwargs):\n image = dict(self.BASE_EMR_IMAGE, **kwargs)\n\n return {k: v for k, v in image.items() if v is not None}","def ReadRecipeFromFile(self, path: str) -> None:\n with io.open(path, 'r', encoding='utf-8') as file_object:\n try:\n recipe = self._ReadRecipeFromFileObject(file_object)\n except json.decoder.JSONDecodeError as exception:\n raise errors.RecipeParseError(\n 'Unable to parse recipe file: {0:s} with error: {1!s}'.format(\n path, exception))\n\n self.RegisterRecipe(recipe)","def process_image_file(self, image_file):\n image = image_util.load_image_from_file(image_file)\n return self.process_image(image)","def _image(filename):\n return TK.PhotoImage(file=filename)","def start_image_builder(Name=None, AppstreamAgentVersion=None):\n pass","def _build_docker_images(self):\n print(f\"+ building {len(self.neurodocker_specs)} Docker images\")\n self.docker_status = []\n for sha1, neurodocker_dict in self.neurodocker_specs.items():\n try:\n print(\"++ building image: {}\".format(neurodocker_dict))\n cg.docker_main(\n self.working_dir,\n neurodocker_dict,\n sha1,\n build_context=self.build_context,\n )\n self.docker_status.append(\"docker ok\")\n except Exception as e:\n self.docker_status.append(\n \"failed to build image with SHA1 {}: {}\".format(sha1, e)\n )","def fit_recipe(recipe):\n # initialize recipe\n new_recipe = copy.deepcopy(recipe)\n\n # initialize and fit the dimensions 'fov', 'r', 'c' and 'z'\n for key in ['fov', 'r', 'c', 'z']:\n if key not in new_recipe:\n new_recipe[key] = [None]\n value = new_recipe[key]\n if isinstance(value, str):\n new_recipe[key] = [value]\n\n # initialize the dimensions 'ext', 'opt'\n for key in ['ext', 'opt']:\n if key not in new_recipe:\n new_recipe[key] = \"\"\n\n return new_recipe","def build_nighthawk_binary_image_from_source(manager: source_manager.SourceManager) -> None:\n # TODO: Inject the builder object into this method\n builder = nighthawk_builder.NightHawkBuilder(manager)\n builder.build_nighthawk_binary_image()","def dockerfile() -> co.Exec:\n image = co.Image(dockerfile=\"./docker/Dockerfile.simple\")\n return co.Exec(\n f\"python -c '{pretty_table_script}'\", image=image, doc=co.util.magic_doc()\n )","def create_hero(infile: str) -> str:\n outfile = re.search(\"(.*)\\.[a-zA-Z]{3,4}\", infile)[1] + \"-hero.png\"\n\n im = Image.open(infile)\n width = im.size[0]\n height = im.size[1]\n aspect = width / float(height)\n required_aspect = REQUIRED_WIDTH / float(REQUIRED_WIDTH)\n\n if width >= REQUIRED_WIDTH:\n if aspect > required_aspect:\n new_width = int(required_aspect * height)\n offset = (width - new_width) / 2\n resize = (0, 0, width - (offset * 2), height)\n else:\n new_height = int(width / required_aspect)\n offset = (height - new_height) / 2\n resize = (0, 0, width, height - (offset * 2))\n\n hero = im.crop(resize).resize(\n (REQUIRED_WIDTH, REQUIRED_HEIGHT), resample=Image.ANTIALIAS\n )\n sharpen = ImageEnhance.Sharpness(hero)\n sharpen.enhance(1.5).save(outfile, \"PNG\")\n return outfile\n else:\n print(\"Image must be at least 640px wide\")","def build(name, file, config, backend, debug):\n setup_lithops_logger(logging.DEBUG)\n\n verify_runtime_name(name)\n\n if config:\n config = load_yaml_config(config)\n\n config_ow = set_config_ow(backend, runtime_name=name)\n config = default_config(config, config_ow, load_storage_config=False)\n\n if config['lithops']['mode'] != SERVERLESS:\n raise Exception('\"lithops build\" command is only valid for serverless backends')\n\n compute_config = extract_serverless_config(config)\n compute_handler = ServerlessHandler(compute_config, None)\n compute_handler.build_runtime(name, file)","def __instructions_runfile(self):\n\n pkg = os.path.basename(self.__runfile)\n\n install_cmds = [\n 'sh ./{} --nox11 -- -noprompt -targetpath={}'.format(\n pkg, self.__prefix)\n ]\n\n # Commands needed to predeploy target-specific files. When\n # connecting through the GUI on another machine to the\n # container, this removes the need to copy the files over.\n install_cmds += [\n 'mkdir -p /tmp/var/target',\n 'ln -sf {}/target/* /tmp/var/target'.format(self.__prefix),\n 'ln -sf {}/sections /tmp/var/'.format(self.__prefix),\n 'chmod -R a+w /tmp/var'\n ]\n\n kwargs = {}\n if self.__runfile.strip().startswith(('http://', 'https://')):\n kwargs['url'] = self.__runfile\n else:\n kwargs['package'] = self.__runfile\n\n self.__bb = generic_build(\n annotations={'runfile': pkg},\n base_annotation=self.__class__.__name__,\n comment = False,\n devel_environment={'PATH': '{}:$PATH'.format(self.__prefix)},\n directory=self.__wd,\n install=install_cmds,\n unpack=False,\n wd=self.__wd,\n **kwargs\n )\n\n self += comment('NVIDIA Nsight Compute {}'.format(pkg), reformat=False)\n self += packages(ospackages=self.__ospackages)\n self += self.__bb\n self += environment(variables=self.environment_variables)","def make_image():\n click.echo(\"make_image\")","def make_montage(infile):\n\n #create a blank image\n blank = Image.new(\"RGB\", OUTPIC_SIZE ,(25,23,24,255))\n\n #paste the background\n bg = Image.open(BACKGROUND_FILENAME)\n blank.paste(bg,(0,0))\n \n #paste the raw picture\n im = Image.open(infile)\n \n # past on background\n blank.paste(im,OUTPIC_PHOTO_OFFSET)\n\n #paste the stickers layer on the pic (a transparent png)\n stickers = Image.open(STICKERS_FILENAME)\n blank.paste(stickers,(0,0),stickers)\n\n return blank","def cli(ctx, image_file):\n if not image_file:\n return\n for pull_image in image_file.readline():\n pull_image = pull_image.rstrip('\\n')\n if len(pull_image) == 0:\n continue\n docker.pull(pull_image)\n push_image = '%s/%s/%s' % (DEFAULT_REGISTRY,\n DEFAULR_NAMESPACE,\n pull_image.split('/')[-1])\n docker.tag(pull_image, push_image)\n docker.push(push_image)","def build(parser):\n parser.add_argument(\n '-i', '--identity-file',\n help=(\n 'A SSH private key file which may be used to pull down '\n 'repositories when building.'\n ),\n )\n parser.add_argument(\n '-e', '--env',\n action='append',\n default=[],\n help=(\n 'Add environ variables to the build. These may be accessed in '\n 'the build scripts. Each variable should be of the format '\n 'KEY=VALUE. This may be used to pass in credentials required '\n 'to access private repositories. May be specified more than once.'\n ),\n )\n parser.add_argument(\n '-b', '--build-dir',\n default=os.getcwd(),\n help=(\n 'This folder should be accessible from the docker instance.'\n ),\n )\n parser.add_argument(\n '--archive',\n help=(\n 'Archive the build files into a local tarball.'\n ),\n )\n parser.add_argument(\n '--archive-only',\n action='store_true',\n default=False,\n help=(\n 'Skip tagging and building the runner image.'\n ),\n )\n parser.add_argument(\n '-t', '--tag',\n help=(\n 'Tag to apply to the built image. '\n 'This will default to the current date/time.'\n ),\n )\n parser.add_argument(\n '--no-cache',\n dest='use_cache',\n action='store_false',\n default=True,\n help=(\n 'Do not mount a cache volume when compiling the app.'\n ),\n )\n parser.add_argument(\n '--cache',\n metavar='CONTAINER:PATH',\n help=(\n 'An optional volume or location for the cache. The format is '\n '\"<volume_id>:<path>\" where the \"volume_id\" must be the '\n 'name or hash of an existing volume. The \"path\" is an absolute '\n 'path to the cache folder/volume within the build container.'\n '\\n\\n'\n 'By default a container will be created by mangling the name of '\n 'the app by appending \"__buildcache\" (e.g. \"myapp__buildcache\").'\n '\\n\\n'\n 'This option is ignored if --no-cache is specified.'\n '\\n\\n'\n 'The \"volume_id\" may be an absolute path on the host filesystem.'\n '\\n\\n'\n 'The \"path\" may be dropped, in which case it will default to '\n '/tmp/cache inside the build container.'\n '\\n\\n'\n 'Examples:'\n '\\n\\n'\n ' # custom volume with default path\\n'\n ' --cache my_cache'\n '\\n\\n'\n ' # custom path inside of volume\\n'\n ' --cache my_cache:/tmp/cache'\n '\\n\\n'\n ' # host filesystem\\n'\n ' --cache /tmp/cache'\n ),\n )\n parser.add_argument(\n '--rebuild-cache',\n action='store_true',\n default=False,\n help=(\n 'Delete any cached artifacts prior to building.'\n ),\n )\n parser.add_argument(\n '--skip-cleanup',\n action='store_true',\n default=False,\n help=(\n 'Skip removal of images and containers.'\n ),\n )\n parser.add_argument(\n 'app',\n help=(\n 'Path to an application folder with a meta.yml file'\n ),\n )","def build(filename, verbose):\n set_verbosity(verbose)\n hokusai.build(filename)","def from_file(self, filename):\n if os.path.exists(filename): # read the image from file\n self.image = io.imread(filename)\n self.filename = filename\n if os.path.exists(filename[:-4] + '.csv'):\n self.metadata.read_from_csv(filename[:-4] + '.csv')\n else:\n warnings.warn(\"Metadata file is not found!\", Warning)\n else:\n raise ValueError('File does not exist!')","def build_tale_image(task, tale_id, force=False):\n logging.info('Building image for Tale %s', tale_id)\n\n task.job_manager.updateProgress(\n message='Building image', total=BUILD_TALE_IMAGE_STEP_TOTAL,\n current=1, forceFlush=True)\n\n tic = time.time()\n tale = task.girder_client.get('/tale/%s' % tale_id)\n while tale[\"status\"] != TaleStatus.READY:\n time.sleep(2)\n tale = task.girder_client.get('/tale/{_id}'.format(**tale))\n if tale[\"status\"] == TaleStatus.ERROR:\n raise ValueError(\"Cannot build image for a Tale in error state.\")\n if time.time() - tic > 5 * 60.0:\n raise ValueError(\"Cannot build image. Tale preparing for more than 5 minutes.\")\n\n last_build_time = -1\n try:\n last_build_time = tale['imageInfo']['last_build']\n except KeyError:\n pass\n\n logging.info('Last build time {}'.format(last_build_time))\n image_builder = ImageBuilder(task.girder_client, tale=tale)\n image_builder.pull_r2d()\n\n tag = image_builder.get_tag(force=force)\n\n logging.info(\"Computed tag: %s (taleId:%s)\", tag, tale_id)\n\n # Use the current time as the image build time and tag\n build_time = int(time.time())\n\n # Check if image already exists\n if not force and (image := image_builder.cached_image(tag)):\n print('Cached image exists for this Tale. Skipping build.')\n task.job_manager.updateProgress(\n message='Tale not modified, no need to build',\n total=BUILD_TALE_IMAGE_STEP_TOTAL,\n current=BUILD_TALE_IMAGE_STEP_TOTAL,\n forceFlush=True\n )\n return {\n 'image_digest': f\"{tag}@{image['Descriptor']['digest']}\",\n 'repo2docker_version': image_builder.container_config.repo2docker_version,\n 'last_build': last_build_time\n }\n\n print(\"Forcing build.\")\n\n # Prepare build context\n ret, _ = image_builder.run_r2d(tag, task=task)\n if task.canceled:\n task.request.chain = None\n logging.info(\"Build canceled.\")\n return\n\n if ret[\"StatusCode\"] != 0:\n # repo2docker build failed\n raise ValueError('Error building tale {}'.format(tale_id))\n\n for line in image_builder.dh.apicli.push(tag, stream=True):\n print(line.decode('utf-8'))\n\n # Get the built image digest\n image = image_builder.dh.cli.images.get(tag)\n digest = next((_ for _ in image.attrs['RepoDigests']\n if _.startswith(urlparse(DEPLOYMENT.registry_url).netloc)), None)\n\n task.job_manager.updateProgress(\n message='Image build succeeded', total=BUILD_TALE_IMAGE_STEP_TOTAL,\n current=BUILD_TALE_IMAGE_STEP_TOTAL, forceFlush=True)\n\n logging.info('Successfully built image %s' % image.attrs['RepoDigests'][0])\n\n # Image digest used by updateBuildStatus handler\n return {\n 'image_digest': digest,\n 'repo2docker_version': image_builder.container_config.repo2docker_version,\n 'last_build': build_time\n }","def build_imagebuilder_template(image_config: ImageBuilderConfig, image_id: str, bucket: S3Bucket):\n from aws_cdk.core import App # pylint: disable=C0415\n\n from pcluster.templates.imagebuilder_stack import ImageBuilderCdkStack # pylint: disable=C0415\n\n with tempfile.TemporaryDirectory() as tempdir:\n output_file = \"imagebuilder\"\n app = App(outdir=str(tempdir))\n ImageBuilderCdkStack(app, output_file, image_config, image_id, bucket)\n app.synth()\n generated_template = load_yaml_dict(os.path.join(tempdir, f\"{output_file}.template.json\"))\n\n return generated_template"],"string":"[\n \"def build(image_name, path='.'):\\n try:\\n doxy.images.build(path=path, tag=image_name)\\n message = '[*] Image {} built.'\\n print message.format(image_name)\\n except Exception as err:\\n print err\\n raise\",\n \"def parseRecipe (self,filename):\\r\\n return RecipeFileObject(os.path.join(main.settings.RECIPE_DIR,filename)).getParsed(self.actualTopology, self.anlage)\",\n \"def build_container(client):\\n client.images.build(path=os.path.join(os.path.abspath(\\\"\\\"), \\\"docker\\\"), tag=\\\"scrape_light\\\")\",\n \"def create_image(name: str, distribution: str, apt_repo: str, release_track: str, release_label: str, flavour: str,\\n organization: str, docker_registry: str, rosdistro_path: pathlib.Path, timestamp:str,\\n publish: bool = False):\\n\\n # Read configuration files\\n common_config = yaml.safe_load((rosdistro_path / 'config/recipes.yaml').open())['common']\\n recipe = yaml.safe_load((rosdistro_path / 'config/images.yaml').open())['images']\\n distro = recipe[name]['distro']\\n build_type = recipe[name]['build_type']\\n env = source_file(f'{os.environ[\\\"BUNDLE_ROOT\\\"]}/{distro}/setup.bash')\\n today = timestamp\\n extra_vars: List[Any] = []\\n\\n try:\\n package = recipe[name]['package']\\n provision_file = recipe[name]['provision_file']\\n except KeyError:\\n package = '/tailor-image'\\n provision_file = f'{build_type}.yaml'\\n\\n env['ANSIBLE_CONFIG'] = find_package(package, 'ansible.cfg', env)\\n template_path = f'/tailor-image/environment/image_recipes/{build_type}/{build_type}.json'\\n provision_file_path = find_package(package, 'playbooks/' + provision_file, env)\\n\\n optional_vars = []\\n optional_var_names = ['username', 'password', 'extra_arguments_ansible',\\n 'ansible_command', 'description', 'disk_size', 'group']\\n\\n for var in optional_var_names:\\n if var in recipe[name]:\\n optional_vars.extend(['-var', f'{var}={recipe[name][var]}'])\\n\\n if build_type == 'docker':\\n image_name = f'tailor-image-{name}-{distribution}-{release_label}'\\n docker_registry_data = docker_registry.replace('https://', '').split('/')\\n ecr_server = docker_registry_data[0]\\n ecr_repository = docker_registry_data[1]\\n extra_vars = [\\n '-var', f'type={build_type}',\\n '-var', f'bundle_flavour={flavour}',\\n '-var', f'image_name={image_name}',\\n '-var', f'ecr_server={ecr_server}',\\n '-var', f'os_version={distribution}',\\n '-var', f'ecr_repository={ecr_repository}',\\n '-var', f'aws_access_key={os.environ[\\\"AWS_ACCESS_KEY_ID\\\"]}',\\n '-var', f'aws_secret_key={os.environ[\\\"AWS_SECRET_ACCESS_KEY\\\"]}'\\n ]\\n\\n if not publish:\\n extra_vars += ['-except', 'publish']\\n\\n # Make sure we remove old containers before creting new ones\\n run_command(['docker', 'rm', '-f', 'default'], check=False)\\n\\n elif build_type in ['bare_metal', 'lxd'] and publish:\\n # Get information about base image\\n base_image = recipe[name]['base_image'].replace('$distribution', distribution)\\n\\n # Get disk size to use\\n disk_size = recipe[name].get('disk_size', 9) # In GB\\n\\n # Get base image\\n base_image_local_path = '/tmp/' + base_image\\n base_image_key = release_label + '/images/' + base_image\\n click.echo(f'Downloading image from {base_image_key}')\\n try:\\n boto3.resource('s3').Bucket(apt_repo).download_file(base_image_key, base_image_local_path)\\n except botocore.exceptions.ClientError:\\n click.echo(f'Unable to download base image from {base_image_key}, creating a new one')\\n run_command(['bash',\\n '/tailor-image/environment/create_base_image.bash',\\n f'{base_image_local_path}',\\n f'{distribution}'])\\n boto3.resource('s3').Bucket(apt_repo).upload_file(base_image_local_path, base_image_key)\\n\\n # Enable nbd kernel module, necesary for qemu's packer chroot builder\\n run_command(['modprobe', 'nbd'])\\n\\n # Resize image\\n run_command(['qemu-img', 'resize', base_image_local_path, '30G'])\\n\\n # Copy image\\n tmp_image = base_image_local_path.replace('disk1', 'disk1-resized')\\n run_command(['cp', base_image_local_path, tmp_image])\\n\\n # Resize partition inside qcow image\\n run_command(['virt-resize', '--expand', '/dev/sda1', base_image_local_path, tmp_image])\\n run_command(['mv', tmp_image, base_image_local_path])\\n\\n # Generate image name\\n image_name = f'{organization}_{name}_{distribution}_{release_label}_{today}'\\n\\n extra_vars = [\\n '-var', f'image_name={image_name}',\\n '-var', f's3_bucket={apt_repo}',\\n '-var', f'iso_image={base_image_local_path}',\\n '-var', f'distribution={distribution}',\\n '-var', f'disk_size={disk_size}'\\n ]\\n\\n # Make sure to clean old image builds\\n run_command(['rm', '-rf', '/tmp/images'])\\n\\n elif build_type == 'ami':\\n image_name = f'{organization}_{name}_{distribution}_ami_{release_label}'\\n # Get ami-id for base image\\n source_ami_id = recipe[name]['source_ami'].get(distribution)\\n\\n if not source_ami_id:\\n click.echo(f'You need to specify a bas AMI for the desired distribution {distribution}')\\n sys.exit(1)\\n\\n # Increase fow how long we wait for image to be ready. Default is 30 minutes, sometime it might take longer\\n env['AWS_MAX_ATTEMPTS'] = '90' # minutes\\n env['AWS_POLL_DELAY_SECONDS'] = '60' # Poll for status every minute\\n\\n extra_vars = [\\n '-var', f'build_date={today}',\\n '-var', f'image_name={image_name}',\\n '-var', f'name={name}',\\n '-var', f'source_ami_id={source_ami_id}',\\n '-var', f'distribution={distribution}',\\n '-var', f'release_label={release_label}',\\n '-var', f'aws_access_key={os.environ[\\\"AWS_ACCESS_KEY_ID\\\"]}',\\n '-var', f'aws_secret_key={os.environ[\\\"AWS_SECRET_ACCESS_KEY\\\"]}'\\n ]\\n else:\\n return 0\\n\\n extra_vars.extend(optional_vars)\\n\\n click.echo(f'Building {build_type} image with: {provision_file}', err=True)\\n\\n command = ['packer', 'build',\\n '-var', f'playbook_file={provision_file_path}',\\n '-var', f'organization={organization}',\\n '-var', f'bundle_track={release_track}',\\n '-var', f'bundle_version={release_label}'] + extra_vars + ['-timestamp-ui', template_path]\\n\\n run_command(command, env=env, cwd='/tmp')\\n\\n if build_type in ['bare_metal', 'lxd'] and publish:\\n update_image_index(release_label, apt_repo, common_config, image_name)\",\n \"def _ReadRecipeFromFileObject(\\n self, file_object: Union[StringIO, TextIOWrapper, TextIO]) -> Recipe:\\n json_dict = json.load(file_object)\\n\\n description = json_dict['description']\\n del json_dict['description']\\n\\n args = []\\n for arg_list in json_dict['args']:\\n args.append(RecipeArgs(*arg_list))\\n del json_dict['args']\\n\\n return resources.Recipe(description, json_dict, args)\",\n \"def build_runtime(self, docker_image_name, dockerfile):\\n logger.debug('Building new docker image from Dockerfile')\\n logger.debug('Docker image name: {}'.format(docker_image_name))\\n\\n entry_point = os.path.join(os.path.dirname(__file__), 'entry_point.py')\\n create_handler_zip(k8s_config.FH_ZIP_LOCATION, entry_point, 'lithopsentry.py')\\n\\n if dockerfile:\\n cmd = '{} build -t {} -f {} .'.format(k8s_config.DOCKER_PATH,\\n docker_image_name,\\n dockerfile)\\n else:\\n cmd = '{} build -t {} .'.format(k8s_config.DOCKER_PATH, docker_image_name)\\n\\n if logger.getEffectiveLevel() != logging.DEBUG:\\n cmd = cmd + \\\" >{} 2>&1\\\".format(os.devnull)\\n\\n logger.info('Building runtime')\\n res = os.system(cmd)\\n if res != 0:\\n raise Exception('There was an error building the runtime')\\n\\n self._delete_function_handler_zip()\\n\\n cmd = '{} push {}'.format(k8s_config.DOCKER_PATH, docker_image_name)\\n if logger.getEffectiveLevel() != logging.DEBUG:\\n cmd = cmd + \\\" >{} 2>&1\\\".format(os.devnull)\\n res = os.system(cmd)\\n if res != 0:\\n raise Exception('There was an error pushing the runtime to the container registry')\\n logger.debug('Building done!')\",\n \"def build(config, version_tag):\\n config_dict = get_config(config)\\n image_name = config_dict['IMAGE'].split(':')[0]\\n image = '{}:{}'.format(image_name, version_tag)\\n base_image = config_dict['BASE_IMAGE']\\n\\n cmd = 'docker build -t {image} --build-arg base_image={base_image} .'.format(image=image,\\n base_image=base_image)\\n with cd(env.project_dir):\\n run(cmd)\\n return image\",\n \"def build(self, source, config, repo, tag):\\n check_blacklist(repo)\\n env = ' '.join(\\\"{}='{}'\\\".format(\\n k, v.encode('unicode-escape').replace(\\\"'\\\", \\\"\\\\\\\\'\\\")) for k, v in config.viewitems())\\n dockerfile = \\\"FROM {}\\\\nENV {}\\\".format(source, env)\\n f = io.BytesIO(dockerfile.encode('utf-8'))\\n target_repo = \\\"{}/{}:{}\\\".format(self.registry, repo, tag)\\n logger.info(\\\"Building Docker image {}\\\".format(target_repo))\\n with SimpleFlock(self.FLOCKFILE, timeout=1200):\\n stream = self.client.build(fileobj=f, tag=target_repo, stream=True, rm=True)\\n log_output(stream)\",\n \"def _build(build_context,\\n image_tag,\\n image_name,\\n nocache,\\n credstore_env=None,\\n registries=None):\\n _logger.info('Starting build ...')\\n\\n # Build the image\\n docker_builder = DockerBuilder(\\n build_context=build_context,\\n image_name=image_name,\\n image_tag=image_tag,\\n credstore_env=credstore_env,\\n registries=registries,\\n )\\n docker_builder.login_private_registries()\\n if docker_builder.check_image():\\n # Image already built\\n docker_builder.clean()\\n return docker_builder\\n if not docker_builder.build(nocache=nocache):\\n docker_builder.clean()\\n raise BuildException('The docker image could not be built.')\\n return docker_builder\",\n \"def make_image(self):\\n\\n if self.type == 'passthrough':\\n return\\n render_template(\\n os.path.dirname(self.main_module),\\n os.path.basename(self.main_module_path),\\n self.language,\\n self.requirements,\\n self.whitelist,\\n self.type,\\n into=self.code_dir)\\n self.build()\",\n \"def build_docker(c):\\n tag = c.run('git describe', hide=True)\\n docker_img = f'{docker_repo}:{tag.stdout.strip()}'\\n c.run(f'docker build -t {docker_img} .')\",\n \"def build_image(self, df_path):\\n cmd = \\\"{} build -f {} -t {} .\\\".format(\\n self.binary, df_path, self.vars['image'])\\n LOG.info(\\\"Running: {}\\\".format(cmd))\\n res = subprocess.run(cmd, shell=True)\\n if res.returncode != 0:\\n sys.exit(2)\\n return res\",\n \"def _build(registry, image_name, git_sha):\\n\\n image = f\\\"{registry}/{image_name}\\\"\\n\\n # If this image is not in our registry, nothing we can do.\\n if registry != config.DOCKER_REGISTRY:\\n return \\\"\\\"\\n\\n # If image:{git_sha} already exists, then return.\\n logger.info(\\n f\\\"Pulling git_sha tag {image}:{git_sha} to check if it already exists\\\"\\n )\\n if (\\n not args.dry_run\\n and docker_silent.pull(f\\\"{image}:{git_sha}\\\").returncode == 0\\n ):\\n # better to find the hash-{hash} of this image, and return hash\\n # but currently, it is not easy to find all tags of the same image digest through registry API.\\n # so we return image digest instead.\\n digest = get_image_digest(f\\\"{image}:{git_sha}\\\")\\n logger.info(\\n f\\\"git_sha tag {image}:{git_sha} already exists, digest: %s\\\", digest\\n )\\n if not digest:\\n raise Exception(\\\"Failed to get digest for existing image\\\")\\n _tag_to_extra_tags(args, image, git_sha)\\n return digest\\n\\n # Enter build context directory if it is specified\\n build_context = enter_build_context(image_name)\\n\\n # Parse .dockerignore in build context\\n dockerignore_files_set = parse_dockerignore(build_context)\\n\\n # Check if the dockerfile exists\\n dockerfile_path = locate_dockerfile(image_name)\\n if not os.path.isfile(dockerfile_path):\\n logger.error(\\n \\\"%s not exists or is not a file, so %s cannot get build\\\",\\n dockerfile_path,\\n image_name,\\n )\\n raise Exception(\\\"Building cannot continue\\\")\\n\\n dockerfile = Dockerfile(dockerfile_path, build_arg=build_arg)\\n\\n hasher = sha256()\\n\\n # Build parents, and calc parents hash\\n for parent_image in dockerfile.parent_images:\\n (\\n parent_image_registry,\\n parent_image_name,\\n parent_image_tag,\\n ) = parse_docker_image_identity(parent_image)\\n parent_digest = _build(\\n parent_image_registry, parent_image_name, parent_image_tag\\n )\\n if parent_digest is None:\\n raise Exception(f\\\"Failed to get parent_digest for {image}\\\")\\n hasher.update(parent_digest.encode())\\n hash_logger.info(\\n \\\"parent: %s, digest: (%s, %s), hash: %s\\\",\\n parent_image,\\n parent_digest,\\n parent_digest.encode(),\\n hasher.hexdigest(),\\n )\\n\\n # Calc current image files hash\\n\\n def update_file_hash(f):\\n if not os.path.isfile(f):\\n return\\n if f in dockerignore_files_set:\\n hash_logger.debug(\\\"ignore: %s\\\", f)\\n return\\n with open(f, \\\"rb\\\") as open_file:\\n buf = open_file.read(config.READ_FILE_BLOCKSIZE)\\n while len(buf) > 0:\\n hasher.update(buf)\\n buf = open_file.read(config.READ_FILE_BLOCKSIZE)\\n hash_logger.info(\\\"update: %s, hash: %s\\\", f, hasher.hexdigest())\\n\\n srcs = [dockerfile_path] + dockerfile.copied_srcs + dockerfile.added_srcs\\n # TODO: if the src is a url, download it and hash it (even crane didn't do that)\\n for src in srcs:\\n for f in sorted(glob.glob(src)):\\n # We match every file in a directory recursively\\n if os.path.isdir(f):\\n for sub_f in sorted(glob.glob(f\\\"{f}/**\\\", recursive=True)):\\n update_file_hash(sub_f)\\n else:\\n update_file_hash(f)\\n\\n files_hash = hasher.hexdigest()\\n hash_logger.info(\\\"image: %s, hash: %s\\\", image, files_hash)\\n\\n hash_tag = config.FILES_HASH_TAG_PATTERN.format(files_hash=files_hash)\\n # FIXME(harry): hack, remove this\\n old_hash_image = f\\\"docker-registry.example.com:5000/{image_name}:{hash_tag}\\\"\\n\\n logger.info(\\n f\\\"Pulling files_hash tag {image}:{hash_tag} to check if it already exists\\\"\\n )\\n # If image:hash-{hash} already exists,\\n # then content didn't change, return.\\n # We just need to tag it to latest code version.\\n if (\\n not args.dry_run\\n and docker_silent.pull(f\\\"{image}:{hash_tag}\\\").returncode == 0\\n ):\\n logger.info(\\n f\\\"files_hash tag {image}:{hash_tag} already exists, \\\"\\n \\\"it means content didn't change, we can just tag the old image to new git_sha version tag\\\"\\n )\\n # FIXME(harry): hack, remove this\\n elif not args.dry_run and docker_silent.pull(old_hash_image).returncode == 0:\\n logger.info(f\\\"NOTE: files_hash tag {old_hash_image} already exists!\\\")\\n # tag and push this hash image\\n if docker.tag(old_hash_image, f\\\"{image}:{hash_tag}\\\").returncode != 0:\\n logger.error(\\\"Failed to tag old hash image\\\")\\n return\\n if docker.push(f\\\"{image}:{hash_tag}\\\").returncode != 0:\\n logger.error(\\\"Failed to push hash_tag image\\\")\\n return\\n # If image:hash-{hash} not exists, then build it from Dockerfile.\\n else:\\n logger.info(\\n f\\\"files_hash tag {image}:{hash_tag} dosen't exists, \\\"\\n \\\"it means content may changed, gonna build it from Dockerfile\\\"\\n )\\n if build_with_raw_command(args, image, dockerfile_path, hash_tag) != 0:\\n logger.error(f\\\"Failed to build {image}:{hash_tag}\\\")\\n return\\n if docker.push(f\\\"{image}:{hash_tag}\\\").returncode != 0:\\n logger.error(f\\\"Failed to push image\\\")\\n return\\n logger.info(f\\\"image files_hash tag {image}:{hash_tag} is pushed\\\")\\n\\n # tag and push this final image\\n if docker.tag(f\\\"{image}:{hash_tag}\\\", f\\\"{image}:{git_sha}\\\").returncode != 0:\\n logger.error(\\\"Failed to tag image\\\")\\n return\\n _tag_to_extra_tags(args, image, git_sha)\\n if docker.push(f\\\"{image}:{git_sha}\\\").returncode != 0:\\n logger.error(\\\"Failed to push image\\\")\\n return\\n digest = get_image_digest(f\\\"{image}:{git_sha}\\\")\\n if not digest:\\n logger.error(\\\"Failed to get digest for image\\\")\\n return\\n logger.info(f\\\"image {image}:{git_sha} is pushed, digest: {digest}\\\")\\n\\n return digest\",\n \"def newimagefromfile(self, infile):\\n return _image.image_newimagefromfile(self, infile)\",\n \"def build(path, tag):\\n print (\\\"*** BUILDING IS INITIATED\\\")\\n try:\\n response = client.images.build(path = path, tag=tag, rm=True)\\n except Exception as ex:\\n template = \\\"An exception of type {0} occured. Arguments:\\\\n{1!r}\\\"\\n message = template.format(type(ex).__name__, ex.args)\\n click.echo(message)\\n print(\\\"*** BUILD OUTPUT:\\\")\\n for object in response[1]:\\n click.echo(object)\\n click.echo('*** BUILDING FINISHED')\",\n \"def make_image(self, path):\\n\\t\\treturn None\",\n \"def _build_container(\\n self, target_image, odcs, repo_type, repo_list, terminate_event,\\n scratch, record):\\n self.logger.info(\\\"Building image: %s\\\" % target_image)\\n cmd_list = [\\\"rhpkg\\\", \\\"--path=%s\\\" % self.distgit_dir]\\n\\n if self.runtime.user is not None:\\n cmd_list.append(\\\"--user=%s\\\" % self.runtime.user)\\n\\n cmd_list += (\\n \\\"container-build\\\",\\n \\\"--nowait\\\",\\n )\\n\\n if odcs:\\n if odcs == 'signed':\\n odcs = 'release' # convenience option for those used to the old types\\n cmd_list.append('--signing-intent')\\n cmd_list.append(odcs)\\n else:\\n if repo_type:\\n repo_list = list(repo_list) # In case we get a tuple\\n repo_list.append(self.metadata.cgit_url(\\\".oit/\\\" + repo_type + \\\".repo\\\"))\\n\\n if repo_list:\\n # rhpkg supports --repo-url [URL [URL ...]]\\n cmd_list.append(\\\"--repo-url\\\")\\n cmd_list.extend(repo_list)\\n\\n if scratch:\\n cmd_list.append(\\\"--scratch\\\")\\n\\n # Run the build with --nowait so that we can immediately get information about the brew task\\n rc, out, err = exectools.cmd_gather(cmd_list)\\n\\n if rc != 0:\\n # Probably no point in continuing.. can't contact brew?\\n self.logger.info(\\\"Unable to create brew task: out={} ; err={}\\\".format(out, err))\\n return False\\n\\n # Otherwise, we should have a brew task we can monitor listed in the stdout.\\n out_lines = out.splitlines()\\n\\n # Look for a line like: \\\"Created task: 13949050\\\" . Extract the identifier.\\n task_id = next((created_line.split(\\\":\\\")[1]).strip() for created_line in out_lines if\\n created_line.startswith(\\\"Created task:\\\"))\\n\\n record[\\\"task_id\\\"] = task_id\\n\\n # Look for a line like: \\\"Task info: https://brewweb.engineering.redhat.com/brew/taskinfo?taskID=13948942\\\"\\n task_url = next((info_line.split(\\\":\\\", 1)[1]).strip() for info_line in out_lines if\\n info_line.startswith(\\\"Task info:\\\"))\\n\\n self.logger.info(\\\"Build running: {}\\\".format(task_url))\\n\\n record[\\\"task_url\\\"] = task_url\\n\\n # Now that we have the basics about the task, wait for it to complete\\n error = watch_task(self.logger.info, task_id, terminate_event)\\n\\n # Looking for something like the following to conclude the image has already been built:\\n # BuildError: Build for openshift-enterprise-base-v3.7.0-0.117.0.0 already exists, id 588961\\n if error is not None and \\\"already exists\\\" in error:\\n self.logger.info(\\\"Image already built against this dist-git commit (or version-release tag): {}\\\".format(target_image))\\n error = None\\n\\n # Gather brew-logs\\n logs_dir = \\\"%s/%s\\\" % (self.runtime.brew_logs_dir, self.metadata.name)\\n logs_rc, _, logs_err = exectools.cmd_gather([\\\"brew\\\", \\\"download-logs\\\", \\\"-d\\\", logs_dir, task_id])\\n\\n if logs_rc != 0:\\n self.logger.info(\\\"Error downloading build logs from brew for task %s: %s\\\" % (task_id, logs_err))\\n\\n if error is not None:\\n # An error occurred. We don't have a viable build.\\n self.logger.info(\\\"Error building image: {}, {}\\\".format(task_url, error))\\n return False\\n\\n self.logger.info(\\\"Successfully built image: {} ; {}\\\".format(target_image, task_url))\\n return True\",\n \"def render_dockerfile(self):\\n logger.info(\\\"Rendering Dockerfile...\\\")\\n\\n if self._params.get('redhat'):\\n self._inject_redhat_defaults()\\n\\n self.image['pkg_manager'] = self._params.get('package_manager', 'yum')\\n self.image.process_defaults()\\n\\n template_file = os.path.join(os.path.dirname(__file__),\\n '..',\\n 'templates',\\n 'template.jinja')\\n loader = FileSystemLoader(os.path.dirname(template_file))\\n env = Environment(loader=loader, trim_blocks=True, lstrip_blocks=True)\\n env.globals['helper'] = TemplateHelper()\\n env.globals['addhelp'] = self._params.get('addhelp')\\n\\n template = env.get_template(os.path.basename(template_file))\\n\\n dockerfile = os.path.join(self.target,\\n 'image',\\n 'Dockerfile')\\n if not os.path.exists(os.path.dirname(dockerfile)):\\n os.makedirs(os.path.dirname(dockerfile))\\n\\n with open(dockerfile, 'wb') as f:\\n f.write(template.render(\\n self.image).encode('utf-8'))\\n logger.debug(\\\"Dockerfile rendered\\\")\\n\\n if self.image.get('help', {}).get('template', \\\"\\\"):\\n help_template_path = self.image['help']['template']\\n elif self._params.get('help_template'):\\n help_template_path = self._params['help_template']\\n else:\\n help_template_path = os.path.join(os.path.dirname(__file__),\\n '..',\\n 'templates',\\n 'help.jinja')\\n\\n help_dirname, help_basename = os.path.split(help_template_path)\\n loader = FileSystemLoader(help_dirname)\\n env = Environment(loader=loader, trim_blocks=True, lstrip_blocks=True)\\n env.globals['helper'] = TemplateHelper()\\n help_template = env.get_template(help_basename)\\n\\n helpfile = os.path.join(self.target, 'image', 'help.md')\\n with open(helpfile, 'wb') as f:\\n f.write(help_template.render(\\n self.image).encode('utf-8'))\\n logger.debug(\\\"help.md rendered\\\")\",\n \"def test_build_image(self):\\n labels = {\\\"apple\\\": \\\"red\\\", \\\"grape\\\": \\\"green\\\"}\\n self.docker.images.build(\\n path=\\\"test/python/docker/build_labels\\\",\\n labels=labels,\\n tag=\\\"labels\\\",\\n isolation=\\\"default\\\",\\n )\\n image = self.docker.images.get(\\\"labels\\\")\\n self.assertEqual(image.labels[\\\"apple\\\"], labels[\\\"apple\\\"])\\n self.assertEqual(image.labels[\\\"grape\\\"], labels[\\\"grape\\\"])\",\n \"def create_target_image(self, builder, target, base_image, parameters):\",\n \"def build(obj):\\n logger.info(ASCII_LOGO)\\n logger.info(\\\"Started building SageMaker Docker image. It will take some minutes...\\\\n\\\")\\n\\n try:\\n config_file_path = os.path.join('.sagify.json')\\n if not os.path.isfile(config_file_path):\\n raise ValueError()\\n\\n config = ConfigManager(config_file_path).get_config()\\n api_build.build(\\n source_dir=config.sagify_module_dir,\\n requirements_dir=config.requirements_dir,\\n docker_tag=obj['docker_tag'],\\n image_name=config.image_name,\\n python_version=config.python_version)\\n\\n logger.info(\\\"Docker image built successfully!\\\")\\n except ValueError:\\n logger.info(\\\"This is not a sagify directory: {}\\\".format(dir))\\n sys.exit(-1)\\n except subprocess.CalledProcessError as e:\\n logger.debug(e.output)\\n raise\\n except Exception as e:\\n logger.info(\\\"{}\\\".format(e))\\n sys.exit(-1)\",\n \"def _build_final_image(self, image):\\n raise NotImplementedError\",\n \"def load_recipes_from_file(cls, args):\\n with open(args.recipes_file, 'r') as f:\\n reader = csv.DictReader(f)\\n for row in reader:\\n cls._recipes.append(row)\\n cls._add_indices_to_recipes()\\n cls._initialize_recipes_status()\\n logging.info(\\\"Recipes loaded.\\\")\",\n \"def build(project_name, compose_file, container, container_dir_to_copy, env):\\n compose_build(project_name, compose_file, container, container_dir_to_copy, env=env)\",\n \"def main():\\n parser = argparse.ArgumentParser(\\n epilog=main.__doc__, formatter_class=argparse.RawDescriptionHelpFormatter\\n )\\n parser.add_argument(\\n \\\"-d\\\", \\\"--dry-run\\\", action=\\\"store_true\\\", default=0, help=\\\"Dry run mode.\\\"\\n )\\n parser.add_argument(\\n \\\"-v\\\",\\n \\\"--verbose\\\",\\n action=\\\"count\\\",\\n default=0,\\n help=\\\"Verbosity. Default is WARNING level.\\\",\\n )\\n\\n subparsers = parser.add_subparsers(help=\\\"Sub commands\\\", dest=\\\"subparser\\\")\\n subparsers.required = True\\n\\n build_parser = subparsers.add_parser(\\n \\\"build\\\",\\n description=\\\"Build an image from Dockerfile, caching image hierarchy\\\",\\n help=\\\"Build an image from a Dockerfile\\\",\\n )\\n build_parser.add_argument(\\n \\\"path\\\", metavar=\\\"PATH\\\", help=\\\"The build context directory\\\"\\n )\\n build_parser.add_argument(\\n \\\"-f\\\",\\n \\\"--file\\\",\\n help=\\\"Name of the Dockerfile. If not provided, \\\"\\n \\\"will use config.DOCKERFILE_PATH_PATTERN to compute. \\\",\\n )\\n build_parser.add_argument(\\n \\\"-v\\\",\\n \\\"--git-sha\\\",\\n required=True,\\n help=\\\"The version of code to build against, \\\" \\\"will pass as GIT_SHA variable\\\",\\n )\\n build_parser.add_argument(\\n \\\"-n\\\", \\\"--name\\\", required=True, help=\\\"The name of the image to build\\\"\\n )\\n build_parser.add_argument(\\n \\\"--build-arg\\\",\\n metavar=\\\"ARG=VALUE\\\",\\n nargs=\\\"*\\\",\\n default=[],\\n help=\\\"Set extra build-time variables. GIT_SHA, TIMESTAMP will be passed by default.\\\",\\n )\\n build_parser.add_argument(\\n \\\"-r\\\",\\n \\\"--raw\\\",\\n action=\\\"store_true\\\",\\n help=\\\"Whether to use raw docker build command to build, skipping caching logic\\\",\\n )\\n build_parser.add_argument(\\n \\\"--registry\\\",\\n default=config.DOCKER_REGISTRY,\\n help=\\\"Docker registry use to determine the image identity, \\\"\\n \\\"can be set via IMAGE_BUILDER_DOCKER_REGISTRY environment variable, \\\"\\n 'or set DOCKER_REGISTRY in config.py. Default is \\\"%(default)s\\\"',\\n )\\n build_parser.add_argument(\\n \\\"-t\\\",\\n \\\"--tag-pattern\\\",\\n default=config.GIT_SHA_TAG_PATTERN,\\n help=\\\"Tag pattern, can only include one `{git_sha}` placeholder, \\\"\\n 'such as \\\"{git_sha}-new\\\". If the tag exists, we won\\\\'t rebuild it. '\\n 'Default is \\\"%(default)s\\\"',\\n )\\n build_parser.add_argument(\\n \\\"-e\\\",\\n \\\"--extra-tag\\\",\\n nargs=\\\"*\\\",\\n default=[],\\n help=\\\"Extra tags to tag to the final images\\\",\\n )\\n build_parser.add_argument(\\n \\\"--extra-name\\\",\\n nargs=\\\"*\\\",\\n default=[],\\n help=\\\"Extra name and optionally with a tag in the 'name:tag' format\\\",\\n )\\n build_parser.add_argument(\\n \\\"-o\\\", \\\"--output-hash\\\", help=\\\"The output filename of the files hash log.\\\"\\n )\\n build_parser.set_defaults(func=build)\\n\\n args = parser.parse_args()\\n if args.dry_run:\\n # DRY_RUN env will be read in image_builder.libs.process\\n os.environ[\\\"DRY_RUN\\\"] = \\\"1\\\"\\n\\n if args.func == build:\\n args.path = expand_path(args.path)\\n if args.output_hash:\\n args.output_hash = expand_path(args.output_hash)\\n\\n args.file = args.file or locate_dockerfile(args.name)\\n args.file = expand_path(args.file)\\n # set environ for main dockerfile for possibly retrieving later\\n os.environ[\\n config.DOCKERFILE_ENV_PATTERN.format(image_name=args.name)\\n ] = args.file\\n\\n # change CWD to PATH\\n os.chdir(args.path)\\n\\n if not args.registry:\\n parser.error(\\n \\\"--registry should be provied \\\"\\n \\\"or specified by IMAGE_BUILDER_DOCKER_REGISTRY environment variable or set DOCKER_REGISTRY in config.py\\\"\\n )\\n if not all(\\\"=\\\" in kv for kv in args.build_arg):\\n parser.error(\\\"--build_arg must be in ARG=VALUE format\\\")\\n\\n # set git_sha_tag\\n try:\\n args.git_sha_tag = args.tag_pattern.format(git_sha=args.git_sha)\\n except KeyError:\\n parser.error(\\n 'Wrong --tag-pattern provided. Can only include one `{git_sha}` placeholder, such as \\\"{git_sha}-new\\\"'\\n )\\n\\n # setup logging\\n level = logging.WARNING - args.verbose * 10\\n logging.basicConfig(\\n level=level, format=\\\"%(asctime)s %(name)s %(levelname)s %(message)s\\\"\\n )\\n\\n if args.output_hash:\\n h = logging.FileHandler(args.output_hash)\\n h.setLevel(logging.DEBUG)\\n h.setFormatter(logging.Formatter(\\\"%(message)s\\\"))\\n hash_logger.addHandler(h)\\n\\n # Suppress warning when we don't verify ssl\\n import urllib3\\n\\n urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)\\n\\n return args.func(args)\",\n \"def _extract_docker_image_file(*, instance, filename: str):\\n try:\\n with instance.image.open(mode=\\\"rb\\\") as im, tarfile.open(\\n fileobj=im, mode=\\\"r\\\"\\n ) as t:\\n member = dict(zip(t.getnames(), t.getmembers()))[filename]\\n file = t.extractfile(member).read()\\n return file\\n except (KeyError, tarfile.ReadError):\\n raise ValidationError(\\n f\\\"{filename} not found at the root of the container image \\\"\\n f\\\"file. Was this created with docker save?\\\"\\n )\",\n \"def build(self):\\n docker = Client(version='auto')\\n status = docker.build(\\n fileobj=self.tarfile,\\n custom_context=True,\\n tag=self.tag,\\n pull=True,\\n nocache=True,\\n rm=True,\\n )\\n\\n for line in status: # This effectively blocks on `docker build`\\n try:\\n current_app.logger.debug(line)\\n except RuntimeError: # Outside of application context\\n print line\\n if \\\"successfully built\\\" not in line.lower():\\n raise BuildError(\\\"Failed to build {}: {}\\\".format(self.tag, line))\\n\\n self.built = True\",\n \"def build_image(image, build_args):\\n\\n subprocess.check_call(['docker', 'build', '--no-cache', '--force-rm', '-t',\\n image] + build_args)\",\n \"def build_impl(**kwargs: Any) -> None:\\n try:\\n config = configuration.create_transient_build_config(kwargs)\\n except configuration.CLIArgumentError as e:\\n print(e, file=sys.stderr)\\n sys.exit(1)\\n store = image.ImageStore(backend_dir=config.image_backend, frontend_dir=None)\\n builder = build.ImageBuilder(config, store)\\n builder.build()\\n sys.exit(0)\",\n \"def generate_dockerfile_extension(base_image, template_name, config_path):\\n template_path = get_template_path(template_name, config_path)\\n template_file = os.path.join(template_path, \\\"Dockerfile\\\")\\n dockerfile = \\\".Dockerfile.luda\\\"\\n\\n def remove():\\n if os.path.exists(dockerfile):\\n os.remove(dockerfile)\\n\\n with cd(template_path, remove):\\n with open(dockerfile, \\\"w\\\") as output:\\n docker_str = j2docker.render(base_image, template_file).decode().strip()\\n output.write(docker_str)\\n client = docker.from_env()\\n if base_image.startswith(\\\"luda/\\\"):\\n _, _, image_name = base_image.partition(\\\"luda/\\\")\\n image_name, _, tag = image_name.partition(\\\":\\\")\\n image_name = \\\"luda/{0}:{1}-{2}\\\".format(image_name, tag, template_name)\\n else:\\n image_name = \\\"luda/{0}:{1}\\\".format(base_image.replace('/', '-').replace(':', '-'), template_name)\\n click.echo(\\\"Building image: {0} ...\\\".format(image_name))\\n client.images.build(path=os.getcwd(), tag=image_name, dockerfile=dockerfile) # This line doesn't work with Python 3...\\n return image_name\",\n \"def create_base_image(self, builder, template, parameters):\",\n \"def read_dockerfile_for_args(target):\\n import colorama\\n build_args = {}\\n missing_args = {}\\n empty_string = \\\"\\\"\\n\\n # read dockerfile for args that have no value\\n try:\\n with open(target + '/Dockerfile') as dockerfile:\\n for line in dockerfile:\\n if line.startswith(\\\"ARG \\\"):\\n dockerfile_args = line.replace(\\n \\\"ARG \\\", \\\"\\\").strip(\\\"\\\\n\\\").split(\\\"=\\\")\\n\\n arg_name = dockerfile_args[0]\\n arg_value = \\\"\\\"\\n\\n if len(dockerfile_args) > 1:\\n arg_value = dockerfile_args[1].strip(\\\"\\\\n\\\")\\n\\n env_value = os.environ.get(arg_name)\\n\\n build_args[arg_name] = arg_value\\n if not env_value is None:\\n build_args[arg_name] = env_value\\n\\n if build_args[arg_name] is empty_string:\\n missing_args[arg_name] = arg_name\\n except FileNotFoundError:\\n exit(f\\\"Dockerfile not found: {target}/Dockerfile\\\")\\n\\n if len(missing_args) > 1:\\n message = \\\"WARNING: Arguments found with no defined value \\\" \\\\\\n \\\"found in Dockerfile or environment [{}]\\\"\\n print(colorama.Fore.YELLOW + colorama.Style.BRIGHT +\\n message.format(\\\", \\\".join(missing_args)))\\n\\n return build_args\",\n \"def build(self, conanfile):\\n app = ConanApp(self._conan_api.cache_folder)\\n conanfile.folders.set_base_package(conanfile.folders.base_build)\\n conanfile.folders.set_base_pkg_metadata(os.path.join(conanfile.build_folder, \\\"metadata\\\"))\\n run_build_method(conanfile, app.hook_manager)\",\n \"def build_command(self, args):\\n self._validate_common(args)\\n self._set_manifests(args)\\n\\n manifest = self._manager._recipes\\n\\n records = self._get_matching_records(args, manifest)\\n\\n if not len(records):\\n sys.exit(\\\"No matching items found.\\\")\\n\\n for record in records:\\n result = self._manager.build(record['_type'], **record)\\n print('The requested asset has been built: {}'.format(result['_path']))\\n\\n if len(records) > 1:\\n print('All files have been successfully built. Thank you.')\",\n \"def image(self, label, fname):\\n if not os.path.exists(fname):\\n raise OptionError(\\\"%s - no such file or directory\\\" % label)\\n try:\\n return Image(fname)\\n except:\\n raise OptionError(\\\"%s - invalid image file\\\" % label)\",\n \"def generate_build_file(startpath, outfilename='build.yml'):\\n buildfiles = {}\\n buildtables = {}\\n\\n def file_node(ext, fullpath):\\n return fullpath\\n\\n def table_node(ext, fullpath):\\n return [ext.lower(), fullpath]\\n\\n def add_to_contents(contents, nodefunc, path, files):\\n try:\\n safepath = [_pythonize_name(d) if d != '.' else '.' for d in path]\\n except BuildException:\\n warning = \\\"Warning: could not determine a Python-legal name for {path}; skipping.\\\"\\n print(warning.format(path=os.sep.join(path)))\\n return\\n\\n ptr = contents\\n for folder in safepath:\\n ptr = ptr.setdefault(folder, {})\\n\\n for file in files:\\n fullpath = os.path.join(os.path.join(*path), file)\\n name, ext = splitext_no_dot(file)\\n ptr[_pythonize_name(name)] = nodefunc(ext, fullpath)\\n\\n for root, dirs, files in os.walk(startpath):\\n # skip hidden directories\\n for d in dirs:\\n if d.startswith('.') or d == PACKAGE_DIR_NAME:\\n dirs.remove(d)\\n\\n rel_path = os.path.relpath(root, startpath)\\n path = rel_path.split(os.sep)\\n\\n tablefiles = []\\n rawfiles = []\\n for file in files:\\n # skip hidden files\\n if file.startswith('.'):\\n continue\\n\\n name, ext = splitext_no_dot(file)\\n # separate files into tables and raw\\n if ext.lower() in TARGET['pandas']:\\n tablefiles.append(file)\\n else:\\n rawfiles.append(file)\\n\\n if rawfiles:\\n add_to_contents(buildfiles, file_node, path, rawfiles)\\n\\n if tablefiles:\\n add_to_contents(buildtables, table_node, path, tablefiles)\\n\\n for contents in [buildfiles, buildtables]:\\n for node in ['.', '..']:\\n if node in contents:\\n for key in contents[node]:\\n contents[key] = contents[node][key]\\n del contents[node]\\n\\n contents = dict(files=buildfiles, tables=buildtables)\\n buildfilepath = os.path.join(startpath, outfilename)\\n with open(buildfilepath, 'w') as outfile:\\n yaml.dump(contents, outfile)\\n return buildfilepath\",\n \"def build_image_jobspec(runtime, env, gcr_tag, stack_base):\\n basename = \\\"grpc_interop_%s\\\" % runtime\\n tag = \\\"%s/%s:%s\\\" % (args.gcr_path, basename, gcr_tag)\\n build_env = {\\\"INTEROP_IMAGE\\\": tag, \\\"BASE_NAME\\\": basename}\\n build_env.update(env)\\n image_builder_path = _IMAGE_BUILDER\\n if client_matrix.should_build_docker_interop_image_from_release_tag(lang):\\n image_builder_path = os.path.join(stack_base, _IMAGE_BUILDER)\\n build_job = jobset.JobSpec(\\n cmdline=[image_builder_path],\\n environ=build_env,\\n shortname=\\\"build_docker_%s\\\" % runtime,\\n timeout_seconds=30 * 60,\\n )\\n build_job.tag = tag\\n return build_job\",\n \"def create_molns_image(self):\\n file_to_remove = None\\n try:\\n dockerfile, file_to_remove = self._create_dockerfile(installSoftware.InstallSW.get_command_list())\\n image_id = self.docker.build_image(dockerfile)\\n return image_id\\n except Exception as e:\\n logging.exception(e)\\n raise ProviderException(\\\"Failed to create molns image: {0}\\\".format(e))\\n finally:\\n if file_to_remove is not None:\\n os.remove(file_to_remove)\",\n \"def set_image_from_file(self, file):\\n if isinstance(file, str):\\n try:\\n file = self.files.filter(current=True, path=file)[0]\\n except IndexError:\\n return\\n\\n content = file.get_content()\\n format = file.get_format()\\n ext = format.default_extension if format else \\\"\\\"\\n\\n # The file name needs to be unique to bust any caches.\\n file = ContentFile(content)\\n file.name = f\\\"{self.id}-{shortuuid.uuid()}{ext}\\\"\\n\\n self.image_file = file\\n self.image_updated = timezone.now()\\n self.save()\",\n \"def create_recipe(*, recipe_in: RecipeCreate) -> dict:\\n new_entry_id = len(RECIPES) + 1\\n recipe_entry = Recipe(\\n id=new_entry_id,\\n label=recipe_in.label,\\n source=recipe_in.source,\\n url=recipe_in.url,\\n )\\n RECIPES.append(recipe_entry.dict())\\n\\n return recipe_entry\",\n \"def generate_from_file(self, filename):\\n lines = open(filename, 'r').readlines()\\n chip_name = lines[0].strip()\\n input_part = re.sub('\\\\s+', ', ', lines[1].strip())\\n output_part = re.sub('\\\\s+', ', ', lines[2].strip())\\n\\n def name_to_names(name):\\n m = re.match('(.*)\\\\[(\\\\d+)\\\\]', name)\\n if m is None:\\n return [name, ]\\n else:\\n return ['%s[%d]' % (m.group(1), i) for i in range(int(m.group(2)))]\\n\\n def part_to_names(part):\\n names = []\\n for name in part.split(', '):\\n names.extend(name_to_names(name))\\n return names\\n\\n input_names = part_to_names(input_part)\\n output_names = part_to_names(output_part)\\n line_expr = ''.join(lines[3:]).replace('\\\\\\\\\\\\n', '').replace('{', '').replace('}', '')\\n exprs = line_expr.split(', ')\\n exprs = map(lambda expr: expr.strip(), exprs)\\n\\n parts_statement = self.generate(exprs, input_names, output_names)\\n\\n return self.src_code_format % (chip_name, input_part, output_part, parts_statement)\",\n \"def create_from_file(cls, file_path: str) -> \\\"ImageClassifier\\\":\\n base_options = _BaseOptions(file_name=file_path)\\n options = ImageClassifierOptions(base_options=base_options)\\n return cls.create_from_options(options)\",\n \"def build_runtime(self, runtime_name, file):\\n self.compute_handler.build_runtime(runtime_name, file)\",\n \"def load_recipe(root: Path) -> None:\\n module_name = 'dotops.recipes.' + root.name\\n fpath = find_recipe(root)\\n\\n try:\\n _ast = import_file_to_ast(fpath, module_name)\\n mod = imp.new_module(module_name)\\n\\n mod.__file__ = fpath\\n eval(ast_compile(_ast, fpath, \\\"exec\\\"), _globals(root, mod))\\n except (HyTypeError, LexException) as e:\\n if e.source is None:\\n with open(fpath, 'rt') as fp:\\n e.source = fp.read()\\n e.filename = fpath\\n raise\\n except Exception:\\n sys.modules.pop(module_name, None)\\n raise\",\n \"def get_image(filename=None, astrotarget=None,\\n **kwargs):\\n return Image(filename,astrotarget=astrotarget,\\n **kwargs)\",\n \"def prep_image_data(arg_dict):\\n cat_df = pd.read_csv(arg_dict['category_file'],\\n skiprows=1,\\n sep='\\\\s+')\\n bbox_df = pd.read_csv(arg_dict['bbox_file'],\\n skiprows=1,\\n sep='\\\\s+')\\n img_dir = arg_dict['image_dir']\\n\\n combo_df = pd.merge(cat_df, bbox_df, how='outer', on='image_name')\\n combo_df['image_name'] = combo_df['image_name'].apply(\\n lambda x: x[len('img'):-len('.jpg')])\\n labels = Labels(combo_df, img_dir, n_images_loaded=-1)\\n labels.set_data_target('raw_image', chunksize=3000)\\n return labels\",\n \"def pyfunc_build_image(model_uri, extra_args=None):\\n name = uuid.uuid4().hex\\n cmd = [\\\"mlflow\\\", \\\"models\\\", \\\"build-docker\\\", \\\"-m\\\", model_uri, \\\"-n\\\", name]\\n if extra_args:\\n cmd += extra_args\\n p = subprocess.Popen(cmd,)\\n assert p.wait() == 0, \\\"Failed to build docker image to serve model from %s\\\" % model_uri\\n return name\",\n \"def construct_list_of_recipes():\\n sub_list = []\\n with open(args.input_file, 'r') as f:\\n lines = [line if line == '\\\\n' else line.rstrip('\\\\n') for line in f]\\n\\n recipes_list = []\\n for element in lines:\\n if element == '\\\\n':\\n recipes_list.append(sub_list)\\n sub_list = []\\n else:\\n sub_list.append(element)\\n recipes_list.append(sub_list)\\n return recipes_list\",\n \"def build(config_file, install=False):\\n return exec_fn(lambda: _build(config_file, install))\",\n \"def build_docker_image(\\n name: str,\\n version: str,\\n build_args: str = \\\"\\\",\\n docker_image_prefix: str = \\\"\\\",\\n dockerfile: Optional[str] = None,\\n additional_build_args: str = \\\"\\\",\\n exit_on_error: bool = True,\\n) -> subprocess.CompletedProcess:\\n # Check if docker exists on the system\\n build_utils.command_exists(\\\"docker\\\", exit_on_error=exit_on_error)\\n\\n versioned_tag = get_image_name(name=name, tag=version)\\n latest_tag = get_image_name(name=name, tag=\\\"latest\\\")\\n\\n dockerfile_command = \\\"\\\"\\n if dockerfile:\\n dockerfile_command = \\\" -f \\\" + dockerfile\\n\\n completed_process = build_utils.run(\\n \\\"docker build \\\"\\n + dockerfile_command\\n + \\\"-t \\\"\\n + versioned_tag\\n + \\\" -t \\\"\\n + latest_tag\\n + \\\" \\\"\\n + build_args\\n + \\\" ./\\\",\\n exit_on_error=exit_on_error,\\n )\\n\\n if completed_process.returncode > 0:\\n build_utils.log(f\\\"Failed to build Docker image {versioned_tag}\\\")\\n return completed_process\\n\\n if docker_image_prefix:\\n remote_versioned_tag = get_image_name(\\n name=name, tag=version, image_prefix=docker_image_prefix\\n )\\n build_utils.run(\\n \\\"docker tag \\\" + versioned_tag + \\\" \\\" + remote_versioned_tag,\\n exit_on_error=exit_on_error,\\n )\\n\\n return completed_process\",\n \"def docker_build(c):\\n cli_tasks.docker_build.run(c)\",\n \"def from_baked_recipe(\\n cls,\\n recipe: BakedRecipe,\\n arguments: Arguments = Arguments(),\\n labels: Dict = {}\\n ):\\n input_dict = {\\n 'recipe': recipe.to_dict(),\\n 'arguments': arguments.to_dict(),\\n 'labels': labels,\\n }\\n\\n return cls.parse_obj(input_dict)\",\n \"def get_base_docker_image(docker_file):\\n with open(docker_file) as f:\\n from_line = next(\\n line for line in f.read().split(\\\"\\\\n\\\") if line.startswith(\\\"FROM\\\")\\n )\\n _from, base_image = from_line.split()\\n return base_image\",\n \"def build(images, tag, quiet):\\n # images to build\\n build_images = None\\n\\n # list of available images\\n image_list = build_image_map().keys()\\n\\n # image specified: check if it exists\\n if images:\\n build_images = []\\n for check_name in images:\\n if check_name in image_list:\\n build_images.append(check_name)\\n continue\\n raise click.ClickException(\\n \\\"invalid image name '{}'\\\".format(check_name))\\n click.echo('building images {}'.format(', '.join(build_images)))\\n # no image specified: build all\\n else:\\n click.echo('building images {}'.format(', '.join(image_list)))\\n\\n manager = Manager('build', tag, images=build_images, verbose=not quiet)\\n manager.run()\",\n \"def newimage(self, infile):\\n return _image.image_newimage(self, infile)\",\n \"def build (self, buildtype):\\n layout = self.xlayout()\\n\\n # NuGet removes the prefixing v from the version.\\n vless_version = self.kv['version']\\n if vless_version[0] == 'v':\\n vless_version = vless_version[1:]\\n\\n\\n self.stpath = tempfile.mkdtemp(prefix=\\\"out-\\\", suffix=\\\"-%s\\\" % buildtype,\\n dir=\\\".\\\")\\n\\n self.render('librdkafka.redist.nuspec')\\n self.copy_template('librdkafka.redist.targets',\\n destpath=os.path.join('build', 'native'))\\n self.copy_template('librdkafka.redist.props',\\n destpath=os.path.join('build', 'native'))\\n self.copy_template('librdkafka.redist.props',\\n destpath=os.path.join('build', 'net'))\\n for f in default_doc:\\n shutil.copy(f, self.stpath)\\n\\n self.extract_artifacts(layout)\\n\\n print('Tree extracted to %s' % self.stpath)\\n\\n # After creating a bare-bone nupkg layout containing the artifacts\\n # and some spec and props files, call the 'nuget' utility to\\n # make a proper nupkg of it (with all the metadata files).\\n subprocess.check_call(\\\"./nuget.sh pack %s -BasePath '%s' -NonInteractive\\\" % \\\\\\n (os.path.join(self.stpath, 'librdkafka.redist.nuspec'),\\n self.stpath), shell=True)\\n return ['librdkafka.redist.%s.nupkg' % vless_version]\",\n \"def Build(self, docker_client):\\n log.info('Building docker image %s from %s/Dockerfile:',\\n self.tag, self._dockerfile_dir)\\n\\n width, _ = console_attr_os.GetTermSize()\\n log.status.Print(DOCKER_OUTPUT_BEGIN.center(width, DOCKER_OUTPUT_LINE_CHAR))\\n\\n build_res = docker_client.build(\\n path=self._dockerfile_dir, tag=self.tag, quiet=False, fileobj=None,\\n nocache=self._nocache, rm=self._rm, pull=False)\\n\\n info = None\\n error = None\\n error_detail = None\\n log_records = []\\n try:\\n for line in build_res:\\n line = line.strip()\\n if not line:\\n continue\\n log_record = json.loads(line)\\n log_records.append(log_record)\\n if 'stream' in log_record:\\n info = log_record['stream'].strip()\\n log.status.Print(info)\\n if 'error' in log_record:\\n error = log_record['error'].strip()\\n # will be logged to log.error in the thrown exception\\n log.status.Print(error)\\n if 'errorDetail' in log_record:\\n error_detail = log_record['errorDetail']['message'].strip()\\n log.status.Print(error_detail)\\n except docker.errors.APIError as e:\\n log.error(e.explanation)\\n error = e.explanation\\n error_detail = ''\\n finally:\\n log.status.Print(DOCKER_OUTPUT_LINE_CHAR * width + '\\\\n')\\n\\n if not log_records:\\n raise ImageBuildError(\\n 'Error building docker image {0} [with no output]'.format(self.tag))\\n\\n success_message = log_records[-1].get(_STREAM)\\n if success_message:\\n m = _SUCCESSFUL_BUILD_PATTERN.match(success_message)\\n if m:\\n # The build was successful.\\n self._id = m.group(1)\\n log.info('Image %s built, id = %s', self.tag, self.id)\\n return\\n\\n raise ImageBuildError('Docker build aborted: ' + error)\",\n \"def _read_image_from_file(file_name):\\n image_file = open(file_name, 'rb')\\n image = image_file.read()\\n image_file.close()\\n return image\",\n \"def builder_will_create_target_image(self, builder, target, image_id, template, parameters):\",\n \"def build_sonar_img_from_log_file(filename, start_index=49, end_index=149):\\n assert filename.endswith('.bin'), 'filename must be a .bin file'\\n\\n parser = get_bin_file_parser(filename)\\n\\n data_list = []\\n for index, (timestamp, decoded_message) in enumerate(parser):\\n if index >= start_index and index <= end_index:\\n data_list.append(decoded_message.data)\\n\\n jpeg_save_path = os.path.join(os.path.dirname(__file__),\\n 'sampleData', 'Sonar_Image.jpeg')\\n\\n sonar_img = build_sonar_image(data_list, display_results=True,\\n jpeg_save_path=jpeg_save_path)\\n return sonar_img\",\n \"def create_image_builder(Name=None, ImageName=None, ImageArn=None, InstanceType=None, Description=None, DisplayName=None, VpcConfig=None, EnableDefaultInternetAccess=None, DomainJoinInfo=None, AppstreamAgentVersion=None):\\n pass\",\n \"def read_image_with_label(dir, file):\\n assert type(file) == str, \\\"File name is not string.\\\"\\n f = os.path.join(dir, file)\\n info = file.split(\\\"_\\\")\\n try:\\n label = [int(info[x]) for x in range(1, 3)]\\n except:\\n print(\\\"The format of file name is not correct.\\\")\\n else:\\n return Image.open(f), label\",\n \"def _load_image(file: str) -> pyglet.image.AbstractImage:\\n\\n return pyglet.image.load(Config.RES_DIR + \\\"img\\\" + Config.FILE_SEPARATOR + file)\",\n \"def create_from_file(cls, file_name: str) -> \\\"TensorImage\\\":\\n image_data = image_utils.decode_image_from_file(file_name)\\n return cls(image_data, is_from_numpy_array=False)\",\n \"def get_image_and_prep(self,file_path):\\r\\n img = np.array(Image.open(file_path).convert('1'))\\r\\n img = img.reshape(28,28,1)\\r\\n return img\",\n \"def build_images(prefix, images, tag=None, commit_range=None, push=False, chart_version=None, skip_build=False):\\n value_modifications = {}\\n for name, options in images.items():\\n image_path = options.get('contextPath', os.path.join('images', name))\\n image_tag = tag\\n # include chartpress.yaml itself as it can contain build args and\\n # similar that influence the image that would be built\\n paths = list(options.get('paths', [])) + [image_path, 'chartpress.yaml']\\n last_commit = last_modified_commit(*paths)\\n if tag is None:\\n if chart_version:\\n image_tag = \\\"{}-{}\\\".format(chart_version, last_commit)\\n else:\\n image_tag = last_commit\\n image_name = prefix + name\\n image_spec = '{}:{}'.format(image_name, image_tag)\\n\\n value_modifications[options['valuesPath']] = {\\n 'repository': image_name,\\n 'tag': SingleQuotedScalarString(image_tag),\\n }\\n\\n if skip_build:\\n continue\\n\\n template_namespace = {\\n 'LAST_COMMIT': last_commit,\\n 'TAG': image_tag,\\n }\\n\\n if tag or image_needs_building(image_spec):\\n build_args = render_build_args(options, template_namespace)\\n build_image(image_path, image_spec, build_args, options.get('dockerfilePath'))\\n else:\\n print(f\\\"Skipping build for {image_spec}, it already exists\\\")\\n\\n if push:\\n if tag or image_needs_pushing(image_spec):\\n check_call([\\n 'docker', 'push', image_spec\\n ])\\n else:\\n print(f\\\"Skipping push for {image_spec}, already on registry\\\")\\n return value_modifications\",\n \"def read_image(self, item):\\n assert item['image_dtype'] == 'uint16'\\n\\n filename = os.path.join(self.home(item['basename']))\\n s = open(filename, 'rb').read()\\n assert hashlib.md5(s).hexdigest() == item['md5']\\n img = np.fromstring(s, dtype=item['image_dtype']).byteswap()\\n img = img.reshape(item['image_shape'])\\n return img\",\n \"def test_recipe_valid(recipe_file, config_user, monkeypatch):\\n # Mock input files\\n find_files = create_autospec(esmvalcore._data_finder.find_files,\\n spec_set=True)\\n find_files.side_effect = lambda *_, **__: [\\n 'test_0000-1849.nc',\\n 'test_1850-9999.nc',\\n ]\\n monkeypatch.setattr(esmvalcore._data_finder, 'find_files', find_files)\\n\\n # Mock vertical levels\\n levels = create_autospec(esmvalcore._recipe.get_reference_levels,\\n spec_set=True)\\n levels.side_effect = lambda *_, **__: [1, 2]\\n monkeypatch.setattr(esmvalcore._recipe, 'get_reference_levels', levels)\\n\\n # Mock valid NCL version\\n ncl_version = create_autospec(esmvalcore._recipe_checks.ncl_version,\\n spec_set=True)\\n monkeypatch.setattr(esmvalcore._recipe_checks, 'ncl_version', ncl_version)\\n\\n # Mock interpreters installed\\n def which(executable):\\n if executable in ('julia', 'ncl', 'python', 'Rscript'):\\n path = '/path/to/' + executable\\n else:\\n path = None\\n return path\\n\\n monkeypatch.setattr(esmvalcore._task, 'which', which)\\n\\n # Create a shapefile for extract_shape preprocessor if needed\\n recipe = yaml.safe_load(recipe_file.read_text())\\n for preproc in recipe.get('preprocessors', {}).values():\\n extract_shape = preproc.get('extract_shape')\\n if extract_shape and 'shapefile' in extract_shape:\\n filename = Path(\\n config_user['auxiliary_data_dir']) / extract_shape['shapefile']\\n filename.parent.mkdir(parents=True, exist_ok=True)\\n filename.touch()\\n\\n esmvalcore._recipe.read_recipe_file(recipe_file, config_user)\",\n \"def _ensure_image(testkit_path, branch_name, artifacts_path):\\n # Construct Docker image name from branch name\\n image_name = \\\"runner:%s\\\" % branch_name\\n image_path = os.path.join(testkit_path, \\\"runner_image\\\")\\n docker.build_and_tag(image_name, image_path, log_path=artifacts_path)\\n\\n return image_name\",\n \"def buildModelFromFile(fname):\\n directory = os.path.dirname(fname)\\n\\n f = open(fname, \\\"r\\\")\\n in_map = yaml.safe_load(f)\\n f.close()\\n\\n expression = \\\"\\\"\\n\\n return build_model_from_dict(in_map)\",\n \"def getImage(file):\\n image = imread(file)\\n image = crop(image, 64, 64)\\n image = color.rgb2gray(image)\\n\\n return image\",\n \"def build_base():\\n with lcd(env.local_path):\\n put('./requirements.txt', '/srv/build/requirements.txt')\\n\\n with cd('/srv/build'):\\n run('docker build -t {base_image_name} .'.format(\\n base_image_name=env.base_image_name,\\n ))\",\n \"def build():\",\n \"def build(self, file_number, data):\\n pass\",\n \"def create(self):\\n self._finish_creation_settings()\\n return self.project.create_recipe(self.recipe_proto, self.creation_settings)\",\n \"def build_container_image(self) -> None:\\n print_version_of_tools()\\n try:\\n self.fs_watcher.start()\\n runner = PluginsRunner(self,\\n self.plugins_conf,\\n self.plugin_files,\\n self.keep_plugins_running,\\n plugins_results=self.data.plugins_results)\\n runner.run()\\n finally:\\n self.fs_watcher.finish()\",\n \"def startRecipeFromFilename(self, filename):\\r\\n recipe = RecipeFileObject(os.path.join(main.settings.RECIPE_DIR, filename)).getParsed(self.actualTopology, self.anlage)\\r\\n if recipe.isValid:\\r\\n if self.actualRecipeThread == None:\\r\\n thread = RecipeRootThread(sys.stdout, recipe)\\r\\n self.actualRecipeThread = thread\\r\\n thread.start()\\r\\n else:\\r\\n self.message = 'Rezept läuft noch.'\\r\\n return False\\r\\n else:\\r\\n self.message = recipe.message\\r\\n return False\\r\\n return True\",\n \"def load_image(file_path):\\r\\n return Image.open(file_path)\",\n \"def image_generation(formula: str) -> Image:\\n\\n expr = \\\"$\\\\displaystyle \\\" + formula + \\\"$\\\"\\n print(f\\\"Generating image for {formula}\\\")\\n # This creates a ByteIO stream and saves there the output of sympy.preview\\n f = BytesIO()\\n sympy.preview(expr, euler=False,\\n preamble=r\\\"\\\\documentclass[varwidth,convert]{standalone}\\\"\\n r\\\"\\\\usepackage{amsmath,amssymb,amsthm,mathtools}\\\"\\n r\\\"\\\\begin{document}\\\",\\n viewer=\\\"BytesIO\\\", output=\\\"ps\\\", outputbuffer=f)\\n f.seek(0)\\n\\n # Open the image as if it were a file.\\n img = Image.open(f)\\n\\n # Resize the image to look sharper\\n img.load(scale=50)\\n print(f\\\"Generated image for {formula}\\\")\\n return img\",\n \"def load_autopkg_recipe(path):\\n recipe = None\\n\\n if path.endswith(\\\".yaml\\\"):\\n try:\\n # try to read it as yaml\\n with open(path, \\\"rb\\\") as f:\\n recipe = yaml.safe_load(f)\\n except Exception as err:\\n print(\\\"{}: yaml parsing error: {}\\\".format(path, err))\\n elif path.endswith(\\\".json\\\"):\\n try:\\n # try to read it as json\\n with open(path, \\\"rb\\\") as f:\\n recipe = json.load(f)\\n except Exception as err:\\n print(\\\"{}: json parsing error: {}\\\".format(path, err))\\n else:\\n try:\\n # try to read it as a plist\\n with open(path, \\\"rb\\\") as f:\\n recipe = plistlib.load(f)\\n except Exception as err:\\n print(\\\"{}: plist parsing error: {}\\\".format(path, err))\\n\\n return recipe\",\n \"def make_image(self, **kwargs):\\n image = dict(self.BASE_EMR_IMAGE, **kwargs)\\n\\n return {k: v for k, v in image.items() if v is not None}\",\n \"def ReadRecipeFromFile(self, path: str) -> None:\\n with io.open(path, 'r', encoding='utf-8') as file_object:\\n try:\\n recipe = self._ReadRecipeFromFileObject(file_object)\\n except json.decoder.JSONDecodeError as exception:\\n raise errors.RecipeParseError(\\n 'Unable to parse recipe file: {0:s} with error: {1!s}'.format(\\n path, exception))\\n\\n self.RegisterRecipe(recipe)\",\n \"def process_image_file(self, image_file):\\n image = image_util.load_image_from_file(image_file)\\n return self.process_image(image)\",\n \"def _image(filename):\\n return TK.PhotoImage(file=filename)\",\n \"def start_image_builder(Name=None, AppstreamAgentVersion=None):\\n pass\",\n \"def _build_docker_images(self):\\n print(f\\\"+ building {len(self.neurodocker_specs)} Docker images\\\")\\n self.docker_status = []\\n for sha1, neurodocker_dict in self.neurodocker_specs.items():\\n try:\\n print(\\\"++ building image: {}\\\".format(neurodocker_dict))\\n cg.docker_main(\\n self.working_dir,\\n neurodocker_dict,\\n sha1,\\n build_context=self.build_context,\\n )\\n self.docker_status.append(\\\"docker ok\\\")\\n except Exception as e:\\n self.docker_status.append(\\n \\\"failed to build image with SHA1 {}: {}\\\".format(sha1, e)\\n )\",\n \"def fit_recipe(recipe):\\n # initialize recipe\\n new_recipe = copy.deepcopy(recipe)\\n\\n # initialize and fit the dimensions 'fov', 'r', 'c' and 'z'\\n for key in ['fov', 'r', 'c', 'z']:\\n if key not in new_recipe:\\n new_recipe[key] = [None]\\n value = new_recipe[key]\\n if isinstance(value, str):\\n new_recipe[key] = [value]\\n\\n # initialize the dimensions 'ext', 'opt'\\n for key in ['ext', 'opt']:\\n if key not in new_recipe:\\n new_recipe[key] = \\\"\\\"\\n\\n return new_recipe\",\n \"def build_nighthawk_binary_image_from_source(manager: source_manager.SourceManager) -> None:\\n # TODO: Inject the builder object into this method\\n builder = nighthawk_builder.NightHawkBuilder(manager)\\n builder.build_nighthawk_binary_image()\",\n \"def dockerfile() -> co.Exec:\\n image = co.Image(dockerfile=\\\"./docker/Dockerfile.simple\\\")\\n return co.Exec(\\n f\\\"python -c '{pretty_table_script}'\\\", image=image, doc=co.util.magic_doc()\\n )\",\n \"def create_hero(infile: str) -> str:\\n outfile = re.search(\\\"(.*)\\\\.[a-zA-Z]{3,4}\\\", infile)[1] + \\\"-hero.png\\\"\\n\\n im = Image.open(infile)\\n width = im.size[0]\\n height = im.size[1]\\n aspect = width / float(height)\\n required_aspect = REQUIRED_WIDTH / float(REQUIRED_WIDTH)\\n\\n if width >= REQUIRED_WIDTH:\\n if aspect > required_aspect:\\n new_width = int(required_aspect * height)\\n offset = (width - new_width) / 2\\n resize = (0, 0, width - (offset * 2), height)\\n else:\\n new_height = int(width / required_aspect)\\n offset = (height - new_height) / 2\\n resize = (0, 0, width, height - (offset * 2))\\n\\n hero = im.crop(resize).resize(\\n (REQUIRED_WIDTH, REQUIRED_HEIGHT), resample=Image.ANTIALIAS\\n )\\n sharpen = ImageEnhance.Sharpness(hero)\\n sharpen.enhance(1.5).save(outfile, \\\"PNG\\\")\\n return outfile\\n else:\\n print(\\\"Image must be at least 640px wide\\\")\",\n \"def build(name, file, config, backend, debug):\\n setup_lithops_logger(logging.DEBUG)\\n\\n verify_runtime_name(name)\\n\\n if config:\\n config = load_yaml_config(config)\\n\\n config_ow = set_config_ow(backend, runtime_name=name)\\n config = default_config(config, config_ow, load_storage_config=False)\\n\\n if config['lithops']['mode'] != SERVERLESS:\\n raise Exception('\\\"lithops build\\\" command is only valid for serverless backends')\\n\\n compute_config = extract_serverless_config(config)\\n compute_handler = ServerlessHandler(compute_config, None)\\n compute_handler.build_runtime(name, file)\",\n \"def __instructions_runfile(self):\\n\\n pkg = os.path.basename(self.__runfile)\\n\\n install_cmds = [\\n 'sh ./{} --nox11 -- -noprompt -targetpath={}'.format(\\n pkg, self.__prefix)\\n ]\\n\\n # Commands needed to predeploy target-specific files. When\\n # connecting through the GUI on another machine to the\\n # container, this removes the need to copy the files over.\\n install_cmds += [\\n 'mkdir -p /tmp/var/target',\\n 'ln -sf {}/target/* /tmp/var/target'.format(self.__prefix),\\n 'ln -sf {}/sections /tmp/var/'.format(self.__prefix),\\n 'chmod -R a+w /tmp/var'\\n ]\\n\\n kwargs = {}\\n if self.__runfile.strip().startswith(('http://', 'https://')):\\n kwargs['url'] = self.__runfile\\n else:\\n kwargs['package'] = self.__runfile\\n\\n self.__bb = generic_build(\\n annotations={'runfile': pkg},\\n base_annotation=self.__class__.__name__,\\n comment = False,\\n devel_environment={'PATH': '{}:$PATH'.format(self.__prefix)},\\n directory=self.__wd,\\n install=install_cmds,\\n unpack=False,\\n wd=self.__wd,\\n **kwargs\\n )\\n\\n self += comment('NVIDIA Nsight Compute {}'.format(pkg), reformat=False)\\n self += packages(ospackages=self.__ospackages)\\n self += self.__bb\\n self += environment(variables=self.environment_variables)\",\n \"def make_image():\\n click.echo(\\\"make_image\\\")\",\n \"def make_montage(infile):\\n\\n #create a blank image\\n blank = Image.new(\\\"RGB\\\", OUTPIC_SIZE ,(25,23,24,255))\\n\\n #paste the background\\n bg = Image.open(BACKGROUND_FILENAME)\\n blank.paste(bg,(0,0))\\n \\n #paste the raw picture\\n im = Image.open(infile)\\n \\n # past on background\\n blank.paste(im,OUTPIC_PHOTO_OFFSET)\\n\\n #paste the stickers layer on the pic (a transparent png)\\n stickers = Image.open(STICKERS_FILENAME)\\n blank.paste(stickers,(0,0),stickers)\\n\\n return blank\",\n \"def cli(ctx, image_file):\\n if not image_file:\\n return\\n for pull_image in image_file.readline():\\n pull_image = pull_image.rstrip('\\\\n')\\n if len(pull_image) == 0:\\n continue\\n docker.pull(pull_image)\\n push_image = '%s/%s/%s' % (DEFAULT_REGISTRY,\\n DEFAULR_NAMESPACE,\\n pull_image.split('/')[-1])\\n docker.tag(pull_image, push_image)\\n docker.push(push_image)\",\n \"def build(parser):\\n parser.add_argument(\\n '-i', '--identity-file',\\n help=(\\n 'A SSH private key file which may be used to pull down '\\n 'repositories when building.'\\n ),\\n )\\n parser.add_argument(\\n '-e', '--env',\\n action='append',\\n default=[],\\n help=(\\n 'Add environ variables to the build. These may be accessed in '\\n 'the build scripts. Each variable should be of the format '\\n 'KEY=VALUE. This may be used to pass in credentials required '\\n 'to access private repositories. May be specified more than once.'\\n ),\\n )\\n parser.add_argument(\\n '-b', '--build-dir',\\n default=os.getcwd(),\\n help=(\\n 'This folder should be accessible from the docker instance.'\\n ),\\n )\\n parser.add_argument(\\n '--archive',\\n help=(\\n 'Archive the build files into a local tarball.'\\n ),\\n )\\n parser.add_argument(\\n '--archive-only',\\n action='store_true',\\n default=False,\\n help=(\\n 'Skip tagging and building the runner image.'\\n ),\\n )\\n parser.add_argument(\\n '-t', '--tag',\\n help=(\\n 'Tag to apply to the built image. '\\n 'This will default to the current date/time.'\\n ),\\n )\\n parser.add_argument(\\n '--no-cache',\\n dest='use_cache',\\n action='store_false',\\n default=True,\\n help=(\\n 'Do not mount a cache volume when compiling the app.'\\n ),\\n )\\n parser.add_argument(\\n '--cache',\\n metavar='CONTAINER:PATH',\\n help=(\\n 'An optional volume or location for the cache. The format is '\\n '\\\"<volume_id>:<path>\\\" where the \\\"volume_id\\\" must be the '\\n 'name or hash of an existing volume. The \\\"path\\\" is an absolute '\\n 'path to the cache folder/volume within the build container.'\\n '\\\\n\\\\n'\\n 'By default a container will be created by mangling the name of '\\n 'the app by appending \\\"__buildcache\\\" (e.g. \\\"myapp__buildcache\\\").'\\n '\\\\n\\\\n'\\n 'This option is ignored if --no-cache is specified.'\\n '\\\\n\\\\n'\\n 'The \\\"volume_id\\\" may be an absolute path on the host filesystem.'\\n '\\\\n\\\\n'\\n 'The \\\"path\\\" may be dropped, in which case it will default to '\\n '/tmp/cache inside the build container.'\\n '\\\\n\\\\n'\\n 'Examples:'\\n '\\\\n\\\\n'\\n ' # custom volume with default path\\\\n'\\n ' --cache my_cache'\\n '\\\\n\\\\n'\\n ' # custom path inside of volume\\\\n'\\n ' --cache my_cache:/tmp/cache'\\n '\\\\n\\\\n'\\n ' # host filesystem\\\\n'\\n ' --cache /tmp/cache'\\n ),\\n )\\n parser.add_argument(\\n '--rebuild-cache',\\n action='store_true',\\n default=False,\\n help=(\\n 'Delete any cached artifacts prior to building.'\\n ),\\n )\\n parser.add_argument(\\n '--skip-cleanup',\\n action='store_true',\\n default=False,\\n help=(\\n 'Skip removal of images and containers.'\\n ),\\n )\\n parser.add_argument(\\n 'app',\\n help=(\\n 'Path to an application folder with a meta.yml file'\\n ),\\n )\",\n \"def build(filename, verbose):\\n set_verbosity(verbose)\\n hokusai.build(filename)\",\n \"def from_file(self, filename):\\n if os.path.exists(filename): # read the image from file\\n self.image = io.imread(filename)\\n self.filename = filename\\n if os.path.exists(filename[:-4] + '.csv'):\\n self.metadata.read_from_csv(filename[:-4] + '.csv')\\n else:\\n warnings.warn(\\\"Metadata file is not found!\\\", Warning)\\n else:\\n raise ValueError('File does not exist!')\",\n \"def build_tale_image(task, tale_id, force=False):\\n logging.info('Building image for Tale %s', tale_id)\\n\\n task.job_manager.updateProgress(\\n message='Building image', total=BUILD_TALE_IMAGE_STEP_TOTAL,\\n current=1, forceFlush=True)\\n\\n tic = time.time()\\n tale = task.girder_client.get('/tale/%s' % tale_id)\\n while tale[\\\"status\\\"] != TaleStatus.READY:\\n time.sleep(2)\\n tale = task.girder_client.get('/tale/{_id}'.format(**tale))\\n if tale[\\\"status\\\"] == TaleStatus.ERROR:\\n raise ValueError(\\\"Cannot build image for a Tale in error state.\\\")\\n if time.time() - tic > 5 * 60.0:\\n raise ValueError(\\\"Cannot build image. Tale preparing for more than 5 minutes.\\\")\\n\\n last_build_time = -1\\n try:\\n last_build_time = tale['imageInfo']['last_build']\\n except KeyError:\\n pass\\n\\n logging.info('Last build time {}'.format(last_build_time))\\n image_builder = ImageBuilder(task.girder_client, tale=tale)\\n image_builder.pull_r2d()\\n\\n tag = image_builder.get_tag(force=force)\\n\\n logging.info(\\\"Computed tag: %s (taleId:%s)\\\", tag, tale_id)\\n\\n # Use the current time as the image build time and tag\\n build_time = int(time.time())\\n\\n # Check if image already exists\\n if not force and (image := image_builder.cached_image(tag)):\\n print('Cached image exists for this Tale. Skipping build.')\\n task.job_manager.updateProgress(\\n message='Tale not modified, no need to build',\\n total=BUILD_TALE_IMAGE_STEP_TOTAL,\\n current=BUILD_TALE_IMAGE_STEP_TOTAL,\\n forceFlush=True\\n )\\n return {\\n 'image_digest': f\\\"{tag}@{image['Descriptor']['digest']}\\\",\\n 'repo2docker_version': image_builder.container_config.repo2docker_version,\\n 'last_build': last_build_time\\n }\\n\\n print(\\\"Forcing build.\\\")\\n\\n # Prepare build context\\n ret, _ = image_builder.run_r2d(tag, task=task)\\n if task.canceled:\\n task.request.chain = None\\n logging.info(\\\"Build canceled.\\\")\\n return\\n\\n if ret[\\\"StatusCode\\\"] != 0:\\n # repo2docker build failed\\n raise ValueError('Error building tale {}'.format(tale_id))\\n\\n for line in image_builder.dh.apicli.push(tag, stream=True):\\n print(line.decode('utf-8'))\\n\\n # Get the built image digest\\n image = image_builder.dh.cli.images.get(tag)\\n digest = next((_ for _ in image.attrs['RepoDigests']\\n if _.startswith(urlparse(DEPLOYMENT.registry_url).netloc)), None)\\n\\n task.job_manager.updateProgress(\\n message='Image build succeeded', total=BUILD_TALE_IMAGE_STEP_TOTAL,\\n current=BUILD_TALE_IMAGE_STEP_TOTAL, forceFlush=True)\\n\\n logging.info('Successfully built image %s' % image.attrs['RepoDigests'][0])\\n\\n # Image digest used by updateBuildStatus handler\\n return {\\n 'image_digest': digest,\\n 'repo2docker_version': image_builder.container_config.repo2docker_version,\\n 'last_build': build_time\\n }\",\n \"def build_imagebuilder_template(image_config: ImageBuilderConfig, image_id: str, bucket: S3Bucket):\\n from aws_cdk.core import App # pylint: disable=C0415\\n\\n from pcluster.templates.imagebuilder_stack import ImageBuilderCdkStack # pylint: disable=C0415\\n\\n with tempfile.TemporaryDirectory() as tempdir:\\n output_file = \\\"imagebuilder\\\"\\n app = App(outdir=str(tempdir))\\n ImageBuilderCdkStack(app, output_file, image_config, image_id, bucket)\\n app.synth()\\n generated_template = load_yaml_dict(os.path.join(tempdir, f\\\"{output_file}.template.json\\\"))\\n\\n return generated_template\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6221498","0.57881945","0.56212443","0.55962044","0.5468818","0.5449105","0.5435583","0.54293376","0.5417929","0.5412393","0.5368963","0.53490657","0.53334695","0.5324951","0.5318995","0.5296623","0.529369","0.52904105","0.52801436","0.5234523","0.52214396","0.52055365","0.51897955","0.51895255","0.51751995","0.51734054","0.51531184","0.51371986","0.5134525","0.51308066","0.5130367","0.5109236","0.51020694","0.5081953","0.50703406","0.50624424","0.5042069","0.50368065","0.50339884","0.5032992","0.50227875","0.50130635","0.5012934","0.50075865","0.5004808","0.5004803","0.49965906","0.49965113","0.49951148","0.49872416","0.49858835","0.4964951","0.49571294","0.49547145","0.49452144","0.49418312","0.49372092","0.4929961","0.4924691","0.49230668","0.49165156","0.49106255","0.48980528","0.48892877","0.48786423","0.4872667","0.4862203","0.48588696","0.48552856","0.4851081","0.4844867","0.48381597","0.48188752","0.4817374","0.48154983","0.48122227","0.48078185","0.48075345","0.48033416","0.4803188","0.47980037","0.47917128","0.47911033","0.4790246","0.47804615","0.47677475","0.47599763","0.47553772","0.47523382","0.47522137","0.47492558","0.4749086","0.4748775","0.47346404","0.47240746","0.4723697","0.47224253","0.47190815","0.47189912","0.47177494"],"string":"[\n \"0.6221498\",\n \"0.57881945\",\n \"0.56212443\",\n \"0.55962044\",\n \"0.5468818\",\n \"0.5449105\",\n \"0.5435583\",\n \"0.54293376\",\n \"0.5417929\",\n \"0.5412393\",\n \"0.5368963\",\n \"0.53490657\",\n \"0.53334695\",\n \"0.5324951\",\n \"0.5318995\",\n \"0.5296623\",\n \"0.529369\",\n \"0.52904105\",\n \"0.52801436\",\n \"0.5234523\",\n \"0.52214396\",\n \"0.52055365\",\n \"0.51897955\",\n \"0.51895255\",\n \"0.51751995\",\n \"0.51734054\",\n \"0.51531184\",\n \"0.51371986\",\n \"0.5134525\",\n \"0.51308066\",\n \"0.5130367\",\n \"0.5109236\",\n \"0.51020694\",\n \"0.5081953\",\n \"0.50703406\",\n \"0.50624424\",\n \"0.5042069\",\n \"0.50368065\",\n \"0.50339884\",\n \"0.5032992\",\n \"0.50227875\",\n \"0.50130635\",\n \"0.5012934\",\n \"0.50075865\",\n \"0.5004808\",\n \"0.5004803\",\n \"0.49965906\",\n \"0.49965113\",\n \"0.49951148\",\n \"0.49872416\",\n \"0.49858835\",\n \"0.4964951\",\n \"0.49571294\",\n \"0.49547145\",\n \"0.49452144\",\n \"0.49418312\",\n \"0.49372092\",\n \"0.4929961\",\n \"0.4924691\",\n \"0.49230668\",\n \"0.49165156\",\n \"0.49106255\",\n \"0.48980528\",\n \"0.48892877\",\n \"0.48786423\",\n \"0.4872667\",\n \"0.4862203\",\n \"0.48588696\",\n \"0.48552856\",\n \"0.4851081\",\n \"0.4844867\",\n \"0.48381597\",\n \"0.48188752\",\n \"0.4817374\",\n \"0.48154983\",\n \"0.48122227\",\n \"0.48078185\",\n \"0.48075345\",\n \"0.48033416\",\n \"0.4803188\",\n \"0.47980037\",\n \"0.47917128\",\n \"0.47911033\",\n \"0.4790246\",\n \"0.47804615\",\n \"0.47677475\",\n \"0.47599763\",\n \"0.47553772\",\n \"0.47523382\",\n \"0.47522137\",\n \"0.47492558\",\n \"0.4749086\",\n \"0.4748775\",\n \"0.47346404\",\n \"0.47240746\",\n \"0.4723697\",\n \"0.47224253\",\n \"0.47190815\",\n \"0.47189912\",\n \"0.47177494\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.50080234"},"document_rank":{"kind":"string","value":"43"}}},{"rowIdx":17881,"cells":{"query":{"kind":"string","value":"Starts a singularity instance based on the image."},"document":{"kind":"string","value":"def singularity_start(self, image):\n env_vars = self.action.get('env', {})\n\n for s in self.action.get('secrets', []):\n env_vars.update({s: os.environ[s]})\n\n for e, v in self.env.items():\n env_vars.update({e: v})\n\n env_vars.update({'HOME': os.environ['HOME']})\n\n # sets the env variables\n for k, v in env_vars.items():\n Client.setenv(k, v)\n\n e = Client.run(image=self.generate_image_name(image),\n args=' '.join(self.action.get('args', '')),\n return_result=True)\n return e['return_code']"},"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 singularity_build(self, path, image):\n Client.build(os.path.join(\n path, 'singularity.def'\n ), self.generate_image_name(image))","def start_ssm(self, ssm_image):\n pass","def create_instance_by_image(self):\n print '# Start a new instance based on an existing AMI'\n ami = raw_input('Enter AMI (empty to cancel): ')\n\n # Cancel\n if not ami:\n print 'Operation cancelled'\n return\n\n # Start the instance\n if self.compute.create_instance_by_image(ami):\n print 'Instance started!'\n else:\n print 'It was not possible to create an instance with the given AMI'","def start_instance(InstanceId=None):\n pass","def startami(image, instancetype, accesskey, secretkey, pkname):\n if not is_valid_instance_type(image, instancetype):\n raise ValueError(\"Invalid instance type: '%s'\" % instancetype)\n\n conn = EC2Connection(accesskey, secretkey)\n image = conn.get_image(get_image_id(image))\n reservation = image.run(instance_type=instancetype, key_name=pkname)\n instance = reservation.instances[0]\n\n waitForInstanceToRun(instance)\n\n # [AN] call script instanceStartup.py\n return str(instance.dns_name)","def start_instance(self):\n instance_id = self._choose_among_stopped_instances()\n\n # Cancel\n if not instance_id:\n print 'Operation cancelled'\n return\n\n print '# Starting the instance \"%s\"' % instance_id\n if self.compute.start_instance(instance_id):\n print 'The instance has been started'\n else:\n print 'The instance could not be started'","def __init__(self, sl):\n threading.Thread.__init__(self)\n self.alpha = 0.5\n self.running = True\n self.slam = sl\n self.image = None\n self.scale = 5.0\n self.centre = np.zeros(2)\n self.mouse = np.zeros(2)\n self.dimensions = np.array([1920, 1080])\n self.tracking = TrackingMode.FREE\n self.map_mode = MapMode.DIST","def run(self, reuse=False):\n build = True\n if 'shub://' in self.action['uses']:\n image = self.action['uses']\n build = False\n elif './' in self.action['uses']:\n image = 'action/' + os.path.basename(self.action['uses'])\n singularityfile_path = os.path.join(\n os.getcwd(), self.action['uses'])\n else:\n image = '/'.join(self.action['uses'].split('/')[:2])\n singularityfile_path = os.path.join(self.action['repo_dir'],\n self.action['action_dir'])\n\n if not reuse:\n if self.singularity_exists():\n self.singularity_rm()\n if build:\n self.singularity_build(singularityfile_path, image)\n else:\n self.singularity_pull(image)\n else:\n if not self.singularity_exists():\n if build:\n self.singularity_build(singularityfile_path, image)\n else:\n self.singularity_pull(image)\n\n e = self.singularity_start(image)\n\n if e != 0:\n pu.fail('Action {} failed!\\n'.format(self.action['name']))","def start_sml():\n launchfile = basepath + '/launch/teststarter.launch'\n\n uuid = roslaunch.rlutil.get_or_generate_uuid(None, False)\n #print roslaunch.rlutil.check_roslaunch(launchfile)\n #roslaunch.configure_logging(uuid)\n launch = roslaunch.parent.ROSLaunchParent(uuid, [launchfile])\n launch.start()","def makeInstanceFromImage(self , imageid, initialconfig, instancename):\n self.initCreate(initialconfig)\n ip = self.launchMinipadServer()\n if ip:\n self.__server_ip = ip\n disk = self.createDisk(instancename)\n self.attachDiskToMinipad(disk )\n \n if self.startConversion(imageid , self.__server_ip) == False:\n return None\n\n self.detachDiskFromMinipad(disk)\n vm = self.createVM(disk , instancename)\n return vm","def start(self, state):\n return self.brain.start(state)","def do_start(self, arg):\n args = arg.split(\" \")\n self.model.initialise(args[0])\n self.model.run()","def FullSingleSceneInference(self, image_file, USE_MODEL = 'DEFAULT'): \n if USE_MODEL != 'DEFAULT':\n (detector_file, classes_file, map_file, \n model_name, model_type) = self.DownloadModel(USE_MODEL)\n self.SetActiveGraph(detector_graph_file = detector_file,\n model_name = model_name,\n classes_file = classes_file,\n map_file = map_file,\n model_type = model_type\n )\n \n b,s,c = self.AnalyzeSingleImage(image_file = image_file)\n self.DrawImage(image_file = image_file, boxes = b,\n scores = s, classes = c\n )\n return","def launch_instance(cloud):\n js = _get_jetstream_conn()\n\n sgs = ['CloudLaunchDefault']\n kp_name = \"cloudman_key_pair\"\n inst_size = 'm1.small'\n network_id = '86a1c3e8-b1fb-41f3-bcaf-8334567fe989'\n lc = js.compute.instances.create_launch_config()\n lc.add_network_interface(network_id)\n\n img_id = '2cf07e4a-62a8-41c2-9282-f3c53962f296' # Gxy Standalone 161021b01\n name = 'ea-galaxy-{0}'.format(strftime(\"%m-%d-%H-%M\", localtime()))\n\n i = js.compute.instances.create(\n name, img_id, inst_size, security_groups=sgs, launch_config=lc,\n key_pair=kp_name)\n return i","def __init__(self, experiment) -> None:\n self.logger = get_logger()\n np.random.seed(experiment.seed)\n self.gpu_imported = False\n\n from sktime.forecasting.naive import NaiveForecaster # type: ignore\n\n # Disable container if certain features are not supported but enforced ----\n dummy = NaiveForecaster()\n self.active = _check_enforcements(forecaster=dummy, experiment=experiment)\n if not self.active:\n return\n\n self.seasonality_present = experiment.seasonality_present\n self.sp = experiment.primary_sp_to_use\n\n if self.sp == 1:\n self.active = False\n return\n\n args = self._set_args\n tune_args = self._set_tune_args\n tune_grid = self._set_tune_grid\n tune_distributions = self._set_tune_distributions\n leftover_parameters_to_categorical_distributions(tune_grid, tune_distributions)\n\n eq_function = lambda x: type(x) is NaiveForecaster and x.sp != 1\n\n super().__init__(\n id=\"snaive\",\n name=\"Seasonal Naive Forecaster\",\n class_def=NaiveForecaster,\n args=args,\n tune_grid=tune_grid,\n tune_distribution=tune_distributions,\n tune_args=tune_args,\n is_gpu_enabled=self.gpu_imported,\n eq_function=eq_function,\n )","def main():\n original = SimpleImage('images/mt-rainier.jpg')\n original.show()\n reflected = make_reflected('images/mt-rainier.jpg')\n reflected.show()","def _start(self, instance):\n try:\n # Attempt to start the VE.\n # NOTE: The VE will throw a warning that the hostname is invalid\n # if it isn't valid. This is logged in LOG.error and is not\n # an indication of failure.\n _, err = utils.execute('sudo', 'vzctl', 'start', instance['id'])\n if err:\n LOG.error(err)\n except ProcessExecutionError as err:\n LOG.error(err)\n raise exception.Error('Failed to start %d' % instance['id'])\n\n # Set instance state as RUNNING\n db.instance_set_state(context.get_admin_context(),\n instance['id'],\n power_state.RUNNING)\n return True","def __init__(self, image_size):\n super(SiameseDiscriminator, self).__init__()\n self.cnn1 = nn.Sequential(\n nn.ReflectionPad2d(1),\n nn.Conv2d(3, 4, kernel_size=3),\n nn.LeakyReLU(0.1, inplace=True),\n nn.BatchNorm2d(4),\n nn.Dropout2d(p=.2),\n\n nn.ReflectionPad2d(1),\n nn.Conv2d(4, 8, kernel_size=3),\n nn.LeakyReLU(0.1, inplace=True),\n nn.BatchNorm2d(8),\n nn.Dropout2d(p=.2),\n\n nn.ReflectionPad2d(1),\n nn.Conv2d(8, 8, kernel_size=3),\n nn.LeakyReLU(0.1, inplace=True),\n nn.BatchNorm2d(8),\n nn.Dropout2d(p=.2))\n\n self.fc1 = nn.Sequential(\n nn.Linear(8 * image_size * image_size, 500),\n nn.LeakyReLU(0.1, inplace=True),\n\n nn.Linear(500, 500),\n nn.LeakyReLU(0.1, inplace=True),\n\n nn.Linear(500, 15))","def _start(self):\n if self._classifier is None:\n self._classifier = TFSlimClassifier(self.config)\n self._classifier.__enter__()","def start_image_builder(Name=None, AppstreamAgentVersion=None):\n pass","def create_instance(self, image='ami-660c3023', key_name='linuxonEC2', instance_type='t1.micro', security_groups=['default']):\n return self.conn.run_instances(image,\n key_name=key_name,\n instance_type=instance_type,\n security_groups=security_groups).instances[0]","def __init__(self):\n self.svclassifier = SVC(kernel='linear')","def start(update: Update, context: CallbackContext) -> None:\n update.message.reply_text('Hi send an image to classify!')","def start_SLP_server(self):\n import slp\n \n s = self.op.get_value('mode_params')\n if len(s) == 0:\n print 'No parameter received'\n params = {}\n else:\n l_p = self.parse_params(s)\n params = {'action':[np.int(l_p[0])]}\n if len(l_p) == 2:\n params['image_feature_layer_name'] = l_p[1]\n slpserver = slp.SLPServerTrial(self, params)\n slpserver.start()","def start(self: AutoScaler) -> AutoScalerState:\n log.info(f'Autoscale start (policy: {self.policy.name.lower()}, init-size: {self.init_size})')\n log.debug(f'Autoscale launcher: {self.launcher}')\n return AutoScalerState.INIT","def start(self):\n with self._lock:\n if not self.started():\n self._started = None\n getattr(self.factory, 'start_' + self.class_name())(self)","def sex(image):\n \n # run sextractor with different default parameters\n print('running SExtractor to {}...'.format(image))\n P = Popen('sextractor -c goto.sex '+image+' -CATALOG_NAME '+image[:-5]+'.cat', shell=True)\n P.wait()","def init_with_display_image(cls, d, image, is_sub=False):\n # init basic display parameters\n scene = cls()\n scene.illuminant = Illuminant(wave=d.wave)\n scene.dist = d.dist # viewing distance\n\n # compute horizontal field of view\n scene.fov = 2 * rad_to_deg(atan2(image.shape[1] * d.meters_per_dot / 2, d.dist))\n\n # set illuminant as spd of display\n scene.illuminant.photons = energy_to_quanta(d.white_spd, d.wave)\n\n # gamma distortion for the input image\n image = d.lookup_digital((image * (d.n_levels - 1)).astype(int))\n\n # sub-pixel rendering if required\n if is_sub:\n image = d.compute(image)\n\n # compute radiance from image\n out_sz = np.concatenate((np.array(image.shape[0:2]), [d.wave.size]))\n image = rgb_to_xw_format(image)\n scene.photons = energy_to_quanta(np.dot(image, d.spd.T), d.wave)\n\n # add ambient quanta to scene photons\n scene.photons += d.ambient\n\n # reshape photons\n scene.photons = scene.photons.reshape(out_sz, order=\"F\")\n return scene","def create_scene(self, ):\n self.scene = create_scene(\n self.opt.splats_img_size, self.opt.splats_img_size, self.opt.fovy,\n self.opt.focal_length, self.opt.n_splats)","def process_image(self):\n\n detect.main(self.nn_args)","def __init__(self, image):\n self.image = image","def run_visualization(SAMPLE_IMAGE):\n original_im = Image.open(SAMPLE_IMAGE)\n global seg_map\n seg_map = MODEL.run(original_im)\n vis_segmentation(original_im, seg_map)","def __init__(self, image_file, **kwargs):\n\n # Mapping of serial numbers to models for known detectors\n self._sn_to_model = {\n 401: \"Q4U\",\n 402: \"Q4\",\n 414: \"Q4\",\n 423: \"Q4R\",\n 428: \"Q4R\",\n 429: \"Q4\", # or Q4R?\n 441: \"Q210\",\n 442: \"Q210\",\n 443: \"Q210\",\n 444: \"Q210\", # or Q210R?\n 445: \"Q210\",\n 446: \"Q210\",\n 447: \"Q210\",\n 448: \"Q210\",\n 457: \"Q210R\",\n 471: \"Q270\",\n 472: \"Q270\",\n 474: \"Q270\",\n 901: \"Q210\",\n 905: \"Q315\",\n 907: \"Q315R\", # or Q315?\n 913: \"Q315\",\n 917: \"Q315R\",\n 923: \"Q315R\",\n 925: \"Q315\",\n 926: \"Q315R\",\n 928: \"Q315R\",\n 931: \"Q315R\",\n 933: \"Q315R\",\n }\n\n super().__init__(image_file, **kwargs)","def start_test_instance(test_name=None):\n env.warn_only = True\n if test_name is not None:\n instances = [test_name]\n else:\n output = run('ls -1 %s' % env.site_root)\n instances = [x.strip() for x in output.split(\"\\n\")]\n for item in instances:\n sudo(\"start %s\" % item.strip())","def __init__(self, evaluation_only=False, seed=0):\n os = platform.system()\n if os == 'Darwin':\n file_name = 'Soccer.app'\n elif os == 'Linux':\n file_name = 'Soccer_Linux/Soccer.x86_64'\n self.env = UnityEnvironment(file_name='unity_envs/' + file_name, seed=seed)\n self.brain_names = self.env.brain_names\n self.evaluation_only = evaluation_only","def start_kernel(self, kernel_name=None, **kwargs):","def start(self):\n try:\n import os\n import io\n from PIL import Image\n from skimage.io import imsave\n from kespryml_roof_wireframe.inference import WireframeNet\n\n os.environ['KMP_DUPLICATE_LIB_OK']='True'\n except Exception as e:\n print('Not Able to import: ' + e)\n\n try:\n with S3() as s3:\n model_path = s3.get(self.model_path).path\n self.model = WireframeNet(model_weight_path=model_path)\n except Exception as e:\n print('Not able to load the model.' + e)\n \n print(\"Model Loaded...\")\n\n try:\n with S3() as s3:\n ortho_image_path = s3.get(os.path.join(self.source_dir, self.mission_id, 'preview_products', 'orthomosaic.tif')).path\n dsm_color_image_path = s3.get(os.path.join(self.source_dir, self.mission_id, 'preview_products', 'dsm_colored.tif')).path\n\n os.system('gdal_translate -of GTiff -r cubicspline -outsize {} {} {} {}'.format(800, 1024, ortho_image_path, os.path.join(os.path.dirname('__file__'), 'orthomosaic_resized.tif')))\n file_stream = io.BytesIO()\n im = Image.open(os.path.join(os.path.dirname('__file__'), 'orthomosaic_resized.tif'))\n im.save(file_stream, format='tiff')\n s3.put(os.path.join(self.save_path, 'orthomosaic_resized.tif'), file_stream.getvalue(), overwrite=True)\n os.system('rm -rf {}'.format(os.path.join(os.path.dirname('__file__'), 'orthomosaic_resized.tif')))\n\n os.system('gdal_translate -of GTiff -r cubicspline -outsize {} {} {} {}'.format(800, 1024, dsm_color_image_path, os.path.join(os.path.dirname('__file__'), 'dsm_colored_resized.tif')))\n file_stream = io.BytesIO()\n im = Image.open(os.path.join(os.path.dirname('__file__'), 'dsm_colored_resized.tif'))\n im.save(file_stream, format='tiff')\n s3.put(os.path.join(self.save_path, 'dsm_colored_resized.tif'), file_stream.getvalue(), overwrite=True)\n os.system('rm -rf {}'.format(os.path.join(os.path.dirname('__file__'), 'dsm_colored_resized.tif')))\n ortho_image_path = s3.get(os.path.join(self.save_path, 'orthomosaic_resized.tif')).path\n dsm_color_image_path = s3.get(os.path.join(self.save_path, 'dsm_colored_resized.tif')).path\n\n self.mask = self.model.predict(ortho_image_path=ortho_image_path, dsm_col_image_path=dsm_color_image_path)\n\n file_stream = io.BytesIO()\n im = Image.fromarray(self.mask)\n im.save(file_stream, format='tiff')\n s3.put(os.path.join(self.save_path, 'output.tif'), file_stream.getvalue(), overwrite=True)\n \n\n except Exception as e:\n print('Not able preprocess Image.'+ e)\n\n print('Finished Processing.')\n self.next(self.end)","def launch_instance(ec2, ami, itype, kp_name, sec_group_name):\n\n\n instance = ec2.run_instances(\n ami,\n key_name=kp_name,\n instance_type=itype,\n security_groups=[sec_group_name]\n ).instances[0]\n\n while instance.state != 'running':\n sys.stdout.write('Waiting for instance: {}, at DNS: {} to start\\n'.format(instance.id,\n str(instance.dns_name).split('.')[0]))\n time.sleep(5)\n instance.update()\n\n sys.stdout.write('\\nSuccess! EC2 Instance Launched \\nInstance_Type: {} in {}'.format(instance.instance_type,\n instance.placement))\n return instance","def launch_on_jetstream():\n launched = launch_instance(\"Jetstream\")\n session.attributes['instance_id'] = launched.id\n session.attributes['public_ip'] = None\n session.attributes['status'] = None\n\n msg = \"An instance is starting. Would you like to check its status?\"\n return question(msg)","def quick_instance(self, name, image, instance_type, env_tag='dev', zone_tag='starwatts', os_tag='debian', sg_id=None,\n private=True, extra_sg_ids=None, extra_tags=None, terminate_on_shutdown=False,\n debug=False):\n # Debug setting\n if debug:\n logging.basicConfig(level=logging.DEBUG)\n\n # Preliminary tests\n try:\n ami = self.get_image(image_id=image)\n except EC2ResponseError:\n logging.error(\"The image {} could not be found. Aborting.\".format(image))\n return\n print(\"Using AMI {} : {}\".format(image, ami.name))\n if len(self.get_only_instances(filters={'tag:name': name, 'instance-state-name': 'running'})) > 0 or \\\n len(self.get_only_instances(filters={'tag:name': name, 'instance-state-name': 'stopped'})) > 0:\n logging.error(\"An instance with the same name ({}) already exists. Aborting.\".format(name))\n return\n logging.debug(\"Test passed : No instance has the same 'name' tag.\")\n if self.keypair_exists(name):\n logging.error(\"A keypair with the same name ({}) already exists. Aborting.\".format(name))\n return\n logging.debug(\"Test passed : No keypair was found with the same name.\")\n if sg_id is None:\n if self.security_group_exists(name=name):\n logging.error(\"A security group with the same name ({}) already exists. Aborting.\".format(name))\n return\n logging.debug(\"Test passed : No security group was found with the same name.\")\n\n # Tags generation\n logging.debug(\"Generating tags to apply.\")\n tags = dict(name=name, os=os_tag, env=env_tag, zone=zone_tag, privacy='true' if private else 'false')\n if extra_tags is not None:\n tags.update(extra_tags)\n print(\"Tags : {}\".format(tags))\n\n # Fetching needed security groups (bastion and zabbix)\n standard_sg = self.get_all_security_groups(groupnames=['standard'])\n if len(standard_sg) != 1:\n logging.error(\"Multiple or no security group was found for the 'bastion' search. Aborting.\")\n return\n standard_sg = standard_sg[0]\n logging.debug(\"The following security group was found for 'standard : {} {}\".format(standard_sg.id,\n standard_sg.description))\n\n # Security group creation\n if sg_id is None:\n sg = self.create_security_group(name, \"SG applied to {} VM\".format(name))\n sg_id = sg.id\n\n sg_ids = [sg_id, standard_sg.id, ]\n # Using the extra security groups if any\n if extra_sg_ids is not None:\n logging.debug(\"Extra security groups to add : {}\".format(extra_sg_ids))\n sg_ids.extend(extra_sg_ids)\n logging.debug(\"Security Groups : {}\".format(sg_ids))\n\n user_data = \"-----BEGIN OUTSCALE SECTION-----\\nprivate_only=true\\n-----END OUTSCALE SECTION-----\" if private else \"\"\n logging.debug(\"Creating keypair.\")\n kp = self.create_key_pair(key_name=name)\n fp = os.path.join(os.path.expanduser('~/.ssh'), '%s.pem' % kp.name)\n with open(fp, 'wb') as fd:\n fd.write(bytes(kp.material, \"UTF-8\"))\n logging.debug(\"Keypair written to ~/.ssh/{}.pem\".format(name))\n\n resa = self.run_instances(image_id=image, key_name=name, security_groups=sg_ids, instance_type=instance_type,\n user_data=user_data,\n instance_initiated_shutdown_behavior='terminate' if terminate_on_shutdown else 'stop')\n inst = resa.instances[0]\n logging.debug(\"Adding tags to the newly created machine.\")\n inst.add_tags(tags)\n return inst","def __init__(self, isoImagePath):\n nrvr.distros.common.kickstart.DistroIsoImage.__init__(self, isoImagePath)","def start_notebook_instance(NotebookInstanceName=None):\n pass","def main():\n rospy.init_node('image_to_pointcloud')\n rospy.loginfo(\"Starting sonar image to pointcloud node...\")\n converter = ImageConverter()\n\n rate = rospy.Rate(10)\n rospy.sleep(3.0)\n while not rospy.is_shutdown():\n converter.proc_and_pub_pointcloud()\n rate.sleep()","def singledish_observe_image(image, pixel_scale, beam, boundary='fill'):\n\n if hasattr(image, 'wcs'):\n singledish_im = image.convolve_to(beam, boundary=boundary)\n\n else:\n kernel = beam.as_kernel(pixel_scale)\n\n # create the single-dish map by convolving the image with a FWHM=40\" kernel\n # (this interpretation is much easier than the sharp-edged stuff in fourier\n # space because the kernel is created in real space)\n singledish_im = convolution.convolve_fft(image,\n kernel=kernel,\n boundary=boundary,\n fill_value=image.mean())\n\n return singledish_im","def __init__(__self__, *,\n additional_info: Optional[pulumi.Input[str]] = None,\n affinity: Optional[pulumi.Input[str]] = None,\n availability_zone: Optional[pulumi.Input[str]] = None,\n block_device_mappings: Optional[pulumi.Input[Sequence[pulumi.Input['InstanceBlockDeviceMappingArgs']]]] = None,\n cpu_options: Optional[pulumi.Input['InstanceCpuOptionsArgs']] = None,\n credit_specification: Optional[pulumi.Input['InstanceCreditSpecificationArgs']] = None,\n disable_api_termination: Optional[pulumi.Input[bool]] = None,\n ebs_optimized: Optional[pulumi.Input[bool]] = None,\n elastic_gpu_specifications: Optional[pulumi.Input[Sequence[pulumi.Input['InstanceElasticGpuSpecificationArgs']]]] = None,\n elastic_inference_accelerators: Optional[pulumi.Input[Sequence[pulumi.Input['InstanceElasticInferenceAcceleratorArgs']]]] = None,\n enclave_options: Optional[pulumi.Input['InstanceEnclaveOptionsArgs']] = None,\n hibernation_options: Optional[pulumi.Input['InstanceHibernationOptionsArgs']] = None,\n host_id: Optional[pulumi.Input[str]] = None,\n host_resource_group_arn: Optional[pulumi.Input[str]] = None,\n iam_instance_profile: Optional[pulumi.Input[str]] = None,\n image_id: Optional[pulumi.Input[str]] = None,\n instance_initiated_shutdown_behavior: Optional[pulumi.Input[str]] = None,\n instance_type: Optional[pulumi.Input[str]] = None,\n ipv6_address_count: Optional[pulumi.Input[int]] = None,\n ipv6_addresses: Optional[pulumi.Input[Sequence[pulumi.Input['InstanceIpv6AddressArgs']]]] = None,\n kernel_id: Optional[pulumi.Input[str]] = None,\n key_name: Optional[pulumi.Input[str]] = None,\n launch_template: Optional[pulumi.Input['InstanceLaunchTemplateSpecificationArgs']] = None,\n license_specifications: Optional[pulumi.Input[Sequence[pulumi.Input['InstanceLicenseSpecificationArgs']]]] = None,\n monitoring: Optional[pulumi.Input[bool]] = None,\n network_interfaces: Optional[pulumi.Input[Sequence[pulumi.Input['InstanceNetworkInterfaceArgs']]]] = None,\n placement_group_name: Optional[pulumi.Input[str]] = None,\n private_dns_name_options: Optional[pulumi.Input['InstancePrivateDnsNameOptionsArgs']] = None,\n private_ip_address: Optional[pulumi.Input[str]] = None,\n propagate_tags_to_volume_on_creation: Optional[pulumi.Input[bool]] = None,\n ramdisk_id: Optional[pulumi.Input[str]] = None,\n security_group_ids: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,\n security_groups: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,\n source_dest_check: Optional[pulumi.Input[bool]] = None,\n ssm_associations: Optional[pulumi.Input[Sequence[pulumi.Input['InstanceSsmAssociationArgs']]]] = None,\n subnet_id: Optional[pulumi.Input[str]] = None,\n tags: Optional[pulumi.Input[Sequence[pulumi.Input['InstanceTagArgs']]]] = None,\n tenancy: Optional[pulumi.Input[str]] = None,\n user_data: Optional[pulumi.Input[str]] = None,\n volumes: Optional[pulumi.Input[Sequence[pulumi.Input['InstanceVolumeArgs']]]] = None):\n if additional_info is not None:\n pulumi.set(__self__, \"additional_info\", additional_info)\n if affinity is not None:\n pulumi.set(__self__, \"affinity\", affinity)\n if availability_zone is not None:\n pulumi.set(__self__, \"availability_zone\", availability_zone)\n if block_device_mappings is not None:\n pulumi.set(__self__, \"block_device_mappings\", block_device_mappings)\n if cpu_options is not None:\n pulumi.set(__self__, \"cpu_options\", cpu_options)\n if credit_specification is not None:\n pulumi.set(__self__, \"credit_specification\", credit_specification)\n if disable_api_termination is not None:\n pulumi.set(__self__, \"disable_api_termination\", disable_api_termination)\n if ebs_optimized is not None:\n pulumi.set(__self__, \"ebs_optimized\", ebs_optimized)\n if elastic_gpu_specifications is not None:\n pulumi.set(__self__, \"elastic_gpu_specifications\", elastic_gpu_specifications)\n if elastic_inference_accelerators is not None:\n pulumi.set(__self__, \"elastic_inference_accelerators\", elastic_inference_accelerators)\n if enclave_options is not None:\n pulumi.set(__self__, \"enclave_options\", enclave_options)\n if hibernation_options is not None:\n pulumi.set(__self__, \"hibernation_options\", hibernation_options)\n if host_id is not None:\n pulumi.set(__self__, \"host_id\", host_id)\n if host_resource_group_arn is not None:\n pulumi.set(__self__, \"host_resource_group_arn\", host_resource_group_arn)\n if iam_instance_profile is not None:\n pulumi.set(__self__, \"iam_instance_profile\", iam_instance_profile)\n if image_id is not None:\n pulumi.set(__self__, \"image_id\", image_id)\n if instance_initiated_shutdown_behavior is not None:\n pulumi.set(__self__, \"instance_initiated_shutdown_behavior\", instance_initiated_shutdown_behavior)\n if instance_type is not None:\n pulumi.set(__self__, \"instance_type\", instance_type)\n if ipv6_address_count is not None:\n pulumi.set(__self__, \"ipv6_address_count\", ipv6_address_count)\n if ipv6_addresses is not None:\n pulumi.set(__self__, \"ipv6_addresses\", ipv6_addresses)\n if kernel_id is not None:\n pulumi.set(__self__, \"kernel_id\", kernel_id)\n if key_name is not None:\n pulumi.set(__self__, \"key_name\", key_name)\n if launch_template is not None:\n pulumi.set(__self__, \"launch_template\", launch_template)\n if license_specifications is not None:\n pulumi.set(__self__, \"license_specifications\", license_specifications)\n if monitoring is not None:\n pulumi.set(__self__, \"monitoring\", monitoring)\n if network_interfaces is not None:\n pulumi.set(__self__, \"network_interfaces\", network_interfaces)\n if placement_group_name is not None:\n pulumi.set(__self__, \"placement_group_name\", placement_group_name)\n if private_dns_name_options is not None:\n pulumi.set(__self__, \"private_dns_name_options\", private_dns_name_options)\n if private_ip_address is not None:\n pulumi.set(__self__, \"private_ip_address\", private_ip_address)\n if propagate_tags_to_volume_on_creation is not None:\n pulumi.set(__self__, \"propagate_tags_to_volume_on_creation\", propagate_tags_to_volume_on_creation)\n if ramdisk_id is not None:\n pulumi.set(__self__, \"ramdisk_id\", ramdisk_id)\n if security_group_ids is not None:\n pulumi.set(__self__, \"security_group_ids\", security_group_ids)\n if security_groups is not None:\n pulumi.set(__self__, \"security_groups\", security_groups)\n if source_dest_check is not None:\n pulumi.set(__self__, \"source_dest_check\", source_dest_check)\n if ssm_associations is not None:\n pulumi.set(__self__, \"ssm_associations\", ssm_associations)\n if subnet_id is not None:\n pulumi.set(__self__, \"subnet_id\", subnet_id)\n if tags is not None:\n pulumi.set(__self__, \"tags\", tags)\n if tenancy is not None:\n pulumi.set(__self__, \"tenancy\", tenancy)\n if user_data is not None:\n pulumi.set(__self__, \"user_data\", user_data)\n if volumes is not None:\n pulumi.set(__self__, \"volumes\", volumes)","def start(self) -> UnityEnvironment:\n self.env = self.get_env(self.file)\n time.sleep(2)\n self.brain_name = self.env.brain_names[0]\n\n brain = self.env.brains[self.brain_name]\n self.action_size = brain.vector_action_space_size\n env_info = self.env.reset(train_mode=True)[self.brain_name]\n states = env_info.vector_observations\n self.n_agents, self.state_size = states.shape\n return self.env","def __init__(self, batch_size=100):\n \n self.features = None\n self.image_w = 320\n self.image_h = 320\n self.vfov = 60\n # self.featurized_scans = set([key.split(\"_\")[0] for key in list(self.features.keys())])\n self.sims = []\n for i in range(batch_size):\n sim = MatterSim.Simulator()\n # sim.setRenderingEnabled(True)\n sim.setDiscretizedViewingAngles(True) # Set increment/decrement to 30 degree. (otherwise by radians)\n sim.setCameraResolution(self.image_w, self.image_h)\n sim.setCameraVFOV(math.radians(self.vfov))\n sim.init()\n self.sims.append(sim)\n\n print('finished')","async def start_ec2_instance(self, env):\n instanceDef= {\n 'AWS_AMI_ID': os.getenv(\"AWS_AMI_ID\"),\n 'AWS_KEYNAME': os.getenv(\"AWS_KEYNAME\"),\n 'AWS_SECURITY_GROUP': os.getenv('AWS_SECURITY_GROUP'),\n 'AWS_SUBNET': os.getenv(\"AWS_SUBNET\"),\n 'DryRun':False,\n 'AWS_INSTANCE_NAME': 'Jupyter',\n 'AWS_IAM_ARN': os.getenv('AWS_IAM_ARN')\n }\n \n self.log.debug('building instance')\n ip = await self.buildInstance(instanceDef, env)\n return ip","def run_single_camera(cam):\r\n try:\r\n result = True\r\n err = False\r\n\r\n # Retrieve TL device nodemap and print device information\r\n nodemap_tldevice = cam.GetTLDeviceNodeMap()\r\n\r\n #result &= print_device_info(nodemap_tldevice)\r\n #dont need to print all of that\r\n\r\n # Initialize camera\r\n cam.Init()\r\n\r\n # Retrieve GenICam nodemap\r\n nodemap = cam.GetNodeMap()\r\n\r\n #configure settings\r\n if configure_exp_gain(cam,exp,gain) is False:\r\n return False\r\n\r\n # Configure trigger\r\n if configure_trigger(cam) is False:\r\n return False\r\n\r\n # Acquire images\r\n snr = acquire_images(cam, nodemap, nodemap_tldevice)\r\n #print(\"SNR after acquire_images: \", snr)\r\n\r\n # Reset trigger\r\n result &= reset_trigger(nodemap)\r\n\r\n # Deinitialize camera\r\n cam.DeInit()\r\n\r\n except PySpin.SpinnakerException as ex:\r\n print('Error: %s' % ex)\r\n result = False\r\n\r\n return snr","def do_startstcv(self, args):\n if not self._assert_login():\n return\n\n vm_image = None\n ttl_minutes = 60\n socket = False\n desc = None\n instances = 2\n host = None\n cores = 1\n memory = None\n vlan = None\n ntp_server = None\n license_server = None\n share = True\n static_ip = None\n netmask = None\n gateway = None\n external = False\n\n if args:\n args = args.split()\n missing_arg = 'missing value after'\n while args:\n arg = args.pop(0)\n if arg in ('-i', '--image'):\n if not args:\n print(missing_arg, arg, file=sys.stderr)\n return\n vm_image = args.pop(0)\n elif arg in ('-t', '--ttl'):\n if not args:\n print(missing_arg, arg, file=sys.stderr)\n return\n ttl_minutes = int(args.pop(0))\n elif arg in ('-d', '--desc'):\n if not args:\n print(missing_arg, arg, file=sys.stderr)\n return\n desc = args.pop(0)\n elif arg in ('-n', '--number'):\n if not args:\n print(missing_arg, arg, file=sys.stderr)\n return\n instances = int(args.pop(0))\n elif arg in ('-h', '--host'):\n if not args:\n print(missing_arg, arg, file=sys.stderr)\n return\n host = args.pop(0)\n elif arg in ('-c', '--cores'):\n if not args:\n print(missing_arg, arg, file=sys.stderr)\n return\n cores = int(args.pop(0))\n elif arg in ('-m', '--memory'):\n if not args:\n print(missing_arg, arg, file=sys.stderr)\n return\n memory = int(args.pop(0))\n elif arg == '--socket':\n socket = True\n elif arg == '--vlan':\n if not args:\n print(missing_arg, arg, file=sys.stderr)\n return\n vlan = int(args.pop(0))\n elif arg == '--ntp':\n if not args:\n print(missing_arg, arg, file=sys.stderr)\n return\n ntp_server = args.pop(0)\n elif arg == '--license':\n if not args:\n print(missing_arg, arg, file=sys.stderr)\n return\n license_server = args.pop(0)\n elif arg == '--noshare':\n share = False\n elif arg == '--staticip':\n if not args:\n print(missing_arg, arg, file=sys.stderr)\n return\n static_ip = args.pop(0)\n elif arg == '--netmask':\n if not args:\n print(missing_arg, arg, file=sys.stderr)\n return\n netmask = args.pop(0)\n elif arg == '--gateway':\n if not args:\n print(missing_arg, arg, file=sys.stderr)\n return\n gateway = args.pop(0)\n else:\n print('unrecognized option:', arg, file=sys.stderr)\n return\n\n if not vm_image:\n builds = self._qm.get_available_stc_builds()\n if not builds:\n print('unable to find latest build', file=sys.stderr)\n return\n vm_image = '#' + builds[0]\n\n try:\n vm_ids = self._qm.start_stc_vm(\n self._user, vm_image, ttl_minutes, socket, desc, instances,\n host, share, vlan, memory, cores, external, ntp_server,\n license_server, static_ip, netmask, gateway)\n except Exception as e:\n print('ERROR:', e, file=sys.stderr)\n return\n\n print('Started new vm instances of', vm_image)\n print('\\n'.join(vm_ids))","def classifyPhaseImage(fr_nb):\n phase_path = os.path.join(\"..\",'data','microglia','Beacon-1 unst',\"Scene1Interval\"+str(fr_nb)+\"_PHASE.png\")\n \n phase= Image.open(phase_path)\n phase = np.asarray(phase)\n X=phase.reshape(-1,1)\n from sklearn.cluster import KMeans\n kmeans = KMeans(n_clusters=3).fit(X)\n classified = kmeans.labels_\n classified=classified.reshape(phase.shape)\n si2(phase,classified,\"Phase image\",\"Classification\")\n return classified","def start(self):\n if self.preemptableScaler != None:\n self.preemptableScaler.start()\n\n if self.scaler != None:\n self.scaler.start()","def __call__(self,\n prompt,\n image,\n num_inference_steps=20,\n guidance_scale=9.0,\n noise_level=20):\n\n # encode input prompt\n prompt_embeds = self.get_prompt_embeds(prompt)\n\n # preprocess image\n image = self.image_processor.preprocess(image).to(self.device)\n\n # prepare timesteps\n self.scheduler.set_timesteps(num_inference_steps, device=self.device)\n timesteps = self.scheduler.timesteps\n\n # add noise to image\n noise_level = torch.tensor([noise_level], device=self.device)\n noise = torch.randn(image.shape, device=self.device)\n image = self.low_res_scheduler.add_noise(image, noise, noise_level)\n\n # duplicate image for classifier free guidance\n image = torch.cat([image] * 2)\n noise_level = torch.cat([noise_level] * image.shape[0])\n\n # prepare the initial image in the latent space (noise on which we will do reverse diffusion)\n num_channels_latents = self.vae.config.latent_channels\n batch_size = prompt_embeds.shape[0] // 2\n height, width = image.shape[2:]\n latents = self.get_initial_latents(height, width, num_channels_latents, batch_size)\n\n # denoise latents\n latents = self.denoise_latents(prompt_embeds,\n image,\n timesteps,\n latents,\n noise_level,\n guidance_scale)\n\n # decode latents to get the image into pixel space\n latents = latents.to(torch.float16)\n image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]\n\n # convert to PIL Image format\n image = self.transform_image(image.detach()) # detach to remove any computed gradients\n\n return image","def start(self):\r\n self._instagram_api = InstagramAPI(mongo_api=self._mongo_api)\r\n self._inst_run()","async def init_new_kernel_configuration(self, request, image_id):\n\n try:\n self._image = Image(image_id=image_id)\n except ImageDoesNotExist:\n request.ret_error(IMAGE_MISSING)\n\n request.ret(READY)","def start_kernel(self, **kw):","def __init__(self, arch, classes, pretrained):\n single_model = DRNSeg(arch, classes, pretrained_model=None,\n pretrained=False)\n single_model.load_state_dict(torch.load(pretrained))\n self.model = torch.nn.DataParallel(single_model).cuda()\n self.num_classes = classes","def launch_new_instance():\n import IPython\n\n IPython.Shell.start().mainloop()","def start(instance=\"default\"):\n # initialize logging\n global logger_ic\n logger_ic = infrasim_log.get_logger(LoggerType.ipmi_console.value, instance)\n common.init_logger(instance)\n\n # initialize environment\n env.local_env.quit_flag = False\n common.init_env(instance)\n pid_file = \"{}/{}/.ipmi_console.pid\".format(config.infrasim_home, instance)\n daemon.daemonize(pid_file)\n with open(pid_file, \"r\") as fp:\n logger_ic.info(\"ipmi-console of {} start with pid {}\".\n format(instance, fp.read().strip()))\n\n # parse the sdrs and build all sensors\n sdr.parse_sdrs()\n\n # running thread for each threshold based sensor\n _start_monitor(instance)\n _spawn_sensor_thread()\n _start_console(instance)","def __init__(self, image: np.ndarray) -> None:\n self.image = image","def __init__(self, feature_store=None, batch_size=100):\n if feature_store:\n if type(feature_store) is dict: # A silly way to avoid multiple reading\n self.features = feature_store\n self.image_w = 640\n self.image_h = 480\n self.vfov = 60\n self.feature_size = next(iter(self.features.values())).shape[-1]\n # print('The feature size is %d' % self.feature_size)\n else:\n print('Image features not provided')\n self.features = None\n self.image_w = 640\n self.image_h = 480\n self.vfov = 60\n self.featurized_scans = set([key.split(\"_\")[0] for key in list(self.features.keys())])\n self.sims = []\n self.batch_size = batch_size\n for i in range(batch_size):\n sim = MatterSim.Simulator()\n sim.setRenderingEnabled(False)\n sim.setDiscretizedViewingAngles(True) # Set increment/decrement to 30 degree. (otherwise by radians)\n sim.setCameraResolution(self.image_w, self.image_h)\n sim.setCameraVFOV(math.radians(self.vfov))\n sim.init()\n self.sims.append(sim)","def start(self):\n \n self.thread.start()\n self.state = \"running\"","def _submitInstance( self, imageName, workDir ):\n return S_OK()","def __init__(self, nvim):\n self.nvim = nvim","def _create_instance(cls, hook: EC2Hook):\n conn = hook.get_conn()\n try:\n ec2_client = conn.meta.client\n except AttributeError:\n ec2_client = conn\n\n # We need existed AMI Image ID otherwise `moto` will raise DeprecationWarning.\n images = ec2_client.describe_images()[\"Images\"]\n response = ec2_client.run_instances(MaxCount=1, MinCount=1, ImageId=images[0][\"ImageId\"])\n return response[\"Instances\"][0][\"InstanceId\"]","def spawn(self, instance):\n\n # Update state to inform the nova stack that the VE is launching\n db.instance_set_state(context.get_admin_context(),\n instance['id'],\n power_state.NOSTATE,\n 'launching')\n LOG.debug('instance %s: is launching' % instance['name'])\n\n # Go through the steps of creating a container\n # TODO(imsplitbit): Need to add conditionals around this stuff to make\n # it more durable during failure. And roll back changes made leading\n # up to the error.\n self._cache_image(instance)\n self._create_vz(instance)\n self._set_vz_os_hint(instance)\n self._configure_vz(instance)\n self._set_name(instance)\n self._add_netif(instance)\n self._add_ip(instance)\n self._set_hostname(instance)\n self._set_nameserver(instance)\n self._start(instance)\n self._initial_secure_host(instance)\n \n # Begin making our looping async call\n timer = utils.LoopingCall(f=None)\n\n # I stole this from the libvirt driver but it is appropriate to\n # have this looping timer call so that if a VE doesn't start right\n # away we can defer all of this.\n def _wait_for_boot():\n try:\n state = self.get_info(instance['name'])['state']\n db.instance_set_state(context.get_admin_context(),\n instance['id'], state)\n if state == power_state.RUNNING:\n LOG.debug('instance %s: booted' % instance['name'])\n timer.stop()\n\n except:\n LOG.exception('instance %s: failed to boot' %\n instance['name'])\n db.instance_set_state(context.get_admin_context(),\n instance['id'],\n power_state.SHUTDOWN)\n timer.stop()\n\n timer.f = _wait_for_boot\n return timer.start(interval=0.5, now=True)","def run_instance():\n ami_id = \"ami-04876f29fd3a5e8ba\" # AMI Id\n instance_type = \"t2.micro\" # Instance Type\n tag_specs = [\n {\n 'ResourceType': 'instance',\n 'Tags': [\n {\n 'Key': 'Name',\n 'Value': 'BoB10@ProductDev.sdk.8084'\n }\n ]\n }\n ]\n\n # Instantiate the service resource object\n ec2_resource = session.resource('ec2', region_name=region)\n # Run an instance\n\n instances = ec2_resource.create_instances(ImageId=ami_id, InstanceType=instance_type,\n MaxCount=1, MinCount=1, KeyName='BoB10@ProductDev.8084',\n TagSpecifications=tag_specs, \n SecurityGroupIds=['sg-06b757b4bb272d98f'],\n UserData=assemble_userdata().as_string())\n Instance_id = instances[0].id\n print('\\nInstance Id: ' + Instance_id)\n print('Image Id: ' + instances[0].image_id)\n print('Instance Type: ' + instances[0].instance_type)\n print('State: ' + instances[0].state['Name'])\n return Instance_id","def __init__(self, feature_store=None, batch_size=100):\n if feature_store:\n if type(feature_store) is dict: # A silly way to avoid multiple reading\n self.features = feature_store\n self.image_w = 640\n self.image_h = 480\n self.vfov = 60\n self.feature_size = next(iter(self.features.values())).shape[-1]\n print('The feature size is %d' % self.feature_size)\n else:\n print('Image features not provided')\n self.features = None\n self.image_w = 640\n self.image_h = 480\n self.vfov = 60\n self.featurized_scans = set([key.split(\"_\")[0] for key in list(self.features.keys())])\n self.sims = []\n for i in range(batch_size):\n sim = MatterSim.Simulator()\n sim.setRenderingEnabled(False)\n sim.setDiscretizedViewingAngles(True) # Set increment/decrement to 30 degree. (otherwise by radians)\n sim.setCameraResolution(self.image_w, self.image_h)\n sim.setCameraVFOV(math.radians(self.vfov))\n sim.init()\n self.sims.append(sim)","def launch(\n *,\n key_name: Optional[str],\n instance_type: str,\n ami: str,\n ami_user: str,\n tags: Dict[str, str],\n display_name: Optional[str] = None,\n size_gb: int,\n security_group_name: str,\n instance_profile: Optional[str],\n nonce: str,\n delete_after: datetime.datetime,\n) -> Instance:\n\n if display_name:\n tags[\"Name\"] = display_name\n tags[\"scratch-delete-after\"] = str(delete_after.timestamp())\n tags[\"nonce\"] = nonce\n tags[\"git_ref\"] = git.describe()\n tags[\"ami-user\"] = ami_user\n\n ec2 = boto3.client(\"ec2\")\n groups = ec2.describe_security_groups()\n security_group_id = None\n for group in groups[\"SecurityGroups\"]:\n if group[\"GroupName\"] == security_group_name:\n security_group_id = group[\"GroupId\"]\n break\n\n if security_group_id is None:\n vpcs = ec2.describe_vpcs()\n vpc_id = None\n for vpc in vpcs[\"Vpcs\"]:\n if vpc[\"IsDefault\"] == True:\n vpc_id = vpc[\"VpcId\"]\n break\n if vpc_id is None:\n default_vpc = ec2.create_default_vpc()\n vpc_id = default_vpc[\"Vpc\"][\"VpcId\"]\n securitygroup = ec2.create_security_group(\n GroupName=security_group_name,\n Description=\"Allows all.\",\n VpcId=vpc_id,\n )\n security_group_id = securitygroup[\"GroupId\"]\n ec2.authorize_security_group_ingress(\n GroupId=security_group_id,\n CidrIp=\"0.0.0.0/0\",\n IpProtocol=\"tcp\",\n FromPort=22,\n ToPort=22,\n )\n\n network_interface: InstanceNetworkInterfaceSpecificationTypeDef = {\n \"AssociatePublicIpAddress\": True,\n \"DeviceIndex\": 0,\n \"Groups\": [security_group_id],\n }\n\n say(f\"launching instance {display_name or '(unnamed)'}\")\n with open(ROOT / \"misc\" / \"scratch\" / \"provision.bash\") as f:\n provisioning_script = f.read()\n kwargs: RunInstancesRequestRequestTypeDef = {\n \"MinCount\": 1,\n \"MaxCount\": 1,\n \"ImageId\": ami,\n \"InstanceType\": cast(InstanceTypeType, instance_type),\n \"UserData\": provisioning_script,\n \"TagSpecifications\": [\n {\n \"ResourceType\": \"instance\",\n \"Tags\": [{\"Key\": k, \"Value\": v} for (k, v) in tags.items()],\n }\n ],\n \"NetworkInterfaces\": [network_interface],\n \"BlockDeviceMappings\": [\n {\n \"DeviceName\": \"/dev/sda1\",\n \"Ebs\": {\n \"VolumeSize\": size_gb,\n \"VolumeType\": \"gp3\",\n },\n }\n ],\n \"MetadataOptions\": {\n # Allow Docker containers to access IMDSv2.\n \"HttpPutResponseHopLimit\": 2,\n },\n }\n if key_name:\n kwargs[\"KeyName\"] = key_name\n if instance_profile:\n kwargs[\"IamInstanceProfile\"] = {\"Name\": instance_profile}\n i = boto3.resource(\"ec2\").create_instances(**kwargs)[0]\n\n return i","def __init__(self, image, scale):\n\n # Call the parent init\n super().__init__(image, scale)\n\n # Create a variable to hold our speed. 'angle' is created by the parent\n # The put vehicle to init position\n self.speed = 0\n self.max_speed = 5\n self.respawning = 0\n\n # Mark that we are respawning.\n self.respawn()","def __init__(self):\n self.image_subscriber = rospy.Subscriber('/raspicam_node/image/compressed', CompressedImage, self.imageCallback)\n print 'Waiting for classifier service to come up...'\n rospy.wait_for_service('/classifier_node/classify')\n self.classify_client = rospy.ServiceProxy('/classifier_node/classify', Classify)","def __init__(self, img):\n self.img = img","def __init__(self):\n super().__init__()\n import sklearn\n import sklearn.svm\n self.model = sklearn.svm.NuSVR","def run(self):\n self.cancelled = False\n # get version from IDF object or by parsing the IDF file for it\n\n # Move files into place\n self.epw = self.idf.epw.copy(self.run_dir / \"in.epw\").expand()\n self.idfname = Path(self.idf.savecopy(self.run_dir / \"in.idf\")).expand()\n self.idd = self.idf.iddname.copy(self.run_dir).expand()\n\n # Get executable using shutil.which (determines the extension based on\n # the platform, eg: .exe. And copy the executable to tmp\n slab_exe = shutil.which(\"Slab\", path=self.eplus_home)\n if slab_exe is None:\n log(\n f\"The Slab program could not be found at \" f\"'{self.eplus_home}'\",\n lg.WARNING,\n )\n return\n self.slabexe = Path(slab_exe).copy(self.run_dir)\n self.slabidd = (self.eplus_home / \"SlabGHT.idd\").copy(self.run_dir)\n\n # The GHTin.idf file is copied from the self.include list (added by\n # ExpandObjects. If self.include is empty, no need to run Slab.\n self.include = [Path(file).copy(self.run_dir) for file in self.idf.include]\n if not self.include:\n self.cleanup_callback()\n return\n\n # Run Slab Program\n with logging_redirect_tqdm(loggers=[lg.getLogger(self.idf.name)]):\n with tqdm(\n unit_scale=True,\n miniters=1,\n desc=f\"RunSlab #{self.idf.position}-{self.idf.name}\",\n position=self.idf.position,\n ) as progress:\n\n self.p = subprocess.Popen(\n self.cmd,\n stdout=subprocess.PIPE,\n stderr=subprocess.PIPE,\n shell=True, # can use shell\n cwd=self.run_dir.abspath(),\n )\n start_time = time.time()\n self.msg_callback(\"Begin Slab Temperature Calculation processing . . .\")\n for line in self.p.stdout:\n self.msg_callback(line.decode(\"utf-8\").strip(\"\\n\"))\n progress.update()\n\n # We explicitly close stdout\n self.p.stdout.close()\n\n # Wait for process to complete\n self.p.wait()\n\n # Communicate callbacks\n if self.cancelled:\n self.msg_callback(\"RunSlab cancelled\")\n # self.cancelled_callback(self.std_out, self.std_err)\n else:\n if self.p.returncode == 0:\n self.msg_callback(\n \"RunSlab completed in {:,.2f} seconds\".format(\n time.time() - start_time\n )\n )\n self.success_callback()\n for line in self.p.stderr:\n self.msg_callback(line.decode(\"utf-8\"))\n else:\n self.msg_callback(\"RunSlab failed\")\n self.failure_callback()","def train_main(cls):\n launcher = cls()\n launcher.launch()","def run(self) -> None:\n self._hass.turn_on('scene.{0}'.format(self._args['scene']))","def start_core(c):\n with c.cd('images'):\n print('Starting Core database')\n c.run('sudo docker-compose up -d stellar-core-db', hide='stderr')\n sleep(2)\n\n # setup core database\n # https://www.stellar.org/developers/stellar-core/software/commands.html\n print('Initializing Core database')\n c.run('sudo docker-compose run stellar-core --newdb --forcescp', hide='both')\n\n # setup cache history archive\n print('Initializing Core history archive')\n c.run('sudo docker-compose run stellar-core --newhist cache', hide='both')\n\n # start a local private testnet core\n # https://www.stellar.org/developers/stellar-core/software/testnet.html\n print('Starting Core')\n c.run('sudo docker-compose up -d stellar-core', hide='stderr')","def run_example(self):\n\n print(\"Run: NNStreamer example for face detection.\")\n\n # main loop\n self.loop = GObject.MainLoop()\n\n # init pipeline\n # Currently Only only runs video from webcam. More features TBU.\n self.pipeline = Gst.parse_launch(\n 'v4l2src name=cam_src ! videoconvert ! videoscale ! '\n 'video/x-raw,width=640,height=480,format=RGB ! videoconvert ! xvimagesink name=img_tensor'\n )\n\n # bus and message callback\n bus = self.pipeline.get_bus()\n bus.add_signal_watch()\n bus.connect('message', self.on_bus_message)\n\n # start pipeline\n self.pipeline.set_state(Gst.State.PLAYING)\n self.running = True\n\n self.set_window_title('img_tensor', 'NNStreamer Face Detection Example')\n\n # run main loop\n self.loop.run()\n\n # quit when received eos or error message\n self.running = False\n self.pipeline.set_state(Gst.State.NULL)\n\n bus.remove_signal_watch()","def startService(self):\n self.world.start()","def start(self):\n self.frame = 0\n self._init_level(1)\n self.reward = 0\n self.pcontinue = 1\n self.ghost_speed = self.ghost_speed_init\n return self._make_image(), self.reward, self.pcontinue","def __init__(self, verbose=1, data=None, shape=(512, 512, 35)):\n\n if shape is not None and data is not None:\n if data.shape != shape:\n data = resize(data, shape, mode='constant')\n self.image_raw = data\n self.image_filtered = None\n self.spots = []\n self.SNR = None\n self._verbose = verbose\n self.name = None\n self.mixture_model = None\n self.background = None\n self.mask = None","def run_visualization(image):\n original_im = Image.fromarray(image)\n seg_map = model.run(original_im)\n seg_image = label_to_color_image(seg_map).astype(np.uint8)\n\n return seg_image","def start(self, world):\n self.sense(world)","def from_image(cls, image):\n raise NotImplementedError","def __init__(self) -> None:\n self.registration_method = None\n self.fixed_image = None\n self.moving_image = None\n self.transform = None","def start(self):\n logger.debug('Starting controller')\n pass","def start_instance(instance_id):\n # Instantiate the service resource object\n ec2_resource = session.resource('ec2', region_name=region)\n try:\n # Start an instance\n response = ec2_resource.Instance(instance_id).start(DryRun=False)\n print(response)\n print(\"\\nSuccessfully starting instance: \", instance_id)\n except botocore.exceptions.ClientError as e:\n if e.response['Error']['Code'] == \"InvalidInstanceID.Malformed\":\n print(\"Error: Invalid instance id!!\")\n else:\n raise\n return","def CreateInstance(self):\n\n # Create host instances for cuttlefish device. Currently one host instance\n # has one cuttlefish device. In the future, these logics should be modified\n # to support multiple cuttlefish devices per host instance.\n instance = self._compute_client.GenerateInstanceName(\n build_id=self.build_info.build_id, build_target=self._build_target)\n\n if self._cfg.enable_multi_stage:\n remote_build_id = self.build_info.build_id\n else:\n remote_build_id = self._GetGcsBucketBuildId(\n self.build_info.build_id, self.build_info.release_build_id)\n\n if self._cfg.enable_multi_stage:\n remote_system_build_id = self.system_build_info.build_id\n else:\n remote_system_build_id = self._GetGcsBucketBuildId(\n self.system_build_info.build_id, self.system_build_info.release_build_id)\n\n host_image_name = self._compute_client.GetHostImageName(\n self._cfg.stable_host_image_name,\n self._cfg.stable_host_image_family,\n self._cfg.stable_host_image_project)\n # Create an instance from Stable Host Image\n self._compute_client.CreateInstance(\n instance=instance,\n image_name=host_image_name,\n image_project=self._cfg.stable_host_image_project,\n build_target=self.build_info.build_target,\n branch=self.build_info.branch,\n build_id=remote_build_id,\n kernel_branch=self.kernel_build_info.branch,\n kernel_build_id=self.kernel_build_info.build_id,\n kernel_build_target=self.kernel_build_info.build_target,\n blank_data_disk_size_gb=self._blank_data_disk_size_gb,\n extra_scopes=self._extra_scopes,\n system_build_target=self.system_build_info.build_target,\n system_branch=self.system_build_info.branch,\n system_build_id=remote_system_build_id,\n bootloader_build_target=self.bootloader_build_info.build_target,\n bootloader_branch=self.bootloader_build_info.branch,\n bootloader_build_id=self.bootloader_build_info.build_id)\n\n return instance","def start(self):\n self.__init__()\n self.set_n_players()\n self.init_players()\n self.init_territory_selection_phase()\n self.init_troop_deployment_phase()\n # self.game_phase()","def start_ec2_instance(client, instance_id, hibernate=False):\n\n response = client.start_instances(\n InstanceIds=[instance_id],\n )\n return response","def setup_class(cls):\n super().setup_class()\n cls.detector = cls.faceEngine.createFaceDetector(DetectorType.FACE_DET_DEFAULT)\n cls.headPoseEstimator = cls.faceEngine.createHeadPoseEstimator()\n cls.image = VLImage.load(filename=ONE_FACE)\n cls.detection = TestHeadPose.detector.detectOne(cls.image, detect5Landmarks=True, detect68Landmarks=True)","def start_neuron(cls, neuron, parameters_dict=None):\n if neuron.parameters is not None:\n try:\n neuron.parameters = cls._replace_brackets_by_loaded_parameter(neuron.parameters, parameters_dict)\n except NeuronParameterNotAvailable:\n Utils.print_danger(\"The neuron %s cannot be launched\" % neuron.name)\n return None\n instantiated_neuron = NeuronLauncher.launch_neuron(neuron)\n return instantiated_neuron","def run(self, image):\n start = datetime.datetime.now()\n\n width, height = image.size\n resize_ratio = 1.0 * self.INPUT_SIZE / max(width, height)\n target_size = (int(resize_ratio * width), int(resize_ratio * height))\n resized_image = image.convert('RGB').resize(target_size, Image.ANTIALIAS)\n batch_seg_map = self.sess.run(\n self.OUTPUT_TENSOR_NAME,\n feed_dict={self.INPUT_TENSOR_NAME: [np.asarray(resized_image)]})\n seg_map = batch_seg_map[0]\n\n end = datetime.datetime.now()\n\n diff = end - start\n print(\"Time taken to evaluate segmentation is : \" + str(diff))\n\n return resized_image, seg_map","def setUp(self):\r\n center = [1,1]\r\n size = 1\r\n self.imageinfo = ImageInfo(center, size)","def setUp(self):\r\n center = [1,1]\r\n size = 1\r\n self.imageinfo = ImageInfo(center, size)","def setUp(self):\r\n center = [1,1]\r\n size = 1\r\n self.imageinfo = ImageInfo(center, size)","def setUp(self):\r\n center = [1,1]\r\n size = 1\r\n self.imageinfo = ImageInfo(center, size)","def setUp(self):\r\n center = [1,1]\r\n size = 1\r\n self.imageinfo = ImageInfo(center, size)","def launch_vm(self,\n region: str,\n security_group_id: str,\n subnet_id: str,\n image_id: str,\n instance_type: str,\n instance_profile: str,\n **kwargs) -> Tuple[str, str, str, str]:\n raise NotImplementedError()","def launch_sagemaker_local_ec2_instance(image, ami_id, ec2_key_name, region):\n instance_type = assign_sagemaker_local_job_instance_type(image)\n instance_name = image.split(\"/\")[-1]\n instance = ec2_utils.launch_instance(\n ami_id,\n region=region,\n ec2_key_name=ec2_key_name,\n instance_type=instance_type,\n # EIA does not have SM Local test\n ei_accelerator_type=None,\n user_data=None,\n iam_instance_profile_name=ec2_utils.EC2_INSTANCE_ROLE_NAME,\n instance_name=f\"sm-local-{instance_name}\",\n )\n instance_id = instance[\"InstanceId\"]\n public_ip_address = ec2_utils.get_public_ip(instance_id, region=region)\n ec2_utils.check_instance_state(instance_id, state=\"running\", region=region)\n ec2_utils.check_system_state(instance_id, system_status=\"ok\", instance_status=\"ok\", region=region)\n return instance_id, public_ip_address"],"string":"[\n \"def singularity_build(self, path, image):\\n Client.build(os.path.join(\\n path, 'singularity.def'\\n ), self.generate_image_name(image))\",\n \"def start_ssm(self, ssm_image):\\n pass\",\n \"def create_instance_by_image(self):\\n print '# Start a new instance based on an existing AMI'\\n ami = raw_input('Enter AMI (empty to cancel): ')\\n\\n # Cancel\\n if not ami:\\n print 'Operation cancelled'\\n return\\n\\n # Start the instance\\n if self.compute.create_instance_by_image(ami):\\n print 'Instance started!'\\n else:\\n print 'It was not possible to create an instance with the given AMI'\",\n \"def start_instance(InstanceId=None):\\n pass\",\n \"def startami(image, instancetype, accesskey, secretkey, pkname):\\n if not is_valid_instance_type(image, instancetype):\\n raise ValueError(\\\"Invalid instance type: '%s'\\\" % instancetype)\\n\\n conn = EC2Connection(accesskey, secretkey)\\n image = conn.get_image(get_image_id(image))\\n reservation = image.run(instance_type=instancetype, key_name=pkname)\\n instance = reservation.instances[0]\\n\\n waitForInstanceToRun(instance)\\n\\n # [AN] call script instanceStartup.py\\n return str(instance.dns_name)\",\n \"def start_instance(self):\\n instance_id = self._choose_among_stopped_instances()\\n\\n # Cancel\\n if not instance_id:\\n print 'Operation cancelled'\\n return\\n\\n print '# Starting the instance \\\"%s\\\"' % instance_id\\n if self.compute.start_instance(instance_id):\\n print 'The instance has been started'\\n else:\\n print 'The instance could not be started'\",\n \"def __init__(self, sl):\\n threading.Thread.__init__(self)\\n self.alpha = 0.5\\n self.running = True\\n self.slam = sl\\n self.image = None\\n self.scale = 5.0\\n self.centre = np.zeros(2)\\n self.mouse = np.zeros(2)\\n self.dimensions = np.array([1920, 1080])\\n self.tracking = TrackingMode.FREE\\n self.map_mode = MapMode.DIST\",\n \"def run(self, reuse=False):\\n build = True\\n if 'shub://' in self.action['uses']:\\n image = self.action['uses']\\n build = False\\n elif './' in self.action['uses']:\\n image = 'action/' + os.path.basename(self.action['uses'])\\n singularityfile_path = os.path.join(\\n os.getcwd(), self.action['uses'])\\n else:\\n image = '/'.join(self.action['uses'].split('/')[:2])\\n singularityfile_path = os.path.join(self.action['repo_dir'],\\n self.action['action_dir'])\\n\\n if not reuse:\\n if self.singularity_exists():\\n self.singularity_rm()\\n if build:\\n self.singularity_build(singularityfile_path, image)\\n else:\\n self.singularity_pull(image)\\n else:\\n if not self.singularity_exists():\\n if build:\\n self.singularity_build(singularityfile_path, image)\\n else:\\n self.singularity_pull(image)\\n\\n e = self.singularity_start(image)\\n\\n if e != 0:\\n pu.fail('Action {} failed!\\\\n'.format(self.action['name']))\",\n \"def start_sml():\\n launchfile = basepath + '/launch/teststarter.launch'\\n\\n uuid = roslaunch.rlutil.get_or_generate_uuid(None, False)\\n #print roslaunch.rlutil.check_roslaunch(launchfile)\\n #roslaunch.configure_logging(uuid)\\n launch = roslaunch.parent.ROSLaunchParent(uuid, [launchfile])\\n launch.start()\",\n \"def makeInstanceFromImage(self , imageid, initialconfig, instancename):\\n self.initCreate(initialconfig)\\n ip = self.launchMinipadServer()\\n if ip:\\n self.__server_ip = ip\\n disk = self.createDisk(instancename)\\n self.attachDiskToMinipad(disk )\\n \\n if self.startConversion(imageid , self.__server_ip) == False:\\n return None\\n\\n self.detachDiskFromMinipad(disk)\\n vm = self.createVM(disk , instancename)\\n return vm\",\n \"def start(self, state):\\n return self.brain.start(state)\",\n \"def do_start(self, arg):\\n args = arg.split(\\\" \\\")\\n self.model.initialise(args[0])\\n self.model.run()\",\n \"def FullSingleSceneInference(self, image_file, USE_MODEL = 'DEFAULT'): \\n if USE_MODEL != 'DEFAULT':\\n (detector_file, classes_file, map_file, \\n model_name, model_type) = self.DownloadModel(USE_MODEL)\\n self.SetActiveGraph(detector_graph_file = detector_file,\\n model_name = model_name,\\n classes_file = classes_file,\\n map_file = map_file,\\n model_type = model_type\\n )\\n \\n b,s,c = self.AnalyzeSingleImage(image_file = image_file)\\n self.DrawImage(image_file = image_file, boxes = b,\\n scores = s, classes = c\\n )\\n return\",\n \"def launch_instance(cloud):\\n js = _get_jetstream_conn()\\n\\n sgs = ['CloudLaunchDefault']\\n kp_name = \\\"cloudman_key_pair\\\"\\n inst_size = 'm1.small'\\n network_id = '86a1c3e8-b1fb-41f3-bcaf-8334567fe989'\\n lc = js.compute.instances.create_launch_config()\\n lc.add_network_interface(network_id)\\n\\n img_id = '2cf07e4a-62a8-41c2-9282-f3c53962f296' # Gxy Standalone 161021b01\\n name = 'ea-galaxy-{0}'.format(strftime(\\\"%m-%d-%H-%M\\\", localtime()))\\n\\n i = js.compute.instances.create(\\n name, img_id, inst_size, security_groups=sgs, launch_config=lc,\\n key_pair=kp_name)\\n return i\",\n \"def __init__(self, experiment) -> None:\\n self.logger = get_logger()\\n np.random.seed(experiment.seed)\\n self.gpu_imported = False\\n\\n from sktime.forecasting.naive import NaiveForecaster # type: ignore\\n\\n # Disable container if certain features are not supported but enforced ----\\n dummy = NaiveForecaster()\\n self.active = _check_enforcements(forecaster=dummy, experiment=experiment)\\n if not self.active:\\n return\\n\\n self.seasonality_present = experiment.seasonality_present\\n self.sp = experiment.primary_sp_to_use\\n\\n if self.sp == 1:\\n self.active = False\\n return\\n\\n args = self._set_args\\n tune_args = self._set_tune_args\\n tune_grid = self._set_tune_grid\\n tune_distributions = self._set_tune_distributions\\n leftover_parameters_to_categorical_distributions(tune_grid, tune_distributions)\\n\\n eq_function = lambda x: type(x) is NaiveForecaster and x.sp != 1\\n\\n super().__init__(\\n id=\\\"snaive\\\",\\n name=\\\"Seasonal Naive Forecaster\\\",\\n class_def=NaiveForecaster,\\n args=args,\\n tune_grid=tune_grid,\\n tune_distribution=tune_distributions,\\n tune_args=tune_args,\\n is_gpu_enabled=self.gpu_imported,\\n eq_function=eq_function,\\n )\",\n \"def main():\\n original = SimpleImage('images/mt-rainier.jpg')\\n original.show()\\n reflected = make_reflected('images/mt-rainier.jpg')\\n reflected.show()\",\n \"def _start(self, instance):\\n try:\\n # Attempt to start the VE.\\n # NOTE: The VE will throw a warning that the hostname is invalid\\n # if it isn't valid. This is logged in LOG.error and is not\\n # an indication of failure.\\n _, err = utils.execute('sudo', 'vzctl', 'start', instance['id'])\\n if err:\\n LOG.error(err)\\n except ProcessExecutionError as err:\\n LOG.error(err)\\n raise exception.Error('Failed to start %d' % instance['id'])\\n\\n # Set instance state as RUNNING\\n db.instance_set_state(context.get_admin_context(),\\n instance['id'],\\n power_state.RUNNING)\\n return True\",\n \"def __init__(self, image_size):\\n super(SiameseDiscriminator, self).__init__()\\n self.cnn1 = nn.Sequential(\\n nn.ReflectionPad2d(1),\\n nn.Conv2d(3, 4, kernel_size=3),\\n nn.LeakyReLU(0.1, inplace=True),\\n nn.BatchNorm2d(4),\\n nn.Dropout2d(p=.2),\\n\\n nn.ReflectionPad2d(1),\\n nn.Conv2d(4, 8, kernel_size=3),\\n nn.LeakyReLU(0.1, inplace=True),\\n nn.BatchNorm2d(8),\\n nn.Dropout2d(p=.2),\\n\\n nn.ReflectionPad2d(1),\\n nn.Conv2d(8, 8, kernel_size=3),\\n nn.LeakyReLU(0.1, inplace=True),\\n nn.BatchNorm2d(8),\\n nn.Dropout2d(p=.2))\\n\\n self.fc1 = nn.Sequential(\\n nn.Linear(8 * image_size * image_size, 500),\\n nn.LeakyReLU(0.1, inplace=True),\\n\\n nn.Linear(500, 500),\\n nn.LeakyReLU(0.1, inplace=True),\\n\\n nn.Linear(500, 15))\",\n \"def _start(self):\\n if self._classifier is None:\\n self._classifier = TFSlimClassifier(self.config)\\n self._classifier.__enter__()\",\n \"def start_image_builder(Name=None, AppstreamAgentVersion=None):\\n pass\",\n \"def create_instance(self, image='ami-660c3023', key_name='linuxonEC2', instance_type='t1.micro', security_groups=['default']):\\n return self.conn.run_instances(image,\\n key_name=key_name,\\n instance_type=instance_type,\\n security_groups=security_groups).instances[0]\",\n \"def __init__(self):\\n self.svclassifier = SVC(kernel='linear')\",\n \"def start(update: Update, context: CallbackContext) -> None:\\n update.message.reply_text('Hi send an image to classify!')\",\n \"def start_SLP_server(self):\\n import slp\\n \\n s = self.op.get_value('mode_params')\\n if len(s) == 0:\\n print 'No parameter received'\\n params = {}\\n else:\\n l_p = self.parse_params(s)\\n params = {'action':[np.int(l_p[0])]}\\n if len(l_p) == 2:\\n params['image_feature_layer_name'] = l_p[1]\\n slpserver = slp.SLPServerTrial(self, params)\\n slpserver.start()\",\n \"def start(self: AutoScaler) -> AutoScalerState:\\n log.info(f'Autoscale start (policy: {self.policy.name.lower()}, init-size: {self.init_size})')\\n log.debug(f'Autoscale launcher: {self.launcher}')\\n return AutoScalerState.INIT\",\n \"def start(self):\\n with self._lock:\\n if not self.started():\\n self._started = None\\n getattr(self.factory, 'start_' + self.class_name())(self)\",\n \"def sex(image):\\n \\n # run sextractor with different default parameters\\n print('running SExtractor to {}...'.format(image))\\n P = Popen('sextractor -c goto.sex '+image+' -CATALOG_NAME '+image[:-5]+'.cat', shell=True)\\n P.wait()\",\n \"def init_with_display_image(cls, d, image, is_sub=False):\\n # init basic display parameters\\n scene = cls()\\n scene.illuminant = Illuminant(wave=d.wave)\\n scene.dist = d.dist # viewing distance\\n\\n # compute horizontal field of view\\n scene.fov = 2 * rad_to_deg(atan2(image.shape[1] * d.meters_per_dot / 2, d.dist))\\n\\n # set illuminant as spd of display\\n scene.illuminant.photons = energy_to_quanta(d.white_spd, d.wave)\\n\\n # gamma distortion for the input image\\n image = d.lookup_digital((image * (d.n_levels - 1)).astype(int))\\n\\n # sub-pixel rendering if required\\n if is_sub:\\n image = d.compute(image)\\n\\n # compute radiance from image\\n out_sz = np.concatenate((np.array(image.shape[0:2]), [d.wave.size]))\\n image = rgb_to_xw_format(image)\\n scene.photons = energy_to_quanta(np.dot(image, d.spd.T), d.wave)\\n\\n # add ambient quanta to scene photons\\n scene.photons += d.ambient\\n\\n # reshape photons\\n scene.photons = scene.photons.reshape(out_sz, order=\\\"F\\\")\\n return scene\",\n \"def create_scene(self, ):\\n self.scene = create_scene(\\n self.opt.splats_img_size, self.opt.splats_img_size, self.opt.fovy,\\n self.opt.focal_length, self.opt.n_splats)\",\n \"def process_image(self):\\n\\n detect.main(self.nn_args)\",\n \"def __init__(self, image):\\n self.image = image\",\n \"def run_visualization(SAMPLE_IMAGE):\\n original_im = Image.open(SAMPLE_IMAGE)\\n global seg_map\\n seg_map = MODEL.run(original_im)\\n vis_segmentation(original_im, seg_map)\",\n \"def __init__(self, image_file, **kwargs):\\n\\n # Mapping of serial numbers to models for known detectors\\n self._sn_to_model = {\\n 401: \\\"Q4U\\\",\\n 402: \\\"Q4\\\",\\n 414: \\\"Q4\\\",\\n 423: \\\"Q4R\\\",\\n 428: \\\"Q4R\\\",\\n 429: \\\"Q4\\\", # or Q4R?\\n 441: \\\"Q210\\\",\\n 442: \\\"Q210\\\",\\n 443: \\\"Q210\\\",\\n 444: \\\"Q210\\\", # or Q210R?\\n 445: \\\"Q210\\\",\\n 446: \\\"Q210\\\",\\n 447: \\\"Q210\\\",\\n 448: \\\"Q210\\\",\\n 457: \\\"Q210R\\\",\\n 471: \\\"Q270\\\",\\n 472: \\\"Q270\\\",\\n 474: \\\"Q270\\\",\\n 901: \\\"Q210\\\",\\n 905: \\\"Q315\\\",\\n 907: \\\"Q315R\\\", # or Q315?\\n 913: \\\"Q315\\\",\\n 917: \\\"Q315R\\\",\\n 923: \\\"Q315R\\\",\\n 925: \\\"Q315\\\",\\n 926: \\\"Q315R\\\",\\n 928: \\\"Q315R\\\",\\n 931: \\\"Q315R\\\",\\n 933: \\\"Q315R\\\",\\n }\\n\\n super().__init__(image_file, **kwargs)\",\n \"def start_test_instance(test_name=None):\\n env.warn_only = True\\n if test_name is not None:\\n instances = [test_name]\\n else:\\n output = run('ls -1 %s' % env.site_root)\\n instances = [x.strip() for x in output.split(\\\"\\\\n\\\")]\\n for item in instances:\\n sudo(\\\"start %s\\\" % item.strip())\",\n \"def __init__(self, evaluation_only=False, seed=0):\\n os = platform.system()\\n if os == 'Darwin':\\n file_name = 'Soccer.app'\\n elif os == 'Linux':\\n file_name = 'Soccer_Linux/Soccer.x86_64'\\n self.env = UnityEnvironment(file_name='unity_envs/' + file_name, seed=seed)\\n self.brain_names = self.env.brain_names\\n self.evaluation_only = evaluation_only\",\n \"def start_kernel(self, kernel_name=None, **kwargs):\",\n \"def start(self):\\n try:\\n import os\\n import io\\n from PIL import Image\\n from skimage.io import imsave\\n from kespryml_roof_wireframe.inference import WireframeNet\\n\\n os.environ['KMP_DUPLICATE_LIB_OK']='True'\\n except Exception as e:\\n print('Not Able to import: ' + e)\\n\\n try:\\n with S3() as s3:\\n model_path = s3.get(self.model_path).path\\n self.model = WireframeNet(model_weight_path=model_path)\\n except Exception as e:\\n print('Not able to load the model.' + e)\\n \\n print(\\\"Model Loaded...\\\")\\n\\n try:\\n with S3() as s3:\\n ortho_image_path = s3.get(os.path.join(self.source_dir, self.mission_id, 'preview_products', 'orthomosaic.tif')).path\\n dsm_color_image_path = s3.get(os.path.join(self.source_dir, self.mission_id, 'preview_products', 'dsm_colored.tif')).path\\n\\n os.system('gdal_translate -of GTiff -r cubicspline -outsize {} {} {} {}'.format(800, 1024, ortho_image_path, os.path.join(os.path.dirname('__file__'), 'orthomosaic_resized.tif')))\\n file_stream = io.BytesIO()\\n im = Image.open(os.path.join(os.path.dirname('__file__'), 'orthomosaic_resized.tif'))\\n im.save(file_stream, format='tiff')\\n s3.put(os.path.join(self.save_path, 'orthomosaic_resized.tif'), file_stream.getvalue(), overwrite=True)\\n os.system('rm -rf {}'.format(os.path.join(os.path.dirname('__file__'), 'orthomosaic_resized.tif')))\\n\\n os.system('gdal_translate -of GTiff -r cubicspline -outsize {} {} {} {}'.format(800, 1024, dsm_color_image_path, os.path.join(os.path.dirname('__file__'), 'dsm_colored_resized.tif')))\\n file_stream = io.BytesIO()\\n im = Image.open(os.path.join(os.path.dirname('__file__'), 'dsm_colored_resized.tif'))\\n im.save(file_stream, format='tiff')\\n s3.put(os.path.join(self.save_path, 'dsm_colored_resized.tif'), file_stream.getvalue(), overwrite=True)\\n os.system('rm -rf {}'.format(os.path.join(os.path.dirname('__file__'), 'dsm_colored_resized.tif')))\\n ortho_image_path = s3.get(os.path.join(self.save_path, 'orthomosaic_resized.tif')).path\\n dsm_color_image_path = s3.get(os.path.join(self.save_path, 'dsm_colored_resized.tif')).path\\n\\n self.mask = self.model.predict(ortho_image_path=ortho_image_path, dsm_col_image_path=dsm_color_image_path)\\n\\n file_stream = io.BytesIO()\\n im = Image.fromarray(self.mask)\\n im.save(file_stream, format='tiff')\\n s3.put(os.path.join(self.save_path, 'output.tif'), file_stream.getvalue(), overwrite=True)\\n \\n\\n except Exception as e:\\n print('Not able preprocess Image.'+ e)\\n\\n print('Finished Processing.')\\n self.next(self.end)\",\n \"def launch_instance(ec2, ami, itype, kp_name, sec_group_name):\\n\\n\\n instance = ec2.run_instances(\\n ami,\\n key_name=kp_name,\\n instance_type=itype,\\n security_groups=[sec_group_name]\\n ).instances[0]\\n\\n while instance.state != 'running':\\n sys.stdout.write('Waiting for instance: {}, at DNS: {} to start\\\\n'.format(instance.id,\\n str(instance.dns_name).split('.')[0]))\\n time.sleep(5)\\n instance.update()\\n\\n sys.stdout.write('\\\\nSuccess! EC2 Instance Launched \\\\nInstance_Type: {} in {}'.format(instance.instance_type,\\n instance.placement))\\n return instance\",\n \"def launch_on_jetstream():\\n launched = launch_instance(\\\"Jetstream\\\")\\n session.attributes['instance_id'] = launched.id\\n session.attributes['public_ip'] = None\\n session.attributes['status'] = None\\n\\n msg = \\\"An instance is starting. Would you like to check its status?\\\"\\n return question(msg)\",\n \"def quick_instance(self, name, image, instance_type, env_tag='dev', zone_tag='starwatts', os_tag='debian', sg_id=None,\\n private=True, extra_sg_ids=None, extra_tags=None, terminate_on_shutdown=False,\\n debug=False):\\n # Debug setting\\n if debug:\\n logging.basicConfig(level=logging.DEBUG)\\n\\n # Preliminary tests\\n try:\\n ami = self.get_image(image_id=image)\\n except EC2ResponseError:\\n logging.error(\\\"The image {} could not be found. Aborting.\\\".format(image))\\n return\\n print(\\\"Using AMI {} : {}\\\".format(image, ami.name))\\n if len(self.get_only_instances(filters={'tag:name': name, 'instance-state-name': 'running'})) > 0 or \\\\\\n len(self.get_only_instances(filters={'tag:name': name, 'instance-state-name': 'stopped'})) > 0:\\n logging.error(\\\"An instance with the same name ({}) already exists. Aborting.\\\".format(name))\\n return\\n logging.debug(\\\"Test passed : No instance has the same 'name' tag.\\\")\\n if self.keypair_exists(name):\\n logging.error(\\\"A keypair with the same name ({}) already exists. Aborting.\\\".format(name))\\n return\\n logging.debug(\\\"Test passed : No keypair was found with the same name.\\\")\\n if sg_id is None:\\n if self.security_group_exists(name=name):\\n logging.error(\\\"A security group with the same name ({}) already exists. Aborting.\\\".format(name))\\n return\\n logging.debug(\\\"Test passed : No security group was found with the same name.\\\")\\n\\n # Tags generation\\n logging.debug(\\\"Generating tags to apply.\\\")\\n tags = dict(name=name, os=os_tag, env=env_tag, zone=zone_tag, privacy='true' if private else 'false')\\n if extra_tags is not None:\\n tags.update(extra_tags)\\n print(\\\"Tags : {}\\\".format(tags))\\n\\n # Fetching needed security groups (bastion and zabbix)\\n standard_sg = self.get_all_security_groups(groupnames=['standard'])\\n if len(standard_sg) != 1:\\n logging.error(\\\"Multiple or no security group was found for the 'bastion' search. Aborting.\\\")\\n return\\n standard_sg = standard_sg[0]\\n logging.debug(\\\"The following security group was found for 'standard : {} {}\\\".format(standard_sg.id,\\n standard_sg.description))\\n\\n # Security group creation\\n if sg_id is None:\\n sg = self.create_security_group(name, \\\"SG applied to {} VM\\\".format(name))\\n sg_id = sg.id\\n\\n sg_ids = [sg_id, standard_sg.id, ]\\n # Using the extra security groups if any\\n if extra_sg_ids is not None:\\n logging.debug(\\\"Extra security groups to add : {}\\\".format(extra_sg_ids))\\n sg_ids.extend(extra_sg_ids)\\n logging.debug(\\\"Security Groups : {}\\\".format(sg_ids))\\n\\n user_data = \\\"-----BEGIN OUTSCALE SECTION-----\\\\nprivate_only=true\\\\n-----END OUTSCALE SECTION-----\\\" if private else \\\"\\\"\\n logging.debug(\\\"Creating keypair.\\\")\\n kp = self.create_key_pair(key_name=name)\\n fp = os.path.join(os.path.expanduser('~/.ssh'), '%s.pem' % kp.name)\\n with open(fp, 'wb') as fd:\\n fd.write(bytes(kp.material, \\\"UTF-8\\\"))\\n logging.debug(\\\"Keypair written to ~/.ssh/{}.pem\\\".format(name))\\n\\n resa = self.run_instances(image_id=image, key_name=name, security_groups=sg_ids, instance_type=instance_type,\\n user_data=user_data,\\n instance_initiated_shutdown_behavior='terminate' if terminate_on_shutdown else 'stop')\\n inst = resa.instances[0]\\n logging.debug(\\\"Adding tags to the newly created machine.\\\")\\n inst.add_tags(tags)\\n return inst\",\n \"def __init__(self, isoImagePath):\\n nrvr.distros.common.kickstart.DistroIsoImage.__init__(self, isoImagePath)\",\n \"def start_notebook_instance(NotebookInstanceName=None):\\n pass\",\n \"def main():\\n rospy.init_node('image_to_pointcloud')\\n rospy.loginfo(\\\"Starting sonar image to pointcloud node...\\\")\\n converter = ImageConverter()\\n\\n rate = rospy.Rate(10)\\n rospy.sleep(3.0)\\n while not rospy.is_shutdown():\\n converter.proc_and_pub_pointcloud()\\n rate.sleep()\",\n \"def singledish_observe_image(image, pixel_scale, beam, boundary='fill'):\\n\\n if hasattr(image, 'wcs'):\\n singledish_im = image.convolve_to(beam, boundary=boundary)\\n\\n else:\\n kernel = beam.as_kernel(pixel_scale)\\n\\n # create the single-dish map by convolving the image with a FWHM=40\\\" kernel\\n # (this interpretation is much easier than the sharp-edged stuff in fourier\\n # space because the kernel is created in real space)\\n singledish_im = convolution.convolve_fft(image,\\n kernel=kernel,\\n boundary=boundary,\\n fill_value=image.mean())\\n\\n return singledish_im\",\n \"def __init__(__self__, *,\\n additional_info: Optional[pulumi.Input[str]] = None,\\n affinity: Optional[pulumi.Input[str]] = None,\\n availability_zone: Optional[pulumi.Input[str]] = None,\\n block_device_mappings: Optional[pulumi.Input[Sequence[pulumi.Input['InstanceBlockDeviceMappingArgs']]]] = None,\\n cpu_options: Optional[pulumi.Input['InstanceCpuOptionsArgs']] = None,\\n credit_specification: Optional[pulumi.Input['InstanceCreditSpecificationArgs']] = None,\\n disable_api_termination: Optional[pulumi.Input[bool]] = None,\\n ebs_optimized: Optional[pulumi.Input[bool]] = None,\\n elastic_gpu_specifications: Optional[pulumi.Input[Sequence[pulumi.Input['InstanceElasticGpuSpecificationArgs']]]] = None,\\n elastic_inference_accelerators: Optional[pulumi.Input[Sequence[pulumi.Input['InstanceElasticInferenceAcceleratorArgs']]]] = None,\\n enclave_options: Optional[pulumi.Input['InstanceEnclaveOptionsArgs']] = None,\\n hibernation_options: Optional[pulumi.Input['InstanceHibernationOptionsArgs']] = None,\\n host_id: Optional[pulumi.Input[str]] = None,\\n host_resource_group_arn: Optional[pulumi.Input[str]] = None,\\n iam_instance_profile: Optional[pulumi.Input[str]] = None,\\n image_id: Optional[pulumi.Input[str]] = None,\\n instance_initiated_shutdown_behavior: Optional[pulumi.Input[str]] = None,\\n instance_type: Optional[pulumi.Input[str]] = None,\\n ipv6_address_count: Optional[pulumi.Input[int]] = None,\\n ipv6_addresses: Optional[pulumi.Input[Sequence[pulumi.Input['InstanceIpv6AddressArgs']]]] = None,\\n kernel_id: Optional[pulumi.Input[str]] = None,\\n key_name: Optional[pulumi.Input[str]] = None,\\n launch_template: Optional[pulumi.Input['InstanceLaunchTemplateSpecificationArgs']] = None,\\n license_specifications: Optional[pulumi.Input[Sequence[pulumi.Input['InstanceLicenseSpecificationArgs']]]] = None,\\n monitoring: Optional[pulumi.Input[bool]] = None,\\n network_interfaces: Optional[pulumi.Input[Sequence[pulumi.Input['InstanceNetworkInterfaceArgs']]]] = None,\\n placement_group_name: Optional[pulumi.Input[str]] = None,\\n private_dns_name_options: Optional[pulumi.Input['InstancePrivateDnsNameOptionsArgs']] = None,\\n private_ip_address: Optional[pulumi.Input[str]] = None,\\n propagate_tags_to_volume_on_creation: Optional[pulumi.Input[bool]] = None,\\n ramdisk_id: Optional[pulumi.Input[str]] = None,\\n security_group_ids: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,\\n security_groups: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,\\n source_dest_check: Optional[pulumi.Input[bool]] = None,\\n ssm_associations: Optional[pulumi.Input[Sequence[pulumi.Input['InstanceSsmAssociationArgs']]]] = None,\\n subnet_id: Optional[pulumi.Input[str]] = None,\\n tags: Optional[pulumi.Input[Sequence[pulumi.Input['InstanceTagArgs']]]] = None,\\n tenancy: Optional[pulumi.Input[str]] = None,\\n user_data: Optional[pulumi.Input[str]] = None,\\n volumes: Optional[pulumi.Input[Sequence[pulumi.Input['InstanceVolumeArgs']]]] = None):\\n if additional_info is not None:\\n pulumi.set(__self__, \\\"additional_info\\\", additional_info)\\n if affinity is not None:\\n pulumi.set(__self__, \\\"affinity\\\", affinity)\\n if availability_zone is not None:\\n pulumi.set(__self__, \\\"availability_zone\\\", availability_zone)\\n if block_device_mappings is not None:\\n pulumi.set(__self__, \\\"block_device_mappings\\\", block_device_mappings)\\n if cpu_options is not None:\\n pulumi.set(__self__, \\\"cpu_options\\\", cpu_options)\\n if credit_specification is not None:\\n pulumi.set(__self__, \\\"credit_specification\\\", credit_specification)\\n if disable_api_termination is not None:\\n pulumi.set(__self__, \\\"disable_api_termination\\\", disable_api_termination)\\n if ebs_optimized is not None:\\n pulumi.set(__self__, \\\"ebs_optimized\\\", ebs_optimized)\\n if elastic_gpu_specifications is not None:\\n pulumi.set(__self__, \\\"elastic_gpu_specifications\\\", elastic_gpu_specifications)\\n if elastic_inference_accelerators is not None:\\n pulumi.set(__self__, \\\"elastic_inference_accelerators\\\", elastic_inference_accelerators)\\n if enclave_options is not None:\\n pulumi.set(__self__, \\\"enclave_options\\\", enclave_options)\\n if hibernation_options is not None:\\n pulumi.set(__self__, \\\"hibernation_options\\\", hibernation_options)\\n if host_id is not None:\\n pulumi.set(__self__, \\\"host_id\\\", host_id)\\n if host_resource_group_arn is not None:\\n pulumi.set(__self__, \\\"host_resource_group_arn\\\", host_resource_group_arn)\\n if iam_instance_profile is not None:\\n pulumi.set(__self__, \\\"iam_instance_profile\\\", iam_instance_profile)\\n if image_id is not None:\\n pulumi.set(__self__, \\\"image_id\\\", image_id)\\n if instance_initiated_shutdown_behavior is not None:\\n pulumi.set(__self__, \\\"instance_initiated_shutdown_behavior\\\", instance_initiated_shutdown_behavior)\\n if instance_type is not None:\\n pulumi.set(__self__, \\\"instance_type\\\", instance_type)\\n if ipv6_address_count is not None:\\n pulumi.set(__self__, \\\"ipv6_address_count\\\", ipv6_address_count)\\n if ipv6_addresses is not None:\\n pulumi.set(__self__, \\\"ipv6_addresses\\\", ipv6_addresses)\\n if kernel_id is not None:\\n pulumi.set(__self__, \\\"kernel_id\\\", kernel_id)\\n if key_name is not None:\\n pulumi.set(__self__, \\\"key_name\\\", key_name)\\n if launch_template is not None:\\n pulumi.set(__self__, \\\"launch_template\\\", launch_template)\\n if license_specifications is not None:\\n pulumi.set(__self__, \\\"license_specifications\\\", license_specifications)\\n if monitoring is not None:\\n pulumi.set(__self__, \\\"monitoring\\\", monitoring)\\n if network_interfaces is not None:\\n pulumi.set(__self__, \\\"network_interfaces\\\", network_interfaces)\\n if placement_group_name is not None:\\n pulumi.set(__self__, \\\"placement_group_name\\\", placement_group_name)\\n if private_dns_name_options is not None:\\n pulumi.set(__self__, \\\"private_dns_name_options\\\", private_dns_name_options)\\n if private_ip_address is not None:\\n pulumi.set(__self__, \\\"private_ip_address\\\", private_ip_address)\\n if propagate_tags_to_volume_on_creation is not None:\\n pulumi.set(__self__, \\\"propagate_tags_to_volume_on_creation\\\", propagate_tags_to_volume_on_creation)\\n if ramdisk_id is not None:\\n pulumi.set(__self__, \\\"ramdisk_id\\\", ramdisk_id)\\n if security_group_ids is not None:\\n pulumi.set(__self__, \\\"security_group_ids\\\", security_group_ids)\\n if security_groups is not None:\\n pulumi.set(__self__, \\\"security_groups\\\", security_groups)\\n if source_dest_check is not None:\\n pulumi.set(__self__, \\\"source_dest_check\\\", source_dest_check)\\n if ssm_associations is not None:\\n pulumi.set(__self__, \\\"ssm_associations\\\", ssm_associations)\\n if subnet_id is not None:\\n pulumi.set(__self__, \\\"subnet_id\\\", subnet_id)\\n if tags is not None:\\n pulumi.set(__self__, \\\"tags\\\", tags)\\n if tenancy is not None:\\n pulumi.set(__self__, \\\"tenancy\\\", tenancy)\\n if user_data is not None:\\n pulumi.set(__self__, \\\"user_data\\\", user_data)\\n if volumes is not None:\\n pulumi.set(__self__, \\\"volumes\\\", volumes)\",\n \"def start(self) -> UnityEnvironment:\\n self.env = self.get_env(self.file)\\n time.sleep(2)\\n self.brain_name = self.env.brain_names[0]\\n\\n brain = self.env.brains[self.brain_name]\\n self.action_size = brain.vector_action_space_size\\n env_info = self.env.reset(train_mode=True)[self.brain_name]\\n states = env_info.vector_observations\\n self.n_agents, self.state_size = states.shape\\n return self.env\",\n \"def __init__(self, batch_size=100):\\n \\n self.features = None\\n self.image_w = 320\\n self.image_h = 320\\n self.vfov = 60\\n # self.featurized_scans = set([key.split(\\\"_\\\")[0] for key in list(self.features.keys())])\\n self.sims = []\\n for i in range(batch_size):\\n sim = MatterSim.Simulator()\\n # sim.setRenderingEnabled(True)\\n sim.setDiscretizedViewingAngles(True) # Set increment/decrement to 30 degree. (otherwise by radians)\\n sim.setCameraResolution(self.image_w, self.image_h)\\n sim.setCameraVFOV(math.radians(self.vfov))\\n sim.init()\\n self.sims.append(sim)\\n\\n print('finished')\",\n \"async def start_ec2_instance(self, env):\\n instanceDef= {\\n 'AWS_AMI_ID': os.getenv(\\\"AWS_AMI_ID\\\"),\\n 'AWS_KEYNAME': os.getenv(\\\"AWS_KEYNAME\\\"),\\n 'AWS_SECURITY_GROUP': os.getenv('AWS_SECURITY_GROUP'),\\n 'AWS_SUBNET': os.getenv(\\\"AWS_SUBNET\\\"),\\n 'DryRun':False,\\n 'AWS_INSTANCE_NAME': 'Jupyter',\\n 'AWS_IAM_ARN': os.getenv('AWS_IAM_ARN')\\n }\\n \\n self.log.debug('building instance')\\n ip = await self.buildInstance(instanceDef, env)\\n return ip\",\n \"def run_single_camera(cam):\\r\\n try:\\r\\n result = True\\r\\n err = False\\r\\n\\r\\n # Retrieve TL device nodemap and print device information\\r\\n nodemap_tldevice = cam.GetTLDeviceNodeMap()\\r\\n\\r\\n #result &= print_device_info(nodemap_tldevice)\\r\\n #dont need to print all of that\\r\\n\\r\\n # Initialize camera\\r\\n cam.Init()\\r\\n\\r\\n # Retrieve GenICam nodemap\\r\\n nodemap = cam.GetNodeMap()\\r\\n\\r\\n #configure settings\\r\\n if configure_exp_gain(cam,exp,gain) is False:\\r\\n return False\\r\\n\\r\\n # Configure trigger\\r\\n if configure_trigger(cam) is False:\\r\\n return False\\r\\n\\r\\n # Acquire images\\r\\n snr = acquire_images(cam, nodemap, nodemap_tldevice)\\r\\n #print(\\\"SNR after acquire_images: \\\", snr)\\r\\n\\r\\n # Reset trigger\\r\\n result &= reset_trigger(nodemap)\\r\\n\\r\\n # Deinitialize camera\\r\\n cam.DeInit()\\r\\n\\r\\n except PySpin.SpinnakerException as ex:\\r\\n print('Error: %s' % ex)\\r\\n result = False\\r\\n\\r\\n return snr\",\n \"def do_startstcv(self, args):\\n if not self._assert_login():\\n return\\n\\n vm_image = None\\n ttl_minutes = 60\\n socket = False\\n desc = None\\n instances = 2\\n host = None\\n cores = 1\\n memory = None\\n vlan = None\\n ntp_server = None\\n license_server = None\\n share = True\\n static_ip = None\\n netmask = None\\n gateway = None\\n external = False\\n\\n if args:\\n args = args.split()\\n missing_arg = 'missing value after'\\n while args:\\n arg = args.pop(0)\\n if arg in ('-i', '--image'):\\n if not args:\\n print(missing_arg, arg, file=sys.stderr)\\n return\\n vm_image = args.pop(0)\\n elif arg in ('-t', '--ttl'):\\n if not args:\\n print(missing_arg, arg, file=sys.stderr)\\n return\\n ttl_minutes = int(args.pop(0))\\n elif arg in ('-d', '--desc'):\\n if not args:\\n print(missing_arg, arg, file=sys.stderr)\\n return\\n desc = args.pop(0)\\n elif arg in ('-n', '--number'):\\n if not args:\\n print(missing_arg, arg, file=sys.stderr)\\n return\\n instances = int(args.pop(0))\\n elif arg in ('-h', '--host'):\\n if not args:\\n print(missing_arg, arg, file=sys.stderr)\\n return\\n host = args.pop(0)\\n elif arg in ('-c', '--cores'):\\n if not args:\\n print(missing_arg, arg, file=sys.stderr)\\n return\\n cores = int(args.pop(0))\\n elif arg in ('-m', '--memory'):\\n if not args:\\n print(missing_arg, arg, file=sys.stderr)\\n return\\n memory = int(args.pop(0))\\n elif arg == '--socket':\\n socket = True\\n elif arg == '--vlan':\\n if not args:\\n print(missing_arg, arg, file=sys.stderr)\\n return\\n vlan = int(args.pop(0))\\n elif arg == '--ntp':\\n if not args:\\n print(missing_arg, arg, file=sys.stderr)\\n return\\n ntp_server = args.pop(0)\\n elif arg == '--license':\\n if not args:\\n print(missing_arg, arg, file=sys.stderr)\\n return\\n license_server = args.pop(0)\\n elif arg == '--noshare':\\n share = False\\n elif arg == '--staticip':\\n if not args:\\n print(missing_arg, arg, file=sys.stderr)\\n return\\n static_ip = args.pop(0)\\n elif arg == '--netmask':\\n if not args:\\n print(missing_arg, arg, file=sys.stderr)\\n return\\n netmask = args.pop(0)\\n elif arg == '--gateway':\\n if not args:\\n print(missing_arg, arg, file=sys.stderr)\\n return\\n gateway = args.pop(0)\\n else:\\n print('unrecognized option:', arg, file=sys.stderr)\\n return\\n\\n if not vm_image:\\n builds = self._qm.get_available_stc_builds()\\n if not builds:\\n print('unable to find latest build', file=sys.stderr)\\n return\\n vm_image = '#' + builds[0]\\n\\n try:\\n vm_ids = self._qm.start_stc_vm(\\n self._user, vm_image, ttl_minutes, socket, desc, instances,\\n host, share, vlan, memory, cores, external, ntp_server,\\n license_server, static_ip, netmask, gateway)\\n except Exception as e:\\n print('ERROR:', e, file=sys.stderr)\\n return\\n\\n print('Started new vm instances of', vm_image)\\n print('\\\\n'.join(vm_ids))\",\n \"def classifyPhaseImage(fr_nb):\\n phase_path = os.path.join(\\\"..\\\",'data','microglia','Beacon-1 unst',\\\"Scene1Interval\\\"+str(fr_nb)+\\\"_PHASE.png\\\")\\n \\n phase= Image.open(phase_path)\\n phase = np.asarray(phase)\\n X=phase.reshape(-1,1)\\n from sklearn.cluster import KMeans\\n kmeans = KMeans(n_clusters=3).fit(X)\\n classified = kmeans.labels_\\n classified=classified.reshape(phase.shape)\\n si2(phase,classified,\\\"Phase image\\\",\\\"Classification\\\")\\n return classified\",\n \"def start(self):\\n if self.preemptableScaler != None:\\n self.preemptableScaler.start()\\n\\n if self.scaler != None:\\n self.scaler.start()\",\n \"def __call__(self,\\n prompt,\\n image,\\n num_inference_steps=20,\\n guidance_scale=9.0,\\n noise_level=20):\\n\\n # encode input prompt\\n prompt_embeds = self.get_prompt_embeds(prompt)\\n\\n # preprocess image\\n image = self.image_processor.preprocess(image).to(self.device)\\n\\n # prepare timesteps\\n self.scheduler.set_timesteps(num_inference_steps, device=self.device)\\n timesteps = self.scheduler.timesteps\\n\\n # add noise to image\\n noise_level = torch.tensor([noise_level], device=self.device)\\n noise = torch.randn(image.shape, device=self.device)\\n image = self.low_res_scheduler.add_noise(image, noise, noise_level)\\n\\n # duplicate image for classifier free guidance\\n image = torch.cat([image] * 2)\\n noise_level = torch.cat([noise_level] * image.shape[0])\\n\\n # prepare the initial image in the latent space (noise on which we will do reverse diffusion)\\n num_channels_latents = self.vae.config.latent_channels\\n batch_size = prompt_embeds.shape[0] // 2\\n height, width = image.shape[2:]\\n latents = self.get_initial_latents(height, width, num_channels_latents, batch_size)\\n\\n # denoise latents\\n latents = self.denoise_latents(prompt_embeds,\\n image,\\n timesteps,\\n latents,\\n noise_level,\\n guidance_scale)\\n\\n # decode latents to get the image into pixel space\\n latents = latents.to(torch.float16)\\n image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]\\n\\n # convert to PIL Image format\\n image = self.transform_image(image.detach()) # detach to remove any computed gradients\\n\\n return image\",\n \"def start(self):\\r\\n self._instagram_api = InstagramAPI(mongo_api=self._mongo_api)\\r\\n self._inst_run()\",\n \"async def init_new_kernel_configuration(self, request, image_id):\\n\\n try:\\n self._image = Image(image_id=image_id)\\n except ImageDoesNotExist:\\n request.ret_error(IMAGE_MISSING)\\n\\n request.ret(READY)\",\n \"def start_kernel(self, **kw):\",\n \"def __init__(self, arch, classes, pretrained):\\n single_model = DRNSeg(arch, classes, pretrained_model=None,\\n pretrained=False)\\n single_model.load_state_dict(torch.load(pretrained))\\n self.model = torch.nn.DataParallel(single_model).cuda()\\n self.num_classes = classes\",\n \"def launch_new_instance():\\n import IPython\\n\\n IPython.Shell.start().mainloop()\",\n \"def start(instance=\\\"default\\\"):\\n # initialize logging\\n global logger_ic\\n logger_ic = infrasim_log.get_logger(LoggerType.ipmi_console.value, instance)\\n common.init_logger(instance)\\n\\n # initialize environment\\n env.local_env.quit_flag = False\\n common.init_env(instance)\\n pid_file = \\\"{}/{}/.ipmi_console.pid\\\".format(config.infrasim_home, instance)\\n daemon.daemonize(pid_file)\\n with open(pid_file, \\\"r\\\") as fp:\\n logger_ic.info(\\\"ipmi-console of {} start with pid {}\\\".\\n format(instance, fp.read().strip()))\\n\\n # parse the sdrs and build all sensors\\n sdr.parse_sdrs()\\n\\n # running thread for each threshold based sensor\\n _start_monitor(instance)\\n _spawn_sensor_thread()\\n _start_console(instance)\",\n \"def __init__(self, image: np.ndarray) -> None:\\n self.image = image\",\n \"def __init__(self, feature_store=None, batch_size=100):\\n if feature_store:\\n if type(feature_store) is dict: # A silly way to avoid multiple reading\\n self.features = feature_store\\n self.image_w = 640\\n self.image_h = 480\\n self.vfov = 60\\n self.feature_size = next(iter(self.features.values())).shape[-1]\\n # print('The feature size is %d' % self.feature_size)\\n else:\\n print('Image features not provided')\\n self.features = None\\n self.image_w = 640\\n self.image_h = 480\\n self.vfov = 60\\n self.featurized_scans = set([key.split(\\\"_\\\")[0] for key in list(self.features.keys())])\\n self.sims = []\\n self.batch_size = batch_size\\n for i in range(batch_size):\\n sim = MatterSim.Simulator()\\n sim.setRenderingEnabled(False)\\n sim.setDiscretizedViewingAngles(True) # Set increment/decrement to 30 degree. (otherwise by radians)\\n sim.setCameraResolution(self.image_w, self.image_h)\\n sim.setCameraVFOV(math.radians(self.vfov))\\n sim.init()\\n self.sims.append(sim)\",\n \"def start(self):\\n \\n self.thread.start()\\n self.state = \\\"running\\\"\",\n \"def _submitInstance( self, imageName, workDir ):\\n return S_OK()\",\n \"def __init__(self, nvim):\\n self.nvim = nvim\",\n \"def _create_instance(cls, hook: EC2Hook):\\n conn = hook.get_conn()\\n try:\\n ec2_client = conn.meta.client\\n except AttributeError:\\n ec2_client = conn\\n\\n # We need existed AMI Image ID otherwise `moto` will raise DeprecationWarning.\\n images = ec2_client.describe_images()[\\\"Images\\\"]\\n response = ec2_client.run_instances(MaxCount=1, MinCount=1, ImageId=images[0][\\\"ImageId\\\"])\\n return response[\\\"Instances\\\"][0][\\\"InstanceId\\\"]\",\n \"def spawn(self, instance):\\n\\n # Update state to inform the nova stack that the VE is launching\\n db.instance_set_state(context.get_admin_context(),\\n instance['id'],\\n power_state.NOSTATE,\\n 'launching')\\n LOG.debug('instance %s: is launching' % instance['name'])\\n\\n # Go through the steps of creating a container\\n # TODO(imsplitbit): Need to add conditionals around this stuff to make\\n # it more durable during failure. And roll back changes made leading\\n # up to the error.\\n self._cache_image(instance)\\n self._create_vz(instance)\\n self._set_vz_os_hint(instance)\\n self._configure_vz(instance)\\n self._set_name(instance)\\n self._add_netif(instance)\\n self._add_ip(instance)\\n self._set_hostname(instance)\\n self._set_nameserver(instance)\\n self._start(instance)\\n self._initial_secure_host(instance)\\n \\n # Begin making our looping async call\\n timer = utils.LoopingCall(f=None)\\n\\n # I stole this from the libvirt driver but it is appropriate to\\n # have this looping timer call so that if a VE doesn't start right\\n # away we can defer all of this.\\n def _wait_for_boot():\\n try:\\n state = self.get_info(instance['name'])['state']\\n db.instance_set_state(context.get_admin_context(),\\n instance['id'], state)\\n if state == power_state.RUNNING:\\n LOG.debug('instance %s: booted' % instance['name'])\\n timer.stop()\\n\\n except:\\n LOG.exception('instance %s: failed to boot' %\\n instance['name'])\\n db.instance_set_state(context.get_admin_context(),\\n instance['id'],\\n power_state.SHUTDOWN)\\n timer.stop()\\n\\n timer.f = _wait_for_boot\\n return timer.start(interval=0.5, now=True)\",\n \"def run_instance():\\n ami_id = \\\"ami-04876f29fd3a5e8ba\\\" # AMI Id\\n instance_type = \\\"t2.micro\\\" # Instance Type\\n tag_specs = [\\n {\\n 'ResourceType': 'instance',\\n 'Tags': [\\n {\\n 'Key': 'Name',\\n 'Value': 'BoB10@ProductDev.sdk.8084'\\n }\\n ]\\n }\\n ]\\n\\n # Instantiate the service resource object\\n ec2_resource = session.resource('ec2', region_name=region)\\n # Run an instance\\n\\n instances = ec2_resource.create_instances(ImageId=ami_id, InstanceType=instance_type,\\n MaxCount=1, MinCount=1, KeyName='BoB10@ProductDev.8084',\\n TagSpecifications=tag_specs, \\n SecurityGroupIds=['sg-06b757b4bb272d98f'],\\n UserData=assemble_userdata().as_string())\\n Instance_id = instances[0].id\\n print('\\\\nInstance Id: ' + Instance_id)\\n print('Image Id: ' + instances[0].image_id)\\n print('Instance Type: ' + instances[0].instance_type)\\n print('State: ' + instances[0].state['Name'])\\n return Instance_id\",\n \"def __init__(self, feature_store=None, batch_size=100):\\n if feature_store:\\n if type(feature_store) is dict: # A silly way to avoid multiple reading\\n self.features = feature_store\\n self.image_w = 640\\n self.image_h = 480\\n self.vfov = 60\\n self.feature_size = next(iter(self.features.values())).shape[-1]\\n print('The feature size is %d' % self.feature_size)\\n else:\\n print('Image features not provided')\\n self.features = None\\n self.image_w = 640\\n self.image_h = 480\\n self.vfov = 60\\n self.featurized_scans = set([key.split(\\\"_\\\")[0] for key in list(self.features.keys())])\\n self.sims = []\\n for i in range(batch_size):\\n sim = MatterSim.Simulator()\\n sim.setRenderingEnabled(False)\\n sim.setDiscretizedViewingAngles(True) # Set increment/decrement to 30 degree. (otherwise by radians)\\n sim.setCameraResolution(self.image_w, self.image_h)\\n sim.setCameraVFOV(math.radians(self.vfov))\\n sim.init()\\n self.sims.append(sim)\",\n \"def launch(\\n *,\\n key_name: Optional[str],\\n instance_type: str,\\n ami: str,\\n ami_user: str,\\n tags: Dict[str, str],\\n display_name: Optional[str] = None,\\n size_gb: int,\\n security_group_name: str,\\n instance_profile: Optional[str],\\n nonce: str,\\n delete_after: datetime.datetime,\\n) -> Instance:\\n\\n if display_name:\\n tags[\\\"Name\\\"] = display_name\\n tags[\\\"scratch-delete-after\\\"] = str(delete_after.timestamp())\\n tags[\\\"nonce\\\"] = nonce\\n tags[\\\"git_ref\\\"] = git.describe()\\n tags[\\\"ami-user\\\"] = ami_user\\n\\n ec2 = boto3.client(\\\"ec2\\\")\\n groups = ec2.describe_security_groups()\\n security_group_id = None\\n for group in groups[\\\"SecurityGroups\\\"]:\\n if group[\\\"GroupName\\\"] == security_group_name:\\n security_group_id = group[\\\"GroupId\\\"]\\n break\\n\\n if security_group_id is None:\\n vpcs = ec2.describe_vpcs()\\n vpc_id = None\\n for vpc in vpcs[\\\"Vpcs\\\"]:\\n if vpc[\\\"IsDefault\\\"] == True:\\n vpc_id = vpc[\\\"VpcId\\\"]\\n break\\n if vpc_id is None:\\n default_vpc = ec2.create_default_vpc()\\n vpc_id = default_vpc[\\\"Vpc\\\"][\\\"VpcId\\\"]\\n securitygroup = ec2.create_security_group(\\n GroupName=security_group_name,\\n Description=\\\"Allows all.\\\",\\n VpcId=vpc_id,\\n )\\n security_group_id = securitygroup[\\\"GroupId\\\"]\\n ec2.authorize_security_group_ingress(\\n GroupId=security_group_id,\\n CidrIp=\\\"0.0.0.0/0\\\",\\n IpProtocol=\\\"tcp\\\",\\n FromPort=22,\\n ToPort=22,\\n )\\n\\n network_interface: InstanceNetworkInterfaceSpecificationTypeDef = {\\n \\\"AssociatePublicIpAddress\\\": True,\\n \\\"DeviceIndex\\\": 0,\\n \\\"Groups\\\": [security_group_id],\\n }\\n\\n say(f\\\"launching instance {display_name or '(unnamed)'}\\\")\\n with open(ROOT / \\\"misc\\\" / \\\"scratch\\\" / \\\"provision.bash\\\") as f:\\n provisioning_script = f.read()\\n kwargs: RunInstancesRequestRequestTypeDef = {\\n \\\"MinCount\\\": 1,\\n \\\"MaxCount\\\": 1,\\n \\\"ImageId\\\": ami,\\n \\\"InstanceType\\\": cast(InstanceTypeType, instance_type),\\n \\\"UserData\\\": provisioning_script,\\n \\\"TagSpecifications\\\": [\\n {\\n \\\"ResourceType\\\": \\\"instance\\\",\\n \\\"Tags\\\": [{\\\"Key\\\": k, \\\"Value\\\": v} for (k, v) in tags.items()],\\n }\\n ],\\n \\\"NetworkInterfaces\\\": [network_interface],\\n \\\"BlockDeviceMappings\\\": [\\n {\\n \\\"DeviceName\\\": \\\"/dev/sda1\\\",\\n \\\"Ebs\\\": {\\n \\\"VolumeSize\\\": size_gb,\\n \\\"VolumeType\\\": \\\"gp3\\\",\\n },\\n }\\n ],\\n \\\"MetadataOptions\\\": {\\n # Allow Docker containers to access IMDSv2.\\n \\\"HttpPutResponseHopLimit\\\": 2,\\n },\\n }\\n if key_name:\\n kwargs[\\\"KeyName\\\"] = key_name\\n if instance_profile:\\n kwargs[\\\"IamInstanceProfile\\\"] = {\\\"Name\\\": instance_profile}\\n i = boto3.resource(\\\"ec2\\\").create_instances(**kwargs)[0]\\n\\n return i\",\n \"def __init__(self, image, scale):\\n\\n # Call the parent init\\n super().__init__(image, scale)\\n\\n # Create a variable to hold our speed. 'angle' is created by the parent\\n # The put vehicle to init position\\n self.speed = 0\\n self.max_speed = 5\\n self.respawning = 0\\n\\n # Mark that we are respawning.\\n self.respawn()\",\n \"def __init__(self):\\n self.image_subscriber = rospy.Subscriber('/raspicam_node/image/compressed', CompressedImage, self.imageCallback)\\n print 'Waiting for classifier service to come up...'\\n rospy.wait_for_service('/classifier_node/classify')\\n self.classify_client = rospy.ServiceProxy('/classifier_node/classify', Classify)\",\n \"def __init__(self, img):\\n self.img = img\",\n \"def __init__(self):\\n super().__init__()\\n import sklearn\\n import sklearn.svm\\n self.model = sklearn.svm.NuSVR\",\n \"def run(self):\\n self.cancelled = False\\n # get version from IDF object or by parsing the IDF file for it\\n\\n # Move files into place\\n self.epw = self.idf.epw.copy(self.run_dir / \\\"in.epw\\\").expand()\\n self.idfname = Path(self.idf.savecopy(self.run_dir / \\\"in.idf\\\")).expand()\\n self.idd = self.idf.iddname.copy(self.run_dir).expand()\\n\\n # Get executable using shutil.which (determines the extension based on\\n # the platform, eg: .exe. And copy the executable to tmp\\n slab_exe = shutil.which(\\\"Slab\\\", path=self.eplus_home)\\n if slab_exe is None:\\n log(\\n f\\\"The Slab program could not be found at \\\" f\\\"'{self.eplus_home}'\\\",\\n lg.WARNING,\\n )\\n return\\n self.slabexe = Path(slab_exe).copy(self.run_dir)\\n self.slabidd = (self.eplus_home / \\\"SlabGHT.idd\\\").copy(self.run_dir)\\n\\n # The GHTin.idf file is copied from the self.include list (added by\\n # ExpandObjects. If self.include is empty, no need to run Slab.\\n self.include = [Path(file).copy(self.run_dir) for file in self.idf.include]\\n if not self.include:\\n self.cleanup_callback()\\n return\\n\\n # Run Slab Program\\n with logging_redirect_tqdm(loggers=[lg.getLogger(self.idf.name)]):\\n with tqdm(\\n unit_scale=True,\\n miniters=1,\\n desc=f\\\"RunSlab #{self.idf.position}-{self.idf.name}\\\",\\n position=self.idf.position,\\n ) as progress:\\n\\n self.p = subprocess.Popen(\\n self.cmd,\\n stdout=subprocess.PIPE,\\n stderr=subprocess.PIPE,\\n shell=True, # can use shell\\n cwd=self.run_dir.abspath(),\\n )\\n start_time = time.time()\\n self.msg_callback(\\\"Begin Slab Temperature Calculation processing . . .\\\")\\n for line in self.p.stdout:\\n self.msg_callback(line.decode(\\\"utf-8\\\").strip(\\\"\\\\n\\\"))\\n progress.update()\\n\\n # We explicitly close stdout\\n self.p.stdout.close()\\n\\n # Wait for process to complete\\n self.p.wait()\\n\\n # Communicate callbacks\\n if self.cancelled:\\n self.msg_callback(\\\"RunSlab cancelled\\\")\\n # self.cancelled_callback(self.std_out, self.std_err)\\n else:\\n if self.p.returncode == 0:\\n self.msg_callback(\\n \\\"RunSlab completed in {:,.2f} seconds\\\".format(\\n time.time() - start_time\\n )\\n )\\n self.success_callback()\\n for line in self.p.stderr:\\n self.msg_callback(line.decode(\\\"utf-8\\\"))\\n else:\\n self.msg_callback(\\\"RunSlab failed\\\")\\n self.failure_callback()\",\n \"def train_main(cls):\\n launcher = cls()\\n launcher.launch()\",\n \"def run(self) -> None:\\n self._hass.turn_on('scene.{0}'.format(self._args['scene']))\",\n \"def start_core(c):\\n with c.cd('images'):\\n print('Starting Core database')\\n c.run('sudo docker-compose up -d stellar-core-db', hide='stderr')\\n sleep(2)\\n\\n # setup core database\\n # https://www.stellar.org/developers/stellar-core/software/commands.html\\n print('Initializing Core database')\\n c.run('sudo docker-compose run stellar-core --newdb --forcescp', hide='both')\\n\\n # setup cache history archive\\n print('Initializing Core history archive')\\n c.run('sudo docker-compose run stellar-core --newhist cache', hide='both')\\n\\n # start a local private testnet core\\n # https://www.stellar.org/developers/stellar-core/software/testnet.html\\n print('Starting Core')\\n c.run('sudo docker-compose up -d stellar-core', hide='stderr')\",\n \"def run_example(self):\\n\\n print(\\\"Run: NNStreamer example for face detection.\\\")\\n\\n # main loop\\n self.loop = GObject.MainLoop()\\n\\n # init pipeline\\n # Currently Only only runs video from webcam. More features TBU.\\n self.pipeline = Gst.parse_launch(\\n 'v4l2src name=cam_src ! videoconvert ! videoscale ! '\\n 'video/x-raw,width=640,height=480,format=RGB ! videoconvert ! xvimagesink name=img_tensor'\\n )\\n\\n # bus and message callback\\n bus = self.pipeline.get_bus()\\n bus.add_signal_watch()\\n bus.connect('message', self.on_bus_message)\\n\\n # start pipeline\\n self.pipeline.set_state(Gst.State.PLAYING)\\n self.running = True\\n\\n self.set_window_title('img_tensor', 'NNStreamer Face Detection Example')\\n\\n # run main loop\\n self.loop.run()\\n\\n # quit when received eos or error message\\n self.running = False\\n self.pipeline.set_state(Gst.State.NULL)\\n\\n bus.remove_signal_watch()\",\n \"def startService(self):\\n self.world.start()\",\n \"def start(self):\\n self.frame = 0\\n self._init_level(1)\\n self.reward = 0\\n self.pcontinue = 1\\n self.ghost_speed = self.ghost_speed_init\\n return self._make_image(), self.reward, self.pcontinue\",\n \"def __init__(self, verbose=1, data=None, shape=(512, 512, 35)):\\n\\n if shape is not None and data is not None:\\n if data.shape != shape:\\n data = resize(data, shape, mode='constant')\\n self.image_raw = data\\n self.image_filtered = None\\n self.spots = []\\n self.SNR = None\\n self._verbose = verbose\\n self.name = None\\n self.mixture_model = None\\n self.background = None\\n self.mask = None\",\n \"def run_visualization(image):\\n original_im = Image.fromarray(image)\\n seg_map = model.run(original_im)\\n seg_image = label_to_color_image(seg_map).astype(np.uint8)\\n\\n return seg_image\",\n \"def start(self, world):\\n self.sense(world)\",\n \"def from_image(cls, image):\\n raise NotImplementedError\",\n \"def __init__(self) -> None:\\n self.registration_method = None\\n self.fixed_image = None\\n self.moving_image = None\\n self.transform = None\",\n \"def start(self):\\n logger.debug('Starting controller')\\n pass\",\n \"def start_instance(instance_id):\\n # Instantiate the service resource object\\n ec2_resource = session.resource('ec2', region_name=region)\\n try:\\n # Start an instance\\n response = ec2_resource.Instance(instance_id).start(DryRun=False)\\n print(response)\\n print(\\\"\\\\nSuccessfully starting instance: \\\", instance_id)\\n except botocore.exceptions.ClientError as e:\\n if e.response['Error']['Code'] == \\\"InvalidInstanceID.Malformed\\\":\\n print(\\\"Error: Invalid instance id!!\\\")\\n else:\\n raise\\n return\",\n \"def CreateInstance(self):\\n\\n # Create host instances for cuttlefish device. Currently one host instance\\n # has one cuttlefish device. In the future, these logics should be modified\\n # to support multiple cuttlefish devices per host instance.\\n instance = self._compute_client.GenerateInstanceName(\\n build_id=self.build_info.build_id, build_target=self._build_target)\\n\\n if self._cfg.enable_multi_stage:\\n remote_build_id = self.build_info.build_id\\n else:\\n remote_build_id = self._GetGcsBucketBuildId(\\n self.build_info.build_id, self.build_info.release_build_id)\\n\\n if self._cfg.enable_multi_stage:\\n remote_system_build_id = self.system_build_info.build_id\\n else:\\n remote_system_build_id = self._GetGcsBucketBuildId(\\n self.system_build_info.build_id, self.system_build_info.release_build_id)\\n\\n host_image_name = self._compute_client.GetHostImageName(\\n self._cfg.stable_host_image_name,\\n self._cfg.stable_host_image_family,\\n self._cfg.stable_host_image_project)\\n # Create an instance from Stable Host Image\\n self._compute_client.CreateInstance(\\n instance=instance,\\n image_name=host_image_name,\\n image_project=self._cfg.stable_host_image_project,\\n build_target=self.build_info.build_target,\\n branch=self.build_info.branch,\\n build_id=remote_build_id,\\n kernel_branch=self.kernel_build_info.branch,\\n kernel_build_id=self.kernel_build_info.build_id,\\n kernel_build_target=self.kernel_build_info.build_target,\\n blank_data_disk_size_gb=self._blank_data_disk_size_gb,\\n extra_scopes=self._extra_scopes,\\n system_build_target=self.system_build_info.build_target,\\n system_branch=self.system_build_info.branch,\\n system_build_id=remote_system_build_id,\\n bootloader_build_target=self.bootloader_build_info.build_target,\\n bootloader_branch=self.bootloader_build_info.branch,\\n bootloader_build_id=self.bootloader_build_info.build_id)\\n\\n return instance\",\n \"def start(self):\\n self.__init__()\\n self.set_n_players()\\n self.init_players()\\n self.init_territory_selection_phase()\\n self.init_troop_deployment_phase()\\n # self.game_phase()\",\n \"def start_ec2_instance(client, instance_id, hibernate=False):\\n\\n response = client.start_instances(\\n InstanceIds=[instance_id],\\n )\\n return response\",\n \"def setup_class(cls):\\n super().setup_class()\\n cls.detector = cls.faceEngine.createFaceDetector(DetectorType.FACE_DET_DEFAULT)\\n cls.headPoseEstimator = cls.faceEngine.createHeadPoseEstimator()\\n cls.image = VLImage.load(filename=ONE_FACE)\\n cls.detection = TestHeadPose.detector.detectOne(cls.image, detect5Landmarks=True, detect68Landmarks=True)\",\n \"def start_neuron(cls, neuron, parameters_dict=None):\\n if neuron.parameters is not None:\\n try:\\n neuron.parameters = cls._replace_brackets_by_loaded_parameter(neuron.parameters, parameters_dict)\\n except NeuronParameterNotAvailable:\\n Utils.print_danger(\\\"The neuron %s cannot be launched\\\" % neuron.name)\\n return None\\n instantiated_neuron = NeuronLauncher.launch_neuron(neuron)\\n return instantiated_neuron\",\n \"def run(self, image):\\n start = datetime.datetime.now()\\n\\n width, height = image.size\\n resize_ratio = 1.0 * self.INPUT_SIZE / max(width, height)\\n target_size = (int(resize_ratio * width), int(resize_ratio * height))\\n resized_image = image.convert('RGB').resize(target_size, Image.ANTIALIAS)\\n batch_seg_map = self.sess.run(\\n self.OUTPUT_TENSOR_NAME,\\n feed_dict={self.INPUT_TENSOR_NAME: [np.asarray(resized_image)]})\\n seg_map = batch_seg_map[0]\\n\\n end = datetime.datetime.now()\\n\\n diff = end - start\\n print(\\\"Time taken to evaluate segmentation is : \\\" + str(diff))\\n\\n return resized_image, seg_map\",\n \"def setUp(self):\\r\\n center = [1,1]\\r\\n size = 1\\r\\n self.imageinfo = ImageInfo(center, size)\",\n \"def setUp(self):\\r\\n center = [1,1]\\r\\n size = 1\\r\\n self.imageinfo = ImageInfo(center, size)\",\n \"def setUp(self):\\r\\n center = [1,1]\\r\\n size = 1\\r\\n self.imageinfo = ImageInfo(center, size)\",\n \"def setUp(self):\\r\\n center = [1,1]\\r\\n size = 1\\r\\n self.imageinfo = ImageInfo(center, size)\",\n \"def setUp(self):\\r\\n center = [1,1]\\r\\n size = 1\\r\\n self.imageinfo = ImageInfo(center, size)\",\n \"def launch_vm(self,\\n region: str,\\n security_group_id: str,\\n subnet_id: str,\\n image_id: str,\\n instance_type: str,\\n instance_profile: str,\\n **kwargs) -> Tuple[str, str, str, str]:\\n raise NotImplementedError()\",\n \"def launch_sagemaker_local_ec2_instance(image, ami_id, ec2_key_name, region):\\n instance_type = assign_sagemaker_local_job_instance_type(image)\\n instance_name = image.split(\\\"/\\\")[-1]\\n instance = ec2_utils.launch_instance(\\n ami_id,\\n region=region,\\n ec2_key_name=ec2_key_name,\\n instance_type=instance_type,\\n # EIA does not have SM Local test\\n ei_accelerator_type=None,\\n user_data=None,\\n iam_instance_profile_name=ec2_utils.EC2_INSTANCE_ROLE_NAME,\\n instance_name=f\\\"sm-local-{instance_name}\\\",\\n )\\n instance_id = instance[\\\"InstanceId\\\"]\\n public_ip_address = ec2_utils.get_public_ip(instance_id, region=region)\\n ec2_utils.check_instance_state(instance_id, state=\\\"running\\\", region=region)\\n ec2_utils.check_system_state(instance_id, system_status=\\\"ok\\\", instance_status=\\\"ok\\\", region=region)\\n return instance_id, public_ip_address\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.666752","0.6325278","0.6263676","0.614632","0.5845064","0.5794984","0.5747361","0.5733841","0.5721612","0.56165344","0.5603583","0.5509438","0.54767895","0.5428946","0.53604615","0.53470963","0.5332762","0.53182864","0.53123957","0.5296787","0.52888715","0.52811354","0.52584165","0.52493566","0.52435315","0.5218621","0.521427","0.5212473","0.51945853","0.5174413","0.5172378","0.5162008","0.514647","0.5124568","0.51184976","0.51086587","0.5096289","0.50956345","0.5093863","0.5072321","0.5069327","0.50641704","0.5061169","0.5058906","0.5050702","0.5043132","0.5042014","0.50360537","0.50306183","0.5025758","0.5024812","0.5017783","0.50127405","0.50077254","0.4998143","0.4991641","0.49889204","0.49810565","0.4975495","0.49724364","0.49709767","0.49620092","0.4958327","0.4952657","0.495233","0.4949334","0.49420452","0.49414364","0.49382284","0.49353734","0.492166","0.49186802","0.49119836","0.4907851","0.49061036","0.4903941","0.49034438","0.48932087","0.48928005","0.48913845","0.4889364","0.488877","0.4884844","0.4876372","0.48729348","0.48690933","0.48689532","0.48661745","0.48474893","0.48413137","0.48308182","0.48299813","0.4824543","0.48230746","0.48230746","0.48230746","0.48230746","0.48230746","0.4817736","0.48101988"],"string":"[\n \"0.666752\",\n \"0.6325278\",\n \"0.6263676\",\n \"0.614632\",\n \"0.5845064\",\n \"0.5794984\",\n \"0.5747361\",\n \"0.5733841\",\n \"0.5721612\",\n \"0.56165344\",\n \"0.5603583\",\n \"0.5509438\",\n \"0.54767895\",\n \"0.5428946\",\n \"0.53604615\",\n \"0.53470963\",\n \"0.5332762\",\n \"0.53182864\",\n \"0.53123957\",\n \"0.5296787\",\n \"0.52888715\",\n \"0.52811354\",\n \"0.52584165\",\n \"0.52493566\",\n \"0.52435315\",\n \"0.5218621\",\n \"0.521427\",\n \"0.5212473\",\n \"0.51945853\",\n \"0.5174413\",\n \"0.5172378\",\n \"0.5162008\",\n \"0.514647\",\n \"0.5124568\",\n \"0.51184976\",\n \"0.51086587\",\n \"0.5096289\",\n \"0.50956345\",\n \"0.5093863\",\n \"0.5072321\",\n \"0.5069327\",\n \"0.50641704\",\n \"0.5061169\",\n \"0.5058906\",\n \"0.5050702\",\n \"0.5043132\",\n \"0.5042014\",\n \"0.50360537\",\n \"0.50306183\",\n \"0.5025758\",\n \"0.5024812\",\n \"0.5017783\",\n \"0.50127405\",\n \"0.50077254\",\n \"0.4998143\",\n \"0.4991641\",\n \"0.49889204\",\n \"0.49810565\",\n \"0.4975495\",\n \"0.49724364\",\n \"0.49709767\",\n \"0.49620092\",\n \"0.4958327\",\n \"0.4952657\",\n \"0.495233\",\n \"0.4949334\",\n \"0.49420452\",\n \"0.49414364\",\n \"0.49382284\",\n \"0.49353734\",\n \"0.492166\",\n \"0.49186802\",\n \"0.49119836\",\n \"0.4907851\",\n \"0.49061036\",\n \"0.4903941\",\n \"0.49034438\",\n \"0.48932087\",\n \"0.48928005\",\n \"0.48913845\",\n \"0.4889364\",\n \"0.488877\",\n \"0.4884844\",\n \"0.4876372\",\n \"0.48729348\",\n \"0.48690933\",\n \"0.48689532\",\n \"0.48661745\",\n \"0.48474893\",\n \"0.48413137\",\n \"0.48308182\",\n \"0.48299813\",\n \"0.4824543\",\n \"0.48230746\",\n \"0.48230746\",\n \"0.48230746\",\n \"0.48230746\",\n \"0.48230746\",\n \"0.4817736\",\n \"0.48101988\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7294491"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":17882,"cells":{"query":{"kind":"string","value":"Use argparse to parse command line arguments."},"document":{"kind":"string","value":"def get_args():\n parser = argparse.ArgumentParser(description='Runner for tasks')\n parser.add_argument('--db_url', help='Database url string to the db.', required=True)\n return parser.parse_args()"},"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 arg_parse():\n p = ap.ArgumentParser()\n p.add_argument()\n return p.parse_args()","def parse_arguments():\n parser = argparse.ArgumentParser()\n parser.add_argument('-u', '--urls_dirpath', type=unicode)\n parser.add_argument('-r', '--resources_dir', type=unicode)\n parser.add_argument('-t', '--total_docs', type=int)\n parser.add_argument('-m', '--mapping', type=unicode,\n help='File with the yago to lkif mapping')\n\n return parser.parse_args()","def parse_args():\n parser = argparse.ArgumentParser(description=__doc__)\n # If user doesn't specify an input file, read from standard input. Since\n # encodings are the worst thing, we're explicitly expecting std\n parser.add_argument('-i', '--infile',\n type=lambda x: open(x, encoding=ENCODE_IN),\n default=io.TextIOWrapper(\n sys.stdin.buffer, encoding=ENCODE_IN)\n )\n # Same thing goes with the output file.\n parser.add_argument('-o', '--outfile',\n type=lambda x: open(x, 'w', encoding=ENCODE_OUT),\n default=io.TextIOWrapper(\n sys.stdout.buffer, encoding=ENCODE_OUT)\n )\n # Set the verbosity level for the logger. The `-v` option will set it to\n # the debug level, while the `-q` will set it to the warning level.\n # Otherwise use the info level.\n verbosity = parser.add_mutually_exclusive_group()\n verbosity.add_argument('-v', '--verbose', action='store_const',\n const=logging.DEBUG, default=logging.INFO)\n verbosity.add_argument('-q', '--quiet', dest='verbose',\n action='store_const', const=logging.WARNING)\n return parser.parse_args()","def parse_args():\n parser = argparse.ArgumentParser(description='Google reminders cli',\n epilog=usage,\n formatter_class=argparse.RawTextHelpFormatter)\n return parser.parse_args()","def parse_arguments(args):","def parse_command_line(self, argv):\n from optparse import OptionParser\n usage = \"usage: %prog [options]\"\n parser = OptionParser(usage)\n\n (options, args) = parser.parse_args(argv)","def parse_args():\n parser = argparse.ArgumentParser()\n parser.add_argument(\"in_fq\", help=\"The fastq file containing Hi-C reads.\")\n parser.add_argument(\n \"-r\",\n \"--reference\",\n required=True,\n help=\"Path to the reference genome, in FASTA format.\",\n )\n parser.add_argument(\n \"-p\",\n \"--nb_processors\",\n default=1,\n type=int,\n help=\"number of CPUs used for alignment.\",\n )\n parser.add_argument(\n \"-o\",\n \"--out_sam\",\n help=\"Path to the output SAM file for the alignment of in_fq.\",\n )\n parser.add_argument(\n \"-T\",\n \"--tempdir\",\n default=\".\",\n help=\"Directory to write temporary files. Defaults to current directory.\",\n )\n parser.add_argument(\n \"-m\",\n \"--minimap2\",\n default=False,\n action=\"store_true\",\n help=\"Use minimap2 instead of bowtie for the alignment.\",\n )\n parser.add_argument(\n \"-l\",\n \"--min_len\",\n type=int,\n default=20,\n help=\"Minimum length to which reads should be truncated.\",\n )\n return parser.parse_args()","def __parse_args():\n parser = argparse.ArgumentParser()\n parser.add_argument('-f', '--force', action=\"store_true\", default=False,\n help='overwrite existing database files during import')\n parser.add_argument('-e', '--extension', action=\"store\", default='txt',\n help='specify file extension. default is \"txt\"')\n parser.add_argument('-d', '--delimiter', action=\"store\", default='\\t',\n help='specify column delimiter. default is tab (\\\\t)')\n parser.add_argument('-m', '--mark', action=\"store\", default='.',\n help='specify decimal mark for numeric data. default is'\n ' dot (.)')\n parser.add_argument('-o', '--outformat', action=\"store\", default='npz',\n help='specify output database format. default is \"npz\"'\n ' for numpy database. use \"mat\" for matlab '\n ' database format.')\n parser.add_argument('-r', '--recursive', action=\"store_true\", default=False,\n help='recursively walk through all sub-directories of'\n ' current working directory')\n parser.add_argument('-p', '--pcs', action=\"store_true\", default=True,\n help='indicate if files are pcs files.')\n parser.add_argument('-c', '--colheadlines', action=\"store\", default='1',\n help='number of lines spanned by the column headers')\n args = parser.parse_args()\n return args","def parse_arguments():\n parser = argparse.ArgumentParser(prog='AdapterRunner', description='Adapter Runner Application')\n parser.add_argument('-a', '--application', action='store', dest='app_name', help='Application Name',\n metavar='<application_name>')\n parser.add_argument('-fi', '--fetch_interval', action='store', dest='fetch_stats_interval', help='Fetch Stats Interval',\n metavar='<fetch_interval in seconds>')\n return parser.parse_args()","def parse_args():\n parser = argparse.ArgumentParser(\n description=\"CUDAPOA Python API sample program.\")\n parser.add_argument('-m',\n help=\"Run MSA generation. By default consensusis generated.\",\n action='store_true')\n parser.add_argument('-p',\n help=\"Print output MSA or consensus for each POA group.\",\n action='store_true')\n parser.add_argument('-l',\n help=\"Use long or short read sample data.\",\n action='store_true')\n return parser.parse_args()","def parse_args():\n parser = argparse.ArgumentParser(description=__doc__)\n return parser.parse_args()","def parse_args():\n parser = argparse.ArgumentParser()\n parser.add_argument(\n \"-d\",\n \"--debug\",\n help=\"Print lots of debugging statements\",\n action=\"store_const\",\n dest=\"loglevel\",\n const=logging.DEBUG,\n default=logging.ERROR,\n )\n parser.add_argument(\n \"-v\",\n \"--verbose\",\n help=\"Be verbose\",\n action=\"store_const\",\n dest=\"loglevel\",\n const=logging.INFO,\n )\n parser.add_argument(\"runscript\", default=None)\n return parser.parse_args()","def parse_cli_arguments():\n parser = argparse.ArgumentParser('Generates a MANIFEST file used by the '\n 'HMP2 AnADAMA2 workflows.')\n parser.add_argument('-b', '--broad-data-sheet', required=True,\n help='Broad data product status spreadsheet. '\n 'Contains entries indicating new files to be '\n 'processed.')\n parser.add_argument('-o', '--output-manifest', required=True,\n help='Path to desired output manifest file.')\n parser.add_argument('-oi', '--origin-institute', required=True,\n help='Name of institute submitting new files '\n 'to be processed.')\n parser.add_argument('-oc', '--origin-contact', required=True,\n help='Contact person for corresponding origin '\n 'institute.')\n parser.add_argument('-oe', '--origin-contact-email', required=True,\n help='Contact email for contact person.')\n parser.add_argument('-p', '--project-name', dest='project', \n required=True,\n help='Project that sequence files belong too.')\n\n return parser.parse_args()","def parse_args():\n parser = argparse.ArgumentParser()\n\n parser.add_argument('--length', required=True, type=int, help='Test length in seconds')\n parser.add_argument('--txs-per-ledger', required=True, type=int, help='Transaction rate to submit (spam) in parallel for every ledger round')\n parser.add_argument('--prioritizer-seeds-file', required=True, type=str, help='File path to prioritizer seeds file')\n parser.add_argument('--spammer-seeds-file', required=True, type=str, help='File path to spammer seeds file')\n parser.add_argument('--out', default='spam-results-{}.json'.format(str(int(time.time()))), type=str, help='Spam results JSON output')\n parser.add_argument('--avg-block-time', type=int, default=5, help='Average block time. Controls the time delay between every spam round and the one just after that')\n\n parser.add_argument('--passphrase', type=str, help='Network passphrase')\n parser.add_argument('--horizon', action='append',\n help='Horizon endpoint URL (use multiple --horizon flags for multiple addresses)')\n\n return parser.parse_args()","def parse_args():\n parser = argparse.ArgumentParser(description=\"SLOWFAST for AVA Dataset\")\n parser.add_argument(\"--pipeline\", type=str,\n default=\"../data/config/slowfast.pipeline\", help=\"SDK infer pipeline\")\n parser.add_argument(\"--data_dir\", type=str, default=\"../data/input\",\n help=\"Dataset contain frames and ava_annotations\")\n args_opt = parser.parse_args()\n return args_opt","def parse_command_line():\r\n\r\n parser = argparse.ArgumentParser(description='User args')\r\n parser.add_argument(\"--action\", choices=['train', 'predict', 'demo', 'test'], required=True, help=\"Choose action.\")\r\n parser.add_argument(\"--model\", choices=['vgg', 'unet', 'fpn'], required=True, help=\"Choose model.\")\r\n parser.add_argument(\"--dataset\", choices=['full', 'small'], required=True, help=\"Choose dataset.\")\r\n\r\n return parser.parse_args()","def _parse_args():\n parser = argparse.ArgumentParser(description='Run DAFI.')\n parser.add_argument('input_file', help='Name (path) of input file')\n return parser.parse_args()","def arg_parse():\n parser = argparse.ArgumentParser()\n # defines command line arguments\n parser.add_argument('-i', '--input_file', help=\"The input .txt file \" +\n \"within the current directory (the file you \" +\n \"downloaded from VEP\")\n parser.add_argument('-o', '--output_file', help=\"Name for the output \" +\n \".txt file\")\n\n return parser.parse_args()","def parse_args():\n parser = argparse.ArgumentParser()\n parser.add_argument(\"auth\",\n help=\"authentication string for Infermedica API: \"\n \"APP_ID:APP_KEY or path to file containing it.\")\n parser.add_argument(\"--model\",\n help=\"use non-standard Infermedica model/language, \"\n \"e.g. infermedica-es\")\n args = parser.parse_args()\n return args","def parse_arguments():\n\n parser = argparse.ArgumentParser()\n\n parser.add_argument(\"--accessions\", help=\"A json file with old/new family mapppings\")\n parser.add_argument(\"--add-header\", help=\"Print descriptive header\",\n action=\"store_true\", default=False)\n parser.add_argument(\"--add-links\", help=\"Creates hyperlinks to available Rfam html content\",\n action=\"store_true\", default=False)\n return parser","def parse_args():\n parser = argparse.ArgumentParser(description=\"Bandits algorithms on a click-through \"\n \"rate dataset.\")\n parser.add_argument('--plot', action='store_true')\n return parser.parse_args()","def parse_args():\n parser = argparse.ArgumentParser()\n parser.add_argument('n_iter',\n help='number of iteration',\n type=int)\n parser.add_argument('n_processes',\n help='number of processes',\n type=int)\n parser.add_argument('method',\n help='mutual exclusion method')\n parser.add_argument('duration',\n help='Duration of each process',\n type=float)\n return parser.parse_args()","def parse_command_line():\n parser = argparse.ArgumentParser()\n\n # All reference encoders\n parser.add_argument(\"--step\", dest=\"step\", default=\"10\", type=int, help=\"step size\")\n parser.add_argument(\"--repeats\", dest=\"repeats\", type=int, default=1, help=\"repeats\")\n\n parser.add_argument(dest=\"image\", default=None,\n help=\"select the test image to run\")\n\n args = parser.parse_args()\n return args","def parse_command_line():\n parser = argparse.ArgumentParser()\n\n help_str = \\\n 'The collection folder to sort files into. ' \\\n 'If the folder does not exist, it will be created along with the ' \\\n 'necessary contents.'\n parser.add_argument('-c', '--collection', help=help_str)\n\n help_str = \\\n 'The source folder to import files from. Has to exist and ' \\\n 'has to be a folder.'\n parser.add_argument('-s', '--source', help=help_str, required=False)\n\n help_str = \\\n 'View the gallery in random order auto skpping after the' \\\n 'given amount of seconds'\n parser.add_argument('-v', '--view', help=help_str, required=False)\n\n return parser.parse_args()","def parse_command_line_arguments():\n parser = argparse.ArgumentParser(\n description=\"Convert dependency files into list of GitHub links.\",\n epilog=\"For help with this program, contact John Speed at jmeyers@iqt.org.\",\n )\n parser.add_argument(\n \"--python\",\n default=False, # default value is False\n help=\"Convert requirements.txt file into GitHub links.\",\n )\n return parser.parse_args()","def parse_args():\n parser = argparse.ArgumentParser(description=_program_description)\n parser.add_argument('input_file', help=_input_file_description)\n #parser.add_argument('-v', '--verbose', action='store_true', \n # default=False, help='show progress')\n args = parser.parse_args()\n return args","def parse_args():\n parser = argparse.ArgumentParser()\n argparse.ArgumentParser(argument_default=False)\n parser.add_argument('-r', type=str, required=True, metavar='[reference.fa]', help=\"Reference file in fasta format\")\n parser.add_argument('-s', type=int, metavar='number of SNPs', help=\"Number of SNPs to introduce\")\n parser.add_argument('-i', type=int, metavar='number of indels', help=\"Number of small indels to introduce. Indel sizes randomly sampled between 1-100bp with a ratio of 9:1 for size being < 11bp\")\n parser.add_argument('-d', type=int, metavar='number of large deletions', help=\"Number of large deletions to introduce. Deletion size randomly sampled between 100-5000bp\")\n parser.add_argument('-t', type=int, metavar='number of translocations', help=\"number of translocations, regions will be randomly selected ranging between 500-5000bp in size\")\n parser.add_argument('-nc', type=bool, default=False, metavar='add at least one non-conserved translocation', help=\"True/False add one non-conserved translocation. Deletion occurs from end of the translocation. Default=False\")\n parser.add_argument('-f', type=str, required=True, metavar='output fasta filename', help=\"Output fasta filename and file path\")\n parser.add_argument('-v', type=str, required=True, metavar='output vcf file of mutations', help=\"Output vcf filename and file path\")\n\n return parser.parse_args()","def arg_parse():\n parser = argparse.ArgumentParser(description=\"Fetches the current ATT&CK content expressed as STIX2 and creates spreadsheet mapping Techniques with Mitigations, Groups or Software.\")\n parser.add_argument(\"-d\", \"--domain\", type=str, required=True, choices=[\"enterprise_attack\", \"mobile_attack\"], help=\"Which ATT&CK domain to use (Enterprise, Mobile).\")\n parser.add_argument(\"-m\", \"--mapping-type\", type=str, required=True, choices=[\"groups\", \"mitigations\", \"software\"], help=\"Which type of object to output mappings for using ATT&CK content.\")\n parser.add_argument(\"-t\", \"--tactic\", type=str, required=False, help=\" Filter based on this tactic name (e.g. initial-access) \" )\n parser.add_argument(\"-s\", \"--save\", type=str, required=False, help=\"Save the CSV file with a different filename.\")\n return parser","def parse_args():\n parser = argparse.ArgumentParser()\n parser.add_argument(\"auth\",\n help=\"authentication string for Infermedica API: \"\n \"APP_ID:APP_KEY or path to file containing it.\")\n parser.add_argument(\"--model\",\n help=\"use non-standard Infermedica model/language, \"\n \"e.g. infermedica-es\")\n # TODO: Check if `verbose` actually does anything.\n parser.add_argument(\"-v\", \"--verbose\",\n dest=\"verbose\", action=\"store_true\", default=False,\n help=\"dump internal state\")\n args = parser.parse_args()\n return args","def _parse_args():\n parser = argparse.ArgumentParser(description=\"\")\n #parser.add_argument(\"args\", metavar=\"N\", type=str, nargs=\"*\", help=\"Positional arguments.\")\n #parser.add_argument(\"\", dest=\"\", type=\"\", default=, help=)\n #parser.add_argument(\"--version\", action=\"version\", version=\"<the version>\")\n\n return parser.parse_args()","def parse_cmd_arguments():\n parser = argparse.ArgumentParser(description='Process some integers.')\n parser.add_argument('-i', '--input', required=True, help='input JSON file')\n parser.add_argument('-o', '--output', required=True,\n help='ouput JSON file')\n parser.add_argument('-d', '--debug', required=False,\n help='log level. Can be 0-3. Defaults to 0')\n\n return parser.parse_args()","def parse_args():\n\n parser = argparse.ArgumentParser(description='Disk metric sender')\n parser.add_argument('-v', '--verbose', action='store_true', default=None, help='Verbose?')\n parser.add_argument('--debug', action='store_true', default=None, help='Debug?')\n\n return parser.parse_args()","def parse_args():\n\n parser = argparse.ArgumentParser(description='CLI to store Actisense-NGT Gateway values to InfluxDB and publish via MQTT')\n parser.add_argument('--config', '-c', type=str, required=True, help='JSON configuraton file with path')\n return parser.parse_args()","def get_args():\n parser = argparse.ArgumentParser(description=\"Arguments for data exploration\")\n parser.add_argument(\"--tokenize\",\n dest=\"tokenize\",\n action=\"store_true\",\n help=\"Tokenize by words and sentences, counting averages/sd for each.\")\n return parser","def parse_command_line_arguments():\n\n description, epilog = __doc__.split(\"\\n\\n\", 1)\n\n parser = argparse.ArgumentParser(\n formatter_class=argparse.RawDescriptionHelpFormatter,\n description=description,\n epilog=epilog)\n\n parser.add_argument('-s', '--s', dest='s', action='store', type=float, required=True,\n help='Minimum frequency')\n parser.add_argument('-c', '--credentials', dest='credentials', action='store',\n default=\"./.tpass\",\n help='File with Twitter credentials (username and password, separated by a space)')\n\n args = parser.parse_args()\n \n return args","def parse_args():\n parser = ArgumentParser(\n description=\"This is a script for auto apply ipex optimization.\"\n \"\\n################################# Basic usage ############################# \\n\"\n \"\\n 1. Apply ipex optimization with fp32 data type\\n\"\n \"\\n >>> python -m intel_extension_for_pytorch.cpu.auto_ipex python_script args \\n\"\n \"\\n 2. Apply ipex optimization with bf16 data type\\n\"\n \"\\n >>> python -m intel_extension_for_pytorch.cpu.auto_ipex --dtype bfloat16 python_script args \\n\",\n formatter_class=RawTextHelpFormatter,\n )\n\n add_auto_ipex_params(parser, auto_ipex_default_enabled=True)\n\n # positional\n parser.add_argument(\n \"program\",\n type=str,\n help=\"The full path to the proram/script to be launched. \"\n \"followed by all the arguments for the script\",\n )\n # rest from the training program\n parser.add_argument(\"program_args\", nargs=REMAINDER)\n return parser.parse_args()","def parse_args():\n parser = argparse.ArgumentParser(description=\"Hyper parameter\")\n parser.add_argument(\n \"--model\", help=\"Model to use\", default=\"DenseNet169\", type=str)\n parser.add_argument(\n \"--optimizer\", help=\"which optimizer to use\", default=\"Adam\", type=str)\n parser.add_argument(\n \"--lr\", help=\"learning rate\", default=2e-5, type=float)\n\n return parser.parse_args()","def parse_args():\n parser = argparse.ArgumentParser(\n fromfile_prefix_chars='@',\n description='Convert CVAT XML annotations to masks'\n )\n\n parser.add_argument(\n '--cvat-xml', metavar='FILE', required=True,\n help='input file with CVAT annotation in xml format'\n )\n\n parser.add_argument(\n '--background-color', metavar='COLOR_BGR', default=\"0,0,0\",\n help='specify background color (by default: 0,0,0)'\n )\n\n parser.add_argument(\n '--label-color', metavar='LABEL:COLOR_BGR', action='append',\n default=[],\n help=\"specify a label's color (e.g. 255 or 255,0,0). The color will \" +\n \"be interpreted in accordance with the mask format.\"\n )\n\n parser.add_argument(\n '--mask-bitness', type=int, choices=[8, 24], default=8,\n help='choose bitness for masks'\n )\n\n parser.add_argument(\n '--output-dir', metavar='DIRECTORY', required=True,\n help='directory for output masks'\n )\n\n return parser.parse_args()","def parse_arguments(args=sys.argv[1:]):\n \n parser = argparse.ArgumentParser()\n \n parser.add_argument('-i', '--input',\n help=\"Path of input file to read. Default: {d}\".format(d=INPUT_FILE),\n default=INPUT_FILE)\n \n return parser.parse_args(args)","def parse_args():\n parser = argparse.ArgumentParser(description=\"\")\n parser.add_argument(\n \"config_path\",\n type=str,\n help=\"Path to the JSON configuration file containing the image transformation settings.\",\n )\n parser.add_argument(\n \"img_path\",\n type=str,\n help=\"Path to the input image file to apply transformations.\",\n )\n return parser.parse_args()","def parse_args():\n parser = ArgumentParser()\n parser.add_argument('-t', '--timer', action='store_true', \\\n help='Time the first random generation')\n parser.add_argument('-i', '--ibmq', default='', help='IBMQ token')\n parser.add_argument('-b', '--backend', default='', help='IBMQ backend')\n return parser.parse_args()","def parse_args():\n\n parser = argparse.ArgumentParser()\n parser.add_argument(\"-i\", \"--input_path\", required=True)\n parser.add_argument(\"-c\", \"--config\", required=True)\n return parser.parse_args()","def parse_args():\n parser = argparse.ArgumentParser(\"BaselineMembershipInferenceAttack\")\n parser.add_argument(\"--batch_size\",\n type=int, default=128,\n help=\"The batch size of normal training.\")\n parser.add_argument(\"--train_epoch\",\n type=int, default=10,\n help=\"The epoch of training.\")\n parser.add_argument(\"--train_lr\",\n type=float, default=0.0002,\n help=\"The learning rate of training.\")\n args = parser.parse_args()\n return args","def parse_args():\n parser = argparse.ArgumentParser(\"Run arguments for system submitted tasks\")\n\n parser.add_argument(\"-f\", \"--funcs\", type=str, nargs=\"?\", required=True,\n help=\"path to pickle file containing a list of \"\n \"functions/methods that should be run by the \"\n \"submitted process\"\n )\n parser.add_argument(\"-k\", \"--kwargs\", type=str, nargs=\"?\", required=False,\n default=None,\n help=\"path to pickle file containing a dictionary of \"\n \"keyword argumnets that should be passed to the \"\n \"functions\")\n parser.add_argument(\"-e\", \"--environment\", type=str, nargs=\"?\",\n required=False,\n help=\"Optional comma-separated environment variables, \"\n \"which should be given as \"\n \"VARNAME1=value1,VARNAME2=value2 and so on. These \"\n \"will be separated and instantiated into Python's \"\n \"os.environ\")\n\n return parser.parse_args()","def parse_args():\n parser = argparse.ArgumentParser(description='Crawl an Android app store for apk files.')\n parser.add_argument('--store', dest='api', choices=['GooglePlay', 'F-Droid'], required=True,\n help='Specifies the store to crawl. At the moment only Google Play is supported.')\n parser.add_argument('--meta', dest='meta', required=False, action='store_const', default=False, const=True,\n help='If set, no apps will be downloaded, but the meta_data will be saved.')\n parser.add_argument('--basedir', dest='base_dir', type=str, default=os.getenv('HOME'),\n required=False, help='Specifies the base path for both logs and apk_downloads.')\n parser.add_argument('--credentials', dest='credentials', type=str, required=False, default=None,\n help='Specifies the path to a credential file in .toml format.')\n parser.add_argument('--limit', dest='limit', type=int, required=False, default=None,\n help='Specifies the maximum number of apks per category to download.')\n return parser.parse_args()","def parse_args(args=None):\n return AP.parse_args(args=args)","def parse_args():\n parser = argparse.ArgumentParser(\n description='Convert CVAT XML annotations to PASCAL VOC format'\n )\n\n parser.add_argument(\n '--cvat-xml', metavar='FILE', required=True,\n help='input file with CVAT annotation in xml format'\n )\n\n parser.add_argument(\n '--image-dir', metavar='DIRECTORY', required=True,\n help='directory which contains original images'\n )\n\n parser.add_argument(\n '--output-dir', metavar='DIRECTORY', required=True,\n help='directory for output annotations in PASCAL VOC format'\n )\n\n return parser.parse_args()","def parse_args():\n p = argparse.ArgumentParser(\n description='Parse system logs, for fun or something')\n p.add_argument('-l', '--log', dest='log_file', help='The log file')\n p.add_argument('-f', '--filter', dest='filter', help='filter by daemon')\n return p.parse_args()","def arg_parse():\n\n parser = argparse.ArgumentParser()\n\n parser.add_argument(\n \"-f\",\n \"--fpath\",\n type=str,\n required=True,\n help=\"Path to files to generate test data from e.g. /badc/cmip5/data/cmip5/output1/MOHC/HadGEM2-ES/rcp85/mon/atmos/Amon/r1i1p1/latest/tas\",\n )\n\n parser.add_argument(\n \"-t\",\n \"--time_only\",\n default=False,\n help=\"Only generate one time step of this dataset\",\n action=\"store_true\",\n )\n\n parser.add_argument(\n \"-s\",\n \"--step\",\n type=int,\n default=100,\n help=\"Step to select latitude/longitude by. Only relevant when time_only is False\",\n )\n\n parser.add_argument(\n \"-n\",\n \"--number\",\n type=int,\n default=0,\n help=\"Number of files to generate. Default is all files. Only relevant when time_only is False\",\n )\n\n parser.add_argument(\n \"-l\",\n \"--level\",\n type=int,\n default=-1,\n help=\"Number of levels to extract, starting with index 0.\",\n )\n\n parser.add_argument(\n \"-c\", \"--compress\", help=\"Compress the files.\", action=\"store_true\"\n )\n\n return parser.parse_args()","def parse_args():\n parser = argparse.ArgumentParser(\n description='Run the destination IoT program (CTRL-C to exit)')\n\n parser.add_argument('-v',\n '--verbose',\n default=False,\n action='store_true',\n help='Print all debug logs')\n\n parser.add_argument('-p',\n '--port',\n metavar='<port number>',\n default=7777,\n type=int,\n help='Default: 7777')\n\n parser.add_argument('-a',\n '--address',\n metavar='<email_address>',\n nargs='*',\n help='Email address(es) to receive notifications')\n\n args = parser.parse_args()\n return args","def parse_args() -> argparse.ArgumentParser:\n parser = argparse.ArgumentParser(description=__doc__)\n\n # Enable command line arguments\n parser.add_argument('--process',\n action='store_true',\n help='Run data processing')\n parser.add_argument('--classify',\n action='store_true',\n help='Run classification')\n parser.add_argument('--lcs_file',\n type=str,\n help='Path to lcs file')\n parser.add_argument('--datasets_file',\n type=str,\n help='Path to datasets file')\n parser.add_argument('--mode',\n type=str,\n help=('Type of data to classify. r=realtime, f=full, r'\n 'fp=realtime+force_photo, ffp=full+force_photo'))\n parser.add_argument('--results_outfile',\n type=str,\n help='Filename to store results',\n default='KNC-Live_Results.csv')\n parser.add_argument('--results_dir',\n type=str,\n help='Directory to save results',\n default='knc_results/')\n parser.add_argument('--rfc_dir',\n type=str,\n help='Path to directory containing classifiers',\n default='classifiers/')\n parser.add_argument('--id_map_file',\n type=str,\n help='Name of ID map file in classifier directory',\n default='id_map.npy')\n parser.add_argument('--verbose',\n action='store_true',\n help='Print status updates')\n parser.add_argument('--skip_cv',\n action='store_true',\n help='Skip hyperparam optimization')\n parser.add_argument('--distribute',\n action='store_true',\n help='Use multiprocessing')\n\n return parser","def parse_args():\n parser = argparse.ArgumentParser(\n description='Convert CVAT XML annotations to YOLO format'\n )\n\n parser.add_argument(\n '--cvat-xml', metavar='FILE', required=True,\n help='input file with CVAT annotation in xml format'\n )\n\n parser.add_argument(\n '--image-dir', metavar='DIRECTORY', required=False,\n help='directory which contains original images'\n )\n\n parser.add_argument(\n '--output-dir', metavar='DIRECTORY', required=True,\n help='directory for output annotations in YOLO format'\n )\n\n parser.add_argument(\n '--username', metavar='USERNAME', required=False,\n help='Username from CVAT Login page, required to download images'\n )\n\n parser.add_argument(\n '--password', metavar='PASSWORD', required=False,\n help='Password from CVAT Login page, required to download images'\n )\n\n parser.add_argument(\n '--labels', metavar='ILABELS', required=False,\n help='Labels (separated by comma) to extract. Example: car,truck,motorcycle'\n )\n\n return parser.parse_args()","def parse_args():\n parser = argparse.ArgumentParser()\n parser.add_argument(\"-t\", \"-threads\", help=\"specifies a thread count for parallel operations\", type=int)\n return parser.parse_args()","def parse_command_line() -> argparse.Namespace:\n parser = argparse.ArgumentParser()\n parser.add_argument(\n 'pet_database',\n type=str,\n help='path to pet database'\n )\n parser.add_argument(\n '--image_dir',\n default='data/images'\n )\n parser.add_argument(\n '--log',\n default=None,\n help='log file path'\n )\n\n args = parser.parse_args()\n args.pet_database = os.path.abspath(os.path.expanduser(args.pet_database))\n args.image_dir = os.path.abspath(os.path.expanduser(args.image_dir))\n args.log = os.path.abspath(os.path.expanduser(args.log)) if args.log else None\n return args","def parseArgs():\n parser = argparse.ArgumentParser()\n parser.add_argument('--dataset', default='fsod', help='training dataset') # use fsod dataset for default\n parser.add_argument('--cfg', dest='cfg_file', required=True, help='optional config file')\n parser.add_argument('--load_ckpt', help='path to load checkpoint')\n parser.add_argument('--load_detectron', help='path to load detectron weight pickle file')\n parser.add_argument('--output_dir', help='output directory to save the testing results.')\n parser.add_argument('--range', help='[start, end)', type=int, nargs=2)\n parser.add_argument('--visualize', dest='visualize', help='output images of detection', action='store_true')\n return parser.parse_args()","def parse_cmd_args():\n parser = argparse.ArgumentParser()\n parser.add_argument('-m', '--path_model', type=str, help='Path to trained model.')\n parser.add_argument('-t', '--type', type=str, help='Either \"torch\" or \"sklearn\".')\n # parser.add_argument('-l', '--location', type=str, help='Either \"local\", \"midgard\" or \"rattle\"')\n parser.add_argument('-i', '--path_in', type=str, help='Path to input file.')\n parser.add_argument('-o', '--path_out', type=str, help='Path to output file.')\n parser.add_argument('-c', '--path_config', type=str, help='Path to config file.')\n parser.add_argument('-g', '--gpu', type=int, default=0, help='Number of the gpu to be used.')\n return parser.parse_args()","def parse_arguments():\n parser = argparse.ArgumentParser()\n parser.add_argument(\"-i\", \"--input\", dest=\"input_file\", help=\"input file or pattern\", default=\"\")\n parser.add_argument(\"-o\", \"--output\", dest=\"output_file\", help=\"output file or pattern\", default=\"\")\n parser.add_argument(\"-d\", \"--debug\", dest=\"debug\", action='store_true')\n parser.add_argument(\"-v\", \"--verbose\", dest=\"verbose\", action='store_true')\n parser.set_defaults(verbose=False)\n parser.set_defaults(debug=False)\n return parser.parse_args()","def _ParseCommandArguments():\n arg_parser = argparse.ArgumentParser()\n arg_parser.usage = __doc__\n\n arg_parser.add_argument('--download-dir',\n type=str,\n required=True,\n help='Directory into which corpora are downloaded.')\n arg_parser.add_argument('--build-dir',\n required=True,\n type=str,\n help='Directory where fuzzers were built.')\n args = arg_parser.parse_args()\n return args","def parse_args(args):\n\n parser = argparse.ArgumentParser(\n description=\"\"\"Generates and runs an afni_proc.py script to preprocess resting state fMRI data\"\"\",\n formatter_class=argparse.RawDescriptionHelpFormatter)\n\n # Optional Flags\n parser.add_argument(\"-t\", \"--trs_remove\", action=\"store\", default=5, type=int, metavar='TRs',\n help=\"\"\"number of trs to remove at the beginning of the epi data\n (default = 5 trs)\"\"\")\n parser.add_argument(\"-d\", \"--dim_voxel\", action=\"store\", default=2.0, type=float, metavar='MM',\n help=\"voxel dimensions in mm that processed epi will be resampled to (default = 2.0 mm)\")\n parser.add_argument(\"-b\", \"--bandpass\", action=\"store\", default=[0.01, 0.25], nargs=2, type=float, metavar=\"F\",\n help=\"bandpass frequencies lower and upper limits (default = 0.01 0.25)\")\n parser.add_argument(\"-v\", \"--volumes\", action=\"store\", default=0, type=int, metavar=\"V\",\n help=\"\"\"truncate the epi data to the inputted number of volumes, useful if subjects have data \n with different numbers of volumes (default = no truncation)\"\"\")\n parser.add_argument(\"-f\", \"--fwhm\", action=\"store\", default=5.0, type=float, metavar=\"MM\",\n help=\"the full width half maximum that is used when blurring (default = 5.0 mm)\")\n parser.add_argument(\"-c\", \"--cores\", action=\"store\", default=cpu_count(), type=int, metavar=\"C\",\n help=\"number of cores supplied to 3dDeconvolve (default = all cores)\")\n parser.add_argument(\"-s\", \"--subj_id\", action=\"store\", default=\"sub\", metavar=\"SUB\",\n help=\"text file of subject ids (default = sub)\")\n parser.add_argument(\"-T\", \"--time_step\", action=\"store\", default=0, type=float, metavar=\"TS\",\n help=\"set the time step for bandpassing (default = ts in header info\")\n\n parser.add_argument(\"-g\", \"--global_signal_regression\", action=\"store_false\", default=True,\n help=\"do not perform global signal regression (default = perform gsr)\")\n\n parser.add_argument(\"-r\", \"--rerun\", action=\"store_true\", default=False,\n help=\"\"\"rerun preprocessing, override and delete previous results in \n 'Processed' folder (default = don't override)\"\"\")\n parser.add_argument(\"-m\", \"--motion_param\", action=\"store_true\", default=False,\n help=\"use 12 motion parameters for regression (default = 6 motion parameters)\")\n parser.add_argument(\"-G\", \"--gm_blur\", action=\"store_true\", default=False,\n help=\"blur only in grey matter mask (default = blur in whole brain)\")\n parser.add_argument(\"-n\", \"--nl_reg\", action=\"store_true\", default=False,\n help=\"use non-linear warp between anatomical and MNI template (default = linear warp)\")\n\n # Required Inputs\n required = parser.add_argument_group(\"required arguments\")\n required.add_argument(\"-e\", \"--epi\", action=\"store\", required=True,\n help=\"text file of paths to raw epi data\")\n required.add_argument(\"-a\", \"--anat\", action=\"store\", required=True,\n help=\"text file of paths to raw anatomical data\")\n required.add_argument(\"-o\", \"--out_dir\", action=\"store\", required=True, metavar=\"OUT\",\n help=\"text file of paths to output directory\")\n result = parser.parse_args(args)\n\n # Make sure inputted parameters are legal\n assert (os.path.isfile(result.epi)), \"{} does not exist or is not a file\".format(result.epi)\n assert (os.path.isfile(result.anat)), \"{} does not exist or is not a file\".format(result.ant)\n assert (result.trs_remove >= 0), \"Cannot remove negative trs\"\n assert (result.dim_voxel >= 0), \"Cannot have a negative voxel dimension\"\n assert (np.all(np.array(result.bandpass) > 0)), \"Cannot have a negative frequency limit for bandpassing\"\n assert (result.volumes > -1), \"Number of volumes must be greater than 0\"\n assert (result.cores > 0), \"Number of cores used must be greater than 0\"\n assert (result.time_step > -1), \"Time step must be greater than 0\"\n\n return result","def parse_args():\n\tparser = argparse.ArgumentParser(description='Show video statistics.')\n\tparser.add_argument('--sort', metavar='FIELD', choices=['views', 'likes', 'dislikes'],\n\t default='views',\n\t help='sort by the specified field. Options are views, likes and dislikes.')\n\tparser.add_argument('--max', metavar='MAX', type=int, help='show the top MAX entries only.')\n\tparser.add_argument('--csv', action='store_true', default=False,\n\t help='output the data in CSV format.')\n\tparser.add_argument('--table', action='store_true', default=False,\n\t help='output the data in an ascii table.')\n\tparser.add_argument('--workers', type=int, default=8,\n\t help='number of workers to use, 8 by default.')\n\treturn parser.parse_args()","def _parse_args():\n args = sys.argv[1:]\n cmd_parser = argparse.ArgumentParser()\n cmd_parser.add_argument(\n '--produce-sub',\n dest='produce_sub',\n help='Produce submision file',\n default=False,\n action='store_true',\n )\n cmd_parser.add_argument(\n '--search-cv',\n dest='search_cv',\n help='Perform Search of parameters',\n default=False,\n action='store_true',\n )\n cmd_opts = cmd_parser.parse_args(args=args)\n return cmd_opts","def parse_args():\n parser = argparse.ArgumentParser()\n parser.add_argument(\n '--zarr_dir',\n type=str,\n help='path to directory of zarr files',\n )\n parser.add_argument(\n '--tiff_dir',\n type=str,\n help='path to directory of tiff files',\n )\n parser.add_argument(\n '--output_dir',\n type=str,\n help='path to directory for writing',\n )\n parser.add_argument(\n '--config_path',\n type=str,\n default=None,\n help='path to yaml preprocess config file',\n )\n \n args = parser.parse_args()\n return args","def parse_arguments():\n parser = ArgumentParser()\n\n # For development/testing\n parser.add_argument(\"--dev\", help=\"run the code of the developers tag\")\n\n return parser.parse_args()","def parse_args():\n parser = argparse.ArgumentParser(description=\"Sequence Cleaner: Remove Duplicate Sequences, etc\",\n epilog=\"example > sequence_cleaner -q INPUT -o OUTPUT\")\n parser.add_argument('-v', '--version', action='version', version='sequence_cleaner {}'.format(version))\n parser.add_argument(\"-q\", \"--query\", help=\"Path to directory with FAST(A/Q) files\", required=True)\n parser.add_argument(\"-o\", \"--output_directory\", help=\"Path to output files\", required=True)\n parser.add_argument(\"-ml\", \"--minimum_length\", help=\"Minimum length allowed (default=0 - allows all the lengths)\",\n default=\"0\")\n parser.add_argument(\"-mn\", \"--percentage_n\", help=\"Percentage of N is allowed (default=100)\", default=\"100\")\n parser.add_argument('--keep_all_duplicates', help='Keep All Duplicate Sequences', action='store_false', required=False)\n parser.add_argument('--remove_ambiguous', help='Remove any sequence with ambiguous bases', action='store_true', required=False)\n\n parser.add_argument('-l', '--log', help='Path to log file (Default: STDOUT).', required=False)\n\n return parser.parse_args()","def parse_args(args):\n \n # Construct the parser (which is stored in parser)\n # Module docstring lives in __doc__\n # See http://python-forum.com/pythonforum/viewtopic.php?f=3&t=36847\n # And a formatter class so our examples in the docstring look good. Isn't it\n # convenient how we already wrapped it to 80 characters?\n # See http://docs.python.org/library/argparse.html#formatter-class\n parser = argparse.ArgumentParser(description=__doc__, \n formatter_class=argparse.RawDescriptionHelpFormatter)\n \n # General options\n parser.add_argument(\"hal\",\n help=\"HAL file to evaluate\")\n parser.add_argument(\"--truth\",\n help=\"MAF file of a true alignment for precision and recall\")\n parser.add_argument(\"--beds\", nargs=\"*\",\n help=\"BED file(s) of genes on the genomes in the HAL\")\n parser.add_argument(\"--coverage_file\", type=argparse.FileType(\"w\"),\n default = sys.stdout,\n help=\"file to save average coverage vs. the reference in (one number)\")\n parser.add_argument(\"--precision_recall_file\", type=argparse.FileType(\"w\"),\n default = sys.stdout,\n help=\"TSV file to save precision and recall in (two numbers)\")\n parser.add_argument(\"--gene_category_file\", type=argparse.FileType(\"w\"),\n default = sys.stdout,\n help=\"file to save categories and counts for genes in\")\n parser.add_argument(\"--tag\", nargs=\"*\", default=[],\n help=\"extra columns to tag all result TSV lines with at the front\")\n \n # The command line arguments start with the program name, which we don't\n # want to treat as an argument for argparse. So we remove it.\n args = args[1:]\n \n return parser.parse_args(args)","def parse_args(args=None):\n\n\tparser = argparse.ArgumentParser(description=\"A simple python based static generator.\")\n\tparser.add_argument(\"--init\", action=\"store_true\", help=\"Initialize project.\")\n\tparser.add_argument(\"--gen\", action=\"store_true\", help=\"Generate static.\")\n\tparser.add_argument(\"--collect-static\", action=\"store_true\", help=\"Collect static.\")\n\n\tif args:\n\t\treturn parser.parse_args(args)\n\treturn parser.parse_args()","def parse_args():\n global Args\n parser = argparse.ArgumentParser()\n subparsers = parser.add_subparsers()\n pars_simulation(subparsers)\n pars_analyze(subparsers)\n Args = parser.parse_args()","def parse_args():\n \n parser = argparse.ArgumentParser()\n parser.add_argument(\n 'config',\n help='Config file')\n parser.add_argument(\n '--quiet',\n '-q',\n action='store_true',\n help='do not print to console'\n )\n parser.add_argument(\n '--password',\n '-p',\n action='store_true',\n help='Set password in keyring.'\n )\n parser.add_argument(\n '--update',\n '-u',\n action='store_true',\n help='Only add transactions after last date in database.'\n )\n parser.add_argument(\n '--mark_seen',\n '-m',\n action='store_true',\n help='Mark fetched emails as seen.'\n )\n\n return parser.parse_args()","def parse_command_line():\n parser = argparse.ArgumentParser(description='Parses ID\\'s from the DDI compendium search results, and then downloads the html and puts them into a sqlite database.')\n parser.add_argument('-f', '--file', dest='file',\n action='store',\n help='Filenname to be read')\n arg_manager = parser.parse_args()\n return arg_manager","def parse_args():\n parser = argparse.ArgumentParser(description=\"Re-ID feature extractor\")\n parser.add_argument(\n \"--model\",\n default=\"resources/networks/mars-small128.ckpt-68577\",\n help=\"Path to checkpoint file\")\n parser.add_argument(\n \"--loss_mode\", default=\"cosine\", help=\"Network loss training mode\")\n parser.add_argument(\n \"--mot_dir\", help=\"Path to MOTChallenge directory (train or test)\",\n required=True)\n parser.add_argument(\n \"--detection_dir\", help=\"Path to custom detections. Defaults to \"\n \"standard MOT detections Directory structure should be the default \"\n \"MOTChallenge structure: [sequence]/det/det.txt\", default=None)\n parser.add_argument(\n \"--output_dir\", help=\"Output directory. Will be created if it does not\"\n \" exist.\", default=\"detections\")\n return parser.parse_args()","def parse_args():\n help_text = \"\"\"\n Analyzer of the frequency of use of nouns in the headings of posts on hubr.com\n \"\"\"\n parser = argparse.ArgumentParser(\n description=help_text\n )\n parser.add_argument(\n '-p',\n '--pages',\n type=int,\n dest='page_count',\n default=PAGE_COUNT,\n help=f'Number of pages to parse, default is {PAGE_COUNT}.'\n )\n parser.add_argument(\n '-s',\n '--start',\n type=int,\n default=PAGE_START,\n dest='start_page',\n help=f'Start page number, default is {PAGE_START}.',\n )\n parser.add_argument(\n '-t',\n '--top',\n type=int,\n default=TOP_SIZE,\n dest='top_size',\n help=f'The size of the top noun, default is {TOP_SIZE}.',\n )\n\n return parser.parse_args()","def parse_args():\n parser = argparse.ArgumentParser(\n description='Aggregate Elasticsearch Log data.')\n parser.add_argument(\n '--host',\n default='https://logging-es',\n type=str,\n action='store',\n help='Host name or IP of the Elasticsearch server.'\n )\n parser.add_argument(\n '--port',\n default=9200,\n type=int,\n action='store',\n help='Port number of the Elasticsearch server.'\n )\n parser.add_argument(\n '--ca_certs',\n default='secret/admin-ca',\n type=str,\n action='store',\n help='Path to the CA certificates file'\n )\n parser.add_argument(\n '--cert',\n default='secret/admin-cert',\n type=str,\n action='store',\n help='Path to the client certificate file'\n )\n parser.add_argument(\n '--key',\n default='secret/admin-key',\n type=str,\n action='store',\n help='Path to the client key file'\n )\n\n return parser.parse_args()","def _parse_args():\n parser = argparse.ArgumentParser(description='Pure-python command-line calculator.')\n\n parser.add_argument('EXPRESSION', action=\"store\", type=str, help=\"expression string to evaluate\")\n parser.add_argument('-m', '--use-modules', nargs='+', action=\"store\", dest=\"MODULE\", type=str,\n help=\"additional modules to use\")\n\n return parser.parse_args()","def parseargs() -> argparse.ArgumentParser:\n\n parser = worker.parseargs(\"ACT hybrid-analysis.com Client\")\n\n parser.add_argument(\n \"--feed\", action=\"store_true\", help=\"Download the public feed only, no lookup\"\n )\n\n parser.add_argument(\n \"--apikey\", default=\"\", help=\"community apikey for hybrid-analysis.com\"\n )\n\n parser.add_argument(\n \"--user-agent\", default=\"Falcon Sandbox\", help=\"User agent while talking to API\"\n )\n\n parser.add_argument(\n \"--no-check-certificate\",\n action=\"store_true\",\n help=\"Do not check SSL certificate\",\n )\n\n return parser","def parse_args():\n parser = argparse.ArgumentParser(description='baseline Mask R-CNN')\n parser.add_argument('--dataset', required=True,\n metavar=\"/path/to/dataset/\",\n help='Directory of the dataset')\n parser.add_argument('--continue_train', type=str, required=False, default='None',\n metavar=\"/path/to/latest/weights.h5\", help=\"Path to lastest training weights .h5 file\")\n parser.add_argument('--weight', required=False,\n metavar='/path/to/pretrained/weight.h5', help=\"Path to trained weight\")\n parser.add_argument('--image', required=False,\n metavar='/path/to/testing/image/directory', help=\"Path to testing image directory\")\n parser.add_argument('--video', required=False,\n metavar='/path/to/testing/image/directory', help=\"Path to testing image directory\")\n return parser.parse_args()","def parse_args():\n\n parser = argparse.ArgumentParser(\n description='Assignment 1: Map directions.',\n epilog = 'If SERIALPORT is not specified, stdin/stdout are used.')\n parser.add_argument('-s', '--serial',\n help='path to serial port',\n dest='serialport',\n default=None)\n parser.add_argument('-v', dest='verbose',\n help='verbose',\n action='store_true')\n parser.add_argument('-g', '--graph',\n help='path to graph (DEFAULT = \" edmonton_roads.txt\")',\n dest='graphname',\n default='edmonton_roads.txt')\n\n return parser.parse_args()","def parse_args():\n # TODO default for model, conn, socket\n parser = argparse.ArgumentParser(description='Anomaly-based Network Intrusion Detection')\n parser.add_argument('--model', choices=model_choice.keys(),\n help='Model to train & use', metavar='model', required=True) # default=model_choice['Kitsune']\n # NOTE: args for offline models\n parser.add_argument('--csv', help='csv file to read network flow data from')\n parser.add_argument('--csv-dir', help='directory to read csv network flow data from')\n parser.add_argument('--pcap', help='pcap file to read data from')\n parser.add_argument('--pcap-dir', help='directory to read pcaps from')\n parser.add_argument('--parquet', help='parquet directory to read network flow data from')\n parser.add_argument('--parquet-dir', help='parquet directory to multiple parquet directory network flow data from')\n parser.add_argument('--tune', action='store_true', help='tune model before training to find best hyperparameters')\n # NOTE: args for online models\n parser.add_argument('--tsv', help='tsv file to read data from')\n parser.add_argument('--conn', help='Connection audit record file (netcap)') # default='/tmp/Connection.sock'\n parser.add_argument('--arp', help='ARP audit record file (netcap)')\n parser.add_argument('--credentials', help='Credentials audit record file (netcap)')\n parser.add_argument('--device', help='Device Profile audit record file (netcap)')\n parser.add_argument('--dhcpv4', help='DHCPv4 audit record file (netcap)')\n parser.add_argument('--dhcpv6', help='DHCPv6 audit record file (netcap)')\n parser.add_argument('--dns', help='DNS audit record file (netcap)')\n parser.add_argument('--ethernet', help='Ethernet audit record file (netcap)')\n parser.add_argument('--exploit', help='Exploit audit record file (netcap)')\n parser.add_argument('--http', help='HTTP audit record file (netcap)')\n parser.add_argument('--icmpv4', help='ICMPv4 audit record file (netcap)')\n parser.add_argument('--icmpv6', help='ICMPv6 audit record file (netcap)')\n parser.add_argument('--igmp', help='IGMP audit record file (netcap)')\n parser.add_argument('--ipprofile', help='IPProfile audit record file (netcap)')\n parser.add_argument('--ipv4', help='IPv4 audit record file (netcap)')\n parser.add_argument('--ipv6hop', help='IPv6HopByHop audit record file (netcap)')\n parser.add_argument('--ipv6', help='IPv6 audit record file (netcap)')\n parser.add_argument('--ntp', help='NTP audit record file (netcap)')\n parser.add_argument('--service', help='Servie audit record file (netcap)')\n parser.add_argument('--sip', help='SIP audit record file (netcap)')\n parser.add_argument('--software', help='Software audit record file (netcap)')\n parser.add_argument('--tcp', help='TCP audit record file (netcap)')\n parser.add_argument('--tls-client-hello', help='TLSClientHello audit record file(netcap)')\n parser.add_argument('--tls-server-hello', help='TLSServerHello audit record file(netcap)')\n parser.add_argument('--udp', help='UDP audit record file (netcap)')\n parser.add_argument('--vulnerability', help='vulnerability audit record file (netcap)')\n parser.add_argument('--audit', help='Read the given audit record types from unix sockets',\n choices=audit_records, nargs='*')\n parser.add_argument('--encoded', action='store_true', help='Read encoded audit records')\n parser.add_argument('--labelled', action='store_true', help='Read labelled audit records')\n return parser.parse_args()","def parse_args():\n\n parser = argparse.ArgumentParser(description=\"Benchmark Thing WoT server\")\n parser = utils.extend_server_arg_parser(parser)\n\n return parser.parse_args()","def parse_arguments():\n description = \" \".join(__doc__.splitlines()[4:])\n\n epilog = \", \".join(__doc__.splitlines()[1:4])\n parser = argparse.ArgumentParser(description=description, epilog=epilog)\n parser.add_argument(\n \"-i\",\n type=str,\n help=\"\"\"\n the multiple sequence alignment fasta file with extension .aln to\n use in input\n \"\"\",\n metavar=\"<file>\",\n required=True,\n )\n parser.add_argument(\n \"-o\",\n metavar=\"<file>\",\n type=str,\n help=\"the file where to write the profile numpy array\",\n required=True,\n )\n\n args = parser.parse_args()\n\n return args","def parse_args():\n parser = argparse.ArgumentParser(description=\"Deep SORT\")\n parser.add_argument(\n \"--input\", help=\"Path to MOTChallenge sequence directory\",\n default=None, required=True)\n return parser.parse_args()","def parse_cmdline_args():\n parser = argparse.ArgumentParser()\n parser.add_argument('-v', '--verbose',\n action=\"store_true\",\n help=\"Be chatty\")\n parser.add_argument('-d', '--dryrun',\n action=\"store_true\",\n help=\"Do not send emails\")\n foreman_config = ForemanConfig()\n foreman_config.add_standard_args(parser)\n parser.add_argument('-e', '--environments', type=str,\n default=DEFAULT_ENVIRONMENTS_PATH,\n help=\"Clone of it-puppet-environments\")\n args = parser.parse_args()\n return args","def _parse_cmd_args():\n parser = argparse.ArgumentParser()\n\n parser.add_argument(\"--root\", required=True,\n help=\"Root directory of data and logs.\")\n parser.add_argument(\"--log_dir\", default=\"logs\",\n help=\"Sub-directory of tensorboard logs.\")\n parser.add_argument(\"--gpu\", default=\"0\", help=\"GPU device ID.\")\n\n args = parser.parse_args()\n\n return args","def parse_args():\n parser = argparse.ArgumentParser()\n parser.add_argument(\"-t\", \"--thoughtspot_host\", required=True,\n help=\"domain or ip. E.g. http://1.1.1.1\")\n parser.add_argument(\"-u\", \"--username\", required=True,\n help=\"username - must have administrative privileges\")\n parser.add_argument(\"-p\", \"--password\", required=True,\n help=\"password - must have administrative privileges\")\n parser.add_argument(\"-d\", \"--delimiter\", default=',',\n help=\"character to seperate values by. Default to comma\")\n parser.add_argument(\"-c\", \"--csv\", action=\"store_true\",\n help=\"create csv file called permissions.csv\")\n parser.add_argument(\"-s\", \"--share\", action=\"store_true\",\n help=\"output usable format for share api\")\n return parser.parse_args()","def parse_args():\n parser = default_argument_parser()\n parser.add_argument(\"--label-map\",\n dest=\"label_map\",\n type=pathlib.Path,\n help=\"Label map in YAML format which maps from category \"\n \"ID to name.\")\n parser.add_argument(\"--train-csv\",\n dest=\"train_csv\",\n required=True,\n type=pathlib.Path,\n help=\"Path to training data CSV file.\")\n parser.add_argument(\"--valid-csv\",\n dest=\"valid_csv\",\n required=False,\n type=pathlib.Path,\n help=\"Optional path to validation data CSV file.\")\n parser.add_argument(\n \"--image-width\",\n type=int,\n help=\"Image width (optional, used to speed up dataset processing).\")\n parser.add_argument(\n \"--image-height\",\n type=int,\n help=\"Image height (optional, used to speed up dataset processing).\")\n return parser.parse_args()","def parse_args(argv):\n formatter_class = argparse.RawDescriptionHelpFormatter\n parser = argparse.ArgumentParser(description=\"Run torrent-knowledge cli\",\n formatter_class=formatter_class)\n\n parser.add_argument(\"-i\", \"--input_file\",\n type=argparse.FileType('r'),\n required=True,\n help=\"CSV with torrents in format 'info_hash|torrent_title'\")\n\n parser.add_argument(\"-o\", \"--output_file\",\n required=True,\n type=argparse.FileType('w'),\n default=sys.stdout)\n\n parser.add_argument(\"-v\", \"--verbose\", dest=\"verbose\",\n action=\"count\", default=0,\n help=\"increase log verbosity via -vvv\")\n\n parser.add_argument(\"-t\", \"--train-mode\", dest=\"train_mode\",\n action=\"count\", default=0,\n help=\"update features frequency from dataset\")\n\n parser.add_argument(\"-l\", \"--log-dir\", dest=\"log_dir\",\n action=\"store\", default=None,\n help=\"directory for log files (only for -vvv mode)\")\n\n args = parser.parse_args(argv[1:])\n\n if args.log_dir and args.verbose != 3:\n logger.debug(\"You should use -vvv with option -l\")\n exit(-1)\n\n return parser.parse_args(argv[1:])","def parse_args():\n parser = argparse.ArgumentParser()\n parser.add_argument(\n \"--start\",\n type=str,\n default=\"\",\n required=False,\n help=\"The start square of the agent, in the form row,col. If not specified, this is randomized\"\n )\n parser.add_argument(\n \"--actions\",\n type=str,\n required=True,\n nargs = '+',\n help=\"The actions the agent takes, comma delimited\"\n )\n parser.add_argument(\n \"--observations\",\n type=str,\n required=True,\n nargs = '+',\n help=\"The observations the agent makes, comma delimited\"\n )\n return parser.parse_args()","def command_line_parse(iargs=None):\n\n parser = create_parser()\n inps = parser.parse_args(args=iargs)\n\n return inps","def parse_arguments():\n parser = argparse.ArgumentParser(description=\n \"This script receives 'grouped_hits.txt', uses a FASTA file to get the \\\n \\nsequences, uses MAFFT to align each group, then generates a consensus \\\n \\nsequence for each group using biopython.\\n\\n \\\n Example: python {0} -g grouped_hits.txt -f sequences.fasta\".format(argv[0]),\n formatter_class = argparse.RawDescriptionHelpFormatter)\n \n requiredNamed = parser.add_argument_group('required arguments')\n\n requiredNamed.add_argument(\"-g\", \"--GROUPS\", type=str, required=True,\\\n help=\"Grouped hits file generated by group_self_BLAST.py\", action=\"store\")\n\n requiredNamed.add_argument(\"-f\", \"--FASTA\", type=str, required=True,\\\n help=\"FASTA file containing all sequences.\", action=\"store\")\n\n return parser.parse_args()","def __parse_cmd_args():\n parser = argparse.ArgumentParser(description='Python Image Downloader.')\n parser.add_argument(\"-f\", \"--file\",\n help=\"Where the URL file is located.\")\n parser.add_argument(\"-d\", \"--dir\",\n help=\"Where the downloaded files are to be stored.\")\n args = parser.parse_args()\n return args","def parse_args():\n parser = argparse.ArgumentParser()\n\n parser.add_argument('--source-account', required=True, type=str, help='Source account to pay transaction fees')\n parser.add_argument('--channel-seeds-file', required=True, type=str, help='File path to channel seeds file')\n parser.add_argument('--accounts', required=True, type=int, help='Amount of accounts to create')\n parser.add_argument('--passphrase', required=True, type=str, help='Network passphrase')\n parser.add_argument('--horizon', action='append', help='Horizon endpoint URL (use multiple --horizon flags for multiple addresses)')\n parser.add_argument('--json-output', required=False, type=bool, help='Export output to json format')\n return parser.parse_args()","def parse_arguments():\n\n parser = argparse.ArgumentParser()\n\n # Specify if one wants to perform the persistence forecast\n parser.add_argument(\n '-p',\n '--persistence-forecast',\n action='store_true',\n help='if set, perform persistence forecast'\n )\n\n # Specify if one wants to look for the best epoch value\n parser.add_argument(\n '-e',\n '--epoch',\n action='store_true',\n help='if set, look for the best epoch value'\n )\n\n return parser","def parse_args():\n parser = argparse.ArgumentParser()\n parser.add_argument('-e', '--env', default='production',\n help='Environment to check: integration, staging, production.')\n parser.add_argument('-l', '--log_type', default='govuk_assets',\n help='Which logs to check: govuk_assets, govuk_www.')\n parser.add_argument('-c', '--critical_age_minutes', type=int, default=60,\n help='If the newest logs are older than this many minutes, '\n 'return CRITICAL status.')\n parser.add_argument('-F', '--fake_time', type=fromisoformat,\n help='For testing purposes, use the given time as if it\\'s the current '\n 'time. Requires the format YYYY-MM-DDTHH:MM. Assumes UTC.')\n parser.add_argument('-v', '--verbose', action='count',\n help='Show DEBUG log messages.')\n return parser.parse_args()","def process_command_line_arguments() -> Namespace:\n\n parser = build_parser()\n arguments = parser.parse_args()\n\n return arguments","def parseArguments():\n parser = argparse.ArgumentParser()\n parser.add_argument('--output_folder',\n help='Path of the folder where output files should be written.')\n parser.add_argument('--partition_id',\n help='ID of the computer partition to collect data from.')\n parser.add_argument('--collector_db',\n help='The path of slapos collect database.')\n\n return parser.parse_args()","def handle_args():\n parser = argparse.ArgumentParser(\n description=\"\"\"Script to download archives from the NLM public\n FTP server.\n \"\"\")\n # Server settings\n server_settings = parser.add_argument_group('FTP SERVER SETTINGS', '')\n server_settings.add_argument(\n '-n', '--netrc', default='~/.netrc',\n help=\"\"\"netrc file containing login parameters for the NLM\n server. See `man 5 netrc` for details on generating this\n file or read nlm_data_import/netrc/example.netrc.\n \"\"\")\n server_settings.add_argument(\n 'server_data_dir',\n help='Directory containing desired files on the NLM FTP server')\n server_settings.add_argument(\n '-l', '--limit', type=int, default=0,\n help='Only download LIMIT files.')\n\n # Download settings\n local_settings = parser.add_argument_group('LOCAL SETTINGS', '')\n local_settings.add_argument(\n '-d', '--download_database', default='~/.ftp_download_db',\n help='Path to SQLite database detailing past downloads')\n local_settings.add_argument(\n '-o', '--output_dir', default='~/medline_data',\n help='Directory where downloads will be saved')\n local_settings.add_argument(\n '-x', '--export_dir', default='~/medline_data_exports',\n help=\"\"\"Directory where data to be retrieved by the\n `hypothesis_graph application server are staged.\n \"\"\")\n # Sending debug emails (requires the send_ses_messages module - see\n # setup.py)\n debugging_settings = parser.add_argument_group('DEBUGGING SETTINGS', '')\n debugging_settings.add_argument(\n '--email_debugging', default=False, action='store_true',\n help=\"Send debugging emails. Defaults to FALSE.\")\n debugging_settings.add_argument(\n '--from_email', required=False, help=\"FROM field for debugging emails\")\n debugging_settings.add_argument(\n '--to_email', required=False, help=\"TO field for debugging emails\")\n\n return parser.parse_args()","def parse_args(args):\n parser = argparse.ArgumentParser(\n description=\"Lookup table generator for Image Comparison\")\n parser.add_argument(\n \"--version\",\n action=\"version\",\n version=\"lookuptable {ver}\".format(ver=__version__))\n parser.add_argument(\n \"-f\",\n \"--folder\",\n dest=\"imagefolder\",\n help=\"path to image folder\",\n type=str,\n metavar=\"STRING\")\n parser.add_argument(\n \"-v\",\n \"--verbose\",\n dest=\"loglevel\",\n help=\"set loglevel to INFO\",\n action=\"store_const\",\n const=logging.INFO)\n parser.add_argument(\n \"-vv\",\n \"--very-verbose\",\n dest=\"loglevel\",\n help=\"set loglevel to DEBUG\",\n action=\"store_const\",\n const=logging.DEBUG)\n return parser.parse_args(args)","def command_line_arguments():\n\n try:\n parser = argparse.ArgumentParser(description='Log Handler/Cleaner/Copier for Idemia DocAuth')\n\n # Add required arguments.\n parser.add_argument('action', choices=['clean', 'download'], type=str, help='clean or download')\n\n # Parse the arguments\n args = parser.parse_args()\n\n return args\n\n except Exception as err:\n print(err)\n return","def parse_arguments():\n parser = argparse.ArgumentParser()\n parser.add_argument(\n \"--obs_len\",\n default=20,\n type=int,\n help=\"Directory where the sequences (csv files) are saved\",\n )\n parser.add_argument(\n \"--data_dir\",\n default=\"\",\n type=str,\n help=\"Directory where the sequences (csv files) are saved\",\n )\n parser.add_argument(\n \"--feature_dir\",\n default=\"\",\n type=str,\n help=\"Directory where the computed features are saved\",\n )\n parser.add_argument(\"--mode\",\n required=True,\n type=str,\n help=\"train/val/test/compute_all/lanes_only\")\n parser.add_argument(\n \"--sequence_num\",\n default=-1,\n type=int,\n help=\"Specify a specific sequence to visualize.\",\n )\n parser.add_argument(\n \"--batch_start\",\n default=0,\n type=int,\n help=\"Specify the starting row of features to visualize.\",\n )\n parser.add_argument(\n \"--batch_end\",\n default=-1,\n type=int,\n help=\"Specify the last row to visualize, -1 to visualize till end.\",\n )\n parser.add_argument(\n \"--single_figure\",\n default=False,\n action=\"store_true\",\n help=\"Plot all candidates for a scenein one figure.\",\n )\n return parser.parse_args()","def command_line_args(parser):\n AbyssAssembler.command_line_args(parser)\n SpadesAssembler.command_line_args(parser)\n TrinityAssembler.command_line_args(parser)\n VelvetAssembler.command_line_args(parser)","def _parse_arguments():\n import argparse\n\n parser = argparse.ArgumentParser(description=__doc__)\n\n parser.add_argument(\n 'list_of_files', type=str,\n help='Input ASCII file with a list of files to be downloaded')\n\n return parser.parse_args()","def parse_command_line(argv):\n parser = argparse.ArgumentParser(description=__doc__)\n parser.add_argument(\n \"--logfile\",\n default=None,\n type=str,\n help=\"If specified, write logs to this file.\",\n )\n parser.add_argument(\n \"--loglevel\",\n help=\"Set logging level (DEBUG, INFO, WARNING, ERROR, or CRITICAL).\",\n default=\"INFO\",\n )\n return parser.parse_args(argv[1:])"],"string":"[\n \"def arg_parse():\\n p = ap.ArgumentParser()\\n p.add_argument()\\n return p.parse_args()\",\n \"def parse_arguments():\\n parser = argparse.ArgumentParser()\\n parser.add_argument('-u', '--urls_dirpath', type=unicode)\\n parser.add_argument('-r', '--resources_dir', type=unicode)\\n parser.add_argument('-t', '--total_docs', type=int)\\n parser.add_argument('-m', '--mapping', type=unicode,\\n help='File with the yago to lkif mapping')\\n\\n return parser.parse_args()\",\n \"def parse_args():\\n parser = argparse.ArgumentParser(description=__doc__)\\n # If user doesn't specify an input file, read from standard input. Since\\n # encodings are the worst thing, we're explicitly expecting std\\n parser.add_argument('-i', '--infile',\\n type=lambda x: open(x, encoding=ENCODE_IN),\\n default=io.TextIOWrapper(\\n sys.stdin.buffer, encoding=ENCODE_IN)\\n )\\n # Same thing goes with the output file.\\n parser.add_argument('-o', '--outfile',\\n type=lambda x: open(x, 'w', encoding=ENCODE_OUT),\\n default=io.TextIOWrapper(\\n sys.stdout.buffer, encoding=ENCODE_OUT)\\n )\\n # Set the verbosity level for the logger. The `-v` option will set it to\\n # the debug level, while the `-q` will set it to the warning level.\\n # Otherwise use the info level.\\n verbosity = parser.add_mutually_exclusive_group()\\n verbosity.add_argument('-v', '--verbose', action='store_const',\\n const=logging.DEBUG, default=logging.INFO)\\n verbosity.add_argument('-q', '--quiet', dest='verbose',\\n action='store_const', const=logging.WARNING)\\n return parser.parse_args()\",\n \"def parse_args():\\n parser = argparse.ArgumentParser(description='Google reminders cli',\\n epilog=usage,\\n formatter_class=argparse.RawTextHelpFormatter)\\n return parser.parse_args()\",\n \"def parse_arguments(args):\",\n \"def parse_command_line(self, argv):\\n from optparse import OptionParser\\n usage = \\\"usage: %prog [options]\\\"\\n parser = OptionParser(usage)\\n\\n (options, args) = parser.parse_args(argv)\",\n \"def parse_args():\\n parser = argparse.ArgumentParser()\\n parser.add_argument(\\\"in_fq\\\", help=\\\"The fastq file containing Hi-C reads.\\\")\\n parser.add_argument(\\n \\\"-r\\\",\\n \\\"--reference\\\",\\n required=True,\\n help=\\\"Path to the reference genome, in FASTA format.\\\",\\n )\\n parser.add_argument(\\n \\\"-p\\\",\\n \\\"--nb_processors\\\",\\n default=1,\\n type=int,\\n help=\\\"number of CPUs used for alignment.\\\",\\n )\\n parser.add_argument(\\n \\\"-o\\\",\\n \\\"--out_sam\\\",\\n help=\\\"Path to the output SAM file for the alignment of in_fq.\\\",\\n )\\n parser.add_argument(\\n \\\"-T\\\",\\n \\\"--tempdir\\\",\\n default=\\\".\\\",\\n help=\\\"Directory to write temporary files. Defaults to current directory.\\\",\\n )\\n parser.add_argument(\\n \\\"-m\\\",\\n \\\"--minimap2\\\",\\n default=False,\\n action=\\\"store_true\\\",\\n help=\\\"Use minimap2 instead of bowtie for the alignment.\\\",\\n )\\n parser.add_argument(\\n \\\"-l\\\",\\n \\\"--min_len\\\",\\n type=int,\\n default=20,\\n help=\\\"Minimum length to which reads should be truncated.\\\",\\n )\\n return parser.parse_args()\",\n \"def __parse_args():\\n parser = argparse.ArgumentParser()\\n parser.add_argument('-f', '--force', action=\\\"store_true\\\", default=False,\\n help='overwrite existing database files during import')\\n parser.add_argument('-e', '--extension', action=\\\"store\\\", default='txt',\\n help='specify file extension. default is \\\"txt\\\"')\\n parser.add_argument('-d', '--delimiter', action=\\\"store\\\", default='\\\\t',\\n help='specify column delimiter. default is tab (\\\\\\\\t)')\\n parser.add_argument('-m', '--mark', action=\\\"store\\\", default='.',\\n help='specify decimal mark for numeric data. default is'\\n ' dot (.)')\\n parser.add_argument('-o', '--outformat', action=\\\"store\\\", default='npz',\\n help='specify output database format. default is \\\"npz\\\"'\\n ' for numpy database. use \\\"mat\\\" for matlab '\\n ' database format.')\\n parser.add_argument('-r', '--recursive', action=\\\"store_true\\\", default=False,\\n help='recursively walk through all sub-directories of'\\n ' current working directory')\\n parser.add_argument('-p', '--pcs', action=\\\"store_true\\\", default=True,\\n help='indicate if files are pcs files.')\\n parser.add_argument('-c', '--colheadlines', action=\\\"store\\\", default='1',\\n help='number of lines spanned by the column headers')\\n args = parser.parse_args()\\n return args\",\n \"def parse_arguments():\\n parser = argparse.ArgumentParser(prog='AdapterRunner', description='Adapter Runner Application')\\n parser.add_argument('-a', '--application', action='store', dest='app_name', help='Application Name',\\n metavar='<application_name>')\\n parser.add_argument('-fi', '--fetch_interval', action='store', dest='fetch_stats_interval', help='Fetch Stats Interval',\\n metavar='<fetch_interval in seconds>')\\n return parser.parse_args()\",\n \"def parse_args():\\n parser = argparse.ArgumentParser(\\n description=\\\"CUDAPOA Python API sample program.\\\")\\n parser.add_argument('-m',\\n help=\\\"Run MSA generation. By default consensusis generated.\\\",\\n action='store_true')\\n parser.add_argument('-p',\\n help=\\\"Print output MSA or consensus for each POA group.\\\",\\n action='store_true')\\n parser.add_argument('-l',\\n help=\\\"Use long or short read sample data.\\\",\\n action='store_true')\\n return parser.parse_args()\",\n \"def parse_args():\\n parser = argparse.ArgumentParser(description=__doc__)\\n return parser.parse_args()\",\n \"def parse_args():\\n parser = argparse.ArgumentParser()\\n parser.add_argument(\\n \\\"-d\\\",\\n \\\"--debug\\\",\\n help=\\\"Print lots of debugging statements\\\",\\n action=\\\"store_const\\\",\\n dest=\\\"loglevel\\\",\\n const=logging.DEBUG,\\n default=logging.ERROR,\\n )\\n parser.add_argument(\\n \\\"-v\\\",\\n \\\"--verbose\\\",\\n help=\\\"Be verbose\\\",\\n action=\\\"store_const\\\",\\n dest=\\\"loglevel\\\",\\n const=logging.INFO,\\n )\\n parser.add_argument(\\\"runscript\\\", default=None)\\n return parser.parse_args()\",\n \"def parse_cli_arguments():\\n parser = argparse.ArgumentParser('Generates a MANIFEST file used by the '\\n 'HMP2 AnADAMA2 workflows.')\\n parser.add_argument('-b', '--broad-data-sheet', required=True,\\n help='Broad data product status spreadsheet. '\\n 'Contains entries indicating new files to be '\\n 'processed.')\\n parser.add_argument('-o', '--output-manifest', required=True,\\n help='Path to desired output manifest file.')\\n parser.add_argument('-oi', '--origin-institute', required=True,\\n help='Name of institute submitting new files '\\n 'to be processed.')\\n parser.add_argument('-oc', '--origin-contact', required=True,\\n help='Contact person for corresponding origin '\\n 'institute.')\\n parser.add_argument('-oe', '--origin-contact-email', required=True,\\n help='Contact email for contact person.')\\n parser.add_argument('-p', '--project-name', dest='project', \\n required=True,\\n help='Project that sequence files belong too.')\\n\\n return parser.parse_args()\",\n \"def parse_args():\\n parser = argparse.ArgumentParser()\\n\\n parser.add_argument('--length', required=True, type=int, help='Test length in seconds')\\n parser.add_argument('--txs-per-ledger', required=True, type=int, help='Transaction rate to submit (spam) in parallel for every ledger round')\\n parser.add_argument('--prioritizer-seeds-file', required=True, type=str, help='File path to prioritizer seeds file')\\n parser.add_argument('--spammer-seeds-file', required=True, type=str, help='File path to spammer seeds file')\\n parser.add_argument('--out', default='spam-results-{}.json'.format(str(int(time.time()))), type=str, help='Spam results JSON output')\\n parser.add_argument('--avg-block-time', type=int, default=5, help='Average block time. Controls the time delay between every spam round and the one just after that')\\n\\n parser.add_argument('--passphrase', type=str, help='Network passphrase')\\n parser.add_argument('--horizon', action='append',\\n help='Horizon endpoint URL (use multiple --horizon flags for multiple addresses)')\\n\\n return parser.parse_args()\",\n \"def parse_args():\\n parser = argparse.ArgumentParser(description=\\\"SLOWFAST for AVA Dataset\\\")\\n parser.add_argument(\\\"--pipeline\\\", type=str,\\n default=\\\"../data/config/slowfast.pipeline\\\", help=\\\"SDK infer pipeline\\\")\\n parser.add_argument(\\\"--data_dir\\\", type=str, default=\\\"../data/input\\\",\\n help=\\\"Dataset contain frames and ava_annotations\\\")\\n args_opt = parser.parse_args()\\n return args_opt\",\n \"def parse_command_line():\\r\\n\\r\\n parser = argparse.ArgumentParser(description='User args')\\r\\n parser.add_argument(\\\"--action\\\", choices=['train', 'predict', 'demo', 'test'], required=True, help=\\\"Choose action.\\\")\\r\\n parser.add_argument(\\\"--model\\\", choices=['vgg', 'unet', 'fpn'], required=True, help=\\\"Choose model.\\\")\\r\\n parser.add_argument(\\\"--dataset\\\", choices=['full', 'small'], required=True, help=\\\"Choose dataset.\\\")\\r\\n\\r\\n return parser.parse_args()\",\n \"def _parse_args():\\n parser = argparse.ArgumentParser(description='Run DAFI.')\\n parser.add_argument('input_file', help='Name (path) of input file')\\n return parser.parse_args()\",\n \"def arg_parse():\\n parser = argparse.ArgumentParser()\\n # defines command line arguments\\n parser.add_argument('-i', '--input_file', help=\\\"The input .txt file \\\" +\\n \\\"within the current directory (the file you \\\" +\\n \\\"downloaded from VEP\\\")\\n parser.add_argument('-o', '--output_file', help=\\\"Name for the output \\\" +\\n \\\".txt file\\\")\\n\\n return parser.parse_args()\",\n \"def parse_args():\\n parser = argparse.ArgumentParser()\\n parser.add_argument(\\\"auth\\\",\\n help=\\\"authentication string for Infermedica API: \\\"\\n \\\"APP_ID:APP_KEY or path to file containing it.\\\")\\n parser.add_argument(\\\"--model\\\",\\n help=\\\"use non-standard Infermedica model/language, \\\"\\n \\\"e.g. infermedica-es\\\")\\n args = parser.parse_args()\\n return args\",\n \"def parse_arguments():\\n\\n parser = argparse.ArgumentParser()\\n\\n parser.add_argument(\\\"--accessions\\\", help=\\\"A json file with old/new family mapppings\\\")\\n parser.add_argument(\\\"--add-header\\\", help=\\\"Print descriptive header\\\",\\n action=\\\"store_true\\\", default=False)\\n parser.add_argument(\\\"--add-links\\\", help=\\\"Creates hyperlinks to available Rfam html content\\\",\\n action=\\\"store_true\\\", default=False)\\n return parser\",\n \"def parse_args():\\n parser = argparse.ArgumentParser(description=\\\"Bandits algorithms on a click-through \\\"\\n \\\"rate dataset.\\\")\\n parser.add_argument('--plot', action='store_true')\\n return parser.parse_args()\",\n \"def parse_args():\\n parser = argparse.ArgumentParser()\\n parser.add_argument('n_iter',\\n help='number of iteration',\\n type=int)\\n parser.add_argument('n_processes',\\n help='number of processes',\\n type=int)\\n parser.add_argument('method',\\n help='mutual exclusion method')\\n parser.add_argument('duration',\\n help='Duration of each process',\\n type=float)\\n return parser.parse_args()\",\n \"def parse_command_line():\\n parser = argparse.ArgumentParser()\\n\\n # All reference encoders\\n parser.add_argument(\\\"--step\\\", dest=\\\"step\\\", default=\\\"10\\\", type=int, help=\\\"step size\\\")\\n parser.add_argument(\\\"--repeats\\\", dest=\\\"repeats\\\", type=int, default=1, help=\\\"repeats\\\")\\n\\n parser.add_argument(dest=\\\"image\\\", default=None,\\n help=\\\"select the test image to run\\\")\\n\\n args = parser.parse_args()\\n return args\",\n \"def parse_command_line():\\n parser = argparse.ArgumentParser()\\n\\n help_str = \\\\\\n 'The collection folder to sort files into. ' \\\\\\n 'If the folder does not exist, it will be created along with the ' \\\\\\n 'necessary contents.'\\n parser.add_argument('-c', '--collection', help=help_str)\\n\\n help_str = \\\\\\n 'The source folder to import files from. Has to exist and ' \\\\\\n 'has to be a folder.'\\n parser.add_argument('-s', '--source', help=help_str, required=False)\\n\\n help_str = \\\\\\n 'View the gallery in random order auto skpping after the' \\\\\\n 'given amount of seconds'\\n parser.add_argument('-v', '--view', help=help_str, required=False)\\n\\n return parser.parse_args()\",\n \"def parse_command_line_arguments():\\n parser = argparse.ArgumentParser(\\n description=\\\"Convert dependency files into list of GitHub links.\\\",\\n epilog=\\\"For help with this program, contact John Speed at jmeyers@iqt.org.\\\",\\n )\\n parser.add_argument(\\n \\\"--python\\\",\\n default=False, # default value is False\\n help=\\\"Convert requirements.txt file into GitHub links.\\\",\\n )\\n return parser.parse_args()\",\n \"def parse_args():\\n parser = argparse.ArgumentParser(description=_program_description)\\n parser.add_argument('input_file', help=_input_file_description)\\n #parser.add_argument('-v', '--verbose', action='store_true', \\n # default=False, help='show progress')\\n args = parser.parse_args()\\n return args\",\n \"def parse_args():\\n parser = argparse.ArgumentParser()\\n argparse.ArgumentParser(argument_default=False)\\n parser.add_argument('-r', type=str, required=True, metavar='[reference.fa]', help=\\\"Reference file in fasta format\\\")\\n parser.add_argument('-s', type=int, metavar='number of SNPs', help=\\\"Number of SNPs to introduce\\\")\\n parser.add_argument('-i', type=int, metavar='number of indels', help=\\\"Number of small indels to introduce. Indel sizes randomly sampled between 1-100bp with a ratio of 9:1 for size being < 11bp\\\")\\n parser.add_argument('-d', type=int, metavar='number of large deletions', help=\\\"Number of large deletions to introduce. Deletion size randomly sampled between 100-5000bp\\\")\\n parser.add_argument('-t', type=int, metavar='number of translocations', help=\\\"number of translocations, regions will be randomly selected ranging between 500-5000bp in size\\\")\\n parser.add_argument('-nc', type=bool, default=False, metavar='add at least one non-conserved translocation', help=\\\"True/False add one non-conserved translocation. Deletion occurs from end of the translocation. Default=False\\\")\\n parser.add_argument('-f', type=str, required=True, metavar='output fasta filename', help=\\\"Output fasta filename and file path\\\")\\n parser.add_argument('-v', type=str, required=True, metavar='output vcf file of mutations', help=\\\"Output vcf filename and file path\\\")\\n\\n return parser.parse_args()\",\n \"def arg_parse():\\n parser = argparse.ArgumentParser(description=\\\"Fetches the current ATT&CK content expressed as STIX2 and creates spreadsheet mapping Techniques with Mitigations, Groups or Software.\\\")\\n parser.add_argument(\\\"-d\\\", \\\"--domain\\\", type=str, required=True, choices=[\\\"enterprise_attack\\\", \\\"mobile_attack\\\"], help=\\\"Which ATT&CK domain to use (Enterprise, Mobile).\\\")\\n parser.add_argument(\\\"-m\\\", \\\"--mapping-type\\\", type=str, required=True, choices=[\\\"groups\\\", \\\"mitigations\\\", \\\"software\\\"], help=\\\"Which type of object to output mappings for using ATT&CK content.\\\")\\n parser.add_argument(\\\"-t\\\", \\\"--tactic\\\", type=str, required=False, help=\\\" Filter based on this tactic name (e.g. initial-access) \\\" )\\n parser.add_argument(\\\"-s\\\", \\\"--save\\\", type=str, required=False, help=\\\"Save the CSV file with a different filename.\\\")\\n return parser\",\n \"def parse_args():\\n parser = argparse.ArgumentParser()\\n parser.add_argument(\\\"auth\\\",\\n help=\\\"authentication string for Infermedica API: \\\"\\n \\\"APP_ID:APP_KEY or path to file containing it.\\\")\\n parser.add_argument(\\\"--model\\\",\\n help=\\\"use non-standard Infermedica model/language, \\\"\\n \\\"e.g. infermedica-es\\\")\\n # TODO: Check if `verbose` actually does anything.\\n parser.add_argument(\\\"-v\\\", \\\"--verbose\\\",\\n dest=\\\"verbose\\\", action=\\\"store_true\\\", default=False,\\n help=\\\"dump internal state\\\")\\n args = parser.parse_args()\\n return args\",\n \"def _parse_args():\\n parser = argparse.ArgumentParser(description=\\\"\\\")\\n #parser.add_argument(\\\"args\\\", metavar=\\\"N\\\", type=str, nargs=\\\"*\\\", help=\\\"Positional arguments.\\\")\\n #parser.add_argument(\\\"\\\", dest=\\\"\\\", type=\\\"\\\", default=, help=)\\n #parser.add_argument(\\\"--version\\\", action=\\\"version\\\", version=\\\"<the version>\\\")\\n\\n return parser.parse_args()\",\n \"def parse_cmd_arguments():\\n parser = argparse.ArgumentParser(description='Process some integers.')\\n parser.add_argument('-i', '--input', required=True, help='input JSON file')\\n parser.add_argument('-o', '--output', required=True,\\n help='ouput JSON file')\\n parser.add_argument('-d', '--debug', required=False,\\n help='log level. Can be 0-3. Defaults to 0')\\n\\n return parser.parse_args()\",\n \"def parse_args():\\n\\n parser = argparse.ArgumentParser(description='Disk metric sender')\\n parser.add_argument('-v', '--verbose', action='store_true', default=None, help='Verbose?')\\n parser.add_argument('--debug', action='store_true', default=None, help='Debug?')\\n\\n return parser.parse_args()\",\n \"def parse_args():\\n\\n parser = argparse.ArgumentParser(description='CLI to store Actisense-NGT Gateway values to InfluxDB and publish via MQTT')\\n parser.add_argument('--config', '-c', type=str, required=True, help='JSON configuraton file with path')\\n return parser.parse_args()\",\n \"def get_args():\\n parser = argparse.ArgumentParser(description=\\\"Arguments for data exploration\\\")\\n parser.add_argument(\\\"--tokenize\\\",\\n dest=\\\"tokenize\\\",\\n action=\\\"store_true\\\",\\n help=\\\"Tokenize by words and sentences, counting averages/sd for each.\\\")\\n return parser\",\n \"def parse_command_line_arguments():\\n\\n description, epilog = __doc__.split(\\\"\\\\n\\\\n\\\", 1)\\n\\n parser = argparse.ArgumentParser(\\n formatter_class=argparse.RawDescriptionHelpFormatter,\\n description=description,\\n epilog=epilog)\\n\\n parser.add_argument('-s', '--s', dest='s', action='store', type=float, required=True,\\n help='Minimum frequency')\\n parser.add_argument('-c', '--credentials', dest='credentials', action='store',\\n default=\\\"./.tpass\\\",\\n help='File with Twitter credentials (username and password, separated by a space)')\\n\\n args = parser.parse_args()\\n \\n return args\",\n \"def parse_args():\\n parser = ArgumentParser(\\n description=\\\"This is a script for auto apply ipex optimization.\\\"\\n \\\"\\\\n################################# Basic usage ############################# \\\\n\\\"\\n \\\"\\\\n 1. Apply ipex optimization with fp32 data type\\\\n\\\"\\n \\\"\\\\n >>> python -m intel_extension_for_pytorch.cpu.auto_ipex python_script args \\\\n\\\"\\n \\\"\\\\n 2. Apply ipex optimization with bf16 data type\\\\n\\\"\\n \\\"\\\\n >>> python -m intel_extension_for_pytorch.cpu.auto_ipex --dtype bfloat16 python_script args \\\\n\\\",\\n formatter_class=RawTextHelpFormatter,\\n )\\n\\n add_auto_ipex_params(parser, auto_ipex_default_enabled=True)\\n\\n # positional\\n parser.add_argument(\\n \\\"program\\\",\\n type=str,\\n help=\\\"The full path to the proram/script to be launched. \\\"\\n \\\"followed by all the arguments for the script\\\",\\n )\\n # rest from the training program\\n parser.add_argument(\\\"program_args\\\", nargs=REMAINDER)\\n return parser.parse_args()\",\n \"def parse_args():\\n parser = argparse.ArgumentParser(description=\\\"Hyper parameter\\\")\\n parser.add_argument(\\n \\\"--model\\\", help=\\\"Model to use\\\", default=\\\"DenseNet169\\\", type=str)\\n parser.add_argument(\\n \\\"--optimizer\\\", help=\\\"which optimizer to use\\\", default=\\\"Adam\\\", type=str)\\n parser.add_argument(\\n \\\"--lr\\\", help=\\\"learning rate\\\", default=2e-5, type=float)\\n\\n return parser.parse_args()\",\n \"def parse_args():\\n parser = argparse.ArgumentParser(\\n fromfile_prefix_chars='@',\\n description='Convert CVAT XML annotations to masks'\\n )\\n\\n parser.add_argument(\\n '--cvat-xml', metavar='FILE', required=True,\\n help='input file with CVAT annotation in xml format'\\n )\\n\\n parser.add_argument(\\n '--background-color', metavar='COLOR_BGR', default=\\\"0,0,0\\\",\\n help='specify background color (by default: 0,0,0)'\\n )\\n\\n parser.add_argument(\\n '--label-color', metavar='LABEL:COLOR_BGR', action='append',\\n default=[],\\n help=\\\"specify a label's color (e.g. 255 or 255,0,0). The color will \\\" +\\n \\\"be interpreted in accordance with the mask format.\\\"\\n )\\n\\n parser.add_argument(\\n '--mask-bitness', type=int, choices=[8, 24], default=8,\\n help='choose bitness for masks'\\n )\\n\\n parser.add_argument(\\n '--output-dir', metavar='DIRECTORY', required=True,\\n help='directory for output masks'\\n )\\n\\n return parser.parse_args()\",\n \"def parse_arguments(args=sys.argv[1:]):\\n \\n parser = argparse.ArgumentParser()\\n \\n parser.add_argument('-i', '--input',\\n help=\\\"Path of input file to read. Default: {d}\\\".format(d=INPUT_FILE),\\n default=INPUT_FILE)\\n \\n return parser.parse_args(args)\",\n \"def parse_args():\\n parser = argparse.ArgumentParser(description=\\\"\\\")\\n parser.add_argument(\\n \\\"config_path\\\",\\n type=str,\\n help=\\\"Path to the JSON configuration file containing the image transformation settings.\\\",\\n )\\n parser.add_argument(\\n \\\"img_path\\\",\\n type=str,\\n help=\\\"Path to the input image file to apply transformations.\\\",\\n )\\n return parser.parse_args()\",\n \"def parse_args():\\n parser = ArgumentParser()\\n parser.add_argument('-t', '--timer', action='store_true', \\\\\\n help='Time the first random generation')\\n parser.add_argument('-i', '--ibmq', default='', help='IBMQ token')\\n parser.add_argument('-b', '--backend', default='', help='IBMQ backend')\\n return parser.parse_args()\",\n \"def parse_args():\\n\\n parser = argparse.ArgumentParser()\\n parser.add_argument(\\\"-i\\\", \\\"--input_path\\\", required=True)\\n parser.add_argument(\\\"-c\\\", \\\"--config\\\", required=True)\\n return parser.parse_args()\",\n \"def parse_args():\\n parser = argparse.ArgumentParser(\\\"BaselineMembershipInferenceAttack\\\")\\n parser.add_argument(\\\"--batch_size\\\",\\n type=int, default=128,\\n help=\\\"The batch size of normal training.\\\")\\n parser.add_argument(\\\"--train_epoch\\\",\\n type=int, default=10,\\n help=\\\"The epoch of training.\\\")\\n parser.add_argument(\\\"--train_lr\\\",\\n type=float, default=0.0002,\\n help=\\\"The learning rate of training.\\\")\\n args = parser.parse_args()\\n return args\",\n \"def parse_args():\\n parser = argparse.ArgumentParser(\\\"Run arguments for system submitted tasks\\\")\\n\\n parser.add_argument(\\\"-f\\\", \\\"--funcs\\\", type=str, nargs=\\\"?\\\", required=True,\\n help=\\\"path to pickle file containing a list of \\\"\\n \\\"functions/methods that should be run by the \\\"\\n \\\"submitted process\\\"\\n )\\n parser.add_argument(\\\"-k\\\", \\\"--kwargs\\\", type=str, nargs=\\\"?\\\", required=False,\\n default=None,\\n help=\\\"path to pickle file containing a dictionary of \\\"\\n \\\"keyword argumnets that should be passed to the \\\"\\n \\\"functions\\\")\\n parser.add_argument(\\\"-e\\\", \\\"--environment\\\", type=str, nargs=\\\"?\\\",\\n required=False,\\n help=\\\"Optional comma-separated environment variables, \\\"\\n \\\"which should be given as \\\"\\n \\\"VARNAME1=value1,VARNAME2=value2 and so on. These \\\"\\n \\\"will be separated and instantiated into Python's \\\"\\n \\\"os.environ\\\")\\n\\n return parser.parse_args()\",\n \"def parse_args():\\n parser = argparse.ArgumentParser(description='Crawl an Android app store for apk files.')\\n parser.add_argument('--store', dest='api', choices=['GooglePlay', 'F-Droid'], required=True,\\n help='Specifies the store to crawl. At the moment only Google Play is supported.')\\n parser.add_argument('--meta', dest='meta', required=False, action='store_const', default=False, const=True,\\n help='If set, no apps will be downloaded, but the meta_data will be saved.')\\n parser.add_argument('--basedir', dest='base_dir', type=str, default=os.getenv('HOME'),\\n required=False, help='Specifies the base path for both logs and apk_downloads.')\\n parser.add_argument('--credentials', dest='credentials', type=str, required=False, default=None,\\n help='Specifies the path to a credential file in .toml format.')\\n parser.add_argument('--limit', dest='limit', type=int, required=False, default=None,\\n help='Specifies the maximum number of apks per category to download.')\\n return parser.parse_args()\",\n \"def parse_args(args=None):\\n return AP.parse_args(args=args)\",\n \"def parse_args():\\n parser = argparse.ArgumentParser(\\n description='Convert CVAT XML annotations to PASCAL VOC format'\\n )\\n\\n parser.add_argument(\\n '--cvat-xml', metavar='FILE', required=True,\\n help='input file with CVAT annotation in xml format'\\n )\\n\\n parser.add_argument(\\n '--image-dir', metavar='DIRECTORY', required=True,\\n help='directory which contains original images'\\n )\\n\\n parser.add_argument(\\n '--output-dir', metavar='DIRECTORY', required=True,\\n help='directory for output annotations in PASCAL VOC format'\\n )\\n\\n return parser.parse_args()\",\n \"def parse_args():\\n p = argparse.ArgumentParser(\\n description='Parse system logs, for fun or something')\\n p.add_argument('-l', '--log', dest='log_file', help='The log file')\\n p.add_argument('-f', '--filter', dest='filter', help='filter by daemon')\\n return p.parse_args()\",\n \"def arg_parse():\\n\\n parser = argparse.ArgumentParser()\\n\\n parser.add_argument(\\n \\\"-f\\\",\\n \\\"--fpath\\\",\\n type=str,\\n required=True,\\n help=\\\"Path to files to generate test data from e.g. /badc/cmip5/data/cmip5/output1/MOHC/HadGEM2-ES/rcp85/mon/atmos/Amon/r1i1p1/latest/tas\\\",\\n )\\n\\n parser.add_argument(\\n \\\"-t\\\",\\n \\\"--time_only\\\",\\n default=False,\\n help=\\\"Only generate one time step of this dataset\\\",\\n action=\\\"store_true\\\",\\n )\\n\\n parser.add_argument(\\n \\\"-s\\\",\\n \\\"--step\\\",\\n type=int,\\n default=100,\\n help=\\\"Step to select latitude/longitude by. Only relevant when time_only is False\\\",\\n )\\n\\n parser.add_argument(\\n \\\"-n\\\",\\n \\\"--number\\\",\\n type=int,\\n default=0,\\n help=\\\"Number of files to generate. Default is all files. Only relevant when time_only is False\\\",\\n )\\n\\n parser.add_argument(\\n \\\"-l\\\",\\n \\\"--level\\\",\\n type=int,\\n default=-1,\\n help=\\\"Number of levels to extract, starting with index 0.\\\",\\n )\\n\\n parser.add_argument(\\n \\\"-c\\\", \\\"--compress\\\", help=\\\"Compress the files.\\\", action=\\\"store_true\\\"\\n )\\n\\n return parser.parse_args()\",\n \"def parse_args():\\n parser = argparse.ArgumentParser(\\n description='Run the destination IoT program (CTRL-C to exit)')\\n\\n parser.add_argument('-v',\\n '--verbose',\\n default=False,\\n action='store_true',\\n help='Print all debug logs')\\n\\n parser.add_argument('-p',\\n '--port',\\n metavar='<port number>',\\n default=7777,\\n type=int,\\n help='Default: 7777')\\n\\n parser.add_argument('-a',\\n '--address',\\n metavar='<email_address>',\\n nargs='*',\\n help='Email address(es) to receive notifications')\\n\\n args = parser.parse_args()\\n return args\",\n \"def parse_args() -> argparse.ArgumentParser:\\n parser = argparse.ArgumentParser(description=__doc__)\\n\\n # Enable command line arguments\\n parser.add_argument('--process',\\n action='store_true',\\n help='Run data processing')\\n parser.add_argument('--classify',\\n action='store_true',\\n help='Run classification')\\n parser.add_argument('--lcs_file',\\n type=str,\\n help='Path to lcs file')\\n parser.add_argument('--datasets_file',\\n type=str,\\n help='Path to datasets file')\\n parser.add_argument('--mode',\\n type=str,\\n help=('Type of data to classify. r=realtime, f=full, r'\\n 'fp=realtime+force_photo, ffp=full+force_photo'))\\n parser.add_argument('--results_outfile',\\n type=str,\\n help='Filename to store results',\\n default='KNC-Live_Results.csv')\\n parser.add_argument('--results_dir',\\n type=str,\\n help='Directory to save results',\\n default='knc_results/')\\n parser.add_argument('--rfc_dir',\\n type=str,\\n help='Path to directory containing classifiers',\\n default='classifiers/')\\n parser.add_argument('--id_map_file',\\n type=str,\\n help='Name of ID map file in classifier directory',\\n default='id_map.npy')\\n parser.add_argument('--verbose',\\n action='store_true',\\n help='Print status updates')\\n parser.add_argument('--skip_cv',\\n action='store_true',\\n help='Skip hyperparam optimization')\\n parser.add_argument('--distribute',\\n action='store_true',\\n help='Use multiprocessing')\\n\\n return parser\",\n \"def parse_args():\\n parser = argparse.ArgumentParser(\\n description='Convert CVAT XML annotations to YOLO format'\\n )\\n\\n parser.add_argument(\\n '--cvat-xml', metavar='FILE', required=True,\\n help='input file with CVAT annotation in xml format'\\n )\\n\\n parser.add_argument(\\n '--image-dir', metavar='DIRECTORY', required=False,\\n help='directory which contains original images'\\n )\\n\\n parser.add_argument(\\n '--output-dir', metavar='DIRECTORY', required=True,\\n help='directory for output annotations in YOLO format'\\n )\\n\\n parser.add_argument(\\n '--username', metavar='USERNAME', required=False,\\n help='Username from CVAT Login page, required to download images'\\n )\\n\\n parser.add_argument(\\n '--password', metavar='PASSWORD', required=False,\\n help='Password from CVAT Login page, required to download images'\\n )\\n\\n parser.add_argument(\\n '--labels', metavar='ILABELS', required=False,\\n help='Labels (separated by comma) to extract. Example: car,truck,motorcycle'\\n )\\n\\n return parser.parse_args()\",\n \"def parse_args():\\n parser = argparse.ArgumentParser()\\n parser.add_argument(\\\"-t\\\", \\\"-threads\\\", help=\\\"specifies a thread count for parallel operations\\\", type=int)\\n return parser.parse_args()\",\n \"def parse_command_line() -> argparse.Namespace:\\n parser = argparse.ArgumentParser()\\n parser.add_argument(\\n 'pet_database',\\n type=str,\\n help='path to pet database'\\n )\\n parser.add_argument(\\n '--image_dir',\\n default='data/images'\\n )\\n parser.add_argument(\\n '--log',\\n default=None,\\n help='log file path'\\n )\\n\\n args = parser.parse_args()\\n args.pet_database = os.path.abspath(os.path.expanduser(args.pet_database))\\n args.image_dir = os.path.abspath(os.path.expanduser(args.image_dir))\\n args.log = os.path.abspath(os.path.expanduser(args.log)) if args.log else None\\n return args\",\n \"def parseArgs():\\n parser = argparse.ArgumentParser()\\n parser.add_argument('--dataset', default='fsod', help='training dataset') # use fsod dataset for default\\n parser.add_argument('--cfg', dest='cfg_file', required=True, help='optional config file')\\n parser.add_argument('--load_ckpt', help='path to load checkpoint')\\n parser.add_argument('--load_detectron', help='path to load detectron weight pickle file')\\n parser.add_argument('--output_dir', help='output directory to save the testing results.')\\n parser.add_argument('--range', help='[start, end)', type=int, nargs=2)\\n parser.add_argument('--visualize', dest='visualize', help='output images of detection', action='store_true')\\n return parser.parse_args()\",\n \"def parse_cmd_args():\\n parser = argparse.ArgumentParser()\\n parser.add_argument('-m', '--path_model', type=str, help='Path to trained model.')\\n parser.add_argument('-t', '--type', type=str, help='Either \\\"torch\\\" or \\\"sklearn\\\".')\\n # parser.add_argument('-l', '--location', type=str, help='Either \\\"local\\\", \\\"midgard\\\" or \\\"rattle\\\"')\\n parser.add_argument('-i', '--path_in', type=str, help='Path to input file.')\\n parser.add_argument('-o', '--path_out', type=str, help='Path to output file.')\\n parser.add_argument('-c', '--path_config', type=str, help='Path to config file.')\\n parser.add_argument('-g', '--gpu', type=int, default=0, help='Number of the gpu to be used.')\\n return parser.parse_args()\",\n \"def parse_arguments():\\n parser = argparse.ArgumentParser()\\n parser.add_argument(\\\"-i\\\", \\\"--input\\\", dest=\\\"input_file\\\", help=\\\"input file or pattern\\\", default=\\\"\\\")\\n parser.add_argument(\\\"-o\\\", \\\"--output\\\", dest=\\\"output_file\\\", help=\\\"output file or pattern\\\", default=\\\"\\\")\\n parser.add_argument(\\\"-d\\\", \\\"--debug\\\", dest=\\\"debug\\\", action='store_true')\\n parser.add_argument(\\\"-v\\\", \\\"--verbose\\\", dest=\\\"verbose\\\", action='store_true')\\n parser.set_defaults(verbose=False)\\n parser.set_defaults(debug=False)\\n return parser.parse_args()\",\n \"def _ParseCommandArguments():\\n arg_parser = argparse.ArgumentParser()\\n arg_parser.usage = __doc__\\n\\n arg_parser.add_argument('--download-dir',\\n type=str,\\n required=True,\\n help='Directory into which corpora are downloaded.')\\n arg_parser.add_argument('--build-dir',\\n required=True,\\n type=str,\\n help='Directory where fuzzers were built.')\\n args = arg_parser.parse_args()\\n return args\",\n \"def parse_args(args):\\n\\n parser = argparse.ArgumentParser(\\n description=\\\"\\\"\\\"Generates and runs an afni_proc.py script to preprocess resting state fMRI data\\\"\\\"\\\",\\n formatter_class=argparse.RawDescriptionHelpFormatter)\\n\\n # Optional Flags\\n parser.add_argument(\\\"-t\\\", \\\"--trs_remove\\\", action=\\\"store\\\", default=5, type=int, metavar='TRs',\\n help=\\\"\\\"\\\"number of trs to remove at the beginning of the epi data\\n (default = 5 trs)\\\"\\\"\\\")\\n parser.add_argument(\\\"-d\\\", \\\"--dim_voxel\\\", action=\\\"store\\\", default=2.0, type=float, metavar='MM',\\n help=\\\"voxel dimensions in mm that processed epi will be resampled to (default = 2.0 mm)\\\")\\n parser.add_argument(\\\"-b\\\", \\\"--bandpass\\\", action=\\\"store\\\", default=[0.01, 0.25], nargs=2, type=float, metavar=\\\"F\\\",\\n help=\\\"bandpass frequencies lower and upper limits (default = 0.01 0.25)\\\")\\n parser.add_argument(\\\"-v\\\", \\\"--volumes\\\", action=\\\"store\\\", default=0, type=int, metavar=\\\"V\\\",\\n help=\\\"\\\"\\\"truncate the epi data to the inputted number of volumes, useful if subjects have data \\n with different numbers of volumes (default = no truncation)\\\"\\\"\\\")\\n parser.add_argument(\\\"-f\\\", \\\"--fwhm\\\", action=\\\"store\\\", default=5.0, type=float, metavar=\\\"MM\\\",\\n help=\\\"the full width half maximum that is used when blurring (default = 5.0 mm)\\\")\\n parser.add_argument(\\\"-c\\\", \\\"--cores\\\", action=\\\"store\\\", default=cpu_count(), type=int, metavar=\\\"C\\\",\\n help=\\\"number of cores supplied to 3dDeconvolve (default = all cores)\\\")\\n parser.add_argument(\\\"-s\\\", \\\"--subj_id\\\", action=\\\"store\\\", default=\\\"sub\\\", metavar=\\\"SUB\\\",\\n help=\\\"text file of subject ids (default = sub)\\\")\\n parser.add_argument(\\\"-T\\\", \\\"--time_step\\\", action=\\\"store\\\", default=0, type=float, metavar=\\\"TS\\\",\\n help=\\\"set the time step for bandpassing (default = ts in header info\\\")\\n\\n parser.add_argument(\\\"-g\\\", \\\"--global_signal_regression\\\", action=\\\"store_false\\\", default=True,\\n help=\\\"do not perform global signal regression (default = perform gsr)\\\")\\n\\n parser.add_argument(\\\"-r\\\", \\\"--rerun\\\", action=\\\"store_true\\\", default=False,\\n help=\\\"\\\"\\\"rerun preprocessing, override and delete previous results in \\n 'Processed' folder (default = don't override)\\\"\\\"\\\")\\n parser.add_argument(\\\"-m\\\", \\\"--motion_param\\\", action=\\\"store_true\\\", default=False,\\n help=\\\"use 12 motion parameters for regression (default = 6 motion parameters)\\\")\\n parser.add_argument(\\\"-G\\\", \\\"--gm_blur\\\", action=\\\"store_true\\\", default=False,\\n help=\\\"blur only in grey matter mask (default = blur in whole brain)\\\")\\n parser.add_argument(\\\"-n\\\", \\\"--nl_reg\\\", action=\\\"store_true\\\", default=False,\\n help=\\\"use non-linear warp between anatomical and MNI template (default = linear warp)\\\")\\n\\n # Required Inputs\\n required = parser.add_argument_group(\\\"required arguments\\\")\\n required.add_argument(\\\"-e\\\", \\\"--epi\\\", action=\\\"store\\\", required=True,\\n help=\\\"text file of paths to raw epi data\\\")\\n required.add_argument(\\\"-a\\\", \\\"--anat\\\", action=\\\"store\\\", required=True,\\n help=\\\"text file of paths to raw anatomical data\\\")\\n required.add_argument(\\\"-o\\\", \\\"--out_dir\\\", action=\\\"store\\\", required=True, metavar=\\\"OUT\\\",\\n help=\\\"text file of paths to output directory\\\")\\n result = parser.parse_args(args)\\n\\n # Make sure inputted parameters are legal\\n assert (os.path.isfile(result.epi)), \\\"{} does not exist or is not a file\\\".format(result.epi)\\n assert (os.path.isfile(result.anat)), \\\"{} does not exist or is not a file\\\".format(result.ant)\\n assert (result.trs_remove >= 0), \\\"Cannot remove negative trs\\\"\\n assert (result.dim_voxel >= 0), \\\"Cannot have a negative voxel dimension\\\"\\n assert (np.all(np.array(result.bandpass) > 0)), \\\"Cannot have a negative frequency limit for bandpassing\\\"\\n assert (result.volumes > -1), \\\"Number of volumes must be greater than 0\\\"\\n assert (result.cores > 0), \\\"Number of cores used must be greater than 0\\\"\\n assert (result.time_step > -1), \\\"Time step must be greater than 0\\\"\\n\\n return result\",\n \"def parse_args():\\n\\tparser = argparse.ArgumentParser(description='Show video statistics.')\\n\\tparser.add_argument('--sort', metavar='FIELD', choices=['views', 'likes', 'dislikes'],\\n\\t default='views',\\n\\t help='sort by the specified field. Options are views, likes and dislikes.')\\n\\tparser.add_argument('--max', metavar='MAX', type=int, help='show the top MAX entries only.')\\n\\tparser.add_argument('--csv', action='store_true', default=False,\\n\\t help='output the data in CSV format.')\\n\\tparser.add_argument('--table', action='store_true', default=False,\\n\\t help='output the data in an ascii table.')\\n\\tparser.add_argument('--workers', type=int, default=8,\\n\\t help='number of workers to use, 8 by default.')\\n\\treturn parser.parse_args()\",\n \"def _parse_args():\\n args = sys.argv[1:]\\n cmd_parser = argparse.ArgumentParser()\\n cmd_parser.add_argument(\\n '--produce-sub',\\n dest='produce_sub',\\n help='Produce submision file',\\n default=False,\\n action='store_true',\\n )\\n cmd_parser.add_argument(\\n '--search-cv',\\n dest='search_cv',\\n help='Perform Search of parameters',\\n default=False,\\n action='store_true',\\n )\\n cmd_opts = cmd_parser.parse_args(args=args)\\n return cmd_opts\",\n \"def parse_args():\\n parser = argparse.ArgumentParser()\\n parser.add_argument(\\n '--zarr_dir',\\n type=str,\\n help='path to directory of zarr files',\\n )\\n parser.add_argument(\\n '--tiff_dir',\\n type=str,\\n help='path to directory of tiff files',\\n )\\n parser.add_argument(\\n '--output_dir',\\n type=str,\\n help='path to directory for writing',\\n )\\n parser.add_argument(\\n '--config_path',\\n type=str,\\n default=None,\\n help='path to yaml preprocess config file',\\n )\\n \\n args = parser.parse_args()\\n return args\",\n \"def parse_arguments():\\n parser = ArgumentParser()\\n\\n # For development/testing\\n parser.add_argument(\\\"--dev\\\", help=\\\"run the code of the developers tag\\\")\\n\\n return parser.parse_args()\",\n \"def parse_args():\\n parser = argparse.ArgumentParser(description=\\\"Sequence Cleaner: Remove Duplicate Sequences, etc\\\",\\n epilog=\\\"example > sequence_cleaner -q INPUT -o OUTPUT\\\")\\n parser.add_argument('-v', '--version', action='version', version='sequence_cleaner {}'.format(version))\\n parser.add_argument(\\\"-q\\\", \\\"--query\\\", help=\\\"Path to directory with FAST(A/Q) files\\\", required=True)\\n parser.add_argument(\\\"-o\\\", \\\"--output_directory\\\", help=\\\"Path to output files\\\", required=True)\\n parser.add_argument(\\\"-ml\\\", \\\"--minimum_length\\\", help=\\\"Minimum length allowed (default=0 - allows all the lengths)\\\",\\n default=\\\"0\\\")\\n parser.add_argument(\\\"-mn\\\", \\\"--percentage_n\\\", help=\\\"Percentage of N is allowed (default=100)\\\", default=\\\"100\\\")\\n parser.add_argument('--keep_all_duplicates', help='Keep All Duplicate Sequences', action='store_false', required=False)\\n parser.add_argument('--remove_ambiguous', help='Remove any sequence with ambiguous bases', action='store_true', required=False)\\n\\n parser.add_argument('-l', '--log', help='Path to log file (Default: STDOUT).', required=False)\\n\\n return parser.parse_args()\",\n \"def parse_args(args):\\n \\n # Construct the parser (which is stored in parser)\\n # Module docstring lives in __doc__\\n # See http://python-forum.com/pythonforum/viewtopic.php?f=3&t=36847\\n # And a formatter class so our examples in the docstring look good. Isn't it\\n # convenient how we already wrapped it to 80 characters?\\n # See http://docs.python.org/library/argparse.html#formatter-class\\n parser = argparse.ArgumentParser(description=__doc__, \\n formatter_class=argparse.RawDescriptionHelpFormatter)\\n \\n # General options\\n parser.add_argument(\\\"hal\\\",\\n help=\\\"HAL file to evaluate\\\")\\n parser.add_argument(\\\"--truth\\\",\\n help=\\\"MAF file of a true alignment for precision and recall\\\")\\n parser.add_argument(\\\"--beds\\\", nargs=\\\"*\\\",\\n help=\\\"BED file(s) of genes on the genomes in the HAL\\\")\\n parser.add_argument(\\\"--coverage_file\\\", type=argparse.FileType(\\\"w\\\"),\\n default = sys.stdout,\\n help=\\\"file to save average coverage vs. the reference in (one number)\\\")\\n parser.add_argument(\\\"--precision_recall_file\\\", type=argparse.FileType(\\\"w\\\"),\\n default = sys.stdout,\\n help=\\\"TSV file to save precision and recall in (two numbers)\\\")\\n parser.add_argument(\\\"--gene_category_file\\\", type=argparse.FileType(\\\"w\\\"),\\n default = sys.stdout,\\n help=\\\"file to save categories and counts for genes in\\\")\\n parser.add_argument(\\\"--tag\\\", nargs=\\\"*\\\", default=[],\\n help=\\\"extra columns to tag all result TSV lines with at the front\\\")\\n \\n # The command line arguments start with the program name, which we don't\\n # want to treat as an argument for argparse. So we remove it.\\n args = args[1:]\\n \\n return parser.parse_args(args)\",\n \"def parse_args(args=None):\\n\\n\\tparser = argparse.ArgumentParser(description=\\\"A simple python based static generator.\\\")\\n\\tparser.add_argument(\\\"--init\\\", action=\\\"store_true\\\", help=\\\"Initialize project.\\\")\\n\\tparser.add_argument(\\\"--gen\\\", action=\\\"store_true\\\", help=\\\"Generate static.\\\")\\n\\tparser.add_argument(\\\"--collect-static\\\", action=\\\"store_true\\\", help=\\\"Collect static.\\\")\\n\\n\\tif args:\\n\\t\\treturn parser.parse_args(args)\\n\\treturn parser.parse_args()\",\n \"def parse_args():\\n global Args\\n parser = argparse.ArgumentParser()\\n subparsers = parser.add_subparsers()\\n pars_simulation(subparsers)\\n pars_analyze(subparsers)\\n Args = parser.parse_args()\",\n \"def parse_args():\\n \\n parser = argparse.ArgumentParser()\\n parser.add_argument(\\n 'config',\\n help='Config file')\\n parser.add_argument(\\n '--quiet',\\n '-q',\\n action='store_true',\\n help='do not print to console'\\n )\\n parser.add_argument(\\n '--password',\\n '-p',\\n action='store_true',\\n help='Set password in keyring.'\\n )\\n parser.add_argument(\\n '--update',\\n '-u',\\n action='store_true',\\n help='Only add transactions after last date in database.'\\n )\\n parser.add_argument(\\n '--mark_seen',\\n '-m',\\n action='store_true',\\n help='Mark fetched emails as seen.'\\n )\\n\\n return parser.parse_args()\",\n \"def parse_command_line():\\n parser = argparse.ArgumentParser(description='Parses ID\\\\'s from the DDI compendium search results, and then downloads the html and puts them into a sqlite database.')\\n parser.add_argument('-f', '--file', dest='file',\\n action='store',\\n help='Filenname to be read')\\n arg_manager = parser.parse_args()\\n return arg_manager\",\n \"def parse_args():\\n parser = argparse.ArgumentParser(description=\\\"Re-ID feature extractor\\\")\\n parser.add_argument(\\n \\\"--model\\\",\\n default=\\\"resources/networks/mars-small128.ckpt-68577\\\",\\n help=\\\"Path to checkpoint file\\\")\\n parser.add_argument(\\n \\\"--loss_mode\\\", default=\\\"cosine\\\", help=\\\"Network loss training mode\\\")\\n parser.add_argument(\\n \\\"--mot_dir\\\", help=\\\"Path to MOTChallenge directory (train or test)\\\",\\n required=True)\\n parser.add_argument(\\n \\\"--detection_dir\\\", help=\\\"Path to custom detections. Defaults to \\\"\\n \\\"standard MOT detections Directory structure should be the default \\\"\\n \\\"MOTChallenge structure: [sequence]/det/det.txt\\\", default=None)\\n parser.add_argument(\\n \\\"--output_dir\\\", help=\\\"Output directory. Will be created if it does not\\\"\\n \\\" exist.\\\", default=\\\"detections\\\")\\n return parser.parse_args()\",\n \"def parse_args():\\n help_text = \\\"\\\"\\\"\\n Analyzer of the frequency of use of nouns in the headings of posts on hubr.com\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(\\n description=help_text\\n )\\n parser.add_argument(\\n '-p',\\n '--pages',\\n type=int,\\n dest='page_count',\\n default=PAGE_COUNT,\\n help=f'Number of pages to parse, default is {PAGE_COUNT}.'\\n )\\n parser.add_argument(\\n '-s',\\n '--start',\\n type=int,\\n default=PAGE_START,\\n dest='start_page',\\n help=f'Start page number, default is {PAGE_START}.',\\n )\\n parser.add_argument(\\n '-t',\\n '--top',\\n type=int,\\n default=TOP_SIZE,\\n dest='top_size',\\n help=f'The size of the top noun, default is {TOP_SIZE}.',\\n )\\n\\n return parser.parse_args()\",\n \"def parse_args():\\n parser = argparse.ArgumentParser(\\n description='Aggregate Elasticsearch Log data.')\\n parser.add_argument(\\n '--host',\\n default='https://logging-es',\\n type=str,\\n action='store',\\n help='Host name or IP of the Elasticsearch server.'\\n )\\n parser.add_argument(\\n '--port',\\n default=9200,\\n type=int,\\n action='store',\\n help='Port number of the Elasticsearch server.'\\n )\\n parser.add_argument(\\n '--ca_certs',\\n default='secret/admin-ca',\\n type=str,\\n action='store',\\n help='Path to the CA certificates file'\\n )\\n parser.add_argument(\\n '--cert',\\n default='secret/admin-cert',\\n type=str,\\n action='store',\\n help='Path to the client certificate file'\\n )\\n parser.add_argument(\\n '--key',\\n default='secret/admin-key',\\n type=str,\\n action='store',\\n help='Path to the client key file'\\n )\\n\\n return parser.parse_args()\",\n \"def _parse_args():\\n parser = argparse.ArgumentParser(description='Pure-python command-line calculator.')\\n\\n parser.add_argument('EXPRESSION', action=\\\"store\\\", type=str, help=\\\"expression string to evaluate\\\")\\n parser.add_argument('-m', '--use-modules', nargs='+', action=\\\"store\\\", dest=\\\"MODULE\\\", type=str,\\n help=\\\"additional modules to use\\\")\\n\\n return parser.parse_args()\",\n \"def parseargs() -> argparse.ArgumentParser:\\n\\n parser = worker.parseargs(\\\"ACT hybrid-analysis.com Client\\\")\\n\\n parser.add_argument(\\n \\\"--feed\\\", action=\\\"store_true\\\", help=\\\"Download the public feed only, no lookup\\\"\\n )\\n\\n parser.add_argument(\\n \\\"--apikey\\\", default=\\\"\\\", help=\\\"community apikey for hybrid-analysis.com\\\"\\n )\\n\\n parser.add_argument(\\n \\\"--user-agent\\\", default=\\\"Falcon Sandbox\\\", help=\\\"User agent while talking to API\\\"\\n )\\n\\n parser.add_argument(\\n \\\"--no-check-certificate\\\",\\n action=\\\"store_true\\\",\\n help=\\\"Do not check SSL certificate\\\",\\n )\\n\\n return parser\",\n \"def parse_args():\\n parser = argparse.ArgumentParser(description='baseline Mask R-CNN')\\n parser.add_argument('--dataset', required=True,\\n metavar=\\\"/path/to/dataset/\\\",\\n help='Directory of the dataset')\\n parser.add_argument('--continue_train', type=str, required=False, default='None',\\n metavar=\\\"/path/to/latest/weights.h5\\\", help=\\\"Path to lastest training weights .h5 file\\\")\\n parser.add_argument('--weight', required=False,\\n metavar='/path/to/pretrained/weight.h5', help=\\\"Path to trained weight\\\")\\n parser.add_argument('--image', required=False,\\n metavar='/path/to/testing/image/directory', help=\\\"Path to testing image directory\\\")\\n parser.add_argument('--video', required=False,\\n metavar='/path/to/testing/image/directory', help=\\\"Path to testing image directory\\\")\\n return parser.parse_args()\",\n \"def parse_args():\\n\\n parser = argparse.ArgumentParser(\\n description='Assignment 1: Map directions.',\\n epilog = 'If SERIALPORT is not specified, stdin/stdout are used.')\\n parser.add_argument('-s', '--serial',\\n help='path to serial port',\\n dest='serialport',\\n default=None)\\n parser.add_argument('-v', dest='verbose',\\n help='verbose',\\n action='store_true')\\n parser.add_argument('-g', '--graph',\\n help='path to graph (DEFAULT = \\\" edmonton_roads.txt\\\")',\\n dest='graphname',\\n default='edmonton_roads.txt')\\n\\n return parser.parse_args()\",\n \"def parse_args():\\n # TODO default for model, conn, socket\\n parser = argparse.ArgumentParser(description='Anomaly-based Network Intrusion Detection')\\n parser.add_argument('--model', choices=model_choice.keys(),\\n help='Model to train & use', metavar='model', required=True) # default=model_choice['Kitsune']\\n # NOTE: args for offline models\\n parser.add_argument('--csv', help='csv file to read network flow data from')\\n parser.add_argument('--csv-dir', help='directory to read csv network flow data from')\\n parser.add_argument('--pcap', help='pcap file to read data from')\\n parser.add_argument('--pcap-dir', help='directory to read pcaps from')\\n parser.add_argument('--parquet', help='parquet directory to read network flow data from')\\n parser.add_argument('--parquet-dir', help='parquet directory to multiple parquet directory network flow data from')\\n parser.add_argument('--tune', action='store_true', help='tune model before training to find best hyperparameters')\\n # NOTE: args for online models\\n parser.add_argument('--tsv', help='tsv file to read data from')\\n parser.add_argument('--conn', help='Connection audit record file (netcap)') # default='/tmp/Connection.sock'\\n parser.add_argument('--arp', help='ARP audit record file (netcap)')\\n parser.add_argument('--credentials', help='Credentials audit record file (netcap)')\\n parser.add_argument('--device', help='Device Profile audit record file (netcap)')\\n parser.add_argument('--dhcpv4', help='DHCPv4 audit record file (netcap)')\\n parser.add_argument('--dhcpv6', help='DHCPv6 audit record file (netcap)')\\n parser.add_argument('--dns', help='DNS audit record file (netcap)')\\n parser.add_argument('--ethernet', help='Ethernet audit record file (netcap)')\\n parser.add_argument('--exploit', help='Exploit audit record file (netcap)')\\n parser.add_argument('--http', help='HTTP audit record file (netcap)')\\n parser.add_argument('--icmpv4', help='ICMPv4 audit record file (netcap)')\\n parser.add_argument('--icmpv6', help='ICMPv6 audit record file (netcap)')\\n parser.add_argument('--igmp', help='IGMP audit record file (netcap)')\\n parser.add_argument('--ipprofile', help='IPProfile audit record file (netcap)')\\n parser.add_argument('--ipv4', help='IPv4 audit record file (netcap)')\\n parser.add_argument('--ipv6hop', help='IPv6HopByHop audit record file (netcap)')\\n parser.add_argument('--ipv6', help='IPv6 audit record file (netcap)')\\n parser.add_argument('--ntp', help='NTP audit record file (netcap)')\\n parser.add_argument('--service', help='Servie audit record file (netcap)')\\n parser.add_argument('--sip', help='SIP audit record file (netcap)')\\n parser.add_argument('--software', help='Software audit record file (netcap)')\\n parser.add_argument('--tcp', help='TCP audit record file (netcap)')\\n parser.add_argument('--tls-client-hello', help='TLSClientHello audit record file(netcap)')\\n parser.add_argument('--tls-server-hello', help='TLSServerHello audit record file(netcap)')\\n parser.add_argument('--udp', help='UDP audit record file (netcap)')\\n parser.add_argument('--vulnerability', help='vulnerability audit record file (netcap)')\\n parser.add_argument('--audit', help='Read the given audit record types from unix sockets',\\n choices=audit_records, nargs='*')\\n parser.add_argument('--encoded', action='store_true', help='Read encoded audit records')\\n parser.add_argument('--labelled', action='store_true', help='Read labelled audit records')\\n return parser.parse_args()\",\n \"def parse_args():\\n\\n parser = argparse.ArgumentParser(description=\\\"Benchmark Thing WoT server\\\")\\n parser = utils.extend_server_arg_parser(parser)\\n\\n return parser.parse_args()\",\n \"def parse_arguments():\\n description = \\\" \\\".join(__doc__.splitlines()[4:])\\n\\n epilog = \\\", \\\".join(__doc__.splitlines()[1:4])\\n parser = argparse.ArgumentParser(description=description, epilog=epilog)\\n parser.add_argument(\\n \\\"-i\\\",\\n type=str,\\n help=\\\"\\\"\\\"\\n the multiple sequence alignment fasta file with extension .aln to\\n use in input\\n \\\"\\\"\\\",\\n metavar=\\\"<file>\\\",\\n required=True,\\n )\\n parser.add_argument(\\n \\\"-o\\\",\\n metavar=\\\"<file>\\\",\\n type=str,\\n help=\\\"the file where to write the profile numpy array\\\",\\n required=True,\\n )\\n\\n args = parser.parse_args()\\n\\n return args\",\n \"def parse_args():\\n parser = argparse.ArgumentParser(description=\\\"Deep SORT\\\")\\n parser.add_argument(\\n \\\"--input\\\", help=\\\"Path to MOTChallenge sequence directory\\\",\\n default=None, required=True)\\n return parser.parse_args()\",\n \"def parse_cmdline_args():\\n parser = argparse.ArgumentParser()\\n parser.add_argument('-v', '--verbose',\\n action=\\\"store_true\\\",\\n help=\\\"Be chatty\\\")\\n parser.add_argument('-d', '--dryrun',\\n action=\\\"store_true\\\",\\n help=\\\"Do not send emails\\\")\\n foreman_config = ForemanConfig()\\n foreman_config.add_standard_args(parser)\\n parser.add_argument('-e', '--environments', type=str,\\n default=DEFAULT_ENVIRONMENTS_PATH,\\n help=\\\"Clone of it-puppet-environments\\\")\\n args = parser.parse_args()\\n return args\",\n \"def _parse_cmd_args():\\n parser = argparse.ArgumentParser()\\n\\n parser.add_argument(\\\"--root\\\", required=True,\\n help=\\\"Root directory of data and logs.\\\")\\n parser.add_argument(\\\"--log_dir\\\", default=\\\"logs\\\",\\n help=\\\"Sub-directory of tensorboard logs.\\\")\\n parser.add_argument(\\\"--gpu\\\", default=\\\"0\\\", help=\\\"GPU device ID.\\\")\\n\\n args = parser.parse_args()\\n\\n return args\",\n \"def parse_args():\\n parser = argparse.ArgumentParser()\\n parser.add_argument(\\\"-t\\\", \\\"--thoughtspot_host\\\", required=True,\\n help=\\\"domain or ip. E.g. http://1.1.1.1\\\")\\n parser.add_argument(\\\"-u\\\", \\\"--username\\\", required=True,\\n help=\\\"username - must have administrative privileges\\\")\\n parser.add_argument(\\\"-p\\\", \\\"--password\\\", required=True,\\n help=\\\"password - must have administrative privileges\\\")\\n parser.add_argument(\\\"-d\\\", \\\"--delimiter\\\", default=',',\\n help=\\\"character to seperate values by. Default to comma\\\")\\n parser.add_argument(\\\"-c\\\", \\\"--csv\\\", action=\\\"store_true\\\",\\n help=\\\"create csv file called permissions.csv\\\")\\n parser.add_argument(\\\"-s\\\", \\\"--share\\\", action=\\\"store_true\\\",\\n help=\\\"output usable format for share api\\\")\\n return parser.parse_args()\",\n \"def parse_args():\\n parser = default_argument_parser()\\n parser.add_argument(\\\"--label-map\\\",\\n dest=\\\"label_map\\\",\\n type=pathlib.Path,\\n help=\\\"Label map in YAML format which maps from category \\\"\\n \\\"ID to name.\\\")\\n parser.add_argument(\\\"--train-csv\\\",\\n dest=\\\"train_csv\\\",\\n required=True,\\n type=pathlib.Path,\\n help=\\\"Path to training data CSV file.\\\")\\n parser.add_argument(\\\"--valid-csv\\\",\\n dest=\\\"valid_csv\\\",\\n required=False,\\n type=pathlib.Path,\\n help=\\\"Optional path to validation data CSV file.\\\")\\n parser.add_argument(\\n \\\"--image-width\\\",\\n type=int,\\n help=\\\"Image width (optional, used to speed up dataset processing).\\\")\\n parser.add_argument(\\n \\\"--image-height\\\",\\n type=int,\\n help=\\\"Image height (optional, used to speed up dataset processing).\\\")\\n return parser.parse_args()\",\n \"def parse_args(argv):\\n formatter_class = argparse.RawDescriptionHelpFormatter\\n parser = argparse.ArgumentParser(description=\\\"Run torrent-knowledge cli\\\",\\n formatter_class=formatter_class)\\n\\n parser.add_argument(\\\"-i\\\", \\\"--input_file\\\",\\n type=argparse.FileType('r'),\\n required=True,\\n help=\\\"CSV with torrents in format 'info_hash|torrent_title'\\\")\\n\\n parser.add_argument(\\\"-o\\\", \\\"--output_file\\\",\\n required=True,\\n type=argparse.FileType('w'),\\n default=sys.stdout)\\n\\n parser.add_argument(\\\"-v\\\", \\\"--verbose\\\", dest=\\\"verbose\\\",\\n action=\\\"count\\\", default=0,\\n help=\\\"increase log verbosity via -vvv\\\")\\n\\n parser.add_argument(\\\"-t\\\", \\\"--train-mode\\\", dest=\\\"train_mode\\\",\\n action=\\\"count\\\", default=0,\\n help=\\\"update features frequency from dataset\\\")\\n\\n parser.add_argument(\\\"-l\\\", \\\"--log-dir\\\", dest=\\\"log_dir\\\",\\n action=\\\"store\\\", default=None,\\n help=\\\"directory for log files (only for -vvv mode)\\\")\\n\\n args = parser.parse_args(argv[1:])\\n\\n if args.log_dir and args.verbose != 3:\\n logger.debug(\\\"You should use -vvv with option -l\\\")\\n exit(-1)\\n\\n return parser.parse_args(argv[1:])\",\n \"def parse_args():\\n parser = argparse.ArgumentParser()\\n parser.add_argument(\\n \\\"--start\\\",\\n type=str,\\n default=\\\"\\\",\\n required=False,\\n help=\\\"The start square of the agent, in the form row,col. If not specified, this is randomized\\\"\\n )\\n parser.add_argument(\\n \\\"--actions\\\",\\n type=str,\\n required=True,\\n nargs = '+',\\n help=\\\"The actions the agent takes, comma delimited\\\"\\n )\\n parser.add_argument(\\n \\\"--observations\\\",\\n type=str,\\n required=True,\\n nargs = '+',\\n help=\\\"The observations the agent makes, comma delimited\\\"\\n )\\n return parser.parse_args()\",\n \"def command_line_parse(iargs=None):\\n\\n parser = create_parser()\\n inps = parser.parse_args(args=iargs)\\n\\n return inps\",\n \"def parse_arguments():\\n parser = argparse.ArgumentParser(description=\\n \\\"This script receives 'grouped_hits.txt', uses a FASTA file to get the \\\\\\n \\\\nsequences, uses MAFFT to align each group, then generates a consensus \\\\\\n \\\\nsequence for each group using biopython.\\\\n\\\\n \\\\\\n Example: python {0} -g grouped_hits.txt -f sequences.fasta\\\".format(argv[0]),\\n formatter_class = argparse.RawDescriptionHelpFormatter)\\n \\n requiredNamed = parser.add_argument_group('required arguments')\\n\\n requiredNamed.add_argument(\\\"-g\\\", \\\"--GROUPS\\\", type=str, required=True,\\\\\\n help=\\\"Grouped hits file generated by group_self_BLAST.py\\\", action=\\\"store\\\")\\n\\n requiredNamed.add_argument(\\\"-f\\\", \\\"--FASTA\\\", type=str, required=True,\\\\\\n help=\\\"FASTA file containing all sequences.\\\", action=\\\"store\\\")\\n\\n return parser.parse_args()\",\n \"def __parse_cmd_args():\\n parser = argparse.ArgumentParser(description='Python Image Downloader.')\\n parser.add_argument(\\\"-f\\\", \\\"--file\\\",\\n help=\\\"Where the URL file is located.\\\")\\n parser.add_argument(\\\"-d\\\", \\\"--dir\\\",\\n help=\\\"Where the downloaded files are to be stored.\\\")\\n args = parser.parse_args()\\n return args\",\n \"def parse_args():\\n parser = argparse.ArgumentParser()\\n\\n parser.add_argument('--source-account', required=True, type=str, help='Source account to pay transaction fees')\\n parser.add_argument('--channel-seeds-file', required=True, type=str, help='File path to channel seeds file')\\n parser.add_argument('--accounts', required=True, type=int, help='Amount of accounts to create')\\n parser.add_argument('--passphrase', required=True, type=str, help='Network passphrase')\\n parser.add_argument('--horizon', action='append', help='Horizon endpoint URL (use multiple --horizon flags for multiple addresses)')\\n parser.add_argument('--json-output', required=False, type=bool, help='Export output to json format')\\n return parser.parse_args()\",\n \"def parse_arguments():\\n\\n parser = argparse.ArgumentParser()\\n\\n # Specify if one wants to perform the persistence forecast\\n parser.add_argument(\\n '-p',\\n '--persistence-forecast',\\n action='store_true',\\n help='if set, perform persistence forecast'\\n )\\n\\n # Specify if one wants to look for the best epoch value\\n parser.add_argument(\\n '-e',\\n '--epoch',\\n action='store_true',\\n help='if set, look for the best epoch value'\\n )\\n\\n return parser\",\n \"def parse_args():\\n parser = argparse.ArgumentParser()\\n parser.add_argument('-e', '--env', default='production',\\n help='Environment to check: integration, staging, production.')\\n parser.add_argument('-l', '--log_type', default='govuk_assets',\\n help='Which logs to check: govuk_assets, govuk_www.')\\n parser.add_argument('-c', '--critical_age_minutes', type=int, default=60,\\n help='If the newest logs are older than this many minutes, '\\n 'return CRITICAL status.')\\n parser.add_argument('-F', '--fake_time', type=fromisoformat,\\n help='For testing purposes, use the given time as if it\\\\'s the current '\\n 'time. Requires the format YYYY-MM-DDTHH:MM. Assumes UTC.')\\n parser.add_argument('-v', '--verbose', action='count',\\n help='Show DEBUG log messages.')\\n return parser.parse_args()\",\n \"def process_command_line_arguments() -> Namespace:\\n\\n parser = build_parser()\\n arguments = parser.parse_args()\\n\\n return arguments\",\n \"def parseArguments():\\n parser = argparse.ArgumentParser()\\n parser.add_argument('--output_folder',\\n help='Path of the folder where output files should be written.')\\n parser.add_argument('--partition_id',\\n help='ID of the computer partition to collect data from.')\\n parser.add_argument('--collector_db',\\n help='The path of slapos collect database.')\\n\\n return parser.parse_args()\",\n \"def handle_args():\\n parser = argparse.ArgumentParser(\\n description=\\\"\\\"\\\"Script to download archives from the NLM public\\n FTP server.\\n \\\"\\\"\\\")\\n # Server settings\\n server_settings = parser.add_argument_group('FTP SERVER SETTINGS', '')\\n server_settings.add_argument(\\n '-n', '--netrc', default='~/.netrc',\\n help=\\\"\\\"\\\"netrc file containing login parameters for the NLM\\n server. See `man 5 netrc` for details on generating this\\n file or read nlm_data_import/netrc/example.netrc.\\n \\\"\\\"\\\")\\n server_settings.add_argument(\\n 'server_data_dir',\\n help='Directory containing desired files on the NLM FTP server')\\n server_settings.add_argument(\\n '-l', '--limit', type=int, default=0,\\n help='Only download LIMIT files.')\\n\\n # Download settings\\n local_settings = parser.add_argument_group('LOCAL SETTINGS', '')\\n local_settings.add_argument(\\n '-d', '--download_database', default='~/.ftp_download_db',\\n help='Path to SQLite database detailing past downloads')\\n local_settings.add_argument(\\n '-o', '--output_dir', default='~/medline_data',\\n help='Directory where downloads will be saved')\\n local_settings.add_argument(\\n '-x', '--export_dir', default='~/medline_data_exports',\\n help=\\\"\\\"\\\"Directory where data to be retrieved by the\\n `hypothesis_graph application server are staged.\\n \\\"\\\"\\\")\\n # Sending debug emails (requires the send_ses_messages module - see\\n # setup.py)\\n debugging_settings = parser.add_argument_group('DEBUGGING SETTINGS', '')\\n debugging_settings.add_argument(\\n '--email_debugging', default=False, action='store_true',\\n help=\\\"Send debugging emails. Defaults to FALSE.\\\")\\n debugging_settings.add_argument(\\n '--from_email', required=False, help=\\\"FROM field for debugging emails\\\")\\n debugging_settings.add_argument(\\n '--to_email', required=False, help=\\\"TO field for debugging emails\\\")\\n\\n return parser.parse_args()\",\n \"def parse_args(args):\\n parser = argparse.ArgumentParser(\\n description=\\\"Lookup table generator for Image Comparison\\\")\\n parser.add_argument(\\n \\\"--version\\\",\\n action=\\\"version\\\",\\n version=\\\"lookuptable {ver}\\\".format(ver=__version__))\\n parser.add_argument(\\n \\\"-f\\\",\\n \\\"--folder\\\",\\n dest=\\\"imagefolder\\\",\\n help=\\\"path to image folder\\\",\\n type=str,\\n metavar=\\\"STRING\\\")\\n parser.add_argument(\\n \\\"-v\\\",\\n \\\"--verbose\\\",\\n dest=\\\"loglevel\\\",\\n help=\\\"set loglevel to INFO\\\",\\n action=\\\"store_const\\\",\\n const=logging.INFO)\\n parser.add_argument(\\n \\\"-vv\\\",\\n \\\"--very-verbose\\\",\\n dest=\\\"loglevel\\\",\\n help=\\\"set loglevel to DEBUG\\\",\\n action=\\\"store_const\\\",\\n const=logging.DEBUG)\\n return parser.parse_args(args)\",\n \"def command_line_arguments():\\n\\n try:\\n parser = argparse.ArgumentParser(description='Log Handler/Cleaner/Copier for Idemia DocAuth')\\n\\n # Add required arguments.\\n parser.add_argument('action', choices=['clean', 'download'], type=str, help='clean or download')\\n\\n # Parse the arguments\\n args = parser.parse_args()\\n\\n return args\\n\\n except Exception as err:\\n print(err)\\n return\",\n \"def parse_arguments():\\n parser = argparse.ArgumentParser()\\n parser.add_argument(\\n \\\"--obs_len\\\",\\n default=20,\\n type=int,\\n help=\\\"Directory where the sequences (csv files) are saved\\\",\\n )\\n parser.add_argument(\\n \\\"--data_dir\\\",\\n default=\\\"\\\",\\n type=str,\\n help=\\\"Directory where the sequences (csv files) are saved\\\",\\n )\\n parser.add_argument(\\n \\\"--feature_dir\\\",\\n default=\\\"\\\",\\n type=str,\\n help=\\\"Directory where the computed features are saved\\\",\\n )\\n parser.add_argument(\\\"--mode\\\",\\n required=True,\\n type=str,\\n help=\\\"train/val/test/compute_all/lanes_only\\\")\\n parser.add_argument(\\n \\\"--sequence_num\\\",\\n default=-1,\\n type=int,\\n help=\\\"Specify a specific sequence to visualize.\\\",\\n )\\n parser.add_argument(\\n \\\"--batch_start\\\",\\n default=0,\\n type=int,\\n help=\\\"Specify the starting row of features to visualize.\\\",\\n )\\n parser.add_argument(\\n \\\"--batch_end\\\",\\n default=-1,\\n type=int,\\n help=\\\"Specify the last row to visualize, -1 to visualize till end.\\\",\\n )\\n parser.add_argument(\\n \\\"--single_figure\\\",\\n default=False,\\n action=\\\"store_true\\\",\\n help=\\\"Plot all candidates for a scenein one figure.\\\",\\n )\\n return parser.parse_args()\",\n \"def command_line_args(parser):\\n AbyssAssembler.command_line_args(parser)\\n SpadesAssembler.command_line_args(parser)\\n TrinityAssembler.command_line_args(parser)\\n VelvetAssembler.command_line_args(parser)\",\n \"def _parse_arguments():\\n import argparse\\n\\n parser = argparse.ArgumentParser(description=__doc__)\\n\\n parser.add_argument(\\n 'list_of_files', type=str,\\n help='Input ASCII file with a list of files to be downloaded')\\n\\n return parser.parse_args()\",\n \"def parse_command_line(argv):\\n parser = argparse.ArgumentParser(description=__doc__)\\n parser.add_argument(\\n \\\"--logfile\\\",\\n default=None,\\n type=str,\\n help=\\\"If specified, write logs to this file.\\\",\\n )\\n parser.add_argument(\\n \\\"--loglevel\\\",\\n help=\\\"Set logging level (DEBUG, INFO, WARNING, ERROR, or CRITICAL).\\\",\\n default=\\\"INFO\\\",\\n )\\n return parser.parse_args(argv[1:])\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.80362767","0.79380834","0.7909644","0.7786256","0.77640563","0.7757112","0.7753354","0.7752254","0.7741536","0.77328235","0.77010775","0.76901954","0.76706934","0.7655658","0.7648945","0.7648416","0.7646757","0.76460826","0.76380634","0.76354045","0.76352936","0.7624028","0.7606092","0.759907","0.7590268","0.758876","0.7579157","0.75738186","0.75706255","0.75603","0.7559775","0.7555709","0.7550785","0.7548574","0.7544146","0.75351614","0.7524266","0.7519361","0.7516758","0.75156915","0.75140965","0.7511908","0.7507585","0.75030744","0.7500737","0.74900126","0.74889874","0.7477768","0.7470214","0.7467035","0.74600613","0.7459012","0.7451068","0.7450384","0.7450271","0.744337","0.7443221","0.7438756","0.7436586","0.74331594","0.7432106","0.7428143","0.742758","0.7419824","0.74165833","0.7416403","0.7408782","0.7403955","0.73981935","0.73978883","0.73929554","0.7392664","0.7381113","0.73762554","0.7373859","0.73705256","0.7368247","0.7358161","0.7354352","0.73531383","0.73523474","0.7346938","0.7346785","0.7342723","0.73391545","0.7338296","0.7338251","0.73375493","0.7336589","0.7330634","0.73295945","0.7326207","0.73182267","0.73131555","0.73123294","0.73060226","0.7305262","0.729667","0.72965044","0.7291498","0.7291301"],"string":"[\n \"0.80362767\",\n \"0.79380834\",\n \"0.7909644\",\n \"0.7786256\",\n \"0.77640563\",\n \"0.7757112\",\n \"0.7753354\",\n \"0.7752254\",\n \"0.7741536\",\n \"0.77328235\",\n \"0.77010775\",\n \"0.76901954\",\n \"0.76706934\",\n \"0.7655658\",\n \"0.7648945\",\n \"0.7648416\",\n \"0.7646757\",\n \"0.76460826\",\n \"0.76380634\",\n \"0.76354045\",\n \"0.76352936\",\n \"0.7624028\",\n \"0.7606092\",\n \"0.759907\",\n \"0.7590268\",\n \"0.758876\",\n \"0.7579157\",\n \"0.75738186\",\n \"0.75706255\",\n \"0.75603\",\n \"0.7559775\",\n \"0.7555709\",\n \"0.7550785\",\n \"0.7548574\",\n \"0.7544146\",\n \"0.75351614\",\n \"0.7524266\",\n \"0.7519361\",\n \"0.7516758\",\n \"0.75156915\",\n \"0.75140965\",\n \"0.7511908\",\n \"0.7507585\",\n \"0.75030744\",\n \"0.7500737\",\n \"0.74900126\",\n \"0.74889874\",\n \"0.7477768\",\n \"0.7470214\",\n \"0.7467035\",\n \"0.74600613\",\n \"0.7459012\",\n \"0.7451068\",\n \"0.7450384\",\n \"0.7450271\",\n \"0.744337\",\n \"0.7443221\",\n \"0.7438756\",\n \"0.7436586\",\n \"0.74331594\",\n \"0.7432106\",\n \"0.7428143\",\n \"0.742758\",\n \"0.7419824\",\n \"0.74165833\",\n \"0.7416403\",\n \"0.7408782\",\n \"0.7403955\",\n \"0.73981935\",\n \"0.73978883\",\n \"0.73929554\",\n \"0.7392664\",\n \"0.7381113\",\n \"0.73762554\",\n \"0.7373859\",\n \"0.73705256\",\n \"0.7368247\",\n \"0.7358161\",\n \"0.7354352\",\n \"0.73531383\",\n \"0.73523474\",\n \"0.7346938\",\n \"0.7346785\",\n \"0.7342723\",\n \"0.73391545\",\n \"0.7338296\",\n \"0.7338251\",\n \"0.73375493\",\n \"0.7336589\",\n \"0.7330634\",\n \"0.73295945\",\n \"0.7326207\",\n \"0.73182267\",\n \"0.73131555\",\n \"0.73123294\",\n \"0.73060226\",\n \"0.7305262\",\n \"0.729667\",\n \"0.72965044\",\n \"0.7291498\",\n \"0.7291301\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":17883,"cells":{"query":{"kind":"string","value":"Load foia sba datasets"},"document":{"kind":"string","value":"def load_sba_datasets(dbm, direc):\n foia_504_1991_present = pd.read_excel(direc + 'FOIA - 504 (FY1991-Present).xlsx')\n foia_7a_1991_1999 = pd.read_excel(direc + 'FOIA - 7(a) (FY1991-FY1999).xlsx', skiprows=1)\n foia_7a_2000_2009 = pd.read_excel(direc + 'FOIA - 7(a)(FY2000-FY2009).xlsx', skiprows=1)\n foia_7a_2010_present = pd.read_excel(direc + 'FOIA - 7(a) (FY2010-Present).xlsx')\n\n dbm.write_df_table(\n foia_504_1991_present, table_name='sba__foia_504_1991_present', schema='data_ingest')\n dbm.write_df_table(\n foia_7a_1991_1999, table_name='sba__foia_7a_1991_1999', schema='data_ingest')\n dbm.write_df_table(\n foia_7a_2000_2009, table_name='sba__foia_7a_2000_2009', schema='data_ingest')\n dbm.write_df_table(\n foia_7a_2010_present, table_name='sba__foia_7a_2010_present', schema='data_ingest')"},"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 load_kiba_dataset():\n trainn_fold = json.load(\n open(os.path.join('dataset', 'regression', 'benchmark', 'KIBAtest', 'folds', 'train_fold_setting1.txt')))\n train_fold = []\n for e in zip(*trainn_fold):\n for ee in e:\n train_fold.extend(ee)\n #train_fold = [ee for e in trainn_fold for ee in e]\n test_fold = json.load(\n open(os.path.join('dataset', 'regression', 'benchmark', 'KIBAtest', 'folds', 'test_fold_setting1.txt')))\n ligands = json.load(\n open(os.path.join('dataset', 'regression', 'benchmark', 'KIBAtest', 'ligands_can.txt')),\n object_pairs_hook=OrderedDict)\n proteins = json.load(\n open(os.path.join('dataset', 'regression', 'benchmark', 'KIBAtest', 'proteins.txt')),\n object_pairs_hook=OrderedDict)\n \n affinity = pickle.load(open(os.path.join('dataset', 'regression', 'benchmark', 'KIBAtest', 'Y'), \n 'rb'), encoding='latin1')\n smiles_lst, protein_lst = [], []\n\n for k in ligands.keys():\n smiles = ligands[k]\n smiles_lst.append(smiles)\n for k in proteins.keys():\n protein_lst.append(proteins[k])\n\n affinity = np.asarray(affinity)\n \n os.makedirs(os.path.join('dataset', 'regression', 'benchmark', 'KIBAtest', 'processed'), exist_ok=True)\n train_test_dataset = []\n for split in ['train', 'test']:\n split_dir = os.path.join('dataset', 'regression', 'benchmark', 'KIBAtest', 'processed', split)\n os.makedirs(split_dir, exist_ok=True)\n fold = train_fold if split == 'train' else test_fold\n rows, cols = np.where(np.isnan(affinity) == False)\n rows, cols = rows[fold], cols[fold]\n \n data_lst = [[] for _ in range(1)]\n for idx in range(len(rows)):\n data = {}\n data['smiles'] = smiles_lst[rows[idx]]\n data['protein'] = protein_lst[cols[idx]]\n af = affinity[rows[idx], cols[idx]]\n data['aff'] = af\n\n data_lst[idx % 1].append(data)\n random.shuffle(data_lst)\n train_test_dataset.append(data_lst[0])\n return train_test_dataset","def LoadTroikaDataset():\n data_dir = \"./datasets/troika/training_data\"\n data_fls = sorted(glob.glob(data_dir + \"/DATA_*.mat\"))\n ref_fls = sorted(glob.glob(data_dir + \"/REF_*.mat\"))\n return data_fls, ref_fls","def load_data(self):","def load_data(self) -> None:","def load_database(self, fsp='Species'):\n self.df_species = pd.read_csv(fsp + '.csv', header=0,\n index_col=0)","def import_and_save(ADCthres=0, s=False):\n df = import_data(ADCthres, s)\n bus_vec = np.array(range(0,3))\n for bus in bus_vec:\n df_clu = cluster_data(df, bus) \n save_clusters(df_clu, bus)","def load_davis_dataset():\n trainn_fold = json.load(\n open(os.path.join('dataset', 'regression', 'benchmark', 'DAVIStest', 'folds', 'train_fold_setting1.txt')))\n train_fold = []\n for e in zip(*trainn_fold):\n for ee in e:\n train_fold.append(ee)\n #train_fold = [ee for e in trainn_fold for ee in e]\n test_fold = json.load(\n open(os.path.join('dataset', 'regression', 'benchmark', 'DAVIStest', 'folds', 'test_fold_setting1.txt')))\n ligands = json.load(\n open(os.path.join('dataset', 'regression', 'benchmark', 'DAVIStest', 'ligands_can.txt')),\n object_pairs_hook=OrderedDict)\n proteins = json.load(\n open(os.path.join('dataset', 'regression', 'benchmark', 'DAVIStest', 'proteins.txt')),\n object_pairs_hook=OrderedDict)\n \n affinity = pickle.load(open(os.path.join('dataset', 'regression', 'benchmark', 'DAVIStest', 'Y'), \n 'rb'), encoding='latin1')\n smiles_lst, protein_lst = [], []\n\n for k in ligands.keys():\n smiles = ligands[k]\n smiles_lst.append(smiles)\n for k in proteins.keys():\n protein_lst.append(proteins[k])\n\n affinity = [-np.log10(y / 1e9) for y in affinity]\n affinity = np.asarray(affinity)\n \n os.makedirs(os.path.join('dataset', 'regression', 'benchmark', 'DAVIStest', 'processed'), exist_ok=True)\n train_test_dataset = []\n for split in ['train', 'test']:\n split_dir = os.path.join('dataset', 'regression', 'benchmark', 'DAVIStest', 'processed', split)\n os.makedirs(split_dir, exist_ok=True)\n fold = train_fold if split == 'train' else test_fold\n rows, cols = np.where(np.isnan(affinity) == False)\n rows, cols = rows[fold], cols[fold]\n \n data_lst = [[] for _ in range(1)]\n for idx in range(len(rows)):\n data = {}\n data['smiles'] = smiles_lst[rows[idx]]\n data['protein'] = protein_lst[cols[idx]]\n af = affinity[rows[idx], cols[idx]]\n data['aff'] = af\n\n data_lst[idx % 1].append(data)\n random.shuffle(data_lst)\n train_test_dataset.append(data_lst[0])\n return train_test_dataset","def load():\n filepath = dirname(abspath(__file__))\n##### EDIT THE FOLLOWING TO POINT TO DatasetName.csv #####\n data = recfromtxt(open(filepath + '/spector.csv',\"rb\"), delimiter=\" \",\n names=True, dtype=float, usecols=(1,2,3,4))\n names = list(data.dtype.names)\n endog = array(data[names[3]], dtype=float)\n endog_name = names[3]\n exog = column_stack(data[i] for i in names[:3]).astype(float)\n exog_name = names[:3]\n dataset = Dataset(data=data, names=names, endog=endog, exog=exog,\n endog_name = endog_name, exog_name=exog_name)\n return dataset","def load():\n filepath = dirname(abspath(__file__))\n data = recfromtxt(filepath + '/scotvote.csv', delimiter=\",\",\n names=True, dtype=float, usecols=(1,2,3,4,5,6,7,8))\n names = list(data.dtype.names)\n endog = array(data[names[0]], dtype=float)\n endog_name = names[0]\n exog = column_stack(data[i] for i in names[1:]).astype(float)\n exog_name = names[1:]\n dataset = Dataset(data=data, names=names, endog=endog, exog=exog,\n endog_name = endog_name, exog_name=exog_name)\n return dataset","def load_data():\n\n base = 'http://www.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/BSR/'\n fname = 'BSR_bsds500.tgz'\n\n path = get_file(fname,\n origin = base + fname,\n cache_dir = DEFAULT_CACHE_DIR,\n dset_name = 'bsds500')\n\n f = tarfile.open(path)\n\n train_data = []\n test_data = []\n for name in f.getnames():\n if name.startswith('BSR/BSDS500/data/images/train/'):\n try:\n fp = f.extractfile(name)\n img = imageio.imread(fp)\n train_data.append(img)\n except:\n continue\n elif name.startswith('BSR/BSDS500/data/images/test/'):\n try:\n fp = f.extractfile(name)\n img = skimage.io.imread(fp)\n test_data.append(img)\n except:\n continue\n\n\n return (train_data, test_data)","def load_dsb(self, dataset_dir, subset):\n # Add classes. We have only one class to add.\n self.add_class(\"dsb\", 1, \"nucleo\")\n\n # Train or validation dataset?\n if subset == \"train\" or subset == \"val\":\n dataset_dir = os.path.join(dataset_dir, \"TRAINCLAHE/\")\n elif subset == 'test':\n dataset_dir = os.path.join(dataset_dir, \"TEST/\")\n else:\n print(\"Invalid Subset\",subset)\n #Listar quais exames tem\n exames = next(os.walk(dataset_dir))[1]\n\n if subset==\"train\":\n exames = exames[:600]\n elif subset == \"val\":\n exames = exames[600:]\n else:\n # exames = exames\n pass\n\n\n #Acessar a pasta exame/image\n for n, id_ in tqdm(enumerate(exames), total=len(exames)):\n path = dataset_dir + id_\n self.add_image(\n \"dsb\",\n image_id=id_, # use file name as a unique image id\n path=path + '/images/' + id_ + '.png', dir=path,\n )","def _build_datasets(self):\n self._build_datasets_sis3302()\n self._build_datasets_sis3305()","def datasets(self):\n pass","def dataset(options):\n pass","def datasets(self):\n return [Dataset.GWAS_CATALOG, Dataset.CLINVAR, Dataset.EFO]","def loadData(catalog):\n loadArtworks(catalog)\n loadArtists(catalog)","def loadData(catalog):\n loadArtists(catalog)\n loadArtworks(catalog)","def loadData(catalog):\n loadArtists(catalog)\n loadArtworks(catalog)","def test_i_large_datasets(self):\n\n # If foma is not installed, exit.\n if not h.foma_installed(force_check=True):\n return\n\n # Configuration\n\n # The ``old_dump_file`` variable holds the name of a MySQL dump file in /tests/data/datasets\n # that will be used to populate the database.\n old_dump_file = 'blaold.sql'\n backup_dump_file = 'old_test_dump.sql'\n\n # The ``precompiled_morphophonology`` variable holds the name of a compiled foma FST that\n # maps surface representations to sequences of morphemes. A file with this name should be\n # present in /tests/data/morphophonologies or else the variable should be set to None.\n pregenerated_morphophonology = None # 'blaold_morphophonology.script'\n precompiled_morphophonology = None # 'blaold_morphophonology.foma'\n\n # Here we load a whole database from the mysqpl dump file specified in ``tests/data/datasets/<old_dump_file>``.\n old_dump_file_path = os.path.join(self.test_datasets_path, old_dump_file)\n backup_dump_file_path = os.path.join(self.test_datasets_path, backup_dump_file)\n tmp_script_path = os.path.join(self.test_datasets_path, 'tmp.sh')\n if not os.path.isfile(old_dump_file_path):\n return\n config = h.get_config(config_filename='test.ini')\n SQLAlchemyURL = config['sqlalchemy.url']\n if not SQLAlchemyURL.split(':')[0] == 'mysql':\n return\n rdbms, username, password, db_name = SQLAlchemyURL.split(':')\n username = username[2:]\n password = password.split('@')[0]\n db_name = db_name.split('/')[-1]\n # First dump the existing database so we can load it later.\n # Note: the --single-transaction option seems to be required (on Mac MySQL 5.6 using InnoDB tables ...)\n # see http://forums.mysql.com/read.php?10,108835,112951#msg-112951\n with open(tmp_script_path, 'w') as tmpscript:\n tmpscript.write('#!/bin/sh\\nmysqldump -u %s -p%s --single-transaction --no-create-info --result-file=%s %s' % (\n username, password, backup_dump_file_path, db_name))\n os.chmod(tmp_script_path, 0744)\n with open(os.devnull, \"w\") as fnull:\n call([tmp_script_path], stdout=fnull, stderr=fnull)\n # Now load the dump file of the large database (from old_dump_file)\n with open(tmp_script_path, 'w') as tmpscript:\n tmpscript.write('#!/bin/sh\\nmysql -u %s -p%s %s < %s' % (username, password, db_name, old_dump_file_path))\n with open(os.devnull, \"w\") as fnull:\n call([tmp_script_path], stdout=fnull, stderr=fnull)\n\n # Recreate the default users that the loaded dump file deleted\n administrator = h.generate_default_administrator()\n contributor = h.generate_default_contributor()\n viewer = h.generate_default_viewer()\n Session.add_all([administrator, contributor, viewer])\n Session.commit()\n\n\n ################################################################################\n # PHONOLOGY\n ################################################################################\n\n # Create a Blackfoot phonology with the test phonology script\n params = self.phonology_create_params.copy()\n params.update({\n 'name': u'Blackfoot Phonology',\n 'description': u'The phonological rules of Frantz (1997) as FSTs',\n 'script': self.blackfoot_phonology_script\n })\n params = json.dumps(params)\n response = self.app.post(url('phonologies'), params, self.json_headers,\n self.extra_environ_admin)\n resp = json.loads(response.body)\n phonology_id = resp['id']\n\n \"\"\"\n\n\n ################################################################################\n # MORPHOLOGY\n ################################################################################\n\n # Create a lexicon form search and corpus\n # The code below constructs a query that finds a (large) subset of the Blackfoot morphemes.\n # Notes for future morphology creators:\n # 1. the \"oth\" category is a mess: detangle the nominalizer, inchoative, transitive suffixes, etc. from\n # one another and from the numerals and temporal modifiers -- ugh!\n # 2. the \"pro\" category\" is also a mess: clearly pronoun-forming iisto does not have the same distribution \n # as the verbal suffixes aiksi and aistsi! And oht, the LING/means thing, is different again...\n # 3. hkayi, that thing at the end of demonstratives, is not agra, what is it? ...\n # 4. the dim category contains only 'sst' 'DIM' and is not used in any forms ...\n lexical_category_names = ['nan', 'nin', 'nar', 'nir', 'vai', 'vii', 'vta', 'vti', 'vrt', 'adt',\n 'drt', 'prev', 'med', 'fin', 'oth', 'o', 'und', 'pro', 'asp', 'ten', 'mod', 'agra', 'agrb', 'thm', 'whq',\n 'num', 'stp', 'PN']\n durative_morpheme = 15717\n hkayi_morpheme = 23429\n query = {'filter': ['and', [['Form', 'syntactic_category', 'name', 'in', lexical_category_names],\n ['not', ['Form', 'morpheme_break', 'regex', '[ -]']],\n ['not', ['Form', 'id', 'in', [durative_morpheme, hkayi_morpheme]]],\n ['not', ['Form', 'grammaticality', '=', '*']]\n ]]}\n smaller_query_for_rapid_testing = {'filter': ['and', [['Form', 'id', '<', 1000],\n ['Form', 'syntactic_category', 'name', 'in', lexical_category_names]]]}\n params = self.form_search_create_params.copy()\n params.update({\n 'name': u'Blackfoot morphemes',\n 'search': query\n })\n params = json.dumps(params)\n response = self.app.post(url('formsearches'), params, self.json_headers, self.extra_environ_admin)\n lexicon_form_search_id = json.loads(response.body)['id']\n params = self.corpus_create_params.copy()\n params.update({\n 'name': u'Corpus of Blackfoot morphemes',\n 'form_search': lexicon_form_search_id\n })\n params = json.dumps(params)\n response = self.app.post(url('corpora'), params, self.json_headers, self.extra_environ_admin)\n lexicon_corpus_id = json.loads(response.body)['id']\n\n # Create a rules corpus\n\n # Create a corpus of forms containing words -- to be used to estimate ngram probabilities\n # The goal here is to exclude things that look like words but are not really words, i.e., \n # morphemes; as a heuristic we search for grammatical forms categorized as 'sent' or whose\n # transcription value contains a space or a dash.\n query = {'filter': ['and', [['or', [['Form', 'syntactic_category', 'name', '=', u'sent'],\n ['Form', 'morpheme_break', 'like', '% %'],\n ['Form', 'morpheme_break', 'like', '%-%']]],\n ['Form', 'syntactic_category_string', '!=', None],\n ['Form', 'grammaticality', '=', '']]]}\n params = self.form_search_create_params.copy()\n params.update({\n 'name': u'Find Blackfoot sentences',\n 'description': u'Returns all forms containing words',\n 'search': query\n })\n params = json.dumps(params)\n response = self.app.post(url('formsearches'), params, self.json_headers, self.extra_environ_admin)\n rules_form_search_id = json.loads(response.body)['id']\n params = self.corpus_create_params.copy()\n params.update({\n 'name': u'Corpus of Blackfoot sentences',\n 'form_search': rules_form_search_id\n })\n params = json.dumps(params)\n response = self.app.post(url('corpora'), params, self.json_headers, self.extra_environ_admin)\n rules_corpus_id = json.loads(response.body)['id']\n\n # Now we reduce the number of category-based word-formation rules by removing all such\n # rules implicit in the rules corpus that have fewer than two exemplar tokens.\n\n # Get the category sequence types of all of the words in the rules corpus ordered by their counts, minus\n # those with fewer than ``minimum_token_count`` counts.\n minimum_token_count = 2\n params = {'minimum_token_count': minimum_token_count}\n response = self.app.get(url(controller='corpora', action='get_word_category_sequences', id=rules_corpus_id),\n params, self.json_headers, self.extra_environ_admin)\n resp = json.loads(response.body)\n\n word_category_sequences = u' '.join([word_category_sequence for word_category_sequence, ids in resp])\n #word_category_sequences = u'agra-vai vai-agrb'\n\n # Now create a morphology using the lexicon and rules defined by word_category_sequences\n rich_upper = False\n name = u'Morphology of Blackfoot'\n params = self.morphology_create_params.copy()\n params.update({\n 'name': name,\n 'lexicon_corpus': lexicon_corpus_id,\n 'rules': word_category_sequences,\n 'script_type': u'lexc',\n 'extract_morphemes_from_rules_corpus': False,\n 'rich_upper': rich_upper\n })\n params = json.dumps(params)\n response = self.app.post(url('morphologies'), params, self.json_headers, self.extra_environ_admin_appset)\n resp = json.loads(response.body)\n morphology_id = resp['id']\n assert resp['name'] == name\n assert resp['script_type'] == u'lexc'\n\n # Generate the morphology's script without compiling it.\n response = self.app.put(url(controller='morphologies', action='generate',\n id=morphology_id), headers=self.json_headers,\n extra_environ=self.extra_environ_contrib)\n resp = json.loads(response.body)\n generate_attempt = resp['generate_attempt']\n\n # Poll ``GET /morphologies/morphology_id`` until ``generate_attempt`` has changed.\n seconds_elapsed = 0\n wait = 2\n while True:\n response = self.app.get(url('morphology', id=morphology_id),\n headers=self.json_headers, extra_environ=self.extra_environ_contrib)\n resp = json.loads(response.body)\n if generate_attempt != resp['generate_attempt']:\n log.debug('Generate attempt for morphology %d has terminated.' % morphology_id)\n break\n else:\n log.debug('Waiting for morphology %d\\'s script to generate: %s' % (\n morphology_id, self.human_readable_seconds(seconds_elapsed)))\n sleep(wait)\n seconds_elapsed = seconds_elapsed + wait\n\n ################################################################################\n # MORPHEME LANGUAGE MODEL\n ################################################################################\n\n # Create a morpheme language model\n name = u'Blackfoot morpheme language model'\n params = self.morpheme_language_model_create_params.copy()\n params.update({\n 'name': name,\n 'corpus': rules_corpus_id,\n 'toolkit': 'mitlm'\n })\n params = json.dumps(params)\n response = self.app.post(url('morphemelanguagemodels'), params, self.json_headers, self.extra_environ_admin)\n resp = json.loads(response.body)\n morpheme_language_model_id = resp['id']\n assert resp['name'] == name\n assert resp['toolkit'] == u'mitlm'\n assert resp['order'] == 3\n assert resp['smoothing'] == u'' # The ModKN smoothing algorithm is the implicit default with MITLM\n\n # Generate the files of the language model\n response = self.app.put(url(controller='morphemelanguagemodels', action='generate', id=morpheme_language_model_id),\n {}, self.json_headers, self.extra_environ_admin)\n resp = json.loads(response.body)\n lm_generate_attempt = resp['generate_attempt']\n\n # Poll GET /morphemelanguagemodels/id until generate_attempt changes.\n requester = lambda: self.app.get(url('morphemelanguagemodel', id=morpheme_language_model_id),\n headers=self.json_headers, extra_environ=self.extra_environ_admin)\n resp = self.poll(requester, 'generate_attempt', lm_generate_attempt, log, wait=1, vocal=False)\n assert resp['generate_message'] == u'Language model successfully generated.'\n\n ################################################################################\n # MORPHOLOGICAL PARSER\n ################################################################################\n\n # Create a morphological parser for Blackfoot\n params = self.morphological_parser_create_params.copy()\n params.update({\n 'name': u'Morphological parser for Blackfoot',\n 'phonology': phonology_id,\n 'morphology': morphology_id,\n 'language_model': morpheme_language_model_id\n })\n params = json.dumps(params)\n response = self.app.post(url('morphologicalparsers'), params, self.json_headers, self.extra_environ_admin)\n resp = json.loads(response.body)\n morphological_parser_id = resp['id']\n\n # Compile the morphological parser's morphophonology script if necessary, cf. precompiled_morphophonology and pregenerated_morphophonology.\n morphological_parser_directory = os.path.join(self.morphological_parsers_path, 'morphological_parser_%d' % morphological_parser_id)\n morphophonology_binary_filename = 'morphophonology.foma'\n morphophonology_script_filename = 'morphological_parser.script'\n morphophonology_binary_path = os.path.join(morphological_parser_directory, morphophonology_binary_filename )\n morphophonology_script_path = os.path.join(morphological_parser_directory, morphophonology_script_filename )\n try:\n precompiled_morphophonology_path = os.path.join(self.test_morphophonologies_path, precompiled_morphophonology)\n pregenerated_morphophonology_path = os.path.join(self.test_morphophonologies_path, pregenerated_morphophonology)\n except Exception:\n precompiled_morphophonology_path = None\n pregenerated_morphophonology_path = None\n if (precompiled_morphophonology_path and pregenerated_morphophonology_path and \n os.path.exists(precompiled_morphophonology_path) and os.path.exists(pregenerated_morphophonology_path)):\n # Use the precompiled morphophonology script if it's available,\n copyfileobj(open(precompiled_morphophonology_path, 'rb'), open(morphophonology_binary_path, 'wb'))\n copyfileobj(open(pregenerated_morphophonology_path, 'rb'), open(morphophonology_script_path, 'wb'))\n else:\n # Generate the parser's morphophonology FST, compile it and generate the morphemic language model\n response = self.app.put(url(controller='morphologicalparsers', action='generate_and_compile',\n id=morphological_parser_id), headers=self.json_headers, extra_environ=self.extra_environ_admin)\n resp = json.loads(response.body)\n morphological_parser_compile_attempt = resp['compile_attempt']\n\n # Generate the parser's morphophonology FST, compile it and generate the morphemic language model\n response = self.app.put(url(controller='morphologicalparsers', action='generate_and_compile',\n id=morphological_parser_id), headers=self.json_headers, extra_environ=self.extra_environ_admin)\n\n # Poll ``GET /morphologicalparsers/mophological_parser_id`` until ``compile_attempt`` has changed.\n requester = lambda: self.app.get(url('morphologicalparser', id=morphological_parser_id),\n headers=self.json_headers, extra_environ=self.extra_environ_admin)\n resp = self.poll(requester, 'compile_attempt', morphological_parser_compile_attempt, log,\n wait=10, vocal=True, task_descr='compile morphological parser %s' % morphological_parser_id)\n assert resp['compile_message'] == \\\n u'Compilation process terminated successfully and new binary file was written.'\n\n # Poll ``GET /morphologicalparsers/mophological_parser_id`` until ``compile_attempt`` has changed.\n requester = lambda: self.app.get(url('morphologicalparser', id=morphological_parser_id),\n headers=self.json_headers, extra_environ=self.extra_environ_admin)\n resp = self.poll(requester, 'compile_attempt', morphological_parser_compile_attempt, log,\n wait=10, vocal=True, task_descr='compile morphological parser %s' % morphological_parser_id)\n assert resp['compile_message'] == \\\n u'Compilation process terminated successfully and new binary file was written.'\n\n # Some reusable transcriptions and their parses\n transcription1 = u'nitsspiyi'\n transcription1_correct_parse = u'%s-%s' % (\n h.rare_delimiter.join([u'nit', u'1', u'agra']),\n h.rare_delimiter.join([u'ihpiyi', u'dance', u'vai']))\n transcription1_impoverished_parse = u'nit-ihpiyi'\n transcription2 = u'aaniit'\n transcription2_correct_parse = u'%s-%s' % (\n h.rare_delimiter.join([u'waanii', u'say', u'vai']),\n h.rare_delimiter.join([u't', u'IMP', u'agrb']))\n transcription2_impoverished_parse = u'waanii-t'\n\n # Test applyup on the mophological parser's morphophonology FST\n params = json.dumps({'transcriptions': [transcription1, transcription2]})\n response = self.app.put(url(controller='morphologicalparsers', action='applyup',\n id=morphological_parser_id), params, self.json_headers, self.extra_environ_admin)\n resp = json.loads(response.body)\n if rich_upper:\n assert transcription1_correct_parse in resp[transcription1]\n assert transcription2_correct_parse in resp[transcription2]\n else:\n assert transcription1_impoverished_parse in resp[transcription1]\n assert transcription2_impoverished_parse in resp[transcription2]\n\n # Test how well the morphological parser parses some test words.\n params = json.dumps({'transcriptions': [transcription1, transcription2]})\n response = self.app.put(url(controller='morphologicalparsers', action='parse',\n id=morphological_parser_id), params, self.json_headers, self.extra_environ_admin)\n resp = json.loads(response.body)\n assert resp[transcription1] == transcription1_correct_parse\n # aaniit will have waaniit 'scatter' as its most likely parse and the correct parse waanii-t 'say-IMP'\n # as its second most likely...\n assert resp[transcription2] != transcription2_correct_parse\n\n\n\n \"\"\"\n\n\n ################################################################################\n # LOUIE MORPHOLOGY\n ################################################################################\n\n # Create a form search that returns forms containing analyzed words elicited by Louie.\n\n conjuncts = [['or', [['Form', 'syntactic_category', 'name', '=', u'sent'],\n ['Form', 'morpheme_break', 'like', '% %'],\n ['Form', 'morpheme_break', 'like', '%-%']]],\n ['Form', 'syntactic_category_string', '!=', None],\n ['Form', 'grammaticality', '=', ''],\n ['Form', 'elicitor', 'last_name', '=', 'Louie']]\n query = {'filter': ['and', conjuncts]}\n\n params = self.form_search_create_params.copy()\n params.update({\n 'name': u'Forms containing analyzed words elicited by Louie',\n 'search': query\n })\n params = json.dumps(params)\n response = self.app.post(url('formsearches'), params, self.json_headers, self.extra_environ_admin)\n louie_form_search_id = json.loads(response.body)['id']\n\n params = self.corpus_create_params.copy()\n params.update({\n 'name': u'Corpus of forms containing analyzed words elicited by Louie',\n 'form_search': louie_form_search_id\n })\n params = json.dumps(params)\n response = self.app.post(url('corpora'), params, self.json_headers, self.extra_environ_admin)\n louie_corpus_id = json.loads(response.body)['id']\n\n # Now create a morphology using the Louie corpus as both the lexicon and rules corpora.\n rich_upper = False\n name = u'Morphology of Blackfoot based on words elicited by Louie'\n params = self.morphology_create_params.copy()\n params.update({\n 'name': name,\n 'lexicon_corpus': louie_corpus_id,\n 'rules_corpus': louie_corpus_id,\n 'script_type': u'regex',\n 'extract_morphemes_from_rules_corpus': True,\n 'rich_upper': rich_upper\n })\n params = json.dumps(params)\n response = self.app.post(url('morphologies'), params, self.json_headers, self.extra_environ_admin_appset)\n resp = json.loads(response.body)\n louie_morphology_id = resp['id']\n assert resp['name'] == name\n assert resp['script_type'] == u'regex'\n\n # Generate the morphology's script without compiling it.\n response = self.app.put(url(controller='morphologies', action='generate',\n id=louie_morphology_id), headers=self.json_headers,\n extra_environ=self.extra_environ_contrib)\n resp = json.loads(response.body)\n generate_attempt = resp['generate_attempt']\n\n # Poll ``GET /morphologies/morphology_id`` until ``generate_attempt`` has changed.\n seconds_elapsed = 0\n wait = 2\n while True:\n response = self.app.get(url('morphology', id=louie_morphology_id),\n headers=self.json_headers, extra_environ=self.extra_environ_contrib)\n resp = json.loads(response.body)\n if generate_attempt != resp['generate_attempt']:\n log.debug('Generate attempt for morphology %d has terminated.' % louie_morphology_id)\n break\n else:\n log.debug('Waiting for morphology %d\\'s script to generate: %s' % (\n louie_morphology_id, self.human_readable_seconds(seconds_elapsed)))\n sleep(wait)\n seconds_elapsed = seconds_elapsed + wait\n\n\n ################################################################################\n # MORPHEME LANGUAGE MODEL -- LOUIE\n ################################################################################\n\n # Create a morpheme language model based on the data elicited by Louie\n name = u'Blackfoot morpheme language model based on data elicited by Louie'\n params = self.morpheme_language_model_create_params.copy()\n params.update({\n 'name': name,\n 'corpus': louie_corpus_id,\n 'toolkit': 'mitlm'\n })\n params = json.dumps(params)\n response = self.app.post(url('morphemelanguagemodels'), params, self.json_headers, self.extra_environ_admin)\n resp = json.loads(response.body)\n louie_language_model_id = resp['id']\n assert resp['name'] == name\n assert resp['toolkit'] == u'mitlm'\n assert resp['order'] == 3\n assert resp['smoothing'] == u'' # The ModKN smoothing algorithm is the implicit default with MITLM\n\n # Generate the files of the language model\n response = self.app.put(url(controller='morphemelanguagemodels', action='generate',\n id=louie_language_model_id),\n {}, self.json_headers, self.extra_environ_admin)\n resp = json.loads(response.body)\n lm_generate_attempt = resp['generate_attempt']\n\n # Poll GET /morphemelanguagemodels/id until generate_attempt changes.\n requester = lambda: self.app.get(url('morphemelanguagemodel', id=louie_language_model_id),\n headers=self.json_headers, extra_environ=self.extra_environ_admin)\n resp = self.poll(requester, 'generate_attempt', lm_generate_attempt, log, wait=1, vocal=False)\n assert resp['generate_message'] == u'Language model successfully generated.'\n\n ################################################################################\n # MORPHOLOGICAL PARSER -- LOUIE\n ################################################################################\n\n # Create a morphological parser for Blackfoot based on data elicited by Louie\n params = self.morphological_parser_create_params.copy()\n params.update({\n 'name': u'Morphological parser for Blackfoot based on data elicited by Louie',\n 'phonology': phonology_id,\n 'morphology': louie_morphology_id,\n 'language_model': louie_language_model_id\n })\n params = json.dumps(params)\n response = self.app.post(url('morphologicalparsers'), params, self.json_headers,\n self.extra_environ_admin)\n resp = json.loads(response.body)\n louie_parser_id = resp['id']\n\n # Compile the morphological parser's morphophonology script if necessary, cf.\n # precompiled_morphophonology and pregenerated_morphophonology.\n morphological_parser_directory = os.path.join(self.morphological_parsers_path,\n 'morphological_parser_%d' % louie_parser_id)\n morphophonology_binary_filename = 'morphophonology.foma'\n morphophonology_script_filename = 'morphological_parser.script'\n morphophonology_binary_path = os.path.join(morphological_parser_directory,\n morphophonology_binary_filename )\n morphophonology_script_path = os.path.join(morphological_parser_directory,\n morphophonology_script_filename )\n try:\n precompiled_morphophonology_path = os.path.join(self.test_morphophonologies_path,\n precompiled_morphophonology)\n pregenerated_morphophonology_path = os.path.join(self.test_morphophonologies_path,\n pregenerated_morphophonology)\n except Exception:\n precompiled_morphophonology_path = None\n pregenerated_morphophonology_path = None\n if (precompiled_morphophonology_path and pregenerated_morphophonology_path and \n os.path.exists(precompiled_morphophonology_path) and os.path.exists(pregenerated_morphophonology_path)):\n # Use the precompiled morphophonology script if it's available,\n copyfileobj(open(precompiled_morphophonology_path, 'rb'), open(morphophonology_binary_path, 'wb'))\n copyfileobj(open(pregenerated_morphophonology_path, 'rb'), open(morphophonology_script_path, 'wb'))\n else:\n # Generate the parser's morphophonology FST, compile it and generate the morphemic language model\n response = self.app.put(url(controller='morphologicalparsers', action='generate_and_compile',\n id=louie_parser_id), headers=self.json_headers, extra_environ=self.extra_environ_admin)\n resp = json.loads(response.body)\n morphological_parser_compile_attempt = resp['compile_attempt']\n\n # Generate the parser's morphophonology FST, compile it and generate the morphemic language model\n response = self.app.put(url(controller='morphologicalparsers', action='generate_and_compile',\n id=louie_parser_id), headers=self.json_headers, extra_environ=self.extra_environ_admin)\n\n # Poll ``GET /morphologicalparsers/mophological_parser_id`` until ``compile_attempt`` has changed.\n requester = lambda: self.app.get(url('morphologicalparser', id=louie_parser_id),\n headers=self.json_headers, extra_environ=self.extra_environ_admin)\n resp = self.poll(requester, 'compile_attempt', morphological_parser_compile_attempt, log,\n wait=10, vocal=True, task_descr='compile morphological parser %s' % louie_parser_id)\n assert resp['compile_message'] == \\\n u'Compilation process terminated successfully and new binary file was written.'\n\n # Poll ``GET /morphologicalparsers/mophological_parser_id`` until ``compile_attempt`` has changed.\n requester = lambda: self.app.get(url('morphologicalparser', id=louie_parser_id),\n headers=self.json_headers, extra_environ=self.extra_environ_admin)\n resp = self.poll(requester, 'compile_attempt', morphological_parser_compile_attempt, log,\n wait=10, vocal=True, task_descr='compile morphological parser %s' % louie_parser_id)\n assert resp['compile_message'] == \\\n u'Compilation process terminated successfully and new binary file was written.'\n\n # Some reusable transcriptions and their parses\n transcription1 = u'nitsspiyi'\n transcription1_correct_parse = u'%s-%s' % (\n h.rare_delimiter.join([u'nit', u'1', u'agra']),\n h.rare_delimiter.join([u'ihpiyi', u'dance', u'vai']))\n transcription1_impoverished_parse = u'nit-ihpiyi'\n transcription2 = u'aaniit'\n transcription2_correct_parse = u'%s-%s' % (\n h.rare_delimiter.join([u'waanii', u'say', u'vai']),\n h.rare_delimiter.join([u't', u'IMP', u'agrb']))\n transcription2_impoverished_parse = u'waanii-t'\n\n # Test applyup on the mophological parser's morphophonology FST\n params = json.dumps({'transcriptions': [transcription1, transcription2]})\n response = self.app.put(url(controller='morphologicalparsers', action='applyup',\n id=louie_parser_id), params, self.json_headers, self.extra_environ_admin)\n resp = json.loads(response.body)\n if rich_upper:\n assert transcription1_correct_parse in resp[transcription1]\n assert transcription2_correct_parse not in resp[transcription2]\n else:\n assert transcription1_impoverished_parse in resp[transcription1]\n assert transcription2_impoverished_parse not in resp[transcription2]\n\n # Test how well the morphological parser parses some test words.\n params = json.dumps({'transcriptions': [transcription1, transcription2]})\n response = self.app.put(url(controller='morphologicalparsers', action='parse',\n id=louie_parser_id), params, self.json_headers, self.extra_environ_admin)\n resp = json.loads(response.body)\n assert resp[transcription1] == transcription1_correct_parse\n # aaniit will have waaniit 'scatter' as its most likely parse and the correct parse waanii-t 'say-IMP'\n # as its second most likely...\n assert resp[transcription2] != transcription2_correct_parse\n\n\n # Finally, load the original database back in so that subsequent tests can work.\n with open(tmp_script_path, 'w') as tmpscript:\n tmpscript.write('#!/bin/sh\\nmysql -u %s -p%s %s < %s' % (username, password, db_name, backup_dump_file_path))\n with open(os.devnull, \"w\") as fnull:\n call([tmp_script_path], stdout=fnull, stderr=fnull)\n os.remove(tmp_script_path)\n os.remove(backup_dump_file_path)\n\n # Implement category-based class LMs and test them against morpheme-based ones.\n # Build multiple Bf morphological parsers and test them out, find the best one, write a paper on it!","def load_yaafedata(params, \n n_learn_frames=2000,\n use_custom_stft=False):\n\n audio_file_path = getoptions(params, 'location', '/sons/voxforge/data/Learn/')\n # if no number specified, use n_learn_frames\n n_frames = getoptions(params, 'n_frames', n_learn_frames)\n sr = getoptions(params, 'sr', 16000)\n sigma_noise = getoptions(params, 'sigma', 0.0)\n random_seed = getoptions(params, 'shuffle', 1001)\n features = getoptions(params, 'features', [])\n wintime = getoptions(params, 'wintime', 0.032)\n steptime = getoptions(params, 'steptime', 0.008)\n startpoint = getoptions(params, 'startpoint', 0)\n forbid_list = getoptions(params, 'forbidden_names', [])\n mfnpf = getoptions(params, 'frame_num_per_file', 3000)\n# wintime = float(win_size)/float(sr)\n# steptime = float(step_size)/float(sr)\n \n win_size = int(wintime*sr)\n step_size = int(steptime*sr)\n# print wintime, steptime, win_size, step_size\n # apply sub_routine to all the files until a condition is met\n n_frames_reached = 0\n\n all_file_paths = get_filepaths(audio_file_path,\n random_seed,\n forbid_list = forbid_list)\n file_index = 0\n\n specseq = []\n featseq = []\n dataseq = []\n n_files_used = 0\n\n while (n_frames_reached < n_frames):\n file_index = file_index + 1\n filepath = all_file_paths[file_index]\n n_files_used = n_files_used + 1\n\n [loc_magSTFT, loc_Feats, locDatas] = load_data_one_audio_file(\n filepath, sr,\n wintime=wintime,\n steptime=steptime,\n max_frame_num_per_file=mfnpf,\n sigma_noise=sigma_noise,\n startpoint = startpoint,\n features=features)\n# if get_data:\n# [loc_magSTFT, loc_Feats, locDatas] = load_data_one_file_melspec(filepath, sr, sigma_noise, params);\n# Data = [Data , locDatas'];\n# else\n# [loc_magSTFT, loc_Feats, ~] = load_data_one_file_melspec(filepath, sr, sigma_noise, params);\n# end\n if not use_custom_stft:\n specseq.append(loc_magSTFT)\n else:\n specseq.append(np.abs(get_stft(locDatas,\n wsize=win_size,\n tstep=step_size,\n sigma = sigma_noise)).T)\n# print wintime, steptime, win_size, step_size\n# print loc_magSTFT.shape\n# print specseq[-1].shape\n# print locDatas.shape\n featseq.append(loc_Feats)\n dataseq.append(locDatas)\n \n n_frames_reached += min(loc_magSTFT.shape[0], loc_Feats.shape[0])\n print n_frames_reached\n \n Spectrums = np.vstack(specseq)\n Features = np.vstack(featseq)\n Data = np.hstack(dataseq)\n\n n_frames_reached = min(n_frames_reached, n_frames)\n Spectrums = Spectrums[0:n_frames_reached,:]\n Features = Features[0:n_frames_reached,:]\n used_files = all_file_paths[0:n_files_used]\n\n return Features, Spectrums, n_frames_reached, Data, used_files","def load_data():\r\n global labelNames\r\n print(\"Loading Data...\")\r\n\r\n fnpath = \"rawdata\\\\cifar-10-batches-py\"\r\n fnprefix = 'data_batch_'\r\n fnlblnames = 'batches.meta'\r\n fntstbatch = 'test_batch'\r\n\r\n labelNames = unpickle(path.join(fnpath, fnlblnames))\r\n label_names = []\r\n for label in labelNames['label_names']:\r\n label_names.append(\"\".join(map(chr, label)))\r\n labelNames['label_names'] = label_names\r\n\r\n CIFAR_Data.append(unpickle(path.join(fnpath, fntstbatch)))\r\n for n in range(1, 6):\r\n CIFAR_Data.append(unpickle(path.join(fnpath, fnprefix + str(n))))","def dataload():\n\t\n\tglobal A, B, fnA, fnB, lPcnA, lPcnB\n\t\n\tdwd = os.getcwd() # Data WD\n\t\t\n\t# First sample A is loaded. This is the \"calibrating\" sample.\n\t# In this case it is the OGLE III LMC small amplitude RGs.\n\t\n\tfnA = '/LMC-CalSample-cleaned_2.fits'\n\tA = Table.read(dwd+fnA)\n\n\t# Then sample B is loaded. For comparison/testing purposes, this is\n\t# again the OGLE III LMC SARGs.\n\t\n\tfnB = '/LMC-CalSample-cleaned_2.fits'\n\tB = Table.read(dwd+fnB)\n\t\n\t\"\"\" Fix tables so only the stars with all three good periods are \n\tconsidered. \"\"\"\n\t\n\tlPcnA = get_logPcn(A)\n\tlPcnB = get_logPcn(B)\n\t\n\tfor cn in lPcnA:\n\t\tA = A[A[cn]>0]\n\tfor cn in lPcnB:\n\t\tB = B[B[cn]>0]","def load_data(dataset_path: str):\n data = arff.loadarff(dataset_path)\n data_frame = pd.DataFrame(data[0])\n return data_frame","def _load20news_miao():\n DIR = os.path.dirname(os.path.realpath(__file__)).split('vae_sparse')[0]+'vae_sparse/optvaedatasets'\n DIR += '/20news_miao'\n h5file = DIR+'/miao.h5'\n if not os.path.exists(h5file):\n flen = len(open(DIR+'/vocab').readlines())\n print 'DIM: ',flen\n np.random.seed(1)\n TRAIN_VALID_MAT = readSparseFile(DIR+'/train.feat', flen, zeroIndexed=False)\n idx = np.random.permutation(TRAIN_VALID_MAT.shape[0])\n VALIDMAT = TRAIN_VALID_MAT[idx[:500]]\n TRAINMAT = TRAIN_VALID_MAT[idx[500:]]\n TESTMAT = readSparseFile(DIR+'/test.feat', flen, zeroIndexed=False) \n saveSparseHDF5(TRAINMAT,'train', h5file)\n saveSparseHDF5(VALIDMAT,'valid', h5file)\n saveSparseHDF5(TESTMAT, 'test' , h5file)\n dset = {}\n dset['vocabulary']= [k.strip().split(' ')[0] for k in open(DIR+'/vocab').readlines()]\n dset['train'] = loadSparseHDF5('train',h5file)\n dset['valid'] = loadSparseHDF5('valid',h5file)\n dset['test'] = loadSparseHDF5('test',h5file)\n dset['dim_observations'] = dset['train'].shape[1]\n dset['data_type'] = 'bow'\n return dset","def load_datasets():\n idx, data_paths, data_names, desc_paths, descrips, sql_paths, \\\n sql_names, loaded, table_size, \\\n loaded_names = mgr.build_datasets_table()\n return render_template('load_datasets.html',\n zip=zip(idx, data_paths, data_names, desc_paths,\n descrips, sql_paths, sql_names, loaded,\n table_size),\n data_names=loaded_names)","def __loadDataset(self, parameters):\n # self.localConfigured = Settings.instance().readValue( key = 'Common/local-repo' )\n for pr in parameters:\n if pr['type'] == 'dataset':\n if pr['value'].startswith('undefined:/'):\n fileName = pr['value'].split('undefined:/')[1]\n if not os.path.exists( fileName ):\n raise Exception(\"the following test data file is missing: %s \" % fileName)\n\n doc = FileModelTestData.DataModel()\n res = doc.load( absPath = fileName )\n pr['value'] = \"undefined:/%s\" % doc.getRaw()\n elif pr['value'].startswith('local-tests:/'):\n fileName = pr['value'].split('local-tests:/')[1]\n\n if not os.path.exists( fileName ):\n raise Exception(\"the following test data file is missing: %s \" % fileName)\n \n doc = FileModelTestData.DataModel()\n res = doc.load( absPath = fileName )\n pr['value'] = \"local-tests:/%s\" % doc.getRaw()\n else:\n pass","def load_all_data_from_file(self) -> None:\n self.load_gene_data_from_file()\n self.load_ontology_from_file(ontology_type=DataType.GO, ontology_url=self.go_ontology_url,\n ontology_cache_path=self.go_ontology_cache_path,\n config=self.config)\n self.load_associations_from_file(associations_type=DataType.GO, associations_url=self.go_associations_url,\n associations_cache_path=self.go_associations_cache_path, config=self.config)\n self.load_ontology_from_file(ontology_type=DataType.DO, ontology_url=self.do_ontology_url,\n ontology_cache_path=self.do_ontology_cache_path, config=self.config)\n self.load_associations_from_file(associations_type=DataType.DO, associations_url=self.do_associations_url,\n associations_cache_path=self.do_associations_cache_path,\n association_additional_cache_path=self.do_associations_new_cache_path,\n association_additional_url=self.do_associations_new_url, config=self.config)\n self.load_ontology_from_file(ontology_type=DataType.EXPR, ontology_url=self.expression_ontology_url,\n ontology_cache_path=self.expression_ontology_cache_path, config=self.config)\n self.load_associations_from_file(associations_type=DataType.EXPR,\n associations_url=self.expression_associations_url,\n associations_cache_path=self.expression_associations_cache_path,\n config=self.config)\n self.load_orthology_from_file()\n self.load_expression_cluster_data()\n self.load_protein_domain_information()","def test_load_dataset():\n\n # Given\n dataset_file_name = core.config.app_config.TESTING_DATA_FILE\n\n # When\n subject = utils.load_dataset(filename=dataset_file_name)\n\n # Then\n assert isinstance(subject, pd.DataFrame)\n assert subject.shape == (5940, 41)","def load(as_pandas=None):\n return du.as_numpy_dataset(load_pandas(), as_pandas=as_pandas)","def load(as_pandas=None):\n return du.as_numpy_dataset(load_pandas(), as_pandas=as_pandas)","def load(as_pandas=None):\n return du.as_numpy_dataset(load_pandas(), as_pandas=as_pandas)","def load_datasets_from_database(test_run_id):\n logging.info('Loading datasets from database for: {}'.format(test_run_id))\n mbtest_run = MBTestDatabaseService().get_run(test_run_id)\n datasets = mbtest_run.get('datasets')\n logging.info('type(Documents): %r', type(datasets))\n logging.info('Documents: %s', str(datasets)[:200])\n\n if isinstance(datasets, basestring):\n documents = yaml.load(datasets)\n # documents should be seq of dicts -- if str then legacy format\n if isinstance(documents, basestring):\n logging.info('Loading datasets from legacy CSV format')\n reader = csv.reader(StringIO(datasets), delimiter=';')\n documents = mbtest_data_csv_to_yaml_format(reader)\n else:\n pass # datasets is seq of dict\n\n return documents","def loadData(catalog):\n\n loadArtwork(catalog)\n loadArtists(catalog)","def import_dataset(fpath):\r\n data = read_csv(fpath)\r\n print(data.head())\r\n print(data.shape)\r\n return data","def load_data(self, f): \n self.sampling = True\n self.reads = np.load(f)\n self.total = self.reads.shape[0]","def load(self):\n #self.df = read_file(\"../data/yelp_academic_dataset_user.json\") #Full Data.\n self.df = read_file(\"../data/user300.json\") #For local machine.\n #self.get_friend_list()\n #self.save_friend_nodes()","def load_data(self,split='train'):\n return load_arrow_data(self.config,split)","def loadData(catalog):\n loadArtworks(catalog)\n loadArtists(catalog)\n loadAdquires(catalog)\n loadNacionalities(catalog)\n load2DArtworks(catalog)\n loadArtistMediumsTags(catalog)\n loadDptments(catalog)\n catalog['artists'] = sortArtists(catalog, 3)\n fillArtistMediums(catalog)\n fillMostUsedMediums(catalog)\n catalog['artists_tags'] = sortArtistTags(catalog, 3)\n sort_dptments(catalog)","def import_datasets(snli_path):\n print('extract data from snli directory..')\n train = dict(); dev = dict(); test = dict()\n gold_labels = {'entailment': 0, 'neutral': 1, 'contradiction': 2}\n\n for file_type in ['train', 'dev', 'test']:\n path = os.path.join(snli_path, 'snli_1.0_{}.jsonl'.format(file_type))\n with open(path) as file:\n data = [json.loads(line) for line in file]\n eval(file_type)['premise'] = [entry['sentence1'] for entry in data if entry['gold_label'] != '-']\n eval(file_type)['hypothesis'] = [entry['sentence2'] for entry in data if entry['gold_label'] != '-']\n g_labels = np.array([gold_labels[entry['gold_label']] for entry in data if entry['gold_label'] != '-'])\n eval(file_type)['label'] = g_labels\n print('extraction process was finished successfully!')\n return train, dev, test","def test_AFQ_data():\n _, bids_path, _ = get_temp_hardi()\n\n for mapping in [SynMap(use_prealign=False), AffMap()]:\n myafq = api.AFQ(\n bids_path=bids_path,\n dmriprep='vistasoft',\n mapping=mapping)\n npt.assert_equal(nib.load(myafq.b0[\"01\"]).shape,\n nib.load(myafq.dwi_file[\"01\"]).shape[:3])\n npt.assert_equal(nib.load(myafq.b0[\"01\"]).shape,\n nib.load(myafq.dti_params[\"01\"]).shape[:3])\n myafq.rois\n shutil.rmtree(op.join(\n bids_path,\n 'derivatives/afq'))","def prepare_dataset(fpath):\n raise NotImplementedError","def LoadBatch(filename):","def load_dataset():\n try:\n data_path = ROOT_PATH.joinpath('data', 'Complete_TAVG_Daily_LatLong1_1880.nc')\n ds = xarray.open_dataset(data_path)\n return ds\n except FileNotFoundError:\n raise","def loadData(catalog):\r\n controller.loadData(catalog)","def load_synthetic_data():\n\n pickle_object = FM().data_file \n\n with pickle_object.open('rb') as data_file: \n return pickle.load(data_file)","def load_data(data_set_key: str, a2e_data_path: str = '../../../a2e-data/data', cache_dir: str = None) -> BearingDataSet:\n\n if a2e_data_path is not None and not a2e_data_path.startswith('http') and not a2e_data_path.startswith('file://'):\n if os.path.isabs(a2e_data_path):\n a2e_data_path = 'file://' + os.path.abspath(a2e_data_path)\n else:\n bearing_module_path = pathlib.Path(__file__).parent.absolute()\n absolute_data_path = os.path.abspath(os.path.join(bearing_module_path, a2e_data_path))\n if os.name == 'nt':\n absolute_data_path = f'/{absolute_data_path}'.replace('\\\\', '/')\n\n a2e_data_path = 'file://' + absolute_data_path\n\n if not os.path.isdir(a2e_data_path.replace('file://', '')):\n a2e_data_path = 'https://github.com/maechler/a2e-data/raw/master/data/'\n\n if cache_dir is None:\n cache_dir = os.path.join(Path.home(), '.a2e')\n\n if not os.path.exists(cache_dir):\n os.makedirs(cache_dir)\n\n a2e_data_path = a2e_data_path.rstrip('/') + '/'\n data_set_description_origin = f'{a2e_data_path}{data_set_key}.yaml'\n data_set_origin = f'{a2e_data_path}{data_set_key}.csv.gz'\n data_set_description_path = get_file(data_set_key + '.yaml', origin=data_set_description_origin, cache_dir=cache_dir, cache_subdir='datasets/bearing')\n windows = {}\n\n with open(data_set_description_path) as data_set_description_file:\n data_set_description = yaml.load(data_set_description_file, Loader=yaml.FullLoader)\n data_set_path = get_file(data_set_key + '.csv.gz', origin=data_set_origin, cache_dir=cache_dir, cache_subdir='datasets/bearing', file_hash=data_set_description['data']['md5_hash'], hash_algorithm='md5')\n\n with gzip.open(data_set_path, mode='rt') as data_set_file:\n data_frame = pd.read_csv(data_set_file, parse_dates=[data_set_description['data']['index_column']], date_parser=lambda x: timestamp_to_date_time(float(x)), quotechar='\"', sep=',')\n data_frame = data_frame.set_index(data_set_description['data']['index_column'])\n\n for window_key, window_description in data_set_description['windows'].items():\n windows[window_key] = {\n 'mask': (data_frame.index > window_description['start']) & (data_frame.index <= window_description['end']),\n 'label': window_description['label'],\n }\n\n return BearingDataSet(data_set_key, data_frame, windows)","def get_data_loaders():\n dataset_path = \"\"\n dataset_cache = None\n personachat = get_dataset(dataset_path, dataset_cache)\n\n tokenizer_selected = OpenAIGPTTokenizer.from_pretrained('openai-gpt')\n logger.info(\"Build inputs and labels\")\n datasets = {\"train\": defaultdict(list), \"valid\": defaultdict(list)}\n personality = []\n history_complete = []\n count_persona = 0\n with open('data_faiss_pegasus_2sentences_finalgenerated.pkl', 'rb') as f:\n persona_selected_list = pickle.load(f)\n for dataset_name, dataset in personachat.items():\n num_candidates = len(dataset[0][\"utterances\"][0][\"candidates\"])\n if num_candidates > 0 and dataset_name == 'train':\n num_candidates = min(1, num_candidates)\n for dialog in dataset:\n persona = dialog[\"persona_info\"].copy()\n #datasets[personality].append(persona)\n count_history = 0\n for utterance in dialog[\"utterances\"]:\n count_history = count_history + 1\n history = utterance[\"history\"][-(2*2+5):]\n \n #history_complete.append(history)\n if len(persona) == 4:\n if len(history) > (len(persona)+3):\n history_chatbot = history[1::2]\n persona_selected = persona_selected_list[count_persona]\n instance = build_input_from_segments_faiss_2(persona, history_chatbot) \n for input_name, input_array in instance.items():\n datasets[dataset_name][input_name].append(input_array)\n count_persona = count_persona + 1\n return datasets","def loadData(catalog):\n controller.loadData(catalog)","def loadData(catalog):\n controller.loadData(catalog)","def loadData(catalog):\n controller.loadData(catalog)","def loadData(catalog):\n controller.loadData(catalog)","def loadData(catalog):\n controller.loadData(catalog)","def load_tgas():\n tgas = GaiaData(gload.tgas())\n return tgas","def load_dataset(fname, nb_lines):\n import os.path\n if os.path.isfile('safe/Amazon-'+str(nb_lines)+'.p'):\n return util.load('safe/Amazon-'+str(nb_lines)+'.p')\n count = 1\n X = []\n y = []\n with open(fname) as f:\n for line in f:\n text, label = read_line(line)\n #print((label, text))\n X.append(text)\n y.append(label)\n if count >= nb_lines:\n break\n count+=1\n\n #load pretrained dictonary\n dico = util.load('safe/vocab_gensim.p')\n preprocessor = text_preprocessing.Preprocessor(dico=dico)\n X = preprocessor.preprocess(X)\n #save the loaded dataset in a pickle for speeding up next run\n util.save((X,y), 'safe/Amazon-'+str(nb_lines)+'.p')\n return (X, y)","def loadData(catalog):\n loadVideos(catalog)\n loadCategories(catalog)","def import_scikit_data(sralist):\n scikit_data_dict = {}\n for dataset in sralist:\n with open(TMP_DIR+'scikit_'+dataset+'/ALL_genes_profile_dict.json', 'r') as scikit_data:\n scikit_data_dict[dataset] = [json.load(scikit_data)]\n return scikit_data_dict","def load_datasets(self):\n if self.processed_extension == '.npz':\n logger.info(f'Loading sets from npz:')\n \n logger.info(f'train: {self.train_path}')\n self.train_data = sparse.load_npz(self.train_path)\n\n logger.info(f'val: {self.val_path}')\n self.val_data = sparse.load_npz(self.val_path)\n\n logger.info(f'test: {self.test_path}')\n self.test_data = sparse.load_npz(self.test_path)\n \n # Split x and y\n self.train_data = [sparse.lil_matrix(sparse.csr_matrix(self.train_data)[:,:-1]),\n sparse.lil_matrix(sparse.csr_matrix(self.train_data)[:,-1])]\n \n self.val_data = [sparse.lil_matrix(sparse.csr_matrix(self.val_data)[:,:-1]),\n sparse.lil_matrix(sparse.csr_matrix(self.val_data)[:,-1])]\n \n self.test_data = [sparse.lil_matrix(sparse.csr_matrix(self.test_data)[:,:-1]),\n sparse.lil_matrix(sparse.csr_matrix(self.test_data)[:,-1])]\n \n elif self.processed_extension == '.csv':\n logger.info(f'Loading sets from csv:')\n \n logger.info(f'train: {self.train_path}')\n self.train_data = pd.read_csv(self.train_path)\n train_cols = self.train_data.columns\n self.train_data = [self.train_data[train_cols.difference(['TARGET'])],\n self.train_data['TARGET']]\n \n logger.info(f'val: {self.val_path}')\n self.val_data = pd.read_csv(self.val_path)\n self.val_data = [self.val_data[train_cols.difference(['TARGET'])],\n self.val_data['TARGET']]\n \n logger.info(f'test: {self.test_path}')\n self.test_data = pd.read_csv(self.test_path)\n self.test_data = [self.test_data[train_cols.difference(['TARGET'])],\n self.test_data['TARGET']]\n else:\n raise AttributeError(f'Wrong extension: {self.processed_extension}')\n self.n_train = self.train_data[0].shape[0]\n self.n_val = self.val_data[0].shape[0]\n self.n_test = self.test_data[0].shape[0]\n self.input_size = self.train_data[0].shape[1]\n self.n_examples = self.n_train + self.n_val + self.n_test\n \n logger.info(f'Set sizes:')\n logger.info(f'train: {self.n_train}')\n logger.info(f'val: {self.n_val}')\n logger.info(f'test: {self.n_test}')","def load_data(catalog):\n controller.load_data(catalog)","def load(name):\n if name in datasets:\n\n return pd.read_csv(os.path.join(datasets_path, \"%s.csv\" % name))\n else:\n raise ValueError(\"Dataset not found!\")","def get_datasets():\n # Get data\n dataframe = pandas.read_csv('agaricus-lepiota.data')\n\n # Convert data type for all variables\n for column in dataframe:\n dataframe[column] = pandas.Categorical(dataframe[column])\n dataframe[column] = dataframe[column].cat.codes\n\n # Get labels\n target = dataframe.pop('p')\n\n # Get tensors, and split data into training and test sets\n split = int(len(dataframe) * 0.8)\n train_dataset = tf.data.Dataset.from_tensor_slices(\n (dataframe[:split].values, target[:split].values))\n\n train_dataset = train_dataset.shuffle(len(dataframe)).batch(1)\n\n test_dataset = tf.data.Dataset.from_tensor_slices(\n (dataframe[split:].values, target[split:].values))\n\n test_dataset = test_dataset.shuffle(len(dataframe)).batch(1)\n\n return train_dataset, test_dataset","def load_cifa_10():\n train_set_x = np.ndarray([ 50000, 3072 ])\n train_set_y = np.ndarray( [50000] )\n\n batch_size = 10000\n for i in xrange(5):\n batch = open( datapath + \"data_batch_\"+str(i+1), 'rb')\n map = cPickle.load( batch )\n batch.close()\n train_set_x[ i*batch_size : (i+1)*batch_size , : ] = np.asarray( map[ 'data' ], dtype = 'float32' )\n train_set_y[ i*batch_size : (i+1)*batch_size ] = np.asarray( map[ 'labels' ], dtype = 'float32' )\n\n test_file = open( datapath + 'test_batch', 'rb')\n map = cPickle.load( test_file )\n test_file.close()\n \n test_set_x = np.asarray( map['data'], dtype = 'float32' )\n test_set_y = np.asarray( map['labels'], dtype = 'float32' )\n \n\n return train_set_x, train_set_y, test_set_x, test_set_y","def download_data_sets(ad_accounts: [adaccount.AdAccount]):\n download_account_structure(ad_accounts)\n download_ad_performance(ad_accounts)","def load_data(self, task):\n params = self.params\n data = {splt: {} for splt in ['train', 'valid', 'test']}\n dpath = os.path.join(params.data_path, 'eval', task)\n\n self.n_sent = 1 if task in ['SST-2', 'CoLA'] else 2\n\n for splt in ['train', 'valid', 'test']:\n\n # load data and dictionary\n data1 = load_binarized(os.path.join(dpath, '%s.s1.pth' % splt), params)\n data2 = load_binarized(os.path.join(dpath, '%s.s2.pth' % splt), params) if self.n_sent == 2 else None\n data['dico'] = data.get('dico', data1['dico'])\n\n # set dictionary parameters\n set_dico_parameters(params, data, data1['dico'])\n if self.n_sent == 2:\n set_dico_parameters(params, data, data2['dico'])\n\n # create dataset\n if self.n_sent == 1:\n data[splt]['x'] = Dataset(data1['sentences'], data1['positions'], params)\n else:\n data[splt]['x'] = ParallelDataset(\n data1['sentences'], data1['positions'],\n data2['sentences'], data2['positions'],\n params\n )\n\n # load labels\n if splt != 'test' or task in ['MRPC']:\n # read labels from file\n with open(os.path.join(dpath, '%s.label' % splt), 'r') as f:\n lines = [l.rstrip() for l in f]\n # STS-B task\n if task == 'STS-B':\n assert all(0 <= float(x) <= 5 for x in lines)\n y = [float(l) for l in lines]\n # QQP\n elif task == 'QQP':\n UNK_LABEL = 0\n lab2id = {x: i for i, x in enumerate(sorted(set(lines) - set([''])))}\n y = [lab2id.get(x, UNK_LABEL) for x in lines]\n # other tasks\n else:\n lab2id = {x: i for i, x in enumerate(sorted(set(lines)))}\n y = [lab2id[x] for x in lines]\n data[splt]['y'] = torch.LongTensor(y)\n assert len(data[splt]['x']) == len(data[splt]['y'])\n\n # compute weights for weighted training\n if task != 'STS-B' and params.weighted_training:\n weights = torch.FloatTensor([\n 1.0 / (data['train']['y'] == i).sum().item()\n for i in range(len(lab2id))\n ]).npu()\n self.weights = weights / weights.sum()\n else:\n self.weights = None\n\n return data","def _load_data(self):\n\n if not self._cache.exists(config.DATAFRAME_SONG_DATA):\n source_path = os.path.join(config.S3_SONG_DATA, 'A/A/A/*.json') # Note: song database is way big, so we get only a slice of it.\n dataframe = self._get_spark_session().read.json(source_path)\n self._cache.set_source(config.DATAFRAME_SONG_DATA, dataframe)","def setup(self):\n # TODO check if need both dataset together\n self.train_dataset = ABSADataset(data_path=self.train_path, mode=self.in_mode, task=self.task, \n tokenizer=self.tokenizer, vocab=\"bert\")\n self.vocabulary = self.train_dataset.vocabulary\n\n self.eval_dataset = ABSADataset(data_path=self.dev_path, mode=self.in_mode, task=self.task,\n tokenizer=self.tokenizer, vocab=self.vocabulary)\n #self.train_restaurant = ABSADataset(data_path=RESTAURANT_TRAIN)\n #self.eval_restaurant = ABSADataset(data_path=RESTAURANT_DEV)","def load(datastream):","def get_datasets(FIELDS='all'):\n dsinfostr = fork_and_get_output(\"zfs list -H -o {0}\".format(FIELDS).split())\n header = get_zfs_ds_header()\n dsinfo = dsinfostr.splitlines()\n dsobjs = []\n for dsstr in dsinfo:\n dsobjs.append(DataZFS(dsstr, header, 'dataset'))\n return dsobjs","def MSA_to_dataset(msa_path):\n\n from deepchem.data.datasets import NumpyDataset # NumpyDataset depends on utils, so imported here to prevent circular import\n from Bio import SeqIO\n\n with open(msa_path, 'r') as f:\n ids = []\n sequences = []\n for record in SeqIO.parse(f, 'fasta'):\n ids.append(record.id)\n seq = []\n for res in record:\n seq.append(res)\n sequences.append(seq)\n dataset = NumpyDataset(X=sequences, ids=ids)\n return dataset","def load_scutfbp():\n train_loader = torch.utils.data.DataLoader(ScutFBP(transform=data_transforms['train']), batch_size=cfg[\n 'batch_size'],\n shuffle=True, num_workers=4, drop_last=True)\n test_loader = torch.utils.data.DataLoader(ScutFBP(transform=data_transforms['test']), batch_size=cfg['batch_size'],\n shuffle=False, num_workers=4, drop_last=True)\n\n return train_loader, test_loader","def load_data():\n\t\t# load the data\n\t\tDATPATH = \"../data/\"\n\t\t#fnino = DATPATH + \"nino3.csv\" # 1871-2000\n\t\tfnino = DATPATH + \"tas_Amon_IPSL-CM5A-LR_past1000_r1i1p1_0850_1850_nino3_tseries.csv\" # 1871-2016\n\t\t#fnino = DATPATH + \"nino34.long.data\"\n\t\t#nc_data_nino3 = netCDF4.Dataset(fnino)\n\t\t#nino3_load = nc_data_nino3.variables['tas'][:]\n\t\t#dnino = nino3_load.flatten()\n\n\t\tdnino = np.genfromtxt(fnino, delimiter=\",\", dtype=float).flatten()\n\t\t#fismr = DATPATH + \"ismr.csv\" # 1871-2000\n\t\t#fismr = DATPATH + \"psl_Amon_IPSL-CM5A-LR_past1000_r1i1p1_0850_1850_1_india_goswami_2002_tseries.csv\" # 1871-2016\n\t\tfismr = DATPATH + \"pr_Amon_IPSL-CM5A-LR_past1000_r1i1p1_0850_1850_goswami_india_tseries.csv\" # 1871-2016\n\t\tdismr = np.genfromtxt(fismr, delimiter=\",\", dtype=float).flatten()\n\t\t#fvolc = DATPATH + \"robock.txt\" # 1871-2000\n\t\tfvolc = DATPATH + \"sigl.txt\" # 1871-2016\n\t\tdvolc = np.genfromtxt(fvolc, delimiter=\",\", dtype=float).flatten()\n\n\t\tfvolc_source = DATPATH + \"volc_source_850_1850.csv\" # 1871-2016\n\t\tdvolc_source = np.genfromtxt(fvolc_source, delimiter=\",\", dtype=float).flatten()\n\t\t# simple check for data consistency\n\t\tassert dnino.shape == dismr.shape, \"Data sets are unequal!\"\n\t\tassert int(dismr.shape[0]/12) == dvolc.shape[0], \"Data sets are unequal\"\n\t\treturn dnino, dismr, dvolc, dvolc_source","def load_cityscapes(path, fdr):\n dataset = Dataset(path, split='val', mode=\"fine\", target_type=[\"semantic\", \"instance\"])\n\n from PATH import SCRI_PATH as spath\n\n for image, (sseg, inst), name in dataset:\n image = np.array(image)\n sseg = gt_covert(sseg)\n inst = np.array(inst)\n if os.path.exists(spath + \"/\" + fdr + \"/\" + name + \"_scri.png\"):\n scribbles = np.array(Image.open(spath + \"/\" + fdr + \"/\" + name + \"_scri.png\"))\n else:\n scribbles = None\n # scribbles = scribble_convert(scribbles)\n yield name, image, sseg, inst, scribbles","def load_nh_gaia():\n \n dir_gaia = os.path.expanduser('~') + '/Data/Catalogs/Gaia/' \n# file_txt = 'nh16.mega.gaia.rdmpm.txt' # Original catalog, but covers MU69 area as seen from Earth\n file_txt = 'mu69.mega.gaia.rdmspm.txt' # Smaller area, covers MU69 as seen from NH.\n \n file_pickle = file_txt.replace('.txt', '.pkl')\n \n if os.path.isfile(dir_gaia + file_pickle):\n lun = open(dir_gaia + file_pickle, 'rb')\n gaia = pickle.load(lun)\n lun.close()\n print(\"Loaded: \" + file_pickle)\n \n else:\n \n # Read the NH MegaCam-Gaia catalog from disk\n \n gaia = astropy.io.ascii.read(dir_gaia + file_txt, format = 'basic')\n \n # Make a plot of the Gaia stars\n \n plt.plot(gaia['RA'], gaia['Dec'], linestyle='none', marker = '.', ms=0.005)\n plt.xlabel('RA [deg]')\n plt.ylabel('Dec [deg]')\n plt.title('NH MegaCam-Gaia catalog')\n plt.show()\n \n # Save it as a pickle file\n \n lun = open(dir_gaia + file_pickle, 'wb')\n pickle.dump(gaia, lun) \n print(\"Wrote: \" + dir_gaia + file_pickle)\n lun.close()\n \n return gaia","def loader():\n bucket = data_load_variables[\"bucket\"]\n\n if data_load_variables[\"use_lite_dataset\"]:\n dataset_name = data_load_variables[\"lite_dataset_name\"]\n else:\n dataset_name = data_load_variables[\"dataset_name\"]\n\n s3 = boto3.client('s3')\n\n obj = s3.get_object(Bucket=bucket, Key=dataset_name)\n # get object and file (key) from bucket\n\n df = pd.read_csv(obj['Body'])\n return df","def get_local_dataset(\n self, \n file_name: str\n ):\n pd.read_csv(file_name)\n #save","def fetch_baja_bathymetry():\n data_file = POOCH.fetch(\"baja-bathymetry.csv.xz\")\n data = pd.read_csv(data_file, compression=\"xz\")\n return data","def load_openml_data():\n datasets = dict()\n files = os.listdir(_DATA_DIRECTORY.value)\n for file_name in files:\n with open(_DATA_DIRECTORY.value + file_name, \"r\") as ff:\n task = np.loadtxt(ff, delimiter=\",\", skiprows=1)\n np.random.shuffle(task)\n datasets[file_name] = [task]\n return datasets, files","def load_dataset(self):\n # Get all the files in the directory\n file_list = self.get_file_list()\n\n # Concatenate the data corresponding to a list of files\n data = self.concatenate_file_data(file_list)\n\n # Shuffle the data and create the training and the validation datasets\n data = self.shuffle_data_dictionary(data)\n self.training_dataset, self.validation_dataset = self.split_data_into_training_and_validation(data)","def get_data_loaders_4sentence():\n dataset_path = \"\"\n dataset_cache = None\n personachat = get_dataset(dataset_path, dataset_cache)\n\n tokenizer_selected = OpenAIGPTTokenizer.from_pretrained('openai-gpt')\n logger.info(\"Build inputs and labels\")\n datasets = {\"train\": defaultdict(list), \"valid\": defaultdict(list)}\n personality = []\n history_complete = []\n count_persona = 0\n with open('data_faiss_pegasus_1generated.pkl', 'rb') as f:\n persona_selected_list = pickle.load(f)\n for dataset_name, dataset in personachat.items():\n num_candidates = len(dataset[0][\"utterances\"][0][\"candidates\"])\n if num_candidates > 0 and dataset_name == 'train':\n num_candidates = min(1, num_candidates)\n for dialog in dataset:\n persona = dialog[\"persona_info\"].copy()\n #datasets[personality].append(persona)\n count_history = 0\n for utterance in dialog[\"utterances\"]:\n count_history = count_history + 1\n history = utterance[\"history\"]\n #history_complete.append(history)\n if len(history_splitted) > (len(persona)-1):\n history_chatbot = history[1::2]\n persona_selected = persona_selected_list[count_persona]\n instance = build_input_from_segments_faiss(persona_selected, history_chatbot) \n for input_name, input_array in instance.items():\n datasets[dataset_name][input_name].append(input_array)\n count_persona = count_persona + 1\n return datasets","def load_data(path=\"../data/cora/\", dataset=\"cora\"):\n print('Loading {} dataset...'.format(dataset))\n\n idx_features_labels = np.genfromtxt(\"{}{}.content\".format(path, dataset),\n dtype=np.dtype(str))\n features = sp.sparse.csr_matrix(idx_features_labels[:, 1:-1], dtype=np.float32)\n# labels = encode_onehot(idx_features_labels[:, -1])\n values = np.unique(idx_features_labels[:, -1])\n values.sort()\n labels = np.zeros(idx_features_labels.shape[0])\n for i in range(labels.shape[0]):\n labels[i] = np.where(values == idx_features_labels[i, -1])[0][0]\n labels = torch.tensor(labels).long()\n\n # build graph\n idx = np.array(idx_features_labels[:, 0], dtype=np.int32)\n idx_map = {j: i for i, j in enumerate(idx)}\n edges_unordered = np.genfromtxt(\"{}{}.cites\".format(path, dataset),\n dtype=np.int32)\n edges = np.array(list(map(idx_map.get, edges_unordered.flatten())),\n dtype=np.int32).reshape(edges_unordered.shape)\n adj = sp.sparse.coo_matrix((np.ones(edges.shape[0]), (edges[:, 0], edges[:, 1])),\n shape=(labels.shape[0], labels.shape[0]),\n dtype=np.float32)\n\n # build symmetric adjacency matrix\n adj = adj + adj.T.multiply(adj.T > adj) - adj.multiply(adj.T > adj)\n\n# features = normalize(features)\n adj = normalize(adj + sp.sparse.eye(adj.shape[0]))\n\n idx_train = range(140)\n idx_val = range(200, 500)\n idx_test = range(500, 1500)\n\n features = torch.FloatTensor(np.array(features.todense()))\n# labels = torch.LongTensor(np.where(labels)[1])\n adj = sparse_mx_to_torch_sparse_tensor(adj)\n\n idx_train = torch.LongTensor(idx_train)\n idx_val = torch.LongTensor(idx_val)\n idx_test = torch.LongTensor(idx_test)\n\n return adj, features, labels, idx_train, idx_val, idx_test","def _load_cluster(self):","def load(self, source):\n try:\n inputdata = self.__inputmanager.read(source)\n self.__suitables = self.__inputmanager.map(inputdata)\n self.__data = inputdata\n except ValueError as e:\n print (\"Failed to load the dataset: %s\" % e)\n raise\n\n self.__modules = self.import_suitable_visualizations(self.__suitables)\n self.__has_datefields = self.__inputmanager.has_date_points()\n # Converting the datakeys into strings.\n self.__datakeys = [str(i) for i in list(self.__data[0].keys())]\n return self.__suitables","def get_data_loaders_1sentence():\n dataset_path = \"\"\n dataset_cache = None\n personachat = get_dataset(dataset_path, dataset_cache)\n\n tokenizer_selected = OpenAIGPTTokenizer.from_pretrained('openai-gpt')\n logger.info(\"Build inputs and labels\")\n datasets = {\"train\": defaultdict(list), \"valid\": defaultdict(list)}\n personality = []\n history_complete = []\n count_persona = 0\n with open('data_faiss_pegasus_1_sentence_final_generated.pkl', 'rb') as f:\n persona_selected_list = pickle.load(f)\n for dataset_name, dataset in personachat.items():\n num_candidates = len(dataset[0][\"utterances\"][0][\"candidates\"])\n if num_candidates > 0 and dataset_name == 'train':\n num_candidates = min(1, num_candidates)\n for dialog in dataset:\n persona = dialog[\"persona_info\"].copy()\n #datasets[personality].append(persona)\n count_history = 0\n for utterance in dialog[\"utterances\"]:\n count_history = count_history + 1\n history = utterance[\"history\"][-(2*2+1):]\n #history_complete.append(history)\n if len(history) > 3:\n history_chatbot = history[1]\n persona_selected = persona_selected_list[count_persona]\n instance = build_input_from_segments_faiss(persona_selected, history_chatbot) \n for input_name, input_array in instance.items():\n datasets[dataset_name][input_name].append(input_array)\n count_persona = count_persona + 1\n return datasets","def load_data():\n dirname = os.path.join('datasets', 'fashion-mnist')\n base = 'http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/'\n files = [\n 'train-labels-idx1-ubyte.gz', 'train-images-idx3-ubyte.gz',\n 't10k-labels-idx1-ubyte.gz', 't10k-images-idx3-ubyte.gz'\n ]\n\n paths = []\n for fname in files:\n paths.append(get_file(fname, origin=base + fname, cache_subdir=dirname))\n\n with gzip.open(paths[0], 'rb') as lbpath:\n y_train = np.frombuffer(lbpath.read(), np.uint8, offset=8)\n\n with gzip.open(paths[1], 'rb') as imgpath:\n x_train = np.frombuffer(\n imgpath.read(), np.uint8, offset=16).reshape(len(y_train), 28, 28)\n\n with gzip.open(paths[2], 'rb') as lbpath:\n y_test = np.frombuffer(lbpath.read(), np.uint8, offset=8)\n\n with gzip.open(paths[3], 'rb') as imgpath:\n x_test = np.frombuffer(\n imgpath.read(), np.uint8, offset=16).reshape(len(y_test), 28, 28)\n\n return (x_train, y_train), (x_test, y_test)","def get_datasets(load_key=None, maven=False):\n ds_names = {}\n if load_key == 'R2349': \n ds_names['batsrus_mf_lr'] = model_dir+'R2349/batsrus_3d_multi_fluid_lowres.h5'\n ds_names['batsrus_multi_species'] = model_dir+'R2349/batsrus_3d_multi_species.h5'\n ds_names['batsrus_electron_pressure'] = model_dir+'R2349/batsrus_3d_pe.h5'\n ds_names['heliosares'] ='/Volumes/triton/Data/ModelChallenge/R2349/heliosares_multi.h5'\n #ds_names['rhybrid'] ='/Volumes/triton/Data/ModelChallenge/R2349/rhybrid.h5'\n \n ds_types = {'batsrus1':[key for key in ds_names.keys() if 'multi_fluid' in key],\n 'batsrus2':[key for key in ds_names.keys() if 'multi_species' in key],\n 'batsrus3':[key for key in ds_names.keys() if 'electron_pressure' in key],\n 'batsrus4':[key for key in ds_names.keys() if 'mf_lr' in key],\n 'heliosares':[key for key in ds_names.keys() if 'helio' in key],\n 'rhybrid_helio':[key for key in ds_names.keys() if 'rhybrid' in key ]}\n if maven or True:\n ds_names['maven']=orbit_dir+'orbit_2349.csv'\n #ds_names['maven'] = orbit_dir+'orbit_plume_2349.csv'\n ds_types['maven']=['maven']\n elif load_key == 'batsrus_mf_lowres':\n ds_names['batsrus_mf_lr'] = model_dir+'R2349/batsrus_3d_multi_fluid_lowres.h5'\n ds_types = {'batsrus_mf_lr' : ['batsrus_mf_lr']}\n\n\n elif load_key == 'helio_multi':\n ds_names['t00550'] = model_dir+'R2349/Heliosares_Multi/t00550.h5'\n ds_names['t00560'] = model_dir+'R2349/Heliosares_Multi/t00560.h5'\n ds_names['t00570'] = model_dir+'R2349/Heliosares_Multi/t00570.h5'\n ds_names['t00580'] = model_dir+'R2349/Heliosares_Multi/t00580.h5'\n ds_names['t00590'] = model_dir+'R2349/Heliosares_Multi/t00590.h5'\n ds_names['t00600'] = model_dir+'R2349/Heliosares_Multi/t00600.h5'\n ds_names['t00610'] = model_dir+'R2349/Heliosares_Multi/t00610.h5'\n ds_names['t00620'] = model_dir+'R2349/Heliosares_Multi/t00620.h5'\n ds_names['t00630'] = model_dir+'R2349/Heliosares_Multi/t00630.h5'\n ds_names['t00640'] = model_dir+'R2349/Heliosares_Multi/t00640.h5'\n ds_names['t00650'] = model_dir+'R2349/Heliosares_Multi/t00650.h5'\n\n ds_types = {'heliosares':[key for key in ds_names.keys()]}\n if maven:\n #ds_names['maven'] = orbit_dir+'orbit_2349.csv'\n ds_types['maven']=['maven']\n elif load_key == 'SDC_BATS':\n ds_names['LS180_SSL000_max'] = model_dir+'SDC_Archive/BATSRUS/LS180_SSL000_max.h5'\n ds_names['LS270_SSL000_max'] = model_dir+'SDC_Archive/BATSRUS/LS270_SSL000_max.h5'\n ds_names['LS090_SSL000_max'] = model_dir+'SDC_Archive/BATSRUS/LS090_SSL000_max.h5'\n ds_names['LS180_SSL270_max'] = model_dir+'SDC_Archive/BATSRUS/LS180_SSL270_max.h5'\n ds_names['LS270_SSL270_max'] = model_dir+'SDC_Archive/BATSRUS/LS270_SSL270_max.h5'\n ds_names['LS090_SSL270_max'] = model_dir+'SDC_Archive/BATSRUS/LS090_SSL270_max.h5'\n ds_names['LS180_SSL180_max'] = model_dir+'SDC_Archive/BATSRUS/LS180_SSL180_max.h5'\n ds_names['LS270_SSL180_max'] = model_dir+'SDC_Archive/BATSRUS/LS270_SSL180_max.h5'\n ds_names['LS090_SSL180_max'] = model_dir+'SDC_Archive/BATSRUS/LS090_SSL180_max.h5'\n ds_names['LS180_SSL000_min'] = model_dir+'SDC_Archive/BATSRUS/LS180_SSL000_min.h5'\n ds_names['LS270_SSL000_min'] = model_dir+'SDC_Archive/BATSRUS/LS270_SSL000_min.h5'\n ds_names['LS090_SSL000_min'] = model_dir+'SDC_Archive/BATSRUS/LS090_SSL000_min.h5'\n ds_names['LS180_SSL270_min'] = model_dir+'SDC_Archive/BATSRUS/LS180_SSL270_min.h5'\n ds_names['LS270_SSL270_min'] = model_dir+'SDC_Archive/BATSRUS/LS270_SSL270_min.h5'\n ds_names['LS090_SSL270_min'] = model_dir+'SDC_Archive/BATSRUS/LS090_SSL270_min.h5'\n ds_names['LS180_SSL180_min'] = model_dir+'SDC_Archive/BATSRUS/LS180_SSL180_min.h5'\n ds_names['LS270_SSL180_min'] = model_dir+'SDC_Archive/BATSRUS/LS270_SSL180_min.h5'\n ds_names['LS090_SSL180_min'] = model_dir+'SDC_Archive/BATSRUS/LS090_SSL180_min.h5'\n\n ds_types = {'batsrus':[key for key in ds_names.keys()]}\n\n elif load_key == 'SDC_G1':\n #BATSRUS\n ds_names['bats_min_LS270_SSL0'] = \\\n model_dir+'SDC_Archive/BATSRUS/'+'3d__ful_4_n00060000_PERmin-SSLONG0.h5'\n ds_names['bats_min_LS270_SSL180'] = \\\n model_dir+'SDC_Archive/BATSRUS/'+'3d__ful_4_n00060000_PERmin-SSLONG180.h5'\n ds_names['bats_min_LS270_SSL270'] = \\\n model_dir+'SDC_Archive/BATSRUS/'+'3d__ful_4_n00060000_PERmin-SSLONG270.h5' \n \n #HELIOSARES\n #ds_names['helio_1'] = \\\n # model_dir+'SDC_Archive/HELIOSARES/Hybrid/'+'helio_1.h5'\n \n #ds_names['helio_2'] = \\\n # model_dir+'SDC_Archive/HELIOSARES/Hybrid/'+'helio_2.h5'\n \n \n ds_types = {'batsrus1':[key for key in ds_names.keys() if 'bats' in key],\n 'heliosares':[key for key in ds_names.keys() if 'helio' in key]}\n if maven:\n pass\n #ds_names['maven'] = orbit_dir+'orbit_2349.csv'\n #ds_types['maven']=['maven']\n\n elif load_key == 'rhybrid_res':\n ds_names = {'rhybrid240':'/Volumes/triton/Data/ModelChallenge/R2349/rhybrid.h5',\n 'rhybrid120':'/Volumes/triton/Data/ModelChallenge/R2349/HYB/state00030000.h5'}\n ds_types = {'rhybrid1':['rhybrid240'], 'rhybrid2':['rhybrid120']}\n elif load_key == 'batsrus_tseries':\n ds_names = {'batsrus_mf':'/Volumes/triton/Data/ModelChallenge/R2349/BATSRUS/10km_mf/3d__ful_4_n00040000.h5',\n 'batsrus_ms':'/Volumes/triton/Data/ModelChallenge/R2349/BATSRUS/10km_ms/3d__mhd_6_n0050000.h5'}\n ds_types = {'batsrus_mf':['batsrus_mf'], 'batsrus_ms':['batsrus_ms']}\n\n elif load_key == 'maven':\n ds_names, ds_types = {},{}\n ds_names['maven'] = orbit_dir+'orbit_2349.csv'\n ds_types['maven']=['maven']\n elif load_key == 'exo_2349':\n keys = ['2349_1RM_225km','2349_1RM_450km', '2349_2RM_450km',\n '2349_2RM_900km','2349_4RM_900km'] \n ds_names = {k:exo_dir+'/'+k+'/'+k+'.h5' for k in keys}\n ds_types = {k:[k] for k in keys}\n elif load_key == 'exo_comparisonA':\n keys = ['2349_1RM_225km', '2349_2RM_450km',\n '2349_1.5RM_338km'] \n ds_names = {k:exo_dir+'/ComparisonA/'+k+'.h5' for k in keys}\n ds_types = {k:[k] for k in keys}\n elif load_key == 'exo_comparisonB':\n keys = ['2349_1RM_225km', 'T0_1RM_225km', 'T1_1RM_225km', \"T2_1RM_225km\"] \n ds_names = {k:exo_dir+'/ComparisonB/'+k+'.h5' for k in keys}\n ds_types = {k:[k] for k in keys}\n\n elif load_key == 'exo_t1':\n keys = ['T1_1RM_112km', 'T1_1RM_225km', #'T1_1RM_450km',\n 'T1_2RM_225km', 'T1_2RM_450km', #'T1_2RM_900km',\n 'T1_4RM_900km']\n\n ds_names = {k:exo_dir+'/'+k+'/'+k+'.h5' for k in keys}\n ds_types = {k:[k] for k in keys}\n\n else:\n print('No datasets selected')\n \n\n return (ds_names, ds_types)","def read_data(feature_file, label_file):","def ensure_dataset_loaded(self, name):\n if name not in self.datasets:\n print(f'Loading dataset \"{name}\"')\n pd_data = pd.read_excel(self.datafiles[name])\n data = pd.DataFrame.to_dict(pd_data, 'records')\n self.datasets[name] = data","def loadData():\n project_dir = \"/home/c/chandanchowdhury/Documents/CIS-833/CSSearch/indexer/\"\n\n index_file = \"index_file.pkl\"\n link_file = \"link_file.pkl\"\n\n index_data = loadPickle(project_dir+index_file)\n link_data = loadPickle(project_dir+link_file)\n\n return index_data, link_data","def load_dataloaders(args):\n logger.info(\"Loading dataloaders...\")\n p_path = os.path.join(\"./data/\", \"df_unencoded.pkl\")\n train_path = os.path.join(\"./data/\", \"df_encoded.pkl\")\n if (not os.path.isfile(p_path)) and (not os.path.isfile(train_path)):\n df = get_data(args, load_extracted=False)\n elif os.path.isfile(p_path) and (not os.path.isfile(train_path)):\n df = get_data(args, load_extracted=True)\n elif os.path.isfile(train_path):\n df = load_pickle(\"df_encoded.pkl\")\n \n # Train-Test split\n msk = np.random.rand(len(df)) < args.train_test_ratio\n trainset = df[msk]\n testset = df[~msk]\n \n trainset = text_dataset(trainset, args)\n max_features_length = trainset.max_x_len\n max_seq_len = trainset.max_y_len\n train_length = len(trainset)\n train_loader = DataLoader(trainset, batch_size=args.batch_size, shuffle=True,\\\n num_workers=0, collate_fn=Pad_Sequence(), pin_memory=False)\n \n testset = text_dataset(testset, args)\n test_length = len(testset)\n test_loader = DataLoader(testset, batch_size=args.batch_size, shuffle=True,\\\n num_workers=0, collate_fn=Pad_Sequence(), pin_memory=False)\n return train_loader, train_length, max_features_length, max_seq_len, test_loader, test_length","def load_default_atf_data():\n df = load_dataframe(\"oqmd_1.2_voronoi_magpie_fingerprints\")\n return df[df['N_species'] == 2].sample(frac=0.2)","def load_data():\n dictionary = corpora.Dictionary.load(app.config['DICTIONARY'])\n matrix = similarities.MatrixSimilarity.load(app.config['MATRIX'])\n model = models.LsiModel.load(app.config['MODEL'])\n df = pd.read_pickle(app.config['DATA_FRAME'])\n return Data(matrix=matrix, model=model, dictionary=dictionary, data_frame=df)","def loadData(infile,k):\n f = open(infile,'r')\n #f = f.read().split(\"\\n\")\n #raw = json.loads(f[1])\n f = f.read()\n raw = json.loads(f)\n data = np.array(raw)\n dataset = data[k]\n return dataset","def load_data(loc='./data/', sp=None):\n trainA, trainB, devA, devB, testA, testB = [],[],[],[],[],[]\n trainS, devS, testS = [],[],[]\n print('loc', loc)\n with codecs.open(os.path.join(loc, 'SICK_train.txt'), mode='rb', encoding='utf-8') as f:\n for line in f:\n text = unicode_tr(line).lower().strip().split('\\t')\n trainA.append(encode_sentence(text[1], sp))\n trainB.append(encode_sentence(text[2], sp))\n trainS.append(text[3])\n with codecs.open(os.path.join(loc, 'SICK_trial.txt'), mode='rb', encoding='utf-8') as f:\n for line in f:\n text = unicode_tr(line).lower().strip().split('\\t')\n devA.append(encode_sentence(text[1], sp))\n devB.append(encode_sentence(text[2], sp))\n devS.append(text[3])\n with codecs.open(os.path.join(loc, 'SICK_test_annotated.txt'), mode='rb', encoding='utf-8') as f:\n for line in f:\n text = unicode_tr(line).lower().strip().split('\\t')\n testA.append(encode_sentence(text[1], sp))\n testB.append(encode_sentence(text[2], sp))\n testS.append(text[3])\n\n trainS = [float(s) for s in trainS[1:]]\n devS = [float(s) for s in devS[1:]]\n testS = [float(s) for s in testS[1:]]\n\n return [trainA[1:], trainB[1:]], [devA[1:], devB[1:]], [testA[1:], testB[1:]], [trainS, devS, testS]","def load_dataset():\n\n df_ = pd.read_excel(\"D:\\VERİBİLİMİOKULU\\VERİSETLERİ\\post_bilgileri.xlsx\")\n df = df_.copy()\n return df","def load_scutfbp5500_64():\n train_loader = torch.utils.data.DataLoader(FDataset(train=True, transform=data_transforms['train']),\n batch_size=cfg['batch_size'], shuffle=True, num_workers=50,\n drop_last=True, pin_memory=True)\n test_loader = torch.utils.data.DataLoader(FDataset(train=False, transform=data_transforms['test']),\n batch_size=cfg['batch_size'], shuffle=False, num_workers=50,\n drop_last=True, pin_memory=True)\n\n return train_loader, test_loader","def load_data(self,split='train'):\n raise NotImplementedError","def loadData(self, file):\n self.data = batchImport(file, self.ps)","def load(cfg, train_mode, split, shot, query,\n bs, test_bs, num_workers, pin_memory,\n ret_name=False):\n if train_mode == \"train\":\n dataset = COCOTrain(cfg, split, shot, query, ret_name=ret_name)\n data_loader = DataLoader(dataset,\n batch_size=bs,\n shuffle=True,\n num_workers=num_workers,\n pin_memory=pin_memory,\n drop_last=False)\n else:\n dataset = COCOTest(cfg, split, shot, query, ret_name=ret_name)\n data_loader = DataLoader(dataset,\n batch_size=test_bs, # Large batch for evaluation\n shuffle=False,\n num_workers=num_workers,\n pin_memory=pin_memory,\n drop_last=False)\n num_classes = 80\n return dataset, data_loader, num_classes","def load_data(path=\"./data/cora/\", dataset=\"cora\"):\n print('Loading {} dataset...'.format(dataset))\n\n idx_features_labels = np.genfromtxt(\"{}{}.content\".format(path, dataset), dtype=np.dtype(str))\n features = sp.csr_matrix(idx_features_labels[:, 1:-1], dtype=np.float32)\n labels = encode_onehot(idx_features_labels[:, -1])\n\n # build graph\n idx = np.array(idx_features_labels[:, 0], dtype=np.int32)\n idx_map = {j: i for i, j in enumerate(idx)}\n edges_unordered = np.genfromtxt(\"{}{}.cites\".format(path, dataset), dtype=np.int32)\n edges = np.array(list(map(idx_map.get, edges_unordered.flatten())), dtype=np.int32).reshape(edges_unordered.shape)\n adj = sp.coo_matrix((np.ones(edges.shape[0]), (edges[:, 0], edges[:, 1])), shape=(labels.shape[0], labels.shape[0]), dtype=np.float32)\n\n # build symmetric adjacency matrix\n adj = adj + adj.T.multiply(adj.T > adj) - adj.multiply(adj.T > adj)\n\n features = normalize_features(features)\n adj = normalize_adj(adj + sp.eye(adj.shape[0]))\n\n idx_train = range(140)\n idx_val = range(200, 500)\n idx_test = range(500, 1500)\n\n adj = torch.FloatTensor(np.array(adj.todense()))\n features = torch.FloatTensor(np.array(features.todense()))\n labels = torch.LongTensor(np.where(labels)[1])\n\n idx_train = torch.LongTensor(idx_train)\n idx_val = torch.LongTensor(idx_val)\n idx_test = torch.LongTensor(idx_test)\n\n return adj, features, labels, idx_train, idx_val, idx_test","def load_data():\n\n server_node = load_nodes(SERVER_NODE_INFILE)\n road_node = load_nodes(ROAD_NODE_INFILE)\n road_segment_point = load_nodes(ROAD_SEGMENT_POINT_INFILE)\n\n return server_node, road_node, road_segment_point","def load_data(self):\n raise NotImplementedError()"],"string":"[\n \"def load_kiba_dataset():\\n trainn_fold = json.load(\\n open(os.path.join('dataset', 'regression', 'benchmark', 'KIBAtest', 'folds', 'train_fold_setting1.txt')))\\n train_fold = []\\n for e in zip(*trainn_fold):\\n for ee in e:\\n train_fold.extend(ee)\\n #train_fold = [ee for e in trainn_fold for ee in e]\\n test_fold = json.load(\\n open(os.path.join('dataset', 'regression', 'benchmark', 'KIBAtest', 'folds', 'test_fold_setting1.txt')))\\n ligands = json.load(\\n open(os.path.join('dataset', 'regression', 'benchmark', 'KIBAtest', 'ligands_can.txt')),\\n object_pairs_hook=OrderedDict)\\n proteins = json.load(\\n open(os.path.join('dataset', 'regression', 'benchmark', 'KIBAtest', 'proteins.txt')),\\n object_pairs_hook=OrderedDict)\\n \\n affinity = pickle.load(open(os.path.join('dataset', 'regression', 'benchmark', 'KIBAtest', 'Y'), \\n 'rb'), encoding='latin1')\\n smiles_lst, protein_lst = [], []\\n\\n for k in ligands.keys():\\n smiles = ligands[k]\\n smiles_lst.append(smiles)\\n for k in proteins.keys():\\n protein_lst.append(proteins[k])\\n\\n affinity = np.asarray(affinity)\\n \\n os.makedirs(os.path.join('dataset', 'regression', 'benchmark', 'KIBAtest', 'processed'), exist_ok=True)\\n train_test_dataset = []\\n for split in ['train', 'test']:\\n split_dir = os.path.join('dataset', 'regression', 'benchmark', 'KIBAtest', 'processed', split)\\n os.makedirs(split_dir, exist_ok=True)\\n fold = train_fold if split == 'train' else test_fold\\n rows, cols = np.where(np.isnan(affinity) == False)\\n rows, cols = rows[fold], cols[fold]\\n \\n data_lst = [[] for _ in range(1)]\\n for idx in range(len(rows)):\\n data = {}\\n data['smiles'] = smiles_lst[rows[idx]]\\n data['protein'] = protein_lst[cols[idx]]\\n af = affinity[rows[idx], cols[idx]]\\n data['aff'] = af\\n\\n data_lst[idx % 1].append(data)\\n random.shuffle(data_lst)\\n train_test_dataset.append(data_lst[0])\\n return train_test_dataset\",\n \"def LoadTroikaDataset():\\n data_dir = \\\"./datasets/troika/training_data\\\"\\n data_fls = sorted(glob.glob(data_dir + \\\"/DATA_*.mat\\\"))\\n ref_fls = sorted(glob.glob(data_dir + \\\"/REF_*.mat\\\"))\\n return data_fls, ref_fls\",\n \"def load_data(self):\",\n \"def load_data(self) -> None:\",\n \"def load_database(self, fsp='Species'):\\n self.df_species = pd.read_csv(fsp + '.csv', header=0,\\n index_col=0)\",\n \"def import_and_save(ADCthres=0, s=False):\\n df = import_data(ADCthres, s)\\n bus_vec = np.array(range(0,3))\\n for bus in bus_vec:\\n df_clu = cluster_data(df, bus) \\n save_clusters(df_clu, bus)\",\n \"def load_davis_dataset():\\n trainn_fold = json.load(\\n open(os.path.join('dataset', 'regression', 'benchmark', 'DAVIStest', 'folds', 'train_fold_setting1.txt')))\\n train_fold = []\\n for e in zip(*trainn_fold):\\n for ee in e:\\n train_fold.append(ee)\\n #train_fold = [ee for e in trainn_fold for ee in e]\\n test_fold = json.load(\\n open(os.path.join('dataset', 'regression', 'benchmark', 'DAVIStest', 'folds', 'test_fold_setting1.txt')))\\n ligands = json.load(\\n open(os.path.join('dataset', 'regression', 'benchmark', 'DAVIStest', 'ligands_can.txt')),\\n object_pairs_hook=OrderedDict)\\n proteins = json.load(\\n open(os.path.join('dataset', 'regression', 'benchmark', 'DAVIStest', 'proteins.txt')),\\n object_pairs_hook=OrderedDict)\\n \\n affinity = pickle.load(open(os.path.join('dataset', 'regression', 'benchmark', 'DAVIStest', 'Y'), \\n 'rb'), encoding='latin1')\\n smiles_lst, protein_lst = [], []\\n\\n for k in ligands.keys():\\n smiles = ligands[k]\\n smiles_lst.append(smiles)\\n for k in proteins.keys():\\n protein_lst.append(proteins[k])\\n\\n affinity = [-np.log10(y / 1e9) for y in affinity]\\n affinity = np.asarray(affinity)\\n \\n os.makedirs(os.path.join('dataset', 'regression', 'benchmark', 'DAVIStest', 'processed'), exist_ok=True)\\n train_test_dataset = []\\n for split in ['train', 'test']:\\n split_dir = os.path.join('dataset', 'regression', 'benchmark', 'DAVIStest', 'processed', split)\\n os.makedirs(split_dir, exist_ok=True)\\n fold = train_fold if split == 'train' else test_fold\\n rows, cols = np.where(np.isnan(affinity) == False)\\n rows, cols = rows[fold], cols[fold]\\n \\n data_lst = [[] for _ in range(1)]\\n for idx in range(len(rows)):\\n data = {}\\n data['smiles'] = smiles_lst[rows[idx]]\\n data['protein'] = protein_lst[cols[idx]]\\n af = affinity[rows[idx], cols[idx]]\\n data['aff'] = af\\n\\n data_lst[idx % 1].append(data)\\n random.shuffle(data_lst)\\n train_test_dataset.append(data_lst[0])\\n return train_test_dataset\",\n \"def load():\\n filepath = dirname(abspath(__file__))\\n##### EDIT THE FOLLOWING TO POINT TO DatasetName.csv #####\\n data = recfromtxt(open(filepath + '/spector.csv',\\\"rb\\\"), delimiter=\\\" \\\",\\n names=True, dtype=float, usecols=(1,2,3,4))\\n names = list(data.dtype.names)\\n endog = array(data[names[3]], dtype=float)\\n endog_name = names[3]\\n exog = column_stack(data[i] for i in names[:3]).astype(float)\\n exog_name = names[:3]\\n dataset = Dataset(data=data, names=names, endog=endog, exog=exog,\\n endog_name = endog_name, exog_name=exog_name)\\n return dataset\",\n \"def load():\\n filepath = dirname(abspath(__file__))\\n data = recfromtxt(filepath + '/scotvote.csv', delimiter=\\\",\\\",\\n names=True, dtype=float, usecols=(1,2,3,4,5,6,7,8))\\n names = list(data.dtype.names)\\n endog = array(data[names[0]], dtype=float)\\n endog_name = names[0]\\n exog = column_stack(data[i] for i in names[1:]).astype(float)\\n exog_name = names[1:]\\n dataset = Dataset(data=data, names=names, endog=endog, exog=exog,\\n endog_name = endog_name, exog_name=exog_name)\\n return dataset\",\n \"def load_data():\\n\\n base = 'http://www.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/BSR/'\\n fname = 'BSR_bsds500.tgz'\\n\\n path = get_file(fname,\\n origin = base + fname,\\n cache_dir = DEFAULT_CACHE_DIR,\\n dset_name = 'bsds500')\\n\\n f = tarfile.open(path)\\n\\n train_data = []\\n test_data = []\\n for name in f.getnames():\\n if name.startswith('BSR/BSDS500/data/images/train/'):\\n try:\\n fp = f.extractfile(name)\\n img = imageio.imread(fp)\\n train_data.append(img)\\n except:\\n continue\\n elif name.startswith('BSR/BSDS500/data/images/test/'):\\n try:\\n fp = f.extractfile(name)\\n img = skimage.io.imread(fp)\\n test_data.append(img)\\n except:\\n continue\\n\\n\\n return (train_data, test_data)\",\n \"def load_dsb(self, dataset_dir, subset):\\n # Add classes. We have only one class to add.\\n self.add_class(\\\"dsb\\\", 1, \\\"nucleo\\\")\\n\\n # Train or validation dataset?\\n if subset == \\\"train\\\" or subset == \\\"val\\\":\\n dataset_dir = os.path.join(dataset_dir, \\\"TRAINCLAHE/\\\")\\n elif subset == 'test':\\n dataset_dir = os.path.join(dataset_dir, \\\"TEST/\\\")\\n else:\\n print(\\\"Invalid Subset\\\",subset)\\n #Listar quais exames tem\\n exames = next(os.walk(dataset_dir))[1]\\n\\n if subset==\\\"train\\\":\\n exames = exames[:600]\\n elif subset == \\\"val\\\":\\n exames = exames[600:]\\n else:\\n # exames = exames\\n pass\\n\\n\\n #Acessar a pasta exame/image\\n for n, id_ in tqdm(enumerate(exames), total=len(exames)):\\n path = dataset_dir + id_\\n self.add_image(\\n \\\"dsb\\\",\\n image_id=id_, # use file name as a unique image id\\n path=path + '/images/' + id_ + '.png', dir=path,\\n )\",\n \"def _build_datasets(self):\\n self._build_datasets_sis3302()\\n self._build_datasets_sis3305()\",\n \"def datasets(self):\\n pass\",\n \"def dataset(options):\\n pass\",\n \"def datasets(self):\\n return [Dataset.GWAS_CATALOG, Dataset.CLINVAR, Dataset.EFO]\",\n \"def loadData(catalog):\\n loadArtworks(catalog)\\n loadArtists(catalog)\",\n \"def loadData(catalog):\\n loadArtists(catalog)\\n loadArtworks(catalog)\",\n \"def loadData(catalog):\\n loadArtists(catalog)\\n loadArtworks(catalog)\",\n \"def test_i_large_datasets(self):\\n\\n # If foma is not installed, exit.\\n if not h.foma_installed(force_check=True):\\n return\\n\\n # Configuration\\n\\n # The ``old_dump_file`` variable holds the name of a MySQL dump file in /tests/data/datasets\\n # that will be used to populate the database.\\n old_dump_file = 'blaold.sql'\\n backup_dump_file = 'old_test_dump.sql'\\n\\n # The ``precompiled_morphophonology`` variable holds the name of a compiled foma FST that\\n # maps surface representations to sequences of morphemes. A file with this name should be\\n # present in /tests/data/morphophonologies or else the variable should be set to None.\\n pregenerated_morphophonology = None # 'blaold_morphophonology.script'\\n precompiled_morphophonology = None # 'blaold_morphophonology.foma'\\n\\n # Here we load a whole database from the mysqpl dump file specified in ``tests/data/datasets/<old_dump_file>``.\\n old_dump_file_path = os.path.join(self.test_datasets_path, old_dump_file)\\n backup_dump_file_path = os.path.join(self.test_datasets_path, backup_dump_file)\\n tmp_script_path = os.path.join(self.test_datasets_path, 'tmp.sh')\\n if not os.path.isfile(old_dump_file_path):\\n return\\n config = h.get_config(config_filename='test.ini')\\n SQLAlchemyURL = config['sqlalchemy.url']\\n if not SQLAlchemyURL.split(':')[0] == 'mysql':\\n return\\n rdbms, username, password, db_name = SQLAlchemyURL.split(':')\\n username = username[2:]\\n password = password.split('@')[0]\\n db_name = db_name.split('/')[-1]\\n # First dump the existing database so we can load it later.\\n # Note: the --single-transaction option seems to be required (on Mac MySQL 5.6 using InnoDB tables ...)\\n # see http://forums.mysql.com/read.php?10,108835,112951#msg-112951\\n with open(tmp_script_path, 'w') as tmpscript:\\n tmpscript.write('#!/bin/sh\\\\nmysqldump -u %s -p%s --single-transaction --no-create-info --result-file=%s %s' % (\\n username, password, backup_dump_file_path, db_name))\\n os.chmod(tmp_script_path, 0744)\\n with open(os.devnull, \\\"w\\\") as fnull:\\n call([tmp_script_path], stdout=fnull, stderr=fnull)\\n # Now load the dump file of the large database (from old_dump_file)\\n with open(tmp_script_path, 'w') as tmpscript:\\n tmpscript.write('#!/bin/sh\\\\nmysql -u %s -p%s %s < %s' % (username, password, db_name, old_dump_file_path))\\n with open(os.devnull, \\\"w\\\") as fnull:\\n call([tmp_script_path], stdout=fnull, stderr=fnull)\\n\\n # Recreate the default users that the loaded dump file deleted\\n administrator = h.generate_default_administrator()\\n contributor = h.generate_default_contributor()\\n viewer = h.generate_default_viewer()\\n Session.add_all([administrator, contributor, viewer])\\n Session.commit()\\n\\n\\n ################################################################################\\n # PHONOLOGY\\n ################################################################################\\n\\n # Create a Blackfoot phonology with the test phonology script\\n params = self.phonology_create_params.copy()\\n params.update({\\n 'name': u'Blackfoot Phonology',\\n 'description': u'The phonological rules of Frantz (1997) as FSTs',\\n 'script': self.blackfoot_phonology_script\\n })\\n params = json.dumps(params)\\n response = self.app.post(url('phonologies'), params, self.json_headers,\\n self.extra_environ_admin)\\n resp = json.loads(response.body)\\n phonology_id = resp['id']\\n\\n \\\"\\\"\\\"\\n\\n\\n ################################################################################\\n # MORPHOLOGY\\n ################################################################################\\n\\n # Create a lexicon form search and corpus\\n # The code below constructs a query that finds a (large) subset of the Blackfoot morphemes.\\n # Notes for future morphology creators:\\n # 1. the \\\"oth\\\" category is a mess: detangle the nominalizer, inchoative, transitive suffixes, etc. from\\n # one another and from the numerals and temporal modifiers -- ugh!\\n # 2. the \\\"pro\\\" category\\\" is also a mess: clearly pronoun-forming iisto does not have the same distribution \\n # as the verbal suffixes aiksi and aistsi! And oht, the LING/means thing, is different again...\\n # 3. hkayi, that thing at the end of demonstratives, is not agra, what is it? ...\\n # 4. the dim category contains only 'sst' 'DIM' and is not used in any forms ...\\n lexical_category_names = ['nan', 'nin', 'nar', 'nir', 'vai', 'vii', 'vta', 'vti', 'vrt', 'adt',\\n 'drt', 'prev', 'med', 'fin', 'oth', 'o', 'und', 'pro', 'asp', 'ten', 'mod', 'agra', 'agrb', 'thm', 'whq',\\n 'num', 'stp', 'PN']\\n durative_morpheme = 15717\\n hkayi_morpheme = 23429\\n query = {'filter': ['and', [['Form', 'syntactic_category', 'name', 'in', lexical_category_names],\\n ['not', ['Form', 'morpheme_break', 'regex', '[ -]']],\\n ['not', ['Form', 'id', 'in', [durative_morpheme, hkayi_morpheme]]],\\n ['not', ['Form', 'grammaticality', '=', '*']]\\n ]]}\\n smaller_query_for_rapid_testing = {'filter': ['and', [['Form', 'id', '<', 1000],\\n ['Form', 'syntactic_category', 'name', 'in', lexical_category_names]]]}\\n params = self.form_search_create_params.copy()\\n params.update({\\n 'name': u'Blackfoot morphemes',\\n 'search': query\\n })\\n params = json.dumps(params)\\n response = self.app.post(url('formsearches'), params, self.json_headers, self.extra_environ_admin)\\n lexicon_form_search_id = json.loads(response.body)['id']\\n params = self.corpus_create_params.copy()\\n params.update({\\n 'name': u'Corpus of Blackfoot morphemes',\\n 'form_search': lexicon_form_search_id\\n })\\n params = json.dumps(params)\\n response = self.app.post(url('corpora'), params, self.json_headers, self.extra_environ_admin)\\n lexicon_corpus_id = json.loads(response.body)['id']\\n\\n # Create a rules corpus\\n\\n # Create a corpus of forms containing words -- to be used to estimate ngram probabilities\\n # The goal here is to exclude things that look like words but are not really words, i.e., \\n # morphemes; as a heuristic we search for grammatical forms categorized as 'sent' or whose\\n # transcription value contains a space or a dash.\\n query = {'filter': ['and', [['or', [['Form', 'syntactic_category', 'name', '=', u'sent'],\\n ['Form', 'morpheme_break', 'like', '% %'],\\n ['Form', 'morpheme_break', 'like', '%-%']]],\\n ['Form', 'syntactic_category_string', '!=', None],\\n ['Form', 'grammaticality', '=', '']]]}\\n params = self.form_search_create_params.copy()\\n params.update({\\n 'name': u'Find Blackfoot sentences',\\n 'description': u'Returns all forms containing words',\\n 'search': query\\n })\\n params = json.dumps(params)\\n response = self.app.post(url('formsearches'), params, self.json_headers, self.extra_environ_admin)\\n rules_form_search_id = json.loads(response.body)['id']\\n params = self.corpus_create_params.copy()\\n params.update({\\n 'name': u'Corpus of Blackfoot sentences',\\n 'form_search': rules_form_search_id\\n })\\n params = json.dumps(params)\\n response = self.app.post(url('corpora'), params, self.json_headers, self.extra_environ_admin)\\n rules_corpus_id = json.loads(response.body)['id']\\n\\n # Now we reduce the number of category-based word-formation rules by removing all such\\n # rules implicit in the rules corpus that have fewer than two exemplar tokens.\\n\\n # Get the category sequence types of all of the words in the rules corpus ordered by their counts, minus\\n # those with fewer than ``minimum_token_count`` counts.\\n minimum_token_count = 2\\n params = {'minimum_token_count': minimum_token_count}\\n response = self.app.get(url(controller='corpora', action='get_word_category_sequences', id=rules_corpus_id),\\n params, self.json_headers, self.extra_environ_admin)\\n resp = json.loads(response.body)\\n\\n word_category_sequences = u' '.join([word_category_sequence for word_category_sequence, ids in resp])\\n #word_category_sequences = u'agra-vai vai-agrb'\\n\\n # Now create a morphology using the lexicon and rules defined by word_category_sequences\\n rich_upper = False\\n name = u'Morphology of Blackfoot'\\n params = self.morphology_create_params.copy()\\n params.update({\\n 'name': name,\\n 'lexicon_corpus': lexicon_corpus_id,\\n 'rules': word_category_sequences,\\n 'script_type': u'lexc',\\n 'extract_morphemes_from_rules_corpus': False,\\n 'rich_upper': rich_upper\\n })\\n params = json.dumps(params)\\n response = self.app.post(url('morphologies'), params, self.json_headers, self.extra_environ_admin_appset)\\n resp = json.loads(response.body)\\n morphology_id = resp['id']\\n assert resp['name'] == name\\n assert resp['script_type'] == u'lexc'\\n\\n # Generate the morphology's script without compiling it.\\n response = self.app.put(url(controller='morphologies', action='generate',\\n id=morphology_id), headers=self.json_headers,\\n extra_environ=self.extra_environ_contrib)\\n resp = json.loads(response.body)\\n generate_attempt = resp['generate_attempt']\\n\\n # Poll ``GET /morphologies/morphology_id`` until ``generate_attempt`` has changed.\\n seconds_elapsed = 0\\n wait = 2\\n while True:\\n response = self.app.get(url('morphology', id=morphology_id),\\n headers=self.json_headers, extra_environ=self.extra_environ_contrib)\\n resp = json.loads(response.body)\\n if generate_attempt != resp['generate_attempt']:\\n log.debug('Generate attempt for morphology %d has terminated.' % morphology_id)\\n break\\n else:\\n log.debug('Waiting for morphology %d\\\\'s script to generate: %s' % (\\n morphology_id, self.human_readable_seconds(seconds_elapsed)))\\n sleep(wait)\\n seconds_elapsed = seconds_elapsed + wait\\n\\n ################################################################################\\n # MORPHEME LANGUAGE MODEL\\n ################################################################################\\n\\n # Create a morpheme language model\\n name = u'Blackfoot morpheme language model'\\n params = self.morpheme_language_model_create_params.copy()\\n params.update({\\n 'name': name,\\n 'corpus': rules_corpus_id,\\n 'toolkit': 'mitlm'\\n })\\n params = json.dumps(params)\\n response = self.app.post(url('morphemelanguagemodels'), params, self.json_headers, self.extra_environ_admin)\\n resp = json.loads(response.body)\\n morpheme_language_model_id = resp['id']\\n assert resp['name'] == name\\n assert resp['toolkit'] == u'mitlm'\\n assert resp['order'] == 3\\n assert resp['smoothing'] == u'' # The ModKN smoothing algorithm is the implicit default with MITLM\\n\\n # Generate the files of the language model\\n response = self.app.put(url(controller='morphemelanguagemodels', action='generate', id=morpheme_language_model_id),\\n {}, self.json_headers, self.extra_environ_admin)\\n resp = json.loads(response.body)\\n lm_generate_attempt = resp['generate_attempt']\\n\\n # Poll GET /morphemelanguagemodels/id until generate_attempt changes.\\n requester = lambda: self.app.get(url('morphemelanguagemodel', id=morpheme_language_model_id),\\n headers=self.json_headers, extra_environ=self.extra_environ_admin)\\n resp = self.poll(requester, 'generate_attempt', lm_generate_attempt, log, wait=1, vocal=False)\\n assert resp['generate_message'] == u'Language model successfully generated.'\\n\\n ################################################################################\\n # MORPHOLOGICAL PARSER\\n ################################################################################\\n\\n # Create a morphological parser for Blackfoot\\n params = self.morphological_parser_create_params.copy()\\n params.update({\\n 'name': u'Morphological parser for Blackfoot',\\n 'phonology': phonology_id,\\n 'morphology': morphology_id,\\n 'language_model': morpheme_language_model_id\\n })\\n params = json.dumps(params)\\n response = self.app.post(url('morphologicalparsers'), params, self.json_headers, self.extra_environ_admin)\\n resp = json.loads(response.body)\\n morphological_parser_id = resp['id']\\n\\n # Compile the morphological parser's morphophonology script if necessary, cf. precompiled_morphophonology and pregenerated_morphophonology.\\n morphological_parser_directory = os.path.join(self.morphological_parsers_path, 'morphological_parser_%d' % morphological_parser_id)\\n morphophonology_binary_filename = 'morphophonology.foma'\\n morphophonology_script_filename = 'morphological_parser.script'\\n morphophonology_binary_path = os.path.join(morphological_parser_directory, morphophonology_binary_filename )\\n morphophonology_script_path = os.path.join(morphological_parser_directory, morphophonology_script_filename )\\n try:\\n precompiled_morphophonology_path = os.path.join(self.test_morphophonologies_path, precompiled_morphophonology)\\n pregenerated_morphophonology_path = os.path.join(self.test_morphophonologies_path, pregenerated_morphophonology)\\n except Exception:\\n precompiled_morphophonology_path = None\\n pregenerated_morphophonology_path = None\\n if (precompiled_morphophonology_path and pregenerated_morphophonology_path and \\n os.path.exists(precompiled_morphophonology_path) and os.path.exists(pregenerated_morphophonology_path)):\\n # Use the precompiled morphophonology script if it's available,\\n copyfileobj(open(precompiled_morphophonology_path, 'rb'), open(morphophonology_binary_path, 'wb'))\\n copyfileobj(open(pregenerated_morphophonology_path, 'rb'), open(morphophonology_script_path, 'wb'))\\n else:\\n # Generate the parser's morphophonology FST, compile it and generate the morphemic language model\\n response = self.app.put(url(controller='morphologicalparsers', action='generate_and_compile',\\n id=morphological_parser_id), headers=self.json_headers, extra_environ=self.extra_environ_admin)\\n resp = json.loads(response.body)\\n morphological_parser_compile_attempt = resp['compile_attempt']\\n\\n # Generate the parser's morphophonology FST, compile it and generate the morphemic language model\\n response = self.app.put(url(controller='morphologicalparsers', action='generate_and_compile',\\n id=morphological_parser_id), headers=self.json_headers, extra_environ=self.extra_environ_admin)\\n\\n # Poll ``GET /morphologicalparsers/mophological_parser_id`` until ``compile_attempt`` has changed.\\n requester = lambda: self.app.get(url('morphologicalparser', id=morphological_parser_id),\\n headers=self.json_headers, extra_environ=self.extra_environ_admin)\\n resp = self.poll(requester, 'compile_attempt', morphological_parser_compile_attempt, log,\\n wait=10, vocal=True, task_descr='compile morphological parser %s' % morphological_parser_id)\\n assert resp['compile_message'] == \\\\\\n u'Compilation process terminated successfully and new binary file was written.'\\n\\n # Poll ``GET /morphologicalparsers/mophological_parser_id`` until ``compile_attempt`` has changed.\\n requester = lambda: self.app.get(url('morphologicalparser', id=morphological_parser_id),\\n headers=self.json_headers, extra_environ=self.extra_environ_admin)\\n resp = self.poll(requester, 'compile_attempt', morphological_parser_compile_attempt, log,\\n wait=10, vocal=True, task_descr='compile morphological parser %s' % morphological_parser_id)\\n assert resp['compile_message'] == \\\\\\n u'Compilation process terminated successfully and new binary file was written.'\\n\\n # Some reusable transcriptions and their parses\\n transcription1 = u'nitsspiyi'\\n transcription1_correct_parse = u'%s-%s' % (\\n h.rare_delimiter.join([u'nit', u'1', u'agra']),\\n h.rare_delimiter.join([u'ihpiyi', u'dance', u'vai']))\\n transcription1_impoverished_parse = u'nit-ihpiyi'\\n transcription2 = u'aaniit'\\n transcription2_correct_parse = u'%s-%s' % (\\n h.rare_delimiter.join([u'waanii', u'say', u'vai']),\\n h.rare_delimiter.join([u't', u'IMP', u'agrb']))\\n transcription2_impoverished_parse = u'waanii-t'\\n\\n # Test applyup on the mophological parser's morphophonology FST\\n params = json.dumps({'transcriptions': [transcription1, transcription2]})\\n response = self.app.put(url(controller='morphologicalparsers', action='applyup',\\n id=morphological_parser_id), params, self.json_headers, self.extra_environ_admin)\\n resp = json.loads(response.body)\\n if rich_upper:\\n assert transcription1_correct_parse in resp[transcription1]\\n assert transcription2_correct_parse in resp[transcription2]\\n else:\\n assert transcription1_impoverished_parse in resp[transcription1]\\n assert transcription2_impoverished_parse in resp[transcription2]\\n\\n # Test how well the morphological parser parses some test words.\\n params = json.dumps({'transcriptions': [transcription1, transcription2]})\\n response = self.app.put(url(controller='morphologicalparsers', action='parse',\\n id=morphological_parser_id), params, self.json_headers, self.extra_environ_admin)\\n resp = json.loads(response.body)\\n assert resp[transcription1] == transcription1_correct_parse\\n # aaniit will have waaniit 'scatter' as its most likely parse and the correct parse waanii-t 'say-IMP'\\n # as its second most likely...\\n assert resp[transcription2] != transcription2_correct_parse\\n\\n\\n\\n \\\"\\\"\\\"\\n\\n\\n ################################################################################\\n # LOUIE MORPHOLOGY\\n ################################################################################\\n\\n # Create a form search that returns forms containing analyzed words elicited by Louie.\\n\\n conjuncts = [['or', [['Form', 'syntactic_category', 'name', '=', u'sent'],\\n ['Form', 'morpheme_break', 'like', '% %'],\\n ['Form', 'morpheme_break', 'like', '%-%']]],\\n ['Form', 'syntactic_category_string', '!=', None],\\n ['Form', 'grammaticality', '=', ''],\\n ['Form', 'elicitor', 'last_name', '=', 'Louie']]\\n query = {'filter': ['and', conjuncts]}\\n\\n params = self.form_search_create_params.copy()\\n params.update({\\n 'name': u'Forms containing analyzed words elicited by Louie',\\n 'search': query\\n })\\n params = json.dumps(params)\\n response = self.app.post(url('formsearches'), params, self.json_headers, self.extra_environ_admin)\\n louie_form_search_id = json.loads(response.body)['id']\\n\\n params = self.corpus_create_params.copy()\\n params.update({\\n 'name': u'Corpus of forms containing analyzed words elicited by Louie',\\n 'form_search': louie_form_search_id\\n })\\n params = json.dumps(params)\\n response = self.app.post(url('corpora'), params, self.json_headers, self.extra_environ_admin)\\n louie_corpus_id = json.loads(response.body)['id']\\n\\n # Now create a morphology using the Louie corpus as both the lexicon and rules corpora.\\n rich_upper = False\\n name = u'Morphology of Blackfoot based on words elicited by Louie'\\n params = self.morphology_create_params.copy()\\n params.update({\\n 'name': name,\\n 'lexicon_corpus': louie_corpus_id,\\n 'rules_corpus': louie_corpus_id,\\n 'script_type': u'regex',\\n 'extract_morphemes_from_rules_corpus': True,\\n 'rich_upper': rich_upper\\n })\\n params = json.dumps(params)\\n response = self.app.post(url('morphologies'), params, self.json_headers, self.extra_environ_admin_appset)\\n resp = json.loads(response.body)\\n louie_morphology_id = resp['id']\\n assert resp['name'] == name\\n assert resp['script_type'] == u'regex'\\n\\n # Generate the morphology's script without compiling it.\\n response = self.app.put(url(controller='morphologies', action='generate',\\n id=louie_morphology_id), headers=self.json_headers,\\n extra_environ=self.extra_environ_contrib)\\n resp = json.loads(response.body)\\n generate_attempt = resp['generate_attempt']\\n\\n # Poll ``GET /morphologies/morphology_id`` until ``generate_attempt`` has changed.\\n seconds_elapsed = 0\\n wait = 2\\n while True:\\n response = self.app.get(url('morphology', id=louie_morphology_id),\\n headers=self.json_headers, extra_environ=self.extra_environ_contrib)\\n resp = json.loads(response.body)\\n if generate_attempt != resp['generate_attempt']:\\n log.debug('Generate attempt for morphology %d has terminated.' % louie_morphology_id)\\n break\\n else:\\n log.debug('Waiting for morphology %d\\\\'s script to generate: %s' % (\\n louie_morphology_id, self.human_readable_seconds(seconds_elapsed)))\\n sleep(wait)\\n seconds_elapsed = seconds_elapsed + wait\\n\\n\\n ################################################################################\\n # MORPHEME LANGUAGE MODEL -- LOUIE\\n ################################################################################\\n\\n # Create a morpheme language model based on the data elicited by Louie\\n name = u'Blackfoot morpheme language model based on data elicited by Louie'\\n params = self.morpheme_language_model_create_params.copy()\\n params.update({\\n 'name': name,\\n 'corpus': louie_corpus_id,\\n 'toolkit': 'mitlm'\\n })\\n params = json.dumps(params)\\n response = self.app.post(url('morphemelanguagemodels'), params, self.json_headers, self.extra_environ_admin)\\n resp = json.loads(response.body)\\n louie_language_model_id = resp['id']\\n assert resp['name'] == name\\n assert resp['toolkit'] == u'mitlm'\\n assert resp['order'] == 3\\n assert resp['smoothing'] == u'' # The ModKN smoothing algorithm is the implicit default with MITLM\\n\\n # Generate the files of the language model\\n response = self.app.put(url(controller='morphemelanguagemodels', action='generate',\\n id=louie_language_model_id),\\n {}, self.json_headers, self.extra_environ_admin)\\n resp = json.loads(response.body)\\n lm_generate_attempt = resp['generate_attempt']\\n\\n # Poll GET /morphemelanguagemodels/id until generate_attempt changes.\\n requester = lambda: self.app.get(url('morphemelanguagemodel', id=louie_language_model_id),\\n headers=self.json_headers, extra_environ=self.extra_environ_admin)\\n resp = self.poll(requester, 'generate_attempt', lm_generate_attempt, log, wait=1, vocal=False)\\n assert resp['generate_message'] == u'Language model successfully generated.'\\n\\n ################################################################################\\n # MORPHOLOGICAL PARSER -- LOUIE\\n ################################################################################\\n\\n # Create a morphological parser for Blackfoot based on data elicited by Louie\\n params = self.morphological_parser_create_params.copy()\\n params.update({\\n 'name': u'Morphological parser for Blackfoot based on data elicited by Louie',\\n 'phonology': phonology_id,\\n 'morphology': louie_morphology_id,\\n 'language_model': louie_language_model_id\\n })\\n params = json.dumps(params)\\n response = self.app.post(url('morphologicalparsers'), params, self.json_headers,\\n self.extra_environ_admin)\\n resp = json.loads(response.body)\\n louie_parser_id = resp['id']\\n\\n # Compile the morphological parser's morphophonology script if necessary, cf.\\n # precompiled_morphophonology and pregenerated_morphophonology.\\n morphological_parser_directory = os.path.join(self.morphological_parsers_path,\\n 'morphological_parser_%d' % louie_parser_id)\\n morphophonology_binary_filename = 'morphophonology.foma'\\n morphophonology_script_filename = 'morphological_parser.script'\\n morphophonology_binary_path = os.path.join(morphological_parser_directory,\\n morphophonology_binary_filename )\\n morphophonology_script_path = os.path.join(morphological_parser_directory,\\n morphophonology_script_filename )\\n try:\\n precompiled_morphophonology_path = os.path.join(self.test_morphophonologies_path,\\n precompiled_morphophonology)\\n pregenerated_morphophonology_path = os.path.join(self.test_morphophonologies_path,\\n pregenerated_morphophonology)\\n except Exception:\\n precompiled_morphophonology_path = None\\n pregenerated_morphophonology_path = None\\n if (precompiled_morphophonology_path and pregenerated_morphophonology_path and \\n os.path.exists(precompiled_morphophonology_path) and os.path.exists(pregenerated_morphophonology_path)):\\n # Use the precompiled morphophonology script if it's available,\\n copyfileobj(open(precompiled_morphophonology_path, 'rb'), open(morphophonology_binary_path, 'wb'))\\n copyfileobj(open(pregenerated_morphophonology_path, 'rb'), open(morphophonology_script_path, 'wb'))\\n else:\\n # Generate the parser's morphophonology FST, compile it and generate the morphemic language model\\n response = self.app.put(url(controller='morphologicalparsers', action='generate_and_compile',\\n id=louie_parser_id), headers=self.json_headers, extra_environ=self.extra_environ_admin)\\n resp = json.loads(response.body)\\n morphological_parser_compile_attempt = resp['compile_attempt']\\n\\n # Generate the parser's morphophonology FST, compile it and generate the morphemic language model\\n response = self.app.put(url(controller='morphologicalparsers', action='generate_and_compile',\\n id=louie_parser_id), headers=self.json_headers, extra_environ=self.extra_environ_admin)\\n\\n # Poll ``GET /morphologicalparsers/mophological_parser_id`` until ``compile_attempt`` has changed.\\n requester = lambda: self.app.get(url('morphologicalparser', id=louie_parser_id),\\n headers=self.json_headers, extra_environ=self.extra_environ_admin)\\n resp = self.poll(requester, 'compile_attempt', morphological_parser_compile_attempt, log,\\n wait=10, vocal=True, task_descr='compile morphological parser %s' % louie_parser_id)\\n assert resp['compile_message'] == \\\\\\n u'Compilation process terminated successfully and new binary file was written.'\\n\\n # Poll ``GET /morphologicalparsers/mophological_parser_id`` until ``compile_attempt`` has changed.\\n requester = lambda: self.app.get(url('morphologicalparser', id=louie_parser_id),\\n headers=self.json_headers, extra_environ=self.extra_environ_admin)\\n resp = self.poll(requester, 'compile_attempt', morphological_parser_compile_attempt, log,\\n wait=10, vocal=True, task_descr='compile morphological parser %s' % louie_parser_id)\\n assert resp['compile_message'] == \\\\\\n u'Compilation process terminated successfully and new binary file was written.'\\n\\n # Some reusable transcriptions and their parses\\n transcription1 = u'nitsspiyi'\\n transcription1_correct_parse = u'%s-%s' % (\\n h.rare_delimiter.join([u'nit', u'1', u'agra']),\\n h.rare_delimiter.join([u'ihpiyi', u'dance', u'vai']))\\n transcription1_impoverished_parse = u'nit-ihpiyi'\\n transcription2 = u'aaniit'\\n transcription2_correct_parse = u'%s-%s' % (\\n h.rare_delimiter.join([u'waanii', u'say', u'vai']),\\n h.rare_delimiter.join([u't', u'IMP', u'agrb']))\\n transcription2_impoverished_parse = u'waanii-t'\\n\\n # Test applyup on the mophological parser's morphophonology FST\\n params = json.dumps({'transcriptions': [transcription1, transcription2]})\\n response = self.app.put(url(controller='morphologicalparsers', action='applyup',\\n id=louie_parser_id), params, self.json_headers, self.extra_environ_admin)\\n resp = json.loads(response.body)\\n if rich_upper:\\n assert transcription1_correct_parse in resp[transcription1]\\n assert transcription2_correct_parse not in resp[transcription2]\\n else:\\n assert transcription1_impoverished_parse in resp[transcription1]\\n assert transcription2_impoverished_parse not in resp[transcription2]\\n\\n # Test how well the morphological parser parses some test words.\\n params = json.dumps({'transcriptions': [transcription1, transcription2]})\\n response = self.app.put(url(controller='morphologicalparsers', action='parse',\\n id=louie_parser_id), params, self.json_headers, self.extra_environ_admin)\\n resp = json.loads(response.body)\\n assert resp[transcription1] == transcription1_correct_parse\\n # aaniit will have waaniit 'scatter' as its most likely parse and the correct parse waanii-t 'say-IMP'\\n # as its second most likely...\\n assert resp[transcription2] != transcription2_correct_parse\\n\\n\\n # Finally, load the original database back in so that subsequent tests can work.\\n with open(tmp_script_path, 'w') as tmpscript:\\n tmpscript.write('#!/bin/sh\\\\nmysql -u %s -p%s %s < %s' % (username, password, db_name, backup_dump_file_path))\\n with open(os.devnull, \\\"w\\\") as fnull:\\n call([tmp_script_path], stdout=fnull, stderr=fnull)\\n os.remove(tmp_script_path)\\n os.remove(backup_dump_file_path)\\n\\n # Implement category-based class LMs and test them against morpheme-based ones.\\n # Build multiple Bf morphological parsers and test them out, find the best one, write a paper on it!\",\n \"def load_yaafedata(params, \\n n_learn_frames=2000,\\n use_custom_stft=False):\\n\\n audio_file_path = getoptions(params, 'location', '/sons/voxforge/data/Learn/')\\n # if no number specified, use n_learn_frames\\n n_frames = getoptions(params, 'n_frames', n_learn_frames)\\n sr = getoptions(params, 'sr', 16000)\\n sigma_noise = getoptions(params, 'sigma', 0.0)\\n random_seed = getoptions(params, 'shuffle', 1001)\\n features = getoptions(params, 'features', [])\\n wintime = getoptions(params, 'wintime', 0.032)\\n steptime = getoptions(params, 'steptime', 0.008)\\n startpoint = getoptions(params, 'startpoint', 0)\\n forbid_list = getoptions(params, 'forbidden_names', [])\\n mfnpf = getoptions(params, 'frame_num_per_file', 3000)\\n# wintime = float(win_size)/float(sr)\\n# steptime = float(step_size)/float(sr)\\n \\n win_size = int(wintime*sr)\\n step_size = int(steptime*sr)\\n# print wintime, steptime, win_size, step_size\\n # apply sub_routine to all the files until a condition is met\\n n_frames_reached = 0\\n\\n all_file_paths = get_filepaths(audio_file_path,\\n random_seed,\\n forbid_list = forbid_list)\\n file_index = 0\\n\\n specseq = []\\n featseq = []\\n dataseq = []\\n n_files_used = 0\\n\\n while (n_frames_reached < n_frames):\\n file_index = file_index + 1\\n filepath = all_file_paths[file_index]\\n n_files_used = n_files_used + 1\\n\\n [loc_magSTFT, loc_Feats, locDatas] = load_data_one_audio_file(\\n filepath, sr,\\n wintime=wintime,\\n steptime=steptime,\\n max_frame_num_per_file=mfnpf,\\n sigma_noise=sigma_noise,\\n startpoint = startpoint,\\n features=features)\\n# if get_data:\\n# [loc_magSTFT, loc_Feats, locDatas] = load_data_one_file_melspec(filepath, sr, sigma_noise, params);\\n# Data = [Data , locDatas'];\\n# else\\n# [loc_magSTFT, loc_Feats, ~] = load_data_one_file_melspec(filepath, sr, sigma_noise, params);\\n# end\\n if not use_custom_stft:\\n specseq.append(loc_magSTFT)\\n else:\\n specseq.append(np.abs(get_stft(locDatas,\\n wsize=win_size,\\n tstep=step_size,\\n sigma = sigma_noise)).T)\\n# print wintime, steptime, win_size, step_size\\n# print loc_magSTFT.shape\\n# print specseq[-1].shape\\n# print locDatas.shape\\n featseq.append(loc_Feats)\\n dataseq.append(locDatas)\\n \\n n_frames_reached += min(loc_magSTFT.shape[0], loc_Feats.shape[0])\\n print n_frames_reached\\n \\n Spectrums = np.vstack(specseq)\\n Features = np.vstack(featseq)\\n Data = np.hstack(dataseq)\\n\\n n_frames_reached = min(n_frames_reached, n_frames)\\n Spectrums = Spectrums[0:n_frames_reached,:]\\n Features = Features[0:n_frames_reached,:]\\n used_files = all_file_paths[0:n_files_used]\\n\\n return Features, Spectrums, n_frames_reached, Data, used_files\",\n \"def load_data():\\r\\n global labelNames\\r\\n print(\\\"Loading Data...\\\")\\r\\n\\r\\n fnpath = \\\"rawdata\\\\\\\\cifar-10-batches-py\\\"\\r\\n fnprefix = 'data_batch_'\\r\\n fnlblnames = 'batches.meta'\\r\\n fntstbatch = 'test_batch'\\r\\n\\r\\n labelNames = unpickle(path.join(fnpath, fnlblnames))\\r\\n label_names = []\\r\\n for label in labelNames['label_names']:\\r\\n label_names.append(\\\"\\\".join(map(chr, label)))\\r\\n labelNames['label_names'] = label_names\\r\\n\\r\\n CIFAR_Data.append(unpickle(path.join(fnpath, fntstbatch)))\\r\\n for n in range(1, 6):\\r\\n CIFAR_Data.append(unpickle(path.join(fnpath, fnprefix + str(n))))\",\n \"def dataload():\\n\\t\\n\\tglobal A, B, fnA, fnB, lPcnA, lPcnB\\n\\t\\n\\tdwd = os.getcwd() # Data WD\\n\\t\\t\\n\\t# First sample A is loaded. This is the \\\"calibrating\\\" sample.\\n\\t# In this case it is the OGLE III LMC small amplitude RGs.\\n\\t\\n\\tfnA = '/LMC-CalSample-cleaned_2.fits'\\n\\tA = Table.read(dwd+fnA)\\n\\n\\t# Then sample B is loaded. For comparison/testing purposes, this is\\n\\t# again the OGLE III LMC SARGs.\\n\\t\\n\\tfnB = '/LMC-CalSample-cleaned_2.fits'\\n\\tB = Table.read(dwd+fnB)\\n\\t\\n\\t\\\"\\\"\\\" Fix tables so only the stars with all three good periods are \\n\\tconsidered. \\\"\\\"\\\"\\n\\t\\n\\tlPcnA = get_logPcn(A)\\n\\tlPcnB = get_logPcn(B)\\n\\t\\n\\tfor cn in lPcnA:\\n\\t\\tA = A[A[cn]>0]\\n\\tfor cn in lPcnB:\\n\\t\\tB = B[B[cn]>0]\",\n \"def load_data(dataset_path: str):\\n data = arff.loadarff(dataset_path)\\n data_frame = pd.DataFrame(data[0])\\n return data_frame\",\n \"def _load20news_miao():\\n DIR = os.path.dirname(os.path.realpath(__file__)).split('vae_sparse')[0]+'vae_sparse/optvaedatasets'\\n DIR += '/20news_miao'\\n h5file = DIR+'/miao.h5'\\n if not os.path.exists(h5file):\\n flen = len(open(DIR+'/vocab').readlines())\\n print 'DIM: ',flen\\n np.random.seed(1)\\n TRAIN_VALID_MAT = readSparseFile(DIR+'/train.feat', flen, zeroIndexed=False)\\n idx = np.random.permutation(TRAIN_VALID_MAT.shape[0])\\n VALIDMAT = TRAIN_VALID_MAT[idx[:500]]\\n TRAINMAT = TRAIN_VALID_MAT[idx[500:]]\\n TESTMAT = readSparseFile(DIR+'/test.feat', flen, zeroIndexed=False) \\n saveSparseHDF5(TRAINMAT,'train', h5file)\\n saveSparseHDF5(VALIDMAT,'valid', h5file)\\n saveSparseHDF5(TESTMAT, 'test' , h5file)\\n dset = {}\\n dset['vocabulary']= [k.strip().split(' ')[0] for k in open(DIR+'/vocab').readlines()]\\n dset['train'] = loadSparseHDF5('train',h5file)\\n dset['valid'] = loadSparseHDF5('valid',h5file)\\n dset['test'] = loadSparseHDF5('test',h5file)\\n dset['dim_observations'] = dset['train'].shape[1]\\n dset['data_type'] = 'bow'\\n return dset\",\n \"def load_datasets():\\n idx, data_paths, data_names, desc_paths, descrips, sql_paths, \\\\\\n sql_names, loaded, table_size, \\\\\\n loaded_names = mgr.build_datasets_table()\\n return render_template('load_datasets.html',\\n zip=zip(idx, data_paths, data_names, desc_paths,\\n descrips, sql_paths, sql_names, loaded,\\n table_size),\\n data_names=loaded_names)\",\n \"def __loadDataset(self, parameters):\\n # self.localConfigured = Settings.instance().readValue( key = 'Common/local-repo' )\\n for pr in parameters:\\n if pr['type'] == 'dataset':\\n if pr['value'].startswith('undefined:/'):\\n fileName = pr['value'].split('undefined:/')[1]\\n if not os.path.exists( fileName ):\\n raise Exception(\\\"the following test data file is missing: %s \\\" % fileName)\\n\\n doc = FileModelTestData.DataModel()\\n res = doc.load( absPath = fileName )\\n pr['value'] = \\\"undefined:/%s\\\" % doc.getRaw()\\n elif pr['value'].startswith('local-tests:/'):\\n fileName = pr['value'].split('local-tests:/')[1]\\n\\n if not os.path.exists( fileName ):\\n raise Exception(\\\"the following test data file is missing: %s \\\" % fileName)\\n \\n doc = FileModelTestData.DataModel()\\n res = doc.load( absPath = fileName )\\n pr['value'] = \\\"local-tests:/%s\\\" % doc.getRaw()\\n else:\\n pass\",\n \"def load_all_data_from_file(self) -> None:\\n self.load_gene_data_from_file()\\n self.load_ontology_from_file(ontology_type=DataType.GO, ontology_url=self.go_ontology_url,\\n ontology_cache_path=self.go_ontology_cache_path,\\n config=self.config)\\n self.load_associations_from_file(associations_type=DataType.GO, associations_url=self.go_associations_url,\\n associations_cache_path=self.go_associations_cache_path, config=self.config)\\n self.load_ontology_from_file(ontology_type=DataType.DO, ontology_url=self.do_ontology_url,\\n ontology_cache_path=self.do_ontology_cache_path, config=self.config)\\n self.load_associations_from_file(associations_type=DataType.DO, associations_url=self.do_associations_url,\\n associations_cache_path=self.do_associations_cache_path,\\n association_additional_cache_path=self.do_associations_new_cache_path,\\n association_additional_url=self.do_associations_new_url, config=self.config)\\n self.load_ontology_from_file(ontology_type=DataType.EXPR, ontology_url=self.expression_ontology_url,\\n ontology_cache_path=self.expression_ontology_cache_path, config=self.config)\\n self.load_associations_from_file(associations_type=DataType.EXPR,\\n associations_url=self.expression_associations_url,\\n associations_cache_path=self.expression_associations_cache_path,\\n config=self.config)\\n self.load_orthology_from_file()\\n self.load_expression_cluster_data()\\n self.load_protein_domain_information()\",\n \"def test_load_dataset():\\n\\n # Given\\n dataset_file_name = core.config.app_config.TESTING_DATA_FILE\\n\\n # When\\n subject = utils.load_dataset(filename=dataset_file_name)\\n\\n # Then\\n assert isinstance(subject, pd.DataFrame)\\n assert subject.shape == (5940, 41)\",\n \"def load(as_pandas=None):\\n return du.as_numpy_dataset(load_pandas(), as_pandas=as_pandas)\",\n \"def load(as_pandas=None):\\n return du.as_numpy_dataset(load_pandas(), as_pandas=as_pandas)\",\n \"def load(as_pandas=None):\\n return du.as_numpy_dataset(load_pandas(), as_pandas=as_pandas)\",\n \"def load_datasets_from_database(test_run_id):\\n logging.info('Loading datasets from database for: {}'.format(test_run_id))\\n mbtest_run = MBTestDatabaseService().get_run(test_run_id)\\n datasets = mbtest_run.get('datasets')\\n logging.info('type(Documents): %r', type(datasets))\\n logging.info('Documents: %s', str(datasets)[:200])\\n\\n if isinstance(datasets, basestring):\\n documents = yaml.load(datasets)\\n # documents should be seq of dicts -- if str then legacy format\\n if isinstance(documents, basestring):\\n logging.info('Loading datasets from legacy CSV format')\\n reader = csv.reader(StringIO(datasets), delimiter=';')\\n documents = mbtest_data_csv_to_yaml_format(reader)\\n else:\\n pass # datasets is seq of dict\\n\\n return documents\",\n \"def loadData(catalog):\\n\\n loadArtwork(catalog)\\n loadArtists(catalog)\",\n \"def import_dataset(fpath):\\r\\n data = read_csv(fpath)\\r\\n print(data.head())\\r\\n print(data.shape)\\r\\n return data\",\n \"def load_data(self, f): \\n self.sampling = True\\n self.reads = np.load(f)\\n self.total = self.reads.shape[0]\",\n \"def load(self):\\n #self.df = read_file(\\\"../data/yelp_academic_dataset_user.json\\\") #Full Data.\\n self.df = read_file(\\\"../data/user300.json\\\") #For local machine.\\n #self.get_friend_list()\\n #self.save_friend_nodes()\",\n \"def load_data(self,split='train'):\\n return load_arrow_data(self.config,split)\",\n \"def loadData(catalog):\\n loadArtworks(catalog)\\n loadArtists(catalog)\\n loadAdquires(catalog)\\n loadNacionalities(catalog)\\n load2DArtworks(catalog)\\n loadArtistMediumsTags(catalog)\\n loadDptments(catalog)\\n catalog['artists'] = sortArtists(catalog, 3)\\n fillArtistMediums(catalog)\\n fillMostUsedMediums(catalog)\\n catalog['artists_tags'] = sortArtistTags(catalog, 3)\\n sort_dptments(catalog)\",\n \"def import_datasets(snli_path):\\n print('extract data from snli directory..')\\n train = dict(); dev = dict(); test = dict()\\n gold_labels = {'entailment': 0, 'neutral': 1, 'contradiction': 2}\\n\\n for file_type in ['train', 'dev', 'test']:\\n path = os.path.join(snli_path, 'snli_1.0_{}.jsonl'.format(file_type))\\n with open(path) as file:\\n data = [json.loads(line) for line in file]\\n eval(file_type)['premise'] = [entry['sentence1'] for entry in data if entry['gold_label'] != '-']\\n eval(file_type)['hypothesis'] = [entry['sentence2'] for entry in data if entry['gold_label'] != '-']\\n g_labels = np.array([gold_labels[entry['gold_label']] for entry in data if entry['gold_label'] != '-'])\\n eval(file_type)['label'] = g_labels\\n print('extraction process was finished successfully!')\\n return train, dev, test\",\n \"def test_AFQ_data():\\n _, bids_path, _ = get_temp_hardi()\\n\\n for mapping in [SynMap(use_prealign=False), AffMap()]:\\n myafq = api.AFQ(\\n bids_path=bids_path,\\n dmriprep='vistasoft',\\n mapping=mapping)\\n npt.assert_equal(nib.load(myafq.b0[\\\"01\\\"]).shape,\\n nib.load(myafq.dwi_file[\\\"01\\\"]).shape[:3])\\n npt.assert_equal(nib.load(myafq.b0[\\\"01\\\"]).shape,\\n nib.load(myafq.dti_params[\\\"01\\\"]).shape[:3])\\n myafq.rois\\n shutil.rmtree(op.join(\\n bids_path,\\n 'derivatives/afq'))\",\n \"def prepare_dataset(fpath):\\n raise NotImplementedError\",\n \"def LoadBatch(filename):\",\n \"def load_dataset():\\n try:\\n data_path = ROOT_PATH.joinpath('data', 'Complete_TAVG_Daily_LatLong1_1880.nc')\\n ds = xarray.open_dataset(data_path)\\n return ds\\n except FileNotFoundError:\\n raise\",\n \"def loadData(catalog):\\r\\n controller.loadData(catalog)\",\n \"def load_synthetic_data():\\n\\n pickle_object = FM().data_file \\n\\n with pickle_object.open('rb') as data_file: \\n return pickle.load(data_file)\",\n \"def load_data(data_set_key: str, a2e_data_path: str = '../../../a2e-data/data', cache_dir: str = None) -> BearingDataSet:\\n\\n if a2e_data_path is not None and not a2e_data_path.startswith('http') and not a2e_data_path.startswith('file://'):\\n if os.path.isabs(a2e_data_path):\\n a2e_data_path = 'file://' + os.path.abspath(a2e_data_path)\\n else:\\n bearing_module_path = pathlib.Path(__file__).parent.absolute()\\n absolute_data_path = os.path.abspath(os.path.join(bearing_module_path, a2e_data_path))\\n if os.name == 'nt':\\n absolute_data_path = f'/{absolute_data_path}'.replace('\\\\\\\\', '/')\\n\\n a2e_data_path = 'file://' + absolute_data_path\\n\\n if not os.path.isdir(a2e_data_path.replace('file://', '')):\\n a2e_data_path = 'https://github.com/maechler/a2e-data/raw/master/data/'\\n\\n if cache_dir is None:\\n cache_dir = os.path.join(Path.home(), '.a2e')\\n\\n if not os.path.exists(cache_dir):\\n os.makedirs(cache_dir)\\n\\n a2e_data_path = a2e_data_path.rstrip('/') + '/'\\n data_set_description_origin = f'{a2e_data_path}{data_set_key}.yaml'\\n data_set_origin = f'{a2e_data_path}{data_set_key}.csv.gz'\\n data_set_description_path = get_file(data_set_key + '.yaml', origin=data_set_description_origin, cache_dir=cache_dir, cache_subdir='datasets/bearing')\\n windows = {}\\n\\n with open(data_set_description_path) as data_set_description_file:\\n data_set_description = yaml.load(data_set_description_file, Loader=yaml.FullLoader)\\n data_set_path = get_file(data_set_key + '.csv.gz', origin=data_set_origin, cache_dir=cache_dir, cache_subdir='datasets/bearing', file_hash=data_set_description['data']['md5_hash'], hash_algorithm='md5')\\n\\n with gzip.open(data_set_path, mode='rt') as data_set_file:\\n data_frame = pd.read_csv(data_set_file, parse_dates=[data_set_description['data']['index_column']], date_parser=lambda x: timestamp_to_date_time(float(x)), quotechar='\\\"', sep=',')\\n data_frame = data_frame.set_index(data_set_description['data']['index_column'])\\n\\n for window_key, window_description in data_set_description['windows'].items():\\n windows[window_key] = {\\n 'mask': (data_frame.index > window_description['start']) & (data_frame.index <= window_description['end']),\\n 'label': window_description['label'],\\n }\\n\\n return BearingDataSet(data_set_key, data_frame, windows)\",\n \"def get_data_loaders():\\n dataset_path = \\\"\\\"\\n dataset_cache = None\\n personachat = get_dataset(dataset_path, dataset_cache)\\n\\n tokenizer_selected = OpenAIGPTTokenizer.from_pretrained('openai-gpt')\\n logger.info(\\\"Build inputs and labels\\\")\\n datasets = {\\\"train\\\": defaultdict(list), \\\"valid\\\": defaultdict(list)}\\n personality = []\\n history_complete = []\\n count_persona = 0\\n with open('data_faiss_pegasus_2sentences_finalgenerated.pkl', 'rb') as f:\\n persona_selected_list = pickle.load(f)\\n for dataset_name, dataset in personachat.items():\\n num_candidates = len(dataset[0][\\\"utterances\\\"][0][\\\"candidates\\\"])\\n if num_candidates > 0 and dataset_name == 'train':\\n num_candidates = min(1, num_candidates)\\n for dialog in dataset:\\n persona = dialog[\\\"persona_info\\\"].copy()\\n #datasets[personality].append(persona)\\n count_history = 0\\n for utterance in dialog[\\\"utterances\\\"]:\\n count_history = count_history + 1\\n history = utterance[\\\"history\\\"][-(2*2+5):]\\n \\n #history_complete.append(history)\\n if len(persona) == 4:\\n if len(history) > (len(persona)+3):\\n history_chatbot = history[1::2]\\n persona_selected = persona_selected_list[count_persona]\\n instance = build_input_from_segments_faiss_2(persona, history_chatbot) \\n for input_name, input_array in instance.items():\\n datasets[dataset_name][input_name].append(input_array)\\n count_persona = count_persona + 1\\n return datasets\",\n \"def loadData(catalog):\\n controller.loadData(catalog)\",\n \"def loadData(catalog):\\n controller.loadData(catalog)\",\n \"def loadData(catalog):\\n controller.loadData(catalog)\",\n \"def loadData(catalog):\\n controller.loadData(catalog)\",\n \"def loadData(catalog):\\n controller.loadData(catalog)\",\n \"def load_tgas():\\n tgas = GaiaData(gload.tgas())\\n return tgas\",\n \"def load_dataset(fname, nb_lines):\\n import os.path\\n if os.path.isfile('safe/Amazon-'+str(nb_lines)+'.p'):\\n return util.load('safe/Amazon-'+str(nb_lines)+'.p')\\n count = 1\\n X = []\\n y = []\\n with open(fname) as f:\\n for line in f:\\n text, label = read_line(line)\\n #print((label, text))\\n X.append(text)\\n y.append(label)\\n if count >= nb_lines:\\n break\\n count+=1\\n\\n #load pretrained dictonary\\n dico = util.load('safe/vocab_gensim.p')\\n preprocessor = text_preprocessing.Preprocessor(dico=dico)\\n X = preprocessor.preprocess(X)\\n #save the loaded dataset in a pickle for speeding up next run\\n util.save((X,y), 'safe/Amazon-'+str(nb_lines)+'.p')\\n return (X, y)\",\n \"def loadData(catalog):\\n loadVideos(catalog)\\n loadCategories(catalog)\",\n \"def import_scikit_data(sralist):\\n scikit_data_dict = {}\\n for dataset in sralist:\\n with open(TMP_DIR+'scikit_'+dataset+'/ALL_genes_profile_dict.json', 'r') as scikit_data:\\n scikit_data_dict[dataset] = [json.load(scikit_data)]\\n return scikit_data_dict\",\n \"def load_datasets(self):\\n if self.processed_extension == '.npz':\\n logger.info(f'Loading sets from npz:')\\n \\n logger.info(f'train: {self.train_path}')\\n self.train_data = sparse.load_npz(self.train_path)\\n\\n logger.info(f'val: {self.val_path}')\\n self.val_data = sparse.load_npz(self.val_path)\\n\\n logger.info(f'test: {self.test_path}')\\n self.test_data = sparse.load_npz(self.test_path)\\n \\n # Split x and y\\n self.train_data = [sparse.lil_matrix(sparse.csr_matrix(self.train_data)[:,:-1]),\\n sparse.lil_matrix(sparse.csr_matrix(self.train_data)[:,-1])]\\n \\n self.val_data = [sparse.lil_matrix(sparse.csr_matrix(self.val_data)[:,:-1]),\\n sparse.lil_matrix(sparse.csr_matrix(self.val_data)[:,-1])]\\n \\n self.test_data = [sparse.lil_matrix(sparse.csr_matrix(self.test_data)[:,:-1]),\\n sparse.lil_matrix(sparse.csr_matrix(self.test_data)[:,-1])]\\n \\n elif self.processed_extension == '.csv':\\n logger.info(f'Loading sets from csv:')\\n \\n logger.info(f'train: {self.train_path}')\\n self.train_data = pd.read_csv(self.train_path)\\n train_cols = self.train_data.columns\\n self.train_data = [self.train_data[train_cols.difference(['TARGET'])],\\n self.train_data['TARGET']]\\n \\n logger.info(f'val: {self.val_path}')\\n self.val_data = pd.read_csv(self.val_path)\\n self.val_data = [self.val_data[train_cols.difference(['TARGET'])],\\n self.val_data['TARGET']]\\n \\n logger.info(f'test: {self.test_path}')\\n self.test_data = pd.read_csv(self.test_path)\\n self.test_data = [self.test_data[train_cols.difference(['TARGET'])],\\n self.test_data['TARGET']]\\n else:\\n raise AttributeError(f'Wrong extension: {self.processed_extension}')\\n self.n_train = self.train_data[0].shape[0]\\n self.n_val = self.val_data[0].shape[0]\\n self.n_test = self.test_data[0].shape[0]\\n self.input_size = self.train_data[0].shape[1]\\n self.n_examples = self.n_train + self.n_val + self.n_test\\n \\n logger.info(f'Set sizes:')\\n logger.info(f'train: {self.n_train}')\\n logger.info(f'val: {self.n_val}')\\n logger.info(f'test: {self.n_test}')\",\n \"def load_data(catalog):\\n controller.load_data(catalog)\",\n \"def load(name):\\n if name in datasets:\\n\\n return pd.read_csv(os.path.join(datasets_path, \\\"%s.csv\\\" % name))\\n else:\\n raise ValueError(\\\"Dataset not found!\\\")\",\n \"def get_datasets():\\n # Get data\\n dataframe = pandas.read_csv('agaricus-lepiota.data')\\n\\n # Convert data type for all variables\\n for column in dataframe:\\n dataframe[column] = pandas.Categorical(dataframe[column])\\n dataframe[column] = dataframe[column].cat.codes\\n\\n # Get labels\\n target = dataframe.pop('p')\\n\\n # Get tensors, and split data into training and test sets\\n split = int(len(dataframe) * 0.8)\\n train_dataset = tf.data.Dataset.from_tensor_slices(\\n (dataframe[:split].values, target[:split].values))\\n\\n train_dataset = train_dataset.shuffle(len(dataframe)).batch(1)\\n\\n test_dataset = tf.data.Dataset.from_tensor_slices(\\n (dataframe[split:].values, target[split:].values))\\n\\n test_dataset = test_dataset.shuffle(len(dataframe)).batch(1)\\n\\n return train_dataset, test_dataset\",\n \"def load_cifa_10():\\n train_set_x = np.ndarray([ 50000, 3072 ])\\n train_set_y = np.ndarray( [50000] )\\n\\n batch_size = 10000\\n for i in xrange(5):\\n batch = open( datapath + \\\"data_batch_\\\"+str(i+1), 'rb')\\n map = cPickle.load( batch )\\n batch.close()\\n train_set_x[ i*batch_size : (i+1)*batch_size , : ] = np.asarray( map[ 'data' ], dtype = 'float32' )\\n train_set_y[ i*batch_size : (i+1)*batch_size ] = np.asarray( map[ 'labels' ], dtype = 'float32' )\\n\\n test_file = open( datapath + 'test_batch', 'rb')\\n map = cPickle.load( test_file )\\n test_file.close()\\n \\n test_set_x = np.asarray( map['data'], dtype = 'float32' )\\n test_set_y = np.asarray( map['labels'], dtype = 'float32' )\\n \\n\\n return train_set_x, train_set_y, test_set_x, test_set_y\",\n \"def download_data_sets(ad_accounts: [adaccount.AdAccount]):\\n download_account_structure(ad_accounts)\\n download_ad_performance(ad_accounts)\",\n \"def load_data(self, task):\\n params = self.params\\n data = {splt: {} for splt in ['train', 'valid', 'test']}\\n dpath = os.path.join(params.data_path, 'eval', task)\\n\\n self.n_sent = 1 if task in ['SST-2', 'CoLA'] else 2\\n\\n for splt in ['train', 'valid', 'test']:\\n\\n # load data and dictionary\\n data1 = load_binarized(os.path.join(dpath, '%s.s1.pth' % splt), params)\\n data2 = load_binarized(os.path.join(dpath, '%s.s2.pth' % splt), params) if self.n_sent == 2 else None\\n data['dico'] = data.get('dico', data1['dico'])\\n\\n # set dictionary parameters\\n set_dico_parameters(params, data, data1['dico'])\\n if self.n_sent == 2:\\n set_dico_parameters(params, data, data2['dico'])\\n\\n # create dataset\\n if self.n_sent == 1:\\n data[splt]['x'] = Dataset(data1['sentences'], data1['positions'], params)\\n else:\\n data[splt]['x'] = ParallelDataset(\\n data1['sentences'], data1['positions'],\\n data2['sentences'], data2['positions'],\\n params\\n )\\n\\n # load labels\\n if splt != 'test' or task in ['MRPC']:\\n # read labels from file\\n with open(os.path.join(dpath, '%s.label' % splt), 'r') as f:\\n lines = [l.rstrip() for l in f]\\n # STS-B task\\n if task == 'STS-B':\\n assert all(0 <= float(x) <= 5 for x in lines)\\n y = [float(l) for l in lines]\\n # QQP\\n elif task == 'QQP':\\n UNK_LABEL = 0\\n lab2id = {x: i for i, x in enumerate(sorted(set(lines) - set([''])))}\\n y = [lab2id.get(x, UNK_LABEL) for x in lines]\\n # other tasks\\n else:\\n lab2id = {x: i for i, x in enumerate(sorted(set(lines)))}\\n y = [lab2id[x] for x in lines]\\n data[splt]['y'] = torch.LongTensor(y)\\n assert len(data[splt]['x']) == len(data[splt]['y'])\\n\\n # compute weights for weighted training\\n if task != 'STS-B' and params.weighted_training:\\n weights = torch.FloatTensor([\\n 1.0 / (data['train']['y'] == i).sum().item()\\n for i in range(len(lab2id))\\n ]).npu()\\n self.weights = weights / weights.sum()\\n else:\\n self.weights = None\\n\\n return data\",\n \"def _load_data(self):\\n\\n if not self._cache.exists(config.DATAFRAME_SONG_DATA):\\n source_path = os.path.join(config.S3_SONG_DATA, 'A/A/A/*.json') # Note: song database is way big, so we get only a slice of it.\\n dataframe = self._get_spark_session().read.json(source_path)\\n self._cache.set_source(config.DATAFRAME_SONG_DATA, dataframe)\",\n \"def setup(self):\\n # TODO check if need both dataset together\\n self.train_dataset = ABSADataset(data_path=self.train_path, mode=self.in_mode, task=self.task, \\n tokenizer=self.tokenizer, vocab=\\\"bert\\\")\\n self.vocabulary = self.train_dataset.vocabulary\\n\\n self.eval_dataset = ABSADataset(data_path=self.dev_path, mode=self.in_mode, task=self.task,\\n tokenizer=self.tokenizer, vocab=self.vocabulary)\\n #self.train_restaurant = ABSADataset(data_path=RESTAURANT_TRAIN)\\n #self.eval_restaurant = ABSADataset(data_path=RESTAURANT_DEV)\",\n \"def load(datastream):\",\n \"def get_datasets(FIELDS='all'):\\n dsinfostr = fork_and_get_output(\\\"zfs list -H -o {0}\\\".format(FIELDS).split())\\n header = get_zfs_ds_header()\\n dsinfo = dsinfostr.splitlines()\\n dsobjs = []\\n for dsstr in dsinfo:\\n dsobjs.append(DataZFS(dsstr, header, 'dataset'))\\n return dsobjs\",\n \"def MSA_to_dataset(msa_path):\\n\\n from deepchem.data.datasets import NumpyDataset # NumpyDataset depends on utils, so imported here to prevent circular import\\n from Bio import SeqIO\\n\\n with open(msa_path, 'r') as f:\\n ids = []\\n sequences = []\\n for record in SeqIO.parse(f, 'fasta'):\\n ids.append(record.id)\\n seq = []\\n for res in record:\\n seq.append(res)\\n sequences.append(seq)\\n dataset = NumpyDataset(X=sequences, ids=ids)\\n return dataset\",\n \"def load_scutfbp():\\n train_loader = torch.utils.data.DataLoader(ScutFBP(transform=data_transforms['train']), batch_size=cfg[\\n 'batch_size'],\\n shuffle=True, num_workers=4, drop_last=True)\\n test_loader = torch.utils.data.DataLoader(ScutFBP(transform=data_transforms['test']), batch_size=cfg['batch_size'],\\n shuffle=False, num_workers=4, drop_last=True)\\n\\n return train_loader, test_loader\",\n \"def load_data():\\n\\t\\t# load the data\\n\\t\\tDATPATH = \\\"../data/\\\"\\n\\t\\t#fnino = DATPATH + \\\"nino3.csv\\\" # 1871-2000\\n\\t\\tfnino = DATPATH + \\\"tas_Amon_IPSL-CM5A-LR_past1000_r1i1p1_0850_1850_nino3_tseries.csv\\\" # 1871-2016\\n\\t\\t#fnino = DATPATH + \\\"nino34.long.data\\\"\\n\\t\\t#nc_data_nino3 = netCDF4.Dataset(fnino)\\n\\t\\t#nino3_load = nc_data_nino3.variables['tas'][:]\\n\\t\\t#dnino = nino3_load.flatten()\\n\\n\\t\\tdnino = np.genfromtxt(fnino, delimiter=\\\",\\\", dtype=float).flatten()\\n\\t\\t#fismr = DATPATH + \\\"ismr.csv\\\" # 1871-2000\\n\\t\\t#fismr = DATPATH + \\\"psl_Amon_IPSL-CM5A-LR_past1000_r1i1p1_0850_1850_1_india_goswami_2002_tseries.csv\\\" # 1871-2016\\n\\t\\tfismr = DATPATH + \\\"pr_Amon_IPSL-CM5A-LR_past1000_r1i1p1_0850_1850_goswami_india_tseries.csv\\\" # 1871-2016\\n\\t\\tdismr = np.genfromtxt(fismr, delimiter=\\\",\\\", dtype=float).flatten()\\n\\t\\t#fvolc = DATPATH + \\\"robock.txt\\\" # 1871-2000\\n\\t\\tfvolc = DATPATH + \\\"sigl.txt\\\" # 1871-2016\\n\\t\\tdvolc = np.genfromtxt(fvolc, delimiter=\\\",\\\", dtype=float).flatten()\\n\\n\\t\\tfvolc_source = DATPATH + \\\"volc_source_850_1850.csv\\\" # 1871-2016\\n\\t\\tdvolc_source = np.genfromtxt(fvolc_source, delimiter=\\\",\\\", dtype=float).flatten()\\n\\t\\t# simple check for data consistency\\n\\t\\tassert dnino.shape == dismr.shape, \\\"Data sets are unequal!\\\"\\n\\t\\tassert int(dismr.shape[0]/12) == dvolc.shape[0], \\\"Data sets are unequal\\\"\\n\\t\\treturn dnino, dismr, dvolc, dvolc_source\",\n \"def load_cityscapes(path, fdr):\\n dataset = Dataset(path, split='val', mode=\\\"fine\\\", target_type=[\\\"semantic\\\", \\\"instance\\\"])\\n\\n from PATH import SCRI_PATH as spath\\n\\n for image, (sseg, inst), name in dataset:\\n image = np.array(image)\\n sseg = gt_covert(sseg)\\n inst = np.array(inst)\\n if os.path.exists(spath + \\\"/\\\" + fdr + \\\"/\\\" + name + \\\"_scri.png\\\"):\\n scribbles = np.array(Image.open(spath + \\\"/\\\" + fdr + \\\"/\\\" + name + \\\"_scri.png\\\"))\\n else:\\n scribbles = None\\n # scribbles = scribble_convert(scribbles)\\n yield name, image, sseg, inst, scribbles\",\n \"def load_nh_gaia():\\n \\n dir_gaia = os.path.expanduser('~') + '/Data/Catalogs/Gaia/' \\n# file_txt = 'nh16.mega.gaia.rdmpm.txt' # Original catalog, but covers MU69 area as seen from Earth\\n file_txt = 'mu69.mega.gaia.rdmspm.txt' # Smaller area, covers MU69 as seen from NH.\\n \\n file_pickle = file_txt.replace('.txt', '.pkl')\\n \\n if os.path.isfile(dir_gaia + file_pickle):\\n lun = open(dir_gaia + file_pickle, 'rb')\\n gaia = pickle.load(lun)\\n lun.close()\\n print(\\\"Loaded: \\\" + file_pickle)\\n \\n else:\\n \\n # Read the NH MegaCam-Gaia catalog from disk\\n \\n gaia = astropy.io.ascii.read(dir_gaia + file_txt, format = 'basic')\\n \\n # Make a plot of the Gaia stars\\n \\n plt.plot(gaia['RA'], gaia['Dec'], linestyle='none', marker = '.', ms=0.005)\\n plt.xlabel('RA [deg]')\\n plt.ylabel('Dec [deg]')\\n plt.title('NH MegaCam-Gaia catalog')\\n plt.show()\\n \\n # Save it as a pickle file\\n \\n lun = open(dir_gaia + file_pickle, 'wb')\\n pickle.dump(gaia, lun) \\n print(\\\"Wrote: \\\" + dir_gaia + file_pickle)\\n lun.close()\\n \\n return gaia\",\n \"def loader():\\n bucket = data_load_variables[\\\"bucket\\\"]\\n\\n if data_load_variables[\\\"use_lite_dataset\\\"]:\\n dataset_name = data_load_variables[\\\"lite_dataset_name\\\"]\\n else:\\n dataset_name = data_load_variables[\\\"dataset_name\\\"]\\n\\n s3 = boto3.client('s3')\\n\\n obj = s3.get_object(Bucket=bucket, Key=dataset_name)\\n # get object and file (key) from bucket\\n\\n df = pd.read_csv(obj['Body'])\\n return df\",\n \"def get_local_dataset(\\n self, \\n file_name: str\\n ):\\n pd.read_csv(file_name)\\n #save\",\n \"def fetch_baja_bathymetry():\\n data_file = POOCH.fetch(\\\"baja-bathymetry.csv.xz\\\")\\n data = pd.read_csv(data_file, compression=\\\"xz\\\")\\n return data\",\n \"def load_openml_data():\\n datasets = dict()\\n files = os.listdir(_DATA_DIRECTORY.value)\\n for file_name in files:\\n with open(_DATA_DIRECTORY.value + file_name, \\\"r\\\") as ff:\\n task = np.loadtxt(ff, delimiter=\\\",\\\", skiprows=1)\\n np.random.shuffle(task)\\n datasets[file_name] = [task]\\n return datasets, files\",\n \"def load_dataset(self):\\n # Get all the files in the directory\\n file_list = self.get_file_list()\\n\\n # Concatenate the data corresponding to a list of files\\n data = self.concatenate_file_data(file_list)\\n\\n # Shuffle the data and create the training and the validation datasets\\n data = self.shuffle_data_dictionary(data)\\n self.training_dataset, self.validation_dataset = self.split_data_into_training_and_validation(data)\",\n \"def get_data_loaders_4sentence():\\n dataset_path = \\\"\\\"\\n dataset_cache = None\\n personachat = get_dataset(dataset_path, dataset_cache)\\n\\n tokenizer_selected = OpenAIGPTTokenizer.from_pretrained('openai-gpt')\\n logger.info(\\\"Build inputs and labels\\\")\\n datasets = {\\\"train\\\": defaultdict(list), \\\"valid\\\": defaultdict(list)}\\n personality = []\\n history_complete = []\\n count_persona = 0\\n with open('data_faiss_pegasus_1generated.pkl', 'rb') as f:\\n persona_selected_list = pickle.load(f)\\n for dataset_name, dataset in personachat.items():\\n num_candidates = len(dataset[0][\\\"utterances\\\"][0][\\\"candidates\\\"])\\n if num_candidates > 0 and dataset_name == 'train':\\n num_candidates = min(1, num_candidates)\\n for dialog in dataset:\\n persona = dialog[\\\"persona_info\\\"].copy()\\n #datasets[personality].append(persona)\\n count_history = 0\\n for utterance in dialog[\\\"utterances\\\"]:\\n count_history = count_history + 1\\n history = utterance[\\\"history\\\"]\\n #history_complete.append(history)\\n if len(history_splitted) > (len(persona)-1):\\n history_chatbot = history[1::2]\\n persona_selected = persona_selected_list[count_persona]\\n instance = build_input_from_segments_faiss(persona_selected, history_chatbot) \\n for input_name, input_array in instance.items():\\n datasets[dataset_name][input_name].append(input_array)\\n count_persona = count_persona + 1\\n return datasets\",\n \"def load_data(path=\\\"../data/cora/\\\", dataset=\\\"cora\\\"):\\n print('Loading {} dataset...'.format(dataset))\\n\\n idx_features_labels = np.genfromtxt(\\\"{}{}.content\\\".format(path, dataset),\\n dtype=np.dtype(str))\\n features = sp.sparse.csr_matrix(idx_features_labels[:, 1:-1], dtype=np.float32)\\n# labels = encode_onehot(idx_features_labels[:, -1])\\n values = np.unique(idx_features_labels[:, -1])\\n values.sort()\\n labels = np.zeros(idx_features_labels.shape[0])\\n for i in range(labels.shape[0]):\\n labels[i] = np.where(values == idx_features_labels[i, -1])[0][0]\\n labels = torch.tensor(labels).long()\\n\\n # build graph\\n idx = np.array(idx_features_labels[:, 0], dtype=np.int32)\\n idx_map = {j: i for i, j in enumerate(idx)}\\n edges_unordered = np.genfromtxt(\\\"{}{}.cites\\\".format(path, dataset),\\n dtype=np.int32)\\n edges = np.array(list(map(idx_map.get, edges_unordered.flatten())),\\n dtype=np.int32).reshape(edges_unordered.shape)\\n adj = sp.sparse.coo_matrix((np.ones(edges.shape[0]), (edges[:, 0], edges[:, 1])),\\n shape=(labels.shape[0], labels.shape[0]),\\n dtype=np.float32)\\n\\n # build symmetric adjacency matrix\\n adj = adj + adj.T.multiply(adj.T > adj) - adj.multiply(adj.T > adj)\\n\\n# features = normalize(features)\\n adj = normalize(adj + sp.sparse.eye(adj.shape[0]))\\n\\n idx_train = range(140)\\n idx_val = range(200, 500)\\n idx_test = range(500, 1500)\\n\\n features = torch.FloatTensor(np.array(features.todense()))\\n# labels = torch.LongTensor(np.where(labels)[1])\\n adj = sparse_mx_to_torch_sparse_tensor(adj)\\n\\n idx_train = torch.LongTensor(idx_train)\\n idx_val = torch.LongTensor(idx_val)\\n idx_test = torch.LongTensor(idx_test)\\n\\n return adj, features, labels, idx_train, idx_val, idx_test\",\n \"def _load_cluster(self):\",\n \"def load(self, source):\\n try:\\n inputdata = self.__inputmanager.read(source)\\n self.__suitables = self.__inputmanager.map(inputdata)\\n self.__data = inputdata\\n except ValueError as e:\\n print (\\\"Failed to load the dataset: %s\\\" % e)\\n raise\\n\\n self.__modules = self.import_suitable_visualizations(self.__suitables)\\n self.__has_datefields = self.__inputmanager.has_date_points()\\n # Converting the datakeys into strings.\\n self.__datakeys = [str(i) for i in list(self.__data[0].keys())]\\n return self.__suitables\",\n \"def get_data_loaders_1sentence():\\n dataset_path = \\\"\\\"\\n dataset_cache = None\\n personachat = get_dataset(dataset_path, dataset_cache)\\n\\n tokenizer_selected = OpenAIGPTTokenizer.from_pretrained('openai-gpt')\\n logger.info(\\\"Build inputs and labels\\\")\\n datasets = {\\\"train\\\": defaultdict(list), \\\"valid\\\": defaultdict(list)}\\n personality = []\\n history_complete = []\\n count_persona = 0\\n with open('data_faiss_pegasus_1_sentence_final_generated.pkl', 'rb') as f:\\n persona_selected_list = pickle.load(f)\\n for dataset_name, dataset in personachat.items():\\n num_candidates = len(dataset[0][\\\"utterances\\\"][0][\\\"candidates\\\"])\\n if num_candidates > 0 and dataset_name == 'train':\\n num_candidates = min(1, num_candidates)\\n for dialog in dataset:\\n persona = dialog[\\\"persona_info\\\"].copy()\\n #datasets[personality].append(persona)\\n count_history = 0\\n for utterance in dialog[\\\"utterances\\\"]:\\n count_history = count_history + 1\\n history = utterance[\\\"history\\\"][-(2*2+1):]\\n #history_complete.append(history)\\n if len(history) > 3:\\n history_chatbot = history[1]\\n persona_selected = persona_selected_list[count_persona]\\n instance = build_input_from_segments_faiss(persona_selected, history_chatbot) \\n for input_name, input_array in instance.items():\\n datasets[dataset_name][input_name].append(input_array)\\n count_persona = count_persona + 1\\n return datasets\",\n \"def load_data():\\n dirname = os.path.join('datasets', 'fashion-mnist')\\n base = 'http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/'\\n files = [\\n 'train-labels-idx1-ubyte.gz', 'train-images-idx3-ubyte.gz',\\n 't10k-labels-idx1-ubyte.gz', 't10k-images-idx3-ubyte.gz'\\n ]\\n\\n paths = []\\n for fname in files:\\n paths.append(get_file(fname, origin=base + fname, cache_subdir=dirname))\\n\\n with gzip.open(paths[0], 'rb') as lbpath:\\n y_train = np.frombuffer(lbpath.read(), np.uint8, offset=8)\\n\\n with gzip.open(paths[1], 'rb') as imgpath:\\n x_train = np.frombuffer(\\n imgpath.read(), np.uint8, offset=16).reshape(len(y_train), 28, 28)\\n\\n with gzip.open(paths[2], 'rb') as lbpath:\\n y_test = np.frombuffer(lbpath.read(), np.uint8, offset=8)\\n\\n with gzip.open(paths[3], 'rb') as imgpath:\\n x_test = np.frombuffer(\\n imgpath.read(), np.uint8, offset=16).reshape(len(y_test), 28, 28)\\n\\n return (x_train, y_train), (x_test, y_test)\",\n \"def get_datasets(load_key=None, maven=False):\\n ds_names = {}\\n if load_key == 'R2349': \\n ds_names['batsrus_mf_lr'] = model_dir+'R2349/batsrus_3d_multi_fluid_lowres.h5'\\n ds_names['batsrus_multi_species'] = model_dir+'R2349/batsrus_3d_multi_species.h5'\\n ds_names['batsrus_electron_pressure'] = model_dir+'R2349/batsrus_3d_pe.h5'\\n ds_names['heliosares'] ='/Volumes/triton/Data/ModelChallenge/R2349/heliosares_multi.h5'\\n #ds_names['rhybrid'] ='/Volumes/triton/Data/ModelChallenge/R2349/rhybrid.h5'\\n \\n ds_types = {'batsrus1':[key for key in ds_names.keys() if 'multi_fluid' in key],\\n 'batsrus2':[key for key in ds_names.keys() if 'multi_species' in key],\\n 'batsrus3':[key for key in ds_names.keys() if 'electron_pressure' in key],\\n 'batsrus4':[key for key in ds_names.keys() if 'mf_lr' in key],\\n 'heliosares':[key for key in ds_names.keys() if 'helio' in key],\\n 'rhybrid_helio':[key for key in ds_names.keys() if 'rhybrid' in key ]}\\n if maven or True:\\n ds_names['maven']=orbit_dir+'orbit_2349.csv'\\n #ds_names['maven'] = orbit_dir+'orbit_plume_2349.csv'\\n ds_types['maven']=['maven']\\n elif load_key == 'batsrus_mf_lowres':\\n ds_names['batsrus_mf_lr'] = model_dir+'R2349/batsrus_3d_multi_fluid_lowres.h5'\\n ds_types = {'batsrus_mf_lr' : ['batsrus_mf_lr']}\\n\\n\\n elif load_key == 'helio_multi':\\n ds_names['t00550'] = model_dir+'R2349/Heliosares_Multi/t00550.h5'\\n ds_names['t00560'] = model_dir+'R2349/Heliosares_Multi/t00560.h5'\\n ds_names['t00570'] = model_dir+'R2349/Heliosares_Multi/t00570.h5'\\n ds_names['t00580'] = model_dir+'R2349/Heliosares_Multi/t00580.h5'\\n ds_names['t00590'] = model_dir+'R2349/Heliosares_Multi/t00590.h5'\\n ds_names['t00600'] = model_dir+'R2349/Heliosares_Multi/t00600.h5'\\n ds_names['t00610'] = model_dir+'R2349/Heliosares_Multi/t00610.h5'\\n ds_names['t00620'] = model_dir+'R2349/Heliosares_Multi/t00620.h5'\\n ds_names['t00630'] = model_dir+'R2349/Heliosares_Multi/t00630.h5'\\n ds_names['t00640'] = model_dir+'R2349/Heliosares_Multi/t00640.h5'\\n ds_names['t00650'] = model_dir+'R2349/Heliosares_Multi/t00650.h5'\\n\\n ds_types = {'heliosares':[key for key in ds_names.keys()]}\\n if maven:\\n #ds_names['maven'] = orbit_dir+'orbit_2349.csv'\\n ds_types['maven']=['maven']\\n elif load_key == 'SDC_BATS':\\n ds_names['LS180_SSL000_max'] = model_dir+'SDC_Archive/BATSRUS/LS180_SSL000_max.h5'\\n ds_names['LS270_SSL000_max'] = model_dir+'SDC_Archive/BATSRUS/LS270_SSL000_max.h5'\\n ds_names['LS090_SSL000_max'] = model_dir+'SDC_Archive/BATSRUS/LS090_SSL000_max.h5'\\n ds_names['LS180_SSL270_max'] = model_dir+'SDC_Archive/BATSRUS/LS180_SSL270_max.h5'\\n ds_names['LS270_SSL270_max'] = model_dir+'SDC_Archive/BATSRUS/LS270_SSL270_max.h5'\\n ds_names['LS090_SSL270_max'] = model_dir+'SDC_Archive/BATSRUS/LS090_SSL270_max.h5'\\n ds_names['LS180_SSL180_max'] = model_dir+'SDC_Archive/BATSRUS/LS180_SSL180_max.h5'\\n ds_names['LS270_SSL180_max'] = model_dir+'SDC_Archive/BATSRUS/LS270_SSL180_max.h5'\\n ds_names['LS090_SSL180_max'] = model_dir+'SDC_Archive/BATSRUS/LS090_SSL180_max.h5'\\n ds_names['LS180_SSL000_min'] = model_dir+'SDC_Archive/BATSRUS/LS180_SSL000_min.h5'\\n ds_names['LS270_SSL000_min'] = model_dir+'SDC_Archive/BATSRUS/LS270_SSL000_min.h5'\\n ds_names['LS090_SSL000_min'] = model_dir+'SDC_Archive/BATSRUS/LS090_SSL000_min.h5'\\n ds_names['LS180_SSL270_min'] = model_dir+'SDC_Archive/BATSRUS/LS180_SSL270_min.h5'\\n ds_names['LS270_SSL270_min'] = model_dir+'SDC_Archive/BATSRUS/LS270_SSL270_min.h5'\\n ds_names['LS090_SSL270_min'] = model_dir+'SDC_Archive/BATSRUS/LS090_SSL270_min.h5'\\n ds_names['LS180_SSL180_min'] = model_dir+'SDC_Archive/BATSRUS/LS180_SSL180_min.h5'\\n ds_names['LS270_SSL180_min'] = model_dir+'SDC_Archive/BATSRUS/LS270_SSL180_min.h5'\\n ds_names['LS090_SSL180_min'] = model_dir+'SDC_Archive/BATSRUS/LS090_SSL180_min.h5'\\n\\n ds_types = {'batsrus':[key for key in ds_names.keys()]}\\n\\n elif load_key == 'SDC_G1':\\n #BATSRUS\\n ds_names['bats_min_LS270_SSL0'] = \\\\\\n model_dir+'SDC_Archive/BATSRUS/'+'3d__ful_4_n00060000_PERmin-SSLONG0.h5'\\n ds_names['bats_min_LS270_SSL180'] = \\\\\\n model_dir+'SDC_Archive/BATSRUS/'+'3d__ful_4_n00060000_PERmin-SSLONG180.h5'\\n ds_names['bats_min_LS270_SSL270'] = \\\\\\n model_dir+'SDC_Archive/BATSRUS/'+'3d__ful_4_n00060000_PERmin-SSLONG270.h5' \\n \\n #HELIOSARES\\n #ds_names['helio_1'] = \\\\\\n # model_dir+'SDC_Archive/HELIOSARES/Hybrid/'+'helio_1.h5'\\n \\n #ds_names['helio_2'] = \\\\\\n # model_dir+'SDC_Archive/HELIOSARES/Hybrid/'+'helio_2.h5'\\n \\n \\n ds_types = {'batsrus1':[key for key in ds_names.keys() if 'bats' in key],\\n 'heliosares':[key for key in ds_names.keys() if 'helio' in key]}\\n if maven:\\n pass\\n #ds_names['maven'] = orbit_dir+'orbit_2349.csv'\\n #ds_types['maven']=['maven']\\n\\n elif load_key == 'rhybrid_res':\\n ds_names = {'rhybrid240':'/Volumes/triton/Data/ModelChallenge/R2349/rhybrid.h5',\\n 'rhybrid120':'/Volumes/triton/Data/ModelChallenge/R2349/HYB/state00030000.h5'}\\n ds_types = {'rhybrid1':['rhybrid240'], 'rhybrid2':['rhybrid120']}\\n elif load_key == 'batsrus_tseries':\\n ds_names = {'batsrus_mf':'/Volumes/triton/Data/ModelChallenge/R2349/BATSRUS/10km_mf/3d__ful_4_n00040000.h5',\\n 'batsrus_ms':'/Volumes/triton/Data/ModelChallenge/R2349/BATSRUS/10km_ms/3d__mhd_6_n0050000.h5'}\\n ds_types = {'batsrus_mf':['batsrus_mf'], 'batsrus_ms':['batsrus_ms']}\\n\\n elif load_key == 'maven':\\n ds_names, ds_types = {},{}\\n ds_names['maven'] = orbit_dir+'orbit_2349.csv'\\n ds_types['maven']=['maven']\\n elif load_key == 'exo_2349':\\n keys = ['2349_1RM_225km','2349_1RM_450km', '2349_2RM_450km',\\n '2349_2RM_900km','2349_4RM_900km'] \\n ds_names = {k:exo_dir+'/'+k+'/'+k+'.h5' for k in keys}\\n ds_types = {k:[k] for k in keys}\\n elif load_key == 'exo_comparisonA':\\n keys = ['2349_1RM_225km', '2349_2RM_450km',\\n '2349_1.5RM_338km'] \\n ds_names = {k:exo_dir+'/ComparisonA/'+k+'.h5' for k in keys}\\n ds_types = {k:[k] for k in keys}\\n elif load_key == 'exo_comparisonB':\\n keys = ['2349_1RM_225km', 'T0_1RM_225km', 'T1_1RM_225km', \\\"T2_1RM_225km\\\"] \\n ds_names = {k:exo_dir+'/ComparisonB/'+k+'.h5' for k in keys}\\n ds_types = {k:[k] for k in keys}\\n\\n elif load_key == 'exo_t1':\\n keys = ['T1_1RM_112km', 'T1_1RM_225km', #'T1_1RM_450km',\\n 'T1_2RM_225km', 'T1_2RM_450km', #'T1_2RM_900km',\\n 'T1_4RM_900km']\\n\\n ds_names = {k:exo_dir+'/'+k+'/'+k+'.h5' for k in keys}\\n ds_types = {k:[k] for k in keys}\\n\\n else:\\n print('No datasets selected')\\n \\n\\n return (ds_names, ds_types)\",\n \"def read_data(feature_file, label_file):\",\n \"def ensure_dataset_loaded(self, name):\\n if name not in self.datasets:\\n print(f'Loading dataset \\\"{name}\\\"')\\n pd_data = pd.read_excel(self.datafiles[name])\\n data = pd.DataFrame.to_dict(pd_data, 'records')\\n self.datasets[name] = data\",\n \"def loadData():\\n project_dir = \\\"/home/c/chandanchowdhury/Documents/CIS-833/CSSearch/indexer/\\\"\\n\\n index_file = \\\"index_file.pkl\\\"\\n link_file = \\\"link_file.pkl\\\"\\n\\n index_data = loadPickle(project_dir+index_file)\\n link_data = loadPickle(project_dir+link_file)\\n\\n return index_data, link_data\",\n \"def load_dataloaders(args):\\n logger.info(\\\"Loading dataloaders...\\\")\\n p_path = os.path.join(\\\"./data/\\\", \\\"df_unencoded.pkl\\\")\\n train_path = os.path.join(\\\"./data/\\\", \\\"df_encoded.pkl\\\")\\n if (not os.path.isfile(p_path)) and (not os.path.isfile(train_path)):\\n df = get_data(args, load_extracted=False)\\n elif os.path.isfile(p_path) and (not os.path.isfile(train_path)):\\n df = get_data(args, load_extracted=True)\\n elif os.path.isfile(train_path):\\n df = load_pickle(\\\"df_encoded.pkl\\\")\\n \\n # Train-Test split\\n msk = np.random.rand(len(df)) < args.train_test_ratio\\n trainset = df[msk]\\n testset = df[~msk]\\n \\n trainset = text_dataset(trainset, args)\\n max_features_length = trainset.max_x_len\\n max_seq_len = trainset.max_y_len\\n train_length = len(trainset)\\n train_loader = DataLoader(trainset, batch_size=args.batch_size, shuffle=True,\\\\\\n num_workers=0, collate_fn=Pad_Sequence(), pin_memory=False)\\n \\n testset = text_dataset(testset, args)\\n test_length = len(testset)\\n test_loader = DataLoader(testset, batch_size=args.batch_size, shuffle=True,\\\\\\n num_workers=0, collate_fn=Pad_Sequence(), pin_memory=False)\\n return train_loader, train_length, max_features_length, max_seq_len, test_loader, test_length\",\n \"def load_default_atf_data():\\n df = load_dataframe(\\\"oqmd_1.2_voronoi_magpie_fingerprints\\\")\\n return df[df['N_species'] == 2].sample(frac=0.2)\",\n \"def load_data():\\n dictionary = corpora.Dictionary.load(app.config['DICTIONARY'])\\n matrix = similarities.MatrixSimilarity.load(app.config['MATRIX'])\\n model = models.LsiModel.load(app.config['MODEL'])\\n df = pd.read_pickle(app.config['DATA_FRAME'])\\n return Data(matrix=matrix, model=model, dictionary=dictionary, data_frame=df)\",\n \"def loadData(infile,k):\\n f = open(infile,'r')\\n #f = f.read().split(\\\"\\\\n\\\")\\n #raw = json.loads(f[1])\\n f = f.read()\\n raw = json.loads(f)\\n data = np.array(raw)\\n dataset = data[k]\\n return dataset\",\n \"def load_data(loc='./data/', sp=None):\\n trainA, trainB, devA, devB, testA, testB = [],[],[],[],[],[]\\n trainS, devS, testS = [],[],[]\\n print('loc', loc)\\n with codecs.open(os.path.join(loc, 'SICK_train.txt'), mode='rb', encoding='utf-8') as f:\\n for line in f:\\n text = unicode_tr(line).lower().strip().split('\\\\t')\\n trainA.append(encode_sentence(text[1], sp))\\n trainB.append(encode_sentence(text[2], sp))\\n trainS.append(text[3])\\n with codecs.open(os.path.join(loc, 'SICK_trial.txt'), mode='rb', encoding='utf-8') as f:\\n for line in f:\\n text = unicode_tr(line).lower().strip().split('\\\\t')\\n devA.append(encode_sentence(text[1], sp))\\n devB.append(encode_sentence(text[2], sp))\\n devS.append(text[3])\\n with codecs.open(os.path.join(loc, 'SICK_test_annotated.txt'), mode='rb', encoding='utf-8') as f:\\n for line in f:\\n text = unicode_tr(line).lower().strip().split('\\\\t')\\n testA.append(encode_sentence(text[1], sp))\\n testB.append(encode_sentence(text[2], sp))\\n testS.append(text[3])\\n\\n trainS = [float(s) for s in trainS[1:]]\\n devS = [float(s) for s in devS[1:]]\\n testS = [float(s) for s in testS[1:]]\\n\\n return [trainA[1:], trainB[1:]], [devA[1:], devB[1:]], [testA[1:], testB[1:]], [trainS, devS, testS]\",\n \"def load_dataset():\\n\\n df_ = pd.read_excel(\\\"D:\\\\VERİBİLİMİOKULU\\\\VERİSETLERİ\\\\post_bilgileri.xlsx\\\")\\n df = df_.copy()\\n return df\",\n \"def load_scutfbp5500_64():\\n train_loader = torch.utils.data.DataLoader(FDataset(train=True, transform=data_transforms['train']),\\n batch_size=cfg['batch_size'], shuffle=True, num_workers=50,\\n drop_last=True, pin_memory=True)\\n test_loader = torch.utils.data.DataLoader(FDataset(train=False, transform=data_transforms['test']),\\n batch_size=cfg['batch_size'], shuffle=False, num_workers=50,\\n drop_last=True, pin_memory=True)\\n\\n return train_loader, test_loader\",\n \"def load_data(self,split='train'):\\n raise NotImplementedError\",\n \"def loadData(self, file):\\n self.data = batchImport(file, self.ps)\",\n \"def load(cfg, train_mode, split, shot, query,\\n bs, test_bs, num_workers, pin_memory,\\n ret_name=False):\\n if train_mode == \\\"train\\\":\\n dataset = COCOTrain(cfg, split, shot, query, ret_name=ret_name)\\n data_loader = DataLoader(dataset,\\n batch_size=bs,\\n shuffle=True,\\n num_workers=num_workers,\\n pin_memory=pin_memory,\\n drop_last=False)\\n else:\\n dataset = COCOTest(cfg, split, shot, query, ret_name=ret_name)\\n data_loader = DataLoader(dataset,\\n batch_size=test_bs, # Large batch for evaluation\\n shuffle=False,\\n num_workers=num_workers,\\n pin_memory=pin_memory,\\n drop_last=False)\\n num_classes = 80\\n return dataset, data_loader, num_classes\",\n \"def load_data(path=\\\"./data/cora/\\\", dataset=\\\"cora\\\"):\\n print('Loading {} dataset...'.format(dataset))\\n\\n idx_features_labels = np.genfromtxt(\\\"{}{}.content\\\".format(path, dataset), dtype=np.dtype(str))\\n features = sp.csr_matrix(idx_features_labels[:, 1:-1], dtype=np.float32)\\n labels = encode_onehot(idx_features_labels[:, -1])\\n\\n # build graph\\n idx = np.array(idx_features_labels[:, 0], dtype=np.int32)\\n idx_map = {j: i for i, j in enumerate(idx)}\\n edges_unordered = np.genfromtxt(\\\"{}{}.cites\\\".format(path, dataset), dtype=np.int32)\\n edges = np.array(list(map(idx_map.get, edges_unordered.flatten())), dtype=np.int32).reshape(edges_unordered.shape)\\n adj = sp.coo_matrix((np.ones(edges.shape[0]), (edges[:, 0], edges[:, 1])), shape=(labels.shape[0], labels.shape[0]), dtype=np.float32)\\n\\n # build symmetric adjacency matrix\\n adj = adj + adj.T.multiply(adj.T > adj) - adj.multiply(adj.T > adj)\\n\\n features = normalize_features(features)\\n adj = normalize_adj(adj + sp.eye(adj.shape[0]))\\n\\n idx_train = range(140)\\n idx_val = range(200, 500)\\n idx_test = range(500, 1500)\\n\\n adj = torch.FloatTensor(np.array(adj.todense()))\\n features = torch.FloatTensor(np.array(features.todense()))\\n labels = torch.LongTensor(np.where(labels)[1])\\n\\n idx_train = torch.LongTensor(idx_train)\\n idx_val = torch.LongTensor(idx_val)\\n idx_test = torch.LongTensor(idx_test)\\n\\n return adj, features, labels, idx_train, idx_val, idx_test\",\n \"def load_data():\\n\\n server_node = load_nodes(SERVER_NODE_INFILE)\\n road_node = load_nodes(ROAD_NODE_INFILE)\\n road_segment_point = load_nodes(ROAD_SEGMENT_POINT_INFILE)\\n\\n return server_node, road_node, road_segment_point\",\n \"def load_data(self):\\n raise NotImplementedError()\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.629331","0.62574154","0.61739457","0.61300355","0.6085598","0.6068422","0.6059043","0.599034","0.5854947","0.58494705","0.58464795","0.5826532","0.58246195","0.58217824","0.5821119","0.57790554","0.5750343","0.5750343","0.5742792","0.57419103","0.5741909","0.57248306","0.57135147","0.5704186","0.56925964","0.567108","0.5655235","0.56461793","0.5633125","0.5633125","0.5633125","0.5627344","0.56162316","0.5613017","0.5610277","0.56024754","0.5588937","0.55812055","0.55766255","0.556389","0.55574584","0.55539256","0.5552605","0.5543263","0.5527904","0.55161","0.55091625","0.55057997","0.55057997","0.55057997","0.55057997","0.55057997","0.55022824","0.5499496","0.5491077","0.5486199","0.5485403","0.5481235","0.54602534","0.5457056","0.5451061","0.5447392","0.5435697","0.5427174","0.54060835","0.5398092","0.5375757","0.53702325","0.5361366","0.53605795","0.53577626","0.5351519","0.5349324","0.5348192","0.5345167","0.5340242","0.5340164","0.53399116","0.5338885","0.53383076","0.53326607","0.5323301","0.53159547","0.5310285","0.53044605","0.5302166","0.52991945","0.52960926","0.5292428","0.5288831","0.5285742","0.52786297","0.52759206","0.52750057","0.5270406","0.5269472","0.5264224","0.52621835","0.5255432","0.5255286"],"string":"[\n \"0.629331\",\n \"0.62574154\",\n \"0.61739457\",\n \"0.61300355\",\n \"0.6085598\",\n \"0.6068422\",\n \"0.6059043\",\n \"0.599034\",\n \"0.5854947\",\n \"0.58494705\",\n \"0.58464795\",\n \"0.5826532\",\n \"0.58246195\",\n \"0.58217824\",\n \"0.5821119\",\n \"0.57790554\",\n \"0.5750343\",\n \"0.5750343\",\n \"0.5742792\",\n \"0.57419103\",\n \"0.5741909\",\n \"0.57248306\",\n \"0.57135147\",\n \"0.5704186\",\n \"0.56925964\",\n \"0.567108\",\n \"0.5655235\",\n \"0.56461793\",\n \"0.5633125\",\n \"0.5633125\",\n \"0.5633125\",\n \"0.5627344\",\n \"0.56162316\",\n \"0.5613017\",\n \"0.5610277\",\n \"0.56024754\",\n \"0.5588937\",\n \"0.55812055\",\n \"0.55766255\",\n \"0.556389\",\n \"0.55574584\",\n \"0.55539256\",\n \"0.5552605\",\n \"0.5543263\",\n \"0.5527904\",\n \"0.55161\",\n \"0.55091625\",\n \"0.55057997\",\n \"0.55057997\",\n \"0.55057997\",\n \"0.55057997\",\n \"0.55057997\",\n \"0.55022824\",\n \"0.5499496\",\n \"0.5491077\",\n \"0.5486199\",\n \"0.5485403\",\n \"0.5481235\",\n \"0.54602534\",\n \"0.5457056\",\n \"0.5451061\",\n \"0.5447392\",\n \"0.5435697\",\n \"0.5427174\",\n \"0.54060835\",\n \"0.5398092\",\n \"0.5375757\",\n \"0.53702325\",\n \"0.5361366\",\n \"0.53605795\",\n \"0.53577626\",\n \"0.5351519\",\n \"0.5349324\",\n \"0.5348192\",\n \"0.5345167\",\n \"0.5340242\",\n \"0.5340164\",\n \"0.53399116\",\n \"0.5338885\",\n \"0.53383076\",\n \"0.53326607\",\n \"0.5323301\",\n \"0.53159547\",\n \"0.5310285\",\n \"0.53044605\",\n \"0.5302166\",\n \"0.52991945\",\n \"0.52960926\",\n \"0.5292428\",\n \"0.5288831\",\n \"0.5285742\",\n \"0.52786297\",\n \"0.52759206\",\n \"0.52750057\",\n \"0.5270406\",\n \"0.5269472\",\n \"0.5264224\",\n \"0.52621835\",\n \"0.5255432\",\n \"0.5255286\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.76649237"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":17884,"cells":{"query":{"kind":"string","value":"Generate center offset for crop window"},"document":{"kind":"string","value":"def center_crop(image, source=(218, 178, 3), target=128):\n height, width, channel = source\n\n off_h = np.ceil((height - target) / 2).astype(int)\n off_w = np.ceil((width - target) / 2).astype(int)\n return image[off_h: off_h+target, off_w: off_w+target, :]"},"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 center(self):\n # minz to offset the heights to 0\n mz = (self.maxz-self.minz)/2\n #mz = self.minz\n return (self.minx + self.width / 2, self.miny + self.height / 2, mz)","def calculate_window_position(self):\n self.x = SQUARE_SIZE * self.col + SQUARE_SIZE // 2\n self.y = SQUARE_SIZE * self.row + SQUARE_SIZE // 2","def center(self):\n return self._lower + 0.5 * (self._upper - self._lower)","def center(self):\n return np.array([0,0,1/self.C+self.pos()])","def _center(pos, shift):\n x = np.concatenate((pos[0], pos[0] + shift[0]))\n y = np.concatenate((pos[1], pos[1] + shift[1]))\n return (x.max() + x.min()) / 2, (y.max() + y.min()) / 2","def center(window):\n window.update_idletasks()\n\n # Find the screen resolution\n screen_width = window.winfo_screenwidth()\n screen_height = window.winfo_screenheight()\n\n # Find new (x, y) coordinates\n size = tuple(int(_) for _ in window.geometry().split('+')[0].split('x'))\n x = screen_width/2 - 7 * size[0] / 13\n y = screen_height/2 - 6 * size[1] / 11\n\n # Apply new coordinates\n window.geometry(\"+%d+%d\" % (x, y))","def get_center_scr(self):\r\n return self.rect.center","def center(self):\r\n self.centerx = self.screen_rect.centerx \r\n self.centery = self.screen_rect.centery","def center(self):\n xc = (self.x.max() + self.x.min())/2.\n yc = (self.y.max() + self.y.min())/2.\n return (xc, yc)","def center(self):\n return (self.upper_right + self.lower_left) * 0.5","def center(self):\n return self.pos + self.axis / 2.0","def _get_frame_center(self, src):\n w, h = get_size(src)\n x = w / 2\n y = h / 2\n\n return x, y","def center(self):\n\n return (\n self.x() + (self.width() / 2),\n self.y() + (self.height() / 2)\n )","def find_center(self):\n return(Point(self.corner.x + self.width/2.0, self.corner.y + self.height/2.0))","def center(self):\n return (self.centerx, self.centery)","def center(self):\n return self.center_x, self.center_y","def _set_center(self):\n sl_center = np.array(\n [self.sl_list[k].mean_pos for k in range(self.nb_sl)]\n )\n sl_nb_pts = np.array(\n [self.sl_list[k].nb_points for k in range(self.nb_sl)]\n )\n sl_wcenter = [sl_center[k] * sl_nb_pts[k] for k in range(self.nb_sl)]\n self.center = np.sum(sl_wcenter, axis=0) / np.sum(sl_nb_pts)","def center(self):\n return np.sum(self.bbox, 0) / 2","def getCenter(self):\n return [self.tx/self.tw, self.ty/self.tw]","def center_window(self):\n\n\t\tframe_geo = self.frameGeometry()\n\t\tcursor_pos = QtWidgets.QApplication.desktop().cursor().pos()\n\t\tscreen = QtWidgets.QApplication.desktop().screenNumber(cursor_pos)\n\t\tcenter_point = QtWidgets.QApplication.desktop().screenGeometry(screen).center()\n\t\tframe_geo.moveCenter(center_point)\n\t\tself.move(frame_geo.topLeft())","def crop_center(image, cropx, cropy):\n x,y = image.shape\n startx = x//2 - cropx//2\n starty = y//2 - cropy//2 \n return image[startx:startx+cropx, starty:starty+cropy]","def centerx(self):\n return self.left + self.width / 2","def center(self):\n return Point(self.width/2, self.height/2)","def pix_center(self):\n return self._pix_center","def calculateCenter(self):\n y_avg = int(sum(self.points[:,0])/float(len(self.points)))\n x_avg = int(sum(self.points[:,1])/float(len(self.points)))\n self.center = (x_avg, y_avg)\n return(x_avg,y_avg)","def center_crop7(x):\n\n return x[:, :, 2:-2, 2:-2].contiguous()","def GetCenter(self):\n ...","def GetCenter(self):\n ...","def GetCenter(self):\n ...","def GetCenter(self):\n ...","def update_center(self): \r\n \r\n self.grfx[0].center = self.center\r\n\r\n self.update_bbox()","def get_offset(tk_window):\n\n width_offset = int(\n (tk_window.winfo_screenwidth() / 2) - (tk_window.winfo_width() / 2)\n )\n\n height_offset = int(\n (tk_window.winfo_screenheight() / 2) - (tk_window.winfo_height() / 2)\n )\n\n return (width_offset, height_offset)","def howell_center(self,postage_stamp):\n xpixels = np.arange(postage_stamp.shape[1])\n ypixels = np.arange(postage_stamp.shape[0])\n I = np.sum(postage_stamp, axis=0)\n J = np.sum(postage_stamp, axis=1)\n\n Isub = I-np.sum(I)/I.size\n Isub[Isub<0] = 0\n Jsub = J-np.sum(J)/J.size\n Jsub[Jsub<0] = 0\n xc = np.sum(Isub*xpixels)/np.sum(Isub)\n yc = np.sum(Jsub*ypixels)/np.sum(Jsub)\n return xc, yc","def center(self):\n self.root.update_idletasks()\n w = self.root.winfo_screenwidth()\n h = self.root.winfo_screenheight()\n size = tuple(int(_) for _ in self.root.geometry().split('+')[0].split('x'))\n x = w/2 - size[0]/2\n y = h/2 - size[1]/2\n self.root.geometry(\"240x80+%d+%d\" % (x, y))","def center_abs(self, source):\n if self.relative:\n return source.center + self.center\n else:\n return self.center","def center_mario(self):\n self.rect.midbottom = self.screen_rect.midbottom\n self.x, self.y = float(self.rect.x), float(self.rect.y)","def center_ava(self):\n\t\tself.rect.midbottom = self.screen_rect.midbottom\n\t\tself.x = float(self.rect.x)","def center(self):\n return self._center","def center(width, height):\n return width/2, height/2","def crop_offset(trimap, crop_size=320):\r\n trimap_ = np.where(trimap == 255, 0, trimap)\r\n y_indices, x_indices = np.where(trimap_ > 0)\r\n num_unknowns = len(y_indices)\r\n y, x = 0, 0\r\n if num_unknowns > 0:\r\n index = np.random.randint(low=0, high=num_unknowns)\r\n xc = x_indices[index]\r\n yc = y_indices[index]\r\n y = max(0, yc - crop_size // 2)\r\n x = max(0, xc - crop_size // 2)\r\n return y, x","def center_window(top):\n screen_width = top.winfo_screenwidth()\n screen_height = top.winfo_screenheight()\n\n width, height, old_x, old_y = get_geometry(top)\n\n new_x = (screen_width - width) // 2\n new_y = (screen_height - height) // 2\n geom = '{}x{}+{}+{}'.format(width, height, new_x, new_y)\n print(\"new geometry:\", geom)\n top.geometry(geom)","def calculate_y_crop_offset(self):\n\n if self.game_name:\n playing_section = PlayingArea[self.game_name]\n else:\n playing_section = 'bottom'\n\n pre_crop_height = int(round(float(IMAGE_HEIGHT) * CROPPED_SIZE / IMAGE_WIDTH))\n shrink_factor = float(CROPPED_SIZE) / IMAGE_WIDTH\n\n if playing_section == 'top':\n return 0\n elif playing_section == 'bottom':\n return pre_crop_height - CROPPED_SIZE\n elif playing_section in ['centre', 'center']:\n return (pre_crop_height - CROPPED_SIZE) // 2\n else:\n # pixel counts\n if playing_section >= 0:\n return int(round(playing_section * shrink_factor))\n else:\n return pre_crop_height + int(round(playing_section * shrink_factor)) - CROPPED_SIZE","def center(self):\n cp = self.dat.flowsheet.getCenter()\n self.centerOn(cp[0], cp[1])","def center(self):\n return self['center']","def center_crop(\n coords: Tensor, crop_x: float = float(\"inf\"), crop_y: float = float(\"inf\"), crop_z: float = float(\"inf\")\n) -> Tensor:\n bounds = torch.tensor([crop_x, crop_y, crop_z], device=coords.device).float().div_(2)\n return torch.le(coords.abs(), bounds).all(dim=-1)","def crop_center(image: Matrix, crop_x: int, crop_y: int) -> Matrix:\n y, x = image.shape\n start_x = x // 2 - (crop_x // 2)\n start_y = y // 2 - (crop_y // 2)\n return image[start_y:start_y + crop_y, start_x:start_x + crop_x].copy()","def centerWindow(self):\n framegeo = self.frameGeometry()\n center = QtGui.QDesktopWidget().availableGeometry().center()\n framegeo.moveCenter(center)\n self.move(framegeo.topLeft())","def crop_center(self,image):\r\n shape = image.shape\r\n new_shape = min(shape[1], shape[2])\r\n offset_y = max(shape[1] - shape[2], 0) // 2\r\n offset_x = max(shape[2] - shape[1], 0) // 2\r\n image = tf.image.crop_to_bounding_box(\r\n image, offset_y, offset_x, new_shape, new_shape)\r\n return image","def crop_center(im, ps):\n if not type(ps) == int:\n raise TypeError('INPUT ps must be a scalar')\n center = [s/2 for s in im.shape[:2]]\n el = [ps / 2, ps / 2] if ps % 2 == 0 else [ps / 2, ps/2 + 1] # edge length\n return(im[center[0] - el[0] : center[0] + el[1], center[1] - el[0] : center[1] + el[1], :])","def crop_to_center(tensor, border=50):\n return tensor[:, :, border:-border, border:-border]","def calculate_fiber_center(self, x, y, crop_size=15):\n self.logger.info(f'Calculating fiber center using ({x}, {y})')\n image = np.copy(self.camera_fiber.temp_image)\n self.fiber_center_position = self.calculate_gaussian_centroid(image, x, y, crop_size)\n return [x,y] #m","def get_view_center(view_dict):\n bounds = view_dict['bounds']\n return (bounds[0][0] + bounds[1][0]) / 2, (bounds[0][1] + bounds[1][1]) / 2","def center(self):\n x0, y0, width, height = self._rect_bbox\n return x0 + width / 2., y0 + height / 2.","def get_center_coordinates(game):\n \n return math.ceil(game.height / 2), math.ceil(game.width / 2)","def find_window_centroids(self, warped):\n window_width = self.window_width\n window_height = self.window_height\n margin = self.margin\n \n window_centroids = [] # store the (left,right) window centroids positions per level\n window = np.ones(window_width) # Create window templates for doing convolutions\n \n # First find the two starting positions right and left lines by using np.sum to get the vertical image slice\n # and then np.convolve the vertical image slice with the window template\n \n # sum the bottom quarter of image to get slice\n l_sum = np.sum(warped[int(3*warped.shape[0]/4):,:int(3*warped.shape[1]/2)], axis=0)\n l_center = np.argmax(np.convolve(window,l_sum)) - window_width/2\n r_sum = np.sum(warped[int(3*warped.shape[0]/4):int(3*warped.shape[1]/2),:], axis=0)\n r_center = np.argmax(np.convolve(window,r_sum)) - window_width/2 + int(warped.shape[1]/2)\n \n # add what we found for the first layer\n window_centroids.append((l_center,r_center))\n \n # Go through each layer looking for max pixel location\n for level in range(1,(int)(warped.shape[0]/window_height)):\n # convolve the window into the vertical slice of the image\n image_layer = np.sum(warped[int(warped.shape[0]-(level+1)*window_height):int(warped.shape[0]-level*window_height),:], axis=0)\n conv_signal = np.convolve(window,image_layer)\n # Find the best left centroid by using past left center as a reference \n # Use window_width/2 as offset because convolution signal reference is at right side of window, not center of window\n offset = window_width/2\n l_min_index = int(max(l_center+offset-margin,0))\n l_max_index = int(max(l_center+offset+margin,warped.shape[1]))\n l_center = np.argmax(conv_signal[l_min_index:l_max_index]) + l_min_index - offset\n \n # Find the best right centroid by using past right center as a reference\n r_min_index = int(max(r_center+offset-margin,0))\n r_max_index = int(max(r_center+offset+margin,warped.shape[1]))\n r_center = np.argmax(conv_signal[r_min_index:r_max_index]) + r_min_index - offset\n # Add what we found for the layer\n window_centroids.append((l_center,r_center))\n \n self.recent_centers.append(window_centroids)\n # return averaged value of of the line centers, helps to keep the markers from jumping around too much\n average_line_centers = np.average(self.recent_centers[-self.smooth_factor:], axis=0)\n return average_line_centers","def calculate_center(self):\n return [(self.startX + self.endX) / 2., (self.startY + self.endY) / 2.]","def centralize(width, height, root_window):\n screen_width, screen_height = root_window.winfo_screenwidth(), root_window.winfo_screenheight()\n posx, posy = screen_width / 2 - width / 2, screen_height / 2 - height / 2\n root_window.geometry(\"%dx%d+%d+%d\" % (width, height, posx, posy))","def center(self) -> Tuple[float, float]:\n return self.x + self.width / 2, self.y + self.height / 2","def _centered_coords(self):\n\n array = self.detection_base.height_model.array\n height, width = self.detection_base.height_model.cell_size_x * array.shape[0], \\\n self.detection_base.height_model.cell_size_y * array.shape[1]\n min_x, min_y = self.detection_base.height_model._bounding_box[0], \\\n self.detection_base.height_model._bounding_box[2]\n\n coords = self.detection_base._coords_array_single\n coords[:, 0], coords[:,1] = coords[:, 0] - (min_x + (width / 2)), coords[:,1] - (min_y + (height / 2))\n return coords","def center(self):\n # get the compute screen's size\n screen = QDesktopWidget().screenGeometry()\n # get the app windows' size\n size = self.geometry()\n self.move(int((screen.width() - size.width()) / 2), int((screen.height() - size.height()) / 2))","def center(self):\n return self.centralizer(self)","def _get_viewport_offset(self):\n viewport_geometry = ManagedWindow.get_viewport_geometry()\n if not viewport_geometry:\n viewport_geometry = WindowGeometry()\n return {'x': viewport_geometry.x, 'y': viewport_geometry.y}","def center(self):\n qr = self.frameGeometry()\n cp = QtWidgets.QDesktopWidget().availableGeometry().center()\n qr.moveCenter(cp)","def crop_center_3d(img, cropx, cropy, cropz):\n z, y, x = img.shape\n startx = x // 2 - (cropx // 2)\n starty = y // 2 - (cropy // 2)\n startz = z // 2 - (cropz // 2)\n return img[\n startz : startz + cropz, starty : starty + cropy, startx : startx + cropx\n ]","def get_center(self):\n return center_points(np.expand_dims(self.best_box, axis=0))[0]","def center(self):\n if self.pos != 0.0:\n self.pos = 0.0","def cropping_center(x, crop_shape, batch=False):\n orig_shape = x.shape\n if not batch:\n h0 = int((orig_shape[0] - crop_shape[0]) * 0.5)\n w0 = int((orig_shape[1] - crop_shape[1]) * 0.5)\n x = x[h0 : h0 + crop_shape[0], w0 : w0 + crop_shape[1]]\n else:\n h0 = int((orig_shape[1] - crop_shape[0]) * 0.5)\n w0 = int((orig_shape[2] - crop_shape[1]) * 0.5)\n x = x[:, h0 : h0 + crop_shape[0], w0 : w0 + crop_shape[1]]\n return x","def center_lines(self):\n window_center = Rectangle.center(self.window_width, self.window_height)\n for line in self.lines.values():\n line.pos = Vector(*window_center)\n line.set_vertices()","def image_center(image_size=None):\n if image_size is not None:\n yc, xc = np.array(image_size)/2\n return int(xc), int(yc)","def center(self, obj):\n return self.phy2abs.center(obj)","def center_on_mouse(w):\n root=w.get_toplevel().get_root_window()\n (screen, x, y, mod) = root.get_display().get_pointer()\n r = screen.get_monitor_geometry(screen.get_monitor_at_point(x, y))\n\n # Let's try to center the window on the mouse as much as possible.\n width, height = w.get_size()\n\n posx = max(r.x, x - width / 2)\n if posx + width > r.x + r.width:\n posx = r.x + r.width - width\n\n posy = max(r.y, y - height / 2)\n if posy + height > r.y + r.height:\n posy = r.y + r.height - height\n\n w.move(posx, posy)","def center_crop(img:np.array,output_size:List[int])->np.array:\n\n if isinstance(output_size,numbers.Number):\n output_size = (int(output_size),int(output_size))\n elif isinstance(output_size,(tuple,list)) and len(output_size)==1:\n output_size =(output_size[0],output_size[0])\n \n image_height,image_width,_=img.shape\n crop_height,crop_width=output_size\n if crop_width > image_width or crop_height > image_height:\n padding_ltrb = [\n (crop_width - image_width) // 2 if crop_width > image_width else 0,\n (crop_height - image_height) // 2 if crop_height > image_height else 0,\n (crop_width - image_width + 1) // 2 if crop_width > image_width else 0,\n (crop_height - image_height + 1) // 2 if crop_height > image_height else 0,\n ]\n img=cv.copyMakeBorder(img,padding_ltrb[1],padding_ltrb[3],padding_ltrb[0],padding_ltrb[2],cv.BORDER_CONSTANT,value=(0,0,0))\n image_height,image_width,_=img.shape\n if crop_width == image_width and crop_height == image_height:\n return img\n\n crop_top = int(round((image_height - crop_height) / 2.))\n crop_left = int(round((image_width - crop_width) / 2.))\n return img[crop_top:crop_top+crop_height,crop_left:crop_left+crop_width]","def calc_crop_coords(img, dim):\n\n width, length = img.size\n left = 0\n right = width\n bottom = length\n top = 0\n if width > dim:\n delta = (width - dim) / 2\n left = delta\n right = width - delta\n if length > dim:\n delta = (length - dim) / 2\n top = delta\n bottom = length - delta\n return (left, top, right, bottom)","def center(win):\n win.update_idletasks()\n width = 1120\n frm_width = win.winfo_rootx() - win.winfo_x()\n win_width = width + 2 * frm_width\n height = 630\n titlebar_height = win.winfo_rooty() - win.winfo_y()\n win_height = height + titlebar_height + frm_width\n x = win.winfo_screenwidth() // 2 - win_width // 2\n y = win.winfo_screenheight() // 2 - win_height // 2\n win.geometry(\"{}x{}+{}+{}\".format(width, height, x, y))\n win.deiconify()","def center_horizontal_paddle(self):\n self.top_center = self.screen_rect.centerx - (self.screen_rect.centerx/2)\n self.bot_center = self.screen_rect.centerx - (self.screen_rect.centerx/2)","def center(self):\r\n qr = self.frameGeometry()\r\n cp = QtWidgets.QDesktopWidget().availableGeometry().center()\r\n qr.moveCenter(cp)\r\n self.move(qr.topLeft())","def centerAxis():\n dislin.center()","def center_ship(self):\r\n self.center = self.screen_rect.centerx","def center_ship(self):\r\n self.center = self.screen_rect.centerx","def centre(self):\n self.top.update_idletasks()\n # The horizontal position is calculated as (screenwidth - window_width)/2\n hpos = int((self.top.winfo_screenwidth() - self.top.winfo_width())/2)\n # And vertical position the same, but with the height dimensions\n vpos = int((self.top.winfo_screenheight() - self.top.winfo_height())/2)\n # And the move call repositions the window\n self.top.geometry('+{x}+{y}'.format(x=hpos, y=vpos))","def get_center(self):\n return self.center","def center_crop(img, output_size):\n if isinstance(output_size, numbers.Number):\n output_size = (int(output_size), int(output_size))\n image_height, image_width = img.shape[:2]\n crop_height, crop_width = output_size\n crop_top = int(round((image_height - crop_height) / 2.))\n crop_left = int(round((image_width - crop_width) / 2.))\n return crop(img, crop_top, crop_left, crop_height, crop_width)","def center(self):\n bounds = self.bounds\n x = (bounds[1] + bounds[0]) / 2\n y = (bounds[3] + bounds[2]) / 2\n z = (bounds[5] + bounds[4]) / 2\n return [x, y, z]","def center(self):\r\n frameGm = self.frameGeometry()\r\n screen = QtGui.QApplication.desktop().screenNumber(QtGui.QApplication.desktop().cursor().pos())\r\n centerPoint = QtGui.QApplication.desktop().screenGeometry(screen).center()\r\n frameGm.moveCenter(centerPoint)\r\n self.move(frameGm.topLeft())","def _pos(self):\n sw = self.parent.winfo_screenwidth()\n sh = self.parent.winfo_screenheight()\n w = sw * 0.8\n h = sh * 0.8\n x = (sw - w) / 2\n y = (sh - h) / 2\n self.parent.geometry('%dx%d+%d+%d' % (w, h, x, y))","def center_crop(data, shape):\n assert 0 < shape[0] <= data.shape[-2]\n assert 0 < shape[1] <= data.shape[-1]\n w_from = (data.shape[-2] - shape[0]) // 2\n h_from = (data.shape[-1] - shape[1]) // 2\n w_to = w_from + shape[0]\n h_to = h_from + shape[1]\n return data[..., w_from:w_to, h_from:h_to]","def center_crop(data, shape):\n assert 0 < shape[0] <= data.shape[-2]\n assert 0 < shape[1] <= data.shape[-1]\n w_from = (data.shape[-2] - shape[0]) // 2\n h_from = (data.shape[-1] - shape[1]) // 2\n w_to = w_from + shape[0]\n h_to = h_from + shape[1]\n return data[..., w_from:w_to, h_from:h_to]","def get_rect_center(rect):\n x, y, w, h = rect\n return x + w/2, y + h/2","def _center_image(image):\n\n dim_x, dim_y = image.shape[0:2]\n\n x = int(dim_x/2)\n off_x = int(dim_x/4)\n xmin = x - off_x\n xmax = x + off_x\n\n y = int(dim_y/2)\n off_y = int(dim_y/4)\n ymin = y - off_y\n ymax = y + off_y\n\n return(image[xmin:xmax, ymin:ymax])","def circle_center(self):\n return self.container.width / 2, self.container.height / 2","def process_laneOffset(self):\n center_line = np.poly1d(np.mean([self.line_l.get_LinePoly().coeffs, self.line_r.get_LinePoly().coeffs], axis=0))\n # store the center line polynomial\n self.center_poly = center_line\n center_point = IMAGE_WIDTH/2 - center_line(709)\n offset_from_center =center_point* self.line_l.x_pxm\n self.lane_offset = offset_from_center\n return center_point","def _center(pos: ArrayLike, shift: ArrayLike) -> Tuple[float, float]:\n x = np.concatenate((pos[0], pos[0] + shift[0]))\n y = np.concatenate((pos[1], pos[1] + shift[1]))\n return (x.max() + x.min()) / 2, (y.max() + y.min()) / 2","def _get_pose_center(self, landmarks):\n left_hip = landmarks[self._landmark_names.index('left_hip')]\n right_hip = landmarks[self._landmark_names.index('right_hip')]\n center = (left_hip + right_hip) * 0.5\n return center","def getCenter(self):\r\n pixels = np.argwhere(self.array)\r\n center = np.mean(pixels.astype(np.float32), axis=0)\r\n return np.round(center).astype(np.int)","def getcenter(self):\n return self.centro.cartesianas()","def rectCenter(rect):\n return np.array([rect[:2].mean(), rect[2:].mean()])","def center(self):\n return self.map_.geom.center_skydir","def central_crop(image, x_crop=27, y_crop=45, z_crop=27):\n image = image[x_crop:-x_crop, y_crop:-y_crop, z_crop:-z_crop, :]\n\n return image","def centerWindows(root, dim=None, refWindows=None):\n root.update_idletasks()\n if dim is None:\n gw, gh, gx, gy = getGeometry(root)\n else:\n gw, gh = dim\n if refWindows:\n rw, rh, rx, ry = getGeometry(refWindows)\n x = rx + (rw - gw) / 2\n y = ry + (rh - gh) / 2 \n else:\n w = root.winfo_screenwidth()\n h = root.winfo_screenheight()\n x = (w - gw) / 2\n y = (h - gh) / 2\n \n root.geometry(\"%dx%d+%d+%d\" % (gw, gh, x, y))","def get_min_mag_center(self):\r\n\t\treturn self.min_mag + self.bin_width / 2"],"string":"[\n \"def center(self):\\n # minz to offset the heights to 0\\n mz = (self.maxz-self.minz)/2\\n #mz = self.minz\\n return (self.minx + self.width / 2, self.miny + self.height / 2, mz)\",\n \"def calculate_window_position(self):\\n self.x = SQUARE_SIZE * self.col + SQUARE_SIZE // 2\\n self.y = SQUARE_SIZE * self.row + SQUARE_SIZE // 2\",\n \"def center(self):\\n return self._lower + 0.5 * (self._upper - self._lower)\",\n \"def center(self):\\n return np.array([0,0,1/self.C+self.pos()])\",\n \"def _center(pos, shift):\\n x = np.concatenate((pos[0], pos[0] + shift[0]))\\n y = np.concatenate((pos[1], pos[1] + shift[1]))\\n return (x.max() + x.min()) / 2, (y.max() + y.min()) / 2\",\n \"def center(window):\\n window.update_idletasks()\\n\\n # Find the screen resolution\\n screen_width = window.winfo_screenwidth()\\n screen_height = window.winfo_screenheight()\\n\\n # Find new (x, y) coordinates\\n size = tuple(int(_) for _ in window.geometry().split('+')[0].split('x'))\\n x = screen_width/2 - 7 * size[0] / 13\\n y = screen_height/2 - 6 * size[1] / 11\\n\\n # Apply new coordinates\\n window.geometry(\\\"+%d+%d\\\" % (x, y))\",\n \"def get_center_scr(self):\\r\\n return self.rect.center\",\n \"def center(self):\\r\\n self.centerx = self.screen_rect.centerx \\r\\n self.centery = self.screen_rect.centery\",\n \"def center(self):\\n xc = (self.x.max() + self.x.min())/2.\\n yc = (self.y.max() + self.y.min())/2.\\n return (xc, yc)\",\n \"def center(self):\\n return (self.upper_right + self.lower_left) * 0.5\",\n \"def center(self):\\n return self.pos + self.axis / 2.0\",\n \"def _get_frame_center(self, src):\\n w, h = get_size(src)\\n x = w / 2\\n y = h / 2\\n\\n return x, y\",\n \"def center(self):\\n\\n return (\\n self.x() + (self.width() / 2),\\n self.y() + (self.height() / 2)\\n )\",\n \"def find_center(self):\\n return(Point(self.corner.x + self.width/2.0, self.corner.y + self.height/2.0))\",\n \"def center(self):\\n return (self.centerx, self.centery)\",\n \"def center(self):\\n return self.center_x, self.center_y\",\n \"def _set_center(self):\\n sl_center = np.array(\\n [self.sl_list[k].mean_pos for k in range(self.nb_sl)]\\n )\\n sl_nb_pts = np.array(\\n [self.sl_list[k].nb_points for k in range(self.nb_sl)]\\n )\\n sl_wcenter = [sl_center[k] * sl_nb_pts[k] for k in range(self.nb_sl)]\\n self.center = np.sum(sl_wcenter, axis=0) / np.sum(sl_nb_pts)\",\n \"def center(self):\\n return np.sum(self.bbox, 0) / 2\",\n \"def getCenter(self):\\n return [self.tx/self.tw, self.ty/self.tw]\",\n \"def center_window(self):\\n\\n\\t\\tframe_geo = self.frameGeometry()\\n\\t\\tcursor_pos = QtWidgets.QApplication.desktop().cursor().pos()\\n\\t\\tscreen = QtWidgets.QApplication.desktop().screenNumber(cursor_pos)\\n\\t\\tcenter_point = QtWidgets.QApplication.desktop().screenGeometry(screen).center()\\n\\t\\tframe_geo.moveCenter(center_point)\\n\\t\\tself.move(frame_geo.topLeft())\",\n \"def crop_center(image, cropx, cropy):\\n x,y = image.shape\\n startx = x//2 - cropx//2\\n starty = y//2 - cropy//2 \\n return image[startx:startx+cropx, starty:starty+cropy]\",\n \"def centerx(self):\\n return self.left + self.width / 2\",\n \"def center(self):\\n return Point(self.width/2, self.height/2)\",\n \"def pix_center(self):\\n return self._pix_center\",\n \"def calculateCenter(self):\\n y_avg = int(sum(self.points[:,0])/float(len(self.points)))\\n x_avg = int(sum(self.points[:,1])/float(len(self.points)))\\n self.center = (x_avg, y_avg)\\n return(x_avg,y_avg)\",\n \"def center_crop7(x):\\n\\n return x[:, :, 2:-2, 2:-2].contiguous()\",\n \"def GetCenter(self):\\n ...\",\n \"def GetCenter(self):\\n ...\",\n \"def GetCenter(self):\\n ...\",\n \"def GetCenter(self):\\n ...\",\n \"def update_center(self): \\r\\n \\r\\n self.grfx[0].center = self.center\\r\\n\\r\\n self.update_bbox()\",\n \"def get_offset(tk_window):\\n\\n width_offset = int(\\n (tk_window.winfo_screenwidth() / 2) - (tk_window.winfo_width() / 2)\\n )\\n\\n height_offset = int(\\n (tk_window.winfo_screenheight() / 2) - (tk_window.winfo_height() / 2)\\n )\\n\\n return (width_offset, height_offset)\",\n \"def howell_center(self,postage_stamp):\\n xpixels = np.arange(postage_stamp.shape[1])\\n ypixels = np.arange(postage_stamp.shape[0])\\n I = np.sum(postage_stamp, axis=0)\\n J = np.sum(postage_stamp, axis=1)\\n\\n Isub = I-np.sum(I)/I.size\\n Isub[Isub<0] = 0\\n Jsub = J-np.sum(J)/J.size\\n Jsub[Jsub<0] = 0\\n xc = np.sum(Isub*xpixels)/np.sum(Isub)\\n yc = np.sum(Jsub*ypixels)/np.sum(Jsub)\\n return xc, yc\",\n \"def center(self):\\n self.root.update_idletasks()\\n w = self.root.winfo_screenwidth()\\n h = self.root.winfo_screenheight()\\n size = tuple(int(_) for _ in self.root.geometry().split('+')[0].split('x'))\\n x = w/2 - size[0]/2\\n y = h/2 - size[1]/2\\n self.root.geometry(\\\"240x80+%d+%d\\\" % (x, y))\",\n \"def center_abs(self, source):\\n if self.relative:\\n return source.center + self.center\\n else:\\n return self.center\",\n \"def center_mario(self):\\n self.rect.midbottom = self.screen_rect.midbottom\\n self.x, self.y = float(self.rect.x), float(self.rect.y)\",\n \"def center_ava(self):\\n\\t\\tself.rect.midbottom = self.screen_rect.midbottom\\n\\t\\tself.x = float(self.rect.x)\",\n \"def center(self):\\n return self._center\",\n \"def center(width, height):\\n return width/2, height/2\",\n \"def crop_offset(trimap, crop_size=320):\\r\\n trimap_ = np.where(trimap == 255, 0, trimap)\\r\\n y_indices, x_indices = np.where(trimap_ > 0)\\r\\n num_unknowns = len(y_indices)\\r\\n y, x = 0, 0\\r\\n if num_unknowns > 0:\\r\\n index = np.random.randint(low=0, high=num_unknowns)\\r\\n xc = x_indices[index]\\r\\n yc = y_indices[index]\\r\\n y = max(0, yc - crop_size // 2)\\r\\n x = max(0, xc - crop_size // 2)\\r\\n return y, x\",\n \"def center_window(top):\\n screen_width = top.winfo_screenwidth()\\n screen_height = top.winfo_screenheight()\\n\\n width, height, old_x, old_y = get_geometry(top)\\n\\n new_x = (screen_width - width) // 2\\n new_y = (screen_height - height) // 2\\n geom = '{}x{}+{}+{}'.format(width, height, new_x, new_y)\\n print(\\\"new geometry:\\\", geom)\\n top.geometry(geom)\",\n \"def calculate_y_crop_offset(self):\\n\\n if self.game_name:\\n playing_section = PlayingArea[self.game_name]\\n else:\\n playing_section = 'bottom'\\n\\n pre_crop_height = int(round(float(IMAGE_HEIGHT) * CROPPED_SIZE / IMAGE_WIDTH))\\n shrink_factor = float(CROPPED_SIZE) / IMAGE_WIDTH\\n\\n if playing_section == 'top':\\n return 0\\n elif playing_section == 'bottom':\\n return pre_crop_height - CROPPED_SIZE\\n elif playing_section in ['centre', 'center']:\\n return (pre_crop_height - CROPPED_SIZE) // 2\\n else:\\n # pixel counts\\n if playing_section >= 0:\\n return int(round(playing_section * shrink_factor))\\n else:\\n return pre_crop_height + int(round(playing_section * shrink_factor)) - CROPPED_SIZE\",\n \"def center(self):\\n cp = self.dat.flowsheet.getCenter()\\n self.centerOn(cp[0], cp[1])\",\n \"def center(self):\\n return self['center']\",\n \"def center_crop(\\n coords: Tensor, crop_x: float = float(\\\"inf\\\"), crop_y: float = float(\\\"inf\\\"), crop_z: float = float(\\\"inf\\\")\\n) -> Tensor:\\n bounds = torch.tensor([crop_x, crop_y, crop_z], device=coords.device).float().div_(2)\\n return torch.le(coords.abs(), bounds).all(dim=-1)\",\n \"def crop_center(image: Matrix, crop_x: int, crop_y: int) -> Matrix:\\n y, x = image.shape\\n start_x = x // 2 - (crop_x // 2)\\n start_y = y // 2 - (crop_y // 2)\\n return image[start_y:start_y + crop_y, start_x:start_x + crop_x].copy()\",\n \"def centerWindow(self):\\n framegeo = self.frameGeometry()\\n center = QtGui.QDesktopWidget().availableGeometry().center()\\n framegeo.moveCenter(center)\\n self.move(framegeo.topLeft())\",\n \"def crop_center(self,image):\\r\\n shape = image.shape\\r\\n new_shape = min(shape[1], shape[2])\\r\\n offset_y = max(shape[1] - shape[2], 0) // 2\\r\\n offset_x = max(shape[2] - shape[1], 0) // 2\\r\\n image = tf.image.crop_to_bounding_box(\\r\\n image, offset_y, offset_x, new_shape, new_shape)\\r\\n return image\",\n \"def crop_center(im, ps):\\n if not type(ps) == int:\\n raise TypeError('INPUT ps must be a scalar')\\n center = [s/2 for s in im.shape[:2]]\\n el = [ps / 2, ps / 2] if ps % 2 == 0 else [ps / 2, ps/2 + 1] # edge length\\n return(im[center[0] - el[0] : center[0] + el[1], center[1] - el[0] : center[1] + el[1], :])\",\n \"def crop_to_center(tensor, border=50):\\n return tensor[:, :, border:-border, border:-border]\",\n \"def calculate_fiber_center(self, x, y, crop_size=15):\\n self.logger.info(f'Calculating fiber center using ({x}, {y})')\\n image = np.copy(self.camera_fiber.temp_image)\\n self.fiber_center_position = self.calculate_gaussian_centroid(image, x, y, crop_size)\\n return [x,y] #m\",\n \"def get_view_center(view_dict):\\n bounds = view_dict['bounds']\\n return (bounds[0][0] + bounds[1][0]) / 2, (bounds[0][1] + bounds[1][1]) / 2\",\n \"def center(self):\\n x0, y0, width, height = self._rect_bbox\\n return x0 + width / 2., y0 + height / 2.\",\n \"def get_center_coordinates(game):\\n \\n return math.ceil(game.height / 2), math.ceil(game.width / 2)\",\n \"def find_window_centroids(self, warped):\\n window_width = self.window_width\\n window_height = self.window_height\\n margin = self.margin\\n \\n window_centroids = [] # store the (left,right) window centroids positions per level\\n window = np.ones(window_width) # Create window templates for doing convolutions\\n \\n # First find the two starting positions right and left lines by using np.sum to get the vertical image slice\\n # and then np.convolve the vertical image slice with the window template\\n \\n # sum the bottom quarter of image to get slice\\n l_sum = np.sum(warped[int(3*warped.shape[0]/4):,:int(3*warped.shape[1]/2)], axis=0)\\n l_center = np.argmax(np.convolve(window,l_sum)) - window_width/2\\n r_sum = np.sum(warped[int(3*warped.shape[0]/4):int(3*warped.shape[1]/2),:], axis=0)\\n r_center = np.argmax(np.convolve(window,r_sum)) - window_width/2 + int(warped.shape[1]/2)\\n \\n # add what we found for the first layer\\n window_centroids.append((l_center,r_center))\\n \\n # Go through each layer looking for max pixel location\\n for level in range(1,(int)(warped.shape[0]/window_height)):\\n # convolve the window into the vertical slice of the image\\n image_layer = np.sum(warped[int(warped.shape[0]-(level+1)*window_height):int(warped.shape[0]-level*window_height),:], axis=0)\\n conv_signal = np.convolve(window,image_layer)\\n # Find the best left centroid by using past left center as a reference \\n # Use window_width/2 as offset because convolution signal reference is at right side of window, not center of window\\n offset = window_width/2\\n l_min_index = int(max(l_center+offset-margin,0))\\n l_max_index = int(max(l_center+offset+margin,warped.shape[1]))\\n l_center = np.argmax(conv_signal[l_min_index:l_max_index]) + l_min_index - offset\\n \\n # Find the best right centroid by using past right center as a reference\\n r_min_index = int(max(r_center+offset-margin,0))\\n r_max_index = int(max(r_center+offset+margin,warped.shape[1]))\\n r_center = np.argmax(conv_signal[r_min_index:r_max_index]) + r_min_index - offset\\n # Add what we found for the layer\\n window_centroids.append((l_center,r_center))\\n \\n self.recent_centers.append(window_centroids)\\n # return averaged value of of the line centers, helps to keep the markers from jumping around too much\\n average_line_centers = np.average(self.recent_centers[-self.smooth_factor:], axis=0)\\n return average_line_centers\",\n \"def calculate_center(self):\\n return [(self.startX + self.endX) / 2., (self.startY + self.endY) / 2.]\",\n \"def centralize(width, height, root_window):\\n screen_width, screen_height = root_window.winfo_screenwidth(), root_window.winfo_screenheight()\\n posx, posy = screen_width / 2 - width / 2, screen_height / 2 - height / 2\\n root_window.geometry(\\\"%dx%d+%d+%d\\\" % (width, height, posx, posy))\",\n \"def center(self) -> Tuple[float, float]:\\n return self.x + self.width / 2, self.y + self.height / 2\",\n \"def _centered_coords(self):\\n\\n array = self.detection_base.height_model.array\\n height, width = self.detection_base.height_model.cell_size_x * array.shape[0], \\\\\\n self.detection_base.height_model.cell_size_y * array.shape[1]\\n min_x, min_y = self.detection_base.height_model._bounding_box[0], \\\\\\n self.detection_base.height_model._bounding_box[2]\\n\\n coords = self.detection_base._coords_array_single\\n coords[:, 0], coords[:,1] = coords[:, 0] - (min_x + (width / 2)), coords[:,1] - (min_y + (height / 2))\\n return coords\",\n \"def center(self):\\n # get the compute screen's size\\n screen = QDesktopWidget().screenGeometry()\\n # get the app windows' size\\n size = self.geometry()\\n self.move(int((screen.width() - size.width()) / 2), int((screen.height() - size.height()) / 2))\",\n \"def center(self):\\n return self.centralizer(self)\",\n \"def _get_viewport_offset(self):\\n viewport_geometry = ManagedWindow.get_viewport_geometry()\\n if not viewport_geometry:\\n viewport_geometry = WindowGeometry()\\n return {'x': viewport_geometry.x, 'y': viewport_geometry.y}\",\n \"def center(self):\\n qr = self.frameGeometry()\\n cp = QtWidgets.QDesktopWidget().availableGeometry().center()\\n qr.moveCenter(cp)\",\n \"def crop_center_3d(img, cropx, cropy, cropz):\\n z, y, x = img.shape\\n startx = x // 2 - (cropx // 2)\\n starty = y // 2 - (cropy // 2)\\n startz = z // 2 - (cropz // 2)\\n return img[\\n startz : startz + cropz, starty : starty + cropy, startx : startx + cropx\\n ]\",\n \"def get_center(self):\\n return center_points(np.expand_dims(self.best_box, axis=0))[0]\",\n \"def center(self):\\n if self.pos != 0.0:\\n self.pos = 0.0\",\n \"def cropping_center(x, crop_shape, batch=False):\\n orig_shape = x.shape\\n if not batch:\\n h0 = int((orig_shape[0] - crop_shape[0]) * 0.5)\\n w0 = int((orig_shape[1] - crop_shape[1]) * 0.5)\\n x = x[h0 : h0 + crop_shape[0], w0 : w0 + crop_shape[1]]\\n else:\\n h0 = int((orig_shape[1] - crop_shape[0]) * 0.5)\\n w0 = int((orig_shape[2] - crop_shape[1]) * 0.5)\\n x = x[:, h0 : h0 + crop_shape[0], w0 : w0 + crop_shape[1]]\\n return x\",\n \"def center_lines(self):\\n window_center = Rectangle.center(self.window_width, self.window_height)\\n for line in self.lines.values():\\n line.pos = Vector(*window_center)\\n line.set_vertices()\",\n \"def image_center(image_size=None):\\n if image_size is not None:\\n yc, xc = np.array(image_size)/2\\n return int(xc), int(yc)\",\n \"def center(self, obj):\\n return self.phy2abs.center(obj)\",\n \"def center_on_mouse(w):\\n root=w.get_toplevel().get_root_window()\\n (screen, x, y, mod) = root.get_display().get_pointer()\\n r = screen.get_monitor_geometry(screen.get_monitor_at_point(x, y))\\n\\n # Let's try to center the window on the mouse as much as possible.\\n width, height = w.get_size()\\n\\n posx = max(r.x, x - width / 2)\\n if posx + width > r.x + r.width:\\n posx = r.x + r.width - width\\n\\n posy = max(r.y, y - height / 2)\\n if posy + height > r.y + r.height:\\n posy = r.y + r.height - height\\n\\n w.move(posx, posy)\",\n \"def center_crop(img:np.array,output_size:List[int])->np.array:\\n\\n if isinstance(output_size,numbers.Number):\\n output_size = (int(output_size),int(output_size))\\n elif isinstance(output_size,(tuple,list)) and len(output_size)==1:\\n output_size =(output_size[0],output_size[0])\\n \\n image_height,image_width,_=img.shape\\n crop_height,crop_width=output_size\\n if crop_width > image_width or crop_height > image_height:\\n padding_ltrb = [\\n (crop_width - image_width) // 2 if crop_width > image_width else 0,\\n (crop_height - image_height) // 2 if crop_height > image_height else 0,\\n (crop_width - image_width + 1) // 2 if crop_width > image_width else 0,\\n (crop_height - image_height + 1) // 2 if crop_height > image_height else 0,\\n ]\\n img=cv.copyMakeBorder(img,padding_ltrb[1],padding_ltrb[3],padding_ltrb[0],padding_ltrb[2],cv.BORDER_CONSTANT,value=(0,0,0))\\n image_height,image_width,_=img.shape\\n if crop_width == image_width and crop_height == image_height:\\n return img\\n\\n crop_top = int(round((image_height - crop_height) / 2.))\\n crop_left = int(round((image_width - crop_width) / 2.))\\n return img[crop_top:crop_top+crop_height,crop_left:crop_left+crop_width]\",\n \"def calc_crop_coords(img, dim):\\n\\n width, length = img.size\\n left = 0\\n right = width\\n bottom = length\\n top = 0\\n if width > dim:\\n delta = (width - dim) / 2\\n left = delta\\n right = width - delta\\n if length > dim:\\n delta = (length - dim) / 2\\n top = delta\\n bottom = length - delta\\n return (left, top, right, bottom)\",\n \"def center(win):\\n win.update_idletasks()\\n width = 1120\\n frm_width = win.winfo_rootx() - win.winfo_x()\\n win_width = width + 2 * frm_width\\n height = 630\\n titlebar_height = win.winfo_rooty() - win.winfo_y()\\n win_height = height + titlebar_height + frm_width\\n x = win.winfo_screenwidth() // 2 - win_width // 2\\n y = win.winfo_screenheight() // 2 - win_height // 2\\n win.geometry(\\\"{}x{}+{}+{}\\\".format(width, height, x, y))\\n win.deiconify()\",\n \"def center_horizontal_paddle(self):\\n self.top_center = self.screen_rect.centerx - (self.screen_rect.centerx/2)\\n self.bot_center = self.screen_rect.centerx - (self.screen_rect.centerx/2)\",\n \"def center(self):\\r\\n qr = self.frameGeometry()\\r\\n cp = QtWidgets.QDesktopWidget().availableGeometry().center()\\r\\n qr.moveCenter(cp)\\r\\n self.move(qr.topLeft())\",\n \"def centerAxis():\\n dislin.center()\",\n \"def center_ship(self):\\r\\n self.center = self.screen_rect.centerx\",\n \"def center_ship(self):\\r\\n self.center = self.screen_rect.centerx\",\n \"def centre(self):\\n self.top.update_idletasks()\\n # The horizontal position is calculated as (screenwidth - window_width)/2\\n hpos = int((self.top.winfo_screenwidth() - self.top.winfo_width())/2)\\n # And vertical position the same, but with the height dimensions\\n vpos = int((self.top.winfo_screenheight() - self.top.winfo_height())/2)\\n # And the move call repositions the window\\n self.top.geometry('+{x}+{y}'.format(x=hpos, y=vpos))\",\n \"def get_center(self):\\n return self.center\",\n \"def center_crop(img, output_size):\\n if isinstance(output_size, numbers.Number):\\n output_size = (int(output_size), int(output_size))\\n image_height, image_width = img.shape[:2]\\n crop_height, crop_width = output_size\\n crop_top = int(round((image_height - crop_height) / 2.))\\n crop_left = int(round((image_width - crop_width) / 2.))\\n return crop(img, crop_top, crop_left, crop_height, crop_width)\",\n \"def center(self):\\n bounds = self.bounds\\n x = (bounds[1] + bounds[0]) / 2\\n y = (bounds[3] + bounds[2]) / 2\\n z = (bounds[5] + bounds[4]) / 2\\n return [x, y, z]\",\n \"def center(self):\\r\\n frameGm = self.frameGeometry()\\r\\n screen = QtGui.QApplication.desktop().screenNumber(QtGui.QApplication.desktop().cursor().pos())\\r\\n centerPoint = QtGui.QApplication.desktop().screenGeometry(screen).center()\\r\\n frameGm.moveCenter(centerPoint)\\r\\n self.move(frameGm.topLeft())\",\n \"def _pos(self):\\n sw = self.parent.winfo_screenwidth()\\n sh = self.parent.winfo_screenheight()\\n w = sw * 0.8\\n h = sh * 0.8\\n x = (sw - w) / 2\\n y = (sh - h) / 2\\n self.parent.geometry('%dx%d+%d+%d' % (w, h, x, y))\",\n \"def center_crop(data, shape):\\n assert 0 < shape[0] <= data.shape[-2]\\n assert 0 < shape[1] <= data.shape[-1]\\n w_from = (data.shape[-2] - shape[0]) // 2\\n h_from = (data.shape[-1] - shape[1]) // 2\\n w_to = w_from + shape[0]\\n h_to = h_from + shape[1]\\n return data[..., w_from:w_to, h_from:h_to]\",\n \"def center_crop(data, shape):\\n assert 0 < shape[0] <= data.shape[-2]\\n assert 0 < shape[1] <= data.shape[-1]\\n w_from = (data.shape[-2] - shape[0]) // 2\\n h_from = (data.shape[-1] - shape[1]) // 2\\n w_to = w_from + shape[0]\\n h_to = h_from + shape[1]\\n return data[..., w_from:w_to, h_from:h_to]\",\n \"def get_rect_center(rect):\\n x, y, w, h = rect\\n return x + w/2, y + h/2\",\n \"def _center_image(image):\\n\\n dim_x, dim_y = image.shape[0:2]\\n\\n x = int(dim_x/2)\\n off_x = int(dim_x/4)\\n xmin = x - off_x\\n xmax = x + off_x\\n\\n y = int(dim_y/2)\\n off_y = int(dim_y/4)\\n ymin = y - off_y\\n ymax = y + off_y\\n\\n return(image[xmin:xmax, ymin:ymax])\",\n \"def circle_center(self):\\n return self.container.width / 2, self.container.height / 2\",\n \"def process_laneOffset(self):\\n center_line = np.poly1d(np.mean([self.line_l.get_LinePoly().coeffs, self.line_r.get_LinePoly().coeffs], axis=0))\\n # store the center line polynomial\\n self.center_poly = center_line\\n center_point = IMAGE_WIDTH/2 - center_line(709)\\n offset_from_center =center_point* self.line_l.x_pxm\\n self.lane_offset = offset_from_center\\n return center_point\",\n \"def _center(pos: ArrayLike, shift: ArrayLike) -> Tuple[float, float]:\\n x = np.concatenate((pos[0], pos[0] + shift[0]))\\n y = np.concatenate((pos[1], pos[1] + shift[1]))\\n return (x.max() + x.min()) / 2, (y.max() + y.min()) / 2\",\n \"def _get_pose_center(self, landmarks):\\n left_hip = landmarks[self._landmark_names.index('left_hip')]\\n right_hip = landmarks[self._landmark_names.index('right_hip')]\\n center = (left_hip + right_hip) * 0.5\\n return center\",\n \"def getCenter(self):\\r\\n pixels = np.argwhere(self.array)\\r\\n center = np.mean(pixels.astype(np.float32), axis=0)\\r\\n return np.round(center).astype(np.int)\",\n \"def getcenter(self):\\n return self.centro.cartesianas()\",\n \"def rectCenter(rect):\\n return np.array([rect[:2].mean(), rect[2:].mean()])\",\n \"def center(self):\\n return self.map_.geom.center_skydir\",\n \"def central_crop(image, x_crop=27, y_crop=45, z_crop=27):\\n image = image[x_crop:-x_crop, y_crop:-y_crop, z_crop:-z_crop, :]\\n\\n return image\",\n \"def centerWindows(root, dim=None, refWindows=None):\\n root.update_idletasks()\\n if dim is None:\\n gw, gh, gx, gy = getGeometry(root)\\n else:\\n gw, gh = dim\\n if refWindows:\\n rw, rh, rx, ry = getGeometry(refWindows)\\n x = rx + (rw - gw) / 2\\n y = ry + (rh - gh) / 2 \\n else:\\n w = root.winfo_screenwidth()\\n h = root.winfo_screenheight()\\n x = (w - gw) / 2\\n y = (h - gh) / 2\\n \\n root.geometry(\\\"%dx%d+%d+%d\\\" % (gw, gh, x, y))\",\n \"def get_min_mag_center(self):\\r\\n\\t\\treturn self.min_mag + self.bin_width / 2\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6784088","0.6756137","0.6714128","0.6631258","0.6611774","0.6609801","0.6596732","0.6587204","0.6557443","0.6556682","0.6531438","0.6452741","0.64465266","0.64361143","0.6413167","0.6385451","0.63821167","0.6377177","0.6372448","0.6344084","0.6343337","0.6338306","0.63325024","0.632003","0.6311389","0.62785614","0.6277664","0.6277664","0.6277664","0.6277664","0.6256087","0.62515575","0.6235913","0.6228173","0.62216985","0.62171435","0.62044644","0.6179773","0.61598015","0.6153989","0.61482835","0.6143304","0.6139737","0.61389387","0.61232674","0.6113202","0.611199","0.6097385","0.60795707","0.6079018","0.6074437","0.6070473","0.6062821","0.606078","0.6050467","0.60501385","0.60361457","0.60252","0.6017113","0.599918","0.59923416","0.59913874","0.5984927","0.59839743","0.59836084","0.59781986","0.5975439","0.596107","0.5956893","0.594735","0.5945603","0.5940593","0.59388226","0.5927248","0.59272105","0.592629","0.5921581","0.59096766","0.59096766","0.59095263","0.59059215","0.5905385","0.59038657","0.5901855","0.5901414","0.5900321","0.5900321","0.5883905","0.5883345","0.5878035","0.5876942","0.5868442","0.5856775","0.5856651","0.5852431","0.58522165","0.5850736","0.5845626","0.5841439","0.5838015"],"string":"[\n \"0.6784088\",\n \"0.6756137\",\n \"0.6714128\",\n \"0.6631258\",\n \"0.6611774\",\n \"0.6609801\",\n \"0.6596732\",\n \"0.6587204\",\n \"0.6557443\",\n \"0.6556682\",\n \"0.6531438\",\n \"0.6452741\",\n \"0.64465266\",\n \"0.64361143\",\n \"0.6413167\",\n \"0.6385451\",\n \"0.63821167\",\n \"0.6377177\",\n \"0.6372448\",\n \"0.6344084\",\n \"0.6343337\",\n \"0.6338306\",\n \"0.63325024\",\n \"0.632003\",\n \"0.6311389\",\n \"0.62785614\",\n \"0.6277664\",\n \"0.6277664\",\n \"0.6277664\",\n \"0.6277664\",\n \"0.6256087\",\n \"0.62515575\",\n \"0.6235913\",\n \"0.6228173\",\n \"0.62216985\",\n \"0.62171435\",\n \"0.62044644\",\n \"0.6179773\",\n \"0.61598015\",\n \"0.6153989\",\n \"0.61482835\",\n \"0.6143304\",\n \"0.6139737\",\n \"0.61389387\",\n \"0.61232674\",\n \"0.6113202\",\n \"0.611199\",\n \"0.6097385\",\n \"0.60795707\",\n \"0.6079018\",\n \"0.6074437\",\n \"0.6070473\",\n \"0.6062821\",\n \"0.606078\",\n \"0.6050467\",\n \"0.60501385\",\n \"0.60361457\",\n \"0.60252\",\n 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integration."},"document":{"kind":"string","value":"def __init__(\n self,\n model_filepath: str,\n user_validation: bool = True,\n ):\n\n # ensure model filepath is balid, and save as att if it is\n assert model_filepath.endswith(\".fmu\"), \"Provided filepath is not an FMU file: '{}'\".format(model_filepath)\n self.model_filepath = model_filepath\n # config file with config_params, inputs, outputs\n self.sim_config_filepath = SIM_CONFIG_NAME_f(self.model_filepath)\n\n # read the model description\n self.model_description = read_model_description(model_filepath)\n error_log = \"Provided model ({}) doesn't have modelVariables in XLS description file\".format(model_filepath)\n assert len(self.model_description.modelVariables) > 0, error_log\n\n # correct non-alphanumeric tags.\n # note, it doesn't suppose any problem, since interaction with sim uses indices, not names.\n self._clean_non_alphanumeric_chars()\n\n\n # collect the value references (indices)\n # collect the value types (Real, Integer or Enumeration)\n # collect the variables to be initialized and the value to do so at\n self.vars_to_idx = {}\n self.vars_to_type_f = {}\n self.vars_to_ini_vals = {}\n for variable in self.model_description.modelVariables:\n # extract key attributes per variable\n var_idx = variable.valueReference #, variable.causality\n var_name = variable.name\n var_type = variable.type\n var_start = variable.start\n \n # collect type reference\n if var_type == \"Real\":\n self.vars_to_type_f[var_name] = float\n elif var_type == \"Integer\":\n self.vars_to_type_f[var_name] = int\n else:\n # [TODO] Integrate variables of type \"Enumeration\". How do we cast? Define a function for \"self.vars_to_type_f\".\n # [TODO] Integrate variables of type string (need to find correct var_type tag first).\n # [TODO] Integrate variables of type boolean (need to find correct var_type tag first).\n print(f\"Variable '{var_name}' will be skipped. FMU connector cannot currently handle vars of type '{var_type}'.\")\n continue\n \n # collect the value references (indices)\n self.vars_to_idx[var_name] = var_idx\n\n # collect the variables to be initialized and the value to do so at\n if var_start is not None:\n # cast variable prior to storing\n self.vars_to_ini_vals[var_name] = self.vars_to_type_f[var_name](var_start)\n \n\n # initialize sim config\n self.is_model_config_valid = False # Currently unused, since error is raised if model invalid\n self.sim_config_params = []\n self.sim_inputs = []\n self.sim_outputs = []\n self.sim_other_vars = []\n\n # ---------------------------------------------------------------------\n # YAML CONFIG --> check for existing config using SIM_CONFIG_NAME_f --> e.g: \"{model_name}_conf.yaml\"\n valid_config = self._validate_sim_config()\n \n # exit if model is valid, unless validation has been activated\n if valid_config:\n\n # print model config for user reference: config_params, inputs, outputs\n print(self._get_sim_config_str())\n\n if user_validation:\n # prompt user to manually validate model if selected\n validation_asserted = input(\"Is this configuration correct (y|n)? \")\n\n if validation_asserted == \"y\":\n self.is_model_config_valid = True\n return\n \n # reset config if invalid\n self.sim_config_params = []\n self.sim_inputs = []\n self.sim_outputs = []\n self.sim_other_vars = []\n \n else:\n # when no validation is selected, we assume the sim config is valid\n self.is_model_config_valid = True\n return\n \n # ---------------------------------------------------------------------\n # FMI CONFIG --> if model is invalid we look for attributes within the .fmi model definition\n valid_config = self._extract_sim_config_from_fmi_std()\n\n if valid_config:\n\n # print model config for user reference: config_params, inputs, outputs\n print(self._get_sim_config_str())\n \n if user_validation:\n # prompt user to manually validate model if selected\n validation_asserted = input(\"Is this configuration correct (y|n)? \")\n\n if validation_asserted == \"y\":\n self.is_model_config_valid = True\n # dump YMAL file to reuse next time the model is loaded\n self._dump_config_to_yaml_file()\n return\n \n else:\n # when no validation is selected, we assume the sim config is valid\n self.is_model_config_valid = True\n # dump YMAL file to reuse next time the model is loaded\n self._dump_config_to_yaml_file()\n return\n \n # Dump auxiliary YAML config file if user doesn't assert the provided set\n # of config_params/inputs/outputs\n self._dump_config_to_yaml_file(is_aux_yaml = True)\n \n # If neither YAML nor FMI model is sufficient raise error\n error_log = \"MODEL DOES NOT HAVE THE CORRECT CONFIG DEFINED NEITHER ON YAML CONFIG FILE \"\n error_log += \"NOR FMI MODEL DESCRIPTION. A YAML FILE HAS BEEN CREATED FOR YOU TO MODIFY. \"\n error_log += \"THE SIM HAS BEEN FORCED TO EXIT, BUT FEEL FREE TO RERUN ONCE SET-UP IS COMPLETED.\"\n raise Exception(error_log)"},"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 validate():","def data_validation(self):\n print \"Starting basic data validation ...\"\n allattr = dir(bdefile)\n idx = [ii for ii, attr in enumerate(allattr) if \"validate_oee_error_\" in attr]\n vfunclist = []\n for ii in idx:\n vfunclist += [allattr[ii]]\n\n errorcodes = []\n for vfunc in vfunclist:\n errorcodes += [int(vfunc.split('_')[3])]\n\n errorcodes.sort()\n\n for code in errorcodes:\n sys.stdout.write(\"Checking validation rule %d ... \" % code)\n success, lines = (eval('self.validate_oee_error_'+str(code)))()\n if success:\n print \"PASSED\"\n else:\n self.report_error(code, lines)\n return False\n \n print \"Basic data validation succeeded.\\n\"\n return True","def _backtest_model_specific_sanity_checks(self, *args: Any, **kwargs: Any) -> None:\n pass","def test_validate(self):\n pass","def __validate():\n # TODO: implement","def validate_model(self) -> Tuple[T_co, T_co]:\n raise NotImplementedError","def test_admin_freeze_schema():\n legal_filing = {'adminFreeze': ADMIN_FREEZE}\n\n is_valid, errors = validate(legal_filing, 'admin_freeze')\n\n if errors:\n for err in errors:\n print(err.message)\n print(errors)\n\n assert is_valid","def validate(self):","def validate(self):","def validate(self):\n ...","def test_validators():","def _validate_data(self, vms, fw_rules):\n self._validate_vms(vms)\n self._validate_fw_rules(fw_rules)\n self._validated = True","def validate(ob):","def validate(self, args: argparse.Namespace) -> int:\n trestle_root = args.trestle_root # trestle root is set via command line in args. Default is cwd.\n\n # validate by type - all of type or just specified by name\n if 'type' in args and args.type is not None:\n models = []\n if 'name' in args and args.name is not None:\n models = [args.name]\n else:\n models = fs.get_models_of_type(args.type, trestle_root)\n models_path = trestle_root / fs.model_type_to_model_dir(args.type)\n for m in models:\n model_path = models_path / m\n try:\n _, _, model = load_distributed(model_path, trestle_root)\n except TrestleError as e:\n logger.warning(f'File load error {e}')\n return 1\n if not self.model_is_valid(model):\n logger.info(f'INVALID: Model {model_path} did not pass the {self.error_msg()}')\n return 1\n logger.info(f'VALID: Model {model_path} passed the {self.error_msg()}')\n return 0\n\n # validate all\n if 'all' in args and args.all:\n model_tups = fs.get_all_models(trestle_root)\n for mt in model_tups:\n\n model_dir = trestle_root / fs.model_type_to_model_dir(mt[0]) / mt[1]\n extension_type = fs.get_contextual_file_type(model_dir)\n model_path = model_dir / f'{mt[0]}{FileContentType.to_file_extension(extension_type)}'\n _, _, model = load_distributed(model_path, trestle_root)\n if not self.model_is_valid(model):\n logger.info(f'INVALID: Model {model_path} did not pass the {self.error_msg()}')\n return 1\n logger.info(f'VALID: Model {model_path} passed the {self.error_msg()}')\n return 0\n\n # validate file\n if 'file' in args and args.file:\n file_path = trestle_root / args.file\n _, _, model = load_distributed(file_path, trestle_root)\n if not self.model_is_valid(model):\n logger.info(f'INVALID: Model {file_path} did not pass the {self.error_msg()}')\n return 1\n logger.info(f'VALID: Model {file_path} passed the {self.error_msg()}')\n return 0","def _validate(self):\n All = voluptuous.All\n Required = voluptuous.Required\n Length = voluptuous.Length\n Extra = voluptuous.Extra\n\n schema = voluptuous.Schema({\n Required('description'): voluptuous.All(str, Length(min=5)),\n Required('environments'): dict,\n Required('application'): {\n Required('name'): str,\n Required('scenario'): [{\n Required('driver'): str,\n Required('description'): All(str, Length(min=5)),\n Extra: object}]}})\n try:\n schema(self.marmite_tree)\n except voluptuous.MultipleInvalid as e:\n LOG.error(\"Failed to validate %s/marmite.yaml structure: %s\" %\n (self.fs_layer.base_dir, e))\n raise InvalidStructure()","def checkModel(self, model):\n # TODO","def test_validate_gpy_models():\n with pytest.raises(ValueError):\n validate_gpy_model([\"m\"])","def test_multiple_if_combi_nonpass(self):\n schema = yaml.load(self.yaml_ifcombi, Loader=yaml.FullLoader)\n document = {'basisOfRecord': 'HumanObservation', 'collectionCode': ''}\n val = DwcaValidator(schema, error_handler=WhipErrorHandler)\n val.validate(document)\n self.assertEqual(val.errors,\n {'basisOfRecord': [{'if': ['unallowed value '\n 'HumanObservation']}]})","def _validate(self):\n pass","def model_check(blk):\n # Check temperature bounds\n if value(blk.temperature) < blk.temperature.lb:\n _log.error('{} Temperature set below lower bound.'.format(blk.name))\n if value(blk.temperature) > blk.temperature.ub:\n _log.error('{} Temperature set above upper bound.'.format(blk.name))\n\n # Check pressure bounds\n if value(blk.pressure) < blk.pressure.lb:\n _log.error('{} Pressure set below lower bound.'.format(blk.name))\n if value(blk.pressure) > blk.pressure.ub:\n _log.error('{} Pressure set above upper bound.'.format(blk.name))","def is_valid(self, data_model: DataModel) -> bool:","def test_base_validate_models():\n with pytest.raises(ValueError):\n base_validate_models([])\n\n assert [\"m\"] == base_validate_models([\"m\"])","def check_model(self, model):\n self.check_initial_conditions(model)\n self.check_variables(model)","def test_multiple_if_error(self):\n schema = yaml.load(self.yaml_ifif, Loader=yaml.FullLoader)\n document = {'age': '21', 'lifestage': 'juvenile'}\n val = DwcaValidator(schema, error_handler=WhipErrorHandler)\n val.validate(document)\n self.assertEqual(val.errors,\n {'lifestage': [{'if_0': ['unallowed value juvenile',\n 'max length is 6'],\n 'if_2': ['max length is 5']}]})","def validate(self):\n pass","def validate(self):\n pass","def validate(self):\n pass","def validate(self):\n pass","def validate(self):\n pass","def validate(self):\n pass","def validate(self):\n pass","def validate(self):\n pass","def run_parameters_validations(self):\n if self.risk_rule:\n if 'connectApi' not in self.services:\n return_error(\"You entered a risk rule but the 'connectApi' service is not chosen. \"\n \"Add the 'connectApi' service to the list or remove the risk rule.\")\n else:\n for risk_rule in self.risk_rule:\n if not is_valid_risk_rule(self, risk_rule):\n return_error(f\"The given risk rule: {risk_rule} does not exist,\"\n f\"please make sure you entered it correctly. \\n\"\n f\"To see all available risk rules run the '!rf-get-risk-rules' command.\")\n\n if self.fusion_file_path is not None:\n if 'fusion' not in self.services:\n return_error(\"You entered a fusion file path but the 'fusion' service is not chosen. \"\n \"Add the 'fusion' service to the list or remove the fusion file path.\")","def validate_requested_xgb_model(xgboost_model_files, xgboost_model_hashes, model_dir):\n for _, model_file in xgboost_model_files.items():\n if not check_model_presence(\n model_file, xgboost_model_hashes[model_file], model_dir\n ):\n download_model(model_file, xgboost_model_hashes[model_file], model_dir)","def check_validity(self):","async def validate(self):\n pass","def test_schema_validation():\n resolver = RefResolver.from_schema(j, store={\"definitions\": j})\n schema_definitions = j[\"definitions\"]\n validation_models = root_dir / \"validation\" / \"models.yaml\"\n validation_tests = yaml.load(open(validation_models), Loader=yaml.SafeLoader)\n for cls, tests in validation_tests.items():\n for t in tests:\n validate(instance=t[\"in\"],\n schema=schema_definitions[cls],\n resolver=resolver)","def validateRaxmlModels(self, input): \n filename=input+\".mg.modelFromMG.txt\"\n arq = open(filename, \"r\")\n output = \"\"\n conteudo = arq.readlines()\n seqcont = 0\n output = \"True\"\n modelogerado = \"\"\n listamodelos = ['DAYHOFF', 'DCMUT', 'JTT', 'MTREV', 'WAG', 'RTREV', 'CPREV',\n 'VT', 'BLOSUM62', 'MTMAM', 'MTART', 'MTZOA', 'LG', 'PMB',\n 'HIVB', 'HIVW', 'JTTDCMUT', 'FLU','GTR']\n\n if len(conteudo) == 0 or len(conteudo) > 1:\n output = \"False\"\n else:\n modelogerado = conteudo[0].strip()\n if modelogerado.upper() not in listamodelos:\n print \"Teste\"\n output = \"False\" \n \n return {\"MG_MODEL\":modelogerado, \"MG_MODEL_VALID\":output}","def test_firms_conversion_schema():\n legal_filing = {'conversion': FIRMS_CONVERSION}\n\n is_valid, errors = validate(legal_filing, 'conversion')\n\n if errors:\n for err in errors:\n print(err.message)\n print(errors)\n\n assert is_valid","def validate(model: str = \"hog\"):\n for filepath in Path(\"validation\").rglob(\"*\"):\n if filepath.is_file():\n recognize_faces(\n image_location=str(filepath.absolute()), model=model\n )","def verifyModels(self):\r\n\r\n #\r\n # now check that all models have the same poly data in the\r\n # model node as in the display node\r\n #\r\n polyDataInScene = []\r\n fileNamesInScene = []\r\n success = True\r\n numModels = slicer.mrmlScene.GetNumberOfNodesByClass( \"vtkMRMLModelNode\" )\r\n for n in range(numModels):\r\n modelNode = slicer.mrmlScene.GetNthNodeByClass( n, \"vtkMRMLModelNode\" )\r\n polyDataInScene.append(modelNode.GetPolyData())\r\n for dn in range(modelNode.GetNumberOfDisplayNodes()):\r\n displayNode = modelNode.GetNthDisplayNode(dn)\r\n if modelNode.GetPolyData() != displayNode.GetInputPolyData():\r\n self.delayDisplay(\"Model %d does not match its display node %d! (name: %s, ids: %s and %s)\" % (n,dn,modelNode.GetName(), modelNode.GetID(),displayNode.GetID()))\r\n success = False\r\n for sn in range(modelNode.GetNumberOfStorageNodes()):\r\n storageNode = modelNode.GetNthStorageNode(sn)\r\n fileName = storageNode.GetFileName()\r\n fileNamesInScene.append(fileName)\r\n if fileName in fileNamesInScene:\r\n self.delayDisplay(\"Model %d has duplicate file name %s! (ids: %s and %s)\" % (n,fileName,modelNode.GetID(),storageNode.GetID()))\r\n success = False\r\n\r\n\r\n #\r\n # now check that each model has a unique polydata\r\n #\r\n for n in range(numModels):\r\n modelNode = slicer.mrmlScene.GetNthNodeByClass( n, \"vtkMRMLModelNode\" )\r\n if polyDataInScene.count(modelNode.GetPolyData()) > 1:\r\n self.delayDisplay(\"Polydata for Model is duplicated! (id: %s and %s)\" % (n,modelNode.GetID()))\r\n success = False\r\n\r\n return success","def validate(model_path: Path):\n tmpfile = tempfile.NamedTemporaryFile()\n\n if not model_path.exists():\n _eprint(f'ERROR: {model_path} does not exist')\n\n # Try quantization on input models\n print(f'[Step 1] Checking if the model can be transformed into a quantized model ...')\n try:\n quantized_model = post_training_quantization_with_random_calibration(model=onnx.load_model(model_path),\n per_channel=True,\n static=True,\n mode=QuantizationMode.dfg,\n num_data=10)\n except Exception as e:\n _eprint(\"[Step 1] Failed\\n\")\n raise e\n print(f'[Step 1] Passed')\n\n try:\n onnx.save_model(quantized_model, tmpfile.name)\n except Exception as e:\n _eprint(\"[ERROR] Fail to save the model\\n\")\n raise e\n\n print(f'[Step 2] Checking the model can be compiled to a NPU program ...')\n try:\n with open(tmpfile.name, \"rb\") as model_file:\n model = model_file.read()\n session.create(model=model)\n except Exception as e:\n _eprint(\"[Step 2] Failed\\n\")\n raise e\n print(f'[Step 2] Passed')","def validate():\n ve('python manage.py validate')","def test__validate_features__1():\n for input_value in (\n 12.6,\n ):\n with vampytest.assert_raises(TypeError):\n validate_features(input_value)","def test_ensure_valid_model_type(self):\n # Note the \"valid\" type strings for our test\n test_types = [\"bar\", \"foo\", \"Sreeta\", \"Feras\"]\n # Note a set of invalid type strings for the test\n bad_types = [\"Tim\", \"Sam\"]\n\n # Alias the function to be tested\n func = pylogit.pylogit.ensure_valid_model_type\n\n # Make note of part of the error message that should be raised\n partial_error_msg = \"The specified model_type was not valid.\"\n\n # Perform the requisite tests\n for good_example in test_types:\n self.assertIsNone(func(good_example, test_types))\n for bad_example in bad_types:\n self.assertRaisesRegexp(ValueError,\n partial_error_msg,\n func,\n bad_example,\n test_types)\n\n return None","def test_multiple_if_combi(self):\n schema = yaml.load(self.yaml_ifcombi, Loader=yaml.FullLoader)\n document = {'basisOfRecord': 'PreservedSpecimen', 'collectionCode': ''}\n val = DwcaValidator(schema, error_handler=WhipErrorHandler)\n self.assertTrue(val.validate(document))","def fields_validator():\n\n return validator.BrewerySchema()","def validate_model(data_dir, script_dir='../results/saved_scripts', model_dir=None,\n model_name='squeezenet', num_classes=3, batch_size=8, num_epochs=50, feature_extract=True,\n learning_rates=(1e-3,), weight_decays=(1e-5,), viz=None):\n # Make sure model type is valid\n assert model_name in available_models_input_size.keys(), 'Unsupported model type ' + model_name\n print('Using %s ...\\n' % model_name)\n\n # Record start time\n since = time.time()\n\n # Create dataloaders\n print('Loading dataset ...\\n')\n dataloaders, dataset_mean, dataset_std = \\\n create_dataloaders(data_dir, available_models_input_size[model_name], batch_size)\n print('+ Dataset mean:', dataset_mean[0])\n print('+ Dataset standard deviation:', dataset_std[0])\n dataset_sizes = {x: len(dataloaders[x].dataset) for x in ['train', 'val', 'test']}\n print('+ %(train)d samples for training, %(val)d for validation, %(test)d for test.\\n' % dataset_sizes)\n\n # Setup the loss function\n criterion = nn.CrossEntropyLoss()\n\n # Keep record of best val acc and best model\n best_model = None\n best_val_acc = 0.0\n best_lr = None\n best_wd = None\n\n # Validating over hyper-parameters\n print('Validating hyper-parameters ...')\n for learning_rate in learning_rates:\n for weight_decay in weight_decays:\n # Initialize the model for this run\n model, params_to_update = initialize_model(model_name, num_classes, feature_extract)\n\n # Send the model to GPU\n model = model.to(using_device)\n\n # Setup optimizer\n optimizer = optim.Adam(\n params_to_update,\n lr=learning_rate,\n betas=(0.9, 0.999),\n eps=1e-08,\n weight_decay=weight_decay,\n amsgrad=False\n )\n\n # Train and evaluate\n model, val_acc_history, loss_history = train_model(\n model=model,\n dataloaders=dataloaders,\n criterion=criterion,\n optimizer=optimizer,\n num_epochs=num_epochs,\n is_inception=(model_name == \"inception\")\n )\n\n # Plot val acc and loss through learning process\n if viz:\n trace_name = '%s[lr=%g,wd=%g]' % (model_name, learning_rate, weight_decay)\n viz.line(\n X=np.arange(1, 1 + len(val_acc_history)),\n Y=val_acc_history,\n name=trace_name,\n win='Val Acc',\n update='append',\n opts={\n 'title': 'Val Acc',\n 'xlabel': 'epoch',\n 'ylabel': 'val_acc',\n 'showlegend': True,\n 'height': 400,\n 'width': 700\n }\n )\n viz.line(\n X=np.arange(1, 1 + len(loss_history)),\n Y=loss_history,\n name=trace_name,\n win='Loss',\n update='append',\n opts={\n 'title': 'Loss',\n 'xlabel': 'epoch',\n 'ylabel': 'loss',\n 'showlegend': True,\n 'height': 400,\n 'width': 700\n }\n )\n\n # Print val acc for this set of hyper-parameters\n this_val_acc = max(val_acc_history)\n print('\\n* lr = %.4e, wd = %.4e, val_acc=%.4f\\n' % (learning_rate, weight_decay, this_val_acc))\n\n # Update best model info\n if this_val_acc > best_val_acc:\n best_model = model\n best_val_acc = this_val_acc\n best_lr = learning_rate\n best_wd = weight_decay\n\n # Print out train result\n time_elapsed = time.time() - since\n print('Training complete in {:.0f}m {:.0f}s'.format(time_elapsed // 60, time_elapsed % 60))\n print('Model at lr=%e, wd=%e has the highest val acc: %.4f' % (best_lr, best_wd, best_val_acc))\n\n best_model.to('cpu')\n\n # Temperature scaling\n print('\\nComputing temperature ...')\n temperature = compute_temperature(best_model, dataloaders['val'], verbose=True)\n\n # For dev purpose ...\n if model_dir:\n torch.save(best_model.state_dict(), os.path.abspath(os.path.join(model_dir, model_name + '.pt')))\n\n # Test model\n test_acc, _ = test_model(best_model, dataloaders['test'])\n print('\\nTest Acc: %.4f' % test_acc)\n\n # Save the best model to disk\n if script_dir:\n # Convert to torch script for inference efficiency\n best_model = convert_to_torch_script(best_model, available_models_input_size[model_name])\n test_acc, _ = test_model(best_model, dataloaders['test'])\n print('\\nSanity check for sciprt module: Test Acc: %.4f' % test_acc)\n os.makedirs(script_dir, exist_ok=True)\n print('\\nSaving model ...')\n info_list = [\n model_name, # model type\n time.ctime().replace(':', '#'), # timestamp\n '%.3f' % temperature, # temperature of the model\n '%.5f' % dataset_mean[0],\n '%.5f' % dataset_std[0]\n ]\n file_name = '%'.join(info_list) + '.pt'\n file_url = os.path.join(script_dir, file_name)\n best_model.save(file_url)\n print('\\nModel saved to %s\\n' % file_url)\n\n return best_model, best_val_acc, test_acc","def semantic_validate(instance):\n unknown_templates = {}\n for name, requires in instance[\"application\"][\"requires\"].items():\n if name in instance[\"application\"][\"services\"]:\n raise ValidationError(errors=[\n \"/application/requires/{}: the name {} conflicts with service\"\n \" /application/services/{}\".format(name,\n repr(name),\n name),\n ])\n if requires[\"template\"] not in instance[\"local\"][\"templates\"]:\n unknown_templates[\"/application/requires/{}/template\".format(\n name)] = requires[\"template\"]\n for service_name, service in instance[\"application\"][\"services\"].items():\n for name, requires in service[\"requires\"].items():\n if name in instance[\"application\"][\"requires\"]:\n raise ValidationError(errors=[\n \"/application/services/{}/requires/{}: the name {}\"\n \" conflicts with /application/requires/{}\".format(\n service_name, name, repr(name), name)\n ])\n if requires[\"template\"] not in instance[\"local\"][\"templates\"]:\n unknown_templates[\n \"/application/services/{}/requires/{}/template\".\n format(service_name, name)] = requires[\"template\"]\n if unknown_templates:\n raise ValidationError(errors=[\n \"{}: the template {} does not exist \"\n \"in /local/templates\".format(path, repr(name))\n for (path, name) in unknown_templates.items()\n ])","def testYamlModelValidation(self):\n\n _model_file = self.TEST_CLASSES_DIR + '/simple-model.yaml'\n _archive_file = self.TEST_CLASSES_DIR + \"/SingleAppDomain.zip\"\n _method_name = 'testYamlModelValidation'\n\n mw_home = env_helper.getenv('MW_HOME')\n args_map = {\n '-oracle_home': mw_home,\n '-model_file': _model_file,\n '-archive_file': _archive_file\n }\n\n model_context = ModelContext('ValidationTestCase', args_map)\n aliases = Aliases(model_context, wls_version=self._wls_version)\n\n try:\n model_dictionary = FileToPython(model_context.get_model_file()).parse()\n model_validator = Validator(model_context, aliases, wlst_mode=WlstModes.ONLINE)\n return_code = model_validator.validate_in_tool_mode(model_dictionary,\n model_context.get_variable_file(),\n model_context.get_archive_file_name())\n self._logger.info('The Validator.validate_in_tool_mode() call returned {0}',\n Validator.ReturnCode.from_value(return_code),\n class_name=self._class_name, method_name=_method_name)\n except TranslateException, te:\n return_code = Validator.ReturnCode.STOP\n self._logger.severe('WLSDPLY-20009',\n self._program_name,\n model_context.get_model_file(),\n te.getLocalizedMessage(), error=te,\n class_name=self._class_name, method_name=_method_name)\n\n self.assertNotEqual(return_code, Validator.ReturnCode.STOP)","def validate(self, entity, value, tracker, dispatcher, events_custom = None):\n global pincodes_available\n\n df = pd.read_excel('SampleModelSerialGEA.xlsx')\n print(entity, value)\n\n if entity == 'appliance': ###IMP :: can reduce appliance value to one allowed here\n print('Checking appliance')\n if value not in appliances:\n print('error')\n value = None\n\n else:\n idx_lis = df.index[df.loc[:, 'Product Line'].map(lambda x : x.lower()) == value.lower()].tolist()\n\n if tracker.get_slot('serialnumber') and not tracker.get_slot('serialnumber').upper() in [df.loc[i, 'Serial Number'] for i in idx_lis]:\n dispatcher.utter_message(\"For appliance %s serial number %s does not match !!!\" % (value, tracker.get_slot('serialnumber')))\n value = None\n\n elif tracker.get_slot('modelnumber') and not tracker.get_slot('modelnumber').upper() in [df.loc[i, 'Model Number'] for i in idx_lis]:\n dispatcher.utter_message(\"For appliance %s model number %s does not match !!!\" % (value, tracker.get_slot('modelnumber')))\n value = None\n\n\n if entity == 'modelnumber':\n print('Checking modelnumber')\n if not any(df.loc[:, 'Model Number'] == value.upper()):\n print('error')\n dispatcher.utter_message(\"Model no %s not in database !!!\" % tracker.get_slot('modelnumber'))\n value = None\n\n else:\n idx_lis = df.index[df.loc[:, 'Model Number'] == value.upper()].tolist()\n idx = idx_lis[0]\n\n if tracker.get_slot('appliance') and not df.loc[idx, 'Product Line'].lower() == tracker.get_slot('appliance'):\n dispatcher.utter_message(\"For model number %s appliance %s does not match !!!\" % (value, tracker.get_slot('appliance')))\n value = None\n\n elif not tracker.get_slot('appliance'):\n events_custom.extend([SlotSet(\"appliance\", df.loc[idx, 'Product Line'].lower())]) \n dispatcher.utter_message(\"For model number %s appliance %s added from database !!!\" % (value, df.loc[idx, 'Product Line'].lower()))\n\n if tracker.get_slot('serialnumber') and not tracker.get_slot('serialnumber').upper() in [df.loc[i, 'Serial Number'] for i in idx_lis]:\n dispatcher.utter_message(\"For model number %s serial number %s does not match !!!\" % (value, tracker.get_slot('serialnumber')))\n value = None\n\n\n if entity == 'serialnumber':\n print('Checking serialnumber')\n \n # if df[idx]['Serial Number'] == value.upper():\n # print('error')\n # value = None\n if not any(df.loc[:, 'Serial Number'] == value.upper()):\n print('error')\n dispatcher.utter_message(\"Serial no %s not in database !!!\" % tracker.get_slot('serialnumber'))\n value = None\n else:\n idx = df.index[df.loc[:, 'Serial Number'] == value.upper()].tolist()[0]\n \n if tracker.get_slot('modelnumber') and not df.loc[idx, 'Model Number'] == tracker.get_slot('modelnumber').upper():\n dispatcher.utter_message(\"For serial number %s model number %s does not match !!!\" % (value, tracker.get_slot('modelnumber')))\n value = None\n \n elif not tracker.get_slot('modelnumber'):\n print('Adding modelnumber given serialnumber')\n events_custom.extend([SlotSet('modelnumber', df.loc[idx, 'Model Number'].lower())]) \n dispatcher.utter_message(\"For serial number %s model number %s added from database !!!\" % (value, df.loc[idx, 'Model Number'].lower()))\n\n if tracker.get_slot('appliance') and not df.loc[idx, 'Product Line'].lower() == tracker.get_slot('appliance'):\n dispatcher.utter_message(\"For serial number %s appliance %s does not match !!!\" % (value, tracker.get_slot('appliance')))\n value = None\n\n elif not tracker.get_slot('appliance'):\n print('Adding appliance given serialnumber')\n events_custom.extend([SlotSet('appliance', df.loc[idx, 'Product Line'].lower())]) \n dispatcher.utter_message(\"For serial number %s appliance %s added from database !!!\" % (value, df.loc[idx, 'Product Line'].lower()))\n \n\n if entity == 'pincode':\n print(value)\n #print(value.type)\n if value not in pincodes_available:\n print('error')\n dispatcher.utter_message(\"The provided pincode %s is unavailable for service. We are sorry for the inconvinence.\" % value)\n value = None\n\n if entity == 'phonenumber':\n try:\n print('Checking phone number')\n r = requests.get('http://apilayer.net/api/validate?access_key=%s&number=%s&country_code=IN&format=1'%(PHONE_NO_API_KEY, value)).json()\n print(r)\n if not r['valid']:\n value = None\n except Exception as e:\n print(str(e))\n dispatcher.utter_message(\"Phone No is unable to be confirmed !!!! Believing entered info is correct\")\n\n return value","def __validate(self):\n pass","def validate_required_model(program_name, arg_map):\n _method_name = 'validate_required_model'\n\n if CommandLineArgUtil.MODEL_FILE_SWITCH in arg_map:\n model_file_value = arg_map[CommandLineArgUtil.MODEL_FILE_SWITCH]\n model_files = cla_utils.get_model_files(model_file_value)\n\n for model_file in model_files:\n try:\n FileUtils.validateExistingFile(model_file)\n except IllegalArgumentException, iae:\n ex = exception_helper.create_cla_exception(ExitCode.ARG_VALIDATION_ERROR, 'WLSDPLY-20006', program_name,\n model_file, iae.getLocalizedMessage(), error=iae)\n __logger.throwing(ex, class_name=_class_name, method_name=_method_name)\n raise ex\n else:\n ex = exception_helper.create_cla_exception(ExitCode.USAGE_ERROR, 'WLSDPLY-20015', program_name,\n CommandLineArgUtil.MODEL_FILE_SWITCH)\n __logger.throwing(ex, class_name=_class_name, method_name=_method_name)\n raise ex","def test_validation(self):\n self.validationFails()","async def async_step_validation(self, user_input=None):\n errors = {}\n if user_input:\n try:\n await self._gateway.validate_mfa(user_input[CONF_CODE])\n except SENSE_CONNECT_EXCEPTIONS:\n errors[\"base\"] = \"cannot_connect\"\n except SenseAuthenticationException:\n errors[\"base\"] = \"invalid_auth\"\n except Exception: # pylint: disable=broad-except\n _LOGGER.exception(\"Unexpected exception\")\n errors[\"base\"] = \"unknown\"\n else:\n return await self.create_entry_from_data()\n\n return self.async_show_form(\n step_id=\"validation\",\n data_schema=vol.Schema({vol.Required(CONF_CODE): vol.All(str, vol.Strip)}),\n errors=errors,\n )","def validate(self) -> None:\n validate_model_generator_interface_input(self.configs)","def __validate__(self):","def do_validate(self, request, _object):\n\n pass","def test_kyc_get_validation(self):\n pass","def check_models(*, schemas: _oa_types.Schemas) -> types.TModels:\n not_constructables = iterate.not_constructable(schemas=schemas)\n not_constructables_result = map(\n lambda args: (args[0], check_model(schemas, args[1])), not_constructables\n )\n return dict(not_constructables_result)","def test_valid_model(self):\n model_cls = ModelContainer(APP_LABEL, TestModel2._meta.db_table).model_cls\n self.assertTrue(model_cls.__class__.__name__ is models.Model.__class__.__name__)","def test__validate_features__0():\n for input_value, expected_output in (\n (None, None),\n ([], None),\n ([GuildFeature.animated_banner], (GuildFeature.animated_banner, )),\n ([GuildFeature.animated_banner.value], (GuildFeature.animated_banner, )),\n (\n [GuildFeature.animated_banner, GuildFeature.animated_icon],\n (GuildFeature.animated_banner, GuildFeature.animated_icon,),\n ),\n ):\n output = validate_features(input_value)\n vampytest.assert_eq(output, expected_output)","def is_model_valid(self):\n try:\n payload = {\n \"modelurl\": self.model_builder_url + self.model_uuid,\n \"api_key\": self.web2nl_api_key\n }\n\n response = requests.get(self.web2nl_url + \"validations\", params=payload)\n if response.status_code is requests.codes.no_content:\n return True\n elif response.status_code is requests.codes.bad_request:\n self.logger.error(\"Model validation failed: \" + response.text)\n return False\n else:\n self.logger.error(\"Failed while validating model. Will retry in some time\")\n raise RuntimeError(\"Failed while validating model\")\n except requests.exceptions.ConnectionError as errc:\n self.logger.error(\"Error Connecting:\", errc)","def test_validate_gbdt_models():\n from lightgbm import LGBMRegressor # pylint:disable=import-outside-toplevel\n\n lgbm = LGBMRegressor(objective=\"quantile\", alpha=0.1)\n with pytest.raises(ValueError):\n validate_gbdt_models([(lgbm, lgbm), (lgbm, lgbm, lgbm)], 2)\n\n with pytest.raises(ValueError):\n validate_gbdt_models([(LGBMRegressor(), lgbm, lgbm), (lgbm, lgbm, lgbm)], 2)\n\n with pytest.raises(ValueError):\n validate_gbdt_models([(lgbm, lgbm, lgbm), (lgbm, lgbm, LGBMRegressor())], 2)\n\n valid_input = [(lgbm, LGBMRegressor(), lgbm), (lgbm, LGBMRegressor(), lgbm)]\n assert validate_gbdt_models(valid_input, 2) == valid_input","def validate(cls, templates):\n super(Product, cls).validate(templates)\n\n for template in templates:\n template.check_type_and_mode()\n\n template.check_gc_min_max()","def test_multiple_if_pass(self):\n schema = yaml.load(self.yaml_ifif, Loader=yaml.FullLoader)\n val = DwcaValidator(schema, error_handler=WhipErrorHandler)\n document = {'age': '21', 'lifestage': 'adult'} # True\n self.assertTrue(val.validate(document))\n\n document = {'age': '2', 'lifestage': 'juvenile'} # True\n val = DwcaValidator(schema, error_handler=WhipErrorHandler)\n self.assertTrue(val.validate(document))","def __init__(\n self,\n model_filepath: str,\n fmi_version: str = FMI_VERSION,\n start_time = START_TIME,\n stop_time = STOP_TIME,\n step_size = STEP_SIZE,\n user_validation: bool = False,\n use_unzipped_model: bool = False,\n ):\n\n # validate simulation: config_vars (optional), inputs, and outputs\n validated_sim = FMUSimValidation(model_filepath, user_validation)\n \n # extract validated sim configuration\n self.model_filepath = validated_sim.model_filepath\n self.sim_config_filepath = validated_sim.sim_config_filepath\n self.model_description = validated_sim.model_description\n # model variable names structured per type (config, inputs/brain actions, outputs/brain states)\n self.sim_config_params = validated_sim.sim_config_params\n self.sim_inputs = validated_sim.sim_inputs\n self.sim_outputs = validated_sim.sim_outputs\n self.sim_other_vars = validated_sim.sim_other_vars\n # model variable dictionaries with\n self.vars_to_idx = validated_sim.vars_to_idx\n self.vars_to_type_f = validated_sim.vars_to_type_f\n self.vars_to_ini_vals = validated_sim.vars_to_ini_vals\n\n # get parent directory and model name (without .fmu)\n aux_head_and_tail_tup = os.path.split(self.model_filepath)\n self.model_dir = aux_head_and_tail_tup[0]\n self.model_name = aux_head_and_tail_tup[1].replace(\".fmu\", \"\")\n\n # placeholder to prevent accessing methods if initialization hasn't been called first\n # also prevents calling self.fmu.terminate() if initialization hasn't occurred or termination has already been applied\n self._is_initialized = False\n \n # get FMI version\n read_fmi_version = self.model_description.fmiVersion\n if read_fmi_version in [\"1.0\", \"2.0\", \"3.0\"]:\n # Use fmi version from model_description\n print(f\"[FMU Connector] FMU model indicates to be follow fmi version '{read_fmi_version}'.\")\n self.fmi_version = read_fmi_version\n else:\n assert fmi_version in [\"1.0\", \"2.0\", \"3.0\"], f\"fmi version provided ({fmi_version}) is invalid.\"\n # Use fmi version provided by user if the one on model_description is invalid\n print(f\"[FMU Connector] Using fmi version provided by user: v'{fmi_version}'. Model indicates v'{read_fmi_version}' instead.\")\n self.fmi_version = fmi_version\n\n # save time-related data\n error_log = \"Stop time provided ({}) is lower than start time provided ({})\".format(stop_time, start_time)\n assert stop_time > start_time, error_log\n error_log = \"Step size time ({}) is greater than the difference between \".format(step_size)\n error_log += \"stop and start times, ({}) and ({}), respectively\".format(stop_time, start_time)\n assert step_size < stop_time-start_time, error_log\n self.start_time = float(start_time)\n self.stop_time = float(stop_time)\n self.step_size = float(step_size)\n self.sim_time = float(self.start_time)\n \n # retrieve FMU model type, as well as model identifier\n self.model_type = \"None\"\n self.model_identifier = self.model_name\n coSimulation = self.model_description.coSimulation\n if coSimulation is not None:\n self.model_identifier = coSimulation.modelIdentifier\n self.model_type = \"coSimulation\"\n else:\n scheduledExecution = self.model_description.scheduledExecution\n if scheduledExecution is not None:\n self.model_identifier = scheduledExecution.modelIdentifier\n self.model_type = \"scheduledExecution\"\n else:\n modelExchange = self.model_description.modelExchange\n if modelExchange is not None:\n self.model_identifier = modelExchange.modelIdentifier\n self.model_type = \"modelExchange\"\n else:\n raise Exception(\"Model is not of any known type: coSimulation, scheduledExecution, nor modelExchange\")\n\n \n # extract the FMU\n extract_path = os.path.join(self.model_dir, self.model_name + \"_unzipped\")\n if not use_unzipped_model:\n # extract model to subfolder by default\n self.unzipdir = extract(self.model_filepath, unzipdir=extract_path)\n else:\n # use previouslly unzipped model\n self.unzipdir = extract_path\n\n # get unique identifier using timestamp for instance_name (possible conflict with batch)\n self.instance_name = self._get_unique_id()\n \n\n # ---------------------------------------------------------------\n # instance model depending on 'fmi version' and 'fmu model type'\n self.fmu = None\n print(f\"[FMU Connector] Model has been determined to be of type '{self.model_type}' with fmi version == '{self.fmi_version}'.\")\n if self.model_type == \"modelExchange\":\n ## [TODO] test integrations\n print(f\"[FMU Connector] Simulator hasn't been tested for '{self.model_type}' models with fmi version == '{self.fmi_version}'.\")\n if self.fmi_version == \"1.0\":\n self.fmu = fmi1.FMU1Model(guid=self.model_description.guid,\n unzipDirectory=self.unzipdir,\n modelIdentifier=self.model_identifier,\n instanceName=self.instance_name)\n elif self.fmi_version == \"2.0\":\n self.fmu = fmi2.FMU2Model(guid=self.model_description.guid,\n unzipDirectory=self.unzipdir,\n modelIdentifier=self.model_identifier,\n instanceName=self.instance_name)\n elif self.fmi_version == \"3.0\":\n self.fmu = fmi3.FMU3Model(guid=self.model_description.guid,\n unzipDirectory=self.unzipdir,\n modelIdentifier=self.model_identifier,\n instanceName=self.instance_name)\n elif self.model_type == \"coSimulation\":\n if self.fmi_version == \"1.0\":\n ## [TODO] test integrations\n print(f\"[FMU Connector] Simulator hasn't been tested for '{self.model_type}' models with fmi version == '{self.fmi_version}'.\")\n self.fmu = fmi1.FMU1Slave(guid=self.model_description.guid,\n unzipDirectory=self.unzipdir,\n modelIdentifier=self.model_identifier,\n instanceName=self.instance_name)\n elif self.fmi_version == \"2.0\":\n self.fmu = fmi2.FMU2Slave(guid=self.model_description.guid,\n unzipDirectory=self.unzipdir,\n modelIdentifier=self.model_identifier,\n instanceName=self.instance_name)\n elif self.fmi_version == \"3.0\":\n ## [TODO] test integrations\n print(f\"[FMU Connector] Simulator hasn't been tested for '{self.model_type}' models with fmi version == '{self.fmi_version}'.\")\n self.fmu = fmi3.FMU3Slave(guid=self.model_description.guid,\n unzipDirectory=self.unzipdir,\n modelIdentifier=self.model_identifier,\n instanceName=self.instance_name)\n elif self.model_type == \"scheduledExecution\":\n if self.fmi_version == \"1.0\" or self.fmi_version == \"2.0\":\n raise Exception(\"scheduledExecution type only exists in fmi v'3.0', but fmi version '{}' was provided.\".format(self.fmi_version))\n \n print(f\"[FMU Connector] Simulator hasn't been tested for '{self.model_type}' models with fmi version == '{self.fmi_version}'.\")\n ## [TODO] test integrations\n #elif self.fmi_version_int == 3:\n self.fmu = fmi3.FMU3ScheduledExecution(guid=self.model_description.guid,\n unzipDirectory=self.unzipdir,\n modelIdentifier=self.model_identifier,\n instanceName=self.instance_name)\n \n \n # ---------------------------------------------------------------\n return","def _validate(self):\n _models = {'hrrr', 'hrrrak', 'rap'}\n _fields = {'prs', 'sfc', 'nat', 'subh'}\n \n self.date = pd.to_datetime(self.date)\n \n if self.model == 'alaska':\n self.model == 'hrrrak'\n\n assert self.fxx in range(49), \"Forecast lead time `fxx` is too large\"\n assert self.model in _models, f\"`model` must be one of {_models}\"\n if self.model in ['hrrr', 'hrrrak']:\n assert self.field in _fields, f\"`field must be one of {_fields}\"\n else:\n # field is not needed for RAP model.\n self.field = ''\n \n if isinstance(self.priority, str):\n self.priority = [self.priority]\n \n self.priority = [i.lower() for i in self.priority]\n\n # Don't look for data from NOMADS if requested date is earlier\n # than yesterday. NOMADS doesn't keep data that old.\n if 'nomads' in self.priority:\n yesterday = datetime.utcnow() - timedelta(hours=24)\n yesterday = pd.to_datetime(f\"{yesterday:%Y-%m-%d}\")\n if self.date < yesterday:\n self.priority.remove('nomads')","def Validate(self):\n raise NotImplementedError","def metadata_validate(self):\n # Set path to `service_schema` stored in the `resources` directory from cwd of `mpe_service.py`\n current_path = Path(__file__).parent\n relative_path = '../../snet/snet_cli/resources/service_schema'\n path_to_schema = (current_path / relative_path).resolve()\n with open(path_to_schema, 'r') as f:\n schema = json.load(f)\n metadata = load_mpe_service_metadata(self.args.metadata_file)\n try:\n validate(instance=metadata.m, schema=schema)\n except Exception as e:\n docs = \"http://snet-cli-docs.singularitynet.io/service.html\"\n error_message = f\"\\nVisit {docs} for more information.\"\n if e.validator == 'required':\n raise ValidationError(e.message + error_message)\n elif e.validator == 'minLength':\n raise ValidationError(f\"`{e.path[-1]}` -> cannot be empty.\" + error_message)\n elif e.validator == 'minItems':\n raise ValidationError(f\"`{e.path[-1]}` -> minimum 1 item required.\" + error_message)\n elif e.validator == 'type':\n raise ValidationError(f\"`{e.path[-1]}` -> {e.message}\" + error_message)\n elif e.validator == 'enum':\n raise ValidationError(f\"`{e.path[-1]}` -> {e.message}\" + error_message)\n elif e.validator == 'additionalProperties':\n if len(e.path) != 0:\n raise ValidationError(f\"{e.message} in `{e.path[-2]}`.\" + error_message)\n else:\n raise ValidationError(f\"{e.message} in main object.\" + error_message)\n else:\n exit(\"OK. Ready to publish.\")","def question_new_validate():","def validate(model,dataloader,criterions,epoch,plots):\n # switch to evaluate mode\n model.eval()\n\n running_loss = 0.0\n running_oa = 0.0\n running_moa = 0.0\n\n avg_losses={}\n avg_accuracies={}\n avg_moas={}\n for i in range(6):\n avg_losses[i] = AverageMeter()\n avg_accuracies[i] = AverageMeter()\n avg_moas[i] = AverageMeter()\n\n tq_bar = tqdm(enumerate(dataloader),total=len(dataloader),ncols=80,desc='Testing')\n for batch_id, (images, labels_group) in tq_bar:\n # if i>25:\n # break\n if torch.cuda.is_available():\n images = [Variable(image.cuda()) for image in images]\n labels_group = [labels for labels in labels_group]\n else:\n print('Cuda not available')\n images = [Variable(image) for image in images]\n labels_group = [labels for labels in labels_group]\n\n\n batch_losses = []\n batch_accuracies = []\n batch_moas = []\n\n for img, labels in zip(images, labels_group):\n outputs = model(img)\n net_batch_size = outputs[0].size(0)\n if torch.cuda.is_available():\n labels = [Variable(label.cuda()) for label in labels]\n else:\n labels = [Variable(label) for label in labels]\n for i,pair in enumerate(zip(outputs, labels)):\n accuracy = accuracy_dense(pair[0].data, pair[1].data)\n moa,_ = mAP_dense(pair[0].data, pair[1].data)\n batch_losses.append(criterions[i](pair[0], pair[1]))\n batch_accuracies.append(accuracy)\n batch_moas.append(moa)\n\n for i in range(6):\n avg_losses[i].update(batch_losses[i].data[0], net_batch_size)\n avg_accuracies[i].update(batch_accuracies[i], net_batch_size)\n avg_moas[i].update(batch_moas[i], net_batch_size)\n\n ## LOSS COMPUTATION\n # loss_weight = [auto_loss_weight(0,epoch), auto_loss_weight(1,epoch), auto_loss_weight(2,epoch), auto_loss_weight(3,epoch), auto_loss_weight(4,epoch)]\n if epoch < 40:\n loss_weight = [0.1, 0.1, 0.1, 0.1, 0.1, 0.5]\n else:\n loss_weight = [0.5, 0.1, 0.1, 0.1, 0.1, 0.1]\n\n # loss_weight = [1., 0.01, 0.01, 0.01, 0.01, 0.01] # fait converger en OA la HD layer\n loss_weight = [1.,0.7, 0.6, 0.5, 0.1, 0.05, 0.01]\n\n total_batch_loss = 0\n for w, l in zip(loss_weight, batch_losses):\n total_batch_loss += w*l\n\n\n running_loss += total_batch_loss.data[0]\n # running_oa += oa\n # running_hd_moa += hd_moa\n for i in range(6):\n plots.plot(\"Total loss (running)\", \"val \"+str(i), epoch*len(dataloader)+batch_id+1, avg_losses[i].val)\n plots.plot(\"OA (running)\", \"val \"+str(i), epoch*len(dataloader)+batch_id+1, avg_accuracies[i].val)\n plots.plot(\"mOA (running)\", \"val \"+str(i), epoch*len(dataloader)+batch_id+1, avg_moas[i].val)\n for i in range(6):\n plots.plot(\"Total loss (final mean of epoch)\", \"val \"+str(i), epoch+1, avg_losses[i].val)\n plots.plot(\"OA (final mean of epoch)\", \"val \"+str(i), epoch+1, avg_accuracies[i].val)\n plots.plot(\"mOA (final mean of epoch)\", \"val \"+str(i), epoch+1, avg_moas[i].val)","def test_validate_fl_fa_fs_hn_wl(session, desc, valid, reg_type, message_content):\n # setup\n json_data = copy.deepcopy(FINANCING)\n json_data['type'] = reg_type\n del json_data['trustIndenture']\n del json_data['lifeYears']\n json_data['lifeInfinite'] = True\n if desc == DESC_MISSING_GC:\n del json_data['generalCollateral']\n if desc != DESC_INCLUDES_VC:\n del json_data['vehicleCollateral']\n\n error_msg = validator.validate(json_data)\n if valid:\n assert error_msg == ''\n elif message_content:\n # print(error_msg)\n assert error_msg != ''\n assert error_msg.find(message_content) != -1","def validate(self):\n\n\tmissing = []\n\tbadcheck = []\n\tfor name, checkfunc, params in self._required:\n\t try:\n\t\targ = self.make_required(name)\n\t\tif checkfunc is not None:\n\t\t if params is not None:\n\t\t\tparams = (self.param_map[name], arg) + params\n\t\t else:\n\t\t\tparams = (self.param_map[name], arg)\n\t\t try:\n\t\t\tapply(checkfunc, params)\n\t\t except ValidationError, msg:\n\t\t\tbadcheck.append(msg)\n\t except ValidationError, args:\n\t\tmissing.append(args)\n\n\tfor (name, checkfunc, params) in self._optional:\n\t tup = self.make_optional(name)\n\t if tup and checkfunc is not None:\n\t\tif params is not None:\n\t\t params = (self.param_map[name], tup) + params\n\t\telse:\n\t\t params = (self.param_map[name], tup)\n\t\ttry:\n\t\t apply(checkfunc, params)\n\t\texcept ValidationError, msg:\n\t\t badcheck.append(msg)\n\n\tif (missing or badcheck) and self.log_errors:\n\t self.log_error(missing, badcheck)\n\n\tif (missing or badcheck) and self.generate_error_page:\n\t self.generate_HTML(missing, badcheck)\n\n\tself.missing = missing\n\tself.badcheck = badcheck\n\n\treturn not (missing or badcheck)","def __init__(self,\n examples: types.Channel,\n model: types.Channel,\n blessing: Optional[types.Channel] = None,\n name: Optional[Text] = None):\n blessing = blessing or types.Channel(\n type=standard_artifacts.ModelBlessing,\n artifacts=[standard_artifacts.ModelBlessing()])\n spec = ModelValidatorSpec(\n examples=examples,\n model=model,\n blessing=blessing)\n super(ModelValidator, self).__init__(spec=spec, name=name)","def test__validate_owner__1():\n for field_value in (\n 12.6,\n ):\n with vampytest.assert_raises(TypeError):\n validate_owner(field_value)","def test_required_fields_X_normalization(self):\n\n del self.validator.adata.uns[\"X_normalization\"]\n self.validator.validate_adata()\n self.assertEqual(\n self.validator.errors, [\"ERROR: 'X_normalization' in 'uns' is not present.\"]\n )","def validate(cls, data, errors):","def validate_model(model):\n warnings = []\n if model is None:\n model = 'gfs'\n warnings.append('Using default model of %s' % model)\n model = model.lower().strip()\n if not model in _supported_models and model != 'spot':\n raise BadQuery(\"Unsupported model %s\" % model)\n return model, warnings","def validate(self):\r\n validictory.validate(self.data, VALIDATION_MAP[\"bmi\"])","def check_all_user_inputs_valid(self):\n self.check_RNN_layers_valid()\n self.check_activations_valid()\n self.check_embedding_dimensions_valid()\n self.check_initialiser_valid()\n self.check_y_range_values_valid()\n self.check_return_final_seq_only_valid()","def validate(self):\n scenarios = set(self.scenarios)\n model_scenarios = set(\n scenario.name for scenario in self.sos_model.scenario_models\n )\n missing_scenarios = scenarios - model_scenarios\n if missing_scenarios:\n raise SmifModelRunError(\n \"ScenarioSets {} are selected in the ModelRun \"\n \"configuration but not found in the SosModel \"\n \"configuration\".format(missing_scenarios)\n )","def validate(self):\n\n raise NotImplementedError('Ando validation is not implemented yet.')","def validate(self):\n allowed_backbones = ['resnet50', 'resnet101', 'resnet152']\n backbone = self.backbone.split('_')[0]\n\n if backbone not in allowed_backbones:\n raise ValueError('Backbone (\\'{}\\') not in allowed backbones ({}).'.format(backbone, allowed_backbones))","def validate(args, limit_to=None):\n # Deliberately not validating the interrelationship of the columns between\n # the biophysical table and the guilds table as the model itself already\n # does extensive checking for this.\n return validation.validate(args, ARGS_SPEC['args'])","def test_validate_nagl_model_path(model_name):\n model_path = validate_nagl_model_path(model_name)\n assert os.path.exists(model_path)","def validate(self):\r\n # Check KeyError\r\n try:\r\n self.fields[\"product_name_fr\"]\r\n self.fields[\"generic_name\"]\r\n self.fields[\"url\"]\r\n self.fields[\"nutrition_grade_fr\"]\r\n self.fields[\"categories\"]\r\n self.fields[\"stores\"]\r\n self.fields[\"brands\"]\r\n except KeyError:\r\n return False\r\n\r\n # Check empty field and lenght of generic_name\r\n for key, value in self.fields.items():\r\n if value == '':\r\n return False\r\n break\r\n if key == \"generic_name\":\r\n if len(value) > 255:\r\n return False\r\n\r\n try:\r\n self.categories = ProductFromApiToDatabase.clean_tag(\r\n self.fields[\"categories\"], 100)\r\n self.stores = ProductFromApiToDatabase.clean_tag(\r\n self.fields[\"stores\"], 45)\r\n self.brands = ProductFromApiToDatabase.clean_tag(\r\n self.fields[\"brands\"], 45)\r\n self.category_index = self.categories.index(self.category)\r\n except KeyError:\r\n return False\r\n except ValueError:\r\n return False\r\n except AttributeError:\r\n self.errors += 1\r\n print(self.errors)\r\n return False","def __init__(\n self,\n model,\n ipakcb=None,\n intercellt=0,\n laycon=3,\n trpy=1.0,\n hdry=-1e30,\n iwdflg=0,\n wetfct=0.1,\n iwetit=1,\n ihdwet=0,\n ikvflag=0,\n ikcflag=0,\n tran=1.0,\n hy=1.0,\n vcont=1.0,\n kv=1.0,\n anglex=0.0,\n ksat=1.0,\n sf1=1e-5,\n sf2=0.15,\n wetdry=-0.01,\n extension=\"bcf\",\n unitnumber=None,\n filenames=None,\n add_package=True,\n ):\n msg = (\n \"Model object must be of type flopy.mfusg.MfUsg\\n\"\n f\"but received type: {type(model)}.\"\n )\n assert isinstance(model, MfUsg), msg\n\n super().__init__(\n model,\n ipakcb=ipakcb,\n intercellt=intercellt,\n laycon=laycon,\n trpy=trpy,\n hdry=hdry,\n iwdflg=iwdflg,\n wetfct=wetfct,\n iwetit=iwetit,\n ihdwet=ihdwet,\n tran=tran,\n hy=hy,\n vcont=vcont,\n sf1=sf1,\n sf2=sf2,\n wetdry=wetdry,\n extension=extension,\n unitnumber=unitnumber,\n filenames=filenames,\n add_package=False,\n )\n\n dis = model.get_package(\"DIS\")\n if dis is None:\n dis = model.get_package(\"DISU\")\n structured = self.parent.structured\n\n nrow, ncol, nlay, _ = self.parent.nrow_ncol_nlay_nper\n\n self.ikvflag = ikvflag\n self.ikcflag = ikcflag\n self.kv = kv\n self.anglex = anglex\n self.ksat = ksat\n\n if not structured:\n njag = dis.njag\n self.anglex = Util2d(\n model,\n (njag,),\n np.float32,\n anglex,\n \"anglex\",\n locat=self.unit_number[0],\n )\n\n # item 1\n self.kv = Util3d(\n model,\n (nlay, nrow, ncol),\n np.float32,\n kv,\n \"Vertical Hydraulic Conductivity\",\n locat=self.unit_number[0],\n )\n if not structured:\n self.ksat = Util3d(\n model,\n (njag,),\n np.float32,\n ksat,\n \"ksat\",\n locat=self.unit_number[0],\n )\n\n if add_package:\n self.parent.add_package(self)","def validate(self, apiobj, method, api, param, safe):","def test_fleur_create_mag_validation_wrong_inputs(self, fleur_local_code, inpgen_local_code, generate_structure2):\n from aiida.orm import Dict\n\n # prepare input nodes and dicts\n options = {\n 'resources': {\n 'num_machines': 1,\n 'num_mpiprocs_per_machine': 1\n },\n 'max_wallclock_seconds': 5 * 60,\n 'withmpi': False,\n 'custom_scheduler_commands': ''\n }\n options = Dict(dict=options).store()\n\n wf_parameters = Dict({'points': 9, 'step': 0.002, 'guess': 1.00, 'wrong_key': None})\n wf_parameters.store()\n structure = generate_structure2()\n structure.store()\n\n ################\n # Create builders\n # interface of exposed scf is tested elsewhere\n\n # 1. create builder with wrong wf parameters\n builder_additionalkeys = FleurCreateMagneticWorkChain.get_builder()\n builder_additionalkeys.structure = structure\n builder_additionalkeys.wf_parameters = wf_parameters\n builder_additionalkeys.scf.fleur = fleur_local_code\n builder_additionalkeys.scf.inpgen = inpgen_local_code\n\n ###################\n # now run the builders all should fail early with exit codes\n\n # 1. structure and fleurinp given\n out, node = run_get_node(builder_additionalkeys)\n assert out == {}\n assert node.is_finished\n assert not node.is_finished_ok\n assert node.exit_status == 230","def check_models(model1, model2, use_cross_validation=False, op='e'):\n # 1. Check model types\n model1_type = model1.__class__.__name__\n model2_type = model1.__class__.__name__\n assert model1_type is model2_type, \"The model types differ. The first model is of type {0} and the second \" \\\n \"models is of type {1}.\".format(model1_type, model2_type)\n\n # 2. Check model metrics\n if isinstance(model1,H2OBinomialModel): # 2a. Binomial\n # F1\n f1_1 = model1.F1(xval=use_cross_validation)\n f1_2 = model2.F1(xval=use_cross_validation)\n if op == 'e': assert f1_1[0][1] == f1_2[0][1], \"The first model has an F1 of {0} and the second model has an F1 of \" \\\n \"{1}. Expected the first to be == to the second.\".format(f1_1[0][1], f1_2[0][1])\n elif op == 'g': assert f1_1[0][1] > f1_2[0][1], \"The first model has an F1 of {0} and the second model has an F1 of \" \\\n \"{1}. Expected the first to be > than the second.\".format(f1_1[0][1], f1_2[0][1])\n elif op == 'ge': assert f1_1[0][1] >= f1_2[0][1], \"The first model has an F1 of {0} and the second model has an F1 of \" \\\n \"{1}. Expected the first to be >= than the second.\".format(f1_1[0][1], f1_2[0][1])\n elif isinstance(model1,H2ORegressionModel): # 2b. Regression\n # MSE\n mse1 = model1.mse(xval=use_cross_validation)\n mse2 = model2.mse(xval=use_cross_validation)\n if op == 'e': assert mse1 == mse2, \"The first model has an MSE of {0} and the second model has an MSE of \" \\\n \"{1}. Expected the first to be == to the second.\".format(mse1, mse2)\n elif op == 'g': assert mse1 > mse2, \"The first model has an MSE of {0} and the second model has an MSE of \" \\\n \"{1}. Expected the first to be > than the second.\".format(mse1, mse2)\n elif op == 'ge': assert mse1 >= mse2, \"The first model has an MSE of {0} and the second model has an MSE of \" \\\n \"{1}. Expected the first to be >= than the second.\".format(mse1, mse2)\n elif isinstance(model1,H2OMultinomialModel): # 2c. Multinomial\n # hit-ratio\n pass\n elif isinstance(model1,H2OClusteringModel): # 2d. Clustering\n # totss\n totss1 = model1.totss(xval=use_cross_validation)\n totss2 = model2.totss(xval=use_cross_validation)\n if op == 'e': assert totss1 == totss2, \"The first model has an TOTSS of {0} and the second model has an \" \\\n \"TOTSS of {1}. Expected the first to be == to the second.\".format(totss1,\n totss2)\n elif op == 'g': assert totss1 > totss2, \"The first model has an TOTSS of {0} and the second model has an \" \\\n \"TOTSS of {1}. Expected the first to be > than the second.\".format(totss1,\n totss2)\n elif op == 'ge': assert totss1 >= totss2, \"The first model has an TOTSS of {0} and the second model has an \" \\\n \"TOTSS of {1}. Expected the first to be >= than the second.\" \\\n \"\".format(totss1, totss2)","def test_minimal_admin_freeze_schema():\n filing = copy.deepcopy(FILING_HEADER)\n filing['filing']['header']['name'] = 'adminFreeze'\n filing['filing']['adminFreeze'] = ADMIN_FREEZE\n\n is_valid, errors = validate(filing, 'filing')\n\n if errors:\n for err in errors:\n print(err.message)\n print(errors)\n\n assert is_valid","def validate():\n # Switch in object mode \n bpy.ops.object.mode_set(mode='OBJECT')\n \n # Apply modifier\n bpy.ops.object.apply_all_modifiers()\n \n # Get the active objec\n obj = bpy.context.active_object\n print(\"Number of faces\", len(obj.data.polygons))","def validate(self, mode): # pragma: no cover\n pass","def parse(self, form_args):\n for name, i in self.inputs.items():\n if name in form_args:\n self.inputs[name].validate(form_args[name])\n else:\n self.inputs[name].take_default()\n\n\n for name, value in form_args.items():\n self.inputs[name].validate(value)","def validate(self):\n return 1","def validate(model_num=None, validate_name=None):\n if model_num is None:\n model_num, model_name = get_latest_model()\n else:\n model_num = int(model_num)\n model_name = get_model(model_num)\n\n # Model N was trained on games up through model N-2, so the validation set\n # should only be for models through N-2 as well, thus the (model_num - 1)\n # term.\n models = list(\n filter(lambda num_name: num_name[0] < (model_num - 1), get_models()))\n # Run on the most recent 50 generations,\n # TODO(brianklee): make this hyperparameter dependency explicit/not hardcoded\n holdout_dirs = [os.path.join(HOLDOUT_DIR, pair[1])\n for pair in models[-50:]]\n\n main.validate(ESTIMATOR_WORKING_DIR, *holdout_dirs,\n checkpoint_name=os.path.join(MODELS_DIR, model_name),\n validate_name=validate_name)","def test_conversion_schema():\n legal_filing = {'conversion': BEN_CONVERSION}\n is_valid, errors = validate(legal_filing, 'conversion')\n\n if errors:\n for err in errors:\n print(err.message)\n print(errors)\n\n assert is_valid","def test_field_rules():","def validate(self, mesh, mod):\n return True"],"string":"[\n \"def validate():\",\n \"def data_validation(self):\\n print \\\"Starting basic data validation ...\\\"\\n allattr = dir(bdefile)\\n idx = [ii for ii, attr in enumerate(allattr) if \\\"validate_oee_error_\\\" in attr]\\n vfunclist = []\\n for ii in idx:\\n vfunclist += [allattr[ii]]\\n\\n errorcodes = []\\n for vfunc in vfunclist:\\n errorcodes += [int(vfunc.split('_')[3])]\\n\\n errorcodes.sort()\\n\\n for code in errorcodes:\\n sys.stdout.write(\\\"Checking validation rule %d ... \\\" % code)\\n success, lines = (eval('self.validate_oee_error_'+str(code)))()\\n if success:\\n print \\\"PASSED\\\"\\n else:\\n self.report_error(code, lines)\\n return False\\n \\n print \\\"Basic data validation succeeded.\\\\n\\\"\\n return True\",\n \"def _backtest_model_specific_sanity_checks(self, *args: Any, **kwargs: Any) -> None:\\n pass\",\n \"def test_validate(self):\\n pass\",\n \"def __validate():\\n # TODO: implement\",\n \"def validate_model(self) -> Tuple[T_co, T_co]:\\n raise NotImplementedError\",\n \"def test_admin_freeze_schema():\\n legal_filing = {'adminFreeze': ADMIN_FREEZE}\\n\\n is_valid, errors = validate(legal_filing, 'admin_freeze')\\n\\n if errors:\\n for err in errors:\\n print(err.message)\\n print(errors)\\n\\n assert is_valid\",\n \"def validate(self):\",\n \"def validate(self):\",\n \"def validate(self):\\n ...\",\n \"def test_validators():\",\n \"def _validate_data(self, vms, fw_rules):\\n self._validate_vms(vms)\\n self._validate_fw_rules(fw_rules)\\n self._validated = True\",\n \"def validate(ob):\",\n \"def validate(self, args: argparse.Namespace) -> int:\\n trestle_root = args.trestle_root # trestle root is set via command line in args. Default is cwd.\\n\\n # validate by type - all of type or just specified by name\\n if 'type' in args and args.type is not None:\\n models = []\\n if 'name' in args and args.name is not None:\\n models = [args.name]\\n else:\\n models = fs.get_models_of_type(args.type, trestle_root)\\n models_path = trestle_root / fs.model_type_to_model_dir(args.type)\\n for m in models:\\n model_path = models_path / m\\n try:\\n _, _, model = load_distributed(model_path, trestle_root)\\n except TrestleError as e:\\n logger.warning(f'File load error {e}')\\n return 1\\n if not self.model_is_valid(model):\\n logger.info(f'INVALID: Model {model_path} did not pass the {self.error_msg()}')\\n return 1\\n logger.info(f'VALID: Model {model_path} passed the {self.error_msg()}')\\n return 0\\n\\n # validate all\\n if 'all' in args and args.all:\\n model_tups = fs.get_all_models(trestle_root)\\n for mt in model_tups:\\n\\n model_dir = trestle_root / fs.model_type_to_model_dir(mt[0]) / mt[1]\\n extension_type = fs.get_contextual_file_type(model_dir)\\n model_path = model_dir / f'{mt[0]}{FileContentType.to_file_extension(extension_type)}'\\n _, _, model = load_distributed(model_path, trestle_root)\\n if not self.model_is_valid(model):\\n logger.info(f'INVALID: Model {model_path} did not pass the {self.error_msg()}')\\n return 1\\n logger.info(f'VALID: Model {model_path} passed the {self.error_msg()}')\\n return 0\\n\\n # validate file\\n if 'file' in args and args.file:\\n file_path = trestle_root / args.file\\n _, _, model = load_distributed(file_path, trestle_root)\\n if not self.model_is_valid(model):\\n logger.info(f'INVALID: Model {file_path} did not pass the {self.error_msg()}')\\n return 1\\n logger.info(f'VALID: Model {file_path} passed the {self.error_msg()}')\\n return 0\",\n \"def _validate(self):\\n All = voluptuous.All\\n Required = voluptuous.Required\\n Length = voluptuous.Length\\n Extra = voluptuous.Extra\\n\\n schema = voluptuous.Schema({\\n Required('description'): voluptuous.All(str, Length(min=5)),\\n Required('environments'): dict,\\n Required('application'): {\\n Required('name'): str,\\n Required('scenario'): [{\\n Required('driver'): str,\\n Required('description'): All(str, Length(min=5)),\\n Extra: object}]}})\\n try:\\n schema(self.marmite_tree)\\n except voluptuous.MultipleInvalid as e:\\n LOG.error(\\\"Failed to validate %s/marmite.yaml structure: %s\\\" %\\n (self.fs_layer.base_dir, e))\\n raise InvalidStructure()\",\n \"def checkModel(self, model):\\n # TODO\",\n \"def test_validate_gpy_models():\\n with pytest.raises(ValueError):\\n validate_gpy_model([\\\"m\\\"])\",\n \"def test_multiple_if_combi_nonpass(self):\\n schema = yaml.load(self.yaml_ifcombi, Loader=yaml.FullLoader)\\n document = {'basisOfRecord': 'HumanObservation', 'collectionCode': ''}\\n val = DwcaValidator(schema, error_handler=WhipErrorHandler)\\n val.validate(document)\\n self.assertEqual(val.errors,\\n {'basisOfRecord': [{'if': ['unallowed value '\\n 'HumanObservation']}]})\",\n \"def _validate(self):\\n pass\",\n \"def model_check(blk):\\n # Check temperature bounds\\n if value(blk.temperature) < blk.temperature.lb:\\n _log.error('{} Temperature set below lower bound.'.format(blk.name))\\n if value(blk.temperature) > blk.temperature.ub:\\n _log.error('{} Temperature set above upper bound.'.format(blk.name))\\n\\n # Check pressure bounds\\n if value(blk.pressure) < blk.pressure.lb:\\n _log.error('{} Pressure set below lower bound.'.format(blk.name))\\n if value(blk.pressure) > blk.pressure.ub:\\n _log.error('{} Pressure set above upper bound.'.format(blk.name))\",\n \"def is_valid(self, data_model: DataModel) -> bool:\",\n \"def test_base_validate_models():\\n with pytest.raises(ValueError):\\n base_validate_models([])\\n\\n assert [\\\"m\\\"] == base_validate_models([\\\"m\\\"])\",\n \"def check_model(self, model):\\n self.check_initial_conditions(model)\\n self.check_variables(model)\",\n \"def test_multiple_if_error(self):\\n schema = yaml.load(self.yaml_ifif, Loader=yaml.FullLoader)\\n document = {'age': '21', 'lifestage': 'juvenile'}\\n val = DwcaValidator(schema, error_handler=WhipErrorHandler)\\n val.validate(document)\\n self.assertEqual(val.errors,\\n {'lifestage': [{'if_0': ['unallowed value juvenile',\\n 'max length is 6'],\\n 'if_2': ['max length is 5']}]})\",\n \"def validate(self):\\n pass\",\n \"def validate(self):\\n pass\",\n \"def validate(self):\\n pass\",\n \"def validate(self):\\n pass\",\n \"def validate(self):\\n pass\",\n \"def validate(self):\\n pass\",\n \"def validate(self):\\n pass\",\n \"def validate(self):\\n pass\",\n \"def run_parameters_validations(self):\\n if self.risk_rule:\\n if 'connectApi' not in self.services:\\n return_error(\\\"You entered a risk rule but the 'connectApi' service is not chosen. \\\"\\n \\\"Add the 'connectApi' service to the list or remove the risk rule.\\\")\\n else:\\n for risk_rule in self.risk_rule:\\n if not is_valid_risk_rule(self, risk_rule):\\n return_error(f\\\"The given risk rule: {risk_rule} does not exist,\\\"\\n f\\\"please make sure you entered it correctly. \\\\n\\\"\\n f\\\"To see all available risk rules run the '!rf-get-risk-rules' command.\\\")\\n\\n if self.fusion_file_path is not None:\\n if 'fusion' not in self.services:\\n return_error(\\\"You entered a fusion file path but the 'fusion' service is not chosen. \\\"\\n \\\"Add the 'fusion' service to the list or remove the fusion file path.\\\")\",\n \"def validate_requested_xgb_model(xgboost_model_files, xgboost_model_hashes, model_dir):\\n for _, model_file in xgboost_model_files.items():\\n if not check_model_presence(\\n model_file, xgboost_model_hashes[model_file], model_dir\\n ):\\n download_model(model_file, xgboost_model_hashes[model_file], model_dir)\",\n \"def check_validity(self):\",\n \"async def validate(self):\\n pass\",\n \"def test_schema_validation():\\n resolver = RefResolver.from_schema(j, store={\\\"definitions\\\": j})\\n schema_definitions = j[\\\"definitions\\\"]\\n validation_models = root_dir / \\\"validation\\\" / \\\"models.yaml\\\"\\n validation_tests = yaml.load(open(validation_models), Loader=yaml.SafeLoader)\\n for cls, tests in validation_tests.items():\\n for t in tests:\\n validate(instance=t[\\\"in\\\"],\\n schema=schema_definitions[cls],\\n resolver=resolver)\",\n \"def validateRaxmlModels(self, input): \\n filename=input+\\\".mg.modelFromMG.txt\\\"\\n arq = open(filename, \\\"r\\\")\\n output = \\\"\\\"\\n conteudo = arq.readlines()\\n seqcont = 0\\n output = \\\"True\\\"\\n modelogerado = \\\"\\\"\\n listamodelos = ['DAYHOFF', 'DCMUT', 'JTT', 'MTREV', 'WAG', 'RTREV', 'CPREV',\\n 'VT', 'BLOSUM62', 'MTMAM', 'MTART', 'MTZOA', 'LG', 'PMB',\\n 'HIVB', 'HIVW', 'JTTDCMUT', 'FLU','GTR']\\n\\n if len(conteudo) == 0 or len(conteudo) > 1:\\n output = \\\"False\\\"\\n else:\\n modelogerado = conteudo[0].strip()\\n if modelogerado.upper() not in listamodelos:\\n print \\\"Teste\\\"\\n output = \\\"False\\\" \\n \\n return {\\\"MG_MODEL\\\":modelogerado, \\\"MG_MODEL_VALID\\\":output}\",\n \"def test_firms_conversion_schema():\\n legal_filing = {'conversion': FIRMS_CONVERSION}\\n\\n is_valid, errors = validate(legal_filing, 'conversion')\\n\\n if errors:\\n for err in errors:\\n print(err.message)\\n print(errors)\\n\\n assert is_valid\",\n \"def validate(model: str = \\\"hog\\\"):\\n for filepath in Path(\\\"validation\\\").rglob(\\\"*\\\"):\\n if filepath.is_file():\\n recognize_faces(\\n image_location=str(filepath.absolute()), model=model\\n )\",\n \"def verifyModels(self):\\r\\n\\r\\n #\\r\\n # now check that all models have the same poly data in the\\r\\n # model node as in the display node\\r\\n #\\r\\n polyDataInScene = []\\r\\n fileNamesInScene = []\\r\\n success = True\\r\\n numModels = slicer.mrmlScene.GetNumberOfNodesByClass( \\\"vtkMRMLModelNode\\\" )\\r\\n for n in range(numModels):\\r\\n modelNode = slicer.mrmlScene.GetNthNodeByClass( n, \\\"vtkMRMLModelNode\\\" )\\r\\n polyDataInScene.append(modelNode.GetPolyData())\\r\\n for dn in range(modelNode.GetNumberOfDisplayNodes()):\\r\\n displayNode = modelNode.GetNthDisplayNode(dn)\\r\\n if modelNode.GetPolyData() != displayNode.GetInputPolyData():\\r\\n self.delayDisplay(\\\"Model %d does not match its display node %d! (name: %s, ids: %s and %s)\\\" % (n,dn,modelNode.GetName(), modelNode.GetID(),displayNode.GetID()))\\r\\n success = False\\r\\n for sn in range(modelNode.GetNumberOfStorageNodes()):\\r\\n storageNode = modelNode.GetNthStorageNode(sn)\\r\\n fileName = storageNode.GetFileName()\\r\\n fileNamesInScene.append(fileName)\\r\\n if fileName in fileNamesInScene:\\r\\n self.delayDisplay(\\\"Model %d has duplicate file name %s! (ids: %s and %s)\\\" % (n,fileName,modelNode.GetID(),storageNode.GetID()))\\r\\n success = False\\r\\n\\r\\n\\r\\n #\\r\\n # now check that each model has a unique polydata\\r\\n #\\r\\n for n in range(numModels):\\r\\n modelNode = slicer.mrmlScene.GetNthNodeByClass( n, \\\"vtkMRMLModelNode\\\" )\\r\\n if polyDataInScene.count(modelNode.GetPolyData()) > 1:\\r\\n self.delayDisplay(\\\"Polydata for Model is duplicated! (id: %s and %s)\\\" % (n,modelNode.GetID()))\\r\\n success = False\\r\\n\\r\\n return success\",\n \"def validate(model_path: Path):\\n tmpfile = tempfile.NamedTemporaryFile()\\n\\n if not model_path.exists():\\n _eprint(f'ERROR: {model_path} does not exist')\\n\\n # Try quantization on input models\\n print(f'[Step 1] Checking if the model can be transformed into a quantized model ...')\\n try:\\n quantized_model = post_training_quantization_with_random_calibration(model=onnx.load_model(model_path),\\n per_channel=True,\\n static=True,\\n mode=QuantizationMode.dfg,\\n num_data=10)\\n except Exception as e:\\n _eprint(\\\"[Step 1] Failed\\\\n\\\")\\n raise e\\n print(f'[Step 1] Passed')\\n\\n try:\\n onnx.save_model(quantized_model, tmpfile.name)\\n except Exception as e:\\n _eprint(\\\"[ERROR] Fail to save the model\\\\n\\\")\\n raise e\\n\\n print(f'[Step 2] Checking the model can be compiled to a NPU program ...')\\n try:\\n with open(tmpfile.name, \\\"rb\\\") as model_file:\\n model = model_file.read()\\n session.create(model=model)\\n except Exception as e:\\n _eprint(\\\"[Step 2] Failed\\\\n\\\")\\n raise e\\n print(f'[Step 2] Passed')\",\n \"def validate():\\n ve('python manage.py validate')\",\n \"def test__validate_features__1():\\n for input_value in (\\n 12.6,\\n ):\\n with vampytest.assert_raises(TypeError):\\n validate_features(input_value)\",\n \"def test_ensure_valid_model_type(self):\\n # Note the \\\"valid\\\" type strings for our test\\n test_types = [\\\"bar\\\", \\\"foo\\\", \\\"Sreeta\\\", \\\"Feras\\\"]\\n # Note a set of invalid type strings for the test\\n bad_types = [\\\"Tim\\\", \\\"Sam\\\"]\\n\\n # Alias the function to be tested\\n func = pylogit.pylogit.ensure_valid_model_type\\n\\n # Make note of part of the error message that should be raised\\n partial_error_msg = \\\"The specified model_type was not valid.\\\"\\n\\n # Perform the requisite tests\\n for good_example in test_types:\\n self.assertIsNone(func(good_example, test_types))\\n for bad_example in bad_types:\\n self.assertRaisesRegexp(ValueError,\\n partial_error_msg,\\n func,\\n bad_example,\\n test_types)\\n\\n return None\",\n \"def test_multiple_if_combi(self):\\n schema = yaml.load(self.yaml_ifcombi, Loader=yaml.FullLoader)\\n document = {'basisOfRecord': 'PreservedSpecimen', 'collectionCode': ''}\\n val = DwcaValidator(schema, error_handler=WhipErrorHandler)\\n self.assertTrue(val.validate(document))\",\n \"def fields_validator():\\n\\n return validator.BrewerySchema()\",\n \"def validate_model(data_dir, script_dir='../results/saved_scripts', model_dir=None,\\n model_name='squeezenet', num_classes=3, batch_size=8, num_epochs=50, feature_extract=True,\\n learning_rates=(1e-3,), weight_decays=(1e-5,), viz=None):\\n # Make sure model type is valid\\n assert model_name in available_models_input_size.keys(), 'Unsupported model type ' + model_name\\n print('Using %s ...\\\\n' % model_name)\\n\\n # Record start time\\n since = time.time()\\n\\n # Create dataloaders\\n print('Loading dataset ...\\\\n')\\n dataloaders, dataset_mean, dataset_std = \\\\\\n create_dataloaders(data_dir, available_models_input_size[model_name], batch_size)\\n print('+ Dataset mean:', dataset_mean[0])\\n print('+ Dataset standard deviation:', dataset_std[0])\\n dataset_sizes = {x: len(dataloaders[x].dataset) for x in ['train', 'val', 'test']}\\n print('+ %(train)d samples for training, %(val)d for validation, %(test)d for test.\\\\n' % dataset_sizes)\\n\\n # Setup the loss function\\n criterion = nn.CrossEntropyLoss()\\n\\n # Keep record of best val acc and best model\\n best_model = None\\n best_val_acc = 0.0\\n best_lr = None\\n best_wd = None\\n\\n # Validating over hyper-parameters\\n print('Validating hyper-parameters ...')\\n for learning_rate in learning_rates:\\n for weight_decay in weight_decays:\\n # Initialize the model for this run\\n model, params_to_update = initialize_model(model_name, num_classes, feature_extract)\\n\\n # Send the model to GPU\\n model = model.to(using_device)\\n\\n # Setup optimizer\\n optimizer = optim.Adam(\\n params_to_update,\\n lr=learning_rate,\\n betas=(0.9, 0.999),\\n eps=1e-08,\\n weight_decay=weight_decay,\\n amsgrad=False\\n )\\n\\n # Train and evaluate\\n model, val_acc_history, loss_history = train_model(\\n model=model,\\n dataloaders=dataloaders,\\n criterion=criterion,\\n optimizer=optimizer,\\n num_epochs=num_epochs,\\n is_inception=(model_name == \\\"inception\\\")\\n )\\n\\n # Plot val acc and loss through learning process\\n if viz:\\n trace_name = '%s[lr=%g,wd=%g]' % (model_name, learning_rate, weight_decay)\\n viz.line(\\n X=np.arange(1, 1 + len(val_acc_history)),\\n Y=val_acc_history,\\n name=trace_name,\\n win='Val Acc',\\n update='append',\\n opts={\\n 'title': 'Val Acc',\\n 'xlabel': 'epoch',\\n 'ylabel': 'val_acc',\\n 'showlegend': True,\\n 'height': 400,\\n 'width': 700\\n }\\n )\\n viz.line(\\n X=np.arange(1, 1 + len(loss_history)),\\n Y=loss_history,\\n name=trace_name,\\n win='Loss',\\n update='append',\\n opts={\\n 'title': 'Loss',\\n 'xlabel': 'epoch',\\n 'ylabel': 'loss',\\n 'showlegend': True,\\n 'height': 400,\\n 'width': 700\\n }\\n )\\n\\n # Print val acc for this set of hyper-parameters\\n this_val_acc = max(val_acc_history)\\n print('\\\\n* lr = %.4e, wd = %.4e, val_acc=%.4f\\\\n' % (learning_rate, weight_decay, this_val_acc))\\n\\n # Update best model info\\n if this_val_acc > best_val_acc:\\n best_model = model\\n best_val_acc = this_val_acc\\n best_lr = learning_rate\\n best_wd = weight_decay\\n\\n # Print out train result\\n time_elapsed = time.time() - since\\n print('Training complete in {:.0f}m {:.0f}s'.format(time_elapsed // 60, time_elapsed % 60))\\n print('Model at lr=%e, wd=%e has the highest val acc: %.4f' % (best_lr, best_wd, best_val_acc))\\n\\n best_model.to('cpu')\\n\\n # Temperature scaling\\n print('\\\\nComputing temperature ...')\\n temperature = compute_temperature(best_model, dataloaders['val'], verbose=True)\\n\\n # For dev purpose ...\\n if model_dir:\\n torch.save(best_model.state_dict(), os.path.abspath(os.path.join(model_dir, model_name + '.pt')))\\n\\n # Test model\\n test_acc, _ = test_model(best_model, dataloaders['test'])\\n print('\\\\nTest Acc: %.4f' % test_acc)\\n\\n # Save the best model to disk\\n if script_dir:\\n # Convert to torch script for inference efficiency\\n best_model = convert_to_torch_script(best_model, available_models_input_size[model_name])\\n test_acc, _ = test_model(best_model, dataloaders['test'])\\n print('\\\\nSanity check for sciprt module: Test Acc: %.4f' % test_acc)\\n os.makedirs(script_dir, exist_ok=True)\\n print('\\\\nSaving model ...')\\n info_list = [\\n model_name, # model type\\n time.ctime().replace(':', '#'), # timestamp\\n '%.3f' % temperature, # temperature of the model\\n '%.5f' % dataset_mean[0],\\n '%.5f' % dataset_std[0]\\n ]\\n file_name = '%'.join(info_list) + '.pt'\\n file_url = os.path.join(script_dir, file_name)\\n best_model.save(file_url)\\n print('\\\\nModel saved to %s\\\\n' % file_url)\\n\\n return best_model, best_val_acc, test_acc\",\n \"def semantic_validate(instance):\\n unknown_templates = {}\\n for name, requires in instance[\\\"application\\\"][\\\"requires\\\"].items():\\n if name in instance[\\\"application\\\"][\\\"services\\\"]:\\n raise ValidationError(errors=[\\n \\\"/application/requires/{}: the name {} conflicts with service\\\"\\n \\\" /application/services/{}\\\".format(name,\\n repr(name),\\n name),\\n ])\\n if requires[\\\"template\\\"] not in instance[\\\"local\\\"][\\\"templates\\\"]:\\n unknown_templates[\\\"/application/requires/{}/template\\\".format(\\n name)] = requires[\\\"template\\\"]\\n for service_name, service in instance[\\\"application\\\"][\\\"services\\\"].items():\\n for name, requires in service[\\\"requires\\\"].items():\\n if name in instance[\\\"application\\\"][\\\"requires\\\"]:\\n raise ValidationError(errors=[\\n \\\"/application/services/{}/requires/{}: the name {}\\\"\\n \\\" conflicts with /application/requires/{}\\\".format(\\n service_name, name, repr(name), name)\\n ])\\n if requires[\\\"template\\\"] not in instance[\\\"local\\\"][\\\"templates\\\"]:\\n unknown_templates[\\n \\\"/application/services/{}/requires/{}/template\\\".\\n format(service_name, name)] = requires[\\\"template\\\"]\\n if unknown_templates:\\n raise ValidationError(errors=[\\n \\\"{}: the template {} does not exist \\\"\\n \\\"in /local/templates\\\".format(path, repr(name))\\n for (path, name) in unknown_templates.items()\\n ])\",\n \"def testYamlModelValidation(self):\\n\\n _model_file = self.TEST_CLASSES_DIR + '/simple-model.yaml'\\n _archive_file = self.TEST_CLASSES_DIR + \\\"/SingleAppDomain.zip\\\"\\n _method_name = 'testYamlModelValidation'\\n\\n mw_home = env_helper.getenv('MW_HOME')\\n args_map = {\\n '-oracle_home': mw_home,\\n '-model_file': _model_file,\\n '-archive_file': _archive_file\\n }\\n\\n model_context = ModelContext('ValidationTestCase', args_map)\\n aliases = Aliases(model_context, wls_version=self._wls_version)\\n\\n try:\\n model_dictionary = FileToPython(model_context.get_model_file()).parse()\\n model_validator = Validator(model_context, aliases, wlst_mode=WlstModes.ONLINE)\\n return_code = model_validator.validate_in_tool_mode(model_dictionary,\\n model_context.get_variable_file(),\\n model_context.get_archive_file_name())\\n self._logger.info('The Validator.validate_in_tool_mode() call returned {0}',\\n Validator.ReturnCode.from_value(return_code),\\n class_name=self._class_name, method_name=_method_name)\\n except TranslateException, te:\\n return_code = Validator.ReturnCode.STOP\\n self._logger.severe('WLSDPLY-20009',\\n self._program_name,\\n model_context.get_model_file(),\\n te.getLocalizedMessage(), error=te,\\n class_name=self._class_name, method_name=_method_name)\\n\\n self.assertNotEqual(return_code, Validator.ReturnCode.STOP)\",\n \"def validate(self, entity, value, tracker, dispatcher, events_custom = None):\\n global pincodes_available\\n\\n df = pd.read_excel('SampleModelSerialGEA.xlsx')\\n print(entity, value)\\n\\n if entity == 'appliance': ###IMP :: can reduce appliance value to one allowed here\\n print('Checking appliance')\\n if value not in appliances:\\n print('error')\\n value = None\\n\\n else:\\n idx_lis = df.index[df.loc[:, 'Product Line'].map(lambda x : x.lower()) == value.lower()].tolist()\\n\\n if tracker.get_slot('serialnumber') and not tracker.get_slot('serialnumber').upper() in [df.loc[i, 'Serial Number'] for i in idx_lis]:\\n dispatcher.utter_message(\\\"For appliance %s serial number %s does not match !!!\\\" % (value, tracker.get_slot('serialnumber')))\\n value = None\\n\\n elif tracker.get_slot('modelnumber') and not tracker.get_slot('modelnumber').upper() in [df.loc[i, 'Model Number'] for i in idx_lis]:\\n dispatcher.utter_message(\\\"For appliance %s model number %s does not match !!!\\\" % (value, tracker.get_slot('modelnumber')))\\n value = None\\n\\n\\n if entity == 'modelnumber':\\n print('Checking modelnumber')\\n if not any(df.loc[:, 'Model Number'] == value.upper()):\\n print('error')\\n dispatcher.utter_message(\\\"Model no %s not in database !!!\\\" % tracker.get_slot('modelnumber'))\\n value = None\\n\\n else:\\n idx_lis = df.index[df.loc[:, 'Model Number'] == value.upper()].tolist()\\n idx = idx_lis[0]\\n\\n if tracker.get_slot('appliance') and not df.loc[idx, 'Product Line'].lower() == tracker.get_slot('appliance'):\\n dispatcher.utter_message(\\\"For model number %s appliance %s does not match !!!\\\" % (value, tracker.get_slot('appliance')))\\n value = None\\n\\n elif not tracker.get_slot('appliance'):\\n events_custom.extend([SlotSet(\\\"appliance\\\", df.loc[idx, 'Product Line'].lower())]) \\n dispatcher.utter_message(\\\"For model number %s appliance %s added from database !!!\\\" % (value, df.loc[idx, 'Product Line'].lower()))\\n\\n if tracker.get_slot('serialnumber') and not tracker.get_slot('serialnumber').upper() in [df.loc[i, 'Serial Number'] for i in idx_lis]:\\n dispatcher.utter_message(\\\"For model number %s serial number %s does not match !!!\\\" % (value, tracker.get_slot('serialnumber')))\\n value = None\\n\\n\\n if entity == 'serialnumber':\\n print('Checking serialnumber')\\n \\n # if df[idx]['Serial Number'] == value.upper():\\n # print('error')\\n # value = None\\n if not any(df.loc[:, 'Serial Number'] == value.upper()):\\n print('error')\\n dispatcher.utter_message(\\\"Serial no %s not in database !!!\\\" % tracker.get_slot('serialnumber'))\\n value = None\\n else:\\n idx = df.index[df.loc[:, 'Serial Number'] == value.upper()].tolist()[0]\\n \\n if tracker.get_slot('modelnumber') and not df.loc[idx, 'Model Number'] == tracker.get_slot('modelnumber').upper():\\n dispatcher.utter_message(\\\"For serial number %s model number %s does not match !!!\\\" % (value, tracker.get_slot('modelnumber')))\\n value = None\\n \\n elif not tracker.get_slot('modelnumber'):\\n print('Adding modelnumber given serialnumber')\\n events_custom.extend([SlotSet('modelnumber', df.loc[idx, 'Model Number'].lower())]) \\n dispatcher.utter_message(\\\"For serial number %s model number %s added from database !!!\\\" % (value, df.loc[idx, 'Model Number'].lower()))\\n\\n if tracker.get_slot('appliance') and not df.loc[idx, 'Product Line'].lower() == tracker.get_slot('appliance'):\\n dispatcher.utter_message(\\\"For serial number %s appliance %s does not match !!!\\\" % (value, tracker.get_slot('appliance')))\\n value = None\\n\\n elif not tracker.get_slot('appliance'):\\n print('Adding appliance given serialnumber')\\n events_custom.extend([SlotSet('appliance', df.loc[idx, 'Product Line'].lower())]) \\n dispatcher.utter_message(\\\"For serial number %s appliance %s added from database !!!\\\" % (value, df.loc[idx, 'Product Line'].lower()))\\n \\n\\n if entity == 'pincode':\\n print(value)\\n #print(value.type)\\n if value not in pincodes_available:\\n print('error')\\n dispatcher.utter_message(\\\"The provided pincode %s is unavailable for service. We are sorry for the inconvinence.\\\" % value)\\n value = None\\n\\n if entity == 'phonenumber':\\n try:\\n print('Checking phone number')\\n r = requests.get('http://apilayer.net/api/validate?access_key=%s&number=%s&country_code=IN&format=1'%(PHONE_NO_API_KEY, value)).json()\\n print(r)\\n if not r['valid']:\\n value = None\\n except Exception as e:\\n print(str(e))\\n dispatcher.utter_message(\\\"Phone No is unable to be confirmed !!!! Believing entered info is correct\\\")\\n\\n return value\",\n \"def __validate(self):\\n pass\",\n \"def validate_required_model(program_name, arg_map):\\n _method_name = 'validate_required_model'\\n\\n if CommandLineArgUtil.MODEL_FILE_SWITCH in arg_map:\\n model_file_value = arg_map[CommandLineArgUtil.MODEL_FILE_SWITCH]\\n model_files = cla_utils.get_model_files(model_file_value)\\n\\n for model_file in model_files:\\n try:\\n FileUtils.validateExistingFile(model_file)\\n except IllegalArgumentException, iae:\\n ex = exception_helper.create_cla_exception(ExitCode.ARG_VALIDATION_ERROR, 'WLSDPLY-20006', program_name,\\n model_file, iae.getLocalizedMessage(), error=iae)\\n __logger.throwing(ex, class_name=_class_name, method_name=_method_name)\\n raise ex\\n else:\\n ex = exception_helper.create_cla_exception(ExitCode.USAGE_ERROR, 'WLSDPLY-20015', program_name,\\n CommandLineArgUtil.MODEL_FILE_SWITCH)\\n __logger.throwing(ex, class_name=_class_name, method_name=_method_name)\\n raise ex\",\n \"def test_validation(self):\\n self.validationFails()\",\n \"async def async_step_validation(self, user_input=None):\\n errors = {}\\n if user_input:\\n try:\\n await self._gateway.validate_mfa(user_input[CONF_CODE])\\n except SENSE_CONNECT_EXCEPTIONS:\\n errors[\\\"base\\\"] = \\\"cannot_connect\\\"\\n except SenseAuthenticationException:\\n errors[\\\"base\\\"] = \\\"invalid_auth\\\"\\n except Exception: # pylint: disable=broad-except\\n _LOGGER.exception(\\\"Unexpected exception\\\")\\n errors[\\\"base\\\"] = \\\"unknown\\\"\\n else:\\n return await self.create_entry_from_data()\\n\\n return self.async_show_form(\\n step_id=\\\"validation\\\",\\n data_schema=vol.Schema({vol.Required(CONF_CODE): vol.All(str, vol.Strip)}),\\n errors=errors,\\n )\",\n \"def validate(self) -> None:\\n validate_model_generator_interface_input(self.configs)\",\n \"def __validate__(self):\",\n \"def do_validate(self, request, _object):\\n\\n pass\",\n \"def test_kyc_get_validation(self):\\n pass\",\n \"def check_models(*, schemas: _oa_types.Schemas) -> types.TModels:\\n not_constructables = iterate.not_constructable(schemas=schemas)\\n not_constructables_result = map(\\n lambda args: (args[0], check_model(schemas, args[1])), not_constructables\\n )\\n return dict(not_constructables_result)\",\n \"def test_valid_model(self):\\n model_cls = ModelContainer(APP_LABEL, TestModel2._meta.db_table).model_cls\\n self.assertTrue(model_cls.__class__.__name__ is models.Model.__class__.__name__)\",\n \"def test__validate_features__0():\\n for input_value, expected_output in (\\n (None, None),\\n ([], None),\\n ([GuildFeature.animated_banner], (GuildFeature.animated_banner, )),\\n ([GuildFeature.animated_banner.value], (GuildFeature.animated_banner, )),\\n (\\n [GuildFeature.animated_banner, GuildFeature.animated_icon],\\n (GuildFeature.animated_banner, GuildFeature.animated_icon,),\\n ),\\n ):\\n output = validate_features(input_value)\\n vampytest.assert_eq(output, expected_output)\",\n \"def is_model_valid(self):\\n try:\\n payload = {\\n \\\"modelurl\\\": self.model_builder_url + self.model_uuid,\\n \\\"api_key\\\": self.web2nl_api_key\\n }\\n\\n response = requests.get(self.web2nl_url + \\\"validations\\\", params=payload)\\n if response.status_code is requests.codes.no_content:\\n return True\\n elif response.status_code is requests.codes.bad_request:\\n self.logger.error(\\\"Model validation failed: \\\" + response.text)\\n return False\\n else:\\n self.logger.error(\\\"Failed while validating model. Will retry in some time\\\")\\n raise RuntimeError(\\\"Failed while validating model\\\")\\n except requests.exceptions.ConnectionError as errc:\\n self.logger.error(\\\"Error Connecting:\\\", errc)\",\n \"def test_validate_gbdt_models():\\n from lightgbm import LGBMRegressor # pylint:disable=import-outside-toplevel\\n\\n lgbm = LGBMRegressor(objective=\\\"quantile\\\", alpha=0.1)\\n with pytest.raises(ValueError):\\n validate_gbdt_models([(lgbm, lgbm), (lgbm, lgbm, lgbm)], 2)\\n\\n with pytest.raises(ValueError):\\n validate_gbdt_models([(LGBMRegressor(), lgbm, lgbm), (lgbm, lgbm, lgbm)], 2)\\n\\n with pytest.raises(ValueError):\\n validate_gbdt_models([(lgbm, lgbm, lgbm), (lgbm, lgbm, LGBMRegressor())], 2)\\n\\n valid_input = [(lgbm, LGBMRegressor(), lgbm), (lgbm, LGBMRegressor(), lgbm)]\\n assert validate_gbdt_models(valid_input, 2) == valid_input\",\n \"def validate(cls, templates):\\n super(Product, cls).validate(templates)\\n\\n for template in templates:\\n template.check_type_and_mode()\\n\\n template.check_gc_min_max()\",\n \"def test_multiple_if_pass(self):\\n schema = yaml.load(self.yaml_ifif, Loader=yaml.FullLoader)\\n val = DwcaValidator(schema, error_handler=WhipErrorHandler)\\n document = {'age': '21', 'lifestage': 'adult'} # True\\n self.assertTrue(val.validate(document))\\n\\n document = {'age': '2', 'lifestage': 'juvenile'} # True\\n val = DwcaValidator(schema, error_handler=WhipErrorHandler)\\n self.assertTrue(val.validate(document))\",\n \"def __init__(\\n self,\\n model_filepath: str,\\n fmi_version: str = FMI_VERSION,\\n start_time = START_TIME,\\n stop_time = STOP_TIME,\\n step_size = STEP_SIZE,\\n user_validation: bool = False,\\n use_unzipped_model: bool = False,\\n ):\\n\\n # validate simulation: config_vars (optional), inputs, and outputs\\n validated_sim = FMUSimValidation(model_filepath, user_validation)\\n \\n # extract validated sim configuration\\n self.model_filepath = validated_sim.model_filepath\\n self.sim_config_filepath = validated_sim.sim_config_filepath\\n self.model_description = validated_sim.model_description\\n # model variable names structured per type (config, inputs/brain actions, outputs/brain states)\\n self.sim_config_params = validated_sim.sim_config_params\\n self.sim_inputs = validated_sim.sim_inputs\\n self.sim_outputs = validated_sim.sim_outputs\\n self.sim_other_vars = validated_sim.sim_other_vars\\n # model variable dictionaries with\\n self.vars_to_idx = validated_sim.vars_to_idx\\n self.vars_to_type_f = validated_sim.vars_to_type_f\\n self.vars_to_ini_vals = validated_sim.vars_to_ini_vals\\n\\n # get parent directory and model name (without .fmu)\\n aux_head_and_tail_tup = os.path.split(self.model_filepath)\\n self.model_dir = aux_head_and_tail_tup[0]\\n self.model_name = aux_head_and_tail_tup[1].replace(\\\".fmu\\\", \\\"\\\")\\n\\n # placeholder to prevent accessing methods if initialization hasn't been called first\\n # also prevents calling self.fmu.terminate() if initialization hasn't occurred or termination has already been applied\\n self._is_initialized = False\\n \\n # get FMI version\\n read_fmi_version = self.model_description.fmiVersion\\n if read_fmi_version in [\\\"1.0\\\", \\\"2.0\\\", \\\"3.0\\\"]:\\n # Use fmi version from model_description\\n print(f\\\"[FMU Connector] FMU model indicates to be follow fmi version '{read_fmi_version}'.\\\")\\n self.fmi_version = read_fmi_version\\n else:\\n assert fmi_version in [\\\"1.0\\\", \\\"2.0\\\", \\\"3.0\\\"], f\\\"fmi version provided ({fmi_version}) is invalid.\\\"\\n # Use fmi version provided by user if the one on model_description is invalid\\n print(f\\\"[FMU Connector] Using fmi version provided by user: v'{fmi_version}'. Model indicates v'{read_fmi_version}' instead.\\\")\\n self.fmi_version = fmi_version\\n\\n # save time-related data\\n error_log = \\\"Stop time provided ({}) is lower than start time provided ({})\\\".format(stop_time, start_time)\\n assert stop_time > start_time, error_log\\n error_log = \\\"Step size time ({}) is greater than the difference between \\\".format(step_size)\\n error_log += \\\"stop and start times, ({}) and ({}), respectively\\\".format(stop_time, start_time)\\n assert step_size < stop_time-start_time, error_log\\n self.start_time = float(start_time)\\n self.stop_time = float(stop_time)\\n self.step_size = float(step_size)\\n self.sim_time = float(self.start_time)\\n \\n # retrieve FMU model type, as well as model identifier\\n self.model_type = \\\"None\\\"\\n self.model_identifier = self.model_name\\n coSimulation = self.model_description.coSimulation\\n if coSimulation is not None:\\n self.model_identifier = coSimulation.modelIdentifier\\n self.model_type = \\\"coSimulation\\\"\\n else:\\n scheduledExecution = self.model_description.scheduledExecution\\n if scheduledExecution is not None:\\n self.model_identifier = scheduledExecution.modelIdentifier\\n self.model_type = \\\"scheduledExecution\\\"\\n else:\\n modelExchange = self.model_description.modelExchange\\n if modelExchange is not None:\\n self.model_identifier = modelExchange.modelIdentifier\\n self.model_type = \\\"modelExchange\\\"\\n else:\\n raise Exception(\\\"Model is not of any known type: coSimulation, scheduledExecution, nor modelExchange\\\")\\n\\n \\n # extract the FMU\\n extract_path = os.path.join(self.model_dir, self.model_name + \\\"_unzipped\\\")\\n if not use_unzipped_model:\\n # extract model to subfolder by default\\n self.unzipdir = extract(self.model_filepath, unzipdir=extract_path)\\n else:\\n # use previouslly unzipped model\\n self.unzipdir = extract_path\\n\\n # get unique identifier using timestamp for instance_name (possible conflict with batch)\\n self.instance_name = self._get_unique_id()\\n \\n\\n # ---------------------------------------------------------------\\n # instance model depending on 'fmi version' and 'fmu model type'\\n self.fmu = None\\n print(f\\\"[FMU Connector] Model has been determined to be of type '{self.model_type}' with fmi version == '{self.fmi_version}'.\\\")\\n if self.model_type == \\\"modelExchange\\\":\\n ## [TODO] test integrations\\n print(f\\\"[FMU Connector] Simulator hasn't been tested for '{self.model_type}' models with fmi version == '{self.fmi_version}'.\\\")\\n if self.fmi_version == \\\"1.0\\\":\\n self.fmu = fmi1.FMU1Model(guid=self.model_description.guid,\\n unzipDirectory=self.unzipdir,\\n modelIdentifier=self.model_identifier,\\n instanceName=self.instance_name)\\n elif self.fmi_version == \\\"2.0\\\":\\n self.fmu = fmi2.FMU2Model(guid=self.model_description.guid,\\n unzipDirectory=self.unzipdir,\\n modelIdentifier=self.model_identifier,\\n instanceName=self.instance_name)\\n elif self.fmi_version == \\\"3.0\\\":\\n self.fmu = fmi3.FMU3Model(guid=self.model_description.guid,\\n unzipDirectory=self.unzipdir,\\n modelIdentifier=self.model_identifier,\\n instanceName=self.instance_name)\\n elif self.model_type == \\\"coSimulation\\\":\\n if self.fmi_version == \\\"1.0\\\":\\n ## [TODO] test integrations\\n print(f\\\"[FMU Connector] Simulator hasn't been tested for '{self.model_type}' models with fmi version == '{self.fmi_version}'.\\\")\\n self.fmu = fmi1.FMU1Slave(guid=self.model_description.guid,\\n unzipDirectory=self.unzipdir,\\n modelIdentifier=self.model_identifier,\\n instanceName=self.instance_name)\\n elif self.fmi_version == \\\"2.0\\\":\\n self.fmu = fmi2.FMU2Slave(guid=self.model_description.guid,\\n unzipDirectory=self.unzipdir,\\n modelIdentifier=self.model_identifier,\\n instanceName=self.instance_name)\\n elif self.fmi_version == \\\"3.0\\\":\\n ## [TODO] test integrations\\n print(f\\\"[FMU Connector] Simulator hasn't been tested for '{self.model_type}' models with fmi version == '{self.fmi_version}'.\\\")\\n self.fmu = fmi3.FMU3Slave(guid=self.model_description.guid,\\n unzipDirectory=self.unzipdir,\\n modelIdentifier=self.model_identifier,\\n instanceName=self.instance_name)\\n elif self.model_type == \\\"scheduledExecution\\\":\\n if self.fmi_version == \\\"1.0\\\" or self.fmi_version == \\\"2.0\\\":\\n raise Exception(\\\"scheduledExecution type only exists in fmi v'3.0', but fmi version '{}' was provided.\\\".format(self.fmi_version))\\n \\n print(f\\\"[FMU Connector] Simulator hasn't been tested for '{self.model_type}' models with fmi version == '{self.fmi_version}'.\\\")\\n ## [TODO] test integrations\\n #elif self.fmi_version_int == 3:\\n self.fmu = fmi3.FMU3ScheduledExecution(guid=self.model_description.guid,\\n unzipDirectory=self.unzipdir,\\n modelIdentifier=self.model_identifier,\\n instanceName=self.instance_name)\\n \\n \\n # ---------------------------------------------------------------\\n return\",\n \"def _validate(self):\\n _models = {'hrrr', 'hrrrak', 'rap'}\\n _fields = {'prs', 'sfc', 'nat', 'subh'}\\n \\n self.date = pd.to_datetime(self.date)\\n \\n if self.model == 'alaska':\\n self.model == 'hrrrak'\\n\\n assert self.fxx in range(49), \\\"Forecast lead time `fxx` is too large\\\"\\n assert self.model in _models, f\\\"`model` must be one of {_models}\\\"\\n if self.model in ['hrrr', 'hrrrak']:\\n assert self.field in _fields, f\\\"`field must be one of {_fields}\\\"\\n else:\\n # field is not needed for RAP model.\\n self.field = ''\\n \\n if isinstance(self.priority, str):\\n self.priority = [self.priority]\\n \\n self.priority = [i.lower() for i in self.priority]\\n\\n # Don't look for data from NOMADS if requested date is earlier\\n # than yesterday. NOMADS doesn't keep data that old.\\n if 'nomads' in self.priority:\\n yesterday = datetime.utcnow() - timedelta(hours=24)\\n yesterday = pd.to_datetime(f\\\"{yesterday:%Y-%m-%d}\\\")\\n if self.date < yesterday:\\n self.priority.remove('nomads')\",\n \"def Validate(self):\\n raise NotImplementedError\",\n \"def metadata_validate(self):\\n # Set path to `service_schema` stored in the `resources` directory from cwd of `mpe_service.py`\\n current_path = Path(__file__).parent\\n relative_path = '../../snet/snet_cli/resources/service_schema'\\n path_to_schema = (current_path / relative_path).resolve()\\n with open(path_to_schema, 'r') as f:\\n schema = json.load(f)\\n metadata = load_mpe_service_metadata(self.args.metadata_file)\\n try:\\n validate(instance=metadata.m, schema=schema)\\n except Exception as e:\\n docs = \\\"http://snet-cli-docs.singularitynet.io/service.html\\\"\\n error_message = f\\\"\\\\nVisit {docs} for more information.\\\"\\n if e.validator == 'required':\\n raise ValidationError(e.message + error_message)\\n elif e.validator == 'minLength':\\n raise ValidationError(f\\\"`{e.path[-1]}` -> cannot be empty.\\\" + error_message)\\n elif e.validator == 'minItems':\\n raise ValidationError(f\\\"`{e.path[-1]}` -> minimum 1 item required.\\\" + error_message)\\n elif e.validator == 'type':\\n raise ValidationError(f\\\"`{e.path[-1]}` -> {e.message}\\\" + error_message)\\n elif e.validator == 'enum':\\n raise ValidationError(f\\\"`{e.path[-1]}` -> {e.message}\\\" + error_message)\\n elif e.validator == 'additionalProperties':\\n if len(e.path) != 0:\\n raise ValidationError(f\\\"{e.message} in `{e.path[-2]}`.\\\" + error_message)\\n else:\\n raise ValidationError(f\\\"{e.message} in main object.\\\" + error_message)\\n else:\\n exit(\\\"OK. Ready to publish.\\\")\",\n \"def question_new_validate():\",\n \"def validate(model,dataloader,criterions,epoch,plots):\\n # switch to evaluate mode\\n model.eval()\\n\\n running_loss = 0.0\\n running_oa = 0.0\\n running_moa = 0.0\\n\\n avg_losses={}\\n avg_accuracies={}\\n avg_moas={}\\n for i in range(6):\\n avg_losses[i] = AverageMeter()\\n avg_accuracies[i] = AverageMeter()\\n avg_moas[i] = AverageMeter()\\n\\n tq_bar = tqdm(enumerate(dataloader),total=len(dataloader),ncols=80,desc='Testing')\\n for batch_id, (images, labels_group) in tq_bar:\\n # if i>25:\\n # break\\n if torch.cuda.is_available():\\n images = [Variable(image.cuda()) for image in images]\\n labels_group = [labels for labels in labels_group]\\n else:\\n print('Cuda not available')\\n images = [Variable(image) for image in images]\\n labels_group = [labels for labels in labels_group]\\n\\n\\n batch_losses = []\\n batch_accuracies = []\\n batch_moas = []\\n\\n for img, labels in zip(images, labels_group):\\n outputs = model(img)\\n net_batch_size = outputs[0].size(0)\\n if torch.cuda.is_available():\\n labels = [Variable(label.cuda()) for label in labels]\\n else:\\n labels = [Variable(label) for label in labels]\\n for i,pair in enumerate(zip(outputs, labels)):\\n accuracy = accuracy_dense(pair[0].data, pair[1].data)\\n moa,_ = mAP_dense(pair[0].data, pair[1].data)\\n batch_losses.append(criterions[i](pair[0], pair[1]))\\n batch_accuracies.append(accuracy)\\n batch_moas.append(moa)\\n\\n for i in range(6):\\n avg_losses[i].update(batch_losses[i].data[0], net_batch_size)\\n avg_accuracies[i].update(batch_accuracies[i], net_batch_size)\\n avg_moas[i].update(batch_moas[i], net_batch_size)\\n\\n ## LOSS COMPUTATION\\n # loss_weight = [auto_loss_weight(0,epoch), auto_loss_weight(1,epoch), auto_loss_weight(2,epoch), auto_loss_weight(3,epoch), auto_loss_weight(4,epoch)]\\n if epoch < 40:\\n loss_weight = [0.1, 0.1, 0.1, 0.1, 0.1, 0.5]\\n else:\\n loss_weight = [0.5, 0.1, 0.1, 0.1, 0.1, 0.1]\\n\\n # loss_weight = [1., 0.01, 0.01, 0.01, 0.01, 0.01] # fait converger en OA la HD layer\\n loss_weight = [1.,0.7, 0.6, 0.5, 0.1, 0.05, 0.01]\\n\\n total_batch_loss = 0\\n for w, l in zip(loss_weight, batch_losses):\\n total_batch_loss += w*l\\n\\n\\n running_loss += total_batch_loss.data[0]\\n # running_oa += oa\\n # running_hd_moa += hd_moa\\n for i in range(6):\\n plots.plot(\\\"Total loss (running)\\\", \\\"val \\\"+str(i), epoch*len(dataloader)+batch_id+1, avg_losses[i].val)\\n plots.plot(\\\"OA (running)\\\", \\\"val \\\"+str(i), epoch*len(dataloader)+batch_id+1, avg_accuracies[i].val)\\n plots.plot(\\\"mOA (running)\\\", \\\"val \\\"+str(i), epoch*len(dataloader)+batch_id+1, avg_moas[i].val)\\n for i in range(6):\\n plots.plot(\\\"Total loss (final mean of epoch)\\\", \\\"val \\\"+str(i), epoch+1, avg_losses[i].val)\\n plots.plot(\\\"OA (final mean of epoch)\\\", \\\"val \\\"+str(i), epoch+1, avg_accuracies[i].val)\\n plots.plot(\\\"mOA (final mean of epoch)\\\", \\\"val \\\"+str(i), epoch+1, avg_moas[i].val)\",\n \"def test_validate_fl_fa_fs_hn_wl(session, desc, valid, reg_type, message_content):\\n # setup\\n json_data = copy.deepcopy(FINANCING)\\n json_data['type'] = reg_type\\n del json_data['trustIndenture']\\n del json_data['lifeYears']\\n json_data['lifeInfinite'] = True\\n if desc == DESC_MISSING_GC:\\n del json_data['generalCollateral']\\n if desc != DESC_INCLUDES_VC:\\n del json_data['vehicleCollateral']\\n\\n error_msg = validator.validate(json_data)\\n if valid:\\n assert error_msg == ''\\n elif message_content:\\n # print(error_msg)\\n assert error_msg != ''\\n assert error_msg.find(message_content) != -1\",\n \"def validate(self):\\n\\n\\tmissing = []\\n\\tbadcheck = []\\n\\tfor name, checkfunc, params in self._required:\\n\\t try:\\n\\t\\targ = self.make_required(name)\\n\\t\\tif checkfunc is not None:\\n\\t\\t if params is not None:\\n\\t\\t\\tparams = (self.param_map[name], arg) + params\\n\\t\\t else:\\n\\t\\t\\tparams = (self.param_map[name], arg)\\n\\t\\t try:\\n\\t\\t\\tapply(checkfunc, params)\\n\\t\\t except ValidationError, msg:\\n\\t\\t\\tbadcheck.append(msg)\\n\\t except ValidationError, args:\\n\\t\\tmissing.append(args)\\n\\n\\tfor (name, checkfunc, params) in self._optional:\\n\\t tup = self.make_optional(name)\\n\\t if tup and checkfunc is not None:\\n\\t\\tif params is not None:\\n\\t\\t params = (self.param_map[name], tup) + params\\n\\t\\telse:\\n\\t\\t params = (self.param_map[name], tup)\\n\\t\\ttry:\\n\\t\\t apply(checkfunc, params)\\n\\t\\texcept ValidationError, msg:\\n\\t\\t badcheck.append(msg)\\n\\n\\tif (missing or badcheck) and self.log_errors:\\n\\t self.log_error(missing, badcheck)\\n\\n\\tif (missing or badcheck) and self.generate_error_page:\\n\\t self.generate_HTML(missing, badcheck)\\n\\n\\tself.missing = missing\\n\\tself.badcheck = badcheck\\n\\n\\treturn not (missing or badcheck)\",\n \"def __init__(self,\\n examples: types.Channel,\\n model: types.Channel,\\n blessing: Optional[types.Channel] = None,\\n name: Optional[Text] = None):\\n blessing = blessing or types.Channel(\\n type=standard_artifacts.ModelBlessing,\\n artifacts=[standard_artifacts.ModelBlessing()])\\n spec = ModelValidatorSpec(\\n examples=examples,\\n model=model,\\n blessing=blessing)\\n super(ModelValidator, self).__init__(spec=spec, name=name)\",\n \"def test__validate_owner__1():\\n for field_value in (\\n 12.6,\\n ):\\n with vampytest.assert_raises(TypeError):\\n validate_owner(field_value)\",\n \"def test_required_fields_X_normalization(self):\\n\\n del self.validator.adata.uns[\\\"X_normalization\\\"]\\n self.validator.validate_adata()\\n self.assertEqual(\\n self.validator.errors, [\\\"ERROR: 'X_normalization' in 'uns' is not present.\\\"]\\n )\",\n \"def validate(cls, data, errors):\",\n \"def validate_model(model):\\n warnings = []\\n if model is None:\\n model = 'gfs'\\n warnings.append('Using default model of %s' % model)\\n model = model.lower().strip()\\n if not model in _supported_models and model != 'spot':\\n raise BadQuery(\\\"Unsupported model %s\\\" % model)\\n return model, warnings\",\n \"def validate(self):\\r\\n validictory.validate(self.data, VALIDATION_MAP[\\\"bmi\\\"])\",\n \"def check_all_user_inputs_valid(self):\\n self.check_RNN_layers_valid()\\n self.check_activations_valid()\\n self.check_embedding_dimensions_valid()\\n self.check_initialiser_valid()\\n self.check_y_range_values_valid()\\n self.check_return_final_seq_only_valid()\",\n \"def validate(self):\\n scenarios = set(self.scenarios)\\n model_scenarios = set(\\n scenario.name for scenario in self.sos_model.scenario_models\\n )\\n missing_scenarios = scenarios - model_scenarios\\n if missing_scenarios:\\n raise SmifModelRunError(\\n \\\"ScenarioSets {} are selected in the ModelRun \\\"\\n \\\"configuration but not found in the SosModel \\\"\\n \\\"configuration\\\".format(missing_scenarios)\\n )\",\n \"def validate(self):\\n\\n raise NotImplementedError('Ando validation is not implemented yet.')\",\n \"def validate(self):\\n allowed_backbones = ['resnet50', 'resnet101', 'resnet152']\\n backbone = self.backbone.split('_')[0]\\n\\n if backbone not in allowed_backbones:\\n raise ValueError('Backbone (\\\\'{}\\\\') not in allowed backbones ({}).'.format(backbone, allowed_backbones))\",\n \"def validate(args, limit_to=None):\\n # Deliberately not validating the interrelationship of the columns between\\n # the biophysical table and the guilds table as the model itself already\\n # does extensive checking for this.\\n return validation.validate(args, ARGS_SPEC['args'])\",\n \"def test_validate_nagl_model_path(model_name):\\n model_path = validate_nagl_model_path(model_name)\\n assert os.path.exists(model_path)\",\n \"def validate(self):\\r\\n # Check KeyError\\r\\n try:\\r\\n self.fields[\\\"product_name_fr\\\"]\\r\\n self.fields[\\\"generic_name\\\"]\\r\\n self.fields[\\\"url\\\"]\\r\\n self.fields[\\\"nutrition_grade_fr\\\"]\\r\\n self.fields[\\\"categories\\\"]\\r\\n self.fields[\\\"stores\\\"]\\r\\n self.fields[\\\"brands\\\"]\\r\\n except KeyError:\\r\\n return False\\r\\n\\r\\n # Check empty field and lenght of generic_name\\r\\n for key, value in self.fields.items():\\r\\n if value == '':\\r\\n return False\\r\\n break\\r\\n if key == \\\"generic_name\\\":\\r\\n if len(value) > 255:\\r\\n return False\\r\\n\\r\\n try:\\r\\n self.categories = ProductFromApiToDatabase.clean_tag(\\r\\n self.fields[\\\"categories\\\"], 100)\\r\\n self.stores = ProductFromApiToDatabase.clean_tag(\\r\\n self.fields[\\\"stores\\\"], 45)\\r\\n self.brands = ProductFromApiToDatabase.clean_tag(\\r\\n self.fields[\\\"brands\\\"], 45)\\r\\n self.category_index = self.categories.index(self.category)\\r\\n except KeyError:\\r\\n return False\\r\\n except ValueError:\\r\\n return False\\r\\n except AttributeError:\\r\\n self.errors += 1\\r\\n print(self.errors)\\r\\n return False\",\n \"def __init__(\\n self,\\n model,\\n ipakcb=None,\\n intercellt=0,\\n laycon=3,\\n trpy=1.0,\\n hdry=-1e30,\\n iwdflg=0,\\n wetfct=0.1,\\n iwetit=1,\\n ihdwet=0,\\n ikvflag=0,\\n ikcflag=0,\\n tran=1.0,\\n hy=1.0,\\n vcont=1.0,\\n kv=1.0,\\n anglex=0.0,\\n ksat=1.0,\\n sf1=1e-5,\\n sf2=0.15,\\n wetdry=-0.01,\\n extension=\\\"bcf\\\",\\n unitnumber=None,\\n filenames=None,\\n add_package=True,\\n ):\\n msg = (\\n \\\"Model object must be of type flopy.mfusg.MfUsg\\\\n\\\"\\n f\\\"but received type: {type(model)}.\\\"\\n )\\n assert isinstance(model, MfUsg), msg\\n\\n super().__init__(\\n model,\\n ipakcb=ipakcb,\\n intercellt=intercellt,\\n laycon=laycon,\\n trpy=trpy,\\n hdry=hdry,\\n iwdflg=iwdflg,\\n wetfct=wetfct,\\n iwetit=iwetit,\\n ihdwet=ihdwet,\\n tran=tran,\\n hy=hy,\\n vcont=vcont,\\n sf1=sf1,\\n sf2=sf2,\\n wetdry=wetdry,\\n extension=extension,\\n unitnumber=unitnumber,\\n filenames=filenames,\\n add_package=False,\\n )\\n\\n dis = model.get_package(\\\"DIS\\\")\\n if dis is None:\\n dis = model.get_package(\\\"DISU\\\")\\n structured = self.parent.structured\\n\\n nrow, ncol, nlay, _ = self.parent.nrow_ncol_nlay_nper\\n\\n self.ikvflag = ikvflag\\n self.ikcflag = ikcflag\\n self.kv = kv\\n self.anglex = anglex\\n self.ksat = ksat\\n\\n if not structured:\\n njag = dis.njag\\n self.anglex = Util2d(\\n model,\\n (njag,),\\n np.float32,\\n anglex,\\n \\\"anglex\\\",\\n locat=self.unit_number[0],\\n )\\n\\n # item 1\\n self.kv = Util3d(\\n model,\\n (nlay, nrow, ncol),\\n np.float32,\\n kv,\\n \\\"Vertical Hydraulic Conductivity\\\",\\n locat=self.unit_number[0],\\n )\\n if not structured:\\n self.ksat = Util3d(\\n model,\\n (njag,),\\n np.float32,\\n ksat,\\n \\\"ksat\\\",\\n locat=self.unit_number[0],\\n )\\n\\n if add_package:\\n self.parent.add_package(self)\",\n \"def validate(self, apiobj, method, api, param, safe):\",\n \"def test_fleur_create_mag_validation_wrong_inputs(self, fleur_local_code, inpgen_local_code, generate_structure2):\\n from aiida.orm import Dict\\n\\n # prepare input nodes and dicts\\n options = {\\n 'resources': {\\n 'num_machines': 1,\\n 'num_mpiprocs_per_machine': 1\\n },\\n 'max_wallclock_seconds': 5 * 60,\\n 'withmpi': False,\\n 'custom_scheduler_commands': ''\\n }\\n options = Dict(dict=options).store()\\n\\n wf_parameters = Dict({'points': 9, 'step': 0.002, 'guess': 1.00, 'wrong_key': None})\\n wf_parameters.store()\\n structure = generate_structure2()\\n structure.store()\\n\\n ################\\n # Create builders\\n # interface of exposed scf is tested elsewhere\\n\\n # 1. create builder with wrong wf parameters\\n builder_additionalkeys = FleurCreateMagneticWorkChain.get_builder()\\n builder_additionalkeys.structure = structure\\n builder_additionalkeys.wf_parameters = wf_parameters\\n builder_additionalkeys.scf.fleur = fleur_local_code\\n builder_additionalkeys.scf.inpgen = inpgen_local_code\\n\\n ###################\\n # now run the builders all should fail early with exit codes\\n\\n # 1. structure and fleurinp given\\n out, node = run_get_node(builder_additionalkeys)\\n assert out == {}\\n assert node.is_finished\\n assert not node.is_finished_ok\\n assert node.exit_status == 230\",\n \"def check_models(model1, model2, use_cross_validation=False, op='e'):\\n # 1. Check model types\\n model1_type = model1.__class__.__name__\\n model2_type = model1.__class__.__name__\\n assert model1_type is model2_type, \\\"The model types differ. The first model is of type {0} and the second \\\" \\\\\\n \\\"models is of type {1}.\\\".format(model1_type, model2_type)\\n\\n # 2. Check model metrics\\n if isinstance(model1,H2OBinomialModel): # 2a. Binomial\\n # F1\\n f1_1 = model1.F1(xval=use_cross_validation)\\n f1_2 = model2.F1(xval=use_cross_validation)\\n if op == 'e': assert f1_1[0][1] == f1_2[0][1], \\\"The first model has an F1 of {0} and the second model has an F1 of \\\" \\\\\\n \\\"{1}. Expected the first to be == to the second.\\\".format(f1_1[0][1], f1_2[0][1])\\n elif op == 'g': assert f1_1[0][1] > f1_2[0][1], \\\"The first model has an F1 of {0} and the second model has an F1 of \\\" \\\\\\n \\\"{1}. Expected the first to be > than the second.\\\".format(f1_1[0][1], f1_2[0][1])\\n elif op == 'ge': assert f1_1[0][1] >= f1_2[0][1], \\\"The first model has an F1 of {0} and the second model has an F1 of \\\" \\\\\\n \\\"{1}. Expected the first to be >= than the second.\\\".format(f1_1[0][1], f1_2[0][1])\\n elif isinstance(model1,H2ORegressionModel): # 2b. Regression\\n # MSE\\n mse1 = model1.mse(xval=use_cross_validation)\\n mse2 = model2.mse(xval=use_cross_validation)\\n if op == 'e': assert mse1 == mse2, \\\"The first model has an MSE of {0} and the second model has an MSE of \\\" \\\\\\n \\\"{1}. Expected the first to be == to the second.\\\".format(mse1, mse2)\\n elif op == 'g': assert mse1 > mse2, \\\"The first model has an MSE of {0} and the second model has an MSE of \\\" \\\\\\n \\\"{1}. Expected the first to be > than the second.\\\".format(mse1, mse2)\\n elif op == 'ge': assert mse1 >= mse2, \\\"The first model has an MSE of {0} and the second model has an MSE of \\\" \\\\\\n \\\"{1}. Expected the first to be >= than the second.\\\".format(mse1, mse2)\\n elif isinstance(model1,H2OMultinomialModel): # 2c. Multinomial\\n # hit-ratio\\n pass\\n elif isinstance(model1,H2OClusteringModel): # 2d. Clustering\\n # totss\\n totss1 = model1.totss(xval=use_cross_validation)\\n totss2 = model2.totss(xval=use_cross_validation)\\n if op == 'e': assert totss1 == totss2, \\\"The first model has an TOTSS of {0} and the second model has an \\\" \\\\\\n \\\"TOTSS of {1}. Expected the first to be == to the second.\\\".format(totss1,\\n totss2)\\n elif op == 'g': assert totss1 > totss2, \\\"The first model has an TOTSS of {0} and the second model has an \\\" \\\\\\n \\\"TOTSS of {1}. Expected the first to be > than the second.\\\".format(totss1,\\n totss2)\\n elif op == 'ge': assert totss1 >= totss2, \\\"The first model has an TOTSS of {0} and the second model has an \\\" \\\\\\n \\\"TOTSS of {1}. Expected the first to be >= than the second.\\\" \\\\\\n \\\"\\\".format(totss1, totss2)\",\n \"def test_minimal_admin_freeze_schema():\\n filing = copy.deepcopy(FILING_HEADER)\\n filing['filing']['header']['name'] = 'adminFreeze'\\n filing['filing']['adminFreeze'] = ADMIN_FREEZE\\n\\n is_valid, errors = validate(filing, 'filing')\\n\\n if errors:\\n for err in errors:\\n print(err.message)\\n print(errors)\\n\\n assert is_valid\",\n \"def validate():\\n # Switch in object mode \\n bpy.ops.object.mode_set(mode='OBJECT')\\n \\n # Apply modifier\\n bpy.ops.object.apply_all_modifiers()\\n \\n # Get the active objec\\n obj = bpy.context.active_object\\n print(\\\"Number of faces\\\", len(obj.data.polygons))\",\n \"def validate(self, mode): # pragma: no cover\\n pass\",\n \"def parse(self, form_args):\\n for name, i in self.inputs.items():\\n if name in form_args:\\n self.inputs[name].validate(form_args[name])\\n else:\\n self.inputs[name].take_default()\\n\\n\\n for name, value in form_args.items():\\n self.inputs[name].validate(value)\",\n \"def validate(self):\\n return 1\",\n \"def validate(model_num=None, validate_name=None):\\n if model_num is None:\\n model_num, model_name = get_latest_model()\\n else:\\n model_num = int(model_num)\\n model_name = get_model(model_num)\\n\\n # Model N was trained on games up through model N-2, so the validation set\\n # should only be for models through N-2 as well, thus the (model_num - 1)\\n # term.\\n models = list(\\n filter(lambda num_name: num_name[0] < (model_num - 1), get_models()))\\n # Run on the most recent 50 generations,\\n # TODO(brianklee): make this hyperparameter dependency explicit/not hardcoded\\n holdout_dirs = [os.path.join(HOLDOUT_DIR, pair[1])\\n for pair in models[-50:]]\\n\\n main.validate(ESTIMATOR_WORKING_DIR, *holdout_dirs,\\n checkpoint_name=os.path.join(MODELS_DIR, model_name),\\n validate_name=validate_name)\",\n \"def test_conversion_schema():\\n legal_filing = {'conversion': BEN_CONVERSION}\\n is_valid, errors = validate(legal_filing, 'conversion')\\n\\n if errors:\\n for err in errors:\\n print(err.message)\\n print(errors)\\n\\n assert is_valid\",\n \"def test_field_rules():\",\n \"def validate(self, mesh, mod):\\n return True\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.62146026","0.59948707","0.5963622","0.5916925","0.589161","0.5838374","0.58137685","0.58000225","0.58000225","0.573189","0.5726065","0.5707462","0.5697073","0.56887645","0.5651742","0.5627777","0.55742997","0.5545567","0.5542714","0.5541837","0.55222416","0.552062","0.55109525","0.55053365","0.55022013","0.55022013","0.55022013","0.55022013","0.55022013","0.55022013","0.55022013","0.55022013","0.55014354","0.55012906","0.54877514","0.5467284","0.5457241","0.54568326","0.5452605","0.54484546","0.54460746","0.5443464","0.5432224","0.5417669","0.54123867","0.5400222","0.53768873","0.53743774","0.5367727","0.5362813","0.5358634","0.5350985","0.53370917","0.5324992","0.53218716","0.53175205","0.5299485","0.5289261","0.528556","0.52853256","0.5278397","0.5267742","0.52560633","0.52541417","0.5252743","0.52446014","0.52316195","0.5230323","0.5228587","0.5221038","0.52106786","0.5206401","0.52000034","0.5198357","0.5195156","0.5192955","0.5180882","0.51686764","0.5167012","0.51643217","0.516404","0.5161708","0.5160704","0.5157898","0.5155723","0.5138824","0.5132489","0.512897","0.5128756","0.5124359","0.5123525","0.512122","0.51209265","0.5120329","0.5119284","0.51142746","0.5109793","0.5109196","0.51087993","0.51064074"],"string":"[\n \"0.62146026\",\n \"0.59948707\",\n \"0.5963622\",\n \"0.5916925\",\n \"0.589161\",\n \"0.5838374\",\n \"0.58137685\",\n \"0.58000225\",\n \"0.58000225\",\n \"0.573189\",\n \"0.5726065\",\n \"0.5707462\",\n \"0.5697073\",\n \"0.56887645\",\n \"0.5651742\",\n \"0.5627777\",\n \"0.55742997\",\n \"0.5545567\",\n \"0.5542714\",\n \"0.5541837\",\n \"0.55222416\",\n \"0.552062\",\n \"0.55109525\",\n \"0.55053365\",\n \"0.55022013\",\n \"0.55022013\",\n \"0.55022013\",\n \"0.55022013\",\n \"0.55022013\",\n \"0.55022013\",\n \"0.55022013\",\n \"0.55022013\",\n \"0.55014354\",\n \"0.55012906\",\n \"0.54877514\",\n \"0.5467284\",\n \"0.5457241\",\n \"0.54568326\",\n \"0.5452605\",\n \"0.54484546\",\n \"0.54460746\",\n \"0.5443464\",\n \"0.5432224\",\n \"0.5417669\",\n \"0.54123867\",\n \"0.5400222\",\n \"0.53768873\",\n \"0.53743774\",\n \"0.5367727\",\n \"0.5362813\",\n \"0.5358634\",\n \"0.5350985\",\n \"0.53370917\",\n \"0.5324992\",\n \"0.53218716\",\n \"0.53175205\",\n \"0.5299485\",\n \"0.5289261\",\n \"0.528556\",\n \"0.52853256\",\n \"0.5278397\",\n \"0.5267742\",\n \"0.52560633\",\n \"0.52541417\",\n \"0.5252743\",\n \"0.52446014\",\n \"0.52316195\",\n \"0.5230323\",\n \"0.5228587\",\n \"0.5221038\",\n \"0.52106786\",\n \"0.5206401\",\n \"0.52000034\",\n \"0.5198357\",\n \"0.5195156\",\n \"0.5192955\",\n \"0.5180882\",\n \"0.51686764\",\n \"0.5167012\",\n \"0.51643217\",\n \"0.516404\",\n \"0.5161708\",\n \"0.5160704\",\n \"0.5157898\",\n \"0.5155723\",\n \"0.5138824\",\n \"0.5132489\",\n \"0.512897\",\n \"0.5128756\",\n \"0.5124359\",\n \"0.5123525\",\n \"0.512122\",\n \"0.51209265\",\n \"0.5120329\",\n \"0.5119284\",\n \"0.51142746\",\n \"0.5109793\",\n \"0.5109196\",\n \"0.51087993\",\n \"0.51064074\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.52467144"},"document_rank":{"kind":"string","value":"65"}}},{"rowIdx":17886,"cells":{"query":{"kind":"string","value":"Check if configuration file exists, otherwise indicate user to do so Configuration contains sim config_params/inputs/outputs and naming"},"document":{"kind":"string","value":"def _validate_sim_config(self):\n\n\n print(\"\\n[FMU Validator] ---- Looking to see if YAML config file exists ----\")\n\n # use convention to search for config file\n config_file = self.sim_config_filepath\n \n if not os.path.isfile(config_file):\n print(\"[FMU Validator] Configuration file for selected example was NOT found: {}\".format(config_file))\n return False\n\n print(\"[FMU Validator] Sim config file for selected example was found: {}\\n\".format(config_file))\n\n # Open and extract sim config from YAML file\n with open(config_file, 'r') as file:\n #data = yaml.dump(config_file, Loader=yaml.FullLoader)\n simulation_config = yaml.load(file, Loader=yaml.FullLoader)\n \n if 'simulation' not in simulation_config.keys():\n print(\"[FMU Validator] Configuration file for selected example does not have a 'simulation' tag, thus it is omited.\")\n return False\n\n # Extract sim configuration from dict\n sim_config_params = simulation_config['simulation']['config_params']\n sim_inputs = simulation_config['simulation']['inputs']\n sim_outputs = simulation_config['simulation']['outputs']\n sim_other_vars = simulation_config['simulation']['other_vars']\n\n # Validate values extracted\n if len(sim_inputs) == 0:\n print(\"[FMU Validator] Sim config file has no sim-input states, and thus cannot be used\\n\")\n elif len(sim_outputs) == 0:\n print(\"[FMU Validator] Sim config file has no sim-output states, and thus cannot be used\\n\")\n else:\n # Store data extracted as attributes\n self.sim_config_params = sim_config_params\n self.sim_inputs = sim_inputs\n self.sim_outputs = sim_outputs\n self.sim_other_vars = sim_other_vars\n return True\n\n return False"},"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 __check_config(self):\n if not os.path.exists(self.__config_path):\n return False\n else:\n return True","def is_config_exist(self) -> bool:\n pass","def is_config_exist(self) -> bool:\n return True","def check_config_file():\n # Locate and init config.\n default_config = \"config.json\"\n if len(sys.argv) == 2:\n # config from command line\n app_config = config_reader(sys.argv[1])\n else:\n # config should be in default\n app_config = config_reader(default_config)\n # fin\n if not app_config:\n print(\"Exiting due to invalid config file.\")\n return False\n # fin\n return app_config","def _check(self, config: Dict):\n if 'path' not in config:\n raise FileNotFoundError(\"File not found.\")","def _validate_config(self):\n # Simulation ID\n empty_string_check(self._config_dict['@id'])\n \n # Output\n empty_string_check(self._config_dict['output']['@baseDirectory'])\n self._config_dict['output']['@saveInteractionLog'] = parse_boolean(self._config_dict['output']['@saveInteractionLog'])\n self._config_dict['output']['@saveRelevanceJudgments'] = parse_boolean(self._config_dict['output']['@saveRelevanceJudgments'])\n self._config_dict['output']['@trec_eval'] = parse_boolean(self._config_dict['output']['@trec_eval'])\n \n # Topics\n def check_topic(t):\n \"\"\"\n Checks a given topic, t. Looks for a topic ID and a valid topic description file.\n \"\"\"\n empty_string_check(t['@id'])\n filesystem_exists_check(t['@filename'])\n filesystem_exists_check(t['@qrelsFilename'])\n \n if '@backgroundFilename' in t: # A background file was specified.\n filesystem_exists_check(t['@backgroundFilename'])\n else:\n t['@backgroundFilename'] = None # No background file was specified.\n \n topics = self._config_dict['topics']['topic']\n \n if type(topics) == list:\n for topic in topics:\n check_topic(topic)\n else:\n check_topic(topics)\n \n # Users\n users = self._config_dict['users']['user']\n \n if type(users) == list:\n for user in users:\n filesystem_exists_check(user['@configurationFile'])\n else:\n filesystem_exists_check(users['@configurationFile'])\n \n # Search Interface\n empty_string_check(self._config_dict['searchInterface']['@class'])\n check_attributes(self._config_dict['searchInterface'])","def config_input(self):\n answer = simpledialog.askstring(\"Input\", \"Please input the path of the config file.\",\n parent=self._master) # ask for a string\n if answer is not None:\n try:\n self.read_config(answer) # read the config\n if all(k in self._config for k in ('World', 'Player')):\n # check whether have a 'World' and 'Player'\n return True\n else: # if don't have 'World' or 'Player'\n messagebox.showerror(\"Missing necessary heading\", \"Missing World or Player\")\n return False\n except IOError:\n messagebox.showerror(\"Invalid path\",\n \"'\" + answer + \"' is not a valid path!\")\n return False","def check_config(self):\n cfgs = self.__get() \n \n for option in Config.FILE_OPTIONS.keys():\n _default = Config.FILE_OPTIONS[option]\n \n if not cfgs.has_key(option):\n self.log.warn(\"Parameter '%s' is missing in '%s', using default('%s')\" % \\\n (option, self.config_file, _default))\n _file = _default\n else:\n _file = cfgs[option]\n Config.FILE_OPTIONS[option] = _file\n\n if not os.path.exists(_file) and not os.path.isfile(_file):\n self.log.error(\"Paramenter '%s' points to non-existing file '%s')\" % \\\n (option, _file))\n raise ConfigError('File Error', \"Paramenter '%s' points to non-existing file '%s')\" % \\\n (option, _file))\n\n\n for option in Config.PATH_OPTIONS.keys():\n _default = Config.PATH_OPTIONS[option]\n \n if not cfgs.has_key(option):\n self.log.warn(\"Parameter '%s' is missing in '%s', using default('%s')\" % \\\n (option, self.config_file, _default))\n _dir = _default\n else:\n _dir = cfgs[option]\n Config.PATH_OPTIONS[option] = _dir\n\n if not os.path.exists(_dir) and not os.path.isdir(_dir):\n self.log.error(\"Paramenter '%s' points to non-existing directory '%s')\" % \\\n (option, _dir))\n raise ConfigError('File Error', \"Paramenter '%s' points to non-existing directory '%s')\" % \\\n (option, _dir))\n\n \n Config.DB_SFT_OPTIONS['sqlalchemy_sft.url'] = cfgs['sqlalchemy_sft.url']\n Config.DB_NAGIOS_OPTIONS['sqlalchemy_nagios.url'] = cfgs['sqlalchemy_nagios.url']\n\n self.log.debug(\"Configuration successfully checked\")","def init_config():\n config_file = create_config_file.CreateConfigFile()\n config_file.check_if_config_file_exists()","def has_configuration(config_file=CONFIG_FILE):\n return os.path.exists(config_file)","def check_config(self):\n # Check if tool is at all included in workflow\n if \"external\" not in self.config[\"tools\"][\"dna\"]:\n return # External not run, don't check configuration # pragma: no cover","def check_config(cfg):","def _check_config(self):","def check_for_config(conf, confs_path):\n if yes_no('Is there a configuration file you would like to load?'):\n options = os.listdir(confs_path)\n if len(options) > 0:\n options.append('Specify a Path')\n option = ask_options(\n 'Pick a configuration file', options, title=False)\n if option == 'Specify a Path':\n conf['config'] = ask_path(\n 'Enter the path for the configuration file')\n else:\n conf['config'] = os.path.join(confs_path, option)\n else:\n conf['config'] = ask_path(\n 'Enter the path for the configuration file')\n conf = get_config_file(conf)\n conf, _ = confirm_conf(conf)\n conf.pop('config')\n return conf","def check_config(outconfig):\n self.log.info(\"Checking if all the necessary files exist.\")\n\n # Perform necessary checks\n\n log.info(\"All necessary files exist for {} configuration.\".format(outconfig[\"Flavor\"]))\n\n return","def check_config(config):\n pass","def is_config_exist(self) -> bool:\n return os.path.isfile(self.connection_string)","def check_config(self):\n # Check if tool is at all included in workflow\n if self.__class__.name not in self.config[\"tools\"][\"rna\"]:\n return # STAR not run, don't check configuration # pragma: no cover\n\n # Check required configuration settings present\n self.parent.ensure_w_config(\n config_keys=(\"step_config\", \"ngs_mapping\", \"star\", \"path_index\"),\n msg=\"Path to STAR index is required\",\n )\n self.parent.ensure_w_config(\n config_keys=(\"static_data_config\", \"reference\"),\n msg=\"No reference genome FASTA file given\",\n )\n\n # Check validity of the STAR index\n full_path = self.config[\"star\"][\"path_index\"]\n # a lot of files should be in this dir, justtest these\n for indfile in (\"Genome\", \"SA\", \"SAindex\"):\n expected_path = os.path.join(full_path, indfile)\n if not os.path.exists(expected_path): # pragma: no cover\n tpl = \"Expected STAR index file {expected_path} does not exist!\".format(\n expected_path=expected_path\n )\n raise InvalidConfiguration(tpl)","def get_conf(self, file_name):\n try:\n self.conf = Config(file_name)\n except ImportError:\n s = \"No such file in simulation directory: \" + file_name\n self.abort(ImportError, s)","def cloudwatch_config_exists(config, config_type, file_name):\n\n cfg = config.get(\"cloudwatch\", {}).get(config_type, {}).get(file_name)\n if cfg:\n assert os.path.isfile(cfg), \\\n \"Invalid CloudWatch Config File Path: {}\".format(cfg)\n return bool(cfg)","def test_config():\n if not os.path.exists(CONFIG_DIR):\n raise mupub.BadConfiguration('Configuration folder not found.')\n if not os.path.exists(_CONFIG_FNM):\n raise mupub.BadConfiguration('Configuration file not found.')\n if not os.path.exists(getDBPath()):\n raise mupub.BadConfiguration('Local database not found.')\n if len(CONFIG_DICT) == 0:\n raise mupub.BadConfiguration('Configuration was not loaded.')","def check_configs(self):\n\n pass","def _ensure_config_file_exists():\n config_file = Path(ELIBConfig.config_file_path).absolute()\n if not config_file.exists():\n raise ConfigFileNotFoundError(ELIBConfig.config_file_path)","def check() -> bool:\n\ttry:\n\t\twith open(configPath, 'r') as file:\n\t\t\tcfg = json.load(file) # load the file\n\texcept FileNotFoundError:\n\t\treturn False\n\t# check if EVERY config exists\n\tfor i in default_config.keys():\n\t\tif i in cfg.keys():\n\t\t\tcontinue\n\t\treturn False\n\t# final check\n\treturn cfg['config_type'] == default_config['config_type']","def __check_configuration__(self, parser):\n if not parser.has_section('core'):\n self.logger.error('The config file should contain a core section with at least the module_path specified')\n sys.exit(1)\n\n else:\n if parser.get('core', 'modules_path', fallback=None) is None:\n self.logger.error('The configuration file should contain at least the modules_path value in core section.')\n sys.exit(1)\n\n if not parser.has_section('mysql'):\n self.logger.error('The config file should contain a mysql section.')\n sys.exit(1)\n\n else:\n if parser.get('mysql', 'host', fallback=None) is None:\n self.logger.error('The config file should contain the host value in mysql section.')\n sys.exit(1)\n\n if parser.get('mysql', 'port', fallback=None) is None:\n self.logger.error('The config file should contain the port value in mysql section.')\n sys.exit(1)\n\n if parser.get('mysql', 'user', fallback=None) is None:\n self.logger.error('The config file should contain the user in mysql section.')\n sys.exit(1)\n\n if parser.get('mysql', 'password', fallback=None) is None:\n self.logger.error('The config file should contain the password of the user in mysql section.')\n sys.exit(1)\n\n if parser.get('mysql', 'server_id', fallback=None) is None:\n self.logger.error('The config file should contain the server_id in mysql section.')\n sys.exit(1)\n\n if parser.get('mysql', 'tables', fallback=None) is not None:\n tables = [table.strip() for table in parser.get('mysql', 'tables').split(',')]\n for table in tables:\n if not parser.has_section(table):\n self.logger.error('The config file should contain a section about the table : %s' % table)\n exit(1)\n if parser.get(table, 'index_label', fallback=None) is None :\n self.logger.error('The config file should contain a table section with a index_label value.')\n exit(1)\n else:\n self.logger.error('The config file should contain a tables value with all the tables to replicate.')\n exit(1)","def check_configuration(self):\n\n return bool(os.path.isfile(self.config_path) and\n self.validate_configuration_file())","def check_configure_scan(project_path):\n for file_name in CONFIGURE_AC_NAMES:\n file_path = os.path.join(project_path, file_name)\n if os.path.exists(file_path):\n return file_path\n return None","def __test_config_file__(parser, path):\n if not os.path.exists(path):\n return False\n else:\n statinfo = os.stat(path)\n if not os.path.isfile(path) or os.path.islink(path):\n parser.error(\"invalid configuration file {} (it must be a regular file)\".format(path))\n elif stat.filemode(statinfo.st_mode) != '-rw-------':\n parser.error(\"invalid configuration file {} (it must have only read and write permissions for user)\".format(path))\n elif statinfo.st_uid != os.getuid():\n parser.error(\"invalid configuration file {} (the owner must be the user)\".format(path))\n return True","def find_config_file(self):\n filename = self.values.get('config_file', Default('noy.json'))\n\n ignore_missing = False\n if isinstance(filename, Default):\n filename = filename.val\n ignore_missing = True\n\n filename = os.path.abspath(filename)\n if os.path.exists(filename):\n return filename\n elif not ignore_missing:\n raise MissingConfigFile(\"Config file doesn't exist at {}\".format(filename))","def check_config(self):\n # Check if tool is at all included in workflow\n if not (set(self.config[\"tools\"][\"dna_long\"]) & {\"ngmlr\", \"ngmlr_chained\"}):\n return # NGLMR not run, don't check configuration # pragma: no cover\n\n # Check required configuration settings present\n self.parent.ensure_w_config(\n config_keys=(\"step_config\", \"ngs_mapping\", \"ngmlr\", \"path_index\"),\n msg=\"Path to NGMLR index is required\",\n )","def check_configs():\n configs = [os.path.join(CUCKOO_ROOT, \"conf\", \"cuckoo.conf\"),\n os.path.join(CUCKOO_ROOT, \"conf\", \"reporting.conf\")]\n\n for config in configs:\n if not os.path.exists(config):\n raise CuckooStartupError(\"Config file does not exist at path: %s\" % config)\n\n return True","def test_nonexisting_config(self):\n try:\n tempdir = tempfile.mkdtemp()\n filename = os.path.join(tempdir, \"Config.yaml\")\n with self.assertRaises(easydms.config.ErrorNoConfiguration):\n easydms.config.Config(filename)\n\n with self.assertRaises(SystemExit) as cm:\n sys.argv = [\"prog\", \"-c\", filename]\n easydms.cli.main()\n self.assertNotEqual(cm.exception.code, 0)\n\n finally:\n shutil.rmtree(tempdir)","def validate_config(self):\n reference = data_file(\"../config/template/minimum_aiscalator.conf\")\n ref = pyhocon.ConfigFactory.parse_file(reference)\n msg = \"In Global Application Configuration file \"\n _validate_configs(self._app_conf, ref, msg,\n missing_exception=True,\n type_mismatch_exception=True)\n reference = data_file(\"../config/template/aiscalator.conf\")\n ref = pyhocon.ConfigFactory.parse_file(reference)\n msg = \"In Global Application Configuration file \"\n _validate_configs(self._app_conf, ref, msg,\n missing_exception=False,\n type_mismatch_exception=True)\n if self._step_name:\n reference = data_file(\"../config/template/minimum_step.conf\")\n ref = pyhocon.ConfigFactory.parse_file(reference)\n msg = \"in step named \" + self._step_name\n _validate_configs(self._step,\n ref[\"steps\"][\"Untitled\"],\n msg,\n missing_exception=True,\n type_mismatch_exception=True)\n reference = data_file(\"../config/template/step.conf\")\n ref = pyhocon.ConfigFactory.parse_file(reference)\n msg = \"in step named \" + self._step_name\n _validate_configs(self._step,\n ref[\"steps\"][\"Untitled\"],\n msg,\n missing_exception=False,\n type_mismatch_exception=True)\n if self._dag_name:\n reference = data_file(\"../config/template/minimum_dag.conf\")\n ref = pyhocon.ConfigFactory.parse_file(reference)\n msg = \"in dag named \" + self._dag_name\n _validate_configs(self._dag,\n ref[\"dags\"][\"Untitled\"],\n msg,\n missing_exception=True,\n type_mismatch_exception=True)\n reference = data_file(\"../config/template/step.conf\")\n ref = pyhocon.ConfigFactory.parse_file(reference)\n msg = \"in dag named \" + self._dag_name\n _validate_configs(self._dag,\n ref[\"dags\"][\"Untitled\"],\n msg,\n missing_exception=False,\n type_mismatch_exception=True)","def check_config(configd):\n # XXX: verify first that the required config parameters are present\n if not os.path.exists(configd['player']):\n logging.error(\"player %s wasn't found\" % (configd['player'],))\n return False\n if not os.path.isdir(configd['sound_dir']):\n logging.error(\"sound directory %s wasn't found\" % (configd['sound_dir'],))\n return False\n return True","def get_config_file(self):\n\n conf_file = self.args.file\n if conf_file is not None:\n if os.path.isfile(conf_file):\n config_file = open(conf_file, \"r\")\n self.main_file = yaml.load(config_file, Loader=yaml.FullLoader)\n elif os.path.isfile(\n os.path.join(get_path(\"DEFAULT\", \"config_file_path\"), conf_file)\n ):\n fpath = get_path(\"DEFAULT\", \"config_file_path\")\n config_file = open(os.path.join(fpath, conf_file), \"r\")\n self.main_file = yaml.load(config_file, Loader=yaml.FullLoader)\n else:\n self.logger.error(\n colorama.Fore.RED\n + \"ERROR!! Config file '%s' is not present \" % conf_file,\n extra=self.log_detail,\n )\n sys.exit(1)\n else:\n if self.args.hostname and self.args.testfiles:\n temp_dict = {\n \"hosts\": [{\"device\": \"\", \"username\": \"\", \"passwd\": \"\"}],\n \"tests\": [],\n }\n temp_dict[\"hosts\"][0][\"device\"] = self.args.hostname\n temp_dict[\"hosts\"][0][\"username\"] = self.args.login\n temp_dict[\"hosts\"][0][\"passwd\"] = self.args.passwd\n for tfile in self.args.testfiles:\n temp_dict[\"tests\"].append(tfile)\n self.main_file = temp_dict\n\n if (\n self.main_file.__contains__(\"sqlite\")\n and self.main_file[\"sqlite\"]\n and self.main_file[\"sqlite\"][0]\n ):\n self.chk_database(\n self.main_file,\n self.args.pre_snapfile,\n self.args.post_snapfile,\n self.args.check,\n self.args.snap,\n )\n else:\n # if --check option is given for sqlite, then snap file name is not compulsory\n # else exit the function saying arguments not correct\n if self.args.check is True and (\n self.args.pre_snapfile is None or self.args.post_snapfile is None\n ):\n self.logger.error(\n colorama.Fore.RED\n + \"Arguments not given correctly, Please refer help message\",\n extra=self.log_detail,\n )\n self.parser.print_help()\n sys.exit(1)","def path_exists(config_items):\n for section, options in config_items.items():\n if type(options) != dict:\n continue\n for key, val in options.items():\n if key == 'output_pattern':\n continue\n if not type(val) == str:\n continue\n if val.endswith('.nc') and not os.path.exists(val):\n print \"File {key}: {value} does not exist, exiting.\".format(key=key, value=val)\n sys.exit(1)","def exists(self):\n if self.host.exists(self.remote_path):\n print 'Yes, config exists already.'\n return True\n else:\n print 'Config doesn\\'t exist yet'\n return False","def check_configuration(self, configuration):\n super(Pixiv_bot, self).check_configuration(configuration)","def check_configuration(self, configuration):\n # If we specified a LOG_FILE path, check to see if it actually exists\n if configuration.get('LOG_FILE') and not path.isfile(configuration.get('LOG_FILE')):\n raise ValidationException(f'Could not find file {configuration[\"LOG_FILE\"]}')","def setup_config(self):\n\n try:\n with open('config.json') as f:\n config = json.load(f)\n except FileNotFoundError as e:\n log.debug('{}[*]{} {}'.format(DFbase.LOG_ERROR_COLOR,\n DFbase.LOG_INFO_COLOR, e))\n print('{}[*]{} {}'.format(DFbase.LOG_ERROR_COLOR, \n DFbase.LOG_INFO_COLOR, \n 'Please copy config.json.example to confing.json'))\n return False\n except Exception as e:\n log.debug('{}[*]{} {}'.format(DFbase.LOG_ERROR_COLOR, \n DFbase.LOG_INFO_COLOR, e))\n return False\n\n self.artifacts_path = config['ARTIFACTS']['BASE_PATH'].format(self.container_id)\n self.executable_path = config['ARTIFACTS']['EXECUTABLE_PATH']\n self.executable_path = self.executable_path.replace('BASE_PATH', self.artifacts_path)\n self.diff_files_path = config['ARTIFACTS']['DIFF_FILES_PATH']\n self.diff_files_path = self.diff_files_path.replace('BASE_PATH', self.artifacts_path)\n self.log_journald = (True if config['ARTIFACTS']['LOG_JOURNALD_SERVICE'] == \"TRUE\" else False)\n\n for x_path in [self.artifacts_path, self.executable_path, self.diff_files_path]:\n if not os.path.exists(x_path):\n try:\n os.makedirs(x_path, mode=0o700)\n except Exception as e:\n log.debug('{}[*]{} {}'.format(DFbase.LOG_ERROR_COLOR,\n DFbase.LOG_INFO_COLOR, e))\n print('{}[*]{} {}:{}'.format(DFbase.LOG_ERROR_COLOR,\n DFbase.LOG_INFO_COLOR,\n 'Failed when creating directory', x_path))\n return False\n\n return True","def config_in_use(self) -> bool:\n config = {}\n config_files = find_config_files([self.config_dir])\n for config_file in config_files:\n with open(config_file) as stream:\n config.update(yaml.safe_load(stream))\n\n if not config:\n return False\n\n print(config.get(CONFIG_LOCK))\n return config.get(CONFIG_LOCK, True)","def setallparams(self, OUTPUTCONFIG):\n if self.name in OUTPUTCONFIG.sections():\n self.extractparams(OUTPUTCONFIG, self.requiredGenericParams, \"required\")\n self.extractparams(OUTPUTCONFIG, self.requiredSpecificParams, \"required\")\n self.extractparams(OUTPUTCONFIG, self.optionalGenericParams, \"optional\")\n self.extractparams(OUTPUTCONFIG, self.optionalSpecificParams, \"optional\")\n return True\n else:\n msg = \"Missing config section for plugin \" + self.name + \".\"\n print(msg)\n msg += \"This should be found in file: \" + filetocheck\n #msg = format_msg(msg, 'error')\n print(msg)\n raise MissingSection\n return False","def init_cfg(input_path, quiet):\n config_file = getConfigPath(input_path)\n\n if not os.path.exists(config_file) or quiet:\n confirm_overwrite = True\n else:\n confirm_overwrite = click.confirm(\n '\\n{} already exists. Do you want to overwrite it?'.format(config_file))\n if confirm_overwrite:\n configHandler(config_file).resetConfig()\n click.secho('{} created'.format(config_file), fg='green')","def validate_configuration_file(self):\n\n with open(self.config_path, \"r+\") as f_config:\n return bool(re.search(get_configuration_file_re(),\n f_config.read()))","def user_configuration_seems_complete(self):\n if not os.path.exists(os.path.join(self.user_conf_dir(),\n \"speechd.conf\")):\n return False\n\n if not len(os.listdir(self.user_conf_dir())) > 2:\n return False\n\n if not os.path.exists(os.path.join(self.user_conf_dir(), \"modules\")):\n return False\n\n if not os.path.exists(os.path.join(self.user_conf_dir(), \"clients\")):\n return False\n\n return True","def test_config_no_file(self):\n if os.path.isfile(\"test_config.conf\"):\n os.remove(\"test_config.conf\")\n self.assertRaises(ConfigError, Config, \"test_config.conf\")","def check_config():\r\n path = os.path.expanduser(\"~\")\r\n config_path = os.path.join(path, \".twbcfg.ini\")\r\n log_path = os.path.join(path, \"tmp\", \"teradata_logs\")\r\n\r\n if not os.path.exists(config_path):\r\n if not os.path.exists(log_path):\r\n os.mkdir(log_path)\r\n config = f'''CheckpointDirectory='{log_path}' \r\n LogDirectory='{log_path}' '''\r\n with open(config_path, 'w') as f:\r\n f.write(config)","def valid_configuration(self):\n valid = True\n\n if (not self.__config.suffix()) and (self.__config.output_dir() == self.__config.input_dir()):\n print(\"ERROR: output_dir directory cannot be the same as input_dir with an empty suffix!\")\n valid = False\n if not self.__config.public_key():\n print(\"ERROR: public_key not set! Set it through 'pdfworkshop config public_key <your_key>'. \"\n \"A free API key can be obtained from https://developer.ilovepdf.com/\")\n valid = False\n return valid","def check_config(self):\n # Check if tool is at all included in workflow\n if self.__class__.name not in self.config[\"tools\"][\"dna\"]:\n return # BWA not run, don't check configuration # pragma: no cover\n\n # Check required configuration settings present\n self.parent.ensure_w_config(\n config_keys=(\"step_config\", \"ngs_mapping\", \"bwa\", \"path_index\"),\n msg=\"Path to BWA index is required\",\n )\n\n # Check that the path to the BWA index is valid.\n for ext in (\".amb\", \".ann\", \".bwt\", \".pac\", \".sa\"):\n expected_path = self.config[\"bwa\"][\"path_index\"] + ext\n if not os.path.exists(expected_path): # pragma: no cover\n tpl = \"Expected BWA input path {expected_path} does not exist!\".format(\n expected_path=expected_path\n )\n raise InvalidConfiguration(tpl)","def client_has_non_default_configuration(self):\n if not os.path.isfile(YPBIND_CONF_FILE):\n return False\n\n # Filter whitespaces and empty lines\n with open(YPBIND_CONF_FILE) as f:\n lines = [line.strip() for line in f.readlines() if line.strip()]\n\n for line in lines:\n # Checks for any valid configuration entry\n if not line.startswith('#'):\n return True\n return False","def test_config_filename_given_file_exists_wellformed_content():\n config_file_name = get_resource_path(\"config\", \"test_config.ini\")\n config = Config(filename=config_file_name)\n\n assert config.metadata_cache_uri == \"https://custom-aqua.url\"\n assert config.provider_address == \"0x00bd138abd70e2f00903268f3db08f2d25677c9e\"\n assert isinstance(config.gas_limit, int)","def validate_config(location):\n if not os.path.exists(location):\n full_path = os.path.abspath(location)\n logging.fatal('Config not found - expected a configfile at: [{loc}] - exiting.'.format(loc=full_path))\n sys.exit(1)","def test_new_config_already_exists(self, context):\n\n context.config_exists.return_value = True\n\n runner = CliRunner()\n result = runner.invoke(cli_node_new_configuration, [\n \"--name\", \"some-name\",\n \"--environment\", \"application\"\n ])\n\n # check that error is produced\n self.assertEqual(result.output[:7], \"[error]\")\n\n # check non-zero exit code\n self.assertEqual(result.exit_code, 1)","def determine_config() -> str:\n if os.environ.get(PortholeConfig.CONFIG_ENV_NAME) is not None:\n return os.environ.get(PortholeConfig.CONFIG_ENV_NAME)\n if os.path.isfile(PortholeConfig.DEFAULT_CONFIG_FILE):\n return PortholeConfig.DEFAULT_CONFIG_FILE\n for file_path in PortholeConfig.OTHER_ALLOWED_CONFIG_PATHS:\n if os.path.isfile(file_path):\n return file_path\n raise FileNotFoundError(\n \"Porthole is unable to locate a useable config file. \"\n \"Try setting the PORTHOLE_CONFIG environment variable, \"\n \"or creating a porthole.ini file in your main project directory.\"\n )","def test_createsConfigWhenNoneExists(self):\n DataManagerUnitTest.dm.createConfig(\"testConfigFile.json\")\n self.assertTrue(os.path.isfile(\"testConfigFile.json\"))\n os.remove(\"testConfigFile.json\")","def get_config(f_configs, section=None):\n if io.is_file(f_configs) is False:\n warn.warn('configs (INI) file does not exist: ' + f_configs)\n return None","def checkRequiredConfigs(self):\n containmentFolder = self.getAbsContainmentFolder()\n rootFileName = self.app.config.exhale_args[\"rootFileName\"]\n rootFileTitle = self.app.config.exhale_args[\"rootFileTitle\"]\n doxygenStripFromPath = self.app.config.exhale_args[\"doxygenStripFromPath\"]\n\n # validate that the containmentFolder was created\n assert os.path.isdir(containmentFolder)\n # validate that {containmentFolder}/{rootFileName} was created\n assert os.path.isfile(os.path.join(containmentFolder, rootFileName))\n # validate that the title was included\n with open(os.path.join(containmentFolder, rootFileName), \"r\") as root:\n root_contents = root.read()\n root_heading = \"{0}\\n{1}\".format(\n rootFileTitle,\n exhale.utils.heading_mark(rootFileTitle, exhale.configs.SECTION_HEADING_CHAR)\n )\n assert root_heading in root_contents\n\n # TODO: validate doxygenStripFromPath\n if doxygenStripFromPath: # this is only here to avoid a flake8 fail on a todo\n pass","def loadConfig(self, name=None):\n\n configDir = self.mwGlob['configDir']\n # looking for file existence and creating new if necessary\n\n if name is None:\n name = 'config'\n fileName = configDir + '/' + name + '.cfg'\n\n if not os.path.isfile(fileName):\n self.config = self.defaultConfig()\n if name == 'config':\n self.log.warning('Config file {0} not existing'.format(fileName))\n return True\n else:\n return False\n\n # parsing the default file\n try:\n with open(fileName, 'r') as configFile:\n configData = json.load(configFile)\n except Exception as e:\n self.log.critical('Cannot parse: {0}, error: {1}'.format(fileName, e))\n self.config = self.defaultConfig()\n return False\n\n # check if reference ist still to default -> correcting\n if configData.get('reference', '') == 'config':\n del configData['reference']\n elif not configData.get('reference', ''):\n configData['profileName'] = 'config'\n\n # loading default and finishing up\n if configData['profileName'] == 'config':\n self.config = self.convertData(configData)\n return True\n\n # checking if reference to another file is available\n refName = configData.get('reference', 'config')\n if refName != name:\n suc = self.loadConfig(refName)\n else:\n self.config = configData\n return True\n return suc","def validate_config(self):\n pass","def validate_config(self):\n pass","async def check_if_config_exists(self, server_id):\n if not os.path.exists(\n '%s.ini' % (\n os.path.join(\n self.server_settings_path,\n str(server_id)\n )\n )\n ):\n return False\n return True","def available(\n\t\t\tconfig_file):\n\t\treturn","def configure(self, conf):\n return False","def check_config(self):\n try:\n config_metadata = self.dbc.get_metadata(\"config.txt\")\n except rest.ErrorResponse:\n print str(datetime.datetime.now()) \\\n + \": No config.txt in Dropbox directory. Exiting.\"\n sys.exit()\n if config_metadata[\"modified\"] != self.config_date:\n print str(datetime.datetime.now()) + \": Config changed\"\n self.config_date = config_metadata[\"modified\"]\n try:\n self.dbc.get_file(\"config.txt\")\n except rest.ErrorResponse as e:\n print str(datetime.datetime.now()) + e.reason\n return False\n self.config.reload(self.local_directory + \"/\" + \"config.txt\")\n return True\n return False","def load_config(self):\n try:\n self.config = yaml.load(open(roslib.packages.get_pkg_dir('human_activities') + '/config/config.ini', 'r'))\n print \"config loaded:\", self.config.keys()\n\n return True\n except:\n print \"no config file found in /human_activities/config/config.ini\"\n return False","def check_general_settings(self):\n\n self.generate_description_general_settings()\n if not self.config.has_section('description'):\n colorprint('[description] in [{self.settingpath}] did not exist. Created it.'.format(**locals()), 'green')\n\n if not self.config.has_section('credentials'):\n self.config.add_section('credentials')\n self.config.set('credentials', 'world', 'de100')\n self.config.set('credentials', 'username', 'yournamegoeshere')\n self.config.set('credentials', 'password', 'yourpasswordgoeshere')\n self.config.set('credentials', 'captcha_user', 'yourdeathbycaptchaussernamegoeshere')\n self.config.set('credentials', 'captcha_pass', 'yourdeathbycaptchapasswordgoeshere')\n colorprint('[credentials] in [{self.settingpath}] did not exist. Created it.'.format(**locals()), 'green')\n\n if not self.config.has_section('control'):\n self.config.add_section('control')\n self.config.set('control', 'sleep', '300')\n self.config.set('control', 'farmsplit', '5')\n colorprint('[control] in [{self.settingpath}] did not exist. Created it.'.format(**locals()), 'green')\n\n if not self.config.has_section('storage'):\n self.config.add_section('storage')\n self.config.set('storage', 'path', 'data')\n colorprint('[storage] in [{self.settingpath}] did not exist. Created it.'.format(**locals()), 'green')\n\n self.config.write(open(self.settingpath, 'w'))","def fallback_file(cls, filename):\n file_list = (\n filename,\n path.join(path.expanduser('~'), '.config', 'hiromi.json'),\n path.join(path.expanduser('~'), '.hiromi')\n )\n for a_file in file_list:\n if path.exists(a_file):\n return a_file\n print(\n \"Please given a legal config file, or make a config file at\"\n \"~/.hiromi or ~/.config/hiromi.json\"\n )\n raise ConfigNotFoundException()","def check_config():\n\n if not config_instance:\n LOG.error(\"Failed to load the config!\")\n sys.exit(9)\n\n if not hasattr(config_instance, \"CONFIG_VERSION\"):\n LOG.warning( \"The config file does not specify CONFIG_VERSION! I will \"\n \"try to continue anyway, but this field is recommended to allow \"\n \"some internal tests to work. I will assume the value '(1,0)'!\" )\n config_instance.CONFIG_VERSION = (1, 0)\n\n major, minor = config_instance.CONFIG_VERSION\n expected_major, expected_minor = EXPECTED_CONFIG_VERSION\n\n if major < expected_major:\n LOG.critical(\"The config system has undergone a major change! \"\n \"I cannot continue without an upgrade!\")\n sys.exit(9)\n\n if minor < expected_minor:\n LOG.warning(\"The config system has undergone a minor change! \"\n \"It should work, but you still should review the docs!\")\n\n if major == expected_major and minor == expected_minor:\n LOG.debug( \"Config version OK!\" )\n\n if not hasattr(config_instance, \"GENERATORS\"):\n LOG.critical(\"Variable 'GENERATORS' not found in config!\")\n sys.exit(9)\n\n if not hasattr(config_instance, \"TARGETS\"):\n LOG.critical(\"Variable 'TARGETS' not found in config!\")\n sys.exit(9)","def print_configuration_not_exist(cls, config_name):\n print(\n f\"{cls.ERROR_PREFIX} {cls.CONFIGHOLDER_CONFIG_NOT_EXIST} {config_name}.\"\n )","def validate_config_file(args):\r\n # Validate arguments\r\n if len(args) > 2:\r\n print 'Error! --validate_config does not take any arguments.'\r\n return\r\n\r\n # Check if the config.txt exists\r\n if not os.path.exists(utils.CONFIG_FILE_PATH):\r\n print 'Config file does not seem to exist. Do you wish STAPLER to ' \\\r\n 'create it now?'\r\n if raw_input('(y/n): ') == 'y':\r\n update_config_file(AvailableCommands.commands.keys(), [])\r\n else:\r\n 'Create the config file manually or get it from ' \\\r\n 'https://github.com/tyrmi/STAPLER\\n'\r\n return 0\r\n\r\n # Check if config.txt contains proper rows\r\n config_file = utils.get_config_file()\r\n i = 0\r\n config_file_defined_commands = []\r\n commands_to_remove = []\r\n for line in config_file.split('\\n'):\r\n i += 1\r\n line = line.strip()\r\n if not line: continue\r\n line = line.split('\\t')\r\n\r\n # Check the number of columns\r\n if len(line) != 4:\r\n print 'config.txt file should have 4 tab delimited columns on ' \\\r\n 'each row. Found {0} columns on line number {1}:'.format(len(line), i)\r\n print ' '.join(line)\r\n return\r\n\r\n # Skip first (header) line\r\n if i == 1: continue\r\n\r\n # Skip comment lines\r\n if line[0].startswith('#'): continue\r\n\r\n # Check for duplicate rows\r\n if line[0] in config_file_defined_commands:\r\n print 'config.txt row number {0} contains configuration for ' \\\r\n 'command {1}, which has been defined earlier in the ' \\\r\n 'config.txt. Please remove the duplicate rows.'.format(i,\r\n line[0])\r\n return 0\r\n\r\n # Check if the defined command is supported by STAPLER\r\n if line[0] not in AvailableCommands.commands:\r\n commands_to_remove.append(line[0])\r\n else:\r\n # Add the current command to list\r\n config_file_defined_commands.append(line[0])\r\n\r\n # Report the commands that are found in config.txt but are not supported\r\n if commands_to_remove:\r\n print 'config.txt contains configuration for the following commands, ' \\\r\n 'that are not supported by STAPLER:'\r\n print '\\n'.join(commands_to_remove)\r\n print '\\n'\r\n\r\n # Check and report if some commands are missing from the config.txt\r\n commands_to_add = set(AvailableCommands.commands.keys()) - \\\r\n utils.CONFIG_FILE_OMITTED_COMMANDS - \\\r\n set(config_file_defined_commands)\r\n if commands_to_add:\r\n print 'config.txt is missing configuration for the following ' \\\r\n 'commands, that are supported by STAPLER:'\r\n print '\\n'.join(list(commands_to_add))\r\n print '\\n'\r\n\r\n # Prompt user if the issues should be automatically fixed\r\n if commands_to_remove or commands_to_add:\r\n print 'Do you wish STAPLER to automatically update config.txt by ' \\\r\n 'adding missing commands and removing unsupported commands?'\r\n if raw_input('(y/n): ') == 'y':\r\n update_config_file(commands_to_add, commands_to_remove)\r\n config_file_defined_commands += commands_to_add\r\n print 'Done. Continuing validation...\\n'\r\n else:\r\n print 'OK. Continuing validation of supported commands...\\n'\r\n\r\n # Test each command on each row of config.txt\r\n print 'Testing the config.txt commands...'\r\n devnull = open(os.devnull, 'wb')\r\n max_width = max(map(len, config_file_defined_commands))\r\n i = 0\r\n results = []\r\n for cmd in config_file_defined_commands:\r\n i += 1\r\n if cmd in ('CUSTOM', 'CUSTOM_NO_OUTPUT'): continue\r\n check_result = AvailableCommands.commands[cmd].validate_tool_config()\r\n check_result = [string.ljust(cmd, max_width)] + check_result\r\n results.append('\\t'.join(check_result))\r\n #print int(float(i)/len(config_file_defined_commands)*100)\r\n sys.stdout.write(('\\rChecking commands %s%%' % (int(float(i)/len(\r\n config_file_defined_commands)*100))))\r\n sys.stdout.flush()\r\n\r\n # Print results\r\n print '\\n\\nTest results:'\r\n print '\\n'.join(results)\r\n print '\\nNONE: The execute field is \"none\" in the config.txt'\r\n print 'OK: Command has run perfectly.'\r\n print 'FAIL: Running the command has failed.'\r\n print '- : Unable to test command due to failure in load_module\\n'","def test_find_config_nonexist(self):\n with pytest.raises(scuba.config.ConfigError):\n scuba.config.find_config()","def test_files_non_existing_config(self, context):\n\n context.config_exists.return_value = False\n\n runner = CliRunner()\n result = runner.invoke(cli_node_files, ['--name', 'non-existing'])\n\n # Check that error is produced\n self.assertEqual(result.output[:7], \"[error]\")\n\n # check for non zero exit-code\n self.assertNotEqual(result.exit_code, 0)","def load_config(configFile):\r\n\r\n # TODO: Implement this\r\n return False","def check_configuration(self, configuration):\n super(Hipchap, self).check_configuration(configuration)","def check_config(self):\n # Check if tool is at all included in workflow\n if \"gatk_post_bam\" not in (self.config[\"postprocessing\"] or []): # pylint: disable=C0325\n return # GATK BAM postprocessing not enabled, skip\n\n # Check required configuration settings present\n self.parent.ensure_w_config(\n config_keys=(\"step_config\", \"ngs_mapping\", \"gatk_post_bam\", \"paths_known_sites\"),\n msg=\"Known sites list cannot be empty for GATK BAM postprocessing\",\n )\n self.parent.ensure_w_config(\n config_keys=(\"static_data_config\", \"reference\", \"path\"),\n msg=\"Path to reference FASTA required for GATK BAM postprocessing\",\n )","def check_configuration(self):\n self.ensure_one()\n getattr(self, '%s_check_configuration' % self.provider, lambda: None)()","def test_load_configuration_fails_gracefully_when_file_does_not_exist():\n config.load_configuration(invalid_configuration_path, graceful=True)\n assert True","def _validate_config(self):\n pass","def loadConfig(self, apiName=None):\n self._packRoot = getattr(sys, \"_MEIPASS\", path.abspath(path.dirname(__file__)))\n rootDir = path.abspath(path.join(self._packRoot, path.pardir))\n logger.debug(\"Package root directory is: %s\" % rootDir)\n\n if self._lookFolder is None:\n userConf = path.join(rootDir, \"config.json\")\n else:\n userConf = path.join(self._lookFolder, \"config.json\")\n\n if path.isfile(userConf):\n logger.debug(\"Loading file: %s\" % userConf)\n jsonData = {}\n try:\n with open(userConf, mode=\"r\") as inFile:\n jsonData = json.loads(inFile.read())\n\n self.dataPath = jsonData.get(\"dataPath\", \"\")\n self._savedSearches = jsonData.get(\"searches\", {})\n\n if apiName is None:\n apiName = jsonData.get(\"default\", None)\n\n apiList = jsonData.get(\"apis\", None)\n if apiList is None:\n logger.error(\"Could not find any apis in config file\")\n return False\n\n if not isinstance(apiList, list):\n logger.error(\"Invalid apis list in config file\")\n return False\n\n if len(apiList) == 0:\n logger.error(\"Empty apis list in config file\")\n return False\n\n for apiEntry in apiList:\n if apiEntry.get(\"apiName\", None) == apiName:\n self.apiName = apiEntry.get(\"apiName\", \"\")\n self.apiUser = apiEntry.get(\"apiUsername\", \"\")\n self.apiPass = apiEntry.get(\"apiPassword\", \"\")\n self.apiURL = apiEntry.get(\"apiURL\", \"\")\n\n self._confPath = userConf\n\n except Exception as e:\n logger.error(\"Failed to parse config JSON data.\")\n logger.error(str(e))\n return False\n\n else:\n logger.debug(\"No file: %s\" % userConf)\n\n return","def check_configuration(configuration: Dict[str, Any] = None) -> bool:\n configuration = configuration or {}\n if \"wiremock\" not in configuration:\n logger.error(\"Error: wiremock key not found in configuration section\")\n return False\n return True","def _check_for_custom_config(self, standard_conf_path):\n\n ret_val = False\n conf_filename = os.path.basename(standard_conf_path)\n custom_conf_expected_path = CUSTOM_CONFIG_DIR + '/' + self._get_tempdir() + '/' + conf_filename\n\n if os.path.isfile(custom_conf_expected_path):\n ret_val = True\n\n return ret_val","def check_create_settings_file(self):\n path = self.paths[\"settings_file\"]\n from django_swagger_utils.core.utils.check_path_exists import check_path_exists\n settings_file = check_path_exists(path)\n if not settings_file:\n settings_file_contents = \"# '%s' settings\" % self.app_name\n from django_swagger_utils.core.utils.write_to_file import write_to_file\n write_to_file(settings_file_contents, path)","def config_reader(config_file):\n if os.path.exists(config_file):\n with open(config_file, 'r') as cfile:\n app_config = json.load(cfile)\n # end with\n return app_config\n else:\n print(\"The config file: {} does not exist, please try again.\".format(config_file))\n return False\n # fin","def test_validate_config_file(self):\n ingest_mgmr = IngestManager()\n ingest_mgmr.validate_config_file(self.example_config_data)\n assert(ingest_mgmr.config is not None)\n assert (ingest_mgmr.config.config_data is not None)","def validate_missing_information(conf):\n failed = False\n\n for field, _ in REQUIRED_SETTINGS.items():\n if field not in conf:\n print 'error: missing configuration for \"{0}\"'.format(field)\n failed = True\n\n if failed:\n sys.exit(1)","def check_config( config: 'bittensor.Config' ):\n assert config.neuron.batch_size_train > 0, \"batch_size_train must be a positive value\"\n assert config.neuron.learning_rate > 0, \"learning_rate must be a positive value.\"\n bittensor.logging.check_config( config )\n bittensor.wallet.check_config( config )\n bittensor.subtensor.check_config( config )\n bittensor.metagraph.check_config( config )\n bittensor.dataloader.check_config( config )\n bittensor.dendrite.check_config( config )\n bittensor.axon.check_config( config )\n GPT2Nucleus.check_config( config )\n SGMOERouter.check_config( config )\n full_path = os.path.expanduser('{}/{}/{}'.format( config.logging.logging_dir, config.wallet.name + \"-\" + config.wallet.hotkey, config.neuron.name ))\n config.neuron.full_path = os.path.expanduser(full_path)\n config.neuron.tensorboard_dir = config.neuron.full_path + '/tensorboard-' + '-'.join(str(datetime.now()).split())\n if not os.path.exists(config.neuron.full_path):\n os.makedirs(config.neuron.full_path)","def _verify_ini(self, config_file=None):\n\n imgur_values = ['ClientID', 'ClientSecret', 'AccessToken', 'RefreshToken']\n mysql_values = ['Host', 'User', 'Password', 'Database']\n missing_values = []\n\n if not config_file:\n print(\"No Config Filed Supplied. Aborting\")\n sys.exit(1)\n\n for val in imgur_values:\n if val not in config_file['IMGURAPI']:\n missing_values.append('IMGURAPI: ' + val)\n\n for val in mysql_values:\n if val not in config_file['MYSQL']:\n missing_values.append('MYSQL: ' + val)\n\n if missing_values:\n print('ERROR: ini file is missing required values. \\n Missing Values:')\n for val in missing_values:\n print(val)\n sys.exit(1)","def _validate_pf_config(module, config):\n rc, out, err = module.run_command(['ls', config])\n\n # Fail if no config file is present\n if rc != 0:\n msg_err = f'Error: Config file does not exist: {config}'\n module.fail_json(msg=msg_err)","def create():\n config_file = get_config_file()\n if os.path.isfile(config_file):\n logger.info(\"The config file already exists at '%s'\", config_file)\n return\n _create_config()\n logger.info(\"Created config file at '%s'\", config_file)","def retrieve_config(conf_info, flowcell, cfg_filepath, genome_dir):\n options = getCombinedOptions()\n\n if options.url is None:\n LOGGER.error(\"%s or %s missing base_host_url option\" % \\\n (CONFIG_USER, CONFIG_SYSTEM))\n return False\n\n try:\n saveConfigFile(flowcell, options.url, cfg_filepath)\n conf_info.config_filepath = cfg_filepath\n except FlowCellNotFound as e:\n LOGGER.error(e)\n return False\n except WebError404 as e:\n LOGGER.error(e)\n return False\n except IOError as e:\n LOGGER.error(e)\n return False\n except Exception as e:\n LOGGER.error(e)\n return False\n\n f = open(cfg_filepath, 'r')\n data = f.read()\n f.close()\n\n genome_dict = getAvailableGenomes(genome_dir)\n mapper_dict = constructMapperDict(genome_dict)\n\n LOGGER.debug(data)\n\n f = open(cfg_filepath, 'w')\n f.write(data % (mapper_dict))\n f.close()\n\n return True","def check_config_file(path):\n\tif not os.path.exists (path):\n\t\t# create the directories\n\t\ttry:\n\t\t\tos.makedirs (os.path.join (os.path.split (path)[0]))\n\t\texcept os.error:\n\t\t\tprint \"Error while creating neccessary directories: %s\"\\\n\t\t\t\t% (os.error)\n\t\t\treturn False\n\n\t\ttry:\n\t\t\tf = file (path, \"w\")\n\t\texcept BaseException,e:\n\t\t\tprint \"Error while creating config file: %s\" % (e)\n\t\t\treturn False\n\t\telse:\n\t\t\tf.close()\n\n\t\treturn True\n\telse:\n\t\treturn True\n\treturn False","def validate_conf(name, ini_file_path):\n sections = read_config(ini_file_path).sections()\n\n if name in sections:\n raise LogmeError(f\"'{name}' logging config already exists in config file: {ini_file_path}\")\n elif 'logme' not in sections:\n raise LogmeError(f\"{ini_file_path} is not a valid logme.ini file\")","def check():\n # Initialize key variables\n config_directory = os.environ['PATTOO_CONFIGDIR']\n\n # Print Status\n print('??: Checking configuration parameters.')\n\n # Check config (pattoo.yaml)\n config_file = configuration.agent_config_filename('pattoo')\n config = files.read_yaml_file(config_file)\n\n # Check main keys\n keys = ['pattoo', 'pattoo_web_api', 'pattoo_agent_api']\n for key in keys:\n if key not in config:\n log_message = ('''\\\nSection \"{}\" not found in configuration file in directory {}. Please fix.\\\n'''.format(key, config_directory))\n log.log2die_safe(80007, log_message)\n\n # Check secondary keys\n secondaries = [\n 'log_level', 'log_directory', 'cache_directory',\n 'daemon_directory']\n secondary_key_check(config, 'pattoo', secondaries)\n secondaries = ['ip_address', 'ip_bind_port']\n secondary_key_check(config, 'pattoo_agent_api', secondaries)\n secondaries = ['ip_address', 'ip_bind_port']\n secondary_key_check(config, 'pattoo_web_api', secondaries)\n\n # Check config (pattoo_webd.yaml)\n config_file = configuration.agent_config_filename('pattoo_webd')\n config = files.read_yaml_file(config_file)\n\n # Check main keys\n keys = ['pattoo_webd']\n for key in keys:\n if key not in config:\n log_message = ('''\\\nSection \"{}\" not found in configuration file in directory {}. Please fix.\\\n'''.format(key, config_directory))\n log.log2die_safe(80020, log_message)\n\n # Check secondary keys\n secondaries = ['ip_listen_address', 'ip_bind_port']\n secondary_key_check(config, 'pattoo_webd', secondaries)\n\n # Print Status\n print('OK: Configuration parameter check passed.')","def test_config_ok_config(self):\n test_data = (\"[gnupg]\\n\"\n \"recipients = tkl@iki.fi\\n\"\n \"signer = tommi.linnakangas@iki.fi\\n\"\n \"\\n\"\n \"[amazon-s3]\\n\"\n \"access_key = ACCESSKEY\\n\"\n \"secret_access_key = SECRETACCESSKEY\\n\"\n \"\\n\"\n \"[data]\\n\"\n \"\\n\"\n \"bucket = DATABUCKET\\n\"\n \"[metadata]\\n\"\n \"bucket = METADATABUCKET\\n\"\n \"\\n\")\n if os.path.isfile(\"test_config.conf\"):\n os.remove(\"test_config.conf\")\n file(\"test_config.conf\", \"wb\").write(test_data)\n config = Config(\"test_config.conf\")\n self.assertIn(\"gnupg\", config.config.sections())\n self.assertIn(\"amazon-s3\", config.config.sections())\n self.assertEqual(config.config.get(\n \"gnupg\", \"recipients\"), \"tkl@iki.fi\")\n self.assertEqual(config.config.get(\n \"gnupg\", \"signer\"), \"tommi.linnakangas@iki.fi\")\n self.assertEqual(config.config.get(\n \"amazon-s3\", \"access_key\"), \"ACCESSKEY\")\n self.assertEqual(config.config.get(\n \"amazon-s3\", \"secret_access_key\"), \"SECRETACCESSKEY\")\n self.assertEqual(config.config.get(\n \"data\", \"bucket\"), \"DATABUCKET\")\n self.assertEqual(config.config.get(\n \"metadata\", \"bucket\"), \"METADATABUCKET\")\n os.remove(\"test_config.conf\")","def check_for_application_file(self, application):\n # 1. Get the path of the app_config directory\n app_conf_dir = self.sys_conf['configs']['env'][self.env]['app_config_url']\n\n # 2. Get the path for the given application configuration file\n app_conf_dir += '/{file}.yaml'.format(file=application)\n\n # 3. Validate the path exists\n chk = file_check(app_conf_dir)\n\n # 4. Return the result\n return chk","def parse_config():\n config_path = Path(\"config.ini\")\n if config_path.exists():\n config.read(config_path)\n else:\n config[\"database\"] = {\"location\": \"image-database.db\"}\n config[\"images\"] = {\"extensions\": \".jpeg,.jpg,.png,.gif,.tiff\"}\n with open(config_path, \"w\") as configfile:\n config.write(configfile)\n config.read(config_path)","def require_config(self) -> None:\n if not self.has_config():\n fail(f\"No tzk_config.py found in the current directory. \"\n f\"(Try 'tzk init' if you want to create a new one.)\")","def read_config(self, config_filename):","def test_no_config_keyword(self):\n args = self.get_args()\n config = {\n \"site\": {\n \"username\": \"\",\n \"name\": \"\",\n \"ip_address\": \"\",\n \"password\": \"\",\n \"local\": \"\",\n \"use_https\": \"\"\n }\n }\n temp = sys.stdout\n fake_out = FakeStdio()\n sys.stdout = fake_out\n\n config_filename = 'testsuite_cfg.json'\n args.config = config_filename\n config_file = open(config_filename, 'w')\n config_file.write(str(json.dumps(config)))\n config_file.close()\n\n execute_tool(args, test_mode=True)\n sys.stdout = temp\n self.assertTrue(fake_out.verify_output(['%% Invalid configuration file', '\\n']))","def __check_config(config):\n\n for key, value in defaults.CONFIG_REQUIRED_ENTRIES.items():\n if (key not in config.keys()) or (config[key] == \"\" and not value):\n raise ValueError(\"configuration file is missing required key \" + key +\n \" or invalid value was provided\")"],"string":"[\n \"def __check_config(self):\\n if not os.path.exists(self.__config_path):\\n return False\\n else:\\n return True\",\n \"def is_config_exist(self) -> bool:\\n pass\",\n \"def is_config_exist(self) -> bool:\\n return True\",\n \"def check_config_file():\\n # Locate and init config.\\n default_config = \\\"config.json\\\"\\n if len(sys.argv) == 2:\\n # config from command line\\n app_config = config_reader(sys.argv[1])\\n else:\\n # config should be in default\\n app_config = config_reader(default_config)\\n # fin\\n if not app_config:\\n print(\\\"Exiting due to invalid config file.\\\")\\n return False\\n # fin\\n return app_config\",\n \"def _check(self, config: Dict):\\n if 'path' not in config:\\n raise FileNotFoundError(\\\"File not found.\\\")\",\n \"def _validate_config(self):\\n # Simulation ID\\n empty_string_check(self._config_dict['@id'])\\n \\n # Output\\n empty_string_check(self._config_dict['output']['@baseDirectory'])\\n self._config_dict['output']['@saveInteractionLog'] = parse_boolean(self._config_dict['output']['@saveInteractionLog'])\\n self._config_dict['output']['@saveRelevanceJudgments'] = parse_boolean(self._config_dict['output']['@saveRelevanceJudgments'])\\n self._config_dict['output']['@trec_eval'] = parse_boolean(self._config_dict['output']['@trec_eval'])\\n \\n # Topics\\n def check_topic(t):\\n \\\"\\\"\\\"\\n Checks a given topic, t. Looks for a topic ID and a valid topic description file.\\n \\\"\\\"\\\"\\n empty_string_check(t['@id'])\\n filesystem_exists_check(t['@filename'])\\n filesystem_exists_check(t['@qrelsFilename'])\\n \\n if '@backgroundFilename' in t: # A background file was specified.\\n filesystem_exists_check(t['@backgroundFilename'])\\n else:\\n t['@backgroundFilename'] = None # No background file was specified.\\n \\n topics = self._config_dict['topics']['topic']\\n \\n if type(topics) == list:\\n for topic in topics:\\n check_topic(topic)\\n else:\\n check_topic(topics)\\n \\n # Users\\n users = self._config_dict['users']['user']\\n \\n if type(users) == list:\\n for user in users:\\n filesystem_exists_check(user['@configurationFile'])\\n else:\\n filesystem_exists_check(users['@configurationFile'])\\n \\n # Search Interface\\n empty_string_check(self._config_dict['searchInterface']['@class'])\\n check_attributes(self._config_dict['searchInterface'])\",\n \"def config_input(self):\\n answer = simpledialog.askstring(\\\"Input\\\", \\\"Please input the path of the config file.\\\",\\n parent=self._master) # ask for a string\\n if answer is not None:\\n try:\\n self.read_config(answer) # read the config\\n if all(k in self._config for k in ('World', 'Player')):\\n # check whether have a 'World' and 'Player'\\n return True\\n else: # if don't have 'World' or 'Player'\\n messagebox.showerror(\\\"Missing necessary heading\\\", \\\"Missing World or Player\\\")\\n return False\\n except IOError:\\n messagebox.showerror(\\\"Invalid path\\\",\\n \\\"'\\\" + answer + \\\"' is not a valid path!\\\")\\n return False\",\n \"def check_config(self):\\n cfgs = self.__get() \\n \\n for option in Config.FILE_OPTIONS.keys():\\n _default = Config.FILE_OPTIONS[option]\\n \\n if not cfgs.has_key(option):\\n self.log.warn(\\\"Parameter '%s' is missing in '%s', using default('%s')\\\" % \\\\\\n (option, self.config_file, _default))\\n _file = _default\\n else:\\n _file = cfgs[option]\\n Config.FILE_OPTIONS[option] = _file\\n\\n if not os.path.exists(_file) and not os.path.isfile(_file):\\n self.log.error(\\\"Paramenter '%s' points to non-existing file '%s')\\\" % \\\\\\n (option, _file))\\n raise ConfigError('File Error', \\\"Paramenter '%s' points to non-existing file '%s')\\\" % \\\\\\n (option, _file))\\n\\n\\n for option in Config.PATH_OPTIONS.keys():\\n _default = Config.PATH_OPTIONS[option]\\n \\n if not cfgs.has_key(option):\\n self.log.warn(\\\"Parameter '%s' is missing in '%s', using default('%s')\\\" % \\\\\\n (option, self.config_file, _default))\\n _dir = _default\\n else:\\n _dir = cfgs[option]\\n Config.PATH_OPTIONS[option] = _dir\\n\\n if not os.path.exists(_dir) and not os.path.isdir(_dir):\\n self.log.error(\\\"Paramenter '%s' points to non-existing directory '%s')\\\" % \\\\\\n (option, _dir))\\n raise ConfigError('File Error', \\\"Paramenter '%s' points to non-existing directory '%s')\\\" % \\\\\\n (option, _dir))\\n\\n \\n Config.DB_SFT_OPTIONS['sqlalchemy_sft.url'] = cfgs['sqlalchemy_sft.url']\\n Config.DB_NAGIOS_OPTIONS['sqlalchemy_nagios.url'] = cfgs['sqlalchemy_nagios.url']\\n\\n self.log.debug(\\\"Configuration successfully checked\\\")\",\n \"def init_config():\\n config_file = create_config_file.CreateConfigFile()\\n config_file.check_if_config_file_exists()\",\n \"def has_configuration(config_file=CONFIG_FILE):\\n return os.path.exists(config_file)\",\n \"def check_config(self):\\n # Check if tool is at all included in workflow\\n if \\\"external\\\" not in self.config[\\\"tools\\\"][\\\"dna\\\"]:\\n return # External not run, don't check configuration # pragma: no cover\",\n \"def check_config(cfg):\",\n \"def _check_config(self):\",\n \"def check_for_config(conf, confs_path):\\n if yes_no('Is there a configuration file you would like to load?'):\\n options = os.listdir(confs_path)\\n if len(options) > 0:\\n options.append('Specify a Path')\\n option = ask_options(\\n 'Pick a configuration file', options, title=False)\\n if option == 'Specify a Path':\\n conf['config'] = ask_path(\\n 'Enter the path for the configuration file')\\n else:\\n conf['config'] = os.path.join(confs_path, option)\\n else:\\n conf['config'] = ask_path(\\n 'Enter the path for the configuration file')\\n conf = get_config_file(conf)\\n conf, _ = confirm_conf(conf)\\n conf.pop('config')\\n return conf\",\n \"def check_config(outconfig):\\n self.log.info(\\\"Checking if all the necessary files exist.\\\")\\n\\n # Perform necessary checks\\n\\n log.info(\\\"All necessary files exist for {} configuration.\\\".format(outconfig[\\\"Flavor\\\"]))\\n\\n return\",\n \"def check_config(config):\\n pass\",\n \"def is_config_exist(self) -> bool:\\n return os.path.isfile(self.connection_string)\",\n \"def check_config(self):\\n # Check if tool is at all included in workflow\\n if self.__class__.name not in self.config[\\\"tools\\\"][\\\"rna\\\"]:\\n return # STAR not run, don't check configuration # pragma: no cover\\n\\n # Check required configuration settings present\\n self.parent.ensure_w_config(\\n config_keys=(\\\"step_config\\\", \\\"ngs_mapping\\\", \\\"star\\\", \\\"path_index\\\"),\\n msg=\\\"Path to STAR index is required\\\",\\n )\\n self.parent.ensure_w_config(\\n config_keys=(\\\"static_data_config\\\", \\\"reference\\\"),\\n msg=\\\"No reference genome FASTA file given\\\",\\n )\\n\\n # Check validity of the STAR index\\n full_path = self.config[\\\"star\\\"][\\\"path_index\\\"]\\n # a lot of files should be in this dir, justtest these\\n for indfile in (\\\"Genome\\\", \\\"SA\\\", \\\"SAindex\\\"):\\n expected_path = os.path.join(full_path, indfile)\\n if not os.path.exists(expected_path): # pragma: no cover\\n tpl = \\\"Expected STAR index file {expected_path} does not exist!\\\".format(\\n expected_path=expected_path\\n )\\n raise InvalidConfiguration(tpl)\",\n \"def get_conf(self, file_name):\\n try:\\n self.conf = Config(file_name)\\n except ImportError:\\n s = \\\"No such file in simulation directory: \\\" + file_name\\n self.abort(ImportError, s)\",\n \"def cloudwatch_config_exists(config, config_type, file_name):\\n\\n cfg = config.get(\\\"cloudwatch\\\", {}).get(config_type, {}).get(file_name)\\n if cfg:\\n assert os.path.isfile(cfg), \\\\\\n \\\"Invalid CloudWatch Config File Path: {}\\\".format(cfg)\\n return bool(cfg)\",\n \"def test_config():\\n if not os.path.exists(CONFIG_DIR):\\n raise mupub.BadConfiguration('Configuration folder not found.')\\n if not os.path.exists(_CONFIG_FNM):\\n raise mupub.BadConfiguration('Configuration file not found.')\\n if not os.path.exists(getDBPath()):\\n raise mupub.BadConfiguration('Local database not found.')\\n if len(CONFIG_DICT) == 0:\\n raise mupub.BadConfiguration('Configuration was not loaded.')\",\n \"def check_configs(self):\\n\\n pass\",\n \"def _ensure_config_file_exists():\\n config_file = Path(ELIBConfig.config_file_path).absolute()\\n if not config_file.exists():\\n raise ConfigFileNotFoundError(ELIBConfig.config_file_path)\",\n \"def check() -> bool:\\n\\ttry:\\n\\t\\twith open(configPath, 'r') as file:\\n\\t\\t\\tcfg = json.load(file) # load the file\\n\\texcept FileNotFoundError:\\n\\t\\treturn False\\n\\t# check if EVERY config exists\\n\\tfor i in default_config.keys():\\n\\t\\tif i in cfg.keys():\\n\\t\\t\\tcontinue\\n\\t\\treturn False\\n\\t# final check\\n\\treturn cfg['config_type'] == default_config['config_type']\",\n \"def __check_configuration__(self, parser):\\n if not parser.has_section('core'):\\n self.logger.error('The config file should contain a core section with at least the module_path specified')\\n sys.exit(1)\\n\\n else:\\n if parser.get('core', 'modules_path', fallback=None) is None:\\n self.logger.error('The configuration file should contain at least the modules_path value in core section.')\\n sys.exit(1)\\n\\n if not parser.has_section('mysql'):\\n self.logger.error('The config file should contain a mysql section.')\\n sys.exit(1)\\n\\n else:\\n if parser.get('mysql', 'host', fallback=None) is None:\\n self.logger.error('The config file should contain the host value in mysql section.')\\n sys.exit(1)\\n\\n if parser.get('mysql', 'port', fallback=None) is None:\\n self.logger.error('The config file should contain the port value in mysql section.')\\n sys.exit(1)\\n\\n if parser.get('mysql', 'user', fallback=None) is None:\\n self.logger.error('The config file should contain the user in mysql section.')\\n sys.exit(1)\\n\\n if parser.get('mysql', 'password', fallback=None) is None:\\n self.logger.error('The config file should contain the password of the user in mysql section.')\\n sys.exit(1)\\n\\n if parser.get('mysql', 'server_id', fallback=None) is None:\\n self.logger.error('The config file should contain the server_id in mysql section.')\\n sys.exit(1)\\n\\n if parser.get('mysql', 'tables', fallback=None) is not None:\\n tables = [table.strip() for table in parser.get('mysql', 'tables').split(',')]\\n for table in tables:\\n if not parser.has_section(table):\\n self.logger.error('The config file should contain a section about the table : %s' % table)\\n exit(1)\\n if parser.get(table, 'index_label', fallback=None) is None :\\n self.logger.error('The config file should contain a table section with a index_label value.')\\n exit(1)\\n else:\\n self.logger.error('The config file should contain a tables value with all the tables to replicate.')\\n exit(1)\",\n \"def check_configuration(self):\\n\\n return bool(os.path.isfile(self.config_path) and\\n self.validate_configuration_file())\",\n \"def check_configure_scan(project_path):\\n for file_name in CONFIGURE_AC_NAMES:\\n file_path = os.path.join(project_path, file_name)\\n if os.path.exists(file_path):\\n return file_path\\n return None\",\n \"def __test_config_file__(parser, path):\\n if not os.path.exists(path):\\n return False\\n else:\\n statinfo = os.stat(path)\\n if not os.path.isfile(path) or os.path.islink(path):\\n parser.error(\\\"invalid configuration file {} (it must be a regular file)\\\".format(path))\\n elif stat.filemode(statinfo.st_mode) != '-rw-------':\\n parser.error(\\\"invalid configuration file {} (it must have only read and write permissions for user)\\\".format(path))\\n elif statinfo.st_uid != os.getuid():\\n parser.error(\\\"invalid configuration file {} (the owner must be the user)\\\".format(path))\\n return True\",\n \"def find_config_file(self):\\n filename = self.values.get('config_file', Default('noy.json'))\\n\\n ignore_missing = False\\n if isinstance(filename, Default):\\n filename = filename.val\\n ignore_missing = True\\n\\n filename = os.path.abspath(filename)\\n if os.path.exists(filename):\\n return filename\\n elif not ignore_missing:\\n raise MissingConfigFile(\\\"Config file doesn't exist at {}\\\".format(filename))\",\n \"def check_config(self):\\n # Check if tool is at all included in workflow\\n if not (set(self.config[\\\"tools\\\"][\\\"dna_long\\\"]) & {\\\"ngmlr\\\", \\\"ngmlr_chained\\\"}):\\n return # NGLMR not run, don't check configuration # pragma: no cover\\n\\n # Check required configuration settings present\\n self.parent.ensure_w_config(\\n config_keys=(\\\"step_config\\\", \\\"ngs_mapping\\\", \\\"ngmlr\\\", \\\"path_index\\\"),\\n msg=\\\"Path to NGMLR index is required\\\",\\n )\",\n \"def check_configs():\\n configs = [os.path.join(CUCKOO_ROOT, \\\"conf\\\", \\\"cuckoo.conf\\\"),\\n os.path.join(CUCKOO_ROOT, \\\"conf\\\", \\\"reporting.conf\\\")]\\n\\n for config in configs:\\n if not os.path.exists(config):\\n raise CuckooStartupError(\\\"Config file does not exist at path: %s\\\" % config)\\n\\n return True\",\n \"def test_nonexisting_config(self):\\n try:\\n tempdir = tempfile.mkdtemp()\\n filename = os.path.join(tempdir, \\\"Config.yaml\\\")\\n with self.assertRaises(easydms.config.ErrorNoConfiguration):\\n easydms.config.Config(filename)\\n\\n with self.assertRaises(SystemExit) as cm:\\n sys.argv = [\\\"prog\\\", \\\"-c\\\", filename]\\n easydms.cli.main()\\n self.assertNotEqual(cm.exception.code, 0)\\n\\n finally:\\n shutil.rmtree(tempdir)\",\n \"def validate_config(self):\\n reference = data_file(\\\"../config/template/minimum_aiscalator.conf\\\")\\n ref = pyhocon.ConfigFactory.parse_file(reference)\\n msg = \\\"In Global Application Configuration file \\\"\\n _validate_configs(self._app_conf, ref, msg,\\n missing_exception=True,\\n type_mismatch_exception=True)\\n reference = data_file(\\\"../config/template/aiscalator.conf\\\")\\n ref = pyhocon.ConfigFactory.parse_file(reference)\\n msg = \\\"In Global Application Configuration file \\\"\\n _validate_configs(self._app_conf, ref, msg,\\n missing_exception=False,\\n type_mismatch_exception=True)\\n if self._step_name:\\n reference = data_file(\\\"../config/template/minimum_step.conf\\\")\\n ref = pyhocon.ConfigFactory.parse_file(reference)\\n msg = \\\"in step named \\\" + self._step_name\\n _validate_configs(self._step,\\n ref[\\\"steps\\\"][\\\"Untitled\\\"],\\n msg,\\n missing_exception=True,\\n type_mismatch_exception=True)\\n reference = data_file(\\\"../config/template/step.conf\\\")\\n ref = pyhocon.ConfigFactory.parse_file(reference)\\n msg = \\\"in step named \\\" + self._step_name\\n _validate_configs(self._step,\\n ref[\\\"steps\\\"][\\\"Untitled\\\"],\\n msg,\\n missing_exception=False,\\n type_mismatch_exception=True)\\n if self._dag_name:\\n reference = data_file(\\\"../config/template/minimum_dag.conf\\\")\\n ref = pyhocon.ConfigFactory.parse_file(reference)\\n msg = \\\"in dag named \\\" + self._dag_name\\n _validate_configs(self._dag,\\n ref[\\\"dags\\\"][\\\"Untitled\\\"],\\n msg,\\n missing_exception=True,\\n type_mismatch_exception=True)\\n reference = data_file(\\\"../config/template/step.conf\\\")\\n ref = pyhocon.ConfigFactory.parse_file(reference)\\n msg = \\\"in dag named \\\" + self._dag_name\\n _validate_configs(self._dag,\\n ref[\\\"dags\\\"][\\\"Untitled\\\"],\\n msg,\\n missing_exception=False,\\n type_mismatch_exception=True)\",\n \"def check_config(configd):\\n # XXX: verify first that the required config parameters are present\\n if not os.path.exists(configd['player']):\\n logging.error(\\\"player %s wasn't found\\\" % (configd['player'],))\\n return False\\n if not os.path.isdir(configd['sound_dir']):\\n logging.error(\\\"sound directory %s wasn't found\\\" % (configd['sound_dir'],))\\n return False\\n return True\",\n \"def get_config_file(self):\\n\\n conf_file = self.args.file\\n if conf_file is not None:\\n if os.path.isfile(conf_file):\\n config_file = open(conf_file, \\\"r\\\")\\n self.main_file = yaml.load(config_file, Loader=yaml.FullLoader)\\n elif os.path.isfile(\\n os.path.join(get_path(\\\"DEFAULT\\\", \\\"config_file_path\\\"), conf_file)\\n ):\\n fpath = get_path(\\\"DEFAULT\\\", \\\"config_file_path\\\")\\n config_file = open(os.path.join(fpath, conf_file), \\\"r\\\")\\n self.main_file = yaml.load(config_file, Loader=yaml.FullLoader)\\n else:\\n self.logger.error(\\n colorama.Fore.RED\\n + \\\"ERROR!! Config file '%s' is not present \\\" % conf_file,\\n extra=self.log_detail,\\n )\\n sys.exit(1)\\n else:\\n if self.args.hostname and self.args.testfiles:\\n temp_dict = {\\n \\\"hosts\\\": [{\\\"device\\\": \\\"\\\", \\\"username\\\": \\\"\\\", \\\"passwd\\\": \\\"\\\"}],\\n \\\"tests\\\": [],\\n }\\n temp_dict[\\\"hosts\\\"][0][\\\"device\\\"] = self.args.hostname\\n temp_dict[\\\"hosts\\\"][0][\\\"username\\\"] = self.args.login\\n temp_dict[\\\"hosts\\\"][0][\\\"passwd\\\"] = self.args.passwd\\n for tfile in self.args.testfiles:\\n temp_dict[\\\"tests\\\"].append(tfile)\\n self.main_file = temp_dict\\n\\n if (\\n self.main_file.__contains__(\\\"sqlite\\\")\\n and self.main_file[\\\"sqlite\\\"]\\n and self.main_file[\\\"sqlite\\\"][0]\\n ):\\n self.chk_database(\\n self.main_file,\\n self.args.pre_snapfile,\\n self.args.post_snapfile,\\n self.args.check,\\n self.args.snap,\\n )\\n else:\\n # if --check option is given for sqlite, then snap file name is not compulsory\\n # else exit the function saying arguments not correct\\n if self.args.check is True and (\\n self.args.pre_snapfile is None or self.args.post_snapfile is None\\n ):\\n self.logger.error(\\n colorama.Fore.RED\\n + \\\"Arguments not given correctly, Please refer help message\\\",\\n extra=self.log_detail,\\n )\\n self.parser.print_help()\\n sys.exit(1)\",\n \"def path_exists(config_items):\\n for section, options in config_items.items():\\n if type(options) != dict:\\n continue\\n for key, val in options.items():\\n if key == 'output_pattern':\\n continue\\n if not type(val) == str:\\n continue\\n if val.endswith('.nc') and not os.path.exists(val):\\n print \\\"File {key}: {value} does not exist, exiting.\\\".format(key=key, value=val)\\n sys.exit(1)\",\n \"def exists(self):\\n if self.host.exists(self.remote_path):\\n print 'Yes, config exists already.'\\n return True\\n else:\\n print 'Config doesn\\\\'t exist yet'\\n return False\",\n \"def check_configuration(self, configuration):\\n super(Pixiv_bot, self).check_configuration(configuration)\",\n \"def check_configuration(self, configuration):\\n # If we specified a LOG_FILE path, check to see if it actually exists\\n if configuration.get('LOG_FILE') and not path.isfile(configuration.get('LOG_FILE')):\\n raise ValidationException(f'Could not find file {configuration[\\\"LOG_FILE\\\"]}')\",\n \"def setup_config(self):\\n\\n try:\\n with open('config.json') as f:\\n config = json.load(f)\\n except FileNotFoundError as e:\\n log.debug('{}[*]{} {}'.format(DFbase.LOG_ERROR_COLOR,\\n DFbase.LOG_INFO_COLOR, e))\\n print('{}[*]{} {}'.format(DFbase.LOG_ERROR_COLOR, \\n DFbase.LOG_INFO_COLOR, \\n 'Please copy config.json.example to confing.json'))\\n return False\\n except Exception as e:\\n log.debug('{}[*]{} {}'.format(DFbase.LOG_ERROR_COLOR, \\n DFbase.LOG_INFO_COLOR, e))\\n return False\\n\\n self.artifacts_path = config['ARTIFACTS']['BASE_PATH'].format(self.container_id)\\n self.executable_path = config['ARTIFACTS']['EXECUTABLE_PATH']\\n self.executable_path = self.executable_path.replace('BASE_PATH', self.artifacts_path)\\n self.diff_files_path = config['ARTIFACTS']['DIFF_FILES_PATH']\\n self.diff_files_path = self.diff_files_path.replace('BASE_PATH', self.artifacts_path)\\n self.log_journald = (True if config['ARTIFACTS']['LOG_JOURNALD_SERVICE'] == \\\"TRUE\\\" else False)\\n\\n for x_path in [self.artifacts_path, self.executable_path, self.diff_files_path]:\\n if not os.path.exists(x_path):\\n try:\\n os.makedirs(x_path, mode=0o700)\\n except Exception as e:\\n log.debug('{}[*]{} {}'.format(DFbase.LOG_ERROR_COLOR,\\n DFbase.LOG_INFO_COLOR, e))\\n print('{}[*]{} {}:{}'.format(DFbase.LOG_ERROR_COLOR,\\n DFbase.LOG_INFO_COLOR,\\n 'Failed when creating directory', x_path))\\n return False\\n\\n return True\",\n \"def config_in_use(self) -> bool:\\n config = {}\\n config_files = find_config_files([self.config_dir])\\n for config_file in config_files:\\n with open(config_file) as stream:\\n config.update(yaml.safe_load(stream))\\n\\n if not config:\\n return False\\n\\n print(config.get(CONFIG_LOCK))\\n return config.get(CONFIG_LOCK, True)\",\n \"def setallparams(self, OUTPUTCONFIG):\\n if self.name in OUTPUTCONFIG.sections():\\n self.extractparams(OUTPUTCONFIG, self.requiredGenericParams, \\\"required\\\")\\n self.extractparams(OUTPUTCONFIG, self.requiredSpecificParams, \\\"required\\\")\\n self.extractparams(OUTPUTCONFIG, self.optionalGenericParams, \\\"optional\\\")\\n self.extractparams(OUTPUTCONFIG, self.optionalSpecificParams, \\\"optional\\\")\\n return True\\n else:\\n msg = \\\"Missing config section for plugin \\\" + self.name + \\\".\\\"\\n print(msg)\\n msg += \\\"This should be found in file: \\\" + filetocheck\\n #msg = format_msg(msg, 'error')\\n print(msg)\\n raise MissingSection\\n return False\",\n \"def init_cfg(input_path, quiet):\\n config_file = getConfigPath(input_path)\\n\\n if not os.path.exists(config_file) or quiet:\\n confirm_overwrite = True\\n else:\\n confirm_overwrite = click.confirm(\\n '\\\\n{} already exists. Do you want to overwrite it?'.format(config_file))\\n if confirm_overwrite:\\n configHandler(config_file).resetConfig()\\n click.secho('{} created'.format(config_file), fg='green')\",\n \"def validate_configuration_file(self):\\n\\n with open(self.config_path, \\\"r+\\\") as f_config:\\n return bool(re.search(get_configuration_file_re(),\\n f_config.read()))\",\n \"def user_configuration_seems_complete(self):\\n if not os.path.exists(os.path.join(self.user_conf_dir(),\\n \\\"speechd.conf\\\")):\\n return False\\n\\n if not len(os.listdir(self.user_conf_dir())) > 2:\\n return False\\n\\n if not os.path.exists(os.path.join(self.user_conf_dir(), \\\"modules\\\")):\\n return False\\n\\n if not os.path.exists(os.path.join(self.user_conf_dir(), \\\"clients\\\")):\\n return False\\n\\n return True\",\n \"def test_config_no_file(self):\\n if os.path.isfile(\\\"test_config.conf\\\"):\\n os.remove(\\\"test_config.conf\\\")\\n self.assertRaises(ConfigError, Config, \\\"test_config.conf\\\")\",\n \"def check_config():\\r\\n path = os.path.expanduser(\\\"~\\\")\\r\\n config_path = os.path.join(path, \\\".twbcfg.ini\\\")\\r\\n log_path = os.path.join(path, \\\"tmp\\\", \\\"teradata_logs\\\")\\r\\n\\r\\n if not os.path.exists(config_path):\\r\\n if not os.path.exists(log_path):\\r\\n os.mkdir(log_path)\\r\\n config = f'''CheckpointDirectory='{log_path}' \\r\\n LogDirectory='{log_path}' '''\\r\\n with open(config_path, 'w') as f:\\r\\n f.write(config)\",\n \"def valid_configuration(self):\\n valid = True\\n\\n if (not self.__config.suffix()) and (self.__config.output_dir() == self.__config.input_dir()):\\n print(\\\"ERROR: output_dir directory cannot be the same as input_dir with an empty suffix!\\\")\\n valid = False\\n if not self.__config.public_key():\\n print(\\\"ERROR: public_key not set! Set it through 'pdfworkshop config public_key <your_key>'. \\\"\\n \\\"A free API key can be obtained from https://developer.ilovepdf.com/\\\")\\n valid = False\\n return valid\",\n \"def check_config(self):\\n # Check if tool is at all included in workflow\\n if self.__class__.name not in self.config[\\\"tools\\\"][\\\"dna\\\"]:\\n return # BWA not run, don't check configuration # pragma: no cover\\n\\n # Check required configuration settings present\\n self.parent.ensure_w_config(\\n config_keys=(\\\"step_config\\\", \\\"ngs_mapping\\\", \\\"bwa\\\", \\\"path_index\\\"),\\n msg=\\\"Path to BWA index is required\\\",\\n )\\n\\n # Check that the path to the BWA index is valid.\\n for ext in (\\\".amb\\\", \\\".ann\\\", \\\".bwt\\\", \\\".pac\\\", \\\".sa\\\"):\\n expected_path = self.config[\\\"bwa\\\"][\\\"path_index\\\"] + ext\\n if not os.path.exists(expected_path): # pragma: no cover\\n tpl = \\\"Expected BWA input path {expected_path} does not exist!\\\".format(\\n expected_path=expected_path\\n )\\n raise InvalidConfiguration(tpl)\",\n \"def client_has_non_default_configuration(self):\\n if not os.path.isfile(YPBIND_CONF_FILE):\\n return False\\n\\n # Filter whitespaces and empty lines\\n with open(YPBIND_CONF_FILE) as f:\\n lines = [line.strip() for line in f.readlines() if line.strip()]\\n\\n for line in lines:\\n # Checks for any valid configuration entry\\n if not line.startswith('#'):\\n return True\\n return False\",\n \"def test_config_filename_given_file_exists_wellformed_content():\\n config_file_name = get_resource_path(\\\"config\\\", \\\"test_config.ini\\\")\\n config = Config(filename=config_file_name)\\n\\n assert config.metadata_cache_uri == \\\"https://custom-aqua.url\\\"\\n assert config.provider_address == \\\"0x00bd138abd70e2f00903268f3db08f2d25677c9e\\\"\\n assert isinstance(config.gas_limit, int)\",\n \"def validate_config(location):\\n if not os.path.exists(location):\\n full_path = os.path.abspath(location)\\n logging.fatal('Config not found - expected a configfile at: [{loc}] - exiting.'.format(loc=full_path))\\n sys.exit(1)\",\n \"def test_new_config_already_exists(self, context):\\n\\n context.config_exists.return_value = True\\n\\n runner = CliRunner()\\n result = runner.invoke(cli_node_new_configuration, [\\n \\\"--name\\\", \\\"some-name\\\",\\n \\\"--environment\\\", \\\"application\\\"\\n ])\\n\\n # check that error is produced\\n self.assertEqual(result.output[:7], \\\"[error]\\\")\\n\\n # check non-zero exit code\\n self.assertEqual(result.exit_code, 1)\",\n \"def determine_config() -> str:\\n if os.environ.get(PortholeConfig.CONFIG_ENV_NAME) is not None:\\n return os.environ.get(PortholeConfig.CONFIG_ENV_NAME)\\n if os.path.isfile(PortholeConfig.DEFAULT_CONFIG_FILE):\\n return PortholeConfig.DEFAULT_CONFIG_FILE\\n for file_path in PortholeConfig.OTHER_ALLOWED_CONFIG_PATHS:\\n if os.path.isfile(file_path):\\n return file_path\\n raise FileNotFoundError(\\n \\\"Porthole is unable to locate a useable config file. \\\"\\n \\\"Try setting the PORTHOLE_CONFIG environment variable, \\\"\\n \\\"or creating a porthole.ini file in your main project directory.\\\"\\n )\",\n \"def test_createsConfigWhenNoneExists(self):\\n DataManagerUnitTest.dm.createConfig(\\\"testConfigFile.json\\\")\\n self.assertTrue(os.path.isfile(\\\"testConfigFile.json\\\"))\\n os.remove(\\\"testConfigFile.json\\\")\",\n \"def get_config(f_configs, section=None):\\n if io.is_file(f_configs) is False:\\n warn.warn('configs (INI) file does not exist: ' + f_configs)\\n return None\",\n \"def checkRequiredConfigs(self):\\n containmentFolder = self.getAbsContainmentFolder()\\n rootFileName = self.app.config.exhale_args[\\\"rootFileName\\\"]\\n rootFileTitle = self.app.config.exhale_args[\\\"rootFileTitle\\\"]\\n doxygenStripFromPath = self.app.config.exhale_args[\\\"doxygenStripFromPath\\\"]\\n\\n # validate that the containmentFolder was created\\n assert os.path.isdir(containmentFolder)\\n # validate that {containmentFolder}/{rootFileName} was created\\n assert os.path.isfile(os.path.join(containmentFolder, rootFileName))\\n # validate that the title was included\\n with open(os.path.join(containmentFolder, rootFileName), \\\"r\\\") as root:\\n root_contents = root.read()\\n root_heading = \\\"{0}\\\\n{1}\\\".format(\\n rootFileTitle,\\n exhale.utils.heading_mark(rootFileTitle, exhale.configs.SECTION_HEADING_CHAR)\\n )\\n assert root_heading in root_contents\\n\\n # TODO: validate doxygenStripFromPath\\n if doxygenStripFromPath: # this is only here to avoid a flake8 fail on a todo\\n pass\",\n \"def loadConfig(self, name=None):\\n\\n configDir = self.mwGlob['configDir']\\n # looking for file existence and creating new if necessary\\n\\n if name is None:\\n name = 'config'\\n fileName = configDir + '/' + name + '.cfg'\\n\\n if not os.path.isfile(fileName):\\n self.config = self.defaultConfig()\\n if name == 'config':\\n self.log.warning('Config file {0} not existing'.format(fileName))\\n return True\\n else:\\n return False\\n\\n # parsing the default file\\n try:\\n with open(fileName, 'r') as configFile:\\n configData = json.load(configFile)\\n except Exception as e:\\n self.log.critical('Cannot parse: {0}, error: {1}'.format(fileName, e))\\n self.config = self.defaultConfig()\\n return False\\n\\n # check if reference ist still to default -> correcting\\n if configData.get('reference', '') == 'config':\\n del configData['reference']\\n elif not configData.get('reference', ''):\\n configData['profileName'] = 'config'\\n\\n # loading default and finishing up\\n if configData['profileName'] == 'config':\\n self.config = self.convertData(configData)\\n return True\\n\\n # checking if reference to another file is available\\n refName = configData.get('reference', 'config')\\n if refName != name:\\n suc = self.loadConfig(refName)\\n else:\\n self.config = configData\\n return True\\n return suc\",\n \"def validate_config(self):\\n pass\",\n \"def validate_config(self):\\n pass\",\n \"async def check_if_config_exists(self, server_id):\\n if not os.path.exists(\\n '%s.ini' % (\\n os.path.join(\\n self.server_settings_path,\\n str(server_id)\\n )\\n )\\n ):\\n return False\\n return True\",\n \"def available(\\n\\t\\t\\tconfig_file):\\n\\t\\treturn\",\n \"def configure(self, conf):\\n return False\",\n \"def check_config(self):\\n try:\\n config_metadata = self.dbc.get_metadata(\\\"config.txt\\\")\\n except rest.ErrorResponse:\\n print str(datetime.datetime.now()) \\\\\\n + \\\": No config.txt in Dropbox directory. Exiting.\\\"\\n sys.exit()\\n if config_metadata[\\\"modified\\\"] != self.config_date:\\n print str(datetime.datetime.now()) + \\\": Config changed\\\"\\n self.config_date = config_metadata[\\\"modified\\\"]\\n try:\\n self.dbc.get_file(\\\"config.txt\\\")\\n except rest.ErrorResponse as e:\\n print str(datetime.datetime.now()) + e.reason\\n return False\\n self.config.reload(self.local_directory + \\\"/\\\" + \\\"config.txt\\\")\\n return True\\n return False\",\n \"def load_config(self):\\n try:\\n self.config = yaml.load(open(roslib.packages.get_pkg_dir('human_activities') + '/config/config.ini', 'r'))\\n print \\\"config loaded:\\\", self.config.keys()\\n\\n return True\\n except:\\n print \\\"no config file found in /human_activities/config/config.ini\\\"\\n return False\",\n \"def check_general_settings(self):\\n\\n self.generate_description_general_settings()\\n if not self.config.has_section('description'):\\n colorprint('[description] in [{self.settingpath}] did not exist. Created it.'.format(**locals()), 'green')\\n\\n if not self.config.has_section('credentials'):\\n self.config.add_section('credentials')\\n self.config.set('credentials', 'world', 'de100')\\n self.config.set('credentials', 'username', 'yournamegoeshere')\\n self.config.set('credentials', 'password', 'yourpasswordgoeshere')\\n self.config.set('credentials', 'captcha_user', 'yourdeathbycaptchaussernamegoeshere')\\n self.config.set('credentials', 'captcha_pass', 'yourdeathbycaptchapasswordgoeshere')\\n colorprint('[credentials] in [{self.settingpath}] did not exist. Created it.'.format(**locals()), 'green')\\n\\n if not self.config.has_section('control'):\\n self.config.add_section('control')\\n self.config.set('control', 'sleep', '300')\\n self.config.set('control', 'farmsplit', '5')\\n colorprint('[control] in [{self.settingpath}] did not exist. Created it.'.format(**locals()), 'green')\\n\\n if not self.config.has_section('storage'):\\n self.config.add_section('storage')\\n self.config.set('storage', 'path', 'data')\\n colorprint('[storage] in [{self.settingpath}] did not exist. Created it.'.format(**locals()), 'green')\\n\\n self.config.write(open(self.settingpath, 'w'))\",\n \"def fallback_file(cls, filename):\\n file_list = (\\n filename,\\n path.join(path.expanduser('~'), '.config', 'hiromi.json'),\\n path.join(path.expanduser('~'), '.hiromi')\\n )\\n for a_file in file_list:\\n if path.exists(a_file):\\n return a_file\\n print(\\n \\\"Please given a legal config file, or make a config file at\\\"\\n \\\"~/.hiromi or ~/.config/hiromi.json\\\"\\n )\\n raise ConfigNotFoundException()\",\n \"def check_config():\\n\\n if not config_instance:\\n LOG.error(\\\"Failed to load the config!\\\")\\n sys.exit(9)\\n\\n if not hasattr(config_instance, \\\"CONFIG_VERSION\\\"):\\n LOG.warning( \\\"The config file does not specify CONFIG_VERSION! I will \\\"\\n \\\"try to continue anyway, but this field is recommended to allow \\\"\\n \\\"some internal tests to work. I will assume the value '(1,0)'!\\\" )\\n config_instance.CONFIG_VERSION = (1, 0)\\n\\n major, minor = config_instance.CONFIG_VERSION\\n expected_major, expected_minor = EXPECTED_CONFIG_VERSION\\n\\n if major < expected_major:\\n LOG.critical(\\\"The config system has undergone a major change! \\\"\\n \\\"I cannot continue without an upgrade!\\\")\\n sys.exit(9)\\n\\n if minor < expected_minor:\\n LOG.warning(\\\"The config system has undergone a minor change! \\\"\\n \\\"It should work, but you still should review the docs!\\\")\\n\\n if major == expected_major and minor == expected_minor:\\n LOG.debug( \\\"Config version OK!\\\" )\\n\\n if not hasattr(config_instance, \\\"GENERATORS\\\"):\\n LOG.critical(\\\"Variable 'GENERATORS' not found in config!\\\")\\n sys.exit(9)\\n\\n if not hasattr(config_instance, \\\"TARGETS\\\"):\\n LOG.critical(\\\"Variable 'TARGETS' not found in config!\\\")\\n sys.exit(9)\",\n \"def print_configuration_not_exist(cls, config_name):\\n print(\\n f\\\"{cls.ERROR_PREFIX} {cls.CONFIGHOLDER_CONFIG_NOT_EXIST} {config_name}.\\\"\\n )\",\n \"def validate_config_file(args):\\r\\n # Validate arguments\\r\\n if len(args) > 2:\\r\\n print 'Error! --validate_config does not take any arguments.'\\r\\n return\\r\\n\\r\\n # Check if the config.txt exists\\r\\n if not os.path.exists(utils.CONFIG_FILE_PATH):\\r\\n print 'Config file does not seem to exist. Do you wish STAPLER to ' \\\\\\r\\n 'create it now?'\\r\\n if raw_input('(y/n): ') == 'y':\\r\\n update_config_file(AvailableCommands.commands.keys(), [])\\r\\n else:\\r\\n 'Create the config file manually or get it from ' \\\\\\r\\n 'https://github.com/tyrmi/STAPLER\\\\n'\\r\\n return 0\\r\\n\\r\\n # Check if config.txt contains proper rows\\r\\n config_file = utils.get_config_file()\\r\\n i = 0\\r\\n config_file_defined_commands = []\\r\\n commands_to_remove = []\\r\\n for line in config_file.split('\\\\n'):\\r\\n i += 1\\r\\n line = line.strip()\\r\\n if not line: continue\\r\\n line = line.split('\\\\t')\\r\\n\\r\\n # Check the number of columns\\r\\n if len(line) != 4:\\r\\n print 'config.txt file should have 4 tab delimited columns on ' \\\\\\r\\n 'each row. Found {0} columns on line number {1}:'.format(len(line), i)\\r\\n print ' '.join(line)\\r\\n return\\r\\n\\r\\n # Skip first (header) line\\r\\n if i == 1: continue\\r\\n\\r\\n # Skip comment lines\\r\\n if line[0].startswith('#'): continue\\r\\n\\r\\n # Check for duplicate rows\\r\\n if line[0] in config_file_defined_commands:\\r\\n print 'config.txt row number {0} contains configuration for ' \\\\\\r\\n 'command {1}, which has been defined earlier in the ' \\\\\\r\\n 'config.txt. Please remove the duplicate rows.'.format(i,\\r\\n line[0])\\r\\n return 0\\r\\n\\r\\n # Check if the defined command is supported by STAPLER\\r\\n if line[0] not in AvailableCommands.commands:\\r\\n commands_to_remove.append(line[0])\\r\\n else:\\r\\n # Add the current command to list\\r\\n config_file_defined_commands.append(line[0])\\r\\n\\r\\n # Report the commands that are found in config.txt but are not supported\\r\\n if commands_to_remove:\\r\\n print 'config.txt contains configuration for the following commands, ' \\\\\\r\\n 'that are not supported by STAPLER:'\\r\\n print '\\\\n'.join(commands_to_remove)\\r\\n print '\\\\n'\\r\\n\\r\\n # Check and report if some commands are missing from the config.txt\\r\\n commands_to_add = set(AvailableCommands.commands.keys()) - \\\\\\r\\n utils.CONFIG_FILE_OMITTED_COMMANDS - \\\\\\r\\n set(config_file_defined_commands)\\r\\n if commands_to_add:\\r\\n print 'config.txt is missing configuration for the following ' \\\\\\r\\n 'commands, that are supported by STAPLER:'\\r\\n print '\\\\n'.join(list(commands_to_add))\\r\\n print '\\\\n'\\r\\n\\r\\n # Prompt user if the issues should be automatically fixed\\r\\n if commands_to_remove or commands_to_add:\\r\\n print 'Do you wish STAPLER to automatically update config.txt by ' \\\\\\r\\n 'adding missing commands and removing unsupported commands?'\\r\\n if raw_input('(y/n): ') == 'y':\\r\\n update_config_file(commands_to_add, commands_to_remove)\\r\\n config_file_defined_commands += commands_to_add\\r\\n print 'Done. Continuing validation...\\\\n'\\r\\n else:\\r\\n print 'OK. Continuing validation of supported commands...\\\\n'\\r\\n\\r\\n # Test each command on each row of config.txt\\r\\n print 'Testing the config.txt commands...'\\r\\n devnull = open(os.devnull, 'wb')\\r\\n max_width = max(map(len, config_file_defined_commands))\\r\\n i = 0\\r\\n results = []\\r\\n for cmd in config_file_defined_commands:\\r\\n i += 1\\r\\n if cmd in ('CUSTOM', 'CUSTOM_NO_OUTPUT'): continue\\r\\n check_result = AvailableCommands.commands[cmd].validate_tool_config()\\r\\n check_result = [string.ljust(cmd, max_width)] + check_result\\r\\n results.append('\\\\t'.join(check_result))\\r\\n #print int(float(i)/len(config_file_defined_commands)*100)\\r\\n sys.stdout.write(('\\\\rChecking commands %s%%' % (int(float(i)/len(\\r\\n config_file_defined_commands)*100))))\\r\\n sys.stdout.flush()\\r\\n\\r\\n # Print results\\r\\n print '\\\\n\\\\nTest results:'\\r\\n print '\\\\n'.join(results)\\r\\n print '\\\\nNONE: The execute field is \\\"none\\\" in the config.txt'\\r\\n print 'OK: Command has run perfectly.'\\r\\n print 'FAIL: Running the command has failed.'\\r\\n print '- : Unable to test command due to failure in load_module\\\\n'\",\n \"def test_find_config_nonexist(self):\\n with pytest.raises(scuba.config.ConfigError):\\n scuba.config.find_config()\",\n \"def test_files_non_existing_config(self, context):\\n\\n context.config_exists.return_value = False\\n\\n runner = CliRunner()\\n result = runner.invoke(cli_node_files, ['--name', 'non-existing'])\\n\\n # Check that error is produced\\n self.assertEqual(result.output[:7], \\\"[error]\\\")\\n\\n # check for non zero exit-code\\n self.assertNotEqual(result.exit_code, 0)\",\n \"def load_config(configFile):\\r\\n\\r\\n # TODO: Implement this\\r\\n return False\",\n \"def check_configuration(self, configuration):\\n super(Hipchap, self).check_configuration(configuration)\",\n \"def check_config(self):\\n # Check if tool is at all included in workflow\\n if \\\"gatk_post_bam\\\" not in (self.config[\\\"postprocessing\\\"] or []): # pylint: disable=C0325\\n return # GATK BAM postprocessing not enabled, skip\\n\\n # Check required configuration settings present\\n self.parent.ensure_w_config(\\n config_keys=(\\\"step_config\\\", \\\"ngs_mapping\\\", \\\"gatk_post_bam\\\", \\\"paths_known_sites\\\"),\\n msg=\\\"Known sites list cannot be empty for GATK BAM postprocessing\\\",\\n )\\n self.parent.ensure_w_config(\\n config_keys=(\\\"static_data_config\\\", \\\"reference\\\", \\\"path\\\"),\\n msg=\\\"Path to reference FASTA required for GATK BAM postprocessing\\\",\\n )\",\n \"def check_configuration(self):\\n self.ensure_one()\\n getattr(self, '%s_check_configuration' % self.provider, lambda: None)()\",\n \"def test_load_configuration_fails_gracefully_when_file_does_not_exist():\\n config.load_configuration(invalid_configuration_path, graceful=True)\\n assert True\",\n \"def _validate_config(self):\\n pass\",\n \"def loadConfig(self, apiName=None):\\n self._packRoot = getattr(sys, \\\"_MEIPASS\\\", path.abspath(path.dirname(__file__)))\\n rootDir = path.abspath(path.join(self._packRoot, path.pardir))\\n logger.debug(\\\"Package root directory is: %s\\\" % rootDir)\\n\\n if self._lookFolder is None:\\n userConf = path.join(rootDir, \\\"config.json\\\")\\n else:\\n userConf = path.join(self._lookFolder, \\\"config.json\\\")\\n\\n if path.isfile(userConf):\\n logger.debug(\\\"Loading file: %s\\\" % userConf)\\n jsonData = {}\\n try:\\n with open(userConf, mode=\\\"r\\\") as inFile:\\n jsonData = json.loads(inFile.read())\\n\\n self.dataPath = jsonData.get(\\\"dataPath\\\", \\\"\\\")\\n self._savedSearches = jsonData.get(\\\"searches\\\", {})\\n\\n if apiName is None:\\n apiName = jsonData.get(\\\"default\\\", None)\\n\\n apiList = jsonData.get(\\\"apis\\\", None)\\n if apiList is None:\\n logger.error(\\\"Could not find any apis in config file\\\")\\n return False\\n\\n if not isinstance(apiList, list):\\n logger.error(\\\"Invalid apis list in config file\\\")\\n return False\\n\\n if len(apiList) == 0:\\n logger.error(\\\"Empty apis list in config file\\\")\\n return False\\n\\n for apiEntry in apiList:\\n if apiEntry.get(\\\"apiName\\\", None) == apiName:\\n self.apiName = apiEntry.get(\\\"apiName\\\", \\\"\\\")\\n self.apiUser = apiEntry.get(\\\"apiUsername\\\", \\\"\\\")\\n self.apiPass = apiEntry.get(\\\"apiPassword\\\", \\\"\\\")\\n self.apiURL = apiEntry.get(\\\"apiURL\\\", \\\"\\\")\\n\\n self._confPath = userConf\\n\\n except Exception as e:\\n logger.error(\\\"Failed to parse config JSON data.\\\")\\n logger.error(str(e))\\n return False\\n\\n else:\\n logger.debug(\\\"No file: %s\\\" % userConf)\\n\\n return\",\n \"def check_configuration(configuration: Dict[str, Any] = None) -> bool:\\n configuration = configuration or {}\\n if \\\"wiremock\\\" not in configuration:\\n logger.error(\\\"Error: wiremock key not found in configuration section\\\")\\n return False\\n return True\",\n \"def _check_for_custom_config(self, standard_conf_path):\\n\\n ret_val = False\\n conf_filename = os.path.basename(standard_conf_path)\\n custom_conf_expected_path = CUSTOM_CONFIG_DIR + '/' + self._get_tempdir() + '/' + conf_filename\\n\\n if os.path.isfile(custom_conf_expected_path):\\n ret_val = True\\n\\n return ret_val\",\n \"def check_create_settings_file(self):\\n path = self.paths[\\\"settings_file\\\"]\\n from django_swagger_utils.core.utils.check_path_exists import check_path_exists\\n settings_file = check_path_exists(path)\\n if not settings_file:\\n settings_file_contents = \\\"# '%s' settings\\\" % self.app_name\\n from django_swagger_utils.core.utils.write_to_file import write_to_file\\n write_to_file(settings_file_contents, path)\",\n \"def config_reader(config_file):\\n if os.path.exists(config_file):\\n with open(config_file, 'r') as cfile:\\n app_config = json.load(cfile)\\n # end with\\n return app_config\\n else:\\n print(\\\"The config file: {} does not exist, please try again.\\\".format(config_file))\\n return False\\n # fin\",\n \"def test_validate_config_file(self):\\n ingest_mgmr = IngestManager()\\n ingest_mgmr.validate_config_file(self.example_config_data)\\n assert(ingest_mgmr.config is not None)\\n assert (ingest_mgmr.config.config_data is not None)\",\n \"def validate_missing_information(conf):\\n failed = False\\n\\n for field, _ in REQUIRED_SETTINGS.items():\\n if field not in conf:\\n print 'error: missing configuration for \\\"{0}\\\"'.format(field)\\n failed = True\\n\\n if failed:\\n sys.exit(1)\",\n \"def check_config( config: 'bittensor.Config' ):\\n assert config.neuron.batch_size_train > 0, \\\"batch_size_train must be a positive value\\\"\\n assert config.neuron.learning_rate > 0, \\\"learning_rate must be a positive value.\\\"\\n bittensor.logging.check_config( config )\\n bittensor.wallet.check_config( config )\\n bittensor.subtensor.check_config( config )\\n bittensor.metagraph.check_config( config )\\n bittensor.dataloader.check_config( config )\\n bittensor.dendrite.check_config( config )\\n bittensor.axon.check_config( config )\\n GPT2Nucleus.check_config( config )\\n SGMOERouter.check_config( config )\\n full_path = os.path.expanduser('{}/{}/{}'.format( config.logging.logging_dir, config.wallet.name + \\\"-\\\" + config.wallet.hotkey, config.neuron.name ))\\n config.neuron.full_path = os.path.expanduser(full_path)\\n config.neuron.tensorboard_dir = config.neuron.full_path + '/tensorboard-' + '-'.join(str(datetime.now()).split())\\n if not os.path.exists(config.neuron.full_path):\\n os.makedirs(config.neuron.full_path)\",\n \"def _verify_ini(self, config_file=None):\\n\\n imgur_values = ['ClientID', 'ClientSecret', 'AccessToken', 'RefreshToken']\\n mysql_values = ['Host', 'User', 'Password', 'Database']\\n missing_values = []\\n\\n if not config_file:\\n print(\\\"No Config Filed Supplied. Aborting\\\")\\n sys.exit(1)\\n\\n for val in imgur_values:\\n if val not in config_file['IMGURAPI']:\\n missing_values.append('IMGURAPI: ' + val)\\n\\n for val in mysql_values:\\n if val not in config_file['MYSQL']:\\n missing_values.append('MYSQL: ' + val)\\n\\n if missing_values:\\n print('ERROR: ini file is missing required values. \\\\n Missing Values:')\\n for val in missing_values:\\n print(val)\\n sys.exit(1)\",\n \"def _validate_pf_config(module, config):\\n rc, out, err = module.run_command(['ls', config])\\n\\n # Fail if no config file is present\\n if rc != 0:\\n msg_err = f'Error: Config file does not exist: {config}'\\n module.fail_json(msg=msg_err)\",\n \"def create():\\n config_file = get_config_file()\\n if os.path.isfile(config_file):\\n logger.info(\\\"The config file already exists at '%s'\\\", config_file)\\n return\\n _create_config()\\n logger.info(\\\"Created config file at '%s'\\\", config_file)\",\n \"def retrieve_config(conf_info, flowcell, cfg_filepath, genome_dir):\\n options = getCombinedOptions()\\n\\n if options.url is None:\\n LOGGER.error(\\\"%s or %s missing base_host_url option\\\" % \\\\\\n (CONFIG_USER, CONFIG_SYSTEM))\\n return False\\n\\n try:\\n saveConfigFile(flowcell, options.url, cfg_filepath)\\n conf_info.config_filepath = cfg_filepath\\n except FlowCellNotFound as e:\\n LOGGER.error(e)\\n return False\\n except WebError404 as e:\\n LOGGER.error(e)\\n return False\\n except IOError as e:\\n LOGGER.error(e)\\n return False\\n except Exception as e:\\n LOGGER.error(e)\\n return False\\n\\n f = open(cfg_filepath, 'r')\\n data = f.read()\\n f.close()\\n\\n genome_dict = getAvailableGenomes(genome_dir)\\n mapper_dict = constructMapperDict(genome_dict)\\n\\n LOGGER.debug(data)\\n\\n f = open(cfg_filepath, 'w')\\n f.write(data % (mapper_dict))\\n f.close()\\n\\n return True\",\n \"def check_config_file(path):\\n\\tif not os.path.exists (path):\\n\\t\\t# create the directories\\n\\t\\ttry:\\n\\t\\t\\tos.makedirs (os.path.join (os.path.split (path)[0]))\\n\\t\\texcept os.error:\\n\\t\\t\\tprint \\\"Error while creating neccessary directories: %s\\\"\\\\\\n\\t\\t\\t\\t% (os.error)\\n\\t\\t\\treturn False\\n\\n\\t\\ttry:\\n\\t\\t\\tf = file (path, \\\"w\\\")\\n\\t\\texcept BaseException,e:\\n\\t\\t\\tprint \\\"Error while creating config file: %s\\\" % (e)\\n\\t\\t\\treturn False\\n\\t\\telse:\\n\\t\\t\\tf.close()\\n\\n\\t\\treturn True\\n\\telse:\\n\\t\\treturn True\\n\\treturn False\",\n \"def validate_conf(name, ini_file_path):\\n sections = read_config(ini_file_path).sections()\\n\\n if name in sections:\\n raise LogmeError(f\\\"'{name}' logging config already exists in config file: {ini_file_path}\\\")\\n elif 'logme' not in sections:\\n raise LogmeError(f\\\"{ini_file_path} is not a valid logme.ini file\\\")\",\n \"def check():\\n # Initialize key variables\\n config_directory = os.environ['PATTOO_CONFIGDIR']\\n\\n # Print Status\\n print('??: Checking configuration parameters.')\\n\\n # Check config (pattoo.yaml)\\n config_file = configuration.agent_config_filename('pattoo')\\n config = files.read_yaml_file(config_file)\\n\\n # Check main keys\\n keys = ['pattoo', 'pattoo_web_api', 'pattoo_agent_api']\\n for key in keys:\\n if key not in config:\\n log_message = ('''\\\\\\nSection \\\"{}\\\" not found in configuration file in directory {}. Please fix.\\\\\\n'''.format(key, config_directory))\\n log.log2die_safe(80007, log_message)\\n\\n # Check secondary keys\\n secondaries = [\\n 'log_level', 'log_directory', 'cache_directory',\\n 'daemon_directory']\\n secondary_key_check(config, 'pattoo', secondaries)\\n secondaries = ['ip_address', 'ip_bind_port']\\n secondary_key_check(config, 'pattoo_agent_api', secondaries)\\n secondaries = ['ip_address', 'ip_bind_port']\\n secondary_key_check(config, 'pattoo_web_api', secondaries)\\n\\n # Check config (pattoo_webd.yaml)\\n config_file = configuration.agent_config_filename('pattoo_webd')\\n config = files.read_yaml_file(config_file)\\n\\n # Check main keys\\n keys = ['pattoo_webd']\\n for key in keys:\\n if key not in config:\\n log_message = ('''\\\\\\nSection \\\"{}\\\" not found in configuration file in directory {}. Please fix.\\\\\\n'''.format(key, config_directory))\\n log.log2die_safe(80020, log_message)\\n\\n # Check secondary keys\\n secondaries = ['ip_listen_address', 'ip_bind_port']\\n secondary_key_check(config, 'pattoo_webd', secondaries)\\n\\n # Print Status\\n print('OK: Configuration parameter check passed.')\",\n \"def test_config_ok_config(self):\\n test_data = (\\\"[gnupg]\\\\n\\\"\\n \\\"recipients = tkl@iki.fi\\\\n\\\"\\n \\\"signer = tommi.linnakangas@iki.fi\\\\n\\\"\\n \\\"\\\\n\\\"\\n \\\"[amazon-s3]\\\\n\\\"\\n \\\"access_key = ACCESSKEY\\\\n\\\"\\n \\\"secret_access_key = SECRETACCESSKEY\\\\n\\\"\\n \\\"\\\\n\\\"\\n \\\"[data]\\\\n\\\"\\n \\\"\\\\n\\\"\\n \\\"bucket = DATABUCKET\\\\n\\\"\\n \\\"[metadata]\\\\n\\\"\\n \\\"bucket = METADATABUCKET\\\\n\\\"\\n \\\"\\\\n\\\")\\n if os.path.isfile(\\\"test_config.conf\\\"):\\n os.remove(\\\"test_config.conf\\\")\\n file(\\\"test_config.conf\\\", \\\"wb\\\").write(test_data)\\n config = Config(\\\"test_config.conf\\\")\\n self.assertIn(\\\"gnupg\\\", config.config.sections())\\n self.assertIn(\\\"amazon-s3\\\", config.config.sections())\\n self.assertEqual(config.config.get(\\n \\\"gnupg\\\", \\\"recipients\\\"), \\\"tkl@iki.fi\\\")\\n self.assertEqual(config.config.get(\\n \\\"gnupg\\\", \\\"signer\\\"), \\\"tommi.linnakangas@iki.fi\\\")\\n self.assertEqual(config.config.get(\\n \\\"amazon-s3\\\", \\\"access_key\\\"), \\\"ACCESSKEY\\\")\\n self.assertEqual(config.config.get(\\n \\\"amazon-s3\\\", \\\"secret_access_key\\\"), \\\"SECRETACCESSKEY\\\")\\n self.assertEqual(config.config.get(\\n \\\"data\\\", \\\"bucket\\\"), \\\"DATABUCKET\\\")\\n self.assertEqual(config.config.get(\\n \\\"metadata\\\", \\\"bucket\\\"), \\\"METADATABUCKET\\\")\\n os.remove(\\\"test_config.conf\\\")\",\n \"def check_for_application_file(self, application):\\n # 1. Get the path of the app_config directory\\n app_conf_dir = self.sys_conf['configs']['env'][self.env]['app_config_url']\\n\\n # 2. Get the path for the given application configuration file\\n app_conf_dir += '/{file}.yaml'.format(file=application)\\n\\n # 3. Validate the path exists\\n chk = file_check(app_conf_dir)\\n\\n # 4. Return the result\\n return chk\",\n \"def parse_config():\\n config_path = Path(\\\"config.ini\\\")\\n if config_path.exists():\\n config.read(config_path)\\n else:\\n config[\\\"database\\\"] = {\\\"location\\\": \\\"image-database.db\\\"}\\n config[\\\"images\\\"] = {\\\"extensions\\\": \\\".jpeg,.jpg,.png,.gif,.tiff\\\"}\\n with open(config_path, \\\"w\\\") as configfile:\\n config.write(configfile)\\n config.read(config_path)\",\n \"def require_config(self) -> None:\\n if not self.has_config():\\n fail(f\\\"No tzk_config.py found in the current directory. \\\"\\n f\\\"(Try 'tzk init' if you want to create a new one.)\\\")\",\n \"def read_config(self, config_filename):\",\n \"def test_no_config_keyword(self):\\n args = self.get_args()\\n config = {\\n \\\"site\\\": {\\n \\\"username\\\": \\\"\\\",\\n \\\"name\\\": \\\"\\\",\\n \\\"ip_address\\\": \\\"\\\",\\n \\\"password\\\": \\\"\\\",\\n \\\"local\\\": \\\"\\\",\\n \\\"use_https\\\": \\\"\\\"\\n }\\n }\\n temp = sys.stdout\\n fake_out = FakeStdio()\\n sys.stdout = fake_out\\n\\n config_filename = 'testsuite_cfg.json'\\n args.config = config_filename\\n config_file = open(config_filename, 'w')\\n config_file.write(str(json.dumps(config)))\\n config_file.close()\\n\\n execute_tool(args, test_mode=True)\\n sys.stdout = temp\\n self.assertTrue(fake_out.verify_output(['%% Invalid configuration file', '\\\\n']))\",\n \"def __check_config(config):\\n\\n for key, value in defaults.CONFIG_REQUIRED_ENTRIES.items():\\n if (key not in config.keys()) or (config[key] == \\\"\\\" and not value):\\n raise ValueError(\\\"configuration file is missing required key \\\" + key +\\n \\\" or invalid value was provided\\\")\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7276761","0.72518843","0.723697","0.700425","0.6990162","0.68774563","0.68128514","0.6784662","0.677204","0.6760748","0.674573","0.6722314","0.6706433","0.6663527","0.66553557","0.6630366","0.6555873","0.65515465","0.6551169","0.65199214","0.64805055","0.6462288","0.64544386","0.64106965","0.63640356","0.63534814","0.63495857","0.6346757","0.63310486","0.6310882","0.6284334","0.62432206","0.624104","0.62363416","0.62347466","0.6221078","0.6219307","0.6214638","0.62089324","0.6207488","0.6206491","0.61954707","0.6189774","0.61760944","0.6171837","0.616746","0.61642057","0.6140919","0.6119048","0.61183774","0.61139417","0.6111247","0.61030936","0.60906523","0.6086097","0.6079533","0.60743254","0.6063033","0.6060038","0.6060038","0.6057364","0.6055652","0.6050972","0.6046482","0.6046312","0.6043838","0.6040933","0.6036792","0.60352683","0.6031276","0.60287327","0.6026555","0.60179377","0.6017805","0.6007477","0.6005678","0.5993606","0.59845656","0.59791714","0.59767026","0.5970962","0.59699","0.59580827","0.59416884","0.59373915","0.59373","0.59309393","0.5926664","0.592666","0.5925021","0.592464","0.5923671","0.5912336","0.59021926","0.58925134","0.5889511","0.5886874","0.5880649","0.5878651","0.5876284"],"string":"[\n \"0.7276761\",\n \"0.72518843\",\n \"0.723697\",\n \"0.700425\",\n \"0.6990162\",\n \"0.68774563\",\n \"0.68128514\",\n \"0.6784662\",\n \"0.677204\",\n \"0.6760748\",\n \"0.674573\",\n \"0.6722314\",\n \"0.6706433\",\n \"0.6663527\",\n \"0.66553557\",\n \"0.6630366\",\n \"0.6555873\",\n \"0.65515465\",\n \"0.6551169\",\n \"0.65199214\",\n \"0.64805055\",\n \"0.6462288\",\n \"0.64544386\",\n \"0.64106965\",\n \"0.63640356\",\n \"0.63534814\",\n \"0.63495857\",\n \"0.6346757\",\n \"0.63310486\",\n \"0.6310882\",\n \"0.6284334\",\n \"0.62432206\",\n \"0.624104\",\n \"0.62363416\",\n \"0.62347466\",\n \"0.6221078\",\n \"0.6219307\",\n \"0.6214638\",\n \"0.62089324\",\n \"0.6207488\",\n \"0.6206491\",\n \"0.61954707\",\n \"0.6189774\",\n \"0.61760944\",\n \"0.6171837\",\n \"0.616746\",\n \"0.61642057\",\n \"0.6140919\",\n \"0.6119048\",\n \"0.61183774\",\n \"0.61139417\",\n \"0.6111247\",\n \"0.61030936\",\n \"0.60906523\",\n \"0.6086097\",\n \"0.6079533\",\n \"0.60743254\",\n \"0.6063033\",\n \"0.6060038\",\n \"0.6060038\",\n \"0.6057364\",\n \"0.6055652\",\n \"0.6050972\",\n \"0.6046482\",\n \"0.6046312\",\n \"0.6043838\",\n \"0.6040933\",\n \"0.6036792\",\n \"0.60352683\",\n \"0.6031276\",\n \"0.60287327\",\n \"0.6026555\",\n \"0.60179377\",\n \"0.6017805\",\n \"0.6007477\",\n \"0.6005678\",\n \"0.5993606\",\n \"0.59845656\",\n \"0.59791714\",\n \"0.59767026\",\n \"0.5970962\",\n \"0.59699\",\n \"0.59580827\",\n \"0.59416884\",\n \"0.59373915\",\n \"0.59373\",\n \"0.59309393\",\n \"0.5926664\",\n \"0.592666\",\n \"0.5925021\",\n \"0.592464\",\n \"0.5923671\",\n \"0.5912336\",\n \"0.59021926\",\n \"0.58925134\",\n \"0.5889511\",\n \"0.5886874\",\n \"0.5880649\",\n \"0.5878651\",\n \"0.5876284\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.72461706"},"document_rank":{"kind":"string","value":"2"}}},{"rowIdx":17887,"cells":{"query":{"kind":"string","value":"We use the fmi standard to extract the correct set of config_params, inputs, outputs We look into the \"causality\" attribute for each variable in model description"},"document":{"kind":"string","value":"def _extract_sim_config_from_fmi_std(self):\n \n print(\"\\n---- Looking to see if FMU model description contains required 'causality' type definitions ----\")\n\n sim_config_params = []\n sim_inputs = []\n sim_outputs = []\n sim_other_vars = []\n for variable in self.model_description.modelVariables:\n\n # extract causality and append valu\n causality = variable.causality\n\n if causality == \"parameter\":\n sim_config_params.append(variable.name)\n elif causality == \"input\":\n sim_inputs.append(variable.name)\n elif causality == \"output\":\n sim_outputs.append(variable.name)\n else:\n sim_other_vars.append(variable.name)\n \n # Validate values extracted\n if len(sim_inputs) == 0:\n print(\"\\n[FMU Validator] Sim FMU description file has no sim-input states, and thus cannot be used.\")\n elif len(sim_outputs) == 0:\n print(\"\\n[FMU Validator] Sim FMU description file has no sim-output states, and thus cannot be used.\")\n else:\n # Store data extracted as attributes\n self.sim_config_params = sim_config_params\n self.sim_inputs = sim_inputs\n self.sim_outputs = sim_outputs\n self.sim_other_vars = sim_other_vars\n return True\n\n\n # Dump auxiliary YMAL file for user to review/edit\n self._dump_config_to_yaml_file(sim_config_params,\n sim_inputs,\n sim_outputs,\n sim_other_vars,\n is_aux_yaml = True)\n\n return False"},"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 doParametersOfInterest(self):\n \n self.modelBuilder.doVar('expr::cosW(\"0.87681811112\",)')\n self.modelBuilder.doVar('expr::sinW(\"0.48082221247\",)')\n self.modelBuilder.doVar('expr::mZ(\"91.2\",)')\n self.modelBuilder.doVar('expr::Lambda1(\"100.0\",)')\n self.modelBuilder.doVar('expr::e2(\"0.0917\",)')\n self.modelBuilder.doVar('expr::gs2(\"1.533\",)')\n\n # EFT Higgs basis couplings\n\n self.modelBuilder.doVar('cZ[0,-1,1]') \n self.modelBuilder.doVar(\"cZZ[0,-2,2]\") \n self.modelBuilder.doVar(\"cZZt[0,-2,2]\") \n self.modelBuilder.doVar(\"cZB[0,-6,6]\") \n\n poi='cZ,cZZ,cZZt,cZB'\n\n # Amplitude couplings from EFT couplings \n\n self.modelBuilder.doVar('expr::a1(\"@0+1\",cZ)') # (\"2*(@0+1)\",cZ) in AN/Paper but a1 = 1 for signal model and width calculation\n self.modelBuilder.doVar('expr::a2(\"-1*@0*(@1/(2*pow(@2,2)*pow(@3,2)))\",cZZ,e2,sinW,cosW)')\n self.modelBuilder.doVar('expr::a3(\"-1*@0*(@1/(2*pow(@2,2)*pow(@3,2)))\",cZZt,e2,sinW,cosW)')\n self.modelBuilder.doVar('expr::k1(\"@0*(@1*pow(@2,2)/(pow(@3,2)*pow(@4,2)))\",cZB,e2,Lambda1,sinW,mZ)')\n self.modelBuilder.doVar('expr::k1L1(\"@0/pow(@1,2)\",k1,Lambda1)')\n\n ###### gamma_H ########\n\n # SMEFT relationships for VV couplings (Expressed using amplitude couplings)\n\n self.modelBuilder.doVar('expr::kappa(\"1.0\",)')\n self.modelBuilder.doVar('expr::kappa_tilde(\"0.0\",)') \n\n self.modelBuilder.doVar('expr::a1_WW(\"@0\",a1)')\n self.modelBuilder.doVar('expr::a2_WW(\"@0*@0*@1\",cosW,a2)')\n self.modelBuilder.doVar('expr::a3_WW(\"@0*@0*@1\",cosW,a3)')\n self.modelBuilder.doVar('expr::k1_WW(\"(@2 / (@0*@0 - @1*@1) - 2*@1*@1*@3*@4*@4 /(@5*@5*(@0*@0 - @1*@1)))\",cosW,sinW,k1,a2,Lambda1,mZ)')\n self.modelBuilder.doVar('expr::k2_k1(\"2*@0*@1*@2/(@0*@0 - @1*@1)\",cosW,sinW,k1)')\n self.modelBuilder.doVar('expr::k2_a2(\"-2*@0*@1*@3*@4*@4/((@2*@2)*(@0*@0 - @1*@1))\",cosW,sinW,mZ,a2,Lambda1)')\n self.modelBuilder.doVar('expr::k2(\"@0 + @1\",k2_k1,k2_a2)')\n\n # Determine gamma_H from VV couplings\n\n zz_expr = '\"4*(@0*@0/4. + 0.1695*@3*@3 + 0.09076*@1*@1 + 0.03809*@2*@2 + 0.8095*@0*@3/2. + 0.5046*@0*@1/2. + 0.2092*@1*@3 + 0.1023*@4*@4 + 0.1901*@0*@4/2. + 0.07429*@3*@4 + 0.04710*@1*@4) \",a1,a2,a3,k1,k2'\n ww_expr = '\"4*(@0*@0/4. + 0.1320*@3*@3 + 0.1944*@1*@1 + 0.08075*@2*@2 + 0.7204*@0*@3/2. + 0.7437*@0*@1/2. + 0.2774*@3*@1) \",a1_WW,a2_WW,a3_WW,k1_WW'\n zgamma_expr = '\"4*(1.118600*@0*@0/4. +0.0035*@1*@1 - 0.125010*@0*@1/2. + 0.000003*@1*@1 - 0.00018*@1*@1 + 0.003100*@0*@1/2. +0.00126*@2*@2 + 0.000005*@2*@2 -0.00047*@2*@2)\",a1_WW,kappa,kappa_tilde'\n gg_expr = '\"(1.1068*@0*@0 + 0.0082*@0*@0 - 0.1150*@0*@0 + 2.5717*@1*@1 + 0.0091*@1*@1 - 0.1982*@1*@1)\",kappa,kappa_tilde'\n bb_expr = '\"(@0*@0 + @1*@1)\",kappa,kappa_tilde'\n cc_expr = '\"(@0*@0 + @1*@1)\",kappa,kappa_tilde'\n tautau_expr = '\"(@0*@0 + @1*@1)\",kappa,kappa_tilde'\n mumu_expr = '\"(@0*@0 + @1*@1)\",kappa,kappa_tilde'\n gmgm_expr = '\"4*(1.6054*@0*@0/4. + 0.07312*@1*@1 - 0.6854*@0*@1/2. + 0.00002*@1*@1 - 0.0018*@1*@1 + 0.0085*@0*@1/2. + 0.1699*@2*@2 + 0.00002*@2*@2 - 0.0031*@2*@2)\",a1_WW,kappa,kappa_tilde'\n \n self.modelBuilder.doVar('expr::R_WW('+str(ww_expr)+')')\n self.modelBuilder.doVar('expr::R_ZZ('+str(zz_expr)+')')\n self.modelBuilder.doVar('expr::R_Zgamma('+str(zgamma_expr)+')')\n self.modelBuilder.doVar('expr::R_gg('+str(gg_expr)+')')\n self.modelBuilder.doVar('expr::R_bb('+str(bb_expr)+')')\n self.modelBuilder.doVar('expr::R_cc('+str(cc_expr)+')')\n self.modelBuilder.doVar('expr::R_tautau('+str(tautau_expr)+')')\n self.modelBuilder.doVar('expr::R_mumu('+str(mumu_expr)+')')\n self.modelBuilder.doVar('expr:R_gammagamma('+str(gmgm_expr)+')')\n\n self.modelBuilder.doVar('expr::gammaH(\"(0.5824*@0 + 0.2137*@1 + 0.08187*@2 + 0.06272*@3 + 0.02891*@4 + 0.02619*@5 + 0.002270*@6 + 0.001533*@7 + 0.0002176*@8 )/0.9998\",R_bb,R_WW,R_gg,R_tautau,R_cc,R_ZZ,R_gammagamma,R_Zgamma,R_mumu)') \n\n ###########################\n\n self.g1V = GetCoupTerms(1,1,1,-0.0001,\"1V\") # Compensate for scaling of k1 templates \n self.g2V = GetCoupTerms(1,1,1,-0.0001,\"2V\") \n \n self.modelBuilder.doVar(\"expr::g2V_1(\\\"\"+str(self.g2V[0])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T1(\\\"((pow(@0,4))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_1)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T1_Neg(\\\"-1*((pow(@0,4))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_1)\") \n self.modelBuilder.doVar(\"expr::g2V_2(\\\"\"+str(self.g2V[1])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T2(\\\"((pow(@0,3)*@1)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_2)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T2_Neg(\\\"-1*((pow(@0,3)*@1)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_2)\") \n self.modelBuilder.doVar(\"expr::g2V_3(\\\"\"+str(self.g2V[2])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T3(\\\"((pow(@0,2)*pow(@1,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_3)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T3_Neg(\\\"-1*((pow(@0,2)*pow(@1,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_3)\") \n self.modelBuilder.doVar(\"expr::g2V_4(\\\"\"+str(self.g2V[3])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T4(\\\"((@0*pow(@1,3))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_4)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T4_Neg(\\\"-1*((@0*pow(@1,3))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_4)\") \n self.modelBuilder.doVar(\"expr::g2V_5(\\\"\"+str(self.g2V[4])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T5(\\\"((pow(@1,4))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_5)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T5_Neg(\\\"-1*((pow(@1,4))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_5)\") \n self.modelBuilder.doVar(\"expr::g2V_6(\\\"\"+str(self.g2V[5])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T6(\\\"((pow(@0,3)*@2)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_6)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T6_Neg(\\\"-1*((pow(@0,3)*@2)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_6)\") \n self.modelBuilder.doVar(\"expr::g2V_7(\\\"\"+str(self.g2V[6])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T7(\\\"((pow(@0,2)*pow(@2,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_7)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T7_Neg(\\\"-1*((pow(@0,2)*pow(@2,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_7)\") \n self.modelBuilder.doVar(\"expr::g2V_8(\\\"\"+str(self.g2V[7])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T8(\\\"((@0*pow(@2,3))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_8)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T8_Neg(\\\"-1*((@0*pow(@2,3))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_8)\") \n self.modelBuilder.doVar(\"expr::g2V_9(\\\"\"+str(self.g2V[8])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T9(\\\"((pow(@2,4))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_9)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T9_Neg(\\\"-1*((pow(@2,4))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_9)\") \n self.modelBuilder.doVar(\"expr::g2V_10(\\\"\"+str(self.g2V[9])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T10(\\\"((pow(@0,3)*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_10)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T10_Neg(\\\"-1*((pow(@0,3)*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_10)\") \n self.modelBuilder.doVar(\"expr::g2V_11(\\\"\"+str(self.g2V[10])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T11(\\\"((pow(@0,2)*pow(@3,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_11)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T11_Neg(\\\"-1*((pow(@0,2)*pow(@3,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_11)\") \n self.modelBuilder.doVar(\"expr::g2V_12(\\\"\"+str(self.g2V[11])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T12(\\\"((@0*pow(@3,3))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_12)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T12_Neg(\\\"-1*((@0*pow(@3,3))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_12)\") \n self.modelBuilder.doVar(\"expr::g2V_13(\\\"\"+str(self.g2V[12])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T13(\\\"((pow(@3,4))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_13)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T13_Neg(\\\"-1*((pow(@3,4))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_13)\") \n self.modelBuilder.doVar(\"expr::g2V_14(\\\"\"+str(self.g2V[13])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T14(\\\"((pow(@1,3)*@2)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_14)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T14_Neg(\\\"-1*((pow(@1,3)*@2)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_14)\") \n self.modelBuilder.doVar(\"expr::g2V_15(\\\"\"+str(self.g2V[14])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T15(\\\"((pow(@1,2)*pow(@2,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_15)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T15_Neg(\\\"-1*((pow(@1,2)*pow(@2,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_15)\") \n self.modelBuilder.doVar(\"expr::g2V_16(\\\"\"+str(self.g2V[15])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T16(\\\"((@1*pow(@2,3))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_16)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T16_Neg(\\\"-1*((@1*pow(@2,3))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_16)\") \n self.modelBuilder.doVar(\"expr::g2V_17(\\\"\"+str(self.g2V[16])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T17(\\\"((pow(@1,3)*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_17)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T17_Neg(\\\"-1*((pow(@1,3)*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_17)\") \n self.modelBuilder.doVar(\"expr::g2V_18(\\\"\"+str(self.g2V[17])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T18(\\\"((pow(@1,2)*pow(@3,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_18)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T18_Neg(\\\"-1*((pow(@1,2)*pow(@3,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_18)\") \n self.modelBuilder.doVar(\"expr::g2V_19(\\\"\"+str(self.g2V[18])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T19(\\\"((@1*pow(@3,3))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_19)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T19_Neg(\\\"-1*((@1*pow(@3,3))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_19)\") \n self.modelBuilder.doVar(\"expr::g2V_20(\\\"\"+str(self.g2V[19])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T20(\\\"((pow(@2,3)*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_20)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T20_Neg(\\\"-1*((pow(@2,3)*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_20)\") \n self.modelBuilder.doVar(\"expr::g2V_21(\\\"\"+str(self.g2V[20])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T21(\\\"((pow(@2,2)*pow(@3,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_21)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T21_Neg(\\\"-1*((pow(@2,2)*pow(@3,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_21)\") \n self.modelBuilder.doVar(\"expr::g2V_22(\\\"\"+str(self.g2V[21])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T22(\\\"((@2*pow(@3,3))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_22)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T22_Neg(\\\"-1*((@2*pow(@3,3))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_22)\") \n self.modelBuilder.doVar(\"expr::g2V_23(\\\"\"+str(self.g2V[22])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T23(\\\"((@0*@1*pow(@2,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_23)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T23_Neg(\\\"-1*((@0*@1*pow(@2,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_23)\") \n self.modelBuilder.doVar(\"expr::g2V_24(\\\"\"+str(self.g2V[23])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T24(\\\"((@0*pow(@1,2)*@2)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_24)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T24_Neg(\\\"-1*((@0*pow(@1,2)*@2)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_24)\") \n self.modelBuilder.doVar(\"expr::g2V_25(\\\"\"+str(self.g2V[24])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T25(\\\"((pow(@0,2)*@1*@2)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_25)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T25_Neg(\\\"-1*((pow(@0,2)*@1*@2)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_25)\") \n self.modelBuilder.doVar(\"expr::g2V_26(\\\"\"+str(self.g2V[25])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T26(\\\"((@0*@1*pow(@3,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_26)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T26_Neg(\\\"-1*((@0*@1*pow(@3,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_26)\") \n self.modelBuilder.doVar(\"expr::g2V_27(\\\"\"+str(self.g2V[26])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T27(\\\"((@0*pow(@1,2)*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_27)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T27_Neg(\\\"-1*((@0*pow(@1,2)*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_27)\") \n self.modelBuilder.doVar(\"expr::g2V_28(\\\"\"+str(self.g2V[27])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T28(\\\"((pow(@0,2)*@1*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_28)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T28_Neg(\\\"-1*((pow(@0,2)*@1*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_28)\") \n self.modelBuilder.doVar(\"expr::g2V_29(\\\"\"+str(self.g2V[28])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T29(\\\"((@0*@2*pow(@3,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_29)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T29_Neg(\\\"-1*((@0*@2*pow(@3,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_29)\") \n self.modelBuilder.doVar(\"expr::g2V_30(\\\"\"+str(self.g2V[29])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T30(\\\"((@0*pow(@2,2)*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_30)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T30_Neg(\\\"-1*((@0*pow(@2,2)*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_30)\") \n self.modelBuilder.doVar(\"expr::g2V_31(\\\"\"+str(self.g2V[30])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T31(\\\"((pow(@0,2)*@2*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_31)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T31_Neg(\\\"-1*((pow(@0,2)*@2*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_31)\") \n self.modelBuilder.doVar(\"expr::g2V_32(\\\"\"+str(self.g2V[31])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T32(\\\"((@1*@2*pow(@3,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_32)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T32_Neg(\\\"-1*((@1*@2*pow(@3,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_32)\") \n self.modelBuilder.doVar(\"expr::g2V_33(\\\"\"+str(self.g2V[32])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T33(\\\"((@1*pow(@2,2)*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_33)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T33_Neg(\\\"-1*((@1*pow(@2,2)*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_33)\") \n self.modelBuilder.doVar(\"expr::g2V_34(\\\"\"+str(self.g2V[33])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T34(\\\"((pow(@1,2)*@2*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_34)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T34_Neg(\\\"-1*((pow(@1,2)*@2*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_34)\") \n self.modelBuilder.doVar(\"expr::g2V_35(\\\"\"+str(self.g2V[34])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T35(\\\"((@0*@1*@2*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_35)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T35_Neg(\\\"-1*((@0*@1*@2*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_35)\") \n \n self.modelBuilder.doVar(\"expr::g1V_1(\\\"\"+str(self.g1V[0])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T1(\\\"((pow(@0,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_1)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T1_Neg(\\\"-1*((pow(@0,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_1)\") \n self.modelBuilder.doVar(\"expr::g1V_2(\\\"\"+str(self.g1V[1])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T2(\\\"((@0*@1)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_2)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T2_Neg(\\\"-1*((@0*@1)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_2)\") \n self.modelBuilder.doVar(\"expr::g1V_3(\\\"\"+str(self.g1V[2])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T3(\\\"((pow(@1,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_3)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T3_Neg(\\\"-1*((pow(@1,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_3)\") \n self.modelBuilder.doVar(\"expr::g1V_4(\\\"\"+str(self.g1V[3])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T4(\\\"((@0*@2)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_4)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T4_Neg(\\\"-1*((@0*@2)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_4)\") \n self.modelBuilder.doVar(\"expr::g1V_5(\\\"\"+str(self.g1V[4])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T5(\\\"((pow(@2,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_5)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T5_Neg(\\\"-1*((pow(@2,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_5)\") \n self.modelBuilder.doVar(\"expr::g1V_6(\\\"\"+str(self.g1V[5])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T6(\\\"((@0*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_6)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T6_Neg(\\\"-1*((@0*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_6)\") \n self.modelBuilder.doVar(\"expr::g1V_7(\\\"\"+str(self.g1V[6])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T7(\\\"((pow(@3,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_7)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T7_Neg(\\\"-1*((pow(@3,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_7)\") \n self.modelBuilder.doVar(\"expr::g1V_8(\\\"\"+str(self.g1V[7])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T8(\\\"((@1*@2)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_8)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T8_Neg(\\\"-1*((@1*@2)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_8)\") \n self.modelBuilder.doVar(\"expr::g1V_9(\\\"\"+str(self.g1V[8])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T9(\\\"((@1*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_9)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T9_Neg(\\\"-1*((@1*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_9)\") \n self.modelBuilder.doVar(\"expr::g1V_10(\\\"\"+str(self.g1V[9])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T10(\\\"((@2*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_10)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T10_Neg(\\\"-1*((@2*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_10)\") \n \n self.modelBuilder.doSet(\"POI\",poi)","def doParametersOfInterest(self):\r\n if self.fg4fixed:\r\n self.modelBuilder.doVar(\"CMS_zz4l_fg4[0]\")\r\n self.modelBuilder.doVar(\"r[1,0,4]\")\r\n print \"Fixing CMS_zz4l_fg4\"\r\n poi = \"r\"\r\n else:\r\n if self.modelBuilder.out.var(\"CMS_zz4l_fg4\"):\r\n print \"have fg4 inside\"\r\n else:\r\n self.modelBuilder.doVar(\"CMS_zz4l_fg4[0.,-1,1]\")\r\n poi = \"CMS_zz4l_fg4\"\r\n if self.cPOI:\r\n if self.modelBuilder.out.var(\"cww_zz\"):\r\n print \"have czz_ww inside\"\r\n else:\r\n self.modelBuilder.doVar(\"cww_zz[0.5,-10,10]\")\r\n poi += \",cww_zz\"\r\n\r\n if self.fg2POI:\r\n if self.modelBuilder.out.var(\"CMS_zz4l_fg2\"):\r\n print \"have fg2 inside\"\r\n else:\r\n self.modelBuilder.doVar(\"CMS_zz4l_fg2[0.,0,1]\")\r\n poi += \",CMS_zz4l_fg2\"\r\n if self.muFloating:\r\n self.modelBuilder.doVar(\"r[1,0,2000]\")\r\n if self.muAsPOI:\r\n print \"Treating r as a POI\"\r\n poi += \",r\"\r\n else:\r\n self.modelBuilder.out.var(\"r\").setAttribute(\"flatParam\")\r\n if self.phiFloating:\r\n if self.modelBuilder.out.var(\"CMS_zz4l_fg4phi\"):\r\n print \"have fg4phi inside\"\r\n else: \r\n self.modelBuilder.doVar(\"CMS_zz4l_fg4phi[0.,-3.1415926,3.1415926]\")\r\n if self.phiPOI:\r\n poi += \",CMS_zz4l_fg4phi\"\r\n else:\r\n self.modelBuilder.out.var(\"CMS_zz4l_fg4phi\").setAttribute(\"flatParam\")\r\n if self.phi2Floating:\r\n #self.modelBuilder.doVar(\"CMS_zz4l_fg4phi[0.,-math.pi,math.pi]\")\r\n if self.modelBuilder.out.var(\"CMS_zz4l_fg2phi\"):\r\n print \"have fg2phi inside\"\r\n else: \r\n self.modelBuilder.doVar(\"CMS_zz4l_fg2phi[0.,-3.1415926,3.1415926]\")\r\n self.modelBuilder.out.var(\"CMS_zz4l_fg2phi\").setAttribute(\"flatParam\")\r\n \r\n self.modelBuilder.doSet(\"POI\",poi)","def doParametersOfInterest(self):\n\n self.modelBuilder.doVar(\"eAfb[0.6,-0.75,0.75]\");\n self.modelBuilder.doVar(\"eA0[0.05, -1.0, 1.0]\");\n self.modelBuilder.doVar(\"rAfb[1.0,-5.0, 5.0]\");\n self.modelBuilder.doVar(\"rA0[1.0, -5.0, 5.0]\");\n self.modelBuilder.doSet(\"POI\",\"rAfb,rA0\")\n self.modelBuilder.factory_('expr::mAfb(\"@0*@1\",eAfb,rAfb)')\n self.modelBuilder.factory_('expr::mA0(\"(@0*@1)\",eA0,rA0)')\n\n \n self.modelBuilder.factory_('expr::eAlph(\"2.0*@0/(2.0-@0)\",eA0)')\n self.modelBuilder.factory_('expr::eNorm(\"3.0/4.0/(2.0+@0)\",eAlph)')\n self.modelBuilder.factory_('expr::eRAlph(\"@0*@1\",eAlph,eNorm)')\n self.modelBuilder.factory_('expr::eRpl(\"(@0+@1)\",eNorm,eAfb)')\n self.modelBuilder.factory_('expr::eRmn(\"(@0-@1)\",eNorm,eAfb)')\n\n self.modelBuilder.factory_('expr::mAlph(\"2.0*@0/(2.0-@0)\",mA0)')\n self.modelBuilder.factory_('expr::mNorm(\"3.0/4.0/(2.0+@0)\",mAlph)')\n self.modelBuilder.factory_('expr::mRAlph(\"@0*@1\",mAlph,mNorm)')\n self.modelBuilder.factory_('expr::mRpl(\"(@0+@1)\",mNorm,mAfb)')\n self.modelBuilder.factory_('expr::mRmn(\"(@0-@1)\",mNorm,mAfb)')","def doParametersOfInterest(self):\n\n self.modelBuilder.doVar(\"eAfb[0.6,-0.75,0.75]\");\n self.modelBuilder.doVar(\"eA0[0.05, -1.0, 1.0]\");\n self.modelBuilder.doVar(\"dAfb[0.,-0.75,0.75]\");\n self.modelBuilder.doVar(\"dA0[0.0, -1.0, 1.0]\");\n #self.modelBuilder.doSet(\"POI\",\"dAfb,dA0\")\n self.modelBuilder.doSet(\"POI\",\"dAfb\")\n self.modelBuilder.factory_('expr::mAfb(\"@0+@1\",eAfb,dAfb)')\n self.modelBuilder.factory_('expr::mA0(\"(@0+@1)\",eA0,dA0)')\n\n \n self.modelBuilder.factory_('expr::eAlph(\"2.0*@0/(2.0-@0)\",eA0)')\n self.modelBuilder.factory_('expr::eNorm(\"3.0/4.0/(2.0+@0)\",eAlph)')\n self.modelBuilder.factory_('expr::eRAlph(\"@0*@1\",eAlph,eNorm)')\n self.modelBuilder.factory_('expr::eRpl(\"(@0+@1)\",eNorm,eAfb)')\n self.modelBuilder.factory_('expr::eRmn(\"(@0-@1)\",eNorm,eAfb)')\n\n self.modelBuilder.factory_('expr::mAlph(\"2.0*@0/(2.0-@0)\",mA0)')\n self.modelBuilder.factory_('expr::mNorm(\"3.0/4.0/(2.0+@0)\",mAlph)')\n self.modelBuilder.factory_('expr::mRAlph(\"@0*@1\",mAlph,mNorm)')\n self.modelBuilder.factory_('expr::mRpl(\"(@0+@1)\",mNorm,mAfb)')\n self.modelBuilder.factory_('expr::mRmn(\"(@0-@1)\",mNorm,mAfb)')","def doParametersOfInterest(self):\n\n self.modelBuilder.doVar(\"eAfb[0.6,-0.75,0.75]\");\n self.modelBuilder.doVar(\"eA0[0.05, -1.0, 1.0]\");\n self.modelBuilder.doVar(\"mAfb[0.6,-0.75,0.75]\");\n self.modelBuilder.doVar(\"mA0[0.05, -1.0, 1.0]\");\n self.modelBuilder.doSet(\"POI\",\"eAfb,mAfb\")\n\n \n self.modelBuilder.factory_('expr::eAlph(\"2.0*@0/(2.0-@0)\",eA0)')\n self.modelBuilder.factory_('expr::eNorm(\"3.0/4.0/(2.0+@0)\",eAlph)')\n self.modelBuilder.factory_('expr::eRAlph(\"@0*@1\",eAlph,eNorm)')\n self.modelBuilder.factory_('expr::eRpl(\"(@0+@1)\",eNorm,eAfb)')\n self.modelBuilder.factory_('expr::eRmn(\"(@0-@1)\",eNorm,eAfb)')\n\n self.modelBuilder.factory_('expr::mAlph(\"2.0*@0/(2.0-@0)\",mA0)')\n self.modelBuilder.factory_('expr::mNorm(\"3.0/4.0/(2.0+@0)\",mAlph)')\n self.modelBuilder.factory_('expr::mRAlph(\"@0*@1\",mAlph,mNorm)')\n self.modelBuilder.factory_('expr::mRpl(\"(@0+@1)\",mNorm,mAfb)')\n self.modelBuilder.factory_('expr::mRmn(\"(@0-@1)\",mNorm,mAfb)')","def doParametersOfInterest(self):\n\n self.modelBuilder.doVar(\"Afb[0.6,-0.7,0.7]\");\n self.modelBuilder.doVar(\"A0[0.05, -1.0, 1.0]\");\n self.modelBuilder.doSet(\"POI\",\"Afb,A0\")\n\n # ss templates\n self.modelBuilder.doVar(\"R_ee_os_fakes[0.6,0.0,1.0]\");\n self.modelBuilder.doVar(\"ee16_fakes_norm[1.0, 0.01, 10.]\");\n self.modelBuilder.doVar(\"ee17_fakes_norm[1.0, 0.01, 10.]\");\n self.modelBuilder.doVar(\"ee18_fakes_norm[1.0, 0.01, 10.]\");\n #Remember, cant use spaces in these formulas!\n #self.modelBuilder.options.verbose = 10\n self.modelBuilder.factory_('expr::R_ee16_qcd_os(\"@0*@1\",ee16_fakes_norm,R_ee_os_fakes)')\n self.modelBuilder.factory_('expr::R_ee17_qcd_os(\"@0*@1\",ee17_fakes_norm,R_ee_os_fakes)')\n self.modelBuilder.factory_('expr::R_ee18_qcd_os(\"@0*@1\",ee18_fakes_norm,R_ee_os_fakes)')\n self.modelBuilder.factory_('expr::R_ee16_qcd_ss(\"@0*(1.0-@1)\",ee16_fakes_norm,R_ee_os_fakes)')\n self.modelBuilder.factory_('expr::R_ee17_qcd_ss(\"@0*(1.0-@1)\",ee17_fakes_norm,R_ee_os_fakes)')\n self.modelBuilder.factory_('expr::R_ee18_qcd_ss(\"@0*(1.0-@1)\",ee18_fakes_norm,R_ee_os_fakes)')\n \n self.modelBuilder.factory_('expr::Alph(\"2.0*@0/(2.0-@0)\",A0)')\n self.modelBuilder.factory_('expr::Norm(\"3.0/4.0/(2.0+@0)\",Alph)')\n self.modelBuilder.factory_('expr::RAlph(\"@0*@1\",Alph,Norm)')\n self.modelBuilder.factory_('expr::Rpl(\"(@0+@1)\",Norm,Afb)')\n self.modelBuilder.factory_('expr::Rmn(\"(@0-@1)\",Norm,Afb)')","def setup_parameters(self):\n structure = self.ctx.structure_initial_primitive\n ecutwfc = []\n ecutrho = []\n\n for kind in structure.get_kind_names():\n try:\n dual = self.ctx.protocol['pseudo_data'][kind]['dual']\n cutoff = self.ctx.protocol['pseudo_data'][kind]['cutoff']\n cutrho = dual * cutoff\n ecutwfc.append(cutoff)\n ecutrho.append(cutrho)\n except KeyError as exception:\n self.abort_nowait('failed to retrieve the cutoff or dual factor for {}'.format(kind))\n\n natoms = len(structure.sites)\n conv_thr = self.ctx.protocol['convergence_threshold'] * natoms\n\n self.ctx.inputs['parameters'] = {\n 'CONTROL': {\n 'restart_mode': 'from_scratch',\n 'tstress': self.ctx.protocol['tstress'],\n },\n 'SYSTEM': {\n 'ecutwfc': max(ecutwfc),\n 'ecutrho': max(ecutrho),\n 'smearing': self.ctx.protocol['smearing'],\n 'degauss': self.ctx.protocol['degauss'],\n 'occupations': self.ctx.protocol['occupations'],\n },\n 'ELECTRONS': {\n 'conv_thr': conv_thr,\n }\n }","def setup(self):\n declared = []\n spec = get_specs_for_module(self.module_name)\n\n # Inputs\n for entry in spec.cpacs_inout.inputs:\n if entry.var_name in declared:\n log.info(\"Already declared\")\n elif entry.var_name in Rt.optim_var_dict:\n var = Rt.optim_var_dict[entry.var_name]\n if entry.var_name in Rt.optim_var_dict:\n self.add_input(entry.var_name, val=var[1][0])\n declared.append(entry.var_name)\n\n if declared == []:\n self.add_input(self.module_name + \"_in\")\n declared = []\n\n for entry in spec.cpacs_inout.outputs:\n # Replace special characters from the name of the entry and checks for accronyms\n entry.var_name = change_var_name(entry.var_name)\n\n if entry.var_name in declared:\n log.info(\"Already declared\")\n elif entry.var_name in Rt.optim_var_dict:\n var = Rt.optim_var_dict[entry.var_name]\n self.add_output(entry.var_name, val=var[1][0])\n declared.append(entry.var_name)\n elif (\n \"aeromap\" in entry.var_name and self.module_name == Rt.last_am_module\n ): # == 'PyTornado': #not skf^is_skf:\n # Condition to avoid any conflict with skinfriction\n for name in PARAMS:\n if name in Rt.optim_var_dict:\n var = Rt.optim_var_dict[name]\n self.add_input(name, val=var[1][0])\n declared.append(entry.var_name)\n for name in COEFS:\n if name in Rt.optim_var_dict:\n var = Rt.optim_var_dict[name]\n if is_digit(var[1][0]):\n self.add_output(name, val=var[1][0])\n else:\n self.add_output(name)\n declared.append(entry.var_name)\n\n if declared == []:\n self.add_output(self.module_name + \"_out\")","def doParametersOfInterest(self):\n\n self.modelBuilder.doVar(\"Afb[0.6,-0.75,0.75]\");\n self.modelBuilder.doVar(\"A0[0.05, -1.0, 1.0]\");\n self.modelBuilder.doSet(\"POI\",\"Afb,A0\")\n\n \n self.modelBuilder.factory_('expr::Alph(\"2.0*@0/(2.0-@0)\",A0)')\n self.modelBuilder.factory_('expr::Norm(\"3.0/4.0/(2.0+@0)\",Alph)')\n self.modelBuilder.factory_('expr::RAlph(\"@0*@1\",Alph,Norm)')\n self.modelBuilder.factory_('expr::Rpl(\"(@0+@1)\",Norm,Afb)')\n self.modelBuilder.factory_('expr::Rmn(\"(@0-@1)\",Norm,Afb)')","def get_input(self):\n system, configuration = self.get_system_configuration(None)\n references = self.parent.references\n\n P = self.parameters.current_values_to_dict(\n context=seamm.flowchart_variables._data\n )\n\n # The model chemistry, for labeling properties.\n self.model = P[\"hamiltonian\"]\n\n # Have to fix formatting for printing...\n PP = dict(P)\n for key in PP:\n if isinstance(PP[key], units_class):\n PP[key] = \"{:~P}\".format(PP[key])\n\n # Save the description for later printing\n self.description = []\n self.description.append(__(self.description_text(PP), **PP, indent=self.indent))\n\n # Start gathering the keywords\n keywords = copy.deepcopy(P[\"extra keywords\"])\n keywords.append(\"1SCF\")\n keywords.append(P[\"hamiltonian\"])\n\n if P[\"hamiltonian\"] == \"AM1\":\n elements = configuration.atoms.symbols\n references.cite(\n raw=self.parent._bibliography[\"Dewar_1985c\"],\n alias=\"Dewar_1985c\",\n module=\"mopac_step\",\n level=1,\n note=\"Main reference for AM1 + C, H, N, O.\",\n )\n for element in (\"F\", \"Cl\", \"Br\", \"I\"):\n if element in elements:\n references.cite(\n raw=self.parent._bibliography[\"Dewar_1988\"],\n alias=\"Dewar_1988\",\n module=\"mopac_step\",\n level=1,\n note=\"AM1 parameters for F, Cl, Br, I.\",\n )\n break\n if \"Al\" in elements:\n references.cite(\n raw=self.parent._bibliography[\"Dewar_1990\"],\n alias=\"Dewar_1990\",\n module=\"mopac_step\",\n level=1,\n note=\"AM1 parameters for Al.\",\n )\n if \"Si\" in elements:\n references.cite(\n raw=self.parent._bibliography[\"Dewar_1987b\"],\n alias=\"Dewar_1987b\",\n module=\"mopac_step\",\n level=1,\n note=\"AM1 parameters for Si.\",\n )\n if \"P\" in elements:\n references.cite(\n raw=self.parent._bibliography[\"Dewar_1989\"],\n alias=\"Dewar_1989\",\n module=\"mopac_step\",\n level=1,\n note=\"AM1 parameters for P.\",\n )\n if \"S\" in elements:\n references.cite(\n raw=self.parent._bibliography[\"Dewar_1990b\"],\n alias=\"Dewar_1990b\",\n module=\"mopac_step\",\n level=1,\n note=\"AM1 parameters for S.\",\n )\n if \"Zn\" in elements:\n references.cite(\n raw=self.parent._bibliography[\"Dewar_1988b\"],\n alias=\"Dewar_1988b\",\n module=\"mopac_step\",\n level=1,\n note=\"AM1 parameters for Zn.\",\n )\n if \"Ge\" in elements:\n references.cite(\n raw=self.parent._bibliography[\"Dewar_1989b\"],\n alias=\"Dewar_1989b\",\n module=\"mopac_step\",\n level=1,\n note=\"AM1 parameters for Ge.\",\n )\n if \"Mo\" in elements:\n references.cite(\n raw=self.parent._bibliography[\"Voityuk_2000\"],\n alias=\"Voityuk_2000\",\n module=\"mopac_step\",\n level=1,\n note=\"AM1 parameters for Mo.\",\n )\n if \"Hg\" in elements:\n references.cite(\n raw=self.parent._bibliography[\"Dewar_1989c\"],\n alias=\"Dewar_1989c\",\n module=\"mopac_step\",\n level=1,\n note=\"AM1 parameters for Hg.\",\n )\n for element in (\n \"Li\",\n \"Be\",\n \"Na\",\n \"Mg\",\n \"K\",\n \"Ca\",\n \"Ga\",\n \"As\",\n \"Se\",\n \"Rb\",\n \"Sr\",\n \"In\",\n \"Sn\",\n \"Sb\",\n \"Te\",\n \"Cs\",\n \"Ba\",\n \"Pb\",\n \"Bi\",\n ):\n if element in elements:\n references.cite(\n raw=self.parent._bibliography[\"Stewart_2004\"],\n alias=\"Stewart_2004\",\n module=\"mopac_step\",\n level=1,\n note=\"AM1 parameterization for main-group elements.\",\n )\n break\n elif P[\"hamiltonian\"] == \"MNDO\" or P[\"hamiltonian\"] == \"MNDOD\":\n elements = configuration.atoms.symbols\n references.cite(\n raw=self.parent._bibliography[\"Dewar_1977\"],\n alias=\"Dewar_1977\",\n module=\"mopac_step\",\n level=1,\n note=\"Main reference for MNDO + C, H, N, O.\",\n )\n if \"Be\" in elements:\n references.cite(\n raw=self.parent._bibliography[\"Dewar_1978\"],\n alias=\"Dewar_1978\",\n module=\"mopac_step\",\n level=1,\n note=\"MNDO parameters for Be.\",\n )\n if \"B\" in elements or \"Al\" in elements:\n if \"B\" in elements or P[\"hamiltonian\"] == \"MNDO\":\n references.cite(\n raw=self.parent._bibliography[\"Davis_1981\"],\n alias=\"Davis_1981\",\n module=\"mopac_step\",\n level=1,\n note=\"MNDO parameters for B and Al.\",\n )\n if \"F\" in elements:\n references.cite(\n raw=self.parent._bibliography[\"Dewar_1978b\"],\n alias=\"Dewar_1978b\",\n module=\"mopac_step\",\n level=1,\n note=\"MNDO parameters for F.\",\n )\n if \"Si\" in elements and P[\"hamiltonian\"] == \"MNDO\":\n references.cite(\n raw=self.parent._bibliography[\"Dewar_1986\"],\n alias=\"Dewar_1986\",\n module=\"mopac_step\",\n level=1,\n note=\"Revised MNDO parameters for Si.\",\n )\n if \"P\" in elements and P[\"hamiltonian\"] == \"MNDO\":\n references.cite(\n raw=self.parent._bibliography[\"Dewar_1978b\"],\n alias=\"Dewar_1978b\",\n module=\"mopac_step\",\n level=1,\n note=\"MNDO parameters for P.\",\n )\n if \"S\" in elements and P[\"hamiltonian\"] == \"MNDO\":\n references.cite(\n raw=self.parent._bibliography[\"Dewar_1986b\"],\n alias=\"Dewar_1986b\",\n module=\"mopac_step\",\n level=1,\n note=\"MNDO parameters for S.\",\n )\n if \"Cl\" in elements and P[\"hamiltonian\"] == \"MNDO\":\n references.cite(\n raw=self.parent._bibliography[\"Dewar_1983\"],\n alias=\"Dewar_1983\",\n module=\"mopac_step\",\n level=1,\n note=\"MNDO parameters for Cl.\",\n )\n if \"Zn\" in elements and P[\"hamiltonian\"] == \"MNDO\":\n references.cite(\n raw=self.parent._bibliography[\"Dewar_1986c\"],\n alias=\"Dewar_1986c\",\n module=\"mopac_step\",\n level=1,\n note=\"MNDO parameters for Zn.\",\n )\n if \"Ge\" in elements:\n references.cite(\n raw=self.parent._bibliography[\"Dewar_1987\"],\n alias=\"Dewar_1987\",\n module=\"mopac_step\",\n level=1,\n note=\"MNDO parameters for Ge.\",\n )\n if \"Br\" in elements and P[\"hamiltonian\"] == \"MNDO\":\n references.cite(\n raw=self.parent._bibliography[\"Dewar_1983b\"],\n alias=\"Dewar_1983b\",\n module=\"mopac_step\",\n level=1,\n note=\"MNDO parameters for Br.\",\n )\n if \"Sn\" in elements:\n references.cite(\n raw=self.parent._bibliography[\"Dewar_1984\"],\n alias=\"Dewar_1984\",\n module=\"mopac_step\",\n level=1,\n note=\"MNDO parameters for Sn.\",\n )\n if \"I\" in elements and P[\"hamiltonian\"] == \"MNDO\":\n references.cite(\n raw=self.parent._bibliography[\"Dewar_1984b\"],\n alias=\"Dewar_1984b\",\n module=\"mopac_step\",\n level=1,\n note=\"MNDO parameters for I.\",\n )\n if \"Hg\" in elements and P[\"hamiltonian\"] == \"MNDO\":\n references.cite(\n raw=self.parent._bibliography[\"Dewar_1985\"],\n alias=\"Dewar_1985\",\n module=\"mopac_step\",\n level=1,\n note=\"MNDO parameters for Hg.\",\n )\n if \"Pb\" in elements:\n references.cite(\n raw=self.parent._bibliography[\"Dewar_1985b\"],\n alias=\"Dewar_1985b\",\n module=\"mopac_step\",\n level=1,\n note=\"MNDO parameters for Pb.\",\n )\n for element in (\n \"Na\",\n \"Mg\",\n \"K\",\n \"Ca\",\n \"Ga\",\n \"As\",\n \"Se\",\n \"Rb\",\n \"Sr\",\n \"In\",\n \"Sb\",\n \"Te\",\n \"Cs\",\n \"Ba\",\n \"Tl\",\n \"Bi\",\n ):\n if element in elements:\n references.cite(\n raw=self.parent._bibliography[\"Stewart_2004\"],\n alias=\"Stewart_2004\",\n module=\"mopac_step\",\n level=1,\n note=\"MNDO parameterization for main-group elements.\",\n )\n break\n if P[\"hamiltonian\"] == \"MNDOD\":\n for element in (\n \"Al\",\n \"Si\",\n \"P\",\n \"S\",\n \"Cl\",\n \"Br\",\n \"I\",\n \"Zn\",\n \"Cd\",\n \"Hg\",\n ):\n if element in elements:\n references.cite(\n raw=self.parent._bibliography[\"Thiel_1992\"],\n alias=\"Thiel_1992\",\n module=\"mopac_step\",\n level=1,\n note=(\"MNDO-D formalism for d-orbitals.\"),\n )\n references.cite(\n raw=self.parent._bibliography[\"Thiel_1996\"],\n alias=\"Thiel_1996\",\n module=\"mopac_step\",\n level=1,\n note=(\n \"MNDO-D, parameters for Al, Si, P, S, Cl, Br, \"\n \"I, Zn, Cd, and Hg.\"\n ),\n )\n break\n elif P[\"hamiltonian\"] == \"PM3\":\n elements = configuration.atoms.symbols\n references.cite(\n raw=self.parent._bibliography[\"Stewart_1989\"],\n alias=\"Stewart_1989\",\n module=\"mopac_step\",\n level=1,\n note=\"The citation for the MOPAC parameterization.\",\n )\n for element in (\n \"Be\",\n \"Mg\",\n \"Zn\",\n \"Ga\",\n \"Ge\",\n \"As\",\n \"Se\",\n \"Cd\",\n \"In\",\n \"Sn\",\n \"Sb\",\n \"Te\",\n \"Hg\",\n \"Tl\",\n \"Pb\",\n \"Bi\",\n ):\n if element in elements:\n references.cite(\n raw=self.parent._bibliography[\"Stewart_1991\"],\n alias=\"Stewart_1991\",\n module=\"mopac_step\",\n level=1,\n note=\"The citation for the MOPAC parameterization.\",\n )\n break\n if \"Li\" in elements:\n references.cite(\n raw=self.parent._bibliography[\"Anders_1993\"],\n alias=\"Anders_1993\",\n module=\"mopac_step\",\n level=1,\n note=\"The citation for the MOPAC parameterization.\",\n )\n for element in (\"B\", \"Na\", \"K\", \"Ca\", \"Rb\", \"Sr\", \"Cs\", \"Ba\"):\n if element in elements:\n references.cite(\n raw=self.parent._bibliography[\"Stewart_2004\"],\n alias=\"Stewart_2004\",\n module=\"mopac_step\",\n level=1,\n note=\"The citation for the MOPAC parameterization.\",\n )\n break\n elif \"PM6\" in P[\"hamiltonian\"]:\n references.cite(\n raw=self.parent._bibliography[\"Stewart_2007\"],\n alias=\"Stewart_2007\",\n module=\"mopac_step\",\n level=1,\n note=\"The PM6 parameterization in MOPAC.\",\n )\n if P[\"hamiltonian\"] == \"PM6-D3\":\n references.cite(\n raw=self.parent._bibliography[\"Grimme_2010\"],\n alias=\"Grimme_2010\",\n module=\"mopac_step\",\n level=1,\n note=\"Dispersion correction by Grimme, et al.\",\n )\n if P[\"hamiltonian\"] == \"PM6-DH+\":\n references.cite(\n raw=self.parent._bibliography[\"Korth_2010\"],\n alias=\"Korth_2010\",\n module=\"mopac_step\",\n level=1,\n note=\"Hydrogen-bonding correction by Korth.\",\n )\n if \"PM6-DH2\" in P[\"hamiltonian\"]:\n references.cite(\n raw=self.parent._bibliography[\"Korth_2009\"],\n alias=\"Korth_2009\",\n module=\"mopac_step\",\n level=1,\n note=\"Hydrogen-bonding and dispersion correction.\",\n )\n references.cite(\n raw=self.parent._bibliography[\"Rezac_2009\"],\n alias=\"Rezac_2009\",\n module=\"mopac_step\",\n level=1,\n note=\"Hydrogen-bonding and dispersion correction.\",\n )\n if P[\"hamiltonian\"] == \"PM6-DH2x\":\n references.cite(\n raw=self.parent._bibliography[\"Rezac_2011\"],\n alias=\"Rezac_2011\",\n module=\"mopac_step\",\n level=1,\n note=\"Halogen-bonding correction.\",\n )\n if \"PM6-D3H4\" in P[\"hamiltonian\"]:\n references.cite(\n raw=self.parent._bibliography[\"Rezac_2011\"],\n alias=\"Rezac_2011\",\n module=\"mopac_step\",\n level=1,\n note=\"Hydrogen-bonding and dispersion correction.\",\n )\n references.cite(\n raw=self.parent._bibliography[\"Vorlova_2015\"],\n alias=\"Vorlova_2015\",\n module=\"mopac_step\",\n level=1,\n note=\"Hydrogen-hydrogen repulsion correction.\",\n )\n if P[\"hamiltonian\"] == \"PM6-D3H4x\":\n references.cite(\n raw=self.parent._bibliography[\"Brahmkshatriya_2013\"],\n alias=\"Brahmkshatriya_2013\",\n module=\"mopac_step\",\n level=1,\n note=\"Halogen-oxygen and halogen-nitrogen correction.\",\n )\n elif \"PM7\" in P[\"hamiltonian\"]:\n references.cite(\n raw=self.parent._bibliography[\"Stewart_2012\"],\n alias=\"Stewart_2012\",\n module=\"mopac_step\",\n level=1,\n note=\"The PM7 parameterization in MOPAC.\",\n )\n elif P[\"hamiltonian\"] == \"RM1\":\n references.cite(\n raw=self.parent._bibliography[\"Rocha_2006\"],\n alias=\"Rocha_2006\",\n module=\"mopac_step\",\n level=1,\n note=\"RM1 parameterization.\",\n )\n\n # which structure? may need to set default first...\n if P[\"structure\"] == \"default\":\n if self._id[-1] == \"1\":\n structure = \"initial\"\n else:\n structure = \"current\"\n elif self._id[-1] == \"1\":\n structure = \"initial\"\n elif P[\"structure\"] == \"current\":\n structure = \"current\"\n\n if structure == \"current\":\n keywords.append(\"OLDGEO\")\n\n if P[\"convergence\"] == \"normal\":\n pass\n elif P[\"convergence\"] == \"precise\":\n keywords.append(\"PRECISE\")\n elif P[\"convergence\"] == \"relative\":\n keywords.append(\"RELSCF=\" + P[\"relative\"])\n elif P[\"convergence\"] == \"absolute\":\n keywords.append(\"SCFSCRT=\" + P[\"absolute\"])\n else:\n raise RuntimeError(\n \"Don't recognize convergence '{}'\".format(P[\"convergence\"])\n )\n\n if P[\"uhf\"]:\n keywords.append(\"UHF\")\n\n if P[\"MOZYME\"] == \"always\":\n keywords.append(\"MOZYME\")\n elif (\n P[\"MOZYME\"] == \"for larger systems\"\n and configuration.n_atoms >= P[\"nMOZYME\"]\n ):\n keywords.append(\"MOZYME\")\n\n if P[\"COSMO\"]:\n keywords.append(f\"EPS={P['eps']}\")\n rsolve = P[\"rsolve\"].to(\"Å\").magnitude\n keywords.append(f\"RSOLVE={rsolve}\")\n keywords.append(f\"NSPA={P['nspa']}\")\n keywords.append(f\"DISEX={P['disex']}\")\n\n if P[\"calculate gradients\"]:\n keywords.append(\"GRADIENTS\")\n\n if \"yes\" in P[\"bond orders\"]:\n keywords.append(\"BONDS\")\n\n # Add any extra keywords so that they appear at the end\n metadata = self.metadata[\"keywords\"]\n for keyword in P[\"extra keywords\"]:\n if \"=\" in keyword:\n keyword, value = keyword.split(\"=\")\n if keyword not in metadata or \"format\" not in metadata[keyword]:\n keywords.append(keyword + \"=\" + value)\n else:\n keywords.append(metadata[keyword][\"format\"].format(keyword, value))\n\n result = []\n result.append([[*keywords], None, None])\n\n # Handle MOZYME follow-up calculations\n if \"MOZYME\" in keywords:\n follow_up = P[\"MOZYME follow-up\"]\n if \"exact\" in follow_up:\n keywords.remove(\"MOZYME\")\n if \"1SCF\" not in keywords:\n keywords.append(\"1SCF\")\n keywords.append(\"OLDGEO\")\n result.append([[*keywords], None, \"MOZYME follow-up using MOPAC\"])\n elif \"new\" in follow_up:\n if \"1SCF\" not in keywords:\n keywords.append(\"1SCF\")\n keywords.append(\"OLDGEO\")\n result.append([[*keywords], None, \"MOZYME follow-up, reinitializing\"])\n elif follow_up == \"none\":\n pass\n else:\n logger.error(f\"Don't recognize the MOZYME follow-up: '{follow_up}'\")\n\n return result","def __init__(self, dataset):\n\n self.input_names = ['CORCON_nv_lwc_vcol',\n 'CORCON_nv_lwc_icol',\n 'CORCON_nv_lwc_vref',\n 'CORCON_nv_lwc_iref',\n 'CORCON_nv_twc_vcol',\n 'CORCON_nv_twc_icol',\n 'CORCON_nv_twc_vref',\n 'CORCON_nv_twc_iref',\n 'TAS_RVSM',\n 'IAS_RVSM',\n 'PS_RVSM',\n 'WOW_IND',\n 'CLWCIREF', 'CLWCVREF', 'CLWCICOL', 'CLWCVCOL',\n 'CTWCIREF', 'CTWCVREF', 'CTWCICOL', 'CTWCVCOL',\n 'CALNVTWC',\n 'CALNVLWC1',\n 'CALNVLWC2',\n 'CALNVL']\n\n self.outputs = [parameter('NV_TWC_U',\n units='gram m-3',\n frequency=64,\n long_name='Uncorrected total condensed water content from the Nevzorov probe'),\n parameter('NV_LWC1_U',\n units='gram m-3',\n frequency=64,\n long_name='Uncorrected liquid water content from the Nevzorov probe (1st collector)',\n standard_name='mass_concentration_of_liquid_water_in_air'),\n parameter('NV_LWC2_U',\n units='gram m-3',\n frequency=64,\n long_name='Uncorrected liquid water content from the Nevzorov probe (2nd collector)',\n standard_name='mass_concentration_of_liquid_water_in_air'),\n parameter('NV_TWC_C',\n units='gram m-3',\n frequency=64,\n long_name='Corrected total condensed water content from the Nevzorov probe'),\n parameter('NV_LWC1_C',\n units='gram m-3',\n frequency=64,\n long_name='Corrected liquid water content from the Nevzorov probe (1st collector)',\n standard_name='mass_concentration_of_liquid_water_in_air'),\n parameter('NV_LWC2_C',\n units='gram m-3',\n frequency=64,\n long_name='Corrected liquid water content from the Nevzorov probe (2nd collector)',\n standard_name='mass_concentration_of_liquid_water_in_air'),\n parameter('NV_TWC_P',\n units='W',\n frequency=64,\n long_name='TWC collector power'),\n parameter('NV_LWC1_P',\n units='W',\n frequency=64,\n long_name='LWC1 collector power'),\n parameter('NV_LWC2_P',\n units='W',\n frequency=64,\n long_name='LWC2 collector power'),\n parameter('NV_REF_P',\n units='W',\n frequency=64,\n long_name='Reference power')]\n\n self.version = 1.00\n cal_base.__init__(self, dataset)","def doParametersOfInterest(self):\n\n self.modelBuilder.doVar(\"Rdy[1.,0.0,10.0]\");\n self.modelBuilder.doVar(\"Rbk[1.,0.0,10.0]\");\n self.modelBuilder.doVar(\"Rqcd_emu[1,0.0,10.0]\");\n self.modelBuilder.doSet(\"POI\",\"Rbk,Rdy,Rqcd_emu\")","def _handleInput(self, paramInput):\n super()._handleInput(paramInput)\n settings, notFound = paramInput.findNodesAndExtractValues(['C', 'dual', 'penalty', 'l1_ratio', 'tol', 'fit_intercept',\n 'solver','intercept_scaling', 'max_iter', 'multi_class',\n 'class_weight', 'random_state'])\n # notFound must be empty\n assert(not notFound)\n self.initializeModel(settings)","def doParametersOfInterest(self):\n self.modelBuilder.doVar(\"kappa_W[1,0.0,2.0]\") \n self.modelBuilder.doVar(\"kappa_Z[1,0.0,2.0]\") \n self.modelBuilder.doVar(\"kappa_tau[1,0.0,3.0]\")\n self.modelBuilder.doVar(\"kappa_mu[1,0.0,5.0]\") \n self.modelBuilder.factory_(\"expr::kappa_mu_expr(\\\"@0*@1+(1-@0)*@2\\\", CMS_use_kmu[0], kappa_mu, kappa_tau)\")\n self.modelBuilder.doVar(\"kappa_t[1,0.0,4.0]\")\n # additional kappa for the anomalous coupling\n self.modelBuilder.doVar(\"kappa_tilde_t[0.0,0.0,4.0]\")\n self.modelBuilder.doVar(\"kappa_b[1,0.0,3.0]\")\n if not self.resolved:\n self.modelBuilder.doVar(\"kappa_g[1,0.0,2.0]\")\n self.modelBuilder.doVar(\"kappa_gam[1,0.0,2.5]\")\n\tself.modelBuilder.doVar(\"BRinv[0,0,1]\")\n self.modelBuilder.out.var(\"BRinv\").setConstant(True)\n # adding additional kappa to list of parameters of interest\n pois = 'kappa_W,kappa_Z,kappa_tau,kappa_t,kappa_tilde_t,kappa_b'\n if not self.resolved:\n pois += ',kappa_g,kappa_gam'\n self.doMH()\n self.modelBuilder.doSet(\"POI\",pois)\n # use modified Higgs Builder\n self.SMH = AnomalousTopHiggsBuilder(self.modelBuilder)\n self.setup()","def config1() :\n data_name = \"titanic\" ### in data/input/\n model_class = 'AutoML' ### ACTUAL Class name for model_sklearn.py\n n_sample = 1000\n\n def post_process_fun(y): ### After prediction is done\n return int(y)\n\n def pre_process_fun(y): ### Before the prediction is done\n return int(y)\n\n\n model_dict = {'model_pars': {\n ### LightGBM API model #######################################\n 'model_class': model_class\n ,'model_pars' : {\n 'total_time_limit' : 20,\n 'algorithms' : 'auto',\n 'results_path' : root_repo + f'/data/output/{data_name}/{os_get_function_name()}/automl_1',\n 'eval_metric' : 'auto'\n\n # mode='Explain',\n # ml_task='auto', model_time_limit=None, algorithms='auto', train_ensemble=True,\n # stack_models='auto', eval_metric='auto', validation_strategy='auto', explain_level='auto',\n # golden_features='auto', features_selection='auto', start_random_models='auto',\n # hill_climbing_steps='auto', top_models_to_improve='auto', verbose=1, random_state=1234)\n }\n\n , 'post_process_fun' : post_process_fun ### After prediction ##########################################\n , 'pre_process_pars' : {'y_norm_fun' : pre_process_fun , ### Before training ##########################\n\n\n ### Pipeline for data processing ##############################\n 'pipe_list': [\n #### coly target prorcessing\n {'uri': 'source/prepro.py::pd_coly', 'pars': {}, 'cols_family': 'coly', 'cols_out': 'coly', 'type': 'coly' },\n\n\n {'uri': 'source/prepro.py::pd_colnum_bin', 'pars': {}, 'cols_family': 'colnum', 'cols_out': 'colnum_bin', 'type': '' },\n {'uri': 'source/prepro.py::pd_colnum_binto_onehot', 'pars': {}, 'cols_family': 'colnum_bin', 'cols_out': 'colnum_onehot', 'type': '' },\n\n #### catcol INTO integer, colcat into OneHot\n {'uri': 'source/prepro.py::pd_colcat_bin', 'pars': {}, 'cols_family': 'colcat', 'cols_out': 'colcat_bin', 'type': '' },\n # {'uri': 'source/prepro.py::pd_colcat_to_onehot', 'pars': {}, 'cols_family': 'colcat_bin', 'cols_out': 'colcat_onehot', 'type': '' },\n\n\n ### Cross_feat = feat1 X feat2\n # {'uri': 'source/prepro.py::pd_colcross', 'pars': {}, 'cols_family': 'colcross', 'cols_out': 'colcross_pair', 'type': 'cross'},\n\n\n #### Example of Custom processor\n #{'uri': THIS_FILEPATH + '::pd_col_myfun', 'pars': {}, 'cols_family': 'colnum', 'cols_out': 'col_myfun', 'type': '' }, \n\n\n ],\n }\n },\n\n 'compute_pars': { 'metric_list': ['accuracy_score','average_precision_score']\n\n ,'mlflow_pars' : None # {} ### Not empty --> use mlflow\n },\n\n 'data_pars': { 'n_sample' : n_sample,\n\n 'download_pars' : None,\n\n\n 'cols_input_type' : cols_input_type_1,\n ### family of columns for MODEL #########################################################\n # \"colnum\", \"colnum_bin\", \"colnum_onehot\", \"colnum_binmap\", #### Colnum columns\n # \"colcat\", \"colcat_bin\", \"colcat_onehot\", \"colcat_bin_map\", #### colcat columns\n # 'colcross_single_onehot_select', \"colcross_pair_onehot\", 'colcross_pair', #### colcross columns 'coldate', 'coltext',\n 'cols_model_group': [ 'colnum_bin',\n 'colcat_bin',\n # 'coltext',\n # 'coldate',\n #'colcross_pair',\n \n ### example of custom\n # 'col_myfun'\n ]\n\n ### Filter data rows ##################################################################\n ,'filter_pars': { 'ymax' : 2 ,'ymin' : -1 }\n\n }\n }\n\n ##### Filling Global parameters ############################################################\n model_dict = global_pars_update(model_dict, data_name, config_name=os_get_function_name() )\n return model_dict","def doParametersOfInterest(self):\n ''' ref : physicsmodel -> rvf\n self.modelBuilder.out.var(\"MH\").setRange(float(self.mHRange[0]),float(self.mHRange[1]))\n self.modelBuilder.out.var(\"MH\").setConstant(False)\n '''\n\n self.modelBuilder.doVar(\"mu[0,0,1000]\") ##mu is what we want to return (in string) name[starting_value,min,max] \n self.modelBuilder.doVar(\"Fvbf[0,0,1]\") ##mu is what we want to return (in string) name[starting_value,min,max] \n self.modelBuilder.doSet(\"POI\",\"mu,Fvbf\")\n self.modelBuilder.doVar(\"\")\n self.modelBuilder.factory_('expr::ggH_s_func(\"(@0-sqrt(@0))*(1.-@1)\", mu,Fvbf)')\n self.modelBuilder.factory_( 'expr::ggH_b_func(\"(1-sqrt(@0))*(1.-@1)\", mu,Fvbf)')\n self.modelBuilder.factory_( 'expr::ggH_sbi_func(\"sqrt(@0)*(1.-@1)\", mu,Fvbf)')\n\n self.modelBuilder.factory_('expr::vbfH_s_func(\"(@0-sqrt(@0))*(@1)\", mu,Fvbf)')\n self.modelBuilder.factory_( 'expr::vbfH_b_func(\"(1-sqrt(@0))*(@1)\", mu,Fvbf)')\n self.modelBuilder.factory_( 'expr::vbfH_sbi_func(\"sqrt(@0)*(@1)\", mu,Fvbf)')","def __init__(self, encut, magmom, ldaul, Uparam, Jparam, name=\"DFTCL_settings\"):\n\n cl_settings = {\"ISPIN\": 2, \"MAGMOM\": magmom, \"SAXIS\": None, \"LSORBIT\": None, \"LNONCOLLINEAR\": None}\n dftu_settings = {\"LDAU\": \".TRUE.\", \"LDAUU\": Uparam, \"LDATYPE\": 2, \"LDAUL\": ldaul, \"LDAUJ\": Jparam , \"LMAXMIMX\": 4}\n InputParameters.__init__(self, name=name, magnetic_settings=cl_settings, hubbard_settings=dftu_settings)\n self.update_electronic_settings(\"encut\", encut)","def doParametersOfInterest(self):\n\n self.modelBuilder.doVar(\"Afb[0.6,-0.70,0.70]\");\n self.modelBuilder.doSet(\"POI\",\"Afb\")\n\n # ss templates\n self.modelBuilder.doVar(\"Rdy_mumu_ss[1.0,0.0,10.0]\");\n self.modelBuilder.doVar(\"Rdy_ee_ss[1.0,0.0,10.0]\");\n \n self.modelBuilder.factory_('expr::Rpl(\"(1.+@0)\",Afb)')\n self.modelBuilder.factory_('expr::Rmn(\"(1.-@0)\",Afb)')","def doParametersOfInterest(self):\n\n self.modelBuilder.doVar(\"Afb[0.6,-0.75,0.75]\");\n self.modelBuilder.doSet(\"POI\",\"Afb\")\n\n # ss templates\n self.modelBuilder.doVar(\"Dilu_ratio[1.0,0.0,10.0]\");\n self.modelBuilder.doVar(\"Rdy_mumu_ss[1.0,0.0,10.0]\");\n self.modelBuilder.doVar(\"Rdy_ee_ss[1.0,0.0,10.0]\");\n \n self.modelBuilder.factory_('expr::Rpl(\"0.5*(1.+@0*@1)\",Afb, Dilu_ratio)')\n self.modelBuilder.factory_('expr::Rmn(\"0.5*(1.-@0*@1)\",Afb, Dilu_ratio)')","def __init__(\n self,\n model_filepath: str,\n user_validation: bool = True,\n ):\n\n # ensure model filepath is balid, and save as att if it is\n assert model_filepath.endswith(\".fmu\"), \"Provided filepath is not an FMU file: '{}'\".format(model_filepath)\n self.model_filepath = model_filepath\n # config file with config_params, inputs, outputs\n self.sim_config_filepath = SIM_CONFIG_NAME_f(self.model_filepath)\n\n # read the model description\n self.model_description = read_model_description(model_filepath)\n error_log = \"Provided model ({}) doesn't have modelVariables in XLS description file\".format(model_filepath)\n assert len(self.model_description.modelVariables) > 0, error_log\n\n # correct non-alphanumeric tags.\n # note, it doesn't suppose any problem, since interaction with sim uses indices, not names.\n self._clean_non_alphanumeric_chars()\n\n\n # collect the value references (indices)\n # collect the value types (Real, Integer or Enumeration)\n # collect the variables to be initialized and the value to do so at\n self.vars_to_idx = {}\n self.vars_to_type_f = {}\n self.vars_to_ini_vals = {}\n for variable in self.model_description.modelVariables:\n # extract key attributes per variable\n var_idx = variable.valueReference #, variable.causality\n var_name = variable.name\n var_type = variable.type\n var_start = variable.start\n \n # collect type reference\n if var_type == \"Real\":\n self.vars_to_type_f[var_name] = float\n elif var_type == \"Integer\":\n self.vars_to_type_f[var_name] = int\n else:\n # [TODO] Integrate variables of type \"Enumeration\". How do we cast? Define a function for \"self.vars_to_type_f\".\n # [TODO] Integrate variables of type string (need to find correct var_type tag first).\n # [TODO] Integrate variables of type boolean (need to find correct var_type tag first).\n print(f\"Variable '{var_name}' will be skipped. FMU connector cannot currently handle vars of type '{var_type}'.\")\n continue\n \n # collect the value references (indices)\n self.vars_to_idx[var_name] = var_idx\n\n # collect the variables to be initialized and the value to do so at\n if var_start is not None:\n # cast variable prior to storing\n self.vars_to_ini_vals[var_name] = self.vars_to_type_f[var_name](var_start)\n \n\n # initialize sim config\n self.is_model_config_valid = False # Currently unused, since error is raised if model invalid\n self.sim_config_params = []\n self.sim_inputs = []\n self.sim_outputs = []\n self.sim_other_vars = []\n\n # ---------------------------------------------------------------------\n # YAML CONFIG --> check for existing config using SIM_CONFIG_NAME_f --> e.g: \"{model_name}_conf.yaml\"\n valid_config = self._validate_sim_config()\n \n # exit if model is valid, unless validation has been activated\n if valid_config:\n\n # print model config for user reference: config_params, inputs, outputs\n print(self._get_sim_config_str())\n\n if user_validation:\n # prompt user to manually validate model if selected\n validation_asserted = input(\"Is this configuration correct (y|n)? \")\n\n if validation_asserted == \"y\":\n self.is_model_config_valid = True\n return\n \n # reset config if invalid\n self.sim_config_params = []\n self.sim_inputs = []\n self.sim_outputs = []\n self.sim_other_vars = []\n \n else:\n # when no validation is selected, we assume the sim config is valid\n self.is_model_config_valid = True\n return\n \n # ---------------------------------------------------------------------\n # FMI CONFIG --> if model is invalid we look for attributes within the .fmi model definition\n valid_config = self._extract_sim_config_from_fmi_std()\n\n if valid_config:\n\n # print model config for user reference: config_params, inputs, outputs\n print(self._get_sim_config_str())\n \n if user_validation:\n # prompt user to manually validate model if selected\n validation_asserted = input(\"Is this configuration correct (y|n)? \")\n\n if validation_asserted == \"y\":\n self.is_model_config_valid = True\n # dump YMAL file to reuse next time the model is loaded\n self._dump_config_to_yaml_file()\n return\n \n else:\n # when no validation is selected, we assume the sim config is valid\n self.is_model_config_valid = True\n # dump YMAL file to reuse next time the model is loaded\n self._dump_config_to_yaml_file()\n return\n \n # Dump auxiliary YAML config file if user doesn't assert the provided set\n # of config_params/inputs/outputs\n self._dump_config_to_yaml_file(is_aux_yaml = True)\n \n # If neither YAML nor FMI model is sufficient raise error\n error_log = \"MODEL DOES NOT HAVE THE CORRECT CONFIG DEFINED NEITHER ON YAML CONFIG FILE \"\n error_log += \"NOR FMI MODEL DESCRIPTION. A YAML FILE HAS BEEN CREATED FOR YOU TO MODIFY. \"\n error_log += \"THE SIM HAS BEEN FORCED TO EXIT, BUT FEEL FREE TO RERUN ONCE SET-UP IS COMPLETED.\"\n raise Exception(error_log)","def buildVariables(self, model):\n\n \"\"\"\n #Inputs\n \"\"\"\n\n \"\"\"\n #Outputs\n \"\"\"\n #-------- Register Settings Used During Calibration --------\n #auxNDiv (to be put into synth.auxfreq.mmddenom during ir cal only)\n self._addModelVariable(model, 'ircal_auxndiv', int, ModelVariableFormat.DECIMAL, units='bytes', desc='This value is predetermined.')\n #auxLoDiv (to be put into synth.divctrl.auxlodivfreqctrl during ir cal only)\n self._addModelVariable(model, 'ircal_auxlodiv', int, ModelVariableFormat.DECIMAL, units='bytes', desc='This value is predetermined.')\n #rampVal (to be put into modem.rampctrl.rampval during ir cal only)\n self._addModelVariable(model, 'ircal_rampval', int, ModelVariableFormat.DECIMAL, units='bytes', desc='This value is predetermined.')\n #rxAmp_PLL (to be put into rac.auxctrl.rxamp during PLL loopback, ir cal only)\n self._addModelVariable(model, 'ircal_rxamppll', int, ModelVariableFormat.DECIMAL, units='bytes', desc='This value is predetermined.')\n #rxAmp_PA (to be put into rac.auxctrl.rxamp during PA loopback, ir cal only)\n self._addModelVariable(model, 'ircal_rxamppa', int, ModelVariableFormat.DECIMAL, units='bytes', desc='This value is predetermined.')\n \n #-------- Decide Between Calibration Procedures --------\n #diConfigIsValid (true = DI value / PTE value is an option)\n self._addModelVariable(model, 'ircal_manufconfigvalid', bool, ModelVariableFormat.ASCII, 'True = the manufacturing calibration value is saved on the chip')\n #pllLoopbackConfigIsValid (true = PLL loopback is an option)\n self._addModelVariable(model, 'ircal_pllconfigvalid', bool, ModelVariableFormat.ASCII, 'True = PLL loopback is permitted to generate a calibration value')\n #paLoopbackConfigIsValid (true = PA loopback is an option)\n self._addModelVariable(model, 'ircal_paconfigvalid', bool, ModelVariableFormat.ASCII, 'True = PA loopback is permitted to generate a calibration value')\n #recommendedConfig (DI/PTE vs PLL loopback vs PA loopback)\n var = self._addModelVariable(model, 'ircal_bestconfig', Enum, ModelVariableFormat.DECIMAL, 'Specify the best calibration method for this radio configuration.')\n member_data = [\n ['MANUFACTURING', 1, 'Use the calibration value saved during manufacturing, if applicable.'],\n ['PLL', 2, 'Put the part into a PLL loopback to generate a calibration value.'],\n ['PA', 3, 'Put the part into a PA loopback to generate a calibration value.'],\n ['UNSUPPORTED', 4, 'Image rejection calibration not supported.'],\n ]\n var.var_enum = CreateModelVariableEnum(\n 'configType',\n 'Specify how image rejection calibration is to run.',\n member_data)\n\n #-------- Decide Between Software/Hardware RSSI Averaging --------\n self._addModelVariable(model, 'ircal_useswrssiaveraging', bool, ModelVariableFormat.ASCII, 'True = use software RSSI averaging; False = use hardware RSSI averaging')\n self._addModelVariable(model, 'ircal_numrssitoavg', int, ModelVariableFormat.DECIMAL, units='bytes', desc='Number of RSSI values (2^value) to average in software. If value = 3, 8 values will be averaged.')\n self._addModelVariable(model, 'ircal_throwawaybeforerssi', int, ModelVariableFormat.DECIMAL, units='bytes', desc='Number of RSSI values to discard before starting to average RSSI values.')\n self._addModelVariable(model, 'ircal_delayusbeforerssi', int, ModelVariableFormat.DECIMAL, units='bytes', desc='Microsecond delay between applying a calibration value and then reading RSSI values.')\n self._addModelVariable(model, 'ircal_delayusbetweenswrssi', int, ModelVariableFormat.DECIMAL, units='bytes', desc='Microsecond delay between gathering RSSI values. Software RSSI averaging mode only.')\n\n #------ Determine number of raw RSSI values averaged by hardware ------\n #agcRssiPeriod\n self._addModelVariable(model, 'ircal_agcrssiperiod', int, ModelVariableFormat.DECIMAL, units='bytes', desc='Number of raw RSSI values averaged by hardware.')\n\n #------ Registers specific to Jumbo (and new Dumbo) support ------\n self._addModelVariable(model, 'ircal_useswrssiaveraging2', bool, ModelVariableFormat.ASCII, 'True = use software RSSI averaging; False = use hardware RSSI averaging; Jumbo support')\n self._addModelVariable(model, 'ircal_numrssitoavg2', int, ModelVariableFormat.DECIMAL, units='bytes', desc='Number of RSSI values (2^value) to average in software. If value = 3, 8 values will be averaged. Jumbo support')\n self._addModelVariable(model, 'ircal_throwawaybeforerssi2', int, ModelVariableFormat.DECIMAL, units='bytes', desc='Number of RSSI values to discard before starting to average RSSI values. Jumbo support')\n self._addModelVariable(model, 'ircal_delayusbeforerssi2', int, ModelVariableFormat.DECIMAL, units='bytes', desc='Microsecond delay between applying a calibration value and then reading RSSI values. Jumbo support')\n self._addModelVariable(model, 'ircal_delayusbetweenswrssi2', int, ModelVariableFormat.DECIMAL, units='bytes', desc='Microsecond delay between gathering RSSI values. Software RSSI averaging mode only. Jumbo support')\n\n #\n # Bools not allowed as advanced inputs due to GUI constraint. Using enum instead\n var = self._addModelVariable(model, 'ircal_rxtx_path_common', Enum, ModelVariableFormat.DECIMAL, 'RX and TX are on a common/shared circuit, or split. Refer to document AN971.')\n member_data = [\n ['SHARED_RX_TX_PATH' , 0, 'RX and TX circuit paths are common/shared/connected'],\n ['SPLIT_RX_TX_PATH', 1, 'RX and TX circuit paths are separated/not connected'],\n ]\n var.var_enum = CreateModelVariableEnum(\n 'IRCalRXTXPathCommonEnum',\n 'RX and TX are on a common/shared circuit, or split. Refer to document AN971.',\n member_data)\n\n self._addModelVariable(model, 'ircal_power_level', int, ModelVariableFormat.DECIMAL, units='codes', desc='Specify IR cal power level (amplitude) instead of auto (0). Refer to document AN971.')","def __init__(self, encut, ldaul, Uparam, Jparam, name=\"DFTU_settings\"):\n\n dftu_settings = {\"LDAU\": \".TRUE.\" , \"LDAUU\": Uparam, \"LDATYPE\": 2, \"LADAUL\": ldaul, \"LDAUJ\": Jparam , \"LMAXMIX\": 4}\n InputParameters.__init__(self, name=name, hubbard_settings=dftu_settings)\n self.update_electronic_settings(\"ENCUT\", encut)","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 _handleInput(self, paramInput):\n super()._handleInput(paramInput)\n settings, notFound = paramInput.findNodesAndExtractValues(['nu','C', 'kernel', 'degree', 'gamma', 'coef0',\n 'tol', 'cache_size', 'shrinking', 'max_iter'])\n # notFound must be empty\n assert(not notFound)\n self.initializeModel(settings)","def test_config():\n import yaml\n import astropy.units as u\n from tqdm import tqdm\n # Need these for `eval` below\n from numpy import array\n\n # Same test suite as used in test_imsim above.\n # This time, we just use this for the det names.\n with open(DATA_DIR / \"wcs_466749.yaml\", 'r') as f:\n wcss = yaml.safe_load(f)\n\n cmds = {}\n with open(DATA_DIR / \"phosim_cat_466749.txt\", 'r') as f:\n for line in f:\n k, v = line.split()\n try:\n v = int(v)\n except ValueError:\n try:\n v = float(v)\n except ValueError:\n pass\n cmds[k] = v\n\n # Values below (and others) from phosim_cat_466749.txt\n rc = cmds['rightascension']\n dc = cmds['declination']\n boresight = galsim.CelestialCoord(\n rc*galsim.degrees,\n dc*galsim.degrees\n )\n obstime = Time(cmds['mjd'], format='mjd', scale='utc')\n obstime -= 15*u.s\n band = \"ugrizy\"[cmds['filter']]\n wavelength_dict = dict(\n u=365.49,\n g=480.03,\n r=622.20,\n i=754.06,\n z=868.21,\n y=991.66\n )\n wavelength = wavelength_dict[band]\n camera = imsim.get_camera()\n\n rotTelPos = cmds['rottelpos'] * galsim.degrees\n telescope = imsim.load_telescope(f\"LSST_{band}.yaml\", rotTelPos=rotTelPos)\n # Non-default values.\n temperature = 293.\n pressure = 69.0\n H2O_pressure = 2.0\n\n factory = imsim.BatoidWCSFactory(\n boresight, obstime, telescope, wavelength,\n camera,\n temperature=temperature,\n pressure=pressure,\n H2O_pressure=H2O_pressure\n )\n\n config = {\n 'input': {\n 'telescope': {\n 'file_name':f\"LSST_{band}.yaml\",\n 'rotTelPos': rotTelPos\n }\n },\n 'image': {\n 'wcs': {\n 'type': 'Batoid',\n 'boresight': boresight,\n 'camera': 'LsstCam',\n 'obstime': obstime,\n 'wavelength': wavelength,\n 'temperature': temperature,\n 'pressure': pressure,\n 'H2O_pressure': H2O_pressure,\n 'order': 2,\n }\n }\n }\n\n rng = np.random.default_rng(1234)\n for k in tqdm(wcss.keys()):\n name = k[18:25].replace('-', '_')\n det = camera[name]\n\n wcs1 = factory.getWCS(det, order=2)\n config['image']['wcs']['det_name'] = name\n galsim.config.RemoveCurrent(config['image']['wcs'])\n galsim.config.ProcessInput(config)\n wcs2 = galsim.config.BuildWCS(config['image'], 'wcs', config)\n\n # Test points\n xs = rng.uniform(0, 4000, 100)\n ys = rng.uniform(0, 4000, 100)\n ra1, dec1 = wcs1.xyToradec(xs, ys, units='radians')\n ra2, dec2 = wcs2.xyToradec(xs, ys, units='radians')\n np.testing.assert_allclose(ra1, ra2)\n np.testing.assert_allclose(dec1, dec2)\n\n # Test == when identical\n galsim.config.RemoveCurrent(config['image']['wcs'])\n wcs3 = galsim.config.BuildWCS(config['image'], 'wcs', config)\n assert wcs3 == wcs2\n\n # Test that pressure and temperature matter.\n config['image']['wcs']['temperature'] = 250\n galsim.config.RemoveCurrent(config['image']['wcs'])\n wcs4 = galsim.config.BuildWCS(config['image'], 'wcs', config)\n assert wcs4 != wcs2\n\n config['image']['wcs']['temperature'] = temperature\n config['image']['wcs']['pressure'] = 55\n galsim.config.RemoveCurrent(config['image']['wcs'])\n wcs5 = galsim.config.BuildWCS(config['image'], 'wcs', config)\n assert wcs5 != wcs2\n\n config['image']['wcs']['pressure'] = pressure\n config['image']['wcs']['H2O_pressure'] = 10\n galsim.config.RemoveCurrent(config['image']['wcs'])\n wcs6 = galsim.config.BuildWCS(config['image'], 'wcs', config)\n assert wcs6 != wcs2\n\n # Test defaults\n del config['image']['wcs']['temperature']\n del config['image']['wcs']['pressure']\n del config['image']['wcs']['H2O_pressure']\n galsim.config.RemoveCurrent(config['image']['wcs'])\n config = galsim.config.CleanConfig(config)\n galsim.config.ProcessInput(config)\n wcs7 = galsim.config.BuildWCS(config['image'], 'wcs', config)\n default_pressure = 101.325 * (1-2.25577e-5*2715)**5.25588\n wcs7a = imsim.BatoidWCSFactory(\n boresight, obstime, telescope, wavelength, camera,\n temperature=280,\n pressure=default_pressure,\n H2O_pressure=1.0,\n ).getWCS(det, order=2)\n assert wcs7 == wcs7a\n\n # Default wavelength from bandpass\n del config['image']['wcs']['wavelength']\n config['bandpass'] = imsim.RubinBandpass('r')\n galsim.config.RemoveCurrent(config['image']['wcs'])\n config = galsim.config.CleanConfig(config)\n galsim.config.ProcessInput(config)\n wcs8 = galsim.config.BuildWCS(config['image'], 'wcs', config)\n wcs8a = imsim.BatoidWCSFactory(\n boresight, obstime, telescope,\n wavelength=config['bandpass'].effective_wavelength,\n camera=camera,\n temperature=280,\n pressure=default_pressure,\n H2O_pressure=1.0,\n ).getWCS(det, order=2)\n assert wcs8 == wcs8a\n\n del config['bandpass']\n config['image']['bandpass'] = {\n 'type': 'RubinBandpass',\n 'band' : 'r',\n }\n galsim.config.RemoveCurrent(config['image']['wcs'])\n config = galsim.config.CleanConfig(config)\n galsim.config.ProcessInput(config)\n wcs8b = galsim.config.BuildWCS(config['image'], 'wcs', config)\n assert wcs8b == wcs8a\n\n # Obstime can be a string\n print('obstime = ',obstime.to_value('iso'), type(obstime.to_value('iso')))\n config['image']['wcs']['obstime'] = obstime.to_value('iso')\n # Doesn't quite roundtrip perfectly. But within a millisecond.\n obstime = Time(obstime.to_value('iso'), scale='tai')\n print('obstime => ',obstime)\n galsim.config.RemoveCurrent(config['image']['wcs'])\n config = galsim.config.CleanConfig(config)\n galsim.config.ProcessInput(config)\n wcs9 = galsim.config.BuildWCS(config['image'], 'wcs', config)\n wcs9a = imsim.BatoidWCSFactory(\n boresight, obstime, telescope,\n wavelength=config['bandpass'].effective_wavelength,\n camera=camera,\n temperature=280,\n pressure=default_pressure,\n H2O_pressure=1.0,\n ).getWCS(det, order=2)\n assert wcs9 == wcs9a","def doParametersOfInterest(self):\n self.modelBuilder.doVar(\"mu[0,0,1000]\") ##mu is what we want to return (in string) name[starting_value,min,max] \n self.modelBuilder.doSet(\"POI\",\"mu\")\n self.modelBuilder.factory_('expr::vbfH_s_func(\"@0-sqrt(@0)\", mu)')\n self.modelBuilder.factory_( 'expr::vbfH_b_func(\"1-sqrt(@0)\", mu)')\n self.modelBuilder.factory_( 'expr::vbfH_sbi_func(\"sqrt(@0)\", mu)')","def initializeParameters(self):\n \n # 0 - Output Workspace\n # 1 - Output Feature Name\n # 2 - X coordinate (longitude)\n # 3 - Y coordinate (latitude)\n # 4 - Z coordinate (elevation)\n # 5 - Observer offset (OFFSETA)\n # 6 - Terrain offset (OFFSETB)\n # 7 - Near distance (RADIUS1)\n # 8 - Far distance (RADIUS2\n # 9 - Left Azimuth (AZIMUTH1)\n # 10 - Right Azimuth (AZIMUTH2)\n # 11 - Top vertical angle (VERT1)\n # 12 - Bottom vertical angle (VERT2)\n # 13 - Spatial Reference\n # 14 - output point features\n \n \n #GETTING ERROR HERE, DOESN'T VALIDATE CORRECTLY, BUG?????\n #---------------------------\n #Testing get_ParameterInfo\n #---------------------------\n #initializeParameters Execution Error: Runtime error <type 'exceptions.AttributeError'>: 'NoneType' object has no attribute 'Type'\n #---------------------------\n #OK \n #---------------------------\n #\n #\n #self.params[0].Filter.Type = \"Workspace\"\n #self.params[0].Filter.List = [\"FileSystem\", \"LocalDatabase\", \"RemoteDatabase\"]\n #\n\n \n self.params[5].Value = 2.0 # OFFSETA\n self.params[6].Value = 0.0 # OFFSETB\n \n self.params[7].Value = 0.0 # RADIUS1\n self.params[8].Value = 1000.0 # RADIUS2\n \n self.params[9].Filter.Type = \"Range\" \n self.params[9].Filter.List = [0.0,360.0]\n self.params[9].Value = 0.0 # AZIMUTH1\n \n self.params[10].Filter.Type = \"Range\"\n self.params[10].Filter.List = [0.0,360.0]\n self.params[10].Value = 360.0 # AZIMUTH2\n \n self.params[11].Filter.Type = \"Range\"\n self.params[11].Filter.List = [-90.0,90.0]\n self.params[11].Value = 90.0 # VERT1\n \n self.params[12].Filter.Type = \"Range\"\n self.params[12].Filter.List = [-90.0,90.0]\n self.params[12].Value = -90.0 # VERT2\n\n # setting output parameter schema\n self.params[14].Schema.ExtentRule = \"Union\"\n self.params[14].Schema.FeatureTypeRule = \"AsSpecified\"\n self.params[14].Schema.FeatureType = \"Simple\"\n self.params[14].Schema.GeometryTypeRule = \"AsSpecified\"\n self.params[14].Schema.GeometryType = \"Point\"\n self.params[14].Schema.FieldsRule = \"All\" \n #spot_field = self.makeField(\"SPOT\", \"DOUBLE\", \"8\", \"4\", \"12\")\n offseta_field = self.makeField(\"OFFSETA\", \"DOUBLE\", \"8\", \"4\", \"12\")\n offsetb_field = self.makeField(\"OFFSETB\", \"DOUBLE\", \"8\", \"4\", \"12\")\n vert1_field = self.makeField(\"VERT1\", \"DOUBLE\", \"8\", \"4\", \"12\")\n vert2_field = self.makeField(\"VERT2\", \"DOUBLE\", \"8\", \"4\", \"12\")\n azimuth1_field = self.makeField(\"AZIMUTH1\", \"DOUBLE\", \"8\", \"4\", \"12\")\n azimuth2_field = self.makeField(\"AZIMUTH2\", \"DOUBLE\", \"8\", \"4\", \"12\")\n radius1_field = self.makeField(\"RADIUS1\", \"DOUBLE\", \"8\", \"4\", \"12\")\n radius2_field = self.makeField(\"RADIUS2\", \"DOUBLE\", \"8\", \"4\", \"12\")\n field_list = [offseta_field,offsetb_field,vert1_field,vert2_field,azimuth1_field,azimuth2_field,radius1_field,radius2_field]\n self.params[14].Schema.AdditionalFields = field_list\n\n return","def doParametersOfInterest(self):\n self.modelBuilder.doVar(\"mu[1,0,100]\") ##mu is what we want to return (in string) name[starting_value,min,max] \n self.modelBuilder.doSet(\"POI\",\"mu\")\n self.modelBuilder.factory_('expr::ggH_s_func(\"@0-sqrt(@0)\", mu)')\n self.modelBuilder.factory_( 'expr::ggH_b_func(\"1-sqrt(@0)\", mu)')\n self.modelBuilder.factory_( 'expr::ggH_sbi_func(\"sqrt(@0)\", mu)')","def config_params0(data,parameter):\n model = []\n #Range of value of p\n acf = sm.graphics.tsa.acf(data.diff().dropna())\n for i in range(len(acf)):\n acf[i] = abs(acf[i]*10)\n if (ceil(acf[i])) <= 2:\n p = range(ceil(acf[i])-1,ceil(acf[i])+2)\n break\n\n #range of value of q\n pacf = sm.graphics.tsa.pacf(data.diff().dropna())\n for i in range(len(pacf)):\n pacf[i] = abs(pacf[i]*10)\n if (ceil(pacf[i])) <= 2:\n q = range(ceil(pacf[i])-1,ceil(pacf[i])+2)\n break\n\n\t# define config lists\n p_params = p\n d_params = parameter['d']\n q_params = q\n m_params = parameter['m']\n #P_params = p\n #D_params = [0, 1]\n #Q_params = q\n \n pdq_m = list(itertools.product(p_params, d_params, q_params,m_params)) #Generate all different combinations of p, q and q triplets\n params = [[(x[0], x[1], x[2]),(x[0], x[1], x[2], x[3])] for x in pdq_m]\n return params","def split_inputs(self):\n\n lca = self.lca\n inputs = self.inputs\n\n inputs_dict = {} # Only store exchanges with uncertainty\n\n # Keep track of which tech_params and bio_params are already included to the analysis\n # Needed to avoid running sa indices computations twice for the same tech or bio params. \n # Initialize with parameterized exchanges\n if self.parameters != None and self.ParametersModel != None:\n indices_tech_all = self.parameters_dict['tech_params_where']\n indices_bio_all = self.parameters_dict['bio_params_where']\n else:\n indices_tech_all = np.array([], dtype=int)\n indices_bio_all = np.array([], dtype=int)\n\n for input_ in inputs:\n\n if input_ == 'biosphere':\n continue\n\n inputs_dict[input_] = {}\n\n indices_tech = np.array([], dtype=int)\n indices_bio = np.array([], dtype=int)\n\n if input_ == 'technosphere':\n indices_tech = np.where(lca.tech_params['uncertainty_type']!=0)[0]\n if 'biosphere' in inputs:\n indices_bio = np.where(lca.bio_params['uncertainty_type']!=0)[0]\n\n elif input_ == 'demand_exc':\n # Select all products that pertain to activities in the given demand vector\n for act_index in np.nonzero(lca.demand_array)[0]:\n mask_tech = np.all([lca.tech_params['uncertainty_type']!=0, lca.tech_params['col']==act_index], axis=0)\n indices_tech = np.concatenate([indices_tech, np.where(mask_tech)[0]])\n if 'biosphere' in inputs:\n mask_bio = np.all([lca.bio_params['uncertainty_type']!=0, lca.bio_params['col']==act_index], axis=0)\n indices_bio = np.concatenate([indices_bio, np.where(mask_bio)[0]])\n\n elif input_ in self.databases:\n # Select all products and flows that are linked to the given database\n # Indices corresponding to exchanges in the tech_params depending on the given database\n db_act_indices_tech = [val for key,val in lca.activity_dict.items() if key[0]==input_]\n if len(db_act_indices_tech) > 0:\n db_act_index_min_tech = db_act_indices_tech[0]\n db_act_index_max_tech = db_act_indices_tech[-1]\n mask = lambda i : np.all( [lca.tech_params['uncertainty_type']!=0, \n lca.tech_params['col']==i,\n lca.tech_params['amount']!=0], axis=0 )\n indices_tech = [ np.where( mask(i) ) [0] for i in range(db_act_index_min_tech, db_act_index_max_tech+1) ]\n indices_tech = np.concatenate(indices_tech)\n\n # Indices corresponding to flows in the biosphere params depending on the given database\n if 'biosphere' in inputs:\n mask = lambda j : np.all( [lca.bio_params['uncertainty_type']!=0, lca.bio_params['col']==j], axis=0 )\n indices_bio = [ np.where(mask(j))[0] for j in range(db_act_index_min_tech, db_act_index_max_tech+1) ]\n indices_bio = np.concatenate(indices_bio)\n\n indices_tech = np.sort(indices_tech)\n indices_bio = np.sort(indices_bio)\n\n # Do not add indices_tech that are already in the indices_tech_all\n indices_tech_same = np.intersect1d(indices_tech, indices_tech_all)\n pos_tech = np.array([ np.where(indices_tech==s)[0] for s in indices_tech_same ]).flatten()\n indices_tech = np.delete(indices_tech, pos_tech)\n np.append(indices_tech_all, indices_tech)\n\n # Do not add indices_bio that are already in the indices_bio_all\n indices_bio_same = np.intersect1d(indices_bio, indices_bio_all)\n pos_bio = np.array([ np.where(indices_bio==s)[0] for s in indices_bio_same ]).flatten()\n indices_bio = np.delete(indices_bio, pos_bio)\n np.append(indices_bio_all, indices_bio)\n \n inputs_dict[input_]['tech_params'] = lca.tech_params[indices_tech] #TODO maybe remove later, indices should be sufficient\n inputs_dict[input_]['tech_params_where'] = indices_tech\n inputs_dict[input_]['tech_n_params'] = len(indices_tech) #TODO remove later\n\n inputs_dict[input_]['bio_params'] = lca.bio_params[indices_bio] #TODO maybe remove later\n inputs_dict[input_]['bio_params_where'] = indices_bio\n inputs_dict[input_]['bio_n_params'] = len(indices_bio)\n\n\n self.indices_tech_all = indices_tech_all #TODO remove later\n self.indices_bio_all = indices_bio_all\n self.inputs_dict = inputs_dict","def __init__(self, encut, name=\"scf_settings\"):\n InputParameters.__init__(self, name=name)\n self.update_electronic_settings(\"ENCUT\", encut)","def config(language):\n global k # Set The window size\n global ignore_U_activated # Set True to ignore the training data with Unknown senseid\n global vector_0_1 # Set True to convert vectors to \"binary form\" e.g.: [0,3,0,4]->[0,1,0,1]\n global remove_punctuations_activated # Set True to ignore all the punctuation tokens\n global lowercase_activated # Set True to convert all the tokens to lowercase\n global stemming_activated # Set True to do stemming on all the tokens\n global remove_stop_words_activated # Set True to ignore stop words\n global expand_synset_activated # Set True to involve synsets, hypernyms, hypornyms in the model\n global extract_4c_feature_activated # Set True to involve feature introduced in 4c in the requirement\n global extract_chi_square_activated # Set True to involve chi-square feature in 4d in the requirement\n global extract_pmi_activated # Set True to involve PMI in 4d in the requirement\n\n if language.__eq__(\"English\"):\n k = 13\n ignore_U_activated = True\n vector_0_1 = False\n remove_punctuations_activated = True\n lowercase_activated = True\n stemming_activated = True\n remove_stop_words_activated = True\n expand_synset_activated = False\n extract_4c_feature_activated = True\n extract_chi_square_activated = False\n extract_pmi_activated = False\n\n elif language.__eq__(\"Spanish\"):\n k = 13\n ignore_U_activated = True\n vector_0_1 = False\n remove_punctuations_activated = True\n lowercase_activated = True\n stemming_activated = True\n remove_stop_words_activated = True\n expand_synset_activated = False # not applicable to Spanish, set to False\n extract_4c_feature_activated = True\n extract_chi_square_activated = False\n extract_pmi_activated = False\n\n elif language.__eq__(\"Catalan\"):\n k = 13\n ignore_U_activated = True\n vector_0_1 = True\n remove_punctuations_activated = True\n lowercase_activated = True\n stemming_activated = False # not applicable to Catalan, set to False\n remove_stop_words_activated = False # not applicable to Catalan, set to False\n expand_synset_activated = False # not applicable to Catalan, set to False\n extract_4c_feature_activated = True\n extract_chi_square_activated = False\n extract_pmi_activated = True","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 construct_model():\n import lbann\n\n # Layer graph\n input = lbann.Input(target_mode='N/A', name='inp_data')\n # data is 64*64*4 images + 15 scalar + 5 param\n #inp_slice = lbann.Slice(input, axis=0, slice_points=\"0 16399 16404\",name='inp_slice')\n inp_slice = lbann.Slice(input, axis=0, slice_points=str_list([0,args.ydim,args.ydim+5]),name='inp_slice')\n gt_y = lbann.Identity(inp_slice,name='gt_y')\n gt_x = lbann.Identity(inp_slice, name='gt_x') #param not used\n\n zero = lbann.Constant(value=0.0,num_neurons='1',name='zero')\n one = lbann.Constant(value=1.0,num_neurons='1',name='one')\n\n z_dim = 20 #Latent space dim\n\n z = lbann.Gaussian(mean=0.0,stdev=1.0, neuron_dims=\"20\")\n model = macc_models.MACCWAE(args.zdim,args.ydim,cf=args.mcf,use_CNN=args.useCNN)\n d1_real, d1_fake, d_adv, pred_y = model(z,gt_y)\n\n d1_real_bce = lbann.SigmoidBinaryCrossEntropy([d1_real,one],name='d1_real_bce')\n d1_fake_bce = lbann.SigmoidBinaryCrossEntropy([d1_fake,zero],name='d1_fake_bce')\n d_adv_bce = lbann.SigmoidBinaryCrossEntropy([d_adv,one],name='d_adv_bce')\n img_loss = lbann.MeanSquaredError([pred_y,gt_y])\n rec_error = lbann.L2Norm2(lbann.WeightedSum([pred_y,gt_y], scaling_factors=\"1 -1\"))\n\n layers = list(lbann.traverse_layer_graph(input))\n # Setup objective function\n weights = set()\n src_layers = []\n dst_layers = []\n for l in layers:\n if(l.weights and \"disc0\" in l.name and \"instance1\" in l.name):\n src_layers.append(l.name)\n #freeze weights in disc2\n if(l.weights and \"disc1\" in l.name):\n dst_layers.append(l.name)\n for idx in range(len(l.weights)):\n l.weights[idx].optimizer = lbann.NoOptimizer()\n weights.update(l.weights)\n l2_reg = lbann.L2WeightRegularization(weights=weights, scale=1e-4)\n d_adv_bce = lbann.LayerTerm(d_adv_bce,scale=0.01)\n obj = lbann.ObjectiveFunction([d1_real_bce,d1_fake_bce,d_adv_bce,img_loss,rec_error,l2_reg])\n # Initialize check metric callback\n metrics = [lbann.Metric(img_loss, name='recon_error')]\n #pred_y = macc_models.MACCWAE.pred_y_name\n callbacks = [lbann.CallbackPrint(),\n lbann.CallbackTimer(),\n lbann.CallbackSaveModel(dir=args.dump_models),\n lbann.CallbackReplaceWeights(source_layers=list2str(src_layers),\n destination_layers=list2str(dst_layers),\n batch_interval=2)]\n\n if(args.ltfb_batch_interval > 0) :\n callbacks.append(lbann.CallbackLTFB(batch_interval=args.ltfb_batch_interval,metric='recon_error',\n low_score_wins=True,\n exchange_hyperparameters=True))\n\n # Construct model\n return lbann.Model(args.num_epochs,\n serialize_io=True,\n weights=weights,\n layers=layers,\n metrics=metrics,\n objective_function=obj,\n callbacks=callbacks)","def doParametersOfInterest(self):\n\n self.modelBuilder.doVar(\"Rdy[1.,0.0,10.0]\");\n self.modelBuilder.doVar(\"Rqcd[1,0.0,10.0]\");\n self.modelBuilder.doSet(\"POI\",\"Rdy,Rqcd\")","def create_grism_config(conffile=\"\",\n pupil=None,\n module=None,\n author=\"STScI\",\n history=\"\",\n outname=\"\"):\n if not history:\n history = \"Created from {0:s}\".format(conffile)\n\n # if pupil is none get from filename like NIRCAM_modB_R.conf\n if pupil is None:\n pupil = \"GRISM\" + conffile.split(\".\")[0][-1]\n # if module is none get from filename\n if module is None:\n module = conffile.split(\".\")[0][-3]\n print(\"Pupil is {}\".format(pupil))\n\n ref_kw = common_reference_file_keywords(reftype=\"specwcs\",\n title=\"NIRCAM Grism Parameters\",\n description=\"{0:s} dispersion models\".format(pupil),\n exp_type=\"NRC_GRISM\",\n author=author,\n model_type=\"NIRCAMGrismModel\",\n module=module,\n pupil=pupil,\n filename=outname,\n )\n\n\n # get all the key-value pairs from the input file\n conf = dict_from_file(conffile)\n beamdict = split_order_info(conf)\n # beam = re.compile('^(?:[+\\-]){0,1}[a-zA-Z0-9]{0,1}$') # match beam only\n # read in the sensitivity tables to save their content\n # they currently have names like this: NIRCam.A.1st.sensitivity.fits\n # translated as inst.beam/order.param\n temp = dict()\n etoken = re.compile(\"^[a-zA-Z]*_(?:[+\\-]){1,1}[1,2]{1,1}\") # find beam key\n for b, bdict in beamdict.items():\n temp[b] = dict()\n # for key in bdict:\n # if 'SENSITIVITY' in key:\n # print(\"Reading sensitivity from: {0:s}\".format(bdict[key]))\n # sdata = read_sensitivity_file(bdict[key])\n # skeys = sdata.keys()\n # for k in skeys:\n # temp[b][key+\"_\"+k] = sdata[k]\n\n # add the new beam information to beamdict and remove spurious beam info\n for k in temp:\n for kk in temp[k]:\n if etoken.match(kk):\n kk = kk.replace(\"_{}\".format(k), \"\")\n beamdict[k][kk] = temp[k][kk]\n\n # add min and max mag info if not provided\n # also make beam coeff lists\n # wx are the wedge offsets for the filters\n\n # for k, bdict in beamdict.items():\n # if isinstance(bdict, dict):\n # keys = bdict.keys()\n # if \"MMAG_EXTRACT\" not in keys:\n # beamdict[k][\"MMAG_EXTRACT\"] = 99.0\n # if maxmag not in keys:\n # beamdict[k][maxmag] = 0.0\n # if \"wx\" not in keys:\n # beamdict[k]['wx'] = 0.0\n # if \"wy\" not in keys:\n # beamdict[k]['wy'] = 0.0\n # add the model for transforms\n\n\n\n # for NIRCAM, the R and C grism coefficients contain zeros where\n # the dispersion is in the opposite direction. Meaning, the GRISMR,\n # which disperses along ROWS has coefficients of zero in the y models\n # and vice versa.\n #\n # There are separate reference files for each grism. Depending on the grism\n # dispersion direction you either want to use the dx from source center or\n # the dy from source center in the inverse dispersion relationship which is\n # used to calculate the t value needed to calculate the wavelength at that pixel.\n #\n # The model creation here takes all of this into account by looking at the GRISM[R/C]\n # the file is used for and creating a reference model with the appropriate dispersion\n # direction in use. This eliminates having to decide which direction to calculate\n # the dispersion from given the input x,y pixel in the dispersed image.\n orders = beamdict.keys()\n\n # dispersion models valid per order and direction saved to reference file\n # Forward\n invdispl = []\n invdispx = []\n invdispy = []\n # Backward\n displ = []\n dispx = []\n dispy = []\n\n for order in orders:\n # convert the displ wavelengths to microns if the input file is still in angstroms\n l0 = beamdict[order]['DISPL'][0] / 10000.\n l1 = beamdict[order]['DISPL'][1] / 10000.\n\n # create polynomials using the coefficients of each order\n\n # This holds the wavelength lookup coeffs\n # This model is INVDISPL for backward and returns t\n # This model should be DISPL for forward and returns wavelength\n if l1 == 0:\n lmodel = Polynomial1D(1, c0=0, c1=0)\n else:\n lmodel = Polynomial1D(1, c0=-l0/l1, c1=1./l1)\n invdispl.append(lmodel)\n lmodel = Polynomial1D(1, c0=l0, c1=l1)\n displ.append(lmodel)\n\n # This holds the x coefficients, for the R grism this model is the\n # the INVDISPX returning t, for the C grism this model is the DISPX\n x0, x1 = beamdict[order]['DISPX']\n xmodel = Polynomial1D(1, c0=x0, c1=x1)\n dispx.append(xmodel)\n if x1 == 0:\n xmodel = Polynomial1D(1, c0=0, c1=0)\n else:\n xmodel = Polynomial1D(1, c0=-x0/x1, c1=1./x1)\n invdispx.append(xmodel)\n\n # This holds the y coefficients, for the C grism, this model is\n # the INVDISPY, returning t, for the R grism, this model is the DISPY\n y0, y1 = beamdict[order]['DISPY']\n ymodel = Polynomial1D(1, c0=y0, c1=y1)\n dispy.append(ymodel)\n if y1 == 0:\n ymodel = Polynomial1D(1, c0=0, c1=0)\n else:\n ymodel = Polynomial1D(1, c0=-y0/y1, c1=1./y1)\n invdispy.append(ymodel)\n\n # change the orders into translatable integers\n # so that we can look up the order with the proper index\n oo = [int(o) for o in beamdict]\n\n ref = NIRCAMGrismModel()\n ref.meta.update(ref_kw)\n # This reference file is good for NRC_GRISM and TSGRISM modes\n ref.meta.exposure.p_exptype = \"NRC_GRISM|NRC_TSGRISM\"\n ref.meta.input_units = u.micron\n ref.meta.output_units = u.micron\n ref.displ = displ\n ref.dispx = dispx\n ref.dispy = dispy\n ref.invdispx = invdispx\n ref.invdispy = invdispy\n ref.invdispl = invdispl\n ref.orders = oo\n entry = HistoryEntry({'description': history, 'time': datetime.datetime.utcnow()})\n sdict = Software({'name': 'nircam_reftools.py',\n 'author': author,\n 'homepage': 'https://github.com/spacetelescope/jwreftools',\n 'version': '0.7.1'})\n entry['sofware'] = sdict\n ref.history['entries'] = [entry]\n ref.to_asdf(outname)\n ref.validate()","def get_param_scenario3():\n nb_carre_x = 42\n nb_carre_y = 45\n largeur_x = 0.825\n largeur_y = 0.645\n z1 = np.array([[11], [32], [17.26]])\n z2 = np.array([[12], [32], [18.43]])\n z3 = np.array([[13], [32], [23.2]])\n z4 = np.array([[15], [31], [23.4]])\n z5 = np.array([[16], [30], [23.29]])\n z6 = np.array([[18], [29], [22.28]])\n z7 = np.array([[19], [28], [35.79]])\n z8 = np.array([[20], [27], [36.87]])\n z9 = np.array([[21], [26], [33.92]])\n z10 = np.array([[23], [24], [38.11]])\n z11 = np.array([[24], [23], [37.76]])\n z12 = np.array([[25], [22], [45.6]])\n z13 = np.array([[26], [20], [56.4]])\n z14 = np.array([[27], [18], [55.2]])\n z15 = np.array([[28], [16], [57.53]])\n z16 = np.array([[29], [14], [58.64]])\n z17 = np.array([[30], [13], [66.04]])\n z18 = np.array([[30], [11], [70.02]])\n z19 = np.array([[31], [9], [69.64]])\n z20 = np.array([[31], [6], [68.82]])\n\n meas = [z1, z2, z3, z4, z5, z6, z7, z8, z9, z10, z11, z12, z13, z14, z15, z16, z17, z18, z19, z20]\n\n z = [np.array([[x[0][0] * largeur_x / nb_carre_x], [x[1][0] * largeur_y / nb_carre_y], [-x[2][0] * np.pi / 180]])\n for x in meas]\n\n # We take a measure every second\n thymio_speed_to_mms = 0.4753\n Ts = 1\n\n Thymio_speed_left = [73, 92, 94, 102, 100, 89, 92, 103, 97, 100, 100, 104, 95, 97, 103, 101, 100, 101, 92, 98]\n Thymio_speed_right = [67, 96, 102, 89, 105, -45, 102, 101, 104, 71, 100, 93, 94, 105, 96, -45, 95, 103, 88, 94]\n delta_sr_test = [x * Ts / thymio_speed_to_mms / 1000 for x in Thymio_speed_right]\n delta_sl_test = [x * Ts / thymio_speed_to_mms / 1000 for x in Thymio_speed_left]\n\n return delta_sr_test, delta_sl_test, z","def summary(self):\n if self.model_type == 2:\n if self.std is None:\n print(\n dedent(\n f\"\"\"\\\n Oaxaca-Blinder Two-fold Effects\n Unexplained Effect: {self.params[0]:.5f}\n Explained Effect: {self.params[1]:.5f}\n Gap: {self.params[2]:.5f}\"\"\"\n )\n )\n else:\n print(\n dedent(\n \"\"\"\\\n Oaxaca-Blinder Two-fold Effects\n Unexplained Effect: {:.5f}\n Unexplained Standard Error: {:.5f}\n Explained Effect: {:.5f}\n Explained Standard Error: {:.5f}\n Gap: {:.5f}\"\"\".format(\n self.params[0],\n self.std[0],\n self.params[1],\n self.std[1],\n self.params[2],\n )\n )\n )\n if self.model_type == 3:\n if self.std is None:\n print(\n dedent(\n f\"\"\"\\\n Oaxaca-Blinder Three-fold Effects\n Endowment Effect: {self.params[0]:.5f}\n Coefficient Effect: {self.params[1]:.5f}\n Interaction Effect: {self.params[2]:.5f}\n Gap: {self.params[3]:.5f}\"\"\"\n )\n )\n else:\n print(\n dedent(\n f\"\"\"\\\n Oaxaca-Blinder Three-fold Effects\n Endowment Effect: {self.params[0]:.5f}\n Endowment Standard Error: {self.std[0]:.5f}\n Coefficient Effect: {self.params[1]:.5f}\n Coefficient Standard Error: {self.std[1]:.5f}\n Interaction Effect: {self.params[2]:.5f}\n Interaction Standard Error: {self.std[2]:.5f}\n Gap: {self.params[3]:.5f}\"\"\"\n )\n )","def getdefflags(config_nm):\n if config_nm is 'train':\n user_params = user_params_train\n elif config_nm is 'eval':\n user_params = user_params_eval\n elif config_nm is 'tfrecorder':\n user_params = user_params_recorder\n else:\n print('Unrecognized configuration name : %s, exiting ....' % config_nm)\n exit(-1)\n\n\n return mandatory_params+user_params","def prepare_experiment(assumptions):\n print(\"\\nGenerate species parameters\")\n np.random.seed(assumptions['seed']) \n params = MakeParams(assumptions) \n if assumptions[\"selected_function\"] == \"f5_invader_suppression\":\n print(\"\\nDraw invader feature\")\n params = create_invader(params, assumptions)\n print(params[\"c\"])\n \n print(\"\\nDraw per-capita function and cost\")\n f1_species_smooth, f1_species_rugged, f2_species_smooth, f2_species_rugged = draw_species_function(assumptions)\n params.update({\"f1_species_smooth\": f1_species_smooth, \"f1_species_rugged\": f1_species_rugged, \"f2_species_smooth\": f2_species_smooth, \"f2_species_rugged\": f2_species_rugged})\n gi = draw_species_cost(f1_species_smooth, assumptions)\n params.update({\"g\": gi})\n \n print(\"\\nConstruct plate\")\n np.random.seed(assumptions['seed']) \n plate = make_plate(assumptions,params)\n \n print(\"\\nAdd community function to plate\")\n plate = add_community_function(plate, assumptions, params)\n \n if not pd.isnull(assumptions[\"overwrite_plate\"]) :\n print(\"\\nUpdating the initial plate composition by overwrite_plate\")\n plate = overwrite_plate(plate, assumptions)\n \n print(\"\\nPrepare Protocol\")\n #Extract Protocol from protocol database\n algorithms = make_algorithms(assumptions)\n params_algorithm = algorithms[algorithms['algorithm_name'] == assumptions['protocol']]\n \n #Params_simulation by default contains all assumptions not stored in params.\n params_simulation = dict((k, assumptions[k]) for k in assumptions.keys() if k not in params.keys())\n \n return params, params_simulation , params_algorithm, plate","def process_configuration(self):\n print \"I \",self.I","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 parse_model_description(model_description: str) -> ModelDescription:\n root = ET.fromstring(model_description)\n\n defaults = _get_attribute_default_values()\n\n # mandatory p.32\n fmi_version = root.get(\"fmiVersion\")\n model_name = root.get(\"modelName\")\n guid = root.get(\"guid\")\n # optional\n description = root.get(\"description\", default=\"\")\n author = root.get(\"author\", default=\"\")\n copyright = root.get(\"copyright\", default=\"\")\n version = root.get(\"version\", default=\"\")\n license = root.get(\"license\", default=\"\")\n generation_tool = root.get(\"generationTool\", default=\"\")\n generation_date_and_time = root.get(\"generationDateAndTime\", default=\"\")\n variable_naming_convention = root.get(\"variableNamingConvention\", default=\"flat\")\n numberOfEventIndicators = root.get(\"numberOfEventIndicators\", default=0)\n\n model_variables = []\n\n \"\"\" Iterate over model variables:\n <ScalarVariable name=\"real_a\" valueReference=\"0\" variability=\"continuous\" causality=\"input\">\n <Real start=\"0.0\" />\n </ScalarVariable>\n \"\"\"\n for scalarVariable in root.iter(\"ScalarVariable\"):\n\n causality = scalarVariable.get(\"causality\", default=\"local\")\n variability = scalarVariable.get(\"variability\", default=\"continuous\")\n\n initial = scalarVariable.get(\"initial\", default=None)\n # defaults of initial depend on causality and variablilty\n # the combinations lead to 5 different cases denoted A-E on p.50\n if initial is None:\n initial, _ = get_intitial_choices_and_default(causality, variability)\n\n var = list(scalarVariable)[0]\n start = var.get(\"start\", default=None)\n dataType = var.tag\n\n model_variables.append(\n ScalarVariable(\n name=scalarVariable.get(\"name\"),\n valueReference=scalarVariable.get(\"valueReference\"),\n variability=variability,\n causality=causality,\n description=scalarVariable.get(\"description\", default=\"\"),\n initial=initial,\n start=start,\n dataType=dataType,\n )\n )\n\n log_categories = []\n for category in root.iter(\"Category\"):\n log_categories.append(category.get(\"name\"))\n\n model_structure = []\n\n # cosimulation\n cosim_element = root.find(\"CoSimulation\")\n\n modelIdentifier = cosim_element.get(\"modelIdentifier\")\n needsExecutionTool = cosim_element.get(\n \"needsExecutionTool\", default=defaults[\"needsExecutionTool\"]\n )\n canHandleVariableCommunicationStepSize = cosim_element.get(\n \"canHandleVariableCommunicationStepSize\",\n default=defaults[\"canHandleVariableCommunicationStepSize\"],\n )\n canInterpolateInputs = cosim_element.get(\n \"canInterpolateInputs\", default=defaults[\"canInterpolateInputs\"]\n )\n maxOutputDerivativeOrder = cosim_element.get(\n \"maxOutputDerivativeOrder\", default=defaults[\"maxOutputDerivativeOrder\"]\n )\n canRunAsynchronuously = cosim_element.get(\n \"canRunAsynchronuously\", default=defaults[\"canRunAsynchronuously\"]\n )\n canBeInstantiatedOnlyOncePerProcess = cosim_element.get(\n \"canBeInstantiatedOnlyOncePerProcess\",\n default=defaults[\"canBeInstantiatedOnlyOncePerProcess\"],\n )\n canNotUseMemoryManagementFunctions = cosim_element.get(\n \"canNotUseMemoryManagementFunctions\",\n default=defaults[\"canNotUseMemoryManagementFunctions\"],\n )\n canGetAndSetFMUstate = cosim_element.get(\n \"canGetAndSetFMUstate\", default=defaults[\"canGetAndSetFMUstate\"]\n )\n canSerializeFMUstate = cosim_element.get(\n \"canSerializeFMUstate\", default=defaults[\"canSerializeFMUstate\"]\n )\n providesDirectionalDerivative = cosim_element.get(\n \"providesDirectionalDerivative\",\n default=defaults[\"providesDirectionalDerivative\"],\n )\n\n def xs_boolean(s):\n if s is None:\n return None\n if s in {\"false\", \"0\"}:\n return False\n elif s in {\"true\", \"1\"}:\n return True\n else:\n raise ValueError(f\"Unable to convert {s} to xsd boolean\")\n\n def xs_normalized_string(s: str):\n if s is None:\n return None\n if not s.isprintable():\n raise ValueError(r\"normalized string can not contain: \\n, \\t or \\r\")\n return s\n\n def xs_unsigned_int(s: str):\n if s is None:\n return None\n value = int(s)\n if value > 4294967295:\n raise ValueError(\"xs:unsingedInt cannot exceed the value 4294967295\")\n return value\n\n cosimulation = CoSimulation(\n modelIdentifier=modelIdentifier,\n needsExecutionTool=xs_boolean(needsExecutionTool),\n canHandleVariableCommunicationStepSize=xs_boolean(\n canHandleVariableCommunicationStepSize\n ),\n canInterpolateInputs=xs_boolean(canInterpolateInputs),\n maxOutputDerivativeOrder=xs_unsigned_int(maxOutputDerivativeOrder),\n canRunAsynchronuously=xs_boolean(canRunAsynchronuously),\n canBeInstantiatedOnlyOncePerProcess=xs_boolean(\n canBeInstantiatedOnlyOncePerProcess\n ),\n canNotUseMemoryManagementFunctions=xs_boolean(\n canNotUseMemoryManagementFunctions\n ),\n canGetAndSetFMUstate=xs_boolean(canGetAndSetFMUstate),\n canSerializeFMUstate=xs_boolean(canSerializeFMUstate),\n providesDirectionalDerivative=xs_boolean(providesDirectionalDerivative),\n )\n\n return ModelDescription(\n fmiVersion=fmi_version,\n modelName=model_name,\n guid=guid,\n author=author,\n description=description,\n version=version,\n copyright=copyright,\n logCategories=log_categories,\n license=license,\n generationTool=generation_tool,\n generationDateAndTime=generation_date_and_time,\n variableNamingConvention=variable_naming_convention,\n numberOfEventIndicators=numberOfEventIndicators,\n CoSimulation=cosimulation,\n modelVariables=model_variables,\n modelStructure=model_structure,\n )","def get_model_config(model_name, args):\n if model_name == 'WaveGlow':\n model_config = dict(\n n_mel_channels=args.n_mel_channels,\n n_flows=args.flows,\n n_group=args.groups,\n n_early_every=args.early_every,\n n_early_size=args.early_size,\n WN_config=dict(\n n_layers=args.wn_layers,\n kernel_size=args.wn_kernel_size,\n n_channels=args.wn_channels\n )\n )\n return model_config\n elif model_name == 'FastPitch':\n model_config = dict(\n # io\n n_mel_channels=args.n_mel_channels,\n # symbols\n n_symbols=len(get_symbols(args.symbol_set)),\n padding_idx=get_pad_idx(args.symbol_set),\n symbols_embedding_dim=args.symbols_embedding_dim,\n # input FFT\n in_fft_n_layers=args.in_fft_n_layers,\n in_fft_n_heads=args.in_fft_n_heads,\n in_fft_d_head=args.in_fft_d_head,\n in_fft_conv1d_kernel_size=args.in_fft_conv1d_kernel_size,\n in_fft_conv1d_filter_size=args.in_fft_conv1d_filter_size,\n in_fft_output_size=args.in_fft_output_size,\n p_in_fft_dropout=args.p_in_fft_dropout,\n p_in_fft_dropatt=args.p_in_fft_dropatt,\n p_in_fft_dropemb=args.p_in_fft_dropemb,\n # output FFT\n out_fft_n_layers=args.out_fft_n_layers,\n out_fft_n_heads=args.out_fft_n_heads,\n out_fft_d_head=args.out_fft_d_head,\n out_fft_conv1d_kernel_size=args.out_fft_conv1d_kernel_size,\n out_fft_conv1d_filter_size=args.out_fft_conv1d_filter_size,\n out_fft_output_size=args.out_fft_output_size,\n p_out_fft_dropout=args.p_out_fft_dropout,\n p_out_fft_dropatt=args.p_out_fft_dropatt,\n p_out_fft_dropemb=args.p_out_fft_dropemb,\n # duration predictor\n dur_predictor_kernel_size=args.dur_predictor_kernel_size,\n dur_predictor_filter_size=args.dur_predictor_filter_size,\n p_dur_predictor_dropout=args.p_dur_predictor_dropout,\n dur_predictor_n_layers=args.dur_predictor_n_layers,\n # pitch predictor\n pitch_predictor_kernel_size=args.pitch_predictor_kernel_size,\n pitch_predictor_filter_size=args.pitch_predictor_filter_size,\n p_pitch_predictor_dropout=args.p_pitch_predictor_dropout,\n pitch_predictor_n_layers=args.pitch_predictor_n_layers,\n # pitch conditioning\n pitch_embedding_kernel_size=args.pitch_embedding_kernel_size,\n # speakers parameters\n n_speakers=args.n_speakers,\n speaker_emb_weight=args.speaker_emb_weight,\n # energy predictor\n energy_predictor_kernel_size=args.energy_predictor_kernel_size,\n energy_predictor_filter_size=args.energy_predictor_filter_size,\n p_energy_predictor_dropout=args.p_energy_predictor_dropout,\n energy_predictor_n_layers=args.energy_predictor_n_layers,\n # energy conditioning\n energy_conditioning=args.energy_conditioning,\n energy_embedding_kernel_size=args.energy_embedding_kernel_size,\n )\n return model_config\n\n else:\n raise NotImplementedError(model_name)","def update_ci_values(filter_item, config_filename, log_level=logutil.logging.INFO):\n log.setLevel(log_level)\n # Check to see if the CI values need to be updated. If not, simply skip over all of this.\n update_ci_vals = False\n for phot_mode in ['aperture', 'segment']:\n if filter_item.configobj_pars.pars['quality control'].outpars['ci filter'][phot_mode]['lookup_ci_limits_from_table']:\n update_ci_vals = True\n if update_ci_vals:\n # read in the custom SVM param file data\n with open(config_filename) as f:\n json_data = json.load(f)\n for phot_mode in ['aperture', 'segment']:\n if filter_item.configobj_pars.pars['quality control'].outpars['ci filter'][phot_mode]['lookup_ci_limits_from_table']:\n log.info(\"NOTE: The 'lookup_ci_limits_from_table' setting in the 'quality control'>'{}' section of the parameters for filter image {} is set to 'True'. This means that any custom user-tuned values for 'ci_upper_limit' and 'ci_lower_limit' will be overwritten. To prevent this, please set 'lookup_ci_limits_from_table' to 'False' in the custom parameter file {}\".format(phot_mode, filter_item.drizzle_filename, config_filename))\n # set up inputs to ci_table.get_ci_from_file() and execute to get new CI values\n drizzled_image = filter_item.drizzle_filename\n ci_lookup_file_path = \"default_parameters/any\"\n diagnostic_mode = False\n ci_lower_limit = filter_item.configobj_pars.pars['quality control'].outpars['ci filter'][phot_mode]['ci_lower_limit']\n ci_upper_limit = filter_item.configobj_pars.pars['quality control'].outpars['ci filter'][phot_mode]['ci_upper_limit']\n ci_dict = ci_table.get_ci_from_file(drizzled_image, ci_lookup_file_path, log_level,\n diagnostic_mode=diagnostic_mode, ci_lower=ci_lower_limit,\n ci_upper=ci_upper_limit)\n log.debug(\"{} {} CI upper limit updated from {} to {}\".format(filter_item.drizzle_filename,\n phot_mode,\n ci_upper_limit,\n ci_dict[\"ci_upper_limit\"]))\n log.debug(\"{} {} CI lower limit updated from {} to {}\\n\".format(filter_item.drizzle_filename,\n phot_mode,\n ci_lower_limit,\n ci_dict[\"ci_lower_limit\"]))\n # update CI values\n json_data[drizzled_image[:-9]][\"default_values\"][\"quality control\"][\"ci filter\"][phot_mode][\"ci_lower_limit\"] = ci_dict[\"ci_lower_limit\"]\n json_data[drizzled_image[:-9]][\"default_values\"][\"quality control\"][\"ci filter\"][phot_mode][\"ci_upper_limit\"] = ci_dict[\"ci_upper_limit\"]\n\n # Write out the updated custom SVM param file data back to file.\n with open(config_filename, 'w') as f:\n json.dump(json_data, f, indent=4)\n else:\n log.debug(\"Using existing concentration index limits from parameter file\")","def __init__(self, encut, spinaxis, ldaul, Uparam, Jparam, name='DFTCL_settings'):\n ncl_settings = {\"ISPIN\": 2, \"MAGMOM\": None, \"SAXIS\": spinaxis, \"LSORBIT\": \".TRUE.\", \"LNONCOLLINEAR\": \".TRUE.\"}\n dftu_settings = {\"LDAU\": \".TRUE.\", \"LDAUU\": Uparam, \"LDATYPE\": 2, \"LDAUL\": ldaul, \"LDAUJ\": Jparam , \"LMAXMIX\": 4}\n InputParameters.__init__(self, name=name, magnetic_settings=ncl_settings, hubbard_settings=dftu_settings)\n self.update_electronic_settings(\"ENCUT\", encut)","def get_model_config(model_name, args):\n if model_name == 'Tacotron2':\n model_config = dict(\n # optimization\n mask_padding=args.mask_padding,\n # audio\n n_mel_channels=args.n_mel_channels,\n # symbols\n n_symbols=args.n_symbols,\n symbols_embedding_dim=args.symbols_embedding_dim,\n # encoder\n encoder_kernel_size=args.encoder_kernel_size,\n encoder_n_convolutions=args.encoder_n_convolutions,\n encoder_embedding_dim=args.encoder_embedding_dim,\n # attention\n attention_rnn_dim=args.attention_rnn_dim,\n attention_dim=args.attention_dim,\n # attention location\n attention_location_n_filters=args.attention_location_n_filters,\n attention_location_kernel_size=args.attention_location_kernel_size,\n # decoder\n n_frames_per_step=args.n_frames_per_step,\n decoder_rnn_dim=args.decoder_rnn_dim,\n prenet_dim=args.prenet_dim,\n max_decoder_steps=args.max_decoder_steps,\n gate_threshold=args.gate_threshold,\n p_attention_dropout=args.p_attention_dropout,\n p_decoder_dropout=args.p_decoder_dropout,\n # postnet\n postnet_embedding_dim=args.postnet_embedding_dim,\n postnet_kernel_size=args.postnet_kernel_size,\n postnet_n_convolutions=args.postnet_n_convolutions,\n decoder_no_early_stopping=args.decoder_no_early_stopping\n )\n return model_config\n elif model_name == 'WaveGlow':\n model_config = dict(\n n_mel_channels=args.n_mel_channels,\n n_flows=args.flows,\n n_group=args.groups,\n n_early_every=args.early_every,\n n_early_size=args.early_size,\n WN_config=dict(\n n_layers=args.wn_layers,\n kernel_size=args.wn_kernel_size,\n n_channels=args.wn_channels\n )\n )\n return model_config\n else:\n raise NotImplementedError(model_name)","def load(self, name=\"\"):\n\n self.constructed = True\n if name == \"\":\n name = \"/home/unai/Escritorio/MultiNetwork/model/model\"\n\n network_descriptors = {\"Generic\": GenericDescriptor, \"Decoder\": DecoderDescriptor, \"Discrete\": DiscreteDescriptor, \"Convolution\": ConvolutionDescriptor}\n\n if not os.path.isfile(name):\n print(\"Error at loading the model\")\n return None\n\n f = open(name, \"r+\")\n\n lines = f.readlines()\n\n i = 0\n while lines[i] != \"\\n\": # Each component is stored in a line\n ident, n_inp, kind, n_hidden, layers, init, act, cond_rand, taking, producing, depth, reachable, belows = lines[i][:-1].split(\"_\")\n kwargs = {}\n if int(ident[1:]) > self.last_net:\n self.last_net = int(ident[1:])\n\n self.reachable[ident] = reachable.split(\",\")\n self.comps_below[ident] = belows.split(\",\")\n\n if \"onv\" in kind: # Not working right now\n filters, sizes, layers, strides = layers.split(\"*\")\n sizes = sizes.split(\",\")\n s = np.array([[int(sz) for sz in szs.split(\"/\")] for szs in sizes])\n desc = network_descriptors[kind](int(inp), int(outp), int(n_inp), layers.split(\",\"), filters.split(\",\"), [int(x) for x in strides.split(\",\")], s, [int(x) for x in act.split(\",\")], [int(x) for x in init.split(\",\")], kwargs)\n else:\n if len(kwargs) > 0: # Not working right now\n kwargs = kwargs.split(\"-\")\n kwargs[0] = [int(x) for x in kwargs[0].split(\".\") if len(x) > 0]\n kwargs[1] = [int(x) for x in kwargs[1].split(\".\") if len(x) > 0]\n if len(cond_rand) > 0:\n cond_rand = cond_rand.split(\"-\")\n cond_rand[0] = [int(x) for x in cond_rand[0].split(\",\") if len(x) > 0]\n cond_rand[1] = [int(x) for x in cond_rand[1].split(\",\") if len(x) > 0]\n kwargs[\"conds\"] = cond_rand\n desc = network_descriptors[kind](int(taking.split(\",\")[0]), int(producing.split(\",\")[0]), int(n_inp), int(n_hidden), [int(x) for x in layers.split(\",\") if x != \"-1\"], init_functions[[int(x) for x in init.split(\",\") if x != \"-1\"]],\n act_functions[[int(x) for x in act.split(\",\") if x != \"-1\"]], **kwargs)\n\n # print(\"ident\", ident, \"n_inp\", n_inp, \"kind\", kind, \"inp\", inp, \"outp\", outp, \"layers\", layers, \"init\", init, \"act\", act, \"taking\", taking, \"producing\", producing, \"depth\", depth, \"kwargs\", kwargs)\n net = NetworkComp(desc, InOut(size=int(taking.split(\",\")[0]), data_type=taking.split(\",\")[1]), InOut(data_type=producing.split(\",\")[1], size=int(producing.split(\",\")[0])), int(depth))\n\n self.add_net(net, ident)\n i += 1\n\n i += 1\n\n while lines[i] != \"\\n\": # Inputs\n\n ident, size, kind, depth = lines[i].split(\"_\")\n\n self.inputs[ident] = ModelComponent(None, InOut(size=int(size), data_type=kind), int(depth))\n i += 1\n\n i += 1\n\n while lines[i] != \"\\n\": # Outputs\n\n ident, size, kind, depth, belows = lines[i].split(\"_\")\n\n self.outputs[ident] = ModelComponent(InOut(size=int(size), data_type=kind), None, int(depth))\n self.comps_below[ident] = belows.split(\",\")\n i += 1\n\n i += 1\n\n while i < len(lines): # Connections\n name, inp, outp, kind, size = lines[i].split(\"_\")\n\n if int(name[1:]) > self.last_con:\n self.last_con = int(name[1:])\n\n self.connections[name] = Connection(inp, outp, InOut(kind, int(size)), name)\n i += 1\n self.update_below()","def test_configuration(self):\n space = Space()\n space.register(Integer(\"yolo1\", \"uniform\", -3, 6, shape=(2,)))\n space.register(Integer(\"yolo2\", \"uniform\", -3, 6, shape=(2,)))\n space.register(Real(\"yolo3\", \"norm\", 0.9))\n space.register(Categorical(\"yolo4\", (\"asdfa\", 2)))\n\n assert space.configuration == {\n \"yolo1\": \"uniform(-3, 3, shape=(2,), discrete=True)\",\n \"yolo2\": \"uniform(-3, 3, shape=(2,), discrete=True)\",\n \"yolo3\": \"normal(0.9)\",\n \"yolo4\": \"choices(['asdfa', 2])\",\n }","def setup(self):\n\n module = [m for m in Rt.modules if m.name == self.module_name][0]\n\n # Take CPACS file from the optimisation\n cpacs_path = module.cpacs_in\n tixi = open_tixi(cpacs_path)\n self.Model = load_surrogate(tixi)\n tixi.save(cpacs_path)\n\n df = self.Model.df\n df.set_index(\"Name\", inplace=True)\n for name in df.index:\n if df.loc[name, \"type\"] == \"obj\":\n self.add_output(name)\n elif df.loc[name, \"type\"] == \"des\":\n self.add_input(name)\n\n self.xd = df.loc[[name for name in df.index if df.loc[name, \"type\"] == \"des\"]]\n self.yd = df.loc[[name for name in df.index if df.loc[name, \"type\"] == \"obj\"]]","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 input_setup(config):\n print(config.is_train)\n # Load data path\n if config.is_train:\n data, label = prepare_data(dataset=\"Train/DoF_Images (2)\")\n else:\n data, label = prepare_data(dataset=\"Test\")\n\n sub_input_sequence = []\n sub_label_sequence = []\n padding = abs(config.image_size - config.label_size) / 2 # 6\n nx = ny = 0\n\n if config.is_train:\n for i in range(len(data)):\n input_, label_ = preprocess(data[i], label[i], config.scale)\n\n if len(input_.shape) == 3:\n h, w, c = input_.shape\n else:\n h, w = input_.shape\n\n for x in range(0, h-config.image_size+1, config.stride):\n if i == 0:\n nx += 1; ny = 0\n for y in range(0, w-config.image_size+1, config.stride):\n if i == 0:\n ny += 1\n\n sub_input = input_[x:x+config.image_size, y:y+config.image_size] # [33 x 33]\n sub_label = label_[x+int(padding):x+int(padding)+config.label_size\n , y+int(padding):y+int(padding)+config.label_size] # [21 x 21]\n # print(sub_input.shape)\n # print(sub_label.shape)\n # Make channel value\n sub_input = sub_input.reshape([config.image_size, config.image_size, 3])\n sub_label = sub_label.reshape([config.label_size, config.label_size, 3])\n\n sub_input_sequence.append(sub_input)\n sub_label_sequence.append(sub_label)\n\n else:\n nx_l = []\n ny_l = []\n for i in range(len(data)):\n input_, label_ = preprocess(data[i], label[i], config.scale)\n\n if len(input_.shape) == 3:\n h, w, c = input_.shape\n else:\n h, w = input_.shape\n\n if w >= 4000 or h > 4000:\n input_ = cv2.resize(input_, dsize=(int(w/2), int(h/2)), interpolation=cv2.INTER_AREA)\n label_ = cv2.resize(label_, dsize=(int(w/2), int(h/2)), interpolation=cv2.INTER_AREA)\n w = int(w/2)\n h = int(h/2)\n\n # Numbers of sub-images in height and width of image are needed to compute merge operation.\n\n nx = ny = 0\n for x in range(0, h-config.image_size+1, config.stride):\n nx += 1; ny = 0\n for y in range(0, w-config.image_size+1, config.stride):\n ny += 1\n sub_input = input_[x:x+config.image_size, y:y+config.image_size] # [33 x 33]\n sub_label = label_[x+int(padding):x+int(padding)+config.label_size, y+int(padding):y+int(padding)+config.label_size] # [21 x 21]\n\n sub_input = sub_input.reshape([config.image_size, config.image_size, 3])\n sub_label = sub_label.reshape([config.label_size, config.label_size, 3])\n\n sub_input_sequence.append(sub_input)\n sub_label_sequence.append(sub_label)\n #print(\"nx: %d ny: %d\" % (nx, ny))\n nx_l.append(nx)\n ny_l.append(ny)\n \"\"\"\n len(sub_input_sequence) : the number of sub_input (33 x 33 x ch) in one image\n (sub_input_sequence[0]).shape : (33, 33, 1)\n \"\"\"\n # Make list to numpy array. With this transform\n arrdata = np.asarray(sub_input_sequence) # [?, 33, 33, 3]\n arrlabel = np.asarray(sub_label_sequence) # [?, 21, 21, 3]\n\n make_data(config, arrdata, arrlabel)\n print(\"make_data success\")\n if config.is_train:\n return nx, ny\n else:\n return nx_l, ny_l, len(data)","def get_input_info_from_cfg(deploy_cfg: mmengine.Config) -> Dict[str, List]:\n # The partition is not supported now. Set the id of model to 0.\n model_inputs = get_model_inputs(deploy_cfg)[0]\n input_info = model_inputs['opt_shapes']\n ir_config = get_ir_config(deploy_cfg)\n if ir_config is not None:\n input_names = ir_config.get('input_names', None)\n if input_names:\n if not isinstance(input_info, Dict):\n input_info = dict(zip(input_names, input_info))\n input_info = update_input_names(input_info, input_names)\n return input_info","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 step(self):\n\n \"\"\" First updates the variables values of the current time form the environment \"\"\"\n self.update_crispval(self.env.context)\n\n \"\"\"\n here the decision making of the agent\n to determine which activity to suggest to the patient\n i apply the creative controller to the current context\n \"\"\"\n curr_input = sample_inputs(False, 0, self.curr_interaction, self.variables_default_val, self.action_var,\n self.fuzzysets_values, self.variables_universe)\n c_out, rules_activations, is_cc_exception = self.creative_controller.computeOutput(curr_input, False)\n\n \"\"\" i obtain a number of ouput crisp values.\n i determine which one achieves the max expected output w.r.t. the a-rules \"\"\"\n best_a = None\n best_a_val = -1000\n best_a_exphapp = 5\n if self.verbose > Constants.VERBOSE_BASIC:\n print(\"rules activations\")\n for a in rules_activations:\n if rules_activations[a] > 0:\n print(str(a) + \"\\n\\t\\t\\t-> \" + str(rules_activations[a]))\n for item in c_out.items(): # for each pair <activity, crisp output>\n if self.verbose > Constants.VERBOSE_BASIC:\n print(item)\n if not item[\n 0] in self.curr_iter_suggestions: # if i didn't suggest the same activity already in the same interaction\n inputs = dict(curr_input) # I create a copy fo the dict\n inputs[item[0]] = item[1]\n assessor_id = self.actions_to_ti[item[0]]\n self.assessors[assessor_id].feed_inputs(inputs)\n is_ac_exception = False\n assout = []\n try:\n a_out, a_rules_activations, is_ac_exception = self.assessors[assessor_id].compute(verbose=False)\n assout = [a_out[ao] for ao in a_out]\n except:\n is_ac_exception = True\n traceback.print_exc()\n # todo the following assumes that every assessor controller has same eval var\n for v in self.eval_var:\n assout.append(self.variables_default_val[v])\n if len(assout) == 0:\n for v in self.eval_var:\n assout.append(self.variables_default_val[v])\n w_ta = self.weights_therapeutic_interventions[self.actions_to_ti[item[0]]]\n\n avg_credit_rules_that_suggested_action = 1.0\n nr_rules_that_suggested_action = 0\n for r in rules_activations:\n if (rules_activations[r] > 0) and (str(item[0]) in str(r)):\n avg_credit_rules_that_suggested_action = avg_credit_rules_that_suggested_action + \\\n self.rules_credits[str(r)]\n nr_rules_that_suggested_action = nr_rules_that_suggested_action + 1\n if nr_rules_that_suggested_action > 0:\n avg_credit_rules_that_suggested_action = (\n avg_credit_rules_that_suggested_action - 1.0) / nr_rules_that_suggested_action\n repetition_cost = 1.0\n a_val = (mean(assout) * w_ta * avg_credit_rules_that_suggested_action) / repetition_cost\n if (a_val > best_a_val) and (\n item[1] >= (self.variables_default_val[item[0]] + self.range_step[item[0]])):\n best_a = item\n best_a_val = a_val\n best_a_exphapp = mean(assout)\n\n \"\"\"I suggest the activity with best expected outcome and store the information to populate the interactions \n memory \"\"\"\n self.proposeActivity(best_a)\n if not best_a is None:\n if (self.verbose > Constants.VERBOSE_FALSE) and (self.verbose <= Constants.VERBOSE_BASIC):\n print(\"proposing activity\" + str(best_a) + \" which has expected feedback: \" + str(\n best_a_exphapp) + \", which weighted is \" + str(best_a_val))\n self.curr_iter_suggestions.append(best_a[0])\n self.last_suggestion = best_a\n else:\n if (self.verbose > Constants.VERBOSE_FALSE) and (self.verbose <= Constants.VERBOSE_BASIC):\n print(\"the activity proposed is \" + str(\n best_a) + \" so I don't suggest anything. I will ask a question instead\")\n self.last_suggestion = []\n self.expected_feedback = best_a_exphapp\n self.last_context = self.env.context.copy()\n self.last_rules_activations = rules_activations","def _check_model_params(self):\n enc_type = self.configs['encoder_type']\n if enc_type not in ['uni', 'bi', 'gnmt']:\n raise ValueError(\"encoder type must be one of ['uni', 'bi', 'gnmt'].\")\n\n attn = self.configs['attention']\n if attn not in ['', 'luong', 'scaled_luong', 'bahdanau', 'normed_bahdanau']:\n raise ValueError(\n \"attention must be one of \"\n \"['', 'luong', 'scaled_luong', 'bahdanau', 'normed_bahdanau'].\")\n\n num_enc_layers = self.configs['num_encoder_layers']\n num_dec_layers = self.configs['num_decoder_layers']\n if not num_enc_layers or not num_dec_layers:\n raise ValueError(\n \"num_encoder_layers and num_decoder_layers must be greater than 0.\")\n if num_enc_layers != num_dec_layers:\n self.configs['pass_hidden_state'] = False\n\n if enc_type == \"bi\" and num_enc_layers % 2 != 0:\n raise ValueError(\n \"num_encoder_layers must be even when encoder_type is %s.\" % enc_type)\n\n attn_arch = self.configs.get('attention_architecture', None)\n if attn_arch in [\"gnmt\"] and num_enc_layers < 2:\n raise ValueError(\"For gnmt attention architecture, \"\n \"num_encoder_layers: %d should be >= 2.\" %\n num_enc_layers)\n\n infer_mode = self.configs['infer_mode']\n beam_width = self.configs.get(\"beam_width\", 0)\n if infer_mode == \"beam_search\" and beam_width <= 0:\n raise ValueError(\"beam_width must be > 0 if infer_mode is `beam_search`.\")\n\n sample_temp = self.configs.get(\"sampling_temperature\", 0.0)\n if infer_mode == \"sample\" and sample_temp <= 0.0:\n raise ValueError(\n \"sampling_temperature must greater than 0.0 using sample decode.\")\n\n subword_option = self.configs['subword_option']\n if subword_option not in ['', 'bpe', 'spm']:\n raise ValueError(\"subword_option must be one of ['','bpe','spm']\")\n\n num_enc_residual_layers = 0\n num_dec_residual_layers = 0\n if self.configs['residual']:\n if num_enc_layers > 1:\n num_enc_residual_layers = num_enc_layers - 1\n if num_dec_layers > 1:\n num_dec_residual_layers = num_dec_layers - 1\n\n if enc_type == \"gnmt\":\n num_enc_residual_layers = num_enc_layers - 2\n if num_enc_layers == num_dec_layers:\n num_dec_residual_layers = num_enc_residual_layers\n\n self.configs['num_encoder_residual_layers'] = num_enc_residual_layers\n self.configs['num_decoder_residual_layers'] = num_dec_residual_layers","def model_4_parameters(num_features, num_classes, image_info):\n parameters = {}\n parameters['num_features'] = num_features\n parameters['num_classes'] = num_classes\n if image_info['key'][:5] == \"pavia\":\n parameters['C'] = 1.0\n else:\n parameters['C'] = 40.0\n \n return parameters","def test013(testDir, dirDict, pflag):\n shutil.copy(osp.join(dirDict[\"baseConfig\"], \"params_system.cfg\"), testDir)\n shutil.copy(osp.join(dirDict[\"baseConfig\"], \"params_c.cfg\"), testDir)\n shutil.copy(osp.join(dirDict[\"baseConfig\"], \"params_a.cfg\"), testDir)\n psys = osp.join(testDir, \"params_system.cfg\")\n ptrodec = osp.join(testDir, \"params_c.cfg\")\n ptrodea = osp.join(testDir, \"params_a.cfg\")\n P_s = IO.get_config(psys)\n P_s.set(\"Sim Params\", \"profileType\", \"CV\")\n P_s.set(\"Sim Params\", \"Vset\", \"3.8\")\n P_s.set(\"Sim Params\", \"tsteps\", \"25\")\n P_s.set(\"Sim Params\", \"Nvol_c\", \"2\")\n P_s.set(\"Sim Params\", \"Nvol_s\", \"2\")\n P_s.set(\"Sim Params\", \"Nvol_a\", \"2\")\n P_s.set(\"Particles\", \"cs0_c\", \"0.2\")\n P_s.set(\"Particles\", \"cs0_a\", \"0.95\")\n P_s.set(\"Electrolyte\", \"elyteModelType\", \"dilute\")\n IO.write_config_file(P_s, psys)\n P = IO.get_config(ptrodec)\n P.set(\"Particles\", \"type\", \"homog\")\n P.set(\"Particles\", \"shape\", \"sphere\")\n P.set(\"Material\", \"muRfunc\", \"LiMn2O4_ss2\")\n P.set(\"Reactions\", \"rxnType\", \"Marcus\")\n IO.write_config_file(P, ptrodec)\n P = IO.get_config(ptrodea)\n P.set(\"Particles\", \"discretization\", \"2.5e-9\")\n P.set(\"Particles\", \"shape\", \"cylinder\")\n P.set(\"Material\", \"muRfunc\", \"testIS_ss\")\n P.set(\"Reactions\", \"rxnType\", \"BV_raw\")\n IO.write_config_file(P, ptrodea)\n main.main(psys, keepArchive=False)\n shutil.move(dirDict[\"simOut\"], testDir)\n if pflag:\n corePlots(testDir, dirDict)\n elytePlots(testDir, dirDict)\n electrodePlots(testDir, dirDict, \"c\")\n cmpr.bulkpf(testDir, dirDict, \"c\")\n electrodePlots(testDir, dirDict, \"a\")\n cmpr.bulkpf(testDir, dirDict, \"a\")","def get_data_config(self):\n conf_map = {}\n\n if self.alien_alg.currentIndex() == 1:\n conf_map['alien_alg'] = '\"block_aliens\"'\n if len(self.aliens.text()) > 0:\n conf_map['aliens'] = str(self.aliens.text()).replace('\\n', '')\n if self.alien_alg.currentIndex() == 2:\n conf_map['alien_alg'] = '\"alien_file\"'\n if len(self.alien_file.text()) > 0:\n conf_map['alien_file'] = '\"' + str(self.alien_file.text()) + '\"'\n elif self.alien_alg.currentIndex() == 3:\n conf_map['alien_alg'] = '\"AutoAlien1\"'\n if len(self.AA1_size_threshold.text()) > 0:\n conf_map['AA1_size_threshold'] = str(self.AA1_size_threshold.text())\n if len(self.AA1_asym_threshold.text()) > 0:\n conf_map['AA1_asym_threshold'] = str(self.AA1_asym_threshold.text())\n if len(self.AA1_min_pts.text()) > 0:\n conf_map['AA1_min_pts'] = str(self.AA1_min_pts.text())\n if len(self.AA1_eps.text()) > 0:\n conf_map['AA1_eps'] = str(self.AA1_eps.text())\n if len(self.AA1_amp_threshold.text()) > 0:\n conf_map['AA1_amp_threshold'] = str(self.AA1_amp_threshold.text())\n if self.AA1_save_arrs.isChecked():\n conf_map['AA1_save_arrs'] = \"True\"\n if len(self.AA1_expandcleanedsigma.text()) > 0:\n conf_map['AA1_expandcleanedsigma'] = str(self.AA1_expandcleanedsigma.text())\n\n if len(self.amp_intensity.text()) > 0:\n conf_map['amp_threshold'] = str(self.amp_intensity.text())\n if len(self.binning.text()) > 0:\n conf_map['binning'] = str(self.binning.text()).replace('\\n', '')\n if len(self.center_shift.text()) > 0:\n conf_map['center_shift'] = str(self.center_shift.text()).replace('\\n', '')\n if len(self.adjust_dimensions.text()) > 0:\n conf_map['adjust_dimensions'] = str(self.adjust_dimensions.text()).replace('\\n', '')\n\n return conf_map","def generateSexConfig(outFile, configFile,\r\n paramsFile, inputModelsfile, weightImage, sexFile,\r\n matchRadius): \r\n f = open(configFile, 'w')\r\n\r\n \"\"\"\r\n sexConfig = dict(CATALOG_NAME='matched.cat', \r\n CATALOG_TYPE='ASCII_HEAD', \r\n PARAMETERS_NAME='assoc.param', \r\n DETECT_TYPE='CCD',\r\n DETECT_MINAREA='8.78733031674',\r\n DETECT_THRESH='1.25',\r\n ANALYSIS_THRESH='1.25',\r\n FILTER='Y',\r\n FILTER_NAME=SExroot+'tophat_3.0_3x3.conv',\r\n DEBLEND_NTHRESH='64',\r\n DEBLEND_MINCONT='0.0002',\r\n CLEAN='Y',\r\n CLEAN_PARAM='1.',\r\n MASK_TYPE='CORRECT',\r\n PHOT_APERTURES='14.0',\r\n PHOT_AUTOPARAMS='2.5,3.5',\r\n SATUR_LEVEL='50000.0',\r\n MAG_ZEROPOINT='31.2671969775',\r\n MAG_GAMMA='4.0',\r\n GAIN='13.4213208113',\r\n PIXEL_SCALE='0.221',\r\n SEEING_FWHM='1.060202',\r\n STARNNW_NAME=SExroot+'default.nnw',\r\n BACK_SIZE='64',\r\n BACK_FILTERSIZE='3',\r\n BACKPHOTO_TYPE='LOCAL',\r\n BACKPHOTO_THICK='102',\r\n MEMORY_OBJSTACK='15000',\r\n MEMORY_PIXSTACK='2600000',\r\n MEMORY_BUFSIZE='4600',\r\n ASSOC_NAME='input_model.coo',\r\n ASSOC_PARAMS='1,2',\r\n ASSOC_RADIUS='5.0',\r\n ASSOCSELEC_TYPE='MATCHED',\r\n ASSOC_TYPE='NEAREST',\r\n ASSOC_DATA='1,2,3,4,5',\r\n VERBOSE_TYPE='NORMAL',\r\n #WEIGHT_GAIN='Y',\r\n WEIGHT_IMAGE='/home/walter/Dropbox/Completeness/f02p01_F814W_2.swp.weight.fits',\r\n WEIGHT_TYPE='MAP_WEIGHT'\r\n )\r\n \"\"\"\r\n\r\n sexdata = open(sexFile, \"r\")\r\n \r\n sexdict = {l.split()[0] : l.split()[1] for l in sexdata.readlines() if l[0] != \" \"}\r\n sexdata.close()\r\n \r\n sexConfig = sexdict\r\n\r\n #... then change the information with some input, image-specific, info\r\n sexConfig['CATALOG_NAME']=outFile\r\n sexConfig['PARAMETERS_NAME']=paramsFile\r\n sexConfig['ASSOC_RADIUS']=str(matchRadius)\r\n sexConfig['ASSOC_NAME']=inputModelsfile\r\n aperture = float(sexConfig['SEEING_FWHM'])/float(sexConfig['PIXEL_SCALE'])\r\n sexConfig['PHOT_APERTURES']=str(aperture)+','+str(2*aperture)+','+str(3*aperture)\r\n sexConfig['WEIGHT_IMAGE']=str(weightImage)\r\n sexConfig['FILTER_NAME']=SExroot+sexConfig['FILTER_NAME']\r\n sexConfig['STARNNW_NAME']=SExroot+sexConfig['STARNNW_NAME']\r\n sexConfig['ASSOC_PARAMS']='1,2'\r\n sexConfig['ASSOCSELEC_TYPE']='MATCHED'\r\n sexConfig['ASSOC_TYPE']='NEAREST'\r\n sexConfig['ASSOC_DATA']='1,2,3,4,5'\r\n \r\n #Now write them out in a config file\r\n for key, value in sexConfig.iteritems():\r\n f.write(key+' '+value+'\\n')\r\n f.close()","def get_model(summary=False):\n\timage_input=Input(shape=(220,220,5),name='image_input')\n\tbranch1_conv1=Conv2D(64, kernel_size=(3, 3), border_mode='same', input_shape=(220,220,5), activation='relu')(image_input)\n\tbranch1_conv2=Conv2D(64, kernel_size=(1, 1), border_mode='same', activation='relu')(branch1_conv1)\t\n\tbranch1_pool1=MaxPooling2D(pool_size=(3, 3), strides=(2, 2))(branch1_conv1)\n\tbranch2_conv1=Conv2D(128, kernel_size=(3, 3), border_mode='same', activation='relu')(branch1_pool1)\n\tbranch2_conv2=Conv2D(128, kernel_size=(1, 1), border_mode='same', activation='relu')(branch2_conv1)\t\n\tbranch2_pool1=MaxPooling2D(pool_size=(3, 3), strides=(2, 2))(branch2_conv2)\n\tbranch3_conv1=Conv2D(128, kernel_size=(3, 3), border_mode='same', activation='relu')(branch2_pool1)\n\tbranch3_conv2=Conv2D(128, kernel_size=(1, 1), border_mode='same', activation='relu')(branch3_conv1)\t\n\tbranch3_pool1=MaxPooling2D(pool_size=(3, 3), strides=(2, 2))(branch3_conv2)\n\tbranch4_conv1=Conv2D(128, kernel_size=(3, 3), border_mode='same', activation='relu')(branch3_pool1)\n\tbranch4_conv2=Conv2D(128, kernel_size=(1, 1), border_mode='same', activation='relu')(branch4_conv1)\t\n\tbranch4_pool1=MaxPooling2D(pool_size=(3, 3), strides=(2, 2))(branch4_conv2)\n\tbranch5_conv1=Conv2D(128, kernel_size=(3, 3), border_mode='same', activation='relu')(branch4_pool1)\n\tbranch5_conv2=Conv2D(128, kernel_size=(1, 1), border_mode='same', activation='relu')(branch5_conv1)\t\n\tbranch5_pool1=MaxPooling2D(pool_size=(3, 3), strides=(2, 2))(branch5_conv2)\n\tbranch6_conv1=Conv2D(128, kernel_size=(3, 3), border_mode='same', activation='relu')(branch5_pool1)\n\tbranch6_conv2=Conv2D(128, kernel_size=(1, 1), border_mode='same', activation='relu')(branch6_conv1)\t\n\tbranch6_pool1=MaxPooling2D(pool_size=(3, 3), strides=(2, 2))(branch6_conv2)\n\tbranch1_flat=Flatten()(branch6_pool1)\n\tdrop=Dropout(.3)(branch1_flat)\n\t# FC layers group\n\tdense1=Dense(512, activation='relu', name='fc1')(drop)\n\tdrop1=Dropout(.3)(dense1)\n\tdense2=Dense(256, activation='relu', name='fc2')(drop1)\n\tdrop3=Dropout(.3)(dense2)\n\tout=Dense(2, activation='softmax', name='fc4')(drop3)\n\tmodel=Model(inputs=image_input,outputs=out)\n\treturn model","def _configure(self):\n FaultCohesive._configure(self)\n self.eqsrcs = self.inventory.eqsrcs\n self.output = self.inventory.output\n return","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, conf_file_dir, file_format, conf_size, output_size, transform=None):\n #initialize basic dataset variables\n #file path to file which contains all configurations\n #inside a line of numbers rperesents one configuration\n self.conf_file_dir = conf_file_dir\n self.file_format = file_format\n \n \"\"\"INDEX ORDER CONFUSING\"\"\"\n self.conf_size = conf_size\n self.output_size = output_size\n \n #latice size given as array [z_l,y_l,x_l] for example\n self.lat_size = conf_size[2:]\n #if lat_size is given in regular order x,y,z,... -> reverse it\n #self.lat_size = lat_size[::-1]\n #dimension of the lattice\n self.dim = len(self.lat_size)\n\n #number of total sites in the lattice\n self.n_sites = int(np.array(self.lat_size).prod())\n\n #one file contains multiple configurations! delimiter = \\n\n self.conf_file_paths = glob.glob(self.conf_file_dir + self.file_format)\n \n \n \"\"\"read in labels (=chemical potential) from filename\"\"\"\n self.raw_labels = np.zeros(shape=(len(self.conf_file_paths)))\n sample_i = 0\n for i, conf_file_path in enumerate(self.conf_file_paths):\n #separate entire path from filename\n file_name = conf_file_path.split(\"/\")[-1]\n if i == sample_i: print(file_name)\n #isolate chemical potential out of filename. delimiter = -\n #config-mu-id-mu_label-.ext\n label = float(file_name.split(\"-\")[-2])\n self.raw_labels[i] = label\n \n print(self.conf_file_paths[sample_i])\n print(self.raw_labels[sample_i])\n \n self.length = len(self.raw_labels)\n\n #define custom transform\n if transform == \"default\":\n \"\"\"define custom transform\"\"\"\n default_axes = list(range(2,len(self.lat_size)+2))\n print(f\"setting default axes for transforms to {default_axes}\")\n #self.transform = lambda x: self.lat_trans(self.lat_rot(x, axes=default_axes, random=True, rot_par=42), axes=default_axes, random=True, trans_par=42)\n self.transform = lambda x: self.lat_trans(x, axes=default_axes, random=True, trans_par=42)\n elif transform != None:\n self.transform = transform","def _read_custom_pars(self):\n if self.use_defaults:\n param_set = \"default_values\"\n else:\n param_set = \"parameters\"\n self.outpars = self.input_cfg_json_data[param_set][self.step_title]","def main(config):\n input_data = config[\"input_data\"]\n\n # Separate OBS from model datasets\n # (and check there is only one obs dataset)\n obs = [v for v in input_data.values() if v[\"project\"] == \"OBS\"]\n if len(obs) != 1:\n msg = f\"Expected exactly 1 OBS dataset: found {len(obs)}\"\n raise RuntimeError(msg)\n clim_file = obs[0][\"filename\"]\n\n models = group_metadata(\n [v for v in input_data.values() if v[\"project\"] != \"OBS\"],\n \"dataset\")\n\n for model_dataset, group in models.items():\n # 'model_dataset' is the name of the model dataset.\n # 'group' is a list of dictionaries containing metadata.\n logger.info(\"Processing data for %s\", model_dataset)\n model_file = [item[\"filename\"] for item in group]\n\n # Input filenames for provenance\n ancestors = flatten([model_file, clim_file])\n\n # Calculate metrics\n metrics = land_sm_top(clim_file, model_file, model_dataset, config,\n ancestors)\n\n # Write metrics\n metrics_dir = os.path.join(\n config[\"plot_dir\"],\n f\"{config['exp_model']}_vs_{config['control_model']}\",\n config[\"area\"],\n model_dataset,\n )\n\n write_metrics(metrics_dir, metrics, config, ancestors)","def __init__(self, encut, name=\"relax_settings\"):\n\n ionic = {\"EDIFF\": 1E-17, \"NSW\": 20, \"IBRION\": 2,\"ISIF\": 2, \"ISYM\": -1, \"NBLOCK\": 1, \"KBLOCK\": 20}\n InputParameters.__init__(self, ionic_settings=ionic, name=name)\n self.update_electronic_sttings(\"ENCUT\", encut)","def parse_params(self,tokenized_lines):\n\n # extract key-value pairs\n conversions = {\n # Space\n \"nuclide\" : tools.tuple_of(int), # use tuple so parameter is hashable when used as analysis key\n \"A\" : tools.singleton_of(int),\n \"Nsigma\" : tools.singleton_of(float),\n \"Nsigmamax\" : tools.singleton_of(int),\n \"N1v\" : tools.singleton_of(int),\n \"Nmax\" : tools.singleton_of(int),\n # Interaction\n \"interaction\" : tools.singleton_of(str),\n \"use_coulomb\" : tools.singleton_of(tools.bool_from_str),\n # Relative observables\n \"observable_names\" : tools.list_of(str),\n # Calculation\n \"hw\" : tools.singleton_of(float)\n }\n key_value_dict = tools.extract_key_value_pairs(\n tokenized_lines,conversions\n )\n\n # legacy support: force interaction to \"JISP16\" for early runs where\n # interaction field was provided as reserved field but not set to \"JISP16\"\n if (\"interaction\" in key_value_dict):\n if (key_value_dict[\"interaction\"] == \"RESERVED\"):\n key_value_dict[\"interaction\"]=\"JISP16\"\n\n # provide \"coulomb\" as preferred field name to match mfdn results analysis\n if (\"use_coulomb\" in key_value_dict):\n key_value_dict[\"coulomb\"] = key_value_dict[\"use_coulomb\"]\n \n # update to params dictionary\n self.params.update(key_value_dict)","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 write_yaml(self, input):\n instrument = input['Instrument']\n # select the right filter\n if input['detector'] in ['NIS']:\n # if input['APTTemplate'] == 'NirissExternalCalibration': 'NirissImaging':\n filtkey = 'FilterWheel'\n pupilkey = 'PupilWheel'\n # set the FilterWheel and PupilWheel for NIRISS\n if input['APTTemplate'] in ['NirissAmi']:\n filter_name = input['Filter']\n\n # TA and direct images will be set to imaging mode\n # rather than ami mode\n if input['Mode'].lower() == 'imaging':\n if filter_name in NIRISS_PUPIL_WHEEL_ELEMENTS:\n input[pupilkey] = filter_name\n input[filtkey] = 'CLEAR'\n elif filter_name in NIRISS_FILTER_WHEEL_ELEMENTS:\n input[pupilkey] = 'CLEARP'\n input[filtkey] = filter_name\n else:\n input[filtkey] = filter_name\n input[pupilkey] = 'NRM'\n\n elif input['APTTemplate'] not in ['NirissExternalCalibration', 'NirissWfss']:\n filter_name = input['Filter']\n if filter_name in NIRISS_PUPIL_WHEEL_ELEMENTS:\n input[pupilkey] = filter_name\n input[filtkey] = 'CLEAR'\n elif filter_name in NIRISS_FILTER_WHEEL_ELEMENTS:\n input[pupilkey] = 'CLEARP'\n input[filtkey] = filter_name\n else:\n raise RuntimeError('Filter {} not valid'.format(filter_name))\n catkey = ''\n elif input['detector'] in ['FGS']:\n filtkey = 'FilterWheel'\n pupilkey = 'PupilWheel'\n catkey = ''\n elif input['detector'] in ['NRS', 'MIR']:\n filtkey = 'FilterWheel'\n pupilkey = 'PupilWheel'\n catkey = ''\n else:\n if int(input['detector'][-1]) < 5:\n filtkey = 'ShortFilter'\n pupilkey = 'ShortPupil'\n catkey = 'sw'\n else:\n filtkey = 'LongFilter'\n pupilkey = 'LongPupil'\n catkey = 'lw'\n\n outfile = input['outputfits']\n yamlout = input['yamlfile']\n\n yamlout = os.path.join(self.output_dir, yamlout)\n with open(yamlout, 'w') as f:\n f.write('Inst:\\n')\n f.write(' instrument: {} # Instrument name\\n'.format(instrument))\n f.write(' mode: {} # Observation mode (e.g. imaging, WFSS)\\n'.format(input['Mode']))\n f.write(' use_JWST_pipeline: {} # Use pipeline in data transformations\\n'.format(input['use_JWST_pipeline']))\n f.write('\\n')\n f.write('Readout:\\n')\n f.write(' readpatt: {} # Readout pattern (RAPID, BRIGHT2, etc) overrides nframe, nskip unless it is not recognized\\n'.format(input['ReadoutPattern']))\n f.write(' ngroup: {} # Number of groups in integration\\n'.format(input['Groups']))\n f.write(' nint: {} # Number of integrations per exposure\\n'.format(input['Integrations']))\n f.write(' namp: {} # Number of amplifiers used to read out detector\\n'.format(input['namp']))\n f.write(' resets_bet_ints: {} #Number of detector resets between integrations\\n'.format(self.resets_bet_ints))\n\n if instrument.lower() == 'nircam':\n # if input['aperture'] in ['NRCA3_DHSPIL', 'NRCB4_DHSPIL']:\n if 'NRCA3_DHSPIL' in input['aperture'] or 'NRCB4_DHSPIL' in input['aperture']: # in ['NRCA3_DHSPIL', 'NRCB4_DHSPIL']:\n full_ap = input['aperture']\n else:\n apunder = input['aperture'].find('_')\n full_ap = 'NRC' + input['detector'] + '_' + input['aperture'][apunder + 1:]\n if instrument.lower() in ['niriss', 'fgs']:\n full_ap = input['aperture']\n\n possible_apertures = pysiaf.Siaf(instrument).apernames\n if full_ap not in possible_apertures:\n raise ValueError('Unrecognized aperture name: {}'.format(full_ap))\n\n f.write(' array_name: {} # Name of array (FULL, SUB160, SUB64P, etc)\\n'.format(full_ap))\n f.write(' intermediate_aperture: {} # Name of intermediate aperture used in NIRCam Grism time series obs.\\n'.format(input['grismts_intermediate_aperture']))\n f.write(' PPS_aperture: {} # Original aperture value supplied by PPS.\\n'.format(input['pps_aperture']))\n f.write(' filter: {} # Filter of simulated data (F090W, F322W2, etc)\\n'.format(input[filtkey]))\n f.write(' pupil: {} # Pupil element for simulated data (CLEAR, GRISMC, etc)\\n'.format(input[pupilkey]))\n f.write('\\n')\n f.write('Reffiles: # Set to None or leave blank if you wish to skip that step\\n')\n f.write(' dark: {} # Dark current integration used as the base\\n'.format(input['dark']))\n f.write(' linearized_darkfile: {} # Linearized dark ramp to use as input. Supercedes dark above\\n'.format(input['lindark']))\n f.write(' badpixmask: {} # If linearized dark is used, populate output DQ extensions using this file\\n'.format(input['badpixmask']))\n f.write(' superbias: {} # Superbias file. Set to None or leave blank if not using\\n'.format(input['superbias']))\n f.write(' linearity: {} # linearity correction coefficients\\n'.format(input['linearity']))\n f.write(' saturation: {} # well depth reference files\\n'.format(input['saturation']))\n f.write(' gain: {} # Gain map\\n'.format(input['gain']))\n f.write(' pixelflat: {} # Flat field file to use for un-flattening output\\n'.format(input['pixelflat']))\n f.write(' illumflat: None # Illumination flat field file\\n')\n f.write(' astrometric: {} # Astrometric distortion file (asdf)\\n'.format(input['astrometric']))\n f.write(' photom: {} # cal pipeline photom reference file\\n'.format(input['photom']))\n f.write(' ipc: {} # File containing IPC kernel to apply\\n'.format(input['ipc']))\n f.write((' invertIPC: {} # Invert the IPC kernel before the convolution. True or False. Use True if the kernel is '\n 'designed for the removal of IPC effects, like the JWST reference files are.\\n'.format(input['invert_ipc'])))\n f.write(' occult: None # Occulting spots correction image\\n')\n f.write((' transmission: {} # Transmission image containing fractional throughput map. (e.g. to imprint occulters into fov\\n'\n .format(input['transmission'])))\n f.write((' subarray_defs: {} # File that contains a list of all possible subarray names and coordinates\\n'\n .format(input['subarray_def_file'])))\n f.write((' readpattdefs: {} # File that contains a list of all possible readout pattern names and associated '\n 'NFRAME/NSKIP values\\n'.format(input['readpatt_def_file'])))\n f.write(' crosstalk: {} # File containing crosstalk coefficients\\n'.format(input['crosstalk_file']))\n f.write((' filtpupilcombo: {} # File that lists the filter wheel element / pupil wheel element combinations. '\n 'Used only in writing output file\\n'.format(input['filtpupilcombo_file'])))\n f.write((' filter_wheel_positions: {} # File containing resolver wheel positions for each filter/pupil\\n'.format(input['filter_position_file'])))\n f.write((' flux_cal: {} # File that lists flux conversion factor and pivot wavelength for each filter. Only '\n 'used when making direct image outputs to be fed into the grism disperser code.\\n'.format(input['flux_cal_file'] )))\n f.write(' filter_throughput: {} #File containing filter throughput curve\\n'.format(self.configfiles[instrument.lower()]['filter_throughput']))\n f.write(' ')\n f.write('\\n')\n f.write('nonlin:\\n')\n f.write(' limit: 60000.0 # Upper singal limit to which nonlinearity is applied (ADU)\\n')\n f.write(' accuracy: 0.000001 # Non-linearity accuracy threshold\\n')\n f.write(' maxiter: 10 # Maximum number of iterations to use when applying non-linearity\\n')\n f.write(' robberto: False # Use Massimo Robberto type non-linearity coefficients\\n')\n f.write('\\n')\n f.write('cosmicRay:\\n')\n cosmic_ray_path = os.path.join(self.datadir, instrument.lower(), 'cosmic_ray_library')\n f.write(' path: {} # Path to CR library\\n'.format(cosmic_ray_path))\n f.write(' library: {} # Type of cosmic rayenvironment (SUNMAX, SUNMIN, FLARE)\\n'.format(input['CosmicRayLibrary']))\n f.write(' scale: {} # Cosmic ray scaling factor\\n'.format(input['CosmicRayScale']))\n # temporary tweak here to make it work with NIRISS\n detector_label = input['detector']\n\n if instrument.lower() in ['nircam', 'wfsc']:\n # detector_label = input['detector']\n f.write(' suffix: IPC_NIRCam_{} # Suffix of library file names\\n'.format(\n detector_label))\n elif instrument.lower() == 'niriss':\n f.write(' suffix: IPC_NIRISS_{} # Suffix of library file names\\n'.format(\n detector_label))\n elif instrument.lower() == 'fgs':\n if detector_label == 'G1':\n detector_string = 'GUIDER1'\n elif detector_label == 'G2':\n detector_string = 'GUIDER2'\n f.write(' suffix: IPC_FGS_{} # Suffix of library file names\\n'.format(\n detector_string))\n f.write(' seed: {} # Seed for random number generator\\n'.format(np.random.randint(1, 2**32-2)))\n f.write('\\n')\n f.write('simSignals:\\n')\n if instrument.lower() in ['nircam', 'wfsc']:\n PointSourceCatalog = input['{}_ptsrc'.format(catkey)]\n GalaxyCatalog = input['{}_galcat'.format(catkey)]\n ExtendedCatalog = input['{}_ext'.format(catkey)]\n ExtendedScale = input['{}_extscl'.format(catkey)]\n ExtendedCenter = input['{}_extcent'.format(catkey)]\n MovingTargetList = input['{}_movptsrc'.format(catkey)]\n MovingTargetSersic = input['{}_movgal'.format(catkey)]\n MovingTargetExtended = input['{}_movext'.format(catkey)]\n MovingTargetConvolveExtended = input['{}_movconv'.format(catkey)]\n MovingTargetToTrack = input['{}_solarsys'.format(catkey)]\n ImagingTSOCatalog = input['{}_img_tso'.format(catkey)]\n GrismTSOCatalog = input['{}_grism_tso'.format(catkey)]\n BackgroundRate = input['{}_bkgd'.format(catkey)]\n elif instrument.lower() in ['niriss', 'fgs']:\n PointSourceCatalog = input['PointSourceCatalog']\n GalaxyCatalog = input['GalaxyCatalog']\n ExtendedCatalog = input['ExtendedCatalog']\n ExtendedScale = input['ExtendedScale']\n ExtendedCenter = input['ExtendedCenter']\n MovingTargetList = input['MovingTargetList']\n MovingTargetSersic = input['MovingTargetSersic']\n MovingTargetExtended = input['MovingTargetExtended']\n MovingTargetConvolveExtended = input['MovingTargetConvolveExtended']\n MovingTargetToTrack = input['MovingTargetToTrack']\n ImagingTSOCatalog = input['ImagingTSOCatalog']\n GrismTSOCatalog = input['GrismTSOCatalog']\n BackgroundRate = input['BackgroundRate']\n\n f.write((' pointsource: {} #File containing a list of point sources to add (x, y locations and magnitudes)\\n'\n .format(PointSourceCatalog)))\n f.write(' gridded_psf_library_row_padding: 4 # Number of outer rows and columns to avoid when evaluating library. RECOMMEND 4.\\n')\n f.write(' psf_wing_threshold_file: {} # File defining PSF sizes versus magnitude\\n'.format(input['psf_wing_threshold_file']))\n f.write(' add_psf_wings: {} # Whether or not to place the core of the psf from the gridded library into an image of the wings before adding.\\n'.format(self.add_psf_wings))\n f.write(' psfpath: {} #Path to PSF library\\n'.format(input['psfpath']))\n f.write(' psfwfe: {} #PSF WFE value (predicted or requirements)\\n'.format(self.psfwfe))\n f.write(' psfwfegroup: {} #WFE realization group (0 to 4)\\n'.format(self.psfwfegroup))\n f.write((' galaxyListFile: {} #File containing a list of positions/ellipticities/magnitudes of galaxies '\n 'to simulate\\n'.format(GalaxyCatalog)))\n f.write(' extended: {} #Extended emission count rate image file name\\n'.format(ExtendedCatalog))\n f.write(' extendedscale: {} #Scaling factor for extended emission image\\n'.format(ExtendedScale))\n f.write((' extendedCenter: {} #x, y pixel location at which to place the extended image '\n 'if it is smaller than the output array size\\n'.format(ExtendedCenter)))\n f.write((' PSFConvolveExtended: {} #Convolve the extended image with the PSF before adding to the output '\n 'image (True or False)\\n'.format(self.convolve_extended)))\n f.write((' movingTargetList: {} #Name of file containing a list of point source moving targets (e.g. '\n 'KBOs, asteroids) to add.\\n'.format(MovingTargetList)))\n f.write((' movingTargetSersic: {} #ascii file containing a list of 2D sersic profiles to have moving through '\n 'the field\\n'.format(MovingTargetSersic)))\n f.write((' movingTargetExtended: {} #ascii file containing a list of stamp images to add as moving targets '\n '(planets, moons, etc)\\n'.format(MovingTargetExtended)))\n f.write((' movingTargetToTrack: {} #File containing a single moving target which JWST will track during '\n 'observation (e.g. a planet, moon, KBO, asteroid)\tThis file will only be used if mode is set to '\n '\"moving_target\" \\n'.format(MovingTargetToTrack)))\n f.write((' tso_imaging_catalog: {} #Catalog of (generally one) source for Imaging Time Series observation\\n'.format(ImagingTSOCatalog)))\n f.write((' tso_grism_catalog: {} #Catalog of (generally one) source for Grism Time Series observation\\n'.format(GrismTSOCatalog)))\n f.write(' zodiacal: None #Zodiacal light count rate image file \\n')\n f.write(' zodiscale: 1.0 #Zodi scaling factor\\n')\n f.write(' scattered: None #Scattered light count rate image file\\n')\n f.write(' scatteredscale: 1.0 #Scattered light scaling factor\\n')\n f.write((' bkgdrate: {} #Constant background count rate (ADU/sec/pixel in an undispersed image) or '\n '\"high\",\"medium\",\"low\" similar to what is used in the ETC\\n'.format(BackgroundRate)))\n f.write((' poissonseed: {} #Random number generator seed for Poisson simulation)\\n'\n .format(np.random.randint(1, 2**32-2))))\n f.write(' photonyield: True #Apply photon yield in simulation\\n')\n f.write(' pymethod: True #Use double Poisson simulation for photon yield\\n')\n f.write(' expand_catalog_for_segments: {} # Expand catalog for 18 segments and use distinct PSFs\\n'\n .format(self.expand_catalog_for_segments))\n f.write(' use_dateobs_for_background: {} # Use date_obs below to deternine background. If False, bkgdrate is used.\\n'\n .format(self.dateobs_for_background))\n f.write(' signal_low_limit_for_segmap: {} # Lower signal limit to include pix in segmentation map. See below for units.\\n'\n .format(self.segmentation_threshold))\n f.write((' signal_low_limit_for_segmap_units: {} # Units of signal_low_limit_for_segmap. Can be: [ADU/sec, e/sec, MJy/sr, '\n 'ergs/cm2/a, ergs/cm2/hz]\\n'.format(self.segmentation_threshold_units)))\n f.write(' add_ghosts: {} # Add optical ghosts associated with astronomical sources\\n'.format(self.add_ghosts))\n f.write(' PSFConvolveGhosts: {} # Convolve ghost stamp images with instrument PSF before adding\\n'.format(self.convolve_ghosts))\n f.write('\\n')\n f.write('Telescope:\\n')\n f.write(' ra: {} # RA of simulated pointing\\n'.format(input['ra_ref']))\n f.write(' dec: {} # Dec of simulated pointing\\n'.format(input['dec_ref']))\n if 'pav3' in input.keys():\n pav3_value = input['pav3']\n else:\n pav3_value = input['PAV3']\n f.write(' rotation: {} # PA_V3 in degrees, i.e. the position angle of the V3 axis at V1 (V2=0, V3=0) measured from N to E.\\n'.format(pav3_value))\n f.write(' tracking: {} #Telescope tracking. Can be sidereal or non-sidereal\\n'.format(input['Tracking']))\n f.write('\\n')\n f.write('Output:\\n')\n # f.write(' use_stsci_output_name: {} # Output filename should follow STScI naming conventions (True/False)\\n'.format(outtf))\n f.write(' directory: {} # Output directory\\n'.format(self.simdata_output_dir))\n f.write(' file: {} # Output filename\\n'.format(outfile))\n f.write((\" datatype: {} # Type of data to save. 'linear' for linearized ramp. 'raw' for raw ramp. 'linear, \"\n \"raw' for both\\n\".format(self.datatype)))\n f.write(' format: DMS # Output file format Options: DMS, SSR(not yet implemented)\\n')\n f.write(' save_intermediates: False # Save intermediate products separately (point source image, etc)\\n')\n f.write(' grism_source_image: {} # grism\\n'.format(input['grism_source_image']))\n f.write(' unsigned: True # Output unsigned integers? (0-65535 if true. -32768 to 32768 if false)\\n')\n f.write(' dmsOrient: True # Output in DMS orientation (vs. fitswriter orientation).\\n')\n f.write(' program_number: {} # Program Number\\n'.format(input['ProposalID']))\n f.write(' title: {} # Program title\\n'.format(input['Title'].replace(':', ', ')))\n f.write(' PI_Name: {} # Proposal PI Name\\n'.format(input['PI_Name']))\n f.write(' Proposal_category: {} # Proposal category\\n'.format(input['Proposal_category']))\n f.write(' Science_category: {} # Science category\\n'.format(input['Science_category']))\n f.write(' target_name: {} # Name of target\\n'.format(input['TargetID']))\n\n # For now, skip populating the target RA and Dec in WFSC data.\n # The read_xxxxx funtions for these observation types will have\n # to be updated to make use of the proposal_parameter_dictionary\n if np.isreal(input['TargetRA']):\n input['TargetRA'] = str(input['TargetRA'])\n input['TargetDec'] = str(input['TargetDec'])\n if input['TargetRA'] != '0':\n ra_degrees, dec_degrees = utils.parse_RA_Dec(input['TargetRA'], input['TargetDec'])\n else:\n ra_degrees = 0.\n dec_degrees = 0.\n\n f.write(' target_ra: {} # RA of the target, from APT file.\\n'.format(ra_degrees))\n f.write(' target_dec: {} # Dec of the target, from APT file.\\n'.format(dec_degrees))\n f.write(\" observation_number: '{}' # Observation Number\\n\".format(input['obs_num']))\n f.write(' observation_label: {} # User-generated observation Label\\n'.format(input['obs_label'].strip()))\n f.write(\" visit_number: '{}' # Visit Number\\n\".format(input['visit_num']))\n f.write(\" visit_group: '{}' # Visit Group\\n\".format(input['visit_group']))\n f.write(\" visit_id: '{}' # Visit ID\\n\".format(input['visit_id']))\n f.write(\" sequence_id: '{}' # Sequence ID\\n\".format(input['sequence_id']))\n f.write(\" activity_id: '{}' # Activity ID. Increment with each exposure.\\n\".format(input['act_id']))\n f.write(\" exposure_number: '{}' # Exposure Number\\n\".format(input['exposure']))\n f.write(\" obs_id: '{}' # Observation ID number\\n\".format(input['observation_id']))\n f.write(\" date_obs: '{}' # Date of observation\\n\".format(input['date_obs']))\n f.write(\" time_obs: '{}' # Time of observation\\n\".format(input['time_obs']))\n # f.write(\" obs_template: '{}' # Observation template\\n\".format(input['obs_template']))\n f.write(\" primary_dither_type: {} # Primary dither pattern name\\n\".format(input['PrimaryDitherType']))\n f.write(\" total_primary_dither_positions: {} # Total number of primary dither positions\\n\".format(input['PrimaryDithers']))\n f.write(\" primary_dither_position: {} # Primary dither position number\\n\".format(int(input['primary_dither_num'])))\n f.write(\" subpix_dither_type: {} # Subpixel dither pattern name\\n\".format(input['SubpixelDitherType']))\n # For WFSS we need to strip out the '-Points' from\n # the number of subpixel positions entry\n dash = -1\n if isinstance(input['SubpixelPositions'], str):\n dash = input['SubpixelPositions'].find('-')\n if (dash == -1):\n val = input['SubpixelPositions']\n else:\n val = input['SubpixelPositions'][0:dash]\n if val == 'None':\n val = 1\n # try:\n # dash = input['SubpixelPositions'].find('-')\n # val = input['SubpixelPositions'][0:dash]\n # except:\n # val = input['SubpixelPositions']\n f.write(\" total_subpix_dither_positions: {} # Total number of subpixel dither positions\\n\".format(val))\n f.write(\" subpix_dither_position: {} # Subpixel dither position number\\n\".format(int(input['subpix_dither_num'])))\n f.write(\" xoffset: {} # Dither pointing offset in x (arcsec)\\n\".format(input['idlx']))\n f.write(\" yoffset: {} # Dither pointing offset in y (arcsec)\\n\".format(input['idly']))\n\n return yamlout","def main(args):\n ## Starting time\n start_time = datetime.now()\n ## Reading all elements and converting to python dictionary\n param_dict = vars(args)\n ## Checking for correct input\n param_vals_test(param_dict)\n #\n # Creating instance of `ReadML` with the input parameters\n param_dict['ml_args'] = ReadML(**param_dict)\n ## Program message\n prog_msg = param_dict['Prog_msg']\n # Adding additional parameters\n param_dict = add_to_dict(param_dict)\n ##\n ## Creating Folder Structure\n # proj_dict = cwpaths.cookiecutter_paths(__file__)\n proj_dict = param_dict['ml_args'].proj_dict\n proj_dict = directory_skeleton(param_dict, proj_dict)\n ##\n ## Printing out project variables\n print('\\n'+50*'='+'\\n')\n for key, key_val in sorted(param_dict.items()):\n if key != 'Prog_msg':\n print('{0} `{1}`: {2}'.format(prog_msg, key, key_val))\n print('\\n'+50*'='+'\\n')\n ##\n ## Feature keys\n param_dict['feat_cols_dict'] = param_dict['ml_args'].feat_cols_names_dict(\n return_all=True)\n ##\n ## Reading in the main catalogue\n catl_pd = catl_file_read_clean(param_dict, proj_dict)\n ###\n ### ------ Figures ------ ###\n ##\n ## Comparison of estimated group masses via HAM and Dynamical Masses\n frac_diff_model(param_dict, proj_dict, plot_opt=param_dict['plot_opt'])\n #\n # Covariance Matrix\n covariance_plot(catl_pd, param_dict, proj_dict)\n #\n # Traditional methods for estimating masses\n # pred_masses_halo_mass(param_dict, proj_dict)\n #\n # Fractional Difference plots vs True mass of galaxy GROUPS\n # frac_diff_groups_model(param_dict, proj_dict,\n # plot_opt=param_dict['plot_opt'])\n ##\n ## End time for running the catalogues\n end_time = datetime.now()\n total_time = end_time - start_time\n print('{0} Total Time taken (Create): {1}'.format(prog_msg, total_time))","def param_info():\n\n\tgizmo_names = syn.getGizmoNames()\n\n\tfor gizmo in gizmo_names:\n\t\tparams = syn.getParameterNames(gizmo)\n\t#doesnt get all parameters from gizmos i.e. WaveFreq\n\n\t# get all info on the 'WaveFreq' parameter\n\tGIZMO = 'aStim2'\n\tPARAMETER = 'WaveFreq'\n\n\t# info = syn.getParameterInfo(GIZMO, PARAMETER)\n\t#\n\t# # get the array size (should be 100)\n\t# sz = syn.getParameterSize(GIZMO, PARAMETER)\n\t#\n\t# # write values 1 to 50 in second half of buffer\n\t# result = syn.setParameterValues(GIZMO, PARAMETER, np.arange(1, 51), 50)\n\t#\n\t# # read all values from buffer\n\t# syn.getParameterValues(GIZMO, PARAMETER, sz)\n\t#\n\t# # get all info on the 'Go' parameter\n\t# PARAMETER = 'Go'\n\t# info = syn.getParameterInfo(GIZMO, PARAMETER)\n\t#\n\t# # flip the switch\n\t# result = syn.setParameterValue(GIZMO, PARAMETER, 1)\n\t#\n\t# # check the value\n\tfreq = syn.getParameterValue(GIZMO, PARAMETER)\n\tprint('value =', freq)\n\tfreq = [freq]\n\n\t# also verify visually that the switch slipped in the run\n\t# time interface. This state change will be logged just\n\t# like any other variable change and saved with the runtime\n\t# state.\n\n\tnumTrials = 5 #total number of trials across stimuli\n\tISI = [2.0, 3.0, 4.0, 5.0] # ISI in seconds\n\n\t# flash parameters\n\tflash_dur = [.001] # flash durs in seconds (100 ms, 200 ms)\n\tluminance = [[1, 1, 1], [.86, .86, .86], [0, .1, 1]] # white , grayish, purple just for testing\n\n\t# auditory parameters\n\tduration = [.005] # in seconds; pulseDur in TDT\n\tsound_levels = [20.0, 40.0, 60.0, 80.0] # dB; waveAmp in TDT\n\n\t# Auditory on (T/F? if T then A+V, if F then Visual only)\n\tstims = {0: \"auditory_only\",\n\t\t\t 1: \"visual_only\",\n\t\t\t 2: \"A+V\"\n\t\t\t }\n\n\texper = Experiment(numTrials=numTrials, ISI=ISI, flash_dur=flash_dur, luminance=luminance, wave_freq=freq,\n\t\t\t\t\t pulse_dur=duration, wave_amp=sound_levels, stimulus=stims)\n\texper.run_experiment()","def __init__(self, **kwargs):\n\n # Identify the mode to start the model in\n if \"x\" in kwargs and \"y\" in kwargs:\n x = kwargs.get(\"x\")\n y = kwargs.get(\"y\")\n if \"model_name\" not in kwargs:\n self.__mode = \"train\"\n else:\n self.__mode = \"retrain\"\n elif \"model_name\" in kwargs:\n self.__mode = \"test\"\n else:\n raise NameError(\"Cannot infer mode from arguments.\")\n\n print(\"Initializing model in %s mode.\" % self.__mode)\n\n if self.mode == \"train\":\n # Infer input type from type(x)\n if type(x[0]) == np.bytes_:\n print(\"Input type is 'binary mols'.\")\n self.__input_type = \"mols\" # binary RDKit mols\n else:\n print(\"Input type is 'molecular descriptors'.\")\n self.__input_type = \"descriptors\" # other molecular descriptors\n\n # If scaling is required\n if kwargs.get(\"scaling\", False) is True:\n # Normalize the input\n print(\"Applying scaling on input.\")\n self.__scaler = StandardScaler()\n x = self.__scaler.fit_transform(x)\n else:\n self.__scaler = None\n\n # If PCA is required\n if kwargs.get(\"pca\", False) is True:\n print(\"Applying PCA on input.\")\n self.__pca = PCA(\n n_components=x.shape[1]\n ) # n_components=n_features for now\n x = self.__pca.fit_transform(x)\n else:\n self.__pca = None\n\n self.__maxlen = (\n kwargs.get(\"dataset_info\")[\"maxlen\"] + 10\n ) # Extend maxlen to avoid breaks in training\n self.__charset = kwargs.get(\"dataset_info\")[\"charset\"]\n self.__dataset_name = kwargs.get(\"dataset_info\")[\"name\"]\n self.__lstm_dim = kwargs.get(\"lstm_dim\", 256)\n self.__h_activation = kwargs.get(\"h_activation\", \"relu\")\n self.__bn = kwargs.get(\"bn\", True)\n self.__bn_momentum = kwargs.get(\"bn_momentum\", 0.9)\n self.__noise_std = kwargs.get(\"noise_std\", 0.01)\n self.__td_dense_dim = kwargs.get(\n \"td_dense_dim\", 0\n ) # >0 squeezes RNN connections with Dense sandwiches\n self.__batch_size = kwargs.get(\"batch_size\", 256)\n self.__dec_layers = kwargs.get(\"dec_layers\", 2)\n\n if self.input_type == \"descriptors\":\n self.__codelayer_dim = x.shape[1] # features\n if \"codelayer_dim\" in kwargs:\n print(\n \"Ignoring requested codelayer_dim because it is inferred from the cardinality of the descriptors.\"\n )\n else:\n self.__codelayer_dim = kwargs.get(\"codelayer_dim\", 128)\n \n # Create the left/right-padding vectorizers\n self.__smilesvec1 = SmilesVectorizer(\n canonical=False,\n augment=True,\n maxlength=self.maxlen,\n charset=self.charset,\n binary=True,\n )\n\n self.__smilesvec2 = SmilesVectorizer(\n canonical=False,\n augment=True,\n maxlength=self.maxlen,\n charset=self.charset,\n binary=True,\n leftpad=False,\n )\n\n # self.train_gen.next() #This line is needed to set train_gen.dims (to be fixed in HetSmilesGenerator)\n self.__input_shape = self.smilesvec1.dims\n self.__dec_dims = list(self.smilesvec1.dims)\n self.__dec_dims[0] = self.dec_dims[0] - 1\n self.__dec_input_shape = self.dec_dims\n self.__output_len = self.smilesvec1.dims[0] - 1\n self.__output_dims = self.smilesvec1.dims[-1]\n\n # Build all sub-models as untrained models\n if self.input_type == \"mols\":\n self.__build_mol_to_latent_model()\n else:\n self.__mol_to_latent_model = None\n\n self.__build_latent_to_states_model()\n self.__build_batch_model()\n\n # Build data generators\n self.__build_generators(x, y)\n\n # Retrain or Test mode\n else:\n self.__model_name = kwargs.get(\"model_name\")\n\n # Load the model\n self.__load(self.model_name)\n \n if self.mode == \"retrain\":\n # If scaling is required\n if self.scaler is not None:\n print(\"Applying scaling on input.\")\n x = self.scaler.transform(x)\n\n # If PCA is required\n if self.pca is not None:\n print(\"Applying PCA on input.\")\n x = self.pca.transform(x)\n \n # Build data generators\n self.__build_generators(x, y)\n\n # Build full model out of the sub-models\n self.__build_model()\n\n # Show the resulting full model\n print(self.model.summary())","def getParameterInfo(self):\n feature_class = arcpy.Parameter(\n name = 'in_features',\n displayName = 'In Features',\n direction = 'Input',\n datatype = 'GPFeatureLayer',\n parameterType = 'Required')\n\n field_mappings = arcpy.Parameter(\n name = 'in_fields',\n displayName = 'In Fields',\n direction = 'Input',\n datatype = 'GPFieldInfo',\n parameterType = 'Required')\n\n field_mappings.parameterDependencies = [feature_class.name]\n\n output_dir = arcpy.Parameter(\n name = 'output_dir',\n displayName = 'Output folder',\n direction = 'Input',\n datatype = 'DEFolder',\n parameterType = 'Required')\n\n output_name = arcpy.Parameter(\n name = 'output_name',\n displayName = 'Output filename',\n direction = 'Input',\n datatype = 'GPString',\n parameterType = 'Required')\n\n convert_4326 = arcpy.Parameter(\n name = 'convert_4326',\n displayName = 'Convert to WGS84?',\n direction = 'Input',\n datatype = 'Boolean',\n parameterType = 'Optional')\n convert_4326.value = 'True'\n\n convert_geojson = arcpy.Parameter(\n name = 'convert_geojson',\n displayName = 'Convert to GeoJSON?',\n direction = 'Input',\n datatype = 'Boolean',\n parameterType = 'Optional')\n convert_geojson.value = 'True'\n\n convert_kmz = arcpy.Parameter(\n name = 'convert_kmz',\n displayName = 'Convert to KMZ?',\n direction = 'Input',\n datatype = 'Boolean',\n parameterType = 'Optional')\n convert_kmz.value = 'True'\n\n convert_csv = arcpy.Parameter(\n name = 'convert_csv',\n displayName = 'Convert to CSV?',\n direction = 'Input',\n datatype = 'Boolean',\n parameterType = 'Optional')\n\n convert_metadata = arcpy.Parameter(\n name = 'convert_metadata',\n displayName = 'Convert metadata to markdown?',\n direction = 'Input',\n datatype = 'Boolean',\n parameterType = 'Optional')\n\n debug = arcpy.Parameter(\n name = 'debug',\n displayName = 'Debug',\n direction = 'Input',\n datatype = 'Boolean',\n parameterType = 'Optional')\n\n return [feature_class, field_mappings, output_dir, output_name,\n convert_4326, convert_geojson, convert_kmz, convert_csv,\n convert_metadata, debug]","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 readVCTPINPUTS(self): \n #fname= os.environ['VMECFDIR'] +\"/CFG/ctp/DB/VALID.CTPINPUTS\"\n fname= os.environ['VMECFDIR'] +\"/CFG/ctp/DB/ctpinputs.cfg\"\n try:\n database=open(fname,\"r\") \n except IOError:\n print \"Cannot open \",fname\n return None\n else:\n print \"File \",fname,\" open successfuly.\"\n #print \"database= \",database\n lines=database.readlines()\n database.close() \n #print lines,len(lines) \n dbinputs=[]\n count=0\n #print \"look for me if you want different inputs range...\"\n for i in lines:\n if(i[0] == 'l' and i[1] == '0'): continue\n if i == \"\\n\": continue\n if(i[0] != '#'):\n items=string.split(i)\n #print 'items= ',items,len(items)\n if(len(items)<6):\n print \"Error parsing database, not enough items in line:\"\n print items\n continue\n #return None\n if items[3] == 'M': continue\n count=count+1\n #if count<6 or count>11 : continue\n #if count>10 and count<24 : continue\n #if count<16: continue\n #if count > 4 and count < 15: continue\n #if items[3] != '1': continue\n #if items[2] != \"EMCAL\": continue\n #if (items[2] != \"SPD\") and (items[2] != \"T0\"): continue\n flag=1\n for i in self.detectors:\n if items[2].find(i)>=0 or i.find(\"ALL\")>=0:\n flag=0;\n break\n if flag: continue\n # input not connected\n if items[7] == '0' and items[3] == '0': continue\n db={}\n db['name']=items[0]\n db['detector']=items[2]\n db['level']='L'+items[3]\n db['signature']=items[4]\n #db['number']=items[5]\n db['number']=items[7]\n db['numberDIM']=items[6]\n db['ctpnum']=items[5]\n db['Edge'] = items[8]\n db['Delay'] = items[9]\n dbinputs.append(db)\n #print \"Adding: \", db\n return dbinputs","def __call__(self, blocks, with_cab=False):\n\n # for k, v in blocks.items():\n # print(k, v.shape)\n\n #down fpn\n f_down = self.FPN_Down_Fusion(blocks)\n # print(\"f_down shape: {}\".format(f_down.shape))\n #up fpn\n f_up = self.FPN_Up_Fusion(blocks)\n # print(\"f_up shape: {}\".format(f_up.shape))\n #fusion\n f_common = fluid.layers.elementwise_add(x=f_down, y=f_up)\n f_common = fluid.layers.relu(f_common)\n # print(\"f_common: {}\".format(f_common.shape))\n\n if self.with_cab:\n # print('enhence f_common with CAB.')\n f_common = self.cross_attention(f_common)\n\n f_score, f_border = self.SAST_Header1(f_common)\n f_tvo, f_tco = self.SAST_Header2(f_common)\n\n predicts = OrderedDict()\n predicts['f_score'] = f_score\n predicts['f_border'] = f_border\n predicts['f_tvo'] = f_tvo\n predicts['f_tco'] = f_tco\n return predicts","def prepareSystematicsExample(config, samples):\n QFramework.WARN(\"this is just an example implementation of the systematics handling\")\n QFramework.WARN(\"you will need to tailor the systematics handling to fit the needs of your analysis!\")\n\n CLI = config.getFolder(\"CLI+\")\n # flag indicating to run a robust analysis\n robust = CLI.getTagBoolDefault(\"robust\",False)\n # flag indicating to run a dummy analysis\n dummy = CLI.getTagBoolDefault(\"dummy\",False)\n\n if not robust and not dummy:\n subfolders = samples.getListOfSampleFolders(\"?\")\n doNominal = config.getTagBoolDefault(\"xAODdoNominal\",True)\n channels = config.getTagVStandardString(\"channels\")\n mcasvchannels = set([ c for c in channels ])\n for c in channels:\n for sf in subfolders:\n if sf.getTagBoolDefault(\"isData\",False):\n # we're in a data SampleFolder\n # just set some nominal tags for this channel subfolder\n f = sf.getSampleFolder(c)\n if not f: continue\n f.setTagString(\".mcasv.channel\",f.getTagStringDefault(\"channel\",\"\"))\n f.setTagString(\"weightname\",\"nominal\")\n f.setTagString(\"p4suffix\",\"\")\n continue\n # we're in a MC SampleFolder\n f = sf.getSampleFolder(c)\n if not f:\n QFramework.WARN(\"unable to retrieve sample folder '{:s}' from '{:s}'. Is this expected from your path restriction?\".format(c,sf.GetName()))\n sf.printContents()\n continue #if this is not a valid folder, don't do anything with it, you'll get a null pointer exception otherwise!\n\n # right now, we are treating p4 systematics and sf systematics as uncorrelated\n # we do only look at the diagonal terms where one systematic is activated\n # crossover terms are neglected for now, hence we have two separate loops\n for p4syst in config.getTagVStandardString(\"xAODp4Systematics\"):\n # for each p4 systematic, copy the channel folder\n if not p4syst or len(p4syst)<1: continue\n newname=c+\"_\"+p4syst\n newf = f.copy(newname)\n if not newf:\n QFramework.BREAK(\"unable to copy folder {:s} to new name {:s}\".format(f.GetName(),newname))\n sf.addFolder(newf)\n # set the appropriate tags\n candname = f.getTagStringDefault(\"~cand\",\"\")\n if not candname:\n QFramework.BREAK(\"didn't find 'cand' tag on sample folder '{:s}'\".format(f.getPath()))\n newf.setTagString(\"p4Variation\",p4syst)\n newf.setTagString(\"weightname\",\"nominal\")\n newf.setTagString(\"cand\",candname+\"___\"+p4syst)\n newf.setTagString(\"cand.original\",candname)\n newf.setTagString(\".mcasv.channel\",newname)\n newf.setTagString(\"p4suffix\",ROOT.TString(\"___\")+p4syst)\n newf.setTagString(\"sysVariation\",p4syst)\n mcasvchannels.add(newname.Data())\n for sfsyst in config.getTagVString(\"xAODsfSystematics\"):\n # for each sf systematic, copy the channel folder\n if not sfsyst or len(sfsyst)<1: continue\n newname = c+\"_\"+sfsyst\n newf = f.copy(newname)\n if not newf:\n QFramework.BREAK(\"unable to copy folder {:s} to new name {:s}\".format(f.GetName(),newname))\n sf.addFolder(newf)\n # set the appropriate tags\n newf.setTagString(\"sfVariation\",sfsyst)\n newf.setTagString(\"weightname\",sfsyst)\n newf.setTagString(\".mcasv.channel\",newname)\n newf.setTagString(\"p4suffix\",\"\")\n newf.setTagString(\"sysVariation\",sfsyst)\n mcasvchannels.add(newname.Data())\n\n # if no nominal analysis was requested, we can remove the nominal channels\n if not doNominal:\n f.detachFromBase()\n else:\n f.setTagString(\".mcasv.channel\",f.GetName())\n\n # Add some nominal top level tags, even if systematics aren't being added\n samples.setTagString(\"weightname\",\"nominal\")\n samples.setTagString(\"sfVariation\",\"nominal\")\n samples.setTagString(\"p4Variation\",\"nominal\")\n samples.setTagString(\"p4suffix\",\"\")\n samples.setTagString(\"sysVariation\",\"nominal\")\n\n # possibly print how the folder looks like now\n if config.getTagBoolDefault(\"showChannels\",False):\n QFramework.INFO(\"after taking care of channel and systematics setup, your sample folder looks like this:\")\n samples.printContents(\"r2dt\")\n\n # save the whole collection of channels (including now systematics)\n # for access later when creating the MCASV\n runtime = config.getFolder(\"runtime+\")\n for i, channel in enumerate(mcasvchannels):\n if \"ROOT\" in str(type(channel)):\n from QFramework import WARN\n QFramework.WARN(\"Please make sure that every mcasvchannel is only booked once. The python set doesn't help when adding c++ ROOT instances (\" + str(type(channel)) + \".\")\n runtime.setTagString(\"mcasvchannels.\"+str(i), channel)\n\n return","def __call__(cls, nir_paw_image_fname, nir_paw_conf_fname, output_template, conf_limit):\n\n# on with the show\n logger.info('Opening science and confidence frames')\n ifits=fitsio.FITS(nir_paw_image_fname,'r')\n cfits=fitsio.FITS(nir_paw_conf_fname,'r')\n\n#\n# Check that the number of HDUs match\n#\n\n if (len(ifits) != len(cfits)):\n print(\"Number of HDUs/extensions in IMAGE and CONFidence files do not match.\")\n print(\"Aborting\")\n exit(1)\n\n p_ih=ifits[0].read_header()\n p_ch=cfits[0].read_header()\n# Remove reserve keywords\n p_ih.clean()\n\n#\n# Extract some keywords from PRIMARY header to propagate into the individual images.\n#\n base_dict={}\n base_header=[]\n for hkeep in nci.nir_paw_primary_keep:\n if (hkeep in p_ih):\n base_header.append({'name':hkeep,'value':p_ih[hkeep],'comment':p_ih.get_comment(hkeep)})\n base_dict[hkeep]={'value':p_ih[hkeep],'comment':p_ih.get_comment(hkeep)}\n else:\n print(\"Keyword {:s} missing in HDU[{:d}]\".format(hkeep,0))\n#\n# If possible, need too keep track of REQTIME (requested frametime) because sometimes \n# EXPTIME seems to be mispopulated in the CCD image HDUs with TEXPTIME\n#\n if ('TEXPTIME' in p_ih):\n texptime=p_ih['TEXPTIME']\n else:\n texptime=None\n if ('REQTIME' in p_ih):\n reqtime=p_ih['REQTIME']\n else:\n reqtime=None\n#\n# print(base_header)\n \n\n#\n# Step through HDUs... and form \"CCD\" images for each HDU\n#\n ExtList=[]\n for hnum in range(1,len(ifits)):\n print(\"############ Begin work on extnum={:d} ###############\".format(hnum))\n\n# Check that extensions match (after that concentrate on image).\n print(hnum,ifits[hnum].get_extname(),cfits[hnum].get_extname())\n if (ifits[hnum].get_extname() != cfits[hnum].get_extname()):\n print(\"Working on extension {:d}. Extension names (image,conf) of ([{:s}],[{:s}]) do not match!\".format(\n hnum,ifits[hnum].get_extname(),cfits[hnum].get_extname()))\n print(\"Aborting!\")\n exit(1)\n\n f_ih=ifits[hnum].read_header()\n f_ih.clean()\n#\n# Fix occurences where the CCD-level keyword EXPTIME has inherited the value of TEXPTIME\n#\n exptime=f_ih['EXPTIME']\n if (reqtime is not None):\n if (exptime > reqtime):\n print(\"Warning: possible corrupt EXPTIME (total exptime rather than frame time present).\")\n print(\"Attempting to update EXTIME to REQTIME (requested frame time).\")\n print(\" Primary HDU: TEXPTIME: {:}\".format(texptime))\n print(\" Primary HDU: REQTIME: {:}\".format(reqtime))\n print(\" Current HDU: EXPTIME: {:} --> {:}\".format(exptime,reqtime))\n exptime=reqtime\n f_ih['EXPTIME']=reqtime\n#\n# Augment keywords pulled from primary header with keywords from current HDU\n#\n c_header=base_header[:]\n c_dict=dict(base_dict)\n for hkeep in nci.nir_paw_hdu_keep:\n if (hkeep in f_ih):\n# print(hkeep,f_ih[hkeep],f_ih.get_comment(hkeep))\n c_header.append({'name':hkeep,'value':f_ih[hkeep],'comment':f_ih.get_comment(hkeep)})\n if (hkeep in c_dict):\n print(\"Warning: Replacing keyword {:s} with value from hdu={:d}\".format(hkeep,hnum))\n c_dict[hkeep]={'value':f_ih[hkeep],'comment':f_ih.get_comment(hkeep)}\n else:\n print(\"Keyword {:s} missing in HDU[{:d}]\".format(hkeep,hnum))\n\n#\n# Get the CCDNUM from special keyword and propagate\n# Get SKYLEVEL, SKYNOISE, ZEROPOINT and form basis value for the weight plane\n#\n ccdnum=f_ih['HIERARCH ESO DET CHIP NO']\n c_header.append({'name':'CCDNUM','value':ccdnum,'comment':'Unique Detector Number'})\n\n# exptime=f_ih['EXPTIME']\n## Fix occurences where the CCD-level keyword EXPTIME has inherited the value of TEXPTIME\n# if (exptime > reqtime):\n# print(\"Warning: possible corrupt EXPTIME (total exptime rather than frame time present).\")\n# print(\"Attempting to update EXTIME to REQTIME (requested frame time).\")\n# print(\" Primary HDU: TEXPTIME: {:.2f}\".format(texptime))\n# print(\" Primary HDU: REQTIME: {:.2f}\".format(reqtime))\n# print(\" Current HDU: EXPTIME: {:.2f} --> {:.2f}\".format(exptime,reqtime))\n# exptime=reqtime\n# f_ih['EXPTIME']=reqtime\n\n mtime=2.5*np.log10(exptime)\n skylev=f_ih['SKYLEVEL']\n skyrms=f_ih['SKYNOISE']\n seeing=f_ih['SEEING']\n magzpt=f_ih['MAGZPT']\n\n# zeropoint include a correction from VEGA->AB\n# zeropoint in headers was found to have a factor for EXPTIME removed (have to add back in for DES-like processing)\n\n if (p_ih['BAND'] in nci.nir_vega_to_ab):\n magzpt=magzpt+nci.nir_vega_to_ab[p_ih['BAND']]+mtime\n else:\n print(\"Warning! Unknown BAND ({:s}) for conversion of zeropoint from VEGA to AB system\".format(p_ih['BAND']))\n\n c_header.append({'name':'SKYBRITE', 'value':skylev, 'comment':'Sky level estimate from IMCORE'})\n c_header.append({'name':'SKYSIGMA', 'value':skyrms, 'comment':'Sky noise estimate from IMCORE'})\n c_header.append({'name':'SKYVARA', 'value':skyrms*skyrms, 'comment':'Sky noise estimate from IMCORE'})\n c_header.append({'name':'SKYVARB', 'value':skyrms*skyrms, 'comment':'Sky noise estimate from IMCORE'})\n c_header.append({'name':'FWHM', 'value':seeing, 'comment':'Average FWHM (pixels)'})\n c_header.append({'name':'MAG_ZERO', 'value':magzpt, 'comment':'Converted MAGZPT(Vega) to AB system'})\n nite_val=convert_utc_str_to_nite(f_ih['DATE-OBS'])\n c_header.append({'name':'NITE', 'value':nite_val, 'comment':'Observation Nite'})\n c_header.append({'name':'SATURATE', 'value':nci.nircam_satval[ccdnum], 'comment': 'Saturation Level (ADU)'})\n c_header.append({'name':'PIXSCAL1', 'value':0.341, 'comment': 'Fiducial pixel scale (arcsec/pix)'})\n c_header.append({'name':'PIXSCAL2', 'value':0.341, 'comment': 'Fiducial pixel scale (arcsec/pix)'})\n\n# bval=f_ih['BSCALE']\n# print(\"BSCALE was: \",bval)\n print(\"SKYLEVEL was: \",skylev)\n print(\"SKYRMS was: \",skyrms)\n#\n# Searching for a proper WGT prescription\n#\n# This was what I took to be equivalent to DES (but perhaps it does not properly factor in N-image stack\n# wgtval=skylev+(skyrms*skyrms)\n print(\"SKYLEV + (SKYRMS*SKYRMS): \",skylev+(skyrms*skyrms))\n#\n# This was assuming SKYLEVEL does not properly inform stats\n# wgtval=(skyrms*skyrms)\n print(\"(SKYRMS*SKYRMS): \",skyrms*skyrms)\n\n#\n# Read the image data from the science and confidence files.\n#\n sci_data=ifits[hnum].read()\n print(\"Median of data {:.3f} \".format(np.median(sci_data)))\n conf_data=cfits[hnum].read()\n\n#\n# Better seemed to be a re-measurement of STD\n#\n print(\"Attempting an improved SKYRMS with 3-sigma clip to remove objects\")\n avgval, medval, stdval = medclip(sci_data,verbose=3)\n# print(avgval,medval,stdval)\n print(\"stdval^2: \",stdval*stdval)\n wgtval=(stdval*stdval)\n# print(wgtval)\n#\n# Use the new (i.e. chip-based header) to feed a WCS \n# Use image size to feed calculations for center and corners (similar to despyastro.CCD_corners\n#\n print(\"Calculating center/corners assuuming native ZPN projection\")\n w=WCS(fitsio.FITSHDR(c_header))\n\n fnax2=float(sci_data.shape[0])\n fnax1=float(sci_data.shape[1])\n corn_x=np.array([fnax1/2.0,1.,fnax1,fnax1,1.])\n corn_y=np.array([fnax2/2.0,1.,1.,fnax2,fnax2])\n sky = w.pixel_to_world(corn_x,corn_y)\n corn_ra=sky.ra.degree\n corn_dec=sky.dec.degree\n\n c_header.append({'name':'RA_CENT', 'value':corn_ra[0], 'comment':'RA center'})\n c_header.append({'name':'DEC_CENT','value':corn_dec[0],'comment':'DEC center'})\n for i in range(1,5):\n c_header.append({'name':'RAC{:d}'.format(i), 'value':corn_ra[i], 'comment':'RA corner {:d}'.format(i)})\n c_header.append({'name':'DECC{:d}'.format(i),'value':corn_dec[i],'comment':'DEC corner {:d}'.format(i)})\n RACMIN, RACMAX, DECCMIN, DECCMAX, CROSSRA0 = get_DESDM_corners_extent(corn_ra, corn_dec)\n c_header.append({'name':'RACMIN', 'value':RACMIN, 'comment':'Minimum extent of image in RA'})\n c_header.append({'name':'RACMAX', 'value':RACMAX, 'comment':'Maximum extent of image in RA'})\n c_header.append({'name':'DECCMIN', 'value':DECCMIN, 'comment':'Minimum extent of image in Declination'})\n c_header.append({'name':'DECCMAX', 'value':DECCMAX, 'comment':'Maximum extent of image in Declination'})\n c_header.append({'name':'CROSSRA0','value':CROSSRA0,'comment':'Does Image Span RA 0h (Y/N)'})\n c_header.append({'name':'DESEPOCH','value':'NIREPOCH','comment':'Default DES epoch definition for including NIR data'})\n#\n#\n#\n print(\"Stripping ZPN projection from WCS and creating a shift to get a rough TAN\")\n recs_to_delete=[] \n for i, hrec in enumerate(c_header):\n if (hrec['name'] == 'CTYPE1'):\n c_header[i]['value']='RA---TAN'\n if (hrec['name'] == 'CTYPE2'):\n c_header[i]['value']='DEC--TAN'\n\n if (hrec['name'] == 'CRVAL1'):\n c_header[i]['value']=corn_ra[0]\n if (hrec['name'] == 'CRVAL2'):\n c_header[i]['value']=corn_dec[0]\n if (hrec['name'] == 'CRPIX1'):\n c_header[i]['value']=fnax1/2.0\n if (hrec['name'] == 'CRPIX2'):\n c_header[i]['value']=fnax2/2.0\n\n if (hrec['name'] in ['PV2_1','PV2_2','PV2_3','PV2_4','PV2_5']):\n recs_to_delete.append(i)\n if (len(recs_to_delete) > 0):\n for i in sorted(recs_to_delete,reverse=True):\n x=c_header.pop(i)\n print(\"Removing: {:}\".format(x))\n\n whack=WCS(fitsio.FITSHDR(c_header))\n skyhack = whack.pixel_to_world(corn_x,corn_y)\n whack_corn_ra=skyhack.ra.degree\n whack_corn_dec=skyhack.dec.degree\n for i in range(5):\n cosdec=np.cos(corn_dec[i]*np.pi/180.)\n dra=3600.*(corn_ra[i]-whack_corn_ra[i])*cosdec\n ddec=3600.*(corn_dec[i]-whack_corn_dec[i])\n print(\" WCS shift {:d} {:.6f} {:.6f} {:.6f} {:.6f} {:.6f} {:.6f} \".format(ccdnum,corn_ra[i],corn_dec[i],whack_corn_ra[i],whack_corn_dec[i],dra,ddec))\n\n# for i, hrec in enumerate(c_header):\n# print(i,hrec)\n\n#\n# Form the SCI, MSK, and WGT HDUs\n#\n im=DESImage(init_data=True,init_mask=True,init_weight=True,shape=sci_data.shape)\n\n im.data=np.float32(sci_data)\n msk_wsm=np.where(conf_data<conf_limit)\n im.mask[msk_wsm] |= BADPIX_BPM\n im.weight=np.float32(conf_data/100./wgtval)\n#\n# Check for extra conditions where further masking is needed\n# Here is where CCD=6 check was started (now removed and placed \n# in nir_starmask to take advantage of bright object masking\n#\n\n\n#\n# Deal with individual header-isms and write out SCI, MSK, WGT\n# Note this is using fitsio (duplicating some of the DESIMAGE.save \n# but customization was needed to deal with foibles of the current\n#\n fname=re.sub('%02d','{:02d}'.format(ccdnum),output_template,1)\n ofits = fitsio.FITS(fname, 'rw', clobber=True)\n\n im.header=fitsio.FITSHDR(c_header) \n im.header['DES_EXT']='IMAGE'\n im.header = update_hdr_compression(im.header, 'SCI')\n ofits.write(im.data,extname='SCI',header=im.header)\n\n\n im.mask_hdr=fitsio.FITSHDR(c_header) \n im.mask_hdr['DES_EXT']='MASK'\n im.mask_hdr = update_hdr_compression(im.mask_hdr, 'MSK')\n im.mask_hdr['DES_EXT']='MASK'\n ofits.write(im.mask,extname='MSK',header=im.mask_hdr)\n\n# im.weight_hdr=fitsio.FITSHDR(c_header) \n# print(im.weight_hdr)\n im.weight_hdr = update_hdr_compression(im.weight_hdr, 'WGT')\n# print(im.weight_hdr)\n im.weight_hdr['DES_EXT']='WEIGHT'\n ofits.write(im.weight,extname='WGT',header=im.weight_hdr)\n\n ofits.close()\n print(\"Wrote {:s}\".format(fname))\n print(\" \")\n \n\n ifits.close()\n cfits.close()\n\n ret_code = 0\n return ret_code","def __init__(self, model_config, global_config):\n self.messages = []\n self.config = global_config\n\n self.name = model_config[\"name\"] \n self.data_filename = model_config[\"data\"] \n self.clock = model_config.get(\"clock\", \"\")\n self.features = model_config.get(\"features\",[\"*\"])\n self.exclusions = model_config.get(\"exclusions\",None)\n self.constant_feature = False\n self.constant_feature_removed = False\n self.frequencies = model_config.get(\"frequencies\", \"empirical\")\n self.pruned = model_config.get(\"pruned\", False)\n self.rate_variation = model_config.get(\"rate_variation\", False)\n self.feature_rates = model_config.get(\"feature_rates\", None)\n self.ascertained = model_config.get(\"ascertained\", None)\n # Force removal of constant features here\n # This can be set by the user in BinaryModel only\n self.remove_constant_features = True\n self.minimum_data = float(model_config.get(\"minimum_data\", 0))\n self.substitution_name = self.__class__.__name__\n self.data_separator = \",\"\n self.use_robust_eigensystem = model_config.get(\"use_robust_eigensystem\", False)\n\n # Load the entire dataset from the file\n self.data = load_data(self.data_filename, file_format=model_config.get(\"file_format\",None), lang_column=model_config.get(\"language_column\",None))\n # Remove features not wanted in this analysis\n self.build_feature_filter()\n self.apply_feature_filter()\n\n # Keep this around for later...\n self.global_config = global_config","def loadInfo(self):\n params_array = SystemIO.load(self.path.model_info_file)\n return TrnParamsConvolutional(*params_array)","def _prepareArg_covGen(self):\n ## generate length scale field in x, y and z directions, respectively\n lenXField, lenYField, lenZField = self._genLenScale()\n ## read cellVolumn Field as the weight Field for KL expansion\n cellVField = foamOp.readTurbCellVolumeFromFile(self.klDir+'/0/')\n weightField = cellVField[:, 0]\n sigmaField = foamOp.readScalarFromFile(self.klDir+'/0/Sigma')\n #TODO: truncateTol can be specified by user\n #truncateTol = -np.log(1e-10)\n # TruncateTol can be specified by user\n truncateTol = -np.log(self.truncateTol) \n ## assembly arguments dictionary for generation of covariance\n if self.kernelType == 'SqExp': \n Arg_covGen = {\n 'sigmaField': sigmaField,\n 'kernelType': 'SqExp',\n 'lenXField': lenXField,\n 'lenYField': lenYField,\n 'lenZField': lenZField,\n 'weightField':weightField,\n 'truncateTol': truncateTol\n }\n elif self.kernelType == 'givenStructure':\n CovStruct = np.loadtxt('./Corr.dat')\n Arg_covGen = {\n 'sigmaField': sigmaField,\n 'kernelType': 'givenStructure',\n 'CovStruct': CovStruct,\n 'weightField':weightField,\n 'truncateTol': truncateTol\n }\n return Arg_covGen","def initialize(self, args):\n # You must parse model_config. JSON string is not parsed here\n self.model_config = json.loads(args['model_config'])\n print(\"model_config:\", self.model_config)\n\n self.input_names = []\n for input_config in self.model_config[\"input\"]:\n self.input_names.append(input_config[\"name\"])\n print(\"postprocess input names:\", self.input_names)\n\n self.output_names = []\n self.output_dtype = []\n for output_config in self.model_config[\"output\"]:\n self.output_names.append(output_config[\"name\"])\n dtype = pb_utils.triton_string_to_numpy(output_config[\"data_type\"])\n self.output_dtype.append(dtype)\n print(\"postprocess output names:\", self.output_names)\n self.postprocessor = fd.vision.ocr.DBDetectorPostprocessor()\n self.cls_preprocessor = fd.vision.ocr.ClassifierPreprocessor()\n self.rec_preprocessor = fd.vision.ocr.RecognizerPreprocessor()\n self.cls_threshold = 0.9","def sources_extraction(image,sextractor_pars):\n\n cat_name, detect_minarea, detect_thresh, analysis_thresh, phot_aperture, satur_level, ZP, gain, pixelScale,seeing,back_type,back_value,back_size,backphoto_type,backphoto_thick,back_filterthresh,checkimage_type,checkimage_name= sextractor_pars\n sp.run('sex %s.fits -c gft.sex -CATALOG_NAME %s.cat -CATALOG_TYPE ASCII_HEAD -PARAMETERS_NAME gft.param -DETECT_TYPE CCD -DETECT_MINAREA %d -DETECT_THRESH %d -ANALYSIS_THRESH %d -PHOT_APERTURES %d -SATUR_LEVEL %d -MAG_ZEROPOINT %f -GAIN %f -PIXEL_SCALE %f -SEEING_FWHM %f -BACK_TYPE %s -BACK_VALUE %f -BACK_SIZE %d -BACKPHOTO_TYPE %s -BACKPHOTO_THICK %d -BACK_FILTTHRESH %f -CHECKIMAGE_TYPE %s -CHECKIMAGE_NAME %s.fits ' % (image,cat_name, detect_minarea, detect_thresh, analysis_thresh, phot_aperture, satur_level, ZP, gain, pixelScale,seeing,back_type,back_value,back_size,backphoto_type,backphoto_thick,back_filterthresh,checkimage_type,checkimage_name),shell=True)","def sweep_image_model():\n for c1 in [4, 8, 16]:\n for c2 in [2, 4]:\n for c3 in [2, 4]:\n for c4 in [1, 2]:\n flags = flag_reader.read_flag()\n print(c1)\n flags.channel_list = c1 * np.array([1, c2, c2*c3, c2*c3*c4])\n print('channel list = ', flags.channel_list)\n flags.last_dim = flags.channel_list[-1]\n flags.model_name = flags.data_set + '_channel_' + str(flags.channel_list).replace('[','').replace(']','').replace(' ','_') + \\\n '_dim_last_' + str(flags.last_dim) + '_ind_' + str(flags.comp_ind) + \\\n '_lr_{}_decay_{}_reg_{}_bs_{}'.format(flags.lr, flags.lr_decay_rate, flags.reg_scale, flags.batch_size)\n print(flags.model_name)\n training_from_flag(flags)","def check_config(params, dannce_net, prediction):\n check_camnames(params)\n\n if params[\"exp\"] is not None:\n for expdict in params[\"exp\"]:\n check_camnames(expdict)\n\n if dannce_net:\n check_net_expval(params)\n check_vmin_vmax(params)","def input_setup(config):\n # Load data path, if is_train False, get test data\n data = load_data(config.is_train, config.test_img)\n \n # Make sub_input and sub_label, if is_train false more return nx, ny\n if config.is_train:\n \tsub_input_sequence, sub_label_sequence, nx, ny = make_sub_data_train(data, config)\n else:\n\tsub_input_sequence, sub_label_sequence, nx, ny = make_sub_data_test(data, config)\n\n\n # Make list to numpy array. With this transform\n arrinput = np.asarray(sub_input_sequence) # [?, 41, 41, 3]\n arrlabel = np.asarray(sub_label_sequence) # [?, 41, 41, 3]\n make_data_hf(arrinput, arrlabel, config)\n\n return nx, ny","def config():\n file_path = None # path to the input file\n db_path = None # path to the output db\n atomic_properties = (\n \"Properties=species:S:1:pos:R:3\"\n ) # atomic properties of the input file\n molecular_properties = [\"energy\"] # molecular properties of the input file\n overwrite = False","def input_models():\n return [\n PDBFile(\n Path(golden_data, \"protdna_complex_1.pdb\"),\n path=golden_data,\n score=42.0,\n restr_fname=Path(golden_data, \"example_ambig_1.tbl\")\n ),\n PDBFile(\n Path(golden_data, \"protdna_complex_2.pdb\"),\n path=golden_data,\n score=28.0,\n restr_fname=Path(golden_data, \"example_ambig_2.tbl\")\n )]","def check_info(self):\n #define convertissor\n def pass_in_integer(value):\n return int(value)\n def pass_in_logical(value):\n if value in ['1','t','T','.true.']:\n return 1\n else:\n return 0\n def pass_in_float(value):\n return float(value)\n\n for line in open('./Source/MadWeight_File/Python/MW_param_default.inc'):\n line=line.split('#')[0] #remove comment\n splitline=line.split() #split the data\n if len(splitline)!=4:\n continue\n #assign element\n block=splitline[0].lower()\n tag=splitline[1].lower()\n type=splitline[2].lower()\n value=splitline[3]\n #check if exist -> default\n try:\n self[block][tag]\n except:\n try:\n self[block][tag]=value\n except:\n self[block]={tag:value}\n #change type\n if type in ['integer','logical','float']:\n self[block][tag]=eval('pass_in_'+type+'(self[block][tag])')","def info_criteria(indep,\n dep,\n models,\n add_aicc=False):\n num_data = len(indep)\n\n bic_calc = BicCalculator(bic_type=BIC_TYPE.STANDARD)\n bic_calc_bkpt = BicCalculator(bic_type=BIC_TYPE.HYBRID)\n\n #bic_calc_bkpt = bic_calc\n #####bic_calc_bkpt = BicCalculator(bic_type = BIC_TYPE.HOS)\n\n bics = []\n aics = []\n aiccs = []\n for model in models:\n\n if model in [Model.ONE_BKPT, Model.TWO_BKPT]:\n bic_calc_to_use = bic_calc_bkpt\n else:\n bic_calc_to_use = bic_calc\n\n estimator = model.estimator(num_end_to_skip=NUM_END_TO_SKIP,\n num_between_to_skip=NUM_BETWEEN_TO_SKIP)\n estimator.fit(indep, dep)\n\n loglikelihood = estimator.loglikelihood\n num_params = estimator.num_params\n\n bic = bic_calc_to_use.bic(num_params=num_params,\n loglikelihood=loglikelihood,\n num_data=num_data)\n aic = stats_util.aic(num_params=num_params,\n loglikelihood=loglikelihood)\n aicc = stats_util.aicc(num_params=num_params,\n loglikelihood=loglikelihood,\n num_data=num_data)\n\n bics.append(bic)\n aics.append(aic)\n aiccs.append(aicc)\n ic_df = pd.DataFrame({\"BIC\": bics, \"AIC\": aics}, index=models)\n if add_aicc:\n ic_df[\"AICC\"] = aiccs\n\n wts_df = ic_df.apply(stats_util.bma_weights, axis=0)\n wts_cols = [x + \" Model Wt\" for x in wts_df.columns]\n wts_df.columns = wts_cols\n both = pd.concat([ic_df, wts_df], join=\"outer\", axis=1)\n return both","def _handleInput(self, paramInput):\n super()._handleInput(paramInput)\n settings, notFound = paramInput.findNodesAndExtractValues(['epsilon', 'dual', 'loss', 'tol', 'fit_intercept',\n 'intercept_scaling', 'max_iter'])\n # notFound must be empty\n assert(not notFound)\n self.initializeModel(settings)","def main():\n target = 'Coding:Level1'\n output_root = f'problem_5_output/{target.replace(\":\", \"_\")}'\n if not os.path.exists(output_root):\n os.makedirs(output_root, exist_ok=True)\n\n # dictionary of parameter grids, one for each process\n param_grids = {\n 'early_stopping': ParameterGrid([\n {\n 'patience': [15], # , 20, 40]\n },\n ]),\n 'fit': ParameterGrid([\n {\n 'batch_size': [128], # , 64, 128, 256],\n 'epochs': [16], # 20, 50],\n },\n ]),\n 'model_preprocessor': ParameterGrid([\n {\n 'num_unique_words': [5000], # 4000, 1000, 6000, 10000],\n 'max_sequence_length': [150], # 50, 75, 100, 125, 150, 200],\n },\n ]),\n 'model': ParameterGrid([\n # {\n # Dense single hidden layer model hyperparameters:\n # 'name': ['dense_h1'],\n # 'embedded_dims': [8], # , 16, 32, 64, 128, 256],\n # 'num_units_h1': [8], # , 16, 32, 64, 128, 256],\n # 'drop_h1': [None], # , 0.1, 0.2, 0.25, 0.5, 0.75],\n # 'optimizer': ['nadam', 'adam'],\n # 'learning_rate': [None], # , 0.01, 0.001],\n # 'activation': ['relu', 'tanh'],\n # },\n # {\n # Dense double hidden layer model hyperparameters:\n # 'name': ['dense_h2'],\n # 'embedded_dims': [64],\n # 'num_units_h1': [128],\n # 'num_units_h2': [128],\n # 'drop_h1': [None],\n # 'drop_h2': [0.5],\n # 'optimizer': ['nadam'],\n # 'activation': ['relu'],\n # 'learning_rate': [0.01],\n # },\n # {\n # CNN single hidden layer model hyperparameters\n # 'name': ['conv_h1'],\n # 'embedded_dims': [64],\n # 'num_units_h1': [32], # , 64, 256],\n # 'k_conv_h1': [2], # , 3, 4],\n # 'drop_embed': [0.2], # , 0.5],\n # 'activation': ['relu', 'tanh'],\n # 'optimizer': ['adam', 'nadam']\n # },\n # {\n # CNN double hidden layer model hyperparameters\n # 'name': ['conv_h2'],\n # 'embedded_dims': [128], # , 64, 32, 16, 8],\n # 'num_units_h1': [32], # , 64, 128],\n # 'drop_h2': [0.5], # , 0.75, 0.25, 0.1],\n # 'k_conv_h1': [2], # , 3, 4],\n # 'num_units_h2': [128], # , 64, 32, 16, 8],\n # 'drop_embed': [0.2], # , 0.50],\n # 'activation': ['relu'],\n # 'optimizer': ['adam'], # , 'nadam'],\n # },\n # {\n # CNN double hidden layer model hyperparameters\n # 'name': ['conv_h2.1'],\n # 'embedded_dims': [64],\n # 'num_units_h1': [32], # , 64, 128],\n # 'k_conv_h1': [2], # , 3, 4],\n # 'drop_embed': [0.2], # , 0.5],\n # 'activation': ['relu'],\n # 'optimizer': ['adam'], # , 'nadam']\n # },\n # {\n # RNN single hidden layer model hyperparameters\n # 'name': ['rnn_h1'],\n # 'embedded_dims': [64],\n # 'drop_embed': [0.2],\n # 'num_units_h1': [128],\n # 'optimizer': ['nadam'],\n # 'learning_rate': [0.01]\n # },\n # {\n # # LSTM double hidden layer (second layer dense FC) model hyperparameters\n # 'name': ['lstm_h1'],\n # 'embedded_dims': [64],\n # 'drop_embed': [0.2],\n # 'drop_h1': [0.5],\n # 'num_units_h1': [128],\n # 'optimizer': ['nadam'],\n # },\n # {\n # LSTM double hidden layer (second layer dense FC) model hyperparameters\n # 'name': ['lstm_h2'],\n # 'embedded_dims': [64],\n # 'drop_embed': [0.2],\n # 'num_units_h1': [128],\n # 'drop_h1': [0.5],\n # 'num_units_h2': [128],\n # 'optimizer': ['nadam'],\n # 'activation': ['relu']\n # },\n # {\n # # Bi-directional LSTM single hidden layer model hyperparameters\n # 'name': ['bi_lstm_h1'],\n # 'embedded_dims': [32], # , 64, 128],\n # 'drop_embed': [0.2], # , 0.25, 0.5],\n # 'num_units_h1': [32], # , 64, 128],\n # 'drop_h1': [0.2], # , 0.25, 0.5],\n # 'optimizer': ['nadam', 'adam']\n # },\n # {\n # Bi-directional LSTM double hidden layer (second layer Bi-LSTM) model hyperparameters\n # 'name': ['bi_lstm_h2'],\n # 'embedded_dims': [32], # , 64, 128],\n # 'num_units_h1': [32], # , 64, 128],\n # 'num_units_h2': [32], # , 64, 128],\n # 'drop_h1': [0.25, 0.5],\n # 'drop_h2': [0.25, 0.5],\n # 'optimizer': ['nadam', 'adam']\n # },\n {\n # Multi Convolutional model hyperparameters\n 'name': ['multi_conv_h3_s2'],\n 'drop_embed': [0.5], # , 0.3],\n 'embedded_dims': [128], # , 64, 128, 256],\n 'num_units_h1': [128], # , 64, 128, 256],\n 'num_units_h2': [128], # , 64, 128, 256],\n 'num_units_h3': [128], # , 64, 128, 256],\n 'num_units_h4': [128], # , 64, 128, 256],\n 'k_conv_h1': [3],\n 'k_conv_h2': [2],\n 'activation': ['relu'], # , 'tanh'],\n 'drop_h3': [0.2], # , 0.2, 0.25, 0.5, 0.75],\n 'optimizer': ['adam'], # 'nadam']\n },\n # {\n # # Multi Convolutional model hyperparameters\n # 'name': ['multi_conv_h3_s3'],\n # 'drop_embed': [0.5], # , 0.3],\n # 'embedded_dims': [32], # , 64, 128, 256],\n # 'num_units_h1': [32], # , 64, 128, 256],\n # 'num_units_h2': [32], # , 64, 128, 256],\n # 'num_units_h3': [32], # , 64, 128, 256],\n # 'num_units_h4': [32], # , 64, 128, 256],\n # 'k_conv_h1': [3],\n # 'k_conv_h2': [2],\n # 'k_conv_h3': [4],\n # 'k_conv_h4': [4],\n # 'activation': ['relu', 'tanh'],\n # 'drop_h4': [0.1], # , 0.2, 0.25, 0.5, 0.75],\n # 'optimizer': ['adam', 'nadam']\n # },\n ]),\n 'preprocessor': ParameterGrid([\n # {\n # 'do_clean': [False],\n # 'pad_type': ['pre', 'post'],\n # 'trunc_type': ['pre', 'post'],\n # },\n {\n 'do_clean': [True],\n 'pad_type': ['post'], # , 'post'],\n 'trunc_type': ['post'], # 'post'],\n 'omit_stopwords': [False],\n 'ignore_urls': [False],\n 'fix_contractions': [True],\n 'stem': [True],\n 'remove_foreign_characters': [False], # , False],\n 'lower': [True], # , False],\n 'remove_punctuation': [True], # , False],\n 'bigrams': [True], # , False]\n },\n ])\n }\n\n def prod(a):\n if len(a) == 0:\n return 1\n return a[0] * prod(a[1:])\n\n num_models = prod([len(pg) for pg in param_grids.values()])\n\n param_grid_names = sorted(list(param_grids.keys()))\n param_grid_list = [param_grids[k] for k in param_grid_names]\n\n all_params, best_params = assemble_results(output_root)\n\n if CHECK_ONLY:\n for i, params in enumerate(itertools.product(*param_grid_list[3:5])):\n params = {k: v for k, v in zip(param_grid_names[3:5], params)}\n print(i, params)\n Preprocessor(**params['preprocessor'], **params['model_preprocessor'])\n\n for i, params in enumerate(itertools.product(*param_grid_list[2:4])):\n params = {k: v for k, v in zip(param_grid_names[2:4], params)}\n print(i, params)\n build_fn(num_classes=3, **params['model'], **params['model_preprocessor'])\n return\n\n for i, params in enumerate(itertools.product(*param_grid_list)):\n mem = psutil.virtual_memory()\n percent_used = 1 - mem.available / mem.total\n print(f'{percent_used:.2%} memory used')\n if percent_used > 0.80:\n # exit failure\n print('Exiting (-1)')\n exit(-1)\n\n params = {k: v for k, v in zip(param_grid_names, params)}\n print(f'\\n{i + 1}/{num_models}: {params}\\n')\n\n if params in all_params:\n # skip this one because we already ran it.\n continue\n\n if best_params is not None:\n # print best performance so far\n print(f'best params: {best_params}')\n print(f'best val loss: {best_params[\"results\"][\"valid\"][\"loss\"]:.6f}')\n print(f'best val acc: {best_params[\"results\"][\"valid\"][\"accuracy\"]:.4%}')\n\n # create a new output directory with path to model file.\n date = datetime.datetime.utcnow().strftime(\"%Y-%m-%d-%H.%M.%S.%f\")\n output_dir = os.path.join(output_root, date)\n if not os.path.exists(output_dir):\n os.makedirs(output_dir)\n model_file = os.path.join(output_dir, 'model.h5')\n\n # get the preprocessed training and validation data\n preprocess_time = time.time()\n classes, data_sets, set_names = get_xy(Preprocessor(**params['preprocessor'], **params['model_preprocessor']),\n target=target)\n ((x_train, y_train), (x_valid, y_valid)) = data_sets\n preprocess_time -= time.time()\n\n # build and compile model\n model = build_fn(num_classes=len(classes), **params['model'], **params['model_preprocessor'])\n\n # setup callbacks\n early_stopping = EarlyStopping(monitor='val_loss', verbose=1, **params['early_stopping'])\n model_checkpoint = ModelCheckpoint(\n filepath=model_file,\n save_weights_only=False, save_freq='epoch',\n save_best_only=True, monitor='val_loss', verbose=1)\n callbacks = [early_stopping, model_checkpoint]\n\n # Use sample weights to treat classes equally in loss and accuracy.\n sample_weight = get_sample_weight(y_train)\n sample_weight_valid = get_sample_weight(y_valid)\n\n # fit the model\n train_time = time.time()\n model.fit(x=x_train, y=y_train, sample_weight=sample_weight, verbose=1,\n validation_data=(x_valid, y_valid, sample_weight_valid), callbacks=callbacks, **params['fit'])\n train_time -= time.time()\n\n # load the best model (last one saved)\n model = load_model(model_file, compile=True)\n\n # compute results\n results = get_performance(model, data_sets, set_names)\n results['time'] = {'train': train_time, 'preprocess': preprocess_time}\n\n print(pd.DataFrame(data=results).T)\n params['results'] = results\n\n # save params and results\n with open(os.path.join(output_dir, 'params.json'), 'w') as fp:\n json.dump(params, fp)\n\n # save a copy of *this* Python file.\n shutil.copyfile(__file__, os.path.join(output_dir, 'roatan.py'))\n\n # for convenience, show the validation loss and accuracy in a file name in the same directory.\n result_file_name = f'{params[\"results\"][\"valid\"][\"loss\"]:.6f}_{params[\"results\"][\"valid\"][\"accuracy\"]:.4f}.out'\n with open(os.path.join(output_dir, result_file_name), 'w'):\n pass\n\n # check_model(output_dir)\n\n if best_params is None or (params['results']['valid']['loss'] < best_params['results']['valid']['loss']):\n best_params = params\n\n # assemble results from all runs into one CSV file in output root.\n assemble_results(output_root)","def get_parameters(config, version_stack_name, app_stack_name, task_definition, app_name, cluster_name, env, version, aws_hosted_zone, base_path, task_definition_arn): # pylint: disable=too-many-arguments,too-many-locals\n\n print(\"Generating Parmeters for CloudFormation template\")\n container_port = [x['portMappings'][0]['containerPort'] for x in task_definition['containerDefinitions'] if x['name'] == app_name][0]\n\n cloudformation = boto3.client('cloudformation')\n print(\"Determining ALB Rule priority...\")\n priority = None\n listener_rule = None\n try:\n response = cloudformation.describe_stack_resources(\n StackName=version_stack_name,\n LogicalResourceId='ListenerRule'\n )\n listener_rule = response['StackResources'][0]['PhysicalResourceId']\n print(\"Listener Rule already exists, not setting priority.\")\n except (KeyError, IndexError, botocore.exceptions.ClientError):\n print(\"Listener Rule does not already exist, getting priority...\")\n if listener_rule is None:\n rules = get_list_of_rules(app_stack_name)\n priority = get_priority(rules)\n print(\"Rule priority is {}.\".format(priority))\n\n print(\"Determining if ALB is internal or internet-facing...\")\n elbv2 = boto3.client('elbv2')\n response = cloudformation.describe_stack_resources(\n StackName=app_stack_name,\n LogicalResourceId='ALB'\n )\n alb = response['StackResources'][0]['PhysicalResourceId']\n response = elbv2.describe_load_balancers(\n LoadBalancerArns=[alb],\n )\n alb_scheme = response['LoadBalancers'][0]['Scheme']\n print(\"ALB is {}.\".format(alb_scheme))\n\n try:\n autoscaling = str(config['autoscaling'])\n autoscaling_target = str(config['autoscaling_target'])\n autoscaling_max_size = str(config['autoscaling_max_size'])\n autoscaling_min_size = str(config['autoscaling_min_size'])\n except KeyError as exception:\n print(\"Autoscaling parameter missing, disabling autoscaling ({})\".format(exception))\n autoscaling = None\n autoscaling_target = None\n autoscaling_max_size = None\n autoscaling_min_size = None\n\n parameters = [\n {\n \"ParameterKey\": \"Name\",\n \"ParameterValue\": app_name\n },\n {\n \"ParameterKey\": 'ClusterName',\n \"ParameterValue\": cluster_name\n },\n {\n \"ParameterKey\": 'Environment',\n \"ParameterValue\": env\n },\n {\n \"ParameterKey\": 'Version',\n \"ParameterValue\": version\n },\n {\n \"ParameterKey\": 'HealthCheckPath',\n \"ParameterValue\": config['lb_health_check']\n },\n {\n \"ParameterKey\": 'HealthCheckGracePeriod',\n \"ParameterValue\": str(config['lb_health_check_grace_period'])\n },\n {\n \"ParameterKey\": 'HealthCheckTimeout',\n \"ParameterValue\": str(config['lb_health_check_timeout'])\n },\n {\n \"ParameterKey\": 'HealthCheckInterval',\n \"ParameterValue\": str(config['lb_health_check_interval'])\n },\n {\n \"ParameterKey\": 'ContainerPort',\n \"ParameterValue\": str(container_port)\n },\n {\n \"ParameterKey\": 'Domain',\n \"ParameterValue\": aws_hosted_zone\n },\n {\n \"ParameterKey\": 'Path',\n \"ParameterValue\": base_path\n },\n {\n \"ParameterKey\": 'TaskDefinitionArn',\n \"ParameterValue\": task_definition_arn\n },\n {\n \"ParameterKey\": 'AlbScheme',\n \"ParameterValue\": alb_scheme\n },\n {\n \"ParameterKey\": 'Autoscaling',\n \"ParameterValue\": autoscaling\n },\n {\n \"ParameterKey\": 'AutoscalingTargetValue',\n \"ParameterValue\": autoscaling_target\n },\n {\n \"ParameterKey\": 'AutoscalingMaxSize',\n \"ParameterValue\": autoscaling_max_size\n },\n {\n \"ParameterKey\": 'AutoscalingMinSize',\n \"ParameterValue\": autoscaling_min_size\n },\n {\n \"ParameterKey\": 'ECSServiceName',\n \"ParameterValue\": \"{}-ECSService\".format(version_stack_name)\n },\n {\n \"ParameterKey\": 'ECSServiceSecurityClassification',\n \"ParameterValue\": str(config['security_classification'])\n },\n {\n \"ParameterKey\": 'ECSServiceSecurityDataType',\n \"ParameterValue\": str(config['security_data_type'])\n },\n {\n \"ParameterKey\": 'ECSServiceSecurityAccessibility',\n \"ParameterValue\": str(config['security_accessibility'])\n }\n ]\n\n parameters = [x for x in parameters if x['ParameterValue'] is not None] # remove all keys that have a value of None\n\n if priority is not None:\n parameters.append({\n \"ParameterKey\": 'RulePriority',\n \"ParameterValue\": str(priority)\n })\n else:\n parameters.append({\n \"ParameterKey\": 'RulePriority',\n \"UsePreviousValue\": True\n })\n\n print(\"Finished generating parameters:\")\n for param in parameters:\n print(\"{:30}{}\".format(param['ParameterKey'] + ':', param.get('ParameterValue', None)))\n return parameters","def read_setup(inifile):\n # inifile = os.path.join(spathy_path, inifile)\n print(inifile)\n cfg = configparser.ConfigParser()\n cfg.read(inifile)\n\n pp = {}\n for s in cfg.sections():\n section = s.encode('ascii', 'ignore')\n pp[section] = {}\n for k, v in cfg.items(section):\n key = k.encode('ascii', 'ignore')\n val = v.encode('ascii', 'ignore')\n if section == 'General': # 'general' section\n pp[section][key] = val\n else:\n pp[section][key] = float(val)\n pp['General']['dt'] = float(pp['General']['dt'])\n\n pgen = pp['General']\n pcpy = pp['CanopyGrid']\n pbu = pp['BucketGrid']\n ptop = pp['Topmodel']\n\n return pgen, pcpy, pbu, ptop","def visualize_saliency_of_output(FLAGS, model, input_images=[]):\n if len(input_images) == 0:\n # use predefined images\n img_dir='/esat/opal/kkelchte/docker_home/pilot_data/visualization_images'\n input_images=sorted([img_dir+'/'+f for f in os.listdir(img_dir)])\n\n print(\"[tools.py]: extracting saliency maps of {0} in {1}\".format([os.path.basename(i) for i in input_images], os.path.dirname(input_images[0])))\n \n \n inputs = load_images(input_images, model.input_size[1:])\n\n print 'shape: ',inputs.shape\n\n if 'nfc' in FLAGS.network:\n inputs = np.concatenate([inputs]*FLAGS.n_frames, axis=-1)\n \n # extract deconvolution\n import tf_cnnvis\n\n # layers = ['c']\n # layers=['MobilenetV1_1/control/Conv2d_1c_1x1/Conv2D']\n # layers=['MobilenetV1_1/control/Conv2d_1c_1x1/Conv2D','MobilenetV1_1/AvgPool_1a/AvgPool']\n\n # layers = [str(i.name) for i in model.sess.graph.get_operations() if 'outputs' in i.name and not 'activations' in i.name and not 'gradients' in i.name]\n layers = [model.endpoints['eval']['outputs'].name[:-2]] #cut out :0 in the end to change name from tensor to operation name\n # layers = ['outputs']\n \n # results = tf_cnnvis.activation_visualization(sess_graph_path = model.sess, \n # value_feed_dict = {model.inputs : inputs}, \n # layers=layers)\n results = tf_cnnvis.deconv_visualization(sess_graph_path = model.sess, \n value_feed_dict = {model.inputs : inputs}, \n layers=layers)\n\n # Normalize deconvolution within 0:1 range\n num_rows=0\n clean_results={} \n # Loop over layers\n for k in results.keys():\n clean_results[k]=[]\n # Loop over channels\n for c in range(len(results[k])):\n num_rows+=1\n clean_results[k].append(np.zeros((results[k][c].shape[0:3])))\n # Loop over images\n for i in range(results[k][c].shape[0]):\n clean_results[k][c][i]=deprocess_image(results[k][c][i],one_channel=True)\n if num_rows > 6:\n print(\"[tools.py]: There are too many columns to create a proper image.\")\n return\n\n # create one combined image with each input image on each column\n fig, axes = plt.subplots(num_rows+1,min(len(input_images),5),figsize=(23, 4*(2*len(results.keys())+1)))\n # fig, axes = plt.subplots(num_columns+1,min(len(input_images),5),figsize=(23, 4*(2*len(results.keys())+1)))\n # add original images in first row\n for i in range(axes.shape[1]):\n axes[0, i].set_title(os.path.basename(input_images[i]).split('.')[0])\n axes[0, i].imshow(matplotlibprove(inputs[i]), cmap='inferno')\n axes[0, i].axis('off')\n \n # experts=np.asarray([[k]*(FLAGS.action_quantity if FLAGS.discrete else 1) for v in sorted(model.factor_offsets.values()) for k in model.factor_offsets.keys() if model.factor_offsets[k]==v]).flatten()\n\n # add deconvolutions over the columns\n row_index = 1\n for k in results.keys(): # go over layers\n for c in range(len(results[k])): # add each channel in 2 new column\n for i in range(axes.shape[1]): # fill row going over input images\n # axes[row_index, i].set_title(k.split('/')[1]+'/'+k.split('/')[2]+'_'+str(c))\n axes[row_index, i].set_title(k+'_'+str(c))\n # axes[row_index, i].set_title(experts[c])\n \n axes[row_index, i].imshow(np.concatenate((inputs[i],np.expand_dims(clean_results[k][c][i],axis=2)), axis=2))\n axes[row_index, i].axis('off')\n # row_index+=2\n row_index+=1\n # plt.show()\n plt.savefig(FLAGS.summary_dir+FLAGS.log_tag+'/saliency_maps.jpg',bbox_inches='tight')","def mex_parameter_parser(self, mex_xml):\n mex_inputs = mex_xml.xpath('tag[@name=\"inputs\"]')\n if mex_inputs:\n for tag in mex_inputs[0]:\n if tag.tag == 'tag' and tag.attrib['type'] != 'system-input':\n log.debug('Set options with %s as %s'%(tag.attrib['name'],tag.attrib['value']))\n setattr(self.options,tag.attrib['name'],tag.attrib['value'])\n else:\n log.debug('BotanicamFS: No Inputs Found on MEX!')","def process_aci_config(self, acicfgfile, is_cfg_struct=False, isj2=False, vars={}):\n urlparams = {}\n # if file is a j2 template\n if isj2:\n acicfgtemplate = Template(acicfgfile)\n # render final yaml configuration file from j2 template\n acicfgfile = acicfgtemplate.render(vars)\n # print(acicfgfile)\n if is_cfg_struct: # acicfgfile is in fact not a yaml file but a python data structure\n acicfg = acicfgfile\n else:\n # convert yaml file into structured data\n acicfg = self.yaml2struct(acicfgfile)\n # process standard ACI config structure\n if \"imdata\" in acicfg:\n for subtree in acicfg[\"imdata\"]:\n self.process_tree(subtree, urlparams)\n # 'default\" names of parameters which are used in urlparams\n # (aciapidesc)\n # when full config tree is not specifies in aci_tree\n # i.e. fvTenant is not specified\n if \"url_names\" in acicfg:\n urlparams = acicfg[\"url_names\"]\n # extract variables from configuration file\n # if vars var conigured in j2 template, they will be used in\n # child files\n if \"vars\" in acicfg:\n for varitem in acicfg[\"vars\"].keys():\n vars[varitem] = acicfg[\"vars\"][varitem]\n # transofm final yaml config into structured data\n # acicfg = self.yaml2struct(renderedcfg)\n if (\n \"aci_trees\" in acicfg and \"aci_items\" not in acicfg\n ): # tree based configuration\n for subtree in acicfg[\"aci_trees\"]:\n self.process_tree(subtree, urlparams)\n elif (\"aci_items\" in acicfg):\n #self.aciitemcfg[\"aci_items\"] = acicfg[\"aci_items\"] # !!! THIS SHOULD BE DONE BETTER !!!\n self.process_items_cfg(acicfg[\"aci_items\"])\n # config file contains reference to another config file\n if \"aci_cfgfiles\" in acicfg:\n for filename in acicfg[\"aci_cfgfiles\"]:\n # absolute path to the file\n filename = os.path.join(self.configfiledir, filename)\n filename = os.path.normpath(filename)\n aci_cfg = self.read_configfile(filename)\n # is filename j2 template ?\n isj2 = filename.split(\".\")[-1] == \"j2\"\n self.process_aci_config(aci_cfg, False, isj2, vars)","def load_cls_params(self):\n with open('models/Final/linear_svc.p', 'rb') as model_file:\n model = pickle.load(model_file)\n self.svc = model['svc']\n self.X_scaler = model['X_scaler']\n self.parameters = model['parameters']\n\n print(self.parameters)","def setup(self):\n\n for name, infos in Rt.geom_dict.items():\n if name in Rt.optim_var_dict:\n self.add_input(name, val=infos[1][0])"],"string":"[\n \"def doParametersOfInterest(self):\\n \\n self.modelBuilder.doVar('expr::cosW(\\\"0.87681811112\\\",)')\\n self.modelBuilder.doVar('expr::sinW(\\\"0.48082221247\\\",)')\\n self.modelBuilder.doVar('expr::mZ(\\\"91.2\\\",)')\\n self.modelBuilder.doVar('expr::Lambda1(\\\"100.0\\\",)')\\n self.modelBuilder.doVar('expr::e2(\\\"0.0917\\\",)')\\n self.modelBuilder.doVar('expr::gs2(\\\"1.533\\\",)')\\n\\n # EFT Higgs basis couplings\\n\\n self.modelBuilder.doVar('cZ[0,-1,1]') \\n self.modelBuilder.doVar(\\\"cZZ[0,-2,2]\\\") \\n self.modelBuilder.doVar(\\\"cZZt[0,-2,2]\\\") \\n self.modelBuilder.doVar(\\\"cZB[0,-6,6]\\\") \\n\\n poi='cZ,cZZ,cZZt,cZB'\\n\\n # Amplitude couplings from EFT couplings \\n\\n self.modelBuilder.doVar('expr::a1(\\\"@0+1\\\",cZ)') # (\\\"2*(@0+1)\\\",cZ) in AN/Paper but a1 = 1 for signal model and width calculation\\n self.modelBuilder.doVar('expr::a2(\\\"-1*@0*(@1/(2*pow(@2,2)*pow(@3,2)))\\\",cZZ,e2,sinW,cosW)')\\n self.modelBuilder.doVar('expr::a3(\\\"-1*@0*(@1/(2*pow(@2,2)*pow(@3,2)))\\\",cZZt,e2,sinW,cosW)')\\n self.modelBuilder.doVar('expr::k1(\\\"@0*(@1*pow(@2,2)/(pow(@3,2)*pow(@4,2)))\\\",cZB,e2,Lambda1,sinW,mZ)')\\n self.modelBuilder.doVar('expr::k1L1(\\\"@0/pow(@1,2)\\\",k1,Lambda1)')\\n\\n ###### gamma_H ########\\n\\n # SMEFT relationships for VV couplings (Expressed using amplitude couplings)\\n\\n self.modelBuilder.doVar('expr::kappa(\\\"1.0\\\",)')\\n self.modelBuilder.doVar('expr::kappa_tilde(\\\"0.0\\\",)') \\n\\n self.modelBuilder.doVar('expr::a1_WW(\\\"@0\\\",a1)')\\n self.modelBuilder.doVar('expr::a2_WW(\\\"@0*@0*@1\\\",cosW,a2)')\\n self.modelBuilder.doVar('expr::a3_WW(\\\"@0*@0*@1\\\",cosW,a3)')\\n self.modelBuilder.doVar('expr::k1_WW(\\\"(@2 / (@0*@0 - @1*@1) - 2*@1*@1*@3*@4*@4 /(@5*@5*(@0*@0 - @1*@1)))\\\",cosW,sinW,k1,a2,Lambda1,mZ)')\\n self.modelBuilder.doVar('expr::k2_k1(\\\"2*@0*@1*@2/(@0*@0 - @1*@1)\\\",cosW,sinW,k1)')\\n self.modelBuilder.doVar('expr::k2_a2(\\\"-2*@0*@1*@3*@4*@4/((@2*@2)*(@0*@0 - @1*@1))\\\",cosW,sinW,mZ,a2,Lambda1)')\\n self.modelBuilder.doVar('expr::k2(\\\"@0 + @1\\\",k2_k1,k2_a2)')\\n\\n # Determine gamma_H from VV couplings\\n\\n zz_expr = '\\\"4*(@0*@0/4. + 0.1695*@3*@3 + 0.09076*@1*@1 + 0.03809*@2*@2 + 0.8095*@0*@3/2. + 0.5046*@0*@1/2. + 0.2092*@1*@3 + 0.1023*@4*@4 + 0.1901*@0*@4/2. + 0.07429*@3*@4 + 0.04710*@1*@4) \\\",a1,a2,a3,k1,k2'\\n ww_expr = '\\\"4*(@0*@0/4. + 0.1320*@3*@3 + 0.1944*@1*@1 + 0.08075*@2*@2 + 0.7204*@0*@3/2. + 0.7437*@0*@1/2. + 0.2774*@3*@1) \\\",a1_WW,a2_WW,a3_WW,k1_WW'\\n zgamma_expr = '\\\"4*(1.118600*@0*@0/4. +0.0035*@1*@1 - 0.125010*@0*@1/2. + 0.000003*@1*@1 - 0.00018*@1*@1 + 0.003100*@0*@1/2. +0.00126*@2*@2 + 0.000005*@2*@2 -0.00047*@2*@2)\\\",a1_WW,kappa,kappa_tilde'\\n gg_expr = '\\\"(1.1068*@0*@0 + 0.0082*@0*@0 - 0.1150*@0*@0 + 2.5717*@1*@1 + 0.0091*@1*@1 - 0.1982*@1*@1)\\\",kappa,kappa_tilde'\\n bb_expr = '\\\"(@0*@0 + @1*@1)\\\",kappa,kappa_tilde'\\n cc_expr = '\\\"(@0*@0 + @1*@1)\\\",kappa,kappa_tilde'\\n tautau_expr = '\\\"(@0*@0 + @1*@1)\\\",kappa,kappa_tilde'\\n mumu_expr = '\\\"(@0*@0 + @1*@1)\\\",kappa,kappa_tilde'\\n gmgm_expr = '\\\"4*(1.6054*@0*@0/4. + 0.07312*@1*@1 - 0.6854*@0*@1/2. + 0.00002*@1*@1 - 0.0018*@1*@1 + 0.0085*@0*@1/2. + 0.1699*@2*@2 + 0.00002*@2*@2 - 0.0031*@2*@2)\\\",a1_WW,kappa,kappa_tilde'\\n \\n self.modelBuilder.doVar('expr::R_WW('+str(ww_expr)+')')\\n self.modelBuilder.doVar('expr::R_ZZ('+str(zz_expr)+')')\\n self.modelBuilder.doVar('expr::R_Zgamma('+str(zgamma_expr)+')')\\n self.modelBuilder.doVar('expr::R_gg('+str(gg_expr)+')')\\n self.modelBuilder.doVar('expr::R_bb('+str(bb_expr)+')')\\n self.modelBuilder.doVar('expr::R_cc('+str(cc_expr)+')')\\n self.modelBuilder.doVar('expr::R_tautau('+str(tautau_expr)+')')\\n self.modelBuilder.doVar('expr::R_mumu('+str(mumu_expr)+')')\\n self.modelBuilder.doVar('expr:R_gammagamma('+str(gmgm_expr)+')')\\n\\n self.modelBuilder.doVar('expr::gammaH(\\\"(0.5824*@0 + 0.2137*@1 + 0.08187*@2 + 0.06272*@3 + 0.02891*@4 + 0.02619*@5 + 0.002270*@6 + 0.001533*@7 + 0.0002176*@8 )/0.9998\\\",R_bb,R_WW,R_gg,R_tautau,R_cc,R_ZZ,R_gammagamma,R_Zgamma,R_mumu)') \\n\\n ###########################\\n\\n self.g1V = GetCoupTerms(1,1,1,-0.0001,\\\"1V\\\") # Compensate for scaling of k1 templates \\n self.g2V = GetCoupTerms(1,1,1,-0.0001,\\\"2V\\\") \\n \\n self.modelBuilder.doVar(\\\"expr::g2V_1(\\\\\\\"\\\"+str(self.g2V[0])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T1(\\\\\\\"((pow(@0,4))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_1)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T1_Neg(\\\\\\\"-1*((pow(@0,4))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_1)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_2(\\\\\\\"\\\"+str(self.g2V[1])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T2(\\\\\\\"((pow(@0,3)*@1)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_2)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T2_Neg(\\\\\\\"-1*((pow(@0,3)*@1)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_2)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_3(\\\\\\\"\\\"+str(self.g2V[2])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T3(\\\\\\\"((pow(@0,2)*pow(@1,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_3)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T3_Neg(\\\\\\\"-1*((pow(@0,2)*pow(@1,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_3)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_4(\\\\\\\"\\\"+str(self.g2V[3])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T4(\\\\\\\"((@0*pow(@1,3))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_4)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T4_Neg(\\\\\\\"-1*((@0*pow(@1,3))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_4)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_5(\\\\\\\"\\\"+str(self.g2V[4])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T5(\\\\\\\"((pow(@1,4))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_5)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T5_Neg(\\\\\\\"-1*((pow(@1,4))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_5)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_6(\\\\\\\"\\\"+str(self.g2V[5])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T6(\\\\\\\"((pow(@0,3)*@2)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_6)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T6_Neg(\\\\\\\"-1*((pow(@0,3)*@2)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_6)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_7(\\\\\\\"\\\"+str(self.g2V[6])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T7(\\\\\\\"((pow(@0,2)*pow(@2,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_7)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T7_Neg(\\\\\\\"-1*((pow(@0,2)*pow(@2,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_7)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_8(\\\\\\\"\\\"+str(self.g2V[7])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T8(\\\\\\\"((@0*pow(@2,3))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_8)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T8_Neg(\\\\\\\"-1*((@0*pow(@2,3))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_8)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_9(\\\\\\\"\\\"+str(self.g2V[8])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T9(\\\\\\\"((pow(@2,4))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_9)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T9_Neg(\\\\\\\"-1*((pow(@2,4))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_9)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_10(\\\\\\\"\\\"+str(self.g2V[9])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T10(\\\\\\\"((pow(@0,3)*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_10)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T10_Neg(\\\\\\\"-1*((pow(@0,3)*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_10)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_11(\\\\\\\"\\\"+str(self.g2V[10])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T11(\\\\\\\"((pow(@0,2)*pow(@3,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_11)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T11_Neg(\\\\\\\"-1*((pow(@0,2)*pow(@3,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_11)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_12(\\\\\\\"\\\"+str(self.g2V[11])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T12(\\\\\\\"((@0*pow(@3,3))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_12)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T12_Neg(\\\\\\\"-1*((@0*pow(@3,3))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_12)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_13(\\\\\\\"\\\"+str(self.g2V[12])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T13(\\\\\\\"((pow(@3,4))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_13)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T13_Neg(\\\\\\\"-1*((pow(@3,4))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_13)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_14(\\\\\\\"\\\"+str(self.g2V[13])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T14(\\\\\\\"((pow(@1,3)*@2)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_14)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T14_Neg(\\\\\\\"-1*((pow(@1,3)*@2)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_14)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_15(\\\\\\\"\\\"+str(self.g2V[14])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T15(\\\\\\\"((pow(@1,2)*pow(@2,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_15)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T15_Neg(\\\\\\\"-1*((pow(@1,2)*pow(@2,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_15)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_16(\\\\\\\"\\\"+str(self.g2V[15])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T16(\\\\\\\"((@1*pow(@2,3))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_16)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T16_Neg(\\\\\\\"-1*((@1*pow(@2,3))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_16)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_17(\\\\\\\"\\\"+str(self.g2V[16])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T17(\\\\\\\"((pow(@1,3)*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_17)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T17_Neg(\\\\\\\"-1*((pow(@1,3)*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_17)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_18(\\\\\\\"\\\"+str(self.g2V[17])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T18(\\\\\\\"((pow(@1,2)*pow(@3,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_18)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T18_Neg(\\\\\\\"-1*((pow(@1,2)*pow(@3,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_18)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_19(\\\\\\\"\\\"+str(self.g2V[18])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T19(\\\\\\\"((@1*pow(@3,3))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_19)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T19_Neg(\\\\\\\"-1*((@1*pow(@3,3))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_19)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_20(\\\\\\\"\\\"+str(self.g2V[19])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T20(\\\\\\\"((pow(@2,3)*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_20)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T20_Neg(\\\\\\\"-1*((pow(@2,3)*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_20)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_21(\\\\\\\"\\\"+str(self.g2V[20])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T21(\\\\\\\"((pow(@2,2)*pow(@3,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_21)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T21_Neg(\\\\\\\"-1*((pow(@2,2)*pow(@3,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_21)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_22(\\\\\\\"\\\"+str(self.g2V[21])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T22(\\\\\\\"((@2*pow(@3,3))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_22)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T22_Neg(\\\\\\\"-1*((@2*pow(@3,3))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_22)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_23(\\\\\\\"\\\"+str(self.g2V[22])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T23(\\\\\\\"((@0*@1*pow(@2,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_23)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T23_Neg(\\\\\\\"-1*((@0*@1*pow(@2,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_23)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_24(\\\\\\\"\\\"+str(self.g2V[23])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T24(\\\\\\\"((@0*pow(@1,2)*@2)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_24)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T24_Neg(\\\\\\\"-1*((@0*pow(@1,2)*@2)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_24)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_25(\\\\\\\"\\\"+str(self.g2V[24])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T25(\\\\\\\"((pow(@0,2)*@1*@2)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_25)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T25_Neg(\\\\\\\"-1*((pow(@0,2)*@1*@2)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_25)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_26(\\\\\\\"\\\"+str(self.g2V[25])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T26(\\\\\\\"((@0*@1*pow(@3,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_26)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T26_Neg(\\\\\\\"-1*((@0*@1*pow(@3,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_26)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_27(\\\\\\\"\\\"+str(self.g2V[26])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T27(\\\\\\\"((@0*pow(@1,2)*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_27)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T27_Neg(\\\\\\\"-1*((@0*pow(@1,2)*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_27)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_28(\\\\\\\"\\\"+str(self.g2V[27])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T28(\\\\\\\"((pow(@0,2)*@1*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_28)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T28_Neg(\\\\\\\"-1*((pow(@0,2)*@1*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_28)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_29(\\\\\\\"\\\"+str(self.g2V[28])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T29(\\\\\\\"((@0*@2*pow(@3,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_29)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T29_Neg(\\\\\\\"-1*((@0*@2*pow(@3,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_29)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_30(\\\\\\\"\\\"+str(self.g2V[29])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T30(\\\\\\\"((@0*pow(@2,2)*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_30)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T30_Neg(\\\\\\\"-1*((@0*pow(@2,2)*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_30)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_31(\\\\\\\"\\\"+str(self.g2V[30])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T31(\\\\\\\"((pow(@0,2)*@2*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_31)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T31_Neg(\\\\\\\"-1*((pow(@0,2)*@2*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_31)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_32(\\\\\\\"\\\"+str(self.g2V[31])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T32(\\\\\\\"((@1*@2*pow(@3,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_32)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T32_Neg(\\\\\\\"-1*((@1*@2*pow(@3,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_32)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_33(\\\\\\\"\\\"+str(self.g2V[32])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T33(\\\\\\\"((@1*pow(@2,2)*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_33)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T33_Neg(\\\\\\\"-1*((@1*pow(@2,2)*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_33)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_34(\\\\\\\"\\\"+str(self.g2V[33])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T34(\\\\\\\"((pow(@1,2)*@2*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_34)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T34_Neg(\\\\\\\"-1*((pow(@1,2)*@2*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_34)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_35(\\\\\\\"\\\"+str(self.g2V[34])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T35(\\\\\\\"((@0*@1*@2*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_35)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T35_Neg(\\\\\\\"-1*((@0*@1*@2*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_35)\\\") \\n \\n self.modelBuilder.doVar(\\\"expr::g1V_1(\\\\\\\"\\\"+str(self.g1V[0])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T1(\\\\\\\"((pow(@0,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_1)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T1_Neg(\\\\\\\"-1*((pow(@0,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_1)\\\") \\n self.modelBuilder.doVar(\\\"expr::g1V_2(\\\\\\\"\\\"+str(self.g1V[1])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T2(\\\\\\\"((@0*@1)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_2)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T2_Neg(\\\\\\\"-1*((@0*@1)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_2)\\\") \\n self.modelBuilder.doVar(\\\"expr::g1V_3(\\\\\\\"\\\"+str(self.g1V[2])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T3(\\\\\\\"((pow(@1,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_3)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T3_Neg(\\\\\\\"-1*((pow(@1,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_3)\\\") \\n self.modelBuilder.doVar(\\\"expr::g1V_4(\\\\\\\"\\\"+str(self.g1V[3])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T4(\\\\\\\"((@0*@2)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_4)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T4_Neg(\\\\\\\"-1*((@0*@2)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_4)\\\") \\n self.modelBuilder.doVar(\\\"expr::g1V_5(\\\\\\\"\\\"+str(self.g1V[4])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T5(\\\\\\\"((pow(@2,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_5)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T5_Neg(\\\\\\\"-1*((pow(@2,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_5)\\\") \\n self.modelBuilder.doVar(\\\"expr::g1V_6(\\\\\\\"\\\"+str(self.g1V[5])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T6(\\\\\\\"((@0*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_6)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T6_Neg(\\\\\\\"-1*((@0*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_6)\\\") \\n self.modelBuilder.doVar(\\\"expr::g1V_7(\\\\\\\"\\\"+str(self.g1V[6])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T7(\\\\\\\"((pow(@3,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_7)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T7_Neg(\\\\\\\"-1*((pow(@3,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_7)\\\") \\n self.modelBuilder.doVar(\\\"expr::g1V_8(\\\\\\\"\\\"+str(self.g1V[7])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T8(\\\\\\\"((@1*@2)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_8)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T8_Neg(\\\\\\\"-1*((@1*@2)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_8)\\\") \\n self.modelBuilder.doVar(\\\"expr::g1V_9(\\\\\\\"\\\"+str(self.g1V[8])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T9(\\\\\\\"((@1*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_9)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T9_Neg(\\\\\\\"-1*((@1*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_9)\\\") \\n self.modelBuilder.doVar(\\\"expr::g1V_10(\\\\\\\"\\\"+str(self.g1V[9])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T10(\\\\\\\"((@2*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_10)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T10_Neg(\\\\\\\"-1*((@2*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_10)\\\") \\n \\n self.modelBuilder.doSet(\\\"POI\\\",poi)\",\n \"def doParametersOfInterest(self):\\r\\n if self.fg4fixed:\\r\\n self.modelBuilder.doVar(\\\"CMS_zz4l_fg4[0]\\\")\\r\\n self.modelBuilder.doVar(\\\"r[1,0,4]\\\")\\r\\n print \\\"Fixing CMS_zz4l_fg4\\\"\\r\\n poi = \\\"r\\\"\\r\\n else:\\r\\n if self.modelBuilder.out.var(\\\"CMS_zz4l_fg4\\\"):\\r\\n print \\\"have fg4 inside\\\"\\r\\n else:\\r\\n self.modelBuilder.doVar(\\\"CMS_zz4l_fg4[0.,-1,1]\\\")\\r\\n poi = \\\"CMS_zz4l_fg4\\\"\\r\\n if self.cPOI:\\r\\n if self.modelBuilder.out.var(\\\"cww_zz\\\"):\\r\\n print \\\"have czz_ww inside\\\"\\r\\n else:\\r\\n self.modelBuilder.doVar(\\\"cww_zz[0.5,-10,10]\\\")\\r\\n poi += \\\",cww_zz\\\"\\r\\n\\r\\n if self.fg2POI:\\r\\n if self.modelBuilder.out.var(\\\"CMS_zz4l_fg2\\\"):\\r\\n print \\\"have fg2 inside\\\"\\r\\n else:\\r\\n self.modelBuilder.doVar(\\\"CMS_zz4l_fg2[0.,0,1]\\\")\\r\\n poi += \\\",CMS_zz4l_fg2\\\"\\r\\n if self.muFloating:\\r\\n self.modelBuilder.doVar(\\\"r[1,0,2000]\\\")\\r\\n if self.muAsPOI:\\r\\n print \\\"Treating r as a POI\\\"\\r\\n poi += \\\",r\\\"\\r\\n else:\\r\\n self.modelBuilder.out.var(\\\"r\\\").setAttribute(\\\"flatParam\\\")\\r\\n if self.phiFloating:\\r\\n if self.modelBuilder.out.var(\\\"CMS_zz4l_fg4phi\\\"):\\r\\n print \\\"have fg4phi inside\\\"\\r\\n else: \\r\\n self.modelBuilder.doVar(\\\"CMS_zz4l_fg4phi[0.,-3.1415926,3.1415926]\\\")\\r\\n if self.phiPOI:\\r\\n poi += \\\",CMS_zz4l_fg4phi\\\"\\r\\n else:\\r\\n self.modelBuilder.out.var(\\\"CMS_zz4l_fg4phi\\\").setAttribute(\\\"flatParam\\\")\\r\\n if self.phi2Floating:\\r\\n #self.modelBuilder.doVar(\\\"CMS_zz4l_fg4phi[0.,-math.pi,math.pi]\\\")\\r\\n if self.modelBuilder.out.var(\\\"CMS_zz4l_fg2phi\\\"):\\r\\n print \\\"have fg2phi inside\\\"\\r\\n else: \\r\\n self.modelBuilder.doVar(\\\"CMS_zz4l_fg2phi[0.,-3.1415926,3.1415926]\\\")\\r\\n self.modelBuilder.out.var(\\\"CMS_zz4l_fg2phi\\\").setAttribute(\\\"flatParam\\\")\\r\\n \\r\\n self.modelBuilder.doSet(\\\"POI\\\",poi)\",\n \"def doParametersOfInterest(self):\\n\\n self.modelBuilder.doVar(\\\"eAfb[0.6,-0.75,0.75]\\\");\\n self.modelBuilder.doVar(\\\"eA0[0.05, -1.0, 1.0]\\\");\\n self.modelBuilder.doVar(\\\"rAfb[1.0,-5.0, 5.0]\\\");\\n self.modelBuilder.doVar(\\\"rA0[1.0, -5.0, 5.0]\\\");\\n self.modelBuilder.doSet(\\\"POI\\\",\\\"rAfb,rA0\\\")\\n self.modelBuilder.factory_('expr::mAfb(\\\"@0*@1\\\",eAfb,rAfb)')\\n self.modelBuilder.factory_('expr::mA0(\\\"(@0*@1)\\\",eA0,rA0)')\\n\\n \\n self.modelBuilder.factory_('expr::eAlph(\\\"2.0*@0/(2.0-@0)\\\",eA0)')\\n self.modelBuilder.factory_('expr::eNorm(\\\"3.0/4.0/(2.0+@0)\\\",eAlph)')\\n self.modelBuilder.factory_('expr::eRAlph(\\\"@0*@1\\\",eAlph,eNorm)')\\n self.modelBuilder.factory_('expr::eRpl(\\\"(@0+@1)\\\",eNorm,eAfb)')\\n self.modelBuilder.factory_('expr::eRmn(\\\"(@0-@1)\\\",eNorm,eAfb)')\\n\\n self.modelBuilder.factory_('expr::mAlph(\\\"2.0*@0/(2.0-@0)\\\",mA0)')\\n self.modelBuilder.factory_('expr::mNorm(\\\"3.0/4.0/(2.0+@0)\\\",mAlph)')\\n self.modelBuilder.factory_('expr::mRAlph(\\\"@0*@1\\\",mAlph,mNorm)')\\n self.modelBuilder.factory_('expr::mRpl(\\\"(@0+@1)\\\",mNorm,mAfb)')\\n self.modelBuilder.factory_('expr::mRmn(\\\"(@0-@1)\\\",mNorm,mAfb)')\",\n \"def doParametersOfInterest(self):\\n\\n self.modelBuilder.doVar(\\\"eAfb[0.6,-0.75,0.75]\\\");\\n self.modelBuilder.doVar(\\\"eA0[0.05, -1.0, 1.0]\\\");\\n self.modelBuilder.doVar(\\\"dAfb[0.,-0.75,0.75]\\\");\\n self.modelBuilder.doVar(\\\"dA0[0.0, -1.0, 1.0]\\\");\\n #self.modelBuilder.doSet(\\\"POI\\\",\\\"dAfb,dA0\\\")\\n self.modelBuilder.doSet(\\\"POI\\\",\\\"dAfb\\\")\\n self.modelBuilder.factory_('expr::mAfb(\\\"@0+@1\\\",eAfb,dAfb)')\\n self.modelBuilder.factory_('expr::mA0(\\\"(@0+@1)\\\",eA0,dA0)')\\n\\n \\n self.modelBuilder.factory_('expr::eAlph(\\\"2.0*@0/(2.0-@0)\\\",eA0)')\\n self.modelBuilder.factory_('expr::eNorm(\\\"3.0/4.0/(2.0+@0)\\\",eAlph)')\\n self.modelBuilder.factory_('expr::eRAlph(\\\"@0*@1\\\",eAlph,eNorm)')\\n self.modelBuilder.factory_('expr::eRpl(\\\"(@0+@1)\\\",eNorm,eAfb)')\\n self.modelBuilder.factory_('expr::eRmn(\\\"(@0-@1)\\\",eNorm,eAfb)')\\n\\n self.modelBuilder.factory_('expr::mAlph(\\\"2.0*@0/(2.0-@0)\\\",mA0)')\\n self.modelBuilder.factory_('expr::mNorm(\\\"3.0/4.0/(2.0+@0)\\\",mAlph)')\\n self.modelBuilder.factory_('expr::mRAlph(\\\"@0*@1\\\",mAlph,mNorm)')\\n self.modelBuilder.factory_('expr::mRpl(\\\"(@0+@1)\\\",mNorm,mAfb)')\\n self.modelBuilder.factory_('expr::mRmn(\\\"(@0-@1)\\\",mNorm,mAfb)')\",\n \"def doParametersOfInterest(self):\\n\\n self.modelBuilder.doVar(\\\"eAfb[0.6,-0.75,0.75]\\\");\\n self.modelBuilder.doVar(\\\"eA0[0.05, -1.0, 1.0]\\\");\\n self.modelBuilder.doVar(\\\"mAfb[0.6,-0.75,0.75]\\\");\\n self.modelBuilder.doVar(\\\"mA0[0.05, -1.0, 1.0]\\\");\\n self.modelBuilder.doSet(\\\"POI\\\",\\\"eAfb,mAfb\\\")\\n\\n \\n self.modelBuilder.factory_('expr::eAlph(\\\"2.0*@0/(2.0-@0)\\\",eA0)')\\n self.modelBuilder.factory_('expr::eNorm(\\\"3.0/4.0/(2.0+@0)\\\",eAlph)')\\n self.modelBuilder.factory_('expr::eRAlph(\\\"@0*@1\\\",eAlph,eNorm)')\\n self.modelBuilder.factory_('expr::eRpl(\\\"(@0+@1)\\\",eNorm,eAfb)')\\n self.modelBuilder.factory_('expr::eRmn(\\\"(@0-@1)\\\",eNorm,eAfb)')\\n\\n self.modelBuilder.factory_('expr::mAlph(\\\"2.0*@0/(2.0-@0)\\\",mA0)')\\n self.modelBuilder.factory_('expr::mNorm(\\\"3.0/4.0/(2.0+@0)\\\",mAlph)')\\n self.modelBuilder.factory_('expr::mRAlph(\\\"@0*@1\\\",mAlph,mNorm)')\\n self.modelBuilder.factory_('expr::mRpl(\\\"(@0+@1)\\\",mNorm,mAfb)')\\n self.modelBuilder.factory_('expr::mRmn(\\\"(@0-@1)\\\",mNorm,mAfb)')\",\n \"def doParametersOfInterest(self):\\n\\n self.modelBuilder.doVar(\\\"Afb[0.6,-0.7,0.7]\\\");\\n self.modelBuilder.doVar(\\\"A0[0.05, -1.0, 1.0]\\\");\\n self.modelBuilder.doSet(\\\"POI\\\",\\\"Afb,A0\\\")\\n\\n # ss templates\\n self.modelBuilder.doVar(\\\"R_ee_os_fakes[0.6,0.0,1.0]\\\");\\n self.modelBuilder.doVar(\\\"ee16_fakes_norm[1.0, 0.01, 10.]\\\");\\n self.modelBuilder.doVar(\\\"ee17_fakes_norm[1.0, 0.01, 10.]\\\");\\n self.modelBuilder.doVar(\\\"ee18_fakes_norm[1.0, 0.01, 10.]\\\");\\n #Remember, cant use spaces in these formulas!\\n #self.modelBuilder.options.verbose = 10\\n self.modelBuilder.factory_('expr::R_ee16_qcd_os(\\\"@0*@1\\\",ee16_fakes_norm,R_ee_os_fakes)')\\n self.modelBuilder.factory_('expr::R_ee17_qcd_os(\\\"@0*@1\\\",ee17_fakes_norm,R_ee_os_fakes)')\\n self.modelBuilder.factory_('expr::R_ee18_qcd_os(\\\"@0*@1\\\",ee18_fakes_norm,R_ee_os_fakes)')\\n self.modelBuilder.factory_('expr::R_ee16_qcd_ss(\\\"@0*(1.0-@1)\\\",ee16_fakes_norm,R_ee_os_fakes)')\\n self.modelBuilder.factory_('expr::R_ee17_qcd_ss(\\\"@0*(1.0-@1)\\\",ee17_fakes_norm,R_ee_os_fakes)')\\n self.modelBuilder.factory_('expr::R_ee18_qcd_ss(\\\"@0*(1.0-@1)\\\",ee18_fakes_norm,R_ee_os_fakes)')\\n \\n self.modelBuilder.factory_('expr::Alph(\\\"2.0*@0/(2.0-@0)\\\",A0)')\\n self.modelBuilder.factory_('expr::Norm(\\\"3.0/4.0/(2.0+@0)\\\",Alph)')\\n self.modelBuilder.factory_('expr::RAlph(\\\"@0*@1\\\",Alph,Norm)')\\n self.modelBuilder.factory_('expr::Rpl(\\\"(@0+@1)\\\",Norm,Afb)')\\n self.modelBuilder.factory_('expr::Rmn(\\\"(@0-@1)\\\",Norm,Afb)')\",\n \"def setup_parameters(self):\\n structure = self.ctx.structure_initial_primitive\\n ecutwfc = []\\n ecutrho = []\\n\\n for kind in structure.get_kind_names():\\n try:\\n dual = self.ctx.protocol['pseudo_data'][kind]['dual']\\n cutoff = self.ctx.protocol['pseudo_data'][kind]['cutoff']\\n cutrho = dual * cutoff\\n ecutwfc.append(cutoff)\\n ecutrho.append(cutrho)\\n except KeyError as exception:\\n self.abort_nowait('failed to retrieve the cutoff or dual factor for {}'.format(kind))\\n\\n natoms = len(structure.sites)\\n conv_thr = self.ctx.protocol['convergence_threshold'] * natoms\\n\\n self.ctx.inputs['parameters'] = {\\n 'CONTROL': {\\n 'restart_mode': 'from_scratch',\\n 'tstress': self.ctx.protocol['tstress'],\\n },\\n 'SYSTEM': {\\n 'ecutwfc': max(ecutwfc),\\n 'ecutrho': max(ecutrho),\\n 'smearing': self.ctx.protocol['smearing'],\\n 'degauss': self.ctx.protocol['degauss'],\\n 'occupations': self.ctx.protocol['occupations'],\\n },\\n 'ELECTRONS': {\\n 'conv_thr': conv_thr,\\n }\\n }\",\n \"def setup(self):\\n declared = []\\n spec = get_specs_for_module(self.module_name)\\n\\n # Inputs\\n for entry in spec.cpacs_inout.inputs:\\n if entry.var_name in declared:\\n log.info(\\\"Already declared\\\")\\n elif entry.var_name in Rt.optim_var_dict:\\n var = Rt.optim_var_dict[entry.var_name]\\n if entry.var_name in Rt.optim_var_dict:\\n self.add_input(entry.var_name, val=var[1][0])\\n declared.append(entry.var_name)\\n\\n if declared == []:\\n self.add_input(self.module_name + \\\"_in\\\")\\n declared = []\\n\\n for entry in spec.cpacs_inout.outputs:\\n # Replace special characters from the name of the entry and checks for accronyms\\n entry.var_name = change_var_name(entry.var_name)\\n\\n if entry.var_name in declared:\\n log.info(\\\"Already declared\\\")\\n elif entry.var_name in Rt.optim_var_dict:\\n var = Rt.optim_var_dict[entry.var_name]\\n self.add_output(entry.var_name, val=var[1][0])\\n declared.append(entry.var_name)\\n elif (\\n \\\"aeromap\\\" in entry.var_name and self.module_name == Rt.last_am_module\\n ): # == 'PyTornado': #not skf^is_skf:\\n # Condition to avoid any conflict with skinfriction\\n for name in PARAMS:\\n if name in Rt.optim_var_dict:\\n var = Rt.optim_var_dict[name]\\n self.add_input(name, val=var[1][0])\\n declared.append(entry.var_name)\\n for name in COEFS:\\n if name in Rt.optim_var_dict:\\n var = Rt.optim_var_dict[name]\\n if is_digit(var[1][0]):\\n self.add_output(name, val=var[1][0])\\n else:\\n self.add_output(name)\\n declared.append(entry.var_name)\\n\\n if declared == []:\\n self.add_output(self.module_name + \\\"_out\\\")\",\n \"def doParametersOfInterest(self):\\n\\n self.modelBuilder.doVar(\\\"Afb[0.6,-0.75,0.75]\\\");\\n self.modelBuilder.doVar(\\\"A0[0.05, -1.0, 1.0]\\\");\\n self.modelBuilder.doSet(\\\"POI\\\",\\\"Afb,A0\\\")\\n\\n \\n self.modelBuilder.factory_('expr::Alph(\\\"2.0*@0/(2.0-@0)\\\",A0)')\\n self.modelBuilder.factory_('expr::Norm(\\\"3.0/4.0/(2.0+@0)\\\",Alph)')\\n self.modelBuilder.factory_('expr::RAlph(\\\"@0*@1\\\",Alph,Norm)')\\n self.modelBuilder.factory_('expr::Rpl(\\\"(@0+@1)\\\",Norm,Afb)')\\n self.modelBuilder.factory_('expr::Rmn(\\\"(@0-@1)\\\",Norm,Afb)')\",\n \"def get_input(self):\\n system, configuration = self.get_system_configuration(None)\\n references = self.parent.references\\n\\n P = self.parameters.current_values_to_dict(\\n context=seamm.flowchart_variables._data\\n )\\n\\n # The model chemistry, for labeling properties.\\n self.model = P[\\\"hamiltonian\\\"]\\n\\n # Have to fix formatting for printing...\\n PP = dict(P)\\n for key in PP:\\n if isinstance(PP[key], units_class):\\n PP[key] = \\\"{:~P}\\\".format(PP[key])\\n\\n # Save the description for later printing\\n self.description = []\\n self.description.append(__(self.description_text(PP), **PP, indent=self.indent))\\n\\n # Start gathering the keywords\\n keywords = copy.deepcopy(P[\\\"extra keywords\\\"])\\n keywords.append(\\\"1SCF\\\")\\n keywords.append(P[\\\"hamiltonian\\\"])\\n\\n if P[\\\"hamiltonian\\\"] == \\\"AM1\\\":\\n elements = configuration.atoms.symbols\\n references.cite(\\n raw=self.parent._bibliography[\\\"Dewar_1985c\\\"],\\n alias=\\\"Dewar_1985c\\\",\\n module=\\\"mopac_step\\\",\\n level=1,\\n note=\\\"Main reference for AM1 + C, H, N, O.\\\",\\n )\\n for element in (\\\"F\\\", \\\"Cl\\\", \\\"Br\\\", \\\"I\\\"):\\n if element in elements:\\n references.cite(\\n raw=self.parent._bibliography[\\\"Dewar_1988\\\"],\\n alias=\\\"Dewar_1988\\\",\\n module=\\\"mopac_step\\\",\\n level=1,\\n note=\\\"AM1 parameters for F, Cl, Br, I.\\\",\\n )\\n break\\n if \\\"Al\\\" in elements:\\n references.cite(\\n raw=self.parent._bibliography[\\\"Dewar_1990\\\"],\\n alias=\\\"Dewar_1990\\\",\\n module=\\\"mopac_step\\\",\\n level=1,\\n note=\\\"AM1 parameters for Al.\\\",\\n )\\n if \\\"Si\\\" in elements:\\n references.cite(\\n raw=self.parent._bibliography[\\\"Dewar_1987b\\\"],\\n alias=\\\"Dewar_1987b\\\",\\n module=\\\"mopac_step\\\",\\n level=1,\\n note=\\\"AM1 parameters for Si.\\\",\\n )\\n if \\\"P\\\" in elements:\\n references.cite(\\n raw=self.parent._bibliography[\\\"Dewar_1989\\\"],\\n alias=\\\"Dewar_1989\\\",\\n module=\\\"mopac_step\\\",\\n level=1,\\n note=\\\"AM1 parameters for P.\\\",\\n )\\n if \\\"S\\\" in elements:\\n references.cite(\\n raw=self.parent._bibliography[\\\"Dewar_1990b\\\"],\\n alias=\\\"Dewar_1990b\\\",\\n module=\\\"mopac_step\\\",\\n level=1,\\n note=\\\"AM1 parameters for S.\\\",\\n )\\n if \\\"Zn\\\" in elements:\\n references.cite(\\n raw=self.parent._bibliography[\\\"Dewar_1988b\\\"],\\n alias=\\\"Dewar_1988b\\\",\\n module=\\\"mopac_step\\\",\\n level=1,\\n note=\\\"AM1 parameters for Zn.\\\",\\n )\\n if \\\"Ge\\\" in elements:\\n references.cite(\\n raw=self.parent._bibliography[\\\"Dewar_1989b\\\"],\\n alias=\\\"Dewar_1989b\\\",\\n module=\\\"mopac_step\\\",\\n level=1,\\n note=\\\"AM1 parameters for Ge.\\\",\\n )\\n if \\\"Mo\\\" in elements:\\n references.cite(\\n raw=self.parent._bibliography[\\\"Voityuk_2000\\\"],\\n alias=\\\"Voityuk_2000\\\",\\n module=\\\"mopac_step\\\",\\n level=1,\\n note=\\\"AM1 parameters for Mo.\\\",\\n )\\n if \\\"Hg\\\" in elements:\\n references.cite(\\n raw=self.parent._bibliography[\\\"Dewar_1989c\\\"],\\n alias=\\\"Dewar_1989c\\\",\\n module=\\\"mopac_step\\\",\\n level=1,\\n note=\\\"AM1 parameters for Hg.\\\",\\n )\\n for element in (\\n \\\"Li\\\",\\n \\\"Be\\\",\\n \\\"Na\\\",\\n \\\"Mg\\\",\\n \\\"K\\\",\\n \\\"Ca\\\",\\n \\\"Ga\\\",\\n \\\"As\\\",\\n \\\"Se\\\",\\n \\\"Rb\\\",\\n \\\"Sr\\\",\\n \\\"In\\\",\\n \\\"Sn\\\",\\n \\\"Sb\\\",\\n \\\"Te\\\",\\n \\\"Cs\\\",\\n \\\"Ba\\\",\\n \\\"Pb\\\",\\n \\\"Bi\\\",\\n ):\\n if element in elements:\\n references.cite(\\n raw=self.parent._bibliography[\\\"Stewart_2004\\\"],\\n alias=\\\"Stewart_2004\\\",\\n module=\\\"mopac_step\\\",\\n level=1,\\n note=\\\"AM1 parameterization for main-group elements.\\\",\\n )\\n break\\n elif P[\\\"hamiltonian\\\"] == \\\"MNDO\\\" or P[\\\"hamiltonian\\\"] == \\\"MNDOD\\\":\\n elements = configuration.atoms.symbols\\n references.cite(\\n raw=self.parent._bibliography[\\\"Dewar_1977\\\"],\\n alias=\\\"Dewar_1977\\\",\\n module=\\\"mopac_step\\\",\\n level=1,\\n note=\\\"Main reference for MNDO + C, H, N, O.\\\",\\n )\\n if \\\"Be\\\" in elements:\\n references.cite(\\n raw=self.parent._bibliography[\\\"Dewar_1978\\\"],\\n alias=\\\"Dewar_1978\\\",\\n module=\\\"mopac_step\\\",\\n level=1,\\n note=\\\"MNDO parameters for Be.\\\",\\n )\\n if \\\"B\\\" in elements or \\\"Al\\\" in elements:\\n if \\\"B\\\" in elements or P[\\\"hamiltonian\\\"] == \\\"MNDO\\\":\\n references.cite(\\n raw=self.parent._bibliography[\\\"Davis_1981\\\"],\\n alias=\\\"Davis_1981\\\",\\n module=\\\"mopac_step\\\",\\n level=1,\\n note=\\\"MNDO parameters for B and Al.\\\",\\n )\\n if \\\"F\\\" in elements:\\n references.cite(\\n raw=self.parent._bibliography[\\\"Dewar_1978b\\\"],\\n alias=\\\"Dewar_1978b\\\",\\n module=\\\"mopac_step\\\",\\n level=1,\\n note=\\\"MNDO parameters for F.\\\",\\n )\\n if \\\"Si\\\" in elements and P[\\\"hamiltonian\\\"] == \\\"MNDO\\\":\\n references.cite(\\n raw=self.parent._bibliography[\\\"Dewar_1986\\\"],\\n alias=\\\"Dewar_1986\\\",\\n module=\\\"mopac_step\\\",\\n level=1,\\n note=\\\"Revised MNDO parameters for Si.\\\",\\n )\\n if \\\"P\\\" in elements and P[\\\"hamiltonian\\\"] == \\\"MNDO\\\":\\n references.cite(\\n raw=self.parent._bibliography[\\\"Dewar_1978b\\\"],\\n alias=\\\"Dewar_1978b\\\",\\n module=\\\"mopac_step\\\",\\n level=1,\\n note=\\\"MNDO parameters for P.\\\",\\n )\\n if \\\"S\\\" in elements and P[\\\"hamiltonian\\\"] == \\\"MNDO\\\":\\n references.cite(\\n raw=self.parent._bibliography[\\\"Dewar_1986b\\\"],\\n alias=\\\"Dewar_1986b\\\",\\n module=\\\"mopac_step\\\",\\n level=1,\\n note=\\\"MNDO parameters for S.\\\",\\n )\\n if \\\"Cl\\\" in elements and P[\\\"hamiltonian\\\"] == \\\"MNDO\\\":\\n references.cite(\\n raw=self.parent._bibliography[\\\"Dewar_1983\\\"],\\n alias=\\\"Dewar_1983\\\",\\n module=\\\"mopac_step\\\",\\n level=1,\\n note=\\\"MNDO parameters for Cl.\\\",\\n )\\n if \\\"Zn\\\" in elements and P[\\\"hamiltonian\\\"] == \\\"MNDO\\\":\\n references.cite(\\n raw=self.parent._bibliography[\\\"Dewar_1986c\\\"],\\n alias=\\\"Dewar_1986c\\\",\\n module=\\\"mopac_step\\\",\\n level=1,\\n note=\\\"MNDO parameters for Zn.\\\",\\n )\\n if \\\"Ge\\\" in elements:\\n references.cite(\\n raw=self.parent._bibliography[\\\"Dewar_1987\\\"],\\n alias=\\\"Dewar_1987\\\",\\n module=\\\"mopac_step\\\",\\n level=1,\\n note=\\\"MNDO parameters for Ge.\\\",\\n )\\n if \\\"Br\\\" in elements and P[\\\"hamiltonian\\\"] == \\\"MNDO\\\":\\n references.cite(\\n raw=self.parent._bibliography[\\\"Dewar_1983b\\\"],\\n alias=\\\"Dewar_1983b\\\",\\n module=\\\"mopac_step\\\",\\n level=1,\\n note=\\\"MNDO parameters for Br.\\\",\\n )\\n if \\\"Sn\\\" in elements:\\n references.cite(\\n raw=self.parent._bibliography[\\\"Dewar_1984\\\"],\\n alias=\\\"Dewar_1984\\\",\\n module=\\\"mopac_step\\\",\\n level=1,\\n note=\\\"MNDO parameters for Sn.\\\",\\n )\\n if \\\"I\\\" in elements and P[\\\"hamiltonian\\\"] == \\\"MNDO\\\":\\n references.cite(\\n raw=self.parent._bibliography[\\\"Dewar_1984b\\\"],\\n alias=\\\"Dewar_1984b\\\",\\n module=\\\"mopac_step\\\",\\n level=1,\\n note=\\\"MNDO parameters for I.\\\",\\n )\\n if \\\"Hg\\\" in elements and P[\\\"hamiltonian\\\"] == \\\"MNDO\\\":\\n references.cite(\\n raw=self.parent._bibliography[\\\"Dewar_1985\\\"],\\n alias=\\\"Dewar_1985\\\",\\n module=\\\"mopac_step\\\",\\n level=1,\\n note=\\\"MNDO parameters for Hg.\\\",\\n )\\n if \\\"Pb\\\" in elements:\\n references.cite(\\n raw=self.parent._bibliography[\\\"Dewar_1985b\\\"],\\n alias=\\\"Dewar_1985b\\\",\\n module=\\\"mopac_step\\\",\\n level=1,\\n note=\\\"MNDO parameters for Pb.\\\",\\n )\\n for element in (\\n \\\"Na\\\",\\n \\\"Mg\\\",\\n \\\"K\\\",\\n \\\"Ca\\\",\\n \\\"Ga\\\",\\n \\\"As\\\",\\n \\\"Se\\\",\\n \\\"Rb\\\",\\n \\\"Sr\\\",\\n \\\"In\\\",\\n \\\"Sb\\\",\\n \\\"Te\\\",\\n \\\"Cs\\\",\\n \\\"Ba\\\",\\n \\\"Tl\\\",\\n \\\"Bi\\\",\\n ):\\n if element in elements:\\n references.cite(\\n raw=self.parent._bibliography[\\\"Stewart_2004\\\"],\\n alias=\\\"Stewart_2004\\\",\\n module=\\\"mopac_step\\\",\\n level=1,\\n note=\\\"MNDO parameterization for main-group elements.\\\",\\n )\\n break\\n if P[\\\"hamiltonian\\\"] == \\\"MNDOD\\\":\\n for element in (\\n \\\"Al\\\",\\n \\\"Si\\\",\\n \\\"P\\\",\\n \\\"S\\\",\\n \\\"Cl\\\",\\n \\\"Br\\\",\\n \\\"I\\\",\\n \\\"Zn\\\",\\n \\\"Cd\\\",\\n \\\"Hg\\\",\\n ):\\n if element in elements:\\n references.cite(\\n raw=self.parent._bibliography[\\\"Thiel_1992\\\"],\\n alias=\\\"Thiel_1992\\\",\\n module=\\\"mopac_step\\\",\\n level=1,\\n note=(\\\"MNDO-D formalism for d-orbitals.\\\"),\\n )\\n references.cite(\\n raw=self.parent._bibliography[\\\"Thiel_1996\\\"],\\n alias=\\\"Thiel_1996\\\",\\n module=\\\"mopac_step\\\",\\n level=1,\\n note=(\\n \\\"MNDO-D, parameters for Al, Si, P, S, Cl, Br, \\\"\\n \\\"I, Zn, Cd, and Hg.\\\"\\n ),\\n )\\n break\\n elif P[\\\"hamiltonian\\\"] == \\\"PM3\\\":\\n elements = configuration.atoms.symbols\\n references.cite(\\n raw=self.parent._bibliography[\\\"Stewart_1989\\\"],\\n alias=\\\"Stewart_1989\\\",\\n module=\\\"mopac_step\\\",\\n level=1,\\n note=\\\"The citation for the MOPAC parameterization.\\\",\\n )\\n for element in (\\n \\\"Be\\\",\\n \\\"Mg\\\",\\n \\\"Zn\\\",\\n \\\"Ga\\\",\\n \\\"Ge\\\",\\n \\\"As\\\",\\n \\\"Se\\\",\\n \\\"Cd\\\",\\n \\\"In\\\",\\n \\\"Sn\\\",\\n \\\"Sb\\\",\\n \\\"Te\\\",\\n \\\"Hg\\\",\\n \\\"Tl\\\",\\n \\\"Pb\\\",\\n \\\"Bi\\\",\\n ):\\n if element in elements:\\n references.cite(\\n raw=self.parent._bibliography[\\\"Stewart_1991\\\"],\\n alias=\\\"Stewart_1991\\\",\\n module=\\\"mopac_step\\\",\\n level=1,\\n note=\\\"The citation for the MOPAC parameterization.\\\",\\n )\\n break\\n if \\\"Li\\\" in elements:\\n references.cite(\\n raw=self.parent._bibliography[\\\"Anders_1993\\\"],\\n alias=\\\"Anders_1993\\\",\\n module=\\\"mopac_step\\\",\\n level=1,\\n note=\\\"The citation for the MOPAC parameterization.\\\",\\n )\\n for element in (\\\"B\\\", \\\"Na\\\", \\\"K\\\", \\\"Ca\\\", \\\"Rb\\\", \\\"Sr\\\", \\\"Cs\\\", \\\"Ba\\\"):\\n if element in elements:\\n references.cite(\\n raw=self.parent._bibliography[\\\"Stewart_2004\\\"],\\n alias=\\\"Stewart_2004\\\",\\n module=\\\"mopac_step\\\",\\n level=1,\\n note=\\\"The citation for the MOPAC parameterization.\\\",\\n )\\n break\\n elif \\\"PM6\\\" in P[\\\"hamiltonian\\\"]:\\n references.cite(\\n raw=self.parent._bibliography[\\\"Stewart_2007\\\"],\\n alias=\\\"Stewart_2007\\\",\\n module=\\\"mopac_step\\\",\\n level=1,\\n note=\\\"The PM6 parameterization in MOPAC.\\\",\\n )\\n if P[\\\"hamiltonian\\\"] == \\\"PM6-D3\\\":\\n references.cite(\\n raw=self.parent._bibliography[\\\"Grimme_2010\\\"],\\n alias=\\\"Grimme_2010\\\",\\n module=\\\"mopac_step\\\",\\n level=1,\\n note=\\\"Dispersion correction by Grimme, et al.\\\",\\n )\\n if P[\\\"hamiltonian\\\"] == \\\"PM6-DH+\\\":\\n references.cite(\\n raw=self.parent._bibliography[\\\"Korth_2010\\\"],\\n alias=\\\"Korth_2010\\\",\\n module=\\\"mopac_step\\\",\\n level=1,\\n note=\\\"Hydrogen-bonding correction by Korth.\\\",\\n )\\n if \\\"PM6-DH2\\\" in P[\\\"hamiltonian\\\"]:\\n references.cite(\\n raw=self.parent._bibliography[\\\"Korth_2009\\\"],\\n alias=\\\"Korth_2009\\\",\\n module=\\\"mopac_step\\\",\\n level=1,\\n note=\\\"Hydrogen-bonding and dispersion correction.\\\",\\n )\\n references.cite(\\n raw=self.parent._bibliography[\\\"Rezac_2009\\\"],\\n alias=\\\"Rezac_2009\\\",\\n module=\\\"mopac_step\\\",\\n level=1,\\n note=\\\"Hydrogen-bonding and dispersion correction.\\\",\\n )\\n if P[\\\"hamiltonian\\\"] == \\\"PM6-DH2x\\\":\\n references.cite(\\n raw=self.parent._bibliography[\\\"Rezac_2011\\\"],\\n alias=\\\"Rezac_2011\\\",\\n module=\\\"mopac_step\\\",\\n level=1,\\n note=\\\"Halogen-bonding correction.\\\",\\n )\\n if \\\"PM6-D3H4\\\" in P[\\\"hamiltonian\\\"]:\\n references.cite(\\n raw=self.parent._bibliography[\\\"Rezac_2011\\\"],\\n alias=\\\"Rezac_2011\\\",\\n module=\\\"mopac_step\\\",\\n level=1,\\n note=\\\"Hydrogen-bonding and dispersion correction.\\\",\\n )\\n references.cite(\\n raw=self.parent._bibliography[\\\"Vorlova_2015\\\"],\\n alias=\\\"Vorlova_2015\\\",\\n module=\\\"mopac_step\\\",\\n level=1,\\n note=\\\"Hydrogen-hydrogen repulsion correction.\\\",\\n )\\n if P[\\\"hamiltonian\\\"] == \\\"PM6-D3H4x\\\":\\n references.cite(\\n raw=self.parent._bibliography[\\\"Brahmkshatriya_2013\\\"],\\n alias=\\\"Brahmkshatriya_2013\\\",\\n module=\\\"mopac_step\\\",\\n level=1,\\n note=\\\"Halogen-oxygen and halogen-nitrogen correction.\\\",\\n )\\n elif \\\"PM7\\\" in P[\\\"hamiltonian\\\"]:\\n references.cite(\\n raw=self.parent._bibliography[\\\"Stewart_2012\\\"],\\n alias=\\\"Stewart_2012\\\",\\n module=\\\"mopac_step\\\",\\n level=1,\\n note=\\\"The PM7 parameterization in MOPAC.\\\",\\n )\\n elif P[\\\"hamiltonian\\\"] == \\\"RM1\\\":\\n references.cite(\\n raw=self.parent._bibliography[\\\"Rocha_2006\\\"],\\n alias=\\\"Rocha_2006\\\",\\n module=\\\"mopac_step\\\",\\n level=1,\\n note=\\\"RM1 parameterization.\\\",\\n )\\n\\n # which structure? may need to set default first...\\n if P[\\\"structure\\\"] == \\\"default\\\":\\n if self._id[-1] == \\\"1\\\":\\n structure = \\\"initial\\\"\\n else:\\n structure = \\\"current\\\"\\n elif self._id[-1] == \\\"1\\\":\\n structure = \\\"initial\\\"\\n elif P[\\\"structure\\\"] == \\\"current\\\":\\n structure = \\\"current\\\"\\n\\n if structure == \\\"current\\\":\\n keywords.append(\\\"OLDGEO\\\")\\n\\n if P[\\\"convergence\\\"] == \\\"normal\\\":\\n pass\\n elif P[\\\"convergence\\\"] == \\\"precise\\\":\\n keywords.append(\\\"PRECISE\\\")\\n elif P[\\\"convergence\\\"] == \\\"relative\\\":\\n keywords.append(\\\"RELSCF=\\\" + P[\\\"relative\\\"])\\n elif P[\\\"convergence\\\"] == \\\"absolute\\\":\\n keywords.append(\\\"SCFSCRT=\\\" + P[\\\"absolute\\\"])\\n else:\\n raise RuntimeError(\\n \\\"Don't recognize convergence '{}'\\\".format(P[\\\"convergence\\\"])\\n )\\n\\n if P[\\\"uhf\\\"]:\\n keywords.append(\\\"UHF\\\")\\n\\n if P[\\\"MOZYME\\\"] == \\\"always\\\":\\n keywords.append(\\\"MOZYME\\\")\\n elif (\\n P[\\\"MOZYME\\\"] == \\\"for larger systems\\\"\\n and configuration.n_atoms >= P[\\\"nMOZYME\\\"]\\n ):\\n keywords.append(\\\"MOZYME\\\")\\n\\n if P[\\\"COSMO\\\"]:\\n keywords.append(f\\\"EPS={P['eps']}\\\")\\n rsolve = P[\\\"rsolve\\\"].to(\\\"Å\\\").magnitude\\n keywords.append(f\\\"RSOLVE={rsolve}\\\")\\n keywords.append(f\\\"NSPA={P['nspa']}\\\")\\n keywords.append(f\\\"DISEX={P['disex']}\\\")\\n\\n if P[\\\"calculate gradients\\\"]:\\n keywords.append(\\\"GRADIENTS\\\")\\n\\n if \\\"yes\\\" in P[\\\"bond orders\\\"]:\\n keywords.append(\\\"BONDS\\\")\\n\\n # Add any extra keywords so that they appear at the end\\n metadata = self.metadata[\\\"keywords\\\"]\\n for keyword in P[\\\"extra keywords\\\"]:\\n if \\\"=\\\" in keyword:\\n keyword, value = keyword.split(\\\"=\\\")\\n if keyword not in metadata or \\\"format\\\" not in metadata[keyword]:\\n keywords.append(keyword + \\\"=\\\" + value)\\n else:\\n keywords.append(metadata[keyword][\\\"format\\\"].format(keyword, value))\\n\\n result = []\\n result.append([[*keywords], None, None])\\n\\n # Handle MOZYME follow-up calculations\\n if \\\"MOZYME\\\" in keywords:\\n follow_up = P[\\\"MOZYME follow-up\\\"]\\n if \\\"exact\\\" in follow_up:\\n keywords.remove(\\\"MOZYME\\\")\\n if \\\"1SCF\\\" not in keywords:\\n keywords.append(\\\"1SCF\\\")\\n keywords.append(\\\"OLDGEO\\\")\\n result.append([[*keywords], None, \\\"MOZYME follow-up using MOPAC\\\"])\\n elif \\\"new\\\" in follow_up:\\n if \\\"1SCF\\\" not in keywords:\\n keywords.append(\\\"1SCF\\\")\\n keywords.append(\\\"OLDGEO\\\")\\n result.append([[*keywords], None, \\\"MOZYME follow-up, reinitializing\\\"])\\n elif follow_up == \\\"none\\\":\\n pass\\n else:\\n logger.error(f\\\"Don't recognize the MOZYME follow-up: '{follow_up}'\\\")\\n\\n return result\",\n \"def __init__(self, dataset):\\n\\n self.input_names = ['CORCON_nv_lwc_vcol',\\n 'CORCON_nv_lwc_icol',\\n 'CORCON_nv_lwc_vref',\\n 'CORCON_nv_lwc_iref',\\n 'CORCON_nv_twc_vcol',\\n 'CORCON_nv_twc_icol',\\n 'CORCON_nv_twc_vref',\\n 'CORCON_nv_twc_iref',\\n 'TAS_RVSM',\\n 'IAS_RVSM',\\n 'PS_RVSM',\\n 'WOW_IND',\\n 'CLWCIREF', 'CLWCVREF', 'CLWCICOL', 'CLWCVCOL',\\n 'CTWCIREF', 'CTWCVREF', 'CTWCICOL', 'CTWCVCOL',\\n 'CALNVTWC',\\n 'CALNVLWC1',\\n 'CALNVLWC2',\\n 'CALNVL']\\n\\n self.outputs = [parameter('NV_TWC_U',\\n units='gram m-3',\\n frequency=64,\\n long_name='Uncorrected total condensed water content from the Nevzorov probe'),\\n parameter('NV_LWC1_U',\\n units='gram m-3',\\n frequency=64,\\n long_name='Uncorrected liquid water content from the Nevzorov probe (1st collector)',\\n standard_name='mass_concentration_of_liquid_water_in_air'),\\n parameter('NV_LWC2_U',\\n units='gram m-3',\\n frequency=64,\\n long_name='Uncorrected liquid water content from the Nevzorov probe (2nd collector)',\\n standard_name='mass_concentration_of_liquid_water_in_air'),\\n parameter('NV_TWC_C',\\n units='gram m-3',\\n frequency=64,\\n long_name='Corrected total condensed water content from the Nevzorov probe'),\\n parameter('NV_LWC1_C',\\n units='gram m-3',\\n frequency=64,\\n long_name='Corrected liquid water content from the Nevzorov probe (1st collector)',\\n standard_name='mass_concentration_of_liquid_water_in_air'),\\n parameter('NV_LWC2_C',\\n units='gram m-3',\\n frequency=64,\\n long_name='Corrected liquid water content from the Nevzorov probe (2nd collector)',\\n standard_name='mass_concentration_of_liquid_water_in_air'),\\n parameter('NV_TWC_P',\\n units='W',\\n frequency=64,\\n long_name='TWC collector power'),\\n parameter('NV_LWC1_P',\\n units='W',\\n frequency=64,\\n long_name='LWC1 collector power'),\\n parameter('NV_LWC2_P',\\n units='W',\\n frequency=64,\\n long_name='LWC2 collector power'),\\n parameter('NV_REF_P',\\n units='W',\\n frequency=64,\\n long_name='Reference power')]\\n\\n self.version = 1.00\\n cal_base.__init__(self, dataset)\",\n \"def doParametersOfInterest(self):\\n\\n self.modelBuilder.doVar(\\\"Rdy[1.,0.0,10.0]\\\");\\n self.modelBuilder.doVar(\\\"Rbk[1.,0.0,10.0]\\\");\\n self.modelBuilder.doVar(\\\"Rqcd_emu[1,0.0,10.0]\\\");\\n self.modelBuilder.doSet(\\\"POI\\\",\\\"Rbk,Rdy,Rqcd_emu\\\")\",\n \"def _handleInput(self, paramInput):\\n super()._handleInput(paramInput)\\n settings, notFound = paramInput.findNodesAndExtractValues(['C', 'dual', 'penalty', 'l1_ratio', 'tol', 'fit_intercept',\\n 'solver','intercept_scaling', 'max_iter', 'multi_class',\\n 'class_weight', 'random_state'])\\n # notFound must be empty\\n assert(not notFound)\\n self.initializeModel(settings)\",\n \"def doParametersOfInterest(self):\\n self.modelBuilder.doVar(\\\"kappa_W[1,0.0,2.0]\\\") \\n self.modelBuilder.doVar(\\\"kappa_Z[1,0.0,2.0]\\\") \\n self.modelBuilder.doVar(\\\"kappa_tau[1,0.0,3.0]\\\")\\n self.modelBuilder.doVar(\\\"kappa_mu[1,0.0,5.0]\\\") \\n self.modelBuilder.factory_(\\\"expr::kappa_mu_expr(\\\\\\\"@0*@1+(1-@0)*@2\\\\\\\", CMS_use_kmu[0], kappa_mu, kappa_tau)\\\")\\n self.modelBuilder.doVar(\\\"kappa_t[1,0.0,4.0]\\\")\\n # additional kappa for the anomalous coupling\\n self.modelBuilder.doVar(\\\"kappa_tilde_t[0.0,0.0,4.0]\\\")\\n self.modelBuilder.doVar(\\\"kappa_b[1,0.0,3.0]\\\")\\n if not self.resolved:\\n self.modelBuilder.doVar(\\\"kappa_g[1,0.0,2.0]\\\")\\n self.modelBuilder.doVar(\\\"kappa_gam[1,0.0,2.5]\\\")\\n\\tself.modelBuilder.doVar(\\\"BRinv[0,0,1]\\\")\\n self.modelBuilder.out.var(\\\"BRinv\\\").setConstant(True)\\n # adding additional kappa to list of parameters of interest\\n pois = 'kappa_W,kappa_Z,kappa_tau,kappa_t,kappa_tilde_t,kappa_b'\\n if not self.resolved:\\n pois += ',kappa_g,kappa_gam'\\n self.doMH()\\n self.modelBuilder.doSet(\\\"POI\\\",pois)\\n # use modified Higgs Builder\\n self.SMH = AnomalousTopHiggsBuilder(self.modelBuilder)\\n self.setup()\",\n \"def config1() :\\n data_name = \\\"titanic\\\" ### in data/input/\\n model_class = 'AutoML' ### ACTUAL Class name for model_sklearn.py\\n n_sample = 1000\\n\\n def post_process_fun(y): ### After prediction is done\\n return int(y)\\n\\n def pre_process_fun(y): ### Before the prediction is done\\n return int(y)\\n\\n\\n model_dict = {'model_pars': {\\n ### LightGBM API model #######################################\\n 'model_class': model_class\\n ,'model_pars' : {\\n 'total_time_limit' : 20,\\n 'algorithms' : 'auto',\\n 'results_path' : root_repo + f'/data/output/{data_name}/{os_get_function_name()}/automl_1',\\n 'eval_metric' : 'auto'\\n\\n # mode='Explain',\\n # ml_task='auto', model_time_limit=None, algorithms='auto', train_ensemble=True,\\n # stack_models='auto', eval_metric='auto', validation_strategy='auto', explain_level='auto',\\n # golden_features='auto', features_selection='auto', start_random_models='auto',\\n # hill_climbing_steps='auto', top_models_to_improve='auto', verbose=1, random_state=1234)\\n }\\n\\n , 'post_process_fun' : post_process_fun ### After prediction ##########################################\\n , 'pre_process_pars' : {'y_norm_fun' : pre_process_fun , ### Before training ##########################\\n\\n\\n ### Pipeline for data processing ##############################\\n 'pipe_list': [\\n #### coly target prorcessing\\n {'uri': 'source/prepro.py::pd_coly', 'pars': {}, 'cols_family': 'coly', 'cols_out': 'coly', 'type': 'coly' },\\n\\n\\n {'uri': 'source/prepro.py::pd_colnum_bin', 'pars': {}, 'cols_family': 'colnum', 'cols_out': 'colnum_bin', 'type': '' },\\n {'uri': 'source/prepro.py::pd_colnum_binto_onehot', 'pars': {}, 'cols_family': 'colnum_bin', 'cols_out': 'colnum_onehot', 'type': '' },\\n\\n #### catcol INTO integer, colcat into OneHot\\n {'uri': 'source/prepro.py::pd_colcat_bin', 'pars': {}, 'cols_family': 'colcat', 'cols_out': 'colcat_bin', 'type': '' },\\n # {'uri': 'source/prepro.py::pd_colcat_to_onehot', 'pars': {}, 'cols_family': 'colcat_bin', 'cols_out': 'colcat_onehot', 'type': '' },\\n\\n\\n ### Cross_feat = feat1 X feat2\\n # {'uri': 'source/prepro.py::pd_colcross', 'pars': {}, 'cols_family': 'colcross', 'cols_out': 'colcross_pair', 'type': 'cross'},\\n\\n\\n #### Example of Custom processor\\n #{'uri': THIS_FILEPATH + '::pd_col_myfun', 'pars': {}, 'cols_family': 'colnum', 'cols_out': 'col_myfun', 'type': '' }, \\n\\n\\n ],\\n }\\n },\\n\\n 'compute_pars': { 'metric_list': ['accuracy_score','average_precision_score']\\n\\n ,'mlflow_pars' : None # {} ### Not empty --> use mlflow\\n },\\n\\n 'data_pars': { 'n_sample' : n_sample,\\n\\n 'download_pars' : None,\\n\\n\\n 'cols_input_type' : cols_input_type_1,\\n ### family of columns for MODEL #########################################################\\n # \\\"colnum\\\", \\\"colnum_bin\\\", \\\"colnum_onehot\\\", \\\"colnum_binmap\\\", #### Colnum columns\\n # \\\"colcat\\\", \\\"colcat_bin\\\", \\\"colcat_onehot\\\", \\\"colcat_bin_map\\\", #### colcat columns\\n # 'colcross_single_onehot_select', \\\"colcross_pair_onehot\\\", 'colcross_pair', #### colcross columns 'coldate', 'coltext',\\n 'cols_model_group': [ 'colnum_bin',\\n 'colcat_bin',\\n # 'coltext',\\n # 'coldate',\\n #'colcross_pair',\\n \\n ### example of custom\\n # 'col_myfun'\\n ]\\n\\n ### Filter data rows ##################################################################\\n ,'filter_pars': { 'ymax' : 2 ,'ymin' : -1 }\\n\\n }\\n }\\n\\n ##### Filling Global parameters ############################################################\\n model_dict = global_pars_update(model_dict, data_name, config_name=os_get_function_name() )\\n return model_dict\",\n \"def doParametersOfInterest(self):\\n ''' ref : physicsmodel -> rvf\\n self.modelBuilder.out.var(\\\"MH\\\").setRange(float(self.mHRange[0]),float(self.mHRange[1]))\\n self.modelBuilder.out.var(\\\"MH\\\").setConstant(False)\\n '''\\n\\n self.modelBuilder.doVar(\\\"mu[0,0,1000]\\\") ##mu is what we want to return (in string) name[starting_value,min,max] \\n self.modelBuilder.doVar(\\\"Fvbf[0,0,1]\\\") ##mu is what we want to return (in string) name[starting_value,min,max] \\n self.modelBuilder.doSet(\\\"POI\\\",\\\"mu,Fvbf\\\")\\n self.modelBuilder.doVar(\\\"\\\")\\n self.modelBuilder.factory_('expr::ggH_s_func(\\\"(@0-sqrt(@0))*(1.-@1)\\\", mu,Fvbf)')\\n self.modelBuilder.factory_( 'expr::ggH_b_func(\\\"(1-sqrt(@0))*(1.-@1)\\\", mu,Fvbf)')\\n self.modelBuilder.factory_( 'expr::ggH_sbi_func(\\\"sqrt(@0)*(1.-@1)\\\", mu,Fvbf)')\\n\\n self.modelBuilder.factory_('expr::vbfH_s_func(\\\"(@0-sqrt(@0))*(@1)\\\", mu,Fvbf)')\\n self.modelBuilder.factory_( 'expr::vbfH_b_func(\\\"(1-sqrt(@0))*(@1)\\\", mu,Fvbf)')\\n self.modelBuilder.factory_( 'expr::vbfH_sbi_func(\\\"sqrt(@0)*(@1)\\\", mu,Fvbf)')\",\n \"def __init__(self, encut, magmom, ldaul, Uparam, Jparam, name=\\\"DFTCL_settings\\\"):\\n\\n cl_settings = {\\\"ISPIN\\\": 2, \\\"MAGMOM\\\": magmom, \\\"SAXIS\\\": None, \\\"LSORBIT\\\": None, \\\"LNONCOLLINEAR\\\": None}\\n dftu_settings = {\\\"LDAU\\\": \\\".TRUE.\\\", \\\"LDAUU\\\": Uparam, \\\"LDATYPE\\\": 2, \\\"LDAUL\\\": ldaul, \\\"LDAUJ\\\": Jparam , \\\"LMAXMIMX\\\": 4}\\n InputParameters.__init__(self, name=name, magnetic_settings=cl_settings, hubbard_settings=dftu_settings)\\n self.update_electronic_settings(\\\"encut\\\", encut)\",\n \"def doParametersOfInterest(self):\\n\\n self.modelBuilder.doVar(\\\"Afb[0.6,-0.70,0.70]\\\");\\n self.modelBuilder.doSet(\\\"POI\\\",\\\"Afb\\\")\\n\\n # ss templates\\n self.modelBuilder.doVar(\\\"Rdy_mumu_ss[1.0,0.0,10.0]\\\");\\n self.modelBuilder.doVar(\\\"Rdy_ee_ss[1.0,0.0,10.0]\\\");\\n \\n self.modelBuilder.factory_('expr::Rpl(\\\"(1.+@0)\\\",Afb)')\\n self.modelBuilder.factory_('expr::Rmn(\\\"(1.-@0)\\\",Afb)')\",\n \"def doParametersOfInterest(self):\\n\\n self.modelBuilder.doVar(\\\"Afb[0.6,-0.75,0.75]\\\");\\n self.modelBuilder.doSet(\\\"POI\\\",\\\"Afb\\\")\\n\\n # ss templates\\n self.modelBuilder.doVar(\\\"Dilu_ratio[1.0,0.0,10.0]\\\");\\n self.modelBuilder.doVar(\\\"Rdy_mumu_ss[1.0,0.0,10.0]\\\");\\n self.modelBuilder.doVar(\\\"Rdy_ee_ss[1.0,0.0,10.0]\\\");\\n \\n self.modelBuilder.factory_('expr::Rpl(\\\"0.5*(1.+@0*@1)\\\",Afb, Dilu_ratio)')\\n self.modelBuilder.factory_('expr::Rmn(\\\"0.5*(1.-@0*@1)\\\",Afb, Dilu_ratio)')\",\n \"def __init__(\\n self,\\n model_filepath: str,\\n user_validation: bool = True,\\n ):\\n\\n # ensure model filepath is balid, and save as att if it is\\n assert model_filepath.endswith(\\\".fmu\\\"), \\\"Provided filepath is not an FMU file: '{}'\\\".format(model_filepath)\\n self.model_filepath = model_filepath\\n # config file with config_params, inputs, outputs\\n self.sim_config_filepath = SIM_CONFIG_NAME_f(self.model_filepath)\\n\\n # read the model description\\n self.model_description = read_model_description(model_filepath)\\n error_log = \\\"Provided model ({}) doesn't have modelVariables in XLS description file\\\".format(model_filepath)\\n assert len(self.model_description.modelVariables) > 0, error_log\\n\\n # correct non-alphanumeric tags.\\n # note, it doesn't suppose any problem, since interaction with sim uses indices, not names.\\n self._clean_non_alphanumeric_chars()\\n\\n\\n # collect the value references (indices)\\n # collect the value types (Real, Integer or Enumeration)\\n # collect the variables to be initialized and the value to do so at\\n self.vars_to_idx = {}\\n self.vars_to_type_f = {}\\n self.vars_to_ini_vals = {}\\n for variable in self.model_description.modelVariables:\\n # extract key attributes per variable\\n var_idx = variable.valueReference #, variable.causality\\n var_name = variable.name\\n var_type = variable.type\\n var_start = variable.start\\n \\n # collect type reference\\n if var_type == \\\"Real\\\":\\n self.vars_to_type_f[var_name] = float\\n elif var_type == \\\"Integer\\\":\\n self.vars_to_type_f[var_name] = int\\n else:\\n # [TODO] Integrate variables of type \\\"Enumeration\\\". How do we cast? Define a function for \\\"self.vars_to_type_f\\\".\\n # [TODO] Integrate variables of type string (need to find correct var_type tag first).\\n # [TODO] Integrate variables of type boolean (need to find correct var_type tag first).\\n print(f\\\"Variable '{var_name}' will be skipped. FMU connector cannot currently handle vars of type '{var_type}'.\\\")\\n continue\\n \\n # collect the value references (indices)\\n self.vars_to_idx[var_name] = var_idx\\n\\n # collect the variables to be initialized and the value to do so at\\n if var_start is not None:\\n # cast variable prior to storing\\n self.vars_to_ini_vals[var_name] = self.vars_to_type_f[var_name](var_start)\\n \\n\\n # initialize sim config\\n self.is_model_config_valid = False # Currently unused, since error is raised if model invalid\\n self.sim_config_params = []\\n self.sim_inputs = []\\n self.sim_outputs = []\\n self.sim_other_vars = []\\n\\n # ---------------------------------------------------------------------\\n # YAML CONFIG --> check for existing config using SIM_CONFIG_NAME_f --> e.g: \\\"{model_name}_conf.yaml\\\"\\n valid_config = self._validate_sim_config()\\n \\n # exit if model is valid, unless validation has been activated\\n if valid_config:\\n\\n # print model config for user reference: config_params, inputs, outputs\\n print(self._get_sim_config_str())\\n\\n if user_validation:\\n # prompt user to manually validate model if selected\\n validation_asserted = input(\\\"Is this configuration correct (y|n)? \\\")\\n\\n if validation_asserted == \\\"y\\\":\\n self.is_model_config_valid = True\\n return\\n \\n # reset config if invalid\\n self.sim_config_params = []\\n self.sim_inputs = []\\n self.sim_outputs = []\\n self.sim_other_vars = []\\n \\n else:\\n # when no validation is selected, we assume the sim config is valid\\n self.is_model_config_valid = True\\n return\\n \\n # ---------------------------------------------------------------------\\n # FMI CONFIG --> if model is invalid we look for attributes within the .fmi model definition\\n valid_config = self._extract_sim_config_from_fmi_std()\\n\\n if valid_config:\\n\\n # print model config for user reference: config_params, inputs, outputs\\n print(self._get_sim_config_str())\\n \\n if user_validation:\\n # prompt user to manually validate model if selected\\n validation_asserted = input(\\\"Is this configuration correct (y|n)? \\\")\\n\\n if validation_asserted == \\\"y\\\":\\n self.is_model_config_valid = True\\n # dump YMAL file to reuse next time the model is loaded\\n self._dump_config_to_yaml_file()\\n return\\n \\n else:\\n # when no validation is selected, we assume the sim config is valid\\n self.is_model_config_valid = True\\n # dump YMAL file to reuse next time the model is loaded\\n self._dump_config_to_yaml_file()\\n return\\n \\n # Dump auxiliary YAML config file if user doesn't assert the provided set\\n # of config_params/inputs/outputs\\n self._dump_config_to_yaml_file(is_aux_yaml = True)\\n \\n # If neither YAML nor FMI model is sufficient raise error\\n error_log = \\\"MODEL DOES NOT HAVE THE CORRECT CONFIG DEFINED NEITHER ON YAML CONFIG FILE \\\"\\n error_log += \\\"NOR FMI MODEL DESCRIPTION. A YAML FILE HAS BEEN CREATED FOR YOU TO MODIFY. \\\"\\n error_log += \\\"THE SIM HAS BEEN FORCED TO EXIT, BUT FEEL FREE TO RERUN ONCE SET-UP IS COMPLETED.\\\"\\n raise Exception(error_log)\",\n \"def buildVariables(self, model):\\n\\n \\\"\\\"\\\"\\n #Inputs\\n \\\"\\\"\\\"\\n\\n \\\"\\\"\\\"\\n #Outputs\\n \\\"\\\"\\\"\\n #-------- Register Settings Used During Calibration --------\\n #auxNDiv (to be put into synth.auxfreq.mmddenom during ir cal only)\\n self._addModelVariable(model, 'ircal_auxndiv', int, ModelVariableFormat.DECIMAL, units='bytes', desc='This value is predetermined.')\\n #auxLoDiv (to be put into synth.divctrl.auxlodivfreqctrl during ir cal only)\\n self._addModelVariable(model, 'ircal_auxlodiv', int, ModelVariableFormat.DECIMAL, units='bytes', desc='This value is predetermined.')\\n #rampVal (to be put into modem.rampctrl.rampval during ir cal only)\\n self._addModelVariable(model, 'ircal_rampval', int, ModelVariableFormat.DECIMAL, units='bytes', desc='This value is predetermined.')\\n #rxAmp_PLL (to be put into rac.auxctrl.rxamp during PLL loopback, ir cal only)\\n self._addModelVariable(model, 'ircal_rxamppll', int, ModelVariableFormat.DECIMAL, units='bytes', desc='This value is predetermined.')\\n #rxAmp_PA (to be put into rac.auxctrl.rxamp during PA loopback, ir cal only)\\n self._addModelVariable(model, 'ircal_rxamppa', int, ModelVariableFormat.DECIMAL, units='bytes', desc='This value is predetermined.')\\n \\n #-------- Decide Between Calibration Procedures --------\\n #diConfigIsValid (true = DI value / PTE value is an option)\\n self._addModelVariable(model, 'ircal_manufconfigvalid', bool, ModelVariableFormat.ASCII, 'True = the manufacturing calibration value is saved on the chip')\\n #pllLoopbackConfigIsValid (true = PLL loopback is an option)\\n self._addModelVariable(model, 'ircal_pllconfigvalid', bool, ModelVariableFormat.ASCII, 'True = PLL loopback is permitted to generate a calibration value')\\n #paLoopbackConfigIsValid (true = PA loopback is an option)\\n self._addModelVariable(model, 'ircal_paconfigvalid', bool, ModelVariableFormat.ASCII, 'True = PA loopback is permitted to generate a calibration value')\\n #recommendedConfig (DI/PTE vs PLL loopback vs PA loopback)\\n var = self._addModelVariable(model, 'ircal_bestconfig', Enum, ModelVariableFormat.DECIMAL, 'Specify the best calibration method for this radio configuration.')\\n member_data = [\\n ['MANUFACTURING', 1, 'Use the calibration value saved during manufacturing, if applicable.'],\\n ['PLL', 2, 'Put the part into a PLL loopback to generate a calibration value.'],\\n ['PA', 3, 'Put the part into a PA loopback to generate a calibration value.'],\\n ['UNSUPPORTED', 4, 'Image rejection calibration not supported.'],\\n ]\\n var.var_enum = CreateModelVariableEnum(\\n 'configType',\\n 'Specify how image rejection calibration is to run.',\\n member_data)\\n\\n #-------- Decide Between Software/Hardware RSSI Averaging --------\\n self._addModelVariable(model, 'ircal_useswrssiaveraging', bool, ModelVariableFormat.ASCII, 'True = use software RSSI averaging; False = use hardware RSSI averaging')\\n self._addModelVariable(model, 'ircal_numrssitoavg', int, ModelVariableFormat.DECIMAL, units='bytes', desc='Number of RSSI values (2^value) to average in software. If value = 3, 8 values will be averaged.')\\n self._addModelVariable(model, 'ircal_throwawaybeforerssi', int, ModelVariableFormat.DECIMAL, units='bytes', desc='Number of RSSI values to discard before starting to average RSSI values.')\\n self._addModelVariable(model, 'ircal_delayusbeforerssi', int, ModelVariableFormat.DECIMAL, units='bytes', desc='Microsecond delay between applying a calibration value and then reading RSSI values.')\\n self._addModelVariable(model, 'ircal_delayusbetweenswrssi', int, ModelVariableFormat.DECIMAL, units='bytes', desc='Microsecond delay between gathering RSSI values. Software RSSI averaging mode only.')\\n\\n #------ Determine number of raw RSSI values averaged by hardware ------\\n #agcRssiPeriod\\n self._addModelVariable(model, 'ircal_agcrssiperiod', int, ModelVariableFormat.DECIMAL, units='bytes', desc='Number of raw RSSI values averaged by hardware.')\\n\\n #------ Registers specific to Jumbo (and new Dumbo) support ------\\n self._addModelVariable(model, 'ircal_useswrssiaveraging2', bool, ModelVariableFormat.ASCII, 'True = use software RSSI averaging; False = use hardware RSSI averaging; Jumbo support')\\n self._addModelVariable(model, 'ircal_numrssitoavg2', int, ModelVariableFormat.DECIMAL, units='bytes', desc='Number of RSSI values (2^value) to average in software. If value = 3, 8 values will be averaged. Jumbo support')\\n self._addModelVariable(model, 'ircal_throwawaybeforerssi2', int, ModelVariableFormat.DECIMAL, units='bytes', desc='Number of RSSI values to discard before starting to average RSSI values. Jumbo support')\\n self._addModelVariable(model, 'ircal_delayusbeforerssi2', int, ModelVariableFormat.DECIMAL, units='bytes', desc='Microsecond delay between applying a calibration value and then reading RSSI values. Jumbo support')\\n self._addModelVariable(model, 'ircal_delayusbetweenswrssi2', int, ModelVariableFormat.DECIMAL, units='bytes', desc='Microsecond delay between gathering RSSI values. Software RSSI averaging mode only. Jumbo support')\\n\\n #\\n # Bools not allowed as advanced inputs due to GUI constraint. Using enum instead\\n var = self._addModelVariable(model, 'ircal_rxtx_path_common', Enum, ModelVariableFormat.DECIMAL, 'RX and TX are on a common/shared circuit, or split. Refer to document AN971.')\\n member_data = [\\n ['SHARED_RX_TX_PATH' , 0, 'RX and TX circuit paths are common/shared/connected'],\\n ['SPLIT_RX_TX_PATH', 1, 'RX and TX circuit paths are separated/not connected'],\\n ]\\n var.var_enum = CreateModelVariableEnum(\\n 'IRCalRXTXPathCommonEnum',\\n 'RX and TX are on a common/shared circuit, or split. Refer to document AN971.',\\n member_data)\\n\\n self._addModelVariable(model, 'ircal_power_level', int, ModelVariableFormat.DECIMAL, units='codes', desc='Specify IR cal power level (amplitude) instead of auto (0). Refer to document AN971.')\",\n \"def __init__(self, encut, ldaul, Uparam, Jparam, name=\\\"DFTU_settings\\\"):\\n\\n dftu_settings = {\\\"LDAU\\\": \\\".TRUE.\\\" , \\\"LDAUU\\\": Uparam, \\\"LDATYPE\\\": 2, \\\"LADAUL\\\": ldaul, \\\"LDAUJ\\\": Jparam , \\\"LMAXMIX\\\": 4}\\n InputParameters.__init__(self, name=name, hubbard_settings=dftu_settings)\\n self.update_electronic_settings(\\\"ENCUT\\\", encut)\",\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 _handleInput(self, paramInput):\\n super()._handleInput(paramInput)\\n settings, notFound = paramInput.findNodesAndExtractValues(['nu','C', 'kernel', 'degree', 'gamma', 'coef0',\\n 'tol', 'cache_size', 'shrinking', 'max_iter'])\\n # notFound must be empty\\n assert(not notFound)\\n self.initializeModel(settings)\",\n \"def test_config():\\n import yaml\\n import astropy.units as u\\n from tqdm import tqdm\\n # Need these for `eval` below\\n from numpy import array\\n\\n # Same test suite as used in test_imsim above.\\n # This time, we just use this for the det names.\\n with open(DATA_DIR / \\\"wcs_466749.yaml\\\", 'r') as f:\\n wcss = yaml.safe_load(f)\\n\\n cmds = {}\\n with open(DATA_DIR / \\\"phosim_cat_466749.txt\\\", 'r') as f:\\n for line in f:\\n k, v = line.split()\\n try:\\n v = int(v)\\n except ValueError:\\n try:\\n v = float(v)\\n except ValueError:\\n pass\\n cmds[k] = v\\n\\n # Values below (and others) from phosim_cat_466749.txt\\n rc = cmds['rightascension']\\n dc = cmds['declination']\\n boresight = galsim.CelestialCoord(\\n rc*galsim.degrees,\\n dc*galsim.degrees\\n )\\n obstime = Time(cmds['mjd'], format='mjd', scale='utc')\\n obstime -= 15*u.s\\n band = \\\"ugrizy\\\"[cmds['filter']]\\n wavelength_dict = dict(\\n u=365.49,\\n g=480.03,\\n r=622.20,\\n i=754.06,\\n z=868.21,\\n y=991.66\\n )\\n wavelength = wavelength_dict[band]\\n camera = imsim.get_camera()\\n\\n rotTelPos = cmds['rottelpos'] * galsim.degrees\\n telescope = imsim.load_telescope(f\\\"LSST_{band}.yaml\\\", rotTelPos=rotTelPos)\\n # Non-default values.\\n temperature = 293.\\n pressure = 69.0\\n H2O_pressure = 2.0\\n\\n factory = imsim.BatoidWCSFactory(\\n boresight, obstime, telescope, wavelength,\\n camera,\\n temperature=temperature,\\n pressure=pressure,\\n H2O_pressure=H2O_pressure\\n )\\n\\n config = {\\n 'input': {\\n 'telescope': {\\n 'file_name':f\\\"LSST_{band}.yaml\\\",\\n 'rotTelPos': rotTelPos\\n }\\n },\\n 'image': {\\n 'wcs': {\\n 'type': 'Batoid',\\n 'boresight': boresight,\\n 'camera': 'LsstCam',\\n 'obstime': obstime,\\n 'wavelength': wavelength,\\n 'temperature': temperature,\\n 'pressure': pressure,\\n 'H2O_pressure': H2O_pressure,\\n 'order': 2,\\n }\\n }\\n }\\n\\n rng = np.random.default_rng(1234)\\n for k in tqdm(wcss.keys()):\\n name = k[18:25].replace('-', '_')\\n det = camera[name]\\n\\n wcs1 = factory.getWCS(det, order=2)\\n config['image']['wcs']['det_name'] = name\\n galsim.config.RemoveCurrent(config['image']['wcs'])\\n galsim.config.ProcessInput(config)\\n wcs2 = galsim.config.BuildWCS(config['image'], 'wcs', config)\\n\\n # Test points\\n xs = rng.uniform(0, 4000, 100)\\n ys = rng.uniform(0, 4000, 100)\\n ra1, dec1 = wcs1.xyToradec(xs, ys, units='radians')\\n ra2, dec2 = wcs2.xyToradec(xs, ys, units='radians')\\n np.testing.assert_allclose(ra1, ra2)\\n np.testing.assert_allclose(dec1, dec2)\\n\\n # Test == when identical\\n galsim.config.RemoveCurrent(config['image']['wcs'])\\n wcs3 = galsim.config.BuildWCS(config['image'], 'wcs', config)\\n assert wcs3 == wcs2\\n\\n # Test that pressure and temperature matter.\\n config['image']['wcs']['temperature'] = 250\\n galsim.config.RemoveCurrent(config['image']['wcs'])\\n wcs4 = galsim.config.BuildWCS(config['image'], 'wcs', config)\\n assert wcs4 != wcs2\\n\\n config['image']['wcs']['temperature'] = temperature\\n config['image']['wcs']['pressure'] = 55\\n galsim.config.RemoveCurrent(config['image']['wcs'])\\n wcs5 = galsim.config.BuildWCS(config['image'], 'wcs', config)\\n assert wcs5 != wcs2\\n\\n config['image']['wcs']['pressure'] = pressure\\n config['image']['wcs']['H2O_pressure'] = 10\\n galsim.config.RemoveCurrent(config['image']['wcs'])\\n wcs6 = galsim.config.BuildWCS(config['image'], 'wcs', config)\\n assert wcs6 != wcs2\\n\\n # Test defaults\\n del config['image']['wcs']['temperature']\\n del config['image']['wcs']['pressure']\\n del config['image']['wcs']['H2O_pressure']\\n galsim.config.RemoveCurrent(config['image']['wcs'])\\n config = galsim.config.CleanConfig(config)\\n galsim.config.ProcessInput(config)\\n wcs7 = galsim.config.BuildWCS(config['image'], 'wcs', config)\\n default_pressure = 101.325 * (1-2.25577e-5*2715)**5.25588\\n wcs7a = imsim.BatoidWCSFactory(\\n boresight, obstime, telescope, wavelength, camera,\\n temperature=280,\\n pressure=default_pressure,\\n H2O_pressure=1.0,\\n ).getWCS(det, order=2)\\n assert wcs7 == wcs7a\\n\\n # Default wavelength from bandpass\\n del config['image']['wcs']['wavelength']\\n config['bandpass'] = imsim.RubinBandpass('r')\\n galsim.config.RemoveCurrent(config['image']['wcs'])\\n config = galsim.config.CleanConfig(config)\\n galsim.config.ProcessInput(config)\\n wcs8 = galsim.config.BuildWCS(config['image'], 'wcs', config)\\n wcs8a = imsim.BatoidWCSFactory(\\n boresight, obstime, telescope,\\n wavelength=config['bandpass'].effective_wavelength,\\n camera=camera,\\n temperature=280,\\n pressure=default_pressure,\\n H2O_pressure=1.0,\\n ).getWCS(det, order=2)\\n assert wcs8 == wcs8a\\n\\n del config['bandpass']\\n config['image']['bandpass'] = {\\n 'type': 'RubinBandpass',\\n 'band' : 'r',\\n }\\n galsim.config.RemoveCurrent(config['image']['wcs'])\\n config = galsim.config.CleanConfig(config)\\n galsim.config.ProcessInput(config)\\n wcs8b = galsim.config.BuildWCS(config['image'], 'wcs', config)\\n assert wcs8b == wcs8a\\n\\n # Obstime can be a string\\n print('obstime = ',obstime.to_value('iso'), type(obstime.to_value('iso')))\\n config['image']['wcs']['obstime'] = obstime.to_value('iso')\\n # Doesn't quite roundtrip perfectly. But within a millisecond.\\n obstime = Time(obstime.to_value('iso'), scale='tai')\\n print('obstime => ',obstime)\\n galsim.config.RemoveCurrent(config['image']['wcs'])\\n config = galsim.config.CleanConfig(config)\\n galsim.config.ProcessInput(config)\\n wcs9 = galsim.config.BuildWCS(config['image'], 'wcs', config)\\n wcs9a = imsim.BatoidWCSFactory(\\n boresight, obstime, telescope,\\n wavelength=config['bandpass'].effective_wavelength,\\n camera=camera,\\n temperature=280,\\n pressure=default_pressure,\\n H2O_pressure=1.0,\\n ).getWCS(det, order=2)\\n assert wcs9 == wcs9a\",\n \"def doParametersOfInterest(self):\\n self.modelBuilder.doVar(\\\"mu[0,0,1000]\\\") ##mu is what we want to return (in string) name[starting_value,min,max] \\n self.modelBuilder.doSet(\\\"POI\\\",\\\"mu\\\")\\n self.modelBuilder.factory_('expr::vbfH_s_func(\\\"@0-sqrt(@0)\\\", mu)')\\n self.modelBuilder.factory_( 'expr::vbfH_b_func(\\\"1-sqrt(@0)\\\", mu)')\\n self.modelBuilder.factory_( 'expr::vbfH_sbi_func(\\\"sqrt(@0)\\\", mu)')\",\n \"def initializeParameters(self):\\n \\n # 0 - Output Workspace\\n # 1 - Output Feature Name\\n # 2 - X coordinate (longitude)\\n # 3 - Y coordinate (latitude)\\n # 4 - Z coordinate (elevation)\\n # 5 - Observer offset (OFFSETA)\\n # 6 - Terrain offset (OFFSETB)\\n # 7 - Near distance (RADIUS1)\\n # 8 - Far distance (RADIUS2\\n # 9 - Left Azimuth (AZIMUTH1)\\n # 10 - Right Azimuth (AZIMUTH2)\\n # 11 - Top vertical angle (VERT1)\\n # 12 - Bottom vertical angle (VERT2)\\n # 13 - Spatial Reference\\n # 14 - output point features\\n \\n \\n #GETTING ERROR HERE, DOESN'T VALIDATE CORRECTLY, BUG?????\\n #---------------------------\\n #Testing get_ParameterInfo\\n #---------------------------\\n #initializeParameters Execution Error: Runtime error <type 'exceptions.AttributeError'>: 'NoneType' object has no attribute 'Type'\\n #---------------------------\\n #OK \\n #---------------------------\\n #\\n #\\n #self.params[0].Filter.Type = \\\"Workspace\\\"\\n #self.params[0].Filter.List = [\\\"FileSystem\\\", \\\"LocalDatabase\\\", \\\"RemoteDatabase\\\"]\\n #\\n\\n \\n self.params[5].Value = 2.0 # OFFSETA\\n self.params[6].Value = 0.0 # OFFSETB\\n \\n self.params[7].Value = 0.0 # RADIUS1\\n self.params[8].Value = 1000.0 # RADIUS2\\n \\n self.params[9].Filter.Type = \\\"Range\\\" \\n self.params[9].Filter.List = [0.0,360.0]\\n self.params[9].Value = 0.0 # AZIMUTH1\\n \\n self.params[10].Filter.Type = \\\"Range\\\"\\n self.params[10].Filter.List = [0.0,360.0]\\n self.params[10].Value = 360.0 # AZIMUTH2\\n \\n self.params[11].Filter.Type = \\\"Range\\\"\\n self.params[11].Filter.List = [-90.0,90.0]\\n self.params[11].Value = 90.0 # VERT1\\n \\n self.params[12].Filter.Type = \\\"Range\\\"\\n self.params[12].Filter.List = [-90.0,90.0]\\n self.params[12].Value = -90.0 # VERT2\\n\\n # setting output parameter schema\\n self.params[14].Schema.ExtentRule = \\\"Union\\\"\\n self.params[14].Schema.FeatureTypeRule = \\\"AsSpecified\\\"\\n self.params[14].Schema.FeatureType = \\\"Simple\\\"\\n self.params[14].Schema.GeometryTypeRule = \\\"AsSpecified\\\"\\n self.params[14].Schema.GeometryType = \\\"Point\\\"\\n self.params[14].Schema.FieldsRule = \\\"All\\\" \\n #spot_field = self.makeField(\\\"SPOT\\\", \\\"DOUBLE\\\", \\\"8\\\", \\\"4\\\", \\\"12\\\")\\n offseta_field = self.makeField(\\\"OFFSETA\\\", \\\"DOUBLE\\\", \\\"8\\\", \\\"4\\\", \\\"12\\\")\\n offsetb_field = self.makeField(\\\"OFFSETB\\\", \\\"DOUBLE\\\", \\\"8\\\", \\\"4\\\", \\\"12\\\")\\n vert1_field = self.makeField(\\\"VERT1\\\", \\\"DOUBLE\\\", \\\"8\\\", \\\"4\\\", \\\"12\\\")\\n vert2_field = self.makeField(\\\"VERT2\\\", \\\"DOUBLE\\\", \\\"8\\\", \\\"4\\\", \\\"12\\\")\\n azimuth1_field = self.makeField(\\\"AZIMUTH1\\\", \\\"DOUBLE\\\", \\\"8\\\", \\\"4\\\", \\\"12\\\")\\n azimuth2_field = self.makeField(\\\"AZIMUTH2\\\", \\\"DOUBLE\\\", \\\"8\\\", \\\"4\\\", \\\"12\\\")\\n radius1_field = self.makeField(\\\"RADIUS1\\\", \\\"DOUBLE\\\", \\\"8\\\", \\\"4\\\", \\\"12\\\")\\n radius2_field = self.makeField(\\\"RADIUS2\\\", \\\"DOUBLE\\\", \\\"8\\\", \\\"4\\\", \\\"12\\\")\\n field_list = [offseta_field,offsetb_field,vert1_field,vert2_field,azimuth1_field,azimuth2_field,radius1_field,radius2_field]\\n self.params[14].Schema.AdditionalFields = field_list\\n\\n return\",\n \"def doParametersOfInterest(self):\\n self.modelBuilder.doVar(\\\"mu[1,0,100]\\\") ##mu is what we want to return (in string) name[starting_value,min,max] \\n self.modelBuilder.doSet(\\\"POI\\\",\\\"mu\\\")\\n self.modelBuilder.factory_('expr::ggH_s_func(\\\"@0-sqrt(@0)\\\", mu)')\\n self.modelBuilder.factory_( 'expr::ggH_b_func(\\\"1-sqrt(@0)\\\", mu)')\\n self.modelBuilder.factory_( 'expr::ggH_sbi_func(\\\"sqrt(@0)\\\", mu)')\",\n \"def config_params0(data,parameter):\\n model = []\\n #Range of value of p\\n acf = sm.graphics.tsa.acf(data.diff().dropna())\\n for i in range(len(acf)):\\n acf[i] = abs(acf[i]*10)\\n if (ceil(acf[i])) <= 2:\\n p = range(ceil(acf[i])-1,ceil(acf[i])+2)\\n break\\n\\n #range of value of q\\n pacf = sm.graphics.tsa.pacf(data.diff().dropna())\\n for i in range(len(pacf)):\\n pacf[i] = abs(pacf[i]*10)\\n if (ceil(pacf[i])) <= 2:\\n q = range(ceil(pacf[i])-1,ceil(pacf[i])+2)\\n break\\n\\n\\t# define config lists\\n p_params = p\\n d_params = parameter['d']\\n q_params = q\\n m_params = parameter['m']\\n #P_params = p\\n #D_params = [0, 1]\\n #Q_params = q\\n \\n pdq_m = list(itertools.product(p_params, d_params, q_params,m_params)) #Generate all different combinations of p, q and q triplets\\n params = [[(x[0], x[1], x[2]),(x[0], x[1], x[2], x[3])] for x in pdq_m]\\n return params\",\n \"def split_inputs(self):\\n\\n lca = self.lca\\n inputs = self.inputs\\n\\n inputs_dict = {} # Only store exchanges with uncertainty\\n\\n # Keep track of which tech_params and bio_params are already included to the analysis\\n # Needed to avoid running sa indices computations twice for the same tech or bio params. \\n # Initialize with parameterized exchanges\\n if self.parameters != None and self.ParametersModel != None:\\n indices_tech_all = self.parameters_dict['tech_params_where']\\n indices_bio_all = self.parameters_dict['bio_params_where']\\n else:\\n indices_tech_all = np.array([], dtype=int)\\n indices_bio_all = np.array([], dtype=int)\\n\\n for input_ in inputs:\\n\\n if input_ == 'biosphere':\\n continue\\n\\n inputs_dict[input_] = {}\\n\\n indices_tech = np.array([], dtype=int)\\n indices_bio = np.array([], dtype=int)\\n\\n if input_ == 'technosphere':\\n indices_tech = np.where(lca.tech_params['uncertainty_type']!=0)[0]\\n if 'biosphere' in inputs:\\n indices_bio = np.where(lca.bio_params['uncertainty_type']!=0)[0]\\n\\n elif input_ == 'demand_exc':\\n # Select all products that pertain to activities in the given demand vector\\n for act_index in np.nonzero(lca.demand_array)[0]:\\n mask_tech = np.all([lca.tech_params['uncertainty_type']!=0, lca.tech_params['col']==act_index], axis=0)\\n indices_tech = np.concatenate([indices_tech, np.where(mask_tech)[0]])\\n if 'biosphere' in inputs:\\n mask_bio = np.all([lca.bio_params['uncertainty_type']!=0, lca.bio_params['col']==act_index], axis=0)\\n indices_bio = np.concatenate([indices_bio, np.where(mask_bio)[0]])\\n\\n elif input_ in self.databases:\\n # Select all products and flows that are linked to the given database\\n # Indices corresponding to exchanges in the tech_params depending on the given database\\n db_act_indices_tech = [val for key,val in lca.activity_dict.items() if key[0]==input_]\\n if len(db_act_indices_tech) > 0:\\n db_act_index_min_tech = db_act_indices_tech[0]\\n db_act_index_max_tech = db_act_indices_tech[-1]\\n mask = lambda i : np.all( [lca.tech_params['uncertainty_type']!=0, \\n lca.tech_params['col']==i,\\n lca.tech_params['amount']!=0], axis=0 )\\n indices_tech = [ np.where( mask(i) ) [0] for i in range(db_act_index_min_tech, db_act_index_max_tech+1) ]\\n indices_tech = np.concatenate(indices_tech)\\n\\n # Indices corresponding to flows in the biosphere params depending on the given database\\n if 'biosphere' in inputs:\\n mask = lambda j : np.all( [lca.bio_params['uncertainty_type']!=0, lca.bio_params['col']==j], axis=0 )\\n indices_bio = [ np.where(mask(j))[0] for j in range(db_act_index_min_tech, db_act_index_max_tech+1) ]\\n indices_bio = np.concatenate(indices_bio)\\n\\n indices_tech = np.sort(indices_tech)\\n indices_bio = np.sort(indices_bio)\\n\\n # Do not add indices_tech that are already in the indices_tech_all\\n indices_tech_same = np.intersect1d(indices_tech, indices_tech_all)\\n pos_tech = np.array([ np.where(indices_tech==s)[0] for s in indices_tech_same ]).flatten()\\n indices_tech = np.delete(indices_tech, pos_tech)\\n np.append(indices_tech_all, indices_tech)\\n\\n # Do not add indices_bio that are already in the indices_bio_all\\n indices_bio_same = np.intersect1d(indices_bio, indices_bio_all)\\n pos_bio = np.array([ np.where(indices_bio==s)[0] for s in indices_bio_same ]).flatten()\\n indices_bio = np.delete(indices_bio, pos_bio)\\n np.append(indices_bio_all, indices_bio)\\n \\n inputs_dict[input_]['tech_params'] = lca.tech_params[indices_tech] #TODO maybe remove later, indices should be sufficient\\n inputs_dict[input_]['tech_params_where'] = indices_tech\\n inputs_dict[input_]['tech_n_params'] = len(indices_tech) #TODO remove later\\n\\n inputs_dict[input_]['bio_params'] = lca.bio_params[indices_bio] #TODO maybe remove later\\n inputs_dict[input_]['bio_params_where'] = indices_bio\\n inputs_dict[input_]['bio_n_params'] = len(indices_bio)\\n\\n\\n self.indices_tech_all = indices_tech_all #TODO remove later\\n self.indices_bio_all = indices_bio_all\\n self.inputs_dict = inputs_dict\",\n \"def __init__(self, encut, name=\\\"scf_settings\\\"):\\n InputParameters.__init__(self, name=name)\\n self.update_electronic_settings(\\\"ENCUT\\\", encut)\",\n \"def config(language):\\n global k # Set The window size\\n global ignore_U_activated # Set True to ignore the training data with Unknown senseid\\n global vector_0_1 # Set True to convert vectors to \\\"binary form\\\" e.g.: [0,3,0,4]->[0,1,0,1]\\n global remove_punctuations_activated # Set True to ignore all the punctuation tokens\\n global lowercase_activated # Set True to convert all the tokens to lowercase\\n global stemming_activated # Set True to do stemming on all the tokens\\n global remove_stop_words_activated # Set True to ignore stop words\\n global expand_synset_activated # Set True to involve synsets, hypernyms, hypornyms in the model\\n global extract_4c_feature_activated # Set True to involve feature introduced in 4c in the requirement\\n global extract_chi_square_activated # Set True to involve chi-square feature in 4d in the requirement\\n global extract_pmi_activated # Set True to involve PMI in 4d in the requirement\\n\\n if language.__eq__(\\\"English\\\"):\\n k = 13\\n ignore_U_activated = True\\n vector_0_1 = False\\n remove_punctuations_activated = True\\n lowercase_activated = True\\n stemming_activated = True\\n remove_stop_words_activated = True\\n expand_synset_activated = False\\n extract_4c_feature_activated = True\\n extract_chi_square_activated = False\\n extract_pmi_activated = False\\n\\n elif language.__eq__(\\\"Spanish\\\"):\\n k = 13\\n ignore_U_activated = True\\n vector_0_1 = False\\n remove_punctuations_activated = True\\n lowercase_activated = True\\n stemming_activated = True\\n remove_stop_words_activated = True\\n expand_synset_activated = False # not applicable to Spanish, set to False\\n extract_4c_feature_activated = True\\n extract_chi_square_activated = False\\n extract_pmi_activated = False\\n\\n elif language.__eq__(\\\"Catalan\\\"):\\n k = 13\\n ignore_U_activated = True\\n vector_0_1 = True\\n remove_punctuations_activated = True\\n lowercase_activated = True\\n stemming_activated = False # not applicable to Catalan, set to False\\n remove_stop_words_activated = False # not applicable to Catalan, set to False\\n expand_synset_activated = False # not applicable to Catalan, set to False\\n extract_4c_feature_activated = True\\n extract_chi_square_activated = False\\n extract_pmi_activated = True\",\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 construct_model():\\n import lbann\\n\\n # Layer graph\\n input = lbann.Input(target_mode='N/A', name='inp_data')\\n # data is 64*64*4 images + 15 scalar + 5 param\\n #inp_slice = lbann.Slice(input, axis=0, slice_points=\\\"0 16399 16404\\\",name='inp_slice')\\n inp_slice = lbann.Slice(input, axis=0, slice_points=str_list([0,args.ydim,args.ydim+5]),name='inp_slice')\\n gt_y = lbann.Identity(inp_slice,name='gt_y')\\n gt_x = lbann.Identity(inp_slice, name='gt_x') #param not used\\n\\n zero = lbann.Constant(value=0.0,num_neurons='1',name='zero')\\n one = lbann.Constant(value=1.0,num_neurons='1',name='one')\\n\\n z_dim = 20 #Latent space dim\\n\\n z = lbann.Gaussian(mean=0.0,stdev=1.0, neuron_dims=\\\"20\\\")\\n model = macc_models.MACCWAE(args.zdim,args.ydim,cf=args.mcf,use_CNN=args.useCNN)\\n d1_real, d1_fake, d_adv, pred_y = model(z,gt_y)\\n\\n d1_real_bce = lbann.SigmoidBinaryCrossEntropy([d1_real,one],name='d1_real_bce')\\n d1_fake_bce = lbann.SigmoidBinaryCrossEntropy([d1_fake,zero],name='d1_fake_bce')\\n d_adv_bce = lbann.SigmoidBinaryCrossEntropy([d_adv,one],name='d_adv_bce')\\n img_loss = lbann.MeanSquaredError([pred_y,gt_y])\\n rec_error = lbann.L2Norm2(lbann.WeightedSum([pred_y,gt_y], scaling_factors=\\\"1 -1\\\"))\\n\\n layers = list(lbann.traverse_layer_graph(input))\\n # Setup objective function\\n weights = set()\\n src_layers = []\\n dst_layers = []\\n for l in layers:\\n if(l.weights and \\\"disc0\\\" in l.name and \\\"instance1\\\" in l.name):\\n src_layers.append(l.name)\\n #freeze weights in disc2\\n if(l.weights and \\\"disc1\\\" in l.name):\\n dst_layers.append(l.name)\\n for idx in range(len(l.weights)):\\n l.weights[idx].optimizer = lbann.NoOptimizer()\\n weights.update(l.weights)\\n l2_reg = lbann.L2WeightRegularization(weights=weights, scale=1e-4)\\n d_adv_bce = lbann.LayerTerm(d_adv_bce,scale=0.01)\\n obj = lbann.ObjectiveFunction([d1_real_bce,d1_fake_bce,d_adv_bce,img_loss,rec_error,l2_reg])\\n # Initialize check metric callback\\n metrics = [lbann.Metric(img_loss, name='recon_error')]\\n #pred_y = macc_models.MACCWAE.pred_y_name\\n callbacks = [lbann.CallbackPrint(),\\n lbann.CallbackTimer(),\\n lbann.CallbackSaveModel(dir=args.dump_models),\\n lbann.CallbackReplaceWeights(source_layers=list2str(src_layers),\\n destination_layers=list2str(dst_layers),\\n batch_interval=2)]\\n\\n if(args.ltfb_batch_interval > 0) :\\n callbacks.append(lbann.CallbackLTFB(batch_interval=args.ltfb_batch_interval,metric='recon_error',\\n low_score_wins=True,\\n exchange_hyperparameters=True))\\n\\n # Construct model\\n return lbann.Model(args.num_epochs,\\n serialize_io=True,\\n weights=weights,\\n layers=layers,\\n metrics=metrics,\\n objective_function=obj,\\n callbacks=callbacks)\",\n \"def doParametersOfInterest(self):\\n\\n self.modelBuilder.doVar(\\\"Rdy[1.,0.0,10.0]\\\");\\n self.modelBuilder.doVar(\\\"Rqcd[1,0.0,10.0]\\\");\\n self.modelBuilder.doSet(\\\"POI\\\",\\\"Rdy,Rqcd\\\")\",\n \"def create_grism_config(conffile=\\\"\\\",\\n pupil=None,\\n module=None,\\n author=\\\"STScI\\\",\\n history=\\\"\\\",\\n outname=\\\"\\\"):\\n if not history:\\n history = \\\"Created from {0:s}\\\".format(conffile)\\n\\n # if pupil is none get from filename like NIRCAM_modB_R.conf\\n if pupil is None:\\n pupil = \\\"GRISM\\\" + conffile.split(\\\".\\\")[0][-1]\\n # if module is none get from filename\\n if module is None:\\n module = conffile.split(\\\".\\\")[0][-3]\\n print(\\\"Pupil is {}\\\".format(pupil))\\n\\n ref_kw = common_reference_file_keywords(reftype=\\\"specwcs\\\",\\n title=\\\"NIRCAM Grism Parameters\\\",\\n description=\\\"{0:s} dispersion models\\\".format(pupil),\\n exp_type=\\\"NRC_GRISM\\\",\\n author=author,\\n model_type=\\\"NIRCAMGrismModel\\\",\\n module=module,\\n pupil=pupil,\\n filename=outname,\\n )\\n\\n\\n # get all the key-value pairs from the input file\\n conf = dict_from_file(conffile)\\n beamdict = split_order_info(conf)\\n # beam = re.compile('^(?:[+\\\\-]){0,1}[a-zA-Z0-9]{0,1}$') # match beam only\\n # read in the sensitivity tables to save their content\\n # they currently have names like this: NIRCam.A.1st.sensitivity.fits\\n # translated as inst.beam/order.param\\n temp = dict()\\n etoken = re.compile(\\\"^[a-zA-Z]*_(?:[+\\\\-]){1,1}[1,2]{1,1}\\\") # find beam key\\n for b, bdict in beamdict.items():\\n temp[b] = dict()\\n # for key in bdict:\\n # if 'SENSITIVITY' in key:\\n # print(\\\"Reading sensitivity from: {0:s}\\\".format(bdict[key]))\\n # sdata = read_sensitivity_file(bdict[key])\\n # skeys = sdata.keys()\\n # for k in skeys:\\n # temp[b][key+\\\"_\\\"+k] = sdata[k]\\n\\n # add the new beam information to beamdict and remove spurious beam info\\n for k in temp:\\n for kk in temp[k]:\\n if etoken.match(kk):\\n kk = kk.replace(\\\"_{}\\\".format(k), \\\"\\\")\\n beamdict[k][kk] = temp[k][kk]\\n\\n # add min and max mag info if not provided\\n # also make beam coeff lists\\n # wx are the wedge offsets for the filters\\n\\n # for k, bdict in beamdict.items():\\n # if isinstance(bdict, dict):\\n # keys = bdict.keys()\\n # if \\\"MMAG_EXTRACT\\\" not in keys:\\n # beamdict[k][\\\"MMAG_EXTRACT\\\"] = 99.0\\n # if maxmag not in keys:\\n # beamdict[k][maxmag] = 0.0\\n # if \\\"wx\\\" not in keys:\\n # beamdict[k]['wx'] = 0.0\\n # if \\\"wy\\\" not in keys:\\n # beamdict[k]['wy'] = 0.0\\n # add the model for transforms\\n\\n\\n\\n # for NIRCAM, the R and C grism coefficients contain zeros where\\n # the dispersion is in the opposite direction. Meaning, the GRISMR,\\n # which disperses along ROWS has coefficients of zero in the y models\\n # and vice versa.\\n #\\n # There are separate reference files for each grism. Depending on the grism\\n # dispersion direction you either want to use the dx from source center or\\n # the dy from source center in the inverse dispersion relationship which is\\n # used to calculate the t value needed to calculate the wavelength at that pixel.\\n #\\n # The model creation here takes all of this into account by looking at the GRISM[R/C]\\n # the file is used for and creating a reference model with the appropriate dispersion\\n # direction in use. This eliminates having to decide which direction to calculate\\n # the dispersion from given the input x,y pixel in the dispersed image.\\n orders = beamdict.keys()\\n\\n # dispersion models valid per order and direction saved to reference file\\n # Forward\\n invdispl = []\\n invdispx = []\\n invdispy = []\\n # Backward\\n displ = []\\n dispx = []\\n dispy = []\\n\\n for order in orders:\\n # convert the displ wavelengths to microns if the input file is still in angstroms\\n l0 = beamdict[order]['DISPL'][0] / 10000.\\n l1 = beamdict[order]['DISPL'][1] / 10000.\\n\\n # create polynomials using the coefficients of each order\\n\\n # This holds the wavelength lookup coeffs\\n # This model is INVDISPL for backward and returns t\\n # This model should be DISPL for forward and returns wavelength\\n if l1 == 0:\\n lmodel = Polynomial1D(1, c0=0, c1=0)\\n else:\\n lmodel = Polynomial1D(1, c0=-l0/l1, c1=1./l1)\\n invdispl.append(lmodel)\\n lmodel = Polynomial1D(1, c0=l0, c1=l1)\\n displ.append(lmodel)\\n\\n # This holds the x coefficients, for the R grism this model is the\\n # the INVDISPX returning t, for the C grism this model is the DISPX\\n x0, x1 = beamdict[order]['DISPX']\\n xmodel = Polynomial1D(1, c0=x0, c1=x1)\\n dispx.append(xmodel)\\n if x1 == 0:\\n xmodel = Polynomial1D(1, c0=0, c1=0)\\n else:\\n xmodel = Polynomial1D(1, c0=-x0/x1, c1=1./x1)\\n invdispx.append(xmodel)\\n\\n # This holds the y coefficients, for the C grism, this model is\\n # the INVDISPY, returning t, for the R grism, this model is the DISPY\\n y0, y1 = beamdict[order]['DISPY']\\n ymodel = Polynomial1D(1, c0=y0, c1=y1)\\n dispy.append(ymodel)\\n if y1 == 0:\\n ymodel = Polynomial1D(1, c0=0, c1=0)\\n else:\\n ymodel = Polynomial1D(1, c0=-y0/y1, c1=1./y1)\\n invdispy.append(ymodel)\\n\\n # change the orders into translatable integers\\n # so that we can look up the order with the proper index\\n oo = [int(o) for o in beamdict]\\n\\n ref = NIRCAMGrismModel()\\n ref.meta.update(ref_kw)\\n # This reference file is good for NRC_GRISM and TSGRISM modes\\n ref.meta.exposure.p_exptype = \\\"NRC_GRISM|NRC_TSGRISM\\\"\\n ref.meta.input_units = u.micron\\n ref.meta.output_units = u.micron\\n ref.displ = displ\\n ref.dispx = dispx\\n ref.dispy = dispy\\n ref.invdispx = invdispx\\n ref.invdispy = invdispy\\n ref.invdispl = invdispl\\n ref.orders = oo\\n entry = HistoryEntry({'description': history, 'time': datetime.datetime.utcnow()})\\n sdict = Software({'name': 'nircam_reftools.py',\\n 'author': author,\\n 'homepage': 'https://github.com/spacetelescope/jwreftools',\\n 'version': '0.7.1'})\\n entry['sofware'] = sdict\\n ref.history['entries'] = [entry]\\n ref.to_asdf(outname)\\n ref.validate()\",\n \"def get_param_scenario3():\\n nb_carre_x = 42\\n nb_carre_y = 45\\n largeur_x = 0.825\\n largeur_y = 0.645\\n z1 = np.array([[11], [32], [17.26]])\\n z2 = np.array([[12], [32], [18.43]])\\n z3 = np.array([[13], [32], [23.2]])\\n z4 = np.array([[15], [31], [23.4]])\\n z5 = np.array([[16], [30], [23.29]])\\n z6 = np.array([[18], [29], [22.28]])\\n z7 = np.array([[19], [28], [35.79]])\\n z8 = np.array([[20], [27], [36.87]])\\n z9 = np.array([[21], [26], [33.92]])\\n z10 = np.array([[23], [24], [38.11]])\\n z11 = np.array([[24], [23], [37.76]])\\n z12 = np.array([[25], [22], [45.6]])\\n z13 = np.array([[26], [20], [56.4]])\\n z14 = np.array([[27], [18], [55.2]])\\n z15 = np.array([[28], [16], [57.53]])\\n z16 = np.array([[29], [14], [58.64]])\\n z17 = np.array([[30], [13], [66.04]])\\n z18 = np.array([[30], [11], [70.02]])\\n z19 = np.array([[31], [9], [69.64]])\\n z20 = np.array([[31], [6], [68.82]])\\n\\n meas = [z1, z2, z3, z4, z5, z6, z7, z8, z9, z10, z11, z12, z13, z14, z15, z16, z17, z18, z19, z20]\\n\\n z = [np.array([[x[0][0] * largeur_x / nb_carre_x], [x[1][0] * largeur_y / nb_carre_y], [-x[2][0] * np.pi / 180]])\\n for x in meas]\\n\\n # We take a measure every second\\n thymio_speed_to_mms = 0.4753\\n Ts = 1\\n\\n Thymio_speed_left = [73, 92, 94, 102, 100, 89, 92, 103, 97, 100, 100, 104, 95, 97, 103, 101, 100, 101, 92, 98]\\n Thymio_speed_right = [67, 96, 102, 89, 105, -45, 102, 101, 104, 71, 100, 93, 94, 105, 96, -45, 95, 103, 88, 94]\\n delta_sr_test = [x * Ts / thymio_speed_to_mms / 1000 for x in Thymio_speed_right]\\n delta_sl_test = [x * Ts / thymio_speed_to_mms / 1000 for x in Thymio_speed_left]\\n\\n return delta_sr_test, delta_sl_test, z\",\n \"def summary(self):\\n if self.model_type == 2:\\n if self.std is None:\\n print(\\n dedent(\\n f\\\"\\\"\\\"\\\\\\n Oaxaca-Blinder Two-fold Effects\\n Unexplained Effect: {self.params[0]:.5f}\\n Explained Effect: {self.params[1]:.5f}\\n Gap: {self.params[2]:.5f}\\\"\\\"\\\"\\n )\\n )\\n else:\\n print(\\n dedent(\\n \\\"\\\"\\\"\\\\\\n Oaxaca-Blinder Two-fold Effects\\n Unexplained Effect: {:.5f}\\n Unexplained Standard Error: {:.5f}\\n Explained Effect: {:.5f}\\n Explained Standard Error: {:.5f}\\n Gap: {:.5f}\\\"\\\"\\\".format(\\n self.params[0],\\n self.std[0],\\n self.params[1],\\n self.std[1],\\n self.params[2],\\n )\\n )\\n )\\n if self.model_type == 3:\\n if self.std is None:\\n print(\\n dedent(\\n f\\\"\\\"\\\"\\\\\\n Oaxaca-Blinder Three-fold Effects\\n Endowment Effect: {self.params[0]:.5f}\\n Coefficient Effect: {self.params[1]:.5f}\\n Interaction Effect: {self.params[2]:.5f}\\n Gap: {self.params[3]:.5f}\\\"\\\"\\\"\\n )\\n )\\n else:\\n print(\\n dedent(\\n f\\\"\\\"\\\"\\\\\\n Oaxaca-Blinder Three-fold Effects\\n Endowment Effect: {self.params[0]:.5f}\\n Endowment Standard Error: {self.std[0]:.5f}\\n Coefficient Effect: {self.params[1]:.5f}\\n Coefficient Standard Error: {self.std[1]:.5f}\\n Interaction Effect: {self.params[2]:.5f}\\n Interaction Standard Error: {self.std[2]:.5f}\\n Gap: {self.params[3]:.5f}\\\"\\\"\\\"\\n )\\n )\",\n \"def getdefflags(config_nm):\\n if config_nm is 'train':\\n user_params = user_params_train\\n elif config_nm is 'eval':\\n user_params = user_params_eval\\n elif config_nm is 'tfrecorder':\\n user_params = user_params_recorder\\n else:\\n print('Unrecognized configuration name : %s, exiting ....' % config_nm)\\n exit(-1)\\n\\n\\n return mandatory_params+user_params\",\n \"def prepare_experiment(assumptions):\\n print(\\\"\\\\nGenerate species parameters\\\")\\n np.random.seed(assumptions['seed']) \\n params = MakeParams(assumptions) \\n if assumptions[\\\"selected_function\\\"] == \\\"f5_invader_suppression\\\":\\n print(\\\"\\\\nDraw invader feature\\\")\\n params = create_invader(params, assumptions)\\n print(params[\\\"c\\\"])\\n \\n print(\\\"\\\\nDraw per-capita function and cost\\\")\\n f1_species_smooth, f1_species_rugged, f2_species_smooth, f2_species_rugged = draw_species_function(assumptions)\\n params.update({\\\"f1_species_smooth\\\": f1_species_smooth, \\\"f1_species_rugged\\\": f1_species_rugged, \\\"f2_species_smooth\\\": f2_species_smooth, \\\"f2_species_rugged\\\": f2_species_rugged})\\n gi = draw_species_cost(f1_species_smooth, assumptions)\\n params.update({\\\"g\\\": gi})\\n \\n print(\\\"\\\\nConstruct plate\\\")\\n np.random.seed(assumptions['seed']) \\n plate = make_plate(assumptions,params)\\n \\n print(\\\"\\\\nAdd community function to plate\\\")\\n plate = add_community_function(plate, assumptions, params)\\n \\n if not pd.isnull(assumptions[\\\"overwrite_plate\\\"]) :\\n print(\\\"\\\\nUpdating the initial plate composition by overwrite_plate\\\")\\n plate = overwrite_plate(plate, assumptions)\\n \\n print(\\\"\\\\nPrepare Protocol\\\")\\n #Extract Protocol from protocol database\\n algorithms = make_algorithms(assumptions)\\n params_algorithm = algorithms[algorithms['algorithm_name'] == assumptions['protocol']]\\n \\n #Params_simulation by default contains all assumptions not stored in params.\\n params_simulation = dict((k, assumptions[k]) for k in assumptions.keys() if k not in params.keys())\\n \\n return params, params_simulation , params_algorithm, plate\",\n \"def process_configuration(self):\\n print \\\"I \\\",self.I\",\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 parse_model_description(model_description: str) -> ModelDescription:\\n root = ET.fromstring(model_description)\\n\\n defaults = _get_attribute_default_values()\\n\\n # mandatory p.32\\n fmi_version = root.get(\\\"fmiVersion\\\")\\n model_name = root.get(\\\"modelName\\\")\\n guid = root.get(\\\"guid\\\")\\n # optional\\n description = root.get(\\\"description\\\", default=\\\"\\\")\\n author = root.get(\\\"author\\\", default=\\\"\\\")\\n copyright = root.get(\\\"copyright\\\", default=\\\"\\\")\\n version = root.get(\\\"version\\\", default=\\\"\\\")\\n license = root.get(\\\"license\\\", default=\\\"\\\")\\n generation_tool = root.get(\\\"generationTool\\\", default=\\\"\\\")\\n generation_date_and_time = root.get(\\\"generationDateAndTime\\\", default=\\\"\\\")\\n variable_naming_convention = root.get(\\\"variableNamingConvention\\\", default=\\\"flat\\\")\\n numberOfEventIndicators = root.get(\\\"numberOfEventIndicators\\\", default=0)\\n\\n model_variables = []\\n\\n \\\"\\\"\\\" Iterate over model variables:\\n <ScalarVariable name=\\\"real_a\\\" valueReference=\\\"0\\\" variability=\\\"continuous\\\" causality=\\\"input\\\">\\n <Real start=\\\"0.0\\\" />\\n </ScalarVariable>\\n \\\"\\\"\\\"\\n for scalarVariable in root.iter(\\\"ScalarVariable\\\"):\\n\\n causality = scalarVariable.get(\\\"causality\\\", default=\\\"local\\\")\\n variability = scalarVariable.get(\\\"variability\\\", default=\\\"continuous\\\")\\n\\n initial = scalarVariable.get(\\\"initial\\\", default=None)\\n # defaults of initial depend on causality and variablilty\\n # the combinations lead to 5 different cases denoted A-E on p.50\\n if initial is None:\\n initial, _ = get_intitial_choices_and_default(causality, variability)\\n\\n var = list(scalarVariable)[0]\\n start = var.get(\\\"start\\\", default=None)\\n dataType = var.tag\\n\\n model_variables.append(\\n ScalarVariable(\\n name=scalarVariable.get(\\\"name\\\"),\\n valueReference=scalarVariable.get(\\\"valueReference\\\"),\\n variability=variability,\\n causality=causality,\\n description=scalarVariable.get(\\\"description\\\", default=\\\"\\\"),\\n initial=initial,\\n start=start,\\n dataType=dataType,\\n )\\n )\\n\\n log_categories = []\\n for category in root.iter(\\\"Category\\\"):\\n log_categories.append(category.get(\\\"name\\\"))\\n\\n model_structure = []\\n\\n # cosimulation\\n cosim_element = root.find(\\\"CoSimulation\\\")\\n\\n modelIdentifier = cosim_element.get(\\\"modelIdentifier\\\")\\n needsExecutionTool = cosim_element.get(\\n \\\"needsExecutionTool\\\", default=defaults[\\\"needsExecutionTool\\\"]\\n )\\n canHandleVariableCommunicationStepSize = cosim_element.get(\\n \\\"canHandleVariableCommunicationStepSize\\\",\\n default=defaults[\\\"canHandleVariableCommunicationStepSize\\\"],\\n )\\n canInterpolateInputs = cosim_element.get(\\n \\\"canInterpolateInputs\\\", default=defaults[\\\"canInterpolateInputs\\\"]\\n )\\n maxOutputDerivativeOrder = cosim_element.get(\\n \\\"maxOutputDerivativeOrder\\\", default=defaults[\\\"maxOutputDerivativeOrder\\\"]\\n )\\n canRunAsynchronuously = cosim_element.get(\\n \\\"canRunAsynchronuously\\\", default=defaults[\\\"canRunAsynchronuously\\\"]\\n )\\n canBeInstantiatedOnlyOncePerProcess = cosim_element.get(\\n \\\"canBeInstantiatedOnlyOncePerProcess\\\",\\n default=defaults[\\\"canBeInstantiatedOnlyOncePerProcess\\\"],\\n )\\n canNotUseMemoryManagementFunctions = cosim_element.get(\\n \\\"canNotUseMemoryManagementFunctions\\\",\\n default=defaults[\\\"canNotUseMemoryManagementFunctions\\\"],\\n )\\n canGetAndSetFMUstate = cosim_element.get(\\n \\\"canGetAndSetFMUstate\\\", default=defaults[\\\"canGetAndSetFMUstate\\\"]\\n )\\n canSerializeFMUstate = cosim_element.get(\\n \\\"canSerializeFMUstate\\\", default=defaults[\\\"canSerializeFMUstate\\\"]\\n )\\n providesDirectionalDerivative = cosim_element.get(\\n \\\"providesDirectionalDerivative\\\",\\n default=defaults[\\\"providesDirectionalDerivative\\\"],\\n )\\n\\n def xs_boolean(s):\\n if s is None:\\n return None\\n if s in {\\\"false\\\", \\\"0\\\"}:\\n return False\\n elif s in {\\\"true\\\", \\\"1\\\"}:\\n return True\\n else:\\n raise ValueError(f\\\"Unable to convert {s} to xsd boolean\\\")\\n\\n def xs_normalized_string(s: str):\\n if s is None:\\n return None\\n if not s.isprintable():\\n raise ValueError(r\\\"normalized string can not contain: \\\\n, \\\\t or \\\\r\\\")\\n return s\\n\\n def xs_unsigned_int(s: str):\\n if s is None:\\n return None\\n value = int(s)\\n if value > 4294967295:\\n raise ValueError(\\\"xs:unsingedInt cannot exceed the value 4294967295\\\")\\n return value\\n\\n cosimulation = CoSimulation(\\n modelIdentifier=modelIdentifier,\\n needsExecutionTool=xs_boolean(needsExecutionTool),\\n canHandleVariableCommunicationStepSize=xs_boolean(\\n canHandleVariableCommunicationStepSize\\n ),\\n canInterpolateInputs=xs_boolean(canInterpolateInputs),\\n maxOutputDerivativeOrder=xs_unsigned_int(maxOutputDerivativeOrder),\\n canRunAsynchronuously=xs_boolean(canRunAsynchronuously),\\n canBeInstantiatedOnlyOncePerProcess=xs_boolean(\\n canBeInstantiatedOnlyOncePerProcess\\n ),\\n canNotUseMemoryManagementFunctions=xs_boolean(\\n canNotUseMemoryManagementFunctions\\n ),\\n canGetAndSetFMUstate=xs_boolean(canGetAndSetFMUstate),\\n canSerializeFMUstate=xs_boolean(canSerializeFMUstate),\\n providesDirectionalDerivative=xs_boolean(providesDirectionalDerivative),\\n )\\n\\n return ModelDescription(\\n fmiVersion=fmi_version,\\n modelName=model_name,\\n guid=guid,\\n author=author,\\n description=description,\\n version=version,\\n copyright=copyright,\\n logCategories=log_categories,\\n license=license,\\n generationTool=generation_tool,\\n generationDateAndTime=generation_date_and_time,\\n variableNamingConvention=variable_naming_convention,\\n numberOfEventIndicators=numberOfEventIndicators,\\n CoSimulation=cosimulation,\\n modelVariables=model_variables,\\n modelStructure=model_structure,\\n )\",\n \"def get_model_config(model_name, args):\\n if model_name == 'WaveGlow':\\n model_config = dict(\\n n_mel_channels=args.n_mel_channels,\\n n_flows=args.flows,\\n n_group=args.groups,\\n n_early_every=args.early_every,\\n n_early_size=args.early_size,\\n WN_config=dict(\\n n_layers=args.wn_layers,\\n kernel_size=args.wn_kernel_size,\\n n_channels=args.wn_channels\\n )\\n )\\n return model_config\\n elif model_name == 'FastPitch':\\n model_config = dict(\\n # io\\n n_mel_channels=args.n_mel_channels,\\n # symbols\\n n_symbols=len(get_symbols(args.symbol_set)),\\n padding_idx=get_pad_idx(args.symbol_set),\\n symbols_embedding_dim=args.symbols_embedding_dim,\\n # input FFT\\n in_fft_n_layers=args.in_fft_n_layers,\\n in_fft_n_heads=args.in_fft_n_heads,\\n in_fft_d_head=args.in_fft_d_head,\\n in_fft_conv1d_kernel_size=args.in_fft_conv1d_kernel_size,\\n in_fft_conv1d_filter_size=args.in_fft_conv1d_filter_size,\\n in_fft_output_size=args.in_fft_output_size,\\n p_in_fft_dropout=args.p_in_fft_dropout,\\n p_in_fft_dropatt=args.p_in_fft_dropatt,\\n p_in_fft_dropemb=args.p_in_fft_dropemb,\\n # output FFT\\n out_fft_n_layers=args.out_fft_n_layers,\\n out_fft_n_heads=args.out_fft_n_heads,\\n out_fft_d_head=args.out_fft_d_head,\\n out_fft_conv1d_kernel_size=args.out_fft_conv1d_kernel_size,\\n out_fft_conv1d_filter_size=args.out_fft_conv1d_filter_size,\\n out_fft_output_size=args.out_fft_output_size,\\n p_out_fft_dropout=args.p_out_fft_dropout,\\n p_out_fft_dropatt=args.p_out_fft_dropatt,\\n p_out_fft_dropemb=args.p_out_fft_dropemb,\\n # duration predictor\\n dur_predictor_kernel_size=args.dur_predictor_kernel_size,\\n dur_predictor_filter_size=args.dur_predictor_filter_size,\\n p_dur_predictor_dropout=args.p_dur_predictor_dropout,\\n dur_predictor_n_layers=args.dur_predictor_n_layers,\\n # pitch predictor\\n pitch_predictor_kernel_size=args.pitch_predictor_kernel_size,\\n pitch_predictor_filter_size=args.pitch_predictor_filter_size,\\n p_pitch_predictor_dropout=args.p_pitch_predictor_dropout,\\n pitch_predictor_n_layers=args.pitch_predictor_n_layers,\\n # pitch conditioning\\n pitch_embedding_kernel_size=args.pitch_embedding_kernel_size,\\n # speakers parameters\\n n_speakers=args.n_speakers,\\n speaker_emb_weight=args.speaker_emb_weight,\\n # energy predictor\\n energy_predictor_kernel_size=args.energy_predictor_kernel_size,\\n energy_predictor_filter_size=args.energy_predictor_filter_size,\\n p_energy_predictor_dropout=args.p_energy_predictor_dropout,\\n energy_predictor_n_layers=args.energy_predictor_n_layers,\\n # energy conditioning\\n energy_conditioning=args.energy_conditioning,\\n energy_embedding_kernel_size=args.energy_embedding_kernel_size,\\n )\\n return model_config\\n\\n else:\\n raise NotImplementedError(model_name)\",\n \"def update_ci_values(filter_item, config_filename, log_level=logutil.logging.INFO):\\n log.setLevel(log_level)\\n # Check to see if the CI values need to be updated. If not, simply skip over all of this.\\n update_ci_vals = False\\n for phot_mode in ['aperture', 'segment']:\\n if filter_item.configobj_pars.pars['quality control'].outpars['ci filter'][phot_mode]['lookup_ci_limits_from_table']:\\n update_ci_vals = True\\n if update_ci_vals:\\n # read in the custom SVM param file data\\n with open(config_filename) as f:\\n json_data = json.load(f)\\n for phot_mode in ['aperture', 'segment']:\\n if filter_item.configobj_pars.pars['quality control'].outpars['ci filter'][phot_mode]['lookup_ci_limits_from_table']:\\n log.info(\\\"NOTE: The 'lookup_ci_limits_from_table' setting in the 'quality control'>'{}' section of the parameters for filter image {} is set to 'True'. This means that any custom user-tuned values for 'ci_upper_limit' and 'ci_lower_limit' will be overwritten. To prevent this, please set 'lookup_ci_limits_from_table' to 'False' in the custom parameter file {}\\\".format(phot_mode, filter_item.drizzle_filename, config_filename))\\n # set up inputs to ci_table.get_ci_from_file() and execute to get new CI values\\n drizzled_image = filter_item.drizzle_filename\\n ci_lookup_file_path = \\\"default_parameters/any\\\"\\n diagnostic_mode = False\\n ci_lower_limit = filter_item.configobj_pars.pars['quality control'].outpars['ci filter'][phot_mode]['ci_lower_limit']\\n ci_upper_limit = filter_item.configobj_pars.pars['quality control'].outpars['ci filter'][phot_mode]['ci_upper_limit']\\n ci_dict = ci_table.get_ci_from_file(drizzled_image, ci_lookup_file_path, log_level,\\n diagnostic_mode=diagnostic_mode, ci_lower=ci_lower_limit,\\n ci_upper=ci_upper_limit)\\n log.debug(\\\"{} {} CI upper limit updated from {} to {}\\\".format(filter_item.drizzle_filename,\\n phot_mode,\\n ci_upper_limit,\\n ci_dict[\\\"ci_upper_limit\\\"]))\\n log.debug(\\\"{} {} CI lower limit updated from {} to {}\\\\n\\\".format(filter_item.drizzle_filename,\\n phot_mode,\\n ci_lower_limit,\\n ci_dict[\\\"ci_lower_limit\\\"]))\\n # update CI values\\n json_data[drizzled_image[:-9]][\\\"default_values\\\"][\\\"quality control\\\"][\\\"ci filter\\\"][phot_mode][\\\"ci_lower_limit\\\"] = ci_dict[\\\"ci_lower_limit\\\"]\\n json_data[drizzled_image[:-9]][\\\"default_values\\\"][\\\"quality control\\\"][\\\"ci filter\\\"][phot_mode][\\\"ci_upper_limit\\\"] = ci_dict[\\\"ci_upper_limit\\\"]\\n\\n # Write out the updated custom SVM param file data back to file.\\n with open(config_filename, 'w') as f:\\n json.dump(json_data, f, indent=4)\\n else:\\n log.debug(\\\"Using existing concentration index limits from parameter file\\\")\",\n \"def __init__(self, encut, spinaxis, ldaul, Uparam, Jparam, name='DFTCL_settings'):\\n ncl_settings = {\\\"ISPIN\\\": 2, \\\"MAGMOM\\\": None, \\\"SAXIS\\\": spinaxis, \\\"LSORBIT\\\": \\\".TRUE.\\\", \\\"LNONCOLLINEAR\\\": \\\".TRUE.\\\"}\\n dftu_settings = {\\\"LDAU\\\": \\\".TRUE.\\\", \\\"LDAUU\\\": Uparam, \\\"LDATYPE\\\": 2, \\\"LDAUL\\\": ldaul, \\\"LDAUJ\\\": Jparam , \\\"LMAXMIX\\\": 4}\\n InputParameters.__init__(self, name=name, magnetic_settings=ncl_settings, hubbard_settings=dftu_settings)\\n self.update_electronic_settings(\\\"ENCUT\\\", encut)\",\n \"def get_model_config(model_name, args):\\n if model_name == 'Tacotron2':\\n model_config = dict(\\n # optimization\\n mask_padding=args.mask_padding,\\n # audio\\n n_mel_channels=args.n_mel_channels,\\n # symbols\\n n_symbols=args.n_symbols,\\n symbols_embedding_dim=args.symbols_embedding_dim,\\n # encoder\\n encoder_kernel_size=args.encoder_kernel_size,\\n encoder_n_convolutions=args.encoder_n_convolutions,\\n encoder_embedding_dim=args.encoder_embedding_dim,\\n # attention\\n attention_rnn_dim=args.attention_rnn_dim,\\n attention_dim=args.attention_dim,\\n # attention location\\n attention_location_n_filters=args.attention_location_n_filters,\\n attention_location_kernel_size=args.attention_location_kernel_size,\\n # decoder\\n n_frames_per_step=args.n_frames_per_step,\\n decoder_rnn_dim=args.decoder_rnn_dim,\\n prenet_dim=args.prenet_dim,\\n max_decoder_steps=args.max_decoder_steps,\\n gate_threshold=args.gate_threshold,\\n p_attention_dropout=args.p_attention_dropout,\\n p_decoder_dropout=args.p_decoder_dropout,\\n # postnet\\n postnet_embedding_dim=args.postnet_embedding_dim,\\n postnet_kernel_size=args.postnet_kernel_size,\\n postnet_n_convolutions=args.postnet_n_convolutions,\\n decoder_no_early_stopping=args.decoder_no_early_stopping\\n )\\n return model_config\\n elif model_name == 'WaveGlow':\\n model_config = dict(\\n n_mel_channels=args.n_mel_channels,\\n n_flows=args.flows,\\n n_group=args.groups,\\n n_early_every=args.early_every,\\n n_early_size=args.early_size,\\n WN_config=dict(\\n n_layers=args.wn_layers,\\n kernel_size=args.wn_kernel_size,\\n n_channels=args.wn_channels\\n )\\n )\\n return model_config\\n else:\\n raise NotImplementedError(model_name)\",\n \"def load(self, name=\\\"\\\"):\\n\\n self.constructed = True\\n if name == \\\"\\\":\\n name = \\\"/home/unai/Escritorio/MultiNetwork/model/model\\\"\\n\\n network_descriptors = {\\\"Generic\\\": GenericDescriptor, \\\"Decoder\\\": DecoderDescriptor, \\\"Discrete\\\": DiscreteDescriptor, \\\"Convolution\\\": ConvolutionDescriptor}\\n\\n if not os.path.isfile(name):\\n print(\\\"Error at loading the model\\\")\\n return None\\n\\n f = open(name, \\\"r+\\\")\\n\\n lines = f.readlines()\\n\\n i = 0\\n while lines[i] != \\\"\\\\n\\\": # Each component is stored in a line\\n ident, n_inp, kind, n_hidden, layers, init, act, cond_rand, taking, producing, depth, reachable, belows = lines[i][:-1].split(\\\"_\\\")\\n kwargs = {}\\n if int(ident[1:]) > self.last_net:\\n self.last_net = int(ident[1:])\\n\\n self.reachable[ident] = reachable.split(\\\",\\\")\\n self.comps_below[ident] = belows.split(\\\",\\\")\\n\\n if \\\"onv\\\" in kind: # Not working right now\\n filters, sizes, layers, strides = layers.split(\\\"*\\\")\\n sizes = sizes.split(\\\",\\\")\\n s = np.array([[int(sz) for sz in szs.split(\\\"/\\\")] for szs in sizes])\\n desc = network_descriptors[kind](int(inp), int(outp), int(n_inp), layers.split(\\\",\\\"), filters.split(\\\",\\\"), [int(x) for x in strides.split(\\\",\\\")], s, [int(x) for x in act.split(\\\",\\\")], [int(x) for x in init.split(\\\",\\\")], kwargs)\\n else:\\n if len(kwargs) > 0: # Not working right now\\n kwargs = kwargs.split(\\\"-\\\")\\n kwargs[0] = [int(x) for x in kwargs[0].split(\\\".\\\") if len(x) > 0]\\n kwargs[1] = [int(x) for x in kwargs[1].split(\\\".\\\") if len(x) > 0]\\n if len(cond_rand) > 0:\\n cond_rand = cond_rand.split(\\\"-\\\")\\n cond_rand[0] = [int(x) for x in cond_rand[0].split(\\\",\\\") if len(x) > 0]\\n cond_rand[1] = [int(x) for x in cond_rand[1].split(\\\",\\\") if len(x) > 0]\\n kwargs[\\\"conds\\\"] = cond_rand\\n desc = network_descriptors[kind](int(taking.split(\\\",\\\")[0]), int(producing.split(\\\",\\\")[0]), int(n_inp), int(n_hidden), [int(x) for x in layers.split(\\\",\\\") if x != \\\"-1\\\"], init_functions[[int(x) for x in init.split(\\\",\\\") if x != \\\"-1\\\"]],\\n act_functions[[int(x) for x in act.split(\\\",\\\") if x != \\\"-1\\\"]], **kwargs)\\n\\n # print(\\\"ident\\\", ident, \\\"n_inp\\\", n_inp, \\\"kind\\\", kind, \\\"inp\\\", inp, \\\"outp\\\", outp, \\\"layers\\\", layers, \\\"init\\\", init, \\\"act\\\", act, \\\"taking\\\", taking, \\\"producing\\\", producing, \\\"depth\\\", depth, \\\"kwargs\\\", kwargs)\\n net = NetworkComp(desc, InOut(size=int(taking.split(\\\",\\\")[0]), data_type=taking.split(\\\",\\\")[1]), InOut(data_type=producing.split(\\\",\\\")[1], size=int(producing.split(\\\",\\\")[0])), int(depth))\\n\\n self.add_net(net, ident)\\n i += 1\\n\\n i += 1\\n\\n while lines[i] != \\\"\\\\n\\\": # Inputs\\n\\n ident, size, kind, depth = lines[i].split(\\\"_\\\")\\n\\n self.inputs[ident] = ModelComponent(None, InOut(size=int(size), data_type=kind), int(depth))\\n i += 1\\n\\n i += 1\\n\\n while lines[i] != \\\"\\\\n\\\": # Outputs\\n\\n ident, size, kind, depth, belows = lines[i].split(\\\"_\\\")\\n\\n self.outputs[ident] = ModelComponent(InOut(size=int(size), data_type=kind), None, int(depth))\\n self.comps_below[ident] = belows.split(\\\",\\\")\\n i += 1\\n\\n i += 1\\n\\n while i < len(lines): # Connections\\n name, inp, outp, kind, size = lines[i].split(\\\"_\\\")\\n\\n if int(name[1:]) > self.last_con:\\n self.last_con = int(name[1:])\\n\\n self.connections[name] = Connection(inp, outp, InOut(kind, int(size)), name)\\n i += 1\\n self.update_below()\",\n \"def test_configuration(self):\\n space = Space()\\n space.register(Integer(\\\"yolo1\\\", \\\"uniform\\\", -3, 6, shape=(2,)))\\n space.register(Integer(\\\"yolo2\\\", \\\"uniform\\\", -3, 6, shape=(2,)))\\n space.register(Real(\\\"yolo3\\\", \\\"norm\\\", 0.9))\\n space.register(Categorical(\\\"yolo4\\\", (\\\"asdfa\\\", 2)))\\n\\n assert space.configuration == {\\n \\\"yolo1\\\": \\\"uniform(-3, 3, shape=(2,), discrete=True)\\\",\\n \\\"yolo2\\\": \\\"uniform(-3, 3, shape=(2,), discrete=True)\\\",\\n \\\"yolo3\\\": \\\"normal(0.9)\\\",\\n \\\"yolo4\\\": \\\"choices(['asdfa', 2])\\\",\\n }\",\n \"def setup(self):\\n\\n module = [m for m in Rt.modules if m.name == self.module_name][0]\\n\\n # Take CPACS file from the optimisation\\n cpacs_path = module.cpacs_in\\n tixi = open_tixi(cpacs_path)\\n self.Model = load_surrogate(tixi)\\n tixi.save(cpacs_path)\\n\\n df = self.Model.df\\n df.set_index(\\\"Name\\\", inplace=True)\\n for name in df.index:\\n if df.loc[name, \\\"type\\\"] == \\\"obj\\\":\\n self.add_output(name)\\n elif df.loc[name, \\\"type\\\"] == \\\"des\\\":\\n self.add_input(name)\\n\\n self.xd = df.loc[[name for name in df.index if df.loc[name, \\\"type\\\"] == \\\"des\\\"]]\\n self.yd = df.loc[[name for name in df.index if df.loc[name, \\\"type\\\"] == \\\"obj\\\"]]\",\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 input_setup(config):\\n print(config.is_train)\\n # Load data path\\n if config.is_train:\\n data, label = prepare_data(dataset=\\\"Train/DoF_Images (2)\\\")\\n else:\\n data, label = prepare_data(dataset=\\\"Test\\\")\\n\\n sub_input_sequence = []\\n sub_label_sequence = []\\n padding = abs(config.image_size - config.label_size) / 2 # 6\\n nx = ny = 0\\n\\n if config.is_train:\\n for i in range(len(data)):\\n input_, label_ = preprocess(data[i], label[i], config.scale)\\n\\n if len(input_.shape) == 3:\\n h, w, c = input_.shape\\n else:\\n h, w = input_.shape\\n\\n for x in range(0, h-config.image_size+1, config.stride):\\n if i == 0:\\n nx += 1; ny = 0\\n for y in range(0, w-config.image_size+1, config.stride):\\n if i == 0:\\n ny += 1\\n\\n sub_input = input_[x:x+config.image_size, y:y+config.image_size] # [33 x 33]\\n sub_label = label_[x+int(padding):x+int(padding)+config.label_size\\n , y+int(padding):y+int(padding)+config.label_size] # [21 x 21]\\n # print(sub_input.shape)\\n # print(sub_label.shape)\\n # Make channel value\\n sub_input = sub_input.reshape([config.image_size, config.image_size, 3])\\n sub_label = sub_label.reshape([config.label_size, config.label_size, 3])\\n\\n sub_input_sequence.append(sub_input)\\n sub_label_sequence.append(sub_label)\\n\\n else:\\n nx_l = []\\n ny_l = []\\n for i in range(len(data)):\\n input_, label_ = preprocess(data[i], label[i], config.scale)\\n\\n if len(input_.shape) == 3:\\n h, w, c = input_.shape\\n else:\\n h, w = input_.shape\\n\\n if w >= 4000 or h > 4000:\\n input_ = cv2.resize(input_, dsize=(int(w/2), int(h/2)), interpolation=cv2.INTER_AREA)\\n label_ = cv2.resize(label_, dsize=(int(w/2), int(h/2)), interpolation=cv2.INTER_AREA)\\n w = int(w/2)\\n h = int(h/2)\\n\\n # Numbers of sub-images in height and width of image are needed to compute merge operation.\\n\\n nx = ny = 0\\n for x in range(0, h-config.image_size+1, config.stride):\\n nx += 1; ny = 0\\n for y in range(0, w-config.image_size+1, config.stride):\\n ny += 1\\n sub_input = input_[x:x+config.image_size, y:y+config.image_size] # [33 x 33]\\n sub_label = label_[x+int(padding):x+int(padding)+config.label_size, y+int(padding):y+int(padding)+config.label_size] # [21 x 21]\\n\\n sub_input = sub_input.reshape([config.image_size, config.image_size, 3])\\n sub_label = sub_label.reshape([config.label_size, config.label_size, 3])\\n\\n sub_input_sequence.append(sub_input)\\n sub_label_sequence.append(sub_label)\\n #print(\\\"nx: %d ny: %d\\\" % (nx, ny))\\n nx_l.append(nx)\\n ny_l.append(ny)\\n \\\"\\\"\\\"\\n len(sub_input_sequence) : the number of sub_input (33 x 33 x ch) in one image\\n (sub_input_sequence[0]).shape : (33, 33, 1)\\n \\\"\\\"\\\"\\n # Make list to numpy array. With this transform\\n arrdata = np.asarray(sub_input_sequence) # [?, 33, 33, 3]\\n arrlabel = np.asarray(sub_label_sequence) # [?, 21, 21, 3]\\n\\n make_data(config, arrdata, arrlabel)\\n print(\\\"make_data success\\\")\\n if config.is_train:\\n return nx, ny\\n else:\\n return nx_l, ny_l, len(data)\",\n \"def get_input_info_from_cfg(deploy_cfg: mmengine.Config) -> Dict[str, List]:\\n # The partition is not supported now. Set the id of model to 0.\\n model_inputs = get_model_inputs(deploy_cfg)[0]\\n input_info = model_inputs['opt_shapes']\\n ir_config = get_ir_config(deploy_cfg)\\n if ir_config is not None:\\n input_names = ir_config.get('input_names', None)\\n if input_names:\\n if not isinstance(input_info, Dict):\\n input_info = dict(zip(input_names, input_info))\\n input_info = update_input_names(input_info, input_names)\\n return input_info\",\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 step(self):\\n\\n \\\"\\\"\\\" First updates the variables values of the current time form the environment \\\"\\\"\\\"\\n self.update_crispval(self.env.context)\\n\\n \\\"\\\"\\\"\\n here the decision making of the agent\\n to determine which activity to suggest to the patient\\n i apply the creative controller to the current context\\n \\\"\\\"\\\"\\n curr_input = sample_inputs(False, 0, self.curr_interaction, self.variables_default_val, self.action_var,\\n self.fuzzysets_values, self.variables_universe)\\n c_out, rules_activations, is_cc_exception = self.creative_controller.computeOutput(curr_input, False)\\n\\n \\\"\\\"\\\" i obtain a number of ouput crisp values.\\n i determine which one achieves the max expected output w.r.t. the a-rules \\\"\\\"\\\"\\n best_a = None\\n best_a_val = -1000\\n best_a_exphapp = 5\\n if self.verbose > Constants.VERBOSE_BASIC:\\n print(\\\"rules activations\\\")\\n for a in rules_activations:\\n if rules_activations[a] > 0:\\n print(str(a) + \\\"\\\\n\\\\t\\\\t\\\\t-> \\\" + str(rules_activations[a]))\\n for item in c_out.items(): # for each pair <activity, crisp output>\\n if self.verbose > Constants.VERBOSE_BASIC:\\n print(item)\\n if not item[\\n 0] in self.curr_iter_suggestions: # if i didn't suggest the same activity already in the same interaction\\n inputs = dict(curr_input) # I create a copy fo the dict\\n inputs[item[0]] = item[1]\\n assessor_id = self.actions_to_ti[item[0]]\\n self.assessors[assessor_id].feed_inputs(inputs)\\n is_ac_exception = False\\n assout = []\\n try:\\n a_out, a_rules_activations, is_ac_exception = self.assessors[assessor_id].compute(verbose=False)\\n assout = [a_out[ao] for ao in a_out]\\n except:\\n is_ac_exception = True\\n traceback.print_exc()\\n # todo the following assumes that every assessor controller has same eval var\\n for v in self.eval_var:\\n assout.append(self.variables_default_val[v])\\n if len(assout) == 0:\\n for v in self.eval_var:\\n assout.append(self.variables_default_val[v])\\n w_ta = self.weights_therapeutic_interventions[self.actions_to_ti[item[0]]]\\n\\n avg_credit_rules_that_suggested_action = 1.0\\n nr_rules_that_suggested_action = 0\\n for r in rules_activations:\\n if (rules_activations[r] > 0) and (str(item[0]) in str(r)):\\n avg_credit_rules_that_suggested_action = avg_credit_rules_that_suggested_action + \\\\\\n self.rules_credits[str(r)]\\n nr_rules_that_suggested_action = nr_rules_that_suggested_action + 1\\n if nr_rules_that_suggested_action > 0:\\n avg_credit_rules_that_suggested_action = (\\n avg_credit_rules_that_suggested_action - 1.0) / nr_rules_that_suggested_action\\n repetition_cost = 1.0\\n a_val = (mean(assout) * w_ta * avg_credit_rules_that_suggested_action) / repetition_cost\\n if (a_val > best_a_val) and (\\n item[1] >= (self.variables_default_val[item[0]] + self.range_step[item[0]])):\\n best_a = item\\n best_a_val = a_val\\n best_a_exphapp = mean(assout)\\n\\n \\\"\\\"\\\"I suggest the activity with best expected outcome and store the information to populate the interactions \\n memory \\\"\\\"\\\"\\n self.proposeActivity(best_a)\\n if not best_a is None:\\n if (self.verbose > Constants.VERBOSE_FALSE) and (self.verbose <= Constants.VERBOSE_BASIC):\\n print(\\\"proposing activity\\\" + str(best_a) + \\\" which has expected feedback: \\\" + str(\\n best_a_exphapp) + \\\", which weighted is \\\" + str(best_a_val))\\n self.curr_iter_suggestions.append(best_a[0])\\n self.last_suggestion = best_a\\n else:\\n if (self.verbose > Constants.VERBOSE_FALSE) and (self.verbose <= Constants.VERBOSE_BASIC):\\n print(\\\"the activity proposed is \\\" + str(\\n best_a) + \\\" so I don't suggest anything. I will ask a question instead\\\")\\n self.last_suggestion = []\\n self.expected_feedback = best_a_exphapp\\n self.last_context = self.env.context.copy()\\n self.last_rules_activations = rules_activations\",\n \"def _check_model_params(self):\\n enc_type = self.configs['encoder_type']\\n if enc_type not in ['uni', 'bi', 'gnmt']:\\n raise ValueError(\\\"encoder type must be one of ['uni', 'bi', 'gnmt'].\\\")\\n\\n attn = self.configs['attention']\\n if attn not in ['', 'luong', 'scaled_luong', 'bahdanau', 'normed_bahdanau']:\\n raise ValueError(\\n \\\"attention must be one of \\\"\\n \\\"['', 'luong', 'scaled_luong', 'bahdanau', 'normed_bahdanau'].\\\")\\n\\n num_enc_layers = self.configs['num_encoder_layers']\\n num_dec_layers = self.configs['num_decoder_layers']\\n if not num_enc_layers or not num_dec_layers:\\n raise ValueError(\\n \\\"num_encoder_layers and num_decoder_layers must be greater than 0.\\\")\\n if num_enc_layers != num_dec_layers:\\n self.configs['pass_hidden_state'] = False\\n\\n if enc_type == \\\"bi\\\" and num_enc_layers % 2 != 0:\\n raise ValueError(\\n \\\"num_encoder_layers must be even when encoder_type is %s.\\\" % enc_type)\\n\\n attn_arch = self.configs.get('attention_architecture', None)\\n if attn_arch in [\\\"gnmt\\\"] and num_enc_layers < 2:\\n raise ValueError(\\\"For gnmt attention architecture, \\\"\\n \\\"num_encoder_layers: %d should be >= 2.\\\" %\\n num_enc_layers)\\n\\n infer_mode = self.configs['infer_mode']\\n beam_width = self.configs.get(\\\"beam_width\\\", 0)\\n if infer_mode == \\\"beam_search\\\" and beam_width <= 0:\\n raise ValueError(\\\"beam_width must be > 0 if infer_mode is `beam_search`.\\\")\\n\\n sample_temp = self.configs.get(\\\"sampling_temperature\\\", 0.0)\\n if infer_mode == \\\"sample\\\" and sample_temp <= 0.0:\\n raise ValueError(\\n \\\"sampling_temperature must greater than 0.0 using sample decode.\\\")\\n\\n subword_option = self.configs['subword_option']\\n if subword_option not in ['', 'bpe', 'spm']:\\n raise ValueError(\\\"subword_option must be one of ['','bpe','spm']\\\")\\n\\n num_enc_residual_layers = 0\\n num_dec_residual_layers = 0\\n if self.configs['residual']:\\n if num_enc_layers > 1:\\n num_enc_residual_layers = num_enc_layers - 1\\n if num_dec_layers > 1:\\n num_dec_residual_layers = num_dec_layers - 1\\n\\n if enc_type == \\\"gnmt\\\":\\n num_enc_residual_layers = num_enc_layers - 2\\n if num_enc_layers == num_dec_layers:\\n num_dec_residual_layers = num_enc_residual_layers\\n\\n self.configs['num_encoder_residual_layers'] = num_enc_residual_layers\\n self.configs['num_decoder_residual_layers'] = num_dec_residual_layers\",\n \"def model_4_parameters(num_features, num_classes, image_info):\\n parameters = {}\\n parameters['num_features'] = num_features\\n parameters['num_classes'] = num_classes\\n if image_info['key'][:5] == \\\"pavia\\\":\\n parameters['C'] = 1.0\\n else:\\n parameters['C'] = 40.0\\n \\n return parameters\",\n \"def test013(testDir, dirDict, pflag):\\n shutil.copy(osp.join(dirDict[\\\"baseConfig\\\"], \\\"params_system.cfg\\\"), testDir)\\n shutil.copy(osp.join(dirDict[\\\"baseConfig\\\"], \\\"params_c.cfg\\\"), testDir)\\n shutil.copy(osp.join(dirDict[\\\"baseConfig\\\"], \\\"params_a.cfg\\\"), testDir)\\n psys = osp.join(testDir, \\\"params_system.cfg\\\")\\n ptrodec = osp.join(testDir, \\\"params_c.cfg\\\")\\n ptrodea = osp.join(testDir, \\\"params_a.cfg\\\")\\n P_s = IO.get_config(psys)\\n P_s.set(\\\"Sim Params\\\", \\\"profileType\\\", \\\"CV\\\")\\n P_s.set(\\\"Sim Params\\\", \\\"Vset\\\", \\\"3.8\\\")\\n P_s.set(\\\"Sim Params\\\", \\\"tsteps\\\", \\\"25\\\")\\n P_s.set(\\\"Sim Params\\\", \\\"Nvol_c\\\", \\\"2\\\")\\n P_s.set(\\\"Sim Params\\\", \\\"Nvol_s\\\", \\\"2\\\")\\n P_s.set(\\\"Sim Params\\\", \\\"Nvol_a\\\", \\\"2\\\")\\n P_s.set(\\\"Particles\\\", \\\"cs0_c\\\", \\\"0.2\\\")\\n P_s.set(\\\"Particles\\\", \\\"cs0_a\\\", \\\"0.95\\\")\\n P_s.set(\\\"Electrolyte\\\", \\\"elyteModelType\\\", \\\"dilute\\\")\\n IO.write_config_file(P_s, psys)\\n P = IO.get_config(ptrodec)\\n P.set(\\\"Particles\\\", \\\"type\\\", \\\"homog\\\")\\n P.set(\\\"Particles\\\", \\\"shape\\\", \\\"sphere\\\")\\n P.set(\\\"Material\\\", \\\"muRfunc\\\", \\\"LiMn2O4_ss2\\\")\\n P.set(\\\"Reactions\\\", \\\"rxnType\\\", \\\"Marcus\\\")\\n IO.write_config_file(P, ptrodec)\\n P = IO.get_config(ptrodea)\\n P.set(\\\"Particles\\\", \\\"discretization\\\", \\\"2.5e-9\\\")\\n P.set(\\\"Particles\\\", \\\"shape\\\", \\\"cylinder\\\")\\n P.set(\\\"Material\\\", \\\"muRfunc\\\", \\\"testIS_ss\\\")\\n P.set(\\\"Reactions\\\", \\\"rxnType\\\", \\\"BV_raw\\\")\\n IO.write_config_file(P, ptrodea)\\n main.main(psys, keepArchive=False)\\n shutil.move(dirDict[\\\"simOut\\\"], testDir)\\n if pflag:\\n corePlots(testDir, dirDict)\\n elytePlots(testDir, dirDict)\\n electrodePlots(testDir, dirDict, \\\"c\\\")\\n cmpr.bulkpf(testDir, dirDict, \\\"c\\\")\\n electrodePlots(testDir, dirDict, \\\"a\\\")\\n cmpr.bulkpf(testDir, dirDict, \\\"a\\\")\",\n \"def get_data_config(self):\\n conf_map = {}\\n\\n if self.alien_alg.currentIndex() == 1:\\n conf_map['alien_alg'] = '\\\"block_aliens\\\"'\\n if len(self.aliens.text()) > 0:\\n conf_map['aliens'] = str(self.aliens.text()).replace('\\\\n', '')\\n if self.alien_alg.currentIndex() == 2:\\n conf_map['alien_alg'] = '\\\"alien_file\\\"'\\n if len(self.alien_file.text()) > 0:\\n conf_map['alien_file'] = '\\\"' + str(self.alien_file.text()) + '\\\"'\\n elif self.alien_alg.currentIndex() == 3:\\n conf_map['alien_alg'] = '\\\"AutoAlien1\\\"'\\n if len(self.AA1_size_threshold.text()) > 0:\\n conf_map['AA1_size_threshold'] = str(self.AA1_size_threshold.text())\\n if len(self.AA1_asym_threshold.text()) > 0:\\n conf_map['AA1_asym_threshold'] = str(self.AA1_asym_threshold.text())\\n if len(self.AA1_min_pts.text()) > 0:\\n conf_map['AA1_min_pts'] = str(self.AA1_min_pts.text())\\n if len(self.AA1_eps.text()) > 0:\\n conf_map['AA1_eps'] = str(self.AA1_eps.text())\\n if len(self.AA1_amp_threshold.text()) > 0:\\n conf_map['AA1_amp_threshold'] = str(self.AA1_amp_threshold.text())\\n if self.AA1_save_arrs.isChecked():\\n conf_map['AA1_save_arrs'] = \\\"True\\\"\\n if len(self.AA1_expandcleanedsigma.text()) > 0:\\n conf_map['AA1_expandcleanedsigma'] = str(self.AA1_expandcleanedsigma.text())\\n\\n if len(self.amp_intensity.text()) > 0:\\n conf_map['amp_threshold'] = str(self.amp_intensity.text())\\n if len(self.binning.text()) > 0:\\n conf_map['binning'] = str(self.binning.text()).replace('\\\\n', '')\\n if len(self.center_shift.text()) > 0:\\n conf_map['center_shift'] = str(self.center_shift.text()).replace('\\\\n', '')\\n if len(self.adjust_dimensions.text()) > 0:\\n conf_map['adjust_dimensions'] = str(self.adjust_dimensions.text()).replace('\\\\n', '')\\n\\n return conf_map\",\n \"def generateSexConfig(outFile, configFile,\\r\\n paramsFile, inputModelsfile, weightImage, sexFile,\\r\\n matchRadius): \\r\\n f = open(configFile, 'w')\\r\\n\\r\\n \\\"\\\"\\\"\\r\\n sexConfig = dict(CATALOG_NAME='matched.cat', \\r\\n CATALOG_TYPE='ASCII_HEAD', \\r\\n PARAMETERS_NAME='assoc.param', \\r\\n DETECT_TYPE='CCD',\\r\\n DETECT_MINAREA='8.78733031674',\\r\\n DETECT_THRESH='1.25',\\r\\n ANALYSIS_THRESH='1.25',\\r\\n FILTER='Y',\\r\\n FILTER_NAME=SExroot+'tophat_3.0_3x3.conv',\\r\\n DEBLEND_NTHRESH='64',\\r\\n DEBLEND_MINCONT='0.0002',\\r\\n CLEAN='Y',\\r\\n CLEAN_PARAM='1.',\\r\\n MASK_TYPE='CORRECT',\\r\\n PHOT_APERTURES='14.0',\\r\\n PHOT_AUTOPARAMS='2.5,3.5',\\r\\n SATUR_LEVEL='50000.0',\\r\\n MAG_ZEROPOINT='31.2671969775',\\r\\n MAG_GAMMA='4.0',\\r\\n GAIN='13.4213208113',\\r\\n PIXEL_SCALE='0.221',\\r\\n SEEING_FWHM='1.060202',\\r\\n STARNNW_NAME=SExroot+'default.nnw',\\r\\n BACK_SIZE='64',\\r\\n BACK_FILTERSIZE='3',\\r\\n BACKPHOTO_TYPE='LOCAL',\\r\\n BACKPHOTO_THICK='102',\\r\\n MEMORY_OBJSTACK='15000',\\r\\n MEMORY_PIXSTACK='2600000',\\r\\n MEMORY_BUFSIZE='4600',\\r\\n ASSOC_NAME='input_model.coo',\\r\\n ASSOC_PARAMS='1,2',\\r\\n ASSOC_RADIUS='5.0',\\r\\n ASSOCSELEC_TYPE='MATCHED',\\r\\n ASSOC_TYPE='NEAREST',\\r\\n ASSOC_DATA='1,2,3,4,5',\\r\\n VERBOSE_TYPE='NORMAL',\\r\\n #WEIGHT_GAIN='Y',\\r\\n WEIGHT_IMAGE='/home/walter/Dropbox/Completeness/f02p01_F814W_2.swp.weight.fits',\\r\\n WEIGHT_TYPE='MAP_WEIGHT'\\r\\n )\\r\\n \\\"\\\"\\\"\\r\\n\\r\\n sexdata = open(sexFile, \\\"r\\\")\\r\\n \\r\\n sexdict = {l.split()[0] : l.split()[1] for l in sexdata.readlines() if l[0] != \\\" \\\"}\\r\\n sexdata.close()\\r\\n \\r\\n sexConfig = sexdict\\r\\n\\r\\n #... then change the information with some input, image-specific, info\\r\\n sexConfig['CATALOG_NAME']=outFile\\r\\n sexConfig['PARAMETERS_NAME']=paramsFile\\r\\n sexConfig['ASSOC_RADIUS']=str(matchRadius)\\r\\n sexConfig['ASSOC_NAME']=inputModelsfile\\r\\n aperture = float(sexConfig['SEEING_FWHM'])/float(sexConfig['PIXEL_SCALE'])\\r\\n sexConfig['PHOT_APERTURES']=str(aperture)+','+str(2*aperture)+','+str(3*aperture)\\r\\n sexConfig['WEIGHT_IMAGE']=str(weightImage)\\r\\n sexConfig['FILTER_NAME']=SExroot+sexConfig['FILTER_NAME']\\r\\n sexConfig['STARNNW_NAME']=SExroot+sexConfig['STARNNW_NAME']\\r\\n sexConfig['ASSOC_PARAMS']='1,2'\\r\\n sexConfig['ASSOCSELEC_TYPE']='MATCHED'\\r\\n sexConfig['ASSOC_TYPE']='NEAREST'\\r\\n sexConfig['ASSOC_DATA']='1,2,3,4,5'\\r\\n \\r\\n #Now write them out in a config file\\r\\n for key, value in sexConfig.iteritems():\\r\\n f.write(key+' '+value+'\\\\n')\\r\\n f.close()\",\n \"def get_model(summary=False):\\n\\timage_input=Input(shape=(220,220,5),name='image_input')\\n\\tbranch1_conv1=Conv2D(64, kernel_size=(3, 3), border_mode='same', input_shape=(220,220,5), activation='relu')(image_input)\\n\\tbranch1_conv2=Conv2D(64, kernel_size=(1, 1), border_mode='same', activation='relu')(branch1_conv1)\\t\\n\\tbranch1_pool1=MaxPooling2D(pool_size=(3, 3), strides=(2, 2))(branch1_conv1)\\n\\tbranch2_conv1=Conv2D(128, kernel_size=(3, 3), border_mode='same', activation='relu')(branch1_pool1)\\n\\tbranch2_conv2=Conv2D(128, kernel_size=(1, 1), border_mode='same', activation='relu')(branch2_conv1)\\t\\n\\tbranch2_pool1=MaxPooling2D(pool_size=(3, 3), strides=(2, 2))(branch2_conv2)\\n\\tbranch3_conv1=Conv2D(128, kernel_size=(3, 3), border_mode='same', activation='relu')(branch2_pool1)\\n\\tbranch3_conv2=Conv2D(128, kernel_size=(1, 1), border_mode='same', activation='relu')(branch3_conv1)\\t\\n\\tbranch3_pool1=MaxPooling2D(pool_size=(3, 3), strides=(2, 2))(branch3_conv2)\\n\\tbranch4_conv1=Conv2D(128, kernel_size=(3, 3), border_mode='same', activation='relu')(branch3_pool1)\\n\\tbranch4_conv2=Conv2D(128, kernel_size=(1, 1), border_mode='same', activation='relu')(branch4_conv1)\\t\\n\\tbranch4_pool1=MaxPooling2D(pool_size=(3, 3), strides=(2, 2))(branch4_conv2)\\n\\tbranch5_conv1=Conv2D(128, kernel_size=(3, 3), border_mode='same', activation='relu')(branch4_pool1)\\n\\tbranch5_conv2=Conv2D(128, kernel_size=(1, 1), border_mode='same', activation='relu')(branch5_conv1)\\t\\n\\tbranch5_pool1=MaxPooling2D(pool_size=(3, 3), strides=(2, 2))(branch5_conv2)\\n\\tbranch6_conv1=Conv2D(128, kernel_size=(3, 3), border_mode='same', activation='relu')(branch5_pool1)\\n\\tbranch6_conv2=Conv2D(128, kernel_size=(1, 1), border_mode='same', activation='relu')(branch6_conv1)\\t\\n\\tbranch6_pool1=MaxPooling2D(pool_size=(3, 3), strides=(2, 2))(branch6_conv2)\\n\\tbranch1_flat=Flatten()(branch6_pool1)\\n\\tdrop=Dropout(.3)(branch1_flat)\\n\\t# FC layers group\\n\\tdense1=Dense(512, activation='relu', name='fc1')(drop)\\n\\tdrop1=Dropout(.3)(dense1)\\n\\tdense2=Dense(256, activation='relu', name='fc2')(drop1)\\n\\tdrop3=Dropout(.3)(dense2)\\n\\tout=Dense(2, activation='softmax', name='fc4')(drop3)\\n\\tmodel=Model(inputs=image_input,outputs=out)\\n\\treturn model\",\n \"def _configure(self):\\n FaultCohesive._configure(self)\\n self.eqsrcs = self.inventory.eqsrcs\\n self.output = self.inventory.output\\n return\",\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, conf_file_dir, file_format, conf_size, output_size, transform=None):\\n #initialize basic dataset variables\\n #file path to file which contains all configurations\\n #inside a line of numbers rperesents one configuration\\n self.conf_file_dir = conf_file_dir\\n self.file_format = file_format\\n \\n \\\"\\\"\\\"INDEX ORDER CONFUSING\\\"\\\"\\\"\\n self.conf_size = conf_size\\n self.output_size = output_size\\n \\n #latice size given as array [z_l,y_l,x_l] for example\\n self.lat_size = conf_size[2:]\\n #if lat_size is given in regular order x,y,z,... -> reverse it\\n #self.lat_size = lat_size[::-1]\\n #dimension of the lattice\\n self.dim = len(self.lat_size)\\n\\n #number of total sites in the lattice\\n self.n_sites = int(np.array(self.lat_size).prod())\\n\\n #one file contains multiple configurations! delimiter = \\\\n\\n self.conf_file_paths = glob.glob(self.conf_file_dir + self.file_format)\\n \\n \\n \\\"\\\"\\\"read in labels (=chemical potential) from filename\\\"\\\"\\\"\\n self.raw_labels = np.zeros(shape=(len(self.conf_file_paths)))\\n sample_i = 0\\n for i, conf_file_path in enumerate(self.conf_file_paths):\\n #separate entire path from filename\\n file_name = conf_file_path.split(\\\"/\\\")[-1]\\n if i == sample_i: print(file_name)\\n #isolate chemical potential out of filename. delimiter = -\\n #config-mu-id-mu_label-.ext\\n label = float(file_name.split(\\\"-\\\")[-2])\\n self.raw_labels[i] = label\\n \\n print(self.conf_file_paths[sample_i])\\n print(self.raw_labels[sample_i])\\n \\n self.length = len(self.raw_labels)\\n\\n #define custom transform\\n if transform == \\\"default\\\":\\n \\\"\\\"\\\"define custom transform\\\"\\\"\\\"\\n default_axes = list(range(2,len(self.lat_size)+2))\\n print(f\\\"setting default axes for transforms to {default_axes}\\\")\\n #self.transform = lambda x: self.lat_trans(self.lat_rot(x, axes=default_axes, random=True, rot_par=42), axes=default_axes, random=True, trans_par=42)\\n self.transform = lambda x: self.lat_trans(x, axes=default_axes, random=True, trans_par=42)\\n elif transform != None:\\n self.transform = transform\",\n \"def _read_custom_pars(self):\\n if self.use_defaults:\\n param_set = \\\"default_values\\\"\\n else:\\n param_set = \\\"parameters\\\"\\n self.outpars = self.input_cfg_json_data[param_set][self.step_title]\",\n \"def main(config):\\n input_data = config[\\\"input_data\\\"]\\n\\n # Separate OBS from model datasets\\n # (and check there is only one obs dataset)\\n obs = [v for v in input_data.values() if v[\\\"project\\\"] == \\\"OBS\\\"]\\n if len(obs) != 1:\\n msg = f\\\"Expected exactly 1 OBS dataset: found {len(obs)}\\\"\\n raise RuntimeError(msg)\\n clim_file = obs[0][\\\"filename\\\"]\\n\\n models = group_metadata(\\n [v for v in input_data.values() if v[\\\"project\\\"] != \\\"OBS\\\"],\\n \\\"dataset\\\")\\n\\n for model_dataset, group in models.items():\\n # 'model_dataset' is the name of the model dataset.\\n # 'group' is a list of dictionaries containing metadata.\\n logger.info(\\\"Processing data for %s\\\", model_dataset)\\n model_file = [item[\\\"filename\\\"] for item in group]\\n\\n # Input filenames for provenance\\n ancestors = flatten([model_file, clim_file])\\n\\n # Calculate metrics\\n metrics = land_sm_top(clim_file, model_file, model_dataset, config,\\n ancestors)\\n\\n # Write metrics\\n metrics_dir = os.path.join(\\n config[\\\"plot_dir\\\"],\\n f\\\"{config['exp_model']}_vs_{config['control_model']}\\\",\\n config[\\\"area\\\"],\\n model_dataset,\\n )\\n\\n write_metrics(metrics_dir, metrics, config, ancestors)\",\n \"def __init__(self, encut, name=\\\"relax_settings\\\"):\\n\\n ionic = {\\\"EDIFF\\\": 1E-17, \\\"NSW\\\": 20, \\\"IBRION\\\": 2,\\\"ISIF\\\": 2, \\\"ISYM\\\": -1, \\\"NBLOCK\\\": 1, \\\"KBLOCK\\\": 20}\\n InputParameters.__init__(self, ionic_settings=ionic, name=name)\\n self.update_electronic_sttings(\\\"ENCUT\\\", encut)\",\n \"def parse_params(self,tokenized_lines):\\n\\n # extract key-value pairs\\n conversions = {\\n # Space\\n \\\"nuclide\\\" : tools.tuple_of(int), # use tuple so parameter is hashable when used as analysis key\\n \\\"A\\\" : tools.singleton_of(int),\\n \\\"Nsigma\\\" : tools.singleton_of(float),\\n \\\"Nsigmamax\\\" : tools.singleton_of(int),\\n \\\"N1v\\\" : tools.singleton_of(int),\\n \\\"Nmax\\\" : tools.singleton_of(int),\\n # Interaction\\n \\\"interaction\\\" : tools.singleton_of(str),\\n \\\"use_coulomb\\\" : tools.singleton_of(tools.bool_from_str),\\n # Relative observables\\n \\\"observable_names\\\" : tools.list_of(str),\\n # Calculation\\n \\\"hw\\\" : tools.singleton_of(float)\\n }\\n key_value_dict = tools.extract_key_value_pairs(\\n tokenized_lines,conversions\\n )\\n\\n # legacy support: force interaction to \\\"JISP16\\\" for early runs where\\n # interaction field was provided as reserved field but not set to \\\"JISP16\\\"\\n if (\\\"interaction\\\" in key_value_dict):\\n if (key_value_dict[\\\"interaction\\\"] == \\\"RESERVED\\\"):\\n key_value_dict[\\\"interaction\\\"]=\\\"JISP16\\\"\\n\\n # provide \\\"coulomb\\\" as preferred field name to match mfdn results analysis\\n if (\\\"use_coulomb\\\" in key_value_dict):\\n key_value_dict[\\\"coulomb\\\"] = key_value_dict[\\\"use_coulomb\\\"]\\n \\n # update to params dictionary\\n self.params.update(key_value_dict)\",\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 write_yaml(self, input):\\n instrument = input['Instrument']\\n # select the right filter\\n if input['detector'] in ['NIS']:\\n # if input['APTTemplate'] == 'NirissExternalCalibration': 'NirissImaging':\\n filtkey = 'FilterWheel'\\n pupilkey = 'PupilWheel'\\n # set the FilterWheel and PupilWheel for NIRISS\\n if input['APTTemplate'] in ['NirissAmi']:\\n filter_name = input['Filter']\\n\\n # TA and direct images will be set to imaging mode\\n # rather than ami mode\\n if input['Mode'].lower() == 'imaging':\\n if filter_name in NIRISS_PUPIL_WHEEL_ELEMENTS:\\n input[pupilkey] = filter_name\\n input[filtkey] = 'CLEAR'\\n elif filter_name in NIRISS_FILTER_WHEEL_ELEMENTS:\\n input[pupilkey] = 'CLEARP'\\n input[filtkey] = filter_name\\n else:\\n input[filtkey] = filter_name\\n input[pupilkey] = 'NRM'\\n\\n elif input['APTTemplate'] not in ['NirissExternalCalibration', 'NirissWfss']:\\n filter_name = input['Filter']\\n if filter_name in NIRISS_PUPIL_WHEEL_ELEMENTS:\\n input[pupilkey] = filter_name\\n input[filtkey] = 'CLEAR'\\n elif filter_name in NIRISS_FILTER_WHEEL_ELEMENTS:\\n input[pupilkey] = 'CLEARP'\\n input[filtkey] = filter_name\\n else:\\n raise RuntimeError('Filter {} not valid'.format(filter_name))\\n catkey = ''\\n elif input['detector'] in ['FGS']:\\n filtkey = 'FilterWheel'\\n pupilkey = 'PupilWheel'\\n catkey = ''\\n elif input['detector'] in ['NRS', 'MIR']:\\n filtkey = 'FilterWheel'\\n pupilkey = 'PupilWheel'\\n catkey = ''\\n else:\\n if int(input['detector'][-1]) < 5:\\n filtkey = 'ShortFilter'\\n pupilkey = 'ShortPupil'\\n catkey = 'sw'\\n else:\\n filtkey = 'LongFilter'\\n pupilkey = 'LongPupil'\\n catkey = 'lw'\\n\\n outfile = input['outputfits']\\n yamlout = input['yamlfile']\\n\\n yamlout = os.path.join(self.output_dir, yamlout)\\n with open(yamlout, 'w') as f:\\n f.write('Inst:\\\\n')\\n f.write(' instrument: {} # Instrument name\\\\n'.format(instrument))\\n f.write(' mode: {} # Observation mode (e.g. imaging, WFSS)\\\\n'.format(input['Mode']))\\n f.write(' use_JWST_pipeline: {} # Use pipeline in data transformations\\\\n'.format(input['use_JWST_pipeline']))\\n f.write('\\\\n')\\n f.write('Readout:\\\\n')\\n f.write(' readpatt: {} # Readout pattern (RAPID, BRIGHT2, etc) overrides nframe, nskip unless it is not recognized\\\\n'.format(input['ReadoutPattern']))\\n f.write(' ngroup: {} # Number of groups in integration\\\\n'.format(input['Groups']))\\n f.write(' nint: {} # Number of integrations per exposure\\\\n'.format(input['Integrations']))\\n f.write(' namp: {} # Number of amplifiers used to read out detector\\\\n'.format(input['namp']))\\n f.write(' resets_bet_ints: {} #Number of detector resets between integrations\\\\n'.format(self.resets_bet_ints))\\n\\n if instrument.lower() == 'nircam':\\n # if input['aperture'] in ['NRCA3_DHSPIL', 'NRCB4_DHSPIL']:\\n if 'NRCA3_DHSPIL' in input['aperture'] or 'NRCB4_DHSPIL' in input['aperture']: # in ['NRCA3_DHSPIL', 'NRCB4_DHSPIL']:\\n full_ap = input['aperture']\\n else:\\n apunder = input['aperture'].find('_')\\n full_ap = 'NRC' + input['detector'] + '_' + input['aperture'][apunder + 1:]\\n if instrument.lower() in ['niriss', 'fgs']:\\n full_ap = input['aperture']\\n\\n possible_apertures = pysiaf.Siaf(instrument).apernames\\n if full_ap not in possible_apertures:\\n raise ValueError('Unrecognized aperture name: {}'.format(full_ap))\\n\\n f.write(' array_name: {} # Name of array (FULL, SUB160, SUB64P, etc)\\\\n'.format(full_ap))\\n f.write(' intermediate_aperture: {} # Name of intermediate aperture used in NIRCam Grism time series obs.\\\\n'.format(input['grismts_intermediate_aperture']))\\n f.write(' PPS_aperture: {} # Original aperture value supplied by PPS.\\\\n'.format(input['pps_aperture']))\\n f.write(' filter: {} # Filter of simulated data (F090W, F322W2, etc)\\\\n'.format(input[filtkey]))\\n f.write(' pupil: {} # Pupil element for simulated data (CLEAR, GRISMC, etc)\\\\n'.format(input[pupilkey]))\\n f.write('\\\\n')\\n f.write('Reffiles: # Set to None or leave blank if you wish to skip that step\\\\n')\\n f.write(' dark: {} # Dark current integration used as the base\\\\n'.format(input['dark']))\\n f.write(' linearized_darkfile: {} # Linearized dark ramp to use as input. Supercedes dark above\\\\n'.format(input['lindark']))\\n f.write(' badpixmask: {} # If linearized dark is used, populate output DQ extensions using this file\\\\n'.format(input['badpixmask']))\\n f.write(' superbias: {} # Superbias file. Set to None or leave blank if not using\\\\n'.format(input['superbias']))\\n f.write(' linearity: {} # linearity correction coefficients\\\\n'.format(input['linearity']))\\n f.write(' saturation: {} # well depth reference files\\\\n'.format(input['saturation']))\\n f.write(' gain: {} # Gain map\\\\n'.format(input['gain']))\\n f.write(' pixelflat: {} # Flat field file to use for un-flattening output\\\\n'.format(input['pixelflat']))\\n f.write(' illumflat: None # Illumination flat field file\\\\n')\\n f.write(' astrometric: {} # Astrometric distortion file (asdf)\\\\n'.format(input['astrometric']))\\n f.write(' photom: {} # cal pipeline photom reference file\\\\n'.format(input['photom']))\\n f.write(' ipc: {} # File containing IPC kernel to apply\\\\n'.format(input['ipc']))\\n f.write((' invertIPC: {} # Invert the IPC kernel before the convolution. True or False. Use True if the kernel is '\\n 'designed for the removal of IPC effects, like the JWST reference files are.\\\\n'.format(input['invert_ipc'])))\\n f.write(' occult: None # Occulting spots correction image\\\\n')\\n f.write((' transmission: {} # Transmission image containing fractional throughput map. (e.g. to imprint occulters into fov\\\\n'\\n .format(input['transmission'])))\\n f.write((' subarray_defs: {} # File that contains a list of all possible subarray names and coordinates\\\\n'\\n .format(input['subarray_def_file'])))\\n f.write((' readpattdefs: {} # File that contains a list of all possible readout pattern names and associated '\\n 'NFRAME/NSKIP values\\\\n'.format(input['readpatt_def_file'])))\\n f.write(' crosstalk: {} # File containing crosstalk coefficients\\\\n'.format(input['crosstalk_file']))\\n f.write((' filtpupilcombo: {} # File that lists the filter wheel element / pupil wheel element combinations. '\\n 'Used only in writing output file\\\\n'.format(input['filtpupilcombo_file'])))\\n f.write((' filter_wheel_positions: {} # File containing resolver wheel positions for each filter/pupil\\\\n'.format(input['filter_position_file'])))\\n f.write((' flux_cal: {} # File that lists flux conversion factor and pivot wavelength for each filter. Only '\\n 'used when making direct image outputs to be fed into the grism disperser code.\\\\n'.format(input['flux_cal_file'] )))\\n f.write(' filter_throughput: {} #File containing filter throughput curve\\\\n'.format(self.configfiles[instrument.lower()]['filter_throughput']))\\n f.write(' ')\\n f.write('\\\\n')\\n f.write('nonlin:\\\\n')\\n f.write(' limit: 60000.0 # Upper singal limit to which nonlinearity is applied (ADU)\\\\n')\\n f.write(' accuracy: 0.000001 # Non-linearity accuracy threshold\\\\n')\\n f.write(' maxiter: 10 # Maximum number of iterations to use when applying non-linearity\\\\n')\\n f.write(' robberto: False # Use Massimo Robberto type non-linearity coefficients\\\\n')\\n f.write('\\\\n')\\n f.write('cosmicRay:\\\\n')\\n cosmic_ray_path = os.path.join(self.datadir, instrument.lower(), 'cosmic_ray_library')\\n f.write(' path: {} # Path to CR library\\\\n'.format(cosmic_ray_path))\\n f.write(' library: {} # Type of cosmic rayenvironment (SUNMAX, SUNMIN, FLARE)\\\\n'.format(input['CosmicRayLibrary']))\\n f.write(' scale: {} # Cosmic ray scaling factor\\\\n'.format(input['CosmicRayScale']))\\n # temporary tweak here to make it work with NIRISS\\n detector_label = input['detector']\\n\\n if instrument.lower() in ['nircam', 'wfsc']:\\n # detector_label = input['detector']\\n f.write(' suffix: IPC_NIRCam_{} # Suffix of library file names\\\\n'.format(\\n detector_label))\\n elif instrument.lower() == 'niriss':\\n f.write(' suffix: IPC_NIRISS_{} # Suffix of library file names\\\\n'.format(\\n detector_label))\\n elif instrument.lower() == 'fgs':\\n if detector_label == 'G1':\\n detector_string = 'GUIDER1'\\n elif detector_label == 'G2':\\n detector_string = 'GUIDER2'\\n f.write(' suffix: IPC_FGS_{} # Suffix of library file names\\\\n'.format(\\n detector_string))\\n f.write(' seed: {} # Seed for random number generator\\\\n'.format(np.random.randint(1, 2**32-2)))\\n f.write('\\\\n')\\n f.write('simSignals:\\\\n')\\n if instrument.lower() in ['nircam', 'wfsc']:\\n PointSourceCatalog = input['{}_ptsrc'.format(catkey)]\\n GalaxyCatalog = input['{}_galcat'.format(catkey)]\\n ExtendedCatalog = input['{}_ext'.format(catkey)]\\n ExtendedScale = input['{}_extscl'.format(catkey)]\\n ExtendedCenter = input['{}_extcent'.format(catkey)]\\n MovingTargetList = input['{}_movptsrc'.format(catkey)]\\n MovingTargetSersic = input['{}_movgal'.format(catkey)]\\n MovingTargetExtended = input['{}_movext'.format(catkey)]\\n MovingTargetConvolveExtended = input['{}_movconv'.format(catkey)]\\n MovingTargetToTrack = input['{}_solarsys'.format(catkey)]\\n ImagingTSOCatalog = input['{}_img_tso'.format(catkey)]\\n GrismTSOCatalog = input['{}_grism_tso'.format(catkey)]\\n BackgroundRate = input['{}_bkgd'.format(catkey)]\\n elif instrument.lower() in ['niriss', 'fgs']:\\n PointSourceCatalog = input['PointSourceCatalog']\\n GalaxyCatalog = input['GalaxyCatalog']\\n ExtendedCatalog = input['ExtendedCatalog']\\n ExtendedScale = input['ExtendedScale']\\n ExtendedCenter = input['ExtendedCenter']\\n MovingTargetList = input['MovingTargetList']\\n MovingTargetSersic = input['MovingTargetSersic']\\n MovingTargetExtended = input['MovingTargetExtended']\\n MovingTargetConvolveExtended = input['MovingTargetConvolveExtended']\\n MovingTargetToTrack = input['MovingTargetToTrack']\\n ImagingTSOCatalog = input['ImagingTSOCatalog']\\n GrismTSOCatalog = input['GrismTSOCatalog']\\n BackgroundRate = input['BackgroundRate']\\n\\n f.write((' pointsource: {} #File containing a list of point sources to add (x, y locations and magnitudes)\\\\n'\\n .format(PointSourceCatalog)))\\n f.write(' gridded_psf_library_row_padding: 4 # Number of outer rows and columns to avoid when evaluating library. RECOMMEND 4.\\\\n')\\n f.write(' psf_wing_threshold_file: {} # File defining PSF sizes versus magnitude\\\\n'.format(input['psf_wing_threshold_file']))\\n f.write(' add_psf_wings: {} # Whether or not to place the core of the psf from the gridded library into an image of the wings before adding.\\\\n'.format(self.add_psf_wings))\\n f.write(' psfpath: {} #Path to PSF library\\\\n'.format(input['psfpath']))\\n f.write(' psfwfe: {} #PSF WFE value (predicted or requirements)\\\\n'.format(self.psfwfe))\\n f.write(' psfwfegroup: {} #WFE realization group (0 to 4)\\\\n'.format(self.psfwfegroup))\\n f.write((' galaxyListFile: {} #File containing a list of positions/ellipticities/magnitudes of galaxies '\\n 'to simulate\\\\n'.format(GalaxyCatalog)))\\n f.write(' extended: {} #Extended emission count rate image file name\\\\n'.format(ExtendedCatalog))\\n f.write(' extendedscale: {} #Scaling factor for extended emission image\\\\n'.format(ExtendedScale))\\n f.write((' extendedCenter: {} #x, y pixel location at which to place the extended image '\\n 'if it is smaller than the output array size\\\\n'.format(ExtendedCenter)))\\n f.write((' PSFConvolveExtended: {} #Convolve the extended image with the PSF before adding to the output '\\n 'image (True or False)\\\\n'.format(self.convolve_extended)))\\n f.write((' movingTargetList: {} #Name of file containing a list of point source moving targets (e.g. '\\n 'KBOs, asteroids) to add.\\\\n'.format(MovingTargetList)))\\n f.write((' movingTargetSersic: {} #ascii file containing a list of 2D sersic profiles to have moving through '\\n 'the field\\\\n'.format(MovingTargetSersic)))\\n f.write((' movingTargetExtended: {} #ascii file containing a list of stamp images to add as moving targets '\\n '(planets, moons, etc)\\\\n'.format(MovingTargetExtended)))\\n f.write((' movingTargetToTrack: {} #File containing a single moving target which JWST will track during '\\n 'observation (e.g. a planet, moon, KBO, asteroid)\\tThis file will only be used if mode is set to '\\n '\\\"moving_target\\\" \\\\n'.format(MovingTargetToTrack)))\\n f.write((' tso_imaging_catalog: {} #Catalog of (generally one) source for Imaging Time Series observation\\\\n'.format(ImagingTSOCatalog)))\\n f.write((' tso_grism_catalog: {} #Catalog of (generally one) source for Grism Time Series observation\\\\n'.format(GrismTSOCatalog)))\\n f.write(' zodiacal: None #Zodiacal light count rate image file \\\\n')\\n f.write(' zodiscale: 1.0 #Zodi scaling factor\\\\n')\\n f.write(' scattered: None #Scattered light count rate image file\\\\n')\\n f.write(' scatteredscale: 1.0 #Scattered light scaling factor\\\\n')\\n f.write((' bkgdrate: {} #Constant background count rate (ADU/sec/pixel in an undispersed image) or '\\n '\\\"high\\\",\\\"medium\\\",\\\"low\\\" similar to what is used in the ETC\\\\n'.format(BackgroundRate)))\\n f.write((' poissonseed: {} #Random number generator seed for Poisson simulation)\\\\n'\\n .format(np.random.randint(1, 2**32-2))))\\n f.write(' photonyield: True #Apply photon yield in simulation\\\\n')\\n f.write(' pymethod: True #Use double Poisson simulation for photon yield\\\\n')\\n f.write(' expand_catalog_for_segments: {} # Expand catalog for 18 segments and use distinct PSFs\\\\n'\\n .format(self.expand_catalog_for_segments))\\n f.write(' use_dateobs_for_background: {} # Use date_obs below to deternine background. If False, bkgdrate is used.\\\\n'\\n .format(self.dateobs_for_background))\\n f.write(' signal_low_limit_for_segmap: {} # Lower signal limit to include pix in segmentation map. See below for units.\\\\n'\\n .format(self.segmentation_threshold))\\n f.write((' signal_low_limit_for_segmap_units: {} # Units of signal_low_limit_for_segmap. Can be: [ADU/sec, e/sec, MJy/sr, '\\n 'ergs/cm2/a, ergs/cm2/hz]\\\\n'.format(self.segmentation_threshold_units)))\\n f.write(' add_ghosts: {} # Add optical ghosts associated with astronomical sources\\\\n'.format(self.add_ghosts))\\n f.write(' PSFConvolveGhosts: {} # Convolve ghost stamp images with instrument PSF before adding\\\\n'.format(self.convolve_ghosts))\\n f.write('\\\\n')\\n f.write('Telescope:\\\\n')\\n f.write(' ra: {} # RA of simulated pointing\\\\n'.format(input['ra_ref']))\\n f.write(' dec: {} # Dec of simulated pointing\\\\n'.format(input['dec_ref']))\\n if 'pav3' in input.keys():\\n pav3_value = input['pav3']\\n else:\\n pav3_value = input['PAV3']\\n f.write(' rotation: {} # PA_V3 in degrees, i.e. the position angle of the V3 axis at V1 (V2=0, V3=0) measured from N to E.\\\\n'.format(pav3_value))\\n f.write(' tracking: {} #Telescope tracking. Can be sidereal or non-sidereal\\\\n'.format(input['Tracking']))\\n f.write('\\\\n')\\n f.write('Output:\\\\n')\\n # f.write(' use_stsci_output_name: {} # Output filename should follow STScI naming conventions (True/False)\\\\n'.format(outtf))\\n f.write(' directory: {} # Output directory\\\\n'.format(self.simdata_output_dir))\\n f.write(' file: {} # Output filename\\\\n'.format(outfile))\\n f.write((\\\" datatype: {} # Type of data to save. 'linear' for linearized ramp. 'raw' for raw ramp. 'linear, \\\"\\n \\\"raw' for both\\\\n\\\".format(self.datatype)))\\n f.write(' format: DMS # Output file format Options: DMS, SSR(not yet implemented)\\\\n')\\n f.write(' save_intermediates: False # Save intermediate products separately (point source image, etc)\\\\n')\\n f.write(' grism_source_image: {} # grism\\\\n'.format(input['grism_source_image']))\\n f.write(' unsigned: True # Output unsigned integers? (0-65535 if true. -32768 to 32768 if false)\\\\n')\\n f.write(' dmsOrient: True # Output in DMS orientation (vs. fitswriter orientation).\\\\n')\\n f.write(' program_number: {} # Program Number\\\\n'.format(input['ProposalID']))\\n f.write(' title: {} # Program title\\\\n'.format(input['Title'].replace(':', ', ')))\\n f.write(' PI_Name: {} # Proposal PI Name\\\\n'.format(input['PI_Name']))\\n f.write(' Proposal_category: {} # Proposal category\\\\n'.format(input['Proposal_category']))\\n f.write(' Science_category: {} # Science category\\\\n'.format(input['Science_category']))\\n f.write(' target_name: {} # Name of target\\\\n'.format(input['TargetID']))\\n\\n # For now, skip populating the target RA and Dec in WFSC data.\\n # The read_xxxxx funtions for these observation types will have\\n # to be updated to make use of the proposal_parameter_dictionary\\n if np.isreal(input['TargetRA']):\\n input['TargetRA'] = str(input['TargetRA'])\\n input['TargetDec'] = str(input['TargetDec'])\\n if input['TargetRA'] != '0':\\n ra_degrees, dec_degrees = utils.parse_RA_Dec(input['TargetRA'], input['TargetDec'])\\n else:\\n ra_degrees = 0.\\n dec_degrees = 0.\\n\\n f.write(' target_ra: {} # RA of the target, from APT file.\\\\n'.format(ra_degrees))\\n f.write(' target_dec: {} # Dec of the target, from APT file.\\\\n'.format(dec_degrees))\\n f.write(\\\" observation_number: '{}' # Observation Number\\\\n\\\".format(input['obs_num']))\\n f.write(' observation_label: {} # User-generated observation Label\\\\n'.format(input['obs_label'].strip()))\\n f.write(\\\" visit_number: '{}' # Visit Number\\\\n\\\".format(input['visit_num']))\\n f.write(\\\" visit_group: '{}' # Visit Group\\\\n\\\".format(input['visit_group']))\\n f.write(\\\" visit_id: '{}' # Visit ID\\\\n\\\".format(input['visit_id']))\\n f.write(\\\" sequence_id: '{}' # Sequence ID\\\\n\\\".format(input['sequence_id']))\\n f.write(\\\" activity_id: '{}' # Activity ID. Increment with each exposure.\\\\n\\\".format(input['act_id']))\\n f.write(\\\" exposure_number: '{}' # Exposure Number\\\\n\\\".format(input['exposure']))\\n f.write(\\\" obs_id: '{}' # Observation ID number\\\\n\\\".format(input['observation_id']))\\n f.write(\\\" date_obs: '{}' # Date of observation\\\\n\\\".format(input['date_obs']))\\n f.write(\\\" time_obs: '{}' # Time of observation\\\\n\\\".format(input['time_obs']))\\n # f.write(\\\" obs_template: '{}' # Observation template\\\\n\\\".format(input['obs_template']))\\n f.write(\\\" primary_dither_type: {} # Primary dither pattern name\\\\n\\\".format(input['PrimaryDitherType']))\\n f.write(\\\" total_primary_dither_positions: {} # Total number of primary dither positions\\\\n\\\".format(input['PrimaryDithers']))\\n f.write(\\\" primary_dither_position: {} # Primary dither position number\\\\n\\\".format(int(input['primary_dither_num'])))\\n f.write(\\\" subpix_dither_type: {} # Subpixel dither pattern name\\\\n\\\".format(input['SubpixelDitherType']))\\n # For WFSS we need to strip out the '-Points' from\\n # the number of subpixel positions entry\\n dash = -1\\n if isinstance(input['SubpixelPositions'], str):\\n dash = input['SubpixelPositions'].find('-')\\n if (dash == -1):\\n val = input['SubpixelPositions']\\n else:\\n val = input['SubpixelPositions'][0:dash]\\n if val == 'None':\\n val = 1\\n # try:\\n # dash = input['SubpixelPositions'].find('-')\\n # val = input['SubpixelPositions'][0:dash]\\n # except:\\n # val = input['SubpixelPositions']\\n f.write(\\\" total_subpix_dither_positions: {} # Total number of subpixel dither positions\\\\n\\\".format(val))\\n f.write(\\\" subpix_dither_position: {} # Subpixel dither position number\\\\n\\\".format(int(input['subpix_dither_num'])))\\n f.write(\\\" xoffset: {} # Dither pointing offset in x (arcsec)\\\\n\\\".format(input['idlx']))\\n f.write(\\\" yoffset: {} # Dither pointing offset in y (arcsec)\\\\n\\\".format(input['idly']))\\n\\n return yamlout\",\n \"def main(args):\\n ## Starting time\\n start_time = datetime.now()\\n ## Reading all elements and converting to python dictionary\\n param_dict = vars(args)\\n ## Checking for correct input\\n param_vals_test(param_dict)\\n #\\n # Creating instance of `ReadML` with the input parameters\\n param_dict['ml_args'] = ReadML(**param_dict)\\n ## Program message\\n prog_msg = param_dict['Prog_msg']\\n # Adding additional parameters\\n param_dict = add_to_dict(param_dict)\\n ##\\n ## Creating Folder Structure\\n # proj_dict = cwpaths.cookiecutter_paths(__file__)\\n proj_dict = param_dict['ml_args'].proj_dict\\n proj_dict = directory_skeleton(param_dict, proj_dict)\\n ##\\n ## Printing out project variables\\n print('\\\\n'+50*'='+'\\\\n')\\n for key, key_val in sorted(param_dict.items()):\\n if key != 'Prog_msg':\\n print('{0} `{1}`: {2}'.format(prog_msg, key, key_val))\\n print('\\\\n'+50*'='+'\\\\n')\\n ##\\n ## Feature keys\\n param_dict['feat_cols_dict'] = param_dict['ml_args'].feat_cols_names_dict(\\n return_all=True)\\n ##\\n ## Reading in the main catalogue\\n catl_pd = catl_file_read_clean(param_dict, proj_dict)\\n ###\\n ### ------ Figures ------ ###\\n ##\\n ## Comparison of estimated group masses via HAM and Dynamical Masses\\n frac_diff_model(param_dict, proj_dict, plot_opt=param_dict['plot_opt'])\\n #\\n # Covariance Matrix\\n covariance_plot(catl_pd, param_dict, proj_dict)\\n #\\n # Traditional methods for estimating masses\\n # pred_masses_halo_mass(param_dict, proj_dict)\\n #\\n # Fractional Difference plots vs True mass of galaxy GROUPS\\n # frac_diff_groups_model(param_dict, proj_dict,\\n # plot_opt=param_dict['plot_opt'])\\n ##\\n ## End time for running the catalogues\\n end_time = datetime.now()\\n total_time = end_time - start_time\\n print('{0} Total Time taken (Create): {1}'.format(prog_msg, total_time))\",\n \"def param_info():\\n\\n\\tgizmo_names = syn.getGizmoNames()\\n\\n\\tfor gizmo in gizmo_names:\\n\\t\\tparams = syn.getParameterNames(gizmo)\\n\\t#doesnt get all parameters from gizmos i.e. WaveFreq\\n\\n\\t# get all info on the 'WaveFreq' parameter\\n\\tGIZMO = 'aStim2'\\n\\tPARAMETER = 'WaveFreq'\\n\\n\\t# info = syn.getParameterInfo(GIZMO, PARAMETER)\\n\\t#\\n\\t# # get the array size (should be 100)\\n\\t# sz = syn.getParameterSize(GIZMO, PARAMETER)\\n\\t#\\n\\t# # write values 1 to 50 in second half of buffer\\n\\t# result = syn.setParameterValues(GIZMO, PARAMETER, np.arange(1, 51), 50)\\n\\t#\\n\\t# # read all values from buffer\\n\\t# syn.getParameterValues(GIZMO, PARAMETER, sz)\\n\\t#\\n\\t# # get all info on the 'Go' parameter\\n\\t# PARAMETER = 'Go'\\n\\t# info = syn.getParameterInfo(GIZMO, PARAMETER)\\n\\t#\\n\\t# # flip the switch\\n\\t# result = syn.setParameterValue(GIZMO, PARAMETER, 1)\\n\\t#\\n\\t# # check the value\\n\\tfreq = syn.getParameterValue(GIZMO, PARAMETER)\\n\\tprint('value =', freq)\\n\\tfreq = [freq]\\n\\n\\t# also verify visually that the switch slipped in the run\\n\\t# time interface. This state change will be logged just\\n\\t# like any other variable change and saved with the runtime\\n\\t# state.\\n\\n\\tnumTrials = 5 #total number of trials across stimuli\\n\\tISI = [2.0, 3.0, 4.0, 5.0] # ISI in seconds\\n\\n\\t# flash parameters\\n\\tflash_dur = [.001] # flash durs in seconds (100 ms, 200 ms)\\n\\tluminance = [[1, 1, 1], [.86, .86, .86], [0, .1, 1]] # white , grayish, purple just for testing\\n\\n\\t# auditory parameters\\n\\tduration = [.005] # in seconds; pulseDur in TDT\\n\\tsound_levels = [20.0, 40.0, 60.0, 80.0] # dB; waveAmp in TDT\\n\\n\\t# Auditory on (T/F? if T then A+V, if F then Visual only)\\n\\tstims = {0: \\\"auditory_only\\\",\\n\\t\\t\\t 1: \\\"visual_only\\\",\\n\\t\\t\\t 2: \\\"A+V\\\"\\n\\t\\t\\t }\\n\\n\\texper = Experiment(numTrials=numTrials, ISI=ISI, flash_dur=flash_dur, luminance=luminance, wave_freq=freq,\\n\\t\\t\\t\\t\\t pulse_dur=duration, wave_amp=sound_levels, stimulus=stims)\\n\\texper.run_experiment()\",\n \"def __init__(self, **kwargs):\\n\\n # Identify the mode to start the model in\\n if \\\"x\\\" in kwargs and \\\"y\\\" in kwargs:\\n x = kwargs.get(\\\"x\\\")\\n y = kwargs.get(\\\"y\\\")\\n if \\\"model_name\\\" not in kwargs:\\n self.__mode = \\\"train\\\"\\n else:\\n self.__mode = \\\"retrain\\\"\\n elif \\\"model_name\\\" in kwargs:\\n self.__mode = \\\"test\\\"\\n else:\\n raise NameError(\\\"Cannot infer mode from arguments.\\\")\\n\\n print(\\\"Initializing model in %s mode.\\\" % self.__mode)\\n\\n if self.mode == \\\"train\\\":\\n # Infer input type from type(x)\\n if type(x[0]) == np.bytes_:\\n print(\\\"Input type is 'binary mols'.\\\")\\n self.__input_type = \\\"mols\\\" # binary RDKit mols\\n else:\\n print(\\\"Input type is 'molecular descriptors'.\\\")\\n self.__input_type = \\\"descriptors\\\" # other molecular descriptors\\n\\n # If scaling is required\\n if kwargs.get(\\\"scaling\\\", False) is True:\\n # Normalize the input\\n print(\\\"Applying scaling on input.\\\")\\n self.__scaler = StandardScaler()\\n x = self.__scaler.fit_transform(x)\\n else:\\n self.__scaler = None\\n\\n # If PCA is required\\n if kwargs.get(\\\"pca\\\", False) is True:\\n print(\\\"Applying PCA on input.\\\")\\n self.__pca = PCA(\\n n_components=x.shape[1]\\n ) # n_components=n_features for now\\n x = self.__pca.fit_transform(x)\\n else:\\n self.__pca = None\\n\\n self.__maxlen = (\\n kwargs.get(\\\"dataset_info\\\")[\\\"maxlen\\\"] + 10\\n ) # Extend maxlen to avoid breaks in training\\n self.__charset = kwargs.get(\\\"dataset_info\\\")[\\\"charset\\\"]\\n self.__dataset_name = kwargs.get(\\\"dataset_info\\\")[\\\"name\\\"]\\n self.__lstm_dim = kwargs.get(\\\"lstm_dim\\\", 256)\\n self.__h_activation = kwargs.get(\\\"h_activation\\\", \\\"relu\\\")\\n self.__bn = kwargs.get(\\\"bn\\\", True)\\n self.__bn_momentum = kwargs.get(\\\"bn_momentum\\\", 0.9)\\n self.__noise_std = kwargs.get(\\\"noise_std\\\", 0.01)\\n self.__td_dense_dim = kwargs.get(\\n \\\"td_dense_dim\\\", 0\\n ) # >0 squeezes RNN connections with Dense sandwiches\\n self.__batch_size = kwargs.get(\\\"batch_size\\\", 256)\\n self.__dec_layers = kwargs.get(\\\"dec_layers\\\", 2)\\n\\n if self.input_type == \\\"descriptors\\\":\\n self.__codelayer_dim = x.shape[1] # features\\n if \\\"codelayer_dim\\\" in kwargs:\\n print(\\n \\\"Ignoring requested codelayer_dim because it is inferred from the cardinality of the descriptors.\\\"\\n )\\n else:\\n self.__codelayer_dim = kwargs.get(\\\"codelayer_dim\\\", 128)\\n \\n # Create the left/right-padding vectorizers\\n self.__smilesvec1 = SmilesVectorizer(\\n canonical=False,\\n augment=True,\\n maxlength=self.maxlen,\\n charset=self.charset,\\n binary=True,\\n )\\n\\n self.__smilesvec2 = SmilesVectorizer(\\n canonical=False,\\n augment=True,\\n maxlength=self.maxlen,\\n charset=self.charset,\\n binary=True,\\n leftpad=False,\\n )\\n\\n # self.train_gen.next() #This line is needed to set train_gen.dims (to be fixed in HetSmilesGenerator)\\n self.__input_shape = self.smilesvec1.dims\\n self.__dec_dims = list(self.smilesvec1.dims)\\n self.__dec_dims[0] = self.dec_dims[0] - 1\\n self.__dec_input_shape = self.dec_dims\\n self.__output_len = self.smilesvec1.dims[0] - 1\\n self.__output_dims = self.smilesvec1.dims[-1]\\n\\n # Build all sub-models as untrained models\\n if self.input_type == \\\"mols\\\":\\n self.__build_mol_to_latent_model()\\n else:\\n self.__mol_to_latent_model = None\\n\\n self.__build_latent_to_states_model()\\n self.__build_batch_model()\\n\\n # Build data generators\\n self.__build_generators(x, y)\\n\\n # Retrain or Test mode\\n else:\\n self.__model_name = kwargs.get(\\\"model_name\\\")\\n\\n # Load the model\\n self.__load(self.model_name)\\n \\n if self.mode == \\\"retrain\\\":\\n # If scaling is required\\n if self.scaler is not None:\\n print(\\\"Applying scaling on input.\\\")\\n x = self.scaler.transform(x)\\n\\n # If PCA is required\\n if self.pca is not None:\\n print(\\\"Applying PCA on input.\\\")\\n x = self.pca.transform(x)\\n \\n # Build data generators\\n self.__build_generators(x, y)\\n\\n # Build full model out of the sub-models\\n self.__build_model()\\n\\n # Show the resulting full model\\n print(self.model.summary())\",\n \"def getParameterInfo(self):\\n feature_class = arcpy.Parameter(\\n name = 'in_features',\\n displayName = 'In Features',\\n direction = 'Input',\\n datatype = 'GPFeatureLayer',\\n parameterType = 'Required')\\n\\n field_mappings = arcpy.Parameter(\\n name = 'in_fields',\\n displayName = 'In Fields',\\n direction = 'Input',\\n datatype = 'GPFieldInfo',\\n parameterType = 'Required')\\n\\n field_mappings.parameterDependencies = [feature_class.name]\\n\\n output_dir = arcpy.Parameter(\\n name = 'output_dir',\\n displayName = 'Output folder',\\n direction = 'Input',\\n datatype = 'DEFolder',\\n parameterType = 'Required')\\n\\n output_name = arcpy.Parameter(\\n name = 'output_name',\\n displayName = 'Output filename',\\n direction = 'Input',\\n datatype = 'GPString',\\n parameterType = 'Required')\\n\\n convert_4326 = arcpy.Parameter(\\n name = 'convert_4326',\\n displayName = 'Convert to WGS84?',\\n direction = 'Input',\\n datatype = 'Boolean',\\n parameterType = 'Optional')\\n convert_4326.value = 'True'\\n\\n convert_geojson = arcpy.Parameter(\\n name = 'convert_geojson',\\n displayName = 'Convert to GeoJSON?',\\n direction = 'Input',\\n datatype = 'Boolean',\\n parameterType = 'Optional')\\n convert_geojson.value = 'True'\\n\\n convert_kmz = arcpy.Parameter(\\n name = 'convert_kmz',\\n displayName = 'Convert to KMZ?',\\n direction = 'Input',\\n datatype = 'Boolean',\\n parameterType = 'Optional')\\n convert_kmz.value = 'True'\\n\\n convert_csv = arcpy.Parameter(\\n name = 'convert_csv',\\n displayName = 'Convert to CSV?',\\n direction = 'Input',\\n datatype = 'Boolean',\\n parameterType = 'Optional')\\n\\n convert_metadata = arcpy.Parameter(\\n name = 'convert_metadata',\\n displayName = 'Convert metadata to markdown?',\\n direction = 'Input',\\n datatype = 'Boolean',\\n parameterType = 'Optional')\\n\\n debug = arcpy.Parameter(\\n name = 'debug',\\n displayName = 'Debug',\\n direction = 'Input',\\n datatype = 'Boolean',\\n parameterType = 'Optional')\\n\\n return [feature_class, field_mappings, output_dir, output_name,\\n convert_4326, convert_geojson, convert_kmz, convert_csv,\\n convert_metadata, debug]\",\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 readVCTPINPUTS(self): \\n #fname= os.environ['VMECFDIR'] +\\\"/CFG/ctp/DB/VALID.CTPINPUTS\\\"\\n fname= os.environ['VMECFDIR'] +\\\"/CFG/ctp/DB/ctpinputs.cfg\\\"\\n try:\\n database=open(fname,\\\"r\\\") \\n except IOError:\\n print \\\"Cannot open \\\",fname\\n return None\\n else:\\n print \\\"File \\\",fname,\\\" open successfuly.\\\"\\n #print \\\"database= \\\",database\\n lines=database.readlines()\\n database.close() \\n #print lines,len(lines) \\n dbinputs=[]\\n count=0\\n #print \\\"look for me if you want different inputs range...\\\"\\n for i in lines:\\n if(i[0] == 'l' and i[1] == '0'): continue\\n if i == \\\"\\\\n\\\": continue\\n if(i[0] != '#'):\\n items=string.split(i)\\n #print 'items= ',items,len(items)\\n if(len(items)<6):\\n print \\\"Error parsing database, not enough items in line:\\\"\\n print items\\n continue\\n #return None\\n if items[3] == 'M': continue\\n count=count+1\\n #if count<6 or count>11 : continue\\n #if count>10 and count<24 : continue\\n #if count<16: continue\\n #if count > 4 and count < 15: continue\\n #if items[3] != '1': continue\\n #if items[2] != \\\"EMCAL\\\": continue\\n #if (items[2] != \\\"SPD\\\") and (items[2] != \\\"T0\\\"): continue\\n flag=1\\n for i in self.detectors:\\n if items[2].find(i)>=0 or i.find(\\\"ALL\\\")>=0:\\n flag=0;\\n break\\n if flag: continue\\n # input not connected\\n if items[7] == '0' and items[3] == '0': continue\\n db={}\\n db['name']=items[0]\\n db['detector']=items[2]\\n db['level']='L'+items[3]\\n db['signature']=items[4]\\n #db['number']=items[5]\\n db['number']=items[7]\\n db['numberDIM']=items[6]\\n db['ctpnum']=items[5]\\n db['Edge'] = items[8]\\n db['Delay'] = items[9]\\n dbinputs.append(db)\\n #print \\\"Adding: \\\", db\\n return dbinputs\",\n \"def __call__(self, blocks, with_cab=False):\\n\\n # for k, v in blocks.items():\\n # print(k, v.shape)\\n\\n #down fpn\\n f_down = self.FPN_Down_Fusion(blocks)\\n # print(\\\"f_down shape: {}\\\".format(f_down.shape))\\n #up fpn\\n f_up = self.FPN_Up_Fusion(blocks)\\n # print(\\\"f_up shape: {}\\\".format(f_up.shape))\\n #fusion\\n f_common = fluid.layers.elementwise_add(x=f_down, y=f_up)\\n f_common = fluid.layers.relu(f_common)\\n # print(\\\"f_common: {}\\\".format(f_common.shape))\\n\\n if self.with_cab:\\n # print('enhence f_common with CAB.')\\n f_common = self.cross_attention(f_common)\\n\\n f_score, f_border = self.SAST_Header1(f_common)\\n f_tvo, f_tco = self.SAST_Header2(f_common)\\n\\n predicts = OrderedDict()\\n predicts['f_score'] = f_score\\n predicts['f_border'] = f_border\\n predicts['f_tvo'] = f_tvo\\n predicts['f_tco'] = f_tco\\n return predicts\",\n \"def prepareSystematicsExample(config, samples):\\n QFramework.WARN(\\\"this is just an example implementation of the systematics handling\\\")\\n QFramework.WARN(\\\"you will need to tailor the systematics handling to fit the needs of your analysis!\\\")\\n\\n CLI = config.getFolder(\\\"CLI+\\\")\\n # flag indicating to run a robust analysis\\n robust = CLI.getTagBoolDefault(\\\"robust\\\",False)\\n # flag indicating to run a dummy analysis\\n dummy = CLI.getTagBoolDefault(\\\"dummy\\\",False)\\n\\n if not robust and not dummy:\\n subfolders = samples.getListOfSampleFolders(\\\"?\\\")\\n doNominal = config.getTagBoolDefault(\\\"xAODdoNominal\\\",True)\\n channels = config.getTagVStandardString(\\\"channels\\\")\\n mcasvchannels = set([ c for c in channels ])\\n for c in channels:\\n for sf in subfolders:\\n if sf.getTagBoolDefault(\\\"isData\\\",False):\\n # we're in a data SampleFolder\\n # just set some nominal tags for this channel subfolder\\n f = sf.getSampleFolder(c)\\n if not f: continue\\n f.setTagString(\\\".mcasv.channel\\\",f.getTagStringDefault(\\\"channel\\\",\\\"\\\"))\\n f.setTagString(\\\"weightname\\\",\\\"nominal\\\")\\n f.setTagString(\\\"p4suffix\\\",\\\"\\\")\\n continue\\n # we're in a MC SampleFolder\\n f = sf.getSampleFolder(c)\\n if not f:\\n QFramework.WARN(\\\"unable to retrieve sample folder '{:s}' from '{:s}'. Is this expected from your path restriction?\\\".format(c,sf.GetName()))\\n sf.printContents()\\n continue #if this is not a valid folder, don't do anything with it, you'll get a null pointer exception otherwise!\\n\\n # right now, we are treating p4 systematics and sf systematics as uncorrelated\\n # we do only look at the diagonal terms where one systematic is activated\\n # crossover terms are neglected for now, hence we have two separate loops\\n for p4syst in config.getTagVStandardString(\\\"xAODp4Systematics\\\"):\\n # for each p4 systematic, copy the channel folder\\n if not p4syst or len(p4syst)<1: continue\\n newname=c+\\\"_\\\"+p4syst\\n newf = f.copy(newname)\\n if not newf:\\n QFramework.BREAK(\\\"unable to copy folder {:s} to new name {:s}\\\".format(f.GetName(),newname))\\n sf.addFolder(newf)\\n # set the appropriate tags\\n candname = f.getTagStringDefault(\\\"~cand\\\",\\\"\\\")\\n if not candname:\\n QFramework.BREAK(\\\"didn't find 'cand' tag on sample folder '{:s}'\\\".format(f.getPath()))\\n newf.setTagString(\\\"p4Variation\\\",p4syst)\\n newf.setTagString(\\\"weightname\\\",\\\"nominal\\\")\\n newf.setTagString(\\\"cand\\\",candname+\\\"___\\\"+p4syst)\\n newf.setTagString(\\\"cand.original\\\",candname)\\n newf.setTagString(\\\".mcasv.channel\\\",newname)\\n newf.setTagString(\\\"p4suffix\\\",ROOT.TString(\\\"___\\\")+p4syst)\\n newf.setTagString(\\\"sysVariation\\\",p4syst)\\n mcasvchannels.add(newname.Data())\\n for sfsyst in config.getTagVString(\\\"xAODsfSystematics\\\"):\\n # for each sf systematic, copy the channel folder\\n if not sfsyst or len(sfsyst)<1: continue\\n newname = c+\\\"_\\\"+sfsyst\\n newf = f.copy(newname)\\n if not newf:\\n QFramework.BREAK(\\\"unable to copy folder {:s} to new name {:s}\\\".format(f.GetName(),newname))\\n sf.addFolder(newf)\\n # set the appropriate tags\\n newf.setTagString(\\\"sfVariation\\\",sfsyst)\\n newf.setTagString(\\\"weightname\\\",sfsyst)\\n newf.setTagString(\\\".mcasv.channel\\\",newname)\\n newf.setTagString(\\\"p4suffix\\\",\\\"\\\")\\n newf.setTagString(\\\"sysVariation\\\",sfsyst)\\n mcasvchannels.add(newname.Data())\\n\\n # if no nominal analysis was requested, we can remove the nominal channels\\n if not doNominal:\\n f.detachFromBase()\\n else:\\n f.setTagString(\\\".mcasv.channel\\\",f.GetName())\\n\\n # Add some nominal top level tags, even if systematics aren't being added\\n samples.setTagString(\\\"weightname\\\",\\\"nominal\\\")\\n samples.setTagString(\\\"sfVariation\\\",\\\"nominal\\\")\\n samples.setTagString(\\\"p4Variation\\\",\\\"nominal\\\")\\n samples.setTagString(\\\"p4suffix\\\",\\\"\\\")\\n samples.setTagString(\\\"sysVariation\\\",\\\"nominal\\\")\\n\\n # possibly print how the folder looks like now\\n if config.getTagBoolDefault(\\\"showChannels\\\",False):\\n QFramework.INFO(\\\"after taking care of channel and systematics setup, your sample folder looks like this:\\\")\\n samples.printContents(\\\"r2dt\\\")\\n\\n # save the whole collection of channels (including now systematics)\\n # for access later when creating the MCASV\\n runtime = config.getFolder(\\\"runtime+\\\")\\n for i, channel in enumerate(mcasvchannels):\\n if \\\"ROOT\\\" in str(type(channel)):\\n from QFramework import WARN\\n QFramework.WARN(\\\"Please make sure that every mcasvchannel is only booked once. The python set doesn't help when adding c++ ROOT instances (\\\" + str(type(channel)) + \\\".\\\")\\n runtime.setTagString(\\\"mcasvchannels.\\\"+str(i), channel)\\n\\n return\",\n \"def __call__(cls, nir_paw_image_fname, nir_paw_conf_fname, output_template, conf_limit):\\n\\n# on with the show\\n logger.info('Opening science and confidence frames')\\n ifits=fitsio.FITS(nir_paw_image_fname,'r')\\n cfits=fitsio.FITS(nir_paw_conf_fname,'r')\\n\\n#\\n# Check that the number of HDUs match\\n#\\n\\n if (len(ifits) != len(cfits)):\\n print(\\\"Number of HDUs/extensions in IMAGE and CONFidence files do not match.\\\")\\n print(\\\"Aborting\\\")\\n exit(1)\\n\\n p_ih=ifits[0].read_header()\\n p_ch=cfits[0].read_header()\\n# Remove reserve keywords\\n p_ih.clean()\\n\\n#\\n# Extract some keywords from PRIMARY header to propagate into the individual images.\\n#\\n base_dict={}\\n base_header=[]\\n for hkeep in nci.nir_paw_primary_keep:\\n if (hkeep in p_ih):\\n base_header.append({'name':hkeep,'value':p_ih[hkeep],'comment':p_ih.get_comment(hkeep)})\\n base_dict[hkeep]={'value':p_ih[hkeep],'comment':p_ih.get_comment(hkeep)}\\n else:\\n print(\\\"Keyword {:s} missing in HDU[{:d}]\\\".format(hkeep,0))\\n#\\n# If possible, need too keep track of REQTIME (requested frametime) because sometimes \\n# EXPTIME seems to be mispopulated in the CCD image HDUs with TEXPTIME\\n#\\n if ('TEXPTIME' in p_ih):\\n texptime=p_ih['TEXPTIME']\\n else:\\n texptime=None\\n if ('REQTIME' in p_ih):\\n reqtime=p_ih['REQTIME']\\n else:\\n reqtime=None\\n#\\n# print(base_header)\\n \\n\\n#\\n# Step through HDUs... and form \\\"CCD\\\" images for each HDU\\n#\\n ExtList=[]\\n for hnum in range(1,len(ifits)):\\n print(\\\"############ Begin work on extnum={:d} ###############\\\".format(hnum))\\n\\n# Check that extensions match (after that concentrate on image).\\n print(hnum,ifits[hnum].get_extname(),cfits[hnum].get_extname())\\n if (ifits[hnum].get_extname() != cfits[hnum].get_extname()):\\n print(\\\"Working on extension {:d}. Extension names (image,conf) of ([{:s}],[{:s}]) do not match!\\\".format(\\n hnum,ifits[hnum].get_extname(),cfits[hnum].get_extname()))\\n print(\\\"Aborting!\\\")\\n exit(1)\\n\\n f_ih=ifits[hnum].read_header()\\n f_ih.clean()\\n#\\n# Fix occurences where the CCD-level keyword EXPTIME has inherited the value of TEXPTIME\\n#\\n exptime=f_ih['EXPTIME']\\n if (reqtime is not None):\\n if (exptime > reqtime):\\n print(\\\"Warning: possible corrupt EXPTIME (total exptime rather than frame time present).\\\")\\n print(\\\"Attempting to update EXTIME to REQTIME (requested frame time).\\\")\\n print(\\\" Primary HDU: TEXPTIME: {:}\\\".format(texptime))\\n print(\\\" Primary HDU: REQTIME: {:}\\\".format(reqtime))\\n print(\\\" Current HDU: EXPTIME: {:} --> {:}\\\".format(exptime,reqtime))\\n exptime=reqtime\\n f_ih['EXPTIME']=reqtime\\n#\\n# Augment keywords pulled from primary header with keywords from current HDU\\n#\\n c_header=base_header[:]\\n c_dict=dict(base_dict)\\n for hkeep in nci.nir_paw_hdu_keep:\\n if (hkeep in f_ih):\\n# print(hkeep,f_ih[hkeep],f_ih.get_comment(hkeep))\\n c_header.append({'name':hkeep,'value':f_ih[hkeep],'comment':f_ih.get_comment(hkeep)})\\n if (hkeep in c_dict):\\n print(\\\"Warning: Replacing keyword {:s} with value from hdu={:d}\\\".format(hkeep,hnum))\\n c_dict[hkeep]={'value':f_ih[hkeep],'comment':f_ih.get_comment(hkeep)}\\n else:\\n print(\\\"Keyword {:s} missing in HDU[{:d}]\\\".format(hkeep,hnum))\\n\\n#\\n# Get the CCDNUM from special keyword and propagate\\n# Get SKYLEVEL, SKYNOISE, ZEROPOINT and form basis value for the weight plane\\n#\\n ccdnum=f_ih['HIERARCH ESO DET CHIP NO']\\n c_header.append({'name':'CCDNUM','value':ccdnum,'comment':'Unique Detector Number'})\\n\\n# exptime=f_ih['EXPTIME']\\n## Fix occurences where the CCD-level keyword EXPTIME has inherited the value of TEXPTIME\\n# if (exptime > reqtime):\\n# print(\\\"Warning: possible corrupt EXPTIME (total exptime rather than frame time present).\\\")\\n# print(\\\"Attempting to update EXTIME to REQTIME (requested frame time).\\\")\\n# print(\\\" Primary HDU: TEXPTIME: {:.2f}\\\".format(texptime))\\n# print(\\\" Primary HDU: REQTIME: {:.2f}\\\".format(reqtime))\\n# print(\\\" Current HDU: EXPTIME: {:.2f} --> {:.2f}\\\".format(exptime,reqtime))\\n# exptime=reqtime\\n# f_ih['EXPTIME']=reqtime\\n\\n mtime=2.5*np.log10(exptime)\\n skylev=f_ih['SKYLEVEL']\\n skyrms=f_ih['SKYNOISE']\\n seeing=f_ih['SEEING']\\n magzpt=f_ih['MAGZPT']\\n\\n# zeropoint include a correction from VEGA->AB\\n# zeropoint in headers was found to have a factor for EXPTIME removed (have to add back in for DES-like processing)\\n\\n if (p_ih['BAND'] in nci.nir_vega_to_ab):\\n magzpt=magzpt+nci.nir_vega_to_ab[p_ih['BAND']]+mtime\\n else:\\n print(\\\"Warning! Unknown BAND ({:s}) for conversion of zeropoint from VEGA to AB system\\\".format(p_ih['BAND']))\\n\\n c_header.append({'name':'SKYBRITE', 'value':skylev, 'comment':'Sky level estimate from IMCORE'})\\n c_header.append({'name':'SKYSIGMA', 'value':skyrms, 'comment':'Sky noise estimate from IMCORE'})\\n c_header.append({'name':'SKYVARA', 'value':skyrms*skyrms, 'comment':'Sky noise estimate from IMCORE'})\\n c_header.append({'name':'SKYVARB', 'value':skyrms*skyrms, 'comment':'Sky noise estimate from IMCORE'})\\n c_header.append({'name':'FWHM', 'value':seeing, 'comment':'Average FWHM (pixels)'})\\n c_header.append({'name':'MAG_ZERO', 'value':magzpt, 'comment':'Converted MAGZPT(Vega) to AB system'})\\n nite_val=convert_utc_str_to_nite(f_ih['DATE-OBS'])\\n c_header.append({'name':'NITE', 'value':nite_val, 'comment':'Observation Nite'})\\n c_header.append({'name':'SATURATE', 'value':nci.nircam_satval[ccdnum], 'comment': 'Saturation Level (ADU)'})\\n c_header.append({'name':'PIXSCAL1', 'value':0.341, 'comment': 'Fiducial pixel scale (arcsec/pix)'})\\n c_header.append({'name':'PIXSCAL2', 'value':0.341, 'comment': 'Fiducial pixel scale (arcsec/pix)'})\\n\\n# bval=f_ih['BSCALE']\\n# print(\\\"BSCALE was: \\\",bval)\\n print(\\\"SKYLEVEL was: \\\",skylev)\\n print(\\\"SKYRMS was: \\\",skyrms)\\n#\\n# Searching for a proper WGT prescription\\n#\\n# This was what I took to be equivalent to DES (but perhaps it does not properly factor in N-image stack\\n# wgtval=skylev+(skyrms*skyrms)\\n print(\\\"SKYLEV + (SKYRMS*SKYRMS): \\\",skylev+(skyrms*skyrms))\\n#\\n# This was assuming SKYLEVEL does not properly inform stats\\n# wgtval=(skyrms*skyrms)\\n print(\\\"(SKYRMS*SKYRMS): \\\",skyrms*skyrms)\\n\\n#\\n# Read the image data from the science and confidence files.\\n#\\n sci_data=ifits[hnum].read()\\n print(\\\"Median of data {:.3f} \\\".format(np.median(sci_data)))\\n conf_data=cfits[hnum].read()\\n\\n#\\n# Better seemed to be a re-measurement of STD\\n#\\n print(\\\"Attempting an improved SKYRMS with 3-sigma clip to remove objects\\\")\\n avgval, medval, stdval = medclip(sci_data,verbose=3)\\n# print(avgval,medval,stdval)\\n print(\\\"stdval^2: \\\",stdval*stdval)\\n wgtval=(stdval*stdval)\\n# print(wgtval)\\n#\\n# Use the new (i.e. chip-based header) to feed a WCS \\n# Use image size to feed calculations for center and corners (similar to despyastro.CCD_corners\\n#\\n print(\\\"Calculating center/corners assuuming native ZPN projection\\\")\\n w=WCS(fitsio.FITSHDR(c_header))\\n\\n fnax2=float(sci_data.shape[0])\\n fnax1=float(sci_data.shape[1])\\n corn_x=np.array([fnax1/2.0,1.,fnax1,fnax1,1.])\\n corn_y=np.array([fnax2/2.0,1.,1.,fnax2,fnax2])\\n sky = w.pixel_to_world(corn_x,corn_y)\\n corn_ra=sky.ra.degree\\n corn_dec=sky.dec.degree\\n\\n c_header.append({'name':'RA_CENT', 'value':corn_ra[0], 'comment':'RA center'})\\n c_header.append({'name':'DEC_CENT','value':corn_dec[0],'comment':'DEC center'})\\n for i in range(1,5):\\n c_header.append({'name':'RAC{:d}'.format(i), 'value':corn_ra[i], 'comment':'RA corner {:d}'.format(i)})\\n c_header.append({'name':'DECC{:d}'.format(i),'value':corn_dec[i],'comment':'DEC corner {:d}'.format(i)})\\n RACMIN, RACMAX, DECCMIN, DECCMAX, CROSSRA0 = get_DESDM_corners_extent(corn_ra, corn_dec)\\n c_header.append({'name':'RACMIN', 'value':RACMIN, 'comment':'Minimum extent of image in RA'})\\n c_header.append({'name':'RACMAX', 'value':RACMAX, 'comment':'Maximum extent of image in RA'})\\n c_header.append({'name':'DECCMIN', 'value':DECCMIN, 'comment':'Minimum extent of image in Declination'})\\n c_header.append({'name':'DECCMAX', 'value':DECCMAX, 'comment':'Maximum extent of image in Declination'})\\n c_header.append({'name':'CROSSRA0','value':CROSSRA0,'comment':'Does Image Span RA 0h (Y/N)'})\\n c_header.append({'name':'DESEPOCH','value':'NIREPOCH','comment':'Default DES epoch definition for including NIR data'})\\n#\\n#\\n#\\n print(\\\"Stripping ZPN projection from WCS and creating a shift to get a rough TAN\\\")\\n recs_to_delete=[] \\n for i, hrec in enumerate(c_header):\\n if (hrec['name'] == 'CTYPE1'):\\n c_header[i]['value']='RA---TAN'\\n if (hrec['name'] == 'CTYPE2'):\\n c_header[i]['value']='DEC--TAN'\\n\\n if (hrec['name'] == 'CRVAL1'):\\n c_header[i]['value']=corn_ra[0]\\n if (hrec['name'] == 'CRVAL2'):\\n c_header[i]['value']=corn_dec[0]\\n if (hrec['name'] == 'CRPIX1'):\\n c_header[i]['value']=fnax1/2.0\\n if (hrec['name'] == 'CRPIX2'):\\n c_header[i]['value']=fnax2/2.0\\n\\n if (hrec['name'] in ['PV2_1','PV2_2','PV2_3','PV2_4','PV2_5']):\\n recs_to_delete.append(i)\\n if (len(recs_to_delete) > 0):\\n for i in sorted(recs_to_delete,reverse=True):\\n x=c_header.pop(i)\\n print(\\\"Removing: {:}\\\".format(x))\\n\\n whack=WCS(fitsio.FITSHDR(c_header))\\n skyhack = whack.pixel_to_world(corn_x,corn_y)\\n whack_corn_ra=skyhack.ra.degree\\n whack_corn_dec=skyhack.dec.degree\\n for i in range(5):\\n cosdec=np.cos(corn_dec[i]*np.pi/180.)\\n dra=3600.*(corn_ra[i]-whack_corn_ra[i])*cosdec\\n ddec=3600.*(corn_dec[i]-whack_corn_dec[i])\\n print(\\\" WCS shift {:d} {:.6f} {:.6f} {:.6f} {:.6f} {:.6f} {:.6f} \\\".format(ccdnum,corn_ra[i],corn_dec[i],whack_corn_ra[i],whack_corn_dec[i],dra,ddec))\\n\\n# for i, hrec in enumerate(c_header):\\n# print(i,hrec)\\n\\n#\\n# Form the SCI, MSK, and WGT HDUs\\n#\\n im=DESImage(init_data=True,init_mask=True,init_weight=True,shape=sci_data.shape)\\n\\n im.data=np.float32(sci_data)\\n msk_wsm=np.where(conf_data<conf_limit)\\n im.mask[msk_wsm] |= BADPIX_BPM\\n im.weight=np.float32(conf_data/100./wgtval)\\n#\\n# Check for extra conditions where further masking is needed\\n# Here is where CCD=6 check was started (now removed and placed \\n# in nir_starmask to take advantage of bright object masking\\n#\\n\\n\\n#\\n# Deal with individual header-isms and write out SCI, MSK, WGT\\n# Note this is using fitsio (duplicating some of the DESIMAGE.save \\n# but customization was needed to deal with foibles of the current\\n#\\n fname=re.sub('%02d','{:02d}'.format(ccdnum),output_template,1)\\n ofits = fitsio.FITS(fname, 'rw', clobber=True)\\n\\n im.header=fitsio.FITSHDR(c_header) \\n im.header['DES_EXT']='IMAGE'\\n im.header = update_hdr_compression(im.header, 'SCI')\\n ofits.write(im.data,extname='SCI',header=im.header)\\n\\n\\n im.mask_hdr=fitsio.FITSHDR(c_header) \\n im.mask_hdr['DES_EXT']='MASK'\\n im.mask_hdr = update_hdr_compression(im.mask_hdr, 'MSK')\\n im.mask_hdr['DES_EXT']='MASK'\\n ofits.write(im.mask,extname='MSK',header=im.mask_hdr)\\n\\n# im.weight_hdr=fitsio.FITSHDR(c_header) \\n# print(im.weight_hdr)\\n im.weight_hdr = update_hdr_compression(im.weight_hdr, 'WGT')\\n# print(im.weight_hdr)\\n im.weight_hdr['DES_EXT']='WEIGHT'\\n ofits.write(im.weight,extname='WGT',header=im.weight_hdr)\\n\\n ofits.close()\\n print(\\\"Wrote {:s}\\\".format(fname))\\n print(\\\" \\\")\\n \\n\\n ifits.close()\\n cfits.close()\\n\\n ret_code = 0\\n return ret_code\",\n \"def __init__(self, model_config, global_config):\\n self.messages = []\\n self.config = global_config\\n\\n self.name = model_config[\\\"name\\\"] \\n self.data_filename = model_config[\\\"data\\\"] \\n self.clock = model_config.get(\\\"clock\\\", \\\"\\\")\\n self.features = model_config.get(\\\"features\\\",[\\\"*\\\"])\\n self.exclusions = model_config.get(\\\"exclusions\\\",None)\\n self.constant_feature = False\\n self.constant_feature_removed = False\\n self.frequencies = model_config.get(\\\"frequencies\\\", \\\"empirical\\\")\\n self.pruned = model_config.get(\\\"pruned\\\", False)\\n self.rate_variation = model_config.get(\\\"rate_variation\\\", False)\\n self.feature_rates = model_config.get(\\\"feature_rates\\\", None)\\n self.ascertained = model_config.get(\\\"ascertained\\\", None)\\n # Force removal of constant features here\\n # This can be set by the user in BinaryModel only\\n self.remove_constant_features = True\\n self.minimum_data = float(model_config.get(\\\"minimum_data\\\", 0))\\n self.substitution_name = self.__class__.__name__\\n self.data_separator = \\\",\\\"\\n self.use_robust_eigensystem = model_config.get(\\\"use_robust_eigensystem\\\", False)\\n\\n # Load the entire dataset from the file\\n self.data = load_data(self.data_filename, file_format=model_config.get(\\\"file_format\\\",None), lang_column=model_config.get(\\\"language_column\\\",None))\\n # Remove features not wanted in this analysis\\n self.build_feature_filter()\\n self.apply_feature_filter()\\n\\n # Keep this around for later...\\n self.global_config = global_config\",\n \"def loadInfo(self):\\n params_array = SystemIO.load(self.path.model_info_file)\\n return TrnParamsConvolutional(*params_array)\",\n \"def _prepareArg_covGen(self):\\n ## generate length scale field in x, y and z directions, respectively\\n lenXField, lenYField, lenZField = self._genLenScale()\\n ## read cellVolumn Field as the weight Field for KL expansion\\n cellVField = foamOp.readTurbCellVolumeFromFile(self.klDir+'/0/')\\n weightField = cellVField[:, 0]\\n sigmaField = foamOp.readScalarFromFile(self.klDir+'/0/Sigma')\\n #TODO: truncateTol can be specified by user\\n #truncateTol = -np.log(1e-10)\\n # TruncateTol can be specified by user\\n truncateTol = -np.log(self.truncateTol) \\n ## assembly arguments dictionary for generation of covariance\\n if self.kernelType == 'SqExp': \\n Arg_covGen = {\\n 'sigmaField': sigmaField,\\n 'kernelType': 'SqExp',\\n 'lenXField': lenXField,\\n 'lenYField': lenYField,\\n 'lenZField': lenZField,\\n 'weightField':weightField,\\n 'truncateTol': truncateTol\\n }\\n elif self.kernelType == 'givenStructure':\\n CovStruct = np.loadtxt('./Corr.dat')\\n Arg_covGen = {\\n 'sigmaField': sigmaField,\\n 'kernelType': 'givenStructure',\\n 'CovStruct': CovStruct,\\n 'weightField':weightField,\\n 'truncateTol': truncateTol\\n }\\n return Arg_covGen\",\n \"def initialize(self, args):\\n # You must parse model_config. JSON string is not parsed here\\n self.model_config = json.loads(args['model_config'])\\n print(\\\"model_config:\\\", self.model_config)\\n\\n self.input_names = []\\n for input_config in self.model_config[\\\"input\\\"]:\\n self.input_names.append(input_config[\\\"name\\\"])\\n print(\\\"postprocess input names:\\\", self.input_names)\\n\\n self.output_names = []\\n self.output_dtype = []\\n for output_config in self.model_config[\\\"output\\\"]:\\n self.output_names.append(output_config[\\\"name\\\"])\\n dtype = pb_utils.triton_string_to_numpy(output_config[\\\"data_type\\\"])\\n self.output_dtype.append(dtype)\\n print(\\\"postprocess output names:\\\", self.output_names)\\n self.postprocessor = fd.vision.ocr.DBDetectorPostprocessor()\\n self.cls_preprocessor = fd.vision.ocr.ClassifierPreprocessor()\\n self.rec_preprocessor = fd.vision.ocr.RecognizerPreprocessor()\\n self.cls_threshold = 0.9\",\n \"def sources_extraction(image,sextractor_pars):\\n\\n cat_name, detect_minarea, detect_thresh, analysis_thresh, phot_aperture, satur_level, ZP, gain, pixelScale,seeing,back_type,back_value,back_size,backphoto_type,backphoto_thick,back_filterthresh,checkimage_type,checkimage_name= sextractor_pars\\n sp.run('sex %s.fits -c gft.sex -CATALOG_NAME %s.cat -CATALOG_TYPE ASCII_HEAD -PARAMETERS_NAME gft.param -DETECT_TYPE CCD -DETECT_MINAREA %d -DETECT_THRESH %d -ANALYSIS_THRESH %d -PHOT_APERTURES %d -SATUR_LEVEL %d -MAG_ZEROPOINT %f -GAIN %f -PIXEL_SCALE %f -SEEING_FWHM %f -BACK_TYPE %s -BACK_VALUE %f -BACK_SIZE %d -BACKPHOTO_TYPE %s -BACKPHOTO_THICK %d -BACK_FILTTHRESH %f -CHECKIMAGE_TYPE %s -CHECKIMAGE_NAME %s.fits ' % (image,cat_name, detect_minarea, detect_thresh, analysis_thresh, phot_aperture, satur_level, ZP, gain, pixelScale,seeing,back_type,back_value,back_size,backphoto_type,backphoto_thick,back_filterthresh,checkimage_type,checkimage_name),shell=True)\",\n \"def sweep_image_model():\\n for c1 in [4, 8, 16]:\\n for c2 in [2, 4]:\\n for c3 in [2, 4]:\\n for c4 in [1, 2]:\\n flags = flag_reader.read_flag()\\n print(c1)\\n flags.channel_list = c1 * np.array([1, c2, c2*c3, c2*c3*c4])\\n print('channel list = ', flags.channel_list)\\n flags.last_dim = flags.channel_list[-1]\\n flags.model_name = flags.data_set + '_channel_' + str(flags.channel_list).replace('[','').replace(']','').replace(' ','_') + \\\\\\n '_dim_last_' + str(flags.last_dim) + '_ind_' + str(flags.comp_ind) + \\\\\\n '_lr_{}_decay_{}_reg_{}_bs_{}'.format(flags.lr, flags.lr_decay_rate, flags.reg_scale, flags.batch_size)\\n print(flags.model_name)\\n training_from_flag(flags)\",\n \"def check_config(params, dannce_net, prediction):\\n check_camnames(params)\\n\\n if params[\\\"exp\\\"] is not None:\\n for expdict in params[\\\"exp\\\"]:\\n check_camnames(expdict)\\n\\n if dannce_net:\\n check_net_expval(params)\\n check_vmin_vmax(params)\",\n \"def input_setup(config):\\n # Load data path, if is_train False, get test data\\n data = load_data(config.is_train, config.test_img)\\n \\n # Make sub_input and sub_label, if is_train false more return nx, ny\\n if config.is_train:\\n \\tsub_input_sequence, sub_label_sequence, nx, ny = make_sub_data_train(data, config)\\n else:\\n\\tsub_input_sequence, sub_label_sequence, nx, ny = make_sub_data_test(data, config)\\n\\n\\n # Make list to numpy array. With this transform\\n arrinput = np.asarray(sub_input_sequence) # [?, 41, 41, 3]\\n arrlabel = np.asarray(sub_label_sequence) # [?, 41, 41, 3]\\n make_data_hf(arrinput, arrlabel, config)\\n\\n return nx, ny\",\n \"def config():\\n file_path = None # path to the input file\\n db_path = None # path to the output db\\n atomic_properties = (\\n \\\"Properties=species:S:1:pos:R:3\\\"\\n ) # atomic properties of the input file\\n molecular_properties = [\\\"energy\\\"] # molecular properties of the input file\\n overwrite = False\",\n \"def input_models():\\n return [\\n PDBFile(\\n Path(golden_data, \\\"protdna_complex_1.pdb\\\"),\\n path=golden_data,\\n score=42.0,\\n restr_fname=Path(golden_data, \\\"example_ambig_1.tbl\\\")\\n ),\\n PDBFile(\\n Path(golden_data, \\\"protdna_complex_2.pdb\\\"),\\n path=golden_data,\\n score=28.0,\\n restr_fname=Path(golden_data, \\\"example_ambig_2.tbl\\\")\\n )]\",\n \"def check_info(self):\\n #define convertissor\\n def pass_in_integer(value):\\n return int(value)\\n def pass_in_logical(value):\\n if value in ['1','t','T','.true.']:\\n return 1\\n else:\\n return 0\\n def pass_in_float(value):\\n return float(value)\\n\\n for line in open('./Source/MadWeight_File/Python/MW_param_default.inc'):\\n line=line.split('#')[0] #remove comment\\n splitline=line.split() #split the data\\n if len(splitline)!=4:\\n continue\\n #assign element\\n block=splitline[0].lower()\\n tag=splitline[1].lower()\\n type=splitline[2].lower()\\n value=splitline[3]\\n #check if exist -> default\\n try:\\n self[block][tag]\\n except:\\n try:\\n self[block][tag]=value\\n except:\\n self[block]={tag:value}\\n #change type\\n if type in ['integer','logical','float']:\\n self[block][tag]=eval('pass_in_'+type+'(self[block][tag])')\",\n \"def info_criteria(indep,\\n dep,\\n models,\\n add_aicc=False):\\n num_data = len(indep)\\n\\n bic_calc = BicCalculator(bic_type=BIC_TYPE.STANDARD)\\n bic_calc_bkpt = BicCalculator(bic_type=BIC_TYPE.HYBRID)\\n\\n #bic_calc_bkpt = bic_calc\\n #####bic_calc_bkpt = BicCalculator(bic_type = BIC_TYPE.HOS)\\n\\n bics = []\\n aics = []\\n aiccs = []\\n for model in models:\\n\\n if model in [Model.ONE_BKPT, Model.TWO_BKPT]:\\n bic_calc_to_use = bic_calc_bkpt\\n else:\\n bic_calc_to_use = bic_calc\\n\\n estimator = model.estimator(num_end_to_skip=NUM_END_TO_SKIP,\\n num_between_to_skip=NUM_BETWEEN_TO_SKIP)\\n estimator.fit(indep, dep)\\n\\n loglikelihood = estimator.loglikelihood\\n num_params = estimator.num_params\\n\\n bic = bic_calc_to_use.bic(num_params=num_params,\\n loglikelihood=loglikelihood,\\n num_data=num_data)\\n aic = stats_util.aic(num_params=num_params,\\n loglikelihood=loglikelihood)\\n aicc = stats_util.aicc(num_params=num_params,\\n loglikelihood=loglikelihood,\\n num_data=num_data)\\n\\n bics.append(bic)\\n aics.append(aic)\\n aiccs.append(aicc)\\n ic_df = pd.DataFrame({\\\"BIC\\\": bics, \\\"AIC\\\": aics}, index=models)\\n if add_aicc:\\n ic_df[\\\"AICC\\\"] = aiccs\\n\\n wts_df = ic_df.apply(stats_util.bma_weights, axis=0)\\n wts_cols = [x + \\\" Model Wt\\\" for x in wts_df.columns]\\n wts_df.columns = wts_cols\\n both = pd.concat([ic_df, wts_df], join=\\\"outer\\\", axis=1)\\n return both\",\n \"def _handleInput(self, paramInput):\\n super()._handleInput(paramInput)\\n settings, notFound = paramInput.findNodesAndExtractValues(['epsilon', 'dual', 'loss', 'tol', 'fit_intercept',\\n 'intercept_scaling', 'max_iter'])\\n # notFound must be empty\\n assert(not notFound)\\n self.initializeModel(settings)\",\n \"def main():\\n target = 'Coding:Level1'\\n output_root = f'problem_5_output/{target.replace(\\\":\\\", \\\"_\\\")}'\\n if not os.path.exists(output_root):\\n os.makedirs(output_root, exist_ok=True)\\n\\n # dictionary of parameter grids, one for each process\\n param_grids = {\\n 'early_stopping': ParameterGrid([\\n {\\n 'patience': [15], # , 20, 40]\\n },\\n ]),\\n 'fit': ParameterGrid([\\n {\\n 'batch_size': [128], # , 64, 128, 256],\\n 'epochs': [16], # 20, 50],\\n },\\n ]),\\n 'model_preprocessor': ParameterGrid([\\n {\\n 'num_unique_words': [5000], # 4000, 1000, 6000, 10000],\\n 'max_sequence_length': [150], # 50, 75, 100, 125, 150, 200],\\n },\\n ]),\\n 'model': ParameterGrid([\\n # {\\n # Dense single hidden layer model hyperparameters:\\n # 'name': ['dense_h1'],\\n # 'embedded_dims': [8], # , 16, 32, 64, 128, 256],\\n # 'num_units_h1': [8], # , 16, 32, 64, 128, 256],\\n # 'drop_h1': [None], # , 0.1, 0.2, 0.25, 0.5, 0.75],\\n # 'optimizer': ['nadam', 'adam'],\\n # 'learning_rate': [None], # , 0.01, 0.001],\\n # 'activation': ['relu', 'tanh'],\\n # },\\n # {\\n # Dense double hidden layer model hyperparameters:\\n # 'name': ['dense_h2'],\\n # 'embedded_dims': [64],\\n # 'num_units_h1': [128],\\n # 'num_units_h2': [128],\\n # 'drop_h1': [None],\\n # 'drop_h2': [0.5],\\n # 'optimizer': ['nadam'],\\n # 'activation': ['relu'],\\n # 'learning_rate': [0.01],\\n # },\\n # {\\n # CNN single hidden layer model hyperparameters\\n # 'name': ['conv_h1'],\\n # 'embedded_dims': [64],\\n # 'num_units_h1': [32], # , 64, 256],\\n # 'k_conv_h1': [2], # , 3, 4],\\n # 'drop_embed': [0.2], # , 0.5],\\n # 'activation': ['relu', 'tanh'],\\n # 'optimizer': ['adam', 'nadam']\\n # },\\n # {\\n # CNN double hidden layer model hyperparameters\\n # 'name': ['conv_h2'],\\n # 'embedded_dims': [128], # , 64, 32, 16, 8],\\n # 'num_units_h1': [32], # , 64, 128],\\n # 'drop_h2': [0.5], # , 0.75, 0.25, 0.1],\\n # 'k_conv_h1': [2], # , 3, 4],\\n # 'num_units_h2': [128], # , 64, 32, 16, 8],\\n # 'drop_embed': [0.2], # , 0.50],\\n # 'activation': ['relu'],\\n # 'optimizer': ['adam'], # , 'nadam'],\\n # },\\n # {\\n # CNN double hidden layer model hyperparameters\\n # 'name': ['conv_h2.1'],\\n # 'embedded_dims': [64],\\n # 'num_units_h1': [32], # , 64, 128],\\n # 'k_conv_h1': [2], # , 3, 4],\\n # 'drop_embed': [0.2], # , 0.5],\\n # 'activation': ['relu'],\\n # 'optimizer': ['adam'], # , 'nadam']\\n # },\\n # {\\n # RNN single hidden layer model hyperparameters\\n # 'name': ['rnn_h1'],\\n # 'embedded_dims': [64],\\n # 'drop_embed': [0.2],\\n # 'num_units_h1': [128],\\n # 'optimizer': ['nadam'],\\n # 'learning_rate': [0.01]\\n # },\\n # {\\n # # LSTM double hidden layer (second layer dense FC) model hyperparameters\\n # 'name': ['lstm_h1'],\\n # 'embedded_dims': [64],\\n # 'drop_embed': [0.2],\\n # 'drop_h1': [0.5],\\n # 'num_units_h1': [128],\\n # 'optimizer': ['nadam'],\\n # },\\n # {\\n # LSTM double hidden layer (second layer dense FC) model hyperparameters\\n # 'name': ['lstm_h2'],\\n # 'embedded_dims': [64],\\n # 'drop_embed': [0.2],\\n # 'num_units_h1': [128],\\n # 'drop_h1': [0.5],\\n # 'num_units_h2': [128],\\n # 'optimizer': ['nadam'],\\n # 'activation': ['relu']\\n # },\\n # {\\n # # Bi-directional LSTM single hidden layer model hyperparameters\\n # 'name': ['bi_lstm_h1'],\\n # 'embedded_dims': [32], # , 64, 128],\\n # 'drop_embed': [0.2], # , 0.25, 0.5],\\n # 'num_units_h1': [32], # , 64, 128],\\n # 'drop_h1': [0.2], # , 0.25, 0.5],\\n # 'optimizer': ['nadam', 'adam']\\n # },\\n # {\\n # Bi-directional LSTM double hidden layer (second layer Bi-LSTM) model hyperparameters\\n # 'name': ['bi_lstm_h2'],\\n # 'embedded_dims': [32], # , 64, 128],\\n # 'num_units_h1': [32], # , 64, 128],\\n # 'num_units_h2': [32], # , 64, 128],\\n # 'drop_h1': [0.25, 0.5],\\n # 'drop_h2': [0.25, 0.5],\\n # 'optimizer': ['nadam', 'adam']\\n # },\\n {\\n # Multi Convolutional model hyperparameters\\n 'name': ['multi_conv_h3_s2'],\\n 'drop_embed': [0.5], # , 0.3],\\n 'embedded_dims': [128], # , 64, 128, 256],\\n 'num_units_h1': [128], # , 64, 128, 256],\\n 'num_units_h2': [128], # , 64, 128, 256],\\n 'num_units_h3': [128], # , 64, 128, 256],\\n 'num_units_h4': [128], # , 64, 128, 256],\\n 'k_conv_h1': [3],\\n 'k_conv_h2': [2],\\n 'activation': ['relu'], # , 'tanh'],\\n 'drop_h3': [0.2], # , 0.2, 0.25, 0.5, 0.75],\\n 'optimizer': ['adam'], # 'nadam']\\n },\\n # {\\n # # Multi Convolutional model hyperparameters\\n # 'name': ['multi_conv_h3_s3'],\\n # 'drop_embed': [0.5], # , 0.3],\\n # 'embedded_dims': [32], # , 64, 128, 256],\\n # 'num_units_h1': [32], # , 64, 128, 256],\\n # 'num_units_h2': [32], # , 64, 128, 256],\\n # 'num_units_h3': [32], # , 64, 128, 256],\\n # 'num_units_h4': [32], # , 64, 128, 256],\\n # 'k_conv_h1': [3],\\n # 'k_conv_h2': [2],\\n # 'k_conv_h3': [4],\\n # 'k_conv_h4': [4],\\n # 'activation': ['relu', 'tanh'],\\n # 'drop_h4': [0.1], # , 0.2, 0.25, 0.5, 0.75],\\n # 'optimizer': ['adam', 'nadam']\\n # },\\n ]),\\n 'preprocessor': ParameterGrid([\\n # {\\n # 'do_clean': [False],\\n # 'pad_type': ['pre', 'post'],\\n # 'trunc_type': ['pre', 'post'],\\n # },\\n {\\n 'do_clean': [True],\\n 'pad_type': ['post'], # , 'post'],\\n 'trunc_type': ['post'], # 'post'],\\n 'omit_stopwords': [False],\\n 'ignore_urls': [False],\\n 'fix_contractions': [True],\\n 'stem': [True],\\n 'remove_foreign_characters': [False], # , False],\\n 'lower': [True], # , False],\\n 'remove_punctuation': [True], # , False],\\n 'bigrams': [True], # , False]\\n },\\n ])\\n }\\n\\n def prod(a):\\n if len(a) == 0:\\n return 1\\n return a[0] * prod(a[1:])\\n\\n num_models = prod([len(pg) for pg in param_grids.values()])\\n\\n param_grid_names = sorted(list(param_grids.keys()))\\n param_grid_list = [param_grids[k] for k in param_grid_names]\\n\\n all_params, best_params = assemble_results(output_root)\\n\\n if CHECK_ONLY:\\n for i, params in enumerate(itertools.product(*param_grid_list[3:5])):\\n params = {k: v for k, v in zip(param_grid_names[3:5], params)}\\n print(i, params)\\n Preprocessor(**params['preprocessor'], **params['model_preprocessor'])\\n\\n for i, params in enumerate(itertools.product(*param_grid_list[2:4])):\\n params = {k: v for k, v in zip(param_grid_names[2:4], params)}\\n print(i, params)\\n build_fn(num_classes=3, **params['model'], **params['model_preprocessor'])\\n return\\n\\n for i, params in enumerate(itertools.product(*param_grid_list)):\\n mem = psutil.virtual_memory()\\n percent_used = 1 - mem.available / mem.total\\n print(f'{percent_used:.2%} memory used')\\n if percent_used > 0.80:\\n # exit failure\\n print('Exiting (-1)')\\n exit(-1)\\n\\n params = {k: v for k, v in zip(param_grid_names, params)}\\n print(f'\\\\n{i + 1}/{num_models}: {params}\\\\n')\\n\\n if params in all_params:\\n # skip this one because we already ran it.\\n continue\\n\\n if best_params is not None:\\n # print best performance so far\\n print(f'best params: {best_params}')\\n print(f'best val loss: {best_params[\\\"results\\\"][\\\"valid\\\"][\\\"loss\\\"]:.6f}')\\n print(f'best val acc: {best_params[\\\"results\\\"][\\\"valid\\\"][\\\"accuracy\\\"]:.4%}')\\n\\n # create a new output directory with path to model file.\\n date = datetime.datetime.utcnow().strftime(\\\"%Y-%m-%d-%H.%M.%S.%f\\\")\\n output_dir = os.path.join(output_root, date)\\n if not os.path.exists(output_dir):\\n os.makedirs(output_dir)\\n model_file = os.path.join(output_dir, 'model.h5')\\n\\n # get the preprocessed training and validation data\\n preprocess_time = time.time()\\n classes, data_sets, set_names = get_xy(Preprocessor(**params['preprocessor'], **params['model_preprocessor']),\\n target=target)\\n ((x_train, y_train), (x_valid, y_valid)) = data_sets\\n preprocess_time -= time.time()\\n\\n # build and compile model\\n model = build_fn(num_classes=len(classes), **params['model'], **params['model_preprocessor'])\\n\\n # setup callbacks\\n early_stopping = EarlyStopping(monitor='val_loss', verbose=1, **params['early_stopping'])\\n model_checkpoint = ModelCheckpoint(\\n filepath=model_file,\\n save_weights_only=False, save_freq='epoch',\\n save_best_only=True, monitor='val_loss', verbose=1)\\n callbacks = [early_stopping, model_checkpoint]\\n\\n # Use sample weights to treat classes equally in loss and accuracy.\\n sample_weight = get_sample_weight(y_train)\\n sample_weight_valid = get_sample_weight(y_valid)\\n\\n # fit the model\\n train_time = time.time()\\n model.fit(x=x_train, y=y_train, sample_weight=sample_weight, verbose=1,\\n validation_data=(x_valid, y_valid, sample_weight_valid), callbacks=callbacks, **params['fit'])\\n train_time -= time.time()\\n\\n # load the best model (last one saved)\\n model = load_model(model_file, compile=True)\\n\\n # compute results\\n results = get_performance(model, data_sets, set_names)\\n results['time'] = {'train': train_time, 'preprocess': preprocess_time}\\n\\n print(pd.DataFrame(data=results).T)\\n params['results'] = results\\n\\n # save params and results\\n with open(os.path.join(output_dir, 'params.json'), 'w') as fp:\\n json.dump(params, fp)\\n\\n # save a copy of *this* Python file.\\n shutil.copyfile(__file__, os.path.join(output_dir, 'roatan.py'))\\n\\n # for convenience, show the validation loss and accuracy in a file name in the same directory.\\n result_file_name = f'{params[\\\"results\\\"][\\\"valid\\\"][\\\"loss\\\"]:.6f}_{params[\\\"results\\\"][\\\"valid\\\"][\\\"accuracy\\\"]:.4f}.out'\\n with open(os.path.join(output_dir, result_file_name), 'w'):\\n pass\\n\\n # check_model(output_dir)\\n\\n if best_params is None or (params['results']['valid']['loss'] < best_params['results']['valid']['loss']):\\n best_params = params\\n\\n # assemble results from all runs into one CSV file in output root.\\n assemble_results(output_root)\",\n \"def get_parameters(config, version_stack_name, app_stack_name, task_definition, app_name, cluster_name, env, version, aws_hosted_zone, base_path, task_definition_arn): # pylint: disable=too-many-arguments,too-many-locals\\n\\n print(\\\"Generating Parmeters for CloudFormation template\\\")\\n container_port = [x['portMappings'][0]['containerPort'] for x in task_definition['containerDefinitions'] if x['name'] == app_name][0]\\n\\n cloudformation = boto3.client('cloudformation')\\n print(\\\"Determining ALB Rule priority...\\\")\\n priority = None\\n listener_rule = None\\n try:\\n response = cloudformation.describe_stack_resources(\\n StackName=version_stack_name,\\n LogicalResourceId='ListenerRule'\\n )\\n listener_rule = response['StackResources'][0]['PhysicalResourceId']\\n print(\\\"Listener Rule already exists, not setting priority.\\\")\\n except (KeyError, IndexError, botocore.exceptions.ClientError):\\n print(\\\"Listener Rule does not already exist, getting priority...\\\")\\n if listener_rule is None:\\n rules = get_list_of_rules(app_stack_name)\\n priority = get_priority(rules)\\n print(\\\"Rule priority is {}.\\\".format(priority))\\n\\n print(\\\"Determining if ALB is internal or internet-facing...\\\")\\n elbv2 = boto3.client('elbv2')\\n response = cloudformation.describe_stack_resources(\\n StackName=app_stack_name,\\n LogicalResourceId='ALB'\\n )\\n alb = response['StackResources'][0]['PhysicalResourceId']\\n response = elbv2.describe_load_balancers(\\n LoadBalancerArns=[alb],\\n )\\n alb_scheme = response['LoadBalancers'][0]['Scheme']\\n print(\\\"ALB is {}.\\\".format(alb_scheme))\\n\\n try:\\n autoscaling = str(config['autoscaling'])\\n autoscaling_target = str(config['autoscaling_target'])\\n autoscaling_max_size = str(config['autoscaling_max_size'])\\n autoscaling_min_size = str(config['autoscaling_min_size'])\\n except KeyError as exception:\\n print(\\\"Autoscaling parameter missing, disabling autoscaling ({})\\\".format(exception))\\n autoscaling = None\\n autoscaling_target = None\\n autoscaling_max_size = None\\n autoscaling_min_size = None\\n\\n parameters = [\\n {\\n \\\"ParameterKey\\\": \\\"Name\\\",\\n \\\"ParameterValue\\\": app_name\\n },\\n {\\n \\\"ParameterKey\\\": 'ClusterName',\\n \\\"ParameterValue\\\": cluster_name\\n },\\n {\\n \\\"ParameterKey\\\": 'Environment',\\n \\\"ParameterValue\\\": env\\n },\\n {\\n \\\"ParameterKey\\\": 'Version',\\n \\\"ParameterValue\\\": version\\n },\\n {\\n \\\"ParameterKey\\\": 'HealthCheckPath',\\n \\\"ParameterValue\\\": config['lb_health_check']\\n },\\n {\\n \\\"ParameterKey\\\": 'HealthCheckGracePeriod',\\n \\\"ParameterValue\\\": str(config['lb_health_check_grace_period'])\\n },\\n {\\n \\\"ParameterKey\\\": 'HealthCheckTimeout',\\n \\\"ParameterValue\\\": str(config['lb_health_check_timeout'])\\n },\\n {\\n \\\"ParameterKey\\\": 'HealthCheckInterval',\\n \\\"ParameterValue\\\": str(config['lb_health_check_interval'])\\n },\\n {\\n \\\"ParameterKey\\\": 'ContainerPort',\\n \\\"ParameterValue\\\": str(container_port)\\n },\\n {\\n \\\"ParameterKey\\\": 'Domain',\\n \\\"ParameterValue\\\": aws_hosted_zone\\n },\\n {\\n \\\"ParameterKey\\\": 'Path',\\n \\\"ParameterValue\\\": base_path\\n },\\n {\\n \\\"ParameterKey\\\": 'TaskDefinitionArn',\\n \\\"ParameterValue\\\": task_definition_arn\\n },\\n {\\n \\\"ParameterKey\\\": 'AlbScheme',\\n \\\"ParameterValue\\\": alb_scheme\\n },\\n {\\n \\\"ParameterKey\\\": 'Autoscaling',\\n \\\"ParameterValue\\\": autoscaling\\n },\\n {\\n \\\"ParameterKey\\\": 'AutoscalingTargetValue',\\n \\\"ParameterValue\\\": autoscaling_target\\n },\\n {\\n \\\"ParameterKey\\\": 'AutoscalingMaxSize',\\n \\\"ParameterValue\\\": autoscaling_max_size\\n },\\n {\\n \\\"ParameterKey\\\": 'AutoscalingMinSize',\\n \\\"ParameterValue\\\": autoscaling_min_size\\n },\\n {\\n \\\"ParameterKey\\\": 'ECSServiceName',\\n \\\"ParameterValue\\\": \\\"{}-ECSService\\\".format(version_stack_name)\\n },\\n {\\n \\\"ParameterKey\\\": 'ECSServiceSecurityClassification',\\n \\\"ParameterValue\\\": str(config['security_classification'])\\n },\\n {\\n \\\"ParameterKey\\\": 'ECSServiceSecurityDataType',\\n \\\"ParameterValue\\\": str(config['security_data_type'])\\n },\\n {\\n \\\"ParameterKey\\\": 'ECSServiceSecurityAccessibility',\\n \\\"ParameterValue\\\": str(config['security_accessibility'])\\n }\\n ]\\n\\n parameters = [x for x in parameters if x['ParameterValue'] is not None] # remove all keys that have a value of None\\n\\n if priority is not None:\\n parameters.append({\\n \\\"ParameterKey\\\": 'RulePriority',\\n \\\"ParameterValue\\\": str(priority)\\n })\\n else:\\n parameters.append({\\n \\\"ParameterKey\\\": 'RulePriority',\\n \\\"UsePreviousValue\\\": True\\n })\\n\\n print(\\\"Finished generating parameters:\\\")\\n for param in parameters:\\n print(\\\"{:30}{}\\\".format(param['ParameterKey'] + ':', param.get('ParameterValue', None)))\\n return parameters\",\n \"def read_setup(inifile):\\n # inifile = os.path.join(spathy_path, inifile)\\n print(inifile)\\n cfg = configparser.ConfigParser()\\n cfg.read(inifile)\\n\\n pp = {}\\n for s in cfg.sections():\\n section = s.encode('ascii', 'ignore')\\n pp[section] = {}\\n for k, v in cfg.items(section):\\n key = k.encode('ascii', 'ignore')\\n val = v.encode('ascii', 'ignore')\\n if section == 'General': # 'general' section\\n pp[section][key] = val\\n else:\\n pp[section][key] = float(val)\\n pp['General']['dt'] = float(pp['General']['dt'])\\n\\n pgen = pp['General']\\n pcpy = pp['CanopyGrid']\\n pbu = pp['BucketGrid']\\n ptop = pp['Topmodel']\\n\\n return pgen, pcpy, pbu, ptop\",\n \"def visualize_saliency_of_output(FLAGS, model, input_images=[]):\\n if len(input_images) == 0:\\n # use predefined images\\n img_dir='/esat/opal/kkelchte/docker_home/pilot_data/visualization_images'\\n input_images=sorted([img_dir+'/'+f for f in os.listdir(img_dir)])\\n\\n print(\\\"[tools.py]: extracting saliency maps of {0} in {1}\\\".format([os.path.basename(i) for i in input_images], os.path.dirname(input_images[0])))\\n \\n \\n inputs = load_images(input_images, model.input_size[1:])\\n\\n print 'shape: ',inputs.shape\\n\\n if 'nfc' in FLAGS.network:\\n inputs = np.concatenate([inputs]*FLAGS.n_frames, axis=-1)\\n \\n # extract deconvolution\\n import tf_cnnvis\\n\\n # layers = ['c']\\n # layers=['MobilenetV1_1/control/Conv2d_1c_1x1/Conv2D']\\n # layers=['MobilenetV1_1/control/Conv2d_1c_1x1/Conv2D','MobilenetV1_1/AvgPool_1a/AvgPool']\\n\\n # layers = [str(i.name) for i in model.sess.graph.get_operations() if 'outputs' in i.name and not 'activations' in i.name and not 'gradients' in i.name]\\n layers = [model.endpoints['eval']['outputs'].name[:-2]] #cut out :0 in the end to change name from tensor to operation name\\n # layers = ['outputs']\\n \\n # results = tf_cnnvis.activation_visualization(sess_graph_path = model.sess, \\n # value_feed_dict = {model.inputs : inputs}, \\n # layers=layers)\\n results = tf_cnnvis.deconv_visualization(sess_graph_path = model.sess, \\n value_feed_dict = {model.inputs : inputs}, \\n layers=layers)\\n\\n # Normalize deconvolution within 0:1 range\\n num_rows=0\\n clean_results={} \\n # Loop over layers\\n for k in results.keys():\\n clean_results[k]=[]\\n # Loop over channels\\n for c in range(len(results[k])):\\n num_rows+=1\\n clean_results[k].append(np.zeros((results[k][c].shape[0:3])))\\n # Loop over images\\n for i in range(results[k][c].shape[0]):\\n clean_results[k][c][i]=deprocess_image(results[k][c][i],one_channel=True)\\n if num_rows > 6:\\n print(\\\"[tools.py]: There are too many columns to create a proper image.\\\")\\n return\\n\\n # create one combined image with each input image on each column\\n fig, axes = plt.subplots(num_rows+1,min(len(input_images),5),figsize=(23, 4*(2*len(results.keys())+1)))\\n # fig, axes = plt.subplots(num_columns+1,min(len(input_images),5),figsize=(23, 4*(2*len(results.keys())+1)))\\n # add original images in first row\\n for i in range(axes.shape[1]):\\n axes[0, i].set_title(os.path.basename(input_images[i]).split('.')[0])\\n axes[0, i].imshow(matplotlibprove(inputs[i]), cmap='inferno')\\n axes[0, i].axis('off')\\n \\n # experts=np.asarray([[k]*(FLAGS.action_quantity if FLAGS.discrete else 1) for v in sorted(model.factor_offsets.values()) for k in model.factor_offsets.keys() if model.factor_offsets[k]==v]).flatten()\\n\\n # add deconvolutions over the columns\\n row_index = 1\\n for k in results.keys(): # go over layers\\n for c in range(len(results[k])): # add each channel in 2 new column\\n for i in range(axes.shape[1]): # fill row going over input images\\n # axes[row_index, i].set_title(k.split('/')[1]+'/'+k.split('/')[2]+'_'+str(c))\\n axes[row_index, i].set_title(k+'_'+str(c))\\n # axes[row_index, i].set_title(experts[c])\\n \\n axes[row_index, i].imshow(np.concatenate((inputs[i],np.expand_dims(clean_results[k][c][i],axis=2)), axis=2))\\n axes[row_index, i].axis('off')\\n # row_index+=2\\n row_index+=1\\n # plt.show()\\n plt.savefig(FLAGS.summary_dir+FLAGS.log_tag+'/saliency_maps.jpg',bbox_inches='tight')\",\n \"def mex_parameter_parser(self, mex_xml):\\n mex_inputs = mex_xml.xpath('tag[@name=\\\"inputs\\\"]')\\n if mex_inputs:\\n for tag in mex_inputs[0]:\\n if tag.tag == 'tag' and tag.attrib['type'] != 'system-input':\\n log.debug('Set options with %s as %s'%(tag.attrib['name'],tag.attrib['value']))\\n setattr(self.options,tag.attrib['name'],tag.attrib['value'])\\n else:\\n log.debug('BotanicamFS: No Inputs Found on MEX!')\",\n \"def process_aci_config(self, acicfgfile, is_cfg_struct=False, isj2=False, vars={}):\\n urlparams = {}\\n # if file is a j2 template\\n if isj2:\\n acicfgtemplate = Template(acicfgfile)\\n # render final yaml configuration file from j2 template\\n acicfgfile = acicfgtemplate.render(vars)\\n # print(acicfgfile)\\n if is_cfg_struct: # acicfgfile is in fact not a yaml file but a python data structure\\n acicfg = acicfgfile\\n else:\\n # convert yaml file into structured data\\n acicfg = self.yaml2struct(acicfgfile)\\n # process standard ACI config structure\\n if \\\"imdata\\\" in acicfg:\\n for subtree in acicfg[\\\"imdata\\\"]:\\n self.process_tree(subtree, urlparams)\\n # 'default\\\" names of parameters which are used in urlparams\\n # (aciapidesc)\\n # when full config tree is not specifies in aci_tree\\n # i.e. fvTenant is not specified\\n if \\\"url_names\\\" in acicfg:\\n urlparams = acicfg[\\\"url_names\\\"]\\n # extract variables from configuration file\\n # if vars var conigured in j2 template, they will be used in\\n # child files\\n if \\\"vars\\\" in acicfg:\\n for varitem in acicfg[\\\"vars\\\"].keys():\\n vars[varitem] = acicfg[\\\"vars\\\"][varitem]\\n # transofm final yaml config into structured data\\n # acicfg = self.yaml2struct(renderedcfg)\\n if (\\n \\\"aci_trees\\\" in acicfg and \\\"aci_items\\\" not in acicfg\\n ): # tree based configuration\\n for subtree in acicfg[\\\"aci_trees\\\"]:\\n self.process_tree(subtree, urlparams)\\n elif (\\\"aci_items\\\" in acicfg):\\n #self.aciitemcfg[\\\"aci_items\\\"] = acicfg[\\\"aci_items\\\"] # !!! THIS SHOULD BE DONE BETTER !!!\\n self.process_items_cfg(acicfg[\\\"aci_items\\\"])\\n # config file contains reference to another config file\\n if \\\"aci_cfgfiles\\\" in acicfg:\\n for filename in acicfg[\\\"aci_cfgfiles\\\"]:\\n # absolute path to the file\\n filename = os.path.join(self.configfiledir, filename)\\n filename = os.path.normpath(filename)\\n aci_cfg = self.read_configfile(filename)\\n # is filename j2 template ?\\n isj2 = filename.split(\\\".\\\")[-1] == \\\"j2\\\"\\n self.process_aci_config(aci_cfg, False, isj2, vars)\",\n \"def load_cls_params(self):\\n with open('models/Final/linear_svc.p', 'rb') as model_file:\\n model = pickle.load(model_file)\\n self.svc = model['svc']\\n self.X_scaler = model['X_scaler']\\n self.parameters = model['parameters']\\n\\n print(self.parameters)\",\n \"def setup(self):\\n\\n for name, infos in Rt.geom_dict.items():\\n if name in Rt.optim_var_dict:\\n self.add_input(name, val=infos[1][0])\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6243876","0.6197848","0.61577994","0.6136601","0.61252385","0.60175735","0.5826551","0.5813928","0.5751767","0.5715533","0.5710865","0.5692011","0.56493515","0.5560804","0.5558627","0.55206186","0.5514668","0.5512474","0.5510357","0.5506426","0.5503846","0.54862106","0.5482662","0.54141146","0.53913194","0.53725797","0.5369344","0.53609294","0.53330445","0.53189135","0.53158367","0.53113455","0.5277472","0.5272427","0.52617633","0.52615106","0.5259876","0.52548206","0.5254015","0.5242424","0.5241152","0.5228191","0.5221538","0.521251","0.5197557","0.5192576","0.5187683","0.5185795","0.5182152","0.5177049","0.5171649","0.51633465","0.51390344","0.51373917","0.5136592","0.5130044","0.5125069","0.51199996","0.511927","0.51145273","0.51139665","0.5113752","0.50967884","0.5088662","0.5085616","0.5083965","0.50697535","0.50616676","0.506109","0.5060486","0.50578713","0.50570124","0.5053991","0.5053988","0.5053689","0.5053498","0.5052577","0.50490266","0.50442404","0.50410384","0.5031835","0.5024321","0.502284","0.5021395","0.5021242","0.50103104","0.50085163","0.49998963","0.49929553","0.49924728","0.49901575","0.49863163","0.4986242","0.49841815","0.49820557","0.49799263","0.4972195","0.49706933","0.49697557","0.49692827"],"string":"[\n \"0.6243876\",\n \"0.6197848\",\n \"0.61577994\",\n \"0.6136601\",\n \"0.61252385\",\n \"0.60175735\",\n \"0.5826551\",\n \"0.5813928\",\n \"0.5751767\",\n \"0.5715533\",\n \"0.5710865\",\n \"0.5692011\",\n \"0.56493515\",\n \"0.5560804\",\n \"0.5558627\",\n \"0.55206186\",\n \"0.5514668\",\n \"0.5512474\",\n \"0.5510357\",\n \"0.5506426\",\n \"0.5503846\",\n \"0.54862106\",\n \"0.5482662\",\n \"0.54141146\",\n \"0.53913194\",\n \"0.53725797\",\n \"0.5369344\",\n \"0.53609294\",\n \"0.53330445\",\n \"0.53189135\",\n \"0.53158367\",\n \"0.53113455\",\n \"0.5277472\",\n \"0.5272427\",\n \"0.52617633\",\n \"0.52615106\",\n \"0.5259876\",\n \"0.52548206\",\n \"0.5254015\",\n \"0.5242424\",\n \"0.5241152\",\n \"0.5228191\",\n \"0.5221538\",\n \"0.521251\",\n \"0.5197557\",\n \"0.5192576\",\n \"0.5187683\",\n \"0.5185795\",\n \"0.5182152\",\n \"0.5177049\",\n \"0.5171649\",\n \"0.51633465\",\n \"0.51390344\",\n \"0.51373917\",\n \"0.5136592\",\n \"0.5130044\",\n \"0.5125069\",\n \"0.51199996\",\n \"0.511927\",\n \"0.51145273\",\n \"0.51139665\",\n \"0.5113752\",\n \"0.50967884\",\n \"0.5088662\",\n \"0.5085616\",\n \"0.5083965\",\n \"0.50697535\",\n \"0.50616676\",\n \"0.506109\",\n \"0.5060486\",\n \"0.50578713\",\n \"0.50570124\",\n \"0.5053991\",\n \"0.5053988\",\n \"0.5053689\",\n \"0.5053498\",\n \"0.5052577\",\n \"0.50490266\",\n \"0.50442404\",\n \"0.50410384\",\n \"0.5031835\",\n \"0.5024321\",\n \"0.502284\",\n \"0.5021395\",\n \"0.5021242\",\n \"0.50103104\",\n \"0.50085163\",\n \"0.49998963\",\n \"0.49929553\",\n \"0.49924728\",\n \"0.49901575\",\n \"0.49863163\",\n \"0.4986242\",\n \"0.49841815\",\n \"0.49820557\",\n \"0.49799263\",\n \"0.4972195\",\n \"0.49706933\",\n \"0.49697557\",\n \"0.49692827\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6741056"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":17888,"cells":{"query":{"kind":"string","value":"Dump sim's config_params, inputs, and outputs to YAML file By default, we overwrite to main YAML config file."},"document":{"kind":"string","value":"def _dump_config_to_yaml_file(self,\n sim_config_params = None,\n sim_inputs = None,\n sim_outputs = None,\n sim_other_vars = None,\n is_aux_yaml = False):\n\n if sim_config_params is None:\n sim_config_params = self.sim_config_params\n if sim_inputs is None:\n sim_inputs = self.sim_inputs\n if sim_outputs is None:\n sim_outputs = self.sim_outputs\n if sim_other_vars is None:\n sim_other_vars = self.sim_other_vars\n\n if not is_aux_yaml:\n config_file = self.sim_config_filepath\n else:\n config_file = self.sim_config_filepath.replace(\".yaml\", \"_EDIT.yaml\")\n\n # Prepare set of unused data ( to be shared with user for editing )\n full_sim_config = {\"config_params\": sim_config_params,\n \"inputs\": sim_inputs,\n \"outputs\": sim_outputs,\n \"other_vars\": sim_other_vars}\n full_sim_data = {\"simulation\": full_sim_config}\n\n # Dump configuration to YAML file for later reuse (or user editing if \"is_aux_yaml==True\")\n with open(config_file, 'w') as file:\n dump = yaml.dump(full_sim_data, sort_keys = False, default_flow_style=False)\n file.write( dump )\n\n # Raise error, and avoid continuing using model\n log = \"\\n[FMU Validator] A YAML file with bonsai required fields, as well as available \"\n log += \"sim variables, has been created at: \\n --> '{}'\\n\".format(config_file)\n \n if is_aux_yaml:\n log += \"[FMU Validator] Edit the YAML file, and remove the '_EDIT' nametag to use this model.\\n\"\n \n print(log)\n\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 save_config(_config, simulation_dir):\n with open(os.path.join(simulation_dir, 'config.yaml'), 'w') as f:\n yaml.dump(_config, f, default_flow_style=False)","def save():\n print(\"Saving config file..\")\n\n res = yaml.round_trip_dump(_conf, indent=2, block_seq_indent=1)\n\n with open(__config_file, 'w', encoding='utf-8') as stream:\n stream.write(res)","def _save_configuration_to_yml(self):\n data = self.get_configuration_data()\n timestamp = self.model.timestamp\n with open(os.path.join(CHECKPOINTS_DIR, timestamp, 'config_{}.yml'.format(timestamp)), 'w') as outfile:\n yaml.dump(dict(data), outfile, default_flow_style=False)","def write(self):\n print yaml.dump(self._config, default_flow_style=False),","def write(self):\n cfgpath = os.path.join(self.config_dir, CONFIG_FILENAME)\n ofile = open(cfgpath, 'w')\n if ofile:\n log.debug( \"Write config: %s\" % cfgpath )\n cfg = yaml.dump(self.yaml, default_flow_style=False)\n log.debug( \"Config:\\n%s\" % cfg)\n ofile.write(cfg)\n ofile.close()","def dump(self, config_file = 'config.yaml'):\n\n with open(config_file, 'w') as fp:\n yaml.dump(self.__dict__, fp)","def save(self):\n for p, c in self.configs_:\n c.write(p)","def dump(self, config):\n raise NotImplementedError","def dump(self):\n with open(self._config_filename, 'w', encoding='utf-8') as file:\n self._parser.write(file)","def save_config(conf, save_path):\n with open(os.path.join(save_path), \"w\") as f:\n f.write(yaml.dump({'param': conf}, default_flow_style=False))","def showconfig():\n print(yaml.dump(CONFIG))","def save(self) -> None:\n logger.info(\"Saving to config...\")\n yml.save(self._config, self.configpath)","def set_yaml_config(self) -> None:\n\n # LT-248: We can pick Artillery Phase configuration from conf file\n self.yaml_config = {\n \"config\": {\n \"target\": self.get_swagger_url(),\n \"processor\": f\"./{self.OUT_FILE}\",\n \"phases\": [\n {\n \"duration\": settings.DURATION or 1,\n \"arrivalRate\": settings.SPAWN_RATE or 1\n }\n ]\n },\n \"scenarios\": self.task_set.yaml_flow\n }","def dump_config_and_makefile(_config):\n experiment_dir = Path(_config['trainer']['storage_dir'])\n makefile_path = Path(experiment_dir) / \"Makefile\"\n\n if not makefile_path.exists():\n from padertorch.contrib.examples.source_separation.tasnet.templates import \\\n MAKEFILE_TEMPLATE_TRAIN\n\n config_path = experiment_dir / \"config.json\"\n pt.io.dump_config(_config, config_path)\n\n makefile_path.write_text(\n MAKEFILE_TEMPLATE_TRAIN.format(\n main_python_path=pt.configurable.resolve_main_python_path(),\n experiment_name=experiment_name,\n eval_python_path=('.'.join(\n pt.configurable.resolve_main_python_path().split('.')[:-1]\n ) + '.evaluate')\n )\n )","def test_dump_config(self):\n config = easydms.config.Config()\n print(config)","def save_config(self):\n config.save_config(self.config, self.config_file)","def run(self):\n write_config(self.filename)\n print('Wrote default config to', self.filename)","def save(config: dict, out_dir: str, filename: str = \"config.yaml\"):\n assert filename.endswith(\".yaml\")\n with open(os.path.join(out_dir, filename), \"w+\") as f:\n f.write(yaml.dump(config))","def _save_config_log(self, data):\n config_path = os.path.join(self.runtime.working_dir, '.config')\n with open(config_path, 'w') as f:\n yaml.safe_dump(data, f, default_flow_style=False)","def save_config(logdir, config):\n with open(os.path.join(logdir, 'config.yml'), 'w') as f:\n yaml.dump(config.__dict__, f)","def _dumpConfiguration(self):\n print \"Writing configuration:\"\n print \" - title = \" + self.title\n print \" - executablePath = \" + self.exePath\n print \" - configPath = \" + self.configPath\n print \" - outputName = \" + self.outputName\n print \" - branch = \" + self.branch\n print \" - branchName = \" + self.branchName\n print \" - buildid = \" + self.buildid\n print \" - currentDate = \" + self.currentDate\n print \" - testDate = \" + self.testDate\n print \" - resultsServer = \" + self.resultsServer\n print \" - resultsLink = \" + self.resultsLink\n print \" - activeTests = \" + self.activeTests\n if self.symbolsPath:\n print \" - symbolsPath = \" + self.symbolsPath","def updateconfig(self):\n\n # Initialize the yaml data\n ydata = {\"metadata\": self._metadata, \"nodes\": self._nodes}\n\n # Write the system config file\n filename = self._rootdir + self._metadata[\"system_config_file\"]\n with open(filename, \"w\") as yamlfile:\n yaml.dump(ydata, yamlfile)","def test_yaml(self):\n with utils.timer('loading'):\n config = serialization.load_file(join(EXAMPLES, 'complete.yml'))[0]\n s = simulation.from_config(config)\n with utils.timer('serializing'):\n serial = s.to_yaml()\n with utils.timer('recovering'):\n recovered = yaml.load(serial, Loader=yaml.SafeLoader)\n with utils.timer('deleting'):\n del recovered['topology']\n assert config == recovered","def _dump_tf_config(self):\n with open(os.path.join(self._tf_temp_dir.name, \"config.tf.json\"), \"w\") as f:\n json.dump(self.tf_config, f, indent=2)\n\n subprocess.run([\"cat\", os.path.join(self._tf_temp_dir.name, \"config.tf.json\")])\n\n self._init_tf() # Re-init post reconfiguration","def save_config(config: Dict[str, Any], path: str) -> None:\n\n with open(path, 'w+', encoding='utf-8') as stream:\n yaml.dump(config, stream, default_flow_style=False)","def to_yaml(self, **kwargs):\n if not self._is_graph_network:\n raise NotImplementedError\n\n if yaml is None:\n raise ImportError('Requires yaml module installed.')\n return yaml.dump(self._updated_config(), **kwargs)","def save():\n with open(CONFIG_FILE, 'w') as f:\n json.dump(config, f, indent=4, sort_keys=True)","def save(self):\r\n with open(self.filename, 'w') as f:\r\n if self.pretty:\r\n json.dump(self.__config, f, sort_keys=False,\r\n indent=4, separators=(',', ': '))\r\n else:\r\n json.dump(self.__config, f)","def save_config(self):\n\n if not self.__conf.has_section(self.section):\n self.__conf.add_section(self.section)\n\n for key in self._params:\n val = self._params[key]\n self.__conf.set(self.section, key, val)\n\n with open(self.conf_path, 'w') as f:\n self.__conf.write(f)","def dump_default_config():\n output = \"PythiaPlotter_config.py\"\n log.info(\"Dumping config to %s\", output)\n import pythiaplotter.default_config as dc\n shutil.copy(dc.__file__.replace(\".pyc\", \".py\"), output)","async def save_config(self):\n\n # Display info message\n log.info(\"save_config\")\n\n # Send command\n output = await self.send_command(self.cmd_save_config)\n\n # Return the commands of the configuration saving process\n return output","def save_config_file(self):\n wkdir = Path(self.config_dict[\"outputdir\"])\n config_filename = str(wkdir / f\"{self.config_dict['name']}.json\")\n save_config(self.config_dict, config_filename)","def saveConfig():\n with open(_CONFIG_FNM, 'w') as configfile:\n CONFIG_DICT.write(configfile,\n space_around_delimiters=True)","def save_config():\n # Order the load flags using load_keys...\n od_load_flags = OrderedDict()\n for k in load_keys:\n od_load_flags[k] = load_flags[k]\n pawstools.save_cfg(od_load_flags,cfg_file)","def pprint_config(config): # pragma: no cover\n yaml.Dumper.ignore_aliases = lambda *args: True\n print(yaml.dump(config, default_flow_style=False))","def save_config_file(self):\n with open(self.config_file_name, 'w',encoding='utf-8') as outfile:\n json.dump(self._config, outfile,indent=2)","def util_dump_config(conf_file=None, verbose=1, dryrun=False, **override):\n # xx this does not account for the effect of dryrun.\n print(json.dumps(config(conf_file=conf_file, verbose=verbose)))","def dump(filename, worflow_tests_config, file_format=FileFormats.YAML):\n workflows = {}\n config = worflow_tests_config.copy() if isinstance(worflow_tests_config, dict) else {}\n config[\"workflows\"] = workflows\n\n if isinstance(worflow_tests_config, dict):\n worflow_tests_config = worflow_tests_config[\"workflows\"].values()\n elif not isinstance(worflow_tests_config, list):\n raise ValueError(\n \"'workflow_tests_config' must be a configuration dict \"\n \"or a list of 'WorkflowTestCase' instances\")\n\n for worlflow in worflow_tests_config:\n workflows[worlflow.name] = worlflow.to_dict()\n with open(filename, \"w\") as f:\n if FileFormats.is_yaml(file_format):\n _yaml_dump(config, f)\n else:\n f.write(_json_dumps(config, indent=2))\n return config","def save(self):\n # Always write out components in alphabetical order for determinism,\n # especially in tests.\n for function_name in sorted(self._components.keys()):\n self._config_parser[_COMPONENTS_SECTION][\n function_name] = self._components[function_name]\n\n with open(str(self._config_filepath), 'w') as f:\n self._config_parser.write(f)","def dump_config(self):\n with open(path.join(HERE, 'navitron_crons.cfg'), 'r') as cfg_fh:\n base_config = cfg_fh.read()\n\n print(base_config)\n exit()","def write(self, file=sys.stdout):\n d = self.to_dict()\n if d:\n yaml.dump([d], file, default_flow_style=False)","def save_configuration(config):\n with open(cwd + '/configuration.pickle', 'wb') as handle:\n pickle.dump(config, handle, protocol=pickle.HIGHEST_PROTOCOL)","def test_config_yaml_dumps(get_config, write_config_fixture):\n cfg = get_config(Config, yaml_content_as_dict)\n cfg.write()\n\n with open(cfg.config_path, 'r') as fh:\n writed_content = fh.read()\n\n writed = cfg._yaml_load(writed_content)\n pure = cfg._yaml_load(yaml_content)\n assert writed == pure, f\"DID NOT MATCHED:\\n{writed}{'-'*80}{pure}\"","def save_config() -> None:\n with open(_config_file, \"w\", newline=\"\") as config_file:\n json.dump(_config, config_file, indent=4)\n config_file.truncate()","def save_config(ctx, config):\n log.info('Saving configuration')\n if ctx.archive is not None:\n with file(os.path.join(ctx.archive, 'config.yaml'), 'w') as f:\n yaml.safe_dump(ctx.config, f, default_flow_style=False)","def save(self):\n torch.save(self.state_dict(), self.checkpoint_path)\n with open(self.config_path, 'w') as f:\n print(self, file=f)","def test_config_device_save(get_config, config_dict):\n cfg = get_config(DeviceConfig, config_dict)\n cfg.save()\n with open(cfg.config_path, 'r') as fh:\n yml = yaml.load(fh.read(), Loader=get_yaml_loader())\n assert yml == config_dict, f'saved {yml} instead of {config_dict}'","def save(self):\n file = open(self.path, 'w')\n self.config.write(file)\n file.close()","async def save_config(self):\n\n # Display info message\n log.info(\"save_config\")\n\n # Send command to ask for saving config. Wait till the question to overwrite\n # the startup file (\"Overwrite file [startup-config].... (Y/N)[N] ?\")\n output = await self.send_command(self.cmd_save_config, pattern=\"?\")\n\n # Confirm to save the config\n output += await self.send_command(\"Y\")\n\n # Return the commands of the configuration saving process\n return output","def print_settings(self, title=None):\n if title:\n print(title)\n print('Contents of imagenode.yaml:')\n pprint.pprint(self.config)\n print()","def write_config(self):\n xshear_conf=XShearConfig(self['run'])\n xshear_conf.write()","def write_yaml(yaml_config: Dict[str, Any], filename: str) -> None:\n\n with open(filename, 'w') as outfile:\n yaml.dump(yaml_config, outfile, default_flow_style=False,\n sort_keys=False)","def writeConfig(self):\n\n qU.writeConfig(quickLogger=self.logger,\n\t\t curveDictionary=self.curveConf,\n\t\t likelihoodDictionary=self.likelihoodConf,\n commonDictionary=self.commonConf,\n analysisDictionary=self.analysisConf)","def roundtrip(verbose=False):\n pairs = ((path_tuple, {s: dict(cfg.items(s)) for s in cfg.sections()})\n for path_tuple, _, cfg in iterconfig())\n save(pairs, assume_changed=True, verbose=verbose)","def dump_trained_model_configs(\n output_dir: str, trained_cfgs: Dict[str, CfgNode]\n) -> Dict[str, str]:\n trained_model_configs = {}\n trained_model_config_dir = os.path.join(output_dir, \"trained_model_configs\")\n PathManager.mkdirs(trained_model_config_dir)\n for name, trained_cfg in trained_cfgs.items():\n config_file = os.path.join(trained_model_config_dir, \"{}.yaml\".format(name))\n trained_model_configs[name] = config_file\n if comm.is_main_process():\n logger.info(\"Dump trained config file: {}\".format(config_file))\n with PathManager.open(config_file, \"w\") as f:\n f.write(trained_cfg.dump())\n return trained_model_configs","def conf(self):\n if self.config:\n if self.config.exists():\n with open(self.config) as f:\n c: dict = dict(json.load(f))\n self.__dict__.update(c)\n\n else:\n with open(self.config, 'w') as f:\n c = dict()\n c['video_params'] = self.video_params\n c['encoder'] = self.encoder\n c['ffmpeg'] = self.ffmpeg\n c['audio_params'] = self.audio_params\n json.dump(c, f)\n\n # Changing pixel format, bit format\n self.pix_format = f'-strict -1 -pix_fmt {self.pix_format}'\n self.ffmpeg_pipe = f' {self.ffmpeg} {self.pix_format} -f yuv4mpegpipe - |'\n\n # Make sure that vmaf calculated after encoding\n if self.vmaf_target:\n self.vmaf = True\n\n if self.vmaf_path:\n if not Path(self.vmaf_path).exists():\n print(f'No such model: {Path(self.vmaf_path).as_posix()}')\n terminate()","def save(config, path=None):\n if path is None:\n path = settings.HOST_CONFIG_PATH\n\n with open(path, 'w') as output:\n output.write(yaml.safe_dump(config, default_flow_style=False))","def save(self):\r\n with open(self.filename, 'wb') as configfile:\r\n self.write(configfile)","def save_to_config(self) -> None:\n config_path = os.path.join(self.base_path, \"config.json\")\n\n with open(config_path, \"r\") as _json:\n c_dict = json.load(_json)\n\n c_dict[\"mean_similarity_error\"] = self.ME\n c_dict[\"similarity_correlation\"] = self.pearson_corr\n c_dict[\"similarity_spearman_correlation\"] = self.spearman_corr\n\n with open(config_path, \"w\") as _json:\n json.dump(c_dict, _json)","def save(self) -> bool:\n config_file = self.DEFAULT_CONFIG_LOCAL\n for filename in self.CONFIG_LOCAL:\n if os.path.isfile(filename):\n config_file = filename\n break\n\n with open(config_file, \"w\") as f:\n try:\n stream = yaml.dump(self.to_dict(), indent=2, default_flow_style=False)\n f.write(stream)\n\n except Exception as e:\n raise click.ClickException(\n f\"Error while saving config in {config_file}:\\n{str(e)}\"\n )\n return True","def _validate_sim_config(self):\n\n\n print(\"\\n[FMU Validator] ---- Looking to see if YAML config file exists ----\")\n\n # use convention to search for config file\n config_file = self.sim_config_filepath\n \n if not os.path.isfile(config_file):\n print(\"[FMU Validator] Configuration file for selected example was NOT found: {}\".format(config_file))\n return False\n\n print(\"[FMU Validator] Sim config file for selected example was found: {}\\n\".format(config_file))\n\n # Open and extract sim config from YAML file\n with open(config_file, 'r') as file:\n #data = yaml.dump(config_file, Loader=yaml.FullLoader)\n simulation_config = yaml.load(file, Loader=yaml.FullLoader)\n \n if 'simulation' not in simulation_config.keys():\n print(\"[FMU Validator] Configuration file for selected example does not have a 'simulation' tag, thus it is omited.\")\n return False\n\n # Extract sim configuration from dict\n sim_config_params = simulation_config['simulation']['config_params']\n sim_inputs = simulation_config['simulation']['inputs']\n sim_outputs = simulation_config['simulation']['outputs']\n sim_other_vars = simulation_config['simulation']['other_vars']\n\n # Validate values extracted\n if len(sim_inputs) == 0:\n print(\"[FMU Validator] Sim config file has no sim-input states, and thus cannot be used\\n\")\n elif len(sim_outputs) == 0:\n print(\"[FMU Validator] Sim config file has no sim-output states, and thus cannot be used\\n\")\n else:\n # Store data extracted as attributes\n self.sim_config_params = sim_config_params\n self.sim_inputs = sim_inputs\n self.sim_outputs = sim_outputs\n self.sim_other_vars = sim_other_vars\n return True\n\n return False","def save(self, filename=None, file_format=FileFormats.YAML):\n if not filename and not self.filename:\n filename = _os.path.splitext(self.DEFAULT_CONFIG_FILENAME)[0] + \\\n \"json\" if FileFormats.is_json(file_format) else \"yml\"\n if not filename:\n filename, _ = _os.path.splitext(self.filename)\n filename += \"json\" if FileFormats.is_json(file_format) else \"yml\"\n self.dump(filename=filename, worflow_tests_config=self.to_dict(), file_format=file_format)","def write_config_file(self):\n for opt, opt_desc in self.opt_dict.iteritems():\n if 'permanent' in opt_desc and opt_desc['permanent'] == True:\n enabled = 'Always'\n else:\n enabled = opt_desc['enabled'].__str__()\n\n self.file_parser.set(opt, 'enabled', enabled)\n self.file_parser.set(opt, 'implementation',\n opt_desc['selected_imp'])\n self.file_parser.set(opt, 'optype', opt_desc['imptype'])\n\n for config, config_desc in self.config_dict.iteritems():\n enabled = config_desc['enabled'].__str__()\n self.file_parser.set(config, 'enabled', enabled)\n\n scratch_file = self.config_filename + '.scratch'\n with open(scratch_file, 'w') as cfile:\n for config in sorted(self.config_dict.keys()):\n self.write_section(cfile, config)\n\n for opt in sorted(self.opt_dict.keys()):\n self.write_section(cfile, opt)\n\n for imp in sorted(self.imp2opt_dict.keys()):\n self.write_section(cfile, imp)\n\n cfile.write(\"\\n\")\n\n os.rename(scratch_file, self.config_filename)","def save():\n\n env.config.save(env.config_file)","def test_configuration_changes(self):\n config = serialization.load_file(join(EXAMPLES, 'complete.yml'))[0]\n s = simulation.from_config(config)\n for i in range(5):\n s.run_simulation(dry_run=True)\n nconfig = s.to_dict()\n del nconfig['topology']\n assert config == nconfig","def write_config():\n\n e = Element(\"Configuration\")\n r = SubElement(e, \"RepositoryList\")\n r = SubElement(r, \"Repository\", name = \"default\")\n SubElement(r, \"Module\").text = args.driver\n SubElement(r, \"TokenLabel\").text = args.token_label\n SubElement(r, \"PIN\").text = args.pin\n ElementTree(e).write(args.write_config)\n args.write_config.flush()","def print_cfg(self, out=stdout):\n print(self.cmaboss_sim.str_cfg(), file=out)","def write_config(self):\r\n obj = [\r\n [self.ip,\r\n self.gate,\r\n self.mask,\r\n self.name,\r\n self.time]\r\n ]\r\n with open('config.json', 'wt') as jsonfile:\r\n json.dump(obj, jsonfile)","def _dump_config(args):\n if not (args.raw or args.full or args.minimal):\n args.minimal = True\n\n if args.raw + args.full + args.minimal != 1:\n raise ValueError(\"Exactly one of --raw, --full, or --minimal must be set\")\n\n if args.raw and (args.include or args.exclude):\n raise ValueError(\n \"--include and --exclude cannot be used with --raw \"\n \"(use --full or --minimal instead).\"\n )\n\n config = Config()\n config_file = None\n if os.path.isdir(args.source):\n config_file = os.path.join(args.source, \"config.yaml\")\n config.load(config_file)\n elif \".yaml\" in os.path.split(args.source)[-1]:\n config_file = args.source\n config.load(config_file)\n else: # a checkpoint\n checkpoint_file = torch.load(args.source, map_location=\"cpu\")\n if args.raw:\n config = checkpoint_file[\"config\"]\n else:\n config.load_options(checkpoint_file[\"config\"].options)\n\n def print_options(options):\n # drop all arguments that are not included\n if args.include:\n args.include = set(args.include)\n options_copy = copy.deepcopy(options)\n for key in options_copy.keys():\n prefix = key\n keep = False\n while True:\n if prefix in args.include:\n keep = True\n break\n else:\n last_dot_index = prefix.rfind(\".\")\n if last_dot_index < 0:\n break\n else:\n prefix = prefix[:last_dot_index]\n if not keep:\n del options[key]\n\n # remove all arguments that are excluded\n if args.exclude:\n args.exclude = set(args.exclude)\n options_copy = copy.deepcopy(options)\n for key in options_copy.keys():\n prefix = key\n while True:\n if prefix in args.exclude:\n del options[key]\n break\n else:\n last_dot_index = prefix.rfind(\".\")\n if last_dot_index < 0:\n break\n else:\n prefix = prefix[:last_dot_index]\n\n # convert the remaining options to a Config and print it\n config = Config(load_default=False)\n config.set_all(options, create=True)\n print(yaml.dump(config.options))\n\n if args.raw:\n if config_file:\n with open(config_file, \"r\") as f:\n print(f.read())\n else:\n print_options(config.options)\n elif args.full:\n print_options(config.options)\n else: # minimal\n default_config = Config()\n imports = config.get(\"import\")\n if imports is not None:\n if not isinstance(imports, list):\n imports = [imports]\n for module_name in imports:\n default_config._import(module_name)\n default_options = Config.flatten(default_config.options)\n new_options = Config.flatten(config.options)\n minimal_options = {}\n\n for option, value in new_options.items():\n if option not in default_options or default_options[option] != value:\n minimal_options[option] = value\n\n print_options(minimal_options)","def saveConfig(self):\r\n self.config[\"Settings\"] = {}\r\n settings = self.config[\"Settings\"]\r\n settings[\"datapath\"] = self.dataPath\r\n settings[\"videopath\"] = self.videoPath\r\n settings[\"dataoffset\"] = str(self.dataOffset)\r\n settings[\"colblindmode\"] = str(self.colBlindMode)\r\n with open(self.CONFIG_FILE,\"w\") as file:\r\n self.config.write(file)","def writeConfig(self):\n targetFile = \"%s/%s\" % (self.workingDir, self.merge_pset_file)\n handle = open(targetFile, 'w')\n handle.write(self.mergeConfig())\n handle.close()\n return","def write_config_file(workflow_config, workflow_name):\n workflow_dir = \"{0}/.config/clx/{1}\".format(dirname, workflow_name)\n if not os.path.exists(workflow_dir):\n os.makedirs(workflow_dir)\n with open(workflow_dir + \"/workflow.yaml\", \"w\") as f:\n yaml.dump(workflow_config, f)","def save_config(self):\n with open(self.config_file, 'w') as fout:\n json.dump({'name_dict': self._name_dict, 'metric_dict': self._metric_dict, 'credential_path': self.credential_path, 'path_for_worksheet_name': self.path_for_worksheet_name}, fout)","def save(self):\n with open(self._CONFIG_FILE_PATH, 'w') as config_file:\n json.dump(vars(self), config_file)\n return self._CONFIG_FILE_PATH","def _get_sim_config_str(self):\n\n log = \"[FMU Validator] The set of configuration_parameters, inputs, and outputs defined is the following:\\n\"\n log += \"\\n{}: {}\".format(\"Sim Config Params -- Brain Config \", self.sim_config_params)\n log += \"\\n{}: {}\".format(\"Sim Inputs -- Brain Actions \", self.sim_inputs)\n log += \"\\n{}: {}\".format(\"Sim Outputs -- Brain States \", self.sim_outputs)\n log += \"\\n{}: {}\".format(\"Sim Other Vars -- Other Sim States \", self.sim_other_vars)\n \n return log","def save(self, filename):\n with open(filename, 'w') as f:\n yaml.dump(self.to_dict(), f, sort_keys=False)","def save(self):\n self.__config.sync()\n self.__saved = True\n Logger().debug(\"Configuration saved\")","def print_config(_run):\n final_config = _run.config\n config_mods = _run.config_modifications\n print(_format_config(final_config, config_mods))","def generate_config_mixed_first(self):\n\n for model_name in self.profile_models[-1:]:\n del self.config['profile_models'][model_name]\n with open('config-mixed-first.yml', 'w+') as f:\n yaml.dump(self.config, f)","def save(config):\n (fd, name) = tempfile.mkstemp()\n f = os.fdopen(fd, \"a\")\n f.write(yaml.dump(config))\n f.close()\n return name","def write(self):\n self.f.write(yaml.safe_dump(self.data, default_flow_style=False, indent=4))","def setup(self):\n\n default_config = self.read()\n\n self.write(default_config)","def save_settings():\n\n dont_save = ['VISIONEGG_CONFIG_FILE',\n 'VISIONEGG_SYSTEM_DIR',\n 'VISIONEGG_USER_DIR',\n ]\n\n if not VisionEgg.config.VISIONEGG_CONFIG_FILE:\n raise RuntimeError(\"No config file in use.\")\n re_setting_finder = re.compile(r\"^\\s?((?:VISIONEGG_[A-Z_]*)|(?:SYNCLYNC_[A-Z_]*))\\s?=\\s?(\\S*)\\s?$\",re.IGNORECASE)\n\n orig_file = open(VisionEgg.config.VISIONEGG_CONFIG_FILE,\"r\")\n orig_lines = orig_file.readlines()\n\n line_ending = orig_lines[0][-2:]\n if line_ending[0] not in ['\\r','\\n','\\l']:\n line_ending = line_ending[1]\n\n out_file_lines = []\n\n saved_config_vars = []\n\n for line in orig_lines:\n out_line = line # The output is the same as the input unless there's a match\n match = re_setting_finder.match(line)\n if match:\n name = match.group(1).upper()\n if name in VisionEgg.config.__dict__.keys():\n if name not in dont_save:\n # Change the output line\n out_line = (\"%s = %s\"%(name,getattr(VisionEgg.config,name,))) + line_ending\n saved_config_vars.append(name)\n out_file_lines.append(out_line)\n\n # Close and reopen orig_file in write mode\n orig_file.close()\n orig_file = open(VisionEgg.config.VISIONEGG_CONFIG_FILE,\"w\")\n for line in out_file_lines:\n orig_file.write(line)","def save_config_xml(self, fileName: str):\n self._sim.saveConfigXML(fileName)","def cfg_from_file(filename):\n with open(filename, \"r\") as f:\n yaml_cfg = yaml.load(f, Loader=yaml.SafeLoader)\n\n # Update the snapshot path to the corresponding path!\n trainpath = str(filename).split(\"pose_cfg.yaml\")[0]\n yaml_cfg[\"snapshot_prefix\"] = trainpath + \"snapshot\"\n # the default is: \"./snapshot\"\n\n # reloading defaults, as they can bleed over from a previous run otherwise\n cfg = dict()\n\n cfg['stride'] = 8.0\n cfg['weigh_part_predictions'] = False\n cfg['weigh_negatives'] = False\n cfg['fg_fraction'] = 0.25\n\n # imagenet mean for resnet pretraining:\n cfg['mean_pixel'] = [123.68, 116.779, 103.939]\n cfg['shuffle'] = True\n cfg['snapshot_prefix'] = \"./snapshot\"\n cfg['log_dir'] = \"log\"\n cfg['global_scale'] = 1.0\n cfg['location_refinement'] = False\n cfg['locref_stdev'] = 7.2801\n cfg['locref_loss_weight'] = 1.0\n cfg['locref_huber_loss'] = True\n cfg['optimizer'] = \"sgd\"\n cfg['intermediate_supervision'] = False\n cfg['intermediate_supervision_layer'] = 12\n cfg['regularize'] = False\n cfg['weight_decay'] = 0.0001\n cfg['crop_pad'] = 0\n cfg['scoremap_dir'] = \"test\"\n\n cfg['batch_size'] = 1\n\n # types of datasets, see factory: deeplabcut/pose_estimation_tensorflow/dataset/factory.py\n cfg['dataset_type'] = \"imgaug\" # >> imagaug default as of 2.2\n # you can also set this to deterministic, see https://github.com/AlexEMG/DeepLabCut/pull/324\n cfg['deterministic'] = False\n cfg['mirror'] = False\n\n # for DLC 2.2. (here all set False to not use PAFs/pairwise fields)\n cfg['pairwise_huber_loss'] = True\n cfg['weigh_only_present_joints'] = False\n cfg['partaffinityfield_predict'] = False\n cfg['pairwise_predict'] = False\n\n default_cfg = cfg\n _merge_a_into_b(yaml_cfg, default_cfg)\n\n logging.info(\"Config:\\n\" + pprint.pformat(default_cfg))\n return default_cfg # updated","def dump_config(self, agent_config: AgentConfig) -> None:\n agent_loader = self.loader()\n path = Path(DEFAULT_AEA_CONFIG_FILE)\n\n with path.open(mode=\"w\", encoding=\"utf-8\") as fp:\n agent_loader.dump(agent_config, fp)","def save_config(self, filename: str=None):\n if not filename:\n filename = self.config_file\n with open(filename, \"w\") as file_object:\n json.dump(self.config, file_object, indent=4, sort_keys=True)","def yaml_inventory(self):\n inventory_file = 'inventory_file'\n with open(inventory_file, 'w') as invfile:\n yaml.dump(self.inventory_dict, invfile, default_flow_style=False, sort_keys=False)","def saveConfig(self, name=None):\n\n configDir = self.mwGlob['configDir']\n\n if self.config.get('profileName', '') == 'config':\n if 'reference' in self.config:\n del self.config['reference']\n\n # default saving for reference\n if name is None:\n name = self.config.get('reference', 'config')\n\n fileName = configDir + '/' + name + '.cfg'\n with open(fileName, 'w') as outfile:\n json.dump(self.config,\n outfile,\n sort_keys=True,\n indent=4)\n # if we save a reference first, we have to save the config as well\n if name != 'config':\n fileName = configDir + '/config.cfg'\n with open(fileName, 'w') as outfile:\n json.dump(self.config,\n outfile,\n sort_keys=True,\n indent=4)\n return True","def write_config(self):\n logging.debug(\"Writing configuration file: %s\" % self.config_file)\n f = open(self.config_file, \"w\")\n self.config.write(f)\n f.close()","def save_cmake_config(self):\n # backup the original configuration set in setup.py\n self._init_config = dict(\n config=self.config,\n generator=self.generator,\n parallel=self.parallel,\n configure_args=self.configure_args,\n build_args=self.build_args,\n install_args=self.install_args,\n )","def save(self, filename=None):\n name = filename or self.filename\n with open(name, \"w\") as stream:\n yaml.dump(self.data, stream, default_flow_style=False)","def generateDefaultConfig(self):\n\n\t\t# Open config.ini in write mode\n\t\tf = open(self.fileName, \"w\")\n\n\t\t# Set keys to config object\n\t\tself.config.add_section(\"db\")\n\t\tself.config.set(\"db\", \"host\", \"localhost\")\n\t\tself.config.set(\"db\", \"username\", \"root\")\n\t\tself.config.set(\"db\", \"password\", \"\")\n\t\tself.config.set(\"db\", \"database\", \"ripple\")\n\t\tself.config.set(\"db\", \"pingtime\", \"600\")\n\n\t\tself.config.add_section(\"server\")\n\t\tself.config.set(\"server\", \"server\", \"tornado\")\n\t\tself.config.set(\"server\", \"host\", \"0.0.0.0\")\n\t\tself.config.set(\"server\", \"port\", \"5001\")\n\t\tself.config.set(\"server\", \"localizeusers\", \"1\")\n\t\tself.config.set(\"server\", \"outputpackets\", \"0\")\n\t\tself.config.set(\"server\", \"outputrequesttime\", \"0\")\n\t\tself.config.set(\"server\", \"timeoutlooptime\", \"100\")\n\t\tself.config.set(\"server\", \"timeouttime\", \"100\")\n\n\t\tself.config.add_section(\"flask\")\n\t\tself.config.set(\"flask\", \"threaded\", \"1\")\n\t\tself.config.set(\"flask\", \"debug\", \"0\")\n\t\tself.config.set(\"flask\", \"logger\", \"0\")\n\n\t\tself.config.add_section(\"ci\")\n\t\tself.config.set(\"ci\", \"key\", \"changeme\")\n\n\t\t# Write ini to file and close\n\t\tself.config.write(f)\n\t\tf.close()","def save(self):\n with open(self._config, 'w') as f:\n json.dump(self.data, f, indent=2, sort_keys=True)","def test_config_save_restore(self):\n\n config_filename_initial = 'test_configuration'\n config_filename_save = 'save_configuration'\n\n # Get config path\n local_dir = os.path.dirname(__file__)\n config_path_initial = os.path.join(local_dir, config_filename_initial)\n config_path_save = os.path.join(local_dir, config_filename_save)\n\n # Load initial configuration from file\n config_initial = neat.Config(neat.DefaultGenome, neat.DefaultReproduction,\n neat.DefaultSpeciesSet, neat.DefaultStagnation, config_path_initial)\n\n config1 = config_initial.genome_config\n names1 = [p.name for p in config1._params]\n for n in names1:\n assert hasattr(config1, n)\n\n # Save configuration to another file\n config_initial.save(config_path_save)\n\n # Obtain configuration from saved file\n config = neat.Config(neat.DefaultGenome, neat.DefaultReproduction,\n neat.DefaultSpeciesSet, neat.DefaultStagnation, config_path_save)\n\n config2 = config.genome_config\n names2 = [p.name for p in config2._params]\n for n in names2:\n assert hasattr(config2, n)\n\n self.assertEqual(names1, names2)\n\n for n in names1:\n v1 = getattr(config1, n)\n v2 = getattr(config2, n)\n self.assertEqual(v1, v2)","def __write_config(self):\n with open(self.config_file, 'w') as data_file:\n config = {\"ibooks_doc_root\":self.ibooks_doc_root,\n \"library_folder\":self.library_folder,\n \"annotation_folder\":self.annotation_folder,\n \"tmp_dir\":self.tmp_dir\n } \n data = json.dumps(config, ensure_ascii=False)\n data_file.write(data)","def config():\n experiment_dir = './experiments'\n simulation_steps = 1000\n device = 'cpu'\n path_to_molecules = os.path.join(experiment_dir, 'data/ethanol.xyz')\n simulation_dir = os.path.join(experiment_dir, 'simulation')\n training_dir = os.path.join(experiment_dir, 'training')\n model_path = os.path.join(training_dir, 'best_model')\n overwrite = True","def save(self, config_file: typing.TextIO):\n json.dump(self.to_dict(), config_file, indent=4)","def dump_yaml(self, data, output):\n yaml.indent(mapping=MAPPING, sequence=SEQUENCE, offset=OFFSET)\n yaml.dump(data, output)","def setup_dump(self):\n dumpdir = self._dump_dirname\n if not os.path.isdir(dumpdir):\n os.makedirs(dumpdir)\n\n dump = False\n yaml_filename = self._yaml_filename\n\n if not os.path.isfile(yaml_filename):\n dump = True\n else:\n with open(yaml_filename) as f:\n if f.read() != yaml.dump(self):\n logging.warning('Existing step.yaml does not match hash, regenerating')\n dump = True\n\n if dump:\n with open(yaml_filename, 'w') as f:\n yaml.dump(self, f)"],"string":"[\n \"def save_config(_config, simulation_dir):\\n with open(os.path.join(simulation_dir, 'config.yaml'), 'w') as f:\\n yaml.dump(_config, f, default_flow_style=False)\",\n \"def save():\\n print(\\\"Saving config file..\\\")\\n\\n res = yaml.round_trip_dump(_conf, indent=2, block_seq_indent=1)\\n\\n with open(__config_file, 'w', encoding='utf-8') as stream:\\n stream.write(res)\",\n \"def _save_configuration_to_yml(self):\\n data = self.get_configuration_data()\\n timestamp = self.model.timestamp\\n with open(os.path.join(CHECKPOINTS_DIR, timestamp, 'config_{}.yml'.format(timestamp)), 'w') as outfile:\\n yaml.dump(dict(data), outfile, default_flow_style=False)\",\n \"def write(self):\\n print yaml.dump(self._config, default_flow_style=False),\",\n \"def write(self):\\n cfgpath = os.path.join(self.config_dir, CONFIG_FILENAME)\\n ofile = open(cfgpath, 'w')\\n if ofile:\\n log.debug( \\\"Write config: %s\\\" % cfgpath )\\n cfg = yaml.dump(self.yaml, default_flow_style=False)\\n log.debug( \\\"Config:\\\\n%s\\\" % cfg)\\n ofile.write(cfg)\\n ofile.close()\",\n \"def dump(self, config_file = 'config.yaml'):\\n\\n with open(config_file, 'w') as fp:\\n yaml.dump(self.__dict__, fp)\",\n \"def save(self):\\n for p, c in self.configs_:\\n c.write(p)\",\n \"def dump(self, config):\\n raise NotImplementedError\",\n \"def dump(self):\\n with open(self._config_filename, 'w', encoding='utf-8') as file:\\n self._parser.write(file)\",\n \"def save_config(conf, save_path):\\n with open(os.path.join(save_path), \\\"w\\\") as f:\\n f.write(yaml.dump({'param': conf}, default_flow_style=False))\",\n \"def showconfig():\\n print(yaml.dump(CONFIG))\",\n \"def save(self) -> None:\\n logger.info(\\\"Saving to config...\\\")\\n yml.save(self._config, self.configpath)\",\n \"def set_yaml_config(self) -> None:\\n\\n # LT-248: We can pick Artillery Phase configuration from conf file\\n self.yaml_config = {\\n \\\"config\\\": {\\n \\\"target\\\": self.get_swagger_url(),\\n \\\"processor\\\": f\\\"./{self.OUT_FILE}\\\",\\n \\\"phases\\\": [\\n {\\n \\\"duration\\\": settings.DURATION or 1,\\n \\\"arrivalRate\\\": settings.SPAWN_RATE or 1\\n }\\n ]\\n },\\n \\\"scenarios\\\": self.task_set.yaml_flow\\n }\",\n \"def dump_config_and_makefile(_config):\\n experiment_dir = Path(_config['trainer']['storage_dir'])\\n makefile_path = Path(experiment_dir) / \\\"Makefile\\\"\\n\\n if not makefile_path.exists():\\n from padertorch.contrib.examples.source_separation.tasnet.templates import \\\\\\n MAKEFILE_TEMPLATE_TRAIN\\n\\n config_path = experiment_dir / \\\"config.json\\\"\\n pt.io.dump_config(_config, config_path)\\n\\n makefile_path.write_text(\\n MAKEFILE_TEMPLATE_TRAIN.format(\\n main_python_path=pt.configurable.resolve_main_python_path(),\\n experiment_name=experiment_name,\\n eval_python_path=('.'.join(\\n pt.configurable.resolve_main_python_path().split('.')[:-1]\\n ) + '.evaluate')\\n )\\n )\",\n \"def test_dump_config(self):\\n config = easydms.config.Config()\\n print(config)\",\n \"def save_config(self):\\n config.save_config(self.config, self.config_file)\",\n \"def run(self):\\n write_config(self.filename)\\n print('Wrote default config to', self.filename)\",\n \"def save(config: dict, out_dir: str, filename: str = \\\"config.yaml\\\"):\\n assert filename.endswith(\\\".yaml\\\")\\n with open(os.path.join(out_dir, filename), \\\"w+\\\") as f:\\n f.write(yaml.dump(config))\",\n \"def _save_config_log(self, data):\\n config_path = os.path.join(self.runtime.working_dir, '.config')\\n with open(config_path, 'w') as f:\\n yaml.safe_dump(data, f, default_flow_style=False)\",\n \"def save_config(logdir, config):\\n with open(os.path.join(logdir, 'config.yml'), 'w') as f:\\n yaml.dump(config.__dict__, f)\",\n \"def _dumpConfiguration(self):\\n print \\\"Writing configuration:\\\"\\n print \\\" - title = \\\" + self.title\\n print \\\" - executablePath = \\\" + self.exePath\\n print \\\" - configPath = \\\" + self.configPath\\n print \\\" - outputName = \\\" + self.outputName\\n print \\\" - branch = \\\" + self.branch\\n print \\\" - branchName = \\\" + self.branchName\\n print \\\" - buildid = \\\" + self.buildid\\n print \\\" - currentDate = \\\" + self.currentDate\\n print \\\" - testDate = \\\" + self.testDate\\n print \\\" - resultsServer = \\\" + self.resultsServer\\n print \\\" - resultsLink = \\\" + self.resultsLink\\n print \\\" - activeTests = \\\" + self.activeTests\\n if self.symbolsPath:\\n print \\\" - symbolsPath = \\\" + self.symbolsPath\",\n \"def updateconfig(self):\\n\\n # Initialize the yaml data\\n ydata = {\\\"metadata\\\": self._metadata, \\\"nodes\\\": self._nodes}\\n\\n # Write the system config file\\n filename = self._rootdir + self._metadata[\\\"system_config_file\\\"]\\n with open(filename, \\\"w\\\") as yamlfile:\\n yaml.dump(ydata, yamlfile)\",\n \"def test_yaml(self):\\n with utils.timer('loading'):\\n config = serialization.load_file(join(EXAMPLES, 'complete.yml'))[0]\\n s = simulation.from_config(config)\\n with utils.timer('serializing'):\\n serial = s.to_yaml()\\n with utils.timer('recovering'):\\n recovered = yaml.load(serial, Loader=yaml.SafeLoader)\\n with utils.timer('deleting'):\\n del recovered['topology']\\n assert config == recovered\",\n \"def _dump_tf_config(self):\\n with open(os.path.join(self._tf_temp_dir.name, \\\"config.tf.json\\\"), \\\"w\\\") as f:\\n json.dump(self.tf_config, f, indent=2)\\n\\n subprocess.run([\\\"cat\\\", os.path.join(self._tf_temp_dir.name, \\\"config.tf.json\\\")])\\n\\n self._init_tf() # Re-init post reconfiguration\",\n \"def save_config(config: Dict[str, Any], path: str) -> None:\\n\\n with open(path, 'w+', encoding='utf-8') as stream:\\n yaml.dump(config, stream, default_flow_style=False)\",\n \"def to_yaml(self, **kwargs):\\n if not self._is_graph_network:\\n raise NotImplementedError\\n\\n if yaml is None:\\n raise ImportError('Requires yaml module installed.')\\n return yaml.dump(self._updated_config(), **kwargs)\",\n \"def save():\\n with open(CONFIG_FILE, 'w') as f:\\n json.dump(config, f, indent=4, sort_keys=True)\",\n \"def save(self):\\r\\n with open(self.filename, 'w') as f:\\r\\n if self.pretty:\\r\\n json.dump(self.__config, f, sort_keys=False,\\r\\n indent=4, separators=(',', ': '))\\r\\n else:\\r\\n json.dump(self.__config, f)\",\n \"def save_config(self):\\n\\n if not self.__conf.has_section(self.section):\\n self.__conf.add_section(self.section)\\n\\n for key in self._params:\\n val = self._params[key]\\n self.__conf.set(self.section, key, val)\\n\\n with open(self.conf_path, 'w') as f:\\n self.__conf.write(f)\",\n \"def dump_default_config():\\n output = \\\"PythiaPlotter_config.py\\\"\\n log.info(\\\"Dumping config to %s\\\", output)\\n import pythiaplotter.default_config as dc\\n shutil.copy(dc.__file__.replace(\\\".pyc\\\", \\\".py\\\"), output)\",\n \"async def save_config(self):\\n\\n # Display info message\\n log.info(\\\"save_config\\\")\\n\\n # Send command\\n output = await self.send_command(self.cmd_save_config)\\n\\n # Return the commands of the configuration saving process\\n return output\",\n \"def save_config_file(self):\\n wkdir = Path(self.config_dict[\\\"outputdir\\\"])\\n config_filename = str(wkdir / f\\\"{self.config_dict['name']}.json\\\")\\n save_config(self.config_dict, config_filename)\",\n \"def saveConfig():\\n with open(_CONFIG_FNM, 'w') as configfile:\\n CONFIG_DICT.write(configfile,\\n space_around_delimiters=True)\",\n \"def save_config():\\n # Order the load flags using load_keys...\\n od_load_flags = OrderedDict()\\n for k in load_keys:\\n od_load_flags[k] = load_flags[k]\\n pawstools.save_cfg(od_load_flags,cfg_file)\",\n \"def pprint_config(config): # pragma: no cover\\n yaml.Dumper.ignore_aliases = lambda *args: True\\n print(yaml.dump(config, default_flow_style=False))\",\n \"def save_config_file(self):\\n with open(self.config_file_name, 'w',encoding='utf-8') as outfile:\\n json.dump(self._config, outfile,indent=2)\",\n \"def util_dump_config(conf_file=None, verbose=1, dryrun=False, **override):\\n # xx this does not account for the effect of dryrun.\\n print(json.dumps(config(conf_file=conf_file, verbose=verbose)))\",\n \"def dump(filename, worflow_tests_config, file_format=FileFormats.YAML):\\n workflows = {}\\n config = worflow_tests_config.copy() if isinstance(worflow_tests_config, dict) else {}\\n config[\\\"workflows\\\"] = workflows\\n\\n if isinstance(worflow_tests_config, dict):\\n worflow_tests_config = worflow_tests_config[\\\"workflows\\\"].values()\\n elif not isinstance(worflow_tests_config, list):\\n raise ValueError(\\n \\\"'workflow_tests_config' must be a configuration dict \\\"\\n \\\"or a list of 'WorkflowTestCase' instances\\\")\\n\\n for worlflow in worflow_tests_config:\\n workflows[worlflow.name] = worlflow.to_dict()\\n with open(filename, \\\"w\\\") as f:\\n if FileFormats.is_yaml(file_format):\\n _yaml_dump(config, f)\\n else:\\n f.write(_json_dumps(config, indent=2))\\n return config\",\n \"def save(self):\\n # Always write out components in alphabetical order for determinism,\\n # especially in tests.\\n for function_name in sorted(self._components.keys()):\\n self._config_parser[_COMPONENTS_SECTION][\\n function_name] = self._components[function_name]\\n\\n with open(str(self._config_filepath), 'w') as f:\\n self._config_parser.write(f)\",\n \"def dump_config(self):\\n with open(path.join(HERE, 'navitron_crons.cfg'), 'r') as cfg_fh:\\n base_config = cfg_fh.read()\\n\\n print(base_config)\\n exit()\",\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 save_configuration(config):\\n with open(cwd + '/configuration.pickle', 'wb') as handle:\\n pickle.dump(config, handle, protocol=pickle.HIGHEST_PROTOCOL)\",\n \"def test_config_yaml_dumps(get_config, write_config_fixture):\\n cfg = get_config(Config, yaml_content_as_dict)\\n cfg.write()\\n\\n with open(cfg.config_path, 'r') as fh:\\n writed_content = fh.read()\\n\\n writed = cfg._yaml_load(writed_content)\\n pure = cfg._yaml_load(yaml_content)\\n assert writed == pure, f\\\"DID NOT MATCHED:\\\\n{writed}{'-'*80}{pure}\\\"\",\n \"def save_config() -> None:\\n with open(_config_file, \\\"w\\\", newline=\\\"\\\") as config_file:\\n json.dump(_config, config_file, indent=4)\\n config_file.truncate()\",\n \"def save_config(ctx, config):\\n log.info('Saving configuration')\\n if ctx.archive is not None:\\n with file(os.path.join(ctx.archive, 'config.yaml'), 'w') as f:\\n yaml.safe_dump(ctx.config, f, default_flow_style=False)\",\n \"def save(self):\\n torch.save(self.state_dict(), self.checkpoint_path)\\n with open(self.config_path, 'w') as f:\\n print(self, file=f)\",\n \"def test_config_device_save(get_config, config_dict):\\n cfg = get_config(DeviceConfig, config_dict)\\n cfg.save()\\n with open(cfg.config_path, 'r') as fh:\\n yml = yaml.load(fh.read(), Loader=get_yaml_loader())\\n assert yml == config_dict, f'saved {yml} instead of {config_dict}'\",\n \"def save(self):\\n file = open(self.path, 'w')\\n self.config.write(file)\\n file.close()\",\n \"async def save_config(self):\\n\\n # Display info message\\n log.info(\\\"save_config\\\")\\n\\n # Send command to ask for saving config. Wait till the question to overwrite\\n # the startup file (\\\"Overwrite file [startup-config].... (Y/N)[N] ?\\\")\\n output = await self.send_command(self.cmd_save_config, pattern=\\\"?\\\")\\n\\n # Confirm to save the config\\n output += await self.send_command(\\\"Y\\\")\\n\\n # Return the commands of the configuration saving process\\n return output\",\n \"def print_settings(self, title=None):\\n if title:\\n print(title)\\n print('Contents of imagenode.yaml:')\\n pprint.pprint(self.config)\\n print()\",\n \"def write_config(self):\\n xshear_conf=XShearConfig(self['run'])\\n xshear_conf.write()\",\n \"def write_yaml(yaml_config: Dict[str, Any], filename: str) -> None:\\n\\n with open(filename, 'w') as outfile:\\n yaml.dump(yaml_config, outfile, default_flow_style=False,\\n sort_keys=False)\",\n \"def writeConfig(self):\\n\\n qU.writeConfig(quickLogger=self.logger,\\n\\t\\t curveDictionary=self.curveConf,\\n\\t\\t likelihoodDictionary=self.likelihoodConf,\\n commonDictionary=self.commonConf,\\n analysisDictionary=self.analysisConf)\",\n \"def roundtrip(verbose=False):\\n pairs = ((path_tuple, {s: dict(cfg.items(s)) for s in cfg.sections()})\\n for path_tuple, _, cfg in iterconfig())\\n save(pairs, assume_changed=True, verbose=verbose)\",\n \"def dump_trained_model_configs(\\n output_dir: str, trained_cfgs: Dict[str, CfgNode]\\n) -> Dict[str, str]:\\n trained_model_configs = {}\\n trained_model_config_dir = os.path.join(output_dir, \\\"trained_model_configs\\\")\\n PathManager.mkdirs(trained_model_config_dir)\\n for name, trained_cfg in trained_cfgs.items():\\n config_file = os.path.join(trained_model_config_dir, \\\"{}.yaml\\\".format(name))\\n trained_model_configs[name] = config_file\\n if comm.is_main_process():\\n logger.info(\\\"Dump trained config file: {}\\\".format(config_file))\\n with PathManager.open(config_file, \\\"w\\\") as f:\\n f.write(trained_cfg.dump())\\n return trained_model_configs\",\n \"def conf(self):\\n if self.config:\\n if self.config.exists():\\n with open(self.config) as f:\\n c: dict = dict(json.load(f))\\n self.__dict__.update(c)\\n\\n else:\\n with open(self.config, 'w') as f:\\n c = dict()\\n c['video_params'] = self.video_params\\n c['encoder'] = self.encoder\\n c['ffmpeg'] = self.ffmpeg\\n c['audio_params'] = self.audio_params\\n json.dump(c, f)\\n\\n # Changing pixel format, bit format\\n self.pix_format = f'-strict -1 -pix_fmt {self.pix_format}'\\n self.ffmpeg_pipe = f' {self.ffmpeg} {self.pix_format} -f yuv4mpegpipe - |'\\n\\n # Make sure that vmaf calculated after encoding\\n if self.vmaf_target:\\n self.vmaf = True\\n\\n if self.vmaf_path:\\n if not Path(self.vmaf_path).exists():\\n print(f'No such model: {Path(self.vmaf_path).as_posix()}')\\n terminate()\",\n \"def save(config, path=None):\\n if path is None:\\n path = settings.HOST_CONFIG_PATH\\n\\n with open(path, 'w') as output:\\n output.write(yaml.safe_dump(config, default_flow_style=False))\",\n \"def save(self):\\r\\n with open(self.filename, 'wb') as configfile:\\r\\n self.write(configfile)\",\n \"def save_to_config(self) -> None:\\n config_path = os.path.join(self.base_path, \\\"config.json\\\")\\n\\n with open(config_path, \\\"r\\\") as _json:\\n c_dict = json.load(_json)\\n\\n c_dict[\\\"mean_similarity_error\\\"] = self.ME\\n c_dict[\\\"similarity_correlation\\\"] = self.pearson_corr\\n c_dict[\\\"similarity_spearman_correlation\\\"] = self.spearman_corr\\n\\n with open(config_path, \\\"w\\\") as _json:\\n json.dump(c_dict, _json)\",\n \"def save(self) -> bool:\\n config_file = self.DEFAULT_CONFIG_LOCAL\\n for filename in self.CONFIG_LOCAL:\\n if os.path.isfile(filename):\\n config_file = filename\\n break\\n\\n with open(config_file, \\\"w\\\") as f:\\n try:\\n stream = yaml.dump(self.to_dict(), indent=2, default_flow_style=False)\\n f.write(stream)\\n\\n except Exception as e:\\n raise click.ClickException(\\n f\\\"Error while saving config in {config_file}:\\\\n{str(e)}\\\"\\n )\\n return True\",\n \"def _validate_sim_config(self):\\n\\n\\n print(\\\"\\\\n[FMU Validator] ---- Looking to see if YAML config file exists ----\\\")\\n\\n # use convention to search for config file\\n config_file = self.sim_config_filepath\\n \\n if not os.path.isfile(config_file):\\n print(\\\"[FMU Validator] Configuration file for selected example was NOT found: {}\\\".format(config_file))\\n return False\\n\\n print(\\\"[FMU Validator] Sim config file for selected example was found: {}\\\\n\\\".format(config_file))\\n\\n # Open and extract sim config from YAML file\\n with open(config_file, 'r') as file:\\n #data = yaml.dump(config_file, Loader=yaml.FullLoader)\\n simulation_config = yaml.load(file, Loader=yaml.FullLoader)\\n \\n if 'simulation' not in simulation_config.keys():\\n print(\\\"[FMU Validator] Configuration file for selected example does not have a 'simulation' tag, thus it is omited.\\\")\\n return False\\n\\n # Extract sim configuration from dict\\n sim_config_params = simulation_config['simulation']['config_params']\\n sim_inputs = simulation_config['simulation']['inputs']\\n sim_outputs = simulation_config['simulation']['outputs']\\n sim_other_vars = simulation_config['simulation']['other_vars']\\n\\n # Validate values extracted\\n if len(sim_inputs) == 0:\\n print(\\\"[FMU Validator] Sim config file has no sim-input states, and thus cannot be used\\\\n\\\")\\n elif len(sim_outputs) == 0:\\n print(\\\"[FMU Validator] Sim config file has no sim-output states, and thus cannot be used\\\\n\\\")\\n else:\\n # Store data extracted as attributes\\n self.sim_config_params = sim_config_params\\n self.sim_inputs = sim_inputs\\n self.sim_outputs = sim_outputs\\n self.sim_other_vars = sim_other_vars\\n return True\\n\\n return False\",\n \"def save(self, filename=None, file_format=FileFormats.YAML):\\n if not filename and not self.filename:\\n filename = _os.path.splitext(self.DEFAULT_CONFIG_FILENAME)[0] + \\\\\\n \\\"json\\\" if FileFormats.is_json(file_format) else \\\"yml\\\"\\n if not filename:\\n filename, _ = _os.path.splitext(self.filename)\\n filename += \\\"json\\\" if FileFormats.is_json(file_format) else \\\"yml\\\"\\n self.dump(filename=filename, worflow_tests_config=self.to_dict(), file_format=file_format)\",\n \"def write_config_file(self):\\n for opt, opt_desc in self.opt_dict.iteritems():\\n if 'permanent' in opt_desc and opt_desc['permanent'] == True:\\n enabled = 'Always'\\n else:\\n enabled = opt_desc['enabled'].__str__()\\n\\n self.file_parser.set(opt, 'enabled', enabled)\\n self.file_parser.set(opt, 'implementation',\\n opt_desc['selected_imp'])\\n self.file_parser.set(opt, 'optype', opt_desc['imptype'])\\n\\n for config, config_desc in self.config_dict.iteritems():\\n enabled = config_desc['enabled'].__str__()\\n self.file_parser.set(config, 'enabled', enabled)\\n\\n scratch_file = self.config_filename + '.scratch'\\n with open(scratch_file, 'w') as cfile:\\n for config in sorted(self.config_dict.keys()):\\n self.write_section(cfile, config)\\n\\n for opt in sorted(self.opt_dict.keys()):\\n self.write_section(cfile, opt)\\n\\n for imp in sorted(self.imp2opt_dict.keys()):\\n self.write_section(cfile, imp)\\n\\n cfile.write(\\\"\\\\n\\\")\\n\\n os.rename(scratch_file, self.config_filename)\",\n \"def save():\\n\\n env.config.save(env.config_file)\",\n \"def test_configuration_changes(self):\\n config = serialization.load_file(join(EXAMPLES, 'complete.yml'))[0]\\n s = simulation.from_config(config)\\n for i in range(5):\\n s.run_simulation(dry_run=True)\\n nconfig = s.to_dict()\\n del nconfig['topology']\\n assert config == nconfig\",\n \"def write_config():\\n\\n e = Element(\\\"Configuration\\\")\\n r = SubElement(e, \\\"RepositoryList\\\")\\n r = SubElement(r, \\\"Repository\\\", name = \\\"default\\\")\\n SubElement(r, \\\"Module\\\").text = args.driver\\n SubElement(r, \\\"TokenLabel\\\").text = args.token_label\\n SubElement(r, \\\"PIN\\\").text = args.pin\\n ElementTree(e).write(args.write_config)\\n args.write_config.flush()\",\n \"def print_cfg(self, out=stdout):\\n print(self.cmaboss_sim.str_cfg(), file=out)\",\n \"def write_config(self):\\r\\n obj = [\\r\\n [self.ip,\\r\\n self.gate,\\r\\n self.mask,\\r\\n self.name,\\r\\n self.time]\\r\\n ]\\r\\n with open('config.json', 'wt') as jsonfile:\\r\\n json.dump(obj, jsonfile)\",\n \"def _dump_config(args):\\n if not (args.raw or args.full or args.minimal):\\n args.minimal = True\\n\\n if args.raw + args.full + args.minimal != 1:\\n raise ValueError(\\\"Exactly one of --raw, --full, or --minimal must be set\\\")\\n\\n if args.raw and (args.include or args.exclude):\\n raise ValueError(\\n \\\"--include and --exclude cannot be used with --raw \\\"\\n \\\"(use --full or --minimal instead).\\\"\\n )\\n\\n config = Config()\\n config_file = None\\n if os.path.isdir(args.source):\\n config_file = os.path.join(args.source, \\\"config.yaml\\\")\\n config.load(config_file)\\n elif \\\".yaml\\\" in os.path.split(args.source)[-1]:\\n config_file = args.source\\n config.load(config_file)\\n else: # a checkpoint\\n checkpoint_file = torch.load(args.source, map_location=\\\"cpu\\\")\\n if args.raw:\\n config = checkpoint_file[\\\"config\\\"]\\n else:\\n config.load_options(checkpoint_file[\\\"config\\\"].options)\\n\\n def print_options(options):\\n # drop all arguments that are not included\\n if args.include:\\n args.include = set(args.include)\\n options_copy = copy.deepcopy(options)\\n for key in options_copy.keys():\\n prefix = key\\n keep = False\\n while True:\\n if prefix in args.include:\\n keep = True\\n break\\n else:\\n last_dot_index = prefix.rfind(\\\".\\\")\\n if last_dot_index < 0:\\n break\\n else:\\n prefix = prefix[:last_dot_index]\\n if not keep:\\n del options[key]\\n\\n # remove all arguments that are excluded\\n if args.exclude:\\n args.exclude = set(args.exclude)\\n options_copy = copy.deepcopy(options)\\n for key in options_copy.keys():\\n prefix = key\\n while True:\\n if prefix in args.exclude:\\n del options[key]\\n break\\n else:\\n last_dot_index = prefix.rfind(\\\".\\\")\\n if last_dot_index < 0:\\n break\\n else:\\n prefix = prefix[:last_dot_index]\\n\\n # convert the remaining options to a Config and print it\\n config = Config(load_default=False)\\n config.set_all(options, create=True)\\n print(yaml.dump(config.options))\\n\\n if args.raw:\\n if config_file:\\n with open(config_file, \\\"r\\\") as f:\\n print(f.read())\\n else:\\n print_options(config.options)\\n elif args.full:\\n print_options(config.options)\\n else: # minimal\\n default_config = Config()\\n imports = config.get(\\\"import\\\")\\n if imports is not None:\\n if not isinstance(imports, list):\\n imports = [imports]\\n for module_name in imports:\\n default_config._import(module_name)\\n default_options = Config.flatten(default_config.options)\\n new_options = Config.flatten(config.options)\\n minimal_options = {}\\n\\n for option, value in new_options.items():\\n if option not in default_options or default_options[option] != value:\\n minimal_options[option] = value\\n\\n print_options(minimal_options)\",\n \"def saveConfig(self):\\r\\n self.config[\\\"Settings\\\"] = {}\\r\\n settings = self.config[\\\"Settings\\\"]\\r\\n settings[\\\"datapath\\\"] = self.dataPath\\r\\n settings[\\\"videopath\\\"] = self.videoPath\\r\\n settings[\\\"dataoffset\\\"] = str(self.dataOffset)\\r\\n settings[\\\"colblindmode\\\"] = str(self.colBlindMode)\\r\\n with open(self.CONFIG_FILE,\\\"w\\\") as file:\\r\\n self.config.write(file)\",\n \"def writeConfig(self):\\n targetFile = \\\"%s/%s\\\" % (self.workingDir, self.merge_pset_file)\\n handle = open(targetFile, 'w')\\n handle.write(self.mergeConfig())\\n handle.close()\\n return\",\n \"def write_config_file(workflow_config, workflow_name):\\n workflow_dir = \\\"{0}/.config/clx/{1}\\\".format(dirname, workflow_name)\\n if not os.path.exists(workflow_dir):\\n os.makedirs(workflow_dir)\\n with open(workflow_dir + \\\"/workflow.yaml\\\", \\\"w\\\") as f:\\n yaml.dump(workflow_config, f)\",\n \"def save_config(self):\\n with open(self.config_file, 'w') as fout:\\n json.dump({'name_dict': self._name_dict, 'metric_dict': self._metric_dict, 'credential_path': self.credential_path, 'path_for_worksheet_name': self.path_for_worksheet_name}, fout)\",\n \"def save(self):\\n with open(self._CONFIG_FILE_PATH, 'w') as config_file:\\n json.dump(vars(self), config_file)\\n return self._CONFIG_FILE_PATH\",\n \"def _get_sim_config_str(self):\\n\\n log = \\\"[FMU Validator] The set of configuration_parameters, inputs, and outputs defined is the following:\\\\n\\\"\\n log += \\\"\\\\n{}: {}\\\".format(\\\"Sim Config Params -- Brain Config \\\", self.sim_config_params)\\n log += \\\"\\\\n{}: {}\\\".format(\\\"Sim Inputs -- Brain Actions \\\", self.sim_inputs)\\n log += \\\"\\\\n{}: {}\\\".format(\\\"Sim Outputs -- Brain States \\\", self.sim_outputs)\\n log += \\\"\\\\n{}: {}\\\".format(\\\"Sim Other Vars -- Other Sim States \\\", self.sim_other_vars)\\n \\n return log\",\n \"def save(self, filename):\\n with open(filename, 'w') as f:\\n yaml.dump(self.to_dict(), f, sort_keys=False)\",\n \"def save(self):\\n self.__config.sync()\\n self.__saved = True\\n Logger().debug(\\\"Configuration saved\\\")\",\n \"def print_config(_run):\\n final_config = _run.config\\n config_mods = _run.config_modifications\\n print(_format_config(final_config, config_mods))\",\n \"def generate_config_mixed_first(self):\\n\\n for model_name in self.profile_models[-1:]:\\n del self.config['profile_models'][model_name]\\n with open('config-mixed-first.yml', 'w+') as f:\\n yaml.dump(self.config, f)\",\n \"def save(config):\\n (fd, name) = tempfile.mkstemp()\\n f = os.fdopen(fd, \\\"a\\\")\\n f.write(yaml.dump(config))\\n f.close()\\n return name\",\n \"def write(self):\\n self.f.write(yaml.safe_dump(self.data, default_flow_style=False, indent=4))\",\n \"def setup(self):\\n\\n default_config = self.read()\\n\\n self.write(default_config)\",\n \"def save_settings():\\n\\n dont_save = ['VISIONEGG_CONFIG_FILE',\\n 'VISIONEGG_SYSTEM_DIR',\\n 'VISIONEGG_USER_DIR',\\n ]\\n\\n if not VisionEgg.config.VISIONEGG_CONFIG_FILE:\\n raise RuntimeError(\\\"No config file in use.\\\")\\n re_setting_finder = re.compile(r\\\"^\\\\s?((?:VISIONEGG_[A-Z_]*)|(?:SYNCLYNC_[A-Z_]*))\\\\s?=\\\\s?(\\\\S*)\\\\s?$\\\",re.IGNORECASE)\\n\\n orig_file = open(VisionEgg.config.VISIONEGG_CONFIG_FILE,\\\"r\\\")\\n orig_lines = orig_file.readlines()\\n\\n line_ending = orig_lines[0][-2:]\\n if line_ending[0] not in ['\\\\r','\\\\n','\\\\l']:\\n line_ending = line_ending[1]\\n\\n out_file_lines = []\\n\\n saved_config_vars = []\\n\\n for line in orig_lines:\\n out_line = line # The output is the same as the input unless there's a match\\n match = re_setting_finder.match(line)\\n if match:\\n name = match.group(1).upper()\\n if name in VisionEgg.config.__dict__.keys():\\n if name not in dont_save:\\n # Change the output line\\n out_line = (\\\"%s = %s\\\"%(name,getattr(VisionEgg.config,name,))) + line_ending\\n saved_config_vars.append(name)\\n out_file_lines.append(out_line)\\n\\n # Close and reopen orig_file in write mode\\n orig_file.close()\\n orig_file = open(VisionEgg.config.VISIONEGG_CONFIG_FILE,\\\"w\\\")\\n for line in out_file_lines:\\n orig_file.write(line)\",\n \"def save_config_xml(self, fileName: str):\\n self._sim.saveConfigXML(fileName)\",\n \"def cfg_from_file(filename):\\n with open(filename, \\\"r\\\") as f:\\n yaml_cfg = yaml.load(f, Loader=yaml.SafeLoader)\\n\\n # Update the snapshot path to the corresponding path!\\n trainpath = str(filename).split(\\\"pose_cfg.yaml\\\")[0]\\n yaml_cfg[\\\"snapshot_prefix\\\"] = trainpath + \\\"snapshot\\\"\\n # the default is: \\\"./snapshot\\\"\\n\\n # reloading defaults, as they can bleed over from a previous run otherwise\\n cfg = dict()\\n\\n cfg['stride'] = 8.0\\n cfg['weigh_part_predictions'] = False\\n cfg['weigh_negatives'] = False\\n cfg['fg_fraction'] = 0.25\\n\\n # imagenet mean for resnet pretraining:\\n cfg['mean_pixel'] = [123.68, 116.779, 103.939]\\n cfg['shuffle'] = True\\n cfg['snapshot_prefix'] = \\\"./snapshot\\\"\\n cfg['log_dir'] = \\\"log\\\"\\n cfg['global_scale'] = 1.0\\n cfg['location_refinement'] = False\\n cfg['locref_stdev'] = 7.2801\\n cfg['locref_loss_weight'] = 1.0\\n cfg['locref_huber_loss'] = True\\n cfg['optimizer'] = \\\"sgd\\\"\\n cfg['intermediate_supervision'] = False\\n cfg['intermediate_supervision_layer'] = 12\\n cfg['regularize'] = False\\n cfg['weight_decay'] = 0.0001\\n cfg['crop_pad'] = 0\\n cfg['scoremap_dir'] = \\\"test\\\"\\n\\n cfg['batch_size'] = 1\\n\\n # types of datasets, see factory: deeplabcut/pose_estimation_tensorflow/dataset/factory.py\\n cfg['dataset_type'] = \\\"imgaug\\\" # >> imagaug default as of 2.2\\n # you can also set this to deterministic, see https://github.com/AlexEMG/DeepLabCut/pull/324\\n cfg['deterministic'] = False\\n cfg['mirror'] = False\\n\\n # for DLC 2.2. (here all set False to not use PAFs/pairwise fields)\\n cfg['pairwise_huber_loss'] = True\\n cfg['weigh_only_present_joints'] = False\\n cfg['partaffinityfield_predict'] = False\\n cfg['pairwise_predict'] = False\\n\\n default_cfg = cfg\\n _merge_a_into_b(yaml_cfg, default_cfg)\\n\\n logging.info(\\\"Config:\\\\n\\\" + pprint.pformat(default_cfg))\\n return default_cfg # updated\",\n \"def dump_config(self, agent_config: AgentConfig) -> None:\\n agent_loader = self.loader()\\n path = Path(DEFAULT_AEA_CONFIG_FILE)\\n\\n with path.open(mode=\\\"w\\\", encoding=\\\"utf-8\\\") as fp:\\n agent_loader.dump(agent_config, fp)\",\n \"def save_config(self, filename: str=None):\\n if not filename:\\n filename = self.config_file\\n with open(filename, \\\"w\\\") as file_object:\\n json.dump(self.config, file_object, indent=4, sort_keys=True)\",\n \"def yaml_inventory(self):\\n inventory_file = 'inventory_file'\\n with open(inventory_file, 'w') as invfile:\\n yaml.dump(self.inventory_dict, invfile, default_flow_style=False, sort_keys=False)\",\n \"def saveConfig(self, name=None):\\n\\n configDir = self.mwGlob['configDir']\\n\\n if self.config.get('profileName', '') == 'config':\\n if 'reference' in self.config:\\n del self.config['reference']\\n\\n # default saving for reference\\n if name is None:\\n name = self.config.get('reference', 'config')\\n\\n fileName = configDir + '/' + name + '.cfg'\\n with open(fileName, 'w') as outfile:\\n json.dump(self.config,\\n outfile,\\n sort_keys=True,\\n indent=4)\\n # if we save a reference first, we have to save the config as well\\n if name != 'config':\\n fileName = configDir + '/config.cfg'\\n with open(fileName, 'w') as outfile:\\n json.dump(self.config,\\n outfile,\\n sort_keys=True,\\n indent=4)\\n return True\",\n \"def write_config(self):\\n logging.debug(\\\"Writing configuration file: %s\\\" % self.config_file)\\n f = open(self.config_file, \\\"w\\\")\\n self.config.write(f)\\n f.close()\",\n \"def save_cmake_config(self):\\n # backup the original configuration set in setup.py\\n self._init_config = dict(\\n config=self.config,\\n generator=self.generator,\\n parallel=self.parallel,\\n configure_args=self.configure_args,\\n build_args=self.build_args,\\n install_args=self.install_args,\\n )\",\n \"def save(self, filename=None):\\n name = filename or self.filename\\n with open(name, \\\"w\\\") as stream:\\n yaml.dump(self.data, stream, default_flow_style=False)\",\n \"def generateDefaultConfig(self):\\n\\n\\t\\t# Open config.ini in write mode\\n\\t\\tf = open(self.fileName, \\\"w\\\")\\n\\n\\t\\t# Set keys to config object\\n\\t\\tself.config.add_section(\\\"db\\\")\\n\\t\\tself.config.set(\\\"db\\\", \\\"host\\\", \\\"localhost\\\")\\n\\t\\tself.config.set(\\\"db\\\", \\\"username\\\", \\\"root\\\")\\n\\t\\tself.config.set(\\\"db\\\", \\\"password\\\", \\\"\\\")\\n\\t\\tself.config.set(\\\"db\\\", \\\"database\\\", \\\"ripple\\\")\\n\\t\\tself.config.set(\\\"db\\\", \\\"pingtime\\\", \\\"600\\\")\\n\\n\\t\\tself.config.add_section(\\\"server\\\")\\n\\t\\tself.config.set(\\\"server\\\", \\\"server\\\", \\\"tornado\\\")\\n\\t\\tself.config.set(\\\"server\\\", \\\"host\\\", \\\"0.0.0.0\\\")\\n\\t\\tself.config.set(\\\"server\\\", \\\"port\\\", \\\"5001\\\")\\n\\t\\tself.config.set(\\\"server\\\", \\\"localizeusers\\\", \\\"1\\\")\\n\\t\\tself.config.set(\\\"server\\\", \\\"outputpackets\\\", \\\"0\\\")\\n\\t\\tself.config.set(\\\"server\\\", \\\"outputrequesttime\\\", \\\"0\\\")\\n\\t\\tself.config.set(\\\"server\\\", \\\"timeoutlooptime\\\", \\\"100\\\")\\n\\t\\tself.config.set(\\\"server\\\", \\\"timeouttime\\\", \\\"100\\\")\\n\\n\\t\\tself.config.add_section(\\\"flask\\\")\\n\\t\\tself.config.set(\\\"flask\\\", \\\"threaded\\\", \\\"1\\\")\\n\\t\\tself.config.set(\\\"flask\\\", \\\"debug\\\", \\\"0\\\")\\n\\t\\tself.config.set(\\\"flask\\\", \\\"logger\\\", \\\"0\\\")\\n\\n\\t\\tself.config.add_section(\\\"ci\\\")\\n\\t\\tself.config.set(\\\"ci\\\", \\\"key\\\", \\\"changeme\\\")\\n\\n\\t\\t# Write ini to file and close\\n\\t\\tself.config.write(f)\\n\\t\\tf.close()\",\n \"def save(self):\\n with open(self._config, 'w') as f:\\n json.dump(self.data, f, indent=2, sort_keys=True)\",\n \"def test_config_save_restore(self):\\n\\n config_filename_initial = 'test_configuration'\\n config_filename_save = 'save_configuration'\\n\\n # Get config path\\n local_dir = os.path.dirname(__file__)\\n config_path_initial = os.path.join(local_dir, config_filename_initial)\\n config_path_save = os.path.join(local_dir, config_filename_save)\\n\\n # Load initial configuration from file\\n config_initial = neat.Config(neat.DefaultGenome, neat.DefaultReproduction,\\n neat.DefaultSpeciesSet, neat.DefaultStagnation, config_path_initial)\\n\\n config1 = config_initial.genome_config\\n names1 = [p.name for p in config1._params]\\n for n in names1:\\n assert hasattr(config1, n)\\n\\n # Save configuration to another file\\n config_initial.save(config_path_save)\\n\\n # Obtain configuration from saved file\\n config = neat.Config(neat.DefaultGenome, neat.DefaultReproduction,\\n neat.DefaultSpeciesSet, neat.DefaultStagnation, config_path_save)\\n\\n config2 = config.genome_config\\n names2 = [p.name for p in config2._params]\\n for n in names2:\\n assert hasattr(config2, n)\\n\\n self.assertEqual(names1, names2)\\n\\n for n in names1:\\n v1 = getattr(config1, n)\\n v2 = getattr(config2, n)\\n self.assertEqual(v1, v2)\",\n \"def __write_config(self):\\n with open(self.config_file, 'w') as data_file:\\n config = {\\\"ibooks_doc_root\\\":self.ibooks_doc_root,\\n \\\"library_folder\\\":self.library_folder,\\n \\\"annotation_folder\\\":self.annotation_folder,\\n \\\"tmp_dir\\\":self.tmp_dir\\n } \\n data = json.dumps(config, ensure_ascii=False)\\n data_file.write(data)\",\n \"def config():\\n experiment_dir = './experiments'\\n simulation_steps = 1000\\n device = 'cpu'\\n path_to_molecules = os.path.join(experiment_dir, 'data/ethanol.xyz')\\n simulation_dir = os.path.join(experiment_dir, 'simulation')\\n training_dir = os.path.join(experiment_dir, 'training')\\n model_path = os.path.join(training_dir, 'best_model')\\n overwrite = True\",\n \"def save(self, config_file: typing.TextIO):\\n json.dump(self.to_dict(), config_file, indent=4)\",\n \"def dump_yaml(self, data, output):\\n yaml.indent(mapping=MAPPING, sequence=SEQUENCE, offset=OFFSET)\\n yaml.dump(data, output)\",\n \"def setup_dump(self):\\n dumpdir = self._dump_dirname\\n if not os.path.isdir(dumpdir):\\n os.makedirs(dumpdir)\\n\\n dump = False\\n yaml_filename = self._yaml_filename\\n\\n if not os.path.isfile(yaml_filename):\\n dump = True\\n else:\\n with open(yaml_filename) as f:\\n if f.read() != yaml.dump(self):\\n logging.warning('Existing step.yaml does not match hash, regenerating')\\n dump = True\\n\\n if dump:\\n with open(yaml_filename, 'w') as f:\\n yaml.dump(self, f)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.730918","0.699196","0.68951195","0.67941695","0.6579302","0.6568991","0.6554705","0.64560366","0.64269376","0.64144593","0.6292432","0.6266346","0.6234814","0.62344337","0.62238747","0.617985","0.6164769","0.6161922","0.6141542","0.6134677","0.6123942","0.61237407","0.609949","0.60739744","0.60521775","0.60447997","0.6012628","0.6007977","0.5958405","0.5957956","0.5939652","0.5931556","0.59180033","0.59094524","0.5883508","0.5875645","0.5862373","0.5810528","0.58095396","0.578128","0.5748566","0.5748555","0.5741406","0.57403195","0.5737631","0.5736045","0.572872","0.5725916","0.5722297","0.57183623","0.5709431","0.5700812","0.5693068","0.5690034","0.56798035","0.56726027","0.5660145","0.5659227","0.56421876","0.56213814","0.56181645","0.5612221","0.56062967","0.56046325","0.56042135","0.56026006","0.5602406","0.56004685","0.5588491","0.5577636","0.55641687","0.5561558","0.5560723","0.5551363","0.55476236","0.55436224","0.5539202","0.5538033","0.5532047","0.5528563","0.5520654","0.55026734","0.5502586","0.55025303","0.5502063","0.5501807","0.5498267","0.5492757","0.5492223","0.5487145","0.54694635","0.5459264","0.5452511","0.5452262","0.5451841","0.5451311","0.54476494","0.5445401","0.5445292","0.5440807"],"string":"[\n \"0.730918\",\n \"0.699196\",\n \"0.68951195\",\n \"0.67941695\",\n \"0.6579302\",\n \"0.6568991\",\n \"0.6554705\",\n \"0.64560366\",\n \"0.64269376\",\n \"0.64144593\",\n \"0.6292432\",\n \"0.6266346\",\n \"0.6234814\",\n \"0.62344337\",\n \"0.62238747\",\n \"0.617985\",\n \"0.6164769\",\n \"0.6161922\",\n \"0.6141542\",\n \"0.6134677\",\n \"0.6123942\",\n \"0.61237407\",\n \"0.609949\",\n \"0.60739744\",\n \"0.60521775\",\n \"0.60447997\",\n \"0.6012628\",\n \"0.6007977\",\n \"0.5958405\",\n \"0.5957956\",\n \"0.5939652\",\n \"0.5931556\",\n \"0.59180033\",\n \"0.59094524\",\n \"0.5883508\",\n \"0.5875645\",\n \"0.5862373\",\n \"0.5810528\",\n \"0.58095396\",\n \"0.578128\",\n \"0.5748566\",\n \"0.5748555\",\n \"0.5741406\",\n \"0.57403195\",\n \"0.5737631\",\n \"0.5736045\",\n \"0.572872\",\n \"0.5725916\",\n \"0.5722297\",\n \"0.57183623\",\n \"0.5709431\",\n \"0.5700812\",\n \"0.5693068\",\n \"0.5690034\",\n \"0.56798035\",\n \"0.56726027\",\n \"0.5660145\",\n \"0.5659227\",\n \"0.56421876\",\n \"0.56213814\",\n \"0.56181645\",\n \"0.5612221\",\n \"0.56062967\",\n \"0.56046325\",\n \"0.56042135\",\n \"0.56026006\",\n \"0.5602406\",\n \"0.56004685\",\n \"0.5588491\",\n \"0.5577636\",\n \"0.55641687\",\n \"0.5561558\",\n \"0.5560723\",\n \"0.5551363\",\n \"0.55476236\",\n \"0.55436224\",\n \"0.5539202\",\n \"0.5538033\",\n \"0.5532047\",\n \"0.5528563\",\n \"0.5520654\",\n \"0.55026734\",\n \"0.5502586\",\n \"0.55025303\",\n \"0.5502063\",\n \"0.5501807\",\n \"0.5498267\",\n \"0.5492757\",\n \"0.5492223\",\n \"0.5487145\",\n \"0.54694635\",\n \"0.5459264\",\n \"0.5452511\",\n \"0.5452262\",\n \"0.5451841\",\n \"0.5451311\",\n \"0.54476494\",\n \"0.5445401\",\n \"0.5445292\",\n \"0.5440807\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.81717044"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":17889,"cells":{"query":{"kind":"string","value":"Get string with the sim's config_params, inputs, and outputs for the model"},"document":{"kind":"string","value":"def _get_sim_config_str(self):\n\n log = \"[FMU Validator] The set of configuration_parameters, inputs, and outputs defined is the following:\\n\"\n log += \"\\n{}: {}\".format(\"Sim Config Params -- Brain Config \", self.sim_config_params)\n log += \"\\n{}: {}\".format(\"Sim Inputs -- Brain Actions \", self.sim_inputs)\n log += \"\\n{}: {}\".format(\"Sim Outputs -- Brain States \", self.sim_outputs)\n log += \"\\n{}: {}\".format(\"Sim Other Vars -- Other Sim States \", self.sim_other_vars)\n \n return log"},"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_model_config(model_name, args):\n if model_name == 'Tacotron2':\n model_config = dict(\n # optimization\n mask_padding=args.mask_padding,\n # audio\n n_mel_channels=args.n_mel_channels,\n # symbols\n n_symbols=args.n_symbols,\n symbols_embedding_dim=args.symbols_embedding_dim,\n # encoder\n encoder_kernel_size=args.encoder_kernel_size,\n encoder_n_convolutions=args.encoder_n_convolutions,\n encoder_embedding_dim=args.encoder_embedding_dim,\n # attention\n attention_rnn_dim=args.attention_rnn_dim,\n attention_dim=args.attention_dim,\n # attention location\n attention_location_n_filters=args.attention_location_n_filters,\n attention_location_kernel_size=args.attention_location_kernel_size,\n # decoder\n n_frames_per_step=args.n_frames_per_step,\n decoder_rnn_dim=args.decoder_rnn_dim,\n prenet_dim=args.prenet_dim,\n max_decoder_steps=args.max_decoder_steps,\n gate_threshold=args.gate_threshold,\n p_attention_dropout=args.p_attention_dropout,\n p_decoder_dropout=args.p_decoder_dropout,\n # postnet\n postnet_embedding_dim=args.postnet_embedding_dim,\n postnet_kernel_size=args.postnet_kernel_size,\n postnet_n_convolutions=args.postnet_n_convolutions,\n decoder_no_early_stopping=args.decoder_no_early_stopping\n )\n return model_config\n elif model_name == 'WaveGlow':\n model_config = dict(\n n_mel_channels=args.n_mel_channels,\n n_flows=args.flows,\n n_group=args.groups,\n n_early_every=args.early_every,\n n_early_size=args.early_size,\n WN_config=dict(\n n_layers=args.wn_layers,\n kernel_size=args.wn_kernel_size,\n n_channels=args.wn_channels\n )\n )\n return model_config\n else:\n raise NotImplementedError(model_name)","def get_model_config(self, model_num=0):\n return [], resources.get_file(\n \"config/tests/methods/unsupervised/train_test.gin\")","def get_configuration_parameters_values(self):\n return (self.timestamp, self.model_name, self.model.WEIGHT_PATH, self.model.FIT_HISTORY_PATH,\n self.learning_rate, self.load_weights, self.freeze_layers, self.margin,\n self.hard_sampling_batch_size, self.batch_size, self.number_of_validation_imgs,\n self.input_shape)","def __str__(self):\n \n res = ['>>> Model %(model_name)s <<<']\n res.append('')\n res.append('Independent parameters:')\n res.append('-----------------------')\n res.append('')","def get_model_config(model_name, args):\n if model_name == 'WaveGlow':\n model_config = dict(\n n_mel_channels=args.n_mel_channels,\n n_flows=args.flows,\n n_group=args.groups,\n n_early_every=args.early_every,\n n_early_size=args.early_size,\n WN_config=dict(\n n_layers=args.wn_layers,\n kernel_size=args.wn_kernel_size,\n n_channels=args.wn_channels\n )\n )\n return model_config\n elif model_name == 'FastPitch':\n model_config = dict(\n # io\n n_mel_channels=args.n_mel_channels,\n # symbols\n n_symbols=len(get_symbols(args.symbol_set)),\n padding_idx=get_pad_idx(args.symbol_set),\n symbols_embedding_dim=args.symbols_embedding_dim,\n # input FFT\n in_fft_n_layers=args.in_fft_n_layers,\n in_fft_n_heads=args.in_fft_n_heads,\n in_fft_d_head=args.in_fft_d_head,\n in_fft_conv1d_kernel_size=args.in_fft_conv1d_kernel_size,\n in_fft_conv1d_filter_size=args.in_fft_conv1d_filter_size,\n in_fft_output_size=args.in_fft_output_size,\n p_in_fft_dropout=args.p_in_fft_dropout,\n p_in_fft_dropatt=args.p_in_fft_dropatt,\n p_in_fft_dropemb=args.p_in_fft_dropemb,\n # output FFT\n out_fft_n_layers=args.out_fft_n_layers,\n out_fft_n_heads=args.out_fft_n_heads,\n out_fft_d_head=args.out_fft_d_head,\n out_fft_conv1d_kernel_size=args.out_fft_conv1d_kernel_size,\n out_fft_conv1d_filter_size=args.out_fft_conv1d_filter_size,\n out_fft_output_size=args.out_fft_output_size,\n p_out_fft_dropout=args.p_out_fft_dropout,\n p_out_fft_dropatt=args.p_out_fft_dropatt,\n p_out_fft_dropemb=args.p_out_fft_dropemb,\n # duration predictor\n dur_predictor_kernel_size=args.dur_predictor_kernel_size,\n dur_predictor_filter_size=args.dur_predictor_filter_size,\n p_dur_predictor_dropout=args.p_dur_predictor_dropout,\n dur_predictor_n_layers=args.dur_predictor_n_layers,\n # pitch predictor\n pitch_predictor_kernel_size=args.pitch_predictor_kernel_size,\n pitch_predictor_filter_size=args.pitch_predictor_filter_size,\n p_pitch_predictor_dropout=args.p_pitch_predictor_dropout,\n pitch_predictor_n_layers=args.pitch_predictor_n_layers,\n # pitch conditioning\n pitch_embedding_kernel_size=args.pitch_embedding_kernel_size,\n # speakers parameters\n n_speakers=args.n_speakers,\n speaker_emb_weight=args.speaker_emb_weight,\n # energy predictor\n energy_predictor_kernel_size=args.energy_predictor_kernel_size,\n energy_predictor_filter_size=args.energy_predictor_filter_size,\n p_energy_predictor_dropout=args.p_energy_predictor_dropout,\n energy_predictor_n_layers=args.energy_predictor_n_layers,\n # energy conditioning\n energy_conditioning=args.energy_conditioning,\n energy_embedding_kernel_size=args.energy_embedding_kernel_size,\n )\n return model_config\n\n else:\n raise NotImplementedError(model_name)","def _get_model(self):\n\n parameters = {keys._topology:self.topology,\n keys._size:self.size,\n keys._name:self.name,\n #keys._output_activation:self._outActiv_fun_key,\n #keys._hidden_activation:self._hiddenActiv_fun_key,\n keys._learning_rate:self.learningRate,\n keys._momentum:self.momentum}\n\n return parameters","def config(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"config\")","def config(self):\n return {\"input_dims\": self.dims, \"output_dims\": self.output_dims, \"mapping\": self.mapping}","def get_config(ctx):\n global HISTORY_LOGS, EXPERIMENT_ID #Ugly hack, make it better at some point, may be ;)\n id = ctx.job.id\n EXPERIMENT_ID = hash(id)\n\n import montezuma_env\n\n ctx.job.register_action(\"Set starting point procssor:\",\n lambda str: set_motezuma_env_options(str, montezuma_env.STARTING_POINT_SELECTOR))\n ctx.job.register_action(\"Set rewards:\",\n lambda str: set_motezuma_env_options(str, montezuma_env.REWARDS_FILE))\n\n logger.auto_set_dir(suffix=id)\n\n # (self, parameters, number_of_actions, input_shape)\n\n M = EXPERIMENT_MODEL\n\n name_base = str(uuid.uuid1())[:6]\n PIPE_DIR = os.environ.get('TENSORPACK_PIPEDIR_{}'.format(id), '.').rstrip('/')\n namec2s = 'ipc://{}/sim-c2s-{}-{}'.format(PIPE_DIR, name_base, id)\n names2c = 'ipc://{}/sim-s2c-{}-{}'.format(PIPE_DIR, name_base, id)\n procs = [MySimulatorWorker(k, namec2s, names2c) for k in range(SIMULATOR_PROC)]\n ensure_proc_terminate(procs)\n start_proc_mask_signal(procs)\n\n master = MySimulatorMaster(namec2s, names2c, M)\n dataflow = BatchData(DataFromQueue(master.queue), BATCH_SIZE)\n\n # My stuff - PM\n neptuneLogger = NeptuneLogger.get_instance()\n lr = tf.Variable(0.001, trainable=False, name='learning_rate')\n tf.scalar_summary('learning_rate', lr)\n num_epochs = get_atribute(ctx, \"num_epochs\", 100)\n\n rewards_str = get_atribute(ctx, \"rewards\", \"5 1 -200\")\n with open(montezuma_env.REWARDS_FILE, \"w\") as file:\n file.write(rewards_str)\n\n\n if hasattr(ctx.params, \"learning_rate_schedule\"):\n schedule_str = str(ctx.params.learning_rate_schedule)\n else: #Default value inhereted from tensorpack\n schedule_str = \"[[80, 0.0003], [120, 0.0001]]\"\n logger.info(\"Setting learing rate schedule:{}\".format(schedule_str))\n learning_rate_scheduler = ScheduledHyperParamSetter('learning_rate', json.loads(schedule_str))\n\n if hasattr(ctx.params, \"entropy_beta_schedule\"):\n schedule_str = str(ctx.params.entropy_beta_schedule)\n else: #Default value inhereted from tensorpack\n schedule_str = \"[[80, 0.0003], [120, 0.0001]]\"\n logger.info(\"Setting entropy beta schedule:{}\".format(schedule_str))\n entropy_beta_scheduler = ScheduledHyperParamSetter('entropy_beta', json.loads(schedule_str))\n\n if hasattr(ctx.params, \"explore_factor_schedule\"):\n schedule_str = str(ctx.params.explore_factor_schedule)\n else: #Default value inhereted from tensorpack\n schedule_str = \"[[80, 2], [100, 3], [120, 4], [140, 5]]\"\n logger.info(\"Setting explore factor schedule:{}\".format(schedule_str))\n explore_factor_scheduler = ScheduledHyperParamSetter('explore_factor', json.loads(schedule_str))\n\n\n\n return TrainConfig(\n dataset=dataflow,\n optimizer=tf.train.AdamOptimizer(lr, epsilon=1e-3),\n callbacks=Callbacks([\n StatPrinter(), ModelSaver(),\n learning_rate_scheduler, entropy_beta_scheduler, explore_factor_scheduler,\n HumanHyperParamSetter('learning_rate'),\n HumanHyperParamSetter('entropy_beta'),\n HumanHyperParamSetter('explore_factor'),\n NeputneHyperParamSetter('learning_rate', ctx),\n NeputneHyperParamSetter('entropy_beta', ctx),\n NeputneHyperParamSetter('explore_factor', ctx),\n master,\n StartProcOrThread(master),\n PeriodicCallback(Evaluator(EVAL_EPISODE, ['state'], ['logits'], neptuneLogger, HISTORY_LOGS), 1),\n neptuneLogger,\n ]),\n session_config=get_default_sess_config(0.5),\n model=M,\n step_per_epoch=STEP_PER_EPOCH,\n max_epoch=num_epochs,\n )","def params():\n return utils.Params('../experiments/base-model/params.json')","def __repr__(self):\n\n # info string\n info = self.model.__repr__()\n info += \"\\n=========================\\n\"\n info += f\"Train data length:\\t\\t{ len(self.train_dataset) }\\n\"\n info += f\"Eval sata length:\\t\\t{ len(self.eval_dataset) }\\n\"\n info += f\"Optimizer:\\t\\t\\t\\t{ str(self.optimizer).split('(')[0] }\\n\"\n info += f\"Criterion:\\t\\t\\t\\t{ str(self.criterion).split('(')[0] }\\n\"\n info += f\"Training Environment:\\t{ self.device.type }\\n\"\n info += f\"Show information:\\t\\t{ 'True' if self.info else 'False' }\\n\"\n info += \"=========================\\n\"\n\n return info","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 print_network(self, model, name):\n num_params = 0\n for p in model.parameters():\n num_params += p.numel()\n #print(model)\n print(name)\n print(\"The number of parameters: {}\".format(num_params))\n\n with open(os.path.join(self.train_dir,'model_arch.txt'), 'a') as fp:\n print(model, file=fp)\n print(name, file=fp)\n print(\"The number of parameters: {}\".format(num_params),file=fp)","def info(self):\r\n\r\n return self.sim_info","def __str__(self):\n model_parameters = filter(lambda p: p.requires_grad, self.parameters())\n params = sum([np.prod(p.size()) for p in model_parameters])\n return super(BaseModel, self).__str__() + '\\nTrainable parameters: {}'.format(params)\n # print(super(BaseModel, self))","def extractModelParam(self):\n copasi_filename = self.genPathCopasi(\"extractor\")\n self.recentModel = model.loada(self.antString, copasi_filename)\n return self.recentModel.parameters.copy().squeeze().to_dict()","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 get_model_output(self):\n\n return self.model_output_file","def get_info_string(config):\n if config.train.folds is not None:\n return f\"folds_{config.train.folds}\"\n elif config.train.validation_portion is not None:\n return \"valportion_{}\".format(config.train.validation_portion)\n return \"\"","def make_params(config):\n params = copy.deepcopy(config.view.params)\n params.t2bins = np.arange(0, params.t2bin_max + 1e-4, params.t2bin_stepsize)\n params.out = make_Bunch(\"State and output of detection processing\") # outputs are not parameters, maybe separate \n return params","def get_input_info_from_cfg(deploy_cfg: mmengine.Config) -> Dict[str, List]:\n # The partition is not supported now. Set the id of model to 0.\n model_inputs = get_model_inputs(deploy_cfg)[0]\n input_info = model_inputs['opt_shapes']\n ir_config = get_ir_config(deploy_cfg)\n if ir_config is not None:\n input_names = ir_config.get('input_names', None)\n if input_names:\n if not isinstance(input_info, Dict):\n input_info = dict(zip(input_names, input_info))\n input_info = update_input_names(input_info, input_names)\n return input_info","def getParamsForWhizard(self, model):\n params = ''\n if model == 'sm':\n params = \"\"\"<GF type=\"float\" value=\"1.16639E-5\">\n<!-- Fermi constant -->\n</GF>\n<mZ type=\"float\" value=\"91.1882\">\n<!-- Z-boson mass -->\n</mZ>\n<mW type=\"float\" value=\"80.419\">\n<!-- W-boson mass -->\n</mW>\n<mH type=\"float\" value=\"120\">\n<!-- Higgs mass -->\n</mH>\n<alphas type=\"float\" value=\"0.1178\">\n<!-- Strong coupling constant alpha_s(MZ) -->\n</alphas>\n<me type=\"float\" value=\"0.\">\n<!-- electron mass -->\n</me>\n<mmu type=\"float\" value=\"0.1066\">\n<!-- muon mass -->\n</mmu>\n<mtau type=\"float\" value=\"1.777\">\n<!-- tau-lepton mass -->\n</mtau>\n<ms type=\"float\" value=\"0.\">\n<!-- s-quark mass -->\n</ms>\n<mc type=\"float\" value=\"0.54\">\n<!-- c-quark mass -->\n</mc>\n<mb type=\"float\" value=\"2.9\">\n<!-- b-quark mass -->\n</mb>\n<mtop type=\"float\" value=\"174\">\n<!-- t-quark mass -->\n</mtop>\n<wtop type=\"float\" value=\"1.523\">\n<!-- t-quark width -->\n</wtop>\n<wZ type=\"float\" value=\"2.443\">\n<!-- Z-boson width -->\n</wZ>\n<wW type=\"float\" value=\"2.049\">\n<!-- W-boson width -->\n</wW>\n<wH type=\"float\" value=\"0.3605E-02\">\n<!-- Higgs width -->\n</wH>\n<vckm11 type=\"float\" value=\"0.97383\">\n<!-- Vud -->\n</vckm11>\n<vckm12 type=\"float\" value=\"0.2272\">\n<!-- Vus -->\n</vckm12>\n<vckm13 type=\"float\" value=\"0.00396\">\n<!-- Vub -->\n</vckm13>\n<vckm21 type=\"float\" value=\"-0.2271\">\n<!-- Vcd -->\n</vckm21>\n<vckm22 type=\"float\" value=\"0.97296\">\n<!-- Vcs -->\n</vckm22>\n<vckm23 type=\"float\" value=\"0.04221\">\n<!-- Vcb -->\n</vckm23>\n<vckm31 type=\"float\" value=\"0.00814\">\n<!-- Vtd -->\n</vckm31>\n<vckm32 type=\"float\" value=\"-0.04161\">\n<!-- Vts -->\n</vckm32>\n<vckm33 type=\"float\" value=\"0.99910\">\n<!-- Vtb -->\n</vckm33>\n<khgaz type=\"float\" value=\"1.000\">\n<!-- anomaly Higgs coupling K factors -->\n</khgaz>\n<khgaga type=\"float\" value=\"1.000\">\n<!-- anomaly Higgs coupling K factors -->\n</khgaga>\n<khgg type=\"float\" value=\"1.000\">\n<!-- anomaly Higgs coupling K factors -->\n</khgg>\n\"\"\"\n else:\n params = \"\"\"\n \"\"\"\n return S_OK(params)","def get_configuration_parameters_names(self):\n return (\n 'timestamp', 'model_name', 'weight_path', 'fit_history_path', 'learning_rate', 'load_weights',\n 'freeze_layers', 'margin', 'hard_sampling_batch_size', 'batch_size',\n 'number_of_validation_imgs', 'input_shape')","def __str__(self):\n string = 'input dim: {} \\noutput dim: {} \\n'.format(\n self.dim_inputs, self.dim_outputs\n )\n string += 'sequence length: {} \\n'.format(\n self.tensors[0].shape[1]\n )\n key = 'train' if self.train else 'test'\n string += '{}_samples: {} \\n{}_sequences: {} \\n'.format(\n key, self.experiment_length, key, self.tensors[0].shape[0]\n )\n return string","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_parameter(config):\n\n selected_event = config['selected_event']\n datasource_raw_data = config['datasource_raw_data']['database']\n measurement_raw = config['datasource_raw_data']['measurement']\n measurement_enriched = config['datasource_enriched_data']['measurement']\n datasource_enriched_data = config['datasource_enriched_data']['database']\n datasource_marked_data = config['datasource_marked_data']['database']\n datasource_predicted_data = config['datasource_predicted_data']['database']\n start_time = config['timeframe'][0]\n end_time = config['timeframe'][1]\n register_dict = config['register_dict']\n required_registers = config[f\"{selected_event}_register\"]\n events = config[selected_event]\n measurement_predicted = config['datasource_predicted_data']['measurement']\n return selected_event, datasource_raw_data, measurement_raw, start_time, end_time, register_dict, \\\n required_registers, datasource_enriched_data, datasource_marked_data, \\\n measurement_enriched, events, datasource_predicted_data, measurement_predicted","def getModelParams(self):\n log.info(\"Importing model params from %s\" % self.modelParamsPath)\n moduleName = os.path.basename(self.modelParamsPath)\n importedModelParams = imp.load_source(moduleName, self.modelParamsPath)\n return importedModelParams.MODEL_PARAMS","def display_sim_parameters(self):\n pprint.pprint(vars(self))\n return","def configuration(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"configuration\")","def config():\n experiment_dir = './experiments'\n simulation_steps = 1000\n device = 'cpu'\n path_to_molecules = os.path.join(experiment_dir, 'data/ethanol.xyz')\n simulation_dir = os.path.join(experiment_dir, 'simulation')\n training_dir = os.path.join(experiment_dir, 'training')\n model_path = os.path.join(training_dir, 'best_model')\n overwrite = True","def _config(self):\r\n return (\r\n self.destructive,\r\n self.output_type,\r\n self.seed,\r\n )","def output_parameters(self):\n output_params = get_data_node(\n 'parameter',\n dict={\n 'stress': self.vasprun_obj.ionic_steps[-1]['stress'],\n 'efermi': self.vasprun_obj.efermi,\n 'energy': self.vasprun_obj.final_energy\n })\n return output_params","def get_model_info(y_test, model_name, model_parameters, model_preprocessing, sequence_origin, primers_origin,\n taxonomy_level, selected_primer, test_size, logger) -> int:\n\n # Global information on the model\n logger.log(title='Parameter information for {}'.format(model_name))\n # Data Origins\n logger.log(subtitle='Data Origins')\n logger.log(text='Sequence origin: {}'.format(sequence_origin))\n logger.log(text='Primers origin: {}'.format(primers_origin))\n # Chosen levels for classification\n logger.log(subtitle='Chosen HyperVariable Region and Taxonomy Rank')\n logger.log(text='HyperVariable Region: {}'.format(str(selected_primer)))\n logger.log(text='Taxonomy Rank: {}'.format(str(taxonomy_level)))\n # Applied Preprocessing\n logger.log(subtitle='Preprocessing')\n logger.log(text='Preprocessing description: ' + model_preprocessing)\n # Model parameters\n logger.log(subtitle='Model parameters')\n logger.log(text='Parameter dict: {}'.format(str(model_parameters)))\n logger.log(text='Size of test set: {}'.format(len(y_test)))\n logger.log(text='Part of test size compared to total: {}'.format(test_size))\n\n return len(y_test)","def get_config(parse=True, **optional_kwargs):\n parser = argparse.ArgumentParser()\n\n # Mode\n parser.add_argument('--mode', type=str, default='train')\n parser.add_argument('--runs', type=int, default=5)\n\n # Bert\n parser.add_argument('--use_bert', type=str2bool, default=True)\n parser.add_argument('--use_cmd_sim', type=str2bool, default=True)\n\n # Train\n time_now = datetime.now().strftime('%Y-%m-%d_%H:%M:%S')\n parser.add_argument('--name', type=str, default=f\"{time_now}\")\n parser.add_argument('--num_classes', type=int, default=0)\n parser.add_argument('--batch_size', type=int, default=128)\n parser.add_argument('--eval_batch_size', type=int, default=10)\n parser.add_argument('--n_epoch', type=int, default=500)\n parser.add_argument('--patience', type=int, default=6)\n\n parser.add_argument('--diff_weight', type=float, default=0.3)\n parser.add_argument('--sim_weight', type=float, default=1.0)\n parser.add_argument('--sp_weight', type=float, default=0.0)\n parser.add_argument('--recon_weight', type=float, default=1.0)\n\n parser.add_argument('--learning_rate', type=float, default=1e-4)\n parser.add_argument('--optimizer', type=str, default='Adam')\n parser.add_argument('--clip', type=float, default=1.0)\n\n parser.add_argument('--rnncell', type=str, default='lstm')\n parser.add_argument('--embedding_size', type=int, default=300)\n parser.add_argument('--hidden_size', type=int, default=128)\n parser.add_argument('--dropout', type=float, default=0.5)\n parser.add_argument('--reverse_grad_weight', type=float, default=1.0)\n # Selectin activation from 'elu', \"hardshrink\", \"hardtanh\", \"leakyrelu\", \"prelu\", \"relu\", \"rrelu\", \"tanh\"\n parser.add_argument('--activation', type=str, default='relu')\n\n # Model\n parser.add_argument('--model', type=str,\n default='MISA', help='one of {MISA, }')\n\n # Data\n parser.add_argument('--data', type=str, default='mosi')\n\n # Parse arguments\n if parse:\n kwargs = parser.parse_args()\n else:\n kwargs = parser.parse_known_args()[0]\n\n print(kwargs.data)\n if kwargs.data == \"mosi\":\n kwargs.num_classes = 1\n kwargs.batch_size = 64\n elif kwargs.data == \"mosei\":\n kwargs.num_classes = 1\n kwargs.batch_size = 16\n elif kwargs.data == \"ur_funny\":\n kwargs.num_classes = 2\n kwargs.batch_size = 32\n else:\n print(\"No dataset mentioned\")\n exit()\n\n # Namespace => Dictionary\n kwargs = vars(kwargs)\n kwargs.update(optional_kwargs)\n\n return Config(**kwargs)","async def get_model(self):\n\n # Display info message\n log.info(\"get_model\")\n\n # Get model\n output = await self.send_command(self.cmd_get_model)\n\n # Display info message\n log.info(f\"get_model: output: '{output}'\")\n\n # Remove the useless information in the returned string\n output = output.split('\"')[3]\n\n # Display info message\n log.info(f\"get_model: model found: '{output}'\")\n\n # Return the model of the device\n return output","def __str__(self):\n status = \"height = {}\\n\".format(self.height)\n status += \"width = {}\\n\".format(self.width)\n status += \"channels = {}\\n\".format(self.channels)\n status += \"input_dim = {}\\n\".format(self.input_dim)\n status += \"architecture = {}\\n\".format(self.architecture)\n status += \"activations = {}\\n\".format(self.activations)\n status += \"batch_size = {}\\n\".format(self.batch_size)\n status += \"epochs = {}\\n\".format(self.epochs)\n status += \"save_step = {}\\n\".format(self.save_step)\n status += \"learning_rate = {}\\n\".format(self.learning_rate)\n status += \"momentum = {}\\n\".format(self.momentum)\n return status","def __str__():\n return str(_config)","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 _build_output_dict_single(model):\n model_name = model.name\n\n param_dict = {}\n for name, value in zip(model.param_names, model.parameters):\n param_dict[name] = str(value)\n\n constraints = _build_constraints_dict(model)\n\n model_dict = {\n 'name': model_name,\n 'class': _get_model_class_name(model),\n 'parameters': param_dict,\n 'constraints': constraints}\n\n return model_name, model_dict","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 get_config(self):\n return {\"name\": self.name, \"tunable\": self.tunable}","def fetch_predictor_config():\n try:\n f = open(os.path.join(CONFIG['results_path'], CONFIG['predictor_model_dir'], 'config.csv'), 'r')\n f.readline()\n values = f.readline().split(',')\n f.close()\n return int(values[0]), str(values[2]).strip()\n except:\n # If this happens, check the path.\n print('ERROR in fetching predictor config. Default values used. These may fail.')\n return CONFIG['training_img_size'], CONFIG['training_set_image_type']","def config(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"config\")","def get_config(self):\n return {'reduction': self.reduction, 'name': self.name}","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_details(self):\n\n return {\n \"embedder\": \"GloVe\",\n \"algorithm\": \"RNN-LSTM\"\n }","def _extract_sim_config_from_fmi_std(self):\n \n print(\"\\n---- Looking to see if FMU model description contains required 'causality' type definitions ----\")\n\n sim_config_params = []\n sim_inputs = []\n sim_outputs = []\n sim_other_vars = []\n for variable in self.model_description.modelVariables:\n\n # extract causality and append valu\n causality = variable.causality\n\n if causality == \"parameter\":\n sim_config_params.append(variable.name)\n elif causality == \"input\":\n sim_inputs.append(variable.name)\n elif causality == \"output\":\n sim_outputs.append(variable.name)\n else:\n sim_other_vars.append(variable.name)\n \n # Validate values extracted\n if len(sim_inputs) == 0:\n print(\"\\n[FMU Validator] Sim FMU description file has no sim-input states, and thus cannot be used.\")\n elif len(sim_outputs) == 0:\n print(\"\\n[FMU Validator] Sim FMU description file has no sim-output states, and thus cannot be used.\")\n else:\n # Store data extracted as attributes\n self.sim_config_params = sim_config_params\n self.sim_inputs = sim_inputs\n self.sim_outputs = sim_outputs\n self.sim_other_vars = sim_other_vars\n return True\n\n\n # Dump auxiliary YMAL file for user to review/edit\n self._dump_config_to_yaml_file(sim_config_params,\n sim_inputs,\n sim_outputs,\n sim_other_vars,\n is_aux_yaml = True)\n\n return False","def sim(self):\n return self.mujoco_simulation.sim","def get_config_filepath(config: configs.Config) -> str:\n return os.path.join(config.model_training.dir_out, configs.DEFAULT_FILENAME_CONFIG)","def __str__(self) -> str:\n model_str = [\"\\nModel info:\\n\", \" Unimodal encoder:\\n\"]\n\n for modality in range(self.num_modalities):\n model_str.append(f\" ({modality + 1}) {self.unimodal_encoder[modality]}\")\n\n model_str.append(\"\\n\\n Unimodal decoder:\\n\")\n for modality in range(self.num_modalities):\n model_str.append(f\" ({modality + 1}) {self.unimodal_decoder[modality]}\")\n\n if self.multimodal_decoder is not None:\n model_str.append(\"\\n\\n Multimodal decoder:\\n\")\n model_str.append(f\" {self.multimodal_decoder}\")\n\n return \"\".join(model_str)","def get_model_filename(config):\n base = os.path.splitext(config['corpus'])[0]\n return '%s--%dT.model' % (base, config['T'])","def __str__(self) -> str:\n return (\n f\"GlacierFlowModel '{self.model_name}' \"\n f\"{'' if self.steady_state else 'not '}in steady state with:\"\n f\"\\n - m: {self.m:20.5f} [m/m]\"\n f\"\\n - ela: {self.ela:20.2f} [m MSL]\"\n f\"\\n - resolution: {self.res:20.2f} [m]\"\n f\"\\n - extent: min max\"\n f\"\\n {self.extent[0]:10.1f} \"\n f\"{self.extent[1]:10.1f} [x]\"\n f\"\\n {self.extent[2]:10.1f} \"\n f\"{self.extent[3]:10.1f} [y]\"\n )","def __str__(self):\n status = \"height = {}\\n\".format(self.height)\n status += \"width = {}\\n\".format(self.width)\n status += \"channels = {}\\n\".format(self.channels)\n status += \"architecture = {}\\n\".format(self.architecture)\n status += \"activations = {}\\n\".format(self.activations)\n status += \"conv_activations = {}\\n\".format(self.conv_activations)\n status += \"conv_architecture = {}\\n\".format(self.conv_architecture)\n status += \"kernel_sizes = {}\\n\".format(self.kernel_sizes)\n status += \"pool_kernel = {}\\n\".format(self.pool_kernel)\n status += \"batch_size = {}\\n\".format(self.batch_size)\n status += \"epochs = {}\\n\".format(self.epochs)\n status += \"save_step = {}\\n\".format(self.save_step)\n status += \"learning_rate = {}\\n\".format(self.learning_rate)\n status += \"momentum = {}\\n\".format(self.momentum)\n return status","def get_model_filepath(config: configs.Config) -> str:\n return os.path.join(config.model_training.dir_out, models.DEFAULT_FILENAME_MODEL)","def get_parameters(self): \n audio_emotions_topic = rospy.get_param(\"~audio_emotions_topic\")\n gcp_name = rospy.get_param(\"~gcp_name\")\n gcp_project = rospy.get_param(\"~gcp_project\")\n gcp_version = rospy.get_param(\"~gcp_version\")\n json_path = rospy.get_param(\"~json_path\")\n model_path = rospy.get_param(\"~model_path\")\n emotions_logfile = rospy.get_param(\"~emotions_logfile\")\n robot_ip = rospy.get_param(\"~robot_IP\")\n s2t_topic = rospy.get_param(\"~s2t_topic\")\n pred_mode = rospy.get_param(\"~pred_mode\")\n raw_audio_topic = rospy.get_param(\"~raw_audio_topic\")\n dest_num_channels = rospy.get_param(\"~dest_num_channels\")\n dest_rate = rospy.get_param(\"~dest_rate\")\n max_iter = rospy.get_param(\"~max_iter\") \n sound_path = rospy.get_param(\"~sound_path\")\n wav_topic = rospy.get_param(\"~wav_topic\")\n stats_logfile = rospy.get_param(\"~stats_logfile\")\n stats_topic = rospy.get_param(\"~stats_topic\")\n return (audio_emotions_topic, gcp_name, gcp_project, gcp_version, json_path, model_path, emotions_logfile, robot_ip, s2t_topic, pred_mode, raw_audio_topic, dest_num_channels, dest_rate, max_iter, sound_path, wav_topic, stats_logfile, stats_topic)","def describe_model():\n train_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES)\n msg = [\"\"]\n total = 0\n for v in train_vars:\n shape = v.get_shape()\n ele = shape.num_elements()\n total += ele\n msg.append(\"{}: shape={}, dim={}\".format(\n v.name, shape.as_list(), ele))\n size_mb = total * 4 / 1024.0**2\n msg.append(colored(\n \"Total param={} ({:01f} MB assuming all float32)\".format(total, size_mb), 'cyan'))\n logger.info(colored(\"Model Parameters: \", 'cyan') + '\\n'.join(msg))","def load_config():\n model_type, run_name, run_comment, epoch, verbose = get_args()\n name = run_name + '-' + run_comment\n if model_type == \"s2s\": \n run_title = \"seq2seq\"\n else:\n run_title = \"def2vec\"\n path = \"outputs/{}/logs/{}/config.json\".format(run_title, name)\n config = None\n with open(path) as f:\n config = dict(json.load(f))\n config = load_config(eval=True)\n return (config, name, model_type)","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 get(self):\n # Num frame, norm log probability and model name\n return (str(self.n_frame), str(self.best_log_probability[1]), str(self.best_log_probability[0]))","def get_config(self): # To support serialization\n return {\"mean\": self.mean, \"stddev\": self.stddev}","def config(self):\n return \"\\n\".join([ c.config(True) for p, c in self.configs_ ])","def get_model_configuration(request):\n text = text_cache['model_configuration']\n return HttpResponse(text, content_type='text/plain')","def get_config():\r\n config = argparser()\r\n\r\n # use the configurations to reproduce the experiments\r\n if not config.reproduct_mode == \"free\":\r\n config = reproduct_config(config)\r\n \r\n print(config)\r\n\r\n with open(config.out_dir + \"/test_results.txt\", \"w\") as txt_f:\r\n txt_f.write(str(config) + \"\\n\")\r\n\r\n return config","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 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 get_config(self):\n return {'name': self.name, 'dtype': self.dtype}","def _est_config(self):\n return self._est_method.config","def print_cfg(self, out=stdout):\n print(self.cmaboss_sim.str_cfg(), file=out)","def _config_str(config: Config) -> str:\n _C = config\n\n __C: CN = CN({\"RANDOM_SEED\": _C.random_seed})\n common_string: str = str(__C) + \"\\n\"\n\n return common_string","def config(self) -> Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]]:\n return pulumi.get(self, \"config\")","def config(self) -> Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]]:\n return pulumi.get(self, \"config\")","def parameters(self):\n return NeuralNetwork.flatten([module.parameters() for module in self.modules])","def __str__(self):\n return 'Tensor product {}: {} params, wires {}'.format([i.name for i in self.obs], len(self.params), self.wires)","def parameters(self):\n params = dict()\n try:\n params['voltage'] = self.voltage()\n params['output'] = self.output()\n return params\n except:\n return \"Disconnected\"","def _dump_config_to_yaml_file(self,\n sim_config_params = None,\n sim_inputs = None,\n sim_outputs = None,\n sim_other_vars = None,\n is_aux_yaml = False):\n\n if sim_config_params is None:\n sim_config_params = self.sim_config_params\n if sim_inputs is None:\n sim_inputs = self.sim_inputs\n if sim_outputs is None:\n sim_outputs = self.sim_outputs\n if sim_other_vars is None:\n sim_other_vars = self.sim_other_vars\n\n if not is_aux_yaml:\n config_file = self.sim_config_filepath\n else:\n config_file = self.sim_config_filepath.replace(\".yaml\", \"_EDIT.yaml\")\n\n # Prepare set of unused data ( to be shared with user for editing )\n full_sim_config = {\"config_params\": sim_config_params,\n \"inputs\": sim_inputs,\n \"outputs\": sim_outputs,\n \"other_vars\": sim_other_vars}\n full_sim_data = {\"simulation\": full_sim_config}\n\n # Dump configuration to YAML file for later reuse (or user editing if \"is_aux_yaml==True\")\n with open(config_file, 'w') as file:\n dump = yaml.dump(full_sim_data, sort_keys = False, default_flow_style=False)\n file.write( dump )\n\n # Raise error, and avoid continuing using model\n log = \"\\n[FMU Validator] A YAML file with bonsai required fields, as well as available \"\n log += \"sim variables, has been created at: \\n --> '{}'\\n\".format(config_file)\n \n if is_aux_yaml:\n log += \"[FMU Validator] Edit the YAML file, and remove the '_EDIT' nametag to use this model.\\n\"\n \n print(log)\n\n return","def _get_model(self) -> str:\n return str(self.hass.data[DOMAIN][self._config_entry.entry_id][ATTR_MODEL])","def get_config(self):\n\n # Use topological sort to get the correct order of modules.\n self.dag_topology_sort()\n mconfig = {}\n module_connection = {}\n for mod in self.mod_wrapper:\n # Generate pipeline configuration.\n mconf = {}\n output_conf = []\n module = self.mod_wrapper[mod]\n for _, binding in module.output_bindings.bindings.items():\n dep_conf = []\n output = {}\n if binding.bindings:\n for dep in binding.bindings:\n dep_item = {}\n _, dname = dep.get_name()\n if dep.is_pipeline_executor_interface():\n dep_item[\"global_output_index\"] = int(dname)\n else:\n dep_item[\"mod_idx\"] = dep.get_owner_idx()\n dep_item[\"input_name\"] = dname\n dep_conf.append(dep_item)\n\n # The value of output_idx start from 0.\n output[\"output_idx\"] = int(binding.name)\n output[\"dependencies\"] = dep_conf\n output_conf.append(output)\n\n mconf[\"mod_idx\"] = module.idx\n mconf[\"cpu_affinity\"] = module.cpu_affinity\n mconf[\"output\"] = output_conf\n\n module_connection[mod] = {\n \"pipeline\": mconf,\n \"target_host\": module.target_host,\n \"mod_name\": \"default\",\n \"build\": module.build_func,\n \"params\": module.params,\n \"target\": module.target,\n \"fcompile\": module.fcompile,\n \"dev\": module.dev,\n \"export_cc\": module.export_cc,\n }\n\n # Creating a map including pipeline inputs and subgraph inputs.\n input_connection = []\n for input_name in self.input_bindings.bindings:\n input_dict = self.input_bindings.bindings[input_name].get_binding_dict()\n if \"interface_name\" not in input_dict[\"connection\"][0]:\n raise RuntimeError(\"interface_name is missing in connection config!\")\n # Creating the map including global interfaces and subgraph interfaces.\n input_map = {\n \"global_interface_name\": input_dict[\"interface_name\"],\n \"mod_idx\": input_dict[\"connection\"][0][\"mod_idx\"],\n \"module_interface_name\": input_dict[\"connection\"][0][\"interface_name\"],\n }\n input_connection.append(input_map)\n\n # Create a map including global parameters groups and modules.\n param_connection = []\n for param_name in self.param_group_bindings.bindings:\n param_dict = self.param_group_bindings.bindings[param_name].get_binding_dict()\n param_map = {\n \"global_param_name\": param_dict[\"interface_name\"],\n \"mod_idx\": param_dict[\"connection\"][0][\"mod_idx\"],\n }\n param_connection.append(param_map)\n\n mconfig[\"module_connection\"] = module_connection\n mconfig[\"input_connection\"] = input_connection\n mconfig[\"param_connection\"] = param_connection\n return mconfig","def print_network(self, model, name):\r\n num_params = 0\r\n for p in model.parameters():\r\n num_params += p.numel()\r\n print(model)\r\n print(name)\r\n print(\"The number of parameters: {}\".format(num_params))","def print_network(self, model, name):\r\n num_params = 0\r\n for p in model.parameters():\r\n num_params += p.numel()\r\n print(model)\r\n print(name)\r\n print(\"The number of parameters: {}\".format(num_params))","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 config(self) -> pulumi.Input['ConfigArgs']:\n return pulumi.get(self, \"config\")","def print_network(self, model, name):\n num_params = 0\n for p in model.parameters():\n num_params += p.numel()\n print(model)\n print(name)\n print(\"The number of parameters: {}\".format(num_params))","def print_network(self, model, name):\n num_params = 0\n for p in model.parameters():\n num_params += p.numel()\n print(model)\n print(name)\n print(\"The number of parameters: {}\".format(num_params))","def print_network(self, model, name):\n num_params = 0\n for p in model.parameters():\n num_params += p.numel()\n print(model)\n print(name)\n print(\"The number of parameters: {}\".format(num_params))","def print_network(self, model, name):\n num_params = 0\n for p in model.parameters():\n num_params += p.numel()\n print(model)\n print(name)\n print(\"The number of parameters: {}\".format(num_params))","def print_network(self, model, name):\n num_params = 0\n for p in model.parameters():\n num_params += p.numel()\n print(\"\\nModel Name: \\\"{}\\\"\".format(name))\n print(model)\n print(\"The number of parameters: {}\".format(num_params))","def print_network(self, model, name):\n num_params = 0\n for p in model.parameters():\n num_params += p.numel()\n print(\"\\nModel Name: \\\"{}\\\"\".format(name))\n print(model)\n print(\"The number of parameters: {}\".format(num_params))","def __str__(self):\n return \"{}: {} params, wires {}\".format(self.name, len(self.params), self.wires)","def export_model_config_with_inputs(self, inputs):\n structure_exporter_tensors = self.sess.run(\n self.exporter_eval.tensors, feed_dict={self.inputs: inputs})\n self.exporter_eval.populate_tensor_values(structure_exporter_tensors)\n path = self.exporter_eval.create_file_and_save_alive_counts(\n self.log_dir, self.global_step)\n return path","def print_network(self, model, name):\n num_params = 0\n for p in model.parameters():\n num_params += p.numel()\n print(model)\n print(name)\n print(\"The number of parameters: {}\".format(num_params))","def config(self) -> pulumi.Output['outputs.ConfigResponse']:\n return pulumi.get(self, \"config\")","def get_model_parameter_names():\n params = ['mu', 'rho']\n return params","def __str__(self):\n d = {}\n d[\"tuner_number\"] = self.tuner_number\n d[\"output_format\"] = self.output_format\n d[\"output_source\"] = self.output_source\n return str(d)","def __str__(self):\n return ' '.join([self.source, self.name, str(self.outputs)])","def get_model_params(self):\n w1 = self.w1\n b1 = self.b1\n w2 = self.w2\n b2 = self.b2\n w3 = self.w3\n b3 = self.b3\n w4 = self.w4\n b4 = self.b4\n w5 = self.w5\n b5 = self.b5\n w6 = self.w6\n b6 = self.b6\n\n return w1, b1, w2, b2, w3, b3, w4, b4, w5, b5, w6, b6","def __str__(self):\n\n print(\"\")\n s = \"NAME : \"+self._name+\"\\n\\n\"\n s += \"PARAMS :\"\n print(s)\n\n for key, val in self.params.items():\n l = (21-len(key))//7\n print(\"{0}\".format(key)+\"\\t\"*l+\":\\t{0}\".format(val))\n\n s = \"\\nRuns stored in DEFAULT_RUNS = \"+str(len(self.default_runs))\n print(s)\n\n s = \"\\nRuns stored in MOD_RUNS = \"+str(len(self.mod_runs))\n print(s)\n\n return \"\"","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 getConfig(self):\n pass","def GetModelDefinitionString(filename, model, forhpp):\n #Get the model by reading from the filename\n\n #Size is defined by the number of ODEs\n odes_dict = translator.GetOdesDictionary(model)\n rules_dict = translator.GetRulesDictionary(model)\n num_species = len(odes_dict) + len(rules_dict)\n\n model_defn_string = \"\"\n\n ode_name = GetOdeSystemName(filename)\n model_name = GetModelName(filename, model)\n\n #Define some common names depending on whether or not the model is a SRN or a CCM\n is_srn_model = translator.IsSrnModel(model)\n if (is_srn_model):\n wrapper = \"SbmlSrnWrapperModel\"\n\n model_defn_string += translator.GetBlockCommentDefinition(0, \"Define SRN model using Wrappers.\", True)\n else:\n wrapper = \"SbmlCcmWrapperModel\"\n\n model_defn_string += translator.GetBlockCommentDefinition(0, \"Define cell cycle model using Wrappers.\", True)\n\n model_defn_string += (IncludeString(\"\\\"\" + wrapper + \".hpp\\\"\") + \n IncludeString(\"\\\"\" + wrapper + \".cpp\\\"\") + \"\\n\" + \n \"typedef \" + wrapper + \"<\" + ode_name + \",\" + str(num_species) + \"> \" + model_name + \";\\n\" + \n \"\\n\")\n\n model_defn_string += translator.GetCommentDefinition(0, \"Declare identifiers for the serializer\", True)\n if ( forhpp ):\n model_defn_string += IncludeString(\"\\\"SerializationExportWrapper.hpp\\\"\")\n else:\n model_defn_string += IncludeString(\"\\\"SerializationExportWrapperForCpp.hpp\\\"\")\n\n model_defn_string += (\"CHASTE_CLASS_EXPORT(\" + ode_name + \")\\n\" + \n \"EXPORT_TEMPLATE_CLASS2(\" + wrapper + \", \" + ode_name + \", \" + str(num_species) + \")\\n\" + \n \"\\n\" + \n IncludeString(\"\\\"CellCycleModelOdeSolverExportWrapper.hpp\\\"\") + \n \"EXPORT_CELL_CYCLE_MODEL_ODE_SOLVER(\" + model_name + \")\\n\\n\" )\n\n return model_defn_string","def __str__(self) -> str:\n st = \"<Output> \"\n if self.inst_out:\n st += f'instance:{self.inst_out};'\n st += f'''{self.output} -> {self.target or '\"\"'} -> '''\n if self.inst_in:\n st += f\"instance:{self.inst_in};\"\n st += self.input\n\n if self.params and not self.inst_in:\n st += f\" ({self.params})\"\n if self.delay != 0:\n st += f\" after {self.delay} seconds\"\n if self.times != -1:\n st += \" (once only)\" if self.times == 1 else f\" ({self.times!s} times only)\"\n return st"],"string":"[\n \"def get_model_config(model_name, args):\\n if model_name == 'Tacotron2':\\n model_config = dict(\\n # optimization\\n mask_padding=args.mask_padding,\\n # audio\\n n_mel_channels=args.n_mel_channels,\\n # symbols\\n n_symbols=args.n_symbols,\\n symbols_embedding_dim=args.symbols_embedding_dim,\\n # encoder\\n encoder_kernel_size=args.encoder_kernel_size,\\n encoder_n_convolutions=args.encoder_n_convolutions,\\n encoder_embedding_dim=args.encoder_embedding_dim,\\n # attention\\n attention_rnn_dim=args.attention_rnn_dim,\\n attention_dim=args.attention_dim,\\n # attention location\\n attention_location_n_filters=args.attention_location_n_filters,\\n attention_location_kernel_size=args.attention_location_kernel_size,\\n # decoder\\n n_frames_per_step=args.n_frames_per_step,\\n decoder_rnn_dim=args.decoder_rnn_dim,\\n prenet_dim=args.prenet_dim,\\n max_decoder_steps=args.max_decoder_steps,\\n gate_threshold=args.gate_threshold,\\n p_attention_dropout=args.p_attention_dropout,\\n p_decoder_dropout=args.p_decoder_dropout,\\n # postnet\\n postnet_embedding_dim=args.postnet_embedding_dim,\\n postnet_kernel_size=args.postnet_kernel_size,\\n postnet_n_convolutions=args.postnet_n_convolutions,\\n decoder_no_early_stopping=args.decoder_no_early_stopping\\n )\\n return model_config\\n elif model_name == 'WaveGlow':\\n model_config = dict(\\n n_mel_channels=args.n_mel_channels,\\n n_flows=args.flows,\\n n_group=args.groups,\\n n_early_every=args.early_every,\\n n_early_size=args.early_size,\\n WN_config=dict(\\n n_layers=args.wn_layers,\\n kernel_size=args.wn_kernel_size,\\n n_channels=args.wn_channels\\n )\\n )\\n return model_config\\n else:\\n raise NotImplementedError(model_name)\",\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_configuration_parameters_values(self):\\n return (self.timestamp, self.model_name, self.model.WEIGHT_PATH, self.model.FIT_HISTORY_PATH,\\n self.learning_rate, self.load_weights, self.freeze_layers, self.margin,\\n self.hard_sampling_batch_size, self.batch_size, self.number_of_validation_imgs,\\n self.input_shape)\",\n \"def __str__(self):\\n \\n res = ['>>> Model %(model_name)s <<<']\\n res.append('')\\n res.append('Independent parameters:')\\n res.append('-----------------------')\\n res.append('')\",\n \"def get_model_config(model_name, args):\\n if model_name == 'WaveGlow':\\n model_config = dict(\\n n_mel_channels=args.n_mel_channels,\\n n_flows=args.flows,\\n n_group=args.groups,\\n n_early_every=args.early_every,\\n n_early_size=args.early_size,\\n WN_config=dict(\\n n_layers=args.wn_layers,\\n kernel_size=args.wn_kernel_size,\\n n_channels=args.wn_channels\\n )\\n )\\n return model_config\\n elif model_name == 'FastPitch':\\n model_config = dict(\\n # io\\n n_mel_channels=args.n_mel_channels,\\n # symbols\\n n_symbols=len(get_symbols(args.symbol_set)),\\n padding_idx=get_pad_idx(args.symbol_set),\\n symbols_embedding_dim=args.symbols_embedding_dim,\\n # input FFT\\n in_fft_n_layers=args.in_fft_n_layers,\\n in_fft_n_heads=args.in_fft_n_heads,\\n in_fft_d_head=args.in_fft_d_head,\\n in_fft_conv1d_kernel_size=args.in_fft_conv1d_kernel_size,\\n in_fft_conv1d_filter_size=args.in_fft_conv1d_filter_size,\\n in_fft_output_size=args.in_fft_output_size,\\n p_in_fft_dropout=args.p_in_fft_dropout,\\n p_in_fft_dropatt=args.p_in_fft_dropatt,\\n p_in_fft_dropemb=args.p_in_fft_dropemb,\\n # output FFT\\n out_fft_n_layers=args.out_fft_n_layers,\\n out_fft_n_heads=args.out_fft_n_heads,\\n out_fft_d_head=args.out_fft_d_head,\\n out_fft_conv1d_kernel_size=args.out_fft_conv1d_kernel_size,\\n out_fft_conv1d_filter_size=args.out_fft_conv1d_filter_size,\\n out_fft_output_size=args.out_fft_output_size,\\n p_out_fft_dropout=args.p_out_fft_dropout,\\n p_out_fft_dropatt=args.p_out_fft_dropatt,\\n p_out_fft_dropemb=args.p_out_fft_dropemb,\\n # duration predictor\\n dur_predictor_kernel_size=args.dur_predictor_kernel_size,\\n dur_predictor_filter_size=args.dur_predictor_filter_size,\\n p_dur_predictor_dropout=args.p_dur_predictor_dropout,\\n dur_predictor_n_layers=args.dur_predictor_n_layers,\\n # pitch predictor\\n pitch_predictor_kernel_size=args.pitch_predictor_kernel_size,\\n pitch_predictor_filter_size=args.pitch_predictor_filter_size,\\n p_pitch_predictor_dropout=args.p_pitch_predictor_dropout,\\n pitch_predictor_n_layers=args.pitch_predictor_n_layers,\\n # pitch conditioning\\n pitch_embedding_kernel_size=args.pitch_embedding_kernel_size,\\n # speakers parameters\\n n_speakers=args.n_speakers,\\n speaker_emb_weight=args.speaker_emb_weight,\\n # energy predictor\\n energy_predictor_kernel_size=args.energy_predictor_kernel_size,\\n energy_predictor_filter_size=args.energy_predictor_filter_size,\\n p_energy_predictor_dropout=args.p_energy_predictor_dropout,\\n energy_predictor_n_layers=args.energy_predictor_n_layers,\\n # energy conditioning\\n energy_conditioning=args.energy_conditioning,\\n energy_embedding_kernel_size=args.energy_embedding_kernel_size,\\n )\\n return model_config\\n\\n else:\\n raise NotImplementedError(model_name)\",\n \"def _get_model(self):\\n\\n parameters = {keys._topology:self.topology,\\n keys._size:self.size,\\n keys._name:self.name,\\n #keys._output_activation:self._outActiv_fun_key,\\n #keys._hidden_activation:self._hiddenActiv_fun_key,\\n keys._learning_rate:self.learningRate,\\n keys._momentum:self.momentum}\\n\\n return parameters\",\n \"def config(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"config\\\")\",\n \"def config(self):\\n return {\\\"input_dims\\\": self.dims, \\\"output_dims\\\": self.output_dims, \\\"mapping\\\": self.mapping}\",\n \"def get_config(ctx):\\n global HISTORY_LOGS, EXPERIMENT_ID #Ugly hack, make it better at some point, may be ;)\\n id = ctx.job.id\\n EXPERIMENT_ID = hash(id)\\n\\n import montezuma_env\\n\\n ctx.job.register_action(\\\"Set starting point procssor:\\\",\\n lambda str: set_motezuma_env_options(str, montezuma_env.STARTING_POINT_SELECTOR))\\n ctx.job.register_action(\\\"Set rewards:\\\",\\n lambda str: set_motezuma_env_options(str, montezuma_env.REWARDS_FILE))\\n\\n logger.auto_set_dir(suffix=id)\\n\\n # (self, parameters, number_of_actions, input_shape)\\n\\n M = EXPERIMENT_MODEL\\n\\n name_base = str(uuid.uuid1())[:6]\\n PIPE_DIR = os.environ.get('TENSORPACK_PIPEDIR_{}'.format(id), '.').rstrip('/')\\n namec2s = 'ipc://{}/sim-c2s-{}-{}'.format(PIPE_DIR, name_base, id)\\n names2c = 'ipc://{}/sim-s2c-{}-{}'.format(PIPE_DIR, name_base, id)\\n procs = [MySimulatorWorker(k, namec2s, names2c) for k in range(SIMULATOR_PROC)]\\n ensure_proc_terminate(procs)\\n start_proc_mask_signal(procs)\\n\\n master = MySimulatorMaster(namec2s, names2c, M)\\n dataflow = BatchData(DataFromQueue(master.queue), BATCH_SIZE)\\n\\n # My stuff - PM\\n neptuneLogger = NeptuneLogger.get_instance()\\n lr = tf.Variable(0.001, trainable=False, name='learning_rate')\\n tf.scalar_summary('learning_rate', lr)\\n num_epochs = get_atribute(ctx, \\\"num_epochs\\\", 100)\\n\\n rewards_str = get_atribute(ctx, \\\"rewards\\\", \\\"5 1 -200\\\")\\n with open(montezuma_env.REWARDS_FILE, \\\"w\\\") as file:\\n file.write(rewards_str)\\n\\n\\n if hasattr(ctx.params, \\\"learning_rate_schedule\\\"):\\n schedule_str = str(ctx.params.learning_rate_schedule)\\n else: #Default value inhereted from tensorpack\\n schedule_str = \\\"[[80, 0.0003], [120, 0.0001]]\\\"\\n logger.info(\\\"Setting learing rate schedule:{}\\\".format(schedule_str))\\n learning_rate_scheduler = ScheduledHyperParamSetter('learning_rate', json.loads(schedule_str))\\n\\n if hasattr(ctx.params, \\\"entropy_beta_schedule\\\"):\\n schedule_str = str(ctx.params.entropy_beta_schedule)\\n else: #Default value inhereted from tensorpack\\n schedule_str = \\\"[[80, 0.0003], [120, 0.0001]]\\\"\\n logger.info(\\\"Setting entropy beta schedule:{}\\\".format(schedule_str))\\n entropy_beta_scheduler = ScheduledHyperParamSetter('entropy_beta', json.loads(schedule_str))\\n\\n if hasattr(ctx.params, \\\"explore_factor_schedule\\\"):\\n schedule_str = str(ctx.params.explore_factor_schedule)\\n else: #Default value inhereted from tensorpack\\n schedule_str = \\\"[[80, 2], [100, 3], [120, 4], [140, 5]]\\\"\\n logger.info(\\\"Setting explore factor schedule:{}\\\".format(schedule_str))\\n explore_factor_scheduler = ScheduledHyperParamSetter('explore_factor', json.loads(schedule_str))\\n\\n\\n\\n return TrainConfig(\\n dataset=dataflow,\\n optimizer=tf.train.AdamOptimizer(lr, epsilon=1e-3),\\n callbacks=Callbacks([\\n StatPrinter(), ModelSaver(),\\n learning_rate_scheduler, entropy_beta_scheduler, explore_factor_scheduler,\\n HumanHyperParamSetter('learning_rate'),\\n HumanHyperParamSetter('entropy_beta'),\\n HumanHyperParamSetter('explore_factor'),\\n NeputneHyperParamSetter('learning_rate', ctx),\\n NeputneHyperParamSetter('entropy_beta', ctx),\\n NeputneHyperParamSetter('explore_factor', ctx),\\n master,\\n StartProcOrThread(master),\\n PeriodicCallback(Evaluator(EVAL_EPISODE, ['state'], ['logits'], neptuneLogger, HISTORY_LOGS), 1),\\n neptuneLogger,\\n ]),\\n session_config=get_default_sess_config(0.5),\\n model=M,\\n step_per_epoch=STEP_PER_EPOCH,\\n max_epoch=num_epochs,\\n )\",\n \"def params():\\n return utils.Params('../experiments/base-model/params.json')\",\n \"def __repr__(self):\\n\\n # info string\\n info = self.model.__repr__()\\n info += \\\"\\\\n=========================\\\\n\\\"\\n info += f\\\"Train data length:\\\\t\\\\t{ len(self.train_dataset) }\\\\n\\\"\\n info += f\\\"Eval sata length:\\\\t\\\\t{ len(self.eval_dataset) }\\\\n\\\"\\n info += f\\\"Optimizer:\\\\t\\\\t\\\\t\\\\t{ str(self.optimizer).split('(')[0] }\\\\n\\\"\\n info += f\\\"Criterion:\\\\t\\\\t\\\\t\\\\t{ str(self.criterion).split('(')[0] }\\\\n\\\"\\n info += f\\\"Training Environment:\\\\t{ self.device.type }\\\\n\\\"\\n info += f\\\"Show information:\\\\t\\\\t{ 'True' if self.info else 'False' }\\\\n\\\"\\n info += \\\"=========================\\\\n\\\"\\n\\n return info\",\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 print_network(self, model, name):\\n num_params = 0\\n for p in model.parameters():\\n num_params += p.numel()\\n #print(model)\\n print(name)\\n print(\\\"The number of parameters: {}\\\".format(num_params))\\n\\n with open(os.path.join(self.train_dir,'model_arch.txt'), 'a') as fp:\\n print(model, file=fp)\\n print(name, file=fp)\\n print(\\\"The number of parameters: {}\\\".format(num_params),file=fp)\",\n \"def info(self):\\r\\n\\r\\n return self.sim_info\",\n \"def __str__(self):\\n model_parameters = filter(lambda p: p.requires_grad, self.parameters())\\n params = sum([np.prod(p.size()) for p in model_parameters])\\n return super(BaseModel, self).__str__() + '\\\\nTrainable parameters: {}'.format(params)\\n # print(super(BaseModel, self))\",\n \"def extractModelParam(self):\\n copasi_filename = self.genPathCopasi(\\\"extractor\\\")\\n self.recentModel = model.loada(self.antString, copasi_filename)\\n return self.recentModel.parameters.copy().squeeze().to_dict()\",\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 get_model_output(self):\\n\\n return self.model_output_file\",\n \"def get_info_string(config):\\n if config.train.folds is not None:\\n return f\\\"folds_{config.train.folds}\\\"\\n elif config.train.validation_portion is not None:\\n return \\\"valportion_{}\\\".format(config.train.validation_portion)\\n return \\\"\\\"\",\n \"def make_params(config):\\n params = copy.deepcopy(config.view.params)\\n params.t2bins = np.arange(0, params.t2bin_max + 1e-4, params.t2bin_stepsize)\\n params.out = make_Bunch(\\\"State and output of detection processing\\\") # outputs are not parameters, maybe separate \\n return params\",\n \"def get_input_info_from_cfg(deploy_cfg: mmengine.Config) -> Dict[str, List]:\\n # The partition is not supported now. Set the id of model to 0.\\n model_inputs = get_model_inputs(deploy_cfg)[0]\\n input_info = model_inputs['opt_shapes']\\n ir_config = get_ir_config(deploy_cfg)\\n if ir_config is not None:\\n input_names = ir_config.get('input_names', None)\\n if input_names:\\n if not isinstance(input_info, Dict):\\n input_info = dict(zip(input_names, input_info))\\n input_info = update_input_names(input_info, input_names)\\n return input_info\",\n \"def getParamsForWhizard(self, model):\\n params = ''\\n if model == 'sm':\\n params = \\\"\\\"\\\"<GF type=\\\"float\\\" value=\\\"1.16639E-5\\\">\\n<!-- Fermi constant -->\\n</GF>\\n<mZ type=\\\"float\\\" value=\\\"91.1882\\\">\\n<!-- Z-boson mass -->\\n</mZ>\\n<mW type=\\\"float\\\" value=\\\"80.419\\\">\\n<!-- W-boson mass -->\\n</mW>\\n<mH type=\\\"float\\\" value=\\\"120\\\">\\n<!-- Higgs mass -->\\n</mH>\\n<alphas type=\\\"float\\\" value=\\\"0.1178\\\">\\n<!-- Strong coupling constant alpha_s(MZ) -->\\n</alphas>\\n<me type=\\\"float\\\" value=\\\"0.\\\">\\n<!-- electron mass -->\\n</me>\\n<mmu type=\\\"float\\\" value=\\\"0.1066\\\">\\n<!-- muon mass -->\\n</mmu>\\n<mtau type=\\\"float\\\" value=\\\"1.777\\\">\\n<!-- tau-lepton mass -->\\n</mtau>\\n<ms type=\\\"float\\\" value=\\\"0.\\\">\\n<!-- s-quark mass -->\\n</ms>\\n<mc type=\\\"float\\\" value=\\\"0.54\\\">\\n<!-- c-quark mass -->\\n</mc>\\n<mb type=\\\"float\\\" value=\\\"2.9\\\">\\n<!-- b-quark mass -->\\n</mb>\\n<mtop type=\\\"float\\\" value=\\\"174\\\">\\n<!-- t-quark mass -->\\n</mtop>\\n<wtop type=\\\"float\\\" value=\\\"1.523\\\">\\n<!-- t-quark width -->\\n</wtop>\\n<wZ type=\\\"float\\\" value=\\\"2.443\\\">\\n<!-- Z-boson width -->\\n</wZ>\\n<wW type=\\\"float\\\" value=\\\"2.049\\\">\\n<!-- W-boson width -->\\n</wW>\\n<wH type=\\\"float\\\" value=\\\"0.3605E-02\\\">\\n<!-- Higgs width -->\\n</wH>\\n<vckm11 type=\\\"float\\\" value=\\\"0.97383\\\">\\n<!-- Vud -->\\n</vckm11>\\n<vckm12 type=\\\"float\\\" value=\\\"0.2272\\\">\\n<!-- Vus -->\\n</vckm12>\\n<vckm13 type=\\\"float\\\" value=\\\"0.00396\\\">\\n<!-- Vub -->\\n</vckm13>\\n<vckm21 type=\\\"float\\\" value=\\\"-0.2271\\\">\\n<!-- Vcd -->\\n</vckm21>\\n<vckm22 type=\\\"float\\\" value=\\\"0.97296\\\">\\n<!-- Vcs -->\\n</vckm22>\\n<vckm23 type=\\\"float\\\" value=\\\"0.04221\\\">\\n<!-- Vcb -->\\n</vckm23>\\n<vckm31 type=\\\"float\\\" value=\\\"0.00814\\\">\\n<!-- Vtd -->\\n</vckm31>\\n<vckm32 type=\\\"float\\\" value=\\\"-0.04161\\\">\\n<!-- Vts -->\\n</vckm32>\\n<vckm33 type=\\\"float\\\" value=\\\"0.99910\\\">\\n<!-- Vtb -->\\n</vckm33>\\n<khgaz type=\\\"float\\\" value=\\\"1.000\\\">\\n<!-- anomaly Higgs coupling K factors -->\\n</khgaz>\\n<khgaga type=\\\"float\\\" value=\\\"1.000\\\">\\n<!-- anomaly Higgs coupling K factors -->\\n</khgaga>\\n<khgg type=\\\"float\\\" value=\\\"1.000\\\">\\n<!-- anomaly Higgs coupling K factors -->\\n</khgg>\\n\\\"\\\"\\\"\\n else:\\n params = \\\"\\\"\\\"\\n \\\"\\\"\\\"\\n return S_OK(params)\",\n \"def get_configuration_parameters_names(self):\\n return (\\n 'timestamp', 'model_name', 'weight_path', 'fit_history_path', 'learning_rate', 'load_weights',\\n 'freeze_layers', 'margin', 'hard_sampling_batch_size', 'batch_size',\\n 'number_of_validation_imgs', 'input_shape')\",\n \"def __str__(self):\\n string = 'input dim: {} \\\\noutput dim: {} \\\\n'.format(\\n self.dim_inputs, self.dim_outputs\\n )\\n string += 'sequence length: {} \\\\n'.format(\\n self.tensors[0].shape[1]\\n )\\n key = 'train' if self.train else 'test'\\n string += '{}_samples: {} \\\\n{}_sequences: {} \\\\n'.format(\\n key, self.experiment_length, key, self.tensors[0].shape[0]\\n )\\n return string\",\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_parameter(config):\\n\\n selected_event = config['selected_event']\\n datasource_raw_data = config['datasource_raw_data']['database']\\n measurement_raw = config['datasource_raw_data']['measurement']\\n measurement_enriched = config['datasource_enriched_data']['measurement']\\n datasource_enriched_data = config['datasource_enriched_data']['database']\\n datasource_marked_data = config['datasource_marked_data']['database']\\n datasource_predicted_data = config['datasource_predicted_data']['database']\\n start_time = config['timeframe'][0]\\n end_time = config['timeframe'][1]\\n register_dict = config['register_dict']\\n required_registers = config[f\\\"{selected_event}_register\\\"]\\n events = config[selected_event]\\n measurement_predicted = config['datasource_predicted_data']['measurement']\\n return selected_event, datasource_raw_data, measurement_raw, start_time, end_time, register_dict, \\\\\\n required_registers, datasource_enriched_data, datasource_marked_data, \\\\\\n measurement_enriched, events, datasource_predicted_data, measurement_predicted\",\n \"def getModelParams(self):\\n log.info(\\\"Importing model params from %s\\\" % self.modelParamsPath)\\n moduleName = os.path.basename(self.modelParamsPath)\\n importedModelParams = imp.load_source(moduleName, self.modelParamsPath)\\n return importedModelParams.MODEL_PARAMS\",\n \"def display_sim_parameters(self):\\n pprint.pprint(vars(self))\\n return\",\n \"def configuration(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"configuration\\\")\",\n \"def config():\\n experiment_dir = './experiments'\\n simulation_steps = 1000\\n device = 'cpu'\\n path_to_molecules = os.path.join(experiment_dir, 'data/ethanol.xyz')\\n simulation_dir = os.path.join(experiment_dir, 'simulation')\\n training_dir = os.path.join(experiment_dir, 'training')\\n model_path = os.path.join(training_dir, 'best_model')\\n overwrite = True\",\n \"def _config(self):\\r\\n return (\\r\\n self.destructive,\\r\\n self.output_type,\\r\\n self.seed,\\r\\n )\",\n \"def output_parameters(self):\\n output_params = get_data_node(\\n 'parameter',\\n dict={\\n 'stress': self.vasprun_obj.ionic_steps[-1]['stress'],\\n 'efermi': self.vasprun_obj.efermi,\\n 'energy': self.vasprun_obj.final_energy\\n })\\n return output_params\",\n \"def get_model_info(y_test, model_name, model_parameters, model_preprocessing, sequence_origin, primers_origin,\\n taxonomy_level, selected_primer, test_size, logger) -> int:\\n\\n # Global information on the model\\n logger.log(title='Parameter information for {}'.format(model_name))\\n # Data Origins\\n logger.log(subtitle='Data Origins')\\n logger.log(text='Sequence origin: {}'.format(sequence_origin))\\n logger.log(text='Primers origin: {}'.format(primers_origin))\\n # Chosen levels for classification\\n logger.log(subtitle='Chosen HyperVariable Region and Taxonomy Rank')\\n logger.log(text='HyperVariable Region: {}'.format(str(selected_primer)))\\n logger.log(text='Taxonomy Rank: {}'.format(str(taxonomy_level)))\\n # Applied Preprocessing\\n logger.log(subtitle='Preprocessing')\\n logger.log(text='Preprocessing description: ' + model_preprocessing)\\n # Model parameters\\n logger.log(subtitle='Model parameters')\\n logger.log(text='Parameter dict: {}'.format(str(model_parameters)))\\n logger.log(text='Size of test set: {}'.format(len(y_test)))\\n logger.log(text='Part of test size compared to total: {}'.format(test_size))\\n\\n return len(y_test)\",\n \"def get_config(parse=True, **optional_kwargs):\\n parser = argparse.ArgumentParser()\\n\\n # Mode\\n parser.add_argument('--mode', type=str, default='train')\\n parser.add_argument('--runs', type=int, default=5)\\n\\n # Bert\\n parser.add_argument('--use_bert', type=str2bool, default=True)\\n parser.add_argument('--use_cmd_sim', type=str2bool, default=True)\\n\\n # Train\\n time_now = datetime.now().strftime('%Y-%m-%d_%H:%M:%S')\\n parser.add_argument('--name', type=str, default=f\\\"{time_now}\\\")\\n parser.add_argument('--num_classes', type=int, default=0)\\n parser.add_argument('--batch_size', type=int, default=128)\\n parser.add_argument('--eval_batch_size', type=int, default=10)\\n parser.add_argument('--n_epoch', type=int, default=500)\\n parser.add_argument('--patience', type=int, default=6)\\n\\n parser.add_argument('--diff_weight', type=float, default=0.3)\\n parser.add_argument('--sim_weight', type=float, default=1.0)\\n parser.add_argument('--sp_weight', type=float, default=0.0)\\n parser.add_argument('--recon_weight', type=float, default=1.0)\\n\\n parser.add_argument('--learning_rate', type=float, default=1e-4)\\n parser.add_argument('--optimizer', type=str, default='Adam')\\n parser.add_argument('--clip', type=float, default=1.0)\\n\\n parser.add_argument('--rnncell', type=str, default='lstm')\\n parser.add_argument('--embedding_size', type=int, default=300)\\n parser.add_argument('--hidden_size', type=int, default=128)\\n parser.add_argument('--dropout', type=float, default=0.5)\\n parser.add_argument('--reverse_grad_weight', type=float, default=1.0)\\n # Selectin activation from 'elu', \\\"hardshrink\\\", \\\"hardtanh\\\", \\\"leakyrelu\\\", \\\"prelu\\\", \\\"relu\\\", \\\"rrelu\\\", \\\"tanh\\\"\\n parser.add_argument('--activation', type=str, default='relu')\\n\\n # Model\\n parser.add_argument('--model', type=str,\\n default='MISA', help='one of {MISA, }')\\n\\n # Data\\n parser.add_argument('--data', type=str, default='mosi')\\n\\n # Parse arguments\\n if parse:\\n kwargs = parser.parse_args()\\n else:\\n kwargs = parser.parse_known_args()[0]\\n\\n print(kwargs.data)\\n if kwargs.data == \\\"mosi\\\":\\n kwargs.num_classes = 1\\n kwargs.batch_size = 64\\n elif kwargs.data == \\\"mosei\\\":\\n kwargs.num_classes = 1\\n kwargs.batch_size = 16\\n elif kwargs.data == \\\"ur_funny\\\":\\n kwargs.num_classes = 2\\n kwargs.batch_size = 32\\n else:\\n print(\\\"No dataset mentioned\\\")\\n exit()\\n\\n # Namespace => Dictionary\\n kwargs = vars(kwargs)\\n kwargs.update(optional_kwargs)\\n\\n return Config(**kwargs)\",\n \"async def get_model(self):\\n\\n # Display info message\\n log.info(\\\"get_model\\\")\\n\\n # Get model\\n output = await self.send_command(self.cmd_get_model)\\n\\n # Display info message\\n log.info(f\\\"get_model: output: '{output}'\\\")\\n\\n # Remove the useless information in the returned string\\n output = output.split('\\\"')[3]\\n\\n # Display info message\\n log.info(f\\\"get_model: model found: '{output}'\\\")\\n\\n # Return the model of the device\\n return output\",\n \"def __str__(self):\\n status = \\\"height = {}\\\\n\\\".format(self.height)\\n status += \\\"width = {}\\\\n\\\".format(self.width)\\n status += \\\"channels = {}\\\\n\\\".format(self.channels)\\n status += \\\"input_dim = {}\\\\n\\\".format(self.input_dim)\\n status += \\\"architecture = {}\\\\n\\\".format(self.architecture)\\n status += \\\"activations = {}\\\\n\\\".format(self.activations)\\n status += \\\"batch_size = {}\\\\n\\\".format(self.batch_size)\\n status += \\\"epochs = {}\\\\n\\\".format(self.epochs)\\n status += \\\"save_step = {}\\\\n\\\".format(self.save_step)\\n status += \\\"learning_rate = {}\\\\n\\\".format(self.learning_rate)\\n status += \\\"momentum = {}\\\\n\\\".format(self.momentum)\\n return status\",\n \"def __str__():\\n return str(_config)\",\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 _build_output_dict_single(model):\\n model_name = model.name\\n\\n param_dict = {}\\n for name, value in zip(model.param_names, model.parameters):\\n param_dict[name] = str(value)\\n\\n constraints = _build_constraints_dict(model)\\n\\n model_dict = {\\n 'name': model_name,\\n 'class': _get_model_class_name(model),\\n 'parameters': param_dict,\\n 'constraints': constraints}\\n\\n return model_name, model_dict\",\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 get_config(self):\\n return {\\\"name\\\": self.name, \\\"tunable\\\": self.tunable}\",\n \"def fetch_predictor_config():\\n try:\\n f = open(os.path.join(CONFIG['results_path'], CONFIG['predictor_model_dir'], 'config.csv'), 'r')\\n f.readline()\\n values = f.readline().split(',')\\n f.close()\\n return int(values[0]), str(values[2]).strip()\\n except:\\n # If this happens, check the path.\\n print('ERROR in fetching predictor config. Default values used. These may fail.')\\n return CONFIG['training_img_size'], CONFIG['training_set_image_type']\",\n \"def config(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"config\\\")\",\n \"def get_config(self):\\n return {'reduction': self.reduction, 'name': self.name}\",\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_details(self):\\n\\n return {\\n \\\"embedder\\\": \\\"GloVe\\\",\\n \\\"algorithm\\\": \\\"RNN-LSTM\\\"\\n }\",\n \"def _extract_sim_config_from_fmi_std(self):\\n \\n print(\\\"\\\\n---- Looking to see if FMU model description contains required 'causality' type definitions ----\\\")\\n\\n sim_config_params = []\\n sim_inputs = []\\n sim_outputs = []\\n sim_other_vars = []\\n for variable in self.model_description.modelVariables:\\n\\n # extract causality and append valu\\n causality = variable.causality\\n\\n if causality == \\\"parameter\\\":\\n sim_config_params.append(variable.name)\\n elif causality == \\\"input\\\":\\n sim_inputs.append(variable.name)\\n elif causality == \\\"output\\\":\\n sim_outputs.append(variable.name)\\n else:\\n sim_other_vars.append(variable.name)\\n \\n # Validate values extracted\\n if len(sim_inputs) == 0:\\n print(\\\"\\\\n[FMU Validator] Sim FMU description file has no sim-input states, and thus cannot be used.\\\")\\n elif len(sim_outputs) == 0:\\n print(\\\"\\\\n[FMU Validator] Sim FMU description file has no sim-output states, and thus cannot be used.\\\")\\n else:\\n # Store data extracted as attributes\\n self.sim_config_params = sim_config_params\\n self.sim_inputs = sim_inputs\\n self.sim_outputs = sim_outputs\\n self.sim_other_vars = sim_other_vars\\n return True\\n\\n\\n # Dump auxiliary YMAL file for user to review/edit\\n self._dump_config_to_yaml_file(sim_config_params,\\n sim_inputs,\\n sim_outputs,\\n sim_other_vars,\\n is_aux_yaml = True)\\n\\n return False\",\n \"def sim(self):\\n return self.mujoco_simulation.sim\",\n \"def get_config_filepath(config: configs.Config) -> str:\\n return os.path.join(config.model_training.dir_out, configs.DEFAULT_FILENAME_CONFIG)\",\n \"def __str__(self) -> str:\\n model_str = [\\\"\\\\nModel info:\\\\n\\\", \\\" Unimodal encoder:\\\\n\\\"]\\n\\n for modality in range(self.num_modalities):\\n model_str.append(f\\\" ({modality + 1}) {self.unimodal_encoder[modality]}\\\")\\n\\n model_str.append(\\\"\\\\n\\\\n Unimodal decoder:\\\\n\\\")\\n for modality in range(self.num_modalities):\\n model_str.append(f\\\" ({modality + 1}) {self.unimodal_decoder[modality]}\\\")\\n\\n if self.multimodal_decoder is not None:\\n model_str.append(\\\"\\\\n\\\\n Multimodal decoder:\\\\n\\\")\\n model_str.append(f\\\" {self.multimodal_decoder}\\\")\\n\\n return \\\"\\\".join(model_str)\",\n \"def get_model_filename(config):\\n base = os.path.splitext(config['corpus'])[0]\\n return '%s--%dT.model' % (base, config['T'])\",\n \"def __str__(self) -> str:\\n return (\\n f\\\"GlacierFlowModel '{self.model_name}' \\\"\\n f\\\"{'' if self.steady_state else 'not '}in steady state with:\\\"\\n f\\\"\\\\n - m: {self.m:20.5f} [m/m]\\\"\\n f\\\"\\\\n - ela: {self.ela:20.2f} [m MSL]\\\"\\n f\\\"\\\\n - resolution: {self.res:20.2f} [m]\\\"\\n f\\\"\\\\n - extent: min max\\\"\\n f\\\"\\\\n {self.extent[0]:10.1f} \\\"\\n f\\\"{self.extent[1]:10.1f} [x]\\\"\\n f\\\"\\\\n {self.extent[2]:10.1f} \\\"\\n f\\\"{self.extent[3]:10.1f} [y]\\\"\\n )\",\n \"def __str__(self):\\n status = \\\"height = {}\\\\n\\\".format(self.height)\\n status += \\\"width = {}\\\\n\\\".format(self.width)\\n status += \\\"channels = {}\\\\n\\\".format(self.channels)\\n status += \\\"architecture = {}\\\\n\\\".format(self.architecture)\\n status += \\\"activations = {}\\\\n\\\".format(self.activations)\\n status += \\\"conv_activations = {}\\\\n\\\".format(self.conv_activations)\\n status += \\\"conv_architecture = {}\\\\n\\\".format(self.conv_architecture)\\n status += \\\"kernel_sizes = {}\\\\n\\\".format(self.kernel_sizes)\\n status += \\\"pool_kernel = {}\\\\n\\\".format(self.pool_kernel)\\n status += \\\"batch_size = {}\\\\n\\\".format(self.batch_size)\\n status += \\\"epochs = {}\\\\n\\\".format(self.epochs)\\n status += \\\"save_step = {}\\\\n\\\".format(self.save_step)\\n status += \\\"learning_rate = {}\\\\n\\\".format(self.learning_rate)\\n status += \\\"momentum = {}\\\\n\\\".format(self.momentum)\\n return status\",\n \"def get_model_filepath(config: configs.Config) -> str:\\n return os.path.join(config.model_training.dir_out, models.DEFAULT_FILENAME_MODEL)\",\n \"def get_parameters(self): \\n audio_emotions_topic = rospy.get_param(\\\"~audio_emotions_topic\\\")\\n gcp_name = rospy.get_param(\\\"~gcp_name\\\")\\n gcp_project = rospy.get_param(\\\"~gcp_project\\\")\\n gcp_version = rospy.get_param(\\\"~gcp_version\\\")\\n json_path = rospy.get_param(\\\"~json_path\\\")\\n model_path = rospy.get_param(\\\"~model_path\\\")\\n emotions_logfile = rospy.get_param(\\\"~emotions_logfile\\\")\\n robot_ip = rospy.get_param(\\\"~robot_IP\\\")\\n s2t_topic = rospy.get_param(\\\"~s2t_topic\\\")\\n pred_mode = rospy.get_param(\\\"~pred_mode\\\")\\n raw_audio_topic = rospy.get_param(\\\"~raw_audio_topic\\\")\\n dest_num_channels = rospy.get_param(\\\"~dest_num_channels\\\")\\n dest_rate = rospy.get_param(\\\"~dest_rate\\\")\\n max_iter = rospy.get_param(\\\"~max_iter\\\") \\n sound_path = rospy.get_param(\\\"~sound_path\\\")\\n wav_topic = rospy.get_param(\\\"~wav_topic\\\")\\n stats_logfile = rospy.get_param(\\\"~stats_logfile\\\")\\n stats_topic = rospy.get_param(\\\"~stats_topic\\\")\\n return (audio_emotions_topic, gcp_name, gcp_project, gcp_version, json_path, model_path, emotions_logfile, robot_ip, s2t_topic, pred_mode, raw_audio_topic, dest_num_channels, dest_rate, max_iter, sound_path, wav_topic, stats_logfile, stats_topic)\",\n \"def describe_model():\\n train_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES)\\n msg = [\\\"\\\"]\\n total = 0\\n for v in train_vars:\\n shape = v.get_shape()\\n ele = shape.num_elements()\\n total += ele\\n msg.append(\\\"{}: shape={}, dim={}\\\".format(\\n v.name, shape.as_list(), ele))\\n size_mb = total * 4 / 1024.0**2\\n msg.append(colored(\\n \\\"Total param={} ({:01f} MB assuming all float32)\\\".format(total, size_mb), 'cyan'))\\n logger.info(colored(\\\"Model Parameters: \\\", 'cyan') + '\\\\n'.join(msg))\",\n \"def load_config():\\n model_type, run_name, run_comment, epoch, verbose = get_args()\\n name = run_name + '-' + run_comment\\n if model_type == \\\"s2s\\\": \\n run_title = \\\"seq2seq\\\"\\n else:\\n run_title = \\\"def2vec\\\"\\n path = \\\"outputs/{}/logs/{}/config.json\\\".format(run_title, name)\\n config = None\\n with open(path) as f:\\n config = dict(json.load(f))\\n config = load_config(eval=True)\\n return (config, name, model_type)\",\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 get(self):\\n # Num frame, norm log probability and model name\\n return (str(self.n_frame), str(self.best_log_probability[1]), str(self.best_log_probability[0]))\",\n \"def get_config(self): # To support serialization\\n return {\\\"mean\\\": self.mean, \\\"stddev\\\": self.stddev}\",\n \"def config(self):\\n return \\\"\\\\n\\\".join([ c.config(True) for p, c in self.configs_ ])\",\n \"def get_model_configuration(request):\\n text = text_cache['model_configuration']\\n return HttpResponse(text, content_type='text/plain')\",\n \"def get_config():\\r\\n config = argparser()\\r\\n\\r\\n # use the configurations to reproduce the experiments\\r\\n if not config.reproduct_mode == \\\"free\\\":\\r\\n config = reproduct_config(config)\\r\\n \\r\\n print(config)\\r\\n\\r\\n with open(config.out_dir + \\\"/test_results.txt\\\", \\\"w\\\") as txt_f:\\r\\n txt_f.write(str(config) + \\\"\\\\n\\\")\\r\\n\\r\\n return config\",\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 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 get_config(self):\\n return {'name': self.name, 'dtype': self.dtype}\",\n \"def _est_config(self):\\n return self._est_method.config\",\n \"def print_cfg(self, out=stdout):\\n print(self.cmaboss_sim.str_cfg(), file=out)\",\n \"def _config_str(config: Config) -> str:\\n _C = config\\n\\n __C: CN = CN({\\\"RANDOM_SEED\\\": _C.random_seed})\\n common_string: str = str(__C) + \\\"\\\\n\\\"\\n\\n return common_string\",\n \"def config(self) -> Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]]:\\n return pulumi.get(self, \\\"config\\\")\",\n \"def config(self) -> Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]]:\\n return pulumi.get(self, \\\"config\\\")\",\n \"def parameters(self):\\n return NeuralNetwork.flatten([module.parameters() for module in self.modules])\",\n \"def __str__(self):\\n return 'Tensor product {}: {} params, wires {}'.format([i.name for i in self.obs], len(self.params), self.wires)\",\n \"def parameters(self):\\n params = dict()\\n try:\\n params['voltage'] = self.voltage()\\n params['output'] = self.output()\\n return params\\n except:\\n return \\\"Disconnected\\\"\",\n \"def _dump_config_to_yaml_file(self,\\n sim_config_params = None,\\n sim_inputs = None,\\n sim_outputs = None,\\n sim_other_vars = None,\\n is_aux_yaml = False):\\n\\n if sim_config_params is None:\\n sim_config_params = self.sim_config_params\\n if sim_inputs is None:\\n sim_inputs = self.sim_inputs\\n if sim_outputs is None:\\n sim_outputs = self.sim_outputs\\n if sim_other_vars is None:\\n sim_other_vars = self.sim_other_vars\\n\\n if not is_aux_yaml:\\n config_file = self.sim_config_filepath\\n else:\\n config_file = self.sim_config_filepath.replace(\\\".yaml\\\", \\\"_EDIT.yaml\\\")\\n\\n # Prepare set of unused data ( to be shared with user for editing )\\n full_sim_config = {\\\"config_params\\\": sim_config_params,\\n \\\"inputs\\\": sim_inputs,\\n \\\"outputs\\\": sim_outputs,\\n \\\"other_vars\\\": sim_other_vars}\\n full_sim_data = {\\\"simulation\\\": full_sim_config}\\n\\n # Dump configuration to YAML file for later reuse (or user editing if \\\"is_aux_yaml==True\\\")\\n with open(config_file, 'w') as file:\\n dump = yaml.dump(full_sim_data, sort_keys = False, default_flow_style=False)\\n file.write( dump )\\n\\n # Raise error, and avoid continuing using model\\n log = \\\"\\\\n[FMU Validator] A YAML file with bonsai required fields, as well as available \\\"\\n log += \\\"sim variables, has been created at: \\\\n --> '{}'\\\\n\\\".format(config_file)\\n \\n if is_aux_yaml:\\n log += \\\"[FMU Validator] Edit the YAML file, and remove the '_EDIT' nametag to use this model.\\\\n\\\"\\n \\n print(log)\\n\\n return\",\n \"def _get_model(self) -> str:\\n return str(self.hass.data[DOMAIN][self._config_entry.entry_id][ATTR_MODEL])\",\n \"def get_config(self):\\n\\n # Use topological sort to get the correct order of modules.\\n self.dag_topology_sort()\\n mconfig = {}\\n module_connection = {}\\n for mod in self.mod_wrapper:\\n # Generate pipeline configuration.\\n mconf = {}\\n output_conf = []\\n module = self.mod_wrapper[mod]\\n for _, binding in module.output_bindings.bindings.items():\\n dep_conf = []\\n output = {}\\n if binding.bindings:\\n for dep in binding.bindings:\\n dep_item = {}\\n _, dname = dep.get_name()\\n if dep.is_pipeline_executor_interface():\\n dep_item[\\\"global_output_index\\\"] = int(dname)\\n else:\\n dep_item[\\\"mod_idx\\\"] = dep.get_owner_idx()\\n dep_item[\\\"input_name\\\"] = dname\\n dep_conf.append(dep_item)\\n\\n # The value of output_idx start from 0.\\n output[\\\"output_idx\\\"] = int(binding.name)\\n output[\\\"dependencies\\\"] = dep_conf\\n output_conf.append(output)\\n\\n mconf[\\\"mod_idx\\\"] = module.idx\\n mconf[\\\"cpu_affinity\\\"] = module.cpu_affinity\\n mconf[\\\"output\\\"] = output_conf\\n\\n module_connection[mod] = {\\n \\\"pipeline\\\": mconf,\\n \\\"target_host\\\": module.target_host,\\n \\\"mod_name\\\": \\\"default\\\",\\n \\\"build\\\": module.build_func,\\n \\\"params\\\": module.params,\\n \\\"target\\\": module.target,\\n \\\"fcompile\\\": module.fcompile,\\n \\\"dev\\\": module.dev,\\n \\\"export_cc\\\": module.export_cc,\\n }\\n\\n # Creating a map including pipeline inputs and subgraph inputs.\\n input_connection = []\\n for input_name in self.input_bindings.bindings:\\n input_dict = self.input_bindings.bindings[input_name].get_binding_dict()\\n if \\\"interface_name\\\" not in input_dict[\\\"connection\\\"][0]:\\n raise RuntimeError(\\\"interface_name is missing in connection config!\\\")\\n # Creating the map including global interfaces and subgraph interfaces.\\n input_map = {\\n \\\"global_interface_name\\\": input_dict[\\\"interface_name\\\"],\\n \\\"mod_idx\\\": input_dict[\\\"connection\\\"][0][\\\"mod_idx\\\"],\\n \\\"module_interface_name\\\": input_dict[\\\"connection\\\"][0][\\\"interface_name\\\"],\\n }\\n input_connection.append(input_map)\\n\\n # Create a map including global parameters groups and modules.\\n param_connection = []\\n for param_name in self.param_group_bindings.bindings:\\n param_dict = self.param_group_bindings.bindings[param_name].get_binding_dict()\\n param_map = {\\n \\\"global_param_name\\\": param_dict[\\\"interface_name\\\"],\\n \\\"mod_idx\\\": param_dict[\\\"connection\\\"][0][\\\"mod_idx\\\"],\\n }\\n param_connection.append(param_map)\\n\\n mconfig[\\\"module_connection\\\"] = module_connection\\n mconfig[\\\"input_connection\\\"] = input_connection\\n mconfig[\\\"param_connection\\\"] = param_connection\\n return mconfig\",\n \"def print_network(self, model, name):\\r\\n num_params = 0\\r\\n for p in model.parameters():\\r\\n num_params += p.numel()\\r\\n print(model)\\r\\n print(name)\\r\\n print(\\\"The number of parameters: {}\\\".format(num_params))\",\n \"def print_network(self, model, name):\\r\\n num_params = 0\\r\\n for p in model.parameters():\\r\\n num_params += p.numel()\\r\\n print(model)\\r\\n print(name)\\r\\n print(\\\"The number of parameters: {}\\\".format(num_params))\",\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 config(self) -> pulumi.Input['ConfigArgs']:\\n return pulumi.get(self, \\\"config\\\")\",\n \"def print_network(self, model, name):\\n num_params = 0\\n for p in model.parameters():\\n num_params += p.numel()\\n print(model)\\n print(name)\\n print(\\\"The number of parameters: {}\\\".format(num_params))\",\n \"def print_network(self, model, name):\\n num_params = 0\\n for p in model.parameters():\\n num_params += p.numel()\\n print(model)\\n print(name)\\n print(\\\"The number of parameters: {}\\\".format(num_params))\",\n \"def print_network(self, model, name):\\n num_params = 0\\n for p in model.parameters():\\n num_params += p.numel()\\n print(model)\\n print(name)\\n print(\\\"The number of parameters: {}\\\".format(num_params))\",\n \"def print_network(self, model, name):\\n num_params = 0\\n for p in model.parameters():\\n num_params += p.numel()\\n print(model)\\n print(name)\\n print(\\\"The number of parameters: {}\\\".format(num_params))\",\n \"def print_network(self, model, name):\\n num_params = 0\\n for p in model.parameters():\\n num_params += p.numel()\\n print(\\\"\\\\nModel Name: \\\\\\\"{}\\\\\\\"\\\".format(name))\\n print(model)\\n print(\\\"The number of parameters: {}\\\".format(num_params))\",\n \"def print_network(self, model, name):\\n num_params = 0\\n for p in model.parameters():\\n num_params += p.numel()\\n print(\\\"\\\\nModel Name: \\\\\\\"{}\\\\\\\"\\\".format(name))\\n print(model)\\n print(\\\"The number of parameters: {}\\\".format(num_params))\",\n \"def __str__(self):\\n return \\\"{}: {} params, wires {}\\\".format(self.name, len(self.params), self.wires)\",\n \"def export_model_config_with_inputs(self, inputs):\\n structure_exporter_tensors = self.sess.run(\\n self.exporter_eval.tensors, feed_dict={self.inputs: inputs})\\n self.exporter_eval.populate_tensor_values(structure_exporter_tensors)\\n path = self.exporter_eval.create_file_and_save_alive_counts(\\n self.log_dir, self.global_step)\\n return path\",\n \"def print_network(self, model, name):\\n num_params = 0\\n for p in model.parameters():\\n num_params += p.numel()\\n print(model)\\n print(name)\\n print(\\\"The number of parameters: {}\\\".format(num_params))\",\n \"def config(self) -> pulumi.Output['outputs.ConfigResponse']:\\n return pulumi.get(self, \\\"config\\\")\",\n \"def get_model_parameter_names():\\n params = ['mu', 'rho']\\n return params\",\n \"def __str__(self):\\n d = {}\\n d[\\\"tuner_number\\\"] = self.tuner_number\\n d[\\\"output_format\\\"] = self.output_format\\n d[\\\"output_source\\\"] = self.output_source\\n return str(d)\",\n \"def __str__(self):\\n return ' '.join([self.source, self.name, str(self.outputs)])\",\n \"def get_model_params(self):\\n w1 = self.w1\\n b1 = self.b1\\n w2 = self.w2\\n b2 = self.b2\\n w3 = self.w3\\n b3 = self.b3\\n w4 = self.w4\\n b4 = self.b4\\n w5 = self.w5\\n b5 = self.b5\\n w6 = self.w6\\n b6 = self.b6\\n\\n return w1, b1, w2, b2, w3, b3, w4, b4, w5, b5, w6, b6\",\n \"def __str__(self):\\n\\n print(\\\"\\\")\\n s = \\\"NAME : \\\"+self._name+\\\"\\\\n\\\\n\\\"\\n s += \\\"PARAMS :\\\"\\n print(s)\\n\\n for key, val in self.params.items():\\n l = (21-len(key))//7\\n print(\\\"{0}\\\".format(key)+\\\"\\\\t\\\"*l+\\\":\\\\t{0}\\\".format(val))\\n\\n s = \\\"\\\\nRuns stored in DEFAULT_RUNS = \\\"+str(len(self.default_runs))\\n print(s)\\n\\n s = \\\"\\\\nRuns stored in MOD_RUNS = \\\"+str(len(self.mod_runs))\\n print(s)\\n\\n return \\\"\\\"\",\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 getConfig(self):\\n pass\",\n \"def GetModelDefinitionString(filename, model, forhpp):\\n #Get the model by reading from the filename\\n\\n #Size is defined by the number of ODEs\\n odes_dict = translator.GetOdesDictionary(model)\\n rules_dict = translator.GetRulesDictionary(model)\\n num_species = len(odes_dict) + len(rules_dict)\\n\\n model_defn_string = \\\"\\\"\\n\\n ode_name = GetOdeSystemName(filename)\\n model_name = GetModelName(filename, model)\\n\\n #Define some common names depending on whether or not the model is a SRN or a CCM\\n is_srn_model = translator.IsSrnModel(model)\\n if (is_srn_model):\\n wrapper = \\\"SbmlSrnWrapperModel\\\"\\n\\n model_defn_string += translator.GetBlockCommentDefinition(0, \\\"Define SRN model using Wrappers.\\\", True)\\n else:\\n wrapper = \\\"SbmlCcmWrapperModel\\\"\\n\\n model_defn_string += translator.GetBlockCommentDefinition(0, \\\"Define cell cycle model using Wrappers.\\\", True)\\n\\n model_defn_string += (IncludeString(\\\"\\\\\\\"\\\" + wrapper + \\\".hpp\\\\\\\"\\\") + \\n IncludeString(\\\"\\\\\\\"\\\" + wrapper + \\\".cpp\\\\\\\"\\\") + \\\"\\\\n\\\" + \\n \\\"typedef \\\" + wrapper + \\\"<\\\" + ode_name + \\\",\\\" + str(num_species) + \\\"> \\\" + model_name + \\\";\\\\n\\\" + \\n \\\"\\\\n\\\")\\n\\n model_defn_string += translator.GetCommentDefinition(0, \\\"Declare identifiers for the serializer\\\", True)\\n if ( forhpp ):\\n model_defn_string += IncludeString(\\\"\\\\\\\"SerializationExportWrapper.hpp\\\\\\\"\\\")\\n else:\\n model_defn_string += IncludeString(\\\"\\\\\\\"SerializationExportWrapperForCpp.hpp\\\\\\\"\\\")\\n\\n model_defn_string += (\\\"CHASTE_CLASS_EXPORT(\\\" + ode_name + \\\")\\\\n\\\" + \\n \\\"EXPORT_TEMPLATE_CLASS2(\\\" + wrapper + \\\", \\\" + ode_name + \\\", \\\" + str(num_species) + \\\")\\\\n\\\" + \\n \\\"\\\\n\\\" + \\n IncludeString(\\\"\\\\\\\"CellCycleModelOdeSolverExportWrapper.hpp\\\\\\\"\\\") + \\n \\\"EXPORT_CELL_CYCLE_MODEL_ODE_SOLVER(\\\" + model_name + \\\")\\\\n\\\\n\\\" )\\n\\n return model_defn_string\",\n \"def __str__(self) -> str:\\n st = \\\"<Output> \\\"\\n if self.inst_out:\\n st += f'instance:{self.inst_out};'\\n st += f'''{self.output} -> {self.target or '\\\"\\\"'} -> '''\\n if self.inst_in:\\n st += f\\\"instance:{self.inst_in};\\\"\\n st += self.input\\n\\n if self.params and not self.inst_in:\\n st += f\\\" ({self.params})\\\"\\n if self.delay != 0:\\n st += f\\\" after {self.delay} seconds\\\"\\n if self.times != -1:\\n st += \\\" (once only)\\\" if self.times == 1 else f\\\" ({self.times!s} times only)\\\"\\n return st\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.63613605","0.6227064","0.6027238","0.6021502","0.59806687","0.59724784","0.58838063","0.5799649","0.5774296","0.57192713","0.5702105","0.57006687","0.568292","0.56661975","0.5660352","0.5649805","0.56424356","0.56185853","0.56185603","0.5617623","0.5615714","0.5590734","0.55902725","0.55628484","0.5554227","0.5527074","0.5506852","0.54998374","0.54964006","0.54950285","0.5494149","0.5492259","0.5477049","0.5458185","0.5455436","0.5450347","0.5448057","0.5445297","0.54390126","0.5438301","0.54374534","0.5428033","0.54267234","0.5410688","0.5402673","0.54024625","0.5387184","0.5387144","0.53814685","0.5378139","0.53710026","0.53704256","0.5365744","0.5361545","0.53501403","0.53481466","0.5339493","0.5331479","0.5327401","0.53258944","0.5325594","0.53206027","0.5318255","0.53141767","0.53122276","0.5296928","0.5296682","0.52928066","0.52886486","0.5275089","0.5275089","0.5274901","0.52683663","0.5265074","0.52596873","0.5250124","0.52427113","0.5240872","0.5240872","0.52357435","0.5228474","0.52254355","0.52254355","0.52254355","0.52254355","0.52225244","0.52225244","0.5220312","0.5216827","0.5214683","0.5213072","0.52127904","0.52086455","0.5208315","0.5204992","0.5202142","0.5183929","0.5182826","0.51822203","0.517936"],"string":"[\n \"0.63613605\",\n \"0.6227064\",\n \"0.6027238\",\n \"0.6021502\",\n \"0.59806687\",\n \"0.59724784\",\n \"0.58838063\",\n \"0.5799649\",\n \"0.5774296\",\n \"0.57192713\",\n \"0.5702105\",\n \"0.57006687\",\n \"0.568292\",\n \"0.56661975\",\n \"0.5660352\",\n \"0.5649805\",\n \"0.56424356\",\n \"0.56185853\",\n \"0.56185603\",\n \"0.5617623\",\n \"0.5615714\",\n \"0.5590734\",\n \"0.55902725\",\n \"0.55628484\",\n \"0.5554227\",\n \"0.5527074\",\n \"0.5506852\",\n \"0.54998374\",\n \"0.54964006\",\n \"0.54950285\",\n \"0.5494149\",\n \"0.5492259\",\n \"0.5477049\",\n \"0.5458185\",\n \"0.5455436\",\n \"0.5450347\",\n \"0.5448057\",\n \"0.5445297\",\n \"0.54390126\",\n \"0.5438301\",\n \"0.54374534\",\n \"0.5428033\",\n \"0.54267234\",\n \"0.5410688\",\n \"0.5402673\",\n \"0.54024625\",\n \"0.5387184\",\n \"0.5387144\",\n \"0.53814685\",\n \"0.5378139\",\n \"0.53710026\",\n \"0.53704256\",\n \"0.5365744\",\n \"0.5361545\",\n \"0.53501403\",\n \"0.53481466\",\n \"0.5339493\",\n \"0.5331479\",\n \"0.5327401\",\n \"0.53258944\",\n \"0.5325594\",\n \"0.53206027\",\n \"0.5318255\",\n \"0.53141767\",\n \"0.53122276\",\n \"0.5296928\",\n \"0.5296682\",\n \"0.52928066\",\n \"0.52886486\",\n \"0.5275089\",\n \"0.5275089\",\n \"0.5274901\",\n \"0.52683663\",\n \"0.5265074\",\n \"0.52596873\",\n \"0.5250124\",\n \"0.52427113\",\n \"0.5240872\",\n \"0.5240872\",\n \"0.52357435\",\n \"0.5228474\",\n \"0.52254355\",\n \"0.52254355\",\n \"0.52254355\",\n \"0.52254355\",\n \"0.52225244\",\n \"0.52225244\",\n \"0.5220312\",\n \"0.5216827\",\n \"0.5214683\",\n \"0.5213072\",\n \"0.52127904\",\n \"0.52086455\",\n \"0.5208315\",\n \"0.5204992\",\n \"0.5202142\",\n \"0.5183929\",\n \"0.5182826\",\n \"0.51822203\",\n \"0.517936\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.737774"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":17890,"cells":{"query":{"kind":"string","value":"Remove nonalphanumeric characters to make them valid with Bonsai interaction."},"document":{"kind":"string","value":"def _clean_non_alphanumeric_chars(self):\n\n for i,variable in enumerate(self.model_description.modelVariables):\n clean_name = re.sub(r'[^a-zA-Z0-9_]', '', variable.name)\n if clean_name != variable.name:\n log = \"Sim variable '{}' has been renamed to '{}' \".format(variable.name, clean_name)\n log += \"to comply with Bonsai naming requirements.\"\n print(log)\n self.model_description.modelVariables[i].name = clean_name\n\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 cleanup_input(data):\n data = re.sub(r'[^0-9A-Za-z ()_,.-:]', '', data)\n return data","def clean_unnecessary_characters(self, tweet):\n tweet = tweet.lstrip(\"\\\"\").rstrip(\"\\\"\")\n tweet = re.sub(self.compiledAlphanumericRegex, ' ', tweet)\n tweet = tweet.replace('_', ' ')\n return tweet","def removeSpecialChars(self) -> None:\n self.text = re.sub('[^a-zA-z0-9\\n\\.\\s]', '', self.text)","def clean(name):\n name = remove_extra(name)\n name = unidecode.unidecode(name) # Remove diacritics\n name = \"\".join(\n list(filter(lambda c: c in (string.ascii_letters + string.digits + \" \"), name))\n )\n name = name.lower().strip()\n return name","def clean_string(value):\n\treturn re.sub(r'[^a-zA-Z0-9_.]', '', str(value))","def remove_specials(sentence):\n sentence = sentence.replace('-', ' ')\n sentence = re.sub(r'[^\\w\\s]', '', sentence)\n return sentence","def remove_non_alphabetic_text(text):\n return RegexFilters.replace_non_alphabetic_text(text, \"\")","def _scrub(self, string):\n if not string.isalnum():\n raise ValueError(\"Table name cannot include non-alphanumerics.\")\n return string","def Clean(s):\n for c in BAD_CHARACTERS:\n s = s.replace(c, '_')\n return s","def remove_non_alpha(self,text):\n \n removelist=\"-\\.\\/\\?\\@\"\n re_alpha_numeric1=r\"[^0-9a-zA-Z\"+removelist+\" ]\"\n clean_text=re.sub(re_alpha_numeric1,'',text)\n clean_text=clean_text.replace('/',' ')\n clean_text=re.sub(' +', ' ', clean_text)\n return clean_text","def fixString(string):\n string = re.sub(r\"[^A-Z-]\", \"\", string)\n return string","def clean_username(value):\n if NO_ASCII_REGEX.search(value):\n value = unidecode(value)\n value = NO_ASCII_REGEX.sub('', value)\n value = NO_SPECIAL_REGEX.sub('', value)\n return value","def replace_bad_characters(self, str):\n\n str = unicode(BeautifulStoneSoup(str,\n convertEntities=BeautifulStoneSoup.HTML_ENTITIES))\n str = unicodedata.normalize('NFKD', str).encode('ascii', 'ignore')\n str = unicode(re.sub('[^\\w\\s-]', '', str).strip().lower())\n str = unicode(str.replace(' ', '-'))\n return str","def _cleanse(text):\n return ''.join([character for character in text\n if character.isalnum()]).lower()","def _clean(self, text):\n if len(self.alph) == 26:\n text = sub('[\\n\\t ' + string.punctuation + ']+?', '', text)\n else:\n text = sub('[\\n\\t]+?', '', text)\n\n text = text.lower()\n text = text.encode('ascii', 'ignore').decode()\n return text","def strip_other_charcter():\n pass","def remove_string_special_characters(s):\n stripped = re.sub('[^\\w\\s]', '', s)\n stripped = re.sub('_', '', stripped)\n stripped = re.sub('\\s+', ' ', stripped)\n stripped = stripped.strip()\n\n return stripped","def clean_user_input(self, user_input):\n legal_chars = re.compile(r'^[a-z0-9]$')\n return filter(lambda c: re.match(legal_chars, c), user_input.lower())","def to_clean_str(s: str) -> str:\n return re.sub(\"[^a-zA-Z0-9]\", \"\", s).lower()","def clean(sent):\n p1 = re.compile('\\W')\n p2 = re.compile('\\s+')\n sent = re.sub(r\"http\\S+\", \"\", sent)\n sent = ReplaceThreeOrMore(sent)\n sent = remove_unicode_diac(sent)\n sent = sent.replace('_', ' ')\n sent = re.sub(r'[A-Za-z0-9]', r'', sent)\n sent = re.sub(p1, ' ', sent)\n sent = re.sub(p2, ' ', sent)\n return sent","def stripword( s ) :\n return re.sub( '[\\W\\d]', '', s )","def remove_special_characters_from_text(text) -> str:\n return re.sub(r'[^\\w\\s]', '', text.strip())","def replace_any_non_letter_or_number_character(text):\n text = text.strip()\n text = re.sub('[^A-Za-z0-9 ]+', '', text)\n return text","def fix_characters(title):\n return re.sub('[^0-9a-zA-Z]+', ' ', title)","def _remove_custom_chars(self, text: str) -> str:\n patterns = \"|\".join([x for x in self.custom_chars])\n return re.sub(patterns, \"\", str(text), flags=re.IGNORECASE)","def clean_word(word: str) -> str:\n\n cleaned_word = ''\n for char in word.lower():\n if char.isalnum():\n cleaned_word = cleaned_word + char\n return cleaned_word","def remove_special_characters(text, remove_digits=False):\n pattern = r'[^a-zA-z0-9\\s]' if not remove_digits else r'[^a-zA-z\\s]'\n text = re.sub(pattern, '', text)\n return text","def remove_bad_characters(self):\n\n self.categorie_name = self.categorie_name.replace(\"\\n\", \"\")","def scrub(input_string):\n return ''.join(k for k in input_string if k.isalnum())","def _replace_non_alnum(self):\n no_punct = [x if x.isalnum() else ' ' for x in self._phrase.lower()]\n return ''.join(no_punct) # Convert an array of char to string","def get_clean_text(messy_text: str) -> str:\n new_text = \"\"\n replace = {\n \"*\": \"\\\"\",\n \"!\": \"?\",\n \"/\": ',',\n \"?\": \"!\"\n }\n remove = \"1234567890&@#$%^()_+|><~\"\n pls_do_upper = False\n for l in messy_text:\n if l in replace:\n new_text += replace[l]\n elif l not in remove:\n if pls_do_upper:\n new_text += l.upper()\n else:\n new_text += l\n return new_text","def sanitize(value):\n from re import sub\n from unicodedata import normalize\n value = normalize('NFKD', value).encode('ascii', 'ignore')\n value = sub('[^\\w\\s\\.-]', '', value.decode('utf-8')).strip().lower()\n return sub('[-_\\s]+', '_', value)","def clean_training_text(txt):\n return re.sub('[^A-Za-z0-9]+', ' ', str(txt)).strip()","def non_letter_removal(text):\n return re.sub('[^a-zA-Z]', ' ', text)","def remove_special_characters(string_list):","def remove_special_chars(s):\n stripped = re.sub('[^\\w\\s]', ' ', s)\n stripped = re.sub('_', ' ', stripped)\n\n # Make all whitespaces only one space\n stripped = re.sub('\\s+', ' ', stripped)\n\n stripped = stripped.strip()\n\n return stripped","def get_strip_string(self, i_str):\n return ''.join(e for e in i_str if e.isalnum())","def fixString(string):\n string = re.sub(r\"[^A-Z-]\", \"\", string)\n string = string.strip(\"\\n\")\n return string","def sanitize_txt(x):\n return '_'.join(smart_split(x.lower()))","def remove_special_characters(text):\n soup = BeautifulSoup(text, \"html.parser\")\n review = soup.get_text()\n review = r\"[^a-zA-z0-9\\s]\"\n review = re.sub(review, \"\", text)\n return review.lower()","def clean_message(message: str) -> str:\n message = message.upper()\n new_message = ''\n\n for char in message:\n\n if char.isalpha():\n new_message += char\n message = new_message\n\n else:\n message = new_message\n\n return message","def sanitize_string(unclean_string: str) -> str:\n return unidecode(unclean_string)","def sanitize(mystr):\n retainlist = \"_-\"\n return re.sub(r'[^\\w' + retainlist + ']', '_', mystr)","def basic_cleaning2(string):\n\n string = string.lower()\n string = re.sub('[0-9\\(\\)\\!\\^\\%\\$\\'\\\"\\.;,-\\?\\{\\}\\[\\]\\\\/]', ' ', string)\n string = re.sub(' +', ' ', string)\n return string","def remove_nonalpha(text):\n text = ''.join(c for c in text if c.isalpha() or c == ' ')\n return re.sub(\" +\", \" \", text)","def remove_punctations_fun(self): \n self.doc = re.sub('[^a-zA-Z0-9]', ' ', self.doc)","def clean_sentence(sentence):\n words = sentence.lower().split()\n clean_sent = \"\"\n for word in words:\n clean_sent += (''.join(list(map(lambda x: x if x in ascii_lowercase or x in \"1234567890\" else '', list(word))))) + \" \"\n return clean_sent[:-1]","def clean_text(text):\n text = str(text).lower()\n text = text.strip(string.punctuation)\n text = re.sub(\"&\", '', text)\n text = re.sub(\"https\", '', text)\n text = re.sub('\\W\\s', '', text)\n text = re.sub('\\s,\\W', '', text)\n text = re.sub('[.!@#$%^&*()_,:;/-]', '', text)\n text = re.sub(\"\\d+\", '', text)\n\n return text","def remove_special_chars(text):\n \n text = re.sub(' +', ' ', re.sub('[^A-Za-z ]+', ' ', text).strip())\n return text","def sanitize(string):\n retval = string.lower()\n retval = re.sub(r\"[^\\w\\s]\", '', retval)\n retval = re.sub(r\"\\s+\", '_', retval)\n return retval","def _sanitizeName(name):\n\n name = name.lower() # lower.\n name = name.replace('.','') # remove periods.\n name = name.replace('-','') # remove dashes.\n name = name.replace(\"'\",'') # remove apostrophies.\n # return it.\n return name","def sanitize_name(name: str) -> str:\n return re.sub(r\"[^A-Za-z0-9_-]\", \"-\", name)[0:128]","def strip_characters(input_string):\n\n allowed_characters = re.compile(\"[^a-zA-Z0-9_-]\")\n subbed_string = allowed_characters.sub(\"_\", input_string)\n\n string_to_shorten = re.compile(\"__+\")\n shortened_string = string_to_shorten.sub(\"_\", subbed_string)\n\n return shortened_string","def safe(s):\n return (\"\".join(c for c in s if c.isalnum() or c == '_' or c == '-').rstrip())","def clean_name(name, allowed_chars):\n ok = identifier_chars + allowed_chars\n newname = \"\".join(c if c in ok else \"-\" for c in name)\n newname = newname.lstrip(\"-\")\n if not newname:\n raise RuntimeError(f\"No valid chars in name '{name}'.\")\n return newname","def cleanName(name):\n return ''.join(char for char in name.lower() if char in (string.digits + string.ascii_lowercase))","def clean(value):\r\n return re.sub('_+', '_', INVALID_CHARS.sub('_', value))","def clean_text(text):\n new_text = \"\"\n text = text.lower()\n for character in text:\n if character.isalpha():\n new_text = new_text + character\n return new_text","def sanitize(instring):\r\n return instring.encode('ascii','replace')","def clean_venue(venue):\n\n return venue.lower().strip('?:!.,;- ')","def sanitize_str(s):\n # throw away unkown characters\n return [c for c in s if c in letters]","def trimAlphaNum(self, value):\n\n while value and value[-1].isalnum():\n value = value[:-1]\n\n while value and value[0].isalnum():\n value = value[1:]\n\n return value","def stripspecialchars(input_str):\n\n import unicodedata\n\n nfkd_form = unicodedata.normalize('NFKD', unicode(input_str))\n\n return ''.join([c for c in nfkd_form if not unicodedata.combining(c)])","def normalize_name(word):\n return word.strip(\"0123456789!@#$%^&*_() +=\\/?<>,.`~;:\").lower().replace(\" \",\"_\")","def _remove_special_chars(self, text: str) -> str:\n pattern = re.compile(self.special_chars_pattern)\n text = re.sub(pattern, \" \", text)\n return text","def cleanString(self, s):\r\n s = s.lower()\r\n for x in s: \r\n if x in punctuation:\r\n s = s.replace(x, '')\r\n return s","def clean_text_from_nonbasic_characters(text):\n text = re.sub(r\"([^\\u0000-\\u007F])\", \" \", text)\n text = replace_newline_with_space(text).strip()\n text = text.replace(\"_\", \"\")\n text = clean_text_from_multiple_consecutive_whitespaces(text)\n return text","def clean_text_from_nonbasic_characters(text):\n text = re.sub(r\"([^\\u0000-\\u007F])\", \" \", text)\n text = replace_newline_with_space(text).strip()\n text = text.replace(\"_\", \"\")\n text = clean_text_from_multiple_consecutive_whitespaces(text)\n return text","def strip_not_alnum_char(str):\n\n i = 0\n # While we don't find a character or a digit,\n # that means it's a special char (logical!)\n if str:\n while not str[i].isalnum() and i < len(str) - 1:\n i += 1\n if i != len(str) - 2:\n str = str[i:]\n return str","def _sanitize(label):\n return re.sub(r'(\\W+| )', '', label)","def clean_text(txt):\n\n cleaned_txt = ''\n for character in txt:\n if character not in 'abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVQXWY ': #punctuation\n character = ''\n cleaned_txt += character\n elif character == character.upper(): #uppercase\n character = character.lower()\n cleaned_txt += character\n else:\n cleaned_txt += character\n return cleaned_txt","def _only_letters(s):\r\n\treturn _regex_non_letters.sub('', s)","def super_clean_str(string):\n return ''.join(x for x in string if x.isalnum()).lower()","def _strip_invalid_characters(self: object) -> None:\n for current_invalid_character in Episode._invalid_characters:\n self.episode_broadcast = self.episode_broadcast.replace(current_invalid_character, \" \").strip()\n self.episode_inspectors = self.episode_inspectors.replace(current_invalid_character, \" \").strip()\n self.episode_name = self.episode_name.replace(current_invalid_character, \" \").strip()\n self.episode_sequence = self.episode_sequence.replace(current_invalid_character, \"-\").strip()","def _strip_remove_non_alpha(stringSentence):\n # type: (str) -> str\n\n _stripped = stringSentence.strip()\n _words = _stripped.split(' ')\n _realWords = [word for word in _words if word.isalpha()]\n\n return ' '.join(_realWords)","def filter_invalid_characters(self, string):\n valid_chars = \"abcdefghijklmnopqrstuvwxyz0123456789-.\"\n newstring = \"\"\n for char in string:\n use_char = char\n if char not in valid_chars:\n use_char = '-'\n newstring = newstring + use_char\n\n return newstring","def sanitize_text(text: str) -> str:\n for r in [RE_NOISE, RE_EMAIL, RE_REFERENCE]:\n text = r.sub(\"\", text)\n return text","def sanitize(name):\n return re.sub(\"\\\\W|^(?=\\\\d)\", \"_\", name)","def remove_special_chars(sentence):\r\n result = re.sub(r\"[^a-zA-Z0-9.]+\", ' ', re.sub('\\.\\.+', ' ', sentence))\r\n return result","def preprocess_input(self, text):\n text = re.sub(r\"([^a-zA-Z0-9 -]+ +[^a-zA-Z0-9 -]*|[^a-zA-Z0-9 -]*\" +\n \" +[^a-zA-Z0-9 -]+)\", ' ', text, flags=re.UNICODE)\n text = re.sub(r\"([^a-zA-Z0-9 -]+$|^[^a-zA-Z0-9 -]+)\", '', text)\n text = re.sub(r\"([a-zA-Z0-9 -]+?)([^a-zA-Z0-9 -])([a-zA-Z0-9 -]+?)\",\n r\"\\1'\\3\", text, flags=re.UNICODE)\n text = re.sub(r\"([\\x00-\\x7F -]+?)([^a-zA-Z0-9 -]+)([\\x00-\\x7F -]+?)\",\n r\"\\1'\\3\", text, flags=re.UNICODE).encode(\"utf-8\")\n return re.sub(r\"([^a-zA-Z0-9 \\-\\'])\", '', text, flags=re.UNICODE)","def to_safe(self, word):\n return re.sub(\"[^A-Za-z0-9\\-]\", \"_\", word)","def clean_str(self,string):\r\n string = re.sub(r\"[^A-Za-z0-9(),!?\\'\\`]\", \" \", string)\r\n string = re.sub(r\"\\'s\", \" \\'s\", string)\r\n string = re.sub(r\"\\'ve\", \" \\'ve\", string)\r\n string = re.sub(r\"n\\'t\", \" n\\'t\", string)\r\n string = re.sub(r\"\\'re\", \" \\'re\", string)\r\n string = re.sub(r\"\\'d\", \" \\'d\", string)\r\n string = re.sub(r\"\\'ll\", \" \\'ll\", string)\r\n string = re.sub(r\",\", \" , \", string)\r\n string = re.sub(r\"!\", \" ! \", string)\r\n string = re.sub(r\"\\(\", \" \\( \", string)\r\n string = re.sub(r\"\\)\", \" \\) \", string)\r\n string = re.sub(r\"\\?\", \" \\? \", string)\r\n string = re.sub(r\"\\s{2,}\", \" \", string)\r\n return string.strip().lower()","def _filter_string(cls, string, extra_chars=\"\"):\n char_white_list = ascii_letters + digits + extra_chars\n return \"\".join([char for char in string if char in char_white_list])","def _filter_string(cls, string, extra_chars=\"\"):\n char_white_list = ascii_letters + digits + extra_chars\n return \"\".join([char for char in string if char in char_white_list])","def clean_data(s):\n s = s.strip()\n s = s.lower()\n return s","def sanitize(text):\n #text = re.sub(r'[*]',r'\\*',text) \n text = re.sub(r'~',r'\\~',text) \n #text = re.sub(r'<',r'\\textless',text) \n #text = re.sub(r'>',r'\\textgreater',text) \n text = re.sub(r'\\|',r'\\|',text) \n text = re.sub(r'_',r'\\\\_',text) \n return text","def cleanWord(word):\r\n newWord = [letter.lower() for letter in word if letter.isalpha()]\r\n return \"\".join(newWord)","def clean_word(self, word):\n return self.filter_pattern.sub(u'', word.lower())","def clean(str):\n str = str.replace(u\"“\",u\"``\")\n str = str.replace(u\"”\",u\"''\")\n str = str.replace(u' \"',u\" ``\")\n str = str.replace(u'\"',u\"''\")\n str = str.replace(u'fi',u\"fi\")\n str = str.replace(u'fl',u\"fl\")\n str = str.replace(u'’',u\"'\")\n str = str.replace(u'–',u\"---\")\n str = str.replace(u'&',u\"\\\\&\")\n str = str.replace(u'#',u\"\\\\#\")\n str = str.replace(u'_',u\"\\\\_\")\n \n return str","def clean_text ( self, text ) :\n text = BeautifulSoup ( text , \"lxml\" ).text # HTML decoding\n text = text.lower ( ) # lowercase text\n text = REPLACE_BY_SPACE_RE.sub ( ' ' , text ) # replace REPLACE_BY_SPACE_RE symbols by space in text\n text = BAD_SYMBOLS_RE.sub ( '' , text ) # delete symbols which are in BAD_SYMBOLS_RE from text\n text = ' '.join ( word for word in text.split ( ) if word not in STOPWORDS ) # delete stopwors from text\n return text","def clean_text ( self, text ) :\n text = BeautifulSoup ( text , \"lxml\" ).text # HTML decoding\n text = text.lower ( ) # lowercase text\n text = REPLACE_BY_SPACE_RE.sub ( ' ' , text ) # replace REPLACE_BY_SPACE_RE symbols by space in text\n text = BAD_SYMBOLS_RE.sub ( '' , text ) # delete symbols which are in BAD_SYMBOLS_RE from text\n text = ' '.join ( word for word in text.split ( ) if word not in STOPWORDS ) # delete stopwors from text\n return text","def clean_text ( self, text ) :\n text = BeautifulSoup ( text , \"lxml\" ).text # HTML decoding\n text = text.lower ( ) # lowercase text\n text = REPLACE_BY_SPACE_RE.sub ( ' ' , text ) # replace REPLACE_BY_SPACE_RE symbols by space in text\n text = BAD_SYMBOLS_RE.sub ( '' , text ) # delete symbols which are in BAD_SYMBOLS_RE from text\n text = ' '.join ( word for word in text.split ( ) if word not in STOPWORDS ) # delete stopwors from text\n return text","def clean_text ( self, text ) :\n text = BeautifulSoup ( text , \"lxml\" ).text # HTML decoding\n text = text.lower ( ) # lowercase text\n text = REPLACE_BY_SPACE_RE.sub ( ' ' , text ) # replace REPLACE_BY_SPACE_RE symbols by space in text\n text = BAD_SYMBOLS_RE.sub ( '' , text ) # delete symbols which are in BAD_SYMBOLS_RE from text\n text = ' '.join ( word for word in text.split ( ) if word not in STOPWORDS ) # delete stopwors from text\n return text","def clean_text ( self, text ) :\n text = BeautifulSoup ( text , \"lxml\" ).text # HTML decoding\n text = text.lower ( ) # lowercase text\n text = REPLACE_BY_SPACE_RE.sub ( ' ' , text ) # replace REPLACE_BY_SPACE_RE symbols by space in text\n text = BAD_SYMBOLS_RE.sub ( '' , text ) # delete symbols which are in BAD_SYMBOLS_RE from text\n text = ' '.join ( word for word in text.split ( ) if word not in STOPWORDS ) # delete stopwors from text\n return text","def clean_text ( self, text ) :\n text = BeautifulSoup ( text , \"lxml\" ).text # HTML decoding\n text = text.lower ( ) # lowercase text\n text = REPLACE_BY_SPACE_RE.sub ( ' ' , text ) # replace REPLACE_BY_SPACE_RE symbols by space in text\n text = BAD_SYMBOLS_RE.sub ( '' , text ) # delete symbols which are in BAD_SYMBOLS_RE from text\n text = ' '.join ( word for word in text.split ( ) if word not in STOPWORDS ) # delete stopwors from text\n return text","def removeNonAsciiFromText(self, text):\n\t\treturn ''.join([i if ord(i) < 128 else '' for i in text])","def sanitize_name(name):\n # For now just change dashes to underscores. Fix this more in the future\n return name.replace(\"-\", \"_\")","def clean_message(message):\n \n new_message = ''\n for char in message:\n if char.isalpha():\n new_message = new_message + char.upper()\n return new_message","def _remove_non_ascii(words):\n new_words = []\n for word in words:\n new_word = unicodedata.normalize('NFKD', word).encode('ascii', 'ignore').decode('utf-8', 'ignore')\n new_words.append(new_word)\n return new_words","def clean_name(s):\n return re.sub('[\\W_]+', '', s).lower()"],"string":"[\n \"def cleanup_input(data):\\n data = re.sub(r'[^0-9A-Za-z ()_,.-:]', '', data)\\n return data\",\n \"def clean_unnecessary_characters(self, tweet):\\n tweet = tweet.lstrip(\\\"\\\\\\\"\\\").rstrip(\\\"\\\\\\\"\\\")\\n tweet = re.sub(self.compiledAlphanumericRegex, ' ', tweet)\\n tweet = tweet.replace('_', ' ')\\n return tweet\",\n \"def removeSpecialChars(self) -> None:\\n self.text = re.sub('[^a-zA-z0-9\\\\n\\\\.\\\\s]', '', self.text)\",\n \"def clean(name):\\n name = remove_extra(name)\\n name = unidecode.unidecode(name) # Remove diacritics\\n name = \\\"\\\".join(\\n list(filter(lambda c: c in (string.ascii_letters + string.digits + \\\" \\\"), name))\\n )\\n name = name.lower().strip()\\n return name\",\n \"def clean_string(value):\\n\\treturn re.sub(r'[^a-zA-Z0-9_.]', '', str(value))\",\n \"def remove_specials(sentence):\\n sentence = sentence.replace('-', ' ')\\n sentence = re.sub(r'[^\\\\w\\\\s]', '', sentence)\\n return sentence\",\n \"def remove_non_alphabetic_text(text):\\n return RegexFilters.replace_non_alphabetic_text(text, \\\"\\\")\",\n \"def _scrub(self, string):\\n if not string.isalnum():\\n raise ValueError(\\\"Table name cannot include non-alphanumerics.\\\")\\n return string\",\n \"def Clean(s):\\n for c in BAD_CHARACTERS:\\n s = s.replace(c, '_')\\n return s\",\n \"def remove_non_alpha(self,text):\\n \\n removelist=\\\"-\\\\.\\\\/\\\\?\\\\@\\\"\\n re_alpha_numeric1=r\\\"[^0-9a-zA-Z\\\"+removelist+\\\" ]\\\"\\n clean_text=re.sub(re_alpha_numeric1,'',text)\\n clean_text=clean_text.replace('/',' ')\\n clean_text=re.sub(' +', ' ', clean_text)\\n return clean_text\",\n \"def fixString(string):\\n string = re.sub(r\\\"[^A-Z-]\\\", \\\"\\\", string)\\n return string\",\n \"def clean_username(value):\\n if NO_ASCII_REGEX.search(value):\\n value = unidecode(value)\\n value = NO_ASCII_REGEX.sub('', value)\\n value = NO_SPECIAL_REGEX.sub('', value)\\n return value\",\n \"def replace_bad_characters(self, str):\\n\\n str = unicode(BeautifulStoneSoup(str,\\n convertEntities=BeautifulStoneSoup.HTML_ENTITIES))\\n str = unicodedata.normalize('NFKD', str).encode('ascii', 'ignore')\\n str = unicode(re.sub('[^\\\\w\\\\s-]', '', str).strip().lower())\\n str = unicode(str.replace(' ', '-'))\\n return str\",\n \"def _cleanse(text):\\n return ''.join([character for character in text\\n if character.isalnum()]).lower()\",\n \"def _clean(self, text):\\n if len(self.alph) == 26:\\n text = sub('[\\\\n\\\\t ' + string.punctuation + ']+?', '', text)\\n else:\\n text = sub('[\\\\n\\\\t]+?', '', text)\\n\\n text = text.lower()\\n text = text.encode('ascii', 'ignore').decode()\\n return text\",\n \"def strip_other_charcter():\\n pass\",\n \"def remove_string_special_characters(s):\\n stripped = re.sub('[^\\\\w\\\\s]', '', s)\\n stripped = re.sub('_', '', stripped)\\n stripped = re.sub('\\\\s+', ' ', stripped)\\n stripped = stripped.strip()\\n\\n return stripped\",\n \"def clean_user_input(self, user_input):\\n legal_chars = re.compile(r'^[a-z0-9]$')\\n return filter(lambda c: re.match(legal_chars, c), user_input.lower())\",\n \"def to_clean_str(s: str) -> str:\\n return re.sub(\\\"[^a-zA-Z0-9]\\\", \\\"\\\", s).lower()\",\n \"def clean(sent):\\n p1 = re.compile('\\\\W')\\n p2 = re.compile('\\\\s+')\\n sent = re.sub(r\\\"http\\\\S+\\\", \\\"\\\", sent)\\n sent = ReplaceThreeOrMore(sent)\\n sent = remove_unicode_diac(sent)\\n sent = sent.replace('_', ' ')\\n sent = re.sub(r'[A-Za-z0-9]', r'', sent)\\n sent = re.sub(p1, ' ', sent)\\n sent = re.sub(p2, ' ', sent)\\n return sent\",\n \"def stripword( s ) :\\n return re.sub( '[\\\\W\\\\d]', '', s )\",\n \"def remove_special_characters_from_text(text) -> str:\\n return re.sub(r'[^\\\\w\\\\s]', '', text.strip())\",\n \"def replace_any_non_letter_or_number_character(text):\\n text = text.strip()\\n text = re.sub('[^A-Za-z0-9 ]+', '', text)\\n return text\",\n \"def fix_characters(title):\\n return re.sub('[^0-9a-zA-Z]+', ' ', title)\",\n \"def _remove_custom_chars(self, text: str) -> str:\\n patterns = \\\"|\\\".join([x for x in self.custom_chars])\\n return re.sub(patterns, \\\"\\\", str(text), flags=re.IGNORECASE)\",\n \"def clean_word(word: str) -> str:\\n\\n cleaned_word = ''\\n for char in word.lower():\\n if char.isalnum():\\n cleaned_word = cleaned_word + char\\n return cleaned_word\",\n \"def remove_special_characters(text, remove_digits=False):\\n pattern = r'[^a-zA-z0-9\\\\s]' if not remove_digits else r'[^a-zA-z\\\\s]'\\n text = re.sub(pattern, '', text)\\n return text\",\n \"def remove_bad_characters(self):\\n\\n self.categorie_name = self.categorie_name.replace(\\\"\\\\n\\\", \\\"\\\")\",\n \"def scrub(input_string):\\n return ''.join(k for k in input_string if k.isalnum())\",\n \"def _replace_non_alnum(self):\\n no_punct = [x if x.isalnum() else ' ' for x in self._phrase.lower()]\\n return ''.join(no_punct) # Convert an array of char to string\",\n \"def get_clean_text(messy_text: str) -> str:\\n new_text = \\\"\\\"\\n replace = {\\n \\\"*\\\": \\\"\\\\\\\"\\\",\\n \\\"!\\\": \\\"?\\\",\\n \\\"/\\\": ',',\\n \\\"?\\\": \\\"!\\\"\\n }\\n remove = \\\"1234567890&@#$%^()_+|><~\\\"\\n pls_do_upper = False\\n for l in messy_text:\\n if l in replace:\\n new_text += replace[l]\\n elif l not in remove:\\n if pls_do_upper:\\n new_text += l.upper()\\n else:\\n new_text += l\\n return new_text\",\n \"def sanitize(value):\\n from re import sub\\n from unicodedata import normalize\\n value = normalize('NFKD', value).encode('ascii', 'ignore')\\n value = sub('[^\\\\w\\\\s\\\\.-]', '', value.decode('utf-8')).strip().lower()\\n return sub('[-_\\\\s]+', '_', value)\",\n \"def clean_training_text(txt):\\n return re.sub('[^A-Za-z0-9]+', ' ', str(txt)).strip()\",\n \"def non_letter_removal(text):\\n return re.sub('[^a-zA-Z]', ' ', text)\",\n \"def remove_special_characters(string_list):\",\n \"def remove_special_chars(s):\\n stripped = re.sub('[^\\\\w\\\\s]', ' ', s)\\n stripped = re.sub('_', ' ', stripped)\\n\\n # Make all whitespaces only one space\\n stripped = re.sub('\\\\s+', ' ', stripped)\\n\\n stripped = stripped.strip()\\n\\n return stripped\",\n \"def get_strip_string(self, i_str):\\n return ''.join(e for e in i_str if e.isalnum())\",\n \"def fixString(string):\\n string = re.sub(r\\\"[^A-Z-]\\\", \\\"\\\", string)\\n string = string.strip(\\\"\\\\n\\\")\\n return string\",\n \"def sanitize_txt(x):\\n return '_'.join(smart_split(x.lower()))\",\n \"def remove_special_characters(text):\\n soup = BeautifulSoup(text, \\\"html.parser\\\")\\n review = soup.get_text()\\n review = r\\\"[^a-zA-z0-9\\\\s]\\\"\\n review = re.sub(review, \\\"\\\", text)\\n return review.lower()\",\n \"def clean_message(message: str) -> str:\\n message = message.upper()\\n new_message = ''\\n\\n for char in message:\\n\\n if char.isalpha():\\n new_message += char\\n message = new_message\\n\\n else:\\n message = new_message\\n\\n return message\",\n \"def sanitize_string(unclean_string: str) -> str:\\n return unidecode(unclean_string)\",\n \"def sanitize(mystr):\\n retainlist = \\\"_-\\\"\\n return re.sub(r'[^\\\\w' + retainlist + ']', '_', mystr)\",\n \"def basic_cleaning2(string):\\n\\n string = string.lower()\\n string = re.sub('[0-9\\\\(\\\\)\\\\!\\\\^\\\\%\\\\$\\\\'\\\\\\\"\\\\.;,-\\\\?\\\\{\\\\}\\\\[\\\\]\\\\\\\\/]', ' ', string)\\n string = re.sub(' +', ' ', string)\\n return string\",\n \"def remove_nonalpha(text):\\n text = ''.join(c for c in text if c.isalpha() or c == ' ')\\n return re.sub(\\\" +\\\", \\\" \\\", text)\",\n \"def remove_punctations_fun(self): \\n self.doc = re.sub('[^a-zA-Z0-9]', ' ', self.doc)\",\n \"def clean_sentence(sentence):\\n words = sentence.lower().split()\\n clean_sent = \\\"\\\"\\n for word in words:\\n clean_sent += (''.join(list(map(lambda x: x if x in ascii_lowercase or x in \\\"1234567890\\\" else '', list(word))))) + \\\" \\\"\\n return clean_sent[:-1]\",\n \"def clean_text(text):\\n text = str(text).lower()\\n text = text.strip(string.punctuation)\\n text = re.sub(\\\"&\\\", '', text)\\n text = re.sub(\\\"https\\\", '', text)\\n text = re.sub('\\\\W\\\\s', '', text)\\n text = re.sub('\\\\s,\\\\W', '', text)\\n text = re.sub('[.!@#$%^&*()_,:;/-]', '', text)\\n text = re.sub(\\\"\\\\d+\\\", '', text)\\n\\n return text\",\n \"def remove_special_chars(text):\\n \\n text = re.sub(' +', ' ', re.sub('[^A-Za-z ]+', ' ', text).strip())\\n return text\",\n \"def sanitize(string):\\n retval = string.lower()\\n retval = re.sub(r\\\"[^\\\\w\\\\s]\\\", '', retval)\\n retval = re.sub(r\\\"\\\\s+\\\", '_', retval)\\n return retval\",\n \"def _sanitizeName(name):\\n\\n name = name.lower() # lower.\\n name = name.replace('.','') # remove periods.\\n name = name.replace('-','') # remove dashes.\\n name = name.replace(\\\"'\\\",'') # remove apostrophies.\\n # return it.\\n return name\",\n \"def sanitize_name(name: str) -> str:\\n return re.sub(r\\\"[^A-Za-z0-9_-]\\\", \\\"-\\\", name)[0:128]\",\n \"def strip_characters(input_string):\\n\\n allowed_characters = re.compile(\\\"[^a-zA-Z0-9_-]\\\")\\n subbed_string = allowed_characters.sub(\\\"_\\\", input_string)\\n\\n string_to_shorten = re.compile(\\\"__+\\\")\\n shortened_string = string_to_shorten.sub(\\\"_\\\", subbed_string)\\n\\n return shortened_string\",\n \"def safe(s):\\n return (\\\"\\\".join(c for c in s if c.isalnum() or c == '_' or c == '-').rstrip())\",\n \"def clean_name(name, allowed_chars):\\n ok = identifier_chars + allowed_chars\\n newname = \\\"\\\".join(c if c in ok else \\\"-\\\" for c in name)\\n newname = newname.lstrip(\\\"-\\\")\\n if not newname:\\n raise RuntimeError(f\\\"No valid chars in name '{name}'.\\\")\\n return newname\",\n \"def cleanName(name):\\n return ''.join(char for char in name.lower() if char in (string.digits + string.ascii_lowercase))\",\n \"def clean(value):\\r\\n return re.sub('_+', '_', INVALID_CHARS.sub('_', value))\",\n \"def clean_text(text):\\n new_text = \\\"\\\"\\n text = text.lower()\\n for character in text:\\n if character.isalpha():\\n new_text = new_text + character\\n return new_text\",\n \"def sanitize(instring):\\r\\n return instring.encode('ascii','replace')\",\n \"def clean_venue(venue):\\n\\n return venue.lower().strip('?:!.,;- ')\",\n \"def sanitize_str(s):\\n # throw away unkown characters\\n return [c for c in s if c in letters]\",\n \"def trimAlphaNum(self, value):\\n\\n while value and value[-1].isalnum():\\n value = value[:-1]\\n\\n while value and value[0].isalnum():\\n value = value[1:]\\n\\n return value\",\n \"def stripspecialchars(input_str):\\n\\n import unicodedata\\n\\n nfkd_form = unicodedata.normalize('NFKD', unicode(input_str))\\n\\n return ''.join([c for c in nfkd_form if not unicodedata.combining(c)])\",\n \"def normalize_name(word):\\n return word.strip(\\\"0123456789!@#$%^&*_() +=\\\\/?<>,.`~;:\\\").lower().replace(\\\" \\\",\\\"_\\\")\",\n \"def _remove_special_chars(self, text: str) -> str:\\n pattern = re.compile(self.special_chars_pattern)\\n text = re.sub(pattern, \\\" \\\", text)\\n return text\",\n \"def cleanString(self, s):\\r\\n s = s.lower()\\r\\n for x in s: \\r\\n if x in punctuation:\\r\\n s = s.replace(x, '')\\r\\n return s\",\n \"def clean_text_from_nonbasic_characters(text):\\n text = re.sub(r\\\"([^\\\\u0000-\\\\u007F])\\\", \\\" \\\", text)\\n text = replace_newline_with_space(text).strip()\\n text = text.replace(\\\"_\\\", \\\"\\\")\\n text = clean_text_from_multiple_consecutive_whitespaces(text)\\n return text\",\n \"def clean_text_from_nonbasic_characters(text):\\n text = re.sub(r\\\"([^\\\\u0000-\\\\u007F])\\\", \\\" \\\", text)\\n text = replace_newline_with_space(text).strip()\\n text = text.replace(\\\"_\\\", \\\"\\\")\\n text = clean_text_from_multiple_consecutive_whitespaces(text)\\n return text\",\n \"def strip_not_alnum_char(str):\\n\\n i = 0\\n # While we don't find a character or a digit,\\n # that means it's a special char (logical!)\\n if str:\\n while not str[i].isalnum() and i < len(str) - 1:\\n i += 1\\n if i != len(str) - 2:\\n str = str[i:]\\n return str\",\n \"def _sanitize(label):\\n return re.sub(r'(\\\\W+| )', '', label)\",\n \"def clean_text(txt):\\n\\n cleaned_txt = ''\\n for character in txt:\\n if character not in 'abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVQXWY ': #punctuation\\n character = ''\\n cleaned_txt += character\\n elif character == character.upper(): #uppercase\\n character = character.lower()\\n cleaned_txt += character\\n else:\\n cleaned_txt += character\\n return cleaned_txt\",\n \"def _only_letters(s):\\r\\n\\treturn _regex_non_letters.sub('', s)\",\n \"def super_clean_str(string):\\n return ''.join(x for x in string if x.isalnum()).lower()\",\n \"def _strip_invalid_characters(self: object) -> None:\\n for current_invalid_character in Episode._invalid_characters:\\n self.episode_broadcast = self.episode_broadcast.replace(current_invalid_character, \\\" \\\").strip()\\n self.episode_inspectors = self.episode_inspectors.replace(current_invalid_character, \\\" \\\").strip()\\n self.episode_name = self.episode_name.replace(current_invalid_character, \\\" \\\").strip()\\n self.episode_sequence = self.episode_sequence.replace(current_invalid_character, \\\"-\\\").strip()\",\n \"def _strip_remove_non_alpha(stringSentence):\\n # type: (str) -> str\\n\\n _stripped = stringSentence.strip()\\n _words = _stripped.split(' ')\\n _realWords = [word for word in _words if word.isalpha()]\\n\\n return ' '.join(_realWords)\",\n \"def filter_invalid_characters(self, string):\\n valid_chars = \\\"abcdefghijklmnopqrstuvwxyz0123456789-.\\\"\\n newstring = \\\"\\\"\\n for char in string:\\n use_char = char\\n if char not in valid_chars:\\n use_char = '-'\\n newstring = newstring + use_char\\n\\n return newstring\",\n \"def sanitize_text(text: str) -> str:\\n for r in [RE_NOISE, RE_EMAIL, RE_REFERENCE]:\\n text = r.sub(\\\"\\\", text)\\n return text\",\n \"def sanitize(name):\\n return re.sub(\\\"\\\\\\\\W|^(?=\\\\\\\\d)\\\", \\\"_\\\", name)\",\n \"def remove_special_chars(sentence):\\r\\n result = re.sub(r\\\"[^a-zA-Z0-9.]+\\\", ' ', re.sub('\\\\.\\\\.+', ' ', sentence))\\r\\n return result\",\n \"def preprocess_input(self, text):\\n text = re.sub(r\\\"([^a-zA-Z0-9 -]+ +[^a-zA-Z0-9 -]*|[^a-zA-Z0-9 -]*\\\" +\\n \\\" +[^a-zA-Z0-9 -]+)\\\", ' ', text, flags=re.UNICODE)\\n text = re.sub(r\\\"([^a-zA-Z0-9 -]+$|^[^a-zA-Z0-9 -]+)\\\", '', text)\\n text = re.sub(r\\\"([a-zA-Z0-9 -]+?)([^a-zA-Z0-9 -])([a-zA-Z0-9 -]+?)\\\",\\n r\\\"\\\\1'\\\\3\\\", text, flags=re.UNICODE)\\n text = re.sub(r\\\"([\\\\x00-\\\\x7F -]+?)([^a-zA-Z0-9 -]+)([\\\\x00-\\\\x7F -]+?)\\\",\\n r\\\"\\\\1'\\\\3\\\", text, flags=re.UNICODE).encode(\\\"utf-8\\\")\\n return re.sub(r\\\"([^a-zA-Z0-9 \\\\-\\\\'])\\\", '', text, flags=re.UNICODE)\",\n \"def to_safe(self, word):\\n return re.sub(\\\"[^A-Za-z0-9\\\\-]\\\", \\\"_\\\", word)\",\n \"def clean_str(self,string):\\r\\n string = re.sub(r\\\"[^A-Za-z0-9(),!?\\\\'\\\\`]\\\", \\\" \\\", string)\\r\\n string = re.sub(r\\\"\\\\'s\\\", \\\" \\\\'s\\\", string)\\r\\n string = re.sub(r\\\"\\\\'ve\\\", \\\" \\\\'ve\\\", string)\\r\\n string = re.sub(r\\\"n\\\\'t\\\", \\\" n\\\\'t\\\", string)\\r\\n string = re.sub(r\\\"\\\\'re\\\", \\\" \\\\'re\\\", string)\\r\\n string = re.sub(r\\\"\\\\'d\\\", \\\" \\\\'d\\\", string)\\r\\n string = re.sub(r\\\"\\\\'ll\\\", \\\" \\\\'ll\\\", string)\\r\\n string = re.sub(r\\\",\\\", \\\" , \\\", string)\\r\\n string = re.sub(r\\\"!\\\", \\\" ! \\\", string)\\r\\n string = re.sub(r\\\"\\\\(\\\", \\\" \\\\( \\\", string)\\r\\n string = re.sub(r\\\"\\\\)\\\", \\\" \\\\) \\\", string)\\r\\n string = re.sub(r\\\"\\\\?\\\", \\\" \\\\? \\\", string)\\r\\n string = re.sub(r\\\"\\\\s{2,}\\\", \\\" \\\", string)\\r\\n return string.strip().lower()\",\n \"def _filter_string(cls, string, extra_chars=\\\"\\\"):\\n char_white_list = ascii_letters + digits + extra_chars\\n return \\\"\\\".join([char for char in string if char in char_white_list])\",\n \"def _filter_string(cls, string, extra_chars=\\\"\\\"):\\n char_white_list = ascii_letters + digits + extra_chars\\n return \\\"\\\".join([char for char in string if char in char_white_list])\",\n \"def clean_data(s):\\n s = s.strip()\\n s = s.lower()\\n return s\",\n \"def sanitize(text):\\n #text = re.sub(r'[*]',r'\\\\*',text) \\n text = re.sub(r'~',r'\\\\~',text) \\n #text = re.sub(r'<',r'\\\\textless',text) \\n #text = re.sub(r'>',r'\\\\textgreater',text) \\n text = re.sub(r'\\\\|',r'\\\\|',text) \\n text = re.sub(r'_',r'\\\\\\\\_',text) \\n return text\",\n \"def cleanWord(word):\\r\\n newWord = [letter.lower() for letter in word if letter.isalpha()]\\r\\n return \\\"\\\".join(newWord)\",\n \"def clean_word(self, word):\\n return self.filter_pattern.sub(u'', word.lower())\",\n \"def clean(str):\\n str = str.replace(u\\\"“\\\",u\\\"``\\\")\\n str = str.replace(u\\\"”\\\",u\\\"''\\\")\\n str = str.replace(u' \\\"',u\\\" ``\\\")\\n str = str.replace(u'\\\"',u\\\"''\\\")\\n str = str.replace(u'fi',u\\\"fi\\\")\\n str = str.replace(u'fl',u\\\"fl\\\")\\n str = str.replace(u'’',u\\\"'\\\")\\n str = str.replace(u'–',u\\\"---\\\")\\n str = str.replace(u'&',u\\\"\\\\\\\\&\\\")\\n str = str.replace(u'#',u\\\"\\\\\\\\#\\\")\\n str = str.replace(u'_',u\\\"\\\\\\\\_\\\")\\n \\n return str\",\n \"def clean_text ( self, text ) :\\n text = BeautifulSoup ( text , \\\"lxml\\\" ).text # HTML decoding\\n text = text.lower ( ) # lowercase text\\n text = REPLACE_BY_SPACE_RE.sub ( ' ' , text ) # replace REPLACE_BY_SPACE_RE symbols by space in text\\n text = BAD_SYMBOLS_RE.sub ( '' , text ) # delete symbols which are in BAD_SYMBOLS_RE from text\\n text = ' '.join ( word for word in text.split ( ) if word not in STOPWORDS ) # delete stopwors from text\\n return text\",\n \"def clean_text ( self, text ) :\\n text = BeautifulSoup ( text , \\\"lxml\\\" ).text # HTML decoding\\n text = text.lower ( ) # lowercase text\\n text = REPLACE_BY_SPACE_RE.sub ( ' ' , text ) # replace REPLACE_BY_SPACE_RE symbols by space in text\\n text = BAD_SYMBOLS_RE.sub ( '' , text ) # delete symbols which are in BAD_SYMBOLS_RE from text\\n text = ' '.join ( word for word in text.split ( ) if word not in STOPWORDS ) # delete stopwors from text\\n return text\",\n \"def clean_text ( self, text ) :\\n text = BeautifulSoup ( text , \\\"lxml\\\" ).text # HTML decoding\\n text = text.lower ( ) # lowercase text\\n text = REPLACE_BY_SPACE_RE.sub ( ' ' , text ) # replace REPLACE_BY_SPACE_RE symbols by space in text\\n text = BAD_SYMBOLS_RE.sub ( '' , text ) # delete symbols which are in BAD_SYMBOLS_RE from text\\n text = ' '.join ( word for word in text.split ( ) if word not in STOPWORDS ) # delete stopwors from text\\n return text\",\n \"def clean_text ( self, text ) :\\n text = BeautifulSoup ( text , \\\"lxml\\\" ).text # HTML decoding\\n text = text.lower ( ) # lowercase text\\n text = REPLACE_BY_SPACE_RE.sub ( ' ' , text ) # replace REPLACE_BY_SPACE_RE symbols by space in text\\n text = BAD_SYMBOLS_RE.sub ( '' , text ) # delete symbols which are in BAD_SYMBOLS_RE from text\\n text = ' '.join ( word for word in text.split ( ) if word not in STOPWORDS ) # delete stopwors from text\\n return text\",\n \"def clean_text ( self, text ) :\\n text = BeautifulSoup ( text , \\\"lxml\\\" ).text # HTML decoding\\n text = text.lower ( ) # lowercase text\\n text = REPLACE_BY_SPACE_RE.sub ( ' ' , text ) # replace REPLACE_BY_SPACE_RE symbols by space in text\\n text = BAD_SYMBOLS_RE.sub ( '' , text ) # delete symbols which are in BAD_SYMBOLS_RE from text\\n text = ' '.join ( word for word in text.split ( ) if word not in STOPWORDS ) # delete stopwors from text\\n return text\",\n \"def clean_text ( self, text ) :\\n text = BeautifulSoup ( text , \\\"lxml\\\" ).text # HTML decoding\\n text = text.lower ( ) # lowercase text\\n text = REPLACE_BY_SPACE_RE.sub ( ' ' , text ) # replace REPLACE_BY_SPACE_RE symbols by space in text\\n text = BAD_SYMBOLS_RE.sub ( '' , text ) # delete symbols which are in BAD_SYMBOLS_RE from text\\n text = ' '.join ( word for word in text.split ( ) if word not in STOPWORDS ) # delete stopwors from text\\n return text\",\n \"def removeNonAsciiFromText(self, text):\\n\\t\\treturn ''.join([i if ord(i) < 128 else '' for i in text])\",\n \"def sanitize_name(name):\\n # For now just change dashes to underscores. Fix this more in the future\\n return name.replace(\\\"-\\\", \\\"_\\\")\",\n \"def clean_message(message):\\n \\n new_message = ''\\n for char in message:\\n if char.isalpha():\\n new_message = new_message + char.upper()\\n return new_message\",\n \"def _remove_non_ascii(words):\\n new_words = []\\n for word in words:\\n new_word = unicodedata.normalize('NFKD', word).encode('ascii', 'ignore').decode('utf-8', 'ignore')\\n new_words.append(new_word)\\n return new_words\",\n \"def clean_name(s):\\n return re.sub('[\\\\W_]+', '', s).lower()\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.73747957","0.73650575","0.73443496","0.7294863","0.72666633","0.72465616","0.7158626","0.7149394","0.713084","0.7125717","0.71202695","0.71086025","0.71029955","0.70836455","0.707941","0.70679945","0.70411354","0.70361745","0.70283735","0.7028341","0.70243955","0.7019881","0.7018226","0.7016978","0.70134974","0.6982579","0.6982041","0.6963868","0.69545656","0.6945799","0.6919488","0.6911822","0.6907064","0.6896959","0.6892414","0.68882674","0.6876","0.6862288","0.684788","0.6844528","0.6840049","0.6826011","0.68152106","0.681144","0.68066037","0.67985","0.67940855","0.67919827","0.6791201","0.6781587","0.67804223","0.6775861","0.6772801","0.67614883","0.6758718","0.67554754","0.67488647","0.67475986","0.67460495","0.6745042","0.6736637","0.67272496","0.6722454","0.6722124","0.6711262","0.66972","0.66922957","0.66922957","0.66912585","0.66832364","0.6676447","0.66710883","0.66655314","0.6661466","0.6654149","0.6651273","0.66489476","0.6648749","0.6627543","0.6626606","0.6619129","0.6611208","0.6609993","0.6609993","0.65993875","0.65954775","0.6594463","0.6593881","0.6593782","0.65927047","0.65927047","0.65927047","0.65927047","0.65927047","0.65927047","0.6584745","0.65830773","0.6582565","0.6573059","0.656765"],"string":"[\n \"0.73747957\",\n \"0.73650575\",\n \"0.73443496\",\n \"0.7294863\",\n \"0.72666633\",\n \"0.72465616\",\n \"0.7158626\",\n \"0.7149394\",\n \"0.713084\",\n \"0.7125717\",\n \"0.71202695\",\n \"0.71086025\",\n \"0.71029955\",\n \"0.70836455\",\n \"0.707941\",\n \"0.70679945\",\n \"0.70411354\",\n \"0.70361745\",\n \"0.70283735\",\n \"0.7028341\",\n \"0.70243955\",\n \"0.7019881\",\n \"0.7018226\",\n \"0.7016978\",\n \"0.70134974\",\n \"0.6982579\",\n \"0.6982041\",\n \"0.6963868\",\n \"0.69545656\",\n \"0.6945799\",\n \"0.6919488\",\n \"0.6911822\",\n \"0.6907064\",\n \"0.6896959\",\n \"0.6892414\",\n \"0.68882674\",\n \"0.6876\",\n \"0.6862288\",\n \"0.684788\",\n \"0.6844528\",\n \"0.6840049\",\n \"0.6826011\",\n \"0.68152106\",\n \"0.681144\",\n \"0.68066037\",\n \"0.67985\",\n \"0.67940855\",\n \"0.67919827\",\n \"0.6791201\",\n \"0.6781587\",\n \"0.67804223\",\n \"0.6775861\",\n \"0.6772801\",\n \"0.67614883\",\n \"0.6758718\",\n \"0.67554754\",\n \"0.67488647\",\n \"0.67475986\",\n \"0.67460495\",\n \"0.6745042\",\n \"0.6736637\",\n \"0.67272496\",\n \"0.6722454\",\n \"0.6722124\",\n \"0.6711262\",\n \"0.66972\",\n \"0.66922957\",\n \"0.66922957\",\n \"0.66912585\",\n \"0.66832364\",\n \"0.6676447\",\n \"0.66710883\",\n \"0.66655314\",\n \"0.6661466\",\n \"0.6654149\",\n \"0.6651273\",\n \"0.66489476\",\n \"0.6648749\",\n \"0.6627543\",\n \"0.6626606\",\n \"0.6619129\",\n \"0.6611208\",\n \"0.6609993\",\n \"0.6609993\",\n \"0.65993875\",\n \"0.65954775\",\n \"0.6594463\",\n \"0.6593881\",\n \"0.6593782\",\n \"0.65927047\",\n \"0.65927047\",\n \"0.65927047\",\n \"0.65927047\",\n \"0.65927047\",\n \"0.65927047\",\n \"0.6584745\",\n \"0.65830773\",\n \"0.6582565\",\n \"0.6573059\",\n \"0.656765\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.744641"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":17891,"cells":{"query":{"kind":"string","value":"Template for simulating FMU models for Bonsai integration. Note, it calls FMUSimValidation to validate the model when first instanced."},"document":{"kind":"string","value":"def __init__(\n self,\n model_filepath: str,\n fmi_version: str = FMI_VERSION,\n start_time = START_TIME,\n stop_time = STOP_TIME,\n step_size = STEP_SIZE,\n user_validation: bool = False,\n use_unzipped_model: bool = False,\n ):\n\n # validate simulation: config_vars (optional), inputs, and outputs\n validated_sim = FMUSimValidation(model_filepath, user_validation)\n \n # extract validated sim configuration\n self.model_filepath = validated_sim.model_filepath\n self.sim_config_filepath = validated_sim.sim_config_filepath\n self.model_description = validated_sim.model_description\n # model variable names structured per type (config, inputs/brain actions, outputs/brain states)\n self.sim_config_params = validated_sim.sim_config_params\n self.sim_inputs = validated_sim.sim_inputs\n self.sim_outputs = validated_sim.sim_outputs\n self.sim_other_vars = validated_sim.sim_other_vars\n # model variable dictionaries with\n self.vars_to_idx = validated_sim.vars_to_idx\n self.vars_to_type_f = validated_sim.vars_to_type_f\n self.vars_to_ini_vals = validated_sim.vars_to_ini_vals\n\n # get parent directory and model name (without .fmu)\n aux_head_and_tail_tup = os.path.split(self.model_filepath)\n self.model_dir = aux_head_and_tail_tup[0]\n self.model_name = aux_head_and_tail_tup[1].replace(\".fmu\", \"\")\n\n # placeholder to prevent accessing methods if initialization hasn't been called first\n # also prevents calling self.fmu.terminate() if initialization hasn't occurred or termination has already been applied\n self._is_initialized = False\n \n # get FMI version\n read_fmi_version = self.model_description.fmiVersion\n if read_fmi_version in [\"1.0\", \"2.0\", \"3.0\"]:\n # Use fmi version from model_description\n print(f\"[FMU Connector] FMU model indicates to be follow fmi version '{read_fmi_version}'.\")\n self.fmi_version = read_fmi_version\n else:\n assert fmi_version in [\"1.0\", \"2.0\", \"3.0\"], f\"fmi version provided ({fmi_version}) is invalid.\"\n # Use fmi version provided by user if the one on model_description is invalid\n print(f\"[FMU Connector] Using fmi version provided by user: v'{fmi_version}'. Model indicates v'{read_fmi_version}' instead.\")\n self.fmi_version = fmi_version\n\n # save time-related data\n error_log = \"Stop time provided ({}) is lower than start time provided ({})\".format(stop_time, start_time)\n assert stop_time > start_time, error_log\n error_log = \"Step size time ({}) is greater than the difference between \".format(step_size)\n error_log += \"stop and start times, ({}) and ({}), respectively\".format(stop_time, start_time)\n assert step_size < stop_time-start_time, error_log\n self.start_time = float(start_time)\n self.stop_time = float(stop_time)\n self.step_size = float(step_size)\n self.sim_time = float(self.start_time)\n \n # retrieve FMU model type, as well as model identifier\n self.model_type = \"None\"\n self.model_identifier = self.model_name\n coSimulation = self.model_description.coSimulation\n if coSimulation is not None:\n self.model_identifier = coSimulation.modelIdentifier\n self.model_type = \"coSimulation\"\n else:\n scheduledExecution = self.model_description.scheduledExecution\n if scheduledExecution is not None:\n self.model_identifier = scheduledExecution.modelIdentifier\n self.model_type = \"scheduledExecution\"\n else:\n modelExchange = self.model_description.modelExchange\n if modelExchange is not None:\n self.model_identifier = modelExchange.modelIdentifier\n self.model_type = \"modelExchange\"\n else:\n raise Exception(\"Model is not of any known type: coSimulation, scheduledExecution, nor modelExchange\")\n\n \n # extract the FMU\n extract_path = os.path.join(self.model_dir, self.model_name + \"_unzipped\")\n if not use_unzipped_model:\n # extract model to subfolder by default\n self.unzipdir = extract(self.model_filepath, unzipdir=extract_path)\n else:\n # use previouslly unzipped model\n self.unzipdir = extract_path\n\n # get unique identifier using timestamp for instance_name (possible conflict with batch)\n self.instance_name = self._get_unique_id()\n \n\n # ---------------------------------------------------------------\n # instance model depending on 'fmi version' and 'fmu model type'\n self.fmu = None\n print(f\"[FMU Connector] Model has been determined to be of type '{self.model_type}' with fmi version == '{self.fmi_version}'.\")\n if self.model_type == \"modelExchange\":\n ## [TODO] test integrations\n print(f\"[FMU Connector] Simulator hasn't been tested for '{self.model_type}' models with fmi version == '{self.fmi_version}'.\")\n if self.fmi_version == \"1.0\":\n self.fmu = fmi1.FMU1Model(guid=self.model_description.guid,\n unzipDirectory=self.unzipdir,\n modelIdentifier=self.model_identifier,\n instanceName=self.instance_name)\n elif self.fmi_version == \"2.0\":\n self.fmu = fmi2.FMU2Model(guid=self.model_description.guid,\n unzipDirectory=self.unzipdir,\n modelIdentifier=self.model_identifier,\n instanceName=self.instance_name)\n elif self.fmi_version == \"3.0\":\n self.fmu = fmi3.FMU3Model(guid=self.model_description.guid,\n unzipDirectory=self.unzipdir,\n modelIdentifier=self.model_identifier,\n instanceName=self.instance_name)\n elif self.model_type == \"coSimulation\":\n if self.fmi_version == \"1.0\":\n ## [TODO] test integrations\n print(f\"[FMU Connector] Simulator hasn't been tested for '{self.model_type}' models with fmi version == '{self.fmi_version}'.\")\n self.fmu = fmi1.FMU1Slave(guid=self.model_description.guid,\n unzipDirectory=self.unzipdir,\n modelIdentifier=self.model_identifier,\n instanceName=self.instance_name)\n elif self.fmi_version == \"2.0\":\n self.fmu = fmi2.FMU2Slave(guid=self.model_description.guid,\n unzipDirectory=self.unzipdir,\n modelIdentifier=self.model_identifier,\n instanceName=self.instance_name)\n elif self.fmi_version == \"3.0\":\n ## [TODO] test integrations\n print(f\"[FMU Connector] Simulator hasn't been tested for '{self.model_type}' models with fmi version == '{self.fmi_version}'.\")\n self.fmu = fmi3.FMU3Slave(guid=self.model_description.guid,\n unzipDirectory=self.unzipdir,\n modelIdentifier=self.model_identifier,\n instanceName=self.instance_name)\n elif self.model_type == \"scheduledExecution\":\n if self.fmi_version == \"1.0\" or self.fmi_version == \"2.0\":\n raise Exception(\"scheduledExecution type only exists in fmi v'3.0', but fmi version '{}' was provided.\".format(self.fmi_version))\n \n print(f\"[FMU Connector] Simulator hasn't been tested for '{self.model_type}' models with fmi version == '{self.fmi_version}'.\")\n ## [TODO] test integrations\n #elif self.fmi_version_int == 3:\n self.fmu = fmi3.FMU3ScheduledExecution(guid=self.model_description.guid,\n unzipDirectory=self.unzipdir,\n modelIdentifier=self.model_identifier,\n instanceName=self.instance_name)\n \n \n # ---------------------------------------------------------------\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 __set_fmu__(self, fmu_file, result_handler, solver, atol, rtol, verbose):\n if self.fmu is None:\n \n # TODO:\n # See what can be done in catching the exception/propagating it\n self.fmu = pyfmi.load_fmu(fmu_file)\n \n # Get the options for the simulation\n self.opts = self.fmu.simulate_options()\n \n # Define the simulation options\n self.set_simulation_options(result_handler, solver, atol, rtol, verbose)\n \n # Define the standard value for the result file\n self.set_result_file(None)\n \n # set the number of states\n self.N_STATES = len(self.get_state())\n \n # get the value references of the state variables\n self.stateValueReferences = self.fmu.get_state_value_references()\n \n # Properties of the FMU\n self.name = str(self.fmu.get_name())\n self.author = str(self.fmu.get_author())\n self.description = str(self.fmu.get_description())\n self.fmu_type = str(self.fmu.__class__.__name__)\n self.version = str(self.fmu.version)\n self.guid = str(self.fmu.get_guid())\n self.tool = str(self.fmu.get_generation_tool())\n [Ncont, Nevt] = self.fmu.get_ode_sizes()\n self.numStates = \"( \"+str(Ncont)+\" , \"+str(Nevt)+\" )\"\n \n # prepare the list of inputs and outputs\n self.__set_inputs__()\n self.__set_outputs__()\n \n else:\n logger.warn(\"The FMU has already been assigned to this model\")","def __init__(\n self,\n model_filepath: str,\n user_validation: bool = True,\n ):\n\n # ensure model filepath is balid, and save as att if it is\n assert model_filepath.endswith(\".fmu\"), \"Provided filepath is not an FMU file: '{}'\".format(model_filepath)\n self.model_filepath = model_filepath\n # config file with config_params, inputs, outputs\n self.sim_config_filepath = SIM_CONFIG_NAME_f(self.model_filepath)\n\n # read the model description\n self.model_description = read_model_description(model_filepath)\n error_log = \"Provided model ({}) doesn't have modelVariables in XLS description file\".format(model_filepath)\n assert len(self.model_description.modelVariables) > 0, error_log\n\n # correct non-alphanumeric tags.\n # note, it doesn't suppose any problem, since interaction with sim uses indices, not names.\n self._clean_non_alphanumeric_chars()\n\n\n # collect the value references (indices)\n # collect the value types (Real, Integer or Enumeration)\n # collect the variables to be initialized and the value to do so at\n self.vars_to_idx = {}\n self.vars_to_type_f = {}\n self.vars_to_ini_vals = {}\n for variable in self.model_description.modelVariables:\n # extract key attributes per variable\n var_idx = variable.valueReference #, variable.causality\n var_name = variable.name\n var_type = variable.type\n var_start = variable.start\n \n # collect type reference\n if var_type == \"Real\":\n self.vars_to_type_f[var_name] = float\n elif var_type == \"Integer\":\n self.vars_to_type_f[var_name] = int\n else:\n # [TODO] Integrate variables of type \"Enumeration\". How do we cast? Define a function for \"self.vars_to_type_f\".\n # [TODO] Integrate variables of type string (need to find correct var_type tag first).\n # [TODO] Integrate variables of type boolean (need to find correct var_type tag first).\n print(f\"Variable '{var_name}' will be skipped. FMU connector cannot currently handle vars of type '{var_type}'.\")\n continue\n \n # collect the value references (indices)\n self.vars_to_idx[var_name] = var_idx\n\n # collect the variables to be initialized and the value to do so at\n if var_start is not None:\n # cast variable prior to storing\n self.vars_to_ini_vals[var_name] = self.vars_to_type_f[var_name](var_start)\n \n\n # initialize sim config\n self.is_model_config_valid = False # Currently unused, since error is raised if model invalid\n self.sim_config_params = []\n self.sim_inputs = []\n self.sim_outputs = []\n self.sim_other_vars = []\n\n # ---------------------------------------------------------------------\n # YAML CONFIG --> check for existing config using SIM_CONFIG_NAME_f --> e.g: \"{model_name}_conf.yaml\"\n valid_config = self._validate_sim_config()\n \n # exit if model is valid, unless validation has been activated\n if valid_config:\n\n # print model config for user reference: config_params, inputs, outputs\n print(self._get_sim_config_str())\n\n if user_validation:\n # prompt user to manually validate model if selected\n validation_asserted = input(\"Is this configuration correct (y|n)? \")\n\n if validation_asserted == \"y\":\n self.is_model_config_valid = True\n return\n \n # reset config if invalid\n self.sim_config_params = []\n self.sim_inputs = []\n self.sim_outputs = []\n self.sim_other_vars = []\n \n else:\n # when no validation is selected, we assume the sim config is valid\n self.is_model_config_valid = True\n return\n \n # ---------------------------------------------------------------------\n # FMI CONFIG --> if model is invalid we look for attributes within the .fmi model definition\n valid_config = self._extract_sim_config_from_fmi_std()\n\n if valid_config:\n\n # print model config for user reference: config_params, inputs, outputs\n print(self._get_sim_config_str())\n \n if user_validation:\n # prompt user to manually validate model if selected\n validation_asserted = input(\"Is this configuration correct (y|n)? \")\n\n if validation_asserted == \"y\":\n self.is_model_config_valid = True\n # dump YMAL file to reuse next time the model is loaded\n self._dump_config_to_yaml_file()\n return\n \n else:\n # when no validation is selected, we assume the sim config is valid\n self.is_model_config_valid = True\n # dump YMAL file to reuse next time the model is loaded\n self._dump_config_to_yaml_file()\n return\n \n # Dump auxiliary YAML config file if user doesn't assert the provided set\n # of config_params/inputs/outputs\n self._dump_config_to_yaml_file(is_aux_yaml = True)\n \n # If neither YAML nor FMI model is sufficient raise error\n error_log = \"MODEL DOES NOT HAVE THE CORRECT CONFIG DEFINED NEITHER ON YAML CONFIG FILE \"\n error_log += \"NOR FMI MODEL DESCRIPTION. A YAML FILE HAS BEEN CREATED FOR YOU TO MODIFY. \"\n error_log += \"THE SIM HAS BEEN FORCED TO EXIT, BUT FEEL FREE TO RERUN ONCE SET-UP IS COMPLETED.\"\n raise Exception(error_log)","def __init__(self, fmu_file = None, result_handler = None, solver = None, atol = 1e-6, rtol = 1e-4, verbose = None, offset = None):\n \n # Reference to the FMU, that will be loaded using pyfmi\n self.fmu = None\n self.fmu_file = fmu_file\n # List of parameters\n self.parameters = []\n # List of state variables\n self.variables = []\n # List of inputs\n self.inputs = []\n # List of outputs\n self.outputs = []\n \n # Initialize the properties of the FMU\n self.name = \"\"\n self.author = \"\"\n self.description = \"\"\n self.fmu_type = \"\"\n self.version = \"\"\n self.guid = \"\"\n self.tool = \"\"\n self.numStates = \"\"\n \n # Number of maximum tries for a simulation to be successfully run\n self.SIMULATION_TRIES = 4\n \n # Empty dictionary that will contain the simulation options\n self.opts = {}\n \n # Set the number of states\n self.N_STATES = 0\n \n # Set the simulation date time offset\n self.offset = offset\n \n # An array that contains the value references for every state variable\n self.stateValueReferences = []\n \n # See what can be done in catching the exception/propagating it\n if fmu_file is not None:\n self.__set_fmu__(fmu_file, result_handler, solver, atol, rtol, verbose)","def run(self):\n msg = sfmt.format(\"Run test\", self.name)\n print(msg)\n\n # Set nam as namefile name without path\n nam = None\n\n # run mf6 models\n exe = str(self.targets[\"mf6\"].absolute())\n msg = sfmt.format(\"using executable\", exe)\n print(msg)\n\n if self.parallel:\n print(\"running parallel on\", self.ncpus, \"processes\")\n try:\n success, buff = self.run_parallel(\n exe,\n )\n except Exception as exc: \n msg = sfmt.format(\"MODFLOW 6 run\", self.name)\n print(msg)\n print(exc)\n success = False\n else:\n try:\n success, buff = flopy.run_model(\n exe,\n nam,\n model_ws=self.simpath,\n silent=False,\n report=True,\n )\n msg = sfmt.format(\"MODFLOW 6 run\", self.name)\n if success:\n print(msg)\n else:\n print(msg)\n except:\n msg = sfmt.format(\"MODFLOW 6 run\", self.name)\n print(msg)\n success = False\n\n # set failure based on success and require_failure setting\n if self.require_failure is None:\n msg = \"MODFLOW 6 model did not terminate normally\"\n if success:\n failure = False\n else:\n failure = True\n else:\n if self.require_failure:\n msg = \"MODFLOW 6 model should have failed\"\n if not success:\n failure = False\n else:\n failure = True\n else:\n msg = \"MODFLOW 6 model should not have failed\"\n if success:\n failure = False\n else:\n failure = True\n\n # print end of mfsim.lst to the screen\n if failure and self.is_CI:\n fpth = os.path.join(self.simpath, \"mfsim.lst\")\n msg = self._get_mfsim_listing(fpth) + msg\n\n # test for failure\n assert not failure, msg\n\n self.nam_cmp = None\n if success:\n if self.action is not None:\n if self.action.lower() == \"compare\":\n msg = sfmt.format(\"Comparison files\", self.name)\n print(msg)\n else:\n cpth = os.path.join(self.simpath, self.action)\n key = self.action.lower().replace(\".cmp\", \"\")\n exe = str(self.targets[key].absolute())\n msg = sfmt.format(\"comparison executable\", exe)\n print(msg)\n if (\n \"mf6\" in key\n or \"libmf6\" in key\n or \"mf6_regression\" in key\n ):\n nam = None\n else:\n npth = get_namefiles(cpth)[0]\n nam = os.path.basename(npth)\n self.nam_cmp = nam\n try:\n if self.api_func is None:\n success_cmp, buff = flopy.run_model(\n exe,\n nam,\n model_ws=cpth,\n silent=False,\n report=True,\n )\n else:\n success_cmp, buff = self.api_func(\n exe, self.idxsim, model_ws=cpth\n )\n msg = sfmt.format(\n \"Comparison run\", self.name + \"/\" + key\n )\n print(msg)\n\n # print end of mfsim.lst to the screen\n if \"mf6\" in key:\n if not success and self.is_CI:\n fpth = os.path.join(cpth, \"mfsim.lst\")\n print(self._get_mfsim_listing(fpth))\n\n except:\n success_cmp = False\n msg = sfmt.format(\n \"Comparison run\", self.name + \"/\" + key\n )\n print(msg)\n\n assert success_cmp, \"Unsuccessful comparison run\"\n\n return","def _simulate(ctx, gui):\n ctx.env['SFFUnits'] = load_SFFUnits(ctx)\n\n \"\"\"\n Creates the directory path and nodes in the build directory.\n Creates a taskgen from each other library in units_hdl\n \"\"\"\n\n top = ctx.env['SFFUnits'].getunit(ctx.env.top_level)\n\n for u in top.synu_deps + top.simu_deps:\n lib = u.script.parent.get_bld().make_node('work_vlib')\n lib.mkdir()\n u.b['vlib'] = lib\n\n if u.use('use'):\n tsk = ModelsimTask(\n name=u.name,\n target=lib,\n source=u.use('src'),\n includes=u.use('includes'),\n after=u.use('use'),\n output=lib,\n scan=SFF_verilog_scan,\n env=ctx.env)\n ctx.add_to_group(tsk)\n else:\n tsk = ModelsimTask(\n name=u.name,\n target=lib,\n source=u.use('src'),\n output=lib,\n includes=u.use('includes'),\n scan=SFF_verilog_scan,\n env=ctx.env)\n ctx.add_to_group(tsk)\n\n\n \"\"\"\n Create the testbench taskgen last as it is always at the top dep\n \"\"\"\n ctx.add_group()\n tb_lib = top.script.parent.get_bld().make_node('work_vlib')\n tb_lib.mkdir()\n top.b['tbvlib'] = tb_lib\n\n tsk = ModelsimTask(\n name=top.use('tb'),\n target=tb_lib,\n source=top.use('tb_src'),\n output=tb_lib,\n includes=top.use('tb_includes'),\n after=ctx.env.top_level,\n scan=SFF_verilog_scan,\n env=ctx.env )\n ctx.add_to_group(tsk)\n ctx.add_group()\n\n \"\"\"\n Run the Modelsim command with gui options provided.\n \"\"\"\n ##Run vsim\n ctx(name='vsim',\n rule='vsim %s -lib %s %s' % (gui,top.b['tbvlib'], top.use('tb')[0]),\n always = True)","def __init__(\n self,\n model,\n ipakcb=None,\n intercellt=0,\n laycon=3,\n trpy=1.0,\n hdry=-1e30,\n iwdflg=0,\n wetfct=0.1,\n iwetit=1,\n ihdwet=0,\n ikvflag=0,\n ikcflag=0,\n tran=1.0,\n hy=1.0,\n vcont=1.0,\n kv=1.0,\n anglex=0.0,\n ksat=1.0,\n sf1=1e-5,\n sf2=0.15,\n wetdry=-0.01,\n extension=\"bcf\",\n unitnumber=None,\n filenames=None,\n add_package=True,\n ):\n msg = (\n \"Model object must be of type flopy.mfusg.MfUsg\\n\"\n f\"but received type: {type(model)}.\"\n )\n assert isinstance(model, MfUsg), msg\n\n super().__init__(\n model,\n ipakcb=ipakcb,\n intercellt=intercellt,\n laycon=laycon,\n trpy=trpy,\n hdry=hdry,\n iwdflg=iwdflg,\n wetfct=wetfct,\n iwetit=iwetit,\n ihdwet=ihdwet,\n tran=tran,\n hy=hy,\n vcont=vcont,\n sf1=sf1,\n sf2=sf2,\n wetdry=wetdry,\n extension=extension,\n unitnumber=unitnumber,\n filenames=filenames,\n add_package=False,\n )\n\n dis = model.get_package(\"DIS\")\n if dis is None:\n dis = model.get_package(\"DISU\")\n structured = self.parent.structured\n\n nrow, ncol, nlay, _ = self.parent.nrow_ncol_nlay_nper\n\n self.ikvflag = ikvflag\n self.ikcflag = ikcflag\n self.kv = kv\n self.anglex = anglex\n self.ksat = ksat\n\n if not structured:\n njag = dis.njag\n self.anglex = Util2d(\n model,\n (njag,),\n np.float32,\n anglex,\n \"anglex\",\n locat=self.unit_number[0],\n )\n\n # item 1\n self.kv = Util3d(\n model,\n (nlay, nrow, ncol),\n np.float32,\n kv,\n \"Vertical Hydraulic Conductivity\",\n locat=self.unit_number[0],\n )\n if not structured:\n self.ksat = Util3d(\n model,\n (njag,),\n np.float32,\n ksat,\n \"ksat\",\n locat=self.unit_number[0],\n )\n\n if add_package:\n self.parent.add_package(self)","def main():\n parser = argparse.ArgumentParser(description=\"Generate standard form system FMUs through commandline\")\n parser.add_argument(\"--name\", default=\"qmodel\", type=str, help=\"Target FMU identifier\")\n parser.add_argument(\"--dir\", default=os.getcwd(), type=str, help=\"Target FMU path\")\n parser.add_argument(\"-v\", \"--verbose\", help=\"Verbose output\", action=\"store_true\")\n parser.add_argument(\"-n\", \"--dry-run\", help=\"Only print system information, use with -v.\", action=\"store_true\")\n\n subparsers = parser.add_subparsers(title=\"System form\", dest=\"subcmd\")\n ss = subparsers.add_parser(\"ss\", help=\"State space model: A, B, C, D\",\n description=\"Define ABCD matrices using string. The string is interpreted as a matrix with commas or spaces separating columns, and semicolons separating rows. e.g. '1,2;3,4' -> 2x2 matrix\")\n ss.add_argument(\"-A\", required=False, type=str, help=\"A matrix\")\n ss.add_argument(\"-B\", required=False, type=str, help=\"B matrix\")\n ss.add_argument(\"-C\", required=False, type=str, help=\"C matrix\")\n ss.add_argument(\"-D\", required=False, type=str, help=\"D matrix\")\n ss.add_argument(\"-x0\", required=False, type=str, help=\"Init state values, zero vector if empty\")\n ss.add_argument(\"-u0\", required=False, type=str, help=\"Init input values, zero vector if empty\")\n\n # tf = subparsers.add_parser(\"tf\", help=\"Transfer function (WIP)\")\n # tf.add_argument(\"-n\", default=\"1,0\", type=str, help=\"Numerator\")\n # tf.add_argument(\"-d\", default=\"1\", type=str, help=\"Denominator\")\n\n try:\n args = parser.parse_args()\n if args.subcmd == \"ss\":\n from qfmu.utils import str_to_1d_array, str_to_2d_array\n A = None if args.A is None or args.A==\"\" else str_to_2d_array(args.A)\n B = None if args.B is None or args.B==\"\" else str_to_2d_array(args.B)\n C = None if args.C is None or args.C==\"\" else str_to_2d_array(args.C)\n D = None if args.D is None or args.D==\"\" else str_to_2d_array(args.D)\n x0 = None if args.x0 is None or args.x0==\"\" else str_to_1d_array(args.x0)\n u0 = None if args.u0 is None or args.u0==\"\" else str_to_1d_array(args.u0)\n ss = StateSpace(A, B, C, D, x0, u0)\n m = Lti(ss, identifier=args.name)\n if args.verbose:\n logging.basicConfig(level=logging.INFO)\n if args.dry_run:\n print(f\"Target FMU:\\n{os.path.join(os.path.abspath(args.dir), args.name)}.fmu\")\n print(f\"System info:\\n{ss}\")\n else:\n m.buildFMU(args.dir)\n else:\n raise Exception(\"Unknown system form\")\n except Exception as ex:\n logging.error(ex)\n return -1\n\n return 0","def setup(self):\n log.debug('Initializing Mex...')\n if (self.options.user and self.options.pwd and self.options.root):\n self.bqSession = BQSession().init_local( self.options.user, self.options.pwd, bisque_root=self.options.root)\n self.options.mexURL = self.bqSession.mex.uri\n\n elif (self.options.mexURL and self.options.token):\n self.bqSession = BQSession().init_mex(self.options.mexURL, self.options.token)\n else:\n return\n \n self.mex_parameter_parser(self.bqSession.mex.xmltree)\n \n #finds and opens model file\n self.bqSession.update_mex('Initializing Classification Model...')\n log.debug('Forming Feature Requests...')\n\n \n #no options currently\n #combo = mex_xml.xpath('tag[@name=\"plant_part\"]/@value')[0]\n combo = 'bush'\n if combo:\n if combo=='bush':\n MODEL_QUERY['tag_query'] = '\"module_identifier\":\"Botanicam\" AND \"Classification Method\":\"Bush Descriptor\"'\n elif combo=='leaf':\n MODEL_QUERY['tag_query'] = '\"module_identifier\":\"Botanicam\" AND \"Classification Method\":\"Leaf Descriptor\"'\n else:\n raise BotanicamError('The incorrect model type was found -> Model Type: %s'%combo)\n else:\n raise BotanicamError('No model type was choosen')\n \n query_xml = self.bqSession.fetchxml('/data_service/file', **MODEL_QUERY)\n\n self.options.model_url = None\n if len(query_xml)>0:\n try:\n model_url = query_xml[0].attrib['uri']\n self.options.model_url = model_url\n log.debug('Fetching Model @ %s' % model_url)\n self.model_xml = self.bqSession.fetchxml(model_url, view='deep')\n self.model_path = os.path.join(self.options.stagingPath, 'model')\n model = self.bqSession.load(model_url)\n model_url = self.bqSession.service_url('blob_service', path=model.resource_uniq)\n self.bqSession.fetchblob(model_url, path=self.model_path+'.zip')\n with zipfile.ZipFile(self.model_path+'.zip') as dirzip:\n dirzip.extractall(self.model_path)\n except BQCommError:\n raise BotanicamError('Model file was not found! Ask admin to set the correct model file')\n else: #run demo classifier model store in the module\n raise BotanicamError('No model file was found. Ask your admin to train a new model with \\\n the Botanicam Trainer.')\n\n self.bqSession.update_mex('Initialized...')\n log.debug('Botanicam: image URL: %s, mexURL: %s, stagingPath: %s, token: %s' % (self.options.image_url, self.options.mexURL, self.options.stagingPath, self.options.token))","def run_models(request):\n job_form_data = request.session['job_form_data']\n job_wrapper = JobWrapper(job_form_data)\n job_wrapper.create_data_file()\n print job_wrapper.job_form_data\n # Must run emits to generate emis_co2.dat - this step is requried to\n # run the models and it's a lot simpler to have it run form here than\n # from a job manager script\n cmd = \"/var/opt/IMOGEN/EMITS/emits\"\n subprocess.call(cmd, shell=True)\n print \"Ran {0} program\".format(cmd)\n # Now submit the models via the job manager\n jr = DRMAAJobRunner()\n return jr.queue_job(job_wrapper)","def test_models_multiclass(model):\n atom = ATOMClassifier(X_class2, y_class2, test_size=0.24, random_state=1)\n atom.run(\n models=model,\n metric=\"f1_micro\",\n n_calls=2,\n n_initial_points=1,\n bo_params={\"base_estimator\": \"rf\", \"cv\": 1},\n )\n assert not atom.errors\n assert hasattr(atom, model)","def build_mod_bpmf_model(train, alpha=2, dim=10, std=0.01):\n n, m = train.shape\n beta_0 = 1 # scaling factor for lambdas; unclear on its use\n\n # Mean value imputation on training data.\n train = train.copy()\n nan_mask = np.isnan(train)\n train[nan_mask] = train[~nan_mask].mean()\n\n # We will use separate priors for sigma and correlation matrix.\n # In order to convert the upper triangular correlation values to a\n # complete correlation matrix, we need to construct an index matrix:\n n_elem = dim * (dim - 1) / 2\n tri_index = np.zeros([dim, dim], dtype=int)\n tri_index[np.triu_indices(dim, k=1)] = np.arange(n_elem)\n tri_index[np.triu_indices(dim, k=1)[::-1]] = np.arange(n_elem)\n\n logging.info('building the BPMF model')\n with pm.Model() as bpmf:\n # Specify user feature matrix\n sigma_u = pm.Uniform('sigma_u', shape=dim)\n corr_triangle_u = pm.LKJCorr(\n 'corr_u', n=1, p=dim,\n testval=np.random.randn(n_elem) * std)\n\n corr_matrix_u = corr_triangle_u[tri_index]\n corr_matrix_u = t.fill_diagonal(corr_matrix_u, 1)\n cov_matrix_u = t.diag(sigma_u).dot(corr_matrix_u.dot(t.diag(sigma_u)))\n lambda_u = t.nlinalg.matrix_inverse(cov_matrix_u)\n\n mu_u = pm.Normal(\n 'mu_u', mu=0, tau=beta_0 * t.diag(lambda_u), shape=dim,\n testval=np.random.randn(dim) * std)\n U = pm.MvNormal(\n 'U', mu=mu_u, tau=lambda_u, shape=(n, dim),\n testval=np.random.randn(n, dim) * std)\n\n # Specify item feature matrix\n sigma_v = pm.Uniform('sigma_v', shape=dim)\n corr_triangle_v = pm.LKJCorr(\n 'corr_v', n=1, p=dim,\n testval=np.random.randn(n_elem) * std)\n\n corr_matrix_v = corr_triangle_v[tri_index]\n corr_matrix_v = t.fill_diagonal(corr_matrix_v, 1)\n cov_matrix_v = t.diag(sigma_v).dot(corr_matrix_v.dot(t.diag(sigma_v)))\n lambda_v = t.nlinalg.matrix_inverse(cov_matrix_v)\n\n mu_v = pm.Normal(\n 'mu_v', mu=0, tau=beta_0 * t.diag(lambda_v), shape=dim,\n testval=np.random.randn(dim) * std)\n V = pm.MvNormal(\n 'V', mu=mu_v, tau=lambda_v, shape=(m, dim),\n testval=np.random.randn(m, dim) * std)\n\n # Specify rating likelihood function\n R = pm.Normal(\n 'R', mu=t.dot(U, V.T), tau=alpha * np.ones((n, m)),\n observed=train)\n\n logging.info('done building the BPMF model')\n return bpmf","def model_test(nu, fsigma_T, fsigma_P, models_in, amps_in, params_in, models_fit, label):\n # Generate fake data with some \"true\" parameters\n (D_vec, Ninv) = gen_data(nu, fsigma_T, fsigma_P, models_in, amps_in, params_in)\n Ninv_sqrt = np.matrix(linalg.sqrtm(Ninv))\n (dust_params, sync_params, cmb_params) = params_in\n (dust_amp, sync_amp, cmb_amp) = amps_in\n \n # Beam model\n beam_mat = np.identity(3*len(nu))\n\n # Set-up MCMC\n dust_guess = np.array([1.6, 20.])\n sync_guess = np.array([-3.])\n cmb_guess = np.array([])\n guess = np.concatenate((dust_guess, sync_guess, cmb_guess))\n #ndim = len(dust_guess) + len(sync_guess) + len(cmb_guess)\n \n # Run MCMC sampler on this model\n t0 = time.time()\n dust_params_out, sync_params_out, cmb_params_out, samples \\\n = mcmc(guess, nu, D_vec, Ninv, beam_mat, models_fit, label)\n print \"MCMC run in %d sec.\" % (time.time() - t0)\n \n # Estimate error on recovered CMB amplitudes\n (F_fg, F_cmb, F) = F_matrix(nu, dust_params_out, sync_params_out, cmb_params_out, models_fit)\n H = F_fg.T*Ninv*F_fg\n x_mat = np.linalg.inv(F.T*beam_mat.T*Ninv*beam_mat*F)*F.T*beam_mat.T*Ninv*D_vec # Equation A3\n \n U, Lambda, VT = np.linalg.svd(Ninv_sqrt*F_fg, full_matrices=False) # Equation A14\n \n print \"-\"*30\n print \"F_cmb.T\", F_cmb.T.shape\n print \"Ninv_sqrt\", Ninv_sqrt.shape\n print \"F_cmb\", F_cmb.shape\n print \"I\", np.identity(U.shape[0]).shape\n print \"U\", U.shape\n print \"U.T\", U.T.shape\n print \"-\"*30\n \n \n \n N_eff_inv_cmb = F_cmb.T*Ninv_sqrt*(np.matrix(np.identity(U.shape[0])) - U*U.T)*Ninv_sqrt*F_cmb # Equation A16\n N_eff_cmb = np.linalg.inv(N_eff_inv_cmb)\n cmb_noise = np.array([N_eff_cmb[0,0], N_eff_cmb[1,1], N_eff_cmb[2,2]])\n\n gls_cmb = x_mat[0:3,0]\n cmb_chisq = (np.matrix(cmb_amp).T - gls_cmb).T*N_eff_inv_cmb*(np.matrix(cmb_amp).T - gls_cmb)\n \n # Output triangle plots for dust\n if label != None:\n if (models_fit[0] == 'mbb' and models_fit[1] == 'pow'):\n if (models_in[0] == 'mbb'):\n fig = corner.corner(samples, truths=[dust_params[0], dust_params[1], sync_params[0]],\n labels=[r\"$\\beta_d$\", r\"$T_d$\",r\"$\\alpha_s$\"])\n else :\n fig = corner.corner(samples, labels=[r\"$\\beta_d$\", r\"$T_d$\",r\"$\\alpha_s$\"])\n else :\n print 'Error! Not configured for this plot!'\n exit()\n fig.savefig('triangle_' + label + '.png')\n plt.close('all')\n \n # Run multinest sampler\n #multinest(nu, D_vec, Ninv, beam_mat, ndim, models_fit, label)\n \n return gls_cmb, cmb_chisq, cmb_noise","def main(**kwargs):\n flowsheet = Flowsheet(name='MB_Model') \n \n # Fix variables\n setInputs(flowsheet) \n\n ts = time.time() \n\n mb = flowsheet.MB_fuel\n \n # Initialize fuel reactor\n flowsheet.MB_fuel._initialize(outlvl=1,\n optarg={\"tol\" : 1e-8,\n \"max_cpu_time\" : 600,\n \"print_level\" : 5,\n \"halt_on_ampl_error\": 'yes'}) \n \n # Create a solver\n opt = SolverFactory('ipopt')\n opt.options = {'tol': 1e-8,\n 'linear_solver' : 'ma27',\n 'bound_push': 1e-8,\n 'max_cpu_time': 600,\n 'print_level': 5}\n \n results = opt.solve(flowsheet,tee=True,symbolic_solver_labels=False,\n keepfiles=False)\n\n #flowsheet.MB_fuel.Solid_In_M.fix(691.4)\n #flowsheet.MB_fuel.Gas_In_y['CO2'].fix(0.03999)\n #flowsheet.MB_fuel.Gas_In_y['H2O'].fix(0.00001)\n #flowsheet.MB_fuel.Gas_In_y['CH4'].fix(0.96)\n\n\n\n #results = opt.solve(flowsheet,tee=True,symbolic_solver_labels=False,\n # keepfiles=False)\n \n \n print(\"\\n\")\n print(\"----------------------------------------------------------\")\n print('Total simulation time: ', value(time.time() - ts), \" s\")\n print(\"----------------------------------------------------------\")\n\n \n # Print some variables \n #print_summary_fuel_reactor(flowsheet) \n\n # Plot some variables \n #results_plot_fuel_reactor(flowsheet) \n\n m = flowsheet.MB_fuel\n if 'Solid_M' in kwargs:\n m.Solid_In_M.fix(kwargs['Solid_M'])\n if 'Solid_T' in kwargs:\n m.Solid_In_Ts[t].fix(kwargs['Solid_T'])\n if 'Solid_x' in kwargs:\n m.Solid_In_x['Fe2O3'].fix(kwargs['Solid_x']['Fe2O3'])\n m.Solid_In_x['Fe3O4'].fix(kwargs['Solid_x']['Fe3O4'])\n m.Solid_In_x['Al2O3'].fix(kwargs['Solid_x']['Al2O3'])\n if 'Gas_F' in kwargs:\n m.Gas_In_F.fix(kwargs['Gas_F'])\n if 'Gas_P' in kwargs:\n m.Gas_In_P.fix(kwargs['Gas_P'])\n if 'Gas_T' in kwargs:\n m.Gas_In_T.fix(kwargs['Gas_T'])\n if 'Gas_y' in kwargs:\n m.Gas_In_y['CO2'].fix(kwargs['Gas_y']['CO2'])\n m.Gas_In_y['H2O'].fix(kwargs['Gas_y']['H2O'])\n m.Gas_In_y['CH4'].fix(kwargs['Gas_y']['CH4'])\n\n results = opt.solve(flowsheet, tee=True)\n\n with open('ss_fs.txt','w') as f:\n flowsheet.display(ostream=f)\n\n dt_Gflux_CO2 = []\n dt_Gflux_H2O = []\n dt_Gflux_CH4 = []\n dt_Sflux_Fe2O3 = []\n dt_Sflux_Fe3O4 = []\n dt_Sflux_Al2O3 = []\n dt_Ctrans_CO2 = []\n dt_Ctrans_H2O = []\n dt_Ctrans_CH4 = []\n dt_qtrans_Fe2O3 = []\n dt_qtrans_Fe3O4 = []\n dt_qtrans_Al2O3 = []\n dt_Ghflux = []\n dt_Ts = []\n dt_TgGS = []\n dt_TsGS = []\n dt_vg = []\n dt_vs = []\n\n# for z in mb.z.get_finite_elements():\n# if z != mb.z.first() and z != mb.z.last():\n#\n# dt_Gflux_CO2.append( (mb.Cg[z,'CO2'].value-mb.Cg[prev,'CO2'].value)/\\\n# (mb.G_flux[z,'CO2'].value-mb.G_flux[prev,'CO2'].value) \\\n# *(z-prev)*mb.eps.value*mb.L.value /(z-prev))\n#\n# dt_Gflux_H2O.append( (mb.Cg[z,'H2O'].value-mb.Cg[prev,'H2O'].value)/\\\n# (mb.G_flux[z,'H2O'].value-mb.G_flux[prev,'H2O'].value) \\\n# *(z-prev)*mb.eps.value*mb.L.value /(z-prev))\n#\n# dt_Gflux_CH4.append( (mb.Cg[z,'CH4'].value-mb.Cg[prev,'CH4'].value)/\\\n# (mb.G_flux[z,'CH4'].value-mb.G_flux[prev,'CH4'].value) \\\n# *(z-prev)*mb.eps.value*mb.L.value /(z-prev))\n#\n# dt_Ctrans_CO2.append( (mb.Cg[z,'CO2'].value-mb.Cg[prev,'CO2'].value)/\\\n# (mb.Ctrans[z,'CO2'].value)* \\\n# #-mv.Ctrans[prev,'CO2'].value)*\\\n# mb.eps.value/(1-mb.eps.value) /(z-prev))\n#\n# dt_Ctrans_H2O.append( (mb.Cg[z,'H2O'].value-mb.Cg[prev,'H2O'].value)/\\\n# (mb.Ctrans[z,'H2O'].value)* \\\n# #-mv.Ctrans[prev,'H2O'].value)*\\\n# mb.eps.value/(1-mb.eps.value) /(z-prev))\n#\n# dt_Ctrans_CH4.append( (mb.Cg[z,'CH4'].value-mb.Cg[prev,'CH4'].value)/\\\n# (mb.Ctrans[z,'CH4'].value)* \\\n# #-mv.Ctrans[prev,'CH4'].value)*\\\n# mb.eps.value/(1-mb.eps.value) /(z-prev))\n#\n# dt_Sflux_Fe2O3.append( (mb.q[z,'Fe2O3'].value-mb.q[prev,'Fe2O3'].value)/\\\n# (mb.S_flux[z,'Fe2O3'].value-mb.S_flux[prev,'Fe2O3'].value)*\\\n# (z-prev)/(1-mb.eps.value)*mb.L.value /(z-prev))\n#\n# dt_Sflux_Fe3O4.append( (mb.q[z,'Fe3O4'].value-mb.q[prev,'Fe3O4'].value)/\\\n# (mb.S_flux[z,'Fe3O4'].value-mb.S_flux[prev,'Fe3O4'].value)*\\\n# (z-prev)/(1-mb.eps.value)*mb.L.value /(z-prev))\n#\n# dt_Sflux_Al2O3.append( (mb.q[z,'Al2O3'].value-mb.q[prev,'Al2O3'].value)/\\\n# (mb.S_flux[z,'Al2O3'].value-mb.S_flux[prev,'Al2O3'].value)*\\\n# (z-prev)/(1-mb.eps.value)*mb.L.value /(z-prev))\n#\n# dt_qtrans_Fe2O3.append( (mb.q[z,'Fe2O3'].value-mb.q[prev,'Fe2O3'].value)/\\\n# (mb.qtrans[z,'Fe2O3'].value )/(z-prev)) \n# #-mb.qtrans[prev,'Fe2O3'].value) )\n#\n# dt_qtrans_Fe3O4.append( (mb.q[z,'Fe3O4'].value-mb.q[prev,'Fe3O4'].value)/\\\n# (mb.qtrans[z,'Fe3O4'].value )/(z-prev)) \n# #-mb.qtrans[prev,'Fe3O4'].value) )\n#\n# dt_qtrans_Al2O3.append( (mb.q[z,'Fe3O4'].value-mb.q[prev,'Fe3O4'].value)/\\\n# (mb.qtrans[z,'Fe3O4'].value )/(z-prev)) \n# #-mb.qtrans[prev,'Fe3O4'].value) )\n#\n# dt_Ghflux.append( (mb.Tg[z].value-mb.Tg[prev].value)/\\\n# (mb.Gh_flux[z].value-mb.Gh_flux[prev].value)* (z-prev)* mb.eps.value*\\\n# mb.L.value* mb.rho_vap[z].value* mb.cp_gas[z].value /(z-prev)) \n#\n# dt_Ts.append( (z-prev)*(1-mb.eps.value)*mb.L.value/mb.vs.value /(z-prev))\n#\n# dt_TgGS.append( (mb.Tg[z].value - mb.Tg[prev].value)/\\\n# mb.Tg_GS[z].value* mb.eps.value* mb.rho_vap[z].value* mb.cp_gas[z].value \n# /(z-prev))\n# \n# dt_TsGS.append( (mb.Ts[z].value - mb.Ts[prev].value)/\\\n# mb.Tg_GS[z].value* (1-mb.eps.value)* mb.rho_sol.value* mb.cp_sol[z].value*1e-3 \n# /(z-prev))\n# \n# dt_vg.append( mb.L.value*(z-prev)/mb.vg[z].value /(z-prev))\n# \n# dt_vs.append( mb.L.value*(z-prev)/mb.vs.value /(z-prev))\n#\n# prev = z\n#\n# with open('dt.txt','w') as f:\n# f.write('dt_Gflux_CO2\\t')\n# for t in dt_Gflux_CO2:\n# f.write('%1.3f'%t +'\\t')\n#\n# f.write('\\ndt_Gflux_H2O\\t')\n# for t in dt_Gflux_H2O:\n# f.write('%1.3f'%t +'\\t')\n#\n# f.write('\\ndt_Gflux_CH4\\t') \n# for t in dt_Gflux_CH4:\n# f.write('%1.3f'%t +'\\t')\n#\n# f.write('\\ndt_Sflux_Fe2O3\\t') \n# for t in dt_Sflux_Fe2O3:\n# f.write('%1.3f'%t +'\\t')\n#\n# f.write('\\ndt_Sflux_Fe3O4\\t') \n# for t in dt_Sflux_Fe3O4:\n# f.write('%1.3f'%t +'\\t')\n#\n# f.write('\\ndt_Sflux_Al2O3\\t') \n# for t in dt_Sflux_Al2O3:\n# f.write('%1.3f'%t +'\\t')\n#\n# f.write('\\ndt_Ctrans_CO2\\t') \n# for t in dt_Ctrans_CO2:\n# f.write('%1.3f'%t +'\\t')\n# \n# f.write('\\ndt_Ctrans_H2O\\t') \n# for t in dt_Ctrans_H2O:\n# f.write('%1.3f'%t +'\\t')\n#\n# f.write('\\ndt_Ctrans_CH4\\t') \n# for t in dt_Ctrans_CH4:\n# f.write('%1.3f'%t +'\\t')\n#\n# f.write('\\ndt_qtrans_Fe2O3\\t') \n# for t in dt_qtrans_Fe2O3:\n# f.write('%1.3f'%t +'\\t')\n#\n# f.write('\\ndt_qtrans_Fe3O4\\t') \n# for t in dt_qtrans_Fe3O4:\n# f.write('%1.3f'%t +'\\t')\n#\n# f.write('\\ndt_qtrans_Al2O3\\t') \n# for t in dt_qtrans_Al2O3:\n# f.write('%1.3f'%t +'\\t')\n#\n# f.write('\\ndt_Ghflux\\t') \n# for t in dt_Ghflux:\n# f.write('%1.3f'%t +'\\t')\n#\n# f.write('\\ndt_Ts\\t\\t') \n# for t in dt_Ts:\n# f.write('%1.3f'%t +'\\t')\n#\n# f.write('\\ndt_TgGS\\t\\t') \n# for t in dt_TgGS:\n# f.write('%1.3f'%t +'\\t')\n#\n# f.write('\\ndt_TsGS\\t\\t') \n# for t in dt_TsGS:\n# f.write('%1.3f'%t +'\\t')\n#\n# f.write('\\ndt_vg\\t\\t') \n# for t in dt_vg:\n# f.write('%1.3f'%t +'\\t')\n#\n# f.write('\\ndt_vs\\t\\t') \n# for t in dt_vs:\n# f.write('%1.3f'%t +'\\t')\n\n\n # Store the flowsheet \n return flowsheet","def cli_simulate(model_file, output_dir, exporter, overwrite, compression,\n confirm, progress: int, progress_tag, output_same,\n simtime_total, simtime_lims, max_sweeps, max_residual, fipy_solver,\n snapshot_interval,\n plot, video, frames, budget, resume, show_eqns):\n\n click.secho('Starting MicroBenthos simulation', fg='green')\n from microbenthos.utils import yaml\n\n click.echo('Loading model from {}'.format(model_file))\n with open(model_file, 'r') as fp:\n defs = yaml.unsafe_load(fp)\n\n if 'model' not in defs and 'domain' in defs:\n # model is not under a separate key, so insert it under \"model\"\n defs = dict(model=defs)\n\n if 'simulation' not in defs:\n defs['simulation'] = {}\n\n # we want to override the keys in the loaded simulation dictionary,\n # so that when it is created the definition stored on the instance and\n # eventually exported to file includes these user overrides\n\n sim_kwargs = dict(\n simtime_total=simtime_total,\n fipy_solver=fipy_solver,\n max_sweeps=max_sweeps,\n simtime_lims=simtime_lims,\n max_residual=max_residual,\n snapshot_interval=snapshot_interval,\n )\n for k, v in sim_kwargs.items():\n if v is None:\n continue\n else:\n defs['simulation'][k] = v\n\n if output_same:\n output_dir = str(Path(model_file).parent)\n click.secho(f'Output directory set to: {output_dir}')\n\n from microbenthos.runners import SimulationRunner\n runner = SimulationRunner(output_dir=output_dir,\n model=defs['model'],\n simulation=defs['simulation'],\n resume=resume,\n overwrite=overwrite,\n confirm=confirm,\n progress=progress,\n progress_tag=progress_tag,\n plot=plot,\n video=video,\n frames=frames,\n budget=budget,\n exporters=exporter,\n show_eqns=show_eqns)\n\n if not runner.get_data_exporters():\n click.secho('No data exporters defined. Adding with compression={}'.format(\n compression), fg='red')\n runner.add_exporter('model_data', output_dir=runner.output_dir,\n compression=compression)\n\n runner.run()","def runModel(quickLogger,\n\t base,\n modelFile=\"\",\n\t irfs=\"P7SOURCE_V6\",\n run=True):\n \n if(modelFile):\n model = modelFile\n else:\n model = base+\"_likeMinuit.xml\"\n\n\n try:\n checkForFiles(quickLogger,\n [base+\"_srcMaps.fits\",\n model,\n base+\"_ltcube.fits\",\n base+\"_BinnedExpMap.fits\"])\n except(FileNotFound):\n quickLogger.critical(\"One or more needed files do not exist.\")\n return\n\n model_map['srcmaps'] = base+\"_srcMaps.fits\"\n model_map['srcmdl'] = model\n model_map['outfile'] = base+\"_modelMap.fits\"\n model_map['expcube'] = base+\"_ltcube.fits\"\n model_map['irfs'] = irfs\n model_map['bexpmap'] = base+\"_BinnedExpMap.fits\"\n \n runCommand(model_map,quickLogger,run)","def test_FEMM_periodicity_time_no_periodicity_a():\n\n SPMSM_015 = load(join(DATA_DIR, \"Machine\", \"SPMSM_015.json\"))\n\n assert SPMSM_015.comp_periodicity() == (9, False, 9, True)\n\n simu = Simu1(name=\"test_FEMM_periodicity_time_no_periodicity_a\", machine=SPMSM_015)\n\n # Definition of the enforced output of the electrical module\n I0_rms = 250 / sqrt(2)\n Phi0 = 140 * pi / 180 # Maximum Torque Per Amp\n\n Id_ref = (I0_rms * exp(1j * Phi0)).real\n Iq_ref = (I0_rms * exp(1j * Phi0)).imag\n\n simu.input = InputCurrent(\n Id_ref=Id_ref,\n Iq_ref=Iq_ref,\n Na_tot=252 * 9,\n Nt_tot=4 * 9,\n N0=1000,\n )\n\n # Definition of the magnetic simulation: with periodicity\n simu.mag = MagFEMM(\n type_BH_stator=1,\n type_BH_rotor=1,\n is_periodicity_a=False,\n is_periodicity_t=True,\n nb_worker=cpu_count(),\n Kmesh_fineness=2,\n )\n simu.force = ForceMT()\n\n # Definition of the magnetic simulation: no periodicity\n simu2 = simu.copy()\n simu2.mag.is_periodicity_t = False\n\n # Run simulations\n out = Output(simu=simu)\n simu.run()\n\n out2 = Output(simu=simu2)\n simu2.run()\n\n # Plot the result\n out.mag.B.plot_2D_Data(\n \"time\",\n \"angle[0]{°}\",\n data_list=[out2.mag.B],\n legend_list=[\"Periodic\", \"Full\"],\n save_path=join(save_path, simu.name + \"_B_time.png\"),\n is_show_fig=False,\n **dict_2D\n )\n\n out.mag.B.plot_2D_Data(\n \"angle{°}\",\n \"time[1]\",\n data_list=[out2.mag.B],\n legend_list=[\"Periodic\", \"Full\"],\n save_path=join(save_path, simu.name + \"_B_space.png\"),\n is_show_fig=False,\n **dict_2D\n )\n\n out.force.AGSF.plot_2D_Data(\n \"wavenumber=[0,100]\",\n \"time[0]\",\n data_list=[out2.force.AGSF],\n legend_list=[\"Periodic\", \"Full\"],\n save_path=join(save_path, simu.name + \"_P_space_fft.png\"),\n is_show_fig=False,\n **dict_2D\n )\n\n out.force.AGSF.plot_2D_Data(\n \"freqs\",\n \"angle[0]\",\n data_list=[out2.force.AGSF],\n legend_list=[\"Periodic\", \"Full\"],\n save_path=join(save_path, simu.name + \"_P_fft2.png\"),\n is_show_fig=False,\n **dict_2D\n )\n\n out.mag.Tem.plot_2D_Data(\n \"time\",\n data_list=[out2.mag.Tem],\n legend_list=[\"Periodic\", \"Full\"],\n save_path=join(save_path, simu.name + \"_Tem_time.png\"),\n is_show_fig=False,\n **dict_2D\n )\n\n out.mag.Phi_wind_stator.plot_2D_Data(\n \"time\",\n \"phase\",\n data_list=[out2.mag.Phi_wind_stator],\n legend_list=[\"Periodic\", \"Full\"],\n save_path=join(save_path, simu.name + \"_Phi_wind_stator_time.png\"),\n is_show_fig=False,\n **dict_2D\n )\n\n Bflux = out.mag.B\n arg_list = [\"time\"]\n result = Bflux.get_rphiz_along(*arg_list)\n Brad = result[\"radial\"]\n time = result[\"time\"]\n\n Bflux2 = out2.mag.B\n arg_list = [\"time\"]\n result2 = Bflux2.get_rphiz_along(*arg_list)\n Brad2 = result2[\"radial\"]\n time2 = result2[\"time\"]\n\n # Compare both simu\n assert_array_almost_equal((Brad - Brad2) / Brad2, 0, decimal=2)\n assert_array_almost_equal(time, time2, decimal=6)\n\n AGSF = out.force.AGSF\n arg_list = [\"time\"]\n result_AGSF = AGSF.get_rphiz_along(*arg_list)\n Prad = result_AGSF[\"radial\"]\n time3 = result_AGSF[\"time\"]\n\n AGSF2 = out2.force.AGSF\n arg_list = [\"time\"]\n result_AGSF2 = AGSF2.get_rphiz_along(*arg_list)\n Prad2 = result_AGSF2[\"radial\"]\n time4 = result_AGSF2[\"time\"]\n\n # Compare both simu\n assert_array_almost_equal((Prad - Prad2) / Prad2, 0, decimal=2)\n assert_array_almost_equal(time3, time4, decimal=6)\n\n return out, out2","def test_FEMM_periodicity_time():\n\n SPMSM_015 = load(join(DATA_DIR, \"Machine\", \"SPMSM_015.json\"))\n\n assert SPMSM_015.comp_periodicity() == (9, False, 9, True)\n\n simu = Simu1(name=\"test_FEMM_periodicity_time\", machine=SPMSM_015)\n\n # Definition of the enforced output of the electrical module\n I0_rms = 250 / sqrt(2)\n Phi0 = 140 * pi / 180 # Maximum Torque Per Amp\n\n Id_ref = (I0_rms * exp(1j * Phi0)).real\n Iq_ref = (I0_rms * exp(1j * Phi0)).imag\n\n simu.input = InputCurrent(\n Id_ref=Id_ref,\n Iq_ref=Iq_ref,\n Na_tot=252 * 9,\n Nt_tot=4 * 9,\n N0=1000,\n )\n\n # Definition of the magnetic simulation: with periodicity\n simu.mag = MagFEMM(\n type_BH_stator=1,\n type_BH_rotor=1,\n is_periodicity_a=True,\n is_periodicity_t=True,\n nb_worker=cpu_count(),\n Kmesh_fineness=2,\n )\n simu.force = ForceMT()\n\n # Definition of the magnetic simulation: no periodicity\n simu2 = simu.copy()\n simu2.mag.is_periodicity_t = False\n\n # Run simulations\n out = Output(simu=simu)\n simu.run()\n\n out2 = Output(simu=simu2)\n simu2.run()\n\n # Plot the result\n out.mag.B.plot_2D_Data(\n \"time\",\n \"angle[0]{°}\",\n data_list=[out2.mag.B],\n legend_list=[\"Periodic\", \"Full\"],\n save_path=join(save_path, simu.name + \"_B_time.png\"),\n is_show_fig=False,\n **dict_2D\n )\n\n out.mag.B.plot_2D_Data(\n \"angle{°}\",\n \"time[1]\",\n data_list=[out2.mag.B],\n legend_list=[\"Periodic\", \"Full\"],\n save_path=join(save_path, simu.name + \"_B_space.png\"),\n is_show_fig=False,\n **dict_2D\n )\n\n out.force.AGSF.plot_2D_Data(\n \"wavenumber=[0,100]\",\n \"time[0]\",\n data_list=[out2.force.AGSF],\n legend_list=[\"Periodic\", \"Full\"],\n save_path=join(save_path, simu.name + \"_P_space_fft.png\"),\n is_show_fig=False,\n **dict_2D\n )\n\n out.force.AGSF.plot_2D_Data(\n \"freqs\",\n \"angle[0]\",\n data_list=[out2.force.AGSF],\n legend_list=[\"Periodic\", \"Full\"],\n save_path=join(save_path, simu.name + \"_P_fft2.png\"),\n is_show_fig=False,\n **dict_2D\n )\n\n out.mag.Tem.plot_2D_Data(\n \"time\",\n data_list=[out2.mag.Tem],\n legend_list=[\"Periodic\", \"Full\"],\n save_path=join(save_path, simu.name + \"_Tem_time.png\"),\n is_show_fig=False,\n **dict_2D\n )\n\n out.mag.Phi_wind_stator.plot_2D_Data(\n \"time\",\n \"phase\",\n data_list=[out2.mag.Phi_wind_stator],\n legend_list=[\"Periodic\", \"Full\"],\n save_path=join(save_path, simu.name + \"_Phi_wind_stator_time.png\"),\n is_show_fig=False,\n **dict_2D\n )\n\n # Compare both simu with B\n Bflux = out.mag.B\n arg_list = [\"time\"]\n result = Bflux.get_rphiz_along(*arg_list)\n Brad = result[\"radial\"]\n time = result[\"time\"]\n\n # Check Flux spatio-temporal reconstruction full\n Bflux2 = out2.mag.B\n arg_list = [\"time\"]\n result2 = Bflux2.get_rphiz_along(*arg_list)\n Brad2 = result2[\"radial\"]\n time = result2[\"time\"]\n\n assert_array_almost_equal(Brad, Brad2, decimal=2)\n\n # Compare both simu with AGSF\n AGSF = out.force.AGSF\n arg_list = [\"time\"]\n result_AGSF = AGSF.get_rphiz_along(*arg_list)\n Prad = result_AGSF[\"radial\"]\n time = result_AGSF[\"time\"]\n\n AGSF2 = out2.force.AGSF\n arg_list = [\"time\"]\n result_AGSF2 = AGSF2.get_rphiz_along(*arg_list)\n Prad2 = result_AGSF2[\"radial\"]\n time = result_AGSF2[\"time\"]\n\n assert_array_almost_equal(Prad / 1000, Prad2 / 1000, decimal=0)\n\n return out, out2","def run(self):\n\n self._logger.debug(\"Starting Dummy Model: modelID=%s;\" % (self._modelID))\n\n # =========================================================================\n # Initialize periodic activities (e.g., for model result updates)\n # =========================================================================\n periodic = self._initPeriodicActivities()\n\n self._optimizedMetricLabel = self._optimizeKeyPattern\n self._reportMetricLabels = [self._optimizeKeyPattern]\n\n # =========================================================================\n # Create our top-level loop-control iterator\n # =========================================================================\n if self._iterations >= 0:\n iterTracker = iter(xrange(self._iterations))\n else:\n iterTracker = iter(itertools.count())\n\n # =========================================================================\n # This gets set in the unit tests. It tells the worker to sys exit\n # the first N models. This is how we generate orphaned models\n doSysExit = False\n if self._sysExitModelRange is not None:\n modelAndCounters = self._jobsDAO.modelsGetUpdateCounters(self._jobID)\n modelIDs = [x[0] for x in modelAndCounters]\n modelIDs.sort()\n (beg,end) = self._sysExitModelRange\n if self._modelID in modelIDs[int(beg):int(end)]:\n doSysExit = True\n\n if self._delayModelRange is not None:\n modelAndCounters = self._jobsDAO.modelsGetUpdateCounters(self._jobID)\n modelIDs = [x[0] for x in modelAndCounters]\n modelIDs.sort()\n (beg,end) = self._delayModelRange\n if self._modelID in modelIDs[int(beg):int(end)]:\n time.sleep(10)\n \n # DEBUG!!!! infinite wait if we have 50 models\n #if len(modelIDs) >= 50:\n # jobCancel = self._jobsDAO.jobGetFields(self._jobID, ['cancel'])[0]\n # while not jobCancel:\n # time.sleep(1)\n # jobCancel = self._jobsDAO.jobGetFields(self._jobID, ['cancel'])[0]\n\n if self._errModelRange is not None:\n modelAndCounters = self._jobsDAO.modelsGetUpdateCounters(self._jobID)\n modelIDs = [x[0] for x in modelAndCounters]\n modelIDs.sort()\n (beg,end) = self._errModelRange\n if self._modelID in modelIDs[int(beg):int(end)]:\n raise RuntimeError(\"Exiting with error due to errModelRange parameter\")\n\n # =========================================================================\n # Delay, if necessary\n if self._delay is not None:\n time.sleep(self._delay)\n\n # =========================================================================\n # Run it!\n # =========================================================================\n self._currentRecordIndex = 0\n while True:\n\n # =========================================================================\n # Check if the model should be stopped\n # =========================================================================\n\n # If killed by a terminator, stop running\n if self._isKilled:\n break\n\n # If job stops or hypersearch ends, stop running\n if self._isCanceled:\n break\n\n # If model is mature, stop running ONLY IF we are not the best model\n # for the job. Otherwise, keep running so we can keep returning\n # predictions to the user\n if self._isMature:\n if not self._isBestModel:\n self._cmpReason = self._jobsDAO.CMPL_REASON_STOPPED\n break\n else:\n self._cmpReason = self._jobsDAO.CMPL_REASON_EOF\n\n # =========================================================================\n # Get the the next record, and \"write it\"\n # =========================================================================\n try:\n self._currentRecordIndex = next(iterTracker)\n except StopIteration:\n break\n\n # \"Write\" a dummy output value. This is used to test that the batched\n # writing works properly\n\n self._writePrediction(ModelResult(None, None, None, None))\n\n periodic.tick()\n\n # =========================================================================\n # Compute wait times. See if model should exit\n # =========================================================================\n\n if self.__shouldSysExit(self._currentRecordIndex):\n sys.exit(1)\n\n # Simulate computation time\n if self._busyWaitTime is not None:\n time.sleep(self._busyWaitTime)\n self.__computeWaitTime()\n\n # Asked to abort after so many iterations?\n if doSysExit:\n sys.exit(1)\n\n # Asked to raise a jobFailException?\n if self._jobFailErr:\n raise utils.JobFailException(\"E10000\",\n \"dummyModel's jobFailErr was True.\")\n\n # =========================================================================\n # Handle final operations\n # =========================================================================\n if self._doFinalize:\n if not self._makeCheckpoint:\n self._model = None\n\n # Delay finalization operation\n if self._finalDelay is not None:\n time.sleep(self._finalDelay)\n\n self._finalize()\n\n self._logger.info(\"Finished: modelID=%r \"% (self._modelID))\n\n return (self._cmpReason, None)","def _backend_run(self):\n if self.vM is not None:\n return self.vM\n else:\n everything = copy.copy(self.model.attrs)\n if hasattr(self,'Iext'):\n everything.update({'Iext':self.Iext})\n\n if 'current_inj' in everything.keys():\n everything.pop('current_inj',None)\n everything = copy.copy(self.model.attrs)\n\n self.model.attrs['celltype'] = round(self.model.attrs['celltype'])\n if self.model.attrs['celltype'] <= 3:\n everything.pop('celltype',None)\n v = get_vm_matlab_one_two_three(**everything)\n else:\n if self.model.attrs['celltype'] == 4:\n v = get_vm_matlab_four(**everything)\n if self.model.attrs['celltype'] == 5:\n v = get_vm_matlab_five(**everything)\n if self.model.attrs['celltype'] == 6:\n v = get_vm_matlab_six(**everything)\n if self.model.attrs['celltype'] == 7:\n #print('gets into multiple regimes',self.attrs['celltype'])\n\n v = get_vm_matlab_seven(**everything)\n\n return AnalogSignal(v, units=pq.mV,\n sampling_period=0.125*pq.ms)","def initialize_model(self, config_param_vals = None):\n self._is_initialized = True\n\n self.fmu.instantiate()\n self.fmu.reset()\n self.fmu.setupExperiment(startTime=self.start_time)\n if config_param_vals is not None:\n self._apply_config(config_param_vals)\n self.fmu.enterInitializationMode()\n self.fmu.exitInitializationMode()\n\n return","def setup_biosafe(self):\n # Generate dummy data in the right format\n species_presence = pd.DataFrame(\n np.random.randint(2, size=len(self.links_law)),\n columns=['speciesPresence'], index=self.links_law.index)\n\n ecotope_area = pd.DataFrame(\n np.ones(len(self.links_eco2.columns)-1) * 1e5,\n columns = ['area_m2'],\n index = self.links_eco2.columns.values[0:-1])\n\n # Simplify ecotope tables to VR ecotopes\n unique_eco = np.unique(\n np.hstack((self.vr_eco.ecotope1.values,\n self.vr_eco.ecotope2.values)))\n links_eco3 = self.links_eco2.reindex(columns=unique_eco)\n ecotope_area = ecotope_area.reindex(index=unique_eco)\n\n # Run a first version of Biosafe\n self.bsf_model = bsf.biosafe(\n self.legal_weights, self.links_law, links_eco3,\n species_presence, ecotope_area)\n\n #PotTax = self.bsf_model.TFI()\n #PotAll = self.bsf_model.FI()\n return","def build_bpmf_model(train, alpha=2, dim=10, std=0.01):\n n, m = train.shape\n beta_0 = 1 # scaling factor for lambdas; unclear on its use\n\n # Mean value imputation on training data.\n train = train.copy()\n nan_mask = np.isnan(train)\n train[nan_mask] = train[~nan_mask].mean()\n\n logging.info('building the BPMF model')\n with pm.Model() as bpmf:\n # Specify user feature matrix\n lambda_u = pm.Wishart(\n 'lambda_u', n=dim, V=np.eye(dim), shape=(dim, dim),\n testval=np.random.randn(dim, dim) * std)\n mu_u = pm.Normal(\n 'mu_u', mu=0, tau=beta_0 * lambda_u, shape=dim,\n testval=np.random.randn(dim) * std)\n U = pm.MvNormal(\n 'U', mu=mu_u, tau=lambda_u, shape=(n, dim),\n testval=np.random.randn(n, dim) * std)\n\n # Specify item feature matrix\n lambda_v = pm.Wishart(\n 'lambda_v', n=dim, V=np.eye(dim), shape=(dim, dim),\n testval=np.random.randn(dim, dim) * std)\n mu_v = pm.Normal(\n 'mu_v', mu=0, tau=beta_0 * lambda_v, shape=dim,\n testval=np.random.randn(dim) * std)\n V = pm.MvNormal(\n 'V', mu=mu_v, tau=lambda_v, shape=(m, dim),\n testval=np.random.randn(m, dim) * std)\n\n # Specify rating likelihood function\n R = pm.Normal(\n 'R', mu=t.dot(U, V.T), tau=alpha * np.ones((n, m)),\n observed=train)\n\n logging.info('done building the BPMF model')\n return bpmf","def run_libfm(train, test, iter=20, std=0.2, dim=8, bias=False,\n outfile=''):\n kwargs = {k: v for k, v in locals().items() if not k in ['train', 'test']}\n args = compose_libfm_args(train, test, **kwargs)\n cmd = ' '.join(args)\n logging.debug(cmd)\n\n proc = sub.Popen(cmd, shell=True, stdout=sub.PIPE)\n retcode = proc.wait()\n if retcode:\n raise LibFMFailed(\"libFM failed to execute.\\n%s\" % cmd)\n\n output = proc.communicate()[0]\n lines = output.split('\\n')\n rows = [row.split('\\t')[1:] for row in lines[-iter:] if row]\n train_err = '%.6f' % float(rows[-1][0].split('=')[1])\n test_err = '%.6f' % float(rows[-1][1].split('=')[1])\n return [train_err, test_err]","def run(mu_v, Sigma_w, Sigma_z, a_mu, l_sensor):\n N = 1000\n # Init tracking\n mu_x = np.zeros(N) # Belief or estimation of hidden state \n F = np.zeros(N) # Free Energy of AI neuron\n mu_y = np.zeros(N) # Belief or prediction of sensory signal \n x = np.zeros(N) # True hidden state\n y = np.zeros(N) # Sensory signal as input to AI neuron\n\n robot_brain = pp_unit(dt, mu_v, Sigma_w, Sigma_z, a_mu) #make pp object\n \n \n\n start_time = time.time()\n for i in np.arange(1, N):\n #Active inference\n y[i] = l_sensor.ambient_light_intensity #take sensor reading\n print('light reading', y[i])\n F[i], mu_x[i], mu_y[i] = robot_brain.inference_step(i, mu_v, y[i])\n\n\n t_elapsed = time.time() - start_time\n\n print(\"Elapsed Time\", t_elapsed, \"sec\")\n return F, mu_x, mu_y, x, y","def test_flim_model(datadir):\n # ifuslot_063\n filename = datadir.join(\"test_hdf.h5\").strpath\n hdcon1 = SensitivityCubeHDF5Container(filename, flim_model=\"hdr1\")\n hdcon2 = SensitivityCubeHDF5Container(filename, flim_model=\"hdr2pt1\")\n\n scube1 = hdcon1.extract_ifu_sensitivity_cube(\"ifuslot_063\")\n scube2 = hdcon2.extract_ifu_sensitivity_cube(\"ifuslot_063\")\n\n s1 = scube1.get_f50(161.4201, 50.8822, 3470.0, 5.5)\n s2 = scube2.get_f50(161.4201, 50.8822, 3470.0, 5.5)\n\n print(s1)\n # if different models passed should be different\n assert abs(s1 - s2) > 1e-19","def test_pregenerated_model(sub_test, case):\n\n if case.startswith(\"sensi2\"):\n model_name = sub_test + \"_o2\"\n else:\n model_name = sub_test\n\n model_swig_folder = str(\n Path(__file__).parents[2]\n / \"build\"\n / \"tests\"\n / \"cpp\"\n / f\"external_{model_name}-prefix\"\n / \"src\"\n / f\"external_{model_name}-build\"\n / \"swig\"\n )\n\n test_model_module = amici.import_model_module(\n module_name=model_name, module_path=model_swig_folder\n )\n model = test_model_module.getModel()\n solver = model.getSolver()\n amici.readModelDataFromHDF5(\n options_file, model.get(), f\"/{sub_test}/{case}/options\"\n )\n amici.readSolverSettingsFromHDF5(\n options_file, solver.get(), f\"/{sub_test}/{case}/options\"\n )\n\n edata = None\n if \"data\" in expected_results[sub_test][case].keys():\n edata = amici.readSimulationExpData(\n str(expected_results_file), f\"/{sub_test}/{case}/data\", model.get()\n )\n rdata = amici.runAmiciSimulation(model, solver, edata)\n\n check_derivative_opts = dict()\n\n if model_name == \"model_nested_events\":\n check_derivative_opts[\"rtol\"] = 1e-2\n elif model_name == \"model_events\":\n check_derivative_opts[\"atol\"] = 1e-3\n\n if (\n edata\n and solver.getSensitivityMethod()\n and solver.getSensitivityOrder()\n and len(model.getParameterList())\n and not model_name.startswith(\"model_neuron\")\n and not case.endswith(\"byhandpreeq\")\n ):\n check_derivatives(model, solver, edata, **check_derivative_opts)\n\n verify_simulation_opts = dict()\n\n if model_name.startswith(\"model_neuron\"):\n verify_simulation_opts[\"atol\"] = 1e-5\n verify_simulation_opts[\"rtol\"] = 1e-2\n\n if model_name.startswith(\"model_robertson\") and case == \"sensiforwardSPBCG\":\n verify_simulation_opts[\"atol\"] = 1e-3\n verify_simulation_opts[\"rtol\"] = 1e-3\n\n verify_simulation_results(\n rdata, expected_results[sub_test][case][\"results\"], **verify_simulation_opts\n )\n\n if model_name == \"model_steadystate\" and case == \"sensiforwarderrorint\":\n edata = amici.amici.ExpData(model.get())\n\n # Test runAmiciSimulations: ensure running twice\n # with same ExpData yields same results\n if (\n edata\n and model_name != \"model_neuron_o2\"\n and not (model_name == \"model_robertson\" and case == \"sensiforwardSPBCG\")\n ):\n if isinstance(edata, amici.amici.ExpData):\n edatas = [edata, edata]\n else:\n edatas = [edata.get(), edata.get()]\n\n rdatas = amici.runAmiciSimulations(\n model, solver, edatas, num_threads=2, failfast=False\n )\n verify_simulation_results(\n rdatas[0],\n expected_results[sub_test][case][\"results\"],\n **verify_simulation_opts,\n )\n verify_simulation_results(\n rdatas[1],\n expected_results[sub_test][case][\"results\"],\n **verify_simulation_opts,\n )\n\n # test residuals mode\n if solver.getSensitivityMethod() == amici.SensitivityMethod.adjoint:\n with pytest.raises(RuntimeError):\n solver.setReturnDataReportingMode(amici.RDataReporting.residuals)\n else:\n solver.setReturnDataReportingMode(amici.RDataReporting.residuals)\n rdata = amici.runAmiciSimulation(model, solver, edata)\n verify_simulation_results(\n rdata,\n expected_results[sub_test][case][\"results\"],\n fields=[\"t\", \"res\", \"sres\", \"y\", \"sy\", \"sigmay\", \"ssigmay\"],\n **verify_simulation_opts,\n )\n with pytest.raises(RuntimeError):\n solver.setSensitivityMethod(amici.SensitivityMethod.adjoint)\n\n chi2_ref = rdata.chi2\n\n # test likelihood mode\n solver.setReturnDataReportingMode(amici.RDataReporting.likelihood)\n rdata = amici.runAmiciSimulation(model, solver, edata)\n verify_simulation_results(\n rdata,\n expected_results[sub_test][case][\"results\"],\n fields=[\"t\", \"llh\", \"sllh\", \"s2llh\", \"FIM\"],\n **verify_simulation_opts,\n )\n\n # test sigma residuals\n\n if (\n model_name == \"model_jakstat_adjoint\"\n and solver.getSensitivityMethod() != amici.SensitivityMethod.adjoint\n ):\n model.setAddSigmaResiduals(True)\n solver.setReturnDataReportingMode(amici.RDataReporting.full)\n rdata = amici.runAmiciSimulation(model, solver, edata)\n # check whether activation changes chi2\n assert chi2_ref != rdata.chi2\n\n if (\n edata\n and solver.getSensitivityMethod()\n and solver.getSensitivityOrder()\n and len(model.getParameterList())\n ):\n check_derivatives(model, solver, edata, **check_derivative_opts)\n\n chi2_ref = rdata.chi2\n res_ref = rdata.res\n\n model.setMinimumSigmaResiduals(100)\n rdata = amici.runAmiciSimulation(model, solver, edata)\n # check whether changing the minimum changes res but not chi2\n assert np.isclose(chi2_ref, rdata.chi2)\n assert not np.allclose(res_ref, rdata.res)\n\n model.setMinimumSigmaResiduals(-10)\n rdata = amici.runAmiciSimulation(model, solver, edata)\n # check whether having a bad minimum results in nan chi2\n assert np.isnan(rdata.chi2)\n\n with pytest.raises(RuntimeError):\n model.getParameterByName(\"thisParameterDoesNotExist\")","def setUp(self):\n # Set Model Parameters\n odeparam = np.array([1, 2])\n y0, y0_unc = 1.0, 0 \n t0, tmax = 0.0, 1.25\n\n # Set Method Parameters\n q = 1\n h = 0.1\n\n # Set up and solve ODE\n ibm = statespace.IBM(q=q, dim=1)\n solver = linsolve.LinearisedODESolver(ibm)\n ivp = linode.LogisticODE(t0, tmax, odeparam, y0, y0_unc)\n tsteps, means, __, rhs_parts, uncerts = solver.solve(ivp, stepsize=h)\n self.mean = odesolver.get_trajectory(means, 0, 0)\n\n # Set up BM and IBM covariance matrices\n evalpt = np.array(tsteps[[-1, -10]])\n derdat = (tsteps, rhs_parts, 0.)\n\n const, jacob = linearisation.compute_linearisation(\n ssm=ibm, initial_value=y0,\n derivative_data=derdat, prdct_tsteps=evalpt)\n\n # Compute GP Estimation of filter mean at t=tmax\n self.postmean = const + np.dot(jacob, odeparam)","def test_FEMM_periodicity_angle():\n\n SPMSM_015 = load(join(DATA_DIR, \"Machine\", \"SPMSM_015.json\"))\n\n assert SPMSM_015.comp_periodicity() == (9, False, 9, True)\n\n simu = Simu1(name=\"test_FEMM_periodicity_angle\", machine=SPMSM_015)\n\n # Definition of the enforced output of the electrical module\n I0_rms = 250 / sqrt(2)\n Phi0 = 140 * pi / 180 # Maximum Torque Per Amp\n\n Id_ref = (I0_rms * exp(1j * Phi0)).real\n Iq_ref = (I0_rms * exp(1j * Phi0)).imag\n\n simu.input = InputCurrent(\n Id_ref=Id_ref,\n Iq_ref=Iq_ref,\n Na_tot=252 * 9,\n Nt_tot=4 * 9,\n N0=1000,\n )\n\n # Definition of the magnetic simulation: with periodicity\n simu.mag = MagFEMM(\n type_BH_stator=1,\n type_BH_rotor=1,\n is_periodicity_a=True,\n is_periodicity_t=False,\n nb_worker=cpu_count(),\n Kmesh_fineness=2,\n )\n simu.force = ForceMT()\n\n # Definition of the magnetic simulation: no periodicity\n # Definition of the magnetic simulation: no periodicity\n simu2 = simu.copy()\n simu2.mag.is_periodicity_a = False\n\n simu2.force = ForceMT()\n\n # Run simulations\n out = Output(simu=simu)\n simu.run()\n\n out2 = Output(simu=simu2)\n simu2.run()\n\n # Plot the result\n out.mag.B.plot_2D_Data(\n \"time\",\n \"angle[0]{°}\",\n data_list=[out2.mag.B],\n legend_list=[\"Periodic\", \"Full\"],\n save_path=join(save_path, simu.name + \"_B_time.png\"),\n is_show_fig=False,\n **dict_2D\n )\n\n out.mag.B.plot_2D_Data(\n \"angle{°}\",\n \"time[1]\",\n data_list=[out2.mag.B],\n legend_list=[\"Periodic\", \"Full\"],\n save_path=join(save_path, simu.name + \"_B_space.png\"),\n is_show_fig=False,\n **dict_2D\n )\n\n out.force.AGSF.plot_2D_Data(\n \"wavenumber=[0,100]\",\n \"time[0]\",\n data_list=[out2.force.AGSF],\n legend_list=[\"Periodic\", \"Full\"],\n save_path=join(save_path, simu.name + \"_P_space_fft.png\"),\n is_show_fig=False,\n **dict_2D\n )\n\n out.force.AGSF.plot_2D_Data(\n \"freqs\",\n \"angle[0]\",\n data_list=[out2.force.AGSF],\n legend_list=[\"Periodic\", \"Full\"],\n save_path=join(save_path, simu.name + \"_P_fft2.png\"),\n is_show_fig=False,\n **dict_2D\n )\n\n out.mag.Tem.plot_2D_Data(\n \"time\",\n data_list=[out2.mag.Tem],\n legend_list=[\"Periodic\", \"Full\"],\n save_path=join(save_path, simu.name + \"_Tem_time.png\"),\n is_show_fig=False,\n **dict_2D\n )\n\n out.mag.Phi_wind_stator.plot_2D_Data(\n \"time\",\n \"phase\",\n data_list=[out2.mag.Phi_wind_stator],\n legend_list=[\"Periodic\", \"Full\"],\n save_path=join(save_path, simu.name + \"_Phi_wind_stator_time.png\"),\n is_show_fig=False,\n **dict_2D\n )\n\n # Compare both simu\n Bflux = out.mag.B\n arg_list = [\"angle\"]\n result = Bflux.get_rphiz_along(*arg_list)\n Brad = result[\"radial\"]\n angle = result[\"angle\"]\n\n Bflux2 = out2.mag.B\n arg_list = [\"angle\"]\n result2 = Bflux2.get_rphiz_along(*arg_list)\n Brad2 = result2[\"radial\"]\n\n assert_array_almost_equal(Brad, Brad2, decimal=1)\n\n return out, out2","def run_model(species, Av, Air, rh, obliquity, temp=-1, verbosity=1):\n\n if isinstance(species, int):\n if species in range(1, len(speciesList) + 1):\n # The species indexing if offset by +1 to make the inputs consistent with the original fortran script.\n species = speciesList[species - 1]\n else:\n logging.error(f\"The inputted index of {species} is not currently supported \\n\"\n f\"Please input one of the following integers or strings: \\n\"\n f\"1: 'H2O', 2: 'H2O-CH4', 3: 'CO2', 4: 'CO'\")\n raise ValueError('Invalid species.')\n if species not in speciesList:\n logging.error(f\"The inputted species of \\\"{species}\\\" is not currently supported \\n\"\n f\"Please input one of the following integers or strings: \\n\"\n f\"1: 'H2O', 2: 'H2O-CH4', 3: 'CO2', 4: 'CO'\")\n raise ValueError('Invalid species.')\n\n if Av < 0:\n logging.error(\n f'A visual albedo of {Av} is not a valid input.'\n ' Please input a value greater than 0.')\n raise ValueError('Invalid visual albedo.')\n\n if verbosity == 0:\n logging.basicConfig(level='WARNING')\n elif verbosity == 1:\n logging.basicConfig(level='INFO')\n else:\n logging.basicConfig(level='DEBUG')\n\n logging.info(\"Input Parameters:\")\n logging.info(\n f'Species = {species}, Avis = {Av}, Air = {Air}, r_H = {rh}, Obl = {obliquity}')\n\n incl = (90 - obliquity) * math.pi / 180\n\n mass, xlt, xltprim, press, pprim, temp = sublime(species, temp)\n root = 1 / math.sqrt(mass * 2 * math.pi * boltz)\n\n nflag = 1\n perc = 0\n gd = None\n for n in range(0, nb):\n temp, gd, perc, nflag = main_loop(\n n, species, Av, Air, rh, obliquity, incl, temp, root, nflag, perc, gd)\n\n zbar = 0.\n for nn in range(0, nb - 1):\n zbar = zbar + 0.5 * (z[nn] + z[nn + 1]) * delsb\n\n zbar = zbar / 2\n zlog = math.log10(zbar)\n rlog = math.log10(rh)\n\n output = {\n \"species\": species,\n \"obliquity\": obliquity,\n \"r_H\": rh,\n \"rlog\": rlog,\n \"Av\": Av,\n \"Air\": Air,\n \"Zbar\": zbar,\n \"Zlog\": zlog,\n }\n\n logging.info(\"Final Results:\")\n logging.info(output)\n\n return output","def execute(cf):\n\n ##Ports and parameters\n train_set = cf.get_input(\"train_set\") #training set. Typically even_file\n test_set = cf.get_input(\"test_set\") #test set. Typically odd_file\n WM1 = cf.get_input(\"WM1\")\n WM2 = cf.get_input(\"WM2\")\n WM3 = cf.get_input(\"WM3\")\n WM4 = cf.get_input(\"WM4\")\n WM5 = cf.get_input(\"WM5\")\n WM6 = cf.get_input(\"WM6\")\n WM7 = cf.get_input(\"WM7\")\n WM8 = cf.get_input(\"WM8\")\n WM9 = cf.get_input(\"WM9\")\n WM10 = cf.get_input(\"WM10\")\n WM11 = cf.get_input(\"WM11\")\n WM12 = cf.get_input(\"WM12\")\n WM13 = cf.get_input(\"WM13\")\n WM14 = cf.get_input(\"WM14\")\n WM15 = cf.get_input(\"WM15\")\n WM16 = cf.get_input(\"WM16\")\n WM17 = cf.get_input(\"WM17\")\n WM18 = cf.get_input(\"WM18\")\n WM19 = cf.get_input(\"WM19\")\n WM20 = cf.get_input(\"WM20\")\n WMdir = cf.get_input(\"WMdir\")\n WMdir2 = cf.get_input(\"WMdir2\")\n basefreqs = cf.get_input(\"BaseFrequencies\")\n ufemodel_path = cf.get_input(\"UFEmodel\")\n\n bestWM = cf.get_output(\"BestWM\")\n log_file = cf.get_output(\"log_file\")\n interm = cf.get_output(\"intermediate\")\n\n genome = cf.get_parameter('genome', 'string')\n motevo_path = cf.get_parameter('motevo_path', 'string')\n aligned = cf.get_parameter(\"aligned\", \"boolean\")\n\n os.mkdir(interm)\n\n\n\n # Read stuff in\n WMs = [i for i in[WM1, WM2, WM3, WM4, WM5, WM6, WM7, WM8, WM9, WM10, WM11, WM12, WM13, WM14, WM15, WM16, WM17, WM18, WM19, WM20] if i]\n\n if WMdir:\n WMs += [os.path.join(WMdir, wm) for wm in os.listdir(WMdir)]\n\n if WMdir2:\n WMs += [os.path.join(WMdir2, wm) for wm in os.listdir(WMdir2)]\n\n f = open(basefreqs)\n ATfreq = float(f.readline().strip().split()[1])\n GCfreq = float(f.readline().strip().split()[1])\n f.close()\n\n\n # Compute stuff: optimal priors and then likelihood of test set\n optpriors = []\n logliks = []\n\n for i, WM in enumerate(WMs):\n\n wmlen = len(open(WM).readlines())-4\n\n # 1. Fit prior on training set with EM\n tag = 'fitP_%i' %(i+1)\n params, sites, priors, loglikfile = giveMotevoParamFile(genome, wmlen, interm, tag, aligned, ufemodel_path, ATfreq, GCfreq, emprior=1, bgorder=0, bgprior=0.99)\n r = runMotevo(motevo_path, train_set, params, WM, interm, tag)\n if r != 0:\n print 'motevo failed ', tag\n sys.exit(1)\n\n # prior file:\n # WM_name final_prior nr_of_sites density\n # /import/bc2/home/nimwegen/GROUP/hseq_pipeline/severin/Anduril/Pipeline/PipeLineSource/TESTRUN/NRF1_Z2/OUTPUT/NRF1_FgBg-runmotevoPG2_1/Logo 0.016554 635.008 0.251863\n # background 0.983446 37724.8 0.748137\n # UFEwm 0 0 0\n\n optprior = float(open(priors).readlines()[1].split()[1])\n bgprior=(1-optprior)\n print bgprior\n\n # 2. Compute log-likelihood on test set with optimal prior from training set and without EM\n tag = 'compLL_%i' %(i+1)\n params, sites, priors, loglikfile = giveMotevoParamFile(genome, wmlen, interm, tag, aligned, ufemodel_path, ATfreq, GCfreq, emprior=0, bgorder=0, bgprior=bgprior)\n runMotevo(motevo_path, train_set, params, WM, interm, tag)\n\n a = loadtxt(loglikfile, usecols=[1])\n ll = sum(a)\n\n logliks.append(ll)\n optpriors.append(optprior)\n\n print logliks\n\n\n\n #replace name in WM file with bestWM\n lines = open(WMs[argmax(logliks)]).readlines()\n lines[1] = 'NA BestWM\\n'\n bwm = open(bestWM, 'w')\n bwm.write(''.join(lines))\n\n\n l = open(log_file, 'w')\n\n l.write('WM_name\\tWM_path\\tlog_likelihood\\topt_prior\\n')\n\n names = ['WM_%i\\t%s\\t%.4f\\t%s' %(i+1, WMs[i], logliks[i], optpriors[i]) for i in arange(len(WMs))]\n\n l.write('\\n'.join(names))\n l.close()\n\n\n return 0","def setUp(self):\n # Set Model Parameters\n odeparam = np.array([1, 2])\n y0, y0_unc = 1.0, 0 \n t0, tmax = 0.0, 1.25\n\n # Set Method Parameters\n q = 1\n h = 0.1\n\n # Set up and solve ODE\n ibm = statespace.IBM(q=q, dim=1)\n solver = linsolve.LinearisedODESolver(ibm)\n ivp = linode.LogisticODE(t0, tmax, odeparam, y0, y0_unc)\n tsteps, means, __, rhs_parts, uncerts = solver.solve(ivp, stepsize=h)\n self.mean = odesolver.get_trajectory(means, 0, 0)\n\n # Set up BM and IBM covariance matrices\n evalpt = np.array(tsteps[[-1]])\n derdat = (tsteps, rhs_parts, 0.)\n\n const, jacob = linearisation.compute_linearisation(\n ssm=ibm, initial_value=y0,\n derivative_data=derdat, prdct_tsteps=evalpt)\n\n # Compute GP Estimation of filter mean at t=tmax\n self.postmean = const + np.dot(jacob, odeparam)","def construct_test_model(self):\n # Set the placeholder for the input episode\n self.inputa = tf.placeholder(tf.float32)\n self.inputb = tf.placeholder(tf.float32)\n self.labela = tf.placeholder(tf.float32)\n self.labelb = tf.placeholder(tf.float32)\n\n with tf.variable_scope('meta-test-model', reuse=None) as training_scope: \n # construct the model weights \n self.ss_weights = ss_weights = self.construct_resnet_ss_weights()\n self.weights = weights = self.construct_resnet_weights()\n self.fc_weights = fc_weights = self.construct_fc_weights()\n\n # Load test base epoch number from FLAGS\n num_updates = FLAGS.test_base_epoch_num\n\n def task_metalearn(inp, reuse=True):\n \"\"\"The function to process one episode in a meta-batch.\n Args:\n inp: the input episode.\n reuse: whether reuse the variables for the normalization.\n Returns:\n A serious outputs like losses and accuracies.\n \"\"\"\n # Seperate inp to different variables\n inputa, inputb, labela, labelb = inp\n # Generate empty list to record accuracies\n accb_list = []\n\n # Embed the input images to embeddings with ss weights\n emb_outputa = self.forward_resnet(inputa, weights, ss_weights, reuse=reuse)\n emb_outputb = self.forward_resnet(inputb, weights, ss_weights, reuse=True)\n\n # This part is similar to the meta-train function, you may refer to the comments above\n outputa = self.forward_fc(emb_outputa, fc_weights)\n lossa = self.loss_func(outputa, labela) \n grads = tf.gradients(lossa, list(fc_weights.values()))\n gradients = dict(zip(fc_weights.keys(), grads))\n fast_fc_weights = dict(zip(fc_weights.keys(), [fc_weights[key] - \\\n self.update_lr*gradients[key] for key in fc_weights.keys()]))\n outputb = self.forward_fc(emb_outputb, fast_fc_weights)\n accb = tf.contrib.metrics.accuracy(tf.argmax(tf.nn.softmax(outputb), 1), tf.argmax(labelb, 1))\n accb_list.append(accb)\n \n for j in range(num_updates - 1):\n lossa = self.loss_func(self.forward_fc(emb_outputa, fast_fc_weights), labela)\n grads = tf.gradients(lossa, list(fast_fc_weights.values()))\n gradients = dict(zip(fast_fc_weights.keys(), grads))\n fast_fc_weights = dict(zip(fast_fc_weights.keys(), [fast_fc_weights[key] - \\\n self.update_lr*gradients[key] for key in fast_fc_weights.keys()]))\n outputb = self.forward_fc(emb_outputb, fast_fc_weights)\n accb = tf.contrib.metrics.accuracy(tf.argmax(tf.nn.softmax(outputb), 1), tf.argmax(labelb, 1))\n accb_list.append(accb)\n\n lossb = self.loss_func(outputb, labelb)\n\n task_output = [lossb, accb, accb_list]\n\n return task_output\n\n if FLAGS.norm is not 'None':\n unused = task_metalearn((self.inputa[0], self.inputb[0], self.labela[0], self.labelb[0]), False)\n\n out_dtype = [tf.float32, tf.float32, [tf.float32]*num_updates]\n\n result = tf.map_fn(task_metalearn, elems=(self.inputa, self.inputb, self.labela, self.labelb), \\\n dtype=out_dtype, parallel_iterations=FLAGS.meta_batch_size)\n lossesb, accsb, accsb_list = result\n\n self.metaval_total_loss = total_loss = tf.reduce_sum(lossesb)\n self.metaval_total_accuracy = total_accuracy = tf.reduce_sum(accsb)\n self.metaval_total_accuracies = total_accuracies =[tf.reduce_sum(accsb_list[j]) for j in range(num_updates)]","def test_construct_f_(self):\n model = roi._core._ContinuousROM('')\n\n # Check that the constructed f takes the right number of arguments.\n model.modelform = \"cA\"\n model.c_, model.A_ = 1, 1\n model.Hc_, model.Gc_, model.B_ = None, None, None\n model._construct_f_()\n with pytest.raises(TypeError) as ex:\n model.f_(1)\n assert ex.value.args[0] == \\\n \"<lambda>() missing 1 required positional argument: 'x_'\"","def test_constructor(self):\n # Record the model types of all the models to be created\n all_model_types = model_type_to_display_name.keys()\n\n # Record the attribute / value pairs that are common to all models.\n common_attr_value_dict = {\"data\": self.fake_df,\n \"name_spec\": self.fake_names,\n \"design\": self.fake_design,\n \"ind_var_names\": self.fake_names[\"x\"],\n \"alt_id_col\": self.alt_id_col,\n \"obs_id_col\": self.obs_id_col,\n \"choice_col\": self.choice_col,\n \"specification\": self.fake_specification,\n \"alt_IDs\": self.fake_df[\"alt_id\"].values,\n \"choices\": self.fake_df[\"choice\"].values}\n\n # Create a shape name dictionary to relate the various models to the\n # names of their shape parameters.\n shape_name_dict = {\"MNL\": None,\n \"Asym\": self.fake_shape_names[:2],\n \"Cloglog\": None,\n \"Scobit\": self.fake_shape_names,\n \"Uneven\": self.fake_shape_names,\n \"Nested Logit\": None,\n \"Mixed Logit\": None}\n\n # Create a shape reference position dictionary to relate the various\n # models to their shape reference positions.\n shape_ref_dict = {}\n for key in shape_name_dict:\n shape_ref_dict[key] = (None if key != \"Asym\" else\n self.fake_shape_ref_pos)\n\n # Create an intercept_names and intercept_ref_position dictionary to\n # relate the various models to their respective kwargs.\n intercept_names_dict = {}\n intercept_ref_dict = {}\n for key in shape_name_dict:\n if key in [\"MNL\", \"Nested Logit\", \"Mixed Logit\"]:\n intercept_names_dict[key] = None\n intercept_ref_dict[key] = None\n else:\n intercept_names_dict[key] = self.fake_intercept_names\n intercept_ref_dict[key] = self.fake_intercept_ref_pos\n\n # Create a nest_names dictionary to relate the various models to their\n # nest_name attributes\n nest_name_dict = {}\n nest_spec_dict = {}\n for key in shape_name_dict:\n if key != \"Nested Logit\":\n nest_name_dict[key] = None\n nest_spec_dict[key] = None\n else:\n nest_name_dict[key] = list(self.fake_nest_spec.keys())\n nest_spec_dict[key] = self.fake_nest_spec\n\n # Create dictionaries for the mixing_id_col, mixing_vars, and\n # mixing_pos attributes\n mixing_id_col_dict = {}\n mixing_vars_dict = {}\n mixing_pos_dict = {}\n\n for key in shape_name_dict:\n if key != \"Mixed Logit\":\n mixing_id_col_dict[key] = None\n mixing_vars_dict[key] = None\n mixing_pos_dict[key] = None\n else:\n mixing_id_col_dict[key] = self.obs_id_col\n mixing_vars_dict[key] = self.fake_names[\"x\"]\n mixing_pos_dict[key] = [0]\n\n # Record the attribute / value pairs that vary across models\n varying_attr_value_dict = {\"model_type\": model_type_to_display_name,\n \"intercept_names\": intercept_names_dict,\n \"intercept_ref_position\":\n intercept_ref_dict,\n \"shape_names\": shape_name_dict,\n \"shape_ref_position\": shape_ref_dict,\n \"nest_names\": nest_name_dict,\n \"nest_spec\": nest_spec_dict,\n \"mixing_id_col\": mixing_id_col_dict,\n \"mixing_vars\": mixing_vars_dict,\n \"mixing_pos\": mixing_pos_dict}\n\n # Set up the keyword arguments that are needed for each of the model\n # types\n variable_kwargs = {}\n for model_name in all_model_types:\n variable_kwargs[model_name] = {}\n variable_kwargs[model_name][\"intercept_names\"] =\\\n intercept_names_dict[model_name]\n variable_kwargs[model_name][\"intercept_ref_pos\"] =\\\n intercept_ref_dict[model_name]\n variable_kwargs[model_name][\"shape_ref_pos\"] =\\\n shape_ref_dict[model_name]\n variable_kwargs[model_name][\"shape_names\"] =\\\n shape_name_dict[model_name]\n variable_kwargs[model_name][\"nest_spec\"] =\\\n nest_spec_dict[model_name]\n variable_kwargs[model_name][\"mixing_id_col\"] =\\\n mixing_id_col_dict[model_name]\n variable_kwargs[model_name][\"mixing_vars\"] =\\\n mixing_vars_dict[model_name]\n\n # Execute the test for each model type\n for model_name in all_model_types:\n # Update the model type in the list of constructor args\n self.constructor_args[-1] = model_name\n\n # Use this specific model's keyword arguments\n self.constructor_kwargs.update(variable_kwargs[model_name])\n\n # Construct the model object\n model_obj = pylogit.create_choice_model(*self.constructor_args,\n **self.constructor_kwargs)\n\n # Make sure that the constructor has all of the required attributes\n for attr in common_attr_value_dict:\n value = common_attr_value_dict[attr]\n if isinstance(value, pd.DataFrame):\n self.assertTrue(value.equals(model_obj.data))\n elif isinstance(value, np.ndarray):\n npt.assert_allclose(value,\n model_obj.__getattribute__(attr))\n else:\n self.assertEqual(value,\n model_obj.__getattribute__(attr))\n\n for attr in varying_attr_value_dict:\n value = varying_attr_value_dict[attr][model_name]\n\n self.assertEqual(value,\n model_obj.__getattribute__(attr))\n\n return None","def __init__(\n self,\n comm,\n model,\n assembler=None,\n gen_output=None,\n thermal_index=0,\n struct_id=None,\n tacs_comm=None,\n override_rotx=False,\n Fvec=None,\n nprocs=None,\n ):\n\n self.comm = comm\n self.tacs_comm = tacs_comm\n self.nprocs = nprocs\n\n # Flag to output heat flux instead of rotx\n self.override_rotx = override_rotx\n\n # const load in TACS, separate and added onto from coupled loading\n self.has_const_load = Fvec is not None\n self.const_force = None\n if self.has_const_load and assembler is not None:\n self.const_force = Fvec.getArray()\n\n # Get the list of active design variables from the FUNtoFEM model. This\n # returns the variables in the FUNtoFEM order. By scenario/body.\n self.variables = model.get_variables()\n\n # Get the structural variables from the global list of variables.\n self.struct_variables = []\n for var in self.variables:\n if var.analysis_type == \"structural\":\n self.struct_variables.append(var)\n\n # Set the assembler object - if it exists or not\n self._initialize_variables(\n model, assembler, thermal_index=thermal_index, struct_id=struct_id\n )\n\n if self.assembler is not None:\n if self.tacs_comm is None:\n self.tacs_comm = self.assembler.getMPIComm()\n\n # Initialize the structural nodes in the bodies\n struct_X = self.struct_X.getArray()\n for body in model.bodies:\n body.initialize_struct_nodes(struct_X, struct_id=struct_id)\n\n # Generate output\n self.gen_output = gen_output\n\n return","def DontuseThis():\n BCM_outputs = ['phi','rho','theta',\n 'r_probabilityMaps','l_probabilityMaps',\n 'models']\n BCM_Models = pe.Node(interface=nio.DataGrabber(input_names=['structures'],\n outfields=BCM_outputs),\n name='10_BCM_Models')\n BCM_Models.inputs.base_directory = atlas_fname_wpath\n BCM_Models.inputs.template_args['phi'] = [['spatialImages','phi','nii.gz']]\n BCM_Models.inputs.template_args['rho'] = [['spatialImages','rho','nii.gz']]\n BCM_Models.inputs.template_args['theta'] = [['spatialImages','theta','nii.gz']]\n BCM_Models.inputs.template_args['r_probabilityMaps'] = [['structures']]\n BCM_Models.inputs.template_args['l_probabilityMaps'] = [['structures']]\n BCM_Models.inputs.template_args['models'] = [['structures']]\n\n BRAINSCut_structures = ['caudate','thalamus','putamen','hippocampus']\n #BRAINSCut_structures = ['caudate','thalamus']\n BCM_Models.iterables = ( 'structures', BRAINSCut_structures )\n BCM_Models.inputs.template = '%s/%s.%s'\n BCM_Models.inputs.field_template = dict(\n r_probabilityMaps='probabilityMaps/r_%s_ProbabilityMap.nii.gz',\n l_probabilityMaps='probabilityMaps/l_%s_ProbabilityMap.nii.gz',\n models='modelFiles/%sModel*',\n )\n\n \"\"\"\n The xml creation and BRAINSCut need to be their own mini-pipeline that gets\n executed once for each of the structures in BRAINSCut_structures. This can be\n accomplished with a map node and a new pipeline.\n \"\"\"\n \"\"\"\n Create xml file for BRAINSCut\n \"\"\"\n\n\n BFitAtlasToSubject = pe.Node(interface=BRAINSFit(),name=\"BFitAtlasToSubject\")\n BFitAtlasToSubject.inputs.costMetric=\"MMI\"\n BFitAtlasToSubject.inputs.maskProcessingMode=\"ROI\"\n BFitAtlasToSubject.inputs.numberOfSamples=100000\n BFitAtlasToSubject.inputs.numberOfIterations=[1500,1500]\n BFitAtlasToSubject.inputs.numberOfHistogramBins=50\n BFitAtlasToSubject.inputs.maximumStepLength=0.2\n BFitAtlasToSubject.inputs.minimumStepLength=[0.005,0.005]\n BFitAtlasToSubject.inputs.transformType= [\"Affine\",\"BSpline\"]\n BFitAtlasToSubject.inputs.maxBSplineDisplacement= 7\n BFitAtlasToSubject.inputs.maskInferiorCutOffFromCenter=65\n BFitAtlasToSubject.inputs.splineGridSize=[28,20,24]\n BFitAtlasToSubject.inputs.outputVolume=\"Trial_Initializer_Output.nii.gz\"\n BFitAtlasToSubject.inputs.outputTransform=\"Trial_Initializer_Output.mat\"\n cutWF.connect(SplitAvgBABC,'avgBABCT1',BFitAtlasToSubject,'fixedVolume')\n cutWF.connect(BABC,'outputLabels',BFitAtlasToSubject,'fixedBinaryVolume')\n cutWF.connect(BAtlas,'template_t1',BFitAtlasToSubject,'movingVolume')\n cutWF.connect(BAtlas,'template_brain',BFitAtlasToSubject,'movingBinaryVolume')\n cutWF.connect(BLI,'outputTransformFilename',BFitAtlasToSubject,'initialTransform')\n\n CreateBRAINSCutXML = pe.Node(Function(input_names=['rho','phi','theta',\n 'model',\n 'r_probabilityMap',\n 'l_probabilityMap',\n 'atlasT1','atlasBrain',\n 'subjT1','subjT2',\n 'subjT1GAD','subjT2GAD',\n 'subjSGGAD','subjBrain',\n 'atlasToSubj','output_dir'],\n output_names=['xml_filename','rl_structure_filename_list'],\n function = create_BRAINSCut_XML),\n overwrite = True,\n name=\"CreateBRAINSCutXML\")\n\n ## HACK Makde better directory\n CreateBRAINSCutXML.inputs.output_dir = \".\" #os.path.join(cutWF.base_dir, \"BRAINSCut_output\")\n cutWF.connect(BCM_Models,'models',CreateBRAINSCutXML,'model')\n cutWF.connect(BCM_Models,'rho',CreateBRAINSCutXML,'rho')\n cutWF.connect(BCM_Models,'phi',CreateBRAINSCutXML,'phi')\n cutWF.connect(BCM_Models,'theta',CreateBRAINSCutXML,'theta')\n cutWF.connect(BCM_Models,'r_probabilityMaps',CreateBRAINSCutXML,'r_probabilityMap')\n cutWF.connect(BCM_Models,'l_probabilityMaps',CreateBRAINSCutXML,'l_probabilityMap')\n cutWF.connect(BAtlas,'template_t1',CreateBRAINSCutXML,'atlasT1')\n cutWF.connect(BAtlas,'template_brain',CreateBRAINSCutXML,'atlasBrain')\n cutWF.connect(SplitAvgBABC,'avgBABCT1',CreateBRAINSCutXML,'subjT1')\n cutWF.connect(SplitAvgBABC,'avgBABCT2',CreateBRAINSCutXML,'subjT2')\n cutWF.connect(GADT1,'outputVolume',CreateBRAINSCutXML,'subjT1GAD')\n cutWF.connect(GADT2,'outputVolume',CreateBRAINSCutXML,'subjT2GAD')\n cutWF.connect(SGI,'outputFileName',CreateBRAINSCutXML,'subjSGGAD')\n cutWF.connect(BABC,'outputLabels',CreateBRAINSCutXML,'subjBrain')\n cutWF.connect(BFitAtlasToSubject,'outputTransform',CreateBRAINSCutXML,'atlasToSubj')\n #CreateBRAINSCutXML.inputs.atlasToSubj = \"INTERNAL_REGISTER.mat\"\n #cutWF.connect(BABC,'atlasToSubjectTransform',CreateBRAINSCutXML,'atlasToSubj')\n\n \"\"\"\n ResampleNACLabels\n \"\"\"\n ResampleAtlasNACLabels=pe.Node(interface=BRAINSResample(),name=\"ResampleAtlasNACLabels\")\n ResampleAtlasNACLabels.inputs.interpolationMode = \"NearestNeighbor\"\n ResampleAtlasNACLabels.inputs.outputVolume = \"atlasToSubjectNACLabels.nii.gz\"\n\n cutWF.connect(cutWF,'OutputSpec.atlasToSubjectTransform',ResampleAtlasNACLabels,'warpTransform')\n cutWF.connect(cutWF,'OutputSpec.t1_corrected',ResampleAtlasNACLabels,'referenceVolume')\n cutWF.connect(BAtlas,'template_nac_lables',ResampleAtlasNACLabels,'inputVolume')\n\n \"\"\"\n BRAINSMush\n \"\"\"\n BMUSH=pe.Node(interface=BRAINSMush(),name=\"BMUSH\")\n BMUSH.inputs.outputVolume = \"MushImage.nii.gz\"\n BMUSH.inputs.outputMask = \"MushMask.nii.gz\"\n BMUSH.inputs.lowerThresholdFactor = 1.2\n BMUSH.inputs.upperThresholdFactor = 0.55\n\n cutWF.connect(myLocalTCWF,'OutputSpec.t1_corrected',BMUSH,'inputFirstVolume')\n cutWF.connect(myLocalTCWF,'OutputSpec.t2_corrected',BMUSH,'inputSecondVolume')\n cutWF.connect(myLocalTCWF,'OutputSpec.outputLabels',BMUSH,'inputMaskVolume')\n\n \"\"\"\n BRAINSROIAuto\n \"\"\"\n BROI = pe.Node(interface=BRAINSROIAuto(), name=\"BRAINSROIAuto\")\n BROI.inputs.closingSize=12\n BROI.inputs.otsuPercentileThreshold=0.01\n BROI.inputs.thresholdCorrectionFactor=1.0\n BROI.inputs.outputROIMaskVolume = \"temproiAuto_t1_ACPC_corrected_BRAINSABC.nii.gz\"\n cutWF.connect(myLocalTCWF,'OutputSpec.t1_corrected',BROI,'inputVolume')\n\n \"\"\"\n Split the implicit outputs of BABCext\n \"\"\"\n SplitAvgBABC = pe.Node(Function(input_names=['in_files','T1_count'], output_names=['avgBABCT1','avgBABCT2'],\n function = get_first_T1_and_T2), run_without_submitting=True, name=\"99_SplitAvgBABC\")\n SplitAvgBABC.inputs.T1_count = 1 ## There is only 1 average T1 image.\n\n cutWF.connect(myLocalTCWF,'OutputSpec.outputAverageImages',SplitAvgBABC,'in_files')\n\n\n\n def printFullPath(outFileFullPath):\n print(\"=\"*80)\n print(\"=\"*80)\n print(\"=\"*80)\n print(\"=\"*80)\n print(\"{0}\".format(outFileFullPath))\n return outFileFullPath\n printOutImage = pe.Node( Function(function=printFullPath, input_names = ['outFileFullPath'], output_names = ['genoutFileFullPath']), run_without_submitting=True, name=\"99_printOutImage\")\n cutWF.connect( GADT2, 'outputVolume', printOutImage, 'outFileFullPath' )","def run(cls, model):\n label = model.label\n print(\"stage1: {label} model: initializing\".format(label=label))\n\n defs_input = model.define_api() # input, original definitions\n\n print(\"stage1: {label} model: analyzing API\".format(label=label))\n\n # Compute any needed derivatives which are not already in the API\n # and for which we have the defs.\n defs = defs_input.copy() # output, final optimized definitions\n for j, key in enumerate(sorted(defs_input.keys(), key=symutil.sortkey), start=1): # sort for progress readability\n name = symutil.derivatives_to_names_in(key) # key is a Symbol or a Derivative\n expr = defs_input[key]\n\n print(\"stage1: ({iteration:d}/{total:d}) {label} model: processing {name}\".format(iteration=j,\n total=len(defs_input.keys()),\n label=label, name=name))\n\n defs[key] = cls.process(expr, defs, model.simplify)\n\n # Delete identically zero definitions\n zero = sy.S.Zero\n defs = {k: v for k, v in defs.items() if v != zero}\n\n print(\"stage1: {label} model: generating code\".format(label=label))\n\n basename = \"mgs_{label}_impl\".format(label=label) # filename without extension\n name_expr_pairs = cls.make_name_expr_pairs(defs)\n generated_code = codegen(name_expr_pairs,\n language=\"f95\",\n project=\"elmer-mgs-galfenol\",\n prefix=basename)\n\n return [(label, filename, cls.finalize(content))\n for filename, content in generated_code]","def setUp(self):\n # Set Model Parameters\n odeparam = np.array([0, 1, 1, 2])\n y0, y0_unc = np.ones(2), 0 * np.ones(2)\n t0, tmax = 0.0, 1.25\n\n # Set Method Parameters\n q = 1\n h = 0.1\n\n # Set up and solve ODE\n ibm = statespace.IBM(q=q, dim=len(y0))\n solver = linsolve.LinearisedODESolver(ibm)\n ivp = linode.LotkaVolterra(t0, tmax, odeparam, y0, y0_unc)\n tsteps, means, __, rhs_parts, uncerts = solver.solve(ivp, stepsize=h)\n self.mean = odesolver.get_trajectory_multidim(means, [0, 1], 0)\n\n # Set up BM and IBM covariance matrices\n evalpt = np.array(tsteps[[-1, -10]])\n derdat = (tsteps, rhs_parts, 0.)\n\n const, jacob = linearisation.compute_linearisation(\n ssm=ibm, initial_value=y0,\n derivative_data=derdat, prdct_tsteps=evalpt)\n\n # Compute GP Estimation of filter mean at t=tmax\n postmean = const + np.dot(jacob, odeparam)\n self.postmean = postmean.reshape((2, 2))","def setUp(self):\n # Set Model Parameters\n odeparam = np.array([0, 1, 1, 2])\n y0, y0_unc = np.ones(2), 0 * np.ones(2)\n t0, tmax = 0.0, 1.25\n\n # Set Method Parameters\n q = 1\n h = 0.1\n\n # Set up and solve ODE\n ibm = statespace.IBM(q=q, dim=len(y0))\n solver = linsolve.LinearisedODESolver(ibm)\n ivp = linode.LotkaVolterra(t0, tmax, odeparam, y0, y0_unc)\n tsteps, means, __, rhs_parts, uncerts = solver.solve(ivp, stepsize=h)\n self.mean = odesolver.get_trajectory_multidim(means, [0, 1], 0)\n\n # Set up BM and IBM covariance matrices\n evalpt = np.array(tsteps[[-1]])\n derdat = (tsteps, rhs_parts, 0.)\n\n const, jacob = linearisation.compute_linearisation(\n ssm=ibm, initial_value=y0,\n derivative_data=derdat, prdct_tsteps=evalpt)\n\n # Compute GP Estimation of filter mean at t=tmax\n postmean = const + np.dot(jacob, odeparam)\n self.postmean = postmean.reshape((2,))","def example():\n num_user, num_item, ratings = build_ticket()\n \n # suffle_data\n np.random.shuffle(ratings)\n\n # split data to training & validation\n train_pct = 0.9\n train_size = int(train_pct * len(ratings))\n train = ratings[:train_size]\n validation = ratings[train_size:]\n\n # params\n num_feature = 5\n bmf_model = BayesianMatrixFactorization(\n num_user, num_item, num_feature, train, validation, max_rating=1, min_rating=0, ratingsMatirx=ratings)\n\n start_time = time.clock()\n bmf_model.estimate(10)\n end_time = time.clock()\n \n mat = np.matrix(bmf_model.item_features)\n with open('../data/ticket/item_feature', 'w') as f:\n for line in mat:\n np.savetxt(f, line, fmt='%.5f')\n\n print \"time spend = %.3f\" % (end_time - start_time)\n\n return bmf_model","def test_fleur_create_mag_validation_wrong_inputs(self, fleur_local_code, inpgen_local_code, generate_structure2):\n from aiida.orm import Dict\n\n # prepare input nodes and dicts\n options = {\n 'resources': {\n 'num_machines': 1,\n 'num_mpiprocs_per_machine': 1\n },\n 'max_wallclock_seconds': 5 * 60,\n 'withmpi': False,\n 'custom_scheduler_commands': ''\n }\n options = Dict(dict=options).store()\n\n wf_parameters = Dict({'points': 9, 'step': 0.002, 'guess': 1.00, 'wrong_key': None})\n wf_parameters.store()\n structure = generate_structure2()\n structure.store()\n\n ################\n # Create builders\n # interface of exposed scf is tested elsewhere\n\n # 1. create builder with wrong wf parameters\n builder_additionalkeys = FleurCreateMagneticWorkChain.get_builder()\n builder_additionalkeys.structure = structure\n builder_additionalkeys.wf_parameters = wf_parameters\n builder_additionalkeys.scf.fleur = fleur_local_code\n builder_additionalkeys.scf.inpgen = inpgen_local_code\n\n ###################\n # now run the builders all should fail early with exit codes\n\n # 1. structure and fleurinp given\n out, node = run_get_node(builder_additionalkeys)\n assert out == {}\n assert node.is_finished\n assert not node.is_finished_ok\n assert node.exit_status == 230","def RunModel(self):\n raise UnimplementedMethodException()","def setup_to_finetune(model):\n\n for layer in model.layers[:NB_VGG_LAYERS_TO_FREEZE]:\n layer.trainable = False\n for layer in model.layers[NB_VGG_LAYERS_TO_FREEZE:]:\n layer.trainable = True\n\n # opt = Adam(lr=0.0001)\n model.compile(optimizer=SGD(lr=0.0001, momentum=0.9),\n loss='categorical_crossentropy',\n metrics=['categorical_accuracy', f1_score])","def turbsim_example(): \n # run TurbSim\n from runTurbSim import runTurbSim\n\n ts = runTurbSim()\n ts.ts_exe = \"/Users/pgraf/opt/windcode-7.31.13/TurbSim/build/TurbSim_glin64\"\n ts.run_dir = \"turbsim_test_run\"\n tscase = 2\n if (tscase == 1):\n ts.ts_dir = \"TurbSimTest\"\n ts.ts_file = \"turbsim_template.inp\"\n else:\n ts.ts_dir = \"/Users/pgraf/work/wese/fatigue12-13/from_gordie/SparFAST3.orig/TurbSim\"\n ts.ts_file = \"TurbSim.inp\"\n \n tmax = 12\n ws=20.0 # wind speed\n\n ts.set_dict({\"URef\": ws, \"AnalysisTime\":tmax, \"UsableTime\":tmax})\n ts.execute()\n\n # then run FAST\n fast = runFAST()\n fast.fst_exe = \"/Users/pgraf/opt/windcode-7.31.13/build/FAST_glin64\"\n case = 2\n if (case==1):\n fast.fst_dir = \"FAST_VT/OC3_Files/\"\n fast.fst_file = \"NRELOffshrBsline5MW_Monopile_RF.fst\" \n elif case ==2:\n fast.fst_dir = \"/Users/pgraf/work/wese/fatigue12-13/from_gordie/SparFAST3.orig\"\n fast.fst_file = \"NRELOffshrBsline5MW_Floating_OC3Hywind.fst\"\n else:\n fast.fst_dir = \"InputFilesToWrite/\"\n fast.fst_file = \"NREL5MW_Monopile_Rigid.v7.02.fst\"\n\n fast.run_dir = \"tsfast_test_run_dir\" ## either abs or rel path ok\n\n # here we link turbsim -> fast\n tswind_file = os.path.join(ts.ts_dir, \"%s.wnd\" % ts.run_name)\n fast.set_wind_file(os.path.abspath(tswind_file))\n\n fast.fstDict['Vhub']=ws\n fast.fstDict['RotSpeed'] = 12.03\n fast.fstDict[\"TMax\"] = tmax\n fast.fstDict['TStart'] = 0.0\n\n fast.setOutputs(['RotPwr'])\n\n fast.execute()\n\n out = fast.getOutputValue(\"RotPwr\")\n\n print \"TurbSim example complete:\"\n print \"max power\"\n print max(out)","def setUp(self):\n # Set Model Parameters\n odeparam = 1.\n y0, y0_unc = 1.0, 0 \n t0, tmax = 0.0, 1.25\n\n # Set Method Parameters\n q = 1\n h = 0.1\n\n # Set up and solve ODE\n ibm = statespace.IBM(q=q, dim=1)\n solver = linsolve.LinearisedODESolver(ibm)\n ivp = linode.LinearODE(t0, tmax, odeparam, y0, y0_unc)\n tsteps, means, __, rhs_parts, uncerts = solver.solve(ivp, stepsize=h)\n self.mean = odesolver.get_trajectory(means, 0, 0)\n\n # Set up BM and IBM covariance matrices\n evalpt = np.array(tsteps[[-1, -10]])\n derdat = (tsteps, rhs_parts, 0.)\n\n const, jacob = linearisation.compute_linearisation(\n ssm=ibm, initial_value=y0,\n derivative_data=derdat, prdct_tsteps=evalpt)\n\n # Compute GP Estimation of filter mean at t=tmax\n self.postmean = const + np.dot(jacob[:, 0], odeparam)","def run(inputs, outdir, paramsfile, model, simulated, beta, diag,\n maxiter, tol, seed, tail, initgamma, initx, max_func, separate, save, prog):\n simulatehuman.cmd_run(inputs, outdir, paramsfile=paramsfile, modeltype=model,\n is_simulation=simulated, savemat=save, saveprog=prog, beta=beta, diag=diag,\n maxiter=maxiter, tol=tol, seed=seed, tail=tail, initgamma=initgamma, initx=initx, smooth=False, h=1,\n max_func=max_func, separate=separate)","def setUp(self):\n # Set Model Parameters\n odeparam = 1.\n y0, y0_unc = 1.0, 0 \n t0, tmax = 0.0, 1.25\n\n # Set Method Parameters\n q = 1\n h = 0.1\n\n # Set up and solve ODE\n ibm = statespace.IBM(q=q, dim=1)\n solver = linsolve.LinearisedODESolver(ibm)\n ivp = linode.LinearODE(t0, tmax, odeparam, y0, y0_unc)\n tsteps, means, __, rhs_parts, uncerts = solver.solve(ivp, stepsize=h)\n self.mean = odesolver.get_trajectory(means, 0, 0)\n\n # Set up BM and IBM covariance matrices\n evalpt = np.array([tsteps[-1]])\n derdat = (tsteps, rhs_parts, 0.)\n\n const, jacob = linearisation.compute_linearisation(\n ssm=ibm, initial_value=y0,\n derivative_data=derdat, prdct_tsteps=evalpt)\n\n # Compute GP Estimation of filter mean at t=tmax\n self.postmean = const + np.dot(jacob[:, 0], odeparam)","def FM(user_feature_columns, item_feature_columns, l2_reg_embedding=1e-6, loss_type='softmax', temperature=0.05,\n sampler_config=None, seed=1024,\n ):\n\n embedding_matrix_dict = create_embedding_matrix(user_feature_columns + item_feature_columns, l2_reg_embedding,\n seed=seed,\n seq_mask_zero=True)\n\n user_features = build_input_features(user_feature_columns)\n user_inputs_list = list(user_features.values())\n user_sparse_embedding_list, _ = input_from_feature_columns(user_features,\n user_feature_columns,\n l2_reg_embedding, seed=seed,\n support_dense=False,\n embedding_matrix_dict=embedding_matrix_dict)\n\n item_features = build_input_features(item_feature_columns)\n item_inputs_list = list(item_features.values())\n item_sparse_embedding_list, _ = input_from_feature_columns(item_features,\n item_feature_columns,\n l2_reg_embedding, seed=seed,\n support_dense=False,\n embedding_matrix_dict=embedding_matrix_dict)\n\n user_dnn_input = concat_func(user_sparse_embedding_list, axis=1)\n user_vector_sum = Lambda(lambda x: reduce_sum(x, axis=1, keep_dims=False))(user_dnn_input)\n user_vector_sum = l2_normalize(user_vector_sum)\n\n item_dnn_input = concat_func(item_sparse_embedding_list, axis=1)\n item_vector_sum = Lambda(lambda x: reduce_sum(x, axis=1, keep_dims=False))(item_dnn_input)\n item_vector_sum = l2_normalize(item_vector_sum)\n\n if loss_type == \"logistic\":\n score = inner_product(user_vector_sum, item_vector_sum, temperature)\n output = PredictionLayer(\"binary\", False)(score)\n\n elif loss_type == \"softmax\":\n output = InBatchSoftmaxLayer(sampler_config._asdict(), temperature)(\n [user_vector_sum, item_vector_sum, item_features[sampler_config.item_name]])\n else:\n raise ValueError(' `loss_type` must be `logistic` or `softmax` ')\n\n model = Model(inputs=user_inputs_list + item_inputs_list, outputs=output)\n\n model.__setattr__(\"user_input\", user_inputs_list)\n model.__setattr__(\"user_embedding\", user_vector_sum)\n\n model.__setattr__(\"item_input\", item_inputs_list)\n model.__setattr__(\"item_embedding\", item_vector_sum)\n\n return model","def run_model(project=None, model=None, raw=None, dyr=None, xls=None, path=None, server='tcp://127.0.0.1:5678'):\n ret = 0\n if (not project) or (not model):\n logging.error('RT-LAB project or model undefined.')\n sys.exit(-1)\n if (not raw) and (not xls):\n logging.error('PSS/E raw file or ePHASORsim Excel file undefined.')\n sys.exit(-1)\n if not dyr:\n logging.debug('PSS/E dyr file not specified')\n\n sim = SimControl(project, model, path)\n\n simulink = os.path.join(path,project, 'simulink')\n models = os.path.join(path,project, 'models')\n if not os.path.isdir(simulink):\n logging.error('No <{}> directory found.'.format(simulink))\n if not os.path.isdir(models):\n logging.error('No <{}> directory found.'.format(models))\n sys.exit(1)\n else:\n logging.info('Using <{}> directory'.format(models))\n modelPath = models\n else:\n logging.info('Using <{}> directory'.format(simulink))\n modelPath = simulink\n\n\n sim_data = LTBSetup(raw=raw, dyr=dyr, xls=xls, path=modelPath, model=model, simObject=sim)\n\n streaming = Streaming(name='sim', server=server, ltb_data=sim_data)\n\n sim.open()\n sim.load()\n\n sim_data.get_sysparam()\n sim_data.get_varheader_idxvgs()\n sim.set_settings(sim_data.Settings)\n # sim_data.Idxvgs['Line'].update(sim.add_branch_power_to_idxvgs())\n # sim_data.Varheader.extend(sim.add_vars_varheader(sim_data.Idxvgs['Line']))\n # sim_data.Idxvgs['Bus'].update(sim.add_bus_power_to_idxvgs())\n # sim_data.Varheader.extend(sim.add_vars_varheader(sim_data.Idxvgs['Bus']))\n streaming.send_init()\n logging.debug('Varheader, SysParam and Idxvgs sent.')\n sleep(0.5)\n\n sim.start()\n\n streaming.run()","def __init__(self, input_model_dict, func_lib, x_list,\n par_add_dict_all={}, QuietMode=False, **kwargs):\n modelDict = OrderedDict()\n modelNameList = input_model_dict.keys()\n for modelName in modelNameList:\n funcName = input_model_dict[modelName][\"function\"]\n funcInfo = func_lib[funcName]\n xName = funcInfo[\"x_name\"]\n #-> Build up the parameter dictionaries\n parFitList = funcInfo[\"param_fit\"]\n parAddList = funcInfo[\"param_add\"]\n parFitDict = OrderedDict()\n parAddDict = {}\n for parName in parFitList:\n parFitDict[parName] = input_model_dict[modelName][parName]\n for parName in parAddList:\n par_add_iterm = par_add_dict_all.get(parName, \"No this parameter\")\n if par_add_iterm == \"No this parameter\":\n pass\n else:\n parAddDict[parName] = par_add_iterm\n #-> Check the consistency if the component is multiply\n multiList = input_model_dict[modelName].get(\"multiply\", None)\n if not multiList is None:\n #--> The \"*\" should be included in the operation list.\n assert \"*\" in funcInfo[\"operation\"]\n if not QuietMode:\n print \"[Model_Generator]: {0} is multiplied to {1}!\".format(modelName, multiList)\n #--> Check further the target models are not multiplicative.\n for tmn in multiList:\n f_mlt = input_model_dict[tmn].get(\"multiply\", None)\n if not f_mlt is None:\n raise ValueError(\"The multiList includes a multiplicative model ({0})!\".format(tmn))\n modelDict[modelName] = ModelFunction(funcName, xName, parFitDict, parAddDict, multiList)\n ModelCombiner.__init__(self, modelDict, x_list, np.complex_, **kwargs)","def test_infer_framework(self):\n\t\t\n\t\tprint(f\"test_infer_framework self.model={self.model}\")\n\t\t\n\t\tnum_files = len(glob.glob(f\"{self.model}/*\"))\n\t\tprint(f\"test_infer_framework found {num_files} tmp files\")\n\t\tself.assertTrue(num_files > 0)\n\t\t\n\t\t\n\t\tnum_pytorch_bin_files = len(glob.glob(f\"{self.model}/*bin\"))\n\t\t#print(f\"test_infer_framework found {num_pytorch_bin_files} tmp pytorch bin files\")\n\t\tself.assertEqual(num_pytorch_bin_files, 0)\n\t\t\n\t\tnum_safetensors_files = len(glob.glob(f\"{self.model}/*safetensors\"))\n\t\t#print(f\"test_infer_framework found {num_pytorch_bin_files} tmp num_safetensors_files files\")\n\t\tself.assertEqual(num_safetensors_files, 1)\n\t\t\n\t\tnum_flat_files = len(glob.glob(f\"{self.model}/*npy\"))\n\t\t#print(f\"test_infer_framework found {num_files} tmp npy flat files\")\n\t\tself.assertEqual(num_flat_files, 0)\n\t\t\n\t\texpected_format, expected_fileglob = ww.WeightWatcher.infer_model_file_format(self.model)\n\t\tprint(f\"infer_model_file_format found {expected_format} expected format\")\n\t\tself.assertEqual(expected_format, MODEL_FILE_FORMATS.SAFETENSORS)\t\t\n\n\t\texpected_framework = ww.WeightWatcher.infer_framework(self.model)\n\t\tprint(f\"infer_model_file_format found {expected_framework} expected_framework \")\n\t\tself.assertEqual(expected_framework, FRAMEWORK.PYSTATEDICT_DIR)\t\n\t\t\n\t\treturn","def setup_to_finetune(model):\r\n # for layer in model.layers[:NB_IV3_LAYERS_TO_FREEZE]:\r\n # layer.trainable = False\r\n for layer in model.layers[:]:\r\n layer.trainable = True\r\n model.compile(optimizer=Adam(lr=0.0001), loss='categorical_crossentropy', metrics=['accuracy'])","def model_fn(model_dir):\n ctx = mx.cpu()\n net = unet.Unet()\n print (\"Loading\", model_dir)\n if path.exists(model_dir+\"/unet_RGB.params\"):\n print (\"Loading RGB Model\")\n net.load_params(model_dir+\"/unet_RGB.params\", ctx)\n print (\"RGB Model Loaded\")\n \n elif path.exists(model_dir+\"/unet_ALL_BANDS.params\"):\n print (\"Loading ALL_BANDS Model\")\n net.load_params(model_dir+\"/unet_ALL_BANDS.params\", ctx)\n print (\"ALL_BANDS Model Loaded\")\n \n else:\n print (\"Model Missing\")\n net=None\n return (net)","def main():\n\n # Create model_dict from arguments\n model_dict = model_dict_create()\n\n # No. of deviations to consider\n no_of_mags = 50\n dev_list = np.linspace(0.1, 5.0, no_of_mags)\n\n # Load dataset specified in model_dict\n print('Loading data...')\n dataset = model_dict['dataset']\n if (dataset == 'MNIST'):\n X_train, y_train, X_val, y_val, X_test, y_test = load_dataset(model_dict)\n # rd_list = [None, 784, 331, 200, 100, 90, 80, 70, 60, 50, 40, 30, 20, 10]\n rd_list = [None, 331, 100, 80, 60, 40, 20]\n # rd_list = [None,784,100]\n elif dataset == 'GTSRB':\n X_train, y_train, X_val, y_val, X_test, y_test = load_dataset(model_dict)\n rd_list = [1024, 338, 200, 100, 90, 80, 70, 60, 50, 40, 33, 30, 20, 10]\n elif dataset == 'HAR':\n X_train, y_train, X_test, y_test = load_dataset(model_dict)\n # rd_list = [561, 200, 100, 90, 80, 70, 60, 50, 40, 30, 20, 10]\n rd_list = [561]\n X_val = None\n y_val = None\n\n mean = np.mean(X_train, axis=0)\n X_train -= mean\n X_test -= mean\n if (dataset == 'MNIST') or (dataset == 'GTSRB'): X_val -= mean\n\n # fig, ax = plt.subplots(nrows=1, ncols=1)\n\n # for rd in rd_list:\n # model_setup_carlini(model_dict, X_train, y_train, X_test, y_test, X_val, y_val, mean, rd, ax)\n\n partial_carlini = partial(model_setup_carlini, model_dict=model_dict, X_train=X_train, y_train=y_train, X_test=X_test, y_test=y_test, X_val=X_val, y_val=y_val,\n mean=mean)\n pool=multiprocessing.Pool(processes=8)\n pool.map(partial_carlini,rd_list,1)\n pool.close()\n pool.join()\n\n # dim_red = model_dict['dim_red']\n # plt.legend()\n # plt.savefig('carlini_l2_hist_'+dim_red+'.png')","def simulate_tanks(model_path, u=U_MAX, t_start=0.0, t_final=50.0,\n h10=20.0, h20=20.0, h30=20.0, tank1_outflow=26.0,\n tank2_outflow=26.0, tank3_outflow=28.0, alpha1=0.5,\n alpha2=0.5, alpha3=0.5, with_full_traj_obj=False,\n with_plots=False):\n # 2. Compute initial guess trajectories by means of simulation\n # Compile the optimization initialization model\n simulation_fmu = compile_fmu(\"TanksPkg.ThreeTanks\", model_path)\n # Load the model\n simulation_model = load_fmu(simulation_fmu)\n set_model_parameters(simulation_model,\n {\"h10\": h10, \"h20\": h20, \"h30\": h30,\n \"C1\": tank1_outflow, \"C2\": tank2_outflow,\n \"C3\": tank3_outflow, \"alpha1\": alpha1,\n \"alpha2\": alpha2, \"alpha3\": alpha3})\n try:\n if hasattr(u, \"__len__\"):\n t = numpy.linspace(0.0, t_final, len(u))\n u_traj = numpy.transpose(numpy.vstack((t, u)))\n init_res = simulation_model.simulate(start_time=t_start,\n final_time=t_final,\n input=('u', u_traj),\n options={\"ncp\": len(u) - 1})\n else:\n simulation_model.set('u', u)\n init_res = simulation_model.simulate(start_time=t_start,\n final_time=t_final)\n except FMUException as e:\n return e\n # Extract variable profiles\n t_init_sim = init_res['time']\n h1_init_sim = init_res['h1']\n h2_init_sim = init_res['h2']\n h3_init_sim = init_res['h3']\n u_init_sim = init_res['u']\n if with_plots:\n # Plot the initial guess trajectories\n plot_results(h1_init_sim, h2_init_sim, h3_init_sim, t_init_sim,\n u_init_sim, title='Trajektorie inicjalizacyjne')\n if with_full_traj_obj:\n return init_res\n else:\n return_dict = {'h1': init_res['h1'], 'h2': init_res['h2'], 'h3':\n init_res['h3'], 'u': init_res['u'],\n 'time': init_res['time']}\n return return_dict","def test_infer_framework(self):\n\t\t\n\t\tprint(f\"test_infer_framework self.model={self.model}\")\n\t\t\n\t\tnum_files = len(glob.glob(f\"{self.model}/*\"))\n\t\tprint(f\"test_infer_framework found {num_files} tmp files\")\n\t\tself.assertTrue(num_files > 0)\n\t\t\n\t\t\n\t\tnum_pytorch_bin_files = len(glob.glob(f\"{self.model}/*bin\"))\n\t\t#print(f\"test_infer_framework found {num_pytorch_bin_files} tmp pytorch bin files\")\n\t\tself.assertEqual(num_pytorch_bin_files, 0)\n\t\t\n\t\tnum_safetensors_files = len(glob.glob(f\"{self.model}/*safetensors\"))\n\t\t#print(f\"test_infer_framework found {num_pytorch_bin_files} tmp num_safetensors_files files\")\n\t\tself.assertEqual(num_safetensors_files, 0)\n\t\t\n\t\tnum_flat_files = len(glob.glob(f\"{self.model}/*npy\"))\n\t\t#print(f\"test_infer_framework found {num_files} tmp npy flat files\")\n\t\tself.assertTrue(num_flat_files > 0)\n\t\t\n\t\texpected_format, expected_fileglob = ww.WeightWatcher.infer_model_file_format(self.model)\n\t\tprint(f\"infer_model_file_format found {expected_format} expected format\")\n\t\tself.assertEqual(expected_format, MODEL_FILE_FORMATS.WW_FLATFILES)\t\t\n\n\t\texpected_framework = ww.WeightWatcher.infer_framework(self.model)\n\t\tprint(f\"infer_model_file_format found {expected_framework} expected_framework \")\n\t\tself.assertEqual(expected_framework, FRAMEWORK.WW_FLATFILES)\t\t\n\t\t\n\t\treturn","def __init__(self, name, quiet=True):\n self.quiet = quiet\n if not name in MODEL_LIST:\n raise NotImplementedError(\n 'Unknown model \"{}\". Valid models are {}.'.format(name, MODEL_LIST))\n self.name = name\n\n # Set up the simulation\n self.solenoid = solenoid_field_map.Solenoid(name, quiet=self.quiet)\n astra_folder = r'\\\\fed.cclrc.ac.uk\\Org\\NLab\\ASTeC-TDL\\Projects\\tdl-1168 CLARA\\CLARA-ASTeC Folder\\Accelerator Physics\\ASTRA'\n if name[:3] == 'Gun':\n cav_fieldmap_file = astra_folder + (r'\\Archive from Delta + CDR\\bas_gun.txt' if name == 'Gun-10' \\\n else r'\\Injector\\fieldmaps\\HRRG_1D_RF.dat')\n\n # Read in RF field map and normalise so peak = 1\n cav_fieldmap = np.loadtxt(cav_fieldmap_file, delimiter='\\t')\n\n self.rf_peak_field = 50 # float(np.max(cav_fieldmap[:, 1]) / 1e6) # set a 'reasonable' value\n # Normalise\n cav_fieldmap[:, 1] /= np.max(cav_fieldmap[:, 1])\n self.norm_E = [solenoid_field_map.interpolate(*cav_fieldmap.T),]\n self.phase_offset = np.zeros(1, dtype='float')\n self.freq = 2998.5 * 1e6 # in Hz\n self.phase = 330.0 # to get optimal acceleration\n\n # Set parameters\n self.dz = 0.5e-3 # in metres - OK to get within 0.5% of final momentum\n self.gamma_start = np.sqrt(1 + abs(1 / epsilon_e)) # 1 eV\n\n self.z_start = 0\n self.z_end = max(cav_fieldmap[-1, 1], self.solenoid.getZMap()[-1])\n\n elif name == 'Linac1':\n linac1_folder = astra_folder + r'\\Injector\\fieldmaps' + '\\\\'\n # Some of this (Mathematica-exported) data is in fraction form (e.g. 2/25), so we need to convert it\n fetch_dat = lambda name: np.loadtxt(linac1_folder + 'L1' + name + 'cell.dat', converters={0: Fraction})\n entrance_data = fetch_dat('entrance')\n single_cell_data = fetch_dat('single')\n exit_data = fetch_dat('exit')\n grad_phase_data = np.loadtxt(linac1_folder + 'RI_linac_grad_phase_error.txt')\n # convert from percentage of first to fraction of max\n rel_grads = grad_phase_data[:, 0] / np.max(grad_phase_data[:, 0])\n self.phase_offset = np.cumsum(np.radians(-grad_phase_data[:, 1]))\n n_cells = len(grad_phase_data)\n\n self.freq = 2998.5 * 1e6 # in Hz\n self.phase = 330.0 # to get optimal acceleration - TODO: not tested\n self.rf_peak_field = 50 # MV/m, just a made-up figure at the moment (TODO)\n\n data_z_length = entrance_data[-1, 0] - entrance_data[0, 0]\n\n interpolate = lambda xy: scipy.interpolate.interp1d(*xy.T, fill_value=0, bounds_error=False)\n ent_interp = interpolate(entrance_data)\n sgl_interp = interpolate(single_cell_data)\n exit_interp = interpolate(exit_data)\n\n cell_length = 0.033327 # from document: file:///\\\\fed.cclrc.ac.uk\\Org\\NLab\\ASTeC-TDL\\Projects\\tdl-1168%20CLARA\\CLARA-ASTeC%20Folder\\Accelerator%20Physics\\ASTRA\\Injector\\CLARA%20v10%20Injector%20Simulations%20v0.3.docx\n self.dz = 0.001\n z_length = n_cells * cell_length + data_z_length # include a bit extra at the ends\n # self.z_start = -z_length / 2\n # self.z_end = z_length / 2\n z_map = self.solenoid.getZMap()\n self.z_start = z_map[0]\n self.z_end = z_map[-1]\n #TODO: self.dz =\n self.norm_E = []\n self.gamma_start = np.sqrt(1 + abs(4e6 / epsilon_e) ** 2) # 4 MeV\n\n n_offset = (n_cells - 1) / 2\n for i in range(n_cells):\n interp = ent_interp if i == 0 else exit_interp if i == n_cells - 1 else sgl_interp\n self.norm_E.append(scipy.interpolate.interp1d(z_map, rel_grads[i] * interp(z_map + (n_offset - i) * cell_length),\n fill_value=0, bounds_error=False))\n\n elif name[:3] == 'gb-':\n self.gamma_start = np.sqrt(1 + abs(1 / epsilon_e)) # 1 eV\n self.z_start = 0\n if name == 'gb-dc-gun':\n self.freq = 0\n self.dz = 1e-3\n self.z_end = 0.6\n self.phase = 0\n elif name == 'gb-rf-gun':\n self.freq = 1.3e9\n self.dz = 1e-4\n self.z_end = 0.3\n self.phase = 295 # to get optimal acceleration\n\n z_list, E_list = np.loadtxt('gb-field-maps/{}_e-field.csv'.format(name), delimiter=',').T\n self.rf_peak_field = float(np.max(E_list))\n # Normalise\n E_list /= self.rf_peak_field\n self.norm_E = [solenoid_field_map.interpolate(z_list, E_list),]\n self.phase_offset = [0,]\n\n self.calc_level = CALC_NONE","def MultiFNN(ANNSetup, test, train):\n\n #One hot encoding\n TrainMultiClass = to_categorical(train.MultiClass)\n TestMultiClass = to_categorical(test.MultiClass)\n\n #ClassWeights = GetClassWeights(train.MultiClass,train.Weights)\n TrainWeights = GetTrainWeights(train.MultiClass,train.Weights) # Transformation of the Monte Carlo weights for training\n\n #Create the model and pass it the data for Callbacks\n model = Sequential()\n model.Y_test = TestMultiClass[:,0]\n model.X_train = train.Events\n model.Y_train = TrainMultiClass[:,0]\n model.W_train = train.Weights #Original weights!\n\n # Build model from configuration (set in FNN.py)\n model.add(Dense(ANNSetup.Neurons[0], activation='selu', input_dim=ANNSetup.InputDim))\n if(ANNSetup.Dropout != None):\n model.add(Dropout(ANNSetup.Dropout))\n for i in range(1,len(ANNSetup.Neurons)):\n if(i == len(ANNSetup.Neurons)-1):\n model.add(Dense(ANNSetup.Neurons[i], activation='softmax'))\n else:\n model.add(Dense(ANNSetup.Neurons[i], activation='selu'))\n\n Opti = GetOpti(ANNSetup.Optimizer,ANNSetup.LearnRate.Lr) # Set optimizer\n lrate = GetLearnRate(ANNSetup.LearnRate,ANNSetup.Epochs) # Set learning rate schedule\n Roc = Histories() # Create history for AUC during training\n Lcallbacks = [Roc,lrate]\n\n model.compile(optimizer=Opti, loss='categorical_crossentropy', metrics=['accuracy'])\n history = model.fit(train.Events, TrainMultiClass, sample_weight=TrainWeights, validation_data=(test.Events, TestMultiClass, test.Weights), epochs=int(ANNSetup.Epochs),\n batch_size=int(ANNSetup.Batch), verbose=2, callbacks=Lcallbacks) #, sample_weight=TrainWeights\n\n LAuc = Roc.TestAucs\n LTrainAuc = Roc.TrainAucs\n print(\"Best Roc {0:.4f} at Epoch {1}\".format(max(LAuc),LAuc.index(max(LAuc))+1))\n print(\"Train Auc {0:.4f}\".format(LTrainAuc[LAuc.index(max(LAuc))]))\n # print(\"Test Rocs: {0}\".format(LAuc))\n # print(\"Test Loss: {0}\".format(Roc.TestLosses))\n # print(\"Train Rocs: {0}\".format(LTrainAuc))\n # print(\"Train Loss: {0}\".format(Roc.TrainLosses))\n\n model.save(ANNSetup.SavePath)\n\n return model, Roc","def test_fer_model(img_folder, model_path=\"saved_models/best_model_Q5.pkl\"):\n preds = None\n\n ### Start your code here\n\n subtract_mean = True\n \n root_path = os.path.dirname(os.path.dirname(os.path.realpath(__file__)))\n model_path = os.path.join(root_path, model_path)\n \n # Load model\n with open(model_path, 'rb') as handle:\n model = pickle.load(handle) \n \n # Sorting the folder names in lexographic order\n images_list = os.listdir(img_folder)\n images_list.sort()\n\n n_pictures = 0\n for filename in images_list:\n n_pictures += 1\n\n # Convert img_folder into preds numpy vector\n images = np.zeros((n_pictures, 48, 48, 1))\n \n i = 0\n for filename in images_list:\n #check this one\n picture_path = os.path.join(img_folder, filename)\n images[i] = imageio.imread(picture_path)[:,:,0].reshape((48,48,1))\n i += 1\n \n # Normalize the data: subtract the mean image\n if subtract_mean:\n mean_image = np.mean(images, axis=0)\n images -= mean_image\n\n images = images.transpose(0, 3, 1, 2).copy()\n \n # Feed pictures into NN and get predictions\n scores = model.loss(images)\n preds = np.argmax(scores, axis=1)\n\n return preds","def load(cls, f, model, ext_unit_dict=None):\n msg = (\n \"Model object must be of type flopy.mfusg.MfUsg\\n\"\n f\"but received type: {type(model)}.\"\n )\n assert isinstance(model, MfUsg), msg\n\n if model.verbose:\n print(\"loading bcf package file...\")\n\n f_obj = get_open_file_object(f, \"r\")\n\n # dataset 0 -- header\n while True:\n line = f_obj.readline()\n if line[0] != \"#\":\n break\n\n # determine problem dimensions\n nlay = model.nlay\n dis = model.get_package(\"DIS\")\n if dis is None:\n dis = model.get_package(\"DISU\")\n njag = dis.njag\n\n # Item 1: ipakcb, HDRY, IWDFLG, WETFCT, IWETIT, IHDWET - line already read above\n if model.verbose:\n print(\" loading ipakcb, HDRY, IWDFLG, WETFCT, IWETIT, IHDWET...\")\n text_list = line_parse(line)\n ipakcb, hdry, iwdflg, wetfct, iwetit, ihdwet = (\n int(text_list[0]),\n float(text_list[1]),\n int(text_list[2]),\n float(text_list[3]),\n int(text_list[4]),\n int(text_list[5]),\n )\n\n ikvflag = type_from_iterable(\n text_list, index=6, _type=int, default_val=0\n )\n ikcflag = type_from_iterable(\n text_list, index=7, _type=int, default_val=0\n )\n\n # LAYCON array\n laycon, intercellt = cls._load_laycon(f_obj, model)\n\n # TRPY array\n if model.verbose:\n print(\" loading TRPY...\")\n trpy = Util2d.load(\n f_obj, model, (nlay,), np.float32, \"trpy\", ext_unit_dict\n )\n\n # property data for each layer based on options\n transient = not dis.steady.all()\n anis = any(t != 1 for t in trpy)\n anglex = 0\n if (not model.structured) and anis:\n if model.verbose:\n print(\"loading ANGLEX...\")\n anglex = Util2d.load(\n f_obj, model, (njag,), np.float32, \"anglex\", ext_unit_dict\n )\n\n # hy, kv, storage\n (sf1, tran, hy, vcont, sf2, wetdry, kv) = cls._load_layer_arrays(\n f_obj,\n model,\n nlay,\n ext_unit_dict,\n transient,\n laycon,\n ikvflag,\n ikcflag,\n iwdflg,\n )\n\n # Ksat mfusg\n ksat = 0\n if (not model.structured) and abs(ikcflag == 1):\n if model.verbose:\n print(\" loading ksat (njag)...\")\n ksat = Util2d.load(\n f_obj, model, (njag,), np.float32, \"ksat\", ext_unit_dict\n )\n\n f_obj.close()\n\n # set package unit number\n unitnumber, filenames = get_unitnumber_from_ext_unit_dict(\n model, cls, ext_unit_dict, ipakcb\n )\n\n # create instance of bcf object\n bcf = cls(\n model,\n ipakcb=ipakcb,\n intercellt=intercellt,\n laycon=laycon,\n trpy=trpy,\n hdry=hdry,\n iwdflg=iwdflg,\n wetfct=wetfct,\n iwetit=iwetit,\n ihdwet=ihdwet,\n ikvflag=ikvflag,\n ikcflag=ikcflag,\n tran=tran,\n hy=hy,\n vcont=vcont,\n kv=kv,\n anglex=anglex,\n ksat=ksat,\n sf1=sf1,\n sf2=sf2,\n wetdry=wetdry,\n unitnumber=unitnumber,\n filenames=filenames,\n )\n\n # return bcf object\n return bcf","def train_models(self, clf, silent, feature_names=None, target_names=None, live=False):\n X_train, X_test, y_train, y_test = self.X_train, self.X_test, self.y_train, self.y_test\n t0 = time()\n clf.fit(X_train, y_train)\n train_time = time() - t0\n pred = clf.predict(X_test)\n test_time = time() - t0\n accuracy = metrics.accuracy_score(y_test, pred)\n fbeta = metrics.fbeta_score(y_test, pred,1,labels=self.dataset['label'].unique(),average='weighted')\n name = clf.name[0]\n if False:\n score_stats = f'Model : {name} | Score : {accuracy} | F-beta : {fbeta}'\n print(score_stats)\n\n if self.best_score_ledger[name][0] < accuracy:\n last = self.best_score_ledger[name][0]\n print(name)\n self.best_score_ledger[name] = [accuracy,fbeta]\n score_stats = f'Model : {name} | Score : {accuracy} | F-beta : {fbeta}'\n print(self.stemmer, ' ', self.transform)\n print(score_stats)\n\n if accuracy > self.best_models[name] and last != 0.0 and self.tuning_depth in ['normal','maximal']:\n new_model,score = self.hyperparameter_tuning(name,clf)\n if score > accuracy:\n self.best_score_ledger[name][0] = score\n clf = new_model\n dump(clf, os.path.join(os.getcwd(), self.file_term, 'models', f'{\"_\".join([self.uid_base, name])}'))\n\n\n\n if not silent:\n if hasattr(clf, 'coef_'):\n print(\"dimensionality: %d\" % clf.coef_.shape[1])\n print(\"density: %f\" % density(clf.coef_))\n\n if True and feature_names is not None:\n print(\"top 10 keywords per class:\")\n for i, label in enumerate(target_names):\n top10 = np.argsort(clf.coef_[i])[-10:]\n print(trim(\"%s: %s\" % (label, \" \".join(feature_names[top10]))))\n print()\n\n if True:\n print(\"classification report:\")\n print(metrics.classification_report(y_test, pred,\n target_names=target_names))\n\n if True:\n print(\"confusion matrix:\")\n print(metrics.confusion_matrix(y_test, pred))\n # if no model exists for the current settings, create one by default. Prevents issues if models are deleted.\n elif not os.path.exists(\n os.path.join(os.getcwd(), self.file_term, 'models', f'{\"_\".join([self.uid_base, name])}')):\n dump(clf, os.path.join(os.getcwd(), self.file_term, 'models', f'{\"_\".join([self.uid_base, name])}'))\n clf_descr = str(clf).split('(')[0]\n return clf_descr, accuracy, train_time, test_time","def Main(ANNSetup,DataSet,BDTSetup=None,BootStrap=('vali',None)):\n # Random seed for the NN cacluations\n np.random.seed(5) \n tf.compat.v1.set_random_seed(5)\n\n train, test, vali = GetSamples(BootStrap,DataSet,ANNSetup.ModelName) # Transform the DataSet into keras readable and aplying bootstrap if needed\n ANNSetup.InputDim = len(DataSet.LVariables) # Set Input dim (#features)\n GuardFNN(ANNSetup) # Protect the setup from typos\n\n # pass the Data to the correct API\n if(ANNSetup.Architecture == 'TMVA'):\n train, test, vali = GetSamples(BootStrap,DataSet,ANNSetup.ModelName,DoTrafo=False) #In Utils\n dataloader, factory, output = Init(train, test, DataSet.LVariables)\n TMVAFNN(ANNSetup, dataloader, factory)\n if(BDTSetup != None):\n BDT(BDTSetup, dataloader, factory)\n #GetRocs(factory, dataloader,\"BDTEven\")\n Finialize(factory, output)\n #GetRocs(factory, dataloader,\"FNN19Even\")\n\n # Direct keras implementation\n elif(ANNSetup.Architecture == 'FNN'):\n train, test, vali = GetSamples(BootStrap,DataSet,ANNSetup.ModelName,DoTrafo=True)\n ANNSetup.InputDim = len(DataSet.LVariables)\n if(BDTSetup != None):\n stdwar(\"BDT is only supported using TMVA\")\n return FNN(ANNSetup, test, train)\n\n # multi classifier (direct keras)\n elif(ANNSetup.Architecture == 'FNNMulti'):\n train, test, vali = GetSamples(BootStrap,DataSet,ANNSetup.ModelName,DoTrafo=True)\n ANNSetup.InputDim = len(DataSet.LVariables)\n if(BDTSetup != None):\n stdwar(\"BDT is only supported using TMVA\")\n Model, Roc = MultiFNN(ANNSetup, test, train)\n if(BootStrap[1] != None):\n stdwar(\"BootStrap for multi has to be implemented\")\n assert 0 == 1\n return Model, Roc","def _simulation_run(model_instance, observations, actions, rewards):\r\n\r\n for observation, action, reward in zip(observations, actions, rewards):\r\n model_instance.observe(observation)\r\n model_instance.overrideActionChoice(action)\r\n model_instance.feedback(reward)\r\n\r\n return model_instance","def init_model(model_filename, doGPU):\n # set model attributes list\n ##print(\"Model-dataset =\", model_ds_name)\n ##if model_ds_name == 'modelRAP':\n ## model_labels = loader_rapdataset_yiqiang.ATTRIBUTES\n ##elif model_ds_name == 'modelPETA':\n ## model_labels = loader_peta_dataset.ATTRIBUTES\n ##elif model_ds_name == 'modelRAPPETA':\n ## model_labels = [peta_label for rap_label,peta_label in loader_rap_plus_peta_dataset.ATTRIBUTES]\n ##else:\n ## print(\"ERROR: unknown model-dataset.\")\n ## sys.exit()\n model_labels = loader_rap_plus_peta_dataset.ATTRIBUTES\n assert (len(model_labels) == 49)\n\n # create model\n person.NO_ATTRIBUTES = len(model_labels) #TODO-elo: ugly, attr. nbr should be a parameter of person.Net.__init__()\n net = person.Net()\n if doGPU:\n net = person.Net().cuda()\n\n # load model\n print('loading model \"' + model_filename + '\"')\n person.load_model(net, model_filename)\n\n return net, model_labels","def build_model_fn(self):","def test_model_processor():\n global model_processor_called\n\n model_str = 'first 34 45 7 A 45 65 B true C \"dfdf\"'\n\n metamodel = metamodel_from_str(grammar)\n metamodel.register_model_processor(model_processor)\n\n metamodel.model_from_str(model_str)\n\n assert model_processor_called","def test_coupledmodels_post(self):\n pass","def run_all_tests():\n model_configs = (model_handler.ModelConfig(\n saved_model_dir=platform_test.test_src_dir_path(\n \"python/compiler/tensorrt/model_tests/sample_model\"),\n default_batch_size=128),)\n if FLAGS.use_tf2:\n model_handler_cls = model_handler.ModelHandlerV2\n trt_model_handeler_cls = model_handler.TrtModelHandlerV2\n default_trt_convert_params = DEFAUL_TRT_CONVERT_PARAMS._replace(\n is_dynamic_op=True)\n else:\n model_handler_cls = model_handler.ModelHandlerV1\n trt_model_handeler_cls = model_handler.TrtModelHandlerV1\n default_trt_convert_params = DEFAUL_TRT_CONVERT_PARAMS._replace(\n is_dynamic_op=False)\n for model_config in model_configs:\n trt_convert_params = default_trt_convert_params._replace(\n max_batch_size=model_config.default_batch_size)\n base_model = model_handler_cls(model_config)\n random_inputs = base_model.generate_random_inputs()\n base_model_result = base_model.run(random_inputs)\n trt_fp32_model_result = trt_model_handeler_cls(\n model_config=model_config,\n trt_convert_params=trt_convert_params._replace(\n precision_mode=trt.TrtPrecisionMode.FP32)).run(random_inputs)\n trt_fp16_model_result = trt_model_handeler_cls(\n model_config=model_config,\n trt_convert_params=trt_convert_params._replace(\n precision_mode=trt.TrtPrecisionMode.FP16)).run(random_inputs)\n\n logging.info(\"Base model latency: %f ms\",\n _get_mean_latency(base_model_result))\n logging.info(\"TensorRT FP32 model latency: %f ms\",\n _get_mean_latency(trt_fp32_model_result))\n logging.info(\"TensorRT FP16 model latency: %f ms\",\n _get_mean_latency(trt_fp16_model_result))","def run(self):\n self.membershipFunction()\n self.interpretingMF()\n self.rules()\n self.standardComposition_Min()\n self.standardComposition_Max()\n self.defuzzification()","def test_infer_framework(self):\n\t\t\n\t\tprint(f\"test_infer_framework self.model={self.model}\")\n\t\t\n\t\tnum_files = len(glob.glob(f\"{self.model}/*\"))\n\t\tprint(f\"test_infer_framework found {num_files} tmp files\")\n\t\tself.assertTrue(num_files > 0)\n\t\t\n\t\t\n\t\tnum_pytorch_bin_files = len(glob.glob(f\"{self.model}/*bin\"))\n\t\t#print(f\"test_infer_framework found {num_pytorch_bin_files} tmp pytorch bin files\")\n\t\tself.assertEqual(num_pytorch_bin_files, 1)\n\t\t\n\t\tnum_safetensors_files = len(glob.glob(f\"{self.model}/*safetensors\"))\n\t\t#print(f\"test_infer_framework found {num_pytorch_bin_files} tmp num_safetensors_files files\")\n\t\tself.assertEqual(num_safetensors_files, 0)\n\t\t\n\t\tnum_flat_files = len(glob.glob(f\"{self.model}/*npy\"))\n\t\t#print(f\"test_infer_framework found {num_files} tmp npy flat files\")\n\t\tself.assertEqual(num_flat_files, 0)\n\t\t\n\t\texpected_format, expected_fileglob = ww.WeightWatcher.infer_model_file_format(self.model)\n\t\tprint(f\"infer_model_file_format found {expected_format} expected format\")\n\t\tself.assertEqual(expected_format, MODEL_FILE_FORMATS.PYTORCH)\t\t\n\n\t\texpected_framework = ww.WeightWatcher.infer_framework(self.model)\n\t\tprint(f\"infer_model_file_format found {expected_framework} expected_framework \")\n\t\tself.assertEqual(expected_framework, FRAMEWORK.PYSTATEDICT_DIR)\t\n\t\t\n\t\treturn","def evaluate_ucf50_fusion():\n accs = np.zeros(3)\n ob_suffix = '-max.feat.npy.gz'\n fv_suffix = '_fv.npy.gz'\n ob_root = '/home/syq/research_final/data/features/ob_ucf50_pooled_python/'\n fv_root = '/home/syq/research_final/data/features/fv_ucf50_python/'\n fv_groups, full, sets = utility.split_data(fv_root,\n suffix=fv_suffix,\n useLooCV=False)\n\n ob_groups, _, _ = utility.split_data(ob_root,\n suffix=ob_suffix,\n useLooCV=False)\n weights = [i / 20.0 for i in range(8, 13)]\n acc_to_weights = {}\n\n for weight in weights:\n print \"Weight: %.2f\" % weight\n accs = np.zeros(2)\n for i in xrange(2):\n ts = time.time()\n Dtrain_fv, Dtest_fv, Ytrain, Ytest = utility.load_groups(\n fv_groups, np.setdiff1d(full, sets[i]),\n sets[i], scale=False, verbose=False)\n\n Dtrain_ob, Dtest_ob, Ytrain, Ytest = utility.load_groups(\n ob_groups, np.setdiff1d(full, sets[i]),\n sets[i], scale=False, verbose=False)\n\n fv_clf = OneVsRestClassifier(estimator=LinearSVC(C=100), n_jobs=8)\n\n ob_clf = OneVsRestClassifier(estimator=SVC(C=10,\n cache_size=1000,\n kernel='linear',\n probability=True),\n n_jobs=-1)\n\n # Get probabilities for late fusion\n Dtrain_fv = fv_clf.fit(Dtrain_fv, Ytrain).decision_function(Dtrain_fv)\n Dtrain_ob = ob_clf.fit(Dtrain_ob, Ytrain).decision_function(Dtrain_ob)\n Dtest_fv = fv_clf.decision_function(Dtest_fv)\n Dtest_ob = ob_clf.decision_function(Dtest_ob)\n\n # Scale decision values b/w 0 and 1\n Dtrain_fv = preprocessing.normalize(Dtrain_fv)\n Dtrain_ob = preprocessing.normalize(Dtrain_ob)\n Dtest_fv = preprocessing.normalize(Dtest_fv)\n Dtest_ob = preprocessing.normalize(Dtest_ob)\n\n # weighted averaging\n scores_train = (Dtrain_fv * weight) + (Dtrain_ob * (1 - weight))\n latefusion_clf = OneVsRestClassifier(estimator=LinearSVC(C=100), n_jobs=-1)\n latefusion_clf.fit(scores_train, Ytrain)\n\n scores_test = (Dtest_fv * weight) + (Dtest_ob * (1 - weight))\n latefusion_acc = latefusion_clf.score(scores_test, Ytest)\n print 'Fold', i, 'late fusion acc', latefusion_acc\n print \"Train & testing time %.3f\" % (time.time() - ts)\n accs[i] = latefusion_acc\n\n print \"Mean accuracy: %.3f\" % accs.mean()\n with open(\"fv_ucf50_accs_5fold_gridsearch.txt\", \"w\") as f:\n for weight, accs in acc_to_weights.items():\n f.write(str(weight) + str(accs) + '\\n')\n return acc_to_weights","def model_qua_launch(self, dict):\r\n list_result = []\r\n if \"SVR\" in dict:\r\n SVR = dict[\"SVR\"]\r\n if SVR[\"Auto\"]:\r\n result = SVR_b(self.ml_data.feature, self.ml_data.target, SVR[\"Auto\"])\r\n model, score, graph, time = result\r\n result_win = Window_Quant()\r\n result_win.setWindowTitle(\"SVR Result Auto\")\r\n result_win.setData(model, score, graph, time)\r\n list_result.append(result_win)\r\n else:\r\n result = SVR_b(self.ml_data.feature, self.ml_data.target, SVR[\"Auto\"], [SVR[\"C\"],\r\n SVR[\"Kernel\"],\r\n SVR[\"Degree\"]])\r\n model, score, graph, time = result\r\n result_win = Window_Quant()\r\n result_win.setWindowTitle(\"SVR Result\")\r\n result_win.setData(model, score, graph, time)\r\n list_result.append(result_win)\r\n if \"LR\" in dict:\r\n LR = dict[\"LR\"]\r\n if LR[\"Auto\"]:\r\n result = regression_lin(self.ml_data.feature, self.ml_data.target, LR[\"Auto\"])\r\n model, score, graph, time = result\r\n result_win = Window_Quant()\r\n result_win.setWindowTitle(\"Linear Regression Result Auto\")\r\n result_win.setData(model, score, graph, time)\r\n list_result.append(result_win)\r\n else:\r\n result = regression_lin(self.ml_data.feature, self.ml_data.target, LR[\"Auto\"], LR[\"fit_intercept\"], LR[\"normalize\"])\r\n model, score, graph, time = result\r\n result_win = Window_Quant()\r\n result_win.setWindowTitle(\"Linear Regression Result\")\r\n result_win.setData(model, score, graph, time)\r\n list_result.append(result_win)\r\n\r\n if \"RT\" in dict:\r\n RT = dict[\"RT\"]\r\n if RT[\"Auto\"]:\r\n result = RegTree(self.ml_data.feature, self.ml_data.target, RT[\"Auto\"])\r\n model, score, graph, time = result\r\n result_win = Window_Quant()\r\n result_win.setWindowTitle(\"Regression Tree Result Auto\")\r\n result_win.setData(model, score, graph, time)\r\n list_result.append(result_win)\r\n else:\r\n result = RegTree(self.ml_data.feature, self.ml_data.target, RT[\"Auto\"], [RT[\"Criterion\"],\r\n RT[\"Min_Samples_Split\"],\r\n RT[\"Min_Samples_Leaf\"]])\r\n model, score, graph, time = result\r\n result_win = Window_Quant()\r\n result_win.setWindowTitle(\"Regression Tree Result\")\r\n result_win.setData(model, score, graph, time)\r\n list_result.append(result_win)\r\n\r\n if \"KNN\" in dict:\r\n KNN = dict[\"KNN\"]\r\n if KNN[\"Auto\"]:\r\n result = knn_class(self.ml_data.feature, self.ml_data.target, KNN[\"Auto\"])\r\n model, matrix, dict_cr, graph, time = result\r\n result_win = Window()\r\n result_win.setWindowTitle(\"KNN Result Auto\")\r\n result_win.setData(model, matrix, dict_cr, graph, time)\r\n list_result.append(result_win)\r\n else:\r\n result = knn_class(self.ml_data.feature, self.ml_data.target, KNN[\"Auto\"],\r\n [KNN[\"leaf_size\"], KNN[\"n_neighbors\"], KNN[\"p\"], KNN[\"metric\"]])\r\n model, matrix, dict_cr, graph, time = result\r\n self.model_quali.close()\r\n result_win = Window()\r\n result_win.setWindowTitle(\"KNN Result\")\r\n result_win.setData(model, matrix, dict_cr, graph, time)\r\n list_result.append(result_win)\r\n if \"LogiR\" in dict:\r\n LogiR = dict[\"LogiR\"]\r\n if LogiR[\"Auto\"]:\r\n result = LogReg(self.ml_data.feature, self.ml_data.target, LogiR[\"Auto\"])\r\n model, matrix, dict_cr, graph, time = result\r\n result_win = Window()\r\n result_win.setWindowTitle(\"Logistic Regression Result Auto\")\r\n result_win.setData(model, matrix, dict_cr, graph, time)\r\n list_result.append(result_win)\r\n else:\r\n result = LogReg(self.ml_data.feature, self.ml_data.target, LogiR[\"Auto\"], [LogiR['C'],\r\n LogiR['penalty']])\r\n model, matrix, dict_cr, graph, time = result\r\n result_win = Window()\r\n result_win.setWindowTitle(\"Logistic Regression Result\")\r\n result_win.setData(model, matrix, dict_cr, graph, time)\r\n list_result.append(result_win)\r\n\r\n if \"DTC\" in dict:\r\n DTC = dict[\"DTC\"]\r\n if DTC[\"Auto\"]:\r\n result = arbre_clas(self.ml_data.feature, self.ml_data.target, DTC[\"Auto\"])\r\n model, matrix, dict_cr, graph, time = result\r\n result_win = Window()\r\n result_win.setWindowTitle(\"Tree Decision Classification Auto\")\r\n result_win.setData(model, matrix, dict_cr, graph, time)\r\n list_result.append(result_win)\r\n\r\n else:\r\n result = arbre_clas(self.ml_data.feature, self.ml_data.target, DTC[\"Auto\"],DTC[\"max_leaf_nodes\"],DTC[\"max_depth\"],DTC[\"min_samples_split\"])\r\n model, matrix, dict_cr, graph, time = result\r\n result_win = Window()\r\n result_win.setWindowTitle(\"Tree Decision Classification\")\r\n result_win.setData(model, matrix, dict_cr, graph, time)\r\n list_result.append(result_win)\r\n\r\n\r\n self.close()\r\n self.trigger_result.emit(list_result)","def synthesize_rf(self, model, mu=1.0):\n ndim = len(model.shape)\n dtype = self._get_dtype()\n \n if(ndim == 1):\n model1 = np.ascontiguousarray(model.reshape((1,1,model.size)), dtype=dtype)\n elif(ndim == 3):\n model1 = model\n else:\n print(\"MilneEddington::synthesize_rf: ERROR, the input model must have 1 or 3 dimensions\")\n return None\n\n if(model1.shape[2] != 9):\n print(\"MilneEddington::synthesize_rf: ERROR, input model has npar={0}, should be 9\".format(model1.shape[2]))\n return None\n\n isContiguous = model1.flags['C_CONTIGUOUS']\n if(not isContiguous or model1.dtype != dtype):\n model1 = np.ascontiguousarray(model1, dtype=dtype)\n\n \n \n return self.Me.synthesize_RF(model, mu=mu)","def run(self):\r\n # Close any open models\r\n self.cleanUp()\r\n # Dynamically select file to preview\r\n self.selectTrial() \r\n # Add adjusted COM (RRA/CMC) model\r\n self.loadAdjustedModel()\r\n # Hide the markers from view\r\n self.hideModelMarkers()\r\n # Load CMC motion to model\r\n self.loadCMCMotion()","def run(self):\n #parse requests\n self.bqSession.update_mex('Calculating Features...')\n log.debug('Forming Feature Requests...')\n #get rectanle gobjects for roi\n r_xml = self.bqSession.fetchxml(self.options.mexURL, view='deep')\n\n rectangles = r_xml.xpath('//tag[@name=\"inputs\"]/tag[@name=\"image_url\"]/gobject[@name=\"roi\"]/rectangle')\n image_xml = self.bqSession.fetchxml(self.options.image_url)\n image_url = self.bqSession.service_url('image_service',path=image_xml.attrib['resource_uniq'])\n if rectangles: #On chooses the first rectangle\n #construct operation node\n x1 = int(float(rectangles[0][0].attrib['x']))\n y1 = int(float(rectangles[0][0].attrib['y']))\n x2 = int(float(rectangles[0][1].attrib['x']))\n y2 = int(float(rectangles[0][1].attrib['y']))\n log.debug('Adding Crop: roi=%s,%s,%s,%s' % (x1, y1, x2, y2))\n image_url = self.bqSession.c.prepare_url(image_url, roi='%s,%s,%s,%s' % (x1, y1, x2, y2))\n \n try:\n feature_vectors = extract_bush_feature(self.bqSession, image_url)\n except FeatureCommError as e:\n raise BotanicamError(str(e))\n \n #parse features\n self.bqSession.update_mex('Classifying Results...')\n log.debug('Classifying Results...')\n results= []\n pca = joblib.load(os.path.join(self.model_path,'pca_model'))\n clf = joblib.load(os.path.join(self.model_path,'svm_model'))\n \n for f in feature_vectors:\n f_norm = pca.transform(f)\n results.append(int(clf.predict(f_norm)))\n \n\n class_count = np.bincount(np.array(results))\n self.class_number = np.argmax(class_count)\n self.confidence = float(class_count[self.class_number])/np.sum(class_count)\n log.debug('Found Class %s'%str(self.class_number))","def main(config):\n\n runModel(config)\n while (config['looping']):\n runModel(config)","def construct_model(self):\n # Set the placeholder for the input episode\n self.inputa = tf.placeholder(tf.float32) # episode train images\n self.inputb = tf.placeholder(tf.float32) # episode test images\n self.labela = tf.placeholder(tf.float32) # episode train labels\n self.labelb = tf.placeholder(tf.float32) # episode test labels\n\n with tf.variable_scope('meta-model', reuse=None) as training_scope:\n # construct the model weights\n self.ss_weights = ss_weights = self.construct_resnet_ss_weights()\n self.weights = weights = self.construct_resnet_weights()\n self.fc_weights = fc_weights = self.construct_fc_weights()\n\n # Load base epoch number from FLAGS\n num_updates = FLAGS.train_base_epoch_num\n\n def task_metalearn(inp, reuse=True):\n \"\"\"The function to process one episode in a meta-batch.\n Args:\n inp: the input episode.\n reuse: whether reuse the variables for the normalization.\n Returns:\n A serious outputs like losses and accuracies.\n \"\"\"\n # Seperate inp to different variables\n inputa, inputb, labela, labelb = inp\n # Generate empty list to record losses\n lossa_list = [] # Base train loss list\n lossb_list = [] # Base test loss list\n\n # Embed the input images to embeddings with ss weights\n emb_outputa = self.forward_resnet(inputa, weights, ss_weights, reuse=reuse) # Embed episode train \n emb_outputb = self.forward_resnet(inputb, weights, ss_weights, reuse=True) # Embed episode test \n\n # Run the first epoch of the base learning\n # Forward fc layer for episode train \n outputa = self.forward_fc(emb_outputa, fc_weights)\n # Calculate base train loss\n lossa = self.loss_func(outputa, labela)\n # Record base train loss\n lossa_list.append(lossa)\n # Forward fc layer for episode test\n outputb = self.forward_fc(emb_outputb, fc_weights)\n # Calculate base test loss\n lossb = self.loss_func(outputb, labelb)\n # Record base test loss\n lossb_list.append(lossb) \n # Calculate the gradients for the fc layer \n grads = tf.gradients(lossa, list(fc_weights.values()))\n gradients = dict(zip(fc_weights.keys(), grads))\n # Use graient descent to update the fc layer\n fast_fc_weights = dict(zip(fc_weights.keys(), [fc_weights[key] - \\\n self.update_lr*gradients[key] for key in fc_weights.keys()]))\n \n for j in range(num_updates - 1):\n # Run the following base epochs, these are similar to the first base epoch\n lossa = self.loss_func(self.forward_fc(emb_outputa, fast_fc_weights), labela)\n lossa_list.append(lossa)\n lossb = self.loss_func(self.forward_fc(emb_outputb, fast_fc_weights), labelb)\n lossb_list.append(lossb) \n grads = tf.gradients(lossa, list(fast_fc_weights.values()))\n gradients = dict(zip(fast_fc_weights.keys(), grads))\n fast_fc_weights = dict(zip(fast_fc_weights.keys(), [fast_fc_weights[key] - \\\n self.update_lr*gradients[key] for key in fast_fc_weights.keys()]))\n\n # Calculate final episode test predictions\n outputb = self.forward_fc(emb_outputb, fast_fc_weights)\n # Calculate the final episode test loss, it is the loss for the episode on meta-train \n final_lossb = self.loss_func(outputb, labelb)\n # Calculate the final episode test accuarcy\n accb = tf.contrib.metrics.accuracy(tf.argmax(tf.nn.softmax(outputb), 1), tf.argmax(labelb, 1))\n\n # Reorganize all the outputs to a list\n task_output = [final_lossb, lossb_list, lossa_list, accb]\n\n return task_output\n\n # Initial the batch normalization weights\n if FLAGS.norm is not 'None':\n unused = task_metalearn((self.inputa[0], self.inputb[0], self.labela[0], self.labelb[0]), False)\n\n # Set the dtype of the outputs\n out_dtype = [tf.float32, [tf.float32]*num_updates, [tf.float32]*num_updates, tf.float32]\n\n # Run two episodes for a meta batch using parallel setting\n result = tf.map_fn(task_metalearn, elems=(self.inputa, self.inputb, self.labela, self.labelb), \\\n dtype=out_dtype, parallel_iterations=FLAGS.meta_batch_size)\n # Seperate the outputs to different variables\n lossb, lossesb, lossesa, accsb = result\n\n # Set the variables to output from the tensorflow graph\n self.total_loss = total_loss = tf.reduce_sum(lossb) / tf.to_float(FLAGS.meta_batch_size)\n self.total_accuracy = total_accuracy = tf.reduce_sum(accsb) / tf.to_float(FLAGS.meta_batch_size)\n self.total_lossa = total_lossa = [tf.reduce_sum(lossesa[j]) / tf.to_float(FLAGS.meta_batch_size) for j in range(num_updates)]\n self.total_lossb = total_lossb = [tf.reduce_sum(lossesb[j]) / tf.to_float(FLAGS.meta_batch_size) for j in range(num_updates)]\n\n # Set the meta-train optimizer\n optimizer = tf.train.AdamOptimizer(self.meta_lr)\n self.metatrain_op = optimizer.minimize(total_loss, var_list=list(ss_weights.values()) + list(fc_weights.values()))\n\n # Set the tensorboard \n self.training_summaries = []\n self.training_summaries.append(tf.summary.scalar('Meta Train Loss', (total_loss / tf.to_float(FLAGS.metatrain_epite_sample_num))))\n self.training_summaries.append(tf.summary.scalar('Meta Train Accuracy', total_accuracy))\n for j in range(num_updates):\n self.training_summaries.append(tf.summary.scalar('Base Train Loss Step' + str(j+1), total_lossa[j]))\n for j in range(num_updates):\n self.training_summaries.append(tf.summary.scalar('Base Val Loss Step' + str(j+1), total_lossb[j]))\n\n self.training_summ_op = tf.summary.merge(self.training_summaries)\n\n self.input_val_loss = tf.placeholder(tf.float32)\n self.input_val_acc = tf.placeholder(tf.float32)\n self.val_summaries = []\n self.val_summaries.append(tf.summary.scalar('Meta Val Loss', self.input_val_loss))\n self.val_summaries.append(tf.summary.scalar('Meta Val Accuracy', self.input_val_acc))\n self.val_summ_op = tf.summary.merge(self.val_summaries)","def define_forms(self):\n\n # Define UFL objects corresponding to the local acceleration\n # if problem is unsteady.\n self.define_ufl_local_inertia()\n self.define_ufl_local_inertia_diff()\n\n # Define UFL objects corresponding to the convective acceleration\n # if problem is formulated with respect to Eulerian coordinates\n self.define_ufl_convec_accel()\n self.define_ufl_convec_accel_diff()\n\n # Define UFL objects corresponding to the stress tensor term.\n # This should always be non-zero for deformable bodies.\n self.define_ufl_stress_work()\n self.define_ufl_stress_work_diff()\n\n # Define UFL object corresponding to the body force term. Assume\n # it is zero if key was not provided.\n self.define_ufl_body_force()\n\n # Define UFL object corresponding to the traction force terms. Assume\n # it is zero if key was not provided.\n self.define_ufl_neumann_bcs()\n self.define_ufl_neumann_bcs_diff()\n\n return None","def makeModel(self):\n\n # Get the script\n modelScript = os.path.join(self.datapath, 'make3FGLxml.py')\n if not os.path.isfile(modelScript):\n # download it\n print(\"\\t=== Downloading make3FGLxml.py ===\")\n os.system('wget https://fermi.gsfc.nasa.gov/ssc/data/analysis/user/make3FGLxml.py -O {}'.format(modelScript))\n\n # Create the model using Tyrel's script\n galModel = os.path.join(self.diffpath, 'gll_iem_v06.fits')\n isoModel = os.path.join(self.diffpath, 'iso_'+self.irf+'_v06.txt')\n if (not os.path.isfile(galModel)) or (not os.path.isfile(isoModel)):\n print(\"\\t=== Unable to find the diffuse models, check the variable '$FERMI_DIR' ===\")\n return\n if not os.path.isdir(self.extpath):\n print(\"\\t=== Unable to find models of extended sources, check the variable '$LATEXTDIR' ===\")\n return\n if not os.path.isfile(self.fermicat):\n # download it\n print(\"\\t=== Downloading 3FGL catalog ===\")\n os.system('wget https://fermi.gsfc.nasa.gov/ssc/data/access/lat/4yr_catalog/gll_psc_v16.fit -O {}'.format(self.fermicat))\n\n os.popen(\"python {} {} {} -o {} -G {} -g 'gll_iem_v06'\\\n -I {} -i 'iso_source_v06' -e {} -r 5 -R 10 -ER 10\\\n -s 9 -m False -GIF False\".format(modelScript, self.fermicat,\n self.ft1, self.model, galModel, isoModel, self.extpath))\n\n # Add the target to the model\n tmpName = self.model + '.tmp'\n rfil = open(self.model, 'r')\n wfil = open(tmpName, 'w')\n # Copy the XML to the temporary model\n wfil.writelines([l for l in rfil.readlines() if not l=='</source_library>']) # copy everything but the last line\n wfil.write(' <source ROI_Center_Distance=\"0.00\" name=\"TARGET\" type=\"PointSource\">\\n')\n wfil.write(' <spectrum type=\"PowerLaw2\">\\n')\n wfil.write(' <parameter free=\"1\" max=\"1000\" min=\"1e-05\" name=\"Integral\" scale=\"1e-08\" value=\"0.3591824258\"/>\\n')\n wfil.write(' <parameter free=\"1\" max=\"1\" min=\"-5\" name=\"Index\" scale=\"1\" value=\"-2.7\"/>\\n')\n wfil.write(' <parameter free=\"0\" max=\"1000000\" min=\"20\" name=\"LowerLimit\" scale=\"1\" value=\"100\"/>\\n')\n wfil.write('<parameter free=\"0\" max=\"1000000\" min=\"20\" name=\"UpperLimit\" scale=\"1\" value=\"100000\"/>\\n')\n wfil.write(' </spectrum>\\n')\n wfil.write(' <spatialModel type=\"SkyDirFunction\">\\n')\n wfil.write(' <parameter free=\"0\" max=\"360.0\" min=\"-360.0\" name=\"RA\" scale=\"1.0\" value=\"'+str(self.ra)+'\"/>\\n')\n wfil.write(' <parameter free=\"0\" max=\"360.0\" min=\"-360.0\" name=\"DEC\" scale=\"1.0\" value=\"'+str(self.dec)+'\"/>\\n')\n wfil.write(' </spatialModel>\\n')\n wfil.write(' </source>\\n')\n wfil.write('</source_library>\\n')\n rfil.close()\n wfil.close()\n\n os.remove(self.model)\n os.rename(tmpName, self.model)\n \n print(\"\\t=== Source model {} added ===\".format(self.model))\n return","def build_model(self):\n if self.args.network_type == 'unet':\n self.shared = models.Unet(self.args)\n else:\n raise NotImplementedError(f'Network type '\n f'`{self.args.network_type}` is not '\n f'defined')\n self.controller = models.Controller(self.args)\n\n if self.args.num_gpu == 1:\n self.shared.cuda()\n self.controller.cuda()\n elif self.args.num_gpu > 1:\n raise NotImplementedError('`num_gpu > 1` is in progress')","def eval_model(config):\n print 'Model directory: %s' % config.model_output\n print 'Running model: %s' % config.model_type\n if config.model_type == 'fully_connected_mlp':\n from models.fully_connected_mlp import model_struct\n elif config.model_type == 'fully_connected_mlp_2l':\n from models.fully_connected_mlp_2l import model_struct\n elif config.model_type == 'fully_connected_conv':\n from models.fully_connected_conv import model_struct\n elif config.model_type == 'vgg_feature_model':\n from models.vgg_feature_model import model_struct\n else:\n raise Exception\n\n # Prepare model training\n dt_stamp = re.split(\n '\\.', str(datetime.now()))[0].\\\n replace(' ', '_').replace(':', '_').replace('-', '_')\n dt_dataset = config.model_type + '_' + dt_stamp + '/'\n config.train_checkpoint = os.path.join(\n config.model_output, dt_dataset) # timestamp this run\n config.summary_dir = os.path.join(\n config.train_summaries, config.model_output, dt_dataset)\n dir_list = [config.train_checkpoint, config.summary_dir]\n [make_dir(d) for d in dir_list]\n\n # Prepare model inputs\n train_data = os.path.join(config.tfrecord_dir, 'train.tfrecords')\n validation_data = os.path.join(config.tfrecord_dir, 'val.tfrecords')\n feat_mean = 0 # np.mean(np.load(config.mean_file)['feat_list'])\n\n # Prepare data on CPU\n with tf.device('/cpu:0'):\n train_images, train_labels = inputs(\n tfrecord_file=train_data,\n batch_size=config.train_batch,\n num_feats=config.n_features,\n sample=config.sample['train'],\n max_pixels_per_image=config.max_pixels_per_image,\n im_size=config.resize,\n model_input_shape=config.resize,\n train=config.data_augmentations,\n num_epochs=config.epochs,\n feat_mean_value=feat_mean)\n val_images, val_labels = inputs(\n tfrecord_file=validation_data,\n batch_size=1,\n num_feats=config.n_features,\n sample=config.sample['val'],\n max_pixels_per_image=config.max_pixels_per_image,\n im_size=config.resize,\n model_input_shape=config.resize,\n train=config.data_augmentations,\n num_epochs=config.epochs,\n feat_mean_value=feat_mean)\n tf.summary.image('validation images', tf.cast(val_labels, tf.float32))\n\n # Prepare model on GPU\n with tf.device('/gpu:0'):\n with tf.variable_scope('cnn') as scope:\n\n model = model_struct()\n train_mode = tf.get_variable(name='training', initializer=True)\n model.build(\n features=train_images,\n output_categories=len(config.labels.keys()),\n train_mode=train_mode, batchnorm=config.batch_norm)\n\n # Prepare the cost function\n cost = softmax_cost(\n model.res_logits, train_labels, ratio=config.ratio,\n label_reshape=[\n config.batch_size * config.max_pixels_per_image])\n train_op = tf.train.AdamOptimizer(config.lr).minimize(cost)\n\n tf.summary.scalar(\"cost\", cost)\n\n train_score = correlation(\n model.prob, train_labels) # training accuracy\n tf.summary.scalar(\"training correlation\", train_score)\n\n # Setup validation op\n if validation_data is not False:\n scope.reuse_variables()\n # Validation graph is the same as training except no batchnorm\n val_model = model_struct()\n val_model.build(\n features=val_images,\n output_categories=len(config.labels.keys()))\n\n # Calculate validation accuracy\n val_pred = tf.cast(\n tf.reshape(\n tf.argmax(\n val_model.prob, axis=1),\n [1, config.resize[0], config.resize[1], 1]),\n tf.float32)\n tf.summary.image('validation prediction', val_pred)\n val_score = correlation(\n val_model.prob, tf.reshape(\n val_labels, [np.prod(config.resize), 1]))\n tf.summary.scalar(\"validation correlation\", val_score)\n\n # Set up summaries and saver\n saver = tf.train.Saver(\n tf.global_variables(), max_to_keep=config.keep_checkpoints)\n summary_op = tf.summary.merge_all()\n\n # Initialize the graph\n sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True))\n\n # Need to initialize both of these if supplying num_epochs to inputs\n sess.run(tf.group(tf.global_variables_initializer(),\n tf.local_variables_initializer()))\n summary_writer = tf.summary.FileWriter(config.summary_dir, sess.graph)\n\n # Set up exemplar threading\n coord = tf.train.Coordinator()\n threads = tf.train.start_queue_runners(sess=sess, coord=coord)\n\n # Start training loop\n np.save(config.train_checkpoint, config)\n step, val_max, losses = 0, 0, []\n train_acc = 0\n try:\n while not coord.should_stop():\n start_time = time.time()\n _, loss_value, train_acc = sess.run([train_op, cost, train_score])\n losses.append(loss_value)\n duration = time.time() - start_time\n assert not np.isnan(loss_value), 'Model diverged with loss = NaN'\n\n if step % 100 == 0 and step % 10 == 0:\n if validation_data is not False:\n _, val_acc, val_frame = sess.run(\n [train_op, val_score, val_pred])\n\n np.save(\n os.path.join(\n config.model_output, '%s_val_image' % step),\n val_frame)\n else:\n val_acc = -1 # Store every checkpoint\n\n # Summaries\n summary_str = sess.run(summary_op)\n summary_writer.add_summary(summary_str, step)\n\n # Training status and validation accuracy\n format_str = (\n '%s: step %d, loss = %.2f (%.1f examples/sec; '\n '%.3f sec/batch) | Training r = %s | '\n 'Validation r = %s | logdir = %s')\n print (format_str % (\n datetime.now(), step, loss_value,\n config.train_batch / duration, float(duration),\n train_acc, val_acc, config.summary_dir))\n\n # Save the model checkpoint if it's the best yet\n if val_acc > val_max:\n saver.save(\n sess, os.path.join(\n config.train_checkpoint,\n 'model_' + str(step) + '.ckpt'), global_step=step)\n\n # Store the new max validation accuracy\n val_max = val_acc\n\n else:\n # Training status\n format_str = ('%s: step %d, loss = %.2f (%.1f examples/sec; '\n '%.3f sec/batch) | Training F = %s')\n print (format_str % (datetime.now(), step, loss_value,\n config.train_batch / duration,\n float(duration), train_acc))\n # End iteration\n step += 1\n\n except tf.errors.OutOfRangeError:\n print('Done training for %d epochs, %d steps.' % (config.epochs, step))\n finally:\n coord.request_stop()\n\n dt_stamp = get_dt() # date-time stamp\n np.save(\n os.path.join(\n config.tfrecord_dir, '%straining_loss' % dt_stamp), losses)\n coord.join(threads)\n sess.close()","def test_models_binary(model):\n atom = ATOMClassifier(X_bin, y_bin, test_size=0.24, random_state=1)\n atom.run(\n models=model,\n metric=\"auc\",\n n_calls=2,\n n_initial_points=1,\n bo_params={\"base_estimator\": \"rf\", \"cv\": 1},\n )\n assert not atom.errors # Assert that the model ran without errors\n assert hasattr(atom, model) # Assert that the model is an attr of the trainer","def __init__(\n self,\n biorbd_model,\n dynamics_type,\n number_shooting_points,\n phase_time,\n X_init,\n U_init,\n X_bounds,\n U_bounds,\n objective_functions=ObjectiveList(),\n constraints=ConstraintList(),\n parameters=ParameterList(),\n external_forces=(),\n ode_solver=OdeSolver.RK,\n nb_integration_steps=5,\n control_type=ControlType.CONSTANT,\n all_generalized_mapping=None,\n q_mapping=None,\n q_dot_mapping=None,\n tau_mapping=None,\n plot_mappings=None,\n state_transitions=StateTransitionList(),\n nb_threads=1,\n use_SX=False,\n ):\n\n if isinstance(biorbd_model, str):\n biorbd_model = [biorbd.Model(biorbd_model)]\n elif isinstance(biorbd_model, biorbd.biorbd.Model):\n biorbd_model = [biorbd_model]\n elif isinstance(biorbd_model, (list, tuple)):\n biorbd_model = [biorbd.Model(m) if isinstance(m, str) else m for m in biorbd_model]\n else:\n raise RuntimeError(\"biorbd_model must either be a string or an instance of biorbd.Model()\")\n self.version = {\"casadi\": casadi.__version__, \"biorbd\": biorbd.__version__, \"biorbd_optim\": __version__}\n self.nb_phases = len(biorbd_model)\n\n biorbd_model_path = [m.path().relativePath().to_string() for m in biorbd_model]\n self.original_values = {\n \"biorbd_model\": biorbd_model_path,\n \"dynamics_type\": dynamics_type,\n \"number_shooting_points\": number_shooting_points,\n \"phase_time\": phase_time,\n \"X_init\": X_init,\n \"U_init\": U_init,\n \"X_bounds\": X_bounds,\n \"U_bounds\": U_bounds,\n \"objective_functions\": ObjectiveList(),\n \"constraints\": ConstraintList(),\n \"parameters\": ParameterList(),\n \"external_forces\": external_forces,\n \"ode_solver\": ode_solver,\n \"nb_integration_steps\": nb_integration_steps,\n \"control_type\": control_type,\n \"all_generalized_mapping\": all_generalized_mapping,\n \"q_mapping\": q_mapping,\n \"q_dot_mapping\": q_dot_mapping,\n \"tau_mapping\": tau_mapping,\n \"plot_mappings\": plot_mappings,\n \"state_transitions\": state_transitions,\n \"nb_threads\": nb_threads,\n \"use_SX\": use_SX,\n }\n\n # Check integrity of arguments\n if not isinstance(nb_threads, int) or isinstance(nb_threads, bool) or nb_threads < 1:\n raise RuntimeError(\"nb_threads should be a positive integer greater or equal than 1\")\n\n if isinstance(dynamics_type, DynamicsTypeOption):\n dynamics_type_tp = DynamicsTypeList()\n dynamics_type_tp.add(dynamics_type)\n dynamics_type = dynamics_type_tp\n elif not isinstance(dynamics_type, DynamicsTypeList):\n raise RuntimeError(\"dynamics_type should be a DynamicsTypeOption or a DynamicsTypeList\")\n\n ns = number_shooting_points\n if not isinstance(ns, int) or ns < 2:\n if isinstance(ns, (tuple, list)):\n if sum([True for i in ns if not isinstance(i, int) and not isinstance(i, bool)]) != 0:\n raise RuntimeError(\n \"number_shooting_points should be a positive integer (or a list of) greater or equal than 2\"\n )\n else:\n raise RuntimeError(\n \"number_shooting_points should be a positive integer (or a list of) greater or equal than 2\"\n )\n nstep = nb_integration_steps\n if not isinstance(nstep, int) or isinstance(nstep, bool) or nstep < 1:\n raise RuntimeError(\"nb_integration_steps should be a positive integer greater or equal than 1\")\n\n if not isinstance(phase_time, (int, float)):\n if isinstance(phase_time, (tuple, list)):\n if sum([True for i in phase_time if not isinstance(i, (int, float))]) != 0:\n raise RuntimeError(\"phase_time should be a number or a list of number\")\n else:\n raise RuntimeError(\"phase_time should be a number or a list of number\")\n\n if isinstance(X_init, InitialConditionsOption):\n X_init_tp = InitialConditionsList()\n X_init_tp.add(X_init)\n X_init = X_init_tp\n elif not isinstance(X_init, InitialConditionsList):\n raise RuntimeError(\"X_init should be built from a InitialConditionsOption or InitialConditionsList\")\n\n if isinstance(U_init, InitialConditionsOption):\n U_init_tp = InitialConditionsList()\n U_init_tp.add(U_init)\n U_init = U_init_tp\n elif not isinstance(U_init, InitialConditionsList):\n raise RuntimeError(\"U_init should be built from a InitialConditionsOption or InitialConditionsList\")\n\n if isinstance(X_bounds, BoundsOption):\n X_bounds_tp = BoundsList()\n X_bounds_tp.add(X_bounds)\n X_bounds = X_bounds_tp\n elif not isinstance(X_bounds, BoundsList):\n raise RuntimeError(\"X_bounds should be built from a BoundOption or a BoundsList\")\n\n if isinstance(U_bounds, BoundsOption):\n U_bounds_tp = BoundsList()\n U_bounds_tp.add(U_bounds)\n U_bounds = U_bounds_tp\n elif not isinstance(U_bounds, BoundsList):\n raise RuntimeError(\"U_bounds should be built from a BoundOption or a BoundsList\")\n\n if isinstance(objective_functions, ObjectiveOption):\n objective_functions_tp = ObjectiveList()\n objective_functions_tp.add(objective_functions)\n objective_functions = objective_functions_tp\n elif not isinstance(objective_functions, ObjectiveList):\n raise RuntimeError(\"objective_functions should be built from an ObjectiveOption or ObjectiveList\")\n\n if isinstance(constraints, ConstraintOption):\n constraints_tp = ConstraintList()\n constraints_tp.add(constraints)\n constraints = constraints_tp\n elif not isinstance(constraints, ConstraintList):\n raise RuntimeError(\"constraints should be built from an ConstraintOption or ConstraintList\")\n\n if not isinstance(parameters, ParameterList):\n raise RuntimeError(\"parameters should be built from an ParameterList\")\n\n if not isinstance(state_transitions, StateTransitionList):\n raise RuntimeError(\"state_transitions should be built from an StateTransitionList\")\n\n if not isinstance(ode_solver, OdeSolver):\n raise RuntimeError(\"ode_solver should be built an instance of OdeSolver\")\n\n if not isinstance(use_SX, bool):\n raise RuntimeError(\"use_SX should be a bool\")\n\n # Declare optimization variables\n self.J = []\n self.g = []\n self.g_bounds = []\n self.V = []\n self.V_bounds = Bounds(interpolation=InterpolationType.CONSTANT)\n self.V_init = InitialConditions(interpolation=InterpolationType.CONSTANT)\n self.param_to_optimize = {}\n\n # nlp is the core of a phase\n self.nlp = [{} for _ in range(self.nb_phases)]\n self.__add_to_nlp(\"model\", biorbd_model, False)\n self.__add_to_nlp(\"phase_idx\", [i for i in range(self.nb_phases)], False)\n\n # Type of CasADi graph\n if use_SX:\n self.CX = SX\n else:\n self.CX = MX\n\n # Define some aliases\n self.__add_to_nlp(\"ns\", number_shooting_points, False)\n for nlp in self.nlp:\n if nlp[\"ns\"] < 1:\n raise RuntimeError(\"Number of shooting points must be at least 1\")\n self.initial_phase_time = phase_time\n phase_time, initial_time_guess, time_min, time_max = self.__init_phase_time(\n phase_time, objective_functions, constraints\n )\n self.__add_to_nlp(\"tf\", phase_time, False)\n self.__add_to_nlp(\"t0\", [0] + [nlp[\"tf\"] for i, nlp in enumerate(self.nlp) if i != len(self.nlp) - 1], False)\n self.__add_to_nlp(\"dt\", [self.nlp[i][\"tf\"] / max(self.nlp[i][\"ns\"], 1) for i in range(self.nb_phases)], False)\n self.nb_threads = nb_threads\n self.__add_to_nlp(\"nb_threads\", nb_threads, True)\n self.solver_type = Solver.NONE\n self.solver = None\n\n # External forces\n if external_forces != ():\n external_forces = BiorbdInterface.convert_array_to_external_forces(external_forces)\n self.__add_to_nlp(\"external_forces\", external_forces, False)\n\n # Compute problem size\n if all_generalized_mapping is not None:\n if q_mapping is not None or q_dot_mapping is not None or tau_mapping is not None:\n raise RuntimeError(\"all_generalized_mapping and a specified mapping cannot be used alongside\")\n q_mapping = q_dot_mapping = tau_mapping = all_generalized_mapping\n self.__add_to_nlp(\"q_mapping\", q_mapping, q_mapping is None, BidirectionalMapping)\n self.__add_to_nlp(\"q_dot_mapping\", q_dot_mapping, q_dot_mapping is None, BidirectionalMapping)\n self.__add_to_nlp(\"tau_mapping\", tau_mapping, tau_mapping is None, BidirectionalMapping)\n plot_mappings = plot_mappings if plot_mappings is not None else {}\n reshaped_plot_mappings = []\n for i in range(self.nb_phases):\n reshaped_plot_mappings.append({})\n for key in plot_mappings:\n reshaped_plot_mappings[i][key] = plot_mappings[key][i]\n self.__add_to_nlp(\"plot_mappings\", reshaped_plot_mappings, False)\n\n # Prepare the parameters to optimize\n self.state_transitions = []\n if len(parameters) > 0:\n self.update_parameters(parameters)\n\n # Declare the time to optimize\n self.__define_variable_time(initial_time_guess, time_min, time_max)\n\n # Prepare the dynamics of the program\n self.__add_to_nlp(\"dynamics_type\", dynamics_type, False)\n self.__add_to_nlp(\"ode_solver\", ode_solver, True)\n self.__add_to_nlp(\"control_type\", control_type, True)\n for i in range(self.nb_phases):\n self.__initialize_nlp(self.nlp[i])\n Problem.initialize(self, self.nlp[i])\n\n # Prepare path constraints\n self.__add_to_nlp(\"X_bounds\", X_bounds, False)\n self.__add_to_nlp(\"U_bounds\", U_bounds, False)\n for i in range(self.nb_phases):\n self.nlp[i][\"X_bounds\"].check_and_adjust_dimensions(self.nlp[i][\"nx\"], self.nlp[i][\"ns\"])\n if self.nlp[i][\"control_type\"] == ControlType.CONSTANT:\n self.nlp[i][\"U_bounds\"].check_and_adjust_dimensions(self.nlp[i][\"nu\"], self.nlp[i][\"ns\"] - 1)\n elif self.nlp[i][\"control_type\"] == ControlType.LINEAR_CONTINUOUS:\n self.nlp[i][\"U_bounds\"].check_and_adjust_dimensions(self.nlp[i][\"nu\"], self.nlp[i][\"ns\"])\n else:\n raise NotImplementedError(f\"Plotting {self.nlp[i]['control_type']} is not implemented yet\")\n\n # Prepare initial guesses\n self.__add_to_nlp(\"X_init\", X_init, False)\n self.__add_to_nlp(\"U_init\", U_init, False)\n for i in range(self.nb_phases):\n self.nlp[i][\"X_init\"].check_and_adjust_dimensions(self.nlp[i][\"nx\"], self.nlp[i][\"ns\"])\n if self.nlp[i][\"control_type\"] == ControlType.CONSTANT:\n self.nlp[i][\"U_init\"].check_and_adjust_dimensions(self.nlp[i][\"nu\"], self.nlp[i][\"ns\"] - 1)\n elif self.nlp[i][\"control_type\"] == ControlType.LINEAR_CONTINUOUS:\n self.nlp[i][\"U_init\"].check_and_adjust_dimensions(self.nlp[i][\"nu\"], self.nlp[i][\"ns\"])\n else:\n raise NotImplementedError(f\"Plotting {self.nlp[i]['control_type']} is not implemented yet\")\n\n # Variables and constraint for the optimization program\n for i in range(self.nb_phases):\n self.__define_multiple_shooting_nodes_per_phase(self.nlp[i], i)\n\n # Define dynamic problem\n self.__add_to_nlp(\n \"nb_integration_steps\", nb_integration_steps, True\n ) # Number of steps of integration (for now only RK4 steps are implemented)\n for i in range(self.nb_phases):\n if self.nlp[0][\"nx\"] != self.nlp[i][\"nx\"] or self.nlp[0][\"nu\"] != self.nlp[i][\"nu\"]:\n raise RuntimeError(\"Dynamics with different nx or nu is not supported yet\")\n self.__prepare_dynamics(self.nlp[i])\n\n # Prepare phase transitions (Reminder, it is important that parameters are declared\n # before, otherwise they will erase the state_transitions)\n self.state_transitions = StateTransitionFunctions.prepare_state_transitions(self, state_transitions)\n\n # Inner- and inter-phase continuity\n ContinuityFunctions.continuity(self)\n\n # Prepare constraints\n self.update_constraints(constraints)\n\n # Prepare objectives\n self.update_objectives(objective_functions)","def load_model(app: FastAPI) -> None:\n\n logging.info(\"Starting up the application\")\n model_path = DATA_MODEL_PATH\n\n if model_path.exists():\n model = FraudDetection(model_path)\n app.state.model = model\n logging.info(f\"Loaded model {model_path}\")\n else:\n app.state.model = FraudDetection()\n logging.warning(f\"No existing model found in {model_path}\")","def test_compute_model(self):\n # Set test tolerances (for infinity norm of transfer function\n # difference)\n tf_abs_tol = 1e-6\n tf_rel_tol = 1e-4\n\n # Set time parameters for discrete-time simulation\n dt = 0.1\n num_time_steps = 1000\n\n # Set size of plant and model. For test, don't reduce the system, just\n # check that it comes back close to the original plant. Also, note that\n # using more than 8 states causes poorly conditioned TF coeffs\n # (https://github.com/scipy/scipy/issues/2980)\n num_states_plant = 8\n num_states_model = num_states_plant\n\n # Loop through different numbers of inputs, numbers of outputs, and\n # sampling intervals\n for num_inputs in [1, 3]:\n for num_outputs in [1, 2]:\n for sample_interval in [1, 2, 4]:\n # Define time steps at which to save data. These will be of\n # the form [0, 1, p, p + 1, 2p, 2p + 1, ...] where p is the\n # sample interval.\n time_steps = make_time_steps(\n num_time_steps, sample_interval)\n # # Create a state space system\n # A_plant, B_plant, C_plant = util.drss(\n # num_states_plant, num_inputs, num_outputs)\n A_plant = util.load_array_text(\n join(self.data_dir, 'A_in%d_out%d.txt') % (\n num_inputs, num_outputs))\n B_plant = util.load_array_text(\n join(self.data_dir, 'B_in%d_out%d.txt') % (\n num_inputs, num_outputs))\n C_plant = util.load_array_text(\n join(self.data_dir, 'C_in%d_out%d.txt') % (\n num_inputs, num_outputs))\n\n # Simulate an impulse response using the state space system.\n # This will generate Markov parameters at all timesteps [0,\n # 1, 2, 3, ...]. Only keep data at the desired time steps,\n # which are separated by a sampling interval (see above\n # comment).\n Markovs = util.impulse(\n A_plant, B_plant, C_plant,\n time_steps[-1] + 1)[time_steps]\n\n # Compute a model using ERA\n my_ERA = era.ERA(verbosity=0)\n A_model, B_model, C_model = my_ERA.compute_model(\n Markovs, num_states_model)\n\n # Save ERA model to disk\n A_path_computed = join(self.out_dir, 'A_computed.txt')\n B_path_computed = join(self.out_dir, 'B_computed.txt')\n C_path_computed = join(self.out_dir, 'C_computed.txt')\n my_ERA.put_model(\n A_path_computed, B_path_computed, C_path_computed)\n\n # Check normalized Markovs\n rtol = 1e-5 # 1e-6\n atol = 1e-5 # 1e-10\n Markovs_model = util.impulse(\n A_model, B_model, C_model,\n time_steps[-1] + 1)[time_steps]\n max_Markov = np.amax(Markovs)\n eigs_plant = np.linalg.eig(A_plant)[0]\n eigs_model = np.linalg.eig(A_model)[0]\n # print 'markovs shape', Markovs.shape\n # print 'max plant eig', np.abs(eigs_plant).max()\n # print 'max model eig', np.abs(eigs_model).max()\n # print 'max plant markov', max_Markov\n # print 'max model markov', np.amax(Markovs_model)\n # print 'markov diffs', (\n # Markovs - Markovs_model).squeeze().max()\n\n '''\n import matplotlib.pyplot as plt\n plt.figure()\n plt.semilogy(np.abs(Markovs).squeeze(), 'b')\n plt.semilogy(np.abs(Markovs_model).squeeze(), 'r--')\n plt.axis(\n [0, time_steps[-1], Markovs.min(), Markovs.max()])\n '''\n\n np.testing.assert_allclose(\n Markovs_model.squeeze(),\n Markovs.squeeze(),\n rtol=rtol, atol=atol)\n\n\n # plt.show()\n '''\n # Use Scipy to check that transfer function of ERA model is\n # close to transfer function of full model. Do so by\n # computing the infinity norm (H_inf) of the difference\n # between the transfer functions. Since Scipy can't handle\n # MIMO transfer functions, loop through each input-output\n # pair individually.\n for input_idx in range(num_inputs):\n for output_idx in range(num_outputs):\n\n # Compute transfer functions\n tf_plant = scipy.signal.StateSpace(\n A_plant, B_plant[:, input_idx:input_idx + 1],\n C_plant[output_idx:output_idx + 1, :],\n 0, dt=dt).to_tf()\n tf_model = scipy.signal.StateSpace(\n A_model,\n B_model[:, input_idx:input_idx + 1],\n C_model[output_idx:output_idx + 1, :],\n 0, dt=dt).to_tf()\n tf_diff = util.sub_transfer_functions(\n tf_plant, tf_model, dt=dt)\n\n # Compute transfer function norms\n tf_plant_inf_norm = util.compute_inf_norm_discrete(\n tf_plant, dt)\n tf_diff_inf_norm = util.compute_inf_norm_discrete(\n tf_diff, dt)\n\n # Test values\n print 'err_frac', (\n tf_diff_inf_norm / tf_plant_inf_norm)\n self.assertTrue(\n tf_diff_inf_norm / tf_plant_inf_norm <\n tf_rel_tol)\n '''\n\n # Also test that saved reduced model mats are equal to those\n # returned in memory\n np.testing.assert_equal(\n util.load_array_text(A_path_computed), A_model)\n np.testing.assert_equal(\n util.load_array_text(B_path_computed), B_model)\n np.testing.assert_equal(\n util.load_array_text(C_path_computed), C_model)","def __init__(self, model_functions, constants):\r\n _debug('Protocol: __init__')\r\n _debug('I never lose. Either I win or learn. – Nelson Mandela')\r\n self.model_functions = model_functions\r\n \r\n self.f0 = model_functions[0]\r\n self.fp = model_functions[1]\r\n self.fm = model_functions[2]\r\n \r\n self.constants = constants\r\n \r\n \r\n \r\n #Variable to be stock for studying the protocol\r\n #Times\r\n self.t_probe_p_s = [] # Times for measuring f0-fp \r\n self.t_probe_m_s = [] # Times for measuring f0-fm \r\n \r\n self.t_pulseSequences_s = [] #This will store the times elapsed in the pulse sequences for an individual iteration\r\n self.t_process_s = [] #This will store the times elapsed in the CPU processing for an individual iteration \r\n self.t_pulseSequences = 0 #Time elapsed in the pulse sequences for an individual iteration\r\n self.t_process = 0 #Time elapsed in the CPU processing for an individual iteration \r\n #Total, accumulated, times\r\n self.t_tot_pulseSequences_s = [] #This will store the total times elapsed for the pulse sequences\r\n self.t_tot_process_s = [] #This will store the total CPU times elapsed for processing the Bayes stuffs\r\n self.t_tot_pulseSequences = 0 #Total times elapsed for the pulse sequences\r\n self.t_tot_process = 0 #Total CPU time elapsed so far for processing the Bayes stuffs\r\n #Stuff on the rates\r\n self.Gp_guess_s = [] #Mean of gamma+ from the posterior\r\n self.Gm_guess_s = [] #Mean of gamma- from the posterior\r\n self.eGp_guess_s = [] #Standard deviation of gamma+ from the posterior\r\n self.eGm_guess_s = [] #Standard deviation of gamma- from the posterior \r\n self.cov_Gp_s = [] #Variance of gamma+ from the posterior\r\n self.cov_Gm_s = [] #Variance of gamma- from the posterior\r\n self.cov_GpGm_s = [] #Covariance of gamma- & gamma- from the posterior \r\n \r\n #Other\r\n self.nb_iteration_s = [] #Stock the number of iteration done so far\r\n self.R_tot_s = [] #Stock Total numbers of readout performed so far\r\n self.R_tot = 0 #Total number of readout for performed so far\r\n \r\n self.iter = 0 #Iterator\r","def test_NMF_parameters(u1_ml100k, pkf):\n\n # The baseline against which to compare.\n algo = NMF(n_factors=1, n_epochs=1, random_state=1)\n rmse_default = cross_validate(algo, u1_ml100k, [\"rmse\"], pkf)[\"test_rmse\"]\n\n # n_factors\n algo = NMF(n_factors=2, n_epochs=1, random_state=1)\n rmse_factors = cross_validate(algo, u1_ml100k, [\"rmse\"], pkf)[\"test_rmse\"]\n assert rmse_default != rmse_factors\n\n # n_epochs\n algo = NMF(n_factors=1, n_epochs=2, random_state=1)\n rmse_n_epochs = cross_validate(algo, u1_ml100k, [\"rmse\"], pkf)[\"test_rmse\"]\n assert rmse_default != rmse_n_epochs\n\n # biased\n algo = NMF(n_factors=1, n_epochs=1, biased=True, random_state=1)\n rmse_biased = cross_validate(algo, u1_ml100k, [\"rmse\"], pkf)[\"test_rmse\"]\n assert rmse_default != rmse_biased\n\n # reg_pu\n algo = NMF(n_factors=1, n_epochs=1, reg_pu=1, random_state=1)\n rmse_reg_pu = cross_validate(algo, u1_ml100k, [\"rmse\"], pkf)[\"test_rmse\"]\n assert rmse_default != rmse_reg_pu\n\n # reg_qi\n algo = NMF(n_factors=1, n_epochs=1, reg_qi=1, random_state=1)\n rmse_reg_qi = cross_validate(algo, u1_ml100k, [\"rmse\"], pkf)[\"test_rmse\"]\n assert rmse_default != rmse_reg_qi\n\n # reg_bu\n algo = NMF(n_factors=1, n_epochs=1, reg_bu=1, biased=True, random_state=1)\n rmse_reg_bu = cross_validate(algo, u1_ml100k, [\"rmse\"], pkf)[\"test_rmse\"]\n assert rmse_default != rmse_reg_bu\n\n # reg_bi\n algo = NMF(n_factors=1, n_epochs=1, reg_bi=1, biased=True, random_state=1)\n rmse_reg_bi = cross_validate(algo, u1_ml100k, [\"rmse\"], pkf)[\"test_rmse\"]\n assert rmse_default != rmse_reg_bi\n\n # lr_bu\n algo = NMF(n_factors=1, n_epochs=1, lr_bu=1, biased=True, random_state=1)\n rmse_lr_bu = cross_validate(algo, u1_ml100k, [\"rmse\"], pkf)[\"test_rmse\"]\n assert rmse_default != rmse_lr_bu\n\n # lr_bi\n algo = NMF(n_factors=1, n_epochs=1, lr_bi=1, biased=True, random_state=1)\n rmse_lr_bi = cross_validate(algo, u1_ml100k, [\"rmse\"], pkf)[\"test_rmse\"]\n assert rmse_default != rmse_lr_bi\n\n # init_low\n algo = NMF(n_factors=1, n_epochs=1, init_low=0.5, random_state=1)\n rmse_init_low = cross_validate(algo, u1_ml100k, [\"rmse\"], pkf)[\"test_rmse\"]\n assert rmse_default != rmse_init_low\n\n # init_low\n with pytest.raises(ValueError):\n algo = NMF(n_factors=1, n_epochs=1, init_low=-1, random_state=1)\n\n # init_high\n algo = NMF(n_factors=1, n_epochs=1, init_high=0.5, random_state=1)\n rmse_init_high = cross_validate(algo, u1_ml100k, [\"rmse\"], pkf)[\"test_rmse\"]\n assert rmse_default != rmse_init_high","def run_models(\n self,\n normal=True,\n interrupt=True,\n run_start=None,\n state_builder=\"acis\",\n hrc=False,\n ):\n if hrc:\n loads = hrc_loads\n else:\n loads = test_loads\n if normal and \"normal\" in loads:\n for load in loads[\"normal\"]:\n self.run_model(\n load_week=load,\n run_start=run_start,\n state_builder=state_builder,\n )\n if interrupt and \"interrupt\" in loads:\n for load in loads[\"interrupt\"]:\n self.run_model(\n load_week=load,\n interrupt=True,\n run_start=run_start,\n state_builder=state_builder,\n )","def entry(self):\n if not os.path.isfile('model'):\n train()\n schedule.every(0.01).seconds.do(predict, self)\n while True:\n schedule.run_pending()","def _doRun(self, model: Model):\n raise Exception(\"Not implemented\")","def initialize_model(model_name, num_classes, feature_extract, verbose=False):\n\n model_ft = None\n\n if model_name == \"resnet\":\n \"\"\" Resnet18\n \"\"\"\n model_ft = models.resnet18(pretrained=True)\n set_parameter_requires_grad(model_ft, feature_extract)\n num_ftrs = model_ft.fc.in_features\n model_ft.fc = nn.Linear(num_ftrs, num_classes)\n\n elif model_name == \"alexnet\":\n \"\"\" Alexnet\n \"\"\"\n model_ft = models.alexnet(pretrained=True)\n set_parameter_requires_grad(model_ft, feature_extract)\n num_ftrs = model_ft.classifier[6].in_features\n model_ft.classifier[6] = nn.Linear(num_ftrs, num_classes)\n\n elif model_name == \"vgg\":\n \"\"\" VGG11_bn\n \"\"\"\n model_ft = models.vgg11_bn(pretrained=True)\n set_parameter_requires_grad(model_ft, feature_extract)\n num_ftrs = model_ft.classifier[6].in_features\n model_ft.classifier[6] = nn.Linear(num_ftrs, num_classes)\n\n elif model_name == \"squeezenet\":\n \"\"\" Squeezenet\n \"\"\"\n with warnings.catch_warnings(): # temporarily suppress warnings about deprecated functions\n warnings.simplefilter(\"ignore\")\n model_ft = models.squeezenet1_0(pretrained=True)\n set_parameter_requires_grad(model_ft, feature_extract)\n model_ft.classifier[1] = nn.Conv2d(512, num_classes, kernel_size=(1, 1), stride=(1, 1))\n model_ft.num_classes = num_classes\n\n elif model_name == \"densenet\":\n \"\"\" Densenet\n \"\"\"\n model_ft = models.densenet121(pretrained=True)\n set_parameter_requires_grad(model_ft, feature_extract)\n num_ftrs = model_ft.classifier.in_features\n model_ft.classifier = nn.Linear(num_ftrs, num_classes)\n\n elif model_name == \"inception\":\n \"\"\" Inception v3\n Be careful, expects (299,299) sized images and has auxiliary output\n \"\"\"\n model_ft = models.inception_v3(pretrained=True)\n set_parameter_requires_grad(model_ft, feature_extract)\n # Handle the auxilary net\n num_ftrs = model_ft.AuxLogits.fc.in_features\n model_ft.AuxLogits.fc = nn.Linear(num_ftrs, num_classes)\n # Handle the primary net\n num_ftrs = model_ft.fc.in_features\n model_ft.fc = nn.Linear(num_ftrs, num_classes)\n\n else: # Unreachable\n exit()\n\n # Gather the parameters to be optimized\n params_to_update = list(filter(lambda p: p.requires_grad, model_ft.parameters()))\n\n # Print model info\n if verbose:\n print()\n print(model_ft)\n print()\n print(\"Params to learn:\")\n for name, param in model_ft.named_parameters():\n if param.requires_grad:\n print('\\t', name)\n\n return model_ft, params_to_update","def train(args):\r\n print('Create generators')\r\n generators = train_valid_test_generators(\r\n valid_proportion=args.valid_proportion,\r\n test_proportion=args.test_proportion,\r\n seed=args.seed,\r\n shape=(args.height, args.width),\r\n batch_size=args.batch_size,\r\n shuffle=True\r\n )\r\n print('Create model')\r\n model = create_mobilenetv2(\r\n input_shape=(args.height, args.width, 3),\r\n alpha=args.alpha,\r\n depth_multiplier=args.depth_multiplier,\r\n l2_reg=args.l2_reg,\r\n seed=args.seed\r\n )\r\n\r\n print('Training freezed model')\r\n freeze_model(model, 'global_max_pooling2d_1')\r\n callbacks = callbacks_factory(\r\n callbacks_list=[\r\n 'early_stopping',\r\n 'tensorboard',\r\n ],\r\n model_mask='mobilenetv2_multiclassification_freezed'\r\n )\r\n model = train_pipeline(\r\n model,\r\n generators['hard_train_generator'],\r\n generators['valid_generator'],\r\n callbacks,\r\n optimizer_lr=args.optimizer_lr,\r\n optimizer_decay=args.optimizer_decay,\r\n epochs=args.epochs\r\n )\r\n\r\n print('Training unfreezed model')\r\n unfreeze_model(model)\r\n callbacks = callbacks_factory(\r\n callbacks_list=[\r\n 'best_model_checkpoint',\r\n 'early_stopping',\r\n 'tensorboard',\r\n 'learning_rate_scheduler'\r\n ],\r\n model_mask='mobilenetv2_multiclassification'\r\n )\r\n model = train_pipeline(\r\n model,\r\n generators['easy_train_generator'],\r\n generators['valid_generator'],\r\n callbacks,\r\n optimizer_lr=args.optimizer_lr,\r\n optimizer_decay=args.optimizer_decay,\r\n epochs=3 * args.epochs\r\n )\r\n\r\n print('Save test evaluation')\r\n results = model.evaluate_generator(generators['test_generator'])\r\n pd.DataFrame({\r\n 'MetricsNames': model.metrics_names,\r\n 'Results': results\r\n }).to_csv(os.path.join('../logs/solution_1_test_generator_evaluation.csv'), index=False)","def validate(model,dataloader,criterions,epoch,plots):\n # switch to evaluate mode\n model.eval()\n\n running_loss = 0.0\n running_oa = 0.0\n running_moa = 0.0\n\n avg_losses={}\n avg_accuracies={}\n avg_moas={}\n for i in range(6):\n avg_losses[i] = AverageMeter()\n avg_accuracies[i] = AverageMeter()\n avg_moas[i] = AverageMeter()\n\n tq_bar = tqdm(enumerate(dataloader),total=len(dataloader),ncols=80,desc='Testing')\n for batch_id, (images, labels_group) in tq_bar:\n # if i>25:\n # break\n if torch.cuda.is_available():\n images = [Variable(image.cuda()) for image in images]\n labels_group = [labels for labels in labels_group]\n else:\n print('Cuda not available')\n images = [Variable(image) for image in images]\n labels_group = [labels for labels in labels_group]\n\n\n batch_losses = []\n batch_accuracies = []\n batch_moas = []\n\n for img, labels in zip(images, labels_group):\n outputs = model(img)\n net_batch_size = outputs[0].size(0)\n if torch.cuda.is_available():\n labels = [Variable(label.cuda()) for label in labels]\n else:\n labels = [Variable(label) for label in labels]\n for i,pair in enumerate(zip(outputs, labels)):\n accuracy = accuracy_dense(pair[0].data, pair[1].data)\n moa,_ = mAP_dense(pair[0].data, pair[1].data)\n batch_losses.append(criterions[i](pair[0], pair[1]))\n batch_accuracies.append(accuracy)\n batch_moas.append(moa)\n\n for i in range(6):\n avg_losses[i].update(batch_losses[i].data[0], net_batch_size)\n avg_accuracies[i].update(batch_accuracies[i], net_batch_size)\n avg_moas[i].update(batch_moas[i], net_batch_size)\n\n ## LOSS COMPUTATION\n # loss_weight = [auto_loss_weight(0,epoch), auto_loss_weight(1,epoch), auto_loss_weight(2,epoch), auto_loss_weight(3,epoch), auto_loss_weight(4,epoch)]\n if epoch < 40:\n loss_weight = [0.1, 0.1, 0.1, 0.1, 0.1, 0.5]\n else:\n loss_weight = [0.5, 0.1, 0.1, 0.1, 0.1, 0.1]\n\n # loss_weight = [1., 0.01, 0.01, 0.01, 0.01, 0.01] # fait converger en OA la HD layer\n loss_weight = [1.,0.7, 0.6, 0.5, 0.1, 0.05, 0.01]\n\n total_batch_loss = 0\n for w, l in zip(loss_weight, batch_losses):\n total_batch_loss += w*l\n\n\n running_loss += total_batch_loss.data[0]\n # running_oa += oa\n # running_hd_moa += hd_moa\n for i in range(6):\n plots.plot(\"Total loss (running)\", \"val \"+str(i), epoch*len(dataloader)+batch_id+1, avg_losses[i].val)\n plots.plot(\"OA (running)\", \"val \"+str(i), epoch*len(dataloader)+batch_id+1, avg_accuracies[i].val)\n plots.plot(\"mOA (running)\", \"val \"+str(i), epoch*len(dataloader)+batch_id+1, avg_moas[i].val)\n for i in range(6):\n plots.plot(\"Total loss (final mean of epoch)\", \"val \"+str(i), epoch+1, avg_losses[i].val)\n plots.plot(\"OA (final mean of epoch)\", \"val \"+str(i), epoch+1, avg_accuracies[i].val)\n plots.plot(\"mOA (final mean of epoch)\", \"val \"+str(i), epoch+1, avg_moas[i].val)","def run(self) -> None:\n self.model = self.trainer.train_model(self.model, self.data)","def test_class():\n riskfree = .03\n maturity = 30/365\n moneyness = np.linspace(-.04, .04, 10)\n premium = np.ones_like(moneyness) * .05\n call = True\n data = {'riskfree': riskfree, 'maturity': maturity,\n 'moneyness': moneyness, 'call': call, 'premium': premium}\n\n sigma = .13\n bsm = BSmodel(sigma, data)\n\n weights = [.63]\n means = [-.01, .09]\n stds = [.16, .05]\n param = weights + means + stds\n mbs = MBSmodel(param, data)\n\n param_a, param_p, param_c = 4, 1.5, -.05\n gb2 = GB2model([param_a, param_p, param_c], data)\n print(gb2.get_pnames())\n\n plt.figure()\n for model in [bsm, mbs, gb2]:\n plt.plot(moneyness, model.density(moneyness), label=model.get_name())\n plt.legend()\n plt.show()\n\n plt.figure()\n for model in [bsm, mbs, gb2]:\n plt.plot(moneyness, model.premium(), label=model.get_name())\n plt.legend()\n plt.show()\n\n plt.figure()\n for model in [bsm, mbs, gb2]:\n plt.plot(moneyness, model.impvol(), label=model.get_name())\n plt.legend()\n plt.show()\n\n print('BS objective function = %.4f' % bsm.objective(sigma))\n print('GB2 objective function = %.4f'\n % gb2.objective([param_a, param_p, param_c]))","def setup(self):\n print(\"setup\")\n \n self.modelToUse = 1\n if self.train:\n print(\"train\")\n else:\n print(\"no train\")\n \n device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n self.envSize = 17\n \n #init model\n if self.train or not os.path.isfile(\"my-saved-model.pt\"):\n self.logger.info(\"Setting up model from scratch.\")\n if self.modelToUse == 0:\n self.policy_net = Model_global_view(self.envSize, self.envSize, 6).to(device)\n self.model = Model_global_view(self.envSize, self.envSize, 6).to(device)\n elif self.modelToUse == 1:\n self.policy_net = Model_local_view(self.envSize, self.envSize, 6).to(device)\n self.model = Model_local_view(self.envSize, self.envSize, 6).to(device)\n else:\n self.policy_net = Model_combined_view(self.envSize, self.envSize, 6).to(device)\n self.model = Model_combined_view(self.envSize, self.envSize, 6).to(device)\n self.model.load_state_dict(self.policy_net.state_dict())\n self.model.eval()\n else:\n self.logger.info(\"Loading model from saved state.\")\n with open(\"my-saved-model.pt\", \"rb\") as file:\n if self.modelToUse == 0:\n self.model = Model_global_view(self.envSize, self.envSize, 6)\n elif self.modelToUse == 1:\n self.model = Model_local_view(self.envSize, self.envSize, 6)\n else:\n self.model = Model_combined_view(self.envSize, self.envSize, 6)\n if torch.cuda.is_available():\n self.model.load_state_dict(torch.load(file))\n self.model.to(device)\n else:\n self.model.load_state_dict(torch.load(file, map_location=device))","def run(self, model, verbose=2, device='cpu', dtype=torch.float32):\n old_verbose = self.verbose\n self.verbose = verbose\n\n state = torchbearer.State()\n state.update({torchbearer.MODEL: model, torchbearer.DEVICE: device, torchbearer.DATA_TYPE: dtype})\n self.process(state)\n self.verbose = old_verbose","def _run(self):\n\n self.init_simulation()\n\n # Inject ourselves into the core\n self.simthread.inject()\n\n # register steppables in queue\n for _ in range(len(self._steppable_queue)):\n steppable, frequency = self._steppable_queue.pop(0)\n if not isinstance(steppable, SteppablePy):\n steppable = steppable(frequency=frequency)\n CompuCellSetup.register_steppable(steppable=steppable)\n for _ in range(len(self._corespecs_queue)):\n CompuCellSetup.register_specs(self._corespecs_queue.pop(0))\n\n # Load model specs from file if specified\n if self.cc3d_sim_fname is not None:\n assert os.path.isfile(self.cc3d_sim_fname), f\"Could not find simulation file: {self.cc3d_sim_fname}\"\n rollback_importer = RollbackImporter()\n CompuCellSetup.run_cc3d_project(self.cc3d_sim_fname)\n rollback_importer.uninstall()\n\n # Call startup if loaded simulation file didn't already do it\n if CompuCellSetup.persistent_globals.simulator is None:\n CompuCellSetup.run()","def test_sim(self):\n nxfe = 4\n ipopt = get_solver(\"ipopt\")\n\n m_steady = self.make_steady_model(nfe=nxfe)\n self.fix_model_inlets(m_steady, inlet_pressure=50.0 * pyo.units.bar)\n m_steady.fs.compressor.boost_pressure[:].fix(7.0 * pyo.units.bar)\n ipopt.solve(m_steady, tee=True)\n time_steady = m_steady.fs.time\n scalar_data = self.get_scalar_data_from_model(m_steady, time_steady)\n initial_data = self.get_data_from_model_at_time(m_steady, time_steady)\n\n m = pyo.ConcreteModel()\n default = {\n \"dynamic\": True,\n \"time_set\": [0.0, 20.0],\n \"time_units\": pyo.units.hr,\n }\n m.fs = idaes.FlowsheetBlock(**default)\n m.fs.properties = NaturalGasParameterBlock()\n pipeline_config = {\n \"property_package\": m.fs.properties,\n \"finite_elements\": nxfe,\n }\n m.fs.pipeline = GasPipeline(**pipeline_config)\n pipeline = m.fs.pipeline\n compressor_config = {\"property_package\": m.fs.properties}\n m.fs.compressor = Compressor(**compressor_config)\n compressor = m.fs.compressor\n m._compressor_to_pipeline = Arc(\n ports=(compressor.outlet_port, pipeline.inlet_port),\n )\n expand_arcs = pyo.TransformationFactory(\"network.expand_arcs\")\n expand_arcs.apply_to(m)\n\n cv = m.fs.pipeline.control_volume\n assert_units_consistent(m)\n\n disc = pyo.TransformationFactory(\"dae.finite_difference\")\n ntfe = 20\n disc.apply_to(m, nfe=ntfe, wrt=m.fs.time, scheme=\"BACKWARD\")\n\n time = m.fs.time\n t0 = m.fs.time.first()\n x0 = cv.length_domain.first()\n xf = cv.length_domain.last()\n j = next(iter(m.fs.properties.component_list))\n\n # Fix geometry variables\n m.fs.pipeline.diameter.fix(0.92 * pyo.units.m)\n cv.length.fix(300.0 * pyo.units.km)\n\n # Fix boost pressure\n compressor.boost_pressure[:].fix()\n\n # Inlets to the compressor are fixed, except for flow, where\n # the outlet is fixed.\n state = compressor.inlet_state\n state[:].pressure.fix()\n state[:].mole_frac_comp[j].fix()\n state[:].temperature.fix()\n cv.flow_mass[:, xf].fix()\n\n # Fix initial conditions. Here, pressure and volume for all\n # non-specified points.\n for x in cv.length_domain:\n if x != x0:\n cv.pressure[t0, x].fix()\n if x != xf:\n cv.flow_mass[t0, x].fix()\n\n # I want to deactivate differential equations at (t0, xf)\n # Material balance already doesn't exist here.\n cv.momentum_balance[t0, xf].deactivate()\n\n self.assertEqual(degrees_of_freedom(m), 0)\n\n # Load initial steady state into model at all time points.\n for name, val in initial_data.items():\n var = m.find_component(name)\n for t in time:\n var[t].set_value(val)\n # Load scalar data from initial steady state\n # (initialize area, basically)\n for name, val in scalar_data.items():\n var = m.find_component(name)\n var.set_value(val)\n\n cv.material_accumulation[...].set_value(0.0)\n cv.flow_mass_dt[...].set_value(0.0)\n\n for con in large_residuals_set(m):\n resid = pyo.value(con.body - con.upper)\n print(resid, con.name)\n ipopt.solve(m, tee=True)\n\n # Load input sequence into model\n sample_points = [4.0, 20.0]\n input_name = \"fs.pipeline.control_volume.flow_mass[*,1.0]\"\n nominal_density = 0.72\n val = 12.0 * 1e6 / 24 * nominal_density # 12 (1e6 SCM)/day\n input_series_data = (\n sample_points,\n {input_name: [val, val]},\n )\n input_interval_data = interval_data_from_time_series(input_series_data)\n load_inputs_into_model(m, time, input_interval_data)\n # Solve with loaded inputs\n res = ipopt.solve(m, tee=True)\n self.assertIs(\n res.solver.termination_condition,\n pyo.TerminationCondition.optimal,\n )\n\n # These predicted values come from a simulation of a single pipeline\n # model from the Pyomo DAE example. flow_mass has been converted\n # to kg/hr from (1e4 SCM/hr) by a factor of 0.72*1e4, where\n # 0.72 kg/m**3 is the gas density at standard conditions.\n pred_values = (\n list(time),\n {\n \"fs.pipeline.control_volume.flow_mass[*,%s]\"\n % x0: [\n 3.000e5,\n 2.999e5,\n 2.999e5,\n 2.999e5,\n 3.000e5,\n 3.174e5,\n 3.301e5,\n 3.389e5,\n 3.449e5,\n 3.492e5,\n 3.523e5,\n 3.544e5,\n 3.560e5,\n 3.571e5,\n 3.579e5,\n 3.585e5,\n 3.589e5,\n 3.592e5,\n 3.594e5,\n 3.595e5,\n 3.597e5,\n ],\n \"fs.pipeline.control_volume.pressure[*,%s]\"\n % xf: [\n 50.90,\n 50.90,\n 50.90,\n 50.90,\n 50.90,\n 49.83,\n 49.31,\n 48.95,\n 48.69,\n 48.51,\n 48.38,\n 48.29,\n 48.22,\n 48.17,\n 48.14,\n 48.11,\n 48.10,\n 48.08,\n 48.07,\n 48.07,\n 48.06,\n ],\n \"fs.compressor.power[*]\": [\n 1.590e3,\n 1.590e3,\n 1.590e3,\n 1.590e3,\n 1.590e3,\n 1.682e3,\n 1.750e3,\n 1.796e3,\n 1.828e3,\n 1.851e3,\n 1.867e3,\n 1.878e3,\n 1.887e3,\n 1.892e3,\n 1.897e3,\n 1.900e3,\n 1.902e3,\n 1.904e3,\n 1.905e3,\n 1.906e3,\n 1.906e3,\n ],\n },\n )\n output_names = [\n \"fs.pipeline.control_volume.flow_mass[*,%s]\" % x0,\n \"fs.pipeline.control_volume.pressure[*,%s]\" % xf,\n \"fs.compressor.power[*]\",\n ]\n actual_values = (\n list(time),\n {\n name: [var.value for var in m.find_component(name).values()]\n for name in output_names\n },\n )\n # Note: We fail with a reltol of 0.01, due to flow rate discrepancies\n # in positions 6, 7, 8, and 9. A reltol of 0.02 seems reasonable to me.\n self.assertStructuredAlmostEqual(pred_values, actual_values, reltol=0.02)","def set_up_and_parameterise_model_for_experiment(self):\n self.experiment_unique_steps_to_model = {}\n for op_number, op in enumerate(self.experiment.unique_steps):\n new_model = self.model.new_copy()\n new_parameter_values = self.parameter_values.copy()\n\n if op.type != \"current\":\n # Voltage or power control\n # Create a new model where the current density is now a variable\n # To do so, we replace all instances of the current density in the\n # model with a current density variable, which is obtained from the\n # FunctionControl submodel\n # check which kind of external circuit model we need (differential\n # or algebraic)\n if op.type == \"voltage\":\n submodel_class = pybamm.external_circuit.VoltageFunctionControl\n elif op.type == \"power\":\n submodel_class = pybamm.external_circuit.PowerFunctionControl\n\n # Build the new submodel and update the model with it\n submodel = submodel_class(new_model.param, new_model.options)\n variables = new_model.variables\n submodel.variables = submodel.get_fundamental_variables()\n variables.update(submodel.variables)\n submodel.variables.update(submodel.get_coupled_variables(variables))\n variables.update(submodel.variables)\n submodel.set_rhs(variables)\n submodel.set_algebraic(variables)\n submodel.set_initial_conditions(variables)\n new_model.rhs.update(submodel.rhs)\n new_model.algebraic.update(submodel.algebraic)\n new_model.initial_conditions.update(submodel.initial_conditions)\n\n # Set the \"current function\" to be the variable defined in the submodel\n new_parameter_values[\"Current function [A]\"] = submodel.variables[\n \"Current [A]\"\n ]\n self.update_new_model_events(new_model, op)\n # Update parameter values\n self._original_temperature = new_parameter_values[\"Ambient temperature [K]\"]\n experiment_parameter_values = self.get_experiment_parameter_values(\n op, op_number\n )\n new_parameter_values.update(\n experiment_parameter_values, check_already_exists=False\n )\n parameterised_model = new_parameter_values.process_model(\n new_model, inplace=False\n )\n self.experiment_unique_steps_to_model[repr(op)] = parameterised_model\n\n # Set up rest model if experiment has start times\n if self.experiment.initial_start_time:\n new_model = self.model.new_copy()\n # Update parameter values\n new_parameter_values = self.parameter_values.copy()\n self._original_temperature = new_parameter_values[\"Ambient temperature [K]\"]\n new_parameter_values.update(\n {\"Current function [A]\": 0, \"Ambient temperature [K]\": \"[input]\"},\n check_already_exists=False,\n )\n parameterised_model = new_parameter_values.process_model(\n new_model, inplace=False\n )\n self.experiment_unique_steps_to_model[\n \"Rest for padding\"\n ] = parameterised_model"],"string":"[\n \"def __set_fmu__(self, fmu_file, result_handler, solver, atol, rtol, verbose):\\n if self.fmu is None:\\n \\n # TODO:\\n # See what can be done in catching the exception/propagating it\\n self.fmu = pyfmi.load_fmu(fmu_file)\\n \\n # Get the options for the simulation\\n self.opts = self.fmu.simulate_options()\\n \\n # Define the simulation options\\n self.set_simulation_options(result_handler, solver, atol, rtol, verbose)\\n \\n # Define the standard value for the result file\\n self.set_result_file(None)\\n \\n # set the number of states\\n self.N_STATES = len(self.get_state())\\n \\n # get the value references of the state variables\\n self.stateValueReferences = self.fmu.get_state_value_references()\\n \\n # Properties of the FMU\\n self.name = str(self.fmu.get_name())\\n self.author = str(self.fmu.get_author())\\n self.description = str(self.fmu.get_description())\\n self.fmu_type = str(self.fmu.__class__.__name__)\\n self.version = str(self.fmu.version)\\n self.guid = str(self.fmu.get_guid())\\n self.tool = str(self.fmu.get_generation_tool())\\n [Ncont, Nevt] = self.fmu.get_ode_sizes()\\n self.numStates = \\\"( \\\"+str(Ncont)+\\\" , \\\"+str(Nevt)+\\\" )\\\"\\n \\n # prepare the list of inputs and outputs\\n self.__set_inputs__()\\n self.__set_outputs__()\\n \\n else:\\n logger.warn(\\\"The FMU has already been assigned to this model\\\")\",\n \"def __init__(\\n self,\\n model_filepath: str,\\n user_validation: bool = True,\\n ):\\n\\n # ensure model filepath is balid, and save as att if it is\\n assert model_filepath.endswith(\\\".fmu\\\"), \\\"Provided filepath is not an FMU file: '{}'\\\".format(model_filepath)\\n self.model_filepath = model_filepath\\n # config file with config_params, inputs, outputs\\n self.sim_config_filepath = SIM_CONFIG_NAME_f(self.model_filepath)\\n\\n # read the model description\\n self.model_description = read_model_description(model_filepath)\\n error_log = \\\"Provided model ({}) doesn't have modelVariables in XLS description file\\\".format(model_filepath)\\n assert len(self.model_description.modelVariables) > 0, error_log\\n\\n # correct non-alphanumeric tags.\\n # note, it doesn't suppose any problem, since interaction with sim uses indices, not names.\\n self._clean_non_alphanumeric_chars()\\n\\n\\n # collect the value references (indices)\\n # collect the value types (Real, Integer or Enumeration)\\n # collect the variables to be initialized and the value to do so at\\n self.vars_to_idx = {}\\n self.vars_to_type_f = {}\\n self.vars_to_ini_vals = {}\\n for variable in self.model_description.modelVariables:\\n # extract key attributes per variable\\n var_idx = variable.valueReference #, variable.causality\\n var_name = variable.name\\n var_type = variable.type\\n var_start = variable.start\\n \\n # collect type reference\\n if var_type == \\\"Real\\\":\\n self.vars_to_type_f[var_name] = float\\n elif var_type == \\\"Integer\\\":\\n self.vars_to_type_f[var_name] = int\\n else:\\n # [TODO] Integrate variables of type \\\"Enumeration\\\". How do we cast? Define a function for \\\"self.vars_to_type_f\\\".\\n # [TODO] Integrate variables of type string (need to find correct var_type tag first).\\n # [TODO] Integrate variables of type boolean (need to find correct var_type tag first).\\n print(f\\\"Variable '{var_name}' will be skipped. FMU connector cannot currently handle vars of type '{var_type}'.\\\")\\n continue\\n \\n # collect the value references (indices)\\n self.vars_to_idx[var_name] = var_idx\\n\\n # collect the variables to be initialized and the value to do so at\\n if var_start is not None:\\n # cast variable prior to storing\\n self.vars_to_ini_vals[var_name] = self.vars_to_type_f[var_name](var_start)\\n \\n\\n # initialize sim config\\n self.is_model_config_valid = False # Currently unused, since error is raised if model invalid\\n self.sim_config_params = []\\n self.sim_inputs = []\\n self.sim_outputs = []\\n self.sim_other_vars = []\\n\\n # ---------------------------------------------------------------------\\n # YAML CONFIG --> check for existing config using SIM_CONFIG_NAME_f --> e.g: \\\"{model_name}_conf.yaml\\\"\\n valid_config = self._validate_sim_config()\\n \\n # exit if model is valid, unless validation has been activated\\n if valid_config:\\n\\n # print model config for user reference: config_params, inputs, outputs\\n print(self._get_sim_config_str())\\n\\n if user_validation:\\n # prompt user to manually validate model if selected\\n validation_asserted = input(\\\"Is this configuration correct (y|n)? \\\")\\n\\n if validation_asserted == \\\"y\\\":\\n self.is_model_config_valid = True\\n return\\n \\n # reset config if invalid\\n self.sim_config_params = []\\n self.sim_inputs = []\\n self.sim_outputs = []\\n self.sim_other_vars = []\\n \\n else:\\n # when no validation is selected, we assume the sim config is valid\\n self.is_model_config_valid = True\\n return\\n \\n # ---------------------------------------------------------------------\\n # FMI CONFIG --> if model is invalid we look for attributes within the .fmi model definition\\n valid_config = self._extract_sim_config_from_fmi_std()\\n\\n if valid_config:\\n\\n # print model config for user reference: config_params, inputs, outputs\\n print(self._get_sim_config_str())\\n \\n if user_validation:\\n # prompt user to manually validate model if selected\\n validation_asserted = input(\\\"Is this configuration correct (y|n)? \\\")\\n\\n if validation_asserted == \\\"y\\\":\\n self.is_model_config_valid = True\\n # dump YMAL file to reuse next time the model is loaded\\n self._dump_config_to_yaml_file()\\n return\\n \\n else:\\n # when no validation is selected, we assume the sim config is valid\\n self.is_model_config_valid = True\\n # dump YMAL file to reuse next time the model is loaded\\n self._dump_config_to_yaml_file()\\n return\\n \\n # Dump auxiliary YAML config file if user doesn't assert the provided set\\n # of config_params/inputs/outputs\\n self._dump_config_to_yaml_file(is_aux_yaml = True)\\n \\n # If neither YAML nor FMI model is sufficient raise error\\n error_log = \\\"MODEL DOES NOT HAVE THE CORRECT CONFIG DEFINED NEITHER ON YAML CONFIG FILE \\\"\\n error_log += \\\"NOR FMI MODEL DESCRIPTION. A YAML FILE HAS BEEN CREATED FOR YOU TO MODIFY. \\\"\\n error_log += \\\"THE SIM HAS BEEN FORCED TO EXIT, BUT FEEL FREE TO RERUN ONCE SET-UP IS COMPLETED.\\\"\\n raise Exception(error_log)\",\n \"def __init__(self, fmu_file = None, result_handler = None, solver = None, atol = 1e-6, rtol = 1e-4, verbose = None, offset = None):\\n \\n # Reference to the FMU, that will be loaded using pyfmi\\n self.fmu = None\\n self.fmu_file = fmu_file\\n # List of parameters\\n self.parameters = []\\n # List of state variables\\n self.variables = []\\n # List of inputs\\n self.inputs = []\\n # List of outputs\\n self.outputs = []\\n \\n # Initialize the properties of the FMU\\n self.name = \\\"\\\"\\n self.author = \\\"\\\"\\n self.description = \\\"\\\"\\n self.fmu_type = \\\"\\\"\\n self.version = \\\"\\\"\\n self.guid = \\\"\\\"\\n self.tool = \\\"\\\"\\n self.numStates = \\\"\\\"\\n \\n # Number of maximum tries for a simulation to be successfully run\\n self.SIMULATION_TRIES = 4\\n \\n # Empty dictionary that will contain the simulation options\\n self.opts = {}\\n \\n # Set the number of states\\n self.N_STATES = 0\\n \\n # Set the simulation date time offset\\n self.offset = offset\\n \\n # An array that contains the value references for every state variable\\n self.stateValueReferences = []\\n \\n # See what can be done in catching the exception/propagating it\\n if fmu_file is not None:\\n self.__set_fmu__(fmu_file, result_handler, solver, atol, rtol, verbose)\",\n \"def run(self):\\n msg = sfmt.format(\\\"Run test\\\", self.name)\\n print(msg)\\n\\n # Set nam as namefile name without path\\n nam = None\\n\\n # run mf6 models\\n exe = str(self.targets[\\\"mf6\\\"].absolute())\\n msg = sfmt.format(\\\"using executable\\\", exe)\\n print(msg)\\n\\n if self.parallel:\\n print(\\\"running parallel on\\\", self.ncpus, \\\"processes\\\")\\n try:\\n success, buff = self.run_parallel(\\n exe,\\n )\\n except Exception as exc: \\n msg = sfmt.format(\\\"MODFLOW 6 run\\\", self.name)\\n print(msg)\\n print(exc)\\n success = False\\n else:\\n try:\\n success, buff = flopy.run_model(\\n exe,\\n nam,\\n model_ws=self.simpath,\\n silent=False,\\n report=True,\\n )\\n msg = sfmt.format(\\\"MODFLOW 6 run\\\", self.name)\\n if success:\\n print(msg)\\n else:\\n print(msg)\\n except:\\n msg = sfmt.format(\\\"MODFLOW 6 run\\\", self.name)\\n print(msg)\\n success = False\\n\\n # set failure based on success and require_failure setting\\n if self.require_failure is None:\\n msg = \\\"MODFLOW 6 model did not terminate normally\\\"\\n if success:\\n failure = False\\n else:\\n failure = True\\n else:\\n if self.require_failure:\\n msg = \\\"MODFLOW 6 model should have failed\\\"\\n if not success:\\n failure = False\\n else:\\n failure = True\\n else:\\n msg = \\\"MODFLOW 6 model should not have failed\\\"\\n if success:\\n failure = False\\n else:\\n failure = True\\n\\n # print end of mfsim.lst to the screen\\n if failure and self.is_CI:\\n fpth = os.path.join(self.simpath, \\\"mfsim.lst\\\")\\n msg = self._get_mfsim_listing(fpth) + msg\\n\\n # test for failure\\n assert not failure, msg\\n\\n self.nam_cmp = None\\n if success:\\n if self.action is not None:\\n if self.action.lower() == \\\"compare\\\":\\n msg = sfmt.format(\\\"Comparison files\\\", self.name)\\n print(msg)\\n else:\\n cpth = os.path.join(self.simpath, self.action)\\n key = self.action.lower().replace(\\\".cmp\\\", \\\"\\\")\\n exe = str(self.targets[key].absolute())\\n msg = sfmt.format(\\\"comparison executable\\\", exe)\\n print(msg)\\n if (\\n \\\"mf6\\\" in key\\n or \\\"libmf6\\\" in key\\n or \\\"mf6_regression\\\" in key\\n ):\\n nam = None\\n else:\\n npth = get_namefiles(cpth)[0]\\n nam = os.path.basename(npth)\\n self.nam_cmp = nam\\n try:\\n if self.api_func is None:\\n success_cmp, buff = flopy.run_model(\\n exe,\\n nam,\\n model_ws=cpth,\\n silent=False,\\n report=True,\\n )\\n else:\\n success_cmp, buff = self.api_func(\\n exe, self.idxsim, model_ws=cpth\\n )\\n msg = sfmt.format(\\n \\\"Comparison run\\\", self.name + \\\"/\\\" + key\\n )\\n print(msg)\\n\\n # print end of mfsim.lst to the screen\\n if \\\"mf6\\\" in key:\\n if not success and self.is_CI:\\n fpth = os.path.join(cpth, \\\"mfsim.lst\\\")\\n print(self._get_mfsim_listing(fpth))\\n\\n except:\\n success_cmp = False\\n msg = sfmt.format(\\n \\\"Comparison run\\\", self.name + \\\"/\\\" + key\\n )\\n print(msg)\\n\\n assert success_cmp, \\\"Unsuccessful comparison run\\\"\\n\\n return\",\n \"def _simulate(ctx, gui):\\n ctx.env['SFFUnits'] = load_SFFUnits(ctx)\\n\\n \\\"\\\"\\\"\\n Creates the directory path and nodes in the build directory.\\n Creates a taskgen from each other library in units_hdl\\n \\\"\\\"\\\"\\n\\n top = ctx.env['SFFUnits'].getunit(ctx.env.top_level)\\n\\n for u in top.synu_deps + top.simu_deps:\\n lib = u.script.parent.get_bld().make_node('work_vlib')\\n lib.mkdir()\\n u.b['vlib'] = lib\\n\\n if u.use('use'):\\n tsk = ModelsimTask(\\n name=u.name,\\n target=lib,\\n source=u.use('src'),\\n includes=u.use('includes'),\\n after=u.use('use'),\\n output=lib,\\n scan=SFF_verilog_scan,\\n env=ctx.env)\\n ctx.add_to_group(tsk)\\n else:\\n tsk = ModelsimTask(\\n name=u.name,\\n target=lib,\\n source=u.use('src'),\\n output=lib,\\n includes=u.use('includes'),\\n scan=SFF_verilog_scan,\\n env=ctx.env)\\n ctx.add_to_group(tsk)\\n\\n\\n \\\"\\\"\\\"\\n Create the testbench taskgen last as it is always at the top dep\\n \\\"\\\"\\\"\\n ctx.add_group()\\n tb_lib = top.script.parent.get_bld().make_node('work_vlib')\\n tb_lib.mkdir()\\n top.b['tbvlib'] = tb_lib\\n\\n tsk = ModelsimTask(\\n name=top.use('tb'),\\n target=tb_lib,\\n source=top.use('tb_src'),\\n output=tb_lib,\\n includes=top.use('tb_includes'),\\n after=ctx.env.top_level,\\n scan=SFF_verilog_scan,\\n env=ctx.env )\\n ctx.add_to_group(tsk)\\n ctx.add_group()\\n\\n \\\"\\\"\\\"\\n Run the Modelsim command with gui options provided.\\n \\\"\\\"\\\"\\n ##Run vsim\\n ctx(name='vsim',\\n rule='vsim %s -lib %s %s' % (gui,top.b['tbvlib'], top.use('tb')[0]),\\n always = True)\",\n \"def __init__(\\n self,\\n model,\\n ipakcb=None,\\n intercellt=0,\\n laycon=3,\\n trpy=1.0,\\n hdry=-1e30,\\n iwdflg=0,\\n wetfct=0.1,\\n iwetit=1,\\n ihdwet=0,\\n ikvflag=0,\\n ikcflag=0,\\n tran=1.0,\\n hy=1.0,\\n vcont=1.0,\\n kv=1.0,\\n anglex=0.0,\\n ksat=1.0,\\n sf1=1e-5,\\n sf2=0.15,\\n wetdry=-0.01,\\n extension=\\\"bcf\\\",\\n unitnumber=None,\\n filenames=None,\\n add_package=True,\\n ):\\n msg = (\\n \\\"Model object must be of type flopy.mfusg.MfUsg\\\\n\\\"\\n f\\\"but received type: {type(model)}.\\\"\\n )\\n assert isinstance(model, MfUsg), msg\\n\\n super().__init__(\\n model,\\n ipakcb=ipakcb,\\n intercellt=intercellt,\\n laycon=laycon,\\n trpy=trpy,\\n hdry=hdry,\\n iwdflg=iwdflg,\\n wetfct=wetfct,\\n iwetit=iwetit,\\n ihdwet=ihdwet,\\n tran=tran,\\n hy=hy,\\n vcont=vcont,\\n sf1=sf1,\\n sf2=sf2,\\n wetdry=wetdry,\\n extension=extension,\\n unitnumber=unitnumber,\\n filenames=filenames,\\n add_package=False,\\n )\\n\\n dis = model.get_package(\\\"DIS\\\")\\n if dis is None:\\n dis = model.get_package(\\\"DISU\\\")\\n structured = self.parent.structured\\n\\n nrow, ncol, nlay, _ = self.parent.nrow_ncol_nlay_nper\\n\\n self.ikvflag = ikvflag\\n self.ikcflag = ikcflag\\n self.kv = kv\\n self.anglex = anglex\\n self.ksat = ksat\\n\\n if not structured:\\n njag = dis.njag\\n self.anglex = Util2d(\\n model,\\n (njag,),\\n np.float32,\\n anglex,\\n \\\"anglex\\\",\\n locat=self.unit_number[0],\\n )\\n\\n # item 1\\n self.kv = Util3d(\\n model,\\n (nlay, nrow, ncol),\\n np.float32,\\n kv,\\n \\\"Vertical Hydraulic Conductivity\\\",\\n locat=self.unit_number[0],\\n )\\n if not structured:\\n self.ksat = Util3d(\\n model,\\n (njag,),\\n np.float32,\\n ksat,\\n \\\"ksat\\\",\\n locat=self.unit_number[0],\\n )\\n\\n if add_package:\\n self.parent.add_package(self)\",\n \"def main():\\n parser = argparse.ArgumentParser(description=\\\"Generate standard form system FMUs through commandline\\\")\\n parser.add_argument(\\\"--name\\\", default=\\\"qmodel\\\", type=str, help=\\\"Target FMU identifier\\\")\\n parser.add_argument(\\\"--dir\\\", default=os.getcwd(), type=str, help=\\\"Target FMU path\\\")\\n parser.add_argument(\\\"-v\\\", \\\"--verbose\\\", help=\\\"Verbose output\\\", action=\\\"store_true\\\")\\n parser.add_argument(\\\"-n\\\", \\\"--dry-run\\\", help=\\\"Only print system information, use with -v.\\\", action=\\\"store_true\\\")\\n\\n subparsers = parser.add_subparsers(title=\\\"System form\\\", dest=\\\"subcmd\\\")\\n ss = subparsers.add_parser(\\\"ss\\\", help=\\\"State space model: A, B, C, D\\\",\\n description=\\\"Define ABCD matrices using string. The string is interpreted as a matrix with commas or spaces separating columns, and semicolons separating rows. e.g. '1,2;3,4' -> 2x2 matrix\\\")\\n ss.add_argument(\\\"-A\\\", required=False, type=str, help=\\\"A matrix\\\")\\n ss.add_argument(\\\"-B\\\", required=False, type=str, help=\\\"B matrix\\\")\\n ss.add_argument(\\\"-C\\\", required=False, type=str, help=\\\"C matrix\\\")\\n ss.add_argument(\\\"-D\\\", required=False, type=str, help=\\\"D matrix\\\")\\n ss.add_argument(\\\"-x0\\\", required=False, type=str, help=\\\"Init state values, zero vector if empty\\\")\\n ss.add_argument(\\\"-u0\\\", required=False, type=str, help=\\\"Init input values, zero vector if empty\\\")\\n\\n # tf = subparsers.add_parser(\\\"tf\\\", help=\\\"Transfer function (WIP)\\\")\\n # tf.add_argument(\\\"-n\\\", default=\\\"1,0\\\", type=str, help=\\\"Numerator\\\")\\n # tf.add_argument(\\\"-d\\\", default=\\\"1\\\", type=str, help=\\\"Denominator\\\")\\n\\n try:\\n args = parser.parse_args()\\n if args.subcmd == \\\"ss\\\":\\n from qfmu.utils import str_to_1d_array, str_to_2d_array\\n A = None if args.A is None or args.A==\\\"\\\" else str_to_2d_array(args.A)\\n B = None if args.B is None or args.B==\\\"\\\" else str_to_2d_array(args.B)\\n C = None if args.C is None or args.C==\\\"\\\" else str_to_2d_array(args.C)\\n D = None if args.D is None or args.D==\\\"\\\" else str_to_2d_array(args.D)\\n x0 = None if args.x0 is None or args.x0==\\\"\\\" else str_to_1d_array(args.x0)\\n u0 = None if args.u0 is None or args.u0==\\\"\\\" else str_to_1d_array(args.u0)\\n ss = StateSpace(A, B, C, D, x0, u0)\\n m = Lti(ss, identifier=args.name)\\n if args.verbose:\\n logging.basicConfig(level=logging.INFO)\\n if args.dry_run:\\n print(f\\\"Target FMU:\\\\n{os.path.join(os.path.abspath(args.dir), args.name)}.fmu\\\")\\n print(f\\\"System info:\\\\n{ss}\\\")\\n else:\\n m.buildFMU(args.dir)\\n else:\\n raise Exception(\\\"Unknown system form\\\")\\n except Exception as ex:\\n logging.error(ex)\\n return -1\\n\\n return 0\",\n \"def setup(self):\\n log.debug('Initializing Mex...')\\n if (self.options.user and self.options.pwd and self.options.root):\\n self.bqSession = BQSession().init_local( self.options.user, self.options.pwd, bisque_root=self.options.root)\\n self.options.mexURL = self.bqSession.mex.uri\\n\\n elif (self.options.mexURL and self.options.token):\\n self.bqSession = BQSession().init_mex(self.options.mexURL, self.options.token)\\n else:\\n return\\n \\n self.mex_parameter_parser(self.bqSession.mex.xmltree)\\n \\n #finds and opens model file\\n self.bqSession.update_mex('Initializing Classification Model...')\\n log.debug('Forming Feature Requests...')\\n\\n \\n #no options currently\\n #combo = mex_xml.xpath('tag[@name=\\\"plant_part\\\"]/@value')[0]\\n combo = 'bush'\\n if combo:\\n if combo=='bush':\\n MODEL_QUERY['tag_query'] = '\\\"module_identifier\\\":\\\"Botanicam\\\" AND \\\"Classification Method\\\":\\\"Bush Descriptor\\\"'\\n elif combo=='leaf':\\n MODEL_QUERY['tag_query'] = '\\\"module_identifier\\\":\\\"Botanicam\\\" AND \\\"Classification Method\\\":\\\"Leaf Descriptor\\\"'\\n else:\\n raise BotanicamError('The incorrect model type was found -> Model Type: %s'%combo)\\n else:\\n raise BotanicamError('No model type was choosen')\\n \\n query_xml = self.bqSession.fetchxml('/data_service/file', **MODEL_QUERY)\\n\\n self.options.model_url = None\\n if len(query_xml)>0:\\n try:\\n model_url = query_xml[0].attrib['uri']\\n self.options.model_url = model_url\\n log.debug('Fetching Model @ %s' % model_url)\\n self.model_xml = self.bqSession.fetchxml(model_url, view='deep')\\n self.model_path = os.path.join(self.options.stagingPath, 'model')\\n model = self.bqSession.load(model_url)\\n model_url = self.bqSession.service_url('blob_service', path=model.resource_uniq)\\n self.bqSession.fetchblob(model_url, path=self.model_path+'.zip')\\n with zipfile.ZipFile(self.model_path+'.zip') as dirzip:\\n dirzip.extractall(self.model_path)\\n except BQCommError:\\n raise BotanicamError('Model file was not found! Ask admin to set the correct model file')\\n else: #run demo classifier model store in the module\\n raise BotanicamError('No model file was found. Ask your admin to train a new model with \\\\\\n the Botanicam Trainer.')\\n\\n self.bqSession.update_mex('Initialized...')\\n log.debug('Botanicam: image URL: %s, mexURL: %s, stagingPath: %s, token: %s' % (self.options.image_url, self.options.mexURL, self.options.stagingPath, self.options.token))\",\n \"def run_models(request):\\n job_form_data = request.session['job_form_data']\\n job_wrapper = JobWrapper(job_form_data)\\n job_wrapper.create_data_file()\\n print job_wrapper.job_form_data\\n # Must run emits to generate emis_co2.dat - this step is requried to\\n # run the models and it's a lot simpler to have it run form here than\\n # from a job manager script\\n cmd = \\\"/var/opt/IMOGEN/EMITS/emits\\\"\\n subprocess.call(cmd, shell=True)\\n print \\\"Ran {0} program\\\".format(cmd)\\n # Now submit the models via the job manager\\n jr = DRMAAJobRunner()\\n return jr.queue_job(job_wrapper)\",\n \"def test_models_multiclass(model):\\n atom = ATOMClassifier(X_class2, y_class2, test_size=0.24, random_state=1)\\n atom.run(\\n models=model,\\n metric=\\\"f1_micro\\\",\\n n_calls=2,\\n n_initial_points=1,\\n bo_params={\\\"base_estimator\\\": \\\"rf\\\", \\\"cv\\\": 1},\\n )\\n assert not atom.errors\\n assert hasattr(atom, model)\",\n \"def build_mod_bpmf_model(train, alpha=2, dim=10, std=0.01):\\n n, m = train.shape\\n beta_0 = 1 # scaling factor for lambdas; unclear on its use\\n\\n # Mean value imputation on training data.\\n train = train.copy()\\n nan_mask = np.isnan(train)\\n train[nan_mask] = train[~nan_mask].mean()\\n\\n # We will use separate priors for sigma and correlation matrix.\\n # In order to convert the upper triangular correlation values to a\\n # complete correlation matrix, we need to construct an index matrix:\\n n_elem = dim * (dim - 1) / 2\\n tri_index = np.zeros([dim, dim], dtype=int)\\n tri_index[np.triu_indices(dim, k=1)] = np.arange(n_elem)\\n tri_index[np.triu_indices(dim, k=1)[::-1]] = np.arange(n_elem)\\n\\n logging.info('building the BPMF model')\\n with pm.Model() as bpmf:\\n # Specify user feature matrix\\n sigma_u = pm.Uniform('sigma_u', shape=dim)\\n corr_triangle_u = pm.LKJCorr(\\n 'corr_u', n=1, p=dim,\\n testval=np.random.randn(n_elem) * std)\\n\\n corr_matrix_u = corr_triangle_u[tri_index]\\n corr_matrix_u = t.fill_diagonal(corr_matrix_u, 1)\\n cov_matrix_u = t.diag(sigma_u).dot(corr_matrix_u.dot(t.diag(sigma_u)))\\n lambda_u = t.nlinalg.matrix_inverse(cov_matrix_u)\\n\\n mu_u = pm.Normal(\\n 'mu_u', mu=0, tau=beta_0 * t.diag(lambda_u), shape=dim,\\n testval=np.random.randn(dim) * std)\\n U = pm.MvNormal(\\n 'U', mu=mu_u, tau=lambda_u, shape=(n, dim),\\n testval=np.random.randn(n, dim) * std)\\n\\n # Specify item feature matrix\\n sigma_v = pm.Uniform('sigma_v', shape=dim)\\n corr_triangle_v = pm.LKJCorr(\\n 'corr_v', n=1, p=dim,\\n testval=np.random.randn(n_elem) * std)\\n\\n corr_matrix_v = corr_triangle_v[tri_index]\\n corr_matrix_v = t.fill_diagonal(corr_matrix_v, 1)\\n cov_matrix_v = t.diag(sigma_v).dot(corr_matrix_v.dot(t.diag(sigma_v)))\\n lambda_v = t.nlinalg.matrix_inverse(cov_matrix_v)\\n\\n mu_v = pm.Normal(\\n 'mu_v', mu=0, tau=beta_0 * t.diag(lambda_v), shape=dim,\\n testval=np.random.randn(dim) * std)\\n V = pm.MvNormal(\\n 'V', mu=mu_v, tau=lambda_v, shape=(m, dim),\\n testval=np.random.randn(m, dim) * std)\\n\\n # Specify rating likelihood function\\n R = pm.Normal(\\n 'R', mu=t.dot(U, V.T), tau=alpha * np.ones((n, m)),\\n observed=train)\\n\\n logging.info('done building the BPMF model')\\n return bpmf\",\n \"def model_test(nu, fsigma_T, fsigma_P, models_in, amps_in, params_in, models_fit, label):\\n # Generate fake data with some \\\"true\\\" parameters\\n (D_vec, Ninv) = gen_data(nu, fsigma_T, fsigma_P, models_in, amps_in, params_in)\\n Ninv_sqrt = np.matrix(linalg.sqrtm(Ninv))\\n (dust_params, sync_params, cmb_params) = params_in\\n (dust_amp, sync_amp, cmb_amp) = amps_in\\n \\n # Beam model\\n beam_mat = np.identity(3*len(nu))\\n\\n # Set-up MCMC\\n dust_guess = np.array([1.6, 20.])\\n sync_guess = np.array([-3.])\\n cmb_guess = np.array([])\\n guess = np.concatenate((dust_guess, sync_guess, cmb_guess))\\n #ndim = len(dust_guess) + len(sync_guess) + len(cmb_guess)\\n \\n # Run MCMC sampler on this model\\n t0 = time.time()\\n dust_params_out, sync_params_out, cmb_params_out, samples \\\\\\n = mcmc(guess, nu, D_vec, Ninv, beam_mat, models_fit, label)\\n print \\\"MCMC run in %d sec.\\\" % (time.time() - t0)\\n \\n # Estimate error on recovered CMB amplitudes\\n (F_fg, F_cmb, F) = F_matrix(nu, dust_params_out, sync_params_out, cmb_params_out, models_fit)\\n H = F_fg.T*Ninv*F_fg\\n x_mat = np.linalg.inv(F.T*beam_mat.T*Ninv*beam_mat*F)*F.T*beam_mat.T*Ninv*D_vec # Equation A3\\n \\n U, Lambda, VT = np.linalg.svd(Ninv_sqrt*F_fg, full_matrices=False) # Equation A14\\n \\n print \\\"-\\\"*30\\n print \\\"F_cmb.T\\\", F_cmb.T.shape\\n print \\\"Ninv_sqrt\\\", Ninv_sqrt.shape\\n print \\\"F_cmb\\\", F_cmb.shape\\n print \\\"I\\\", np.identity(U.shape[0]).shape\\n print \\\"U\\\", U.shape\\n print \\\"U.T\\\", U.T.shape\\n print \\\"-\\\"*30\\n \\n \\n \\n N_eff_inv_cmb = F_cmb.T*Ninv_sqrt*(np.matrix(np.identity(U.shape[0])) - U*U.T)*Ninv_sqrt*F_cmb # Equation A16\\n N_eff_cmb = np.linalg.inv(N_eff_inv_cmb)\\n cmb_noise = np.array([N_eff_cmb[0,0], N_eff_cmb[1,1], N_eff_cmb[2,2]])\\n\\n gls_cmb = x_mat[0:3,0]\\n cmb_chisq = (np.matrix(cmb_amp).T - gls_cmb).T*N_eff_inv_cmb*(np.matrix(cmb_amp).T - gls_cmb)\\n \\n # Output triangle plots for dust\\n if label != None:\\n if (models_fit[0] == 'mbb' and models_fit[1] == 'pow'):\\n if (models_in[0] == 'mbb'):\\n fig = corner.corner(samples, truths=[dust_params[0], dust_params[1], sync_params[0]],\\n labels=[r\\\"$\\\\beta_d$\\\", r\\\"$T_d$\\\",r\\\"$\\\\alpha_s$\\\"])\\n else :\\n fig = corner.corner(samples, labels=[r\\\"$\\\\beta_d$\\\", r\\\"$T_d$\\\",r\\\"$\\\\alpha_s$\\\"])\\n else :\\n print 'Error! Not configured for this plot!'\\n exit()\\n fig.savefig('triangle_' + label + '.png')\\n plt.close('all')\\n \\n # Run multinest sampler\\n #multinest(nu, D_vec, Ninv, beam_mat, ndim, models_fit, label)\\n \\n return gls_cmb, cmb_chisq, cmb_noise\",\n \"def main(**kwargs):\\n flowsheet = Flowsheet(name='MB_Model') \\n \\n # Fix variables\\n setInputs(flowsheet) \\n\\n ts = time.time() \\n\\n mb = flowsheet.MB_fuel\\n \\n # Initialize fuel reactor\\n flowsheet.MB_fuel._initialize(outlvl=1,\\n optarg={\\\"tol\\\" : 1e-8,\\n \\\"max_cpu_time\\\" : 600,\\n \\\"print_level\\\" : 5,\\n \\\"halt_on_ampl_error\\\": 'yes'}) \\n \\n # Create a solver\\n opt = SolverFactory('ipopt')\\n opt.options = {'tol': 1e-8,\\n 'linear_solver' : 'ma27',\\n 'bound_push': 1e-8,\\n 'max_cpu_time': 600,\\n 'print_level': 5}\\n \\n results = opt.solve(flowsheet,tee=True,symbolic_solver_labels=False,\\n keepfiles=False)\\n\\n #flowsheet.MB_fuel.Solid_In_M.fix(691.4)\\n #flowsheet.MB_fuel.Gas_In_y['CO2'].fix(0.03999)\\n #flowsheet.MB_fuel.Gas_In_y['H2O'].fix(0.00001)\\n #flowsheet.MB_fuel.Gas_In_y['CH4'].fix(0.96)\\n\\n\\n\\n #results = opt.solve(flowsheet,tee=True,symbolic_solver_labels=False,\\n # keepfiles=False)\\n \\n \\n print(\\\"\\\\n\\\")\\n print(\\\"----------------------------------------------------------\\\")\\n print('Total simulation time: ', value(time.time() - ts), \\\" s\\\")\\n print(\\\"----------------------------------------------------------\\\")\\n\\n \\n # Print some variables \\n #print_summary_fuel_reactor(flowsheet) \\n\\n # Plot some variables \\n #results_plot_fuel_reactor(flowsheet) \\n\\n m = flowsheet.MB_fuel\\n if 'Solid_M' in kwargs:\\n m.Solid_In_M.fix(kwargs['Solid_M'])\\n if 'Solid_T' in kwargs:\\n m.Solid_In_Ts[t].fix(kwargs['Solid_T'])\\n if 'Solid_x' in kwargs:\\n m.Solid_In_x['Fe2O3'].fix(kwargs['Solid_x']['Fe2O3'])\\n m.Solid_In_x['Fe3O4'].fix(kwargs['Solid_x']['Fe3O4'])\\n m.Solid_In_x['Al2O3'].fix(kwargs['Solid_x']['Al2O3'])\\n if 'Gas_F' in kwargs:\\n m.Gas_In_F.fix(kwargs['Gas_F'])\\n if 'Gas_P' in kwargs:\\n m.Gas_In_P.fix(kwargs['Gas_P'])\\n if 'Gas_T' in kwargs:\\n m.Gas_In_T.fix(kwargs['Gas_T'])\\n if 'Gas_y' in kwargs:\\n m.Gas_In_y['CO2'].fix(kwargs['Gas_y']['CO2'])\\n m.Gas_In_y['H2O'].fix(kwargs['Gas_y']['H2O'])\\n m.Gas_In_y['CH4'].fix(kwargs['Gas_y']['CH4'])\\n\\n results = opt.solve(flowsheet, tee=True)\\n\\n with open('ss_fs.txt','w') as f:\\n flowsheet.display(ostream=f)\\n\\n dt_Gflux_CO2 = []\\n dt_Gflux_H2O = []\\n dt_Gflux_CH4 = []\\n dt_Sflux_Fe2O3 = []\\n dt_Sflux_Fe3O4 = []\\n dt_Sflux_Al2O3 = []\\n dt_Ctrans_CO2 = []\\n dt_Ctrans_H2O = []\\n dt_Ctrans_CH4 = []\\n dt_qtrans_Fe2O3 = []\\n dt_qtrans_Fe3O4 = []\\n dt_qtrans_Al2O3 = []\\n dt_Ghflux = []\\n dt_Ts = []\\n dt_TgGS = []\\n dt_TsGS = []\\n dt_vg = []\\n dt_vs = []\\n\\n# for z in mb.z.get_finite_elements():\\n# if z != mb.z.first() and z != mb.z.last():\\n#\\n# dt_Gflux_CO2.append( (mb.Cg[z,'CO2'].value-mb.Cg[prev,'CO2'].value)/\\\\\\n# (mb.G_flux[z,'CO2'].value-mb.G_flux[prev,'CO2'].value) \\\\\\n# *(z-prev)*mb.eps.value*mb.L.value /(z-prev))\\n#\\n# dt_Gflux_H2O.append( (mb.Cg[z,'H2O'].value-mb.Cg[prev,'H2O'].value)/\\\\\\n# (mb.G_flux[z,'H2O'].value-mb.G_flux[prev,'H2O'].value) \\\\\\n# *(z-prev)*mb.eps.value*mb.L.value /(z-prev))\\n#\\n# dt_Gflux_CH4.append( (mb.Cg[z,'CH4'].value-mb.Cg[prev,'CH4'].value)/\\\\\\n# (mb.G_flux[z,'CH4'].value-mb.G_flux[prev,'CH4'].value) \\\\\\n# *(z-prev)*mb.eps.value*mb.L.value /(z-prev))\\n#\\n# dt_Ctrans_CO2.append( (mb.Cg[z,'CO2'].value-mb.Cg[prev,'CO2'].value)/\\\\\\n# (mb.Ctrans[z,'CO2'].value)* \\\\\\n# #-mv.Ctrans[prev,'CO2'].value)*\\\\\\n# mb.eps.value/(1-mb.eps.value) /(z-prev))\\n#\\n# dt_Ctrans_H2O.append( (mb.Cg[z,'H2O'].value-mb.Cg[prev,'H2O'].value)/\\\\\\n# (mb.Ctrans[z,'H2O'].value)* \\\\\\n# #-mv.Ctrans[prev,'H2O'].value)*\\\\\\n# mb.eps.value/(1-mb.eps.value) /(z-prev))\\n#\\n# dt_Ctrans_CH4.append( (mb.Cg[z,'CH4'].value-mb.Cg[prev,'CH4'].value)/\\\\\\n# (mb.Ctrans[z,'CH4'].value)* \\\\\\n# #-mv.Ctrans[prev,'CH4'].value)*\\\\\\n# mb.eps.value/(1-mb.eps.value) /(z-prev))\\n#\\n# dt_Sflux_Fe2O3.append( (mb.q[z,'Fe2O3'].value-mb.q[prev,'Fe2O3'].value)/\\\\\\n# (mb.S_flux[z,'Fe2O3'].value-mb.S_flux[prev,'Fe2O3'].value)*\\\\\\n# (z-prev)/(1-mb.eps.value)*mb.L.value /(z-prev))\\n#\\n# dt_Sflux_Fe3O4.append( (mb.q[z,'Fe3O4'].value-mb.q[prev,'Fe3O4'].value)/\\\\\\n# (mb.S_flux[z,'Fe3O4'].value-mb.S_flux[prev,'Fe3O4'].value)*\\\\\\n# (z-prev)/(1-mb.eps.value)*mb.L.value /(z-prev))\\n#\\n# dt_Sflux_Al2O3.append( (mb.q[z,'Al2O3'].value-mb.q[prev,'Al2O3'].value)/\\\\\\n# (mb.S_flux[z,'Al2O3'].value-mb.S_flux[prev,'Al2O3'].value)*\\\\\\n# (z-prev)/(1-mb.eps.value)*mb.L.value /(z-prev))\\n#\\n# dt_qtrans_Fe2O3.append( (mb.q[z,'Fe2O3'].value-mb.q[prev,'Fe2O3'].value)/\\\\\\n# (mb.qtrans[z,'Fe2O3'].value )/(z-prev)) \\n# #-mb.qtrans[prev,'Fe2O3'].value) )\\n#\\n# dt_qtrans_Fe3O4.append( (mb.q[z,'Fe3O4'].value-mb.q[prev,'Fe3O4'].value)/\\\\\\n# (mb.qtrans[z,'Fe3O4'].value )/(z-prev)) \\n# #-mb.qtrans[prev,'Fe3O4'].value) )\\n#\\n# dt_qtrans_Al2O3.append( (mb.q[z,'Fe3O4'].value-mb.q[prev,'Fe3O4'].value)/\\\\\\n# (mb.qtrans[z,'Fe3O4'].value )/(z-prev)) \\n# #-mb.qtrans[prev,'Fe3O4'].value) )\\n#\\n# dt_Ghflux.append( (mb.Tg[z].value-mb.Tg[prev].value)/\\\\\\n# (mb.Gh_flux[z].value-mb.Gh_flux[prev].value)* (z-prev)* mb.eps.value*\\\\\\n# mb.L.value* mb.rho_vap[z].value* mb.cp_gas[z].value /(z-prev)) \\n#\\n# dt_Ts.append( (z-prev)*(1-mb.eps.value)*mb.L.value/mb.vs.value /(z-prev))\\n#\\n# dt_TgGS.append( (mb.Tg[z].value - mb.Tg[prev].value)/\\\\\\n# mb.Tg_GS[z].value* mb.eps.value* mb.rho_vap[z].value* mb.cp_gas[z].value \\n# /(z-prev))\\n# \\n# dt_TsGS.append( (mb.Ts[z].value - mb.Ts[prev].value)/\\\\\\n# mb.Tg_GS[z].value* (1-mb.eps.value)* mb.rho_sol.value* mb.cp_sol[z].value*1e-3 \\n# /(z-prev))\\n# \\n# dt_vg.append( mb.L.value*(z-prev)/mb.vg[z].value /(z-prev))\\n# \\n# dt_vs.append( mb.L.value*(z-prev)/mb.vs.value /(z-prev))\\n#\\n# prev = z\\n#\\n# with open('dt.txt','w') as f:\\n# f.write('dt_Gflux_CO2\\\\t')\\n# for t in dt_Gflux_CO2:\\n# f.write('%1.3f'%t +'\\\\t')\\n#\\n# f.write('\\\\ndt_Gflux_H2O\\\\t')\\n# for t in dt_Gflux_H2O:\\n# f.write('%1.3f'%t +'\\\\t')\\n#\\n# f.write('\\\\ndt_Gflux_CH4\\\\t') \\n# for t in dt_Gflux_CH4:\\n# f.write('%1.3f'%t +'\\\\t')\\n#\\n# f.write('\\\\ndt_Sflux_Fe2O3\\\\t') \\n# for t in dt_Sflux_Fe2O3:\\n# f.write('%1.3f'%t +'\\\\t')\\n#\\n# f.write('\\\\ndt_Sflux_Fe3O4\\\\t') \\n# for t in dt_Sflux_Fe3O4:\\n# f.write('%1.3f'%t +'\\\\t')\\n#\\n# f.write('\\\\ndt_Sflux_Al2O3\\\\t') \\n# for t in dt_Sflux_Al2O3:\\n# f.write('%1.3f'%t +'\\\\t')\\n#\\n# f.write('\\\\ndt_Ctrans_CO2\\\\t') \\n# for t in dt_Ctrans_CO2:\\n# f.write('%1.3f'%t +'\\\\t')\\n# \\n# f.write('\\\\ndt_Ctrans_H2O\\\\t') \\n# for t in dt_Ctrans_H2O:\\n# f.write('%1.3f'%t +'\\\\t')\\n#\\n# f.write('\\\\ndt_Ctrans_CH4\\\\t') \\n# for t in dt_Ctrans_CH4:\\n# f.write('%1.3f'%t +'\\\\t')\\n#\\n# f.write('\\\\ndt_qtrans_Fe2O3\\\\t') \\n# for t in dt_qtrans_Fe2O3:\\n# f.write('%1.3f'%t +'\\\\t')\\n#\\n# f.write('\\\\ndt_qtrans_Fe3O4\\\\t') \\n# for t in dt_qtrans_Fe3O4:\\n# f.write('%1.3f'%t +'\\\\t')\\n#\\n# f.write('\\\\ndt_qtrans_Al2O3\\\\t') \\n# for t in dt_qtrans_Al2O3:\\n# f.write('%1.3f'%t +'\\\\t')\\n#\\n# f.write('\\\\ndt_Ghflux\\\\t') \\n# for t in dt_Ghflux:\\n# f.write('%1.3f'%t +'\\\\t')\\n#\\n# f.write('\\\\ndt_Ts\\\\t\\\\t') \\n# for t in dt_Ts:\\n# f.write('%1.3f'%t +'\\\\t')\\n#\\n# f.write('\\\\ndt_TgGS\\\\t\\\\t') \\n# for t in dt_TgGS:\\n# f.write('%1.3f'%t +'\\\\t')\\n#\\n# f.write('\\\\ndt_TsGS\\\\t\\\\t') \\n# for t in dt_TsGS:\\n# f.write('%1.3f'%t +'\\\\t')\\n#\\n# f.write('\\\\ndt_vg\\\\t\\\\t') \\n# for t in dt_vg:\\n# f.write('%1.3f'%t +'\\\\t')\\n#\\n# f.write('\\\\ndt_vs\\\\t\\\\t') \\n# for t in dt_vs:\\n# f.write('%1.3f'%t +'\\\\t')\\n\\n\\n # Store the flowsheet \\n return flowsheet\",\n \"def cli_simulate(model_file, output_dir, exporter, overwrite, compression,\\n confirm, progress: int, progress_tag, output_same,\\n simtime_total, simtime_lims, max_sweeps, max_residual, fipy_solver,\\n snapshot_interval,\\n plot, video, frames, budget, resume, show_eqns):\\n\\n click.secho('Starting MicroBenthos simulation', fg='green')\\n from microbenthos.utils import yaml\\n\\n click.echo('Loading model from {}'.format(model_file))\\n with open(model_file, 'r') as fp:\\n defs = yaml.unsafe_load(fp)\\n\\n if 'model' not in defs and 'domain' in defs:\\n # model is not under a separate key, so insert it under \\\"model\\\"\\n defs = dict(model=defs)\\n\\n if 'simulation' not in defs:\\n defs['simulation'] = {}\\n\\n # we want to override the keys in the loaded simulation dictionary,\\n # so that when it is created the definition stored on the instance and\\n # eventually exported to file includes these user overrides\\n\\n sim_kwargs = dict(\\n simtime_total=simtime_total,\\n fipy_solver=fipy_solver,\\n max_sweeps=max_sweeps,\\n simtime_lims=simtime_lims,\\n max_residual=max_residual,\\n snapshot_interval=snapshot_interval,\\n )\\n for k, v in sim_kwargs.items():\\n if v is None:\\n continue\\n else:\\n defs['simulation'][k] = v\\n\\n if output_same:\\n output_dir = str(Path(model_file).parent)\\n click.secho(f'Output directory set to: {output_dir}')\\n\\n from microbenthos.runners import SimulationRunner\\n runner = SimulationRunner(output_dir=output_dir,\\n model=defs['model'],\\n simulation=defs['simulation'],\\n resume=resume,\\n overwrite=overwrite,\\n confirm=confirm,\\n progress=progress,\\n progress_tag=progress_tag,\\n plot=plot,\\n video=video,\\n frames=frames,\\n budget=budget,\\n exporters=exporter,\\n show_eqns=show_eqns)\\n\\n if not runner.get_data_exporters():\\n click.secho('No data exporters defined. Adding with compression={}'.format(\\n compression), fg='red')\\n runner.add_exporter('model_data', output_dir=runner.output_dir,\\n compression=compression)\\n\\n runner.run()\",\n \"def runModel(quickLogger,\\n\\t base,\\n modelFile=\\\"\\\",\\n\\t irfs=\\\"P7SOURCE_V6\\\",\\n run=True):\\n \\n if(modelFile):\\n model = modelFile\\n else:\\n model = base+\\\"_likeMinuit.xml\\\"\\n\\n\\n try:\\n checkForFiles(quickLogger,\\n [base+\\\"_srcMaps.fits\\\",\\n model,\\n base+\\\"_ltcube.fits\\\",\\n base+\\\"_BinnedExpMap.fits\\\"])\\n except(FileNotFound):\\n quickLogger.critical(\\\"One or more needed files do not exist.\\\")\\n return\\n\\n model_map['srcmaps'] = base+\\\"_srcMaps.fits\\\"\\n model_map['srcmdl'] = model\\n model_map['outfile'] = base+\\\"_modelMap.fits\\\"\\n model_map['expcube'] = base+\\\"_ltcube.fits\\\"\\n model_map['irfs'] = irfs\\n model_map['bexpmap'] = base+\\\"_BinnedExpMap.fits\\\"\\n \\n runCommand(model_map,quickLogger,run)\",\n \"def test_FEMM_periodicity_time_no_periodicity_a():\\n\\n SPMSM_015 = load(join(DATA_DIR, \\\"Machine\\\", \\\"SPMSM_015.json\\\"))\\n\\n assert SPMSM_015.comp_periodicity() == (9, False, 9, True)\\n\\n simu = Simu1(name=\\\"test_FEMM_periodicity_time_no_periodicity_a\\\", machine=SPMSM_015)\\n\\n # Definition of the enforced output of the electrical module\\n I0_rms = 250 / sqrt(2)\\n Phi0 = 140 * pi / 180 # Maximum Torque Per Amp\\n\\n Id_ref = (I0_rms * exp(1j * Phi0)).real\\n Iq_ref = (I0_rms * exp(1j * Phi0)).imag\\n\\n simu.input = InputCurrent(\\n Id_ref=Id_ref,\\n Iq_ref=Iq_ref,\\n Na_tot=252 * 9,\\n Nt_tot=4 * 9,\\n N0=1000,\\n )\\n\\n # Definition of the magnetic simulation: with periodicity\\n simu.mag = MagFEMM(\\n type_BH_stator=1,\\n type_BH_rotor=1,\\n is_periodicity_a=False,\\n is_periodicity_t=True,\\n nb_worker=cpu_count(),\\n Kmesh_fineness=2,\\n )\\n simu.force = ForceMT()\\n\\n # Definition of the magnetic simulation: no periodicity\\n simu2 = simu.copy()\\n simu2.mag.is_periodicity_t = False\\n\\n # Run simulations\\n out = Output(simu=simu)\\n simu.run()\\n\\n out2 = Output(simu=simu2)\\n simu2.run()\\n\\n # Plot the result\\n out.mag.B.plot_2D_Data(\\n \\\"time\\\",\\n \\\"angle[0]{°}\\\",\\n data_list=[out2.mag.B],\\n legend_list=[\\\"Periodic\\\", \\\"Full\\\"],\\n save_path=join(save_path, simu.name + \\\"_B_time.png\\\"),\\n is_show_fig=False,\\n **dict_2D\\n )\\n\\n out.mag.B.plot_2D_Data(\\n \\\"angle{°}\\\",\\n \\\"time[1]\\\",\\n data_list=[out2.mag.B],\\n legend_list=[\\\"Periodic\\\", \\\"Full\\\"],\\n save_path=join(save_path, simu.name + \\\"_B_space.png\\\"),\\n is_show_fig=False,\\n **dict_2D\\n )\\n\\n out.force.AGSF.plot_2D_Data(\\n \\\"wavenumber=[0,100]\\\",\\n \\\"time[0]\\\",\\n data_list=[out2.force.AGSF],\\n legend_list=[\\\"Periodic\\\", \\\"Full\\\"],\\n save_path=join(save_path, simu.name + \\\"_P_space_fft.png\\\"),\\n is_show_fig=False,\\n **dict_2D\\n )\\n\\n out.force.AGSF.plot_2D_Data(\\n \\\"freqs\\\",\\n \\\"angle[0]\\\",\\n data_list=[out2.force.AGSF],\\n legend_list=[\\\"Periodic\\\", \\\"Full\\\"],\\n save_path=join(save_path, simu.name + \\\"_P_fft2.png\\\"),\\n is_show_fig=False,\\n **dict_2D\\n )\\n\\n out.mag.Tem.plot_2D_Data(\\n \\\"time\\\",\\n data_list=[out2.mag.Tem],\\n legend_list=[\\\"Periodic\\\", \\\"Full\\\"],\\n save_path=join(save_path, simu.name + \\\"_Tem_time.png\\\"),\\n is_show_fig=False,\\n **dict_2D\\n )\\n\\n out.mag.Phi_wind_stator.plot_2D_Data(\\n \\\"time\\\",\\n \\\"phase\\\",\\n data_list=[out2.mag.Phi_wind_stator],\\n legend_list=[\\\"Periodic\\\", \\\"Full\\\"],\\n save_path=join(save_path, simu.name + \\\"_Phi_wind_stator_time.png\\\"),\\n is_show_fig=False,\\n **dict_2D\\n )\\n\\n Bflux = out.mag.B\\n arg_list = [\\\"time\\\"]\\n result = Bflux.get_rphiz_along(*arg_list)\\n Brad = result[\\\"radial\\\"]\\n time = result[\\\"time\\\"]\\n\\n Bflux2 = out2.mag.B\\n arg_list = [\\\"time\\\"]\\n result2 = Bflux2.get_rphiz_along(*arg_list)\\n Brad2 = result2[\\\"radial\\\"]\\n time2 = result2[\\\"time\\\"]\\n\\n # Compare both simu\\n assert_array_almost_equal((Brad - Brad2) / Brad2, 0, decimal=2)\\n assert_array_almost_equal(time, time2, decimal=6)\\n\\n AGSF = out.force.AGSF\\n arg_list = [\\\"time\\\"]\\n result_AGSF = AGSF.get_rphiz_along(*arg_list)\\n Prad = result_AGSF[\\\"radial\\\"]\\n time3 = result_AGSF[\\\"time\\\"]\\n\\n AGSF2 = out2.force.AGSF\\n arg_list = [\\\"time\\\"]\\n result_AGSF2 = AGSF2.get_rphiz_along(*arg_list)\\n Prad2 = result_AGSF2[\\\"radial\\\"]\\n time4 = result_AGSF2[\\\"time\\\"]\\n\\n # Compare both simu\\n assert_array_almost_equal((Prad - Prad2) / Prad2, 0, decimal=2)\\n assert_array_almost_equal(time3, time4, decimal=6)\\n\\n return out, out2\",\n \"def test_FEMM_periodicity_time():\\n\\n SPMSM_015 = load(join(DATA_DIR, \\\"Machine\\\", \\\"SPMSM_015.json\\\"))\\n\\n assert SPMSM_015.comp_periodicity() == (9, False, 9, True)\\n\\n simu = Simu1(name=\\\"test_FEMM_periodicity_time\\\", machine=SPMSM_015)\\n\\n # Definition of the enforced output of the electrical module\\n I0_rms = 250 / sqrt(2)\\n Phi0 = 140 * pi / 180 # Maximum Torque Per Amp\\n\\n Id_ref = (I0_rms * exp(1j * Phi0)).real\\n Iq_ref = (I0_rms * exp(1j * Phi0)).imag\\n\\n simu.input = InputCurrent(\\n Id_ref=Id_ref,\\n Iq_ref=Iq_ref,\\n Na_tot=252 * 9,\\n Nt_tot=4 * 9,\\n N0=1000,\\n )\\n\\n # Definition of the magnetic simulation: with periodicity\\n simu.mag = MagFEMM(\\n type_BH_stator=1,\\n type_BH_rotor=1,\\n is_periodicity_a=True,\\n is_periodicity_t=True,\\n nb_worker=cpu_count(),\\n Kmesh_fineness=2,\\n )\\n simu.force = ForceMT()\\n\\n # Definition of the magnetic simulation: no periodicity\\n simu2 = simu.copy()\\n simu2.mag.is_periodicity_t = False\\n\\n # Run simulations\\n out = Output(simu=simu)\\n simu.run()\\n\\n out2 = Output(simu=simu2)\\n simu2.run()\\n\\n # Plot the result\\n out.mag.B.plot_2D_Data(\\n \\\"time\\\",\\n \\\"angle[0]{°}\\\",\\n data_list=[out2.mag.B],\\n legend_list=[\\\"Periodic\\\", \\\"Full\\\"],\\n save_path=join(save_path, simu.name + \\\"_B_time.png\\\"),\\n is_show_fig=False,\\n **dict_2D\\n )\\n\\n out.mag.B.plot_2D_Data(\\n \\\"angle{°}\\\",\\n \\\"time[1]\\\",\\n data_list=[out2.mag.B],\\n legend_list=[\\\"Periodic\\\", \\\"Full\\\"],\\n save_path=join(save_path, simu.name + \\\"_B_space.png\\\"),\\n is_show_fig=False,\\n **dict_2D\\n )\\n\\n out.force.AGSF.plot_2D_Data(\\n \\\"wavenumber=[0,100]\\\",\\n \\\"time[0]\\\",\\n data_list=[out2.force.AGSF],\\n legend_list=[\\\"Periodic\\\", \\\"Full\\\"],\\n save_path=join(save_path, simu.name + \\\"_P_space_fft.png\\\"),\\n is_show_fig=False,\\n **dict_2D\\n )\\n\\n out.force.AGSF.plot_2D_Data(\\n \\\"freqs\\\",\\n \\\"angle[0]\\\",\\n data_list=[out2.force.AGSF],\\n legend_list=[\\\"Periodic\\\", \\\"Full\\\"],\\n save_path=join(save_path, simu.name + \\\"_P_fft2.png\\\"),\\n is_show_fig=False,\\n **dict_2D\\n )\\n\\n out.mag.Tem.plot_2D_Data(\\n \\\"time\\\",\\n data_list=[out2.mag.Tem],\\n legend_list=[\\\"Periodic\\\", \\\"Full\\\"],\\n save_path=join(save_path, simu.name + \\\"_Tem_time.png\\\"),\\n is_show_fig=False,\\n **dict_2D\\n )\\n\\n out.mag.Phi_wind_stator.plot_2D_Data(\\n \\\"time\\\",\\n \\\"phase\\\",\\n data_list=[out2.mag.Phi_wind_stator],\\n legend_list=[\\\"Periodic\\\", \\\"Full\\\"],\\n save_path=join(save_path, simu.name + \\\"_Phi_wind_stator_time.png\\\"),\\n is_show_fig=False,\\n **dict_2D\\n )\\n\\n # Compare both simu with B\\n Bflux = out.mag.B\\n arg_list = [\\\"time\\\"]\\n result = Bflux.get_rphiz_along(*arg_list)\\n Brad = result[\\\"radial\\\"]\\n time = result[\\\"time\\\"]\\n\\n # Check Flux spatio-temporal reconstruction full\\n Bflux2 = out2.mag.B\\n arg_list = [\\\"time\\\"]\\n result2 = Bflux2.get_rphiz_along(*arg_list)\\n Brad2 = result2[\\\"radial\\\"]\\n time = result2[\\\"time\\\"]\\n\\n assert_array_almost_equal(Brad, Brad2, decimal=2)\\n\\n # Compare both simu with AGSF\\n AGSF = out.force.AGSF\\n arg_list = [\\\"time\\\"]\\n result_AGSF = AGSF.get_rphiz_along(*arg_list)\\n Prad = result_AGSF[\\\"radial\\\"]\\n time = result_AGSF[\\\"time\\\"]\\n\\n AGSF2 = out2.force.AGSF\\n arg_list = [\\\"time\\\"]\\n result_AGSF2 = AGSF2.get_rphiz_along(*arg_list)\\n Prad2 = result_AGSF2[\\\"radial\\\"]\\n time = result_AGSF2[\\\"time\\\"]\\n\\n assert_array_almost_equal(Prad / 1000, Prad2 / 1000, decimal=0)\\n\\n return out, out2\",\n \"def run(self):\\n\\n self._logger.debug(\\\"Starting Dummy Model: modelID=%s;\\\" % (self._modelID))\\n\\n # =========================================================================\\n # Initialize periodic activities (e.g., for model result updates)\\n # =========================================================================\\n periodic = self._initPeriodicActivities()\\n\\n self._optimizedMetricLabel = self._optimizeKeyPattern\\n self._reportMetricLabels = [self._optimizeKeyPattern]\\n\\n # =========================================================================\\n # Create our top-level loop-control iterator\\n # =========================================================================\\n if self._iterations >= 0:\\n iterTracker = iter(xrange(self._iterations))\\n else:\\n iterTracker = iter(itertools.count())\\n\\n # =========================================================================\\n # This gets set in the unit tests. It tells the worker to sys exit\\n # the first N models. This is how we generate orphaned models\\n doSysExit = False\\n if self._sysExitModelRange is not None:\\n modelAndCounters = self._jobsDAO.modelsGetUpdateCounters(self._jobID)\\n modelIDs = [x[0] for x in modelAndCounters]\\n modelIDs.sort()\\n (beg,end) = self._sysExitModelRange\\n if self._modelID in modelIDs[int(beg):int(end)]:\\n doSysExit = True\\n\\n if self._delayModelRange is not None:\\n modelAndCounters = self._jobsDAO.modelsGetUpdateCounters(self._jobID)\\n modelIDs = [x[0] for x in modelAndCounters]\\n modelIDs.sort()\\n (beg,end) = self._delayModelRange\\n if self._modelID in modelIDs[int(beg):int(end)]:\\n time.sleep(10)\\n \\n # DEBUG!!!! infinite wait if we have 50 models\\n #if len(modelIDs) >= 50:\\n # jobCancel = self._jobsDAO.jobGetFields(self._jobID, ['cancel'])[0]\\n # while not jobCancel:\\n # time.sleep(1)\\n # jobCancel = self._jobsDAO.jobGetFields(self._jobID, ['cancel'])[0]\\n\\n if self._errModelRange is not None:\\n modelAndCounters = self._jobsDAO.modelsGetUpdateCounters(self._jobID)\\n modelIDs = [x[0] for x in modelAndCounters]\\n modelIDs.sort()\\n (beg,end) = self._errModelRange\\n if self._modelID in modelIDs[int(beg):int(end)]:\\n raise RuntimeError(\\\"Exiting with error due to errModelRange parameter\\\")\\n\\n # =========================================================================\\n # Delay, if necessary\\n if self._delay is not None:\\n time.sleep(self._delay)\\n\\n # =========================================================================\\n # Run it!\\n # =========================================================================\\n self._currentRecordIndex = 0\\n while True:\\n\\n # =========================================================================\\n # Check if the model should be stopped\\n # =========================================================================\\n\\n # If killed by a terminator, stop running\\n if self._isKilled:\\n break\\n\\n # If job stops or hypersearch ends, stop running\\n if self._isCanceled:\\n break\\n\\n # If model is mature, stop running ONLY IF we are not the best model\\n # for the job. Otherwise, keep running so we can keep returning\\n # predictions to the user\\n if self._isMature:\\n if not self._isBestModel:\\n self._cmpReason = self._jobsDAO.CMPL_REASON_STOPPED\\n break\\n else:\\n self._cmpReason = self._jobsDAO.CMPL_REASON_EOF\\n\\n # =========================================================================\\n # Get the the next record, and \\\"write it\\\"\\n # =========================================================================\\n try:\\n self._currentRecordIndex = next(iterTracker)\\n except StopIteration:\\n break\\n\\n # \\\"Write\\\" a dummy output value. This is used to test that the batched\\n # writing works properly\\n\\n self._writePrediction(ModelResult(None, None, None, None))\\n\\n periodic.tick()\\n\\n # =========================================================================\\n # Compute wait times. See if model should exit\\n # =========================================================================\\n\\n if self.__shouldSysExit(self._currentRecordIndex):\\n sys.exit(1)\\n\\n # Simulate computation time\\n if self._busyWaitTime is not None:\\n time.sleep(self._busyWaitTime)\\n self.__computeWaitTime()\\n\\n # Asked to abort after so many iterations?\\n if doSysExit:\\n sys.exit(1)\\n\\n # Asked to raise a jobFailException?\\n if self._jobFailErr:\\n raise utils.JobFailException(\\\"E10000\\\",\\n \\\"dummyModel's jobFailErr was True.\\\")\\n\\n # =========================================================================\\n # Handle final operations\\n # =========================================================================\\n if self._doFinalize:\\n if not self._makeCheckpoint:\\n self._model = None\\n\\n # Delay finalization operation\\n if self._finalDelay is not None:\\n time.sleep(self._finalDelay)\\n\\n self._finalize()\\n\\n self._logger.info(\\\"Finished: modelID=%r \\\"% (self._modelID))\\n\\n return (self._cmpReason, None)\",\n \"def _backend_run(self):\\n if self.vM is not None:\\n return self.vM\\n else:\\n everything = copy.copy(self.model.attrs)\\n if hasattr(self,'Iext'):\\n everything.update({'Iext':self.Iext})\\n\\n if 'current_inj' in everything.keys():\\n everything.pop('current_inj',None)\\n everything = copy.copy(self.model.attrs)\\n\\n self.model.attrs['celltype'] = round(self.model.attrs['celltype'])\\n if self.model.attrs['celltype'] <= 3:\\n everything.pop('celltype',None)\\n v = get_vm_matlab_one_two_three(**everything)\\n else:\\n if self.model.attrs['celltype'] == 4:\\n v = get_vm_matlab_four(**everything)\\n if self.model.attrs['celltype'] == 5:\\n v = get_vm_matlab_five(**everything)\\n if self.model.attrs['celltype'] == 6:\\n v = get_vm_matlab_six(**everything)\\n if self.model.attrs['celltype'] == 7:\\n #print('gets into multiple regimes',self.attrs['celltype'])\\n\\n v = get_vm_matlab_seven(**everything)\\n\\n return AnalogSignal(v, units=pq.mV,\\n sampling_period=0.125*pq.ms)\",\n \"def initialize_model(self, config_param_vals = None):\\n self._is_initialized = True\\n\\n self.fmu.instantiate()\\n self.fmu.reset()\\n self.fmu.setupExperiment(startTime=self.start_time)\\n if config_param_vals is not None:\\n self._apply_config(config_param_vals)\\n self.fmu.enterInitializationMode()\\n self.fmu.exitInitializationMode()\\n\\n return\",\n \"def setup_biosafe(self):\\n # Generate dummy data in the right format\\n species_presence = pd.DataFrame(\\n np.random.randint(2, size=len(self.links_law)),\\n columns=['speciesPresence'], index=self.links_law.index)\\n\\n ecotope_area = pd.DataFrame(\\n np.ones(len(self.links_eco2.columns)-1) * 1e5,\\n columns = ['area_m2'],\\n index = self.links_eco2.columns.values[0:-1])\\n\\n # Simplify ecotope tables to VR ecotopes\\n unique_eco = np.unique(\\n np.hstack((self.vr_eco.ecotope1.values,\\n self.vr_eco.ecotope2.values)))\\n links_eco3 = self.links_eco2.reindex(columns=unique_eco)\\n ecotope_area = ecotope_area.reindex(index=unique_eco)\\n\\n # Run a first version of Biosafe\\n self.bsf_model = bsf.biosafe(\\n self.legal_weights, self.links_law, links_eco3,\\n species_presence, ecotope_area)\\n\\n #PotTax = self.bsf_model.TFI()\\n #PotAll = self.bsf_model.FI()\\n return\",\n \"def build_bpmf_model(train, alpha=2, dim=10, std=0.01):\\n n, m = train.shape\\n beta_0 = 1 # scaling factor for lambdas; unclear on its use\\n\\n # Mean value imputation on training data.\\n train = train.copy()\\n nan_mask = np.isnan(train)\\n train[nan_mask] = train[~nan_mask].mean()\\n\\n logging.info('building the BPMF model')\\n with pm.Model() as bpmf:\\n # Specify user feature matrix\\n lambda_u = pm.Wishart(\\n 'lambda_u', n=dim, V=np.eye(dim), shape=(dim, dim),\\n testval=np.random.randn(dim, dim) * std)\\n mu_u = pm.Normal(\\n 'mu_u', mu=0, tau=beta_0 * lambda_u, shape=dim,\\n testval=np.random.randn(dim) * std)\\n U = pm.MvNormal(\\n 'U', mu=mu_u, tau=lambda_u, shape=(n, dim),\\n testval=np.random.randn(n, dim) * std)\\n\\n # Specify item feature matrix\\n lambda_v = pm.Wishart(\\n 'lambda_v', n=dim, V=np.eye(dim), shape=(dim, dim),\\n testval=np.random.randn(dim, dim) * std)\\n mu_v = pm.Normal(\\n 'mu_v', mu=0, tau=beta_0 * lambda_v, shape=dim,\\n testval=np.random.randn(dim) * std)\\n V = pm.MvNormal(\\n 'V', mu=mu_v, tau=lambda_v, shape=(m, dim),\\n testval=np.random.randn(m, dim) * std)\\n\\n # Specify rating likelihood function\\n R = pm.Normal(\\n 'R', mu=t.dot(U, V.T), tau=alpha * np.ones((n, m)),\\n observed=train)\\n\\n logging.info('done building the BPMF model')\\n return bpmf\",\n \"def run_libfm(train, test, iter=20, std=0.2, dim=8, bias=False,\\n outfile=''):\\n kwargs = {k: v for k, v in locals().items() if not k in ['train', 'test']}\\n args = compose_libfm_args(train, test, **kwargs)\\n cmd = ' '.join(args)\\n logging.debug(cmd)\\n\\n proc = sub.Popen(cmd, shell=True, stdout=sub.PIPE)\\n retcode = proc.wait()\\n if retcode:\\n raise LibFMFailed(\\\"libFM failed to execute.\\\\n%s\\\" % cmd)\\n\\n output = proc.communicate()[0]\\n lines = output.split('\\\\n')\\n rows = [row.split('\\\\t')[1:] for row in lines[-iter:] if row]\\n train_err = '%.6f' % float(rows[-1][0].split('=')[1])\\n test_err = '%.6f' % float(rows[-1][1].split('=')[1])\\n return [train_err, test_err]\",\n \"def run(mu_v, Sigma_w, Sigma_z, a_mu, l_sensor):\\n N = 1000\\n # Init tracking\\n mu_x = np.zeros(N) # Belief or estimation of hidden state \\n F = np.zeros(N) # Free Energy of AI neuron\\n mu_y = np.zeros(N) # Belief or prediction of sensory signal \\n x = np.zeros(N) # True hidden state\\n y = np.zeros(N) # Sensory signal as input to AI neuron\\n\\n robot_brain = pp_unit(dt, mu_v, Sigma_w, Sigma_z, a_mu) #make pp object\\n \\n \\n\\n start_time = time.time()\\n for i in np.arange(1, N):\\n #Active inference\\n y[i] = l_sensor.ambient_light_intensity #take sensor reading\\n print('light reading', y[i])\\n F[i], mu_x[i], mu_y[i] = robot_brain.inference_step(i, mu_v, y[i])\\n\\n\\n t_elapsed = time.time() - start_time\\n\\n print(\\\"Elapsed Time\\\", t_elapsed, \\\"sec\\\")\\n return F, mu_x, mu_y, x, y\",\n \"def test_flim_model(datadir):\\n # ifuslot_063\\n filename = datadir.join(\\\"test_hdf.h5\\\").strpath\\n hdcon1 = SensitivityCubeHDF5Container(filename, flim_model=\\\"hdr1\\\")\\n hdcon2 = SensitivityCubeHDF5Container(filename, flim_model=\\\"hdr2pt1\\\")\\n\\n scube1 = hdcon1.extract_ifu_sensitivity_cube(\\\"ifuslot_063\\\")\\n scube2 = hdcon2.extract_ifu_sensitivity_cube(\\\"ifuslot_063\\\")\\n\\n s1 = scube1.get_f50(161.4201, 50.8822, 3470.0, 5.5)\\n s2 = scube2.get_f50(161.4201, 50.8822, 3470.0, 5.5)\\n\\n print(s1)\\n # if different models passed should be different\\n assert abs(s1 - s2) > 1e-19\",\n \"def test_pregenerated_model(sub_test, case):\\n\\n if case.startswith(\\\"sensi2\\\"):\\n model_name = sub_test + \\\"_o2\\\"\\n else:\\n model_name = sub_test\\n\\n model_swig_folder = str(\\n Path(__file__).parents[2]\\n / \\\"build\\\"\\n / \\\"tests\\\"\\n / \\\"cpp\\\"\\n / f\\\"external_{model_name}-prefix\\\"\\n / \\\"src\\\"\\n / f\\\"external_{model_name}-build\\\"\\n / \\\"swig\\\"\\n )\\n\\n test_model_module = amici.import_model_module(\\n module_name=model_name, module_path=model_swig_folder\\n )\\n model = test_model_module.getModel()\\n solver = model.getSolver()\\n amici.readModelDataFromHDF5(\\n options_file, model.get(), f\\\"/{sub_test}/{case}/options\\\"\\n )\\n amici.readSolverSettingsFromHDF5(\\n options_file, solver.get(), f\\\"/{sub_test}/{case}/options\\\"\\n )\\n\\n edata = None\\n if \\\"data\\\" in expected_results[sub_test][case].keys():\\n edata = amici.readSimulationExpData(\\n str(expected_results_file), f\\\"/{sub_test}/{case}/data\\\", model.get()\\n )\\n rdata = amici.runAmiciSimulation(model, solver, edata)\\n\\n check_derivative_opts = dict()\\n\\n if model_name == \\\"model_nested_events\\\":\\n check_derivative_opts[\\\"rtol\\\"] = 1e-2\\n elif model_name == \\\"model_events\\\":\\n check_derivative_opts[\\\"atol\\\"] = 1e-3\\n\\n if (\\n edata\\n and solver.getSensitivityMethod()\\n and solver.getSensitivityOrder()\\n and len(model.getParameterList())\\n and not model_name.startswith(\\\"model_neuron\\\")\\n and not case.endswith(\\\"byhandpreeq\\\")\\n ):\\n check_derivatives(model, solver, edata, **check_derivative_opts)\\n\\n verify_simulation_opts = dict()\\n\\n if model_name.startswith(\\\"model_neuron\\\"):\\n verify_simulation_opts[\\\"atol\\\"] = 1e-5\\n verify_simulation_opts[\\\"rtol\\\"] = 1e-2\\n\\n if model_name.startswith(\\\"model_robertson\\\") and case == \\\"sensiforwardSPBCG\\\":\\n verify_simulation_opts[\\\"atol\\\"] = 1e-3\\n verify_simulation_opts[\\\"rtol\\\"] = 1e-3\\n\\n verify_simulation_results(\\n rdata, expected_results[sub_test][case][\\\"results\\\"], **verify_simulation_opts\\n )\\n\\n if model_name == \\\"model_steadystate\\\" and case == \\\"sensiforwarderrorint\\\":\\n edata = amici.amici.ExpData(model.get())\\n\\n # Test runAmiciSimulations: ensure running twice\\n # with same ExpData yields same results\\n if (\\n edata\\n and model_name != \\\"model_neuron_o2\\\"\\n and not (model_name == \\\"model_robertson\\\" and case == \\\"sensiforwardSPBCG\\\")\\n ):\\n if isinstance(edata, amici.amici.ExpData):\\n edatas = [edata, edata]\\n else:\\n edatas = [edata.get(), edata.get()]\\n\\n rdatas = amici.runAmiciSimulations(\\n model, solver, edatas, num_threads=2, failfast=False\\n )\\n verify_simulation_results(\\n rdatas[0],\\n expected_results[sub_test][case][\\\"results\\\"],\\n **verify_simulation_opts,\\n )\\n verify_simulation_results(\\n rdatas[1],\\n expected_results[sub_test][case][\\\"results\\\"],\\n **verify_simulation_opts,\\n )\\n\\n # test residuals mode\\n if solver.getSensitivityMethod() == amici.SensitivityMethod.adjoint:\\n with pytest.raises(RuntimeError):\\n solver.setReturnDataReportingMode(amici.RDataReporting.residuals)\\n else:\\n solver.setReturnDataReportingMode(amici.RDataReporting.residuals)\\n rdata = amici.runAmiciSimulation(model, solver, edata)\\n verify_simulation_results(\\n rdata,\\n expected_results[sub_test][case][\\\"results\\\"],\\n fields=[\\\"t\\\", \\\"res\\\", \\\"sres\\\", \\\"y\\\", \\\"sy\\\", \\\"sigmay\\\", \\\"ssigmay\\\"],\\n **verify_simulation_opts,\\n )\\n with pytest.raises(RuntimeError):\\n solver.setSensitivityMethod(amici.SensitivityMethod.adjoint)\\n\\n chi2_ref = rdata.chi2\\n\\n # test likelihood mode\\n solver.setReturnDataReportingMode(amici.RDataReporting.likelihood)\\n rdata = amici.runAmiciSimulation(model, solver, edata)\\n verify_simulation_results(\\n rdata,\\n expected_results[sub_test][case][\\\"results\\\"],\\n fields=[\\\"t\\\", \\\"llh\\\", \\\"sllh\\\", \\\"s2llh\\\", \\\"FIM\\\"],\\n **verify_simulation_opts,\\n )\\n\\n # test sigma residuals\\n\\n if (\\n model_name == \\\"model_jakstat_adjoint\\\"\\n and solver.getSensitivityMethod() != amici.SensitivityMethod.adjoint\\n ):\\n model.setAddSigmaResiduals(True)\\n solver.setReturnDataReportingMode(amici.RDataReporting.full)\\n rdata = amici.runAmiciSimulation(model, solver, edata)\\n # check whether activation changes chi2\\n assert chi2_ref != rdata.chi2\\n\\n if (\\n edata\\n and solver.getSensitivityMethod()\\n and solver.getSensitivityOrder()\\n and len(model.getParameterList())\\n ):\\n check_derivatives(model, solver, edata, **check_derivative_opts)\\n\\n chi2_ref = rdata.chi2\\n res_ref = rdata.res\\n\\n model.setMinimumSigmaResiduals(100)\\n rdata = amici.runAmiciSimulation(model, solver, edata)\\n # check whether changing the minimum changes res but not chi2\\n assert np.isclose(chi2_ref, rdata.chi2)\\n assert not np.allclose(res_ref, rdata.res)\\n\\n model.setMinimumSigmaResiduals(-10)\\n rdata = amici.runAmiciSimulation(model, solver, edata)\\n # check whether having a bad minimum results in nan chi2\\n assert np.isnan(rdata.chi2)\\n\\n with pytest.raises(RuntimeError):\\n model.getParameterByName(\\\"thisParameterDoesNotExist\\\")\",\n \"def setUp(self):\\n # Set Model Parameters\\n odeparam = np.array([1, 2])\\n y0, y0_unc = 1.0, 0 \\n t0, tmax = 0.0, 1.25\\n\\n # Set Method Parameters\\n q = 1\\n h = 0.1\\n\\n # Set up and solve ODE\\n ibm = statespace.IBM(q=q, dim=1)\\n solver = linsolve.LinearisedODESolver(ibm)\\n ivp = linode.LogisticODE(t0, tmax, odeparam, y0, y0_unc)\\n tsteps, means, __, rhs_parts, uncerts = solver.solve(ivp, stepsize=h)\\n self.mean = odesolver.get_trajectory(means, 0, 0)\\n\\n # Set up BM and IBM covariance matrices\\n evalpt = np.array(tsteps[[-1, -10]])\\n derdat = (tsteps, rhs_parts, 0.)\\n\\n const, jacob = linearisation.compute_linearisation(\\n ssm=ibm, initial_value=y0,\\n derivative_data=derdat, prdct_tsteps=evalpt)\\n\\n # Compute GP Estimation of filter mean at t=tmax\\n self.postmean = const + np.dot(jacob, odeparam)\",\n \"def test_FEMM_periodicity_angle():\\n\\n SPMSM_015 = load(join(DATA_DIR, \\\"Machine\\\", \\\"SPMSM_015.json\\\"))\\n\\n assert SPMSM_015.comp_periodicity() == (9, False, 9, True)\\n\\n simu = Simu1(name=\\\"test_FEMM_periodicity_angle\\\", machine=SPMSM_015)\\n\\n # Definition of the enforced output of the electrical module\\n I0_rms = 250 / sqrt(2)\\n Phi0 = 140 * pi / 180 # Maximum Torque Per Amp\\n\\n Id_ref = (I0_rms * exp(1j * Phi0)).real\\n Iq_ref = (I0_rms * exp(1j * Phi0)).imag\\n\\n simu.input = InputCurrent(\\n Id_ref=Id_ref,\\n Iq_ref=Iq_ref,\\n Na_tot=252 * 9,\\n Nt_tot=4 * 9,\\n N0=1000,\\n )\\n\\n # Definition of the magnetic simulation: with periodicity\\n simu.mag = MagFEMM(\\n type_BH_stator=1,\\n type_BH_rotor=1,\\n is_periodicity_a=True,\\n is_periodicity_t=False,\\n nb_worker=cpu_count(),\\n Kmesh_fineness=2,\\n )\\n simu.force = ForceMT()\\n\\n # Definition of the magnetic simulation: no periodicity\\n # Definition of the magnetic simulation: no periodicity\\n simu2 = simu.copy()\\n simu2.mag.is_periodicity_a = False\\n\\n simu2.force = ForceMT()\\n\\n # Run simulations\\n out = Output(simu=simu)\\n simu.run()\\n\\n out2 = Output(simu=simu2)\\n simu2.run()\\n\\n # Plot the result\\n out.mag.B.plot_2D_Data(\\n \\\"time\\\",\\n \\\"angle[0]{°}\\\",\\n data_list=[out2.mag.B],\\n legend_list=[\\\"Periodic\\\", \\\"Full\\\"],\\n save_path=join(save_path, simu.name + \\\"_B_time.png\\\"),\\n is_show_fig=False,\\n **dict_2D\\n )\\n\\n out.mag.B.plot_2D_Data(\\n \\\"angle{°}\\\",\\n \\\"time[1]\\\",\\n data_list=[out2.mag.B],\\n legend_list=[\\\"Periodic\\\", \\\"Full\\\"],\\n save_path=join(save_path, simu.name + \\\"_B_space.png\\\"),\\n is_show_fig=False,\\n **dict_2D\\n )\\n\\n out.force.AGSF.plot_2D_Data(\\n \\\"wavenumber=[0,100]\\\",\\n \\\"time[0]\\\",\\n data_list=[out2.force.AGSF],\\n legend_list=[\\\"Periodic\\\", \\\"Full\\\"],\\n save_path=join(save_path, simu.name + \\\"_P_space_fft.png\\\"),\\n is_show_fig=False,\\n **dict_2D\\n )\\n\\n out.force.AGSF.plot_2D_Data(\\n \\\"freqs\\\",\\n \\\"angle[0]\\\",\\n data_list=[out2.force.AGSF],\\n legend_list=[\\\"Periodic\\\", \\\"Full\\\"],\\n save_path=join(save_path, simu.name + \\\"_P_fft2.png\\\"),\\n is_show_fig=False,\\n **dict_2D\\n )\\n\\n out.mag.Tem.plot_2D_Data(\\n \\\"time\\\",\\n data_list=[out2.mag.Tem],\\n legend_list=[\\\"Periodic\\\", \\\"Full\\\"],\\n save_path=join(save_path, simu.name + \\\"_Tem_time.png\\\"),\\n is_show_fig=False,\\n **dict_2D\\n )\\n\\n out.mag.Phi_wind_stator.plot_2D_Data(\\n \\\"time\\\",\\n \\\"phase\\\",\\n data_list=[out2.mag.Phi_wind_stator],\\n legend_list=[\\\"Periodic\\\", \\\"Full\\\"],\\n save_path=join(save_path, simu.name + \\\"_Phi_wind_stator_time.png\\\"),\\n is_show_fig=False,\\n **dict_2D\\n )\\n\\n # Compare both simu\\n Bflux = out.mag.B\\n arg_list = [\\\"angle\\\"]\\n result = Bflux.get_rphiz_along(*arg_list)\\n Brad = result[\\\"radial\\\"]\\n angle = result[\\\"angle\\\"]\\n\\n Bflux2 = out2.mag.B\\n arg_list = [\\\"angle\\\"]\\n result2 = Bflux2.get_rphiz_along(*arg_list)\\n Brad2 = result2[\\\"radial\\\"]\\n\\n assert_array_almost_equal(Brad, Brad2, decimal=1)\\n\\n return out, out2\",\n \"def run_model(species, Av, Air, rh, obliquity, temp=-1, verbosity=1):\\n\\n if isinstance(species, int):\\n if species in range(1, len(speciesList) + 1):\\n # The species indexing if offset by +1 to make the inputs consistent with the original fortran script.\\n species = speciesList[species - 1]\\n else:\\n logging.error(f\\\"The inputted index of {species} is not currently supported \\\\n\\\"\\n f\\\"Please input one of the following integers or strings: \\\\n\\\"\\n f\\\"1: 'H2O', 2: 'H2O-CH4', 3: 'CO2', 4: 'CO'\\\")\\n raise ValueError('Invalid species.')\\n if species not in speciesList:\\n logging.error(f\\\"The inputted species of \\\\\\\"{species}\\\\\\\" is not currently supported \\\\n\\\"\\n f\\\"Please input one of the following integers or strings: \\\\n\\\"\\n f\\\"1: 'H2O', 2: 'H2O-CH4', 3: 'CO2', 4: 'CO'\\\")\\n raise ValueError('Invalid species.')\\n\\n if Av < 0:\\n logging.error(\\n f'A visual albedo of {Av} is not a valid input.'\\n ' Please input a value greater than 0.')\\n raise ValueError('Invalid visual albedo.')\\n\\n if verbosity == 0:\\n logging.basicConfig(level='WARNING')\\n elif verbosity == 1:\\n logging.basicConfig(level='INFO')\\n else:\\n logging.basicConfig(level='DEBUG')\\n\\n logging.info(\\\"Input Parameters:\\\")\\n logging.info(\\n f'Species = {species}, Avis = {Av}, Air = {Air}, r_H = {rh}, Obl = {obliquity}')\\n\\n incl = (90 - obliquity) * math.pi / 180\\n\\n mass, xlt, xltprim, press, pprim, temp = sublime(species, temp)\\n root = 1 / math.sqrt(mass * 2 * math.pi * boltz)\\n\\n nflag = 1\\n perc = 0\\n gd = None\\n for n in range(0, nb):\\n temp, gd, perc, nflag = main_loop(\\n n, species, Av, Air, rh, obliquity, incl, temp, root, nflag, perc, gd)\\n\\n zbar = 0.\\n for nn in range(0, nb - 1):\\n zbar = zbar + 0.5 * (z[nn] + z[nn + 1]) * delsb\\n\\n zbar = zbar / 2\\n zlog = math.log10(zbar)\\n rlog = math.log10(rh)\\n\\n output = {\\n \\\"species\\\": species,\\n \\\"obliquity\\\": obliquity,\\n \\\"r_H\\\": rh,\\n \\\"rlog\\\": rlog,\\n \\\"Av\\\": Av,\\n \\\"Air\\\": Air,\\n \\\"Zbar\\\": zbar,\\n \\\"Zlog\\\": zlog,\\n }\\n\\n logging.info(\\\"Final Results:\\\")\\n logging.info(output)\\n\\n return output\",\n \"def execute(cf):\\n\\n ##Ports and parameters\\n train_set = cf.get_input(\\\"train_set\\\") #training set. Typically even_file\\n test_set = cf.get_input(\\\"test_set\\\") #test set. Typically odd_file\\n WM1 = cf.get_input(\\\"WM1\\\")\\n WM2 = cf.get_input(\\\"WM2\\\")\\n WM3 = cf.get_input(\\\"WM3\\\")\\n WM4 = cf.get_input(\\\"WM4\\\")\\n WM5 = cf.get_input(\\\"WM5\\\")\\n WM6 = cf.get_input(\\\"WM6\\\")\\n WM7 = cf.get_input(\\\"WM7\\\")\\n WM8 = cf.get_input(\\\"WM8\\\")\\n WM9 = cf.get_input(\\\"WM9\\\")\\n WM10 = cf.get_input(\\\"WM10\\\")\\n WM11 = cf.get_input(\\\"WM11\\\")\\n WM12 = cf.get_input(\\\"WM12\\\")\\n WM13 = cf.get_input(\\\"WM13\\\")\\n WM14 = cf.get_input(\\\"WM14\\\")\\n WM15 = cf.get_input(\\\"WM15\\\")\\n WM16 = cf.get_input(\\\"WM16\\\")\\n WM17 = cf.get_input(\\\"WM17\\\")\\n WM18 = cf.get_input(\\\"WM18\\\")\\n WM19 = cf.get_input(\\\"WM19\\\")\\n WM20 = cf.get_input(\\\"WM20\\\")\\n WMdir = cf.get_input(\\\"WMdir\\\")\\n WMdir2 = cf.get_input(\\\"WMdir2\\\")\\n basefreqs = cf.get_input(\\\"BaseFrequencies\\\")\\n ufemodel_path = cf.get_input(\\\"UFEmodel\\\")\\n\\n bestWM = cf.get_output(\\\"BestWM\\\")\\n log_file = cf.get_output(\\\"log_file\\\")\\n interm = cf.get_output(\\\"intermediate\\\")\\n\\n genome = cf.get_parameter('genome', 'string')\\n motevo_path = cf.get_parameter('motevo_path', 'string')\\n aligned = cf.get_parameter(\\\"aligned\\\", \\\"boolean\\\")\\n\\n os.mkdir(interm)\\n\\n\\n\\n # Read stuff in\\n WMs = [i for i in[WM1, WM2, WM3, WM4, WM5, WM6, WM7, WM8, WM9, WM10, WM11, WM12, WM13, WM14, WM15, WM16, WM17, WM18, WM19, WM20] if i]\\n\\n if WMdir:\\n WMs += [os.path.join(WMdir, wm) for wm in os.listdir(WMdir)]\\n\\n if WMdir2:\\n WMs += [os.path.join(WMdir2, wm) for wm in os.listdir(WMdir2)]\\n\\n f = open(basefreqs)\\n ATfreq = float(f.readline().strip().split()[1])\\n GCfreq = float(f.readline().strip().split()[1])\\n f.close()\\n\\n\\n # Compute stuff: optimal priors and then likelihood of test set\\n optpriors = []\\n logliks = []\\n\\n for i, WM in enumerate(WMs):\\n\\n wmlen = len(open(WM).readlines())-4\\n\\n # 1. Fit prior on training set with EM\\n tag = 'fitP_%i' %(i+1)\\n params, sites, priors, loglikfile = giveMotevoParamFile(genome, wmlen, interm, tag, aligned, ufemodel_path, ATfreq, GCfreq, emprior=1, bgorder=0, bgprior=0.99)\\n r = runMotevo(motevo_path, train_set, params, WM, interm, tag)\\n if r != 0:\\n print 'motevo failed ', tag\\n sys.exit(1)\\n\\n # prior file:\\n # WM_name final_prior nr_of_sites density\\n # /import/bc2/home/nimwegen/GROUP/hseq_pipeline/severin/Anduril/Pipeline/PipeLineSource/TESTRUN/NRF1_Z2/OUTPUT/NRF1_FgBg-runmotevoPG2_1/Logo 0.016554 635.008 0.251863\\n # background 0.983446 37724.8 0.748137\\n # UFEwm 0 0 0\\n\\n optprior = float(open(priors).readlines()[1].split()[1])\\n bgprior=(1-optprior)\\n print bgprior\\n\\n # 2. Compute log-likelihood on test set with optimal prior from training set and without EM\\n tag = 'compLL_%i' %(i+1)\\n params, sites, priors, loglikfile = giveMotevoParamFile(genome, wmlen, interm, tag, aligned, ufemodel_path, ATfreq, GCfreq, emprior=0, bgorder=0, bgprior=bgprior)\\n runMotevo(motevo_path, train_set, params, WM, interm, tag)\\n\\n a = loadtxt(loglikfile, usecols=[1])\\n ll = sum(a)\\n\\n logliks.append(ll)\\n optpriors.append(optprior)\\n\\n print logliks\\n\\n\\n\\n #replace name in WM file with bestWM\\n lines = open(WMs[argmax(logliks)]).readlines()\\n lines[1] = 'NA BestWM\\\\n'\\n bwm = open(bestWM, 'w')\\n bwm.write(''.join(lines))\\n\\n\\n l = open(log_file, 'w')\\n\\n l.write('WM_name\\\\tWM_path\\\\tlog_likelihood\\\\topt_prior\\\\n')\\n\\n names = ['WM_%i\\\\t%s\\\\t%.4f\\\\t%s' %(i+1, WMs[i], logliks[i], optpriors[i]) for i in arange(len(WMs))]\\n\\n l.write('\\\\n'.join(names))\\n l.close()\\n\\n\\n return 0\",\n \"def setUp(self):\\n # Set Model Parameters\\n odeparam = np.array([1, 2])\\n y0, y0_unc = 1.0, 0 \\n t0, tmax = 0.0, 1.25\\n\\n # Set Method Parameters\\n q = 1\\n h = 0.1\\n\\n # Set up and solve ODE\\n ibm = statespace.IBM(q=q, dim=1)\\n solver = linsolve.LinearisedODESolver(ibm)\\n ivp = linode.LogisticODE(t0, tmax, odeparam, y0, y0_unc)\\n tsteps, means, __, rhs_parts, uncerts = solver.solve(ivp, stepsize=h)\\n self.mean = odesolver.get_trajectory(means, 0, 0)\\n\\n # Set up BM and IBM covariance matrices\\n evalpt = np.array(tsteps[[-1]])\\n derdat = (tsteps, rhs_parts, 0.)\\n\\n const, jacob = linearisation.compute_linearisation(\\n ssm=ibm, initial_value=y0,\\n derivative_data=derdat, prdct_tsteps=evalpt)\\n\\n # Compute GP Estimation of filter mean at t=tmax\\n self.postmean = const + np.dot(jacob, odeparam)\",\n \"def construct_test_model(self):\\n # Set the placeholder for the input episode\\n self.inputa = tf.placeholder(tf.float32)\\n self.inputb = tf.placeholder(tf.float32)\\n self.labela = tf.placeholder(tf.float32)\\n self.labelb = tf.placeholder(tf.float32)\\n\\n with tf.variable_scope('meta-test-model', reuse=None) as training_scope: \\n # construct the model weights \\n self.ss_weights = ss_weights = self.construct_resnet_ss_weights()\\n self.weights = weights = self.construct_resnet_weights()\\n self.fc_weights = fc_weights = self.construct_fc_weights()\\n\\n # Load test base epoch number from FLAGS\\n num_updates = FLAGS.test_base_epoch_num\\n\\n def task_metalearn(inp, reuse=True):\\n \\\"\\\"\\\"The function to process one episode in a meta-batch.\\n Args:\\n inp: the input episode.\\n reuse: whether reuse the variables for the normalization.\\n Returns:\\n A serious outputs like losses and accuracies.\\n \\\"\\\"\\\"\\n # Seperate inp to different variables\\n inputa, inputb, labela, labelb = inp\\n # Generate empty list to record accuracies\\n accb_list = []\\n\\n # Embed the input images to embeddings with ss weights\\n emb_outputa = self.forward_resnet(inputa, weights, ss_weights, reuse=reuse)\\n emb_outputb = self.forward_resnet(inputb, weights, ss_weights, reuse=True)\\n\\n # This part is similar to the meta-train function, you may refer to the comments above\\n outputa = self.forward_fc(emb_outputa, fc_weights)\\n lossa = self.loss_func(outputa, labela) \\n grads = tf.gradients(lossa, list(fc_weights.values()))\\n gradients = dict(zip(fc_weights.keys(), grads))\\n fast_fc_weights = dict(zip(fc_weights.keys(), [fc_weights[key] - \\\\\\n self.update_lr*gradients[key] for key in fc_weights.keys()]))\\n outputb = self.forward_fc(emb_outputb, fast_fc_weights)\\n accb = tf.contrib.metrics.accuracy(tf.argmax(tf.nn.softmax(outputb), 1), tf.argmax(labelb, 1))\\n accb_list.append(accb)\\n \\n for j in range(num_updates - 1):\\n lossa = self.loss_func(self.forward_fc(emb_outputa, fast_fc_weights), labela)\\n grads = tf.gradients(lossa, list(fast_fc_weights.values()))\\n gradients = dict(zip(fast_fc_weights.keys(), grads))\\n fast_fc_weights = dict(zip(fast_fc_weights.keys(), [fast_fc_weights[key] - \\\\\\n self.update_lr*gradients[key] for key in fast_fc_weights.keys()]))\\n outputb = self.forward_fc(emb_outputb, fast_fc_weights)\\n accb = tf.contrib.metrics.accuracy(tf.argmax(tf.nn.softmax(outputb), 1), tf.argmax(labelb, 1))\\n accb_list.append(accb)\\n\\n lossb = self.loss_func(outputb, labelb)\\n\\n task_output = [lossb, accb, accb_list]\\n\\n return task_output\\n\\n if FLAGS.norm is not 'None':\\n unused = task_metalearn((self.inputa[0], self.inputb[0], self.labela[0], self.labelb[0]), False)\\n\\n out_dtype = [tf.float32, tf.float32, [tf.float32]*num_updates]\\n\\n result = tf.map_fn(task_metalearn, elems=(self.inputa, self.inputb, self.labela, self.labelb), \\\\\\n dtype=out_dtype, parallel_iterations=FLAGS.meta_batch_size)\\n lossesb, accsb, accsb_list = result\\n\\n self.metaval_total_loss = total_loss = tf.reduce_sum(lossesb)\\n self.metaval_total_accuracy = total_accuracy = tf.reduce_sum(accsb)\\n self.metaval_total_accuracies = total_accuracies =[tf.reduce_sum(accsb_list[j]) for j in range(num_updates)]\",\n \"def test_construct_f_(self):\\n model = roi._core._ContinuousROM('')\\n\\n # Check that the constructed f takes the right number of arguments.\\n model.modelform = \\\"cA\\\"\\n model.c_, model.A_ = 1, 1\\n model.Hc_, model.Gc_, model.B_ = None, None, None\\n model._construct_f_()\\n with pytest.raises(TypeError) as ex:\\n model.f_(1)\\n assert ex.value.args[0] == \\\\\\n \\\"<lambda>() missing 1 required positional argument: 'x_'\\\"\",\n \"def test_constructor(self):\\n # Record the model types of all the models to be created\\n all_model_types = model_type_to_display_name.keys()\\n\\n # Record the attribute / value pairs that are common to all models.\\n common_attr_value_dict = {\\\"data\\\": self.fake_df,\\n \\\"name_spec\\\": self.fake_names,\\n \\\"design\\\": self.fake_design,\\n \\\"ind_var_names\\\": self.fake_names[\\\"x\\\"],\\n \\\"alt_id_col\\\": self.alt_id_col,\\n \\\"obs_id_col\\\": self.obs_id_col,\\n \\\"choice_col\\\": self.choice_col,\\n \\\"specification\\\": self.fake_specification,\\n \\\"alt_IDs\\\": self.fake_df[\\\"alt_id\\\"].values,\\n \\\"choices\\\": self.fake_df[\\\"choice\\\"].values}\\n\\n # Create a shape name dictionary to relate the various models to the\\n # names of their shape parameters.\\n shape_name_dict = {\\\"MNL\\\": None,\\n \\\"Asym\\\": self.fake_shape_names[:2],\\n \\\"Cloglog\\\": None,\\n \\\"Scobit\\\": self.fake_shape_names,\\n \\\"Uneven\\\": self.fake_shape_names,\\n \\\"Nested Logit\\\": None,\\n \\\"Mixed Logit\\\": None}\\n\\n # Create a shape reference position dictionary to relate the various\\n # models to their shape reference positions.\\n shape_ref_dict = {}\\n for key in shape_name_dict:\\n shape_ref_dict[key] = (None if key != \\\"Asym\\\" else\\n self.fake_shape_ref_pos)\\n\\n # Create an intercept_names and intercept_ref_position dictionary to\\n # relate the various models to their respective kwargs.\\n intercept_names_dict = {}\\n intercept_ref_dict = {}\\n for key in shape_name_dict:\\n if key in [\\\"MNL\\\", \\\"Nested Logit\\\", \\\"Mixed Logit\\\"]:\\n intercept_names_dict[key] = None\\n intercept_ref_dict[key] = None\\n else:\\n intercept_names_dict[key] = self.fake_intercept_names\\n intercept_ref_dict[key] = self.fake_intercept_ref_pos\\n\\n # Create a nest_names dictionary to relate the various models to their\\n # nest_name attributes\\n nest_name_dict = {}\\n nest_spec_dict = {}\\n for key in shape_name_dict:\\n if key != \\\"Nested Logit\\\":\\n nest_name_dict[key] = None\\n nest_spec_dict[key] = None\\n else:\\n nest_name_dict[key] = list(self.fake_nest_spec.keys())\\n nest_spec_dict[key] = self.fake_nest_spec\\n\\n # Create dictionaries for the mixing_id_col, mixing_vars, and\\n # mixing_pos attributes\\n mixing_id_col_dict = {}\\n mixing_vars_dict = {}\\n mixing_pos_dict = {}\\n\\n for key in shape_name_dict:\\n if key != \\\"Mixed Logit\\\":\\n mixing_id_col_dict[key] = None\\n mixing_vars_dict[key] = None\\n mixing_pos_dict[key] = None\\n else:\\n mixing_id_col_dict[key] = self.obs_id_col\\n mixing_vars_dict[key] = self.fake_names[\\\"x\\\"]\\n mixing_pos_dict[key] = [0]\\n\\n # Record the attribute / value pairs that vary across models\\n varying_attr_value_dict = {\\\"model_type\\\": model_type_to_display_name,\\n \\\"intercept_names\\\": intercept_names_dict,\\n \\\"intercept_ref_position\\\":\\n intercept_ref_dict,\\n \\\"shape_names\\\": shape_name_dict,\\n \\\"shape_ref_position\\\": shape_ref_dict,\\n \\\"nest_names\\\": nest_name_dict,\\n \\\"nest_spec\\\": nest_spec_dict,\\n \\\"mixing_id_col\\\": mixing_id_col_dict,\\n \\\"mixing_vars\\\": mixing_vars_dict,\\n \\\"mixing_pos\\\": mixing_pos_dict}\\n\\n # Set up the keyword arguments that are needed for each of the model\\n # types\\n variable_kwargs = {}\\n for model_name in all_model_types:\\n variable_kwargs[model_name] = {}\\n variable_kwargs[model_name][\\\"intercept_names\\\"] =\\\\\\n intercept_names_dict[model_name]\\n variable_kwargs[model_name][\\\"intercept_ref_pos\\\"] =\\\\\\n intercept_ref_dict[model_name]\\n variable_kwargs[model_name][\\\"shape_ref_pos\\\"] =\\\\\\n shape_ref_dict[model_name]\\n variable_kwargs[model_name][\\\"shape_names\\\"] =\\\\\\n shape_name_dict[model_name]\\n variable_kwargs[model_name][\\\"nest_spec\\\"] =\\\\\\n nest_spec_dict[model_name]\\n variable_kwargs[model_name][\\\"mixing_id_col\\\"] =\\\\\\n mixing_id_col_dict[model_name]\\n variable_kwargs[model_name][\\\"mixing_vars\\\"] =\\\\\\n mixing_vars_dict[model_name]\\n\\n # Execute the test for each model type\\n for model_name in all_model_types:\\n # Update the model type in the list of constructor args\\n self.constructor_args[-1] = model_name\\n\\n # Use this specific model's keyword arguments\\n self.constructor_kwargs.update(variable_kwargs[model_name])\\n\\n # Construct the model object\\n model_obj = pylogit.create_choice_model(*self.constructor_args,\\n **self.constructor_kwargs)\\n\\n # Make sure that the constructor has all of the required attributes\\n for attr in common_attr_value_dict:\\n value = common_attr_value_dict[attr]\\n if isinstance(value, pd.DataFrame):\\n self.assertTrue(value.equals(model_obj.data))\\n elif isinstance(value, np.ndarray):\\n npt.assert_allclose(value,\\n model_obj.__getattribute__(attr))\\n else:\\n self.assertEqual(value,\\n model_obj.__getattribute__(attr))\\n\\n for attr in varying_attr_value_dict:\\n value = varying_attr_value_dict[attr][model_name]\\n\\n self.assertEqual(value,\\n model_obj.__getattribute__(attr))\\n\\n return None\",\n \"def __init__(\\n self,\\n comm,\\n model,\\n assembler=None,\\n gen_output=None,\\n thermal_index=0,\\n struct_id=None,\\n tacs_comm=None,\\n override_rotx=False,\\n Fvec=None,\\n nprocs=None,\\n ):\\n\\n self.comm = comm\\n self.tacs_comm = tacs_comm\\n self.nprocs = nprocs\\n\\n # Flag to output heat flux instead of rotx\\n self.override_rotx = override_rotx\\n\\n # const load in TACS, separate and added onto from coupled loading\\n self.has_const_load = Fvec is not None\\n self.const_force = None\\n if self.has_const_load and assembler is not None:\\n self.const_force = Fvec.getArray()\\n\\n # Get the list of active design variables from the FUNtoFEM model. This\\n # returns the variables in the FUNtoFEM order. By scenario/body.\\n self.variables = model.get_variables()\\n\\n # Get the structural variables from the global list of variables.\\n self.struct_variables = []\\n for var in self.variables:\\n if var.analysis_type == \\\"structural\\\":\\n self.struct_variables.append(var)\\n\\n # Set the assembler object - if it exists or not\\n self._initialize_variables(\\n model, assembler, thermal_index=thermal_index, struct_id=struct_id\\n )\\n\\n if self.assembler is not None:\\n if self.tacs_comm is None:\\n self.tacs_comm = self.assembler.getMPIComm()\\n\\n # Initialize the structural nodes in the bodies\\n struct_X = self.struct_X.getArray()\\n for body in model.bodies:\\n body.initialize_struct_nodes(struct_X, struct_id=struct_id)\\n\\n # Generate output\\n self.gen_output = gen_output\\n\\n return\",\n \"def DontuseThis():\\n BCM_outputs = ['phi','rho','theta',\\n 'r_probabilityMaps','l_probabilityMaps',\\n 'models']\\n BCM_Models = pe.Node(interface=nio.DataGrabber(input_names=['structures'],\\n outfields=BCM_outputs),\\n name='10_BCM_Models')\\n BCM_Models.inputs.base_directory = atlas_fname_wpath\\n BCM_Models.inputs.template_args['phi'] = [['spatialImages','phi','nii.gz']]\\n BCM_Models.inputs.template_args['rho'] = [['spatialImages','rho','nii.gz']]\\n BCM_Models.inputs.template_args['theta'] = [['spatialImages','theta','nii.gz']]\\n BCM_Models.inputs.template_args['r_probabilityMaps'] = [['structures']]\\n BCM_Models.inputs.template_args['l_probabilityMaps'] = [['structures']]\\n BCM_Models.inputs.template_args['models'] = [['structures']]\\n\\n BRAINSCut_structures = ['caudate','thalamus','putamen','hippocampus']\\n #BRAINSCut_structures = ['caudate','thalamus']\\n BCM_Models.iterables = ( 'structures', BRAINSCut_structures )\\n BCM_Models.inputs.template = '%s/%s.%s'\\n BCM_Models.inputs.field_template = dict(\\n r_probabilityMaps='probabilityMaps/r_%s_ProbabilityMap.nii.gz',\\n l_probabilityMaps='probabilityMaps/l_%s_ProbabilityMap.nii.gz',\\n models='modelFiles/%sModel*',\\n )\\n\\n \\\"\\\"\\\"\\n The xml creation and BRAINSCut need to be their own mini-pipeline that gets\\n executed once for each of the structures in BRAINSCut_structures. This can be\\n accomplished with a map node and a new pipeline.\\n \\\"\\\"\\\"\\n \\\"\\\"\\\"\\n Create xml file for BRAINSCut\\n \\\"\\\"\\\"\\n\\n\\n BFitAtlasToSubject = pe.Node(interface=BRAINSFit(),name=\\\"BFitAtlasToSubject\\\")\\n BFitAtlasToSubject.inputs.costMetric=\\\"MMI\\\"\\n BFitAtlasToSubject.inputs.maskProcessingMode=\\\"ROI\\\"\\n BFitAtlasToSubject.inputs.numberOfSamples=100000\\n BFitAtlasToSubject.inputs.numberOfIterations=[1500,1500]\\n BFitAtlasToSubject.inputs.numberOfHistogramBins=50\\n BFitAtlasToSubject.inputs.maximumStepLength=0.2\\n BFitAtlasToSubject.inputs.minimumStepLength=[0.005,0.005]\\n BFitAtlasToSubject.inputs.transformType= [\\\"Affine\\\",\\\"BSpline\\\"]\\n BFitAtlasToSubject.inputs.maxBSplineDisplacement= 7\\n BFitAtlasToSubject.inputs.maskInferiorCutOffFromCenter=65\\n BFitAtlasToSubject.inputs.splineGridSize=[28,20,24]\\n BFitAtlasToSubject.inputs.outputVolume=\\\"Trial_Initializer_Output.nii.gz\\\"\\n BFitAtlasToSubject.inputs.outputTransform=\\\"Trial_Initializer_Output.mat\\\"\\n cutWF.connect(SplitAvgBABC,'avgBABCT1',BFitAtlasToSubject,'fixedVolume')\\n cutWF.connect(BABC,'outputLabels',BFitAtlasToSubject,'fixedBinaryVolume')\\n cutWF.connect(BAtlas,'template_t1',BFitAtlasToSubject,'movingVolume')\\n cutWF.connect(BAtlas,'template_brain',BFitAtlasToSubject,'movingBinaryVolume')\\n cutWF.connect(BLI,'outputTransformFilename',BFitAtlasToSubject,'initialTransform')\\n\\n CreateBRAINSCutXML = pe.Node(Function(input_names=['rho','phi','theta',\\n 'model',\\n 'r_probabilityMap',\\n 'l_probabilityMap',\\n 'atlasT1','atlasBrain',\\n 'subjT1','subjT2',\\n 'subjT1GAD','subjT2GAD',\\n 'subjSGGAD','subjBrain',\\n 'atlasToSubj','output_dir'],\\n output_names=['xml_filename','rl_structure_filename_list'],\\n function = create_BRAINSCut_XML),\\n overwrite = True,\\n name=\\\"CreateBRAINSCutXML\\\")\\n\\n ## HACK Makde better directory\\n CreateBRAINSCutXML.inputs.output_dir = \\\".\\\" #os.path.join(cutWF.base_dir, \\\"BRAINSCut_output\\\")\\n cutWF.connect(BCM_Models,'models',CreateBRAINSCutXML,'model')\\n cutWF.connect(BCM_Models,'rho',CreateBRAINSCutXML,'rho')\\n cutWF.connect(BCM_Models,'phi',CreateBRAINSCutXML,'phi')\\n cutWF.connect(BCM_Models,'theta',CreateBRAINSCutXML,'theta')\\n cutWF.connect(BCM_Models,'r_probabilityMaps',CreateBRAINSCutXML,'r_probabilityMap')\\n cutWF.connect(BCM_Models,'l_probabilityMaps',CreateBRAINSCutXML,'l_probabilityMap')\\n cutWF.connect(BAtlas,'template_t1',CreateBRAINSCutXML,'atlasT1')\\n cutWF.connect(BAtlas,'template_brain',CreateBRAINSCutXML,'atlasBrain')\\n cutWF.connect(SplitAvgBABC,'avgBABCT1',CreateBRAINSCutXML,'subjT1')\\n cutWF.connect(SplitAvgBABC,'avgBABCT2',CreateBRAINSCutXML,'subjT2')\\n cutWF.connect(GADT1,'outputVolume',CreateBRAINSCutXML,'subjT1GAD')\\n cutWF.connect(GADT2,'outputVolume',CreateBRAINSCutXML,'subjT2GAD')\\n cutWF.connect(SGI,'outputFileName',CreateBRAINSCutXML,'subjSGGAD')\\n cutWF.connect(BABC,'outputLabels',CreateBRAINSCutXML,'subjBrain')\\n cutWF.connect(BFitAtlasToSubject,'outputTransform',CreateBRAINSCutXML,'atlasToSubj')\\n #CreateBRAINSCutXML.inputs.atlasToSubj = \\\"INTERNAL_REGISTER.mat\\\"\\n #cutWF.connect(BABC,'atlasToSubjectTransform',CreateBRAINSCutXML,'atlasToSubj')\\n\\n \\\"\\\"\\\"\\n ResampleNACLabels\\n \\\"\\\"\\\"\\n ResampleAtlasNACLabels=pe.Node(interface=BRAINSResample(),name=\\\"ResampleAtlasNACLabels\\\")\\n ResampleAtlasNACLabels.inputs.interpolationMode = \\\"NearestNeighbor\\\"\\n ResampleAtlasNACLabels.inputs.outputVolume = \\\"atlasToSubjectNACLabels.nii.gz\\\"\\n\\n cutWF.connect(cutWF,'OutputSpec.atlasToSubjectTransform',ResampleAtlasNACLabels,'warpTransform')\\n cutWF.connect(cutWF,'OutputSpec.t1_corrected',ResampleAtlasNACLabels,'referenceVolume')\\n cutWF.connect(BAtlas,'template_nac_lables',ResampleAtlasNACLabels,'inputVolume')\\n\\n \\\"\\\"\\\"\\n BRAINSMush\\n \\\"\\\"\\\"\\n BMUSH=pe.Node(interface=BRAINSMush(),name=\\\"BMUSH\\\")\\n BMUSH.inputs.outputVolume = \\\"MushImage.nii.gz\\\"\\n BMUSH.inputs.outputMask = \\\"MushMask.nii.gz\\\"\\n BMUSH.inputs.lowerThresholdFactor = 1.2\\n BMUSH.inputs.upperThresholdFactor = 0.55\\n\\n cutWF.connect(myLocalTCWF,'OutputSpec.t1_corrected',BMUSH,'inputFirstVolume')\\n cutWF.connect(myLocalTCWF,'OutputSpec.t2_corrected',BMUSH,'inputSecondVolume')\\n cutWF.connect(myLocalTCWF,'OutputSpec.outputLabels',BMUSH,'inputMaskVolume')\\n\\n \\\"\\\"\\\"\\n BRAINSROIAuto\\n \\\"\\\"\\\"\\n BROI = pe.Node(interface=BRAINSROIAuto(), name=\\\"BRAINSROIAuto\\\")\\n BROI.inputs.closingSize=12\\n BROI.inputs.otsuPercentileThreshold=0.01\\n BROI.inputs.thresholdCorrectionFactor=1.0\\n BROI.inputs.outputROIMaskVolume = \\\"temproiAuto_t1_ACPC_corrected_BRAINSABC.nii.gz\\\"\\n cutWF.connect(myLocalTCWF,'OutputSpec.t1_corrected',BROI,'inputVolume')\\n\\n \\\"\\\"\\\"\\n Split the implicit outputs of BABCext\\n \\\"\\\"\\\"\\n SplitAvgBABC = pe.Node(Function(input_names=['in_files','T1_count'], output_names=['avgBABCT1','avgBABCT2'],\\n function = get_first_T1_and_T2), run_without_submitting=True, name=\\\"99_SplitAvgBABC\\\")\\n SplitAvgBABC.inputs.T1_count = 1 ## There is only 1 average T1 image.\\n\\n cutWF.connect(myLocalTCWF,'OutputSpec.outputAverageImages',SplitAvgBABC,'in_files')\\n\\n\\n\\n def printFullPath(outFileFullPath):\\n print(\\\"=\\\"*80)\\n print(\\\"=\\\"*80)\\n print(\\\"=\\\"*80)\\n print(\\\"=\\\"*80)\\n print(\\\"{0}\\\".format(outFileFullPath))\\n return outFileFullPath\\n printOutImage = pe.Node( Function(function=printFullPath, input_names = ['outFileFullPath'], output_names = ['genoutFileFullPath']), run_without_submitting=True, name=\\\"99_printOutImage\\\")\\n cutWF.connect( GADT2, 'outputVolume', printOutImage, 'outFileFullPath' )\",\n \"def run(cls, model):\\n label = model.label\\n print(\\\"stage1: {label} model: initializing\\\".format(label=label))\\n\\n defs_input = model.define_api() # input, original definitions\\n\\n print(\\\"stage1: {label} model: analyzing API\\\".format(label=label))\\n\\n # Compute any needed derivatives which are not already in the API\\n # and for which we have the defs.\\n defs = defs_input.copy() # output, final optimized definitions\\n for j, key in enumerate(sorted(defs_input.keys(), key=symutil.sortkey), start=1): # sort for progress readability\\n name = symutil.derivatives_to_names_in(key) # key is a Symbol or a Derivative\\n expr = defs_input[key]\\n\\n print(\\\"stage1: ({iteration:d}/{total:d}) {label} model: processing {name}\\\".format(iteration=j,\\n total=len(defs_input.keys()),\\n label=label, name=name))\\n\\n defs[key] = cls.process(expr, defs, model.simplify)\\n\\n # Delete identically zero definitions\\n zero = sy.S.Zero\\n defs = {k: v for k, v in defs.items() if v != zero}\\n\\n print(\\\"stage1: {label} model: generating code\\\".format(label=label))\\n\\n basename = \\\"mgs_{label}_impl\\\".format(label=label) # filename without extension\\n name_expr_pairs = cls.make_name_expr_pairs(defs)\\n generated_code = codegen(name_expr_pairs,\\n language=\\\"f95\\\",\\n project=\\\"elmer-mgs-galfenol\\\",\\n prefix=basename)\\n\\n return [(label, filename, cls.finalize(content))\\n for filename, content in generated_code]\",\n \"def setUp(self):\\n # Set Model Parameters\\n odeparam = np.array([0, 1, 1, 2])\\n y0, y0_unc = np.ones(2), 0 * np.ones(2)\\n t0, tmax = 0.0, 1.25\\n\\n # Set Method Parameters\\n q = 1\\n h = 0.1\\n\\n # Set up and solve ODE\\n ibm = statespace.IBM(q=q, dim=len(y0))\\n solver = linsolve.LinearisedODESolver(ibm)\\n ivp = linode.LotkaVolterra(t0, tmax, odeparam, y0, y0_unc)\\n tsteps, means, __, rhs_parts, uncerts = solver.solve(ivp, stepsize=h)\\n self.mean = odesolver.get_trajectory_multidim(means, [0, 1], 0)\\n\\n # Set up BM and IBM covariance matrices\\n evalpt = np.array(tsteps[[-1, -10]])\\n derdat = (tsteps, rhs_parts, 0.)\\n\\n const, jacob = linearisation.compute_linearisation(\\n ssm=ibm, initial_value=y0,\\n derivative_data=derdat, prdct_tsteps=evalpt)\\n\\n # Compute GP Estimation of filter mean at t=tmax\\n postmean = const + np.dot(jacob, odeparam)\\n self.postmean = postmean.reshape((2, 2))\",\n \"def setUp(self):\\n # Set Model Parameters\\n odeparam = np.array([0, 1, 1, 2])\\n y0, y0_unc = np.ones(2), 0 * np.ones(2)\\n t0, tmax = 0.0, 1.25\\n\\n # Set Method Parameters\\n q = 1\\n h = 0.1\\n\\n # Set up and solve ODE\\n ibm = statespace.IBM(q=q, dim=len(y0))\\n solver = linsolve.LinearisedODESolver(ibm)\\n ivp = linode.LotkaVolterra(t0, tmax, odeparam, y0, y0_unc)\\n tsteps, means, __, rhs_parts, uncerts = solver.solve(ivp, stepsize=h)\\n self.mean = odesolver.get_trajectory_multidim(means, [0, 1], 0)\\n\\n # Set up BM and IBM covariance matrices\\n evalpt = np.array(tsteps[[-1]])\\n derdat = (tsteps, rhs_parts, 0.)\\n\\n const, jacob = linearisation.compute_linearisation(\\n ssm=ibm, initial_value=y0,\\n derivative_data=derdat, prdct_tsteps=evalpt)\\n\\n # Compute GP Estimation of filter mean at t=tmax\\n postmean = const + np.dot(jacob, odeparam)\\n self.postmean = postmean.reshape((2,))\",\n \"def example():\\n num_user, num_item, ratings = build_ticket()\\n \\n # suffle_data\\n np.random.shuffle(ratings)\\n\\n # split data to training & validation\\n train_pct = 0.9\\n train_size = int(train_pct * len(ratings))\\n train = ratings[:train_size]\\n validation = ratings[train_size:]\\n\\n # params\\n num_feature = 5\\n bmf_model = BayesianMatrixFactorization(\\n num_user, num_item, num_feature, train, validation, max_rating=1, min_rating=0, ratingsMatirx=ratings)\\n\\n start_time = time.clock()\\n bmf_model.estimate(10)\\n end_time = time.clock()\\n \\n mat = np.matrix(bmf_model.item_features)\\n with open('../data/ticket/item_feature', 'w') as f:\\n for line in mat:\\n np.savetxt(f, line, fmt='%.5f')\\n\\n print \\\"time spend = %.3f\\\" % (end_time - start_time)\\n\\n return bmf_model\",\n \"def test_fleur_create_mag_validation_wrong_inputs(self, fleur_local_code, inpgen_local_code, generate_structure2):\\n from aiida.orm import Dict\\n\\n # prepare input nodes and dicts\\n options = {\\n 'resources': {\\n 'num_machines': 1,\\n 'num_mpiprocs_per_machine': 1\\n },\\n 'max_wallclock_seconds': 5 * 60,\\n 'withmpi': False,\\n 'custom_scheduler_commands': ''\\n }\\n options = Dict(dict=options).store()\\n\\n wf_parameters = Dict({'points': 9, 'step': 0.002, 'guess': 1.00, 'wrong_key': None})\\n wf_parameters.store()\\n structure = generate_structure2()\\n structure.store()\\n\\n ################\\n # Create builders\\n # interface of exposed scf is tested elsewhere\\n\\n # 1. create builder with wrong wf parameters\\n builder_additionalkeys = FleurCreateMagneticWorkChain.get_builder()\\n builder_additionalkeys.structure = structure\\n builder_additionalkeys.wf_parameters = wf_parameters\\n builder_additionalkeys.scf.fleur = fleur_local_code\\n builder_additionalkeys.scf.inpgen = inpgen_local_code\\n\\n ###################\\n # now run the builders all should fail early with exit codes\\n\\n # 1. structure and fleurinp given\\n out, node = run_get_node(builder_additionalkeys)\\n assert out == {}\\n assert node.is_finished\\n assert not node.is_finished_ok\\n assert node.exit_status == 230\",\n \"def RunModel(self):\\n raise UnimplementedMethodException()\",\n \"def setup_to_finetune(model):\\n\\n for layer in model.layers[:NB_VGG_LAYERS_TO_FREEZE]:\\n layer.trainable = False\\n for layer in model.layers[NB_VGG_LAYERS_TO_FREEZE:]:\\n layer.trainable = True\\n\\n # opt = Adam(lr=0.0001)\\n model.compile(optimizer=SGD(lr=0.0001, momentum=0.9),\\n loss='categorical_crossentropy',\\n metrics=['categorical_accuracy', f1_score])\",\n \"def turbsim_example(): \\n # run TurbSim\\n from runTurbSim import runTurbSim\\n\\n ts = runTurbSim()\\n ts.ts_exe = \\\"/Users/pgraf/opt/windcode-7.31.13/TurbSim/build/TurbSim_glin64\\\"\\n ts.run_dir = \\\"turbsim_test_run\\\"\\n tscase = 2\\n if (tscase == 1):\\n ts.ts_dir = \\\"TurbSimTest\\\"\\n ts.ts_file = \\\"turbsim_template.inp\\\"\\n else:\\n ts.ts_dir = \\\"/Users/pgraf/work/wese/fatigue12-13/from_gordie/SparFAST3.orig/TurbSim\\\"\\n ts.ts_file = \\\"TurbSim.inp\\\"\\n \\n tmax = 12\\n ws=20.0 # wind speed\\n\\n ts.set_dict({\\\"URef\\\": ws, \\\"AnalysisTime\\\":tmax, \\\"UsableTime\\\":tmax})\\n ts.execute()\\n\\n # then run FAST\\n fast = runFAST()\\n fast.fst_exe = \\\"/Users/pgraf/opt/windcode-7.31.13/build/FAST_glin64\\\"\\n case = 2\\n if (case==1):\\n fast.fst_dir = \\\"FAST_VT/OC3_Files/\\\"\\n fast.fst_file = \\\"NRELOffshrBsline5MW_Monopile_RF.fst\\\" \\n elif case ==2:\\n fast.fst_dir = \\\"/Users/pgraf/work/wese/fatigue12-13/from_gordie/SparFAST3.orig\\\"\\n fast.fst_file = \\\"NRELOffshrBsline5MW_Floating_OC3Hywind.fst\\\"\\n else:\\n fast.fst_dir = \\\"InputFilesToWrite/\\\"\\n fast.fst_file = \\\"NREL5MW_Monopile_Rigid.v7.02.fst\\\"\\n\\n fast.run_dir = \\\"tsfast_test_run_dir\\\" ## either abs or rel path ok\\n\\n # here we link turbsim -> fast\\n tswind_file = os.path.join(ts.ts_dir, \\\"%s.wnd\\\" % ts.run_name)\\n fast.set_wind_file(os.path.abspath(tswind_file))\\n\\n fast.fstDict['Vhub']=ws\\n fast.fstDict['RotSpeed'] = 12.03\\n fast.fstDict[\\\"TMax\\\"] = tmax\\n fast.fstDict['TStart'] = 0.0\\n\\n fast.setOutputs(['RotPwr'])\\n\\n fast.execute()\\n\\n out = fast.getOutputValue(\\\"RotPwr\\\")\\n\\n print \\\"TurbSim example complete:\\\"\\n print \\\"max power\\\"\\n print max(out)\",\n \"def setUp(self):\\n # Set Model Parameters\\n odeparam = 1.\\n y0, y0_unc = 1.0, 0 \\n t0, tmax = 0.0, 1.25\\n\\n # Set Method Parameters\\n q = 1\\n h = 0.1\\n\\n # Set up and solve ODE\\n ibm = statespace.IBM(q=q, dim=1)\\n solver = linsolve.LinearisedODESolver(ibm)\\n ivp = linode.LinearODE(t0, tmax, odeparam, y0, y0_unc)\\n tsteps, means, __, rhs_parts, uncerts = solver.solve(ivp, stepsize=h)\\n self.mean = odesolver.get_trajectory(means, 0, 0)\\n\\n # Set up BM and IBM covariance matrices\\n evalpt = np.array(tsteps[[-1, -10]])\\n derdat = (tsteps, rhs_parts, 0.)\\n\\n const, jacob = linearisation.compute_linearisation(\\n ssm=ibm, initial_value=y0,\\n derivative_data=derdat, prdct_tsteps=evalpt)\\n\\n # Compute GP Estimation of filter mean at t=tmax\\n self.postmean = const + np.dot(jacob[:, 0], odeparam)\",\n \"def run(inputs, outdir, paramsfile, model, simulated, beta, diag,\\n maxiter, tol, seed, tail, initgamma, initx, max_func, separate, save, prog):\\n simulatehuman.cmd_run(inputs, outdir, paramsfile=paramsfile, modeltype=model,\\n is_simulation=simulated, savemat=save, saveprog=prog, beta=beta, diag=diag,\\n maxiter=maxiter, tol=tol, seed=seed, tail=tail, initgamma=initgamma, initx=initx, smooth=False, h=1,\\n max_func=max_func, separate=separate)\",\n \"def setUp(self):\\n # Set Model Parameters\\n odeparam = 1.\\n y0, y0_unc = 1.0, 0 \\n t0, tmax = 0.0, 1.25\\n\\n # Set Method Parameters\\n q = 1\\n h = 0.1\\n\\n # Set up and solve ODE\\n ibm = statespace.IBM(q=q, dim=1)\\n solver = linsolve.LinearisedODESolver(ibm)\\n ivp = linode.LinearODE(t0, tmax, odeparam, y0, y0_unc)\\n tsteps, means, __, rhs_parts, uncerts = solver.solve(ivp, stepsize=h)\\n self.mean = odesolver.get_trajectory(means, 0, 0)\\n\\n # Set up BM and IBM covariance matrices\\n evalpt = np.array([tsteps[-1]])\\n derdat = (tsteps, rhs_parts, 0.)\\n\\n const, jacob = linearisation.compute_linearisation(\\n ssm=ibm, initial_value=y0,\\n derivative_data=derdat, prdct_tsteps=evalpt)\\n\\n # Compute GP Estimation of filter mean at t=tmax\\n self.postmean = const + np.dot(jacob[:, 0], odeparam)\",\n \"def FM(user_feature_columns, item_feature_columns, l2_reg_embedding=1e-6, loss_type='softmax', temperature=0.05,\\n sampler_config=None, seed=1024,\\n ):\\n\\n embedding_matrix_dict = create_embedding_matrix(user_feature_columns + item_feature_columns, l2_reg_embedding,\\n seed=seed,\\n seq_mask_zero=True)\\n\\n user_features = build_input_features(user_feature_columns)\\n user_inputs_list = list(user_features.values())\\n user_sparse_embedding_list, _ = input_from_feature_columns(user_features,\\n user_feature_columns,\\n l2_reg_embedding, seed=seed,\\n support_dense=False,\\n embedding_matrix_dict=embedding_matrix_dict)\\n\\n item_features = build_input_features(item_feature_columns)\\n item_inputs_list = list(item_features.values())\\n item_sparse_embedding_list, _ = input_from_feature_columns(item_features,\\n item_feature_columns,\\n l2_reg_embedding, seed=seed,\\n support_dense=False,\\n embedding_matrix_dict=embedding_matrix_dict)\\n\\n user_dnn_input = concat_func(user_sparse_embedding_list, axis=1)\\n user_vector_sum = Lambda(lambda x: reduce_sum(x, axis=1, keep_dims=False))(user_dnn_input)\\n user_vector_sum = l2_normalize(user_vector_sum)\\n\\n item_dnn_input = concat_func(item_sparse_embedding_list, axis=1)\\n item_vector_sum = Lambda(lambda x: reduce_sum(x, axis=1, keep_dims=False))(item_dnn_input)\\n item_vector_sum = l2_normalize(item_vector_sum)\\n\\n if loss_type == \\\"logistic\\\":\\n score = inner_product(user_vector_sum, item_vector_sum, temperature)\\n output = PredictionLayer(\\\"binary\\\", False)(score)\\n\\n elif loss_type == \\\"softmax\\\":\\n output = InBatchSoftmaxLayer(sampler_config._asdict(), temperature)(\\n [user_vector_sum, item_vector_sum, item_features[sampler_config.item_name]])\\n else:\\n raise ValueError(' `loss_type` must be `logistic` or `softmax` ')\\n\\n model = Model(inputs=user_inputs_list + item_inputs_list, outputs=output)\\n\\n model.__setattr__(\\\"user_input\\\", user_inputs_list)\\n model.__setattr__(\\\"user_embedding\\\", user_vector_sum)\\n\\n model.__setattr__(\\\"item_input\\\", item_inputs_list)\\n model.__setattr__(\\\"item_embedding\\\", item_vector_sum)\\n\\n return model\",\n \"def run_model(project=None, model=None, raw=None, dyr=None, xls=None, path=None, server='tcp://127.0.0.1:5678'):\\n ret = 0\\n if (not project) or (not model):\\n logging.error('RT-LAB project or model undefined.')\\n sys.exit(-1)\\n if (not raw) and (not xls):\\n logging.error('PSS/E raw file or ePHASORsim Excel file undefined.')\\n sys.exit(-1)\\n if not dyr:\\n logging.debug('PSS/E dyr file not specified')\\n\\n sim = SimControl(project, model, path)\\n\\n simulink = os.path.join(path,project, 'simulink')\\n models = os.path.join(path,project, 'models')\\n if not os.path.isdir(simulink):\\n logging.error('No <{}> directory found.'.format(simulink))\\n if not os.path.isdir(models):\\n logging.error('No <{}> directory found.'.format(models))\\n sys.exit(1)\\n else:\\n logging.info('Using <{}> directory'.format(models))\\n modelPath = models\\n else:\\n logging.info('Using <{}> directory'.format(simulink))\\n modelPath = simulink\\n\\n\\n sim_data = LTBSetup(raw=raw, dyr=dyr, xls=xls, path=modelPath, model=model, simObject=sim)\\n\\n streaming = Streaming(name='sim', server=server, ltb_data=sim_data)\\n\\n sim.open()\\n sim.load()\\n\\n sim_data.get_sysparam()\\n sim_data.get_varheader_idxvgs()\\n sim.set_settings(sim_data.Settings)\\n # sim_data.Idxvgs['Line'].update(sim.add_branch_power_to_idxvgs())\\n # sim_data.Varheader.extend(sim.add_vars_varheader(sim_data.Idxvgs['Line']))\\n # sim_data.Idxvgs['Bus'].update(sim.add_bus_power_to_idxvgs())\\n # sim_data.Varheader.extend(sim.add_vars_varheader(sim_data.Idxvgs['Bus']))\\n streaming.send_init()\\n logging.debug('Varheader, SysParam and Idxvgs sent.')\\n sleep(0.5)\\n\\n sim.start()\\n\\n streaming.run()\",\n \"def __init__(self, input_model_dict, func_lib, x_list,\\n par_add_dict_all={}, QuietMode=False, **kwargs):\\n modelDict = OrderedDict()\\n modelNameList = input_model_dict.keys()\\n for modelName in modelNameList:\\n funcName = input_model_dict[modelName][\\\"function\\\"]\\n funcInfo = func_lib[funcName]\\n xName = funcInfo[\\\"x_name\\\"]\\n #-> Build up the parameter dictionaries\\n parFitList = funcInfo[\\\"param_fit\\\"]\\n parAddList = funcInfo[\\\"param_add\\\"]\\n parFitDict = OrderedDict()\\n parAddDict = {}\\n for parName in parFitList:\\n parFitDict[parName] = input_model_dict[modelName][parName]\\n for parName in parAddList:\\n par_add_iterm = par_add_dict_all.get(parName, \\\"No this parameter\\\")\\n if par_add_iterm == \\\"No this parameter\\\":\\n pass\\n else:\\n parAddDict[parName] = par_add_iterm\\n #-> Check the consistency if the component is multiply\\n multiList = input_model_dict[modelName].get(\\\"multiply\\\", None)\\n if not multiList is None:\\n #--> The \\\"*\\\" should be included in the operation list.\\n assert \\\"*\\\" in funcInfo[\\\"operation\\\"]\\n if not QuietMode:\\n print \\\"[Model_Generator]: {0} is multiplied to {1}!\\\".format(modelName, multiList)\\n #--> Check further the target models are not multiplicative.\\n for tmn in multiList:\\n f_mlt = input_model_dict[tmn].get(\\\"multiply\\\", None)\\n if not f_mlt is None:\\n raise ValueError(\\\"The multiList includes a multiplicative model ({0})!\\\".format(tmn))\\n modelDict[modelName] = ModelFunction(funcName, xName, parFitDict, parAddDict, multiList)\\n ModelCombiner.__init__(self, modelDict, x_list, np.complex_, **kwargs)\",\n \"def test_infer_framework(self):\\n\\t\\t\\n\\t\\tprint(f\\\"test_infer_framework self.model={self.model}\\\")\\n\\t\\t\\n\\t\\tnum_files = len(glob.glob(f\\\"{self.model}/*\\\"))\\n\\t\\tprint(f\\\"test_infer_framework found {num_files} tmp files\\\")\\n\\t\\tself.assertTrue(num_files > 0)\\n\\t\\t\\n\\t\\t\\n\\t\\tnum_pytorch_bin_files = len(glob.glob(f\\\"{self.model}/*bin\\\"))\\n\\t\\t#print(f\\\"test_infer_framework found {num_pytorch_bin_files} tmp pytorch bin files\\\")\\n\\t\\tself.assertEqual(num_pytorch_bin_files, 0)\\n\\t\\t\\n\\t\\tnum_safetensors_files = len(glob.glob(f\\\"{self.model}/*safetensors\\\"))\\n\\t\\t#print(f\\\"test_infer_framework found {num_pytorch_bin_files} tmp num_safetensors_files files\\\")\\n\\t\\tself.assertEqual(num_safetensors_files, 1)\\n\\t\\t\\n\\t\\tnum_flat_files = len(glob.glob(f\\\"{self.model}/*npy\\\"))\\n\\t\\t#print(f\\\"test_infer_framework found {num_files} tmp npy flat files\\\")\\n\\t\\tself.assertEqual(num_flat_files, 0)\\n\\t\\t\\n\\t\\texpected_format, expected_fileglob = ww.WeightWatcher.infer_model_file_format(self.model)\\n\\t\\tprint(f\\\"infer_model_file_format found {expected_format} expected format\\\")\\n\\t\\tself.assertEqual(expected_format, MODEL_FILE_FORMATS.SAFETENSORS)\\t\\t\\n\\n\\t\\texpected_framework = ww.WeightWatcher.infer_framework(self.model)\\n\\t\\tprint(f\\\"infer_model_file_format found {expected_framework} expected_framework \\\")\\n\\t\\tself.assertEqual(expected_framework, FRAMEWORK.PYSTATEDICT_DIR)\\t\\n\\t\\t\\n\\t\\treturn\",\n \"def setup_to_finetune(model):\\r\\n # for layer in model.layers[:NB_IV3_LAYERS_TO_FREEZE]:\\r\\n # layer.trainable = False\\r\\n for layer in model.layers[:]:\\r\\n layer.trainable = True\\r\\n model.compile(optimizer=Adam(lr=0.0001), loss='categorical_crossentropy', metrics=['accuracy'])\",\n \"def model_fn(model_dir):\\n ctx = mx.cpu()\\n net = unet.Unet()\\n print (\\\"Loading\\\", model_dir)\\n if path.exists(model_dir+\\\"/unet_RGB.params\\\"):\\n print (\\\"Loading RGB Model\\\")\\n net.load_params(model_dir+\\\"/unet_RGB.params\\\", ctx)\\n print (\\\"RGB Model Loaded\\\")\\n \\n elif path.exists(model_dir+\\\"/unet_ALL_BANDS.params\\\"):\\n print (\\\"Loading ALL_BANDS Model\\\")\\n net.load_params(model_dir+\\\"/unet_ALL_BANDS.params\\\", ctx)\\n print (\\\"ALL_BANDS Model Loaded\\\")\\n \\n else:\\n print (\\\"Model Missing\\\")\\n net=None\\n return (net)\",\n \"def main():\\n\\n # Create model_dict from arguments\\n model_dict = model_dict_create()\\n\\n # No. of deviations to consider\\n no_of_mags = 50\\n dev_list = np.linspace(0.1, 5.0, no_of_mags)\\n\\n # Load dataset specified in model_dict\\n print('Loading data...')\\n dataset = model_dict['dataset']\\n if (dataset == 'MNIST'):\\n X_train, y_train, X_val, y_val, X_test, y_test = load_dataset(model_dict)\\n # rd_list = [None, 784, 331, 200, 100, 90, 80, 70, 60, 50, 40, 30, 20, 10]\\n rd_list = [None, 331, 100, 80, 60, 40, 20]\\n # rd_list = [None,784,100]\\n elif dataset == 'GTSRB':\\n X_train, y_train, X_val, y_val, X_test, y_test = load_dataset(model_dict)\\n rd_list = [1024, 338, 200, 100, 90, 80, 70, 60, 50, 40, 33, 30, 20, 10]\\n elif dataset == 'HAR':\\n X_train, y_train, X_test, y_test = load_dataset(model_dict)\\n # rd_list = [561, 200, 100, 90, 80, 70, 60, 50, 40, 30, 20, 10]\\n rd_list = [561]\\n X_val = None\\n y_val = None\\n\\n mean = np.mean(X_train, axis=0)\\n X_train -= mean\\n X_test -= mean\\n if (dataset == 'MNIST') or (dataset == 'GTSRB'): X_val -= mean\\n\\n # fig, ax = plt.subplots(nrows=1, ncols=1)\\n\\n # for rd in rd_list:\\n # model_setup_carlini(model_dict, X_train, y_train, X_test, y_test, X_val, y_val, mean, rd, ax)\\n\\n partial_carlini = partial(model_setup_carlini, model_dict=model_dict, X_train=X_train, y_train=y_train, X_test=X_test, y_test=y_test, X_val=X_val, y_val=y_val,\\n mean=mean)\\n pool=multiprocessing.Pool(processes=8)\\n pool.map(partial_carlini,rd_list,1)\\n pool.close()\\n pool.join()\\n\\n # dim_red = model_dict['dim_red']\\n # plt.legend()\\n # plt.savefig('carlini_l2_hist_'+dim_red+'.png')\",\n \"def simulate_tanks(model_path, u=U_MAX, t_start=0.0, t_final=50.0,\\n h10=20.0, h20=20.0, h30=20.0, tank1_outflow=26.0,\\n tank2_outflow=26.0, tank3_outflow=28.0, alpha1=0.5,\\n alpha2=0.5, alpha3=0.5, with_full_traj_obj=False,\\n with_plots=False):\\n # 2. Compute initial guess trajectories by means of simulation\\n # Compile the optimization initialization model\\n simulation_fmu = compile_fmu(\\\"TanksPkg.ThreeTanks\\\", model_path)\\n # Load the model\\n simulation_model = load_fmu(simulation_fmu)\\n set_model_parameters(simulation_model,\\n {\\\"h10\\\": h10, \\\"h20\\\": h20, \\\"h30\\\": h30,\\n \\\"C1\\\": tank1_outflow, \\\"C2\\\": tank2_outflow,\\n \\\"C3\\\": tank3_outflow, \\\"alpha1\\\": alpha1,\\n \\\"alpha2\\\": alpha2, \\\"alpha3\\\": alpha3})\\n try:\\n if hasattr(u, \\\"__len__\\\"):\\n t = numpy.linspace(0.0, t_final, len(u))\\n u_traj = numpy.transpose(numpy.vstack((t, u)))\\n init_res = simulation_model.simulate(start_time=t_start,\\n final_time=t_final,\\n input=('u', u_traj),\\n options={\\\"ncp\\\": len(u) - 1})\\n else:\\n simulation_model.set('u', u)\\n init_res = simulation_model.simulate(start_time=t_start,\\n final_time=t_final)\\n except FMUException as e:\\n return e\\n # Extract variable profiles\\n t_init_sim = init_res['time']\\n h1_init_sim = init_res['h1']\\n h2_init_sim = init_res['h2']\\n h3_init_sim = init_res['h3']\\n u_init_sim = init_res['u']\\n if with_plots:\\n # Plot the initial guess trajectories\\n plot_results(h1_init_sim, h2_init_sim, h3_init_sim, t_init_sim,\\n u_init_sim, title='Trajektorie inicjalizacyjne')\\n if with_full_traj_obj:\\n return init_res\\n else:\\n return_dict = {'h1': init_res['h1'], 'h2': init_res['h2'], 'h3':\\n init_res['h3'], 'u': init_res['u'],\\n 'time': init_res['time']}\\n return return_dict\",\n \"def test_infer_framework(self):\\n\\t\\t\\n\\t\\tprint(f\\\"test_infer_framework self.model={self.model}\\\")\\n\\t\\t\\n\\t\\tnum_files = len(glob.glob(f\\\"{self.model}/*\\\"))\\n\\t\\tprint(f\\\"test_infer_framework found {num_files} tmp files\\\")\\n\\t\\tself.assertTrue(num_files > 0)\\n\\t\\t\\n\\t\\t\\n\\t\\tnum_pytorch_bin_files = len(glob.glob(f\\\"{self.model}/*bin\\\"))\\n\\t\\t#print(f\\\"test_infer_framework found {num_pytorch_bin_files} tmp pytorch bin files\\\")\\n\\t\\tself.assertEqual(num_pytorch_bin_files, 0)\\n\\t\\t\\n\\t\\tnum_safetensors_files = len(glob.glob(f\\\"{self.model}/*safetensors\\\"))\\n\\t\\t#print(f\\\"test_infer_framework found {num_pytorch_bin_files} tmp num_safetensors_files files\\\")\\n\\t\\tself.assertEqual(num_safetensors_files, 0)\\n\\t\\t\\n\\t\\tnum_flat_files = len(glob.glob(f\\\"{self.model}/*npy\\\"))\\n\\t\\t#print(f\\\"test_infer_framework found {num_files} tmp npy flat files\\\")\\n\\t\\tself.assertTrue(num_flat_files > 0)\\n\\t\\t\\n\\t\\texpected_format, expected_fileglob = ww.WeightWatcher.infer_model_file_format(self.model)\\n\\t\\tprint(f\\\"infer_model_file_format found {expected_format} expected format\\\")\\n\\t\\tself.assertEqual(expected_format, MODEL_FILE_FORMATS.WW_FLATFILES)\\t\\t\\n\\n\\t\\texpected_framework = ww.WeightWatcher.infer_framework(self.model)\\n\\t\\tprint(f\\\"infer_model_file_format found {expected_framework} expected_framework \\\")\\n\\t\\tself.assertEqual(expected_framework, FRAMEWORK.WW_FLATFILES)\\t\\t\\n\\t\\t\\n\\t\\treturn\",\n \"def __init__(self, name, quiet=True):\\n self.quiet = quiet\\n if not name in MODEL_LIST:\\n raise NotImplementedError(\\n 'Unknown model \\\"{}\\\". Valid models are {}.'.format(name, MODEL_LIST))\\n self.name = name\\n\\n # Set up the simulation\\n self.solenoid = solenoid_field_map.Solenoid(name, quiet=self.quiet)\\n astra_folder = r'\\\\\\\\fed.cclrc.ac.uk\\\\Org\\\\NLab\\\\ASTeC-TDL\\\\Projects\\\\tdl-1168 CLARA\\\\CLARA-ASTeC Folder\\\\Accelerator Physics\\\\ASTRA'\\n if name[:3] == 'Gun':\\n cav_fieldmap_file = astra_folder + (r'\\\\Archive from Delta + CDR\\\\bas_gun.txt' if name == 'Gun-10' \\\\\\n else r'\\\\Injector\\\\fieldmaps\\\\HRRG_1D_RF.dat')\\n\\n # Read in RF field map and normalise so peak = 1\\n cav_fieldmap = np.loadtxt(cav_fieldmap_file, delimiter='\\\\t')\\n\\n self.rf_peak_field = 50 # float(np.max(cav_fieldmap[:, 1]) / 1e6) # set a 'reasonable' value\\n # Normalise\\n cav_fieldmap[:, 1] /= np.max(cav_fieldmap[:, 1])\\n self.norm_E = [solenoid_field_map.interpolate(*cav_fieldmap.T),]\\n self.phase_offset = np.zeros(1, dtype='float')\\n self.freq = 2998.5 * 1e6 # in Hz\\n self.phase = 330.0 # to get optimal acceleration\\n\\n # Set parameters\\n self.dz = 0.5e-3 # in metres - OK to get within 0.5% of final momentum\\n self.gamma_start = np.sqrt(1 + abs(1 / epsilon_e)) # 1 eV\\n\\n self.z_start = 0\\n self.z_end = max(cav_fieldmap[-1, 1], self.solenoid.getZMap()[-1])\\n\\n elif name == 'Linac1':\\n linac1_folder = astra_folder + r'\\\\Injector\\\\fieldmaps' + '\\\\\\\\'\\n # Some of this (Mathematica-exported) data is in fraction form (e.g. 2/25), so we need to convert it\\n fetch_dat = lambda name: np.loadtxt(linac1_folder + 'L1' + name + 'cell.dat', converters={0: Fraction})\\n entrance_data = fetch_dat('entrance')\\n single_cell_data = fetch_dat('single')\\n exit_data = fetch_dat('exit')\\n grad_phase_data = np.loadtxt(linac1_folder + 'RI_linac_grad_phase_error.txt')\\n # convert from percentage of first to fraction of max\\n rel_grads = grad_phase_data[:, 0] / np.max(grad_phase_data[:, 0])\\n self.phase_offset = np.cumsum(np.radians(-grad_phase_data[:, 1]))\\n n_cells = len(grad_phase_data)\\n\\n self.freq = 2998.5 * 1e6 # in Hz\\n self.phase = 330.0 # to get optimal acceleration - TODO: not tested\\n self.rf_peak_field = 50 # MV/m, just a made-up figure at the moment (TODO)\\n\\n data_z_length = entrance_data[-1, 0] - entrance_data[0, 0]\\n\\n interpolate = lambda xy: scipy.interpolate.interp1d(*xy.T, fill_value=0, bounds_error=False)\\n ent_interp = interpolate(entrance_data)\\n sgl_interp = interpolate(single_cell_data)\\n exit_interp = interpolate(exit_data)\\n\\n cell_length = 0.033327 # from document: file:///\\\\\\\\fed.cclrc.ac.uk\\\\Org\\\\NLab\\\\ASTeC-TDL\\\\Projects\\\\tdl-1168%20CLARA\\\\CLARA-ASTeC%20Folder\\\\Accelerator%20Physics\\\\ASTRA\\\\Injector\\\\CLARA%20v10%20Injector%20Simulations%20v0.3.docx\\n self.dz = 0.001\\n z_length = n_cells * cell_length + data_z_length # include a bit extra at the ends\\n # self.z_start = -z_length / 2\\n # self.z_end = z_length / 2\\n z_map = self.solenoid.getZMap()\\n self.z_start = z_map[0]\\n self.z_end = z_map[-1]\\n #TODO: self.dz =\\n self.norm_E = []\\n self.gamma_start = np.sqrt(1 + abs(4e6 / epsilon_e) ** 2) # 4 MeV\\n\\n n_offset = (n_cells - 1) / 2\\n for i in range(n_cells):\\n interp = ent_interp if i == 0 else exit_interp if i == n_cells - 1 else sgl_interp\\n self.norm_E.append(scipy.interpolate.interp1d(z_map, rel_grads[i] * interp(z_map + (n_offset - i) * cell_length),\\n fill_value=0, bounds_error=False))\\n\\n elif name[:3] == 'gb-':\\n self.gamma_start = np.sqrt(1 + abs(1 / epsilon_e)) # 1 eV\\n self.z_start = 0\\n if name == 'gb-dc-gun':\\n self.freq = 0\\n self.dz = 1e-3\\n self.z_end = 0.6\\n self.phase = 0\\n elif name == 'gb-rf-gun':\\n self.freq = 1.3e9\\n self.dz = 1e-4\\n self.z_end = 0.3\\n self.phase = 295 # to get optimal acceleration\\n\\n z_list, E_list = np.loadtxt('gb-field-maps/{}_e-field.csv'.format(name), delimiter=',').T\\n self.rf_peak_field = float(np.max(E_list))\\n # Normalise\\n E_list /= self.rf_peak_field\\n self.norm_E = [solenoid_field_map.interpolate(z_list, E_list),]\\n self.phase_offset = [0,]\\n\\n self.calc_level = CALC_NONE\",\n \"def MultiFNN(ANNSetup, test, train):\\n\\n #One hot encoding\\n TrainMultiClass = to_categorical(train.MultiClass)\\n TestMultiClass = to_categorical(test.MultiClass)\\n\\n #ClassWeights = GetClassWeights(train.MultiClass,train.Weights)\\n TrainWeights = GetTrainWeights(train.MultiClass,train.Weights) # Transformation of the Monte Carlo weights for training\\n\\n #Create the model and pass it the data for Callbacks\\n model = Sequential()\\n model.Y_test = TestMultiClass[:,0]\\n model.X_train = train.Events\\n model.Y_train = TrainMultiClass[:,0]\\n model.W_train = train.Weights #Original weights!\\n\\n # Build model from configuration (set in FNN.py)\\n model.add(Dense(ANNSetup.Neurons[0], activation='selu', input_dim=ANNSetup.InputDim))\\n if(ANNSetup.Dropout != None):\\n model.add(Dropout(ANNSetup.Dropout))\\n for i in range(1,len(ANNSetup.Neurons)):\\n if(i == len(ANNSetup.Neurons)-1):\\n model.add(Dense(ANNSetup.Neurons[i], activation='softmax'))\\n else:\\n model.add(Dense(ANNSetup.Neurons[i], activation='selu'))\\n\\n Opti = GetOpti(ANNSetup.Optimizer,ANNSetup.LearnRate.Lr) # Set optimizer\\n lrate = GetLearnRate(ANNSetup.LearnRate,ANNSetup.Epochs) # Set learning rate schedule\\n Roc = Histories() # Create history for AUC during training\\n Lcallbacks = [Roc,lrate]\\n\\n model.compile(optimizer=Opti, loss='categorical_crossentropy', metrics=['accuracy'])\\n history = model.fit(train.Events, TrainMultiClass, sample_weight=TrainWeights, validation_data=(test.Events, TestMultiClass, test.Weights), epochs=int(ANNSetup.Epochs),\\n batch_size=int(ANNSetup.Batch), verbose=2, callbacks=Lcallbacks) #, sample_weight=TrainWeights\\n\\n LAuc = Roc.TestAucs\\n LTrainAuc = Roc.TrainAucs\\n print(\\\"Best Roc {0:.4f} at Epoch {1}\\\".format(max(LAuc),LAuc.index(max(LAuc))+1))\\n print(\\\"Train Auc {0:.4f}\\\".format(LTrainAuc[LAuc.index(max(LAuc))]))\\n # print(\\\"Test Rocs: {0}\\\".format(LAuc))\\n # print(\\\"Test Loss: {0}\\\".format(Roc.TestLosses))\\n # print(\\\"Train Rocs: {0}\\\".format(LTrainAuc))\\n # print(\\\"Train Loss: {0}\\\".format(Roc.TrainLosses))\\n\\n model.save(ANNSetup.SavePath)\\n\\n return model, Roc\",\n \"def test_fer_model(img_folder, model_path=\\\"saved_models/best_model_Q5.pkl\\\"):\\n preds = None\\n\\n ### Start your code here\\n\\n subtract_mean = True\\n \\n root_path = os.path.dirname(os.path.dirname(os.path.realpath(__file__)))\\n model_path = os.path.join(root_path, model_path)\\n \\n # Load model\\n with open(model_path, 'rb') as handle:\\n model = pickle.load(handle) \\n \\n # Sorting the folder names in lexographic order\\n images_list = os.listdir(img_folder)\\n images_list.sort()\\n\\n n_pictures = 0\\n for filename in images_list:\\n n_pictures += 1\\n\\n # Convert img_folder into preds numpy vector\\n images = np.zeros((n_pictures, 48, 48, 1))\\n \\n i = 0\\n for filename in images_list:\\n #check this one\\n picture_path = os.path.join(img_folder, filename)\\n images[i] = imageio.imread(picture_path)[:,:,0].reshape((48,48,1))\\n i += 1\\n \\n # Normalize the data: subtract the mean image\\n if subtract_mean:\\n mean_image = np.mean(images, axis=0)\\n images -= mean_image\\n\\n images = images.transpose(0, 3, 1, 2).copy()\\n \\n # Feed pictures into NN and get predictions\\n scores = model.loss(images)\\n preds = np.argmax(scores, axis=1)\\n\\n return preds\",\n \"def load(cls, f, model, ext_unit_dict=None):\\n msg = (\\n \\\"Model object must be of type flopy.mfusg.MfUsg\\\\n\\\"\\n f\\\"but received type: {type(model)}.\\\"\\n )\\n assert isinstance(model, MfUsg), msg\\n\\n if model.verbose:\\n print(\\\"loading bcf package file...\\\")\\n\\n f_obj = get_open_file_object(f, \\\"r\\\")\\n\\n # dataset 0 -- header\\n while True:\\n line = f_obj.readline()\\n if line[0] != \\\"#\\\":\\n break\\n\\n # determine problem dimensions\\n nlay = model.nlay\\n dis = model.get_package(\\\"DIS\\\")\\n if dis is None:\\n dis = model.get_package(\\\"DISU\\\")\\n njag = dis.njag\\n\\n # Item 1: ipakcb, HDRY, IWDFLG, WETFCT, IWETIT, IHDWET - line already read above\\n if model.verbose:\\n print(\\\" loading ipakcb, HDRY, IWDFLG, WETFCT, IWETIT, IHDWET...\\\")\\n text_list = line_parse(line)\\n ipakcb, hdry, iwdflg, wetfct, iwetit, ihdwet = (\\n int(text_list[0]),\\n float(text_list[1]),\\n int(text_list[2]),\\n float(text_list[3]),\\n int(text_list[4]),\\n int(text_list[5]),\\n )\\n\\n ikvflag = type_from_iterable(\\n text_list, index=6, _type=int, default_val=0\\n )\\n ikcflag = type_from_iterable(\\n text_list, index=7, _type=int, default_val=0\\n )\\n\\n # LAYCON array\\n laycon, intercellt = cls._load_laycon(f_obj, model)\\n\\n # TRPY array\\n if model.verbose:\\n print(\\\" loading TRPY...\\\")\\n trpy = Util2d.load(\\n f_obj, model, (nlay,), np.float32, \\\"trpy\\\", ext_unit_dict\\n )\\n\\n # property data for each layer based on options\\n transient = not dis.steady.all()\\n anis = any(t != 1 for t in trpy)\\n anglex = 0\\n if (not model.structured) and anis:\\n if model.verbose:\\n print(\\\"loading ANGLEX...\\\")\\n anglex = Util2d.load(\\n f_obj, model, (njag,), np.float32, \\\"anglex\\\", ext_unit_dict\\n )\\n\\n # hy, kv, storage\\n (sf1, tran, hy, vcont, sf2, wetdry, kv) = cls._load_layer_arrays(\\n f_obj,\\n model,\\n nlay,\\n ext_unit_dict,\\n transient,\\n laycon,\\n ikvflag,\\n ikcflag,\\n iwdflg,\\n )\\n\\n # Ksat mfusg\\n ksat = 0\\n if (not model.structured) and abs(ikcflag == 1):\\n if model.verbose:\\n print(\\\" loading ksat (njag)...\\\")\\n ksat = Util2d.load(\\n f_obj, model, (njag,), np.float32, \\\"ksat\\\", ext_unit_dict\\n )\\n\\n f_obj.close()\\n\\n # set package unit number\\n unitnumber, filenames = get_unitnumber_from_ext_unit_dict(\\n model, cls, ext_unit_dict, ipakcb\\n )\\n\\n # create instance of bcf object\\n bcf = cls(\\n model,\\n ipakcb=ipakcb,\\n intercellt=intercellt,\\n laycon=laycon,\\n trpy=trpy,\\n hdry=hdry,\\n iwdflg=iwdflg,\\n wetfct=wetfct,\\n iwetit=iwetit,\\n ihdwet=ihdwet,\\n ikvflag=ikvflag,\\n ikcflag=ikcflag,\\n tran=tran,\\n hy=hy,\\n vcont=vcont,\\n kv=kv,\\n anglex=anglex,\\n ksat=ksat,\\n sf1=sf1,\\n sf2=sf2,\\n wetdry=wetdry,\\n unitnumber=unitnumber,\\n filenames=filenames,\\n )\\n\\n # return bcf object\\n return bcf\",\n \"def train_models(self, clf, silent, feature_names=None, target_names=None, live=False):\\n X_train, X_test, y_train, y_test = self.X_train, self.X_test, self.y_train, self.y_test\\n t0 = time()\\n clf.fit(X_train, y_train)\\n train_time = time() - t0\\n pred = clf.predict(X_test)\\n test_time = time() - t0\\n accuracy = metrics.accuracy_score(y_test, pred)\\n fbeta = metrics.fbeta_score(y_test, pred,1,labels=self.dataset['label'].unique(),average='weighted')\\n name = clf.name[0]\\n if False:\\n score_stats = f'Model : {name} | Score : {accuracy} | F-beta : {fbeta}'\\n print(score_stats)\\n\\n if self.best_score_ledger[name][0] < accuracy:\\n last = self.best_score_ledger[name][0]\\n print(name)\\n self.best_score_ledger[name] = [accuracy,fbeta]\\n score_stats = f'Model : {name} | Score : {accuracy} | F-beta : {fbeta}'\\n print(self.stemmer, ' ', self.transform)\\n print(score_stats)\\n\\n if accuracy > self.best_models[name] and last != 0.0 and self.tuning_depth in ['normal','maximal']:\\n new_model,score = self.hyperparameter_tuning(name,clf)\\n if score > accuracy:\\n self.best_score_ledger[name][0] = score\\n clf = new_model\\n dump(clf, os.path.join(os.getcwd(), self.file_term, 'models', f'{\\\"_\\\".join([self.uid_base, name])}'))\\n\\n\\n\\n if not silent:\\n if hasattr(clf, 'coef_'):\\n print(\\\"dimensionality: %d\\\" % clf.coef_.shape[1])\\n print(\\\"density: %f\\\" % density(clf.coef_))\\n\\n if True and feature_names is not None:\\n print(\\\"top 10 keywords per class:\\\")\\n for i, label in enumerate(target_names):\\n top10 = np.argsort(clf.coef_[i])[-10:]\\n print(trim(\\\"%s: %s\\\" % (label, \\\" \\\".join(feature_names[top10]))))\\n print()\\n\\n if True:\\n print(\\\"classification report:\\\")\\n print(metrics.classification_report(y_test, pred,\\n target_names=target_names))\\n\\n if True:\\n print(\\\"confusion matrix:\\\")\\n print(metrics.confusion_matrix(y_test, pred))\\n # if no model exists for the current settings, create one by default. Prevents issues if models are deleted.\\n elif not os.path.exists(\\n os.path.join(os.getcwd(), self.file_term, 'models', f'{\\\"_\\\".join([self.uid_base, name])}')):\\n dump(clf, os.path.join(os.getcwd(), self.file_term, 'models', f'{\\\"_\\\".join([self.uid_base, name])}'))\\n clf_descr = str(clf).split('(')[0]\\n return clf_descr, accuracy, train_time, test_time\",\n \"def Main(ANNSetup,DataSet,BDTSetup=None,BootStrap=('vali',None)):\\n # Random seed for the NN cacluations\\n np.random.seed(5) \\n tf.compat.v1.set_random_seed(5)\\n\\n train, test, vali = GetSamples(BootStrap,DataSet,ANNSetup.ModelName) # Transform the DataSet into keras readable and aplying bootstrap if needed\\n ANNSetup.InputDim = len(DataSet.LVariables) # Set Input dim (#features)\\n GuardFNN(ANNSetup) # Protect the setup from typos\\n\\n # pass the Data to the correct API\\n if(ANNSetup.Architecture == 'TMVA'):\\n train, test, vali = GetSamples(BootStrap,DataSet,ANNSetup.ModelName,DoTrafo=False) #In Utils\\n dataloader, factory, output = Init(train, test, DataSet.LVariables)\\n TMVAFNN(ANNSetup, dataloader, factory)\\n if(BDTSetup != None):\\n BDT(BDTSetup, dataloader, factory)\\n #GetRocs(factory, dataloader,\\\"BDTEven\\\")\\n Finialize(factory, output)\\n #GetRocs(factory, dataloader,\\\"FNN19Even\\\")\\n\\n # Direct keras implementation\\n elif(ANNSetup.Architecture == 'FNN'):\\n train, test, vali = GetSamples(BootStrap,DataSet,ANNSetup.ModelName,DoTrafo=True)\\n ANNSetup.InputDim = len(DataSet.LVariables)\\n if(BDTSetup != None):\\n stdwar(\\\"BDT is only supported using TMVA\\\")\\n return FNN(ANNSetup, test, train)\\n\\n # multi classifier (direct keras)\\n elif(ANNSetup.Architecture == 'FNNMulti'):\\n train, test, vali = GetSamples(BootStrap,DataSet,ANNSetup.ModelName,DoTrafo=True)\\n ANNSetup.InputDim = len(DataSet.LVariables)\\n if(BDTSetup != None):\\n stdwar(\\\"BDT is only supported using TMVA\\\")\\n Model, Roc = MultiFNN(ANNSetup, test, train)\\n if(BootStrap[1] != None):\\n stdwar(\\\"BootStrap for multi has to be implemented\\\")\\n assert 0 == 1\\n return Model, Roc\",\n \"def _simulation_run(model_instance, observations, actions, rewards):\\r\\n\\r\\n for observation, action, reward in zip(observations, actions, rewards):\\r\\n model_instance.observe(observation)\\r\\n model_instance.overrideActionChoice(action)\\r\\n model_instance.feedback(reward)\\r\\n\\r\\n return model_instance\",\n \"def init_model(model_filename, doGPU):\\n # set model attributes list\\n ##print(\\\"Model-dataset =\\\", model_ds_name)\\n ##if model_ds_name == 'modelRAP':\\n ## model_labels = loader_rapdataset_yiqiang.ATTRIBUTES\\n ##elif model_ds_name == 'modelPETA':\\n ## model_labels = loader_peta_dataset.ATTRIBUTES\\n ##elif model_ds_name == 'modelRAPPETA':\\n ## model_labels = [peta_label for rap_label,peta_label in loader_rap_plus_peta_dataset.ATTRIBUTES]\\n ##else:\\n ## print(\\\"ERROR: unknown model-dataset.\\\")\\n ## sys.exit()\\n model_labels = loader_rap_plus_peta_dataset.ATTRIBUTES\\n assert (len(model_labels) == 49)\\n\\n # create model\\n person.NO_ATTRIBUTES = len(model_labels) #TODO-elo: ugly, attr. nbr should be a parameter of person.Net.__init__()\\n net = person.Net()\\n if doGPU:\\n net = person.Net().cuda()\\n\\n # load model\\n print('loading model \\\"' + model_filename + '\\\"')\\n person.load_model(net, model_filename)\\n\\n return net, model_labels\",\n \"def build_model_fn(self):\",\n \"def test_model_processor():\\n global model_processor_called\\n\\n model_str = 'first 34 45 7 A 45 65 B true C \\\"dfdf\\\"'\\n\\n metamodel = metamodel_from_str(grammar)\\n metamodel.register_model_processor(model_processor)\\n\\n metamodel.model_from_str(model_str)\\n\\n assert model_processor_called\",\n \"def test_coupledmodels_post(self):\\n pass\",\n \"def run_all_tests():\\n model_configs = (model_handler.ModelConfig(\\n saved_model_dir=platform_test.test_src_dir_path(\\n \\\"python/compiler/tensorrt/model_tests/sample_model\\\"),\\n default_batch_size=128),)\\n if FLAGS.use_tf2:\\n model_handler_cls = model_handler.ModelHandlerV2\\n trt_model_handeler_cls = model_handler.TrtModelHandlerV2\\n default_trt_convert_params = DEFAUL_TRT_CONVERT_PARAMS._replace(\\n is_dynamic_op=True)\\n else:\\n model_handler_cls = model_handler.ModelHandlerV1\\n trt_model_handeler_cls = model_handler.TrtModelHandlerV1\\n default_trt_convert_params = DEFAUL_TRT_CONVERT_PARAMS._replace(\\n is_dynamic_op=False)\\n for model_config in model_configs:\\n trt_convert_params = default_trt_convert_params._replace(\\n max_batch_size=model_config.default_batch_size)\\n base_model = model_handler_cls(model_config)\\n random_inputs = base_model.generate_random_inputs()\\n base_model_result = base_model.run(random_inputs)\\n trt_fp32_model_result = trt_model_handeler_cls(\\n model_config=model_config,\\n trt_convert_params=trt_convert_params._replace(\\n precision_mode=trt.TrtPrecisionMode.FP32)).run(random_inputs)\\n trt_fp16_model_result = trt_model_handeler_cls(\\n model_config=model_config,\\n trt_convert_params=trt_convert_params._replace(\\n precision_mode=trt.TrtPrecisionMode.FP16)).run(random_inputs)\\n\\n logging.info(\\\"Base model latency: %f ms\\\",\\n _get_mean_latency(base_model_result))\\n logging.info(\\\"TensorRT FP32 model latency: %f ms\\\",\\n _get_mean_latency(trt_fp32_model_result))\\n logging.info(\\\"TensorRT FP16 model latency: %f ms\\\",\\n _get_mean_latency(trt_fp16_model_result))\",\n \"def run(self):\\n self.membershipFunction()\\n self.interpretingMF()\\n self.rules()\\n self.standardComposition_Min()\\n self.standardComposition_Max()\\n self.defuzzification()\",\n \"def test_infer_framework(self):\\n\\t\\t\\n\\t\\tprint(f\\\"test_infer_framework self.model={self.model}\\\")\\n\\t\\t\\n\\t\\tnum_files = len(glob.glob(f\\\"{self.model}/*\\\"))\\n\\t\\tprint(f\\\"test_infer_framework found {num_files} tmp files\\\")\\n\\t\\tself.assertTrue(num_files > 0)\\n\\t\\t\\n\\t\\t\\n\\t\\tnum_pytorch_bin_files = len(glob.glob(f\\\"{self.model}/*bin\\\"))\\n\\t\\t#print(f\\\"test_infer_framework found {num_pytorch_bin_files} tmp pytorch bin files\\\")\\n\\t\\tself.assertEqual(num_pytorch_bin_files, 1)\\n\\t\\t\\n\\t\\tnum_safetensors_files = len(glob.glob(f\\\"{self.model}/*safetensors\\\"))\\n\\t\\t#print(f\\\"test_infer_framework found {num_pytorch_bin_files} tmp num_safetensors_files files\\\")\\n\\t\\tself.assertEqual(num_safetensors_files, 0)\\n\\t\\t\\n\\t\\tnum_flat_files = len(glob.glob(f\\\"{self.model}/*npy\\\"))\\n\\t\\t#print(f\\\"test_infer_framework found {num_files} tmp npy flat files\\\")\\n\\t\\tself.assertEqual(num_flat_files, 0)\\n\\t\\t\\n\\t\\texpected_format, expected_fileglob = ww.WeightWatcher.infer_model_file_format(self.model)\\n\\t\\tprint(f\\\"infer_model_file_format found {expected_format} expected format\\\")\\n\\t\\tself.assertEqual(expected_format, MODEL_FILE_FORMATS.PYTORCH)\\t\\t\\n\\n\\t\\texpected_framework = ww.WeightWatcher.infer_framework(self.model)\\n\\t\\tprint(f\\\"infer_model_file_format found {expected_framework} expected_framework \\\")\\n\\t\\tself.assertEqual(expected_framework, FRAMEWORK.PYSTATEDICT_DIR)\\t\\n\\t\\t\\n\\t\\treturn\",\n \"def evaluate_ucf50_fusion():\\n accs = np.zeros(3)\\n ob_suffix = '-max.feat.npy.gz'\\n fv_suffix = '_fv.npy.gz'\\n ob_root = '/home/syq/research_final/data/features/ob_ucf50_pooled_python/'\\n fv_root = '/home/syq/research_final/data/features/fv_ucf50_python/'\\n fv_groups, full, sets = utility.split_data(fv_root,\\n suffix=fv_suffix,\\n useLooCV=False)\\n\\n ob_groups, _, _ = utility.split_data(ob_root,\\n suffix=ob_suffix,\\n useLooCV=False)\\n weights = [i / 20.0 for i in range(8, 13)]\\n acc_to_weights = {}\\n\\n for weight in weights:\\n print \\\"Weight: %.2f\\\" % weight\\n accs = np.zeros(2)\\n for i in xrange(2):\\n ts = time.time()\\n Dtrain_fv, Dtest_fv, Ytrain, Ytest = utility.load_groups(\\n fv_groups, np.setdiff1d(full, sets[i]),\\n sets[i], scale=False, verbose=False)\\n\\n Dtrain_ob, Dtest_ob, Ytrain, Ytest = utility.load_groups(\\n ob_groups, np.setdiff1d(full, sets[i]),\\n sets[i], scale=False, verbose=False)\\n\\n fv_clf = OneVsRestClassifier(estimator=LinearSVC(C=100), n_jobs=8)\\n\\n ob_clf = OneVsRestClassifier(estimator=SVC(C=10,\\n cache_size=1000,\\n kernel='linear',\\n probability=True),\\n n_jobs=-1)\\n\\n # Get probabilities for late fusion\\n Dtrain_fv = fv_clf.fit(Dtrain_fv, Ytrain).decision_function(Dtrain_fv)\\n Dtrain_ob = ob_clf.fit(Dtrain_ob, Ytrain).decision_function(Dtrain_ob)\\n Dtest_fv = fv_clf.decision_function(Dtest_fv)\\n Dtest_ob = ob_clf.decision_function(Dtest_ob)\\n\\n # Scale decision values b/w 0 and 1\\n Dtrain_fv = preprocessing.normalize(Dtrain_fv)\\n Dtrain_ob = preprocessing.normalize(Dtrain_ob)\\n Dtest_fv = preprocessing.normalize(Dtest_fv)\\n Dtest_ob = preprocessing.normalize(Dtest_ob)\\n\\n # weighted averaging\\n scores_train = (Dtrain_fv * weight) + (Dtrain_ob * (1 - weight))\\n latefusion_clf = OneVsRestClassifier(estimator=LinearSVC(C=100), n_jobs=-1)\\n latefusion_clf.fit(scores_train, Ytrain)\\n\\n scores_test = (Dtest_fv * weight) + (Dtest_ob * (1 - weight))\\n latefusion_acc = latefusion_clf.score(scores_test, Ytest)\\n print 'Fold', i, 'late fusion acc', latefusion_acc\\n print \\\"Train & testing time %.3f\\\" % (time.time() - ts)\\n accs[i] = latefusion_acc\\n\\n print \\\"Mean accuracy: %.3f\\\" % accs.mean()\\n with open(\\\"fv_ucf50_accs_5fold_gridsearch.txt\\\", \\\"w\\\") as f:\\n for weight, accs in acc_to_weights.items():\\n f.write(str(weight) + str(accs) + '\\\\n')\\n return acc_to_weights\",\n \"def model_qua_launch(self, dict):\\r\\n list_result = []\\r\\n if \\\"SVR\\\" in dict:\\r\\n SVR = dict[\\\"SVR\\\"]\\r\\n if SVR[\\\"Auto\\\"]:\\r\\n result = SVR_b(self.ml_data.feature, self.ml_data.target, SVR[\\\"Auto\\\"])\\r\\n model, score, graph, time = result\\r\\n result_win = Window_Quant()\\r\\n result_win.setWindowTitle(\\\"SVR Result Auto\\\")\\r\\n result_win.setData(model, score, graph, time)\\r\\n list_result.append(result_win)\\r\\n else:\\r\\n result = SVR_b(self.ml_data.feature, self.ml_data.target, SVR[\\\"Auto\\\"], [SVR[\\\"C\\\"],\\r\\n SVR[\\\"Kernel\\\"],\\r\\n SVR[\\\"Degree\\\"]])\\r\\n model, score, graph, time = result\\r\\n result_win = Window_Quant()\\r\\n result_win.setWindowTitle(\\\"SVR Result\\\")\\r\\n result_win.setData(model, score, graph, time)\\r\\n list_result.append(result_win)\\r\\n if \\\"LR\\\" in dict:\\r\\n LR = dict[\\\"LR\\\"]\\r\\n if LR[\\\"Auto\\\"]:\\r\\n result = regression_lin(self.ml_data.feature, self.ml_data.target, LR[\\\"Auto\\\"])\\r\\n model, score, graph, time = result\\r\\n result_win = Window_Quant()\\r\\n result_win.setWindowTitle(\\\"Linear Regression Result Auto\\\")\\r\\n result_win.setData(model, score, graph, time)\\r\\n list_result.append(result_win)\\r\\n else:\\r\\n result = regression_lin(self.ml_data.feature, self.ml_data.target, LR[\\\"Auto\\\"], LR[\\\"fit_intercept\\\"], LR[\\\"normalize\\\"])\\r\\n model, score, graph, time = result\\r\\n result_win = Window_Quant()\\r\\n result_win.setWindowTitle(\\\"Linear Regression Result\\\")\\r\\n result_win.setData(model, score, graph, time)\\r\\n list_result.append(result_win)\\r\\n\\r\\n if \\\"RT\\\" in dict:\\r\\n RT = dict[\\\"RT\\\"]\\r\\n if RT[\\\"Auto\\\"]:\\r\\n result = RegTree(self.ml_data.feature, self.ml_data.target, RT[\\\"Auto\\\"])\\r\\n model, score, graph, time = result\\r\\n result_win = Window_Quant()\\r\\n result_win.setWindowTitle(\\\"Regression Tree Result Auto\\\")\\r\\n result_win.setData(model, score, graph, time)\\r\\n list_result.append(result_win)\\r\\n else:\\r\\n result = RegTree(self.ml_data.feature, self.ml_data.target, RT[\\\"Auto\\\"], [RT[\\\"Criterion\\\"],\\r\\n RT[\\\"Min_Samples_Split\\\"],\\r\\n RT[\\\"Min_Samples_Leaf\\\"]])\\r\\n model, score, graph, time = result\\r\\n result_win = Window_Quant()\\r\\n result_win.setWindowTitle(\\\"Regression Tree Result\\\")\\r\\n result_win.setData(model, score, graph, time)\\r\\n list_result.append(result_win)\\r\\n\\r\\n if \\\"KNN\\\" in dict:\\r\\n KNN = dict[\\\"KNN\\\"]\\r\\n if KNN[\\\"Auto\\\"]:\\r\\n result = knn_class(self.ml_data.feature, self.ml_data.target, KNN[\\\"Auto\\\"])\\r\\n model, matrix, dict_cr, graph, time = result\\r\\n result_win = Window()\\r\\n result_win.setWindowTitle(\\\"KNN Result Auto\\\")\\r\\n result_win.setData(model, matrix, dict_cr, graph, time)\\r\\n list_result.append(result_win)\\r\\n else:\\r\\n result = knn_class(self.ml_data.feature, self.ml_data.target, KNN[\\\"Auto\\\"],\\r\\n [KNN[\\\"leaf_size\\\"], KNN[\\\"n_neighbors\\\"], KNN[\\\"p\\\"], KNN[\\\"metric\\\"]])\\r\\n model, matrix, dict_cr, graph, time = result\\r\\n self.model_quali.close()\\r\\n result_win = Window()\\r\\n result_win.setWindowTitle(\\\"KNN Result\\\")\\r\\n result_win.setData(model, matrix, dict_cr, graph, time)\\r\\n list_result.append(result_win)\\r\\n if \\\"LogiR\\\" in dict:\\r\\n LogiR = dict[\\\"LogiR\\\"]\\r\\n if LogiR[\\\"Auto\\\"]:\\r\\n result = LogReg(self.ml_data.feature, self.ml_data.target, LogiR[\\\"Auto\\\"])\\r\\n model, matrix, dict_cr, graph, time = result\\r\\n result_win = Window()\\r\\n result_win.setWindowTitle(\\\"Logistic Regression Result Auto\\\")\\r\\n result_win.setData(model, matrix, dict_cr, graph, time)\\r\\n list_result.append(result_win)\\r\\n else:\\r\\n result = LogReg(self.ml_data.feature, self.ml_data.target, LogiR[\\\"Auto\\\"], [LogiR['C'],\\r\\n LogiR['penalty']])\\r\\n model, matrix, dict_cr, graph, time = result\\r\\n result_win = Window()\\r\\n result_win.setWindowTitle(\\\"Logistic Regression Result\\\")\\r\\n result_win.setData(model, matrix, dict_cr, graph, time)\\r\\n list_result.append(result_win)\\r\\n\\r\\n if \\\"DTC\\\" in dict:\\r\\n DTC = dict[\\\"DTC\\\"]\\r\\n if DTC[\\\"Auto\\\"]:\\r\\n result = arbre_clas(self.ml_data.feature, self.ml_data.target, DTC[\\\"Auto\\\"])\\r\\n model, matrix, dict_cr, graph, time = result\\r\\n result_win = Window()\\r\\n result_win.setWindowTitle(\\\"Tree Decision Classification Auto\\\")\\r\\n result_win.setData(model, matrix, dict_cr, graph, time)\\r\\n list_result.append(result_win)\\r\\n\\r\\n else:\\r\\n result = arbre_clas(self.ml_data.feature, self.ml_data.target, DTC[\\\"Auto\\\"],DTC[\\\"max_leaf_nodes\\\"],DTC[\\\"max_depth\\\"],DTC[\\\"min_samples_split\\\"])\\r\\n model, matrix, dict_cr, graph, time = result\\r\\n result_win = Window()\\r\\n result_win.setWindowTitle(\\\"Tree Decision Classification\\\")\\r\\n result_win.setData(model, matrix, dict_cr, graph, time)\\r\\n list_result.append(result_win)\\r\\n\\r\\n\\r\\n self.close()\\r\\n self.trigger_result.emit(list_result)\",\n \"def synthesize_rf(self, model, mu=1.0):\\n ndim = len(model.shape)\\n dtype = self._get_dtype()\\n \\n if(ndim == 1):\\n model1 = np.ascontiguousarray(model.reshape((1,1,model.size)), dtype=dtype)\\n elif(ndim == 3):\\n model1 = model\\n else:\\n print(\\\"MilneEddington::synthesize_rf: ERROR, the input model must have 1 or 3 dimensions\\\")\\n return None\\n\\n if(model1.shape[2] != 9):\\n print(\\\"MilneEddington::synthesize_rf: ERROR, input model has npar={0}, should be 9\\\".format(model1.shape[2]))\\n return None\\n\\n isContiguous = model1.flags['C_CONTIGUOUS']\\n if(not isContiguous or model1.dtype != dtype):\\n model1 = np.ascontiguousarray(model1, dtype=dtype)\\n\\n \\n \\n return self.Me.synthesize_RF(model, mu=mu)\",\n \"def run(self):\\r\\n # Close any open models\\r\\n self.cleanUp()\\r\\n # Dynamically select file to preview\\r\\n self.selectTrial() \\r\\n # Add adjusted COM (RRA/CMC) model\\r\\n self.loadAdjustedModel()\\r\\n # Hide the markers from view\\r\\n self.hideModelMarkers()\\r\\n # Load CMC motion to model\\r\\n self.loadCMCMotion()\",\n \"def run(self):\\n #parse requests\\n self.bqSession.update_mex('Calculating Features...')\\n log.debug('Forming Feature Requests...')\\n #get rectanle gobjects for roi\\n r_xml = self.bqSession.fetchxml(self.options.mexURL, view='deep')\\n\\n rectangles = r_xml.xpath('//tag[@name=\\\"inputs\\\"]/tag[@name=\\\"image_url\\\"]/gobject[@name=\\\"roi\\\"]/rectangle')\\n image_xml = self.bqSession.fetchxml(self.options.image_url)\\n image_url = self.bqSession.service_url('image_service',path=image_xml.attrib['resource_uniq'])\\n if rectangles: #On chooses the first rectangle\\n #construct operation node\\n x1 = int(float(rectangles[0][0].attrib['x']))\\n y1 = int(float(rectangles[0][0].attrib['y']))\\n x2 = int(float(rectangles[0][1].attrib['x']))\\n y2 = int(float(rectangles[0][1].attrib['y']))\\n log.debug('Adding Crop: roi=%s,%s,%s,%s' % (x1, y1, x2, y2))\\n image_url = self.bqSession.c.prepare_url(image_url, roi='%s,%s,%s,%s' % (x1, y1, x2, y2))\\n \\n try:\\n feature_vectors = extract_bush_feature(self.bqSession, image_url)\\n except FeatureCommError as e:\\n raise BotanicamError(str(e))\\n \\n #parse features\\n self.bqSession.update_mex('Classifying Results...')\\n log.debug('Classifying Results...')\\n results= []\\n pca = joblib.load(os.path.join(self.model_path,'pca_model'))\\n clf = joblib.load(os.path.join(self.model_path,'svm_model'))\\n \\n for f in feature_vectors:\\n f_norm = pca.transform(f)\\n results.append(int(clf.predict(f_norm)))\\n \\n\\n class_count = np.bincount(np.array(results))\\n self.class_number = np.argmax(class_count)\\n self.confidence = float(class_count[self.class_number])/np.sum(class_count)\\n log.debug('Found Class %s'%str(self.class_number))\",\n \"def main(config):\\n\\n runModel(config)\\n while (config['looping']):\\n runModel(config)\",\n \"def construct_model(self):\\n # Set the placeholder for the input episode\\n self.inputa = tf.placeholder(tf.float32) # episode train images\\n self.inputb = tf.placeholder(tf.float32) # episode test images\\n self.labela = tf.placeholder(tf.float32) # episode train labels\\n self.labelb = tf.placeholder(tf.float32) # episode test labels\\n\\n with tf.variable_scope('meta-model', reuse=None) as training_scope:\\n # construct the model weights\\n self.ss_weights = ss_weights = self.construct_resnet_ss_weights()\\n self.weights = weights = self.construct_resnet_weights()\\n self.fc_weights = fc_weights = self.construct_fc_weights()\\n\\n # Load base epoch number from FLAGS\\n num_updates = FLAGS.train_base_epoch_num\\n\\n def task_metalearn(inp, reuse=True):\\n \\\"\\\"\\\"The function to process one episode in a meta-batch.\\n Args:\\n inp: the input episode.\\n reuse: whether reuse the variables for the normalization.\\n Returns:\\n A serious outputs like losses and accuracies.\\n \\\"\\\"\\\"\\n # Seperate inp to different variables\\n inputa, inputb, labela, labelb = inp\\n # Generate empty list to record losses\\n lossa_list = [] # Base train loss list\\n lossb_list = [] # Base test loss list\\n\\n # Embed the input images to embeddings with ss weights\\n emb_outputa = self.forward_resnet(inputa, weights, ss_weights, reuse=reuse) # Embed episode train \\n emb_outputb = self.forward_resnet(inputb, weights, ss_weights, reuse=True) # Embed episode test \\n\\n # Run the first epoch of the base learning\\n # Forward fc layer for episode train \\n outputa = self.forward_fc(emb_outputa, fc_weights)\\n # Calculate base train loss\\n lossa = self.loss_func(outputa, labela)\\n # Record base train loss\\n lossa_list.append(lossa)\\n # Forward fc layer for episode test\\n outputb = self.forward_fc(emb_outputb, fc_weights)\\n # Calculate base test loss\\n lossb = self.loss_func(outputb, labelb)\\n # Record base test loss\\n lossb_list.append(lossb) \\n # Calculate the gradients for the fc layer \\n grads = tf.gradients(lossa, list(fc_weights.values()))\\n gradients = dict(zip(fc_weights.keys(), grads))\\n # Use graient descent to update the fc layer\\n fast_fc_weights = dict(zip(fc_weights.keys(), [fc_weights[key] - \\\\\\n self.update_lr*gradients[key] for key in fc_weights.keys()]))\\n \\n for j in range(num_updates - 1):\\n # Run the following base epochs, these are similar to the first base epoch\\n lossa = self.loss_func(self.forward_fc(emb_outputa, fast_fc_weights), labela)\\n lossa_list.append(lossa)\\n lossb = self.loss_func(self.forward_fc(emb_outputb, fast_fc_weights), labelb)\\n lossb_list.append(lossb) \\n grads = tf.gradients(lossa, list(fast_fc_weights.values()))\\n gradients = dict(zip(fast_fc_weights.keys(), grads))\\n fast_fc_weights = dict(zip(fast_fc_weights.keys(), [fast_fc_weights[key] - \\\\\\n self.update_lr*gradients[key] for key in fast_fc_weights.keys()]))\\n\\n # Calculate final episode test predictions\\n outputb = self.forward_fc(emb_outputb, fast_fc_weights)\\n # Calculate the final episode test loss, it is the loss for the episode on meta-train \\n final_lossb = self.loss_func(outputb, labelb)\\n # Calculate the final episode test accuarcy\\n accb = tf.contrib.metrics.accuracy(tf.argmax(tf.nn.softmax(outputb), 1), tf.argmax(labelb, 1))\\n\\n # Reorganize all the outputs to a list\\n task_output = [final_lossb, lossb_list, lossa_list, accb]\\n\\n return task_output\\n\\n # Initial the batch normalization weights\\n if FLAGS.norm is not 'None':\\n unused = task_metalearn((self.inputa[0], self.inputb[0], self.labela[0], self.labelb[0]), False)\\n\\n # Set the dtype of the outputs\\n out_dtype = [tf.float32, [tf.float32]*num_updates, [tf.float32]*num_updates, tf.float32]\\n\\n # Run two episodes for a meta batch using parallel setting\\n result = tf.map_fn(task_metalearn, elems=(self.inputa, self.inputb, self.labela, self.labelb), \\\\\\n dtype=out_dtype, parallel_iterations=FLAGS.meta_batch_size)\\n # Seperate the outputs to different variables\\n lossb, lossesb, lossesa, accsb = result\\n\\n # Set the variables to output from the tensorflow graph\\n self.total_loss = total_loss = tf.reduce_sum(lossb) / tf.to_float(FLAGS.meta_batch_size)\\n self.total_accuracy = total_accuracy = tf.reduce_sum(accsb) / tf.to_float(FLAGS.meta_batch_size)\\n self.total_lossa = total_lossa = [tf.reduce_sum(lossesa[j]) / tf.to_float(FLAGS.meta_batch_size) for j in range(num_updates)]\\n self.total_lossb = total_lossb = [tf.reduce_sum(lossesb[j]) / tf.to_float(FLAGS.meta_batch_size) for j in range(num_updates)]\\n\\n # Set the meta-train optimizer\\n optimizer = tf.train.AdamOptimizer(self.meta_lr)\\n self.metatrain_op = optimizer.minimize(total_loss, var_list=list(ss_weights.values()) + list(fc_weights.values()))\\n\\n # Set the tensorboard \\n self.training_summaries = []\\n self.training_summaries.append(tf.summary.scalar('Meta Train Loss', (total_loss / tf.to_float(FLAGS.metatrain_epite_sample_num))))\\n self.training_summaries.append(tf.summary.scalar('Meta Train Accuracy', total_accuracy))\\n for j in range(num_updates):\\n self.training_summaries.append(tf.summary.scalar('Base Train Loss Step' + str(j+1), total_lossa[j]))\\n for j in range(num_updates):\\n self.training_summaries.append(tf.summary.scalar('Base Val Loss Step' + str(j+1), total_lossb[j]))\\n\\n self.training_summ_op = tf.summary.merge(self.training_summaries)\\n\\n self.input_val_loss = tf.placeholder(tf.float32)\\n self.input_val_acc = tf.placeholder(tf.float32)\\n self.val_summaries = []\\n self.val_summaries.append(tf.summary.scalar('Meta Val Loss', self.input_val_loss))\\n self.val_summaries.append(tf.summary.scalar('Meta Val Accuracy', self.input_val_acc))\\n self.val_summ_op = tf.summary.merge(self.val_summaries)\",\n \"def define_forms(self):\\n\\n # Define UFL objects corresponding to the local acceleration\\n # if problem is unsteady.\\n self.define_ufl_local_inertia()\\n self.define_ufl_local_inertia_diff()\\n\\n # Define UFL objects corresponding to the convective acceleration\\n # if problem is formulated with respect to Eulerian coordinates\\n self.define_ufl_convec_accel()\\n self.define_ufl_convec_accel_diff()\\n\\n # Define UFL objects corresponding to the stress tensor term.\\n # This should always be non-zero for deformable bodies.\\n self.define_ufl_stress_work()\\n self.define_ufl_stress_work_diff()\\n\\n # Define UFL object corresponding to the body force term. Assume\\n # it is zero if key was not provided.\\n self.define_ufl_body_force()\\n\\n # Define UFL object corresponding to the traction force terms. Assume\\n # it is zero if key was not provided.\\n self.define_ufl_neumann_bcs()\\n self.define_ufl_neumann_bcs_diff()\\n\\n return None\",\n \"def makeModel(self):\\n\\n # Get the script\\n modelScript = os.path.join(self.datapath, 'make3FGLxml.py')\\n if not os.path.isfile(modelScript):\\n # download it\\n print(\\\"\\\\t=== Downloading make3FGLxml.py ===\\\")\\n os.system('wget https://fermi.gsfc.nasa.gov/ssc/data/analysis/user/make3FGLxml.py -O {}'.format(modelScript))\\n\\n # Create the model using Tyrel's script\\n galModel = os.path.join(self.diffpath, 'gll_iem_v06.fits')\\n isoModel = os.path.join(self.diffpath, 'iso_'+self.irf+'_v06.txt')\\n if (not os.path.isfile(galModel)) or (not os.path.isfile(isoModel)):\\n print(\\\"\\\\t=== Unable to find the diffuse models, check the variable '$FERMI_DIR' ===\\\")\\n return\\n if not os.path.isdir(self.extpath):\\n print(\\\"\\\\t=== Unable to find models of extended sources, check the variable '$LATEXTDIR' ===\\\")\\n return\\n if not os.path.isfile(self.fermicat):\\n # download it\\n print(\\\"\\\\t=== Downloading 3FGL catalog ===\\\")\\n os.system('wget https://fermi.gsfc.nasa.gov/ssc/data/access/lat/4yr_catalog/gll_psc_v16.fit -O {}'.format(self.fermicat))\\n\\n os.popen(\\\"python {} {} {} -o {} -G {} -g 'gll_iem_v06'\\\\\\n -I {} -i 'iso_source_v06' -e {} -r 5 -R 10 -ER 10\\\\\\n -s 9 -m False -GIF False\\\".format(modelScript, self.fermicat,\\n self.ft1, self.model, galModel, isoModel, self.extpath))\\n\\n # Add the target to the model\\n tmpName = self.model + '.tmp'\\n rfil = open(self.model, 'r')\\n wfil = open(tmpName, 'w')\\n # Copy the XML to the temporary model\\n wfil.writelines([l for l in rfil.readlines() if not l=='</source_library>']) # copy everything but the last line\\n wfil.write(' <source ROI_Center_Distance=\\\"0.00\\\" name=\\\"TARGET\\\" type=\\\"PointSource\\\">\\\\n')\\n wfil.write(' <spectrum type=\\\"PowerLaw2\\\">\\\\n')\\n wfil.write(' <parameter free=\\\"1\\\" max=\\\"1000\\\" min=\\\"1e-05\\\" name=\\\"Integral\\\" scale=\\\"1e-08\\\" value=\\\"0.3591824258\\\"/>\\\\n')\\n wfil.write(' <parameter free=\\\"1\\\" max=\\\"1\\\" min=\\\"-5\\\" name=\\\"Index\\\" scale=\\\"1\\\" value=\\\"-2.7\\\"/>\\\\n')\\n wfil.write(' <parameter free=\\\"0\\\" max=\\\"1000000\\\" min=\\\"20\\\" name=\\\"LowerLimit\\\" scale=\\\"1\\\" value=\\\"100\\\"/>\\\\n')\\n wfil.write('<parameter free=\\\"0\\\" max=\\\"1000000\\\" min=\\\"20\\\" name=\\\"UpperLimit\\\" scale=\\\"1\\\" value=\\\"100000\\\"/>\\\\n')\\n wfil.write(' </spectrum>\\\\n')\\n wfil.write(' <spatialModel type=\\\"SkyDirFunction\\\">\\\\n')\\n wfil.write(' <parameter free=\\\"0\\\" max=\\\"360.0\\\" min=\\\"-360.0\\\" name=\\\"RA\\\" scale=\\\"1.0\\\" value=\\\"'+str(self.ra)+'\\\"/>\\\\n')\\n wfil.write(' <parameter free=\\\"0\\\" max=\\\"360.0\\\" min=\\\"-360.0\\\" name=\\\"DEC\\\" scale=\\\"1.0\\\" value=\\\"'+str(self.dec)+'\\\"/>\\\\n')\\n wfil.write(' </spatialModel>\\\\n')\\n wfil.write(' </source>\\\\n')\\n wfil.write('</source_library>\\\\n')\\n rfil.close()\\n wfil.close()\\n\\n os.remove(self.model)\\n os.rename(tmpName, self.model)\\n \\n print(\\\"\\\\t=== Source model {} added ===\\\".format(self.model))\\n return\",\n \"def build_model(self):\\n if self.args.network_type == 'unet':\\n self.shared = models.Unet(self.args)\\n else:\\n raise NotImplementedError(f'Network type '\\n f'`{self.args.network_type}` is not '\\n f'defined')\\n self.controller = models.Controller(self.args)\\n\\n if self.args.num_gpu == 1:\\n self.shared.cuda()\\n self.controller.cuda()\\n elif self.args.num_gpu > 1:\\n raise NotImplementedError('`num_gpu > 1` is in progress')\",\n \"def eval_model(config):\\n print 'Model directory: %s' % config.model_output\\n print 'Running model: %s' % config.model_type\\n if config.model_type == 'fully_connected_mlp':\\n from models.fully_connected_mlp import model_struct\\n elif config.model_type == 'fully_connected_mlp_2l':\\n from models.fully_connected_mlp_2l import model_struct\\n elif config.model_type == 'fully_connected_conv':\\n from models.fully_connected_conv import model_struct\\n elif config.model_type == 'vgg_feature_model':\\n from models.vgg_feature_model import model_struct\\n else:\\n raise Exception\\n\\n # Prepare model training\\n dt_stamp = re.split(\\n '\\\\.', str(datetime.now()))[0].\\\\\\n replace(' ', '_').replace(':', '_').replace('-', '_')\\n dt_dataset = config.model_type + '_' + dt_stamp + '/'\\n config.train_checkpoint = os.path.join(\\n config.model_output, dt_dataset) # timestamp this run\\n config.summary_dir = os.path.join(\\n config.train_summaries, config.model_output, dt_dataset)\\n dir_list = [config.train_checkpoint, config.summary_dir]\\n [make_dir(d) for d in dir_list]\\n\\n # Prepare model inputs\\n train_data = os.path.join(config.tfrecord_dir, 'train.tfrecords')\\n validation_data = os.path.join(config.tfrecord_dir, 'val.tfrecords')\\n feat_mean = 0 # np.mean(np.load(config.mean_file)['feat_list'])\\n\\n # Prepare data on CPU\\n with tf.device('/cpu:0'):\\n train_images, train_labels = inputs(\\n tfrecord_file=train_data,\\n batch_size=config.train_batch,\\n num_feats=config.n_features,\\n sample=config.sample['train'],\\n max_pixels_per_image=config.max_pixels_per_image,\\n im_size=config.resize,\\n model_input_shape=config.resize,\\n train=config.data_augmentations,\\n num_epochs=config.epochs,\\n feat_mean_value=feat_mean)\\n val_images, val_labels = inputs(\\n tfrecord_file=validation_data,\\n batch_size=1,\\n num_feats=config.n_features,\\n sample=config.sample['val'],\\n max_pixels_per_image=config.max_pixels_per_image,\\n im_size=config.resize,\\n model_input_shape=config.resize,\\n train=config.data_augmentations,\\n num_epochs=config.epochs,\\n feat_mean_value=feat_mean)\\n tf.summary.image('validation images', tf.cast(val_labels, tf.float32))\\n\\n # Prepare model on GPU\\n with tf.device('/gpu:0'):\\n with tf.variable_scope('cnn') as scope:\\n\\n model = model_struct()\\n train_mode = tf.get_variable(name='training', initializer=True)\\n model.build(\\n features=train_images,\\n output_categories=len(config.labels.keys()),\\n train_mode=train_mode, batchnorm=config.batch_norm)\\n\\n # Prepare the cost function\\n cost = softmax_cost(\\n model.res_logits, train_labels, ratio=config.ratio,\\n label_reshape=[\\n config.batch_size * config.max_pixels_per_image])\\n train_op = tf.train.AdamOptimizer(config.lr).minimize(cost)\\n\\n tf.summary.scalar(\\\"cost\\\", cost)\\n\\n train_score = correlation(\\n model.prob, train_labels) # training accuracy\\n tf.summary.scalar(\\\"training correlation\\\", train_score)\\n\\n # Setup validation op\\n if validation_data is not False:\\n scope.reuse_variables()\\n # Validation graph is the same as training except no batchnorm\\n val_model = model_struct()\\n val_model.build(\\n features=val_images,\\n output_categories=len(config.labels.keys()))\\n\\n # Calculate validation accuracy\\n val_pred = tf.cast(\\n tf.reshape(\\n tf.argmax(\\n val_model.prob, axis=1),\\n [1, config.resize[0], config.resize[1], 1]),\\n tf.float32)\\n tf.summary.image('validation prediction', val_pred)\\n val_score = correlation(\\n val_model.prob, tf.reshape(\\n val_labels, [np.prod(config.resize), 1]))\\n tf.summary.scalar(\\\"validation correlation\\\", val_score)\\n\\n # Set up summaries and saver\\n saver = tf.train.Saver(\\n tf.global_variables(), max_to_keep=config.keep_checkpoints)\\n summary_op = tf.summary.merge_all()\\n\\n # Initialize the graph\\n sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True))\\n\\n # Need to initialize both of these if supplying num_epochs to inputs\\n sess.run(tf.group(tf.global_variables_initializer(),\\n tf.local_variables_initializer()))\\n summary_writer = tf.summary.FileWriter(config.summary_dir, sess.graph)\\n\\n # Set up exemplar threading\\n coord = tf.train.Coordinator()\\n threads = tf.train.start_queue_runners(sess=sess, coord=coord)\\n\\n # Start training loop\\n np.save(config.train_checkpoint, config)\\n step, val_max, losses = 0, 0, []\\n train_acc = 0\\n try:\\n while not coord.should_stop():\\n start_time = time.time()\\n _, loss_value, train_acc = sess.run([train_op, cost, train_score])\\n losses.append(loss_value)\\n duration = time.time() - start_time\\n assert not np.isnan(loss_value), 'Model diverged with loss = NaN'\\n\\n if step % 100 == 0 and step % 10 == 0:\\n if validation_data is not False:\\n _, val_acc, val_frame = sess.run(\\n [train_op, val_score, val_pred])\\n\\n np.save(\\n os.path.join(\\n config.model_output, '%s_val_image' % step),\\n val_frame)\\n else:\\n val_acc = -1 # Store every checkpoint\\n\\n # Summaries\\n summary_str = sess.run(summary_op)\\n summary_writer.add_summary(summary_str, step)\\n\\n # Training status and validation accuracy\\n format_str = (\\n '%s: step %d, loss = %.2f (%.1f examples/sec; '\\n '%.3f sec/batch) | Training r = %s | '\\n 'Validation r = %s | logdir = %s')\\n print (format_str % (\\n datetime.now(), step, loss_value,\\n config.train_batch / duration, float(duration),\\n train_acc, val_acc, config.summary_dir))\\n\\n # Save the model checkpoint if it's the best yet\\n if val_acc > val_max:\\n saver.save(\\n sess, os.path.join(\\n config.train_checkpoint,\\n 'model_' + str(step) + '.ckpt'), global_step=step)\\n\\n # Store the new max validation accuracy\\n val_max = val_acc\\n\\n else:\\n # Training status\\n format_str = ('%s: step %d, loss = %.2f (%.1f examples/sec; '\\n '%.3f sec/batch) | Training F = %s')\\n print (format_str % (datetime.now(), step, loss_value,\\n config.train_batch / duration,\\n float(duration), train_acc))\\n # End iteration\\n step += 1\\n\\n except tf.errors.OutOfRangeError:\\n print('Done training for %d epochs, %d steps.' % (config.epochs, step))\\n finally:\\n coord.request_stop()\\n\\n dt_stamp = get_dt() # date-time stamp\\n np.save(\\n os.path.join(\\n config.tfrecord_dir, '%straining_loss' % dt_stamp), losses)\\n coord.join(threads)\\n sess.close()\",\n \"def test_models_binary(model):\\n atom = ATOMClassifier(X_bin, y_bin, test_size=0.24, random_state=1)\\n atom.run(\\n models=model,\\n metric=\\\"auc\\\",\\n n_calls=2,\\n n_initial_points=1,\\n bo_params={\\\"base_estimator\\\": \\\"rf\\\", \\\"cv\\\": 1},\\n )\\n assert not atom.errors # Assert that the model ran without errors\\n assert hasattr(atom, model) # Assert that the model is an attr of the trainer\",\n \"def __init__(\\n self,\\n biorbd_model,\\n dynamics_type,\\n number_shooting_points,\\n phase_time,\\n X_init,\\n U_init,\\n X_bounds,\\n U_bounds,\\n objective_functions=ObjectiveList(),\\n constraints=ConstraintList(),\\n parameters=ParameterList(),\\n external_forces=(),\\n ode_solver=OdeSolver.RK,\\n nb_integration_steps=5,\\n control_type=ControlType.CONSTANT,\\n all_generalized_mapping=None,\\n q_mapping=None,\\n q_dot_mapping=None,\\n tau_mapping=None,\\n plot_mappings=None,\\n state_transitions=StateTransitionList(),\\n nb_threads=1,\\n use_SX=False,\\n ):\\n\\n if isinstance(biorbd_model, str):\\n biorbd_model = [biorbd.Model(biorbd_model)]\\n elif isinstance(biorbd_model, biorbd.biorbd.Model):\\n biorbd_model = [biorbd_model]\\n elif isinstance(biorbd_model, (list, tuple)):\\n biorbd_model = [biorbd.Model(m) if isinstance(m, str) else m for m in biorbd_model]\\n else:\\n raise RuntimeError(\\\"biorbd_model must either be a string or an instance of biorbd.Model()\\\")\\n self.version = {\\\"casadi\\\": casadi.__version__, \\\"biorbd\\\": biorbd.__version__, \\\"biorbd_optim\\\": __version__}\\n self.nb_phases = len(biorbd_model)\\n\\n biorbd_model_path = [m.path().relativePath().to_string() for m in biorbd_model]\\n self.original_values = {\\n \\\"biorbd_model\\\": biorbd_model_path,\\n \\\"dynamics_type\\\": dynamics_type,\\n \\\"number_shooting_points\\\": number_shooting_points,\\n \\\"phase_time\\\": phase_time,\\n \\\"X_init\\\": X_init,\\n \\\"U_init\\\": U_init,\\n \\\"X_bounds\\\": X_bounds,\\n \\\"U_bounds\\\": U_bounds,\\n \\\"objective_functions\\\": ObjectiveList(),\\n \\\"constraints\\\": ConstraintList(),\\n \\\"parameters\\\": ParameterList(),\\n \\\"external_forces\\\": external_forces,\\n \\\"ode_solver\\\": ode_solver,\\n \\\"nb_integration_steps\\\": nb_integration_steps,\\n \\\"control_type\\\": control_type,\\n \\\"all_generalized_mapping\\\": all_generalized_mapping,\\n \\\"q_mapping\\\": q_mapping,\\n \\\"q_dot_mapping\\\": q_dot_mapping,\\n \\\"tau_mapping\\\": tau_mapping,\\n \\\"plot_mappings\\\": plot_mappings,\\n \\\"state_transitions\\\": state_transitions,\\n \\\"nb_threads\\\": nb_threads,\\n \\\"use_SX\\\": use_SX,\\n }\\n\\n # Check integrity of arguments\\n if not isinstance(nb_threads, int) or isinstance(nb_threads, bool) or nb_threads < 1:\\n raise RuntimeError(\\\"nb_threads should be a positive integer greater or equal than 1\\\")\\n\\n if isinstance(dynamics_type, DynamicsTypeOption):\\n dynamics_type_tp = DynamicsTypeList()\\n dynamics_type_tp.add(dynamics_type)\\n dynamics_type = dynamics_type_tp\\n elif not isinstance(dynamics_type, DynamicsTypeList):\\n raise RuntimeError(\\\"dynamics_type should be a DynamicsTypeOption or a DynamicsTypeList\\\")\\n\\n ns = number_shooting_points\\n if not isinstance(ns, int) or ns < 2:\\n if isinstance(ns, (tuple, list)):\\n if sum([True for i in ns if not isinstance(i, int) and not isinstance(i, bool)]) != 0:\\n raise RuntimeError(\\n \\\"number_shooting_points should be a positive integer (or a list of) greater or equal than 2\\\"\\n )\\n else:\\n raise RuntimeError(\\n \\\"number_shooting_points should be a positive integer (or a list of) greater or equal than 2\\\"\\n )\\n nstep = nb_integration_steps\\n if not isinstance(nstep, int) or isinstance(nstep, bool) or nstep < 1:\\n raise RuntimeError(\\\"nb_integration_steps should be a positive integer greater or equal than 1\\\")\\n\\n if not isinstance(phase_time, (int, float)):\\n if isinstance(phase_time, (tuple, list)):\\n if sum([True for i in phase_time if not isinstance(i, (int, float))]) != 0:\\n raise RuntimeError(\\\"phase_time should be a number or a list of number\\\")\\n else:\\n raise RuntimeError(\\\"phase_time should be a number or a list of number\\\")\\n\\n if isinstance(X_init, InitialConditionsOption):\\n X_init_tp = InitialConditionsList()\\n X_init_tp.add(X_init)\\n X_init = X_init_tp\\n elif not isinstance(X_init, InitialConditionsList):\\n raise RuntimeError(\\\"X_init should be built from a InitialConditionsOption or InitialConditionsList\\\")\\n\\n if isinstance(U_init, InitialConditionsOption):\\n U_init_tp = InitialConditionsList()\\n U_init_tp.add(U_init)\\n U_init = U_init_tp\\n elif not isinstance(U_init, InitialConditionsList):\\n raise RuntimeError(\\\"U_init should be built from a InitialConditionsOption or InitialConditionsList\\\")\\n\\n if isinstance(X_bounds, BoundsOption):\\n X_bounds_tp = BoundsList()\\n X_bounds_tp.add(X_bounds)\\n X_bounds = X_bounds_tp\\n elif not isinstance(X_bounds, BoundsList):\\n raise RuntimeError(\\\"X_bounds should be built from a BoundOption or a BoundsList\\\")\\n\\n if isinstance(U_bounds, BoundsOption):\\n U_bounds_tp = BoundsList()\\n U_bounds_tp.add(U_bounds)\\n U_bounds = U_bounds_tp\\n elif not isinstance(U_bounds, BoundsList):\\n raise RuntimeError(\\\"U_bounds should be built from a BoundOption or a BoundsList\\\")\\n\\n if isinstance(objective_functions, ObjectiveOption):\\n objective_functions_tp = ObjectiveList()\\n objective_functions_tp.add(objective_functions)\\n objective_functions = objective_functions_tp\\n elif not isinstance(objective_functions, ObjectiveList):\\n raise RuntimeError(\\\"objective_functions should be built from an ObjectiveOption or ObjectiveList\\\")\\n\\n if isinstance(constraints, ConstraintOption):\\n constraints_tp = ConstraintList()\\n constraints_tp.add(constraints)\\n constraints = constraints_tp\\n elif not isinstance(constraints, ConstraintList):\\n raise RuntimeError(\\\"constraints should be built from an ConstraintOption or ConstraintList\\\")\\n\\n if not isinstance(parameters, ParameterList):\\n raise RuntimeError(\\\"parameters should be built from an ParameterList\\\")\\n\\n if not isinstance(state_transitions, StateTransitionList):\\n raise RuntimeError(\\\"state_transitions should be built from an StateTransitionList\\\")\\n\\n if not isinstance(ode_solver, OdeSolver):\\n raise RuntimeError(\\\"ode_solver should be built an instance of OdeSolver\\\")\\n\\n if not isinstance(use_SX, bool):\\n raise RuntimeError(\\\"use_SX should be a bool\\\")\\n\\n # Declare optimization variables\\n self.J = []\\n self.g = []\\n self.g_bounds = []\\n self.V = []\\n self.V_bounds = Bounds(interpolation=InterpolationType.CONSTANT)\\n self.V_init = InitialConditions(interpolation=InterpolationType.CONSTANT)\\n self.param_to_optimize = {}\\n\\n # nlp is the core of a phase\\n self.nlp = [{} for _ in range(self.nb_phases)]\\n self.__add_to_nlp(\\\"model\\\", biorbd_model, False)\\n self.__add_to_nlp(\\\"phase_idx\\\", [i for i in range(self.nb_phases)], False)\\n\\n # Type of CasADi graph\\n if use_SX:\\n self.CX = SX\\n else:\\n self.CX = MX\\n\\n # Define some aliases\\n self.__add_to_nlp(\\\"ns\\\", number_shooting_points, False)\\n for nlp in self.nlp:\\n if nlp[\\\"ns\\\"] < 1:\\n raise RuntimeError(\\\"Number of shooting points must be at least 1\\\")\\n self.initial_phase_time = phase_time\\n phase_time, initial_time_guess, time_min, time_max = self.__init_phase_time(\\n phase_time, objective_functions, constraints\\n )\\n self.__add_to_nlp(\\\"tf\\\", phase_time, False)\\n self.__add_to_nlp(\\\"t0\\\", [0] + [nlp[\\\"tf\\\"] for i, nlp in enumerate(self.nlp) if i != len(self.nlp) - 1], False)\\n self.__add_to_nlp(\\\"dt\\\", [self.nlp[i][\\\"tf\\\"] / max(self.nlp[i][\\\"ns\\\"], 1) for i in range(self.nb_phases)], False)\\n self.nb_threads = nb_threads\\n self.__add_to_nlp(\\\"nb_threads\\\", nb_threads, True)\\n self.solver_type = Solver.NONE\\n self.solver = None\\n\\n # External forces\\n if external_forces != ():\\n external_forces = BiorbdInterface.convert_array_to_external_forces(external_forces)\\n self.__add_to_nlp(\\\"external_forces\\\", external_forces, False)\\n\\n # Compute problem size\\n if all_generalized_mapping is not None:\\n if q_mapping is not None or q_dot_mapping is not None or tau_mapping is not None:\\n raise RuntimeError(\\\"all_generalized_mapping and a specified mapping cannot be used alongside\\\")\\n q_mapping = q_dot_mapping = tau_mapping = all_generalized_mapping\\n self.__add_to_nlp(\\\"q_mapping\\\", q_mapping, q_mapping is None, BidirectionalMapping)\\n self.__add_to_nlp(\\\"q_dot_mapping\\\", q_dot_mapping, q_dot_mapping is None, BidirectionalMapping)\\n self.__add_to_nlp(\\\"tau_mapping\\\", tau_mapping, tau_mapping is None, BidirectionalMapping)\\n plot_mappings = plot_mappings if plot_mappings is not None else {}\\n reshaped_plot_mappings = []\\n for i in range(self.nb_phases):\\n reshaped_plot_mappings.append({})\\n for key in plot_mappings:\\n reshaped_plot_mappings[i][key] = plot_mappings[key][i]\\n self.__add_to_nlp(\\\"plot_mappings\\\", reshaped_plot_mappings, False)\\n\\n # Prepare the parameters to optimize\\n self.state_transitions = []\\n if len(parameters) > 0:\\n self.update_parameters(parameters)\\n\\n # Declare the time to optimize\\n self.__define_variable_time(initial_time_guess, time_min, time_max)\\n\\n # Prepare the dynamics of the program\\n self.__add_to_nlp(\\\"dynamics_type\\\", dynamics_type, False)\\n self.__add_to_nlp(\\\"ode_solver\\\", ode_solver, True)\\n self.__add_to_nlp(\\\"control_type\\\", control_type, True)\\n for i in range(self.nb_phases):\\n self.__initialize_nlp(self.nlp[i])\\n Problem.initialize(self, self.nlp[i])\\n\\n # Prepare path constraints\\n self.__add_to_nlp(\\\"X_bounds\\\", X_bounds, False)\\n self.__add_to_nlp(\\\"U_bounds\\\", U_bounds, False)\\n for i in range(self.nb_phases):\\n self.nlp[i][\\\"X_bounds\\\"].check_and_adjust_dimensions(self.nlp[i][\\\"nx\\\"], self.nlp[i][\\\"ns\\\"])\\n if self.nlp[i][\\\"control_type\\\"] == ControlType.CONSTANT:\\n self.nlp[i][\\\"U_bounds\\\"].check_and_adjust_dimensions(self.nlp[i][\\\"nu\\\"], self.nlp[i][\\\"ns\\\"] - 1)\\n elif self.nlp[i][\\\"control_type\\\"] == ControlType.LINEAR_CONTINUOUS:\\n self.nlp[i][\\\"U_bounds\\\"].check_and_adjust_dimensions(self.nlp[i][\\\"nu\\\"], self.nlp[i][\\\"ns\\\"])\\n else:\\n raise NotImplementedError(f\\\"Plotting {self.nlp[i]['control_type']} is not implemented yet\\\")\\n\\n # Prepare initial guesses\\n self.__add_to_nlp(\\\"X_init\\\", X_init, False)\\n self.__add_to_nlp(\\\"U_init\\\", U_init, False)\\n for i in range(self.nb_phases):\\n self.nlp[i][\\\"X_init\\\"].check_and_adjust_dimensions(self.nlp[i][\\\"nx\\\"], self.nlp[i][\\\"ns\\\"])\\n if self.nlp[i][\\\"control_type\\\"] == ControlType.CONSTANT:\\n self.nlp[i][\\\"U_init\\\"].check_and_adjust_dimensions(self.nlp[i][\\\"nu\\\"], self.nlp[i][\\\"ns\\\"] - 1)\\n elif self.nlp[i][\\\"control_type\\\"] == ControlType.LINEAR_CONTINUOUS:\\n self.nlp[i][\\\"U_init\\\"].check_and_adjust_dimensions(self.nlp[i][\\\"nu\\\"], self.nlp[i][\\\"ns\\\"])\\n else:\\n raise NotImplementedError(f\\\"Plotting {self.nlp[i]['control_type']} is not implemented yet\\\")\\n\\n # Variables and constraint for the optimization program\\n for i in range(self.nb_phases):\\n self.__define_multiple_shooting_nodes_per_phase(self.nlp[i], i)\\n\\n # Define dynamic problem\\n self.__add_to_nlp(\\n \\\"nb_integration_steps\\\", nb_integration_steps, True\\n ) # Number of steps of integration (for now only RK4 steps are implemented)\\n for i in range(self.nb_phases):\\n if self.nlp[0][\\\"nx\\\"] != self.nlp[i][\\\"nx\\\"] or self.nlp[0][\\\"nu\\\"] != self.nlp[i][\\\"nu\\\"]:\\n raise RuntimeError(\\\"Dynamics with different nx or nu is not supported yet\\\")\\n self.__prepare_dynamics(self.nlp[i])\\n\\n # Prepare phase transitions (Reminder, it is important that parameters are declared\\n # before, otherwise they will erase the state_transitions)\\n self.state_transitions = StateTransitionFunctions.prepare_state_transitions(self, state_transitions)\\n\\n # Inner- and inter-phase continuity\\n ContinuityFunctions.continuity(self)\\n\\n # Prepare constraints\\n self.update_constraints(constraints)\\n\\n # Prepare objectives\\n self.update_objectives(objective_functions)\",\n \"def load_model(app: FastAPI) -> None:\\n\\n logging.info(\\\"Starting up the application\\\")\\n model_path = DATA_MODEL_PATH\\n\\n if model_path.exists():\\n model = FraudDetection(model_path)\\n app.state.model = model\\n logging.info(f\\\"Loaded model {model_path}\\\")\\n else:\\n app.state.model = FraudDetection()\\n logging.warning(f\\\"No existing model found in {model_path}\\\")\",\n \"def test_compute_model(self):\\n # Set test tolerances (for infinity norm of transfer function\\n # difference)\\n tf_abs_tol = 1e-6\\n tf_rel_tol = 1e-4\\n\\n # Set time parameters for discrete-time simulation\\n dt = 0.1\\n num_time_steps = 1000\\n\\n # Set size of plant and model. For test, don't reduce the system, just\\n # check that it comes back close to the original plant. Also, note that\\n # using more than 8 states causes poorly conditioned TF coeffs\\n # (https://github.com/scipy/scipy/issues/2980)\\n num_states_plant = 8\\n num_states_model = num_states_plant\\n\\n # Loop through different numbers of inputs, numbers of outputs, and\\n # sampling intervals\\n for num_inputs in [1, 3]:\\n for num_outputs in [1, 2]:\\n for sample_interval in [1, 2, 4]:\\n # Define time steps at which to save data. These will be of\\n # the form [0, 1, p, p + 1, 2p, 2p + 1, ...] where p is the\\n # sample interval.\\n time_steps = make_time_steps(\\n num_time_steps, sample_interval)\\n # # Create a state space system\\n # A_plant, B_plant, C_plant = util.drss(\\n # num_states_plant, num_inputs, num_outputs)\\n A_plant = util.load_array_text(\\n join(self.data_dir, 'A_in%d_out%d.txt') % (\\n num_inputs, num_outputs))\\n B_plant = util.load_array_text(\\n join(self.data_dir, 'B_in%d_out%d.txt') % (\\n num_inputs, num_outputs))\\n C_plant = util.load_array_text(\\n join(self.data_dir, 'C_in%d_out%d.txt') % (\\n num_inputs, num_outputs))\\n\\n # Simulate an impulse response using the state space system.\\n # This will generate Markov parameters at all timesteps [0,\\n # 1, 2, 3, ...]. Only keep data at the desired time steps,\\n # which are separated by a sampling interval (see above\\n # comment).\\n Markovs = util.impulse(\\n A_plant, B_plant, C_plant,\\n time_steps[-1] + 1)[time_steps]\\n\\n # Compute a model using ERA\\n my_ERA = era.ERA(verbosity=0)\\n A_model, B_model, C_model = my_ERA.compute_model(\\n Markovs, num_states_model)\\n\\n # Save ERA model to disk\\n A_path_computed = join(self.out_dir, 'A_computed.txt')\\n B_path_computed = join(self.out_dir, 'B_computed.txt')\\n C_path_computed = join(self.out_dir, 'C_computed.txt')\\n my_ERA.put_model(\\n A_path_computed, B_path_computed, C_path_computed)\\n\\n # Check normalized Markovs\\n rtol = 1e-5 # 1e-6\\n atol = 1e-5 # 1e-10\\n Markovs_model = util.impulse(\\n A_model, B_model, C_model,\\n time_steps[-1] + 1)[time_steps]\\n max_Markov = np.amax(Markovs)\\n eigs_plant = np.linalg.eig(A_plant)[0]\\n eigs_model = np.linalg.eig(A_model)[0]\\n # print 'markovs shape', Markovs.shape\\n # print 'max plant eig', np.abs(eigs_plant).max()\\n # print 'max model eig', np.abs(eigs_model).max()\\n # print 'max plant markov', max_Markov\\n # print 'max model markov', np.amax(Markovs_model)\\n # print 'markov diffs', (\\n # Markovs - Markovs_model).squeeze().max()\\n\\n '''\\n import matplotlib.pyplot as plt\\n plt.figure()\\n plt.semilogy(np.abs(Markovs).squeeze(), 'b')\\n plt.semilogy(np.abs(Markovs_model).squeeze(), 'r--')\\n plt.axis(\\n [0, time_steps[-1], Markovs.min(), Markovs.max()])\\n '''\\n\\n np.testing.assert_allclose(\\n Markovs_model.squeeze(),\\n Markovs.squeeze(),\\n rtol=rtol, atol=atol)\\n\\n\\n # plt.show()\\n '''\\n # Use Scipy to check that transfer function of ERA model is\\n # close to transfer function of full model. Do so by\\n # computing the infinity norm (H_inf) of the difference\\n # between the transfer functions. Since Scipy can't handle\\n # MIMO transfer functions, loop through each input-output\\n # pair individually.\\n for input_idx in range(num_inputs):\\n for output_idx in range(num_outputs):\\n\\n # Compute transfer functions\\n tf_plant = scipy.signal.StateSpace(\\n A_plant, B_plant[:, input_idx:input_idx + 1],\\n C_plant[output_idx:output_idx + 1, :],\\n 0, dt=dt).to_tf()\\n tf_model = scipy.signal.StateSpace(\\n A_model,\\n B_model[:, input_idx:input_idx + 1],\\n C_model[output_idx:output_idx + 1, :],\\n 0, dt=dt).to_tf()\\n tf_diff = util.sub_transfer_functions(\\n tf_plant, tf_model, dt=dt)\\n\\n # Compute transfer function norms\\n tf_plant_inf_norm = util.compute_inf_norm_discrete(\\n tf_plant, dt)\\n tf_diff_inf_norm = util.compute_inf_norm_discrete(\\n tf_diff, dt)\\n\\n # Test values\\n print 'err_frac', (\\n tf_diff_inf_norm / tf_plant_inf_norm)\\n self.assertTrue(\\n tf_diff_inf_norm / tf_plant_inf_norm <\\n tf_rel_tol)\\n '''\\n\\n # Also test that saved reduced model mats are equal to those\\n # returned in memory\\n np.testing.assert_equal(\\n util.load_array_text(A_path_computed), A_model)\\n np.testing.assert_equal(\\n util.load_array_text(B_path_computed), B_model)\\n np.testing.assert_equal(\\n util.load_array_text(C_path_computed), C_model)\",\n \"def __init__(self, model_functions, constants):\\r\\n _debug('Protocol: __init__')\\r\\n _debug('I never lose. Either I win or learn. – Nelson Mandela')\\r\\n self.model_functions = model_functions\\r\\n \\r\\n self.f0 = model_functions[0]\\r\\n self.fp = model_functions[1]\\r\\n self.fm = model_functions[2]\\r\\n \\r\\n self.constants = constants\\r\\n \\r\\n \\r\\n \\r\\n #Variable to be stock for studying the protocol\\r\\n #Times\\r\\n self.t_probe_p_s = [] # Times for measuring f0-fp \\r\\n self.t_probe_m_s = [] # Times for measuring f0-fm \\r\\n \\r\\n self.t_pulseSequences_s = [] #This will store the times elapsed in the pulse sequences for an individual iteration\\r\\n self.t_process_s = [] #This will store the times elapsed in the CPU processing for an individual iteration \\r\\n self.t_pulseSequences = 0 #Time elapsed in the pulse sequences for an individual iteration\\r\\n self.t_process = 0 #Time elapsed in the CPU processing for an individual iteration \\r\\n #Total, accumulated, times\\r\\n self.t_tot_pulseSequences_s = [] #This will store the total times elapsed for the pulse sequences\\r\\n self.t_tot_process_s = [] #This will store the total CPU times elapsed for processing the Bayes stuffs\\r\\n self.t_tot_pulseSequences = 0 #Total times elapsed for the pulse sequences\\r\\n self.t_tot_process = 0 #Total CPU time elapsed so far for processing the Bayes stuffs\\r\\n #Stuff on the rates\\r\\n self.Gp_guess_s = [] #Mean of gamma+ from the posterior\\r\\n self.Gm_guess_s = [] #Mean of gamma- from the posterior\\r\\n self.eGp_guess_s = [] #Standard deviation of gamma+ from the posterior\\r\\n self.eGm_guess_s = [] #Standard deviation of gamma- from the posterior \\r\\n self.cov_Gp_s = [] #Variance of gamma+ from the posterior\\r\\n self.cov_Gm_s = [] #Variance of gamma- from the posterior\\r\\n self.cov_GpGm_s = [] #Covariance of gamma- & gamma- from the posterior \\r\\n \\r\\n #Other\\r\\n self.nb_iteration_s = [] #Stock the number of iteration done so far\\r\\n self.R_tot_s = [] #Stock Total numbers of readout performed so far\\r\\n self.R_tot = 0 #Total number of readout for performed so far\\r\\n \\r\\n self.iter = 0 #Iterator\\r\",\n \"def test_NMF_parameters(u1_ml100k, pkf):\\n\\n # The baseline against which to compare.\\n algo = NMF(n_factors=1, n_epochs=1, random_state=1)\\n rmse_default = cross_validate(algo, u1_ml100k, [\\\"rmse\\\"], pkf)[\\\"test_rmse\\\"]\\n\\n # n_factors\\n algo = NMF(n_factors=2, n_epochs=1, random_state=1)\\n rmse_factors = cross_validate(algo, u1_ml100k, [\\\"rmse\\\"], pkf)[\\\"test_rmse\\\"]\\n assert rmse_default != rmse_factors\\n\\n # n_epochs\\n algo = NMF(n_factors=1, n_epochs=2, random_state=1)\\n rmse_n_epochs = cross_validate(algo, u1_ml100k, [\\\"rmse\\\"], pkf)[\\\"test_rmse\\\"]\\n assert rmse_default != rmse_n_epochs\\n\\n # biased\\n algo = NMF(n_factors=1, n_epochs=1, biased=True, random_state=1)\\n rmse_biased = cross_validate(algo, u1_ml100k, [\\\"rmse\\\"], pkf)[\\\"test_rmse\\\"]\\n assert rmse_default != rmse_biased\\n\\n # reg_pu\\n algo = NMF(n_factors=1, n_epochs=1, reg_pu=1, random_state=1)\\n rmse_reg_pu = cross_validate(algo, u1_ml100k, [\\\"rmse\\\"], pkf)[\\\"test_rmse\\\"]\\n assert rmse_default != rmse_reg_pu\\n\\n # reg_qi\\n algo = NMF(n_factors=1, n_epochs=1, reg_qi=1, random_state=1)\\n rmse_reg_qi = cross_validate(algo, u1_ml100k, [\\\"rmse\\\"], pkf)[\\\"test_rmse\\\"]\\n assert rmse_default != rmse_reg_qi\\n\\n # reg_bu\\n algo = NMF(n_factors=1, n_epochs=1, reg_bu=1, biased=True, random_state=1)\\n rmse_reg_bu = cross_validate(algo, u1_ml100k, [\\\"rmse\\\"], pkf)[\\\"test_rmse\\\"]\\n assert rmse_default != rmse_reg_bu\\n\\n # reg_bi\\n algo = NMF(n_factors=1, n_epochs=1, reg_bi=1, biased=True, random_state=1)\\n rmse_reg_bi = cross_validate(algo, u1_ml100k, [\\\"rmse\\\"], pkf)[\\\"test_rmse\\\"]\\n assert rmse_default != rmse_reg_bi\\n\\n # lr_bu\\n algo = NMF(n_factors=1, n_epochs=1, lr_bu=1, biased=True, random_state=1)\\n rmse_lr_bu = cross_validate(algo, u1_ml100k, [\\\"rmse\\\"], pkf)[\\\"test_rmse\\\"]\\n assert rmse_default != rmse_lr_bu\\n\\n # lr_bi\\n algo = NMF(n_factors=1, n_epochs=1, lr_bi=1, biased=True, random_state=1)\\n rmse_lr_bi = cross_validate(algo, u1_ml100k, [\\\"rmse\\\"], pkf)[\\\"test_rmse\\\"]\\n assert rmse_default != rmse_lr_bi\\n\\n # init_low\\n algo = NMF(n_factors=1, n_epochs=1, init_low=0.5, random_state=1)\\n rmse_init_low = cross_validate(algo, u1_ml100k, [\\\"rmse\\\"], pkf)[\\\"test_rmse\\\"]\\n assert rmse_default != rmse_init_low\\n\\n # init_low\\n with pytest.raises(ValueError):\\n algo = NMF(n_factors=1, n_epochs=1, init_low=-1, random_state=1)\\n\\n # init_high\\n algo = NMF(n_factors=1, n_epochs=1, init_high=0.5, random_state=1)\\n rmse_init_high = cross_validate(algo, u1_ml100k, [\\\"rmse\\\"], pkf)[\\\"test_rmse\\\"]\\n assert rmse_default != rmse_init_high\",\n \"def run_models(\\n self,\\n normal=True,\\n interrupt=True,\\n run_start=None,\\n state_builder=\\\"acis\\\",\\n hrc=False,\\n ):\\n if hrc:\\n loads = hrc_loads\\n else:\\n loads = test_loads\\n if normal and \\\"normal\\\" in loads:\\n for load in loads[\\\"normal\\\"]:\\n self.run_model(\\n load_week=load,\\n run_start=run_start,\\n state_builder=state_builder,\\n )\\n if interrupt and \\\"interrupt\\\" in loads:\\n for load in loads[\\\"interrupt\\\"]:\\n self.run_model(\\n load_week=load,\\n interrupt=True,\\n run_start=run_start,\\n state_builder=state_builder,\\n )\",\n \"def entry(self):\\n if not os.path.isfile('model'):\\n train()\\n schedule.every(0.01).seconds.do(predict, self)\\n while True:\\n schedule.run_pending()\",\n \"def _doRun(self, model: Model):\\n raise Exception(\\\"Not implemented\\\")\",\n \"def initialize_model(model_name, num_classes, feature_extract, verbose=False):\\n\\n model_ft = None\\n\\n if model_name == \\\"resnet\\\":\\n \\\"\\\"\\\" Resnet18\\n \\\"\\\"\\\"\\n model_ft = models.resnet18(pretrained=True)\\n set_parameter_requires_grad(model_ft, feature_extract)\\n num_ftrs = model_ft.fc.in_features\\n model_ft.fc = nn.Linear(num_ftrs, num_classes)\\n\\n elif model_name == \\\"alexnet\\\":\\n \\\"\\\"\\\" Alexnet\\n \\\"\\\"\\\"\\n model_ft = models.alexnet(pretrained=True)\\n set_parameter_requires_grad(model_ft, feature_extract)\\n num_ftrs = model_ft.classifier[6].in_features\\n model_ft.classifier[6] = nn.Linear(num_ftrs, num_classes)\\n\\n elif model_name == \\\"vgg\\\":\\n \\\"\\\"\\\" VGG11_bn\\n \\\"\\\"\\\"\\n model_ft = models.vgg11_bn(pretrained=True)\\n set_parameter_requires_grad(model_ft, feature_extract)\\n num_ftrs = model_ft.classifier[6].in_features\\n model_ft.classifier[6] = nn.Linear(num_ftrs, num_classes)\\n\\n elif model_name == \\\"squeezenet\\\":\\n \\\"\\\"\\\" Squeezenet\\n \\\"\\\"\\\"\\n with warnings.catch_warnings(): # temporarily suppress warnings about deprecated functions\\n warnings.simplefilter(\\\"ignore\\\")\\n model_ft = models.squeezenet1_0(pretrained=True)\\n set_parameter_requires_grad(model_ft, feature_extract)\\n model_ft.classifier[1] = nn.Conv2d(512, num_classes, kernel_size=(1, 1), stride=(1, 1))\\n model_ft.num_classes = num_classes\\n\\n elif model_name == \\\"densenet\\\":\\n \\\"\\\"\\\" Densenet\\n \\\"\\\"\\\"\\n model_ft = models.densenet121(pretrained=True)\\n set_parameter_requires_grad(model_ft, feature_extract)\\n num_ftrs = model_ft.classifier.in_features\\n model_ft.classifier = nn.Linear(num_ftrs, num_classes)\\n\\n elif model_name == \\\"inception\\\":\\n \\\"\\\"\\\" Inception v3\\n Be careful, expects (299,299) sized images and has auxiliary output\\n \\\"\\\"\\\"\\n model_ft = models.inception_v3(pretrained=True)\\n set_parameter_requires_grad(model_ft, feature_extract)\\n # Handle the auxilary net\\n num_ftrs = model_ft.AuxLogits.fc.in_features\\n model_ft.AuxLogits.fc = nn.Linear(num_ftrs, num_classes)\\n # Handle the primary net\\n num_ftrs = model_ft.fc.in_features\\n model_ft.fc = nn.Linear(num_ftrs, num_classes)\\n\\n else: # Unreachable\\n exit()\\n\\n # Gather the parameters to be optimized\\n params_to_update = list(filter(lambda p: p.requires_grad, model_ft.parameters()))\\n\\n # Print model info\\n if verbose:\\n print()\\n print(model_ft)\\n print()\\n print(\\\"Params to learn:\\\")\\n for name, param in model_ft.named_parameters():\\n if param.requires_grad:\\n print('\\\\t', name)\\n\\n return model_ft, params_to_update\",\n \"def train(args):\\r\\n print('Create generators')\\r\\n generators = train_valid_test_generators(\\r\\n valid_proportion=args.valid_proportion,\\r\\n test_proportion=args.test_proportion,\\r\\n seed=args.seed,\\r\\n shape=(args.height, args.width),\\r\\n batch_size=args.batch_size,\\r\\n shuffle=True\\r\\n )\\r\\n print('Create model')\\r\\n model = create_mobilenetv2(\\r\\n input_shape=(args.height, args.width, 3),\\r\\n alpha=args.alpha,\\r\\n depth_multiplier=args.depth_multiplier,\\r\\n l2_reg=args.l2_reg,\\r\\n seed=args.seed\\r\\n )\\r\\n\\r\\n print('Training freezed model')\\r\\n freeze_model(model, 'global_max_pooling2d_1')\\r\\n callbacks = callbacks_factory(\\r\\n callbacks_list=[\\r\\n 'early_stopping',\\r\\n 'tensorboard',\\r\\n ],\\r\\n model_mask='mobilenetv2_multiclassification_freezed'\\r\\n )\\r\\n model = train_pipeline(\\r\\n model,\\r\\n generators['hard_train_generator'],\\r\\n generators['valid_generator'],\\r\\n callbacks,\\r\\n optimizer_lr=args.optimizer_lr,\\r\\n optimizer_decay=args.optimizer_decay,\\r\\n epochs=args.epochs\\r\\n )\\r\\n\\r\\n print('Training unfreezed model')\\r\\n unfreeze_model(model)\\r\\n callbacks = callbacks_factory(\\r\\n callbacks_list=[\\r\\n 'best_model_checkpoint',\\r\\n 'early_stopping',\\r\\n 'tensorboard',\\r\\n 'learning_rate_scheduler'\\r\\n ],\\r\\n model_mask='mobilenetv2_multiclassification'\\r\\n )\\r\\n model = train_pipeline(\\r\\n model,\\r\\n generators['easy_train_generator'],\\r\\n generators['valid_generator'],\\r\\n callbacks,\\r\\n optimizer_lr=args.optimizer_lr,\\r\\n optimizer_decay=args.optimizer_decay,\\r\\n epochs=3 * args.epochs\\r\\n )\\r\\n\\r\\n print('Save test evaluation')\\r\\n results = model.evaluate_generator(generators['test_generator'])\\r\\n pd.DataFrame({\\r\\n 'MetricsNames': model.metrics_names,\\r\\n 'Results': results\\r\\n }).to_csv(os.path.join('../logs/solution_1_test_generator_evaluation.csv'), index=False)\",\n \"def validate(model,dataloader,criterions,epoch,plots):\\n # switch to evaluate mode\\n model.eval()\\n\\n running_loss = 0.0\\n running_oa = 0.0\\n running_moa = 0.0\\n\\n avg_losses={}\\n avg_accuracies={}\\n avg_moas={}\\n for i in range(6):\\n avg_losses[i] = AverageMeter()\\n avg_accuracies[i] = AverageMeter()\\n avg_moas[i] = AverageMeter()\\n\\n tq_bar = tqdm(enumerate(dataloader),total=len(dataloader),ncols=80,desc='Testing')\\n for batch_id, (images, labels_group) in tq_bar:\\n # if i>25:\\n # break\\n if torch.cuda.is_available():\\n images = [Variable(image.cuda()) for image in images]\\n labels_group = [labels for labels in labels_group]\\n else:\\n print('Cuda not available')\\n images = [Variable(image) for image in images]\\n labels_group = [labels for labels in labels_group]\\n\\n\\n batch_losses = []\\n batch_accuracies = []\\n batch_moas = []\\n\\n for img, labels in zip(images, labels_group):\\n outputs = model(img)\\n net_batch_size = outputs[0].size(0)\\n if torch.cuda.is_available():\\n labels = [Variable(label.cuda()) for label in labels]\\n else:\\n labels = [Variable(label) for label in labels]\\n for i,pair in enumerate(zip(outputs, labels)):\\n accuracy = accuracy_dense(pair[0].data, pair[1].data)\\n moa,_ = mAP_dense(pair[0].data, pair[1].data)\\n batch_losses.append(criterions[i](pair[0], pair[1]))\\n batch_accuracies.append(accuracy)\\n batch_moas.append(moa)\\n\\n for i in range(6):\\n avg_losses[i].update(batch_losses[i].data[0], net_batch_size)\\n avg_accuracies[i].update(batch_accuracies[i], net_batch_size)\\n avg_moas[i].update(batch_moas[i], net_batch_size)\\n\\n ## LOSS COMPUTATION\\n # loss_weight = [auto_loss_weight(0,epoch), auto_loss_weight(1,epoch), auto_loss_weight(2,epoch), auto_loss_weight(3,epoch), auto_loss_weight(4,epoch)]\\n if epoch < 40:\\n loss_weight = [0.1, 0.1, 0.1, 0.1, 0.1, 0.5]\\n else:\\n loss_weight = [0.5, 0.1, 0.1, 0.1, 0.1, 0.1]\\n\\n # loss_weight = [1., 0.01, 0.01, 0.01, 0.01, 0.01] # fait converger en OA la HD layer\\n loss_weight = [1.,0.7, 0.6, 0.5, 0.1, 0.05, 0.01]\\n\\n total_batch_loss = 0\\n for w, l in zip(loss_weight, batch_losses):\\n total_batch_loss += w*l\\n\\n\\n running_loss += total_batch_loss.data[0]\\n # running_oa += oa\\n # running_hd_moa += hd_moa\\n for i in range(6):\\n plots.plot(\\\"Total loss (running)\\\", \\\"val \\\"+str(i), epoch*len(dataloader)+batch_id+1, avg_losses[i].val)\\n plots.plot(\\\"OA (running)\\\", \\\"val \\\"+str(i), epoch*len(dataloader)+batch_id+1, avg_accuracies[i].val)\\n plots.plot(\\\"mOA (running)\\\", \\\"val \\\"+str(i), epoch*len(dataloader)+batch_id+1, avg_moas[i].val)\\n for i in range(6):\\n plots.plot(\\\"Total loss (final mean of epoch)\\\", \\\"val \\\"+str(i), epoch+1, avg_losses[i].val)\\n plots.plot(\\\"OA (final mean of epoch)\\\", \\\"val \\\"+str(i), epoch+1, avg_accuracies[i].val)\\n plots.plot(\\\"mOA (final mean of epoch)\\\", \\\"val \\\"+str(i), epoch+1, avg_moas[i].val)\",\n \"def run(self) -> None:\\n self.model = self.trainer.train_model(self.model, self.data)\",\n \"def test_class():\\n riskfree = .03\\n maturity = 30/365\\n moneyness = np.linspace(-.04, .04, 10)\\n premium = np.ones_like(moneyness) * .05\\n call = True\\n data = {'riskfree': riskfree, 'maturity': maturity,\\n 'moneyness': moneyness, 'call': call, 'premium': premium}\\n\\n sigma = .13\\n bsm = BSmodel(sigma, data)\\n\\n weights = [.63]\\n means = [-.01, .09]\\n stds = [.16, .05]\\n param = weights + means + stds\\n mbs = MBSmodel(param, data)\\n\\n param_a, param_p, param_c = 4, 1.5, -.05\\n gb2 = GB2model([param_a, param_p, param_c], data)\\n print(gb2.get_pnames())\\n\\n plt.figure()\\n for model in [bsm, mbs, gb2]:\\n plt.plot(moneyness, model.density(moneyness), label=model.get_name())\\n plt.legend()\\n plt.show()\\n\\n plt.figure()\\n for model in [bsm, mbs, gb2]:\\n plt.plot(moneyness, model.premium(), label=model.get_name())\\n plt.legend()\\n plt.show()\\n\\n plt.figure()\\n for model in [bsm, mbs, gb2]:\\n plt.plot(moneyness, model.impvol(), label=model.get_name())\\n plt.legend()\\n plt.show()\\n\\n print('BS objective function = %.4f' % bsm.objective(sigma))\\n print('GB2 objective function = %.4f'\\n % gb2.objective([param_a, param_p, param_c]))\",\n \"def setup(self):\\n print(\\\"setup\\\")\\n \\n self.modelToUse = 1\\n if self.train:\\n print(\\\"train\\\")\\n else:\\n print(\\\"no train\\\")\\n \\n device = torch.device(\\\"cuda\\\" if torch.cuda.is_available() else \\\"cpu\\\")\\n self.envSize = 17\\n \\n #init model\\n if self.train or not os.path.isfile(\\\"my-saved-model.pt\\\"):\\n self.logger.info(\\\"Setting up model from scratch.\\\")\\n if self.modelToUse == 0:\\n self.policy_net = Model_global_view(self.envSize, self.envSize, 6).to(device)\\n self.model = Model_global_view(self.envSize, self.envSize, 6).to(device)\\n elif self.modelToUse == 1:\\n self.policy_net = Model_local_view(self.envSize, self.envSize, 6).to(device)\\n self.model = Model_local_view(self.envSize, self.envSize, 6).to(device)\\n else:\\n self.policy_net = Model_combined_view(self.envSize, self.envSize, 6).to(device)\\n self.model = Model_combined_view(self.envSize, self.envSize, 6).to(device)\\n self.model.load_state_dict(self.policy_net.state_dict())\\n self.model.eval()\\n else:\\n self.logger.info(\\\"Loading model from saved state.\\\")\\n with open(\\\"my-saved-model.pt\\\", \\\"rb\\\") as file:\\n if self.modelToUse == 0:\\n self.model = Model_global_view(self.envSize, self.envSize, 6)\\n elif self.modelToUse == 1:\\n self.model = Model_local_view(self.envSize, self.envSize, 6)\\n else:\\n self.model = Model_combined_view(self.envSize, self.envSize, 6)\\n if torch.cuda.is_available():\\n self.model.load_state_dict(torch.load(file))\\n self.model.to(device)\\n else:\\n self.model.load_state_dict(torch.load(file, map_location=device))\",\n \"def run(self, model, verbose=2, device='cpu', dtype=torch.float32):\\n old_verbose = self.verbose\\n self.verbose = verbose\\n\\n state = torchbearer.State()\\n state.update({torchbearer.MODEL: model, torchbearer.DEVICE: device, torchbearer.DATA_TYPE: dtype})\\n self.process(state)\\n self.verbose = old_verbose\",\n \"def _run(self):\\n\\n self.init_simulation()\\n\\n # Inject ourselves into the core\\n self.simthread.inject()\\n\\n # register steppables in queue\\n for _ in range(len(self._steppable_queue)):\\n steppable, frequency = self._steppable_queue.pop(0)\\n if not isinstance(steppable, SteppablePy):\\n steppable = steppable(frequency=frequency)\\n CompuCellSetup.register_steppable(steppable=steppable)\\n for _ in range(len(self._corespecs_queue)):\\n CompuCellSetup.register_specs(self._corespecs_queue.pop(0))\\n\\n # Load model specs from file if specified\\n if self.cc3d_sim_fname is not None:\\n assert os.path.isfile(self.cc3d_sim_fname), f\\\"Could not find simulation file: {self.cc3d_sim_fname}\\\"\\n rollback_importer = RollbackImporter()\\n CompuCellSetup.run_cc3d_project(self.cc3d_sim_fname)\\n rollback_importer.uninstall()\\n\\n # Call startup if loaded simulation file didn't already do it\\n if CompuCellSetup.persistent_globals.simulator is None:\\n CompuCellSetup.run()\",\n \"def test_sim(self):\\n nxfe = 4\\n ipopt = get_solver(\\\"ipopt\\\")\\n\\n m_steady = self.make_steady_model(nfe=nxfe)\\n self.fix_model_inlets(m_steady, inlet_pressure=50.0 * pyo.units.bar)\\n m_steady.fs.compressor.boost_pressure[:].fix(7.0 * pyo.units.bar)\\n ipopt.solve(m_steady, tee=True)\\n time_steady = m_steady.fs.time\\n scalar_data = self.get_scalar_data_from_model(m_steady, time_steady)\\n initial_data = self.get_data_from_model_at_time(m_steady, time_steady)\\n\\n m = pyo.ConcreteModel()\\n default = {\\n \\\"dynamic\\\": True,\\n \\\"time_set\\\": [0.0, 20.0],\\n \\\"time_units\\\": pyo.units.hr,\\n }\\n m.fs = idaes.FlowsheetBlock(**default)\\n m.fs.properties = NaturalGasParameterBlock()\\n pipeline_config = {\\n \\\"property_package\\\": m.fs.properties,\\n \\\"finite_elements\\\": nxfe,\\n }\\n m.fs.pipeline = GasPipeline(**pipeline_config)\\n pipeline = m.fs.pipeline\\n compressor_config = {\\\"property_package\\\": m.fs.properties}\\n m.fs.compressor = Compressor(**compressor_config)\\n compressor = m.fs.compressor\\n m._compressor_to_pipeline = Arc(\\n ports=(compressor.outlet_port, pipeline.inlet_port),\\n )\\n expand_arcs = pyo.TransformationFactory(\\\"network.expand_arcs\\\")\\n expand_arcs.apply_to(m)\\n\\n cv = m.fs.pipeline.control_volume\\n assert_units_consistent(m)\\n\\n disc = pyo.TransformationFactory(\\\"dae.finite_difference\\\")\\n ntfe = 20\\n disc.apply_to(m, nfe=ntfe, wrt=m.fs.time, scheme=\\\"BACKWARD\\\")\\n\\n time = m.fs.time\\n t0 = m.fs.time.first()\\n x0 = cv.length_domain.first()\\n xf = cv.length_domain.last()\\n j = next(iter(m.fs.properties.component_list))\\n\\n # Fix geometry variables\\n m.fs.pipeline.diameter.fix(0.92 * pyo.units.m)\\n cv.length.fix(300.0 * pyo.units.km)\\n\\n # Fix boost pressure\\n compressor.boost_pressure[:].fix()\\n\\n # Inlets to the compressor are fixed, except for flow, where\\n # the outlet is fixed.\\n state = compressor.inlet_state\\n state[:].pressure.fix()\\n state[:].mole_frac_comp[j].fix()\\n state[:].temperature.fix()\\n cv.flow_mass[:, xf].fix()\\n\\n # Fix initial conditions. Here, pressure and volume for all\\n # non-specified points.\\n for x in cv.length_domain:\\n if x != x0:\\n cv.pressure[t0, x].fix()\\n if x != xf:\\n cv.flow_mass[t0, x].fix()\\n\\n # I want to deactivate differential equations at (t0, xf)\\n # Material balance already doesn't exist here.\\n cv.momentum_balance[t0, xf].deactivate()\\n\\n self.assertEqual(degrees_of_freedom(m), 0)\\n\\n # Load initial steady state into model at all time points.\\n for name, val in initial_data.items():\\n var = m.find_component(name)\\n for t in time:\\n var[t].set_value(val)\\n # Load scalar data from initial steady state\\n # (initialize area, basically)\\n for name, val in scalar_data.items():\\n var = m.find_component(name)\\n var.set_value(val)\\n\\n cv.material_accumulation[...].set_value(0.0)\\n cv.flow_mass_dt[...].set_value(0.0)\\n\\n for con in large_residuals_set(m):\\n resid = pyo.value(con.body - con.upper)\\n print(resid, con.name)\\n ipopt.solve(m, tee=True)\\n\\n # Load input sequence into model\\n sample_points = [4.0, 20.0]\\n input_name = \\\"fs.pipeline.control_volume.flow_mass[*,1.0]\\\"\\n nominal_density = 0.72\\n val = 12.0 * 1e6 / 24 * nominal_density # 12 (1e6 SCM)/day\\n input_series_data = (\\n sample_points,\\n {input_name: [val, val]},\\n )\\n input_interval_data = interval_data_from_time_series(input_series_data)\\n load_inputs_into_model(m, time, input_interval_data)\\n # Solve with loaded inputs\\n res = ipopt.solve(m, tee=True)\\n self.assertIs(\\n res.solver.termination_condition,\\n pyo.TerminationCondition.optimal,\\n )\\n\\n # These predicted values come from a simulation of a single pipeline\\n # model from the Pyomo DAE example. flow_mass has been converted\\n # to kg/hr from (1e4 SCM/hr) by a factor of 0.72*1e4, where\\n # 0.72 kg/m**3 is the gas density at standard conditions.\\n pred_values = (\\n list(time),\\n {\\n \\\"fs.pipeline.control_volume.flow_mass[*,%s]\\\"\\n % x0: [\\n 3.000e5,\\n 2.999e5,\\n 2.999e5,\\n 2.999e5,\\n 3.000e5,\\n 3.174e5,\\n 3.301e5,\\n 3.389e5,\\n 3.449e5,\\n 3.492e5,\\n 3.523e5,\\n 3.544e5,\\n 3.560e5,\\n 3.571e5,\\n 3.579e5,\\n 3.585e5,\\n 3.589e5,\\n 3.592e5,\\n 3.594e5,\\n 3.595e5,\\n 3.597e5,\\n ],\\n \\\"fs.pipeline.control_volume.pressure[*,%s]\\\"\\n % xf: [\\n 50.90,\\n 50.90,\\n 50.90,\\n 50.90,\\n 50.90,\\n 49.83,\\n 49.31,\\n 48.95,\\n 48.69,\\n 48.51,\\n 48.38,\\n 48.29,\\n 48.22,\\n 48.17,\\n 48.14,\\n 48.11,\\n 48.10,\\n 48.08,\\n 48.07,\\n 48.07,\\n 48.06,\\n ],\\n \\\"fs.compressor.power[*]\\\": [\\n 1.590e3,\\n 1.590e3,\\n 1.590e3,\\n 1.590e3,\\n 1.590e3,\\n 1.682e3,\\n 1.750e3,\\n 1.796e3,\\n 1.828e3,\\n 1.851e3,\\n 1.867e3,\\n 1.878e3,\\n 1.887e3,\\n 1.892e3,\\n 1.897e3,\\n 1.900e3,\\n 1.902e3,\\n 1.904e3,\\n 1.905e3,\\n 1.906e3,\\n 1.906e3,\\n ],\\n },\\n )\\n output_names = [\\n \\\"fs.pipeline.control_volume.flow_mass[*,%s]\\\" % x0,\\n \\\"fs.pipeline.control_volume.pressure[*,%s]\\\" % xf,\\n \\\"fs.compressor.power[*]\\\",\\n ]\\n actual_values = (\\n list(time),\\n {\\n name: [var.value for var in m.find_component(name).values()]\\n for name in output_names\\n },\\n )\\n # Note: We fail with a reltol of 0.01, due to flow rate discrepancies\\n # in positions 6, 7, 8, and 9. A reltol of 0.02 seems reasonable to me.\\n self.assertStructuredAlmostEqual(pred_values, actual_values, reltol=0.02)\",\n \"def set_up_and_parameterise_model_for_experiment(self):\\n self.experiment_unique_steps_to_model = {}\\n for op_number, op in enumerate(self.experiment.unique_steps):\\n new_model = self.model.new_copy()\\n new_parameter_values = self.parameter_values.copy()\\n\\n if op.type != \\\"current\\\":\\n # Voltage or power control\\n # Create a new model where the current density is now a variable\\n # To do so, we replace all instances of the current density in the\\n # model with a current density variable, which is obtained from the\\n # FunctionControl submodel\\n # check which kind of external circuit model we need (differential\\n # or algebraic)\\n if op.type == \\\"voltage\\\":\\n submodel_class = pybamm.external_circuit.VoltageFunctionControl\\n elif op.type == \\\"power\\\":\\n submodel_class = pybamm.external_circuit.PowerFunctionControl\\n\\n # Build the new submodel and update the model with it\\n submodel = submodel_class(new_model.param, new_model.options)\\n variables = new_model.variables\\n submodel.variables = submodel.get_fundamental_variables()\\n variables.update(submodel.variables)\\n submodel.variables.update(submodel.get_coupled_variables(variables))\\n variables.update(submodel.variables)\\n submodel.set_rhs(variables)\\n submodel.set_algebraic(variables)\\n submodel.set_initial_conditions(variables)\\n new_model.rhs.update(submodel.rhs)\\n new_model.algebraic.update(submodel.algebraic)\\n new_model.initial_conditions.update(submodel.initial_conditions)\\n\\n # Set the \\\"current function\\\" to be the variable defined in the submodel\\n new_parameter_values[\\\"Current function [A]\\\"] = submodel.variables[\\n \\\"Current [A]\\\"\\n ]\\n self.update_new_model_events(new_model, op)\\n # Update parameter values\\n self._original_temperature = new_parameter_values[\\\"Ambient temperature [K]\\\"]\\n experiment_parameter_values = self.get_experiment_parameter_values(\\n op, op_number\\n )\\n new_parameter_values.update(\\n experiment_parameter_values, check_already_exists=False\\n )\\n parameterised_model = new_parameter_values.process_model(\\n new_model, inplace=False\\n )\\n self.experiment_unique_steps_to_model[repr(op)] = parameterised_model\\n\\n # Set up rest model if experiment has start times\\n if self.experiment.initial_start_time:\\n new_model = self.model.new_copy()\\n # Update parameter values\\n new_parameter_values = self.parameter_values.copy()\\n self._original_temperature = new_parameter_values[\\\"Ambient temperature [K]\\\"]\\n new_parameter_values.update(\\n {\\\"Current function [A]\\\": 0, \\\"Ambient temperature [K]\\\": \\\"[input]\\\"},\\n check_already_exists=False,\\n )\\n parameterised_model = new_parameter_values.process_model(\\n new_model, inplace=False\\n )\\n self.experiment_unique_steps_to_model[\\n \\\"Rest for padding\\\"\\n ] = parameterised_model\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.629378","0.6192165","0.60618526","0.6044517","0.60348445","0.6019483","0.5948904","0.5907536","0.5832203","0.58019847","0.572914","0.5647409","0.5641304","0.5582606","0.55634594","0.55448914","0.5505208","0.5491149","0.54623306","0.5438175","0.53968614","0.53924066","0.53631943","0.53469366","0.5322211","0.5309065","0.5281697","0.5276423","0.527359","0.5273153","0.5265908","0.52653825","0.5260761","0.5254837","0.52497345","0.52391714","0.5238611","0.5226908","0.52249116","0.5222676","0.52215976","0.52138776","0.5207607","0.52052194","0.51959914","0.5190843","0.5185363","0.5159796","0.5155722","0.5152218","0.5129453","0.51193744","0.5105512","0.5100964","0.510075","0.5094649","0.50940704","0.5072318","0.50690174","0.5068688","0.506632","0.50650185","0.5060383","0.505271","0.5048612","0.5042663","0.5038688","0.5038258","0.50379777","0.5037678","0.5034631","0.5034389","0.50333774","0.5027214","0.50243235","0.5023971","0.502265","0.5020386","0.5020358","0.5019008","0.50182384","0.5018093","0.50152606","0.50140303","0.5013655","0.50108045","0.5009861","0.50089246","0.5007638","0.5007554","0.50071186","0.49995357","0.4998061","0.49976218","0.49957335","0.4992287","0.49893433","0.49890423","0.49878368","0.4976275"],"string":"[\n \"0.629378\",\n \"0.6192165\",\n \"0.60618526\",\n \"0.6044517\",\n \"0.60348445\",\n \"0.6019483\",\n \"0.5948904\",\n \"0.5907536\",\n \"0.5832203\",\n \"0.58019847\",\n \"0.572914\",\n \"0.5647409\",\n \"0.5641304\",\n \"0.5582606\",\n \"0.55634594\",\n \"0.55448914\",\n \"0.5505208\",\n \"0.5491149\",\n \"0.54623306\",\n \"0.5438175\",\n \"0.53968614\",\n \"0.53924066\",\n \"0.53631943\",\n \"0.53469366\",\n \"0.5322211\",\n \"0.5309065\",\n \"0.5281697\",\n \"0.5276423\",\n \"0.527359\",\n \"0.5273153\",\n \"0.5265908\",\n \"0.52653825\",\n \"0.5260761\",\n \"0.5254837\",\n \"0.52497345\",\n \"0.52391714\",\n \"0.5238611\",\n \"0.5226908\",\n \"0.52249116\",\n \"0.5222676\",\n \"0.52215976\",\n \"0.52138776\",\n \"0.5207607\",\n \"0.52052194\",\n \"0.51959914\",\n \"0.5190843\",\n \"0.5185363\",\n \"0.5159796\",\n \"0.5155722\",\n \"0.5152218\",\n \"0.5129453\",\n \"0.51193744\",\n \"0.5105512\",\n \"0.5100964\",\n \"0.510075\",\n \"0.5094649\",\n \"0.50940704\",\n \"0.5072318\",\n \"0.50690174\",\n \"0.5068688\",\n \"0.506632\",\n \"0.50650185\",\n \"0.5060383\",\n \"0.505271\",\n \"0.5048612\",\n \"0.5042663\",\n \"0.5038688\",\n \"0.5038258\",\n \"0.50379777\",\n \"0.5037678\",\n \"0.5034631\",\n \"0.5034389\",\n \"0.50333774\",\n \"0.5027214\",\n \"0.50243235\",\n \"0.5023971\",\n \"0.502265\",\n \"0.5020386\",\n \"0.5020358\",\n \"0.5019008\",\n \"0.50182384\",\n \"0.5018093\",\n \"0.50152606\",\n \"0.50140303\",\n \"0.5013655\",\n \"0.50108045\",\n \"0.5009861\",\n \"0.50089246\",\n \"0.5007638\",\n \"0.5007554\",\n \"0.50071186\",\n \"0.49995357\",\n \"0.4998061\",\n \"0.49976218\",\n \"0.49957335\",\n \"0.4992287\",\n \"0.49893433\",\n \"0.49890423\",\n \"0.49878368\",\n \"0.4976275\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.70113957"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":17892,"cells":{"query":{"kind":"string","value":"Initialize model in the sequential manner required."},"document":{"kind":"string","value":"def initialize_model(self, config_param_vals = None):\n self._is_initialized = True\n\n self.fmu.instantiate()\n self.fmu.reset()\n self.fmu.setupExperiment(startTime=self.start_time)\n if config_param_vals is not None:\n self._apply_config(config_param_vals)\n self.fmu.enterInitializationMode()\n self.fmu.exitInitializationMode()\n\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 initialize_model(self):\n pass","def init_model(self):\n pass","def initialize(self, model):\n pass","def initialize(self):\n LOG.info(\"Initializing Model.\")\n self.model = self.convert(df=self.training_df)\n if self.bootstraps is not None:\n LOG.info(\"Bootstrapping Data.\")\n self.bootstrap_data()","def initialize(self):\n for key in self.parameter_dict:\n self.models[key] = self._create_model(key)","def initialisation(self):\n self.create_variables()\n self.create_placeholders()\n self.build_model()\n self.reset_lr(None, True)\n self.build_loss()\n self.initialised = True","def basic_model_init(model_args, task_infos, tokenizer):\n config = AutoConfig.from_pretrained(\n model_args.model_name_or_path,\n num_labels=task_infos.num_labels,\n cache_dir=model_args.model_cache_dir,\n id2label=task_infos.id2label,\n label2id=task_infos.label2id,\n )\n model_cls = getattr(mod, model_args.architectures,\n AutoModelForSequenceClassification)\n model = model_cls.from_pretrained(\n model_args.model_name_or_path,\n config=config,\n cache_dir=model_args.model_cache_dir,\n )\n if model.config.vocab_size < len(tokenizer):\n print(\"resize...\")\n model.resize_token_embeddings(len(tokenizer))\n return model","def initialize(self) -> None:\n self.model = load(self.path)","def _initialize_model(rngs):\n init_model_state, init_params = model_def.init(\n rngs, *dummy_input, train=False, debug=False).pop('params')\n # Set bias in the head to low value, such that loss is small initially.\n if config.get('init_head_bias', None) is not None:\n init_params = flax.core.unfreeze(init_params)\n init_params['output_projection'] = optimizers.tree_map_with_names(\n lambda p: jnp.full_like(p, config.init_head_bias),\n init_params['output_projection'],\n match_name_fn=lambda name: 'bias' in name)\n init_params = flax.core.freeze(init_params)\n return init_params, init_model_state","def _setup_model(self) -> torch.nn.Sequential:\r\n\r\n # setting up model\r\n ids_ = self.get_hyperparam().get_dim_ids()\r\n if self.get_hyperparam().get_value(ids_[13]):\r\n init_ = lambda mod: self._default_weight_bias_init(mod,\r\n self.get_hyperparam().get_value(ids_[14]),\r\n self.get_hyperparam().get_value(ids_[15]),\r\n self.get_hyperparam().get_value(ids_[16]))\r\n\r\n modules = []\r\n for hd in range(int(self.get_hyperparam().get_value(ids_[3]))+1):\r\n if hd == 0:\r\n act_input_size = self.get_hyperparam().get_value(ids_[0])\r\n output_size = self.get_hyperparam().get_value(ids_[4])[hd]\r\n act_fct = self.get_hyperparam().get_value(ids_[5])[hd]()\r\n elif hd == self.get_hyperparam().get_value(ids_[3]):\r\n act_input_size = self.get_hyperparam().get_value(ids_[4])[hd-1]\r\n output_size = self.get_hyperparam().get_value(ids_[1])\r\n act_fct = self.get_hyperparam().get_value(ids_[6])()\r\n else:\r\n act_input_size = self.get_hyperparam().get_value(ids_[4])[hd-1]\r\n output_size = self.get_hyperparam().get_value(ids_[4])[hd]\r\n act_fct = self.get_hyperparam().get_value(ids_[5])[hd]()\r\n \r\n if self.get_hyperparam().get_value(ids_[13]):\r\n modules.append(init_(torch.nn.Linear(int(act_input_size), int(output_size))))\r\n else:\r\n modules.append(torch.nn.Linear(int(act_input_size), int(output_size)))\r\n modules.append(act_fct)\r\n\r\n model = torch.nn.Sequential(*modules)\r\n \r\n # add process to the model\r\n try:\r\n model = self._add_init(model)\r\n except:\r\n pass \r\n \r\n self._loss_fct = self.get_hyperparam().get_value(ids_[8])()\r\n self._optimizer = self.get_hyperparam().get_value(ids_[7])(model.parameters(), lr=self.get_hyperparam().get_value(ids_[12]))\r\n self._sampling_seed = self.get_hyperparam().get_value(ids_[11])\r\n \r\n return model","def _initialize_model(rngs):\n init_model_state, init_params = nn.init(\n fn=init_fn, module=model_def)(rngs).pop('params')\n # Set bias in the head to low value, such that loss is small initially.\n if (config.get('init_head_bias', None) is not None and\n 'output_projection' in init_params):\n init_params = flax.core.unfreeze(init_params)\n init_params['output_projection'] = optimizers.tree_map_with_names(\n lambda p: jnp.full_like(p, config.init_head_bias),\n init_params['output_projection'],\n match_name_fn=lambda name: 'bias' in name)\n init_params = flax.core.freeze(init_params)\n return init_params, init_model_state","def init_batch(self):\n pass","def init_model(self):\n cxnlib.CXNNetInitModel(self.handle)","def initiate(self):\n # if self.opt.checkpoint_encoder:\n # self.load(self.opt.checkpoint_encoder, self.opt.checkpoint_decoder)\n # else:\n # start fresh.\n self.model = Transformer(\n self.opt.src_vocab_size,\n self.opt.tgt_vocab_size,\n self.opt.max_token_seq_len,\n tgt_emb_prj_weight_sharing=self.opt.proj_share_weight,\n emb_src_tgt_weight_sharing=self.opt.embs_share_weight,\n d_k=self.opt.d_k,\n d_v=self.opt.d_v,\n d_model=self.opt.d_model,\n d_word_vec=self.opt.d_word_vec,\n d_inner=self.opt.d_inner_hid,\n n_layers=self.opt.layers,\n n_head=self.opt.n_head,\n dropout=self.opt.dropout).to(self.device)\n \n for p in self.model.parameters():\n if p.dim() > 1:\n nn.init.xavier_uniform_(p)","def initialize(self):\n # FIX: INITIALIZE PROCESS INPUTS??\n for mech, value in self.initial_values.items():\n mech.initialize(value)","def initialize_trainer(self):\n self.initialize_matrices()\n self.initialize_model()\n self.initialize_optimizers()\n return self","def init_model(model):\n model(tf.random.uniform((1, 512, 512, 3)))","def initialize_first_model(self, wait=True, input_data_s3_prefix=None):\n # Sync experiment state if required\n self._sync_experiment_state_with_ddb()\n\n # experiment only allow one training job at a time,\n # validate no other training request is in progress\n if (\n self.experiment_record._training_state is not None\n and self.experiment_record._training_state.endswith(\"ING\")\n ):\n logger.error(\n f\"A training request with model id '{self.experiment_record._next_model_to_train_id}' \"\n f\"was in the state of '{self.experiment_record._training_state}'. \"\n \"Wait until the training job finished or canceled the request.\"\n )\n raise InvalidUsageException(\n \"Please wait for old Training Job to Complete before requesting a new one!\"\n )\n else:\n # update next_model_to_train_id and training state\n next_model_to_train_id = ModelManager.name_next_model(experiment_id=self.experiment_id)\n logger.info(f\"Next Model name would be {next_model_to_train_id}\")\n self.exp_db_client.update_experiment_next_model_to_train_id(\n self.experiment_id, next_model_to_train_id\n )\n self.exp_db_client.update_experiment_training_state(\n self.experiment_id, TrainingState.PENDING\n )\n logger.info(f\"Start training job for model '{next_model_to_train_id}''\")\n\n # generate manifest file if input is a list\n manifest_file_path = None\n if isinstance(input_data_s3_prefix, list):\n # generate manifest file and upload to s3\n manifest_file_path = self._generate_manifest(input_data_s3_prefix)\n\n # init model for training, update model table\n try:\n self.next_model_to_train = ModelManager(\n model_db_client=self.model_db_client,\n experiment_id=self.experiment_id,\n model_id=next_model_to_train_id,\n image=self.image,\n role=self.resource_manager.iam_role_arn,\n instance_config=self.resource_manager.training_fleet_config,\n boto_session=self.boto_session,\n algor_config=self.algor_config,\n )\n\n self.next_model_to_train.fit(\n wait=wait,\n input_model_id=None,\n input_data_s3_prefix=input_data_s3_prefix,\n manifest_file_path=manifest_file_path,\n logs=wait,\n )\n except Exception as e:\n logger.error(\n f\"Failed to start new Model Training job for\"\n \" ModelId {next_model_to_train_id}\"\n )\n logger.error(e)\n pass\n\n # wait until ExperimentDb state is updated\n if self.local_mode or wait:\n trained_state = (\n self.experiment_record._training_state == TrainingState.TRAINED\n and self.experiment_record._last_trained_model_id == next_model_to_train_id\n and self.experiment_record._next_model_to_train_id is None\n )\n num_retries = 0\n\n while not trained_state:\n # Sync experiment state if required\n self._sync_experiment_state_with_ddb()\n logger.debug(\"Waiting for experiment table training status to be updated...\")\n time.sleep(2 * (2 ** num_retries))\n trained_state = (\n self.experiment_record._training_state == TrainingState.TRAINED\n and self.experiment_record._last_trained_model_id == next_model_to_train_id\n and self.experiment_record._next_model_to_train_id is None\n )\n num_retries += 1\n if num_retries >= 5:\n raise UnhandledWorkflowException(\n f\"Training job '{self.experiment_record._next_model_to_train_id}' \"\n f\"was in state of '{self.experiment_record._training_state}'. Expected it to be TRAINED.\"\n )\n if (\n self.experiment_record._training_state == TrainingState.FAILED\n or self.experiment_record._training_state == TrainingState.STOPPED\n ):\n raise SageMakerTrainingJobException(\n f\"Training job '{self.experiment_record._next_model_to_train_id}' \"\n f\"ended in state of '{self.experiment_record._training_state}'. Please check Sagemaker logs for \"\n \"more information.\"\n )","def _init_model_params(self):\n super()._init_model_params()\n\n if 'e' in self.init_params:\n if self.init_type == 'uniform':\n if self.nr_no_train_de == 0:\n self.B = [\n np.full(\n (self.n_states, self.n_features[i]), 1.0 / self.n_features[i])\n for i in range(self.n_emissions)\n ]\n else:\n check_if_attributes_set(self, attr='e')\n else:\n if self.nr_no_train_de == 0:\n self.B = [\n np.random.rand(self.n_states, self.n_features[i])\n for i in range(self.n_emissions)\n ]\n for i in range(self.n_emissions):\n normalise(self.B[i], axis=1)\n\n else:\n check_if_attributes_set(self, attr='e')","def re_init(self):\n self.latent.re_init()\n if 're_init' in dir(self.inference_model):\n self.inference_model.re_init()\n if 're_init' in dir(self.generative_model):\n self.generative_model.re_init()","def init_model(self, model_name, config=None):\n ModelDirectory.init_model(model_name, pipeline=self, config=config)\n return self\n #self._action_list.append({'name': INIT_MODEL_ID, 'model_name': model_name, 'config': config})\n #return self.append_action()","def init_model(self, num_inputs, num_outputs):\n self.number_inputs = num_inputs\n\n self.number_outputs = num_outputs\n\n self.last_state = None\n\n self.model.start_nn(num_inputs, num_outputs)\n \n self.is_model_init = True","def initialize_model(self, initial_data):\n # EDIT THIS METHOD TO RETURN A MINIMAX MODEL ###\n return None","def initialize_model(epochs=4):\n bert_classifier = BertClassifier(freeze_bert=False)\n\n bert_classifier.to(device)\n\n optimizer = AdamW(bert_classifier.parameters(),lr=0.0001,eps=1e-8)\n\n # Total number of training steps\n total_steps = len(train_dataloader) * epochs\n\n # Set up the learning rate scheduler\n scheduler = get_linear_schedule_with_warmup(optimizer,\n num_warmup_steps=0, # Default value\n num_training_steps=total_steps)\n return bert_classifier, optimizer, scheduler","def _add_init(self, p_model):\r\n\r\n raise NotImplementedError","def _init_model(self, forrest):\n rels = self.get_rels(forrest)\n self._model = RDPModel(rels)","def init_models(self) -> None:\n self.models = AKSManagedClusterModels(self.cmd, self.resource_type)","def init_models(self) -> None:\n self.models = AKSManagedClusterModels(self.cmd, self.resource_type)","def init(self):\n inputs = self.inputs()\n outputs = self.outputs(inputs)\n self.model = tf.keras.Model(inputs=inputs, outputs=outputs)\n self.model.compile(optimizer=self.optimizer() or self.config.get('optimizer'),\n loss=self.loss() or None,\n metrics=self.metrics() or None,\n loss_weights=self.loss_weights() or None,\n weighted_metrics=self.weighted_metrics() or None,\n target_tensors=self.target_tensors() or None)\n if self.config.get('debug'):\n self.model.summary()","def __init__(self, num_models: int, num_classes: int):\n self.nun_models = num_models\n self.num_classes = num_classes\n self.model: keras.Model = self.init_model()","def __init__(self, initialState, runningModels, terminalModel):\n self.T = len(runningModels)\n self.initialState = initialState\n self.runningModels = runningModels\n self.runningDatas = [m.createData() for m in runningModels]\n self.terminalModel = terminalModel\n self.terminalData = terminalModel.createData()","def init(self, rng_key, num_warmup, init_params, model_args, model_kwargs):\n raise NotImplementedError","def initialize_data_iterator(self, model_idx=None):\n if model_idx and not isinstance(model_idx, (list, tuple)):\n raise TypeError(\"`model_idx` must be list-like structure\")\n\n model_indices = (\n # if model_idx is None\n # model_idx = [all_models_indices]\n model_idx if model_idx is not None\n else range(self.num_models))\n\n for idx in model_indices:\n try:\n print(\"Initializing %d data\" % idx)\n self._sess.run(self._data[idx].initializer)\n except AttributeError:\n # wraps the error with more informative message\n raise AttributeError(\"Model #%d has no `_data` attribute\")","def _initial_setup(self, **train_kwargs):\n self._update(time_step=0., **train_kwargs)","def prepare_for_model_run(self, model_time):\n\n pass","def __init__(self):\n self.model = None","def __init__(self):\n self.model = None","def init(default_model_key: str = None, install_requirements: bool = True):\n\n global default_model\n global install_req\n global initialized\n\n log.info(\"Initializing Model Handler\")\n\n if default_model_key:\n log.info(\"Preload model: \" + default_model_key)\n default_model = get_model(default_model_key)\n elif DEFAULT_MODEL_ENV in os.environ:\n model_key = os.environ[DEFAULT_MODEL_ENV]\n log.info(\"Preload model: \" + model_key)\n default_model = get_model(model_key)\n\n install_req = install_requirements\n if INSTALL_REQUIREMENTS_ENV in os.environ:\n if not bool(os.environ[INSTALL_REQUIREMENTS_ENV]):\n install_req = False\n\n initialized = True","def new(self):\n self.define_layers()\n self.model = nn.Sequential(*self.layers)\n self.model.cuda()\n self.model = orthogonal_init(self.model)\n\n # Re-count N\n self.count_params()","def init():\n global neural_network\n global labels\n\n # load objects required by run() for inferencing\n model_dir = Model.get_model_path(\"mnist-fashion\")\n # neural model\n neural_network = keras.models.load_model(f\"{model_dir}/neural-network.h5\")\n # labels\n with open(f\"{model_dir}/labels.jsonpickle\", \"r\") as labels_file:\n labels = jsonpickle.decode(labels_file.read())","def __init__(self, **kwargs):\n super(Model, self).__init__(**kwargs)\n self._params = self.find_params()","def initialize_main_model(self, model, **kwargs):\n return NotImplementedError(\n \"Initializer has not implemented an initialize_main_model method. Derived classes \"\n \"are required to overload this.\"\n )","def initialize(self):\n \n #initialize the variables\n init = tf.global_variables_initializer()\n self.session.run(init)\n \n #initialize the data iterators\n self.session.run(self.data_iterator.initializer)","def _construct_model(self):\n self.model = AutoEncoderConvolutional(self.n_latent_features, self.reduced_size)\n self.model = self.model.to(self.device, non_blocking=True)","def do_init(self):\n\n pass","def test_model_initialization():\n MyModel(\"model\", SkillContext())","def params_init(self) -> None:\n # Initialize weights and biases with uniform distribution.\n nn.init.uniform_(self.emb.weight, self.init_lower, self.init_upper)\n nn.init.uniform_(self.fc_e2h[1].weight, self.init_lower, self.init_upper)\n nn.init.uniform_(self.fc_e2h[1].bias, self.init_lower, self.init_upper)\n for lyr in range(self.n_lyr):\n self.stack_rnn[2 * lyr].params_init()\n nn.init.uniform_(self.fc_h2e[0].weight, self.init_lower, self.init_upper)\n nn.init.uniform_(self.fc_h2e[0].bias, self.init_lower, self.init_upper)","def init_model(model_filename, doGPU):\n # set model attributes list\n ##print(\"Model-dataset =\", model_ds_name)\n ##if model_ds_name == 'modelRAP':\n ## model_labels = loader_rapdataset_yiqiang.ATTRIBUTES\n ##elif model_ds_name == 'modelPETA':\n ## model_labels = loader_peta_dataset.ATTRIBUTES\n ##elif model_ds_name == 'modelRAPPETA':\n ## model_labels = [peta_label for rap_label,peta_label in loader_rap_plus_peta_dataset.ATTRIBUTES]\n ##else:\n ## print(\"ERROR: unknown model-dataset.\")\n ## sys.exit()\n model_labels = loader_rap_plus_peta_dataset.ATTRIBUTES\n assert (len(model_labels) == 49)\n\n # create model\n person.NO_ATTRIBUTES = len(model_labels) #TODO-elo: ugly, attr. nbr should be a parameter of person.Net.__init__()\n net = person.Net()\n if doGPU:\n net = person.Net().cuda()\n\n # load model\n print('loading model \"' + model_filename + '\"')\n person.load_model(net, model_filename)\n\n return net, model_labels","def init_model(engine):\n # Must use the same engine as the session fixture used by this module.\n initialize_model(mapper_registry, engine)","def _initialize_model_params(self):\n\n if 'model' not in self._raw_data_dict:\n raise Error('The \"model\" key is not found in the configuration file. Looks like the parsed file is not '\n 'Object Detection API model configuration file.')\n params = list(self._raw_data_dict['model'].values())[0]\n for rule in mapping_rules:\n self._update_param_using_rule(params, rule)","def initialize(self):\n logging.info(\"Loading model.\")\n\n self._bleurt_graph = tf.Graph()\n with self._bleurt_graph.as_default():\n\n imported = tf.saved_model.load(self.checkpoint)\n bleurt_model_ops = imported.signatures[\"serving_default\"]\n self._bleurt_ops = bleurt_model_ops(\n input_ids=tf.compat.v1.placeholder(tf.int64, name=\"input_ids\"),\n input_mask=tf.compat.v1.placeholder(tf.int64, name=\"input_mask\"),\n segment_ids=tf.compat.v1.placeholder(tf.int64, name=\"segment_ids\"))\n\n init_op = tf.group(tf.compat.v1.global_variables_initializer(),\n tf.compat.v1.tables_initializer())\n\n self.session = tf.compat.v1.Session(graph=self._bleurt_graph)\n self.session.run(init_op)\n\n logging.info(\"Done.\")","def init_model(engine):\n # Must use the same engine as the session fixture used by this module.\n initialize_model(model.mapper_registry, engine)","def initialization_step(self):\n # Update where agents are\n self.update_agent_location_vector()\n # update task locations\n self.update_task_location_vector()\n # update deadlines\n self.populate_deadline_vector()\n # update distances to each task and orientation to each task\n self.update_agent_distances_vector()\n self.update_agent_orientation_vector()","def initialize_network(self):\n self.sess = tf.InteractiveSession()\n sys.stderr.write(\"------\\n\")\n self.model.create_model()\n self._initialize_trainer()\n self.sess.run(tf.initialize_all_variables())\n self.saver = tf.train.Saver()","def initialize_setup(self, init_lr):\n param_list = []\n for name, param in self.model.named_parameters():\n if param.requires_grad:\n param_list.append(param)\n\n self.optimizer = torch.optim.AdamW(\n param_list, lr=init_lr, eps=1e-6)\n\n self.optim_scheduler = get_linear_schedule_with_warmup(\n self.optimizer, num_warmup_steps=0,\n num_training_steps=len(self.train_examples) * self.max_epochs)\n\n if not path.exists(self.model_path):\n torch.manual_seed(self.seed)\n np.random.seed(self.seed)\n # Try to initialize the mention model part\n if path.exists(self.pretrained_mention_model):\n print(\"Found pretrained model!!\")\n checkpoint = torch.load(self.pretrained_mention_model)\n self.model.load_state_dict(checkpoint['model'], strict=False)\n else:\n logger.info('Loading previous model: %s' % self.model_path)\n # Load model\n self.load_model(self.model_path)","def _manually_initialize(self) -> None:\n # XXX: maybe refactor, this is actually part of the public interface\n pass","def __init__(self):\n self.scaler = None\n self.model = None\n self.encoder = {}\n\n self._load_model()\n return","def __init__(self, model):\n self._model = model","def initialize_model(self):\n args = self.args\n\n if self.args.search_space == 'nasbench':\n self.model_fn = NasBenchNetSearchDarts\n self.fixmodel_fn = NasBenchNet\n model = self.model_fn(args)\n utils = darts_nasbench_utils\n else:\n raise NotImplementedError(\"Not supported\")\n # finialize model update\n if args.gpus > 0:\n if self.args.gpus == 1:\n model = model.cuda()\n self.parallel_model = model\n else:\n self.model = model\n self.parallel_model = nn.DataParallel(self.model).cuda()\n # IPython.embed(header='checking replicas and others.')\n else:\n self.parallel_model = model\n\n darts = DartsArchitect(model, args=args)\n model = self.parallel_model\n # logging.info(\"DARTS param size = %fMB\", utils.count_parameters_in_MB(darts))\n self.train_fn = partial(darts_train_model, args=args, architect=darts, sampler=None)\n self.eval_fn = partial(darts_model_validation, args=args, verbose=True)\n self.controller = darts\n\n logging.info(\"param size = %fMB\", utils.count_parameters_in_MB(model))\n optimizer = torch.optim.SGD(\n model.parameters(),\n args.learning_rate,\n momentum=args.momentum,\n weight_decay=args.weight_decay,\n )\n\n # scheduler as Cosine.\n scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, float(args.epochs), args.learning_rate_min)\n return model, optimizer, scheduler, darts, None","def init(self, train):\n return","def init(self, train):\n return","def init(self, train):\n return","def init(self, train):\n return","def init(self, train):\n return","def validate_and_initialize(self, model_dir=environment.model_dir, context=None):\n if not self._initialized:\n self._context = context\n self._environment = environment.Environment()\n self._validate_user_module_and_set_functions()\n\n if self._pre_model_fn is not None:\n self._run_handler_function(self._pre_model_fn, *(model_dir,))\n\n self._model = self._run_handler_function(self._model_fn, *(model_dir,))\n\n if self._model_warmup_fn is not None:\n self._run_handler_function(self._model_warmup_fn, *(model_dir, self._model))\n\n self._initialized = True","def __init__(self, model: Model1D):\n self._model = model","def __init__(self, k_folds, n_days):\n self.n_days = n_days\n self.k_folds = k_folds\n\n # Makes a Sequential() model\n super(CustomSequential, self).__init__()","def __init__(self, model: str, **kwargs):\n\n super().__init__(model=model, **kwargs)\n logger.info('load model done')","def __init__(self, **kwargs):\n super(Model, self).__init__(**kwargs)","def initialize(self):\n self.initialize_edges()\n self.initialize_prob()\n self.initialize_total_input_dict()\n\n self.initialize_fpmusigv_dict()","def __init__(self, depth, identifier):\n self.model = Sequential()\n self.identifier = identifier\n self.model_filename = f'models/{self.identifier}.model'\n self.model.add(LSTM(4, input_shape=(2, 2), return_sequences=True))\n for i in range(depth):\n self.model.add(LSTM(8, return_sequences=True))\n self.model.add(LSTM(2, return_sequences=True))\n self.model.compile(optimizer='rmsprop',\n loss='mse')","def keras_setup(self):\n\n # Interpret the request data based on the expected row and column structure\n row_template = ['strData', 'numData', 'strData', 'strData', 'strData']\n col_headers = ['model_name', 'sort_order', 'layer_type', 'args', 'kwargs']\n \n # Create a Pandas Data Frame for the request data\n self.request_df = utils.request_df(self.request, row_template, col_headers)\n \n # Create a model that can be persisted to disk\n self.model = PersistentModel()\n \n # Get the model name from the request dataframe\n self.model.name = self.request_df.loc[0, 'model_name']\n \n # Get the model from cache or disk\n self._get_model()\n \n # Debug information is printed to the terminal and logs if the paramater debug = true\n if self.model.debug:\n self._print_log(3)\n\n # Set a flag which will let us know that this is a Keras model\n self.model.using_keras = True\n\n # Sort the layers, drop unnecessart columns and save the model architecture to a new data frame\n architecture = self.request_df.sort_values(by=['sort_order']).reset_index(drop=True).drop(labels=['model_name', 'sort_order'], axis = 1)\n\n # Convert args to a list and kwargs to a dictionary\n architecture['args'] = architecture['args'].apply(utils.get_args_by_type)\n architecture['kwargs'] = architecture['kwargs'].apply(utils.get_kwargs).apply(utils.get_kwargs_by_type) \n\n # Add the architecture to the model\n self.model.architecture = architecture\n\n # Add the first layer's kwargs as a property for easy reference\n self.model.first_layer_kwargs = self.model.architecture.iloc[0, 2]\n\n # Debug information is printed to the terminal and logs if the paramater debug = true\n if self.model.debug:\n self._print_log(10)\n\n # Persist the model to disk\n self.model = self.model.save(self.model.name, self.path, overwrite=self.model.overwrite, compress=self.model.compress)\n \n # Update the cache to keep this model in memory\n self._update_cache()\n \n # Prepare the output\n message = [[self.model.name, 'Keras model architecture saved to disk',\\\n time.strftime('%X %x %Z', time.localtime(self.model.state_timestamp))]]\n self.response = pd.DataFrame(message, columns=['model_name', 'result', 'time_stamp'])\n \n # Send the reponse table description to Qlik\n self._send_table_description(\"setup\")\n \n # Debug information is printed to the terminal and logs if the paramater debug = true\n if self.model.debug:\n self._print_log(4)\n \n # Finally send the response\n return self.response","def test_init_variants():\n # get data\n X, _, _, _ = get_data()\n # set seed and kernel initialisers for repeatability\n tf.random.set_seed(12345)\n initializer = tf.keras.initializers.Zeros()\n\n # define model architecture\n input_data = Input(shape=X[0].shape)\n xx = Dense(32, activation=\"relu\", kernel_initializer=initializer)(input_data)\n output = Dense(n_classes, activation=\"softmax\", kernel_initializer=initializer)(xx)\n\n # standard way\n model = SafeKerasModel(\n inputs=input_data,\n outputs=output,\n name=\"test\",\n num_samples=X.shape[0],\n epochs=EPOCHS,\n )\n check_init_completed(model)\n\n # inputs and outputs not in kwargs\n model2 = SafeKerasModel(\n input_data,\n output,\n \"test\",\n num_samples=X.shape[0],\n epochs=EPOCHS,\n )\n check_init_completed(model2)\n\n # batch size zero\n model3 = SafeKerasModel(\n inputs=input_data,\n outputs=output,\n name=\"test\",\n num_samples=X.shape[0],\n epochs=EPOCHS,\n batch_size=0,\n )\n errstr = \"failed to correct batch_size=0 in init\"\n assert model3.batch_size == 32, errstr","def _init_model(self):\n try:\n model_config = self._conf.get(PredictConstance.BASE_CONFIG,\n PredictConstance.MODEL_CONFIG)\n conf = configparser.ConfigParser()\n conf.read(model_config)\n self._model = model_factory.ModelFactory.create_model(config=conf)\n return True\n except Exception as err:\n self.managerlogger.logger.error(\"init model error: %s\" % err)\n self.errorlogger.logger.error(\"init model error: \\n %s\" % traceback.format_exc())\n return False","def init_training(self):\n\n if not os.path.exists(self._model_root_path):\n os.makedirs(self._model_root_path)\n\n # Only initialize once!\n if self._model is None:\n self._model = TrainableAimbotModel(self._config, self._fov,\n os.path.join(self._model_root_path, 'aimbot_model.tf'))\n\n if not os.path.isfile(self._train_data_tfrecord_path) and not os.path.isfile(self._test_data_tfrecord_path):\n # Only create if not existing\n images_labels = _get_annotations_and_images(self._image_path)\n images_labels_train, images_labels_test = train_test_split(images_labels, shuffle=True, test_size=0.20)\n\n self._model.create_tfrecords(self._train_data_tfrecord_path, images_labels_train)\n self._model.create_tfrecords(self._test_data_tfrecord_path, images_labels_test)\n\n self._train_data_set = self._model.create_dataset(self._train_data_tfrecord_path, augment=True, shuffle=True)\n self._test_data_set = self._model.create_dataset(self._train_data_tfrecord_path)","def _lazy_init(self) -> None:\n # Initialize _is_root and setup streams. These steps would ideally\n # happen in __init__, but _is_root can only be determined after the\n # entire model hierarchy is setup, thus we run it lazily.\n if self._is_root is None:\n self._set_is_root()\n self._setup_output_hook_and_backward_opt_barrier_lists()\n\n if self._is_root and self.disable_reshard_on_root:\n # Don't free the full params for the outer-most (root) instance,\n # since those params will be needed immediately after for the\n # backward pass.\n self.reshard_after_forward = False","def __init__(self):\n self.train(positivity_files, 0)\n self.train(subjectivity_files, 1)","def run(self) -> None:\n self.model = self.trainer.train_model(self.model, self.data)","def initialize_model(self):\n model = self.model_class()\n return model","def __init__(self, config):\n self.model = None\n self.config = config\n self.batch_size = config.get('batch_size')\n self.epochs = config.get('epochs')\n self.steps_per_epoch = config.get('steps_per_epoch')\n self.validation_steps = config.get('validation_steps')\n self.distributed = config.get('distributed', False)\n \n # init model\n self.init()","def initialize_model(self):\n\n input_layer = Input(\n shape=(self.input_length,), \n dtype='int32', \n name='input'\n )\n\n if self.embedding_matrix is None:\n embedding = Embedding(\n output_dim=self.embedding_size,\n input_dim=self.vocabulary_size + 1, # for mask\n input_length=self.input_length,\n mask_zero=True,\n name='embedding'\n )(input_layer)\n else:\n embedding = Embedding(\n output_dim=self.embedding_size,\n input_dim=self.vocabulary_size + 1,\n input_length=self.input_length,\n mask_zero=True,\n weights=[np.vstack((np.zeros((1, self.embedding_size)),\n self.embedding_matrix))],\n name='embedding'\n )(input_layer)\n\n encoder = self.recurrent_cell(\n self.latent_dim,\n dropout=self.dropout,\n recurrent_dropout=self.dropout,\n name='encoder',\n recurrent_regularizer=l1_l2(*self.regularization)\n )\n\n if self.use_bidirection:\n encoder = Bidirectional(\n encoder,\n merge_mode='concat'\n )\n\n encoder = encoder(embedding)\n\n dense_1 = Dense(\n 1024,\n activation='tanh',\n name='dense_1',\n kernel_regularizer=l1_l2(*self.regularization)\n )(encoder)\n\n dense_2 = Dense(\n 512,\n activation='tanh',\n name='dense_2',\n kernel_regularizer=l1_l2(*self.regularization)\n )(dense_1)\n\n dropout = Dropout(self.dropout)(\n dense_2\n )\n\n prediction = Dense(\n 1,\n activation='sigmoid',\n name='prediction'\n )(dropout)\n\n model = Model(inputs=input_layer, outputs=prediction)\n\n # sparse_categorical_crossentropy\n model.compile(optimizer=Adam(lr=self.learning_rate),\n loss='binary_crossentropy',\n metrics=['acc'])\n\n self.model = model\n\n if self.verbose > 0:\n model.summary()\n\n return [model]","def _initialize(self) -> None:\n p = self.params\n # We make self.input public so that users can access its methods like\n # IdsToStrings if needed.\n with py_utils.infeed_context_scope(\n infeed_host_index=p.infeed_host_index,\n num_infeed_hosts=p.num_infeed_hosts):\n self.input = p.input.Instantiate()\n\n if hasattr(self.input, 'datasource') and isinstance(\n self.input.datasource, datasource.TFDatasetSource):\n # For the special case when the input is implemented by a tf.data.Dataset,\n # use it directly. Otherwise roundtrip adaptions may result in returning\n # duplciate batches.\n self._get_next_fn = self.input.datasource.GetNext\n else:\n self._get_next_fn = tf.function(self._get_batch)\n self._num_batches_produced = 0","def initialization_routine(\n self, model: Block, plugin_initializer_args: dict = None, **kwargs\n ):\n if plugin_initializer_args is None:\n plugin_initializer_args = {}\n\n # Get logger\n _log = self.get_logger(model)\n\n # Prepare plug-ins for initialization\n sub_initializers, plugin_initializer_args = self.prepare_plugins(\n model, plugin_initializer_args\n )\n _log.info_high(\"Step 1: preparation complete.\")\n\n # Initialize model and sub-models\n results = self.initialize_submodels(\n model, plugin_initializer_args, sub_initializers, **kwargs\n )\n _log.info_high(\"Step 2: sub-model initialization complete.\")\n\n # Solve full model including plug-ins\n results = self.solve_full_model(model, results)\n _log.info_high(\n f\"Step 3: full model initialization {idaeslog.condition(results)}.\"\n )\n\n # Clean up plug-ins\n self.cleanup(model, plugin_initializer_args, sub_initializers)\n _log.info_high(\"Step 4: clean up completed.\")\n\n return results","def initialise(self):\n self.set_up()","def model_setup(self):\n self.DNN = SganMLP(self.settings.number_of_bins)\n self.D = SganMLP(self.settings.number_of_bins)\n self.G = Generator()","def _prepare_model(model):\n\n # Ensure there is at least 1 load combination to solve if the user didn't define any\n if model.LoadCombos == {}:\n # Create and add a default load combination to the dictionary of load combinations\n model.LoadCombos['Combo 1'] = LoadCombo('Combo 1', factors={'Case 1':1.0})\n \n # Generate all meshes\n for mesh in model.Meshes.values():\n if mesh.is_generated == False:\n mesh.generate()\n\n # Activate all springs and members for all load combinations\n for spring in model.Springs.values():\n for combo_name in model.LoadCombos.keys():\n spring.active[combo_name] = True\n \n # Activate all physical members for all load combinations\n for phys_member in model.Members.values():\n for combo_name in model.LoadCombos.keys():\n phys_member.active[combo_name] = True\n \n # Assign an internal ID to all nodes and elements in the model. This number is different from the name used by the user to identify nodes and elements.\n _renumber(model)","def __init__(self, **kwargs):\n\n # Identify the mode to start the model in\n if \"x\" in kwargs and \"y\" in kwargs:\n x = kwargs.get(\"x\")\n y = kwargs.get(\"y\")\n if \"model_name\" not in kwargs:\n self.__mode = \"train\"\n else:\n self.__mode = \"retrain\"\n elif \"model_name\" in kwargs:\n self.__mode = \"test\"\n else:\n raise NameError(\"Cannot infer mode from arguments.\")\n\n print(\"Initializing model in %s mode.\" % self.__mode)\n\n if self.mode == \"train\":\n # Infer input type from type(x)\n if type(x[0]) == np.bytes_:\n print(\"Input type is 'binary mols'.\")\n self.__input_type = \"mols\" # binary RDKit mols\n else:\n print(\"Input type is 'molecular descriptors'.\")\n self.__input_type = \"descriptors\" # other molecular descriptors\n\n # If scaling is required\n if kwargs.get(\"scaling\", False) is True:\n # Normalize the input\n print(\"Applying scaling on input.\")\n self.__scaler = StandardScaler()\n x = self.__scaler.fit_transform(x)\n else:\n self.__scaler = None\n\n # If PCA is required\n if kwargs.get(\"pca\", False) is True:\n print(\"Applying PCA on input.\")\n self.__pca = PCA(\n n_components=x.shape[1]\n ) # n_components=n_features for now\n x = self.__pca.fit_transform(x)\n else:\n self.__pca = None\n\n self.__maxlen = (\n kwargs.get(\"dataset_info\")[\"maxlen\"] + 10\n ) # Extend maxlen to avoid breaks in training\n self.__charset = kwargs.get(\"dataset_info\")[\"charset\"]\n self.__dataset_name = kwargs.get(\"dataset_info\")[\"name\"]\n self.__lstm_dim = kwargs.get(\"lstm_dim\", 256)\n self.__h_activation = kwargs.get(\"h_activation\", \"relu\")\n self.__bn = kwargs.get(\"bn\", True)\n self.__bn_momentum = kwargs.get(\"bn_momentum\", 0.9)\n self.__noise_std = kwargs.get(\"noise_std\", 0.01)\n self.__td_dense_dim = kwargs.get(\n \"td_dense_dim\", 0\n ) # >0 squeezes RNN connections with Dense sandwiches\n self.__batch_size = kwargs.get(\"batch_size\", 256)\n self.__dec_layers = kwargs.get(\"dec_layers\", 2)\n\n if self.input_type == \"descriptors\":\n self.__codelayer_dim = x.shape[1] # features\n if \"codelayer_dim\" in kwargs:\n print(\n \"Ignoring requested codelayer_dim because it is inferred from the cardinality of the descriptors.\"\n )\n else:\n self.__codelayer_dim = kwargs.get(\"codelayer_dim\", 128)\n \n # Create the left/right-padding vectorizers\n self.__smilesvec1 = SmilesVectorizer(\n canonical=False,\n augment=True,\n maxlength=self.maxlen,\n charset=self.charset,\n binary=True,\n )\n\n self.__smilesvec2 = SmilesVectorizer(\n canonical=False,\n augment=True,\n maxlength=self.maxlen,\n charset=self.charset,\n binary=True,\n leftpad=False,\n )\n\n # self.train_gen.next() #This line is needed to set train_gen.dims (to be fixed in HetSmilesGenerator)\n self.__input_shape = self.smilesvec1.dims\n self.__dec_dims = list(self.smilesvec1.dims)\n self.__dec_dims[0] = self.dec_dims[0] - 1\n self.__dec_input_shape = self.dec_dims\n self.__output_len = self.smilesvec1.dims[0] - 1\n self.__output_dims = self.smilesvec1.dims[-1]\n\n # Build all sub-models as untrained models\n if self.input_type == \"mols\":\n self.__build_mol_to_latent_model()\n else:\n self.__mol_to_latent_model = None\n\n self.__build_latent_to_states_model()\n self.__build_batch_model()\n\n # Build data generators\n self.__build_generators(x, y)\n\n # Retrain or Test mode\n else:\n self.__model_name = kwargs.get(\"model_name\")\n\n # Load the model\n self.__load(self.model_name)\n \n if self.mode == \"retrain\":\n # If scaling is required\n if self.scaler is not None:\n print(\"Applying scaling on input.\")\n x = self.scaler.transform(x)\n\n # If PCA is required\n if self.pca is not None:\n print(\"Applying PCA on input.\")\n x = self.pca.transform(x)\n \n # Build data generators\n self.__build_generators(x, y)\n\n # Build full model out of the sub-models\n self.__build_model()\n\n # Show the resulting full model\n print(self.model.summary())","def initialize(self):\n pass","def initialize(self):\n pass","def initialize(self):\n pass","def initialize(self):\n pass","def initialize(self):\n pass","def epoch_begin(self, model):\n pass","def initialize(self):\n if not self._ready:\n self._real_initialize()\n self._ready = True","def init_components(\n self, model_fn=None, criterion_fn=None, optimizer_fn=None, scheduler_fn=None\n ):\n model = model_fn()\n model = self.sync_device(model)\n if self._sync_bn:\n model = apex.parallel.convert_syncbn_model(model)\n\n criterion = criterion_fn()\n criterion = self.sync_device(criterion)\n\n optimizer = optimizer_fn()\n optimizer = self.sync_device(optimizer)\n\n model, optimizer = amp.initialize(model, optimizer, **self.apex_kwargs)\n model = ApexDistributedDataParallel(model, **self.ddp_kwargs)\n\n scheduler = scheduler_fn()\n scheduler = self.sync_device(scheduler)\n return model, criterion, optimizer, scheduler","def __init__(self , model_file_name ):\n logging.set_verbosity(logging.ERROR)\n with TheTFGraph.as_default():\n with TheTFSession.as_default():\n self.model = keras.models.load_model( model_file_name + \".hdf5\" , compile=False )\n JSON = json.load( open(model_file_name + \".json\" ) )\n self.all_sites = list(JSON['all_sites'])\n self.all_errors = list(JSON['all_errors'])\n self.all_actions = list(JSON['all_actions'])\n self.IsBinary = bool(JSON['IsBinary'])\n self.TiersOnly = bool(JSON['TiersOnly'])\n self.Task = Task({} , \"TaskLoader\" , self)\n self.Name = model_file_name.split('/')[-1]\n self.ModelID = int( JSON['model'] )\n self.InputTrainingDataID = int( JSON['trainingdata'])\n\n self.Prediction = Prediction.Prediction( self.ModelID , self.InputTrainingDataID )","def init_model(session, model):\n # If there is a checkpoint, load it\n if not tf.gfile.Exists(FLAGS.train_dir):\n tf.gfile.MkDir(FLAGS.train_dir)\n ckpt = tf.train.get_checkpoint_state(FLAGS.train_dir)\n if ckpt and tf.train.checkpoint_exists(ckpt.model_checkpoint_path):\n print(\"Reading model parameters from %s\" % ckpt.model_checkpoint_path)\n model.saver.restore(session, ckpt.model_checkpoint_path)\n\n # Else initialize the variables\n else:\n if FLAGS.decode:\n input(\"You sure you want to talk to an untrained chatbot? Press Ctrl-C to stop, Return to continue \")\n print(\"Fine.\")\n\n print(\"Creating model with fresh parameters.\")\n session.run(tf.global_variables_initializer())","def __init__(self, model):\n\t\tself.model = model","def initialize(self, model):\n # Retrieve all parameters on which to act\n self.set_pruning_parameters(model)\n # Create a set of masks for each layer\n mask = [None] * len(self.prune_parameters)\n for step, (name, param) in enumerate(self.prune_parameters):\n mask[step] = torch.ones_like(param.data).detach()#.cpu().numpy()\n # Save mask\n self.mask = mask\n # Save the current model weights\n self.initial_state_dict = None","def initialise_sim(self):\n pass"],"string":"[\n \"def initialize_model(self):\\n pass\",\n \"def init_model(self):\\n pass\",\n \"def initialize(self, model):\\n pass\",\n \"def initialize(self):\\n LOG.info(\\\"Initializing Model.\\\")\\n self.model = self.convert(df=self.training_df)\\n if self.bootstraps is not None:\\n LOG.info(\\\"Bootstrapping Data.\\\")\\n self.bootstrap_data()\",\n \"def initialize(self):\\n for key in self.parameter_dict:\\n self.models[key] = self._create_model(key)\",\n \"def initialisation(self):\\n self.create_variables()\\n self.create_placeholders()\\n self.build_model()\\n self.reset_lr(None, True)\\n self.build_loss()\\n self.initialised = True\",\n \"def basic_model_init(model_args, task_infos, tokenizer):\\n config = AutoConfig.from_pretrained(\\n model_args.model_name_or_path,\\n num_labels=task_infos.num_labels,\\n cache_dir=model_args.model_cache_dir,\\n id2label=task_infos.id2label,\\n label2id=task_infos.label2id,\\n )\\n model_cls = getattr(mod, model_args.architectures,\\n AutoModelForSequenceClassification)\\n model = model_cls.from_pretrained(\\n model_args.model_name_or_path,\\n config=config,\\n cache_dir=model_args.model_cache_dir,\\n )\\n if model.config.vocab_size < len(tokenizer):\\n print(\\\"resize...\\\")\\n model.resize_token_embeddings(len(tokenizer))\\n return model\",\n \"def initialize(self) -> None:\\n self.model = load(self.path)\",\n \"def _initialize_model(rngs):\\n init_model_state, init_params = model_def.init(\\n rngs, *dummy_input, train=False, debug=False).pop('params')\\n # Set bias in the head to low value, such that loss is small initially.\\n if config.get('init_head_bias', None) is not None:\\n init_params = flax.core.unfreeze(init_params)\\n init_params['output_projection'] = optimizers.tree_map_with_names(\\n lambda p: jnp.full_like(p, config.init_head_bias),\\n init_params['output_projection'],\\n match_name_fn=lambda name: 'bias' in name)\\n init_params = flax.core.freeze(init_params)\\n return init_params, init_model_state\",\n \"def _setup_model(self) -> torch.nn.Sequential:\\r\\n\\r\\n # setting up model\\r\\n ids_ = self.get_hyperparam().get_dim_ids()\\r\\n if self.get_hyperparam().get_value(ids_[13]):\\r\\n init_ = lambda mod: self._default_weight_bias_init(mod,\\r\\n self.get_hyperparam().get_value(ids_[14]),\\r\\n self.get_hyperparam().get_value(ids_[15]),\\r\\n self.get_hyperparam().get_value(ids_[16]))\\r\\n\\r\\n modules = []\\r\\n for hd in range(int(self.get_hyperparam().get_value(ids_[3]))+1):\\r\\n if hd == 0:\\r\\n act_input_size = self.get_hyperparam().get_value(ids_[0])\\r\\n output_size = self.get_hyperparam().get_value(ids_[4])[hd]\\r\\n act_fct = self.get_hyperparam().get_value(ids_[5])[hd]()\\r\\n elif hd == self.get_hyperparam().get_value(ids_[3]):\\r\\n act_input_size = self.get_hyperparam().get_value(ids_[4])[hd-1]\\r\\n output_size = self.get_hyperparam().get_value(ids_[1])\\r\\n act_fct = self.get_hyperparam().get_value(ids_[6])()\\r\\n else:\\r\\n act_input_size = self.get_hyperparam().get_value(ids_[4])[hd-1]\\r\\n output_size = self.get_hyperparam().get_value(ids_[4])[hd]\\r\\n act_fct = self.get_hyperparam().get_value(ids_[5])[hd]()\\r\\n \\r\\n if self.get_hyperparam().get_value(ids_[13]):\\r\\n modules.append(init_(torch.nn.Linear(int(act_input_size), int(output_size))))\\r\\n else:\\r\\n modules.append(torch.nn.Linear(int(act_input_size), int(output_size)))\\r\\n modules.append(act_fct)\\r\\n\\r\\n model = torch.nn.Sequential(*modules)\\r\\n \\r\\n # add process to the model\\r\\n try:\\r\\n model = self._add_init(model)\\r\\n except:\\r\\n pass \\r\\n \\r\\n self._loss_fct = self.get_hyperparam().get_value(ids_[8])()\\r\\n self._optimizer = self.get_hyperparam().get_value(ids_[7])(model.parameters(), lr=self.get_hyperparam().get_value(ids_[12]))\\r\\n self._sampling_seed = self.get_hyperparam().get_value(ids_[11])\\r\\n \\r\\n return model\",\n \"def _initialize_model(rngs):\\n init_model_state, init_params = nn.init(\\n fn=init_fn, module=model_def)(rngs).pop('params')\\n # Set bias in the head to low value, such that loss is small initially.\\n if (config.get('init_head_bias', None) is not None and\\n 'output_projection' in init_params):\\n init_params = flax.core.unfreeze(init_params)\\n init_params['output_projection'] = optimizers.tree_map_with_names(\\n lambda p: jnp.full_like(p, config.init_head_bias),\\n init_params['output_projection'],\\n match_name_fn=lambda name: 'bias' in name)\\n init_params = flax.core.freeze(init_params)\\n return init_params, init_model_state\",\n \"def init_batch(self):\\n pass\",\n \"def init_model(self):\\n cxnlib.CXNNetInitModel(self.handle)\",\n \"def initiate(self):\\n # if self.opt.checkpoint_encoder:\\n # self.load(self.opt.checkpoint_encoder, self.opt.checkpoint_decoder)\\n # else:\\n # start fresh.\\n self.model = Transformer(\\n self.opt.src_vocab_size,\\n self.opt.tgt_vocab_size,\\n self.opt.max_token_seq_len,\\n tgt_emb_prj_weight_sharing=self.opt.proj_share_weight,\\n emb_src_tgt_weight_sharing=self.opt.embs_share_weight,\\n d_k=self.opt.d_k,\\n d_v=self.opt.d_v,\\n d_model=self.opt.d_model,\\n d_word_vec=self.opt.d_word_vec,\\n d_inner=self.opt.d_inner_hid,\\n n_layers=self.opt.layers,\\n n_head=self.opt.n_head,\\n dropout=self.opt.dropout).to(self.device)\\n \\n for p in self.model.parameters():\\n if p.dim() > 1:\\n nn.init.xavier_uniform_(p)\",\n \"def initialize(self):\\n # FIX: INITIALIZE PROCESS INPUTS??\\n for mech, value in self.initial_values.items():\\n mech.initialize(value)\",\n \"def initialize_trainer(self):\\n self.initialize_matrices()\\n self.initialize_model()\\n self.initialize_optimizers()\\n return self\",\n \"def init_model(model):\\n model(tf.random.uniform((1, 512, 512, 3)))\",\n \"def initialize_first_model(self, wait=True, input_data_s3_prefix=None):\\n # Sync experiment state if required\\n self._sync_experiment_state_with_ddb()\\n\\n # experiment only allow one training job at a time,\\n # validate no other training request is in progress\\n if (\\n self.experiment_record._training_state is not None\\n and self.experiment_record._training_state.endswith(\\\"ING\\\")\\n ):\\n logger.error(\\n f\\\"A training request with model id '{self.experiment_record._next_model_to_train_id}' \\\"\\n f\\\"was in the state of '{self.experiment_record._training_state}'. \\\"\\n \\\"Wait until the training job finished or canceled the request.\\\"\\n )\\n raise InvalidUsageException(\\n \\\"Please wait for old Training Job to Complete before requesting a new one!\\\"\\n )\\n else:\\n # update next_model_to_train_id and training state\\n next_model_to_train_id = ModelManager.name_next_model(experiment_id=self.experiment_id)\\n logger.info(f\\\"Next Model name would be {next_model_to_train_id}\\\")\\n self.exp_db_client.update_experiment_next_model_to_train_id(\\n self.experiment_id, next_model_to_train_id\\n )\\n self.exp_db_client.update_experiment_training_state(\\n self.experiment_id, TrainingState.PENDING\\n )\\n logger.info(f\\\"Start training job for model '{next_model_to_train_id}''\\\")\\n\\n # generate manifest file if input is a list\\n manifest_file_path = None\\n if isinstance(input_data_s3_prefix, list):\\n # generate manifest file and upload to s3\\n manifest_file_path = self._generate_manifest(input_data_s3_prefix)\\n\\n # init model for training, update model table\\n try:\\n self.next_model_to_train = ModelManager(\\n model_db_client=self.model_db_client,\\n experiment_id=self.experiment_id,\\n model_id=next_model_to_train_id,\\n image=self.image,\\n role=self.resource_manager.iam_role_arn,\\n instance_config=self.resource_manager.training_fleet_config,\\n boto_session=self.boto_session,\\n algor_config=self.algor_config,\\n )\\n\\n self.next_model_to_train.fit(\\n wait=wait,\\n input_model_id=None,\\n input_data_s3_prefix=input_data_s3_prefix,\\n manifest_file_path=manifest_file_path,\\n logs=wait,\\n )\\n except Exception as e:\\n logger.error(\\n f\\\"Failed to start new Model Training job for\\\"\\n \\\" ModelId {next_model_to_train_id}\\\"\\n )\\n logger.error(e)\\n pass\\n\\n # wait until ExperimentDb state is updated\\n if self.local_mode or wait:\\n trained_state = (\\n self.experiment_record._training_state == TrainingState.TRAINED\\n and self.experiment_record._last_trained_model_id == next_model_to_train_id\\n and self.experiment_record._next_model_to_train_id is None\\n )\\n num_retries = 0\\n\\n while not trained_state:\\n # Sync experiment state if required\\n self._sync_experiment_state_with_ddb()\\n logger.debug(\\\"Waiting for experiment table training status to be updated...\\\")\\n time.sleep(2 * (2 ** num_retries))\\n trained_state = (\\n self.experiment_record._training_state == TrainingState.TRAINED\\n and self.experiment_record._last_trained_model_id == next_model_to_train_id\\n and self.experiment_record._next_model_to_train_id is None\\n )\\n num_retries += 1\\n if num_retries >= 5:\\n raise UnhandledWorkflowException(\\n f\\\"Training job '{self.experiment_record._next_model_to_train_id}' \\\"\\n f\\\"was in state of '{self.experiment_record._training_state}'. Expected it to be TRAINED.\\\"\\n )\\n if (\\n self.experiment_record._training_state == TrainingState.FAILED\\n or self.experiment_record._training_state == TrainingState.STOPPED\\n ):\\n raise SageMakerTrainingJobException(\\n f\\\"Training job '{self.experiment_record._next_model_to_train_id}' \\\"\\n f\\\"ended in state of '{self.experiment_record._training_state}'. Please check Sagemaker logs for \\\"\\n \\\"more information.\\\"\\n )\",\n \"def _init_model_params(self):\\n super()._init_model_params()\\n\\n if 'e' in self.init_params:\\n if self.init_type == 'uniform':\\n if self.nr_no_train_de == 0:\\n self.B = [\\n np.full(\\n (self.n_states, self.n_features[i]), 1.0 / self.n_features[i])\\n for i in range(self.n_emissions)\\n ]\\n else:\\n check_if_attributes_set(self, attr='e')\\n else:\\n if self.nr_no_train_de == 0:\\n self.B = [\\n np.random.rand(self.n_states, self.n_features[i])\\n for i in range(self.n_emissions)\\n ]\\n for i in range(self.n_emissions):\\n normalise(self.B[i], axis=1)\\n\\n else:\\n check_if_attributes_set(self, attr='e')\",\n \"def re_init(self):\\n self.latent.re_init()\\n if 're_init' in dir(self.inference_model):\\n self.inference_model.re_init()\\n if 're_init' in dir(self.generative_model):\\n self.generative_model.re_init()\",\n \"def init_model(self, model_name, config=None):\\n ModelDirectory.init_model(model_name, pipeline=self, config=config)\\n return self\\n #self._action_list.append({'name': INIT_MODEL_ID, 'model_name': model_name, 'config': config})\\n #return self.append_action()\",\n \"def init_model(self, num_inputs, num_outputs):\\n self.number_inputs = num_inputs\\n\\n self.number_outputs = num_outputs\\n\\n self.last_state = None\\n\\n self.model.start_nn(num_inputs, num_outputs)\\n \\n self.is_model_init = True\",\n \"def initialize_model(self, initial_data):\\n # EDIT THIS METHOD TO RETURN A MINIMAX MODEL ###\\n return None\",\n \"def initialize_model(epochs=4):\\n bert_classifier = BertClassifier(freeze_bert=False)\\n\\n bert_classifier.to(device)\\n\\n optimizer = AdamW(bert_classifier.parameters(),lr=0.0001,eps=1e-8)\\n\\n # Total number of training steps\\n total_steps = len(train_dataloader) * epochs\\n\\n # Set up the learning rate scheduler\\n scheduler = get_linear_schedule_with_warmup(optimizer,\\n num_warmup_steps=0, # Default value\\n num_training_steps=total_steps)\\n return bert_classifier, optimizer, scheduler\",\n \"def _add_init(self, p_model):\\r\\n\\r\\n raise NotImplementedError\",\n \"def _init_model(self, forrest):\\n rels = self.get_rels(forrest)\\n self._model = RDPModel(rels)\",\n \"def init_models(self) -> None:\\n self.models = AKSManagedClusterModels(self.cmd, self.resource_type)\",\n \"def init_models(self) -> None:\\n self.models = AKSManagedClusterModels(self.cmd, self.resource_type)\",\n \"def init(self):\\n inputs = self.inputs()\\n outputs = self.outputs(inputs)\\n self.model = tf.keras.Model(inputs=inputs, outputs=outputs)\\n self.model.compile(optimizer=self.optimizer() or self.config.get('optimizer'),\\n loss=self.loss() or None,\\n metrics=self.metrics() or None,\\n loss_weights=self.loss_weights() or None,\\n weighted_metrics=self.weighted_metrics() or None,\\n target_tensors=self.target_tensors() or None)\\n if self.config.get('debug'):\\n self.model.summary()\",\n \"def __init__(self, num_models: int, num_classes: int):\\n self.nun_models = num_models\\n self.num_classes = num_classes\\n self.model: keras.Model = self.init_model()\",\n \"def __init__(self, initialState, runningModels, terminalModel):\\n self.T = len(runningModels)\\n self.initialState = initialState\\n self.runningModels = runningModels\\n self.runningDatas = [m.createData() for m in runningModels]\\n self.terminalModel = terminalModel\\n self.terminalData = terminalModel.createData()\",\n \"def init(self, rng_key, num_warmup, init_params, model_args, model_kwargs):\\n raise NotImplementedError\",\n \"def initialize_data_iterator(self, model_idx=None):\\n if model_idx and not isinstance(model_idx, (list, tuple)):\\n raise TypeError(\\\"`model_idx` must be list-like structure\\\")\\n\\n model_indices = (\\n # if model_idx is None\\n # model_idx = [all_models_indices]\\n model_idx if model_idx is not None\\n else range(self.num_models))\\n\\n for idx in model_indices:\\n try:\\n print(\\\"Initializing %d data\\\" % idx)\\n self._sess.run(self._data[idx].initializer)\\n except AttributeError:\\n # wraps the error with more informative message\\n raise AttributeError(\\\"Model #%d has no `_data` attribute\\\")\",\n \"def _initial_setup(self, **train_kwargs):\\n self._update(time_step=0., **train_kwargs)\",\n \"def prepare_for_model_run(self, model_time):\\n\\n pass\",\n \"def __init__(self):\\n self.model = None\",\n \"def __init__(self):\\n self.model = None\",\n \"def init(default_model_key: str = None, install_requirements: bool = True):\\n\\n global default_model\\n global install_req\\n global initialized\\n\\n log.info(\\\"Initializing Model Handler\\\")\\n\\n if default_model_key:\\n log.info(\\\"Preload model: \\\" + default_model_key)\\n default_model = get_model(default_model_key)\\n elif DEFAULT_MODEL_ENV in os.environ:\\n model_key = os.environ[DEFAULT_MODEL_ENV]\\n log.info(\\\"Preload model: \\\" + model_key)\\n default_model = get_model(model_key)\\n\\n install_req = install_requirements\\n if INSTALL_REQUIREMENTS_ENV in os.environ:\\n if not bool(os.environ[INSTALL_REQUIREMENTS_ENV]):\\n install_req = False\\n\\n initialized = True\",\n \"def new(self):\\n self.define_layers()\\n self.model = nn.Sequential(*self.layers)\\n self.model.cuda()\\n self.model = orthogonal_init(self.model)\\n\\n # Re-count N\\n self.count_params()\",\n \"def init():\\n global neural_network\\n global labels\\n\\n # load objects required by run() for inferencing\\n model_dir = Model.get_model_path(\\\"mnist-fashion\\\")\\n # neural model\\n neural_network = keras.models.load_model(f\\\"{model_dir}/neural-network.h5\\\")\\n # labels\\n with open(f\\\"{model_dir}/labels.jsonpickle\\\", \\\"r\\\") as labels_file:\\n labels = jsonpickle.decode(labels_file.read())\",\n \"def __init__(self, **kwargs):\\n super(Model, self).__init__(**kwargs)\\n self._params = self.find_params()\",\n \"def initialize_main_model(self, model, **kwargs):\\n return NotImplementedError(\\n \\\"Initializer has not implemented an initialize_main_model method. Derived classes \\\"\\n \\\"are required to overload this.\\\"\\n )\",\n \"def initialize(self):\\n \\n #initialize the variables\\n init = tf.global_variables_initializer()\\n self.session.run(init)\\n \\n #initialize the data iterators\\n self.session.run(self.data_iterator.initializer)\",\n \"def _construct_model(self):\\n self.model = AutoEncoderConvolutional(self.n_latent_features, self.reduced_size)\\n self.model = self.model.to(self.device, non_blocking=True)\",\n \"def do_init(self):\\n\\n pass\",\n \"def test_model_initialization():\\n MyModel(\\\"model\\\", SkillContext())\",\n \"def params_init(self) -> None:\\n # Initialize weights and biases with uniform distribution.\\n nn.init.uniform_(self.emb.weight, self.init_lower, self.init_upper)\\n nn.init.uniform_(self.fc_e2h[1].weight, self.init_lower, self.init_upper)\\n nn.init.uniform_(self.fc_e2h[1].bias, self.init_lower, self.init_upper)\\n for lyr in range(self.n_lyr):\\n self.stack_rnn[2 * lyr].params_init()\\n nn.init.uniform_(self.fc_h2e[0].weight, self.init_lower, self.init_upper)\\n nn.init.uniform_(self.fc_h2e[0].bias, self.init_lower, self.init_upper)\",\n \"def init_model(model_filename, doGPU):\\n # set model attributes list\\n ##print(\\\"Model-dataset =\\\", model_ds_name)\\n ##if model_ds_name == 'modelRAP':\\n ## model_labels = loader_rapdataset_yiqiang.ATTRIBUTES\\n ##elif model_ds_name == 'modelPETA':\\n ## model_labels = loader_peta_dataset.ATTRIBUTES\\n ##elif model_ds_name == 'modelRAPPETA':\\n ## model_labels = [peta_label for rap_label,peta_label in loader_rap_plus_peta_dataset.ATTRIBUTES]\\n ##else:\\n ## print(\\\"ERROR: unknown model-dataset.\\\")\\n ## sys.exit()\\n model_labels = loader_rap_plus_peta_dataset.ATTRIBUTES\\n assert (len(model_labels) == 49)\\n\\n # create model\\n person.NO_ATTRIBUTES = len(model_labels) #TODO-elo: ugly, attr. nbr should be a parameter of person.Net.__init__()\\n net = person.Net()\\n if doGPU:\\n net = person.Net().cuda()\\n\\n # load model\\n print('loading model \\\"' + model_filename + '\\\"')\\n person.load_model(net, model_filename)\\n\\n return net, model_labels\",\n \"def init_model(engine):\\n # Must use the same engine as the session fixture used by this module.\\n initialize_model(mapper_registry, engine)\",\n \"def _initialize_model_params(self):\\n\\n if 'model' not in self._raw_data_dict:\\n raise Error('The \\\"model\\\" key is not found in the configuration file. Looks like the parsed file is not '\\n 'Object Detection API model configuration file.')\\n params = list(self._raw_data_dict['model'].values())[0]\\n for rule in mapping_rules:\\n self._update_param_using_rule(params, rule)\",\n \"def initialize(self):\\n logging.info(\\\"Loading model.\\\")\\n\\n self._bleurt_graph = tf.Graph()\\n with self._bleurt_graph.as_default():\\n\\n imported = tf.saved_model.load(self.checkpoint)\\n bleurt_model_ops = imported.signatures[\\\"serving_default\\\"]\\n self._bleurt_ops = bleurt_model_ops(\\n input_ids=tf.compat.v1.placeholder(tf.int64, name=\\\"input_ids\\\"),\\n input_mask=tf.compat.v1.placeholder(tf.int64, name=\\\"input_mask\\\"),\\n segment_ids=tf.compat.v1.placeholder(tf.int64, name=\\\"segment_ids\\\"))\\n\\n init_op = tf.group(tf.compat.v1.global_variables_initializer(),\\n tf.compat.v1.tables_initializer())\\n\\n self.session = tf.compat.v1.Session(graph=self._bleurt_graph)\\n self.session.run(init_op)\\n\\n logging.info(\\\"Done.\\\")\",\n \"def init_model(engine):\\n # Must use the same engine as the session fixture used by this module.\\n initialize_model(model.mapper_registry, engine)\",\n \"def initialization_step(self):\\n # Update where agents are\\n self.update_agent_location_vector()\\n # update task locations\\n self.update_task_location_vector()\\n # update deadlines\\n self.populate_deadline_vector()\\n # update distances to each task and orientation to each task\\n self.update_agent_distances_vector()\\n self.update_agent_orientation_vector()\",\n \"def initialize_network(self):\\n self.sess = tf.InteractiveSession()\\n sys.stderr.write(\\\"------\\\\n\\\")\\n self.model.create_model()\\n self._initialize_trainer()\\n self.sess.run(tf.initialize_all_variables())\\n self.saver = tf.train.Saver()\",\n \"def initialize_setup(self, init_lr):\\n param_list = []\\n for name, param in self.model.named_parameters():\\n if param.requires_grad:\\n param_list.append(param)\\n\\n self.optimizer = torch.optim.AdamW(\\n param_list, lr=init_lr, eps=1e-6)\\n\\n self.optim_scheduler = get_linear_schedule_with_warmup(\\n self.optimizer, num_warmup_steps=0,\\n num_training_steps=len(self.train_examples) * self.max_epochs)\\n\\n if not path.exists(self.model_path):\\n torch.manual_seed(self.seed)\\n np.random.seed(self.seed)\\n # Try to initialize the mention model part\\n if path.exists(self.pretrained_mention_model):\\n print(\\\"Found pretrained model!!\\\")\\n checkpoint = torch.load(self.pretrained_mention_model)\\n self.model.load_state_dict(checkpoint['model'], strict=False)\\n else:\\n logger.info('Loading previous model: %s' % self.model_path)\\n # Load model\\n self.load_model(self.model_path)\",\n \"def _manually_initialize(self) -> None:\\n # XXX: maybe refactor, this is actually part of the public interface\\n pass\",\n \"def __init__(self):\\n self.scaler = None\\n self.model = None\\n self.encoder = {}\\n\\n self._load_model()\\n return\",\n \"def __init__(self, model):\\n self._model = model\",\n \"def initialize_model(self):\\n args = self.args\\n\\n if self.args.search_space == 'nasbench':\\n self.model_fn = NasBenchNetSearchDarts\\n self.fixmodel_fn = NasBenchNet\\n model = self.model_fn(args)\\n utils = darts_nasbench_utils\\n else:\\n raise NotImplementedError(\\\"Not supported\\\")\\n # finialize model update\\n if args.gpus > 0:\\n if self.args.gpus == 1:\\n model = model.cuda()\\n self.parallel_model = model\\n else:\\n self.model = model\\n self.parallel_model = nn.DataParallel(self.model).cuda()\\n # IPython.embed(header='checking replicas and others.')\\n else:\\n self.parallel_model = model\\n\\n darts = DartsArchitect(model, args=args)\\n model = self.parallel_model\\n # logging.info(\\\"DARTS param size = %fMB\\\", utils.count_parameters_in_MB(darts))\\n self.train_fn = partial(darts_train_model, args=args, architect=darts, sampler=None)\\n self.eval_fn = partial(darts_model_validation, args=args, verbose=True)\\n self.controller = darts\\n\\n logging.info(\\\"param size = %fMB\\\", utils.count_parameters_in_MB(model))\\n optimizer = torch.optim.SGD(\\n model.parameters(),\\n args.learning_rate,\\n momentum=args.momentum,\\n weight_decay=args.weight_decay,\\n )\\n\\n # scheduler as Cosine.\\n scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, float(args.epochs), args.learning_rate_min)\\n return model, optimizer, scheduler, darts, None\",\n \"def init(self, train):\\n return\",\n \"def init(self, train):\\n return\",\n \"def init(self, train):\\n return\",\n \"def init(self, train):\\n return\",\n \"def init(self, train):\\n return\",\n \"def validate_and_initialize(self, model_dir=environment.model_dir, context=None):\\n if not self._initialized:\\n self._context = context\\n self._environment = environment.Environment()\\n self._validate_user_module_and_set_functions()\\n\\n if self._pre_model_fn is not None:\\n self._run_handler_function(self._pre_model_fn, *(model_dir,))\\n\\n self._model = self._run_handler_function(self._model_fn, *(model_dir,))\\n\\n if self._model_warmup_fn is not None:\\n self._run_handler_function(self._model_warmup_fn, *(model_dir, self._model))\\n\\n self._initialized = True\",\n \"def __init__(self, model: Model1D):\\n self._model = model\",\n \"def __init__(self, k_folds, n_days):\\n self.n_days = n_days\\n self.k_folds = k_folds\\n\\n # Makes a Sequential() model\\n super(CustomSequential, self).__init__()\",\n \"def __init__(self, model: str, **kwargs):\\n\\n super().__init__(model=model, **kwargs)\\n logger.info('load model done')\",\n \"def __init__(self, **kwargs):\\n super(Model, self).__init__(**kwargs)\",\n \"def initialize(self):\\n self.initialize_edges()\\n self.initialize_prob()\\n self.initialize_total_input_dict()\\n\\n self.initialize_fpmusigv_dict()\",\n \"def __init__(self, depth, identifier):\\n self.model = Sequential()\\n self.identifier = identifier\\n self.model_filename = f'models/{self.identifier}.model'\\n self.model.add(LSTM(4, input_shape=(2, 2), return_sequences=True))\\n for i in range(depth):\\n self.model.add(LSTM(8, return_sequences=True))\\n self.model.add(LSTM(2, return_sequences=True))\\n self.model.compile(optimizer='rmsprop',\\n loss='mse')\",\n \"def keras_setup(self):\\n\\n # Interpret the request data based on the expected row and column structure\\n row_template = ['strData', 'numData', 'strData', 'strData', 'strData']\\n col_headers = ['model_name', 'sort_order', 'layer_type', 'args', 'kwargs']\\n \\n # Create a Pandas Data Frame for the request data\\n self.request_df = utils.request_df(self.request, row_template, col_headers)\\n \\n # Create a model that can be persisted to disk\\n self.model = PersistentModel()\\n \\n # Get the model name from the request dataframe\\n self.model.name = self.request_df.loc[0, 'model_name']\\n \\n # Get the model from cache or disk\\n self._get_model()\\n \\n # Debug information is printed to the terminal and logs if the paramater debug = true\\n if self.model.debug:\\n self._print_log(3)\\n\\n # Set a flag which will let us know that this is a Keras model\\n self.model.using_keras = True\\n\\n # Sort the layers, drop unnecessart columns and save the model architecture to a new data frame\\n architecture = self.request_df.sort_values(by=['sort_order']).reset_index(drop=True).drop(labels=['model_name', 'sort_order'], axis = 1)\\n\\n # Convert args to a list and kwargs to a dictionary\\n architecture['args'] = architecture['args'].apply(utils.get_args_by_type)\\n architecture['kwargs'] = architecture['kwargs'].apply(utils.get_kwargs).apply(utils.get_kwargs_by_type) \\n\\n # Add the architecture to the model\\n self.model.architecture = architecture\\n\\n # Add the first layer's kwargs as a property for easy reference\\n self.model.first_layer_kwargs = self.model.architecture.iloc[0, 2]\\n\\n # Debug information is printed to the terminal and logs if the paramater debug = true\\n if self.model.debug:\\n self._print_log(10)\\n\\n # Persist the model to disk\\n self.model = self.model.save(self.model.name, self.path, overwrite=self.model.overwrite, compress=self.model.compress)\\n \\n # Update the cache to keep this model in memory\\n self._update_cache()\\n \\n # Prepare the output\\n message = [[self.model.name, 'Keras model architecture saved to disk',\\\\\\n time.strftime('%X %x %Z', time.localtime(self.model.state_timestamp))]]\\n self.response = pd.DataFrame(message, columns=['model_name', 'result', 'time_stamp'])\\n \\n # Send the reponse table description to Qlik\\n self._send_table_description(\\\"setup\\\")\\n \\n # Debug information is printed to the terminal and logs if the paramater debug = true\\n if self.model.debug:\\n self._print_log(4)\\n \\n # Finally send the response\\n return self.response\",\n \"def test_init_variants():\\n # get data\\n X, _, _, _ = get_data()\\n # set seed and kernel initialisers for repeatability\\n tf.random.set_seed(12345)\\n initializer = tf.keras.initializers.Zeros()\\n\\n # define model architecture\\n input_data = Input(shape=X[0].shape)\\n xx = Dense(32, activation=\\\"relu\\\", kernel_initializer=initializer)(input_data)\\n output = Dense(n_classes, activation=\\\"softmax\\\", kernel_initializer=initializer)(xx)\\n\\n # standard way\\n model = SafeKerasModel(\\n inputs=input_data,\\n outputs=output,\\n name=\\\"test\\\",\\n num_samples=X.shape[0],\\n epochs=EPOCHS,\\n )\\n check_init_completed(model)\\n\\n # inputs and outputs not in kwargs\\n model2 = SafeKerasModel(\\n input_data,\\n output,\\n \\\"test\\\",\\n num_samples=X.shape[0],\\n epochs=EPOCHS,\\n )\\n check_init_completed(model2)\\n\\n # batch size zero\\n model3 = SafeKerasModel(\\n inputs=input_data,\\n outputs=output,\\n name=\\\"test\\\",\\n num_samples=X.shape[0],\\n epochs=EPOCHS,\\n batch_size=0,\\n )\\n errstr = \\\"failed to correct batch_size=0 in init\\\"\\n assert model3.batch_size == 32, errstr\",\n \"def _init_model(self):\\n try:\\n model_config = self._conf.get(PredictConstance.BASE_CONFIG,\\n PredictConstance.MODEL_CONFIG)\\n conf = configparser.ConfigParser()\\n conf.read(model_config)\\n self._model = model_factory.ModelFactory.create_model(config=conf)\\n return True\\n except Exception as err:\\n self.managerlogger.logger.error(\\\"init model error: %s\\\" % err)\\n self.errorlogger.logger.error(\\\"init model error: \\\\n %s\\\" % traceback.format_exc())\\n return False\",\n \"def init_training(self):\\n\\n if not os.path.exists(self._model_root_path):\\n os.makedirs(self._model_root_path)\\n\\n # Only initialize once!\\n if self._model is None:\\n self._model = TrainableAimbotModel(self._config, self._fov,\\n os.path.join(self._model_root_path, 'aimbot_model.tf'))\\n\\n if not os.path.isfile(self._train_data_tfrecord_path) and not os.path.isfile(self._test_data_tfrecord_path):\\n # Only create if not existing\\n images_labels = _get_annotations_and_images(self._image_path)\\n images_labels_train, images_labels_test = train_test_split(images_labels, shuffle=True, test_size=0.20)\\n\\n self._model.create_tfrecords(self._train_data_tfrecord_path, images_labels_train)\\n self._model.create_tfrecords(self._test_data_tfrecord_path, images_labels_test)\\n\\n self._train_data_set = self._model.create_dataset(self._train_data_tfrecord_path, augment=True, shuffle=True)\\n self._test_data_set = self._model.create_dataset(self._train_data_tfrecord_path)\",\n \"def _lazy_init(self) -> None:\\n # Initialize _is_root and setup streams. These steps would ideally\\n # happen in __init__, but _is_root can only be determined after the\\n # entire model hierarchy is setup, thus we run it lazily.\\n if self._is_root is None:\\n self._set_is_root()\\n self._setup_output_hook_and_backward_opt_barrier_lists()\\n\\n if self._is_root and self.disable_reshard_on_root:\\n # Don't free the full params for the outer-most (root) instance,\\n # since those params will be needed immediately after for the\\n # backward pass.\\n self.reshard_after_forward = False\",\n \"def __init__(self):\\n self.train(positivity_files, 0)\\n self.train(subjectivity_files, 1)\",\n \"def run(self) -> None:\\n self.model = self.trainer.train_model(self.model, self.data)\",\n \"def initialize_model(self):\\n model = self.model_class()\\n return model\",\n \"def __init__(self, config):\\n self.model = None\\n self.config = config\\n self.batch_size = config.get('batch_size')\\n self.epochs = config.get('epochs')\\n self.steps_per_epoch = config.get('steps_per_epoch')\\n self.validation_steps = config.get('validation_steps')\\n self.distributed = config.get('distributed', False)\\n \\n # init model\\n self.init()\",\n \"def initialize_model(self):\\n\\n input_layer = Input(\\n shape=(self.input_length,), \\n dtype='int32', \\n name='input'\\n )\\n\\n if self.embedding_matrix is None:\\n embedding = Embedding(\\n output_dim=self.embedding_size,\\n input_dim=self.vocabulary_size + 1, # for mask\\n input_length=self.input_length,\\n mask_zero=True,\\n name='embedding'\\n )(input_layer)\\n else:\\n embedding = Embedding(\\n output_dim=self.embedding_size,\\n input_dim=self.vocabulary_size + 1,\\n input_length=self.input_length,\\n mask_zero=True,\\n weights=[np.vstack((np.zeros((1, self.embedding_size)),\\n self.embedding_matrix))],\\n name='embedding'\\n )(input_layer)\\n\\n encoder = self.recurrent_cell(\\n self.latent_dim,\\n dropout=self.dropout,\\n recurrent_dropout=self.dropout,\\n name='encoder',\\n recurrent_regularizer=l1_l2(*self.regularization)\\n )\\n\\n if self.use_bidirection:\\n encoder = Bidirectional(\\n encoder,\\n merge_mode='concat'\\n )\\n\\n encoder = encoder(embedding)\\n\\n dense_1 = Dense(\\n 1024,\\n activation='tanh',\\n name='dense_1',\\n kernel_regularizer=l1_l2(*self.regularization)\\n )(encoder)\\n\\n dense_2 = Dense(\\n 512,\\n activation='tanh',\\n name='dense_2',\\n kernel_regularizer=l1_l2(*self.regularization)\\n )(dense_1)\\n\\n dropout = Dropout(self.dropout)(\\n dense_2\\n )\\n\\n prediction = Dense(\\n 1,\\n activation='sigmoid',\\n name='prediction'\\n )(dropout)\\n\\n model = Model(inputs=input_layer, outputs=prediction)\\n\\n # sparse_categorical_crossentropy\\n model.compile(optimizer=Adam(lr=self.learning_rate),\\n loss='binary_crossentropy',\\n metrics=['acc'])\\n\\n self.model = model\\n\\n if self.verbose > 0:\\n model.summary()\\n\\n return [model]\",\n \"def _initialize(self) -> None:\\n p = self.params\\n # We make self.input public so that users can access its methods like\\n # IdsToStrings if needed.\\n with py_utils.infeed_context_scope(\\n infeed_host_index=p.infeed_host_index,\\n num_infeed_hosts=p.num_infeed_hosts):\\n self.input = p.input.Instantiate()\\n\\n if hasattr(self.input, 'datasource') and isinstance(\\n self.input.datasource, datasource.TFDatasetSource):\\n # For the special case when the input is implemented by a tf.data.Dataset,\\n # use it directly. Otherwise roundtrip adaptions may result in returning\\n # duplciate batches.\\n self._get_next_fn = self.input.datasource.GetNext\\n else:\\n self._get_next_fn = tf.function(self._get_batch)\\n self._num_batches_produced = 0\",\n \"def initialization_routine(\\n self, model: Block, plugin_initializer_args: dict = None, **kwargs\\n ):\\n if plugin_initializer_args is None:\\n plugin_initializer_args = {}\\n\\n # Get logger\\n _log = self.get_logger(model)\\n\\n # Prepare plug-ins for initialization\\n sub_initializers, plugin_initializer_args = self.prepare_plugins(\\n model, plugin_initializer_args\\n )\\n _log.info_high(\\\"Step 1: preparation complete.\\\")\\n\\n # Initialize model and sub-models\\n results = self.initialize_submodels(\\n model, plugin_initializer_args, sub_initializers, **kwargs\\n )\\n _log.info_high(\\\"Step 2: sub-model initialization complete.\\\")\\n\\n # Solve full model including plug-ins\\n results = self.solve_full_model(model, results)\\n _log.info_high(\\n f\\\"Step 3: full model initialization {idaeslog.condition(results)}.\\\"\\n )\\n\\n # Clean up plug-ins\\n self.cleanup(model, plugin_initializer_args, sub_initializers)\\n _log.info_high(\\\"Step 4: clean up completed.\\\")\\n\\n return results\",\n \"def initialise(self):\\n self.set_up()\",\n \"def model_setup(self):\\n self.DNN = SganMLP(self.settings.number_of_bins)\\n self.D = SganMLP(self.settings.number_of_bins)\\n self.G = Generator()\",\n \"def _prepare_model(model):\\n\\n # Ensure there is at least 1 load combination to solve if the user didn't define any\\n if model.LoadCombos == {}:\\n # Create and add a default load combination to the dictionary of load combinations\\n model.LoadCombos['Combo 1'] = LoadCombo('Combo 1', factors={'Case 1':1.0})\\n \\n # Generate all meshes\\n for mesh in model.Meshes.values():\\n if mesh.is_generated == False:\\n mesh.generate()\\n\\n # Activate all springs and members for all load combinations\\n for spring in model.Springs.values():\\n for combo_name in model.LoadCombos.keys():\\n spring.active[combo_name] = True\\n \\n # Activate all physical members for all load combinations\\n for phys_member in model.Members.values():\\n for combo_name in model.LoadCombos.keys():\\n phys_member.active[combo_name] = True\\n \\n # Assign an internal ID to all nodes and elements in the model. This number is different from the name used by the user to identify nodes and elements.\\n _renumber(model)\",\n \"def __init__(self, **kwargs):\\n\\n # Identify the mode to start the model in\\n if \\\"x\\\" in kwargs and \\\"y\\\" in kwargs:\\n x = kwargs.get(\\\"x\\\")\\n y = kwargs.get(\\\"y\\\")\\n if \\\"model_name\\\" not in kwargs:\\n self.__mode = \\\"train\\\"\\n else:\\n self.__mode = \\\"retrain\\\"\\n elif \\\"model_name\\\" in kwargs:\\n self.__mode = \\\"test\\\"\\n else:\\n raise NameError(\\\"Cannot infer mode from arguments.\\\")\\n\\n print(\\\"Initializing model in %s mode.\\\" % self.__mode)\\n\\n if self.mode == \\\"train\\\":\\n # Infer input type from type(x)\\n if type(x[0]) == np.bytes_:\\n print(\\\"Input type is 'binary mols'.\\\")\\n self.__input_type = \\\"mols\\\" # binary RDKit mols\\n else:\\n print(\\\"Input type is 'molecular descriptors'.\\\")\\n self.__input_type = \\\"descriptors\\\" # other molecular descriptors\\n\\n # If scaling is required\\n if kwargs.get(\\\"scaling\\\", False) is True:\\n # Normalize the input\\n print(\\\"Applying scaling on input.\\\")\\n self.__scaler = StandardScaler()\\n x = self.__scaler.fit_transform(x)\\n else:\\n self.__scaler = None\\n\\n # If PCA is required\\n if kwargs.get(\\\"pca\\\", False) is True:\\n print(\\\"Applying PCA on input.\\\")\\n self.__pca = PCA(\\n n_components=x.shape[1]\\n ) # n_components=n_features for now\\n x = self.__pca.fit_transform(x)\\n else:\\n self.__pca = None\\n\\n self.__maxlen = (\\n kwargs.get(\\\"dataset_info\\\")[\\\"maxlen\\\"] + 10\\n ) # Extend maxlen to avoid breaks in training\\n self.__charset = kwargs.get(\\\"dataset_info\\\")[\\\"charset\\\"]\\n self.__dataset_name = kwargs.get(\\\"dataset_info\\\")[\\\"name\\\"]\\n self.__lstm_dim = kwargs.get(\\\"lstm_dim\\\", 256)\\n self.__h_activation = kwargs.get(\\\"h_activation\\\", \\\"relu\\\")\\n self.__bn = kwargs.get(\\\"bn\\\", True)\\n self.__bn_momentum = kwargs.get(\\\"bn_momentum\\\", 0.9)\\n self.__noise_std = kwargs.get(\\\"noise_std\\\", 0.01)\\n self.__td_dense_dim = kwargs.get(\\n \\\"td_dense_dim\\\", 0\\n ) # >0 squeezes RNN connections with Dense sandwiches\\n self.__batch_size = kwargs.get(\\\"batch_size\\\", 256)\\n self.__dec_layers = kwargs.get(\\\"dec_layers\\\", 2)\\n\\n if self.input_type == \\\"descriptors\\\":\\n self.__codelayer_dim = x.shape[1] # features\\n if \\\"codelayer_dim\\\" in kwargs:\\n print(\\n \\\"Ignoring requested codelayer_dim because it is inferred from the cardinality of the descriptors.\\\"\\n )\\n else:\\n self.__codelayer_dim = kwargs.get(\\\"codelayer_dim\\\", 128)\\n \\n # Create the left/right-padding vectorizers\\n self.__smilesvec1 = SmilesVectorizer(\\n canonical=False,\\n augment=True,\\n maxlength=self.maxlen,\\n charset=self.charset,\\n binary=True,\\n )\\n\\n self.__smilesvec2 = SmilesVectorizer(\\n canonical=False,\\n augment=True,\\n maxlength=self.maxlen,\\n charset=self.charset,\\n binary=True,\\n leftpad=False,\\n )\\n\\n # self.train_gen.next() #This line is needed to set train_gen.dims (to be fixed in HetSmilesGenerator)\\n self.__input_shape = self.smilesvec1.dims\\n self.__dec_dims = list(self.smilesvec1.dims)\\n self.__dec_dims[0] = self.dec_dims[0] - 1\\n self.__dec_input_shape = self.dec_dims\\n self.__output_len = self.smilesvec1.dims[0] - 1\\n self.__output_dims = self.smilesvec1.dims[-1]\\n\\n # Build all sub-models as untrained models\\n if self.input_type == \\\"mols\\\":\\n self.__build_mol_to_latent_model()\\n else:\\n self.__mol_to_latent_model = None\\n\\n self.__build_latent_to_states_model()\\n self.__build_batch_model()\\n\\n # Build data generators\\n self.__build_generators(x, y)\\n\\n # Retrain or Test mode\\n else:\\n self.__model_name = kwargs.get(\\\"model_name\\\")\\n\\n # Load the model\\n self.__load(self.model_name)\\n \\n if self.mode == \\\"retrain\\\":\\n # If scaling is required\\n if self.scaler is not None:\\n print(\\\"Applying scaling on input.\\\")\\n x = self.scaler.transform(x)\\n\\n # If PCA is required\\n if self.pca is not None:\\n print(\\\"Applying PCA on input.\\\")\\n x = self.pca.transform(x)\\n \\n # Build data generators\\n self.__build_generators(x, y)\\n\\n # Build full model out of the sub-models\\n self.__build_model()\\n\\n # Show the resulting full model\\n print(self.model.summary())\",\n \"def initialize(self):\\n pass\",\n \"def initialize(self):\\n pass\",\n \"def initialize(self):\\n pass\",\n \"def initialize(self):\\n pass\",\n \"def initialize(self):\\n pass\",\n \"def epoch_begin(self, model):\\n pass\",\n \"def initialize(self):\\n if not self._ready:\\n self._real_initialize()\\n self._ready = True\",\n \"def init_components(\\n self, model_fn=None, criterion_fn=None, optimizer_fn=None, scheduler_fn=None\\n ):\\n model = model_fn()\\n model = self.sync_device(model)\\n if self._sync_bn:\\n model = apex.parallel.convert_syncbn_model(model)\\n\\n criterion = criterion_fn()\\n criterion = self.sync_device(criterion)\\n\\n optimizer = optimizer_fn()\\n optimizer = self.sync_device(optimizer)\\n\\n model, optimizer = amp.initialize(model, optimizer, **self.apex_kwargs)\\n model = ApexDistributedDataParallel(model, **self.ddp_kwargs)\\n\\n scheduler = scheduler_fn()\\n scheduler = self.sync_device(scheduler)\\n return model, criterion, optimizer, scheduler\",\n \"def __init__(self , model_file_name ):\\n logging.set_verbosity(logging.ERROR)\\n with TheTFGraph.as_default():\\n with TheTFSession.as_default():\\n self.model = keras.models.load_model( model_file_name + \\\".hdf5\\\" , compile=False )\\n JSON = json.load( open(model_file_name + \\\".json\\\" ) )\\n self.all_sites = list(JSON['all_sites'])\\n self.all_errors = list(JSON['all_errors'])\\n self.all_actions = list(JSON['all_actions'])\\n self.IsBinary = bool(JSON['IsBinary'])\\n self.TiersOnly = bool(JSON['TiersOnly'])\\n self.Task = Task({} , \\\"TaskLoader\\\" , self)\\n self.Name = model_file_name.split('/')[-1]\\n self.ModelID = int( JSON['model'] )\\n self.InputTrainingDataID = int( JSON['trainingdata'])\\n\\n self.Prediction = Prediction.Prediction( self.ModelID , self.InputTrainingDataID )\",\n \"def init_model(session, model):\\n # If there is a checkpoint, load it\\n if not tf.gfile.Exists(FLAGS.train_dir):\\n tf.gfile.MkDir(FLAGS.train_dir)\\n ckpt = tf.train.get_checkpoint_state(FLAGS.train_dir)\\n if ckpt and tf.train.checkpoint_exists(ckpt.model_checkpoint_path):\\n print(\\\"Reading model parameters from %s\\\" % ckpt.model_checkpoint_path)\\n model.saver.restore(session, ckpt.model_checkpoint_path)\\n\\n # Else initialize the variables\\n else:\\n if FLAGS.decode:\\n input(\\\"You sure you want to talk to an untrained chatbot? Press Ctrl-C to stop, Return to continue \\\")\\n print(\\\"Fine.\\\")\\n\\n print(\\\"Creating model with fresh parameters.\\\")\\n session.run(tf.global_variables_initializer())\",\n \"def __init__(self, model):\\n\\t\\tself.model = model\",\n \"def initialize(self, model):\\n # Retrieve all parameters on which to act\\n self.set_pruning_parameters(model)\\n # Create a set of masks for each layer\\n mask = [None] * len(self.prune_parameters)\\n for step, (name, param) in enumerate(self.prune_parameters):\\n mask[step] = torch.ones_like(param.data).detach()#.cpu().numpy()\\n # Save mask\\n self.mask = mask\\n # Save the current model weights\\n self.initial_state_dict = None\",\n \"def initialise_sim(self):\\n pass\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7742976","0.7590463","0.759012","0.74840647","0.7275883","0.70921814","0.6888844","0.68874913","0.68849003","0.6871453","0.68196464","0.6760278","0.67510974","0.6709929","0.66639215","0.6652089","0.6573573","0.6572885","0.6565885","0.6531906","0.6519555","0.65169865","0.6511854","0.6499241","0.64968294","0.64903563","0.6465642","0.6465642","0.6426285","0.6421225","0.64125043","0.64082724","0.6365586","0.6360184","0.6356359","0.63478136","0.63478136","0.6345134","0.63411117","0.63381106","0.6333344","0.6328821","0.63104165","0.63071257","0.6293513","0.6287571","0.62853813","0.6285124","0.62744176","0.6271724","0.626911","0.62666124","0.6265203","0.62644583","0.62586856","0.62518835","0.62438416","0.6226208","0.6222509","0.6221743","0.6221743","0.6221743","0.6221743","0.6221743","0.6220865","0.6209564","0.61928636","0.6192694","0.6191587","0.6186852","0.61851656","0.6182652","0.61803454","0.61785406","0.617542","0.61711097","0.617062","0.6146223","0.6136699","0.61291033","0.6114843","0.6113407","0.6107929","0.61045367","0.6103998","0.6098544","0.6088249","0.60869074","0.60869074","0.60869074","0.60869074","0.60869074","0.6077392","0.6073722","0.6062191","0.6058335","0.60546535","0.6052721","0.60508883","0.6050622"],"string":"[\n \"0.7742976\",\n \"0.7590463\",\n \"0.759012\",\n \"0.74840647\",\n \"0.7275883\",\n \"0.70921814\",\n \"0.6888844\",\n \"0.68874913\",\n \"0.68849003\",\n \"0.6871453\",\n \"0.68196464\",\n \"0.6760278\",\n \"0.67510974\",\n \"0.6709929\",\n \"0.66639215\",\n \"0.6652089\",\n \"0.6573573\",\n \"0.6572885\",\n \"0.6565885\",\n \"0.6531906\",\n \"0.6519555\",\n \"0.65169865\",\n \"0.6511854\",\n \"0.6499241\",\n \"0.64968294\",\n \"0.64903563\",\n \"0.6465642\",\n \"0.6465642\",\n \"0.6426285\",\n \"0.6421225\",\n \"0.64125043\",\n \"0.64082724\",\n \"0.6365586\",\n \"0.6360184\",\n \"0.6356359\",\n \"0.63478136\",\n \"0.63478136\",\n \"0.6345134\",\n \"0.63411117\",\n \"0.63381106\",\n \"0.6333344\",\n \"0.6328821\",\n \"0.63104165\",\n \"0.63071257\",\n \"0.6293513\",\n \"0.6287571\",\n \"0.62853813\",\n \"0.6285124\",\n \"0.62744176\",\n \"0.6271724\",\n \"0.626911\",\n \"0.62666124\",\n \"0.6265203\",\n \"0.62644583\",\n \"0.62586856\",\n \"0.62518835\",\n \"0.62438416\",\n \"0.6226208\",\n \"0.6222509\",\n \"0.6221743\",\n \"0.6221743\",\n \"0.6221743\",\n \"0.6221743\",\n \"0.6221743\",\n \"0.6220865\",\n \"0.6209564\",\n \"0.61928636\",\n \"0.6192694\",\n \"0.6191587\",\n \"0.6186852\",\n \"0.61851656\",\n \"0.6182652\",\n \"0.61803454\",\n \"0.61785406\",\n \"0.617542\",\n \"0.61711097\",\n \"0.617062\",\n \"0.6146223\",\n \"0.6136699\",\n \"0.61291033\",\n \"0.6114843\",\n \"0.6113407\",\n \"0.6107929\",\n \"0.61045367\",\n \"0.6103998\",\n \"0.6098544\",\n \"0.6088249\",\n \"0.60869074\",\n \"0.60869074\",\n \"0.60869074\",\n \"0.60869074\",\n \"0.60869074\",\n \"0.6077392\",\n \"0.6073722\",\n \"0.6062191\",\n \"0.6058335\",\n \"0.60546535\",\n \"0.6052721\",\n \"0.60508883\",\n \"0.6050622\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6732077"},"document_rank":{"kind":"string","value":"13"}}},{"rowIdx":17893,"cells":{"query":{"kind":"string","value":"Move one step forward."},"document":{"kind":"string","value":"def run_step(self):\n \n # Ensure model has been initialized at least once\n self._model_has_been_initialized(\"run_step\")\n\n # Check if sim is steady-state (doesn't contain \"doStep\" method)\n if \"doStep\" not in dir(self.fmu):\n error_log = \"[run_step] FMU model cannot be run one step-forward, since it is a steady-state sim. \"\n error_log += \"No step advance will be applied.\"\n print(error_log)\n return\n\n self.fmu.doStep(currentCommunicationPoint=self.sim_time, communicationStepSize=self.step_size)\n self.sim_time += self.step_size\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 step_forward(self):","def forward(self):\n self.position += 1","def move_forward():\n pass","def step(self, move):","def forward(self):\n self.cursor.forward()","def _step(self) -> None:","def back(self, step):\r\n self.forward(-step)","def move_forward(self, distance):\r\n return self.move('forward', distance)","def goForward(self):\r\n if self.currLoc + 1 < len(self.history):\r\n self.currLoc += 1\r\n return self.history[self.currLoc]","def _step(self):\n pass","def next_step(self):\n self.proceed()\n self.execute_current()","def next_step(self):\n logging.debug(u\"Moving to next step\")\n\n if not self.steps or len(self.steps) < 1:\n logging.debug(u\"- no steps have ben set\")\n return None\n\n index = self.get('_index')\n\n if index is None:\n index = 0\n elif index < len(self.steps)-1:\n index += 1\n else:\n logging.debug(u\"- all steps have ben consumed\")\n return None\n\n current = self.current_step\n if current:\n current.stop()\n\n logging.debug(u\"- triggering step #{}\".format(index+1))\n self.set('_index', index)\n step = self.steps[index]\n step.trigger(bot=self.bot)\n return step","def move_forward(self):\n self.x, self.y = self.compute_positions()","def forward(self, step):\r\n x = self.pos_x + math.cos(math.radians(self.rotation)) * step\r\n y = self.pos_y + math.sin(math.radians(self.rotation)) * step\r\n prev_brush_state = self.brush_on\r\n self.brush_on = True\r\n self.move(x, y)\r\n self.brush_on = prev_brush_state","def advance(self) -> None:\n pass","def _goForward(self) -> None:\n if self._stackIndex < -1:\n self._stackIndex += 1\n self._openPath(path=self._pathStack[self._stackIndex], ignoreStack=True)","def _advance(self):\n self._prev, self._current = self._current, self._prev + self._current","def forward( self ):\n self._has_change = True\n print( \"Forward\" )","def move_down(self):\n self.move_step(1)","def step_forward(self):\n self.read_value = self.write_value\n self.write_value = None","def next(self):\n while not self.is_stable():\n self.step()","def move_forward(self):\n self.pos += 1\n if self.pos > len(self.text) - 1:\n self.current_char = None\n else:\n self.current_char = self.text[self.pos]","def go_forward(self, distance, speed=0.1):\n while (self._last_odom_msg == None):\n\t rospy.sleep(1.0)\n start = copy.deepcopy(self._last_odom_msg.pose.pose.position)\n rate = rospy.Rate(10)\n while self.distance_fn(self._last_odom_msg.pose.pose.position, start) < math.fabs(distance):\n direction = -1 if distance < 0 else 1\n self.move(direction * speed, 0)\n rate.sleep()","def do_step(self) -> None:","def next(self):\n self.jumpahead(1)","def one_step_forward(self):\n if(self.row+1>=len(self.maze)):\n return False\n elif(self.battery==0):\n return False\n elif(self.maze[self.row+1][self.column]==False):\n return False\n else:\n self.row+=1\n self.battery-=1\n return True","def advance(self):\n self._state = self._next_state","def step(self):\n self.latent.step()","def move_next(self, step=1):\n if self._index is not None and len(self) > self._index + step:\n self._index += step\n # if index >= end index of current frame --> recalculate findex\n if self._index >= self._findex * self._flen + self._flen:\n self._findex += int(math.ceil(step / float(self._flen)))\n return self[self._index]\n return None","def step(self):\n\n pass","def advance(self, step=1):\n self.set_progress(self._step + step)","def forward(self, *args: Any, **kwargs: Any) -> None:\n self._check_for_increment(\"forward\")\n return self[-1](*args, **kwargs)","def left_forward(self):\n self.left_motor.run_forever(speed_sp=self.MAX_SPEED)","def _advance(self):\n self._current += 1","def next_step(self):\n if self.time_point + 1 >= len(self.data):\n print(\"Error: at last time point\")\n else:\n self.time_point = self.time_point + 1\n self.load_frame()","def MoveLeftStep(self):\n if self.facing == 0:\n self.facing = 3\n self.x -= self.stepLeft\n elif self.facing == 1:\n self.facing = 0\n self.y -= self.stepUp\n elif self.facing == 2:\n self.facing = 1\n self.x += self.stepRight\n elif self.facing == 3:\n self.facing = 2\n self.y += self.stepDown","def go_to_start(self):\n self.go_to(0)","def moveStep(self):\n\t\tif self.pos[0] <= self.boundsX[0] or \\\n\t\t(self.pos[0]+ 2*(self.radius)) >= self.boundsX[1]:\n\t\t\tself.dir[0] *= -1\n\t\t\t\n\t\tself.pos[0] += self.dir[0]*self.speed\n\t\tself.pos[1] += self.dir[1]*self.speed","def step(self):\n try:\n current_symbol = self._tape[self._head]\n next_symbol, direction, self._current_state = self._transitions.get(\n self._current_state\n ).get(current_symbol)\n except:\n return True\n\n self._tape[self._head] = next_symbol\n self._head += 1 if direction else -1\n\n if self._head < 0:\n self._tape.insert(0, self._blank_symbol)\n self._head = 0\n elif self._head >= len(self._tape):\n self._tape.append(self._blank_symbol)\n self._head = len(self._tape) - 1\n\n return False","def __step(self, p):\n action = self.__action(p)\n temp_state = self.state\n\n if self.state == 0:\n if action == 1:\n self.state += 1\n elif self.state == 1:\n if action == 1:\n self.state -= 1\n else:\n self.state += 1\n else:\n if action == 1:\n self.state += 1\n else:\n self.state -= 1\n \n self.trajectory.append([temp_state, action, self.__reward(self.state)])","def moveStep(self):\n\t\tif self.pos[0] < self.boundsX[0] or \\\n\t\t\tself.pos[0] > (self.boundsX[1] - self.width):\n\t\t\t\tself.dir[0] *= -1\n\t\tif self.pos[1] < self.boundsY[0] or \\\n\t\t self.pos[1] > (self.boundsY[1] - self.height):\n\t\t\t\tself.dir[1] *= -1\n\t\t\t\n\t\tself.pos[0] += self.dir[0]*self.speed\n\t\tself.pos[1] += self.dir[1]*self.speed","def take_step(self):\n if self.facing == 0:\n self.new_loc = (self.new_loc[0], self.new_loc[1] + 1)\n elif self.facing == 1:\n self.new_loc = (self.new_loc[0] + 1, self.new_loc[1])\n elif self.facing == 2:\n self.new_loc = (self.new_loc[0], self.new_loc[1] - 1)\n else:\n self.new_loc = (self.new_loc[0] - 1, self.new_loc[1])","def move_forward(self, distance):\n quad_offset = self.quad_offset_mapping['forward']\n client.moveByVelocityAsync(self.velocity * quad_offset[0], self.velocity * quad_offset[1],\n 0.15, distance/self.velocity).join()\n # if self.logging:\n # self.log_arr.append(\"forward\")","def move_up(self):\n self.move_step(-1)","def step(self):\r\n\r\n self.velocity = 1\r\n new_pos = self.pos\r\n self.model.space.move_agent(self, new_pos)","def move_left(self):\n self.yaw_motor.step_backward()","def goForward(current, bck, fwd):\n if fwd.size() < 1:\n print(\"Cannot go forward.\")\n else:\n bck.push(current)\n current = fwd.pop()\n return current","def up(self):\n self.forward(MOVE_DISTANCE)","def step(self, state):","def step(self, step=None):\n pass","def step_to(self, direction):\n s = self\n s.steps += s.speed\n s.physics.move_bomberman(self, direction)\n s.update_pos()","def go_to_next_state(self):\n pass","def move_forward(self, steps):\n\t\tif self.movement <= steps:\n\t\t\tif self.heading == 0:\n\t\t\t\tself.grid_y -= steps\n\t\t\telif self.heading == 90:\n\t\t\t\tself.grid_x += steps\n\t\t\telif self.heading == 180:\n\t\t\t\tself.grid_y += steps\n\t\t\telif self.heading == 270:\n\t\t\t\tself.grid_x -= steps","def decide_next_move(self):\n pass","def move_step(self, direction):\n x = self.objects[0].x\n y = self.objects[0].y\n if direction == 0 and y >= 1:\n self.objects[0].y -= 1\n elif direction == 1 and y <= self.size_y - 2:\n self.objects[0].y += 1\n elif direction == 2 and x >= 1:\n self.objects[0].x -= 1\n elif direction == 3 and x <= self.size_x - 2:\n self.objects[0].x += 1","def _advance(self):\n self._prev, self._current = self._current, abs(self._prev - self._current)","def moveForward(self):\n if self.onGround:\n self.vx = 4","def step(self):\n raise NotImplementedError","def go_forward(self):\n command = _build_robovac_command(RobovacModes.GO_FORWARD, RobovacCommands.MOVE)\n message = self._build_command_user_data_message(command)\n\n self._send_packet(message, False)","def go_forward(self):\n if not self._items:\n raise ValueError(\"Empty navigation history\")\n\n if self.can_go_forward():\n self._pointer += 1\n return self._items[self._pointer]","def step(self):\r\n raise NotImplementedError","def step(self):\n self.game.step()","def forward(self):\n pass","def forward(self):\n pass","def move(self):\n pass","def backward_step():\n #print 'a step backward'\n maze.turn_left()\n maze.turn_left()\n if maze.found():\n return maze.found()\n maze.go()\n maze.turn_left()\n maze.turn_left()","def forward(self):\n raise NotImplemented","def forward(self):\n raise NotImplemented","def forward(self):\n raise NotImplemented","def move_up(self):\n self.pitch_motor.step_backward()","def forward(self, speed):\n self.controller.forward(speed)","def step(self):\n raise NotImplementedError()","def step(self):\n raise NotImplementedError()","def step(self):\n raise NotImplementedError()","def move(self): # AH note. Swich move with extra_steps?\n if self.adjustment < 0:\n self.position += self.extra_steps\n super().move()\n self.no_moves += 1\n # Do the regular move","def go_to_next_spike(self, ):\n self._jump_to_spike(+1)","def move_down(self):\n self.pitch_motor.step_forward()","def step(self, move):\r\n self.board.push_uci(move)\r\n self.num_halfmoves += 1","def forward(self, amount):\n newX = self._x + round(amount * math.sin(math.radians(self._rotation)), 2)\n newY = self._y - round(amount * math.cos(math.radians(self._rotation)), 2)\n self.goto(newX, newY)","def _step(self, whence):\n pass","def move_prev(self, step=1):\n if self._index is not None and self._index - step >= 0:\n self._index -= step\n # if index <= start index of current frame --> recalculate findex\n if self._index < self._findex * self._flen:\n self._findex -= int(math.ceil(step / float(self._flen)))\n return self[self._index]\n return None","def forward(self, distance):\n self.logger.debug(\"forward \" + str(distance))","def drive_forward(self):\n print(f\"{self.make.title()} is now driving forward.\")","def forward_character():\r\n set_point(point()+1)","def forward(self, x):\n pass","def go_forward():\n raw_input( \"Press ENTER to continue: \")\n print \"\\n\"","def next(self):\n if self.step ==0:\n raise StopIteration\n self.step -= 1\n return self.step","def move_head_looking_forward():\n return _move_head(degrees(0))","def first_order_posint(self, timestep):\n self.prev_pos = self.position\n self.position = self.position + (self.velocity * timestep)","def next(self):\n if self.step == 0:\n raise StopIteration\n self.step -= 1\n return self.step","def __next__(self):\n if self.step <= 0:\n raise StopIteration\n self.step -= 1\n return self.step","def open_up(self):\n\n self.move(self.__max_step__)","def move(self, head, steps):\n self.turn(head)\n if self.direction == 0:\n self.x += int(steps)\n if self.direction == 1:\n self.y += int(steps)\n if self.direction == 2:\n self.x -= int(steps)\n if self.direction == 3:\n self.y -= int(steps)","def step(self, action):\n self._action = action\n if action[0] < 0: # Only allow forward direction\n action[0] = 0\n\n # Publish action via ROS\n msg = ackermann_msgs.msg.AckermannDriveStamped()\n msg.drive.speed = action[0]\n msg.drive.steering_angle = action[1]\n msg.header.stamp = self._sensor_stamp\n self.publisher.publish(msg)\n\n # Wait for next state readings\n self._num_states_received = self._num_states_needed\n while self._num_states_received > 0:\n time.sleep(0.00001)\n nextstate = self._current_state\n next_observation = self._state_to_observation(nextstate)\n self._state = nextstate\n\n reward, info = self.get_reward(nextstate, action)\n return Step(observation=next_observation, reward=reward,\n done=False, dist=info['dist'], vel=info['vel'],\n kappa=self._model.kappa)","def move_backward():\n pass","def on_reset(self):\n\n current = self.current_step\n if current:\n current.stop()\n\n logging.debug(u\"- seeking back before first step\")\n self.set('_index', None)","def bring_down(self):\n\n self.move(self.__min_step__)","def _advance(self):\n self._current += self._increment # Accessing the superclass's field","def Advance():\n warp.step()","def advance(self):\n raise NotImplementedError(\"Fragment.advance\")","def skip_forward(self) -> None:\n self.keyboard.press(Key.right)\n self.keyboard.release(Key.right)"],"string":"[\n \"def step_forward(self):\",\n \"def forward(self):\\n self.position += 1\",\n \"def move_forward():\\n pass\",\n \"def step(self, move):\",\n \"def forward(self):\\n self.cursor.forward()\",\n \"def _step(self) -> None:\",\n \"def back(self, step):\\r\\n self.forward(-step)\",\n \"def move_forward(self, distance):\\r\\n return self.move('forward', distance)\",\n \"def goForward(self):\\r\\n if self.currLoc + 1 < len(self.history):\\r\\n self.currLoc += 1\\r\\n return self.history[self.currLoc]\",\n \"def _step(self):\\n pass\",\n \"def next_step(self):\\n self.proceed()\\n self.execute_current()\",\n \"def next_step(self):\\n logging.debug(u\\\"Moving to next step\\\")\\n\\n if not self.steps or len(self.steps) < 1:\\n logging.debug(u\\\"- no steps have ben set\\\")\\n return None\\n\\n index = self.get('_index')\\n\\n if index is None:\\n index = 0\\n elif index < len(self.steps)-1:\\n index += 1\\n else:\\n logging.debug(u\\\"- all steps have ben consumed\\\")\\n return None\\n\\n current = self.current_step\\n if current:\\n current.stop()\\n\\n logging.debug(u\\\"- triggering step #{}\\\".format(index+1))\\n self.set('_index', index)\\n step = self.steps[index]\\n step.trigger(bot=self.bot)\\n return step\",\n \"def move_forward(self):\\n self.x, self.y = self.compute_positions()\",\n \"def forward(self, step):\\r\\n x = self.pos_x + math.cos(math.radians(self.rotation)) * step\\r\\n y = self.pos_y + math.sin(math.radians(self.rotation)) * step\\r\\n prev_brush_state = self.brush_on\\r\\n self.brush_on = True\\r\\n self.move(x, y)\\r\\n self.brush_on = prev_brush_state\",\n \"def advance(self) -> None:\\n pass\",\n \"def _goForward(self) -> None:\\n if self._stackIndex < -1:\\n self._stackIndex += 1\\n self._openPath(path=self._pathStack[self._stackIndex], ignoreStack=True)\",\n \"def _advance(self):\\n self._prev, self._current = self._current, self._prev + self._current\",\n \"def forward( self ):\\n self._has_change = True\\n print( \\\"Forward\\\" )\",\n \"def move_down(self):\\n self.move_step(1)\",\n \"def step_forward(self):\\n self.read_value = self.write_value\\n self.write_value = None\",\n \"def next(self):\\n while not self.is_stable():\\n self.step()\",\n \"def move_forward(self):\\n self.pos += 1\\n if self.pos > len(self.text) - 1:\\n self.current_char = None\\n else:\\n self.current_char = self.text[self.pos]\",\n \"def go_forward(self, distance, speed=0.1):\\n while (self._last_odom_msg == None):\\n\\t rospy.sleep(1.0)\\n start = copy.deepcopy(self._last_odom_msg.pose.pose.position)\\n rate = rospy.Rate(10)\\n while self.distance_fn(self._last_odom_msg.pose.pose.position, start) < math.fabs(distance):\\n direction = -1 if distance < 0 else 1\\n self.move(direction * speed, 0)\\n rate.sleep()\",\n \"def do_step(self) -> None:\",\n \"def next(self):\\n self.jumpahead(1)\",\n \"def one_step_forward(self):\\n if(self.row+1>=len(self.maze)):\\n return False\\n elif(self.battery==0):\\n return False\\n elif(self.maze[self.row+1][self.column]==False):\\n return False\\n else:\\n self.row+=1\\n self.battery-=1\\n return True\",\n \"def advance(self):\\n self._state = self._next_state\",\n \"def step(self):\\n self.latent.step()\",\n \"def move_next(self, step=1):\\n if self._index is not None and len(self) > self._index + step:\\n self._index += step\\n # if index >= end index of current frame --> recalculate findex\\n if self._index >= self._findex * self._flen + self._flen:\\n self._findex += int(math.ceil(step / float(self._flen)))\\n return self[self._index]\\n return None\",\n \"def step(self):\\n\\n pass\",\n \"def advance(self, step=1):\\n self.set_progress(self._step + step)\",\n \"def forward(self, *args: Any, **kwargs: Any) -> None:\\n self._check_for_increment(\\\"forward\\\")\\n return self[-1](*args, **kwargs)\",\n \"def left_forward(self):\\n self.left_motor.run_forever(speed_sp=self.MAX_SPEED)\",\n \"def _advance(self):\\n self._current += 1\",\n \"def next_step(self):\\n if self.time_point + 1 >= len(self.data):\\n print(\\\"Error: at last time point\\\")\\n else:\\n self.time_point = self.time_point + 1\\n self.load_frame()\",\n \"def MoveLeftStep(self):\\n if self.facing == 0:\\n self.facing = 3\\n self.x -= self.stepLeft\\n elif self.facing == 1:\\n self.facing = 0\\n self.y -= self.stepUp\\n elif self.facing == 2:\\n self.facing = 1\\n self.x += self.stepRight\\n elif self.facing == 3:\\n self.facing = 2\\n self.y += self.stepDown\",\n \"def go_to_start(self):\\n self.go_to(0)\",\n \"def moveStep(self):\\n\\t\\tif self.pos[0] <= self.boundsX[0] or \\\\\\n\\t\\t(self.pos[0]+ 2*(self.radius)) >= self.boundsX[1]:\\n\\t\\t\\tself.dir[0] *= -1\\n\\t\\t\\t\\n\\t\\tself.pos[0] += self.dir[0]*self.speed\\n\\t\\tself.pos[1] += self.dir[1]*self.speed\",\n \"def step(self):\\n try:\\n current_symbol = self._tape[self._head]\\n next_symbol, direction, self._current_state = self._transitions.get(\\n self._current_state\\n ).get(current_symbol)\\n except:\\n return True\\n\\n self._tape[self._head] = next_symbol\\n self._head += 1 if direction else -1\\n\\n if self._head < 0:\\n self._tape.insert(0, self._blank_symbol)\\n self._head = 0\\n elif self._head >= len(self._tape):\\n self._tape.append(self._blank_symbol)\\n self._head = len(self._tape) - 1\\n\\n return False\",\n \"def __step(self, p):\\n action = self.__action(p)\\n temp_state = self.state\\n\\n if self.state == 0:\\n if action == 1:\\n self.state += 1\\n elif self.state == 1:\\n if action == 1:\\n self.state -= 1\\n else:\\n self.state += 1\\n else:\\n if action == 1:\\n self.state += 1\\n else:\\n self.state -= 1\\n \\n self.trajectory.append([temp_state, action, self.__reward(self.state)])\",\n \"def moveStep(self):\\n\\t\\tif self.pos[0] < self.boundsX[0] or \\\\\\n\\t\\t\\tself.pos[0] > (self.boundsX[1] - self.width):\\n\\t\\t\\t\\tself.dir[0] *= -1\\n\\t\\tif self.pos[1] < self.boundsY[0] or \\\\\\n\\t\\t self.pos[1] > (self.boundsY[1] - self.height):\\n\\t\\t\\t\\tself.dir[1] *= -1\\n\\t\\t\\t\\n\\t\\tself.pos[0] += self.dir[0]*self.speed\\n\\t\\tself.pos[1] += self.dir[1]*self.speed\",\n \"def take_step(self):\\n if self.facing == 0:\\n self.new_loc = (self.new_loc[0], self.new_loc[1] + 1)\\n elif self.facing == 1:\\n self.new_loc = (self.new_loc[0] + 1, self.new_loc[1])\\n elif self.facing == 2:\\n self.new_loc = (self.new_loc[0], self.new_loc[1] - 1)\\n else:\\n self.new_loc = (self.new_loc[0] - 1, self.new_loc[1])\",\n \"def move_forward(self, distance):\\n quad_offset = self.quad_offset_mapping['forward']\\n client.moveByVelocityAsync(self.velocity * quad_offset[0], self.velocity * quad_offset[1],\\n 0.15, distance/self.velocity).join()\\n # if self.logging:\\n # self.log_arr.append(\\\"forward\\\")\",\n \"def move_up(self):\\n self.move_step(-1)\",\n \"def step(self):\\r\\n\\r\\n self.velocity = 1\\r\\n new_pos = self.pos\\r\\n self.model.space.move_agent(self, new_pos)\",\n \"def move_left(self):\\n self.yaw_motor.step_backward()\",\n \"def goForward(current, bck, fwd):\\n if fwd.size() < 1:\\n print(\\\"Cannot go forward.\\\")\\n else:\\n bck.push(current)\\n current = fwd.pop()\\n return current\",\n \"def up(self):\\n self.forward(MOVE_DISTANCE)\",\n \"def step(self, state):\",\n \"def step(self, step=None):\\n pass\",\n \"def step_to(self, direction):\\n s = self\\n s.steps += s.speed\\n s.physics.move_bomberman(self, direction)\\n s.update_pos()\",\n \"def go_to_next_state(self):\\n pass\",\n \"def move_forward(self, steps):\\n\\t\\tif self.movement <= steps:\\n\\t\\t\\tif self.heading == 0:\\n\\t\\t\\t\\tself.grid_y -= steps\\n\\t\\t\\telif self.heading == 90:\\n\\t\\t\\t\\tself.grid_x += steps\\n\\t\\t\\telif self.heading == 180:\\n\\t\\t\\t\\tself.grid_y += steps\\n\\t\\t\\telif self.heading == 270:\\n\\t\\t\\t\\tself.grid_x -= steps\",\n \"def decide_next_move(self):\\n pass\",\n \"def move_step(self, direction):\\n x = self.objects[0].x\\n y = self.objects[0].y\\n if direction == 0 and y >= 1:\\n self.objects[0].y -= 1\\n elif direction == 1 and y <= self.size_y - 2:\\n self.objects[0].y += 1\\n elif direction == 2 and x >= 1:\\n self.objects[0].x -= 1\\n elif direction == 3 and x <= self.size_x - 2:\\n self.objects[0].x += 1\",\n \"def _advance(self):\\n self._prev, self._current = self._current, abs(self._prev - self._current)\",\n \"def moveForward(self):\\n if self.onGround:\\n self.vx = 4\",\n \"def step(self):\\n raise NotImplementedError\",\n \"def go_forward(self):\\n command = _build_robovac_command(RobovacModes.GO_FORWARD, RobovacCommands.MOVE)\\n message = self._build_command_user_data_message(command)\\n\\n self._send_packet(message, False)\",\n \"def go_forward(self):\\n if not self._items:\\n raise ValueError(\\\"Empty navigation history\\\")\\n\\n if self.can_go_forward():\\n self._pointer += 1\\n return self._items[self._pointer]\",\n \"def step(self):\\r\\n raise NotImplementedError\",\n \"def step(self):\\n self.game.step()\",\n \"def forward(self):\\n pass\",\n \"def forward(self):\\n pass\",\n \"def move(self):\\n pass\",\n \"def backward_step():\\n #print 'a step backward'\\n maze.turn_left()\\n maze.turn_left()\\n if maze.found():\\n return maze.found()\\n maze.go()\\n maze.turn_left()\\n maze.turn_left()\",\n \"def forward(self):\\n raise NotImplemented\",\n \"def forward(self):\\n raise NotImplemented\",\n \"def forward(self):\\n raise NotImplemented\",\n \"def move_up(self):\\n self.pitch_motor.step_backward()\",\n \"def forward(self, speed):\\n self.controller.forward(speed)\",\n \"def step(self):\\n raise NotImplementedError()\",\n \"def step(self):\\n raise NotImplementedError()\",\n \"def step(self):\\n raise NotImplementedError()\",\n \"def move(self): # AH note. Swich move with extra_steps?\\n if self.adjustment < 0:\\n self.position += self.extra_steps\\n super().move()\\n self.no_moves += 1\\n # Do the regular move\",\n \"def go_to_next_spike(self, ):\\n self._jump_to_spike(+1)\",\n \"def move_down(self):\\n self.pitch_motor.step_forward()\",\n \"def step(self, move):\\r\\n self.board.push_uci(move)\\r\\n self.num_halfmoves += 1\",\n \"def forward(self, amount):\\n newX = self._x + round(amount * math.sin(math.radians(self._rotation)), 2)\\n newY = self._y - round(amount * math.cos(math.radians(self._rotation)), 2)\\n self.goto(newX, newY)\",\n \"def _step(self, whence):\\n pass\",\n \"def move_prev(self, step=1):\\n if self._index is not None and self._index - step >= 0:\\n self._index -= step\\n # if index <= start index of current frame --> recalculate findex\\n if self._index < self._findex * self._flen:\\n self._findex -= int(math.ceil(step / float(self._flen)))\\n return self[self._index]\\n return None\",\n \"def forward(self, distance):\\n self.logger.debug(\\\"forward \\\" + str(distance))\",\n \"def drive_forward(self):\\n print(f\\\"{self.make.title()} is now driving forward.\\\")\",\n \"def forward_character():\\r\\n set_point(point()+1)\",\n \"def forward(self, x):\\n pass\",\n \"def go_forward():\\n raw_input( \\\"Press ENTER to continue: \\\")\\n print \\\"\\\\n\\\"\",\n \"def next(self):\\n if self.step ==0:\\n raise StopIteration\\n self.step -= 1\\n return self.step\",\n \"def move_head_looking_forward():\\n return _move_head(degrees(0))\",\n \"def first_order_posint(self, timestep):\\n self.prev_pos = self.position\\n self.position = self.position + (self.velocity * timestep)\",\n \"def next(self):\\n if self.step == 0:\\n raise StopIteration\\n self.step -= 1\\n return self.step\",\n \"def __next__(self):\\n if self.step <= 0:\\n raise StopIteration\\n self.step -= 1\\n return self.step\",\n \"def open_up(self):\\n\\n self.move(self.__max_step__)\",\n \"def move(self, head, steps):\\n self.turn(head)\\n if self.direction == 0:\\n self.x += int(steps)\\n if self.direction == 1:\\n self.y += int(steps)\\n if self.direction == 2:\\n self.x -= int(steps)\\n if self.direction == 3:\\n self.y -= int(steps)\",\n \"def step(self, action):\\n self._action = action\\n if action[0] < 0: # Only allow forward direction\\n action[0] = 0\\n\\n # Publish action via ROS\\n msg = ackermann_msgs.msg.AckermannDriveStamped()\\n msg.drive.speed = action[0]\\n msg.drive.steering_angle = action[1]\\n msg.header.stamp = self._sensor_stamp\\n self.publisher.publish(msg)\\n\\n # Wait for next state readings\\n self._num_states_received = self._num_states_needed\\n while self._num_states_received > 0:\\n time.sleep(0.00001)\\n nextstate = self._current_state\\n next_observation = self._state_to_observation(nextstate)\\n self._state = nextstate\\n\\n reward, info = self.get_reward(nextstate, action)\\n return Step(observation=next_observation, reward=reward,\\n done=False, dist=info['dist'], vel=info['vel'],\\n kappa=self._model.kappa)\",\n \"def move_backward():\\n pass\",\n \"def on_reset(self):\\n\\n current = self.current_step\\n if current:\\n current.stop()\\n\\n logging.debug(u\\\"- seeking back before first step\\\")\\n self.set('_index', None)\",\n \"def bring_down(self):\\n\\n self.move(self.__min_step__)\",\n \"def _advance(self):\\n self._current += self._increment # Accessing the superclass's field\",\n \"def Advance():\\n warp.step()\",\n \"def advance(self):\\n raise NotImplementedError(\\\"Fragment.advance\\\")\",\n \"def skip_forward(self) -> None:\\n self.keyboard.press(Key.right)\\n self.keyboard.release(Key.right)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.84211636","0.80904514","0.7710274","0.76850307","0.74500424","0.71530104","0.7122298","0.7110573","0.7092961","0.7079883","0.70217985","0.7018611","0.6964649","0.69422424","0.6910966","0.6876663","0.68695754","0.6869501","0.68424183","0.68298566","0.68058646","0.67889196","0.6786553","0.67825943","0.6774914","0.6749851","0.6747196","0.6746621","0.6731763","0.67281353","0.6716733","0.6710979","0.6680253","0.66559","0.6653773","0.6650982","0.6647275","0.66468084","0.6644357","0.6635155","0.66309345","0.6630818","0.6628977","0.6618082","0.6615188","0.65961325","0.6582345","0.6574577","0.65572995","0.65565014","0.6555005","0.654678","0.65213156","0.65104103","0.6505302","0.64988935","0.6487826","0.6485791","0.6485674","0.6480762","0.6468025","0.64646274","0.6463538","0.6463538","0.6462747","0.64537996","0.64458954","0.64458954","0.64458954","0.6429727","0.6428239","0.6420397","0.6420397","0.6420397","0.6414217","0.64118373","0.6380343","0.63791484","0.63744503","0.6363261","0.6360259","0.63537526","0.633868","0.6332536","0.6331989","0.63314354","0.6327455","0.6310899","0.6302276","0.62946177","0.6289774","0.62666523","0.62660813","0.62655455","0.62634075","0.62619805","0.62605715","0.6260305","0.62582994","0.6258192","0.62517744"],"string":"[\n \"0.84211636\",\n \"0.80904514\",\n \"0.7710274\",\n \"0.76850307\",\n \"0.74500424\",\n \"0.71530104\",\n \"0.7122298\",\n \"0.7110573\",\n \"0.7092961\",\n \"0.7079883\",\n \"0.70217985\",\n \"0.7018611\",\n \"0.6964649\",\n \"0.69422424\",\n \"0.6910966\",\n \"0.6876663\",\n \"0.68695754\",\n \"0.6869501\",\n \"0.68424183\",\n \"0.68298566\",\n \"0.68058646\",\n \"0.67889196\",\n \"0.6786553\",\n \"0.67825943\",\n \"0.6774914\",\n \"0.6749851\",\n \"0.6747196\",\n \"0.6746621\",\n \"0.6731763\",\n \"0.67281353\",\n \"0.6716733\",\n \"0.6710979\",\n \"0.6680253\",\n \"0.66559\",\n \"0.6653773\",\n \"0.6650982\",\n \"0.6647275\",\n \"0.66468084\",\n \"0.6644357\",\n \"0.6635155\",\n \"0.66309345\",\n \"0.6630818\",\n \"0.6628977\",\n \"0.6618082\",\n \"0.6615188\",\n \"0.65961325\",\n \"0.6582345\",\n \"0.6574577\",\n \"0.65572995\",\n \"0.65565014\",\n \"0.6555005\",\n \"0.654678\",\n \"0.65213156\",\n \"0.65104103\",\n \"0.6505302\",\n \"0.64988935\",\n \"0.6487826\",\n \"0.6485791\",\n \"0.6485674\",\n \"0.6480762\",\n \"0.6468025\",\n \"0.64646274\",\n \"0.6463538\",\n \"0.6463538\",\n \"0.6462747\",\n \"0.64537996\",\n \"0.64458954\",\n \"0.64458954\",\n \"0.64458954\",\n \"0.6429727\",\n \"0.6428239\",\n \"0.6420397\",\n \"0.6420397\",\n \"0.6420397\",\n \"0.6414217\",\n \"0.64118373\",\n \"0.6380343\",\n \"0.63791484\",\n \"0.63744503\",\n \"0.6363261\",\n \"0.6360259\",\n \"0.63537526\",\n \"0.633868\",\n \"0.6332536\",\n \"0.6331989\",\n \"0.63314354\",\n \"0.6327455\",\n \"0.6310899\",\n \"0.6302276\",\n \"0.62946177\",\n \"0.6289774\",\n \"0.62666523\",\n \"0.62660813\",\n \"0.62655455\",\n \"0.62634075\",\n \"0.62619805\",\n \"0.62605715\",\n \"0.6260305\",\n \"0.62582994\",\n \"0.6258192\",\n \"0.62517744\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":17894,"cells":{"query":{"kind":"string","value":"Reset model with new config (if given)."},"document":{"kind":"string","value":"def reset(self, config_param_vals: Dict[str, Any] = None):\n \n # Ensure model has been initialized at least once\n self._model_has_been_initialized(\"reset\")\n\n # Terminate and re-initialize\n self._terminate_model()\n self.initialize_model(config_param_vals)\n \n # Reset time\n self.sim_time = float(self.start_time)\n\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 reset_model(self):\n raise NotImplementedError","def reset_model(self):\n raise NotImplementedError","def reset_model(self):\n raise NotImplementedError","def reset(self, model):\n self.reset_strategy(model)","def reset(self, config, **kwargs):\n pass","def reset_config():\n return _set_config(_gen_config())","def reset_config():\r\n # TODO implement configuration reset\r\n pass","def _reset(self):\n self._model._reset()\n super(RDPAnalyzer, self)._reset()","def reset(self):\n checkpoint = torch.load(\n 'model_lr_finder.pth.tar',\n map_location=self.device)\n self.model.load_state_dict(checkpoint['state_dict'])\n self.optimizer.load_state_dict(checkpoint['optimizer'])\n self.model.to(self.device)\n self.model.train()","def reset( self ):\n self.conf = self.defaults","def reset_config():\n\n Config.config().update({\"coerce\": True, \"debug\": True, \"active\": True})","def test_model_reset_correctly(tmpdir):\n tutils.reset_seed()\n\n model = EvalModelTemplate()\n\n # logger file to get meta\n trainer = Trainer(\n default_root_dir=tmpdir,\n max_epochs=1,\n )\n\n before_state_dict = deepcopy(model.state_dict())\n\n trainer.tuner.scale_batch_size(model, max_trials=5)\n\n after_state_dict = model.state_dict()\n\n for key in before_state_dict.keys():\n assert torch.all(torch.eq(before_state_dict[key], after_state_dict[key])), \\\n 'Model was not reset correctly after scaling batch size'","def reset(self):\n urdf=os.path.join(os.environ[\"YUMI_PUSH_MODELS\"],\"robot_hand.urdf\")\n self._model = self._world.add_model(\n model_path=urdf,\n position=[-10.0, -10.0, 0.0],\n orientation=[0.0, 0.0, 0.0, 1.0],\n is_robot=True)\n self._model.set_dynamics(mass=self._config.get(\"act_mass\", 10.0),\n lateralFriction=0,spinningFriction=10,rollingFriction=10,\n linearDamping=0,angularDamping=0)","def reset(self):\n logging.info(\"Resetting DINTModel.\")\n if self.classifier:\n self.server.remove_model(self.classifier)\n # for ds in self.server.datasets:\n # self.server.remove_dataset(ds)\n # TODO: remove datasets?\n self.classifier = None","def reset(self, model):\n def replace_parameters(module, target_weight, target_bias=None):\n module.weight = nn.Parameter(torch.from_numpy(target_weight).to(self.args.device))\n if (hasattr(module, 'bias')):\n module.bias = nn.Parameter(torch.from_numpy(target_bias).to(self.args.device))\n def replace_recurrent(module, l, cur_idx, prev_kept):\n # Retrieve parameters\n cur_ih_m = getattr(module, 'weight_ih_l' + str(l))\n cur_ih = cur_ih_m.data.cpu().numpy()\n cur_hh_m = getattr(module, 'weight_hh_l' + str(l))\n cur_hh = cur_hh_m.data.cpu().numpy()\n cur_bih = getattr(module, 'bias_ih_l' + str(l)).data.cpu().numpy()\n cur_bhh = getattr(module, 'bias_hh_l' + str(l)).data.cpu().numpy()\n cur_hidden = cur_hh.shape[1]\n if (prev_kept is not None): \n cur_ih = cur_ih[:, prev_kept]\n cur_ih_m.mask = cur_ih_m.mask[:, prev_kept]\n cur_hh = cur_hh[:, cur_idx[0]]\n cur_hh_m.mask = cur_hh_m.mask[:, cur_idx[0]]\n rep_id0, rep_id1 = cur_idx[0], cur_idx[1]\n # Handle repetitions for LSTM and GRU\n if (module.__class__ in [nn.LSTM, nn.GRU]):\n n_reps = {nn.LSTM:4, nn.GRU:3}[module.__class__]\n final_id0, final_id1 = [], []\n for i in range(n_reps):\n final_id0.extend(rep_id0 + (cur_hidden * i))\n final_id1.extend(rep_id1 + (cur_hidden * i))\n rep_id0, rep_id1 = final_id0, final_id1\n # Finally replace parameters\n cur_ih = nn.Parameter(torch.from_numpy(cur_ih[rep_id0]).to(self.args.device))\n cur_hh = nn.Parameter(torch.from_numpy(cur_hh[rep_id1]).to(self.args.device))\n cur_ih_m.mask = cur_ih_m.mask[rep_id0]\n cur_hh_m.mask = cur_hh_m.mask[rep_id1]\n cur_bih = nn.Parameter(torch.from_numpy(cur_bih[rep_id0]).to(self.args.device))\n cur_bhh = nn.Parameter(torch.from_numpy(cur_bhh[rep_id1]).to(self.args.device))\n setattr(module, 'weight_ih_l' + str(l), cur_ih)\n setattr(module, 'weight_hh_l' + str(l), cur_hh)\n setattr(module, 'bias_ih_l' + str(l), cur_bih)\n setattr(module, 'bias_hh_l' + str(l), cur_bhh)\n module.hidden_size = len(cur_idx[1])\n # Possibility to reinit\n if self.prune_reset == 'reinit': \n model.apply(self.initializer)\n else: \n model.apply(self.initializer)\n # Rewind the weights to the saved state dict\n for name, param in model.named_parameters(): \n param.data = self.rewind_state_dict[name]\n # Need to track previous modules\n prev_kept = None\n for (name, m) in self.leaf_modules:\n if (hasattr(m, 'unprune_idx')):\n if ('Transpose' in str(m.__class__)):\n kept_weights = m.weight.data.cpu().numpy()[:, m.unprune_idx]\n if (hasattr(m.weight, 'mask')):\n m.weight.mask = m.weight.mask[:, m.unprune_idx]\n if (prev_kept is not None and len(kept_weights.shape) > 1):\n kept_weights = kept_weights[prev_kept]\n if (hasattr(m.weight, 'mask')):\n m.weight.mask = m.weight.mask[prev_kept]\n elif (m.__class__ in [nn.LSTM, nn.GRU, nn.RNN]):\n for l in range(32):\n if (not hasattr(m, 'weight_ih_l' + str(l))):\n break\n replace_recurrent(m, l, m.unprune_idx[l], prev_kept)\n prev_kept = m.unprune_idx[l][1]\n continue\n else:\n kept_weights = m.weight.data.cpu().numpy()[m.unprune_idx]\n if (hasattr(m.weight, 'mask')):\n m.weight.mask = m.weight.mask[m.unprune_idx]\n if (prev_kept is not None and len(kept_weights.shape) > 1):\n kept_weights = kept_weights[:, prev_kept]\n if (hasattr(m.weight, 'mask')):\n m.weight.mask = m.weight.mask[:, prev_kept]\n if (hasattr(m, 'bias')):\n kept_biases = m.bias.data.cpu().numpy()[m.unprune_idx]\n replace_parameters(m, kept_weights, kept_biases)\n prev_kept = m.unprune_idx\n if ('Norm' in str(m.__class__)):\n running_mean = m.running_mean.cpu().numpy()[m.unprune_idx]\n m.running_mean.data = torch.from_numpy(running_mean).to(self.args.device)\n running_var = m.running_var.cpu().numpy()[m.unprune_idx]\n m.running_var.data = torch.from_numpy(running_var).to(self.args.device)\n elif (hasattr(m, 'weight') and (m.weight is not None) and prev_kept is not None):\n kept_weights = m.weight.data.cpu().numpy()\n if (prev_kept is not None):\n if ('Transpose' in str(m.__class__) or 'Norm' in str(m.__class__)):\n kept_weights = kept_weights[prev_kept]\n if (hasattr(m.weight, 'mask')):\n m.weight.mask = m.weight.mask[prev_kept]\n else:\n kept_weights = kept_weights[:, prev_kept]\n if (hasattr(m.weight, 'mask')):\n m.weight.mask = m.weight.mask[:, prev_kept]\n kept_biases = m.bias.data.cpu().numpy() \n if ('Norm' in str(m.__class__)):\n running_mean = m.running_mean.cpu().numpy()[prev_kept]\n m.running_mean.data = torch.from_numpy(running_mean).to(self.args.device)\n running_var = m.running_var.cpu().numpy()[prev_kept]\n m.running_var.data = torch.from_numpy(running_var).to(self.args.device)\n kept_biases = kept_biases[prev_kept]\n else:\n prev_kept = None\n replace_parameters(m, kept_weights, kept_biases)\n # Skip non-prunable layers\n if (hasattr(m, 'unprunable') and m.unprunable):\n prev_kept = None\n continue\n self.switch_magnitude()\n # Replace mask by the trimmed ones\n tmp_mask = {}\n for step, (name, param) in enumerate(self.prune_parameters):\n tmp_mask[step] = param.mask\n # Reupdate the prune parameters list\n self.set_pruning_parameters(model)\n for step, (name, param) in enumerate(self.prune_parameters):\n self.mask[step] = tmp_mask[step]\n param.mask = tmp_mask[step]\n # Call the masking step and reset function\n PruningMasking.step(self, model)\n # Apply mask through the layers\n for step, (name, param) in enumerate(self.prune_parameters):\n weight_dev = param.device\n param.data = torch.from_numpy(param.data.cpu().numpy() * self.mask[step]).to(weight_dev)\n # Reset dictionnary\n self.rewind_state_dict = copy.deepcopy(model.state_dict())\n self.unswitch_magnitude()\n return model","def reset(self, model):\n def replace_parameters(module, target_weight, target_bias=None):\n module.weight = nn.Parameter(target_weight)#torch.from_numpy(target_weight).to(self.args.device))\n if (hasattr(module, 'bias')):\n module.bias = nn.Parameter(target_bias)#torch.from_numpy(target_bias).to(self.args.device))\n def replace_recurrent(module, l, cur_idx, prev_kept):\n # Retrieve parameters\n cur_ih = getattr(module, 'weight_ih_l' + str(l)).data#.cpu().numpy()\n cur_hh = getattr(module, 'weight_hh_l' + str(l)).data#.cpu().numpy()\n cur_bih = getattr(module, 'bias_ih_l' + str(l)).data#.cpu().numpy()\n cur_bhh = getattr(module, 'bias_hh_l' + str(l)).data#.cpu().numpy()\n cur_hidden = cur_hh.shape[1]\n if (prev_kept is not None): \n cur_ih = cur_ih[:, prev_kept]\n if (len(cur_idx[0]) < 3):\n n_hid = cur_ih.shape[0] / {nn.LSTM:4, nn.GRU:3}[module.__class__]\n cur_idx[0] = np.linspace(0, n_hid - 1, n_hid).astype('int')\n setattr(module, 'weight_ih_l' + str(l), cur_ih)\n return\n if (len(cur_idx[1]) < 3):\n cur_idx[1] = np.linspace(0, cur_hh.shape[0] - 1, cur_hh.shape[0]).astype('int')\n setattr(module, 'weight_ih_l' + str(l), cur_ih)\n return\n cur_hh = cur_hh[:, cur_idx[0]]\n rep_id0, rep_id1 = cur_idx[0], cur_idx[1]\n # Handle repetitions for LSTM and GRU\n if (module.__class__ in [nn.LSTM, nn.GRU]):\n n_reps = {nn.LSTM:4, nn.GRU:3}[module.__class__]\n final_id0, final_id1 = [], []\n for i in range(n_reps):\n final_id0.extend(rep_id0 + (cur_hidden * i))\n final_id1.extend(rep_id1 + (cur_hidden * i))\n rep_id0, rep_id1 = final_id0, final_id1\n # Finally replace parameters\n cur_ih = nn.Parameter(cur_ih[rep_id0])#torch.from_numpy(cur_ih[rep_id0]).to(self.args.device))\n cur_hh = nn.Parameter(cur_hh[rep_id1])#torch.from_numpy(cur_hh[rep_id1]).to(self.args.device))\n cur_bih = nn.Parameter(cur_bih[rep_id0])#torch.from_numpy(cur_bih[rep_id0]).to(self.args.device))\n cur_bhh = nn.Parameter(cur_bhh[rep_id1])#torch.from_numpy(cur_bhh[rep_id1]).to(self.args.device))\n setattr(module, 'weight_ih_l' + str(l), cur_ih)\n setattr(module, 'weight_hh_l' + str(l), cur_hh)\n setattr(module, 'bias_ih_l' + str(l), cur_bih)\n setattr(module, 'bias_hh_l' + str(l), cur_bhh)\n module.hidden_size = len(cur_idx[1])\n # Possibility to reinit\n if self.prune_reset == 'reinit': \n model.apply(self.initializer)\n else:\n model.apply(self.initializer)\n # Rewind the weights to the saved state dict\n for name, param in model.named_parameters():\n param.data = self.rewind_state_dict[name]\n # Need to track previous modules\n prev_kept = None\n for (name, m) in self.leaf_modules:\n if (hasattr(m, 'unprune_idx')):\n if ('Transpose' in str(m.__class__)):\n kept_weights = m.weight.data[:, m.unprune_idx]#.cpu().numpy()[:, m.unprune_idx]\n if (prev_kept is not None and len(kept_weights.shape) > 1):\n kept_weights = kept_weights[prev_kept]\n elif (m.__class__ in [nn.LSTM, nn.GRU, nn.RNN]):\n for l in range(32):\n if (not hasattr(m, 'weight_ih_l' + str(l))):\n break\n replace_recurrent(m, l, m.unprune_idx[l], prev_kept)\n prev_kept = m.unprune_idx[l][1]\n continue\n else:\n kept_weights = m.weight.data[m.unprune_idx]#.cpu().numpy()[m.unprune_idx]\n if (prev_kept is not None and len(kept_weights.shape) > 1):\n kept_weights = kept_weights[:, prev_kept]\n if (hasattr(m, 'bias')):\n kept_biases = m.bias.data[m.unprune_idx]#.cpu().numpy()[m.unprune_idx]\n replace_parameters(m, kept_weights, kept_biases)\n prev_kept = m.unprune_idx\n if ('Norm' in str(m.__class__) and (not 'LayerNorm' in str(m.__class__))):\n running_mean = m.running_mean[m.unprune_idx]#.cpu().numpy()[m.unprune_idx]\n m.running_mean.data = running_mean#torch.from_numpy(running_mean).to(self.args.device)\n running_var = m.running_var[m.unprune_idx]#.cpu().numpy()[m.unprune_idx]\n m.running_var.data = running_var#torch.from_numpy(running_var).to(self.args.device)\n if ('LayerNorm' in str(m.__class__)):\n m.normalized_shape = (m.unprune_idx.shape[0],)\n elif (hasattr(m, 'weight') and (m.weight is not None) and prev_kept is not None):\n kept_weights = m.weight.data#.cpu().numpy()\n if (prev_kept is not None):\n if ('Transpose' in str(m.__class__) or 'Norm' in str(m.__class__)):\n kept_weights = kept_weights[prev_kept]\n else:\n kept_weights = kept_weights[:, prev_kept]\n kept_biases = m.bias.data#.cpu().numpy() \n if ('Norm' in str(m.__class__)):\n if (not 'LayerNorm' in str(m.__class__)):\n running_mean = m.running_mean[prev_kept]#.cpu().numpy()[prev_kept]\n m.running_mean.data = running_mean#torch.from_numpy(running_mean).to(self.args.device)\n running_var = m.running_var[prev_kept]#.cpu().numpy()[prev_kept]\n m.running_var.data = running_var#torch.from_numpy(running_var).to(self.args.device)\n kept_biases = kept_biases[prev_kept]\n if ('LayerNorm' in str(m.__class__)):\n m.normalized_shape = (prev_kept.shape[0],)\n else:\n prev_kept = None\n replace_parameters(m, kept_weights, kept_biases)\n # Skip non-prunable layers\n if (hasattr(m, 'unprunable') and m.unprunable):\n prev_kept = None\n continue\n self.rewind_state_dict = copy.deepcopy(model.state_dict())\n return model","def _reset(self):\n self.loss_history = []\n self.optim_configs = {}\n for p in self.model.params:\n d = {k: v for k, v in self.optim_config.items()}\n self.optim_configs[p] = d","def reset_parameters(model: torch.nn.Module) -> None:\n logging.info(\"Resetting model parameters.\")\n for _, module in model.named_modules():\n reset_params = getattr(module, \"reset_parameters\", None)\n if callable(reset_params):\n reset_params()","def reset(args):\n if os.path.exists(args.config):\n os.remove(args.config)\n return","def reset_model(model):\n\n\tfor layer in model.layers:\n\t\t# Note: these are custom depending on the layer type\n\t\tif '.MoleculeConv' in str(layer):\n\t\t\tW_inner = layer.init_inner((layer.inner_dim, layer.inner_dim))\n\t\t\tb_inner = np.zeros((1, layer.inner_dim))\n\t\t\t# Inner weights\n\t\t\tlayer.W_inner.set_value((T.tile(W_inner, (layer.depth + 1, 1, 1)).eval() + \\\n\t\t\t\tinitializations.uniform((layer.depth + 1, layer.inner_dim, layer.inner_dim)).eval()).astype(np.float32))\n\t\t\tlayer.b_inner.set_value((T.tile(b_inner, (layer.depth + 1, 1, 1)).eval() + \\\n\t\t\t\tinitializations.uniform((layer.depth + 1, 1, layer.inner_dim)).eval()).astype(np.float32))\n\n\t\t\t# Outer weights\n\t\t\tW_output = layer.init_output((layer.inner_dim, layer.units), scale = layer.scale_output)\n\t\t\tb_output = np.zeros((1, layer.units))\n\t\t\t# Initialize weights tensor\n\t\t\tlayer.W_output.set_value((T.tile(W_output, (layer.depth + 1, 1, 1)).eval()).astype(np.float32))\n\t\t\tlayer.b_output.set_value((T.tile(b_output, (layer.depth + 1, 1, 1)).eval()).astype(np.float32))\n\t\t\tlogging.info('graphFP layer reset')\n\n\t\telif '.Dense' in str(layer):\n\t\t\tlayer.W.set_value((layer.init(layer.W.shape.eval()).eval()).astype(np.float32))\n\t\t\tlayer.b.set_value(np.zeros(layer.b.shape.eval(), dtype=np.float32))\n\t\t\tlogging.info('dense layer reset')\n\n\t\telif '.Dropout' in str(layer):\n\t\t\tlogging.info('dropout unchanged')\n\t\telse:\n\t\t\traise ValueError('Unknown layer {}, cannot reset weights'.format(str(layer)))\n\tlogging.info('Reset model weights')\n\treturn model","def clear(self):\r\n del self.__config\r\n self.__config = {}\r\n self.save()","def reset(self):\n self.params.resetParams()","def reset_cfg():\n _C.merge_from_other_cfg(_CFG_DEFAULT)","async def reset_config(self):\n self.config = {\"enable_auto_gen\": False, \"enable_world_barrier\": False}\n await shared.event_handler.call_async(\"world:reset_config\")\n self.gamerule_handler = mcpython.common.world.GameRule.GameRuleHandler(self)","def reset(self):\n self.satisfiability = Satisfiability.UNTESTED\n self.model = None\n self.unsatCore = []","def reset(self):\n self.manager.delete_all()\n for name, val in DEFAULT_SETTINGS.items():\n val['name'] = name\n val['default_value'] = val['value']\n self.manager.from_dict(val)","def reset_to(self, state):\n should_ret = False\n if \"model\" in state:\n self.reset()\n xml = postprocess_model_xml(state[\"model\"])\n self.env.reset_from_xml_string(xml)\n self.env.sim.reset()\n if not self._is_v1:\n # hide teleop visualization after restoring from model\n self.env.sim.model.site_rgba[self.env.eef_site_id] = np.array([0., 0., 0., 0.])\n self.env.sim.model.site_rgba[self.env.eef_cylinder_id] = np.array([0., 0., 0., 0.])\n if \"states\" in state:\n self.env.sim.set_state_from_flattened(state[\"states\"])\n self.env.sim.forward()\n should_ret = True\n\n if \"goal\" in state:\n self.set_goal(**state[\"goal\"])\n if should_ret:\n # only return obs if we've done a forward call - otherwise the observations will be garbage\n return self.get_observation()\n return None","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):\n self.vna.write(edit_list(self.model))\n self.vna.write(clear_list(self.model))","def reset_states(self):\n self.model.reset_states()","def re_init(self):\n self.latent.re_init()\n if 're_init' in dir(self.inference_model):\n self.inference_model.re_init()\n if 're_init' in dir(self.generative_model):\n self.generative_model.re_init()","def reset(self):\n self.best_model = None\n self.best_res = -1","def reset_parameters(self):\n self.lstm.reset_parameters()","def reset(self, model):\n for obs_func in self.observation_functions:\n obs_func.reset(model)","def setOldModel(self,model):\n self.modelName = model\n oldModel = load_model(self.modelPath + \"/\" + model + \".hdf5\",\n custom_objects={'recall': recall,\n 'precision': precision,\n 'f1Score':f1Score})\n\n self.old_weights = oldModel.get_weights()\n self.input_shape = oldModel.inputs[0].get_shape().as_list()[1:]\n self.n_classes = oldModel.outputs[0].get_shape().as_list()[-1]\n self.conv_depth = oldModel.layers[1].output_shape[-1]\n self.model = oldModel","def antenny_config_reset(self):\n return self.antenny_config.reset_default_config()","def reset(self, **kwargs):\n return self.env.reset(**kwargs)","def reset_from(self, other_model):\n self.wv.vocab = other_model.wv.vocab\n self.wv.index2word = other_model.wv.index2word\n self.vocabulary.cum_table = other_model.vocabulary.cum_table\n self.corpus_count = other_model.corpus_count\n self.docvecs.count = other_model.docvecs.count\n self.docvecs.doctags = other_model.docvecs.doctags\n self.docvecs.offset2doctag = other_model.docvecs.offset2doctag\n self.trainables.reset_weights(self.hs, self.negative, self.wv, self.docvecs)","def reset(self):\n\n def reset_function(module):\n if isinstance(m, nn.Conv2d) or isinstance(m, nn.Linear):\n m.reset_parameters()\n\n self.apply(reset_function)","def reset(self, **kwargs):\n # '_reset' depends on the 'backend'\n self._reset(**kwargs)","def reset(self):\n # must NOT reset color map here, otherwise we loose provided configs by user,\n # which are more important in this case for result images vs whatever the model task specified\n self.class_names = None\n self._map = None","def resetmodel(self):\n for key, value in self._dentsvertsdata.items():\n value.free()\n self._dentsvertsdata.clear()","def reset(self):\r\n # TODO: have reset flag such that it forces all the bottom changes\r\n self.pwm_freq = self._default[\"pwm_freq\"]\r\n self.gate_logic = self._default[\"gate_logic\"]\r\n self.max_pwm = self._default[\"max_pwm\"]\r\n self.lase_on_power_up = self._default[\"lase_on_power_up\"]\r\n\r\n self.mode = self._default[\"mode\"]\r\n self.lase = self._default[\"lase\"]\r\n self.percent = self._default[\"percent\"] # in percent\r","def reset(self):\n self.train_loss.reset_states()\n self.train_accuracy.reset_states()\n self.val_loss.reset_states()\n self.val_accuracy.reset_states()\n self.train_mIoU.reset_states()\n self.val_mIoU.reset_states()","def _reset(self, *args):\n self.dirty = True","def reset(self, model):\n for obs_func in self.observation_functions.values():\n obs_func.reset(model)","def reset(self):\n self.data = self._defaults","def update_model(self):\n pass","async def gpt2_set_model(self, ctx, *, arg=None):\n print('Command gpt2_set_model triggered')\n if arg:\n if arg in VALID_DEFAULT_MODELS:\n self.update_config(model_name=arg)\n else:\n await ctx.send(f\"ERROR: Invalid model name {arg}\")\n else:\n await ctx.send(\"ERROR: Argument required\")","def reset_settings():\n settings = Settings()\n settings.reset()\n settings.save()","def reset():\n teardown_db()\n build()","def ClearModels(self):\n self._modelFileNames = []\n self._models = []\n self.Modified(readAgain=True)","def reset_values(self):\n self.parse_config_file()","def reset_config() -> None:\r\n self.ui.labelCloneListsLocation.setText(str(pathlib.Path(config.path_clone_list).resolve()))\r\n self.ui.labelMetadataLocation.setText(str(pathlib.Path(config.path_metadata).resolve()))\r\n self.ui.lineEditCloneListDownloadLocation.setText(config.clone_list_metadata_download_location)\r\n parent.clone_lists_folder = config.path_clone_list\r\n parent.metadata_folder = config.path_metadata\r\n parent.clone_list_metadata_url = config.clone_list_metadata_download_location\r\n write_config(parent, dat_details, config, self)","def _reinit(self):\n cfg_path = self[CONFIG_FILE_KEY] if CONFIG_FILE_KEY in self else None\n for attr in list(self.keys()):\n del self[attr]\n self.__init__(cfg=cfg_path)","def unload_model(app: FastAPI) -> None:\n\n logging.info(\"Shuting down the app\")\n app.state.model = None","def restore_model_state(self, model: Block):\n if model in self.initial_state:\n from_json(model, sd=self.initial_state[model], wts=StoreState)\n else:\n self._update_summary(model, \"status\", InitializationStatus.Error)\n raise ValueError(\"No initial state stored.\")","def reset(self):\n \n s = self\n s.step_counter = 0\n \n # TODO: initialize first layer activations here, and not everywhere else\n # self.model.initialize_local_vars()\n # self.model.initialize_global_vars()\n\n ops = []\n\n for var in self.model.trainable_vars:\n if self.needs_correction(var):\n A_svd = s[var].A.svd\n B2_svd = s[var].B2.svd \n ops.extend(A_svd.init_ops)\n ops.extend(B2_svd.init_ops)\n ops.append(s[var].A.cov.initializer)\n ops.append(s[var].B2.cov.initializer)\n\n # in new TensorFlow this breaks, probably because of\n # https://github.com/tensorflow/tensorflow/commit/07adc2ea910de715d31e16a019fcbcccb575e931\n # sometimes get \"need to feed\" placeholder error\n # sometimes do not get this error, but spend two minutes inside\n # _build_initializer_expr\n s.run(ops)","def reset(self, *args, **kwargs):\n ...","def reset_button(self):\n\n # Update the parameter values and start a new model\n self.time_series = []\n self.res_ended = False\n self.school_ended = False\n self.params = self.update_pars()\n self.model = CompassModel(**self.params)\n\n # Stop the model when it is still running while reset is clicked.\n if self.run.label == 'Stop' and self.callback_obj is not None:\n self.doc.remove_periodic_callback(self.callback_obj)\n self.run.label = 'Run'\n self.run.button_type = 'success'\n\n self.reset_data()\n self.doc.clear()\n self.layout()","def reset_train(self):\n\n self.model.apply(self._reset_weights)\n self.epoch_loss.reset()\n self.epoch = 0\n del self.batch_process\n self.batch_process = None","def reset_parameters(self):\n \n for i in range(self.num_layers):\n getattr(self, 'LSTMCell%i'%(i+1)).reset_parameters()","def reset(self, *args, **kwargs):\n pass","def create_model(self):\n self.model = None\n pass","def reset(self):\n # Clear mutable data, but leave the immutables intact\n self.train_data = {}\n self.val_data = {}\n self.test_data = {}\n self.model_files = []\n self.custom_data = {}\n # Remove all the physical assets\n for item in os.scandir(self.root_path):\n os.remove(item.path)\n # Reserialize\n self.serialize()","def reset_parameters(self, reset_mode='glorot_uniform'):\n\n if reset_mode == 'glorot_uniform':\n if self.weight_decomp == 'block':\n nn.init.xavier_uniform_(self.blocks, gain=nn.init.calculate_gain('relu'))\n elif self.weight_decomp == 'basis':\n nn.init.xavier_uniform_(self.bases, gain=nn.init.calculate_gain('relu'))\n nn.init.xavier_uniform_(self.comps, gain=nn.init.calculate_gain('relu'))\n else:\n nn.init.xavier_uniform_(self.weights, gain=nn.init.calculate_gain('relu'))\n\n if self.bias is not None:\n torch.nn.init.zeros_(self.bias)\n elif reset_mode == 'schlichtkrull':\n if self.weight_decomp == 'block':\n nn.init.xavier_uniform_(self.blocks, gain=nn.init.calculate_gain('relu'))\n elif self.weight_decomp == 'basis':\n nn.init.xavier_uniform_(self.bases, gain=nn.init.calculate_gain('relu'))\n nn.init.xavier_uniform_(self.comps, gain=nn.init.calculate_gain('relu'))\n else:\n nn.init.xavier_uniform_(self.weights, gain=nn.init.calculate_gain('relu'))\n\n if self.bias is not None:\n torch.nn.init.zeros_(self.bias)\n elif reset_mode == 'uniform':\n stdv = 1.0 / math.sqrt(self.weights.size(1))\n if self.weight_decomp == 'block':\n self.blocks.data.uniform_(-stdv, stdv)\n elif self.weight_decomp == 'basis':\n self.bases.data.uniform_(-stdv, stdv)\n self.comps.data.uniform_(-stdv, stdv)\n else:\n self.weights.data.uniform_(-stdv, stdv)\n\n if self.bias is not None:\n self.bias.data.uniform_(-stdv, stdv)\n else:\n raise NotImplementedError(f'{reset_mode} parameter initialisation method has not been implemented')","def reload_config(self):\n pass","def refresh_config(self):\n self._user_config = UserConfig(None)","def reset_program_state_dict(args, model, state_dict, pretrained_state_dict):\n reset_state_dict = {}\n scale = model.initializer_range if hasattr(model, \"initializer_range\")\\\n else getattr(model, args.model_type).config[\"initializer_range\"]\n reset_parameter_names = []\n for n, p in state_dict.items():\n if n in pretrained_state_dict:\n reset_state_dict[p.name] = np.array(pretrained_state_dict[n])\n reset_parameter_names.append(n)\n elif p.name in pretrained_state_dict and \"bert\" in n:\n reset_state_dict[p.name] = np.array(pretrained_state_dict[p.name])\n reset_parameter_names.append(n)\n else:\n dtype_str = \"float32\"\n if str(p.dtype) == \"VarType.FP64\":\n dtype_str = \"float64\"\n reset_state_dict[p.name] = np.random.normal(\n loc=0.0, scale=scale, size=p.shape).astype(dtype_str)\n logger.info(\"the following parameter had reset, please check. {}\".format(\n reset_parameter_names))\n return reset_state_dict","def unsetModel(self):\n return _libsbml.L3ParserSettings_unsetModel(self)","def restore_config(self):\n self._clear_previous_windows_assigment()\n self._restart_i3_config()","def reset(self):\n self.pred = None\n self.target = None","def reset(self):\n self.pred = None\n self.target = None","def reset(self):\n if self._key:\n self._lib.StObjectReset(self._key)\n os.chdir(self._cwd)\n self._layers.clear() # layer: index\n self._substrate = None\n self._experiments.clear() # analyzed experiments\n self._tmpstandards.clear()","def update_model(self):\n pass # TODO: Implement this.","def _reset(self):\n self._set(\"_n_init_features\", None)\n self._set(\"_n_output_features\", None)\n self._set(\"_n_intervals\", None)\n self._set(\"_mapper\", {})\n self._set(\"_cpp_preprocessor\", None)\n self._set(\"_fitted\", False)","def reload(self):\n self.load_config()\n # Seems we need to explicitly refresh this\n if self.main_instance:\n self.main_instance.config = self.config","def reset(self):\n self.mode = 0\n self.graphs = [[], [], []]\n self.coefficients = []\n self.sample = []","def updateModel(self):\n pass","def reset_parameters(self):\n self.rnn.reset_parameters()\n self.action_lookup.reset_parameters()\n\n # self.state_dict_lookup.reset_parameters()\n self.own_c_lookup.reset.parameters()\n self.own_s_lookup.reset_parameters()\n\n self.th_1_lookup.reset_parameters()\n self.th_2_lookup.reset_parameters()\n self.th_3_lookup.reset_parameters()\n self.f_1_lookup.reset.parameters()\n self.f_2_lookup.reset_parameters()\n self.f_3_lookup.reset_parameters()\n self.f_4_lookup.reset_parameters()\n\n self.bu_msg_lookup.reset_parameters()\n\n self.i_t_lookup.reset_parameters()\n self.lives_lookup.reset_parameters()\n\n self.prev_action_lookup.reset_parameters()\n # self.message.apply(weight_reset)\n self.output.apply(weight_reset)\n for p in self.rnn.parameters():\n p.data.uniform_(*self.init_param_range)","def reset(runtime, cfg, inputs, state, outputs):\n state['page_title'] = cfg['page_title']\n state['id_resource_page'] = cfg['id_resource']\n state['id_topic'] = cfg['id_topic']","def reset(self, *args, **kwargs):","def applyModelSource(self, modelSource):\n modelSource.setEdit(False)\n if modelSource.isLoaded():\n self._reload()\n else:\n self._loadModelSource(modelSource)","def test_config_device_reset(get_config, monkeypatch):\n monkeypatch.setattr(DeviceConfig, 'minimal_essential_conf', {'test': 'conf'})\n cfg = get_config(DeviceConfig, base_config)\n cfg.save()\n cfg.write_default()\n new_conf = cfg.load()\n\n assert cfg.data == cfg.minimal_essential_conf, 'failed to apply default config'\n assert new_conf == cfg.minimal_essential_conf, 'failed to load default config'","def reset_config(modem, disable_auto_linking, monitor_mode, auto_led, deadman):\n modem.configuration[DISABLE_AUTO_LINKING].set_value(not disable_auto_linking)\n modem.configuration[MONITOR_MODE].set_value(not monitor_mode)\n modem.configuration[AUTO_LED].set_value(not auto_led)\n modem.configuration[DEADMAN].set_value(not deadman)","def resetSettings(self):\n\n # it does this 4 times because for some reason it would not grab everything one time through. Investigate\n for i in range(4):\n\n networkNode = self.returnNetworkNode\n attrs = cmds.listAttr(networkNode, ud=True)\n\n for attr in attrs:\n attrType = str(cmds.getAttr(networkNode + \".\" + attr, type=True))\n\n if attrType == \"double\":\n cmds.setAttr(networkNode + \".\" + attr, lock=False)\n cmds.setAttr(networkNode + \".\" + attr, 0, lock=True)\n\n if attrType == \"bool\":\n cmds.setAttr(networkNode + \".\" + attr, lock=False)\n cmds.setAttr(networkNode + \".\" + attr, True, lock=True)\n\n if attrType == \"enum\":\n cmds.setAttr(networkNode + \".\" + attr, lock=False)\n cmds.setAttr(networkNode + \".\" + attr, 0, lock=True)\n\n # relaunch the UI\n self.updateSettingsUI()\n self.applyModuleChanges(self)","def reset(self):\n ih = (param for name, param in self.named_parameters() if 'weight_ih' in name)\n hh = (param for name, param in self.named_parameters() if 'weight_hh' in name)\n b = (param for name, param in self.named_parameters() if 'bias' in name)\n for t in ih:\n torch.nn.init.xavier_uniform_(t)\n for t in hh:\n torch.nn.init.orthogonal_(t)\n for t in b:\n torch.nn.init.constant_(t, 0)","def reset(self):\n self.__init__()","def reset(self):\n self.__init__()","def reset(self):\n self.__init__()","def revalidate(self):\n *_, validation_error = validate_model(self.__class__, self.__dict__)\n if validation_error:\n raise validation_error\n self.validate_config()","def clear_model_cache():\n global __model_cache\n __model_cache = {}","def reset_state(self, name=None):\n if name is None:\n self.solver.reset_solver(self.initial_state.copy())\n else:\n # TODO: Raise a nice ProtocolError if state not defined\n self.solver.reset_solver(self.saved_states[name].copy())","def set_Model(newModel):\n global model\n model = newModel\n print(\"model is set\")\n print(model)","def restore(self):\n if os.path.isfile( \\\n os.path.join(self.network_path,'net_parameters.nnprm.index')):\n self.load_network_parameters(\n file_name='net_parameters', file_path=self.network_path)\n else:\n self.log(\"Could not load previous network parameters from:\\n{}\".format(\\\n os.path.join(self.network_path,'net_parameters.nnprm') ))\n self.log(\"Starting with untrained parameters\")","def set_load_model_parameters(self):\n\n self.controller.set_new_model_test_input_path(self.test_input.get())\n self.controller.set_new_model_results_input_path(self.results_input.get())\n self.controller.set_new_model_running(False)","def reset(self, pure=True, **kwargs):\n cutoff = kwargs.get('cutoff_dim', self.circuit._trunc)\n self._modemap.reset()\n self.circuit.reset(pure, num_subsystems=self._init_modes, cutoff_dim=cutoff)","def reset(self):\n self._setupObjects()","def resetSelfWithDefaults( self ):\n self.__dict__.update( self._defDict )","def set_model(self):\n self.model = self.get_model()"],"string":"[\n \"def reset_model(self):\\n raise NotImplementedError\",\n \"def reset_model(self):\\n raise NotImplementedError\",\n \"def reset_model(self):\\n raise NotImplementedError\",\n \"def reset(self, model):\\n self.reset_strategy(model)\",\n \"def reset(self, config, **kwargs):\\n pass\",\n \"def reset_config():\\n return _set_config(_gen_config())\",\n \"def reset_config():\\r\\n # TODO implement configuration reset\\r\\n pass\",\n \"def _reset(self):\\n self._model._reset()\\n super(RDPAnalyzer, self)._reset()\",\n \"def reset(self):\\n checkpoint = torch.load(\\n 'model_lr_finder.pth.tar',\\n map_location=self.device)\\n self.model.load_state_dict(checkpoint['state_dict'])\\n self.optimizer.load_state_dict(checkpoint['optimizer'])\\n self.model.to(self.device)\\n self.model.train()\",\n \"def reset( self ):\\n self.conf = self.defaults\",\n \"def reset_config():\\n\\n Config.config().update({\\\"coerce\\\": True, \\\"debug\\\": True, \\\"active\\\": True})\",\n \"def test_model_reset_correctly(tmpdir):\\n tutils.reset_seed()\\n\\n model = EvalModelTemplate()\\n\\n # logger file to get meta\\n trainer = Trainer(\\n default_root_dir=tmpdir,\\n max_epochs=1,\\n )\\n\\n before_state_dict = deepcopy(model.state_dict())\\n\\n trainer.tuner.scale_batch_size(model, max_trials=5)\\n\\n after_state_dict = model.state_dict()\\n\\n for key in before_state_dict.keys():\\n assert torch.all(torch.eq(before_state_dict[key], after_state_dict[key])), \\\\\\n 'Model was not reset correctly after scaling batch size'\",\n \"def reset(self):\\n urdf=os.path.join(os.environ[\\\"YUMI_PUSH_MODELS\\\"],\\\"robot_hand.urdf\\\")\\n self._model = self._world.add_model(\\n model_path=urdf,\\n position=[-10.0, -10.0, 0.0],\\n orientation=[0.0, 0.0, 0.0, 1.0],\\n is_robot=True)\\n self._model.set_dynamics(mass=self._config.get(\\\"act_mass\\\", 10.0),\\n lateralFriction=0,spinningFriction=10,rollingFriction=10,\\n linearDamping=0,angularDamping=0)\",\n \"def reset(self):\\n logging.info(\\\"Resetting DINTModel.\\\")\\n if self.classifier:\\n self.server.remove_model(self.classifier)\\n # for ds in self.server.datasets:\\n # self.server.remove_dataset(ds)\\n # TODO: remove datasets?\\n self.classifier = None\",\n \"def reset(self, model):\\n def replace_parameters(module, target_weight, target_bias=None):\\n module.weight = nn.Parameter(torch.from_numpy(target_weight).to(self.args.device))\\n if (hasattr(module, 'bias')):\\n module.bias = nn.Parameter(torch.from_numpy(target_bias).to(self.args.device))\\n def replace_recurrent(module, l, cur_idx, prev_kept):\\n # Retrieve parameters\\n cur_ih_m = getattr(module, 'weight_ih_l' + str(l))\\n cur_ih = cur_ih_m.data.cpu().numpy()\\n cur_hh_m = getattr(module, 'weight_hh_l' + str(l))\\n cur_hh = cur_hh_m.data.cpu().numpy()\\n cur_bih = getattr(module, 'bias_ih_l' + str(l)).data.cpu().numpy()\\n cur_bhh = getattr(module, 'bias_hh_l' + str(l)).data.cpu().numpy()\\n cur_hidden = cur_hh.shape[1]\\n if (prev_kept is not None): \\n cur_ih = cur_ih[:, prev_kept]\\n cur_ih_m.mask = cur_ih_m.mask[:, prev_kept]\\n cur_hh = cur_hh[:, cur_idx[0]]\\n cur_hh_m.mask = cur_hh_m.mask[:, cur_idx[0]]\\n rep_id0, rep_id1 = cur_idx[0], cur_idx[1]\\n # Handle repetitions for LSTM and GRU\\n if (module.__class__ in [nn.LSTM, nn.GRU]):\\n n_reps = {nn.LSTM:4, nn.GRU:3}[module.__class__]\\n final_id0, final_id1 = [], []\\n for i in range(n_reps):\\n final_id0.extend(rep_id0 + (cur_hidden * i))\\n final_id1.extend(rep_id1 + (cur_hidden * i))\\n rep_id0, rep_id1 = final_id0, final_id1\\n # Finally replace parameters\\n cur_ih = nn.Parameter(torch.from_numpy(cur_ih[rep_id0]).to(self.args.device))\\n cur_hh = nn.Parameter(torch.from_numpy(cur_hh[rep_id1]).to(self.args.device))\\n cur_ih_m.mask = cur_ih_m.mask[rep_id0]\\n cur_hh_m.mask = cur_hh_m.mask[rep_id1]\\n cur_bih = nn.Parameter(torch.from_numpy(cur_bih[rep_id0]).to(self.args.device))\\n cur_bhh = nn.Parameter(torch.from_numpy(cur_bhh[rep_id1]).to(self.args.device))\\n setattr(module, 'weight_ih_l' + str(l), cur_ih)\\n setattr(module, 'weight_hh_l' + str(l), cur_hh)\\n setattr(module, 'bias_ih_l' + str(l), cur_bih)\\n setattr(module, 'bias_hh_l' + str(l), cur_bhh)\\n module.hidden_size = len(cur_idx[1])\\n # Possibility to reinit\\n if self.prune_reset == 'reinit': \\n model.apply(self.initializer)\\n else: \\n model.apply(self.initializer)\\n # Rewind the weights to the saved state dict\\n for name, param in model.named_parameters(): \\n param.data = self.rewind_state_dict[name]\\n # Need to track previous modules\\n prev_kept = None\\n for (name, m) in self.leaf_modules:\\n if (hasattr(m, 'unprune_idx')):\\n if ('Transpose' in str(m.__class__)):\\n kept_weights = m.weight.data.cpu().numpy()[:, m.unprune_idx]\\n if (hasattr(m.weight, 'mask')):\\n m.weight.mask = m.weight.mask[:, m.unprune_idx]\\n if (prev_kept is not None and len(kept_weights.shape) > 1):\\n kept_weights = kept_weights[prev_kept]\\n if (hasattr(m.weight, 'mask')):\\n m.weight.mask = m.weight.mask[prev_kept]\\n elif (m.__class__ in [nn.LSTM, nn.GRU, nn.RNN]):\\n for l in range(32):\\n if (not hasattr(m, 'weight_ih_l' + str(l))):\\n break\\n replace_recurrent(m, l, m.unprune_idx[l], prev_kept)\\n prev_kept = m.unprune_idx[l][1]\\n continue\\n else:\\n kept_weights = m.weight.data.cpu().numpy()[m.unprune_idx]\\n if (hasattr(m.weight, 'mask')):\\n m.weight.mask = m.weight.mask[m.unprune_idx]\\n if (prev_kept is not None and len(kept_weights.shape) > 1):\\n kept_weights = kept_weights[:, prev_kept]\\n if (hasattr(m.weight, 'mask')):\\n m.weight.mask = m.weight.mask[:, prev_kept]\\n if (hasattr(m, 'bias')):\\n kept_biases = m.bias.data.cpu().numpy()[m.unprune_idx]\\n replace_parameters(m, kept_weights, kept_biases)\\n prev_kept = m.unprune_idx\\n if ('Norm' in str(m.__class__)):\\n running_mean = m.running_mean.cpu().numpy()[m.unprune_idx]\\n m.running_mean.data = torch.from_numpy(running_mean).to(self.args.device)\\n running_var = m.running_var.cpu().numpy()[m.unprune_idx]\\n m.running_var.data = torch.from_numpy(running_var).to(self.args.device)\\n elif (hasattr(m, 'weight') and (m.weight is not None) and prev_kept is not None):\\n kept_weights = m.weight.data.cpu().numpy()\\n if (prev_kept is not None):\\n if ('Transpose' in str(m.__class__) or 'Norm' in str(m.__class__)):\\n kept_weights = kept_weights[prev_kept]\\n if (hasattr(m.weight, 'mask')):\\n m.weight.mask = m.weight.mask[prev_kept]\\n else:\\n kept_weights = kept_weights[:, prev_kept]\\n if (hasattr(m.weight, 'mask')):\\n m.weight.mask = m.weight.mask[:, prev_kept]\\n kept_biases = m.bias.data.cpu().numpy() \\n if ('Norm' in str(m.__class__)):\\n running_mean = m.running_mean.cpu().numpy()[prev_kept]\\n m.running_mean.data = torch.from_numpy(running_mean).to(self.args.device)\\n running_var = m.running_var.cpu().numpy()[prev_kept]\\n m.running_var.data = torch.from_numpy(running_var).to(self.args.device)\\n kept_biases = kept_biases[prev_kept]\\n else:\\n prev_kept = None\\n replace_parameters(m, kept_weights, kept_biases)\\n # Skip non-prunable layers\\n if (hasattr(m, 'unprunable') and m.unprunable):\\n prev_kept = None\\n continue\\n self.switch_magnitude()\\n # Replace mask by the trimmed ones\\n tmp_mask = {}\\n for step, (name, param) in enumerate(self.prune_parameters):\\n tmp_mask[step] = param.mask\\n # Reupdate the prune parameters list\\n self.set_pruning_parameters(model)\\n for step, (name, param) in enumerate(self.prune_parameters):\\n self.mask[step] = tmp_mask[step]\\n param.mask = tmp_mask[step]\\n # Call the masking step and reset function\\n PruningMasking.step(self, model)\\n # Apply mask through the layers\\n for step, (name, param) in enumerate(self.prune_parameters):\\n weight_dev = param.device\\n param.data = torch.from_numpy(param.data.cpu().numpy() * self.mask[step]).to(weight_dev)\\n # Reset dictionnary\\n self.rewind_state_dict = copy.deepcopy(model.state_dict())\\n self.unswitch_magnitude()\\n return model\",\n \"def reset(self, model):\\n def replace_parameters(module, target_weight, target_bias=None):\\n module.weight = nn.Parameter(target_weight)#torch.from_numpy(target_weight).to(self.args.device))\\n if (hasattr(module, 'bias')):\\n module.bias = nn.Parameter(target_bias)#torch.from_numpy(target_bias).to(self.args.device))\\n def replace_recurrent(module, l, cur_idx, prev_kept):\\n # Retrieve parameters\\n cur_ih = getattr(module, 'weight_ih_l' + str(l)).data#.cpu().numpy()\\n cur_hh = getattr(module, 'weight_hh_l' + str(l)).data#.cpu().numpy()\\n cur_bih = getattr(module, 'bias_ih_l' + str(l)).data#.cpu().numpy()\\n cur_bhh = getattr(module, 'bias_hh_l' + str(l)).data#.cpu().numpy()\\n cur_hidden = cur_hh.shape[1]\\n if (prev_kept is not None): \\n cur_ih = cur_ih[:, prev_kept]\\n if (len(cur_idx[0]) < 3):\\n n_hid = cur_ih.shape[0] / {nn.LSTM:4, nn.GRU:3}[module.__class__]\\n cur_idx[0] = np.linspace(0, n_hid - 1, n_hid).astype('int')\\n setattr(module, 'weight_ih_l' + str(l), cur_ih)\\n return\\n if (len(cur_idx[1]) < 3):\\n cur_idx[1] = np.linspace(0, cur_hh.shape[0] - 1, cur_hh.shape[0]).astype('int')\\n setattr(module, 'weight_ih_l' + str(l), cur_ih)\\n return\\n cur_hh = cur_hh[:, cur_idx[0]]\\n rep_id0, rep_id1 = cur_idx[0], cur_idx[1]\\n # Handle repetitions for LSTM and GRU\\n if (module.__class__ in [nn.LSTM, nn.GRU]):\\n n_reps = {nn.LSTM:4, nn.GRU:3}[module.__class__]\\n final_id0, final_id1 = [], []\\n for i in range(n_reps):\\n final_id0.extend(rep_id0 + (cur_hidden * i))\\n final_id1.extend(rep_id1 + (cur_hidden * i))\\n rep_id0, rep_id1 = final_id0, final_id1\\n # Finally replace parameters\\n cur_ih = nn.Parameter(cur_ih[rep_id0])#torch.from_numpy(cur_ih[rep_id0]).to(self.args.device))\\n cur_hh = nn.Parameter(cur_hh[rep_id1])#torch.from_numpy(cur_hh[rep_id1]).to(self.args.device))\\n cur_bih = nn.Parameter(cur_bih[rep_id0])#torch.from_numpy(cur_bih[rep_id0]).to(self.args.device))\\n cur_bhh = nn.Parameter(cur_bhh[rep_id1])#torch.from_numpy(cur_bhh[rep_id1]).to(self.args.device))\\n setattr(module, 'weight_ih_l' + str(l), cur_ih)\\n setattr(module, 'weight_hh_l' + str(l), cur_hh)\\n setattr(module, 'bias_ih_l' + str(l), cur_bih)\\n setattr(module, 'bias_hh_l' + str(l), cur_bhh)\\n module.hidden_size = len(cur_idx[1])\\n # Possibility to reinit\\n if self.prune_reset == 'reinit': \\n model.apply(self.initializer)\\n else:\\n model.apply(self.initializer)\\n # Rewind the weights to the saved state dict\\n for name, param in model.named_parameters():\\n param.data = self.rewind_state_dict[name]\\n # Need to track previous modules\\n prev_kept = None\\n for (name, m) in self.leaf_modules:\\n if (hasattr(m, 'unprune_idx')):\\n if ('Transpose' in str(m.__class__)):\\n kept_weights = m.weight.data[:, m.unprune_idx]#.cpu().numpy()[:, m.unprune_idx]\\n if (prev_kept is not None and len(kept_weights.shape) > 1):\\n kept_weights = kept_weights[prev_kept]\\n elif (m.__class__ in [nn.LSTM, nn.GRU, nn.RNN]):\\n for l in range(32):\\n if (not hasattr(m, 'weight_ih_l' + str(l))):\\n break\\n replace_recurrent(m, l, m.unprune_idx[l], prev_kept)\\n prev_kept = m.unprune_idx[l][1]\\n continue\\n else:\\n kept_weights = m.weight.data[m.unprune_idx]#.cpu().numpy()[m.unprune_idx]\\n if (prev_kept is not None and len(kept_weights.shape) > 1):\\n kept_weights = kept_weights[:, prev_kept]\\n if (hasattr(m, 'bias')):\\n kept_biases = m.bias.data[m.unprune_idx]#.cpu().numpy()[m.unprune_idx]\\n replace_parameters(m, kept_weights, kept_biases)\\n prev_kept = m.unprune_idx\\n if ('Norm' in str(m.__class__) and (not 'LayerNorm' in str(m.__class__))):\\n running_mean = m.running_mean[m.unprune_idx]#.cpu().numpy()[m.unprune_idx]\\n m.running_mean.data = running_mean#torch.from_numpy(running_mean).to(self.args.device)\\n running_var = m.running_var[m.unprune_idx]#.cpu().numpy()[m.unprune_idx]\\n m.running_var.data = running_var#torch.from_numpy(running_var).to(self.args.device)\\n if ('LayerNorm' in str(m.__class__)):\\n m.normalized_shape = (m.unprune_idx.shape[0],)\\n elif (hasattr(m, 'weight') and (m.weight is not None) and prev_kept is not None):\\n kept_weights = m.weight.data#.cpu().numpy()\\n if (prev_kept is not None):\\n if ('Transpose' in str(m.__class__) or 'Norm' in str(m.__class__)):\\n kept_weights = kept_weights[prev_kept]\\n else:\\n kept_weights = kept_weights[:, prev_kept]\\n kept_biases = m.bias.data#.cpu().numpy() \\n if ('Norm' in str(m.__class__)):\\n if (not 'LayerNorm' in str(m.__class__)):\\n running_mean = m.running_mean[prev_kept]#.cpu().numpy()[prev_kept]\\n m.running_mean.data = running_mean#torch.from_numpy(running_mean).to(self.args.device)\\n running_var = m.running_var[prev_kept]#.cpu().numpy()[prev_kept]\\n m.running_var.data = running_var#torch.from_numpy(running_var).to(self.args.device)\\n kept_biases = kept_biases[prev_kept]\\n if ('LayerNorm' in str(m.__class__)):\\n m.normalized_shape = (prev_kept.shape[0],)\\n else:\\n prev_kept = None\\n replace_parameters(m, kept_weights, kept_biases)\\n # Skip non-prunable layers\\n if (hasattr(m, 'unprunable') and m.unprunable):\\n prev_kept = None\\n continue\\n self.rewind_state_dict = copy.deepcopy(model.state_dict())\\n return model\",\n \"def _reset(self):\\n self.loss_history = []\\n self.optim_configs = {}\\n for p in self.model.params:\\n d = {k: v for k, v in self.optim_config.items()}\\n self.optim_configs[p] = d\",\n \"def reset_parameters(model: torch.nn.Module) -> None:\\n logging.info(\\\"Resetting model parameters.\\\")\\n for _, module in model.named_modules():\\n reset_params = getattr(module, \\\"reset_parameters\\\", None)\\n if callable(reset_params):\\n reset_params()\",\n \"def reset(args):\\n if os.path.exists(args.config):\\n os.remove(args.config)\\n return\",\n \"def reset_model(model):\\n\\n\\tfor layer in model.layers:\\n\\t\\t# Note: these are custom depending on the layer type\\n\\t\\tif '.MoleculeConv' in str(layer):\\n\\t\\t\\tW_inner = layer.init_inner((layer.inner_dim, layer.inner_dim))\\n\\t\\t\\tb_inner = np.zeros((1, layer.inner_dim))\\n\\t\\t\\t# Inner weights\\n\\t\\t\\tlayer.W_inner.set_value((T.tile(W_inner, (layer.depth + 1, 1, 1)).eval() + \\\\\\n\\t\\t\\t\\tinitializations.uniform((layer.depth + 1, layer.inner_dim, layer.inner_dim)).eval()).astype(np.float32))\\n\\t\\t\\tlayer.b_inner.set_value((T.tile(b_inner, (layer.depth + 1, 1, 1)).eval() + \\\\\\n\\t\\t\\t\\tinitializations.uniform((layer.depth + 1, 1, layer.inner_dim)).eval()).astype(np.float32))\\n\\n\\t\\t\\t# Outer weights\\n\\t\\t\\tW_output = layer.init_output((layer.inner_dim, layer.units), scale = layer.scale_output)\\n\\t\\t\\tb_output = np.zeros((1, layer.units))\\n\\t\\t\\t# Initialize weights tensor\\n\\t\\t\\tlayer.W_output.set_value((T.tile(W_output, (layer.depth + 1, 1, 1)).eval()).astype(np.float32))\\n\\t\\t\\tlayer.b_output.set_value((T.tile(b_output, (layer.depth + 1, 1, 1)).eval()).astype(np.float32))\\n\\t\\t\\tlogging.info('graphFP layer reset')\\n\\n\\t\\telif '.Dense' in str(layer):\\n\\t\\t\\tlayer.W.set_value((layer.init(layer.W.shape.eval()).eval()).astype(np.float32))\\n\\t\\t\\tlayer.b.set_value(np.zeros(layer.b.shape.eval(), dtype=np.float32))\\n\\t\\t\\tlogging.info('dense layer reset')\\n\\n\\t\\telif '.Dropout' in str(layer):\\n\\t\\t\\tlogging.info('dropout unchanged')\\n\\t\\telse:\\n\\t\\t\\traise ValueError('Unknown layer {}, cannot reset weights'.format(str(layer)))\\n\\tlogging.info('Reset model weights')\\n\\treturn model\",\n \"def clear(self):\\r\\n del self.__config\\r\\n self.__config = {}\\r\\n self.save()\",\n \"def reset(self):\\n self.params.resetParams()\",\n \"def reset_cfg():\\n _C.merge_from_other_cfg(_CFG_DEFAULT)\",\n \"async def reset_config(self):\\n self.config = {\\\"enable_auto_gen\\\": False, \\\"enable_world_barrier\\\": False}\\n await shared.event_handler.call_async(\\\"world:reset_config\\\")\\n self.gamerule_handler = mcpython.common.world.GameRule.GameRuleHandler(self)\",\n \"def reset(self):\\n self.satisfiability = Satisfiability.UNTESTED\\n self.model = None\\n self.unsatCore = []\",\n \"def reset(self):\\n self.manager.delete_all()\\n for name, val in DEFAULT_SETTINGS.items():\\n val['name'] = name\\n val['default_value'] = val['value']\\n self.manager.from_dict(val)\",\n \"def reset_to(self, state):\\n should_ret = False\\n if \\\"model\\\" in state:\\n self.reset()\\n xml = postprocess_model_xml(state[\\\"model\\\"])\\n self.env.reset_from_xml_string(xml)\\n self.env.sim.reset()\\n if not self._is_v1:\\n # hide teleop visualization after restoring from model\\n self.env.sim.model.site_rgba[self.env.eef_site_id] = np.array([0., 0., 0., 0.])\\n self.env.sim.model.site_rgba[self.env.eef_cylinder_id] = np.array([0., 0., 0., 0.])\\n if \\\"states\\\" in state:\\n self.env.sim.set_state_from_flattened(state[\\\"states\\\"])\\n self.env.sim.forward()\\n should_ret = True\\n\\n if \\\"goal\\\" in state:\\n self.set_goal(**state[\\\"goal\\\"])\\n if should_ret:\\n # only return obs if we've done a forward call - otherwise the observations will be garbage\\n return self.get_observation()\\n return None\",\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):\\n self.vna.write(edit_list(self.model))\\n self.vna.write(clear_list(self.model))\",\n \"def reset_states(self):\\n self.model.reset_states()\",\n \"def re_init(self):\\n self.latent.re_init()\\n if 're_init' in dir(self.inference_model):\\n self.inference_model.re_init()\\n if 're_init' in dir(self.generative_model):\\n self.generative_model.re_init()\",\n \"def reset(self):\\n self.best_model = None\\n self.best_res = -1\",\n \"def reset_parameters(self):\\n self.lstm.reset_parameters()\",\n \"def reset(self, model):\\n for obs_func in self.observation_functions:\\n obs_func.reset(model)\",\n \"def setOldModel(self,model):\\n self.modelName = model\\n oldModel = load_model(self.modelPath + \\\"/\\\" + model + \\\".hdf5\\\",\\n custom_objects={'recall': recall,\\n 'precision': precision,\\n 'f1Score':f1Score})\\n\\n self.old_weights = oldModel.get_weights()\\n self.input_shape = oldModel.inputs[0].get_shape().as_list()[1:]\\n self.n_classes = oldModel.outputs[0].get_shape().as_list()[-1]\\n self.conv_depth = oldModel.layers[1].output_shape[-1]\\n self.model = oldModel\",\n \"def antenny_config_reset(self):\\n return self.antenny_config.reset_default_config()\",\n \"def reset(self, **kwargs):\\n return self.env.reset(**kwargs)\",\n \"def reset_from(self, other_model):\\n self.wv.vocab = other_model.wv.vocab\\n self.wv.index2word = other_model.wv.index2word\\n self.vocabulary.cum_table = other_model.vocabulary.cum_table\\n self.corpus_count = other_model.corpus_count\\n self.docvecs.count = other_model.docvecs.count\\n self.docvecs.doctags = other_model.docvecs.doctags\\n self.docvecs.offset2doctag = other_model.docvecs.offset2doctag\\n self.trainables.reset_weights(self.hs, self.negative, self.wv, self.docvecs)\",\n \"def reset(self):\\n\\n def reset_function(module):\\n if isinstance(m, nn.Conv2d) or isinstance(m, nn.Linear):\\n m.reset_parameters()\\n\\n self.apply(reset_function)\",\n \"def reset(self, **kwargs):\\n # '_reset' depends on the 'backend'\\n self._reset(**kwargs)\",\n \"def reset(self):\\n # must NOT reset color map here, otherwise we loose provided configs by user,\\n # which are more important in this case for result images vs whatever the model task specified\\n self.class_names = None\\n self._map = None\",\n \"def resetmodel(self):\\n for key, value in self._dentsvertsdata.items():\\n value.free()\\n self._dentsvertsdata.clear()\",\n \"def reset(self):\\r\\n # TODO: have reset flag such that it forces all the bottom changes\\r\\n self.pwm_freq = self._default[\\\"pwm_freq\\\"]\\r\\n self.gate_logic = self._default[\\\"gate_logic\\\"]\\r\\n self.max_pwm = self._default[\\\"max_pwm\\\"]\\r\\n self.lase_on_power_up = self._default[\\\"lase_on_power_up\\\"]\\r\\n\\r\\n self.mode = self._default[\\\"mode\\\"]\\r\\n self.lase = self._default[\\\"lase\\\"]\\r\\n self.percent = self._default[\\\"percent\\\"] # in percent\\r\",\n \"def reset(self):\\n self.train_loss.reset_states()\\n self.train_accuracy.reset_states()\\n self.val_loss.reset_states()\\n self.val_accuracy.reset_states()\\n self.train_mIoU.reset_states()\\n self.val_mIoU.reset_states()\",\n \"def _reset(self, *args):\\n self.dirty = True\",\n \"def reset(self, model):\\n for obs_func in self.observation_functions.values():\\n obs_func.reset(model)\",\n \"def reset(self):\\n self.data = self._defaults\",\n \"def update_model(self):\\n pass\",\n \"async def gpt2_set_model(self, ctx, *, arg=None):\\n print('Command gpt2_set_model triggered')\\n if arg:\\n if arg in VALID_DEFAULT_MODELS:\\n self.update_config(model_name=arg)\\n else:\\n await ctx.send(f\\\"ERROR: Invalid model name {arg}\\\")\\n else:\\n await ctx.send(\\\"ERROR: Argument required\\\")\",\n \"def reset_settings():\\n settings = Settings()\\n settings.reset()\\n settings.save()\",\n \"def reset():\\n teardown_db()\\n build()\",\n \"def ClearModels(self):\\n self._modelFileNames = []\\n self._models = []\\n self.Modified(readAgain=True)\",\n \"def reset_values(self):\\n self.parse_config_file()\",\n \"def reset_config() -> None:\\r\\n self.ui.labelCloneListsLocation.setText(str(pathlib.Path(config.path_clone_list).resolve()))\\r\\n self.ui.labelMetadataLocation.setText(str(pathlib.Path(config.path_metadata).resolve()))\\r\\n self.ui.lineEditCloneListDownloadLocation.setText(config.clone_list_metadata_download_location)\\r\\n parent.clone_lists_folder = config.path_clone_list\\r\\n parent.metadata_folder = config.path_metadata\\r\\n parent.clone_list_metadata_url = config.clone_list_metadata_download_location\\r\\n write_config(parent, dat_details, config, self)\",\n \"def _reinit(self):\\n cfg_path = self[CONFIG_FILE_KEY] if CONFIG_FILE_KEY in self else None\\n for attr in list(self.keys()):\\n del self[attr]\\n self.__init__(cfg=cfg_path)\",\n \"def unload_model(app: FastAPI) -> None:\\n\\n logging.info(\\\"Shuting down the app\\\")\\n app.state.model = None\",\n \"def restore_model_state(self, model: Block):\\n if model in self.initial_state:\\n from_json(model, sd=self.initial_state[model], wts=StoreState)\\n else:\\n self._update_summary(model, \\\"status\\\", InitializationStatus.Error)\\n raise ValueError(\\\"No initial state stored.\\\")\",\n \"def reset(self):\\n \\n s = self\\n s.step_counter = 0\\n \\n # TODO: initialize first layer activations here, and not everywhere else\\n # self.model.initialize_local_vars()\\n # self.model.initialize_global_vars()\\n\\n ops = []\\n\\n for var in self.model.trainable_vars:\\n if self.needs_correction(var):\\n A_svd = s[var].A.svd\\n B2_svd = s[var].B2.svd \\n ops.extend(A_svd.init_ops)\\n ops.extend(B2_svd.init_ops)\\n ops.append(s[var].A.cov.initializer)\\n ops.append(s[var].B2.cov.initializer)\\n\\n # in new TensorFlow this breaks, probably because of\\n # https://github.com/tensorflow/tensorflow/commit/07adc2ea910de715d31e16a019fcbcccb575e931\\n # sometimes get \\\"need to feed\\\" placeholder error\\n # sometimes do not get this error, but spend two minutes inside\\n # _build_initializer_expr\\n s.run(ops)\",\n \"def reset(self, *args, **kwargs):\\n ...\",\n \"def reset_button(self):\\n\\n # Update the parameter values and start a new model\\n self.time_series = []\\n self.res_ended = False\\n self.school_ended = False\\n self.params = self.update_pars()\\n self.model = CompassModel(**self.params)\\n\\n # Stop the model when it is still running while reset is clicked.\\n if self.run.label == 'Stop' and self.callback_obj is not None:\\n self.doc.remove_periodic_callback(self.callback_obj)\\n self.run.label = 'Run'\\n self.run.button_type = 'success'\\n\\n self.reset_data()\\n self.doc.clear()\\n self.layout()\",\n \"def reset_train(self):\\n\\n self.model.apply(self._reset_weights)\\n self.epoch_loss.reset()\\n self.epoch = 0\\n del self.batch_process\\n self.batch_process = None\",\n \"def reset_parameters(self):\\n \\n for i in range(self.num_layers):\\n getattr(self, 'LSTMCell%i'%(i+1)).reset_parameters()\",\n \"def reset(self, *args, **kwargs):\\n pass\",\n \"def create_model(self):\\n self.model = None\\n pass\",\n \"def reset(self):\\n # Clear mutable data, but leave the immutables intact\\n self.train_data = {}\\n self.val_data = {}\\n self.test_data = {}\\n self.model_files = []\\n self.custom_data = {}\\n # Remove all the physical assets\\n for item in os.scandir(self.root_path):\\n os.remove(item.path)\\n # Reserialize\\n self.serialize()\",\n \"def reset_parameters(self, reset_mode='glorot_uniform'):\\n\\n if reset_mode == 'glorot_uniform':\\n if self.weight_decomp == 'block':\\n nn.init.xavier_uniform_(self.blocks, gain=nn.init.calculate_gain('relu'))\\n elif self.weight_decomp == 'basis':\\n nn.init.xavier_uniform_(self.bases, gain=nn.init.calculate_gain('relu'))\\n nn.init.xavier_uniform_(self.comps, gain=nn.init.calculate_gain('relu'))\\n else:\\n nn.init.xavier_uniform_(self.weights, gain=nn.init.calculate_gain('relu'))\\n\\n if self.bias is not None:\\n torch.nn.init.zeros_(self.bias)\\n elif reset_mode == 'schlichtkrull':\\n if self.weight_decomp == 'block':\\n nn.init.xavier_uniform_(self.blocks, gain=nn.init.calculate_gain('relu'))\\n elif self.weight_decomp == 'basis':\\n nn.init.xavier_uniform_(self.bases, gain=nn.init.calculate_gain('relu'))\\n nn.init.xavier_uniform_(self.comps, gain=nn.init.calculate_gain('relu'))\\n else:\\n nn.init.xavier_uniform_(self.weights, gain=nn.init.calculate_gain('relu'))\\n\\n if self.bias is not None:\\n torch.nn.init.zeros_(self.bias)\\n elif reset_mode == 'uniform':\\n stdv = 1.0 / math.sqrt(self.weights.size(1))\\n if self.weight_decomp == 'block':\\n self.blocks.data.uniform_(-stdv, stdv)\\n elif self.weight_decomp == 'basis':\\n self.bases.data.uniform_(-stdv, stdv)\\n self.comps.data.uniform_(-stdv, stdv)\\n else:\\n self.weights.data.uniform_(-stdv, stdv)\\n\\n if self.bias is not None:\\n self.bias.data.uniform_(-stdv, stdv)\\n else:\\n raise NotImplementedError(f'{reset_mode} parameter initialisation method has not been implemented')\",\n \"def reload_config(self):\\n pass\",\n \"def refresh_config(self):\\n self._user_config = UserConfig(None)\",\n \"def reset_program_state_dict(args, model, state_dict, pretrained_state_dict):\\n reset_state_dict = {}\\n scale = model.initializer_range if hasattr(model, \\\"initializer_range\\\")\\\\\\n else getattr(model, args.model_type).config[\\\"initializer_range\\\"]\\n reset_parameter_names = []\\n for n, p in state_dict.items():\\n if n in pretrained_state_dict:\\n reset_state_dict[p.name] = np.array(pretrained_state_dict[n])\\n reset_parameter_names.append(n)\\n elif p.name in pretrained_state_dict and \\\"bert\\\" in n:\\n reset_state_dict[p.name] = np.array(pretrained_state_dict[p.name])\\n reset_parameter_names.append(n)\\n else:\\n dtype_str = \\\"float32\\\"\\n if str(p.dtype) == \\\"VarType.FP64\\\":\\n dtype_str = \\\"float64\\\"\\n reset_state_dict[p.name] = np.random.normal(\\n loc=0.0, scale=scale, size=p.shape).astype(dtype_str)\\n logger.info(\\\"the following parameter had reset, please check. {}\\\".format(\\n reset_parameter_names))\\n return reset_state_dict\",\n \"def unsetModel(self):\\n return _libsbml.L3ParserSettings_unsetModel(self)\",\n \"def restore_config(self):\\n self._clear_previous_windows_assigment()\\n self._restart_i3_config()\",\n \"def reset(self):\\n self.pred = None\\n self.target = None\",\n \"def reset(self):\\n self.pred = None\\n self.target = None\",\n \"def reset(self):\\n if self._key:\\n self._lib.StObjectReset(self._key)\\n os.chdir(self._cwd)\\n self._layers.clear() # layer: index\\n self._substrate = None\\n self._experiments.clear() # analyzed experiments\\n self._tmpstandards.clear()\",\n \"def update_model(self):\\n pass # TODO: Implement this.\",\n \"def _reset(self):\\n self._set(\\\"_n_init_features\\\", None)\\n self._set(\\\"_n_output_features\\\", None)\\n self._set(\\\"_n_intervals\\\", None)\\n self._set(\\\"_mapper\\\", {})\\n self._set(\\\"_cpp_preprocessor\\\", None)\\n self._set(\\\"_fitted\\\", False)\",\n \"def reload(self):\\n self.load_config()\\n # Seems we need to explicitly refresh this\\n if self.main_instance:\\n self.main_instance.config = self.config\",\n \"def reset(self):\\n self.mode = 0\\n self.graphs = [[], [], []]\\n self.coefficients = []\\n self.sample = []\",\n \"def updateModel(self):\\n pass\",\n \"def reset_parameters(self):\\n self.rnn.reset_parameters()\\n self.action_lookup.reset_parameters()\\n\\n # self.state_dict_lookup.reset_parameters()\\n self.own_c_lookup.reset.parameters()\\n self.own_s_lookup.reset_parameters()\\n\\n self.th_1_lookup.reset_parameters()\\n self.th_2_lookup.reset_parameters()\\n self.th_3_lookup.reset_parameters()\\n self.f_1_lookup.reset.parameters()\\n self.f_2_lookup.reset_parameters()\\n self.f_3_lookup.reset_parameters()\\n self.f_4_lookup.reset_parameters()\\n\\n self.bu_msg_lookup.reset_parameters()\\n\\n self.i_t_lookup.reset_parameters()\\n self.lives_lookup.reset_parameters()\\n\\n self.prev_action_lookup.reset_parameters()\\n # self.message.apply(weight_reset)\\n self.output.apply(weight_reset)\\n for p in self.rnn.parameters():\\n p.data.uniform_(*self.init_param_range)\",\n \"def reset(runtime, cfg, inputs, state, outputs):\\n state['page_title'] = cfg['page_title']\\n state['id_resource_page'] = cfg['id_resource']\\n state['id_topic'] = cfg['id_topic']\",\n \"def reset(self, *args, **kwargs):\",\n \"def applyModelSource(self, modelSource):\\n modelSource.setEdit(False)\\n if modelSource.isLoaded():\\n self._reload()\\n else:\\n self._loadModelSource(modelSource)\",\n \"def test_config_device_reset(get_config, monkeypatch):\\n monkeypatch.setattr(DeviceConfig, 'minimal_essential_conf', {'test': 'conf'})\\n cfg = get_config(DeviceConfig, base_config)\\n cfg.save()\\n cfg.write_default()\\n new_conf = cfg.load()\\n\\n assert cfg.data == cfg.minimal_essential_conf, 'failed to apply default config'\\n assert new_conf == cfg.minimal_essential_conf, 'failed to load default config'\",\n \"def reset_config(modem, disable_auto_linking, monitor_mode, auto_led, deadman):\\n modem.configuration[DISABLE_AUTO_LINKING].set_value(not disable_auto_linking)\\n modem.configuration[MONITOR_MODE].set_value(not monitor_mode)\\n modem.configuration[AUTO_LED].set_value(not auto_led)\\n modem.configuration[DEADMAN].set_value(not deadman)\",\n \"def resetSettings(self):\\n\\n # it does this 4 times because for some reason it would not grab everything one time through. Investigate\\n for i in range(4):\\n\\n networkNode = self.returnNetworkNode\\n attrs = cmds.listAttr(networkNode, ud=True)\\n\\n for attr in attrs:\\n attrType = str(cmds.getAttr(networkNode + \\\".\\\" + attr, type=True))\\n\\n if attrType == \\\"double\\\":\\n cmds.setAttr(networkNode + \\\".\\\" + attr, lock=False)\\n cmds.setAttr(networkNode + \\\".\\\" + attr, 0, lock=True)\\n\\n if attrType == \\\"bool\\\":\\n cmds.setAttr(networkNode + \\\".\\\" + attr, lock=False)\\n cmds.setAttr(networkNode + \\\".\\\" + attr, True, lock=True)\\n\\n if attrType == \\\"enum\\\":\\n cmds.setAttr(networkNode + \\\".\\\" + attr, lock=False)\\n cmds.setAttr(networkNode + \\\".\\\" + attr, 0, lock=True)\\n\\n # relaunch the UI\\n self.updateSettingsUI()\\n self.applyModuleChanges(self)\",\n \"def reset(self):\\n ih = (param for name, param in self.named_parameters() if 'weight_ih' in name)\\n hh = (param for name, param in self.named_parameters() if 'weight_hh' in name)\\n b = (param for name, param in self.named_parameters() if 'bias' in name)\\n for t in ih:\\n torch.nn.init.xavier_uniform_(t)\\n for t in hh:\\n torch.nn.init.orthogonal_(t)\\n for t in b:\\n torch.nn.init.constant_(t, 0)\",\n \"def reset(self):\\n self.__init__()\",\n \"def reset(self):\\n self.__init__()\",\n \"def reset(self):\\n self.__init__()\",\n \"def revalidate(self):\\n *_, validation_error = validate_model(self.__class__, self.__dict__)\\n if validation_error:\\n raise validation_error\\n self.validate_config()\",\n \"def clear_model_cache():\\n global __model_cache\\n __model_cache = {}\",\n \"def reset_state(self, name=None):\\n if name is None:\\n self.solver.reset_solver(self.initial_state.copy())\\n else:\\n # TODO: Raise a nice ProtocolError if state not defined\\n self.solver.reset_solver(self.saved_states[name].copy())\",\n \"def set_Model(newModel):\\n global model\\n model = newModel\\n print(\\\"model is set\\\")\\n print(model)\",\n \"def restore(self):\\n if os.path.isfile( \\\\\\n os.path.join(self.network_path,'net_parameters.nnprm.index')):\\n self.load_network_parameters(\\n file_name='net_parameters', file_path=self.network_path)\\n else:\\n self.log(\\\"Could not load previous network parameters from:\\\\n{}\\\".format(\\\\\\n os.path.join(self.network_path,'net_parameters.nnprm') ))\\n self.log(\\\"Starting with untrained parameters\\\")\",\n \"def set_load_model_parameters(self):\\n\\n self.controller.set_new_model_test_input_path(self.test_input.get())\\n self.controller.set_new_model_results_input_path(self.results_input.get())\\n self.controller.set_new_model_running(False)\",\n \"def reset(self, pure=True, **kwargs):\\n cutoff = kwargs.get('cutoff_dim', self.circuit._trunc)\\n self._modemap.reset()\\n self.circuit.reset(pure, num_subsystems=self._init_modes, cutoff_dim=cutoff)\",\n \"def reset(self):\\n self._setupObjects()\",\n \"def resetSelfWithDefaults( self ):\\n self.__dict__.update( self._defDict )\",\n \"def set_model(self):\\n self.model = self.get_model()\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.77136797","0.77136797","0.77136797","0.731388","0.7217995","0.7177466","0.6790846","0.6759494","0.67429775","0.668906","0.66128105","0.65749794","0.6570066","0.65698665","0.65314436","0.6444437","0.63590646","0.6338237","0.6334196","0.627672","0.62158126","0.6215003","0.61957103","0.6163648","0.615972","0.6152384","0.613745","0.6135079","0.61166966","0.611125","0.6095492","0.6040328","0.6039232","0.6032554","0.60285795","0.6013403","0.6006295","0.5996823","0.59947026","0.59817874","0.59712356","0.59703374","0.59646064","0.5944897","0.5917905","0.5909062","0.5875529","0.5872126","0.58670324","0.58503693","0.58436704","0.5836806","0.58201355","0.5817543","0.5809922","0.58091956","0.5799511","0.57961476","0.5795308","0.5790054","0.5782721","0.5778077","0.5775901","0.57733256","0.5769972","0.57686365","0.5766696","0.5764548","0.5760176","0.57595557","0.5756263","0.5745654","0.5745654","0.5744803","0.57308227","0.5723593","0.5714712","0.5690924","0.5688569","0.56764865","0.56705445","0.5659695","0.56487495","0.5647343","0.5646273","0.5641932","0.5640623","0.5637097","0.5637097","0.5637097","0.5622349","0.56192327","0.56139386","0.56098527","0.5602831","0.56023365","0.5598874","0.55982155","0.55948883","0.5594819"],"string":"[\n \"0.77136797\",\n \"0.77136797\",\n \"0.77136797\",\n \"0.731388\",\n \"0.7217995\",\n \"0.7177466\",\n \"0.6790846\",\n \"0.6759494\",\n \"0.67429775\",\n \"0.668906\",\n \"0.66128105\",\n \"0.65749794\",\n \"0.6570066\",\n \"0.65698665\",\n \"0.65314436\",\n \"0.6444437\",\n \"0.63590646\",\n \"0.6338237\",\n \"0.6334196\",\n \"0.627672\",\n \"0.62158126\",\n \"0.6215003\",\n \"0.61957103\",\n \"0.6163648\",\n \"0.615972\",\n \"0.6152384\",\n \"0.613745\",\n \"0.6135079\",\n \"0.61166966\",\n \"0.611125\",\n \"0.6095492\",\n \"0.6040328\",\n \"0.6039232\",\n \"0.6032554\",\n \"0.60285795\",\n \"0.6013403\",\n \"0.6006295\",\n \"0.5996823\",\n \"0.59947026\",\n \"0.59817874\",\n \"0.59712356\",\n \"0.59703374\",\n \"0.59646064\",\n \"0.5944897\",\n \"0.5917905\",\n \"0.5909062\",\n \"0.5875529\",\n \"0.5872126\",\n \"0.58670324\",\n \"0.58503693\",\n \"0.58436704\",\n \"0.5836806\",\n \"0.58201355\",\n \"0.5817543\",\n \"0.5809922\",\n \"0.58091956\",\n \"0.5799511\",\n \"0.57961476\",\n \"0.5795308\",\n \"0.5790054\",\n \"0.5782721\",\n \"0.5778077\",\n \"0.5775901\",\n \"0.57733256\",\n \"0.5769972\",\n \"0.57686365\",\n \"0.5766696\",\n \"0.5764548\",\n \"0.5760176\",\n \"0.57595557\",\n \"0.5756263\",\n \"0.5745654\",\n \"0.5745654\",\n \"0.5744803\",\n \"0.57308227\",\n \"0.5723593\",\n \"0.5714712\",\n \"0.5690924\",\n \"0.5688569\",\n \"0.56764865\",\n \"0.56705445\",\n \"0.5659695\",\n \"0.56487495\",\n \"0.5647343\",\n \"0.5646273\",\n \"0.5641932\",\n \"0.5640623\",\n \"0.5637097\",\n \"0.5637097\",\n \"0.5637097\",\n \"0.5622349\",\n \"0.56192327\",\n \"0.56139386\",\n \"0.56098527\",\n \"0.5602831\",\n \"0.56023365\",\n \"0.5598874\",\n \"0.55982155\",\n \"0.55948883\",\n \"0.5594819\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7440945"},"document_rank":{"kind":"string","value":"3"}}},{"rowIdx":17895,"cells":{"query":{"kind":"string","value":"Close model and remove unzipped model from temporary folder."},"document":{"kind":"string","value":"def close_model(self):\n \n # Ensure model has been initialized at least once\n self._model_has_been_initialized(\"close_model\")\n\n # terminate fmu model\n # - avoids error from calling self.fmu.terminate if termination has already been performed\n self._terminate_model()\n\n # free fmu\n self.fmu.freeInstance()\n # clean up\n # [TODO] enforce clean up even when exceptions are thrown, or after keyboard interruption\n shutil.rmtree(self.unzipdir, ignore_errors=True)\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 clean_up_temp_files():\n global __tmp_model_dir\n\n if __tmp_model_dir is not None:\n FileUtils.deleteDirectory(__tmp_model_dir)\n __tmp_model_dir = None","def delete_model(self):\n os.remove(self.filepath)\n self.cmodel = None","def cleanUp(self):\r\n # Close any open models\r\n openModels = getAllModels()\r\n if len(openModels):\r\n for model in openModels:\r\n setCurrentModel(model)\r\n performAction(\"FileClose\")\r\n # Wait \r\n time.sleep(1)","def __exit__(self, exc_type, exc_value, traceback):\n if self.cleanup_model_file:\n os.unlink(self.model_file)","def delete_best_model(self):\n if self.best_model_path.exists():\n # not using `missing_ok=True` because we are running this code on pythin 3.7\n self.best_model_path.unlink()","def close(self):\n\n sp.call([\"convert\", \"{}_*\".format(self.tmp_prefix),\n self.filename])\n\n sp.call(\"rm {}_*\".format(self.tmp_prefix), shell=True)\n sp.call([\"rmdir\", self.tmp_dir])","def tearDown(self):\n self.model.close()\n os.remove(self.DATABASE_PATH)","def cleanup(self):\n if os.path.exists(f\"{self.save_path}{self.name}\"):\n shutil.rmtree(f\"{self.save_path}{self.name}\")","def dispose(self):\n rmtree(self._temp_path)","def shutdown(self):\n del self.model\n del self.train_dataset\n del self.test_dataset","def tearDownClass(self):\n remove('temp_mol_file.csv')","def shutdown(self):\n path = self.opt.get('model_file', None)\n if path is not None:\n self.save(path + '.shutdown_state')\n super().shutdown()","def tearDown(self):\r\n remove_files(self.files_to_remove, False)\r\n if self.tmpdir:\r\n rmtree(self.tmpdir)\r\n\r\n # clean up the file from init_flowgram_file\r\n if (hasattr(self, \"tmp_filename\") and exists(self.tmp_filename)):\r\n remove(self.tmp_filename)","def clean(self):\n os.remove(\"temp.py\") # Delete the file \"temp.py\", to free up disk space","def clean_up(model_path):\n cmds = [\"rm */grad*.pickle\",\n \"rm -r checkpoints\",\n \"rm */train_len\",\n \"rm log_human_read.csv\",\n \"rm */log_human_read.csv\",\n \"rm -r best_model\",\n \"rm */*epoch*\"]\n\n for cmd in cmds:\n os.system(\"cd {} && {}\".format(model_path, cmd))","def teardown(self):\n super(TestCisObjInput, self).teardown()\n if os.path.isfile(self.tempfile):\n os.remove(self.tempfile)","def teardown(self):\n super(TestCisPickleInput, self).teardown()\n if os.path.isfile(self.tempfile):\n os.remove(self.tempfile)","def clean(self):\n\t\tself.archiver.closeFile()","def tearDown(self) -> None:\n\n self.temp_env_file.close()\n os.remove(self.temp_env_file.name)\n\n del self.temp_env_file\n del self.test_name\n del self.helper","def close(self):\n\t\tif os.path.exists(self.name): os.system(\"rm -rf %s\" % (self.name))\n\t\tos.system(\"mkdir %s\" % (self.name))\n\t\told_path = os.getcwd()\n\t\tos.chdir(self.name)\n\t\tfor i in self.objects.keys():\n\t\t\t# dill will taken care of down the line\n\t\t\tpickled_object(self.objects[i], name = i,\n\t\t\t\tdefault = self._default).save()\n\t\tos.chdir(old_path)","def teardown(self):\n super(TestCisPickleOutput, self).teardown()\n if os.path.isfile(self.tempfile):\n os.remove(self.tempfile)","def __deleteSave(self) -> None:\n os.remove(self.save_location)","def tearDown(self):\n self.db.close()\n self.dbfile.close()\n os.unlink(self.path)","def cleanup(self):\r\n if self.tempDirectory != None:\r\n shutil.rmtree(self.tempDirectory, True)\r\n self.tempDirectory = None","def tearDown(self):\n utils.rm_rf(TMP_DIR_PATH)","def unload_model(app: FastAPI) -> None:\n\n logging.info(\"Shuting down the app\")\n app.state.model = None","def shutdown(self):\n path = self.opt.get('model_file', None)\n if path is not None and hasattr(self, 'optimizer'):\n self.save(path + '.shutdown_state')\n super().shutdown()","def close(self):\r\n if self._session:\r\n self._session.close()\r\n self._session = None\r\n try:\r\n self._writer.remove_file()\r\n self._reader.remove_file()\r\n except Oct2PyError:\r\n pass","def cleanup(self):\n if os.path.exists(self.tgzfile):\n os.remove(self.tgzfile)\n\n if os.path.exists(self.dirname):\n shutil.rmtree(self.dirname)","def teardown(self):\n super(TestCisObjOutput, self).teardown()\n if os.path.isfile(self.tempfile):\n os.remove(self.tempfile)","def delete_training_files(cls, model_file):\n try:\n os.remove('%s.vec' % model_file)\n os.remove('%s.bin' % model_file)\n except FileNotFoundError:\n logger.debug('Training files %s not found when attempting to delete', model_file)\n pass","def tearDown(self):\n os.remove(self._file)","def __del__(self):\n\t\tif self.temp_dir:\n\t\t\tself.temp_dir.cleanup()","def tearDown(self):\n self.tempdir.cleanup()","def tearDown(self):\n\t\ttry:\n\t\t\tos.remove(self.filename)\n\t\texcept:\n\t\t\tpass","def tearDown(self):\n if self.workspace is not None:\n rmtree(self.workspace.workspace, ignore_errors=True)\n Path.cwd().joinpath(\"workspace.tar.gz\").unlink(missing_ok=True)\n for item in self.items:\n if item.is_dir():\n rmtree(item)\n elif item.is_file():\n item.unlink()\n self.workspace = None","def tearDown(self):\n try:\n os.remove(self.filename)\n except:\n pass","def tearDown(self):\n try:\n os.remove(self.filename)\n except:\n pass","def tearDown(self):\n try:\n os.remove(self.filename)\n except:\n pass","def tearDown(self):\n if os.path.exists(self.temp):\n shutil.rmtree(self.temp)","async def unload_model(\n self,\n model_name: str,\n headers: dict[str, t.Any] = ...,\n unload_dependents: bool = ...,\n ) -> None:","def tearDown(self):\r\n shutil.rmtree(self.temp_dir_path)","def clean(self):\n if os.path.exists(self.initial):\n if os.path.exists(self.path) and os.stat(self.path).st_size == os.stat(\n self.initial).st_size:\n os.remove(self.initial)\n else:\n # if it doesn't match, something probably crashed; rename the temporary file and\n # it'll get uploaded at some point\n self.auto_filename()\n self.rename()\n self.connect()\n os.remove(self.initial)\n if os.path.exists(self.path):\n os.remove(self.path)\n self.filename_set = False","def unzip_model_file(path):\n with zipfile.ZipFile(path) as zfile:\n (dirname, ext) = os.path.splitext(path)\n for name in zfile.namelist():\n #idenfity model name\n if not os.path.splitext(name)[1]:\n model_name = name\n if not os.path.exists(dirname):\n os.makedirs(dirname)\n zfile.extract(name, dirname)\n return os.path.join(dirname, model_name)","def teardown(self):\n super(TestCisPlyInput, self).teardown()\n if os.path.isfile(self.tempfile):\n os.remove(self.tempfile)","def delete_model_package(ModelPackageName=None):\n pass","def cleanup(self):\n try:\n self.wc = os.path.dirname(self.wc)\n rmtree2(self.wc)\n except IOError, err:\n self.log.exception(\"Can't remove working copy %s: %s\" % (self.wc, err))","def cleanup(self): \n if os.path.exists(self.inpms):\n shutil.rmtree(self.inpms)","def __del__(self) -> None:\n try:\n shutil.rmtree(self.temp_path)\n except FileNotFoundError:\n pass","def tearDown(self):\n self.tmp.cleanup()","def cleanup(self):\n self._tmp_obj.cleanup()","def tearDown(self):\n with contextlib.suppress(FileNotFoundError):\n Path(\"test.xlsx\").absolute().unlink()","def load_model(self, tmp_dir):\n pass","def delete(self):\n self.stop()\n try:\n self.skil.api.delete_model(self.deployment.id, self.model.id)\n except api_exception as e:\n self.skil.printer.pprint(\n \">>> Exception when calling delete_model_instance: %s\\n\" % e)","def tearDown(self):\n rmtree(getcwd(), ignore_errors=True)","def tearDown(self):\n rmtree(getcwd(), ignore_errors=True)","def destroy(self):\n self.db = None\n self.gen_cursor = None\n self.map = None\n self.fmap = None\n self.smap = None\n FlatBaseModel.destroy(self)","def close(self) -> None:\n os.remove(self.FILE_NAME)","def tearDownClass(cls):\n\n os.remove(cls.temp_file_input_csv.name)\n os.remove(cls.temp_file_input_csv_larger.name)\n os.remove(cls.temp_file_input_csv_confusion.name)\n os.remove(cls.temp_file_output_series.name)\n os.remove(cls.temp_file_output_seriessuggest.name)\n os.remove(cls.temp_file_output_seriessuggest2.name)\n os.remove(cls.temp_file_output_autosearch.name)","def delete(self, name):\n if (self.model_dir / (str(name) + '.pkl')).exists():\n (self.model_dir / (str(name) + '.pkl')).unlink()","def _delete_temp():\n global _TEMP_NAME\n\n try:\n database.delete_temp(_TEMP_NAME)\n outputtools.delete_temp(_TEMP_NAME)\n except:\n raise","def close(self):\n if self.comm is not None:\n _instances.pop(self.model_id, None)\n self.comm.close()\n self.comm = None\n self._repr_mimebundle_ = None","def tearDownClass(cls):\n with contextlib.suppress(FileNotFoundError):\n Path(\"test.xlsx\").absolute().unlink()","def __del__(self):\n shutil.rmtree('tmp')\n self.quit_browser()","def teardown(self):\n super(TestCisPandasInput, self).teardown()\n if os.path.isfile(self.tempfile):\n os.remove(self.tempfile)","def clean_up(self):\n directory = os.path.join(os.getcwd(), self.TMP_FOLDER)\n if os.path.exists(directory) and os.path.isdir(directory):\n shutil.rmtree(directory)","def close(self):\n if self._key is not None:\n logger.debug('StObjectDelete(key)')\n self._lib.StObjectDelete(self._key)\n self._key = None\n\n for filepath in self._tmpstandards:\n os.remove(filepath)\n logger.debug('Remove temporary standard: %s', filepath)\n\n self.reset()","def tteardown_class(self):\n os.close(self.db_fd)\n os.unlink(app.config['DATABASE'])","def remove(self):\n \n dbpath, config = self._start() \n desc_file = check_file(config.model_descriptions, dbpath,\n \"model_descriptions\", allow_none=False) \n self.logger.msg1(\"Reading model ids\")\n ids = values_in_column(desc_file, \"id\")\n self.logger.msg1(\"Deleting models: \"+str(len(ids)))\n delete_models(dbpath, ids)\n self._end()","def tearDown(self):\n if 'turbinia-test-tmp' in self.tmp_dir:\n shutil.rmtree(self.tmp_dir)","def teardown(self):\n try:\n self._close(True)\n except:\n pass\n try:\n os.unlink(os.path.join(self.home_dir, DB_FILE))\n except FileNotFoundError as _:\n pass","def CleanUp(self, path):\n try:\n if os.path.exists(path):\n os.remove(path)\n except (OSError, IOError) as e:\n logging.info(\"Failed to remove temporary file %s. Err: %s\", path, e)","def tearDown(self):\n logging.info(\"Delete %s\", self._temp_dir)\n shutil.rmtree(self._temp_dir)","def _cleanup(self):\n try:\n tmpdir = self.tmpdir\n except AttributeError:\n # Don't need to do anything if the temp dir isn't set\n return\n shutil.rmtree(tmpdir)","def tearDownClass(cls):\n del cls.my_model.name\n del cls.my_model.my_number\n del cls.my_model_json\n del cls.my_new_model\n del cls.my_model","def __del__(self):\r\n self.save()\r\n self.close()","def teardown(self):\n os.chdir(self.cwd)\n try:\n shutil.rmtree(self.t)\n except (OSError, IOError):\n pass","def tearDown(self):\n rmtree(self.out_dir, ignore_errors=True)","def remove(self):\n self.model_or_sim.remove_package(self)","def tearDown(self):\n try:\n os.remove(self.fixtureFile)\n except OSError:\n pass","def tearDown(self):\n shutil.rmtree(self.working_directory)","def finalize(self):\r\n self.outfile_param.close()\r\n self.outfile_sim.close()","def finalise(self):\n self.logger.info(\"Saving final versions of model...\")\n self.save_checkpoint(filename='final.pth.tar')","def tearDown(self):\n try:\n os.remove(self.fixture_file)\n except OSError:\n pass","def cleanup(self):\n self.all_wav_to_mp3()\n self.past_songs_db.close()\n self.move_tracks_to_music_folder( )\n self.delete_leftovers()\n print \"Cleanup finished\"","def tearDown(self):\n\n self.h5file.close()\n self.h5file = None\n Path(self.h5fname).unlink() # comment this for debug only\n super().tearDown()","def tearDown(self):\r\n shutil.rmtree(self.working_directory)","def close(self):\n self.myzip.close()","def teardown(self):\n super(TestCisPlyOutput, self).teardown()\n if os.path.isfile(self.tempfile):\n os.remove(self.tempfile)","def _clean_up_temporary_files(dataset_dir):\n filename = _DATA_URL.split('/')[-1]\n filepath = os.path.join(dataset_dir, filename)\n tf.gfile.Remove(filepath)\n\n tmp_dir = os.path.join(dataset_dir, 'cifar-100-python')\n tf.gfile.DeleteRecursively(tmp_dir)","def remove(self):\n self.remove_file()","def tearDown(self):\n if self.workdir and os.path.exists(self.workdir):\n shutil.rmtree(self.workdir)","def tearDown(self):\n shutil.rmtree(self.workspace_dir)","def tearDown(self):\n shutil.rmtree(self.workspace_dir)","def tearDown(self):\n shutil.rmtree(self.workspace_dir)","def tearDown(self):\n shutil.rmtree(self.workspace_dir)","def tearDown(self):\n shutil.rmtree(self.workspace_dir)","def tearDown(self):\n shutil.rmtree(self.workspace_dir)","def tearDown(self):\n shutil.rmtree(self.workspace_dir)","def clearTemp():\n Installer.tempDir.rmtree(safety='Temp')"],"string":"[\n \"def clean_up_temp_files():\\n global __tmp_model_dir\\n\\n if __tmp_model_dir is not None:\\n FileUtils.deleteDirectory(__tmp_model_dir)\\n __tmp_model_dir = None\",\n \"def delete_model(self):\\n os.remove(self.filepath)\\n self.cmodel = None\",\n \"def cleanUp(self):\\r\\n # Close any open models\\r\\n openModels = getAllModels()\\r\\n if len(openModels):\\r\\n for model in openModels:\\r\\n setCurrentModel(model)\\r\\n performAction(\\\"FileClose\\\")\\r\\n # Wait \\r\\n time.sleep(1)\",\n \"def __exit__(self, exc_type, exc_value, traceback):\\n if self.cleanup_model_file:\\n os.unlink(self.model_file)\",\n \"def delete_best_model(self):\\n if self.best_model_path.exists():\\n # not using `missing_ok=True` because we are running this code on pythin 3.7\\n self.best_model_path.unlink()\",\n \"def close(self):\\n\\n sp.call([\\\"convert\\\", \\\"{}_*\\\".format(self.tmp_prefix),\\n self.filename])\\n\\n sp.call(\\\"rm {}_*\\\".format(self.tmp_prefix), shell=True)\\n sp.call([\\\"rmdir\\\", self.tmp_dir])\",\n \"def tearDown(self):\\n self.model.close()\\n os.remove(self.DATABASE_PATH)\",\n \"def cleanup(self):\\n if os.path.exists(f\\\"{self.save_path}{self.name}\\\"):\\n shutil.rmtree(f\\\"{self.save_path}{self.name}\\\")\",\n \"def dispose(self):\\n rmtree(self._temp_path)\",\n \"def shutdown(self):\\n del self.model\\n del self.train_dataset\\n del self.test_dataset\",\n \"def tearDownClass(self):\\n remove('temp_mol_file.csv')\",\n \"def shutdown(self):\\n path = self.opt.get('model_file', None)\\n if path is not None:\\n self.save(path + '.shutdown_state')\\n super().shutdown()\",\n \"def tearDown(self):\\r\\n remove_files(self.files_to_remove, False)\\r\\n if self.tmpdir:\\r\\n rmtree(self.tmpdir)\\r\\n\\r\\n # clean up the file from init_flowgram_file\\r\\n if (hasattr(self, \\\"tmp_filename\\\") and exists(self.tmp_filename)):\\r\\n remove(self.tmp_filename)\",\n \"def clean(self):\\n os.remove(\\\"temp.py\\\") # Delete the file \\\"temp.py\\\", to free up disk space\",\n \"def clean_up(model_path):\\n cmds = [\\\"rm */grad*.pickle\\\",\\n \\\"rm -r checkpoints\\\",\\n \\\"rm */train_len\\\",\\n \\\"rm log_human_read.csv\\\",\\n \\\"rm */log_human_read.csv\\\",\\n \\\"rm -r best_model\\\",\\n \\\"rm */*epoch*\\\"]\\n\\n for cmd in cmds:\\n os.system(\\\"cd {} && {}\\\".format(model_path, cmd))\",\n \"def teardown(self):\\n super(TestCisObjInput, self).teardown()\\n if os.path.isfile(self.tempfile):\\n os.remove(self.tempfile)\",\n \"def teardown(self):\\n super(TestCisPickleInput, self).teardown()\\n if os.path.isfile(self.tempfile):\\n os.remove(self.tempfile)\",\n \"def clean(self):\\n\\t\\tself.archiver.closeFile()\",\n \"def tearDown(self) -> None:\\n\\n self.temp_env_file.close()\\n os.remove(self.temp_env_file.name)\\n\\n del self.temp_env_file\\n del self.test_name\\n del self.helper\",\n \"def close(self):\\n\\t\\tif os.path.exists(self.name): os.system(\\\"rm -rf %s\\\" % (self.name))\\n\\t\\tos.system(\\\"mkdir %s\\\" % (self.name))\\n\\t\\told_path = os.getcwd()\\n\\t\\tos.chdir(self.name)\\n\\t\\tfor i in self.objects.keys():\\n\\t\\t\\t# dill will taken care of down the line\\n\\t\\t\\tpickled_object(self.objects[i], name = i,\\n\\t\\t\\t\\tdefault = self._default).save()\\n\\t\\tos.chdir(old_path)\",\n \"def teardown(self):\\n super(TestCisPickleOutput, self).teardown()\\n if os.path.isfile(self.tempfile):\\n os.remove(self.tempfile)\",\n \"def __deleteSave(self) -> None:\\n os.remove(self.save_location)\",\n \"def tearDown(self):\\n self.db.close()\\n self.dbfile.close()\\n os.unlink(self.path)\",\n \"def cleanup(self):\\r\\n if self.tempDirectory != None:\\r\\n shutil.rmtree(self.tempDirectory, True)\\r\\n self.tempDirectory = None\",\n \"def tearDown(self):\\n utils.rm_rf(TMP_DIR_PATH)\",\n \"def unload_model(app: FastAPI) -> None:\\n\\n logging.info(\\\"Shuting down the app\\\")\\n app.state.model = None\",\n \"def shutdown(self):\\n path = self.opt.get('model_file', None)\\n if path is not None and hasattr(self, 'optimizer'):\\n self.save(path + '.shutdown_state')\\n super().shutdown()\",\n \"def close(self):\\r\\n if self._session:\\r\\n self._session.close()\\r\\n self._session = None\\r\\n try:\\r\\n self._writer.remove_file()\\r\\n self._reader.remove_file()\\r\\n except Oct2PyError:\\r\\n pass\",\n \"def cleanup(self):\\n if os.path.exists(self.tgzfile):\\n os.remove(self.tgzfile)\\n\\n if os.path.exists(self.dirname):\\n shutil.rmtree(self.dirname)\",\n \"def teardown(self):\\n super(TestCisObjOutput, self).teardown()\\n if os.path.isfile(self.tempfile):\\n os.remove(self.tempfile)\",\n \"def delete_training_files(cls, model_file):\\n try:\\n os.remove('%s.vec' % model_file)\\n os.remove('%s.bin' % model_file)\\n except FileNotFoundError:\\n logger.debug('Training files %s not found when attempting to delete', model_file)\\n pass\",\n \"def tearDown(self):\\n os.remove(self._file)\",\n \"def __del__(self):\\n\\t\\tif self.temp_dir:\\n\\t\\t\\tself.temp_dir.cleanup()\",\n \"def tearDown(self):\\n self.tempdir.cleanup()\",\n \"def tearDown(self):\\n\\t\\ttry:\\n\\t\\t\\tos.remove(self.filename)\\n\\t\\texcept:\\n\\t\\t\\tpass\",\n \"def tearDown(self):\\n if self.workspace is not None:\\n rmtree(self.workspace.workspace, ignore_errors=True)\\n Path.cwd().joinpath(\\\"workspace.tar.gz\\\").unlink(missing_ok=True)\\n for item in self.items:\\n if item.is_dir():\\n rmtree(item)\\n elif item.is_file():\\n item.unlink()\\n self.workspace = None\",\n \"def tearDown(self):\\n try:\\n os.remove(self.filename)\\n except:\\n pass\",\n \"def tearDown(self):\\n try:\\n os.remove(self.filename)\\n except:\\n pass\",\n \"def tearDown(self):\\n try:\\n os.remove(self.filename)\\n except:\\n pass\",\n \"def tearDown(self):\\n if os.path.exists(self.temp):\\n shutil.rmtree(self.temp)\",\n \"async def unload_model(\\n self,\\n model_name: str,\\n headers: dict[str, t.Any] = ...,\\n unload_dependents: bool = ...,\\n ) -> None:\",\n \"def tearDown(self):\\r\\n shutil.rmtree(self.temp_dir_path)\",\n \"def clean(self):\\n if os.path.exists(self.initial):\\n if os.path.exists(self.path) and os.stat(self.path).st_size == os.stat(\\n self.initial).st_size:\\n os.remove(self.initial)\\n else:\\n # if it doesn't match, something probably crashed; rename the temporary file and\\n # it'll get uploaded at some point\\n self.auto_filename()\\n self.rename()\\n self.connect()\\n os.remove(self.initial)\\n if os.path.exists(self.path):\\n os.remove(self.path)\\n self.filename_set = False\",\n \"def unzip_model_file(path):\\n with zipfile.ZipFile(path) as zfile:\\n (dirname, ext) = os.path.splitext(path)\\n for name in zfile.namelist():\\n #idenfity model name\\n if not os.path.splitext(name)[1]:\\n model_name = name\\n if not os.path.exists(dirname):\\n os.makedirs(dirname)\\n zfile.extract(name, dirname)\\n return os.path.join(dirname, model_name)\",\n \"def teardown(self):\\n super(TestCisPlyInput, self).teardown()\\n if os.path.isfile(self.tempfile):\\n os.remove(self.tempfile)\",\n \"def delete_model_package(ModelPackageName=None):\\n pass\",\n \"def cleanup(self):\\n try:\\n self.wc = os.path.dirname(self.wc)\\n rmtree2(self.wc)\\n except IOError, err:\\n self.log.exception(\\\"Can't remove working copy %s: %s\\\" % (self.wc, err))\",\n \"def cleanup(self): \\n if os.path.exists(self.inpms):\\n shutil.rmtree(self.inpms)\",\n \"def __del__(self) -> None:\\n try:\\n shutil.rmtree(self.temp_path)\\n except FileNotFoundError:\\n pass\",\n \"def tearDown(self):\\n self.tmp.cleanup()\",\n \"def cleanup(self):\\n self._tmp_obj.cleanup()\",\n \"def tearDown(self):\\n with contextlib.suppress(FileNotFoundError):\\n Path(\\\"test.xlsx\\\").absolute().unlink()\",\n \"def load_model(self, tmp_dir):\\n pass\",\n \"def delete(self):\\n self.stop()\\n try:\\n self.skil.api.delete_model(self.deployment.id, self.model.id)\\n except api_exception as e:\\n self.skil.printer.pprint(\\n \\\">>> Exception when calling delete_model_instance: %s\\\\n\\\" % e)\",\n \"def tearDown(self):\\n rmtree(getcwd(), ignore_errors=True)\",\n \"def tearDown(self):\\n rmtree(getcwd(), ignore_errors=True)\",\n \"def destroy(self):\\n self.db = None\\n self.gen_cursor = None\\n self.map = None\\n self.fmap = None\\n self.smap = None\\n FlatBaseModel.destroy(self)\",\n \"def close(self) -> None:\\n os.remove(self.FILE_NAME)\",\n \"def tearDownClass(cls):\\n\\n os.remove(cls.temp_file_input_csv.name)\\n os.remove(cls.temp_file_input_csv_larger.name)\\n os.remove(cls.temp_file_input_csv_confusion.name)\\n os.remove(cls.temp_file_output_series.name)\\n os.remove(cls.temp_file_output_seriessuggest.name)\\n os.remove(cls.temp_file_output_seriessuggest2.name)\\n os.remove(cls.temp_file_output_autosearch.name)\",\n \"def delete(self, name):\\n if (self.model_dir / (str(name) + '.pkl')).exists():\\n (self.model_dir / (str(name) + '.pkl')).unlink()\",\n \"def _delete_temp():\\n global _TEMP_NAME\\n\\n try:\\n database.delete_temp(_TEMP_NAME)\\n outputtools.delete_temp(_TEMP_NAME)\\n except:\\n raise\",\n \"def close(self):\\n if self.comm is not None:\\n _instances.pop(self.model_id, None)\\n self.comm.close()\\n self.comm = None\\n self._repr_mimebundle_ = None\",\n \"def tearDownClass(cls):\\n with contextlib.suppress(FileNotFoundError):\\n Path(\\\"test.xlsx\\\").absolute().unlink()\",\n \"def __del__(self):\\n shutil.rmtree('tmp')\\n self.quit_browser()\",\n \"def teardown(self):\\n super(TestCisPandasInput, self).teardown()\\n if os.path.isfile(self.tempfile):\\n os.remove(self.tempfile)\",\n \"def clean_up(self):\\n directory = os.path.join(os.getcwd(), self.TMP_FOLDER)\\n if os.path.exists(directory) and os.path.isdir(directory):\\n shutil.rmtree(directory)\",\n \"def close(self):\\n if self._key is not None:\\n logger.debug('StObjectDelete(key)')\\n self._lib.StObjectDelete(self._key)\\n self._key = None\\n\\n for filepath in self._tmpstandards:\\n os.remove(filepath)\\n logger.debug('Remove temporary standard: %s', filepath)\\n\\n self.reset()\",\n \"def tteardown_class(self):\\n os.close(self.db_fd)\\n os.unlink(app.config['DATABASE'])\",\n \"def remove(self):\\n \\n dbpath, config = self._start() \\n desc_file = check_file(config.model_descriptions, dbpath,\\n \\\"model_descriptions\\\", allow_none=False) \\n self.logger.msg1(\\\"Reading model ids\\\")\\n ids = values_in_column(desc_file, \\\"id\\\")\\n self.logger.msg1(\\\"Deleting models: \\\"+str(len(ids)))\\n delete_models(dbpath, ids)\\n self._end()\",\n \"def tearDown(self):\\n if 'turbinia-test-tmp' in self.tmp_dir:\\n shutil.rmtree(self.tmp_dir)\",\n \"def teardown(self):\\n try:\\n self._close(True)\\n except:\\n pass\\n try:\\n os.unlink(os.path.join(self.home_dir, DB_FILE))\\n except FileNotFoundError as _:\\n pass\",\n \"def CleanUp(self, path):\\n try:\\n if os.path.exists(path):\\n os.remove(path)\\n except (OSError, IOError) as e:\\n logging.info(\\\"Failed to remove temporary file %s. Err: %s\\\", path, e)\",\n \"def tearDown(self):\\n logging.info(\\\"Delete %s\\\", self._temp_dir)\\n shutil.rmtree(self._temp_dir)\",\n \"def _cleanup(self):\\n try:\\n tmpdir = self.tmpdir\\n except AttributeError:\\n # Don't need to do anything if the temp dir isn't set\\n return\\n shutil.rmtree(tmpdir)\",\n \"def tearDownClass(cls):\\n del cls.my_model.name\\n del cls.my_model.my_number\\n del cls.my_model_json\\n del cls.my_new_model\\n del cls.my_model\",\n \"def __del__(self):\\r\\n self.save()\\r\\n self.close()\",\n \"def teardown(self):\\n os.chdir(self.cwd)\\n try:\\n shutil.rmtree(self.t)\\n except (OSError, IOError):\\n pass\",\n \"def tearDown(self):\\n rmtree(self.out_dir, ignore_errors=True)\",\n \"def remove(self):\\n self.model_or_sim.remove_package(self)\",\n \"def tearDown(self):\\n try:\\n os.remove(self.fixtureFile)\\n except OSError:\\n pass\",\n \"def tearDown(self):\\n shutil.rmtree(self.working_directory)\",\n \"def finalize(self):\\r\\n self.outfile_param.close()\\r\\n self.outfile_sim.close()\",\n \"def finalise(self):\\n self.logger.info(\\\"Saving final versions of model...\\\")\\n self.save_checkpoint(filename='final.pth.tar')\",\n \"def tearDown(self):\\n try:\\n os.remove(self.fixture_file)\\n except OSError:\\n pass\",\n \"def cleanup(self):\\n self.all_wav_to_mp3()\\n self.past_songs_db.close()\\n self.move_tracks_to_music_folder( )\\n self.delete_leftovers()\\n print \\\"Cleanup finished\\\"\",\n \"def tearDown(self):\\n\\n self.h5file.close()\\n self.h5file = None\\n Path(self.h5fname).unlink() # comment this for debug only\\n super().tearDown()\",\n \"def tearDown(self):\\r\\n shutil.rmtree(self.working_directory)\",\n \"def close(self):\\n self.myzip.close()\",\n \"def teardown(self):\\n super(TestCisPlyOutput, self).teardown()\\n if os.path.isfile(self.tempfile):\\n os.remove(self.tempfile)\",\n \"def _clean_up_temporary_files(dataset_dir):\\n filename = _DATA_URL.split('/')[-1]\\n filepath = os.path.join(dataset_dir, filename)\\n tf.gfile.Remove(filepath)\\n\\n tmp_dir = os.path.join(dataset_dir, 'cifar-100-python')\\n tf.gfile.DeleteRecursively(tmp_dir)\",\n \"def remove(self):\\n self.remove_file()\",\n \"def tearDown(self):\\n if self.workdir and os.path.exists(self.workdir):\\n shutil.rmtree(self.workdir)\",\n \"def tearDown(self):\\n shutil.rmtree(self.workspace_dir)\",\n \"def tearDown(self):\\n shutil.rmtree(self.workspace_dir)\",\n \"def tearDown(self):\\n shutil.rmtree(self.workspace_dir)\",\n \"def tearDown(self):\\n shutil.rmtree(self.workspace_dir)\",\n \"def tearDown(self):\\n shutil.rmtree(self.workspace_dir)\",\n \"def tearDown(self):\\n shutil.rmtree(self.workspace_dir)\",\n \"def tearDown(self):\\n shutil.rmtree(self.workspace_dir)\",\n \"def clearTemp():\\n Installer.tempDir.rmtree(safety='Temp')\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.71848893","0.7168014","0.69020855","0.6778169","0.66447943","0.6597088","0.63135016","0.6312071","0.63115525","0.62721944","0.6262568","0.6260771","0.6243184","0.61231935","0.60675275","0.60634017","0.6029513","0.59984267","0.5996684","0.599638","0.5995369","0.5994043","0.59669465","0.591555","0.5895086","0.5891867","0.5832516","0.5832041","0.58318305","0.5831473","0.5826178","0.5821391","0.58072263","0.5802294","0.5798321","0.5795143","0.57912785","0.57912785","0.57912785","0.5778547","0.5775937","0.5771814","0.5762829","0.5761849","0.5759724","0.5757793","0.57425857","0.57416713","0.5728818","0.5719009","0.5717212","0.57161576","0.5713396","0.57104737","0.57014215","0.57014215","0.5688273","0.5676532","0.56740373","0.5665783","0.56656575","0.5664915","0.5662137","0.56611085","0.56366765","0.56338525","0.5614811","0.5609148","0.56072205","0.56054366","0.5604938","0.56011814","0.559314","0.55825084","0.5556132","0.5555251","0.555094","0.5550877","0.55400646","0.5531272","0.55290794","0.55257237","0.55249053","0.5521804","0.5515441","0.55148846","0.55144864","0.55126876","0.55125743","0.5505707","0.5503999","0.5492688","0.54765904","0.54765904","0.54765904","0.54765904","0.54765904","0.54765904","0.54765904","0.5472721"],"string":"[\n \"0.71848893\",\n \"0.7168014\",\n \"0.69020855\",\n \"0.6778169\",\n \"0.66447943\",\n \"0.6597088\",\n \"0.63135016\",\n \"0.6312071\",\n \"0.63115525\",\n \"0.62721944\",\n \"0.6262568\",\n \"0.6260771\",\n \"0.6243184\",\n \"0.61231935\",\n \"0.60675275\",\n \"0.60634017\",\n \"0.6029513\",\n \"0.59984267\",\n \"0.5996684\",\n \"0.599638\",\n \"0.5995369\",\n \"0.5994043\",\n \"0.59669465\",\n \"0.591555\",\n \"0.5895086\",\n \"0.5891867\",\n \"0.5832516\",\n \"0.5832041\",\n \"0.58318305\",\n \"0.5831473\",\n \"0.5826178\",\n \"0.5821391\",\n \"0.58072263\",\n \"0.5802294\",\n \"0.5798321\",\n \"0.5795143\",\n \"0.57912785\",\n \"0.57912785\",\n \"0.57912785\",\n \"0.5778547\",\n \"0.5775937\",\n \"0.5771814\",\n \"0.5762829\",\n \"0.5761849\",\n \"0.5759724\",\n \"0.5757793\",\n \"0.57425857\",\n \"0.57416713\",\n \"0.5728818\",\n \"0.5719009\",\n \"0.5717212\",\n \"0.57161576\",\n \"0.5713396\",\n \"0.57104737\",\n \"0.57014215\",\n \"0.57014215\",\n \"0.5688273\",\n \"0.5676532\",\n \"0.56740373\",\n \"0.5665783\",\n \"0.56656575\",\n \"0.5664915\",\n \"0.5662137\",\n \"0.56611085\",\n \"0.56366765\",\n \"0.56338525\",\n \"0.5614811\",\n \"0.5609148\",\n \"0.56072205\",\n \"0.56054366\",\n \"0.5604938\",\n \"0.56011814\",\n \"0.559314\",\n \"0.55825084\",\n \"0.5556132\",\n \"0.5555251\",\n \"0.555094\",\n \"0.5550877\",\n \"0.55400646\",\n \"0.5531272\",\n \"0.55290794\",\n \"0.55257237\",\n \"0.55249053\",\n \"0.5521804\",\n \"0.5515441\",\n \"0.55148846\",\n \"0.55144864\",\n \"0.55126876\",\n \"0.55125743\",\n \"0.5505707\",\n \"0.5503999\",\n \"0.5492688\",\n \"0.54765904\",\n \"0.54765904\",\n \"0.54765904\",\n \"0.54765904\",\n \"0.54765904\",\n \"0.54765904\",\n \"0.54765904\",\n \"0.5472721\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7889111"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":17896,"cells":{"query":{"kind":"string","value":"Get var indices for each (valid) var name provided in list. If none are provided, all outputs are returned."},"document":{"kind":"string","value":"def get_states(self, sim_outputs: List = None):\n \n # Ensure model has been initialized at least once\n self._model_has_been_initialized(\"get_states\")\n\n if sim_outputs is None:\n sim_outputs = self.sim_outputs\n elif not len(sim_outputs) > 0:\n sim_outputs = self.sim_outputs\n\n states_dict = self._get_variables(sim_outputs)\n \n # Check if more than one index has been found\n if not len(states_dict.keys()) > 0:\n print(\"[get_states] No valid state names have been provided. No states are returned.\")\n return {}\n\n return states_dict"},"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 _var_names_to_indices(self, var_names: List):\n\n if type(var_names) is not type([]):\n # Return empty array if input is not 'list' type\n print(\"[_var_names_to_indices] Provided input is not of type list.\")\n return []\n\n indices_array = []\n names_array = []\n for name in var_names:\n if name not in self.vars_to_idx.keys():\n print(\"[_var_names_to_indices] Invalid variable name '{}' has been skipped.\".format(name))\n continue\n indices_array.append(self.vars_to_idx[name])\n names_array.append(name)\n\n if not len(var_names) > 0:\n print(\"[_var_names_to_indices] No (valid) states have been provided.\")\n\n return indices_array, names_array","def indices_of_var(v):\n name = v.varName\n indices = name[2:].split(',')\n i, j = int(indices[0]), int(indices[1])\n return i, j","def index(self, variables):\n return [self._variables.index(v) for v in variables]","def read_variables(var_or_list):\n session = ph.get_session()\n return session.run(var_or_list)","def compute_variable_indexes(path, overwrite=True, multiproc=False):\n if multiproc is True:\n tf.keras.backend.clear_session()\n set_cpu_option()\n\n gin_bindings = [\n \"evaluation.evaluation_fn = @variables_idx\",\n \"variables_idx.num_train = 10000\", \"evaluation.random_seed = 2051556033\",\n \"dataset.name='auto'\", \"evaluation.name = 'variables index'\"\n ]\n path = pathlib.Path(path)\n result_path = path.parent.parent / \"metrics\" / \"variance\" / \"filtered_variables\"\n logger.info(\"Computing variable indexes of {}\".format(path.parent.parent))\n gin_evaluation(path, result_path, overwrite, gin_bindings)","def vars(self):\n return [Var(i,self.dims[i]) for i in range(self.nvar)] # TODO: use stored state info (=1 sometimes)","def index(self, varname):\n if not isinstance(varname, str):\n raise TypeError(\"argument must be str\")\n varname = self._find_vars(varname, empty_ok=False, single=True)[0]\n return self._varlist.index(varname)","def vars(svars):\n return np.array([pm.var(var) for var in svars.split()])","def __splitVariableNames(self, name, indexes):\n if name == 'x':\n var = self.xCoordinates[indexes[0]][indexes[1]]\n elif name == 'y':\n var = self.yCoordinates[indexes[0]][indexes[1]]\n elif name == 'z':\n var = self.zCoordinates[indexes[0]][indexes[1]]\n elif name == 'colorMap':\n var = self.colorMapCoordinates[indexes[0]][indexes[1]]\n elif name == 'clusterLabels':\n var = self.clusterLabels[indexes[0]][indexes[1]]\n elif name == 'mixtureLabels':\n var = self.mixtureLabels[indexes[0]][indexes[1]]\n elif name == 'mixtureMeans':\n var = self.mixtureMeans[indexes[0]][indexes[1]]\n elif name == 'mixtureCovars':\n var = self.mixtureCovars[indexes[0]][indexes[1]]\n\n # The variable can contain brackets {} (when the symbol \"|\" is present in\n # the variable name), e.g.:\n # DataName|Input|{RavenAuxiliary|variableName|initial_value}\n # or it can look like:\n # DataName|Input|variableName\n\n if var is not None:\n result = [None] * 3\n if '|input|' in var.lower():\n match = re.search(r\"(\\|input\\|)\", var.lower())\n elif '|output|' in var.lower():\n match = re.search(r\"(\\|output\\|)\", var.lower())\n else:\n self.raiseAnError(IOError, f'In Plot {self.name}, the input coordinate {name} has not specified an \"Input\" or \"Output\" (case insensitive). e.g., sourceName|Input|aVariable) in {var}')\n startLoc, endLoc = match.start(), match.end()\n result = [var[:startLoc].strip(), var[startLoc+1:endLoc-1].strip(), var[endLoc:].strip()]\n if '{' in result[-1] and '}' in result[-1]:\n locLower, locUpper = result[-1].find(\"{\"), result[-1].rfind(\"}\")\n result[-1] = result[-1][locLower + 1:locUpper].strip()\n else:\n result = None\n\n return result","def variables_used (self) :\r\n\t\t## These names do not contain dimension specification (everything in brackets\r\n\t\t## that comes after a name is am array index - either the arry was declared\r\n\t\t## correctly or it is wrong anyway, there is no implicit declaration of arrays) !\r\n\r\n\t\tresult = []\r\n\r\n\t\tfor l in self.equ_lists :\r\n\t\t\tfor var_name in l :\r\n\t\t\t\tresult.append(var_name[0])\r\n\t\treturn result","def variable_parser(var_list, prefix):\r\n ret_list = []\r\n for var in var_list:\r\n varname = var.name\r\n varprefix = varname.split('/')[0]\r\n if varprefix == prefix:\r\n ret_list.append(var)\r\n return ret_list","def get_list_vars(my_vars):\n lists = []\n for var in my_vars:\n try:\n temp = my_vars[var].getValue()\n #print var + '=' + str(temp)\n except ValueError:\n lists.append(var)\n return lists","def variable_parser(var_list, prefix):\r\n ret_list = []\r\n for var in var_list:\r\n varname = var.name\r\n varprefix = varname.split('/')[0]\r\n if varprefix == prefix:\r\n ret_list.append(var)\r\n elif prefix in varname:\r\n ret_list.append(var)\r\n return ret_list","def get_indexes(self, variable, *args):\n\n return [get_subset_idxs(data, min, max)\n for data, (min, max) in args]","def _find_vars(self, varnames, unique=False, evars=False, all_ok=False, \n empty_ok=False, single=False):\n if isinstance(varnames, str):\n varnames = (varnames,)\n elif not isinstance(varnames, collections.Iterable):\n raise TypeError(\"variable names should be str or iterable of str\")\n \n # first split into list of single abbrevs per str\n split_names = []\n for name in varnames:\n if not isinstance(name, str):\n raise TypeError(\"must specify variables as string(s)\")\n split_names += name.split()\n nnames = len(split_names)\n \n # check for _all, check for proper usage, and return copy of varlist\n # if evars==False or ['_dta'] + varlist if evars==True\n all_specified = False\n if '_all' in split_names:\n if not all_ok:\n raise ValueError(\"\\\"_all\\\" not allowed in this context\")\n elif not nnames == 1:\n raise ValueError(\n \"\\\"_all\\\" may not be combined with other names\")\n all_specified = True\n all_names = (['_dta'] if evars else []) + list(self._varlist)\n nnames = len(all_names)\n \n # check that more than 0 names specified if empty_ok==False, and\n # ignore extras (with message) if single==True\n if not empty_ok and nnames == 0:\n raise ValueError(\"no variables specified\")\n if single and nnames > 1:\n if not self._quiet:\n smcl = \"{err}\" if IN_STATA else \"\"\n msg = smcl + \"only one {}varname allowed; ignoring the rest\"\n print(msg.format('e' if evars else ''))\n split_names = split_names[:1]\n \n # if all_specified, return aleady-constructed all_names\n if all_specified:\n return all_names\n \n # Create match list of [abbrev, match1, match2, ...].\n # The loops below identify when exact varname given, but that varname\n # happens to be abbreviation of other varnames.\n varlist = self._varlist\n matches = []\n append = matches.append\n if evars:\n for name in split_names:\n if name == \"_dta\":\n append([name, name])\n else:\n match = [var for var in varlist if var.startswith(name)]\n append([name, name] if name in match else [name] + match)\n else:\n for name in split_names:\n match = [var for var in varlist if var.startswith(name)]\n append([name, name] if name in match else [name] + match)\n \n # abbreviation was a good, unambiguous abbreviation if exactly\n # one match found, i.e. if the corresponding entry in -matches- \n # is [abbrev, match1]\n if not all(len(m) == 2 for m in matches):\n # there were unmatched or ambiguous abbreviations\n zeros = \" \".join([m[0] for m in matches if len(m) == 1])\n twos = \" \".join([m[0] for m in matches if len(m) >= 3])\n if zeros != \"\" and twos != \"\":\n msg = \"no variables found for {}; multiple found for {}\"\n raise ValueError(msg.format(zeros, twos))\n if zeros != \"\":\n raise ValueError(\n \"no variables found for {}\".format(zeros, twos))\n # if getting here, twos != \"\" and zeros == \"\"\n raise ValueError(\"multiple variables found for '{}'\".format(twos))\n \n if not unique:\n return [m[1] for m in matches]\n seen = set()\n # if name has not been encountered, add to list and set of encountered\n return [m[1] for m in matches \n if m[1] not in seen and not seen.add(m[1])]","def map_to_scope(var_list):\n return {var.op.name.split('/', 1)[1]: var for var in var_list}","def _get_variables(self, sim_outputs: List = None):\n \n # Ensure model has been initialized at least once\n self._model_has_been_initialized(\"_get_variables\")\n\n # Ensure array is not empty\n if sim_outputs is None:\n return {}\n elif not len(sim_outputs) > 0:\n #print(\"[_get_variables] No var names were provided. No vars are returned.\")\n return {}\n\n\n sim_output_indices, sim_output_names = self._var_names_to_indices(sim_outputs)\n \n # Check if more than one index has been found\n if not len(sim_output_indices) > 0:\n #print(\"[_get_variables] No valid var names have been provided. No vars are returned.\")\n return {}\n \n outputs_dict = dict(zip(sim_output_names, self.fmu.getReal(sim_output_indices)))\n\n return outputs_dict","def get_input_var_names(self):\n return self._input_var_names","def get_vars_by_prefix(self, prefix):\n\n t_vars = tf.global_variables()\n return [var for var in t_vars if prefix in var.name]","def _get_indices(self, indices: VecEnvIndices) -> Iterable[int]:\n if indices is None:\n indices = range(self.num_envs)\n elif isinstance(indices, int):\n indices = [indices]\n return indices","def get_varnams(self, varnam_list):\n self.varnams = [[v.name, v.composer, v.talam] for v in varnam_list\n if v.raga == self.name]","def get_input_names(self):\n inputNames = []\n for inVar in self.inputs:\n # inVar is of type InOutVar and the object that it contains is a PyFMI variable\n inputNames.append(inVar.get_object().name)\n return inputNames","def variables(self):\n return [i.name for i in self.inputs + self.outputs]","def coord_indices_of(self, v_list):\n return [self.coord_index_of(v) for v in v_list]","def get_vars(self):\n return [self.mu, self.var]","def get_name_list(msh, varname):\n return [str(chartostring(v)) for v in msh.variables[varname]]","def variable_names(self):\n \n return [x['variable'] for x in self.variable_dicts()]","def get_indexes(from_list, find_list):\n\n df_find = pd.DataFrame(find_list, columns=['value'])\n df_from = pd.DataFrame(list(zip(from_list, np.arange(len(from_list)))), columns=['value', 'index'])\n indexes = pd.merge(df_from, df_find, on='value', how='inner')['index'].values\n return indexes","def get_predicate_indices(tags: List[str]) -> List[int]:\n return [ind for ind, tag in enumerate(tags) if \"V\" in tag]","def get_vars(scope=''):\n return [x for x in tf.trainable_variables() if scope in x.name]","def varlist(data):\n \n Bxm=data[\"Bx\"].mean()\n Bym=data[\"By\"].mean()\n Bzm=data[\"Bz\"].mean()\n Bxsqm=(data[\"Bx\"]**2).mean()\n Bysqm=(data[\"By\"]**2).mean()\n Bzsqm=(data[\"Bz\"]**2).mean()\n Bxym=(data[\"Bx\"]*data[\"By\"]).mean()\n Bxzm=(data[\"Bx\"]*data[\"Bz\"]).mean()\n Byzm=(data[\"By\"]*data[\"Bz\"]).mean()\n \n Varx= Bxsqm-Bxm**2\n Varxy=Bxym-Bxm*Bym\n Varxz=Bxzm-Bxm*Bzm\n Vary=Bysqm-Bym**2\n Varyz=Byzm-Bym*Bzm\n Varz=Bzsqm-Bzm**2\n var=[Varx,Varxy, Varxz,Varxy,Vary,Varyz,Varxz,Varyz,Varz]\n var=np.array(var)\n var=var.reshape((3,3))\n return var","def find_varying(params, nvmax):\n print('Finding variable parameters')\n if nvmax < 0:\n raise ValueError(f'nvmax ({nvmax}) must be positive')\n\n var = []\n cnt = 0\n for p in params:\n if len(params[p]) == 2:\n if (params[p][1] - params[p][0]) < 0:\n raise ValueError(f'range is inverted for param: {p}')\n elif cnt >= nvmax:\n raise ValueError(f'too many param ranges were given. Expected {nvmax}')\n else:\n var.append(p)\n cnt += 1\n return var","def GetListVariable(self, name):\n var = self._makefile.variables.get(name, expand=True)[2]\n if not var:\n return []\n return var.resolvesplit(self._makefile, self._makefile.variables)","def getOqiVarNames( self ):\n\n if self.oqiVarNames:\n return self.oqiVarNames.keys()\n\n n = self.adb.get( \"nOqiVars\" )\n for indx in xrange( n ):\n name = self.adb.get( \"oqiVarName\",\n indx ) \n self.oqiVarNames[name] = indx\n\n return self.oqiVarNames.keys()","def get_list_slice_vars(list_):\n\n # Dump unwanted portions\n array, list_ = list_.split(\"[\")\n list_ = list_.split(\"]\", 1)[0]\n\n # Split at ':'\n variables = list_.split(\":\")\n var_count = len(variables)\n\n step = \"\"\n\n # If step provided\n if var_count == 3:\n\n # If provided, store provided values\n start, stop, step = variables\n else:\n\n # Else store start, stop with default step\n start, stop = variables\n\n # If values are not provided by user, fall back to defaults\n\n # Set start default to 0\n if not start:\n start = \"0\"\n\n # Set stop default to array length\n if not stop:\n stop = \"Array.length\"\n\n # Set step default to 1\n if not step:\n step = \"1\"\n\n # Return stripped array with extracted values\n return array, start, stop, step","def get_output_names(self):\n outputNames = []\n for outVar in self.outputs:\n # outVar is of type InOutVar and the object that it contains is a PyFMI variable\n outputNames.append(outVar.get_object().name)\n return outputNames","def get_variable_names(self):\n return [var[1] for var in self.variables]","def stats_variable_names(res):\n def varname(s):\n pos = s.find(':')\n return s if pos==-1 else s[0:pos]\n return set( [ varname(key) for key in res.keys()] )","def var_index(self, code=1, s=False):\n if s:\n code = 2\n\n index = None\n for i, var in enumerate(self.primary_header['variables']):\n if var['Variable code'] == code:\n assert index is None, 'Appears to be two sets of same data in profile'\n index = i\n return index","def getVar2FactorsMap(self):\r\n V = self.getAllNodes()\r\n return list(list(idx for idx,f in enumerate(self.factors) if i in f.var) for i in V)","def get_variable_values(self, vars):\n raise NotImplementedError()","def getVariableList(dataset):\n variables = [v for v in dataset.variables.keys() if v not in dataset.dimensions.keys()]\n for d in dataset.dimensions.keys():\n try:\n variables.pop(variables.index(dataset.variables[d].getncattr(\"bounds\")))\n except:\n pass\n return variables","def get_column_indices(indices, inputs, multiple):\n if multiple:\n res = OrderedDict()\n for p in indices:\n ov, onnx_i = get_column_index(p, inputs)\n if ov not in res:\n res[ov] = []\n res[ov].append(onnx_i)\n return res\n\n onnx_var = None\n onnx_is = []\n for p in indices:\n ov, onnx_i = get_column_index(p, inputs)\n onnx_is.append(onnx_i)\n if onnx_var is None:\n onnx_var = ov\n elif onnx_var != ov:\n cols = [onnx_var, ov]\n raise NotImplementedError( # pragma: no cover\n \"sklearn-onnx is not able to merge multiple columns from \"\n \"multiple variables ({0}). You should think about merging \"\n \"initial types.\".format(cols))\n return onnx_var, onnx_is","def _get_dependent_variables(input_ops, output_ops):\n\n # avoids the edge-case when input_ops == output_ops.\n output_ops = nest.map_structure(gen_array_ops.identity, output_ops)\n inbetween_ops = op_selector.get_backward_walk_ops(\n seed_ops=output_ops,\n stop_at_ts=input_ops,\n inclusive=False,\n only_differentiable=True)\n var_ops = (op for op in inbetween_ops if op.type in VAR_OP_TYPES)\n var_names = (op.name for op in var_ops)\n tf_vars = (get_variable_by_name(var_name) for var_name in var_names)\n tf_vars = [v for v in tf_vars if v is not None]\n return tf_vars","def inputs(variable_list, blockers=None):\r\n vlist = ancestors(variable_list, blockers)\r\n rval = [r for r in vlist if r.owner is None]\r\n return rval","def extract_var_data(self, var_names):\n variable_dict = {} # Declaring dictionary used to store key-val pairs, var_name as key and the array as the value\n try:\n for var in var_names:\n variable_dict[var] = self.dataset[var].values\n return variable_dict\n except Exception as e:\n print(\"An Error occured:\", e)\n raise e","def GetVariableArrayDetails(line: str, variables: dict, report) -> (str, int):\n\n if re.sub(r\" (\\d+)$\", \"\", line ) in variables:\n var_name = re.sub(r\" (\\d+)$\", \"\", line )\n if not var_name in variables:\n raise Exception(f\"Couldn't find variable {var_name}\")\n if not variables[ var_name ]._isArray():\n raise Exception(f\"Variable {var_name} isn't an array\")\n try:\n var_index = int( re.search(r\" (\\d+$)\", line).group(0).strip() )\n except:\n raise Exception(\"Invalid array index\")\n return var_name, var_index-1\n elif \" of \" in line:\n line_split = line.split(' of ')\n var_name_index = line_split[0].strip()\n var_name = line_split[1].strip()\n\n if not var_name in variables:\n raise Exception(f\"Couldn't find variable {var_name}\")\n if not variables[ var_name ]._isArray():\n raise Exception(f\"Variable {var_name} isn't an array\")\n\n arith_check, arith_value = FCalc.FiMArithmetic.IsArithmetic( var_name_index )\n if arith_check:\n if not arith_value.is_prefix:\n arith = FCalc.FiMArithmetic(var_name_index, arith_value)\n arith.GetValues(variables, report)\n var_index = arith.Calculate()\n else:\n if not var_name_index in variables:\n raise Exception(f\"Couldn't find variable {var_name_index}\")\n if variables[ var_name_index ].Type != FClass.FiMVariableTypes.NUMBER:\n raise Exception(f\"Variable {var_name_index} isn't a number\")\n if _isFloat( variables[ var_name_index ].getv() ):\n raise Exception(f\"Cannot use floating-point numbers as array index\")\n var_index = variables[ var_name_index ].getv()\n return var_name, var_index-1\n\n return None, None","def __get_vars_list(self, template_idx, log):\n template = self.templates[template_idx].split()\n log = log.split()\n variables = []\n pt = pl = 0\n while pt < len(template) and pl < len(log):\n if template[pt] == log[pl]:\n pt += 1\n pl += 1\n continue\n elif template[pt] == '*':\n # found a variable\n while pt < len(template) and template[pt] == '*':\n # in case there are many variables together\n pt += 1\n if pt >= len(template):\n # it's the end of the template\n variables.append(' '.join(log[pl:]))\n break\n else:\n variable_tokens = []\n while pl < len(log) and log[pl] != template[pt]:\n variable_tokens.append(log[pl])\n pl += 1\n # it duplicates when many variables together for a correct output\n variables.append(' '.join(variable_tokens))\n else:\n # it is a variable not covered by the template asterisks\n # we move on on the log but stay on the template token\n pl += 1\n return variables","def get_n_params(var_list):\n return int(np.sum([np.product(\n [x.value for x in var.get_shape()]) for var in var_list]))","def getDummyVars(input_dict, feature_list, prefix=\"DummyVars_\"):\n\theader_list = []\n\tfeat_list = []\n\tfor ind,feat in enumerate(feature_list):\n\t\theader_list.append(prefix+str(ind))\n\t\tif input_dict.has_key(feat):\n\t\t\tfeat_list.append(input_dict[feat])\n\t\telse:\n\t\t\tfeat_list.append(0)\n\treturn feat_list, header_list","def getVariableInfo(self, variables, name):\r\n\r\n return [var.return_variable_dict() for var in variables if var.name == name][0]","def cnf_variables(cnf):\n variabs = set()\n\n for clause in cnf:\n for var in clause:\n var = abs(var)\n\n if var not in variabs:\n variabs.add(var)\n\n return variabs","def convert_vars(self, input):\n input = self.convert(input)\n if not isinstance(input, list):\n input = [input]\n converted = []\n for item in input:\n if item in self._variables:\n converted.append(self._variables[item])\n else:\n converted.append(item)\n if len(converted) == 1:\n converted = converted[0]\n return converted","def get_valid(self, names):\n\n ret = [None] * len(names)\n posdict = dict([(name, i) for i, name in enumerate(names)])\n\n for compname, varnames in partition_names_by_comp(names).items():\n if compname is None:\n vals = super(Assembly, self).get_valid(varnames)\n for i, val in enumerate(vals):\n ret[posdict[varnames[i]]] = val\n else:\n comp = getattr(self, compname)\n if isinstance(comp, Component):\n vals = comp.get_valid(varnames)\n else:\n vals = [self._valid_dict['.'.join([compname, vname])] for vname in varnames]\n for i, val in enumerate(vals):\n full = '.'.join([compname, varnames[i]])\n ret[posdict[full]] = val\n return ret","def _instantiate_vars(labels: np.ndarray):\n n = len(labels)\n if len(set(labels)) == n:\n index_seed = np.arange(n)\n index_remain = np.arange(n)\n else:\n index_seed = np.argwhere(labels >= 0).ravel()\n index_remain = np.argwhere(labels < 0).ravel()\n labels = labels[index_seed]\n return index_seed.astype(np.int32), index_remain.astype(np.int32), labels.astype(np.int32)","def getOhcVarNames( self ):\n\n if self.ohcVarNames:\n return self.ohcVarNames.keys()\n \n n = self.adb.get( \"nOhcVars\" )\n for indx in xrange( n ):\n name = self.adb.get( \"ohcVarName\",\n indx ) \n self.ohcVarNames[name] = indx\n\n return self.ohcVarNames.keys()","def var_names(self):\n return self._var_names","def get_variable_names(self):\n varNames = []\n for var in self.variables:\n # EstimationVariable\n varNames.append(var.name)\n return varNames","def getOthVarNames( self ):\n\n if self.othVarNames:\n return self.othVarNames.keys()\n\n n = self.adb.get( \"nOthVars\" )\n for indx in range( n ):\n name = self.adb.get( \"othVarName\",\n indx ) \n self.othVarNames[ name ] = indx\n\n return self.othVarNames.keys()","def get_wind_indices(predictor_names):\n\n try:\n u_wind_index = predictor_names.index(\n processed_narr_io.U_WIND_GRID_RELATIVE_NAME)\n except ValueError:\n error_string = (\n '\\n{0:s}\\nPredictor names (shown above) must include \"{1:s}\".'\n ).format(str(predictor_names),\n processed_narr_io.U_WIND_GRID_RELATIVE_NAME)\n\n raise ValueError(error_string)\n\n try:\n v_wind_index = predictor_names.index(\n processed_narr_io.V_WIND_GRID_RELATIVE_NAME)\n except ValueError:\n error_string = (\n '\\n{0:s}\\nPredictor names (shown above) must include \"{1:s}\".'\n ).format(str(predictor_names),\n processed_narr_io.V_WIND_GRID_RELATIVE_NAME)\n\n raise ValueError(error_string)\n\n return u_wind_index, v_wind_index","def variables_used (self) :\r\n\t\treturn [i[0] for i in self.parameters]","def getOfcVarNames( self ):\n\n if self.ofcVarNames:\n return self.ofcVarNames.keys()\n \n n = self.adb.get( \"nOfcVars\" )\n for indx in xrange( n ):\n name = self.adb.get( \"ofcVarName\",\n indx ) \n self.ofcVarNames[name] = indx\n\n return self.ofcVarNames.keys()","def getvarnameindex(self,somename_): # 3\n res,resargs = self.__obj.getvarnameindex(somename_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _asgn_return_value,_index_return_value = resargs\n return _asgn_return_value,_index_return_value","def _find_regressors(self,eqn,varname) :\n\t\ttrue_params = self.ss._ss_params\n\t\tparams = true_params[varname][eqn-1]\t\t\n\t\tregressors = [ii+1 for ii,p in enumerate(params) if p!=0 ]\t\t\n\t\treturn regressors","def variables(self):\n return np.array(list(self._match_result_dict.keys()))","def means_of_variables(dataset, name_of_variable):\r\n list_of_means = []\r\n for num in range(len(dataset)):\r\n sum_of_variables = 0\r\n for row in dataset[num].items():\r\n if name_of_variable in row[0]:\r\n if row[1].strip() != \"\":\r\n sum_of_variables += int(row[1])\r\n if sum_of_variables != 0:\r\n list_of_means.append(float(sum_of_variables) / number_of_variables(dataset, name_of_variable)) \r\n return list_of_means","def getDefinedVars(self):\n numTabs = self.tabWidget.count()\n varList = []\n \n for i in range(numTabs):\n var = self.tabWidget.widget(i).getVar()\n name = self.tabWidget.tabText(i)\n varList.append([name, var])\n\n return varList","def getOsiVarNames( self ):\n\n if self.osiVarNames:\n return self.osiVarNames.keys()\n \n n = self.adb.get( \"nOsiVars\" )\n for indx in xrange( n ):\n name = self.adb.get( \"osiVarName\",\n indx ) \n self.osiVarNames[ name ]= indx\n\n return self.osiVarNames.keys()","def _returnSplitIndex(self, var, pltIndex, ind):\n\n firstSplit = self.__splitVariableNames(var, (pltIndex, ind))\n if firstSplit[2].strip() not in self.sourceData[pltIndex].getVars(firstSplit[1].lower()):\n self.raiseAnError(IOError, f'Variable \"{firstSplit[2]}\" not found in \"{firstSplit[1]}\" of DataObject \"{self.sourceData[pltIndex].name}\"!')\n split = firstSplit[2]\n\n return split","def input_names(self) -> List[Union[str, int]]:\n return [x.name or i for i, x in enumerate(self.inputs)]","def findall_var(formula, variable):\n res = []\n s = Solver()\n s.add(formula)\n while True:\n if s.check() == sat:\n m = s.model()\n res.append(m)\n value = m[variable]\n if value == None:\n return res\n s.add(variable != value)\n else:\n return res","def getOeiVarNames( self ):\n\n if self.oeiVarNames:\n return self.oeiVarNames.keys()\n\n n = self.adb.get( \"nOeiVars\" )\n for indx in xrange( n ):\n name = self.adb.get( \"oeiVarName\",\n indx ) \n self.oeiVarNames[name] = indx\n\n return self.oeiVarNames.keys()","def get_yields(wsp, binname, yield_names):\n yields = []\n for name in yield_names:\n var_name = '_'.join([name, binname])\n yields.append(get_var(wsp, var_name).getVal())\n\n return np.sum(yields)","def get_swp_values(self, var: str) -> List[Any]:\n return self._sweep_params[var]","def __setVarNames(self):\n result = set()\n\n # detecting variables\n for templatePart in self.inputString().split(\"{\"):\n if templatePart is '' or \"}\" not in templatePart:\n continue\n\n endIndex = templatePart.find('}')\n result.add(templatePart[:endIndex])\n\n self.__varNames = list(result)","def _returnSplitIndexHS(self, var, pltIndex, ind):\n\n Split = self.__splitVariableNames(var, (pltIndex, ind))\n outputIndexes = self.sourceData[pltIndex].indexes if Split[1].lower() == 'output' else []\n if Split[2].strip() not in self.sourceData[pltIndex].getVars(Split[1].lower())+outputIndexes:\n self.raiseAnError(IOError, f'Variable \"{Split[2]}\" not found in \"{Split[1]}\" of DataObject \"{self.sourceData[pltIndex].name}\"!')\n split = Split[2]\n\n return split","def trainable_variables(self, name=None):\n\n _m = [self.modules.get(name)] if name else self.modules.values()\n _var = []\n for i in _m:\n _var += filter(lambda p: p.requires_grad, i.parameters())\n return _var","def vars(*tensor_types):\n return map(var, tensor_types)","def indices_of_split(self, split_name='train'):\n return self.indices_of('split', split_name)","def data_variable_names(self):\n data_names = []\n mesh = self.mesh_names()\n prefix = mesh[0]+'_'\n for vname in self.nc.variables.keys():\n if vname.startswith(prefix):\n if self.nc.dimensions.has_key(vname):\n continue\n if hasattr(self.nc.variables[vname],'cf_role'):\n continue\n data_names.append( vname[len(prefix):] )\n return data_names","def VarData(i, sim_dict, data_folder=\".\", iter_list = []):\n for num in iter_list: # now num is the variable\n temp_dict = _fix_vars(sim_dict, num)\n\n ReMapSim(temp_dict)\n sim = temp_dict['Simulation']\n\n file_name = \"{}/data{}.txt\".format(data_folder, i)\n sim.runVar(\n temp_dict['horizon'],\n temp_dict['cycles'],\n file_name\n )\n\n return None","def get_indexes(self, x):\n indexes = []\n for index_hashes in self.hash_functions:\n combined_index = []\n for idx_spec, hash_func in zip(self.config.index_specs, index_hashes):\n combined_index.append(idx_spec.distribution.get_index(hash_func(x)))\n indexes.append(tuple(combined_index))\n return indexes","def varname_generator(id_name, code_name, code_range):\n # generate variable names according to the ones mentioned in the respective dataset coding report\n select_vars = [id_name]\n for i in range(1, (code_range + 1)):\n # creates a varname and adds an integer to it\n var_name = code_name + '{!s}'.format(i)\n select_vars.append(var_name)\n # finally, return the varname list\n return select_vars","def get_variable_groups(all_inputs):\n row_length = len(all_inputs[0])\n for single_input in all_inputs[1:]:\n if len(single_input) != row_length:\n raise ValueError(\n \"Please make sure the length is the same if you want to input multiple values when the type of variables is t_array or t_mapping\")\n\n final_groups = list()\n row_length = len(all_inputs[0])\n col_length = len(all_inputs)\n for i in range(1, row_length):\n temp_list = list()\n for j in range(col_length):\n temp_list.append((all_inputs[j][0], all_inputs[j][i]))\n final_groups.append(temp_list)\n return final_groups","def variables(s):\n result = set([])\n def walk(s):\n if is_variable(s):\n result.add(s)\n else:\n for arg in s.args:\n walk(arg)\n walk(s)\n return result","def getOriVarNames( self ):\n\n if self.oriVarNames:\n return self.oriVarNames.keys()\n\n n = self.adb.get( \"nOriVars\" )\n for indx in xrange( n ):\n name = self.adb.get( \"oriVarName\",\n indx ) \n self.oriVarNames[name] = indx\n\n return self.oriVarNames.keys()","def get_collections(scope_list):\n variables = [tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope)\n for scope in scope_list]\n return list(chain(*variables))","def _populate_var_to_app_to_idx(outputs, wrt, consider_constant):\n\n # Validate and format consider_constant\n if consider_constant is None:\n consider_constant = []\n else:\n # error checking on consider_constant: verify that it is a collection\n # of aesara variables\n # this is important, if someone accidentally passes a nested data\n # structure with aesara variables at the leaves, only the root will\n # be properly considered constant\n try:\n iter(consider_constant)\n except TypeError:\n raise TypeError(\n \"consider_constant must be an iterable collection,\"\n \" got \" + str(type(consider_constant))\n )\n for elem in consider_constant:\n if not isinstance(elem, Variable):\n raise TypeError(\n \"Elements of consider_constant must be \"\n \"variables, but got \" + str(type(elem))\n )\n\n # var_to_app_to_idx[var][node] = [i,j] means node has\n # var as input at positions i and j\n var_to_app_to_idx = dict()\n\n # Set of variables that have been added to their true parents\n # ('true' here means that the elements of the variable are a function\n # of the elements of the parent, according to the op's\n # connection_pattern)\n # Note: we need to revisit the apply nodes repeatedly, because\n # different outputs of the apply node are connected to\n # different subsets of the inputs.\n accounted_for = set()\n\n def account_for(var):\n # Don't visit the same variable twice\n if var in accounted_for:\n return\n accounted_for.add(var)\n\n # Constants are not a function of anything\n if var in consider_constant:\n return\n\n # Recursively add the variables that this variable is\n # a function of.\n if var.owner is not None:\n app = var.owner\n\n connection_pattern = _node_to_pattern(app)\n\n var_idx = app.outputs.index(var)\n\n for i, ipt in enumerate(app.inputs):\n # don't process ipt if it is not a true\n # parent of var\n if not connection_pattern[i][var_idx]:\n continue\n\n if ipt not in var_to_app_to_idx:\n # This object here *must* be ordered, because\n # we iterate over its keys when adding up the terms of the\n # gradient on ipt. If it is a regular dict, the grad method\n # will return something that is analytically correct, but\n # whose order of doing additions depends on the memory\n # location of the apply nodes.\n var_to_app_to_idx[ipt] = {}\n app_to_idx = var_to_app_to_idx[ipt]\n if app not in app_to_idx:\n app_to_idx[app] = []\n idx = app_to_idx[app]\n if i not in idx:\n idx.append(i)\n account_for(ipt)\n\n # add all variables that are true ancestors of the cost\n for output in outputs:\n account_for(output)\n\n # determine which variables have elements of wrt as a true\n # ancestor. Do this with an upward pass starting from wrt,\n # following only true connections\n visited = set()\n\n def visit(var):\n if var in visited:\n return\n if var not in var_to_app_to_idx:\n return\n visited.add(var)\n nodes = var_to_app_to_idx[var]\n for node in nodes:\n connection_pattern = _node_to_pattern(node)\n for idx in nodes[node]:\n for ii, output in enumerate(node.outputs):\n if connection_pattern[idx][ii]:\n visit(output)\n\n for elem in wrt:\n visit(elem)\n\n # Remove variables that don't have wrt as a true ancestor\n orig_vars = list(var_to_app_to_idx.keys())\n for var in orig_vars:\n if var not in visited:\n del var_to_app_to_idx[var]\n\n return var_to_app_to_idx","def basisVar(bases):\n \n var = [] # The ordered list of active coordinates \n k = 0\n for b in bases:\n if np.isscalar(b):\n # A singleton, inactive coordinate\n k += 1\n else:\n # An active basis\n for i in range(b.nd):\n var.append(k)\n k += 1 \n return var","def evaluate(self, function_name, x):\n stats = None\n for ii in range(self.nunique_vars):\n var = self.unique_variables[ii]\n indices = self.unique_variable_indices[ii]\n for jj in indices:\n stats_jj = np.atleast_1d(getattr(var, function_name)(x[jj, :]))\n assert stats_jj.ndim == 1\n if stats is None:\n stats = np.empty((self.num_vars(), stats_jj.shape[0]))\n stats[jj] = stats_jj\n return stats","def varNames(self):\n return self.__varNames","def indexAll(inputList=None, value=None):\r\n if not isinstance(inputList, list):\r\n raise TypeError('Input list must be a list object.')\r\n return [i for i, x in enumerate(inputList) if x == value]","def get_variables_by_name(given_name, scope=None):\n suffix = '/' + given_name + ':|^' + given_name + ':'\n return get_variables(scope=scope, suffix=suffix)","def _extract_inputs(self, inputs, input_names):\n # inputs is either a dict or a list, where index matches\n # the input banks.\n if isinstance(inputs, dict):\n return [inputs[name] for name in input_names]\n else:\n return [\n inputs[index]\n for index in [self.input_bank_order.index(name) for name in input_names]\n ]","def names_to_indices(names, ordered_names):\r\n indices = []\r\n names_list = list(names)\r\n for ordered_name in ordered_names:\r\n if ordered_name in names_list:\r\n indices.append(names_list.index(ordered_name))\r\n return array(indices)","def extract_cols(lst,indexes):\n return (lst[i] for i in indexes)","def variables(self):\n return [term.variable for term in self.terms]","def get_state(self, var_list):\n raise NotImplementedError()","def get_variable_names(self):\n return [VariableString(s) for s in\n self._design.GetVariables()+self._design.GetPostProcessingVariables()]","def get_output_var_names(self, var_val):\n var_val = self._output_var_names\n return True","def getvarnameindex(self,somename_):\n if isinstance(somename_,unicode):\n somename_ = somename_.encode(\"utf-8\",errors=\"replace\")\n asgn_ = ctypes.c_int32()\n index_ = ctypes.c_int32()\n res = __library__.MSK_XX_getvarnameindex(self.__nativep,somename_,ctypes.byref(asgn_),ctypes.byref(index_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n asgn_ = asgn_.value\n _asgn_return_value = asgn_\n index_ = index_.value\n _index_return_value = index_\n return (_asgn_return_value,_index_return_value)"],"string":"[\n \"def _var_names_to_indices(self, var_names: List):\\n\\n if type(var_names) is not type([]):\\n # Return empty array if input is not 'list' type\\n print(\\\"[_var_names_to_indices] Provided input is not of type list.\\\")\\n return []\\n\\n indices_array = []\\n names_array = []\\n for name in var_names:\\n if name not in self.vars_to_idx.keys():\\n print(\\\"[_var_names_to_indices] Invalid variable name '{}' has been skipped.\\\".format(name))\\n continue\\n indices_array.append(self.vars_to_idx[name])\\n names_array.append(name)\\n\\n if not len(var_names) > 0:\\n print(\\\"[_var_names_to_indices] No (valid) states have been provided.\\\")\\n\\n return indices_array, names_array\",\n \"def indices_of_var(v):\\n name = v.varName\\n indices = name[2:].split(',')\\n i, j = int(indices[0]), int(indices[1])\\n return i, j\",\n \"def index(self, variables):\\n return [self._variables.index(v) for v in variables]\",\n \"def read_variables(var_or_list):\\n session = ph.get_session()\\n return session.run(var_or_list)\",\n \"def compute_variable_indexes(path, overwrite=True, multiproc=False):\\n if multiproc is True:\\n tf.keras.backend.clear_session()\\n set_cpu_option()\\n\\n gin_bindings = [\\n \\\"evaluation.evaluation_fn = @variables_idx\\\",\\n \\\"variables_idx.num_train = 10000\\\", \\\"evaluation.random_seed = 2051556033\\\",\\n \\\"dataset.name='auto'\\\", \\\"evaluation.name = 'variables index'\\\"\\n ]\\n path = pathlib.Path(path)\\n result_path = path.parent.parent / \\\"metrics\\\" / \\\"variance\\\" / \\\"filtered_variables\\\"\\n logger.info(\\\"Computing variable indexes of {}\\\".format(path.parent.parent))\\n gin_evaluation(path, result_path, overwrite, gin_bindings)\",\n \"def vars(self):\\n return [Var(i,self.dims[i]) for i in range(self.nvar)] # TODO: use stored state info (=1 sometimes)\",\n \"def index(self, varname):\\n if not isinstance(varname, str):\\n raise TypeError(\\\"argument must be str\\\")\\n varname = self._find_vars(varname, empty_ok=False, single=True)[0]\\n return self._varlist.index(varname)\",\n \"def vars(svars):\\n return np.array([pm.var(var) for var in svars.split()])\",\n \"def __splitVariableNames(self, name, indexes):\\n if name == 'x':\\n var = self.xCoordinates[indexes[0]][indexes[1]]\\n elif name == 'y':\\n var = self.yCoordinates[indexes[0]][indexes[1]]\\n elif name == 'z':\\n var = self.zCoordinates[indexes[0]][indexes[1]]\\n elif name == 'colorMap':\\n var = self.colorMapCoordinates[indexes[0]][indexes[1]]\\n elif name == 'clusterLabels':\\n var = self.clusterLabels[indexes[0]][indexes[1]]\\n elif name == 'mixtureLabels':\\n var = self.mixtureLabels[indexes[0]][indexes[1]]\\n elif name == 'mixtureMeans':\\n var = self.mixtureMeans[indexes[0]][indexes[1]]\\n elif name == 'mixtureCovars':\\n var = self.mixtureCovars[indexes[0]][indexes[1]]\\n\\n # The variable can contain brackets {} (when the symbol \\\"|\\\" is present in\\n # the variable name), e.g.:\\n # DataName|Input|{RavenAuxiliary|variableName|initial_value}\\n # or it can look like:\\n # DataName|Input|variableName\\n\\n if var is not None:\\n result = [None] * 3\\n if '|input|' in var.lower():\\n match = re.search(r\\\"(\\\\|input\\\\|)\\\", var.lower())\\n elif '|output|' in var.lower():\\n match = re.search(r\\\"(\\\\|output\\\\|)\\\", var.lower())\\n else:\\n self.raiseAnError(IOError, f'In Plot {self.name}, the input coordinate {name} has not specified an \\\"Input\\\" or \\\"Output\\\" (case insensitive). e.g., sourceName|Input|aVariable) in {var}')\\n startLoc, endLoc = match.start(), match.end()\\n result = [var[:startLoc].strip(), var[startLoc+1:endLoc-1].strip(), var[endLoc:].strip()]\\n if '{' in result[-1] and '}' in result[-1]:\\n locLower, locUpper = result[-1].find(\\\"{\\\"), result[-1].rfind(\\\"}\\\")\\n result[-1] = result[-1][locLower + 1:locUpper].strip()\\n else:\\n result = None\\n\\n return result\",\n \"def variables_used (self) :\\r\\n\\t\\t## These names do not contain dimension specification (everything in brackets\\r\\n\\t\\t## that comes after a name is am array index - either the arry was declared\\r\\n\\t\\t## correctly or it is wrong anyway, there is no implicit declaration of arrays) !\\r\\n\\r\\n\\t\\tresult = []\\r\\n\\r\\n\\t\\tfor l in self.equ_lists :\\r\\n\\t\\t\\tfor var_name in l :\\r\\n\\t\\t\\t\\tresult.append(var_name[0])\\r\\n\\t\\treturn result\",\n \"def variable_parser(var_list, prefix):\\r\\n ret_list = []\\r\\n for var in var_list:\\r\\n varname = var.name\\r\\n varprefix = varname.split('/')[0]\\r\\n if varprefix == prefix:\\r\\n ret_list.append(var)\\r\\n return ret_list\",\n \"def get_list_vars(my_vars):\\n lists = []\\n for var in my_vars:\\n try:\\n temp = my_vars[var].getValue()\\n #print var + '=' + str(temp)\\n except ValueError:\\n lists.append(var)\\n return lists\",\n \"def variable_parser(var_list, prefix):\\r\\n ret_list = []\\r\\n for var in var_list:\\r\\n varname = var.name\\r\\n varprefix = varname.split('/')[0]\\r\\n if varprefix == prefix:\\r\\n ret_list.append(var)\\r\\n elif prefix in varname:\\r\\n ret_list.append(var)\\r\\n return ret_list\",\n \"def get_indexes(self, variable, *args):\\n\\n return [get_subset_idxs(data, min, max)\\n for data, (min, max) in args]\",\n \"def _find_vars(self, varnames, unique=False, evars=False, all_ok=False, \\n empty_ok=False, single=False):\\n if isinstance(varnames, str):\\n varnames = (varnames,)\\n elif not isinstance(varnames, collections.Iterable):\\n raise TypeError(\\\"variable names should be str or iterable of str\\\")\\n \\n # first split into list of single abbrevs per str\\n split_names = []\\n for name in varnames:\\n if not isinstance(name, str):\\n raise TypeError(\\\"must specify variables as string(s)\\\")\\n split_names += name.split()\\n nnames = len(split_names)\\n \\n # check for _all, check for proper usage, and return copy of varlist\\n # if evars==False or ['_dta'] + varlist if evars==True\\n all_specified = False\\n if '_all' in split_names:\\n if not all_ok:\\n raise ValueError(\\\"\\\\\\\"_all\\\\\\\" not allowed in this context\\\")\\n elif not nnames == 1:\\n raise ValueError(\\n \\\"\\\\\\\"_all\\\\\\\" may not be combined with other names\\\")\\n all_specified = True\\n all_names = (['_dta'] if evars else []) + list(self._varlist)\\n nnames = len(all_names)\\n \\n # check that more than 0 names specified if empty_ok==False, and\\n # ignore extras (with message) if single==True\\n if not empty_ok and nnames == 0:\\n raise ValueError(\\\"no variables specified\\\")\\n if single and nnames > 1:\\n if not self._quiet:\\n smcl = \\\"{err}\\\" if IN_STATA else \\\"\\\"\\n msg = smcl + \\\"only one {}varname allowed; ignoring the rest\\\"\\n print(msg.format('e' if evars else ''))\\n split_names = split_names[:1]\\n \\n # if all_specified, return aleady-constructed all_names\\n if all_specified:\\n return all_names\\n \\n # Create match list of [abbrev, match1, match2, ...].\\n # The loops below identify when exact varname given, but that varname\\n # happens to be abbreviation of other varnames.\\n varlist = self._varlist\\n matches = []\\n append = matches.append\\n if evars:\\n for name in split_names:\\n if name == \\\"_dta\\\":\\n append([name, name])\\n else:\\n match = [var for var in varlist if var.startswith(name)]\\n append([name, name] if name in match else [name] + match)\\n else:\\n for name in split_names:\\n match = [var for var in varlist if var.startswith(name)]\\n append([name, name] if name in match else [name] + match)\\n \\n # abbreviation was a good, unambiguous abbreviation if exactly\\n # one match found, i.e. if the corresponding entry in -matches- \\n # is [abbrev, match1]\\n if not all(len(m) == 2 for m in matches):\\n # there were unmatched or ambiguous abbreviations\\n zeros = \\\" \\\".join([m[0] for m in matches if len(m) == 1])\\n twos = \\\" \\\".join([m[0] for m in matches if len(m) >= 3])\\n if zeros != \\\"\\\" and twos != \\\"\\\":\\n msg = \\\"no variables found for {}; multiple found for {}\\\"\\n raise ValueError(msg.format(zeros, twos))\\n if zeros != \\\"\\\":\\n raise ValueError(\\n \\\"no variables found for {}\\\".format(zeros, twos))\\n # if getting here, twos != \\\"\\\" and zeros == \\\"\\\"\\n raise ValueError(\\\"multiple variables found for '{}'\\\".format(twos))\\n \\n if not unique:\\n return [m[1] for m in matches]\\n seen = set()\\n # if name has not been encountered, add to list and set of encountered\\n return [m[1] for m in matches \\n if m[1] not in seen and not seen.add(m[1])]\",\n \"def map_to_scope(var_list):\\n return {var.op.name.split('/', 1)[1]: var for var in var_list}\",\n \"def _get_variables(self, sim_outputs: List = None):\\n \\n # Ensure model has been initialized at least once\\n self._model_has_been_initialized(\\\"_get_variables\\\")\\n\\n # Ensure array is not empty\\n if sim_outputs is None:\\n return {}\\n elif not len(sim_outputs) > 0:\\n #print(\\\"[_get_variables] No var names were provided. No vars are returned.\\\")\\n return {}\\n\\n\\n sim_output_indices, sim_output_names = self._var_names_to_indices(sim_outputs)\\n \\n # Check if more than one index has been found\\n if not len(sim_output_indices) > 0:\\n #print(\\\"[_get_variables] No valid var names have been provided. No vars are returned.\\\")\\n return {}\\n \\n outputs_dict = dict(zip(sim_output_names, self.fmu.getReal(sim_output_indices)))\\n\\n return outputs_dict\",\n \"def get_input_var_names(self):\\n return self._input_var_names\",\n \"def get_vars_by_prefix(self, prefix):\\n\\n t_vars = tf.global_variables()\\n return [var for var in t_vars if prefix in var.name]\",\n \"def _get_indices(self, indices: VecEnvIndices) -> Iterable[int]:\\n if indices is None:\\n indices = range(self.num_envs)\\n elif isinstance(indices, int):\\n indices = [indices]\\n return indices\",\n \"def get_varnams(self, varnam_list):\\n self.varnams = [[v.name, v.composer, v.talam] for v in varnam_list\\n if v.raga == self.name]\",\n \"def get_input_names(self):\\n inputNames = []\\n for inVar in self.inputs:\\n # inVar is of type InOutVar and the object that it contains is a PyFMI variable\\n inputNames.append(inVar.get_object().name)\\n return inputNames\",\n \"def variables(self):\\n return [i.name for i in self.inputs + self.outputs]\",\n \"def coord_indices_of(self, v_list):\\n return [self.coord_index_of(v) for v in v_list]\",\n \"def get_vars(self):\\n return [self.mu, self.var]\",\n \"def get_name_list(msh, varname):\\n return [str(chartostring(v)) for v in msh.variables[varname]]\",\n \"def variable_names(self):\\n \\n return [x['variable'] for x in self.variable_dicts()]\",\n \"def get_indexes(from_list, find_list):\\n\\n df_find = pd.DataFrame(find_list, columns=['value'])\\n df_from = pd.DataFrame(list(zip(from_list, np.arange(len(from_list)))), columns=['value', 'index'])\\n indexes = pd.merge(df_from, df_find, on='value', how='inner')['index'].values\\n return indexes\",\n \"def get_predicate_indices(tags: List[str]) -> List[int]:\\n return [ind for ind, tag in enumerate(tags) if \\\"V\\\" in tag]\",\n \"def get_vars(scope=''):\\n return [x for x in tf.trainable_variables() if scope in x.name]\",\n \"def varlist(data):\\n \\n Bxm=data[\\\"Bx\\\"].mean()\\n Bym=data[\\\"By\\\"].mean()\\n Bzm=data[\\\"Bz\\\"].mean()\\n Bxsqm=(data[\\\"Bx\\\"]**2).mean()\\n Bysqm=(data[\\\"By\\\"]**2).mean()\\n Bzsqm=(data[\\\"Bz\\\"]**2).mean()\\n Bxym=(data[\\\"Bx\\\"]*data[\\\"By\\\"]).mean()\\n Bxzm=(data[\\\"Bx\\\"]*data[\\\"Bz\\\"]).mean()\\n Byzm=(data[\\\"By\\\"]*data[\\\"Bz\\\"]).mean()\\n \\n Varx= Bxsqm-Bxm**2\\n Varxy=Bxym-Bxm*Bym\\n Varxz=Bxzm-Bxm*Bzm\\n Vary=Bysqm-Bym**2\\n Varyz=Byzm-Bym*Bzm\\n Varz=Bzsqm-Bzm**2\\n var=[Varx,Varxy, Varxz,Varxy,Vary,Varyz,Varxz,Varyz,Varz]\\n var=np.array(var)\\n var=var.reshape((3,3))\\n return var\",\n \"def find_varying(params, nvmax):\\n print('Finding variable parameters')\\n if nvmax < 0:\\n raise ValueError(f'nvmax ({nvmax}) must be positive')\\n\\n var = []\\n cnt = 0\\n for p in params:\\n if len(params[p]) == 2:\\n if (params[p][1] - params[p][0]) < 0:\\n raise ValueError(f'range is inverted for param: {p}')\\n elif cnt >= nvmax:\\n raise ValueError(f'too many param ranges were given. Expected {nvmax}')\\n else:\\n var.append(p)\\n cnt += 1\\n return var\",\n \"def GetListVariable(self, name):\\n var = self._makefile.variables.get(name, expand=True)[2]\\n if not var:\\n return []\\n return var.resolvesplit(self._makefile, self._makefile.variables)\",\n \"def getOqiVarNames( self ):\\n\\n if self.oqiVarNames:\\n return self.oqiVarNames.keys()\\n\\n n = self.adb.get( \\\"nOqiVars\\\" )\\n for indx in xrange( n ):\\n name = self.adb.get( \\\"oqiVarName\\\",\\n indx ) \\n self.oqiVarNames[name] = indx\\n\\n return self.oqiVarNames.keys()\",\n \"def get_list_slice_vars(list_):\\n\\n # Dump unwanted portions\\n array, list_ = list_.split(\\\"[\\\")\\n list_ = list_.split(\\\"]\\\", 1)[0]\\n\\n # Split at ':'\\n variables = list_.split(\\\":\\\")\\n var_count = len(variables)\\n\\n step = \\\"\\\"\\n\\n # If step provided\\n if var_count == 3:\\n\\n # If provided, store provided values\\n start, stop, step = variables\\n else:\\n\\n # Else store start, stop with default step\\n start, stop = variables\\n\\n # If values are not provided by user, fall back to defaults\\n\\n # Set start default to 0\\n if not start:\\n start = \\\"0\\\"\\n\\n # Set stop default to array length\\n if not stop:\\n stop = \\\"Array.length\\\"\\n\\n # Set step default to 1\\n if not step:\\n step = \\\"1\\\"\\n\\n # Return stripped array with extracted values\\n return array, start, stop, step\",\n \"def get_output_names(self):\\n outputNames = []\\n for outVar in self.outputs:\\n # outVar is of type InOutVar and the object that it contains is a PyFMI variable\\n outputNames.append(outVar.get_object().name)\\n return outputNames\",\n \"def get_variable_names(self):\\n return [var[1] for var in self.variables]\",\n \"def stats_variable_names(res):\\n def varname(s):\\n pos = s.find(':')\\n return s if pos==-1 else s[0:pos]\\n return set( [ varname(key) for key in res.keys()] )\",\n \"def var_index(self, code=1, s=False):\\n if s:\\n code = 2\\n\\n index = None\\n for i, var in enumerate(self.primary_header['variables']):\\n if var['Variable code'] == code:\\n assert index is None, 'Appears to be two sets of same data in profile'\\n index = i\\n return index\",\n \"def getVar2FactorsMap(self):\\r\\n V = self.getAllNodes()\\r\\n return list(list(idx for idx,f in enumerate(self.factors) if i in f.var) for i in V)\",\n \"def get_variable_values(self, vars):\\n raise NotImplementedError()\",\n \"def getVariableList(dataset):\\n variables = [v for v in dataset.variables.keys() if v not in dataset.dimensions.keys()]\\n for d in dataset.dimensions.keys():\\n try:\\n variables.pop(variables.index(dataset.variables[d].getncattr(\\\"bounds\\\")))\\n except:\\n pass\\n return variables\",\n \"def get_column_indices(indices, inputs, multiple):\\n if multiple:\\n res = OrderedDict()\\n for p in indices:\\n ov, onnx_i = get_column_index(p, inputs)\\n if ov not in res:\\n res[ov] = []\\n res[ov].append(onnx_i)\\n return res\\n\\n onnx_var = None\\n onnx_is = []\\n for p in indices:\\n ov, onnx_i = get_column_index(p, inputs)\\n onnx_is.append(onnx_i)\\n if onnx_var is None:\\n onnx_var = ov\\n elif onnx_var != ov:\\n cols = [onnx_var, ov]\\n raise NotImplementedError( # pragma: no cover\\n \\\"sklearn-onnx is not able to merge multiple columns from \\\"\\n \\\"multiple variables ({0}). You should think about merging \\\"\\n \\\"initial types.\\\".format(cols))\\n return onnx_var, onnx_is\",\n \"def _get_dependent_variables(input_ops, output_ops):\\n\\n # avoids the edge-case when input_ops == output_ops.\\n output_ops = nest.map_structure(gen_array_ops.identity, output_ops)\\n inbetween_ops = op_selector.get_backward_walk_ops(\\n seed_ops=output_ops,\\n stop_at_ts=input_ops,\\n inclusive=False,\\n only_differentiable=True)\\n var_ops = (op for op in inbetween_ops if op.type in VAR_OP_TYPES)\\n var_names = (op.name for op in var_ops)\\n tf_vars = (get_variable_by_name(var_name) for var_name in var_names)\\n tf_vars = [v for v in tf_vars if v is not None]\\n return tf_vars\",\n \"def inputs(variable_list, blockers=None):\\r\\n vlist = ancestors(variable_list, blockers)\\r\\n rval = [r for r in vlist if r.owner is None]\\r\\n return rval\",\n \"def extract_var_data(self, var_names):\\n variable_dict = {} # Declaring dictionary used to store key-val pairs, var_name as key and the array as the value\\n try:\\n for var in var_names:\\n variable_dict[var] = self.dataset[var].values\\n return variable_dict\\n except Exception as e:\\n print(\\\"An Error occured:\\\", e)\\n raise e\",\n \"def GetVariableArrayDetails(line: str, variables: dict, report) -> (str, int):\\n\\n if re.sub(r\\\" (\\\\d+)$\\\", \\\"\\\", line ) in variables:\\n var_name = re.sub(r\\\" (\\\\d+)$\\\", \\\"\\\", line )\\n if not var_name in variables:\\n raise Exception(f\\\"Couldn't find variable {var_name}\\\")\\n if not variables[ var_name ]._isArray():\\n raise Exception(f\\\"Variable {var_name} isn't an array\\\")\\n try:\\n var_index = int( re.search(r\\\" (\\\\d+$)\\\", line).group(0).strip() )\\n except:\\n raise Exception(\\\"Invalid array index\\\")\\n return var_name, var_index-1\\n elif \\\" of \\\" in line:\\n line_split = line.split(' of ')\\n var_name_index = line_split[0].strip()\\n var_name = line_split[1].strip()\\n\\n if not var_name in variables:\\n raise Exception(f\\\"Couldn't find variable {var_name}\\\")\\n if not variables[ var_name ]._isArray():\\n raise Exception(f\\\"Variable {var_name} isn't an array\\\")\\n\\n arith_check, arith_value = FCalc.FiMArithmetic.IsArithmetic( var_name_index )\\n if arith_check:\\n if not arith_value.is_prefix:\\n arith = FCalc.FiMArithmetic(var_name_index, arith_value)\\n arith.GetValues(variables, report)\\n var_index = arith.Calculate()\\n else:\\n if not var_name_index in variables:\\n raise Exception(f\\\"Couldn't find variable {var_name_index}\\\")\\n if variables[ var_name_index ].Type != FClass.FiMVariableTypes.NUMBER:\\n raise Exception(f\\\"Variable {var_name_index} isn't a number\\\")\\n if _isFloat( variables[ var_name_index ].getv() ):\\n raise Exception(f\\\"Cannot use floating-point numbers as array index\\\")\\n var_index = variables[ var_name_index ].getv()\\n return var_name, var_index-1\\n\\n return None, None\",\n \"def __get_vars_list(self, template_idx, log):\\n template = self.templates[template_idx].split()\\n log = log.split()\\n variables = []\\n pt = pl = 0\\n while pt < len(template) and pl < len(log):\\n if template[pt] == log[pl]:\\n pt += 1\\n pl += 1\\n continue\\n elif template[pt] == '*':\\n # found a variable\\n while pt < len(template) and template[pt] == '*':\\n # in case there are many variables together\\n pt += 1\\n if pt >= len(template):\\n # it's the end of the template\\n variables.append(' '.join(log[pl:]))\\n break\\n else:\\n variable_tokens = []\\n while pl < len(log) and log[pl] != template[pt]:\\n variable_tokens.append(log[pl])\\n pl += 1\\n # it duplicates when many variables together for a correct output\\n variables.append(' '.join(variable_tokens))\\n else:\\n # it is a variable not covered by the template asterisks\\n # we move on on the log but stay on the template token\\n pl += 1\\n return variables\",\n \"def get_n_params(var_list):\\n return int(np.sum([np.product(\\n [x.value for x in var.get_shape()]) for var in var_list]))\",\n \"def getDummyVars(input_dict, feature_list, prefix=\\\"DummyVars_\\\"):\\n\\theader_list = []\\n\\tfeat_list = []\\n\\tfor ind,feat in enumerate(feature_list):\\n\\t\\theader_list.append(prefix+str(ind))\\n\\t\\tif input_dict.has_key(feat):\\n\\t\\t\\tfeat_list.append(input_dict[feat])\\n\\t\\telse:\\n\\t\\t\\tfeat_list.append(0)\\n\\treturn feat_list, header_list\",\n \"def getVariableInfo(self, variables, name):\\r\\n\\r\\n return [var.return_variable_dict() for var in variables if var.name == name][0]\",\n \"def cnf_variables(cnf):\\n variabs = set()\\n\\n for clause in cnf:\\n for var in clause:\\n var = abs(var)\\n\\n if var not in variabs:\\n variabs.add(var)\\n\\n return variabs\",\n \"def convert_vars(self, input):\\n input = self.convert(input)\\n if not isinstance(input, list):\\n input = [input]\\n converted = []\\n for item in input:\\n if item in self._variables:\\n converted.append(self._variables[item])\\n else:\\n converted.append(item)\\n if len(converted) == 1:\\n converted = converted[0]\\n return converted\",\n \"def get_valid(self, names):\\n\\n ret = [None] * len(names)\\n posdict = dict([(name, i) for i, name in enumerate(names)])\\n\\n for compname, varnames in partition_names_by_comp(names).items():\\n if compname is None:\\n vals = super(Assembly, self).get_valid(varnames)\\n for i, val in enumerate(vals):\\n ret[posdict[varnames[i]]] = val\\n else:\\n comp = getattr(self, compname)\\n if isinstance(comp, Component):\\n vals = comp.get_valid(varnames)\\n else:\\n vals = [self._valid_dict['.'.join([compname, vname])] for vname in varnames]\\n for i, val in enumerate(vals):\\n full = '.'.join([compname, varnames[i]])\\n ret[posdict[full]] = val\\n return ret\",\n \"def _instantiate_vars(labels: np.ndarray):\\n n = len(labels)\\n if len(set(labels)) == n:\\n index_seed = np.arange(n)\\n index_remain = np.arange(n)\\n else:\\n index_seed = np.argwhere(labels >= 0).ravel()\\n index_remain = np.argwhere(labels < 0).ravel()\\n labels = labels[index_seed]\\n return index_seed.astype(np.int32), index_remain.astype(np.int32), labels.astype(np.int32)\",\n \"def getOhcVarNames( self ):\\n\\n if self.ohcVarNames:\\n return self.ohcVarNames.keys()\\n \\n n = self.adb.get( \\\"nOhcVars\\\" )\\n for indx in xrange( n ):\\n name = self.adb.get( \\\"ohcVarName\\\",\\n indx ) \\n self.ohcVarNames[name] = indx\\n\\n return self.ohcVarNames.keys()\",\n \"def var_names(self):\\n return self._var_names\",\n \"def get_variable_names(self):\\n varNames = []\\n for var in self.variables:\\n # EstimationVariable\\n varNames.append(var.name)\\n return varNames\",\n \"def getOthVarNames( self ):\\n\\n if self.othVarNames:\\n return self.othVarNames.keys()\\n\\n n = self.adb.get( \\\"nOthVars\\\" )\\n for indx in range( n ):\\n name = self.adb.get( \\\"othVarName\\\",\\n indx ) \\n self.othVarNames[ name ] = indx\\n\\n return self.othVarNames.keys()\",\n \"def get_wind_indices(predictor_names):\\n\\n try:\\n u_wind_index = predictor_names.index(\\n processed_narr_io.U_WIND_GRID_RELATIVE_NAME)\\n except ValueError:\\n error_string = (\\n '\\\\n{0:s}\\\\nPredictor names (shown above) must include \\\"{1:s}\\\".'\\n ).format(str(predictor_names),\\n processed_narr_io.U_WIND_GRID_RELATIVE_NAME)\\n\\n raise ValueError(error_string)\\n\\n try:\\n v_wind_index = predictor_names.index(\\n processed_narr_io.V_WIND_GRID_RELATIVE_NAME)\\n except ValueError:\\n error_string = (\\n '\\\\n{0:s}\\\\nPredictor names (shown above) must include \\\"{1:s}\\\".'\\n ).format(str(predictor_names),\\n processed_narr_io.V_WIND_GRID_RELATIVE_NAME)\\n\\n raise ValueError(error_string)\\n\\n return u_wind_index, v_wind_index\",\n \"def variables_used (self) :\\r\\n\\t\\treturn [i[0] for i in self.parameters]\",\n \"def getOfcVarNames( self ):\\n\\n if self.ofcVarNames:\\n return self.ofcVarNames.keys()\\n \\n n = self.adb.get( \\\"nOfcVars\\\" )\\n for indx in xrange( n ):\\n name = self.adb.get( \\\"ofcVarName\\\",\\n indx ) \\n self.ofcVarNames[name] = indx\\n\\n return self.ofcVarNames.keys()\",\n \"def getvarnameindex(self,somename_): # 3\\n res,resargs = self.__obj.getvarnameindex(somename_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _asgn_return_value,_index_return_value = resargs\\n return _asgn_return_value,_index_return_value\",\n \"def _find_regressors(self,eqn,varname) :\\n\\t\\ttrue_params = self.ss._ss_params\\n\\t\\tparams = true_params[varname][eqn-1]\\t\\t\\n\\t\\tregressors = [ii+1 for ii,p in enumerate(params) if p!=0 ]\\t\\t\\n\\t\\treturn regressors\",\n \"def variables(self):\\n return np.array(list(self._match_result_dict.keys()))\",\n \"def means_of_variables(dataset, name_of_variable):\\r\\n list_of_means = []\\r\\n for num in range(len(dataset)):\\r\\n sum_of_variables = 0\\r\\n for row in dataset[num].items():\\r\\n if name_of_variable in row[0]:\\r\\n if row[1].strip() != \\\"\\\":\\r\\n sum_of_variables += int(row[1])\\r\\n if sum_of_variables != 0:\\r\\n list_of_means.append(float(sum_of_variables) / number_of_variables(dataset, name_of_variable)) \\r\\n return list_of_means\",\n \"def getDefinedVars(self):\\n numTabs = self.tabWidget.count()\\n varList = []\\n \\n for i in range(numTabs):\\n var = self.tabWidget.widget(i).getVar()\\n name = self.tabWidget.tabText(i)\\n varList.append([name, var])\\n\\n return varList\",\n \"def getOsiVarNames( self ):\\n\\n if self.osiVarNames:\\n return self.osiVarNames.keys()\\n \\n n = self.adb.get( \\\"nOsiVars\\\" )\\n for indx in xrange( n ):\\n name = self.adb.get( \\\"osiVarName\\\",\\n indx ) \\n self.osiVarNames[ name ]= indx\\n\\n return self.osiVarNames.keys()\",\n \"def _returnSplitIndex(self, var, pltIndex, ind):\\n\\n firstSplit = self.__splitVariableNames(var, (pltIndex, ind))\\n if firstSplit[2].strip() not in self.sourceData[pltIndex].getVars(firstSplit[1].lower()):\\n self.raiseAnError(IOError, f'Variable \\\"{firstSplit[2]}\\\" not found in \\\"{firstSplit[1]}\\\" of DataObject \\\"{self.sourceData[pltIndex].name}\\\"!')\\n split = firstSplit[2]\\n\\n return split\",\n \"def input_names(self) -> List[Union[str, int]]:\\n return [x.name or i for i, x in enumerate(self.inputs)]\",\n \"def findall_var(formula, variable):\\n res = []\\n s = Solver()\\n s.add(formula)\\n while True:\\n if s.check() == sat:\\n m = s.model()\\n res.append(m)\\n value = m[variable]\\n if value == None:\\n return res\\n s.add(variable != value)\\n else:\\n return res\",\n \"def getOeiVarNames( self ):\\n\\n if self.oeiVarNames:\\n return self.oeiVarNames.keys()\\n\\n n = self.adb.get( \\\"nOeiVars\\\" )\\n for indx in xrange( n ):\\n name = self.adb.get( \\\"oeiVarName\\\",\\n indx ) \\n self.oeiVarNames[name] = indx\\n\\n return self.oeiVarNames.keys()\",\n \"def get_yields(wsp, binname, yield_names):\\n yields = []\\n for name in yield_names:\\n var_name = '_'.join([name, binname])\\n yields.append(get_var(wsp, var_name).getVal())\\n\\n return np.sum(yields)\",\n \"def get_swp_values(self, var: str) -> List[Any]:\\n return self._sweep_params[var]\",\n \"def __setVarNames(self):\\n result = set()\\n\\n # detecting variables\\n for templatePart in self.inputString().split(\\\"{\\\"):\\n if templatePart is '' or \\\"}\\\" not in templatePart:\\n continue\\n\\n endIndex = templatePart.find('}')\\n result.add(templatePart[:endIndex])\\n\\n self.__varNames = list(result)\",\n \"def _returnSplitIndexHS(self, var, pltIndex, ind):\\n\\n Split = self.__splitVariableNames(var, (pltIndex, ind))\\n outputIndexes = self.sourceData[pltIndex].indexes if Split[1].lower() == 'output' else []\\n if Split[2].strip() not in self.sourceData[pltIndex].getVars(Split[1].lower())+outputIndexes:\\n self.raiseAnError(IOError, f'Variable \\\"{Split[2]}\\\" not found in \\\"{Split[1]}\\\" of DataObject \\\"{self.sourceData[pltIndex].name}\\\"!')\\n split = Split[2]\\n\\n return split\",\n \"def trainable_variables(self, name=None):\\n\\n _m = [self.modules.get(name)] if name else self.modules.values()\\n _var = []\\n for i in _m:\\n _var += filter(lambda p: p.requires_grad, i.parameters())\\n return _var\",\n \"def vars(*tensor_types):\\n return map(var, tensor_types)\",\n \"def indices_of_split(self, split_name='train'):\\n return self.indices_of('split', split_name)\",\n \"def data_variable_names(self):\\n data_names = []\\n mesh = self.mesh_names()\\n prefix = mesh[0]+'_'\\n for vname in self.nc.variables.keys():\\n if vname.startswith(prefix):\\n if self.nc.dimensions.has_key(vname):\\n continue\\n if hasattr(self.nc.variables[vname],'cf_role'):\\n continue\\n data_names.append( vname[len(prefix):] )\\n return data_names\",\n \"def VarData(i, sim_dict, data_folder=\\\".\\\", iter_list = []):\\n for num in iter_list: # now num is the variable\\n temp_dict = _fix_vars(sim_dict, num)\\n\\n ReMapSim(temp_dict)\\n sim = temp_dict['Simulation']\\n\\n file_name = \\\"{}/data{}.txt\\\".format(data_folder, i)\\n sim.runVar(\\n temp_dict['horizon'],\\n temp_dict['cycles'],\\n file_name\\n )\\n\\n return None\",\n \"def get_indexes(self, x):\\n indexes = []\\n for index_hashes in self.hash_functions:\\n combined_index = []\\n for idx_spec, hash_func in zip(self.config.index_specs, index_hashes):\\n combined_index.append(idx_spec.distribution.get_index(hash_func(x)))\\n indexes.append(tuple(combined_index))\\n return indexes\",\n \"def varname_generator(id_name, code_name, code_range):\\n # generate variable names according to the ones mentioned in the respective dataset coding report\\n select_vars = [id_name]\\n for i in range(1, (code_range + 1)):\\n # creates a varname and adds an integer to it\\n var_name = code_name + '{!s}'.format(i)\\n select_vars.append(var_name)\\n # finally, return the varname list\\n return select_vars\",\n \"def get_variable_groups(all_inputs):\\n row_length = len(all_inputs[0])\\n for single_input in all_inputs[1:]:\\n if len(single_input) != row_length:\\n raise ValueError(\\n \\\"Please make sure the length is the same if you want to input multiple values when the type of variables is t_array or t_mapping\\\")\\n\\n final_groups = list()\\n row_length = len(all_inputs[0])\\n col_length = len(all_inputs)\\n for i in range(1, row_length):\\n temp_list = list()\\n for j in range(col_length):\\n temp_list.append((all_inputs[j][0], all_inputs[j][i]))\\n final_groups.append(temp_list)\\n return final_groups\",\n \"def variables(s):\\n result = set([])\\n def walk(s):\\n if is_variable(s):\\n result.add(s)\\n else:\\n for arg in s.args:\\n walk(arg)\\n walk(s)\\n return result\",\n \"def getOriVarNames( self ):\\n\\n if self.oriVarNames:\\n return self.oriVarNames.keys()\\n\\n n = self.adb.get( \\\"nOriVars\\\" )\\n for indx in xrange( n ):\\n name = self.adb.get( \\\"oriVarName\\\",\\n indx ) \\n self.oriVarNames[name] = indx\\n\\n return self.oriVarNames.keys()\",\n \"def get_collections(scope_list):\\n variables = [tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope)\\n for scope in scope_list]\\n return list(chain(*variables))\",\n \"def _populate_var_to_app_to_idx(outputs, wrt, consider_constant):\\n\\n # Validate and format consider_constant\\n if consider_constant is None:\\n consider_constant = []\\n else:\\n # error checking on consider_constant: verify that it is a collection\\n # of aesara variables\\n # this is important, if someone accidentally passes a nested data\\n # structure with aesara variables at the leaves, only the root will\\n # be properly considered constant\\n try:\\n iter(consider_constant)\\n except TypeError:\\n raise TypeError(\\n \\\"consider_constant must be an iterable collection,\\\"\\n \\\" got \\\" + str(type(consider_constant))\\n )\\n for elem in consider_constant:\\n if not isinstance(elem, Variable):\\n raise TypeError(\\n \\\"Elements of consider_constant must be \\\"\\n \\\"variables, but got \\\" + str(type(elem))\\n )\\n\\n # var_to_app_to_idx[var][node] = [i,j] means node has\\n # var as input at positions i and j\\n var_to_app_to_idx = dict()\\n\\n # Set of variables that have been added to their true parents\\n # ('true' here means that the elements of the variable are a function\\n # of the elements of the parent, according to the op's\\n # connection_pattern)\\n # Note: we need to revisit the apply nodes repeatedly, because\\n # different outputs of the apply node are connected to\\n # different subsets of the inputs.\\n accounted_for = set()\\n\\n def account_for(var):\\n # Don't visit the same variable twice\\n if var in accounted_for:\\n return\\n accounted_for.add(var)\\n\\n # Constants are not a function of anything\\n if var in consider_constant:\\n return\\n\\n # Recursively add the variables that this variable is\\n # a function of.\\n if var.owner is not None:\\n app = var.owner\\n\\n connection_pattern = _node_to_pattern(app)\\n\\n var_idx = app.outputs.index(var)\\n\\n for i, ipt in enumerate(app.inputs):\\n # don't process ipt if it is not a true\\n # parent of var\\n if not connection_pattern[i][var_idx]:\\n continue\\n\\n if ipt not in var_to_app_to_idx:\\n # This object here *must* be ordered, because\\n # we iterate over its keys when adding up the terms of the\\n # gradient on ipt. If it is a regular dict, the grad method\\n # will return something that is analytically correct, but\\n # whose order of doing additions depends on the memory\\n # location of the apply nodes.\\n var_to_app_to_idx[ipt] = {}\\n app_to_idx = var_to_app_to_idx[ipt]\\n if app not in app_to_idx:\\n app_to_idx[app] = []\\n idx = app_to_idx[app]\\n if i not in idx:\\n idx.append(i)\\n account_for(ipt)\\n\\n # add all variables that are true ancestors of the cost\\n for output in outputs:\\n account_for(output)\\n\\n # determine which variables have elements of wrt as a true\\n # ancestor. Do this with an upward pass starting from wrt,\\n # following only true connections\\n visited = set()\\n\\n def visit(var):\\n if var in visited:\\n return\\n if var not in var_to_app_to_idx:\\n return\\n visited.add(var)\\n nodes = var_to_app_to_idx[var]\\n for node in nodes:\\n connection_pattern = _node_to_pattern(node)\\n for idx in nodes[node]:\\n for ii, output in enumerate(node.outputs):\\n if connection_pattern[idx][ii]:\\n visit(output)\\n\\n for elem in wrt:\\n visit(elem)\\n\\n # Remove variables that don't have wrt as a true ancestor\\n orig_vars = list(var_to_app_to_idx.keys())\\n for var in orig_vars:\\n if var not in visited:\\n del var_to_app_to_idx[var]\\n\\n return var_to_app_to_idx\",\n \"def basisVar(bases):\\n \\n var = [] # The ordered list of active coordinates \\n k = 0\\n for b in bases:\\n if np.isscalar(b):\\n # A singleton, inactive coordinate\\n k += 1\\n else:\\n # An active basis\\n for i in range(b.nd):\\n var.append(k)\\n k += 1 \\n return var\",\n \"def evaluate(self, function_name, x):\\n stats = None\\n for ii in range(self.nunique_vars):\\n var = self.unique_variables[ii]\\n indices = self.unique_variable_indices[ii]\\n for jj in indices:\\n stats_jj = np.atleast_1d(getattr(var, function_name)(x[jj, :]))\\n assert stats_jj.ndim == 1\\n if stats is None:\\n stats = np.empty((self.num_vars(), stats_jj.shape[0]))\\n stats[jj] = stats_jj\\n return stats\",\n \"def varNames(self):\\n return self.__varNames\",\n \"def indexAll(inputList=None, value=None):\\r\\n if not isinstance(inputList, list):\\r\\n raise TypeError('Input list must be a list object.')\\r\\n return [i for i, x in enumerate(inputList) if x == value]\",\n \"def get_variables_by_name(given_name, scope=None):\\n suffix = '/' + given_name + ':|^' + given_name + ':'\\n return get_variables(scope=scope, suffix=suffix)\",\n \"def _extract_inputs(self, inputs, input_names):\\n # inputs is either a dict or a list, where index matches\\n # the input banks.\\n if isinstance(inputs, dict):\\n return [inputs[name] for name in input_names]\\n else:\\n return [\\n inputs[index]\\n for index in [self.input_bank_order.index(name) for name in input_names]\\n ]\",\n \"def names_to_indices(names, ordered_names):\\r\\n indices = []\\r\\n names_list = list(names)\\r\\n for ordered_name in ordered_names:\\r\\n if ordered_name in names_list:\\r\\n indices.append(names_list.index(ordered_name))\\r\\n return array(indices)\",\n \"def extract_cols(lst,indexes):\\n return (lst[i] for i in indexes)\",\n \"def variables(self):\\n return [term.variable for term in self.terms]\",\n \"def get_state(self, var_list):\\n raise NotImplementedError()\",\n \"def get_variable_names(self):\\n return [VariableString(s) for s in\\n self._design.GetVariables()+self._design.GetPostProcessingVariables()]\",\n \"def get_output_var_names(self, var_val):\\n var_val = self._output_var_names\\n return True\",\n \"def getvarnameindex(self,somename_):\\n if isinstance(somename_,unicode):\\n somename_ = somename_.encode(\\\"utf-8\\\",errors=\\\"replace\\\")\\n asgn_ = ctypes.c_int32()\\n index_ = ctypes.c_int32()\\n res = __library__.MSK_XX_getvarnameindex(self.__nativep,somename_,ctypes.byref(asgn_),ctypes.byref(index_))\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n asgn_ = asgn_.value\\n _asgn_return_value = asgn_\\n index_ = index_.value\\n _index_return_value = index_\\n return (_asgn_return_value,_index_return_value)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7106208","0.6794905","0.6497141","0.6025208","0.5968013","0.5913243","0.5865424","0.5832427","0.57964826","0.57409185","0.5729248","0.57017255","0.5681039","0.5604614","0.5604506","0.5603223","0.5594183","0.55620307","0.5516843","0.54871476","0.5463308","0.5418181","0.54151684","0.5408232","0.53889376","0.5388129","0.5386848","0.53856224","0.535468","0.534495","0.5342808","0.5338461","0.5335573","0.5330713","0.53214467","0.5297877","0.529635","0.5294422","0.52873135","0.52773637","0.5271834","0.526611","0.5239361","0.5225201","0.5214027","0.51942813","0.51846063","0.5180044","0.51781386","0.51724905","0.5169947","0.5162136","0.51614","0.51524097","0.5134379","0.51310384","0.51218957","0.51171476","0.5115208","0.5100074","0.5087273","0.50849915","0.50793636","0.5079111","0.5074284","0.506472","0.505323","0.5035826","0.50319993","0.50260764","0.50245714","0.5021952","0.5016017","0.5015458","0.50150144","0.50131845","0.5007738","0.5005364","0.50028116","0.4994798","0.49939355","0.49916622","0.4990771","0.4988983","0.49778315","0.49767762","0.49747506","0.49737662","0.497211","0.49717724","0.496963","0.4964712","0.4964391","0.49585682","0.49580002","0.4953417","0.49489355","0.49474257","0.49455446","0.49454075","0.49357343"],"string":"[\n \"0.7106208\",\n \"0.6794905\",\n \"0.6497141\",\n \"0.6025208\",\n \"0.5968013\",\n \"0.5913243\",\n \"0.5865424\",\n \"0.5832427\",\n \"0.57964826\",\n \"0.57409185\",\n \"0.5729248\",\n \"0.57017255\",\n \"0.5681039\",\n \"0.5604614\",\n \"0.5604506\",\n \"0.5603223\",\n \"0.5594183\",\n \"0.55620307\",\n \"0.5516843\",\n \"0.54871476\",\n \"0.5463308\",\n \"0.5418181\",\n \"0.54151684\",\n \"0.5408232\",\n \"0.53889376\",\n \"0.5388129\",\n \"0.5386848\",\n \"0.53856224\",\n \"0.535468\",\n \"0.534495\",\n \"0.5342808\",\n \"0.5338461\",\n \"0.5335573\",\n \"0.5330713\",\n \"0.53214467\",\n \"0.5297877\",\n \"0.529635\",\n \"0.5294422\",\n \"0.52873135\",\n \"0.52773637\",\n \"0.5271834\",\n \"0.526611\",\n \"0.5239361\",\n \"0.5225201\",\n \"0.5214027\",\n \"0.51942813\",\n \"0.51846063\",\n \"0.5180044\",\n \"0.51781386\",\n \"0.51724905\",\n \"0.5169947\",\n \"0.5162136\",\n \"0.51614\",\n \"0.51524097\",\n \"0.5134379\",\n \"0.51310384\",\n \"0.51218957\",\n \"0.51171476\",\n \"0.5115208\",\n \"0.5100074\",\n \"0.5087273\",\n \"0.50849915\",\n \"0.50793636\",\n \"0.5079111\",\n \"0.5074284\",\n \"0.506472\",\n \"0.505323\",\n \"0.5035826\",\n \"0.50319993\",\n \"0.50260764\",\n \"0.50245714\",\n \"0.5021952\",\n \"0.5016017\",\n \"0.5015458\",\n \"0.50150144\",\n \"0.50131845\",\n \"0.5007738\",\n \"0.5005364\",\n \"0.50028116\",\n \"0.4994798\",\n \"0.49939355\",\n \"0.49916622\",\n \"0.4990771\",\n \"0.4988983\",\n \"0.49778315\",\n \"0.49767762\",\n \"0.49747506\",\n \"0.49737662\",\n \"0.497211\",\n \"0.49717724\",\n \"0.496963\",\n \"0.4964712\",\n \"0.4964391\",\n \"0.49585682\",\n \"0.49580002\",\n \"0.4953417\",\n \"0.49489355\",\n \"0.49474257\",\n \"0.49455446\",\n \"0.49454075\",\n \"0.49357343\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":17897,"cells":{"query":{"kind":"string","value":"Apply brain actions to simulation inputs."},"document":{"kind":"string","value":"def apply_actions(self, b_action_vals: Dict[str, Any] = {}):\n \n # Ensure model has been initialized at least once\n self._model_has_been_initialized(\"apply_actions\")\n\n # Ensure action dict is not empty\n if not len(b_action_vals.items()) > 0:\n print(\"[apply_actions] Provided action dict is empty. No action changes will be applied.\")\n return False\n \n # We forward the configuration values provided\n applied_actions_bool = self._set_variables(b_action_vals)\n\n if not applied_actions_bool:\n print(\"[apply_actions] No valid action parameters were found. No actions applied.\")\n\n return applied_actions_bool"},"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 apply(self, inputs):\n raise NotImplementedError()","def _simulation_run(model_instance, observations, actions, rewards):\r\n\r\n for observation, action, reward in zip(observations, actions, rewards):\r\n model_instance.observe(observation)\r\n model_instance.overrideActionChoice(action)\r\n model_instance.feedback(reward)\r\n\r\n return model_instance","def control_law( self, inputs ):","def process_inputs(self, inputs):","def apply_neurons(self):\n for neuron in range(self.n_outputs):\n self.uf_activate(neuron)","def learning_Utility(self):\n # Shape the input that we give to the neural network with the value of sensors, the previous actions the life of the agent \n # Get the results from the sensors according the different movement executed by the agent \n sensors_result_N = self.agent.sensors(self, direction=3) + self.agent.get_energy_coarsed()+\\\n self.agent.rotate_previousAction(3)+[self.agent.get_previous_collision()]\n sensors_result_O = self.agent.sensors(self, direction=2) + self.agent.get_energy_coarsed()+\\\n self.agent.rotate_previousAction(2) + [self.agent.get_previous_collision()]\n sensors_result_S = self.agent.sensors(self, direction=1) + self.agent.get_energy_coarsed()+\\\n self.agent.rotate_previousAction(1) + [self.agent.get_previous_collision()]\n sensors_result_E = self.agent.sensors(self, direction=0) + self.agent.get_energy_coarsed()+\\\n self.agent.rotate_previousAction(0) + [self.agent.get_previous_collision()]\n\n input_nn_N = np.asarray(sensors_result_N).astype(int) # input when the Nord action is performed \n input_nn_O = np.asarray(sensors_result_O).astype(int) # input when the West action is performed\n input_nn_S = np.asarray(sensors_result_S).astype(int) # input when the South action is performed\n input_nn_E = np.asarray(sensors_result_E).astype(int) # input when the West action is performed\n\n self.input_list = [input_nn_E.reshape(1,145),\n input_nn_S.reshape(1,145),\n input_nn_O.reshape(1,145),\n input_nn_N.reshape(1,145)]\n self.U_list = [self.nn.predict(i) for i in self.input_list ] #The utility according the different acts performed \n return self.actionSelector() #Select the action acording a propbabilitics distribution given in the paper","def apply_action(self, physics, action, random_state):\n del random_state\n physics.bind(self.actuators).ctrl = action","def perform_split_brain_actions(self):\n proximity = self.grid.proximity_to_apple()\n inputs = [\n torch.tensor([\n [\n proximity,\n self.grid.safe_cells_up_global(),\n self.grid.safe_cells_up(),\n self.grid.apple_is_up_safe(0.5)\n ]\n ]),\n torch.tensor([\n [\n proximity,\n self.grid.safe_cells_down_global(),\n self.grid.safe_cells_down(),\n self.grid.apple_is_down_safe(0.5)\n ]\n ]),\n torch.tensor([\n [\n proximity,\n self.grid.safe_cells_left_global(),\n self.grid.safe_cells_left(),\n self.grid.apple_is_left_safe(0.5)\n ]\n ]),\n torch.tensor([\n [\n proximity,\n self.grid.safe_cells_right_global(),\n self.grid.safe_cells_right(),\n self.grid.apple_is_right_safe(0.5)\n ]\n ])\n ]\n\n new_direction = self.split_brain_network.eval(inputs)\n self.grid.change_direction(new_direction)\n got_apple = self.grid.next_frame()\n new_proximity = self.grid.proximity_to_apple()\n\n if self.is_training:\n reward = torch.tensor([-0.5])\n\n if self.grid.snake_died():\n reward = torch.tensor([-1.0])\n elif got_apple:\n reward = torch.tensor([1.0])\n elif new_proximity > proximity:\n reward = torch.tensor([0.8])\n\n self.split_brain_network.update(reward)","def quick_run(self, *args):\n self.inputs(*args)\n self.run()","def __call__(self, inputs: np.ndarray):\n # Denote the impact the inputs have directly on the outputs\n output_inputs: np.ndarray = np.matmul(self.in2out, inputs.transpose()).transpose()\n \n # Denote the impact hidden nodes have on the outputs, if there are hidden nodes\n if self.n_hidden > 0:\n # Nice to know:\n # - np.transpose() will transpose the tensor\n # - np.matmul(tensor1, tensor2) will perform a matrix multiplication between tensor and tensor2\n \n # The activation is defined by:\n # - the inputs mapping to the hidden nodes\n # - the hidden nodes mapping to themselves\n # - the hidden nodes' biases\n \n # 1) Propagate the hidden nodes\n self.hidden_act = self.act_f(np.matmul(self.in2hid, inputs.transpose()).transpose() +\n np.matmul(self.hid2hid, self.hidden_act.transpose()).transpose() +\n self.hidden_biases)\n \n # 2) Execute the RNN nodes if they exists (updating current hidden state)\n for i, rnn_idx in enumerate(self.rnn_idx):\n self.rnn_state[:, i] = self.rnn_array[i](\n np.concatenate((self.in2hid[rnn_idx] * inputs,\n self.hid2hid[rnn_idx] * self.hidden_act),\n axis=1)[self.rnn_map[i]].reshape(self.bs, self.rnn_array[i].input_size)\n )\n self.hidden_act[:, rnn_idx] = self.rnn_state[:, i, 0]\n \n # 3) Propagate hidden-values to the outputs\n output_inputs += np.matmul(self.hid2out, self.hidden_act.transpose()).transpose()\n \n # Define the values of the outputs, which is the sum of their received inputs and their corresponding bias\n self.output_act = self.act_f(output_inputs + self.output_biases)\n return self.output_act","def run(self):\n self.assign_inputs()\n self.execute()\n self.collect_outputs()","def processInputs(self):","def do_manipulations(self, *args, **kwargs):\n pass","def step(self, action):\n actions = action.reshape(2, 2)\n action = {'GoalieBrain': actions[0], 'StrikerBrain': actions[1]}\n #action = {'GoalieBrain': [5, 5], 'StrikerBrain': [2, 2]}\n #print(action)\n info = self.env.step(action)\n\n state0 = info[self.brain_names[0]].vector_observations\n state1 = info[self.brain_names[1]].vector_observations\n state = np.vstack((state0, state1))\n reward0 = info[self.brain_names[0]].rewards\n reward1 = info[self.brain_names[1]].rewards\n reward = reward0 + reward1\n done0 = info[self.brain_names[0]].local_done\n done1 = info[self.brain_names[1]].local_done\n done = done0 + done1\n return state, reward, done","def apply_actions_to_body_parts(self, actions_dict):\n\n if self._noise:\n self.current_actions = self._apply_noise(actions_dict)\n else:\n self.current_actions = actions_dict\n\n for actuator, value in self.current_actions.items():\n\n for body_part in self.parts:\n body_part.apply_action(actuator, value)","def call(self, inputs):\n raise NotImplementedError","def call(self, inputs, state):\n raise NotImplementedError","def run_net(inputs, **parameter):\n\n # ---- set numpy random state for each run----\n np.random.set_state(np_state)\n\n # -----parameter setting-------\n n_ex = 1600\n n_inh = int(n_ex / 4)\n n_input = MNIST_shape[1] * coding_n\n n_read = n_ex + n_inh\n\n R = parameter['R']\n f_in = parameter['f_in']\n f_EE = parameter['f_EE']\n f_EI = parameter['f_EI']\n f_IE = parameter['f_IE']\n f_II = parameter['f_II']\n\n A_EE = 60 * f_EE\n A_EI = 60 * f_EI\n A_IE = 60 * f_IE\n A_II = 60 * f_II\n A_inE = 60 * f_in\n A_inI = 60 * f_in\n\n tau_ex = parameter['tau_ex'] * coding_duration\n tau_inh = parameter['tau_inh'] * coding_duration\n tau_read = 30\n\n p_inE = parameter['p_in'] * 0.1\n p_inI = parameter['p_in'] * 0.1\n\n # ------definition of equation-------------\n neuron_in = '''\n I = stimulus(t,i) : 1\n '''\n\n neuron = '''\n tau : 1\n dv/dt = (I-v) / (tau*ms) : 1 (unless refractory)\n dg/dt = (-g)/(3*ms) : 1\n dh/dt = (-h)/(6*ms) : 1\n I = (g+h)+13.5: 1\n x : 1\n y : 1\n z : 1\n '''\n\n neuron_read = '''\n tau : 1\n dv/dt = (I-v) / (tau*ms) : 1\n dg/dt = (-g)/(3*ms) : 1 \n dh/dt = (-h)/(6*ms) : 1\n I = (g+h): 1\n '''\n\n synapse = '''\n w : 1\n '''\n\n on_pre_ex = '''\n g+=w\n '''\n\n on_pre_inh = '''\n h-=w\n '''\n\n # -----Neurons and Synapses setting-------\n Input = NeuronGroup(n_input, neuron_in, threshold='I > 0', method='euler', refractory=0 * ms,\n name='neurongroup_input')\n\n G_ex = NeuronGroup(n_ex, neuron, threshold='v > 15', reset='v = 13.5', method='euler', refractory=3 * ms,\n name='neurongroup_ex')\n\n G_inh = NeuronGroup(n_inh, neuron, threshold='v > 15', reset='v = 13.5', method='euler', refractory=2 * ms,\n name='neurongroup_in')\n\n G_readout = NeuronGroup(n_read, neuron_read, method='euler', name='neurongroup_read')\n\n S_inE = Synapses(Input, G_ex, synapse, on_pre=on_pre_ex, method='euler', name='synapses_inE')\n\n S_inI = Synapses(Input, G_inh, synapse, on_pre=on_pre_ex, method='euler', name='synapses_inI')\n\n S_EE = Synapses(G_ex, G_ex, synapse, on_pre=on_pre_ex, method='euler', name='synapses_EE')\n\n S_EI = Synapses(G_ex, G_inh, synapse, on_pre=on_pre_ex, method='euler', name='synapses_EI')\n\n S_IE = Synapses(G_inh, G_ex, synapse, on_pre=on_pre_inh, method='euler', name='synapses_IE')\n\n S_II = Synapses(G_inh, G_inh, synapse, on_pre=on_pre_inh, method='euler', name='synapses_I')\n\n S_E_readout = Synapses(G_ex, G_readout, 'w = 1 : 1', on_pre=on_pre_ex, method='euler')\n\n S_I_readout = Synapses(G_inh, G_readout, 'w = 1 : 1', on_pre=on_pre_inh, method='euler')\n\n # -------initialization of neuron parameters----------\n G_ex.v = '13.5+1.5*rand()'\n G_inh.v = '13.5+1.5*rand()'\n G_readout.v = '0'\n G_ex.g = '0'\n G_inh.g = '0'\n G_readout.g = '0'\n G_ex.h = '0'\n G_inh.h = '0'\n G_readout.h = '0'\n G_ex.tau = tau_ex\n G_inh.tau = tau_inh\n G_readout.tau = tau_read\n [G_ex, G_in] = base.allocate([G_ex, G_inh], 10, 10, 20)\n\n # -------initialization of network topology and synapses parameters----------\n S_inE.connect(condition='j<0.3*N_post', p=p_inE)\n S_inI.connect(condition='j<0.3*N_post', p=p_inI)\n S_EE.connect(condition='i != j', p='0.3*exp(-((x_pre-x_post)**2+(y_pre-y_post)**2+(z_pre-z_post)**2)/R**2)')\n S_EI.connect(p='0.2*exp(-((x_pre-x_post)**2+(y_pre-y_post)**2+(z_pre-z_post)**2)/R**2)')\n S_IE.connect(p='0.4*exp(-((x_pre-x_post)**2+(y_pre-y_post)**2+(z_pre-z_post)**2)/R**2)')\n S_II.connect(condition='i != j', p='0.1*exp(-((x_pre-x_post)**2+(y_pre-y_post)**2+(z_pre-z_post)**2)/R**2)')\n S_E_readout.connect(j='i')\n S_I_readout.connect(j='i+n_ex')\n\n S_inE.w = function.gamma(A_inE, S_inE.w.shape)\n S_inI.w = function.gamma(A_inI, S_inI.w.shape)\n S_EE.w = function.gamma(A_EE, S_EE.w.shape)\n S_IE.w = function.gamma(A_IE, S_IE.w.shape)\n S_EI.w = function.gamma(A_EI, S_EI.w.shape)\n S_II.w = function.gamma(A_II, S_II.w.shape)\n\n S_EE.pre.delay = '1.5*ms'\n S_EI.pre.delay = '0.8*ms'\n S_IE.pre.delay = '0.8*ms'\n S_II.pre.delay = '0.8*ms'\n\n # ------create network-------------\n net = Network(collect())\n net.store('init')\n\n # ------run network-------------\n stimulus = TimedArray(inputs[0], dt=Dt)\n net.run(duration * Dt)\n states = net.get_states()['neurongroup_read']['v']\n net.restore('init')\n return (states, inputs[1])","def run_net(inputs, **parameter):\n\n #---- set numpy random state for each run----\n np.random.set_state(np_state)\n\n # -----parameter setting-------\n n_ex = 1600\n n_inh = int(n_ex/4)\n n_input = MNIST_shape[1]*coding_n\n n_read = n_ex+n_inh\n\n R = parameter['R']\n f_in = parameter['f_in']\n f_EE = parameter['f_EE']\n f_EI = parameter['f_EI']\n f_IE = parameter['f_IE']\n f_II = parameter['f_II']\n\n A_EE = 60*f_EE\n A_EI = 60*f_EI\n A_IE = 60*f_IE\n A_II = 60*f_II\n A_inE = 60*f_in\n A_inI = 60*f_in\n\n tau_ex = parameter['tau_ex']*coding_duration\n tau_inh = parameter['tau_inh']*coding_duration\n tau_read= 30\n\n p_inE = parameter['p_in']*0.1\n p_inI = parameter['p_in']*0.1\n\n #------definition of equation-------------\n neuron_in = '''\n I = stimulus(t,i) : 1\n '''\n\n neuron = '''\n tau : 1\n dv/dt = (I-v) / (tau*ms) : 1 (unless refractory)\n dg/dt = (-g)/(3*ms) : 1\n dh/dt = (-h)/(6*ms) : 1\n I = (g+h)+13.5: 1\n x : 1\n y : 1\n z : 1\n '''\n\n neuron_read = '''\n tau : 1\n dv/dt = (I-v) / (tau*ms) : 1\n dg/dt = (-g)/(3*ms) : 1 \n dh/dt = (-h)/(6*ms) : 1\n I = (g+h): 1\n '''\n\n synapse = '''\n w : 1\n '''\n\n on_pre_ex = '''\n g+=w\n '''\n\n on_pre_inh = '''\n h-=w\n '''\n\n # -----Neurons and Synapses setting-------\n Input = NeuronGroup(n_input, neuron_in, threshold='I > 0', method='euler', refractory=0 * ms,\n name = 'neurongroup_input')\n\n G_ex = NeuronGroup(n_ex, neuron, threshold='v > 15', reset='v = 13.5', method='euler', refractory=3 * ms,\n name ='neurongroup_ex')\n\n G_inh = NeuronGroup(n_inh, neuron, threshold='v > 15', reset='v = 13.5', method='euler', refractory=2 * ms,\n name ='neurongroup_in')\n\n G_readout = NeuronGroup(n_read, neuron_read, method='euler', name='neurongroup_read')\n\n S_inE = Synapses(Input, G_ex, synapse, on_pre = on_pre_ex ,method='euler', name='synapses_inE')\n\n S_inI = Synapses(Input, G_inh, synapse, on_pre = on_pre_ex ,method='euler', name='synapses_inI')\n\n S_EE = Synapses(G_ex, G_ex, synapse, on_pre = on_pre_ex ,method='euler', name='synapses_EE')\n\n S_EI = Synapses(G_ex, G_inh, synapse, on_pre = on_pre_ex ,method='euler', name='synapses_EI')\n\n S_IE = Synapses(G_inh, G_ex, synapse, on_pre = on_pre_inh ,method='euler', name='synapses_IE')\n\n S_II = Synapses(G_inh, G_inh, synapse, on_pre = on_pre_inh ,method='euler', name='synapses_I')\n\n S_E_readout = Synapses(G_ex, G_readout, 'w = 1 : 1', on_pre=on_pre_ex, method='euler')\n\n S_I_readout = Synapses(G_inh, G_readout, 'w = 1 : 1', on_pre=on_pre_inh, method='euler')\n\n #-------initialization of neuron parameters----------\n G_ex.v = '13.5+1.5*rand()'\n G_inh.v = '13.5+1.5*rand()'\n G_readout.v = '0'\n G_ex.g = '0'\n G_inh.g = '0'\n G_readout.g = '0'\n G_ex.h = '0'\n G_inh.h = '0'\n G_readout.h = '0'\n G_ex.tau = tau_ex\n G_inh.tau = tau_inh\n G_readout.tau = tau_read\n\n [G_ex,G_in] = base.allocate([G_ex,G_inh],10,10,20)\n\n # -------initialization of network topology and synapses parameters----------\n S_inE.connect(condition='j<0.3*N_post', p = p_inE)\n S_inI.connect(condition='j<0.3*N_post', p = p_inI)\n S_EE.connect(condition='i != j', p='0.3*exp(-((x_pre-x_post)**2+(y_pre-y_post)**2+(z_pre-z_post)**2)/R**2)')\n S_EI.connect(p='0.2*exp(-((x_pre-x_post)**2+(y_pre-y_post)**2+(z_pre-z_post)**2)/R**2)')\n S_IE.connect(p='0.4*exp(-((x_pre-x_post)**2+(y_pre-y_post)**2+(z_pre-z_post)**2)/R**2)')\n S_II.connect(condition='i != j', p='0.1*exp(-((x_pre-x_post)**2+(y_pre-y_post)**2+(z_pre-z_post)**2)/R**2)')\n S_E_readout.connect(j='i')\n S_I_readout.connect(j='i+n_ex')\n\n S_inE.w = function.gamma(A_inE, S_inE.w.shape)\n S_inI.w = function.gamma(A_inI, S_inI.w.shape)\n S_EE.w = function.gamma(A_EE, S_EE.w.shape)\n S_IE.w = function.gamma(A_IE, S_IE.w.shape)\n S_EI.w = function.gamma(A_EI, S_EI.w.shape)\n S_II.w = function.gamma(A_II, S_II.w.shape)\n\n S_EE.pre.delay = '1.5*ms'\n S_EI.pre.delay = '0.8*ms'\n S_IE.pre.delay = '0.8*ms'\n S_II.pre.delay = '0.8*ms'\n\n # ------create network-------------\n net = Network(collect())\n net.store('init')\n\n # ------run network-------------\n stimulus = TimedArray(inputs[0], dt=Dt)\n net.run(duration * Dt)\n states = net.get_states()['neurongroup_read']['v']\n net.restore('init')\n return (states, inputs[1])","def __call__(self, inputs):\n for layer in self.prenet_layers:\n f = layer(inputs)\n if self._enable_dropout:\n inputs = tf.layers.dropout(\n f, rate=self._dropout, training=True\n )\n else:\n inputs = tf.layers.batch_normalization(\n f, momentum=0.1, epsilon=1e-5\n )\n inputs = self._activation_fn(inputs)\n return inputs","def forward(self, inputs):\n raise NotImplementedError","def __call__(self, inputs):\n return self._hidden_activation(inputs)","def call(self, inputs):\n for index,layer in enumerate(self.list_cnn):\n if index == 0:\n out = layer(inputs)\n else:\n if self.residual:\n prev_out = out\n cur_out = layer(out)\n if self.use_batchnorm:\n cur_out = layers.BatchNormalization()(cur_out)\n if self.use_dropout:\n cur_out = layers.Dropout(0.2)(cur_out)\n out = prev_out + cur_out\n else:\n out = layer(out)\n out = self.output_layer(out)\n if self.use_pool:\n out = self.pool(out)\n out = self.flatten(out)\n out = self.classify(out)\n return out","def _run_apply(self):\n self.ip = self.ip + 1\n operator_id = self.bytecode.code[self.ip]\n\n # Fetch the operator details so we know two things: which operator to execute and how many bytes to read for the input\n input_size, operator = self._operators[operator_id]\n \n # Read the input\n inputs = []\n input_index = 0\n input_data = None\n self.ip = self.ip + 2\n while input_index < input_size:\n input_data = (self.bytecode.code[self.ip + input_index], self.bytecode.code[self.ip + input_index + 1])\n\n # Read the output\n self.ip = self.ip + input_index + 1\n output_data = (self.bytecode.code[self.ip], self.bytecode.code[self.ip + 1])\n\n # Validate the output\n # The output type must always be a register\n if output_data[0] != OpType.REGISTER:\n raise ValueError(\"Unexpected output operand at offset <\" + str(self.bytecode.code[self.ip]) + \">. Expected a register.\")\n # The output register must be within the VM register count\n if output_data[1] >= len(self._registers):\n raise ValueError(\"Output register outside of VM register count. Byte at offset <\" + str(self.bytecode.code[self.ip + 1]) + \">.\")\n\n # Execute the instruction\n print(\"Executing the APPLY instruction.\")\n\n # Update the instruction pointer to the next instruction\n self.ip = self.ip + 2","def call(self, inputs, **kwargs):\n xs = self._apply_post_hooks_for_inputs(inputs)\n with get_current_context().enter(in_call=True, wrap_ops=True, model=self):\n outputs = self._model(xs, **kwargs)\n return outputs","def call(self, inputs, training=None, mask=None):\n # pylint: disable=arguments-differ\n raise NotImplementedError()","def call(self, inputs, states, training):\n raise NotImplementedError(\"Please implement this method\")","def act(self, states, add_noise=True):\n \n actions = []\n for agent, state in zip(self.agents, states):\n action = agent.act(state, noise_weight=self.noise_weight, add_noise=self.enable_noise)\n actions.append(action)\n self.noise_weight *= self.noise_decay\n return np.array(actions).reshape(1, -1) # flatten","def run(self, inputs, **kwargs):\n graph = self.model.graph\n # last_layers means we run this model until the last #N layers\n last_layers = kwargs.get('last_layers', len(self.singa_ops))\n if last_layers != len(self.singa_ops):\n final_outputs = self.singa_ops[last_layers-1].op.outputs\n else:\n final_outputs = [outp.name for outp in graph.output]\n # whether return all outputs\n all_outputs = kwargs.get('all_outputs', False)\n # get a specific op by its name\n op_name = kwargs.get('op_name', None)\n # record the tensor we added from input\n tmp_tensor_map = {name: val for name, val in self.tensor_map.items()}\n\n # the dict will be returned\n ret_outputs = OrderedDict()\n if self.keep_initializers_as_inputs:\n require_input_len = len(graph.input) - len(graph.initializer)\n actual_input_len = len(inputs)\n else:\n require_input_len = len(graph.input)\n actual_input_len = len(inputs)\n assert require_input_len == actual_input_len, \"The length of graph input is different from the tensor input: %d, %d\" % (\n require_input_len, actual_input_len)\n # run the handle by the order of the list(the list is Topological Sorting)\n for inp in graph.input:\n if inp.name not in tmp_tensor_map:\n tmp_tensor_map[inp.name] = inputs.pop(0)\n\n for _, op, handle, forward in self.singa_ops[:last_layers]:\n if len(op.consumed_inputs) != 0:\n # because if op has consumed_inputs, it means it moved some inputs into attributes\n # so when running, we should update these attributes\n handle, forward = get_op(op,\n [tmp_tensor_map[x] for x in op.inputs])\n inputs = [\n tmp_tensor_map[x]\n for x in op.inputs\n if x not in op.consumed_inputs\n ]\n outputs = _run_node(op, inputs, handle, forward)\n for key, val in outputs.items():\n tmp_tensor_map[key] = val\n ret_outputs[key] = val\n\n if op_name is not None:\n if op_name in outputs:\n return outputs[op_name]\n else:\n raise RuntimeError(\n \"The op_name {} does not exist, please check. The available op_names are: {}\"\n .format(op_name, [val for key, val in op_name.items()]))\n\n # return all outputs if all_outputs==True\n # else return last outputs\n if all_outputs:\n return ret_outputs\n else:\n return [ret_outputs[outp] for outp in final_outputs]","def post(self, scenario, bodies):\n if self.tacs_proc:\n # Save the solution vector\n self.scenario_data[scenario].u.copyValues(self.ans)\n\n if self.override_rotx:\n # Override rotx to be heat flux\n ans_array = self.ans.getArray()\n ndof = self.assembler.getVarsPerNode()\n\n for body in bodies:\n struct_flux = body.get_struct_heat_flux(scenario)\n if struct_flux is not None:\n ans_array[3::ndof] = struct_flux[:]\n\n self.assembler.setVariables(self.ans)\n\n if self.gen_output is not None:\n self.gen_output()\n\n return","def forward(self, *inputs):\n raise NotImplementedError","def _forwardImplementation(self, inbuf, outbuf):\n assert self.module\n \n values = self.module.getActionValues(self.state) \n \n actions = []\n if random() <= self.exploration:\n for i in range(self.shield_options):\n new_action = choice(range(len(values))) \n np.delete(values, new_action)\n actions.append(new_action)\n else:\n for i in range(self.shield_options):\n new_action = where(values == max(values))[0]\n new_action = choice(new_action) \n np.delete(values, new_action)\n actions.append(new_action)\n \n while len(actions) < self.outdim:\n actions.append(-1)\n \n outbuf[:] = actions","def forward(self, inputs):\n action_pred_input, self.actions = self.sample_tsteps(inputs.states, inputs.action_targets)\n a_pred = self.s_encoder.forward(action_pred_input)\n return AttrDict(a_pred=a_pred)","def run(self, inputs):\n for layer in self._layers:\n inputs = layer.calc(inputs)\n return inputs","def _run_actions(self):\n\n if \"install-bento\" in self.actions:\n self._do_action_bento_setup()\n\n if \"create-tables\" in self.actions:\n self._do_action_tables_create()\n\n if \"import-ratings\" in self.actions:\n self._do_action_import_ratings()\n\n if \"import-user-info\" in self.actions:\n self._do_action_import_user_info()\n\n if \"import-movie-info\" in self.actions:\n self._do_action_import_movie_info()\n\n if \"train-item-item-cf\" in self.actions:\n self._do_action_train()\n\n if \"register-freshener\" in self.actions:\n self._do_action_register_freshener()","def call(self, inputs):\r\n\r\n if self.share_weights:\r\n hat_inputs = K.conv1d(inputs, self.kernel)\r\n else:\r\n hat_inputs = K.local_conv1d(inputs, self.kernel, [1], [1])\r\n\r\n batch_size = K.shape(inputs)[0]\r\n input_num_capsule = K.shape(inputs)[1]\r\n hat_inputs = K.reshape(hat_inputs,\r\n (batch_size, input_num_capsule,\r\n self.num_capsule, self.dim_capsule))\r\n hat_inputs = K.permute_dimensions(hat_inputs, (0, 2, 1, 3))\r\n\r\n b = K.zeros_like(hat_inputs[:, :, :, 0])\r\n for i in range(self.routings):\r\n c = softmax(b, 1)\r\n \r\n if K.backend() == 'theano':\r\n o = K.sum(o, axis=1)\r\n o = self.activation(K.batch_dot(c, hat_inputs, [2, 2]))\r\n \r\n if i < self.routings - 1:\r\n b = K.batch_dot(o, hat_inputs, [2, 3])\r\n if K.backend() == 'theano':\r\n o = K.sum(o, axis=1)\r\n\r\n return o","def run(self, **inputs):\n if self.cached_model is None:\n self.cache_model(**inputs)\n for name, data in inputs.items():\n self.shared_vars[name].set_value(data)\n if self.mb and self.variational:\n self.minibatches = zip(self._mb_generator(inputs['gwas_gen']),\n self._mb_generator(inputs['gwas_phen']))\n self.trace = self._inference()\n return self.trace","def act(self, state, training):\n\n action = self.actor_local(state)\n if training:\n action += self.noise.sample()\n else:\n action = self.actor_target(state)\n\n return action","def __call__(self, inputs, states, **kwargs):\n raise NotImplementedError()","def act(self, action_values: Tensor) -> Tensor:\n ...","def Action(self, states, eps=0, isRandom=False):\n action = []\n # No gradient needed\n with T.no_grad():\n states = T.Tensor(states).float().to(device) # Send states to GPU\n [a.eval() for a in self.Actor] # Eval Mode\n # Loop Each Actor Model to get Action as np.array()\n for i in range(len(self.Actor)):\n action.append(self.Actor[i](states[i]).cpu().data.numpy())\n # If less Actor Models the Env. Agents: use Agent[0] to calc the rest\n if len(self.Actor)<len(states):\n act2 = self.Actor[0](states[len(self.Actor):]).cpu().data.numpy() # Get Action\n action = np.append(action, act2, axis=0) # Append to Action Array\n else: action = np.array(action) # Else convert List to np.array()\n [a.train() for a in self.Actor] # Back to Train Mode\n # Add Epsilon Greedy Noise\n action += self.Noise(1 if isRandom else eps, action.shape)\n return action","def act(self, states, add_noise=True):\n\n states = torch.from_numpy(states).float().to(device)\n actions = np.empty([self.agents_count, self.action_size])\n\n self.actor_local.eval()\n with torch.no_grad():\n for a in range(self.agents_count):\n actions[a] = self.actor_local(states[a]).cpu().data.numpy()\n self.actor_local.train()\n if add_noise:\n actions += self.noise.sample()\n return np.clip(actions, -1, 1)","def apply_action(self, action):\n return self.__environment.step(action)","def call(self, inputs, training = False):\n \"\"\" includes training argument as batch_norm functions differently during training and during inference \"\"\"\n \n # input --> block_1 --> block_2 --> block_3 \n x = self.block_1(inputs, training = training)\n x = self.block_2(x, training = training)\n x = self.block_3(x, training = training)\n\n # output for MAA2C or DDPG \n # block_3 --> outputs \n x = self.outputs(x)\n\n return x","def _action(self, wloops: Any, beta: Any) -> Any:\n pass","def __call__(self, inputs):\n self.inputs = inputs\n self.process_inputs()\n self.init_mesh()\n mesh_modified = self.run_tasks()\n self.write_output_mesh(mesh_modified)","def forward(self, inputs):\n _, state = self.core(inputs)\n return state","def run_ai(self):\n state = self.brainstate\n me = self.owner\n if not self.owner.alive:\n return\n self._acquire_target()\n if 'target' in state:\n self._move_towards_target()\n else:\n # No target, wander around\n if random.random() > 0.3:\n possible_directions = [i for i in\n ([-1, -1], [-1, 0], [-1, 1], [0, -1],\n [0, 1], [1, -1], [1, 0], [1, 1])\n if afr.map.map.tile_is_traversable(\n me.x + i[0],\n me.y + i[1]\n )\n ]\n logging.debug(\"possible directions: %s\", possible_directions)\n movement = random.choice(possible_directions)\n me.x += movement[0]\n me.y += movement[1]","def actions(self, states, agent_indices):\n return NotImplementedError()","def step(self):\n if not self.is_done():\n actions = [ agent.program(self.percept(agent)) for agent in self.agents ]\n for agent, action in zip(self.agents, actions):\n self.execute_action(agent, action)\n\n self.exogenous_change()","def run_game_logic(self):\n pass","def ApplyInputs(ss, en):\n ss.Net.InitExt()\n\n lays = [\"Input\", \"Output\"]\n for lnm in lays :\n ly = leabra.Layer(ss.Net.LayerByName(lnm))\n pats = en.State(ly.Nm)\n if pats != 0:\n ly.ApplyExt(pats)","def mutate(self):\n #First, mutate masses\n for neuronNum in range(self.neuronCounter - 1):\n if self.learningRate > random.random():\n self.neurons[neuronNum].mutate()\n else:\n continue\n\n #Now determine physical mutations\n if random.random() < 0.2:\n try:\n physMutation = random.choice(['a','l','c'])\n if physMutation == 'a':\n self.addNeuron(random.choice([0,1,2]))\n elif physMutation == 'l':\n begin = random.randint(1,self.neuronCounter - 1)\n end = random.randint(1, self.neuronCounter - 1)\n self.link(begin, end)\n else:\n begin = random.randint(1,self.neuronCounter - 1)\n end = random.choice(self.neurons[begin].outDic.keys())\n self.cut(begin, end)\n except:\n return self\n return self","def call(self, inputs, training=True):\n pass","def _send(self, action: List[np.ndarray]) -> None:","def run(self):\n count = self.neuron_count\n for i in range(0, count):\n self.run(i)","def runMasking(self,roiNode,inputVolumes,inPlace):\n\n self.delayDisplay('Running the algorithm')\n\n for vol in inputVolumes:\n self.maskVolume(vol, roiNode, inPlace)\n\n return True\n\n self.delayDisplay('Running the aglorithm')","def calculate(self, inputs:[bool]):\n\n w_som = 0\n outputs = []\n for i in range(len(inputs)):# iterate through the index inputs e.g [0,0]\n weight = self.weights[i] # get weight \n x = inputs[i] # get x\n\n w_som += (weight*x) # increment w_som with the multiplication of weighti and xi\n output = self.activation(w_som) # apply the step function to w_Som\n #print(outputs)\n return output","def run(self):\n if self.next_state == \"initialize_rexarm\":\n self.initialize_rexarm()\n\n if self.next_state == \"idle\":\n self.idle()\n\n if self.next_state == \"estop\":\n self.estop()\n\n if self.next_state == \"execute_tp\":\n self.execute_tp()\n\n if self.next_state == \"execute\":\n self.execute()\n\n if self.next_state == \"calibrate\":\n self.calibrate()\n\n if self.next_state == \"manual\":\n self.manual()\n\n if self.next_state == \"learn\":\n self.learn()\n\n if self.next_state == \"remember\":\n self.remember()\n\n if self.next_state == \"write\":\n self.write()\n\n if self.next_state == \"get_color\":\n self.get_color()\n\n if self.next_state == \"find_blocks\":\n self.find_blocks()\n\n # if self.next_state == \"dance\":\n # self.execute_dance()","def apply_rl_actions(self, rl_actions):\n pass","def process(self, inputs):\n output = None\n return output","def _step(self, action):\n\n\n assert action.ndim == 2, 'Action must be a two dimensional array with shape (num_models, num_stocks + 1)'\n assert action.shape[1] == len(self.sim[0].asset_names) + 1\n assert action.shape[0] == len(self.model_names)\n\n print(\"before normalisation\",action)\n\n #Let's ALLOW THE SHORT!!!\n # normalise just in case\n #action = np.clip(action, 0, 1)\n action = np.clip(action, -1, 1)\n\n weights = action # np.array([cash_bias] + list(action)) # [w0, w1...]\n #weights /= (np.sum(weights, axis=1, keepdims=True) + eps)\n\n # so if weights are all zeros we normalise to [1,0...]\n weights[:, 0] += np.clip(1 - np.sum(weights, axis=1), 0, 1)\n #assert ((action >= 0) * (action <= 1)).all(), 'all action values should be between 0 and 1. Not %s' % action\n\n #assert ((action >= 0) * (action <= 1)).all(), 'all action values should be between 0 and 1. Not %s' % action\n\n\n pos_w = [abs(number) for number in weights]\n\n\n np.testing.assert_almost_equal(np.sum(pos_w, axis=1), np.ones(shape=(weights.shape[0])), 3,\n err_msg='weights should sum to 1. action=\"%s\"' % weights)\n\n observation, done1, ground_truth_obs = self.src._step()\n\n # concatenate observation with ones\n cash_observation = np.ones((1, self.window_length, observation.shape[2]))\n observation = np.concatenate((cash_observation, observation), axis=0)\n\n cash_ground_truth = np.ones((1, 1, ground_truth_obs.shape[2]))\n ground_truth_obs = np.concatenate((cash_ground_truth, ground_truth_obs), axis=0)\n\n\n # relative price vector of last observation day (close/open)\n\n\n close_price_vector = observation[:, -1, 3]\n open_price_vector = observation[:, -1, 0]\n\n\n y1_long = close_price_vector / open_price_vector\n\n y1_short= open_price_vector/close_price_vector\n #for i in len(range(observation.shape[0])):\n # if\n\n #print(\"THIS IS Y1\",y1)\n #print(\"y1 shape\",y1.shape)\n #print(\"wieh\",weights.shape)\n\n rewards = np.empty(shape=(weights.shape[0]))\n info = {}\n dones = np.empty(shape=(weights.shape[0]), dtype=bool)\n for i in range(weights.shape[0]):\n reward, current_info, done2 = self.sim[i]._step(weights[i], y1_long,y1_short)\n rewards[i] = reward\n info[self.model_names[i]] = current_info['portfolio_value']\n info['return'] = current_info['return']\n dones[i] = done2\n\n # calculate return for buy and hold a bit of each asset\n info['market_value'] = np.cumprod([inf[\"return\"] for inf in self.infos + [info]])[-1]\n # add dates\n info['date'] = index_to_date(self.start_idx + self.src.idx + self.src.step)\n info['steps'] = self.src.step\n info['next_obs'] = ground_truth_obs\n\n self.infos.append(info)\n\n #print (info)\n\n #print(\"My action is :\",action)\n\n\n\n return observation, rewards, np.all(dones) or done1, info","def _step(self, rl_actions):\n self.timer += 1\n # pdb.set_trace()\n # perform acceleration and (optionally) lane change actions for\n # traci-controlled human-driven vehicles\n if len(self.controlled_ids) > 0:\n accel = []\n for veh_id in self.controlled_ids:\n # acceleration action\n action = self.vehicles.get_acc_controller(veh_id).get_action(\n self)\n accel.append(action)\n\n # lane changing action\n lane_flag = 0\n if type(self.scenario.lanes) is dict:\n if any(v > 1 for v in self.scenario.lanes.values()):\n lane_flag = 1\n else:\n if self.scenario.lanes > 1:\n lane_flag = 1\n\n if self.vehicles.get_lane_changing_controller(veh_id) \\\n is not None and lane_flag:\n new_lane = \\\n self.vehicles.get_lane_changing_controller(\n veh_id).get_action(self)\n self.apply_lane_change([veh_id], target_lane=[new_lane])\n\n self.apply_acceleration(self.controlled_ids, acc=accel)\n\n # perform (optionally) lane change actions for sumo-controlled\n # human-driven vehicles\n if len(self.sumo_ids) > 0:\n for veh_id in self.sumo_ids:\n # lane changing action\n if self.scenario.lanes > 1:\n lc_contr = self.vehicles.get_lane_changing_controller(\n veh_id)\n if lc_contr is not None:\n new_lane = lc_contr.get_action(self)\n self.apply_lane_change([veh_id], target_lane=[new_lane])\n\n # perform (optionally) routing actions for all vehicle in the network,\n # including rl vehicles\n routing_ids = []\n routing_actions = []\n for veh_id in self.ids:\n if self.vehicles.get_routing_controller(veh_id) is not None:\n routing_ids.append(veh_id)\n route_contr = self.vehicles.get_routing_controller(veh_id)\n routing_actions.append(route_contr.choose_route(self))\n\n self.choose_routes(veh_ids=routing_ids, route_choices=routing_actions)\n\n self.apply_rl_actions(rl_actions)\n\n self.additional_command()\n # pdb.set_trace()\n self.traci_connection.simulationStep()\n #Error being thrown from sumo/src/utils/foxtools/MFXImageHelper.cpp\n # self.traci_connection.gui.screenshot(\"View #0\", \"/home/mkoren/Pictures/a\"+str(self.timer)+\".png\")\n # pdb.set_trace()\n # store new observations in the network after traci simulation step\n network_observations = \\\n self.traci_connection.vehicle.getSubscriptionResults()\n crash = False\n for veh_id in self.ids:\n prev_pos = self.get_x_by_id(veh_id)\n prev_lane = self.vehicles.get_lane(veh_id)\n try:\n self.vehicles.set_position(\n veh_id, network_observations[veh_id][tc.VAR_LANEPOSITION])\n except KeyError:\n continue\n self.vehicles.set_edge(\n veh_id, network_observations[veh_id][tc.VAR_ROAD_ID])\n self.vehicles.set_lane(\n veh_id, network_observations[veh_id][tc.VAR_LANE_INDEX])\n if network_observations[veh_id][tc.VAR_LANE_INDEX] != prev_lane \\\n and veh_id in self.rl_ids:\n self.vehicles.set_state(veh_id, \"last_lc\", self.timer)\n self.vehicles.set_speed(\n veh_id, network_observations[veh_id][tc.VAR_SPEED])\n\n try:\n change = self.get_x_by_id(veh_id) - prev_pos\n if change < 0:\n change += self.scenario.length\n new_abs_pos = self.vehicles.get_absolute_position(\n veh_id) + change\n self.vehicles.set_absolute_position(veh_id, new_abs_pos)\n except ValueError or TypeError:\n self.vehicles.set_absolute_position(veh_id, -1001)\n\n if self.vehicles.get_position(veh_id) < 0 or \\\n self.vehicles.get_speed(veh_id) < 0:\n crash = True\n\n # collect headway, leader id, and follower id data\n headway_data = \\\n self.get_headway_dict(network_observations=network_observations)\n\n for veh_id in self.ids:\n try:\n self.vehicles.set_headway(\n veh_id, headway_data[veh_id][\"headway\"])\n self.vehicles.set_leader(\n veh_id, headway_data[veh_id][\"leader\"])\n self.vehicles.set_follower(\n veh_id, headway_data[veh_id][\"follower\"])\n except KeyError:\n # occurs in the case of crashes, so headway is assumed to\n # be very small\n self.vehicles.set_headway(veh_id, 1e-3)\n self.vehicles.set_leader(veh_id, None)\n self.vehicles.set_follower(veh_id, None)\n\n # collect list of sorted vehicle ids\n self.sorted_ids, self.sorted_extra_data = self.sort_by_position()\n\n # collect information of the state of the network based on the\n # environment class used\n if self.vehicles.num_rl_vehicles > 0:\n self.state = self.get_state()\n # rllab requires non-multi agent to have state shape as\n # num-states x num_vehicles\n if not self.multi_agent:\n self.state = self.state.T\n else:\n self.state = []\n # collect observation new state associated with action\n\n next_observation = list(self.state)\n\n # crash encodes whether sumo experienced a crash\n crash = \\\n crash \\\n or self.traci_connection.simulation.getEndingTeleportNumber() != 0 \\\n or self.traci_connection.simulation.getStartingTeleportNumber() != 0\n\n # compute the reward\n if self.vehicles.num_rl_vehicles > 0:\n reward = self.compute_reward(self.state, rl_actions, fail=crash)\n else:\n reward = 0\n\n # Are we in a multi-agent scenario? If so, the action space is a list.\n if self.multi_agent:\n done_n = self.vehicles.num_rl_vehicles * [0]\n info_n = {'n': []}\n\n if self.shared_reward:\n info_n['reward_n'] = [reward] * len(self.action_space)\n else:\n info_n['reward_n'] = reward\n\n if crash:\n done_n = self.vehicles.num_rl_vehicles * [1]\n if self.fail_safe:\n logging.error(\"Crash has occurred! Check your failsafes\")\n\n info_n['done_n'] = done_n\n info_n['state'] = self.state\n done = np.all(done_n)\n return self.state, sum(reward), done, info_n\n\n else:\n if crash:\n logging.info(\"Crash has occurred! Check failsafes!\")\n return Step(observation=next_observation, reward=reward,\n done=True)\n else:\n return Step(observation=next_observation, reward=reward,\n done=False)","def act(self, obs_all_agents, noise=0.0):\n actions = [agent.act(obs, noise) for agent, obs in zip(self.maddpg_agent, obs_all_agents)]\n return actions","def act(self, obs_all_agents, noise=0.0):\n actions = [agent.act(obs, noise) for agent, obs in zip(self.maddpg_agent, obs_all_agents)]\n return actions","def simulate(self, data):\n # Get the current timestep\n now = data.current_timestep\n self.logger.info(\"{model_name_cap}Wrapper received inputs in %s\", now)\n\n # Get model parameters\n {model_parameters}\n # Get model inputs\n {model_inputs}\n # Write results to data handler\n {model_outputs}\n self.logger.info(\"{model_name_cap}Wrapper produced outputs in %s\", now)","def execute_actions(self, actions):\n execute_actions(self.board, self.agent_locs, actions)","def __call__(self, inputs):\n inputs = jnp.asarray(inputs, self.dtype)\n\n bias_shape = inputs.shape[-1]\n bias = self.param('bias', self.bias_init, bias_shape)\n bias = jnp.asarray(bias, self.dtype)\n bias = jnp.broadcast_to(bias, inputs.shape)\n\n return inputs + bias","def run(self, steps):\n self.sim.run(steps)","def call(self, inputs, training=False):\n features, predicted = inputs\n enc = self.encoder(features, training=training)\n pred, _ = self.predict_net(predicted, training=training)\n outputs = self.joint_net([enc, pred], training=training)\n return outputs","def act(\n self, obs: Any, possible_actions_mask: Optional[torch.Tensor] = None\n ) -> rlt.ActorOutput:\n scorer_inputs = (obs,)\n if possible_actions_mask is not None:\n scorer_inputs += (possible_actions_mask,)\n model_output = self.scorer(*scorer_inputs)\n scores = model_output[\"ucb\"]\n actor_output = self.sampler.sample_action(scores)\n return actor_output.cpu().detach()","def act(self, mean, *args, **kwargs):\n pass","def step(self):\n if not self.is_done():\n actions = []\n for agent in self.agents:\n if agent.alive:\n actions.append(agent.program(self.percept(agent)))\n else:\n actions.append(\"\")\n for (agent, action) in zip(self.agents, actions):\n self.execute_action(agent, action)\n self.exogenous_change()","def step(self):\n if not self.is_done():\n actions = []\n for agent in self.agents:\n if agent.alive:\n actions.append(agent.program(self.percept(agent)))\n else:\n actions.append(\"\")\n for (agent, action) in zip(self.agents, actions):\n self.execute_action(agent, action)\n self.exogenous_change()","def feedForward(self, inputs):\n self.ai = np.array(inputs)\n self.ah1 = tanh(self.ai.dot(self.wi))\n self.ah2 = tanh(self.ah1.dot(self.wh))\n self.ao = softmax(self.ah2.dot(self.wo))","def act(self, kb, args):\n # check if the preconditions are satisfied\n if not self.check_precond(kb, args):\n raise Exception(\"Action pre-conditions not satisfied\")\n # remove negative literals\n for clause in self.effect_rem:\n kb.retract(self.substitute(clause, args))\n # add positive literals\n for clause in self.effect_add:\n kb.tell(self.substitute(clause, args))","def call(self, inputs):\n\n x = tf.matmul(inputs, self.w) + self.b\n x = self.activation(x)\n\n return x","def transform(self, inputs):\n msg = 'The function transform() is not available in the class ' \\\n '`EnsembleDetector`.'\n LOGGER.error(TAG, msg)\n raise NotImplementedError(msg)","def forward(self, input_dict):\n if not self.flag:\n print_total_parameters(self._net)\n self.flag = True\n\n agent_num = len(input_dict[\"agents\"])\n if \"update_target_net\" in input_dict and input_dict[\"update_target_net\"]:\n self._update_target_net()\n\n for i in range(agent_num):\n p = input_dict[\"agents\"][i]\n entities = p[\"obs\"][\"entities\"]\n localmap = p[\"obs\"][\"localmap\"]\n globalmap = p[\"obs\"][\"globalmap\"]\n p[\"atoms\"] = self.atoms.repeat(entities.shape[0], 1)\n p[\"q_dist\"] = self.policy_net(entities, localmap, globalmap)\n p[\"qs\"] = self._cal_q(p[\"q_dist\"])\n\n if \"train\" in input_dict and input_dict[\"train\"]:\n if not self.n_step:\n next_entities = p[\"next_obs\"][\"entities\"]\n next_localmap = p[\"next_obs\"][\"localmap\"]\n next_globalmap = p[\"next_obs\"][\"globalmap\"]\n with torch.no_grad():\n p[\"next_target_q_dist\"] = self.target_net(\n next_entities, next_localmap, next_globalmap\n )\n p[\"next_target_qs\"] = self._cal_q(p[\"next_target_q_dist\"])\n if self.double:\n p[\"next_q_dist\"] = self.policy_net(\n next_entities, next_localmap, next_globalmap\n )\n p[\"next_qs\"] = self._cal_q(p[\"next_q_dist\"])\n else:\n n_step_entities = p[\"n_step_obs\"][\"entities\"]\n n_step_localmap = p[\"n_step_obs\"][\"localmap\"]\n n_step_globalmap = p[\"n_step_obs\"][\"globalmap\"]\n with torch.no_grad():\n p[\"n_step_target_q_dist\"] = self.target_net(\n n_step_entities, n_step_localmap, n_step_globalmap\n )\n p[\"n_step_target_qs\"] = self._cal_q(p[\"n_step_target_q_dist\"])\n if self.double:\n p[\"n_step_q_dist\"] = self.policy_net(\n n_step_entities, n_step_localmap, n_step_globalmap\n )\n p[\"n_step_qs\"] = self._cal_q(p[\"n_step_q_dist\"])\n\n return input_dict","def call(self, inputs):\n # Feed attention\n norm_attention = self.norm_attention(inputs)\n\n attention = self.attention(query=norm_attention, value=norm_attention)\n\n # Skip Connection\n attention += inputs\n\n # Feed MLP\n outputs = self.mlp(self.norm_mlp(attention))\n\n # Skip Connection\n outputs += attention\n\n return outputs","def act(self, obs_all_agents, noise=0.0):\n actions = [agent.act(obs, noise) for agent, obs in zip(self.maddpg_agent, obs_all_agents)]\n return np.array(actions)","def run(self, xs):\n \"*** YOUR CODE HERE ***\"\n previousOutput = None\n for letter in xs:\n inputLayer = self.runInputLayer(letter)\n if previousOutput is not None:\n previousOutputLayer = self.runPreviousOutputLayer(previousOutput)\n inputLayer = nn.Add(inputLayer, previousOutputLayer)\n outputLayer = self.runOutputLayer(inputLayer)\n previousOutput = inputLayer\n return outputLayer","def act(self):\n self.features = self.next_features\n self.choose_random = np.random.choice(2,p=(1-self.epsilon,self.epsilon)) # Chooses whether to explore or exploit with probability 1-self.epsilon\n # Selects the best action index in current state\n if self.choose_random:\n self.chosenA = np.random.choice(4)\n else:\n self.chosenA = self.argmaxQsa(self.features)\n # Records reward for printing and performs action\n self.action = self.idx2act[self.chosenA]\n # Execute the action and get the received reward signal\n self.reward = self.move(self.action)\n self.total_reward += self.reward\n # IMPORTANT NOTE:\n # 'action' must be one of the values in the actions set,\n # i.e. Action.LEFT, Action.RIGHT, Action.ACCELERATE or Action.BRAKE\n # Do not use plain integers between 0 - 3 as it will not work","def calculate(self, inputs):\r\n output = inputs\r\n for layer in self.layers:\r\n output = layer.calculate(output)\r\n return output","def call(self, inputs):\n if self.backward_compatible:\n kernel = tf.keras.backend.reshape(self.kernel, self.kernel_shape)\n bias = (tf.keras.backend.reshape(self.bias, self.bias_shape)\n if self.use_bias else None)\n else:\n kernel = self.kernel\n bias = self.bias\n\n if self.output_projection:\n ret = tf.einsum(\"abcd,cde->abe\", inputs, kernel)\n else:\n ret = tf.einsum(\"abc,cde->abde\", inputs, kernel)\n if self.use_bias:\n ret += bias\n if self.activation is not None:\n return self.activation(ret)\n return ret","def _forwardImplementation(self, inbuf, outbuf):\n assert self.module\n \n values = self.module.getActionValues(self.state) \n n_values = self.n_values.getActionValues(self.state)\n values = map(lambda x, y: x + self.exploration * (sqrt(2 * log(self.experiment.stepid, 2) / y) if y > 0 else 1000), values, n_values);\n \n actions = []\n for i in range(self.shield_options):\n new_action = where(values == max(values))[0]\n new_action = choice(new_action) \n values[new_action] = -10000\n actions.append(new_action)\n \n while len(actions) < self.outdim:\n actions.append(-1)\n \n outbuf[:] = actions","def act(self, states, add_noise=True):\n actions = np.zeros([self.num_agents, self.action_size])\n for index, agent in enumerate(self.agents):\n actions[index, :] = agent.act(states[index], add_noise)\n return actions","def interact(self):\n x, mu = self.update_position_direction(self.l_int)\n mu_mean = self.calculate_mean_mu(self.x, x, self.l_int)\n self.update_estimators(self.l_int, mu_mean)\n\n self.is_absorbed = True\n self.is_active = False","def set_inputs(self,inputs):\n raise NotImplementedError(\"Robot.set_inputs\")","def engage(self):\n # no sigmoid for the inputs and bias\n if layer != 0:\n self.outputValue = sigmoid(inputSum);\n\n for connection in self.outputConnections:\n if connection.enabled == True:\n #connection will have toNode\n connection.toNode.inputSum += connection.weight * self.outputValue;","def apply(self, gameState):\n pass","def exploit_act(self, states_ns):\n raise NotImplementedError","def call(self, states, hand):\n states = tf.convert_to_tensor(states)\n hand = tf.convert_to_tensor(hand)\n # 1. Raise each state in states with an embedding layer.\n state_embedding = self.state_embedding(states)\n # 2. Raise the hand with a different embedding layer.\n hand_embedding = self.hand_embedding(hand)\n # 3. Feed states through a GRU, and get the last hidden state.\n gru_out, gru_state = self.gru(inputs=state_embedding)\n # 4. Feed hand through a Dense layer.\n dense_hand = self.hand_dense_layer(hand_embedding)\n # 5. Concat output of GRU and output of Dense layer.\n concatted = tf.concat(gru_out, dense_hand)\n # 6. Feed concat output through one (or more) Dense layer(s).\n concatted_dense_1 = self.concatted_dense_1(concatted)\n # 7. Feed output through Dense layer to size num_actions (61), and call softmax.\n final_dense = self.final_dense(concatted_dense_1)\n # 8 TODO: gotta be train/test I think (maybe here, or maybe in train/test). Mask out illegal calls, and re-normalize.\n # 9. Return matrix of probability distribution over actions.\n return final_dense","def apply(self):\n pass","def apply(self):\n pass","def forward(self, screen, minimap, flat, available_actions):\n # push each input through the network\n screen = self.screen_features(screen)\n minimap = self.minimap_features(minimap)\n flat = self.flat_features(flat)\n\n flattened_screen = screen.view(1, -1)\n flattened_mm = minimap.view(1, -1)\n\n latent_vector = torch.cat([flat, flattened_screen, flattened_mm], 1)\n features = self.combined_features(latent_vector)\n\n value = self.value_predictor(features)\n action = self.policy_action(features)\n\n policy_args = dict()\n for arg in actions.TYPES:\n for dim, size in enumerate(arg.sizes):\n module_name = self.get_argument_module_name(arg, dim)\n operator = getattr(self, module_name)\n policy_args[module_name] = operator(features)\n\n return action, policy_args, value","def act(self, obs_all_agents, noise=0.0):\n actions_next = [agent.act(obs, noise) for agent, obs in zip(self.maddpg_agent, obs_all_agents)]\n return actions_next","def act(self, states: np.ndarray, eps: float = 0.0) -> List[np.ndarray]:\n actions = [\n agent.act(state.reshape(-1, 1).T, eps)\n for agent, state in zip(self.agents, states)\n ]\n return actions","def simulate(self, inputs = None, bit_str = None):\n # Eliminating basic outlier conditions\n if (inputs is None and bit_str is not None) or (inputs is not None and bit_str is None):\n print('Please enter both inputs and bit_str. Exiting simulation...')\n return\n if inputs is not None and bit_str is not None:\n if len(inputs) != len(bit_str):\n print('inputs and bit_str not of same length. Exiting simulation...')\n return\n # setting primary inputs\n for i in range(0, len(inputs)):\n self.setIpValue(inputs[i], int(bit_str[i]))\n \n self.__simSetup()\n\n # iterating all cfg_blcks\n blck_ids = [blck[0] for blck in self.listCfgBlcks()]\n\n for cfg_id in blck_ids:\n if self.dGrph[cfg_id][1][1] == None:\n if(self.__processBlcks(cfg_id)):\n print('Processed cfg_blck: ', cfg_id)\n else:\n print('Some error in processing cfg_blck: ', cfg_id)\n \n # iterating all ari_blcks\n blck_ids = [blck[0] for blck in self.listAriBlcks()]\n\n for ari_id in blck_ids:\n # checking if one output is None because all outputs are set simultaneously\n if self.dGrph[ari_id][1][0][1] == None:\n if(self.__processBlcks(ari_id)):\n print('Processed ari_blck: ', ari_id)\n else:\n print('Some error in processing ari_blck: ', ari_id)\n \n # iterating all tribufs\n blck_ids = [blck[0] for blck in self.listTribufs()]\n\n for tri_id in blck_ids:\n if self.dGrph[tri_id][2][1] == None:\n if(self.__processBlcks(tri_id)):\n print('Processed tribuf: ', tri_id)\n else:\n print('Some error in processing tribuf: ', tri_id)\n \n # iterating all gates\n blck_ids = [blck[0] for blck in self.listGates()]\n\n for gate_id in blck_ids:\n if self.dGrph[gate_id][1][2] == None:\n if(self.__processBlcks(gate_id)):\n print('Processed gate: ', gate_id)\n else:\n print('Some error in processing gate: ', gate_id)","def forward(self, inputs):\r\n #print (len(inputs))\r\n out = self.fc1(inputs)\r\n out = self.fc2(out)\r\n self.out = out\r\n return out\r\n #raise NotImplementedError('Implement the forward method of the model')\r","def compute(self, inputs):\n\t\tres = inputs\n\t\tfor layer in range(self.layersNumber):\n\t\t\tweight = self.weights[layer]\n\t\t\tbias = self.biases[layer]\n\t\t\tres = fActivation(np.dot(weight, res) + bias)\n\t\treturn res"],"string":"[\n \"def apply(self, inputs):\\n raise NotImplementedError()\",\n \"def _simulation_run(model_instance, observations, actions, rewards):\\r\\n\\r\\n for observation, action, reward in zip(observations, actions, rewards):\\r\\n model_instance.observe(observation)\\r\\n model_instance.overrideActionChoice(action)\\r\\n model_instance.feedback(reward)\\r\\n\\r\\n return model_instance\",\n \"def control_law( self, inputs ):\",\n \"def process_inputs(self, inputs):\",\n \"def apply_neurons(self):\\n for neuron in range(self.n_outputs):\\n self.uf_activate(neuron)\",\n \"def learning_Utility(self):\\n # Shape the input that we give to the neural network with the value of sensors, the previous actions the life of the agent \\n # Get the results from the sensors according the different movement executed by the agent \\n sensors_result_N = self.agent.sensors(self, direction=3) + self.agent.get_energy_coarsed()+\\\\\\n self.agent.rotate_previousAction(3)+[self.agent.get_previous_collision()]\\n sensors_result_O = self.agent.sensors(self, direction=2) + self.agent.get_energy_coarsed()+\\\\\\n self.agent.rotate_previousAction(2) + [self.agent.get_previous_collision()]\\n sensors_result_S = self.agent.sensors(self, direction=1) + self.agent.get_energy_coarsed()+\\\\\\n self.agent.rotate_previousAction(1) + [self.agent.get_previous_collision()]\\n sensors_result_E = self.agent.sensors(self, direction=0) + self.agent.get_energy_coarsed()+\\\\\\n self.agent.rotate_previousAction(0) + [self.agent.get_previous_collision()]\\n\\n input_nn_N = np.asarray(sensors_result_N).astype(int) # input when the Nord action is performed \\n input_nn_O = np.asarray(sensors_result_O).astype(int) # input when the West action is performed\\n input_nn_S = np.asarray(sensors_result_S).astype(int) # input when the South action is performed\\n input_nn_E = np.asarray(sensors_result_E).astype(int) # input when the West action is performed\\n\\n self.input_list = [input_nn_E.reshape(1,145),\\n input_nn_S.reshape(1,145),\\n input_nn_O.reshape(1,145),\\n input_nn_N.reshape(1,145)]\\n self.U_list = [self.nn.predict(i) for i in self.input_list ] #The utility according the different acts performed \\n return self.actionSelector() #Select the action acording a propbabilitics distribution given in the paper\",\n \"def apply_action(self, physics, action, random_state):\\n del random_state\\n physics.bind(self.actuators).ctrl = action\",\n \"def perform_split_brain_actions(self):\\n proximity = self.grid.proximity_to_apple()\\n inputs = [\\n torch.tensor([\\n [\\n proximity,\\n self.grid.safe_cells_up_global(),\\n self.grid.safe_cells_up(),\\n self.grid.apple_is_up_safe(0.5)\\n ]\\n ]),\\n torch.tensor([\\n [\\n proximity,\\n self.grid.safe_cells_down_global(),\\n self.grid.safe_cells_down(),\\n self.grid.apple_is_down_safe(0.5)\\n ]\\n ]),\\n torch.tensor([\\n [\\n proximity,\\n self.grid.safe_cells_left_global(),\\n self.grid.safe_cells_left(),\\n self.grid.apple_is_left_safe(0.5)\\n ]\\n ]),\\n torch.tensor([\\n [\\n proximity,\\n self.grid.safe_cells_right_global(),\\n self.grid.safe_cells_right(),\\n self.grid.apple_is_right_safe(0.5)\\n ]\\n ])\\n ]\\n\\n new_direction = self.split_brain_network.eval(inputs)\\n self.grid.change_direction(new_direction)\\n got_apple = self.grid.next_frame()\\n new_proximity = self.grid.proximity_to_apple()\\n\\n if self.is_training:\\n reward = torch.tensor([-0.5])\\n\\n if self.grid.snake_died():\\n reward = torch.tensor([-1.0])\\n elif got_apple:\\n reward = torch.tensor([1.0])\\n elif new_proximity > proximity:\\n reward = torch.tensor([0.8])\\n\\n self.split_brain_network.update(reward)\",\n \"def quick_run(self, *args):\\n self.inputs(*args)\\n self.run()\",\n \"def __call__(self, inputs: np.ndarray):\\n # Denote the impact the inputs have directly on the outputs\\n output_inputs: np.ndarray = np.matmul(self.in2out, inputs.transpose()).transpose()\\n \\n # Denote the impact hidden nodes have on the outputs, if there are hidden nodes\\n if self.n_hidden > 0:\\n # Nice to know:\\n # - np.transpose() will transpose the tensor\\n # - np.matmul(tensor1, tensor2) will perform a matrix multiplication between tensor and tensor2\\n \\n # The activation is defined by:\\n # - the inputs mapping to the hidden nodes\\n # - the hidden nodes mapping to themselves\\n # - the hidden nodes' biases\\n \\n # 1) Propagate the hidden nodes\\n self.hidden_act = self.act_f(np.matmul(self.in2hid, inputs.transpose()).transpose() +\\n np.matmul(self.hid2hid, self.hidden_act.transpose()).transpose() +\\n self.hidden_biases)\\n \\n # 2) Execute the RNN nodes if they exists (updating current hidden state)\\n for i, rnn_idx in enumerate(self.rnn_idx):\\n self.rnn_state[:, i] = self.rnn_array[i](\\n np.concatenate((self.in2hid[rnn_idx] * inputs,\\n self.hid2hid[rnn_idx] * self.hidden_act),\\n axis=1)[self.rnn_map[i]].reshape(self.bs, self.rnn_array[i].input_size)\\n )\\n self.hidden_act[:, rnn_idx] = self.rnn_state[:, i, 0]\\n \\n # 3) Propagate hidden-values to the outputs\\n output_inputs += np.matmul(self.hid2out, self.hidden_act.transpose()).transpose()\\n \\n # Define the values of the outputs, which is the sum of their received inputs and their corresponding bias\\n self.output_act = self.act_f(output_inputs + self.output_biases)\\n return self.output_act\",\n \"def run(self):\\n self.assign_inputs()\\n self.execute()\\n self.collect_outputs()\",\n \"def processInputs(self):\",\n \"def do_manipulations(self, *args, **kwargs):\\n pass\",\n \"def step(self, action):\\n actions = action.reshape(2, 2)\\n action = {'GoalieBrain': actions[0], 'StrikerBrain': actions[1]}\\n #action = {'GoalieBrain': [5, 5], 'StrikerBrain': [2, 2]}\\n #print(action)\\n info = self.env.step(action)\\n\\n state0 = info[self.brain_names[0]].vector_observations\\n state1 = info[self.brain_names[1]].vector_observations\\n state = np.vstack((state0, state1))\\n reward0 = info[self.brain_names[0]].rewards\\n reward1 = info[self.brain_names[1]].rewards\\n reward = reward0 + reward1\\n done0 = info[self.brain_names[0]].local_done\\n done1 = info[self.brain_names[1]].local_done\\n done = done0 + done1\\n return state, reward, done\",\n \"def apply_actions_to_body_parts(self, actions_dict):\\n\\n if self._noise:\\n self.current_actions = self._apply_noise(actions_dict)\\n else:\\n self.current_actions = actions_dict\\n\\n for actuator, value in self.current_actions.items():\\n\\n for body_part in self.parts:\\n body_part.apply_action(actuator, value)\",\n \"def call(self, inputs):\\n raise NotImplementedError\",\n \"def call(self, inputs, state):\\n raise NotImplementedError\",\n \"def run_net(inputs, **parameter):\\n\\n # ---- set numpy random state for each run----\\n np.random.set_state(np_state)\\n\\n # -----parameter setting-------\\n n_ex = 1600\\n n_inh = int(n_ex / 4)\\n n_input = MNIST_shape[1] * coding_n\\n n_read = n_ex + n_inh\\n\\n R = parameter['R']\\n f_in = parameter['f_in']\\n f_EE = parameter['f_EE']\\n f_EI = parameter['f_EI']\\n f_IE = parameter['f_IE']\\n f_II = parameter['f_II']\\n\\n A_EE = 60 * f_EE\\n A_EI = 60 * f_EI\\n A_IE = 60 * f_IE\\n A_II = 60 * f_II\\n A_inE = 60 * f_in\\n A_inI = 60 * f_in\\n\\n tau_ex = parameter['tau_ex'] * coding_duration\\n tau_inh = parameter['tau_inh'] * coding_duration\\n tau_read = 30\\n\\n p_inE = parameter['p_in'] * 0.1\\n p_inI = parameter['p_in'] * 0.1\\n\\n # ------definition of equation-------------\\n neuron_in = '''\\n I = stimulus(t,i) : 1\\n '''\\n\\n neuron = '''\\n tau : 1\\n dv/dt = (I-v) / (tau*ms) : 1 (unless refractory)\\n dg/dt = (-g)/(3*ms) : 1\\n dh/dt = (-h)/(6*ms) : 1\\n I = (g+h)+13.5: 1\\n x : 1\\n y : 1\\n z : 1\\n '''\\n\\n neuron_read = '''\\n tau : 1\\n dv/dt = (I-v) / (tau*ms) : 1\\n dg/dt = (-g)/(3*ms) : 1 \\n dh/dt = (-h)/(6*ms) : 1\\n I = (g+h): 1\\n '''\\n\\n synapse = '''\\n w : 1\\n '''\\n\\n on_pre_ex = '''\\n g+=w\\n '''\\n\\n on_pre_inh = '''\\n h-=w\\n '''\\n\\n # -----Neurons and Synapses setting-------\\n Input = NeuronGroup(n_input, neuron_in, threshold='I > 0', method='euler', refractory=0 * ms,\\n name='neurongroup_input')\\n\\n G_ex = NeuronGroup(n_ex, neuron, threshold='v > 15', reset='v = 13.5', method='euler', refractory=3 * ms,\\n name='neurongroup_ex')\\n\\n G_inh = NeuronGroup(n_inh, neuron, threshold='v > 15', reset='v = 13.5', method='euler', refractory=2 * ms,\\n name='neurongroup_in')\\n\\n G_readout = NeuronGroup(n_read, neuron_read, method='euler', name='neurongroup_read')\\n\\n S_inE = Synapses(Input, G_ex, synapse, on_pre=on_pre_ex, method='euler', name='synapses_inE')\\n\\n S_inI = Synapses(Input, G_inh, synapse, on_pre=on_pre_ex, method='euler', name='synapses_inI')\\n\\n S_EE = Synapses(G_ex, G_ex, synapse, on_pre=on_pre_ex, method='euler', name='synapses_EE')\\n\\n S_EI = Synapses(G_ex, G_inh, synapse, on_pre=on_pre_ex, method='euler', name='synapses_EI')\\n\\n S_IE = Synapses(G_inh, G_ex, synapse, on_pre=on_pre_inh, method='euler', name='synapses_IE')\\n\\n S_II = Synapses(G_inh, G_inh, synapse, on_pre=on_pre_inh, method='euler', name='synapses_I')\\n\\n S_E_readout = Synapses(G_ex, G_readout, 'w = 1 : 1', on_pre=on_pre_ex, method='euler')\\n\\n S_I_readout = Synapses(G_inh, G_readout, 'w = 1 : 1', on_pre=on_pre_inh, method='euler')\\n\\n # -------initialization of neuron parameters----------\\n G_ex.v = '13.5+1.5*rand()'\\n G_inh.v = '13.5+1.5*rand()'\\n G_readout.v = '0'\\n G_ex.g = '0'\\n G_inh.g = '0'\\n G_readout.g = '0'\\n G_ex.h = '0'\\n G_inh.h = '0'\\n G_readout.h = '0'\\n G_ex.tau = tau_ex\\n G_inh.tau = tau_inh\\n G_readout.tau = tau_read\\n [G_ex, G_in] = base.allocate([G_ex, G_inh], 10, 10, 20)\\n\\n # -------initialization of network topology and synapses parameters----------\\n S_inE.connect(condition='j<0.3*N_post', p=p_inE)\\n S_inI.connect(condition='j<0.3*N_post', p=p_inI)\\n S_EE.connect(condition='i != j', p='0.3*exp(-((x_pre-x_post)**2+(y_pre-y_post)**2+(z_pre-z_post)**2)/R**2)')\\n S_EI.connect(p='0.2*exp(-((x_pre-x_post)**2+(y_pre-y_post)**2+(z_pre-z_post)**2)/R**2)')\\n S_IE.connect(p='0.4*exp(-((x_pre-x_post)**2+(y_pre-y_post)**2+(z_pre-z_post)**2)/R**2)')\\n S_II.connect(condition='i != j', p='0.1*exp(-((x_pre-x_post)**2+(y_pre-y_post)**2+(z_pre-z_post)**2)/R**2)')\\n S_E_readout.connect(j='i')\\n S_I_readout.connect(j='i+n_ex')\\n\\n S_inE.w = function.gamma(A_inE, S_inE.w.shape)\\n S_inI.w = function.gamma(A_inI, S_inI.w.shape)\\n S_EE.w = function.gamma(A_EE, S_EE.w.shape)\\n S_IE.w = function.gamma(A_IE, S_IE.w.shape)\\n S_EI.w = function.gamma(A_EI, S_EI.w.shape)\\n S_II.w = function.gamma(A_II, S_II.w.shape)\\n\\n S_EE.pre.delay = '1.5*ms'\\n S_EI.pre.delay = '0.8*ms'\\n S_IE.pre.delay = '0.8*ms'\\n S_II.pre.delay = '0.8*ms'\\n\\n # ------create network-------------\\n net = Network(collect())\\n net.store('init')\\n\\n # ------run network-------------\\n stimulus = TimedArray(inputs[0], dt=Dt)\\n net.run(duration * Dt)\\n states = net.get_states()['neurongroup_read']['v']\\n net.restore('init')\\n return (states, inputs[1])\",\n \"def run_net(inputs, **parameter):\\n\\n #---- set numpy random state for each run----\\n np.random.set_state(np_state)\\n\\n # -----parameter setting-------\\n n_ex = 1600\\n n_inh = int(n_ex/4)\\n n_input = MNIST_shape[1]*coding_n\\n n_read = n_ex+n_inh\\n\\n R = parameter['R']\\n f_in = parameter['f_in']\\n f_EE = parameter['f_EE']\\n f_EI = parameter['f_EI']\\n f_IE = parameter['f_IE']\\n f_II = parameter['f_II']\\n\\n A_EE = 60*f_EE\\n A_EI = 60*f_EI\\n A_IE = 60*f_IE\\n A_II = 60*f_II\\n A_inE = 60*f_in\\n A_inI = 60*f_in\\n\\n tau_ex = parameter['tau_ex']*coding_duration\\n tau_inh = parameter['tau_inh']*coding_duration\\n tau_read= 30\\n\\n p_inE = parameter['p_in']*0.1\\n p_inI = parameter['p_in']*0.1\\n\\n #------definition of equation-------------\\n neuron_in = '''\\n I = stimulus(t,i) : 1\\n '''\\n\\n neuron = '''\\n tau : 1\\n dv/dt = (I-v) / (tau*ms) : 1 (unless refractory)\\n dg/dt = (-g)/(3*ms) : 1\\n dh/dt = (-h)/(6*ms) : 1\\n I = (g+h)+13.5: 1\\n x : 1\\n y : 1\\n z : 1\\n '''\\n\\n neuron_read = '''\\n tau : 1\\n dv/dt = (I-v) / (tau*ms) : 1\\n dg/dt = (-g)/(3*ms) : 1 \\n dh/dt = (-h)/(6*ms) : 1\\n I = (g+h): 1\\n '''\\n\\n synapse = '''\\n w : 1\\n '''\\n\\n on_pre_ex = '''\\n g+=w\\n '''\\n\\n on_pre_inh = '''\\n h-=w\\n '''\\n\\n # -----Neurons and Synapses setting-------\\n Input = NeuronGroup(n_input, neuron_in, threshold='I > 0', method='euler', refractory=0 * ms,\\n name = 'neurongroup_input')\\n\\n G_ex = NeuronGroup(n_ex, neuron, threshold='v > 15', reset='v = 13.5', method='euler', refractory=3 * ms,\\n name ='neurongroup_ex')\\n\\n G_inh = NeuronGroup(n_inh, neuron, threshold='v > 15', reset='v = 13.5', method='euler', refractory=2 * ms,\\n name ='neurongroup_in')\\n\\n G_readout = NeuronGroup(n_read, neuron_read, method='euler', name='neurongroup_read')\\n\\n S_inE = Synapses(Input, G_ex, synapse, on_pre = on_pre_ex ,method='euler', name='synapses_inE')\\n\\n S_inI = Synapses(Input, G_inh, synapse, on_pre = on_pre_ex ,method='euler', name='synapses_inI')\\n\\n S_EE = Synapses(G_ex, G_ex, synapse, on_pre = on_pre_ex ,method='euler', name='synapses_EE')\\n\\n S_EI = Synapses(G_ex, G_inh, synapse, on_pre = on_pre_ex ,method='euler', name='synapses_EI')\\n\\n S_IE = Synapses(G_inh, G_ex, synapse, on_pre = on_pre_inh ,method='euler', name='synapses_IE')\\n\\n S_II = Synapses(G_inh, G_inh, synapse, on_pre = on_pre_inh ,method='euler', name='synapses_I')\\n\\n S_E_readout = Synapses(G_ex, G_readout, 'w = 1 : 1', on_pre=on_pre_ex, method='euler')\\n\\n S_I_readout = Synapses(G_inh, G_readout, 'w = 1 : 1', on_pre=on_pre_inh, method='euler')\\n\\n #-------initialization of neuron parameters----------\\n G_ex.v = '13.5+1.5*rand()'\\n G_inh.v = '13.5+1.5*rand()'\\n G_readout.v = '0'\\n G_ex.g = '0'\\n G_inh.g = '0'\\n G_readout.g = '0'\\n G_ex.h = '0'\\n G_inh.h = '0'\\n G_readout.h = '0'\\n G_ex.tau = tau_ex\\n G_inh.tau = tau_inh\\n G_readout.tau = tau_read\\n\\n [G_ex,G_in] = base.allocate([G_ex,G_inh],10,10,20)\\n\\n # -------initialization of network topology and synapses parameters----------\\n S_inE.connect(condition='j<0.3*N_post', p = p_inE)\\n S_inI.connect(condition='j<0.3*N_post', p = p_inI)\\n S_EE.connect(condition='i != j', p='0.3*exp(-((x_pre-x_post)**2+(y_pre-y_post)**2+(z_pre-z_post)**2)/R**2)')\\n S_EI.connect(p='0.2*exp(-((x_pre-x_post)**2+(y_pre-y_post)**2+(z_pre-z_post)**2)/R**2)')\\n S_IE.connect(p='0.4*exp(-((x_pre-x_post)**2+(y_pre-y_post)**2+(z_pre-z_post)**2)/R**2)')\\n S_II.connect(condition='i != j', p='0.1*exp(-((x_pre-x_post)**2+(y_pre-y_post)**2+(z_pre-z_post)**2)/R**2)')\\n S_E_readout.connect(j='i')\\n S_I_readout.connect(j='i+n_ex')\\n\\n S_inE.w = function.gamma(A_inE, S_inE.w.shape)\\n S_inI.w = function.gamma(A_inI, S_inI.w.shape)\\n S_EE.w = function.gamma(A_EE, S_EE.w.shape)\\n S_IE.w = function.gamma(A_IE, S_IE.w.shape)\\n S_EI.w = function.gamma(A_EI, S_EI.w.shape)\\n S_II.w = function.gamma(A_II, S_II.w.shape)\\n\\n S_EE.pre.delay = '1.5*ms'\\n S_EI.pre.delay = '0.8*ms'\\n S_IE.pre.delay = '0.8*ms'\\n S_II.pre.delay = '0.8*ms'\\n\\n # ------create network-------------\\n net = Network(collect())\\n net.store('init')\\n\\n # ------run network-------------\\n stimulus = TimedArray(inputs[0], dt=Dt)\\n net.run(duration * Dt)\\n states = net.get_states()['neurongroup_read']['v']\\n net.restore('init')\\n return (states, inputs[1])\",\n \"def __call__(self, inputs):\\n for layer in self.prenet_layers:\\n f = layer(inputs)\\n if self._enable_dropout:\\n inputs = tf.layers.dropout(\\n f, rate=self._dropout, training=True\\n )\\n else:\\n inputs = tf.layers.batch_normalization(\\n f, momentum=0.1, epsilon=1e-5\\n )\\n inputs = self._activation_fn(inputs)\\n return inputs\",\n \"def forward(self, inputs):\\n raise NotImplementedError\",\n \"def __call__(self, inputs):\\n return self._hidden_activation(inputs)\",\n \"def call(self, inputs):\\n for index,layer in enumerate(self.list_cnn):\\n if index == 0:\\n out = layer(inputs)\\n else:\\n if self.residual:\\n prev_out = out\\n cur_out = layer(out)\\n if self.use_batchnorm:\\n cur_out = layers.BatchNormalization()(cur_out)\\n if self.use_dropout:\\n cur_out = layers.Dropout(0.2)(cur_out)\\n out = prev_out + cur_out\\n else:\\n out = layer(out)\\n out = self.output_layer(out)\\n if self.use_pool:\\n out = self.pool(out)\\n out = self.flatten(out)\\n out = self.classify(out)\\n return out\",\n \"def _run_apply(self):\\n self.ip = self.ip + 1\\n operator_id = self.bytecode.code[self.ip]\\n\\n # Fetch the operator details so we know two things: which operator to execute and how many bytes to read for the input\\n input_size, operator = self._operators[operator_id]\\n \\n # Read the input\\n inputs = []\\n input_index = 0\\n input_data = None\\n self.ip = self.ip + 2\\n while input_index < input_size:\\n input_data = (self.bytecode.code[self.ip + input_index], self.bytecode.code[self.ip + input_index + 1])\\n\\n # Read the output\\n self.ip = self.ip + input_index + 1\\n output_data = (self.bytecode.code[self.ip], self.bytecode.code[self.ip + 1])\\n\\n # Validate the output\\n # The output type must always be a register\\n if output_data[0] != OpType.REGISTER:\\n raise ValueError(\\\"Unexpected output operand at offset <\\\" + str(self.bytecode.code[self.ip]) + \\\">. Expected a register.\\\")\\n # The output register must be within the VM register count\\n if output_data[1] >= len(self._registers):\\n raise ValueError(\\\"Output register outside of VM register count. Byte at offset <\\\" + str(self.bytecode.code[self.ip + 1]) + \\\">.\\\")\\n\\n # Execute the instruction\\n print(\\\"Executing the APPLY instruction.\\\")\\n\\n # Update the instruction pointer to the next instruction\\n self.ip = self.ip + 2\",\n \"def call(self, inputs, **kwargs):\\n xs = self._apply_post_hooks_for_inputs(inputs)\\n with get_current_context().enter(in_call=True, wrap_ops=True, model=self):\\n outputs = self._model(xs, **kwargs)\\n return outputs\",\n \"def call(self, inputs, training=None, mask=None):\\n # pylint: disable=arguments-differ\\n raise NotImplementedError()\",\n \"def call(self, inputs, states, training):\\n raise NotImplementedError(\\\"Please implement this method\\\")\",\n \"def act(self, states, add_noise=True):\\n \\n actions = []\\n for agent, state in zip(self.agents, states):\\n action = agent.act(state, noise_weight=self.noise_weight, add_noise=self.enable_noise)\\n actions.append(action)\\n self.noise_weight *= self.noise_decay\\n return np.array(actions).reshape(1, -1) # flatten\",\n \"def run(self, inputs, **kwargs):\\n graph = self.model.graph\\n # last_layers means we run this model until the last #N layers\\n last_layers = kwargs.get('last_layers', len(self.singa_ops))\\n if last_layers != len(self.singa_ops):\\n final_outputs = self.singa_ops[last_layers-1].op.outputs\\n else:\\n final_outputs = [outp.name for outp in graph.output]\\n # whether return all outputs\\n all_outputs = kwargs.get('all_outputs', False)\\n # get a specific op by its name\\n op_name = kwargs.get('op_name', None)\\n # record the tensor we added from input\\n tmp_tensor_map = {name: val for name, val in self.tensor_map.items()}\\n\\n # the dict will be returned\\n ret_outputs = OrderedDict()\\n if self.keep_initializers_as_inputs:\\n require_input_len = len(graph.input) - len(graph.initializer)\\n actual_input_len = len(inputs)\\n else:\\n require_input_len = len(graph.input)\\n actual_input_len = len(inputs)\\n assert require_input_len == actual_input_len, \\\"The length of graph input is different from the tensor input: %d, %d\\\" % (\\n require_input_len, actual_input_len)\\n # run the handle by the order of the list(the list is Topological Sorting)\\n for inp in graph.input:\\n if inp.name not in tmp_tensor_map:\\n tmp_tensor_map[inp.name] = inputs.pop(0)\\n\\n for _, op, handle, forward in self.singa_ops[:last_layers]:\\n if len(op.consumed_inputs) != 0:\\n # because if op has consumed_inputs, it means it moved some inputs into attributes\\n # so when running, we should update these attributes\\n handle, forward = get_op(op,\\n [tmp_tensor_map[x] for x in op.inputs])\\n inputs = [\\n tmp_tensor_map[x]\\n for x in op.inputs\\n if x not in op.consumed_inputs\\n ]\\n outputs = _run_node(op, inputs, handle, forward)\\n for key, val in outputs.items():\\n tmp_tensor_map[key] = val\\n ret_outputs[key] = val\\n\\n if op_name is not None:\\n if op_name in outputs:\\n return outputs[op_name]\\n else:\\n raise RuntimeError(\\n \\\"The op_name {} does not exist, please check. The available op_names are: {}\\\"\\n .format(op_name, [val for key, val in op_name.items()]))\\n\\n # return all outputs if all_outputs==True\\n # else return last outputs\\n if all_outputs:\\n return ret_outputs\\n else:\\n return [ret_outputs[outp] for outp in final_outputs]\",\n \"def post(self, scenario, bodies):\\n if self.tacs_proc:\\n # Save the solution vector\\n self.scenario_data[scenario].u.copyValues(self.ans)\\n\\n if self.override_rotx:\\n # Override rotx to be heat flux\\n ans_array = self.ans.getArray()\\n ndof = self.assembler.getVarsPerNode()\\n\\n for body in bodies:\\n struct_flux = body.get_struct_heat_flux(scenario)\\n if struct_flux is not None:\\n ans_array[3::ndof] = struct_flux[:]\\n\\n self.assembler.setVariables(self.ans)\\n\\n if self.gen_output is not None:\\n self.gen_output()\\n\\n return\",\n \"def forward(self, *inputs):\\n raise NotImplementedError\",\n \"def _forwardImplementation(self, inbuf, outbuf):\\n assert self.module\\n \\n values = self.module.getActionValues(self.state) \\n \\n actions = []\\n if random() <= self.exploration:\\n for i in range(self.shield_options):\\n new_action = choice(range(len(values))) \\n np.delete(values, new_action)\\n actions.append(new_action)\\n else:\\n for i in range(self.shield_options):\\n new_action = where(values == max(values))[0]\\n new_action = choice(new_action) \\n np.delete(values, new_action)\\n actions.append(new_action)\\n \\n while len(actions) < self.outdim:\\n actions.append(-1)\\n \\n outbuf[:] = actions\",\n \"def forward(self, inputs):\\n action_pred_input, self.actions = self.sample_tsteps(inputs.states, inputs.action_targets)\\n a_pred = self.s_encoder.forward(action_pred_input)\\n return AttrDict(a_pred=a_pred)\",\n \"def run(self, inputs):\\n for layer in self._layers:\\n inputs = layer.calc(inputs)\\n return inputs\",\n \"def _run_actions(self):\\n\\n if \\\"install-bento\\\" in self.actions:\\n self._do_action_bento_setup()\\n\\n if \\\"create-tables\\\" in self.actions:\\n self._do_action_tables_create()\\n\\n if \\\"import-ratings\\\" in self.actions:\\n self._do_action_import_ratings()\\n\\n if \\\"import-user-info\\\" in self.actions:\\n self._do_action_import_user_info()\\n\\n if \\\"import-movie-info\\\" in self.actions:\\n self._do_action_import_movie_info()\\n\\n if \\\"train-item-item-cf\\\" in self.actions:\\n self._do_action_train()\\n\\n if \\\"register-freshener\\\" in self.actions:\\n self._do_action_register_freshener()\",\n \"def call(self, inputs):\\r\\n\\r\\n if self.share_weights:\\r\\n hat_inputs = K.conv1d(inputs, self.kernel)\\r\\n else:\\r\\n hat_inputs = K.local_conv1d(inputs, self.kernel, [1], [1])\\r\\n\\r\\n batch_size = K.shape(inputs)[0]\\r\\n input_num_capsule = K.shape(inputs)[1]\\r\\n hat_inputs = K.reshape(hat_inputs,\\r\\n (batch_size, input_num_capsule,\\r\\n self.num_capsule, self.dim_capsule))\\r\\n hat_inputs = K.permute_dimensions(hat_inputs, (0, 2, 1, 3))\\r\\n\\r\\n b = K.zeros_like(hat_inputs[:, :, :, 0])\\r\\n for i in range(self.routings):\\r\\n c = softmax(b, 1)\\r\\n \\r\\n if K.backend() == 'theano':\\r\\n o = K.sum(o, axis=1)\\r\\n o = self.activation(K.batch_dot(c, hat_inputs, [2, 2]))\\r\\n \\r\\n if i < self.routings - 1:\\r\\n b = K.batch_dot(o, hat_inputs, [2, 3])\\r\\n if K.backend() == 'theano':\\r\\n o = K.sum(o, axis=1)\\r\\n\\r\\n return o\",\n \"def run(self, **inputs):\\n if self.cached_model is None:\\n self.cache_model(**inputs)\\n for name, data in inputs.items():\\n self.shared_vars[name].set_value(data)\\n if self.mb and self.variational:\\n self.minibatches = zip(self._mb_generator(inputs['gwas_gen']),\\n self._mb_generator(inputs['gwas_phen']))\\n self.trace = self._inference()\\n return self.trace\",\n \"def act(self, state, training):\\n\\n action = self.actor_local(state)\\n if training:\\n action += self.noise.sample()\\n else:\\n action = self.actor_target(state)\\n\\n return action\",\n \"def __call__(self, inputs, states, **kwargs):\\n raise NotImplementedError()\",\n \"def act(self, action_values: Tensor) -> Tensor:\\n ...\",\n \"def Action(self, states, eps=0, isRandom=False):\\n action = []\\n # No gradient needed\\n with T.no_grad():\\n states = T.Tensor(states).float().to(device) # Send states to GPU\\n [a.eval() for a in self.Actor] # Eval Mode\\n # Loop Each Actor Model to get Action as np.array()\\n for i in range(len(self.Actor)):\\n action.append(self.Actor[i](states[i]).cpu().data.numpy())\\n # If less Actor Models the Env. Agents: use Agent[0] to calc the rest\\n if len(self.Actor)<len(states):\\n act2 = self.Actor[0](states[len(self.Actor):]).cpu().data.numpy() # Get Action\\n action = np.append(action, act2, axis=0) # Append to Action Array\\n else: action = np.array(action) # Else convert List to np.array()\\n [a.train() for a in self.Actor] # Back to Train Mode\\n # Add Epsilon Greedy Noise\\n action += self.Noise(1 if isRandom else eps, action.shape)\\n return action\",\n \"def act(self, states, add_noise=True):\\n\\n states = torch.from_numpy(states).float().to(device)\\n actions = np.empty([self.agents_count, self.action_size])\\n\\n self.actor_local.eval()\\n with torch.no_grad():\\n for a in range(self.agents_count):\\n actions[a] = self.actor_local(states[a]).cpu().data.numpy()\\n self.actor_local.train()\\n if add_noise:\\n actions += self.noise.sample()\\n return np.clip(actions, -1, 1)\",\n \"def apply_action(self, action):\\n return self.__environment.step(action)\",\n \"def call(self, inputs, training = False):\\n \\\"\\\"\\\" includes training argument as batch_norm functions differently during training and during inference \\\"\\\"\\\"\\n \\n # input --> block_1 --> block_2 --> block_3 \\n x = self.block_1(inputs, training = training)\\n x = self.block_2(x, training = training)\\n x = self.block_3(x, training = training)\\n\\n # output for MAA2C or DDPG \\n # block_3 --> outputs \\n x = self.outputs(x)\\n\\n return x\",\n \"def _action(self, wloops: Any, beta: Any) -> Any:\\n pass\",\n \"def __call__(self, inputs):\\n self.inputs = inputs\\n self.process_inputs()\\n self.init_mesh()\\n mesh_modified = self.run_tasks()\\n self.write_output_mesh(mesh_modified)\",\n \"def forward(self, inputs):\\n _, state = self.core(inputs)\\n return state\",\n \"def run_ai(self):\\n state = self.brainstate\\n me = self.owner\\n if not self.owner.alive:\\n return\\n self._acquire_target()\\n if 'target' in state:\\n self._move_towards_target()\\n else:\\n # No target, wander around\\n if random.random() > 0.3:\\n possible_directions = [i for i in\\n ([-1, -1], [-1, 0], [-1, 1], [0, -1],\\n [0, 1], [1, -1], [1, 0], [1, 1])\\n if afr.map.map.tile_is_traversable(\\n me.x + i[0],\\n me.y + i[1]\\n )\\n ]\\n logging.debug(\\\"possible directions: %s\\\", possible_directions)\\n movement = random.choice(possible_directions)\\n me.x += movement[0]\\n me.y += movement[1]\",\n \"def actions(self, states, agent_indices):\\n return NotImplementedError()\",\n \"def step(self):\\n if not self.is_done():\\n actions = [ agent.program(self.percept(agent)) for agent in self.agents ]\\n for agent, action in zip(self.agents, actions):\\n self.execute_action(agent, action)\\n\\n self.exogenous_change()\",\n \"def run_game_logic(self):\\n pass\",\n \"def ApplyInputs(ss, en):\\n ss.Net.InitExt()\\n\\n lays = [\\\"Input\\\", \\\"Output\\\"]\\n for lnm in lays :\\n ly = leabra.Layer(ss.Net.LayerByName(lnm))\\n pats = en.State(ly.Nm)\\n if pats != 0:\\n ly.ApplyExt(pats)\",\n \"def mutate(self):\\n #First, mutate masses\\n for neuronNum in range(self.neuronCounter - 1):\\n if self.learningRate > random.random():\\n self.neurons[neuronNum].mutate()\\n else:\\n continue\\n\\n #Now determine physical mutations\\n if random.random() < 0.2:\\n try:\\n physMutation = random.choice(['a','l','c'])\\n if physMutation == 'a':\\n self.addNeuron(random.choice([0,1,2]))\\n elif physMutation == 'l':\\n begin = random.randint(1,self.neuronCounter - 1)\\n end = random.randint(1, self.neuronCounter - 1)\\n self.link(begin, end)\\n else:\\n begin = random.randint(1,self.neuronCounter - 1)\\n end = random.choice(self.neurons[begin].outDic.keys())\\n self.cut(begin, end)\\n except:\\n return self\\n return self\",\n \"def call(self, inputs, training=True):\\n pass\",\n \"def _send(self, action: List[np.ndarray]) -> None:\",\n \"def run(self):\\n count = self.neuron_count\\n for i in range(0, count):\\n self.run(i)\",\n \"def runMasking(self,roiNode,inputVolumes,inPlace):\\n\\n self.delayDisplay('Running the algorithm')\\n\\n for vol in inputVolumes:\\n self.maskVolume(vol, roiNode, inPlace)\\n\\n return True\\n\\n self.delayDisplay('Running the aglorithm')\",\n \"def calculate(self, inputs:[bool]):\\n\\n w_som = 0\\n outputs = []\\n for i in range(len(inputs)):# iterate through the index inputs e.g [0,0]\\n weight = self.weights[i] # get weight \\n x = inputs[i] # get x\\n\\n w_som += (weight*x) # increment w_som with the multiplication of weighti and xi\\n output = self.activation(w_som) # apply the step function to w_Som\\n #print(outputs)\\n return output\",\n \"def run(self):\\n if self.next_state == \\\"initialize_rexarm\\\":\\n self.initialize_rexarm()\\n\\n if self.next_state == \\\"idle\\\":\\n self.idle()\\n\\n if self.next_state == \\\"estop\\\":\\n self.estop()\\n\\n if self.next_state == \\\"execute_tp\\\":\\n self.execute_tp()\\n\\n if self.next_state == \\\"execute\\\":\\n self.execute()\\n\\n if self.next_state == \\\"calibrate\\\":\\n self.calibrate()\\n\\n if self.next_state == \\\"manual\\\":\\n self.manual()\\n\\n if self.next_state == \\\"learn\\\":\\n self.learn()\\n\\n if self.next_state == \\\"remember\\\":\\n self.remember()\\n\\n if self.next_state == \\\"write\\\":\\n self.write()\\n\\n if self.next_state == \\\"get_color\\\":\\n self.get_color()\\n\\n if self.next_state == \\\"find_blocks\\\":\\n self.find_blocks()\\n\\n # if self.next_state == \\\"dance\\\":\\n # self.execute_dance()\",\n \"def apply_rl_actions(self, rl_actions):\\n pass\",\n \"def process(self, inputs):\\n output = None\\n return output\",\n \"def _step(self, action):\\n\\n\\n assert action.ndim == 2, 'Action must be a two dimensional array with shape (num_models, num_stocks + 1)'\\n assert action.shape[1] == len(self.sim[0].asset_names) + 1\\n assert action.shape[0] == len(self.model_names)\\n\\n print(\\\"before normalisation\\\",action)\\n\\n #Let's ALLOW THE SHORT!!!\\n # normalise just in case\\n #action = np.clip(action, 0, 1)\\n action = np.clip(action, -1, 1)\\n\\n weights = action # np.array([cash_bias] + list(action)) # [w0, w1...]\\n #weights /= (np.sum(weights, axis=1, keepdims=True) + eps)\\n\\n # so if weights are all zeros we normalise to [1,0...]\\n weights[:, 0] += np.clip(1 - np.sum(weights, axis=1), 0, 1)\\n #assert ((action >= 0) * (action <= 1)).all(), 'all action values should be between 0 and 1. Not %s' % action\\n\\n #assert ((action >= 0) * (action <= 1)).all(), 'all action values should be between 0 and 1. Not %s' % action\\n\\n\\n pos_w = [abs(number) for number in weights]\\n\\n\\n np.testing.assert_almost_equal(np.sum(pos_w, axis=1), np.ones(shape=(weights.shape[0])), 3,\\n err_msg='weights should sum to 1. action=\\\"%s\\\"' % weights)\\n\\n observation, done1, ground_truth_obs = self.src._step()\\n\\n # concatenate observation with ones\\n cash_observation = np.ones((1, self.window_length, observation.shape[2]))\\n observation = np.concatenate((cash_observation, observation), axis=0)\\n\\n cash_ground_truth = np.ones((1, 1, ground_truth_obs.shape[2]))\\n ground_truth_obs = np.concatenate((cash_ground_truth, ground_truth_obs), axis=0)\\n\\n\\n # relative price vector of last observation day (close/open)\\n\\n\\n close_price_vector = observation[:, -1, 3]\\n open_price_vector = observation[:, -1, 0]\\n\\n\\n y1_long = close_price_vector / open_price_vector\\n\\n y1_short= open_price_vector/close_price_vector\\n #for i in len(range(observation.shape[0])):\\n # if\\n\\n #print(\\\"THIS IS Y1\\\",y1)\\n #print(\\\"y1 shape\\\",y1.shape)\\n #print(\\\"wieh\\\",weights.shape)\\n\\n rewards = np.empty(shape=(weights.shape[0]))\\n info = {}\\n dones = np.empty(shape=(weights.shape[0]), dtype=bool)\\n for i in range(weights.shape[0]):\\n reward, current_info, done2 = self.sim[i]._step(weights[i], y1_long,y1_short)\\n rewards[i] = reward\\n info[self.model_names[i]] = current_info['portfolio_value']\\n info['return'] = current_info['return']\\n dones[i] = done2\\n\\n # calculate return for buy and hold a bit of each asset\\n info['market_value'] = np.cumprod([inf[\\\"return\\\"] for inf in self.infos + [info]])[-1]\\n # add dates\\n info['date'] = index_to_date(self.start_idx + self.src.idx + self.src.step)\\n info['steps'] = self.src.step\\n info['next_obs'] = ground_truth_obs\\n\\n self.infos.append(info)\\n\\n #print (info)\\n\\n #print(\\\"My action is :\\\",action)\\n\\n\\n\\n return observation, rewards, np.all(dones) or done1, info\",\n \"def _step(self, rl_actions):\\n self.timer += 1\\n # pdb.set_trace()\\n # perform acceleration and (optionally) lane change actions for\\n # traci-controlled human-driven vehicles\\n if len(self.controlled_ids) > 0:\\n accel = []\\n for veh_id in self.controlled_ids:\\n # acceleration action\\n action = self.vehicles.get_acc_controller(veh_id).get_action(\\n self)\\n accel.append(action)\\n\\n # lane changing action\\n lane_flag = 0\\n if type(self.scenario.lanes) is dict:\\n if any(v > 1 for v in self.scenario.lanes.values()):\\n lane_flag = 1\\n else:\\n if self.scenario.lanes > 1:\\n lane_flag = 1\\n\\n if self.vehicles.get_lane_changing_controller(veh_id) \\\\\\n is not None and lane_flag:\\n new_lane = \\\\\\n self.vehicles.get_lane_changing_controller(\\n veh_id).get_action(self)\\n self.apply_lane_change([veh_id], target_lane=[new_lane])\\n\\n self.apply_acceleration(self.controlled_ids, acc=accel)\\n\\n # perform (optionally) lane change actions for sumo-controlled\\n # human-driven vehicles\\n if len(self.sumo_ids) > 0:\\n for veh_id in self.sumo_ids:\\n # lane changing action\\n if self.scenario.lanes > 1:\\n lc_contr = self.vehicles.get_lane_changing_controller(\\n veh_id)\\n if lc_contr is not None:\\n new_lane = lc_contr.get_action(self)\\n self.apply_lane_change([veh_id], target_lane=[new_lane])\\n\\n # perform (optionally) routing actions for all vehicle in the network,\\n # including rl vehicles\\n routing_ids = []\\n routing_actions = []\\n for veh_id in self.ids:\\n if self.vehicles.get_routing_controller(veh_id) is not None:\\n routing_ids.append(veh_id)\\n route_contr = self.vehicles.get_routing_controller(veh_id)\\n routing_actions.append(route_contr.choose_route(self))\\n\\n self.choose_routes(veh_ids=routing_ids, route_choices=routing_actions)\\n\\n self.apply_rl_actions(rl_actions)\\n\\n self.additional_command()\\n # pdb.set_trace()\\n self.traci_connection.simulationStep()\\n #Error being thrown from sumo/src/utils/foxtools/MFXImageHelper.cpp\\n # self.traci_connection.gui.screenshot(\\\"View #0\\\", \\\"/home/mkoren/Pictures/a\\\"+str(self.timer)+\\\".png\\\")\\n # pdb.set_trace()\\n # store new observations in the network after traci simulation step\\n network_observations = \\\\\\n self.traci_connection.vehicle.getSubscriptionResults()\\n crash = False\\n for veh_id in self.ids:\\n prev_pos = self.get_x_by_id(veh_id)\\n prev_lane = self.vehicles.get_lane(veh_id)\\n try:\\n self.vehicles.set_position(\\n veh_id, network_observations[veh_id][tc.VAR_LANEPOSITION])\\n except KeyError:\\n continue\\n self.vehicles.set_edge(\\n veh_id, network_observations[veh_id][tc.VAR_ROAD_ID])\\n self.vehicles.set_lane(\\n veh_id, network_observations[veh_id][tc.VAR_LANE_INDEX])\\n if network_observations[veh_id][tc.VAR_LANE_INDEX] != prev_lane \\\\\\n and veh_id in self.rl_ids:\\n self.vehicles.set_state(veh_id, \\\"last_lc\\\", self.timer)\\n self.vehicles.set_speed(\\n veh_id, network_observations[veh_id][tc.VAR_SPEED])\\n\\n try:\\n change = self.get_x_by_id(veh_id) - prev_pos\\n if change < 0:\\n change += self.scenario.length\\n new_abs_pos = self.vehicles.get_absolute_position(\\n veh_id) + change\\n self.vehicles.set_absolute_position(veh_id, new_abs_pos)\\n except ValueError or TypeError:\\n self.vehicles.set_absolute_position(veh_id, -1001)\\n\\n if self.vehicles.get_position(veh_id) < 0 or \\\\\\n self.vehicles.get_speed(veh_id) < 0:\\n crash = True\\n\\n # collect headway, leader id, and follower id data\\n headway_data = \\\\\\n self.get_headway_dict(network_observations=network_observations)\\n\\n for veh_id in self.ids:\\n try:\\n self.vehicles.set_headway(\\n veh_id, headway_data[veh_id][\\\"headway\\\"])\\n self.vehicles.set_leader(\\n veh_id, headway_data[veh_id][\\\"leader\\\"])\\n self.vehicles.set_follower(\\n veh_id, headway_data[veh_id][\\\"follower\\\"])\\n except KeyError:\\n # occurs in the case of crashes, so headway is assumed to\\n # be very small\\n self.vehicles.set_headway(veh_id, 1e-3)\\n self.vehicles.set_leader(veh_id, None)\\n self.vehicles.set_follower(veh_id, None)\\n\\n # collect list of sorted vehicle ids\\n self.sorted_ids, self.sorted_extra_data = self.sort_by_position()\\n\\n # collect information of the state of the network based on the\\n # environment class used\\n if self.vehicles.num_rl_vehicles > 0:\\n self.state = self.get_state()\\n # rllab requires non-multi agent to have state shape as\\n # num-states x num_vehicles\\n if not self.multi_agent:\\n self.state = self.state.T\\n else:\\n self.state = []\\n # collect observation new state associated with action\\n\\n next_observation = list(self.state)\\n\\n # crash encodes whether sumo experienced a crash\\n crash = \\\\\\n crash \\\\\\n or self.traci_connection.simulation.getEndingTeleportNumber() != 0 \\\\\\n or self.traci_connection.simulation.getStartingTeleportNumber() != 0\\n\\n # compute the reward\\n if self.vehicles.num_rl_vehicles > 0:\\n reward = self.compute_reward(self.state, rl_actions, fail=crash)\\n else:\\n reward = 0\\n\\n # Are we in a multi-agent scenario? If so, the action space is a list.\\n if self.multi_agent:\\n done_n = self.vehicles.num_rl_vehicles * [0]\\n info_n = {'n': []}\\n\\n if self.shared_reward:\\n info_n['reward_n'] = [reward] * len(self.action_space)\\n else:\\n info_n['reward_n'] = reward\\n\\n if crash:\\n done_n = self.vehicles.num_rl_vehicles * [1]\\n if self.fail_safe:\\n logging.error(\\\"Crash has occurred! Check your failsafes\\\")\\n\\n info_n['done_n'] = done_n\\n info_n['state'] = self.state\\n done = np.all(done_n)\\n return self.state, sum(reward), done, info_n\\n\\n else:\\n if crash:\\n logging.info(\\\"Crash has occurred! Check failsafes!\\\")\\n return Step(observation=next_observation, reward=reward,\\n done=True)\\n else:\\n return Step(observation=next_observation, reward=reward,\\n done=False)\",\n \"def act(self, obs_all_agents, noise=0.0):\\n actions = [agent.act(obs, noise) for agent, obs in zip(self.maddpg_agent, obs_all_agents)]\\n return actions\",\n \"def act(self, obs_all_agents, noise=0.0):\\n actions = [agent.act(obs, noise) for agent, obs in zip(self.maddpg_agent, obs_all_agents)]\\n return actions\",\n \"def simulate(self, data):\\n # Get the current timestep\\n now = data.current_timestep\\n self.logger.info(\\\"{model_name_cap}Wrapper received inputs in %s\\\", now)\\n\\n # Get model parameters\\n {model_parameters}\\n # Get model inputs\\n {model_inputs}\\n # Write results to data handler\\n {model_outputs}\\n self.logger.info(\\\"{model_name_cap}Wrapper produced outputs in %s\\\", now)\",\n \"def execute_actions(self, actions):\\n execute_actions(self.board, self.agent_locs, actions)\",\n \"def __call__(self, inputs):\\n inputs = jnp.asarray(inputs, self.dtype)\\n\\n bias_shape = inputs.shape[-1]\\n bias = self.param('bias', self.bias_init, bias_shape)\\n bias = jnp.asarray(bias, self.dtype)\\n bias = jnp.broadcast_to(bias, inputs.shape)\\n\\n return inputs + bias\",\n \"def run(self, steps):\\n self.sim.run(steps)\",\n \"def call(self, inputs, training=False):\\n features, predicted = inputs\\n enc = self.encoder(features, training=training)\\n pred, _ = self.predict_net(predicted, training=training)\\n outputs = self.joint_net([enc, pred], training=training)\\n return outputs\",\n \"def act(\\n self, obs: Any, possible_actions_mask: Optional[torch.Tensor] = None\\n ) -> rlt.ActorOutput:\\n scorer_inputs = (obs,)\\n if possible_actions_mask is not None:\\n scorer_inputs += (possible_actions_mask,)\\n model_output = self.scorer(*scorer_inputs)\\n scores = model_output[\\\"ucb\\\"]\\n actor_output = self.sampler.sample_action(scores)\\n return actor_output.cpu().detach()\",\n \"def act(self, mean, *args, **kwargs):\\n pass\",\n \"def step(self):\\n if not self.is_done():\\n actions = []\\n for agent in self.agents:\\n if agent.alive:\\n actions.append(agent.program(self.percept(agent)))\\n else:\\n actions.append(\\\"\\\")\\n for (agent, action) in zip(self.agents, actions):\\n self.execute_action(agent, action)\\n self.exogenous_change()\",\n \"def step(self):\\n if not self.is_done():\\n actions = []\\n for agent in self.agents:\\n if agent.alive:\\n actions.append(agent.program(self.percept(agent)))\\n else:\\n actions.append(\\\"\\\")\\n for (agent, action) in zip(self.agents, actions):\\n self.execute_action(agent, action)\\n self.exogenous_change()\",\n \"def feedForward(self, inputs):\\n self.ai = np.array(inputs)\\n self.ah1 = tanh(self.ai.dot(self.wi))\\n self.ah2 = tanh(self.ah1.dot(self.wh))\\n self.ao = softmax(self.ah2.dot(self.wo))\",\n \"def act(self, kb, args):\\n # check if the preconditions are satisfied\\n if not self.check_precond(kb, args):\\n raise Exception(\\\"Action pre-conditions not satisfied\\\")\\n # remove negative literals\\n for clause in self.effect_rem:\\n kb.retract(self.substitute(clause, args))\\n # add positive literals\\n for clause in self.effect_add:\\n kb.tell(self.substitute(clause, args))\",\n \"def call(self, inputs):\\n\\n x = tf.matmul(inputs, self.w) + self.b\\n x = self.activation(x)\\n\\n return x\",\n \"def transform(self, inputs):\\n msg = 'The function transform() is not available in the class ' \\\\\\n '`EnsembleDetector`.'\\n LOGGER.error(TAG, msg)\\n raise NotImplementedError(msg)\",\n \"def forward(self, input_dict):\\n if not self.flag:\\n print_total_parameters(self._net)\\n self.flag = True\\n\\n agent_num = len(input_dict[\\\"agents\\\"])\\n if \\\"update_target_net\\\" in input_dict and input_dict[\\\"update_target_net\\\"]:\\n self._update_target_net()\\n\\n for i in range(agent_num):\\n p = input_dict[\\\"agents\\\"][i]\\n entities = p[\\\"obs\\\"][\\\"entities\\\"]\\n localmap = p[\\\"obs\\\"][\\\"localmap\\\"]\\n globalmap = p[\\\"obs\\\"][\\\"globalmap\\\"]\\n p[\\\"atoms\\\"] = self.atoms.repeat(entities.shape[0], 1)\\n p[\\\"q_dist\\\"] = self.policy_net(entities, localmap, globalmap)\\n p[\\\"qs\\\"] = self._cal_q(p[\\\"q_dist\\\"])\\n\\n if \\\"train\\\" in input_dict and input_dict[\\\"train\\\"]:\\n if not self.n_step:\\n next_entities = p[\\\"next_obs\\\"][\\\"entities\\\"]\\n next_localmap = p[\\\"next_obs\\\"][\\\"localmap\\\"]\\n next_globalmap = p[\\\"next_obs\\\"][\\\"globalmap\\\"]\\n with torch.no_grad():\\n p[\\\"next_target_q_dist\\\"] = self.target_net(\\n next_entities, next_localmap, next_globalmap\\n )\\n p[\\\"next_target_qs\\\"] = self._cal_q(p[\\\"next_target_q_dist\\\"])\\n if self.double:\\n p[\\\"next_q_dist\\\"] = self.policy_net(\\n next_entities, next_localmap, next_globalmap\\n )\\n p[\\\"next_qs\\\"] = self._cal_q(p[\\\"next_q_dist\\\"])\\n else:\\n n_step_entities = p[\\\"n_step_obs\\\"][\\\"entities\\\"]\\n n_step_localmap = p[\\\"n_step_obs\\\"][\\\"localmap\\\"]\\n n_step_globalmap = p[\\\"n_step_obs\\\"][\\\"globalmap\\\"]\\n with torch.no_grad():\\n p[\\\"n_step_target_q_dist\\\"] = self.target_net(\\n n_step_entities, n_step_localmap, n_step_globalmap\\n )\\n p[\\\"n_step_target_qs\\\"] = self._cal_q(p[\\\"n_step_target_q_dist\\\"])\\n if self.double:\\n p[\\\"n_step_q_dist\\\"] = self.policy_net(\\n n_step_entities, n_step_localmap, n_step_globalmap\\n )\\n p[\\\"n_step_qs\\\"] = self._cal_q(p[\\\"n_step_q_dist\\\"])\\n\\n return input_dict\",\n \"def call(self, inputs):\\n # Feed attention\\n norm_attention = self.norm_attention(inputs)\\n\\n attention = self.attention(query=norm_attention, value=norm_attention)\\n\\n # Skip Connection\\n attention += inputs\\n\\n # Feed MLP\\n outputs = self.mlp(self.norm_mlp(attention))\\n\\n # Skip Connection\\n outputs += attention\\n\\n return outputs\",\n \"def act(self, obs_all_agents, noise=0.0):\\n actions = [agent.act(obs, noise) for agent, obs in zip(self.maddpg_agent, obs_all_agents)]\\n return np.array(actions)\",\n \"def run(self, xs):\\n \\\"*** YOUR CODE HERE ***\\\"\\n previousOutput = None\\n for letter in xs:\\n inputLayer = self.runInputLayer(letter)\\n if previousOutput is not None:\\n previousOutputLayer = self.runPreviousOutputLayer(previousOutput)\\n inputLayer = nn.Add(inputLayer, previousOutputLayer)\\n outputLayer = self.runOutputLayer(inputLayer)\\n previousOutput = inputLayer\\n return outputLayer\",\n \"def act(self):\\n self.features = self.next_features\\n self.choose_random = np.random.choice(2,p=(1-self.epsilon,self.epsilon)) # Chooses whether to explore or exploit with probability 1-self.epsilon\\n # Selects the best action index in current state\\n if self.choose_random:\\n self.chosenA = np.random.choice(4)\\n else:\\n self.chosenA = self.argmaxQsa(self.features)\\n # Records reward for printing and performs action\\n self.action = self.idx2act[self.chosenA]\\n # Execute the action and get the received reward signal\\n self.reward = self.move(self.action)\\n self.total_reward += self.reward\\n # IMPORTANT NOTE:\\n # 'action' must be one of the values in the actions set,\\n # i.e. Action.LEFT, Action.RIGHT, Action.ACCELERATE or Action.BRAKE\\n # Do not use plain integers between 0 - 3 as it will not work\",\n \"def calculate(self, inputs):\\r\\n output = inputs\\r\\n for layer in self.layers:\\r\\n output = layer.calculate(output)\\r\\n return output\",\n \"def call(self, inputs):\\n if self.backward_compatible:\\n kernel = tf.keras.backend.reshape(self.kernel, self.kernel_shape)\\n bias = (tf.keras.backend.reshape(self.bias, self.bias_shape)\\n if self.use_bias else None)\\n else:\\n kernel = self.kernel\\n bias = self.bias\\n\\n if self.output_projection:\\n ret = tf.einsum(\\\"abcd,cde->abe\\\", inputs, kernel)\\n else:\\n ret = tf.einsum(\\\"abc,cde->abde\\\", inputs, kernel)\\n if self.use_bias:\\n ret += bias\\n if self.activation is not None:\\n return self.activation(ret)\\n return ret\",\n \"def _forwardImplementation(self, inbuf, outbuf):\\n assert self.module\\n \\n values = self.module.getActionValues(self.state) \\n n_values = self.n_values.getActionValues(self.state)\\n values = map(lambda x, y: x + self.exploration * (sqrt(2 * log(self.experiment.stepid, 2) / y) if y > 0 else 1000), values, n_values);\\n \\n actions = []\\n for i in range(self.shield_options):\\n new_action = where(values == max(values))[0]\\n new_action = choice(new_action) \\n values[new_action] = -10000\\n actions.append(new_action)\\n \\n while len(actions) < self.outdim:\\n actions.append(-1)\\n \\n outbuf[:] = actions\",\n \"def act(self, states, add_noise=True):\\n actions = np.zeros([self.num_agents, self.action_size])\\n for index, agent in enumerate(self.agents):\\n actions[index, :] = agent.act(states[index], add_noise)\\n return actions\",\n \"def interact(self):\\n x, mu = self.update_position_direction(self.l_int)\\n mu_mean = self.calculate_mean_mu(self.x, x, self.l_int)\\n self.update_estimators(self.l_int, mu_mean)\\n\\n self.is_absorbed = True\\n self.is_active = False\",\n \"def set_inputs(self,inputs):\\n raise NotImplementedError(\\\"Robot.set_inputs\\\")\",\n \"def engage(self):\\n # no sigmoid for the inputs and bias\\n if layer != 0:\\n self.outputValue = sigmoid(inputSum);\\n\\n for connection in self.outputConnections:\\n if connection.enabled == True:\\n #connection will have toNode\\n connection.toNode.inputSum += connection.weight * self.outputValue;\",\n \"def apply(self, gameState):\\n pass\",\n \"def exploit_act(self, states_ns):\\n raise NotImplementedError\",\n \"def call(self, states, hand):\\n states = tf.convert_to_tensor(states)\\n hand = tf.convert_to_tensor(hand)\\n # 1. Raise each state in states with an embedding layer.\\n state_embedding = self.state_embedding(states)\\n # 2. Raise the hand with a different embedding layer.\\n hand_embedding = self.hand_embedding(hand)\\n # 3. Feed states through a GRU, and get the last hidden state.\\n gru_out, gru_state = self.gru(inputs=state_embedding)\\n # 4. Feed hand through a Dense layer.\\n dense_hand = self.hand_dense_layer(hand_embedding)\\n # 5. Concat output of GRU and output of Dense layer.\\n concatted = tf.concat(gru_out, dense_hand)\\n # 6. Feed concat output through one (or more) Dense layer(s).\\n concatted_dense_1 = self.concatted_dense_1(concatted)\\n # 7. Feed output through Dense layer to size num_actions (61), and call softmax.\\n final_dense = self.final_dense(concatted_dense_1)\\n # 8 TODO: gotta be train/test I think (maybe here, or maybe in train/test). Mask out illegal calls, and re-normalize.\\n # 9. Return matrix of probability distribution over actions.\\n return final_dense\",\n \"def apply(self):\\n pass\",\n \"def apply(self):\\n pass\",\n \"def forward(self, screen, minimap, flat, available_actions):\\n # push each input through the network\\n screen = self.screen_features(screen)\\n minimap = self.minimap_features(minimap)\\n flat = self.flat_features(flat)\\n\\n flattened_screen = screen.view(1, -1)\\n flattened_mm = minimap.view(1, -1)\\n\\n latent_vector = torch.cat([flat, flattened_screen, flattened_mm], 1)\\n features = self.combined_features(latent_vector)\\n\\n value = self.value_predictor(features)\\n action = self.policy_action(features)\\n\\n policy_args = dict()\\n for arg in actions.TYPES:\\n for dim, size in enumerate(arg.sizes):\\n module_name = self.get_argument_module_name(arg, dim)\\n operator = getattr(self, module_name)\\n policy_args[module_name] = operator(features)\\n\\n return action, policy_args, value\",\n \"def act(self, obs_all_agents, noise=0.0):\\n actions_next = [agent.act(obs, noise) for agent, obs in zip(self.maddpg_agent, obs_all_agents)]\\n return actions_next\",\n \"def act(self, states: np.ndarray, eps: float = 0.0) -> List[np.ndarray]:\\n actions = [\\n agent.act(state.reshape(-1, 1).T, eps)\\n for agent, state in zip(self.agents, states)\\n ]\\n return actions\",\n \"def simulate(self, inputs = None, bit_str = None):\\n # Eliminating basic outlier conditions\\n if (inputs is None and bit_str is not None) or (inputs is not None and bit_str is None):\\n print('Please enter both inputs and bit_str. Exiting simulation...')\\n return\\n if inputs is not None and bit_str is not None:\\n if len(inputs) != len(bit_str):\\n print('inputs and bit_str not of same length. Exiting simulation...')\\n return\\n # setting primary inputs\\n for i in range(0, len(inputs)):\\n self.setIpValue(inputs[i], int(bit_str[i]))\\n \\n self.__simSetup()\\n\\n # iterating all cfg_blcks\\n blck_ids = [blck[0] for blck in self.listCfgBlcks()]\\n\\n for cfg_id in blck_ids:\\n if self.dGrph[cfg_id][1][1] == None:\\n if(self.__processBlcks(cfg_id)):\\n print('Processed cfg_blck: ', cfg_id)\\n else:\\n print('Some error in processing cfg_blck: ', cfg_id)\\n \\n # iterating all ari_blcks\\n blck_ids = [blck[0] for blck in self.listAriBlcks()]\\n\\n for ari_id in blck_ids:\\n # checking if one output is None because all outputs are set simultaneously\\n if self.dGrph[ari_id][1][0][1] == None:\\n if(self.__processBlcks(ari_id)):\\n print('Processed ari_blck: ', ari_id)\\n else:\\n print('Some error in processing ari_blck: ', ari_id)\\n \\n # iterating all tribufs\\n blck_ids = [blck[0] for blck in self.listTribufs()]\\n\\n for tri_id in blck_ids:\\n if self.dGrph[tri_id][2][1] == None:\\n if(self.__processBlcks(tri_id)):\\n print('Processed tribuf: ', tri_id)\\n else:\\n print('Some error in processing tribuf: ', tri_id)\\n \\n # iterating all gates\\n blck_ids = [blck[0] for blck in self.listGates()]\\n\\n for gate_id in blck_ids:\\n if self.dGrph[gate_id][1][2] == None:\\n if(self.__processBlcks(gate_id)):\\n print('Processed gate: ', gate_id)\\n else:\\n print('Some error in processing gate: ', gate_id)\",\n \"def forward(self, inputs):\\r\\n #print (len(inputs))\\r\\n out = self.fc1(inputs)\\r\\n out = self.fc2(out)\\r\\n self.out = out\\r\\n return out\\r\\n #raise NotImplementedError('Implement the forward method of the model')\\r\",\n \"def compute(self, inputs):\\n\\t\\tres = inputs\\n\\t\\tfor layer in range(self.layersNumber):\\n\\t\\t\\tweight = self.weights[layer]\\n\\t\\t\\tbias = self.biases[layer]\\n\\t\\t\\tres = fActivation(np.dot(weight, res) + bias)\\n\\t\\treturn res\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.64400464","0.5957464","0.58729064","0.5810304","0.57571757","0.57132226","0.5649076","0.56446207","0.56303746","0.5490612","0.5457731","0.545238","0.5415022","0.54109704","0.5397137","0.5371112","0.5361186","0.5359599","0.53587407","0.53278804","0.53256255","0.53020144","0.5300632","0.5289866","0.52792037","0.527792","0.52774423","0.5272559","0.5263101","0.5192686","0.5185794","0.51838535","0.5181504","0.5181114","0.5174507","0.51726496","0.51706475","0.51681256","0.5157721","0.51506805","0.5149785","0.51451063","0.5129795","0.5126124","0.5124142","0.5111401","0.5099345","0.5098104","0.5098004","0.50972337","0.50956136","0.50932896","0.5091694","0.5080764","0.5080477","0.5073929","0.5071932","0.50614876","0.5059004","0.50484955","0.50387955","0.50267535","0.50263834","0.50263613","0.50263613","0.50233513","0.5012812","0.5009456","0.5005261","0.4996327","0.4993866","0.49884418","0.498283","0.498283","0.49787828","0.497231","0.49653092","0.49638313","0.4960962","0.49592534","0.49583006","0.4954774","0.4951122","0.49473754","0.4945938","0.49444667","0.4938157","0.4933503","0.4933354","0.49314505","0.49272084","0.49236104","0.49219194","0.49195918","0.49195918","0.4912315","0.4905091","0.49039063","0.48884246","0.4885767","0.48849538"],"string":"[\n \"0.64400464\",\n \"0.5957464\",\n \"0.58729064\",\n \"0.5810304\",\n \"0.57571757\",\n \"0.57132226\",\n \"0.5649076\",\n \"0.56446207\",\n \"0.56303746\",\n \"0.5490612\",\n \"0.5457731\",\n \"0.545238\",\n \"0.5415022\",\n \"0.54109704\",\n \"0.5397137\",\n \"0.5371112\",\n \"0.5361186\",\n \"0.5359599\",\n \"0.53587407\",\n \"0.53278804\",\n \"0.53256255\",\n \"0.53020144\",\n \"0.5300632\",\n \"0.5289866\",\n \"0.52792037\",\n \"0.527792\",\n \"0.52774423\",\n \"0.5272559\",\n \"0.5263101\",\n \"0.5192686\",\n \"0.5185794\",\n \"0.51838535\",\n \"0.5181504\",\n \"0.5181114\",\n \"0.5174507\",\n \"0.51726496\",\n \"0.51706475\",\n \"0.51681256\",\n \"0.5157721\",\n \"0.51506805\",\n \"0.5149785\",\n \"0.51451063\",\n \"0.5129795\",\n \"0.5126124\",\n \"0.5124142\",\n \"0.5111401\",\n \"0.5099345\",\n \"0.5098104\",\n \"0.5098004\",\n \"0.50972337\",\n \"0.50956136\",\n \"0.50932896\",\n \"0.5091694\",\n \"0.5080764\",\n \"0.5080477\",\n \"0.5073929\",\n \"0.5071932\",\n \"0.50614876\",\n \"0.5059004\",\n \"0.50484955\",\n \"0.50387955\",\n \"0.50267535\",\n \"0.50263834\",\n \"0.50263613\",\n \"0.50263613\",\n \"0.50233513\",\n \"0.5012812\",\n \"0.5009456\",\n \"0.5005261\",\n \"0.4996327\",\n \"0.4993866\",\n \"0.49884418\",\n \"0.498283\",\n \"0.498283\",\n \"0.49787828\",\n \"0.497231\",\n \"0.49653092\",\n \"0.49638313\",\n \"0.4960962\",\n \"0.49592534\",\n \"0.49583006\",\n \"0.4954774\",\n \"0.4951122\",\n \"0.49473754\",\n \"0.4945938\",\n \"0.49444667\",\n \"0.4938157\",\n \"0.4933503\",\n \"0.4933354\",\n \"0.49314505\",\n \"0.49272084\",\n \"0.49236104\",\n \"0.49219194\",\n \"0.49195918\",\n \"0.49195918\",\n \"0.4912315\",\n \"0.4905091\",\n \"0.49039063\",\n \"0.48884246\",\n \"0.4885767\",\n \"0.48849538\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":17898,"cells":{"query":{"kind":"string","value":"Get a list of all variables in the sim (removing duplicates, if any). Note, list is kept the same from first time this method is called."},"document":{"kind":"string","value":"def get_all_var_names(self):\n\n if hasattr(self, \"all_var_names\"):\n return self.all_var_names\n\n # Append all variables in model (defined in YAML).\n aux_all_var_names = []\n aux_all_var_names.extend(self.sim_config_params)\n aux_all_var_names.extend(self.sim_inputs)\n aux_all_var_names.extend(self.sim_outputs)\n aux_all_var_names.extend(self.sim_other_vars)\n\n # Remove duplicates (if any) -- Keeping initial order\n all_var_names = [aux_all_var_names[i] for i in range(len(aux_all_var_names)) \\\n if aux_all_var_names[i] not in aux_all_var_names[:i]]\n\n # Store for following calls\n self.all_var_names = all_var_names\n return self.all_var_names"},"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_all_variables(self):\n return []","def get_all_variables(self):\n out = []\n for i in self.items:\n out += i.get_all_variables()\n return out","def get_all_variables(self):\n out = []\n for i in self.items:\n out += i.get_all_variables()\n return out","def get_all_variables(self):\n out = []\n for i in self.items:\n out += i.get_all_variables()\n return out","def get_variables(self):\n return [self.variables[key] for key in sorted(self.variables)]","def get_all_variables(self):\n return [self.item]","def vars(self):\n return [Var(i,self.dims[i]) for i in range(self.nvar)] # TODO: use stored state info (=1 sometimes)","def get_vars(self):\n return [self.mu, self.var]","def variables(self):\n return sorted(set(self._variables))","def getVariables(self):\n return [x for x in self.variables.values() if x is not None]","def getVariables(self):\n statVars = [self[vn] for vn in self.statVars]\n timeVars = [self[vn] for vn in self.timeVars]\n return statVars + timeVars","def get_all_variables(self):\n return self.item.get_all_variables()","def variables(self):\n return {u for u in self if u.type == 'var'}","def get_variables(self):\n return [self.g_t, self.m_t]","def variables_used (self) :\r\n\t\treturn [i[0] for i in self.parameters]","def variables(self):\n return [term.variable for term in self.terms]","def variables(self):\n return [i.name for i in self.inputs + self.outputs]","def get_variables(self):\n return set(self._head_vars)","def marginals(self):\n all_variables = [None for ii in range(self.nvars)]\n for ii in range(self.nunique_vars):\n for jj in self.unique_variable_indices[ii]:\n all_variables[jj] = self.unique_variables[ii]\n return all_variables","def to_list(self):\n return copy.deepcopy(self._varvals)","def get_variables(self):\n\n self._enforce_coupling()\n\n dv = []\n for scenario in self.scenarios:\n if scenario.group_master:\n dv.extend(scenario.active_variables())\n else:\n dv.extend(scenario.uncoupled_variables())\n\n for body in self.bodies:\n if body.group_master:\n dv.extend(body.active_variables())\n else:\n dv.extend(body.uncoupled_variables())\n\n return dv","def get_variables_list(self):\n variables = self.variables.values()\n # handle reference variables\n for variable in variables:\n name = variable['name']\n if name in self.references:\n variable['data'] = self.references[name]\n return variables","def all_variables(formula):\n return collect_unique_nodes(formula, lambda x: isinstance(x, Variable))","def get_variables(self):\n\t\treturn self.variables","def get_all_variables(self):\n raise NotImplementedError()","def variables_used (self) :\r\n\t\t## These names do not contain dimension specification (everything in brackets\r\n\t\t## that comes after a name is am array index - either the arry was declared\r\n\t\t## correctly or it is wrong anyway, there is no implicit declaration of arrays) !\r\n\r\n\t\tresult = []\r\n\r\n\t\tfor l in self.equ_lists :\r\n\t\t\tfor var_name in l :\r\n\t\t\t\tresult.append(var_name[0])\r\n\t\treturn result","def get_all_variables(self):\n return self._properties.copy()","def setOfVariables(self):\n return set(self.dictOfVariables().keys())","def get_all_variables(self):\n return self.start.get_all_variables() + self.end.get_all_variables()","def get_all_variables_names(self):\n return self.project.get_variable_names() + self.design.get_variable_names()","def get_list_vars(my_vars):\n lists = []\n for var in my_vars:\n try:\n temp = my_vars[var].getValue()\n #print var + '=' + str(temp)\n except ValueError:\n lists.append(var)\n return lists","def get_variable_names(self):\n return [var[1] for var in self.variables]","def variable_names(self):\n \n return [x['variable'] for x in self.variable_dicts()]","def variables(self):\n if save_context.in_save_context():\n return [self._vars[0]]\n return self._vars","def get_variables(self) -> typing.List:\n parts = (self.neural_net.encoder, self.neural_net.predictor, self.neural_net.dynamics)\n return [v for v_list in map(lambda n: n.weights, parts) for v in v_list]","def get_vehicle_variables_list(self):\n return self.get('vehicles/GetVehicleVariableList')","def get_variable_names(self):\n varNames = []\n for var in self.variables:\n # EstimationVariable\n varNames.append(var.name)\n return varNames","def get_metric_variables(self) -> List[Any]:\n with self._lock:\n return self._get_metric_variables()","def get_variables(self):\n return self.variables","def get_variable_names(self):\n return [VariableString(s) for s in\n self._design.GetVariables()+self._design.GetPostProcessingVariables()]","def getVars(self):\n return self.__vars","def variables(self) -> AbstractSet[Variable]:\n return self._variables","def variables_used (self) :\r\n\t\treturn []","def list(self):\n if self.handle == None: return []\n return self.handle.variables.keys()","def getVariableList(dataset):\n variables = [v for v in dataset.variables.keys() if v not in dataset.dimensions.keys()]\n for d in dataset.dimensions.keys():\n try:\n variables.pop(variables.index(dataset.variables[d].getncattr(\"bounds\")))\n except:\n pass\n return variables","def list_variables(self):\n return list(self._properties.keys())","def list_of_vars_in_e3sm_diags():\n\n # Get all of the 'variables' parameter from each file.\n vars_used = []\n try:\n logger.info(\"Using user arguments.\")\n parameters = get_parameters(parser)\n except Exception as e:\n logger.error(e)\n # Looks for these files in their installed location.\n pth = os.path.join(e3sm_diags.INSTALL_PATH)\n # The first '*' is the folder of the set, the second is the actual file.\n # Ex: {e3sm_diags.INSTALL_PATH}/lat_lon/lat_lon_model_vs_obs.cfg\n file_paths = [p for p in glob.glob(pth + \"*/*.cfg\")]\n # NOT NEEDED:\n # parser.add_argument('path') # Needed so the filename can be passed in.\n # parser.add_args_and_values([DUMMY_FILE_PATH])\n parameters = parser.get_cfg_parameters(\n files_to_open=file_paths, check_values=False\n )\n\n for p in parameters:\n logger.info(f\"p.variables {p.variables}\")\n vars_used.extend(p.variables)\n\n logger.info(f\"Variables used: {sorted(list(set(vars_used)))}\")\n return set(vars_used)","def variables(self):\n return [i for (i, j) in self.qubo.keys() if i == j]","def var_names(self):\n return self._var_names","def variables(s):\n result = set([])\n def walk(s):\n if is_variable(s):\n result.add(s)\n else:\n for arg in s.args:\n walk(arg)\n walk(s)\n return result","def variables(self):\n return self._variables","def variables(self):\n return self._.vars","def varNames(self):\n return self.__varNames","def getDefinedVars(self):\n numTabs = self.tabWidget.count()\n varList = []\n \n for i in range(numTabs):\n var = self.tabWidget.widget(i).getVar()\n name = self.tabWidget.tabText(i)\n varList.append([name, var])\n\n return varList","def get(self) -> Set[MemoryVariable]:\n return self.variables","def vars(self):\n return self.v","def variables(self):\n # created variable from `get_variable`\n allname = [name for _, (name, t) in self._variable_info.iteritems()\n if t == 'variable']\n allvars = [v for v in K.get_all_variables() if v.name in allname]\n # related variables to all `Tensor`\n tensors = [self.get_variable(name)\n for name, (info, t) in self._variable_info.iteritems()\n if t == 'tensor']\n tensors = K.ComputationGraph(tensors).variables\n # all variables within the scope\n scope_vars = K.get_all_variables(scope=self.name)\n return sorted(set(allvars + tensors + scope_vars),\n key=lambda x: x.name)","def get(self):\n return self.vars","def get_model_variables():\n g = tf.get_default_graph()\n return set(g.get_collection(tf.GraphKeys.MODEL_VARIABLES))","def variables(self):\n return self.dataset.data_vars","def fetch_variables(self):\r\n fgraph = self.fgraph\r\n self.inputs = fgraph.inputs\r\n self.outputs = fgraph.outputs\r\n\r\n # list(fgraph.variables)\r\n # We need to include the not used inputs in our variables,\r\n # otherwise we can't pass them to the module.\r\n self.variables = [var for var in self.inputs if not len(var.clients)]\r\n self.variables += graph.variables(self.inputs, self.outputs)\r\n\r\n # The orphans field is listified to ensure a consistent order.\r\n #list(fgraph.orphans.difference(self.outputs))\r\n self.orphans = list(r for r in self.variables\r\n if isinstance(r, graph.Constant) and\r\n r not in self.inputs)\r\n self.temps = list(set(self.variables).difference(\r\n self.inputs).difference(self.outputs).difference(self.orphans))\r\n self.consts = []\r\n self.node_order = self.schedule(fgraph)","def vars(cls):\n for key in dir(cls):\n if key.startswith('var_'):\n yield key[4:]","def getSelectedVariables(self):\r\n\r\n variables = []\r\n\r\n if self.ui.variablesStackedWidget.currentWidget() == self.ui.treePage:\r\n for index in self.ui.treeView.selectionModel().selectedRows():\r\n sourceIndex = self.treeFilterModel.mapToSource(index)\r\n treeItem = sourceIndex.internalPointer()\r\n if treeItem.variable is not None:\r\n variables.append(treeItem.variable)\r\n else:\r\n for index in self.ui.tableView.selectionModel().selectedRows():\r\n sourceIndex = self.tableFilterModel.mapToSource(index)\r\n variable = sourceIndex.internalPointer()\r\n variables.append(variable)\r\n\r\n return variables","def vars(self):\n return self._return_if('_vars')","def variable_selection(self):\n X = []\n\n if self.cfg.variables == 'X1':\n X.append({\"name\": \"X1\", \"variables\": ['DepDelay', 'TaxiOut']})\n elif self.cfg.variables == 'all':\n X.append({\"name\": \"X1\", \"variables\": ['DepDelay', 'TaxiOut']})\n X.append({\"name\": \"X2\", \"variables\": ['DepDelay', 'TaxiOut', 'HotDepTime']})\n X.append({\"name\": \"X3\", \"variables\": ['DepDelay', 'TaxiOut', 'HotDayOfWeek', 'Speed']})\n X.append({\"name\": \"X4\", \"variables\": ['DepDelay', 'TaxiOut', 'HotDayOfWeek', 'Speed', 'HotMonth']})\n X.append({\"name\": \"X5\", \"variables\": ['DepDelay', 'TaxiOut', 'Speed', 'HotDepTime', 'HotCRSCatArrTime']})\n elif self.cfg.variables == 'best':\n X.append({\"name\": \"X5\", \"variables\": ['DepDelay', 'TaxiOut', 'Speed', 'HotDepTime', 'HotCRSCatArrTime']})\n return X","def get_transform_vars(self):\n return [v for v in (self.rotation_vars + self.translation_vars)\n if isinstance(v, tf.Variable)]","def remove_variables(self):\n self.variables = []","def get_variables(self) -> np.array:\n return np.array([self.a, self.b, self.c])","def get_variable_values(self, vars):\n raise NotImplementedError()","def variables(model: Model) -> AbstractSet[str]:\n assert is_model(model)\n return model.keys()","def variables(self):\r\n return self.get_field('variable')","def get_variables(self):\n local_variables = self._design.GetVariables(\n )+self._design.GetPostProcessingVariables()\n return {lv: self.get_variable_value(lv) for lv in local_variables}","def get_variables(self) -> np.array:\n pass","def variables(model: Model) -> AbstractSet[str]:\r\n assert is_model(model)\r\n return model.keys()","def free_variables(self):\n\n free_vars = set()\n self.free_variables_helper(free_vars)\n return free_vars\n # Task 7.6","def list_variables(self, request, context):\n response = ListVariablesResponse()\n for variable in self._delegator.list_variables(request.component):\n response.variables.append(variable)\n return response","def get_variables(self) -> list[str]:\n if self._cnf_formula is None:\n raise Exception(\"CNF Model not initialized. Use `from_cnf` method first.\") \n return self._variables","def variables(self):\n return np.array(list(self._match_result_dict.keys()))","def findall_var(formula, variable):\n res = []\n s = Solver()\n s.add(formula)\n while True:\n if s.check() == sat:\n m = s.model()\n res.append(m)\n value = m[variable]\n if value == None:\n return res\n s.add(variable != value)\n else:\n return res","def phis_lst ( self ) :\n return self.__phi_list","def variables(self):\n return self._variablesDef","def values(self):\n return [ self[x] for x in self ]","def vars(self, scope: str = '') -> VarCollection:\n return self.__wrapped__.vars(scope=scope)","def output_farm_vars(self, algo):\n return self._tvars","def variables(self):\n for name in self._nodes:\n if isinstance(self._nodes[name], RandomVariable):\n yield name","def get_output_variables(self):\n variables = set()\n for sub_effect in self._sub_effects:\n variables.add(sub_effect.get_variable())\n return variables","def coords(self, deep=False):\n variables = list(self.vars(deep))\n coords = [\n coord\n for coord in variables\n for var in variables\n if coord in self[var].dims\n ]\n return list(set(coords))","def draw_variables(self): \n z = self.q[0].draw_variable_local(self.sims)\n for i in range(1,len(self.q)):\n z = np.vstack((z,self.q[i].draw_variable_local(self.sims)))\n return z","def variables(self):\n return tuple(flatten([a.variables for a in self.args]))","def getRRDVariables(self, upToPoint=None):\n cmds = self.getFakeGraphCmds(upToPoint=upToPoint)\n names = [line[line.find(':')+1:line.find('=')]\n for line in cmds.split('\\n')\n if line[:line.find(':')] in ('DEF', 'CDEF', 'VDEF')]\n nameSet = set(names)\n result = []\n for name in names:\n #only allow -raw variables if a corresponding -rpn variable is present\n if name.endswith('-raw') and name.replace('-raw', '-rpn') in nameSet or not name.endswith('-raw'):\n result.append(name)\n return result","def get_swp_values(self, var: str) -> List[Any]:\n return self._sweep_params[var]","def getMeteorologicalVariableNames(self, product):\r\n return []","def PrimaryVars(self):\n res = []\n for subsector in self.subsectors:\n res.append(subsector.pvar)\n return res","def get_varnams(self, varnam_list):\n self.varnams = [[v.name, v.composer, v.talam] for v in varnam_list\n if v.raga == self.name]","def all_q_variables(self):\n\n return self.main_q_variables() + (self.twin_q_variables() if self.twin_q_net else [])","def all_values(cls) -> List[str]:\n return list(member.value for member in cls.__members__.values())","def dump_var_map(self):\n vars = []\n vars.append(\"--------- VARIABLES ---------\")\n for var in self.var_map:\n vars.append(var)\n\n return vars","def listall(self, varName=None, all=None):\n\n if varName is None:\n return None\n var = self.getVariable(varName)\n return var.listall(all=all)","def get_variable_names(filepath):\n variables = set()\n with open(filepath, \"r\") as f:\n previous = \"\"\n for line in f.readlines():\n if line[0] == \"#\":\n previous = line\n var_names = None\n continue\n if var_names is not None:\n continue\n var_names = previous.split()[1:]\n while \"vs\" in var_names:\n var_names.remove(\"vs\")\n for name in var_names:\n variables.add(name)\n return list(variables)","def getMeteorologicalVariableNames(self, product):\r\n\r\n meteorological_variable_names = []\r\n\r\n return meteorological_variable_names"],"string":"[\n \"def get_all_variables(self):\\n return []\",\n \"def get_all_variables(self):\\n out = []\\n for i in self.items:\\n out += i.get_all_variables()\\n return out\",\n \"def get_all_variables(self):\\n out = []\\n for i in self.items:\\n out += i.get_all_variables()\\n return out\",\n \"def get_all_variables(self):\\n out = []\\n for i in self.items:\\n out += i.get_all_variables()\\n return out\",\n \"def get_variables(self):\\n return [self.variables[key] for key in sorted(self.variables)]\",\n \"def get_all_variables(self):\\n return [self.item]\",\n \"def vars(self):\\n return [Var(i,self.dims[i]) for i in range(self.nvar)] # TODO: use stored state info (=1 sometimes)\",\n \"def get_vars(self):\\n return [self.mu, self.var]\",\n \"def variables(self):\\n return sorted(set(self._variables))\",\n \"def getVariables(self):\\n return [x for x in self.variables.values() if x is not None]\",\n \"def getVariables(self):\\n statVars = [self[vn] for vn in self.statVars]\\n timeVars = [self[vn] for vn in self.timeVars]\\n return statVars + timeVars\",\n \"def get_all_variables(self):\\n return self.item.get_all_variables()\",\n \"def variables(self):\\n return {u for u in self if u.type == 'var'}\",\n \"def get_variables(self):\\n return [self.g_t, self.m_t]\",\n \"def variables_used (self) :\\r\\n\\t\\treturn [i[0] for i in self.parameters]\",\n \"def variables(self):\\n return [term.variable for term in self.terms]\",\n \"def variables(self):\\n return [i.name for i in self.inputs + self.outputs]\",\n \"def get_variables(self):\\n return set(self._head_vars)\",\n \"def marginals(self):\\n all_variables = [None for ii in range(self.nvars)]\\n for ii in range(self.nunique_vars):\\n for jj in self.unique_variable_indices[ii]:\\n all_variables[jj] = self.unique_variables[ii]\\n return all_variables\",\n \"def to_list(self):\\n return copy.deepcopy(self._varvals)\",\n \"def get_variables(self):\\n\\n self._enforce_coupling()\\n\\n dv = []\\n for scenario in self.scenarios:\\n if scenario.group_master:\\n dv.extend(scenario.active_variables())\\n else:\\n dv.extend(scenario.uncoupled_variables())\\n\\n for body in self.bodies:\\n if body.group_master:\\n dv.extend(body.active_variables())\\n else:\\n dv.extend(body.uncoupled_variables())\\n\\n return dv\",\n \"def get_variables_list(self):\\n variables = self.variables.values()\\n # handle reference variables\\n for variable in variables:\\n name = variable['name']\\n if name in self.references:\\n variable['data'] = self.references[name]\\n return variables\",\n \"def all_variables(formula):\\n return collect_unique_nodes(formula, lambda x: isinstance(x, Variable))\",\n \"def get_variables(self):\\n\\t\\treturn self.variables\",\n \"def get_all_variables(self):\\n raise NotImplementedError()\",\n \"def variables_used (self) :\\r\\n\\t\\t## These names do not contain dimension specification (everything in brackets\\r\\n\\t\\t## that comes after a name is am array index - either the arry was declared\\r\\n\\t\\t## correctly or it is wrong anyway, there is no implicit declaration of arrays) !\\r\\n\\r\\n\\t\\tresult = []\\r\\n\\r\\n\\t\\tfor l in self.equ_lists :\\r\\n\\t\\t\\tfor var_name in l :\\r\\n\\t\\t\\t\\tresult.append(var_name[0])\\r\\n\\t\\treturn result\",\n \"def get_all_variables(self):\\n return self._properties.copy()\",\n \"def setOfVariables(self):\\n return set(self.dictOfVariables().keys())\",\n \"def get_all_variables(self):\\n return self.start.get_all_variables() + self.end.get_all_variables()\",\n \"def get_all_variables_names(self):\\n return self.project.get_variable_names() + self.design.get_variable_names()\",\n \"def get_list_vars(my_vars):\\n lists = []\\n for var in my_vars:\\n try:\\n temp = my_vars[var].getValue()\\n #print var + '=' + str(temp)\\n except ValueError:\\n lists.append(var)\\n return lists\",\n \"def get_variable_names(self):\\n return [var[1] for var in self.variables]\",\n \"def variable_names(self):\\n \\n return [x['variable'] for x in self.variable_dicts()]\",\n \"def variables(self):\\n if save_context.in_save_context():\\n return [self._vars[0]]\\n return self._vars\",\n \"def get_variables(self) -> typing.List:\\n parts = (self.neural_net.encoder, self.neural_net.predictor, self.neural_net.dynamics)\\n return [v for v_list in map(lambda n: n.weights, parts) for v in v_list]\",\n \"def get_vehicle_variables_list(self):\\n return self.get('vehicles/GetVehicleVariableList')\",\n \"def get_variable_names(self):\\n varNames = []\\n for var in self.variables:\\n # EstimationVariable\\n varNames.append(var.name)\\n return varNames\",\n \"def get_metric_variables(self) -> List[Any]:\\n with self._lock:\\n return self._get_metric_variables()\",\n \"def get_variables(self):\\n return self.variables\",\n \"def get_variable_names(self):\\n return [VariableString(s) for s in\\n self._design.GetVariables()+self._design.GetPostProcessingVariables()]\",\n \"def getVars(self):\\n return self.__vars\",\n \"def variables(self) -> AbstractSet[Variable]:\\n return self._variables\",\n \"def variables_used (self) :\\r\\n\\t\\treturn []\",\n \"def list(self):\\n if self.handle == None: return []\\n return self.handle.variables.keys()\",\n \"def getVariableList(dataset):\\n variables = [v for v in dataset.variables.keys() if v not in dataset.dimensions.keys()]\\n for d in dataset.dimensions.keys():\\n try:\\n variables.pop(variables.index(dataset.variables[d].getncattr(\\\"bounds\\\")))\\n except:\\n pass\\n return variables\",\n \"def list_variables(self):\\n return list(self._properties.keys())\",\n \"def list_of_vars_in_e3sm_diags():\\n\\n # Get all of the 'variables' parameter from each file.\\n vars_used = []\\n try:\\n logger.info(\\\"Using user arguments.\\\")\\n parameters = get_parameters(parser)\\n except Exception as e:\\n logger.error(e)\\n # Looks for these files in their installed location.\\n pth = os.path.join(e3sm_diags.INSTALL_PATH)\\n # The first '*' is the folder of the set, the second is the actual file.\\n # Ex: {e3sm_diags.INSTALL_PATH}/lat_lon/lat_lon_model_vs_obs.cfg\\n file_paths = [p for p in glob.glob(pth + \\\"*/*.cfg\\\")]\\n # NOT NEEDED:\\n # parser.add_argument('path') # Needed so the filename can be passed in.\\n # parser.add_args_and_values([DUMMY_FILE_PATH])\\n parameters = parser.get_cfg_parameters(\\n files_to_open=file_paths, check_values=False\\n )\\n\\n for p in parameters:\\n logger.info(f\\\"p.variables {p.variables}\\\")\\n vars_used.extend(p.variables)\\n\\n logger.info(f\\\"Variables used: {sorted(list(set(vars_used)))}\\\")\\n return set(vars_used)\",\n \"def variables(self):\\n return [i for (i, j) in self.qubo.keys() if i == j]\",\n \"def var_names(self):\\n return self._var_names\",\n \"def variables(s):\\n result = set([])\\n def walk(s):\\n if is_variable(s):\\n result.add(s)\\n else:\\n for arg in s.args:\\n walk(arg)\\n walk(s)\\n return result\",\n \"def variables(self):\\n return self._variables\",\n \"def variables(self):\\n return self._.vars\",\n \"def varNames(self):\\n return self.__varNames\",\n \"def getDefinedVars(self):\\n numTabs = self.tabWidget.count()\\n varList = []\\n \\n for i in range(numTabs):\\n var = self.tabWidget.widget(i).getVar()\\n name = self.tabWidget.tabText(i)\\n varList.append([name, var])\\n\\n return varList\",\n \"def get(self) -> Set[MemoryVariable]:\\n return self.variables\",\n \"def vars(self):\\n return self.v\",\n \"def variables(self):\\n # created variable from `get_variable`\\n allname = [name for _, (name, t) in self._variable_info.iteritems()\\n if t == 'variable']\\n allvars = [v for v in K.get_all_variables() if v.name in allname]\\n # related variables to all `Tensor`\\n tensors = [self.get_variable(name)\\n for name, (info, t) in self._variable_info.iteritems()\\n if t == 'tensor']\\n tensors = K.ComputationGraph(tensors).variables\\n # all variables within the scope\\n scope_vars = K.get_all_variables(scope=self.name)\\n return sorted(set(allvars + tensors + scope_vars),\\n key=lambda x: x.name)\",\n \"def get(self):\\n return self.vars\",\n \"def get_model_variables():\\n g = tf.get_default_graph()\\n return set(g.get_collection(tf.GraphKeys.MODEL_VARIABLES))\",\n \"def variables(self):\\n return self.dataset.data_vars\",\n \"def fetch_variables(self):\\r\\n fgraph = self.fgraph\\r\\n self.inputs = fgraph.inputs\\r\\n self.outputs = fgraph.outputs\\r\\n\\r\\n # list(fgraph.variables)\\r\\n # We need to include the not used inputs in our variables,\\r\\n # otherwise we can't pass them to the module.\\r\\n self.variables = [var for var in self.inputs if not len(var.clients)]\\r\\n self.variables += graph.variables(self.inputs, self.outputs)\\r\\n\\r\\n # The orphans field is listified to ensure a consistent order.\\r\\n #list(fgraph.orphans.difference(self.outputs))\\r\\n self.orphans = list(r for r in self.variables\\r\\n if isinstance(r, graph.Constant) and\\r\\n r not in self.inputs)\\r\\n self.temps = list(set(self.variables).difference(\\r\\n self.inputs).difference(self.outputs).difference(self.orphans))\\r\\n self.consts = []\\r\\n self.node_order = self.schedule(fgraph)\",\n \"def vars(cls):\\n for key in dir(cls):\\n if key.startswith('var_'):\\n yield key[4:]\",\n \"def getSelectedVariables(self):\\r\\n\\r\\n variables = []\\r\\n\\r\\n if self.ui.variablesStackedWidget.currentWidget() == self.ui.treePage:\\r\\n for index in self.ui.treeView.selectionModel().selectedRows():\\r\\n sourceIndex = self.treeFilterModel.mapToSource(index)\\r\\n treeItem = sourceIndex.internalPointer()\\r\\n if treeItem.variable is not None:\\r\\n variables.append(treeItem.variable)\\r\\n else:\\r\\n for index in self.ui.tableView.selectionModel().selectedRows():\\r\\n sourceIndex = self.tableFilterModel.mapToSource(index)\\r\\n variable = sourceIndex.internalPointer()\\r\\n variables.append(variable)\\r\\n\\r\\n return variables\",\n \"def vars(self):\\n return self._return_if('_vars')\",\n \"def variable_selection(self):\\n X = []\\n\\n if self.cfg.variables == 'X1':\\n X.append({\\\"name\\\": \\\"X1\\\", \\\"variables\\\": ['DepDelay', 'TaxiOut']})\\n elif self.cfg.variables == 'all':\\n X.append({\\\"name\\\": \\\"X1\\\", \\\"variables\\\": ['DepDelay', 'TaxiOut']})\\n X.append({\\\"name\\\": \\\"X2\\\", \\\"variables\\\": ['DepDelay', 'TaxiOut', 'HotDepTime']})\\n X.append({\\\"name\\\": \\\"X3\\\", \\\"variables\\\": ['DepDelay', 'TaxiOut', 'HotDayOfWeek', 'Speed']})\\n X.append({\\\"name\\\": \\\"X4\\\", \\\"variables\\\": ['DepDelay', 'TaxiOut', 'HotDayOfWeek', 'Speed', 'HotMonth']})\\n X.append({\\\"name\\\": \\\"X5\\\", \\\"variables\\\": ['DepDelay', 'TaxiOut', 'Speed', 'HotDepTime', 'HotCRSCatArrTime']})\\n elif self.cfg.variables == 'best':\\n X.append({\\\"name\\\": \\\"X5\\\", \\\"variables\\\": ['DepDelay', 'TaxiOut', 'Speed', 'HotDepTime', 'HotCRSCatArrTime']})\\n return X\",\n \"def get_transform_vars(self):\\n return [v for v in (self.rotation_vars + self.translation_vars)\\n if isinstance(v, tf.Variable)]\",\n \"def remove_variables(self):\\n self.variables = []\",\n \"def get_variables(self) -> np.array:\\n return np.array([self.a, self.b, self.c])\",\n \"def get_variable_values(self, vars):\\n raise NotImplementedError()\",\n \"def variables(model: Model) -> AbstractSet[str]:\\n assert is_model(model)\\n return model.keys()\",\n \"def variables(self):\\r\\n return self.get_field('variable')\",\n \"def get_variables(self):\\n local_variables = self._design.GetVariables(\\n )+self._design.GetPostProcessingVariables()\\n return {lv: self.get_variable_value(lv) for lv in local_variables}\",\n \"def get_variables(self) -> np.array:\\n pass\",\n \"def variables(model: Model) -> AbstractSet[str]:\\r\\n assert is_model(model)\\r\\n return model.keys()\",\n \"def free_variables(self):\\n\\n free_vars = set()\\n self.free_variables_helper(free_vars)\\n return free_vars\\n # Task 7.6\",\n \"def list_variables(self, request, context):\\n response = ListVariablesResponse()\\n for variable in self._delegator.list_variables(request.component):\\n response.variables.append(variable)\\n return response\",\n \"def get_variables(self) -> list[str]:\\n if self._cnf_formula is None:\\n raise Exception(\\\"CNF Model not initialized. Use `from_cnf` method first.\\\") \\n return self._variables\",\n \"def variables(self):\\n return np.array(list(self._match_result_dict.keys()))\",\n \"def findall_var(formula, variable):\\n res = []\\n s = Solver()\\n s.add(formula)\\n while True:\\n if s.check() == sat:\\n m = s.model()\\n res.append(m)\\n value = m[variable]\\n if value == None:\\n return res\\n s.add(variable != value)\\n else:\\n return res\",\n \"def phis_lst ( self ) :\\n return self.__phi_list\",\n \"def variables(self):\\n return self._variablesDef\",\n \"def values(self):\\n return [ self[x] for x in self ]\",\n \"def vars(self, scope: str = '') -> VarCollection:\\n return self.__wrapped__.vars(scope=scope)\",\n \"def output_farm_vars(self, algo):\\n return self._tvars\",\n \"def variables(self):\\n for name in self._nodes:\\n if isinstance(self._nodes[name], RandomVariable):\\n yield name\",\n \"def get_output_variables(self):\\n variables = set()\\n for sub_effect in self._sub_effects:\\n variables.add(sub_effect.get_variable())\\n return variables\",\n \"def coords(self, deep=False):\\n variables = list(self.vars(deep))\\n coords = [\\n coord\\n for coord in variables\\n for var in variables\\n if coord in self[var].dims\\n ]\\n return list(set(coords))\",\n \"def draw_variables(self): \\n z = self.q[0].draw_variable_local(self.sims)\\n for i in range(1,len(self.q)):\\n z = np.vstack((z,self.q[i].draw_variable_local(self.sims)))\\n return z\",\n \"def variables(self):\\n return tuple(flatten([a.variables for a in self.args]))\",\n \"def getRRDVariables(self, upToPoint=None):\\n cmds = self.getFakeGraphCmds(upToPoint=upToPoint)\\n names = [line[line.find(':')+1:line.find('=')]\\n for line in cmds.split('\\\\n')\\n if line[:line.find(':')] in ('DEF', 'CDEF', 'VDEF')]\\n nameSet = set(names)\\n result = []\\n for name in names:\\n #only allow -raw variables if a corresponding -rpn variable is present\\n if name.endswith('-raw') and name.replace('-raw', '-rpn') in nameSet or not name.endswith('-raw'):\\n result.append(name)\\n return result\",\n \"def get_swp_values(self, var: str) -> List[Any]:\\n return self._sweep_params[var]\",\n \"def getMeteorologicalVariableNames(self, product):\\r\\n return []\",\n \"def PrimaryVars(self):\\n res = []\\n for subsector in self.subsectors:\\n res.append(subsector.pvar)\\n return res\",\n \"def get_varnams(self, varnam_list):\\n self.varnams = [[v.name, v.composer, v.talam] for v in varnam_list\\n if v.raga == self.name]\",\n \"def all_q_variables(self):\\n\\n return self.main_q_variables() + (self.twin_q_variables() if self.twin_q_net else [])\",\n \"def all_values(cls) -> List[str]:\\n return list(member.value for member in cls.__members__.values())\",\n \"def dump_var_map(self):\\n vars = []\\n vars.append(\\\"--------- VARIABLES ---------\\\")\\n for var in self.var_map:\\n vars.append(var)\\n\\n return vars\",\n \"def listall(self, varName=None, all=None):\\n\\n if varName is None:\\n return None\\n var = self.getVariable(varName)\\n return var.listall(all=all)\",\n \"def get_variable_names(filepath):\\n variables = set()\\n with open(filepath, \\\"r\\\") as f:\\n previous = \\\"\\\"\\n for line in f.readlines():\\n if line[0] == \\\"#\\\":\\n previous = line\\n var_names = None\\n continue\\n if var_names is not None:\\n continue\\n var_names = previous.split()[1:]\\n while \\\"vs\\\" in var_names:\\n var_names.remove(\\\"vs\\\")\\n for name in var_names:\\n variables.add(name)\\n return list(variables)\",\n \"def getMeteorologicalVariableNames(self, product):\\r\\n\\r\\n meteorological_variable_names = []\\r\\n\\r\\n return meteorological_variable_names\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7587621","0.74283415","0.74283415","0.74283415","0.74034715","0.73497486","0.73163354","0.71726215","0.7025907","0.6996247","0.6937989","0.6923813","0.67796665","0.6772739","0.67677814","0.67526543","0.6719864","0.6713113","0.6711427","0.67106885","0.6696938","0.6681457","0.6674172","0.66414714","0.66307503","0.6598537","0.6590695","0.65682864","0.6561983","0.65334386","0.6531532","0.65269697","0.6473798","0.6472403","0.6469376","0.6456928","0.6453075","0.6450786","0.64401925","0.63921136","0.6383315","0.6381924","0.6373861","0.637357","0.6360343","0.6351113","0.63446116","0.6330722","0.6327722","0.6309606","0.62908024","0.62884855","0.62797904","0.62497056","0.6247741","0.62443703","0.6232441","0.615042","0.6150198","0.61407727","0.61402446","0.6125439","0.6109759","0.6097131","0.60951436","0.60827106","0.60725087","0.6071832","0.6062651","0.6059146","0.60491174","0.60409063","0.6038236","0.6024336","0.6000731","0.59591794","0.5942346","0.59360546","0.59170973","0.5912645","0.59005797","0.5897239","0.58916295","0.58894575","0.58893096","0.5883775","0.5882951","0.58706117","0.5867664","0.5843743","0.58375615","0.58339703","0.5833614","0.5830395","0.5830022","0.5825088","0.582458","0.58233464","0.5819377","0.5816668"],"string":"[\n \"0.7587621\",\n \"0.74283415\",\n \"0.74283415\",\n \"0.74283415\",\n \"0.74034715\",\n \"0.73497486\",\n \"0.73163354\",\n \"0.71726215\",\n \"0.7025907\",\n \"0.6996247\",\n \"0.6937989\",\n \"0.6923813\",\n \"0.67796665\",\n \"0.6772739\",\n \"0.67677814\",\n \"0.67526543\",\n \"0.6719864\",\n \"0.6713113\",\n \"0.6711427\",\n \"0.67106885\",\n \"0.6696938\",\n \"0.6681457\",\n \"0.6674172\",\n \"0.66414714\",\n \"0.66307503\",\n \"0.6598537\",\n \"0.6590695\",\n \"0.65682864\",\n \"0.6561983\",\n \"0.65334386\",\n \"0.6531532\",\n \"0.65269697\",\n \"0.6473798\",\n \"0.6472403\",\n \"0.6469376\",\n \"0.6456928\",\n \"0.6453075\",\n \"0.6450786\",\n \"0.64401925\",\n \"0.63921136\",\n \"0.6383315\",\n \"0.6381924\",\n \"0.6373861\",\n \"0.637357\",\n \"0.6360343\",\n \"0.6351113\",\n \"0.63446116\",\n \"0.6330722\",\n \"0.6327722\",\n \"0.6309606\",\n \"0.62908024\",\n \"0.62884855\",\n \"0.62797904\",\n \"0.62497056\",\n \"0.6247741\",\n \"0.62443703\",\n \"0.6232441\",\n \"0.615042\",\n \"0.6150198\",\n \"0.61407727\",\n \"0.61402446\",\n \"0.6125439\",\n \"0.6109759\",\n \"0.6097131\",\n \"0.60951436\",\n \"0.60827106\",\n \"0.60725087\",\n \"0.6071832\",\n \"0.6062651\",\n \"0.6059146\",\n \"0.60491174\",\n \"0.60409063\",\n \"0.6038236\",\n \"0.6024336\",\n \"0.6000731\",\n \"0.59591794\",\n \"0.5942346\",\n \"0.59360546\",\n \"0.59170973\",\n \"0.5912645\",\n \"0.59005797\",\n \"0.5897239\",\n \"0.58916295\",\n \"0.58894575\",\n \"0.58893096\",\n \"0.5883775\",\n \"0.5882951\",\n \"0.58706117\",\n \"0.5867664\",\n \"0.5843743\",\n \"0.58375615\",\n \"0.58339703\",\n \"0.5833614\",\n \"0.5830395\",\n \"0.5830022\",\n \"0.5825088\",\n \"0.582458\",\n \"0.58233464\",\n \"0.5819377\",\n \"0.5816668\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7627399"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":17899,"cells":{"query":{"kind":"string","value":"Get var indices for each (valid) var name provided in list."},"document":{"kind":"string","value":"def _get_variables(self, sim_outputs: List = None):\n \n # Ensure model has been initialized at least once\n self._model_has_been_initialized(\"_get_variables\")\n\n # Ensure array is not empty\n if sim_outputs is None:\n return {}\n elif not len(sim_outputs) > 0:\n #print(\"[_get_variables] No var names were provided. No vars are returned.\")\n return {}\n\n\n sim_output_indices, sim_output_names = self._var_names_to_indices(sim_outputs)\n \n # Check if more than one index has been found\n if not len(sim_output_indices) > 0:\n #print(\"[_get_variables] No valid var names have been provided. No vars are returned.\")\n return {}\n \n outputs_dict = dict(zip(sim_output_names, self.fmu.getReal(sim_output_indices)))\n\n return outputs_dict"},"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 indices_of_var(v):\n name = v.varName\n indices = name[2:].split(',')\n i, j = int(indices[0]), int(indices[1])\n return i, j","def _var_names_to_indices(self, var_names: List):\n\n if type(var_names) is not type([]):\n # Return empty array if input is not 'list' type\n print(\"[_var_names_to_indices] Provided input is not of type list.\")\n return []\n\n indices_array = []\n names_array = []\n for name in var_names:\n if name not in self.vars_to_idx.keys():\n print(\"[_var_names_to_indices] Invalid variable name '{}' has been skipped.\".format(name))\n continue\n indices_array.append(self.vars_to_idx[name])\n names_array.append(name)\n\n if not len(var_names) > 0:\n print(\"[_var_names_to_indices] No (valid) states have been provided.\")\n\n return indices_array, names_array","def index(self, variables):\n return [self._variables.index(v) for v in variables]","def variables_used (self) :\r\n\t\t## These names do not contain dimension specification (everything in brackets\r\n\t\t## that comes after a name is am array index - either the arry was declared\r\n\t\t## correctly or it is wrong anyway, there is no implicit declaration of arrays) !\r\n\r\n\t\tresult = []\r\n\r\n\t\tfor l in self.equ_lists :\r\n\t\t\tfor var_name in l :\r\n\t\t\t\tresult.append(var_name[0])\r\n\t\treturn result","def vars(svars):\n return np.array([pm.var(var) for var in svars.split()])","def vars(self):\n return [Var(i,self.dims[i]) for i in range(self.nvar)] # TODO: use stored state info (=1 sometimes)","def index(self, varname):\n if not isinstance(varname, str):\n raise TypeError(\"argument must be str\")\n varname = self._find_vars(varname, empty_ok=False, single=True)[0]\n return self._varlist.index(varname)","def variable_parser(var_list, prefix):\r\n ret_list = []\r\n for var in var_list:\r\n varname = var.name\r\n varprefix = varname.split('/')[0]\r\n if varprefix == prefix:\r\n ret_list.append(var)\r\n return ret_list","def variable_parser(var_list, prefix):\r\n ret_list = []\r\n for var in var_list:\r\n varname = var.name\r\n varprefix = varname.split('/')[0]\r\n if varprefix == prefix:\r\n ret_list.append(var)\r\n elif prefix in varname:\r\n ret_list.append(var)\r\n return ret_list","def get_list_vars(my_vars):\n lists = []\n for var in my_vars:\n try:\n temp = my_vars[var].getValue()\n #print var + '=' + str(temp)\n except ValueError:\n lists.append(var)\n return lists","def coord_indices_of(self, v_list):\n return [self.coord_index_of(v) for v in v_list]","def get_varnams(self, varnam_list):\n self.varnams = [[v.name, v.composer, v.talam] for v in varnam_list\n if v.raga == self.name]","def __splitVariableNames(self, name, indexes):\n if name == 'x':\n var = self.xCoordinates[indexes[0]][indexes[1]]\n elif name == 'y':\n var = self.yCoordinates[indexes[0]][indexes[1]]\n elif name == 'z':\n var = self.zCoordinates[indexes[0]][indexes[1]]\n elif name == 'colorMap':\n var = self.colorMapCoordinates[indexes[0]][indexes[1]]\n elif name == 'clusterLabels':\n var = self.clusterLabels[indexes[0]][indexes[1]]\n elif name == 'mixtureLabels':\n var = self.mixtureLabels[indexes[0]][indexes[1]]\n elif name == 'mixtureMeans':\n var = self.mixtureMeans[indexes[0]][indexes[1]]\n elif name == 'mixtureCovars':\n var = self.mixtureCovars[indexes[0]][indexes[1]]\n\n # The variable can contain brackets {} (when the symbol \"|\" is present in\n # the variable name), e.g.:\n # DataName|Input|{RavenAuxiliary|variableName|initial_value}\n # or it can look like:\n # DataName|Input|variableName\n\n if var is not None:\n result = [None] * 3\n if '|input|' in var.lower():\n match = re.search(r\"(\\|input\\|)\", var.lower())\n elif '|output|' in var.lower():\n match = re.search(r\"(\\|output\\|)\", var.lower())\n else:\n self.raiseAnError(IOError, f'In Plot {self.name}, the input coordinate {name} has not specified an \"Input\" or \"Output\" (case insensitive). e.g., sourceName|Input|aVariable) in {var}')\n startLoc, endLoc = match.start(), match.end()\n result = [var[:startLoc].strip(), var[startLoc+1:endLoc-1].strip(), var[endLoc:].strip()]\n if '{' in result[-1] and '}' in result[-1]:\n locLower, locUpper = result[-1].find(\"{\"), result[-1].rfind(\"}\")\n result[-1] = result[-1][locLower + 1:locUpper].strip()\n else:\n result = None\n\n return result","def map_to_scope(var_list):\n return {var.op.name.split('/', 1)[1]: var for var in var_list}","def get_predicate_indices(tags: List[str]) -> List[int]:\n return [ind for ind, tag in enumerate(tags) if \"V\" in tag]","def variable_names(self):\n \n return [x['variable'] for x in self.variable_dicts()]","def read_variables(var_or_list):\n session = ph.get_session()\n return session.run(var_or_list)","def compute_variable_indexes(path, overwrite=True, multiproc=False):\n if multiproc is True:\n tf.keras.backend.clear_session()\n set_cpu_option()\n\n gin_bindings = [\n \"evaluation.evaluation_fn = @variables_idx\",\n \"variables_idx.num_train = 10000\", \"evaluation.random_seed = 2051556033\",\n \"dataset.name='auto'\", \"evaluation.name = 'variables index'\"\n ]\n path = pathlib.Path(path)\n result_path = path.parent.parent / \"metrics\" / \"variance\" / \"filtered_variables\"\n logger.info(\"Computing variable indexes of {}\".format(path.parent.parent))\n gin_evaluation(path, result_path, overwrite, gin_bindings)","def getOqiVarNames( self ):\n\n if self.oqiVarNames:\n return self.oqiVarNames.keys()\n\n n = self.adb.get( \"nOqiVars\" )\n for indx in xrange( n ):\n name = self.adb.get( \"oqiVarName\",\n indx ) \n self.oqiVarNames[name] = indx\n\n return self.oqiVarNames.keys()","def get_vars_by_prefix(self, prefix):\n\n t_vars = tf.global_variables()\n return [var for var in t_vars if prefix in var.name]","def _find_vars(self, varnames, unique=False, evars=False, all_ok=False, \n empty_ok=False, single=False):\n if isinstance(varnames, str):\n varnames = (varnames,)\n elif not isinstance(varnames, collections.Iterable):\n raise TypeError(\"variable names should be str or iterable of str\")\n \n # first split into list of single abbrevs per str\n split_names = []\n for name in varnames:\n if not isinstance(name, str):\n raise TypeError(\"must specify variables as string(s)\")\n split_names += name.split()\n nnames = len(split_names)\n \n # check for _all, check for proper usage, and return copy of varlist\n # if evars==False or ['_dta'] + varlist if evars==True\n all_specified = False\n if '_all' in split_names:\n if not all_ok:\n raise ValueError(\"\\\"_all\\\" not allowed in this context\")\n elif not nnames == 1:\n raise ValueError(\n \"\\\"_all\\\" may not be combined with other names\")\n all_specified = True\n all_names = (['_dta'] if evars else []) + list(self._varlist)\n nnames = len(all_names)\n \n # check that more than 0 names specified if empty_ok==False, and\n # ignore extras (with message) if single==True\n if not empty_ok and nnames == 0:\n raise ValueError(\"no variables specified\")\n if single and nnames > 1:\n if not self._quiet:\n smcl = \"{err}\" if IN_STATA else \"\"\n msg = smcl + \"only one {}varname allowed; ignoring the rest\"\n print(msg.format('e' if evars else ''))\n split_names = split_names[:1]\n \n # if all_specified, return aleady-constructed all_names\n if all_specified:\n return all_names\n \n # Create match list of [abbrev, match1, match2, ...].\n # The loops below identify when exact varname given, but that varname\n # happens to be abbreviation of other varnames.\n varlist = self._varlist\n matches = []\n append = matches.append\n if evars:\n for name in split_names:\n if name == \"_dta\":\n append([name, name])\n else:\n match = [var for var in varlist if var.startswith(name)]\n append([name, name] if name in match else [name] + match)\n else:\n for name in split_names:\n match = [var for var in varlist if var.startswith(name)]\n append([name, name] if name in match else [name] + match)\n \n # abbreviation was a good, unambiguous abbreviation if exactly\n # one match found, i.e. if the corresponding entry in -matches- \n # is [abbrev, match1]\n if not all(len(m) == 2 for m in matches):\n # there were unmatched or ambiguous abbreviations\n zeros = \" \".join([m[0] for m in matches if len(m) == 1])\n twos = \" \".join([m[0] for m in matches if len(m) >= 3])\n if zeros != \"\" and twos != \"\":\n msg = \"no variables found for {}; multiple found for {}\"\n raise ValueError(msg.format(zeros, twos))\n if zeros != \"\":\n raise ValueError(\n \"no variables found for {}\".format(zeros, twos))\n # if getting here, twos != \"\" and zeros == \"\"\n raise ValueError(\"multiple variables found for '{}'\".format(twos))\n \n if not unique:\n return [m[1] for m in matches]\n seen = set()\n # if name has not been encountered, add to list and set of encountered\n return [m[1] for m in matches \n if m[1] not in seen and not seen.add(m[1])]","def get_name_list(msh, varname):\n return [str(chartostring(v)) for v in msh.variables[varname]]","def get_indexes(from_list, find_list):\n\n df_find = pd.DataFrame(find_list, columns=['value'])\n df_from = pd.DataFrame(list(zip(from_list, np.arange(len(from_list)))), columns=['value', 'index'])\n indexes = pd.merge(df_from, df_find, on='value', how='inner')['index'].values\n return indexes","def getVar2FactorsMap(self):\r\n V = self.getAllNodes()\r\n return list(list(idx for idx,f in enumerate(self.factors) if i in f.var) for i in V)","def var_index(self, code=1, s=False):\n if s:\n code = 2\n\n index = None\n for i, var in enumerate(self.primary_header['variables']):\n if var['Variable code'] == code:\n assert index is None, 'Appears to be two sets of same data in profile'\n index = i\n return index","def stats_variable_names(res):\n def varname(s):\n pos = s.find(':')\n return s if pos==-1 else s[0:pos]\n return set( [ varname(key) for key in res.keys()] )","def names_to_indices(names, ordered_names):\r\n indices = []\r\n names_list = list(names)\r\n for ordered_name in ordered_names:\r\n if ordered_name in names_list:\r\n indices.append(names_list.index(ordered_name))\r\n return array(indices)","def _get_indices(self, indices: VecEnvIndices) -> Iterable[int]:\n if indices is None:\n indices = range(self.num_envs)\n elif isinstance(indices, int):\n indices = [indices]\n return indices","def getVariableList(dataset):\n variables = [v for v in dataset.variables.keys() if v not in dataset.dimensions.keys()]\n for d in dataset.dimensions.keys():\n try:\n variables.pop(variables.index(dataset.variables[d].getncattr(\"bounds\")))\n except:\n pass\n return variables","def get_indexes(self, variable, *args):\n\n return [get_subset_idxs(data, min, max)\n for data, (min, max) in args]","def getOfcVarNames( self ):\n\n if self.ofcVarNames:\n return self.ofcVarNames.keys()\n \n n = self.adb.get( \"nOfcVars\" )\n for indx in xrange( n ):\n name = self.adb.get( \"ofcVarName\",\n indx ) \n self.ofcVarNames[name] = indx\n\n return self.ofcVarNames.keys()","def get_variable_names(self):\n return [var[1] for var in self.variables]","def getDefinedVars(self):\n numTabs = self.tabWidget.count()\n varList = []\n \n for i in range(numTabs):\n var = self.tabWidget.widget(i).getVar()\n name = self.tabWidget.tabText(i)\n varList.append([name, var])\n\n return varList","def get_valid(self, names):\n\n ret = [None] * len(names)\n posdict = dict([(name, i) for i, name in enumerate(names)])\n\n for compname, varnames in partition_names_by_comp(names).items():\n if compname is None:\n vals = super(Assembly, self).get_valid(varnames)\n for i, val in enumerate(vals):\n ret[posdict[varnames[i]]] = val\n else:\n comp = getattr(self, compname)\n if isinstance(comp, Component):\n vals = comp.get_valid(varnames)\n else:\n vals = [self._valid_dict['.'.join([compname, vname])] for vname in varnames]\n for i, val in enumerate(vals):\n full = '.'.join([compname, varnames[i]])\n ret[posdict[full]] = val\n return ret","def getOhcVarNames( self ):\n\n if self.ohcVarNames:\n return self.ohcVarNames.keys()\n \n n = self.adb.get( \"nOhcVars\" )\n for indx in xrange( n ):\n name = self.adb.get( \"ohcVarName\",\n indx ) \n self.ohcVarNames[name] = indx\n\n return self.ohcVarNames.keys()","def GetListVariable(self, name):\n var = self._makefile.variables.get(name, expand=True)[2]\n if not var:\n return []\n return var.resolvesplit(self._makefile, self._makefile.variables)","def get_input_var_names(self):\n return self._input_var_names","def cnf_variables(cnf):\n variabs = set()\n\n for clause in cnf:\n for var in clause:\n var = abs(var)\n\n if var not in variabs:\n variabs.add(var)\n\n return variabs","def getOeiVarNames( self ):\n\n if self.oeiVarNames:\n return self.oeiVarNames.keys()\n\n n = self.adb.get( \"nOeiVars\" )\n for indx in xrange( n ):\n name = self.adb.get( \"oeiVarName\",\n indx ) \n self.oeiVarNames[name] = indx\n\n return self.oeiVarNames.keys()","def data_variable_names(self):\n data_names = []\n mesh = self.mesh_names()\n prefix = mesh[0]+'_'\n for vname in self.nc.variables.keys():\n if vname.startswith(prefix):\n if self.nc.dimensions.has_key(vname):\n continue\n if hasattr(self.nc.variables[vname],'cf_role'):\n continue\n data_names.append( vname[len(prefix):] )\n return data_names","def getOsiVarNames( self ):\n\n if self.osiVarNames:\n return self.osiVarNames.keys()\n \n n = self.adb.get( \"nOsiVars\" )\n for indx in xrange( n ):\n name = self.adb.get( \"osiVarName\",\n indx ) \n self.osiVarNames[ name ]= indx\n\n return self.osiVarNames.keys()","def var_names(self):\n return self._var_names","def get_list_slice_vars(list_):\n\n # Dump unwanted portions\n array, list_ = list_.split(\"[\")\n list_ = list_.split(\"]\", 1)[0]\n\n # Split at ':'\n variables = list_.split(\":\")\n var_count = len(variables)\n\n step = \"\"\n\n # If step provided\n if var_count == 3:\n\n # If provided, store provided values\n start, stop, step = variables\n else:\n\n # Else store start, stop with default step\n start, stop = variables\n\n # If values are not provided by user, fall back to defaults\n\n # Set start default to 0\n if not start:\n start = \"0\"\n\n # Set stop default to array length\n if not stop:\n stop = \"Array.length\"\n\n # Set step default to 1\n if not step:\n step = \"1\"\n\n # Return stripped array with extracted values\n return array, start, stop, step","def __setVarNames(self):\n result = set()\n\n # detecting variables\n for templatePart in self.inputString().split(\"{\"):\n if templatePart is '' or \"}\" not in templatePart:\n continue\n\n endIndex = templatePart.find('}')\n result.add(templatePart[:endIndex])\n\n self.__varNames = list(result)","def getIndices(schema_attr_levels):\n return np.array(list(schema_attr_levels.values())).flatten().tolist()","def getOthVarNames( self ):\n\n if self.othVarNames:\n return self.othVarNames.keys()\n\n n = self.adb.get( \"nOthVars\" )\n for indx in range( n ):\n name = self.adb.get( \"othVarName\",\n indx ) \n self.othVarNames[ name ] = indx\n\n return self.othVarNames.keys()","def _instantiate_vars(labels: np.ndarray):\n n = len(labels)\n if len(set(labels)) == n:\n index_seed = np.arange(n)\n index_remain = np.arange(n)\n else:\n index_seed = np.argwhere(labels >= 0).ravel()\n index_remain = np.argwhere(labels < 0).ravel()\n labels = labels[index_seed]\n return index_seed.astype(np.int32), index_remain.astype(np.int32), labels.astype(np.int32)","def get_input_names(self):\n inputNames = []\n for inVar in self.inputs:\n # inVar is of type InOutVar and the object that it contains is a PyFMI variable\n inputNames.append(inVar.get_object().name)\n return inputNames","def getOriVarNames( self ):\n\n if self.oriVarNames:\n return self.oriVarNames.keys()\n\n n = self.adb.get( \"nOriVars\" )\n for indx in xrange( n ):\n name = self.adb.get( \"oriVarName\",\n indx ) \n self.oriVarNames[name] = indx\n\n return self.oriVarNames.keys()","def get_variable_names(self):\n varNames = []\n for var in self.variables:\n # EstimationVariable\n varNames.append(var.name)\n return varNames","def varNames(self):\n return self.__varNames","def __checkNrVars(self):\n variables = set()\n for q in self.__quantifierList:\n for var in q.getVariableNames():\n variables.add(\"%s\" % var)\n for c in self.__clauseList:\n for var in c.getVariableNames():\n variables.add(\"%s\" % var)\n \n return len(variables)","def extract_var_data(self, var_names):\n variable_dict = {} # Declaring dictionary used to store key-val pairs, var_name as key and the array as the value\n try:\n for var in var_names:\n variable_dict[var] = self.dataset[var].values\n return variable_dict\n except Exception as e:\n print(\"An Error occured:\", e)\n raise e","def varlist(data):\n \n Bxm=data[\"Bx\"].mean()\n Bym=data[\"By\"].mean()\n Bzm=data[\"Bz\"].mean()\n Bxsqm=(data[\"Bx\"]**2).mean()\n Bysqm=(data[\"By\"]**2).mean()\n Bzsqm=(data[\"Bz\"]**2).mean()\n Bxym=(data[\"Bx\"]*data[\"By\"]).mean()\n Bxzm=(data[\"Bx\"]*data[\"Bz\"]).mean()\n Byzm=(data[\"By\"]*data[\"Bz\"]).mean()\n \n Varx= Bxsqm-Bxm**2\n Varxy=Bxym-Bxm*Bym\n Varxz=Bxzm-Bxm*Bzm\n Vary=Bysqm-Bym**2\n Varyz=Byzm-Bym*Bzm\n Varz=Bzsqm-Bzm**2\n var=[Varx,Varxy, Varxz,Varxy,Vary,Varyz,Varxz,Varyz,Varz]\n var=np.array(var)\n var=var.reshape((3,3))\n return var","def number_of_variables(dataset, name_of_variable):\r\n first_row = dataset[0].keys()\r\n num = 0\r\n for variable in first_row:\r\n if name_of_variable in variable:\r\n num += 1 \r\n return num","def get_variable_names(filepath):\n variables = set()\n with open(filepath, \"r\") as f:\n previous = \"\"\n for line in f.readlines():\n if line[0] == \"#\":\n previous = line\n var_names = None\n continue\n if var_names is not None:\n continue\n var_names = previous.split()[1:]\n while \"vs\" in var_names:\n var_names.remove(\"vs\")\n for name in var_names:\n variables.add(name)\n return list(variables)","def get_vars(self):\n return [self.mu, self.var]","def get_vars(scope=''):\n return [x for x in tf.trainable_variables() if scope in x.name]","def varname_generator(id_name, code_name, code_range):\n # generate variable names according to the ones mentioned in the respective dataset coding report\n select_vars = [id_name]\n for i in range(1, (code_range + 1)):\n # creates a varname and adds an integer to it\n var_name = code_name + '{!s}'.format(i)\n select_vars.append(var_name)\n # finally, return the varname list\n return select_vars","def get_variable_names(self):\n return [VariableString(s) for s in\n self._design.GetVariables()+self._design.GetPostProcessingVariables()]","def getvarnameindex(self,somename_): # 3\n res,resargs = self.__obj.getvarnameindex(somename_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n _asgn_return_value,_index_return_value = resargs\n return _asgn_return_value,_index_return_value","def get_variable_names(text):\n names = []\n if '@@' in text:\n matches = _property_pattern.findall(text)\n for token, key in matches:\n names.append(key)\n\n return names","def find_varying(params, nvmax):\n print('Finding variable parameters')\n if nvmax < 0:\n raise ValueError(f'nvmax ({nvmax}) must be positive')\n\n var = []\n cnt = 0\n for p in params:\n if len(params[p]) == 2:\n if (params[p][1] - params[p][0]) < 0:\n raise ValueError(f'range is inverted for param: {p}')\n elif cnt >= nvmax:\n raise ValueError(f'too many param ranges were given. Expected {nvmax}')\n else:\n var.append(p)\n cnt += 1\n return var","def variables(self):\n return {u for u in self if u.type == 'var'}","def count_vars(scope=''):\n v = get_vars(scope)\n return sum([np.prod(var.shape.as_list()) for var in v])","def list_item_indexes(list_arg: list, item: Any) -> Tuple[int, ...]:\n indexes = [index for index, value in enumerate(list_arg) if value == item]\n return indexes","def __get_vars_list(self, template_idx, log):\n template = self.templates[template_idx].split()\n log = log.split()\n variables = []\n pt = pl = 0\n while pt < len(template) and pl < len(log):\n if template[pt] == log[pl]:\n pt += 1\n pl += 1\n continue\n elif template[pt] == '*':\n # found a variable\n while pt < len(template) and template[pt] == '*':\n # in case there are many variables together\n pt += 1\n if pt >= len(template):\n # it's the end of the template\n variables.append(' '.join(log[pl:]))\n break\n else:\n variable_tokens = []\n while pl < len(log) and log[pl] != template[pt]:\n variable_tokens.append(log[pl])\n pl += 1\n # it duplicates when many variables together for a correct output\n variables.append(' '.join(variable_tokens))\n else:\n # it is a variable not covered by the template asterisks\n # we move on on the log but stay on the template token\n pl += 1\n return variables","def getDummyVars(input_dict, feature_list, prefix=\"DummyVars_\"):\n\theader_list = []\n\tfeat_list = []\n\tfor ind,feat in enumerate(feature_list):\n\t\theader_list.append(prefix+str(ind))\n\t\tif input_dict.has_key(feat):\n\t\t\tfeat_list.append(input_dict[feat])\n\t\telse:\n\t\t\tfeat_list.append(0)\n\treturn feat_list, header_list","def variables(self):\n return [term.variable for term in self.terms]","def getLinIterVarNames( self ):\n\n self.updateAdb( )\n\n return self.iterNames.keys()","def getvarnameindex(self,somename_):\n if isinstance(somename_,unicode):\n somename_ = somename_.encode(\"utf-8\",errors=\"replace\")\n asgn_ = ctypes.c_int32()\n index_ = ctypes.c_int32()\n res = __library__.MSK_XX_getvarnameindex(self.__nativep,somename_,ctypes.byref(asgn_),ctypes.byref(index_))\n if res != 0:\n _,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n asgn_ = asgn_.value\n _asgn_return_value = asgn_\n index_ = index_.value\n _index_return_value = index_\n return (_asgn_return_value,_index_return_value)","def basisVar(bases):\n \n var = [] # The ordered list of active coordinates \n k = 0\n for b in bases:\n if np.isscalar(b):\n # A singleton, inactive coordinate\n k += 1\n else:\n # An active basis\n for i in range(b.nd):\n var.append(k)\n k += 1 \n return var","def GetVariableArrayDetails(line: str, variables: dict, report) -> (str, int):\n\n if re.sub(r\" (\\d+)$\", \"\", line ) in variables:\n var_name = re.sub(r\" (\\d+)$\", \"\", line )\n if not var_name in variables:\n raise Exception(f\"Couldn't find variable {var_name}\")\n if not variables[ var_name ]._isArray():\n raise Exception(f\"Variable {var_name} isn't an array\")\n try:\n var_index = int( re.search(r\" (\\d+$)\", line).group(0).strip() )\n except:\n raise Exception(\"Invalid array index\")\n return var_name, var_index-1\n elif \" of \" in line:\n line_split = line.split(' of ')\n var_name_index = line_split[0].strip()\n var_name = line_split[1].strip()\n\n if not var_name in variables:\n raise Exception(f\"Couldn't find variable {var_name}\")\n if not variables[ var_name ]._isArray():\n raise Exception(f\"Variable {var_name} isn't an array\")\n\n arith_check, arith_value = FCalc.FiMArithmetic.IsArithmetic( var_name_index )\n if arith_check:\n if not arith_value.is_prefix:\n arith = FCalc.FiMArithmetic(var_name_index, arith_value)\n arith.GetValues(variables, report)\n var_index = arith.Calculate()\n else:\n if not var_name_index in variables:\n raise Exception(f\"Couldn't find variable {var_name_index}\")\n if variables[ var_name_index ].Type != FClass.FiMVariableTypes.NUMBER:\n raise Exception(f\"Variable {var_name_index} isn't a number\")\n if _isFloat( variables[ var_name_index ].getv() ):\n raise Exception(f\"Cannot use floating-point numbers as array index\")\n var_index = variables[ var_name_index ].getv()\n return var_name, var_index-1\n\n return None, None","def indices_of_split(self, split_name='train'):\n return self.indices_of('split', split_name)","def get_indexes(self, items: Iterable[_T]) -> List[int]:\n return [self.get_index(item) for item in items]","def test_get_varnames(self):\n the_names = self.get_list_of_varnames()\n # I know that the current test file should contain the following\n ok_(\"I5\" in the_names)\n ok_(\">>\" in the_names)\n ok_(\"^^\" in the_names)","def get_n_params(var_list):\n return int(np.sum([np.product(\n [x.value for x in var.get_shape()]) for var in var_list]))","def variables(s):\n result = set([])\n def walk(s):\n if is_variable(s):\n result.add(s)\n else:\n for arg in s.args:\n walk(arg)\n walk(s)\n return result","def func_var_names(func):\n names = func.__code__.co_varnames[:func.__code__.co_argcount]\n return names","def _parse_indices(self):\n v_name = self.prior_token\n v_indices = []\n\n while self.token in (',', '('):\n v_indices.append(self._parse_index(v_name))\n\n return v_indices","def getIndexes(self, val):\n # Find where this value is listed. \n valNdx = (self.values == val).nonzero()[0]\n \n # If this value is not actually in those listed, then we \n # must return empty indexes\n if len(valNdx) == 0:\n start = 0\n end = 0\n else:\n # The index into counts, etc. for this value. \n valNdx = valNdx[0]\n start = self.start[valNdx]\n end = self.end[valNdx]\n \n # Create a tuple of index arrays, one for each index of the original array. \n ndx = ()\n for i in range(self.nDims):\n ndx += (self.indexes[start:end, i], )\n return ndx","def get_34index_list(self):\n msk = self.load_mask()\n return [i for (i,v) in enumerate(msk) if v==1]","def get_indexes(self, x):\n indexes = []\n for index_hashes in self.hash_functions:\n combined_index = []\n for idx_spec, hash_func in zip(self.config.index_specs, index_hashes):\n combined_index.append(idx_spec.distribution.get_index(hash_func(x)))\n indexes.append(tuple(combined_index))\n return indexes","def getAtomIndices( structure, resname ):\n atom_indices_ligand = []\n topology = structure.topology\n for atom in topology.atoms():\n if str(resname) in atom.residue.name:\n atom_indices_ligand.append(atom.index)\n\n return atom_indices_ligand","def var_name(i, j):\n return \"x_\" + str(i) + \",\" + str(j)","def get_name_by(indices: List[int]) -> List[str]:\n return [wiki_data[\"name\"][i] for i in indices]","def countVarFreq(list_models_vars_freq):\n list_variables_total = []\n for model_var_freq in list_models_vars_freq:\n variables_names = list(model_var_freq.dict_freq_var.keys())\n list_variables_total.extend(variables_names)\n \n counter_frec_variables = Counter(list_variables_total)\n dict_frec_variables = dict(counter_frec_variables)\n return dict_frec_variables","def var_slice(colnames, x):\n # TODO: produces bahavior similar to df.loc[:, \"V1\":\"V3\"], but can reverse\n # TODO: make DRY\n # TODO: reverse not including end points\n if isinstance(x.start, Var):\n start_indx = (colnames == x.start.name).idxmax()\n elif isinstance(x.start, str):\n start_indx = (colnames == x.start).idxmax()\n else:\n start_indx = x.start or 0\n\n if isinstance(x.stop, Var):\n stop_indx = (colnames == x.stop.name).idxmax() + 1\n elif isinstance(x.stop, str):\n stop_indx = (colnames == x.stop).idxmax() + 1\n else:\n stop_indx = x.stop or len(colnames)\n\n if start_indx > stop_indx:\n return stop_indx, start_indx\n else:\n return start_indx, stop_indx","def get_wind_indices(predictor_names):\n\n try:\n u_wind_index = predictor_names.index(\n processed_narr_io.U_WIND_GRID_RELATIVE_NAME)\n except ValueError:\n error_string = (\n '\\n{0:s}\\nPredictor names (shown above) must include \"{1:s}\".'\n ).format(str(predictor_names),\n processed_narr_io.U_WIND_GRID_RELATIVE_NAME)\n\n raise ValueError(error_string)\n\n try:\n v_wind_index = predictor_names.index(\n processed_narr_io.V_WIND_GRID_RELATIVE_NAME)\n except ValueError:\n error_string = (\n '\\n{0:s}\\nPredictor names (shown above) must include \"{1:s}\".'\n ).format(str(predictor_names),\n processed_narr_io.V_WIND_GRID_RELATIVE_NAME)\n\n raise ValueError(error_string)\n\n return u_wind_index, v_wind_index","def variables(self):\n return np.array(list(self._match_result_dict.keys()))","def means_of_variables(dataset, name_of_variable):\r\n list_of_means = []\r\n for num in range(len(dataset)):\r\n sum_of_variables = 0\r\n for row in dataset[num].items():\r\n if name_of_variable in row[0]:\r\n if row[1].strip() != \"\":\r\n sum_of_variables += int(row[1])\r\n if sum_of_variables != 0:\r\n list_of_means.append(float(sum_of_variables) / number_of_variables(dataset, name_of_variable)) \r\n return list_of_means","def get_variable_values(self, vars):\n raise NotImplementedError()","def give_variables_names(variables):\r\n names = map(lambda var: var.name, variables)\r\n h = hist(names)\r\n bad_var = lambda var: not var.name or h[var.name] > 1\r\n\r\n for i, var in enumerate(filter(bad_var, variables)):\r\n var.name = (var.name or \"\") + \"_%d\" % i\r\n\r\n if not unique(map(str, variables)):\r\n raise ValueError(\"Not all variables have unique names.\"\r\n \"Maybe you've named some of the variables identically\")\r\n\r\n return variables","def parse_vars(items):\n return dict((parse_var(item) for item in items))","def setVariableIndices(self, indicesOfVariables):\n if self.name in indicesOfVariables:\n self.variableId = indicesOfVariables[self.name]","def get_collections(scope_list):\n variables = [tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope)\n for scope in scope_list]\n return list(chain(*variables))","def vars(cls):\n for key in dir(cls):\n if key.startswith('var_'):\n yield key[4:]","def getVariableInfo(self, variables, name):\r\n\r\n return [var.return_variable_dict() for var in variables if var.name == name][0]","def indices_of_label(self, label_name):\n return self.indices_of('label', label_name)","def variables(self):\n return [i.name for i in self.inputs + self.outputs]","def extract_cols(lst,indexes):\n return (lst[i] for i in indexes)"],"string":"[\n \"def indices_of_var(v):\\n name = v.varName\\n indices = name[2:].split(',')\\n i, j = int(indices[0]), int(indices[1])\\n return i, j\",\n \"def _var_names_to_indices(self, var_names: List):\\n\\n if type(var_names) is not type([]):\\n # Return empty array if input is not 'list' type\\n print(\\\"[_var_names_to_indices] Provided input is not of type list.\\\")\\n return []\\n\\n indices_array = []\\n names_array = []\\n for name in var_names:\\n if name not in self.vars_to_idx.keys():\\n print(\\\"[_var_names_to_indices] Invalid variable name '{}' has been skipped.\\\".format(name))\\n continue\\n indices_array.append(self.vars_to_idx[name])\\n names_array.append(name)\\n\\n if not len(var_names) > 0:\\n print(\\\"[_var_names_to_indices] No (valid) states have been provided.\\\")\\n\\n return indices_array, names_array\",\n \"def index(self, variables):\\n return [self._variables.index(v) for v in variables]\",\n \"def variables_used (self) :\\r\\n\\t\\t## These names do not contain dimension specification (everything in brackets\\r\\n\\t\\t## that comes after a name is am array index - either the arry was declared\\r\\n\\t\\t## correctly or it is wrong anyway, there is no implicit declaration of arrays) !\\r\\n\\r\\n\\t\\tresult = []\\r\\n\\r\\n\\t\\tfor l in self.equ_lists :\\r\\n\\t\\t\\tfor var_name in l :\\r\\n\\t\\t\\t\\tresult.append(var_name[0])\\r\\n\\t\\treturn result\",\n \"def vars(svars):\\n return np.array([pm.var(var) for var in svars.split()])\",\n \"def vars(self):\\n return [Var(i,self.dims[i]) for i in range(self.nvar)] # TODO: use stored state info (=1 sometimes)\",\n \"def index(self, varname):\\n if not isinstance(varname, str):\\n raise TypeError(\\\"argument must be str\\\")\\n varname = self._find_vars(varname, empty_ok=False, single=True)[0]\\n return self._varlist.index(varname)\",\n \"def variable_parser(var_list, prefix):\\r\\n ret_list = []\\r\\n for var in var_list:\\r\\n varname = var.name\\r\\n varprefix = varname.split('/')[0]\\r\\n if varprefix == prefix:\\r\\n ret_list.append(var)\\r\\n return ret_list\",\n \"def variable_parser(var_list, prefix):\\r\\n ret_list = []\\r\\n for var in var_list:\\r\\n varname = var.name\\r\\n varprefix = varname.split('/')[0]\\r\\n if varprefix == prefix:\\r\\n ret_list.append(var)\\r\\n elif prefix in varname:\\r\\n ret_list.append(var)\\r\\n return ret_list\",\n \"def get_list_vars(my_vars):\\n lists = []\\n for var in my_vars:\\n try:\\n temp = my_vars[var].getValue()\\n #print var + '=' + str(temp)\\n except ValueError:\\n lists.append(var)\\n return lists\",\n \"def coord_indices_of(self, v_list):\\n return [self.coord_index_of(v) for v in v_list]\",\n \"def get_varnams(self, varnam_list):\\n self.varnams = [[v.name, v.composer, v.talam] for v in varnam_list\\n if v.raga == self.name]\",\n \"def __splitVariableNames(self, name, indexes):\\n if name == 'x':\\n var = self.xCoordinates[indexes[0]][indexes[1]]\\n elif name == 'y':\\n var = self.yCoordinates[indexes[0]][indexes[1]]\\n elif name == 'z':\\n var = self.zCoordinates[indexes[0]][indexes[1]]\\n elif name == 'colorMap':\\n var = self.colorMapCoordinates[indexes[0]][indexes[1]]\\n elif name == 'clusterLabels':\\n var = self.clusterLabels[indexes[0]][indexes[1]]\\n elif name == 'mixtureLabels':\\n var = self.mixtureLabels[indexes[0]][indexes[1]]\\n elif name == 'mixtureMeans':\\n var = self.mixtureMeans[indexes[0]][indexes[1]]\\n elif name == 'mixtureCovars':\\n var = self.mixtureCovars[indexes[0]][indexes[1]]\\n\\n # The variable can contain brackets {} (when the symbol \\\"|\\\" is present in\\n # the variable name), e.g.:\\n # DataName|Input|{RavenAuxiliary|variableName|initial_value}\\n # or it can look like:\\n # DataName|Input|variableName\\n\\n if var is not None:\\n result = [None] * 3\\n if '|input|' in var.lower():\\n match = re.search(r\\\"(\\\\|input\\\\|)\\\", var.lower())\\n elif '|output|' in var.lower():\\n match = re.search(r\\\"(\\\\|output\\\\|)\\\", var.lower())\\n else:\\n self.raiseAnError(IOError, f'In Plot {self.name}, the input coordinate {name} has not specified an \\\"Input\\\" or \\\"Output\\\" (case insensitive). e.g., sourceName|Input|aVariable) in {var}')\\n startLoc, endLoc = match.start(), match.end()\\n result = [var[:startLoc].strip(), var[startLoc+1:endLoc-1].strip(), var[endLoc:].strip()]\\n if '{' in result[-1] and '}' in result[-1]:\\n locLower, locUpper = result[-1].find(\\\"{\\\"), result[-1].rfind(\\\"}\\\")\\n result[-1] = result[-1][locLower + 1:locUpper].strip()\\n else:\\n result = None\\n\\n return result\",\n \"def map_to_scope(var_list):\\n return {var.op.name.split('/', 1)[1]: var for var in var_list}\",\n \"def get_predicate_indices(tags: List[str]) -> List[int]:\\n return [ind for ind, tag in enumerate(tags) if \\\"V\\\" in tag]\",\n \"def variable_names(self):\\n \\n return [x['variable'] for x in self.variable_dicts()]\",\n \"def read_variables(var_or_list):\\n session = ph.get_session()\\n return session.run(var_or_list)\",\n \"def compute_variable_indexes(path, overwrite=True, multiproc=False):\\n if multiproc is True:\\n tf.keras.backend.clear_session()\\n set_cpu_option()\\n\\n gin_bindings = [\\n \\\"evaluation.evaluation_fn = @variables_idx\\\",\\n \\\"variables_idx.num_train = 10000\\\", \\\"evaluation.random_seed = 2051556033\\\",\\n \\\"dataset.name='auto'\\\", \\\"evaluation.name = 'variables index'\\\"\\n ]\\n path = pathlib.Path(path)\\n result_path = path.parent.parent / \\\"metrics\\\" / \\\"variance\\\" / \\\"filtered_variables\\\"\\n logger.info(\\\"Computing variable indexes of {}\\\".format(path.parent.parent))\\n gin_evaluation(path, result_path, overwrite, gin_bindings)\",\n \"def getOqiVarNames( self ):\\n\\n if self.oqiVarNames:\\n return self.oqiVarNames.keys()\\n\\n n = self.adb.get( \\\"nOqiVars\\\" )\\n for indx in xrange( n ):\\n name = self.adb.get( \\\"oqiVarName\\\",\\n indx ) \\n self.oqiVarNames[name] = indx\\n\\n return self.oqiVarNames.keys()\",\n \"def get_vars_by_prefix(self, prefix):\\n\\n t_vars = tf.global_variables()\\n return [var for var in t_vars if prefix in var.name]\",\n \"def _find_vars(self, varnames, unique=False, evars=False, all_ok=False, \\n empty_ok=False, single=False):\\n if isinstance(varnames, str):\\n varnames = (varnames,)\\n elif not isinstance(varnames, collections.Iterable):\\n raise TypeError(\\\"variable names should be str or iterable of str\\\")\\n \\n # first split into list of single abbrevs per str\\n split_names = []\\n for name in varnames:\\n if not isinstance(name, str):\\n raise TypeError(\\\"must specify variables as string(s)\\\")\\n split_names += name.split()\\n nnames = len(split_names)\\n \\n # check for _all, check for proper usage, and return copy of varlist\\n # if evars==False or ['_dta'] + varlist if evars==True\\n all_specified = False\\n if '_all' in split_names:\\n if not all_ok:\\n raise ValueError(\\\"\\\\\\\"_all\\\\\\\" not allowed in this context\\\")\\n elif not nnames == 1:\\n raise ValueError(\\n \\\"\\\\\\\"_all\\\\\\\" may not be combined with other names\\\")\\n all_specified = True\\n all_names = (['_dta'] if evars else []) + list(self._varlist)\\n nnames = len(all_names)\\n \\n # check that more than 0 names specified if empty_ok==False, and\\n # ignore extras (with message) if single==True\\n if not empty_ok and nnames == 0:\\n raise ValueError(\\\"no variables specified\\\")\\n if single and nnames > 1:\\n if not self._quiet:\\n smcl = \\\"{err}\\\" if IN_STATA else \\\"\\\"\\n msg = smcl + \\\"only one {}varname allowed; ignoring the rest\\\"\\n print(msg.format('e' if evars else ''))\\n split_names = split_names[:1]\\n \\n # if all_specified, return aleady-constructed all_names\\n if all_specified:\\n return all_names\\n \\n # Create match list of [abbrev, match1, match2, ...].\\n # The loops below identify when exact varname given, but that varname\\n # happens to be abbreviation of other varnames.\\n varlist = self._varlist\\n matches = []\\n append = matches.append\\n if evars:\\n for name in split_names:\\n if name == \\\"_dta\\\":\\n append([name, name])\\n else:\\n match = [var for var in varlist if var.startswith(name)]\\n append([name, name] if name in match else [name] + match)\\n else:\\n for name in split_names:\\n match = [var for var in varlist if var.startswith(name)]\\n append([name, name] if name in match else [name] + match)\\n \\n # abbreviation was a good, unambiguous abbreviation if exactly\\n # one match found, i.e. if the corresponding entry in -matches- \\n # is [abbrev, match1]\\n if not all(len(m) == 2 for m in matches):\\n # there were unmatched or ambiguous abbreviations\\n zeros = \\\" \\\".join([m[0] for m in matches if len(m) == 1])\\n twos = \\\" \\\".join([m[0] for m in matches if len(m) >= 3])\\n if zeros != \\\"\\\" and twos != \\\"\\\":\\n msg = \\\"no variables found for {}; multiple found for {}\\\"\\n raise ValueError(msg.format(zeros, twos))\\n if zeros != \\\"\\\":\\n raise ValueError(\\n \\\"no variables found for {}\\\".format(zeros, twos))\\n # if getting here, twos != \\\"\\\" and zeros == \\\"\\\"\\n raise ValueError(\\\"multiple variables found for '{}'\\\".format(twos))\\n \\n if not unique:\\n return [m[1] for m in matches]\\n seen = set()\\n # if name has not been encountered, add to list and set of encountered\\n return [m[1] for m in matches \\n if m[1] not in seen and not seen.add(m[1])]\",\n \"def get_name_list(msh, varname):\\n return [str(chartostring(v)) for v in msh.variables[varname]]\",\n \"def get_indexes(from_list, find_list):\\n\\n df_find = pd.DataFrame(find_list, columns=['value'])\\n df_from = pd.DataFrame(list(zip(from_list, np.arange(len(from_list)))), columns=['value', 'index'])\\n indexes = pd.merge(df_from, df_find, on='value', how='inner')['index'].values\\n return indexes\",\n \"def getVar2FactorsMap(self):\\r\\n V = self.getAllNodes()\\r\\n return list(list(idx for idx,f in enumerate(self.factors) if i in f.var) for i in V)\",\n \"def var_index(self, code=1, s=False):\\n if s:\\n code = 2\\n\\n index = None\\n for i, var in enumerate(self.primary_header['variables']):\\n if var['Variable code'] == code:\\n assert index is None, 'Appears to be two sets of same data in profile'\\n index = i\\n return index\",\n \"def stats_variable_names(res):\\n def varname(s):\\n pos = s.find(':')\\n return s if pos==-1 else s[0:pos]\\n return set( [ varname(key) for key in res.keys()] )\",\n \"def names_to_indices(names, ordered_names):\\r\\n indices = []\\r\\n names_list = list(names)\\r\\n for ordered_name in ordered_names:\\r\\n if ordered_name in names_list:\\r\\n indices.append(names_list.index(ordered_name))\\r\\n return array(indices)\",\n \"def _get_indices(self, indices: VecEnvIndices) -> Iterable[int]:\\n if indices is None:\\n indices = range(self.num_envs)\\n elif isinstance(indices, int):\\n indices = [indices]\\n return indices\",\n \"def getVariableList(dataset):\\n variables = [v for v in dataset.variables.keys() if v not in dataset.dimensions.keys()]\\n for d in dataset.dimensions.keys():\\n try:\\n variables.pop(variables.index(dataset.variables[d].getncattr(\\\"bounds\\\")))\\n except:\\n pass\\n return variables\",\n \"def get_indexes(self, variable, *args):\\n\\n return [get_subset_idxs(data, min, max)\\n for data, (min, max) in args]\",\n \"def getOfcVarNames( self ):\\n\\n if self.ofcVarNames:\\n return self.ofcVarNames.keys()\\n \\n n = self.adb.get( \\\"nOfcVars\\\" )\\n for indx in xrange( n ):\\n name = self.adb.get( \\\"ofcVarName\\\",\\n indx ) \\n self.ofcVarNames[name] = indx\\n\\n return self.ofcVarNames.keys()\",\n \"def get_variable_names(self):\\n return [var[1] for var in self.variables]\",\n \"def getDefinedVars(self):\\n numTabs = self.tabWidget.count()\\n varList = []\\n \\n for i in range(numTabs):\\n var = self.tabWidget.widget(i).getVar()\\n name = self.tabWidget.tabText(i)\\n varList.append([name, var])\\n\\n return varList\",\n \"def get_valid(self, names):\\n\\n ret = [None] * len(names)\\n posdict = dict([(name, i) for i, name in enumerate(names)])\\n\\n for compname, varnames in partition_names_by_comp(names).items():\\n if compname is None:\\n vals = super(Assembly, self).get_valid(varnames)\\n for i, val in enumerate(vals):\\n ret[posdict[varnames[i]]] = val\\n else:\\n comp = getattr(self, compname)\\n if isinstance(comp, Component):\\n vals = comp.get_valid(varnames)\\n else:\\n vals = [self._valid_dict['.'.join([compname, vname])] for vname in varnames]\\n for i, val in enumerate(vals):\\n full = '.'.join([compname, varnames[i]])\\n ret[posdict[full]] = val\\n return ret\",\n \"def getOhcVarNames( self ):\\n\\n if self.ohcVarNames:\\n return self.ohcVarNames.keys()\\n \\n n = self.adb.get( \\\"nOhcVars\\\" )\\n for indx in xrange( n ):\\n name = self.adb.get( \\\"ohcVarName\\\",\\n indx ) \\n self.ohcVarNames[name] = indx\\n\\n return self.ohcVarNames.keys()\",\n \"def GetListVariable(self, name):\\n var = self._makefile.variables.get(name, expand=True)[2]\\n if not var:\\n return []\\n return var.resolvesplit(self._makefile, self._makefile.variables)\",\n \"def get_input_var_names(self):\\n return self._input_var_names\",\n \"def cnf_variables(cnf):\\n variabs = set()\\n\\n for clause in cnf:\\n for var in clause:\\n var = abs(var)\\n\\n if var not in variabs:\\n variabs.add(var)\\n\\n return variabs\",\n \"def getOeiVarNames( self ):\\n\\n if self.oeiVarNames:\\n return self.oeiVarNames.keys()\\n\\n n = self.adb.get( \\\"nOeiVars\\\" )\\n for indx in xrange( n ):\\n name = self.adb.get( \\\"oeiVarName\\\",\\n indx ) \\n self.oeiVarNames[name] = indx\\n\\n return self.oeiVarNames.keys()\",\n \"def data_variable_names(self):\\n data_names = []\\n mesh = self.mesh_names()\\n prefix = mesh[0]+'_'\\n for vname in self.nc.variables.keys():\\n if vname.startswith(prefix):\\n if self.nc.dimensions.has_key(vname):\\n continue\\n if hasattr(self.nc.variables[vname],'cf_role'):\\n continue\\n data_names.append( vname[len(prefix):] )\\n return data_names\",\n \"def getOsiVarNames( self ):\\n\\n if self.osiVarNames:\\n return self.osiVarNames.keys()\\n \\n n = self.adb.get( \\\"nOsiVars\\\" )\\n for indx in xrange( n ):\\n name = self.adb.get( \\\"osiVarName\\\",\\n indx ) \\n self.osiVarNames[ name ]= indx\\n\\n return self.osiVarNames.keys()\",\n \"def var_names(self):\\n return self._var_names\",\n \"def get_list_slice_vars(list_):\\n\\n # Dump unwanted portions\\n array, list_ = list_.split(\\\"[\\\")\\n list_ = list_.split(\\\"]\\\", 1)[0]\\n\\n # Split at ':'\\n variables = list_.split(\\\":\\\")\\n var_count = len(variables)\\n\\n step = \\\"\\\"\\n\\n # If step provided\\n if var_count == 3:\\n\\n # If provided, store provided values\\n start, stop, step = variables\\n else:\\n\\n # Else store start, stop with default step\\n start, stop = variables\\n\\n # If values are not provided by user, fall back to defaults\\n\\n # Set start default to 0\\n if not start:\\n start = \\\"0\\\"\\n\\n # Set stop default to array length\\n if not stop:\\n stop = \\\"Array.length\\\"\\n\\n # Set step default to 1\\n if not step:\\n step = \\\"1\\\"\\n\\n # Return stripped array with extracted values\\n return array, start, stop, step\",\n \"def __setVarNames(self):\\n result = set()\\n\\n # detecting variables\\n for templatePart in self.inputString().split(\\\"{\\\"):\\n if templatePart is '' or \\\"}\\\" not in templatePart:\\n continue\\n\\n endIndex = templatePart.find('}')\\n result.add(templatePart[:endIndex])\\n\\n self.__varNames = list(result)\",\n \"def getIndices(schema_attr_levels):\\n return np.array(list(schema_attr_levels.values())).flatten().tolist()\",\n \"def getOthVarNames( self ):\\n\\n if self.othVarNames:\\n return self.othVarNames.keys()\\n\\n n = self.adb.get( \\\"nOthVars\\\" )\\n for indx in range( n ):\\n name = self.adb.get( \\\"othVarName\\\",\\n indx ) \\n self.othVarNames[ name ] = indx\\n\\n return self.othVarNames.keys()\",\n \"def _instantiate_vars(labels: np.ndarray):\\n n = len(labels)\\n if len(set(labels)) == n:\\n index_seed = np.arange(n)\\n index_remain = np.arange(n)\\n else:\\n index_seed = np.argwhere(labels >= 0).ravel()\\n index_remain = np.argwhere(labels < 0).ravel()\\n labels = labels[index_seed]\\n return index_seed.astype(np.int32), index_remain.astype(np.int32), labels.astype(np.int32)\",\n \"def get_input_names(self):\\n inputNames = []\\n for inVar in self.inputs:\\n # inVar is of type InOutVar and the object that it contains is a PyFMI variable\\n inputNames.append(inVar.get_object().name)\\n return inputNames\",\n \"def getOriVarNames( self ):\\n\\n if self.oriVarNames:\\n return self.oriVarNames.keys()\\n\\n n = self.adb.get( \\\"nOriVars\\\" )\\n for indx in xrange( n ):\\n name = self.adb.get( \\\"oriVarName\\\",\\n indx ) \\n self.oriVarNames[name] = indx\\n\\n return self.oriVarNames.keys()\",\n \"def get_variable_names(self):\\n varNames = []\\n for var in self.variables:\\n # EstimationVariable\\n varNames.append(var.name)\\n return varNames\",\n \"def varNames(self):\\n return self.__varNames\",\n \"def __checkNrVars(self):\\n variables = set()\\n for q in self.__quantifierList:\\n for var in q.getVariableNames():\\n variables.add(\\\"%s\\\" % var)\\n for c in self.__clauseList:\\n for var in c.getVariableNames():\\n variables.add(\\\"%s\\\" % var)\\n \\n return len(variables)\",\n \"def extract_var_data(self, var_names):\\n variable_dict = {} # Declaring dictionary used to store key-val pairs, var_name as key and the array as the value\\n try:\\n for var in var_names:\\n variable_dict[var] = self.dataset[var].values\\n return variable_dict\\n except Exception as e:\\n print(\\\"An Error occured:\\\", e)\\n raise e\",\n \"def varlist(data):\\n \\n Bxm=data[\\\"Bx\\\"].mean()\\n Bym=data[\\\"By\\\"].mean()\\n Bzm=data[\\\"Bz\\\"].mean()\\n Bxsqm=(data[\\\"Bx\\\"]**2).mean()\\n Bysqm=(data[\\\"By\\\"]**2).mean()\\n Bzsqm=(data[\\\"Bz\\\"]**2).mean()\\n Bxym=(data[\\\"Bx\\\"]*data[\\\"By\\\"]).mean()\\n Bxzm=(data[\\\"Bx\\\"]*data[\\\"Bz\\\"]).mean()\\n Byzm=(data[\\\"By\\\"]*data[\\\"Bz\\\"]).mean()\\n \\n Varx= Bxsqm-Bxm**2\\n Varxy=Bxym-Bxm*Bym\\n Varxz=Bxzm-Bxm*Bzm\\n Vary=Bysqm-Bym**2\\n Varyz=Byzm-Bym*Bzm\\n Varz=Bzsqm-Bzm**2\\n var=[Varx,Varxy, Varxz,Varxy,Vary,Varyz,Varxz,Varyz,Varz]\\n var=np.array(var)\\n var=var.reshape((3,3))\\n return var\",\n \"def number_of_variables(dataset, name_of_variable):\\r\\n first_row = dataset[0].keys()\\r\\n num = 0\\r\\n for variable in first_row:\\r\\n if name_of_variable in variable:\\r\\n num += 1 \\r\\n return num\",\n \"def get_variable_names(filepath):\\n variables = set()\\n with open(filepath, \\\"r\\\") as f:\\n previous = \\\"\\\"\\n for line in f.readlines():\\n if line[0] == \\\"#\\\":\\n previous = line\\n var_names = None\\n continue\\n if var_names is not None:\\n continue\\n var_names = previous.split()[1:]\\n while \\\"vs\\\" in var_names:\\n var_names.remove(\\\"vs\\\")\\n for name in var_names:\\n variables.add(name)\\n return list(variables)\",\n \"def get_vars(self):\\n return [self.mu, self.var]\",\n \"def get_vars(scope=''):\\n return [x for x in tf.trainable_variables() if scope in x.name]\",\n \"def varname_generator(id_name, code_name, code_range):\\n # generate variable names according to the ones mentioned in the respective dataset coding report\\n select_vars = [id_name]\\n for i in range(1, (code_range + 1)):\\n # creates a varname and adds an integer to it\\n var_name = code_name + '{!s}'.format(i)\\n select_vars.append(var_name)\\n # finally, return the varname list\\n return select_vars\",\n \"def get_variable_names(self):\\n return [VariableString(s) for s in\\n self._design.GetVariables()+self._design.GetPostProcessingVariables()]\",\n \"def getvarnameindex(self,somename_): # 3\\n res,resargs = self.__obj.getvarnameindex(somename_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n _asgn_return_value,_index_return_value = resargs\\n return _asgn_return_value,_index_return_value\",\n \"def get_variable_names(text):\\n names = []\\n if '@@' in text:\\n matches = _property_pattern.findall(text)\\n for token, key in matches:\\n names.append(key)\\n\\n return names\",\n \"def find_varying(params, nvmax):\\n print('Finding variable parameters')\\n if nvmax < 0:\\n raise ValueError(f'nvmax ({nvmax}) must be positive')\\n\\n var = []\\n cnt = 0\\n for p in params:\\n if len(params[p]) == 2:\\n if (params[p][1] - params[p][0]) < 0:\\n raise ValueError(f'range is inverted for param: {p}')\\n elif cnt >= nvmax:\\n raise ValueError(f'too many param ranges were given. Expected {nvmax}')\\n else:\\n var.append(p)\\n cnt += 1\\n return var\",\n \"def variables(self):\\n return {u for u in self if u.type == 'var'}\",\n \"def count_vars(scope=''):\\n v = get_vars(scope)\\n return sum([np.prod(var.shape.as_list()) for var in v])\",\n \"def list_item_indexes(list_arg: list, item: Any) -> Tuple[int, ...]:\\n indexes = [index for index, value in enumerate(list_arg) if value == item]\\n return indexes\",\n \"def __get_vars_list(self, template_idx, log):\\n template = self.templates[template_idx].split()\\n log = log.split()\\n variables = []\\n pt = pl = 0\\n while pt < len(template) and pl < len(log):\\n if template[pt] == log[pl]:\\n pt += 1\\n pl += 1\\n continue\\n elif template[pt] == '*':\\n # found a variable\\n while pt < len(template) and template[pt] == '*':\\n # in case there are many variables together\\n pt += 1\\n if pt >= len(template):\\n # it's the end of the template\\n variables.append(' '.join(log[pl:]))\\n break\\n else:\\n variable_tokens = []\\n while pl < len(log) and log[pl] != template[pt]:\\n variable_tokens.append(log[pl])\\n pl += 1\\n # it duplicates when many variables together for a correct output\\n variables.append(' '.join(variable_tokens))\\n else:\\n # it is a variable not covered by the template asterisks\\n # we move on on the log but stay on the template token\\n pl += 1\\n return variables\",\n \"def getDummyVars(input_dict, feature_list, prefix=\\\"DummyVars_\\\"):\\n\\theader_list = []\\n\\tfeat_list = []\\n\\tfor ind,feat in enumerate(feature_list):\\n\\t\\theader_list.append(prefix+str(ind))\\n\\t\\tif input_dict.has_key(feat):\\n\\t\\t\\tfeat_list.append(input_dict[feat])\\n\\t\\telse:\\n\\t\\t\\tfeat_list.append(0)\\n\\treturn feat_list, header_list\",\n \"def variables(self):\\n return [term.variable for term in self.terms]\",\n \"def getLinIterVarNames( self ):\\n\\n self.updateAdb( )\\n\\n return self.iterNames.keys()\",\n \"def getvarnameindex(self,somename_):\\n if isinstance(somename_,unicode):\\n somename_ = somename_.encode(\\\"utf-8\\\",errors=\\\"replace\\\")\\n asgn_ = ctypes.c_int32()\\n index_ = ctypes.c_int32()\\n res = __library__.MSK_XX_getvarnameindex(self.__nativep,somename_,ctypes.byref(asgn_),ctypes.byref(index_))\\n if res != 0:\\n _,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n asgn_ = asgn_.value\\n _asgn_return_value = asgn_\\n index_ = index_.value\\n _index_return_value = index_\\n return (_asgn_return_value,_index_return_value)\",\n \"def basisVar(bases):\\n \\n var = [] # The ordered list of active coordinates \\n k = 0\\n for b in bases:\\n if np.isscalar(b):\\n # A singleton, inactive coordinate\\n k += 1\\n else:\\n # An active basis\\n for i in range(b.nd):\\n var.append(k)\\n k += 1 \\n return var\",\n \"def GetVariableArrayDetails(line: str, variables: dict, report) -> (str, int):\\n\\n if re.sub(r\\\" (\\\\d+)$\\\", \\\"\\\", line ) in variables:\\n var_name = re.sub(r\\\" (\\\\d+)$\\\", \\\"\\\", line )\\n if not var_name in variables:\\n raise Exception(f\\\"Couldn't find variable {var_name}\\\")\\n if not variables[ var_name ]._isArray():\\n raise Exception(f\\\"Variable {var_name} isn't an array\\\")\\n try:\\n var_index = int( re.search(r\\\" (\\\\d+$)\\\", line).group(0).strip() )\\n except:\\n raise Exception(\\\"Invalid array index\\\")\\n return var_name, var_index-1\\n elif \\\" of \\\" in line:\\n line_split = line.split(' of ')\\n var_name_index = line_split[0].strip()\\n var_name = line_split[1].strip()\\n\\n if not var_name in variables:\\n raise Exception(f\\\"Couldn't find variable {var_name}\\\")\\n if not variables[ var_name ]._isArray():\\n raise Exception(f\\\"Variable {var_name} isn't an array\\\")\\n\\n arith_check, arith_value = FCalc.FiMArithmetic.IsArithmetic( var_name_index )\\n if arith_check:\\n if not arith_value.is_prefix:\\n arith = FCalc.FiMArithmetic(var_name_index, arith_value)\\n arith.GetValues(variables, report)\\n var_index = arith.Calculate()\\n else:\\n if not var_name_index in variables:\\n raise Exception(f\\\"Couldn't find variable {var_name_index}\\\")\\n if variables[ var_name_index ].Type != FClass.FiMVariableTypes.NUMBER:\\n raise Exception(f\\\"Variable {var_name_index} isn't a number\\\")\\n if _isFloat( variables[ var_name_index ].getv() ):\\n raise Exception(f\\\"Cannot use floating-point numbers as array index\\\")\\n var_index = variables[ var_name_index ].getv()\\n return var_name, var_index-1\\n\\n return None, None\",\n \"def indices_of_split(self, split_name='train'):\\n return self.indices_of('split', split_name)\",\n \"def get_indexes(self, items: Iterable[_T]) -> List[int]:\\n return [self.get_index(item) for item in items]\",\n \"def test_get_varnames(self):\\n the_names = self.get_list_of_varnames()\\n # I know that the current test file should contain the following\\n ok_(\\\"I5\\\" in the_names)\\n ok_(\\\">>\\\" in the_names)\\n ok_(\\\"^^\\\" in the_names)\",\n \"def get_n_params(var_list):\\n return int(np.sum([np.product(\\n [x.value for x in var.get_shape()]) for var in var_list]))\",\n \"def variables(s):\\n result = set([])\\n def walk(s):\\n if is_variable(s):\\n result.add(s)\\n else:\\n for arg in s.args:\\n walk(arg)\\n walk(s)\\n return result\",\n \"def func_var_names(func):\\n names = func.__code__.co_varnames[:func.__code__.co_argcount]\\n return names\",\n \"def _parse_indices(self):\\n v_name = self.prior_token\\n v_indices = []\\n\\n while self.token in (',', '('):\\n v_indices.append(self._parse_index(v_name))\\n\\n return v_indices\",\n \"def getIndexes(self, val):\\n # Find where this value is listed. \\n valNdx = (self.values == val).nonzero()[0]\\n \\n # If this value is not actually in those listed, then we \\n # must return empty indexes\\n if len(valNdx) == 0:\\n start = 0\\n end = 0\\n else:\\n # The index into counts, etc. for this value. \\n valNdx = valNdx[0]\\n start = self.start[valNdx]\\n end = self.end[valNdx]\\n \\n # Create a tuple of index arrays, one for each index of the original array. \\n ndx = ()\\n for i in range(self.nDims):\\n ndx += (self.indexes[start:end, i], )\\n return ndx\",\n \"def get_34index_list(self):\\n msk = self.load_mask()\\n return [i for (i,v) in enumerate(msk) if v==1]\",\n \"def get_indexes(self, x):\\n indexes = []\\n for index_hashes in self.hash_functions:\\n combined_index = []\\n for idx_spec, hash_func in zip(self.config.index_specs, index_hashes):\\n combined_index.append(idx_spec.distribution.get_index(hash_func(x)))\\n indexes.append(tuple(combined_index))\\n return indexes\",\n \"def getAtomIndices( structure, resname ):\\n atom_indices_ligand = []\\n topology = structure.topology\\n for atom in topology.atoms():\\n if str(resname) in atom.residue.name:\\n atom_indices_ligand.append(atom.index)\\n\\n return atom_indices_ligand\",\n \"def var_name(i, j):\\n return \\\"x_\\\" + str(i) + \\\",\\\" + str(j)\",\n \"def get_name_by(indices: List[int]) -> List[str]:\\n return [wiki_data[\\\"name\\\"][i] for i in indices]\",\n \"def countVarFreq(list_models_vars_freq):\\n list_variables_total = []\\n for model_var_freq in list_models_vars_freq:\\n variables_names = list(model_var_freq.dict_freq_var.keys())\\n list_variables_total.extend(variables_names)\\n \\n counter_frec_variables = Counter(list_variables_total)\\n dict_frec_variables = dict(counter_frec_variables)\\n return dict_frec_variables\",\n \"def var_slice(colnames, x):\\n # TODO: produces bahavior similar to df.loc[:, \\\"V1\\\":\\\"V3\\\"], but can reverse\\n # TODO: make DRY\\n # TODO: reverse not including end points\\n if isinstance(x.start, Var):\\n start_indx = (colnames == x.start.name).idxmax()\\n elif isinstance(x.start, str):\\n start_indx = (colnames == x.start).idxmax()\\n else:\\n start_indx = x.start or 0\\n\\n if isinstance(x.stop, Var):\\n stop_indx = (colnames == x.stop.name).idxmax() + 1\\n elif isinstance(x.stop, str):\\n stop_indx = (colnames == x.stop).idxmax() + 1\\n else:\\n stop_indx = x.stop or len(colnames)\\n\\n if start_indx > stop_indx:\\n return stop_indx, start_indx\\n else:\\n return start_indx, stop_indx\",\n \"def get_wind_indices(predictor_names):\\n\\n try:\\n u_wind_index = predictor_names.index(\\n processed_narr_io.U_WIND_GRID_RELATIVE_NAME)\\n except ValueError:\\n error_string = (\\n '\\\\n{0:s}\\\\nPredictor names (shown above) must include \\\"{1:s}\\\".'\\n ).format(str(predictor_names),\\n processed_narr_io.U_WIND_GRID_RELATIVE_NAME)\\n\\n raise ValueError(error_string)\\n\\n try:\\n v_wind_index = predictor_names.index(\\n processed_narr_io.V_WIND_GRID_RELATIVE_NAME)\\n except ValueError:\\n error_string = (\\n '\\\\n{0:s}\\\\nPredictor names (shown above) must include \\\"{1:s}\\\".'\\n ).format(str(predictor_names),\\n processed_narr_io.V_WIND_GRID_RELATIVE_NAME)\\n\\n raise ValueError(error_string)\\n\\n return u_wind_index, v_wind_index\",\n \"def variables(self):\\n return np.array(list(self._match_result_dict.keys()))\",\n \"def means_of_variables(dataset, name_of_variable):\\r\\n list_of_means = []\\r\\n for num in range(len(dataset)):\\r\\n sum_of_variables = 0\\r\\n for row in dataset[num].items():\\r\\n if name_of_variable in row[0]:\\r\\n if row[1].strip() != \\\"\\\":\\r\\n sum_of_variables += int(row[1])\\r\\n if sum_of_variables != 0:\\r\\n list_of_means.append(float(sum_of_variables) / number_of_variables(dataset, name_of_variable)) \\r\\n return list_of_means\",\n \"def get_variable_values(self, vars):\\n raise NotImplementedError()\",\n \"def give_variables_names(variables):\\r\\n names = map(lambda var: var.name, variables)\\r\\n h = hist(names)\\r\\n bad_var = lambda var: not var.name or h[var.name] > 1\\r\\n\\r\\n for i, var in enumerate(filter(bad_var, variables)):\\r\\n var.name = (var.name or \\\"\\\") + \\\"_%d\\\" % i\\r\\n\\r\\n if not unique(map(str, variables)):\\r\\n raise ValueError(\\\"Not all variables have unique names.\\\"\\r\\n \\\"Maybe you've named some of the variables identically\\\")\\r\\n\\r\\n return variables\",\n \"def parse_vars(items):\\n return dict((parse_var(item) for item in items))\",\n \"def setVariableIndices(self, indicesOfVariables):\\n if self.name in indicesOfVariables:\\n self.variableId = indicesOfVariables[self.name]\",\n \"def get_collections(scope_list):\\n variables = [tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope)\\n for scope in scope_list]\\n return list(chain(*variables))\",\n \"def vars(cls):\\n for key in dir(cls):\\n if key.startswith('var_'):\\n yield key[4:]\",\n \"def getVariableInfo(self, variables, name):\\r\\n\\r\\n return [var.return_variable_dict() for var in variables if var.name == name][0]\",\n \"def indices_of_label(self, label_name):\\n return self.indices_of('label', label_name)\",\n \"def variables(self):\\n return [i.name for i in self.inputs + self.outputs]\",\n \"def extract_cols(lst,indexes):\\n return (lst[i] for i in indexes)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7444971","0.7400322","0.6825618","0.61766595","0.6171719","0.6141269","0.604002","0.6002675","0.5954537","0.58911085","0.5872941","0.5856526","0.5843315","0.58359766","0.57936394","0.579151","0.5744204","0.5717505","0.57010794","0.56933683","0.5685582","0.5678396","0.56728923","0.5644376","0.56342816","0.5617331","0.561411","0.56125355","0.5595388","0.5583206","0.55766547","0.5576318","0.55696803","0.5567434","0.5517854","0.5514313","0.550876","0.5494308","0.54811436","0.54745543","0.5459479","0.5450277","0.54431313","0.5441712","0.5425963","0.5425013","0.5422706","0.5412134","0.5394093","0.53930753","0.5381008","0.5379181","0.5363689","0.53626454","0.53584975","0.53573066","0.5356441","0.5355779","0.53494763","0.53312147","0.5320095","0.52850026","0.52719986","0.5269608","0.5251154","0.52409357","0.52264255","0.5223139","0.5217217","0.5214436","0.521432","0.5206486","0.5204216","0.5202807","0.52012557","0.5193804","0.5186952","0.51711714","0.51670384","0.5159038","0.51541656","0.51437265","0.5135495","0.5131441","0.51313287","0.5128818","0.5125019","0.51182836","0.51141536","0.51108795","0.51106864","0.51048875","0.5101378","0.50948906","0.509255","0.5089629","0.5084956","0.5078712","0.50671434","0.5066216","0.5060016"],"string":"[\n \"0.7444971\",\n \"0.7400322\",\n \"0.6825618\",\n \"0.61766595\",\n \"0.6171719\",\n \"0.6141269\",\n \"0.604002\",\n \"0.6002675\",\n \"0.5954537\",\n \"0.58911085\",\n \"0.5872941\",\n \"0.5856526\",\n \"0.5843315\",\n \"0.58359766\",\n \"0.57936394\",\n \"0.579151\",\n \"0.5744204\",\n \"0.5717505\",\n \"0.57010794\",\n \"0.56933683\",\n \"0.5685582\",\n \"0.5678396\",\n \"0.56728923\",\n \"0.5644376\",\n \"0.56342816\",\n \"0.5617331\",\n \"0.561411\",\n \"0.56125355\",\n \"0.5595388\",\n \"0.5583206\",\n \"0.55766547\",\n \"0.5576318\",\n \"0.55696803\",\n \"0.5567434\",\n \"0.5517854\",\n \"0.5514313\",\n \"0.550876\",\n \"0.5494308\",\n \"0.54811436\",\n \"0.54745543\",\n \"0.5459479\",\n \"0.5450277\",\n \"0.54431313\",\n \"0.5441712\",\n \"0.5425963\",\n \"0.5425013\",\n \"0.5422706\",\n \"0.5412134\",\n \"0.5394093\",\n \"0.53930753\",\n \"0.5381008\",\n \"0.5379181\",\n \"0.5363689\",\n \"0.53626454\",\n \"0.53584975\",\n \"0.53573066\",\n \"0.5356441\",\n \"0.5355779\",\n \"0.53494763\",\n \"0.53312147\",\n \"0.5320095\",\n \"0.52850026\",\n \"0.52719986\",\n \"0.5269608\",\n \"0.5251154\",\n \"0.52409357\",\n \"0.52264255\",\n \"0.5223139\",\n \"0.5217217\",\n \"0.5214436\",\n \"0.521432\",\n \"0.5206486\",\n \"0.5204216\",\n \"0.5202807\",\n \"0.52012557\",\n \"0.5193804\",\n \"0.5186952\",\n \"0.51711714\",\n \"0.51670384\",\n \"0.5159038\",\n \"0.51541656\",\n \"0.51437265\",\n \"0.5135495\",\n \"0.5131441\",\n \"0.51313287\",\n \"0.5128818\",\n \"0.5125019\",\n \"0.51182836\",\n \"0.51141536\",\n \"0.51108795\",\n \"0.51106864\",\n \"0.51048875\",\n \"0.5101378\",\n \"0.50948906\",\n \"0.509255\",\n \"0.5089629\",\n \"0.5084956\",\n \"0.5078712\",\n \"0.50671434\",\n \"0.5066216\",\n \"0.5060016\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}}],"truncated":false,"partial":true},"paginationData":{"pageIndex":178,"numItemsPerPage":100,"numTotalItems":95772,"offset":17800,"length":100}},"jwt":"eyJhbGciOiJFZERTQSIsImtpZCI6IjVHZDBvd0g5MTM2eDZjc1FvbE1zcktNWUZoRVFoUm5PVVVybEpjOUhaUEEifQ.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sImlhdCI6MTc4NjI0MDgzNCwianRpIjoiMmFiNzJlNTYtYzZjMi00MTY3LTgyNTMtZjFhYTI1ZmI0NmQ3Iiwic3ViIjoiL2RhdGFzZXRzL25vbWljLWFpL2Nvcm5zdGFjay1weXRob24tdjEiLCJleHAiOjE3ODYyNDQ0MzQsImlzcyI6Imh0dHBzOi8vaHVnZ2luZ2ZhY2UuY28ifQ.IQqR09--lQa346yYuRfERi79UnOzQxwXiyRzPwcQMk-5azXHGU7LPCit5hiBPNRL7ev8yxAD2h2cL6ceGeBLCw","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 |
|---|---|---|---|---|---|---|
Restrict most types of constant except for numeric types and constant strings Picks up some obvious conversions such as None and Bools | def _Constant(self, t):
value = t.value
if isinstance(value, tuple):
self.RaiseError(t, "Tuples not supported")
if isinstance(value, dict):
self.RaiseError(t, "Dictionaries not supported")
if isinstance(value, list):
self.RaiseError(t, "Lists not suppo... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def smart_coerce(value: str) -> ValueType:\n try:\n return int(value)\n except ValueError:\n pass\n try:\n return float(value)\n except ValueError:\n pass\n if value.lower() in ('null', 'none', ):\n return None\n elif value.lower() in ('true', ):\n return... | [
"0.6328382",
"0.6033366",
"0.5924364",
"0.5904759",
"0.5871673",
"0.582811",
"0.57898164",
"0.5732572",
"0.57246566",
"0.57075286",
"0.5638559",
"0.559512",
"0.5559636",
"0.55591905",
"0.54913414",
"0.54847956",
"0.5479766",
"0.547383",
"0.54578835",
"0.54506004",
"0.544511",... | 0.5639109 | 10 |
Equivalent to a ternary operator | def _IfExp(self, t):
self.dispatch(t.test)
self.write(" ? ")
self.dispatch(t.body)
self.write(" : ")
self.dispatch(t.orelse) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def conditional_value(self) -> global___Expression.ConditionalOperator:",
"def get_truefalse(truefalse):\n return 'True' if truefalse else 'False'",
"def toggle(condition, if_true, if_false):\n return (if_true if condition else if_false)",
"def ifelse(test, if_true, if_false):\n if test:\n re... | [
"0.6408623",
"0.60773087",
"0.60416794",
"0.6040853",
"0.5728847",
"0.5722783",
"0.5698696",
"0.5694586",
"0.5692849",
"0.5692849",
"0.56510156",
"0.56408584",
"0.5613775",
"0.55266976",
"0.551668",
"0.5453018",
"0.54519844",
"0.544794",
"0.5433943",
"0.5424278",
"0.53960836"... | 0.5033665 | 59 |
Translate to C equivalent opertaors | def _UnaryOp(self, t):
self.write("(")
self.write(self.unop[t.op.__class__.__name__])
self.dispatch(t.operand)
self.write(")") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def CL(self):",
"def C(self, u, v):\n pass",
"def lin_o_func(self):\n return self.hx",
"def c(self):\n pass",
"def c(self):\n pass",
"def exo2():",
"def _make_array(self, c):\n return (c * ctypes.py_object)()",
"def topoc2ant_c_wrapped(el, az):\n gam = ct.c_double()\... | [
"0.55931556",
"0.55788374",
"0.54712087",
"0.545349",
"0.545349",
"0.5416191",
"0.53282946",
"0.53185683",
"0.52821124",
"0.5247868",
"0.52405506",
"0.519369",
"0.5176467",
"0.5163417",
"0.5161596",
"0.5116109",
"0.51085824",
"0.5087596",
"0.50612295",
"0.50414175",
"0.503272... | 0.0 | -1 |
Python style pow and floordiv are not supported so translate to a function call. No matrix mul support. | def _BinOp(self, t):
op_name = t.op.__class__.__name__
# translate pow into function call (no float version)
if op_name == "Pow":
self.write("pow(")
self.dispatch(t.left)
self.write(", ")
self.dispatch(t.right)
self.write(")")
#... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __pow__(self, ???):",
"def power(a, b):\n \n return a**b",
"def __pow__(self,*args):\r\n pass",
"def py_pow(x, p, op_version=None):\n return x ** p",
"def pow(x, y):\n return 0.0",
"def __rpow__(self, ???):",
"def pow(space, w_base, w_exponent, w_modulus):\n return space.pow(w_base, ... | [
"0.7005062",
"0.68626165",
"0.68304825",
"0.67894477",
"0.6744171",
"0.6733175",
"0.6722",
"0.67047685",
"0.6682735",
"0.66395473",
"0.6584284",
"0.6565196",
"0.65598845",
"0.655792",
"0.655061",
"0.655061",
"0.655061",
"0.655061",
"0.655061",
"0.655061",
"0.655061",
"0.655... | 0.0 | -1 |
Translate to logical and/or operators in C | def _BoolOp(self, t):
self.write("(")
s = " %s " % self.boolops[t.op.__class__]
interleave(lambda: self.write(s), self.dispatch, t.values)
self.write(")") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def and_(a, b):",
"def _and(it):\n return 1 if it[0]==1 and it[1]==1 else 0",
"def _logical_and(*args):\n args_ = [_static_value(x) for x in args]\n if any(x is not None and not bool(x) for x in args_):\n return constant_op.constant(False)\n if all(x is not None and bool(x) for x in args_):\n retur... | [
"0.80580425",
"0.74413097",
"0.74133176",
"0.7363188",
"0.7348686",
"0.72836405",
"0.7277993",
"0.7163794",
"0.7146615",
"0.7092901",
"0.7062364",
"0.6979023",
"0.6877739",
"0.6872799",
"0.6869939",
"0.6856394",
"0.6841215",
"0.68358576",
"0.6812308",
"0.6753586",
"0.6708629"... | 0.0 | -1 |
A very limited set of attributes are supported so these are fully evaluated here. Other places where attribute type expressions may occur will also evaluate them fully rather than recursively call this function. Attributes supported are only; pyflamegpu.attribute a supported attribute e.g. pyflamegpu.ALIVE. This will b... | def _Attribute(self,t):
# Only a limited set of globals supported
func_dict = None
# pyflamegpu singleton
if isinstance(t.value, ast.Name):
if t.value.id == "pyflamegpu":
if t.attr in self.fgpu_attrs:
# proceed
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def testattributes(self):\n for attr in ('ST', 'DX', 'IQ', 'MA', 'Dam', 'Hit'):\n AttributeAbility([attr,])",
"def check_common_attrs(self, ast):\n declarator = ast.declarator\n attrs = declarator.attrs\n meta = declarator.metaattrs\n ntypemap = ast.typemap\n ... | [
"0.6042245",
"0.5977239",
"0.5950845",
"0.5936985",
"0.59264654",
"0.5886172",
"0.5826526",
"0.5790564",
"0.5755268",
"0.567972",
"0.56774443",
"0.5620047",
"0.553324",
"0.55215055",
"0.54968786",
"0.5450834",
"0.5426266",
"0.5415868",
"0.5412917",
"0.5388492",
"0.53772295",
... | 0.713576 | 0 |
Some basic checks are undertaken on calls to ensure that the function being called is either a builtin or defined device function. A special dispatcher is required | def _Call(self, t):
# check calls but let attributes check in their own dispatcher
funcs = self._device_functions + self.pythonbuiltins + [self._input_message_var] # message_input variable is a valid function name as certain message types have arguments on iterator
if isinstance(t.func, ast.Name... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __call__(fun_name):",
"def test_dispatchUnknown(self):\n disp = Dispatcher()\n name = \"missing\"\n args = (1, 2)\n res = disp.dispatch(name, *args)\n self.assertEqual(res, (name,) + args)",
"def validate_universal_calls(cls):\n assert True == cls.universal... | [
"0.5971725",
"0.5816666",
"0.56597704",
"0.565735",
"0.56368434",
"0.56075937",
"0.560516",
"0.5587939",
"0.55403495",
"0.54888415",
"0.542656",
"0.5398054",
"0.53934664",
"0.53790116",
"0.5372327",
"0.53566486",
"0.53564227",
"0.5351131",
"0.53422856",
"0.5330329",
"0.532966... | 0.5911068 | 1 |
Arrays are not supported but subscript allows accessing array like variables which is required for macro environment properties (e.g. a[0][1][2]) | def _Subscript(self, t):
self.dispatch(t.value)
self.write("[")
self.dispatch(t.slice)
self.write("]") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_test_arraypointertype(self):\n input = \"\"\"\n void main () {\n float arr[3];\n arr[2]=1.5;\n foo(arr);\n arr[2] = foo(arr)[2] + 1.1;\n putFloatLn(arr[2]);\n }\n float[] foo(float x[]){\n x[2] = 5.1;\n return x;\n ... | [
"0.5226535",
"0.52079296",
"0.51937526",
"0.5155106",
"0.51182663",
"0.5075775",
"0.505575",
"0.50432193",
"0.4988872",
"0.4984631",
"0.4984631",
"0.49769264",
"0.49674764",
"0.49674764",
"0.4951684",
"0.48429304",
"0.48406672",
"0.48227164",
"0.47971278",
"0.47961617",
"0.47... | 0.0 | -1 |
Arguments should be processed by a custom dispatcher and it should not be possible to get here | def _arg(self, t):
self.RaiseError(t, "Arguments should already have been processed") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __call__(self, args):",
"def handle_arguments(self, args):\n debug(\"BloomGenerator.handle_arguments: got args -> \" + str(args))",
"def _post_argument_parsing(self):\n pass",
"def dispatcher(self):\n pass # pragma: no cover",
"def __call__(self, args, kwargs):\n raise NotI... | [
"0.69573575",
"0.6790874",
"0.66730875",
"0.66662073",
"0.6665857",
"0.6653361",
"0.66326094",
"0.6623602",
"0.655339",
"0.65198654",
"0.6508264",
"0.6471017",
"0.6397061",
"0.63866425",
"0.63720274",
"0.63668007",
"0.6358945",
"0.6358945",
"0.6345801",
"0.6319397",
"0.631502... | 0.0 | -1 |
Arguments should be processed by a custom dispatcher and it should not be possible to get here | def _arguments(self, t):
self.RaiseError(t, "Arguments should already have been processed") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __call__(self, args):",
"def handle_arguments(self, args):\n debug(\"BloomGenerator.handle_arguments: got args -> \" + str(args))",
"def _post_argument_parsing(self):\n pass",
"def __call__(self, args, kwargs):\n raise NotImplementedError",
"def dispatcher(self):\n pass # p... | [
"0.695645",
"0.67914146",
"0.6672667",
"0.6665095",
"0.6663948",
"0.6652526",
"0.6631388",
"0.66237223",
"0.65519965",
"0.65194076",
"0.65079045",
"0.6469331",
"0.63968253",
"0.6386184",
"0.63703763",
"0.63667357",
"0.63573086",
"0.63573086",
"0.63446873",
"0.6319109",
"0.631... | 0.0 | -1 |
A function to visualize pymatgen Structure objects in jupyter notebook using chemview package. | def quick_view(structure, bonds=True, conventional=False, transform=None, show_box=True, bond_tol=0.2, stick_radius=0.1):
s = structure.copy()
if conventional:
s = SpacegroupAnalyzer(s).get_conventional_standard_structure()
if transform:
s.make_supercell(transform)
atom_types = [i.symb... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_ipython_repr_no_nglview(self):\n molecule = Molecule().from_smiles(\"CCO\")\n molecule._ipython_display_()",
"def jupyter():",
"def test_visualize_openeye(self):\n import IPython\n\n mol = Molecule().from_smiles(\"CCO\")\n\n assert isinstance(mol.visualize(backend=\"... | [
"0.64746124",
"0.6093616",
"0.6001484",
"0.6001484",
"0.5946787",
"0.58996487",
"0.58758247",
"0.5842013",
"0.5826114",
"0.5824589",
"0.5681209",
"0.56638604",
"0.56560427",
"0.5653275",
"0.5592476",
"0.5589071",
"0.5589071",
"0.557641",
"0.5573647",
"0.5561114",
"0.55604887"... | 0.6274498 | 1 |
Sets the latitude of the center of the map (in degrees North). | def lat(self):
return self['lat'] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def set_center_position(self, lon, lat):\n self.lon_center = lon\n self.lat_center = lat",
"def center_map(self):\n self.folium_map.location = _get_center_coords(self.locations)",
"def setLonLat(self, longitude, latitude):\n self._lon = longitude\n self._lat = latitude\n ... | [
"0.79386646",
"0.6982322",
"0.69601065",
"0.69601065",
"0.6958233",
"0.67998946",
"0.6750116",
"0.6718834",
"0.6718834",
"0.6718834",
"0.6657234",
"0.6521176",
"0.65120494",
"0.6503709",
"0.64779633",
"0.6468823",
"0.6463587",
"0.6361164",
"0.63329315",
"0.63317585",
"0.62532... | 0.52226204 | 85 |
Sets the longitude of the center of the map (in degrees East). | def lon(self):
return self['lon'] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def set_center_position(self, lon, lat):\n self.lon_center = lon\n self.lat_center = lat",
"def center_map(self):\n self.folium_map.location = _get_center_coords(self.locations)",
"def set_lon(self, lon):\n self._set_sub_text('lon', text=str(lon))\n return self",
"def longi... | [
"0.7713446",
"0.6939537",
"0.6829773",
"0.6798695",
"0.6644435",
"0.6644435",
"0.6644435",
"0.6521517",
"0.6521517",
"0.63733596",
"0.6272971",
"0.6196717",
"0.6163853",
"0.61490005",
"0.6020683",
"0.60018146",
"0.59674734",
"0.5966749",
"0.5953392",
"0.59224313",
"0.59046423... | 0.5577709 | 36 |
Construct a new Center object | def __init__(self, arg=None, lat=None, lon=None, **kwargs):
super(Center, self).__init__('center')
# Validate arg
# ------------
if arg is None:
arg = {}
elif isinstance(arg, self.__class__):
arg = arg.to_plotly_json()
elif isinstance(arg, dict):
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __init__(self, centre):\n super().__init__()\n self.centre = centre",
"def __init__(self, center=None, radius=1):\n if center is None:\n center = Point()\n self.center = center\n self.radius = radius",
"def __init__( self , center , radius ):\r\n self.ce... | [
"0.72278726",
"0.722529",
"0.71776736",
"0.6926115",
"0.6846305",
"0.67975366",
"0.67975366",
"0.67975366",
"0.67975366",
"0.664468",
"0.664468",
"0.6639922",
"0.64834195",
"0.64597523",
"0.64532304",
"0.64329094",
"0.64091885",
"0.6394365",
"0.63900244",
"0.6376003",
"0.6372... | 0.7048143 | 3 |
Remove the sprite from all lists and cancel the update event. | def remove_from_sprite_lists(self):
super().remove_from_sprite_lists()
# It is very important to call this to prevent potential
# issues such as crashes or excess memory use from failed
# garbage collection.
pyglet.clock.unschedule(self.update) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def update(self):\n # delete sprite if fired\n if not self.player.state == 'USE_A':\n self.game.all_sprites.remove(self)",
"def remove_sprites(self, *sprites):\r\n with self.lock:\r\n self.sprites_to_unload.update(sprites)",
"def _remove_texture(self):\n # Retrieve the i... | [
"0.766939",
"0.72574145",
"0.6607035",
"0.65096223",
"0.64890796",
"0.64082247",
"0.63706535",
"0.63706535",
"0.6319077",
"0.6264712",
"0.62010026",
"0.61186045",
"0.60208344",
"0.60023475",
"0.59555334",
"0.59235364",
"0.5921738",
"0.5916878",
"0.5907688",
"0.5894691",
"0.58... | 0.82141757 | 0 |
Update the graph by redrawing the internal texture data. | def update_graph(self, delta_time: float):
# Skip update if there is no SpriteList that can draw this graph
if self.sprite_lists is None or len(self.sprite_lists) == 0:
return
sprite_list = self.sprite_lists[0]
# Clear and return if timings are disabled
if not arca... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _redraw_graph(self) -> None:\n self._clear_drawing()\n self.draw_graph(graph=self.graph, axes=self.subplot)\n self.draw_graph(graph=self.graph2, axes=self.subplot2)\n self.draw_mappings(self.mapping)",
"def _update_current_graph(self, **kwargs):\n\n self.current_graph.redra... | [
"0.73301584",
"0.7203513",
"0.7171285",
"0.7122468",
"0.6981086",
"0.682984",
"0.6811901",
"0.6716891",
"0.66613007",
"0.65672034",
"0.6497443",
"0.6478494",
"0.6450372",
"0.64425576",
"0.63823295",
"0.6361715",
"0.6361715",
"0.63509274",
"0.634727",
"0.6336588",
"0.6336588",... | 0.6214974 | 28 |
Make sure that the wavelength solution gives same results on different runs. | def test_regression_determine_wavelength_solution(
ad, params, caplog, change_working_dir, path_to_refs, request):
caplog.set_level(logging.INFO, logger="geminidr")
with change_working_dir():
logutils.config(file_name='log_regress_{:s}.txt'.format(ad.data_label()))
p = GNIRSLongslit([ad... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def runWavelengthDependency():\n RunData([getFiles(mintime=(15, 39, 58), maxtime=(15, 47, 58), folder='data/30Jul/')[0],], out='I600nmwave',\n wavelength='l600')\n RunData([getFiles(mintime=(17, 48, 35), maxtime=(17, 56, 03), folder='data/30Jul/')[0],], out='I700nmwave',\n wavelength='l... | [
"0.6326733",
"0.62901336",
"0.62077874",
"0.6200481",
"0.61979157",
"0.61383563",
"0.6129043",
"0.61258394",
"0.61127585",
"0.6074986",
"0.606553",
"0.6044059",
"0.60430026",
"0.60421956",
"0.6021372",
"0.60169053",
"0.597903",
"0.59567064",
"0.5934162",
"0.59200054",
"0.5919... | 0.6112688 | 9 |
Returns the preprocessed spectrum file. | def ad(path_to_inputs, request):
filename = request.param
path = os.path.join(path_to_inputs, filename)
if os.path.exists(path):
ad = astrodata.open(path)
else:
raise FileNotFoundError(path)
return ad | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def preprocessing(filename):\n reporting(\"Preprocessing file...\", True)\n chdir(path.dirname(filename))\n (rate, sig) = wavefile.load(path.split(filename)[1])\n signal = sig[0]\n\n duration = len(signal) / rate\n reporting(f\"Done. Duration={duration}\")\n return signal",
"def spectrum(sel... | [
"0.6432614",
"0.6161507",
"0.60673326",
"0.5921605",
"0.59183276",
"0.58777124",
"0.5702783",
"0.56843203",
"0.5673299",
"0.56465805",
"0.5645744",
"0.56218576",
"0.55903924",
"0.5574906",
"0.55568427",
"0.5550424",
"0.5543234",
"0.55300075",
"0.55147284",
"0.55121267",
"0.54... | 0.0 | -1 |
Generate text file with test details. | def do_report(ad, ref_ad, failed):
output_dir = ("../DRAGONS_tests/geminidr/gnirs/longslit/"
"test_determine_wavelength_solution")
os.makedirs(output_dir, exist_ok=True)
report_filename = 'report.txt'
report_path = os.path.join(output_dir, report_filename)
ref_wavecal_model = am.g... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def generate_test_txt(name, path):\n with open(path + '/test.txt', 'a') as file:\n file.write('data/test/' + name + '\\n')",
"def write_tests(project_name, root_dir):\r\n test_path = get_file_path(root_dir, \"tests\", \"%s_tests.py\" % project_name) #Get the path for setup.py\r\n test_content = g... | [
"0.78161335",
"0.7021593",
"0.685751",
"0.6783171",
"0.6773141",
"0.6696793",
"0.65754807",
"0.6556282",
"0.65421546",
"0.652261",
"0.651013",
"0.65013415",
"0.64712375",
"0.64608437",
"0.641307",
"0.63824457",
"0.6368995",
"0.6334157",
"0.6326425",
"0.62804115",
"0.62656343"... | 0.0 | -1 |
Creates input data for tests using preprocessed standard star and its calibration files. The raw files will be downloaded and saved inside the path stored in the `$DRAGONS_TEST/raw_inputs` directory. Processed files will be stored inside a new folder called "dragons_test_inputs". The subdirectory structure should refle... | def create_inputs_recipe():
module_name, _ = os.path.splitext(os.path.basename(__file__))
path = os.path.join(CREATED_INPUTS_PATH_FOR_TESTS, module_name)
os.makedirs(path, exist_ok=True)
os.chdir(path)
os.makedirs("inputs/", exist_ok=True)
print('Current working directory:\n {:s}'.format(os.g... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def prepare_test_inputs(input_dir):\n # Prepare input parameters\n parameters = Dict(dict={})\n # example structure: bcc Fe\n structure = StructureData(cell=[[1.42002584, 1.42002584, 1.42002584],\n [1.42002584, -1.42002584, -1.42002584],\n ... | [
"0.6538091",
"0.6146276",
"0.607586",
"0.5977594",
"0.5945953",
"0.59351915",
"0.5910153",
"0.5886085",
"0.5845465",
"0.5794927",
"0.576355",
"0.574656",
"0.57456464",
"0.57011575",
"0.5694419",
"0.569211",
"0.5688554",
"0.5685619",
"0.56332463",
"0.56278634",
"0.5609137",
... | 0.7445018 | 0 |
This function checks that the ordering of the samples matches between the expression file and the metadata file. This ordering is used for calculating DEGs. | def compare_and_reorder_samples(expression_file, metadata_file):
# Check ordering of sample ids is consistent between gene expression data and metadata
metadata = pd.read_csv(metadata_file, sep="\t", header=0, index_col=0)
metadata_sample_ids = metadata.index
expression_data = pd.read_csv(expression_fi... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _check_file_sorting(observable_config_path: list[Path]) -> None:\n _names = list(map(lambda f: f.name, observable_config_path))\n _names_sorted = list(\n sorted(_names, key=lambda f: re.findall(r\"(\\d+).bin\", f)[0])\n )\n _is_match = [f0 == f1 for f0, f1 in zip(_names, _names_sorted)]\n ... | [
"0.57153094",
"0.5681696",
"0.56747234",
"0.56506944",
"0.56446517",
"0.5587243",
"0.5585911",
"0.5583389",
"0.5577927",
"0.5573355",
"0.55560887",
"0.55500454",
"0.5520259",
"0.5505986",
"0.5488156",
"0.546695",
"0.5445724",
"0.54166615",
"0.5415728",
"0.54112035",
"0.539252... | 0.8004621 | 0 |
This function reads in pseudomonas pathway data from `pathway_DB_filename` and formats and outputs it to `output_filename` in order to be used in GSEA_analysis.R | def format_pseudomonas_pathway_DB(pathway_DB_filename, local_dir, out_filename):
# Read in pathway data
pa_pathway_DB = pd.read_csv(
pathway_DB_filename,
names=["pathway id", "num genes", "genes"],
sep="\t",
header=None,
)
# Drop extra column
pa_pathway_DB.drop(colum... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def read_ripser_output(output_path,max_dim,output_name=None):\n # \\todo add persistence by density (columns pers by threshold and column pers by dens) ## only needed if input weighted network\n output_file_path =os.path.join(output_path,'output_ripser.txt')\n data = open(output_file_path,'rb').readlines(... | [
"0.5886894",
"0.5863955",
"0.5786592",
"0.5592899",
"0.558136",
"0.5495973",
"0.5433662",
"0.5431351",
"0.5404583",
"0.53899485",
"0.53796154",
"0.53388137",
"0.5241749",
"0.52406466",
"0.52103275",
"0.5181477",
"0.5170354",
"0.51633763",
"0.5160058",
"0.513753",
"0.5134894",... | 0.78855044 | 0 |
This function processes samples in the template and simulated experiments to prepare for DE analysis using DESeq. | def process_samples_for_limma(
expression_filename,
grp_metadata_filename,
out_expression_filename=None,
process_metadata_filename=None,
):
# Read data
expression = pd.read_csv(expression_filename, sep="\t", index_col=0, header=0)
if process_metadata_filename is not None:
process_me... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def prepare(params, samples):\r\n return",
"def _insertAllSteps(self): \n self.uMics = self.inputCoordinatesTiltedPairs.get().getUntilted().getMicrographs()\n self.tMics = self.inputCoordinatesTiltedPairs.get().getTilted().getMicrographs()\n\n self.inputMics = self._createSetOfParti... | [
"0.5883362",
"0.5755976",
"0.57489634",
"0.56756586",
"0.5667148",
"0.56626016",
"0.5629808",
"0.5613106",
"0.55905974",
"0.5588201",
"0.5555386",
"0.555308",
"0.55530614",
"0.55265236",
"0.5500799",
"0.5494181",
"0.54867214",
"0.54662234",
"0.5421914",
"0.54192924",
"0.54029... | 0.0 | -1 |
This function processes samples in the template and simulated experiments to prepare for DE analysis using DESeq. | def process_samples_for_DESeq(
expression_filename,
grp_metadata_filename,
out_expression_filename=None,
count_threshold=None,
process_metadata_filename=None,
):
# Read data
expression = pd.read_csv(expression_filename, sep="\t", index_col=0, header=0)
if process_metadata_filename is no... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def prepare(params, samples):\r\n return",
"def _insertAllSteps(self): \n self.uMics = self.inputCoordinatesTiltedPairs.get().getUntilted().getMicrographs()\n self.tMics = self.inputCoordinatesTiltedPairs.get().getTilted().getMicrographs()\n\n self.inputMics = self._createSetOfParti... | [
"0.5884892",
"0.5756187",
"0.57483244",
"0.5675142",
"0.5668464",
"0.56622994",
"0.5629105",
"0.5612436",
"0.5591457",
"0.5587859",
"0.5554776",
"0.55546945",
"0.5554384",
"0.552562",
"0.5499063",
"0.5493435",
"0.548793",
"0.5467298",
"0.54216987",
"0.54175216",
"0.5401552",
... | 0.5284362 | 33 |
Compute the hash of a parsed JSON value using the given hash object. This function does not hash the JSON value, it hashes the object tree that is the result of parsing a string in JSON format. Hashables (JSON objects) are hashed entry by entry in order of the lexicographical ordering on the keys. Iterables are hashed ... | def hash_json( hash_obj, value ):
try:
items = iter(list(value.items( )))
except AttributeError:
# Must check for string before testing iterability since strings are iterable
if isinstance( value, str ):
_hash_string( hash_obj, value )
else:
try:
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_hash_json(self):\n # pre-sorted str object\n self.assertEqual('5348ed1f4cd2f73e576bb66b866f2800', \\\n comparator.hash_json('{\"a_1\": [{\"a_2\": 2, \"f_2\": 3, \"g_2\": 1}], \"c_3\": 1}'))\n # pre-sorted dict object\n self.assertEqual('5348ed1f4cd2f73e576bb66b866f28... | [
"0.65041846",
"0.6385206",
"0.6255319",
"0.62435746",
"0.6187472",
"0.6180679",
"0.6165562",
"0.6159233",
"0.6080034",
"0.5938248",
"0.59185636",
"0.5907186",
"0.5907186",
"0.5907186",
"0.5907186",
"0.5907186",
"0.58739746",
"0.58451295",
"0.58451295",
"0.58178645",
"0.581098... | 0.79588073 | 0 |
Remove selected items from the tree. Because data is stored separately also need to deal with it, but deleting the matching items from the data list and updating all of the data indexes is a bit of a headache, so just make them empty. | def remove_treeItem(browser, tree):
items = tree.selectedItems()
for item in items:
if item.listIndex: # Only dataset items have a listIndex
browser.ui.workingDataTree.dataItems[item.listIndex] = []
sip.delete(item) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def remove_selected(self):\n if not self.tree_widget.selectedItems():\n self.configuration_widgets.logger.warning('Nothing has been selected. Please select an item and try again.')\n return\n _selected_items = self.tree_widget.selectedItems()\n root = self.tree_widget.inv... | [
"0.72222024",
"0.67945594",
"0.6788877",
"0.6749571",
"0.66043264",
"0.6599819",
"0.65675104",
"0.6546712",
"0.6505644",
"0.634928",
"0.6347942",
"0.6327042",
"0.632324",
"0.63223594",
"0.62746567",
"0.6272785",
"0.6232611",
"0.6196286",
"0.61239",
"0.6118763",
"0.6097092",
... | 0.72647107 | 0 |
Clone h5 item. Useful for Drag & Drop | def clone_item(item):
i = h5Item(item.text(0))
i.path = item.path
i.listIndex = item.dataIndex
i.originalIndex = item.originalIndex
i.data = item.data
return i | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def clone(self):",
"def clone(self):\n raise NotImplementedError",
"def copy(self, h5file=None):\n h5 = qpimage.core.copyh5(self.h5, h5file)\n return FLImage(h5file=h5, h5dtype=self.h5dtype)",
"def copy(self):\n new_h5 = FileHDFio(file_name=self.file_name, h5_path=self.h5_path)\n ... | [
"0.6020959",
"0.58370215",
"0.5827161",
"0.5780741",
"0.57302743",
"0.57141834",
"0.568242",
"0.5659609",
"0.5652022",
"0.56075025",
"0.55323535",
"0.5526989",
"0.5505679",
"0.5489802",
"0.54832345",
"0.5482787",
"0.5463926",
"0.53882384",
"0.5372098",
"0.5372057",
"0.533904"... | 0.74541837 | 0 |
Helper function to convert SPARQL results into a Pandas data frame. | def get_sparql_dataframe(query, service = "https://query.wikidata.org/sparql"):
sparql = SPARQLWrapper(service)
sparql.setQuery(query)
sparql.setReturnFormat(JSON)
result = sparql.query()
processed_results = json.load(result.response)
cols = processed_results['head']['vars']
out = []
f... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_sparql_dataframe(service, query):\n sparql = SPARQLWrapper(service)\n sparql.setQuery(query)\n sparql.setReturnFormat(JSON)\n result = sparql.query()\n\n processed_results = json.load(result.response)\n cols = processed_results['head']['vars']\n\n out = []\n... | [
"0.7584528",
"0.7129623",
"0.7129623",
"0.711426",
"0.7094009",
"0.7050637",
"0.70471936",
"0.6952182",
"0.6945529",
"0.6940668",
"0.6868899",
"0.6827423",
"0.6801506",
"0.6724016",
"0.67137694",
"0.6685681",
"0.6672927",
"0.6663773",
"0.66266656",
"0.6613739",
"0.6602476",
... | 0.7614764 | 0 |
Build a dictionary describing a music album | def make_album(artist_name, album_title):
music_album = {
'Artist': artist_name.title(),
'Album': album_title.title()
}
return music_album | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def make_album(artist, title, songs=None):\n album = {}\n album['artist'] = artist\n album['title'] = title\n if songs:\n album['songs'] = songs\n return album",
"def make_album(artist, title):\n album_dict = {\n 'artist': artist.title(),\n 'title': title.title(),\n ... | [
"0.78110856",
"0.77839744",
"0.7685824",
"0.76828676",
"0.76828676",
"0.7542805",
"0.75344175",
"0.7530572",
"0.7466792",
"0.7411964",
"0.735979",
"0.73411816",
"0.73353547",
"0.73207825",
"0.730691",
"0.728729",
"0.7213423",
"0.71799314",
"0.710353",
"0.7094194",
"0.7069114"... | 0.7782399 | 2 |
Build a dictionary describing a music album | def make_album_two(artist_name, album_title, number_of_songs= None):
music_album = {'Artist': artist_name.title(),
'Album': album_title.title()}
if number_of_songs:
music_album['Number of Songs'] = number_of_songs
return music_album | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def make_album(artist, title, songs=None):\n album = {}\n album['artist'] = artist\n album['title'] = title\n if songs:\n album['songs'] = songs\n return album",
"def make_album(artist, title):\n album_dict = {\n 'artist': artist.title(),\n 'title': title.title(),\n ... | [
"0.78110856",
"0.77839744",
"0.7782399",
"0.7685824",
"0.76828676",
"0.76828676",
"0.7542805",
"0.75344175",
"0.7530572",
"0.7466792",
"0.7411964",
"0.735979",
"0.73411816",
"0.73353547",
"0.73207825",
"0.730691",
"0.728729",
"0.7213423",
"0.71799314",
"0.710353",
"0.7094194"... | 0.68839395 | 23 |
Build a set of resources who are available for a given time. It might make more sense to work based on a given restricted resource set. | def avail(self, time, resource_group):
a = set()
for r in self.resource_group.resources:
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _filter_resources_by_age(self, resources: [], resource_age_minutes: int):\n all_resources = []\n for resource in resources:\n if resource_age_minutes:\n start = self._to_utc_datetime(resource.updated_on)\n end = datetime.utcnow().replace(tzinfo=pytz.UTC)\n... | [
"0.6184426",
"0.60298663",
"0.5953174",
"0.5767035",
"0.5638926",
"0.5493595",
"0.54747224",
"0.5458828",
"0.54446715",
"0.5438135",
"0.53841877",
"0.53665984",
"0.5353068",
"0.53493536",
"0.53424084",
"0.53357965",
"0.53341115",
"0.5326668",
"0.53145623",
"0.5271738",
"0.526... | 0.73468834 | 0 |
Test to see if the mongodb client logger can persist a log entry to the database | def test_mongo_logging_client_persists_log():
error_message = "This is a test message."
logger = LoggingService(console_output=True)
result = logger.log(LogEntry(LogLevel.ERROR, __name__, error_message))
logger.log(LogEntry(LogLevel.WARN, __name__, error_message))
logger.log(LogEntry(LogLevel.INFO... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_create_log(self):\n message = \"Message is {0}\".format(random.random())\n resp = gracedb.writeLog(eventId, message)\n self.assertEqual(resp.status, 201)\n new_log_uri = resp.getheader('Location')\n new_log = resp.json()\n self.assertEqual(new_log_uri, new_log['se... | [
"0.6045933",
"0.586778",
"0.5836898",
"0.5810724",
"0.57567275",
"0.56929016",
"0.56574714",
"0.5656057",
"0.5636488",
"0.5636488",
"0.5576545",
"0.55317235",
"0.5509709",
"0.54728764",
"0.5472665",
"0.5462667",
"0.54399234",
"0.53963417",
"0.53956026",
"0.5391641",
"0.538321... | 0.6278423 | 0 |
API.Java is the file with most revisions and fixes in the bug introducing change. | def test_revisions_weight(self):
solution = FeatureWeightLearner(self.repository, generations=70).learn()
self.assertGreater(solution["revisions"], solution["fixes"]) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def api(self) -> str:",
"def patch_sdk():",
"def patch_sdk():",
"def patch_sdk():",
"def write_updated_content(filename, updated_jdoc):\n\n with open(filename, 'r+') as f:\n java_doc_location = find_javadoc(filename)\n original_text = f.read()\n\n if java_doc_location is None:\n ... | [
"0.59695005",
"0.5921598",
"0.5921598",
"0.5921598",
"0.5599607",
"0.5551672",
"0.5551672",
"0.549483",
"0.5482901",
"0.54320264",
"0.533171",
"0.533171",
"0.533171",
"0.533171",
"0.533171",
"0.5311816",
"0.526028",
"0.5249579",
"0.5245414",
"0.5222134",
"0.52081716",
"0.51... | 0.0 | -1 |
Place the piece on a board at the provided linear position. | def __init__(self, board, index):
self.board = board
self.index = index
self._x, self._y = None, None | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def place_at(self, row, col, piece):\n self.board[row + PADDING][col + PADDING] = piece",
"def place_piece(piece, px, py, pc):\n \n\n for i, j in piece:\n x = px + i\n y = py + j\n if not (0 <= x < BOARD_WIDTH):\n continue\n if not (0 <= y < BOARD_HEIGHT):\n ... | [
"0.76690376",
"0.7182792",
"0.7109724",
"0.70972294",
"0.68987334",
"0.68776935",
"0.68297994",
"0.6791927",
"0.6777104",
"0.66752934",
"0.66641605",
"0.65767026",
"0.6527156",
"0.6510956",
"0.6510956",
"0.6492618",
"0.64556587",
"0.6382593",
"0.637511",
"0.63017607",
"0.6263... | 0.0 | -1 |
Display all relevant object internals. | def __repr__(self):
return (
'<{}: uid={}; label={}, symbol={}; x={}, y={}; index={}>'.format(
self.__class__.__name__,
self.uid, self.label, self.symbol,
self.x, self.y, self.index)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def print_object_details(obj: object) -> None:\n print_section(obj, 'Type', print_type)\n print_section(obj, 'Documentation', print_documentation)\n print_section(obj, 'Attributes', print_attributes)\n print_section(obj, 'Methods', print_methods)\n print_section_delimiter()",
"def print_objects(se... | [
"0.7177779",
"0.7122019",
"0.70775235",
"0.6984967",
"0.67244166",
"0.6650569",
"0.66377944",
"0.662285",
"0.6608439",
"0.65655065",
"0.6540253",
"0.6531621",
"0.6511373",
"0.6501881",
"0.6458379",
"0.64282763",
"0.6425996",
"0.6378596",
"0.6353891",
"0.6325332",
"0.6325332",... | 0.0 | -1 |
Compute 2D coordinates of the piece. | def compute_coordinates(self):
self._x, self._y = self.board.index_to_coordinates(self.index) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _coord(self, x, y):\n gridEdge = 7 # originally 5\n y = gridEdge - y\n cx = 100 * (x - 1) + 50\n cy = 100 * (y - 1) + 50\n r = 20\n return (cx - r, cy - r, cx + r, cy + r)",
"def coords2D(self):\n return (self.x, self.y)",
"def pixel2coords(self, x... | [
"0.73788416",
"0.7368451",
"0.69669753",
"0.69285214",
"0.6886795",
"0.68646175",
"0.68372095",
"0.67921597",
"0.6789865",
"0.6781801",
"0.67475146",
"0.6724371",
"0.66918415",
"0.6677662",
"0.66761553",
"0.6613176",
"0.6612401",
"0.6603495",
"0.6580332",
"0.656608",
"0.65619... | 0.71683186 | 2 |
Return the piece's horizontal position. Property is used here so we only compute position once when needed. | def x(self):
if self._x is None:
self.compute_coordinates()
return self._x | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_horizontal(self, position):\n pass",
"def get_horizontal(self, x, y):\n\n return self._board[y]",
"def horizontal(self):\n return self._horizontal",
"def _get_x(self):\n return self.position.x",
"def get_x_position(self):\n return self.rect.x",
"def horiz_center... | [
"0.7555169",
"0.71737736",
"0.68906856",
"0.67113984",
"0.66651046",
"0.6657736",
"0.6657736",
"0.66271603",
"0.6555104",
"0.6521626",
"0.64567375",
"0.63844174",
"0.6338153",
"0.6338153",
"0.6300035",
"0.6295312",
"0.6227775",
"0.6223017",
"0.62109965",
"0.620136",
"0.615026... | 0.0 | -1 |
Return the piece's vertical position. Property is used here so we only compute position once when needed. | def y(self):
if self._y is None:
self.compute_coordinates()
return self._y | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_y_position(self): \n return self.rect.y",
"def _get_y(self):\n return self.position.y",
"def get_y(self):\n return self.posY",
"def get_pos_y(self):\n return self.__pos_y",
"def get_y_position(self):\n return self.actual_coordinates[1]",
"def y(self):\r\n... | [
"0.72299945",
"0.71392894",
"0.70769536",
"0.69982135",
"0.6946308",
"0.69392604",
"0.67431444",
"0.6726614",
"0.6718979",
"0.67096394",
"0.668686",
"0.66658616",
"0.6653479",
"0.6539394",
"0.65354824",
"0.6437881",
"0.6415131",
"0.6410589",
"0.6389794",
"0.63825667",
"0.6372... | 0.0 | -1 |
Number of squares separating the piece from board's bottom edge. | def bottom_distance(self):
return self.board.height - 1 - self.y | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_num_black_pieces(self):\n return self.num_black_pieces",
"def columns(self) -> int:\n return self.__squares[0].__len__()",
"def bottom_height_px(self):\n return self.bottom_pieces * PipePair.PIECE_HEIGHT",
"def get_size(self):\n return len(self.board)",
"def _adjacent_bl... | [
"0.69117856",
"0.6843006",
"0.6691296",
"0.65614504",
"0.6522529",
"0.65193826",
"0.6504261",
"0.646451",
"0.6464343",
"0.6431834",
"0.6429351",
"0.6374707",
"0.63672584",
"0.6351437",
"0.63387626",
"0.6334059",
"0.6305897",
"0.6291991",
"0.6206564",
"0.62018037",
"0.6156334"... | 0.58802783 | 45 |
Number of squares separating the piece from board's right edge. | def right_distance(self):
return self.board.length - 1 - self.x | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def columns(self) -> int:\n return self.__squares[0].__len__()",
"def num_pieces_left(self):\n return self.num_white_pieces + self.num_black_pieces",
"def number_of_their_pieces_to_right(column):\n row = __get_top_of_stack(column)\n return number_pieces_of_type_in_direction(column, row, THE... | [
"0.68224204",
"0.67955893",
"0.67566025",
"0.6721409",
"0.67032707",
"0.6690386",
"0.6673122",
"0.6567716",
"0.65650105",
"0.6469845",
"0.63960135",
"0.63398033",
"0.6330938",
"0.63127047",
"0.6299541",
"0.62951994",
"0.6247025",
"0.6244428",
"0.618134",
"0.61776143",
"0.6147... | 0.6083372 | 25 |
Number of squares separating the piece from board's top edge. | def top_distance(self):
return self.y | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_num_black_pieces(self):\n return self.num_black_pieces",
"def num_pieces_left(self):\n return self.num_white_pieces + self.num_black_pieces",
"def columns(self) -> int:\n return self.__squares[0].__len__()",
"def get_num_white_pieces(self):\n return self.num_white_pieces",... | [
"0.714511",
"0.6862219",
"0.6851472",
"0.67452055",
"0.6723331",
"0.66431844",
"0.65715116",
"0.6539787",
"0.6536414",
"0.64877564",
"0.6469646",
"0.64569587",
"0.64565516",
"0.64425284",
"0.6439542",
"0.6425374",
"0.6407991",
"0.63659966",
"0.63487965",
"0.6333705",
"0.63302... | 0.0 | -1 |
Number of squares separating the piece from board's left edge. | def left_distance(self):
return self.x | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def num_pieces_left(self):\n return self.num_white_pieces + self.num_black_pieces",
"def get_pieces_left(board, piece):\r\n\tpieces = 0\r\n\tfor row in board:\r\n\t\tfor col in row:\r\n\t\t\tif col == piece:\r\n\t\t\t\tpieces += 1\r\n\r\n\treturn pieces",
"def number_of_their_pieces_to_left(column):\n ... | [
"0.79140216",
"0.7175402",
"0.6958002",
"0.6864814",
"0.6846879",
"0.6766236",
"0.6750894",
"0.66248316",
"0.65929973",
"0.65737724",
"0.64028585",
"0.6343601",
"0.6329777",
"0.6307301",
"0.6228556",
"0.62279946",
"0.6163106",
"0.6096209",
"0.6093313",
"0.6085473",
"0.607297"... | 0.0 | -1 |
All horizontal squares from the piece's point of view. Returns a list of relative movements up to the board's bound. | def horizontals(self):
horizontal_shifts = set(izip_longest(map(
lambda i: i - self.x, range(self.board.length)), [], fillvalue=0))
horizontal_shifts.discard((0, 0))
return horizontal_shifts | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def spanning_squares(self):\n spanning = []\n for i in range(self.length):\n # Assume ACROSS and DOWN are the only valid directions\n if self.direction == \"ACROSS\":\n spanning.append((self.start_x + i, self.start_y))\n else:\n spanning.... | [
"0.6494722",
"0.64518917",
"0.6354893",
"0.63426304",
"0.63299483",
"0.6264752",
"0.6235491",
"0.6193277",
"0.6177784",
"0.6174575",
"0.61528724",
"0.6148699",
"0.61453056",
"0.6122243",
"0.6096462",
"0.6087461",
"0.60867596",
"0.60618335",
"0.6006694",
"0.59964144",
"0.59660... | 0.7379711 | 0 |
All vertical squares from the piece's point of view. Returns a list of relative movements up to the board's bound. | def verticals(self):
vertical_shifts = set(izip_longest([], map(
lambda i: i - self.y, range(self.board.height)), fillvalue=0))
vertical_shifts.discard((0, 0))
return vertical_shifts | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_vertical(self, x, y):\n\n return [row[x] for row in self._board]",
"def spanning_squares(self):\n spanning = []\n for i in range(self.length):\n # Assume ACROSS and DOWN are the only valid directions\n if self.direction == \"ACROSS\":\n spanning.a... | [
"0.6549495",
"0.65164524",
"0.6475518",
"0.6370953",
"0.63641816",
"0.63362104",
"0.6315978",
"0.62523115",
"0.6206709",
"0.6104576",
"0.6071588",
"0.60655314",
"0.6062132",
"0.60403407",
"0.60398203",
"0.6025194",
"0.5976983",
"0.5959147",
"0.59579796",
"0.5956464",
"0.59492... | 0.7238823 | 0 |
All diagonal squares from the piece's point of view. Returns a list of relative movements up to the board's bound. | def diagonals(self):
left_top_shifts = map(lambda i: (-(i + 1), -(i + 1)), range(min(
self.left_distance, self.top_distance)))
left_bottom_shifts = map(lambda i: (-(i + 1), +(i + 1)), range(min(
self.left_distance, self.bottom_distance)))
right_top_shifts = map(lambda i: ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_legal_moves(self):\n moves = []\n if self.player_locations[self.whose_turn] is None:\n return self.get_blank_locations()\n matrix = [(1,0), (-1,0), (0,1), (0,-1), (1,1), (1,-1), (-1, 1), (-1,-1)]\n\n for dx, dy in matrix:\n x,y = self.player_locations[self.... | [
"0.7041069",
"0.68426985",
"0.6820618",
"0.67042565",
"0.6688481",
"0.66436976",
"0.6637318",
"0.6608051",
"0.66066635",
"0.65922946",
"0.65870273",
"0.6556292",
"0.65425926",
"0.6470519",
"0.64307237",
"0.64034235",
"0.63913053",
"0.6317035",
"0.6294141",
"0.6290895",
"0.628... | 0.6688425 | 5 |
Return list of relative movements allowed. | def movements(self):
raise NotImplementedError | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getMovableRange(self, unit):\n CostArr_mod = modifyMovCost(CostArr, ability)\n Obstacles = self.getUnpassable(player) # units that are not passable....\n pos_list, path_list = UCS_solve(unit.pos, CostArr_mod, unit.MovPnt)\n return pos_list, path_list",
"def all_rel_actions(self, p... | [
"0.6335595",
"0.6219791",
"0.61731637",
"0.6113557",
"0.6098801",
"0.6082442",
"0.6076151",
"0.60303247",
"0.59810054",
"0.59727407",
"0.59463936",
"0.59408045",
"0.5901733",
"0.58617115",
"0.58573633",
"0.58515835",
"0.58431864",
"0.58416694",
"0.5836999",
"0.5812548",
"0.58... | 0.6397189 | 0 |
Return the cached territory occupied by the piece. | def territory(self):
cache_key = (
self.board.length, self.board.height, self.uid, self.index)
if cache_key not in self.territory_cache:
vector = self.compute_territory()
self.territory_cache[cache_key] = vector
else:
vector = self.territory_cache[... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def compute_territory(self):\n # Initialize the square occupancy vector of the board.\n vector = self.board.new_vector()\n\n # Mark current position as reachable.\n vector[self.index] = True\n\n # List all places reacheable by the piece from its current position.\n for x_s... | [
"0.6026444",
"0.59270763",
"0.5713847",
"0.56876725",
"0.5581824",
"0.5523887",
"0.5491418",
"0.54348814",
"0.54129577",
"0.5392716",
"0.5293671",
"0.5249759",
"0.5236839",
"0.5229331",
"0.5221467",
"0.5203362",
"0.51952547",
"0.5169698",
"0.5168036",
"0.5155035",
"0.51431674... | 0.6969132 | 0 |
Compute territory reachable by the piece from its current position. Returns a list of boolean flags of squares indexed linearly, for which a True means the square is reachable. | def compute_territory(self):
# Initialize the square occupancy vector of the board.
vector = self.board.new_vector()
# Mark current position as reachable.
vector[self.index] = True
# List all places reacheable by the piece from its current position.
for x_shift, y_shift... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def find_moves(self):\n\n from itertools import product\n free_position = self.find_free()\n return [list(free_position+i) for i in [[0,1],[1,0],[-1,0],[0,-1]] if tuple(i+free_position) in product(range(self.size),repeat=2)]",
"def _get_rules_possibles_moves(cell, board_shape):\n retu... | [
"0.5903241",
"0.5804372",
"0.57658273",
"0.57489616",
"0.5739321",
"0.5720743",
"0.5670181",
"0.5648418",
"0.5639911",
"0.5635544",
"0.5632291",
"0.5622091",
"0.5598855",
"0.559796",
"0.5573405",
"0.55529225",
"0.5552635",
"0.5536598",
"0.55041766",
"0.55024284",
"0.54945725"... | 0.6670306 | 0 |
Generate M3U file for the given software into out_dir | def generate(software, out_dir, suffix, dry_run):
m3u_filename = software.name + (suffix if suffix else '') + '.m3u'
if not dry_run:
m3u_fd = open(os.path.join(out_dir, m3u_filename), 'w')
for i in software.images():
image_rel_path = os.path.relpath(i.path, out_dir)
if not dry_run... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_output_matrix_files(self, year, max_zone_id):\r\n from opus_emme2.travel_model_output import TravelModelOutput\r\n tm_output = TravelModelOutput(self.emme_cmd)\r\n year_config = self.config['travel_model_configuration'][year]\r\n for x in 1,2,3:\r\n if \"bank%i\" %... | [
"0.5642974",
"0.5558042",
"0.5551027",
"0.5361542",
"0.53559524",
"0.5301094",
"0.5252762",
"0.5241855",
"0.5204623",
"0.5158686",
"0.51586396",
"0.5136852",
"0.51365227",
"0.51075536",
"0.50907314",
"0.5074185",
"0.50671935",
"0.50445044",
"0.5027117",
"0.5024382",
"0.502100... | 0.8317543 | 0 |
Generate M3U file for the list of softwares into out_dir | def generate_all(softwares, out_dir, suffix, dry_run):
if not dry_run:
if not out_dir.exists():
out_dir.mkdir(parents=True)
multi_images_softwares = (x for x in softwares if x.nb_images() > 1)
for i in multi_images_softwares:
try:
generate(i, out_dir, suf... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def generate(software, out_dir, suffix, dry_run):\n m3u_filename = software.name + (suffix if suffix else '') + '.m3u'\n\n if not dry_run:\n m3u_fd = open(os.path.join(out_dir, m3u_filename), 'w')\n\n for i in software.images():\n image_rel_path = os.path.relpath(i.path, out_dir)\n\n ... | [
"0.7786916",
"0.5748073",
"0.5662895",
"0.5528735",
"0.55101776",
"0.5463695",
"0.5421654",
"0.5380845",
"0.53311723",
"0.5303696",
"0.52977276",
"0.52798694",
"0.5260766",
"0.52486426",
"0.5233821",
"0.51944816",
"0.5123264",
"0.5093341",
"0.5092315",
"0.50845486",
"0.506893... | 0.59200364 | 1 |
Set up Wiser climate device. | async def async_setup_entry(hass, config_entry, async_add_entities):
data = hass.data[DOMAIN][config_entry.entry_id][DATA] # Get Handler
wiser_numbers = []
_LOGGER.debug("Setting up Away Mode setpoint setter")
wiser_numbers.extend(
[WiserAwayModeTempNumber(data, "Away Mode Target Temperature")... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _setup_sensor ( self ):\n self.spectral = Spectral ( np.array([450, 520, 630, 770., 1550, 2090.] ),\n np.array([ 520, 600, 690, 900., 1750., 2350.] ) )",
"def _setup_sensor ( self ):\n self.spectral = Spectral ( np.array([500, 610, 780, 1580.] ),\n ... | [
"0.59758526",
"0.5945028",
"0.5865419",
"0.5819859",
"0.5804272",
"0.5767758",
"0.57342315",
"0.56519085",
"0.56142",
"0.5603683",
"0.55886334",
"0.55398846",
"0.5516714",
"0.55021566",
"0.54558253",
"0.5444716",
"0.5435281",
"0.5406201",
"0.53743196",
"0.5360252",
"0.5341247... | 0.0 | -1 |
Handle updated data from the coordinator. | def _handle_coordinator_update(self) -> None:
_LOGGER.debug(f"{self.name} updating")
self._value = self._data.wiserhub.system.away_mode_target_temperature
# Support prior to 2022.7.0 Versions without deprecation warning
if hasattr(self, "_attr_value"):
self._attr_value = self... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _handle_coordinator_update(self) -> None:\n self._update_data()\n self.async_write_ha_state()",
"def _handle_coordinator_update(self) -> None:\n self._update_from_rest_data()\n self.async_write_ha_state()",
"def _handle_coordinator_update(self) -> None:\n self.update_from... | [
"0.8382766",
"0.83799595",
"0.8251621",
"0.79713356",
"0.7916281",
"0.7603569",
"0.7557993",
"0.7358644",
"0.7336086",
"0.7178118",
"0.7178118",
"0.7178118",
"0.7178118",
"0.7113253",
"0.6962891",
"0.6899875",
"0.6793388",
"0.67592835",
"0.67307264",
"0.66545016",
"0.6622291"... | 0.7118628 | 13 |
Return the minimum value. | def native_min_value(self) -> float:
return TEMP_MINIMUM | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _get_minimum(self):\n return self._minimum",
"def find_min(self):\n return self.min",
"def find_min(self):\n return self.min",
"def min(self) -> pulumi.Input[int]:\n return pulumi.get(self, \"min\")",
"def min(self) -> pulumi.Input[int]:\n return pulumi.get(self, \"mi... | [
"0.87291557",
"0.8631156",
"0.8631156",
"0.85999566",
"0.85999566",
"0.85038835",
"0.8491322",
"0.8482031",
"0.8466204",
"0.8466204",
"0.8432562",
"0.8430932",
"0.8430932",
"0.8421418",
"0.8405366",
"0.8311868",
"0.82909995",
"0.8286746",
"0.8284653",
"0.8281578",
"0.8255062"... | 0.8165567 | 22 |
Return the maximum value. | def native_max_value(self) -> float:
return TEMP_MAXIMUM | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_max_value(self):\n max_value = max(self.values)\n return max_value",
"def _get_maximum(self):\n return self._maximum",
"def get_max(self):\n return self._max",
"def get_max(self):\n return self.max[-1]",
"def _get_maximum_value(self):\n if hasattr(self, '_m... | [
"0.8933136",
"0.88841957",
"0.87473977",
"0.861131",
"0.8606676",
"0.85935724",
"0.8586842",
"0.8532539",
"0.8532539",
"0.8495911",
"0.8495911",
"0.8488697",
"0.8487578",
"0.8399635",
"0.83845997",
"0.836424",
"0.83359325",
"0.8262177",
"0.8261219",
"0.8261219",
"0.8256761",
... | 0.8079731 | 31 |
Return the mode of the entity. | def mode(self) -> NumberMode:
return NumberMode.AUTO | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_mode(self, ):\n return self.get_parameter('mode')",
"def get_mode(self):\r\n return self.mode",
"def mode(self):\n return self._lift(\"mode\")",
"def mode(self):\n return self._data.get('mode', None)",
"def getmode(self):\n return self.mode",
"def mode(self):\n ... | [
"0.79294884",
"0.7913022",
"0.7889855",
"0.7879603",
"0.78356427",
"0.78341234",
"0.78341234",
"0.78341234",
"0.7831721",
"0.7831721",
"0.7831721",
"0.781534",
"0.78104544",
"0.7787036",
"0.7783009",
"0.777116",
"0.7756425",
"0.7699104",
"0.7604584",
"0.7604584",
"0.7558141",... | 0.0 | -1 |
Return Name of device. | def name(self):
return f"{get_device_name(self._data, 0, self._name)}" | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def name(self):\n return self._device.name",
"def name(self):\n return self._device.name",
"def name(self):\n return self._device.name",
"def name(self):\n return self.device.name()",
"def name(self):\n return self.device.device_data[self.device_id]['name']",
"def devic... | [
"0.891022",
"0.891022",
"0.891022",
"0.8865886",
"0.87226844",
"0.8687004",
"0.8687004",
"0.8627558",
"0.8609878",
"0.8509157",
"0.8509157",
"0.8476352",
"0.84732556",
"0.8449398",
"0.8261127",
"0.8234234",
"0.8221397",
"0.81503457",
"0.81250376",
"0.8083982",
"0.79021084",
... | 0.8831564 | 4 |
Return device specific attributes. | def device_info(self):
return {
"name": get_device_name(self._data, 0),
"identifiers": {(DOMAIN, get_identifier(self._data, 0))},
"manufacturer": MANUFACTURER,
"model": self._data.wiserhub.system.product_type,
"sw_version": self._data.wiserhub.system.f... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def attributes(self, device_id=0):\n\t\t\treturn cuda.Device(device_id).get_attributes()",
"def device_info(self) -> Optional[Dict[str, Any]]:\n return {ATTR_NAME: self.name, \"identifiers\": {(DOMAIN, self._device.device_id)}}",
"def device_state_attributes(self):\n attrs = {\n ATTR_A... | [
"0.80724686",
"0.7450437",
"0.742771",
"0.7409806",
"0.7293218",
"0.72523683",
"0.72523683",
"0.719905",
"0.7175151",
"0.7155777",
"0.7155777",
"0.71476424",
"0.7145971",
"0.71375567",
"0.7095317",
"0.7051197",
"0.703671",
"0.7035435",
"0.7017325",
"0.7013673",
"0.70050514",
... | 0.6574845 | 83 |
Handle updated data from the coordinator. | def _handle_coordinator_update(self) -> None:
_LOGGER.debug(f"{self.name} updating")
self._value = getattr(self._actuator.floor_temperature_sensor, self._name)
# Support prior to 2022.7.0 Versions without deprecation warning
if hasattr(self, "_attr_value"):
self._attr_value =... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _handle_coordinator_update(self) -> None:\n self._update_data()\n self.async_write_ha_state()",
"def _handle_coordinator_update(self) -> None:\n self._update_from_rest_data()\n self.async_write_ha_state()",
"def _handle_coordinator_update(self) -> None:\n self.update_from... | [
"0.8382766",
"0.83799595",
"0.8251621",
"0.79713356",
"0.7916281",
"0.7603569",
"0.7557993",
"0.7358644",
"0.7178118",
"0.7178118",
"0.7178118",
"0.7178118",
"0.7118628",
"0.7113253",
"0.6962891",
"0.6899875",
"0.6793388",
"0.67592835",
"0.67307264",
"0.66545016",
"0.6622291"... | 0.7336086 | 8 |
Return the minimum value. | def native_min_value(self) -> float:
return -9 | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _get_minimum(self):\n return self._minimum",
"def find_min(self):\n return self.min",
"def find_min(self):\n return self.min",
"def min(self) -> pulumi.Input[int]:\n return pulumi.get(self, \"min\")",
"def min(self) -> pulumi.Input[int]:\n return pulumi.get(self, \"mi... | [
"0.87291557",
"0.8631156",
"0.8631156",
"0.85999566",
"0.85999566",
"0.85038835",
"0.8491322",
"0.8482031",
"0.8466204",
"0.8466204",
"0.8432562",
"0.8430932",
"0.8430932",
"0.8421418",
"0.8405366",
"0.8311868",
"0.82909995",
"0.8286746",
"0.8284653",
"0.8281578",
"0.8255062"... | 0.7826625 | 38 |
Return the maximum value. | def native_max_value(self) -> float:
return 9 | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_max_value(self):\n max_value = max(self.values)\n return max_value",
"def _get_maximum(self):\n return self._maximum",
"def get_max(self):\n return self._max",
"def get_max(self):\n return self.max[-1]",
"def _get_maximum_value(self):\n if hasattr(self, '_m... | [
"0.8933136",
"0.88841957",
"0.87473977",
"0.861131",
"0.8606676",
"0.85935724",
"0.8586842",
"0.8532539",
"0.8532539",
"0.8495911",
"0.8495911",
"0.8488697",
"0.8487578",
"0.8399635",
"0.83845997",
"0.836424",
"0.83359325",
"0.8262177",
"0.8261219",
"0.8261219",
"0.8256761",
... | 0.7767184 | 55 |
Return the mode of the entity. | def mode(self) -> NumberMode:
return NumberMode.AUTO | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_mode(self, ):\n return self.get_parameter('mode')",
"def get_mode(self):\r\n return self.mode",
"def mode(self):\n return self._lift(\"mode\")",
"def mode(self):\n return self._data.get('mode', None)",
"def getmode(self):\n return self.mode",
"def mode(self):\n ... | [
"0.79352784",
"0.7918545",
"0.78954023",
"0.7884875",
"0.7841136",
"0.78398794",
"0.78398794",
"0.78398794",
"0.7837497",
"0.7837497",
"0.7837497",
"0.7821151",
"0.781643",
"0.7792414",
"0.7788912",
"0.77766544",
"0.77633715",
"0.7705739",
"0.76101565",
"0.76101565",
"0.75625... | 0.0 | -1 |
Return Name of device. | def name(self):
return f"{get_device_name(self._data, self._actuator.id)} Floor Temp Offset" | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def name(self):\n return self._device.name",
"def name(self):\n return self._device.name",
"def name(self):\n return self._device.name",
"def name(self):\n return self.device.name()",
"def name(self):\n return f\"{get_device_name(self._data, 0, self._name)}\"",
"def nam... | [
"0.891022",
"0.891022",
"0.891022",
"0.8865886",
"0.8831564",
"0.87226844",
"0.8687004",
"0.8687004",
"0.8627558",
"0.8609878",
"0.8509157",
"0.8509157",
"0.8476352",
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"0.8449398",
"0.8261127",
"0.8234234",
"0.8221397",
"0.81503457",
"0.81250376",
"0.8083982",
... | 0.0 | -1 |
Return device specific attributes. | def device_info(self):
return {
"name": get_device_name(self._data, self._actuator.id),
"identifiers": {(DOMAIN, get_identifier(self._data, self._actuator.id))},
"via_device": (DOMAIN, self._data.wiserhub.system.name),
} | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def attributes(self, device_id=0):\n\t\t\treturn cuda.Device(device_id).get_attributes()",
"def device_info(self) -> Optional[Dict[str, Any]]:\n return {ATTR_NAME: self.name, \"identifiers\": {(DOMAIN, self._device.device_id)}}",
"def device_state_attributes(self):\n attrs = {\n ATTR_A... | [
"0.80724686",
"0.7450437",
"0.742771",
"0.7409806",
"0.7293218",
"0.72523683",
"0.72523683",
"0.719905",
"0.7175151",
"0.7155777",
"0.7155777",
"0.71476424",
"0.7145971",
"0.71375567",
"0.7095317",
"0.7051197",
"0.703671",
"0.7035435",
"0.7017325",
"0.7013673",
"0.70050514",
... | 0.6660786 | 72 |
Recursively parses XML contents to python dict. We assume that `object` tags are the only ones that can appear multiple times at the same level of a tree. | def recursive_parse_xml_to_dict(xml):
if not xml:
return {xml.tag: xml.text}
result = {}
for child in xml:
child_result = recursive_parse_xml_to_dict(child)
if child.tag != 'object':
result[child.tag] = child_result[child.tag]
else:
if child.tag not in... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def recursive_parse_xml_to_dict(xml):\n if not xml:\n return {xml.tag: xml.text}\n result = {}\n for child in xml:\n child_result = recursive_parse_xml_to_dict(child)\n if child.tag != 'object':\n result[child.tag] = child_result[child.tag]\n else:\n if child.tag not in result:\n re... | [
"0.76405007",
"0.6487578",
"0.6464231",
"0.6301873",
"0.6218455",
"0.6149198",
"0.6135709",
"0.607625",
"0.60481364",
"0.58940786",
"0.5876097",
"0.5762465",
"0.5756617",
"0.5738174",
"0.5733136",
"0.57227683",
"0.5722584",
"0.56979394",
"0.5683315",
"0.5661643",
"0.5641846",... | 0.7581804 | 1 |
Key to sort hosts / domains alphabetically, by domain name. | def domain_sort_key(domain):
import re
domain_expr = r'(.*\.)?(.*\.)(.*)' # Eg: (www.)(google.)(com)
domain_search = re.search(domain_expr, domain)
if domain_search and domain_search.group(1):
# sort by domain name and then everything left of
# Eg: google, com, www
domain_valu... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def subdomain_sorting_key(hostname):\n parts = hostname.split('.')[::-1]\n if parts[-1] == 'www':\n return parts[:-1], 1\n return parts, 0",
"def list_domain_names(self) -> Dict:\n pass",
"def get_hosts(self):\n\n return sorted(self.host_data.keys())",
"def sort_... | [
"0.77395064",
"0.5994397",
"0.59496844",
"0.5851569",
"0.5843953",
"0.5838",
"0.58089083",
"0.5635496",
"0.56327444",
"0.55446255",
"0.5542909",
"0.5542909",
"0.5528308",
"0.55238223",
"0.5503389",
"0.5494649",
"0.54859453",
"0.5457136",
"0.5452885",
"0.544919",
"0.54136103",... | 0.8291568 | 0 |
This function adjusts matplotlib settings so that all figures in the textbook have a uniform format and look. | def setup_text_plots(fontsize=8, usetex=True):
from distutils.version import LooseVersion
matplotlib.rc('legend', fontsize=fontsize, handlelength=3)
matplotlib.rc('axes', titlesize=fontsize)
matplotlib.rc('axes', labelsize=fontsize)
matplotlib.rc('xtick', labelsize=fontsize)
matplotlib.rc('ytick... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def adjust_matplotlib_settings():\n font = {\"weight\": \"bold\", \"size\": 22, \"family\": \"sans-serif\"}\n matplotlib.rc(\"font\", **font)\n matplotlib.rc(\"text\", usetex=True)\n matplotlib.rcParams[\"mathtext.fontset\"] = \"dejavusans\"",
"def set_style(usetex=False):\n plt.rc('text', usetex=... | [
"0.7674264",
"0.7047249",
"0.69050926",
"0.676554",
"0.6711467",
"0.6589669",
"0.6553633",
"0.6522526",
"0.650684",
"0.6426557",
"0.6402421",
"0.6368734",
"0.6247413",
"0.6218916",
"0.61540663",
"0.6130522",
"0.608861",
"0.5950496",
"0.59500664",
"0.59225714",
"0.5862739",
... | 0.63753253 | 11 |
draw and label a cube. edges is a list of numbers between 1 and 12, specifying which of the 12 cube edges to draw | def draw_cube(ax, xy, size, depth=0.3,
edges=None, label=None, label_kwargs=None, **kwargs):
if edges is None:
edges = range(1, 13)
x, y = xy
y -= size # set left/up corner as the first (0,0) for one cube
# first plot background edges
if 9 in edges:
ax.plot([x + d... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def writeCube(c) :\n print(\"Edge Length =\",c.getLength())\n print(\"Volume =\",c.volume())\n print(\"Surface Area =\",c.surfaceArea())\n print(\"Face Diagonal =\",c.faceDiagonal())\n print(\"Space Diagonal =\",c.spaceDiagonal())",
"def main() :\n c1 = Cube(5.3) # cube with edge length of 5.3\... | [
"0.6443449",
"0.6413569",
"0.6355094",
"0.63017035",
"0.6130413",
"0.6115143",
"0.60516644",
"0.59508514",
"0.59508514",
"0.59094137",
"0.58450186",
"0.5810668",
"0.5770342",
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"0.5727801",
"0.5716166",
"0.57069796",
"0.56948054",
"0.5686037",
"0.5677185",
"0.5661... | 0.73245275 | 0 |
Shift an image by the specified amount Uses interpolation to do subpixel shifts if desired. Missing data should be represented with numpy.nans. | def shift(data, offset, header=None, variance=None, order=None,
crpix=None, resize=False, no_shift=False, missing=np.nan,
**kwargs):
if not isinstance(header, fits.header.Header):
header = fits.header.Header()
var = variance.copy() if isinstance(variance, np.ndarray) else None
if... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def shift(image,shift_x,shift_y):\n return np.roll(np.roll(image,shift_y,axis=0),shift_x,axis=1)",
"def shift(self):\n r = self.std\n mid = self.mid_pixel #center pixel index of 384x384 image\n delta = self.size - self.mid_pixel - r\n \n x = np.random.randint(low=-1*delta,hi... | [
"0.7026287",
"0.68541515",
"0.66203284",
"0.6425733",
"0.6338593",
"0.63167566",
"0.62013304",
"0.6186507",
"0.6057882",
"0.6040801",
"0.5912511",
"0.5905362",
"0.58581746",
"0.58155656",
"0.5812555",
"0.5750415",
"0.5727573",
"0.5655622",
"0.56455517",
"0.5634604",
"0.562525... | 0.56988156 | 17 |
Validates the .workflow file. | def validate_syntax(self):
resolves_present = False
uses_present = False
if not self.wf.get('workflow', None):
pu.fail('A workflow block must be present\n')
else:
for _, wf_block in dict(self.wf['workflow']).items():
if wf_block.get('resolves', Non... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _validate(self):\n if not self._contents.has_key('type'):\n raise ValidationFailed(\"Metadata file %s contains no type field\" % (self._filename))\n \n if not self._contents.has_key('version'):\n raise ValidationFailed(\"Metadata file %s contains no version field\" %\... | [
"0.6631906",
"0.6614255",
"0.6606154",
"0.6537318",
"0.63448745",
"0.62795186",
"0.62735194",
"0.6186111",
"0.61825204",
"0.6171616",
"0.61469364",
"0.61425644",
"0.61318344",
"0.6077196",
"0.6075853",
"0.6061646",
"0.60611504",
"0.6057122",
"0.60558885",
"0.6055866",
"0.6049... | 0.67150223 | 0 |
normalize the dictionary representation of the workflow | def normalize(self):
# modify from this:
#
# "workflow": {
# "test-and-deploy": {
# "resolves": "deploy"
# }
# }
#
# to this:
#
# "workflow": {
# "name": "test-and-deploy",
# "on": "push"... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def normalise_workflow(workflow_dict):\n normalise_process(workflow_dict)\n if not 'steps' in workflow_dict:\n exit_perm_fail(\"No steps in Workflow\")\n\n if isinstance(workflow_dict['steps'], dict):\n new_steps = []\n for step_id, step in workflow_dict['steps'].items():\n ... | [
"0.7474329",
"0.5918155",
"0.5898275",
"0.58240426",
"0.5808958",
"0.57993853",
"0.57069623",
"0.5687853",
"0.5675693",
"0.559138",
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"0.55547684",
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"0.55311835",
"0.5512876",
"0.55118704",
"0.549156",
"0.5486343",
"0.54750293",
"0.54630303... | 0.6584143 | 1 |
A GHA workflow is defined by specifying edges that point to the previous nodes they depend on. To make the workflow easier to process, we add forward edges. We also obtains the root nodes. | def complete_graph(self):
root_nodes = set()
for name, a_block in self.wf['action'].items():
a_block['name'] = name
for n in a_block.get('needs', []):
if not self.wf['action'][n].get('next', None):
self.wf['action'][n]['next'] = set()
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def forward_graph(self):\n raise NotImplementedError",
"def get_forward_init(node, graph):\n\tedges = []\n\tfor e in node.edges:\n\t\tif node.label <= graph.nodes[e.to].label:\n\t\t\tedges.append(e)\n\treturn edges",
"def _bfs_forward(self, start_node):\n visited = {node: (False) for node in self... | [
"0.68631876",
"0.59310347",
"0.5865252",
"0.57889354",
"0.57486254",
"0.57264847",
"0.56896424",
"0.5673367",
"0.5652373",
"0.5643193",
"0.5587451",
"0.55842507",
"0.55828464",
"0.5574014",
"0.5573729",
"0.55621177",
"0.55612457",
"0.5532347",
"0.5529374",
"0.55181307",
"0.55... | 0.6231825 | 1 |
Clone actions that reference a repository. | def download_actions(self):
cloned = set()
infoed = False
for _, a in self.wf['action'].items():
if ('docker://' in a['uses'] or
'shub://' in a['uses'] or
'./' in a['uses']):
continue
action = None
if a['us... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def clone(repo, src, dest, shallow):\n print('Repo: %s' % repo)\n print('Source: %s' % src)\n print('Destination: %s' % dest)\n print('Shallow: %s' % shallow)",
"def clone_repo():\n with settings(warn_only=True):\n run('git clone %(repository_url)s %(repo_path)s' % env)",
"def GetClone(se... | [
"0.6880847",
"0.6472116",
"0.6390989",
"0.6377147",
"0.63734037",
"0.6366674",
"0.62699676",
"0.62628525",
"0.6210326",
"0.61976963",
"0.6163758",
"0.6146929",
"0.61124015",
"0.6028663",
"0.60193825",
"0.5995425",
"0.5988343",
"0.5987096",
"0.5978216",
"0.59648484",
"0.594991... | 0.54043 | 61 |
Factory of ActionRunner instances, one for each action | def instantiate_runners(self):
for _, a in self.wf['action'].items():
if 'docker://' in a['uses']:
a['runner'] = DockerRunner(
a, self.workspace, self.env,
self.quiet, self.debug, self.dry_run)
continue
if 'shub://'... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_clients():\n clients = {}\n\n rospy.loginfo(\"Waiting for rubble detector\")\n clients['rubble_detect'] = actionlib.SimpleActionClient('rubble_detect',\n RubbleDetectAction)\n\n rospy.loginfo(\"Waiting for rubble checker\")\n clie... | [
"0.56596607",
"0.56475294",
"0.5616994",
"0.5510894",
"0.54630816",
"0.5457942",
"0.54218817",
"0.5406142",
"0.53818655",
"0.5351243",
"0.53502804",
"0.5345953",
"0.532772",
"0.5279873",
"0.527048",
"0.523934",
"0.521685",
"0.51686555",
"0.515931",
"0.5137021",
"0.5137021",
... | 0.69310266 | 0 |
Run the pipeline or a specific action | def run(self, action_name=None, reuse=False, parallel=False):
os.environ['WORKSPACE'] = self.workspace
self.download_actions()
self.instantiate_runners()
if action_name:
self.wf['action'][action_name]['runner'].run(reuse)
else:
for s in self.get_stages()... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def run(self):\n\n self._action.execute()",
"def action_run(self):\n pass",
"def apply_action(self, action):\n return self.__environment.step(action)",
"def _run_actions(self):\n\n if \"install-bento\" in self.actions:\n self._do_action_bento_setup()\n\n if \"create-... | [
"0.6953901",
"0.6952061",
"0.6602551",
"0.6561766",
"0.6464926",
"0.64080197",
"0.6405319",
"0.63914955",
"0.63781947",
"0.63781947",
"0.6359134",
"0.63469976",
"0.63318545",
"0.63070595",
"0.62973326",
"0.6241613",
"0.6240744",
"0.6202429",
"0.6176801",
"0.6176801",
"0.61743... | 0.6402181 | 7 |
Generator of stages. A stages is a list of actions that can be executed in parallel. | def get_stages(self):
current_stage = self.wf['root']
while current_stage:
yield current_stage
next_stage = set()
for n in current_stage:
next_stage.update(self.wf['action'][n].get('next', set()))
current_stage = next_stage | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def stages(self):\n return StageManager(session=self._session)",
"def stages(self):\r\n return pipelines.Stages(self)",
"def run(stages, maxsize=0):\n\n if isinstance(stages, list) and len(stages) == 0:\n raise ValueError(\"Expected at least 1 stage to run\")\n\n elif isinstance(stag... | [
"0.6442683",
"0.63786435",
"0.6202054",
"0.6113744",
"0.6091911",
"0.60464627",
"0.60097075",
"0.60097075",
"0.5929696",
"0.5866913",
"0.5734935",
"0.56417054",
"0.56399095",
"0.56399095",
"0.56399095",
"0.56128675",
"0.55290216",
"0.5513827",
"0.54971427",
"0.5468624",
"0.54... | 0.71270746 | 0 |
Runs the singularity action | def run(self, reuse=False):
build = True
if 'shub://' in self.action['uses']:
image = self.action['uses']
build = False
elif './' in self.action['uses']:
image = 'action/' + os.path.basename(self.action['uses'])
singularityfile_path = os.path.join(... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_smoke(self):\n\t\tinit_state = torch.tensor(0.0)\n\t\ttotal_time = torch.tensor(4.0)\n\t\tprint('Agent state trajectory and actions:')\n\t\tAgent().play(init_state, total_time)\n\t\tpyro.clear_param_store()",
"def singularity_start(self, image):\n env_vars = self.action.get('env', {})\n\n ... | [
"0.5975073",
"0.5935403",
"0.59130096",
"0.57898796",
"0.5710506",
"0.56999266",
"0.56843907",
"0.5681977",
"0.55953836",
"0.55517167",
"0.5515142",
"0.55127794",
"0.5501622",
"0.5477187",
"0.5396195",
"0.53956157",
"0.5391933",
"0.53681904",
"0.53640187",
"0.53324854",
"0.53... | 0.6079829 | 0 |
Generates the image name from the image url. | def generate_image_name(self, image):
return image.replace('shub://', '').replace('/', '-') + '.simg' | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_generated_image_name(full_image_url):\r\n\r\n logging.debug('get_generated_image_name({})'.format(full_image_url))\r\n\r\n image_name = datetime.datetime.now().strftime(\"%Y%m%d%H%M%S\")\r\n image_extension = full_image_url.split(\".\")[-1]\r\n image_name = image_name + \".\" + image_extension\... | [
"0.8253151",
"0.7566465",
"0.73151815",
"0.7306501",
"0.72865564",
"0.72370976",
"0.7237034",
"0.7213149",
"0.7112175",
"0.71032536",
"0.7042305",
"0.70135736",
"0.7007883",
"0.6985449",
"0.6945476",
"0.6920966",
"0.68803626",
"0.68720126",
"0.6842178",
"0.68296844",
"0.68112... | 0.8002894 | 1 |
Check whether an instance exists or not. | def singularity_exists(self):
instances = Client.instances(quiet=self.quiet)
for instance in instances:
if self.pid in instance.name:
return True
return False | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _Exists(self, instance_only: bool = False) -> bool:\n cmd = util.GcloudCommand(self, 'spanner', 'instances', 'describe',\n self.name)\n\n # Do not log error or warning when checking existence.\n _, _, retcode = cmd.Issue(suppress_warning=True, raise_on_failure=False)\n i... | [
"0.7803968",
"0.76079255",
"0.70579004",
"0.7034308",
"0.7027742",
"0.7027742",
"0.6946009",
"0.689386",
"0.67451686",
"0.6722067",
"0.6699531",
"0.66554385",
"0.6641805",
"0.66386664",
"0.66077375",
"0.6606492",
"0.6554289",
"0.65404296",
"0.6523959",
"0.651226",
"0.6500812"... | 0.77773875 | 1 |
Stops and removes an instance. | def singularity_rm(self):
Client.instances(self.pid, quiet=self.quiet).stop() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def stop_instance(self):\n instance_id = self._choose_among_running_instances()\n\n # Cancel\n if not instance_id:\n print 'Operation cancelled'\n return\n\n print '# Stopping the instance \"%s\"' % instance_id\n self.compute.stop_instance(instance_id)\n ... | [
"0.74867284",
"0.7001102",
"0.68385786",
"0.6705385",
"0.6695328",
"0.66951686",
"0.667478",
"0.6628798",
"0.66176504",
"0.660671",
"0.6593816",
"0.65497553",
"0.6535843",
"0.6532505",
"0.64770555",
"0.64421254",
"0.6439605",
"0.6426153",
"0.6410027",
"0.6365153",
"0.635097",... | 0.63447744 | 22 |
Pulls an docker or singularity images from hub. | def singularity_pull(self, image):
Client.pull(image) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def pull(self) -> None:\n cached_zip_filepath = None\n try:\n scheme, name, tag, secret = parse_hub_uri(self.args.uri)\n\n executor = HubIO.fetch(name, tag=tag, secret=secret)\n\n if not tag:\n tag = executor.tag\n\n uuid = executor.uuid\n ... | [
"0.6917054",
"0.66231585",
"0.6467475",
"0.64548194",
"0.64449644",
"0.6421621",
"0.63432056",
"0.631559",
"0.63136727",
"0.631053",
"0.630868",
"0.62965673",
"0.6283755",
"0.6199952",
"0.619788",
"0.61943036",
"0.61171836",
"0.6069643",
"0.6063285",
"0.6025357",
"0.59519005"... | 0.6308105 | 11 |
Builds an image from a recipefile. | def singularity_build(self, path, image):
Client.build(os.path.join(
path, 'singularity.def'
), self.generate_image_name(image)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def build(image_name, path='.'):\n try:\n doxy.images.build(path=path, tag=image_name)\n message = '[*] Image {} built.'\n print message.format(image_name)\n except Exception as err:\n print err\n raise",
"def parseRecipe (self,filename):\r\n return RecipeFileO... | [
"0.6221498",
"0.57881945",
"0.56212443",
"0.55962044",
"0.5468818",
"0.5449105",
"0.5435583",
"0.54293376",
"0.5417929",
"0.5412393",
"0.5368963",
"0.53490657",
"0.53334695",
"0.5324951",
"0.5318995",
"0.5296623",
"0.529369",
"0.52904105",
"0.52801436",
"0.5234523",
"0.522143... | 0.50080234 | 43 |
Starts a singularity instance based on the image. | def singularity_start(self, image):
env_vars = self.action.get('env', {})
for s in self.action.get('secrets', []):
env_vars.update({s: os.environ[s]})
for e, v in self.env.items():
env_vars.update({e: v})
env_vars.update({'HOME': os.environ['HOME']})
#... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def singularity_build(self, path, image):\n Client.build(os.path.join(\n path, 'singularity.def'\n ), self.generate_image_name(image))",
"def start_ssm(self, ssm_image):\n pass",
"def create_instance_by_image(self):\n print '# Start a new instance based on an existing AMI... | [
"0.666752",
"0.6325278",
"0.6263676",
"0.614632",
"0.5845064",
"0.5794984",
"0.5747361",
"0.5733841",
"0.5721612",
"0.56165344",
"0.5603583",
"0.5509438",
"0.54767895",
"0.5428946",
"0.53604615",
"0.53470963",
"0.5332762",
"0.53182864",
"0.53123957",
"0.5296787",
"0.52888715"... | 0.7294491 | 0 |
Use argparse to parse command line arguments. | def get_args():
parser = argparse.ArgumentParser(description='Runner for tasks')
parser.add_argument('--db_url', help='Database url string to the db.', required=True)
return parser.parse_args() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def arg_parse():\n p = ap.ArgumentParser()\n p.add_argument()\n return p.parse_args()",
"def parse_arguments():\n parser = argparse.ArgumentParser()\n parser.add_argument('-u', '--urls_dirpath', type=unicode)\n parser.add_argument('-r', '--resources_dir', type=unicode)\n parser.add_argument(... | [
"0.80362767",
"0.79380834",
"0.7909644",
"0.7786256",
"0.77640563",
"0.7757112",
"0.7753354",
"0.7752254",
"0.7741536",
"0.77328235",
"0.77010775",
"0.76901954",
"0.76706934",
"0.7655658",
"0.7648945",
"0.7648416",
"0.7646757",
"0.76460826",
"0.76380634",
"0.76354045",
"0.763... | 0.0 | -1 |
Load foia sba datasets | def load_sba_datasets(dbm, direc):
foia_504_1991_present = pd.read_excel(direc + 'FOIA - 504 (FY1991-Present).xlsx')
foia_7a_1991_1999 = pd.read_excel(direc + 'FOIA - 7(a) (FY1991-FY1999).xlsx', skiprows=1)
foia_7a_2000_2009 = pd.read_excel(direc + 'FOIA - 7(a)(FY2000-FY2009).xlsx', skiprows=1)
foia_7a_... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def load_kiba_dataset():\n trainn_fold = json.load(\n open(os.path.join('dataset', 'regression', 'benchmark', 'KIBAtest', 'folds', 'train_fold_setting1.txt')))\n train_fold = []\n for e in zip(*trainn_fold):\n for ee in e:\n train_fold.extend(ee)\n #train_fold = [ee for e in tr... | [
"0.629331",
"0.62574154",
"0.61739457",
"0.61300355",
"0.6085598",
"0.6068422",
"0.6059043",
"0.599034",
"0.5854947",
"0.58494705",
"0.58464795",
"0.5826532",
"0.58246195",
"0.58217824",
"0.5821119",
"0.57790554",
"0.5750343",
"0.5750343",
"0.5742792",
"0.57419103",
"0.574190... | 0.76649237 | 0 |
Generate center offset for crop window | def center_crop(image, source=(218, 178, 3), target=128):
height, width, channel = source
off_h = np.ceil((height - target) / 2).astype(int)
off_w = np.ceil((width - target) / 2).astype(int)
return image[off_h: off_h+target, off_w: off_w+target, :] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def center(self):\n # minz to offset the heights to 0\n mz = (self.maxz-self.minz)/2\n #mz = self.minz\n return (self.minx + self.width / 2, self.miny + self.height / 2, mz)",
"def calculate_window_position(self):\n self.x = SQUARE_SIZE * self.col + SQUARE_SIZE // 2\n se... | [
"0.6784088",
"0.6756137",
"0.6714128",
"0.6631258",
"0.6611774",
"0.6609801",
"0.6596732",
"0.6587204",
"0.6557443",
"0.6556682",
"0.6531438",
"0.6452741",
"0.64465266",
"0.64361143",
"0.6413167",
"0.6385451",
"0.63821167",
"0.6377177",
"0.6372448",
"0.6344084",
"0.6343337",
... | 0.6065056 | 52 |
Template for validating FMU models for Bonsai integration. | def __init__(
self,
model_filepath: str,
user_validation: bool = True,
):
# ensure model filepath is balid, and save as att if it is
assert model_filepath.endswith(".fmu"), "Provided filepath is not an FMU file: '{}'".format(model_filepath)
self.model_filepath = mode... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def validate():",
"def data_validation(self):\n print \"Starting basic data validation ...\"\n allattr = dir(bdefile)\n idx = [ii for ii, attr in enumerate(allattr) if \"validate_oee_error_\" in attr]\n vfunclist = []\n for ii in idx:\n vfunclist += [allattr[ii]]\n\n... | [
"0.62146026",
"0.59948707",
"0.5963622",
"0.5916925",
"0.589161",
"0.5838374",
"0.58137685",
"0.58000225",
"0.58000225",
"0.573189",
"0.5726065",
"0.5707462",
"0.5697073",
"0.56887645",
"0.5651742",
"0.5627777",
"0.55742997",
"0.5545567",
"0.5542714",
"0.5541837",
"0.55222416... | 0.52467144 | 65 |
Check if configuration file exists, otherwise indicate user to do so Configuration contains sim config_params/inputs/outputs and naming | def _validate_sim_config(self):
print("\n[FMU Validator] ---- Looking to see if YAML config file exists ----")
# use convention to search for config file
config_file = self.sim_config_filepath
if not os.path.isfile(config_file):
print("[FMU Validator] Configuratio... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __check_config(self):\n if not os.path.exists(self.__config_path):\n return False\n else:\n return True",
"def is_config_exist(self) -> bool:\n pass",
"def is_config_exist(self) -> bool:\n return True",
"def check_config_file():\n # Locate and init con... | [
"0.7276761",
"0.72518843",
"0.723697",
"0.700425",
"0.6990162",
"0.68774563",
"0.68128514",
"0.6784662",
"0.677204",
"0.6760748",
"0.674573",
"0.6722314",
"0.6706433",
"0.6663527",
"0.66553557",
"0.6630366",
"0.6555873",
"0.65515465",
"0.6551169",
"0.65199214",
"0.64805055",
... | 0.72461706 | 2 |
We use the fmi standard to extract the correct set of config_params, inputs, outputs We look into the "causality" attribute for each variable in model description | def _extract_sim_config_from_fmi_std(self):
print("\n---- Looking to see if FMU model description contains required 'causality' type definitions ----")
sim_config_params = []
sim_inputs = []
sim_outputs = []
sim_other_vars = []
for variable in self.model_descrip... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def doParametersOfInterest(self):\n \n self.modelBuilder.doVar('expr::cosW(\"0.87681811112\",)')\n self.modelBuilder.doVar('expr::sinW(\"0.48082221247\",)')\n self.modelBuilder.doVar('expr::mZ(\"91.2\",)')\n self.modelBuilder.doVar('expr::Lambda1(\"100.0\",)')\n self.modelBui... | [
"0.6243876",
"0.6197848",
"0.61577994",
"0.6136601",
"0.61252385",
"0.60175735",
"0.5826551",
"0.5813928",
"0.5751767",
"0.5715533",
"0.5710865",
"0.5692011",
"0.56493515",
"0.5560804",
"0.5558627",
"0.55206186",
"0.5514668",
"0.5512474",
"0.5510357",
"0.5506426",
"0.5503846"... | 0.6741056 | 0 |
Dump sim's config_params, inputs, and outputs to YAML file By default, we overwrite to main YAML config file. | def _dump_config_to_yaml_file(self,
sim_config_params = None,
sim_inputs = None,
sim_outputs = None,
sim_other_vars = None,
is_aux_yaml = False):
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def save_config(_config, simulation_dir):\n with open(os.path.join(simulation_dir, 'config.yaml'), 'w') as f:\n yaml.dump(_config, f, default_flow_style=False)",
"def save():\n print(\"Saving config file..\")\n\n res = yaml.round_trip_dump(_conf, indent=2, block_seq_indent=1)\n\n with open(__c... | [
"0.730918",
"0.699196",
"0.68951195",
"0.67941695",
"0.6579302",
"0.6568991",
"0.6554705",
"0.64560366",
"0.64269376",
"0.64144593",
"0.6292432",
"0.6266346",
"0.6234814",
"0.62344337",
"0.62238747",
"0.617985",
"0.6164769",
"0.6161922",
"0.6141542",
"0.6134677",
"0.6123942",... | 0.81717044 | 0 |
Get string with the sim's config_params, inputs, and outputs for the model | def _get_sim_config_str(self):
log = "[FMU Validator] The set of configuration_parameters, inputs, and outputs defined is the following:\n"
log += "\n{}: {}".format("Sim Config Params -- Brain Config ", self.sim_config_params)
log += "\n{}: {}".format("Sim Inputs -- Brain Act... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_model_config(model_name, args):\n if model_name == 'Tacotron2':\n model_config = dict(\n # optimization\n mask_padding=args.mask_padding,\n # audio\n n_mel_channels=args.n_mel_channels,\n # symbols\n n_symbols=args.n_symbols,\n ... | [
"0.63613605",
"0.6227064",
"0.6027238",
"0.6021502",
"0.59806687",
"0.59724784",
"0.58838063",
"0.5799649",
"0.5774296",
"0.57192713",
"0.5702105",
"0.57006687",
"0.568292",
"0.56661975",
"0.5660352",
"0.5649805",
"0.56424356",
"0.56185853",
"0.56185603",
"0.5617623",
"0.5615... | 0.737774 | 0 |
Remove nonalphanumeric characters to make them valid with Bonsai interaction. | def _clean_non_alphanumeric_chars(self):
for i,variable in enumerate(self.model_description.modelVariables):
clean_name = re.sub(r'[^a-zA-Z0-9_]', '', variable.name)
if clean_name != variable.name:
log = "Sim variable '{}' has been renamed to '{}' ".format(variable.name,... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def cleanup_input(data):\n data = re.sub(r'[^0-9A-Za-z ()_,.-:]', '', data)\n return data",
"def clean_unnecessary_characters(self, tweet):\n tweet = tweet.lstrip(\"\\\"\").rstrip(\"\\\"\")\n tweet = re.sub(self.compiledAlphanumericRegex, ' ', tweet)\n tweet = tweet.replace('_', ' ')\n... | [
"0.73747957",
"0.73650575",
"0.73443496",
"0.7294863",
"0.72666633",
"0.72465616",
"0.7158626",
"0.7149394",
"0.713084",
"0.7125717",
"0.71202695",
"0.71086025",
"0.71029955",
"0.70836455",
"0.707941",
"0.70679945",
"0.70411354",
"0.70361745",
"0.70283735",
"0.7028341",
"0.70... | 0.744641 | 0 |
Template for simulating FMU models for Bonsai integration. Note, it calls FMUSimValidation to validate the model when first instanced. | def __init__(
self,
model_filepath: str,
fmi_version: str = FMI_VERSION,
start_time = START_TIME,
stop_time = STOP_TIME,
step_size = STEP_SIZE,
user_validation: bool = False,
use_unzipped_model: bool = False,
):
# validate simulation: config_v... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __set_fmu__(self, fmu_file, result_handler, solver, atol, rtol, verbose):\n if self.fmu is None:\n \n # TODO:\n # See what can be done in catching the exception/propagating it\n self.fmu = pyfmi.load_fmu(fmu_file)\n \n # Get the optio... | [
"0.629378",
"0.6192165",
"0.60618526",
"0.6044517",
"0.60348445",
"0.6019483",
"0.5948904",
"0.5907536",
"0.5832203",
"0.58019847",
"0.572914",
"0.5647409",
"0.5641304",
"0.5582606",
"0.55634594",
"0.55448914",
"0.5505208",
"0.5491149",
"0.54623306",
"0.5438175",
"0.53968614"... | 0.70113957 | 0 |
Initialize model in the sequential manner required. | def initialize_model(self, config_param_vals = None):
self._is_initialized = True
self.fmu.instantiate()
self.fmu.reset()
self.fmu.setupExperiment(startTime=self.start_time)
if config_param_vals is not None:
self._apply_config(config_param_vals)
self.fmu.ente... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def initialize_model(self):\n pass",
"def init_model(self):\n pass",
"def initialize(self, model):\n pass",
"def initialize(self):\n LOG.info(\"Initializing Model.\")\n self.model = self.convert(df=self.training_df)\n if self.bootstraps is not None:\n LOG.... | [
"0.7742976",
"0.7590463",
"0.759012",
"0.74840647",
"0.7275883",
"0.70921814",
"0.6888844",
"0.68874913",
"0.68849003",
"0.6871453",
"0.68196464",
"0.6760278",
"0.67510974",
"0.6709929",
"0.66639215",
"0.6652089",
"0.6573573",
"0.6572885",
"0.6565885",
"0.6531906",
"0.6519555... | 0.6732077 | 13 |
Move one step forward. | def run_step(self):
# Ensure model has been initialized at least once
self._model_has_been_initialized("run_step")
# Check if sim is steady-state (doesn't contain "doStep" method)
if "doStep" not in dir(self.fmu):
error_log = "[run_step] FMU model cannot be run on... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def step_forward(self):",
"def forward(self):\n self.position += 1",
"def move_forward():\n pass",
"def step(self, move):",
"def forward(self):\n self.cursor.forward()",
"def _step(self) -> None:",
"def back(self, step):\r\n self.forward(-step)",
"def move_forward(self, distan... | [
"0.84211636",
"0.80904514",
"0.7710274",
"0.76850307",
"0.74500424",
"0.71530104",
"0.7122298",
"0.7110573",
"0.7092961",
"0.7079883",
"0.70217985",
"0.7018611",
"0.6964649",
"0.69422424",
"0.6910966",
"0.6876663",
"0.68695754",
"0.6869501",
"0.68424183",
"0.68298566",
"0.680... | 0.0 | -1 |
Reset model with new config (if given). | def reset(self, config_param_vals: Dict[str, Any] = None):
# Ensure model has been initialized at least once
self._model_has_been_initialized("reset")
# Terminate and re-initialize
self._terminate_model()
self.initialize_model(config_param_vals)
# Reset... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def reset_model(self):\n raise NotImplementedError",
"def reset_model(self):\n raise NotImplementedError",
"def reset_model(self):\n raise NotImplementedError",
"def reset(self, model):\n self.reset_strategy(model)",
"def reset(self, config, **kwargs):\n pass",
"def res... | [
"0.77136797",
"0.77136797",
"0.77136797",
"0.731388",
"0.7217995",
"0.7177466",
"0.6790846",
"0.6759494",
"0.67429775",
"0.668906",
"0.66128105",
"0.65749794",
"0.6570066",
"0.65698665",
"0.65314436",
"0.6444437",
"0.63590646",
"0.6338237",
"0.6334196",
"0.627672",
"0.6215812... | 0.7440945 | 3 |
Close model and remove unzipped model from temporary folder. | def close_model(self):
# Ensure model has been initialized at least once
self._model_has_been_initialized("close_model")
# terminate fmu model
# - avoids error from calling self.fmu.terminate if termination has already been performed
self._terminate_model()
# f... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def clean_up_temp_files():\n global __tmp_model_dir\n\n if __tmp_model_dir is not None:\n FileUtils.deleteDirectory(__tmp_model_dir)\n __tmp_model_dir = None",
"def delete_model(self):\n os.remove(self.filepath)\n self.cmodel = None",
"def cleanUp(self):\r\n # Close any... | [
"0.71848893",
"0.7168014",
"0.69020855",
"0.6778169",
"0.66447943",
"0.6597088",
"0.63135016",
"0.6312071",
"0.63115525",
"0.62721944",
"0.6262568",
"0.6260771",
"0.6243184",
"0.61231935",
"0.60675275",
"0.60634017",
"0.6029513",
"0.59984267",
"0.5996684",
"0.599638",
"0.5995... | 0.7889111 | 0 |
Get var indices for each (valid) var name provided in list. If none are provided, all outputs are returned. | def get_states(self, sim_outputs: List = None):
# Ensure model has been initialized at least once
self._model_has_been_initialized("get_states")
if sim_outputs is None:
sim_outputs = self.sim_outputs
elif not len(sim_outputs) > 0:
sim_outputs = self.sim_... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _var_names_to_indices(self, var_names: List):\n\n if type(var_names) is not type([]):\n # Return empty array if input is not 'list' type\n print(\"[_var_names_to_indices] Provided input is not of type list.\")\n return []\n\n indices_array = []\n names_arra... | [
"0.7106208",
"0.6794905",
"0.6497141",
"0.6025208",
"0.5968013",
"0.5913243",
"0.5865424",
"0.5832427",
"0.57964826",
"0.57409185",
"0.5729248",
"0.57017255",
"0.5681039",
"0.5604614",
"0.5604506",
"0.5603223",
"0.5594183",
"0.55620307",
"0.5516843",
"0.54871476",
"0.5463308"... | 0.0 | -1 |
Apply brain actions to simulation inputs. | def apply_actions(self, b_action_vals: Dict[str, Any] = {}):
# Ensure model has been initialized at least once
self._model_has_been_initialized("apply_actions")
# Ensure action dict is not empty
if not len(b_action_vals.items()) > 0:
print("[apply_actions] Provided ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def apply(self, inputs):\n raise NotImplementedError()",
"def _simulation_run(model_instance, observations, actions, rewards):\r\n\r\n for observation, action, reward in zip(observations, actions, rewards):\r\n model_instance.observe(observation)\r\n model_instance.overrideAct... | [
"0.64400464",
"0.5957464",
"0.58729064",
"0.5810304",
"0.57571757",
"0.57132226",
"0.5649076",
"0.56446207",
"0.56303746",
"0.5490612",
"0.5457731",
"0.545238",
"0.5415022",
"0.54109704",
"0.5397137",
"0.5371112",
"0.5361186",
"0.5359599",
"0.53587407",
"0.53278804",
"0.53256... | 0.0 | -1 |
Get a list of all variables in the sim (removing duplicates, if any). Note, list is kept the same from first time this method is called. | def get_all_var_names(self):
if hasattr(self, "all_var_names"):
return self.all_var_names
# Append all variables in model (defined in YAML).
aux_all_var_names = []
aux_all_var_names.extend(self.sim_config_params)
aux_all_var_names.extend(self.sim_inputs)
aux... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_all_variables(self):\n return []",
"def get_all_variables(self):\n out = []\n for i in self.items:\n out += i.get_all_variables()\n return out",
"def get_all_variables(self):\n out = []\n for i in self.items:\n out += i.get_all_variables()... | [
"0.7587621",
"0.74283415",
"0.74283415",
"0.74283415",
"0.74034715",
"0.73497486",
"0.73163354",
"0.71726215",
"0.7025907",
"0.6996247",
"0.6937989",
"0.6923813",
"0.67796665",
"0.6772739",
"0.67677814",
"0.67526543",
"0.6719864",
"0.6713113",
"0.6711427",
"0.67106885",
"0.66... | 0.7627399 | 0 |
Get var indices for each (valid) var name provided in list. | def _get_variables(self, sim_outputs: List = None):
# Ensure model has been initialized at least once
self._model_has_been_initialized("_get_variables")
# Ensure array is not empty
if sim_outputs is None:
return {}
elif not len(sim_outputs) > 0:
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def indices_of_var(v):\n name = v.varName\n indices = name[2:].split(',')\n i, j = int(indices[0]), int(indices[1])\n return i, j",
"def _var_names_to_indices(self, var_names: List):\n\n if type(var_names) is not type([]):\n # Return empty array if input is not 'list... | [
"0.7444971",
"0.7400322",
"0.6825618",
"0.61766595",
"0.6171719",
"0.6141269",
"0.604002",
"0.6002675",
"0.5954537",
"0.58911085",
"0.5872941",
"0.5856526",
"0.5843315",
"0.58359766",
"0.57936394",
"0.579151",
"0.5744204",
"0.5717505",
"0.57010794",
"0.56933683",
"0.5685582",... | 0.0 | -1 |
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