0:\\n # Check ParamArray arguments\\n paramarray = False\\n if script[0] == '@':\\n script = script[1:]\\n paramarray = True\\n scope = ''\\n if '#' in script:\\n scope, script = script.split('#')\\n if '.py$' in script.lower(): # Python\\n if len(scope) == 0:\\n scope = 'share' # Default for Python\\n # Provide an alternate helper script depending on test context\\n if script.startswith(cls.pythonhelpermodule) and hasattr(cls, 'pythonhelpermodule2'):\\n script = cls.pythonhelpermodule2 + script[len(cls.pythonhelpermodule):]\\n if '#' in script:\\n scope, script = script.split('#')\\n uri = 'vnd.sun.star.script:{0}?language=Python&location={1}'.format(script, scope)\\n else: # Basic\\n if len(scope) == 0:\\n scope = 'application' # Default for Basic\\n lib = ''\\n if len(script.split('.')) < 3:\\n lib = cls.library + '.' # Default library = ScriptForge\\n uri = 'vnd.sun.star.script:{0}{1}?language=Basic&location={2}'.format(lib, script, scope)\\n # Get the script object\\n fullscript = ('@' if paramarray else '') + scope + ':' + script\\n try:\\n xscript = cls.scriptprovider.getScript(uri)\\n except Exception:\\n raise RuntimeError(\\n 'The script \\\\'{0}\\\\' could not be located in your LibreOffice installation'.format(script))\\n else: # Should not happen\\n return None\\n\\n # At 1st execution of the common Basic dispatcher, buffer xscript\\n if fullscript == ScriptForge.basicdispatcher and cls.servicesdispatcher is None:\\n cls.servicesdispatcher = xscript\\n\\n # Execute the script with the given arguments\\n # Packaging for script provider depends on presence of ParamArray arguments in the called Basic script\\n if paramarray:\\n scriptreturn = xscript.invoke(args[0], (), ())\\n else:\\n scriptreturn = xscript.invoke(args, (), ())\\n\\n #\\n return scriptreturn[0] # Updatable arguments passed by reference are ignored\",\n \"def get_results_from_script(self, script):\\n raise NotImplementedError()\",\n \"def execute(self, sql, sql_args=None):\\n # Check that sql arguments have the correct type\\n self._check_sql_args(sql_args)\\n # Execute the query\\n try:\\n pgcursor = self.get_postgres_cursor()\\n pgcursor.execute(sql, sql_args)\\n self._connection.commit()\\n except PostgresError, e:\\n self._connection.rollback()\\n raise RuntimeError(\\\"Error running SQL query: %s\\\", str(e))\\n finally:\\n pgcursor.close()\",\n \"def _run(self, script, args):\\n proc = subprocess.Popen([script] + args,\\n stdout=subprocess.PIPE\\n )\\n\\n stdout = proc.communicate()[0]\\n retcode = proc.returncode\\n\\n return stdout, retcode\",\n \"def ASQL(*rest):\\n if str(acm.Class()) == \\\"FTmServer\\\":\\n acm.RunModuleWithParameters(__name__, acm.GetDefaultContext().Name())\\n return 'SUCCESS'\\n else:\\n return 'FAILED'\",\n \"def execute_script(self, action, *args):\\n self.host.cmd(('./%s' + len(args) * ' %s') % (action, *args))\",\n \"def call_script(name, args=[]):\\n def fn(): lib.call([env.script(name)] + args)\\n time_duration(fn)\",\n \"def register_script(self, script):\\n return self.conn.register_script(script)\",\n \"def execute_script(self, script_on_self: str, *arguments):\\n driver: WebDriver = self.config.driver\\n webelement = self()\\n # TODO: should we wrap it in wait or not?\\n # TODO: should we add additional it and/or its aliases for element?\\n return driver.execute_script(\\n f'''\\n let element = arguments[0]\\n let self = arguments[0]\\n return (function(...args) {{\\n {script_on_self}\\n }})(...arguments[1])\\n ''',\\n webelement,\\n arguments,\\n )\",\n \"def execute(script, data, outfile):\\n parsed = load_script(script)\\n input_data = pd.read_csv(data)\\n\\n output_data = parsed.execute(input_data)\\n output_data.to_csv(outfile, index=False)\",\n \"def call_script(self, script):\\n filename, callable = script.rsplit(':', 1)\\n filename = os.path.abspath(filename)\\n module = imp.load_source('script', filename)\\n script = getattr(module, callable.strip())\\n\\n try:\\n script(self.options, self.buildout, self.augmented_environment())\\n except TypeError:\\n # BBB: Support hook scripts that do not take the environment as\\n # the third parameter\\n script(self.options, self.buildout)\",\n \"def test_script(self):\\n self.script(\\\"# script.py\\\\n\\\"\\n \\\"a = 2\\\\n\\\"\\n \\\"# other\\\")\\n self.compile()\\n\\n script = self.find_code_component(name=\\\"script.py\\\")\\n self.assertEqual(script.type, \\\"script\\\")\\n self.assertEqual(script.mode, \\\"w\\\")\\n self.assertEqual(script.first_char_line, 1)\\n self.assertEqual(script.first_char_column, 0)\\n self.assertEqual(script.last_char_line, 3)\\n self.assertEqual(script.last_char_column, 7)\\n self.assertEqual(script.container_id, -1)\\n\\n script_block = self.metascript.code_blocks_store[script.id]\\n self.assertEqual(script_block.code, \\\"# script.py\\\\na = 2\\\\n# other\\\")\\n self.assertEqual(script_block.docstring, \\\"\\\")\\n self.assertTrue(bool(script_block.code_hash))\",\n \"def run_sql(self, sql):\\n def mk_run_sql_q(sql):\\n return {\\n 'type' : 'run_sql',\\n 'args': {\\n 'sql' : sql\\n }\\n }\\n return self.v1q(mk_run_sql_q(sql))\",\n \"def test_script(self):\\r\\n\\r\\n # Calculate the answer using a script\\r\\n script = \\\"calculated_ans = 'x+x'\\\"\\r\\n\\r\\n # Sample x in the range [-10,10]\\r\\n sample_dict = {'x': (-10, 10)}\\r\\n\\r\\n # The expected solution is numerically equivalent to 2*x\\r\\n problem = self.build_problem(sample_dict=sample_dict,\\r\\n num_samples=10,\\r\\n tolerance=0.01,\\r\\n answer=\\\"$calculated_ans\\\",\\r\\n script=script)\\r\\n\\r\\n # Expect that the inputs are graded correctly\\r\\n self.assert_grade(problem, '2*x', 'correct')\\r\\n self.assert_grade(problem, '3*x', 'incorrect')\",\n \"def run_script(script, stdin=None):\\n # Note: by using a list here (['bash', ...]) you avoid quoting issues, as the \\n # arguments are passed in exactly this order (spaces, quotes, and newlines won't\\n # cause problems):\\n proc = subprocess.Popen(['bash', '-c', script],\\n stdout=subprocess.PIPE, stderr=subprocess.PIPE,\\n stdin=subprocess.PIPE)\\n stdout, stderr = proc.communicate()\\n if proc.returncode:\\n raise ScriptException(proc.returncode, stdout, stderr, script)\\n return stdout, stderr\",\n \"def script_load(self, script):\\n return self._execute([b'SCRIPT', b'LOAD', script])\",\n \"def run_setup_script(self, script_path):\\n try:\\n f = open(script_path, 'r')\\n setup_script = f.read()\\n # print(setup_script)\\n c = self.conn.cursor()\\n c.executescript(setup_script)\\n except (Error, IOError) as e:\\n print('[Datanase] Error:')\\n print(e)\",\n \"def executeScript(self, script, scriptroot=None, asexec=True):\\n if self.instream:\\n asexec = False\\n if not scriptroot:\\n scriptroot = self.scriptroot\\n if not scriptroot or not os.path.isdir(scriptroot):\\n return self.sendNotFound(script)\\n\\n script = script.strip(os.sep)\\n path = os.path.join(scriptroot, script)\\n pathinfo = ''\\n if os.path.isdir(path):\\n print >> self.estrm, \\\\\\n \\\"vaologin: Attempted to execute a directory: \\\" + path\\n return self.sendNotFound(script)\\n \\n while path and not os.path.exists(path):\\n if not pathinfo:\\n pathinfo = \\\"/\\\" + os.path.basename(path)\\n else:\\n pathinfo = \\\"/\\\" + os.path.basename(path) + pathinfo\\n \\n path = os.path.dirname(path)\\n if not path or not os.path.isfile(path):\\n print >> self.estrm, \\\"vaologin: script not found: \\\" + path\\n return self.sendNotFound(\\\"/\\\"+script)\\n\\n env = os.environ.copy()\\n env['PATH_INFO'] = pathinfo\\n scriptname = os.environ['SCRIPT_NAME']\\n if self.scriptalias:\\n scriptname += self.scriptalias.rstrip('/')\\n scriptname = \\\"%s/%s\\\" % (scriptname, script)\\n env['SCRIPT_NAME'] = scriptname\\n\\n # load the environment with what we know\\n cgienv = self.getCGIEnv(self.request.getSession())\\n for key in cgienv.keys():\\n env[key] = cgienv[key]\\n\\n self._sendheader()\\n if asexec:\\n print >> self.estrm, \\\"executing %s via exec\\\" % path\\n os.execle(path, env)\\n else:\\n print >> self.estrm, \\\"executing %s via pipe\\\" % path\\n sessiondir = self.config.get('vaologin.auth.sessionsdir', '/tmp')\\n instrm = self.instream or sys.stdin\\n outfile = os.path.join(sessiondir, self.sessionid+'.out')\\n errfile = os.path.join(sessiondir, self.sessionid+'.err')\\n with open(outfile,'w') as out:\\n with open(errfile,'w') as err:\\n child = Popen(path, stdout=out, stderr=err, stdin=instrm, \\n env=env)\\n child.wait()\\n with open(outfile) as out:\\n for line in out:\\n self.ostrm.write(line)\\n os.remove(outfile)\\n os.remove(errfile)\\n if self.instream:\\n self.instream.close()\\n os.remove(self.instream.name)\\n self.instream = None\",\n \"def xj(self, script):\\n self.pbug(\\\"Executing > {0}\\\".format(script))\\n script = \\\"try {{ {0} }} catch(e) {{ miteErrorCallback(String(e), e, 'execute', '', '');}}\\\".format(script)\\n return self.browser.ExecuteJavascript(script)\",\n \"def execute(self, template, arguments):\\n raise NotImplementedError()\",\n \"def run(*script_args):\\n\\n # Parse the arguments\\n argv = []\\n if script_args:\\n argv = shlex.split(script_args[0])\\n\\n # Default values for the arguments\\n debug = False\\n email_column_name = 'email'\\n make_instructors = False\\n password_default = 'changethepassword'\\n password = password_default\\n\\n # Parse options\\n try:\\n opts, args = getopt.getopt(argv, \\\"de:ip:\\\")\\n except getopt.GetoptError as e:\\n print(e.msg)\\n print(run.__doc__)\\n sys.exit(2)\\n\\n # Store option values\\n for optstr, value in opts:\\n if optstr == \\\"-d\\\":\\n debug = True\\n elif optstr == \\\"-i\\\":\\n make_instructors = True\\n elif optstr == \\\"-e\\\":\\n email_column_name = value\\n elif optstr == \\\"-p\\\":\\n password = value\\n\\n filenames = args\\n\\n if debug:\\n print('Options:')\\n print(' Debug:' + str(debug))\\n print(' Email column name:', email_column_name)\\n print(' Make instructors:', str(make_instructors))\\n print(\\n ' Default password: ',\\n 'default' if password == password_default else 'GIVEN')\\n print(' Files: ' + ', '.join(filenames))\\n\\n if debug:\\n print('Step: Creating the instructor group')\\n group = Group.objects.filter(name='instructor').first()\\n # Create the instructor group if it does not exist\\n if not group:\\n group = Group(name='instructor')\\n group.save()\\n elif debug:\\n print('Group already exists. Bypassing.')\\n if debug:\\n print('Done')\\n\\n if not make_instructors:\\n group = None\\n\\n # If there is no argument we are done.\\n if len(script_args) == 0:\\n return\\n\\n if debug:\\n print('Step: Creating users')\\n\\n if not make_instructors:\\n group = None\\n\\n not_present = [x for x in filenames if not os.path.exists(x)]\\n if not_present:\\n print('These files were not found: ', ', '.join(not_present))\\n filenames = [x for x in filenames if x not in not_present]\\n\\n if not filenames:\\n print('No files provided to create users. Terminating')\\n return\\n\\n # Get the emails\\n emails = get_column_value_list(filenames, email_column_name, debug)\\n\\n create_users(emails, password, group, debug)\",\n \"def run(script, restart=False, subscript=True, triggerAlarm=False, **options) :\\n\\n global fullScriptName\\n global scriptName\\n global scriptOptions\\n global scriptKeyVals\\n # For backward compatibility\\n scriptOptions = \\\"\\\"\\n \\n # Note to the programmer:\\n # Scripts that are run by this function can expect the global variable\\n # restartScript to be defined.\\n\\n # Make sure that the script name has no embedded whitespace.\\n if len(script.strip().split()) != 1:\\n msg = \\\"The script name must be a string with no embedded.\\\\n \\\"\\n msg += \\\"white space.\\\\n\\\"\\n msg += __doc__\\n raise Exception, msg\\n\\n # Check input parameter type\\n if not isinstance(script, str) :\\n raise Exception, \\\"script must be a string\\\"\\n\\n # Stash away current script name if one is already running...\\n state = s.getScriptState()\\n nested = state == RUN\\n if nested: previousScript = fullScriptName\\n\\n restartScript = restart # restartScript is used externally\\n scriptName = script.split()[0]\\n scriptKeyVals = dict(options)\\n # This will throw an exception if the script cannot be found\\n fullScriptName = helpers.getScriptFullPathname(scriptName)\\n\\n s.setScriptName(fullScriptName)\\n s.setScriptState(carma.control.RUNNING)\\n logmsg = \\\"Beginning \\\" + fullScriptName + \\\" with \\\"\\n if len(scriptKeyVals) > 0 :\\n logmsg += \\\"options\\\"\\n scriptsOptions = \\\"\\\"\\n prefix = ' '\\n for i in scriptKeyVals.items():\\n scriptOptions += prefix + i[0] + \\\"=\\\" + str(i[1])\\n prefix = ', '\\n else :\\n scriptOptions = \\\"no options\\\"\\n logmsg += scriptOptions \\n scriptlog(logmsg)\\n print logmsg\\n if (not restart) and (not subscript): s.clearScriptAll()\\n try :\\n # Execute the script using identical globals and locals dictionaries.\\n # This prevents the current scope type (function scope) from \\\"leaking\\\"\\n # into the execution environment of the script.\\n #\\n # By passing identical dictionaries, global variables will work\\n # as expected in the script.\\n #\\n # In addition, a copy of the globals() + locals() dictionary is used,\\n # which avoids \\\"leaking\\\" import statements from the script back into\\n # this execution environment.\\n d = dict(globals(), **locals())\\n execfile(fullScriptName, d, d)\\n except ScriptReturn, ex:\\n #print \\\"Quick and quiet return from script has been called\\\"\\n pass\\n except ScriptInputError, ex:\\n s.setScriptState(carma.control.CRASHED)\\n print \\\"Script input error!!\\\"\\n if triggerAlarm: s.alarm(True, \\\"scriptError\\\")\\n return False\\n # Don't print out the exception because we have already done that\\n #print ex\\n except ScriptError, ex:\\n s.setScriptState(carma.control.CRASHED)\\n print \\\"Script error!!\\\"\\n print ex\\n if triggerAlarm: s.alarm(True, \\\"scriptError\\\")\\n cancel() # Stop any integrations that might be running...\\n return False\\n except (carma.util.CancelException,KeyboardInterrupt, SuccessfulCancel):\\n s.setScriptState(carma.control.CANCELED)\\n print \\\"Script canceled.\\\"\\n rtdComment(\\\"Script canceled.\\\")\\n scriptlog(\\\"Script \\\" + fullScriptName + \\\" has been canceled.\\\" )\\n cancel() # Stop any integrations that might be running...\\n return False\\n except carma.util.UserException, userEx:\\n s.setScriptState( carma.control.CRASHED )\\n if triggerAlarm: s.alarm( True, \\\"scriptError\\\" )\\n errMsg = \\\"\\\\nScript crashed due to RTS runtime exception: \\\\n\\\"\\n errMsg += \\\"\\\\n\\\" + userEx.errorMsg + \\\".\\\"\\n printInColor( errMsg, 'red', linefeed=True )\\n try:\\n exceptionType, exceptionValue, exceptionTraceback = sys.exc_info()\\n scriptlog( \\\"Exception: \\\" )\\n tbstrs = traceback.format_exception(exceptionType, \\n exceptionValue, \\n exceptionTraceback)\\n for t in tbstrs: scriptlog(t) \\n printInColor( \\\"\\\\nDetailed context information has been sent to the logs.\\\\n\\\", \\n 'red', \\n linefeed=True )\\n except: \\n printInColor( \\\"\\\\nUnable to send detailed context information to the logs.\\\", \\n 'red', \\n linefeed=True )\\n return False\\n except Exception, ex:\\n s.setScriptState(carma.control.CRASHED)\\n if triggerAlarm: s.alarm(True, \\\"scriptError\\\")\\n print \\\"Script error!!\\\"\\n print ex\\n exceptionType, exceptionValue, exceptionTraceback = sys.exc_info()\\n traceback.print_exception(exceptionType,\\n exceptionValue, exceptionTraceback)\\n scriptlog(\\\"Exception: \\\" + str(ex))\\n tbstrs = traceback.format_exception(exceptionType, \\n exceptionValue, exceptionTraceback)\\n for t in tbstrs: scriptlog(t) \\n scriptlog(\\\"Script \\\" + fullScriptName + \\\" crashed!!\\\")\\n\\n cancel() # Stop any integrations that might be running...\\n if s.getScriptBool(odi.INDX_BOOL_PACS): \\n pacs.cleanup(s.getScriptString(odi.INDX_STR_PACS_OBSBLOCK))\\n return False\\n except :\\n # Anything else represents an unknown exception...\\n s.setScriptState(carma.control.CRASHED)\\n print \\\"Script error!!\\\"\\n if triggerAlarm: s.alarm(True, \\\"scriptError\\\")\\n scriptlog(\\\"Script \\\" + fullScriptName + \\\" crashed!!\\\" )\\n cancel() # Stop any integrations that might be running...\\n raise\\n scriptlog(\\\"Script \\\" + fullScriptName + \\\" completed\\\")\\n if nested:\\n fullScriptName = previousScript\\n s.setScriptName(previousScript)\\n else: s.setScriptState(carma.control.COMPLETED)\\n return True\",\n \"def execute(self, instruction: str, arguments: List[Any]) -> None:\\n self._validate_configurations()\\n validate_raw_endpoint(self.__endpoints[instruction])\\n request = generate_request(self.__endpoints[instruction], arguments)\\n response = execute(self.__metadata[\\\"server\\\"], request)\\n self.__handle_response(response)\",\n \"def run_script(script_file: str, config_file: str, **kwargs: Any) -> None:\\n # Add config path and current working directory to sys.path to correctly load the configuration\\n script_filepath = Path(script_file)\\n config_filepath = Path(config_file)\\n sys.path.insert(0, script_filepath.resolve().parent.as_posix())\\n sys.path.insert(0, config_filepath.resolve().parent.as_posix())\\n sys.path.insert(0, os.getcwd())\\n\\n module = load_module(script_filepath)\\n _check_script(module)\\n\\n run_fn = module.__dict__[\\\"run\\\"]\\n\\n # Lazy setup configuration\\n config = ConfigObject(config_filepath, script_filepath=script_filepath)\\n\\n run_fn(config, **kwargs)\",\n \"def execute(self, parseString, *args):\\r\\n debug.write('[SourceRPG] Executing SQL String: %s' % parseString, 1, True)\\r\\n self.cursor.execute(parseString, args)\\r\\n debug.write('[SourceRPG] SQL String executed successfully', 2, True)\",\n \"def run_script(self,\\n\\t script,\\n\\t shutit_pexpect_child=None,\\n\\t in_shell=True,\\n\\t echo=None,\\n\\t note=None,\\n\\t loglevel=logging.DEBUG):\\n\\t\\tshutit_global.shutit_global_object.yield_to_draw()\\n\\t\\tshutit_pexpect_child = shutit_pexpect_child or self.get_current_shutit_pexpect_session().pexpect_child\\n\\t\\tshutit_pexpect_session = self.get_shutit_pexpect_session_from_child(shutit_pexpect_child)\\n\\t\\treturn shutit_pexpect_session.run_script(script,\\n\\t\\t in_shell=in_shell,\\n\\t\\t echo=echo,\\n\\t\\t note=note,\\n\\t\\t loglevel=loglevel)\",\n \"def run(self, sql, *args):\\n return self.database.execute(sql, args)\",\n \"def execute(self, code, environment = dict()):\\r\\n if not self.config.get('scripting', 'enable') and type(code) == str:\\r\\n self.send(code, log = False)\\r\\n else:\\r\\n if type(code) == str:\\r\\n c = compile(code, 'errors.log', 'exec')\\r\\n else:\\r\\n c = code\\r\\n eval(c, self.getEnvironment(environment))\",\n \"def RunScript(code):\\n with ScriptContext() as script_module:\\n try:\\n exec code in script_module.__dict__\\n except:\\n # Get exception output as close to exec as possible.\\n # We don't take the first entry in the traceback because it just contains\\n # \\\"exec\\\". Everything after that is the submitted code.\\n try:\\n etype, evalue, tb = sys.exc_info()\\n traceback.print_exception(etype,\\n evalue,\\n tb.tb_next, # one frame up\\n file=sys.stderr)\\n finally:\\n del tb # break circular references when using exc_info\\n\\n return sys.stdout.getvalue(), sys.stderr.getvalue()\",\n \"def with_script(self, script):\\n self.script = script\\n return self\",\n \"def exec_script(self, script):\\n filename = os.path.join(self.script_dir, script + \\\".sh\\\")\\n # http://docs.python.org/library/os.html#os.X_OK\\n if os.access(filename, os.X_OK):\\n with open(filename):\\n subprocess.call(filename)\\n self.vibrate(0.1)\",\n \"def run_script(input_file, script_name, interpreter='python'):\\r\\n from paver.easy import sh\\r\\n from paver.path import path\\r\\n rundir = path(input_file).dirname()\\r\\n output_text = sh('cd %(rundir)s && %(interpreter)s %(script_name)s 2>&1' % vars(), capture=True)\\r\\n response = '\\\\n::\\\\n\\\\n\\\\t$ %(interpreter)s %(script_name)s\\\\n\\\\t' % vars()\\r\\n response += '\\\\n\\\\t'.join(output_text.splitlines())\\r\\n while not response.endswith('\\\\n\\\\n'):\\r\\n response += '\\\\n'\\r\\n return response\",\n \"def execute(cls, commandline_arguments, check_return_code=True):\\n from infi.execute import execute\\n arguments = [cls.path()]\\n arguments.extend(commandline_arguments)\\n logger.debug(\\\"Executing {!r}\\\".format(arguments))\\n process = execute(arguments)\\n process.wait()\\n if process.get_returncode() != 0 and check_return_code:\\n raise RuntimeError(arguments, process.get_returncode(), process.get_stdout(), process.get_stderr())\\n logger.debug(\\\"Result: {!r}\\\".format(process.get_stdout()))\\n return process.get_stdout()\",\n \"def execute():\",\n \"def isScriptExecuted(self, timeout=20.0, commandId=None):\\n TestAdapterLib.check_timeout(caller=TestAdapterLib.caller(), timeout=timeout)\\n \\n return self.isActionAccepted(timeout=timeout, commandName=Command.EXECUTE_SCRIPT, \\n commandId=commandId)\",\n \"def script_run(ctx: click.Context, name, script_arguments):\\n subcommand_script.cmd_run(ctx.obj, name, script_arguments)\",\n \"def scriptNode(*args, afterScript: Union[AnyStr, bool]=\\\"\\\", beforeScript: Union[AnyStr, bool]=\\\"\\\",\\n executeAfter: bool=True, executeBefore: bool=True, ignoreReferenceEdits:\\n bool=True, name: AnyStr=\\\"\\\", scriptType: Union[int, bool]=0, sourceType:\\n Union[AnyStr, bool]=\\\"\\\", q=True, query=True, e=True, edit=True,\\n **kwargs)->Union[None, Any]:\\n pass\",\n \"def checkscript(self, content):\\n if \\\"VERSION\\\" not in self.__capabilities:\\n raise NotImplementedError(\\n \\\"server does not support CHECKSCRIPT command\\\")\\n content = self.__prepare_content(content)\\n code, data = self.__send_command(\\\"CHECKSCRIPT\\\", [content])\\n if code == \\\"OK\\\":\\n return True\\n return False\",\n \"def ExecuteScript(script):\\n os.system(\\\"%s > /dev/null 2>&1\\\" % script)\",\n \"def run_script(self, script_name, script_args=None, node_paths=None):\\n # TODO: consider add a pants.util function to manipulate command line.\\n package_manager_args = self._get_run_script_args()\\n package_manager_args.append(script_name)\\n if script_args:\\n package_manager_args.append('--')\\n package_manager_args.extend(script_args)\\n return self.run_command(args=package_manager_args, node_paths=node_paths)\",\n \"def process_arguments(\\n script_path: str,\\n arguments: Dict[str, str],\\n scheduler_arguments: Dict[str, str] = [],\\n ):\\n # maps scheduler specific args with commands\\n scheduler_header_command_dict = {\\n \\\"time\\\": \\\"-l walltime={value}\\\",\\n \\\"job_name\\\": \\\"-N {value}\\\",\\n }\\n scheduler_args_commands = \\\"\\\"\\n if \\\"nodes\\\" in scheduler_arguments and \\\"ncpus\\\" in scheduler_arguments:\\n scheduler_args_commands += f\\\"-l select={scheduler_arguments['nodes']}:ncpus={scheduler_arguments['ncpus']}\\\"\\n for key, value in scheduler_arguments.items():\\n if key in scheduler_header_command_dict.keys():\\n scheduler_args_commands = (\\n scheduler_args_commands\\n + \\\" \\\"\\n + scheduler_header_command_dict[key].format(value=value)\\n )\\n # script related args\\n # construct a string\\n args_string = \\\",\\\".join([f'{k}=\\\"{v}\\\"' for k, v in arguments.items()])\\n return (\\n f\\\"{scheduler_args_commands} -W umask=002 -v {args_string} -V {script_path} \\\"\\n )\",\n \"def script(self, code):\\r\\n LOG(\\\"Executing script \\\" + repr(code))\\r\\n cmd = MsgHelper.createMessage(Messages.CMD_SCRIPT)\\r\\n cmd[Messages.FIELD_SCRIPT] = code\\r\\n cmd[Messages.FIELD_FORCE] = True\\r\\n self.mailbox.push( cmd, high_priority = False )\",\n \"def test_script_integrity(capsys):\\n script = os.path.abspath(\\\"examples/scikitlearn-iris/main.py\\\")\\n\\n return_code = subprocess.call([\\\"python\\\", script, \\\"0.1\\\"])\\n\\n assert return_code != 2, \\\"The example script does not exists.\\\"\\n assert return_code != 1, \\\"The example script did not terminates its execution.\\\"\\n assert (\\n return_code == 0 and not capsys.readouterr().err\\n ), \\\"The example script encountered an error during its execution.\\\"\",\n \"def execute(self):\\n if self.sql is None:\\n self.sql = self.construct_query()\\n # Only SQL strings can be split, not (e.g.) SQLAlchemy statements.\\n if self.multiple_statements and isinstance(self.sql, str):\\n statements = self._split_sql()\\n else:\\n statements = [self.sql]\\n single_statement = True if len(statements) == 1 and self.filename else False\\n try:\\n for statement in statements:\\n result_proxy = self.cm.conn.execute(statement)\\n log_string = self.filename if single_statement else str(statement)[:25]\\n self.logger.info(\\\"Executed {} against {}\\\".format(log_string, self.cm.db))\\n if result_proxy.cursor:\\n return self.fetch_results(result_proxy)\\n except Exception as e:\\n self.logger.exception(e)\\n raise\",\n \"async def _call_script(\\n self, cmd_args: List[str], env: Optional[Dict[str, str]] = None, cwd: Optional[Path] = None\\n ) -> None:\\n process = await asyncio.create_subprocess_exec( # wait for process creation to finish\\n self._executable,\\n *cmd_args,\\n stderr=asyncio.subprocess.PIPE, # we capture the stderr for errors\\n env=env,\\n cwd=cwd,\\n )\\n # print(f\\\"Started: {cmd_args!r} (pid = {process.pid})\\\")\\n\\n try: # wait for process itself to finish\\n one_hour = 3600\\n _, stderr = await asyncio.wait_for(process.communicate(), one_hour)\\n except asyncio.TimeoutError as error:\\n raise RuntimeError(\\\"The script timed out.\\\") from error\\n\\n if process.returncode != 0:\\n print(f\\\"Failure: {cmd_args!r} (pid = {process.pid})\\\")\\n if stderr:\\n print(stderr.decode().strip())\\n raise RuntimeError(\\n f\\\"The script failed. Supplied arguments: {cmd_args} with exec: {self._executable}\\\"\\n )\",\n \"def execute():\\n pass\",\n \"def run(self, _):\\n ida_kernwin.warning(\\\"IDACpp cannot be run as a script\\\")\\n return False\",\n \"def do_pyscript(self, arg, opts=None):\\n if not arg:\\n self.perror(\\\"pyscript command requires at least 1 argument ...\\\", traceback_war=False)\\n self.do_help('pyscript')\\n return\\n\\n if not USE_ARG_LIST:\\n arg = shlex.split(arg, posix=POSIX_SHLEX)\\n\\n # Get the absolute path of the script\\n script_path = os.path.expanduser(arg[0])\\n\\n # Save current command line arguments\\n orig_args = sys.argv\\n\\n # Overwrite sys.argv to allow the script to take command line arguments\\n sys.argv = [script_path]\\n sys.argv.extend(arg[1:])\\n\\n # Run the script - use repr formatting to escape things which need to be escaped to prevent issues on Windows\\n self.do_py(\\\"run({!r})\\\".format(script_path))\\n\\n # Restore command line arguments to original state\\n sys.argv = orig_args\",\n \"def script(self, script):\\n\\n self._script = script\",\n \"def RunScript(self):\\r\\n if (not os.path.isfile(self.configDict[\\\"xsiPath\\\"])):\\r\\n print \\\"XSI does not exist\\\"\\r\\n return True\\r\\n \\r\\n print (\\\"start running \\\" + os.path.basename(self.__script.name))\\r\\n returnValue = self.RunApplication(self.configDict[\\\"xsiPath\\\"] + \\r\\n \\\" -script \\\\\\\"\\\" + self.__script.name + \\\"\\\\\\\" > NUL 2>>&1\\\", \\r\\n self.__workingDir)\\r\\n \\r\\n if (returnValue == 0):\\r\\n print \\\"finished running \\\" + os.path.basename(self.__script.name)\\r\\n else:\\r\\n print \\\"crashed running \\\" + os.path.basename(self.__script.name)\\r\\n # since may not be able to do anything with the generated files\\r\\n return False \\r\\n \\r\\n # XSI generates unicode logs -- convert to UTF-8\\r\\n (encoder, decoder, reader, writer) = codecs.lookup(\\\"utf-16-le\\\")\\r\\n for logfile in self.__logFiles:\\r\\n logInMemory = \\\"\\\"\\r\\n log = reader(open(logfile))\\r\\n line = log.readline()\\r\\n while (line):\\r\\n logInMemory = logInMemory + line\\r\\n line = log.readline()\\r\\n log.close()\\r\\n logInMemory.encode(\\\"utf-8\\\")\\r\\n log = open(logfile, \\\"w\\\")\\r\\n log.write(logInMemory)\\r\\n log.close()\\r\\n \\r\\n for logfile in self.__importLogFiles:\\r\\n logInMemory = \\\"\\\"\\r\\n log = open(logfile)\\r\\n line = log.readline()\\r\\n while (line):\\r\\n warningFind = line.find(\\\"WARNING\\\")\\r\\n ctfPathFind = line.find(FXsi.__REPLACE_PATH)\\r\\n if ((warningFind != -1) and (ctfPathFind != -1)):\\r\\n line = line.replace(\\\"WARNING\\\", \\\"CTF_OVERRIDE\\\")\\r\\n logInMemory = logInMemory + line\\r\\n line = log.readline()\\r\\n log.close()\\r\\n log = open(logfile, \\\"w\\\")\\r\\n log.write(logInMemory)\\r\\n log.close()\\r\\n \\r\\n # since XSI has to save in its own project tree, need to relocate files\\r\\n for projectPath, testProcedurePath in self.__pathMap:\\r\\n for entry in os.listdir(projectPath):\\r\\n shutil.move(os.path.join(projectPath, entry), \\r\\n os.path.join(testProcedurePath, entry))\\r\\n \\r\\n return True\",\n \"def run_query(self, sql_query='', *parameters):\\n if not self.cursor:\\n raise BaseException(\\\"Database not selected\\\")\\n\\n return self.cursor.execute(sql_query, parameters)\",\n \"def run_script(script_id, params=None):\\n # I p1 script id\\n # I p2 number of parameters (0-10)\\n ## (optional) extension ##\\n # I[] params\\n\\n if params is not None:\\n msg = \\\"\\\"\\n for p in params:\\n msg += struct.pack(\\\"I\\\", p)\\n nump = len(params)\\n extents = [msg]\\n else:\\n nump = 0\\n extents = []\\n\\n return _u2i(_pigpio_command_ext(\\n _control, _PI_CMD_PROCR, script_id, nump, extents))\",\n \"def runscript(self, path):\\n self.rpc.call(MsfRpcMethod.SessionMeterpreterScript, [self.sid, path])\\n return self.read()\",\n \"def execute_script_from_file(self, filename):\\n filename = os.path.join(self.curr_dir, filename)\\n # Connect to db\\n conn = sqlite3.connect(self.db_path)\\n cursor = conn.cursor()\\n with open(filename, \\\"r\\\", encoding=\\\"utf-8\\\") as sql_file:\\n sql_script = sql_file.read()\\n\\n # all SQL commands (split on ';')\\n sql_commands = filter(None, sql_script.split(\\\";\\\"))\\n # Execute every command from the input file\\n for command in sql_commands:\\n # This will skip and report errors\\n # For example, if the tables do not yet exist, this will skip over\\n # the DROP TABLE commands\\n try:\\n cursor.execute(command)\\n except OperationalError as msg:\\n print(\\\"Command skipped: \\\", msg)\\n conn.commit()\\n conn.close()\",\n \"def run_script_block(json_response):\\r\\n scripts = request.values['scripts']\\r\\n exec_id = request.values['exec_id']\\r\\n ds_name = request.values['datasource']\\r\\n user_id = current_user.login_name\\r\\n result = executor.run_script_block(exec_id, scripts, ds_name, user_id)\\r\\n return json_response(result=result)\",\n \"def execute(self, isd_as: ISD_AS, cmd: str, *args: str) -> str:\\n pass\",\n \"def run_script_on_vm(script, arguments=None):\\n\\n # Stop the program if no init has occurred.\\n Vagrant.stop_if_not_init()\\n\\n # What is the path to the script files on the VM?\\n path = \\\"sudo -u vagrant bash \\\" + Settings.scripts_on_vm + script\\n\\n # Are there any arguments?\\n if arguments:\\n path = path + ' ' + arguments\\n\\n # Make sure the cabal path is in the environment's PATH.\\n path = \\\"export PATH=/home/vagrant/.cabal/bin:$PATH; \\\" + path\\n\\n # Run vagrant ssh -c \\\"<commands>\\\" from the vagrant folder.\\n command = ['vagrant', 'ssh', '-c', path]\\n cwd = Settings.devbox_folder\\n try:\\n result = subprocess.check_call(command, cwd=cwd)\\n except subprocess.CalledProcessError:\\n Utilities.log(\\\"Running \\\" + script + \\\" on the VM failed.\\\")\\n exit(1)\",\n \"def ExecuteSql(session, sql):\\n client = apis.GetClientInstance('spanner', 'v1')\\n msgs = apis.GetMessagesModule('spanner', 'v1')\\n\\n # TODO(b/33482229): remove this workaround\\n def _ToJson(msg):\\n return extra_types.JsonProtoEncoder(\\n extra_types.JsonArray(entries=msg.entry))\\n def _FromJson(data):\\n return msgs.ResultSet.RowsValueListEntry(\\n entry=extra_types.JsonProtoDecoder(data).entries)\\n encoding.RegisterCustomMessageCodec(\\n encoder=_ToJson, decoder=_FromJson)(\\n msgs.ResultSet.RowsValueListEntry)\\n\\n req = msgs.SpannerProjectsInstancesDatabasesSessionsExecuteSqlRequest(\\n session=session.name,\\n executeSqlRequest=msgs.ExecuteSqlRequest(\\n sql=sql))\\n resp = client.projects_instances_databases_sessions.ExecuteSql(req)\\n return resp\",\n \"def shell_script(scriptfile, args=None):\\n # retrieve executable for this script.\\n try:\\n cmdargs = EXECS[scriptfile[-3:]]\\n except Exception as ex:\\n print_fail('Cannot locate executable for this script: '\\n '{}'.format(scriptfile),\\n exc=ex)\\n # build command list for subprocess\\n cmdlst = list(cmdargs)\\n cmdlst.append(scriptfile)\\n if args:\\n cmdlst.extend(args)\\n\\n # run/create process\\n print('\\\\nRunning: {}\\\\n'.format(' '.join(cmdlst)))\\n # Shell the process, returns the subprocess.Popen() proc object.\\n proc = run_process(cmdlst)\\n # Print stdout/stderr\\n retcode = print_proc_output(proc)\\n\\n return retcode\",\n \"def execute(self, *sql):\\n # assemble the command and pass it on to the connection\\n return self.postgres.execute(self.connection, \\\"\\\\n\\\".join(sql))\",\n \"def run_script(command):\\n process = subprocess.Popen(command, shell=True, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, universal_newlines=True)\\n output = process.communicate()[0]\\n success = process.poll() == 0\\n return (success, output)\",\n \"def run_athena_query(self, **kwargs):\\n LOGGER.debug('Executing query: %s', kwargs['query'])\\n query_execution_resp = self.athena_client.start_query_execution(\\n QueryString=kwargs['query'], QueryExecutionContext={\\n 'Database': kwargs.get(\\n 'database', self.DATABASE_DEFAULT)}, ResultConfiguration={\\n 'OutputLocation': '{}/{}'.format(\\n self.athena_results_bucket, self.athena_results_key)})\\n\\n query_execution_result = self.check_query_status(\\n query_execution_resp['QueryExecutionId'])\\n\\n if query_execution_result != 'SUCCEEDED':\\n LOGGER.error(\\n 'The query %s returned %s, exiting!',\\n kwargs['query'],\\n query_execution_result)\\n return False, {}\\n\\n query_results_resp = self.athena_client.get_query_results(\\n QueryExecutionId=query_execution_resp['QueryExecutionId'],\\n )\\n\\n # The idea here is to leave the processing logic to the calling functions.\\n # No data being returned isn't always an indication that something is wrong.\\n # When handling the query result data, iterate over each element in the Row,\\n # and parse the Data key.\\n # Reference: https://bit.ly/2tWOQ2N\\n if not query_results_resp['ResultSet']['Rows']:\\n LOGGER.debug('The query %s returned empty rows of data', kwargs['query'])\\n\\n return True, query_results_resp\",\n \"def _execute(script, prefix=None, path=None):\\n path = tempfile.gettempdir() if path is None else path\\n result = 1\\n try:\\n fh = tempfile.NamedTemporaryFile('w', delete=False)\\n fh.write(script)\\n fh.close()\\n print('Executing script below with cwd=%s\\\\n{{{\\\\n%s\\\\n}}}\\\\n' %\\n (path, script))\\n try:\\n os.chmod(fh.name, stat.S_IRWXU)\\n env = os.environ.copy()\\n if prefix is not None:\\n env['COLCON_BUNDLE_INSTALL_PREFIX'] = prefix\\n result = subprocess.run(\\n fh.name, cwd=path, env=env, stdout=PIPE, stderr=PIPE,\\n universal_newlines=True)\\n if result.stdout is not None:\\n logger.debug('stdout output: \\\\n' + result.stdout)\\n if result.stderr is not None:\\n logger.warn('stderr output: \\\\n' + result.stderr)\\n except OSError as ex:\\n print('Execution failed with OSError: %s' % ex)\\n finally:\\n if os.path.exists(fh.name):\\n os.remove(fh.name)\\n logger.info('Return code was: %s' % result)\\n return result.returncode == 0\",\n \"def clirun(command, arguments=None):\\n if arguments == '':\\n return 'Was soll ich denn damit anfangen? :man-facepalming: Da musst du schon mehr eingeben ...'\\n elif arguments is None:\\n run_cmd = ' '.join([IDOITCLI, command])\\n else:\\n run_cmd = '{} {} \\\"{}\\\"'.format(IDOITCLI, command, arguments)\\n out = subprocess.Popen(run_cmd, shell=True, stdin=subprocess.PIPE, stdout=subprocess.PIPE, stderr=subprocess.PIPE)\\n (stdout, stderr) = out.communicate()\\n return_code = out.returncode\\n if return_code > 0:\\n return return_code, 'Da ist wohl was schief gelaufen: ```' + stderr.strip().decode() + '```'\\n else:\\n return return_code, stdout.strip().decode()\",\n \"def execute_script(file_name):\\n conn = psycopg2.connect(config['SQLALCHEMY_DATABASE_URI'])\\n cur = conn.cursor()\\n sql_file = os.path.join(os.path.dirname(os.path.abspath(__file__)), file_name)\\n cur.execute(open(sql_file, 'r').read())\\n conn.commit()\\n cur.close()\\n conn.close()\",\n \"def run_script(input_file, run_dir, script_name, interpreter='python'):\\n from paver.runtime import sh\\n from paver.path import path\\n docdir = path(input_file).dirname()\\n output_text = sh('cd %(docdir)s/%(run_dir)s;%(interpreter)s %(script_name)s 2>&1' % vars(),\\n capture=True)\\n response = '\\\\n::\\\\n\\\\n\\\\t$ %(interpreter)s %(script_name)s\\\\n\\\\t' % vars()\\n response += '\\\\n\\\\t'.join(output_text.splitlines())\\n while not response.endswith('\\\\n\\\\n'):\\n response += '\\\\n'\\n return response\",\n \"def execute(in_file, resolution, width, height, raster_mode):\\n\\n raster_tmpfile = tempfile.NamedTemporaryFile()\\n\\n # Convert width and height to DPI.\\n width = (width/72) * resolution\\n height = (height/72) * resolution\\n\\n args = [config.gs,\\n \\\"-q\\\",\\n \\\"-dBATCH\\\",\\n \\\"-dNOPAUSE\\\",\\n \\\"-r%s\\\" % resolution,\\n \\\"-g%dx%d\\\" % (width, height),\\n \\\"-sDEVICE=%s\\\" % raster_mode,\\n \\\"-sOutputFile=%s\\\" % raster_tmpfile.name,\\n \\\"-\\\",\\n ]\\n # Execute ghostscript\\n log.debug(\\\"Opening ghostscript process with args: %s\\\" % args)\\n process = subprocess.Popen(args, cwd=config.tmp_dir,\\n stdout=subprocess.PIPE,\\n stderr=subprocess.PIPE,\\n stdin=subprocess.PIPE)\\n (stdoutdata, stderrdata) = process.communicate(in_file.read())\\n log.debug(\\\"Waiting on ghostscript to terminate.\\\")\\n process.wait()\\n\\n # Check that ghostscript functioned correctly.\\n if process.returncode != 0:\\n log.crit(\\\"Ghostscript failed during execution with returncode = %s.\\\"\\n % process.returncode)\\n\\n # Get raster information into a StringIO\\n log.debug(\\\"Rewinding ghostscript raster output and storing it in StringIO\\\")\\n raster_tmpfile.seek(0)\\n raster = raster_tmpfile\\n\\n # Gather the vector information into a StringIO\\n log.debug(\\\"Gathering the vector output from ghostscript.\\\")\\n vector = StringIO()\\n vector.write(stdoutdata)\\n vector.seek(0)\\n\\n log.debug(\\\"Returning ghostscript raster and vector output.\\\")\\n return (raster, vector)\",\n \"def executeQuery(args):\\n\\n if 'dbname' not in args and 'query' not in args and 'type' not in args:\\n print \\\"invalid executeQuery options\\\"\\n else:\\n querytype = args['type']\\n connection = connect({ 'dbname' : args['dbname'] })\\n if connection:\\n cursor = connection.cursor()\\n query = args['query']\\n\\n # FIND QUERY\\n if(querytype == 'find'):\\n if 'values' not in args:\\n cursor.execute(query)\\n else:\\n cursor.execute(query,args['values'])\\n if cursor.rowcount > 0:\\n result = cursor.fetchall()\\n else:\\n result = 0\\n\\n # DELETE QUERY\\n if(querytype == 'delete'):\\n cursor.execute(query)\\n result = cursor.rowcount\\n\\n # INSERT QUERY\\n if(querytype == 'insert'):\\n if 'values' not in args:\\n print \\\"no values to insert\\\"\\n else:\\n cursor.execute(query,args['values'])\\n result = cursor.rowcount\\n\\n connection.commit()\\n cursor.close()\\n connection.close()\\n return result\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7854742","0.61550087","0.59776443","0.5792648","0.5619949","0.55894285","0.55582255","0.5536295","0.54101485","0.536589","0.5349416","0.53441995","0.5326733","0.53031874","0.5293181","0.529026","0.52394783","0.5232753","0.5228306","0.521149","0.52065","0.5166577","0.5133957","0.5074078","0.50738883","0.5069975","0.50101393","0.5008663","0.4966692","0.49546844","0.49478316","0.4909066","0.4906714","0.48677677","0.4836271","0.48259005","0.48256093","0.48243502","0.48199207","0.48158145","0.48088658","0.47972777","0.47940502","0.4793551","0.47863737","0.47659332","0.47393522","0.47291145","0.47022","0.47007212","0.46840724","0.46769062","0.46717718","0.46694276","0.46629128","0.46422133","0.46420518","0.4637277","0.4636574","0.4628902","0.46180606","0.46155095","0.46153948","0.46098787","0.45948306","0.45851517","0.4571546","0.45712525","0.4565024","0.4562521","0.45592618","0.45570368","0.4546463","0.45458975","0.45421818","0.45321718","0.4529533","0.45160735","0.4513471","0.451017","0.45033208","0.44900736","0.4488184","0.4479337","0.44781053","0.44762442","0.44736272","0.44731405","0.44690666","0.44665697","0.44556808","0.44457725","0.44420165","0.44354472","0.44284937","0.44144723","0.4413763","0.44094992","0.43946484","0.4393568"],"string":"[\n \"0.7854742\",\n \"0.61550087\",\n \"0.59776443\",\n \"0.5792648\",\n \"0.5619949\",\n \"0.55894285\",\n \"0.55582255\",\n \"0.5536295\",\n \"0.54101485\",\n \"0.536589\",\n \"0.5349416\",\n \"0.53441995\",\n \"0.5326733\",\n \"0.53031874\",\n \"0.5293181\",\n \"0.529026\",\n \"0.52394783\",\n \"0.5232753\",\n \"0.5228306\",\n \"0.521149\",\n \"0.52065\",\n \"0.5166577\",\n \"0.5133957\",\n \"0.5074078\",\n \"0.50738883\",\n \"0.5069975\",\n \"0.50101393\",\n \"0.5008663\",\n \"0.4966692\",\n \"0.49546844\",\n \"0.49478316\",\n \"0.4909066\",\n \"0.4906714\",\n \"0.48677677\",\n \"0.4836271\",\n \"0.48259005\",\n \"0.48256093\",\n \"0.48243502\",\n \"0.48199207\",\n \"0.48158145\",\n \"0.48088658\",\n \"0.47972777\",\n \"0.47940502\",\n \"0.4793551\",\n \"0.47863737\",\n \"0.47659332\",\n \"0.47393522\",\n \"0.47291145\",\n \"0.47022\",\n \"0.47007212\",\n \"0.46840724\",\n \"0.46769062\",\n \"0.46717718\",\n \"0.46694276\",\n \"0.46629128\",\n \"0.46422133\",\n \"0.46420518\",\n \"0.4637277\",\n \"0.4636574\",\n \"0.4628902\",\n \"0.46180606\",\n \"0.46155095\",\n \"0.46153948\",\n \"0.46098787\",\n \"0.45948306\",\n \"0.45851517\",\n \"0.4571546\",\n \"0.45712525\",\n \"0.4565024\",\n \"0.4562521\",\n \"0.45592618\",\n \"0.45570368\",\n \"0.4546463\",\n \"0.45458975\",\n \"0.45421818\",\n \"0.45321718\",\n \"0.4529533\",\n \"0.45160735\",\n \"0.4513471\",\n \"0.451017\",\n \"0.45033208\",\n \"0.44900736\",\n \"0.4488184\",\n \"0.4479337\",\n \"0.44781053\",\n \"0.44762442\",\n \"0.44736272\",\n \"0.44731405\",\n \"0.44690666\",\n \"0.44665697\",\n \"0.44556808\",\n \"0.44457725\",\n \"0.44420165\",\n \"0.44354472\",\n \"0.44284937\",\n \"0.44144723\",\n \"0.4413763\",\n \"0.44094992\",\n \"0.43946484\",\n \"0.4393568\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.68645024"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":26088,"cells":{"query":{"kind":"string","value":"Construct the environment for executing the target program."},"document":{"kind":"string","value":"def MakeEnvironment(self, context):\n\n # Start with any environment variables that are already present\n # in the environment.\n environment = os.environ.copy()\n # Copy context variables into the environment.\n for key, value in context.items():\n name = \"QMV_\" + key\n environment[name] = value\n # Extract additional environment variable assignments from the\n # 'Environment' field.\n for assignment in self.environment:\n if \"=\" in assignment:\n # Break the assignment at the first equals sign.\n variable, value = string.split(assignment, \"=\", 1)\n environment[variable] = value\n else:\n raise ValueError, \\\n qm.error(\"invalid environment assignment\",\n assignment=assignment)\n return environment"},"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_python_environment(self):\n # build command for creating the python environment\n cmd_args = {\n 'exe': self.env_executable,\n 'cmds': \" \".join(self.env_commands),\n 'flags': \" \".join(self.env_flags),\n 'args': \" \".join(self.env_arguments),\n }\n cmd_create_env = self.cmd_env.format(**cmd_args)\n print(\"Building new python environment ({}) ... \"\n .format(self.proj_name))\n with click_spinner.spinner():\n errno, stdout, stderr = utils.run_command(cmd_create_env, env=None,\n shell=True)\n if errno:\n raise Exception(\"Environment setup failed (STDERR: {})\"\n .format(stderr))","def setup_environment(self):\n self.run_command(\"cd {}\".format(quote(str(self.builddir))))\n env_vars = self._build_env_variables_string()\n if env_vars:\n env_vars = quote(env_vars)\n command = \"{} DISTRO={} MACHINE={} . {} build-{}\".format(\n env_vars,\n quote(self.distro),\n quote(self.machine),\n quote(self.init_env_file),\n quote(self.distro),\n )\n self.run_command(command)","def __MakeEnvironment(self):\n environment= os.environ.copy()\n\n for key, value in self.__context.items():\n if type(value) is str:\n name = \"QMV_\" + key.replace(\".\", \"__\")\n environment[name]= value\n\n return environment","def _create_environment(config):\n if isinstance(config.env, str):\n env = gym.make(config.env)\n else:\n env = config.env()\n if config.max_length:\n env = tools.wrappers.LimitDuration(env, config.max_length)\n env = tools.wrappers.RangeNormalize(env)\n env = tools.wrappers.ClipAction(env)\n env = tools.wrappers.ConvertTo32Bit(env)\n return env","def make_env():\n return {\n 'init': init,\n 'step': step,\n 'is_terminal': is_terminal,\n 'state_as_example': state_as_example,\n }","def build_env(self, instance, private_data_dir, private_data_files=None):\n env = {}\n # Add ANSIBLE_* settings to the subprocess environment.\n for attr in dir(settings):\n if attr == attr.upper() and attr.startswith('ANSIBLE_'):\n env[attr] = str(getattr(settings, attr))\n # Also set environment variables configured in AWX_TASK_ENV setting.\n for key, value in settings.AWX_TASK_ENV.items():\n env[key] = str(value)\n\n env['AWX_PRIVATE_DATA_DIR'] = private_data_dir\n\n if self.instance.execution_environment is None:\n raise RuntimeError(f'The {self.model.__name__} could not run because there is no Execution Environment.')\n\n return env","def generate_environment(self):\n try:\n if self._environment is None:\n self._environment = Environment.fromfilepath(self._environmentName,\n self._configuration.environment_file_path)\n except Exception:\n raise","def _setup_environment_vars(self, opts):\n # Check that these directories actually exist\n assert os.path.isdir(opts.movie_advisor_home)\n\n #if not 'install-bento' in self.actions: assert os.path.isdir(opts.bento_home)\n\n self.movie_advisor_home = opts.movie_advisor_home\n self.bento_home = opts.bento_home\n self.bento_tgz = opts.bento_tgz\n self.kiji_uri = \"kiji://.env/tutorial\"\n\n # \"express job\" takes a jar file as an argument\n assert os.path.isfile(os.path.join(self.movie_advisor_home, self.express_jar))\n\n # Set the classpath for all of the commands that we'll run\n jarsFullPaths = [os.path.join(self.movie_advisor_home, j) for j in self.jars]\n for jar in jarsFullPaths: assert os.path.isfile(jar)\n\n classpath = \":\".join(jarsFullPaths)\n os.environ['KIJI_CLASSPATH'] = classpath\n\n if opts.show_classpath:\n print(\"export KIJI_CLASSPATH=%s\" % classpath)\n sys.exit(0)","def create(self):\n os.environ.update(self.env)\n return self.core(*self.args)","def create_aiida_project_environment(self):\n # mock the virtualenv activation procedure\n venv_prefix = self.env_folder / self.proj_name\n current_env = os.environ.copy()\n current_env.pop('PYTHONHOME', None)\n current_env['VIRTUAL_ENV'] = venv_prefix\n old_path = current_env['PATH']\n new_path = str(venv_prefix / 'bin') + os.pathsep + old_path\n current_env['PATH'] = new_path\n try:\n self.create_folder_structure()\n self.build_python_environment()\n self.install_packages_from_index(env=current_env)\n self.install_packages_from_source(env=current_env)\n except Exception:\n self.exit_on_exception()\n raise\n self.create_spec_entry()","def make_environment(\n evaluation: bool = False,\n task: str = 'MountainCarContinuous-v0') -> dm_env.Environment:\n del evaluation\n\n # Load the gym environment.\n environment = gym.make(task)\n\n # Make sure the environment obeys the dm_env.Environment interface.\n environment = wrappers.GymWrapper(environment)\n # Clip the action returned by the agent to the environment spec.\n environment = wrappers.CanonicalSpecWrapper(environment, clip=True)\n environment = wrappers.SinglePrecisionWrapper(environment)\n\n return environment","def initialise(self, args, environ):","def get_environ():\n # Manually set environment.\n if FLAGS.env is not None:\n return BasicEnvironment.from_json(open(FLAGS.env, \"r\").read())\n\n if FLAGS.data_folder is None:\n data_folder = FLAGS.dataset\n else:\n data_folder = FLAGS.data_folder\n exp_id = \"exp_\" + FLAGS.dataset + \"_\" + FLAGS.model\n if FLAGS.id is None:\n exp_id = gen_id(exp_id)\n else:\n exp_id = FLAGS.id\n return BasicEnvironment(\n device=get_device(FLAGS.gpu),\n dataset=FLAGS.dataset,\n data_folder=data_folder,\n logs_folder=FLAGS.logs,\n save_folder=FLAGS.results,\n run_validation=FLAGS.validation,\n verbose=FLAGS.verbose,\n exp_id=exp_id,\n description=FLAGS.description,\n valid_num_fold=FLAGS.valid_num_fold,\n valid_fold_id=FLAGS.valid_fold_id)","def MakeEnvironment(self, context):\n\n # Start with any environment variables that are already present\n # in the environment.\n environment = os.environ.copy()\n # Copy context variables into the environment.\n for key, value in context.items():\n name = \"QMV_\" + key\n environment[name] = value\n return environment","def _run_env(self):\n raise NotImplementedError()","def set_env(self, propagated_env_vars={}):\n os.environ['BUILD_ROOT'] = self.build_root\n # This is how we tell run-test.sh what set of C++ binaries to use for mini-clusters in Java\n # tests.\n for env_var_name, env_var_value in propagated_env_vars.iteritems():\n os.environ[env_var_name] = env_var_value","def make_environment(seed, task_horizon):\n # Load the gym environment.\n environment = CartPoleEnv()\n environment = gym_wrappers.TimeLimit(environment, task_horizon)\n environment.seed(seed)\n environment = wrappers.GymWrapper(environment)\n environment = wrappers.SinglePrecisionWrapper(environment)\n return environment","def _build_env(target, *, orig=os.environ):\n overlay = dict(\n PYTHONPATH=_path_insert(orig.get('PYTHONPATH', ''), os.fspath(target)),\n PATH=_path_insert(orig.get('PATH', ''), os.fspath(target / 'bin')),\n )\n return {**orig, **overlay}","def __setup(self):\n\n build_environment = []\n\n # The download URL has the format contains vMAJOR.MINOR in the\n # path and the tarball contains MAJOR.MINOR.REVISION, so pull\n # apart the full version to get the MAJOR and MINOR components.\n match = re.match(r'(?P<major>\\d+)\\.(?P<minor>\\d+)', self.version)\n major_minor = 'v{0}.{1}'.format(match.groupdict()['major'],\n match.groupdict()['minor'])\n tarball = 'openmpi-{}.tar.bz2'.format(self.version)\n url = '{0}/{1}/downloads/{2}'.format(self.baseurl, major_minor,\n tarball)\n\n # CUDA\n if self.cuda:\n if self.__toolchain.CUDA_HOME:\n self.configure_opts.append(\n '--with-cuda={}'.format(self.__toolchain.CUDA_HOME))\n else:\n self.configure_opts.append('--with-cuda')\n else:\n self.configure_opts.append('--without-cuda')\n\n # InfiniBand\n if self.infiniband:\n self.configure_opts.append('--with-verbs')\n else:\n self.configure_opts.append('--without-verbs')\n\n # UCX\n if self.__ucx:\n if isinstance(self.__ucx, string_types):\n # Use specified path\n self.configure_opts.append('--with-ucx={}'.format(self.__ucx))\n else:\n self.configure_opts.append('--with-ucx')\n\n # If UCX was built with CUDA support, it is linked with\n # libcuda.so.1, which is not available during the\n # build stage. Assume that if OpenMPI is built with\n # CUDA support, then UCX was as well...\n if self.cuda:\n cuda_home = \"/usr/local/cuda\"\n if self.__toolchain.CUDA_HOME:\n cuda_home = self.__toolchain.CUDA_HOME\n self.__commands.append('ln -s {0} {1}'.format(\n os.path.join(cuda_home, 'lib64', 'stubs', 'libcuda.so'),\n os.path.join(cuda_home, 'lib64', 'stubs', 'libcuda.so.1')))\n if not self.__toolchain.LD_LIBRARY_PATH:\n build_environment.append('LD_LIBRARY_PATH=\"{}:$LD_LIBRARY_PATH\"'.format(os.path.join(cuda_home, 'lib64', 'stubs')))\n\n if self.directory:\n # Use source from local build context\n self.__commands.append(self.configure_step(\n directory=os.path.join(self.__wd, self.directory),\n toolchain=self.__toolchain))\n else:\n # Download source from web\n self.__commands.append(self.download_step(url=url,\n directory=self.__wd))\n self.__commands.append(self.untar_step(\n tarball=os.path.join(self.__wd, tarball), directory=self.__wd))\n self.__commands.append(self.configure_step(\n directory=os.path.join(self.__wd,\n 'openmpi-{}'.format(self.version)),\n environment=build_environment,\n toolchain=self.__toolchain))\n\n self.__commands.append(self.build_step())\n\n if self.__check:\n self.__commands.append(self.check_step())\n\n self.__commands.append(self.install_step())\n\n # Set library path\n libpath = os.path.join(self.prefix, 'lib')\n if self.ldconfig:\n self.__commands.append(self.ldcache_step(directory=libpath))\n else:\n self.__environment_variables['LD_LIBRARY_PATH'] = '{}:$LD_LIBRARY_PATH'.format(libpath)\n\n if self.directory:\n # Using source from local build context, cleanup directory\n self.__commands.append(self.cleanup_step(\n items=[os.path.join(self.__wd, self.directory)]))\n else:\n # Using downloaded source, cleanup tarball and directory\n self.__commands.append(self.cleanup_step(\n items=[os.path.join(self.__wd, tarball),\n os.path.join(self.__wd,\n 'openmpi-{}'.format(self.version))]))","def createEnvironment(self, _):\r\n if self._namespaces:\r\n raise InternalError('The environment can have only one namespace '\r\n 'at a time.')\r\n\r\n environment = Environment(self)\r\n return self._avatar.callRemote('setupNamespace', environment)","def create_aiida_project_environment(self):\n try:\n self.create_folder_structure()\n self.build_python_environment()\n self.install_packages_from_index()\n except Exception:\n self.exit_on_exception()\n raise\n self.create_spec_entry()","def env(parser, args):\n action = subcommand_functions[args.env_command]\n action(args)","def init():\n env = Environment(5, 5, 20, [10, 20, 10, 5])\n return env","def _init_env_variables(self):\n raise NotImplementedError()","def _init_env_variables(self):\n raise NotImplementedError()","def _init_env_variables(self):\n raise NotImplementedError()","def _init_env_variables(self):\n raise NotImplementedError()","def _init_env_variables(self):\n raise NotImplementedError()","def _init_env_variables(self):\n raise NotImplementedError()","def makeenv(outer=None):\n\n retval = {'outer': outer}\n return retval","def build_env(self, ad_hoc_command, private_data_dir, private_data_files=None):\n env = super(RunAdHocCommand, self).build_env(ad_hoc_command, private_data_dir, private_data_files=private_data_files)\n # Set environment variables needed for inventory and ad hoc event\n # callbacks to work.\n env['AD_HOC_COMMAND_ID'] = str(ad_hoc_command.pk)\n env['INVENTORY_ID'] = str(ad_hoc_command.inventory.pk)\n env['INVENTORY_HOSTVARS'] = str(True)\n env['ANSIBLE_LOAD_CALLBACK_PLUGINS'] = '1'\n env['ANSIBLE_SFTP_BATCH_MODE'] = 'False'\n\n return env","def _setenv(self):\n tokens = {}\n tokens[\"CT_TIMESTAMP\"] = self._timestamp\n tokens[\"CT_SUBMITTER\"] = self._node.name()\n # tokens[\"CT_HIPBASE\"] = self._file[\"hipbase\"]\n tokens[\"CT_SCENE\"] = self._scene\n tokens[\"CT_PROJECT\"] = self.project_name\n\n for token in tokens:\n hou.putenv(token, tokens[token])\n\n return tokens","def generic_env_configure_vars(self, verbose=False):\n\n if self.settings.os == \"Windows\":\n self.output.fatal(\"Cannot build on Windows, sorry!\")\n return\n\n if self.settings.os == \"Linux\" or self.settings.os == \"Macos\":\n libs = 'LIBS=\"%s\"' % \" \".join([\"-l%s\" % lib for lib in self.deps_cpp_info.libs])\n ldflags = 'LDFLAGS=\"%s\"' % \" \".join([\"-L%s\" % lib for lib in self.deps_cpp_info.lib_paths]) \n archflag = \"-m32\" if self.settings.arch == \"x86\" else \"\"\n cflags = 'CFLAGS=\"-fPIC %s %s\"' % (archflag, \" \".join(self.deps_cpp_info.cflags))\n cpp_flags = 'CPPFLAGS=\"%s %s\"' % (archflag, \" \".join(self.deps_cpp_info.cppflags))\n command = \"env %s %s %s %s\" % (libs, ldflags, cflags, cpp_flags)\n # elif self.settings.os == \"Windows\" and self.settings.compiler == \"Visual Studio\":\n # cl_args = \" \".join(['/I\"%s\"' % lib for lib in self.deps_cpp_info.include_paths])\n # lib_paths= \";\".join(['\"%s\"' % lib for lib in self.deps_cpp_info.lib_paths])\n # command = \"SET LIB=%s;%%LIB%% && SET CL=%s\" % (lib_paths, cl_args)\n # if verbose:\n # command += \" && SET LINK=/VERBOSE\"\n \n return command","def initFromEnv(self):\n #self.command = 'scram' # SB I think this line is not needed\n self[\"SCRAM_ARCH\"] = None\n\n if 'SCRAM_ARCH' in os.environ:\n self[\"SCRAM_ARCH\"] = os.environ[\"SCRAM_ARCH\"]\n else:\n stdout, _, _ = execute_command(command='scram arch')\n self[\"SCRAM_ARCH\"] = stdout\n\n try:\n self[\"CMSSW_BASE\"] = os.environ[\"CMSSW_BASE\"]\n self[\"CMSSW_VERSION\"] = os.environ[\"CMSSW_VERSION\"]\n# Commenting these two out. I don't think they are really needed\n# self.cmsswReleaseBase = os.environ[\"CMSSW_RELEASE_BASE\"]\n# self.localRT = os.environ[\"LOCALRT\"]\n except KeyError as ke:\n self[\"CMSSW_BASE\"] = None\n self[\"CMSSW_VERSION\"] = None\n# self.cmsswReleaseBase = None\n# self.localRT = None\n msg = \"Please make sure you have setup the CMS enviroment (cmsenv). Cannot find %s in your env\" % str(ke)\n msg += \"\\nPlease refer to https://twiki.cern.ch/twiki/bin/view/CMSPublic/WorkBookCRAB3Tutorial#CMS_environment for how to setup the CMS enviroment.\"\n raise EnvironmentException(msg)","def main(self):\n\n env = self.env\n\n # Initial setup\n self.parse_args()\n self.find_taskfile()\n\n if self.taskfile is None:\n env.abort(\"Unable to find {0}\".format(self.cmdline.file))\n\n # Set command line NAME=VALUE variables before loading the file\n (tasks, params) = self.get_tasks_params()\n env.update(**params)\n\n env[\"_TOP_\"] = os.path.dirname(self.taskfile)\n env[\"_ABSTOP_\"] = os.path.abspath(env[\"_TOP_\"])\n env[\"_CWD_\"] = os.path.abspath(self.cwd)\n env._load(self.taskfile)\n\n # Print tasks list if requested\n if self.cmdline.list:\n for name in env._tasks:\n if not name.startswith(\"_\"):\n env.outputln(name)\n env.exit()\n\n # Print var/task help if requested\n if self.cmdline.varhelp:\n self.show_varhelp()\n\n if self.cmdline.taskhelp:\n names = sorted(set([i[0] for i in tasks]))\n self.show_taskhelp(names)\n\n if self.cmdline.taskhelp or self.cmdline.varhelp:\n env.exit()\n\n # Execute the requested tasks setting task specific variables\n for (task, params) in tasks:\n env.calltask(task, **params)","def _set_environment_vars(self):\n os.environ[\"PATH\"] = os.path.join(self.source_folder, \"depot_tools\") + os.pathsep + os.environ[\"PATH\"]\n os.environ[\"DEPOT_TOOLS_PATH\"] = os.path.join(self.source_folder, \"depot_tools\")\n if tools.os_info.is_windows:\n os.environ[\"DEPOT_TOOLS_WIN_TOOLCHAIN\"] = \"0\"\n os.environ[\"GYP_MSVS_VERSION\"] = \"2017\" if str(self.settings.compiler.version) == \"15\" else \"2019\"","def __init__(self): \n\t\n\t # get the environment\n\t\tself.env = env()","def build_env(env_id, inst=0, **all_args):\n env_keys = ['dt', 'timing']\n if env_id == 'RDM-v0':\n env_keys.append('stimEv')\n env_args = {x: all_args[x] for x in env_keys}\n\n if all_args['combine']:\n env1 = gym.make(env_id, **env_args)\n env_args['timing'] = all_args['timing2']\n env2 = gym.make(all_args['env2'], **env_args)\n env = combine.combine(env1, env2, delay=all_args['delay'])\n else:\n env = gym.make(env_id, **env_args)\n if all_args['trial_hist']:\n env = trial_hist.TrialHistory(env, rep_prob=all_args['rep_prob'],\n block_dur=all_args['bl_dur'],\n blk_ch_prob=all_args['blk_ch_prob'])\n elif all_args['side_bias']:\n env = side_bias.SideBias(env, prob=all_args['rep_prob'],\n block_dur=all_args['bl_dur'])\n if all_args['catch_trials']:\n env = catch_trials.CatchTrials(env,\n catch_prob=all_args['catch_prob'],\n stim_th=all_args['stim_th'])\n if all_args['reaction_time']:\n env = reaction_time.ReactionTime(env)\n if all_args['pass_reward']:\n env = pass_reward.PassReward(env)\n if all_args['pass_action']:\n env = pass_action.PassAction(env)\n env = manage_data.manage_data(env, inst=inst, plt_tr=all_args['figs'],\n folder=all_args['save_path'])\n return env","def __create_test_environment(self):\n os.chdir(self.wd)\n temp_dir = tempfile.gettempdir()\n self.test_root = os.path.join(temp_dir, \"test-grpc\")\n print(\"Creating testing environment in {}\".format(self.test_root))\n if os.path.exists(self.test_root):\n # delete any previous environment\n shutil.rmtree(self.test_root)\n # create root directory\n os.makedirs(self.test_root)\n def copy_app(name):\n app_root = os.path.join(self.test_root, name)\n os.makedirs(app_root)\n filename = \"grpc-{}\".format(name)\n src = os.path.join(self.args.bin, filename)\n dst = os.path.join(app_root, filename)\n shutil.copy(src, dst)\n return dst\n # copy client and server into the new test environment\n self.server_path = copy_app(\"server\")\n self.client_path = copy_app(\"client\")","def create_environment(game_name, seed, is_test=False):\n\n # Note, you may want to use a level cache to speed of compilation of\n # environment maps. See the documentation for the Python interface of DeepMind\n # Lab.\n config = {\n \"width\": FLAGS.width,\n \"height\": FLAGS.height,\n \"logLevel\": \"WARN\",\n \"n_input_frames\": FLAGS.history,\n \"show_display\": FLAGS.display,\n \"save_video\": FLAGS.save_video,\n }\n if is_test:\n config[\"allowHoldOutLevels\"] = \"true\"\n # Mixer seed for evalution, see\n # https://github.com/deepmind/lab/blob/master/docs/users/python_api.md\n config[\"mixerSeed\"] = 0x600D5EED\n\n p = py_process.PyProcess(\n environments.PyProcessAtari,\n game_name,\n config,\n FLAGS.num_action_repeats,\n seed,\n )\n return environments.FlowEnvironment(p.proxy)","def _generate_environment(self):\n envvars = {}\n for key in self.envvars:\n try:\n envvars[key] = os.environ[key]\n except KeyError:\n continue\n\n # Warn the user that we cannot support secrets\n if envvars:\n logger.warning(\"This API does not support environment secrets.\")\n return envvars","def _env_create(name_or_path, init_file=None, dir=False, with_view=None, keep_relative=False):\n if dir:\n env = ev.Environment(name_or_path, init_file, with_view, keep_relative)\n env.write()\n tty.msg(\"Created environment in %s\" % env.path)\n tty.msg(\"You can activate this environment with:\")\n tty.msg(\" spack env activate %s\" % env.path)\n else:\n env = ev.create(name_or_path, init_file, with_view, keep_relative)\n env.write()\n tty.msg(\"Created environment '%s' in %s\" % (name_or_path, env.path))\n tty.msg(\"You can activate this environment with:\")\n tty.msg(\" spack env activate %s\" % (name_or_path))\n return env","def SetupEnvironment(self):\n pass","def setEnv(self, origin, checked, simOnly=False):\n\n # resolve circular dependencies\n if( self.name in checked ):\n return\n else:\n checked.append( self.name )\n\n # set values to strings\n for k in self.env.keys():\n self.env[k]=str(self.env[k])\n \n # set environment variables\n if( simOnly ):\n if( len( checked ) == 1 ):\n if( len(self.parent.env) != 0 ):\n print \"\\n + Global Environment variables:\"\n for k, v in self.parent.env.iteritems():\n print \"\\t* \" + k + \": \" + str(v)\n\n print \"\\n + Environment variables set by \" + self.name + \":\"\n \n for k, v in self.env.iteritems():\n print \"\\t* \" + k + \": \" + str(v)\n else:\n # first set the priority values\n for k in self.envorder:\n if( self.env[k].find('$') != -1 ):\n os.environ[k]=os.path.expandvars(self.env[k])\n else:\n os.environ[k] = self.env[k]\n # then set the rest\n for k, v in self.env.iteritems():\n if k not in self.envorder:\n if( v.find('$') != -1 ):\n os.environ[k] = os.path.expandvars(v)\n else:\n os.environ[k] = v\n\n # print path and build environment variables\n if( simOnly ):\n for k, v in self.envpath.iteritems():\n if( len(v) != 0 ):\n print \"\\t* \" + k + \": \" + str(v)\n\n # set environment for dependencies\n if( len( checked ) > 1 ):\n mods = self.optmodules + self.reqmodules\n else:\n # buildonly modules are only used for the package were they are needed\n mods = self.optmodules + self.reqmodules + self.reqmodules_buildonly + self.reqmodules_external\n \n for modname in mods:\n self.parent.module(modname).setEnv(origin, checked, simOnly)\n\n # list of \"trivial\" paths we do not want to add again to PATH and co\n ignorepaths = ['/usr/bin','/usr/lib','/sbin','/usr/sbin']\n # set path environment variables\n for k, v in self.envpath.iteritems():\n if( len(v) != 0 ):\n env = getenv( k )\n newvalues = \"\"\n for i in v:\n rpath = fixPath(i)\n if rpath in ignorepaths:\n continue\n newvalues = newvalues + rpath + ':'\n os.environ[k] = newvalues + env","def initialize():\n environment = Environment()\n environment.setup()","def wsSetupEnvironment(self, nj=0):\n psetName = 'pset.py'\n\n # Prepare JobType-independent part\n txt = '\\n#Written by cms_cmssw::wsSetupEnvironment\\n'\n txt += 'echo \">>> setup environment\"\\n'\n txt += 'echo \"set SCRAM ARCH to ' + self.executable_arch + '\"\\n'\n txt += 'export SCRAM_ARCH=' + self.executable_arch + '\\n'\n txt += 'echo \"SCRAM_ARCH = $SCRAM_ARCH\"\\n'\n txt += 'if [ $middleware == LCG ] || [ $middleware == CAF ] || [ $middleware == LSF ]; then \\n'\n txt += self.wsSetupCMSLCGEnvironment_()\n txt += 'elif [ $middleware == OSG ]; then\\n'\n txt += ' WORKING_DIR=`/bin/mktemp -d $OSG_WN_TMP/cms_XXXXXXXXXXXX`\\n'\n txt += ' if [ ! $? == 0 ] ;then\\n'\n txt += ' echo \"ERROR ==> OSG $WORKING_DIR could not be created on WN `hostname`\"\\n'\n txt += ' job_exit_code=10016\\n'\n txt += ' func_exit\\n'\n txt += ' fi\\n'\n txt += ' echo \">>> Created working directory: $WORKING_DIR\"\\n'\n txt += '\\n'\n txt += ' echo \"Change to working directory: $WORKING_DIR\"\\n'\n txt += ' cd $WORKING_DIR\\n'\n txt += ' echo \">>> current directory (WORKING_DIR): $WORKING_DIR\"\\n'\n txt += self.wsSetupCMSOSGEnvironment_()\n #Setup SGE Environment\n txt += 'elif [ $middleware == SGE ]; then\\n'\n txt += self.wsSetupCMSLCGEnvironment_()\n\n txt += 'elif [ $middleware == ARC ]; then\\n'\n txt += self.wsSetupCMSLCGEnvironment_()\n\n #Setup PBS Environment\n txt += 'elif [ $middleware == PBS ] || [ $middleware == PBSV2 ] || [ $middleware == SLURM ]; then\\n'\n txt += self.wsSetupCMSLCGEnvironment_()\n\n txt += 'fi\\n'\n\n # Prepare JobType-specific part\n scram = self.scram.commandName()\n txt += '\\n\\n'\n txt += 'echo \">>> specific cmssw setup environment:\"\\n'\n txt += 'echo \"CMSSW_VERSION = '+self.version+'\"\\n'\n txt += scram+' project CMSSW '+self.version+'\\n'\n txt += 'status=$?\\n'\n txt += 'if [ $status != 0 ] ; then\\n'\n txt += ' echo \"ERROR ==> CMSSW '+self.version+' not found on `hostname`\" \\n'\n txt += ' job_exit_code=10034\\n'\n txt += ' func_exit\\n'\n txt += 'fi \\n'\n txt += 'cd '+self.version+'\\n'\n txt += 'SOFTWARE_DIR=`pwd`; export SOFTWARE_DIR\\n'\n txt += 'echo \">>> current directory (SOFTWARE_DIR): $SOFTWARE_DIR\" \\n'\n txt += 'eval `'+scram+' runtime -sh | grep -v SCRAMRT_LSB_JOBNAME`\\n'\n txt += 'if [ $? != 0 ] ; then\\n'\n txt += ' echo \"ERROR ==> Problem with the command: \"\\n'\n txt += ' echo \"eval \\`'+scram+' runtime -sh | grep -v SCRAMRT_LSB_JOBNAME \\` at `hostname`\"\\n'\n txt += ' job_exit_code=10034\\n'\n txt += ' func_exit\\n'\n txt += 'fi \\n'\n # Handle the arguments:\n txt += \"\\n\"\n txt += \"## number of arguments (first argument always jobnumber, the second is the resubmission number)\\n\"\n txt += \"\\n\"\n txt += \"if [ $nargs -lt \"+str(self.argsList)+\" ]\\n\"\n txt += \"then\\n\"\n txt += \" echo 'ERROR ==> Too few arguments' +$nargs+ \\n\"\n txt += ' job_exit_code=50113\\n'\n txt += \" func_exit\\n\"\n txt += \"fi\\n\"\n txt += \"\\n\"\n\n # Prepare job-specific part\n job = common.job_list[nj]\n if (self.datasetPath):\n txt += '\\n'\n txt += 'DatasetPath='+self.datasetPath+'\\n'\n\n txt += 'PrimaryDataset='+self.primaryDataset +'\\n'\n txt += 'DataTier='+self.dataTier+'\\n'\n txt += 'ApplicationFamily=cmsRun\\n'\n\n else:\n txt += 'DatasetPath=MCDataTier\\n'\n txt += 'PrimaryDataset=null\\n'\n txt += 'DataTier=null\\n'\n txt += 'ApplicationFamily=MCDataTier\\n'\n if self.pset != None:\n pset = os.path.basename(job.configFilename())\n pkl = os.path.basename(job.configFilename()) + '.pkl'\n txt += '\\n'\n txt += 'cp $RUNTIME_AREA/'+pset+' .\\n'\n txt += 'cp $RUNTIME_AREA/'+pkl+' .\\n'\n\n txt += 'PreserveSeeds=' + ','.join(self.preserveSeeds) + '; export PreserveSeeds\\n'\n txt += 'IncrementSeeds=' + ','.join(self.incrementSeeds) + '; export IncrementSeeds\\n'\n txt += 'echo \"PreserveSeeds: <$PreserveSeeds>\"\\n'\n txt += 'echo \"IncrementSeeds:<$IncrementSeeds>\"\\n'\n\n txt += 'mv -f ' + pset + ' ' + psetName + '\\n'\n #if self.var_filter:\n # #print \"self.var_filter = \",self.var_filter\n # txt += \"export var_filter=\"+\"'\"+self.var_filter+\"'\\n\"\n # txt += 'echo $var_filter'\n else:\n txt += '\\n'\n if self.AdditionalArgs: txt += 'export AdditionalArgs=\\\"%s\\\"\\n'%(self.AdditionalArgs)\n if int(self.NumEvents) != 0: txt += 'export MaxEvents=%s\\n'%str(self.NumEvents)\n return txt","def set_envs(self):\n # pylint:disable=protected-access\n # Need to call sys.__getframe() to get the filename and method/func\n # for logging information.\n\n # Useful for logging\n # Logging output: TIME UTC |TYPE (DEBUG, INFO, WARNING, etc.) |\n # [File : function]| Message\n cur_filename = sys._getframe().f_code.co_filename\n cur_function = sys._getframe().f_code.co_name\n\n self.logger.info('Setting env variables from config file...')\n # Set all the environment variables that are needed by the\n # MET config file.\n\n tmp_amodel = self.c_dict['AMODEL']\n if tmp_amodel:\n # Replace any single quotes with double quotes and remove any\n # whitespace\n tmp_amodel_str = str(tmp_amodel).replace(\"\\'\", \"\\\"\")\n tmp_amodel = ''.join(tmp_amodel_str.split())\n self.add_env_var('AMODEL', tmp_amodel)\n else:\n self.add_env_var('AMODEL', \"[]\")\n\n tmp_bmodel = self.c_dict['BMODEL']\n if tmp_bmodel:\n # Replace any single quotes with double quotes and remove any\n # whitespace\n tmp_bmodel_str = str(tmp_bmodel).replace(\"\\'\", \"\\\"\")\n tmp_bmodel = ''.join(tmp_bmodel_str.split())\n self.add_env_var('BMODEL', tmp_bmodel)\n else:\n self.add_env_var('BMODEL', \"[]\")\n\n tmp_desc = self.c_dict['DESC']\n if tmp_desc:\n # Replace any single quotes with double quotes and remove any\n # whitespace\n tmp_desc_str = str(tmp_desc).replace(\"\\'\", \"\\\"\")\n tmp_desc = ''.join(tmp_desc_str.split())\n self.add_env_var('DESC', tmp_desc)\n else:\n self.add_env_var('DESC', \"[]\")\n\n tmp_storm_id = self.c_dict['STORM_ID']\n if tmp_storm_id:\n # Replace any single quotes with double quotes and remove any\n # whitespace\n tmp_storm_id_str = str(tmp_storm_id).replace(\"\\'\", \"\\\"\")\n tmp_storm_id = ''.join(tmp_storm_id_str.split())\n self.add_env_var('STORM_ID', tmp_storm_id)\n else:\n self.add_env_var('STORM_ID', \"[]\")\n\n tmp_basin = self.c_dict['BASIN']\n if tmp_basin:\n # Replace any single quotes with double quotes and remove any\n # whitespace\n tmp_basin_str = str(tmp_basin).replace(\"\\'\", \"\\\"\")\n tmp_basin = ''.join(tmp_basin_str.split())\n self.add_env_var('BASIN', tmp_basin)\n else:\n self.add_env_var('BASIN', \"[]\")\n\n tmp_cyclone = self.c_dict['CYCLONE']\n if tmp_cyclone:\n # Replace any single quotes with double quotes and remove any\n # whitespace\n tmp_cyclone_str = str(tmp_cyclone).replace(\"\\'\", \"\\\"\")\n tmp_cyclone = ''.join(tmp_cyclone_str.strip())\n self.add_env_var('CYCLONE', tmp_cyclone)\n else:\n self.add_env_var('CYCLONE', \"[]\")\n\n tmp_storm_name = self.c_dict['STORM_NAME']\n if tmp_storm_name:\n # Replace any single quotes with double quotes and remove any\n # whitespace\n tmp_storm_name_str = str(tmp_storm_name).replace(\"\\'\", \"\\\"\")\n tmp_storm_name = ''.join(tmp_storm_name_str.strip())\n self.add_env_var('STORM_NAME', tmp_storm_name)\n else:\n self.add_env_var('STORM_NAME', \"[]\")\n\n if self.c_dict['INIT_BEG']:\n self.add_env_var('INIT_BEG', self.c_dict['INIT_BEG'])\n else:\n self.add_env_var('INIT_BEG', \"\")\n\n if self.c_dict['INIT_END']:\n self.add_env_var('INIT_END', self.c_dict['INIT_END'])\n else:\n self.add_env_var('INIT_END', \"\")\n\n tmp_init_include = self.c_dict['INIT_INCLUDE']\n if tmp_init_include:\n # Replace any single quotes with double quotes and remove any\n # whitespace\n tmp_init_include_str = str(tmp_init_include).replace(\"\\'\", \"\\\"\")\n tmp_init_include = ''.join(tmp_init_include_str.strip())\n self.add_env_var('INIT_INCLUDE', tmp_init_include)\n else:\n self.add_env_var('INIT_INCLUDE', \"[]\")\n\n tmp_init_exclude = self.c_dict['INIT_EXCLUDE']\n if tmp_init_exclude:\n # Replace any single quotes with double quotes and remove any\n # whitespace\n tmp_init_exclude_str = str(tmp_init_exclude).replace(\"\\'\", \"\\\"\")\n tmp_init_exclude = ''.join(tmp_init_exclude_str.strip())\n self.add_env_var('INIT_EXCLUDE', tmp_init_exclude)\n else:\n self.add_env_var('INIT_EXCLUDE', \"[]\")\n\n tmp_init_hour = self.c_dict['INIT_HOUR']\n if tmp_init_hour:\n # Replace any single quotes with double quotes and remove any\n # whitespace\n tmp_init_hour_str = str(tmp_init_hour).replace(\"\\'\", \"\\\"\")\n tmp_init_hour = ''.join(tmp_init_hour_str.split())\n self.add_env_var('INIT_HOUR', tmp_init_hour)\n else:\n self.add_env_var('INIT_HOUR', \"[]\")\n\n tmp_valid_begin = self.c_dict['VALID_BEG']\n if tmp_valid_begin:\n # Replace any single quotes with double quotes and remove any\n # whitespace\n tmp_valid_begin_str = str(tmp_valid_begin).replace(\"\\'\", \"\\\"\")\n tmp_valid_begin = ''.join(tmp_valid_begin_str.strip())\n self.add_env_var('VALID_BEG', tmp_valid_begin)\n else:\n self.add_env_var('VALID_BEG', '')\n\n tmp_valid_end = self.c_dict['VALID_END']\n if tmp_valid_end:\n # Replace any single quotes with double quotes and remove any\n # whitespace\n tmp_valid_end_str = str(tmp_valid_end).replace(\"\\'\", \"\\\"\")\n tmp_valid_end = ''.join(tmp_valid_end_str.strip())\n self.add_env_var('VALID_END', tmp_valid_end)\n else:\n self.add_env_var('VALID_END', \"\")\n\n tmp_valid_include = self.c_dict['VALID_INCLUDE']\n if tmp_valid_include:\n # Replace any single quotes with double quotes and remove any\n # whitespace\n tmp_valid_include_str = str(tmp_valid_include).replace(\"\\'\", \"\\\"\")\n tmp_valid_include = ''.join(tmp_valid_include_str.strip())\n self.add_env_var('VALID_INCLUDE', tmp_valid_include)\n else:\n self.add_env_var('VALID_INCLUDE', \"[]\")\n\n tmp_valid_exclude = self.c_dict['VALID_EXCLUDE']\n if tmp_valid_exclude:\n # Replace any single quotes with double quotes and remove any\n # whitespace\n tmp_valid_exclude_str = str(tmp_valid_exclude).replace(\"\\'\", \"\\\"\")\n tmp_valid_exclude = ''.join(tmp_valid_exclude_str.strip())\n self.add_env_var('VALID_EXCLUDE', tmp_valid_exclude)\n else:\n self.add_env_var('VALID_EXCLUDE', \"[]\")\n\n tmp_valid_hour = self.c_dict['VALID_HOUR']\n if tmp_valid_hour:\n # Replace any single quotes with double quotes and remove any\n # whitespace\n tmp_valid_hour_str = str(tmp_valid_hour).replace(\"\\'\", \"\\\"\")\n tmp_valid_hour = ''.join(tmp_valid_hour_str.strip())\n self.add_env_var('VALID_HOUR', tmp_valid_hour)\n else:\n self.add_env_var('VALID_HOUR', \"[]\")\n\n tmp_lead_req = self.c_dict['LEAD_REQ']\n if tmp_lead_req:\n # Replace any single quotes with double quotes and remove any\n # whitespace\n tmp_lead_req_str = str(tmp_lead_req).replace(\"\\'\", \"\\\"\")\n tmp_lead_req = ''.join(tmp_lead_req_str.strip())\n self.add_env_var('LEAD_REQ', tmp_lead_req)\n else:\n self.add_env_var('LEAD_REQ', \"[]\")\n\n tmp_lead = self.c_dict['LEAD']\n if tmp_lead:\n # Replace any single quotes with double quotes and remove any\n # whitespace\n tmp_lead_str = str(tmp_lead).replace(\"\\'\", \"\\\"\")\n tmp_lead = ''.join(tmp_lead_str.strip())\n self.add_env_var('LEAD', tmp_lead)\n else:\n self.add_env_var('LEAD', \"[]\")\n\n tmp_init_mask = self.c_dict['INIT_MASK']\n if tmp_init_mask:\n # Replace any single quotes with double quotes and remove any\n # whitespace\n tmp_init_mask_str = str(tmp_init_mask).replace(\"\\'\", \"\\\"\")\n tmp_init_mask = ''.join(tmp_init_mask_str.strip())\n self.add_env_var('INIT_MASK', tmp_init_mask)\n else:\n self.add_env_var('INIT_MASK', \"[]\")\n\n tmp_valid_mask = self.c_dict['VALID_MASK']\n if tmp_valid_mask:\n # Replace any single quotes with double quotes and remove any\n # whitespace\n tmp_valid_mask_str = str(tmp_valid_mask).replace(\"\\'\", \"\\\"\")\n tmp_valid_mask = ''.join(tmp_valid_mask_str.strip())\n self.add_env_var('VALID_MASK', tmp_valid_mask)\n else:\n self.add_env_var('VALID_MASK', \"[]\")\n\n tmp_track_watch_warn = self.c_dict['TRACK_WATCH_WARN']\n if tmp_track_watch_warn:\n # Replace any single quotes with double quotes and remove any\n # whitespace\n tmp_track_watch_warn_str = str(tmp_track_watch_warn).replace(\"\\'\",\n \"\\\"\")\n tmp_track_watch_warn = ''.join(tmp_track_watch_warn_str.strip())\n self.add_env_var('TRACK_WATCH_WARN', tmp_track_watch_warn)\n else:\n self.add_env_var('TRACK_WATCH_WARN', \"[]\")\n\n tmp_column_thresh_name = self.c_dict['COLUMN_THRESH_NAME']\n if tmp_column_thresh_name:\n # Replace any single quotes with double quotes and remove any\n # whitespace\n tmp_column_thresh_name_str = str(tmp_column_thresh_name).replace(\n \"\\'\", \"\\\"\")\n tmp_column_thresh_name = ''.join(tmp_column_thresh_name_str.strip())\n self.add_env_var('COLUMN_THRESH_NAME', tmp_column_thresh_name)\n else:\n self.add_env_var('COLUMN_THRESH_NAME', \"[]\")\n\n tmp_column_thresh_val = self.c_dict['COLUMN_THRESH_VAL']\n if tmp_column_thresh_val:\n # Replace any single quotes with double quotes and remove any\n # whitespace\n tmp_column_thresh_val_str = str(tmp_column_thresh_val).replace(\"\\'\",\n \"\\\"\")\n tmp_column_thresh_val = ''.join(tmp_column_thresh_val_str.strip())\n self.add_env_var('COLUMN_THRESH_VAL', tmp_column_thresh_val)\n else:\n self.add_env_var('COLUMN_THRESH_VAL', \"[]\")\n\n tmp_column_str_name = self.c_dict['COLUMN_STR_NAME']\n if tmp_column_str_name:\n # Replace any single quotes with double quotes and remove any\n # whitespace\n tmp_column_str_name = str(tmp_column_str_name).replace(\"\\'\",\n \"\\\"\")\n tmp_column_str_name = ''.join(tmp_column_str_name.strip())\n self.add_env_var('COLUMN_STR_NAME', tmp_column_str_name)\n else:\n self.add_env_var('COLUMN_STR_NAME', \"[]\")\n\n tmp_column_str_val = self.c_dict['COLUMN_STR_VAL']\n if tmp_column_str_val:\n # Replace any single quotes with double quotes and remove any\n # whitespace\n tmp_column_str_val_str = str(tmp_column_str_val).replace(\"\\'\", \"\\\"\")\n tmp_column_str_val = ''.join(tmp_column_str_val_str.strip())\n self.add_env_var('COLUMN_STR_VAL', tmp_column_str_val)\n else:\n self.add_env_var('COLUMN_STR_VAL', \"[]\")\n\n tmp_init_thresh_name = self.c_dict['INIT_THRESH_NAME']\n if tmp_init_thresh_name:\n # Replace any single quotes with double quotes and remove any\n # whitespace\n tmp_init_thresh_name_str = str(tmp_init_thresh_name).replace(\"\\'\",\n \"\\\"\")\n tmp_init_thresh_name = ''.join(tmp_init_thresh_name_str.strip())\n\n self.add_env_var('INIT_THRESH_NAME', tmp_init_thresh_name)\n\n else:\n self.add_env_var('INIT_THRESH_NAME', \"[]\")\n\n tmp_init_thresh_val = self.c_dict['INIT_THRESH_VAL']\n if tmp_init_thresh_val:\n # Replace any single quotes with double quotes and remove any\n # whitespace\n tmp_init_thresh_val_str = str(tmp_init_thresh_val).replace(\"\\'\",\n \"\\\"\")\n tmp_init_thresh_val = ''.join(tmp_init_thresh_val_str.strip())\n self.add_env_var('INIT_THRESH_VAL', tmp_init_thresh_val)\n else:\n self.add_env_var('INIT_THRESH_VAL', \"[]\")\n\n tmp_init_str_name = self.c_dict['INIT_STR_NAME']\n if tmp_init_str_name:\n # Replace any single quotes with double quotes and remove any\n # whitespace\n tmp_init_str_name_str = str(tmp_init_str_name).replace(\"\\'\", \"\\\"\")\n tmp_init_str_name = ''.join(tmp_init_str_name_str.strip())\n self.add_env_var('INIT_STR_NAME', tmp_init_str_name)\n else:\n self.add_env_var('INIT_STR_NAME', \"[]\")\n\n tmp_init_str_val = self.c_dict['INIT_STR_VAL']\n if tmp_init_str_val:\n # Replace any single quotes with double quotes and remove any\n # whitespace\n tmp_init_str_val_str = str(tmp_init_str_val).replace(\"\\'\", \"\\\"\")\n tmp_init_str_val = ''.join(tmp_init_str_val_str.strip())\n self.add_env_var('INIT_STR_VAL', tmp_init_str_val)\n else:\n self.add_env_var('INIT_STR_VAL', \"[]\")\n\n # boolean values for WATER_ONLY\n if self.c_dict['WATER_ONLY']:\n flag = \"TRUE\"\n else:\n flag = \"FALSE\"\n self.add_env_var('WATER_ONLY', flag)\n\n # boolean value for LANDFALL\n if self.c_dict['LANDFALL']:\n flag = \"TRUE\"\n else:\n flag = \"FALSE\"\n self.add_env_var('LANDFALL', flag)\n\n if self.c_dict['LANDFALL_BEG']:\n self.add_env_var('LANDFALL_BEG',\n self.c_dict['LANDFALL_BEG'])\n else:\n # Set to default\n self.add_env_var('LANDFALL_BEG', '-24')\n\n if self.c_dict['LANDFALL_END']:\n self.add_env_var('LANDFALL_END',\n self.c_dict['LANDFALL_END'])\n else:\n # Set to default\n self.add_env_var('LANDFALL_END', '00')\n\n # boolean value for MATCH_POINTS\n if self.c_dict['MATCH_POINTS'] == 'true':\n flag = \"TRUE\"\n else:\n flag = \"FALSE\"\n self.add_env_var('MATCH_POINTS', flag)\n\n if self.c_dict['CONFIG_FILE']:\n self.add_env_var('CONFIG_FILE',\n self.c_dict['CONFIG_FILE'])\n else:\n self.log_error(\n cur_filename + '|' + cur_function +\n ': no MET TC-Stat config file found. Exiting')\n sys.exit(1)\n\n jobs_list_tmp = self.c_dict['JOBS_LIST']\n if jobs_list_tmp:\n # MET is expecting a string\n jobs_list_str = '\"' + jobs_list_tmp + '\"'\n self.add_env_var('JOBS', jobs_list_str)\n else:\n self.log_error('No jobs list defined. Please check your METplus'\n 'config file. Exiting...')\n sys.exit(1)\n return 0","def as_invocation_environment_dict(self):\n lib_env_var = self._platform.resolve_platform_specific({\n 'darwin': lambda: 'DYLD_LIBRARY_PATH',\n 'linux': lambda: 'LD_LIBRARY_PATH',\n })\n return {\n 'PATH': create_path_env_var(self.path_entries),\n lib_env_var: create_path_env_var(self.library_dirs),\n }","def generate(env):\n\n # FIXME: this is already too late\n #if env.get('quiet', False):\n # quietCommandLines(env)\n\n # shortcuts\n debug = env['debug']\n machine = env['machine']\n platform = env['platform']\n x86 = env['machine'] == 'x86'\n gcc = env['platform'] in ('linux', 'freebsd', 'darwin')\n msvc = env['platform'] in ('windows', 'winddk', 'wince')\n\n # Tool\n if platform == 'winddk':\n env.Tool('winddk')\n elif platform == 'wince':\n env.Tool('wcesdk')\n else:\n env.Tool('default')\n\n # Put build output in a separate dir, which depends on the current\n # configuration. See also http://www.scons.org/wiki/AdvancedBuildExample\n build_topdir = 'build'\n build_subdir = env['platform']\n if env['dri']:\n build_subdir += \"-dri\"\n if env['llvm']:\n build_subdir += \"-llvm\"\n if env['machine'] != 'generic':\n build_subdir += '-' + env['machine']\n if env['debug']:\n build_subdir += \"-debug\"\n if env['profile']:\n build_subdir += \"-profile\"\n build_dir = os.path.join(build_topdir, build_subdir)\n # Place the .sconsign file in the build dir too, to avoid issues with\n # different scons versions building the same source file\n env['build'] = build_dir\n env.SConsignFile(os.path.join(build_dir, '.sconsign'))\n\n # C preprocessor options\n cppdefines = []\n if debug:\n cppdefines += ['DEBUG']\n else:\n cppdefines += ['NDEBUG']\n if env['profile']:\n cppdefines += ['PROFILE']\n if platform == 'windows':\n cppdefines += [\n 'WIN32',\n '_WINDOWS',\n '_UNICODE',\n 'UNICODE',\n # http://msdn2.microsoft.com/en-us/library/6dwk3a1z.aspx,\n 'WIN32_LEAN_AND_MEAN',\n 'VC_EXTRALEAN',\n '_CRT_SECURE_NO_DEPRECATE',\n ]\n if debug:\n cppdefines += ['_DEBUG']\n if platform == 'winddk':\n # Mimic WINDDK's builtin flags. See also:\n # - WINDDK's bin/makefile.new i386mk.inc for more info.\n # - buildchk_wxp_x86.log files, generated by the WINDDK's build\n # - http://alter.org.ua/docs/nt_kernel/vc8_proj/\n cppdefines += [\n ('_X86_', '1'),\n ('i386', '1'),\n 'STD_CALL',\n ('CONDITION_HANDLING', '1'),\n ('NT_INST', '0'),\n ('WIN32', '100'),\n ('_NT1X_', '100'),\n ('WINNT', '1'),\n ('_WIN32_WINNT', '0x0501'), # minimum required OS version\n ('WINVER', '0x0501'),\n ('_WIN32_IE', '0x0603'),\n ('WIN32_LEAN_AND_MEAN', '1'),\n ('DEVL', '1'),\n ('__BUILDMACHINE__', 'WinDDK'),\n ('FPO', '0'),\n ]\n if debug:\n cppdefines += [('DBG', 1)]\n if platform == 'wince':\n cppdefines += [\n '_CRT_SECURE_NO_DEPRECATE',\n '_USE_32BIT_TIME_T',\n 'UNICODE',\n '_UNICODE',\n ('UNDER_CE', '600'),\n ('_WIN32_WCE', '0x600'),\n 'WINCEOEM',\n 'WINCEINTERNAL',\n 'WIN32',\n 'STRICT',\n 'x86',\n '_X86_',\n 'INTERNATIONAL',\n ('INTLMSG_CODEPAGE', '1252'),\n ]\n if platform == 'windows':\n cppdefines += ['PIPE_SUBSYSTEM_WINDOWS_USER']\n if platform == 'winddk':\n cppdefines += ['PIPE_SUBSYSTEM_WINDOWS_DISPLAY']\n if platform == 'wince':\n cppdefines += ['PIPE_SUBSYSTEM_WINDOWS_CE']\n env.Append(CPPDEFINES = cppdefines)\n\n # C preprocessor includes\n if platform == 'winddk':\n env.Append(CPPPATH = [\n env['SDK_INC_PATH'],\n env['DDK_INC_PATH'],\n env['WDM_INC_PATH'],\n env['CRT_INC_PATH'],\n ])\n\n # C compiler options\n cflags = []\n if gcc:\n if debug:\n cflags += ['-O0', '-g3']\n else:\n cflags += ['-O3', '-g3']\n if env['profile']:\n cflags += ['-pg']\n if env['machine'] == 'x86':\n cflags += [\n '-m32',\n #'-march=pentium4',\n '-mmmx', '-msse', '-msse2', # enable SIMD intrinsics\n #'-mfpmath=sse',\n ]\n if env['machine'] == 'x86_64':\n cflags += ['-m64']\n cflags += [\n '-Wall',\n '-Wmissing-prototypes',\n '-Wno-long-long',\n '-ffast-math',\n '-pedantic',\n '-fmessage-length=0', # be nice to Eclipse\n ]\n if msvc:\n # See also:\n # - http://msdn.microsoft.com/en-us/library/19z1t1wy.aspx\n # - cl /?\n if debug:\n cflags += [\n '/Od', # disable optimizations\n '/Oi', # enable intrinsic functions\n '/Oy-', # disable frame pointer omission\n ]\n else:\n cflags += [\n '/Ox', # maximum optimizations\n '/Oi', # enable intrinsic functions\n '/Ot', # favor code speed\n #'/fp:fast', # fast floating point \n ]\n if env['profile']:\n cflags += [\n '/Gh', # enable _penter hook function\n '/GH', # enable _pexit hook function\n ]\n cflags += [\n '/W3', # warning level\n #'/Wp64', # enable 64 bit porting warnings\n ]\n if env['machine'] == 'x86':\n cflags += [\n #'/QIfist', # Suppress _ftol\n #'/arch:SSE2', # use the SSE2 instructions\n ]\n if platform == 'windows':\n cflags += [\n # TODO\n ]\n if platform == 'winddk':\n cflags += [\n '/Zl', # omit default library name in .OBJ\n '/Zp8', # 8bytes struct member alignment\n '/Gy', # separate functions for linker\n '/Gm-', # disable minimal rebuild\n '/WX', # treat warnings as errors\n '/Gz', # __stdcall Calling convention\n '/GX-', # disable C++ EH\n '/GR-', # disable C++ RTTI\n '/GF', # enable read-only string pooling\n '/G6', # optimize for PPro, P-II, P-III\n '/Ze', # enable extensions\n '/Gi-', # disable incremental compilation\n '/QIfdiv-', # disable Pentium FDIV fix\n '/hotpatch', # prepares an image for hotpatching.\n #'/Z7', #enable old-style debug info\n ]\n if platform == 'wince':\n # See also C:\\WINCE600\\public\\common\\oak\\misc\\makefile.def\n cflags += [\n '/Zl', # omit default library name in .OBJ\n '/GF', # enable read-only string pooling\n '/GR-', # disable C++ RTTI\n '/GS', # enable security checks\n # Allow disabling language conformance to maintain backward compat\n #'/Zc:wchar_t-', # don't force wchar_t as native type, instead of typedef\n #'/Zc:forScope-', # don't enforce Standard C++ for scoping rules\n #'/wd4867',\n #'/wd4430',\n #'/MT',\n #'/U_MT',\n ]\n # Automatic pdb generation\n # See http://scons.tigris.org/issues/show_bug.cgi?id=1656\n env.EnsureSConsVersion(0, 98, 0)\n env['PDB'] = '${TARGET.base}.pdb'\n env.Append(CFLAGS = cflags)\n env.Append(CXXFLAGS = cflags)\n\n # Assembler options\n if gcc:\n if env['machine'] == 'x86':\n env.Append(ASFLAGS = ['-m32'])\n if env['machine'] == 'x86_64':\n env.Append(ASFLAGS = ['-m64'])\n\n # Linker options\n linkflags = []\n if gcc:\n if env['machine'] == 'x86':\n linkflags += ['-m32']\n if env['machine'] == 'x86_64':\n linkflags += ['-m64']\n if platform == 'winddk':\n # See also:\n # - http://msdn2.microsoft.com/en-us/library/y0zzbyt4.aspx\n linkflags += [\n '/merge:_PAGE=PAGE',\n '/merge:_TEXT=.text',\n '/section:INIT,d',\n '/opt:ref',\n '/opt:icf',\n '/ignore:4198,4010,4037,4039,4065,4070,4078,4087,4089,4221',\n '/incremental:no',\n '/fullbuild',\n '/release',\n '/nodefaultlib',\n '/wx',\n '/debug',\n '/debugtype:cv',\n '/version:5.1',\n '/osversion:5.1',\n '/functionpadmin:5',\n '/safeseh',\n '/pdbcompress',\n '/stack:0x40000,0x1000',\n '/driver',\n '/align:0x80',\n '/subsystem:native,5.01',\n '/base:0x10000',\n\n '/entry:DrvEnableDriver',\n ]\n if env['profile']:\n linkflags += [\n '/MAP', # http://msdn.microsoft.com/en-us/library/k7xkk3e2.aspx\n ]\n if platform == 'wince':\n linkflags += [\n '/nodefaultlib',\n #'/incremental:no',\n #'/fullbuild',\n '/entry:_DllMainCRTStartup',\n ]\n env.Append(LINKFLAGS = linkflags)\n\n # Default libs\n env.Append(LIBS = [])\n\n # Custom builders and methods\n createConvenienceLibBuilder(env)\n createCodeGenerateMethod(env)\n createInstallMethods(env)\n\n # for debugging\n #print env.Dump()","def getEnvironmentVars(self):\n my_env = os.environ.copy()\n my_env[\"MODE\"] = self.model.mode\n my_env[\"ALGORITHM\"] = self.model.algorithm\n my_env[\"INPUT_FILE\"] = self.model.inputFile\n my_env[\"TARGET_FILE\"] = self.model.targetFile\n my_env[\"DEVICE\"] = self.model.device\n my_env[\"SIZE\"] = str(self.model.size)\n my_env[\"ROLE\"] = self.model.role\n my_env[\"WAIT_SIZE\"] = str(self.model.waitSize)\n my_env[\"STEP_SIZE\"] = str(self.model.stepSize)\n my_env[\"TUI_CONNECTION\"] = str(True)\n return my_env","def make_env(env_name):\n \n env = gym.make(env_name) \n return env","def __create_execution_engine(self):\n target = self.binding.Target.from_default_triple()\n target_machine = target.create_target_machine()\n backing_mod = binding.parse_assembly(\"\")\n engine = binding.create_mcjit_compiler(backing_mod, target_machine)\n self.engine = engine","def setup_environment():\n os.environ['QUEUE_OVERRIDE'] = 'LINUX_UNTRUSTED'\n os.environ['WORKER_ROOT_DIR'] = os.path.join(MNT_DIR, 'clusterfuzz')\n os.environ['WORKER_BOT_TMPDIR'] = os.path.join(MNT_DIR, 'tmp')\n\n if not os.path.exists(BOT_BASEDIR):\n os.mkdir(BOT_BASEDIR)","def _make_environment(\n self, problem_type, curriculum_sample, wrapper_type, backend_type=None):\n if backend_type is None:\n backend_type = FLAGS.backend\n return dm_construction.get_environment(\n problem_type,\n unity_environment=self._unity_envs[backend_type],\n wrapper_type=wrapper_type,\n curriculum_sample=curriculum_sample)","def setup_app(command, conf, vars):\n load_environment(conf.global_conf, conf.local_conf)","def _env_setup(self, initial_qpos):\n raise NotImplementedError()","def __init__(self, *args):\n self.env = os.environ.copy()\n \"\"\"Environment variables (:class:`dict`)\"\"\"\n command = \"modulecmd python \"+' '.join(args)\n p = subprocess.Popen(command, shell=True, stdout=subprocess.PIPE)\n retval = p.communicate()\n self._parse(retval)","def make_env(\n args,\n num_envs=None,\n include_norm=False,\n norm_reward=True,\n **kwargs,\n):\n if num_envs is None:\n num_envs = args.num_envs\n\n # `kwargs` given via `args`\n args_kwargs = {}\n for arg in [\n 'M',\n 'dt',\n 'restol',\n 'seed',\n\n 'lambda_real_interval',\n 'lambda_imag_interval',\n 'lambda_real_interpolation_interval',\n\n 'norm_factor',\n 'residual_weight',\n 'step_penalty',\n 'reward_iteration_only',\n 'reward_strategy',\n 'collect_states',\n ]:\n args_kwargs[arg] = kwargs.pop(arg, getattr(args, arg))\n all_kwargs = {**kwargs, **args_kwargs}\n\n # SAC does not support float64\n if args.model_class == 'SAC':\n all_kwargs['use_doubles'] = False\n\n return gym.make(\n args.envname,\n **all_kwargs,\n )","def _set_executables(self):\n\n # add path from argument to env\n if self.home_path:\n if self.env:\n self.env += f\":{self.home_path}\"\n else:\n self.env = self.home_path\n\n # set fuzzer_exe \n self.fuzzer_exe = self._search_for_executable(self.fuzzer_exe)\n L.debug(\"Will use %s as fuzzer executable.\", self.fuzzer_exe)\n\n # set compiler_exe\n if self.compiler_exe:\n self.compiler_exe = self._search_for_executable(self.compiler_exe)\n L.debug(\"Will use %s as fuzzer compiler.\", self.compiler_exe)\n\n # set additional executables\n for exe_name, exe_file in self.EXECUTABLES.items():\n self.EXECUTABLES[exe_name] = self._search_for_executable(exe_file)","def init_env(self, env_info):\n raise NotImplementedError","def get_environment() -> dict:\n go_path = subprocess.check_output([\"go\", \"env\", \"GOPATH\"]).decode().strip()\n setup_script_path = f\"{go_path}/src/github.com/harmony-one/harmony/scripts/setup_bls_build_flags.sh\"\n response = subprocess.check_output([\"bash\", setup_script_path, \"-v\"], timeout=5)\n environment = json.loads(response)\n environment[\"HOME\"] = os.environ.get(\"HOME\")\n return environment","def build_env(self, job, private_data_dir, private_data_files=None):\n env = super(RunJob, self).build_env(job, private_data_dir, private_data_files=private_data_files)\n if private_data_files is None:\n private_data_files = {}\n # Set environment variables needed for inventory and job event\n # callbacks to work.\n env['JOB_ID'] = str(job.pk)\n env['INVENTORY_ID'] = str(job.inventory.pk)\n if job.project:\n env['PROJECT_REVISION'] = job.project.scm_revision\n env['ANSIBLE_RETRY_FILES_ENABLED'] = \"False\"\n env['MAX_EVENT_RES'] = str(settings.MAX_EVENT_RES_DATA)\n if hasattr(settings, 'AWX_ANSIBLE_CALLBACK_PLUGINS') and settings.AWX_ANSIBLE_CALLBACK_PLUGINS:\n env['ANSIBLE_CALLBACK_PLUGINS'] = ':'.join(settings.AWX_ANSIBLE_CALLBACK_PLUGINS)\n env['AWX_HOST'] = settings.TOWER_URL_BASE\n\n # Create a directory for ControlPath sockets that is unique to each job\n cp_dir = os.path.join(private_data_dir, 'cp')\n if not os.path.exists(cp_dir):\n os.mkdir(cp_dir, 0o700)\n # FIXME: more elegant way to manage this path in container\n env['ANSIBLE_SSH_CONTROL_PATH_DIR'] = '/runner/cp'\n\n # Set environment variables for cloud credentials.\n cred_files = private_data_files.get('credentials', {})\n for cloud_cred in job.cloud_credentials:\n if cloud_cred and cloud_cred.credential_type.namespace == 'openstack' and cred_files.get(cloud_cred, ''):\n env['OS_CLIENT_CONFIG_FILE'] = to_container_path(cred_files.get(cloud_cred, ''), private_data_dir)\n\n for network_cred in job.network_credentials:\n env['ANSIBLE_NET_USERNAME'] = network_cred.get_input('username', default='')\n env['ANSIBLE_NET_PASSWORD'] = network_cred.get_input('password', default='')\n\n ssh_keyfile = cred_files.get(network_cred, '')\n if ssh_keyfile:\n env['ANSIBLE_NET_SSH_KEYFILE'] = ssh_keyfile\n\n authorize = network_cred.get_input('authorize', default=False)\n env['ANSIBLE_NET_AUTHORIZE'] = str(int(authorize))\n if authorize:\n env['ANSIBLE_NET_AUTH_PASS'] = network_cred.get_input('authorize_password', default='')\n\n path_vars = (\n ('ANSIBLE_COLLECTIONS_PATHS', 'collections_paths', 'requirements_collections', '~/.ansible/collections:/usr/share/ansible/collections'),\n ('ANSIBLE_ROLES_PATH', 'roles_path', 'requirements_roles', '~/.ansible/roles:/usr/share/ansible/roles:/etc/ansible/roles'),\n ('ANSIBLE_COLLECTIONS_PATH', 'collections_path', 'requirements_collections', '~/.ansible/collections:/usr/share/ansible/collections'),\n )\n\n config_values = read_ansible_config(os.path.join(private_data_dir, 'project'), list(map(lambda x: x[1], path_vars)))\n\n for env_key, config_setting, folder, default in path_vars:\n paths = default.split(':')\n if env_key in env:\n for path in env[env_key].split(':'):\n if path not in paths:\n paths = [env[env_key]] + paths\n elif config_setting in config_values:\n for path in config_values[config_setting].split(':'):\n if path not in paths:\n paths = [config_values[config_setting]] + paths\n paths = [os.path.join(CONTAINER_ROOT, folder)] + paths\n env[env_key] = os.pathsep.join(paths)\n\n return env","def init_environment(self):\n # import outside of cell so we don't get a traceback\n from sage import all_cmdline\n from sage.repl.user_globals import initialize_globals\n initialize_globals(all_cmdline, self.shell.user_ns)\n self.run_init()","def get_environmentals(self):\n for k, v in utils.slurm_envs(default.SBATCH_VARS_FOR_WORKFLOW).items():\n setattr(self, k, v)","def create_virtual_environment(self):\n cmd_venv = \"python\" + self.python_version + \" -m venv --prompt \\\"\" + self.project_name + \"\\\" \" + TaskCreator.venv_dir\n return {\n \"actions\": [cmd_venv],\n \"verbosity\": 2\n }","def prepare(self):\n # This is a no-op; the native subprocess environment is ready-to-use.\n pass","def setup_environment():\n global _ENV_SETUP_DONE\n if _ENV_SETUP_DONE:\n return\n _ENV_SETUP_DONE = True\n\n _configure_libraries()\n\n custom_module_path = os.environ.get(\"DETECTRON2_ENV_MODULE\")\n\n if custom_module_path:\n setup_custom_environment(custom_module_path)\n else:\n # The default setup is a no-op\n pass","def shell_source(script):\n import subprocess, os\n pipe = subprocess.Popen(\". %s; env\" % script, stdout=subprocess.PIPE, shell=True)\n output = pipe.communicate()[0]\n env = dict((line.split(\"=\", 1) for line in output.splitlines()))\n os.environ.update(env)","def shell_source(script):\n import subprocess, os\n pipe = subprocess.Popen(\". %s; env\" % script, stdout=subprocess.PIPE, shell=True)\n output = pipe.communicate()[0]\n env = dict((line.split(\"=\", 1) for line in output.splitlines()))\n os.environ.update(env)","def test_environment_scripts_generated_envvars(require_run):\n consumer_pkg = textwrap.dedent(r\"\"\"\n from conan import ConanFile\n from conan.tools.env import VirtualBuildEnv, VirtualRunEnv\n class Pkg(ConanFile):\n settings = \"os\"\n requires = \"require_pkg/1.0\"\n tool_requires = \"build_require_pkg/1.0\"\n generators = \"VirtualRunEnv\", \"VirtualBuildEnv\"\n \"\"\")\n\n client = TestClient()\n conanfile_br = (GenConanfile().with_package_file(\"bin/myapp\", \"myexe\")\n .with_package_file(\"lib/mylib\", \"mylibcontent\")\n .with_settings(\"os\"))\n conanfile_require = (GenConanfile().with_package_file(\"bin/myapp\", \"myexe\")\n .with_package_file(\"lib/mylib\", \"mylibcontent\")\n .with_settings(\"os\"))\n if require_run:\n conanfile_require.with_package_type(\"application\")\n client.save({\"build_require_pkg/conanfile.py\": conanfile_br,\n \"require_pkg/conanfile.py\": conanfile_require,\n \"consumer_pkg/conanfile.py\": consumer_pkg})\n\n client.run(\"export build_require_pkg --name=build_require_pkg --version=1.0\")\n client.run(\"export require_pkg --name=require_pkg --version=1.0\")\n\n client.run(\"install consumer_pkg --build='*'\")\n if platform.system() == \"Windows\":\n conanbuildenv = client.load(\"consumer_pkg/conanbuildenv.bat\")\n if require_run:\n conanrunenv = client.load(\"consumer_pkg/conanrunenv.bat\")\n assert \"LD_LIBRARY_PATH\" not in conanbuildenv\n assert \"LD_LIBRARY_PATH\" not in conanrunenv\n else:\n assert not os.path.exists(\"consumer_pkg/conanrunenv.bat\")\n else:\n if require_run:\n conanbuildenv = client.load(\"consumer_pkg/conanbuildenv.sh\")\n conanrunenv = client.load(\"consumer_pkg/conanrunenv.sh\")\n assert \"LD_LIBRARY_PATH\" in conanbuildenv\n assert \"LD_LIBRARY_PATH\" in conanrunenv\n else:\n assert not os.path.exists(\"consumer_pkg/conanrunenv.sh\")\n\n if require_run:\n # Build context LINUX - Host context LINUX\n client.run(\"install consumer_pkg -s:b os=Linux -s:h os=Linux --build='*'\")\n conanbuildenv = client.load(\"consumer_pkg/conanbuildenv.sh\")\n conanrunenv = client.load(\"consumer_pkg/conanrunenv.sh\")\n assert \"LD_LIBRARY_PATH\" in conanbuildenv\n assert \"LD_LIBRARY_PATH\" in conanrunenv\n\n # Build context WINDOWS - Host context WINDOWS\n client.run(\"install consumer_pkg -s:b os=Windows -s:h os=Windows --build='*'\")\n conanbuildenv = client.load(\"consumer_pkg/conanbuildenv.bat\")\n conanrunenv = client.load(\"consumer_pkg/conanrunenv.bat\")\n assert \"LD_LIBRARY_PATH\" not in conanbuildenv\n assert \"LD_LIBRARY_PATH\" not in conanrunenv\n\n # Build context LINUX - Host context WINDOWS\n client.run(\"install consumer_pkg -s:b os=Linux -s:h os=Windows --build='*'\")\n conanbuildenv = client.load(\"consumer_pkg/conanbuildenv.sh\")\n conanrunenv = client.load(\"consumer_pkg/conanrunenv.bat\")\n assert \"LD_LIBRARY_PATH\" in conanbuildenv\n assert \"LD_LIBRARY_PATH\" not in conanrunenv\n\n # Build context WINDOWS - Host context LINUX\n client.run(\"install consumer_pkg -s:b os=Windows -s:h os=Linux --build='*'\")\n conanbuildenv = client.load(\"consumer_pkg/conanbuildenv.bat\")\n conanrunenv = client.load(\"consumer_pkg/conanrunenv.sh\")\n assert \"LD_LIBRARY_PATH\" not in conanbuildenv\n assert \"LD_LIBRARY_PATH\" in conanrunenv","def getEnvironment(self):\n pass","def create_environment(args):\n env.username = args.user\n env.password = args.password\n env.service_url = args.service_url\n env.quiet = args.quiet\n env.verbose = args.verbose\n env.manifest = args.manifest\n env.debug = args.debug\n env.always_confirm = args.yes\n env.args = args\n env.api = ravello.RavelloClient(env.username, env.password, env.service_url)","def _setup_env():\n env.home_path = os.path.expanduser('~')\n env.env_path = os.getenv('WORKON_HOME')\n\n if not env.env_path:\n warn(\"You should set the WORKON_HOME environment variable to\" \\\n \" the root directory for your virtual environments.\")\n env.env_path = env.sites_path\n\n env.project_path = join(env.sites_path, env.project_name)\n env.ve_path = join(env.env_path, env.project_name)\n env.activate_path = join(env.ve_path, 'bin', 'activate')","def _create_extra_environment(self):\n return {}","def create(self):\n if self._is_setup:\n return\n\n if not self.check_presence():\n if os.path.exists(self._path):\n util.long_path_rmtree(self._path)\n\n if not os.path.exists(self._env_dir):\n try:\n os.makedirs(self._env_dir)\n except OSError:\n # Environment.create may be called in parallel for\n # environments with different self._path, but same\n # self._env_dir. This causes a race condition for\n # the above makedirs() call --- but not for the\n # rest of the processing. Therefore, we will just\n # ignore the error here, as things will fail at a\n # later stage if there is really was a problem.\n pass\n\n try:\n self._setup()\n except Exception:\n log.error(f\"Failure creating environment for {self.name}\")\n if os.path.exists(self._path):\n util.long_path_rmtree(self._path)\n raise\n\n self.save_info_file(self._path)\n\n self._is_setup = True","def set_up(self):\n self.path.state = self.path.gen.joinpath(\"state\")\n if self.path.state.exists():\n self.path.state.rmtree(ignore_errors=True)\n self.path.state.mkdir()\n\n for script in self.given.get(\"scripts\", []):\n script_path = self.path.state.joinpath(script)\n\n if not script_path.dirname().exists():\n script_path.dirname().makedirs()\n\n script_path.write_text(self.given[\"scripts\"][script])\n script_path.chmod(\"u+x\")\n\n for filename, contents in self.given.get(\"files\", {}).items():\n self.path.state.joinpath(filename).write_text(contents)\n\n self.python = hitchpylibrarytoolkit.project_build(\n \"commandlib\", self.path, self.given[\"python version\"]\n ).bin.python\n\n self.example_py_code = (\n ExamplePythonCode(self.python, self.path.state)\n .with_code(self.given.get(\"code\", \"\"))\n .with_setup_code(self.given.get(\"setup\", \"\"))\n )","def get_env(self, *args):\n m = module(*args)\n return m.env","def executor_env(self, key=None, value=None, pairs=None):\n if (key is not None and pairs is not None) or (key is None and pairs is None):\n raise Exception(\"Either pass one key-value pair or a list of pairs\")\n elif key is not None:\n self.environmentVariables[key] = value\n elif pairs is not None:\n for (k, v) in pairs:\n self.environmentVariables[k] = v\n return self","def generate_environment(dataset, tmpdir):\n\n print(\">>> Test environment:\")\n print(\"dataset:\", dataset)\n print(\"tmpdir:\", tmpdir)\n\n generate_test_environment(tmpdir, dataset)\n\n return { 'dataset': dataset, 'tmpdir': tmpdir }","def setup_env(app_dir, app_id, version, module_id, remote_api=False):\n # GCS library behaves differently when running under remote_api. It uses\n # SERVER_SOFTWARE to figure this out. See cloudstorage/common.py, local_run().\n if remote_api:\n os.environ['SERVER_SOFTWARE'] = 'remote_api'\n else:\n os.environ['SERVER_SOFTWARE'] = 'Development yo dawg/1.0'\n if app_dir:\n app_id = app_id or Application(app_dir).app_id\n version = version or 'default-version'\n if app_id:\n os.environ['APPLICATION_ID'] = app_id\n if version:\n os.environ['CURRENT_VERSION_ID'] = '%s.%d' % (\n version, int(time.time()) << 28)\n if module_id:\n os.environ['CURRENT_MODULE_ID'] = module_id","def environment(self):\n return self._environ","def __init__(self, env, system=None):\n self._env = env\n self._system = system if system is not None else {}","def env_init(self, environ) -> None:\n environ.update(self._env)","def CreateEnv(self):\n if len(self.input.get()) > 0:\n VirtualEnvApp().CreateEnv(self.input.get())\n self.restart_program()\n else:\n messagebox.showinfo('Error', 'Please Enter the Name of the ENV')","def init_vars(self):\n\n load_dotenv()\n self.smart_cube = True if os.environ.get(\"SMART_CUBE\") == \"True\" else False\n self.gen_parsed_to_cubedb = True if os.environ.get(\"GEN_PARSED_TO_CUBEDB\") == \"True\" else False\n self.name_of_solve = os.environ.get(\"NAME_OF_SOLVE\")\n self.time_solve = os.environ.get(\"TIME_SOLVE\")\n self.comms_unparsed_bool = True if os.environ.get(\"COMMS_UNPARSED\") == \"True\" else False\n self.gen_with_move_count = True if os.environ.get(\"GEN_WITH_MOVE_COUNT\") == \"True\" else False\n self.diff_to_solved_state = float(os.environ.get(\"DIFF_BETWEEN_ALGS\"))\n self.parse_to_lp = True if os.environ.get(\"PARSE_TO_LETTER_PAIR\") == \"True\" else False\n self.gen_with_moves = True if os.environ.get(\"GEN_WITH_MOVE_COUNT\") == \"True\" else False\n self.buffer_ed = self.get_buffer_ed(os.environ.get(\"EDGES_BUFFER\"))\n self.buffer_cor = self.get_buffer_cor(os.environ.get(\"CORNER_BUFFER\"))\n self.path_to_lp = os.environ.get(\"PATH_LETTER_PAIR_FILE\")\n self.dict_lp = self.load_letter_pairs_dict()","def make_env(\n\t\tdomain_name,\n\t\ttask_name,\n\t\tseed=0,\n\t\tepisode_length=1000,\n\t\tframe_stack=3,\n\t\taction_repeat=4,\n\t\timage_size=100,\n\t\tmode='original'\n\t):\n\tassert mode in {'original', 'color_easy', 'color_hard', 'video_easy', 'video_hard'}, \\\n\t\tf'specified mode \"{mode}\" is not supported'\n\n\tenv = dmc2gym.make(\n\t\tdomain_name=domain_name,\n\t\ttask_name=task_name,\n\t\tseed=seed,\n\t\tvisualize_reward=False,\n\t\tfrom_pixels=True,\n\t\theight=image_size,\n\t\twidth=image_size,\n\t\tepisode_length=episode_length,\n\t\tframe_skip=action_repeat\n\t)\n\tenv = VideoWrapper(env, mode, seed)\n\tenv = FrameStack(env, frame_stack)\n\tenv = RandomizationWrapper(env, mode, seed)\n\n\treturn env","def construct_envs(\n config,\n env_class,\n workers_ignore_signals=False,\n):\n\n num_processes = config.NUM_ENVIRONMENTS\n configs = []\n env_classes = [env_class for _ in range(num_processes)]\n dataset = habitat.datasets.make_dataset(config.TASK_CONFIG.DATASET.TYPE)\n scenes = config.TASK_CONFIG.DATASET.CONTENT_SCENES\n if \"*\" in config.TASK_CONFIG.DATASET.CONTENT_SCENES:\n scenes = dataset.get_scenes_to_load(config.TASK_CONFIG.DATASET)\n\n if num_processes > 1:\n if len(scenes) == 0:\n raise RuntimeError(\n \"No scenes to load, multiple process logic relies on being able to split scenes uniquely between processes\"\n )\n\n if len(scenes) < num_processes:\n scenes = scenes * num_processes\n\n random.shuffle(scenes)\n\n scene_splits = [[] for _ in range(num_processes)]\n for idx, scene in enumerate(scenes):\n scene_splits[idx % len(scene_splits)].append(scene)\n\n assert sum(map(len, scene_splits)) == len(scenes)\n\n for i in range(num_processes):\n proc_config = config.clone()\n proc_config.defrost()\n\n task_config = proc_config.TASK_CONFIG\n task_config.SEED = task_config.SEED + i\n if len(scenes) > 0:\n task_config.DATASET.CONTENT_SCENES = scene_splits[i]\n\n task_config.SIMULATOR.HABITAT_SIM_V0.GPU_DEVICE_ID = (\n config.SIMULATOR_GPU_ID\n )\n\n task_config.SIMULATOR.AGENT_0.SENSORS = config.SENSORS\n\n proc_config.freeze()\n configs.append(proc_config)\n\n envs = habitat.ThreadedVectorEnv(\n make_env_fn=make_env_fn,\n env_fn_args=tuple(zip(configs, env_classes)),\n workers_ignore_signals=workers_ignore_signals,\n )\n return envs","def env(config, args):\n print config.template(\"scripts/env.sh\", project=args.project)","def make_env(rank, agents, seed=10000):\n\tdef _init():\n\t\tenv = TradingGameEnv.TradingGameEnv(player_count = NUM_PLAYERS, other_agent_list = agents,\n\t\t\tseq_per_day = SEQ_PER_DAY, cards_per_suit = CARDS_PER_SUIT, player_hand_count = HAND_COUNT,\n\t\t\trandom_seq = True, self_play = SELF_PLAY, policy_type = POLICY_TYPE, self_copy_freq = SELF_COPY_FREQ,\n\t\t\tobs_transaction_history_size=TRANSACTION_HISTORY_SIZE)\n\t\tenv.seed(seed + rank)\n\t\treturn env\n\tset_global_seeds(seed)\n\treturn _init","def wrapper_environment(args):\n\n return {\n ENVIRONMENT_KEY: json.dumps({\n 'verbose': args.verbose,\n 'cc': shlex.split(args.cc),\n 'cxx': shlex.split(args.cxx)\n })\n }","def make_as_global(self):\n return setup(env=self)","def start_env(config: OmegaConf, debug_env=False) -> gym.Env:\n if debug_env:\n env = MineRLDebugEnv(config)\n else:\n environment = config.env.name\n env = gym.make(environment)\n env = ActionShaping(env, config)\n env = ObservationWrapper(env, config)\n return env","def get_elinos_environment():\n result = {}\n for key in (\"project\", \"cdk\", \"target\"):\n var = \"ELINOS_\" + key.upper()\n if var in os.environ:\n result[key] = os.environ[var]\n else:\n warn(\"%s not set\" % var)\n result[key] = None\n\n if result[\"project\"] is not None:\n result[\"xenomai\"] = glob.glob(result[\"project\"] + \"/xenomai-[0-9.]*\")\n else:\n result[\"xenomai\"] = []\n\n return result","def setup(self):\n\n if os.name == 'nt':\n windows_env_start.print_banner(bootstrap=True, no_shell_file=False)\n else:\n enable_colors()\n\n steps = [\n ('CIPD package manager', self.cipd),\n ('Python environment', self.virtualenv),\n ('Host tools', self.host_tools),\n ]\n\n if self._is_windows:\n steps.append((\"Windows scripts\", self.win_scripts))\n\n self._log(\n Color.bold('Downloading and installing packages into local '\n 'source directory:\\n'))\n\n max_name_len = max(len(name) for name, _ in steps)\n\n self._env.comment('''\nThis file is automatically generated. DO NOT EDIT!\nFor details, see $PW_ROOT/pw_env_setup/py/pw_env_setup/env_setup.py and\n$PW_ROOT/pw_env_setup/py/pw_env_setup/environment.py.\n'''.strip())\n\n if not self._is_windows:\n self._env.comment('''\nFor help debugging errors in this script, uncomment the next line.\nset -x\nThen use `set +x` to go back to normal.\n'''.strip())\n\n self._env.echo(\n Color.bold(\n 'Activating environment (setting environment variables):'))\n self._env.echo('')\n\n for name, step in steps:\n self._log(' Setting up {name:.<{width}}...'.format(\n name=name, width=max_name_len),\n end='',\n flush=True)\n self._env.echo(\n ' Setting environment variables for {name:.<{width}}...'.\n format(name=name, width=max_name_len),\n newline=False,\n )\n\n start = time.time()\n spin = spinner.Spinner()\n with spin():\n result = step(spin)\n stop = time.time()\n\n self._log(result.status_str(stop - start))\n\n self._env.echo(result.status_str())\n for message in result.messages():\n sys.stderr.write('{}\\n'.format(message))\n self._env.echo(message)\n\n if not result.ok():\n return -1\n\n self._log('')\n self._env.echo('')\n\n self._env.finalize()\n\n self._env.echo(Color.bold('Checking the environment:'))\n self._env.echo()\n\n self._env.doctor()\n self._env.echo()\n\n self._env.echo(\n Color.bold('Environment looks good, you are ready to go!'))\n self._env.echo()\n\n with open(self._shell_file, 'w') as outs:\n self._env.write(outs)\n\n deactivate = os.path.join(\n self._install_dir,\n 'deactivate{}'.format(os.path.splitext(self._shell_file)[1]))\n with open(deactivate, 'w') as outs:\n self._env.write_deactivate(outs)\n\n config = {\n # Skipping sysname and nodename in os.uname(). nodename could change\n # based on the current network. sysname won't change, but is\n # redundant because it's contained in release or version, and\n # skipping it here simplifies logic.\n 'uname': ' '.join(getattr(os, 'uname', lambda: ())()[2:]),\n 'os': os.name,\n }\n\n with open(os.path.join(self._install_dir, 'config.json'), 'w') as outs:\n outs.write(\n json.dumps(config, indent=4, separators=(',', ': ')) + '\\n')\n\n if self._json_file is not None:\n with open(self._json_file, 'w') as outs:\n self._env.json(outs)\n\n return 0","def base_env(*args, **kwargs):\n try:\n # regular gym\n env = gym.make(*args, **kwargs)\n except:\n try:\n # gym retro\n env = retro.make(*args, **kwargs)\n except:\n # gym-super-mario-bros\n env = gym_super_mario_bros.make(*args, **kwargs)\n env.recognized = None\n return env","def setUpEnv(self):\n \n robot = Robot('atrv')\n\n pose = Sensor('pose')\n robot.append(pose)\n pose.configure_mw('yarp')\n\n motion = Actuator('v_omega')\n robot.append(motion)\n motion.configure_mw('yarp')\n \n env = Environment('indoors-1/indoor-1')\n env.configure_service('socket')","def _prep_subprocess_environment(self) -> None:\n os.makedirs('dist/ba_root', exist_ok=True)\n if os.path.exists('dist/ba_root/config.json'):\n with open('dist/ba_root/config.json') as infile:\n bincfg = json.loads(infile.read())\n else:\n bincfg = {}\n\n # Some of our config values translate directly into the\n # ballisticacore config file; the rest we pass at runtime.\n bincfg['Port'] = self._config.port\n bincfg['Auto Balance Teams'] = self._config.auto_balance_teams\n bincfg['Show Tutorial'] = False\n bincfg['Idle Exit Minutes'] = self._config.idle_exit_minutes\n with open('dist/ba_root/config.json', 'w') as outfile:\n outfile.write(json.dumps(bincfg))","def create_environment(loader):\n loaders = (loader, PackageLoader('pynuts', 'templates'))\n environment = Environment(\n loader=ChoiceLoader(loaders), extensions=[ShowOnMatch])\n environment.globals.update({'url_for': flask.url_for})\n environment.filters['data'] = filters.data\n environment.filters['base64'] = filters.base64\n return environment","def construct_parent_shell(eval_output_with, print_script_to):\n if eval_output_with:\n environment_ctor = {\n \"bash\": BashParentEnvironment,\n \"powershell\": PowershellParentEnvironment\n }\n\n printers = {\n \"bash\": escaped_printer_with_character(\"\\\\\", print_script_to),\n \"powershell\": escaped_printer_with_character(\"`\",\n print_script_to)\n }\n\n printer = printers[eval_output_with]\n return environment_ctor[eval_output_with](printer)\n else:\n return BashParentEnvironment(lambda _: None)","def collect_env():\n env_info = mmengine_collect_env()\n\n # MMEngine does not add the hipcc compiler information when collecting\n # environment information, so it is added here. When MMEngine v0.3.0 is\n # released, the code here can be removed.\n cuda_available = torch.cuda.is_available()\n if cuda_available and env_info.get('NVCC') == 'Not Available':\n CUDA_HOME = env_info['CUDA_HOME']\n if CUDA_HOME is not None and osp.isdir(CUDA_HOME):\n if CUDA_HOME == '/opt/rocm':\n try:\n nvcc = osp.join(CUDA_HOME, 'hip/bin/hipcc')\n nvcc = subprocess.check_output(\n f'\"{nvcc}\" --version', shell=True)\n nvcc = nvcc.decode('utf-8').strip()\n release = nvcc.rfind('HIP version:')\n build = nvcc.rfind('')\n nvcc = nvcc[release:build].strip()\n except subprocess.SubprocessError:\n nvcc = 'Not Available'\n else:\n try:\n nvcc = osp.join(CUDA_HOME, 'bin/nvcc')\n nvcc = subprocess.check_output(f'\"{nvcc}\" -V', shell=True)\n nvcc = nvcc.decode('utf-8').strip()\n release = nvcc.rfind('Cuda compilation tools')\n build = nvcc.rfind('Build ')\n nvcc = nvcc[release:build].strip()\n except subprocess.SubprocessError:\n nvcc = 'Not Available'\n env_info['NVCC'] = nvcc\n\n env_info['MMCV'] = mmcv.__version__\n\n try:\n from mmcv.ops import get_compiler_version, get_compiling_cuda_version\n except ModuleNotFoundError:\n env_info['MMCV Compiler'] = 'n/a'\n env_info['MMCV CUDA Compiler'] = 'n/a'\n else:\n env_info['MMCV Compiler'] = get_compiler_version()\n env_info['MMCV CUDA Compiler'] = get_compiling_cuda_version()\n\n return env_info"],"string":"[\n \"def build_python_environment(self):\\n # build command for creating the python environment\\n cmd_args = {\\n 'exe': self.env_executable,\\n 'cmds': \\\" \\\".join(self.env_commands),\\n 'flags': \\\" \\\".join(self.env_flags),\\n 'args': \\\" \\\".join(self.env_arguments),\\n }\\n cmd_create_env = self.cmd_env.format(**cmd_args)\\n print(\\\"Building new python environment ({}) ... \\\"\\n .format(self.proj_name))\\n with click_spinner.spinner():\\n errno, stdout, stderr = utils.run_command(cmd_create_env, env=None,\\n shell=True)\\n if errno:\\n raise Exception(\\\"Environment setup failed (STDERR: {})\\\"\\n .format(stderr))\",\n \"def setup_environment(self):\\n self.run_command(\\\"cd {}\\\".format(quote(str(self.builddir))))\\n env_vars = self._build_env_variables_string()\\n if env_vars:\\n env_vars = quote(env_vars)\\n command = \\\"{} DISTRO={} MACHINE={} . {} build-{}\\\".format(\\n env_vars,\\n quote(self.distro),\\n quote(self.machine),\\n quote(self.init_env_file),\\n quote(self.distro),\\n )\\n self.run_command(command)\",\n \"def __MakeEnvironment(self):\\n environment= os.environ.copy()\\n\\n for key, value in self.__context.items():\\n if type(value) is str:\\n name = \\\"QMV_\\\" + key.replace(\\\".\\\", \\\"__\\\")\\n environment[name]= value\\n\\n return environment\",\n \"def _create_environment(config):\\n if isinstance(config.env, str):\\n env = gym.make(config.env)\\n else:\\n env = config.env()\\n if config.max_length:\\n env = tools.wrappers.LimitDuration(env, config.max_length)\\n env = tools.wrappers.RangeNormalize(env)\\n env = tools.wrappers.ClipAction(env)\\n env = tools.wrappers.ConvertTo32Bit(env)\\n return env\",\n \"def make_env():\\n return {\\n 'init': init,\\n 'step': step,\\n 'is_terminal': is_terminal,\\n 'state_as_example': state_as_example,\\n }\",\n \"def build_env(self, instance, private_data_dir, private_data_files=None):\\n env = {}\\n # Add ANSIBLE_* settings to the subprocess environment.\\n for attr in dir(settings):\\n if attr == attr.upper() and attr.startswith('ANSIBLE_'):\\n env[attr] = str(getattr(settings, attr))\\n # Also set environment variables configured in AWX_TASK_ENV setting.\\n for key, value in settings.AWX_TASK_ENV.items():\\n env[key] = str(value)\\n\\n env['AWX_PRIVATE_DATA_DIR'] = private_data_dir\\n\\n if self.instance.execution_environment is None:\\n raise RuntimeError(f'The {self.model.__name__} could not run because there is no Execution Environment.')\\n\\n return env\",\n \"def generate_environment(self):\\n try:\\n if self._environment is None:\\n self._environment = Environment.fromfilepath(self._environmentName,\\n self._configuration.environment_file_path)\\n except Exception:\\n raise\",\n \"def _setup_environment_vars(self, opts):\\n # Check that these directories actually exist\\n assert os.path.isdir(opts.movie_advisor_home)\\n\\n #if not 'install-bento' in self.actions: assert os.path.isdir(opts.bento_home)\\n\\n self.movie_advisor_home = opts.movie_advisor_home\\n self.bento_home = opts.bento_home\\n self.bento_tgz = opts.bento_tgz\\n self.kiji_uri = \\\"kiji://.env/tutorial\\\"\\n\\n # \\\"express job\\\" takes a jar file as an argument\\n assert os.path.isfile(os.path.join(self.movie_advisor_home, self.express_jar))\\n\\n # Set the classpath for all of the commands that we'll run\\n jarsFullPaths = [os.path.join(self.movie_advisor_home, j) for j in self.jars]\\n for jar in jarsFullPaths: assert os.path.isfile(jar)\\n\\n classpath = \\\":\\\".join(jarsFullPaths)\\n os.environ['KIJI_CLASSPATH'] = classpath\\n\\n if opts.show_classpath:\\n print(\\\"export KIJI_CLASSPATH=%s\\\" % classpath)\\n sys.exit(0)\",\n \"def create(self):\\n os.environ.update(self.env)\\n return self.core(*self.args)\",\n \"def create_aiida_project_environment(self):\\n # mock the virtualenv activation procedure\\n venv_prefix = self.env_folder / self.proj_name\\n current_env = os.environ.copy()\\n current_env.pop('PYTHONHOME', None)\\n current_env['VIRTUAL_ENV'] = venv_prefix\\n old_path = current_env['PATH']\\n new_path = str(venv_prefix / 'bin') + os.pathsep + old_path\\n current_env['PATH'] = new_path\\n try:\\n self.create_folder_structure()\\n self.build_python_environment()\\n self.install_packages_from_index(env=current_env)\\n self.install_packages_from_source(env=current_env)\\n except Exception:\\n self.exit_on_exception()\\n raise\\n self.create_spec_entry()\",\n \"def make_environment(\\n evaluation: bool = False,\\n task: str = 'MountainCarContinuous-v0') -> dm_env.Environment:\\n del evaluation\\n\\n # Load the gym environment.\\n environment = gym.make(task)\\n\\n # Make sure the environment obeys the dm_env.Environment interface.\\n environment = wrappers.GymWrapper(environment)\\n # Clip the action returned by the agent to the environment spec.\\n environment = wrappers.CanonicalSpecWrapper(environment, clip=True)\\n environment = wrappers.SinglePrecisionWrapper(environment)\\n\\n return environment\",\n \"def initialise(self, args, environ):\",\n \"def get_environ():\\n # Manually set environment.\\n if FLAGS.env is not None:\\n return BasicEnvironment.from_json(open(FLAGS.env, \\\"r\\\").read())\\n\\n if FLAGS.data_folder is None:\\n data_folder = FLAGS.dataset\\n else:\\n data_folder = FLAGS.data_folder\\n exp_id = \\\"exp_\\\" + FLAGS.dataset + \\\"_\\\" + FLAGS.model\\n if FLAGS.id is None:\\n exp_id = gen_id(exp_id)\\n else:\\n exp_id = FLAGS.id\\n return BasicEnvironment(\\n device=get_device(FLAGS.gpu),\\n dataset=FLAGS.dataset,\\n data_folder=data_folder,\\n logs_folder=FLAGS.logs,\\n save_folder=FLAGS.results,\\n run_validation=FLAGS.validation,\\n verbose=FLAGS.verbose,\\n exp_id=exp_id,\\n description=FLAGS.description,\\n valid_num_fold=FLAGS.valid_num_fold,\\n valid_fold_id=FLAGS.valid_fold_id)\",\n \"def MakeEnvironment(self, context):\\n\\n # Start with any environment variables that are already present\\n # in the environment.\\n environment = os.environ.copy()\\n # Copy context variables into the environment.\\n for key, value in context.items():\\n name = \\\"QMV_\\\" + key\\n environment[name] = value\\n return environment\",\n \"def _run_env(self):\\n raise NotImplementedError()\",\n \"def set_env(self, propagated_env_vars={}):\\n os.environ['BUILD_ROOT'] = self.build_root\\n # This is how we tell run-test.sh what set of C++ binaries to use for mini-clusters in Java\\n # tests.\\n for env_var_name, env_var_value in propagated_env_vars.iteritems():\\n os.environ[env_var_name] = env_var_value\",\n \"def make_environment(seed, task_horizon):\\n # Load the gym environment.\\n environment = CartPoleEnv()\\n environment = gym_wrappers.TimeLimit(environment, task_horizon)\\n environment.seed(seed)\\n environment = wrappers.GymWrapper(environment)\\n environment = wrappers.SinglePrecisionWrapper(environment)\\n return environment\",\n \"def _build_env(target, *, orig=os.environ):\\n overlay = dict(\\n PYTHONPATH=_path_insert(orig.get('PYTHONPATH', ''), os.fspath(target)),\\n PATH=_path_insert(orig.get('PATH', ''), os.fspath(target / 'bin')),\\n )\\n return {**orig, **overlay}\",\n \"def __setup(self):\\n\\n build_environment = []\\n\\n # The download URL has the format contains vMAJOR.MINOR in the\\n # path and the tarball contains MAJOR.MINOR.REVISION, so pull\\n # apart the full version to get the MAJOR and MINOR components.\\n match = re.match(r'(?P<major>\\\\d+)\\\\.(?P<minor>\\\\d+)', self.version)\\n major_minor = 'v{0}.{1}'.format(match.groupdict()['major'],\\n match.groupdict()['minor'])\\n tarball = 'openmpi-{}.tar.bz2'.format(self.version)\\n url = '{0}/{1}/downloads/{2}'.format(self.baseurl, major_minor,\\n tarball)\\n\\n # CUDA\\n if self.cuda:\\n if self.__toolchain.CUDA_HOME:\\n self.configure_opts.append(\\n '--with-cuda={}'.format(self.__toolchain.CUDA_HOME))\\n else:\\n self.configure_opts.append('--with-cuda')\\n else:\\n self.configure_opts.append('--without-cuda')\\n\\n # InfiniBand\\n if self.infiniband:\\n self.configure_opts.append('--with-verbs')\\n else:\\n self.configure_opts.append('--without-verbs')\\n\\n # UCX\\n if self.__ucx:\\n if isinstance(self.__ucx, string_types):\\n # Use specified path\\n self.configure_opts.append('--with-ucx={}'.format(self.__ucx))\\n else:\\n self.configure_opts.append('--with-ucx')\\n\\n # If UCX was built with CUDA support, it is linked with\\n # libcuda.so.1, which is not available during the\\n # build stage. Assume that if OpenMPI is built with\\n # CUDA support, then UCX was as well...\\n if self.cuda:\\n cuda_home = \\\"/usr/local/cuda\\\"\\n if self.__toolchain.CUDA_HOME:\\n cuda_home = self.__toolchain.CUDA_HOME\\n self.__commands.append('ln -s {0} {1}'.format(\\n os.path.join(cuda_home, 'lib64', 'stubs', 'libcuda.so'),\\n os.path.join(cuda_home, 'lib64', 'stubs', 'libcuda.so.1')))\\n if not self.__toolchain.LD_LIBRARY_PATH:\\n build_environment.append('LD_LIBRARY_PATH=\\\"{}:$LD_LIBRARY_PATH\\\"'.format(os.path.join(cuda_home, 'lib64', 'stubs')))\\n\\n if self.directory:\\n # Use source from local build context\\n self.__commands.append(self.configure_step(\\n directory=os.path.join(self.__wd, self.directory),\\n toolchain=self.__toolchain))\\n else:\\n # Download source from web\\n self.__commands.append(self.download_step(url=url,\\n directory=self.__wd))\\n self.__commands.append(self.untar_step(\\n tarball=os.path.join(self.__wd, tarball), directory=self.__wd))\\n self.__commands.append(self.configure_step(\\n directory=os.path.join(self.__wd,\\n 'openmpi-{}'.format(self.version)),\\n environment=build_environment,\\n toolchain=self.__toolchain))\\n\\n self.__commands.append(self.build_step())\\n\\n if self.__check:\\n self.__commands.append(self.check_step())\\n\\n self.__commands.append(self.install_step())\\n\\n # Set library path\\n libpath = os.path.join(self.prefix, 'lib')\\n if self.ldconfig:\\n self.__commands.append(self.ldcache_step(directory=libpath))\\n else:\\n self.__environment_variables['LD_LIBRARY_PATH'] = '{}:$LD_LIBRARY_PATH'.format(libpath)\\n\\n if self.directory:\\n # Using source from local build context, cleanup directory\\n self.__commands.append(self.cleanup_step(\\n items=[os.path.join(self.__wd, self.directory)]))\\n else:\\n # Using downloaded source, cleanup tarball and directory\\n self.__commands.append(self.cleanup_step(\\n items=[os.path.join(self.__wd, tarball),\\n os.path.join(self.__wd,\\n 'openmpi-{}'.format(self.version))]))\",\n \"def createEnvironment(self, _):\\r\\n if self._namespaces:\\r\\n raise InternalError('The environment can have only one namespace '\\r\\n 'at a time.')\\r\\n\\r\\n environment = Environment(self)\\r\\n return self._avatar.callRemote('setupNamespace', environment)\",\n \"def create_aiida_project_environment(self):\\n try:\\n self.create_folder_structure()\\n self.build_python_environment()\\n self.install_packages_from_index()\\n except Exception:\\n self.exit_on_exception()\\n raise\\n self.create_spec_entry()\",\n \"def env(parser, args):\\n action = subcommand_functions[args.env_command]\\n action(args)\",\n \"def init():\\n env = Environment(5, 5, 20, [10, 20, 10, 5])\\n return env\",\n \"def _init_env_variables(self):\\n raise NotImplementedError()\",\n \"def _init_env_variables(self):\\n raise NotImplementedError()\",\n \"def _init_env_variables(self):\\n raise NotImplementedError()\",\n \"def _init_env_variables(self):\\n raise NotImplementedError()\",\n \"def _init_env_variables(self):\\n raise NotImplementedError()\",\n \"def _init_env_variables(self):\\n raise NotImplementedError()\",\n \"def makeenv(outer=None):\\n\\n retval = {'outer': outer}\\n return retval\",\n \"def build_env(self, ad_hoc_command, private_data_dir, private_data_files=None):\\n env = super(RunAdHocCommand, self).build_env(ad_hoc_command, private_data_dir, private_data_files=private_data_files)\\n # Set environment variables needed for inventory and ad hoc event\\n # callbacks to work.\\n env['AD_HOC_COMMAND_ID'] = str(ad_hoc_command.pk)\\n env['INVENTORY_ID'] = str(ad_hoc_command.inventory.pk)\\n env['INVENTORY_HOSTVARS'] = str(True)\\n env['ANSIBLE_LOAD_CALLBACK_PLUGINS'] = '1'\\n env['ANSIBLE_SFTP_BATCH_MODE'] = 'False'\\n\\n return env\",\n \"def _setenv(self):\\n tokens = {}\\n tokens[\\\"CT_TIMESTAMP\\\"] = self._timestamp\\n tokens[\\\"CT_SUBMITTER\\\"] = self._node.name()\\n # tokens[\\\"CT_HIPBASE\\\"] = self._file[\\\"hipbase\\\"]\\n tokens[\\\"CT_SCENE\\\"] = self._scene\\n tokens[\\\"CT_PROJECT\\\"] = self.project_name\\n\\n for token in tokens:\\n hou.putenv(token, tokens[token])\\n\\n return tokens\",\n \"def generic_env_configure_vars(self, verbose=False):\\n\\n if self.settings.os == \\\"Windows\\\":\\n self.output.fatal(\\\"Cannot build on Windows, sorry!\\\")\\n return\\n\\n if self.settings.os == \\\"Linux\\\" or self.settings.os == \\\"Macos\\\":\\n libs = 'LIBS=\\\"%s\\\"' % \\\" \\\".join([\\\"-l%s\\\" % lib for lib in self.deps_cpp_info.libs])\\n ldflags = 'LDFLAGS=\\\"%s\\\"' % \\\" \\\".join([\\\"-L%s\\\" % lib for lib in self.deps_cpp_info.lib_paths]) \\n archflag = \\\"-m32\\\" if self.settings.arch == \\\"x86\\\" else \\\"\\\"\\n cflags = 'CFLAGS=\\\"-fPIC %s %s\\\"' % (archflag, \\\" \\\".join(self.deps_cpp_info.cflags))\\n cpp_flags = 'CPPFLAGS=\\\"%s %s\\\"' % (archflag, \\\" \\\".join(self.deps_cpp_info.cppflags))\\n command = \\\"env %s %s %s %s\\\" % (libs, ldflags, cflags, cpp_flags)\\n # elif self.settings.os == \\\"Windows\\\" and self.settings.compiler == \\\"Visual Studio\\\":\\n # cl_args = \\\" \\\".join(['/I\\\"%s\\\"' % lib for lib in self.deps_cpp_info.include_paths])\\n # lib_paths= \\\";\\\".join(['\\\"%s\\\"' % lib for lib in self.deps_cpp_info.lib_paths])\\n # command = \\\"SET LIB=%s;%%LIB%% && SET CL=%s\\\" % (lib_paths, cl_args)\\n # if verbose:\\n # command += \\\" && SET LINK=/VERBOSE\\\"\\n \\n return command\",\n \"def initFromEnv(self):\\n #self.command = 'scram' # SB I think this line is not needed\\n self[\\\"SCRAM_ARCH\\\"] = None\\n\\n if 'SCRAM_ARCH' in os.environ:\\n self[\\\"SCRAM_ARCH\\\"] = os.environ[\\\"SCRAM_ARCH\\\"]\\n else:\\n stdout, _, _ = execute_command(command='scram arch')\\n self[\\\"SCRAM_ARCH\\\"] = stdout\\n\\n try:\\n self[\\\"CMSSW_BASE\\\"] = os.environ[\\\"CMSSW_BASE\\\"]\\n self[\\\"CMSSW_VERSION\\\"] = os.environ[\\\"CMSSW_VERSION\\\"]\\n# Commenting these two out. I don't think they are really needed\\n# self.cmsswReleaseBase = os.environ[\\\"CMSSW_RELEASE_BASE\\\"]\\n# self.localRT = os.environ[\\\"LOCALRT\\\"]\\n except KeyError as ke:\\n self[\\\"CMSSW_BASE\\\"] = None\\n self[\\\"CMSSW_VERSION\\\"] = None\\n# self.cmsswReleaseBase = None\\n# self.localRT = None\\n msg = \\\"Please make sure you have setup the CMS enviroment (cmsenv). Cannot find %s in your env\\\" % str(ke)\\n msg += \\\"\\\\nPlease refer to https://twiki.cern.ch/twiki/bin/view/CMSPublic/WorkBookCRAB3Tutorial#CMS_environment for how to setup the CMS enviroment.\\\"\\n raise EnvironmentException(msg)\",\n \"def main(self):\\n\\n env = self.env\\n\\n # Initial setup\\n self.parse_args()\\n self.find_taskfile()\\n\\n if self.taskfile is None:\\n env.abort(\\\"Unable to find {0}\\\".format(self.cmdline.file))\\n\\n # Set command line NAME=VALUE variables before loading the file\\n (tasks, params) = self.get_tasks_params()\\n env.update(**params)\\n\\n env[\\\"_TOP_\\\"] = os.path.dirname(self.taskfile)\\n env[\\\"_ABSTOP_\\\"] = os.path.abspath(env[\\\"_TOP_\\\"])\\n env[\\\"_CWD_\\\"] = os.path.abspath(self.cwd)\\n env._load(self.taskfile)\\n\\n # Print tasks list if requested\\n if self.cmdline.list:\\n for name in env._tasks:\\n if not name.startswith(\\\"_\\\"):\\n env.outputln(name)\\n env.exit()\\n\\n # Print var/task help if requested\\n if self.cmdline.varhelp:\\n self.show_varhelp()\\n\\n if self.cmdline.taskhelp:\\n names = sorted(set([i[0] for i in tasks]))\\n self.show_taskhelp(names)\\n\\n if self.cmdline.taskhelp or self.cmdline.varhelp:\\n env.exit()\\n\\n # Execute the requested tasks setting task specific variables\\n for (task, params) in tasks:\\n env.calltask(task, **params)\",\n \"def _set_environment_vars(self):\\n os.environ[\\\"PATH\\\"] = os.path.join(self.source_folder, \\\"depot_tools\\\") + os.pathsep + os.environ[\\\"PATH\\\"]\\n os.environ[\\\"DEPOT_TOOLS_PATH\\\"] = os.path.join(self.source_folder, \\\"depot_tools\\\")\\n if tools.os_info.is_windows:\\n os.environ[\\\"DEPOT_TOOLS_WIN_TOOLCHAIN\\\"] = \\\"0\\\"\\n os.environ[\\\"GYP_MSVS_VERSION\\\"] = \\\"2017\\\" if str(self.settings.compiler.version) == \\\"15\\\" else \\\"2019\\\"\",\n \"def __init__(self): \\n\\t\\n\\t # get the environment\\n\\t\\tself.env = env()\",\n \"def build_env(env_id, inst=0, **all_args):\\n env_keys = ['dt', 'timing']\\n if env_id == 'RDM-v0':\\n env_keys.append('stimEv')\\n env_args = {x: all_args[x] for x in env_keys}\\n\\n if all_args['combine']:\\n env1 = gym.make(env_id, **env_args)\\n env_args['timing'] = all_args['timing2']\\n env2 = gym.make(all_args['env2'], **env_args)\\n env = combine.combine(env1, env2, delay=all_args['delay'])\\n else:\\n env = gym.make(env_id, **env_args)\\n if all_args['trial_hist']:\\n env = trial_hist.TrialHistory(env, rep_prob=all_args['rep_prob'],\\n block_dur=all_args['bl_dur'],\\n blk_ch_prob=all_args['blk_ch_prob'])\\n elif all_args['side_bias']:\\n env = side_bias.SideBias(env, prob=all_args['rep_prob'],\\n block_dur=all_args['bl_dur'])\\n if all_args['catch_trials']:\\n env = catch_trials.CatchTrials(env,\\n catch_prob=all_args['catch_prob'],\\n stim_th=all_args['stim_th'])\\n if all_args['reaction_time']:\\n env = reaction_time.ReactionTime(env)\\n if all_args['pass_reward']:\\n env = pass_reward.PassReward(env)\\n if all_args['pass_action']:\\n env = pass_action.PassAction(env)\\n env = manage_data.manage_data(env, inst=inst, plt_tr=all_args['figs'],\\n folder=all_args['save_path'])\\n return env\",\n \"def __create_test_environment(self):\\n os.chdir(self.wd)\\n temp_dir = tempfile.gettempdir()\\n self.test_root = os.path.join(temp_dir, \\\"test-grpc\\\")\\n print(\\\"Creating testing environment in {}\\\".format(self.test_root))\\n if os.path.exists(self.test_root):\\n # delete any previous environment\\n shutil.rmtree(self.test_root)\\n # create root directory\\n os.makedirs(self.test_root)\\n def copy_app(name):\\n app_root = os.path.join(self.test_root, name)\\n os.makedirs(app_root)\\n filename = \\\"grpc-{}\\\".format(name)\\n src = os.path.join(self.args.bin, filename)\\n dst = os.path.join(app_root, filename)\\n shutil.copy(src, dst)\\n return dst\\n # copy client and server into the new test environment\\n self.server_path = copy_app(\\\"server\\\")\\n self.client_path = copy_app(\\\"client\\\")\",\n \"def create_environment(game_name, seed, is_test=False):\\n\\n # Note, you may want to use a level cache to speed of compilation of\\n # environment maps. See the documentation for the Python interface of DeepMind\\n # Lab.\\n config = {\\n \\\"width\\\": FLAGS.width,\\n \\\"height\\\": FLAGS.height,\\n \\\"logLevel\\\": \\\"WARN\\\",\\n \\\"n_input_frames\\\": FLAGS.history,\\n \\\"show_display\\\": FLAGS.display,\\n \\\"save_video\\\": FLAGS.save_video,\\n }\\n if is_test:\\n config[\\\"allowHoldOutLevels\\\"] = \\\"true\\\"\\n # Mixer seed for evalution, see\\n # https://github.com/deepmind/lab/blob/master/docs/users/python_api.md\\n config[\\\"mixerSeed\\\"] = 0x600D5EED\\n\\n p = py_process.PyProcess(\\n environments.PyProcessAtari,\\n game_name,\\n config,\\n FLAGS.num_action_repeats,\\n seed,\\n )\\n return environments.FlowEnvironment(p.proxy)\",\n \"def _generate_environment(self):\\n envvars = {}\\n for key in self.envvars:\\n try:\\n envvars[key] = os.environ[key]\\n except KeyError:\\n continue\\n\\n # Warn the user that we cannot support secrets\\n if envvars:\\n logger.warning(\\\"This API does not support environment secrets.\\\")\\n return envvars\",\n \"def _env_create(name_or_path, init_file=None, dir=False, with_view=None, keep_relative=False):\\n if dir:\\n env = ev.Environment(name_or_path, init_file, with_view, keep_relative)\\n env.write()\\n tty.msg(\\\"Created environment in %s\\\" % env.path)\\n tty.msg(\\\"You can activate this environment with:\\\")\\n tty.msg(\\\" spack env activate %s\\\" % env.path)\\n else:\\n env = ev.create(name_or_path, init_file, with_view, keep_relative)\\n env.write()\\n tty.msg(\\\"Created environment '%s' in %s\\\" % (name_or_path, env.path))\\n tty.msg(\\\"You can activate this environment with:\\\")\\n tty.msg(\\\" spack env activate %s\\\" % (name_or_path))\\n return env\",\n \"def SetupEnvironment(self):\\n pass\",\n \"def setEnv(self, origin, checked, simOnly=False):\\n\\n # resolve circular dependencies\\n if( self.name in checked ):\\n return\\n else:\\n checked.append( self.name )\\n\\n # set values to strings\\n for k in self.env.keys():\\n self.env[k]=str(self.env[k])\\n \\n # set environment variables\\n if( simOnly ):\\n if( len( checked ) == 1 ):\\n if( len(self.parent.env) != 0 ):\\n print \\\"\\\\n + Global Environment variables:\\\"\\n for k, v in self.parent.env.iteritems():\\n print \\\"\\\\t* \\\" + k + \\\": \\\" + str(v)\\n\\n print \\\"\\\\n + Environment variables set by \\\" + self.name + \\\":\\\"\\n \\n for k, v in self.env.iteritems():\\n print \\\"\\\\t* \\\" + k + \\\": \\\" + str(v)\\n else:\\n # first set the priority values\\n for k in self.envorder:\\n if( self.env[k].find('$') != -1 ):\\n os.environ[k]=os.path.expandvars(self.env[k])\\n else:\\n os.environ[k] = self.env[k]\\n # then set the rest\\n for k, v in self.env.iteritems():\\n if k not in self.envorder:\\n if( v.find('$') != -1 ):\\n os.environ[k] = os.path.expandvars(v)\\n else:\\n os.environ[k] = v\\n\\n # print path and build environment variables\\n if( simOnly ):\\n for k, v in self.envpath.iteritems():\\n if( len(v) != 0 ):\\n print \\\"\\\\t* \\\" + k + \\\": \\\" + str(v)\\n\\n # set environment for dependencies\\n if( len( checked ) > 1 ):\\n mods = self.optmodules + self.reqmodules\\n else:\\n # buildonly modules are only used for the package were they are needed\\n mods = self.optmodules + self.reqmodules + self.reqmodules_buildonly + self.reqmodules_external\\n \\n for modname in mods:\\n self.parent.module(modname).setEnv(origin, checked, simOnly)\\n\\n # list of \\\"trivial\\\" paths we do not want to add again to PATH and co\\n ignorepaths = ['/usr/bin','/usr/lib','/sbin','/usr/sbin']\\n # set path environment variables\\n for k, v in self.envpath.iteritems():\\n if( len(v) != 0 ):\\n env = getenv( k )\\n newvalues = \\\"\\\"\\n for i in v:\\n rpath = fixPath(i)\\n if rpath in ignorepaths:\\n continue\\n newvalues = newvalues + rpath + ':'\\n os.environ[k] = newvalues + env\",\n \"def initialize():\\n environment = Environment()\\n environment.setup()\",\n \"def wsSetupEnvironment(self, nj=0):\\n psetName = 'pset.py'\\n\\n # Prepare JobType-independent part\\n txt = '\\\\n#Written by cms_cmssw::wsSetupEnvironment\\\\n'\\n txt += 'echo \\\">>> setup environment\\\"\\\\n'\\n txt += 'echo \\\"set SCRAM ARCH to ' + self.executable_arch + '\\\"\\\\n'\\n txt += 'export SCRAM_ARCH=' + self.executable_arch + '\\\\n'\\n txt += 'echo \\\"SCRAM_ARCH = $SCRAM_ARCH\\\"\\\\n'\\n txt += 'if [ $middleware == LCG ] || [ $middleware == CAF ] || [ $middleware == LSF ]; then \\\\n'\\n txt += self.wsSetupCMSLCGEnvironment_()\\n txt += 'elif [ $middleware == OSG ]; then\\\\n'\\n txt += ' WORKING_DIR=`/bin/mktemp -d $OSG_WN_TMP/cms_XXXXXXXXXXXX`\\\\n'\\n txt += ' if [ ! $? == 0 ] ;then\\\\n'\\n txt += ' echo \\\"ERROR ==> OSG $WORKING_DIR could not be created on WN `hostname`\\\"\\\\n'\\n txt += ' job_exit_code=10016\\\\n'\\n txt += ' func_exit\\\\n'\\n txt += ' fi\\\\n'\\n txt += ' echo \\\">>> Created working directory: $WORKING_DIR\\\"\\\\n'\\n txt += '\\\\n'\\n txt += ' echo \\\"Change to working directory: $WORKING_DIR\\\"\\\\n'\\n txt += ' cd $WORKING_DIR\\\\n'\\n txt += ' echo \\\">>> current directory (WORKING_DIR): $WORKING_DIR\\\"\\\\n'\\n txt += self.wsSetupCMSOSGEnvironment_()\\n #Setup SGE Environment\\n txt += 'elif [ $middleware == SGE ]; then\\\\n'\\n txt += self.wsSetupCMSLCGEnvironment_()\\n\\n txt += 'elif [ $middleware == ARC ]; then\\\\n'\\n txt += self.wsSetupCMSLCGEnvironment_()\\n\\n #Setup PBS Environment\\n txt += 'elif [ $middleware == PBS ] || [ $middleware == PBSV2 ] || [ $middleware == SLURM ]; then\\\\n'\\n txt += self.wsSetupCMSLCGEnvironment_()\\n\\n txt += 'fi\\\\n'\\n\\n # Prepare JobType-specific part\\n scram = self.scram.commandName()\\n txt += '\\\\n\\\\n'\\n txt += 'echo \\\">>> specific cmssw setup environment:\\\"\\\\n'\\n txt += 'echo \\\"CMSSW_VERSION = '+self.version+'\\\"\\\\n'\\n txt += scram+' project CMSSW '+self.version+'\\\\n'\\n txt += 'status=$?\\\\n'\\n txt += 'if [ $status != 0 ] ; then\\\\n'\\n txt += ' echo \\\"ERROR ==> CMSSW '+self.version+' not found on `hostname`\\\" \\\\n'\\n txt += ' job_exit_code=10034\\\\n'\\n txt += ' func_exit\\\\n'\\n txt += 'fi \\\\n'\\n txt += 'cd '+self.version+'\\\\n'\\n txt += 'SOFTWARE_DIR=`pwd`; export SOFTWARE_DIR\\\\n'\\n txt += 'echo \\\">>> current directory (SOFTWARE_DIR): $SOFTWARE_DIR\\\" \\\\n'\\n txt += 'eval `'+scram+' runtime -sh | grep -v SCRAMRT_LSB_JOBNAME`\\\\n'\\n txt += 'if [ $? != 0 ] ; then\\\\n'\\n txt += ' echo \\\"ERROR ==> Problem with the command: \\\"\\\\n'\\n txt += ' echo \\\"eval \\\\`'+scram+' runtime -sh | grep -v SCRAMRT_LSB_JOBNAME \\\\` at `hostname`\\\"\\\\n'\\n txt += ' job_exit_code=10034\\\\n'\\n txt += ' func_exit\\\\n'\\n txt += 'fi \\\\n'\\n # Handle the arguments:\\n txt += \\\"\\\\n\\\"\\n txt += \\\"## number of arguments (first argument always jobnumber, the second is the resubmission number)\\\\n\\\"\\n txt += \\\"\\\\n\\\"\\n txt += \\\"if [ $nargs -lt \\\"+str(self.argsList)+\\\" ]\\\\n\\\"\\n txt += \\\"then\\\\n\\\"\\n txt += \\\" echo 'ERROR ==> Too few arguments' +$nargs+ \\\\n\\\"\\n txt += ' job_exit_code=50113\\\\n'\\n txt += \\\" func_exit\\\\n\\\"\\n txt += \\\"fi\\\\n\\\"\\n txt += \\\"\\\\n\\\"\\n\\n # Prepare job-specific part\\n job = common.job_list[nj]\\n if (self.datasetPath):\\n txt += '\\\\n'\\n txt += 'DatasetPath='+self.datasetPath+'\\\\n'\\n\\n txt += 'PrimaryDataset='+self.primaryDataset +'\\\\n'\\n txt += 'DataTier='+self.dataTier+'\\\\n'\\n txt += 'ApplicationFamily=cmsRun\\\\n'\\n\\n else:\\n txt += 'DatasetPath=MCDataTier\\\\n'\\n txt += 'PrimaryDataset=null\\\\n'\\n txt += 'DataTier=null\\\\n'\\n txt += 'ApplicationFamily=MCDataTier\\\\n'\\n if self.pset != None:\\n pset = os.path.basename(job.configFilename())\\n pkl = os.path.basename(job.configFilename()) + '.pkl'\\n txt += '\\\\n'\\n txt += 'cp $RUNTIME_AREA/'+pset+' .\\\\n'\\n txt += 'cp $RUNTIME_AREA/'+pkl+' .\\\\n'\\n\\n txt += 'PreserveSeeds=' + ','.join(self.preserveSeeds) + '; export PreserveSeeds\\\\n'\\n txt += 'IncrementSeeds=' + ','.join(self.incrementSeeds) + '; export IncrementSeeds\\\\n'\\n txt += 'echo \\\"PreserveSeeds: <$PreserveSeeds>\\\"\\\\n'\\n txt += 'echo \\\"IncrementSeeds:<$IncrementSeeds>\\\"\\\\n'\\n\\n txt += 'mv -f ' + pset + ' ' + psetName + '\\\\n'\\n #if self.var_filter:\\n # #print \\\"self.var_filter = \\\",self.var_filter\\n # txt += \\\"export var_filter=\\\"+\\\"'\\\"+self.var_filter+\\\"'\\\\n\\\"\\n # txt += 'echo $var_filter'\\n else:\\n txt += '\\\\n'\\n if self.AdditionalArgs: txt += 'export AdditionalArgs=\\\\\\\"%s\\\\\\\"\\\\n'%(self.AdditionalArgs)\\n if int(self.NumEvents) != 0: txt += 'export MaxEvents=%s\\\\n'%str(self.NumEvents)\\n return txt\",\n \"def set_envs(self):\\n # pylint:disable=protected-access\\n # Need to call sys.__getframe() to get the filename and method/func\\n # for logging information.\\n\\n # Useful for logging\\n # Logging output: TIME UTC |TYPE (DEBUG, INFO, WARNING, etc.) |\\n # [File : function]| Message\\n cur_filename = sys._getframe().f_code.co_filename\\n cur_function = sys._getframe().f_code.co_name\\n\\n self.logger.info('Setting env variables from config file...')\\n # Set all the environment variables that are needed by the\\n # MET config file.\\n\\n tmp_amodel = self.c_dict['AMODEL']\\n if tmp_amodel:\\n # Replace any single quotes with double quotes and remove any\\n # whitespace\\n tmp_amodel_str = str(tmp_amodel).replace(\\\"\\\\'\\\", \\\"\\\\\\\"\\\")\\n tmp_amodel = ''.join(tmp_amodel_str.split())\\n self.add_env_var('AMODEL', tmp_amodel)\\n else:\\n self.add_env_var('AMODEL', \\\"[]\\\")\\n\\n tmp_bmodel = self.c_dict['BMODEL']\\n if tmp_bmodel:\\n # Replace any single quotes with double quotes and remove any\\n # whitespace\\n tmp_bmodel_str = str(tmp_bmodel).replace(\\\"\\\\'\\\", \\\"\\\\\\\"\\\")\\n tmp_bmodel = ''.join(tmp_bmodel_str.split())\\n self.add_env_var('BMODEL', tmp_bmodel)\\n else:\\n self.add_env_var('BMODEL', \\\"[]\\\")\\n\\n tmp_desc = self.c_dict['DESC']\\n if tmp_desc:\\n # Replace any single quotes with double quotes and remove any\\n # whitespace\\n tmp_desc_str = str(tmp_desc).replace(\\\"\\\\'\\\", \\\"\\\\\\\"\\\")\\n tmp_desc = ''.join(tmp_desc_str.split())\\n self.add_env_var('DESC', tmp_desc)\\n else:\\n self.add_env_var('DESC', \\\"[]\\\")\\n\\n tmp_storm_id = self.c_dict['STORM_ID']\\n if tmp_storm_id:\\n # Replace any single quotes with double quotes and remove any\\n # whitespace\\n tmp_storm_id_str = str(tmp_storm_id).replace(\\\"\\\\'\\\", \\\"\\\\\\\"\\\")\\n tmp_storm_id = ''.join(tmp_storm_id_str.split())\\n self.add_env_var('STORM_ID', tmp_storm_id)\\n else:\\n self.add_env_var('STORM_ID', \\\"[]\\\")\\n\\n tmp_basin = self.c_dict['BASIN']\\n if tmp_basin:\\n # Replace any single quotes with double quotes and remove any\\n # whitespace\\n tmp_basin_str = str(tmp_basin).replace(\\\"\\\\'\\\", \\\"\\\\\\\"\\\")\\n tmp_basin = ''.join(tmp_basin_str.split())\\n self.add_env_var('BASIN', tmp_basin)\\n else:\\n self.add_env_var('BASIN', \\\"[]\\\")\\n\\n tmp_cyclone = self.c_dict['CYCLONE']\\n if tmp_cyclone:\\n # Replace any single quotes with double quotes and remove any\\n # whitespace\\n tmp_cyclone_str = str(tmp_cyclone).replace(\\\"\\\\'\\\", \\\"\\\\\\\"\\\")\\n tmp_cyclone = ''.join(tmp_cyclone_str.strip())\\n self.add_env_var('CYCLONE', tmp_cyclone)\\n else:\\n self.add_env_var('CYCLONE', \\\"[]\\\")\\n\\n tmp_storm_name = self.c_dict['STORM_NAME']\\n if tmp_storm_name:\\n # Replace any single quotes with double quotes and remove any\\n # whitespace\\n tmp_storm_name_str = str(tmp_storm_name).replace(\\\"\\\\'\\\", \\\"\\\\\\\"\\\")\\n tmp_storm_name = ''.join(tmp_storm_name_str.strip())\\n self.add_env_var('STORM_NAME', tmp_storm_name)\\n else:\\n self.add_env_var('STORM_NAME', \\\"[]\\\")\\n\\n if self.c_dict['INIT_BEG']:\\n self.add_env_var('INIT_BEG', self.c_dict['INIT_BEG'])\\n else:\\n self.add_env_var('INIT_BEG', \\\"\\\")\\n\\n if self.c_dict['INIT_END']:\\n self.add_env_var('INIT_END', self.c_dict['INIT_END'])\\n else:\\n self.add_env_var('INIT_END', \\\"\\\")\\n\\n tmp_init_include = self.c_dict['INIT_INCLUDE']\\n if tmp_init_include:\\n # Replace any single quotes with double quotes and remove any\\n # whitespace\\n tmp_init_include_str = str(tmp_init_include).replace(\\\"\\\\'\\\", \\\"\\\\\\\"\\\")\\n tmp_init_include = ''.join(tmp_init_include_str.strip())\\n self.add_env_var('INIT_INCLUDE', tmp_init_include)\\n else:\\n self.add_env_var('INIT_INCLUDE', \\\"[]\\\")\\n\\n tmp_init_exclude = self.c_dict['INIT_EXCLUDE']\\n if tmp_init_exclude:\\n # Replace any single quotes with double quotes and remove any\\n # whitespace\\n tmp_init_exclude_str = str(tmp_init_exclude).replace(\\\"\\\\'\\\", \\\"\\\\\\\"\\\")\\n tmp_init_exclude = ''.join(tmp_init_exclude_str.strip())\\n self.add_env_var('INIT_EXCLUDE', tmp_init_exclude)\\n else:\\n self.add_env_var('INIT_EXCLUDE', \\\"[]\\\")\\n\\n tmp_init_hour = self.c_dict['INIT_HOUR']\\n if tmp_init_hour:\\n # Replace any single quotes with double quotes and remove any\\n # whitespace\\n tmp_init_hour_str = str(tmp_init_hour).replace(\\\"\\\\'\\\", \\\"\\\\\\\"\\\")\\n tmp_init_hour = ''.join(tmp_init_hour_str.split())\\n self.add_env_var('INIT_HOUR', tmp_init_hour)\\n else:\\n self.add_env_var('INIT_HOUR', \\\"[]\\\")\\n\\n tmp_valid_begin = self.c_dict['VALID_BEG']\\n if tmp_valid_begin:\\n # Replace any single quotes with double quotes and remove any\\n # whitespace\\n tmp_valid_begin_str = str(tmp_valid_begin).replace(\\\"\\\\'\\\", \\\"\\\\\\\"\\\")\\n tmp_valid_begin = ''.join(tmp_valid_begin_str.strip())\\n self.add_env_var('VALID_BEG', tmp_valid_begin)\\n else:\\n self.add_env_var('VALID_BEG', '')\\n\\n tmp_valid_end = self.c_dict['VALID_END']\\n if tmp_valid_end:\\n # Replace any single quotes with double quotes and remove any\\n # whitespace\\n tmp_valid_end_str = str(tmp_valid_end).replace(\\\"\\\\'\\\", \\\"\\\\\\\"\\\")\\n tmp_valid_end = ''.join(tmp_valid_end_str.strip())\\n self.add_env_var('VALID_END', tmp_valid_end)\\n else:\\n self.add_env_var('VALID_END', \\\"\\\")\\n\\n tmp_valid_include = self.c_dict['VALID_INCLUDE']\\n if tmp_valid_include:\\n # Replace any single quotes with double quotes and remove any\\n # whitespace\\n tmp_valid_include_str = str(tmp_valid_include).replace(\\\"\\\\'\\\", \\\"\\\\\\\"\\\")\\n tmp_valid_include = ''.join(tmp_valid_include_str.strip())\\n self.add_env_var('VALID_INCLUDE', tmp_valid_include)\\n else:\\n self.add_env_var('VALID_INCLUDE', \\\"[]\\\")\\n\\n tmp_valid_exclude = self.c_dict['VALID_EXCLUDE']\\n if tmp_valid_exclude:\\n # Replace any single quotes with double quotes and remove any\\n # whitespace\\n tmp_valid_exclude_str = str(tmp_valid_exclude).replace(\\\"\\\\'\\\", \\\"\\\\\\\"\\\")\\n tmp_valid_exclude = ''.join(tmp_valid_exclude_str.strip())\\n self.add_env_var('VALID_EXCLUDE', tmp_valid_exclude)\\n else:\\n self.add_env_var('VALID_EXCLUDE', \\\"[]\\\")\\n\\n tmp_valid_hour = self.c_dict['VALID_HOUR']\\n if tmp_valid_hour:\\n # Replace any single quotes with double quotes and remove any\\n # whitespace\\n tmp_valid_hour_str = str(tmp_valid_hour).replace(\\\"\\\\'\\\", \\\"\\\\\\\"\\\")\\n tmp_valid_hour = ''.join(tmp_valid_hour_str.strip())\\n self.add_env_var('VALID_HOUR', tmp_valid_hour)\\n else:\\n self.add_env_var('VALID_HOUR', \\\"[]\\\")\\n\\n tmp_lead_req = self.c_dict['LEAD_REQ']\\n if tmp_lead_req:\\n # Replace any single quotes with double quotes and remove any\\n # whitespace\\n tmp_lead_req_str = str(tmp_lead_req).replace(\\\"\\\\'\\\", \\\"\\\\\\\"\\\")\\n tmp_lead_req = ''.join(tmp_lead_req_str.strip())\\n self.add_env_var('LEAD_REQ', tmp_lead_req)\\n else:\\n self.add_env_var('LEAD_REQ', \\\"[]\\\")\\n\\n tmp_lead = self.c_dict['LEAD']\\n if tmp_lead:\\n # Replace any single quotes with double quotes and remove any\\n # whitespace\\n tmp_lead_str = str(tmp_lead).replace(\\\"\\\\'\\\", \\\"\\\\\\\"\\\")\\n tmp_lead = ''.join(tmp_lead_str.strip())\\n self.add_env_var('LEAD', tmp_lead)\\n else:\\n self.add_env_var('LEAD', \\\"[]\\\")\\n\\n tmp_init_mask = self.c_dict['INIT_MASK']\\n if tmp_init_mask:\\n # Replace any single quotes with double quotes and remove any\\n # whitespace\\n tmp_init_mask_str = str(tmp_init_mask).replace(\\\"\\\\'\\\", \\\"\\\\\\\"\\\")\\n tmp_init_mask = ''.join(tmp_init_mask_str.strip())\\n self.add_env_var('INIT_MASK', tmp_init_mask)\\n else:\\n self.add_env_var('INIT_MASK', \\\"[]\\\")\\n\\n tmp_valid_mask = self.c_dict['VALID_MASK']\\n if tmp_valid_mask:\\n # Replace any single quotes with double quotes and remove any\\n # whitespace\\n tmp_valid_mask_str = str(tmp_valid_mask).replace(\\\"\\\\'\\\", \\\"\\\\\\\"\\\")\\n tmp_valid_mask = ''.join(tmp_valid_mask_str.strip())\\n self.add_env_var('VALID_MASK', tmp_valid_mask)\\n else:\\n self.add_env_var('VALID_MASK', \\\"[]\\\")\\n\\n tmp_track_watch_warn = self.c_dict['TRACK_WATCH_WARN']\\n if tmp_track_watch_warn:\\n # Replace any single quotes with double quotes and remove any\\n # whitespace\\n tmp_track_watch_warn_str = str(tmp_track_watch_warn).replace(\\\"\\\\'\\\",\\n \\\"\\\\\\\"\\\")\\n tmp_track_watch_warn = ''.join(tmp_track_watch_warn_str.strip())\\n self.add_env_var('TRACK_WATCH_WARN', tmp_track_watch_warn)\\n else:\\n self.add_env_var('TRACK_WATCH_WARN', \\\"[]\\\")\\n\\n tmp_column_thresh_name = self.c_dict['COLUMN_THRESH_NAME']\\n if tmp_column_thresh_name:\\n # Replace any single quotes with double quotes and remove any\\n # whitespace\\n tmp_column_thresh_name_str = str(tmp_column_thresh_name).replace(\\n \\\"\\\\'\\\", \\\"\\\\\\\"\\\")\\n tmp_column_thresh_name = ''.join(tmp_column_thresh_name_str.strip())\\n self.add_env_var('COLUMN_THRESH_NAME', tmp_column_thresh_name)\\n else:\\n self.add_env_var('COLUMN_THRESH_NAME', \\\"[]\\\")\\n\\n tmp_column_thresh_val = self.c_dict['COLUMN_THRESH_VAL']\\n if tmp_column_thresh_val:\\n # Replace any single quotes with double quotes and remove any\\n # whitespace\\n tmp_column_thresh_val_str = str(tmp_column_thresh_val).replace(\\\"\\\\'\\\",\\n \\\"\\\\\\\"\\\")\\n tmp_column_thresh_val = ''.join(tmp_column_thresh_val_str.strip())\\n self.add_env_var('COLUMN_THRESH_VAL', tmp_column_thresh_val)\\n else:\\n self.add_env_var('COLUMN_THRESH_VAL', \\\"[]\\\")\\n\\n tmp_column_str_name = self.c_dict['COLUMN_STR_NAME']\\n if tmp_column_str_name:\\n # Replace any single quotes with double quotes and remove any\\n # whitespace\\n tmp_column_str_name = str(tmp_column_str_name).replace(\\\"\\\\'\\\",\\n \\\"\\\\\\\"\\\")\\n tmp_column_str_name = ''.join(tmp_column_str_name.strip())\\n self.add_env_var('COLUMN_STR_NAME', tmp_column_str_name)\\n else:\\n self.add_env_var('COLUMN_STR_NAME', \\\"[]\\\")\\n\\n tmp_column_str_val = self.c_dict['COLUMN_STR_VAL']\\n if tmp_column_str_val:\\n # Replace any single quotes with double quotes and remove any\\n # whitespace\\n tmp_column_str_val_str = str(tmp_column_str_val).replace(\\\"\\\\'\\\", \\\"\\\\\\\"\\\")\\n tmp_column_str_val = ''.join(tmp_column_str_val_str.strip())\\n self.add_env_var('COLUMN_STR_VAL', tmp_column_str_val)\\n else:\\n self.add_env_var('COLUMN_STR_VAL', \\\"[]\\\")\\n\\n tmp_init_thresh_name = self.c_dict['INIT_THRESH_NAME']\\n if tmp_init_thresh_name:\\n # Replace any single quotes with double quotes and remove any\\n # whitespace\\n tmp_init_thresh_name_str = str(tmp_init_thresh_name).replace(\\\"\\\\'\\\",\\n \\\"\\\\\\\"\\\")\\n tmp_init_thresh_name = ''.join(tmp_init_thresh_name_str.strip())\\n\\n self.add_env_var('INIT_THRESH_NAME', tmp_init_thresh_name)\\n\\n else:\\n self.add_env_var('INIT_THRESH_NAME', \\\"[]\\\")\\n\\n tmp_init_thresh_val = self.c_dict['INIT_THRESH_VAL']\\n if tmp_init_thresh_val:\\n # Replace any single quotes with double quotes and remove any\\n # whitespace\\n tmp_init_thresh_val_str = str(tmp_init_thresh_val).replace(\\\"\\\\'\\\",\\n \\\"\\\\\\\"\\\")\\n tmp_init_thresh_val = ''.join(tmp_init_thresh_val_str.strip())\\n self.add_env_var('INIT_THRESH_VAL', tmp_init_thresh_val)\\n else:\\n self.add_env_var('INIT_THRESH_VAL', \\\"[]\\\")\\n\\n tmp_init_str_name = self.c_dict['INIT_STR_NAME']\\n if tmp_init_str_name:\\n # Replace any single quotes with double quotes and remove any\\n # whitespace\\n tmp_init_str_name_str = str(tmp_init_str_name).replace(\\\"\\\\'\\\", \\\"\\\\\\\"\\\")\\n tmp_init_str_name = ''.join(tmp_init_str_name_str.strip())\\n self.add_env_var('INIT_STR_NAME', tmp_init_str_name)\\n else:\\n self.add_env_var('INIT_STR_NAME', \\\"[]\\\")\\n\\n tmp_init_str_val = self.c_dict['INIT_STR_VAL']\\n if tmp_init_str_val:\\n # Replace any single quotes with double quotes and remove any\\n # whitespace\\n tmp_init_str_val_str = str(tmp_init_str_val).replace(\\\"\\\\'\\\", \\\"\\\\\\\"\\\")\\n tmp_init_str_val = ''.join(tmp_init_str_val_str.strip())\\n self.add_env_var('INIT_STR_VAL', tmp_init_str_val)\\n else:\\n self.add_env_var('INIT_STR_VAL', \\\"[]\\\")\\n\\n # boolean values for WATER_ONLY\\n if self.c_dict['WATER_ONLY']:\\n flag = \\\"TRUE\\\"\\n else:\\n flag = \\\"FALSE\\\"\\n self.add_env_var('WATER_ONLY', flag)\\n\\n # boolean value for LANDFALL\\n if self.c_dict['LANDFALL']:\\n flag = \\\"TRUE\\\"\\n else:\\n flag = \\\"FALSE\\\"\\n self.add_env_var('LANDFALL', flag)\\n\\n if self.c_dict['LANDFALL_BEG']:\\n self.add_env_var('LANDFALL_BEG',\\n self.c_dict['LANDFALL_BEG'])\\n else:\\n # Set to default\\n self.add_env_var('LANDFALL_BEG', '-24')\\n\\n if self.c_dict['LANDFALL_END']:\\n self.add_env_var('LANDFALL_END',\\n self.c_dict['LANDFALL_END'])\\n else:\\n # Set to default\\n self.add_env_var('LANDFALL_END', '00')\\n\\n # boolean value for MATCH_POINTS\\n if self.c_dict['MATCH_POINTS'] == 'true':\\n flag = \\\"TRUE\\\"\\n else:\\n flag = \\\"FALSE\\\"\\n self.add_env_var('MATCH_POINTS', flag)\\n\\n if self.c_dict['CONFIG_FILE']:\\n self.add_env_var('CONFIG_FILE',\\n self.c_dict['CONFIG_FILE'])\\n else:\\n self.log_error(\\n cur_filename + '|' + cur_function +\\n ': no MET TC-Stat config file found. Exiting')\\n sys.exit(1)\\n\\n jobs_list_tmp = self.c_dict['JOBS_LIST']\\n if jobs_list_tmp:\\n # MET is expecting a string\\n jobs_list_str = '\\\"' + jobs_list_tmp + '\\\"'\\n self.add_env_var('JOBS', jobs_list_str)\\n else:\\n self.log_error('No jobs list defined. Please check your METplus'\\n 'config file. Exiting...')\\n sys.exit(1)\\n return 0\",\n \"def as_invocation_environment_dict(self):\\n lib_env_var = self._platform.resolve_platform_specific({\\n 'darwin': lambda: 'DYLD_LIBRARY_PATH',\\n 'linux': lambda: 'LD_LIBRARY_PATH',\\n })\\n return {\\n 'PATH': create_path_env_var(self.path_entries),\\n lib_env_var: create_path_env_var(self.library_dirs),\\n }\",\n \"def generate(env):\\n\\n # FIXME: this is already too late\\n #if env.get('quiet', False):\\n # quietCommandLines(env)\\n\\n # shortcuts\\n debug = env['debug']\\n machine = env['machine']\\n platform = env['platform']\\n x86 = env['machine'] == 'x86'\\n gcc = env['platform'] in ('linux', 'freebsd', 'darwin')\\n msvc = env['platform'] in ('windows', 'winddk', 'wince')\\n\\n # Tool\\n if platform == 'winddk':\\n env.Tool('winddk')\\n elif platform == 'wince':\\n env.Tool('wcesdk')\\n else:\\n env.Tool('default')\\n\\n # Put build output in a separate dir, which depends on the current\\n # configuration. See also http://www.scons.org/wiki/AdvancedBuildExample\\n build_topdir = 'build'\\n build_subdir = env['platform']\\n if env['dri']:\\n build_subdir += \\\"-dri\\\"\\n if env['llvm']:\\n build_subdir += \\\"-llvm\\\"\\n if env['machine'] != 'generic':\\n build_subdir += '-' + env['machine']\\n if env['debug']:\\n build_subdir += \\\"-debug\\\"\\n if env['profile']:\\n build_subdir += \\\"-profile\\\"\\n build_dir = os.path.join(build_topdir, build_subdir)\\n # Place the .sconsign file in the build dir too, to avoid issues with\\n # different scons versions building the same source file\\n env['build'] = build_dir\\n env.SConsignFile(os.path.join(build_dir, '.sconsign'))\\n\\n # C preprocessor options\\n cppdefines = []\\n if debug:\\n cppdefines += ['DEBUG']\\n else:\\n cppdefines += ['NDEBUG']\\n if env['profile']:\\n cppdefines += ['PROFILE']\\n if platform == 'windows':\\n cppdefines += [\\n 'WIN32',\\n '_WINDOWS',\\n '_UNICODE',\\n 'UNICODE',\\n # http://msdn2.microsoft.com/en-us/library/6dwk3a1z.aspx,\\n 'WIN32_LEAN_AND_MEAN',\\n 'VC_EXTRALEAN',\\n '_CRT_SECURE_NO_DEPRECATE',\\n ]\\n if debug:\\n cppdefines += ['_DEBUG']\\n if platform == 'winddk':\\n # Mimic WINDDK's builtin flags. See also:\\n # - WINDDK's bin/makefile.new i386mk.inc for more info.\\n # - buildchk_wxp_x86.log files, generated by the WINDDK's build\\n # - http://alter.org.ua/docs/nt_kernel/vc8_proj/\\n cppdefines += [\\n ('_X86_', '1'),\\n ('i386', '1'),\\n 'STD_CALL',\\n ('CONDITION_HANDLING', '1'),\\n ('NT_INST', '0'),\\n ('WIN32', '100'),\\n ('_NT1X_', '100'),\\n ('WINNT', '1'),\\n ('_WIN32_WINNT', '0x0501'), # minimum required OS version\\n ('WINVER', '0x0501'),\\n ('_WIN32_IE', '0x0603'),\\n ('WIN32_LEAN_AND_MEAN', '1'),\\n ('DEVL', '1'),\\n ('__BUILDMACHINE__', 'WinDDK'),\\n ('FPO', '0'),\\n ]\\n if debug:\\n cppdefines += [('DBG', 1)]\\n if platform == 'wince':\\n cppdefines += [\\n '_CRT_SECURE_NO_DEPRECATE',\\n '_USE_32BIT_TIME_T',\\n 'UNICODE',\\n '_UNICODE',\\n ('UNDER_CE', '600'),\\n ('_WIN32_WCE', '0x600'),\\n 'WINCEOEM',\\n 'WINCEINTERNAL',\\n 'WIN32',\\n 'STRICT',\\n 'x86',\\n '_X86_',\\n 'INTERNATIONAL',\\n ('INTLMSG_CODEPAGE', '1252'),\\n ]\\n if platform == 'windows':\\n cppdefines += ['PIPE_SUBSYSTEM_WINDOWS_USER']\\n if platform == 'winddk':\\n cppdefines += ['PIPE_SUBSYSTEM_WINDOWS_DISPLAY']\\n if platform == 'wince':\\n cppdefines += ['PIPE_SUBSYSTEM_WINDOWS_CE']\\n env.Append(CPPDEFINES = cppdefines)\\n\\n # C preprocessor includes\\n if platform == 'winddk':\\n env.Append(CPPPATH = [\\n env['SDK_INC_PATH'],\\n env['DDK_INC_PATH'],\\n env['WDM_INC_PATH'],\\n env['CRT_INC_PATH'],\\n ])\\n\\n # C compiler options\\n cflags = []\\n if gcc:\\n if debug:\\n cflags += ['-O0', '-g3']\\n else:\\n cflags += ['-O3', '-g3']\\n if env['profile']:\\n cflags += ['-pg']\\n if env['machine'] == 'x86':\\n cflags += [\\n '-m32',\\n #'-march=pentium4',\\n '-mmmx', '-msse', '-msse2', # enable SIMD intrinsics\\n #'-mfpmath=sse',\\n ]\\n if env['machine'] == 'x86_64':\\n cflags += ['-m64']\\n cflags += [\\n '-Wall',\\n '-Wmissing-prototypes',\\n '-Wno-long-long',\\n '-ffast-math',\\n '-pedantic',\\n '-fmessage-length=0', # be nice to Eclipse\\n ]\\n if msvc:\\n # See also:\\n # - http://msdn.microsoft.com/en-us/library/19z1t1wy.aspx\\n # - cl /?\\n if debug:\\n cflags += [\\n '/Od', # disable optimizations\\n '/Oi', # enable intrinsic functions\\n '/Oy-', # disable frame pointer omission\\n ]\\n else:\\n cflags += [\\n '/Ox', # maximum optimizations\\n '/Oi', # enable intrinsic functions\\n '/Ot', # favor code speed\\n #'/fp:fast', # fast floating point \\n ]\\n if env['profile']:\\n cflags += [\\n '/Gh', # enable _penter hook function\\n '/GH', # enable _pexit hook function\\n ]\\n cflags += [\\n '/W3', # warning level\\n #'/Wp64', # enable 64 bit porting warnings\\n ]\\n if env['machine'] == 'x86':\\n cflags += [\\n #'/QIfist', # Suppress _ftol\\n #'/arch:SSE2', # use the SSE2 instructions\\n ]\\n if platform == 'windows':\\n cflags += [\\n # TODO\\n ]\\n if platform == 'winddk':\\n cflags += [\\n '/Zl', # omit default library name in .OBJ\\n '/Zp8', # 8bytes struct member alignment\\n '/Gy', # separate functions for linker\\n '/Gm-', # disable minimal rebuild\\n '/WX', # treat warnings as errors\\n '/Gz', # __stdcall Calling convention\\n '/GX-', # disable C++ EH\\n '/GR-', # disable C++ RTTI\\n '/GF', # enable read-only string pooling\\n '/G6', # optimize for PPro, P-II, P-III\\n '/Ze', # enable extensions\\n '/Gi-', # disable incremental compilation\\n '/QIfdiv-', # disable Pentium FDIV fix\\n '/hotpatch', # prepares an image for hotpatching.\\n #'/Z7', #enable old-style debug info\\n ]\\n if platform == 'wince':\\n # See also C:\\\\WINCE600\\\\public\\\\common\\\\oak\\\\misc\\\\makefile.def\\n cflags += [\\n '/Zl', # omit default library name in .OBJ\\n '/GF', # enable read-only string pooling\\n '/GR-', # disable C++ RTTI\\n '/GS', # enable security checks\\n # Allow disabling language conformance to maintain backward compat\\n #'/Zc:wchar_t-', # don't force wchar_t as native type, instead of typedef\\n #'/Zc:forScope-', # don't enforce Standard C++ for scoping rules\\n #'/wd4867',\\n #'/wd4430',\\n #'/MT',\\n #'/U_MT',\\n ]\\n # Automatic pdb generation\\n # See http://scons.tigris.org/issues/show_bug.cgi?id=1656\\n env.EnsureSConsVersion(0, 98, 0)\\n env['PDB'] = '${TARGET.base}.pdb'\\n env.Append(CFLAGS = cflags)\\n env.Append(CXXFLAGS = cflags)\\n\\n # Assembler options\\n if gcc:\\n if env['machine'] == 'x86':\\n env.Append(ASFLAGS = ['-m32'])\\n if env['machine'] == 'x86_64':\\n env.Append(ASFLAGS = ['-m64'])\\n\\n # Linker options\\n linkflags = []\\n if gcc:\\n if env['machine'] == 'x86':\\n linkflags += ['-m32']\\n if env['machine'] == 'x86_64':\\n linkflags += ['-m64']\\n if platform == 'winddk':\\n # See also:\\n # - http://msdn2.microsoft.com/en-us/library/y0zzbyt4.aspx\\n linkflags += [\\n '/merge:_PAGE=PAGE',\\n '/merge:_TEXT=.text',\\n '/section:INIT,d',\\n '/opt:ref',\\n '/opt:icf',\\n '/ignore:4198,4010,4037,4039,4065,4070,4078,4087,4089,4221',\\n '/incremental:no',\\n '/fullbuild',\\n '/release',\\n '/nodefaultlib',\\n '/wx',\\n '/debug',\\n '/debugtype:cv',\\n '/version:5.1',\\n '/osversion:5.1',\\n '/functionpadmin:5',\\n '/safeseh',\\n '/pdbcompress',\\n '/stack:0x40000,0x1000',\\n '/driver',\\n '/align:0x80',\\n '/subsystem:native,5.01',\\n '/base:0x10000',\\n\\n '/entry:DrvEnableDriver',\\n ]\\n if env['profile']:\\n linkflags += [\\n '/MAP', # http://msdn.microsoft.com/en-us/library/k7xkk3e2.aspx\\n ]\\n if platform == 'wince':\\n linkflags += [\\n '/nodefaultlib',\\n #'/incremental:no',\\n #'/fullbuild',\\n '/entry:_DllMainCRTStartup',\\n ]\\n env.Append(LINKFLAGS = linkflags)\\n\\n # Default libs\\n env.Append(LIBS = [])\\n\\n # Custom builders and methods\\n createConvenienceLibBuilder(env)\\n createCodeGenerateMethod(env)\\n createInstallMethods(env)\\n\\n # for debugging\\n #print env.Dump()\",\n \"def getEnvironmentVars(self):\\n my_env = os.environ.copy()\\n my_env[\\\"MODE\\\"] = self.model.mode\\n my_env[\\\"ALGORITHM\\\"] = self.model.algorithm\\n my_env[\\\"INPUT_FILE\\\"] = self.model.inputFile\\n my_env[\\\"TARGET_FILE\\\"] = self.model.targetFile\\n my_env[\\\"DEVICE\\\"] = self.model.device\\n my_env[\\\"SIZE\\\"] = str(self.model.size)\\n my_env[\\\"ROLE\\\"] = self.model.role\\n my_env[\\\"WAIT_SIZE\\\"] = str(self.model.waitSize)\\n my_env[\\\"STEP_SIZE\\\"] = str(self.model.stepSize)\\n my_env[\\\"TUI_CONNECTION\\\"] = str(True)\\n return my_env\",\n \"def make_env(env_name):\\n \\n env = gym.make(env_name) \\n return env\",\n \"def __create_execution_engine(self):\\n target = self.binding.Target.from_default_triple()\\n target_machine = target.create_target_machine()\\n backing_mod = binding.parse_assembly(\\\"\\\")\\n engine = binding.create_mcjit_compiler(backing_mod, target_machine)\\n self.engine = engine\",\n \"def setup_environment():\\n os.environ['QUEUE_OVERRIDE'] = 'LINUX_UNTRUSTED'\\n os.environ['WORKER_ROOT_DIR'] = os.path.join(MNT_DIR, 'clusterfuzz')\\n os.environ['WORKER_BOT_TMPDIR'] = os.path.join(MNT_DIR, 'tmp')\\n\\n if not os.path.exists(BOT_BASEDIR):\\n os.mkdir(BOT_BASEDIR)\",\n \"def _make_environment(\\n self, problem_type, curriculum_sample, wrapper_type, backend_type=None):\\n if backend_type is None:\\n backend_type = FLAGS.backend\\n return dm_construction.get_environment(\\n problem_type,\\n unity_environment=self._unity_envs[backend_type],\\n wrapper_type=wrapper_type,\\n curriculum_sample=curriculum_sample)\",\n \"def setup_app(command, conf, vars):\\n load_environment(conf.global_conf, conf.local_conf)\",\n \"def _env_setup(self, initial_qpos):\\n raise NotImplementedError()\",\n \"def __init__(self, *args):\\n self.env = os.environ.copy()\\n \\\"\\\"\\\"Environment variables (:class:`dict`)\\\"\\\"\\\"\\n command = \\\"modulecmd python \\\"+' '.join(args)\\n p = subprocess.Popen(command, shell=True, stdout=subprocess.PIPE)\\n retval = p.communicate()\\n self._parse(retval)\",\n \"def make_env(\\n args,\\n num_envs=None,\\n include_norm=False,\\n norm_reward=True,\\n **kwargs,\\n):\\n if num_envs is None:\\n num_envs = args.num_envs\\n\\n # `kwargs` given via `args`\\n args_kwargs = {}\\n for arg in [\\n 'M',\\n 'dt',\\n 'restol',\\n 'seed',\\n\\n 'lambda_real_interval',\\n 'lambda_imag_interval',\\n 'lambda_real_interpolation_interval',\\n\\n 'norm_factor',\\n 'residual_weight',\\n 'step_penalty',\\n 'reward_iteration_only',\\n 'reward_strategy',\\n 'collect_states',\\n ]:\\n args_kwargs[arg] = kwargs.pop(arg, getattr(args, arg))\\n all_kwargs = {**kwargs, **args_kwargs}\\n\\n # SAC does not support float64\\n if args.model_class == 'SAC':\\n all_kwargs['use_doubles'] = False\\n\\n return gym.make(\\n args.envname,\\n **all_kwargs,\\n )\",\n \"def _set_executables(self):\\n\\n # add path from argument to env\\n if self.home_path:\\n if self.env:\\n self.env += f\\\":{self.home_path}\\\"\\n else:\\n self.env = self.home_path\\n\\n # set fuzzer_exe \\n self.fuzzer_exe = self._search_for_executable(self.fuzzer_exe)\\n L.debug(\\\"Will use %s as fuzzer executable.\\\", self.fuzzer_exe)\\n\\n # set compiler_exe\\n if self.compiler_exe:\\n self.compiler_exe = self._search_for_executable(self.compiler_exe)\\n L.debug(\\\"Will use %s as fuzzer compiler.\\\", self.compiler_exe)\\n\\n # set additional executables\\n for exe_name, exe_file in self.EXECUTABLES.items():\\n self.EXECUTABLES[exe_name] = self._search_for_executable(exe_file)\",\n \"def init_env(self, env_info):\\n raise NotImplementedError\",\n \"def get_environment() -> dict:\\n go_path = subprocess.check_output([\\\"go\\\", \\\"env\\\", \\\"GOPATH\\\"]).decode().strip()\\n setup_script_path = f\\\"{go_path}/src/github.com/harmony-one/harmony/scripts/setup_bls_build_flags.sh\\\"\\n response = subprocess.check_output([\\\"bash\\\", setup_script_path, \\\"-v\\\"], timeout=5)\\n environment = json.loads(response)\\n environment[\\\"HOME\\\"] = os.environ.get(\\\"HOME\\\")\\n return environment\",\n \"def build_env(self, job, private_data_dir, private_data_files=None):\\n env = super(RunJob, self).build_env(job, private_data_dir, private_data_files=private_data_files)\\n if private_data_files is None:\\n private_data_files = {}\\n # Set environment variables needed for inventory and job event\\n # callbacks to work.\\n env['JOB_ID'] = str(job.pk)\\n env['INVENTORY_ID'] = str(job.inventory.pk)\\n if job.project:\\n env['PROJECT_REVISION'] = job.project.scm_revision\\n env['ANSIBLE_RETRY_FILES_ENABLED'] = \\\"False\\\"\\n env['MAX_EVENT_RES'] = str(settings.MAX_EVENT_RES_DATA)\\n if hasattr(settings, 'AWX_ANSIBLE_CALLBACK_PLUGINS') and settings.AWX_ANSIBLE_CALLBACK_PLUGINS:\\n env['ANSIBLE_CALLBACK_PLUGINS'] = ':'.join(settings.AWX_ANSIBLE_CALLBACK_PLUGINS)\\n env['AWX_HOST'] = settings.TOWER_URL_BASE\\n\\n # Create a directory for ControlPath sockets that is unique to each job\\n cp_dir = os.path.join(private_data_dir, 'cp')\\n if not os.path.exists(cp_dir):\\n os.mkdir(cp_dir, 0o700)\\n # FIXME: more elegant way to manage this path in container\\n env['ANSIBLE_SSH_CONTROL_PATH_DIR'] = '/runner/cp'\\n\\n # Set environment variables for cloud credentials.\\n cred_files = private_data_files.get('credentials', {})\\n for cloud_cred in job.cloud_credentials:\\n if cloud_cred and cloud_cred.credential_type.namespace == 'openstack' and cred_files.get(cloud_cred, ''):\\n env['OS_CLIENT_CONFIG_FILE'] = to_container_path(cred_files.get(cloud_cred, ''), private_data_dir)\\n\\n for network_cred in job.network_credentials:\\n env['ANSIBLE_NET_USERNAME'] = network_cred.get_input('username', default='')\\n env['ANSIBLE_NET_PASSWORD'] = network_cred.get_input('password', default='')\\n\\n ssh_keyfile = cred_files.get(network_cred, '')\\n if ssh_keyfile:\\n env['ANSIBLE_NET_SSH_KEYFILE'] = ssh_keyfile\\n\\n authorize = network_cred.get_input('authorize', default=False)\\n env['ANSIBLE_NET_AUTHORIZE'] = str(int(authorize))\\n if authorize:\\n env['ANSIBLE_NET_AUTH_PASS'] = network_cred.get_input('authorize_password', default='')\\n\\n path_vars = (\\n ('ANSIBLE_COLLECTIONS_PATHS', 'collections_paths', 'requirements_collections', '~/.ansible/collections:/usr/share/ansible/collections'),\\n ('ANSIBLE_ROLES_PATH', 'roles_path', 'requirements_roles', '~/.ansible/roles:/usr/share/ansible/roles:/etc/ansible/roles'),\\n ('ANSIBLE_COLLECTIONS_PATH', 'collections_path', 'requirements_collections', '~/.ansible/collections:/usr/share/ansible/collections'),\\n )\\n\\n config_values = read_ansible_config(os.path.join(private_data_dir, 'project'), list(map(lambda x: x[1], path_vars)))\\n\\n for env_key, config_setting, folder, default in path_vars:\\n paths = default.split(':')\\n if env_key in env:\\n for path in env[env_key].split(':'):\\n if path not in paths:\\n paths = [env[env_key]] + paths\\n elif config_setting in config_values:\\n for path in config_values[config_setting].split(':'):\\n if path not in paths:\\n paths = [config_values[config_setting]] + paths\\n paths = [os.path.join(CONTAINER_ROOT, folder)] + paths\\n env[env_key] = os.pathsep.join(paths)\\n\\n return env\",\n \"def init_environment(self):\\n # import outside of cell so we don't get a traceback\\n from sage import all_cmdline\\n from sage.repl.user_globals import initialize_globals\\n initialize_globals(all_cmdline, self.shell.user_ns)\\n self.run_init()\",\n \"def get_environmentals(self):\\n for k, v in utils.slurm_envs(default.SBATCH_VARS_FOR_WORKFLOW).items():\\n setattr(self, k, v)\",\n \"def create_virtual_environment(self):\\n cmd_venv = \\\"python\\\" + self.python_version + \\\" -m venv --prompt \\\\\\\"\\\" + self.project_name + \\\"\\\\\\\" \\\" + TaskCreator.venv_dir\\n return {\\n \\\"actions\\\": [cmd_venv],\\n \\\"verbosity\\\": 2\\n }\",\n \"def prepare(self):\\n # This is a no-op; the native subprocess environment is ready-to-use.\\n pass\",\n \"def setup_environment():\\n global _ENV_SETUP_DONE\\n if _ENV_SETUP_DONE:\\n return\\n _ENV_SETUP_DONE = True\\n\\n _configure_libraries()\\n\\n custom_module_path = os.environ.get(\\\"DETECTRON2_ENV_MODULE\\\")\\n\\n if custom_module_path:\\n setup_custom_environment(custom_module_path)\\n else:\\n # The default setup is a no-op\\n pass\",\n \"def shell_source(script):\\n import subprocess, os\\n pipe = subprocess.Popen(\\\". %s; env\\\" % script, stdout=subprocess.PIPE, shell=True)\\n output = pipe.communicate()[0]\\n env = dict((line.split(\\\"=\\\", 1) for line in output.splitlines()))\\n os.environ.update(env)\",\n \"def shell_source(script):\\n import subprocess, os\\n pipe = subprocess.Popen(\\\". %s; env\\\" % script, stdout=subprocess.PIPE, shell=True)\\n output = pipe.communicate()[0]\\n env = dict((line.split(\\\"=\\\", 1) for line in output.splitlines()))\\n os.environ.update(env)\",\n \"def test_environment_scripts_generated_envvars(require_run):\\n consumer_pkg = textwrap.dedent(r\\\"\\\"\\\"\\n from conan import ConanFile\\n from conan.tools.env import VirtualBuildEnv, VirtualRunEnv\\n class Pkg(ConanFile):\\n settings = \\\"os\\\"\\n requires = \\\"require_pkg/1.0\\\"\\n tool_requires = \\\"build_require_pkg/1.0\\\"\\n generators = \\\"VirtualRunEnv\\\", \\\"VirtualBuildEnv\\\"\\n \\\"\\\"\\\")\\n\\n client = TestClient()\\n conanfile_br = (GenConanfile().with_package_file(\\\"bin/myapp\\\", \\\"myexe\\\")\\n .with_package_file(\\\"lib/mylib\\\", \\\"mylibcontent\\\")\\n .with_settings(\\\"os\\\"))\\n conanfile_require = (GenConanfile().with_package_file(\\\"bin/myapp\\\", \\\"myexe\\\")\\n .with_package_file(\\\"lib/mylib\\\", \\\"mylibcontent\\\")\\n .with_settings(\\\"os\\\"))\\n if require_run:\\n conanfile_require.with_package_type(\\\"application\\\")\\n client.save({\\\"build_require_pkg/conanfile.py\\\": conanfile_br,\\n \\\"require_pkg/conanfile.py\\\": conanfile_require,\\n \\\"consumer_pkg/conanfile.py\\\": consumer_pkg})\\n\\n client.run(\\\"export build_require_pkg --name=build_require_pkg --version=1.0\\\")\\n client.run(\\\"export require_pkg --name=require_pkg --version=1.0\\\")\\n\\n client.run(\\\"install consumer_pkg --build='*'\\\")\\n if platform.system() == \\\"Windows\\\":\\n conanbuildenv = client.load(\\\"consumer_pkg/conanbuildenv.bat\\\")\\n if require_run:\\n conanrunenv = client.load(\\\"consumer_pkg/conanrunenv.bat\\\")\\n assert \\\"LD_LIBRARY_PATH\\\" not in conanbuildenv\\n assert \\\"LD_LIBRARY_PATH\\\" not in conanrunenv\\n else:\\n assert not os.path.exists(\\\"consumer_pkg/conanrunenv.bat\\\")\\n else:\\n if require_run:\\n conanbuildenv = client.load(\\\"consumer_pkg/conanbuildenv.sh\\\")\\n conanrunenv = client.load(\\\"consumer_pkg/conanrunenv.sh\\\")\\n assert \\\"LD_LIBRARY_PATH\\\" in conanbuildenv\\n assert \\\"LD_LIBRARY_PATH\\\" in conanrunenv\\n else:\\n assert not os.path.exists(\\\"consumer_pkg/conanrunenv.sh\\\")\\n\\n if require_run:\\n # Build context LINUX - Host context LINUX\\n client.run(\\\"install consumer_pkg -s:b os=Linux -s:h os=Linux --build='*'\\\")\\n conanbuildenv = client.load(\\\"consumer_pkg/conanbuildenv.sh\\\")\\n conanrunenv = client.load(\\\"consumer_pkg/conanrunenv.sh\\\")\\n assert \\\"LD_LIBRARY_PATH\\\" in conanbuildenv\\n assert \\\"LD_LIBRARY_PATH\\\" in conanrunenv\\n\\n # Build context WINDOWS - Host context WINDOWS\\n client.run(\\\"install consumer_pkg -s:b os=Windows -s:h os=Windows --build='*'\\\")\\n conanbuildenv = client.load(\\\"consumer_pkg/conanbuildenv.bat\\\")\\n conanrunenv = client.load(\\\"consumer_pkg/conanrunenv.bat\\\")\\n assert \\\"LD_LIBRARY_PATH\\\" not in conanbuildenv\\n assert \\\"LD_LIBRARY_PATH\\\" not in conanrunenv\\n\\n # Build context LINUX - Host context WINDOWS\\n client.run(\\\"install consumer_pkg -s:b os=Linux -s:h os=Windows --build='*'\\\")\\n conanbuildenv = client.load(\\\"consumer_pkg/conanbuildenv.sh\\\")\\n conanrunenv = client.load(\\\"consumer_pkg/conanrunenv.bat\\\")\\n assert \\\"LD_LIBRARY_PATH\\\" in conanbuildenv\\n assert \\\"LD_LIBRARY_PATH\\\" not in conanrunenv\\n\\n # Build context WINDOWS - Host context LINUX\\n client.run(\\\"install consumer_pkg -s:b os=Windows -s:h os=Linux --build='*'\\\")\\n conanbuildenv = client.load(\\\"consumer_pkg/conanbuildenv.bat\\\")\\n conanrunenv = client.load(\\\"consumer_pkg/conanrunenv.sh\\\")\\n assert \\\"LD_LIBRARY_PATH\\\" not in conanbuildenv\\n assert \\\"LD_LIBRARY_PATH\\\" in conanrunenv\",\n \"def getEnvironment(self):\\n pass\",\n \"def create_environment(args):\\n env.username = args.user\\n env.password = args.password\\n env.service_url = args.service_url\\n env.quiet = args.quiet\\n env.verbose = args.verbose\\n env.manifest = args.manifest\\n env.debug = args.debug\\n env.always_confirm = args.yes\\n env.args = args\\n env.api = ravello.RavelloClient(env.username, env.password, env.service_url)\",\n \"def _setup_env():\\n env.home_path = os.path.expanduser('~')\\n env.env_path = os.getenv('WORKON_HOME')\\n\\n if not env.env_path:\\n warn(\\\"You should set the WORKON_HOME environment variable to\\\" \\\\\\n \\\" the root directory for your virtual environments.\\\")\\n env.env_path = env.sites_path\\n\\n env.project_path = join(env.sites_path, env.project_name)\\n env.ve_path = join(env.env_path, env.project_name)\\n env.activate_path = join(env.ve_path, 'bin', 'activate')\",\n \"def _create_extra_environment(self):\\n return {}\",\n \"def create(self):\\n if self._is_setup:\\n return\\n\\n if not self.check_presence():\\n if os.path.exists(self._path):\\n util.long_path_rmtree(self._path)\\n\\n if not os.path.exists(self._env_dir):\\n try:\\n os.makedirs(self._env_dir)\\n except OSError:\\n # Environment.create may be called in parallel for\\n # environments with different self._path, but same\\n # self._env_dir. This causes a race condition for\\n # the above makedirs() call --- but not for the\\n # rest of the processing. Therefore, we will just\\n # ignore the error here, as things will fail at a\\n # later stage if there is really was a problem.\\n pass\\n\\n try:\\n self._setup()\\n except Exception:\\n log.error(f\\\"Failure creating environment for {self.name}\\\")\\n if os.path.exists(self._path):\\n util.long_path_rmtree(self._path)\\n raise\\n\\n self.save_info_file(self._path)\\n\\n self._is_setup = True\",\n \"def set_up(self):\\n self.path.state = self.path.gen.joinpath(\\\"state\\\")\\n if self.path.state.exists():\\n self.path.state.rmtree(ignore_errors=True)\\n self.path.state.mkdir()\\n\\n for script in self.given.get(\\\"scripts\\\", []):\\n script_path = self.path.state.joinpath(script)\\n\\n if not script_path.dirname().exists():\\n script_path.dirname().makedirs()\\n\\n script_path.write_text(self.given[\\\"scripts\\\"][script])\\n script_path.chmod(\\\"u+x\\\")\\n\\n for filename, contents in self.given.get(\\\"files\\\", {}).items():\\n self.path.state.joinpath(filename).write_text(contents)\\n\\n self.python = hitchpylibrarytoolkit.project_build(\\n \\\"commandlib\\\", self.path, self.given[\\\"python version\\\"]\\n ).bin.python\\n\\n self.example_py_code = (\\n ExamplePythonCode(self.python, self.path.state)\\n .with_code(self.given.get(\\\"code\\\", \\\"\\\"))\\n .with_setup_code(self.given.get(\\\"setup\\\", \\\"\\\"))\\n )\",\n \"def get_env(self, *args):\\n m = module(*args)\\n return m.env\",\n \"def executor_env(self, key=None, value=None, pairs=None):\\n if (key is not None and pairs is not None) or (key is None and pairs is None):\\n raise Exception(\\\"Either pass one key-value pair or a list of pairs\\\")\\n elif key is not None:\\n self.environmentVariables[key] = value\\n elif pairs is not None:\\n for (k, v) in pairs:\\n self.environmentVariables[k] = v\\n return self\",\n \"def generate_environment(dataset, tmpdir):\\n\\n print(\\\">>> Test environment:\\\")\\n print(\\\"dataset:\\\", dataset)\\n print(\\\"tmpdir:\\\", tmpdir)\\n\\n generate_test_environment(tmpdir, dataset)\\n\\n return { 'dataset': dataset, 'tmpdir': tmpdir }\",\n \"def setup_env(app_dir, app_id, version, module_id, remote_api=False):\\n # GCS library behaves differently when running under remote_api. It uses\\n # SERVER_SOFTWARE to figure this out. See cloudstorage/common.py, local_run().\\n if remote_api:\\n os.environ['SERVER_SOFTWARE'] = 'remote_api'\\n else:\\n os.environ['SERVER_SOFTWARE'] = 'Development yo dawg/1.0'\\n if app_dir:\\n app_id = app_id or Application(app_dir).app_id\\n version = version or 'default-version'\\n if app_id:\\n os.environ['APPLICATION_ID'] = app_id\\n if version:\\n os.environ['CURRENT_VERSION_ID'] = '%s.%d' % (\\n version, int(time.time()) << 28)\\n if module_id:\\n os.environ['CURRENT_MODULE_ID'] = module_id\",\n \"def environment(self):\\n return self._environ\",\n \"def __init__(self, env, system=None):\\n self._env = env\\n self._system = system if system is not None else {}\",\n \"def env_init(self, environ) -> None:\\n environ.update(self._env)\",\n \"def CreateEnv(self):\\n if len(self.input.get()) > 0:\\n VirtualEnvApp().CreateEnv(self.input.get())\\n self.restart_program()\\n else:\\n messagebox.showinfo('Error', 'Please Enter the Name of the ENV')\",\n \"def init_vars(self):\\n\\n load_dotenv()\\n self.smart_cube = True if os.environ.get(\\\"SMART_CUBE\\\") == \\\"True\\\" else False\\n self.gen_parsed_to_cubedb = True if os.environ.get(\\\"GEN_PARSED_TO_CUBEDB\\\") == \\\"True\\\" else False\\n self.name_of_solve = os.environ.get(\\\"NAME_OF_SOLVE\\\")\\n self.time_solve = os.environ.get(\\\"TIME_SOLVE\\\")\\n self.comms_unparsed_bool = True if os.environ.get(\\\"COMMS_UNPARSED\\\") == \\\"True\\\" else False\\n self.gen_with_move_count = True if os.environ.get(\\\"GEN_WITH_MOVE_COUNT\\\") == \\\"True\\\" else False\\n self.diff_to_solved_state = float(os.environ.get(\\\"DIFF_BETWEEN_ALGS\\\"))\\n self.parse_to_lp = True if os.environ.get(\\\"PARSE_TO_LETTER_PAIR\\\") == \\\"True\\\" else False\\n self.gen_with_moves = True if os.environ.get(\\\"GEN_WITH_MOVE_COUNT\\\") == \\\"True\\\" else False\\n self.buffer_ed = self.get_buffer_ed(os.environ.get(\\\"EDGES_BUFFER\\\"))\\n self.buffer_cor = self.get_buffer_cor(os.environ.get(\\\"CORNER_BUFFER\\\"))\\n self.path_to_lp = os.environ.get(\\\"PATH_LETTER_PAIR_FILE\\\")\\n self.dict_lp = self.load_letter_pairs_dict()\",\n \"def make_env(\\n\\t\\tdomain_name,\\n\\t\\ttask_name,\\n\\t\\tseed=0,\\n\\t\\tepisode_length=1000,\\n\\t\\tframe_stack=3,\\n\\t\\taction_repeat=4,\\n\\t\\timage_size=100,\\n\\t\\tmode='original'\\n\\t):\\n\\tassert mode in {'original', 'color_easy', 'color_hard', 'video_easy', 'video_hard'}, \\\\\\n\\t\\tf'specified mode \\\"{mode}\\\" is not supported'\\n\\n\\tenv = dmc2gym.make(\\n\\t\\tdomain_name=domain_name,\\n\\t\\ttask_name=task_name,\\n\\t\\tseed=seed,\\n\\t\\tvisualize_reward=False,\\n\\t\\tfrom_pixels=True,\\n\\t\\theight=image_size,\\n\\t\\twidth=image_size,\\n\\t\\tepisode_length=episode_length,\\n\\t\\tframe_skip=action_repeat\\n\\t)\\n\\tenv = VideoWrapper(env, mode, seed)\\n\\tenv = FrameStack(env, frame_stack)\\n\\tenv = RandomizationWrapper(env, mode, seed)\\n\\n\\treturn env\",\n \"def construct_envs(\\n config,\\n env_class,\\n workers_ignore_signals=False,\\n):\\n\\n num_processes = config.NUM_ENVIRONMENTS\\n configs = []\\n env_classes = [env_class for _ in range(num_processes)]\\n dataset = habitat.datasets.make_dataset(config.TASK_CONFIG.DATASET.TYPE)\\n scenes = config.TASK_CONFIG.DATASET.CONTENT_SCENES\\n if \\\"*\\\" in config.TASK_CONFIG.DATASET.CONTENT_SCENES:\\n scenes = dataset.get_scenes_to_load(config.TASK_CONFIG.DATASET)\\n\\n if num_processes > 1:\\n if len(scenes) == 0:\\n raise RuntimeError(\\n \\\"No scenes to load, multiple process logic relies on being able to split scenes uniquely between processes\\\"\\n )\\n\\n if len(scenes) < num_processes:\\n scenes = scenes * num_processes\\n\\n random.shuffle(scenes)\\n\\n scene_splits = [[] for _ in range(num_processes)]\\n for idx, scene in enumerate(scenes):\\n scene_splits[idx % len(scene_splits)].append(scene)\\n\\n assert sum(map(len, scene_splits)) == len(scenes)\\n\\n for i in range(num_processes):\\n proc_config = config.clone()\\n proc_config.defrost()\\n\\n task_config = proc_config.TASK_CONFIG\\n task_config.SEED = task_config.SEED + i\\n if len(scenes) > 0:\\n task_config.DATASET.CONTENT_SCENES = scene_splits[i]\\n\\n task_config.SIMULATOR.HABITAT_SIM_V0.GPU_DEVICE_ID = (\\n config.SIMULATOR_GPU_ID\\n )\\n\\n task_config.SIMULATOR.AGENT_0.SENSORS = config.SENSORS\\n\\n proc_config.freeze()\\n configs.append(proc_config)\\n\\n envs = habitat.ThreadedVectorEnv(\\n make_env_fn=make_env_fn,\\n env_fn_args=tuple(zip(configs, env_classes)),\\n workers_ignore_signals=workers_ignore_signals,\\n )\\n return envs\",\n \"def env(config, args):\\n print config.template(\\\"scripts/env.sh\\\", project=args.project)\",\n \"def make_env(rank, agents, seed=10000):\\n\\tdef _init():\\n\\t\\tenv = TradingGameEnv.TradingGameEnv(player_count = NUM_PLAYERS, other_agent_list = agents,\\n\\t\\t\\tseq_per_day = SEQ_PER_DAY, cards_per_suit = CARDS_PER_SUIT, player_hand_count = HAND_COUNT,\\n\\t\\t\\trandom_seq = True, self_play = SELF_PLAY, policy_type = POLICY_TYPE, self_copy_freq = SELF_COPY_FREQ,\\n\\t\\t\\tobs_transaction_history_size=TRANSACTION_HISTORY_SIZE)\\n\\t\\tenv.seed(seed + rank)\\n\\t\\treturn env\\n\\tset_global_seeds(seed)\\n\\treturn _init\",\n \"def wrapper_environment(args):\\n\\n return {\\n ENVIRONMENT_KEY: json.dumps({\\n 'verbose': args.verbose,\\n 'cc': shlex.split(args.cc),\\n 'cxx': shlex.split(args.cxx)\\n })\\n }\",\n \"def make_as_global(self):\\n return setup(env=self)\",\n \"def start_env(config: OmegaConf, debug_env=False) -> gym.Env:\\n if debug_env:\\n env = MineRLDebugEnv(config)\\n else:\\n environment = config.env.name\\n env = gym.make(environment)\\n env = ActionShaping(env, config)\\n env = ObservationWrapper(env, config)\\n return env\",\n \"def get_elinos_environment():\\n result = {}\\n for key in (\\\"project\\\", \\\"cdk\\\", \\\"target\\\"):\\n var = \\\"ELINOS_\\\" + key.upper()\\n if var in os.environ:\\n result[key] = os.environ[var]\\n else:\\n warn(\\\"%s not set\\\" % var)\\n result[key] = None\\n\\n if result[\\\"project\\\"] is not None:\\n result[\\\"xenomai\\\"] = glob.glob(result[\\\"project\\\"] + \\\"/xenomai-[0-9.]*\\\")\\n else:\\n result[\\\"xenomai\\\"] = []\\n\\n return result\",\n \"def setup(self):\\n\\n if os.name == 'nt':\\n windows_env_start.print_banner(bootstrap=True, no_shell_file=False)\\n else:\\n enable_colors()\\n\\n steps = [\\n ('CIPD package manager', self.cipd),\\n ('Python environment', self.virtualenv),\\n ('Host tools', self.host_tools),\\n ]\\n\\n if self._is_windows:\\n steps.append((\\\"Windows scripts\\\", self.win_scripts))\\n\\n self._log(\\n Color.bold('Downloading and installing packages into local '\\n 'source directory:\\\\n'))\\n\\n max_name_len = max(len(name) for name, _ in steps)\\n\\n self._env.comment('''\\nThis file is automatically generated. DO NOT EDIT!\\nFor details, see $PW_ROOT/pw_env_setup/py/pw_env_setup/env_setup.py and\\n$PW_ROOT/pw_env_setup/py/pw_env_setup/environment.py.\\n'''.strip())\\n\\n if not self._is_windows:\\n self._env.comment('''\\nFor help debugging errors in this script, uncomment the next line.\\nset -x\\nThen use `set +x` to go back to normal.\\n'''.strip())\\n\\n self._env.echo(\\n Color.bold(\\n 'Activating environment (setting environment variables):'))\\n self._env.echo('')\\n\\n for name, step in steps:\\n self._log(' Setting up {name:.<{width}}...'.format(\\n name=name, width=max_name_len),\\n end='',\\n flush=True)\\n self._env.echo(\\n ' Setting environment variables for {name:.<{width}}...'.\\n format(name=name, width=max_name_len),\\n newline=False,\\n )\\n\\n start = time.time()\\n spin = spinner.Spinner()\\n with spin():\\n result = step(spin)\\n stop = time.time()\\n\\n self._log(result.status_str(stop - start))\\n\\n self._env.echo(result.status_str())\\n for message in result.messages():\\n sys.stderr.write('{}\\\\n'.format(message))\\n self._env.echo(message)\\n\\n if not result.ok():\\n return -1\\n\\n self._log('')\\n self._env.echo('')\\n\\n self._env.finalize()\\n\\n self._env.echo(Color.bold('Checking the environment:'))\\n self._env.echo()\\n\\n self._env.doctor()\\n self._env.echo()\\n\\n self._env.echo(\\n Color.bold('Environment looks good, you are ready to go!'))\\n self._env.echo()\\n\\n with open(self._shell_file, 'w') as outs:\\n self._env.write(outs)\\n\\n deactivate = os.path.join(\\n self._install_dir,\\n 'deactivate{}'.format(os.path.splitext(self._shell_file)[1]))\\n with open(deactivate, 'w') as outs:\\n self._env.write_deactivate(outs)\\n\\n config = {\\n # Skipping sysname and nodename in os.uname(). nodename could change\\n # based on the current network. sysname won't change, but is\\n # redundant because it's contained in release or version, and\\n # skipping it here simplifies logic.\\n 'uname': ' '.join(getattr(os, 'uname', lambda: ())()[2:]),\\n 'os': os.name,\\n }\\n\\n with open(os.path.join(self._install_dir, 'config.json'), 'w') as outs:\\n outs.write(\\n json.dumps(config, indent=4, separators=(',', ': ')) + '\\\\n')\\n\\n if self._json_file is not None:\\n with open(self._json_file, 'w') as outs:\\n self._env.json(outs)\\n\\n return 0\",\n \"def base_env(*args, **kwargs):\\n try:\\n # regular gym\\n env = gym.make(*args, **kwargs)\\n except:\\n try:\\n # gym retro\\n env = retro.make(*args, **kwargs)\\n except:\\n # gym-super-mario-bros\\n env = gym_super_mario_bros.make(*args, **kwargs)\\n env.recognized = None\\n return env\",\n \"def setUpEnv(self):\\n \\n robot = Robot('atrv')\\n\\n pose = Sensor('pose')\\n robot.append(pose)\\n pose.configure_mw('yarp')\\n\\n motion = Actuator('v_omega')\\n robot.append(motion)\\n motion.configure_mw('yarp')\\n \\n env = Environment('indoors-1/indoor-1')\\n env.configure_service('socket')\",\n \"def _prep_subprocess_environment(self) -> None:\\n os.makedirs('dist/ba_root', exist_ok=True)\\n if os.path.exists('dist/ba_root/config.json'):\\n with open('dist/ba_root/config.json') as infile:\\n bincfg = json.loads(infile.read())\\n else:\\n bincfg = {}\\n\\n # Some of our config values translate directly into the\\n # ballisticacore config file; the rest we pass at runtime.\\n bincfg['Port'] = self._config.port\\n bincfg['Auto Balance Teams'] = self._config.auto_balance_teams\\n bincfg['Show Tutorial'] = False\\n bincfg['Idle Exit Minutes'] = self._config.idle_exit_minutes\\n with open('dist/ba_root/config.json', 'w') as outfile:\\n outfile.write(json.dumps(bincfg))\",\n \"def create_environment(loader):\\n loaders = (loader, PackageLoader('pynuts', 'templates'))\\n environment = Environment(\\n loader=ChoiceLoader(loaders), extensions=[ShowOnMatch])\\n environment.globals.update({'url_for': flask.url_for})\\n environment.filters['data'] = filters.data\\n environment.filters['base64'] = filters.base64\\n return environment\",\n \"def construct_parent_shell(eval_output_with, print_script_to):\\n if eval_output_with:\\n environment_ctor = {\\n \\\"bash\\\": BashParentEnvironment,\\n \\\"powershell\\\": PowershellParentEnvironment\\n }\\n\\n printers = {\\n \\\"bash\\\": escaped_printer_with_character(\\\"\\\\\\\\\\\", print_script_to),\\n \\\"powershell\\\": escaped_printer_with_character(\\\"`\\\",\\n print_script_to)\\n }\\n\\n printer = printers[eval_output_with]\\n return environment_ctor[eval_output_with](printer)\\n else:\\n return BashParentEnvironment(lambda _: None)\",\n \"def collect_env():\\n env_info = mmengine_collect_env()\\n\\n # MMEngine does not add the hipcc compiler information when collecting\\n # environment information, so it is added here. When MMEngine v0.3.0 is\\n # released, the code here can be removed.\\n cuda_available = torch.cuda.is_available()\\n if cuda_available and env_info.get('NVCC') == 'Not Available':\\n CUDA_HOME = env_info['CUDA_HOME']\\n if CUDA_HOME is not None and osp.isdir(CUDA_HOME):\\n if CUDA_HOME == '/opt/rocm':\\n try:\\n nvcc = osp.join(CUDA_HOME, 'hip/bin/hipcc')\\n nvcc = subprocess.check_output(\\n f'\\\"{nvcc}\\\" --version', shell=True)\\n nvcc = nvcc.decode('utf-8').strip()\\n release = nvcc.rfind('HIP version:')\\n build = nvcc.rfind('')\\n nvcc = nvcc[release:build].strip()\\n except subprocess.SubprocessError:\\n nvcc = 'Not Available'\\n else:\\n try:\\n nvcc = osp.join(CUDA_HOME, 'bin/nvcc')\\n nvcc = subprocess.check_output(f'\\\"{nvcc}\\\" -V', shell=True)\\n nvcc = nvcc.decode('utf-8').strip()\\n release = nvcc.rfind('Cuda compilation tools')\\n build = nvcc.rfind('Build ')\\n nvcc = nvcc[release:build].strip()\\n except subprocess.SubprocessError:\\n nvcc = 'Not Available'\\n env_info['NVCC'] = nvcc\\n\\n env_info['MMCV'] = mmcv.__version__\\n\\n try:\\n from mmcv.ops import get_compiler_version, get_compiling_cuda_version\\n except ModuleNotFoundError:\\n env_info['MMCV Compiler'] = 'n/a'\\n env_info['MMCV CUDA Compiler'] = 'n/a'\\n else:\\n env_info['MMCV Compiler'] = get_compiler_version()\\n env_info['MMCV CUDA Compiler'] = get_compiling_cuda_version()\\n\\n return env_info\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6866149","0.67887974","0.6682207","0.665503","0.6565945","0.6451024","0.64138275","0.6385889","0.62477255","0.6221224","0.62101907","0.62013006","0.6196314","0.6132033","0.610246","0.60736305","0.6043668","0.60429454","0.6000888","0.5997547","0.5982757","0.5969678","0.5963436","0.59562224","0.59562224","0.59562224","0.59562224","0.59562224","0.59562224","0.59357154","0.59280086","0.5920841","0.5905678","0.5903768","0.58922315","0.58640903","0.5862575","0.585113","0.5836444","0.58210135","0.5804941","0.5800124","0.57911205","0.5789892","0.57858706","0.57801247","0.57698345","0.5760529","0.57549024","0.5732965","0.5715627","0.5708546","0.5705368","0.57047457","0.5687339","0.5685152","0.56639147","0.5663153","0.5658861","0.5648923","0.5641926","0.5639063","0.5625225","0.5621499","0.5614997","0.5609131","0.56031996","0.5602163","0.5602163","0.55759025","0.55737007","0.5569247","0.5568979","0.5562748","0.55580574","0.55440986","0.5542676","0.5539606","0.5536458","0.5535629","0.55237913","0.5520327","0.55185544","0.5513079","0.5510408","0.5508138","0.5502825","0.5495197","0.549116","0.5487497","0.54844415","0.5470194","0.5470095","0.5470046","0.5469581","0.546894","0.54594976","0.5458945","0.54580724","0.5457068"],"string":"[\n \"0.6866149\",\n \"0.67887974\",\n \"0.6682207\",\n \"0.665503\",\n \"0.6565945\",\n \"0.6451024\",\n \"0.64138275\",\n \"0.6385889\",\n \"0.62477255\",\n \"0.6221224\",\n \"0.62101907\",\n \"0.62013006\",\n \"0.6196314\",\n \"0.6132033\",\n \"0.610246\",\n \"0.60736305\",\n \"0.6043668\",\n \"0.60429454\",\n \"0.6000888\",\n \"0.5997547\",\n \"0.5982757\",\n \"0.5969678\",\n \"0.5963436\",\n \"0.59562224\",\n \"0.59562224\",\n \"0.59562224\",\n \"0.59562224\",\n \"0.59562224\",\n \"0.59562224\",\n \"0.59357154\",\n \"0.59280086\",\n \"0.5920841\",\n \"0.5905678\",\n \"0.5903768\",\n \"0.58922315\",\n \"0.58640903\",\n \"0.5862575\",\n \"0.585113\",\n \"0.5836444\",\n \"0.58210135\",\n \"0.5804941\",\n \"0.5800124\",\n \"0.57911205\",\n \"0.5789892\",\n \"0.57858706\",\n \"0.57801247\",\n \"0.57698345\",\n \"0.5760529\",\n \"0.57549024\",\n \"0.5732965\",\n \"0.5715627\",\n \"0.5708546\",\n \"0.5705368\",\n \"0.57047457\",\n \"0.5687339\",\n \"0.5685152\",\n \"0.56639147\",\n \"0.5663153\",\n \"0.5658861\",\n \"0.5648923\",\n \"0.5641926\",\n \"0.5639063\",\n \"0.5625225\",\n \"0.5621499\",\n \"0.5614997\",\n \"0.5609131\",\n \"0.56031996\",\n \"0.5602163\",\n \"0.5602163\",\n \"0.55759025\",\n \"0.55737007\",\n \"0.5569247\",\n \"0.5568979\",\n \"0.5562748\",\n \"0.55580574\",\n \"0.55440986\",\n \"0.5542676\",\n \"0.5539606\",\n \"0.5536458\",\n \"0.5535629\",\n \"0.55237913\",\n \"0.5520327\",\n \"0.55185544\",\n \"0.5513079\",\n \"0.5510408\",\n \"0.5508138\",\n \"0.5502825\",\n \"0.5495197\",\n \"0.549116\",\n \"0.5487497\",\n \"0.54844415\",\n \"0.5470194\",\n \"0.5470095\",\n \"0.5470046\",\n \"0.5469581\",\n \"0.546894\",\n \"0.54594976\",\n \"0.5458945\",\n \"0.54580724\",\n \"0.5457068\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.60146767"},"document_rank":{"kind":"string","value":"18"}}},{"rowIdx":26089,"cells":{"query":{"kind":"string","value":"Run the 'program'. 'program' The path to the program to run. 'arguments' A list of the arguments to the program. This list must contain a first argument corresponding to 'argv[0]'. 'context' A 'Context' giving runtime parameters to the test. 'result' A 'Result' object. The outcome will be 'Result.PASS' when this method is called. The 'result' may be modified by this method to indicate outcomes other than 'Result.PASS' or to add annotations."},"document":{"kind":"string","value":"def RunProgram(self, program, arguments, context, result):\n\n # Construct the environment.\n environment = self.MakeEnvironment(context)\n e_stdin = self.stdin\n c = {}\n for pair in context.items():\n c[pair[0]] = pair[1]\n for substitution in c.keys():\n pattern = \"$(\"+substitution.upper()+\")\"\n replacement = context[substitution]\n e_stdin = e_stdin.replace(pattern, replacement)\n\n basename = os.path.split(arguments[0])[-1]\n qm_exec = qm.executable.Filter(e_stdin, -2)\n\n try:\n exit_status= qm_exec.Run(arguments, environment)\n stdout = qm_exec.stdout\n stderr = qm_exec.stderr\n causes = []\n\n if sys.platform != \"win32\":\n if os.WIFEXITED(exit_status):\n if exit_status != self.exit_code:\n causes.append(\"exit_code\")\n result[\"RunProgram.exit_code\"] = str(exit_status)\n elif os.WIFSIGNALED(exit_status):\n self.__cause= \"Process %s terminated by signal %d.\" % (basename, os.WTERMSIG(exit_status))\n\n elif os.WIFSTOPPED(exit_status):\n self.__cause= \"Process %s stopped by signal %d.\" % (basename, os.WSTOPSIG(exit_status))\n\n else:\n self.__cause= \"Process %s terminated abnormally.\" % basename\n\n # Check to see if the standard output matches.\n # First strip out ISQL junk\n stdout_stripped = re.sub(\"Database:.*\\n\",\"\",stdout)\n stdout_stripped = re.sub(\"SQL>\\s*\",\"\",stdout_stripped)\n stdout_stripped = re.sub(\"CON>\\s*\",\"\",stdout_stripped)\n stdout_stripped = re.sub(\"-->\\s*\",\"\",stdout_stripped)\n stdout_stripped = self.__PerformSubstitutions(stdout_stripped)\n stdout_stripped = re.compile(\"^\\s+\",re.I+re.M).sub(\"\",stdout_stripped)\n stdout_stripped = re.compile(\"\\s+$\",re.I+re.M).sub(\"\",stdout_stripped)\n\n self.stdout_stripped = re.sub(\"Database:.*\\n\",\"\",self.stdout)\n self.stdout_stripped = re.sub(\"SQL>\\s*\",\"\",self.stdout_stripped)\n self.stdout_stripped = re.sub(\"CON>\\s*\",\"\",self.stdout_stripped)\n self.stdout_stripped = re.sub(\"-->\\s*\",\"\",self.stdout_stripped)\n self.stdout_stripped = self.__PerformSubstitutions(self.stdout_stripped)\n self.stdout_stripped = re.compile(\"^\\s+\",re.I+re.M).sub(\"\",self.stdout_stripped)\n self.stdout_stripped = re.compile(\"\\s+$\",re.I+re.M).sub(\"\",self.stdout_stripped)\n\n if stdout_stripped != self.stdout_stripped:\n causes.append(\"standard output\")\n result[\"ExecTest.stdin\"] = \"<pre>\" + e_stdin + \"</pre>\"\n result[\"ExecTest.stdout_expected\"] = \"<pre>\" + self.stdout + \"</pre>\"\n result[\"ExecTest.stdout\"] = \"<pre>\" + stdout + \"</pre>\"\n result[\"ExecTest.stdout_stripped\"] = \"<pre>\" + stdout_stripped + \"</pre>\"\n result[\"ExecTest.stdout_stripped_expected\"] = \"<pre>\" + self.stdout_stripped + \"</pre>\"\n result[\"ExecTest.stripped_diff\"] = \"<pre>\"+'\\n'.join(difflib.ndiff(stdout_stripped.splitlines(0),self.stdout_stripped.splitlines(0)))+\"</pre>\"\n # Check to see that the standard error matches.\n stderr_stripped = re.sub(\"Use CONNECT or CREATE DATABASE to specify a database.*\\n\",\"\",stderr)\n if stderr_stripped != self.stderr:\n causes.append(\"standard error\")\n result[\"ExecTest.stdin\"] = \"<pre>\" + e_stdin + \"</pre>\"\n result[\"ExecTest.stderr\"] = \"<pre>\" + stderr + \"</pre>\"\n result[\"ExecTest.expected_stderr\"] = \"<pre>\" + self.stderr + \"</pre>\"\n # If anything went wrong, the test failed.\n if causes:\n result.Fail(\"Unexpected %s.\" % string.join(causes, \", \"))\n except:\n result.NoteException()"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def RunProgram(self, program, arguments, stdin, context, result):\n\n # Construct the environment.\n environment = self.MakeEnvironment(context)\n e_stdin = stdin\n c = {}\n for pair in context.items():\n c[pair[0]] = pair[1]\n for substitution in c.keys():\n pattern = \"$(\"+substitution.upper()+\")\"\n replacement = context[substitution]\n e_stdin = e_stdin.replace(pattern, replacement)\n basename = os.path.split(arguments[0])[-1]\n qm_exec = qm.executable.Filter(e_stdin, -2)\n\n try:\n exit_status= qm_exec.Run(arguments, environment)\n stdout = qm_exec.stdout\n stderr = qm_exec.stderr\n causes = []\n\n if sys.platform != \"win32\":\n if os.WIFEXITED(exit_status):\n if exit_status != self.exit_code:\n causes.append(\"exit_code\")\n result[\"RunProgram.exit_code\"] = str(exit_status)\n elif os.WIFSIGNALED(exit_status):\n self.__cause= \"Process %s terminated by signal %d.\" % (basename, os.WTERMSIG(exit_status))\n\n elif os.WIFSTOPPED(exit_status):\n self.__cause= \"Process %s stopped by signal %d.\" % (basename, os.WSTOPSIG(exit_status))\n\n else:\n self.__cause= \"Process %s terminated abnormally.\" % basename\n\n # Check to see that the standard error matches.\n if stderr:\n causes.append(\"standard error\")\n result[\"RunProgram.stderr\"] = \"'''\" + stderr + \"'''\"\n # If anything went wrong, the test failed.\n if causes:\n result.Fail(\"Unexpected %s.\" % string.join(causes, \", \"))\n except:\n result.NoteException()","def execute (self, program, cache=False):\n ast = None\n if cache:\n requests_cache.install_cache('demo_cache',\n allowable_methods=('GET', 'POST', ))\n else:\n requests_cache.disabled()\n\n if isinstance(program, str):\n ast = self.parse (program)\n if not ast:\n raise ValueError (f\"Unhandled type: {type(program)}\")\n for statement in ast.statements:\n logger.debug (f\"execute: {statement} type={type(statement).__name__}\")\n statement.execute (interpreter=self)\n return self.context","def run(name, program, arguments, cache_enabled, filename):\n output = None\n if cache_enabled:\n output = get_output_from_cache(name, filename)\n\n if output is None:\n call_arguments = [program] + arguments + [filename]\n try:\n output = subprocess.check_output(\n call_arguments, stderr=subprocess.STDOUT)\n except subprocess.CalledProcessError as error:\n output = error.output\n except OSError:\n return {\n filename: {\n 'error': [('Could not execute \"%s\".%sMake sure all ' +\n 'required programs are installed') %\n (' '.join(call_arguments), os.linesep)]\n }\n }\n output = output.decode('utf-8')\n if cache_enabled:\n save_output_in_cache(name, filename, output)\n return output","def run_program_from_python_test(program, args, stdin=''):\n #environ = self.__MakeEnvironment()\n # PC: fix values so they are strings. Needed for Windows.\n #for key in environ.iterkeys():\n #environ[key] = str(environ[key])\n # provide full path for standard tools\n program = context.environment.get('%s_path' % program, program)\n basename = os.path.split(program)[-1]\n args.insert(0,program)\n\n if self.connection_character_set:\n args.extend(['-ch',self.connection_character_set])\n script = stdin.encode(DB_CHAR_SET_NAME_TO_PYTHON_ENCODING_MAP[self.connection_character_set])\n else:\n script = stdin.encode('ascii')\n script = substitute_macros(script)\n try:\n return_code, stdout, stderr = runProgram(args,[],stdin=script)\n sys.stdout.writelines(stdout)\n sys.stderr.writelines(stderr)\n except:\n result.note_exception(cause=\"Python test: Exception raised while running external program from Python test.\")\n result[\"failing_program\"] = program\n #cleanup()","def execute_program(args: List[str], mode: TestMode) -> None:\n args_str = ' '.join(args)\n\n def check_output(result: CompletedProcess):\n stdout = strip_ascii_codes(result.stdout.decode())\n error_string = f'Failure:\\nMode: {mode.value}\\nCommand: {args_str}\\nExit code: {result.returncode:X}\\nOutput:\\n{stdout}\\n'\n if mode == TestMode.STUB:\n if result.returncode not in (1, 2) or 'Test FAILED' not in stdout:\n raise RuntimeError(error_string)\n elif mode == TestMode.SOLUTION:\n if (result.returncode != 0 or\n \"*** You've passed ALL tests. Congratulations! ***\" not in stdout):\n raise RuntimeError(error_string)\n else:\n print('Invalid mode: {}'.format(mode))\n sys.exit(1)\n\n try:\n result = subprocess.run(args, stdout=subprocess.PIPE, timeout=300)\n check_output(result)\n except RuntimeError as e:\n print(e)\n sys.exit(1)\n except TimeoutExpired:\n print(f\"{mode} > {args_str}: TIMEOUT\")\n sys.exit(1)","def run_program(\n environment='emulator',\n block_device=None,\n dump_file=None,\n dump_base=None,\n dump_length=None,\n timeout=60,\n flush_l2=False,\n trace=False,\n executable=None):\n if not executable:\n executable = HEX_FILE\n\n if environment == 'emulator':\n args = [BIN_DIR + 'emulator']\n args += [ '-a' ] # Enable thread scheduling randomization by default\n if block_device:\n args += ['-b', block_device]\n\n if dump_file:\n args += ['-d', dump_file + ',' +\n hex(dump_base) + ',' + hex(dump_length)]\n\n args += [executable]\n return _run_test_with_timeout(args, timeout)\n elif environment == 'verilator':\n args = [BIN_DIR + 'verilator_model']\n if block_device:\n args += ['+block=' + block_device]\n\n if dump_file:\n args += ['+memdumpfile=' + dump_file,\n '+memdumpbase=' + hex(dump_base)[2:],\n '+memdumplen=' + hex(dump_length)[2:]]\n\n if flush_l2:\n args += ['+autoflushl2=1']\n\n if trace:\n args += ['+trace']\n\n args += ['+bin=' + executable]\n output = _run_test_with_timeout(args, timeout)\n if '***HALTED***' not in output:\n raise TestException(output + '\\nProgram did not halt normally')\n\n return output\n else:\n raise TestException('Unknown execution environment')","def main(*arguments):\n\n args = parse_args(arguments)\n\n if args.test_suite is not None:\n test_suite = report_manager.load_test_suite_conf(args.test_suite)\n for i, test in enumerate(test_suite):\n args = parse_args(test)\n process_args_and_run(args, test_suite_iter=i)\n else:\n process_args_and_run(args)","def execute_file (self, program):\n with open (program, \"r\") as stream:\n self.execute (stream.read ())\n return self.context","def exec(self, program, *args, cwd=os.getcwd(), **kwargs):\n if len(args) > 0:\n raise RuntimeError(\"Program arguments are not supported for real hardware devices\")\n\n assert self.platform is not None, \"TVM targets need a platform to execute programs\"\n\n if self.timeout_sec > 0:\n raise NotImplementedError\n\n ret = self.platform.run(program, self)\n return ret","def Run(self, cli, args):\n metrics.Loaded()\n\n tool_context = {}\n if self._parent_group:\n self._parent_group.RunGroupFilter(tool_context, args)\n\n command_instance = self._common_type(cli=cli, context=tool_context)\n\n log.debug('Running %s with %s.', self.dotted_name, args)\n resources = command_instance.Run(args)\n resources = display.Displayer(command_instance, args, resources,\n display_info=self.ai.display_info).Display()\n metrics.Ran()\n\n if command_instance.exit_code != 0:\n raise exceptions.ExitCodeNoError(exit_code=command_instance.exit_code)\n\n return resources","def test_with_command_line_arguments(self, arguments):\n fixed_arguments = self.get_argument_string(arguments)\n result = self.run(\n arguments=fixed_arguments,\n timeout=self.full_timeout,\n use_fresh_profile=True)\n return self._handle_test_result(result)","def Run(self, context, result):\n\n # Was the program not specified?\n\n self.program = context[\"isql_path\"]\n\n if context.has_key(\"database_path\"):\n database = context[\"database_path\"]\n else:\n database = \"\"\n self.RunProgram(self.program,\n\t\t\t[ self.program , database ,\n \"-user\", context[\"user_name\"], \"-password\", context[\"user_password\"] ],\n context, result)","def runTool(self, filename, expected_out, args):\n\n input_path = os.path.join(self.inputs_dir, filename)\n return_value, actual_output = create_subprocess(self.executable_binary, args + [input_path] + ['--'])\n actual_output = actual_output.decode('utf-8')\n\n self.assertEqual(return_value, 0)\n self.evaluate(expected_out, actual_output, command=f'{[self.executable_binary] + args} {filename}')","def main(self, argv=None):\n\n p = self.build_parser()\n\n args = p.parse_args(argv)\n\n try:\n return self.run(args) or 0\n except CommandFailed as e:\n print(str(e))\n return 1","def main(args: List[Union[str, bytes]] = sys.argv,):\n\tprogram_name, *args = args\n\targs = decode_raw_args(args, str)\n\n\tgen = Generator(*args)\n\tgen.generate_data()\n\tgen.print_return_list()","def run_program(program, args=None, **subprocess_kwargs):\n if 'shell' in subprocess_kwargs and subprocess_kwargs['shell']:\n raise ProgramError(\n \"This function is only for non-shell programs, \"\n \"use run_shell_command() instead.\")\n fullcmd = find_program(program)\n if not fullcmd:\n raise ProgramError(\"Program %s was not found\" % program)\n # As per subprocess, we make a complete list of prog+args\n fullcmd = [fullcmd] + (args or [])\n for stream in ['stdin', 'stdout', 'stderr']:\n subprocess_kwargs.setdefault(stream, subprocess.PIPE)\n subprocess_kwargs = alter_subprocess_kwargs_by_platform(\n **subprocess_kwargs)\n return subprocess.Popen(fullcmd, **subprocess_kwargs)","def _test_program(self, result_success=True, verbose=False):\n\n # set testing parameters\n if self.sim_type is InstructionListJointsFlags.Position:\n self.program.simulation_type = self.sim_type\n self.program.max_mm_step = self.sim_step_mm\n self.program.max_deg_step = self.sim_step_deg\n elif self.sim_type is InstructionListJointsFlags.TimeBased:\n self.program.simulation_type = self.sim_type\n self.program.max_time_step = self.sim_step_time\n elif self.sim_type is None:\n raise ValueError(\"No 'sim_type' provided\")\n\n self.program.load_to_robodk()\n self.program.simulate()\n\n if verbose:\n self.program.print()\n self.program.simulation_result.add_to_robodk()\n\n if not result_success:\n # other checks don't make sense\n return\n\n # perform checks on simulation result\n self._test_if_result_message_is_success()\n self._test_for_playback_frame_errors()\n self._test_for_missing_move_ids()\n self._test_for_valid_move_ids()\n self._test_max_simulation_step()\n self._test_if_stop_points_reached()","def run(self, argv):\n global console\n\n console = init_console()\n\n command, options = self.parse_options(argv)\n\n # We call out to things like the test runner, which expect to operate\n # off of sys.argv. We want to simulate that now that we've parsed\n # options. We'll restore sys.argv after the command finishes.\n old_argv = sys.argv\n sys.argv = argv\n\n try:\n return command.run(options)\n except Exception as e:\n logger.exception('Unexpected exception when running command '\n '\"%s\": %s',\n command.name, e)\n return 1\n finally:\n sys.argv = old_argv","def main():\n args = get_args()\n prg = args.program\n\n if not os.path.isfile(prg):\n die('Missing expected program \"{}\"'.format(prg))\n\n for name in args.name:\n cmd = '{} \"{}\"'.format(prg, name)\n rv, out = getstatusoutput(cmd)\n if rv != 0:\n warn('Failed to run: {}\\nError: {}'.format(cmd, out))\n else:\n print('Success: \"{}\"'.format(out))\n\n print('Done.')","def run_program(program):\n halt = False\n instruction_pointer = 0\n\n while not halt:\n halt = process_instruction(instruction_pointer, program)\n instruction_pointer += STEP_SIZE\n\n return program","def test_run(prog, correct, inp=None):\n print(f\"Testing [{prog2str(prog)}] (inp={inp})...\", end=\"\")\n out = list()\n run_prog(prog, inp, out)\n if (out and out == correct) or (not out and prog == correct):\n print(\" OK!\")\n else:\n fail_str = prog2str(out if out else prog)\n print(f\" Failed: [{fail_str}] != [{prog2str(correct)}]!\")","def testCreateProgram(self):\n try:\n contextID, retErr = PyOpenCLInterface.CreateContext(self.testResources.listDevicesIDs, self.testResources.dictProperties)\n self.assertEqual(retErr, 0)\n # create mem program\n programID, retErr = PyOpenCLInterface.CreateProgram(contextID, self.testResources.programCodeStrings)\n self.assertEqual(retErr, 0)\n listPrograms = PyOpenCLInterface.ListPrograms()\n self.assertEqual(listPrograms, [programID])\n programProperty, retErr = PyOpenCLInterface.GetProgramProperties(programID)\n self.assertEqual(programProperty['id'], programID)\n self.assertEqual(programProperty['Devices'], self.testResources.listDevicesIDs)\n self.assertEqual(programProperty['Context'], contextID)\n retErr = PyOpenCLInterface.ReleaseProgram(programID)\n self.assertEqual(retErr, 0)\n listPrograms = PyOpenCLInterface.ListPrograms()\n self.assertEqual(listPrograms, [])\n retErr = PyOpenCLInterface.ReleaseContext(contextID)\n self.assertEqual(retErr, 0)\n except:\n print \"Exception caught:\", sys.exc_info()[0]","def run(context, path=\"\"):\n common.success(f\"Tests {path} running \")\n return start.run_python(\n context,\n f\"-m pytest {path}\"\n )","def main(cls, *args, **kwargs):\n assert not (bool(args) and bool(kwargs))\n if args:\n return cls._run_args(args)\n elif kwargs:\n return cls._run_kwargs(kwargs)\n else:\n return cls._run_args(None)","def main():\n\n parser = argparse.ArgumentParser(prog=\"run_test.py\",\n formatter_class=argparse.RawTextHelpFormatter)\n parser.add_argument('id', help=\"Id of a test\")\n args = parser.parse_args()\n\n configure_logger()\n\n test_info = TESTS.get(args.id, None)\n if not test_info:\n test_info.log.error(f'{args.id} does not exist')\n exit(ErrorCode.CRITICAL)\n os.environ['DISPLAY'] = \":0.0\"\n\n test = Test(args.id, test_info)\n result = test.run()\n\n test.log.info('#' * 80)\n if not result:\n test.log.error('TEST FAILED')\n else:\n test.log.info('TEST PASSED')\n test.log.info('#' * 80)\n exit(not result)","def main(argv=sys.argv):\n log = _setup_logging()\n log.info(\"Starting {f} version {v} dataset manipulator\".format(\n f=__file__, v=__VERSION__))\n parser = get_parser()\n args = parser.parse_args()\n if args.debug:\n log.setLevel(logging.DEBUG)\n return args.func(args)\n #return main_runner_default(argv[1:], get_parser(), log)","def main(self):\n try:\n self.parse_args()\n self.run()\n return 0\n except AnalysisBackendError as e:\n L.error(e)\n return 1","def execute_tool(description, *args):\n command_line = list(args) + files_and_directories\n click.echo(f\"{description}: {' '.join(command_line)}\")\n rv = call(command_line)\n if rv != 0:\n exit(rv)","def main():\n args, subparser = parse_args()\n if not args:\n sys.exit(1)\n\n # configure logging\n Log.config(args[0].log_level, args[0].log_file)\n\n # display GeneFlow version\n Log.some().info('GeneFlow %s', __version__)\n\n # call the appropriate command\n if not args[0].func(\n args=args[0],\n other_args=args[1],\n subparser=subparser\n ):\n sys.exit(1)\n\n sys.exit(0)","def main(cli_args=None):\n # build an arg parser\n parser = get_arg_parser()\n\n # run the parser on cli args\n args = parser.parse_args(cli_args)\n\n print(f\"Running script with arguments: {args}\")\n test_input(args.raw_training_data)\n test_input(args.raw_testing_data)\n test_output(args.train_output)\n test_output(args.test_output)","def testBuildProgramSuccess(self):\n try:\n contextID, retErr = PyOpenCLInterface.CreateContext(self.testResources.listDevicesIDs, self.testResources.dictProperties)\n self.assertEqual(retErr, 0)\n # create mem program\n programID, retErr = PyOpenCLInterface.CreateProgram(contextID, self.testResources.programCodeStrings)\n self.assertEqual(retErr, 0)\n buildOptions = \"\"\n retErr = PyOpenCLInterface.BuildProgram(programID, self.testResources.listDevicesIDs, buildOptions)\n self.assertEqual(retErr, 0)\n buildInfo = \"CL_PROGRAM_BUILD_STATUS\"\n dictResp, retErr = PyOpenCLInterface.GetProgramBuildInfo(programID, self.testResources.listDevicesIDs[0], buildInfo);\n self.assertEqual(retErr, 0)\n self.assertEqual(dictResp['CL_PROGRAM_BUILD_STATUS'], 'CL_BUILD_SUCCESS')\n retErr = PyOpenCLInterface.ReleaseProgram(programID)\n self.assertEqual(retErr, 0)\n listPrograms = PyOpenCLInterface.ListPrograms()\n self.assertEqual(listPrograms, [])\n retErr = PyOpenCLInterface.ReleaseContext(contextID)\n self.assertEqual(retErr, 0)\n except:\n print \"Exception caught:\", sys.exc_info()[0]","def main(argv):\n global g_test_root_dir\n global g_temp_filename\n\n if len(argv) < 2:\n print(\"invoke this script as python collectUnitTestRunTime.py 10 'python run.py_path/run.py --wipe \"\n \"--test dir_to_test/test1,python run.py_path/run.py --wipe --test dir_to_test2/test2,...' True\\n\")\n sys.exit(1)\n else: # we may be in business\n repeat_number = int(argv[1]) # number of times to run a unit test\n command_lists = argv[2] # list of unit tests to run\n\n for command in command_lists.split(','): # for each command in the list\n # run command repeat_number of times and collect results into result_dict\n run_commands(command, repeat_number, g_temp_filename)","def main(argv=None):\n # __package__ should be `development.main`\n run_example_local('examples.run_rl', argv)","def run_test_case(project, test_case_name, environment):\n timestamp = utils.get_timestamp()\n param_list = ['golem','run', project, test_case_name,\n '--timestamp', timestamp]\n if environment:\n param_list.append('--environments')\n param_list.append(environment)\n subprocess.Popen(param_list)\n return timestamp","def run(self, inp=\"\"):\n result = self.run_process(inp)\n if result.returncode == 0:\n outcome = self.PASS\n elif result.returncode < 0:\n outcome = self.FAIL\n else:\n outcome = self.UNRESOLVED\n return (result, outcome)","def main():\n run_program()","def main():\n program_name = os.path.basename(sys.argv[0])\n\n _initialize_debugging(program_name)\n _handle_signals()\n _process_environment_variables()\n arguments = _process_command_line()\n\n exit_status = 1 # no match\n if arguments:\n for filename in arguments:\n if os.path.isfile(filename):\n if not parameters[\"No formatting\"]:\n print(filename + \":\")\n for _, printable_string in strings.strings(filename):\n if what_in_string(printable_string):\n exit_status = 0 # match found\n if parameters[\"First match only\"]:\n break\n else:\n logging.error('\"%s\": No such file or directory', filename)\n elif parameters[\"Stdin unused\"]:\n logging.critical(\"At least one filename expected\")\n else:\n for _, printable_string in strings.strings():\n if what_in_string(printable_string):\n exit_status = 0 # match found\n if parameters[\"First match only\"]:\n break\n\n sys.exit(exit_status)","def runScript(self, script):\n data = FilePath(__file__).parent().child('data')\n sample_file = data.child('1.input.ofx')\n\n args = (script, [sample_file.path])\n log.msg('executing %r' % (args,))\n out, err, rc = yield utils.getProcessOutputAndValue(*args, env=None)\n log.msg('rc: %r' % (rc,))\n log.msg('out: %r' % (out,))\n log.msg('err: %r' % (err,))\n if rc != 0:\n self.fail(\"Failed: %s\\n\\n%s\" % (out, err))","def main():\n\n parser = get_populated_argparser()\n cli_dict = generate_cli_dictionary()\n\n # Parse arguments.\n if len(sys.argv) > 1:\n args = parser.parse_args(sys.argv[1:])\n\n # Validate arguments.\n args = cli_dict[args.tool].validate_args(args)\n\n # Run the tool.\n cli_dict[args.tool].run(args)\n\n else:\n\n parser.print_help()","def run(ctx, problem,\n hparam_set: str = None,\n progress: bool = False,\n cuda: bool = False,\n device: str = None,\n log_dir: str = None,\n checkpoint_prefix: str = 'checkpoint',\n **kwargs):\n\n # Read custom transformation arbitrary CLI key value pairs.\n extra_hparams = ctx.obj.get('extra_hparams')\n kwargs.pop('extra_hparams')\n\n do_run(problem,\n hparam_set=hparam_set,\n extra_hparams=extra_hparams,\n progress=progress,\n cuda=cuda,\n device=device,\n log_dir=log_dir,\n checkpoint_prefix=checkpoint_prefix,\n **kwargs)","def ExecuteFile(python_program, main_filename, args, env, module_space,\n coverage_entrypoint, workspace):\n # type: (str, str, list[str], dict[str, str], str, str|None, str|None) -> ...\n # We want to use os.execv instead of subprocess.call, which causes\n # problems with signal passing (making it difficult to kill\n # Bazel). However, these conditions force us to run via\n # subprocess.call instead:\n #\n # - On Windows, os.execv doesn't handle arguments with spaces\n # correctly, and it actually starts a subprocess just like\n # subprocess.call.\n # - When running in a workspace (i.e., if we're running from a zip),\n # we need to clean up the workspace after the process finishes so\n # control must return here.\n # - If we may need to emit a host config warning after execution, we\n # can't execv because we need control to return here. This only\n # happens for targets built in the host config.\n # - For coverage targets, at least coveragepy requires running in\n # two invocations, which also requires control to return here.\n #\n if not (IsWindows() or workspace or coverage_entrypoint):\n _RunExecv(python_program, main_filename, args, env)\n\n if coverage_entrypoint is not None:\n ret_code = _RunForCoverage(python_program, main_filename, args, env,\n coverage_entrypoint, workspace)\n else:\n ret_code = subprocess.call(\n [python_program, main_filename] + args,\n env=env,\n cwd=workspace\n )\n\n if workspace:\n shutil.rmtree(os.path.dirname(module_space), True)\n sys.exit(ret_code)","def run(argv=None):\n program_conf = get_program_conf()\n\n # Test usage of utils... do something with ddl\n print(get_ddl_list('dev')[2])\n\n # Parse args\n program_args = program_conf[PROGRAM_ARGS_CONF]\n pipeline_args = program_conf[PIPELINE_ARGS_CONF]\n pipeline_options = PipelineOptions(pipeline_args)\n pipeline_options.view_as(SetupOptions).save_main_session = True\n\n # Get schema path key for validation\n gcs_client = storage.Client()\n etl_config_dict = get_gcs_json_as_dict(program_args[ETL_CONFIG_PATH_CONF], gcs_client)\n schema_dict = get_gcs_json_as_dict(program_args[SCHEMA_PATH_KEY], gcs_client)\n input_file_path = program_args[INPUT_CONF]\n\n with beam.Pipeline(options=pipeline_options) as pipeline:\n validated_records = (\n pipeline \n | 'read' >> ReadFromText(input_file_path)\n | 'validate' >> beam.ParDo(ValidateRecord(schema_dict, input_file_path, etl_config_dict))\n | 'filter_data' >> beam.Filter(lambda x: x[1] and x[2] and x[3])\n | 'recover_data' >> beam.Map(lambda x: x[0])\n # | beam.Map(print)\n )\n if IS_VALID_FILE:\n validated_records | 'write_success' >> WriteToText(program_args[OUTPUT_CONF]) \n else:\n validated_records | 'write_reject' >> WriteToText(program_args[REJECT_CONF])","def run_program(self, sAppName, hArgsList, hEnvsList, nFlags = consts.PRPM_RUN_PROGRAM_AND_RETURN_IMMEDIATELY, nStdin = consts.PRL_INVALID_FILE_DESCRIPTOR, nStdout = consts.PRL_INVALID_FILE_DESCRIPTOR, nStderr = consts.PRL_INVALID_FILE_DESCRIPTOR):\n\t\treturn Job(SDK.PrlVmGuest_RunProgram(self.handle, sAppName, conv_handle_arg(hArgsList), conv_handle_arg(hEnvsList), nFlags, nStdin, nStdout, nStderr)[0])","def Run(self, context, result):\n\n # check if the test can be run on the current platform\n if not self.SupportedPlatform(context, result):\n return\n\n # If the context specifies a shell, use it.\n if context.has_key(\"ShellCommandTest.command_shell\"):\n # Split the context value to build the argument list.\n shell = qm.common.split_argument_list(\n context[\"ShellCommandTest.command_shell\"])\n else:\n # Otherwise, use a platform-specific default.\n shell = qm.platform.get_shell_for_command()\n\n # Environment for the nightlies (user lcgnight)\n if sys.platform.startswith(\"win\") \\\n and \"USERNAME\" in os.environ \\\n and os.environ[\"USERNAME\"] == \"lcgnight\" :\n # Take TNS_ADMIN from DFS on Win (bug #86450)\n os.environ[\"TNS_ADMIN\"]=r\"\\\\cern.ch\\dfs\\Applications\\Oracle\\ADMIN\"\n # Use credentials from %USERPROFILE%\\private on Win (bug #86463)\n private = os.path.join(os.environ[\"USERPROFILE\"],\"private\")\n os.environ[\"CORAL_AUTH_PATH\"] = private\n os.environ[\"CORAL_DBLOOKUP_PATH\"] = os.environ[\"CORAL_AUTH_PATH\"]\n elif sys.platform.startswith(\"darwin\") \\\n and \"USER\" in os.environ \\\n and os.environ[\"USER\"] == \"lcgnight\" :\n # Use credentials from $HOME/private on Mac (bug #86463)\n private = os.path.join(os.environ[\"HOME\"],\"private\")\n os.environ[\"CORAL_AUTH_PATH\"] = private\n os.environ[\"CORAL_DBLOOKUP_PATH\"] = os.environ[\"CORAL_AUTH_PATH\"]\n elif sys.platform.startswith(\"linux\") \\\n and \"USER\" in os.environ \\\n and os.environ[\"USER\"] == \"lcgnight\" :\n # Use credentials from POOL AFS area on Linux (bug #86463)\n os.environ[\"CORAL_AUTH_PATH\"] = \"/afs/cern.ch/sw/lcg/app/pool/db\"\n os.environ[\"CORAL_DBLOOKUP_PATH\"] = os.environ[\"CORAL_AUTH_PATH\"]\n\n # Special handling if this is a python script (from COOLTests.py)\n if sys.platform.startswith(\"win\") and \\\n self.command.split()[0].endswith(\".py\"):\n # We need to run a python test and not a standard executable\n [cmd, arg] = self.command.split(' ',1)\n for d in os.environ[\"PATH\"].split(os.path.pathsep):\n if os.path.isdir(d) and cmd in os.listdir(d):\n # we found the script in the path\n self.command = 'python \"%s\" %s'%(os.path.join(d,cmd),arg)\n break\n\n # Append the command at the end of the argument list.\n arguments = shell + [ self.command ]\n self.RunProgram(arguments[0], arguments, context, result)","def execute_program(prgm, existing_env=None):\n line_mgr, env = convert_program_to_lines(prgm, existing_env)\n execution.execute_lines(line_mgr, env)","def Run(main):\n # Parse command-line arguments:\n program_name = GetProgramName()\n if not FLAGS.Parse(sys.argv[1:]):\n FLAGS.PrintUsage()\n return os.EX_USAGE\n\n # Initialize the logging system:\n try:\n log_root_level = ParseLogLevelFlag(FLAGS.log_level)\n log_console_level = ParseLogLevelFlag(FLAGS.log_console_level)\n log_file_level = ParseLogLevelFlag(FLAGS.log_file_level)\n except Error as err:\n print(err)\n return os.EX_USAGE\n\n log_formatter = logging.Formatter(\n '%(asctime)s %(levelname)s %(filename)s:%(lineno)s : %(message)s')\n\n logging.root.setLevel(log_root_level)\n\n console_handler = logging.StreamHandler()\n console_handler.setFormatter(log_formatter)\n console_handler.setLevel(log_console_level)\n logging.root.addHandler(console_handler)\n\n # Initialize log dir:\n timestamp = Timestamp()\n pid = os.getpid()\n\n if FLAGS.log_dir is None:\n tmp_dir = os.path.join('/tmp', program_name)\n if not os.path.exists(tmp_dir): os.makedirs(tmp_dir)\n FLAGS.log_dir = tempfile.mkdtemp(\n prefix='%s.%d.' % (timestamp, pid),\n dir=tmp_dir)\n logging.info('Using log dir: %s', FLAGS.log_dir)\n if not os.path.exists(FLAGS.log_dir):\n os.makedirs(FLAGS.log_dir)\n\n log_file = os.path.join(FLAGS.log_dir,\n '%s.%s.%d.log' % (program_name, timestamp, pid))\n\n file_handler = logging.FileHandler(filename=log_file)\n file_handler.setFormatter(log_formatter)\n file_handler.setLevel(log_file_level)\n logging.root.addHandler(file_handler)\n\n # Run program:\n sys.exit(main(FLAGS.GetUnparsed()))","def test_execute_with_context(self):\n pass","def _run_command(self, prog_name, args):\n\n # Build the command with the fullpath to the executable\n\n command = [os.path.join(VorpatestLibrary._bin_dir, prog_name)] + args\n\n # Debug: uncomment the following to debug the library for valgrind\n # executions.\n # command = ['/home/vorpatest/bin/dummy_vorpaline.sh'] + command\n\n # Check if execution is controlled by a wrapper\n\n if VorpatestLibrary._exec_wrapper != None:\n command = VorpatestLibrary._exec_wrapper.wrap_command(\n prog_name, command, self._preserve_files\n )\n\n self._log(\"Run command: %s\" % command)\n self._status = -1\n\n try:\n output = subprocess.check_output(command, stderr=subprocess.STDOUT)\n self._log(\"Command passed\")\n self._log(\"Output: %s\" % output)\n except subprocess.CalledProcessError as e:\n self._log(\"Command failed!\")\n self._log(\"Return code: %s\" % e.returncode)\n self._log(\"Exception: %s\" % e)\n self._log(\"Output: %s\" % e.output)\n raise\n except:\n (exc_type, exc_value) = sys.exc_info()[:2]\n self._log(\"Command failed!\")\n self._log(\"Exception type: %s\" % exc_type)\n self._log(\"Exception value: %s\" % exc_value)\n raise\n\n self._status = 0","def run(self, specs, experiment = None, dirname = 'target'):\n self.generate(specs = specs, experiment = experiment, dirname = dirname)\n cwd = os.getcwd()\n os.chdir(dirname)\n status = os.system('stack build')\n if status == 0:\n status = os.system('stack exec modeling-project-exe')\n os.chdir(cwd)\n if (status == 0) and (not (experiment is None)):\n experiment.open()\n return status","def cli(ctx, runnable_identifier, job_path, **kwds):\n runnable_identifier = translate_alias(ctx, runnable_identifier, kwds.get('profile'))\n path = uri_to_path(ctx, runnable_identifier)\n if os.path.exists(path):\n runnable = for_path(path)\n else: # assume galaxy workflow id\n runnable = for_id(runnable_identifier)\n\n # TODO: do a better test of cwl.\n is_cwl = path.endswith(\".cwl\")\n kwds[\"cwl\"] = is_cwl\n if kwds.get(\"engine\", None) is None:\n if is_cwl:\n kwds[\"engine\"] = \"cwltool\"\n elif kwds.get('galaxy_url', None):\n kwds[\"engine\"] = \"external_galaxy\"\n else:\n kwds[\"engine\"] = \"galaxy\"\n with engine_context(ctx, **kwds) as engine:\n run_result = engine.run(runnable, job_path)\n if not run_result.was_successful:\n warn(\"Run failed [%s]\" % unicodify(run_result))\n ctx.exit(1)\n outputs_dict = run_result.outputs_dict\n output_json = kwds.get(\"output_json\", None)\n if output_json:\n with open(output_json, \"w\") as f:\n json.dump(outputs_dict, f)\n\n return 0","def main(execution_type = \"Native\", qmetry_data = {}):\r\n try:\r\n prog = dellunit.TestProgram(LOGTIME, execution_type, qmetry_data)\r\n jsonfile = ('report_%s.json'%logtime if (prog.timestamp_report == 1) else 'report.json')\r\n htmlfile = ('report_%s.html'%logtime if (prog.timestamp_report == 1) else 'report.html')\r\n JSON_FILE = os.path.abspath(os.path.join('logs', jsonfile))\r\n REPORT_FILE = os.path.abspath(os.path.join('logs', htmlfile))\r\n\r\n invalid_tests_json = []\r\n for test in prog.invalid_tests:\r\n test_json = {}\r\n test_json[\"testcase\"] = test\r\n test_json[\"invalid\"] = True\r\n test_json[\"status\"] = \"error\"\r\n test_json[\"description\"] = util.getTestcaseInformation(test)[\"Testcase Name\"]\r\n invalid_tests_json.append(test_json)\r\n\r\n except:\r\n sys.stdout == sys.__stdout__\r\n sys.stderr == sys.__stderr__\r\n logging.exception('Fatal error during test, skipping report generation')\r\n os.chdir(CURRENT_DIR)\r\n sys.exit(1)\r\n\r\n with open(JSON_FILE, 'r') as outfile_read:\r\n data = json.load(outfile_read)\r\n with open(JSON_FILE, 'w') as outfile_write:\r\n data[\"results\"][0][\"tests\"].extend(invalid_tests_json)\r\n json.dump(data, outfile_write)\r\n logging.info('Creating the report.html file')\r\n report.render_report(JSON_FILE, REPORT_FILE)\r\n os.chdir(CURRENT_DIR)","def run_program(\n args: Union[str, List[str]],\n error_ok: bool = False,\n max_error_lines: int = 100,\n cwd: Optional[str] = None,\n log_command: bool = False) -> ProgramResult:\n if not isinstance(args, list):\n args = [args]\n if log_command:\n if cwd is None:\n dir_for_logging = os.getcwd()\n else:\n dir_for_logging = cwd\n logging.info(\"Running command: %s (in directory: %s)\", shlex_join(args), dir_for_logging)\n try:\n program_subprocess = subprocess.Popen(\n args,\n stdout=subprocess.PIPE,\n stderr=subprocess.PIPE,\n cwd=cwd)\n except OSError:\n logging.error(\"Failed to run program {}\".format(args))\n raise\n\n program_stdout, program_stderr = program_subprocess.communicate()\n error_msg = None\n if program_subprocess.returncode != 0:\n error_msg = \"Non-zero exit code {} from: {} ; stdout: '{}' stderr: '{}'\".format(\n program_subprocess.returncode, shlex_join(args),\n trim_output(program_stdout.strip(), max_error_lines),\n trim_output(program_stderr.strip(), max_error_lines))\n if not error_ok:\n raise RuntimeError(error_msg)\n return ProgramResult(\n cmd_line=args,\n returncode=program_subprocess.returncode,\n stdout=program_stdout.strip().decode('utf-8'),\n stderr=program_stderr.strip().decode('utf-8'),\n error_msg=error_msg,\n program_path=os.path.realpath(args[0])\n )","def main():\n long_description = (\n \"Computing a deflex scenario. By default the name of the result file \"\n \"is derived from the name of the input file but by adding '--results` \"\n \"it is possible to define a custom path. The results will be of the \"\n \"same file format as the input scenario.\\n\"\n \"Optionally a dump-file can be stored. If no path is given the path \"\n \"is derived from the path of the input scenario. The suffix of the \"\n \"dump is '.dflx'.\"\n )\n parser = argparse.ArgumentParser(\n prog=\"deflex-compute\",\n description=long_description,\n formatter_class=argparse.RawDescriptionHelpFormatter,\n )\n parser.add_argument(\n \"--version\", action=\"version\", version=f\"deflex {deflex.__version__}\"\n )\n parser.add_argument(\"path\", type=str, help=\"Input file or directory.\")\n parser.add_argument(\n \"--results\",\n dest=\"results\",\n const=True,\n default=True,\n nargs=\"?\",\n help=(\n \"The name of the results file or directory or False to get no \"\n \"result file. By default the path is derived from scenario path.\"\n ),\n )\n parser.add_argument(\n \"--dump\",\n dest=\"dump\",\n const=True,\n default=None,\n nargs=\"?\",\n help=(\n \"The name of the dump file. Leave empty for the default file name.\"\n ),\n )\n parser.add_argument(\n \"--solver\",\n dest=\"solver\",\n const=\"cbc\",\n default=\"cbc\",\n nargs=\"?\",\n help=\"Solver to use for computing (default: cbc)\",\n )\n\n args = parser.parse_args()\n\n deflex.use_logging()\n\n deflex.scripts.model_scenario(**vars(args))","def ez_run(cls, program_string):\n res = Cpu('test')\n res.load(program_string.split('\\n'))\n res.run()\n return res","def execute(self, code: int = 0, args: Optional[List[str]] = None, fail: str = \"\", verbose: bool = False):\n assert fail in AssemblyTest._can_fail, f\"Invalid fail={fail}. Can only fail: {list(AssemblyTest._can_fail)}\"\n\n \"\"\" As soon as this function is called, the AssemblyTest is considered \"executed\" for the duration of the life cycle of this test and should be treated as such. \"\"\"\n self._has_executed = True\n\n # turn function to fail into a define\n if len(fail) == 0:\n defines = []\n else:\n ret = 0 if fail == 'malloc' else -1\n defines = [\"--def\", f\"#{fail.upper()}_RETURN_HOOK=li a0 {ret}\"]\n\n # check arguments\n if args is not None:\n # TODO: check to see if any args clash with venus arguments\n assert len(args) > 0, \"use None if you don't want to pass any arguments\"\n for a in args:\n assert not a.startswith('-'), f\"argument '{a}' starting with '-' is not allowed\"\n # all arguments could potentially be filenames that we write to, so let's just add them\n self._write_files |= set(args)\n else:\n # ensure that args is always a list\n args = []\n\n lines = []\n\n lines += [f\".import ../../src/{i}\" for i in self._imports]\n lines += [\"\", \".data\"] + self.data\n lines += [\"\", \".globl main_test\", \".text\", \"# main_test function for testing\", \"main_test:\"]\n\n # prologue\n if len(self._output_regs) > 0:\n assert len(self._output_regs) < 13, f\"Too many output registers: {len(self._output_regs)}!\"\n p = [\"# Prologue\", f\"addi sp, sp, -{4 * (len(self._output_regs) + 1)}\", \"sw ra, 0(sp)\"]\n p += [f\"sw s{i}, {(i+1) * 4}(sp)\" for i in range(len(self._output_regs))]\n lines += _indent(p + [\"\"])\n\n\n lines += _indent(self._args)\n\n assert self._call is not None, \"No function was called!\"\n foo_call = [\"\", f\"# call {self._call} function\", f\"jal ra {self._call}\"]\n lines += _indent(foo_call)\n\n if len(self._output_regs) > 0:\n lines += _indent([\"\", \"# save all return values in the save registers\"])\n lines += _indent([f\"mv s{i} a{i}\" for i in self._output_regs] + [\"\"])\n\n lines += _indent(self._checks)\n if code != 0:\n lines += _indent([f\"# we expect {self._call} to exit early with code {code}\"])\n\n lines += _indent([\"\", \"# exit normally\"])\n # epilogue\n if len(self._output_regs) > 0:\n p = [\"# Epilogue\", \"lw ra, 0(sp)\"]\n p += [f\"lw s{i}, {(i + 1) * 4}(sp)\" for i in range(len(self._output_regs))]\n p += [f\"addi sp, sp, {4 * (len(self._output_regs) + 1)}\"]\n lines += _indent(p + [\"\"])\n # lines += _indent([\"mv a0, zero\", \"ret\"])\n lines += _indent([\"jal exit\"])\n lines += [\"\"]\n\n if verbose: print()\n filename = save_assembly(self.name, '\\n'.join(lines), verbose=verbose)\n r, coverage = run_venus(filename, self.check_calling_convention, defines, args, verbose=verbose)\n _process_coverage(coverage, self._assembly)\n self._program_executed = True\n self._std_out = r.stdout.decode('UTF-8')\n if r.returncode != code:\n self._print_failure(r, code)","def _call(self,\n\t\tconsole = ExtendedConsole,\n\t\tcontext = None\n\t):\n\t\tsys.modules['__main__'] = self.module__main__\n\t\tmd = self.module__main__.__dict__\n\n\t\t# Establish execution context in the locals;\n\t\t# iterate over all the loaders in self.context and\n\t\tfor path, ldesc in self.context:\n\t\t\tltitle, loader, xpath = ldesc\n\t\t\trpath = xpath(path)\n\t\t\tli = loader(rpath)\n\t\t\tif li is None:\n\t\t\t\tsys.stderr.write(\n\t\t\t\t\t\"%s %r does not exist or cannot be read%s\" %(\n\t\t\t\t\t\tltitle, rpath, os.linesep\n\t\t\t\t\t)\n\t\t\t\t)\n\t\t\t\treturn 1\n\t\t\ttry:\n\t\t\t\tcode = li.get_code(rpath)\n\t\t\texcept:\n\t\t\t\tprint_exception(*sys.exc_info())\n\t\t\t\treturn 1\n\t\t\tself.module__main__.__file__ = getattr(\n\t\t\t\tli, 'get_filename', lambda x: x\n\t\t\t)(rpath)\n\t\t\tself.module__main__.__loader__ = li\n\t\t\ttry:\n\t\t\t\texec(code, md, md)\n\t\t\texcept:\n\t\t\t\te, v, tb = sys.exc_info()\n\t\t\t\tprint_exception(e, v, tb.tb_next or tb)\n\t\t\t\treturn 1\n\n\t\tif self.main == (None, None):\n\t\t\t# It's interactive.\n\t\t\tsys.argv = self.args or ['<console>']\n\n\t\t\t# Use readline if available\n\t\t\ttry:\n\t\t\t\timport readline\n\t\t\texcept ImportError:\n\t\t\t\tpass\n\n\t\t\tic = console(locals = md)\n\t\t\ttry:\n\t\t\t\tic.interact()\n\t\t\texcept SystemExit as e:\n\t\t\t\treturn e.code\n\t\t\treturn 0\n\t\telse:\n\t\t\t# It's ultimately a code object.\n\t\t\tpath, loader = self.main\n\t\t\tself.module__main__.__file__ = getattr(\n\t\t\t\tloader, 'get_filename', lambda x: x\n\t\t\t)(path)\n\t\t\tsys.argv = list(self.args)\n\t\t\tsys.argv.insert(0, self.module__main__.__file__)\n\t\t\ttry:\n\t\t\t\tcode = loader.get_code(path)\n\t\t\texcept:\n\t\t\t\tprint_exception(*sys.exc_info())\n\t\t\t\treturn 1\n\n\t\t\trv = 0\n\t\t\texe_exception = False\n\t\t\ttry:\n\t\t\t\tif context is not None:\n\t\t\t\t\twith context:\n\t\t\t\t\t\ttry:\n\t\t\t\t\t\t\texec(code, md, md)\n\t\t\t\t\t\texcept:\n\t\t\t\t\t\t\texe_exception = True\n\t\t\t\t\t\t\traise\n\t\t\t\telse:\n\t\t\t\t\ttry:\n\t\t\t\t\t\texec(code, md, md)\n\t\t\t\t\texcept:\n\t\t\t\t\t\texe_exception = True\n\t\t\t\t\t\traise\n\n\t\t\texcept SystemExit as e:\n\t\t\t\t# Assume it's an exe_exception as anything ran in `context`\n\t\t\t\t# shouldn't cause an exception.\n\t\t\t\trv = e.code\n\t\t\t\te, v, tb = sys.exc_info()\n\t\t\t\tsys.last_traceback = (tb.tb_next or tb)\n\t\t\texcept:\n\t\t\t\tif exe_exception is False:\n\t\t\t\t\traise\n\t\t\t\trv = 1\n\t\t\t\te, v, tb = sys.exc_info()\n\t\t\t\tprint_exception(e, v, tb.tb_next or tb)\n\t\t\t\tsys.last_traceback = (tb.tb_next or tb)\n\n\t\t\treturn rv","def cli_main():\n\n\n if len(sys.argv) > 1 and sys.argv[1].endswith('.xmind'):\n xmind_file = sys.argv[1]\n xmind_file = get_absolute_path(xmind_file)\n logging.info('Start to convert XMind file: %s', xmind_file)\n\n if len(sys.argv) == 3 and sys.argv[2] == '-json':\n testlink_json_file = xmind_testcase_to_json_file(xmind_file)\n logging.info('Convert XMind file to testcase json file successfully: %s', testlink_json_file)\n elif len(sys.argv) == 3 and sys.argv[2] == '-xml':\n testlink_xml_file = xmind_to_testlink_xml_file(xmind_file)\n logging.info('Convert XMind file to testlink xml files successfully: %s', testlink_xml_file)\n elif len(sys.argv) == 3 and sys.argv[2] == '-csv':\n zentao_csv_file = xmind_to_zentao_csv_file(xmind_file)\n logging.info('Convert XMind file to zentao csv file successfully: %s', zentao_csv_file)\n elif len(sys.argv) == 3 and sys.argv[2] == '-xlsx':\n excel_xlsx_file = xmind_to_xlsx_file(xmind_file)\n logging.info('Convert XMind file to zentao csv file successfully: %s', excel_xlsx_file)\n else:\n testlink_json_file = xmind_testcase_to_json_file(xmind_file)\n testlink_xml_file = xmind_to_testlink_xml_file(xmind_file)\n zentao_csv_file = xmind_to_zentao_csv_file(xmind_file)\n logging.info('Convert XMind file successfully: \\n'\n '1、 testcase json file(%s)\\n'\n '2、 testlink xml file(%s)\\n'\n '3、 zentao csv file(%s)',\n testlink_json_file,\n testlink_xml_file,\n zentao_csv_file)\n\n\n else:\n print(__doc__)\n logging.error('%s', __doc__)","def test_runSuccess(self):\n builder = BookBuilder()\n self.assertEquals(\n builder.run([\n sys.executable, '-c',\n 'import sys; '\n 'sys.stdout.write(\"hi\\\\n\"); '\n 'sys.stdout.flush(); '\n 'sys.stderr.write(\"bye\\\\n\"); '\n 'sys.stderr.flush()']),\n \"hi\\nbye\\n\")","def run_program ( name, command ):\n\n # Create temporary file to write stdout, stderr of the program\n tmpfile,tmpname = tempfile.mkstemp()\n ret_code = subprocess.call ( command, shell = True , stdout=tmpfile, stderr=tmpfile)\n # Catch some error codes\n if ret_code == 127:\n msg = \"Unable to find %s executable, please make sure this is present in your PATH.\"%name\n logger.error(msg) \n raise Exception ( msg )\n if ret_code == 1:\n # Get the error message from the temporary file\n errmsg = \"\\n\".join(line for line in open(tmpname).readlines())\n os.remove(tmpname)\n msg = \"%s was not able to run successfully. Please check output. Error message was:\\n%s\"%(name,errmsg)\n raise Exception ( msg )\n\n os.remove(tmpname)","def main():\n parser = ArgumentParser(description=__doc__,\n formatter_class=RawTextHelpFormatter)\n parser.add_argument('code', help='Python code to execute')\n group = parser.add_mutually_exclusive_group()\n group.add_argument('-3', action='store_const', dest='python',\n const='python3', help='Explicitly use Python 3')\n group.add_argument('-2', action='store_const', dest='python',\n const='python2', help='Explicitly use Python 2')\n group.add_argument('-p', '--python', help='Specify python interpreter')\n args = parser.parse_args()\n if args.python is not None:\n call([args.python, __file__, args.code])\n else:\n InteractiveInterpreter(LocalsImportDict()).runsource(args.code)","def call_prog(args):\n # Just dump the entirety of the command so that\n # the user can specify whatever arguments they want\n call(args)","def run(argv: Optional[list[str]] = None) -> tuple[str, str]:\n argv = argv if argv is not None else []\n\n with PipeStream() as stdin:\n stdin.writer.close()\n\n with PipeStream() as stdout:\n with PipeStream() as stderr:\n gada.main(\n [\"gada\"] + argv,\n stdin=stdin.reader,\n stdout=stdout.writer,\n stderr=stderr.writer,\n )\n stdout.writer.close()\n stderr.writer.close()\n return (\n stdout.reader.read().decode(errors=\"ignore\"),\n stderr.reader.read().decode(errors=\"ignore\"),\n )","def run(argv: List[str], stdout: TextIO, stderr: TextIO) -> int:\n parser = _make_argument_parser()\n args, out, err = _parse_args(parser=parser, argv=argv)\n if len(out) > 0:\n stdout.write(out)\n\n if len(err) > 0:\n stderr.write(err)\n\n if args is None:\n return 1\n\n command, errors = _parse_args_to_params(args=args)\n if errors:\n for error in errors:\n print(error, file=stderr)\n return 1\n\n assert command is not None\n\n if not command.scenarios_dir.exists():\n print(\n f\"The directory you specified in --scenarios_dir does not exist \"\n f\"on your system: {command.scenarios_dir}\",\n file=stderr,\n )\n return 1\n\n if not command.scenarios_dir.is_dir():\n print(\n f\"The path you specified in --scenarios_dir is expected to be a directory, \"\n f\"but it is not: {command.scenarios_dir}\",\n file=stderr,\n )\n return 1\n\n if isinstance(command, Once):\n errors = rasaeco.render.once(scenarios_dir=command.scenarios_dir)\n elif isinstance(command, Continuously):\n server = None # type: Optional[ThreadedServer]\n\n with contextlib.ExitStack() as exit_stack:\n if command.port is not None:\n server = ThreadedServer(\n port=command.port,\n scenarios_dir=command.scenarios_dir,\n stdout=stdout,\n stderr=stderr,\n )\n server.start()\n exit_stack.push(server)\n\n stop = queue.Queue() # type: queue.Queue[bool]\n\n work_thread = threading.Thread(\n target=_render_continuously,\n args=(stdout, stderr, command.scenarios_dir, stop),\n )\n\n prefix = \"In the main\"\n work_thread.start()\n try:\n while True:\n time.sleep(0.5)\n except KeyboardInterrupt:\n print(f\"{prefix}: Got a keyboard interrupt.\", file=stdout)\n finally:\n print(f\"{prefix}: Sending a stop from the main thread...\", file=stdout)\n stop.put(True)\n print(\n f\"{prefix}: Waiting for the work thread in main to join...\",\n file=stdout,\n )\n work_thread.join()\n\n else:\n raise AssertionError(\"Unhandled command: {}\".format(command))\n\n if errors:\n for error in errors:\n print(error, file=stderr)\n return 1\n\n return 0","def execute(self):\n if self._cli_arguments.get('<samplename>') == 'cfn':\n generate_sample_cfn_module(self.env_root)\n elif self._cli_arguments.get('<samplename>') == 'static-angular':\n generate_sample_static_angular(self.env_root)\n elif self._cli_arguments.get('<samplename>') == 'static-react':\n generate_sample_static_react(self.env_root)\n elif self._cli_arguments.get('<samplename>') == 'sls-py':\n generate_sample_sls_module(self.env_root, 'sls-py')\n elif self._cli_arguments.get('<samplename>') == 'sls-tsc':\n generate_sample_sls_module(self.env_root, 'sls-tsc')\n elif self._cli_arguments.get('<samplename>') == 'stacker':\n generate_sample_stacker_module(self.env_root)\n elif self._cli_arguments.get('<samplename>') == 'tf':\n generate_sample_tf_module(self.env_root)\n elif self._cli_arguments.get('<samplename>') == 'k8s-cfn-repo':\n generate_sample_k8s_cfn_repo(self.env_root)\n elif self._cli_arguments.get('<samplename>') == 'k8s-tf-repo':\n generate_sample_k8s_tf_repo(self.env_root)\n elif self._cli_arguments.get('<samplename>') == 'cdk-tsc':\n generate_sample_cdk_tsc_module(self.env_root)\n elif self._cli_arguments.get('<samplename>') == 'cdk-py':\n generate_sample_cdk_py_module(self.env_root)\n elif self._cli_arguments.get('<samplename>') == 'cdk-csharp':\n generate_sample_cdk_cs_module(self.env_root)\n else:\n LOGGER.info(\"Available samples to generate:\")\n for i in ['cfn', 'static-angular', 'static-react', 'sls-tsc',\n 'sls-py', 'tf', 'k8s-cfn-repo', 'k8s-tf-repo',\n 'stacker', 'cdk-tsc', 'cdk-py', 'cdk-csharp']:\n print(i)","def __call__(self, arguments):\n filtered_arguments = self._filter_unused_argument_dash_m(arguments)\n configuration = load_configuration()\n\n if len(filtered_arguments) > 1:\n filtered_arguments = self._handle_debug_argument(filtered_arguments)\n self._handle_version_argument(filtered_arguments)\n self._handle_help_argument(filtered_arguments)\n\n if configuration.execute_before:\n LOGGER.debug('Executing command \"%s\" before doing anything else.', configuration.execute_before)\n command_and_arguments = configuration.execute_before.split()\n execute_command(command_and_arguments[0], *command_and_arguments[1:])\n\n try:\n self.function(filtered_arguments, configuration)\n return exit(0)\n\n except errors.CommitterError as committer_exception:\n LOGGER.error(committer_exception.message)\n return exit(committer_exception.error_code)\n\n except KeyboardInterrupt:\n LOGGER.error('Interrupted by user.\\n')\n return exit(1)","def execute(self, args=None):\n parser = argparse.ArgumentParser(description='run tests on jupyter notebook')\n\n parser.add_argument('notebook', type=str, help='the jupyter notebook to test')\n parser.add_argument('-t', '--target', type=str, metavar='', help='where to store results')\n parser.add_argument('-c', '--context', type=str, metavar='', help='context directory')\n parser.add_argument('-v', '--verbose', action='count', default=0, help='verbosity level')\n\n args = parser.parse_args(args)\n\n logger.setLevel(loglevel(args.verbose))\n logger.debug(f'args: {args}')\n\n results = self._grade_notebook(\n Path(args.notebook).absolute(),\n target_dir=Path(args.target).absolute() if args.target else None,\n context=Path(args.context).absolute() if args.context else None\n )\n\n return results.summary().failed","def parse_and_test(program: Text,\n name: Text,\n *,\n filename: Text,\n additional_search_paths: Tuple[str, ...] = (),\n raise_on_error: bool = True,\n test_filter: Optional[Text] = None,\n trace_all: bool = False,\n compare_jit: bool = True,\n seed: Optional[int] = None) -> bool:\n did_fail = False\n test_name = None\n type_info = None\n\n importer = import_helpers.Importer(additional_search_paths)\n ran = 0\n\n try:\n module = Parser(Scanner(filename, program), name).parse_module()\n type_info = cpp_typecheck.check_module(module, importer.cache,\n importer.additional_search_paths)\n\n ir_package = (\n ir_converter.convert_module_to_package(\n module, type_info, importer.cache, traverse_tests=True)\n if compare_jit else None)\n\n interpreter = Interpreter(\n module,\n type_info,\n importer.typecheck,\n importer.additional_search_paths,\n importer.cache,\n trace_all=trace_all,\n ir_package=ir_package)\n for test_name in module.get_test_names():\n if not _matches(test_name, test_filter):\n continue\n ran += 1\n print('[ RUN UNITTEST ]', test_name, file=sys.stderr)\n interpreter.run_test(test_name)\n print('[ OK ]', test_name, file=sys.stderr)\n\n if ir_package and module.get_quickchecks():\n if seed is None:\n # We want to guarantee non-determinism by default. See\n # https://abseil.io/docs/cpp/guides/random#stability-of-generated-sequences\n # for rationale.\n seed = int(os.getpid() * time.time())\n print(f'[ SEED: {seed} ]')\n for quickcheck in module.get_quickchecks():\n test_name = quickcheck.f.name.identifier\n print('[ RUN QUICKCHECK ]', test_name, file=sys.stderr)\n quickcheck_helpers.run_quickcheck(\n interpreter, ir_package, quickcheck, seed=seed)\n print('[ OK ]', test_name, file=sys.stderr)\n\n except (PositionalError, FailureError, CppParseError, ScanError,\n TypeInferenceError, XlsTypeError) as e:\n did_fail = True\n parser_helpers.pprint_positional_error(\n e, output=cast(io.IOBase, sys.stderr))\n if test_name:\n print(\n '[ FAILED ]',\n test_name,\n e.__class__.__name__,\n file=sys.stderr)\n if raise_on_error:\n raise\n finally:\n if type_info is not None:\n type_info.clear_type_info_refs_for_gc()\n\n print('[==================]', ran, 'test(s) ran.', file=sys.stderr)\n return did_fail","def main():\n log.configureLogger(log=log.testlog, fileLog=log.TESTLOGFILE, format=log.TESTFORMAT, truncate=True)\n log.configureLogger(log=log.log, fileLog=log.LOGFILE)\n try:\n opts, args = getopt.getopt(sys.argv[1:], \"h\", [\"help\", \"file=\", \"verbosity=\",\n \"settings=\"]) \n # process options\n testfile = None\n inifile = None\n\n verbosity = 2 \n for o, a in opts:\n if o in (\"-h\", \"--help\"):\n print __doc__\n sys.exit(0)\n if o in (\"--file\",):\n testfile = a\n logger.debug(\"starting %s\" % testfile)\n if o in (\"--settings\",):\n inifile = a\n if o in (\"--verbosity\",):\n verbosity = a\n\n if testfile is None:\n sys.stderr.write(\"Error, missing testfile\")\n print __doc__\n sys.exit(2)\n\n suite = regressionTest(testfile, inifile)\n tester = TESTER(suite, verbosity)\n # TODO: runTest gives a return value\n tester.runTest()\n tester.generateFailures()\n\n sys.exit(0)\n\t\n except getopt.error, msg:\n sys.stderr.write(\"%s\" % msg)\n sys.stderr.write(\"for help use --help\")\n sys.exit(2)","def do_work():\n args = sys.argv\n args = args[1:] # First element of args is the file name\n\n if len(args) == 0:\n print('You have not passed any commands in!')\n else:\n for a in args:\n if a == '--help':\n print('AWS - EKS - Terraform : Demo Tool')\n print('Options:')\n print(' --help -> show this help menu.')\n print(' --tf -> execute terraform validate and apply.')\n print(' --passtest -> pass unit test - flask deployment')\n print(' --failtest -> fail unit test - flask deployment')\n print(' --prepare -> prepare tests')\n elif a == '--passtest':\n os.system(\"python python_test.py\")\n elif a == '--failtest':\n os.system(\"python python_test_fail.py\")\n elif a == '--tf':\n os.system(\"cd terraform && terraform validate && terraform apply -auto-approve && cd ..\")\n elif a == '--prepare':\n os.system(\"aws eks update-kubeconfig --name flugeldev-cluster && kubectl delete -f curl.yml && kubectl apply -f curl.yml\")\n else:\n print('Unrecognised argument.')","def run(\n *args,\n module=None,\n plugins=(),\n run_in_thread=default,\n raise_on_error=default,\n addopts=default,\n defopts=default,\n display_columns=default,\n):\n import ipytest\n\n run_in_thread = default.unwrap(run_in_thread, current_config[\"run_in_thread\"])\n raise_on_error = default.unwrap(raise_on_error, current_config[\"raise_on_error\"])\n addopts = default.unwrap(addopts, current_config[\"addopts\"])\n defopts = default.unwrap(defopts, current_config[\"defopts\"])\n display_columns = default.unwrap(display_columns, current_config[\"display_columns\"])\n\n if module is None:\n import __main__ as module\n\n run = run_func_in_thread if run_in_thread else run_func_direct\n exit_code = run(\n _run_impl,\n *args,\n module=module,\n plugins=plugins,\n addopts=addopts,\n defopts=defopts,\n display_columns=display_columns,\n )\n\n ipytest.exit_code = exit_code\n\n if raise_on_error is True and exit_code != 0:\n raise Error(exit_code)\n\n return exit_code","def Main(args=None):\n util.ChangeStdoutEncoding()\n\n # Support for setuptools console wrappers.\n if args is None:\n args = sys.argv[1:]\n\n options = Options()\n try:\n args = options.ReadOptions(args) # args may be shorter after this\n except getopt.GetoptError as e:\n print(\"grit:\", str(e))\n print(\"Try running 'grit help' for valid options.\")\n return 1\n if not args:\n print(\"No tool provided. Try running 'grit help' for a list of tools.\")\n return 2\n\n tool = args[0]\n if tool == 'help':\n if len(args) == 1:\n PrintUsage()\n return 0\n else:\n tool = args[1]\n if not _GetToolInfo(tool):\n print(\"No such tool. Try running 'grit help' for a list of tools.\")\n return 2\n\n print(\"Help for 'grit %s' (for general help, run 'grit help'):\\n\" %\n (tool,))\n _GetToolInfo(tool)[_FACTORY]().ShowUsage()\n return 0\n if not _GetToolInfo(tool):\n print(\"No such tool. Try running 'grit help' for a list of tools.\")\n return 2\n\n try:\n if _GetToolInfo(tool)[_REQUIRES_INPUT]:\n os.stat(options.input)\n except OSError:\n print('Input file %s not found.\\n'\n 'To specify a different input file:\\n'\n ' 1. Use the GRIT_INPUT environment variable.\\n'\n ' 2. Use the -i command-line option. This overrides '\n 'GRIT_INPUT.\\n'\n ' 3. Specify neither GRIT_INPUT or -i and GRIT will try to load '\n \"'resource.grd'\\n\"\n ' from the current directory.' % options.input)\n return 2\n\n if options.hash:\n grit.extern.FP.UseUnsignedFingerPrintFromModule(options.hash)\n\n try:\n toolobject = _GetToolInfo(tool)[_FACTORY]()\n if options.profile_dest:\n import hotshot\n prof = hotshot.Profile(options.profile_dest)\n return prof.runcall(toolobject.Run, options, args[1:])\n else:\n return toolobject.Run(options, args[1:])\n except getopt.GetoptError as e:\n print(\"grit: %s: %s\" % (tool, str(e)))\n print(\"Try running 'grit help %s' for valid options.\" % (tool,))\n return 1","def run(executable, arguments=[], verbose=True, shell=False, drop_cache=False):\r\n if drop_cache:\r\n dropcache()\r\n if verbose:\r\n print(\"%s %s\" % (os.path.basename(executable), \" \".join([pipes.quote(arg) for arg in arguments])))\r\n arguments.insert(0, executable)\r\n if shell:\r\n arguments = \" \".join([pipes.quote(arg) for arg in arguments])\r\n p = subprocess.Popen(args=arguments, stdout=subprocess.PIPE,\r\n stderr=subprocess.PIPE, shell=shell)\r\n stdout, stderr = p.communicate()\r\n\r\n if p.returncode != EXIT_SUCCESS:\r\n print('STDERR: ' + stderr.decode(), file=sys.stderr)\r\n\r\n return (p.returncode, stdout.decode(), stderr.decode())","def run_from_argv(self, argv):\n parser = self.create_parser(argv[0], argv[1])\n options, args = parser.parse_args(argv[2:])\n handle_default_options(options)\n self.execute(*args, **options.__dict__) # pylint: disable-msg=W0142","def run ( self, argv = sys.argv ) :\n\n self._options, self._args = self._parser.parse_args(argv[1:])\n\n appName = self.appName()\n\n # setup logger\n if self._installLogger :\n logging.addLevelName ( LOGGING_TRACE_LEVEL, 'TRACE' )\n rootlog = logging.getLogger()\n hdlr = logging.StreamHandler( sys.stdout )\n formatter = logging.Formatter(self._logfmt)\n hdlr.setFormatter(formatter)\n rootlog.addHandler(hdlr)\n logLevels = { 0 : logging.WARNING, 1 : logging.INFO, 2 : LOGGING_TRACE_LEVEL }\n rootlog.setLevel( logLevels.get (self._options.verbose,logging.DEBUG) )\n self._log = logging.getLogger(appName)\n else :\n self._log = logging.getLogger()\n\n return self._run()","def execute():\n if PRINT:\n print(\"Start Child Process\")\n\n # 1. Receive inputs as you want\n parser = argparse.ArgumentParser()\n parser.add_argument(\"-x\", type=float, required=True)\n parser.add_argument(\"--test-env\", action=\"store_true\")\n parser.add_argument(\"--experiment-id\", type=str)\n parser.add_argument(\"--experiment-name\", type=str)\n parser.add_argument(\"--experiment-version\", type=str)\n parser.add_argument(\"--trial-id\", type=str)\n parser.add_argument(\"--working-dir\", type=str)\n\n inputs = parser.parse_args()\n\n if inputs.test_env:\n assert inputs.experiment_id == os.environ[\"ORION_EXPERIMENT_ID\"]\n assert inputs.experiment_name == os.environ[\"ORION_EXPERIMENT_NAME\"]\n assert inputs.experiment_version == os.environ[\"ORION_EXPERIMENT_VERSION\"]\n assert inputs.trial_id == os.environ[\"ORION_TRIAL_ID\"]\n assert inputs.working_dir == os.environ[\"ORION_WORKING_DIR\"]\n\n # 2. Perform computations\n y, dy = function(inputs.x)\n\n if PRINT:\n print(\"Child Error\", file=sys.stderr)\n\n # 3. Gather and report results\n results = list()\n results.append(dict(name=\"example_objective\", type=\"objective\", value=y))\n results.append(dict(name=\"example_gradient\", type=\"gradient\", value=[dy]))\n\n report_results(results)\n\n if PRINT:\n print(\"End Child Process\")","def main( argv = None ):\n\n if not argv: argv = sys.argv\n\n # setup command line parser\n parser = optparse.OptionParser( version = \"%prog version: $Id$\",\n usage = globals()[\"__doc__\"] )\n\n parser.add_option( \"-p\", \"--proc\", dest=\"processors\", type=\"int\",\n help = \"use # processors [%default]\" )\n\n parser.set_defaults(\n processors = 1 )\n\n\n options, args = E.Start( parser, argv = argv )\n\n t1 = Test( RunnerGat, \n small_test_segmented_workspaces(), \n [ ValidatorNumSamples,\n ValidatorSegmentDistribution ] )\n\n t1.run( options.stdout, \n processors = options.processors )\n\n E.Stop()","def run_from_argv(self, prog, subcommand, global_options, argv):\r\n self.prog_name = prog\r\n if self.skip_options:\r\n parser = NewOptionParser(prog=self.prog_name,\r\n usage=self.usage(subcommand),\r\n version='',\r\n formatter = NewFormatter(),\r\n add_help_option = False,\r\n option_list=self.option_list)\r\n else:\r\n parser = self.create_parser(prog, subcommand)\r\n options, args = parser.parse_args(argv)\r\n self.execute(args, options, global_options)","def run(path):\n config = conf.get_yaml_field(gl.configFile)\n exe_con = config['ENABLE_EXECUTION']\n exe_num = config['EXECUTION_NUM']\n rerun = config['ENABLE_RERUN']\n reruns_nums = config['RERUN_NUM']\n repeat = config['ENABLE_REPEAT']\n repeat_num = config['REPEAT_NUM']\n exec_mode = config['ENABLE_EXEC_MODE']\n debug_mode = config['ENABLE_DEBUG_MODE']\n last_failed = config['ENABLE_LAST_FAILED']\n failed_first = config['ENABLE_FAILED_FIRST']\n\n # custom function\n RunTestCase.copy_custom_function()\n\n # failed first\n failed_first_args = (' --ff ' if failed_first else '') if not last_failed else ''\n\n # last failed\n last_failed_args = (' --lf ' if last_failed else '') if not failed_first else ''\n\n # Enable repeat case.\n repeat_args = ' --count={} '.format(repeat_num) if repeat else ''\n\n # Enable CPU concurrency\n py_args = ' -n {} '.format(exe_num) if exe_con else ''\n\n # Enable failed retry\n reruns_args = ' --reruns {} '.format(reruns_nums) if rerun else ''\n\n # debug mode print debug info.\n debug = '' if debug_mode else '--tb=no'\n\n \"\"\"\n Load the pytest framework,\n which must be written here or DDT will be loaded first.\n from httptesting.case import test_load_case\n \"\"\"\n case_path = gl.loadcasePath\n # Output mode console or report.\n if exec_mode:\n cmd = 'cd {} && py.test -q -s {} {} {} {}'.format(\n case_path, reruns_args, 'test_load_case.py',\n repeat_args, debug\n )\n else:\n cmd = 'cd {} && py.test {} {} {} {} {} {} --html={} {} --self-contained-html'.format(\n case_path,\n py_args,\n reruns_args,\n last_failed_args,\n failed_first_args,\n 'test_load_case.py',\n repeat_args,\n path,\n debug\n )\n try:\n os.system(cmd)\n except (KeyboardInterrupt, SystemExit):\n print('已终止执行.')","def main(argv):\n ret = 0\n parser = utils.parser.createParser()\n\n opt = parser.parse_args(argv)\n\n try:\n # Stop execution if help flag is on\n if opt.help:\n raise ShowHelpException()\n\n # Read materials we need for later processing\n cfg = auxiliary.readConfig(opt.config)\n rawJson = auxiliary.readJson(opt.input[0])\n\n # List we are going to manipulate :)\n commands = rawJson['commands']\n\n # Pump commands and instruction for filtering, sorting etc\n rawJson['commands'] = pump(commands, cfg)\n\n # Write final result\n auxiliary.writeJSONFile(rawJson, opt.output[0])\n\n except ShowHelpException:\n parser.print_help()\n ret = 0\n except FilterException, e:\n ret = 1\n print('ERROR')\n print(e)\n\n return ret","def main(args=None):\n args = args or sys.argv[1:]\n parser = parse_options()\n common.main_cli(experiment_parse_and_run, parser, args)","def main():\n tester = Tester()\n # parse args, load configuration and create all required objects.\n tester.setup_experiment()\n # GO!\n tester.run_experiment()","def run(self):\n\n input_args = {}\n self._execute(input_args, self.args)","def _run_command(*argv):\n del argv # Unused\n\n # Set the program name to 'earthengine' for proper help text display.\n parser = argparse.ArgumentParser(\n prog='earthengine', description='Earth Engine Command Line Interface.')\n parser.add_argument(\n '--ee_config', help='Path to the earthengine configuration file. '\n 'Defaults to \"~/%s\".' % utils.DEFAULT_EE_CONFIG_FILE_RELATIVE)\n parser.add_argument(\n '--service_account_file', help='Path to a service account credentials'\n 'file. Overrides any ee_config if specified.')\n parser.add_argument(\n '--project',\n help='Specifies a Google Cloud Platform Project id to override the call.',\n dest='project_override')\n\n dispatcher = CommandDispatcher(parser)\n\n # Print the list of commands if the user supplied no arguments at all.\n if len(sys.argv) == 1:\n parser.print_help()\n return\n\n args = parser.parse_args()\n config = utils.CommandLineConfig(\n args.ee_config, args.service_account_file, args.project_override\n )\n\n # Catch EEException errors, which wrap server-side Earth Engine\n # errors, and print the error message without the irrelevant local\n # stack trace. (Individual commands may also catch EEException if\n # they want to be able to continue despite errors.)\n try:\n dispatcher.run(args, config)\n except ee.EEException as e:\n print(e)\n sys.exit(1)","def main(argv=None):\n if argv is None:\n argv = sys.argv\n\n import doctest\n verbose = (len(argv) >= 2 and argv[1] == \"--Test\")\n if verbose:\n print(\"Running doctests (verbose mode)\")\n else:\n print(\"Running doctests\")\n doctest.testmod(verbose=verbose)\n return 0","def run(self, script, *args, **kwargs):\n return self._run('run', script, *args, **kwargs)","def main():\n vunit = vunit_pkg.VUnit.from_argv()\n vunit = map_sources(vunit)\n run_tests(vunit)","def parse (self, program):\n return self.parser.parse (program)","def run(self, argv):\r\n try:\r\n index = 0\r\n command_pos = -1\r\n help_pos = -1\r\n help_command_pos = -1\r\n for arg in argv:\r\n if arg == 'bash-completion':\r\n self._bash_completion()\r\n return 0\r\n if arg in self.commands[self.api_version]:\r\n if command_pos == -1:\r\n command_pos = index\r\n elif arg in ('-h', '--help'):\r\n if help_pos == -1:\r\n help_pos = index\r\n elif arg == 'help':\r\n if help_command_pos == -1:\r\n help_command_pos = index\r\n index = index + 1\r\n if command_pos > -1 and help_pos > command_pos:\r\n argv = ['help', argv[command_pos]]\r\n if help_command_pos > -1 and command_pos == -1:\r\n argv[help_command_pos] = '--help'\r\n self.options, remainder = self.parser.parse_known_args(argv)\r\n self.configure_logging()\r\n self.interactive_mode = not remainder\r\n self.initialize_app(remainder)\r\n except Exception as err:\r\n if self.options.verbose_level == self.DEBUG_LEVEL:\r\n self.log.exception(unicode(err))\r\n raise\r\n else:\r\n self.log.error(unicode(err))\r\n return 1\r\n result = 1\r\n if self.interactive_mode:\r\n _argv = [sys.argv[0]]\r\n sys.argv = _argv\r\n result = self.interact()\r\n else:\r\n result = self.run_subcommand(remainder)\r\n return result","def main():\n parser = argparse.ArgumentParser()\n subparsers = parser.add_subparsers(dest='mode')\n\n # Add sub-parser for feature extraction\n parser_extract = subparsers.add_parser('extract')\n parser_extract.add_argument('dataset',\n choices=['training', 'validation', 'test'],\n )\n\n # Add sub-parser for training\n subparsers.add_parser('train')\n\n # Add sub-parser for inference\n parser_predict = subparsers.add_parser('predict')\n parser_predict.add_argument('dataset',\n nargs='?',\n choices=['validation', 'test'],\n default='test',\n )\n\n # Add sub-parser for evaluation\n parser_evaluate = subparsers.add_parser('evaluate')\n parser_evaluate.add_argument('task',\n nargs='?',\n choices=['tagging', 'sed', 'all'],\n default='all',\n )\n parser_evaluate.add_argument('dataset',\n nargs='?',\n choices=['validation', 'test'],\n default='test',\n )\n parser_evaluate.add_argument('--thresholds', action='store_true')\n\n args = parser.parse_args()\n\n if args.mode == 'extract':\n extract(cfg.to_dataset(args.dataset))\n elif args.mode == 'train':\n train()\n elif args.mode == 'predict':\n predict(cfg.to_dataset(args.dataset))\n elif args.mode == 'evaluate':\n eval_all = args.task == 'all'\n dataset = cfg.to_dataset(args.dataset)\n if args.task == 'tagging' or eval_all:\n evaluate_audio_tagging(dataset, args.thresholds)\n if args.task == 'sed' or eval_all:\n evaluate_sed(dataset)","def run_experiment(arguments):\n\n logging.info('Arguments: %s', arguments)\n\n # Get estimator\n estimator = get_estimator(arguments)\n # my_module.\n\n # Run training and evaluation\n _train_and_evaluate(estimator, arguments.job_dir)","def subprocess_run(self, *args):\n return self.testdir.runpytest_subprocess(*args)","def main(argv=None):\n if argv is None:\n argv = sys.argv\n\n global _pgm\n _pgm = \"%s:\" % os.path.basename(argv[0]) # colon for easy use by print\n\n parser = argparse.ArgumentParser(description=\"The redim_autochunk_2 test.\")\n parser.add_argument('-c', '--host', default=None,\n help='Target host for iquery commands.')\n parser.add_argument('-p', '--port', default=None,\n help='SciDB port on target host for iquery commands.')\n parser.add_argument('-r', '--run-id', type=int, default=0,\n help='Unique run identifier.')\n parser.add_argument('-k', '--keep-arrays', action='store_true',\n help='Do not remove test arrays during cleanup.')\n parser.add_argument('-v', '--verbosity', default=0, action='count',\n help='Increase debug logs. 1=info, 2=debug, 3=debug+')\n\n global _args\n _args = parser.parse_args(argv[1:])\n IQuery.setenv(_args)\n\n global _iquery\n _iquery = IQuery(afl=True, format='tsv')\n\n try:\n tt = TheTest()\n return tt.run()\n except AppError as e:\n print >>sys.stderr, _pgm, e\n return 1\n except Exception as e:\n print >>sys.stderr, _pgm, \"Unhandled exception:\", e\n traceback.print_exc() # always want this for unexpected exceptions\n return 2","def RunScript(self, database, script, arguments, result):\n\n self.RunProgram(\"\\\"\"+self.__context[\"isql_path\"]+\"\\\"\",\n [ self.__context[\"isql_path\"] ] + [ database ] + arguments,\n script, self.__context, result)","def main(argv):\n\n # Setup Directory\n experiment_dir = os.path.join(FLAGS.dir, FLAGS.id)\n if not os.path.exists(experiment_dir):\n os.makedirs(os.path.join(experiment_dir, \"logs\"), exist_ok=True)\n\n # Setup Logging\n FLAGS.alsologtostderr = True\n logging.get_absl_handler().use_absl_log_file(FLAGS.logfile, os.path.join(experiment_dir, \"logs\"))\n\n # Setup seeds\n if FLAGS.random_seed:\n np.random.seed(FLAGS.random_seed)\n tf.random.set_seed(FLAGS.random_seed)\n\n # Log Flags\n log_flags(FLAGS)\n\n try:\n experiment()\n except:\n exception = traceback.format_exc()\n logging.info(exception)","def main(args):\n module = args.module\n\n if args.step not in STEP_OPTIONS:\n raise ValueError(\n f\"{args.step} is an unknown option. Your options are {STEP_OPTIONS}.\"\n )\n\n if module == \"structure_plan\":\n run_module_structure_plan(args)\n elif module == \"floor_plan\":\n run_module_floor_plan(args)\n elif module == \"complete_floorplan\":\n run_module_complete_floorplan(args)\n elif module == \"ground_plan\":\n run_module_ground_plan(args)\n elif module == \"text_to_gdf\":\n run_module_text_to_gdf(args)\n else:\n raise ValueError(\n f\"{module} is an unknown option. Your options are {MODULE_OPTIONS}.\"\n )","def run(self, graph, context):\n ginf = GraphInferrer(self.engine.tracks['type'],\n graph, Context.empty(),\n broaden=False)\n\n argrefs = [self.engine.ref(p, context)\n for p in graph.parameters]\n\n return self.engine.run_coroutine(\n self._specialize(None, ginf, argrefs)\n )","def executable():\n\n if len(sys.argv) == 1:\n arguments.get_help()\n sys.exit('\\nGive me something to do and I will do it\\n')\n else:\n # Parse the Arguments that have been provided\n args = arguments.get_args()\n\n # Load The System Logger\n log = logger.load_in(log_level=args.get('log_level', 'info'))\n log.debug('Used Arguments %s', args)\n const(log_method=log)\n\n # Begin Work\n start(set_args=args)","def Run(self, device):\n margin = 8\n print '[ %-*s ] %s' % (margin, 'RUN', self.tagged_name)\n logging.info('Running linker test: %s', self.tagged_name)\n adb = android_commands.AndroidCommands(device)\n\n # Create command-line file on device.\n command_line_flags = ''\n if self.is_low_memory:\n command_line_flags = '--low-memory-device'\n _WriteCommandLineFile(adb, command_line_flags, _COMMAND_LINE_FILE)\n\n # Run the test.\n status, logs = self._RunTest(adb)\n\n result_text = 'OK'\n if status == ResultType.FAIL:\n result_text = 'FAILED'\n elif status == ResultType.TIMEOUT:\n result_text = 'TIMEOUT'\n print '[ %*s ] %s' % (margin, result_text, self.tagged_name)\n\n results = base_test_result.TestRunResults()\n results.AddResult(\n base_test_result.BaseTestResult(\n self.tagged_name,\n status,\n logs))\n\n return results","def main():\n argument_parser = argparse.ArgumentParser(add_help=True)\n argument_parser.add_argument(\"directory\", type=str,\n help=\"Directory to detect test smells.\")\n args = argument_parser.parse_args()\n \n if len(sys.argv) < 1:\n \n argument_parser.print_help()\n \n else:\n \n if os.path.exists(args.directory) or os.path.isdir(args.directory):\n\n #Stage 1: project level rule checking\n files = python_parser.get_python_files(os.path.abspath(args.directory))\n results_list = project_rule_runner(files)\n \n #Stage 2: test case level rule checking\n #test_case_pairs_list is a list of test cases paired with their file of origin\n filtered_files = python_parser.filter_python_files(files)\n test_case_pairs_list = python_parser.get_test_case_asts(filtered_files)\n \n for test_case_pair in test_case_pairs_list:\n results_list = results_list + test_case_rule_runner(test_case_pair)\n \n #Stage 3: test method level rule checking\n test_method_list = list()\n \n for test_case_pair in test_case_pairs_list:\n test_method_list = test_method_list + python_parser.get_test_asts(test_case_pair)\n \n for test_method in test_method_list: \n results_list = results_list + test_method_rule_runner(test_method)\n \n #Output formatting\n format_output(results_list)\n \n else:\n print(\"Invalid path given.\")","def run(self, args=None):\n # Disable \"Too many branches\" and \"Too many return statemets\" warnings\n # pylint: disable=R0912,R0911\n retcodesummary = ExitCodes.EXIT_SUCCESS\n self.args = args if args else self.args\n\n if not self.check_args():\n return retcodesummary\n\n if self.args.clean:\n if not self.args.tc and not self.args.suite:\n return retcodesummary\n\n # If called with --version print version and exit\n version = get_fw_version()\n if self.args.version and version:\n print(version)\n return retcodesummary\n elif self.args.version and not version:\n print(\"Unable to get version. Have you installed Icetea correctly?\")\n return retcodesummary\n\n self.logger.info(\"Using Icetea version {}\".format(version) if version\n else \"Unable to get Icetea version. Is Icetea installed?\")\n\n # If cloud set, import cloud, get parameters from environment, initialize cloud\n cloud = self._init_cloud(self.args.cloud)\n\n # Check if called with listsuites. If so, print out suites either from cloud or from local\n if self.args.listsuites:\n table = self.list_suites(self.args.suitedir, cloud)\n if table is None:\n self.logger.error(\"No suites found!\")\n retcodesummary = ExitCodes.EXIT_FAIL\n else:\n print(table)\n return retcodesummary\n\n try:\n testsuite = TestSuite(logger=self.logger, cloud_module=cloud, args=self.args)\n except SuiteException as error:\n self.logger.error(\"Something went wrong in suite creation! {}\".format(error))\n retcodesummary = ExitCodes.EXIT_INCONC\n return retcodesummary\n\n if self.args.list:\n if self.args.cloud:\n testsuite.update_testcases()\n testcases = testsuite.list_testcases()\n print(testcases)\n return retcodesummary\n\n results = self.runtestsuite(testsuite=testsuite)\n\n if not results:\n retcodesummary = ExitCodes.EXIT_SUCCESS\n elif results.failure_count() and self.args.failure_return_value is True:\n retcodesummary = ExitCodes.EXIT_FAIL\n elif results.inconclusive_count() and self.args.failure_return_value is True:\n retcodesummary = ExitCodes.EXIT_INCONC\n\n return retcodesummary"],"string":"[\n \"def RunProgram(self, program, arguments, stdin, context, result):\\n\\n # Construct the environment.\\n environment = self.MakeEnvironment(context)\\n e_stdin = stdin\\n c = {}\\n for pair in context.items():\\n c[pair[0]] = pair[1]\\n for substitution in c.keys():\\n pattern = \\\"$(\\\"+substitution.upper()+\\\")\\\"\\n replacement = context[substitution]\\n e_stdin = e_stdin.replace(pattern, replacement)\\n basename = os.path.split(arguments[0])[-1]\\n qm_exec = qm.executable.Filter(e_stdin, -2)\\n\\n try:\\n exit_status= qm_exec.Run(arguments, environment)\\n stdout = qm_exec.stdout\\n stderr = qm_exec.stderr\\n causes = []\\n\\n if sys.platform != \\\"win32\\\":\\n if os.WIFEXITED(exit_status):\\n if exit_status != self.exit_code:\\n causes.append(\\\"exit_code\\\")\\n result[\\\"RunProgram.exit_code\\\"] = str(exit_status)\\n elif os.WIFSIGNALED(exit_status):\\n self.__cause= \\\"Process %s terminated by signal %d.\\\" % (basename, os.WTERMSIG(exit_status))\\n\\n elif os.WIFSTOPPED(exit_status):\\n self.__cause= \\\"Process %s stopped by signal %d.\\\" % (basename, os.WSTOPSIG(exit_status))\\n\\n else:\\n self.__cause= \\\"Process %s terminated abnormally.\\\" % basename\\n\\n # Check to see that the standard error matches.\\n if stderr:\\n causes.append(\\\"standard error\\\")\\n result[\\\"RunProgram.stderr\\\"] = \\\"'''\\\" + stderr + \\\"'''\\\"\\n # If anything went wrong, the test failed.\\n if causes:\\n result.Fail(\\\"Unexpected %s.\\\" % string.join(causes, \\\", \\\"))\\n except:\\n result.NoteException()\",\n \"def execute (self, program, cache=False):\\n ast = None\\n if cache:\\n requests_cache.install_cache('demo_cache',\\n allowable_methods=('GET', 'POST', ))\\n else:\\n requests_cache.disabled()\\n\\n if isinstance(program, str):\\n ast = self.parse (program)\\n if not ast:\\n raise ValueError (f\\\"Unhandled type: {type(program)}\\\")\\n for statement in ast.statements:\\n logger.debug (f\\\"execute: {statement} type={type(statement).__name__}\\\")\\n statement.execute (interpreter=self)\\n return self.context\",\n \"def run(name, program, arguments, cache_enabled, filename):\\n output = None\\n if cache_enabled:\\n output = get_output_from_cache(name, filename)\\n\\n if output is None:\\n call_arguments = [program] + arguments + [filename]\\n try:\\n output = subprocess.check_output(\\n call_arguments, stderr=subprocess.STDOUT)\\n except subprocess.CalledProcessError as error:\\n output = error.output\\n except OSError:\\n return {\\n filename: {\\n 'error': [('Could not execute \\\"%s\\\".%sMake sure all ' +\\n 'required programs are installed') %\\n (' '.join(call_arguments), os.linesep)]\\n }\\n }\\n output = output.decode('utf-8')\\n if cache_enabled:\\n save_output_in_cache(name, filename, output)\\n return output\",\n \"def run_program_from_python_test(program, args, stdin=''):\\n #environ = self.__MakeEnvironment()\\n # PC: fix values so they are strings. Needed for Windows.\\n #for key in environ.iterkeys():\\n #environ[key] = str(environ[key])\\n # provide full path for standard tools\\n program = context.environment.get('%s_path' % program, program)\\n basename = os.path.split(program)[-1]\\n args.insert(0,program)\\n\\n if self.connection_character_set:\\n args.extend(['-ch',self.connection_character_set])\\n script = stdin.encode(DB_CHAR_SET_NAME_TO_PYTHON_ENCODING_MAP[self.connection_character_set])\\n else:\\n script = stdin.encode('ascii')\\n script = substitute_macros(script)\\n try:\\n return_code, stdout, stderr = runProgram(args,[],stdin=script)\\n sys.stdout.writelines(stdout)\\n sys.stderr.writelines(stderr)\\n except:\\n result.note_exception(cause=\\\"Python test: Exception raised while running external program from Python test.\\\")\\n result[\\\"failing_program\\\"] = program\\n #cleanup()\",\n \"def execute_program(args: List[str], mode: TestMode) -> None:\\n args_str = ' '.join(args)\\n\\n def check_output(result: CompletedProcess):\\n stdout = strip_ascii_codes(result.stdout.decode())\\n error_string = f'Failure:\\\\nMode: {mode.value}\\\\nCommand: {args_str}\\\\nExit code: {result.returncode:X}\\\\nOutput:\\\\n{stdout}\\\\n'\\n if mode == TestMode.STUB:\\n if result.returncode not in (1, 2) or 'Test FAILED' not in stdout:\\n raise RuntimeError(error_string)\\n elif mode == TestMode.SOLUTION:\\n if (result.returncode != 0 or\\n \\\"*** You've passed ALL tests. Congratulations! ***\\\" not in stdout):\\n raise RuntimeError(error_string)\\n else:\\n print('Invalid mode: {}'.format(mode))\\n sys.exit(1)\\n\\n try:\\n result = subprocess.run(args, stdout=subprocess.PIPE, timeout=300)\\n check_output(result)\\n except RuntimeError as e:\\n print(e)\\n sys.exit(1)\\n except TimeoutExpired:\\n print(f\\\"{mode} > {args_str}: TIMEOUT\\\")\\n sys.exit(1)\",\n \"def run_program(\\n environment='emulator',\\n block_device=None,\\n dump_file=None,\\n dump_base=None,\\n dump_length=None,\\n timeout=60,\\n flush_l2=False,\\n trace=False,\\n executable=None):\\n if not executable:\\n executable = HEX_FILE\\n\\n if environment == 'emulator':\\n args = [BIN_DIR + 'emulator']\\n args += [ '-a' ] # Enable thread scheduling randomization by default\\n if block_device:\\n args += ['-b', block_device]\\n\\n if dump_file:\\n args += ['-d', dump_file + ',' +\\n hex(dump_base) + ',' + hex(dump_length)]\\n\\n args += [executable]\\n return _run_test_with_timeout(args, timeout)\\n elif environment == 'verilator':\\n args = [BIN_DIR + 'verilator_model']\\n if block_device:\\n args += ['+block=' + block_device]\\n\\n if dump_file:\\n args += ['+memdumpfile=' + dump_file,\\n '+memdumpbase=' + hex(dump_base)[2:],\\n '+memdumplen=' + hex(dump_length)[2:]]\\n\\n if flush_l2:\\n args += ['+autoflushl2=1']\\n\\n if trace:\\n args += ['+trace']\\n\\n args += ['+bin=' + executable]\\n output = _run_test_with_timeout(args, timeout)\\n if '***HALTED***' not in output:\\n raise TestException(output + '\\\\nProgram did not halt normally')\\n\\n return output\\n else:\\n raise TestException('Unknown execution environment')\",\n \"def main(*arguments):\\n\\n args = parse_args(arguments)\\n\\n if args.test_suite is not None:\\n test_suite = report_manager.load_test_suite_conf(args.test_suite)\\n for i, test in enumerate(test_suite):\\n args = parse_args(test)\\n process_args_and_run(args, test_suite_iter=i)\\n else:\\n process_args_and_run(args)\",\n \"def execute_file (self, program):\\n with open (program, \\\"r\\\") as stream:\\n self.execute (stream.read ())\\n return self.context\",\n \"def exec(self, program, *args, cwd=os.getcwd(), **kwargs):\\n if len(args) > 0:\\n raise RuntimeError(\\\"Program arguments are not supported for real hardware devices\\\")\\n\\n assert self.platform is not None, \\\"TVM targets need a platform to execute programs\\\"\\n\\n if self.timeout_sec > 0:\\n raise NotImplementedError\\n\\n ret = self.platform.run(program, self)\\n return ret\",\n \"def Run(self, cli, args):\\n metrics.Loaded()\\n\\n tool_context = {}\\n if self._parent_group:\\n self._parent_group.RunGroupFilter(tool_context, args)\\n\\n command_instance = self._common_type(cli=cli, context=tool_context)\\n\\n log.debug('Running %s with %s.', self.dotted_name, args)\\n resources = command_instance.Run(args)\\n resources = display.Displayer(command_instance, args, resources,\\n display_info=self.ai.display_info).Display()\\n metrics.Ran()\\n\\n if command_instance.exit_code != 0:\\n raise exceptions.ExitCodeNoError(exit_code=command_instance.exit_code)\\n\\n return resources\",\n \"def test_with_command_line_arguments(self, arguments):\\n fixed_arguments = self.get_argument_string(arguments)\\n result = self.run(\\n arguments=fixed_arguments,\\n timeout=self.full_timeout,\\n use_fresh_profile=True)\\n return self._handle_test_result(result)\",\n \"def Run(self, context, result):\\n\\n # Was the program not specified?\\n\\n self.program = context[\\\"isql_path\\\"]\\n\\n if context.has_key(\\\"database_path\\\"):\\n database = context[\\\"database_path\\\"]\\n else:\\n database = \\\"\\\"\\n self.RunProgram(self.program,\\n\\t\\t\\t[ self.program , database ,\\n \\\"-user\\\", context[\\\"user_name\\\"], \\\"-password\\\", context[\\\"user_password\\\"] ],\\n context, result)\",\n \"def runTool(self, filename, expected_out, args):\\n\\n input_path = os.path.join(self.inputs_dir, filename)\\n return_value, actual_output = create_subprocess(self.executable_binary, args + [input_path] + ['--'])\\n actual_output = actual_output.decode('utf-8')\\n\\n self.assertEqual(return_value, 0)\\n self.evaluate(expected_out, actual_output, command=f'{[self.executable_binary] + args} {filename}')\",\n \"def main(self, argv=None):\\n\\n p = self.build_parser()\\n\\n args = p.parse_args(argv)\\n\\n try:\\n return self.run(args) or 0\\n except CommandFailed as e:\\n print(str(e))\\n return 1\",\n \"def main(args: List[Union[str, bytes]] = sys.argv,):\\n\\tprogram_name, *args = args\\n\\targs = decode_raw_args(args, str)\\n\\n\\tgen = Generator(*args)\\n\\tgen.generate_data()\\n\\tgen.print_return_list()\",\n \"def run_program(program, args=None, **subprocess_kwargs):\\n if 'shell' in subprocess_kwargs and subprocess_kwargs['shell']:\\n raise ProgramError(\\n \\\"This function is only for non-shell programs, \\\"\\n \\\"use run_shell_command() instead.\\\")\\n fullcmd = find_program(program)\\n if not fullcmd:\\n raise ProgramError(\\\"Program %s was not found\\\" % program)\\n # As per subprocess, we make a complete list of prog+args\\n fullcmd = [fullcmd] + (args or [])\\n for stream in ['stdin', 'stdout', 'stderr']:\\n subprocess_kwargs.setdefault(stream, subprocess.PIPE)\\n subprocess_kwargs = alter_subprocess_kwargs_by_platform(\\n **subprocess_kwargs)\\n return subprocess.Popen(fullcmd, **subprocess_kwargs)\",\n \"def _test_program(self, result_success=True, verbose=False):\\n\\n # set testing parameters\\n if self.sim_type is InstructionListJointsFlags.Position:\\n self.program.simulation_type = self.sim_type\\n self.program.max_mm_step = self.sim_step_mm\\n self.program.max_deg_step = self.sim_step_deg\\n elif self.sim_type is InstructionListJointsFlags.TimeBased:\\n self.program.simulation_type = self.sim_type\\n self.program.max_time_step = self.sim_step_time\\n elif self.sim_type is None:\\n raise ValueError(\\\"No 'sim_type' provided\\\")\\n\\n self.program.load_to_robodk()\\n self.program.simulate()\\n\\n if verbose:\\n self.program.print()\\n self.program.simulation_result.add_to_robodk()\\n\\n if not result_success:\\n # other checks don't make sense\\n return\\n\\n # perform checks on simulation result\\n self._test_if_result_message_is_success()\\n self._test_for_playback_frame_errors()\\n self._test_for_missing_move_ids()\\n self._test_for_valid_move_ids()\\n self._test_max_simulation_step()\\n self._test_if_stop_points_reached()\",\n \"def run(self, argv):\\n global console\\n\\n console = init_console()\\n\\n command, options = self.parse_options(argv)\\n\\n # We call out to things like the test runner, which expect to operate\\n # off of sys.argv. We want to simulate that now that we've parsed\\n # options. We'll restore sys.argv after the command finishes.\\n old_argv = sys.argv\\n sys.argv = argv\\n\\n try:\\n return command.run(options)\\n except Exception as e:\\n logger.exception('Unexpected exception when running command '\\n '\\\"%s\\\": %s',\\n command.name, e)\\n return 1\\n finally:\\n sys.argv = old_argv\",\n \"def main():\\n args = get_args()\\n prg = args.program\\n\\n if not os.path.isfile(prg):\\n die('Missing expected program \\\"{}\\\"'.format(prg))\\n\\n for name in args.name:\\n cmd = '{} \\\"{}\\\"'.format(prg, name)\\n rv, out = getstatusoutput(cmd)\\n if rv != 0:\\n warn('Failed to run: {}\\\\nError: {}'.format(cmd, out))\\n else:\\n print('Success: \\\"{}\\\"'.format(out))\\n\\n print('Done.')\",\n \"def run_program(program):\\n halt = False\\n instruction_pointer = 0\\n\\n while not halt:\\n halt = process_instruction(instruction_pointer, program)\\n instruction_pointer += STEP_SIZE\\n\\n return program\",\n \"def test_run(prog, correct, inp=None):\\n print(f\\\"Testing [{prog2str(prog)}] (inp={inp})...\\\", end=\\\"\\\")\\n out = list()\\n run_prog(prog, inp, out)\\n if (out and out == correct) or (not out and prog == correct):\\n print(\\\" OK!\\\")\\n else:\\n fail_str = prog2str(out if out else prog)\\n print(f\\\" Failed: [{fail_str}] != [{prog2str(correct)}]!\\\")\",\n \"def testCreateProgram(self):\\n try:\\n contextID, retErr = PyOpenCLInterface.CreateContext(self.testResources.listDevicesIDs, self.testResources.dictProperties)\\n self.assertEqual(retErr, 0)\\n # create mem program\\n programID, retErr = PyOpenCLInterface.CreateProgram(contextID, self.testResources.programCodeStrings)\\n self.assertEqual(retErr, 0)\\n listPrograms = PyOpenCLInterface.ListPrograms()\\n self.assertEqual(listPrograms, [programID])\\n programProperty, retErr = PyOpenCLInterface.GetProgramProperties(programID)\\n self.assertEqual(programProperty['id'], programID)\\n self.assertEqual(programProperty['Devices'], self.testResources.listDevicesIDs)\\n self.assertEqual(programProperty['Context'], contextID)\\n retErr = PyOpenCLInterface.ReleaseProgram(programID)\\n self.assertEqual(retErr, 0)\\n listPrograms = PyOpenCLInterface.ListPrograms()\\n self.assertEqual(listPrograms, [])\\n retErr = PyOpenCLInterface.ReleaseContext(contextID)\\n self.assertEqual(retErr, 0)\\n except:\\n print \\\"Exception caught:\\\", sys.exc_info()[0]\",\n \"def run(context, path=\\\"\\\"):\\n common.success(f\\\"Tests {path} running \\\")\\n return start.run_python(\\n context,\\n f\\\"-m pytest {path}\\\"\\n )\",\n \"def main(cls, *args, **kwargs):\\n assert not (bool(args) and bool(kwargs))\\n if args:\\n return cls._run_args(args)\\n elif kwargs:\\n return cls._run_kwargs(kwargs)\\n else:\\n return cls._run_args(None)\",\n \"def main():\\n\\n parser = argparse.ArgumentParser(prog=\\\"run_test.py\\\",\\n formatter_class=argparse.RawTextHelpFormatter)\\n parser.add_argument('id', help=\\\"Id of a test\\\")\\n args = parser.parse_args()\\n\\n configure_logger()\\n\\n test_info = TESTS.get(args.id, None)\\n if not test_info:\\n test_info.log.error(f'{args.id} does not exist')\\n exit(ErrorCode.CRITICAL)\\n os.environ['DISPLAY'] = \\\":0.0\\\"\\n\\n test = Test(args.id, test_info)\\n result = test.run()\\n\\n test.log.info('#' * 80)\\n if not result:\\n test.log.error('TEST FAILED')\\n else:\\n test.log.info('TEST PASSED')\\n test.log.info('#' * 80)\\n exit(not result)\",\n \"def main(argv=sys.argv):\\n log = _setup_logging()\\n log.info(\\\"Starting {f} version {v} dataset manipulator\\\".format(\\n f=__file__, v=__VERSION__))\\n parser = get_parser()\\n args = parser.parse_args()\\n if args.debug:\\n log.setLevel(logging.DEBUG)\\n return args.func(args)\\n #return main_runner_default(argv[1:], get_parser(), log)\",\n \"def main(self):\\n try:\\n self.parse_args()\\n self.run()\\n return 0\\n except AnalysisBackendError as e:\\n L.error(e)\\n return 1\",\n \"def execute_tool(description, *args):\\n command_line = list(args) + files_and_directories\\n click.echo(f\\\"{description}: {' '.join(command_line)}\\\")\\n rv = call(command_line)\\n if rv != 0:\\n exit(rv)\",\n \"def main():\\n args, subparser = parse_args()\\n if not args:\\n sys.exit(1)\\n\\n # configure logging\\n Log.config(args[0].log_level, args[0].log_file)\\n\\n # display GeneFlow version\\n Log.some().info('GeneFlow %s', __version__)\\n\\n # call the appropriate command\\n if not args[0].func(\\n args=args[0],\\n other_args=args[1],\\n subparser=subparser\\n ):\\n sys.exit(1)\\n\\n sys.exit(0)\",\n \"def main(cli_args=None):\\n # build an arg parser\\n parser = get_arg_parser()\\n\\n # run the parser on cli args\\n args = parser.parse_args(cli_args)\\n\\n print(f\\\"Running script with arguments: {args}\\\")\\n test_input(args.raw_training_data)\\n test_input(args.raw_testing_data)\\n test_output(args.train_output)\\n test_output(args.test_output)\",\n \"def testBuildProgramSuccess(self):\\n try:\\n contextID, retErr = PyOpenCLInterface.CreateContext(self.testResources.listDevicesIDs, self.testResources.dictProperties)\\n self.assertEqual(retErr, 0)\\n # create mem program\\n programID, retErr = PyOpenCLInterface.CreateProgram(contextID, self.testResources.programCodeStrings)\\n self.assertEqual(retErr, 0)\\n buildOptions = \\\"\\\"\\n retErr = PyOpenCLInterface.BuildProgram(programID, self.testResources.listDevicesIDs, buildOptions)\\n self.assertEqual(retErr, 0)\\n buildInfo = \\\"CL_PROGRAM_BUILD_STATUS\\\"\\n dictResp, retErr = PyOpenCLInterface.GetProgramBuildInfo(programID, self.testResources.listDevicesIDs[0], buildInfo);\\n self.assertEqual(retErr, 0)\\n self.assertEqual(dictResp['CL_PROGRAM_BUILD_STATUS'], 'CL_BUILD_SUCCESS')\\n retErr = PyOpenCLInterface.ReleaseProgram(programID)\\n self.assertEqual(retErr, 0)\\n listPrograms = PyOpenCLInterface.ListPrograms()\\n self.assertEqual(listPrograms, [])\\n retErr = PyOpenCLInterface.ReleaseContext(contextID)\\n self.assertEqual(retErr, 0)\\n except:\\n print \\\"Exception caught:\\\", sys.exc_info()[0]\",\n \"def main(argv):\\n global g_test_root_dir\\n global g_temp_filename\\n\\n if len(argv) < 2:\\n print(\\\"invoke this script as python collectUnitTestRunTime.py 10 'python run.py_path/run.py --wipe \\\"\\n \\\"--test dir_to_test/test1,python run.py_path/run.py --wipe --test dir_to_test2/test2,...' True\\\\n\\\")\\n sys.exit(1)\\n else: # we may be in business\\n repeat_number = int(argv[1]) # number of times to run a unit test\\n command_lists = argv[2] # list of unit tests to run\\n\\n for command in command_lists.split(','): # for each command in the list\\n # run command repeat_number of times and collect results into result_dict\\n run_commands(command, repeat_number, g_temp_filename)\",\n \"def main(argv=None):\\n # __package__ should be `development.main`\\n run_example_local('examples.run_rl', argv)\",\n \"def run_test_case(project, test_case_name, environment):\\n timestamp = utils.get_timestamp()\\n param_list = ['golem','run', project, test_case_name,\\n '--timestamp', timestamp]\\n if environment:\\n param_list.append('--environments')\\n param_list.append(environment)\\n subprocess.Popen(param_list)\\n return timestamp\",\n \"def run(self, inp=\\\"\\\"):\\n result = self.run_process(inp)\\n if result.returncode == 0:\\n outcome = self.PASS\\n elif result.returncode < 0:\\n outcome = self.FAIL\\n else:\\n outcome = self.UNRESOLVED\\n return (result, outcome)\",\n \"def main():\\n run_program()\",\n \"def main():\\n program_name = os.path.basename(sys.argv[0])\\n\\n _initialize_debugging(program_name)\\n _handle_signals()\\n _process_environment_variables()\\n arguments = _process_command_line()\\n\\n exit_status = 1 # no match\\n if arguments:\\n for filename in arguments:\\n if os.path.isfile(filename):\\n if not parameters[\\\"No formatting\\\"]:\\n print(filename + \\\":\\\")\\n for _, printable_string in strings.strings(filename):\\n if what_in_string(printable_string):\\n exit_status = 0 # match found\\n if parameters[\\\"First match only\\\"]:\\n break\\n else:\\n logging.error('\\\"%s\\\": No such file or directory', filename)\\n elif parameters[\\\"Stdin unused\\\"]:\\n logging.critical(\\\"At least one filename expected\\\")\\n else:\\n for _, printable_string in strings.strings():\\n if what_in_string(printable_string):\\n exit_status = 0 # match found\\n if parameters[\\\"First match only\\\"]:\\n break\\n\\n sys.exit(exit_status)\",\n \"def runScript(self, script):\\n data = FilePath(__file__).parent().child('data')\\n sample_file = data.child('1.input.ofx')\\n\\n args = (script, [sample_file.path])\\n log.msg('executing %r' % (args,))\\n out, err, rc = yield utils.getProcessOutputAndValue(*args, env=None)\\n log.msg('rc: %r' % (rc,))\\n log.msg('out: %r' % (out,))\\n log.msg('err: %r' % (err,))\\n if rc != 0:\\n self.fail(\\\"Failed: %s\\\\n\\\\n%s\\\" % (out, err))\",\n \"def main():\\n\\n parser = get_populated_argparser()\\n cli_dict = generate_cli_dictionary()\\n\\n # Parse arguments.\\n if len(sys.argv) > 1:\\n args = parser.parse_args(sys.argv[1:])\\n\\n # Validate arguments.\\n args = cli_dict[args.tool].validate_args(args)\\n\\n # Run the tool.\\n cli_dict[args.tool].run(args)\\n\\n else:\\n\\n parser.print_help()\",\n \"def run(ctx, problem,\\n hparam_set: str = None,\\n progress: bool = False,\\n cuda: bool = False,\\n device: str = None,\\n log_dir: str = None,\\n checkpoint_prefix: str = 'checkpoint',\\n **kwargs):\\n\\n # Read custom transformation arbitrary CLI key value pairs.\\n extra_hparams = ctx.obj.get('extra_hparams')\\n kwargs.pop('extra_hparams')\\n\\n do_run(problem,\\n hparam_set=hparam_set,\\n extra_hparams=extra_hparams,\\n progress=progress,\\n cuda=cuda,\\n device=device,\\n log_dir=log_dir,\\n checkpoint_prefix=checkpoint_prefix,\\n **kwargs)\",\n \"def ExecuteFile(python_program, main_filename, args, env, module_space,\\n coverage_entrypoint, workspace):\\n # type: (str, str, list[str], dict[str, str], str, str|None, str|None) -> ...\\n # We want to use os.execv instead of subprocess.call, which causes\\n # problems with signal passing (making it difficult to kill\\n # Bazel). However, these conditions force us to run via\\n # subprocess.call instead:\\n #\\n # - On Windows, os.execv doesn't handle arguments with spaces\\n # correctly, and it actually starts a subprocess just like\\n # subprocess.call.\\n # - When running in a workspace (i.e., if we're running from a zip),\\n # we need to clean up the workspace after the process finishes so\\n # control must return here.\\n # - If we may need to emit a host config warning after execution, we\\n # can't execv because we need control to return here. This only\\n # happens for targets built in the host config.\\n # - For coverage targets, at least coveragepy requires running in\\n # two invocations, which also requires control to return here.\\n #\\n if not (IsWindows() or workspace or coverage_entrypoint):\\n _RunExecv(python_program, main_filename, args, env)\\n\\n if coverage_entrypoint is not None:\\n ret_code = _RunForCoverage(python_program, main_filename, args, env,\\n coverage_entrypoint, workspace)\\n else:\\n ret_code = subprocess.call(\\n [python_program, main_filename] + args,\\n env=env,\\n cwd=workspace\\n )\\n\\n if workspace:\\n shutil.rmtree(os.path.dirname(module_space), True)\\n sys.exit(ret_code)\",\n \"def run(argv=None):\\n program_conf = get_program_conf()\\n\\n # Test usage of utils... do something with ddl\\n print(get_ddl_list('dev')[2])\\n\\n # Parse args\\n program_args = program_conf[PROGRAM_ARGS_CONF]\\n pipeline_args = program_conf[PIPELINE_ARGS_CONF]\\n pipeline_options = PipelineOptions(pipeline_args)\\n pipeline_options.view_as(SetupOptions).save_main_session = True\\n\\n # Get schema path key for validation\\n gcs_client = storage.Client()\\n etl_config_dict = get_gcs_json_as_dict(program_args[ETL_CONFIG_PATH_CONF], gcs_client)\\n schema_dict = get_gcs_json_as_dict(program_args[SCHEMA_PATH_KEY], gcs_client)\\n input_file_path = program_args[INPUT_CONF]\\n\\n with beam.Pipeline(options=pipeline_options) as pipeline:\\n validated_records = (\\n pipeline \\n | 'read' >> ReadFromText(input_file_path)\\n | 'validate' >> beam.ParDo(ValidateRecord(schema_dict, input_file_path, etl_config_dict))\\n | 'filter_data' >> beam.Filter(lambda x: x[1] and x[2] and x[3])\\n | 'recover_data' >> beam.Map(lambda x: x[0])\\n # | beam.Map(print)\\n )\\n if IS_VALID_FILE:\\n validated_records | 'write_success' >> WriteToText(program_args[OUTPUT_CONF]) \\n else:\\n validated_records | 'write_reject' >> WriteToText(program_args[REJECT_CONF])\",\n \"def run_program(self, sAppName, hArgsList, hEnvsList, nFlags = consts.PRPM_RUN_PROGRAM_AND_RETURN_IMMEDIATELY, nStdin = consts.PRL_INVALID_FILE_DESCRIPTOR, nStdout = consts.PRL_INVALID_FILE_DESCRIPTOR, nStderr = consts.PRL_INVALID_FILE_DESCRIPTOR):\\n\\t\\treturn Job(SDK.PrlVmGuest_RunProgram(self.handle, sAppName, conv_handle_arg(hArgsList), conv_handle_arg(hEnvsList), nFlags, nStdin, nStdout, nStderr)[0])\",\n \"def Run(self, context, result):\\n\\n # check if the test can be run on the current platform\\n if not self.SupportedPlatform(context, result):\\n return\\n\\n # If the context specifies a shell, use it.\\n if context.has_key(\\\"ShellCommandTest.command_shell\\\"):\\n # Split the context value to build the argument list.\\n shell = qm.common.split_argument_list(\\n context[\\\"ShellCommandTest.command_shell\\\"])\\n else:\\n # Otherwise, use a platform-specific default.\\n shell = qm.platform.get_shell_for_command()\\n\\n # Environment for the nightlies (user lcgnight)\\n if sys.platform.startswith(\\\"win\\\") \\\\\\n and \\\"USERNAME\\\" in os.environ \\\\\\n and os.environ[\\\"USERNAME\\\"] == \\\"lcgnight\\\" :\\n # Take TNS_ADMIN from DFS on Win (bug #86450)\\n os.environ[\\\"TNS_ADMIN\\\"]=r\\\"\\\\\\\\cern.ch\\\\dfs\\\\Applications\\\\Oracle\\\\ADMIN\\\"\\n # Use credentials from %USERPROFILE%\\\\private on Win (bug #86463)\\n private = os.path.join(os.environ[\\\"USERPROFILE\\\"],\\\"private\\\")\\n os.environ[\\\"CORAL_AUTH_PATH\\\"] = private\\n os.environ[\\\"CORAL_DBLOOKUP_PATH\\\"] = os.environ[\\\"CORAL_AUTH_PATH\\\"]\\n elif sys.platform.startswith(\\\"darwin\\\") \\\\\\n and \\\"USER\\\" in os.environ \\\\\\n and os.environ[\\\"USER\\\"] == \\\"lcgnight\\\" :\\n # Use credentials from $HOME/private on Mac (bug #86463)\\n private = os.path.join(os.environ[\\\"HOME\\\"],\\\"private\\\")\\n os.environ[\\\"CORAL_AUTH_PATH\\\"] = private\\n os.environ[\\\"CORAL_DBLOOKUP_PATH\\\"] = os.environ[\\\"CORAL_AUTH_PATH\\\"]\\n elif sys.platform.startswith(\\\"linux\\\") \\\\\\n and \\\"USER\\\" in os.environ \\\\\\n and os.environ[\\\"USER\\\"] == \\\"lcgnight\\\" :\\n # Use credentials from POOL AFS area on Linux (bug #86463)\\n os.environ[\\\"CORAL_AUTH_PATH\\\"] = \\\"/afs/cern.ch/sw/lcg/app/pool/db\\\"\\n os.environ[\\\"CORAL_DBLOOKUP_PATH\\\"] = os.environ[\\\"CORAL_AUTH_PATH\\\"]\\n\\n # Special handling if this is a python script (from COOLTests.py)\\n if sys.platform.startswith(\\\"win\\\") and \\\\\\n self.command.split()[0].endswith(\\\".py\\\"):\\n # We need to run a python test and not a standard executable\\n [cmd, arg] = self.command.split(' ',1)\\n for d in os.environ[\\\"PATH\\\"].split(os.path.pathsep):\\n if os.path.isdir(d) and cmd in os.listdir(d):\\n # we found the script in the path\\n self.command = 'python \\\"%s\\\" %s'%(os.path.join(d,cmd),arg)\\n break\\n\\n # Append the command at the end of the argument list.\\n arguments = shell + [ self.command ]\\n self.RunProgram(arguments[0], arguments, context, result)\",\n \"def execute_program(prgm, existing_env=None):\\n line_mgr, env = convert_program_to_lines(prgm, existing_env)\\n execution.execute_lines(line_mgr, env)\",\n \"def Run(main):\\n # Parse command-line arguments:\\n program_name = GetProgramName()\\n if not FLAGS.Parse(sys.argv[1:]):\\n FLAGS.PrintUsage()\\n return os.EX_USAGE\\n\\n # Initialize the logging system:\\n try:\\n log_root_level = ParseLogLevelFlag(FLAGS.log_level)\\n log_console_level = ParseLogLevelFlag(FLAGS.log_console_level)\\n log_file_level = ParseLogLevelFlag(FLAGS.log_file_level)\\n except Error as err:\\n print(err)\\n return os.EX_USAGE\\n\\n log_formatter = logging.Formatter(\\n '%(asctime)s %(levelname)s %(filename)s:%(lineno)s : %(message)s')\\n\\n logging.root.setLevel(log_root_level)\\n\\n console_handler = logging.StreamHandler()\\n console_handler.setFormatter(log_formatter)\\n console_handler.setLevel(log_console_level)\\n logging.root.addHandler(console_handler)\\n\\n # Initialize log dir:\\n timestamp = Timestamp()\\n pid = os.getpid()\\n\\n if FLAGS.log_dir is None:\\n tmp_dir = os.path.join('/tmp', program_name)\\n if not os.path.exists(tmp_dir): os.makedirs(tmp_dir)\\n FLAGS.log_dir = tempfile.mkdtemp(\\n prefix='%s.%d.' % (timestamp, pid),\\n dir=tmp_dir)\\n logging.info('Using log dir: %s', FLAGS.log_dir)\\n if not os.path.exists(FLAGS.log_dir):\\n os.makedirs(FLAGS.log_dir)\\n\\n log_file = os.path.join(FLAGS.log_dir,\\n '%s.%s.%d.log' % (program_name, timestamp, pid))\\n\\n file_handler = logging.FileHandler(filename=log_file)\\n file_handler.setFormatter(log_formatter)\\n file_handler.setLevel(log_file_level)\\n logging.root.addHandler(file_handler)\\n\\n # Run program:\\n sys.exit(main(FLAGS.GetUnparsed()))\",\n \"def test_execute_with_context(self):\\n pass\",\n \"def _run_command(self, prog_name, args):\\n\\n # Build the command with the fullpath to the executable\\n\\n command = [os.path.join(VorpatestLibrary._bin_dir, prog_name)] + args\\n\\n # Debug: uncomment the following to debug the library for valgrind\\n # executions.\\n # command = ['/home/vorpatest/bin/dummy_vorpaline.sh'] + command\\n\\n # Check if execution is controlled by a wrapper\\n\\n if VorpatestLibrary._exec_wrapper != None:\\n command = VorpatestLibrary._exec_wrapper.wrap_command(\\n prog_name, command, self._preserve_files\\n )\\n\\n self._log(\\\"Run command: %s\\\" % command)\\n self._status = -1\\n\\n try:\\n output = subprocess.check_output(command, stderr=subprocess.STDOUT)\\n self._log(\\\"Command passed\\\")\\n self._log(\\\"Output: %s\\\" % output)\\n except subprocess.CalledProcessError as e:\\n self._log(\\\"Command failed!\\\")\\n self._log(\\\"Return code: %s\\\" % e.returncode)\\n self._log(\\\"Exception: %s\\\" % e)\\n self._log(\\\"Output: %s\\\" % e.output)\\n raise\\n except:\\n (exc_type, exc_value) = sys.exc_info()[:2]\\n self._log(\\\"Command failed!\\\")\\n self._log(\\\"Exception type: %s\\\" % exc_type)\\n self._log(\\\"Exception value: %s\\\" % exc_value)\\n raise\\n\\n self._status = 0\",\n \"def run(self, specs, experiment = None, dirname = 'target'):\\n self.generate(specs = specs, experiment = experiment, dirname = dirname)\\n cwd = os.getcwd()\\n os.chdir(dirname)\\n status = os.system('stack build')\\n if status == 0:\\n status = os.system('stack exec modeling-project-exe')\\n os.chdir(cwd)\\n if (status == 0) and (not (experiment is None)):\\n experiment.open()\\n return status\",\n \"def cli(ctx, runnable_identifier, job_path, **kwds):\\n runnable_identifier = translate_alias(ctx, runnable_identifier, kwds.get('profile'))\\n path = uri_to_path(ctx, runnable_identifier)\\n if os.path.exists(path):\\n runnable = for_path(path)\\n else: # assume galaxy workflow id\\n runnable = for_id(runnable_identifier)\\n\\n # TODO: do a better test of cwl.\\n is_cwl = path.endswith(\\\".cwl\\\")\\n kwds[\\\"cwl\\\"] = is_cwl\\n if kwds.get(\\\"engine\\\", None) is None:\\n if is_cwl:\\n kwds[\\\"engine\\\"] = \\\"cwltool\\\"\\n elif kwds.get('galaxy_url', None):\\n kwds[\\\"engine\\\"] = \\\"external_galaxy\\\"\\n else:\\n kwds[\\\"engine\\\"] = \\\"galaxy\\\"\\n with engine_context(ctx, **kwds) as engine:\\n run_result = engine.run(runnable, job_path)\\n if not run_result.was_successful:\\n warn(\\\"Run failed [%s]\\\" % unicodify(run_result))\\n ctx.exit(1)\\n outputs_dict = run_result.outputs_dict\\n output_json = kwds.get(\\\"output_json\\\", None)\\n if output_json:\\n with open(output_json, \\\"w\\\") as f:\\n json.dump(outputs_dict, f)\\n\\n return 0\",\n \"def main(execution_type = \\\"Native\\\", qmetry_data = {}):\\r\\n try:\\r\\n prog = dellunit.TestProgram(LOGTIME, execution_type, qmetry_data)\\r\\n jsonfile = ('report_%s.json'%logtime if (prog.timestamp_report == 1) else 'report.json')\\r\\n htmlfile = ('report_%s.html'%logtime if (prog.timestamp_report == 1) else 'report.html')\\r\\n JSON_FILE = os.path.abspath(os.path.join('logs', jsonfile))\\r\\n REPORT_FILE = os.path.abspath(os.path.join('logs', htmlfile))\\r\\n\\r\\n invalid_tests_json = []\\r\\n for test in prog.invalid_tests:\\r\\n test_json = {}\\r\\n test_json[\\\"testcase\\\"] = test\\r\\n test_json[\\\"invalid\\\"] = True\\r\\n test_json[\\\"status\\\"] = \\\"error\\\"\\r\\n test_json[\\\"description\\\"] = util.getTestcaseInformation(test)[\\\"Testcase Name\\\"]\\r\\n invalid_tests_json.append(test_json)\\r\\n\\r\\n except:\\r\\n sys.stdout == sys.__stdout__\\r\\n sys.stderr == sys.__stderr__\\r\\n logging.exception('Fatal error during test, skipping report generation')\\r\\n os.chdir(CURRENT_DIR)\\r\\n sys.exit(1)\\r\\n\\r\\n with open(JSON_FILE, 'r') as outfile_read:\\r\\n data = json.load(outfile_read)\\r\\n with open(JSON_FILE, 'w') as outfile_write:\\r\\n data[\\\"results\\\"][0][\\\"tests\\\"].extend(invalid_tests_json)\\r\\n json.dump(data, outfile_write)\\r\\n logging.info('Creating the report.html file')\\r\\n report.render_report(JSON_FILE, REPORT_FILE)\\r\\n os.chdir(CURRENT_DIR)\",\n \"def run_program(\\n args: Union[str, List[str]],\\n error_ok: bool = False,\\n max_error_lines: int = 100,\\n cwd: Optional[str] = None,\\n log_command: bool = False) -> ProgramResult:\\n if not isinstance(args, list):\\n args = [args]\\n if log_command:\\n if cwd is None:\\n dir_for_logging = os.getcwd()\\n else:\\n dir_for_logging = cwd\\n logging.info(\\\"Running command: %s (in directory: %s)\\\", shlex_join(args), dir_for_logging)\\n try:\\n program_subprocess = subprocess.Popen(\\n args,\\n stdout=subprocess.PIPE,\\n stderr=subprocess.PIPE,\\n cwd=cwd)\\n except OSError:\\n logging.error(\\\"Failed to run program {}\\\".format(args))\\n raise\\n\\n program_stdout, program_stderr = program_subprocess.communicate()\\n error_msg = None\\n if program_subprocess.returncode != 0:\\n error_msg = \\\"Non-zero exit code {} from: {} ; stdout: '{}' stderr: '{}'\\\".format(\\n program_subprocess.returncode, shlex_join(args),\\n trim_output(program_stdout.strip(), max_error_lines),\\n trim_output(program_stderr.strip(), max_error_lines))\\n if not error_ok:\\n raise RuntimeError(error_msg)\\n return ProgramResult(\\n cmd_line=args,\\n returncode=program_subprocess.returncode,\\n stdout=program_stdout.strip().decode('utf-8'),\\n stderr=program_stderr.strip().decode('utf-8'),\\n error_msg=error_msg,\\n program_path=os.path.realpath(args[0])\\n )\",\n \"def main():\\n long_description = (\\n \\\"Computing a deflex scenario. By default the name of the result file \\\"\\n \\\"is derived from the name of the input file but by adding '--results` \\\"\\n \\\"it is possible to define a custom path. The results will be of the \\\"\\n \\\"same file format as the input scenario.\\\\n\\\"\\n \\\"Optionally a dump-file can be stored. If no path is given the path \\\"\\n \\\"is derived from the path of the input scenario. The suffix of the \\\"\\n \\\"dump is '.dflx'.\\\"\\n )\\n parser = argparse.ArgumentParser(\\n prog=\\\"deflex-compute\\\",\\n description=long_description,\\n formatter_class=argparse.RawDescriptionHelpFormatter,\\n )\\n parser.add_argument(\\n \\\"--version\\\", action=\\\"version\\\", version=f\\\"deflex {deflex.__version__}\\\"\\n )\\n parser.add_argument(\\\"path\\\", type=str, help=\\\"Input file or directory.\\\")\\n parser.add_argument(\\n \\\"--results\\\",\\n dest=\\\"results\\\",\\n const=True,\\n default=True,\\n nargs=\\\"?\\\",\\n help=(\\n \\\"The name of the results file or directory or False to get no \\\"\\n \\\"result file. By default the path is derived from scenario path.\\\"\\n ),\\n )\\n parser.add_argument(\\n \\\"--dump\\\",\\n dest=\\\"dump\\\",\\n const=True,\\n default=None,\\n nargs=\\\"?\\\",\\n help=(\\n \\\"The name of the dump file. Leave empty for the default file name.\\\"\\n ),\\n )\\n parser.add_argument(\\n \\\"--solver\\\",\\n dest=\\\"solver\\\",\\n const=\\\"cbc\\\",\\n default=\\\"cbc\\\",\\n nargs=\\\"?\\\",\\n help=\\\"Solver to use for computing (default: cbc)\\\",\\n )\\n\\n args = parser.parse_args()\\n\\n deflex.use_logging()\\n\\n deflex.scripts.model_scenario(**vars(args))\",\n \"def ez_run(cls, program_string):\\n res = Cpu('test')\\n res.load(program_string.split('\\\\n'))\\n res.run()\\n return res\",\n \"def execute(self, code: int = 0, args: Optional[List[str]] = None, fail: str = \\\"\\\", verbose: bool = False):\\n assert fail in AssemblyTest._can_fail, f\\\"Invalid fail={fail}. Can only fail: {list(AssemblyTest._can_fail)}\\\"\\n\\n \\\"\\\"\\\" As soon as this function is called, the AssemblyTest is considered \\\"executed\\\" for the duration of the life cycle of this test and should be treated as such. \\\"\\\"\\\"\\n self._has_executed = True\\n\\n # turn function to fail into a define\\n if len(fail) == 0:\\n defines = []\\n else:\\n ret = 0 if fail == 'malloc' else -1\\n defines = [\\\"--def\\\", f\\\"#{fail.upper()}_RETURN_HOOK=li a0 {ret}\\\"]\\n\\n # check arguments\\n if args is not None:\\n # TODO: check to see if any args clash with venus arguments\\n assert len(args) > 0, \\\"use None if you don't want to pass any arguments\\\"\\n for a in args:\\n assert not a.startswith('-'), f\\\"argument '{a}' starting with '-' is not allowed\\\"\\n # all arguments could potentially be filenames that we write to, so let's just add them\\n self._write_files |= set(args)\\n else:\\n # ensure that args is always a list\\n args = []\\n\\n lines = []\\n\\n lines += [f\\\".import ../../src/{i}\\\" for i in self._imports]\\n lines += [\\\"\\\", \\\".data\\\"] + self.data\\n lines += [\\\"\\\", \\\".globl main_test\\\", \\\".text\\\", \\\"# main_test function for testing\\\", \\\"main_test:\\\"]\\n\\n # prologue\\n if len(self._output_regs) > 0:\\n assert len(self._output_regs) < 13, f\\\"Too many output registers: {len(self._output_regs)}!\\\"\\n p = [\\\"# Prologue\\\", f\\\"addi sp, sp, -{4 * (len(self._output_regs) + 1)}\\\", \\\"sw ra, 0(sp)\\\"]\\n p += [f\\\"sw s{i}, {(i+1) * 4}(sp)\\\" for i in range(len(self._output_regs))]\\n lines += _indent(p + [\\\"\\\"])\\n\\n\\n lines += _indent(self._args)\\n\\n assert self._call is not None, \\\"No function was called!\\\"\\n foo_call = [\\\"\\\", f\\\"# call {self._call} function\\\", f\\\"jal ra {self._call}\\\"]\\n lines += _indent(foo_call)\\n\\n if len(self._output_regs) > 0:\\n lines += _indent([\\\"\\\", \\\"# save all return values in the save registers\\\"])\\n lines += _indent([f\\\"mv s{i} a{i}\\\" for i in self._output_regs] + [\\\"\\\"])\\n\\n lines += _indent(self._checks)\\n if code != 0:\\n lines += _indent([f\\\"# we expect {self._call} to exit early with code {code}\\\"])\\n\\n lines += _indent([\\\"\\\", \\\"# exit normally\\\"])\\n # epilogue\\n if len(self._output_regs) > 0:\\n p = [\\\"# Epilogue\\\", \\\"lw ra, 0(sp)\\\"]\\n p += [f\\\"lw s{i}, {(i + 1) * 4}(sp)\\\" for i in range(len(self._output_regs))]\\n p += [f\\\"addi sp, sp, {4 * (len(self._output_regs) + 1)}\\\"]\\n lines += _indent(p + [\\\"\\\"])\\n # lines += _indent([\\\"mv a0, zero\\\", \\\"ret\\\"])\\n lines += _indent([\\\"jal exit\\\"])\\n lines += [\\\"\\\"]\\n\\n if verbose: print()\\n filename = save_assembly(self.name, '\\\\n'.join(lines), verbose=verbose)\\n r, coverage = run_venus(filename, self.check_calling_convention, defines, args, verbose=verbose)\\n _process_coverage(coverage, self._assembly)\\n self._program_executed = True\\n self._std_out = r.stdout.decode('UTF-8')\\n if r.returncode != code:\\n self._print_failure(r, code)\",\n \"def _call(self,\\n\\t\\tconsole = ExtendedConsole,\\n\\t\\tcontext = None\\n\\t):\\n\\t\\tsys.modules['__main__'] = self.module__main__\\n\\t\\tmd = self.module__main__.__dict__\\n\\n\\t\\t# Establish execution context in the locals;\\n\\t\\t# iterate over all the loaders in self.context and\\n\\t\\tfor path, ldesc in self.context:\\n\\t\\t\\tltitle, loader, xpath = ldesc\\n\\t\\t\\trpath = xpath(path)\\n\\t\\t\\tli = loader(rpath)\\n\\t\\t\\tif li is None:\\n\\t\\t\\t\\tsys.stderr.write(\\n\\t\\t\\t\\t\\t\\\"%s %r does not exist or cannot be read%s\\\" %(\\n\\t\\t\\t\\t\\t\\tltitle, rpath, os.linesep\\n\\t\\t\\t\\t\\t)\\n\\t\\t\\t\\t)\\n\\t\\t\\t\\treturn 1\\n\\t\\t\\ttry:\\n\\t\\t\\t\\tcode = li.get_code(rpath)\\n\\t\\t\\texcept:\\n\\t\\t\\t\\tprint_exception(*sys.exc_info())\\n\\t\\t\\t\\treturn 1\\n\\t\\t\\tself.module__main__.__file__ = getattr(\\n\\t\\t\\t\\tli, 'get_filename', lambda x: x\\n\\t\\t\\t)(rpath)\\n\\t\\t\\tself.module__main__.__loader__ = li\\n\\t\\t\\ttry:\\n\\t\\t\\t\\texec(code, md, md)\\n\\t\\t\\texcept:\\n\\t\\t\\t\\te, v, tb = sys.exc_info()\\n\\t\\t\\t\\tprint_exception(e, v, tb.tb_next or tb)\\n\\t\\t\\t\\treturn 1\\n\\n\\t\\tif self.main == (None, None):\\n\\t\\t\\t# It's interactive.\\n\\t\\t\\tsys.argv = self.args or ['<console>']\\n\\n\\t\\t\\t# Use readline if available\\n\\t\\t\\ttry:\\n\\t\\t\\t\\timport readline\\n\\t\\t\\texcept ImportError:\\n\\t\\t\\t\\tpass\\n\\n\\t\\t\\tic = console(locals = md)\\n\\t\\t\\ttry:\\n\\t\\t\\t\\tic.interact()\\n\\t\\t\\texcept SystemExit as e:\\n\\t\\t\\t\\treturn e.code\\n\\t\\t\\treturn 0\\n\\t\\telse:\\n\\t\\t\\t# It's ultimately a code object.\\n\\t\\t\\tpath, loader = self.main\\n\\t\\t\\tself.module__main__.__file__ = getattr(\\n\\t\\t\\t\\tloader, 'get_filename', lambda x: x\\n\\t\\t\\t)(path)\\n\\t\\t\\tsys.argv = list(self.args)\\n\\t\\t\\tsys.argv.insert(0, self.module__main__.__file__)\\n\\t\\t\\ttry:\\n\\t\\t\\t\\tcode = loader.get_code(path)\\n\\t\\t\\texcept:\\n\\t\\t\\t\\tprint_exception(*sys.exc_info())\\n\\t\\t\\t\\treturn 1\\n\\n\\t\\t\\trv = 0\\n\\t\\t\\texe_exception = False\\n\\t\\t\\ttry:\\n\\t\\t\\t\\tif context is not None:\\n\\t\\t\\t\\t\\twith context:\\n\\t\\t\\t\\t\\t\\ttry:\\n\\t\\t\\t\\t\\t\\t\\texec(code, md, md)\\n\\t\\t\\t\\t\\t\\texcept:\\n\\t\\t\\t\\t\\t\\t\\texe_exception = True\\n\\t\\t\\t\\t\\t\\t\\traise\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\ttry:\\n\\t\\t\\t\\t\\t\\texec(code, md, md)\\n\\t\\t\\t\\t\\texcept:\\n\\t\\t\\t\\t\\t\\texe_exception = True\\n\\t\\t\\t\\t\\t\\traise\\n\\n\\t\\t\\texcept SystemExit as e:\\n\\t\\t\\t\\t# Assume it's an exe_exception as anything ran in `context`\\n\\t\\t\\t\\t# shouldn't cause an exception.\\n\\t\\t\\t\\trv = e.code\\n\\t\\t\\t\\te, v, tb = sys.exc_info()\\n\\t\\t\\t\\tsys.last_traceback = (tb.tb_next or tb)\\n\\t\\t\\texcept:\\n\\t\\t\\t\\tif exe_exception is False:\\n\\t\\t\\t\\t\\traise\\n\\t\\t\\t\\trv = 1\\n\\t\\t\\t\\te, v, tb = sys.exc_info()\\n\\t\\t\\t\\tprint_exception(e, v, tb.tb_next or tb)\\n\\t\\t\\t\\tsys.last_traceback = (tb.tb_next or tb)\\n\\n\\t\\t\\treturn rv\",\n \"def cli_main():\\n\\n\\n if len(sys.argv) > 1 and sys.argv[1].endswith('.xmind'):\\n xmind_file = sys.argv[1]\\n xmind_file = get_absolute_path(xmind_file)\\n logging.info('Start to convert XMind file: %s', xmind_file)\\n\\n if len(sys.argv) == 3 and sys.argv[2] == '-json':\\n testlink_json_file = xmind_testcase_to_json_file(xmind_file)\\n logging.info('Convert XMind file to testcase json file successfully: %s', testlink_json_file)\\n elif len(sys.argv) == 3 and sys.argv[2] == '-xml':\\n testlink_xml_file = xmind_to_testlink_xml_file(xmind_file)\\n logging.info('Convert XMind file to testlink xml files successfully: %s', testlink_xml_file)\\n elif len(sys.argv) == 3 and sys.argv[2] == '-csv':\\n zentao_csv_file = xmind_to_zentao_csv_file(xmind_file)\\n logging.info('Convert XMind file to zentao csv file successfully: %s', zentao_csv_file)\\n elif len(sys.argv) == 3 and sys.argv[2] == '-xlsx':\\n excel_xlsx_file = xmind_to_xlsx_file(xmind_file)\\n logging.info('Convert XMind file to zentao csv file successfully: %s', excel_xlsx_file)\\n else:\\n testlink_json_file = xmind_testcase_to_json_file(xmind_file)\\n testlink_xml_file = xmind_to_testlink_xml_file(xmind_file)\\n zentao_csv_file = xmind_to_zentao_csv_file(xmind_file)\\n logging.info('Convert XMind file successfully: \\\\n'\\n '1、 testcase json file(%s)\\\\n'\\n '2、 testlink xml file(%s)\\\\n'\\n '3、 zentao csv file(%s)',\\n testlink_json_file,\\n testlink_xml_file,\\n zentao_csv_file)\\n\\n\\n else:\\n print(__doc__)\\n logging.error('%s', __doc__)\",\n \"def test_runSuccess(self):\\n builder = BookBuilder()\\n self.assertEquals(\\n builder.run([\\n sys.executable, '-c',\\n 'import sys; '\\n 'sys.stdout.write(\\\"hi\\\\\\\\n\\\"); '\\n 'sys.stdout.flush(); '\\n 'sys.stderr.write(\\\"bye\\\\\\\\n\\\"); '\\n 'sys.stderr.flush()']),\\n \\\"hi\\\\nbye\\\\n\\\")\",\n \"def run_program ( name, command ):\\n\\n # Create temporary file to write stdout, stderr of the program\\n tmpfile,tmpname = tempfile.mkstemp()\\n ret_code = subprocess.call ( command, shell = True , stdout=tmpfile, stderr=tmpfile)\\n # Catch some error codes\\n if ret_code == 127:\\n msg = \\\"Unable to find %s executable, please make sure this is present in your PATH.\\\"%name\\n logger.error(msg) \\n raise Exception ( msg )\\n if ret_code == 1:\\n # Get the error message from the temporary file\\n errmsg = \\\"\\\\n\\\".join(line for line in open(tmpname).readlines())\\n os.remove(tmpname)\\n msg = \\\"%s was not able to run successfully. Please check output. Error message was:\\\\n%s\\\"%(name,errmsg)\\n raise Exception ( msg )\\n\\n os.remove(tmpname)\",\n \"def main():\\n parser = ArgumentParser(description=__doc__,\\n formatter_class=RawTextHelpFormatter)\\n parser.add_argument('code', help='Python code to execute')\\n group = parser.add_mutually_exclusive_group()\\n group.add_argument('-3', action='store_const', dest='python',\\n const='python3', help='Explicitly use Python 3')\\n group.add_argument('-2', action='store_const', dest='python',\\n const='python2', help='Explicitly use Python 2')\\n group.add_argument('-p', '--python', help='Specify python interpreter')\\n args = parser.parse_args()\\n if args.python is not None:\\n call([args.python, __file__, args.code])\\n else:\\n InteractiveInterpreter(LocalsImportDict()).runsource(args.code)\",\n \"def call_prog(args):\\n # Just dump the entirety of the command so that\\n # the user can specify whatever arguments they want\\n call(args)\",\n \"def run(argv: Optional[list[str]] = None) -> tuple[str, str]:\\n argv = argv if argv is not None else []\\n\\n with PipeStream() as stdin:\\n stdin.writer.close()\\n\\n with PipeStream() as stdout:\\n with PipeStream() as stderr:\\n gada.main(\\n [\\\"gada\\\"] + argv,\\n stdin=stdin.reader,\\n stdout=stdout.writer,\\n stderr=stderr.writer,\\n )\\n stdout.writer.close()\\n stderr.writer.close()\\n return (\\n stdout.reader.read().decode(errors=\\\"ignore\\\"),\\n stderr.reader.read().decode(errors=\\\"ignore\\\"),\\n )\",\n \"def run(argv: List[str], stdout: TextIO, stderr: TextIO) -> int:\\n parser = _make_argument_parser()\\n args, out, err = _parse_args(parser=parser, argv=argv)\\n if len(out) > 0:\\n stdout.write(out)\\n\\n if len(err) > 0:\\n stderr.write(err)\\n\\n if args is None:\\n return 1\\n\\n command, errors = _parse_args_to_params(args=args)\\n if errors:\\n for error in errors:\\n print(error, file=stderr)\\n return 1\\n\\n assert command is not None\\n\\n if not command.scenarios_dir.exists():\\n print(\\n f\\\"The directory you specified in --scenarios_dir does not exist \\\"\\n f\\\"on your system: {command.scenarios_dir}\\\",\\n file=stderr,\\n )\\n return 1\\n\\n if not command.scenarios_dir.is_dir():\\n print(\\n f\\\"The path you specified in --scenarios_dir is expected to be a directory, \\\"\\n f\\\"but it is not: {command.scenarios_dir}\\\",\\n file=stderr,\\n )\\n return 1\\n\\n if isinstance(command, Once):\\n errors = rasaeco.render.once(scenarios_dir=command.scenarios_dir)\\n elif isinstance(command, Continuously):\\n server = None # type: Optional[ThreadedServer]\\n\\n with contextlib.ExitStack() as exit_stack:\\n if command.port is not None:\\n server = ThreadedServer(\\n port=command.port,\\n scenarios_dir=command.scenarios_dir,\\n stdout=stdout,\\n stderr=stderr,\\n )\\n server.start()\\n exit_stack.push(server)\\n\\n stop = queue.Queue() # type: queue.Queue[bool]\\n\\n work_thread = threading.Thread(\\n target=_render_continuously,\\n args=(stdout, stderr, command.scenarios_dir, stop),\\n )\\n\\n prefix = \\\"In the main\\\"\\n work_thread.start()\\n try:\\n while True:\\n time.sleep(0.5)\\n except KeyboardInterrupt:\\n print(f\\\"{prefix}: Got a keyboard interrupt.\\\", file=stdout)\\n finally:\\n print(f\\\"{prefix}: Sending a stop from the main thread...\\\", file=stdout)\\n stop.put(True)\\n print(\\n f\\\"{prefix}: Waiting for the work thread in main to join...\\\",\\n file=stdout,\\n )\\n work_thread.join()\\n\\n else:\\n raise AssertionError(\\\"Unhandled command: {}\\\".format(command))\\n\\n if errors:\\n for error in errors:\\n print(error, file=stderr)\\n return 1\\n\\n return 0\",\n \"def execute(self):\\n if self._cli_arguments.get('<samplename>') == 'cfn':\\n generate_sample_cfn_module(self.env_root)\\n elif self._cli_arguments.get('<samplename>') == 'static-angular':\\n generate_sample_static_angular(self.env_root)\\n elif self._cli_arguments.get('<samplename>') == 'static-react':\\n generate_sample_static_react(self.env_root)\\n elif self._cli_arguments.get('<samplename>') == 'sls-py':\\n generate_sample_sls_module(self.env_root, 'sls-py')\\n elif self._cli_arguments.get('<samplename>') == 'sls-tsc':\\n generate_sample_sls_module(self.env_root, 'sls-tsc')\\n elif self._cli_arguments.get('<samplename>') == 'stacker':\\n generate_sample_stacker_module(self.env_root)\\n elif self._cli_arguments.get('<samplename>') == 'tf':\\n generate_sample_tf_module(self.env_root)\\n elif self._cli_arguments.get('<samplename>') == 'k8s-cfn-repo':\\n generate_sample_k8s_cfn_repo(self.env_root)\\n elif self._cli_arguments.get('<samplename>') == 'k8s-tf-repo':\\n generate_sample_k8s_tf_repo(self.env_root)\\n elif self._cli_arguments.get('<samplename>') == 'cdk-tsc':\\n generate_sample_cdk_tsc_module(self.env_root)\\n elif self._cli_arguments.get('<samplename>') == 'cdk-py':\\n generate_sample_cdk_py_module(self.env_root)\\n elif self._cli_arguments.get('<samplename>') == 'cdk-csharp':\\n generate_sample_cdk_cs_module(self.env_root)\\n else:\\n LOGGER.info(\\\"Available samples to generate:\\\")\\n for i in ['cfn', 'static-angular', 'static-react', 'sls-tsc',\\n 'sls-py', 'tf', 'k8s-cfn-repo', 'k8s-tf-repo',\\n 'stacker', 'cdk-tsc', 'cdk-py', 'cdk-csharp']:\\n print(i)\",\n \"def __call__(self, arguments):\\n filtered_arguments = self._filter_unused_argument_dash_m(arguments)\\n configuration = load_configuration()\\n\\n if len(filtered_arguments) > 1:\\n filtered_arguments = self._handle_debug_argument(filtered_arguments)\\n self._handle_version_argument(filtered_arguments)\\n self._handle_help_argument(filtered_arguments)\\n\\n if configuration.execute_before:\\n LOGGER.debug('Executing command \\\"%s\\\" before doing anything else.', configuration.execute_before)\\n command_and_arguments = configuration.execute_before.split()\\n execute_command(command_and_arguments[0], *command_and_arguments[1:])\\n\\n try:\\n self.function(filtered_arguments, configuration)\\n return exit(0)\\n\\n except errors.CommitterError as committer_exception:\\n LOGGER.error(committer_exception.message)\\n return exit(committer_exception.error_code)\\n\\n except KeyboardInterrupt:\\n LOGGER.error('Interrupted by user.\\\\n')\\n return exit(1)\",\n \"def execute(self, args=None):\\n parser = argparse.ArgumentParser(description='run tests on jupyter notebook')\\n\\n parser.add_argument('notebook', type=str, help='the jupyter notebook to test')\\n parser.add_argument('-t', '--target', type=str, metavar='', help='where to store results')\\n parser.add_argument('-c', '--context', type=str, metavar='', help='context directory')\\n parser.add_argument('-v', '--verbose', action='count', default=0, help='verbosity level')\\n\\n args = parser.parse_args(args)\\n\\n logger.setLevel(loglevel(args.verbose))\\n logger.debug(f'args: {args}')\\n\\n results = self._grade_notebook(\\n Path(args.notebook).absolute(),\\n target_dir=Path(args.target).absolute() if args.target else None,\\n context=Path(args.context).absolute() if args.context else None\\n )\\n\\n return results.summary().failed\",\n \"def parse_and_test(program: Text,\\n name: Text,\\n *,\\n filename: Text,\\n additional_search_paths: Tuple[str, ...] = (),\\n raise_on_error: bool = True,\\n test_filter: Optional[Text] = None,\\n trace_all: bool = False,\\n compare_jit: bool = True,\\n seed: Optional[int] = None) -> bool:\\n did_fail = False\\n test_name = None\\n type_info = None\\n\\n importer = import_helpers.Importer(additional_search_paths)\\n ran = 0\\n\\n try:\\n module = Parser(Scanner(filename, program), name).parse_module()\\n type_info = cpp_typecheck.check_module(module, importer.cache,\\n importer.additional_search_paths)\\n\\n ir_package = (\\n ir_converter.convert_module_to_package(\\n module, type_info, importer.cache, traverse_tests=True)\\n if compare_jit else None)\\n\\n interpreter = Interpreter(\\n module,\\n type_info,\\n importer.typecheck,\\n importer.additional_search_paths,\\n importer.cache,\\n trace_all=trace_all,\\n ir_package=ir_package)\\n for test_name in module.get_test_names():\\n if not _matches(test_name, test_filter):\\n continue\\n ran += 1\\n print('[ RUN UNITTEST ]', test_name, file=sys.stderr)\\n interpreter.run_test(test_name)\\n print('[ OK ]', test_name, file=sys.stderr)\\n\\n if ir_package and module.get_quickchecks():\\n if seed is None:\\n # We want to guarantee non-determinism by default. See\\n # https://abseil.io/docs/cpp/guides/random#stability-of-generated-sequences\\n # for rationale.\\n seed = int(os.getpid() * time.time())\\n print(f'[ SEED: {seed} ]')\\n for quickcheck in module.get_quickchecks():\\n test_name = quickcheck.f.name.identifier\\n print('[ RUN QUICKCHECK ]', test_name, file=sys.stderr)\\n quickcheck_helpers.run_quickcheck(\\n interpreter, ir_package, quickcheck, seed=seed)\\n print('[ OK ]', test_name, file=sys.stderr)\\n\\n except (PositionalError, FailureError, CppParseError, ScanError,\\n TypeInferenceError, XlsTypeError) as e:\\n did_fail = True\\n parser_helpers.pprint_positional_error(\\n e, output=cast(io.IOBase, sys.stderr))\\n if test_name:\\n print(\\n '[ FAILED ]',\\n test_name,\\n e.__class__.__name__,\\n file=sys.stderr)\\n if raise_on_error:\\n raise\\n finally:\\n if type_info is not None:\\n type_info.clear_type_info_refs_for_gc()\\n\\n print('[==================]', ran, 'test(s) ran.', file=sys.stderr)\\n return did_fail\",\n \"def main():\\n log.configureLogger(log=log.testlog, fileLog=log.TESTLOGFILE, format=log.TESTFORMAT, truncate=True)\\n log.configureLogger(log=log.log, fileLog=log.LOGFILE)\\n try:\\n opts, args = getopt.getopt(sys.argv[1:], \\\"h\\\", [\\\"help\\\", \\\"file=\\\", \\\"verbosity=\\\",\\n \\\"settings=\\\"]) \\n # process options\\n testfile = None\\n inifile = None\\n\\n verbosity = 2 \\n for o, a in opts:\\n if o in (\\\"-h\\\", \\\"--help\\\"):\\n print __doc__\\n sys.exit(0)\\n if o in (\\\"--file\\\",):\\n testfile = a\\n logger.debug(\\\"starting %s\\\" % testfile)\\n if o in (\\\"--settings\\\",):\\n inifile = a\\n if o in (\\\"--verbosity\\\",):\\n verbosity = a\\n\\n if testfile is None:\\n sys.stderr.write(\\\"Error, missing testfile\\\")\\n print __doc__\\n sys.exit(2)\\n\\n suite = regressionTest(testfile, inifile)\\n tester = TESTER(suite, verbosity)\\n # TODO: runTest gives a return value\\n tester.runTest()\\n tester.generateFailures()\\n\\n sys.exit(0)\\n\\t\\n except getopt.error, msg:\\n sys.stderr.write(\\\"%s\\\" % msg)\\n sys.stderr.write(\\\"for help use --help\\\")\\n sys.exit(2)\",\n \"def do_work():\\n args = sys.argv\\n args = args[1:] # First element of args is the file name\\n\\n if len(args) == 0:\\n print('You have not passed any commands in!')\\n else:\\n for a in args:\\n if a == '--help':\\n print('AWS - EKS - Terraform : Demo Tool')\\n print('Options:')\\n print(' --help -> show this help menu.')\\n print(' --tf -> execute terraform validate and apply.')\\n print(' --passtest -> pass unit test - flask deployment')\\n print(' --failtest -> fail unit test - flask deployment')\\n print(' --prepare -> prepare tests')\\n elif a == '--passtest':\\n os.system(\\\"python python_test.py\\\")\\n elif a == '--failtest':\\n os.system(\\\"python python_test_fail.py\\\")\\n elif a == '--tf':\\n os.system(\\\"cd terraform && terraform validate && terraform apply -auto-approve && cd ..\\\")\\n elif a == '--prepare':\\n os.system(\\\"aws eks update-kubeconfig --name flugeldev-cluster && kubectl delete -f curl.yml && kubectl apply -f curl.yml\\\")\\n else:\\n print('Unrecognised argument.')\",\n \"def run(\\n *args,\\n module=None,\\n plugins=(),\\n run_in_thread=default,\\n raise_on_error=default,\\n addopts=default,\\n defopts=default,\\n display_columns=default,\\n):\\n import ipytest\\n\\n run_in_thread = default.unwrap(run_in_thread, current_config[\\\"run_in_thread\\\"])\\n raise_on_error = default.unwrap(raise_on_error, current_config[\\\"raise_on_error\\\"])\\n addopts = default.unwrap(addopts, current_config[\\\"addopts\\\"])\\n defopts = default.unwrap(defopts, current_config[\\\"defopts\\\"])\\n display_columns = default.unwrap(display_columns, current_config[\\\"display_columns\\\"])\\n\\n if module is None:\\n import __main__ as module\\n\\n run = run_func_in_thread if run_in_thread else run_func_direct\\n exit_code = run(\\n _run_impl,\\n *args,\\n module=module,\\n plugins=plugins,\\n addopts=addopts,\\n defopts=defopts,\\n display_columns=display_columns,\\n )\\n\\n ipytest.exit_code = exit_code\\n\\n if raise_on_error is True and exit_code != 0:\\n raise Error(exit_code)\\n\\n return exit_code\",\n \"def Main(args=None):\\n util.ChangeStdoutEncoding()\\n\\n # Support for setuptools console wrappers.\\n if args is None:\\n args = sys.argv[1:]\\n\\n options = Options()\\n try:\\n args = options.ReadOptions(args) # args may be shorter after this\\n except getopt.GetoptError as e:\\n print(\\\"grit:\\\", str(e))\\n print(\\\"Try running 'grit help' for valid options.\\\")\\n return 1\\n if not args:\\n print(\\\"No tool provided. Try running 'grit help' for a list of tools.\\\")\\n return 2\\n\\n tool = args[0]\\n if tool == 'help':\\n if len(args) == 1:\\n PrintUsage()\\n return 0\\n else:\\n tool = args[1]\\n if not _GetToolInfo(tool):\\n print(\\\"No such tool. Try running 'grit help' for a list of tools.\\\")\\n return 2\\n\\n print(\\\"Help for 'grit %s' (for general help, run 'grit help'):\\\\n\\\" %\\n (tool,))\\n _GetToolInfo(tool)[_FACTORY]().ShowUsage()\\n return 0\\n if not _GetToolInfo(tool):\\n print(\\\"No such tool. Try running 'grit help' for a list of tools.\\\")\\n return 2\\n\\n try:\\n if _GetToolInfo(tool)[_REQUIRES_INPUT]:\\n os.stat(options.input)\\n except OSError:\\n print('Input file %s not found.\\\\n'\\n 'To specify a different input file:\\\\n'\\n ' 1. Use the GRIT_INPUT environment variable.\\\\n'\\n ' 2. Use the -i command-line option. This overrides '\\n 'GRIT_INPUT.\\\\n'\\n ' 3. Specify neither GRIT_INPUT or -i and GRIT will try to load '\\n \\\"'resource.grd'\\\\n\\\"\\n ' from the current directory.' % options.input)\\n return 2\\n\\n if options.hash:\\n grit.extern.FP.UseUnsignedFingerPrintFromModule(options.hash)\\n\\n try:\\n toolobject = _GetToolInfo(tool)[_FACTORY]()\\n if options.profile_dest:\\n import hotshot\\n prof = hotshot.Profile(options.profile_dest)\\n return prof.runcall(toolobject.Run, options, args[1:])\\n else:\\n return toolobject.Run(options, args[1:])\\n except getopt.GetoptError as e:\\n print(\\\"grit: %s: %s\\\" % (tool, str(e)))\\n print(\\\"Try running 'grit help %s' for valid options.\\\" % (tool,))\\n return 1\",\n \"def run(executable, arguments=[], verbose=True, shell=False, drop_cache=False):\\r\\n if drop_cache:\\r\\n dropcache()\\r\\n if verbose:\\r\\n print(\\\"%s %s\\\" % (os.path.basename(executable), \\\" \\\".join([pipes.quote(arg) for arg in arguments])))\\r\\n arguments.insert(0, executable)\\r\\n if shell:\\r\\n arguments = \\\" \\\".join([pipes.quote(arg) for arg in arguments])\\r\\n p = subprocess.Popen(args=arguments, stdout=subprocess.PIPE,\\r\\n stderr=subprocess.PIPE, shell=shell)\\r\\n stdout, stderr = p.communicate()\\r\\n\\r\\n if p.returncode != EXIT_SUCCESS:\\r\\n print('STDERR: ' + stderr.decode(), file=sys.stderr)\\r\\n\\r\\n return (p.returncode, stdout.decode(), stderr.decode())\",\n \"def run_from_argv(self, argv):\\n parser = self.create_parser(argv[0], argv[1])\\n options, args = parser.parse_args(argv[2:])\\n handle_default_options(options)\\n self.execute(*args, **options.__dict__) # pylint: disable-msg=W0142\",\n \"def run ( self, argv = sys.argv ) :\\n\\n self._options, self._args = self._parser.parse_args(argv[1:])\\n\\n appName = self.appName()\\n\\n # setup logger\\n if self._installLogger :\\n logging.addLevelName ( LOGGING_TRACE_LEVEL, 'TRACE' )\\n rootlog = logging.getLogger()\\n hdlr = logging.StreamHandler( sys.stdout )\\n formatter = logging.Formatter(self._logfmt)\\n hdlr.setFormatter(formatter)\\n rootlog.addHandler(hdlr)\\n logLevels = { 0 : logging.WARNING, 1 : logging.INFO, 2 : LOGGING_TRACE_LEVEL }\\n rootlog.setLevel( logLevels.get (self._options.verbose,logging.DEBUG) )\\n self._log = logging.getLogger(appName)\\n else :\\n self._log = logging.getLogger()\\n\\n return self._run()\",\n \"def execute():\\n if PRINT:\\n print(\\\"Start Child Process\\\")\\n\\n # 1. Receive inputs as you want\\n parser = argparse.ArgumentParser()\\n parser.add_argument(\\\"-x\\\", type=float, required=True)\\n parser.add_argument(\\\"--test-env\\\", action=\\\"store_true\\\")\\n parser.add_argument(\\\"--experiment-id\\\", type=str)\\n parser.add_argument(\\\"--experiment-name\\\", type=str)\\n parser.add_argument(\\\"--experiment-version\\\", type=str)\\n parser.add_argument(\\\"--trial-id\\\", type=str)\\n parser.add_argument(\\\"--working-dir\\\", type=str)\\n\\n inputs = parser.parse_args()\\n\\n if inputs.test_env:\\n assert inputs.experiment_id == os.environ[\\\"ORION_EXPERIMENT_ID\\\"]\\n assert inputs.experiment_name == os.environ[\\\"ORION_EXPERIMENT_NAME\\\"]\\n assert inputs.experiment_version == os.environ[\\\"ORION_EXPERIMENT_VERSION\\\"]\\n assert inputs.trial_id == os.environ[\\\"ORION_TRIAL_ID\\\"]\\n assert inputs.working_dir == os.environ[\\\"ORION_WORKING_DIR\\\"]\\n\\n # 2. Perform computations\\n y, dy = function(inputs.x)\\n\\n if PRINT:\\n print(\\\"Child Error\\\", file=sys.stderr)\\n\\n # 3. Gather and report results\\n results = list()\\n results.append(dict(name=\\\"example_objective\\\", type=\\\"objective\\\", value=y))\\n results.append(dict(name=\\\"example_gradient\\\", type=\\\"gradient\\\", value=[dy]))\\n\\n report_results(results)\\n\\n if PRINT:\\n print(\\\"End Child Process\\\")\",\n \"def main( argv = None ):\\n\\n if not argv: argv = sys.argv\\n\\n # setup command line parser\\n parser = optparse.OptionParser( version = \\\"%prog version: $Id$\\\",\\n usage = globals()[\\\"__doc__\\\"] )\\n\\n parser.add_option( \\\"-p\\\", \\\"--proc\\\", dest=\\\"processors\\\", type=\\\"int\\\",\\n help = \\\"use # processors [%default]\\\" )\\n\\n parser.set_defaults(\\n processors = 1 )\\n\\n\\n options, args = E.Start( parser, argv = argv )\\n\\n t1 = Test( RunnerGat, \\n small_test_segmented_workspaces(), \\n [ ValidatorNumSamples,\\n ValidatorSegmentDistribution ] )\\n\\n t1.run( options.stdout, \\n processors = options.processors )\\n\\n E.Stop()\",\n \"def run_from_argv(self, prog, subcommand, global_options, argv):\\r\\n self.prog_name = prog\\r\\n if self.skip_options:\\r\\n parser = NewOptionParser(prog=self.prog_name,\\r\\n usage=self.usage(subcommand),\\r\\n version='',\\r\\n formatter = NewFormatter(),\\r\\n add_help_option = False,\\r\\n option_list=self.option_list)\\r\\n else:\\r\\n parser = self.create_parser(prog, subcommand)\\r\\n options, args = parser.parse_args(argv)\\r\\n self.execute(args, options, global_options)\",\n \"def run(path):\\n config = conf.get_yaml_field(gl.configFile)\\n exe_con = config['ENABLE_EXECUTION']\\n exe_num = config['EXECUTION_NUM']\\n rerun = config['ENABLE_RERUN']\\n reruns_nums = config['RERUN_NUM']\\n repeat = config['ENABLE_REPEAT']\\n repeat_num = config['REPEAT_NUM']\\n exec_mode = config['ENABLE_EXEC_MODE']\\n debug_mode = config['ENABLE_DEBUG_MODE']\\n last_failed = config['ENABLE_LAST_FAILED']\\n failed_first = config['ENABLE_FAILED_FIRST']\\n\\n # custom function\\n RunTestCase.copy_custom_function()\\n\\n # failed first\\n failed_first_args = (' --ff ' if failed_first else '') if not last_failed else ''\\n\\n # last failed\\n last_failed_args = (' --lf ' if last_failed else '') if not failed_first else ''\\n\\n # Enable repeat case.\\n repeat_args = ' --count={} '.format(repeat_num) if repeat else ''\\n\\n # Enable CPU concurrency\\n py_args = ' -n {} '.format(exe_num) if exe_con else ''\\n\\n # Enable failed retry\\n reruns_args = ' --reruns {} '.format(reruns_nums) if rerun else ''\\n\\n # debug mode print debug info.\\n debug = '' if debug_mode else '--tb=no'\\n\\n \\\"\\\"\\\"\\n Load the pytest framework,\\n which must be written here or DDT will be loaded first.\\n from httptesting.case import test_load_case\\n \\\"\\\"\\\"\\n case_path = gl.loadcasePath\\n # Output mode console or report.\\n if exec_mode:\\n cmd = 'cd {} && py.test -q -s {} {} {} {}'.format(\\n case_path, reruns_args, 'test_load_case.py',\\n repeat_args, debug\\n )\\n else:\\n cmd = 'cd {} && py.test {} {} {} {} {} {} --html={} {} --self-contained-html'.format(\\n case_path,\\n py_args,\\n reruns_args,\\n last_failed_args,\\n failed_first_args,\\n 'test_load_case.py',\\n repeat_args,\\n path,\\n debug\\n )\\n try:\\n os.system(cmd)\\n except (KeyboardInterrupt, SystemExit):\\n print('已终止执行.')\",\n \"def main(argv):\\n ret = 0\\n parser = utils.parser.createParser()\\n\\n opt = parser.parse_args(argv)\\n\\n try:\\n # Stop execution if help flag is on\\n if opt.help:\\n raise ShowHelpException()\\n\\n # Read materials we need for later processing\\n cfg = auxiliary.readConfig(opt.config)\\n rawJson = auxiliary.readJson(opt.input[0])\\n\\n # List we are going to manipulate :)\\n commands = rawJson['commands']\\n\\n # Pump commands and instruction for filtering, sorting etc\\n rawJson['commands'] = pump(commands, cfg)\\n\\n # Write final result\\n auxiliary.writeJSONFile(rawJson, opt.output[0])\\n\\n except ShowHelpException:\\n parser.print_help()\\n ret = 0\\n except FilterException, e:\\n ret = 1\\n print('ERROR')\\n print(e)\\n\\n return ret\",\n \"def main(args=None):\\n args = args or sys.argv[1:]\\n parser = parse_options()\\n common.main_cli(experiment_parse_and_run, parser, args)\",\n \"def main():\\n tester = Tester()\\n # parse args, load configuration and create all required objects.\\n tester.setup_experiment()\\n # GO!\\n tester.run_experiment()\",\n \"def run(self):\\n\\n input_args = {}\\n self._execute(input_args, self.args)\",\n \"def _run_command(*argv):\\n del argv # Unused\\n\\n # Set the program name to 'earthengine' for proper help text display.\\n parser = argparse.ArgumentParser(\\n prog='earthengine', description='Earth Engine Command Line Interface.')\\n parser.add_argument(\\n '--ee_config', help='Path to the earthengine configuration file. '\\n 'Defaults to \\\"~/%s\\\".' % utils.DEFAULT_EE_CONFIG_FILE_RELATIVE)\\n parser.add_argument(\\n '--service_account_file', help='Path to a service account credentials'\\n 'file. Overrides any ee_config if specified.')\\n parser.add_argument(\\n '--project',\\n help='Specifies a Google Cloud Platform Project id to override the call.',\\n dest='project_override')\\n\\n dispatcher = CommandDispatcher(parser)\\n\\n # Print the list of commands if the user supplied no arguments at all.\\n if len(sys.argv) == 1:\\n parser.print_help()\\n return\\n\\n args = parser.parse_args()\\n config = utils.CommandLineConfig(\\n args.ee_config, args.service_account_file, args.project_override\\n )\\n\\n # Catch EEException errors, which wrap server-side Earth Engine\\n # errors, and print the error message without the irrelevant local\\n # stack trace. (Individual commands may also catch EEException if\\n # they want to be able to continue despite errors.)\\n try:\\n dispatcher.run(args, config)\\n except ee.EEException as e:\\n print(e)\\n sys.exit(1)\",\n \"def main(argv=None):\\n if argv is None:\\n argv = sys.argv\\n\\n import doctest\\n verbose = (len(argv) >= 2 and argv[1] == \\\"--Test\\\")\\n if verbose:\\n print(\\\"Running doctests (verbose mode)\\\")\\n else:\\n print(\\\"Running doctests\\\")\\n doctest.testmod(verbose=verbose)\\n return 0\",\n \"def run(self, script, *args, **kwargs):\\n return self._run('run', script, *args, **kwargs)\",\n \"def main():\\n vunit = vunit_pkg.VUnit.from_argv()\\n vunit = map_sources(vunit)\\n run_tests(vunit)\",\n \"def parse (self, program):\\n return self.parser.parse (program)\",\n \"def run(self, argv):\\r\\n try:\\r\\n index = 0\\r\\n command_pos = -1\\r\\n help_pos = -1\\r\\n help_command_pos = -1\\r\\n for arg in argv:\\r\\n if arg == 'bash-completion':\\r\\n self._bash_completion()\\r\\n return 0\\r\\n if arg in self.commands[self.api_version]:\\r\\n if command_pos == -1:\\r\\n command_pos = index\\r\\n elif arg in ('-h', '--help'):\\r\\n if help_pos == -1:\\r\\n help_pos = index\\r\\n elif arg == 'help':\\r\\n if help_command_pos == -1:\\r\\n help_command_pos = index\\r\\n index = index + 1\\r\\n if command_pos > -1 and help_pos > command_pos:\\r\\n argv = ['help', argv[command_pos]]\\r\\n if help_command_pos > -1 and command_pos == -1:\\r\\n argv[help_command_pos] = '--help'\\r\\n self.options, remainder = self.parser.parse_known_args(argv)\\r\\n self.configure_logging()\\r\\n self.interactive_mode = not remainder\\r\\n self.initialize_app(remainder)\\r\\n except Exception as err:\\r\\n if self.options.verbose_level == self.DEBUG_LEVEL:\\r\\n self.log.exception(unicode(err))\\r\\n raise\\r\\n else:\\r\\n self.log.error(unicode(err))\\r\\n return 1\\r\\n result = 1\\r\\n if self.interactive_mode:\\r\\n _argv = [sys.argv[0]]\\r\\n sys.argv = _argv\\r\\n result = self.interact()\\r\\n else:\\r\\n result = self.run_subcommand(remainder)\\r\\n return result\",\n \"def main():\\n parser = argparse.ArgumentParser()\\n subparsers = parser.add_subparsers(dest='mode')\\n\\n # Add sub-parser for feature extraction\\n parser_extract = subparsers.add_parser('extract')\\n parser_extract.add_argument('dataset',\\n choices=['training', 'validation', 'test'],\\n )\\n\\n # Add sub-parser for training\\n subparsers.add_parser('train')\\n\\n # Add sub-parser for inference\\n parser_predict = subparsers.add_parser('predict')\\n parser_predict.add_argument('dataset',\\n nargs='?',\\n choices=['validation', 'test'],\\n default='test',\\n )\\n\\n # Add sub-parser for evaluation\\n parser_evaluate = subparsers.add_parser('evaluate')\\n parser_evaluate.add_argument('task',\\n nargs='?',\\n choices=['tagging', 'sed', 'all'],\\n default='all',\\n )\\n parser_evaluate.add_argument('dataset',\\n nargs='?',\\n choices=['validation', 'test'],\\n default='test',\\n )\\n parser_evaluate.add_argument('--thresholds', action='store_true')\\n\\n args = parser.parse_args()\\n\\n if args.mode == 'extract':\\n extract(cfg.to_dataset(args.dataset))\\n elif args.mode == 'train':\\n train()\\n elif args.mode == 'predict':\\n predict(cfg.to_dataset(args.dataset))\\n elif args.mode == 'evaluate':\\n eval_all = args.task == 'all'\\n dataset = cfg.to_dataset(args.dataset)\\n if args.task == 'tagging' or eval_all:\\n evaluate_audio_tagging(dataset, args.thresholds)\\n if args.task == 'sed' or eval_all:\\n evaluate_sed(dataset)\",\n \"def run_experiment(arguments):\\n\\n logging.info('Arguments: %s', arguments)\\n\\n # Get estimator\\n estimator = get_estimator(arguments)\\n # my_module.\\n\\n # Run training and evaluation\\n _train_and_evaluate(estimator, arguments.job_dir)\",\n \"def subprocess_run(self, *args):\\n return self.testdir.runpytest_subprocess(*args)\",\n \"def main(argv=None):\\n if argv is None:\\n argv = sys.argv\\n\\n global _pgm\\n _pgm = \\\"%s:\\\" % os.path.basename(argv[0]) # colon for easy use by print\\n\\n parser = argparse.ArgumentParser(description=\\\"The redim_autochunk_2 test.\\\")\\n parser.add_argument('-c', '--host', default=None,\\n help='Target host for iquery commands.')\\n parser.add_argument('-p', '--port', default=None,\\n help='SciDB port on target host for iquery commands.')\\n parser.add_argument('-r', '--run-id', type=int, default=0,\\n help='Unique run identifier.')\\n parser.add_argument('-k', '--keep-arrays', action='store_true',\\n help='Do not remove test arrays during cleanup.')\\n parser.add_argument('-v', '--verbosity', default=0, action='count',\\n help='Increase debug logs. 1=info, 2=debug, 3=debug+')\\n\\n global _args\\n _args = parser.parse_args(argv[1:])\\n IQuery.setenv(_args)\\n\\n global _iquery\\n _iquery = IQuery(afl=True, format='tsv')\\n\\n try:\\n tt = TheTest()\\n return tt.run()\\n except AppError as e:\\n print >>sys.stderr, _pgm, e\\n return 1\\n except Exception as e:\\n print >>sys.stderr, _pgm, \\\"Unhandled exception:\\\", e\\n traceback.print_exc() # always want this for unexpected exceptions\\n return 2\",\n \"def RunScript(self, database, script, arguments, result):\\n\\n self.RunProgram(\\\"\\\\\\\"\\\"+self.__context[\\\"isql_path\\\"]+\\\"\\\\\\\"\\\",\\n [ self.__context[\\\"isql_path\\\"] ] + [ database ] + arguments,\\n script, self.__context, result)\",\n \"def main(argv):\\n\\n # Setup Directory\\n experiment_dir = os.path.join(FLAGS.dir, FLAGS.id)\\n if not os.path.exists(experiment_dir):\\n os.makedirs(os.path.join(experiment_dir, \\\"logs\\\"), exist_ok=True)\\n\\n # Setup Logging\\n FLAGS.alsologtostderr = True\\n logging.get_absl_handler().use_absl_log_file(FLAGS.logfile, os.path.join(experiment_dir, \\\"logs\\\"))\\n\\n # Setup seeds\\n if FLAGS.random_seed:\\n np.random.seed(FLAGS.random_seed)\\n tf.random.set_seed(FLAGS.random_seed)\\n\\n # Log Flags\\n log_flags(FLAGS)\\n\\n try:\\n experiment()\\n except:\\n exception = traceback.format_exc()\\n logging.info(exception)\",\n \"def main(args):\\n module = args.module\\n\\n if args.step not in STEP_OPTIONS:\\n raise ValueError(\\n f\\\"{args.step} is an unknown option. Your options are {STEP_OPTIONS}.\\\"\\n )\\n\\n if module == \\\"structure_plan\\\":\\n run_module_structure_plan(args)\\n elif module == \\\"floor_plan\\\":\\n run_module_floor_plan(args)\\n elif module == \\\"complete_floorplan\\\":\\n run_module_complete_floorplan(args)\\n elif module == \\\"ground_plan\\\":\\n run_module_ground_plan(args)\\n elif module == \\\"text_to_gdf\\\":\\n run_module_text_to_gdf(args)\\n else:\\n raise ValueError(\\n f\\\"{module} is an unknown option. Your options are {MODULE_OPTIONS}.\\\"\\n )\",\n \"def run(self, graph, context):\\n ginf = GraphInferrer(self.engine.tracks['type'],\\n graph, Context.empty(),\\n broaden=False)\\n\\n argrefs = [self.engine.ref(p, context)\\n for p in graph.parameters]\\n\\n return self.engine.run_coroutine(\\n self._specialize(None, ginf, argrefs)\\n )\",\n \"def executable():\\n\\n if len(sys.argv) == 1:\\n arguments.get_help()\\n sys.exit('\\\\nGive me something to do and I will do it\\\\n')\\n else:\\n # Parse the Arguments that have been provided\\n args = arguments.get_args()\\n\\n # Load The System Logger\\n log = logger.load_in(log_level=args.get('log_level', 'info'))\\n log.debug('Used Arguments %s', args)\\n const(log_method=log)\\n\\n # Begin Work\\n start(set_args=args)\",\n \"def Run(self, device):\\n margin = 8\\n print '[ %-*s ] %s' % (margin, 'RUN', self.tagged_name)\\n logging.info('Running linker test: %s', self.tagged_name)\\n adb = android_commands.AndroidCommands(device)\\n\\n # Create command-line file on device.\\n command_line_flags = ''\\n if self.is_low_memory:\\n command_line_flags = '--low-memory-device'\\n _WriteCommandLineFile(adb, command_line_flags, _COMMAND_LINE_FILE)\\n\\n # Run the test.\\n status, logs = self._RunTest(adb)\\n\\n result_text = 'OK'\\n if status == ResultType.FAIL:\\n result_text = 'FAILED'\\n elif status == ResultType.TIMEOUT:\\n result_text = 'TIMEOUT'\\n print '[ %*s ] %s' % (margin, result_text, self.tagged_name)\\n\\n results = base_test_result.TestRunResults()\\n results.AddResult(\\n base_test_result.BaseTestResult(\\n self.tagged_name,\\n status,\\n logs))\\n\\n return results\",\n \"def main():\\n argument_parser = argparse.ArgumentParser(add_help=True)\\n argument_parser.add_argument(\\\"directory\\\", type=str,\\n help=\\\"Directory to detect test smells.\\\")\\n args = argument_parser.parse_args()\\n \\n if len(sys.argv) < 1:\\n \\n argument_parser.print_help()\\n \\n else:\\n \\n if os.path.exists(args.directory) or os.path.isdir(args.directory):\\n\\n #Stage 1: project level rule checking\\n files = python_parser.get_python_files(os.path.abspath(args.directory))\\n results_list = project_rule_runner(files)\\n \\n #Stage 2: test case level rule checking\\n #test_case_pairs_list is a list of test cases paired with their file of origin\\n filtered_files = python_parser.filter_python_files(files)\\n test_case_pairs_list = python_parser.get_test_case_asts(filtered_files)\\n \\n for test_case_pair in test_case_pairs_list:\\n results_list = results_list + test_case_rule_runner(test_case_pair)\\n \\n #Stage 3: test method level rule checking\\n test_method_list = list()\\n \\n for test_case_pair in test_case_pairs_list:\\n test_method_list = test_method_list + python_parser.get_test_asts(test_case_pair)\\n \\n for test_method in test_method_list: \\n results_list = results_list + test_method_rule_runner(test_method)\\n \\n #Output formatting\\n format_output(results_list)\\n \\n else:\\n print(\\\"Invalid path given.\\\")\",\n \"def run(self, args=None):\\n # Disable \\\"Too many branches\\\" and \\\"Too many return statemets\\\" warnings\\n # pylint: disable=R0912,R0911\\n retcodesummary = ExitCodes.EXIT_SUCCESS\\n self.args = args if args else self.args\\n\\n if not self.check_args():\\n return retcodesummary\\n\\n if self.args.clean:\\n if not self.args.tc and not self.args.suite:\\n return retcodesummary\\n\\n # If called with --version print version and exit\\n version = get_fw_version()\\n if self.args.version and version:\\n print(version)\\n return retcodesummary\\n elif self.args.version and not version:\\n print(\\\"Unable to get version. Have you installed Icetea correctly?\\\")\\n return retcodesummary\\n\\n self.logger.info(\\\"Using Icetea version {}\\\".format(version) if version\\n else \\\"Unable to get Icetea version. Is Icetea installed?\\\")\\n\\n # If cloud set, import cloud, get parameters from environment, initialize cloud\\n cloud = self._init_cloud(self.args.cloud)\\n\\n # Check if called with listsuites. If so, print out suites either from cloud or from local\\n if self.args.listsuites:\\n table = self.list_suites(self.args.suitedir, cloud)\\n if table is None:\\n self.logger.error(\\\"No suites found!\\\")\\n retcodesummary = ExitCodes.EXIT_FAIL\\n else:\\n print(table)\\n return retcodesummary\\n\\n try:\\n testsuite = TestSuite(logger=self.logger, cloud_module=cloud, args=self.args)\\n except SuiteException as error:\\n self.logger.error(\\\"Something went wrong in suite creation! {}\\\".format(error))\\n retcodesummary = ExitCodes.EXIT_INCONC\\n return retcodesummary\\n\\n if self.args.list:\\n if self.args.cloud:\\n testsuite.update_testcases()\\n testcases = testsuite.list_testcases()\\n print(testcases)\\n return retcodesummary\\n\\n results = self.runtestsuite(testsuite=testsuite)\\n\\n if not results:\\n retcodesummary = ExitCodes.EXIT_SUCCESS\\n elif results.failure_count() and self.args.failure_return_value is True:\\n retcodesummary = ExitCodes.EXIT_FAIL\\n elif results.inconclusive_count() and self.args.failure_return_value is True:\\n retcodesummary = ExitCodes.EXIT_INCONC\\n\\n return retcodesummary\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.77603376","0.6088054","0.60775167","0.58894455","0.5795883","0.57625437","0.5644391","0.5612917","0.5577483","0.54712975","0.54391956","0.5397551","0.5342152","0.53416663","0.53210723","0.5283056","0.5247545","0.5224499","0.51938117","0.51841336","0.5168212","0.5164869","0.51189053","0.50694346","0.5053899","0.50491637","0.50486887","0.5039772","0.5035316","0.5018021","0.5004843","0.49741417","0.49698216","0.4966712","0.49663436","0.49638265","0.49621338","0.4953759","0.49390936","0.49376997","0.4935141","0.49190196","0.49080446","0.4896623","0.48787236","0.4878654","0.4858541","0.48455176","0.48420855","0.4830018","0.48207688","0.4820358","0.4819804","0.48195162","0.48123035","0.4808784","0.48000208","0.4798526","0.47877544","0.47863087","0.47780836","0.4777645","0.47713575","0.47698835","0.4768967","0.47665784","0.47511384","0.47446284","0.4740796","0.47406608","0.47368163","0.4732664","0.47301942","0.47298717","0.47282568","0.47222477","0.47131157","0.4708626","0.47023654","0.4702131","0.46971476","0.46967867","0.4689607","0.46825993","0.46824294","0.46807337","0.46788868","0.46718267","0.46670866","0.46647364","0.4664311","0.46641088","0.46557996","0.46507886","0.46507308","0.46468905","0.46453652","0.46439472","0.46424678","0.46350673"],"string":"[\n \"0.77603376\",\n \"0.6088054\",\n \"0.60775167\",\n \"0.58894455\",\n \"0.5795883\",\n \"0.57625437\",\n \"0.5644391\",\n \"0.5612917\",\n \"0.5577483\",\n \"0.54712975\",\n \"0.54391956\",\n \"0.5397551\",\n \"0.5342152\",\n \"0.53416663\",\n \"0.53210723\",\n \"0.5283056\",\n \"0.5247545\",\n \"0.5224499\",\n \"0.51938117\",\n \"0.51841336\",\n \"0.5168212\",\n \"0.5164869\",\n \"0.51189053\",\n \"0.50694346\",\n \"0.5053899\",\n \"0.50491637\",\n \"0.50486887\",\n \"0.5039772\",\n \"0.5035316\",\n \"0.5018021\",\n \"0.5004843\",\n \"0.49741417\",\n \"0.49698216\",\n \"0.4966712\",\n \"0.49663436\",\n \"0.49638265\",\n \"0.49621338\",\n \"0.4953759\",\n \"0.49390936\",\n \"0.49376997\",\n \"0.4935141\",\n \"0.49190196\",\n \"0.49080446\",\n \"0.4896623\",\n \"0.48787236\",\n \"0.4878654\",\n \"0.4858541\",\n \"0.48455176\",\n \"0.48420855\",\n \"0.4830018\",\n \"0.48207688\",\n \"0.4820358\",\n \"0.4819804\",\n \"0.48195162\",\n \"0.48123035\",\n \"0.4808784\",\n \"0.48000208\",\n \"0.4798526\",\n \"0.47877544\",\n \"0.47863087\",\n \"0.47780836\",\n \"0.4777645\",\n \"0.47713575\",\n \"0.47698835\",\n \"0.4768967\",\n \"0.47665784\",\n \"0.47511384\",\n \"0.47446284\",\n \"0.4740796\",\n \"0.47406608\",\n \"0.47368163\",\n \"0.4732664\",\n \"0.47301942\",\n \"0.47298717\",\n \"0.47282568\",\n \"0.47222477\",\n \"0.47131157\",\n \"0.4708626\",\n \"0.47023654\",\n \"0.4702131\",\n \"0.46971476\",\n \"0.46967867\",\n \"0.4689607\",\n \"0.46825993\",\n \"0.46824294\",\n \"0.46807337\",\n \"0.46788868\",\n \"0.46718267\",\n \"0.46670866\",\n \"0.46647364\",\n \"0.4664311\",\n \"0.46641088\",\n \"0.46557996\",\n \"0.46507886\",\n \"0.46507308\",\n \"0.46468905\",\n \"0.46453652\",\n \"0.46439472\",\n \"0.46424678\",\n \"0.46350673\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.69340014"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":26090,"cells":{"query":{"kind":"string","value":"Split a value of this field into the pattern and replacement string. 'value' A value for this field. returns A pair '(pattern, replacement_string)'."},"document":{"kind":"string","value":"def SplitValue(self, value):\n\n # Be lenient about an empty string.\n if value == \"\":\n return (\"\", \"\")\n # Break it in half.\n elements = string.split(value, \";\", 1)\n # Unescape semicolons in both halves.\n elements = map(lambda e: string.replace(e, r\"\\;\", \";\"), elements) \n return elements"},"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 _splitValue(self, value):\n m = ConfigParser.varRe.match(value)\n if m == None:\n raise Exception(self.fh.name + \":\" + str(self.lineNum) + \": bug, can't split line \")\n (beforeRef, junk, varName, afterRef) = m.groups()\n return (beforeRef, varName, afterRef)","def setSplitPattern(self, value):\n return self._set(splitPattern=value)","def setSplitPattern(self, value):\n return self._set(splitPattern=value)","def split(value, delimiter):\n return value.split(delimiter)","def prepare_value(self, value):\n if value is None:\n return value\n value = value.replace(\" \", \"\").replace(\".\", \"\")\n if value:\n return \"%s.%s.%s.%s\" % (value[0:3], value[3:7], value[7:11], value[11:])\n return value","def format_value(value):\n return value.replace('|', ' ')","def substitute(self, field_values):\n\n\t\tdef substituteFunc(mo):\n\t\t\tname = mo.group('braced')\n\t\t\tif name is not None:\n\t\t\t\tif name in field_values:\n\t\t\t\t\treturn str(field_values[name])\n\t\t\t\telse:\n\t\t\t\t\treturn self._DELIMITER + '{' + name + '}'\n\n\t\t\t# regexp could catch either 'braced' or 'escaped' substring\n\t\t\t# if it is not 'braced', it is 'escaped'\n\t\t\treturn self._DELIMITER\n\n\t\treturn self._PATTERN.sub(substituteFunc, self._template)","def split_search(self, value: str, adapter: str = AGG_ADAPTER_NAME) -> Tuple[str, List[str]]:\n search = value.strip().lower()\n\n # Problem are fields containing \":\" from adapters that populate dynamic fields\n # PBUG: special check for 'custom attributes' coming from esx adapter.\n if \":\" in search and 'custom attributes' not in search:\n adapter_split, field = [x.strip() for x in search.split(\":\", 1)]\n if not adapter_split:\n adapter_split = adapter\n else:\n field = search\n adapter_split = adapter\n qual_check = re.match(r\"adapters_data\\.(.*?)\\.\", field)\n if qual_check and len(qual_check.groups()) == 1:\n adapter_split = qual_check.groups()[0]\n\n adapter_split = strip_right(obj=adapter_split.lower().strip(), fix=\"_adapter\")\n\n fields = split_str(\n obj=field,\n split=\",\",\n strip=None,\n do_strip=True,\n lower=True,\n empty=False,\n )\n\n if not fields:\n raise ApiError(f\"No fields provided in {value!r}, format must be 'adapter:field'\")\n return adapter_split, fields","def split(value, key):\n return str(value).split(key)","def _splitFieldValue(self, line):\n found = self.FIELDVALUE.findall(line)\n if found:\n fieldName, value = found[0]\n if fieldName in self.C.ADAPTER_COMMAFIELDS:\n value = self.COMMASPLIT.findall(value)[:-1] # Split and remove last empty part\n return fieldName, value\n return None, None # No field name match on this line.","def split(value: str, sep: str = \":\") -> Tuple:\n left, _, right = value.partition(sep)\n return (left, right) if right else (None, left)","def sub_values(content: str, **kwargs) -> str:\n\n match_re = re.compile(\"(?<={{ .Values.)[a-zA-Z0-9]+?(?= }})\")\n found = set()\n\n def replace_fn(match):\n varname = match_re.search(match.group(0)).group(0)\n found.add(varname)\n try:\n return str(kwargs[varname])\n except KeyError:\n raise ValueError(f\"No value provided for wildcard {varname}\")\n\n content = re.sub(\"{{ .Values.[a-zA-Z0-9]+? }}\", replace_fn, content)\n\n missing = set(kwargs) - found\n if missing:\n raise ValueError(\n \"Provided unused wildcard values: {}\".format(\", \".join(missing))\n )\n return content","def get_prep_value(self, value):\n if isinstance(value, tuple) and len(value) == 2:\n return '[%s,%s]' % value\n return value","def find_value(code, value):\n value_pattern = re.compile(rf\"{re.escape(value)} ?= ?([^=][a-zA-Z0-9\\.'/_)(]*)\")\n\n target = None\n for line in code:\n if value_pattern.search(line):\n target = re.findall(value_pattern, line)\n break\n\n return target[0] if target is not None else value","def linebreaks(value):\n value = normalize_newlines(value)\n paras = re.split('\\n{2,}', str(value))\n paras = ['<p>%s</p>' % p.replace('\\n', '<br>') for p in paras]\n return '\\n\\n'.join(paras)","def parse(cls, value: str) -> Tuple[str, Dict[str, str]]:\n raw_value = read_value_from_path(value)\n args: Dict[str, str] = {}\n\n if \"@\" in raw_value:\n args[\"region\"], raw_value = raw_value.split(\"@\", 1)\n\n # now find any other arguments that can be filters\n matches = re.findall(r\"([0-9a-zA-z_-]+:[^\\s$]+)\", raw_value)\n for match in matches:\n k, v = match.split(\":\", 1)\n args[k] = v\n\n return args.pop(\"name_regex\"), args","def format_regex_string(self, name, value):\n\n if value is not None and isinstance(value, str):\n string = '%s = %s\\n' % (name, self.simple_format_string(value, force_raw=True))\n else:\n string = '%s = None\\n' % name\n return string","def csv_to_field_Urls(entity, value):\n if value is None or value == '':\n return\n splitter = re.compile(url_splitter)\n entity.string = splitter.split(value)","def _expand_value(self, value, multiple=False):\n if multiple:\n return tuple(self._expand_value(item) for item in value)\n elif isinstance(value, str):\n expanded_value = value\n for match in re.finditer(r'\\${([\\w\\d.]+)}', value):\n key = match.group(1)\n substring = '${{{key}}}'.format(key=key)\n\n if key in self._config:\n expanded_value = value.replace(substring, str(self._raw_get(key)))\n else:\n msg = 'Error expanding value {value}. No value found for reference {ref}'.format(\n value=value, ref=key)\n raise InvalidReference(msg)\n\n return expanded_value\n else:\n return value","def exptostr(value):\n return re.sub(r'`(.*)`', r'\\g<1>', value)","def replace( scr ):\n d = ReplaceDialog(scr)\n value = d.main()\n if not \"pattern\" in value:\n return (None,None)\n else:\n return (value[\"pattern\"],value[\"replace\"])","def _extractTuples(self, value, pos=0):\n matchValues = self._patternValues.match(value)\n infections, _, deaths = matchValues.groups()\n infections = int(infections)\n deaths = int(deaths)\n return infections, deaths","def split_values(self, value):\n if value:\n return [s.strip() for s in value.split(',')]\n else:\n return []","def format_value(content):\n try:\n content += \"\"\n except TypeError:\n content = \", \".join(content)\n return remove_break_lines_characters(\n content).strip().replace('^', PRESERVECIRC)","def addInfixPattern(self, value):\n try:\n infix_patterns = self.getInfixPatterns()\n except KeyError:\n infix_patterns = []\n infix_patterns.insert(0, value)\n return self._set(infixPatterns=infix_patterns)","def regex_replace_value(val, val_new, pattern,\n val_exception=np.nan):\n try:\n if not bool(re.match(pattern, val)):\n return val_new\n else:\n return val\n except:\n return val_exception","def addSplitChars(self, value):\n try:\n split_chars = self.getSplitChars()\n except KeyError:\n split_chars = []\n split_chars.append(value)\n return self._set(splitChars=split_chars)","def addSplitChars(self, value):\n try:\n split_chars = self.getSplitChars()\n except KeyError:\n split_chars = []\n split_chars.append(value)\n return self._set(splitChars=split_chars)","def regex_sub_value(val, pattern, val_sub='',\n val_exception=np.nan, val_none=np.nan):\n try:\n stripped = re.sub(pattern, val_sub, val)\n new_val = val_none if stripped == '' else stripped\n return new_val\n except:\n return val_exception","def _eval_str_tuple(value):\n if not (value.startswith('(') and value.endswith(')')):\n raise ValueError(value)\n\n orig_value = value\n value = value[1:-1]\n\n result = []\n while value:\n m = _strs.match(value)\n if m is None:\n raise ValueError(orig_value)\n\n result.append(m.group(1))\n value = value[len(m.group(0)):]\n\n return tuple(result)","def search_pattern(self, value, pattern):\n _pattern = re.compile(pattern)\n _match = _pattern.search(value)\n return _match","def setPattern(self, value):\n return self._set(pattern=value)","def __build_value(self, v, info):\n\n if not v:\n return \"\"\n\n # Find all placeholders\n attrs = {}\n for attr in re.findall(r\"%\\(([^)]+)\\)s\", v):\n\n # Extract ordinary attributes\n if attr in info:\n attrs[attr] = \", \".join(info[attr])\n\n # Check for result renderers\n elif attr in self.__value_extender:\n attrs[attr] = self.__value_extender[attr](info)\n\n # Fallback - just set nothing\n else:\n attrs[attr] = \"\"\n\n # Assemble and remove empty lines and multiple whitespaces\n res = v % attrs\n res = re.sub(r\"(<br>)+\", \"<br>\", res)\n res = re.sub(r\"^<br>\", \"\", res)\n res = re.sub(r\"<br>$\", \"\", res)\n return \"<br>\".join([s.strip() for s in res.split(\"<br>\")])","def linebreaks(value):\n value = re.sub(r'\\r\\n|\\r|\\n', '\\n', value) # normalize newlines\n paras = re.split('\\n{2,}', value)\n try:\n paras = [u'<p>%s</p>' % p.encode('ascii', errors='ignore').replace('\\n', '<br />') for p in paras]\n except Exception, e:\n logger.error('Exception: {}'.format(e))\n pass\n try:\n paras = u'\\n\\n'.join(paras)\n except Exception, e:\n logger.error('Exception: {}'.format(e))\n pass\n return Markup(paras)","def parse_entry(\n entry,\n regex=r\"(\\s)*?(?P<value>[\\.\\w]+)(\\s)*?\",\n delimiter=\",\",\n values_only=True,\n first_only=True,\n errors=None,\n replace_nan=\"None\",\n):\n\n if isinstance(entry, str):\n pattern = re.compile(regex)\n matches = []\n if not delimiter or (delimiter is None):\n subparts = [entry]\n else:\n subparts = entry.split(delimiter)\n\n for _l in subparts:\n _m = pattern.match(_l)\n if _m:\n _d = dict(value=_m.group(\"value\"))\n # Add other groups\n _d.update(\n {\n k: _m.group(k)\n for (k, ind) in pattern.groupindex.items()\n if not k == \"value\"\n }\n )\n\n else:\n _d = dict(value=replace_nan)\n # Add other groups\n _d.update(\n {\n k: replace_nan\n for (k, ind) in pattern.groupindex.items()\n if not k == \"value\"\n }\n )\n matches.append(_d)\n\n if values_only:\n matches = [m[\"value\"] for m in matches]\n\n if first_only:\n return matches[0]\n\n return matches\n else:\n if entry is None:\n entry = replace_nan\n elif isinstance(entry, float):\n if np.isnan(entry):\n entry = replace_nan\n if first_only:\n return entry\n else:\n return [entry]","def placeholder_to_regex(placeholder,encloser='%',matcher='(.+)'):\n pattern = placeholder\n pattern = pattern.replace('\\\\','/')\n if pattern.count('%') == 0 or pattern.count('%') % 2 != 0:\n return '',[]\n else:\n borders = pattern.split(encloser)[::2]\n fields = pattern.split(encloser)[1::2]\n for field in fields:\n pattern = pattern.replace(encloser+field+encloser, matcher, 1)\n pattern = pattern.replace('/','\\\\/')\n return pattern,fields","def processArgument(self, value):\n if not isinstance(value, basestring):\n return value\n \n value = self._RE_FIND.subn(self._replaceFind, value)[0]\n value = self._RE_ENV.subn(self._replaceEnv, value)[0]\n \n return value","def _callback(self, matcher):\n matched_field = matcher.group(self.field)\n replacement = self.lookup.get(matched_field)\n if not replacement:\n return matcher.group(0)\n\n fields = list(f or \"\" for f in matcher.groups())\n fields[self.field - 1] = replacement\n\n return \"\".join(fields)","def parse_value(cls, value):\n choice, value = value.split('=')\n value = cls.VALUES_MAP[value]\n\n return choice, value","def _string_subst_partial(self, val):\n def repl(m):\n k = m.group('id')\n replacement = self.bib_database.strings[k.lower()] if k.lower() in self.bib_database.strings else k\n pre = '\"' if m.group('pre') != '\"' else ''\n post = '\"' if m.group('post') != '\"' else ''\n return pre + replacement + post\n\n logger.debug('Substitute string definitions inside larger expressions')\n if '#' not in val:\n return val\n\n # TODO?: Does not match two subsequent variables or strings, such as \"start\" # foo # bar # \"end\" or \"start\" # \"end\".\n # TODO: Does not support braces instead of quotes, e.g.: {start} # foo # {bar}\n # TODO: Does not support strings like: \"te#s#t\"\n return self.replace_all_re.sub(repl, val)","def processArgument(self, value):\r\n if not isinstance(value, basestring):\r\n return value\r\n\r\n value = self._RE_FIND.subn(self._replaceFind, value)[0]\r\n value = self._RE_ENV.subn(self._replaceEnv, value)[0]\r\n\r\n return value","def _rsplit(value, sep, maxsplit=None):\n\tstr_parts = value.split(sep)\n\tif (maxsplit is not None) and (len(str_parts) > 1):\n\t\treturn [str.join(sep, str_parts[:-maxsplit])] + str_parts[-maxsplit:]\n\treturn str_parts","def regex_splitter(input_string: str) -> str:\n #grouping_list: list = re.compile(REGEX_PATTERN).split(input_string)\n compiled_regex = re.compile(REGEX_PATTERN)\n mo = compiled_regex.findall(input_string)\n \n #print(\"Current found matches are:\" + str(mo))\n \n result = evaluator_indefinite(mo)\n \n #print(\"Dictionary of evaluations\" + str(result))\n \n new_string = input_string\n\n for match in mo:\n new_string = new_string.replace(str(match), str(result[match]))\n #print(\"Current string modified with new value: \" + new_string)\n\n return new_string","def parse_field_expression(value):\n if not value:\n return None\n\n mo = regex.match(value)\n if not mo:\n return None\n return mo.groups()","def clean(self, value):\n return [f.clean(v) for v,f in zip(value, self.fields)]","def parse_list(value: str) -> list[str]:\n segments = _QUOTED_SEGMENT_RE.findall(value)\n for segment in segments:\n left, match, right = value.partition(segment)\n value = ''.join([left, match.replace(',', '\\000'), right])\n return [_dequote(x.strip()).replace('\\000', ',') for x in value.split(',')]","def parse_value(value: str) -> Tuple[str, str, str]:\n value_pattern = r'^(usb|pci)\\(([^:]{4}):([^:]{4})\\)$'\n matches = re.match(value_pattern, value)\n assert matches, value\n ilk, vendor, device = matches.group(1), matches.group(2), matches.group(3)\n return ilk, vendor, device","def get_val_tuple(self, value):\n group_id = ([(idx1, idx2)\n for idx1, category in enumerate(self.groups)\n for idx2, val in enumerate(category) if val == value])\n return group_id[0]","def __splitVersionPattern(versionPattern):\n prefix = ''\n padding = ''\n suffix = ''\n for char in versionPattern:\n if char != '#' and not padding:\n prefix += char\n elif char != '#' and padding:\n suffix += char\n else:\n padding += char\n\n assert len(padding), 'Padding pattern representation (#) not found in version pattern: {}'.format(versionPattern)\n return {\n 'prefix': prefix,\n 'padding': padding,\n 'suffix': suffix\n }","def convert(cls, value: Any) -> Optional[VarNames]:\n # Can be optional\n if value is None:\n return None\n\n if isinstance(value, str):\n return to_list(value)\n\n if not isinstance(value, list):\n raise ValueError('Variable name pattern can only be a string or a'\n ' list of strings.')\n\n for item in value:\n if not isinstance(item, str):\n raise ValueError('Variable name pattern can only be a string'\n ' or a list of strings.')\n\n return value.copy()","def slugify(value, delimiter='-'):\n words = slug_pattern.split(value)\n return delimiter.join(filter(truth, words)).lower()","def filter_re_replace(val: AnyStr, pattern: str, repl: str) -> str:\n return re.sub(pattern, repl, str(val))","def test_regex_replace_value(self):\n inputs = ['1234', '12345', '123a5', '', 1234, None, np.nan]\n outputs = ['1234', 'test', 'test', 'test', np.nan, np.nan, np.nan]\n pattern = '[0-9]{4}$'\n outputs_test = [\n regex_replace_value(input_val, 'test', pattern)\n for input_val in inputs\n ]\n self.assertEqual(outputs, outputs_test)","def fn_sub(self, value):\n\n if isinstance(value, list):\n value, variables = value\n else:\n # only template parameter names, resource logical IDs, and resource attributes, will be parsed\n value, variables = value, {}\n\n for name, target in variables.items():\n value = value.replace('${{{}}}'.format(name), target)\n\n return Functions.SUB_VARIABLE_PATTERN.sub(self._sub_variable, value)","def split_words(value: str) -> List[str]:\n words: List[str] = []\n buffer: List[str] = []\n previous = None\n\n def flush():\n if buffer:\n words.append(\"\".join(buffer))\n buffer.clear()\n\n for char in value:\n tp = classify(char)\n if tp == StringType.OTHER:\n flush()\n elif not previous or tp == previous:\n buffer.append(char)\n elif tp == StringType.UPPER and previous != StringType.UPPER:\n flush()\n buffer.append(char)\n else:\n buffer.append(char)\n\n previous = tp\n\n flush()\n return words","def replace_tag_value( msg, tag, value ) :\n _TAG_RE = re.compile('\\x01' + str(tag) + '=' + '[\\s\\w:-]*' + '\\x01')\n msg = _TAG_RE.sub('\\x01' + str(tag) + '=' + str(value) + '\\x01', msg,1)\n return msg","def linebreaks(value, autoescape=False):\n value = normalize_newlines(value)\n paras = re.split('\\n{2,}', value)\n if autoescape:\n paras = ['<p>%s</p>' % escape(p).replace('\\n', '<br />') for p in\n paras]\n else:\n paras = ['<p>%s</p>' % p.replace('\\n', '<br />') for p in paras]\n return '\\n\\n'.join(paras)","def subst(self, value, filter=None):\n\n if isinstance(value, Literal):\n return value._value\n elif isinstance(value, tuple):\n return tuple(self.subst(i, filter) for i in value)\n elif isinstance(value, list):\n return list(self.subst(i, filter) for i in value)\n elif isinstance(value, dict):\n return {i: self.subst(value[i], filter) for i in value}\n elif isinstance(value, StringTypes):\n def subfn(mo):\n var = mo.group(0)\n\n if var == \"$$\":\n return \"$\"\n\n # Apply variable filters\n parts = var[2:-1].split(\"|\")\n value = self.evaluate(parts[0])\n\n if len(parts) > 1:\n # Filters supplied directly\n for part in parts[1:]:\n if len(part) == 0:\n # Empty filter can be used to disable auto filter\n continue\n else:\n value = self.callfilter(part, value)\n elif filter:\n # Use auto-filter if specified\n for part in filter.split(\"|\"):\n value = self.callfilter(part, value)\n\n return value\n return re.sub(r\"\\$\\$|\\$\\(.*?\\)\", subfn, value)\n else:\n return value","def texify(value):\n for k, v in REPLACEMENTS.items():\n value = value.replace(k, v)\n return mark_safe(value)","def _extract_parts_from_final_value(self, full_encrypted_value, **options):\n\n empty, handler, encrypted_part = full_encrypted_value.split(\n self._get_separator(), self._get_separator_count())\n\n return handler.decode(self._encoding), self._decode_encrypted_part(encrypted_part)","def split_tuple(value: tuple[str, ...] | str | None) -> tuple[str, ...] | None:\n if value is None:\n return None\n if TYPE_CHECKING:\n assert isinstance(value, str)\n return tuple(value.split(\".\"))","def sub(self, value, conversion = str):\n if value.startswith(\"$(eval\") and value.endswith(\")\"):\n result = UnresolvedValue()\n result.append((\"eval\", value[7:-1]))\n return result\n if self.ERROR_PATTERN.search(value):\n raise SubstitutionError(\"'$' cannot appear within expression\")\n match = self.PATTERN.search(value)\n if not match:\n return self.convert_str(value, conversion)\n result = UnresolvedValue()\n rest = value\n while match:\n # this map is to avoid python 2/3 issues\n parts = list(map(str, match.group(1).split(None, 1)))\n parts = list(map(str.strip, parts))\n parts = list(filter(bool, parts))\n\n assert len(parts) == 1 or len(parts) == 2\n if not parts[0] in self.COMMANDS:\n raise SubstitutionError(\"invalid command: \" + parts[0])\n prefix = rest[:match.start()]\n if prefix:\n if parts[0] == \"eval\":\n raise SubstitutionError(\"eval must appear at the start\")\n result.append(prefix)\n cmd = getattr(self, \"_\" + parts[0])\n result.append(cmd(parts))\n rest = rest[match.end():]\n match = self.PATTERN.search(rest)\n if rest:\n result.append(rest)\n return self.convert_unresolved(result, conversion)","def find_values_to_replace(self):\n regexp = re.compile(self.raw_pattern)\n self.to_replace = regexp.findall(self.raw_sql)","def pathify(urlpattern, **context):\n\n repl = lambda match: context[match.group(1)]\n path = re.sub(r':([a-z]+)', repl, urlpattern)\n return tuple(path[1:].split('/'))","def parse_from_placeholder(string,pattern,encloser='%',matcher='(.+)'):\n pattern,fields = placeholder_to_regex(pattern,encloser,matcher)\n return parse_from_regex(string,pattern,fields)","def field_values(s):\n if s: \n for f in s.split(','):\n if f.find('=') > 0:\n (field, value) = f.split('=')\n else:\n field = f\n value = None\n yield (field, value)","def get_values_re():\n\n regex = r\"([1-9]|1[0-9]) \" +\\\n r\"(localhost) \" +\\\n r\"(\\d{1,5}) \" +\\\n r\"(\\d{1,5}) \" +\\\n r\"(YES|NO) \" +\\\n r\"(YES|NO) \" +\\\n r\"(YES|NO) \" + \\\n r\"(YES|NO)\"\n\n regex = regex.split(\" \")\n\n return regex","def get_expanded_values(self, value: Union[str, List[str]]) -> List[str]:\n if isinstance(value, str):\n value = [value]\n _out = []\n for v in value:\n try:\n v = self.biolink_model_toolkit.get_descendants(v, reflexive=True, formatted=True)\n v = [x.split(\":\")[-1] for x in v] # remove biolink: prefix\n except ValueError:\n v = [v]\n _out.extend(v)\n return _out","def parse_template(template):\n field_name = None\n field_value = []\n\n for line in template.strip().split('\\n') + ['end:']:\n if line.startswith('#'):\n continue\n match = RE_TEMPLATE_FIELD_LINE.match(line)\n if match:\n if field_name is not None:\n yield (field_name, '\\n'.join(field_value).strip())\n elif len(field_value) > 0:\n logging.warning('Ignoring lines: %r', field_value)\n\n field_name = match.group(1)\n field_value = [match.group(2)]\n else:\n field_value.append(line)","def lister(list_value):\n\n for item in list_value:\n if isinstance(item, basestring):\n index = list_value.index(item)\n rep, fact = replacer(replace_value=item)\n if fact is True:\n list_value[index] = rep\n\n elif isinstance(item, list):\n lister(list_value=item)\n\n elif isinstance(item, dict):\n _super_munger(mungie=item)","def setPatterns(self, value):\n return self._set(patterns=value)","def generate_regex(self, pattern_to_glob):\n\n # First thing, replace '.' with '\\.' and make sure we're going\n # to match the start and the end of the pattern string.\n pattern_to_glob = re.sub(r\"\\.\", r\"\\.\", pattern_to_glob)\n pattern_to_glob = r\"^\" + pattern_to_glob + r\"$\"\n\n found_values = []\n new_val = pattern_to_glob\n\n # Find all the named attributes\n all_matches = re.findall(r\"%.+?%\", pattern_to_glob)\n\n for match_thing in all_matches:\n name = match_thing[1:-1]\n\n if match_thing not in found_values:\n # Change to a named group.\n group_string = r\"(?P<\" + name + r\">.+?)\"\n new_val = re.sub(match_thing, group_string, new_val, count=1)\n found_values.append(match_thing)\n else:\n # Use the existing group.\n group_string = r\"(?P=\" + name + r\")\"\n new_val = re.sub(match_thing, group_string, new_val, count=1)\n\n return new_val","def MakePattern(self,content):\n return self.register(Pattern(content,reg=self))","def separate_content_and_variables(text, boundary='{# /variables #}'):\n\n # Find boundary\n pos = text.find(boundary)\n\n # Text contains variables\n if pos > 0:\n return(text[:pos].strip(), text[(pos + len(boundary)):].strip())\n\n # Text does not contain variables\n return ('', text)","def string_value(cls, json_field: str, comparison: str, value: str) -> \"JsonPattern\":\n return jsii.sinvoke(cls, \"stringValue\", [json_field, comparison, value])","def replace_unwanted_value_by_value_grouped(tX_grouped, unwanted_value, value):\n tX_grouped_new = []\n for i in range(len(tX_grouped)):\n tX_grouped_new.append(\n replace_unwanted_value_by_value(tX_grouped[i], unwanted_value, value)\n )\n return tX_grouped_new","def output_space(value):\n tpl_args = value.split(':')\n template = tpl_args[0]\n spec = {}\n for modifier in tpl_args[1:]:\n mitems = modifier.split('-', 1)\n spec[mitems[0]] = len(mitems) == 1 or mitems[1]\n\n return template, spec","def run(self, content):\n parts = []\n offset = 0\n for match in self.regexp.finditer(content):\n parts.append(content[offset:match.start(0)])\n parts.append(self.replace(match))\n offset = match.end(0)\n parts.append(content[offset:])\n return ''.join(parts)","def format_field(self, value, spec):\n cache = Cache()\n if spec == \"co\":\n # if cache(re.match(\"(.*)co$\", spec)):\n value = co_join(value)\n spec = \"s\"\n # cache.output.group(1) + \"s\"\n elif cache(re.match(r\"^sub(\\d?)_?(.*)$\", spec)):\n depth = (1 if cache.output.group(1) == \"\" else\n int(cache.output.group(1)))\n value = \"\\n\".join([\n \"{0}{1} = {2}\".format(depth * \" \", key, val)\n for key, val in value.items()])\n if cache.output.group(2) != \"\":\n value = (\n depth * \"[\" + cache.output.group(2) + depth * \"]\" + \"\\n\" +\n value)\n spec = \"s\"\n return super(Format, self).format_field(value, spec)","def get_value(value):\n if value:\n return value.split('\\n')[0]\n else:\n return None","def linebreaks(value, autoescape=False):\n value = normalize_newlines(value)\n paras = re.split('\\n{2,}', value)\n if autoescape:\n paras = ['<p data-parnum=\"%s\">%s</p>' % (i, escape(p).replace('\\n', '<br />')) for i, p in enumerate(paras)]\n else:\n paras = ['<p data-parnum=\"%s\">%s</p>' % (i, p.replace('\\n', '<br />')) for i, p in enumerate(paras)]\n return '\\n\\n'.join(paras)","def _substitute(self, value, group=None, namespace=None):\n if isinstance(value, list):\n return [self._substitute(i, group=group, namespace=namespace)\n for i in value]\n elif isinstance(value, str):\n # Treat a backslash followed by the dollar sign \"\\$\"\n # the same as the string template escape \"$$\" as it is\n # a bit more natural for users\n if r'\\$' in value:\n value = value.replace(r'\\$', '$$')\n tmpl = self.Template(value)\n ret = tmpl.safe_substitute(\n self.StrSubWrapper(self, group=group, namespace=namespace))\n return ret\n elif isinstance(value, dict):\n # Substitute template variables in both key and value\n return {self._substitute(key, group=group, namespace=namespace):\n self._substitute(val, group=group, namespace=namespace)\n for key, val in value.items()}\n else:\n return value","def _format_field_val(\n self,\n field: str,\n field_type: str,\n value: Any,\n ) -> str | int | bool | list | None:\n\n # If the field is empty, no need to format.\n if value is None:\n return None\n\n # TODO(DanielRyanSmith): Write checks to ensure enum values are valid.\n if field_type == 'emails' or field_type == 'split_str':\n list_val = self._split_list_input(field, field_type, value, ',')\n if field == 'blink_components' and len(value) == 0:\n return [settings.DEFAULT_COMPONENT]\n return list_val\n elif field_type == 'link':\n return self._extract_link(value)\n elif field_type == 'links':\n list_val = self._split_list_input(field, field_type, value)\n # Filter out any URLs that do not conform to the proper pattern.\n return [self._extract_link(link)\n for link in list_val if link]\n elif field_type == 'int':\n # Int fields can be unset by giving null or nothing in the input field.\n if value == '' or value is None:\n return None\n try:\n return int(value)\n except ValueError:\n self._abort_invalid_data_type(field, field_type, value)\n elif field_type == 'bool':\n return bool(value)\n return str(value)","def replacer(replace_value, key=None):\n\n if key is not None:\n if key not in exempt:\n return check_replace(replace_value)\n else:\n return replace_value, False\n else:\n return check_replace(replace_value)","def map(self, key, value):\n namelist = value.strip().split()\n namelist.insert(0, '')\n namelist.append('')\n n = len(namelist)\n for i in range(1, n - 1):\n self.outputcollector.collect(namelist[i], \\\n tuple2str((namelist[i-1] ,namelist[i+1])))","def setValueSplitSymbol(self, value):\n return self._set(valueSplitSymbol=value)","def valueformat(value, format_list):\n\n # print(\"\\n\", format_list, value)\n concat_key = format_list.split('.')\n # Pass in either the key of the field\n # or pass in resource.key to enable a resource lookup.\n key = \"\"\n resource = \"\"\n member_id = \"\"\n key_sequence = [key, resource, member_id]\n count = 0\n for r in reversed(concat_key):\n key_sequence[count] = r\n count += 1\n\n # print(\"Concat_key:\", concat_key)\n key = key_sequence[0]\n resource = key_sequence[1]\n member_id = key_sequence[2]\n\n # print(\"Key:\", key)\n\n if key:\n if key.lower() == \"address\":\n return dt_address(value)\n\n elif key.lower() == \"telecom\":\n return dt_telecom(value)\n\n elif key.lower() == \"name\":\n return dt_name(value)\n elif key.lower() == 'dosage':\n return dt_dosage(value)\n elif key.lower() == 'medicationreference':\n # print(\"Working on\", key, \": \", value)\n # f_value = value\n # lookup field_formats\n # concat_key should have a resource name\n # print(\"\\n\\nRESOURCE:\", resource)\n # print(\"calling dt_medicationreference with Resource:\", resource, \", value:\", value)\n return dt_medicationreference(value, member_id, resource)\n elif key.lower() == 'dataabsentreason':\n if isinstance(value, dict):\n return value['coding'][0]['display']\n else:\n return value\n elif key.lower() == 'valuequantity':\n # return str(value['value']) + \" \" + value['unit']\n return dt_valuequantity(value)\n elif key.lower() == 'valuestring':\n return value\n elif key.lower() == 'interpretation':\n return value['coding'][0]['display']\n elif key.lower() == 'referencerange':\n return dt_referencerange(value)\n elif key.lower() == 'requester':\n if 'display' in value['agent']:\n return dt_reference(value['agent'], member_id)\n elif key.lower() == 'practitioner':\n if 'display' in value:\n return dt_reference(value, member_id)\n elif key.lower() == 'organization':\n if 'display' in value:\n return dt_reference(value, member_id)\n # elif key.lower() == \"result\":\n # return dt_reference(value[0], member_id)\n elif key.lower() == 'practitioner':\n if 'display' in value:\n return dt_reference(value, member_id)\n elif key.lower() == 'organization':\n if 'display' in value:\n return dt_reference(value, member_id)\n elif key.lower() == 'participant':\n if 'display' in value[0]['individual']:\n return dt_reference(value[0]['individual'], member_id)\n elif key.lower() == 'location':\n if 'display' in value[0]['location']:\n return dt_reference(value[0]['location'], member_id)\n elif key.lower() == 'communication':\n return dt_communication(value)\n else:\n # print(\"value:\", value, \" type:\", type(value), \" for: \", key)\n return value","def _value_to_matcher(value, add_quotes=False):\n if isinstance(value, tf.Operation):\n return _OpMatcher(str(value.node_def.name))\n if isinstance(value, tf.Tensor):\n return _TensorMatcher(str(value.name))\n if isinstance(value, (six.text_type, str, bytes)):\n if add_quotes:\n return '\\'{}\\''.format(value)\n else:\n return value\n raise ValueError('Cannot get a matcher for: {}, {}'.format(\n type(value), value))","def implode(self, label_pattern, value_pattern):\n import hxl.filters\n return hxl.filters.ImplodeFilter(self, label_pattern=label_pattern, value_pattern=value_pattern)","def process_result_value(self, value: str, dialect):\n pb = self._cls()\n pb.ParseFromString(value)\n return pb","def extract_values(line):\n key, value = line.split(':')\n key, value = key.strip(), value.strip()\n key = key.replace(' ', '_')\n\n # values as lists\n if key.lower() in ('flags', 'bugs'):\n value = value.split()\n return key.lower(), value","def macro_files_pattern(self, val: Pattern):\n self[\"macro_files_pattern\"] = str(val)","def line_replacer(config,change_this_line,key):\n for arg in config['HyperParameter'][key]: \n pattern=r'{}[ ]*=.*,'.format(arg)\n replace_value=config['HyperParameter'][key][arg][counter]\n if type(replace_value) is str:\n replace_value=\"'\"+replace_value+\"'\"\n change_this_line=re.sub(pattern,\"{}= {},\".format(arg,replace_value),change_this_line)\n return change_this_line","def components(self, value):\n if value.shape != self._components.shape:\n raise ValueError(\n \"Trying to replace components of shape {} with some of \"\n \"shape {}\".format(self.components.shape, value.shape)\n )\n else:\n np.copyto(self._components, value, casting=\"safe\")","def value_to_string(self, value, type_class, param_info=None):\n if isinstance(value, Entry):\n var = self.get_variable(value.code_entry)\n if isinstance(value.target, list):\n return \"tuple(%s)\" % var\n return var\n else:\n if type_class == TypeClass.STRING:\n return '\"%s\"' % value\n elif type_class == TypeClass.ENUM:\n name = value.typeName\n suffix = self.get_last_part(name)\n upper_chars = [c for c in suffix if c.isupper()]\n as_name = \"%s_%s\" % (\"\".join(upper_chars), value.value)\n self.add_import('%s.%s' % (value.typeName, value.value), as_name)\n #return value.value\n return as_name\n elif type_class == TypeClass.CHAR:\n return \"uno.Char(\\\"%s\\\")\" % value.value\n elif type_class == TypeClass.SEQUENCE:\n comp_type, n = self.parse_seq(param_info)\n _comp_type_class = comp_type.getTypeClass()\n str_val = [self.value_to_string(v, _comp_type_class) for v in value]\n return \"(%s)\" % \", \".join(str_val)\n else:\n return str(value)","def make_safe(value, delimiter):\n if isinstance(value,list):\n return delimiter.join(map(lambda x: make_safe(x, delimiter),value))\n return str(value)","def preprocess(self, value):\n for val_type in self._value_types:\n try:\n value = val_type(value)\n break\n except ValueError:\n pass\n return value","def str_map(values, formatting):\n return [formatting % v for v in values]","def patmod(self, value):\n self._custom_setter('patmod', value)"],"string":"[\n \"def _splitValue(self, value):\\n m = ConfigParser.varRe.match(value)\\n if m == None:\\n raise Exception(self.fh.name + \\\":\\\" + str(self.lineNum) + \\\": bug, can't split line \\\")\\n (beforeRef, junk, varName, afterRef) = m.groups()\\n return (beforeRef, varName, afterRef)\",\n \"def setSplitPattern(self, value):\\n return self._set(splitPattern=value)\",\n \"def setSplitPattern(self, value):\\n return self._set(splitPattern=value)\",\n \"def split(value, delimiter):\\n return value.split(delimiter)\",\n \"def prepare_value(self, value):\\n if value is None:\\n return value\\n value = value.replace(\\\" \\\", \\\"\\\").replace(\\\".\\\", \\\"\\\")\\n if value:\\n return \\\"%s.%s.%s.%s\\\" % (value[0:3], value[3:7], value[7:11], value[11:])\\n return value\",\n \"def format_value(value):\\n return value.replace('|', ' ')\",\n \"def substitute(self, field_values):\\n\\n\\t\\tdef substituteFunc(mo):\\n\\t\\t\\tname = mo.group('braced')\\n\\t\\t\\tif name is not None:\\n\\t\\t\\t\\tif name in field_values:\\n\\t\\t\\t\\t\\treturn str(field_values[name])\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\treturn self._DELIMITER + '{' + name + '}'\\n\\n\\t\\t\\t# regexp could catch either 'braced' or 'escaped' substring\\n\\t\\t\\t# if it is not 'braced', it is 'escaped'\\n\\t\\t\\treturn self._DELIMITER\\n\\n\\t\\treturn self._PATTERN.sub(substituteFunc, self._template)\",\n \"def split_search(self, value: str, adapter: str = AGG_ADAPTER_NAME) -> Tuple[str, List[str]]:\\n search = value.strip().lower()\\n\\n # Problem are fields containing \\\":\\\" from adapters that populate dynamic fields\\n # PBUG: special check for 'custom attributes' coming from esx adapter.\\n if \\\":\\\" in search and 'custom attributes' not in search:\\n adapter_split, field = [x.strip() for x in search.split(\\\":\\\", 1)]\\n if not adapter_split:\\n adapter_split = adapter\\n else:\\n field = search\\n adapter_split = adapter\\n qual_check = re.match(r\\\"adapters_data\\\\.(.*?)\\\\.\\\", field)\\n if qual_check and len(qual_check.groups()) == 1:\\n adapter_split = qual_check.groups()[0]\\n\\n adapter_split = strip_right(obj=adapter_split.lower().strip(), fix=\\\"_adapter\\\")\\n\\n fields = split_str(\\n obj=field,\\n split=\\\",\\\",\\n strip=None,\\n do_strip=True,\\n lower=True,\\n empty=False,\\n )\\n\\n if not fields:\\n raise ApiError(f\\\"No fields provided in {value!r}, format must be 'adapter:field'\\\")\\n return adapter_split, fields\",\n \"def split(value, key):\\n return str(value).split(key)\",\n \"def _splitFieldValue(self, line):\\n found = self.FIELDVALUE.findall(line)\\n if found:\\n fieldName, value = found[0]\\n if fieldName in self.C.ADAPTER_COMMAFIELDS:\\n value = self.COMMASPLIT.findall(value)[:-1] # Split and remove last empty part\\n return fieldName, value\\n return None, None # No field name match on this line.\",\n \"def split(value: str, sep: str = \\\":\\\") -> Tuple:\\n left, _, right = value.partition(sep)\\n return (left, right) if right else (None, left)\",\n \"def sub_values(content: str, **kwargs) -> str:\\n\\n match_re = re.compile(\\\"(?<={{ .Values.)[a-zA-Z0-9]+?(?= }})\\\")\\n found = set()\\n\\n def replace_fn(match):\\n varname = match_re.search(match.group(0)).group(0)\\n found.add(varname)\\n try:\\n return str(kwargs[varname])\\n except KeyError:\\n raise ValueError(f\\\"No value provided for wildcard {varname}\\\")\\n\\n content = re.sub(\\\"{{ .Values.[a-zA-Z0-9]+? }}\\\", replace_fn, content)\\n\\n missing = set(kwargs) - found\\n if missing:\\n raise ValueError(\\n \\\"Provided unused wildcard values: {}\\\".format(\\\", \\\".join(missing))\\n )\\n return content\",\n \"def get_prep_value(self, value):\\n if isinstance(value, tuple) and len(value) == 2:\\n return '[%s,%s]' % value\\n return value\",\n \"def find_value(code, value):\\n value_pattern = re.compile(rf\\\"{re.escape(value)} ?= ?([^=][a-zA-Z0-9\\\\.'/_)(]*)\\\")\\n\\n target = None\\n for line in code:\\n if value_pattern.search(line):\\n target = re.findall(value_pattern, line)\\n break\\n\\n return target[0] if target is not None else value\",\n \"def linebreaks(value):\\n value = normalize_newlines(value)\\n paras = re.split('\\\\n{2,}', str(value))\\n paras = ['<p>%s</p>' % p.replace('\\\\n', '<br>') for p in paras]\\n return '\\\\n\\\\n'.join(paras)\",\n \"def parse(cls, value: str) -> Tuple[str, Dict[str, str]]:\\n raw_value = read_value_from_path(value)\\n args: Dict[str, str] = {}\\n\\n if \\\"@\\\" in raw_value:\\n args[\\\"region\\\"], raw_value = raw_value.split(\\\"@\\\", 1)\\n\\n # now find any other arguments that can be filters\\n matches = re.findall(r\\\"([0-9a-zA-z_-]+:[^\\\\s$]+)\\\", raw_value)\\n for match in matches:\\n k, v = match.split(\\\":\\\", 1)\\n args[k] = v\\n\\n return args.pop(\\\"name_regex\\\"), args\",\n \"def format_regex_string(self, name, value):\\n\\n if value is not None and isinstance(value, str):\\n string = '%s = %s\\\\n' % (name, self.simple_format_string(value, force_raw=True))\\n else:\\n string = '%s = None\\\\n' % name\\n return string\",\n \"def csv_to_field_Urls(entity, value):\\n if value is None or value == '':\\n return\\n splitter = re.compile(url_splitter)\\n entity.string = splitter.split(value)\",\n \"def _expand_value(self, value, multiple=False):\\n if multiple:\\n return tuple(self._expand_value(item) for item in value)\\n elif isinstance(value, str):\\n expanded_value = value\\n for match in re.finditer(r'\\\\${([\\\\w\\\\d.]+)}', value):\\n key = match.group(1)\\n substring = '${{{key}}}'.format(key=key)\\n\\n if key in self._config:\\n expanded_value = value.replace(substring, str(self._raw_get(key)))\\n else:\\n msg = 'Error expanding value {value}. No value found for reference {ref}'.format(\\n value=value, ref=key)\\n raise InvalidReference(msg)\\n\\n return expanded_value\\n else:\\n return value\",\n \"def exptostr(value):\\n return re.sub(r'`(.*)`', r'\\\\g<1>', value)\",\n \"def replace( scr ):\\n d = ReplaceDialog(scr)\\n value = d.main()\\n if not \\\"pattern\\\" in value:\\n return (None,None)\\n else:\\n return (value[\\\"pattern\\\"],value[\\\"replace\\\"])\",\n \"def _extractTuples(self, value, pos=0):\\n matchValues = self._patternValues.match(value)\\n infections, _, deaths = matchValues.groups()\\n infections = int(infections)\\n deaths = int(deaths)\\n return infections, deaths\",\n \"def split_values(self, value):\\n if value:\\n return [s.strip() for s in value.split(',')]\\n else:\\n return []\",\n \"def format_value(content):\\n try:\\n content += \\\"\\\"\\n except TypeError:\\n content = \\\", \\\".join(content)\\n return remove_break_lines_characters(\\n content).strip().replace('^', PRESERVECIRC)\",\n \"def addInfixPattern(self, value):\\n try:\\n infix_patterns = self.getInfixPatterns()\\n except KeyError:\\n infix_patterns = []\\n infix_patterns.insert(0, value)\\n return self._set(infixPatterns=infix_patterns)\",\n \"def regex_replace_value(val, val_new, pattern,\\n val_exception=np.nan):\\n try:\\n if not bool(re.match(pattern, val)):\\n return val_new\\n else:\\n return val\\n except:\\n return val_exception\",\n \"def addSplitChars(self, value):\\n try:\\n split_chars = self.getSplitChars()\\n except KeyError:\\n split_chars = []\\n split_chars.append(value)\\n return self._set(splitChars=split_chars)\",\n \"def addSplitChars(self, value):\\n try:\\n split_chars = self.getSplitChars()\\n except KeyError:\\n split_chars = []\\n split_chars.append(value)\\n return self._set(splitChars=split_chars)\",\n \"def regex_sub_value(val, pattern, val_sub='',\\n val_exception=np.nan, val_none=np.nan):\\n try:\\n stripped = re.sub(pattern, val_sub, val)\\n new_val = val_none if stripped == '' else stripped\\n return new_val\\n except:\\n return val_exception\",\n \"def _eval_str_tuple(value):\\n if not (value.startswith('(') and value.endswith(')')):\\n raise ValueError(value)\\n\\n orig_value = value\\n value = value[1:-1]\\n\\n result = []\\n while value:\\n m = _strs.match(value)\\n if m is None:\\n raise ValueError(orig_value)\\n\\n result.append(m.group(1))\\n value = value[len(m.group(0)):]\\n\\n return tuple(result)\",\n \"def search_pattern(self, value, pattern):\\n _pattern = re.compile(pattern)\\n _match = _pattern.search(value)\\n return _match\",\n \"def setPattern(self, value):\\n return self._set(pattern=value)\",\n \"def __build_value(self, v, info):\\n\\n if not v:\\n return \\\"\\\"\\n\\n # Find all placeholders\\n attrs = {}\\n for attr in re.findall(r\\\"%\\\\(([^)]+)\\\\)s\\\", v):\\n\\n # Extract ordinary attributes\\n if attr in info:\\n attrs[attr] = \\\", \\\".join(info[attr])\\n\\n # Check for result renderers\\n elif attr in self.__value_extender:\\n attrs[attr] = self.__value_extender[attr](info)\\n\\n # Fallback - just set nothing\\n else:\\n attrs[attr] = \\\"\\\"\\n\\n # Assemble and remove empty lines and multiple whitespaces\\n res = v % attrs\\n res = re.sub(r\\\"(<br>)+\\\", \\\"<br>\\\", res)\\n res = re.sub(r\\\"^<br>\\\", \\\"\\\", res)\\n res = re.sub(r\\\"<br>$\\\", \\\"\\\", res)\\n return \\\"<br>\\\".join([s.strip() for s in res.split(\\\"<br>\\\")])\",\n \"def linebreaks(value):\\n value = re.sub(r'\\\\r\\\\n|\\\\r|\\\\n', '\\\\n', value) # normalize newlines\\n paras = re.split('\\\\n{2,}', value)\\n try:\\n paras = [u'<p>%s</p>' % p.encode('ascii', errors='ignore').replace('\\\\n', '<br />') for p in paras]\\n except Exception, e:\\n logger.error('Exception: {}'.format(e))\\n pass\\n try:\\n paras = u'\\\\n\\\\n'.join(paras)\\n except Exception, e:\\n logger.error('Exception: {}'.format(e))\\n pass\\n return Markup(paras)\",\n \"def parse_entry(\\n entry,\\n regex=r\\\"(\\\\s)*?(?P<value>[\\\\.\\\\w]+)(\\\\s)*?\\\",\\n delimiter=\\\",\\\",\\n values_only=True,\\n first_only=True,\\n errors=None,\\n replace_nan=\\\"None\\\",\\n):\\n\\n if isinstance(entry, str):\\n pattern = re.compile(regex)\\n matches = []\\n if not delimiter or (delimiter is None):\\n subparts = [entry]\\n else:\\n subparts = entry.split(delimiter)\\n\\n for _l in subparts:\\n _m = pattern.match(_l)\\n if _m:\\n _d = dict(value=_m.group(\\\"value\\\"))\\n # Add other groups\\n _d.update(\\n {\\n k: _m.group(k)\\n for (k, ind) in pattern.groupindex.items()\\n if not k == \\\"value\\\"\\n }\\n )\\n\\n else:\\n _d = dict(value=replace_nan)\\n # Add other groups\\n _d.update(\\n {\\n k: replace_nan\\n for (k, ind) in pattern.groupindex.items()\\n if not k == \\\"value\\\"\\n }\\n )\\n matches.append(_d)\\n\\n if values_only:\\n matches = [m[\\\"value\\\"] for m in matches]\\n\\n if first_only:\\n return matches[0]\\n\\n return matches\\n else:\\n if entry is None:\\n entry = replace_nan\\n elif isinstance(entry, float):\\n if np.isnan(entry):\\n entry = replace_nan\\n if first_only:\\n return entry\\n else:\\n return [entry]\",\n \"def placeholder_to_regex(placeholder,encloser='%',matcher='(.+)'):\\n pattern = placeholder\\n pattern = pattern.replace('\\\\\\\\','/')\\n if pattern.count('%') == 0 or pattern.count('%') % 2 != 0:\\n return '',[]\\n else:\\n borders = pattern.split(encloser)[::2]\\n fields = pattern.split(encloser)[1::2]\\n for field in fields:\\n pattern = pattern.replace(encloser+field+encloser, matcher, 1)\\n pattern = pattern.replace('/','\\\\\\\\/')\\n return pattern,fields\",\n \"def processArgument(self, value):\\n if not isinstance(value, basestring):\\n return value\\n \\n value = self._RE_FIND.subn(self._replaceFind, value)[0]\\n value = self._RE_ENV.subn(self._replaceEnv, value)[0]\\n \\n return value\",\n \"def _callback(self, matcher):\\n matched_field = matcher.group(self.field)\\n replacement = self.lookup.get(matched_field)\\n if not replacement:\\n return matcher.group(0)\\n\\n fields = list(f or \\\"\\\" for f in matcher.groups())\\n fields[self.field - 1] = replacement\\n\\n return \\\"\\\".join(fields)\",\n \"def parse_value(cls, value):\\n choice, value = value.split('=')\\n value = cls.VALUES_MAP[value]\\n\\n return choice, value\",\n \"def _string_subst_partial(self, val):\\n def repl(m):\\n k = m.group('id')\\n replacement = self.bib_database.strings[k.lower()] if k.lower() in self.bib_database.strings else k\\n pre = '\\\"' if m.group('pre') != '\\\"' else ''\\n post = '\\\"' if m.group('post') != '\\\"' else ''\\n return pre + replacement + post\\n\\n logger.debug('Substitute string definitions inside larger expressions')\\n if '#' not in val:\\n return val\\n\\n # TODO?: Does not match two subsequent variables or strings, such as \\\"start\\\" # foo # bar # \\\"end\\\" or \\\"start\\\" # \\\"end\\\".\\n # TODO: Does not support braces instead of quotes, e.g.: {start} # foo # {bar}\\n # TODO: Does not support strings like: \\\"te#s#t\\\"\\n return self.replace_all_re.sub(repl, val)\",\n \"def processArgument(self, value):\\r\\n if not isinstance(value, basestring):\\r\\n return value\\r\\n\\r\\n value = self._RE_FIND.subn(self._replaceFind, value)[0]\\r\\n value = self._RE_ENV.subn(self._replaceEnv, value)[0]\\r\\n\\r\\n return value\",\n \"def _rsplit(value, sep, maxsplit=None):\\n\\tstr_parts = value.split(sep)\\n\\tif (maxsplit is not None) and (len(str_parts) > 1):\\n\\t\\treturn [str.join(sep, str_parts[:-maxsplit])] + str_parts[-maxsplit:]\\n\\treturn str_parts\",\n \"def regex_splitter(input_string: str) -> str:\\n #grouping_list: list = re.compile(REGEX_PATTERN).split(input_string)\\n compiled_regex = re.compile(REGEX_PATTERN)\\n mo = compiled_regex.findall(input_string)\\n \\n #print(\\\"Current found matches are:\\\" + str(mo))\\n \\n result = evaluator_indefinite(mo)\\n \\n #print(\\\"Dictionary of evaluations\\\" + str(result))\\n \\n new_string = input_string\\n\\n for match in mo:\\n new_string = new_string.replace(str(match), str(result[match]))\\n #print(\\\"Current string modified with new value: \\\" + new_string)\\n\\n return new_string\",\n \"def parse_field_expression(value):\\n if not value:\\n return None\\n\\n mo = regex.match(value)\\n if not mo:\\n return None\\n return mo.groups()\",\n \"def clean(self, value):\\n return [f.clean(v) for v,f in zip(value, self.fields)]\",\n \"def parse_list(value: str) -> list[str]:\\n segments = _QUOTED_SEGMENT_RE.findall(value)\\n for segment in segments:\\n left, match, right = value.partition(segment)\\n value = ''.join([left, match.replace(',', '\\\\000'), right])\\n return [_dequote(x.strip()).replace('\\\\000', ',') for x in value.split(',')]\",\n \"def parse_value(value: str) -> Tuple[str, str, str]:\\n value_pattern = r'^(usb|pci)\\\\(([^:]{4}):([^:]{4})\\\\)$'\\n matches = re.match(value_pattern, value)\\n assert matches, value\\n ilk, vendor, device = matches.group(1), matches.group(2), matches.group(3)\\n return ilk, vendor, device\",\n \"def get_val_tuple(self, value):\\n group_id = ([(idx1, idx2)\\n for idx1, category in enumerate(self.groups)\\n for idx2, val in enumerate(category) if val == value])\\n return group_id[0]\",\n \"def __splitVersionPattern(versionPattern):\\n prefix = ''\\n padding = ''\\n suffix = ''\\n for char in versionPattern:\\n if char != '#' and not padding:\\n prefix += char\\n elif char != '#' and padding:\\n suffix += char\\n else:\\n padding += char\\n\\n assert len(padding), 'Padding pattern representation (#) not found in version pattern: {}'.format(versionPattern)\\n return {\\n 'prefix': prefix,\\n 'padding': padding,\\n 'suffix': suffix\\n }\",\n \"def convert(cls, value: Any) -> Optional[VarNames]:\\n # Can be optional\\n if value is None:\\n return None\\n\\n if isinstance(value, str):\\n return to_list(value)\\n\\n if not isinstance(value, list):\\n raise ValueError('Variable name pattern can only be a string or a'\\n ' list of strings.')\\n\\n for item in value:\\n if not isinstance(item, str):\\n raise ValueError('Variable name pattern can only be a string'\\n ' or a list of strings.')\\n\\n return value.copy()\",\n \"def slugify(value, delimiter='-'):\\n words = slug_pattern.split(value)\\n return delimiter.join(filter(truth, words)).lower()\",\n \"def filter_re_replace(val: AnyStr, pattern: str, repl: str) -> str:\\n return re.sub(pattern, repl, str(val))\",\n \"def test_regex_replace_value(self):\\n inputs = ['1234', '12345', '123a5', '', 1234, None, np.nan]\\n outputs = ['1234', 'test', 'test', 'test', np.nan, np.nan, np.nan]\\n pattern = '[0-9]{4}$'\\n outputs_test = [\\n regex_replace_value(input_val, 'test', pattern)\\n for input_val in inputs\\n ]\\n self.assertEqual(outputs, outputs_test)\",\n \"def fn_sub(self, value):\\n\\n if isinstance(value, list):\\n value, variables = value\\n else:\\n # only template parameter names, resource logical IDs, and resource attributes, will be parsed\\n value, variables = value, {}\\n\\n for name, target in variables.items():\\n value = value.replace('${{{}}}'.format(name), target)\\n\\n return Functions.SUB_VARIABLE_PATTERN.sub(self._sub_variable, value)\",\n \"def split_words(value: str) -> List[str]:\\n words: List[str] = []\\n buffer: List[str] = []\\n previous = None\\n\\n def flush():\\n if buffer:\\n words.append(\\\"\\\".join(buffer))\\n buffer.clear()\\n\\n for char in value:\\n tp = classify(char)\\n if tp == StringType.OTHER:\\n flush()\\n elif not previous or tp == previous:\\n buffer.append(char)\\n elif tp == StringType.UPPER and previous != StringType.UPPER:\\n flush()\\n buffer.append(char)\\n else:\\n buffer.append(char)\\n\\n previous = tp\\n\\n flush()\\n return words\",\n \"def replace_tag_value( msg, tag, value ) :\\n _TAG_RE = re.compile('\\\\x01' + str(tag) + '=' + '[\\\\s\\\\w:-]*' + '\\\\x01')\\n msg = _TAG_RE.sub('\\\\x01' + str(tag) + '=' + str(value) + '\\\\x01', msg,1)\\n return msg\",\n \"def linebreaks(value, autoescape=False):\\n value = normalize_newlines(value)\\n paras = re.split('\\\\n{2,}', value)\\n if autoescape:\\n paras = ['<p>%s</p>' % escape(p).replace('\\\\n', '<br />') for p in\\n paras]\\n else:\\n paras = ['<p>%s</p>' % p.replace('\\\\n', '<br />') for p in paras]\\n return '\\\\n\\\\n'.join(paras)\",\n \"def subst(self, value, filter=None):\\n\\n if isinstance(value, Literal):\\n return value._value\\n elif isinstance(value, tuple):\\n return tuple(self.subst(i, filter) for i in value)\\n elif isinstance(value, list):\\n return list(self.subst(i, filter) for i in value)\\n elif isinstance(value, dict):\\n return {i: self.subst(value[i], filter) for i in value}\\n elif isinstance(value, StringTypes):\\n def subfn(mo):\\n var = mo.group(0)\\n\\n if var == \\\"$$\\\":\\n return \\\"$\\\"\\n\\n # Apply variable filters\\n parts = var[2:-1].split(\\\"|\\\")\\n value = self.evaluate(parts[0])\\n\\n if len(parts) > 1:\\n # Filters supplied directly\\n for part in parts[1:]:\\n if len(part) == 0:\\n # Empty filter can be used to disable auto filter\\n continue\\n else:\\n value = self.callfilter(part, value)\\n elif filter:\\n # Use auto-filter if specified\\n for part in filter.split(\\\"|\\\"):\\n value = self.callfilter(part, value)\\n\\n return value\\n return re.sub(r\\\"\\\\$\\\\$|\\\\$\\\\(.*?\\\\)\\\", subfn, value)\\n else:\\n return value\",\n \"def texify(value):\\n for k, v in REPLACEMENTS.items():\\n value = value.replace(k, v)\\n return mark_safe(value)\",\n \"def _extract_parts_from_final_value(self, full_encrypted_value, **options):\\n\\n empty, handler, encrypted_part = full_encrypted_value.split(\\n self._get_separator(), self._get_separator_count())\\n\\n return handler.decode(self._encoding), self._decode_encrypted_part(encrypted_part)\",\n \"def split_tuple(value: tuple[str, ...] | str | None) -> tuple[str, ...] | None:\\n if value is None:\\n return None\\n if TYPE_CHECKING:\\n assert isinstance(value, str)\\n return tuple(value.split(\\\".\\\"))\",\n \"def sub(self, value, conversion = str):\\n if value.startswith(\\\"$(eval\\\") and value.endswith(\\\")\\\"):\\n result = UnresolvedValue()\\n result.append((\\\"eval\\\", value[7:-1]))\\n return result\\n if self.ERROR_PATTERN.search(value):\\n raise SubstitutionError(\\\"'$' cannot appear within expression\\\")\\n match = self.PATTERN.search(value)\\n if not match:\\n return self.convert_str(value, conversion)\\n result = UnresolvedValue()\\n rest = value\\n while match:\\n # this map is to avoid python 2/3 issues\\n parts = list(map(str, match.group(1).split(None, 1)))\\n parts = list(map(str.strip, parts))\\n parts = list(filter(bool, parts))\\n\\n assert len(parts) == 1 or len(parts) == 2\\n if not parts[0] in self.COMMANDS:\\n raise SubstitutionError(\\\"invalid command: \\\" + parts[0])\\n prefix = rest[:match.start()]\\n if prefix:\\n if parts[0] == \\\"eval\\\":\\n raise SubstitutionError(\\\"eval must appear at the start\\\")\\n result.append(prefix)\\n cmd = getattr(self, \\\"_\\\" + parts[0])\\n result.append(cmd(parts))\\n rest = rest[match.end():]\\n match = self.PATTERN.search(rest)\\n if rest:\\n result.append(rest)\\n return self.convert_unresolved(result, conversion)\",\n \"def find_values_to_replace(self):\\n regexp = re.compile(self.raw_pattern)\\n self.to_replace = regexp.findall(self.raw_sql)\",\n \"def pathify(urlpattern, **context):\\n\\n repl = lambda match: context[match.group(1)]\\n path = re.sub(r':([a-z]+)', repl, urlpattern)\\n return tuple(path[1:].split('/'))\",\n \"def parse_from_placeholder(string,pattern,encloser='%',matcher='(.+)'):\\n pattern,fields = placeholder_to_regex(pattern,encloser,matcher)\\n return parse_from_regex(string,pattern,fields)\",\n \"def field_values(s):\\n if s: \\n for f in s.split(','):\\n if f.find('=') > 0:\\n (field, value) = f.split('=')\\n else:\\n field = f\\n value = None\\n yield (field, value)\",\n \"def get_values_re():\\n\\n regex = r\\\"([1-9]|1[0-9]) \\\" +\\\\\\n r\\\"(localhost) \\\" +\\\\\\n r\\\"(\\\\d{1,5}) \\\" +\\\\\\n r\\\"(\\\\d{1,5}) \\\" +\\\\\\n r\\\"(YES|NO) \\\" +\\\\\\n r\\\"(YES|NO) \\\" +\\\\\\n r\\\"(YES|NO) \\\" + \\\\\\n r\\\"(YES|NO)\\\"\\n\\n regex = regex.split(\\\" \\\")\\n\\n return regex\",\n \"def get_expanded_values(self, value: Union[str, List[str]]) -> List[str]:\\n if isinstance(value, str):\\n value = [value]\\n _out = []\\n for v in value:\\n try:\\n v = self.biolink_model_toolkit.get_descendants(v, reflexive=True, formatted=True)\\n v = [x.split(\\\":\\\")[-1] for x in v] # remove biolink: prefix\\n except ValueError:\\n v = [v]\\n _out.extend(v)\\n return _out\",\n \"def parse_template(template):\\n field_name = None\\n field_value = []\\n\\n for line in template.strip().split('\\\\n') + ['end:']:\\n if line.startswith('#'):\\n continue\\n match = RE_TEMPLATE_FIELD_LINE.match(line)\\n if match:\\n if field_name is not None:\\n yield (field_name, '\\\\n'.join(field_value).strip())\\n elif len(field_value) > 0:\\n logging.warning('Ignoring lines: %r', field_value)\\n\\n field_name = match.group(1)\\n field_value = [match.group(2)]\\n else:\\n field_value.append(line)\",\n \"def lister(list_value):\\n\\n for item in list_value:\\n if isinstance(item, basestring):\\n index = list_value.index(item)\\n rep, fact = replacer(replace_value=item)\\n if fact is True:\\n list_value[index] = rep\\n\\n elif isinstance(item, list):\\n lister(list_value=item)\\n\\n elif isinstance(item, dict):\\n _super_munger(mungie=item)\",\n \"def setPatterns(self, value):\\n return self._set(patterns=value)\",\n \"def generate_regex(self, pattern_to_glob):\\n\\n # First thing, replace '.' with '\\\\.' and make sure we're going\\n # to match the start and the end of the pattern string.\\n pattern_to_glob = re.sub(r\\\"\\\\.\\\", r\\\"\\\\.\\\", pattern_to_glob)\\n pattern_to_glob = r\\\"^\\\" + pattern_to_glob + r\\\"$\\\"\\n\\n found_values = []\\n new_val = pattern_to_glob\\n\\n # Find all the named attributes\\n all_matches = re.findall(r\\\"%.+?%\\\", pattern_to_glob)\\n\\n for match_thing in all_matches:\\n name = match_thing[1:-1]\\n\\n if match_thing not in found_values:\\n # Change to a named group.\\n group_string = r\\\"(?P<\\\" + name + r\\\">.+?)\\\"\\n new_val = re.sub(match_thing, group_string, new_val, count=1)\\n found_values.append(match_thing)\\n else:\\n # Use the existing group.\\n group_string = r\\\"(?P=\\\" + name + r\\\")\\\"\\n new_val = re.sub(match_thing, group_string, new_val, count=1)\\n\\n return new_val\",\n \"def MakePattern(self,content):\\n return self.register(Pattern(content,reg=self))\",\n \"def separate_content_and_variables(text, boundary='{# /variables #}'):\\n\\n # Find boundary\\n pos = text.find(boundary)\\n\\n # Text contains variables\\n if pos > 0:\\n return(text[:pos].strip(), text[(pos + len(boundary)):].strip())\\n\\n # Text does not contain variables\\n return ('', text)\",\n \"def string_value(cls, json_field: str, comparison: str, value: str) -> \\\"JsonPattern\\\":\\n return jsii.sinvoke(cls, \\\"stringValue\\\", [json_field, comparison, value])\",\n \"def replace_unwanted_value_by_value_grouped(tX_grouped, unwanted_value, value):\\n tX_grouped_new = []\\n for i in range(len(tX_grouped)):\\n tX_grouped_new.append(\\n replace_unwanted_value_by_value(tX_grouped[i], unwanted_value, value)\\n )\\n return tX_grouped_new\",\n \"def output_space(value):\\n tpl_args = value.split(':')\\n template = tpl_args[0]\\n spec = {}\\n for modifier in tpl_args[1:]:\\n mitems = modifier.split('-', 1)\\n spec[mitems[0]] = len(mitems) == 1 or mitems[1]\\n\\n return template, spec\",\n \"def run(self, content):\\n parts = []\\n offset = 0\\n for match in self.regexp.finditer(content):\\n parts.append(content[offset:match.start(0)])\\n parts.append(self.replace(match))\\n offset = match.end(0)\\n parts.append(content[offset:])\\n return ''.join(parts)\",\n \"def format_field(self, value, spec):\\n cache = Cache()\\n if spec == \\\"co\\\":\\n # if cache(re.match(\\\"(.*)co$\\\", spec)):\\n value = co_join(value)\\n spec = \\\"s\\\"\\n # cache.output.group(1) + \\\"s\\\"\\n elif cache(re.match(r\\\"^sub(\\\\d?)_?(.*)$\\\", spec)):\\n depth = (1 if cache.output.group(1) == \\\"\\\" else\\n int(cache.output.group(1)))\\n value = \\\"\\\\n\\\".join([\\n \\\"{0}{1} = {2}\\\".format(depth * \\\" \\\", key, val)\\n for key, val in value.items()])\\n if cache.output.group(2) != \\\"\\\":\\n value = (\\n depth * \\\"[\\\" + cache.output.group(2) + depth * \\\"]\\\" + \\\"\\\\n\\\" +\\n value)\\n spec = \\\"s\\\"\\n return super(Format, self).format_field(value, spec)\",\n \"def get_value(value):\\n if value:\\n return value.split('\\\\n')[0]\\n else:\\n return None\",\n \"def linebreaks(value, autoescape=False):\\n value = normalize_newlines(value)\\n paras = re.split('\\\\n{2,}', value)\\n if autoescape:\\n paras = ['<p data-parnum=\\\"%s\\\">%s</p>' % (i, escape(p).replace('\\\\n', '<br />')) for i, p in enumerate(paras)]\\n else:\\n paras = ['<p data-parnum=\\\"%s\\\">%s</p>' % (i, p.replace('\\\\n', '<br />')) for i, p in enumerate(paras)]\\n return '\\\\n\\\\n'.join(paras)\",\n \"def _substitute(self, value, group=None, namespace=None):\\n if isinstance(value, list):\\n return [self._substitute(i, group=group, namespace=namespace)\\n for i in value]\\n elif isinstance(value, str):\\n # Treat a backslash followed by the dollar sign \\\"\\\\$\\\"\\n # the same as the string template escape \\\"$$\\\" as it is\\n # a bit more natural for users\\n if r'\\\\$' in value:\\n value = value.replace(r'\\\\$', '$$')\\n tmpl = self.Template(value)\\n ret = tmpl.safe_substitute(\\n self.StrSubWrapper(self, group=group, namespace=namespace))\\n return ret\\n elif isinstance(value, dict):\\n # Substitute template variables in both key and value\\n return {self._substitute(key, group=group, namespace=namespace):\\n self._substitute(val, group=group, namespace=namespace)\\n for key, val in value.items()}\\n else:\\n return value\",\n \"def _format_field_val(\\n self,\\n field: str,\\n field_type: str,\\n value: Any,\\n ) -> str | int | bool | list | None:\\n\\n # If the field is empty, no need to format.\\n if value is None:\\n return None\\n\\n # TODO(DanielRyanSmith): Write checks to ensure enum values are valid.\\n if field_type == 'emails' or field_type == 'split_str':\\n list_val = self._split_list_input(field, field_type, value, ',')\\n if field == 'blink_components' and len(value) == 0:\\n return [settings.DEFAULT_COMPONENT]\\n return list_val\\n elif field_type == 'link':\\n return self._extract_link(value)\\n elif field_type == 'links':\\n list_val = self._split_list_input(field, field_type, value)\\n # Filter out any URLs that do not conform to the proper pattern.\\n return [self._extract_link(link)\\n for link in list_val if link]\\n elif field_type == 'int':\\n # Int fields can be unset by giving null or nothing in the input field.\\n if value == '' or value is None:\\n return None\\n try:\\n return int(value)\\n except ValueError:\\n self._abort_invalid_data_type(field, field_type, value)\\n elif field_type == 'bool':\\n return bool(value)\\n return str(value)\",\n \"def replacer(replace_value, key=None):\\n\\n if key is not None:\\n if key not in exempt:\\n return check_replace(replace_value)\\n else:\\n return replace_value, False\\n else:\\n return check_replace(replace_value)\",\n \"def map(self, key, value):\\n namelist = value.strip().split()\\n namelist.insert(0, '')\\n namelist.append('')\\n n = len(namelist)\\n for i in range(1, n - 1):\\n self.outputcollector.collect(namelist[i], \\\\\\n tuple2str((namelist[i-1] ,namelist[i+1])))\",\n \"def setValueSplitSymbol(self, value):\\n return self._set(valueSplitSymbol=value)\",\n \"def valueformat(value, format_list):\\n\\n # print(\\\"\\\\n\\\", format_list, value)\\n concat_key = format_list.split('.')\\n # Pass in either the key of the field\\n # or pass in resource.key to enable a resource lookup.\\n key = \\\"\\\"\\n resource = \\\"\\\"\\n member_id = \\\"\\\"\\n key_sequence = [key, resource, member_id]\\n count = 0\\n for r in reversed(concat_key):\\n key_sequence[count] = r\\n count += 1\\n\\n # print(\\\"Concat_key:\\\", concat_key)\\n key = key_sequence[0]\\n resource = key_sequence[1]\\n member_id = key_sequence[2]\\n\\n # print(\\\"Key:\\\", key)\\n\\n if key:\\n if key.lower() == \\\"address\\\":\\n return dt_address(value)\\n\\n elif key.lower() == \\\"telecom\\\":\\n return dt_telecom(value)\\n\\n elif key.lower() == \\\"name\\\":\\n return dt_name(value)\\n elif key.lower() == 'dosage':\\n return dt_dosage(value)\\n elif key.lower() == 'medicationreference':\\n # print(\\\"Working on\\\", key, \\\": \\\", value)\\n # f_value = value\\n # lookup field_formats\\n # concat_key should have a resource name\\n # print(\\\"\\\\n\\\\nRESOURCE:\\\", resource)\\n # print(\\\"calling dt_medicationreference with Resource:\\\", resource, \\\", value:\\\", value)\\n return dt_medicationreference(value, member_id, resource)\\n elif key.lower() == 'dataabsentreason':\\n if isinstance(value, dict):\\n return value['coding'][0]['display']\\n else:\\n return value\\n elif key.lower() == 'valuequantity':\\n # return str(value['value']) + \\\" \\\" + value['unit']\\n return dt_valuequantity(value)\\n elif key.lower() == 'valuestring':\\n return value\\n elif key.lower() == 'interpretation':\\n return value['coding'][0]['display']\\n elif key.lower() == 'referencerange':\\n return dt_referencerange(value)\\n elif key.lower() == 'requester':\\n if 'display' in value['agent']:\\n return dt_reference(value['agent'], member_id)\\n elif key.lower() == 'practitioner':\\n if 'display' in value:\\n return dt_reference(value, member_id)\\n elif key.lower() == 'organization':\\n if 'display' in value:\\n return dt_reference(value, member_id)\\n # elif key.lower() == \\\"result\\\":\\n # return dt_reference(value[0], member_id)\\n elif key.lower() == 'practitioner':\\n if 'display' in value:\\n return dt_reference(value, member_id)\\n elif key.lower() == 'organization':\\n if 'display' in value:\\n return dt_reference(value, member_id)\\n elif key.lower() == 'participant':\\n if 'display' in value[0]['individual']:\\n return dt_reference(value[0]['individual'], member_id)\\n elif key.lower() == 'location':\\n if 'display' in value[0]['location']:\\n return dt_reference(value[0]['location'], member_id)\\n elif key.lower() == 'communication':\\n return dt_communication(value)\\n else:\\n # print(\\\"value:\\\", value, \\\" type:\\\", type(value), \\\" for: \\\", key)\\n return value\",\n \"def _value_to_matcher(value, add_quotes=False):\\n if isinstance(value, tf.Operation):\\n return _OpMatcher(str(value.node_def.name))\\n if isinstance(value, tf.Tensor):\\n return _TensorMatcher(str(value.name))\\n if isinstance(value, (six.text_type, str, bytes)):\\n if add_quotes:\\n return '\\\\'{}\\\\''.format(value)\\n else:\\n return value\\n raise ValueError('Cannot get a matcher for: {}, {}'.format(\\n type(value), value))\",\n \"def implode(self, label_pattern, value_pattern):\\n import hxl.filters\\n return hxl.filters.ImplodeFilter(self, label_pattern=label_pattern, value_pattern=value_pattern)\",\n \"def process_result_value(self, value: str, dialect):\\n pb = self._cls()\\n pb.ParseFromString(value)\\n return pb\",\n \"def extract_values(line):\\n key, value = line.split(':')\\n key, value = key.strip(), value.strip()\\n key = key.replace(' ', '_')\\n\\n # values as lists\\n if key.lower() in ('flags', 'bugs'):\\n value = value.split()\\n return key.lower(), value\",\n \"def macro_files_pattern(self, val: Pattern):\\n self[\\\"macro_files_pattern\\\"] = str(val)\",\n \"def line_replacer(config,change_this_line,key):\\n for arg in config['HyperParameter'][key]: \\n pattern=r'{}[ ]*=.*,'.format(arg)\\n replace_value=config['HyperParameter'][key][arg][counter]\\n if type(replace_value) is str:\\n replace_value=\\\"'\\\"+replace_value+\\\"'\\\"\\n change_this_line=re.sub(pattern,\\\"{}= {},\\\".format(arg,replace_value),change_this_line)\\n return change_this_line\",\n \"def components(self, value):\\n if value.shape != self._components.shape:\\n raise ValueError(\\n \\\"Trying to replace components of shape {} with some of \\\"\\n \\\"shape {}\\\".format(self.components.shape, value.shape)\\n )\\n else:\\n np.copyto(self._components, value, casting=\\\"safe\\\")\",\n \"def value_to_string(self, value, type_class, param_info=None):\\n if isinstance(value, Entry):\\n var = self.get_variable(value.code_entry)\\n if isinstance(value.target, list):\\n return \\\"tuple(%s)\\\" % var\\n return var\\n else:\\n if type_class == TypeClass.STRING:\\n return '\\\"%s\\\"' % value\\n elif type_class == TypeClass.ENUM:\\n name = value.typeName\\n suffix = self.get_last_part(name)\\n upper_chars = [c for c in suffix if c.isupper()]\\n as_name = \\\"%s_%s\\\" % (\\\"\\\".join(upper_chars), value.value)\\n self.add_import('%s.%s' % (value.typeName, value.value), as_name)\\n #return value.value\\n return as_name\\n elif type_class == TypeClass.CHAR:\\n return \\\"uno.Char(\\\\\\\"%s\\\\\\\")\\\" % value.value\\n elif type_class == TypeClass.SEQUENCE:\\n comp_type, n = self.parse_seq(param_info)\\n _comp_type_class = comp_type.getTypeClass()\\n str_val = [self.value_to_string(v, _comp_type_class) for v in value]\\n return \\\"(%s)\\\" % \\\", \\\".join(str_val)\\n else:\\n return str(value)\",\n \"def make_safe(value, delimiter):\\n if isinstance(value,list):\\n return delimiter.join(map(lambda x: make_safe(x, delimiter),value))\\n return str(value)\",\n \"def preprocess(self, value):\\n for val_type in self._value_types:\\n try:\\n value = val_type(value)\\n break\\n except ValueError:\\n pass\\n return value\",\n \"def str_map(values, formatting):\\n return [formatting % v for v in values]\",\n \"def patmod(self, value):\\n self._custom_setter('patmod', value)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6626543","0.5880005","0.5880005","0.5834636","0.5569926","0.530701","0.5272915","0.51757324","0.5105574","0.5092262","0.50849164","0.50815874","0.5064315","0.49831337","0.49724522","0.49412143","0.49400538","0.4932219","0.49313912","0.4915455","0.49036115","0.49005428","0.4879037","0.48759058","0.4873342","0.48699373","0.48297873","0.48297873","0.48236546","0.48190948","0.47954798","0.47914416","0.47890586","0.4770153","0.47673374","0.4734357","0.47325537","0.4718478","0.4717365","0.4705481","0.46980736","0.46586445","0.46452633","0.46302193","0.46101692","0.45922482","0.45912376","0.45870718","0.4583453","0.4563049","0.45600516","0.45569742","0.45566916","0.4516965","0.45043865","0.45015106","0.44991517","0.44907752","0.4490026","0.44898117","0.44823357","0.4466902","0.44559732","0.44547367","0.44532183","0.44495475","0.44448224","0.44392246","0.44372076","0.4434139","0.4428304","0.44279492","0.44222227","0.44215596","0.44118413","0.44095093","0.44076836","0.4403834","0.43906346","0.43893158","0.4381765","0.4377537","0.43663716","0.43577537","0.43563145","0.43524244","0.43331113","0.43316737","0.43218896","0.43188643","0.43168208","0.4313009","0.43014914","0.43008086","0.4300383","0.4294021","0.42856216","0.42853478","0.42817515"],"string":"[\n \"0.6626543\",\n \"0.5880005\",\n \"0.5880005\",\n \"0.5834636\",\n \"0.5569926\",\n \"0.530701\",\n \"0.5272915\",\n \"0.51757324\",\n \"0.5105574\",\n \"0.5092262\",\n \"0.50849164\",\n \"0.50815874\",\n \"0.5064315\",\n \"0.49831337\",\n \"0.49724522\",\n \"0.49412143\",\n \"0.49400538\",\n \"0.4932219\",\n \"0.49313912\",\n \"0.4915455\",\n \"0.49036115\",\n \"0.49005428\",\n \"0.4879037\",\n \"0.48759058\",\n \"0.4873342\",\n \"0.48699373\",\n \"0.48297873\",\n \"0.48297873\",\n \"0.48236546\",\n \"0.48190948\",\n \"0.47954798\",\n \"0.47914416\",\n \"0.47890586\",\n \"0.4770153\",\n \"0.47673374\",\n \"0.4734357\",\n \"0.47325537\",\n \"0.4718478\",\n \"0.4717365\",\n \"0.4705481\",\n \"0.46980736\",\n \"0.46586445\",\n \"0.46452633\",\n \"0.46302193\",\n \"0.46101692\",\n \"0.45922482\",\n \"0.45912376\",\n \"0.45870718\",\n \"0.4583453\",\n \"0.4563049\",\n \"0.45600516\",\n \"0.45569742\",\n \"0.45566916\",\n \"0.4516965\",\n \"0.45043865\",\n \"0.45015106\",\n \"0.44991517\",\n \"0.44907752\",\n \"0.4490026\",\n \"0.44898117\",\n \"0.44823357\",\n \"0.4466902\",\n \"0.44559732\",\n \"0.44547367\",\n \"0.44532183\",\n \"0.44495475\",\n \"0.44448224\",\n \"0.44392246\",\n \"0.44372076\",\n \"0.4434139\",\n \"0.4428304\",\n \"0.44279492\",\n \"0.44222227\",\n \"0.44215596\",\n \"0.44118413\",\n \"0.44095093\",\n \"0.44076836\",\n \"0.4403834\",\n \"0.43906346\",\n \"0.43893158\",\n \"0.4381765\",\n \"0.4377537\",\n \"0.43663716\",\n \"0.43577537\",\n \"0.43563145\",\n \"0.43524244\",\n \"0.43331113\",\n \"0.43316737\",\n \"0.43218896\",\n \"0.43188643\",\n \"0.43168208\",\n \"0.4313009\",\n \"0.43014914\",\n \"0.43008086\",\n \"0.4300383\",\n \"0.4294021\",\n \"0.42856216\",\n \"0.42853478\",\n \"0.42817515\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.5778173"},"document_rank":{"kind":"string","value":"4"}}},{"rowIdx":26091,"cells":{"query":{"kind":"string","value":"Perform substitutions on a body of text. returns The string 'text', processed with the substitutions configured for this test instance."},"document":{"kind":"string","value":"def __PerformSubstitutions(self, text):\n\n for substitution in self.substitutions:\n pattern, replacement = self.SplitValue(substitution)\n text = re.compile(pattern,re.M).sub(replacement, text)\n return text"},"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 postprocess(self, text):\r\n return text","def preprocess(self, text):\r\n return text","def post_process_text(self, text):\n\t\treturn text","def substitution(plainText, key):\n return plainText","def apply(self, text):","def applyRegularExpressions(strText, substitutionPatternList, languageId, debug=False):\n # print substitutionPatternList\n if debug:\n RegularExpressionFormula.logger.info(\n \"Applying regular expressions to transcript ...\")\n\n # For successive regular expressions\n strText = RegularExpressionFormula.normalizeSpaces(strText, True)\n\n if debug:\n RegularExpressionFormula.logger.info(\n \"Initial transcript: \" + strText)\n\n # For each known regular expression\n for regex, alternate, regexType, regexLanguageId in substitutionPatternList:\n regexLanguageId = int(regexLanguageId)\n\n # Does it match the text language\n if regexLanguageId != languageId and \\\n regexLanguageId != 0:\n continue\n\n # Convert from type\n regexListForType = \\\n RegexType.typeToRegularExpressions(\n regex, alternate, int(regexType))\n\n # Get regular expressions for the given type\n for regexForType in regexListForType:\n regexPattern = regexForType[0] # What to match\n regexSubstitution = regexForType[1] # What to substitute\n\n strLineOriginal = strText\n\n # Is it some python code\n if alternate.startswith(\"lambda\"):\n # Use alternate version\n strText = re.sub(regexPattern, eval(\n alternate), strText, flags=re.UNICODE | re.MULTILINE)\n else:\n # print regexPattern, regexSubstitution\n # No ignore case available\n # print regexPattern, \" --> \", strText\n strText = re.sub(regexPattern, regexSubstitution,\n strText, flags=re.UNICODE | re.MULTILINE)\n\n if debug:\n if strText.encode('utf-8') != strLineOriginal.encode('utf-8'):\n sys.stdout.write(\n \" --> Original string: >\" + strLineOriginal.encode('utf-8') + \"<\\n\")\n sys.stdout.write(\" Match pattern: >\" + regexPattern.encode('utf-8') + \"<\"\n \"\\n Substitution: >\" + regexSubstitution.encode('utf-8') + \"<\")\n sys.stdout.write(\n \"\\n >\" + strText.encode('utf-8') + \"<\\n\")\n\n strText = RegularExpressionFormula.normalizeSpaces(strText)\n\n if debug:\n sys.stdout.flush()\n RegularExpressionFormula.logger.info(\n \"Final transcript: \" + strText + \"\\n\")\n\n return strText","def translate_text(target, text):\n return text","def process_text(self, text, language):","def run(self, text):\r\n for i in range(self.markdown.htmlStash.html_counter):\r\n html, safe = self.markdown.htmlStash.rawHtmlBlocks[i]\r\n if self.markdown.safeMode and not safe:\r\n if str(self.markdown.safeMode).lower() == 'escape':\r\n html = self.escape(html)\r\n elif str(self.markdown.safeMode).lower() == 'remove':\r\n html = ''\r\n else:\r\n html = self.markdown.html_replacement_text\r\n if self.isblocklevel(html) and (safe or not self.markdown.safeMode):\r\n text = text.replace(\"<p>%s</p>\" % \r\n (self.markdown.htmlStash.get_placeholder(i)),\r\n html + \"\\n\")\r\n text = text.replace(self.markdown.htmlStash.get_placeholder(i), \r\n html)\r\n return text","def evaluateText(compiled_expression):","def post_get_convert(self, site, getText):\n return getText","def apply(\n self,\n text: str,\n *,\n limit: int = 2000,\n context_id: Any = None,\n ) -> str:\n\n context = self.get_context(text=text, context_id=context_id)\n\n for replacement in self._replacements:\n text = replacement.apply(\n text,\n severity=self.severity,\n limit=limit,\n context=context,\n )\n\n return text","def contextualize_text(text, context): # private\r\n if not text:\r\n return text\r\n for key in sorted(context, lambda x, y: cmp(len(y), len(x))):\r\n # TODO (vshnayder): This whole replacement thing is a big hack\r\n # right now--context contains not just the vars defined in the\r\n # program, but also e.g. a reference to the numpy module.\r\n # Should be a separate dict of variables that should be\r\n # replaced.\r\n if '$' + key in text:\r\n try:\r\n s = str(context[key])\r\n except UnicodeEncodeError:\r\n s = context[key].encode('utf8', errors='ignore')\r\n text = text.replace('$' + key, s)\r\n return text","def refined_text(text):\n import re\n text = text.replace('<e1>','')\n text = text.replace('</e1>','')\n text = text.replace('<e2>','')\n text = text.replace('</e2>','')\n\n text = text[1:-1] # trim quotes\n # text = text.replace('\"','')\n # text = text.replace(',','')\n # text = text.replace('.','')\n # text = text.replace(';','')\n # text = text.replace('`','')\n # text = text.replace('\\'','')\n # text = text.replace('(','')\n # text = text.replace(')','')\n # text = text.replace('/','')\n\n return text","def plain_text_body(self, val: str):\n self._plain_text_body = val","def _text(self, text):\r\n URL_REGEX.sub(self._parse_urls, text)\r\n USERNAME_REGEX.sub(self._parse_users, text)\r\n LIST_REGEX.sub(self._parse_lists, text)\r\n HASHTAG_REGEX.sub(self._parse_tags, text)\r\n return None","def recept(self, text, *args, **kwargs):\n return text","def materialize(template, substitutions):\n\n script_str = template\n for param, value in substitutions.items():\n script_str = re.sub(param, str(value), script_str)\n\n return script_str","def adjustText(cls, text):\n\t\t\n\t\t\"\"\"Adjust dates so to transform strings such as '21 August' to 'August\n\t\t 21' and have them recognized by the SCNLP tools\"\"\"\n\t\tmonths = (u'January|February|March|April|May|June|July'\n\t\t\t\t\t'August|September|October|November|December')\n\t\tdates = re.compile('(?P<day>\\d{1,2})\\s+(?P<month>%s)(\\s+(?P<year>(\\d{2,4})))?' % months)\n\t\ttext = dates.sub(cls.normalizeDate, text)\n\t\t# Strip any remaining HTML (WikiExtractor is not perfect)\n\t\thtmlTags = re.compile('<[^>]+>')\n\t\t\n\t\ttext = htmlTags.sub(\"\", text)\n\t\t\n\t\treturn text","def run(self, content):\n parts = []\n offset = 0\n for match in self.regexp.finditer(content):\n parts.append(content[offset:match.start(0)])\n parts.append(self.replace(match))\n offset = match.end(0)\n parts.append(content[offset:])\n return ''.join(parts)","def _prepare_text(body):\n text = body.lower()\n text = text.replace('\\n', ' ')\n regex = re.compile('[^a-z ]')\n return regex.sub('', text)","def textfrombodies(self) -> str:\n type_priority = [\"plain\", \"html\", \"other\"] # TODO: Make configurable\n\n for texttype in type_priority:\n if texttype == \"plain\" and texttype in self.textbodies:\n \"\"\"Text is plain, so it can be used verbatim\"\"\"\n return self.textbodies[texttype]\n if texttype == \"html\" and texttype in self.textbodies:\n \"\"\"HTML text. Convert to markup with html2text and remove extra spaces\"\"\"\n text = html2text.html2text(self.textbodies[texttype])\n # Remove every second newline which is added to distinguish between paragraphs in Markdown, but makes\n # the jira ticket hard to read.\n return re.sub(\"(\\n.*?)\\n\", \"\\g<1>\", text)\n if texttype == \"other\" and len(self.textbodies):\n # If no other text is found, return the first available body if any.\n return self.textbodies[list(self.textbodies.keys())[0]]\n return \"The email contained no text bodies.\"","def process_text(txt):\n\n # Make text all lowercase, remove line breaks and tabs\n txt = txt.lower()\n txt = sub(\"\\n\", \" \", txt)\n txt = sub(\"\\t\", \" \", txt)\n txt = sub(\"/\", \" \", txt)\n txt = sub(\"’\", \"\", txt)\n\n # Convert numbers, urls, email addresses, and dollar signs\n txt = sub(\"[0-9]+\", \"number\", txt)\n txt = sub(\"(http|https)://[^\\s]*\", \"httpaddr\", txt)\n txt = sub(\"[^\\s]+@[^\\s]+\", \"emailaddr\", txt)\n txt = sub(\"[$]+\", \"dollar\", txt)\n\n # Remove additional punctuation\n table = str.maketrans({key: None for key in punctuation})\n txt = txt.translate(table)\n\n return txt","def tr(self, text, gender=None):\n _LOG.debug(\"text = %r\", text)\n if self._tr:\n variants = [text]\n if gender:\n variants = [text + \"#\" + gender] + variants\n _LOG.debug(\"variants = %r\", variants)\n for txt in variants:\n _LOG.debug(\"variant = %r\", txt)\n if hasattr(self._tr, \"ugettext\"):\n tr_text = self._tr.ugettext(txt)\n else:\n tr_text = self._tr.gettext(txt)\n _LOG.debug(\"translation = %r\", tr_text)\n if tr_text:\n return tr_text\n _LOG.debug(\"return original = %r\", text)\n return text","def apply(self, strText, languageId, debug=False):\n if len(self.substitutionPatternList) == 0:\n if self.rulesFile == None:\n return strText\n else:\n self.logger.info(\"Loading regexes from %s\" %\n str(self.rulesFile))\n self.substitutionPatternList = \\\n RegexList.loadFromFile(self.rulesFile)\n\n return RegularExpressionFormula.applyRegularExpressions(strText,\n self.substitutionPatternList, languageId, debug)","def _html(self, text):\r\n html = URL_REGEX.sub(self._parse_urls, text)\r\n html = USERNAME_REGEX.sub(self._parse_users, html)\r\n html = LIST_REGEX.sub(self._parse_lists, html)\r\n return HASHTAG_REGEX.sub(self._parse_tags, html)","def convert(self, text):\r\n # Main function. The order in which other subs are called here is\r\n # essential. Link and image substitutions need to happen before\r\n # _EscapeSpecialChars(), so that any *'s or _'s in the <a>\r\n # and <img> tags get encoded.\r\n\r\n # Clear the global hashes. If we don't clear these, you get conflicts\r\n # from other articles when generating a page which contains more than\r\n # one article (e.g. an index page that shows the N most recent\r\n # articles):\r\n self.reset()\r\n\r\n if not isinstance(text, unicode):\r\n #TODO: perhaps shouldn't presume UTF-8 for string input?\r\n text = unicode(text, 'utf-8')\r\n\r\n if self.use_file_vars:\r\n # Look for emacs-style file variable hints.\r\n emacs_vars = self._get_emacs_vars(text)\r\n if \"markdown-extras\" in emacs_vars:\r\n splitter = re.compile(\"[ ,]+\")\r\n for e in splitter.split(emacs_vars[\"markdown-extras\"]):\r\n if '=' in e:\r\n ename, earg = e.split('=', 1)\r\n try:\r\n earg = int(earg)\r\n except ValueError:\r\n pass\r\n else:\r\n ename, earg = e, None\r\n self.extras[ename] = earg\r\n\r\n # Standardize line endings:\r\n text = re.sub(\"\\r\\n|\\r\", \"\\n\", text)\r\n\r\n # Make sure $text ends with a couple of newlines:\r\n text += \"\\n\\n\"\r\n\r\n # Convert all tabs to spaces.\r\n text = self._detab(text)\r\n\r\n # Strip any lines consisting only of spaces and tabs.\r\n # This makes subsequent regexen easier to write, because we can\r\n # match consecutive blank lines with /\\n+/ instead of something\r\n # contorted like /[ \\t]*\\n+/ .\r\n text = self._ws_only_line_re.sub(\"\", text)\r\n\r\n # strip metadata from head and extract\r\n if \"metadata\" in self.extras:\r\n text = self._extract_metadata(text)\r\n\r\n text = self.preprocess(text)\r\n\r\n if self.safe_mode:\r\n text = self._hash_html_spans(text)\r\n\r\n # Turn block-level HTML blocks into hash entries\r\n text = self._hash_html_blocks(text, raw=True)\r\n\r\n # Strip link definitions, store in hashes.\r\n if \"footnotes\" in self.extras:\r\n # Must do footnotes first because an unlucky footnote defn\r\n # looks like a link defn:\r\n # [^4]: this \"looks like a link defn\"\r\n text = self._strip_footnote_definitions(text)\r\n text = self._strip_link_definitions(text)\r\n\r\n text = self._run_block_gamut(text)\r\n\r\n if \"footnotes\" in self.extras:\r\n text = self._add_footnotes(text)\r\n\r\n text = self.postprocess(text)\r\n\r\n text = self._unescape_special_chars(text)\r\n\r\n if self.safe_mode:\r\n text = self._unhash_html_spans(text)\r\n\r\n text += \"\\n\"\r\n\r\n rv = UnicodeWithAttrs(text)\r\n if \"toc\" in self.extras:\r\n rv._toc = self._toc\r\n if \"metadata\" in self.extras:\r\n rv.metadata = self.metadata\r\n return rv","def inner_text(self, text) -> None:\n logging.info(f\"inner text. {self.desc}\")\n js = f\"\"\"var elm = document.querySelectorAll(\"{self.css}\")[{self.index}];\n elm.innerText=\"{text}\";\"\"\"\n self._execute_javascript(js)","def parse_text(self, text):\n self._text_paragraph = text.split(\"\\n\")\n self._render()","def process_text(document):\n return preprocess_string(document,\n filters=[strip_tags, strip_punctuation,\n strip_multiple_whitespaces,\n strip_numeric, remove_stopwords,\n strip_short]\n )","def process_text(self):\n prp1 = preprocessor.Preprocess()\n processed_text = prp1.clean_data(self.text)\n self.vec1 = self.vec.transform(pd.Series(processed_text))","def replace_text(self,\n\t text,\n\t fname,\n\t pattern=None,\n\t expect=None,\n\t shutit_pexpect_child=None,\n\t note=None,\n\t before=False,\n\t force=False,\n\t line_oriented=True,\n\t loglevel=logging.DEBUG):\n\t\tshutit_global.shutit_global_object.yield_to_draw()\n\t\treturn self.change_text(text,\n\t\t fname,\n\t\t pattern,\n\t\t expect,\n\t\t shutit_pexpect_child,\n\t\t before,\n\t\t force,\n\t\t note=note,\n\t\t line_oriented=line_oriented,\n\t\t replace=True,\n\t\t loglevel=loglevel)","def _preprocess(self, body):\n return body","def tidy_text(self, text: List[str],\n ) -> List[str]:\n \n if self.case_fold:\n text = list(map(lambda t: t.lower(), text))\n \n if self.special_syms:\n text = list(map(self.handle_special_symbols, text))\n \n text = list(map(self.handle_kw_phrases, text))\n text = list(map(self.depluralise_keywords, text))\n\n if self.tokenise:\n text = list(map(word_tokenize, text))\n \n if self.stem:\n text = list(map(self.stemming, text))\n \n if self.lemmatise:\n text = list(map(self.lemmatisation, text))\n \n if self.del_stop_words:\n stop_words = subsample_frequent_words(text, set(self.target_words))\n text = list(map(\n lambda t: self.handle_stop_words(t, stop_words), text\n ))\n \n return text","def run(self, text):\r\n pass","def render_text(self, text, *args, **kwargs):\n global TEXT_TEMPLATE\n self.render(TEXT_TEMPLATE, text=text, *args, **kwargs)","def replace(self, text):\n if self.should_skip(text):\n return text\n return self.replacer.sub(self._callback, text)","def contraction_expansion(text):\n\n global patterns\n for (pattern, repl) in patterns:\n (text, _) = re.subn(pattern, repl, text)\n return text","def process_text(str_in):\n str_out = get_model().replace(str_in)\n get_model().inc_call_counter()\n return str_out","def test_substitutions_in_word(self):\n m = strutils.MultiReplace({r'cat': 'kedi', r'purple': 'mor', })\n self.assertEqual(m.sub('Thecatispurple'), 'Thekediismor')","def preprocess(self, text):\n if self.model_name == \"bert-base-arabert\":\n return self._old_preprocess(\n text,\n do_farasa_tokenization=True,\n )\n\n if self.model_name == \"bert-base-arabertv01\":\n return self._old_preprocess(text, do_farasa_tokenization=False)\n\n text = str(text)\n text = html.unescape(text)\n if self.strip_tashkeel:\n text = araby.strip_tashkeel(text)\n if self.strip_tatweel:\n text = araby.strip_tatweel(text)\n\n if self.replace_urls_emails_mentions:\n # replace all possible URLs\n for reg in url_regexes:\n text = re.sub(reg, \" [رابط] \", text)\n # REplace Emails with [بريد]\n for reg in email_regexes:\n text = re.sub(reg, \" [بريد] \", text)\n # replace mentions with [مستخدم]\n text = re.sub(user_mention_regex, \" [مستخدم] \", text)\n\n if self.remove_html_markup:\n # remove html line breaks\n text = re.sub(\"<br />\", \" \", text)\n # remove html markup\n text = re.sub(\"</?[^>]+>\", \" \", text)\n\n # remove repeated characters >2\n if self.remove_elongation:\n text = self._remove_elongation(text)\n\n # insert whitespace before and after all non Arabic digits or English Digits and Alphabet and the 2 brackets\n if self.insert_white_spaces:\n text = re.sub(\n \"([^0-9\\u0621-\\u063A\\u0641-\\u064A\\u0660-\\u0669a-zA-Z\\[\\]])\",\n r\" \\1 \",\n text,\n )\n\n # insert whitespace between words and numbers or numbers and words\n text = re.sub(\n \"(\\d+)([\\u0621-\\u063A\\u0641-\\u064A\\u0660-\\u066C]+)\", r\" \\1 \\2 \", text\n )\n text = re.sub(\n \"([\\u0621-\\u063A\\u0641-\\u064A\\u0660-\\u066C]+)(\\d+)\", r\" \\1 \\2 \", text\n )\n\n # remove unwanted characters\n if self.keep_emojis:\n emoji_regex = \"\".join(list(self.emoji.UNICODE_EMOJI[\"en\"].keys()))\n rejected_chars_regex2 = \"[^%s%s]\" % (chars_regex, emoji_regex)\n text = re.sub(rejected_chars_regex2, \" \", text)\n else:\n text = re.sub(rejected_chars_regex, \" \", text)\n\n # remove extra spaces\n text = \" \".join(text.replace(\"\\uFE0F\", \"\").split())\n\n if (\n self.model_name == \"bert-base-arabertv2\"\n or self.model_name == \"bert-large-arabertv2\"\n ):\n if self.keep_emojis:\n new_text = []\n for word in text.split():\n if word in list(self.emoji.UNICODE_EMOJI[\"en\"].keys()):\n new_text.append(word)\n else:\n new_text.append(self.farasa_segmenter.segment(word))\n text = \" \".join(new_text)\n else:\n text = self.farasa_segmenter.segment(text)\n return self._farasa_segment(text)\n\n # ALl the other models dont require Farasa Segmentation\n return text","def convert_text(s):\n for d in config.repl: # loaded from config.py\n if \"flags\" in d:\n s = re.sub(d[\"ptrn\"], d[\"repl\"], s, flags=d[\"flags\"])\n else:\n s = re.sub(d[\"ptrn\"], d[\"repl\"], s)\n return s","def result(target_text):\n\n display_text(target_text)\n readability(target_text)","def set_text_body(self, body):\n self._text_body = body","def reach_process_text():\n response = request.body.read().decode('utf-8')\n body = json.loads(response)\n text = body.get('text')\n rp = reach.process_text(text)\n if rp and rp.statements:\n stmts = stmts_to_json(rp.statements)\n res = {'statements': stmts}\n return res\n else:\n res = {'statements': []}\n return res","def post_process(text):\n # XXX update to spit out HTML - no need for requests GDocs can take html\n verbose = False\n request_list = []\n chars = iter(text)\n normal_text = []\n knownsigils = {\"end\":('',\"NONE\"),\n \"^\": (\"0123456789+-\",\"SUPERSCRIPT\"),\n \"_\": (\"0123456789\",\"SUBSCRIPT\")\n }\n c = next(chars, \"end\")\n while (True):\n if (c in knownsigils.keys()):\n if len(normal_text): request_list.append((''.join(normal_text), \"NORMAL\"))\n normal_text.clear()\n (c,token) = _gettoken(c,chars,knownsigils)\n if (token is not None): request_list.append(token)\n if (c==\"end\"):\n break\n else:\n continue\n else:\n normal_text.append(c)\n c = next(chars, \"end\")\n return request_list","def replace_with_text(self):\r\n self.parser.stripTags(self.get_top_node(), 'b', 'strong', 'i', 'br', 'sup')","def escape_tex(value):\n newval = value\n for pattern, replacement in LATEX_SUBS:\n newval = pattern.sub(replacement, newval)\n return newval","def process_text(text, args):\n if args.uppercase:\n text = convert_to_uppercase(text)\n\n if args.spaces:\n text = add_spaces(text)\n\n if not args.uppercase and not args.spaces:\n text = add_spaces(text)\n\n return text","def __filter( self, text ):\n return text","def _convert_to_fancypants(self, markdown_text: str) -> dict: # noqa: ANN001\n text_data = {\"output_mode\": \"rtjson\", \"markdown_text\": markdown_text}\n return self._reddit.post(API_PATH[\"convert_rte_body\"], data=text_data)[\"output\"]","def strtr(text, items):\n regex = re.compile(\"(%s)\" % \"|\".join(map(re.escape, items.keys())))\n out = regex.sub(lambda mo: items[mo.string[mo.start():mo.end()]], text)\n return out","def addContent(text):","def substitute_value(text, variables, model_context):\n method_name = 'substitute_value'\n error_info = {'errorCount': 0}\n result = _substitute(text, variables, model_context, error_info)\n error_count = error_info['errorCount']\n if error_count:\n ex = exception_helper.create_variable_exception(\"WLSDPLY-01740\", error_count)\n _logger.throwing(ex, class_name=_class_name, method_name=method_name)\n raise ex\n return result","def normalize(self, text: str) -> str:","def refang(self, text: str):","def process_text(text):\n fix_dict = {'fig.': 'fig', 'fig .': 'fig ', 'Fig.': 'Fig', 'Fig .': 'Fig ',\n 'figure.': 'figure', 'figure .': 'figure ', 'Figure.': 'Fig', 'Figure .': 'Fig ',\n 'et al.': 'et al', 'III': '3', 'II': '2', 'I': '1'}\n\n for old_pattern in fix_dict.keys():\n text = text.replace(old_pattern, fix_dict[old_pattern])\n return text","def process_text(text):\n text = re.sub(r'<@>\\s+|<s>\\s+|</s>\\s+|<p>\\s+|</p>\\s+|\\s+\\,|\\'s|\\'|\\;|\\(|\\)|\\-\\-\\s+|\\s+\\.', '', text)\n text = re.sub(r'\\.\\,', '. ,', text)\n text = re.sub(r'\\,', '', text)\n text = re.sub(r'\\$', '$ ', text)\n text = re.sub(r'\\%', ' %', text)\n text = re.sub(r'\\s\\\"\\s', ' ', text)\n text = re.sub(r'\\.\\s+', '. ', text)\n text = text.lower()\n return text","def test_simple_substitutions(self):\n m = strutils.MultiReplace({r'cat': 'kedi', r'purple': 'mor', })\n self.assertEqual(m.sub('The cat is purple'), 'The kedi is mor')","def reformat_text(self, text):\n xml = BeautifulSoup(text)\n self.remove_header_and_footer(xml)\n self.process_superscripts(xml)\n self.remove_footnotes(xml)\n text = xml.get_text() # Strip XML tags.\n text = self.join_hyphenated_words(text)\n text = self.remove_linebreaks(text)\n return text","def correction():\n text = request.args.get('text', '')\n text = TextBlob(text)\n return jsonify(text=unicode(text.correct()))","def substitute_words_with_dict(text: str, substitutions_dict: Union[str, dict]) -> str:\n\n if not ((type(substitutions_dict) is str) or\n (type(substitutions_dict) is dict)):\n raise TypeError('The substitutions_dict type must be dict or str. '\n 'If it is str, it has to be the path to the dict stored in json format.')\n\n # If substitutions_dict is a str, then it's the path to a file containing the dict in json format.\n # So, we have to load the dict from the hard drive to memory.\n if type(substitutions_dict) is str:\n with open(substitutions_dict) as f:\n substitutions_dict = json.load(f)\n\n # If it's a dict (it was at the beginning or it was converted above) we make the substitutions.\n for word, substitute_by in substitutions_dict.items():\n text = substitute_word(text, word, substitute_by)\n\n return text","def inspect_text(self, page_text, terms):\n\n # convert all text to lower case to\n # increase likelihood of match\n page_text_lower = page_text.lower()\n page_text_lower = page_text_lower.replace('\\r', '').replace('\\n', '')\n\n payload = [\n # populate with match dicts\n ]\n\n for term in terms:\n sentences = [sentence + '.' for sentence in page_text_lower.split('.') if term in sentence]\n if len(sentences) > 0:\n logger.info('found \"{0}\" x{1}'.format(term, len(sentences)))\n for sentence in sentences:\n payload.append({\n 'term': term,\n 'sentence': sentence\n })\n\n return payload","def _apply_filters(self, text, tag):\n\n # The order of the filters below is important\n # and should not be changed\n\n # intial_quotes needs to happen at this point so that\n # attribute values introduced later on do not get affected\n text = self.initial_quotes(text)\n text = self.smarty_pants(text)\n text = self.amp(text)\n text = self.caps(text)\n\n return text","def replace_texts(self, replace, text_key=None):\n if not text_key: text_key = self.valid_tks\n text_func = self._replace_from_dict\n args = ()\n kwargs = {'replace_map': replace,\n 'text_key': text_key}\n DataSet._apply_to_texts(text_func, self._meta, args, kwargs)\n return None","def multi_replace(text, replace_dict):\n new_text = text\n for search, replace in list(replace_dict.items()):\n new_text = new_text.replace(search, str(replace))\n return new_text","def processText(text):\n print(type(text))\n for line in text:\n print(line)\n return text","def preprocess_text(self):\n self.text_received = self.text_received.replace(\" \", \"\").lower()","def clean_text(text2, project_key):\n\n text = text2\n text = return_text_without_headlines(text)\n # remove text written between double curly braces\n text = re.sub(r\"{{code}}.*{{code}}\", \"code.\", text)\n text = re.sub(r\"{code.*{code}\", \"code.\", text)\n text = re.sub(r\"{code:java}.*{code:java}\", \"code.\", text)\n text = re.sub(r\"{noformat}.*{noformat}\", \"code.\", text)\n text = re.sub(r\"{{monospaced}}.*{{monospaced}}\", \"code.\", text)\n text = re.sub(r'<script type=\"text/javascript\">.*</noscript>', 'code.', text)\n text = re.sub(r\"'''.*'''\", \"code\", text)\n text = text.replace('<p> </p>', \"\")\n text = text.replace('<div> </div>', \"\")\n text = text.replace(' ', \" \")\n # remove URLs link\n text = re.sub(r\"<a href=.*</a>\", \"url. \", text)\n text = re.sub(r\"http\\S+\", \"url. \", text)\n text = re.sub(r\"hdfs://\\S+\", \"url. \", text)\n text = re.sub(r\"tcp://\\S+\", \"url. \", text)\n text = re.sub(r\"webhdfs://\\S+\", \"url. \", text)\n text = re.sub(r\":/\\S+\", \"url. \", text)\n text = re.sub(r\"\\S+.com \", \"url. \", text)\n text = re.sub(r\"N/A]\", \" \", text)\n text = \" \".join(x for x in text.split() if not x.endswith('.com'))\n text = \" \".join(x for x in text.split() if not x.endswith('.com*'))\n text = \" \".join(x for x in text.split() if not x.endswith('.org'))\n text = \" \".join(x for x in text.split() if not x.endswith('.xml'))\n text = \" \".join(x for x in text.split() if not x.startswith('*javax.xml.'))\n text = \" \".join(x for x in text.split() if not x.startswith('javax.xml.'))\n # remove Image attachments\n text = re.sub(r\"<p><img alt=.></p>\", \"image.\", text)\n text = re.sub(r\"{}-\\d+\".format(project_key), \"issue\", text)\n # remove date\n text = re.sub(r'(\\w{4})-(\\d{1,2})-(\\d{1,2}) ', 'date.', text)\n text = re.sub(r'(\\w{3,4,5})-(\\d{1,2})-(\\d{4})', 'date.', text)\n text = re.sub(r'(\\d{1,2})/(\\d{1,2})/(\\d{4})', 'date.', text)\n text = re.sub(r'(\\w{3}). (\\d{1,2}), (\\d{4})', 'date.', text)\n text = re.sub(r'(\\w{3}). (\\d{1,2}) (\\d{4})', 'date.', text)\n text = re.sub(r'<= Today’s Date AND', 'date.', text)\n text = re.sub(r'yyyy-mm-dd', 'date', text)\n # remove text written between small braces\n text = re.sub(r'<.+?>', \"\", text)\n text = text.replace(\"e.g.,\", \" \")\n text = text.replace(\"e.g.\", \" \")\n text = text.replace(\"i.e.,\", \" \")\n text = text.replace(\"i.e.\", \" \")\n # replace non-breaking space with regular space\n text = text.replace(u'\\xa0', u' ')\n # replace all punctuations with space\n text = text.replace('-->', \" \")\n text = text.replace('--', \" \")\n text = text.replace('-', \" \")\n text = text.replace('/', \" \")\n text = text.replace('&', \" \")\n text = text.replace(' * ', \". \")\n text = re.sub(r\"\\\"|\\#|\\“|\\*|\\'|\\]|\\^|\\`|\\(|\\)|\\~\", \"\", text)\n text = re.sub(r\"\\\"|\\$|\\%|\\&|\\/|\\|\\=|\\>|\\<|\\@|\\[|\\\\|\\]|\\{|\\||\\}\", \" \", text)\n text = text.replace('$', \"\")\n text = text.replace('?', \".\")\n text = text.replace('+', \" \")\n text = re.sub(r\" \\d\\.\\d\\.N \", \" \", text)\n text = re.sub(r\" \\d\\.\\d\\.b.\", \" \", text)\n text = re.sub(r\" \\d\\.\\d\\.b \", \" \", text)\n text = re.sub(r\"\\d\\.\\d\\.N\", \" \", text)\n text = re.sub(r\"\\d\\.\\d\\.X\", \" \", text)\n text = re.sub(r\"v\\d\\.\\d\\.\\d+\", \" \", text)\n text = re.sub(r\"V\\d\\.\\d\\.\\d+\", \" \", text)\n text = re.sub(r\"v\\d\\.\\d+\", \" \", text)\n text = re.sub(r\"V\\d\\.\\d+\", \" \", text)\n text = re.sub(r\"\\d\\.\\d+\", \" \", text)\n text = re.sub(r\"\\d\\.\\d\\.\\d+\", \" \", text)\n text = text.replace(\"V1\", \" \")\n text = text.replace(\"v1\", \" \")\n # remove digits from text\n text = re.sub(r\"\\d+\", \"\", text)\n text = text.replace('lt;=', \" \")\n text = text.replace('.!', \".\")\n text = text.replace('!.', \".\")\n text = text.replace('!', \".\")\n text = text.replace('... ', \". \")\n text = text.replace('.. ', \". \")\n text = text.replace('..', \".\")\n text = text.replace('. . . ', \". \")\n text = text.replace('. . ', \". \")\n text = text.replace('. . ', \". \")\n text = text.replace(' .', \".\")\n text = text.replace('. . ', \". \")\n text = text.replace('. . ', \". \")\n text = text.replace(':.', \".\")\n text = text.replace(' :', \" \")\n text = text.lower()\n text = text.replace('..', \".\")\n text = ' '.join(text.split())\n\n return text","def replace_docx_inline_text(doc: docx.Document, data: Dict[str, str]) -> docx.Document:\n logging.debug(\" --- starting docx_replace\")\n\n if convert_vars.making_template:\n replacement_values = sort_keys_longest_to_shortest(data)\n else:\n replacement_values = list(data.items())\n\n paragraphs = get_document_paragraphs(doc)\n for p in paragraphs:\n runs_text = \"\".join(r.text for r in p.runs)\n if runs_text.strip() == \"\" or (\n convert_vars.making_template and re.search(re.escape(\"${\") + \".*\" + re.escape(\"}\"), runs_text)\n ):\n continue\n for key, val in replacement_values:\n replaced_key = False\n for i, run in enumerate(p.runs):\n if run.text.find(key) != -1:\n p.runs[i].text = run.text.replace(key, val)\n replaced_key = True\n runs_text = runs_text.replace(key, val)\n if not replaced_key:\n if runs_text.find(key) != -1:\n runs_text = runs_text.replace(key, val)\n for i, r in enumerate(p.runs):\n p.runs[i].text = \"\"\n p.runs[0].text = runs_text\n\n logging.debug(\" --- finished replacing text in doc\")\n return doc","def markup_text(self, text):\n for moniker, name in S['names'].items():\n text = text.replace('${0}'.format(moniker.split('_')[1]), name)\n return text","def setText(*args):","def process(self, message: Message, **kwargs: Any) -> None:\n\n try:\n textdata = message.data[\"text\"]\n # print(\"text :::\" + textdata)\n textdata = textdata.split()\n new_message = \" \".join(spell.correction(w) for w in textdata)\n # print(\"after correction text :::\" + new_message)\n message.data[\"text\"] = new_message\n except KeyError:\n pass","def trans(monitext):\n result = ''\n last_line = 'empty'\n\n while monitext:\n # newline character or empty line(s)\n matched = re.match(r'\\n+', monitext, re.M)\n\n if matched:\n result += matched.group()\n if len(matched.group()) > 1:\n last_line = 'empty'\n elif last_line == 'title':\n result += '\\n'\n last_line = 'empty'\n monitext = monitext[matched.end():]\n continue\n\n # code block\n matched = re.match(r'{{{.*?\\n((\\n|.)*?)\\n}}}', monitext, re.M)\n\n if matched:\n body = matched.groups()[0]\n result += '\\n\\t' + '\\n\\t'.join(body.split('\\n'))\n monitext = monitext[matched.end():]\n last_line = 'code'\n continue\n\n # header\n matched = re.match(r'^(=+) (.+) (=+)', monitext)\n\n if matched:\n title = matched.groups()[1]\n level = len(matched.groups()[0])\n\n if last_line != 'empty':\n result += '\\n'\n\n if level < 4:\n underscore = {2 : '=', 3 : '-'}[level] * mbstrlen(title)\n result += title + os.linesep + underscore\n else:\n result += ('#' * level) + \" \" + title\n monitext = monitext[matched.end():]\n\n last_line = 'title'\n\n continue\n\n # link\n matched = re.match(r'(.*)\\[([^\\s]+[ \\t]+)?(.+)\\]', monitext)\n\n if matched:\n pre = matched.groups()[0]\n url = matched.groups()[1]\n if url:\n url = url.strip()\n name = matched.groups()[2]\n\n if url:\n replaced = \"%s[%s](%s)\" % (pre, name, url)\n else:\n replaced = \"%s[%s](%s)\" % (pre, name, name)\n\n monitext = monitext[:matched.start()] + replaced\\\n + monitext[matched.end():]\n\n # important\n monitext = re.sub(r'\\'\\'\\'(.*?)\\'\\'\\'', r'**\\1**', monitext)\n\n # italic\n monitext = re.sub(r'\\'\\'(.*?)\\'\\'', r'_\\1_', monitext)\n\n # list\n matched = re.match(r'^(\\s*)\\* (.*)', monitext)\n\n if matched:\n depth = len(matched.groups()[0])\n body = matched.groups()[1]\n result += (depth - 1) * '\\t' + '* ' + body\n monitext = monitext[matched.end():]\n\n last_line = 'others'\n\n try:\n # Go to the next line\n index = monitext.index('\\n')\n result += monitext[:index]\n monitext = monitext[index:]\n except ValueError:\n result += monitext\n break\n\n return result","def translateText(text):\r\n\treturn translator.translate(text, src='en', dest='ro')","def process_body(nlp, body: str, replacements=None, require_labels=False):\n\n if replacements is None:\n replacements = []\n\n entry = {\"ents\": [],\n \"cats\": [],\n \"replacements\": [],\n \"text\": None,\n \"docstrings\": []}\n\n body_ = body.lstrip()\n initial_strip = body[:len(body) - len(body_)]\n\n replacements = correct_entities(replacements, [(0, len(initial_strip))])\n\n docsting_offsets = get_docstring(body_)\n\n body_, replacements, docstrings = remove_offsets(body_, replacements, docsting_offsets)\n entry['docstrings'].extend(docstrings)\n\n was_valid = body_valid(body_)\n initial_labels = get_initial_labels(body_)\n\n if require_labels and initial_labels is None:\n return None\n\n returns, return_cuts = unpack_returns(body_, initial_labels)\n annotations, annotation_cuts = unpack_annotations(body_, initial_labels)\n\n body_, replacements_annotations, _ = remove_offsets(body_, replacements + annotations,\n return_cuts + annotation_cuts)\n is_valid = body_valid(body_)\n if was_valid != is_valid:\n print(\"Failed processing\")\n return None\n # raise Exception()\n\n replacements_annotations = adjust_offsets2(replacements_annotations, len(initial_strip))\n body_ = initial_strip + body_\n\n entry['replacements'].extend(list(filter(lambda x: isint(x[2]), replacements_annotations)))\n entry['ents'].extend(list(filter(lambda x: not isint(x[2]), replacements_annotations)))\n entry['cats'].extend(returns)\n entry['text'] = body_\n\n entry['replacements'] = resolve_self_collisions2(entry['replacements'])\n\n # assert isvalid(nlp, body_, entry['replacements'])\n assert isvalid(nlp, body_, entry['ents'])\n\n return entry","def paragraph(self, text):\n return [text]","def multiple_replace(dict, text): \n\n # Create a regular expression from the dictionary keys\n regex = re.compile(\"(%s)\" % \"|\".join(map(re.escape, dict.keys())))\n\n # For each match, look-up corresponding value in dictionary\n return regex.sub(lambda mo: dict[mo.string[mo.start():mo.end()]], text)","def _string_subst_partial(self, val):\n def repl(m):\n k = m.group('id')\n replacement = self.bib_database.strings[k.lower()] if k.lower() in self.bib_database.strings else k\n pre = '\"' if m.group('pre') != '\"' else ''\n post = '\"' if m.group('post') != '\"' else ''\n return pre + replacement + post\n\n logger.debug('Substitute string definitions inside larger expressions')\n if '#' not in val:\n return val\n\n # TODO?: Does not match two subsequent variables or strings, such as \"start\" # foo # bar # \"end\" or \"start\" # \"end\".\n # TODO: Does not support braces instead of quotes, e.g.: {start} # foo # {bar}\n # TODO: Does not support strings like: \"te#s#t\"\n return self.replace_all_re.sub(repl, val)","def replace(self, text):\n for key, val in self.env.items():\n text = text.replace(\"$%s\" % key, val)\n return text","def preprocess_text(self, seq):\n if self.text_preprocess_fn:\n seq = list(map(self.text_preprocess_fn, seq))\n return seq","def svn_fs_apply_text(*args):\r\n return _fs.svn_fs_apply_text(*args)","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 pre_put_convert(self, site, putText):\n return putText","def preprocess(self,text):\n return preprocess.get_tokens(text)","def getContents(self):\n normal_body_regex = re.compile(r'[ \\n\\r\\t]+')\n return normal_body_regex.sub(' ', self.contents)","def process_quote_text(quote_text):\n quote_text = quote_text.replace('―', '').replace('\\n\\n', '\\n')\n quote_text = quote_text[:-1] if quote_text[-1] == '\\n' else quote_text\n for char in HTML:\n quote_text = quote_text.replace(*char)\n return quote_text","def get_text(text_input):\r\n return text_input","def render(self, text, apply_spammer_limits=False):\n if False:\n regex = re.compile(\"&(?!nbsp;)\");\n body = regex.sub( \"&\", text )\n regex = re.compile(\"<(/?)([a-zA-Z]+?)( .*?)?/?>\")\n return regex.sub( htmltag_replace, body )\n return \"\"","def interpolate_replacements(text, expr_replacements):\n if not expr_replacements:\n return [text]\n\n replacement_strings = list(expr_replacements.keys())\n splitter = re.compile(\n \"({0})\".format(\"|\".join(re.escape(r) for r in replacement_strings))\n )\n split_text = [p for p in splitter.split(text) if p]\n return [expr_replacements.get(t, t) for t in split_text]","def plain_text_body(self):\n return self._plain_text_body","def obtain_text():\n pass","def trips_process_text():\n response = request.body.read().decode('utf-8')\n body = json.loads(response)\n text = body.get('text')\n tp = trips.process_text(text)\n if tp and tp.statements:\n stmts = stmts_to_json(tp.statements)\n res = {'statements': stmts}\n return res\n else:\n res = {'statements': []}\n return res","def update_html(self, plain_text):\n ENGINE.reset()\n self.html = ENGINE.convert(self.content)\n self.save()","def getPlainText(key, default=None, replaceFontTags=True):\n\treturn getText(key, (), default, replaceFontTags)","def cleanup(self, body):\n body = re.sub(\">\", \">\", body) # Recode HTML codes\n body = re.sub(\"<\", \"<\", body)\n body = re.sub(\"&\", \"&\", body)\n body = re.sub(\" \", \" \", body)\n # body = re.sub(\"^\\[deleted\\]$\", \"\", body) # [deleted] and [removed]: delete entire row from dataframe\n body = re.sub(\"http\\S+\", \" \", body) # Remove URL\n body = re.sub(\"/r/\\S+|/u/\\S+\", \" \", body) # Remove /r/subreddit, /u/user\n # body = re.sub(\"(>.*?\\\\n\\\\n)+\", \" \", body) # Remove quoted comments\n # body = re.sub(\"[[:cntrl:]]\", \" \", body) # Remove control characters (\\n, \\b) doesn't work for unicode\n body = \"\".join(ch for ch in body if unicodedata.category(ch)[0]!=\"C\") # Remove control characters (\\n, \\b) etc.\n body = re.sub(\"'\", \"\", body) # Remove single quotation marks (contractions)\n # body = re.sub(\"[[:punct:]]\", \" \", body) # Remove punctuation\n body = \"\".join(ch for ch in body if unicodedata.category(ch)[0]!=\"P\") # Remove punctuation\n body = re.sub(\"\\\\s+\", \" \", body) # Replace multiple spaces with single space\n body = body.strip()\n body = body.lower() # Lower case\n return body # Return body (cleaned up text)","def preprocessing(sample):\n\n content = ' '.join(sample)\n content = re.sub(r\"\\'ll\", \" will\", content)\n content = re.sub(r\"\\'d\", \" would\", content)\n content = re.sub(r\"\\'s\", \" is\", content)\n content = re.sub(r\"\\'m\", \" am\", content)\n content = re.sub(r\"\\'ve\", \" have\", content)\n content = re.sub(r\"\\'re\", \" are\", content)\n content = content.replace('&', 'and')\n content = content.replace('$', '')\n content = content.split()\n return content","def render_text(self):\n if self.text_type == 1:\n return markdown.markdown(self.text)\n else:\n return self.text","def preprocess(text):\n text = remove_space(text)\n text = clean_special_punctuations(text)\n text = clean_number(text)\n text = decontracted(text)\n text = correct_spelling(text)\n text = spacing_punctuation(text)\n text = spacing_some_connect_words(text)\n text = clean_repeat_words(text)\n text = remove_space(text)\n text = text.lower()\n return text","def getText():"],"string":"[\n \"def postprocess(self, text):\\r\\n return text\",\n \"def preprocess(self, text):\\r\\n return text\",\n \"def post_process_text(self, text):\\n\\t\\treturn text\",\n \"def substitution(plainText, key):\\n return plainText\",\n \"def apply(self, text):\",\n \"def applyRegularExpressions(strText, substitutionPatternList, languageId, debug=False):\\n # print substitutionPatternList\\n if debug:\\n RegularExpressionFormula.logger.info(\\n \\\"Applying regular expressions to transcript ...\\\")\\n\\n # For successive regular expressions\\n strText = RegularExpressionFormula.normalizeSpaces(strText, True)\\n\\n if debug:\\n RegularExpressionFormula.logger.info(\\n \\\"Initial transcript: \\\" + strText)\\n\\n # For each known regular expression\\n for regex, alternate, regexType, regexLanguageId in substitutionPatternList:\\n regexLanguageId = int(regexLanguageId)\\n\\n # Does it match the text language\\n if regexLanguageId != languageId and \\\\\\n regexLanguageId != 0:\\n continue\\n\\n # Convert from type\\n regexListForType = \\\\\\n RegexType.typeToRegularExpressions(\\n regex, alternate, int(regexType))\\n\\n # Get regular expressions for the given type\\n for regexForType in regexListForType:\\n regexPattern = regexForType[0] # What to match\\n regexSubstitution = regexForType[1] # What to substitute\\n\\n strLineOriginal = strText\\n\\n # Is it some python code\\n if alternate.startswith(\\\"lambda\\\"):\\n # Use alternate version\\n strText = re.sub(regexPattern, eval(\\n alternate), strText, flags=re.UNICODE | re.MULTILINE)\\n else:\\n # print regexPattern, regexSubstitution\\n # No ignore case available\\n # print regexPattern, \\\" --> \\\", strText\\n strText = re.sub(regexPattern, regexSubstitution,\\n strText, flags=re.UNICODE | re.MULTILINE)\\n\\n if debug:\\n if strText.encode('utf-8') != strLineOriginal.encode('utf-8'):\\n sys.stdout.write(\\n \\\" --> Original string: >\\\" + strLineOriginal.encode('utf-8') + \\\"<\\\\n\\\")\\n sys.stdout.write(\\\" Match pattern: >\\\" + regexPattern.encode('utf-8') + \\\"<\\\"\\n \\\"\\\\n Substitution: >\\\" + regexSubstitution.encode('utf-8') + \\\"<\\\")\\n sys.stdout.write(\\n \\\"\\\\n >\\\" + strText.encode('utf-8') + \\\"<\\\\n\\\")\\n\\n strText = RegularExpressionFormula.normalizeSpaces(strText)\\n\\n if debug:\\n sys.stdout.flush()\\n RegularExpressionFormula.logger.info(\\n \\\"Final transcript: \\\" + strText + \\\"\\\\n\\\")\\n\\n return strText\",\n \"def translate_text(target, text):\\n return text\",\n \"def process_text(self, text, language):\",\n \"def run(self, text):\\r\\n for i in range(self.markdown.htmlStash.html_counter):\\r\\n html, safe = self.markdown.htmlStash.rawHtmlBlocks[i]\\r\\n if self.markdown.safeMode and not safe:\\r\\n if str(self.markdown.safeMode).lower() == 'escape':\\r\\n html = self.escape(html)\\r\\n elif str(self.markdown.safeMode).lower() == 'remove':\\r\\n html = ''\\r\\n else:\\r\\n html = self.markdown.html_replacement_text\\r\\n if self.isblocklevel(html) and (safe or not self.markdown.safeMode):\\r\\n text = text.replace(\\\"<p>%s</p>\\\" % \\r\\n (self.markdown.htmlStash.get_placeholder(i)),\\r\\n html + \\\"\\\\n\\\")\\r\\n text = text.replace(self.markdown.htmlStash.get_placeholder(i), \\r\\n html)\\r\\n return text\",\n \"def evaluateText(compiled_expression):\",\n \"def post_get_convert(self, site, getText):\\n return getText\",\n \"def apply(\\n self,\\n text: str,\\n *,\\n limit: int = 2000,\\n context_id: Any = None,\\n ) -> str:\\n\\n context = self.get_context(text=text, context_id=context_id)\\n\\n for replacement in self._replacements:\\n text = replacement.apply(\\n text,\\n severity=self.severity,\\n limit=limit,\\n context=context,\\n )\\n\\n return text\",\n \"def contextualize_text(text, context): # private\\r\\n if not text:\\r\\n return text\\r\\n for key in sorted(context, lambda x, y: cmp(len(y), len(x))):\\r\\n # TODO (vshnayder): This whole replacement thing is a big hack\\r\\n # right now--context contains not just the vars defined in the\\r\\n # program, but also e.g. a reference to the numpy module.\\r\\n # Should be a separate dict of variables that should be\\r\\n # replaced.\\r\\n if '$' + key in text:\\r\\n try:\\r\\n s = str(context[key])\\r\\n except UnicodeEncodeError:\\r\\n s = context[key].encode('utf8', errors='ignore')\\r\\n text = text.replace('$' + key, s)\\r\\n return text\",\n \"def refined_text(text):\\n import re\\n text = text.replace('<e1>','')\\n text = text.replace('</e1>','')\\n text = text.replace('<e2>','')\\n text = text.replace('</e2>','')\\n\\n text = text[1:-1] # trim quotes\\n # text = text.replace('\\\"','')\\n # text = text.replace(',','')\\n # text = text.replace('.','')\\n # text = text.replace(';','')\\n # text = text.replace('`','')\\n # text = text.replace('\\\\'','')\\n # text = text.replace('(','')\\n # text = text.replace(')','')\\n # text = text.replace('/','')\\n\\n return text\",\n \"def plain_text_body(self, val: str):\\n self._plain_text_body = val\",\n \"def _text(self, text):\\r\\n URL_REGEX.sub(self._parse_urls, text)\\r\\n USERNAME_REGEX.sub(self._parse_users, text)\\r\\n LIST_REGEX.sub(self._parse_lists, text)\\r\\n HASHTAG_REGEX.sub(self._parse_tags, text)\\r\\n return None\",\n \"def recept(self, text, *args, **kwargs):\\n return text\",\n \"def materialize(template, substitutions):\\n\\n script_str = template\\n for param, value in substitutions.items():\\n script_str = re.sub(param, str(value), script_str)\\n\\n return script_str\",\n \"def adjustText(cls, text):\\n\\t\\t\\n\\t\\t\\\"\\\"\\\"Adjust dates so to transform strings such as '21 August' to 'August\\n\\t\\t 21' and have them recognized by the SCNLP tools\\\"\\\"\\\"\\n\\t\\tmonths = (u'January|February|March|April|May|June|July'\\n\\t\\t\\t\\t\\t'August|September|October|November|December')\\n\\t\\tdates = re.compile('(?P<day>\\\\d{1,2})\\\\s+(?P<month>%s)(\\\\s+(?P<year>(\\\\d{2,4})))?' % months)\\n\\t\\ttext = dates.sub(cls.normalizeDate, text)\\n\\t\\t# Strip any remaining HTML (WikiExtractor is not perfect)\\n\\t\\thtmlTags = re.compile('<[^>]+>')\\n\\t\\t\\n\\t\\ttext = htmlTags.sub(\\\"\\\", text)\\n\\t\\t\\n\\t\\treturn text\",\n \"def run(self, content):\\n parts = []\\n offset = 0\\n for match in self.regexp.finditer(content):\\n parts.append(content[offset:match.start(0)])\\n parts.append(self.replace(match))\\n offset = match.end(0)\\n parts.append(content[offset:])\\n return ''.join(parts)\",\n \"def _prepare_text(body):\\n text = body.lower()\\n text = text.replace('\\\\n', ' ')\\n regex = re.compile('[^a-z ]')\\n return regex.sub('', text)\",\n \"def textfrombodies(self) -> str:\\n type_priority = [\\\"plain\\\", \\\"html\\\", \\\"other\\\"] # TODO: Make configurable\\n\\n for texttype in type_priority:\\n if texttype == \\\"plain\\\" and texttype in self.textbodies:\\n \\\"\\\"\\\"Text is plain, so it can be used verbatim\\\"\\\"\\\"\\n return self.textbodies[texttype]\\n if texttype == \\\"html\\\" and texttype in self.textbodies:\\n \\\"\\\"\\\"HTML text. Convert to markup with html2text and remove extra spaces\\\"\\\"\\\"\\n text = html2text.html2text(self.textbodies[texttype])\\n # Remove every second newline which is added to distinguish between paragraphs in Markdown, but makes\\n # the jira ticket hard to read.\\n return re.sub(\\\"(\\\\n.*?)\\\\n\\\", \\\"\\\\g<1>\\\", text)\\n if texttype == \\\"other\\\" and len(self.textbodies):\\n # If no other text is found, return the first available body if any.\\n return self.textbodies[list(self.textbodies.keys())[0]]\\n return \\\"The email contained no text bodies.\\\"\",\n \"def process_text(txt):\\n\\n # Make text all lowercase, remove line breaks and tabs\\n txt = txt.lower()\\n txt = sub(\\\"\\\\n\\\", \\\" \\\", txt)\\n txt = sub(\\\"\\\\t\\\", \\\" \\\", txt)\\n txt = sub(\\\"/\\\", \\\" \\\", txt)\\n txt = sub(\\\"’\\\", \\\"\\\", txt)\\n\\n # Convert numbers, urls, email addresses, and dollar signs\\n txt = sub(\\\"[0-9]+\\\", \\\"number\\\", txt)\\n txt = sub(\\\"(http|https)://[^\\\\s]*\\\", \\\"httpaddr\\\", txt)\\n txt = sub(\\\"[^\\\\s]+@[^\\\\s]+\\\", \\\"emailaddr\\\", txt)\\n txt = sub(\\\"[$]+\\\", \\\"dollar\\\", txt)\\n\\n # Remove additional punctuation\\n table = str.maketrans({key: None for key in punctuation})\\n txt = txt.translate(table)\\n\\n return txt\",\n \"def tr(self, text, gender=None):\\n _LOG.debug(\\\"text = %r\\\", text)\\n if self._tr:\\n variants = [text]\\n if gender:\\n variants = [text + \\\"#\\\" + gender] + variants\\n _LOG.debug(\\\"variants = %r\\\", variants)\\n for txt in variants:\\n _LOG.debug(\\\"variant = %r\\\", txt)\\n if hasattr(self._tr, \\\"ugettext\\\"):\\n tr_text = self._tr.ugettext(txt)\\n else:\\n tr_text = self._tr.gettext(txt)\\n _LOG.debug(\\\"translation = %r\\\", tr_text)\\n if tr_text:\\n return tr_text\\n _LOG.debug(\\\"return original = %r\\\", text)\\n return text\",\n \"def apply(self, strText, languageId, debug=False):\\n if len(self.substitutionPatternList) == 0:\\n if self.rulesFile == None:\\n return strText\\n else:\\n self.logger.info(\\\"Loading regexes from %s\\\" %\\n str(self.rulesFile))\\n self.substitutionPatternList = \\\\\\n RegexList.loadFromFile(self.rulesFile)\\n\\n return RegularExpressionFormula.applyRegularExpressions(strText,\\n self.substitutionPatternList, languageId, debug)\",\n \"def _html(self, text):\\r\\n html = URL_REGEX.sub(self._parse_urls, text)\\r\\n html = USERNAME_REGEX.sub(self._parse_users, html)\\r\\n html = LIST_REGEX.sub(self._parse_lists, html)\\r\\n return HASHTAG_REGEX.sub(self._parse_tags, html)\",\n \"def convert(self, text):\\r\\n # Main function. The order in which other subs are called here is\\r\\n # essential. Link and image substitutions need to happen before\\r\\n # _EscapeSpecialChars(), so that any *'s or _'s in the <a>\\r\\n # and <img> tags get encoded.\\r\\n\\r\\n # Clear the global hashes. If we don't clear these, you get conflicts\\r\\n # from other articles when generating a page which contains more than\\r\\n # one article (e.g. an index page that shows the N most recent\\r\\n # articles):\\r\\n self.reset()\\r\\n\\r\\n if not isinstance(text, unicode):\\r\\n #TODO: perhaps shouldn't presume UTF-8 for string input?\\r\\n text = unicode(text, 'utf-8')\\r\\n\\r\\n if self.use_file_vars:\\r\\n # Look for emacs-style file variable hints.\\r\\n emacs_vars = self._get_emacs_vars(text)\\r\\n if \\\"markdown-extras\\\" in emacs_vars:\\r\\n splitter = re.compile(\\\"[ ,]+\\\")\\r\\n for e in splitter.split(emacs_vars[\\\"markdown-extras\\\"]):\\r\\n if '=' in e:\\r\\n ename, earg = e.split('=', 1)\\r\\n try:\\r\\n earg = int(earg)\\r\\n except ValueError:\\r\\n pass\\r\\n else:\\r\\n ename, earg = e, None\\r\\n self.extras[ename] = earg\\r\\n\\r\\n # Standardize line endings:\\r\\n text = re.sub(\\\"\\\\r\\\\n|\\\\r\\\", \\\"\\\\n\\\", text)\\r\\n\\r\\n # Make sure $text ends with a couple of newlines:\\r\\n text += \\\"\\\\n\\\\n\\\"\\r\\n\\r\\n # Convert all tabs to spaces.\\r\\n text = self._detab(text)\\r\\n\\r\\n # Strip any lines consisting only of spaces and tabs.\\r\\n # This makes subsequent regexen easier to write, because we can\\r\\n # match consecutive blank lines with /\\\\n+/ instead of something\\r\\n # contorted like /[ \\\\t]*\\\\n+/ .\\r\\n text = self._ws_only_line_re.sub(\\\"\\\", text)\\r\\n\\r\\n # strip metadata from head and extract\\r\\n if \\\"metadata\\\" in self.extras:\\r\\n text = self._extract_metadata(text)\\r\\n\\r\\n text = self.preprocess(text)\\r\\n\\r\\n if self.safe_mode:\\r\\n text = self._hash_html_spans(text)\\r\\n\\r\\n # Turn block-level HTML blocks into hash entries\\r\\n text = self._hash_html_blocks(text, raw=True)\\r\\n\\r\\n # Strip link definitions, store in hashes.\\r\\n if \\\"footnotes\\\" in self.extras:\\r\\n # Must do footnotes first because an unlucky footnote defn\\r\\n # looks like a link defn:\\r\\n # [^4]: this \\\"looks like a link defn\\\"\\r\\n text = self._strip_footnote_definitions(text)\\r\\n text = self._strip_link_definitions(text)\\r\\n\\r\\n text = self._run_block_gamut(text)\\r\\n\\r\\n if \\\"footnotes\\\" in self.extras:\\r\\n text = self._add_footnotes(text)\\r\\n\\r\\n text = self.postprocess(text)\\r\\n\\r\\n text = self._unescape_special_chars(text)\\r\\n\\r\\n if self.safe_mode:\\r\\n text = self._unhash_html_spans(text)\\r\\n\\r\\n text += \\\"\\\\n\\\"\\r\\n\\r\\n rv = UnicodeWithAttrs(text)\\r\\n if \\\"toc\\\" in self.extras:\\r\\n rv._toc = self._toc\\r\\n if \\\"metadata\\\" in self.extras:\\r\\n rv.metadata = self.metadata\\r\\n return rv\",\n \"def inner_text(self, text) -> None:\\n logging.info(f\\\"inner text. {self.desc}\\\")\\n js = f\\\"\\\"\\\"var elm = document.querySelectorAll(\\\"{self.css}\\\")[{self.index}];\\n elm.innerText=\\\"{text}\\\";\\\"\\\"\\\"\\n self._execute_javascript(js)\",\n \"def parse_text(self, text):\\n self._text_paragraph = text.split(\\\"\\\\n\\\")\\n self._render()\",\n \"def process_text(document):\\n return preprocess_string(document,\\n filters=[strip_tags, strip_punctuation,\\n strip_multiple_whitespaces,\\n strip_numeric, remove_stopwords,\\n strip_short]\\n )\",\n \"def process_text(self):\\n prp1 = preprocessor.Preprocess()\\n processed_text = prp1.clean_data(self.text)\\n self.vec1 = self.vec.transform(pd.Series(processed_text))\",\n \"def replace_text(self,\\n\\t text,\\n\\t fname,\\n\\t pattern=None,\\n\\t expect=None,\\n\\t shutit_pexpect_child=None,\\n\\t note=None,\\n\\t before=False,\\n\\t force=False,\\n\\t line_oriented=True,\\n\\t loglevel=logging.DEBUG):\\n\\t\\tshutit_global.shutit_global_object.yield_to_draw()\\n\\t\\treturn self.change_text(text,\\n\\t\\t fname,\\n\\t\\t pattern,\\n\\t\\t expect,\\n\\t\\t shutit_pexpect_child,\\n\\t\\t before,\\n\\t\\t force,\\n\\t\\t note=note,\\n\\t\\t line_oriented=line_oriented,\\n\\t\\t replace=True,\\n\\t\\t loglevel=loglevel)\",\n \"def _preprocess(self, body):\\n return body\",\n \"def tidy_text(self, text: List[str],\\n ) -> List[str]:\\n \\n if self.case_fold:\\n text = list(map(lambda t: t.lower(), text))\\n \\n if self.special_syms:\\n text = list(map(self.handle_special_symbols, text))\\n \\n text = list(map(self.handle_kw_phrases, text))\\n text = list(map(self.depluralise_keywords, text))\\n\\n if self.tokenise:\\n text = list(map(word_tokenize, text))\\n \\n if self.stem:\\n text = list(map(self.stemming, text))\\n \\n if self.lemmatise:\\n text = list(map(self.lemmatisation, text))\\n \\n if self.del_stop_words:\\n stop_words = subsample_frequent_words(text, set(self.target_words))\\n text = list(map(\\n lambda t: self.handle_stop_words(t, stop_words), text\\n ))\\n \\n return text\",\n \"def run(self, text):\\r\\n pass\",\n \"def render_text(self, text, *args, **kwargs):\\n global TEXT_TEMPLATE\\n self.render(TEXT_TEMPLATE, text=text, *args, **kwargs)\",\n \"def replace(self, text):\\n if self.should_skip(text):\\n return text\\n return self.replacer.sub(self._callback, text)\",\n \"def contraction_expansion(text):\\n\\n global patterns\\n for (pattern, repl) in patterns:\\n (text, _) = re.subn(pattern, repl, text)\\n return text\",\n \"def process_text(str_in):\\n str_out = get_model().replace(str_in)\\n get_model().inc_call_counter()\\n return str_out\",\n \"def test_substitutions_in_word(self):\\n m = strutils.MultiReplace({r'cat': 'kedi', r'purple': 'mor', })\\n self.assertEqual(m.sub('Thecatispurple'), 'Thekediismor')\",\n \"def preprocess(self, text):\\n if self.model_name == \\\"bert-base-arabert\\\":\\n return self._old_preprocess(\\n text,\\n do_farasa_tokenization=True,\\n )\\n\\n if self.model_name == \\\"bert-base-arabertv01\\\":\\n return self._old_preprocess(text, do_farasa_tokenization=False)\\n\\n text = str(text)\\n text = html.unescape(text)\\n if self.strip_tashkeel:\\n text = araby.strip_tashkeel(text)\\n if self.strip_tatweel:\\n text = araby.strip_tatweel(text)\\n\\n if self.replace_urls_emails_mentions:\\n # replace all possible URLs\\n for reg in url_regexes:\\n text = re.sub(reg, \\\" [رابط] \\\", text)\\n # REplace Emails with [بريد]\\n for reg in email_regexes:\\n text = re.sub(reg, \\\" [بريد] \\\", text)\\n # replace mentions with [مستخدم]\\n text = re.sub(user_mention_regex, \\\" [مستخدم] \\\", text)\\n\\n if self.remove_html_markup:\\n # remove html line breaks\\n text = re.sub(\\\"<br />\\\", \\\" \\\", text)\\n # remove html markup\\n text = re.sub(\\\"</?[^>]+>\\\", \\\" \\\", text)\\n\\n # remove repeated characters >2\\n if self.remove_elongation:\\n text = self._remove_elongation(text)\\n\\n # insert whitespace before and after all non Arabic digits or English Digits and Alphabet and the 2 brackets\\n if self.insert_white_spaces:\\n text = re.sub(\\n \\\"([^0-9\\\\u0621-\\\\u063A\\\\u0641-\\\\u064A\\\\u0660-\\\\u0669a-zA-Z\\\\[\\\\]])\\\",\\n r\\\" \\\\1 \\\",\\n text,\\n )\\n\\n # insert whitespace between words and numbers or numbers and words\\n text = re.sub(\\n \\\"(\\\\d+)([\\\\u0621-\\\\u063A\\\\u0641-\\\\u064A\\\\u0660-\\\\u066C]+)\\\", r\\\" \\\\1 \\\\2 \\\", text\\n )\\n text = re.sub(\\n \\\"([\\\\u0621-\\\\u063A\\\\u0641-\\\\u064A\\\\u0660-\\\\u066C]+)(\\\\d+)\\\", r\\\" \\\\1 \\\\2 \\\", text\\n )\\n\\n # remove unwanted characters\\n if self.keep_emojis:\\n emoji_regex = \\\"\\\".join(list(self.emoji.UNICODE_EMOJI[\\\"en\\\"].keys()))\\n rejected_chars_regex2 = \\\"[^%s%s]\\\" % (chars_regex, emoji_regex)\\n text = re.sub(rejected_chars_regex2, \\\" \\\", text)\\n else:\\n text = re.sub(rejected_chars_regex, \\\" \\\", text)\\n\\n # remove extra spaces\\n text = \\\" \\\".join(text.replace(\\\"\\\\uFE0F\\\", \\\"\\\").split())\\n\\n if (\\n self.model_name == \\\"bert-base-arabertv2\\\"\\n or self.model_name == \\\"bert-large-arabertv2\\\"\\n ):\\n if self.keep_emojis:\\n new_text = []\\n for word in text.split():\\n if word in list(self.emoji.UNICODE_EMOJI[\\\"en\\\"].keys()):\\n new_text.append(word)\\n else:\\n new_text.append(self.farasa_segmenter.segment(word))\\n text = \\\" \\\".join(new_text)\\n else:\\n text = self.farasa_segmenter.segment(text)\\n return self._farasa_segment(text)\\n\\n # ALl the other models dont require Farasa Segmentation\\n return text\",\n \"def convert_text(s):\\n for d in config.repl: # loaded from config.py\\n if \\\"flags\\\" in d:\\n s = re.sub(d[\\\"ptrn\\\"], d[\\\"repl\\\"], s, flags=d[\\\"flags\\\"])\\n else:\\n s = re.sub(d[\\\"ptrn\\\"], d[\\\"repl\\\"], s)\\n return s\",\n \"def result(target_text):\\n\\n display_text(target_text)\\n readability(target_text)\",\n \"def set_text_body(self, body):\\n self._text_body = body\",\n \"def reach_process_text():\\n response = request.body.read().decode('utf-8')\\n body = json.loads(response)\\n text = body.get('text')\\n rp = reach.process_text(text)\\n if rp and rp.statements:\\n stmts = stmts_to_json(rp.statements)\\n res = {'statements': stmts}\\n return res\\n else:\\n res = {'statements': []}\\n return res\",\n \"def post_process(text):\\n # XXX update to spit out HTML - no need for requests GDocs can take html\\n verbose = False\\n request_list = []\\n chars = iter(text)\\n normal_text = []\\n knownsigils = {\\\"end\\\":('',\\\"NONE\\\"),\\n \\\"^\\\": (\\\"0123456789+-\\\",\\\"SUPERSCRIPT\\\"),\\n \\\"_\\\": (\\\"0123456789\\\",\\\"SUBSCRIPT\\\")\\n }\\n c = next(chars, \\\"end\\\")\\n while (True):\\n if (c in knownsigils.keys()):\\n if len(normal_text): request_list.append((''.join(normal_text), \\\"NORMAL\\\"))\\n normal_text.clear()\\n (c,token) = _gettoken(c,chars,knownsigils)\\n if (token is not None): request_list.append(token)\\n if (c==\\\"end\\\"):\\n break\\n else:\\n continue\\n else:\\n normal_text.append(c)\\n c = next(chars, \\\"end\\\")\\n return request_list\",\n \"def replace_with_text(self):\\r\\n self.parser.stripTags(self.get_top_node(), 'b', 'strong', 'i', 'br', 'sup')\",\n \"def escape_tex(value):\\n newval = value\\n for pattern, replacement in LATEX_SUBS:\\n newval = pattern.sub(replacement, newval)\\n return newval\",\n \"def process_text(text, args):\\n if args.uppercase:\\n text = convert_to_uppercase(text)\\n\\n if args.spaces:\\n text = add_spaces(text)\\n\\n if not args.uppercase and not args.spaces:\\n text = add_spaces(text)\\n\\n return text\",\n \"def __filter( self, text ):\\n return text\",\n \"def _convert_to_fancypants(self, markdown_text: str) -> dict: # noqa: ANN001\\n text_data = {\\\"output_mode\\\": \\\"rtjson\\\", \\\"markdown_text\\\": markdown_text}\\n return self._reddit.post(API_PATH[\\\"convert_rte_body\\\"], data=text_data)[\\\"output\\\"]\",\n \"def strtr(text, items):\\n regex = re.compile(\\\"(%s)\\\" % \\\"|\\\".join(map(re.escape, items.keys())))\\n out = regex.sub(lambda mo: items[mo.string[mo.start():mo.end()]], text)\\n return out\",\n \"def addContent(text):\",\n \"def substitute_value(text, variables, model_context):\\n method_name = 'substitute_value'\\n error_info = {'errorCount': 0}\\n result = _substitute(text, variables, model_context, error_info)\\n error_count = error_info['errorCount']\\n if error_count:\\n ex = exception_helper.create_variable_exception(\\\"WLSDPLY-01740\\\", error_count)\\n _logger.throwing(ex, class_name=_class_name, method_name=method_name)\\n raise ex\\n return result\",\n \"def normalize(self, text: str) -> str:\",\n \"def refang(self, text: str):\",\n \"def process_text(text):\\n fix_dict = {'fig.': 'fig', 'fig .': 'fig ', 'Fig.': 'Fig', 'Fig .': 'Fig ',\\n 'figure.': 'figure', 'figure .': 'figure ', 'Figure.': 'Fig', 'Figure .': 'Fig ',\\n 'et al.': 'et al', 'III': '3', 'II': '2', 'I': '1'}\\n\\n for old_pattern in fix_dict.keys():\\n text = text.replace(old_pattern, fix_dict[old_pattern])\\n return text\",\n \"def process_text(text):\\n text = re.sub(r'<@>\\\\s+|<s>\\\\s+|</s>\\\\s+|<p>\\\\s+|</p>\\\\s+|\\\\s+\\\\,|\\\\'s|\\\\'|\\\\;|\\\\(|\\\\)|\\\\-\\\\-\\\\s+|\\\\s+\\\\.', '', text)\\n text = re.sub(r'\\\\.\\\\,', '. ,', text)\\n text = re.sub(r'\\\\,', '', text)\\n text = re.sub(r'\\\\$', '$ ', text)\\n text = re.sub(r'\\\\%', ' %', text)\\n text = re.sub(r'\\\\s\\\\\\\"\\\\s', ' ', text)\\n text = re.sub(r'\\\\.\\\\s+', '. ', text)\\n text = text.lower()\\n return text\",\n \"def test_simple_substitutions(self):\\n m = strutils.MultiReplace({r'cat': 'kedi', r'purple': 'mor', })\\n self.assertEqual(m.sub('The cat is purple'), 'The kedi is mor')\",\n \"def reformat_text(self, text):\\n xml = BeautifulSoup(text)\\n self.remove_header_and_footer(xml)\\n self.process_superscripts(xml)\\n self.remove_footnotes(xml)\\n text = xml.get_text() # Strip XML tags.\\n text = self.join_hyphenated_words(text)\\n text = self.remove_linebreaks(text)\\n return text\",\n \"def correction():\\n text = request.args.get('text', '')\\n text = TextBlob(text)\\n return jsonify(text=unicode(text.correct()))\",\n \"def substitute_words_with_dict(text: str, substitutions_dict: Union[str, dict]) -> str:\\n\\n if not ((type(substitutions_dict) is str) or\\n (type(substitutions_dict) is dict)):\\n raise TypeError('The substitutions_dict type must be dict or str. '\\n 'If it is str, it has to be the path to the dict stored in json format.')\\n\\n # If substitutions_dict is a str, then it's the path to a file containing the dict in json format.\\n # So, we have to load the dict from the hard drive to memory.\\n if type(substitutions_dict) is str:\\n with open(substitutions_dict) as f:\\n substitutions_dict = json.load(f)\\n\\n # If it's a dict (it was at the beginning or it was converted above) we make the substitutions.\\n for word, substitute_by in substitutions_dict.items():\\n text = substitute_word(text, word, substitute_by)\\n\\n return text\",\n \"def inspect_text(self, page_text, terms):\\n\\n # convert all text to lower case to\\n # increase likelihood of match\\n page_text_lower = page_text.lower()\\n page_text_lower = page_text_lower.replace('\\\\r', '').replace('\\\\n', '')\\n\\n payload = [\\n # populate with match dicts\\n ]\\n\\n for term in terms:\\n sentences = [sentence + '.' for sentence in page_text_lower.split('.') if term in sentence]\\n if len(sentences) > 0:\\n logger.info('found \\\"{0}\\\" x{1}'.format(term, len(sentences)))\\n for sentence in sentences:\\n payload.append({\\n 'term': term,\\n 'sentence': sentence\\n })\\n\\n return payload\",\n \"def _apply_filters(self, text, tag):\\n\\n # The order of the filters below is important\\n # and should not be changed\\n\\n # intial_quotes needs to happen at this point so that\\n # attribute values introduced later on do not get affected\\n text = self.initial_quotes(text)\\n text = self.smarty_pants(text)\\n text = self.amp(text)\\n text = self.caps(text)\\n\\n return text\",\n \"def replace_texts(self, replace, text_key=None):\\n if not text_key: text_key = self.valid_tks\\n text_func = self._replace_from_dict\\n args = ()\\n kwargs = {'replace_map': replace,\\n 'text_key': text_key}\\n DataSet._apply_to_texts(text_func, self._meta, args, kwargs)\\n return None\",\n \"def multi_replace(text, replace_dict):\\n new_text = text\\n for search, replace in list(replace_dict.items()):\\n new_text = new_text.replace(search, str(replace))\\n return new_text\",\n \"def processText(text):\\n print(type(text))\\n for line in text:\\n print(line)\\n return text\",\n \"def preprocess_text(self):\\n self.text_received = self.text_received.replace(\\\" \\\", \\\"\\\").lower()\",\n \"def clean_text(text2, project_key):\\n\\n text = text2\\n text = return_text_without_headlines(text)\\n # remove text written between double curly braces\\n text = re.sub(r\\\"{{code}}.*{{code}}\\\", \\\"code.\\\", text)\\n text = re.sub(r\\\"{code.*{code}\\\", \\\"code.\\\", text)\\n text = re.sub(r\\\"{code:java}.*{code:java}\\\", \\\"code.\\\", text)\\n text = re.sub(r\\\"{noformat}.*{noformat}\\\", \\\"code.\\\", text)\\n text = re.sub(r\\\"{{monospaced}}.*{{monospaced}}\\\", \\\"code.\\\", text)\\n text = re.sub(r'<script type=\\\"text/javascript\\\">.*</noscript>', 'code.', text)\\n text = re.sub(r\\\"'''.*'''\\\", \\\"code\\\", text)\\n text = text.replace('<p> </p>', \\\"\\\")\\n text = text.replace('<div> </div>', \\\"\\\")\\n text = text.replace(' ', \\\" \\\")\\n # remove URLs link\\n text = re.sub(r\\\"<a href=.*</a>\\\", \\\"url. \\\", text)\\n text = re.sub(r\\\"http\\\\S+\\\", \\\"url. \\\", text)\\n text = re.sub(r\\\"hdfs://\\\\S+\\\", \\\"url. \\\", text)\\n text = re.sub(r\\\"tcp://\\\\S+\\\", \\\"url. \\\", text)\\n text = re.sub(r\\\"webhdfs://\\\\S+\\\", \\\"url. \\\", text)\\n text = re.sub(r\\\":/\\\\S+\\\", \\\"url. \\\", text)\\n text = re.sub(r\\\"\\\\S+.com \\\", \\\"url. \\\", text)\\n text = re.sub(r\\\"N/A]\\\", \\\" \\\", text)\\n text = \\\" \\\".join(x for x in text.split() if not x.endswith('.com'))\\n text = \\\" \\\".join(x for x in text.split() if not x.endswith('.com*'))\\n text = \\\" \\\".join(x for x in text.split() if not x.endswith('.org'))\\n text = \\\" \\\".join(x for x in text.split() if not x.endswith('.xml'))\\n text = \\\" \\\".join(x for x in text.split() if not x.startswith('*javax.xml.'))\\n text = \\\" \\\".join(x for x in text.split() if not x.startswith('javax.xml.'))\\n # remove Image attachments\\n text = re.sub(r\\\"<p><img alt=.></p>\\\", \\\"image.\\\", text)\\n text = re.sub(r\\\"{}-\\\\d+\\\".format(project_key), \\\"issue\\\", text)\\n # remove date\\n text = re.sub(r'(\\\\w{4})-(\\\\d{1,2})-(\\\\d{1,2}) ', 'date.', text)\\n text = re.sub(r'(\\\\w{3,4,5})-(\\\\d{1,2})-(\\\\d{4})', 'date.', text)\\n text = re.sub(r'(\\\\d{1,2})/(\\\\d{1,2})/(\\\\d{4})', 'date.', text)\\n text = re.sub(r'(\\\\w{3}). (\\\\d{1,2}), (\\\\d{4})', 'date.', text)\\n text = re.sub(r'(\\\\w{3}). (\\\\d{1,2}) (\\\\d{4})', 'date.', text)\\n text = re.sub(r'<= Today’s Date AND', 'date.', text)\\n text = re.sub(r'yyyy-mm-dd', 'date', text)\\n # remove text written between small braces\\n text = re.sub(r'<.+?>', \\\"\\\", text)\\n text = text.replace(\\\"e.g.,\\\", \\\" \\\")\\n text = text.replace(\\\"e.g.\\\", \\\" \\\")\\n text = text.replace(\\\"i.e.,\\\", \\\" \\\")\\n text = text.replace(\\\"i.e.\\\", \\\" \\\")\\n # replace non-breaking space with regular space\\n text = text.replace(u'\\\\xa0', u' ')\\n # replace all punctuations with space\\n text = text.replace('-->', \\\" \\\")\\n text = text.replace('--', \\\" \\\")\\n text = text.replace('-', \\\" \\\")\\n text = text.replace('/', \\\" \\\")\\n text = text.replace('&', \\\" \\\")\\n text = text.replace(' * ', \\\". \\\")\\n text = re.sub(r\\\"\\\\\\\"|\\\\#|\\\\“|\\\\*|\\\\'|\\\\]|\\\\^|\\\\`|\\\\(|\\\\)|\\\\~\\\", \\\"\\\", text)\\n text = re.sub(r\\\"\\\\\\\"|\\\\$|\\\\%|\\\\&|\\\\/|\\\\|\\\\=|\\\\>|\\\\<|\\\\@|\\\\[|\\\\\\\\|\\\\]|\\\\{|\\\\||\\\\}\\\", \\\" \\\", text)\\n text = text.replace('$', \\\"\\\")\\n text = text.replace('?', \\\".\\\")\\n text = text.replace('+', \\\" \\\")\\n text = re.sub(r\\\" \\\\d\\\\.\\\\d\\\\.N \\\", \\\" \\\", text)\\n text = re.sub(r\\\" \\\\d\\\\.\\\\d\\\\.b.\\\", \\\" \\\", text)\\n text = re.sub(r\\\" \\\\d\\\\.\\\\d\\\\.b \\\", \\\" \\\", text)\\n text = re.sub(r\\\"\\\\d\\\\.\\\\d\\\\.N\\\", \\\" \\\", text)\\n text = re.sub(r\\\"\\\\d\\\\.\\\\d\\\\.X\\\", \\\" \\\", text)\\n text = re.sub(r\\\"v\\\\d\\\\.\\\\d\\\\.\\\\d+\\\", \\\" \\\", text)\\n text = re.sub(r\\\"V\\\\d\\\\.\\\\d\\\\.\\\\d+\\\", \\\" \\\", text)\\n text = re.sub(r\\\"v\\\\d\\\\.\\\\d+\\\", \\\" \\\", text)\\n text = re.sub(r\\\"V\\\\d\\\\.\\\\d+\\\", \\\" \\\", text)\\n text = re.sub(r\\\"\\\\d\\\\.\\\\d+\\\", \\\" \\\", text)\\n text = re.sub(r\\\"\\\\d\\\\.\\\\d\\\\.\\\\d+\\\", \\\" \\\", text)\\n text = text.replace(\\\"V1\\\", \\\" \\\")\\n text = text.replace(\\\"v1\\\", \\\" \\\")\\n # remove digits from text\\n text = re.sub(r\\\"\\\\d+\\\", \\\"\\\", text)\\n text = text.replace('lt;=', \\\" \\\")\\n text = text.replace('.!', \\\".\\\")\\n text = text.replace('!.', \\\".\\\")\\n text = text.replace('!', \\\".\\\")\\n text = text.replace('... ', \\\". \\\")\\n text = text.replace('.. ', \\\". \\\")\\n text = text.replace('..', \\\".\\\")\\n text = text.replace('. . . ', \\\". \\\")\\n text = text.replace('. . ', \\\". \\\")\\n text = text.replace('. . ', \\\". \\\")\\n text = text.replace(' .', \\\".\\\")\\n text = text.replace('. . ', \\\". \\\")\\n text = text.replace('. . ', \\\". \\\")\\n text = text.replace(':.', \\\".\\\")\\n text = text.replace(' :', \\\" \\\")\\n text = text.lower()\\n text = text.replace('..', \\\".\\\")\\n text = ' '.join(text.split())\\n\\n return text\",\n \"def replace_docx_inline_text(doc: docx.Document, data: Dict[str, str]) -> docx.Document:\\n logging.debug(\\\" --- starting docx_replace\\\")\\n\\n if convert_vars.making_template:\\n replacement_values = sort_keys_longest_to_shortest(data)\\n else:\\n replacement_values = list(data.items())\\n\\n paragraphs = get_document_paragraphs(doc)\\n for p in paragraphs:\\n runs_text = \\\"\\\".join(r.text for r in p.runs)\\n if runs_text.strip() == \\\"\\\" or (\\n convert_vars.making_template and re.search(re.escape(\\\"${\\\") + \\\".*\\\" + re.escape(\\\"}\\\"), runs_text)\\n ):\\n continue\\n for key, val in replacement_values:\\n replaced_key = False\\n for i, run in enumerate(p.runs):\\n if run.text.find(key) != -1:\\n p.runs[i].text = run.text.replace(key, val)\\n replaced_key = True\\n runs_text = runs_text.replace(key, val)\\n if not replaced_key:\\n if runs_text.find(key) != -1:\\n runs_text = runs_text.replace(key, val)\\n for i, r in enumerate(p.runs):\\n p.runs[i].text = \\\"\\\"\\n p.runs[0].text = runs_text\\n\\n logging.debug(\\\" --- finished replacing text in doc\\\")\\n return doc\",\n \"def markup_text(self, text):\\n for moniker, name in S['names'].items():\\n text = text.replace('${0}'.format(moniker.split('_')[1]), name)\\n return text\",\n \"def setText(*args):\",\n \"def process(self, message: Message, **kwargs: Any) -> None:\\n\\n try:\\n textdata = message.data[\\\"text\\\"]\\n # print(\\\"text :::\\\" + textdata)\\n textdata = textdata.split()\\n new_message = \\\" \\\".join(spell.correction(w) for w in textdata)\\n # print(\\\"after correction text :::\\\" + new_message)\\n message.data[\\\"text\\\"] = new_message\\n except KeyError:\\n pass\",\n \"def trans(monitext):\\n result = ''\\n last_line = 'empty'\\n\\n while monitext:\\n # newline character or empty line(s)\\n matched = re.match(r'\\\\n+', monitext, re.M)\\n\\n if matched:\\n result += matched.group()\\n if len(matched.group()) > 1:\\n last_line = 'empty'\\n elif last_line == 'title':\\n result += '\\\\n'\\n last_line = 'empty'\\n monitext = monitext[matched.end():]\\n continue\\n\\n # code block\\n matched = re.match(r'{{{.*?\\\\n((\\\\n|.)*?)\\\\n}}}', monitext, re.M)\\n\\n if matched:\\n body = matched.groups()[0]\\n result += '\\\\n\\\\t' + '\\\\n\\\\t'.join(body.split('\\\\n'))\\n monitext = monitext[matched.end():]\\n last_line = 'code'\\n continue\\n\\n # header\\n matched = re.match(r'^(=+) (.+) (=+)', monitext)\\n\\n if matched:\\n title = matched.groups()[1]\\n level = len(matched.groups()[0])\\n\\n if last_line != 'empty':\\n result += '\\\\n'\\n\\n if level < 4:\\n underscore = {2 : '=', 3 : '-'}[level] * mbstrlen(title)\\n result += title + os.linesep + underscore\\n else:\\n result += ('#' * level) + \\\" \\\" + title\\n monitext = monitext[matched.end():]\\n\\n last_line = 'title'\\n\\n continue\\n\\n # link\\n matched = re.match(r'(.*)\\\\[([^\\\\s]+[ \\\\t]+)?(.+)\\\\]', monitext)\\n\\n if matched:\\n pre = matched.groups()[0]\\n url = matched.groups()[1]\\n if url:\\n url = url.strip()\\n name = matched.groups()[2]\\n\\n if url:\\n replaced = \\\"%s[%s](%s)\\\" % (pre, name, url)\\n else:\\n replaced = \\\"%s[%s](%s)\\\" % (pre, name, name)\\n\\n monitext = monitext[:matched.start()] + replaced\\\\\\n + monitext[matched.end():]\\n\\n # important\\n monitext = re.sub(r'\\\\'\\\\'\\\\'(.*?)\\\\'\\\\'\\\\'', r'**\\\\1**', monitext)\\n\\n # italic\\n monitext = re.sub(r'\\\\'\\\\'(.*?)\\\\'\\\\'', r'_\\\\1_', monitext)\\n\\n # list\\n matched = re.match(r'^(\\\\s*)\\\\* (.*)', monitext)\\n\\n if matched:\\n depth = len(matched.groups()[0])\\n body = matched.groups()[1]\\n result += (depth - 1) * '\\\\t' + '* ' + body\\n monitext = monitext[matched.end():]\\n\\n last_line = 'others'\\n\\n try:\\n # Go to the next line\\n index = monitext.index('\\\\n')\\n result += monitext[:index]\\n monitext = monitext[index:]\\n except ValueError:\\n result += monitext\\n break\\n\\n return result\",\n \"def translateText(text):\\r\\n\\treturn translator.translate(text, src='en', dest='ro')\",\n \"def process_body(nlp, body: str, replacements=None, require_labels=False):\\n\\n if replacements is None:\\n replacements = []\\n\\n entry = {\\\"ents\\\": [],\\n \\\"cats\\\": [],\\n \\\"replacements\\\": [],\\n \\\"text\\\": None,\\n \\\"docstrings\\\": []}\\n\\n body_ = body.lstrip()\\n initial_strip = body[:len(body) - len(body_)]\\n\\n replacements = correct_entities(replacements, [(0, len(initial_strip))])\\n\\n docsting_offsets = get_docstring(body_)\\n\\n body_, replacements, docstrings = remove_offsets(body_, replacements, docsting_offsets)\\n entry['docstrings'].extend(docstrings)\\n\\n was_valid = body_valid(body_)\\n initial_labels = get_initial_labels(body_)\\n\\n if require_labels and initial_labels is None:\\n return None\\n\\n returns, return_cuts = unpack_returns(body_, initial_labels)\\n annotations, annotation_cuts = unpack_annotations(body_, initial_labels)\\n\\n body_, replacements_annotations, _ = remove_offsets(body_, replacements + annotations,\\n return_cuts + annotation_cuts)\\n is_valid = body_valid(body_)\\n if was_valid != is_valid:\\n print(\\\"Failed processing\\\")\\n return None\\n # raise Exception()\\n\\n replacements_annotations = adjust_offsets2(replacements_annotations, len(initial_strip))\\n body_ = initial_strip + body_\\n\\n entry['replacements'].extend(list(filter(lambda x: isint(x[2]), replacements_annotations)))\\n entry['ents'].extend(list(filter(lambda x: not isint(x[2]), replacements_annotations)))\\n entry['cats'].extend(returns)\\n entry['text'] = body_\\n\\n entry['replacements'] = resolve_self_collisions2(entry['replacements'])\\n\\n # assert isvalid(nlp, body_, entry['replacements'])\\n assert isvalid(nlp, body_, entry['ents'])\\n\\n return entry\",\n \"def paragraph(self, text):\\n return [text]\",\n \"def multiple_replace(dict, text): \\n\\n # Create a regular expression from the dictionary keys\\n regex = re.compile(\\\"(%s)\\\" % \\\"|\\\".join(map(re.escape, dict.keys())))\\n\\n # For each match, look-up corresponding value in dictionary\\n return regex.sub(lambda mo: dict[mo.string[mo.start():mo.end()]], text)\",\n \"def _string_subst_partial(self, val):\\n def repl(m):\\n k = m.group('id')\\n replacement = self.bib_database.strings[k.lower()] if k.lower() in self.bib_database.strings else k\\n pre = '\\\"' if m.group('pre') != '\\\"' else ''\\n post = '\\\"' if m.group('post') != '\\\"' else ''\\n return pre + replacement + post\\n\\n logger.debug('Substitute string definitions inside larger expressions')\\n if '#' not in val:\\n return val\\n\\n # TODO?: Does not match two subsequent variables or strings, such as \\\"start\\\" # foo # bar # \\\"end\\\" or \\\"start\\\" # \\\"end\\\".\\n # TODO: Does not support braces instead of quotes, e.g.: {start} # foo # {bar}\\n # TODO: Does not support strings like: \\\"te#s#t\\\"\\n return self.replace_all_re.sub(repl, val)\",\n \"def replace(self, text):\\n for key, val in self.env.items():\\n text = text.replace(\\\"$%s\\\" % key, val)\\n return text\",\n \"def preprocess_text(self, seq):\\n if self.text_preprocess_fn:\\n seq = list(map(self.text_preprocess_fn, seq))\\n return seq\",\n \"def svn_fs_apply_text(*args):\\r\\n return _fs.svn_fs_apply_text(*args)\",\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 pre_put_convert(self, site, putText):\\n return putText\",\n \"def preprocess(self,text):\\n return preprocess.get_tokens(text)\",\n \"def getContents(self):\\n normal_body_regex = re.compile(r'[ \\\\n\\\\r\\\\t]+')\\n return normal_body_regex.sub(' ', self.contents)\",\n \"def process_quote_text(quote_text):\\n quote_text = quote_text.replace('―', '').replace('\\\\n\\\\n', '\\\\n')\\n quote_text = quote_text[:-1] if quote_text[-1] == '\\\\n' else quote_text\\n for char in HTML:\\n quote_text = quote_text.replace(*char)\\n return quote_text\",\n \"def get_text(text_input):\\r\\n return text_input\",\n \"def render(self, text, apply_spammer_limits=False):\\n if False:\\n regex = re.compile(\\\"&(?!nbsp;)\\\");\\n body = regex.sub( \\\"&\\\", text )\\n regex = re.compile(\\\"<(/?)([a-zA-Z]+?)( .*?)?/?>\\\")\\n return regex.sub( htmltag_replace, body )\\n return \\\"\\\"\",\n \"def interpolate_replacements(text, expr_replacements):\\n if not expr_replacements:\\n return [text]\\n\\n replacement_strings = list(expr_replacements.keys())\\n splitter = re.compile(\\n \\\"({0})\\\".format(\\\"|\\\".join(re.escape(r) for r in replacement_strings))\\n )\\n split_text = [p for p in splitter.split(text) if p]\\n return [expr_replacements.get(t, t) for t in split_text]\",\n \"def plain_text_body(self):\\n return self._plain_text_body\",\n \"def obtain_text():\\n pass\",\n \"def trips_process_text():\\n response = request.body.read().decode('utf-8')\\n body = json.loads(response)\\n text = body.get('text')\\n tp = trips.process_text(text)\\n if tp and tp.statements:\\n stmts = stmts_to_json(tp.statements)\\n res = {'statements': stmts}\\n return res\\n else:\\n res = {'statements': []}\\n return res\",\n \"def update_html(self, plain_text):\\n ENGINE.reset()\\n self.html = ENGINE.convert(self.content)\\n self.save()\",\n \"def getPlainText(key, default=None, replaceFontTags=True):\\n\\treturn getText(key, (), default, replaceFontTags)\",\n \"def cleanup(self, body):\\n body = re.sub(\\\">\\\", \\\">\\\", body) # Recode HTML codes\\n body = re.sub(\\\"<\\\", \\\"<\\\", body)\\n body = re.sub(\\\"&\\\", \\\"&\\\", body)\\n body = re.sub(\\\" \\\", \\\" \\\", body)\\n # body = re.sub(\\\"^\\\\[deleted\\\\]$\\\", \\\"\\\", body) # [deleted] and [removed]: delete entire row from dataframe\\n body = re.sub(\\\"http\\\\S+\\\", \\\" \\\", body) # Remove URL\\n body = re.sub(\\\"/r/\\\\S+|/u/\\\\S+\\\", \\\" \\\", body) # Remove /r/subreddit, /u/user\\n # body = re.sub(\\\"(>.*?\\\\\\\\n\\\\\\\\n)+\\\", \\\" \\\", body) # Remove quoted comments\\n # body = re.sub(\\\"[[:cntrl:]]\\\", \\\" \\\", body) # Remove control characters (\\\\n, \\\\b) doesn't work for unicode\\n body = \\\"\\\".join(ch for ch in body if unicodedata.category(ch)[0]!=\\\"C\\\") # Remove control characters (\\\\n, \\\\b) etc.\\n body = re.sub(\\\"'\\\", \\\"\\\", body) # Remove single quotation marks (contractions)\\n # body = re.sub(\\\"[[:punct:]]\\\", \\\" \\\", body) # Remove punctuation\\n body = \\\"\\\".join(ch for ch in body if unicodedata.category(ch)[0]!=\\\"P\\\") # Remove punctuation\\n body = re.sub(\\\"\\\\\\\\s+\\\", \\\" \\\", body) # Replace multiple spaces with single space\\n body = body.strip()\\n body = body.lower() # Lower case\\n return body # Return body (cleaned up text)\",\n \"def preprocessing(sample):\\n\\n content = ' '.join(sample)\\n content = re.sub(r\\\"\\\\'ll\\\", \\\" will\\\", content)\\n content = re.sub(r\\\"\\\\'d\\\", \\\" would\\\", content)\\n content = re.sub(r\\\"\\\\'s\\\", \\\" is\\\", content)\\n content = re.sub(r\\\"\\\\'m\\\", \\\" am\\\", content)\\n content = re.sub(r\\\"\\\\'ve\\\", \\\" have\\\", content)\\n content = re.sub(r\\\"\\\\'re\\\", \\\" are\\\", content)\\n content = content.replace('&', 'and')\\n content = content.replace('$', '')\\n content = content.split()\\n return content\",\n \"def render_text(self):\\n if self.text_type == 1:\\n return markdown.markdown(self.text)\\n else:\\n return self.text\",\n \"def preprocess(text):\\n text = remove_space(text)\\n text = clean_special_punctuations(text)\\n text = clean_number(text)\\n text = decontracted(text)\\n text = correct_spelling(text)\\n text = spacing_punctuation(text)\\n text = spacing_some_connect_words(text)\\n text = clean_repeat_words(text)\\n text = remove_space(text)\\n text = text.lower()\\n return text\",\n \"def getText():\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.65611976","0.6435312","0.6424799","0.6276254","0.6202044","0.61083364","0.6037628","0.6034791","0.59750575","0.57668275","0.5727748","0.57236975","0.5721389","0.5685642","0.56766623","0.5662096","0.5638508","0.56205267","0.5587311","0.55449075","0.5541788","0.55329967","0.55227274","0.5517054","0.5494322","0.5493339","0.54426795","0.5434009","0.5430204","0.5426383","0.54234296","0.5403354","0.53911","0.5388199","0.5387887","0.53847647","0.5377782","0.5371517","0.5369222","0.5354359","0.5353925","0.53406745","0.53353363","0.53244615","0.5315649","0.5313266","0.5294096","0.52928936","0.5289224","0.52826774","0.52754796","0.527513","0.52714133","0.5267921","0.52666825","0.52656883","0.5247876","0.5245312","0.52366954","0.52306783","0.5220473","0.52191347","0.5217126","0.5217002","0.5208903","0.52085507","0.5205883","0.5186228","0.5185358","0.51551086","0.515263","0.5139556","0.5131503","0.5128844","0.5126651","0.5123871","0.51228064","0.51174194","0.51166993","0.5093048","0.5085159","0.5080711","0.5080672","0.507571","0.5075641","0.5074114","0.5073711","0.5069282","0.5064461","0.5063696","0.5063627","0.50590545","0.5059008","0.50558716","0.50495684","0.50490415","0.50454396","0.50434905","0.5042043","0.50385046"],"string":"[\n \"0.65611976\",\n \"0.6435312\",\n \"0.6424799\",\n \"0.6276254\",\n \"0.6202044\",\n \"0.61083364\",\n \"0.6037628\",\n \"0.6034791\",\n \"0.59750575\",\n \"0.57668275\",\n \"0.5727748\",\n \"0.57236975\",\n \"0.5721389\",\n \"0.5685642\",\n \"0.56766623\",\n \"0.5662096\",\n \"0.5638508\",\n \"0.56205267\",\n \"0.5587311\",\n \"0.55449075\",\n \"0.5541788\",\n \"0.55329967\",\n \"0.55227274\",\n \"0.5517054\",\n \"0.5494322\",\n \"0.5493339\",\n \"0.54426795\",\n \"0.5434009\",\n \"0.5430204\",\n \"0.5426383\",\n \"0.54234296\",\n \"0.5403354\",\n \"0.53911\",\n \"0.5388199\",\n \"0.5387887\",\n \"0.53847647\",\n \"0.5377782\",\n \"0.5371517\",\n \"0.5369222\",\n \"0.5354359\",\n \"0.5353925\",\n \"0.53406745\",\n \"0.53353363\",\n \"0.53244615\",\n \"0.5315649\",\n \"0.5313266\",\n \"0.5294096\",\n \"0.52928936\",\n \"0.5289224\",\n \"0.52826774\",\n \"0.52754796\",\n \"0.527513\",\n \"0.52714133\",\n \"0.5267921\",\n \"0.52666825\",\n \"0.52656883\",\n \"0.5247876\",\n \"0.5245312\",\n \"0.52366954\",\n \"0.52306783\",\n \"0.5220473\",\n \"0.52191347\",\n \"0.5217126\",\n \"0.5217002\",\n \"0.5208903\",\n \"0.52085507\",\n \"0.5205883\",\n \"0.5186228\",\n \"0.5185358\",\n \"0.51551086\",\n \"0.515263\",\n \"0.5139556\",\n \"0.5131503\",\n \"0.5128844\",\n \"0.5126651\",\n \"0.5123871\",\n \"0.51228064\",\n \"0.51174194\",\n \"0.51166993\",\n \"0.5093048\",\n \"0.5085159\",\n \"0.5080711\",\n \"0.5080672\",\n \"0.507571\",\n \"0.5075641\",\n \"0.5074114\",\n \"0.5073711\",\n \"0.5069282\",\n \"0.5064461\",\n \"0.5063696\",\n \"0.5063627\",\n \"0.50590545\",\n \"0.5059008\",\n \"0.50558716\",\n \"0.50495684\",\n \"0.50490415\",\n \"0.50454396\",\n \"0.50434905\",\n \"0.5042043\",\n \"0.50385046\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7525169"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":26092,"cells":{"query":{"kind":"string","value":"Run the test. 'context' A 'Context' giving runtime parameters to the test. 'result' A 'Result' object. The outcome will be 'Result.PASS' when this method is called. The 'result' may be modified by this method to indicate outcomes other than 'Result.PASS' or to add annotations."},"document":{"kind":"string","value":"def Run(self, context, result):\n\n # Was the program not specified?\n\n self.program = context[\"isql_path\"]\n\n if context.has_key(\"database_path\"):\n database = context[\"database_path\"]\n else:\n database = \"\"\n self.RunProgram(self.program,\n\t\t\t[ self.program , database ,\n \"-user\", context[\"user_name\"], \"-password\", context[\"user_password\"] ],\n context, result)"},"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 run(self, context):\n TestStepBase.run(self, context)\n\n if self._device is None:\n self._raise_config_exception(\"%s is not specified in the bench configuration\" % self._pars.device,\n AcsConfigException.INVALID_BENCH_CONFIG)\n\n # Insert log into device logs as info \"i\"\n self._device.inject_device_log(\"i\", \"ACS_TESTSTEP\", \"RUNTESTSTEP - %s\" % self._pars.id)","def test_execute_with_context(self):\n pass","def run(context, path=\"\"):\n common.success(f\"Tests {path} running \")\n return start.run_python(\n context,\n f\"-m pytest {path}\"\n )","def Run(self, context, result):\n\n # check if the test can be run on the current platform\n if not self.SupportedPlatform(context, result):\n return\n\n # If the context specifies a shell, use it.\n if context.has_key(\"ShellCommandTest.command_shell\"):\n # Split the context value to build the argument list.\n shell = qm.common.split_argument_list(\n context[\"ShellCommandTest.command_shell\"])\n else:\n # Otherwise, use a platform-specific default.\n shell = qm.platform.get_shell_for_command()\n\n # Environment for the nightlies (user lcgnight)\n if sys.platform.startswith(\"win\") \\\n and \"USERNAME\" in os.environ \\\n and os.environ[\"USERNAME\"] == \"lcgnight\" :\n # Take TNS_ADMIN from DFS on Win (bug #86450)\n os.environ[\"TNS_ADMIN\"]=r\"\\\\cern.ch\\dfs\\Applications\\Oracle\\ADMIN\"\n # Use credentials from %USERPROFILE%\\private on Win (bug #86463)\n private = os.path.join(os.environ[\"USERPROFILE\"],\"private\")\n os.environ[\"CORAL_AUTH_PATH\"] = private\n os.environ[\"CORAL_DBLOOKUP_PATH\"] = os.environ[\"CORAL_AUTH_PATH\"]\n elif sys.platform.startswith(\"darwin\") \\\n and \"USER\" in os.environ \\\n and os.environ[\"USER\"] == \"lcgnight\" :\n # Use credentials from $HOME/private on Mac (bug #86463)\n private = os.path.join(os.environ[\"HOME\"],\"private\")\n os.environ[\"CORAL_AUTH_PATH\"] = private\n os.environ[\"CORAL_DBLOOKUP_PATH\"] = os.environ[\"CORAL_AUTH_PATH\"]\n elif sys.platform.startswith(\"linux\") \\\n and \"USER\" in os.environ \\\n and os.environ[\"USER\"] == \"lcgnight\" :\n # Use credentials from POOL AFS area on Linux (bug #86463)\n os.environ[\"CORAL_AUTH_PATH\"] = \"/afs/cern.ch/sw/lcg/app/pool/db\"\n os.environ[\"CORAL_DBLOOKUP_PATH\"] = os.environ[\"CORAL_AUTH_PATH\"]\n\n # Special handling if this is a python script (from COOLTests.py)\n if sys.platform.startswith(\"win\") and \\\n self.command.split()[0].endswith(\".py\"):\n # We need to run a python test and not a standard executable\n [cmd, arg] = self.command.split(' ',1)\n for d in os.environ[\"PATH\"].split(os.path.pathsep):\n if os.path.isdir(d) and cmd in os.listdir(d):\n # we found the script in the path\n self.command = 'python \"%s\" %s'%(os.path.join(d,cmd),arg)\n break\n\n # Append the command at the end of the argument list.\n arguments = shell + [ self.command ]\n self.RunProgram(arguments[0], arguments, context, result)","def test(ctx):\n click.echo('testing')\n ctx.test()\n click.echo('done')","def run(self, result=None):\n\n with self.env_wrap():\n super(RelengToolTestCase, self).run(result)","def execute(self, context: Any) -> Any:\n pass","def run( self, test ):\n\n result = self._makeResult()\n test( result )\n result.printErrors()\n self.stream.writeln( result.separator2 )\n run = result.testsRun\n self.stream.writeln()\n\n if not result.wasSuccessful():\n self.stream.write( \"FAILED (\" )\n failed, errored = map( len, ( result.failures, result.errors ) )\n if failed:\n self.stream.write( \"failures=%d\" % failed )\n if errored:\n if failed: self.stream.write( \", \" )\n self.stream.write( \"errors=%d\" % errored )\n self.stream.writeln( \")\" )\n else:\n self.stream.writeln( \"OK\" )\n \n return result","def run_tests(event, context):\n try:\n jobId = event['CodePipeline.job']['id']\n user_parameters = json.loads(event['CodePipeline.job']['data']['actionConfiguration']['configuration']['UserParameters'])\n runscope_trigger_url = user_parameters['runscopeTriggerUrl']\n runscope_access_token = user_parameters['runscopeAccessToken']\n\n tests = start_tests(runscope_trigger_url)\n aggregate_status = wait_for_tests_to_complete(tests, runscope_access_token)\n if aggregate_status == \"pass\":\n code_pipeline.put_job_success_result(jobId=jobId)\n else:\n code_pipeline.put_job_failure_result(jobId=jobId, failureDetails={\n 'type': 'JobFailed',\n 'message': 'One or more tests failed'\n })\n except:\n code_pipeline.put_job_failure_result(jobId=jobId, failureDetails={\n 'type': 'JobFailed',\n 'message': 'Unhandled exception during Runscope tests execution'\n })","def execute(self):\n\n self._status = 'Running'\n\n try:\n self._init_staf_handle()\n self._setup_results()\n self._stage_test_step()\n self._execute_test_step()\n sleep(self._post_wait)\n self._get_remote_results()\n self._status = 'Pass'\n except CoreError as e:\n self._status = 'Fail'\n self._message = e.msg\n finally:\n self._close_staf_handle()\n\n #Notify TestCase that a failure occurred.\n if self._status == 'Fail': raise Failure(self._message)","def test(self):\n return self._test(result_count=1, failure_amount=1)","def test(self):\n return self._test(result_count=1, failure_amount=1)","def tests(context):\n black(context)\n isort(context)\n flake8(context)\n pylint(context)\n yamllint(context)\n pydocstyle(context)\n bandit(context)\n pytest(context)\n\n print(\"All tests have passed!\")","def excecute(self):\r\n self.initialize()\r\n self.addteststeps()\r\n for teststep in self.test_steps_list:\r\n if teststep.run() == TestStatus.PASS:\r\n logging.info(\"test {} passed the test\".format(teststep.stepname))\r\n self.result = TestStatus.PASS\r\n else:\r\n logging.warn(\"test {} failed the test\".format(teststep.stepname))\r\n self.result = TestStatus.FAIL\r\n self.cleanup()\r\n return self.result","def run(self, context):\n TestStepBase.run(self, context)\n\n # Looks for the key in the context, raise an error if it doesn't exist\n info = context.get_info(self._key)\n if info is None:\n return_message = \"%s is not found in the context\" % self._key\n self._logger.error(return_message)\n raise AcsConfigException(AcsConfigException.OPERATION_FAILED, return_message)","def check_result(context, expected):\n assert context.result == expected, \"Wrong result: {r} != {e}\".format(\n r=context.result, e=expected\n )","def __call__(self, example):\n return self.test(example)","def run_quick_test(self, context: ResourceCommandContext, test: str) -> None:\n self.handler.run_quick_test(context, test)","def when_query_pipeline(context):\n result = context.stage.runTest('testing stage endpoint')\n print('Result = {}'.format(result))\n context.result = result","def execute(self, args=None):\n parser = argparse.ArgumentParser(description='run tests on jupyter notebook')\n\n parser.add_argument('notebook', type=str, help='the jupyter notebook to test')\n parser.add_argument('-t', '--target', type=str, metavar='', help='where to store results')\n parser.add_argument('-c', '--context', type=str, metavar='', help='context directory')\n parser.add_argument('-v', '--verbose', action='count', default=0, help='verbosity level')\n\n args = parser.parse_args(args)\n\n logger.setLevel(loglevel(args.verbose))\n logger.debug(f'args: {args}')\n\n results = self._grade_notebook(\n Path(args.notebook).absolute(),\n target_dir=Path(args.target).absolute() if args.target else None,\n context=Path(args.context).absolute() if args.context else None\n )\n\n return results.summary().failed","def execute(self, driver, context):\n raise NotImplementedError","def __call__(self, result = None):\r\n if result != None:\r\n self.result = result\r\n else:\r\n self.result = core.FW_conf['connection'].getResult()\r\n assert self.result, 'Internal error: test result is missing from Phone Manager instance!'\r\n\r\n core.FW_conf['connection'].currentTcId = self.tcId\r\n for remote in core.FW_conf['remote_connection']:\r\n remote.setResult(self.result)\r\n remote.currentTcId = self.tcId\r\n\r\n self.result.startTest(self)\r\n\r\n # add comment to test report whether we are in WhiteBox/HW Assisted WhiteBox mode\r\n self.comment('Executing test case in %s mode' % (core.FW_conf['blackbox_enabled'] and 'HW Assisted WhiteBox' or 'WhiteBox'))\r\n\r\n # add test case information for bltue report\r\n self.addBltueTestCase(self.tcId)\r\n\r\n try:\r\n # Code inside the next try-except -block must not raise exceptions.\r\n # All exception handling must be done within the logApply method.\r\n try:\r\n # return if test execution should be stopped\r\n if core.FW_conf['should_stop']:\r\n return\r\n\r\n # execute _setUp\r\n self.comment('%s MARBLE SETUP %s' % ((15 * '='), (15 * '=')))\r\n try:\r\n self._inSetUp = True\r\n resp = self.logApply(self._setUp)\r\n finally:\r\n self._inSetUp = False\r\n\r\n # return if test execution should be stopped\r\n if core.FW_conf['should_stop']:\r\n return\r\n\r\n if resp == False or self.result.wasSkipped():\r\n # execute _tearDown\r\n self.comment('%s MARBLE TEARDOWN %s' % ((15 * '='), (15 * '=')))\r\n try:\r\n self._inTearDown = True\r\n resp = self.logApply(self._tearDown)\r\n finally:\r\n self._inTearDown = False\r\n\r\n # return because setup failed\r\n return\r\n\r\n # return if test execution should be stopped\r\n if core.FW_conf['should_stop']:\r\n return\r\n\r\n # execute possible test case's setUp\r\n if 'setUp' in dir(self):\r\n self.comment('%s TEST CASE SETUP %s' % ((15 * '='), (15 * '=')))\r\n try:\r\n self._inTestCaseSetUp = True\r\n resp = self.logApply(self.setUp)\r\n finally:\r\n self._inTestCaseSetUp = False\r\n\r\n # return if test execution should be stopped\r\n if core.FW_conf['should_stop']:\r\n return\r\n\r\n if resp == False or self.result.wasSkipped():\r\n # test case setup failed\r\n\r\n # execute _tearDown\r\n self.comment('%s MARBLE TEARDOWN %s' % ((15 * '='), (15 * '=')))\r\n try:\r\n self._inTearDown = True\r\n resp = self.logApply(self._tearDown)\r\n finally:\r\n self._inTearDown = False\r\n\r\n # return if test execution should be stopped\r\n if core.FW_conf['should_stop']:\r\n return\r\n\r\n # execute _postTearDown\r\n try:\r\n self._inPostTearDown = True\r\n resp = self.logApply(self._postTearDown)\r\n finally:\r\n self._inPostTearDown = False\r\n\r\n # return if test execution should be stopped\r\n if core.FW_conf['should_stop']:\r\n return\r\n\r\n # execute possible test case's tearDown\r\n if 'tearDown' in dir(self):\r\n self.comment('%s TEST CASE TEARDOWN %s' % ((15 * '='), (15 * '=')))\r\n try:\r\n self._inTestCaseTearDown = True\r\n resp = self.logApply(self.tearDown)\r\n finally:\r\n self._inTestCaseTearDown = False\r\n\r\n if resp == False:\r\n self._warn('Failure during test case teardown!')\r\n\r\n # return because test case setup failed\r\n return\r\n\r\n # return if test execution should be stopped\r\n if core.FW_conf['should_stop']:\r\n return\r\n\r\n # execute _postSetUp\r\n try:\r\n self._inPostSetUp = True\r\n resp = self.logApply(self._postSetUp)\r\n finally:\r\n self._inPostSetUp = False\r\n\r\n # return if post setup fails\r\n if resp == False:\r\n self._warn('Failure during post setup!')\r\n return\r\n\r\n # return if test execution should be stopped\r\n if core.FW_conf['should_stop']:\r\n return\r\n\r\n # execute the test method\r\n testMethod = getattr(self, self.testMethodName)\r\n self.comment('%s TEST CASE %s' % ((15 * '='), (15 * '=')))\r\n try:\r\n self._inTestCase = True\r\n resp = self.logApply(testMethod)\r\n finally:\r\n self._inTestCase = False\r\n\r\n ok = resp != False\r\n\r\n # return if test execution should be stopped\r\n if core.FW_conf['should_stop']:\r\n return\r\n\r\n # execute _tearDown\r\n self.comment('%s MARBLE TEARDOWN %s' % ((15 * '='), (15 * '=')))\r\n try:\r\n self._inTearDown = True\r\n resp = self.logApply(self._tearDown)\r\n finally:\r\n self._inTearDown = False\r\n\r\n if ok: ok = resp != False\r\n\r\n # return if test execution should be stopped\r\n if core.FW_conf['should_stop']:\r\n return\r\n\r\n # execute possible test case's tearDown\r\n if 'tearDown' in dir(self):\r\n self.comment('%s TEST CASE TEARDOWN %s' % ((15 * '='), (15 * '=')))\r\n try:\r\n self._inTestCaseTearDown = True\r\n resp = self.logApply(self.tearDown)\r\n finally:\r\n self._inTestCaseTearDown = False\r\n\r\n if resp == False:\r\n self._warn('Failure during test case teardown!')\r\n\r\n # return if test execution should be stopped\r\n if core.FW_conf['should_stop']:\r\n return\r\n\r\n # execute _postTearDown\r\n try:\r\n self._inPostTearDown = True\r\n resp = self.logApply(self._postTearDown)\r\n finally:\r\n self._inPostTearDown = False\r\n\r\n if ok: ok = resp != False\r\n\r\n # return if test execution should be stopped\r\n if core.FW_conf['should_stop']:\r\n return\r\n\r\n # Success!\r\n if not self.result.getError():\r\n if ok:\r\n self.result.addSuccess(self)\r\n if core.FW_conf['delete_success_blx'] and core.FW_conf['tracing_enabled'] and not core.FW_conf['memory_leak_detection']:\r\n #Delete successfull testcase blx if user doesn't need those\r\n core.FW_conf['trace'].deleteBlx = True\r\n except SystemExit:\r\n # put self._raiseSystemExit flag to True so that SystemExit\r\n # can be raised in finally:\r\n self._raiseSystemExit = True\r\n except Exception, e:\r\n # This is usually caused by an internal error, but could also\r\n # be a KeyboardInterrupt. In either case, stop testing is\r\n # needed here.\r\n self.logApply(self.stop, e)\r\n finally:\r\n # raise SystemExit if needed\r\n if self._raiseSystemExit:\r\n raise\r\n\r\n for phone in core.FW_conf['connection']:\r\n if phone._createBugReport:\r\n phone.createBugReport()\r\n\r\n if core.FW_conf['settings'].TestRun.TakeHeapDumps:\r\n core.FW_conf['connection'].takeHeapDumps()\r\n\r\n self._stopTest()","def test(ctx):\n pass","def runtest(self):","def execute(self):\n\n self._status = 'Running'\n\n try:\n self._init_staf_handle()\n self._setup_results()\n self._stage()\n self._install()\n self._get_remote_results()\n self._status = 'Pass'\n except CoreError as e:\n self._status = 'Fatal'\n self._message = e.msg\n finally:\n self._close_staf_handle()\n\n #Notify TestCase that a failure occurred.\n if self._status == 'Fatal': raise FatalError(self._message)","def run(self, test, env):\n\n raise NotImplementedError","def execute(self, context: dict):\n conf = copy.deepcopy(context[\"dag_run\"].conf)\n logger.debug(f\"Got conf {conf}.\")\n execution_context = conf[\"execution_context\"]\n if \"Payload\" in execution_context:\n payload_context = Context.populate(execution_context)\n else:\n payload_context = Context(data_partition_id=execution_context[\"data-partition-id\"],\n app_key=execution_context.get(\"AppKey\", \"\"))\n workflow_name = conf[\"workflow_name\"]\n run_id = conf[\"run_id\"]\n status = self.status.value\n status_updater = UpdateStatus(\n workflow_name=workflow_name,\n workflow_url=Variable.get(\"core__service__workflow__host\"),\n workflow_id=\"\",\n run_id=run_id,\n status=status,\n token_refresher=AirflowTokenRefresher(),\n context=payload_context\n )\n status_updater.update_workflow_status()\n\n if self._show_skipped_ids:\n skipped_ids, saved_record_ids = self._create_skipped_report(context)\n context[\"ti\"].xcom_push(key=\"skipped_ids\", value=skipped_ids)\n context[\"ti\"].xcom_push(key=\"saved_record_ids\", value=saved_record_ids)\n\n if self.status is self.prev_ti_state.FAILED:\n raise PipelineFailedError(\"Dag failed\")","def __call__(self, result):\n result.startTest(self)\n\n if self._line.skip:\n result.addSkip(None, self._line.directive.reason)\n return\n\n if self._line.todo:\n if self._line.ok:\n result.addUnexpectedSuccess(self)\n else:\n result.addExpectedFailure(self, (Exception, Exception(), None))\n return\n\n if self._line.ok:\n result.addSuccess(self)\n else:\n self.addFailure(result)","def run(self, result=None):\n self.result = result\n\n # === Decorate the setUp and tearDown methods ===\n setup_method = getattr(self, self.SETUP_METHOD_NAME)\n setattr(self, self.SETUP_METHOD_NAME,\n self._decorate_setup(setup_method))\n\n teardown_method = getattr(self, self.TEARDOWN_METHOD_NAME)\n setattr(self, self.TEARDOWN_METHOD_NAME,\n self._decorate_teardown(teardown_method))\n\n try:\n super(AbstractFlowComponent, self).run(result)\n\n except JumpException as e:\n self.data.exception_type = TestOutcome.SKIPPED\n if e.jump_target is not self.parent:\n raise","def run(self, result, debug=False):\n rv = unittest.TestSuite.run(self, result, debug)\n sys.stdout.flush()\n return rv","def _run_scenario(self, cls, method_name, context, args, config):","def _RunTest(self, adb):\n return ResultType.FAIL, 'Unimplemented _RunTest() method!'","def run_test(self):\n raise NotImplementedError","def run(self, test):\n result = _TestResult(self.verbosity)\n\n test(result)\n self.stopTime = datetime.datetime.now()\n self._write_data(result)\n print(sys.stderr, '\\nTime Elapsed: %s' % (self.stopTime-self.startTime))\n return result","def __call__(self, result=None, runcondition=None, options=None):\n if result is None:\n result = self.defaultTestResult()\n self._options_ = options\n # if result.cvg:\n # result.cvg.start()\n testMethod = self._get_test_method()\n if (getattr(self.__class__, \"__unittest_skip__\", False) or\n getattr(testMethod, \"__unittest_skip__\", False)):\n # If the class or method was skipped.\n try:\n skip_why = (getattr(self.__class__, '__unittest_skip_why__', '')\n or getattr(testMethod, '__unittest_skip_why__', ''))\n if hasattr(result, 'addSkip'):\n result.addSkip(self, skip_why)\n else:\n warnings.warn(\"TestResult has no addSkip method, skips not reported\",\n RuntimeWarning, 2)\n result.addSuccess(self)\n finally:\n result.stopTest(self)\n return\n if runcondition and not runcondition(testMethod):\n return # test is skipped\n result.startTest(self)\n try:\n if not self.quiet_run(result, self.setUp):\n return\n generative = isgeneratorfunction(testMethod)\n # generative tests\n if generative:\n self._proceed_generative(result, testMethod,\n runcondition)\n else:\n status = self._proceed(result, testMethod)\n success = (status == 0)\n if not self.quiet_run(result, self.tearDown):\n return\n if not generative and success:\n result.addSuccess(self)\n finally:\n # if result.cvg:\n # result.cvg.stop()\n result.stopTest(self)","def run(self, test):\n result = _TestResult(self.verbosity)\n\n self.start_time = datetime.datetime.now()\n test(result)\n self.stop_time = datetime.datetime.now()\n self.elapsed_time = self.stop_time - self.start_time\n print >>sys.stderr, '\\nTime Elapsed: %s' % self.elapsed_time\n\n report = self.generate_report(result)\n self.stream.write(report.encode('utf8'))\n\n return result","def run(self):\n if self.verbose:\n print(f'Running {self.name} tests...')\n\n # try running setup if there is one\n if self.setup:\n self.__process_setup()\n\n final_report = [None] * len(self.tests)\n\n for test_in, test_out in sorted(self.tests.items()):\n # increment total num of tests\n self.total += 1\n\n if self.verbose:\n print(f'#{self.total}')\n\n # evaluate test input w/ setup vars, if any\n try:\n inp = eval(test_in, self.vars)\n except Exception as err:\n print(f'Issue during evaluation of test input: {err}')\n final_report[self.total - 1] = 'input eval error'\n if self.verbose:\n print(f'Test input was: {test_in}')\n print('Vars from execution: {}'.format({k : v for k, v in self.vars.items() if k != '__builtins__'}))\n continue\n\n \n # checking if function input has more than one arg\n if type(inp) in (list, tuple):\n try:\n student_out = self.student_function(*inp)\n except Exception as err:\n print(f'Issue while running student code: {err}')\n final_report[self.total - 1] = f'student code error: {err}; input: {inp}; func_name: {self.name}'\n if self.verbose:\n print(f'Function being run was: {self.name}')\n print(f'Inputs were: {inp}')\n continue\n else:\n try:\n student_out = self.student_function(inp)\n except Exception as err:\n print(f'Issue while running student code: {err}')\n final_report[self.total - 1] = f'student code error: {err}; input: {inp}; func_name: {self.name}'\n if self.verbose:\n print(f'Function being run was: {self.name}')\n print(f'Input was: {inp}')\n continue\n\n # ans alias for ease of answer checking\n self.vars['ans'] = student_out\n\n if self.schema:\n format_vals = eval(test_out, self.vars)\n results, maybe_failed_schema = self.__process_schema(format_vals)\n if all(results):\n self.correct += 1\n final_report[self.total - 1] = 'PASSED'\n else:\n # failed at least one of the tests\n failed_str = \" and \".join([\", \".join(maybe_failed_schema[:-1]),maybe_failed_schema[-1]] if len(maybe_failed_schema) > 2 else maybe_failed_schema)\n final_report[self.total - 1] = f'FAILED; failed following assertion(s): {failed_str}'\n else:\n expected_ans = eval(test_out, self.vars)\n if student_out == expected_ans:\n self.correct += 1\n final_report[self.total - 1] = 'PASSED'\n else:\n # failed the only test\n final_report[self.total - 1] = f'FAILED; got {repr(student_out)} but expected {repr(expected_ans)}'\n\n # run callback function, if there is one\n if self.callback:\n if self.verbose:\n print('Running callback...')\n print('call back is:', self.callback)\n\n # once done, put the final report on the queue\n self.queue.put((self.student_username, self.name, f'{self.correct}/{self.total}', final_report))","def run_case(self, **kwargs):\n module_name = kwargs.get('module_name', None)\n if self.result:\n self.success_msg.append('>>>%s PASSED' % module_name or sys.modules[__name__])\n else:\n self.fail_msg.insert(0, '>>>%s FAILED' % module_name or sys.modules[__name__])","def test_example_runs(self):\n run_example(\n verbose=False,\n testapp=self.testapp,\n )","def pytest(context):\n exec_cmd = \"pytest\"\n run_cmd(context, exec_cmd)","def __call__(self, result=None):\n try:\n self._pre_setup()\n super(TestCase, self).__call__(result)\n finally:\n self._post_teardown()","def run(self, **kwargs):\n self.start_time = time.time()\n\n try:\n # Make sure that Tempest is configured\n if not self.tempestconf:\n self.generate_conf()\n self.run_defcore_default()\n self.parse_refstack_result()\n res = testcase.TestCase.EX_OK\n except Exception: # pylint: disable=broad-except\n LOGGER.exception(\"Error with run\")\n res = testcase.TestCase.EX_RUN_ERROR\n finally:\n self.tempestconf.clean()\n\n self.stop_time = time.time()\n return res","def runTests(self):\n \n pass","def _execute_test_step(self):\n\n test_step_command = '{0}'.format(self._test_exec) if self._interpreter == '' else \\\n '{0} {1}'.format(self._interpreter, self._test_exec)\n\n # TODO: Expand user and environment variables.\n test_params = ['{0} {1}'.format(x,y) if y != '' else '{0}'.format(x)\n for x,y in self._test_params.items()]\n\n exit_code, out = self._staf_start_proc(test_step_command,\n self._remote_target_path,\n self._timeout,\n test_params,\n location=self._sut.network_address)\n\n if exit_code != 0:\n raise CoreError('Test step \"{0}\" failed: {1}'.format(self._description, out))","def run_suite(self, name=\"_\", result=None):\n result = result if result else self.result\n self.mod_suites[name].run(result)","def run(run_type, module, config):\n print(\" -----------------------------------------------------------------\")\n print(\" Beginning \" + run_type.lower() + \" test suite \")\n print(\" -----------------------------------------------------------------\")\n print(\"\")\n summary = run_quiet(run_type, module, config)\n print(\" -----------------------------------------------------------------\")\n print(\" \" + run_type.capitalize() + \" test suite complete \")\n print(\" -----------------------------------------------------------------\")\n print(\"\")\n return summary","def _super_run_modified(self, result=None):\n\n orig_result = result\n if result is None:\n result = self.defaultTestResult()\n startTestRun = getattr(result, 'startTestRun', None)\n if startTestRun is not None:\n startTestRun()\n\n result.startTest(self)\n\n testMethod = getattr(self, self._testMethodName)\n if (getattr(self.__class__, \"__unittest_skip__\", False) or\n getattr(testMethod, \"__unittest_skip__\", False)):\n # If the class or method was skipped.\n try:\n skip_why = (getattr(self.__class__, '__unittest_skip_why__', '')\n or getattr(testMethod, '__unittest_skip_why__', ''))\n self._addSkip(result, self, skip_why)\n finally:\n result.stopTest(self)\n return\n expecting_failure_method = getattr(testMethod,\n \"__unittest_expecting_failure__\", False)\n expecting_failure_class = getattr(self,\n \"__unittest_expecting_failure__\", False)\n expecting_failure = expecting_failure_class or expecting_failure_method\n outcome = Outcome(result)\n try:\n self._outcome = outcome\n\n with outcome.testPartExecutor(self):\n self.setUp()\n if outcome.success:\n outcome.expecting_failure = expecting_failure\n with outcome.testPartExecutor(self, isTest=True):\n testMethod()\n\n # 当前用例失败时触发on_errors回调\n if not outcome.success:\n with outcome.testPartExecutor(self):\n self.on_errors(outcome.errors)\n\n outcome.expecting_failure = False\n with outcome.testPartExecutor(self):\n self.tearDown()\n\n self.doCleanups()\n for test, reason in outcome.skipped:\n self._addSkip(result, test, reason)\n self._feedErrorsToResult(result, outcome.errors)\n if outcome.success:\n if expecting_failure:\n if outcome.expectedFailure:\n self._addExpectedFailure(result, outcome.expectedFailure)\n else:\n self._addUnexpectedSuccess(result)\n else:\n result.addSuccess(self)\n return result\n finally:\n result.stopTest(self)\n if orig_result is None:\n stopTestRun = getattr(result, 'stopTestRun', None)\n if stopTestRun is not None:\n stopTestRun()\n\n # explicitly break reference cycles:\n # outcome.errors -> frame -> outcome -> outcome.errors\n # outcome.expectedFailure -> frame -> outcome -> outcome.expectedFailure\n del outcome.errors[:] # equivalent to [].clear in py3\n outcome.expectedFailure = None\n\n # clear the outcome, no more needed\n self._outcome = None","def execute_test(self):\n try:\n self._execute()\n except Exception as ex:\n self._fail(exception=ex)\n raise","def run(self, **kwargs):\n try:\n name = kwargs[\"name\"]\n try:\n self.suite = unittest.TestLoader().loadTestsFromName(name)\n except ImportError:\n self.__logger.error(\"Can not import %s\", name)\n return testcase.TestCase.EX_RUN_ERROR\n except KeyError:\n pass\n try:\n assert self.suite\n self.start_time = time.time()\n stream = six.StringIO()\n result = unittest.TextTestRunner(\n stream=stream, verbosity=2).run(self.suite)\n self.__logger.debug(\"\\n\\n%s\", stream.getvalue())\n self.stop_time = time.time()\n self.details = {\n \"testsRun\": result.testsRun,\n \"failures\": len(result.failures),\n \"errors\": len(result.errors),\n \"stream\": stream.getvalue()}\n self.result = 100 * (\n (result.testsRun - (len(result.failures) +\n len(result.errors))) /\n result.testsRun)\n return testcase.TestCase.EX_OK\n except AssertionError:\n self.__logger.error(\"No suite is defined\")\n return testcase.TestCase.EX_RUN_ERROR\n except ZeroDivisionError:\n self.__logger.error(\"No test has been run\")\n return testcase.TestCase.EX_RUN_ERROR","async def run_test_with_values(\n self,\n test_name: str,\n source_entity_name: str,\n\n expected_context_default: 'AttributeContextExpectedValue',\n expected_context_normalized: 'AttributeContextExpectedValue',\n expected_context_reference_only: 'AttributeContextExpectedValue',\n expected_context_structured: 'AttributeContextExpectedValue',\n expected_context_normalized_structured: 'AttributeContextExpectedValue',\n expected_context_reference_only_normalized: 'AttributeContextExpectedValue',\n expected_context_reference_only_structured: 'AttributeContextExpectedValue',\n expected_context_reference_only_normalized_structured: 'AttributeContextExpectedValue',\n\n expected_default: 'List[AttributeExpectedValue]',\n expected_normalized: 'List[AttributeExpectedValue]',\n expected_reference_only: 'List[AttributeExpectedValue]',\n expected_structured: 'List[AttributeExpectedValue]',\n expected_normalized_structured: 'List[AttributeExpectedValue]',\n expected_reference_only_normalized: 'List[AttributeExpectedValue]',\n expected_reference_only_structured: 'List[AttributeExpectedValue]',\n expected_reference_only_normalized_structured: 'List[AttributeExpectedValue]'\n ) -> None:\n try:\n test_input_path = TestHelper.get_input_folder_path(self.tests_subpath, test_name)\n\n corpus = CdmCorpusDefinition()\n corpus.ctx.report_at_level = CdmStatusLevel.WARNING\n corpus.storage.mount('localInput', LocalAdapter(test_input_path))\n corpus.storage.mount('cdm', LocalAdapter(self.schema_docs_path))\n corpus.storage.default_namespace = 'localInput'\n\n src_entity_def = await corpus.fetch_object_async('localInput:/{}.cdm.json/{}'.format(source_entity_name, source_entity_name))\n self.assertTrue(src_entity_def is not None)\n\n res_opt = ResolveOptions(wrt_doc=src_entity_def.in_document)\n\n resolved_entity_def = None # type: CdmEntityDefinition\n output_entity_name = ''\n output_entity_file_name = ''\n entity_file_name = ''\n\n if expected_context_default and expected_default:\n entity_file_name = 'default'\n res_opt.directives = AttributeResolutionDirectiveSet(set())\n output_entity_name = '{}_Resolved_{}'.format(source_entity_name, entity_file_name)\n output_entity_file_name = '{}.cdm.json'.format(output_entity_name)\n resolved_entity_def = await src_entity_def.create_resolved_entity_async(output_entity_name, res_opt)\n self.validate_output_with_values(expected_context_default, expected_default, resolved_entity_def)\n\n if expected_context_normalized and expected_normalized:\n entity_file_name = 'normalized'\n res_opt.directives = AttributeResolutionDirectiveSet(set({'normalized'}))\n output_entity_name = '{}_Resolved_{}'.format(source_entity_name, entity_file_name)\n output_entity_file_name = '{}.cdm.json'.format(output_entity_name)\n resolved_entity_def = await src_entity_def.create_resolved_entity_async(output_entity_name, res_opt)\n self.validate_output_with_values(expected_context_normalized, expected_normalized, resolved_entity_def)\n\n if expected_context_reference_only and expected_reference_only:\n entity_file_name = 'referenceOnly'\n res_opt.directives = AttributeResolutionDirectiveSet(set({'referenceOnly'}))\n output_entity_name = '{}_Resolved_{}'.format(source_entity_name, entity_file_name)\n output_entity_file_name = '{}.cdm.json'.format(output_entity_name)\n resolved_entity_def = await src_entity_def.create_resolved_entity_async(output_entity_name, res_opt)\n self.validate_output_with_values(expected_context_reference_only, expected_reference_only, resolved_entity_def)\n\n if expected_context_structured and expected_structured:\n entity_file_name = 'structured'\n res_opt.directives = AttributeResolutionDirectiveSet(set({'structured'}))\n output_entity_name = '{}_Resolved_{}'.format(source_entity_name, entity_file_name)\n output_entity_file_name = '{}.cdm.json'.format(output_entity_name)\n resolved_entity_def = await src_entity_def.create_resolved_entity_async(output_entity_name, res_opt)\n self.validate_output_with_values(expected_context_structured, expected_structured, resolved_entity_def)\n\n if expected_context_normalized_structured and expected_normalized_structured:\n entity_file_name = 'normalized_structured'\n res_opt.directives = AttributeResolutionDirectiveSet(set({'normalized', 'structured'}))\n output_entity_name = '{}_Resolved_{}'.format(source_entity_name, entity_file_name)\n output_entity_file_name = '{}.cdm.json'.format(output_entity_name)\n resolved_entity_def = await src_entity_def.create_resolved_entity_async(output_entity_name, res_opt)\n self.validate_output_with_values(expected_context_normalized_structured, expected_normalized_structured, resolved_entity_def)\n\n if expected_context_reference_only_normalized and expected_reference_only_normalized:\n entity_file_name = 'referenceOnly_normalized'\n res_opt.directives = AttributeResolutionDirectiveSet(set({'referenceOnly', 'normalized'}))\n output_entity_name = '{}_Resolved_{}'.format(source_entity_name, entity_file_name)\n output_entity_file_name = '{}.cdm.json'.format(output_entity_name)\n resolved_entity_def = await src_entity_def.create_resolved_entity_async(output_entity_name, res_opt)\n self.validate_output_with_values(expected_context_reference_only_normalized, expected_reference_only_normalized, resolved_entity_def)\n\n if expected_context_reference_only_structured and expected_reference_only_structured:\n entity_file_name = 'referenceOnly_structured'\n res_opt.directives = AttributeResolutionDirectiveSet(set({'referenceOnly', 'structured'}))\n output_entity_name = '{}_Resolved_{}'.format(source_entity_name, entity_file_name)\n output_entity_file_name = '{}.cdm.json'.format(output_entity_name)\n resolved_entity_def = await src_entity_def.create_resolved_entity_async(output_entity_name, res_opt)\n self.validate_output_with_values(expected_context_reference_only_structured, expected_reference_only_structured, resolved_entity_def)\n\n if expected_context_reference_only_normalized_structured and expected_reference_only_normalized_structured:\n entity_file_name = 'referenceOnly_normalized_structured'\n res_opt.directives = AttributeResolutionDirectiveSet(set({'referenceOnly', 'normalized', 'structured'}))\n output_entity_name = '{}_Resolved_{}'.format(source_entity_name, entity_file_name)\n output_entity_file_name = '{}.cdm.json'.format(output_entity_name)\n resolved_entity_def = await src_entity_def.create_resolved_entity_async(output_entity_name, res_opt)\n self.validate_output_with_values(expected_context_reference_only_normalized_structured, expected_reference_only_normalized_structured, resolved_entity_def)\n except Exception as e:\n self.fail(e)","def main():\n\n parser = argparse.ArgumentParser(prog=\"run_test.py\",\n formatter_class=argparse.RawTextHelpFormatter)\n parser.add_argument('id', help=\"Id of a test\")\n args = parser.parse_args()\n\n configure_logger()\n\n test_info = TESTS.get(args.id, None)\n if not test_info:\n test_info.log.error(f'{args.id} does not exist')\n exit(ErrorCode.CRITICAL)\n os.environ['DISPLAY'] = \":0.0\"\n\n test = Test(args.id, test_info)\n result = test.run()\n\n test.log.info('#' * 80)\n if not result:\n test.log.error('TEST FAILED')\n else:\n test.log.info('TEST PASSED')\n test.log.info('#' * 80)\n exit(not result)","def test_passed():\n pass","def test(\n context,\n user=get_local_user(),\n remote=False,\n instance=None,\n stack=None,\n app=None,\n coverage=False,\n):\n command = \"pytest\"\n\n if app:\n command = f\"{command} {app}\"\n\n if coverage:\n command = f\"coverage run -m {command}\"\n\n command = f\"run --rm django {command}\"\n run_command(context, user, remote, instance, stack, command)","def run_test(skip_run):\n # First off, we need the 'official' test data downloaded and unpacked\n # if necessary.\n if ref_test_data.install_and_check_test_files() != 0:\n return 1\n if install_inputs() != 0:\n return 1\n\n if not skip_run:\n sys.stdout.write(\"Executing CCC code...\\n\")\n ret = run.main()\n if ret != 0:\n return 1\n\n test_result_dir = os.path.join(ref_test_data.test_data_dir, \"result\")\n sys.stdout.write(\"Comparing CCC results with 'official' results...\\n\")\n ret = compare_results.main([sys.argv[0], \"result\", test_result_dir])\n if ret != 0:\n return 1","def then_app_running_stage(context):\n result = context.result\n result | should.equal('Success').desc(\"Application is reachable in the Stage stage.\")","def run(self, job_result):\n self.logger.info(f\"Running report\")\n job_result.status = JobResultStatusChoices.STATUS_RUNNING\n job_result.save()\n\n try:\n\n for method_name in self.test_methods:\n self.active_test = method_name\n test_method = getattr(self, self.test_methods[method_name])\n test_method()\n\n if self.failed:\n self.logger.warning(\"Report failed\")\n job_result.status = JobResultStatusChoices.STATUS_FAILED\n else:\n self.logger.info(\"Report completed successfully\")\n job_result.status = JobResultStatusChoices.STATUS_COMPLETED\n\n except Exception as e:\n stacktrace = traceback.format_exc()\n self.log_failure(None, f\"An exception occurred: {type(e).__name__}: {e} <pre>{stacktrace}</pre>\")\n logger.error(f\"Exception raised during report execution: {e}\")\n job_result.set_status(JobResultStatusChoices.STATUS_ERRORED)\n\n job_result.data = self._results\n job_result.completed = timezone.now()\n job_result.save()\n\n # Perform any post-run tasks\n self.post_run()","def test(self, args=None, init_args=None, config=None, verbose=False):\n self.ctest(args=args, init_args=init_args, config=config, verbose=verbose)","def test_basic_execution(self):","def set_test_passed(self):\n self.set_result(Status.PASSED)","def sample(self, context: Context) -> T:\n ...","def test_run(self):\n class MockProvider(BaseCoverageProvider):\n SERVICE_NAME = \"I do nothing\"\n was_run = False\n\n def run_once_and_update_timestamp(self):\n \"\"\"Set a variable.\"\"\"\n self.was_run = True\n return None\n\n provider = MockProvider(self._db)\n result = provider.run()\n\n # run_once_and_update_timestamp() was called.\n assert True == provider.was_run\n\n # run() returned a CoverageProviderProgress with basic\n # timing information, since run_once_and_update_timestamp()\n # didn't provide anything.\n assert isinstance(result, CoverageProviderProgress)\n now = utc_now()\n assert result.start < result.finish\n for time in (result.start, result.finish):\n assert (now - time).total_seconds() < 5","def runTest(self):\n self.setUp()\n success = False\n message = ''\n try:\n self.command = [self.test_manager.executable_path]\n self.command.extend(self.test.spirv_args)\n\n process = subprocess.Popen(\n args=self.command,\n stdin=subprocess.PIPE,\n stdout=subprocess.PIPE,\n stderr=subprocess.PIPE,\n cwd=self.directory)\n output = process.communicate(self.stdin_shader)\n test_status = TestStatus(self.test_manager, process.returncode, output[0],\n output[1], self.directory, self.inputs,\n self.file_shaders)\n run_results = [\n getattr(self.test, test_method)(test_status)\n for test_method in get_all_test_methods(self.test.__class__)\n ]\n success, message = zip(*run_results)\n success = all(success)\n message = '\\n'.join(message)\n except Exception as e:\n success = False\n message = str(e)\n self.test_manager.notify_result(\n self, success,\n message + '\\nSTDOUT:\\n%s\\nSTDERR:\\n%s' % (output[0], output[1]))\n self.tearDown()","def run(self):\n\n output_to_pipe(self._pipe_in)\n\n error = self.lib.iperf_run_client(self._test)\n\n if error:\n data = '{\"error\": \"%s\"}' % self._error_to_string(self._errno)\n else:\n data = read_pipe(self._pipe_out)\n\n output_to_screen(self._stdout_fd, self._stderr_fd)\n\n return TestResult(data)","def _handler_test_run_tests(self, *args, **kwargs):\n next_state = None\n result = None\n\n tc_pass = False\n tt_pass = False\n tp_pass = False\n tc_result = None\n tt_result = None\n tp_result = None\n\n test_result = {}\n\n try:\n tc_pass, tc_result = self._do_cmd_resp('tc', timeout=200)\n tt_pass, tt_result = self._do_cmd_resp('tt', timeout=200)\n tp_pass, tp_result = self._do_cmd_resp('tp', timeout=200)\n \n except Exception as e:\n test_result['exception'] = e\n test_result['message'] = 'Error running instrument tests.'\n \n finally:\n test_result['cond_test'] = 'Passed' if tc_pass else 'Failed'\n test_result['cond_data'] = tc_result\n test_result['temp_test'] = 'Passed' if tt_pass else 'Failed'\n test_result['temp_data'] = tt_result\n test_result['pres_test'] = 'Passed' if tp_pass else 'Failed'\n test_result['pres_data'] = tp_result\n test_result['success'] = 'Passed' if (tc_pass and tt_pass and tp_pass) else 'Failed'\n \n self._driver_event(DriverAsyncEvent.TEST_RESULT, test_result)\n next_state = SBE37ProtocolState.COMMAND\n \n return (next_state, result)","def test_execute(self):\n context = dict()\n cmd = pycell.python_cell(\n source='print(2+2)',\n validate=True\n )\n controller = FakeWorkflowController()\n self.backend.execute_async(\n task=TaskHandle(\n task_id='000',\n project_id=self.PROJECT_ID,\n controller=controller\n ),\n command=cmd,\n artifacts=context\n )\n time.sleep(3)\n self.assertEqual(controller.task_id, '000')\n self.assertEqual(controller.state, 'SUCCESS')\n self.assertEqual(controller.outputs.stdout[0].value, '4')","def run(self):\n if self.all:\n cmd = self.apply_options(self.test_all_cmd)\n self.call_and_exit(cmd)\n else:\n cmds = (self.apply_options(self.unit_test_cmd, (\"coverage\",)),)\n if self.coverage:\n cmds += (self.apply_options(self.coverage_cmd),)\n self.call_in_sequence(cmds)","def runTestCase(self):\n \n #Login\n self.login() \n \n #Performing Configure Resources \n ResultCR, statusCR = self.test_configureResourec()\n \n if statusCR:\n self.succeed(\"Configure Resources Step Successfully Completed %s\"%ResultCR)\n \n else:\n self.failure(\"Failed to Configure Resources Step %s\"%ResultCR)\n \n time.sleep(120)","def run(self, context):\n self.start()\n while not context.is_finished():\n self.update_predictions(context)\n new_results, instances = self.evaluate()\n if new_results:\n self.write_results(new_results, instances)\n self.update_plot(new_results, instances)\n\n if self.plot_file:\n self.visualizer.savefig(self.plot_file)\n if self.show_plot:\n self.visualizer.show()\n self.end()","def step_run(environment):\n cmd = \"/bin/sh %(testname)s\" % environment\n return execute(environment, cmd, timeout=10)","def execute(self, context):\n result = Result(result='success')\n\n for tag in self._tags:\n if isinstance(self._tags, (dict, OrderedDict)):\n # we have a dictionary of items.\n LOGGER.debug(\"Setting environment variables for tag.\")\n for key, value in self._tags[tag].items():\n if context:\n value = render(value, **context.variables.last())\n else: # noqa: no-cover\n raise RuntimeError(\"Context is required.\")\n\n LOGGER.debug(\"Setting '%s' to '%s', in the system environment.\", key, value)\n os.putenv(key, value)\n os.environ[key] = value\n\n with matrix_scoped_variables(context, tag):\n matrix_list = []\n matrix_tags = []\n if context and len(context.matrix_tags) > 0:\n matrix_list = context.variables.last()[\n 'matrix_list'] if 'matrix_list' in context.variables.last() else []\n matrix_tags = context.matrix_tags\n\n if len(matrix_tags) > 0 and not all(\n [fnmatch.fnmatch(x[1], x[0]) for x in zip(matrix_tags, matrix_list)]):\n LOGGER.debug(\"Skipping because this matrix item does not have a user-selected matrix tag.\")\n LOGGER.debug(\"matrix_list=%r matrix_tags=%r\", matrix_list, matrix_tags)\n result = Result(result='skipped')\n else:\n LOGGER.debug('Beginning matrix entry: %s', tag)\n result = self._execute_tasks(context)\n LOGGER.debug('Completed matrix entry: %s', tag)\n\n if isinstance(self._tags, (dict, OrderedDict)):\n # we have a dictionary of items.\n LOGGER.debug(\"Unsetting environment variables for tag.\")\n for key, _ in self._tags[tag].items():\n try:\n os.unsetenv(key)\n except AttributeError: # noqa: no-cover\n pass # noqa: no-cover\n del os.environ[key]\n\n if not result:\n break\n\n if result['result'] in ['skipped', 'continue']:\n result = Result(result='success')\n\n return result","def execute(self):\n\n self._status = 'Running'\n\n try:\n self._init_staf_handle()\n self._prep_vm()\n self._ping()\n self._install_bespoke()\n sleep(self._post_wait)\n self._status = 'Pass'\n except CoreError as e:\n self._status = 'Fatal'\n self._message = e.msg\n finally:\n self._close_staf_handle()\n\n #Notify TestCase that a failure occurred.\n if self._status == 'Fatal': raise FatalError(self._message)","def test(self, dataset):\n\n outputs, errors = self.use(dataset)\n\n ## PUT CODE HERE ##\n # I put the code in the \"use\" function, seems better :-)\n\n return outputs, errors","def process_ResultCheck(self):\n try:\n cmd = self.ExecutionTask.get_param().split(',')\n logging.debug(\"%s-%s-%s-%s-%s\" % ( TestScriptSymbolTable.get_value_from_sym_tab(cmd[0], TestScriptSymbolTable.test_script_sym_tab),cmd[0], cmd[1], cmd[2], cmd[3]))\n\n checkval = cmd[0].split('!') \n \n cval = TestScriptSymbolTable.get_value_from_sym_tab(checkval[1], TestScriptSymbolTable.capi_cmd_ret_sym_tab)\n\n if int(cval) >= int(cmd[1]):\n result = cmd[2]\n else:\n result = cmd[3]\n\n logging.info(\"\\nRESULT CHECK---> %15s\", result) \n self.setTestResult(result)\n \n #if result == 'FAIL':\n if 'FAIL' in result:\n self.setStatus('STOP')\n self.setTestResult('FAIL')\n except OSError:\n logging.info(\"\\nException - ResultCheck\")","def evaluate(self, test_data):\n result = self.model.run(test_data)\n self._save_result(result)","def run(self):\n list_test_scenarios = self.__get_list_scenarios_in_folder()\n\n if not list_test_scenarios:\n utils.print_error(\n \"\\n{}\\n\".format(constant.ERR_CANNOT_FIND_ANY_TEST_SCENARIOS))\n exit(1)\n\n (tests_pass, tests_fail) = self.__execute_tests(list_test_scenarios)\n\n complete_message = constant.INFO_TEST_PASS_FAIL.format(\n tests_pass, tests_fail)\n\n print(complete_message)\n\n self.__execute_reporter()","def execute(self):\n\n self._status = 'Running'\n\n self._checkout_resources()\n\n for test in self._tests:\n try:\n self._update_resource_timeouts(test.timeout)\n test.execute()\n except Failure as e:\n self._status = 'Fail'\n self._message = ('The \"{0}\" test in the test case \"{1}\" failed with the '\n 'message: \"{2}\"'.format(test.name, self.name, e.msg))\n except FatalError as e:\n self._checkin_resources()\n self._status = 'Fatal'\n self._message = ('The \"{0}\" test in the test case \"{1}\" encountered the fatal '\n 'error: \"{2}\"'.format(test.name, self.name, e.msg))\n raise FatalError(self._message)\n\n if self._status == 'Fail':\n self._checkin_resources()\n raise Failure(self._message)\n\n self._status = 'Pass'\n self._checkin_resources()","def run(self, result=None):\n method = getattr(self, self._testMethodName)\n\n if getattr(method, 'multi_graph', False) and not getattr(self, 'graphname', False):\n for graph in self.test_graphs:\n self.graphname = graph\n self.run(result=result)\n self.graphname = None\n else:\n super(BaseRexProTestCase, self).run(result=result)","def _proceed(self, result, testfunc, args=(), kwargs=None):\n kwargs = kwargs or {}\n try:\n testfunc(*args, **kwargs)\n except self.failureException:\n result.addFailure(self, self.__exc_info())\n return 1\n except KeyboardInterrupt:\n raise\n except InnerTestSkipped as e:\n result.addSkip(self, e)\n return 1\n except SkipTest as e:\n result.addSkip(self, e)\n return 0\n except:\n result.addError(self, self.__exc_info())\n return 2\n return 0","def execute(self, activity, context):\n\n raise NotImplementedError","def test_run(self):\n sut = ExperimentEmail()\n train = os.path.join(os.path.dirname(__file__), \"data\", \"sample.csv\")\n val = os.path.join(os.path.dirname(__file__), \"data\", \"sample.csv\")\n outdir = tempfile.mkdtemp()\n\n # Act\n sut.run(train, val, outdir, batch_size=32, epochs=2)","def __call__(self, result=None):\n self._pre_setup()\n super(TestCase, self).__call__(result)\n self._post_tearDown()","def run_tests(self, test_labels):\n import pytest\n\n argv = []\n if self.verbosity == 0:\n argv.append('--quiet')\n if self.verbosity == 2:\n argv.append('--verbose')\n if self.verbosity == 3:\n argv.append('-vv')\n if self.failfast:\n argv.append('--exitfirst')\n if self.keepdb:\n argv.append('--reuse-db')\n\n argv.extend(test_labels)\n return pytest.main(argv)","def __call__(self):\n\n if 'timed out' in self.result:\n self.code = 3\n\n elif not self.is_pov and self.polls_failed > 0:\n self.code = 6\n codes_error[self.code] = codes_error[self.code] + str(self.polls_failed) + \"\\n\"\n\n elif 'not ok - pov did not negotiate' in self.result:\n self.code = 2\n\n # If the test failed to run, consider it failed\n elif 'TOTAL TESTS' not in self.result:\n self.code = 1\n\n elif 'TOTAL TESTS: ' in self.result:\n # Parse out results\n self.code = 0\n self.total = int(self.result.split('TOTAL TESTS: ')[1].split('\\n')[0])\n self.passed = int(self.result.split('TOTAL PASSED: ')[1].split('\\n')[0])\n\n if self.is_pov:\n self.passed ^= 1\n\n if self.is_sig() and self.passed:\n self.passed = 0\n\n if 'should have cored, and did not' in self.result:\n self.code = 7\n else:\n self.code = 5\n\n codes_error[self.code] = codes_error[self.code] + str(self.sig) + \"\\n\"\n\n elif self.is_sig():\n if self.passed:\n self.passed = 0\n\n self.code = 5\n codes_error[self.code] = codes_error[self.code] + str(self.sig) + \"\\n\"\n\n else:\n self.code = 4\n\n self.error = codes_error[self.code]","def testing(self):\n print('test successful')","def test_perform_action(self):\n\n # Prepare.\n app = self.factory()\n request = self.getRequest(app)\n context = model.factory()\n\n # Create a dummy event and get it back.\n event_id = boilerplate.createEvent(context)\n event = repo.LookupActivityEvent()(event_id)\n\n # Check we're allowed.\n state_changer = request.state_changer\n with transaction.manager:\n bm.Session.add(context)\n self.assertTrue(state_changer.can_perform(context, a.START))\n context_id = context.id\n\n # Perform the action.\n # We have to use a transaction manager because perform creates\n # a new event on state change.\n context = model.Model.query.get(context_id)\n with transaction.manager:\n bm.Session.add(event)\n bm.Session.add(context)\n _ = state_changer.perform(context, a.START, event)\n status = context.work_status.value\n\n # The context is now in the configured state.\n self.assertEqual(status, s.STARTED)","def execute(self, context, obj):\n \n # if there are results, then processthem, otherwise stop the machine\n if obj.get(CONTINUATION_RESULTS_KEY):\n \n entities, data = obj[CONTINUATION_RESULTS_KEY], []\n \n # process each entity in turn\n for entity in entities:\n \n # extracting entity specific data\n csvData = self.generateCsvData(context, obj, entity)\n if csvData:\n data.append(csvData)\n \n if data:\n context[DATA_PARAM] = data\n \n # and then extracting data from a batch of entities (ie. sums, averages etc.)\n aggData = self.generateAggregatedCsvData(context, obj, entities)\n if aggData:\n context[AGG_DATA_PARAM] = aggData\n \n # this stores the total number of entities processed by this Task\n context[NUM_ENTITIES_PARAM] = len(entities)\n \n # keep going in all cases, even when the continuation is done\n return OK_EVENT","def execute(self):\n\n self._status = 'Running'\n\n try:\n self._init_staf_handle()\n self._ping()\n\n if self._sut.os == 'Linux':\n self._linux_power_control()\n elif self._sut.os == 'Windows':\n self._windows_power_control()\n else:\n raise CoreError(\"Unknown OS platform: {0}\".format(self._sut.os))\n\n if self._wait:\n sleep(BespokeGlobals.VM_BOOT_WAIT)\n\n self._status = 'Pass'\n except CoreError as e:\n self._status = 'Fatal'\n self._message = e.msg\n finally:\n self._close_staf_handle()\n\n #Notify TestCase that a failure occurred.\n if self._status == 'Fatal': raise FatalError(self._message)","def setup(self,context,result):\n pass","def execute(self, context):\n redshift_hook = PostgresHook(postgres_conn_id=self.redshift_conn_id)\n\n for data_check in self.data_checks:\n records = redshift_hook.get_records(data_check[\"sql\"])\n num_records = records[0][0]\n\n assert (num_records != data_check[\"expected_result\"]) , \\\n f\"Data quality test returned unexpected result.\"\n\n self.log.info('DataQualityOperator not implemented yet')","def end_test(self, name: str, attributes: dict) -> None:\n test_case_key = tm4j_config_helpers.get_tm4j_test_case_key(attributes)\n\n tm4j_api.configure_tm4j_api(api_access_key=self.tm4j_access_key, project_key=self.tm4j_project_key)\n\n try:\n if not self.tm4j_test_cycle_key:\n self.tm4j_test_cycle_key = tm4j_test_cycle_helpers.get_tm4j_test_cycle_key(\n parallel_execution=self.tm4j_parallel_execution_support,\n test_cycle_key_file_path=self.tm4j_shared_test_cycle_key_file_path,\n tm4j_test_cycle_name=self.tm4j_test_cycle_name\n )\n\n tm4j_api.create_test_execution_result(\n test_cycle_key=self.tm4j_test_cycle_key,\n test_case_key=test_case_key,\n execution_status=attributes[\"status\"],\n actual_end_date=datetime.strptime(attributes[\"endtime\"], \"%Y%m%d %H:%M:%S.%f\").strftime(\n \"%Y-%m-%dT%H:%M:%SZ\"\n ),\n execution_time=attributes[\"elapsedtime\"],\n comment=attributes[\"message\"],\n )\n except tm4j_response_exceptions.TM4JResponseException as e:\n print(f\"Sorry, test execution reporting for {test_case_key} didn't go well because of: {e.message}\")\n\n return None","async def test_successful_samples(self):\n self.set_source_parameter(\"test_result\", [\"success\"])\n response = await self.collect(get_request_json_return_value=self.JMETER_JSON)\n self.assert_measurement(response, value=\"242\", entities=[])","def run(ceph_cluster, **kw):\n log.info(run.__doc__)\n config = kw[\"config\"]\n test_pass = 0\n run_iterations = config[\"run_iteration\"]\n min_pass = 1\n\n log.info(f\"Test is configured to run {run_iterations} iterations\")\n log.info(\n f\"Due to unpredictability of ceph osd df stats,\"\n f\" and varying changes observed in stats of other OSDs and nodes, \"\n f\" it is difficult to verify the changes with a fixed degree of allowed deviation, \"\n f\" therefore, the test is expected to pass at least 70% of time.\"\n f\" \\n Minimum number of pass runs: {min_pass}\"\n )\n for i in range(run_iterations):\n log.info(f\"--------- Test Iteration: {i+1} -----------\")\n test_result = run_test(ceph_cluster, **kw)\n if not test_result:\n test_pass += 1\n break\n\n summary_text = (\n f\"Test could only pass {test_pass} out of {run_iterations} of times,\"\n f\" expected min pass: {min_pass}\"\n )\n if test_pass < min_pass:\n log.error(summary_text)\n return 1\n log.info(summary_text)\n return 0","def do_test(self):\n\t\tshutit_global.shutit_global_object.yield_to_draw()\n\t\tif not self.build['dotest']:\n\t\t\tself.log('Tests configured off, not running',level=logging.DEBUG)\n\t\t\treturn\n\t\t# Test in reverse order\n\t\tself.log('PHASE: test', level=logging.DEBUG)\n\t\tself.stop_all()\n\t\tself.start_all()\n\t\tfor module_id in self.module_ids(rev=True):\n\t\t\t# Only test if it's installed.\n\t\t\tif self.is_installed(self.shutit_map[module_id]):\n\t\t\t\tself.log('RUNNING TEST ON: ' + module_id, level=logging.DEBUG)\n\t\t\t\tself.login(prompt_prefix=module_id,command=shutit_global.shutit_global_object.bash_startup_command,echo=False)\n\t\t\t\tif not self.shutit_map[module_id].test(self):\n\t\t\t\t\tself.fail(module_id + ' failed on test', shutit_pexpect_child=self.get_shutit_pexpect_session_from_id('target_child').pexpect_child) # pragma: no cover\n\t\t\t\tself.logout(echo=False)","def test_run_process(self):\n workflow = self.get_workflow(\n \"\"\"file://result <- file://source\n echo result > result\n \"\"\")\n process = workflow._processes[0]\n create_tuttle_dirs()\n workflow.run_process(process)\n assert path.isfile(\"result\")","def test_run(make_runner: Callable[..., TargetFunctionRunner]) -> None:\n runner = make_runner(target, use_instances=True)\n run_info = TrialInfo(config=2, instance=\"test\", seed=0, budget=0.0)\n\n # submit runs! then get the value\n runner.submit_trial(run_info)\n result = next(runner.iter_results(), None)\n\n assert result is not None\n\n run_info, run_value = result\n\n assert run_value.cost == 4\n assert run_value.status == StatusType.SUCCESS","def execute(self):\n\n self._status = 'Running'\n\n for test_case in self._test_cases.values():\n try:\n test_case.execute()\n except Failure as e:\n self._status = 'Fail'\n self._message = ('The \"{0}\" test case in the test plan \"{1}\" failed with the '\n 'message: \"{2}\"'.format(test_case.name, self.name, e.msg))\n except FatalError as e:\n self._status = 'Fail'\n self._message = ('The \"{0}\" test case in the test plan \"{1}\" encountered the fatal '\n 'error: \"{2}\"'.format(test_case.name, self.name, e.msg))\n raise FatalError(self._message)\n\n if self._status == 'Fail':\n raise Failure(self._message)\n\n self._status = 'Pass'","def execute (self, context):\n # call the main import function. This function should work\n # independent of this context-manager/operator logic.\n category = context.scene.dsf_category\n filepath = self.properties.filepath\n (libdir, srpath) = self.split_scene_filepath (filepath)\n drpath = self.construct_data_path (srpath, category)\n data_rpath = os.path.join (\"/\", drpath)\n scene_rpath = os.path.join (\"/\", srpath)\n log.info (\"libdir: %s\", libdir)\n log.info (\"scene_rpath: %s\", scene_rpath)\n log.info (\"data_rpath: %s\", data_rpath)\n scale = context.scene.dsf_scale\n bpy.ops.dsf.export_props (scene_path = scene_rpath,\\\n data_path = data_rpath, base_dir = libdir, scale = scale)\n return { 'FINISHED' }","def runTest(self):\n try:\n print('TestCase runTest===>')\n self.test_runner.run_test(self.testcase_dict)\n\n finally:\n self.meta_data = getattr(self.test_runner.http_client_session, \"meta_data\", {})","def test_job_pass(self):\n with self.settings(JOBS_ROOT=os.path.join(settings.BASE_DIR, \"extras/tests/dummy_jobs\")):\n\n module = \"test_pass\"\n name = \"TestPass\"\n job_class = get_job(f\"local/{module}/{name}\")\n\n job_result = JobResult.objects.create(\n name=job_class.class_path,\n obj_type=self.job_content_type,\n user=None,\n job_id=uuid.uuid4(),\n )\n\n run_job(data={}, request=None, commit=False, job_result_pk=job_result.pk)\n job_result.refresh_from_db()\n self.assertEqual(job_result.status, JobResultStatusChoices.STATUS_COMPLETED)","def run_test_tool(self, cmd, expected_status=0 ):\n\n status, output = self.target.run(cmd)\n self.assertEqual(status, expected_status, msg='\\n'.join([cmd, output]))"],"string":"[\n \"def run(self, context):\\n TestStepBase.run(self, context)\\n\\n if self._device is None:\\n self._raise_config_exception(\\\"%s is not specified in the bench configuration\\\" % self._pars.device,\\n AcsConfigException.INVALID_BENCH_CONFIG)\\n\\n # Insert log into device logs as info \\\"i\\\"\\n self._device.inject_device_log(\\\"i\\\", \\\"ACS_TESTSTEP\\\", \\\"RUNTESTSTEP - %s\\\" % self._pars.id)\",\n \"def test_execute_with_context(self):\\n pass\",\n \"def run(context, path=\\\"\\\"):\\n common.success(f\\\"Tests {path} running \\\")\\n return start.run_python(\\n context,\\n f\\\"-m pytest {path}\\\"\\n )\",\n \"def Run(self, context, result):\\n\\n # check if the test can be run on the current platform\\n if not self.SupportedPlatform(context, result):\\n return\\n\\n # If the context specifies a shell, use it.\\n if context.has_key(\\\"ShellCommandTest.command_shell\\\"):\\n # Split the context value to build the argument list.\\n shell = qm.common.split_argument_list(\\n context[\\\"ShellCommandTest.command_shell\\\"])\\n else:\\n # Otherwise, use a platform-specific default.\\n shell = qm.platform.get_shell_for_command()\\n\\n # Environment for the nightlies (user lcgnight)\\n if sys.platform.startswith(\\\"win\\\") \\\\\\n and \\\"USERNAME\\\" in os.environ \\\\\\n and os.environ[\\\"USERNAME\\\"] == \\\"lcgnight\\\" :\\n # Take TNS_ADMIN from DFS on Win (bug #86450)\\n os.environ[\\\"TNS_ADMIN\\\"]=r\\\"\\\\\\\\cern.ch\\\\dfs\\\\Applications\\\\Oracle\\\\ADMIN\\\"\\n # Use credentials from %USERPROFILE%\\\\private on Win (bug #86463)\\n private = os.path.join(os.environ[\\\"USERPROFILE\\\"],\\\"private\\\")\\n os.environ[\\\"CORAL_AUTH_PATH\\\"] = private\\n os.environ[\\\"CORAL_DBLOOKUP_PATH\\\"] = os.environ[\\\"CORAL_AUTH_PATH\\\"]\\n elif sys.platform.startswith(\\\"darwin\\\") \\\\\\n and \\\"USER\\\" in os.environ \\\\\\n and os.environ[\\\"USER\\\"] == \\\"lcgnight\\\" :\\n # Use credentials from $HOME/private on Mac (bug #86463)\\n private = os.path.join(os.environ[\\\"HOME\\\"],\\\"private\\\")\\n os.environ[\\\"CORAL_AUTH_PATH\\\"] = private\\n os.environ[\\\"CORAL_DBLOOKUP_PATH\\\"] = os.environ[\\\"CORAL_AUTH_PATH\\\"]\\n elif sys.platform.startswith(\\\"linux\\\") \\\\\\n and \\\"USER\\\" in os.environ \\\\\\n and os.environ[\\\"USER\\\"] == \\\"lcgnight\\\" :\\n # Use credentials from POOL AFS area on Linux (bug #86463)\\n os.environ[\\\"CORAL_AUTH_PATH\\\"] = \\\"/afs/cern.ch/sw/lcg/app/pool/db\\\"\\n os.environ[\\\"CORAL_DBLOOKUP_PATH\\\"] = os.environ[\\\"CORAL_AUTH_PATH\\\"]\\n\\n # Special handling if this is a python script (from COOLTests.py)\\n if sys.platform.startswith(\\\"win\\\") and \\\\\\n self.command.split()[0].endswith(\\\".py\\\"):\\n # We need to run a python test and not a standard executable\\n [cmd, arg] = self.command.split(' ',1)\\n for d in os.environ[\\\"PATH\\\"].split(os.path.pathsep):\\n if os.path.isdir(d) and cmd in os.listdir(d):\\n # we found the script in the path\\n self.command = 'python \\\"%s\\\" %s'%(os.path.join(d,cmd),arg)\\n break\\n\\n # Append the command at the end of the argument list.\\n arguments = shell + [ self.command ]\\n self.RunProgram(arguments[0], arguments, context, result)\",\n \"def test(ctx):\\n click.echo('testing')\\n ctx.test()\\n click.echo('done')\",\n \"def run(self, result=None):\\n\\n with self.env_wrap():\\n super(RelengToolTestCase, self).run(result)\",\n \"def execute(self, context: Any) -> Any:\\n pass\",\n \"def run( self, test ):\\n\\n result = self._makeResult()\\n test( result )\\n result.printErrors()\\n self.stream.writeln( result.separator2 )\\n run = result.testsRun\\n self.stream.writeln()\\n\\n if not result.wasSuccessful():\\n self.stream.write( \\\"FAILED (\\\" )\\n failed, errored = map( len, ( result.failures, result.errors ) )\\n if failed:\\n self.stream.write( \\\"failures=%d\\\" % failed )\\n if errored:\\n if failed: self.stream.write( \\\", \\\" )\\n self.stream.write( \\\"errors=%d\\\" % errored )\\n self.stream.writeln( \\\")\\\" )\\n else:\\n self.stream.writeln( \\\"OK\\\" )\\n \\n return result\",\n \"def run_tests(event, context):\\n try:\\n jobId = event['CodePipeline.job']['id']\\n user_parameters = json.loads(event['CodePipeline.job']['data']['actionConfiguration']['configuration']['UserParameters'])\\n runscope_trigger_url = user_parameters['runscopeTriggerUrl']\\n runscope_access_token = user_parameters['runscopeAccessToken']\\n\\n tests = start_tests(runscope_trigger_url)\\n aggregate_status = wait_for_tests_to_complete(tests, runscope_access_token)\\n if aggregate_status == \\\"pass\\\":\\n code_pipeline.put_job_success_result(jobId=jobId)\\n else:\\n code_pipeline.put_job_failure_result(jobId=jobId, failureDetails={\\n 'type': 'JobFailed',\\n 'message': 'One or more tests failed'\\n })\\n except:\\n code_pipeline.put_job_failure_result(jobId=jobId, failureDetails={\\n 'type': 'JobFailed',\\n 'message': 'Unhandled exception during Runscope tests execution'\\n })\",\n \"def execute(self):\\n\\n self._status = 'Running'\\n\\n try:\\n self._init_staf_handle()\\n self._setup_results()\\n self._stage_test_step()\\n self._execute_test_step()\\n sleep(self._post_wait)\\n self._get_remote_results()\\n self._status = 'Pass'\\n except CoreError as e:\\n self._status = 'Fail'\\n self._message = e.msg\\n finally:\\n self._close_staf_handle()\\n\\n #Notify TestCase that a failure occurred.\\n if self._status == 'Fail': raise Failure(self._message)\",\n \"def test(self):\\n return self._test(result_count=1, failure_amount=1)\",\n \"def test(self):\\n return self._test(result_count=1, failure_amount=1)\",\n \"def tests(context):\\n black(context)\\n isort(context)\\n flake8(context)\\n pylint(context)\\n yamllint(context)\\n pydocstyle(context)\\n bandit(context)\\n pytest(context)\\n\\n print(\\\"All tests have passed!\\\")\",\n \"def excecute(self):\\r\\n self.initialize()\\r\\n self.addteststeps()\\r\\n for teststep in self.test_steps_list:\\r\\n if teststep.run() == TestStatus.PASS:\\r\\n logging.info(\\\"test {} passed the test\\\".format(teststep.stepname))\\r\\n self.result = TestStatus.PASS\\r\\n else:\\r\\n logging.warn(\\\"test {} failed the test\\\".format(teststep.stepname))\\r\\n self.result = TestStatus.FAIL\\r\\n self.cleanup()\\r\\n return self.result\",\n \"def run(self, context):\\n TestStepBase.run(self, context)\\n\\n # Looks for the key in the context, raise an error if it doesn't exist\\n info = context.get_info(self._key)\\n if info is None:\\n return_message = \\\"%s is not found in the context\\\" % self._key\\n self._logger.error(return_message)\\n raise AcsConfigException(AcsConfigException.OPERATION_FAILED, return_message)\",\n \"def check_result(context, expected):\\n assert context.result == expected, \\\"Wrong result: {r} != {e}\\\".format(\\n r=context.result, e=expected\\n )\",\n \"def __call__(self, example):\\n return self.test(example)\",\n \"def run_quick_test(self, context: ResourceCommandContext, test: str) -> None:\\n self.handler.run_quick_test(context, test)\",\n \"def when_query_pipeline(context):\\n result = context.stage.runTest('testing stage endpoint')\\n print('Result = {}'.format(result))\\n context.result = result\",\n \"def execute(self, args=None):\\n parser = argparse.ArgumentParser(description='run tests on jupyter notebook')\\n\\n parser.add_argument('notebook', type=str, help='the jupyter notebook to test')\\n parser.add_argument('-t', '--target', type=str, metavar='', help='where to store results')\\n parser.add_argument('-c', '--context', type=str, metavar='', help='context directory')\\n parser.add_argument('-v', '--verbose', action='count', default=0, help='verbosity level')\\n\\n args = parser.parse_args(args)\\n\\n logger.setLevel(loglevel(args.verbose))\\n logger.debug(f'args: {args}')\\n\\n results = self._grade_notebook(\\n Path(args.notebook).absolute(),\\n target_dir=Path(args.target).absolute() if args.target else None,\\n context=Path(args.context).absolute() if args.context else None\\n )\\n\\n return results.summary().failed\",\n \"def execute(self, driver, context):\\n raise NotImplementedError\",\n \"def __call__(self, result = None):\\r\\n if result != None:\\r\\n self.result = result\\r\\n else:\\r\\n self.result = core.FW_conf['connection'].getResult()\\r\\n assert self.result, 'Internal error: test result is missing from Phone Manager instance!'\\r\\n\\r\\n core.FW_conf['connection'].currentTcId = self.tcId\\r\\n for remote in core.FW_conf['remote_connection']:\\r\\n remote.setResult(self.result)\\r\\n remote.currentTcId = self.tcId\\r\\n\\r\\n self.result.startTest(self)\\r\\n\\r\\n # add comment to test report whether we are in WhiteBox/HW Assisted WhiteBox mode\\r\\n self.comment('Executing test case in %s mode' % (core.FW_conf['blackbox_enabled'] and 'HW Assisted WhiteBox' or 'WhiteBox'))\\r\\n\\r\\n # add test case information for bltue report\\r\\n self.addBltueTestCase(self.tcId)\\r\\n\\r\\n try:\\r\\n # Code inside the next try-except -block must not raise exceptions.\\r\\n # All exception handling must be done within the logApply method.\\r\\n try:\\r\\n # return if test execution should be stopped\\r\\n if core.FW_conf['should_stop']:\\r\\n return\\r\\n\\r\\n # execute _setUp\\r\\n self.comment('%s MARBLE SETUP %s' % ((15 * '='), (15 * '=')))\\r\\n try:\\r\\n self._inSetUp = True\\r\\n resp = self.logApply(self._setUp)\\r\\n finally:\\r\\n self._inSetUp = False\\r\\n\\r\\n # return if test execution should be stopped\\r\\n if core.FW_conf['should_stop']:\\r\\n return\\r\\n\\r\\n if resp == False or self.result.wasSkipped():\\r\\n # execute _tearDown\\r\\n self.comment('%s MARBLE TEARDOWN %s' % ((15 * '='), (15 * '=')))\\r\\n try:\\r\\n self._inTearDown = True\\r\\n resp = self.logApply(self._tearDown)\\r\\n finally:\\r\\n self._inTearDown = False\\r\\n\\r\\n # return because setup failed\\r\\n return\\r\\n\\r\\n # return if test execution should be stopped\\r\\n if core.FW_conf['should_stop']:\\r\\n return\\r\\n\\r\\n # execute possible test case's setUp\\r\\n if 'setUp' in dir(self):\\r\\n self.comment('%s TEST CASE SETUP %s' % ((15 * '='), (15 * '=')))\\r\\n try:\\r\\n self._inTestCaseSetUp = True\\r\\n resp = self.logApply(self.setUp)\\r\\n finally:\\r\\n self._inTestCaseSetUp = False\\r\\n\\r\\n # return if test execution should be stopped\\r\\n if core.FW_conf['should_stop']:\\r\\n return\\r\\n\\r\\n if resp == False or self.result.wasSkipped():\\r\\n # test case setup failed\\r\\n\\r\\n # execute _tearDown\\r\\n self.comment('%s MARBLE TEARDOWN %s' % ((15 * '='), (15 * '=')))\\r\\n try:\\r\\n self._inTearDown = True\\r\\n resp = self.logApply(self._tearDown)\\r\\n finally:\\r\\n self._inTearDown = False\\r\\n\\r\\n # return if test execution should be stopped\\r\\n if core.FW_conf['should_stop']:\\r\\n return\\r\\n\\r\\n # execute _postTearDown\\r\\n try:\\r\\n self._inPostTearDown = True\\r\\n resp = self.logApply(self._postTearDown)\\r\\n finally:\\r\\n self._inPostTearDown = False\\r\\n\\r\\n # return if test execution should be stopped\\r\\n if core.FW_conf['should_stop']:\\r\\n return\\r\\n\\r\\n # execute possible test case's tearDown\\r\\n if 'tearDown' in dir(self):\\r\\n self.comment('%s TEST CASE TEARDOWN %s' % ((15 * '='), (15 * '=')))\\r\\n try:\\r\\n self._inTestCaseTearDown = True\\r\\n resp = self.logApply(self.tearDown)\\r\\n finally:\\r\\n self._inTestCaseTearDown = False\\r\\n\\r\\n if resp == False:\\r\\n self._warn('Failure during test case teardown!')\\r\\n\\r\\n # return because test case setup failed\\r\\n return\\r\\n\\r\\n # return if test execution should be stopped\\r\\n if core.FW_conf['should_stop']:\\r\\n return\\r\\n\\r\\n # execute _postSetUp\\r\\n try:\\r\\n self._inPostSetUp = True\\r\\n resp = self.logApply(self._postSetUp)\\r\\n finally:\\r\\n self._inPostSetUp = False\\r\\n\\r\\n # return if post setup fails\\r\\n if resp == False:\\r\\n self._warn('Failure during post setup!')\\r\\n return\\r\\n\\r\\n # return if test execution should be stopped\\r\\n if core.FW_conf['should_stop']:\\r\\n return\\r\\n\\r\\n # execute the test method\\r\\n testMethod = getattr(self, self.testMethodName)\\r\\n self.comment('%s TEST CASE %s' % ((15 * '='), (15 * '=')))\\r\\n try:\\r\\n self._inTestCase = True\\r\\n resp = self.logApply(testMethod)\\r\\n finally:\\r\\n self._inTestCase = False\\r\\n\\r\\n ok = resp != False\\r\\n\\r\\n # return if test execution should be stopped\\r\\n if core.FW_conf['should_stop']:\\r\\n return\\r\\n\\r\\n # execute _tearDown\\r\\n self.comment('%s MARBLE TEARDOWN %s' % ((15 * '='), (15 * '=')))\\r\\n try:\\r\\n self._inTearDown = True\\r\\n resp = self.logApply(self._tearDown)\\r\\n finally:\\r\\n self._inTearDown = False\\r\\n\\r\\n if ok: ok = resp != False\\r\\n\\r\\n # return if test execution should be stopped\\r\\n if core.FW_conf['should_stop']:\\r\\n return\\r\\n\\r\\n # execute possible test case's tearDown\\r\\n if 'tearDown' in dir(self):\\r\\n self.comment('%s TEST CASE TEARDOWN %s' % ((15 * '='), (15 * '=')))\\r\\n try:\\r\\n self._inTestCaseTearDown = True\\r\\n resp = self.logApply(self.tearDown)\\r\\n finally:\\r\\n self._inTestCaseTearDown = False\\r\\n\\r\\n if resp == False:\\r\\n self._warn('Failure during test case teardown!')\\r\\n\\r\\n # return if test execution should be stopped\\r\\n if core.FW_conf['should_stop']:\\r\\n return\\r\\n\\r\\n # execute _postTearDown\\r\\n try:\\r\\n self._inPostTearDown = True\\r\\n resp = self.logApply(self._postTearDown)\\r\\n finally:\\r\\n self._inPostTearDown = False\\r\\n\\r\\n if ok: ok = resp != False\\r\\n\\r\\n # return if test execution should be stopped\\r\\n if core.FW_conf['should_stop']:\\r\\n return\\r\\n\\r\\n # Success!\\r\\n if not self.result.getError():\\r\\n if ok:\\r\\n self.result.addSuccess(self)\\r\\n if core.FW_conf['delete_success_blx'] and core.FW_conf['tracing_enabled'] and not core.FW_conf['memory_leak_detection']:\\r\\n #Delete successfull testcase blx if user doesn't need those\\r\\n core.FW_conf['trace'].deleteBlx = True\\r\\n except SystemExit:\\r\\n # put self._raiseSystemExit flag to True so that SystemExit\\r\\n # can be raised in finally:\\r\\n self._raiseSystemExit = True\\r\\n except Exception, e:\\r\\n # This is usually caused by an internal error, but could also\\r\\n # be a KeyboardInterrupt. In either case, stop testing is\\r\\n # needed here.\\r\\n self.logApply(self.stop, e)\\r\\n finally:\\r\\n # raise SystemExit if needed\\r\\n if self._raiseSystemExit:\\r\\n raise\\r\\n\\r\\n for phone in core.FW_conf['connection']:\\r\\n if phone._createBugReport:\\r\\n phone.createBugReport()\\r\\n\\r\\n if core.FW_conf['settings'].TestRun.TakeHeapDumps:\\r\\n core.FW_conf['connection'].takeHeapDumps()\\r\\n\\r\\n self._stopTest()\",\n \"def test(ctx):\\n pass\",\n \"def runtest(self):\",\n \"def execute(self):\\n\\n self._status = 'Running'\\n\\n try:\\n self._init_staf_handle()\\n self._setup_results()\\n self._stage()\\n self._install()\\n self._get_remote_results()\\n self._status = 'Pass'\\n except CoreError as e:\\n self._status = 'Fatal'\\n self._message = e.msg\\n finally:\\n self._close_staf_handle()\\n\\n #Notify TestCase that a failure occurred.\\n if self._status == 'Fatal': raise FatalError(self._message)\",\n \"def run(self, test, env):\\n\\n raise NotImplementedError\",\n \"def execute(self, context: dict):\\n conf = copy.deepcopy(context[\\\"dag_run\\\"].conf)\\n logger.debug(f\\\"Got conf {conf}.\\\")\\n execution_context = conf[\\\"execution_context\\\"]\\n if \\\"Payload\\\" in execution_context:\\n payload_context = Context.populate(execution_context)\\n else:\\n payload_context = Context(data_partition_id=execution_context[\\\"data-partition-id\\\"],\\n app_key=execution_context.get(\\\"AppKey\\\", \\\"\\\"))\\n workflow_name = conf[\\\"workflow_name\\\"]\\n run_id = conf[\\\"run_id\\\"]\\n status = self.status.value\\n status_updater = UpdateStatus(\\n workflow_name=workflow_name,\\n workflow_url=Variable.get(\\\"core__service__workflow__host\\\"),\\n workflow_id=\\\"\\\",\\n run_id=run_id,\\n status=status,\\n token_refresher=AirflowTokenRefresher(),\\n context=payload_context\\n )\\n status_updater.update_workflow_status()\\n\\n if self._show_skipped_ids:\\n skipped_ids, saved_record_ids = self._create_skipped_report(context)\\n context[\\\"ti\\\"].xcom_push(key=\\\"skipped_ids\\\", value=skipped_ids)\\n context[\\\"ti\\\"].xcom_push(key=\\\"saved_record_ids\\\", value=saved_record_ids)\\n\\n if self.status is self.prev_ti_state.FAILED:\\n raise PipelineFailedError(\\\"Dag failed\\\")\",\n \"def __call__(self, result):\\n result.startTest(self)\\n\\n if self._line.skip:\\n result.addSkip(None, self._line.directive.reason)\\n return\\n\\n if self._line.todo:\\n if self._line.ok:\\n result.addUnexpectedSuccess(self)\\n else:\\n result.addExpectedFailure(self, (Exception, Exception(), None))\\n return\\n\\n if self._line.ok:\\n result.addSuccess(self)\\n else:\\n self.addFailure(result)\",\n \"def run(self, result=None):\\n self.result = result\\n\\n # === Decorate the setUp and tearDown methods ===\\n setup_method = getattr(self, self.SETUP_METHOD_NAME)\\n setattr(self, self.SETUP_METHOD_NAME,\\n self._decorate_setup(setup_method))\\n\\n teardown_method = getattr(self, self.TEARDOWN_METHOD_NAME)\\n setattr(self, self.TEARDOWN_METHOD_NAME,\\n self._decorate_teardown(teardown_method))\\n\\n try:\\n super(AbstractFlowComponent, self).run(result)\\n\\n except JumpException as e:\\n self.data.exception_type = TestOutcome.SKIPPED\\n if e.jump_target is not self.parent:\\n raise\",\n \"def run(self, result, debug=False):\\n rv = unittest.TestSuite.run(self, result, debug)\\n sys.stdout.flush()\\n return rv\",\n \"def _run_scenario(self, cls, method_name, context, args, config):\",\n \"def _RunTest(self, adb):\\n return ResultType.FAIL, 'Unimplemented _RunTest() method!'\",\n \"def run_test(self):\\n raise NotImplementedError\",\n \"def run(self, test):\\n result = _TestResult(self.verbosity)\\n\\n test(result)\\n self.stopTime = datetime.datetime.now()\\n self._write_data(result)\\n print(sys.stderr, '\\\\nTime Elapsed: %s' % (self.stopTime-self.startTime))\\n return result\",\n \"def __call__(self, result=None, runcondition=None, options=None):\\n if result is None:\\n result = self.defaultTestResult()\\n self._options_ = options\\n # if result.cvg:\\n # result.cvg.start()\\n testMethod = self._get_test_method()\\n if (getattr(self.__class__, \\\"__unittest_skip__\\\", False) or\\n getattr(testMethod, \\\"__unittest_skip__\\\", False)):\\n # If the class or method was skipped.\\n try:\\n skip_why = (getattr(self.__class__, '__unittest_skip_why__', '')\\n or getattr(testMethod, '__unittest_skip_why__', ''))\\n if hasattr(result, 'addSkip'):\\n result.addSkip(self, skip_why)\\n else:\\n warnings.warn(\\\"TestResult has no addSkip method, skips not reported\\\",\\n RuntimeWarning, 2)\\n result.addSuccess(self)\\n finally:\\n result.stopTest(self)\\n return\\n if runcondition and not runcondition(testMethod):\\n return # test is skipped\\n result.startTest(self)\\n try:\\n if not self.quiet_run(result, self.setUp):\\n return\\n generative = isgeneratorfunction(testMethod)\\n # generative tests\\n if generative:\\n self._proceed_generative(result, testMethod,\\n runcondition)\\n else:\\n status = self._proceed(result, testMethod)\\n success = (status == 0)\\n if not self.quiet_run(result, self.tearDown):\\n return\\n if not generative and success:\\n result.addSuccess(self)\\n finally:\\n # if result.cvg:\\n # result.cvg.stop()\\n result.stopTest(self)\",\n \"def run(self, test):\\n result = _TestResult(self.verbosity)\\n\\n self.start_time = datetime.datetime.now()\\n test(result)\\n self.stop_time = datetime.datetime.now()\\n self.elapsed_time = self.stop_time - self.start_time\\n print >>sys.stderr, '\\\\nTime Elapsed: %s' % self.elapsed_time\\n\\n report = self.generate_report(result)\\n self.stream.write(report.encode('utf8'))\\n\\n return result\",\n \"def run(self):\\n if self.verbose:\\n print(f'Running {self.name} tests...')\\n\\n # try running setup if there is one\\n if self.setup:\\n self.__process_setup()\\n\\n final_report = [None] * len(self.tests)\\n\\n for test_in, test_out in sorted(self.tests.items()):\\n # increment total num of tests\\n self.total += 1\\n\\n if self.verbose:\\n print(f'#{self.total}')\\n\\n # evaluate test input w/ setup vars, if any\\n try:\\n inp = eval(test_in, self.vars)\\n except Exception as err:\\n print(f'Issue during evaluation of test input: {err}')\\n final_report[self.total - 1] = 'input eval error'\\n if self.verbose:\\n print(f'Test input was: {test_in}')\\n print('Vars from execution: {}'.format({k : v for k, v in self.vars.items() if k != '__builtins__'}))\\n continue\\n\\n \\n # checking if function input has more than one arg\\n if type(inp) in (list, tuple):\\n try:\\n student_out = self.student_function(*inp)\\n except Exception as err:\\n print(f'Issue while running student code: {err}')\\n final_report[self.total - 1] = f'student code error: {err}; input: {inp}; func_name: {self.name}'\\n if self.verbose:\\n print(f'Function being run was: {self.name}')\\n print(f'Inputs were: {inp}')\\n continue\\n else:\\n try:\\n student_out = self.student_function(inp)\\n except Exception as err:\\n print(f'Issue while running student code: {err}')\\n final_report[self.total - 1] = f'student code error: {err}; input: {inp}; func_name: {self.name}'\\n if self.verbose:\\n print(f'Function being run was: {self.name}')\\n print(f'Input was: {inp}')\\n continue\\n\\n # ans alias for ease of answer checking\\n self.vars['ans'] = student_out\\n\\n if self.schema:\\n format_vals = eval(test_out, self.vars)\\n results, maybe_failed_schema = self.__process_schema(format_vals)\\n if all(results):\\n self.correct += 1\\n final_report[self.total - 1] = 'PASSED'\\n else:\\n # failed at least one of the tests\\n failed_str = \\\" and \\\".join([\\\", \\\".join(maybe_failed_schema[:-1]),maybe_failed_schema[-1]] if len(maybe_failed_schema) > 2 else maybe_failed_schema)\\n final_report[self.total - 1] = f'FAILED; failed following assertion(s): {failed_str}'\\n else:\\n expected_ans = eval(test_out, self.vars)\\n if student_out == expected_ans:\\n self.correct += 1\\n final_report[self.total - 1] = 'PASSED'\\n else:\\n # failed the only test\\n final_report[self.total - 1] = f'FAILED; got {repr(student_out)} but expected {repr(expected_ans)}'\\n\\n # run callback function, if there is one\\n if self.callback:\\n if self.verbose:\\n print('Running callback...')\\n print('call back is:', self.callback)\\n\\n # once done, put the final report on the queue\\n self.queue.put((self.student_username, self.name, f'{self.correct}/{self.total}', final_report))\",\n \"def run_case(self, **kwargs):\\n module_name = kwargs.get('module_name', None)\\n if self.result:\\n self.success_msg.append('>>>%s PASSED' % module_name or sys.modules[__name__])\\n else:\\n self.fail_msg.insert(0, '>>>%s FAILED' % module_name or sys.modules[__name__])\",\n \"def test_example_runs(self):\\n run_example(\\n verbose=False,\\n testapp=self.testapp,\\n )\",\n \"def pytest(context):\\n exec_cmd = \\\"pytest\\\"\\n run_cmd(context, exec_cmd)\",\n \"def __call__(self, result=None):\\n try:\\n self._pre_setup()\\n super(TestCase, self).__call__(result)\\n finally:\\n self._post_teardown()\",\n \"def run(self, **kwargs):\\n self.start_time = time.time()\\n\\n try:\\n # Make sure that Tempest is configured\\n if not self.tempestconf:\\n self.generate_conf()\\n self.run_defcore_default()\\n self.parse_refstack_result()\\n res = testcase.TestCase.EX_OK\\n except Exception: # pylint: disable=broad-except\\n LOGGER.exception(\\\"Error with run\\\")\\n res = testcase.TestCase.EX_RUN_ERROR\\n finally:\\n self.tempestconf.clean()\\n\\n self.stop_time = time.time()\\n return res\",\n \"def runTests(self):\\n \\n pass\",\n \"def _execute_test_step(self):\\n\\n test_step_command = '{0}'.format(self._test_exec) if self._interpreter == '' else \\\\\\n '{0} {1}'.format(self._interpreter, self._test_exec)\\n\\n # TODO: Expand user and environment variables.\\n test_params = ['{0} {1}'.format(x,y) if y != '' else '{0}'.format(x)\\n for x,y in self._test_params.items()]\\n\\n exit_code, out = self._staf_start_proc(test_step_command,\\n self._remote_target_path,\\n self._timeout,\\n test_params,\\n location=self._sut.network_address)\\n\\n if exit_code != 0:\\n raise CoreError('Test step \\\"{0}\\\" failed: {1}'.format(self._description, out))\",\n \"def run_suite(self, name=\\\"_\\\", result=None):\\n result = result if result else self.result\\n self.mod_suites[name].run(result)\",\n \"def run(run_type, module, config):\\n print(\\\" -----------------------------------------------------------------\\\")\\n print(\\\" Beginning \\\" + run_type.lower() + \\\" test suite \\\")\\n print(\\\" -----------------------------------------------------------------\\\")\\n print(\\\"\\\")\\n summary = run_quiet(run_type, module, config)\\n print(\\\" -----------------------------------------------------------------\\\")\\n print(\\\" \\\" + run_type.capitalize() + \\\" test suite complete \\\")\\n print(\\\" -----------------------------------------------------------------\\\")\\n print(\\\"\\\")\\n return summary\",\n \"def _super_run_modified(self, result=None):\\n\\n orig_result = result\\n if result is None:\\n result = self.defaultTestResult()\\n startTestRun = getattr(result, 'startTestRun', None)\\n if startTestRun is not None:\\n startTestRun()\\n\\n result.startTest(self)\\n\\n testMethod = getattr(self, self._testMethodName)\\n if (getattr(self.__class__, \\\"__unittest_skip__\\\", False) or\\n getattr(testMethod, \\\"__unittest_skip__\\\", False)):\\n # If the class or method was skipped.\\n try:\\n skip_why = (getattr(self.__class__, '__unittest_skip_why__', '')\\n or getattr(testMethod, '__unittest_skip_why__', ''))\\n self._addSkip(result, self, skip_why)\\n finally:\\n result.stopTest(self)\\n return\\n expecting_failure_method = getattr(testMethod,\\n \\\"__unittest_expecting_failure__\\\", False)\\n expecting_failure_class = getattr(self,\\n \\\"__unittest_expecting_failure__\\\", False)\\n expecting_failure = expecting_failure_class or expecting_failure_method\\n outcome = Outcome(result)\\n try:\\n self._outcome = outcome\\n\\n with outcome.testPartExecutor(self):\\n self.setUp()\\n if outcome.success:\\n outcome.expecting_failure = expecting_failure\\n with outcome.testPartExecutor(self, isTest=True):\\n testMethod()\\n\\n # 当前用例失败时触发on_errors回调\\n if not outcome.success:\\n with outcome.testPartExecutor(self):\\n self.on_errors(outcome.errors)\\n\\n outcome.expecting_failure = False\\n with outcome.testPartExecutor(self):\\n self.tearDown()\\n\\n self.doCleanups()\\n for test, reason in outcome.skipped:\\n self._addSkip(result, test, reason)\\n self._feedErrorsToResult(result, outcome.errors)\\n if outcome.success:\\n if expecting_failure:\\n if outcome.expectedFailure:\\n self._addExpectedFailure(result, outcome.expectedFailure)\\n else:\\n self._addUnexpectedSuccess(result)\\n else:\\n result.addSuccess(self)\\n return result\\n finally:\\n result.stopTest(self)\\n if orig_result is None:\\n stopTestRun = getattr(result, 'stopTestRun', None)\\n if stopTestRun is not None:\\n stopTestRun()\\n\\n # explicitly break reference cycles:\\n # outcome.errors -> frame -> outcome -> outcome.errors\\n # outcome.expectedFailure -> frame -> outcome -> outcome.expectedFailure\\n del outcome.errors[:] # equivalent to [].clear in py3\\n outcome.expectedFailure = None\\n\\n # clear the outcome, no more needed\\n self._outcome = None\",\n \"def execute_test(self):\\n try:\\n self._execute()\\n except Exception as ex:\\n self._fail(exception=ex)\\n raise\",\n \"def run(self, **kwargs):\\n try:\\n name = kwargs[\\\"name\\\"]\\n try:\\n self.suite = unittest.TestLoader().loadTestsFromName(name)\\n except ImportError:\\n self.__logger.error(\\\"Can not import %s\\\", name)\\n return testcase.TestCase.EX_RUN_ERROR\\n except KeyError:\\n pass\\n try:\\n assert self.suite\\n self.start_time = time.time()\\n stream = six.StringIO()\\n result = unittest.TextTestRunner(\\n stream=stream, verbosity=2).run(self.suite)\\n self.__logger.debug(\\\"\\\\n\\\\n%s\\\", stream.getvalue())\\n self.stop_time = time.time()\\n self.details = {\\n \\\"testsRun\\\": result.testsRun,\\n \\\"failures\\\": len(result.failures),\\n \\\"errors\\\": len(result.errors),\\n \\\"stream\\\": stream.getvalue()}\\n self.result = 100 * (\\n (result.testsRun - (len(result.failures) +\\n len(result.errors))) /\\n result.testsRun)\\n return testcase.TestCase.EX_OK\\n except AssertionError:\\n self.__logger.error(\\\"No suite is defined\\\")\\n return testcase.TestCase.EX_RUN_ERROR\\n except ZeroDivisionError:\\n self.__logger.error(\\\"No test has been run\\\")\\n return testcase.TestCase.EX_RUN_ERROR\",\n \"async def run_test_with_values(\\n self,\\n test_name: str,\\n source_entity_name: str,\\n\\n expected_context_default: 'AttributeContextExpectedValue',\\n expected_context_normalized: 'AttributeContextExpectedValue',\\n expected_context_reference_only: 'AttributeContextExpectedValue',\\n expected_context_structured: 'AttributeContextExpectedValue',\\n expected_context_normalized_structured: 'AttributeContextExpectedValue',\\n expected_context_reference_only_normalized: 'AttributeContextExpectedValue',\\n expected_context_reference_only_structured: 'AttributeContextExpectedValue',\\n expected_context_reference_only_normalized_structured: 'AttributeContextExpectedValue',\\n\\n expected_default: 'List[AttributeExpectedValue]',\\n expected_normalized: 'List[AttributeExpectedValue]',\\n expected_reference_only: 'List[AttributeExpectedValue]',\\n expected_structured: 'List[AttributeExpectedValue]',\\n expected_normalized_structured: 'List[AttributeExpectedValue]',\\n expected_reference_only_normalized: 'List[AttributeExpectedValue]',\\n expected_reference_only_structured: 'List[AttributeExpectedValue]',\\n expected_reference_only_normalized_structured: 'List[AttributeExpectedValue]'\\n ) -> None:\\n try:\\n test_input_path = TestHelper.get_input_folder_path(self.tests_subpath, test_name)\\n\\n corpus = CdmCorpusDefinition()\\n corpus.ctx.report_at_level = CdmStatusLevel.WARNING\\n corpus.storage.mount('localInput', LocalAdapter(test_input_path))\\n corpus.storage.mount('cdm', LocalAdapter(self.schema_docs_path))\\n corpus.storage.default_namespace = 'localInput'\\n\\n src_entity_def = await corpus.fetch_object_async('localInput:/{}.cdm.json/{}'.format(source_entity_name, source_entity_name))\\n self.assertTrue(src_entity_def is not None)\\n\\n res_opt = ResolveOptions(wrt_doc=src_entity_def.in_document)\\n\\n resolved_entity_def = None # type: CdmEntityDefinition\\n output_entity_name = ''\\n output_entity_file_name = ''\\n entity_file_name = ''\\n\\n if expected_context_default and expected_default:\\n entity_file_name = 'default'\\n res_opt.directives = AttributeResolutionDirectiveSet(set())\\n output_entity_name = '{}_Resolved_{}'.format(source_entity_name, entity_file_name)\\n output_entity_file_name = '{}.cdm.json'.format(output_entity_name)\\n resolved_entity_def = await src_entity_def.create_resolved_entity_async(output_entity_name, res_opt)\\n self.validate_output_with_values(expected_context_default, expected_default, resolved_entity_def)\\n\\n if expected_context_normalized and expected_normalized:\\n entity_file_name = 'normalized'\\n res_opt.directives = AttributeResolutionDirectiveSet(set({'normalized'}))\\n output_entity_name = '{}_Resolved_{}'.format(source_entity_name, entity_file_name)\\n output_entity_file_name = '{}.cdm.json'.format(output_entity_name)\\n resolved_entity_def = await src_entity_def.create_resolved_entity_async(output_entity_name, res_opt)\\n self.validate_output_with_values(expected_context_normalized, expected_normalized, resolved_entity_def)\\n\\n if expected_context_reference_only and expected_reference_only:\\n entity_file_name = 'referenceOnly'\\n res_opt.directives = AttributeResolutionDirectiveSet(set({'referenceOnly'}))\\n output_entity_name = '{}_Resolved_{}'.format(source_entity_name, entity_file_name)\\n output_entity_file_name = '{}.cdm.json'.format(output_entity_name)\\n resolved_entity_def = await src_entity_def.create_resolved_entity_async(output_entity_name, res_opt)\\n self.validate_output_with_values(expected_context_reference_only, expected_reference_only, resolved_entity_def)\\n\\n if expected_context_structured and expected_structured:\\n entity_file_name = 'structured'\\n res_opt.directives = AttributeResolutionDirectiveSet(set({'structured'}))\\n output_entity_name = '{}_Resolved_{}'.format(source_entity_name, entity_file_name)\\n output_entity_file_name = '{}.cdm.json'.format(output_entity_name)\\n resolved_entity_def = await src_entity_def.create_resolved_entity_async(output_entity_name, res_opt)\\n self.validate_output_with_values(expected_context_structured, expected_structured, resolved_entity_def)\\n\\n if expected_context_normalized_structured and expected_normalized_structured:\\n entity_file_name = 'normalized_structured'\\n res_opt.directives = AttributeResolutionDirectiveSet(set({'normalized', 'structured'}))\\n output_entity_name = '{}_Resolved_{}'.format(source_entity_name, entity_file_name)\\n output_entity_file_name = '{}.cdm.json'.format(output_entity_name)\\n resolved_entity_def = await src_entity_def.create_resolved_entity_async(output_entity_name, res_opt)\\n self.validate_output_with_values(expected_context_normalized_structured, expected_normalized_structured, resolved_entity_def)\\n\\n if expected_context_reference_only_normalized and expected_reference_only_normalized:\\n entity_file_name = 'referenceOnly_normalized'\\n res_opt.directives = AttributeResolutionDirectiveSet(set({'referenceOnly', 'normalized'}))\\n output_entity_name = '{}_Resolved_{}'.format(source_entity_name, entity_file_name)\\n output_entity_file_name = '{}.cdm.json'.format(output_entity_name)\\n resolved_entity_def = await src_entity_def.create_resolved_entity_async(output_entity_name, res_opt)\\n self.validate_output_with_values(expected_context_reference_only_normalized, expected_reference_only_normalized, resolved_entity_def)\\n\\n if expected_context_reference_only_structured and expected_reference_only_structured:\\n entity_file_name = 'referenceOnly_structured'\\n res_opt.directives = AttributeResolutionDirectiveSet(set({'referenceOnly', 'structured'}))\\n output_entity_name = '{}_Resolved_{}'.format(source_entity_name, entity_file_name)\\n output_entity_file_name = '{}.cdm.json'.format(output_entity_name)\\n resolved_entity_def = await src_entity_def.create_resolved_entity_async(output_entity_name, res_opt)\\n self.validate_output_with_values(expected_context_reference_only_structured, expected_reference_only_structured, resolved_entity_def)\\n\\n if expected_context_reference_only_normalized_structured and expected_reference_only_normalized_structured:\\n entity_file_name = 'referenceOnly_normalized_structured'\\n res_opt.directives = AttributeResolutionDirectiveSet(set({'referenceOnly', 'normalized', 'structured'}))\\n output_entity_name = '{}_Resolved_{}'.format(source_entity_name, entity_file_name)\\n output_entity_file_name = '{}.cdm.json'.format(output_entity_name)\\n resolved_entity_def = await src_entity_def.create_resolved_entity_async(output_entity_name, res_opt)\\n self.validate_output_with_values(expected_context_reference_only_normalized_structured, expected_reference_only_normalized_structured, resolved_entity_def)\\n except Exception as e:\\n self.fail(e)\",\n \"def main():\\n\\n parser = argparse.ArgumentParser(prog=\\\"run_test.py\\\",\\n formatter_class=argparse.RawTextHelpFormatter)\\n parser.add_argument('id', help=\\\"Id of a test\\\")\\n args = parser.parse_args()\\n\\n configure_logger()\\n\\n test_info = TESTS.get(args.id, None)\\n if not test_info:\\n test_info.log.error(f'{args.id} does not exist')\\n exit(ErrorCode.CRITICAL)\\n os.environ['DISPLAY'] = \\\":0.0\\\"\\n\\n test = Test(args.id, test_info)\\n result = test.run()\\n\\n test.log.info('#' * 80)\\n if not result:\\n test.log.error('TEST FAILED')\\n else:\\n test.log.info('TEST PASSED')\\n test.log.info('#' * 80)\\n exit(not result)\",\n \"def test_passed():\\n pass\",\n \"def test(\\n context,\\n user=get_local_user(),\\n remote=False,\\n instance=None,\\n stack=None,\\n app=None,\\n coverage=False,\\n):\\n command = \\\"pytest\\\"\\n\\n if app:\\n command = f\\\"{command} {app}\\\"\\n\\n if coverage:\\n command = f\\\"coverage run -m {command}\\\"\\n\\n command = f\\\"run --rm django {command}\\\"\\n run_command(context, user, remote, instance, stack, command)\",\n \"def run_test(skip_run):\\n # First off, we need the 'official' test data downloaded and unpacked\\n # if necessary.\\n if ref_test_data.install_and_check_test_files() != 0:\\n return 1\\n if install_inputs() != 0:\\n return 1\\n\\n if not skip_run:\\n sys.stdout.write(\\\"Executing CCC code...\\\\n\\\")\\n ret = run.main()\\n if ret != 0:\\n return 1\\n\\n test_result_dir = os.path.join(ref_test_data.test_data_dir, \\\"result\\\")\\n sys.stdout.write(\\\"Comparing CCC results with 'official' results...\\\\n\\\")\\n ret = compare_results.main([sys.argv[0], \\\"result\\\", test_result_dir])\\n if ret != 0:\\n return 1\",\n \"def then_app_running_stage(context):\\n result = context.result\\n result | should.equal('Success').desc(\\\"Application is reachable in the Stage stage.\\\")\",\n \"def run(self, job_result):\\n self.logger.info(f\\\"Running report\\\")\\n job_result.status = JobResultStatusChoices.STATUS_RUNNING\\n job_result.save()\\n\\n try:\\n\\n for method_name in self.test_methods:\\n self.active_test = method_name\\n test_method = getattr(self, self.test_methods[method_name])\\n test_method()\\n\\n if self.failed:\\n self.logger.warning(\\\"Report failed\\\")\\n job_result.status = JobResultStatusChoices.STATUS_FAILED\\n else:\\n self.logger.info(\\\"Report completed successfully\\\")\\n job_result.status = JobResultStatusChoices.STATUS_COMPLETED\\n\\n except Exception as e:\\n stacktrace = traceback.format_exc()\\n self.log_failure(None, f\\\"An exception occurred: {type(e).__name__}: {e} <pre>{stacktrace}</pre>\\\")\\n logger.error(f\\\"Exception raised during report execution: {e}\\\")\\n job_result.set_status(JobResultStatusChoices.STATUS_ERRORED)\\n\\n job_result.data = self._results\\n job_result.completed = timezone.now()\\n job_result.save()\\n\\n # Perform any post-run tasks\\n self.post_run()\",\n \"def test(self, args=None, init_args=None, config=None, verbose=False):\\n self.ctest(args=args, init_args=init_args, config=config, verbose=verbose)\",\n \"def test_basic_execution(self):\",\n \"def set_test_passed(self):\\n self.set_result(Status.PASSED)\",\n \"def sample(self, context: Context) -> T:\\n ...\",\n \"def test_run(self):\\n class MockProvider(BaseCoverageProvider):\\n SERVICE_NAME = \\\"I do nothing\\\"\\n was_run = False\\n\\n def run_once_and_update_timestamp(self):\\n \\\"\\\"\\\"Set a variable.\\\"\\\"\\\"\\n self.was_run = True\\n return None\\n\\n provider = MockProvider(self._db)\\n result = provider.run()\\n\\n # run_once_and_update_timestamp() was called.\\n assert True == provider.was_run\\n\\n # run() returned a CoverageProviderProgress with basic\\n # timing information, since run_once_and_update_timestamp()\\n # didn't provide anything.\\n assert isinstance(result, CoverageProviderProgress)\\n now = utc_now()\\n assert result.start < result.finish\\n for time in (result.start, result.finish):\\n assert (now - time).total_seconds() < 5\",\n \"def runTest(self):\\n self.setUp()\\n success = False\\n message = ''\\n try:\\n self.command = [self.test_manager.executable_path]\\n self.command.extend(self.test.spirv_args)\\n\\n process = subprocess.Popen(\\n args=self.command,\\n stdin=subprocess.PIPE,\\n stdout=subprocess.PIPE,\\n stderr=subprocess.PIPE,\\n cwd=self.directory)\\n output = process.communicate(self.stdin_shader)\\n test_status = TestStatus(self.test_manager, process.returncode, output[0],\\n output[1], self.directory, self.inputs,\\n self.file_shaders)\\n run_results = [\\n getattr(self.test, test_method)(test_status)\\n for test_method in get_all_test_methods(self.test.__class__)\\n ]\\n success, message = zip(*run_results)\\n success = all(success)\\n message = '\\\\n'.join(message)\\n except Exception as e:\\n success = False\\n message = str(e)\\n self.test_manager.notify_result(\\n self, success,\\n message + '\\\\nSTDOUT:\\\\n%s\\\\nSTDERR:\\\\n%s' % (output[0], output[1]))\\n self.tearDown()\",\n \"def run(self):\\n\\n output_to_pipe(self._pipe_in)\\n\\n error = self.lib.iperf_run_client(self._test)\\n\\n if error:\\n data = '{\\\"error\\\": \\\"%s\\\"}' % self._error_to_string(self._errno)\\n else:\\n data = read_pipe(self._pipe_out)\\n\\n output_to_screen(self._stdout_fd, self._stderr_fd)\\n\\n return TestResult(data)\",\n \"def _handler_test_run_tests(self, *args, **kwargs):\\n next_state = None\\n result = None\\n\\n tc_pass = False\\n tt_pass = False\\n tp_pass = False\\n tc_result = None\\n tt_result = None\\n tp_result = None\\n\\n test_result = {}\\n\\n try:\\n tc_pass, tc_result = self._do_cmd_resp('tc', timeout=200)\\n tt_pass, tt_result = self._do_cmd_resp('tt', timeout=200)\\n tp_pass, tp_result = self._do_cmd_resp('tp', timeout=200)\\n \\n except Exception as e:\\n test_result['exception'] = e\\n test_result['message'] = 'Error running instrument tests.'\\n \\n finally:\\n test_result['cond_test'] = 'Passed' if tc_pass else 'Failed'\\n test_result['cond_data'] = tc_result\\n test_result['temp_test'] = 'Passed' if tt_pass else 'Failed'\\n test_result['temp_data'] = tt_result\\n test_result['pres_test'] = 'Passed' if tp_pass else 'Failed'\\n test_result['pres_data'] = tp_result\\n test_result['success'] = 'Passed' if (tc_pass and tt_pass and tp_pass) else 'Failed'\\n \\n self._driver_event(DriverAsyncEvent.TEST_RESULT, test_result)\\n next_state = SBE37ProtocolState.COMMAND\\n \\n return (next_state, result)\",\n \"def test_execute(self):\\n context = dict()\\n cmd = pycell.python_cell(\\n source='print(2+2)',\\n validate=True\\n )\\n controller = FakeWorkflowController()\\n self.backend.execute_async(\\n task=TaskHandle(\\n task_id='000',\\n project_id=self.PROJECT_ID,\\n controller=controller\\n ),\\n command=cmd,\\n artifacts=context\\n )\\n time.sleep(3)\\n self.assertEqual(controller.task_id, '000')\\n self.assertEqual(controller.state, 'SUCCESS')\\n self.assertEqual(controller.outputs.stdout[0].value, '4')\",\n \"def run(self):\\n if self.all:\\n cmd = self.apply_options(self.test_all_cmd)\\n self.call_and_exit(cmd)\\n else:\\n cmds = (self.apply_options(self.unit_test_cmd, (\\\"coverage\\\",)),)\\n if self.coverage:\\n cmds += (self.apply_options(self.coverage_cmd),)\\n self.call_in_sequence(cmds)\",\n \"def runTestCase(self):\\n \\n #Login\\n self.login() \\n \\n #Performing Configure Resources \\n ResultCR, statusCR = self.test_configureResourec()\\n \\n if statusCR:\\n self.succeed(\\\"Configure Resources Step Successfully Completed %s\\\"%ResultCR)\\n \\n else:\\n self.failure(\\\"Failed to Configure Resources Step %s\\\"%ResultCR)\\n \\n time.sleep(120)\",\n \"def run(self, context):\\n self.start()\\n while not context.is_finished():\\n self.update_predictions(context)\\n new_results, instances = self.evaluate()\\n if new_results:\\n self.write_results(new_results, instances)\\n self.update_plot(new_results, instances)\\n\\n if self.plot_file:\\n self.visualizer.savefig(self.plot_file)\\n if self.show_plot:\\n self.visualizer.show()\\n self.end()\",\n \"def step_run(environment):\\n cmd = \\\"/bin/sh %(testname)s\\\" % environment\\n return execute(environment, cmd, timeout=10)\",\n \"def execute(self, context):\\n result = Result(result='success')\\n\\n for tag in self._tags:\\n if isinstance(self._tags, (dict, OrderedDict)):\\n # we have a dictionary of items.\\n LOGGER.debug(\\\"Setting environment variables for tag.\\\")\\n for key, value in self._tags[tag].items():\\n if context:\\n value = render(value, **context.variables.last())\\n else: # noqa: no-cover\\n raise RuntimeError(\\\"Context is required.\\\")\\n\\n LOGGER.debug(\\\"Setting '%s' to '%s', in the system environment.\\\", key, value)\\n os.putenv(key, value)\\n os.environ[key] = value\\n\\n with matrix_scoped_variables(context, tag):\\n matrix_list = []\\n matrix_tags = []\\n if context and len(context.matrix_tags) > 0:\\n matrix_list = context.variables.last()[\\n 'matrix_list'] if 'matrix_list' in context.variables.last() else []\\n matrix_tags = context.matrix_tags\\n\\n if len(matrix_tags) > 0 and not all(\\n [fnmatch.fnmatch(x[1], x[0]) for x in zip(matrix_tags, matrix_list)]):\\n LOGGER.debug(\\\"Skipping because this matrix item does not have a user-selected matrix tag.\\\")\\n LOGGER.debug(\\\"matrix_list=%r matrix_tags=%r\\\", matrix_list, matrix_tags)\\n result = Result(result='skipped')\\n else:\\n LOGGER.debug('Beginning matrix entry: %s', tag)\\n result = self._execute_tasks(context)\\n LOGGER.debug('Completed matrix entry: %s', tag)\\n\\n if isinstance(self._tags, (dict, OrderedDict)):\\n # we have a dictionary of items.\\n LOGGER.debug(\\\"Unsetting environment variables for tag.\\\")\\n for key, _ in self._tags[tag].items():\\n try:\\n os.unsetenv(key)\\n except AttributeError: # noqa: no-cover\\n pass # noqa: no-cover\\n del os.environ[key]\\n\\n if not result:\\n break\\n\\n if result['result'] in ['skipped', 'continue']:\\n result = Result(result='success')\\n\\n return result\",\n \"def execute(self):\\n\\n self._status = 'Running'\\n\\n try:\\n self._init_staf_handle()\\n self._prep_vm()\\n self._ping()\\n self._install_bespoke()\\n sleep(self._post_wait)\\n self._status = 'Pass'\\n except CoreError as e:\\n self._status = 'Fatal'\\n self._message = e.msg\\n finally:\\n self._close_staf_handle()\\n\\n #Notify TestCase that a failure occurred.\\n if self._status == 'Fatal': raise FatalError(self._message)\",\n \"def test(self, dataset):\\n\\n outputs, errors = self.use(dataset)\\n\\n ## PUT CODE HERE ##\\n # I put the code in the \\\"use\\\" function, seems better :-)\\n\\n return outputs, errors\",\n \"def process_ResultCheck(self):\\n try:\\n cmd = self.ExecutionTask.get_param().split(',')\\n logging.debug(\\\"%s-%s-%s-%s-%s\\\" % ( TestScriptSymbolTable.get_value_from_sym_tab(cmd[0], TestScriptSymbolTable.test_script_sym_tab),cmd[0], cmd[1], cmd[2], cmd[3]))\\n\\n checkval = cmd[0].split('!') \\n \\n cval = TestScriptSymbolTable.get_value_from_sym_tab(checkval[1], TestScriptSymbolTable.capi_cmd_ret_sym_tab)\\n\\n if int(cval) >= int(cmd[1]):\\n result = cmd[2]\\n else:\\n result = cmd[3]\\n\\n logging.info(\\\"\\\\nRESULT CHECK---> %15s\\\", result) \\n self.setTestResult(result)\\n \\n #if result == 'FAIL':\\n if 'FAIL' in result:\\n self.setStatus('STOP')\\n self.setTestResult('FAIL')\\n except OSError:\\n logging.info(\\\"\\\\nException - ResultCheck\\\")\",\n \"def evaluate(self, test_data):\\n result = self.model.run(test_data)\\n self._save_result(result)\",\n \"def run(self):\\n list_test_scenarios = self.__get_list_scenarios_in_folder()\\n\\n if not list_test_scenarios:\\n utils.print_error(\\n \\\"\\\\n{}\\\\n\\\".format(constant.ERR_CANNOT_FIND_ANY_TEST_SCENARIOS))\\n exit(1)\\n\\n (tests_pass, tests_fail) = self.__execute_tests(list_test_scenarios)\\n\\n complete_message = constant.INFO_TEST_PASS_FAIL.format(\\n tests_pass, tests_fail)\\n\\n print(complete_message)\\n\\n self.__execute_reporter()\",\n \"def execute(self):\\n\\n self._status = 'Running'\\n\\n self._checkout_resources()\\n\\n for test in self._tests:\\n try:\\n self._update_resource_timeouts(test.timeout)\\n test.execute()\\n except Failure as e:\\n self._status = 'Fail'\\n self._message = ('The \\\"{0}\\\" test in the test case \\\"{1}\\\" failed with the '\\n 'message: \\\"{2}\\\"'.format(test.name, self.name, e.msg))\\n except FatalError as e:\\n self._checkin_resources()\\n self._status = 'Fatal'\\n self._message = ('The \\\"{0}\\\" test in the test case \\\"{1}\\\" encountered the fatal '\\n 'error: \\\"{2}\\\"'.format(test.name, self.name, e.msg))\\n raise FatalError(self._message)\\n\\n if self._status == 'Fail':\\n self._checkin_resources()\\n raise Failure(self._message)\\n\\n self._status = 'Pass'\\n self._checkin_resources()\",\n \"def run(self, result=None):\\n method = getattr(self, self._testMethodName)\\n\\n if getattr(method, 'multi_graph', False) and not getattr(self, 'graphname', False):\\n for graph in self.test_graphs:\\n self.graphname = graph\\n self.run(result=result)\\n self.graphname = None\\n else:\\n super(BaseRexProTestCase, self).run(result=result)\",\n \"def _proceed(self, result, testfunc, args=(), kwargs=None):\\n kwargs = kwargs or {}\\n try:\\n testfunc(*args, **kwargs)\\n except self.failureException:\\n result.addFailure(self, self.__exc_info())\\n return 1\\n except KeyboardInterrupt:\\n raise\\n except InnerTestSkipped as e:\\n result.addSkip(self, e)\\n return 1\\n except SkipTest as e:\\n result.addSkip(self, e)\\n return 0\\n except:\\n result.addError(self, self.__exc_info())\\n return 2\\n return 0\",\n \"def execute(self, activity, context):\\n\\n raise NotImplementedError\",\n \"def test_run(self):\\n sut = ExperimentEmail()\\n train = os.path.join(os.path.dirname(__file__), \\\"data\\\", \\\"sample.csv\\\")\\n val = os.path.join(os.path.dirname(__file__), \\\"data\\\", \\\"sample.csv\\\")\\n outdir = tempfile.mkdtemp()\\n\\n # Act\\n sut.run(train, val, outdir, batch_size=32, epochs=2)\",\n \"def __call__(self, result=None):\\n self._pre_setup()\\n super(TestCase, self).__call__(result)\\n self._post_tearDown()\",\n \"def run_tests(self, test_labels):\\n import pytest\\n\\n argv = []\\n if self.verbosity == 0:\\n argv.append('--quiet')\\n if self.verbosity == 2:\\n argv.append('--verbose')\\n if self.verbosity == 3:\\n argv.append('-vv')\\n if self.failfast:\\n argv.append('--exitfirst')\\n if self.keepdb:\\n argv.append('--reuse-db')\\n\\n argv.extend(test_labels)\\n return pytest.main(argv)\",\n \"def __call__(self):\\n\\n if 'timed out' in self.result:\\n self.code = 3\\n\\n elif not self.is_pov and self.polls_failed > 0:\\n self.code = 6\\n codes_error[self.code] = codes_error[self.code] + str(self.polls_failed) + \\\"\\\\n\\\"\\n\\n elif 'not ok - pov did not negotiate' in self.result:\\n self.code = 2\\n\\n # If the test failed to run, consider it failed\\n elif 'TOTAL TESTS' not in self.result:\\n self.code = 1\\n\\n elif 'TOTAL TESTS: ' in self.result:\\n # Parse out results\\n self.code = 0\\n self.total = int(self.result.split('TOTAL TESTS: ')[1].split('\\\\n')[0])\\n self.passed = int(self.result.split('TOTAL PASSED: ')[1].split('\\\\n')[0])\\n\\n if self.is_pov:\\n self.passed ^= 1\\n\\n if self.is_sig() and self.passed:\\n self.passed = 0\\n\\n if 'should have cored, and did not' in self.result:\\n self.code = 7\\n else:\\n self.code = 5\\n\\n codes_error[self.code] = codes_error[self.code] + str(self.sig) + \\\"\\\\n\\\"\\n\\n elif self.is_sig():\\n if self.passed:\\n self.passed = 0\\n\\n self.code = 5\\n codes_error[self.code] = codes_error[self.code] + str(self.sig) + \\\"\\\\n\\\"\\n\\n else:\\n self.code = 4\\n\\n self.error = codes_error[self.code]\",\n \"def testing(self):\\n print('test successful')\",\n \"def test_perform_action(self):\\n\\n # Prepare.\\n app = self.factory()\\n request = self.getRequest(app)\\n context = model.factory()\\n\\n # Create a dummy event and get it back.\\n event_id = boilerplate.createEvent(context)\\n event = repo.LookupActivityEvent()(event_id)\\n\\n # Check we're allowed.\\n state_changer = request.state_changer\\n with transaction.manager:\\n bm.Session.add(context)\\n self.assertTrue(state_changer.can_perform(context, a.START))\\n context_id = context.id\\n\\n # Perform the action.\\n # We have to use a transaction manager because perform creates\\n # a new event on state change.\\n context = model.Model.query.get(context_id)\\n with transaction.manager:\\n bm.Session.add(event)\\n bm.Session.add(context)\\n _ = state_changer.perform(context, a.START, event)\\n status = context.work_status.value\\n\\n # The context is now in the configured state.\\n self.assertEqual(status, s.STARTED)\",\n \"def execute(self, context, obj):\\n \\n # if there are results, then processthem, otherwise stop the machine\\n if obj.get(CONTINUATION_RESULTS_KEY):\\n \\n entities, data = obj[CONTINUATION_RESULTS_KEY], []\\n \\n # process each entity in turn\\n for entity in entities:\\n \\n # extracting entity specific data\\n csvData = self.generateCsvData(context, obj, entity)\\n if csvData:\\n data.append(csvData)\\n \\n if data:\\n context[DATA_PARAM] = data\\n \\n # and then extracting data from a batch of entities (ie. sums, averages etc.)\\n aggData = self.generateAggregatedCsvData(context, obj, entities)\\n if aggData:\\n context[AGG_DATA_PARAM] = aggData\\n \\n # this stores the total number of entities processed by this Task\\n context[NUM_ENTITIES_PARAM] = len(entities)\\n \\n # keep going in all cases, even when the continuation is done\\n return OK_EVENT\",\n \"def execute(self):\\n\\n self._status = 'Running'\\n\\n try:\\n self._init_staf_handle()\\n self._ping()\\n\\n if self._sut.os == 'Linux':\\n self._linux_power_control()\\n elif self._sut.os == 'Windows':\\n self._windows_power_control()\\n else:\\n raise CoreError(\\\"Unknown OS platform: {0}\\\".format(self._sut.os))\\n\\n if self._wait:\\n sleep(BespokeGlobals.VM_BOOT_WAIT)\\n\\n self._status = 'Pass'\\n except CoreError as e:\\n self._status = 'Fatal'\\n self._message = e.msg\\n finally:\\n self._close_staf_handle()\\n\\n #Notify TestCase that a failure occurred.\\n if self._status == 'Fatal': raise FatalError(self._message)\",\n \"def setup(self,context,result):\\n pass\",\n \"def execute(self, context):\\n redshift_hook = PostgresHook(postgres_conn_id=self.redshift_conn_id)\\n\\n for data_check in self.data_checks:\\n records = redshift_hook.get_records(data_check[\\\"sql\\\"])\\n num_records = records[0][0]\\n\\n assert (num_records != data_check[\\\"expected_result\\\"]) , \\\\\\n f\\\"Data quality test returned unexpected result.\\\"\\n\\n self.log.info('DataQualityOperator not implemented yet')\",\n \"def end_test(self, name: str, attributes: dict) -> None:\\n test_case_key = tm4j_config_helpers.get_tm4j_test_case_key(attributes)\\n\\n tm4j_api.configure_tm4j_api(api_access_key=self.tm4j_access_key, project_key=self.tm4j_project_key)\\n\\n try:\\n if not self.tm4j_test_cycle_key:\\n self.tm4j_test_cycle_key = tm4j_test_cycle_helpers.get_tm4j_test_cycle_key(\\n parallel_execution=self.tm4j_parallel_execution_support,\\n test_cycle_key_file_path=self.tm4j_shared_test_cycle_key_file_path,\\n tm4j_test_cycle_name=self.tm4j_test_cycle_name\\n )\\n\\n tm4j_api.create_test_execution_result(\\n test_cycle_key=self.tm4j_test_cycle_key,\\n test_case_key=test_case_key,\\n execution_status=attributes[\\\"status\\\"],\\n actual_end_date=datetime.strptime(attributes[\\\"endtime\\\"], \\\"%Y%m%d %H:%M:%S.%f\\\").strftime(\\n \\\"%Y-%m-%dT%H:%M:%SZ\\\"\\n ),\\n execution_time=attributes[\\\"elapsedtime\\\"],\\n comment=attributes[\\\"message\\\"],\\n )\\n except tm4j_response_exceptions.TM4JResponseException as e:\\n print(f\\\"Sorry, test execution reporting for {test_case_key} didn't go well because of: {e.message}\\\")\\n\\n return None\",\n \"async def test_successful_samples(self):\\n self.set_source_parameter(\\\"test_result\\\", [\\\"success\\\"])\\n response = await self.collect(get_request_json_return_value=self.JMETER_JSON)\\n self.assert_measurement(response, value=\\\"242\\\", entities=[])\",\n \"def run(ceph_cluster, **kw):\\n log.info(run.__doc__)\\n config = kw[\\\"config\\\"]\\n test_pass = 0\\n run_iterations = config[\\\"run_iteration\\\"]\\n min_pass = 1\\n\\n log.info(f\\\"Test is configured to run {run_iterations} iterations\\\")\\n log.info(\\n f\\\"Due to unpredictability of ceph osd df stats,\\\"\\n f\\\" and varying changes observed in stats of other OSDs and nodes, \\\"\\n f\\\" it is difficult to verify the changes with a fixed degree of allowed deviation, \\\"\\n f\\\" therefore, the test is expected to pass at least 70% of time.\\\"\\n f\\\" \\\\n Minimum number of pass runs: {min_pass}\\\"\\n )\\n for i in range(run_iterations):\\n log.info(f\\\"--------- Test Iteration: {i+1} -----------\\\")\\n test_result = run_test(ceph_cluster, **kw)\\n if not test_result:\\n test_pass += 1\\n break\\n\\n summary_text = (\\n f\\\"Test could only pass {test_pass} out of {run_iterations} of times,\\\"\\n f\\\" expected min pass: {min_pass}\\\"\\n )\\n if test_pass < min_pass:\\n log.error(summary_text)\\n return 1\\n log.info(summary_text)\\n return 0\",\n \"def do_test(self):\\n\\t\\tshutit_global.shutit_global_object.yield_to_draw()\\n\\t\\tif not self.build['dotest']:\\n\\t\\t\\tself.log('Tests configured off, not running',level=logging.DEBUG)\\n\\t\\t\\treturn\\n\\t\\t# Test in reverse order\\n\\t\\tself.log('PHASE: test', level=logging.DEBUG)\\n\\t\\tself.stop_all()\\n\\t\\tself.start_all()\\n\\t\\tfor module_id in self.module_ids(rev=True):\\n\\t\\t\\t# Only test if it's installed.\\n\\t\\t\\tif self.is_installed(self.shutit_map[module_id]):\\n\\t\\t\\t\\tself.log('RUNNING TEST ON: ' + module_id, level=logging.DEBUG)\\n\\t\\t\\t\\tself.login(prompt_prefix=module_id,command=shutit_global.shutit_global_object.bash_startup_command,echo=False)\\n\\t\\t\\t\\tif not self.shutit_map[module_id].test(self):\\n\\t\\t\\t\\t\\tself.fail(module_id + ' failed on test', shutit_pexpect_child=self.get_shutit_pexpect_session_from_id('target_child').pexpect_child) # pragma: no cover\\n\\t\\t\\t\\tself.logout(echo=False)\",\n \"def test_run_process(self):\\n workflow = self.get_workflow(\\n \\\"\\\"\\\"file://result <- file://source\\n echo result > result\\n \\\"\\\"\\\")\\n process = workflow._processes[0]\\n create_tuttle_dirs()\\n workflow.run_process(process)\\n assert path.isfile(\\\"result\\\")\",\n \"def test_run(make_runner: Callable[..., TargetFunctionRunner]) -> None:\\n runner = make_runner(target, use_instances=True)\\n run_info = TrialInfo(config=2, instance=\\\"test\\\", seed=0, budget=0.0)\\n\\n # submit runs! then get the value\\n runner.submit_trial(run_info)\\n result = next(runner.iter_results(), None)\\n\\n assert result is not None\\n\\n run_info, run_value = result\\n\\n assert run_value.cost == 4\\n assert run_value.status == StatusType.SUCCESS\",\n \"def execute(self):\\n\\n self._status = 'Running'\\n\\n for test_case in self._test_cases.values():\\n try:\\n test_case.execute()\\n except Failure as e:\\n self._status = 'Fail'\\n self._message = ('The \\\"{0}\\\" test case in the test plan \\\"{1}\\\" failed with the '\\n 'message: \\\"{2}\\\"'.format(test_case.name, self.name, e.msg))\\n except FatalError as e:\\n self._status = 'Fail'\\n self._message = ('The \\\"{0}\\\" test case in the test plan \\\"{1}\\\" encountered the fatal '\\n 'error: \\\"{2}\\\"'.format(test_case.name, self.name, e.msg))\\n raise FatalError(self._message)\\n\\n if self._status == 'Fail':\\n raise Failure(self._message)\\n\\n self._status = 'Pass'\",\n \"def execute (self, context):\\n # call the main import function. This function should work\\n # independent of this context-manager/operator logic.\\n category = context.scene.dsf_category\\n filepath = self.properties.filepath\\n (libdir, srpath) = self.split_scene_filepath (filepath)\\n drpath = self.construct_data_path (srpath, category)\\n data_rpath = os.path.join (\\\"/\\\", drpath)\\n scene_rpath = os.path.join (\\\"/\\\", srpath)\\n log.info (\\\"libdir: %s\\\", libdir)\\n log.info (\\\"scene_rpath: %s\\\", scene_rpath)\\n log.info (\\\"data_rpath: %s\\\", data_rpath)\\n scale = context.scene.dsf_scale\\n bpy.ops.dsf.export_props (scene_path = scene_rpath,\\\\\\n data_path = data_rpath, base_dir = libdir, scale = scale)\\n return { 'FINISHED' }\",\n \"def runTest(self):\\n try:\\n print('TestCase runTest===>')\\n self.test_runner.run_test(self.testcase_dict)\\n\\n finally:\\n self.meta_data = getattr(self.test_runner.http_client_session, \\\"meta_data\\\", {})\",\n \"def test_job_pass(self):\\n with self.settings(JOBS_ROOT=os.path.join(settings.BASE_DIR, \\\"extras/tests/dummy_jobs\\\")):\\n\\n module = \\\"test_pass\\\"\\n name = \\\"TestPass\\\"\\n job_class = get_job(f\\\"local/{module}/{name}\\\")\\n\\n job_result = JobResult.objects.create(\\n name=job_class.class_path,\\n obj_type=self.job_content_type,\\n user=None,\\n job_id=uuid.uuid4(),\\n )\\n\\n run_job(data={}, request=None, commit=False, job_result_pk=job_result.pk)\\n job_result.refresh_from_db()\\n self.assertEqual(job_result.status, JobResultStatusChoices.STATUS_COMPLETED)\",\n \"def run_test_tool(self, cmd, expected_status=0 ):\\n\\n status, output = self.target.run(cmd)\\n self.assertEqual(status, expected_status, msg='\\\\n'.join([cmd, output]))\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6657738","0.65140593","0.6481782","0.6363065","0.62938815","0.6101981","0.60296535","0.6014694","0.5991694","0.5981408","0.59577173","0.59577173","0.59303653","0.5891765","0.58808815","0.5873598","0.5869656","0.5845078","0.57987034","0.57243335","0.5679734","0.56615424","0.5643614","0.5642287","0.56356597","0.5628104","0.5623559","0.56075996","0.55094033","0.5507437","0.54964745","0.54524094","0.544309","0.54350275","0.54334205","0.5431525","0.54312664","0.54011106","0.5400395","0.5382198","0.53752744","0.5374881","0.53477013","0.5345715","0.5344904","0.53417015","0.5335482","0.5335296","0.53098506","0.5307982","0.53038013","0.5294966","0.5292073","0.52855474","0.5284107","0.52729625","0.5271837","0.5269617","0.5263064","0.52582383","0.52529883","0.5252384","0.52507293","0.52427024","0.52419245","0.52059525","0.52056336","0.5204252","0.5196329","0.5196234","0.5182673","0.51765275","0.51726925","0.51710725","0.51673496","0.51657176","0.51651424","0.51619726","0.51521116","0.5149044","0.5146795","0.51437145","0.5136465","0.5130385","0.51260054","0.51258147","0.5124069","0.5104864","0.51003075","0.50991386","0.50974095","0.5097251","0.5096845","0.509379","0.509301","0.50920177","0.508349","0.50713265","0.5070484","0.5069886"],"string":"[\n \"0.6657738\",\n \"0.65140593\",\n \"0.6481782\",\n \"0.6363065\",\n \"0.62938815\",\n \"0.6101981\",\n \"0.60296535\",\n \"0.6014694\",\n \"0.5991694\",\n \"0.5981408\",\n \"0.59577173\",\n \"0.59577173\",\n \"0.59303653\",\n \"0.5891765\",\n \"0.58808815\",\n \"0.5873598\",\n \"0.5869656\",\n \"0.5845078\",\n \"0.57987034\",\n \"0.57243335\",\n \"0.5679734\",\n \"0.56615424\",\n \"0.5643614\",\n \"0.5642287\",\n \"0.56356597\",\n \"0.5628104\",\n \"0.5623559\",\n \"0.56075996\",\n \"0.55094033\",\n \"0.5507437\",\n \"0.54964745\",\n \"0.54524094\",\n \"0.544309\",\n \"0.54350275\",\n \"0.54334205\",\n \"0.5431525\",\n \"0.54312664\",\n \"0.54011106\",\n \"0.5400395\",\n \"0.5382198\",\n \"0.53752744\",\n \"0.5374881\",\n \"0.53477013\",\n \"0.5345715\",\n \"0.5344904\",\n \"0.53417015\",\n \"0.5335482\",\n \"0.5335296\",\n \"0.53098506\",\n \"0.5307982\",\n \"0.53038013\",\n \"0.5294966\",\n \"0.5292073\",\n \"0.52855474\",\n \"0.5284107\",\n \"0.52729625\",\n \"0.5271837\",\n \"0.5269617\",\n \"0.5263064\",\n \"0.52582383\",\n \"0.52529883\",\n \"0.5252384\",\n \"0.52507293\",\n \"0.52427024\",\n \"0.52419245\",\n \"0.52059525\",\n \"0.52056336\",\n \"0.5204252\",\n \"0.5196329\",\n \"0.5196234\",\n \"0.5182673\",\n \"0.51765275\",\n \"0.51726925\",\n \"0.51710725\",\n \"0.51673496\",\n \"0.51657176\",\n \"0.51651424\",\n \"0.51619726\",\n \"0.51521116\",\n \"0.5149044\",\n \"0.5146795\",\n \"0.51437145\",\n \"0.5136465\",\n \"0.5130385\",\n \"0.51260054\",\n \"0.51258147\",\n \"0.5124069\",\n \"0.5104864\",\n \"0.51003075\",\n \"0.50991386\",\n \"0.50974095\",\n \"0.5097251\",\n \"0.5096845\",\n \"0.509379\",\n \"0.509301\",\n \"0.50920177\",\n \"0.508349\",\n \"0.50713265\",\n \"0.5070484\",\n \"0.5069886\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.5427483"},"document_rank":{"kind":"string","value":"37"}}},{"rowIdx":26093,"cells":{"query":{"kind":"string","value":"Returns the sum of each individual digit in a given integer n"},"document":{"kind":"string","value":"def sum_of_digits(n):\n return sum(int(c) for c in str(n))"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def digitSum ( n ) :\n return sum ( map ( int , str ( n ) ) )","def digit_sum(n):\n\treturn sum(int(c) for c in str(n))","def digit_sum(n):\n sum_of_digits = 0\n for c in str(n):\n sum_of_digits += int(c)\n return sum_of_digits","def digit_sum(n):\n s = 0\n while n:\n s += n % 10\n n //= 10\n return s","def sum_of_digits_in_number(n: int) -> int:\n return sum(int(digit) for digit in str(n))","def sum_digits(n):\n digits = [int(i) for i in str(n)]\n return sum(digits)","def sum_digits(n):\n sum = 0\n while n > 0:\n num = n % 10\n sum += num\n n //= 10\n return sum","def sum_of_digits(n):\n rest_of_num, last_num = split(n)\n if rest_of_num < 10:\n \treturn last_num + rest_of_num\n return last_num + sum_of_digits(rest_of_num)","def digital_sum(n):\n r = 0\n while n:\n r, n = r + n % 10, n // 10\n return r","def sum_digits(n):\n if (n < 10):\n return n\n else:\n all_but_last, last = split(n)\n return sum_digits(all_but_last) + last","def sum_integer(n):\n return sum([int(elem) for elem in str(n)])","def factorial_digit_sum(n):\n sum = 0\n factStr = str(factorial(n))\n for digit in factStr:\n sum += int(digit)\n return sum","def add_digits(n):\n return sum([int(d) for d in str(n)])","def sum_digits(n):\n \"*** YOUR CODE HERE ***\"\n count=0\n length=len(str(n))\n last=0\n sum=0\n while count<length:\n last=n%10\n n//=10\n sum+=last\n count+=1\n return sum","def sum_digits(n):\n num = n\n incTen = 1\n summy = 0\n if num > 10:\n while incTen * 10 < num:\n incTen = incTen * 10\n while incTen >= 10:\n summy += num // incTen\n num = num % incTen\n incTen = incTen // 10\n summy += num\n return summy\n elif num == 10:\n return 1\n else:\n return num","def sum_to_n(n):\n total = 0\n for i in range(1,n+1):\n total += i\n return total","def integer_sum(n):\n\n sum = 0\n k = 0\n\n # INVARIANT\n # The sum of far is equal to the sum of the first k integer numbers\n # VARIANT: n-k\n #\n while (k!=n):\n k += 1\n sum += k\n\n return sum","def sum_to(n):\n the_sum = 0\n for counter in range(n+1):\n the_sum = the_sum + counter\n return the_sum","def sum_of_digit_powers_in_number(n: int, power: int) -> int:\n return sum(int(digit) ** power for digit in str(n))","def sumDigit():","def sumTo(n):\n\n sum_all = (n * (n+1))/2\n\n return sum_all","def sumTo(n):\n \n the_sum = 0 #current sum\n a_number = 1 #where we are\n while a_number <= n:\n the_sum += a_number\n a_number += 1\n return the_sum","def square_digit_sum(number):\n return sum(precomputed_digit_squares[digit] for digit in str(number))","def sum_n_numbers():\n n=int(input(\"Enter a number:\"))\n s=0\n for i in range(n+1):\n s+=i\n print(\"sum=\",s)","def sum_of_nth( n ):\n if n > 0:\n return sum( range(n + 1) )\n else:\n return 0","def sumTotal(n):\n\n sum_total = 0\n\n for i in range(1, n+1):\n sum_total = sum_total + i\n\n return sum_total","def fn(n):\n digits = [int(x) for x in str(n)]\n for i in reversed(range(len(digits)//2+1)): \n if digits[i] < 9: break \n else: return 10*n + 11\n digits[i] = digits[~i] = digits[i] + 1\n for ii in range(i): \n digits[~ii] = digits[ii]\n for ii in range(i+1, len(digits)//2+1): \n digits[ii] = digits[~ii] = 0\n return int(\"\".join(map(str, digits)))","def digit(number: int, n: int) -> int:\n return number // 10 ** n % 10","def subtract_product_and_sum(n):\n product = 1\n addition = 0\n\n while n > 0:\n element = n % 10\n n = n // 10\n\n product *= element\n addition += element\n \n return product - addition","def luhn_sum(n):\n rest_of_num, last_num = split(n)\n\n if rest_of_num == 0:\n \treturn last_num\n else:\n \treturn last_num + luhn_double_sum(rest_of_num)","def get_sum_of_digits(number):\n return sum(int(digit) for digit in str(number))","def digit_sum(x):\n s = 0\n while x>0:\n s = s+(x%10)\n x = x//10\n\n return s","def total(n):\n if n < 0:\n return None\n else:\n result = 0\n for i in range(n + 1):\n result += i\n return result","def total(n):\n if n < 0:\n return None\n else:\n result = 0\n for i in range(n + 1):\n result += i\n return result","def luhn_digits(n):\n\n digits = [int(i) for i in str(n)]\n\n # First, reverse the list of digits.\n digits.reverse()\n\n # Double the value of every second digit.\n digits = apply_to_odd_positions(double, digits)\n\n # If the result of this doubling operation is greater than 9 then\n # add the digits of the result.\n digits = apply_to_odd_positions(sum_digits, digits)\n\n return digits","def calculate_digits_sum(number: int) -> int:\n\n # Return value\n ret = 0\n\n while number != 0:\n # Extract the last digit number and add it to ret\n ret += number % 10\n\n # Delete the last digit of the number\n number //= 10\n\n return ret","def sum_numbers_one_to_ten():\n sum=0\n for num in range(1,11):\n sum=sum+num\n return sum\n pass","def twenty():\r\n \r\n n = 100\r\n factorial = 1\r\n sum = 0\r\n \r\n while n > 0:\r\n factorial *= n\r\n n -= 1\r\n \r\n for c in str(factorial):\r\n sum += int(c)\r\n \r\n return sum","def sum_of_squares(n):\n result = i = 0\n while i < n:\n result += i\n i += 1\n return result","def sum(n):\n if n == 0:\n return 0\n return sum(n - 1) + n","def countDigits(n):\n digits = [0]*10\n while n > 0:\n digits[n%10] += 1\n n = n//10\n return digits","def sum_natural(n):\n total, curr = 0 , 1\n\n while curr <= n:\n total, curr = total + curr, curr + 1\n return total","def luhn_double_sum(n):\n rest_of_num, last_num = split(n)\n\n doubled_last_num = last_num*2\n if (doubled_last_num) >= 10:\n \tdoubled_last_num = sum_of_digits(doubled_last_num)\n if rest_of_num == 0:\n \treturn doubled_last_num\n return doubled_last_num + luhn_sum(rest_of_num)","def sum_of_digits(number):\n \n if number < 0:\n number = -number\n \n digit_sum = 0\n while True:\n if number == 0:\n break\n digit = number % 10 # Get the digit\n digit_sum = digit_sum + digit # Accumulate it into the sum\n number = number // 10 # Get ready for the next digit\n \n return digit_sum","def sum_of_digits(number):\n # Students: While you are welcome to try to understand this\n # function definition, all you have to do is trust\n # that the green doc-string is correct (it is!).\n if number < 0:\n number = -number\n\n digit_sum = 0\n while True:\n if number == 0:\n break\n digit = number % 10 # Get the digit\n digit_sum = digit_sum + digit # Accumulate it into the sum\n number = number // 10 # Get ready for the next digit\n\n return digit_sum","def ndigits(n):\n return len(str(abs(n)))","def sum_of_squares(n):\n sum = 0\n\n for i in range(0,n):\n sum += i*i\n\n return sum","def count_digits(n):\n count = 0\n n=abs(n)\n while n!=0:\n count += 1\n n = n // 10\n return count","def sum_squares(num):\n sum = 0\n while (num != 0):\n sum += math.pow((num % 10), 2)\n num = num/10\n return int(sum)","def sum_n_m(n, m):\n total = 0\n for i in range(n, m+1):\n total += i\n return total","def digits(n, base=10):\n if n == 0:\n return 1\n\n n = abs(n)\n if base != 10:\n digits = math.log(n, base)\n else:\n digits = math.log10(n)\n return int(digits) + 1","def calculate(x: int) -> int:\n\n digits = list(map(int, list(str(x))))\n return sum(list(map(lambda a: a**2, digits)))","def count_digits(n):\n return len(str(n))","def row_sum_odd_numbers(n):\n return n**3","def sum_of_squares(n):\n return (n * (n+1) * (2*n + 1)) / 6","def Sum_Numbers_x_Power_Digits(x):\n totalSum = 0 \n for i in xrange(10, 999999):\n if i == sum([int(j)**x for j in str(i)]):\n totalSum += i\n return totalSum","def digitDegree(n, i=0):\n\n\n if len(str(n)) == 1:\n return i\n \n next_n = 0\n \n for char in str(n):\n next_n += int(char)\n \n i += 1\n \n return digitDegree(next_n, i)","def summation( n, exponent ):\n result = 0\n num = 1\n while num < n + 1:\n result += num ** exponent\n num += 1\n return result","def sumDigits(s):\n sum_digit = 0\n\n for e in s:\n try:\n sum_digit += int(e)\n except ValueError:\n continue\n return sum_digit","def cut_number(n):\n i = 0\n while True:\n cur = n % 10\n ahead = n // 10 % 10\n if cur > ahead:\n i += 1\n break\n i += 1\n n //= 10\n return i","def summation_i_squared(n):\n if (type(n) is not int) or (n is None) or (n < 1):\n return None\n else:\n numbers = range(1, n + 1)\n result = 0\n result = map(lambda i: i ** 2, numbers)\n return sum(result)","def solution(n):\n i = 1\n j = 2\n sum = 0\n while j <= n:\n if j % 2 == 0:\n sum += j\n i, j = j, i + j\n\n return sum","def sumDigits(s):\n _sum = 0\n for char in s:\n try:\n _sum += int(char)\n except:\n pass\n return _sum","def square_of_sum(n):\n return ((n * (n+1)) / 2)**2","def sum_odds( n ):\n result = 0\n num = 1\n while num < n + 1:\n result += num\n num += 2\n return result","def get_digit(n):\n \"\"\"\n 9 - 9\n 10-99 - 2*90 \n 100-999 - 3*900\n \"\"\"\n i = 0\n start = 0\n done = False\n while not done:\n step = (i+1)*9*(10**i)\n if start + step > n:\n done = True\n else:\n start += step\n i += 1\n\n num_digits = i + 1\n\n offset = n - start\n number = offset // num_digits + \\\n (10**(num_digits-1) if (num_digits - 1) else 0)\n _i = (offset - 1) % num_digits\n return int(str(number)[_i])","def fakultet (n = 1):\n sum = 1\n for i in range(n, 1, -1):\n sum *= i\n return sum","def cbd(n):\n return reduce(lambda a,b: a+b, \\\n [int(n) * (2 ** int(i)) for n,i in enumerate(str(n)[::-1])])","def get_nth_digit_hex(n: int) -> int:\n res = (\n 4 * get_partial_sum(n, 1)\n - 2 * get_partial_sum(n, 4)\n - get_partial_sum(n, 5)\n - get_partial_sum(n, 6)\n )\n res = res - math.floor(res)\n\n return math.floor(res * 16)","def sumDigits(s):\n sum = 0\n for c in s:\n try: \n sum += int(c)\n except ValueError:\n continue\n return sum","def find_digits(a,b,n):\n res = []\n\n c = a//b\n res.append(c)\n i = 0 \n while i < n:\n a -= b*c \n if a == 0:\n return res\n if a < b:\n a *= 10\n c = a//b\n res.append(c)\n i += 1\n \n return res","def square_digits(num):\n nums = ''\n for n in str(num):\n nums += str(int(n)* int(n))\n return int(nums)","def ten_pairs(n):\n def digit_in_n(num, i):\n if num < 10:\n return 1 if num == i else 0\n else:\n if num % 10 == i:\n return 1 + digit_in_n(num // 10, i)\n else:\n return digit_in_n(num // 10, i)\n\n def pairs_calc(num):\n if num <= 0:\n return 0\n return digit_in_n(num // 10, 10 - num % 10) + pairs_calc(num // 10)\n return pairs_calc(n)","def get_integers(n: int) -> int:\n l = [int(sqrt(n))]\n val = l[0] * l[0]\n index = 0\n while val != n:\n val = sum([x*x for x in l])\n if val > n:\n l[index] -= 1\n elif val < n:\n index += 1\n l.append(l[index - 1])\n\n return len(l)","def consecutiveNumbersSum(self, N):\n\n count = 0\n # nmax = int(-1 + sqrt(1+8*N)/2)\n # print(nmax)\n n = 1\n n2 = n*(n-1)/2 + n\n while n2 <= N:\n if (N-n2) % n == 0:\n # print(n)\n count += 1\n n += 1\n n2 = n*(n-1)/2 + n\n\n # Note N-(n2-n) % n == N-n2 % n\n return count","def zernike_num_coeff(n):\n \n\tif not (n>=0):\n\t\tprint('Input parameter must be >= 0')\n\t\traise AssertionError() \n \n\treturn sum(xrange(n+1)) + n+1","def sum_of_n():\n try:\n input_value = int(input(\"Enter value for n: \"))\n except:\n return None\n \n sum_int = 0\n\n try:\n for number in range(1, input_value+1): # Positive numbers\n sum_int += number\n \n print(\"The result is:\", sum_int)\n return None\n except:\n return None","def linear_sum(S, n):\n if n == 0:\n return 0\n else:\n return linear_sum(S, n - 1) + S[n - 1]","def digits_product(n):\n out = []\n if n == 1: return 11\n if n == 0: return 10\n while True:\n if n > 7 and isprime(n):return -1\n for i in range(9, 0, -1):\n if i == 1 and len(out) == 0:\n return -1\n if n % i:\n continue\n out.append(str(i))\n n //= i\n break\n if n == 1 and len(out) == 1:\n out.append(str(n))\n break\n elif n == 1:break\n return int(''.join(sorted(out)))","def linear_sum(S, n):\n if n == 0:\n return 0\n else:\n return linear_sum(S, n-1) + S[n-1]","def unique_digits(n):\n \"*** YOUR CODE HERE ***\"\n count = 0\n for k in range(0, 10):\n if has_digit(n, k):\n count = count + 1\n return count","def solve(n=10):\n return sum(M_N_S(n, d)[2] for d in range(10))","def sum_of_proper_divisors(n):\n\n\tpd = find_divisors(n)\n\n\treturn sum(pd)","def sum_square_difference(n):\n\tdifference = (n-1)*(n)*(n+1)*(3*n+2)/12\n\treturn difference","def last_n_digits(num, n):\n return num%(10**n)","def first_n_digits(num, n):\n return num // 10 ** (int(math.log(num, 10)) - n + 1)","def get_nth_digit(n, digit):\n n = n / 10**(digit-1)\n return n % 10","def sum_series(n, a=0, b=1):\n\tseq = []\n\tnth_term = 0\n\t\n\tfor i in range(0,n+1):\n\t\tif i == 0:\n\t\t\tseq.append(a)\n\t\tif i == 1:\n\t\t\tseq.append(b)\n\t\tif i > 1:\n\t\t\tnth_term = seq[-1] + seq[-2]\n\t\t\tseq.append(nth_term)\n\t\n\tprint(seq)\n\tprint(seq[n])\n\treturn(seq[n])","def get_squares(n):\n\n return sum([i * i for i in range(n)])","def sum_square_dif(n):\n sum_square = 0\n square_sum = 0\n for i in range(1, n+1):\n sum_square += i**2\n square_sum += i\n return (square_sum ** 2) - sum_square","def ndigits(x):\n if type(x) != int: # basic error handling of non-int\n print('The value of x is not an integer')\n return None\n if abs(x) < 10:\n return 1 # single digit case\n x_recursive = int((x - x % 10)/10) # remove last digit using modulo\n return 1 + ndigits(x_recursive)","def multiples_sum_3_5(n):\n\tsum = 0\n\tfor x in range(1,n):\n\t\tif (x%3==0) or (x%5==0):\n\t\t\tsum += x\n\treturn sum","def sum_of_numbers(numbers):\r\n return sum(numbers)","def get_n_digit(num):\n cnt = 0\n while num & 1 != 1:\n num >>= 1\n cnt += 1\n # print(cnt)\n return cnt","def powerize(n, p):\n return sum(int(d)**p for d in str(n))","def fn(n):\n if n == 0: return 1\n return sum(fn(i)*fn(n-i-1) for i in range(n))","def get_total_complexity(n):\n total = 0\n for i in range(1, n + 1):\n total += get_complexity(i)\n return total","def count_divisible_digits(n, m):\n result = 0\n if m == 0:\n return 0\n n = abs(n)\n while n != 0:\n temp = n % 10\n if temp % m == 0:\n result += 1\n n = n // 10\n return result","def count_digit(x, i):\n \"\"\" GRAPSTE TON KWDIKA SAS APO KATW \"\"\"\n su = 0\n s = 0\n k = x\n while(i>1):\n x = x//10\n i = i-1\n s = x%10\n while(k>0):\n if((k%10)==s):\n su = su + 1\n k = k//10\n return su","def sum_series(n,v1=0,v2=1):\n\tL1=v2\n\tL2=v1\n\tif n<0:\n\t\tprint(\"please enter positive int value\")\n\n\telif n==0:\n\t\treturn v1\n\n\telif n==1:\n\t\treturn v2\n\n\telse:\n\t\tfor i in range(n-1):\n\t\t\tC=L1+L2\n\t\t\tL2=L1\n\t\t\tL1=C\n\t\treturn C"],"string":"[\n \"def digitSum ( n ) :\\n return sum ( map ( int , str ( n ) ) )\",\n \"def digit_sum(n):\\n\\treturn sum(int(c) for c in str(n))\",\n \"def digit_sum(n):\\n sum_of_digits = 0\\n for c in str(n):\\n sum_of_digits += int(c)\\n return sum_of_digits\",\n \"def digit_sum(n):\\n s = 0\\n while n:\\n s += n % 10\\n n //= 10\\n return s\",\n \"def sum_of_digits_in_number(n: int) -> int:\\n return sum(int(digit) for digit in str(n))\",\n \"def sum_digits(n):\\n digits = [int(i) for i in str(n)]\\n return sum(digits)\",\n \"def sum_digits(n):\\n sum = 0\\n while n > 0:\\n num = n % 10\\n sum += num\\n n //= 10\\n return sum\",\n \"def sum_of_digits(n):\\n rest_of_num, last_num = split(n)\\n if rest_of_num < 10:\\n \\treturn last_num + rest_of_num\\n return last_num + sum_of_digits(rest_of_num)\",\n \"def digital_sum(n):\\n r = 0\\n while n:\\n r, n = r + n % 10, n // 10\\n return r\",\n \"def sum_digits(n):\\n if (n < 10):\\n return n\\n else:\\n all_but_last, last = split(n)\\n return sum_digits(all_but_last) + last\",\n \"def sum_integer(n):\\n return sum([int(elem) for elem in str(n)])\",\n \"def factorial_digit_sum(n):\\n sum = 0\\n factStr = str(factorial(n))\\n for digit in factStr:\\n sum += int(digit)\\n return sum\",\n \"def add_digits(n):\\n return sum([int(d) for d in str(n)])\",\n \"def sum_digits(n):\\n \\\"*** YOUR CODE HERE ***\\\"\\n count=0\\n length=len(str(n))\\n last=0\\n sum=0\\n while count<length:\\n last=n%10\\n n//=10\\n sum+=last\\n count+=1\\n return sum\",\n \"def sum_digits(n):\\n num = n\\n incTen = 1\\n summy = 0\\n if num > 10:\\n while incTen * 10 < num:\\n incTen = incTen * 10\\n while incTen >= 10:\\n summy += num // incTen\\n num = num % incTen\\n incTen = incTen // 10\\n summy += num\\n return summy\\n elif num == 10:\\n return 1\\n else:\\n return num\",\n \"def sum_to_n(n):\\n total = 0\\n for i in range(1,n+1):\\n total += i\\n return total\",\n \"def integer_sum(n):\\n\\n sum = 0\\n k = 0\\n\\n # INVARIANT\\n # The sum of far is equal to the sum of the first k integer numbers\\n # VARIANT: n-k\\n #\\n while (k!=n):\\n k += 1\\n sum += k\\n\\n return sum\",\n \"def sum_to(n):\\n the_sum = 0\\n for counter in range(n+1):\\n the_sum = the_sum + counter\\n return the_sum\",\n \"def sum_of_digit_powers_in_number(n: int, power: int) -> int:\\n return sum(int(digit) ** power for digit in str(n))\",\n \"def sumDigit():\",\n \"def sumTo(n):\\n\\n sum_all = (n * (n+1))/2\\n\\n return sum_all\",\n \"def sumTo(n):\\n \\n the_sum = 0 #current sum\\n a_number = 1 #where we are\\n while a_number <= n:\\n the_sum += a_number\\n a_number += 1\\n return the_sum\",\n \"def square_digit_sum(number):\\n return sum(precomputed_digit_squares[digit] for digit in str(number))\",\n \"def sum_n_numbers():\\n n=int(input(\\\"Enter a number:\\\"))\\n s=0\\n for i in range(n+1):\\n s+=i\\n print(\\\"sum=\\\",s)\",\n \"def sum_of_nth( n ):\\n if n > 0:\\n return sum( range(n + 1) )\\n else:\\n return 0\",\n \"def sumTotal(n):\\n\\n sum_total = 0\\n\\n for i in range(1, n+1):\\n sum_total = sum_total + i\\n\\n return sum_total\",\n \"def fn(n):\\n digits = [int(x) for x in str(n)]\\n for i in reversed(range(len(digits)//2+1)): \\n if digits[i] < 9: break \\n else: return 10*n + 11\\n digits[i] = digits[~i] = digits[i] + 1\\n for ii in range(i): \\n digits[~ii] = digits[ii]\\n for ii in range(i+1, len(digits)//2+1): \\n digits[ii] = digits[~ii] = 0\\n return int(\\\"\\\".join(map(str, digits)))\",\n \"def digit(number: int, n: int) -> int:\\n return number // 10 ** n % 10\",\n \"def subtract_product_and_sum(n):\\n product = 1\\n addition = 0\\n\\n while n > 0:\\n element = n % 10\\n n = n // 10\\n\\n product *= element\\n addition += element\\n \\n return product - addition\",\n \"def luhn_sum(n):\\n rest_of_num, last_num = split(n)\\n\\n if rest_of_num == 0:\\n \\treturn last_num\\n else:\\n \\treturn last_num + luhn_double_sum(rest_of_num)\",\n \"def get_sum_of_digits(number):\\n return sum(int(digit) for digit in str(number))\",\n \"def digit_sum(x):\\n s = 0\\n while x>0:\\n s = s+(x%10)\\n x = x//10\\n\\n return s\",\n \"def total(n):\\n if n < 0:\\n return None\\n else:\\n result = 0\\n for i in range(n + 1):\\n result += i\\n return result\",\n \"def total(n):\\n if n < 0:\\n return None\\n else:\\n result = 0\\n for i in range(n + 1):\\n result += i\\n return result\",\n \"def luhn_digits(n):\\n\\n digits = [int(i) for i in str(n)]\\n\\n # First, reverse the list of digits.\\n digits.reverse()\\n\\n # Double the value of every second digit.\\n digits = apply_to_odd_positions(double, digits)\\n\\n # If the result of this doubling operation is greater than 9 then\\n # add the digits of the result.\\n digits = apply_to_odd_positions(sum_digits, digits)\\n\\n return digits\",\n \"def calculate_digits_sum(number: int) -> int:\\n\\n # Return value\\n ret = 0\\n\\n while number != 0:\\n # Extract the last digit number and add it to ret\\n ret += number % 10\\n\\n # Delete the last digit of the number\\n number //= 10\\n\\n return ret\",\n \"def sum_numbers_one_to_ten():\\n sum=0\\n for num in range(1,11):\\n sum=sum+num\\n return sum\\n pass\",\n \"def twenty():\\r\\n \\r\\n n = 100\\r\\n factorial = 1\\r\\n sum = 0\\r\\n \\r\\n while n > 0:\\r\\n factorial *= n\\r\\n n -= 1\\r\\n \\r\\n for c in str(factorial):\\r\\n sum += int(c)\\r\\n \\r\\n return sum\",\n \"def sum_of_squares(n):\\n result = i = 0\\n while i < n:\\n result += i\\n i += 1\\n return result\",\n \"def sum(n):\\n if n == 0:\\n return 0\\n return sum(n - 1) + n\",\n \"def countDigits(n):\\n digits = [0]*10\\n while n > 0:\\n digits[n%10] += 1\\n n = n//10\\n return digits\",\n \"def sum_natural(n):\\n total, curr = 0 , 1\\n\\n while curr <= n:\\n total, curr = total + curr, curr + 1\\n return total\",\n \"def luhn_double_sum(n):\\n rest_of_num, last_num = split(n)\\n\\n doubled_last_num = last_num*2\\n if (doubled_last_num) >= 10:\\n \\tdoubled_last_num = sum_of_digits(doubled_last_num)\\n if rest_of_num == 0:\\n \\treturn doubled_last_num\\n return doubled_last_num + luhn_sum(rest_of_num)\",\n \"def sum_of_digits(number):\\n \\n if number < 0:\\n number = -number\\n \\n digit_sum = 0\\n while True:\\n if number == 0:\\n break\\n digit = number % 10 # Get the digit\\n digit_sum = digit_sum + digit # Accumulate it into the sum\\n number = number // 10 # Get ready for the next digit\\n \\n return digit_sum\",\n \"def sum_of_digits(number):\\n # Students: While you are welcome to try to understand this\\n # function definition, all you have to do is trust\\n # that the green doc-string is correct (it is!).\\n if number < 0:\\n number = -number\\n\\n digit_sum = 0\\n while True:\\n if number == 0:\\n break\\n digit = number % 10 # Get the digit\\n digit_sum = digit_sum + digit # Accumulate it into the sum\\n number = number // 10 # Get ready for the next digit\\n\\n return digit_sum\",\n \"def ndigits(n):\\n return len(str(abs(n)))\",\n \"def sum_of_squares(n):\\n sum = 0\\n\\n for i in range(0,n):\\n sum += i*i\\n\\n return sum\",\n \"def count_digits(n):\\n count = 0\\n n=abs(n)\\n while n!=0:\\n count += 1\\n n = n // 10\\n return count\",\n \"def sum_squares(num):\\n sum = 0\\n while (num != 0):\\n sum += math.pow((num % 10), 2)\\n num = num/10\\n return int(sum)\",\n \"def sum_n_m(n, m):\\n total = 0\\n for i in range(n, m+1):\\n total += i\\n return total\",\n \"def digits(n, base=10):\\n if n == 0:\\n return 1\\n\\n n = abs(n)\\n if base != 10:\\n digits = math.log(n, base)\\n else:\\n digits = math.log10(n)\\n return int(digits) + 1\",\n \"def calculate(x: int) -> int:\\n\\n digits = list(map(int, list(str(x))))\\n return sum(list(map(lambda a: a**2, digits)))\",\n \"def count_digits(n):\\n return len(str(n))\",\n \"def row_sum_odd_numbers(n):\\n return n**3\",\n \"def sum_of_squares(n):\\n return (n * (n+1) * (2*n + 1)) / 6\",\n \"def Sum_Numbers_x_Power_Digits(x):\\n totalSum = 0 \\n for i in xrange(10, 999999):\\n if i == sum([int(j)**x for j in str(i)]):\\n totalSum += i\\n return totalSum\",\n \"def digitDegree(n, i=0):\\n\\n\\n if len(str(n)) == 1:\\n return i\\n \\n next_n = 0\\n \\n for char in str(n):\\n next_n += int(char)\\n \\n i += 1\\n \\n return digitDegree(next_n, i)\",\n \"def summation( n, exponent ):\\n result = 0\\n num = 1\\n while num < n + 1:\\n result += num ** exponent\\n num += 1\\n return result\",\n \"def sumDigits(s):\\n sum_digit = 0\\n\\n for e in s:\\n try:\\n sum_digit += int(e)\\n except ValueError:\\n continue\\n return sum_digit\",\n \"def cut_number(n):\\n i = 0\\n while True:\\n cur = n % 10\\n ahead = n // 10 % 10\\n if cur > ahead:\\n i += 1\\n break\\n i += 1\\n n //= 10\\n return i\",\n \"def summation_i_squared(n):\\n if (type(n) is not int) or (n is None) or (n < 1):\\n return None\\n else:\\n numbers = range(1, n + 1)\\n result = 0\\n result = map(lambda i: i ** 2, numbers)\\n return sum(result)\",\n \"def solution(n):\\n i = 1\\n j = 2\\n sum = 0\\n while j <= n:\\n if j % 2 == 0:\\n sum += j\\n i, j = j, i + j\\n\\n return sum\",\n \"def sumDigits(s):\\n _sum = 0\\n for char in s:\\n try:\\n _sum += int(char)\\n except:\\n pass\\n return _sum\",\n \"def square_of_sum(n):\\n return ((n * (n+1)) / 2)**2\",\n \"def sum_odds( n ):\\n result = 0\\n num = 1\\n while num < n + 1:\\n result += num\\n num += 2\\n return result\",\n \"def get_digit(n):\\n \\\"\\\"\\\"\\n 9 - 9\\n 10-99 - 2*90 \\n 100-999 - 3*900\\n \\\"\\\"\\\"\\n i = 0\\n start = 0\\n done = False\\n while not done:\\n step = (i+1)*9*(10**i)\\n if start + step > n:\\n done = True\\n else:\\n start += step\\n i += 1\\n\\n num_digits = i + 1\\n\\n offset = n - start\\n number = offset // num_digits + \\\\\\n (10**(num_digits-1) if (num_digits - 1) else 0)\\n _i = (offset - 1) % num_digits\\n return int(str(number)[_i])\",\n \"def fakultet (n = 1):\\n sum = 1\\n for i in range(n, 1, -1):\\n sum *= i\\n return sum\",\n \"def cbd(n):\\n return reduce(lambda a,b: a+b, \\\\\\n [int(n) * (2 ** int(i)) for n,i in enumerate(str(n)[::-1])])\",\n \"def get_nth_digit_hex(n: int) -> int:\\n res = (\\n 4 * get_partial_sum(n, 1)\\n - 2 * get_partial_sum(n, 4)\\n - get_partial_sum(n, 5)\\n - get_partial_sum(n, 6)\\n )\\n res = res - math.floor(res)\\n\\n return math.floor(res * 16)\",\n \"def sumDigits(s):\\n sum = 0\\n for c in s:\\n try: \\n sum += int(c)\\n except ValueError:\\n continue\\n return sum\",\n \"def find_digits(a,b,n):\\n res = []\\n\\n c = a//b\\n res.append(c)\\n i = 0 \\n while i < n:\\n a -= b*c \\n if a == 0:\\n return res\\n if a < b:\\n a *= 10\\n c = a//b\\n res.append(c)\\n i += 1\\n \\n return res\",\n \"def square_digits(num):\\n nums = ''\\n for n in str(num):\\n nums += str(int(n)* int(n))\\n return int(nums)\",\n \"def ten_pairs(n):\\n def digit_in_n(num, i):\\n if num < 10:\\n return 1 if num == i else 0\\n else:\\n if num % 10 == i:\\n return 1 + digit_in_n(num // 10, i)\\n else:\\n return digit_in_n(num // 10, i)\\n\\n def pairs_calc(num):\\n if num <= 0:\\n return 0\\n return digit_in_n(num // 10, 10 - num % 10) + pairs_calc(num // 10)\\n return pairs_calc(n)\",\n \"def get_integers(n: int) -> int:\\n l = [int(sqrt(n))]\\n val = l[0] * l[0]\\n index = 0\\n while val != n:\\n val = sum([x*x for x in l])\\n if val > n:\\n l[index] -= 1\\n elif val < n:\\n index += 1\\n l.append(l[index - 1])\\n\\n return len(l)\",\n \"def consecutiveNumbersSum(self, N):\\n\\n count = 0\\n # nmax = int(-1 + sqrt(1+8*N)/2)\\n # print(nmax)\\n n = 1\\n n2 = n*(n-1)/2 + n\\n while n2 <= N:\\n if (N-n2) % n == 0:\\n # print(n)\\n count += 1\\n n += 1\\n n2 = n*(n-1)/2 + n\\n\\n # Note N-(n2-n) % n == N-n2 % n\\n return count\",\n \"def zernike_num_coeff(n):\\n \\n\\tif not (n>=0):\\n\\t\\tprint('Input parameter must be >= 0')\\n\\t\\traise AssertionError() \\n \\n\\treturn sum(xrange(n+1)) + n+1\",\n \"def sum_of_n():\\n try:\\n input_value = int(input(\\\"Enter value for n: \\\"))\\n except:\\n return None\\n \\n sum_int = 0\\n\\n try:\\n for number in range(1, input_value+1): # Positive numbers\\n sum_int += number\\n \\n print(\\\"The result is:\\\", sum_int)\\n return None\\n except:\\n return None\",\n \"def linear_sum(S, n):\\n if n == 0:\\n return 0\\n else:\\n return linear_sum(S, n - 1) + S[n - 1]\",\n \"def digits_product(n):\\n out = []\\n if n == 1: return 11\\n if n == 0: return 10\\n while True:\\n if n > 7 and isprime(n):return -1\\n for i in range(9, 0, -1):\\n if i == 1 and len(out) == 0:\\n return -1\\n if n % i:\\n continue\\n out.append(str(i))\\n n //= i\\n break\\n if n == 1 and len(out) == 1:\\n out.append(str(n))\\n break\\n elif n == 1:break\\n return int(''.join(sorted(out)))\",\n \"def linear_sum(S, n):\\n if n == 0:\\n return 0\\n else:\\n return linear_sum(S, n-1) + S[n-1]\",\n \"def unique_digits(n):\\n \\\"*** YOUR CODE HERE ***\\\"\\n count = 0\\n for k in range(0, 10):\\n if has_digit(n, k):\\n count = count + 1\\n return count\",\n \"def solve(n=10):\\n return sum(M_N_S(n, d)[2] for d in range(10))\",\n \"def sum_of_proper_divisors(n):\\n\\n\\tpd = find_divisors(n)\\n\\n\\treturn sum(pd)\",\n \"def sum_square_difference(n):\\n\\tdifference = (n-1)*(n)*(n+1)*(3*n+2)/12\\n\\treturn difference\",\n \"def last_n_digits(num, n):\\n return num%(10**n)\",\n \"def first_n_digits(num, n):\\n return num // 10 ** (int(math.log(num, 10)) - n + 1)\",\n \"def get_nth_digit(n, digit):\\n n = n / 10**(digit-1)\\n return n % 10\",\n \"def sum_series(n, a=0, b=1):\\n\\tseq = []\\n\\tnth_term = 0\\n\\t\\n\\tfor i in range(0,n+1):\\n\\t\\tif i == 0:\\n\\t\\t\\tseq.append(a)\\n\\t\\tif i == 1:\\n\\t\\t\\tseq.append(b)\\n\\t\\tif i > 1:\\n\\t\\t\\tnth_term = seq[-1] + seq[-2]\\n\\t\\t\\tseq.append(nth_term)\\n\\t\\n\\tprint(seq)\\n\\tprint(seq[n])\\n\\treturn(seq[n])\",\n \"def get_squares(n):\\n\\n return sum([i * i for i in range(n)])\",\n \"def sum_square_dif(n):\\n sum_square = 0\\n square_sum = 0\\n for i in range(1, n+1):\\n sum_square += i**2\\n square_sum += i\\n return (square_sum ** 2) - sum_square\",\n \"def ndigits(x):\\n if type(x) != int: # basic error handling of non-int\\n print('The value of x is not an integer')\\n return None\\n if abs(x) < 10:\\n return 1 # single digit case\\n x_recursive = int((x - x % 10)/10) # remove last digit using modulo\\n return 1 + ndigits(x_recursive)\",\n \"def multiples_sum_3_5(n):\\n\\tsum = 0\\n\\tfor x in range(1,n):\\n\\t\\tif (x%3==0) or (x%5==0):\\n\\t\\t\\tsum += x\\n\\treturn sum\",\n \"def sum_of_numbers(numbers):\\r\\n return sum(numbers)\",\n \"def get_n_digit(num):\\n cnt = 0\\n while num & 1 != 1:\\n num >>= 1\\n cnt += 1\\n # print(cnt)\\n return cnt\",\n \"def powerize(n, p):\\n return sum(int(d)**p for d in str(n))\",\n \"def fn(n):\\n if n == 0: return 1\\n return sum(fn(i)*fn(n-i-1) for i in range(n))\",\n \"def get_total_complexity(n):\\n total = 0\\n for i in range(1, n + 1):\\n total += get_complexity(i)\\n return total\",\n \"def count_divisible_digits(n, m):\\n result = 0\\n if m == 0:\\n return 0\\n n = abs(n)\\n while n != 0:\\n temp = n % 10\\n if temp % m == 0:\\n result += 1\\n n = n // 10\\n return result\",\n \"def count_digit(x, i):\\n \\\"\\\"\\\" GRAPSTE TON KWDIKA SAS APO KATW \\\"\\\"\\\"\\n su = 0\\n s = 0\\n k = x\\n while(i>1):\\n x = x//10\\n i = i-1\\n s = x%10\\n while(k>0):\\n if((k%10)==s):\\n su = su + 1\\n k = k//10\\n return su\",\n \"def sum_series(n,v1=0,v2=1):\\n\\tL1=v2\\n\\tL2=v1\\n\\tif n<0:\\n\\t\\tprint(\\\"please enter positive int value\\\")\\n\\n\\telif n==0:\\n\\t\\treturn v1\\n\\n\\telif n==1:\\n\\t\\treturn v2\\n\\n\\telse:\\n\\t\\tfor i in range(n-1):\\n\\t\\t\\tC=L1+L2\\n\\t\\t\\tL2=L1\\n\\t\\t\\tL1=C\\n\\t\\treturn C\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.8983617","0.896262","0.88677377","0.8782588","0.87734056","0.8756498","0.85132176","0.84619224","0.83623177","0.83091414","0.82763064","0.8267195","0.8232159","0.80869514","0.80285734","0.7945917","0.7804985","0.77949435","0.77158666","0.7711733","0.7626172","0.75637996","0.7537441","0.74777824","0.7463601","0.74371994","0.74042183","0.7392572","0.7356597","0.73214257","0.7302476","0.72888315","0.7210026","0.7210026","0.72044814","0.72029805","0.7118994","0.706191","0.7041345","0.70137465","0.7003241","0.69785565","0.6959308","0.6912167","0.6896757","0.6884645","0.6881855","0.6774011","0.6731954","0.6727343","0.6717641","0.671726","0.6683874","0.6646687","0.66084814","0.6590129","0.658901","0.65855324","0.6555936","0.65510654","0.6530666","0.6528159","0.6510315","0.6505371","0.65007395","0.64899266","0.64853704","0.6481104","0.6480919","0.6458629","0.6446945","0.64417243","0.6425482","0.6420677","0.6420237","0.6418937","0.64167744","0.64008135","0.63934064","0.6393207","0.63917315","0.6383329","0.6366231","0.6365968","0.6357264","0.6357207","0.63566196","0.6353212","0.6329816","0.6326796","0.6311411","0.6302547","0.6286425","0.62840146","0.6268948","0.62583846","0.62487453","0.62473404","0.62468964","0.62456024"],"string":"[\n \"0.8983617\",\n \"0.896262\",\n \"0.88677377\",\n \"0.8782588\",\n \"0.87734056\",\n \"0.8756498\",\n \"0.85132176\",\n \"0.84619224\",\n \"0.83623177\",\n \"0.83091414\",\n \"0.82763064\",\n \"0.8267195\",\n \"0.8232159\",\n \"0.80869514\",\n \"0.80285734\",\n \"0.7945917\",\n \"0.7804985\",\n \"0.77949435\",\n \"0.77158666\",\n \"0.7711733\",\n \"0.7626172\",\n \"0.75637996\",\n \"0.7537441\",\n \"0.74777824\",\n \"0.7463601\",\n \"0.74371994\",\n \"0.74042183\",\n \"0.7392572\",\n \"0.7356597\",\n \"0.73214257\",\n \"0.7302476\",\n \"0.72888315\",\n \"0.7210026\",\n \"0.7210026\",\n \"0.72044814\",\n \"0.72029805\",\n \"0.7118994\",\n \"0.706191\",\n \"0.7041345\",\n \"0.70137465\",\n \"0.7003241\",\n \"0.69785565\",\n \"0.6959308\",\n \"0.6912167\",\n \"0.6896757\",\n \"0.6884645\",\n \"0.6881855\",\n \"0.6774011\",\n \"0.6731954\",\n \"0.6727343\",\n \"0.6717641\",\n \"0.671726\",\n \"0.6683874\",\n \"0.6646687\",\n \"0.66084814\",\n \"0.6590129\",\n \"0.658901\",\n \"0.65855324\",\n \"0.6555936\",\n \"0.65510654\",\n \"0.6530666\",\n \"0.6528159\",\n \"0.6510315\",\n \"0.6505371\",\n \"0.65007395\",\n \"0.64899266\",\n \"0.64853704\",\n \"0.6481104\",\n \"0.6480919\",\n \"0.6458629\",\n \"0.6446945\",\n \"0.64417243\",\n \"0.6425482\",\n \"0.6420677\",\n \"0.6420237\",\n \"0.6418937\",\n \"0.64167744\",\n \"0.64008135\",\n \"0.63934064\",\n \"0.6393207\",\n \"0.63917315\",\n \"0.6383329\",\n \"0.6366231\",\n \"0.6365968\",\n \"0.6357264\",\n \"0.6357207\",\n \"0.63566196\",\n \"0.6353212\",\n \"0.6329816\",\n \"0.6326796\",\n \"0.6311411\",\n \"0.6302547\",\n \"0.6286425\",\n \"0.62840146\",\n \"0.6268948\",\n \"0.62583846\",\n \"0.62487453\",\n \"0.62473404\",\n \"0.62468964\",\n \"0.62456024\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.8821321"},"document_rank":{"kind":"string","value":"3"}}},{"rowIdx":26094,"cells":{"query":{"kind":"string","value":"Compute softmax values for each sets of scores in x."},"document":{"kind":"string","value":"def softmax(x):\n # Compute and return softmax(x)\n denom = sum(np.exp(x))\n return [ np.exp(xi)/denom for xi in x ]"},"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 softmax(self, scores):\n\n\n # for each sample, for each class ,caclulate\n # np.exp(scores) : still (n_samples, n_classes)\n\n # axis = 1\n # a00, a01, a02 as a sinlge one to perfrom np_sum\n # which is the same sample \n # sum_exp : still (n_samples, 1)\n\n # softmax = (n_samples, n_classes) / (n_samples, 1) = (n_samples, n_classes) \n\n sum_exp = np.sum(np.exp(scores), axis=1, keepdims=True)\n softmax = np.exp(scores) / sum_exp\n \n return softmax","def softmax(scores):\n exp_score = np.exp(scores)\n return exp_score / np.sum(exp_score)","def softmax(x):\n scoreMatExp = np.exp(np.asarray(x))\n return scoreMatExp / scoreMatExp.sum(0)","def _compute_softmax(scores):\n if not scores:\n return []\n\n max_score = None\n for score in scores:\n if max_score is None or score > max_score:\n max_score = score\n\n exp_scores = []\n total_sum = 0.0\n for score in scores:\n x = np.exp(score - max_score)\n exp_scores.append(x)\n total_sum += x\n\n probs = []\n for score in exp_scores:\n probs.append(score / total_sum)\n return probs","def _compute_softmax(scores):\n if not scores:\n return []\n\n max_score = None\n for score in scores:\n if max_score is None or score > max_score:\n max_score = score\n\n exp_scores = []\n total_sum = 0.0\n for score in scores:\n x = math.exp(score - max_score)\n exp_scores.append(x)\n total_sum += x\n\n probs = []\n for score in exp_scores:\n probs.append(score / total_sum)\n return probs","def _compute_softmax(scores):\n if not scores:\n return []\n\n max_score = None\n for score in scores:\n if max_score is None or score > max_score:\n max_score = score\n\n exp_scores = []\n total_sum = 0.0\n for score in scores:\n x = math.exp(score - max_score)\n exp_scores.append(x)\n total_sum += x\n\n probs = []\n for score in exp_scores:\n probs.append(score / total_sum)\n return probs","def _compute_softmax(scores):\n if not scores:\n return []\n\n max_score = None\n for score in scores:\n if max_score is None or score > max_score:\n max_score = score\n\n exp_scores = []\n total_sum = 0.0\n for score in scores:\n x = math.exp(score - max_score)\n exp_scores.append(x)\n total_sum += x\n\n probs = []\n for score in exp_scores:\n probs.append(score / total_sum)\n return probs","def _compute_softmax(scores):\n if not scores:\n return []\n\n max_score = None\n for score in scores:\n if max_score is None or score > max_score:\n max_score = score\n\n exp_scores = []\n total_sum = 0.0\n for score in scores:\n x = math.exp(score - max_score)\n exp_scores.append(x)\n total_sum += x\n\n probs = []\n for score in exp_scores:\n probs.append(score / total_sum)\n return probs","def softmax(self, x):\n\n out = np.zeros(x.shape)\n for i in range(x.shape[0]):\n max_x = x[i] - np.max(x[i])\n out[i] = np.exp(max_x) / np.sum(np.exp(max_x), axis=0)\n\n return out","def softmax(x):\n x = np.array(x)\n if x.ndim == 1:\n return column_softmax(x)\n else:\n ret_arr = np.array([column_softmax(col) for col in x.T])\n return ret_arr.T","def softmax(x):\n # (n_samples, n_classes)\n if len(x.shape) == 2:\n row_max = np.max(x, axis=1)\n x -= row_max.reshape((x.shape[0], 1))\n x = np.exp(x)\n row_sum = np.sum(x, axis=1)\n x /= row_sum.reshape((x.shape[0], 1))\n # (n_samples, n_tasks, n_classes)\n elif len(x.shape) == 3:\n row_max = np.max(x, axis=2)\n x -= row_max.reshape(x.shape[:2] + (1,))\n x = np.exp(x)\n row_sum = np.sum(x, axis=2)\n x /= row_sum.reshape(x.shape[:2] + (1,))\n return x","def softmax(self, x):\n\n #############################################################################\n # TODO: Implement the softmax function. #\n #############################################################################\n meanValue = np.mean(x, axis=1)\n x = (x.T - meanValue).T\n ExpValue = np.exp(x)\n totalExpValue = np.sum(np.exp(x), axis=1)\n\n probs = (ExpValue.T / totalExpValue).T\n #############################################################################\n # END OF YOUR CODE #\n #############################################################################\n\n return probs","def softmax(x: npt.NDArray) -> npt.NDArray:\n row_wise_max = np.max(x, axis=1).reshape(-1, 1)\n exp_x = np.exp(x - row_wise_max)\n return exp_x / np.sum(exp_x, axis=1).reshape(-1, 1)","def softmax(x):\n xx = x\n x = x.reshape((-1, x.shape[-1]))\n e_x = np.exp(x - np.max(x, 1).reshape(-1, 1))\n res = e_x / e_x.sum(axis=1).reshape(-1, 1)\n return res.reshape(xx.shape)","def softmax(x):\n e_x = np.exp((x.transpose()-x.max(axis=1)).transpose())\n return e_x / np.sum(e_x,axis=1)[:,None]","def softmax(self, x):\n if x.ndim == 1:\n x = x.reshape((1, -1))\n max_x = np.max(x, axis=1).reshape((-1, 1))\n exp_x = np.exp(x - max_x)\n return exp_x / np.sum(exp_x, axis=1).reshape((-1, 1))","def softmax(x):\n e_x = np.exp(x - np.max(x))\n return (e_x / e_x.sum()).tolist()","def softmax(x):\n \"\"\"\"\"\"\n return exp(x) / sum(exp(x), axis=0)","def softmax(x):\n output = []\n denomSum = 0\n for i in range(0, len(x)):\n denomSum += np.exp(x[i])\n\n for j in range(0, len(x)):\n output.append(np.exp(x[j]) / denomSum)\n return np.asarray(output)","def softmax(x):\n #pass # TODO: Compute and return softmax(x)\n return np.exp(x) / np.sum(np.exp(x), axis=0)","def softmax(x):\n e_x = np.exp(x - np.max(x))\n return e_x / e_x.sum(axis=0)","def softmax(x): \n e_x = np.exp(x - np.max(x)) \n return e_x / e_x.sum()","def softmax(x):\n e_x = np.exp(x - np.max(x))\n return e_x / e_x.sum()","def softmax(x):\n pass # TODO: Compute and return softmax(x)\n\n exp_x = np.exp(x)\n sum_x = np.sum(exp_x, axis=0)\n softmax = exp_x/sum_x\n \n return softmax","def softmax(x):\n shape = x.shape\n probs = np.exp(x - np.max(x, axis=len(shape) - 1, keepdims=True))\n probs /= np.sum(probs, axis=len(shape) - 1, keepdims=True)\n return probs","def _softmax(self, x):\n return np.exp(x - np.max(x)) / np.sum(np.exp(x - np.max(x)))","def softmax(x):\n e_x = np.exp(x - np.max(x))\n return e_x / e_x.sum(axis=0)","def softmax(x):\r\n e_x = np.exp(x - np.max(x))\r\n return e_x / e_x.sum()","def softmax(x):\r\n e_x = np.exp(x - np.max(x))\r\n return e_x / e_x.sum()","def softmax(x):\r\n sum_c = np.sum(np.exp(x), axis=1)\r\n sum_c = np.expand_dims(sum_c, axis=1)\r\n pred_x = np.divide(np.exp(x), sum_c)\r\n return pred_x","def softmax(input):\n list_value = []\n len_compute = input.shape[-1]\n shape_input = input.shape\n for x in input.reshape(-1, len_compute):\n # print(x)\n e_x = np.exp(x - np.max(x))\n res = e_x / e_x.sum(axis=0)\n list_value.append(res)\n\n return np.array(list_value).reshape(shape_input)","def softmax(x):\n\n e_x = np.exp(x - np.max(x))\n return e_x / e_x.sum()","def softmax(x):\n shape = np.shape(x)\n numDim = len(shape)\n result = np.zeros_like(x, dtype=float)\n \n if numDim == 1:\n totalSum = 0\n # Sum (e^yi)\n for i in range(len(x)):\n totalSum = totalSum + exp(x[i])\n # e^yi / Sum (e^yi)\n for i in range(len(x)):\n result[i] = exp(x[i])/totalSum\n else:\n xDim = len(x[0])\n totalSum = np.zeros(xDim, dtype=float)\n \n # e^yi\n for i in range(len(x)):\n for j in range(len(x[i])):\n result[i,j] = exp(x[i,j])\n totalSum[j] = totalSum[j] + result[i,j]\n # e^yi / Sum (e^yi)\n for i in range(len(result)):\n for j in range(len(result[i])):\n result[i,j] = result[i,j] / totalSum[j]\n \n return result","def softmax(x):\n e_x = np.exp(x - np.max(x))\n return e_x / e_x.sum(axis=0) # only difference","def softmax(x):\n npX = np.array(x)\n expX = np.exp(x)\n\n return expX/sum(expX)","def _softmax(self,x):\n e_x = np.exp(x - np.max(x))\n return np.nan_to_num(e_x / np.nan_to_num(e_x.sum(axis=0)))","def softmax(x):\n e_x = np.exp(x - np.max(x, axis=1).reshape(-1, 1))\n return e_x / np.sum(e_x, axis=1).reshape(-1, 1)","def softmax(x):\n # https://stackoverflow.com/questions/34968722/softmax-function-python\n return np.exp(x) / np.sum(np.exp(x), axis=0)","def softmax(x):\n return np.exp(x) / np.sum(np.exp(x), axis=0)","def softmax(x):\n e_x = np.exp(x - np.max(x))\n return e_x / e_x.sum()","def softmax(x):\n e_x = np.exp(x - np.max(x))\n return e_x / e_x.sum()","def softmax(x):\n e_x = np.exp(x - np.max(x))\n return e_x / e_x.sum()","def softmax(x):\n e_x = np.exp(x - np.max(x))\n return e_x / e_x.sum()","def softmax(x):\n e_x = np.exp(x - np.max(x))\n return e_x / e_x.sum()","def softmax(x):\n e_x = np.exp(x - np.max(x))\n return e_x / e_x.sum()","def softmax(x):\n e_x = np.exp(x - np.max(x))\n return e_x / e_x.sum()","def softmax(x):\n e_x = np.exp(x - np.max(x))\n return e_x / e_x.sum()","def softmax(x):\n e_x = np.exp(x - np.max(x))\n return e_x / e_x.sum()","def softmax(x):\n e_x = np.exp(x - np.max(x))\n return e_x / e_x.sum()","def softmax(x):\n e_x = np.exp(x - np.max(x))\n return e_x / e_x.sum()","def softmax(x):\n e_x = np.exp(x - np.max(x))\n return e_x / e_x.sum()","def softmax(x):\n e_x = np.exp(x - np.max(x))\n return e_x / e_x.sum()","def softmax(x):\n e_x = np.exp(x - np.max(x))\n return e_x / e_x.sum()","def softmax(x):\n e_x = np.exp(x - np.max(x))\n return e_x / e_x.sum()","def softmax(x):\n e_x = np.exp(x - np.max(x))\n return e_x / e_x.sum()","def softmax(x):\n e_x = np.exp(x - np.max(x))\n return e_x / e_x.sum()","def softmax(x):\n e_x = np.exp(x - np.max(x))\n return e_x / e_x.sum()","def softmax(x):\n e_x = np.exp(x - np.max(x))\n return e_x / e_x.sum()","def softmax(x):\n e_x = np.exp(x - np.max(x))\n return e_x / e_x.sum()","def softmax(x: np.array) -> Tuple[int, List[float]]:\n dist = np.exp(x) / np.sum(np.exp(x))\n y = np.argmax(dist)\n return int(y), dist","def softmax(x):\n e_x = np.exp(x - np.max(x))\n return e_x / e_x.sum(axis=1)","def softmax(x):\n e_x = np.exp(x - np.max(x))\n return e_x / e_x.sum(axis=0)","def softmax(x):\n e_x = np.exp(x - np.max(x))\n return e_x / e_x.sum(axis=0)","def softmax(x):\n e_x = np.exp(x - np.max(x))\n return e_x / e_x.sum(axis=0)","def softmax(x):\n e_x = np.exp(x - np.max(x))\n return e_x / e_x.sum(axis=0)","def softmax(x):\n e_x = np.exp(x - np.max(x))\n return e_x / e_x.sum(axis=0)","def softmax(self, x):\n x_max = np.max(x, axis=-1, keepdims=True)\n e_x = np.exp(x - x_max)\n return e_x / e_x.sum(axis=-1, keepdims=True)","def softmax(x):\n x_exp = (x - x.max(1)[0].view(-1, 1)).exp()\n return x_exp / x_exp.sum(1).view(-1, 1)","def softmax(self, x):\n \"\"\" Meaning: avoid very large exponents by reducing the largest to zero\n and everything else to less than that\"\"\"\n ex = np.exp(x - np.max(x))\n return ex / np.sum(ex, axis = 1, keepdims=True) #Sums over rows np.sum([[0, 1], [0, 5]], axis=1) = array([1, 5])","def softmax(x):\r\n e_x = np.exp(x - np.max(x))\r\n return e_x / e_x.sum(axis=0) # only difference\r","def softmax(x): \n return np.exp(x) / np.sum(np.exp(x), axis=0)","def softmax(x):\n exps = np.exp(x - np.max(x, axis=0))\n return exps / exps.sum(axis=0)","def my_softmax(x):\n x = x - np.max(x)\n exp_x = np.exp(x)\n softmax_x = exp_x / np.sum(exp_x)\n return softmax_x","def softmax(x):\n e_x = np.exp(x - np.max(x))\n return e_x / e_x.sum(axis=0) # only difference","def softmax(x):\n e_x = np.exp(x - np.max(x))\n return e_x / e_x.sum(axis=0) # only difference","def softmax(x):\n e_x = np.exp(x - np.max(x))\n return e_x / e_x.sum(axis=0) # only difference","def softmax(x):\n e_x = np.exp(x - np.max(x))\n return e_x / e_x.sum(axis=0) # only difference","def softmax_my(x):\r\n return np.exp(x) / np.sum(np.exp(x), axis=0)","def softmax(x):\n return np.exp(x) / np.sum(np.exp(x), axis=0)","def softmax(x):\n return np.exp(x) / np.sum(np.exp(x), axis=0)","def softmax(x):\n return np.exp(x) / np.sum(np.exp(x), axis=0)","def softmax(x):\n return np.exp(x) / np.sum(np.exp(x), axis=0)","def softmax(x):\n return np.exp(x) / np.sum(np.exp(x), axis=0)","def softmax(x):\n return np.exp(x) / np.sum(np.exp(x), axis=0)","def softmax(x):\n return np.exp(x) / np.sum(np.exp(x), axis=0)","def softmax(x):\n return np.exp(x) / np.sum(np.exp(x), axis=0)","def softmax(x):\n e_x = np.exp(x - np.max(x))\n return e_x / e_x.sum(axis=0) # only difference","def softmax_loss(scores, y):\r\n N = scores.shape[0] # number of input data\r\n\r\n # compute data loss\r\n shifted_logits = scores - np.max(scores, axis=1, keepdims=True)\r\n Z = np.sum(np.exp(shifted_logits), axis=1, keepdims=True)\r\n log_probs = shifted_logits - np.log(Z)\r\n probs = np.exp(log_probs)\r\n loss = -np.sum(log_probs[range(N), y]) / N\r\n\r\n # Compute gradient of loss function w.r.t. scores\r\n dscores = probs.copy()\r\n dscores[range(N), y] -= 1\r\n dscores /= N\r\n \r\n return loss, dscores","def softmax(x):\n return np.exp(x) / np.sum(np.exp(x), axis=1)","def softmax(x):\n return np.exp(x) / np.sum(np.exp(x), axis=0)\n # return ( x / np.sum(x, axis=0) )","def softmax(x):\n e_x = np.exp(x - np.max(x))\n return old_div(e_x, e_x.sum())","def softmax(x):\n if type(x) == list:\n dim=len(x)\n norm = np.sum(np.exp(x))\n for idx in range(dim):\n x[idx] = np.exp(x[idx])/norm\n elif type(x) == np.ndarray:\n dim=x.shape\n for col in range(dim[1]):\n norm = np.sum(np.exp(x[:, col]))\n for idx in range(dim[0]):\n x[idx, col] = np.exp(x[idx, col])/norm\n else:\n raise Exception('incorrect input')\n return x","def softmax(x):\n e_yi = np.exp(x)\n suma_e_yj = sum(e_yi)\n soft = e_yi/suma_e_yj\n return soft","def softmax(x):\n return np.exp(x)/np.sum(np.exp(x),axis=0)","def softmax(x):\n return np.exp(x)/np.sum(np.exp(x),axis=0)","def softmax_my(x):\n dim = 1\n try:\n dim = x.ndim\n except AttributeError:\n return cal_1D_softmax(x)\n if dim == 1:\n return cal_1D_softmax(x)\n elif dim == 2:\n return cal_2D_softmax(x)","def softmax(x):\n e_x = np.exp(x - np.max(x))\n temp=e_x / e_x.sum(axis=0) # only difference\n \n if np.isnan(temp).any()==True:\n return [0.0,1.0,0.0]\n else:\n return temp","def softmax(x):\r\n e_x = np.exp(x - np.expand_dims(np.max(x, axis=-1), axis=-1))\r\n return e_x / np.expand_dims(e_x.sum(axis=-1), axis=-1) # only difference\r","def softmax(x, stable=0):\n if stable:\n e_x = np.exp(x - np.max(x, axis=0))\n return e_x / e_x.sum(axis=0)\n return np.exp(x) / np.sum(np.exp(x), axis=0)","def softmax(x1, x2, x3, beta):\n xs = np.array((x1, x2, x3))\n num = np.exp(xs * beta)\n den = np.exp(xs * beta).sum()\n return num / den"],"string":"[\n \"def softmax(self, scores):\\n\\n\\n # for each sample, for each class ,caclulate\\n # np.exp(scores) : still (n_samples, n_classes)\\n\\n # axis = 1\\n # a00, a01, a02 as a sinlge one to perfrom np_sum\\n # which is the same sample \\n # sum_exp : still (n_samples, 1)\\n\\n # softmax = (n_samples, n_classes) / (n_samples, 1) = (n_samples, n_classes) \\n\\n sum_exp = np.sum(np.exp(scores), axis=1, keepdims=True)\\n softmax = np.exp(scores) / sum_exp\\n \\n return softmax\",\n \"def softmax(scores):\\n exp_score = np.exp(scores)\\n return exp_score / np.sum(exp_score)\",\n \"def softmax(x):\\n scoreMatExp = np.exp(np.asarray(x))\\n return scoreMatExp / scoreMatExp.sum(0)\",\n \"def _compute_softmax(scores):\\n if not scores:\\n return []\\n\\n max_score = None\\n for score in scores:\\n if max_score is None or score > max_score:\\n max_score = score\\n\\n exp_scores = []\\n total_sum = 0.0\\n for score in scores:\\n x = np.exp(score - max_score)\\n exp_scores.append(x)\\n total_sum += x\\n\\n probs = []\\n for score in exp_scores:\\n probs.append(score / total_sum)\\n return probs\",\n \"def _compute_softmax(scores):\\n if not scores:\\n return []\\n\\n max_score = None\\n for score in scores:\\n if max_score is None or score > max_score:\\n max_score = score\\n\\n exp_scores = []\\n total_sum = 0.0\\n for score in scores:\\n x = math.exp(score - max_score)\\n exp_scores.append(x)\\n total_sum += x\\n\\n probs = []\\n for score in exp_scores:\\n probs.append(score / total_sum)\\n return probs\",\n \"def _compute_softmax(scores):\\n if not scores:\\n return []\\n\\n max_score = None\\n for score in scores:\\n if max_score is None or score > max_score:\\n max_score = score\\n\\n exp_scores = []\\n total_sum = 0.0\\n for score in scores:\\n x = math.exp(score - max_score)\\n exp_scores.append(x)\\n total_sum += x\\n\\n probs = []\\n for score in exp_scores:\\n probs.append(score / total_sum)\\n return probs\",\n \"def _compute_softmax(scores):\\n if not scores:\\n return []\\n\\n max_score = None\\n for score in scores:\\n if max_score is None or score > max_score:\\n max_score = score\\n\\n exp_scores = []\\n total_sum = 0.0\\n for score in scores:\\n x = math.exp(score - max_score)\\n exp_scores.append(x)\\n total_sum += x\\n\\n probs = []\\n for score in exp_scores:\\n probs.append(score / total_sum)\\n return probs\",\n \"def _compute_softmax(scores):\\n if not scores:\\n return []\\n\\n max_score = None\\n for score in scores:\\n if max_score is None or score > max_score:\\n max_score = score\\n\\n exp_scores = []\\n total_sum = 0.0\\n for score in scores:\\n x = math.exp(score - max_score)\\n exp_scores.append(x)\\n total_sum += x\\n\\n probs = []\\n for score in exp_scores:\\n probs.append(score / total_sum)\\n return probs\",\n \"def softmax(self, x):\\n\\n out = np.zeros(x.shape)\\n for i in range(x.shape[0]):\\n max_x = x[i] - np.max(x[i])\\n out[i] = np.exp(max_x) / np.sum(np.exp(max_x), axis=0)\\n\\n return out\",\n \"def softmax(x):\\n x = np.array(x)\\n if x.ndim == 1:\\n return column_softmax(x)\\n else:\\n ret_arr = np.array([column_softmax(col) for col in x.T])\\n return ret_arr.T\",\n \"def softmax(x):\\n # (n_samples, n_classes)\\n if len(x.shape) == 2:\\n row_max = np.max(x, axis=1)\\n x -= row_max.reshape((x.shape[0], 1))\\n x = np.exp(x)\\n row_sum = np.sum(x, axis=1)\\n x /= row_sum.reshape((x.shape[0], 1))\\n # (n_samples, n_tasks, n_classes)\\n elif len(x.shape) == 3:\\n row_max = np.max(x, axis=2)\\n x -= row_max.reshape(x.shape[:2] + (1,))\\n x = np.exp(x)\\n row_sum = np.sum(x, axis=2)\\n x /= row_sum.reshape(x.shape[:2] + (1,))\\n return x\",\n \"def softmax(self, x):\\n\\n #############################################################################\\n # TODO: Implement the softmax function. #\\n #############################################################################\\n meanValue = np.mean(x, axis=1)\\n x = (x.T - meanValue).T\\n ExpValue = np.exp(x)\\n totalExpValue = np.sum(np.exp(x), axis=1)\\n\\n probs = (ExpValue.T / totalExpValue).T\\n #############################################################################\\n # END OF YOUR CODE #\\n #############################################################################\\n\\n return probs\",\n \"def softmax(x: npt.NDArray) -> npt.NDArray:\\n row_wise_max = np.max(x, axis=1).reshape(-1, 1)\\n exp_x = np.exp(x - row_wise_max)\\n return exp_x / np.sum(exp_x, axis=1).reshape(-1, 1)\",\n \"def softmax(x):\\n xx = x\\n x = x.reshape((-1, x.shape[-1]))\\n e_x = np.exp(x - np.max(x, 1).reshape(-1, 1))\\n res = e_x / e_x.sum(axis=1).reshape(-1, 1)\\n return res.reshape(xx.shape)\",\n \"def softmax(x):\\n e_x = np.exp((x.transpose()-x.max(axis=1)).transpose())\\n return e_x / np.sum(e_x,axis=1)[:,None]\",\n \"def softmax(self, x):\\n if x.ndim == 1:\\n x = x.reshape((1, -1))\\n max_x = np.max(x, axis=1).reshape((-1, 1))\\n exp_x = np.exp(x - max_x)\\n return exp_x / np.sum(exp_x, axis=1).reshape((-1, 1))\",\n \"def softmax(x):\\n e_x = np.exp(x - np.max(x))\\n return (e_x / e_x.sum()).tolist()\",\n \"def softmax(x):\\n \\\"\\\"\\\"\\\"\\\"\\\"\\n return exp(x) / sum(exp(x), axis=0)\",\n \"def softmax(x):\\n output = []\\n denomSum = 0\\n for i in range(0, len(x)):\\n denomSum += np.exp(x[i])\\n\\n for j in range(0, len(x)):\\n output.append(np.exp(x[j]) / denomSum)\\n return np.asarray(output)\",\n \"def softmax(x):\\n #pass # TODO: Compute and return softmax(x)\\n return np.exp(x) / np.sum(np.exp(x), axis=0)\",\n \"def softmax(x):\\n e_x = np.exp(x - np.max(x))\\n return e_x / e_x.sum(axis=0)\",\n \"def softmax(x): \\n e_x = np.exp(x - np.max(x)) \\n return e_x / e_x.sum()\",\n \"def softmax(x):\\n e_x = np.exp(x - np.max(x))\\n return e_x / e_x.sum()\",\n \"def softmax(x):\\n pass # TODO: Compute and return softmax(x)\\n\\n exp_x = np.exp(x)\\n sum_x = np.sum(exp_x, axis=0)\\n softmax = exp_x/sum_x\\n \\n return softmax\",\n \"def softmax(x):\\n shape = x.shape\\n probs = np.exp(x - np.max(x, axis=len(shape) - 1, keepdims=True))\\n probs /= np.sum(probs, axis=len(shape) - 1, keepdims=True)\\n return probs\",\n \"def _softmax(self, x):\\n return np.exp(x - np.max(x)) / np.sum(np.exp(x - np.max(x)))\",\n \"def softmax(x):\\n e_x = np.exp(x - np.max(x))\\n return e_x / e_x.sum(axis=0)\",\n \"def softmax(x):\\r\\n e_x = np.exp(x - np.max(x))\\r\\n return e_x / e_x.sum()\",\n \"def softmax(x):\\r\\n e_x = np.exp(x - np.max(x))\\r\\n return e_x / e_x.sum()\",\n \"def softmax(x):\\r\\n sum_c = np.sum(np.exp(x), axis=1)\\r\\n sum_c = np.expand_dims(sum_c, axis=1)\\r\\n pred_x = np.divide(np.exp(x), sum_c)\\r\\n return pred_x\",\n \"def softmax(input):\\n list_value = []\\n len_compute = input.shape[-1]\\n shape_input = input.shape\\n for x in input.reshape(-1, len_compute):\\n # print(x)\\n e_x = np.exp(x - np.max(x))\\n res = e_x / e_x.sum(axis=0)\\n list_value.append(res)\\n\\n return np.array(list_value).reshape(shape_input)\",\n \"def softmax(x):\\n\\n e_x = np.exp(x - np.max(x))\\n return e_x / e_x.sum()\",\n \"def softmax(x):\\n shape = np.shape(x)\\n numDim = len(shape)\\n result = np.zeros_like(x, dtype=float)\\n \\n if numDim == 1:\\n totalSum = 0\\n # Sum (e^yi)\\n for i in range(len(x)):\\n totalSum = totalSum + exp(x[i])\\n # e^yi / Sum (e^yi)\\n for i in range(len(x)):\\n result[i] = exp(x[i])/totalSum\\n else:\\n xDim = len(x[0])\\n totalSum = np.zeros(xDim, dtype=float)\\n \\n # e^yi\\n for i in range(len(x)):\\n for j in range(len(x[i])):\\n result[i,j] = exp(x[i,j])\\n totalSum[j] = totalSum[j] + result[i,j]\\n # e^yi / Sum (e^yi)\\n for i in range(len(result)):\\n for j in range(len(result[i])):\\n result[i,j] = result[i,j] / totalSum[j]\\n \\n return result\",\n \"def softmax(x):\\n e_x = np.exp(x - np.max(x))\\n return e_x / e_x.sum(axis=0) # only difference\",\n \"def softmax(x):\\n npX = np.array(x)\\n expX = np.exp(x)\\n\\n return expX/sum(expX)\",\n \"def _softmax(self,x):\\n e_x = np.exp(x - np.max(x))\\n return np.nan_to_num(e_x / np.nan_to_num(e_x.sum(axis=0)))\",\n \"def softmax(x):\\n e_x = np.exp(x - np.max(x, axis=1).reshape(-1, 1))\\n return e_x / np.sum(e_x, axis=1).reshape(-1, 1)\",\n \"def softmax(x):\\n # https://stackoverflow.com/questions/34968722/softmax-function-python\\n return np.exp(x) / np.sum(np.exp(x), axis=0)\",\n \"def softmax(x):\\n return np.exp(x) / np.sum(np.exp(x), axis=0)\",\n \"def softmax(x):\\n e_x = np.exp(x - np.max(x))\\n return e_x / e_x.sum()\",\n \"def softmax(x):\\n e_x = np.exp(x - np.max(x))\\n return e_x / e_x.sum()\",\n \"def softmax(x):\\n e_x = np.exp(x - np.max(x))\\n return e_x / e_x.sum()\",\n \"def softmax(x):\\n e_x = np.exp(x - np.max(x))\\n return e_x / e_x.sum()\",\n \"def softmax(x):\\n e_x = np.exp(x - np.max(x))\\n return e_x / e_x.sum()\",\n \"def softmax(x):\\n e_x = np.exp(x - np.max(x))\\n return e_x / e_x.sum()\",\n \"def softmax(x):\\n e_x = np.exp(x - np.max(x))\\n return e_x / e_x.sum()\",\n \"def softmax(x):\\n e_x = np.exp(x - np.max(x))\\n return e_x / e_x.sum()\",\n \"def softmax(x):\\n e_x = np.exp(x - np.max(x))\\n return e_x / e_x.sum()\",\n \"def softmax(x):\\n e_x = np.exp(x - np.max(x))\\n return e_x / e_x.sum()\",\n \"def softmax(x):\\n e_x = np.exp(x - np.max(x))\\n return e_x / e_x.sum()\",\n \"def softmax(x):\\n e_x = np.exp(x - np.max(x))\\n return e_x / e_x.sum()\",\n \"def softmax(x):\\n e_x = np.exp(x - np.max(x))\\n return e_x / e_x.sum()\",\n \"def softmax(x):\\n e_x = np.exp(x - np.max(x))\\n return e_x / e_x.sum()\",\n \"def softmax(x):\\n e_x = np.exp(x - np.max(x))\\n return e_x / e_x.sum()\",\n \"def softmax(x):\\n e_x = np.exp(x - np.max(x))\\n return e_x / e_x.sum()\",\n \"def softmax(x):\\n e_x = np.exp(x - np.max(x))\\n return e_x / e_x.sum()\",\n \"def softmax(x):\\n e_x = np.exp(x - np.max(x))\\n return e_x / e_x.sum()\",\n \"def softmax(x):\\n e_x = np.exp(x - np.max(x))\\n return e_x / e_x.sum()\",\n \"def softmax(x):\\n e_x = np.exp(x - np.max(x))\\n return e_x / e_x.sum()\",\n \"def softmax(x: np.array) -> Tuple[int, List[float]]:\\n dist = np.exp(x) / np.sum(np.exp(x))\\n y = np.argmax(dist)\\n return int(y), dist\",\n \"def softmax(x):\\n e_x = np.exp(x - np.max(x))\\n return e_x / e_x.sum(axis=1)\",\n \"def softmax(x):\\n e_x = np.exp(x - np.max(x))\\n return e_x / e_x.sum(axis=0)\",\n \"def softmax(x):\\n e_x = np.exp(x - np.max(x))\\n return e_x / e_x.sum(axis=0)\",\n \"def softmax(x):\\n e_x = np.exp(x - np.max(x))\\n return e_x / e_x.sum(axis=0)\",\n \"def softmax(x):\\n e_x = np.exp(x - np.max(x))\\n return e_x / e_x.sum(axis=0)\",\n \"def softmax(x):\\n e_x = np.exp(x - np.max(x))\\n return e_x / e_x.sum(axis=0)\",\n \"def softmax(self, x):\\n x_max = np.max(x, axis=-1, keepdims=True)\\n e_x = np.exp(x - x_max)\\n return e_x / e_x.sum(axis=-1, keepdims=True)\",\n \"def softmax(x):\\n x_exp = (x - x.max(1)[0].view(-1, 1)).exp()\\n return x_exp / x_exp.sum(1).view(-1, 1)\",\n \"def softmax(self, x):\\n \\\"\\\"\\\" Meaning: avoid very large exponents by reducing the largest to zero\\n and everything else to less than that\\\"\\\"\\\"\\n ex = np.exp(x - np.max(x))\\n return ex / np.sum(ex, axis = 1, keepdims=True) #Sums over rows np.sum([[0, 1], [0, 5]], axis=1) = array([1, 5])\",\n \"def softmax(x):\\r\\n e_x = np.exp(x - np.max(x))\\r\\n return e_x / e_x.sum(axis=0) # only difference\\r\",\n \"def softmax(x): \\n return np.exp(x) / np.sum(np.exp(x), axis=0)\",\n \"def softmax(x):\\n exps = np.exp(x - np.max(x, axis=0))\\n return exps / exps.sum(axis=0)\",\n \"def my_softmax(x):\\n x = x - np.max(x)\\n exp_x = np.exp(x)\\n softmax_x = exp_x / np.sum(exp_x)\\n return softmax_x\",\n \"def softmax(x):\\n e_x = np.exp(x - np.max(x))\\n return e_x / e_x.sum(axis=0) # only difference\",\n \"def softmax(x):\\n e_x = np.exp(x - np.max(x))\\n return e_x / e_x.sum(axis=0) # only difference\",\n \"def softmax(x):\\n e_x = np.exp(x - np.max(x))\\n return e_x / e_x.sum(axis=0) # only difference\",\n \"def softmax(x):\\n e_x = np.exp(x - np.max(x))\\n return e_x / e_x.sum(axis=0) # only difference\",\n \"def softmax_my(x):\\r\\n return np.exp(x) / np.sum(np.exp(x), axis=0)\",\n \"def softmax(x):\\n return np.exp(x) / np.sum(np.exp(x), axis=0)\",\n \"def softmax(x):\\n return np.exp(x) / np.sum(np.exp(x), axis=0)\",\n \"def softmax(x):\\n return np.exp(x) / np.sum(np.exp(x), axis=0)\",\n \"def softmax(x):\\n return np.exp(x) / np.sum(np.exp(x), axis=0)\",\n \"def softmax(x):\\n return np.exp(x) / np.sum(np.exp(x), axis=0)\",\n \"def softmax(x):\\n return np.exp(x) / np.sum(np.exp(x), axis=0)\",\n \"def softmax(x):\\n return np.exp(x) / np.sum(np.exp(x), axis=0)\",\n \"def softmax(x):\\n return np.exp(x) / np.sum(np.exp(x), axis=0)\",\n \"def softmax(x):\\n e_x = np.exp(x - np.max(x))\\n return e_x / e_x.sum(axis=0) # only difference\",\n \"def softmax_loss(scores, y):\\r\\n N = scores.shape[0] # number of input data\\r\\n\\r\\n # compute data loss\\r\\n shifted_logits = scores - np.max(scores, axis=1, keepdims=True)\\r\\n Z = np.sum(np.exp(shifted_logits), axis=1, keepdims=True)\\r\\n log_probs = shifted_logits - np.log(Z)\\r\\n probs = np.exp(log_probs)\\r\\n loss = -np.sum(log_probs[range(N), y]) / N\\r\\n\\r\\n # Compute gradient of loss function w.r.t. scores\\r\\n dscores = probs.copy()\\r\\n dscores[range(N), y] -= 1\\r\\n dscores /= N\\r\\n \\r\\n return loss, dscores\",\n \"def softmax(x):\\n return np.exp(x) / np.sum(np.exp(x), axis=1)\",\n \"def softmax(x):\\n return np.exp(x) / np.sum(np.exp(x), axis=0)\\n # return ( x / np.sum(x, axis=0) )\",\n \"def softmax(x):\\n e_x = np.exp(x - np.max(x))\\n return old_div(e_x, e_x.sum())\",\n \"def softmax(x):\\n if type(x) == list:\\n dim=len(x)\\n norm = np.sum(np.exp(x))\\n for idx in range(dim):\\n x[idx] = np.exp(x[idx])/norm\\n elif type(x) == np.ndarray:\\n dim=x.shape\\n for col in range(dim[1]):\\n norm = np.sum(np.exp(x[:, col]))\\n for idx in range(dim[0]):\\n x[idx, col] = np.exp(x[idx, col])/norm\\n else:\\n raise Exception('incorrect input')\\n return x\",\n \"def softmax(x):\\n e_yi = np.exp(x)\\n suma_e_yj = sum(e_yi)\\n soft = e_yi/suma_e_yj\\n return soft\",\n \"def softmax(x):\\n return np.exp(x)/np.sum(np.exp(x),axis=0)\",\n \"def softmax(x):\\n return np.exp(x)/np.sum(np.exp(x),axis=0)\",\n \"def softmax_my(x):\\n dim = 1\\n try:\\n dim = x.ndim\\n except AttributeError:\\n return cal_1D_softmax(x)\\n if dim == 1:\\n return cal_1D_softmax(x)\\n elif dim == 2:\\n return cal_2D_softmax(x)\",\n \"def softmax(x):\\n e_x = np.exp(x - np.max(x))\\n temp=e_x / e_x.sum(axis=0) # only difference\\n \\n if np.isnan(temp).any()==True:\\n return [0.0,1.0,0.0]\\n else:\\n return temp\",\n \"def softmax(x):\\r\\n e_x = np.exp(x - np.expand_dims(np.max(x, axis=-1), axis=-1))\\r\\n return e_x / np.expand_dims(e_x.sum(axis=-1), axis=-1) # only difference\\r\",\n \"def softmax(x, stable=0):\\n if stable:\\n e_x = np.exp(x - np.max(x, axis=0))\\n return e_x / e_x.sum(axis=0)\\n return np.exp(x) / np.sum(np.exp(x), axis=0)\",\n \"def softmax(x1, x2, x3, beta):\\n xs = np.array((x1, x2, x3))\\n num = np.exp(xs * beta)\\n den = np.exp(xs * beta).sum()\\n return num / den\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7800231","0.776332","0.7762483","0.77600384","0.76607925","0.76607925","0.76607925","0.76607925","0.7585753","0.7432371","0.7409777","0.73932534","0.7360278","0.72708684","0.7263948","0.7262435","0.7257905","0.7231808","0.72159433","0.7203519","0.72031534","0.7175359","0.715989","0.71574956","0.71401036","0.71392417","0.7135203","0.7130013","0.7130013","0.71288246","0.71284634","0.7127693","0.7126444","0.7124798","0.71159613","0.7111768","0.710316","0.7102545","0.70968384","0.7092023","0.7092023","0.7092023","0.7092023","0.7092023","0.7092023","0.7092023","0.7092023","0.7092023","0.7092023","0.7092023","0.7092023","0.7092023","0.7092023","0.7092023","0.7092023","0.7092023","0.7092023","0.7092023","0.7092023","0.70679355","0.70665216","0.7063594","0.7063594","0.7063594","0.7063594","0.7063594","0.704956","0.70455515","0.7041385","0.70370984","0.7026458","0.70180935","0.70147467","0.70140433","0.70140433","0.70140433","0.70140433","0.6991182","0.6987456","0.6987456","0.6987456","0.6987456","0.6987456","0.6987456","0.6987456","0.6987456","0.69821763","0.6981148","0.69809437","0.697636","0.69755137","0.69571006","0.6955068","0.6954161","0.6954161","0.6951381","0.69450307","0.69448256","0.69307935","0.6919526"],"string":"[\n \"0.7800231\",\n \"0.776332\",\n \"0.7762483\",\n \"0.77600384\",\n \"0.76607925\",\n \"0.76607925\",\n \"0.76607925\",\n \"0.76607925\",\n \"0.7585753\",\n \"0.7432371\",\n \"0.7409777\",\n \"0.73932534\",\n \"0.7360278\",\n \"0.72708684\",\n \"0.7263948\",\n \"0.7262435\",\n \"0.7257905\",\n \"0.7231808\",\n \"0.72159433\",\n \"0.7203519\",\n \"0.72031534\",\n \"0.7175359\",\n \"0.715989\",\n \"0.71574956\",\n \"0.71401036\",\n \"0.71392417\",\n \"0.7135203\",\n \"0.7130013\",\n \"0.7130013\",\n \"0.71288246\",\n \"0.71284634\",\n \"0.7127693\",\n \"0.7126444\",\n \"0.7124798\",\n \"0.71159613\",\n \"0.7111768\",\n \"0.710316\",\n \"0.7102545\",\n \"0.70968384\",\n \"0.7092023\",\n \"0.7092023\",\n \"0.7092023\",\n \"0.7092023\",\n \"0.7092023\",\n \"0.7092023\",\n \"0.7092023\",\n \"0.7092023\",\n \"0.7092023\",\n \"0.7092023\",\n \"0.7092023\",\n \"0.7092023\",\n \"0.7092023\",\n \"0.7092023\",\n \"0.7092023\",\n \"0.7092023\",\n \"0.7092023\",\n \"0.7092023\",\n \"0.7092023\",\n \"0.7092023\",\n \"0.70679355\",\n \"0.70665216\",\n \"0.7063594\",\n \"0.7063594\",\n \"0.7063594\",\n \"0.7063594\",\n \"0.7063594\",\n \"0.704956\",\n \"0.70455515\",\n \"0.7041385\",\n \"0.70370984\",\n \"0.7026458\",\n \"0.70180935\",\n \"0.70147467\",\n \"0.70140433\",\n \"0.70140433\",\n \"0.70140433\",\n \"0.70140433\",\n \"0.6991182\",\n \"0.6987456\",\n \"0.6987456\",\n \"0.6987456\",\n \"0.6987456\",\n \"0.6987456\",\n \"0.6987456\",\n \"0.6987456\",\n \"0.6987456\",\n \"0.69821763\",\n \"0.6981148\",\n \"0.69809437\",\n \"0.697636\",\n \"0.69755137\",\n \"0.69571006\",\n \"0.6955068\",\n \"0.6954161\",\n \"0.6954161\",\n \"0.6951381\",\n \"0.69450307\",\n \"0.69448256\",\n \"0.69307935\",\n \"0.6919526\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.731833"},"document_rank":{"kind":"string","value":"13"}}},{"rowIdx":26095,"cells":{"query":{"kind":"string","value":"Return the nr_terms most frequent terms in article"},"document":{"kind":"string","value":"def common_terms(self, nr_terms=20):\n tokens = analysis.extra_tokenize(self.fulltext)\n all_to_remove = stopwords + punct + WORDS_FOR_REMOVAL\n coretokens = [t.lower() for t in tokens if t.lower() not in all_to_remove]\n fd = FreqDist(coretokens)\n\n return fd.most_common(nr_terms)"},"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 getNumberTerms(content): \n return Counter(getTerms(content))","def most_frequent(corpus):\n fd = nltk.FreqDist(corpus)\n return fd.most_common(10)","def get_num_terms(self, documents=None):\n terms = []\n if documents == None:\n docs = self.vocab\n else:\n docs = [term for term in self.vocab if term['name'] in documents]\n for doc in docs:\n for term, count in doc['term_counts'].items():\n terms.append(term)\n return len(list(set(terms)))","def count_terms(self, tokens):\n\n terms = [self.term_match(t) for t in tokens ]\n \n terms = [t for t in terms if t != None]\n\n #print terms\n lf = dict(Counter(terms))\n for k in lf:\n lf[k] /= float(len(tokens))\n #lf[k] = 1 # binarize?\n pass\n return lf","def augmented_term_fequency(term,tokens):\n\tterm = processes_and_tokenize(term)[0] #make sure term is in correct form\n\n\tmax_count = max([tokens.count(t) for t in tokens])\n\treturn tokens.count(term)/max_count","def get_10_most_frequent_words(tokens):\n\n return FreqDist(word.lower() for word in tokens).most_common(10)","def getNumTerms(self):\n\n return self._numTerms","def count_tokens(txt_to_tokenise_and_count):\n\n txt_tokens = nltk.word_tokenize(txt_to_tokenise_and_count) \n counts = Counter(txt_tokens)\n\n print (counts.most_common(10))\n return counts","def counterFrequency(text):\n dictText = {}\n maxN = 0\n mostFrequent = \"\"\n for item in text:\n if (item not in dictText):\n dictText[item] = 1\n else: \n dictText[item] +=1\n \n if (dictText[item] > maxN):\n mostFrequent = item\n maxN = dictText[item]\n return mostFrequent","def most_frequent(s):\n\n hist = make_histogram(s)\n\n t = []\n for x, freq in hist.items():\n t.append((freq, x))\n \n t.sort(reverse = True)\n # print(t)\n res = []\n for freq, x in t:\n res.append(x)\n \n return res","def computeWordsFrequencies(self):\n token_stream = self._tokenize(self.readable)\n token_map = self._countTokens(token_stream)\n # print token_map.items()\n return sorted(token_map.items(), key = lambda x : x[1], reverse = True)","def entity_counts(doc):\n \n tags = []\n for token in doc.ents:\n tags.append(token.label_)\n frequency = dict(Counter(tags).most_common())\n\n return frequency","def calc_tf(doc):\r\n tf = {}\r\n for term in doc:\r\n if term not in tf:\r\n tf[term] = doc.count(term)\r\n return tf","def top_terms(self, nterms):\n return self.sql_session.query(Term)\\\n .filter(Term.term != '*')\\\n .order_by(desc(Term.relevance))[:nterms]","def freq(self) -> int:","def compute_frequencies(num_words, documents):\n res = [0 for i in range(num_words)]\n sum = 0\n for word in documents:\n sum += 1\n tmp = set(word)\n for number in tmp:\n res[number] += 1\n \n res = [i / sum for i in res]\n return res","def get_word_frequencies(documents):\n cv_model = CountVectorizer(binary=True)\n tf_matrix = cv_model.fit_transform(documents)\n tf_matrix_transpose = tf_matrix.transpose()\n\n vocabulary = get_vocabulary(documents)\n n_words = len(vocabulary)\n\n word_frequency = {}\n word_frequency_in_documents = {}\n\n for word_idx in range(n_words):\n word = vocabulary[word_idx]\n tf_word = tf_matrix_transpose[word_idx]\n\n # getnnz -> Get the count of explicitly-stored values (nonzeros)\n word_frequency[word] = float(tf_word.getnnz(1))\n # nonzero -> Return the indices of the elements that are non-zero\n word_frequency_in_documents[word] = set(tf_word.nonzero()[1])\n\n return word_frequency, word_frequency_in_documents","def nolog_inverse_document_frequency(term, tokenized_documents_list):\n\tterm = processes_and_tokenize(term)[0]\t#make sure term is in correct form\n\n\tnum_documents = len(tokenized_documents_list)\n\tnum_documents_with_term = len([document for document in tokenized_documents_list if term in document])\n\t\n\tassert num_documents_with_term > 0\n\treturn num_documents / num_documents_with_term","def countFreq(self,document):\n self.document = document\n vocab=['python','js','android','php','django','javascript','oracle','ruby','rails','java']\n cnt_vector = CountVectorizer(vocabulary=vocab)\n self.freq_term_matrix = cnt_vector.fit_transform(self.document)\n return self.freq_term_matrix.toarray()","def get_tf(term, document):\n\n term_list = [term.lower() for term in document.split()]\n num_of_words_in_doc = len(document.split())\n term_count_in_doc = term_list.count(term)\n\n return term_count_in_doc / num_of_words_in_doc","def inverse_document_frequency(term, tokenized_documents_list):\n\tterm = processes_and_tokenize(term)[0]\t#make sure term is in correct form\n\n\tnum_documents = len(tokenized_documents_list)\n\tnum_documents_with_term = len([document for document in tokenized_documents_list if term in document])\n\t\n\tassert num_documents_with_term > 0\n\treturn math.log(num_documents / num_documents_with_term)","def find_frequent_words(words, most_frequent): \n \n # common_words = Counter(sorted(words))\n # print common_words\n common_words = Counter(sorted(words)).most_common(most_frequent)\n print (common_words )\n most_common_words = [w for w, w_count in common_words]\n return most_common_words","def mostFrequentNGram(text,n):\n\n ngram = calculateNGrams(text,n)\n return counterFrequency(ngram)","def get_mostFrequent(self, n=5):\r\n pass","def get_mostFrequent(self, n=5):\r\n pass","def pos_tag_counts(doc):\n tags = []\n for token in doc:\n tags.append(token.pos_)\n frequency = dict(Counter(tags).most_common()) \n return frequency","def keywords(articles, top_n=25):\n\n # compute term idfs\n token_docs = [lemma_tokenize(clean(a.text)) for a in articles]\n local_term_idf = IDF(token_docs)\n\n token_docs, phrases = extract_phrases(token_docs, [a.text for a in articles], global_term_idf)\n\n titles = [a.title for a in articles]\n title_tokens = [lemma_tokenize(clean(t)) for t in titles]\n term_counts = defaultdict(int)\n for doc in token_docs:\n for t in set(doc):\n if t:\n term_counts[t] += 1\n\n title_terms = set()\n for title_tks in title_tokens:\n title_terms = title_terms | set(title_tks)\n for ph in phrases:\n if any(ph in title.lower() for title in titles):\n title_terms.add(ph)\n\n # Score terms\n term_scores = []\n for t, count in term_counts.items():\n # Ignore numbers, they are very specific to a particular event and\n # introduce noise\n try:\n float(t)\n continue\n except ValueError:\n # TODO This is a troublesome token, not sure why it's not filtered out by\n # IDF. needs more investigation\n if t == 'n\\'t':\n continue\n score = count * (global_term_idf[t] - local_term_idf[t])\n if t in title_terms:\n score *= 1.5\n term_scores.append((t, score))\n\n return sorted(term_scores, key=lambda t: t[1], reverse=True)[:top_n]","def partition(list_of_tokens):\n freq_count = defaultdict(int)\n for word in list_of_tokens:\n freq_count[word] += 1\n\n return freq_count","def find_largest_freq():\n words_list = {word for line in lines for word in line} # all words possible\n word_freqs = [(find_freq(word), word) for word in words_list] # list of tuples of words and their frequencies\n max_freq = max(word_freqs)\n return max_freq[0], max_freq[1]","def get_num_tokens(self, documents=None):\n tokens = []\n if documents == None:\n docs = self.vocab\n else:\n docs = [term for term in self.vocab if term['name'] in documents]\n for doc in docs:\n for term, count in doc['term_counts'].items():\n tokens.append(count)\n return sum(tokens)","def get_frequencies(tokens):\n cnt = {}\n\n for word in tokens:\n if word not in cnt:\n cnt[word] = 0\n\n cnt[word] += 1\n\n return cnt","def count_words(s, n):\r\n list_of_words=get_listOfWords(s)\r\n res=wrap_with_freq_toList(list_of_words)\r\n res=sortit(res)\r\n top_n=res[0:n]\r\n return top_n\r\n \r\n # TODO: Count the number of occurences of each word in s\r\n # TODO: Sort the occurences in descending order (alphabetically in case of ties)\r\n # TODO: Return the top n words as a list of tuples (<word>, <count>)\r","def freq(word, document):\n return document.split(None).count(word)","def count_words(data, number_word_frequency_results=40):\n current_max_sentence_size = 0\n count_word_frequency = Counter()\n for entry in data:\n print (entry)\n terms_all = [term for term in entry]\n count_word_frequency.update(terms_all)\n return count_word_frequency.most_common(number_word_frequency_results)","def top_dimensionality(self):\n return self._vocab_size","def freq():","def extract_frequent_words(records, num_words, no_counts=False):\r\n word_counts = FreqDist(records)\r\n frequent_words = word_counts.most_common(num_words)\r\n if no_counts:\r\n frequent_words = [word[0] for word in frequent_words]\r\n print(\"=====The {:d} Most Frequent Words=====\".format(num_words))\r\n print(frequent_words)\r\n return frequent_words","def statistic_tfidf(self):\n\t\t# calculate df-idf for all words\n\t\tcount_dict = {x: self.main_doc.term_dict[x] * self.__count_term_in_env(x) for x in self.main_doc.term_dict}\n\t\t# sort them by df and idf\n\t\treturn sorted(count_dict.items(), key=operator.itemgetter(1), reverse=True)","def extract_terms(document):\n tokens = word_tokenize(document)\n stemmer = PorterStemmer()\n terms = {} # Dictionary {term: appearances}\n word_count = 0 # To return total (meaningful) word count\n for token in tokens:\n token = token.lower() # Lowercase\n token = token.strip(string.punctuation) # Remove punctuation\n if token and token not in stopwords.words(\"english\"): # Remove stopwords\n token = stemmer.stem(token) # Using Porter Stemmer\n if token not in terms:\n terms[token] = 1\n else:\n terms[token] += 1\n word_count += 1\n return terms, word_count","def count_term(self, term):\n term_entry = self.get_term(term)\n if term_entry:\n return term_entry.count\n else:\n return 0","def compute_term_doc_rank(total_doc_count, avg_doc_length, doc_length, doc_freq, term_freq):\n eq_pt1 = float(total_doc_count) / doc_freq\n eq_pt2 = (K1_VALUE + 1) * term_freq\n\n # Handle case where term_freq could be 0\n if term_freq == 0:\n return 0\n\n eq_pt3 = (K1_VALUE * ((1 - B_VALUE) + B_VALUE * (float(doc_length) / avg_doc_length)) + term_freq)\n\n return log10(eq_pt1 * eq_pt2 / eq_pt3)","def idf(term, corpus):\n corpus_size = len(corpus)\n docs_with_term = 0\n\n for document in corpus:\n if term in document:\n docs_with_term += 1\n\n #add 1 to docs_with_term to account for terms that don't occur in the corpus\n #so that a division by zero doesn't occur\n return math.log( corpus_size / (docs_with_term+1) )","def termFreq(self):\n if self._tf is None:\n nwords = len(self)\n self._tf = []\n for t in self.freqDist:\n self._tf.append( ( t[0], t[1] / nwords ) )\n return self._tf","def get_top_tweet_ngrams(corpus, dim=2, n=None):\r\n vec = CountVectorizer(ngram_range=(dim, dim)).fit(corpus)\r\n bag_of_words = vec.transform(corpus)\r\n sum_words = bag_of_words.sum(axis=0) \r\n words_freq = [(word, sum_words[0, idx]) for word, idx in vec.vocabulary_.items()]\r\n words_freq =sorted(words_freq, key = lambda x: x[1], reverse=True)\r\n return words_freq[:n]","def count_doc_frequencies(self, docs):\n frequencyIndex = {}\n doc_id = 0\n for doc in docs:\n for term in doc:\n if term not in frequencyIndex:\n frequencyIndex[term] = [doc_id]\n else:\n for id in frequencyIndex[term]:\n if doc_id == id:\n break\n else:\n frequencyIndex[term].append(doc_id)\n doc_id+=1\n\n for term in frequencyIndex:\n occurences = len(frequencyIndex[term])\n frequencyIndex[term] = occurences\n\n return frequencyIndex","def top_k_frequent(top_k, words, list_of_texts):\n dict_top_freq = {}\n for word in words:\n dict_top_freq[word.lower()] = 0\n for string in list_of_texts:\n if word.lower() in string.lower():\n counter = string.lower().count(word.lower())\n dict_top_freq[word.lower()] += counter\n\n list_top_sorted = sorted(dict_top_freq.items(), key=lambda item: item[1], reverse=True)\n print(list_top_sorted)\n\n list_k = []\n for i in list_top_sorted:\n list_k.append(i[0])\n\n return list_k[:top_k]","def count_term_distinct_documents(self, term):\n term_entry = self.get_term(term)\n if term_entry:\n return term_entry.distinct_docs\n else:\n return 0","def _counts(data):\n table = collections.Counter(iter(data)).most_common()\n if not table:\n return table\n maxfreq = table[0][1]\n for i in range(1, len(table)):\n if table[i][1] != maxfreq:\n table = table[:i]\n break\n return table","def print_most_frequent(ngrams, num=10):\n for n in sorted(ngrams):\n print('----- {} most common {}-grams -----'.format(num, n))\n for gram, count in ngrams[n].most_common(num):\n print('{0}: {1}'.format(' '.join(gram), count))\n print('')","def print_most_frequent(ngrams, num=10):\n for n in sorted(ngrams):\n print('----- {} most common {}-grams -----'.format(num, n))\n for gram, count in ngrams[n].most_common(num):\n print('{0}: {1}'.format(' '.join(gram), count))\n print('')","def find_freq_words(words):\n word_list = [1 for line in f if words in line]\n return reduce(lambda x,y: x+1 if y==1 else x, word_list) # x takes on the type of the elements in the array","def entropy(doc_or_tokens: types.DocOrTokens) -> float:\n words = utils.get_words(doc_or_tokens)\n word_counts = itertoolz.frequencies(word.text for word in words)\n n_words = sum(word_counts.values())\n probs = (count / n_words for count in word_counts.values())\n return -sum(prob * math.log2(prob) for prob in probs)","def test_top_n_freqs():\n ngrams = NgramFrequencies()\n top_list = [(\"d\", 4), (\"c\", 3), (\"b\", 2), (\"a\", 1)]\n top_freq = ngrams.top_n_freq(top_list, 10)\n assert top_freq == [(\"d\", 0.4), (\"c\", 0.3), (\"b\", 0.2), (\"a\", 0.1)]","def ngram_frequency(word):\r\n\tword = word.lower()\r\n\tword = re.sub(r'[^A-Za-z. ]','',word)\r\n\tngram_statistics = {}\r\n\tngram_categorization_model_keys = []\r\n\tngram_categorization_model_occurances = []\r\n\tres = [0 for _ in range(0,300)]\r\n\tfor ituple in ngram_categorization_model:\r\n\t\tngram_categorization_model_keys.append(ituple[0])\r\n\t\tngram_categorization_model_occurances.append(int(ituple[1]))\r\n\tfor grams in range(2,6):\r\n\t\tfor i in range(len(word)-grams+1):\r\n\t\t\tseq = word[i:i+grams]\r\n\t\t\tif seq not in ngram_statistics.keys():\r\n\t\t\t\tngram_statistics.update({seq:1})\r\n\t\t\telse:\r\n\t\t\t\tngram_occurances = ngram_statistics[seq]\r\n\t\t\t\tngram_statistics.update({seq:ngram_occurances+1})\r\n\tngram_frequency_keys = ngram_statistics.keys()\r\n\tngram_frequency_occurances = list(ngram_statistics.values())\r\n\tfor index, val in enumerate(ngram_categorization_model_keys):\r\n\t\tfor index1, val1 in enumerate(ngram_frequency_keys):\r\n\t\t\tif val == val1:\r\n\t\t\t\tres[index] = ngram_categorization_model_occurances[index]*ngram_frequency_occurances[index1]\r\n\treturn res","def term_frequency(ngrams,lang):\n token_dictionary = {}\n for ng in ngrams:\n try:\n token_dictionary[ng] = token_dictionary[ng] + 1\n except KeyError:\n token_dictionary[ng] = 1\n return token_dictionary","def print_most_frequent(ngrams, num=10):\r\n for n in sorted(ngrams):\r\n print('----- {} most common {}-grams -----'.format(num, n))\r\n for gram, count in ngrams[n].most_common(num):\r\n print('{0}: {1}'.format(' '.join(gram), count))\r\n print('')","def term_idf(self, term):\n idf = math.log(2 + self.count_term_distinct_documents(ANY))\\\n - math.log(1 + self.count_term_distinct_documents(term))\n return idf","def most_frequent(x):\n return Counter(x).most_common()[0][0]","def test_top_n_counts():\n ngrams = NgramFrequencies()\n new_dic = {\n \"a\": 1,\n \"b\": 2,\n \"c\": 3,\n \"d\": 4\n }\n top_list = ngrams.top_n_counts(new_dic)\n assert top_list == [(\"d\", 4), (\"c\", 3), (\"b\", 2), (\"a\", 1)]","def most_frequent_train(train_data):\n ### YOUR CODE HERE\n tags_counts_for_each_word = {}\n # Filling a dictionary from words and tag tags to their counters\n # Going over the words and counting their tags appearances\n for sentance in train_data:\n for word, tag in sentance:\n # If first time seeing word, adding it's tags count dictionary\n if word not in tags_counts_for_each_word:\n tags_counts_for_each_word[word] = {}\n # Fetching word tags count dictionary\n word_tags_count_dictionary = tags_counts_for_each_word[word]\n # If tag not in word's tags dictionary, initializing the counter\n if tag not in word_tags_count_dictionary:\n word_tags_count_dictionary[tag] = 0\n # Incrementing word tag counter\n word_tags_count_dictionary[tag] += 1\n \n words_maximal_tags = {}\n # Going over each word and finding it's maximal tag\n for word in tags_counts_for_each_word:\n # Fetching all word tags counts\n word_tags_count_dictionary = tags_counts_for_each_word[word]\n \n maximal_tag, maximal_tag_counter = '', 0\n # Finding word tag with maximal tag counter\n for curent_tag, current_counter in word_tags_count_dictionary.items():\n if current_counter > maximal_tag_counter:\n maximal_tag, maximal_tag_counter = curent_tag, current_counter\n \n # Setting the maximal tag for current word\n words_maximal_tags[word] = maximal_tag\n \n return words_maximal_tags\n ### END CODE HERE","def tf(self, term, text):\n return text.count(term) / len(text)","def print_most_frequent(ngrams, num=10):\n for n in sorted(ngrams):\n print('----- {} most common {}-grams -----'.format(num, n))\n for gram, count in ngrams[n].most_common(num):\n print('{0}: {1}'.format(' '.join(gram), count))\n print('')","def get_frequency(processed_text_list):\n \n word_frequency = FreqDist(word for word in processed_text_list)\n\n sorted_counts = sorted(word_frequency.items() , key = lambda x: x[1] ,\n reverse = True)\n\n return sorted_counts","def maxterms(self):\n return {term for term in self.iter_maxterms()}","def count(self):\n freq = {}\n\n for desc in self.words:\n if desc in freq:\n freq[desc] += 1\n else:\n freq[desc] = 1\n\n return freq","def word_frequency(self, document):\n freq_table = {}\n words = nltk.word_tokenize(document)\n for word in words:\n if word in freq_table:\n freq_table[word] = freq_table.get(word) + 1\n else:\n freq_table[word] = 1\n # cut down the frequency table so that only common words are scored for\n freq_table = sorted(freq_table.items(), key=lambda x: x[1], reverse=True)\n scorable_words = []\n for word, occ in freq_table:\n # set threshold as words appearing x times or more - set to optimal valeue = 0\n # in hindsight this can just be deleted\n if int(occ) > 0:\n scorable_words.append(word)\n else:\n break\n self.sent_pos = self.sent_pos + 1 \n return scorable_words","def most_words(self, n):\n return big_tags","def find_freq(word):\n word_list = [1 for line in lines if word in line] # create a list of 1s that reduce() can use\n return reduce(lambda x,y: x+1 if y==1 else x, word_list) # x takes on the type of the elements in the array","def __getTermDocumentOccurences(col, matrix):\n term_document_occurances = 0\n rows, cols = matrix.shape\n for n in xrange(0, rows):\n if matrix[n][col] > 0: # Term appears in document\n term_document_occurances += 1\n return term_document_occurances","def count_term_in_document(self, term, document):\n doc = self.get_document(document)\n for docterm, value in doc.get_terms():\n if docterm == term:\n return value\n return 0","def summer(n_terms, n_words):\n try:\n math.log(math.log(n_terms)) / math.log(math.log(n_words))\n except ValueError:\n return 0","def compute_most_frequent(dict1, dict2):\r\n list_freq = []\r\n most_freq = []\r\n #different frequencies of each word/n-gram in the dictionnaries will be\r\n #appended to list_freq in the following 2 for loops\r\n for i in dict1:\r\n list_freq.append(dict1[i])\r\n for i in dict2:\r\n list_freq.append(dict2[i])\r\n #Using the maximum value of list_freq, if this maximum value is the \r\n #value of any of the words/n-grams in the dictionnaries, these words\r\n #will be added to the list most_freq\r\n for i in dict1:\r\n if dict1[i] == max(list_freq):\r\n most_freq.append(i)\r\n for i in dict2:\r\n if dict2[i] == max(list_freq):\r\n most_freq.append(i)\r\n return sorted(most_freq)\r\n #use of sorted() as specification establises that the list has to be\r\n #alphabetically ordered\r","def calc_tdf(docs):\r\n terms = set()\r\n for doc in docs:\r\n for term in doc:\r\n terms.add(term)\r\n tdf = {}\r\n for term in terms:\r\n doc_count = 0\r\n for doc in docs:\r\n doc_count += 1 if term in doc else 0\r\n tdf[term] = doc_count\r\n return tdf","def specht(mu):\n return StandardTableaux(mu).cardinality().n()","def basic_ttr(n_terms, n_words):\n if n_words == 0:\n return 0\n return n_terms / n_words","def get_vocabulary_words_with_counts(txt, min_word_freq):\n\n data = txt.split()\n counter = collections.Counter(data)\n count_pairs = sorted(counter.items(), key=lambda x: (-x[1], x[0]))\n # keep words that occur more than min_word_freq\n top_count_pairs = [pair for pair in count_pairs if pair[1] > min_word_freq]\n return top_count_pairs","def get_word_frequency(client, search_term=''):\n\tif not search_term:\n\t\tprint \"Enter a word to count its frequency:\"\n\t\tsearch_term = raw_input()\n\n\tlists = client.get_item_lists()\n\n\tprimary_text = ''\n\t# Iterate through only the personal item list\n\tfor l in lists['own']:\n\t\titem_list = client.get_item_list(l['item_list_url'])\n\t\tfor i in item_list:\n\t\t\t# get item object from the item_url\n\t\t\titem = client.get_item(i)\n\t\t\tprimary_text = primary_text + item.get_primary_text()\n\n\twords = word_tokenize(primary_text)\n\n\tword_frequency = words.count(search_term)\n\tprint word_frequency\n\treturn word_frequency","def term_relevance(self, term):\n words = term.split(' ')\n if isinstance(term, tuple):\n # probably a tag\n return _BIG\n if len(words) == 1:\n return self.count_term(term)\n elif len(words) == 2:\n return bigram_likelihood_ratio(\n self.count_term(term),\n self.count_term(words[0]),\n self.count_term(words[1]),\n self.count_term(ANY)\n )\n else:\n raise NotImplementedError(\n \"I don't know how to handle trigrams or larger\"\n )","def n_words(doc_or_tokens: types.DocOrTokens) -> int:\n words = utils.get_words(doc_or_tokens)\n return itertoolz.count(words)","def get_vocab_length(Series, words=30, title=\"Word Frequency\", show_graph=True):\n corpus = \" \".join(Series.to_list())\n corpus = tokenizer.tokenize(corpus)\n freqdist = FreqDist(corpus)\n if show_graph:\n fig, ax = plt.subplots(nrows=1,ncols=1, figsize=(12,6))\n freqdist.plot(words, title= title)\n print(f\"Current Vocab size is = {len(freqdist)}\")\n return freqdist","def top_terms_tuples(self, num):\n \n sorted_tokens = sorted(\n self.centroid_vector.items(),\n key=itemgetter(1), # (1) is value\n reverse=True)\n\n # count to index\n top_terms = []\n \n for i in xrange(0, min(num, len(sorted_tokens))):\n top_terms.append(sorted_tokens[i])\n\n return top_terms","def calc_idf(docs):\r\n terms = set()\r\n for doc in docs:\r\n for term in doc:\r\n terms.add(term)\r\n idf = {}\r\n for term in terms:\r\n term_count = 0\r\n doc_count = 0\r\n for doc in docs:\r\n doc_count += 1\r\n if term in doc:\r\n term_count += 1\r\n idf[term] = doc_count/term_count\r\n return idf","def most_reducible(wordlist):\n\n\t# We create a memo for reducible words since is_reducible is \n\t# recursive. The keys are the words and the values are the \n\t# number of characters\n\tglobal reducible_words\n\treducible_words = dict()\n\treducible_words['a'], reducible_words['i'] = 1, 1\n\t\n\tword_dict = to_dictionary(wordlist)\n\tfor line in word_dict:\n\t\tis_reducible(line, word_dict)\n\n\t# Varible that will search the memo for the longest word\n\tcurrent_greatest = ''\n\tfor word in reducible_words:\n\t\tif reducible_words[word] > len(current_greatest):\n\t\t\tcurrent_greatest = word\n\tprint(current_greatest)","def get_freqs(self):\n dictionary = {}\n for word in self.word_list:\n if word in dictionary:\n dictionary[word] += 1\n else:\n dictionary[word] = 1\n letter_sorted = sorted(dictionary.items(), key=lambda entry: entry[0]) #sorts dictionary into alphabetized tuples\n count_sorted = sorted(letter_sorted, key=lambda seq: seq[1], reverse=True) #sorts alphabetical tuples into count order\n return count_sorted","def number_rare(freq_counts, gamma=False):\n \n n_rare=0 \n if gamma == True:\n for i, j in enumerate(freq_counts[:rare_threshold+1]):\n n_rare = n_rare + (i*j)*(i-1)\n return n_rare\n \n for i, j in enumerate(freq_counts[:rare_threshold+1]):\n n_rare = n_rare + (i*j)\n return n_rare","def word_frequencies(url):\n\ttexts = get_all_texts(url)\n\tcount = count_words_in_sentence_list(texts)\n\treturn count","def count_terms(equat_orig):\n\tterms = 0\n\tfor pow_group in equat_orig:\n\t\tif pow_group:\n\t\t\tfor _ in pow_group:\n\t\t\t\tterms += 1\n\tprint(f'\\033[1;95mTerms in the polynom: \\033[0m{terms}')","def mostfrequent(self, L):\n L.sort()\n n0, e0 = 0, None\n ep = None\n for e in L:\n if e != ep:\n n = L.count(e)\n if n > n0:\n n0, e0 = n, e\n ep = e\n return e0, n0","def word_frequencies(corpus):\n return frequencies(corpus, 1, to_lower=True)","def print_most_common(hist, num=10):\n t = most_common(hist)\n print 'The most common words are:'\n for freq, word in t[:num]:\n print word, '\\t', freq","def get_idf(term, documents):\n\n number_of_docs = len(documents)\n documents_containing_term = len([document for document in documents if term in document])\n\n idf = math.log10(number_of_docs / documents_containing_term)\n\n return round(idf, 5)","def get_descriptor_term_counts(self):\n\t\tcounts = Counter()\n\t\tdescriptors = self.get_descriptors()\n\t\tif descriptors is None:\n\t\t\treturn counts\n\t\tfor d in descriptors:\n\t\t\tfor term in d:\n\t\t\t\tcounts[term] += 1\n\t\treturn counts","def frequency(self, word):\n if word in self:\n return self[word].tokens\n return 0","def _compute_frequencies(self, word_sent):\n freq = defaultdict(int)\n for s in word_sent:\n for word in s:\n if word not in self._stopwords:\n freq[word] += 1\n # frequencies normalization and fitering\n m = float(max(freq.values()))\n for w in freq.keys():\n freq[w] = freq[w]/m\n if freq[w] >= self._max_cut or freq[w] <= self._min_cut:\n del freq[w]\n return freq","def extract_frequent_words(df:pd.DataFrame):\n x = (pd.pivot_table(df.drop(['text', 'percent_correct'], axis=1),\n index='success_lvl',\n aggfunc=['sum', 'mean']) # Count shows ~50/50 split\n .transpose()\n .loc[:, ['high', 'low']]\n .unstack(level=0))\n\n # Rank the most frequent phrases\n x['high_rank'] = x[('high', 'sum')].rank(method='dense', ascending=False)\n x['low_rank'] = x[('low', 'sum')].rank(method='dense', ascending=False)\n print(x[x.high_rank <= 10.].sort_values('high_rank'))\n print(x[x.low_rank <= 10.].sort_values('low_rank'))","def calcCountDict(TFdict):\n\n countDict = {}\n\n for doc in TFdict:\n for term in doc:\n if term in countDict:\n countDict[term] +=1\n else:\n countDict[term] = 1\n\n return countDict","def frequency(self):\n # BEGIN\n \n freq = {} \n # for word in my_list:\n # for letter in word:\n # keys=freq.keys()\n # if letter in keys:\n # freq[letter]+=1\n # else:\n # freq[letter]=1\n # return freq\n\n whole = ''.join(WordSet(self.text).words())\n \n for m in whole:\n if m in freq:\n freq[m] += 1\n else:\n freq[m] = 1\n return freq\n # END","def calculate_tf(self, book_dict, term):\n term_frequency = 0\n try:\n term_frequency = (\n book_dict[\"SanitizedText\"][term] / book_dict[\"TotalNoOfTerms\"]\n )\n except KeyError:\n print(\"Key Error, Term doesnt exist\")\n return 0\n except ZeroDivisionError:\n print(\"tf division by zero!\")\n return 0\n return term_frequency","def most_influential_words_doc(doc, tfidf_words):\n words_found = []\n for d in doc.split():\n for t in tfidf_words:\n if d == t[0]:\n if d not in words_found:\n words_found.append(d)\n return words_found","def get_word_frequencies(topic_description):\n frequencies = {w:f for w,f in topic_description}\n return frequencies"],"string":"[\n \"def getNumberTerms(content): \\n return Counter(getTerms(content))\",\n \"def most_frequent(corpus):\\n fd = nltk.FreqDist(corpus)\\n return fd.most_common(10)\",\n \"def get_num_terms(self, documents=None):\\n terms = []\\n if documents == None:\\n docs = self.vocab\\n else:\\n docs = [term for term in self.vocab if term['name'] in documents]\\n for doc in docs:\\n for term, count in doc['term_counts'].items():\\n terms.append(term)\\n return len(list(set(terms)))\",\n \"def count_terms(self, tokens):\\n\\n terms = [self.term_match(t) for t in tokens ]\\n \\n terms = [t for t in terms if t != None]\\n\\n #print terms\\n lf = dict(Counter(terms))\\n for k in lf:\\n lf[k] /= float(len(tokens))\\n #lf[k] = 1 # binarize?\\n pass\\n return lf\",\n \"def augmented_term_fequency(term,tokens):\\n\\tterm = processes_and_tokenize(term)[0] #make sure term is in correct form\\n\\n\\tmax_count = max([tokens.count(t) for t in tokens])\\n\\treturn tokens.count(term)/max_count\",\n \"def get_10_most_frequent_words(tokens):\\n\\n return FreqDist(word.lower() for word in tokens).most_common(10)\",\n \"def getNumTerms(self):\\n\\n return self._numTerms\",\n \"def count_tokens(txt_to_tokenise_and_count):\\n\\n txt_tokens = nltk.word_tokenize(txt_to_tokenise_and_count) \\n counts = Counter(txt_tokens)\\n\\n print (counts.most_common(10))\\n return counts\",\n \"def counterFrequency(text):\\n dictText = {}\\n maxN = 0\\n mostFrequent = \\\"\\\"\\n for item in text:\\n if (item not in dictText):\\n dictText[item] = 1\\n else: \\n dictText[item] +=1\\n \\n if (dictText[item] > maxN):\\n mostFrequent = item\\n maxN = dictText[item]\\n return mostFrequent\",\n \"def most_frequent(s):\\n\\n hist = make_histogram(s)\\n\\n t = []\\n for x, freq in hist.items():\\n t.append((freq, x))\\n \\n t.sort(reverse = True)\\n # print(t)\\n res = []\\n for freq, x in t:\\n res.append(x)\\n \\n return res\",\n \"def computeWordsFrequencies(self):\\n token_stream = self._tokenize(self.readable)\\n token_map = self._countTokens(token_stream)\\n # print token_map.items()\\n return sorted(token_map.items(), key = lambda x : x[1], reverse = True)\",\n \"def entity_counts(doc):\\n \\n tags = []\\n for token in doc.ents:\\n tags.append(token.label_)\\n frequency = dict(Counter(tags).most_common())\\n\\n return frequency\",\n \"def calc_tf(doc):\\r\\n tf = {}\\r\\n for term in doc:\\r\\n if term not in tf:\\r\\n tf[term] = doc.count(term)\\r\\n return tf\",\n \"def top_terms(self, nterms):\\n return self.sql_session.query(Term)\\\\\\n .filter(Term.term != '*')\\\\\\n .order_by(desc(Term.relevance))[:nterms]\",\n \"def freq(self) -> int:\",\n \"def compute_frequencies(num_words, documents):\\n res = [0 for i in range(num_words)]\\n sum = 0\\n for word in documents:\\n sum += 1\\n tmp = set(word)\\n for number in tmp:\\n res[number] += 1\\n \\n res = [i / sum for i in res]\\n return res\",\n \"def get_word_frequencies(documents):\\n cv_model = CountVectorizer(binary=True)\\n tf_matrix = cv_model.fit_transform(documents)\\n tf_matrix_transpose = tf_matrix.transpose()\\n\\n vocabulary = get_vocabulary(documents)\\n n_words = len(vocabulary)\\n\\n word_frequency = {}\\n word_frequency_in_documents = {}\\n\\n for word_idx in range(n_words):\\n word = vocabulary[word_idx]\\n tf_word = tf_matrix_transpose[word_idx]\\n\\n # getnnz -> Get the count of explicitly-stored values (nonzeros)\\n word_frequency[word] = float(tf_word.getnnz(1))\\n # nonzero -> Return the indices of the elements that are non-zero\\n word_frequency_in_documents[word] = set(tf_word.nonzero()[1])\\n\\n return word_frequency, word_frequency_in_documents\",\n \"def nolog_inverse_document_frequency(term, tokenized_documents_list):\\n\\tterm = processes_and_tokenize(term)[0]\\t#make sure term is in correct form\\n\\n\\tnum_documents = len(tokenized_documents_list)\\n\\tnum_documents_with_term = len([document for document in tokenized_documents_list if term in document])\\n\\t\\n\\tassert num_documents_with_term > 0\\n\\treturn num_documents / num_documents_with_term\",\n \"def countFreq(self,document):\\n self.document = document\\n vocab=['python','js','android','php','django','javascript','oracle','ruby','rails','java']\\n cnt_vector = CountVectorizer(vocabulary=vocab)\\n self.freq_term_matrix = cnt_vector.fit_transform(self.document)\\n return self.freq_term_matrix.toarray()\",\n \"def get_tf(term, document):\\n\\n term_list = [term.lower() for term in document.split()]\\n num_of_words_in_doc = len(document.split())\\n term_count_in_doc = term_list.count(term)\\n\\n return term_count_in_doc / num_of_words_in_doc\",\n \"def inverse_document_frequency(term, tokenized_documents_list):\\n\\tterm = processes_and_tokenize(term)[0]\\t#make sure term is in correct form\\n\\n\\tnum_documents = len(tokenized_documents_list)\\n\\tnum_documents_with_term = len([document for document in tokenized_documents_list if term in document])\\n\\t\\n\\tassert num_documents_with_term > 0\\n\\treturn math.log(num_documents / num_documents_with_term)\",\n \"def find_frequent_words(words, most_frequent): \\n \\n # common_words = Counter(sorted(words))\\n # print common_words\\n common_words = Counter(sorted(words)).most_common(most_frequent)\\n print (common_words )\\n most_common_words = [w for w, w_count in common_words]\\n return most_common_words\",\n \"def mostFrequentNGram(text,n):\\n\\n ngram = calculateNGrams(text,n)\\n return counterFrequency(ngram)\",\n \"def get_mostFrequent(self, n=5):\\r\\n pass\",\n \"def get_mostFrequent(self, n=5):\\r\\n pass\",\n \"def pos_tag_counts(doc):\\n tags = []\\n for token in doc:\\n tags.append(token.pos_)\\n frequency = dict(Counter(tags).most_common()) \\n return frequency\",\n \"def keywords(articles, top_n=25):\\n\\n # compute term idfs\\n token_docs = [lemma_tokenize(clean(a.text)) for a in articles]\\n local_term_idf = IDF(token_docs)\\n\\n token_docs, phrases = extract_phrases(token_docs, [a.text for a in articles], global_term_idf)\\n\\n titles = [a.title for a in articles]\\n title_tokens = [lemma_tokenize(clean(t)) for t in titles]\\n term_counts = defaultdict(int)\\n for doc in token_docs:\\n for t in set(doc):\\n if t:\\n term_counts[t] += 1\\n\\n title_terms = set()\\n for title_tks in title_tokens:\\n title_terms = title_terms | set(title_tks)\\n for ph in phrases:\\n if any(ph in title.lower() for title in titles):\\n title_terms.add(ph)\\n\\n # Score terms\\n term_scores = []\\n for t, count in term_counts.items():\\n # Ignore numbers, they are very specific to a particular event and\\n # introduce noise\\n try:\\n float(t)\\n continue\\n except ValueError:\\n # TODO This is a troublesome token, not sure why it's not filtered out by\\n # IDF. needs more investigation\\n if t == 'n\\\\'t':\\n continue\\n score = count * (global_term_idf[t] - local_term_idf[t])\\n if t in title_terms:\\n score *= 1.5\\n term_scores.append((t, score))\\n\\n return sorted(term_scores, key=lambda t: t[1], reverse=True)[:top_n]\",\n \"def partition(list_of_tokens):\\n freq_count = defaultdict(int)\\n for word in list_of_tokens:\\n freq_count[word] += 1\\n\\n return freq_count\",\n \"def find_largest_freq():\\n words_list = {word for line in lines for word in line} # all words possible\\n word_freqs = [(find_freq(word), word) for word in words_list] # list of tuples of words and their frequencies\\n max_freq = max(word_freqs)\\n return max_freq[0], max_freq[1]\",\n \"def get_num_tokens(self, documents=None):\\n tokens = []\\n if documents == None:\\n docs = self.vocab\\n else:\\n docs = [term for term in self.vocab if term['name'] in documents]\\n for doc in docs:\\n for term, count in doc['term_counts'].items():\\n tokens.append(count)\\n return sum(tokens)\",\n \"def get_frequencies(tokens):\\n cnt = {}\\n\\n for word in tokens:\\n if word not in cnt:\\n cnt[word] = 0\\n\\n cnt[word] += 1\\n\\n return cnt\",\n \"def count_words(s, n):\\r\\n list_of_words=get_listOfWords(s)\\r\\n res=wrap_with_freq_toList(list_of_words)\\r\\n res=sortit(res)\\r\\n top_n=res[0:n]\\r\\n return top_n\\r\\n \\r\\n # TODO: Count the number of occurences of each word in s\\r\\n # TODO: Sort the occurences in descending order (alphabetically in case of ties)\\r\\n # TODO: Return the top n words as a list of tuples (<word>, <count>)\\r\",\n \"def freq(word, document):\\n return document.split(None).count(word)\",\n \"def count_words(data, number_word_frequency_results=40):\\n current_max_sentence_size = 0\\n count_word_frequency = Counter()\\n for entry in data:\\n print (entry)\\n terms_all = [term for term in entry]\\n count_word_frequency.update(terms_all)\\n return count_word_frequency.most_common(number_word_frequency_results)\",\n \"def top_dimensionality(self):\\n return self._vocab_size\",\n \"def freq():\",\n \"def extract_frequent_words(records, num_words, no_counts=False):\\r\\n word_counts = FreqDist(records)\\r\\n frequent_words = word_counts.most_common(num_words)\\r\\n if no_counts:\\r\\n frequent_words = [word[0] for word in frequent_words]\\r\\n print(\\\"=====The {:d} Most Frequent Words=====\\\".format(num_words))\\r\\n print(frequent_words)\\r\\n return frequent_words\",\n \"def statistic_tfidf(self):\\n\\t\\t# calculate df-idf for all words\\n\\t\\tcount_dict = {x: self.main_doc.term_dict[x] * self.__count_term_in_env(x) for x in self.main_doc.term_dict}\\n\\t\\t# sort them by df and idf\\n\\t\\treturn sorted(count_dict.items(), key=operator.itemgetter(1), reverse=True)\",\n \"def extract_terms(document):\\n tokens = word_tokenize(document)\\n stemmer = PorterStemmer()\\n terms = {} # Dictionary {term: appearances}\\n word_count = 0 # To return total (meaningful) word count\\n for token in tokens:\\n token = token.lower() # Lowercase\\n token = token.strip(string.punctuation) # Remove punctuation\\n if token and token not in stopwords.words(\\\"english\\\"): # Remove stopwords\\n token = stemmer.stem(token) # Using Porter Stemmer\\n if token not in terms:\\n terms[token] = 1\\n else:\\n terms[token] += 1\\n word_count += 1\\n return terms, word_count\",\n \"def count_term(self, term):\\n term_entry = self.get_term(term)\\n if term_entry:\\n return term_entry.count\\n else:\\n return 0\",\n \"def compute_term_doc_rank(total_doc_count, avg_doc_length, doc_length, doc_freq, term_freq):\\n eq_pt1 = float(total_doc_count) / doc_freq\\n eq_pt2 = (K1_VALUE + 1) * term_freq\\n\\n # Handle case where term_freq could be 0\\n if term_freq == 0:\\n return 0\\n\\n eq_pt3 = (K1_VALUE * ((1 - B_VALUE) + B_VALUE * (float(doc_length) / avg_doc_length)) + term_freq)\\n\\n return log10(eq_pt1 * eq_pt2 / eq_pt3)\",\n \"def idf(term, corpus):\\n corpus_size = len(corpus)\\n docs_with_term = 0\\n\\n for document in corpus:\\n if term in document:\\n docs_with_term += 1\\n\\n #add 1 to docs_with_term to account for terms that don't occur in the corpus\\n #so that a division by zero doesn't occur\\n return math.log( corpus_size / (docs_with_term+1) )\",\n \"def termFreq(self):\\n if self._tf is None:\\n nwords = len(self)\\n self._tf = []\\n for t in self.freqDist:\\n self._tf.append( ( t[0], t[1] / nwords ) )\\n return self._tf\",\n \"def get_top_tweet_ngrams(corpus, dim=2, n=None):\\r\\n vec = CountVectorizer(ngram_range=(dim, dim)).fit(corpus)\\r\\n bag_of_words = vec.transform(corpus)\\r\\n sum_words = bag_of_words.sum(axis=0) \\r\\n words_freq = [(word, sum_words[0, idx]) for word, idx in vec.vocabulary_.items()]\\r\\n words_freq =sorted(words_freq, key = lambda x: x[1], reverse=True)\\r\\n return words_freq[:n]\",\n \"def count_doc_frequencies(self, docs):\\n frequencyIndex = {}\\n doc_id = 0\\n for doc in docs:\\n for term in doc:\\n if term not in frequencyIndex:\\n frequencyIndex[term] = [doc_id]\\n else:\\n for id in frequencyIndex[term]:\\n if doc_id == id:\\n break\\n else:\\n frequencyIndex[term].append(doc_id)\\n doc_id+=1\\n\\n for term in frequencyIndex:\\n occurences = len(frequencyIndex[term])\\n frequencyIndex[term] = occurences\\n\\n return frequencyIndex\",\n \"def top_k_frequent(top_k, words, list_of_texts):\\n dict_top_freq = {}\\n for word in words:\\n dict_top_freq[word.lower()] = 0\\n for string in list_of_texts:\\n if word.lower() in string.lower():\\n counter = string.lower().count(word.lower())\\n dict_top_freq[word.lower()] += counter\\n\\n list_top_sorted = sorted(dict_top_freq.items(), key=lambda item: item[1], reverse=True)\\n print(list_top_sorted)\\n\\n list_k = []\\n for i in list_top_sorted:\\n list_k.append(i[0])\\n\\n return list_k[:top_k]\",\n \"def count_term_distinct_documents(self, term):\\n term_entry = self.get_term(term)\\n if term_entry:\\n return term_entry.distinct_docs\\n else:\\n return 0\",\n \"def _counts(data):\\n table = collections.Counter(iter(data)).most_common()\\n if not table:\\n return table\\n maxfreq = table[0][1]\\n for i in range(1, len(table)):\\n if table[i][1] != maxfreq:\\n table = table[:i]\\n break\\n return table\",\n \"def print_most_frequent(ngrams, num=10):\\n for n in sorted(ngrams):\\n print('----- {} most common {}-grams -----'.format(num, n))\\n for gram, count in ngrams[n].most_common(num):\\n print('{0}: {1}'.format(' '.join(gram), count))\\n print('')\",\n \"def print_most_frequent(ngrams, num=10):\\n for n in sorted(ngrams):\\n print('----- {} most common {}-grams -----'.format(num, n))\\n for gram, count in ngrams[n].most_common(num):\\n print('{0}: {1}'.format(' '.join(gram), count))\\n print('')\",\n \"def find_freq_words(words):\\n word_list = [1 for line in f if words in line]\\n return reduce(lambda x,y: x+1 if y==1 else x, word_list) # x takes on the type of the elements in the array\",\n \"def entropy(doc_or_tokens: types.DocOrTokens) -> float:\\n words = utils.get_words(doc_or_tokens)\\n word_counts = itertoolz.frequencies(word.text for word in words)\\n n_words = sum(word_counts.values())\\n probs = (count / n_words for count in word_counts.values())\\n return -sum(prob * math.log2(prob) for prob in probs)\",\n \"def test_top_n_freqs():\\n ngrams = NgramFrequencies()\\n top_list = [(\\\"d\\\", 4), (\\\"c\\\", 3), (\\\"b\\\", 2), (\\\"a\\\", 1)]\\n top_freq = ngrams.top_n_freq(top_list, 10)\\n assert top_freq == [(\\\"d\\\", 0.4), (\\\"c\\\", 0.3), (\\\"b\\\", 0.2), (\\\"a\\\", 0.1)]\",\n \"def ngram_frequency(word):\\r\\n\\tword = word.lower()\\r\\n\\tword = re.sub(r'[^A-Za-z. ]','',word)\\r\\n\\tngram_statistics = {}\\r\\n\\tngram_categorization_model_keys = []\\r\\n\\tngram_categorization_model_occurances = []\\r\\n\\tres = [0 for _ in range(0,300)]\\r\\n\\tfor ituple in ngram_categorization_model:\\r\\n\\t\\tngram_categorization_model_keys.append(ituple[0])\\r\\n\\t\\tngram_categorization_model_occurances.append(int(ituple[1]))\\r\\n\\tfor grams in range(2,6):\\r\\n\\t\\tfor i in range(len(word)-grams+1):\\r\\n\\t\\t\\tseq = word[i:i+grams]\\r\\n\\t\\t\\tif seq not in ngram_statistics.keys():\\r\\n\\t\\t\\t\\tngram_statistics.update({seq:1})\\r\\n\\t\\t\\telse:\\r\\n\\t\\t\\t\\tngram_occurances = ngram_statistics[seq]\\r\\n\\t\\t\\t\\tngram_statistics.update({seq:ngram_occurances+1})\\r\\n\\tngram_frequency_keys = ngram_statistics.keys()\\r\\n\\tngram_frequency_occurances = list(ngram_statistics.values())\\r\\n\\tfor index, val in enumerate(ngram_categorization_model_keys):\\r\\n\\t\\tfor index1, val1 in enumerate(ngram_frequency_keys):\\r\\n\\t\\t\\tif val == val1:\\r\\n\\t\\t\\t\\tres[index] = ngram_categorization_model_occurances[index]*ngram_frequency_occurances[index1]\\r\\n\\treturn res\",\n \"def term_frequency(ngrams,lang):\\n token_dictionary = {}\\n for ng in ngrams:\\n try:\\n token_dictionary[ng] = token_dictionary[ng] + 1\\n except KeyError:\\n token_dictionary[ng] = 1\\n return token_dictionary\",\n \"def print_most_frequent(ngrams, num=10):\\r\\n for n in sorted(ngrams):\\r\\n print('----- {} most common {}-grams -----'.format(num, n))\\r\\n for gram, count in ngrams[n].most_common(num):\\r\\n print('{0}: {1}'.format(' '.join(gram), count))\\r\\n print('')\",\n \"def term_idf(self, term):\\n idf = math.log(2 + self.count_term_distinct_documents(ANY))\\\\\\n - math.log(1 + self.count_term_distinct_documents(term))\\n return idf\",\n \"def most_frequent(x):\\n return Counter(x).most_common()[0][0]\",\n \"def test_top_n_counts():\\n ngrams = NgramFrequencies()\\n new_dic = {\\n \\\"a\\\": 1,\\n \\\"b\\\": 2,\\n \\\"c\\\": 3,\\n \\\"d\\\": 4\\n }\\n top_list = ngrams.top_n_counts(new_dic)\\n assert top_list == [(\\\"d\\\", 4), (\\\"c\\\", 3), (\\\"b\\\", 2), (\\\"a\\\", 1)]\",\n \"def most_frequent_train(train_data):\\n ### YOUR CODE HERE\\n tags_counts_for_each_word = {}\\n # Filling a dictionary from words and tag tags to their counters\\n # Going over the words and counting their tags appearances\\n for sentance in train_data:\\n for word, tag in sentance:\\n # If first time seeing word, adding it's tags count dictionary\\n if word not in tags_counts_for_each_word:\\n tags_counts_for_each_word[word] = {}\\n # Fetching word tags count dictionary\\n word_tags_count_dictionary = tags_counts_for_each_word[word]\\n # If tag not in word's tags dictionary, initializing the counter\\n if tag not in word_tags_count_dictionary:\\n word_tags_count_dictionary[tag] = 0\\n # Incrementing word tag counter\\n word_tags_count_dictionary[tag] += 1\\n \\n words_maximal_tags = {}\\n # Going over each word and finding it's maximal tag\\n for word in tags_counts_for_each_word:\\n # Fetching all word tags counts\\n word_tags_count_dictionary = tags_counts_for_each_word[word]\\n \\n maximal_tag, maximal_tag_counter = '', 0\\n # Finding word tag with maximal tag counter\\n for curent_tag, current_counter in word_tags_count_dictionary.items():\\n if current_counter > maximal_tag_counter:\\n maximal_tag, maximal_tag_counter = curent_tag, current_counter\\n \\n # Setting the maximal tag for current word\\n words_maximal_tags[word] = maximal_tag\\n \\n return words_maximal_tags\\n ### END CODE HERE\",\n \"def tf(self, term, text):\\n return text.count(term) / len(text)\",\n \"def print_most_frequent(ngrams, num=10):\\n for n in sorted(ngrams):\\n print('----- {} most common {}-grams -----'.format(num, n))\\n for gram, count in ngrams[n].most_common(num):\\n print('{0}: {1}'.format(' '.join(gram), count))\\n print('')\",\n \"def get_frequency(processed_text_list):\\n \\n word_frequency = FreqDist(word for word in processed_text_list)\\n\\n sorted_counts = sorted(word_frequency.items() , key = lambda x: x[1] ,\\n reverse = True)\\n\\n return sorted_counts\",\n \"def maxterms(self):\\n return {term for term in self.iter_maxterms()}\",\n \"def count(self):\\n freq = {}\\n\\n for desc in self.words:\\n if desc in freq:\\n freq[desc] += 1\\n else:\\n freq[desc] = 1\\n\\n return freq\",\n \"def word_frequency(self, document):\\n freq_table = {}\\n words = nltk.word_tokenize(document)\\n for word in words:\\n if word in freq_table:\\n freq_table[word] = freq_table.get(word) + 1\\n else:\\n freq_table[word] = 1\\n # cut down the frequency table so that only common words are scored for\\n freq_table = sorted(freq_table.items(), key=lambda x: x[1], reverse=True)\\n scorable_words = []\\n for word, occ in freq_table:\\n # set threshold as words appearing x times or more - set to optimal valeue = 0\\n # in hindsight this can just be deleted\\n if int(occ) > 0:\\n scorable_words.append(word)\\n else:\\n break\\n self.sent_pos = self.sent_pos + 1 \\n return scorable_words\",\n \"def most_words(self, n):\\n return big_tags\",\n \"def find_freq(word):\\n word_list = [1 for line in lines if word in line] # create a list of 1s that reduce() can use\\n return reduce(lambda x,y: x+1 if y==1 else x, word_list) # x takes on the type of the elements in the array\",\n \"def __getTermDocumentOccurences(col, matrix):\\n term_document_occurances = 0\\n rows, cols = matrix.shape\\n for n in xrange(0, rows):\\n if matrix[n][col] > 0: # Term appears in document\\n term_document_occurances += 1\\n return term_document_occurances\",\n \"def count_term_in_document(self, term, document):\\n doc = self.get_document(document)\\n for docterm, value in doc.get_terms():\\n if docterm == term:\\n return value\\n return 0\",\n \"def summer(n_terms, n_words):\\n try:\\n math.log(math.log(n_terms)) / math.log(math.log(n_words))\\n except ValueError:\\n return 0\",\n \"def compute_most_frequent(dict1, dict2):\\r\\n list_freq = []\\r\\n most_freq = []\\r\\n #different frequencies of each word/n-gram in the dictionnaries will be\\r\\n #appended to list_freq in the following 2 for loops\\r\\n for i in dict1:\\r\\n list_freq.append(dict1[i])\\r\\n for i in dict2:\\r\\n list_freq.append(dict2[i])\\r\\n #Using the maximum value of list_freq, if this maximum value is the \\r\\n #value of any of the words/n-grams in the dictionnaries, these words\\r\\n #will be added to the list most_freq\\r\\n for i in dict1:\\r\\n if dict1[i] == max(list_freq):\\r\\n most_freq.append(i)\\r\\n for i in dict2:\\r\\n if dict2[i] == max(list_freq):\\r\\n most_freq.append(i)\\r\\n return sorted(most_freq)\\r\\n #use of sorted() as specification establises that the list has to be\\r\\n #alphabetically ordered\\r\",\n \"def calc_tdf(docs):\\r\\n terms = set()\\r\\n for doc in docs:\\r\\n for term in doc:\\r\\n terms.add(term)\\r\\n tdf = {}\\r\\n for term in terms:\\r\\n doc_count = 0\\r\\n for doc in docs:\\r\\n doc_count += 1 if term in doc else 0\\r\\n tdf[term] = doc_count\\r\\n return tdf\",\n \"def specht(mu):\\n return StandardTableaux(mu).cardinality().n()\",\n \"def basic_ttr(n_terms, n_words):\\n if n_words == 0:\\n return 0\\n return n_terms / n_words\",\n \"def get_vocabulary_words_with_counts(txt, min_word_freq):\\n\\n data = txt.split()\\n counter = collections.Counter(data)\\n count_pairs = sorted(counter.items(), key=lambda x: (-x[1], x[0]))\\n # keep words that occur more than min_word_freq\\n top_count_pairs = [pair for pair in count_pairs if pair[1] > min_word_freq]\\n return top_count_pairs\",\n \"def get_word_frequency(client, search_term=''):\\n\\tif not search_term:\\n\\t\\tprint \\\"Enter a word to count its frequency:\\\"\\n\\t\\tsearch_term = raw_input()\\n\\n\\tlists = client.get_item_lists()\\n\\n\\tprimary_text = ''\\n\\t# Iterate through only the personal item list\\n\\tfor l in lists['own']:\\n\\t\\titem_list = client.get_item_list(l['item_list_url'])\\n\\t\\tfor i in item_list:\\n\\t\\t\\t# get item object from the item_url\\n\\t\\t\\titem = client.get_item(i)\\n\\t\\t\\tprimary_text = primary_text + item.get_primary_text()\\n\\n\\twords = word_tokenize(primary_text)\\n\\n\\tword_frequency = words.count(search_term)\\n\\tprint word_frequency\\n\\treturn word_frequency\",\n \"def term_relevance(self, term):\\n words = term.split(' ')\\n if isinstance(term, tuple):\\n # probably a tag\\n return _BIG\\n if len(words) == 1:\\n return self.count_term(term)\\n elif len(words) == 2:\\n return bigram_likelihood_ratio(\\n self.count_term(term),\\n self.count_term(words[0]),\\n self.count_term(words[1]),\\n self.count_term(ANY)\\n )\\n else:\\n raise NotImplementedError(\\n \\\"I don't know how to handle trigrams or larger\\\"\\n )\",\n \"def n_words(doc_or_tokens: types.DocOrTokens) -> int:\\n words = utils.get_words(doc_or_tokens)\\n return itertoolz.count(words)\",\n \"def get_vocab_length(Series, words=30, title=\\\"Word Frequency\\\", show_graph=True):\\n corpus = \\\" \\\".join(Series.to_list())\\n corpus = tokenizer.tokenize(corpus)\\n freqdist = FreqDist(corpus)\\n if show_graph:\\n fig, ax = plt.subplots(nrows=1,ncols=1, figsize=(12,6))\\n freqdist.plot(words, title= title)\\n print(f\\\"Current Vocab size is = {len(freqdist)}\\\")\\n return freqdist\",\n \"def top_terms_tuples(self, num):\\n \\n sorted_tokens = sorted(\\n self.centroid_vector.items(),\\n key=itemgetter(1), # (1) is value\\n reverse=True)\\n\\n # count to index\\n top_terms = []\\n \\n for i in xrange(0, min(num, len(sorted_tokens))):\\n top_terms.append(sorted_tokens[i])\\n\\n return top_terms\",\n \"def calc_idf(docs):\\r\\n terms = set()\\r\\n for doc in docs:\\r\\n for term in doc:\\r\\n terms.add(term)\\r\\n idf = {}\\r\\n for term in terms:\\r\\n term_count = 0\\r\\n doc_count = 0\\r\\n for doc in docs:\\r\\n doc_count += 1\\r\\n if term in doc:\\r\\n term_count += 1\\r\\n idf[term] = doc_count/term_count\\r\\n return idf\",\n \"def most_reducible(wordlist):\\n\\n\\t# We create a memo for reducible words since is_reducible is \\n\\t# recursive. The keys are the words and the values are the \\n\\t# number of characters\\n\\tglobal reducible_words\\n\\treducible_words = dict()\\n\\treducible_words['a'], reducible_words['i'] = 1, 1\\n\\t\\n\\tword_dict = to_dictionary(wordlist)\\n\\tfor line in word_dict:\\n\\t\\tis_reducible(line, word_dict)\\n\\n\\t# Varible that will search the memo for the longest word\\n\\tcurrent_greatest = ''\\n\\tfor word in reducible_words:\\n\\t\\tif reducible_words[word] > len(current_greatest):\\n\\t\\t\\tcurrent_greatest = word\\n\\tprint(current_greatest)\",\n \"def get_freqs(self):\\n dictionary = {}\\n for word in self.word_list:\\n if word in dictionary:\\n dictionary[word] += 1\\n else:\\n dictionary[word] = 1\\n letter_sorted = sorted(dictionary.items(), key=lambda entry: entry[0]) #sorts dictionary into alphabetized tuples\\n count_sorted = sorted(letter_sorted, key=lambda seq: seq[1], reverse=True) #sorts alphabetical tuples into count order\\n return count_sorted\",\n \"def number_rare(freq_counts, gamma=False):\\n \\n n_rare=0 \\n if gamma == True:\\n for i, j in enumerate(freq_counts[:rare_threshold+1]):\\n n_rare = n_rare + (i*j)*(i-1)\\n return n_rare\\n \\n for i, j in enumerate(freq_counts[:rare_threshold+1]):\\n n_rare = n_rare + (i*j)\\n return n_rare\",\n \"def word_frequencies(url):\\n\\ttexts = get_all_texts(url)\\n\\tcount = count_words_in_sentence_list(texts)\\n\\treturn count\",\n \"def count_terms(equat_orig):\\n\\tterms = 0\\n\\tfor pow_group in equat_orig:\\n\\t\\tif pow_group:\\n\\t\\t\\tfor _ in pow_group:\\n\\t\\t\\t\\tterms += 1\\n\\tprint(f'\\\\033[1;95mTerms in the polynom: \\\\033[0m{terms}')\",\n \"def mostfrequent(self, L):\\n L.sort()\\n n0, e0 = 0, None\\n ep = None\\n for e in L:\\n if e != ep:\\n n = L.count(e)\\n if n > n0:\\n n0, e0 = n, e\\n ep = e\\n return e0, n0\",\n \"def word_frequencies(corpus):\\n return frequencies(corpus, 1, to_lower=True)\",\n \"def print_most_common(hist, num=10):\\n t = most_common(hist)\\n print 'The most common words are:'\\n for freq, word in t[:num]:\\n print word, '\\\\t', freq\",\n \"def get_idf(term, documents):\\n\\n number_of_docs = len(documents)\\n documents_containing_term = len([document for document in documents if term in document])\\n\\n idf = math.log10(number_of_docs / documents_containing_term)\\n\\n return round(idf, 5)\",\n \"def get_descriptor_term_counts(self):\\n\\t\\tcounts = Counter()\\n\\t\\tdescriptors = self.get_descriptors()\\n\\t\\tif descriptors is None:\\n\\t\\t\\treturn counts\\n\\t\\tfor d in descriptors:\\n\\t\\t\\tfor term in d:\\n\\t\\t\\t\\tcounts[term] += 1\\n\\t\\treturn counts\",\n \"def frequency(self, word):\\n if word in self:\\n return self[word].tokens\\n return 0\",\n \"def _compute_frequencies(self, word_sent):\\n freq = defaultdict(int)\\n for s in word_sent:\\n for word in s:\\n if word not in self._stopwords:\\n freq[word] += 1\\n # frequencies normalization and fitering\\n m = float(max(freq.values()))\\n for w in freq.keys():\\n freq[w] = freq[w]/m\\n if freq[w] >= self._max_cut or freq[w] <= self._min_cut:\\n del freq[w]\\n return freq\",\n \"def extract_frequent_words(df:pd.DataFrame):\\n x = (pd.pivot_table(df.drop(['text', 'percent_correct'], axis=1),\\n index='success_lvl',\\n aggfunc=['sum', 'mean']) # Count shows ~50/50 split\\n .transpose()\\n .loc[:, ['high', 'low']]\\n .unstack(level=0))\\n\\n # Rank the most frequent phrases\\n x['high_rank'] = x[('high', 'sum')].rank(method='dense', ascending=False)\\n x['low_rank'] = x[('low', 'sum')].rank(method='dense', ascending=False)\\n print(x[x.high_rank <= 10.].sort_values('high_rank'))\\n print(x[x.low_rank <= 10.].sort_values('low_rank'))\",\n \"def calcCountDict(TFdict):\\n\\n countDict = {}\\n\\n for doc in TFdict:\\n for term in doc:\\n if term in countDict:\\n countDict[term] +=1\\n else:\\n countDict[term] = 1\\n\\n return countDict\",\n \"def frequency(self):\\n # BEGIN\\n \\n freq = {} \\n # for word in my_list:\\n # for letter in word:\\n # keys=freq.keys()\\n # if letter in keys:\\n # freq[letter]+=1\\n # else:\\n # freq[letter]=1\\n # return freq\\n\\n whole = ''.join(WordSet(self.text).words())\\n \\n for m in whole:\\n if m in freq:\\n freq[m] += 1\\n else:\\n freq[m] = 1\\n return freq\\n # END\",\n \"def calculate_tf(self, book_dict, term):\\n term_frequency = 0\\n try:\\n term_frequency = (\\n book_dict[\\\"SanitizedText\\\"][term] / book_dict[\\\"TotalNoOfTerms\\\"]\\n )\\n except KeyError:\\n print(\\\"Key Error, Term doesnt exist\\\")\\n return 0\\n except ZeroDivisionError:\\n print(\\\"tf division by zero!\\\")\\n return 0\\n return term_frequency\",\n \"def most_influential_words_doc(doc, tfidf_words):\\n words_found = []\\n for d in doc.split():\\n for t in tfidf_words:\\n if d == t[0]:\\n if d not in words_found:\\n words_found.append(d)\\n return words_found\",\n \"def get_word_frequencies(topic_description):\\n frequencies = {w:f for w,f in topic_description}\\n return frequencies\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7059205","0.7044106","0.6974766","0.69031334","0.6797572","0.6758672","0.674704","0.6637728","0.656225","0.6542295","0.6528525","0.6483165","0.6466837","0.64654","0.644185","0.641473","0.6407708","0.6399931","0.6389089","0.6380951","0.6369813","0.63587594","0.63326955","0.63292944","0.63292944","0.63134617","0.6310138","0.62998265","0.62857836","0.6285298","0.62631947","0.6251645","0.62425727","0.62284166","0.6222261","0.6217284","0.62104756","0.6182091","0.61792344","0.61265","0.6081852","0.6058314","0.60579973","0.60576874","0.60544086","0.60527515","0.60501564","0.6047651","0.603944","0.603944","0.60339665","0.6033458","0.60330516","0.6030957","0.60307145","0.6024703","0.60193723","0.60122633","0.60115653","0.60040605","0.59978515","0.59939265","0.5986173","0.59697795","0.59672785","0.5963196","0.59625995","0.59616876","0.5949791","0.59491736","0.59272623","0.5923107","0.59183556","0.59161645","0.59116036","0.5910181","0.59095675","0.59092945","0.5908914","0.5903892","0.5902448","0.59019166","0.59008044","0.5899296","0.58915716","0.5890332","0.5889472","0.5883954","0.58818096","0.5879282","0.58758926","0.5866551","0.5866329","0.58660394","0.5861392","0.58574015","0.5850142","0.5848224","0.58475846","0.5845675"],"string":"[\n \"0.7059205\",\n \"0.7044106\",\n \"0.6974766\",\n \"0.69031334\",\n \"0.6797572\",\n \"0.6758672\",\n \"0.674704\",\n \"0.6637728\",\n \"0.656225\",\n \"0.6542295\",\n \"0.6528525\",\n \"0.6483165\",\n \"0.6466837\",\n \"0.64654\",\n \"0.644185\",\n \"0.641473\",\n \"0.6407708\",\n \"0.6399931\",\n \"0.6389089\",\n \"0.6380951\",\n \"0.6369813\",\n \"0.63587594\",\n \"0.63326955\",\n \"0.63292944\",\n \"0.63292944\",\n \"0.63134617\",\n \"0.6310138\",\n \"0.62998265\",\n \"0.62857836\",\n \"0.6285298\",\n \"0.62631947\",\n \"0.6251645\",\n \"0.62425727\",\n \"0.62284166\",\n \"0.6222261\",\n \"0.6217284\",\n \"0.62104756\",\n \"0.6182091\",\n \"0.61792344\",\n \"0.61265\",\n \"0.6081852\",\n \"0.6058314\",\n \"0.60579973\",\n \"0.60576874\",\n \"0.60544086\",\n \"0.60527515\",\n \"0.60501564\",\n \"0.6047651\",\n \"0.603944\",\n \"0.603944\",\n \"0.60339665\",\n \"0.6033458\",\n \"0.60330516\",\n \"0.6030957\",\n \"0.60307145\",\n \"0.6024703\",\n \"0.60193723\",\n \"0.60122633\",\n \"0.60115653\",\n \"0.60040605\",\n \"0.59978515\",\n \"0.59939265\",\n \"0.5986173\",\n \"0.59697795\",\n \"0.59672785\",\n \"0.5963196\",\n \"0.59625995\",\n \"0.59616876\",\n \"0.5949791\",\n \"0.59491736\",\n \"0.59272623\",\n \"0.5923107\",\n \"0.59183556\",\n \"0.59161645\",\n \"0.59116036\",\n \"0.5910181\",\n \"0.59095675\",\n \"0.59092945\",\n \"0.5908914\",\n \"0.5903892\",\n \"0.5902448\",\n \"0.59019166\",\n \"0.59008044\",\n \"0.5899296\",\n \"0.58915716\",\n \"0.5890332\",\n \"0.5889472\",\n \"0.5883954\",\n \"0.58818096\",\n \"0.5879282\",\n \"0.58758926\",\n \"0.5866551\",\n \"0.5866329\",\n \"0.58660394\",\n \"0.5861392\",\n \"0.58574015\",\n \"0.5850142\",\n \"0.5848224\",\n \"0.58475846\",\n \"0.5845675\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6191852"},"document_rank":{"kind":"string","value":"37"}}},{"rowIdx":26096,"cells":{"query":{"kind":"string","value":"Get articles for a gives news source"},"document":{"kind":"string","value":"def for_source(source, articles=None):\n \n if not articles:\n articles = load_articles(nl.read_data())\n \n source_arts = [a for a in articles if a.source == source]\n for art in source_arts:\n yield art"},"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 listsources():\n\tmain_url = \" https://newsapi.org/v2/sources?apiKey=5f81b593f35d42a8980313250c03d7e7\"\n\n\t# fetching data in json format \n\topen_source = requests.get(main_url).json() \n\n\t# getting all articles in a string sources\n\tsource = open_source[\"sources\"] \n\n\t# empty list which will \n\t# contain all trending newssources \n\tresults = [] \n\t\n\tfor k in source: \n results.append(k[\"id\"])\n \n \t\n\tfor w in results[0:4]:\n print(w)","def get_news(category):\n get_news_url = base_url.format(category,api_key)\n\n with urllib.request.urlopen(get_news_url) as url:\n get_news_data = url.read()\n get_news_response= json.loads(get_news_data)\n\n news_results = None\n\n if get_news_response['sources']:\n news_results_list = get_news_response['sources']\n news_results = process_results(news_results_list)\n\n return news_results","def news()->str:#return array[news desc,news link]\n event_log(\"retrieve news data....\",\"\")\n c = 0\n location = read_json(\"news_api\")[0]\n main_url = \"https://newsapi.org/v2/top-headlines?country=\"+location+\"&apiKey=\"+read_json(\"news_api\")[1]+\"\"#add a country selection optin via json\n page = requests.get(main_url).json()\n article = page[\"articles\"]\n news_result = []\n for data in article:\n news_result.append([data[\"title\"],str(data[\"url\"]).replace('\"',\" \")])#exctracts the wanted data from api\n if c == 5:#add this to json file so scalibility\n break\n c+=1\n return news_result","def get_articles(city):\n article_list = []\n\n url = 'https://news.search.yahoo.com/search;?p=' + city\n source = requests.get(url, timeout=5)\n plain_text = source.text\n soup = BeautifulSoup(plain_text, \"html5lib\")\n\n articles = soup.findAll('div', {'class': 'NewsArticle'})\n\n i = 0\n for item in articles:\n if i < 3:\n title = item.find('a', attrs={'class':'thmb'})['title']\n source = item.find('span', attrs={'class':'mr-5'}).text\n header4 = item.find('h4', attrs={'class':'fz-16'})\n link = header4.find('a')['href']\n\n article = format_article_list(title, source, \"| \" + link)\n article_list.append(article)\n\n i += 1\n else:\n break\n\n return article_list","def news(self):\n\n # Get articles with search term, if available, from each News API source\n news_api_articles = pd.DataFrame()\n\n q = urllib.parse.quote(\" OR \".join(self.search_terms), safe='')\n\n response = requests.get(\"https://newsapi.org/v2/everything?q=\" + q + \"&from=\" + datetime.now().strftime(\n \"%Y-%m-%d\") + \"&sortBy=popularity&pageSize=100&apiKey=\" + self.__news_api_key)\n\n if response.status_code == 200:\n data = json.loads(response.text)\n\n source_articles = []\n\n for article in data['articles']:\n source_articles.append([article['title'],\n article['description'],\n article['url'],\n article['publishedAt']])\n\n source_articles = pd.DataFrame(source_articles, columns=['title', 'description', 'url', 'publishedAt'])\n news_api_articles = pd.concat([news_api_articles, source_articles])\n\n news_api_articles = news_api_articles.reset_index(drop='True')\n\n news_api_articles['publishedAt'] = news_api_articles['publishedAt'].apply(pd.to_datetime)\n\n news_api_articles = news_api_articles.fillna(' ')\n\n term_in_title = news_api_articles['title'].apply(self.any_term)\n\n news_api_articles = news_api_articles[term_in_title]\n\n if (len(news_api_articles) > 10):\n news_api_articles = news_api_articles[0:10]\n\n else:\n print(\"News API failed to return any items\")\n\n # Create shortened links using bitly if access token is provided\n if self.__bitly_access_token != '':\n\n bitly_urls = []\n\n for index, article in news_api_articles.iterrows():\n url = article['url']\n bitly_response = requests.get(\"https://api-ssl.bitly.com/v3/shorten\",\n params={'longUrl': url, 'access_token': self.__bitly_access_token})\n\n if bitly_response.status_code == 200:\n data = json.loads(bitly_response.text)\n bitly_urls.append(data['data']['url'])\n\n news_api_articles['url'] = bitly_urls\n\n # Store final list to TwitterBot object\n self.list = news_api_articles\n\n return","def news_fetch(region,news_key):\n #Allows for customizable API key and weather location.\n url = (f\"http://newsapi.org/v2/top-headlines?country={region}&apiKey={news_key}\")\n #Gets API with requests and convert to .json\n news_api = requests.get(url)\n news_json = news_api.json()\n return news_json","def get_news_articles():\n filename = 'inshorts_news_articles.csv'\n\n # checks if file exists if not make a new request and creates\n if os.path.exists(filename):\n return pd.read_csv(filename)\n else:\n return make_new_request()","def get_articles_from_topic(url):\n headers = {'User-Agent': 'Codeup Data Science'}\n response = get(url, headers=headers)\n soup = BeautifulSoup(response.content, 'html.parser')\n\n output = []\n\n articles = soup.select(\".news-card\")\n\n for article in articles: \n title = article.select(\"[itemprop='headline']\")[0].get_text()\n body = article.select(\"[itemprop='articleBody']\")[0].get_text()\n author = article.select(\".author\")[0].get_text()\n published_date = article.select(\".time\")[0][\"content\"]\n category = response.url.split(\"/\")[-1]\n\n article_data = {\n 'title': title,\n 'body': body,\n 'category': category,\n 'author': author,\n 'published_date': published_date,\n }\n output.append(article_data)\n\n\n return output","def get_news(url):\r\n \r\n # parse RSS feed into list of dictionaries\r\n feed = feedparser.parse(url)\r\n\r\n # no RSS feed articles for url\r\n if len(feed['entries']) == 0:\r\n return []\r\n \r\n # get first ten articles from the RSS feed\r\n news = []\r\n i = 0\r\n while True:\r\n if i == len(feed['entries']) or i > 30:\r\n break\r\n \r\n try:\r\n # get link to article\r\n link = feed[\"entries\"][i][\"link\"]\r\n\r\n # get title of article\r\n title = feed[\"entries\"][i][\"title\"]\r\n \r\n try:\r\n # get raw summary of article\r\n summary_raw = feed[\"entries\"][i][\"summary\"]\r\n \r\n # format summary\r\n summary = \"\"\r\n for c in summary_raw:\r\n if c == \"<\":\r\n summary += \"...\"\r\n break\r\n summary += c\r\n except KeyError as e:\r\n logging.error(\"no summary for RSS feed article: {}\".format(link))\r\n summary = \"read more here...\"\r\n \r\n # get raw date \r\n date_raw = feed[\"entries\"][i][\"published_parsed\"]\r\n \r\n if date_raw is None:\r\n date = feed[\"entries\"][i][\"published\"]\r\n \r\n else:\r\n # format date\r\n year = str(date_raw.tm_year)\r\n months = [\"January\", \"February\", \"March\", \"April\", \"May\", \"June\", \"July\", \"August\", \"September\", \"October\", \"November\", \"December\"]\r\n month = months[date_raw.tm_mon - 1]\r\n day = str(date_raw.tm_mday)\r\n weekdays = [\"Monday\", \"Tuesday\", \"Wednesday\", \"Thursday\", \"Friday\", \"Saturday\", \"Sunday\"]\r\n wday = weekdays[date_raw.tm_wday]\r\n hour = str(date_raw.tm_hour)\r\n hour = \"{:2}\".format(hour).format(' ','0')\r\n min = str(date_raw.tm_min)\r\n min = \"{:2}\".format(min).replace(' ','0')\r\n date = hour + \":\" + min + \" - \" + wday + \" \" + month + \" \" + day + \", \" + year\r\n \r\n # compile entry and append to news list\r\n entry = {\"link\":link, \"title\":title, \"date\":date, \"summary\":summary}\r\n \r\n # sanitize entry\r\n for key in entry:\r\n # apostrophe\r\n entry[key] = entry[key].replace(\"'\", \"'\")\r\n # right single quotation mark\r\n entry[key] = entry[key].replace(\"’\", \"’\")\r\n # left single quotation mark\r\n entry[key] = entry[key].replace('\"', \"‘\")\r\n # right double quotation mark\r\n entry[key] = entry[key].replace(\"'\", \"”\")\r\n # left double quotation mark\r\n entry[key] = entry[key].replace(\"'\", \"“\")\r\n # Weird ampersand formatting\r\n entry[key] = entry[key].replace(\"&\", \"&\")\r\n \r\n # prepare entry for sqlite queries\r\n entry[key] = surround(entry[key])\r\n \r\n # add entry to news list\r\n news.append(entry)\r\n \r\n # max 10 entries\r\n if len(news) == 10:\r\n break\r\n i += 1\r\n \r\n except Exception as e:\r\n logging.error(e)\r\n i += 1\r\n pass\r\n \r\n # success\r\n return news","def sourcenews(source):\n\turlnews=urlsource\n\turl=urlnews+source\n\turlapi=url+'&'+'apiKey='\n\turlsour=urlapi+apikey\n\tresponse=requests.get(urlsour)\n\tdata=response.json()\n\treturn data","def fetch(api_key, query='', page=1, from_date=False, to_date=False):\n fetch_articles(api_key, query, page, from_date, to_date)","def get_headlines(newssource):\n \n \n newssource_dict = {}\n url = 'https://newsapi.org/v1/articles?source=' + newssource + '&sortBy=top&apiKey=' + api\n request = http.request('GET',url,timeout=4.0)\n\n headline = json.loads(request.data)\n \n if not headline['articles']:\n return \"NewsAPI can not receive information from\" + newsource + \"right now\"\n \n newssource_dict['url'] = headline['articles'][0]['url']\n newssource_dict['title']= headline['articles'][0]['title']\n newssource_dict['description'] = headline['articles'][0]['description']\n \n \n return newssource_dict","def articles():\n\n # Store the 'geo' part of the URL as a string called 'geo'. Check 'geo' loaded, and produce runtime error if not.\n # e.g. '12589'\n geo = request.args.get(\"geo\")\n if not geo:\n raise RuntimeError(\"missing geo\")\n\n # Run 'geo' through 'lookup()' function, store resulting list of objects in 'rows'.\n # e.g. [{'link':'www.website1.com','title':'article_title1'},{'link':'www.website2.com','title':'article_title2'}]\n rows = lookup(geo)\n\n # Run 'rows' through 'jsonify()'' function, and return resulting dictionary w/ up to 5 objects. The 'jsonify()' function modifies the input to JSON.\n # e.g. [{'link':'www.website1.com','title':'article_title1'},{'link':'www.website2.com','title':'article_title2'}]\n if len(rows) > 5:\n return jsonify(rows[0], rows[1], rows[2], rows[3], rows[4])\n else:\n return jsonify(rows)","def fetch_news(n):\n\n # This is the list we will use the pass back the news information.\n data = []\n\n # Get news stories from the MEN RSS feed.\n response = feedparser.parse('https://www.manchestereveningnews.co.uk/?service=rss')\n\n # Loop through the news items, and the pull out the data we need.\n for news in response.entries[:n]:\n data.append({\n 'headline': news.title,\n 'content': news.description,\n })\n\n return data","async def get_news(q: str = None):\n\treturn aggregate_news(q)","def dashboard_article_sources():\n sources = dict()\n past_30 = offset_time_past(30, str=True)\n articles = mongo.db[app.config['ARTICLES_COLLECTION']]\n results = articles.find({'collected': {'$gt': past_30}}, {'_id': 0})\n for result in results:\n sources[result['source']] = sources.get(result['source'], 0) + 1\n sources = sorted(sources.items(), key=operator.itemgetter(1), reverse=True)\n data = sources[:10]\n return jsonify(data)","def show_sources_all():\n response = requests.get(SOURCE_URL)\n json = response.json()\n for source in json['sources']:\n print(u\"{0}: <{1}> {2}\".format(\"News Code\", source['id'], source['name']))","def articles(news_id):\n #get news based on source id\n articles = get_articles(news_id)\n print(articles)\n title = f'{news_id}'\n return render_template(\"articles.html\", articles = articles, title = title)","async def news(self):\n url = f\"https://newsapi.org/v2/top-headlines?country=nz&apiKey={self.bot.news_api_key}\"\n async with ClientSession() as session:\n async with session.get(url) as response:\n r = await response.json()\n firstArticle = r[\"articles\"][0]\n nSource = firstArticle[\"source\"][\"name\"]\n nTitle = firstArticle[\"title\"]\n nTimestamp = firstArticle[\"publishedAt\"]\n embed = discord.Embed(\n title=f\"News Title: {nTitle}\", description=f\"News Source: {nSource}\"\n )\n embed.add_field(name=\"News Content\", value=firstArticle[\"description\"])\n embed.set_image(url=firstArticle[\"urlToImage\"])\n embed.set_footer(text=f\"News Timestamp: {nTimestamp}\")\n\n channel = self.bot.get_channel(self.bot.main_channel_id)\n await channel.send(embed=embed)","def pull_articles(self, *args, **kwargs):\n tasks.pull_articles()\n return Response({})","def articles ():\n\n offset = int (arg ('offset', '0', re_integer_arg))\n limit = clip (arg ('limit', str (MAX_RESULTS), re_integer_arg), 1, MAX_RESULTS)\n\n with current_app.config.dba.engine.begin () as conn:\n res = execute (conn, r\"\"\"\n SELECT no\n FROM article\n ORDER BY no\n LIMIT :limit\n OFFSET :offset\n \"\"\", { 'offset' : offset, 'limit' : limit })\n\n return make_articles_response (res, limit)","def searchnews(self,\n keywords=None,\n dateStart=None,\n dateEnd=None,\n sortBy=None,\n domains=None,\n page=20,\n sources='abc-news',\n language=None,\n apiKey=None,\n version=None):\n\n # detect version\n version = self.version\n request_params=None\n\n # set up for version 1 articles\n if self.version != 2:\n if sortBy not in ['top','latest','popular']:\n sortBy='top'\n warnings.warn('Version 1 sorts by top, latest, or popular. '\n 'Defaulted to top for this search.')\n\n request_params = {\n \"sortBy\":sortBy,\n \"source\": sources,\n \"apiKey\": self._api_key,\n }\n\n if not keywords and self.version !=1:\n raise ValueError('You must enter a keywords to use the search '\n 'service.')\n\n # retrive the api key if set; otherwise, error\n if not self._api_key:\n raise ValueError(\n 'You must use use an API key; to get a key visit https://news'\n 'api.org/. If you have an API key, set it using the '\n 'Api.SetCredentials method.')\n\n # if api key is there, set the params\n else:\n if not request_params:\n if not sortBy:\n if sortBy not in ['publishedAt', 'relevancy', 'popularity']:\n sortBy='publishedAt'\n warnings.warn('Version 2 can only sort by publishedAt, '\n 'relevancy, or popularity. Defaulted to '\n 'publishedAt for this search.')\n request_params = {\n \"q\": keywords,\n \"dateStart\":dateStart,\n \"dateEnd\":dateEnd,\n \"sortBy\":sortBy,\n \"domains\":domains,\n \"page\":page,\n \"sources\":sources,\n \"language\":language,\n \"apiKey\": self._api_key,\n }\n\n\n # build the url\n url = self.base_url + self.__endpoints['search']\n\n # make the request\n r = requests.get(url,params=request_params,timeout=self._timeout)\n\n print(r.url)\n # return the json\n return r.json()","def get_news(company_name: str) -> list[dict]:\n news_params = {\n \"q\": company_name,\n \"apiKey\": config.NEWS_API_KEY\n }\n response = requests.get(\"https://newsapi.org/v2/everything\", params=news_params)\n response.raise_for_status()\n news_data = response.json()\n return news_data[\"articles\"][:3]","def get_remote_news_items(self):\n items = []\n params = {\n \"base_url\": self.osha_json_url,\n \"lang\": api.portal.get_tool(\"portal_languages\").getPreferredLanguage(),\n \"query_tags\": self.remote_news_query_tags,\n }\n qurl = \"{base_url}/{lang}/services/hw/news/{query_tags}\".format(**params)\n result = urlopen(qurl)\n if result.code == 200:\n json = load(result)\n for node in json.get(\"nodes\"):\n item = node.get(\"node\")\n pd = item.get('publication_date', '')\n items.append({\n 'remote_item': True,\n 'Title': item['title'],\n 'Date': (\n pd and DateTime(pd, datefmt=\"international\").strftime(\n \"%Y/%m/%d %H:%M\") or \"\"),\n 'getURL': item.get('path'),\n 'path': item.get('path'),\n 'Description': item.get('summary', '') or item.get('body', ''),\n 'text': item.get('summary', '') and item.get('body', '') or '',\n 'remote_image': item.get('image', ''),\n 'node_id': item.get('nid'),\n })\n return items","def get_from_db(date, source, connection, logger):\n check_date(date, logger)\n result = execute_news(date, connection, source, logger)\n if len(result) == 0:\n raise SystemExit(f\"Sorry, there are no articles for {date}!\")\n else:\n return result","def articles(self, page=None, per_page=None, sort=None):\r\n params = base.get_params(None, locals())\r\n request = http.Request('GET', self.get_url(), params)\r\n\r\n return request, parsers.parse_json","def scrapeTopic(self, topic, num_articles, sources=list(site.all_sites)): \n pass","def get_news(self, keyword, since=None, to=None, page=None):\n payload = {}\n url = \"https://newsapi.org/v2/everything\"\n payload['q'] = keyword\n if since is not None:\n try:\n start_dt = dateutil.parser.parse(since)\n if to is not None:\n to_dt = dateutil.parser.parse(to)\n else:\n to_dt = datetime.datetime.now()\n except ValueError:\n raise IOError('since parameter can not be converted to datetime')\n payload['from'] = start_dt.isoformat()\n payload['to'] = to_dt.isoformat()\n payload['language'] = 'en'\n payload['pageSize'] = 20\n payload['sortBy'] = 'popularity'\n payload['excludeDomains'] = 'startribune.com'\n if page is not None and type(page) == int and page > 0:\n payload['page'] = page\n r = requests.get(url, auth=self.auth, params=payload)\n return r.content","def articleList():\n articles = get_news(\n 5, since=news.YESTERDAY.strftime(\"%yyyy-%mm-%dd\"), query=\"covid\"\n )\n title_list = []\n desc_list = []\n url_list = []\n image_list = []\n source_list = []\n for art in articles:\n image_list.append(art.image)\n title_list.append(art.title)\n source_list.append(art.source)\n desc_list.append(art.description)\n url_list.append(art.url)\n socketio.emit(\n ARTICLE,\n {\n \"title\": title_list,\n \"desc\": desc_list,\n \"url\": url_list,\n \"img\": image_list,\n \"sources\": source_list,\n },\n )\n return True","def get_news(keywords, news='all'):\n if news is 'all':\n return news_client.get_everything(q=keywords)\n elif news is 'top':\n return news_client.get_top_headlines(q=keywords)\n else:\n raise AttributeError(\"Optional argument news expected 'top' or 'all'\")","def fetch(feed):\n # Fetch the feed data.\n data = feedparser.parse(feed.ext_url)\n new_articles = []\n\n # If the `bozo` value is anything\n # but 0, there was an error parsing (or connecting) to the feed.\n if data.bozo:\n # Some errors are ok.\n if not isinstance(data.bozo_exception, feedparser.CharacterEncodingOverride) and not isinstance(data.bozo_exception, feedparser.NonXMLContentType):\n raise data.bozo_exception\n\n for entry in data.entries:\n\n # URL for this entry.\n url = entry['links'][0]['href']\n\n # Check for an existing Article.\n # If one exists, skip.\n if Article.objects(ext_url=url).first():\n continue\n\n data = extractor.extract(url, existing_data=entry)\n\n if data is None:\n continue\n\n # Secondary check for an existing Article,\n # by checking the title and source.\n existing = Article.objects(title=data['title']).first()\n if existing and existing.feed.source == feed.source:\n continue\n\n data['feed'] = feed\n\n article = Article(**data)\n article.save()\n new_articles.append(article)\n\n return new_articles","def fetch_all_news_codes():\n response = requests.get(SOURCE_URL)\n json = response.json()\n global news_codes\n for source in json['sources']:\n news_codes.append(source['id'])","def get_articles(self, publish_status):\n query_str = (\n \"SELECT Id,KnowledgeArticleId,Title,UrlName FROM {} \"\n \"WHERE PublishStatus='{}' AND language='en_US'\"\n ).format(\n settings.SALESFORCE_ARTICLE_TYPE,\n publish_status,\n )\n result = self.api.query(query_str)\n return result['records']","def news_web():\n get_news()","def NewsArticles():\n health_articles = get_articles('health')\n education_articles = get_articles('technology')\n return render_template('articles.html',health=health_articles, tech =education_articles)","def environmental_science_news():\n\n return general_scraper(['http://mesva.univaq.it/?q=avvisi/cl-clm/52671'])","def fetch_articles(api_key, query, page, from_date, to_date, collection=[]):\n\n # fetch articles\n response = fetch_guardian_articles.fetch(api_key, query, page,\n from_date, to_date)\n\n # do we have results?\n if (response and response['results']):\n articles = response['results']\n\n # add for which query the article\n # make sure we convert result of map to list\n # https://stackoverflow.com/questions/1303347/getting-a-map-to-return-a-list-in-python-3-x\n articles = list(\n map(\n lambda article: add_query_to_article(article, query),\n articles\n )\n )\n\n # concatenate\n collection = collection + articles\n\n # should we fetch additional articles?\n pages = response['pages']\n if (pages and page < pages):\n # load next article\n time.sleep(IN_BETWEEN_REQUESTS_GAP)\n fetch_articles(api_key, query, page + 1,\n from_date, to_date, collection)\n else:\n # all done, return collected articles\n return collection\n else:\n print('Nothing found')","def get_article_URLs(sourceURL):\n sourceURL = sourceURL.encode().decode()\n articleList = []\n soup = bs.BeautifulSoup(urllib.request.urlopen(sourceURL),'lxml')\n #remove any tables\n for table in soup.find_all(\"table\"):\n table.extract()\n #remove any special wiki pages\n for citation in soup.find_all(href=re.compile(\"^/wiki/Wikipedia:|^/wiki/User:|^/wiki/File:|^/wiki/MediaWiki:|^/wiki/Template:|^/wiki/Help:|^/wiki/Category:|^/wiki/Portal:|^/wiki/Draft:|^/wiki/TimedText:|^/wiki/Module:|^/wiki/Special:\")):\n citation.extract()\n a = soup.find('div', {'class':'mw-parser-output'}).find_all('a', href=re.compile(\"^/wiki/\"))\n for link in a:\n articleList.append(link['href'])\n return (articleList)","def articles(self, audience_filter=None):\n articles = ArticlePage.objects.live().descendant_of(self)\n if audience_filter is not None:\n articles = articles.filter(audience__name=audience_filter)\n articles = articles.order_by('-date')\n return articles","def news():\n\n # ensure parameters are present\n # geo = request.args.get(\"geo\")\n geo = '95060'\n if not geo:\n raise RuntimeError(\"missing geo\")\n\n # lookup articles and store them as JSON array\n article_list = lookup(geo)\n\n # TODO\n print(article_list)\n news = jsonify(article_list) \n print(news)\n # return render_template(\"index.html\")\n return article_list","def articles(self):\n return self.get_queryset().filter(content_type__model='article').order_by('-articles__published_at')","def get_news_text(news_url: str):\n print(\"Getting Article Content..\")\n if news_url is None:\n print(\"URL is missing\")\n return None\n else:\n article = Article(news_url)\n article.download()\n article.parse()\n return article.text","def nytArticleSearch(tag):\n nytKey=\"7d9f3b88013d99f9f9ab2a8a82545671:19:73424493\" #new york times article search api key\n url= 'http://api.nytimes.com/svc/search/v2/articlesearch.json?q=%s&fq=type_of_material:(\"News\")&api-key=%s'\n url=url%(tag,nytKey)\n request = urllib2.urlopen(url)\n result = request.read()\n r = json.loads(result)\n return r['response']['docs']","def get_local_news_items(self):\n catalog = api.portal.get_tool(name='portal_catalog')\n default_lang = api.portal.get_tool(\n \"portal_languages\").getDefaultLanguage()\n results = catalog.searchResults(\n portal_type=\"News Item\",\n sort_on=\"effective\",\n sort_order=\"descending\",\n review_state='published',\n Language=[default_lang, ''],\n )\n return results","def search_news(request):\n try:\n query_string = ''\n if request.GET['search_text'].strip() != '':\n query_string = '&title='+request.GET['search_text']\n response = requests.get(APIURL + '/articles/?format=json'+query_string)\n parser = json.loads(response.content)\n return render_to_response('article/search_result.html', {'articlelist':parser})\n except:\n raise Http404(\"Search Item error\")","def get_news(request):\n return get_all_posts(request, PostType.NEWS)","def articles(self, subject_filter=None):\n articles = ArticlePage.objects.live().descendant_of(self)\n if subject_filter is not None:\n articles = articles.filter(\n Q(subject_1=subject_filter) | Q(subject_2=subject_filter))\n articles = articles.order_by('-date')\n return articles","def articles(self):\r\n return articles.Articles(self)","def general_news():\n\n return general_scraper(['http://mesva.univaq.it/'])","def getStories(self, source, alreadyReadList):\n\t\t\n\t\tstories = HackerNewsAPI.getStories(self, source)\n\t\t\n\t\t\n\t\tfor i in range(0, HackerNewsAPI.numberOfStoriesOnFrontPage):\n\t\t\tstory = stories[i]\n\t\t\t\n\t\t\tnewStory = NewsStory()\n\t\t\tnewStory.id = story.id\n\t\t\tnewStory.number = story.number\n\t\t\tnewStory.title = story.title\n\t\t\tnewStory.domain = story.domain\n\t\t\tnewStory.URL = story.URL\n\t\t\tnewStory.score = story.score\n\t\t\tnewStory.submitter = story.submitter\n\t\t\tnewStory.commentCount = story.commentCount\n\t\t\tnewStory.commentsURL = story.commentsURL\n\t\t\tif story.URL in alreadyReadList:\n\t\t\t\tnewStory.hasBeenRead = 1\n\t\t\tstories[i] = newStory\n\t\t\t\t\n\t\treturn stories","def get_article(url):\n \n r = requests.get(url) \n html_soup = BeautifulSoup(r.content, 'lxml')\n return html_soup","def get_remote_news_item(self, node_id):\n items = []\n lang = api.portal.get_tool(\"portal_languages\").getPreferredLanguage()\n\n params = {\n \"base_url\": self.osha_json_url,\n \"lang\": lang,\n \"node_id\": node_id,\n }\n qurl = \"{base_url}/{lang}/services/hw/news1/{node_id}\".format(**params)\n\n result = urlopen(qurl)\n if result.code == 200:\n json = load(result)\n for node in json.get(\"nodes\"):\n item = node.get(\"node\")\n pd = item.get('publication_date', '')\n items.append({\n 'remote_item': True,\n 'Title': item['title'],\n 'Date': (\n pd and DateTime(pd, datefmt=\"international\").strftime(\n \"%Y/%m/%d %H:%M\") or \"\"),\n 'getURL': item.get('path'),\n 'Description': item.get('summary', ''),\n 'text': item.get('body', ''),\n 'remote_image': item.get('image', ''),\n })\n if len(items) > 0:\n return items.pop()","def _fetch_news(self, docid):\n detail_link = Fetch163.detail_link % docid\n try:\n resp = urllib2.urlopen(detail_link)\n except urllib2.URLError as e:\n urllib_error(e)\n else:\n try:\n doc_json = json.load(resp)\n content = doc_json[docid]\n title = content['title']\n except KeyError:\n print \"Error while Fetching docid: %s\" % docid\n except ValueError:\n print \"No JSON obj could be decoded\", resp.read()\n else:\n if self._filter_test(title):\n #print title\n c_num = content['replyCount']\n ptime = content['ptime']\n img_list = content['img']\n if img_list:\n list_pic = img_list[0]['src']\n else:\n list_pic = ''\n body = content['body']\n abstract = self._retrieve_abstract(body)\n body = convert_163(body, img_list)\n return abstract, body, c_num, ptime, list_pic\n return '', '', '', '', list_pic","def get_sources():\n url = base_url + \"sources\"\n params = {\"language\": \"en\"}\n resp = requests.get(url, params=params)\n data = resp.json()\n sources = [src['id'].strip() for src in data['sources']]\n print(\"all the sources\")\n print(sources)\n return sources","def get_news(url, n_pages=1):\n news = []\n while n_pages:\n print(\"Collecting data from page: {}\".format(url))\n response = requests.get(url)\n soup = BeautifulSoup(response.text, \"html.parser\")\n news_list = extract_news(soup)\n next_page = extract_next_page(soup)\n url = \"https://news.ycombinator.com/\" + next_page\n news.extend(news_list)\n n_pages -= 1\n return news","def get_news(self):\n if self.api_key_entry.get() == \"\":\n return None\n api = nac(api_key=self.api_key_entry.get())\n now = datetime.datetime.utcnow()\n two_weeks = (now-datetime.timedelta(days=14))\n #today = now.strftime()\n query = \"\"\n for cat in self.sorted_categories():\n query += f\"{cat},\"\n search = api.get_top_headlines(q=query,\n sources=\"bbc-news,the-verge\",\n language=\"en\")\n news = \"\"\n for article in search[\"articles\"]:\n news += f\"{search['articles'][article]['title']}\\n\"\n self.news_box.delete('1.0', tk.END)\n self.news_box.insert('1.0', news)","def search(self, json_query):\n res = self.es.search(index=self.index, body=json_query)\n #print(json.dumps(res, indent=2))\n # Process the results and return the article objects only\n articel_list = [src['_source'] for src in res['hits']['hits']]\n return articel_list","def get_news(url, n_pages=1):\n news = []\n while n_pages:\n print(\"Collecting data from page: {}\".format(url))\n response = requests.get(url)\n soup = BeautifulSoup(response.text, \"html.parser\")\n news_list = extract_news(soup)\n print(news_list)\n next_page = extract_next_page(soup)\n url = 'https://news.ycombinator.com/' + next_page\n news.extend(news_list)\n n_pages -= 1\n return news","def articles(self):\n return articles.Articles(self)","def pull_articles(ls):\n # pull articles\n doi = self.search_articles(file)\n els_key = self.els_key\n\n for i in doi:\n els_url = 'https://api.elsevier.com/content/article/doi/' + doi + '?APIKey=' + els_key\n r = requests.get(els_url)\n for num in range(len(ls)):\n with open(folder + f'/write_test_els_paper{num}.xml', 'wb') as file:\n file.write(r.content)","def article_list(request):\n try:\n logger.info('Calling the api' + APIURL + '/articles/?format=json&limit=' + str(COUNT))\n response = requests.get(APIURL + '/articles/?format=json&limit=' + str(COUNT))\n parser = json.loads(response.content)\n preview_article = random_article(parser)\n next_read = read_next()\n return render(request, 'article/article_list.html', {'articlelist':parser, 'preview_article': preview_article, 'next_read': next_read})\n except:\n logger.error('Calling the api error in article_list')\n raise Http404(\"Article does not exist\")","def get_article1(url):\n # set agent\n agent = 'codeup ds germain' \n # query\n response = requests.get(url, headers={'User-Agent': agent}) \n # soup\n soup = BeautifulSoup(response.text) \n # get cat\n category = soup.find_all('li', {'class':'active-category selected'})[0].text \n # get raw cards\n cards = soup.select('.news-card') \n # create list of dicts for dataframe\n card_dict_list = [] \n # iterate each card\n for card in cards: \n # headline\n headline = card.find_all('span', {'itemprop':'headline'})[0].text \n # publish time\n publish_time = card.find_all('span', {'class':'time'})[0].text \n # content\n content = card.find_all('div', {'itemprop':'articleBody'})[0].text.strip() \n # create dict\n card_dict = {'headline':headline, 'publish_time':publish_time,\n 'category':category, 'content':content} \n # push dict to list\n card_dict_list.append(card_dict) \n # return dataframe\n return pd.DataFrame(card_dict_list)","def get_sources():\n url = base_url + \"sources\"\n params = {\"language\": \"en\"}\n resp = requests.get(url, params=params)\n data = resp.json()\n sources = [src['id'].strip() for src in data['sources']]\n print(\"all the sources:\")\n print(sources)\n return sources","def execute_news(date, connection, url, logger):\n cursor = connection.cursor()\n if url:\n logger.info(f\"Retrieves news for the selected url ({url}) from database...\")\n cursor.execute('SELECT title, link, full_date, source, description, image, url FROM news WHERE date=:date '\n 'and url=:url', {'date': date, 'url': url})\n else:\n cursor.execute('SELECT title, link, full_date, source, description, image, url FROM news WHERE date=:date',\n {'date': date})\n records = cursor.fetchall()\n articles = []\n for title, link, full_date, source, description, image, url in records:\n articles.append(Article(title, link, full_date, source, description, image))\n return articles","def select_news_article(request):\n global results\n if request.method == \"POST\":\n article_url = request.POST.get('article_url')\n with open('temp/url.txt', 'w') as f:\n f.write(article_url)\n\n return _start_analysis(request)\n else:\n return HttpResponse(\n json.dumps({\"error\": \"error, GET request not supported\"}),\n content_type=\"application/json\"\n )","def get_article_data(self, url):\n article_data = {}\n \n response = requests.get(url, headers=HEADERS)\n \n soup = BeautifulSoup(response.text, 'html.parser')\n script_tags = soup.find_all('script', type=\"application/ld+json\")\n for script in script_tags:\n if '\"@type\":\"NewsArticle\"' in script.string:\n article_object = json.loads(script.string)\n article_data['url'] = article_object.get('url')\n article_data['publish_date'] = article_object.get('datePublished')\n article_data['title'] = article_object.get('headline')\n article_data['text'] = article_object.get('articleBody')\n article_data['video'] = self._download_file(article_object['video'])\n break\n elif '\"@type\":[\"ReportageNewsArticle\"]' in script.string:\n article_object = json.loads(script.string)\n article_data['url'] = article_object.get('mainEntityOfPage')\n article_data['publish_date'] = article_object.get('datePublished')\n article_data['title'] = article_object.get('headline')\n article_data['text'] = article_object.get('articleBody')\n article_data['video'] = \"media/\"\n break\n\n return article_data","def select_news(self):\n data = self.soup.findAll('item')\n for item in data:\n news_data = dict()\n for tag in ['title', 'link']:\n news_data[tag] = item.find(tag).get_text()\n\n news_data['pubDate'] = parse(item.find('pubDate').get_text())\n media = item.find('media:content')\n\n if media:\n news_data['media'] = media.get('url')\n else:\n news_data['media'] = None\n\n yield news_data","def FindArticles(sesh):\n\n rssfeeds = FindRSSFeeds()\n\n found = ScraperUtils.FindArticlesFromRSS( rssfeeds, u'dailymail', ScrubFunc )\n return found","def list_articles():\n\n return template(\"index\", articles=get_articles())","def get(self, request):\n query = Article.objects.exclude(in_menu=True)\n\n article_filter = Q()\n searchable_paths = {}\n\n search_terms = [term for term in request.GET.get(\"query\").split(\" \")]\n for term in search_terms:\n searchable_paths[term] = Q(headline__icontains=term) | Q(\n teaser__icontains=term\n )\n\n for key in searchable_paths.keys():\n article_filter &= searchable_paths[key]\n\n query = query.filter(article_filter).distinct()\n articles = [\n {\n \"updated\": article.updated,\n \"headline\": article.headline,\n \"teaser\": article.teaser,\n \"tags\": [tag.name for tag in article.tags.all()],\n \"url\": reverse(\n \"article.detail\",\n kwargs={\n \"year\": article.created.year,\n \"month\": article.created.month,\n \"slug\": article.slug,\n },\n ),\n }\n for article in query\n ]\n\n return JsonResponse({\"articles\": articles})","def get_news(news_url):\n news_final = []\n try:\n news_handler = urllib.urlopen(news_url)\n news = news_handler.read()\n news = nl2br(news)\n news = string.split(news, '<br/>')\n\n news_array = {}\n value = {}\n for newsweb in news:\n value = string.split(newsweb, '|')\n if len(value[0]) > 1:\n news_array[value[0]] = value[1]\n\n info = {}\n for k in news_array:\n info = k[0:int(k.find(\"http://\") - 1)]\n info = string.split(k, ' - ')\n news_final.append((info[0], info[1], news_array[k]))\n\n news_handler.close()\n except IndexError:\n pass\n except IOError:\n pass\n\n return news_final","def parse_news(news):\n default_value = '---'\n\n news_list = []\n for entry in news:\n title = entry.get('title', default_value)\n link = entry.get('link', default_value)\n published = entry.get('published', default_value)\n source = entry.get('source', default_value)\n description = entry.get('description', default_value)\n media_content = entry.get('media_content', default_value)\n\n source_title = default_value\n if source != default_value:\n source_title = source['title']\n\n image = default_value\n if media_content != image:\n image = media_content[0]['url']\n\n article = Article(title, link, published, source_title, description, image)\n news_list.append(article)\n\n return news_list","def articles(self):\r\n return Articles(self)","def get_news():\n # empty dataframe\n df = pd.DataFrame() \n # read each url in list\n for url in inshorts_urls(): \n # add each dataframe of cards to df\n df = pd.concat([df, get_article(url)])\n # return all urls' cards\n return df","def get_articles(\n self,\n size=None,\n page=None,\n order_type='asc',\n order_by=None\n ):\n params = {}\n\n if order_type and order_by:\n params['order_type'] = order_type\n params['order_by'] = order_by\n if size:\n params['size'] = size\n if page:\n params['page'] = page\n\n params_str = \"&\".join(f'{k}={v}' for k, v in params.items())\n\n r = requests.get(\n f'{self.api_host}/v1/articles',\n params=params_str,\n headers=self.get_request_headers()\n )\n\n response = None\n if r.status_code == 200:\n response = r.json()\n\n return response","def getsources(self,\n category='general',\n language=None,\n country='us',\n apiKey=None,\n version=None):\n\n if self.version != 2:\n\n request_params = {\n \"category\":category,\n \"language\": language,\n \"country\":country,\n \"apiKey\": self._api_key,\n }\n\n # retrive the api key if set; otherwise, error\n if not self._api_key:\n raise ValueError(\n 'You must use use an API key; to get a key visit https://news'\n 'api.org/. If you have an API key, set it using the '\n 'Api.SetCredentials method.')\n\n # if api key is there, set the params\n else:\n request_params = {\n \"category\": category,\n 'language': language,\n \"country\": country,\n \"apiKey\": self._api_key,\n }\n\n\n # build the url\n url = self.base_url + self.__endpoints['source']\n\n # make the request\n r = requests.get(url,params=request_params,timeout=self._timeout)\n\n\n # return the json\n return r.json()","def get_from_url(source):\n try:\n rss_news = feedparser.parse(source)\n result = parse_news(rss_news['entries'])\n except urllib.error.URLError:\n raise SystemExit(\"Source isn't available\")\n else:\n if len(result) == 0:\n raise SystemExit('Please, check if the entered link is correct!')\n else:\n return result","def biological_science_news():\n\n return general_scraper(['http://mesva.univaq.it/?q=avvisi/cl-clm/52672'])","def get_articles(category, base =\"https://inshorts.com/en/read/\"):\n \n # We concatenate our base_url with the category\n url = base + category\n \n # Set the headers\n headers = {\"User-Agent\": \"Mozilla/4.5 (compatible; HTTrack 3.0x; Windows 98)\"}\n\n # Get the http response object from the server\n response = get(url, headers=headers)\n\n # Make soup out of the raw html\n soup = BeautifulSoup(response.text)\n \n # Ignore everything, focusing only on the news cards\n articles = soup.select(\".news-card\")\n \n output = []\n \n # Iterate through every article tag/soup \n for article in articles:\n \n # Returns a dictionary of the article's title, body, and category\n article_data = get_article(article, category) \n \n # Append the dictionary to the list\n output.append(article_data)\n \n # Return the list of dictionaries\n return output\n\n#def get_blog_articles(url):\n '''\n This function takes in a url and pull the necessary elements off the website\n then creates a dictionary with those elements\n '''\n\n # create an empty dictionary to append to\n blog_dict = {}\n \n # fetch the data\n headers = {'User-Agent': 'Codeup Data Science'}\n response = get(url, headers=headers)\n soup = BeautifulSoup(response.text)\n \n # pull the elements from the soup\n article = soup.find('div', class_='jupiterx-post-content') # pulling body of text\n title = soup.find('h1', class_='jupiterx-post-title') # pulling title as text\n \n # append the elements to the dictionary\n blog_dict = {'title': title.text,\n 'content': article.text}\n \n # return dictionary\n return blog_dict","def news_handle(news_json):\n #Sets all the variables to global space to be used later.\n global title_1, title_2, author_1, author_2, source_1, source_2, url_1_final, url_2_final\n #Handles data from API and assigns data to variables.\n news_data = news_json['articles']\n article_1 = news_data[0]\n article_2 = news_data[1]\n title_1 = article_1['title']\n author_1 = article_1['author']\n source_1 = article_1['source']['name']\n url_1 = article_1['url']\n url_1_final = '<a href=\"{}\">Read More</a>'.format(url_1)\n title_2 = article_2['title']\n author_2 = article_2['author']\n source_2 = article_2['source']['name']\n url_2 = article_2['url']\n url_2_final = '<a href=\"{}\">Read More</a>'.format(url_2)\n return","def show_news(code, BASE_URL):\n url = \"?source={news_id}&apiKey=\"\n response = requests.get((BASE_URL+url+api_key).format(news_id=code))\n json = response.json()\n\n article_list = []\n for article in json['articles']:\n print('[{0}] {1}: {2}'.format(len(article_list), \"Title\", article['title']))\n print('{0}: {1}'.format(\"Author\", article['author']))\n print('{0}: {1}'.format(\"Summary\", article['description']))\n print('--------------------------------------------')\n article_list.append(article['url'])\n\n print(\"Would you like to open an article in a web browser? Enter (y/n): \")\n\n choice = (input()).lower()\n if choice not in VALID:\n print(\"Invalid entry.\")\n sys.exit()\n\n if choice == 'y':\n while choice == 'y':\n article_number = input(\"Enter an article number: \")\n while not article_number.isdigit() or not 0 <= int(article_number) <= len(article_list):\n print(\"Invalid article number. Try again.\")\n article_number = input(\"Enter an article number: \")\n\n webbrowser.open(article_list[int(article_number)])\n\n print(\"Would you like to view another article? Enter (y/n): \")\n choice = (input()).lower()\n if choice not in VALID:\n sys.exit()\n if choice == 'y':\n continue","def read_next():\n try:\n response = requests.get(APIURL + '/articles/?format=json&random=yes')\n parser = json.loads(response.content)\n return parser\n except:\n raise Http404(\"Article does not exist\")","def get_articles(self, feed_id: int) -> List[Article]:\n articles = []\n for row in self._sqlite_connection.execute('SELECT identifier, uri, title, updated, author, content, unread, flag FROM articles WHERE feed_id = ? ORDER BY updated DESC LIMIT 200', [feed_id]):\n data = ArticleData()\n data.feed_id = feed_id\n data.identifier = row['identifier']\n data.uri = row['uri']\n data.title = row['title']\n data.updated = datetime.fromtimestamp(row['updated'], timezone.utc)\n data.author = row['author']\n data.content = row['content']\n data.unread = bool(row['unread'])\n data.flag = bool(row['flag'])\n articles.append(Article(data))\n return articles","def make_new_request():\n urls = [\n \"https://inshorts.com/en/read/business\",\n \"https://inshorts.com/en/read/sports\",\n \"https://inshorts.com/en/read/technology\",\n \"https://inshorts.com/en/read/entertainment\"\n ]\n\n output = []\n \n for url in urls:\n # We use .extend in order to make a flat output list.\n output.extend(get_articles_from_topic(url))\n \n df = pd.DataFrame(output)\n df.to_csv('inshorts_news_articles.csv')\n return df","def nfldotcomnews(self, irc, msg, args):\n \n url = self._b64decode('aHR0cDovL3MzLmFtYXpvbmF3cy5jb20vbmZsZ2MvYWxsX25ld3NMaXN0Lmpz')\n \n try:\n req = urllib2.Request(url)\n html = (urllib2.urlopen(req)).read()\n except:\n irc.reply(\"Failed to fetch: %s\" % url)\n return\n \n try:\n jsondata = json.loads(html)['content']\n except:\n irc.reply(\"Failed to parse article json from: %s\" % url)\n return\n \n for article in jsondata[0:6]:\n title = article.get('title', None)\n desc = article.get('description', None)\n link = article.get('linkURL', None)\n date = article.get('date_ago', None)\n \n output = \"{0} - {1}\".format(ircutils.bold(title), self._shortenUrl(link))\n irc.reply(output)","def _extract_articles(self, target):\n feed_response, modified, etag = self._get_recent_feed(target)\n\n # Bozo is a tag which tells that the RSS hasn't been parsed correctly.\n if feed_response.bozo:\n exc = feed_response.bozo_exception\n if not isinstance(exc, self.ALLOWED_EXCEPTIONS):\n raise exc\n\n articles = []\n count = 0\n if self._manage_status(feed_response, target):\n for feed_entry in feed_response.entries:\n if self._limit and count >= self._limit:\n logging.info(\n \"Crawling limit of %d article(s) was reached for this target.\",\n count\n )\n break\n try:\n article = self.extract_article(feed_entry, target)\n except Exception as exc:\n # NOTE(cmiN): On Stackdriver Error Reporting we don't want to catch\n # (with `logging.exception`) \"Not Found\" errors, because they are\n # pretty frequent and usual, therefore ignore-able.\n log_function = (\n logging.error if \"404\" in str(exc) else logging.exception\n )\n log_function(\"Got %s while parsing %r.\", exc, feed_entry.id)\n else:\n articles.append(article)\n count += 1\n target.checkpoint(modified, etag)\n\n return articles","def get_news(self, news_api_key, company):\r\n news_params = {\r\n \"q\": company,\r\n \"apiKey\": news_api_key\r\n }\r\n\r\n news_resp = requests.get(NEWS_API_ENDPOINT, params=news_params)\r\n news_resp.raise_for_status()\r\n news_data = news_resp.json()\r\n return news_data","def get(self):\n return GlobalNews.retrieve()","def process(url):\n feed = feedparser.parse(url)\n entries = feed.entries\n ret = []\n for entry in entries:\n print entry\n guid = entry.guid\n title = translate_html(entry.title)\n link = entry.link\n summary = translate_html(entry.summary)\n try:\n subject = translate_html(entry.tags[0]['term'])\n except AttributeError:\n subject = \"\"\n newsStory = NewsStory(guid, title, subject, summary, link)\n ret.append(newsStory)\n return ret","def topicnews(topic):\n urlnews=urltop\n url=urlnews+topic\n urlapi=url+'&'+'apiKey='\n urlcoun=urlapi+apikey\n response=requests.get(urlcoun)\n data=response.json()\n return data","def getStories(self, source):\n self.numberOfStoriesOnFrontPage = source.count(\"span id=score\")\n # Create the empty stories.\n newsStories = []\n for i in range(0, self.numberOfStoriesOnFrontPage):\n story = HackerNewsStory()\n newsStories.append(story)\n \n soup = BeautifulSoup(source)\n # Gives URLs, Domains and titles.\n story_details = soup.findAll(\"td\", {\"class\" : \"title\"}) \n # Gives score, submitter, comment count and comment URL.\n story_other_details = soup.findAll(\"td\", {\"class\" : \"subtext\"})\n\n # Get story numbers.\n storyNumbers = []\n for i in range(0,len(story_details) - 1, 2):\n story = str(story_details[i]) # otherwise, story_details[i] is a BeautifulSoup-defined object.\n storyNumber = self.getStoryNumber(story)\n storyNumbers.append(storyNumber)\n \n storyURLs = []\n storyDomains = []\n storyTitles = []\n storyScores = []\n storySubmitters = []\n storyCommentCounts = []\n storyCommentURLs = []\n storyIDs = []\n\n for i in range(1, len(story_details), 2): # Every second cell contains a story.\n story = str(story_details[i])\n storyURLs.append(self.getStoryURL(story))\n storyDomains.append(self.getStoryDomain(story))\n storyTitles.append(self.getStoryTitle(story))\n \n for s in story_other_details:\n story = str(s)\n storyScores.append(self.getStoryScore(story))\n storySubmitters.append(self.getSubmitter(story))\n storyCommentCounts.append(self.getCommentCount(story))\n storyCommentURLs.append(self.getCommentsURL(story))\n storyIDs.append(self.getHNID(story))\n \n \n # Associate the values with our newsStories. \n for i in range(0, self.numberOfStoriesOnFrontPage):\n newsStories[i].number = storyNumbers[i]\n newsStories[i].URL = storyURLs[i]\n newsStories[i].domain = storyDomains[i]\n newsStories[i].title = storyTitles[i]\n newsStories[i].score = storyScores[i]\n newsStories[i].submitter = storySubmitters[i]\n newsStories[i].commentCount = storyCommentCounts[i]\n newsStories[i].commentsURL = storyCommentURLs[i]\n newsStories[i].id = storyIDs[i]\n \n return newsStories","def fetch_article_list(self, url):\n print(url)\n\n r = requests.get(url, headers=headers, timeout=10)\n html = r.text\n time.sleep(1)\n\n if r.status_code is not 200:\n print('Server dinied. Status:[%s].'%r.status_code)\n return\n\n # local data test\n #with open('./dataset/sina-blog-list.html', 'r') as f:\n # html = f.read()\n\n #print(html)\n\n soup = BeautifulSoup(html, 'html5lib')\n tags = soup.select('div[class=articleList] > div[class~=articleCell] > p > span[class=atc_title] > a')\n\n for t in tags:\n print('Appened: '+t['href'])\n self.article_urls.append(t['href'])\n\n # Get the url of next blog-list page\n nxpage = soup.select('div[class=SG_page] > ul > li[class=SG_pgnext] > a')\n if len(nxpage) > 0:\n #print ('Next list page: '+nxpage[0]['href'])\n self.fetch_article_list(nxpage[0]['href'])\n else:\n print('Have reached to the botom of blog lists.')\n\n\n # backup lists to local file\n with open(self.path+'/blog-lists.txt', 'w') as f:\n f.write('\\n'.join(self.article_urls))","def get_news(self, until_date=None):\r\n if not self.news:\r\n more_news = True\r\n headers = {\"Content-type\": \"application/x-www-form-urlencoded\", \"Accept\": \"text/plain\",\r\n 'Referer': 'http://' + self.domain + '/login.phtml', \"User-Agent\": user_agent}\r\n req = self.session.get('http://' + self.domain + '/team_news.phtml', headers=headers).content\r\n soup = BeautifulSoup(req, \"html.parser\")\r\n newsheader = soup.find_all('div', {'class', 'newsheader'})[1:]\r\n for index, i in enumerate(soup.find_all('div', {'class', 'article_content_text'})[1:]):\r\n news_date = datetime.strptime(newsheader[index].span['title'][0:8], \"%d.%m.%y\").date()\r\n news_title = newsheader[index].text.split(\">\")[1].strip()\r\n if news_date < until_date:\r\n more_news = None\r\n break\r\n self.news.append([news_date, news_title, i.text])\r\n first_news = 10\r\n while more_news and first_news < 200:\r\n other_news = BeautifulSoup(\r\n self.session.post('http://' + self.domain + '/team_news.phtml', headers=headers,\r\n data={'newsAction': 'reload', 'first_news': first_news}).content, \"html.parser\")\r\n newsheader = other_news.find_all('div', {'class', 'newsheader'})\r\n for index, i in enumerate(other_news.find_all('div', {'class', 'article_content_text'})):\r\n news_date = datetime.strptime(newsheader[index].span['title'][0:8], \"%d.%m.%y\").date()\r\n news_title = newsheader[index].text.split(\">\")[1].strip()\r\n if news_date < until_date:\r\n more_news = None\r\n break\r\n self.news.append([news_date, news_title, i.text])\r\n first_news += 10\r\n time.sleep(1)\r\n return self.news","def get_news(url, n_pages=1):\r\n news = []\r\n while n_pages:\r\n print(\"Collecting data from page: {}\".format(url))\r\n\r\n delay = 2\r\n max_retries = 5\r\n backoff_factor = 0.3\r\n for tryes in range(max_retries):\r\n try:\r\n response = requests.get(url)\r\n except requests.exceptions.RequestException:\r\n if tryes == max_retries - 1:\r\n raise\r\n else:\r\n break\r\n time.sleep(delay)\r\n delay = backoff_factor * (2 ** tryes)\r\n\r\n soup = BeautifulSoup(response.text, \"html.parser\")\r\n news_list = extract_news(soup)\r\n next_page = extract_next_page(soup)\r\n url = \"https://news.ycombinator.com/\" + next_page\r\n news.extend(news_list)\r\n n_pages -= 1\r\n return news","def extract_articles(self, parsed_xml):\n\n # Iterates over every item (article) in xml\n for item in parsed_xml.xpath(\"//item\"):\n\n article = {}\n\n\n article['title'] = self.get_text_or_attr(item, 'title')\n\n\n # The article's categories must be always a list, even if it has\n # only one element.\n categories = self.get_text_or_attr(item, 'category')\n\n if isinstance(categories, str):\n categories = [categories]\n\n article['categories'] = categories\n\n\n url = self.get_text_or_attr(item, 'feedburner:origLink')\n article['url'] = self.remove_query(url)\n\n self.article_url = article['url']\n\n\n # If article's URL is already stored, don't parse it again\n if Article.objects.filter(url=article['url']).count() > 0:\n continue\n\n\n # It is interesting to have the publication date as a `dateutil`\n # object, so we can do whatever manipulation we want.\n pub_date = self.get_text_or_attr(item, 'pubDate')\n article['date'] = self.parse_datetime_passing_errors(pub_date)\n\n\n # Get the author attribute and tries to fetch informations about\n # him/her. An article can have more than one author; on techcrunch's\n # feed, they are separated by a comma.\n author_names = self.get_text_or_attr(item, 'dc:creator').split(',')\n article['authors'] = []\n\n for i, name in enumerate(author_names):\n article['authors'] += [self.get_author(name, i)]\n\n\n # Tries to find the article's thumbnail url\n thumb = self.get_text_or_attr(item, 'media:thumbnail', 'url')\n if thumb and thumb[0]:\n article['thumb'] = self.remove_query(thumb[0])\n\n\n # Gets the article's description and strip all html tags from it\n content = self.clear_text(item.xpath('description'))\n content = content.strip(' Read More').strip(' ').strip()\n\n\n article['content'] = content\n\n\n yield article","def fetchFullText(self, arnumbers):\n if type(arnumbers) != list:\n arnumbers = [arnumbers]\n browser = webdriver.Chrome()\n articles = []\n for num in arnumbers:\n browser.get(\"http://ieeexplore.ieee.org/xpls/icp.jsp?arnumber=\" + str(num))\n # TODO: Ensure the page load is ready\n page = pq(browser.page_source)\n articles.append(page('div#article'))\n browser.quit()\n return articles","def get_articles(request, search_type, search_for):\n\n template_name = \"home/caos_home_page_search.html\"\n unslug_search = search_for.replace('-', ' ')\n\n if 'category' in search_type:\n get_results = (CaosBlogPage.objects.live()\n .search(unslug_search, fields=['category']))\n query = Query.get(unslug_search)\n query.add_hit()\n\n elif 'author' in search_type:\n get_results = (CaosBlogPage.objects.live()\n .search(unslug_search, fields=['author']))\n query = Query.get(unslug_search)\n query.add_hit()\n\n else:\n get_results = CaosBlogPage.objects.none()\n\n context = {\n 'search_results': get_results,\n }\n\n return render(request, template_name, context)","def fetch_article(self):\n dl = download(self.url, max_content_length=settings.PYPO_MAX_CONTENT_LENGTH)\n self.title, self.readable_article = parse(self, content_type=dl.content_type,\n text=dl.text, content=dl.content)","def _mw_fetch_article(self, baseurl, title):\n params = urllib.parse.urlencode({\n 'action': 'parse',\n 'page': title,\n 'prop': 'wikitext|headhtml',\n 'formatversion': 2,\n 'format': 'json',\n 'redirects': True\n })\n api_data = self._mw_api_call(baseurl, params)\n\n page_title = api_data['parse']['title']\n content = api_data['parse']['wikitext']\n html_head = api_data['parse']['headhtml']\n text = formatter.fmt(content, summary=True)\n\n soup = BeautifulSoup(html_head, features=\"lxml\")\n if canonical_link := soup.find('link', rel='canonical'):\n # Wikipedia\n url = canonical_link.attrs['href']\n elif og_url := soup.find('meta', property='og:url'):\n # Fandom\n url = og_url.attrs['content']\n else:\n # Use generic MediaWiki link as fallback (this doesn't look as nice)\n url = baseurl.replace('api.php', 'index.php?' + urllib.parse.urlencode({\n 'title': page_title\n }))\n\n return (text, url)","def get_news(rss_feed):\r\n\r\n class _CurrentData(object):\r\n \"\"\"Class holding a set of current attributes.\"\"\"\r\n item = None\r\n text = None\r\n\r\n def _start_element_handler(name, attrs):\r\n \"\"\"Handle XML start-elements.\"\"\"\r\n if name == 'item':\r\n # Allocate a new item.\r\n current.item = NewsItem()\r\n\r\n def _end_element_handler(name):\r\n \"\"\"Handle XML end-elements.\"\"\"\r\n if name == 'item':\r\n news_items.append(current.item)\r\n elif name in ('title', 'description', 'link', 'category'):\r\n try:\r\n setattr(current.item, name, current.text)\r\n except AttributeError:\r\n # The parser has run into a non-news item.\r\n pass\r\n\r\n def _char_data_handler(data):\r\n \"\"\"Handle XML element character data.\"\"\"\r\n current.text = data\r\n\r\n news_items = list()\r\n current = _CurrentData()\r\n\r\n parser = expat.ParserCreate()\r\n parser.StartElementHandler = _start_element_handler\r\n parser.EndElementHandler = _end_element_handler\r\n parser.CharacterDataHandler = _char_data_handler\r\n\r\n news_handle = urllib2.urlopen(rss_feed)\r\n xml_data = news_handle.read()\r\n \r\n parser.Parse(xml_data)\r\n\r\n return news_items"],"string":"[\n \"def listsources():\\n\\tmain_url = \\\" https://newsapi.org/v2/sources?apiKey=5f81b593f35d42a8980313250c03d7e7\\\"\\n\\n\\t# fetching data in json format \\n\\topen_source = requests.get(main_url).json() \\n\\n\\t# getting all articles in a string sources\\n\\tsource = open_source[\\\"sources\\\"] \\n\\n\\t# empty list which will \\n\\t# contain all trending newssources \\n\\tresults = [] \\n\\t\\n\\tfor k in source: \\n results.append(k[\\\"id\\\"])\\n \\n \\t\\n\\tfor w in results[0:4]:\\n print(w)\",\n \"def get_news(category):\\n get_news_url = base_url.format(category,api_key)\\n\\n with urllib.request.urlopen(get_news_url) as url:\\n get_news_data = url.read()\\n get_news_response= json.loads(get_news_data)\\n\\n news_results = None\\n\\n if get_news_response['sources']:\\n news_results_list = get_news_response['sources']\\n news_results = process_results(news_results_list)\\n\\n return news_results\",\n \"def news()->str:#return array[news desc,news link]\\n event_log(\\\"retrieve news data....\\\",\\\"\\\")\\n c = 0\\n location = read_json(\\\"news_api\\\")[0]\\n main_url = \\\"https://newsapi.org/v2/top-headlines?country=\\\"+location+\\\"&apiKey=\\\"+read_json(\\\"news_api\\\")[1]+\\\"\\\"#add a country selection optin via json\\n page = requests.get(main_url).json()\\n article = page[\\\"articles\\\"]\\n news_result = []\\n for data in article:\\n news_result.append([data[\\\"title\\\"],str(data[\\\"url\\\"]).replace('\\\"',\\\" \\\")])#exctracts the wanted data from api\\n if c == 5:#add this to json file so scalibility\\n break\\n c+=1\\n return news_result\",\n \"def get_articles(city):\\n article_list = []\\n\\n url = 'https://news.search.yahoo.com/search;?p=' + city\\n source = requests.get(url, timeout=5)\\n plain_text = source.text\\n soup = BeautifulSoup(plain_text, \\\"html5lib\\\")\\n\\n articles = soup.findAll('div', {'class': 'NewsArticle'})\\n\\n i = 0\\n for item in articles:\\n if i < 3:\\n title = item.find('a', attrs={'class':'thmb'})['title']\\n source = item.find('span', attrs={'class':'mr-5'}).text\\n header4 = item.find('h4', attrs={'class':'fz-16'})\\n link = header4.find('a')['href']\\n\\n article = format_article_list(title, source, \\\"| \\\" + link)\\n article_list.append(article)\\n\\n i += 1\\n else:\\n break\\n\\n return article_list\",\n \"def news(self):\\n\\n # Get articles with search term, if available, from each News API source\\n news_api_articles = pd.DataFrame()\\n\\n q = urllib.parse.quote(\\\" OR \\\".join(self.search_terms), safe='')\\n\\n response = requests.get(\\\"https://newsapi.org/v2/everything?q=\\\" + q + \\\"&from=\\\" + datetime.now().strftime(\\n \\\"%Y-%m-%d\\\") + \\\"&sortBy=popularity&pageSize=100&apiKey=\\\" + self.__news_api_key)\\n\\n if response.status_code == 200:\\n data = json.loads(response.text)\\n\\n source_articles = []\\n\\n for article in data['articles']:\\n source_articles.append([article['title'],\\n article['description'],\\n article['url'],\\n article['publishedAt']])\\n\\n source_articles = pd.DataFrame(source_articles, columns=['title', 'description', 'url', 'publishedAt'])\\n news_api_articles = pd.concat([news_api_articles, source_articles])\\n\\n news_api_articles = news_api_articles.reset_index(drop='True')\\n\\n news_api_articles['publishedAt'] = news_api_articles['publishedAt'].apply(pd.to_datetime)\\n\\n news_api_articles = news_api_articles.fillna(' ')\\n\\n term_in_title = news_api_articles['title'].apply(self.any_term)\\n\\n news_api_articles = news_api_articles[term_in_title]\\n\\n if (len(news_api_articles) > 10):\\n news_api_articles = news_api_articles[0:10]\\n\\n else:\\n print(\\\"News API failed to return any items\\\")\\n\\n # Create shortened links using bitly if access token is provided\\n if self.__bitly_access_token != '':\\n\\n bitly_urls = []\\n\\n for index, article in news_api_articles.iterrows():\\n url = article['url']\\n bitly_response = requests.get(\\\"https://api-ssl.bitly.com/v3/shorten\\\",\\n params={'longUrl': url, 'access_token': self.__bitly_access_token})\\n\\n if bitly_response.status_code == 200:\\n data = json.loads(bitly_response.text)\\n bitly_urls.append(data['data']['url'])\\n\\n news_api_articles['url'] = bitly_urls\\n\\n # Store final list to TwitterBot object\\n self.list = news_api_articles\\n\\n return\",\n \"def news_fetch(region,news_key):\\n #Allows for customizable API key and weather location.\\n url = (f\\\"http://newsapi.org/v2/top-headlines?country={region}&apiKey={news_key}\\\")\\n #Gets API with requests and convert to .json\\n news_api = requests.get(url)\\n news_json = news_api.json()\\n return news_json\",\n \"def get_news_articles():\\n filename = 'inshorts_news_articles.csv'\\n\\n # checks if file exists if not make a new request and creates\\n if os.path.exists(filename):\\n return pd.read_csv(filename)\\n else:\\n return make_new_request()\",\n \"def get_articles_from_topic(url):\\n headers = {'User-Agent': 'Codeup Data Science'}\\n response = get(url, headers=headers)\\n soup = BeautifulSoup(response.content, 'html.parser')\\n\\n output = []\\n\\n articles = soup.select(\\\".news-card\\\")\\n\\n for article in articles: \\n title = article.select(\\\"[itemprop='headline']\\\")[0].get_text()\\n body = article.select(\\\"[itemprop='articleBody']\\\")[0].get_text()\\n author = article.select(\\\".author\\\")[0].get_text()\\n published_date = article.select(\\\".time\\\")[0][\\\"content\\\"]\\n category = response.url.split(\\\"/\\\")[-1]\\n\\n article_data = {\\n 'title': title,\\n 'body': body,\\n 'category': category,\\n 'author': author,\\n 'published_date': published_date,\\n }\\n output.append(article_data)\\n\\n\\n return output\",\n \"def get_news(url):\\r\\n \\r\\n # parse RSS feed into list of dictionaries\\r\\n feed = feedparser.parse(url)\\r\\n\\r\\n # no RSS feed articles for url\\r\\n if len(feed['entries']) == 0:\\r\\n return []\\r\\n \\r\\n # get first ten articles from the RSS feed\\r\\n news = []\\r\\n i = 0\\r\\n while True:\\r\\n if i == len(feed['entries']) or i > 30:\\r\\n break\\r\\n \\r\\n try:\\r\\n # get link to article\\r\\n link = feed[\\\"entries\\\"][i][\\\"link\\\"]\\r\\n\\r\\n # get title of article\\r\\n title = feed[\\\"entries\\\"][i][\\\"title\\\"]\\r\\n \\r\\n try:\\r\\n # get raw summary of article\\r\\n summary_raw = feed[\\\"entries\\\"][i][\\\"summary\\\"]\\r\\n \\r\\n # format summary\\r\\n summary = \\\"\\\"\\r\\n for c in summary_raw:\\r\\n if c == \\\"<\\\":\\r\\n summary += \\\"...\\\"\\r\\n break\\r\\n summary += c\\r\\n except KeyError as e:\\r\\n logging.error(\\\"no summary for RSS feed article: {}\\\".format(link))\\r\\n summary = \\\"read more here...\\\"\\r\\n \\r\\n # get raw date \\r\\n date_raw = feed[\\\"entries\\\"][i][\\\"published_parsed\\\"]\\r\\n \\r\\n if date_raw is None:\\r\\n date = feed[\\\"entries\\\"][i][\\\"published\\\"]\\r\\n \\r\\n else:\\r\\n # format date\\r\\n year = str(date_raw.tm_year)\\r\\n months = [\\\"January\\\", \\\"February\\\", \\\"March\\\", \\\"April\\\", \\\"May\\\", \\\"June\\\", \\\"July\\\", \\\"August\\\", \\\"September\\\", \\\"October\\\", \\\"November\\\", \\\"December\\\"]\\r\\n month = months[date_raw.tm_mon - 1]\\r\\n day = str(date_raw.tm_mday)\\r\\n weekdays = [\\\"Monday\\\", \\\"Tuesday\\\", \\\"Wednesday\\\", \\\"Thursday\\\", \\\"Friday\\\", \\\"Saturday\\\", \\\"Sunday\\\"]\\r\\n wday = weekdays[date_raw.tm_wday]\\r\\n hour = str(date_raw.tm_hour)\\r\\n hour = \\\"{:2}\\\".format(hour).format(' ','0')\\r\\n min = str(date_raw.tm_min)\\r\\n min = \\\"{:2}\\\".format(min).replace(' ','0')\\r\\n date = hour + \\\":\\\" + min + \\\" - \\\" + wday + \\\" \\\" + month + \\\" \\\" + day + \\\", \\\" + year\\r\\n \\r\\n # compile entry and append to news list\\r\\n entry = {\\\"link\\\":link, \\\"title\\\":title, \\\"date\\\":date, \\\"summary\\\":summary}\\r\\n \\r\\n # sanitize entry\\r\\n for key in entry:\\r\\n # apostrophe\\r\\n entry[key] = entry[key].replace(\\\"'\\\", \\\"'\\\")\\r\\n # right single quotation mark\\r\\n entry[key] = entry[key].replace(\\\"’\\\", \\\"’\\\")\\r\\n # left single quotation mark\\r\\n entry[key] = entry[key].replace('\\\"', \\\"‘\\\")\\r\\n # right double quotation mark\\r\\n entry[key] = entry[key].replace(\\\"'\\\", \\\"”\\\")\\r\\n # left double quotation mark\\r\\n entry[key] = entry[key].replace(\\\"'\\\", \\\"“\\\")\\r\\n # Weird ampersand formatting\\r\\n entry[key] = entry[key].replace(\\\"&\\\", \\\"&\\\")\\r\\n \\r\\n # prepare entry for sqlite queries\\r\\n entry[key] = surround(entry[key])\\r\\n \\r\\n # add entry to news list\\r\\n news.append(entry)\\r\\n \\r\\n # max 10 entries\\r\\n if len(news) == 10:\\r\\n break\\r\\n i += 1\\r\\n \\r\\n except Exception as e:\\r\\n logging.error(e)\\r\\n i += 1\\r\\n pass\\r\\n \\r\\n # success\\r\\n return news\",\n \"def sourcenews(source):\\n\\turlnews=urlsource\\n\\turl=urlnews+source\\n\\turlapi=url+'&'+'apiKey='\\n\\turlsour=urlapi+apikey\\n\\tresponse=requests.get(urlsour)\\n\\tdata=response.json()\\n\\treturn data\",\n \"def fetch(api_key, query='', page=1, from_date=False, to_date=False):\\n fetch_articles(api_key, query, page, from_date, to_date)\",\n \"def get_headlines(newssource):\\n \\n \\n newssource_dict = {}\\n url = 'https://newsapi.org/v1/articles?source=' + newssource + '&sortBy=top&apiKey=' + api\\n request = http.request('GET',url,timeout=4.0)\\n\\n headline = json.loads(request.data)\\n \\n if not headline['articles']:\\n return \\\"NewsAPI can not receive information from\\\" + newsource + \\\"right now\\\"\\n \\n newssource_dict['url'] = headline['articles'][0]['url']\\n newssource_dict['title']= headline['articles'][0]['title']\\n newssource_dict['description'] = headline['articles'][0]['description']\\n \\n \\n return newssource_dict\",\n \"def articles():\\n\\n # Store the 'geo' part of the URL as a string called 'geo'. Check 'geo' loaded, and produce runtime error if not.\\n # e.g. '12589'\\n geo = request.args.get(\\\"geo\\\")\\n if not geo:\\n raise RuntimeError(\\\"missing geo\\\")\\n\\n # Run 'geo' through 'lookup()' function, store resulting list of objects in 'rows'.\\n # e.g. [{'link':'www.website1.com','title':'article_title1'},{'link':'www.website2.com','title':'article_title2'}]\\n rows = lookup(geo)\\n\\n # Run 'rows' through 'jsonify()'' function, and return resulting dictionary w/ up to 5 objects. The 'jsonify()' function modifies the input to JSON.\\n # e.g. [{'link':'www.website1.com','title':'article_title1'},{'link':'www.website2.com','title':'article_title2'}]\\n if len(rows) > 5:\\n return jsonify(rows[0], rows[1], rows[2], rows[3], rows[4])\\n else:\\n return jsonify(rows)\",\n \"def fetch_news(n):\\n\\n # This is the list we will use the pass back the news information.\\n data = []\\n\\n # Get news stories from the MEN RSS feed.\\n response = feedparser.parse('https://www.manchestereveningnews.co.uk/?service=rss')\\n\\n # Loop through the news items, and the pull out the data we need.\\n for news in response.entries[:n]:\\n data.append({\\n 'headline': news.title,\\n 'content': news.description,\\n })\\n\\n return data\",\n \"async def get_news(q: str = None):\\n\\treturn aggregate_news(q)\",\n \"def dashboard_article_sources():\\n sources = dict()\\n past_30 = offset_time_past(30, str=True)\\n articles = mongo.db[app.config['ARTICLES_COLLECTION']]\\n results = articles.find({'collected': {'$gt': past_30}}, {'_id': 0})\\n for result in results:\\n sources[result['source']] = sources.get(result['source'], 0) + 1\\n sources = sorted(sources.items(), key=operator.itemgetter(1), reverse=True)\\n data = sources[:10]\\n return jsonify(data)\",\n \"def show_sources_all():\\n response = requests.get(SOURCE_URL)\\n json = response.json()\\n for source in json['sources']:\\n print(u\\\"{0}: <{1}> {2}\\\".format(\\\"News Code\\\", source['id'], source['name']))\",\n \"def articles(news_id):\\n #get news based on source id\\n articles = get_articles(news_id)\\n print(articles)\\n title = f'{news_id}'\\n return render_template(\\\"articles.html\\\", articles = articles, title = title)\",\n \"async def news(self):\\n url = f\\\"https://newsapi.org/v2/top-headlines?country=nz&apiKey={self.bot.news_api_key}\\\"\\n async with ClientSession() as session:\\n async with session.get(url) as response:\\n r = await response.json()\\n firstArticle = r[\\\"articles\\\"][0]\\n nSource = firstArticle[\\\"source\\\"][\\\"name\\\"]\\n nTitle = firstArticle[\\\"title\\\"]\\n nTimestamp = firstArticle[\\\"publishedAt\\\"]\\n embed = discord.Embed(\\n title=f\\\"News Title: {nTitle}\\\", description=f\\\"News Source: {nSource}\\\"\\n )\\n embed.add_field(name=\\\"News Content\\\", value=firstArticle[\\\"description\\\"])\\n embed.set_image(url=firstArticle[\\\"urlToImage\\\"])\\n embed.set_footer(text=f\\\"News Timestamp: {nTimestamp}\\\")\\n\\n channel = self.bot.get_channel(self.bot.main_channel_id)\\n await channel.send(embed=embed)\",\n \"def pull_articles(self, *args, **kwargs):\\n tasks.pull_articles()\\n return Response({})\",\n \"def articles ():\\n\\n offset = int (arg ('offset', '0', re_integer_arg))\\n limit = clip (arg ('limit', str (MAX_RESULTS), re_integer_arg), 1, MAX_RESULTS)\\n\\n with current_app.config.dba.engine.begin () as conn:\\n res = execute (conn, r\\\"\\\"\\\"\\n SELECT no\\n FROM article\\n ORDER BY no\\n LIMIT :limit\\n OFFSET :offset\\n \\\"\\\"\\\", { 'offset' : offset, 'limit' : limit })\\n\\n return make_articles_response (res, limit)\",\n \"def searchnews(self,\\n keywords=None,\\n dateStart=None,\\n dateEnd=None,\\n sortBy=None,\\n domains=None,\\n page=20,\\n sources='abc-news',\\n language=None,\\n apiKey=None,\\n version=None):\\n\\n # detect version\\n version = self.version\\n request_params=None\\n\\n # set up for version 1 articles\\n if self.version != 2:\\n if sortBy not in ['top','latest','popular']:\\n sortBy='top'\\n warnings.warn('Version 1 sorts by top, latest, or popular. '\\n 'Defaulted to top for this search.')\\n\\n request_params = {\\n \\\"sortBy\\\":sortBy,\\n \\\"source\\\": sources,\\n \\\"apiKey\\\": self._api_key,\\n }\\n\\n if not keywords and self.version !=1:\\n raise ValueError('You must enter a keywords to use the search '\\n 'service.')\\n\\n # retrive the api key if set; otherwise, error\\n if not self._api_key:\\n raise ValueError(\\n 'You must use use an API key; to get a key visit https://news'\\n 'api.org/. If you have an API key, set it using the '\\n 'Api.SetCredentials method.')\\n\\n # if api key is there, set the params\\n else:\\n if not request_params:\\n if not sortBy:\\n if sortBy not in ['publishedAt', 'relevancy', 'popularity']:\\n sortBy='publishedAt'\\n warnings.warn('Version 2 can only sort by publishedAt, '\\n 'relevancy, or popularity. Defaulted to '\\n 'publishedAt for this search.')\\n request_params = {\\n \\\"q\\\": keywords,\\n \\\"dateStart\\\":dateStart,\\n \\\"dateEnd\\\":dateEnd,\\n \\\"sortBy\\\":sortBy,\\n \\\"domains\\\":domains,\\n \\\"page\\\":page,\\n \\\"sources\\\":sources,\\n \\\"language\\\":language,\\n \\\"apiKey\\\": self._api_key,\\n }\\n\\n\\n # build the url\\n url = self.base_url + self.__endpoints['search']\\n\\n # make the request\\n r = requests.get(url,params=request_params,timeout=self._timeout)\\n\\n print(r.url)\\n # return the json\\n return r.json()\",\n \"def get_news(company_name: str) -> list[dict]:\\n news_params = {\\n \\\"q\\\": company_name,\\n \\\"apiKey\\\": config.NEWS_API_KEY\\n }\\n response = requests.get(\\\"https://newsapi.org/v2/everything\\\", params=news_params)\\n response.raise_for_status()\\n news_data = response.json()\\n return news_data[\\\"articles\\\"][:3]\",\n \"def get_remote_news_items(self):\\n items = []\\n params = {\\n \\\"base_url\\\": self.osha_json_url,\\n \\\"lang\\\": api.portal.get_tool(\\\"portal_languages\\\").getPreferredLanguage(),\\n \\\"query_tags\\\": self.remote_news_query_tags,\\n }\\n qurl = \\\"{base_url}/{lang}/services/hw/news/{query_tags}\\\".format(**params)\\n result = urlopen(qurl)\\n if result.code == 200:\\n json = load(result)\\n for node in json.get(\\\"nodes\\\"):\\n item = node.get(\\\"node\\\")\\n pd = item.get('publication_date', '')\\n items.append({\\n 'remote_item': True,\\n 'Title': item['title'],\\n 'Date': (\\n pd and DateTime(pd, datefmt=\\\"international\\\").strftime(\\n \\\"%Y/%m/%d %H:%M\\\") or \\\"\\\"),\\n 'getURL': item.get('path'),\\n 'path': item.get('path'),\\n 'Description': item.get('summary', '') or item.get('body', ''),\\n 'text': item.get('summary', '') and item.get('body', '') or '',\\n 'remote_image': item.get('image', ''),\\n 'node_id': item.get('nid'),\\n })\\n return items\",\n \"def get_from_db(date, source, connection, logger):\\n check_date(date, logger)\\n result = execute_news(date, connection, source, logger)\\n if len(result) == 0:\\n raise SystemExit(f\\\"Sorry, there are no articles for {date}!\\\")\\n else:\\n return result\",\n \"def articles(self, page=None, per_page=None, sort=None):\\r\\n params = base.get_params(None, locals())\\r\\n request = http.Request('GET', self.get_url(), params)\\r\\n\\r\\n return request, parsers.parse_json\",\n \"def scrapeTopic(self, topic, num_articles, sources=list(site.all_sites)): \\n pass\",\n \"def get_news(self, keyword, since=None, to=None, page=None):\\n payload = {}\\n url = \\\"https://newsapi.org/v2/everything\\\"\\n payload['q'] = keyword\\n if since is not None:\\n try:\\n start_dt = dateutil.parser.parse(since)\\n if to is not None:\\n to_dt = dateutil.parser.parse(to)\\n else:\\n to_dt = datetime.datetime.now()\\n except ValueError:\\n raise IOError('since parameter can not be converted to datetime')\\n payload['from'] = start_dt.isoformat()\\n payload['to'] = to_dt.isoformat()\\n payload['language'] = 'en'\\n payload['pageSize'] = 20\\n payload['sortBy'] = 'popularity'\\n payload['excludeDomains'] = 'startribune.com'\\n if page is not None and type(page) == int and page > 0:\\n payload['page'] = page\\n r = requests.get(url, auth=self.auth, params=payload)\\n return r.content\",\n \"def articleList():\\n articles = get_news(\\n 5, since=news.YESTERDAY.strftime(\\\"%yyyy-%mm-%dd\\\"), query=\\\"covid\\\"\\n )\\n title_list = []\\n desc_list = []\\n url_list = []\\n image_list = []\\n source_list = []\\n for art in articles:\\n image_list.append(art.image)\\n title_list.append(art.title)\\n source_list.append(art.source)\\n desc_list.append(art.description)\\n url_list.append(art.url)\\n socketio.emit(\\n ARTICLE,\\n {\\n \\\"title\\\": title_list,\\n \\\"desc\\\": desc_list,\\n \\\"url\\\": url_list,\\n \\\"img\\\": image_list,\\n \\\"sources\\\": source_list,\\n },\\n )\\n return True\",\n \"def get_news(keywords, news='all'):\\n if news is 'all':\\n return news_client.get_everything(q=keywords)\\n elif news is 'top':\\n return news_client.get_top_headlines(q=keywords)\\n else:\\n raise AttributeError(\\\"Optional argument news expected 'top' or 'all'\\\")\",\n \"def fetch(feed):\\n # Fetch the feed data.\\n data = feedparser.parse(feed.ext_url)\\n new_articles = []\\n\\n # If the `bozo` value is anything\\n # but 0, there was an error parsing (or connecting) to the feed.\\n if data.bozo:\\n # Some errors are ok.\\n if not isinstance(data.bozo_exception, feedparser.CharacterEncodingOverride) and not isinstance(data.bozo_exception, feedparser.NonXMLContentType):\\n raise data.bozo_exception\\n\\n for entry in data.entries:\\n\\n # URL for this entry.\\n url = entry['links'][0]['href']\\n\\n # Check for an existing Article.\\n # If one exists, skip.\\n if Article.objects(ext_url=url).first():\\n continue\\n\\n data = extractor.extract(url, existing_data=entry)\\n\\n if data is None:\\n continue\\n\\n # Secondary check for an existing Article,\\n # by checking the title and source.\\n existing = Article.objects(title=data['title']).first()\\n if existing and existing.feed.source == feed.source:\\n continue\\n\\n data['feed'] = feed\\n\\n article = Article(**data)\\n article.save()\\n new_articles.append(article)\\n\\n return new_articles\",\n \"def fetch_all_news_codes():\\n response = requests.get(SOURCE_URL)\\n json = response.json()\\n global news_codes\\n for source in json['sources']:\\n news_codes.append(source['id'])\",\n \"def get_articles(self, publish_status):\\n query_str = (\\n \\\"SELECT Id,KnowledgeArticleId,Title,UrlName FROM {} \\\"\\n \\\"WHERE PublishStatus='{}' AND language='en_US'\\\"\\n ).format(\\n settings.SALESFORCE_ARTICLE_TYPE,\\n publish_status,\\n )\\n result = self.api.query(query_str)\\n return result['records']\",\n \"def news_web():\\n get_news()\",\n \"def NewsArticles():\\n health_articles = get_articles('health')\\n education_articles = get_articles('technology')\\n return render_template('articles.html',health=health_articles, tech =education_articles)\",\n \"def environmental_science_news():\\n\\n return general_scraper(['http://mesva.univaq.it/?q=avvisi/cl-clm/52671'])\",\n \"def fetch_articles(api_key, query, page, from_date, to_date, collection=[]):\\n\\n # fetch articles\\n response = fetch_guardian_articles.fetch(api_key, query, page,\\n from_date, to_date)\\n\\n # do we have results?\\n if (response and response['results']):\\n articles = response['results']\\n\\n # add for which query the article\\n # make sure we convert result of map to list\\n # https://stackoverflow.com/questions/1303347/getting-a-map-to-return-a-list-in-python-3-x\\n articles = list(\\n map(\\n lambda article: add_query_to_article(article, query),\\n articles\\n )\\n )\\n\\n # concatenate\\n collection = collection + articles\\n\\n # should we fetch additional articles?\\n pages = response['pages']\\n if (pages and page < pages):\\n # load next article\\n time.sleep(IN_BETWEEN_REQUESTS_GAP)\\n fetch_articles(api_key, query, page + 1,\\n from_date, to_date, collection)\\n else:\\n # all done, return collected articles\\n return collection\\n else:\\n print('Nothing found')\",\n \"def get_article_URLs(sourceURL):\\n sourceURL = sourceURL.encode().decode()\\n articleList = []\\n soup = bs.BeautifulSoup(urllib.request.urlopen(sourceURL),'lxml')\\n #remove any tables\\n for table in soup.find_all(\\\"table\\\"):\\n table.extract()\\n #remove any special wiki pages\\n for citation in soup.find_all(href=re.compile(\\\"^/wiki/Wikipedia:|^/wiki/User:|^/wiki/File:|^/wiki/MediaWiki:|^/wiki/Template:|^/wiki/Help:|^/wiki/Category:|^/wiki/Portal:|^/wiki/Draft:|^/wiki/TimedText:|^/wiki/Module:|^/wiki/Special:\\\")):\\n citation.extract()\\n a = soup.find('div', {'class':'mw-parser-output'}).find_all('a', href=re.compile(\\\"^/wiki/\\\"))\\n for link in a:\\n articleList.append(link['href'])\\n return (articleList)\",\n \"def articles(self, audience_filter=None):\\n articles = ArticlePage.objects.live().descendant_of(self)\\n if audience_filter is not None:\\n articles = articles.filter(audience__name=audience_filter)\\n articles = articles.order_by('-date')\\n return articles\",\n \"def news():\\n\\n # ensure parameters are present\\n # geo = request.args.get(\\\"geo\\\")\\n geo = '95060'\\n if not geo:\\n raise RuntimeError(\\\"missing geo\\\")\\n\\n # lookup articles and store them as JSON array\\n article_list = lookup(geo)\\n\\n # TODO\\n print(article_list)\\n news = jsonify(article_list) \\n print(news)\\n # return render_template(\\\"index.html\\\")\\n return article_list\",\n \"def articles(self):\\n return self.get_queryset().filter(content_type__model='article').order_by('-articles__published_at')\",\n \"def get_news_text(news_url: str):\\n print(\\\"Getting Article Content..\\\")\\n if news_url is None:\\n print(\\\"URL is missing\\\")\\n return None\\n else:\\n article = Article(news_url)\\n article.download()\\n article.parse()\\n return article.text\",\n \"def nytArticleSearch(tag):\\n nytKey=\\\"7d9f3b88013d99f9f9ab2a8a82545671:19:73424493\\\" #new york times article search api key\\n url= 'http://api.nytimes.com/svc/search/v2/articlesearch.json?q=%s&fq=type_of_material:(\\\"News\\\")&api-key=%s'\\n url=url%(tag,nytKey)\\n request = urllib2.urlopen(url)\\n result = request.read()\\n r = json.loads(result)\\n return r['response']['docs']\",\n \"def get_local_news_items(self):\\n catalog = api.portal.get_tool(name='portal_catalog')\\n default_lang = api.portal.get_tool(\\n \\\"portal_languages\\\").getDefaultLanguage()\\n results = catalog.searchResults(\\n portal_type=\\\"News Item\\\",\\n sort_on=\\\"effective\\\",\\n sort_order=\\\"descending\\\",\\n review_state='published',\\n Language=[default_lang, ''],\\n )\\n return results\",\n \"def search_news(request):\\n try:\\n query_string = ''\\n if request.GET['search_text'].strip() != '':\\n query_string = '&title='+request.GET['search_text']\\n response = requests.get(APIURL + '/articles/?format=json'+query_string)\\n parser = json.loads(response.content)\\n return render_to_response('article/search_result.html', {'articlelist':parser})\\n except:\\n raise Http404(\\\"Search Item error\\\")\",\n \"def get_news(request):\\n return get_all_posts(request, PostType.NEWS)\",\n \"def articles(self, subject_filter=None):\\n articles = ArticlePage.objects.live().descendant_of(self)\\n if subject_filter is not None:\\n articles = articles.filter(\\n Q(subject_1=subject_filter) | Q(subject_2=subject_filter))\\n articles = articles.order_by('-date')\\n return articles\",\n \"def articles(self):\\r\\n return articles.Articles(self)\",\n \"def general_news():\\n\\n return general_scraper(['http://mesva.univaq.it/'])\",\n \"def getStories(self, source, alreadyReadList):\\n\\t\\t\\n\\t\\tstories = HackerNewsAPI.getStories(self, source)\\n\\t\\t\\n\\t\\t\\n\\t\\tfor i in range(0, HackerNewsAPI.numberOfStoriesOnFrontPage):\\n\\t\\t\\tstory = stories[i]\\n\\t\\t\\t\\n\\t\\t\\tnewStory = NewsStory()\\n\\t\\t\\tnewStory.id = story.id\\n\\t\\t\\tnewStory.number = story.number\\n\\t\\t\\tnewStory.title = story.title\\n\\t\\t\\tnewStory.domain = story.domain\\n\\t\\t\\tnewStory.URL = story.URL\\n\\t\\t\\tnewStory.score = story.score\\n\\t\\t\\tnewStory.submitter = story.submitter\\n\\t\\t\\tnewStory.commentCount = story.commentCount\\n\\t\\t\\tnewStory.commentsURL = story.commentsURL\\n\\t\\t\\tif story.URL in alreadyReadList:\\n\\t\\t\\t\\tnewStory.hasBeenRead = 1\\n\\t\\t\\tstories[i] = newStory\\n\\t\\t\\t\\t\\n\\t\\treturn stories\",\n \"def get_article(url):\\n \\n r = requests.get(url) \\n html_soup = BeautifulSoup(r.content, 'lxml')\\n return html_soup\",\n \"def get_remote_news_item(self, node_id):\\n items = []\\n lang = api.portal.get_tool(\\\"portal_languages\\\").getPreferredLanguage()\\n\\n params = {\\n \\\"base_url\\\": self.osha_json_url,\\n \\\"lang\\\": lang,\\n \\\"node_id\\\": node_id,\\n }\\n qurl = \\\"{base_url}/{lang}/services/hw/news1/{node_id}\\\".format(**params)\\n\\n result = urlopen(qurl)\\n if result.code == 200:\\n json = load(result)\\n for node in json.get(\\\"nodes\\\"):\\n item = node.get(\\\"node\\\")\\n pd = item.get('publication_date', '')\\n items.append({\\n 'remote_item': True,\\n 'Title': item['title'],\\n 'Date': (\\n pd and DateTime(pd, datefmt=\\\"international\\\").strftime(\\n \\\"%Y/%m/%d %H:%M\\\") or \\\"\\\"),\\n 'getURL': item.get('path'),\\n 'Description': item.get('summary', ''),\\n 'text': item.get('body', ''),\\n 'remote_image': item.get('image', ''),\\n })\\n if len(items) > 0:\\n return items.pop()\",\n \"def _fetch_news(self, docid):\\n detail_link = Fetch163.detail_link % docid\\n try:\\n resp = urllib2.urlopen(detail_link)\\n except urllib2.URLError as e:\\n urllib_error(e)\\n else:\\n try:\\n doc_json = json.load(resp)\\n content = doc_json[docid]\\n title = content['title']\\n except KeyError:\\n print \\\"Error while Fetching docid: %s\\\" % docid\\n except ValueError:\\n print \\\"No JSON obj could be decoded\\\", resp.read()\\n else:\\n if self._filter_test(title):\\n #print title\\n c_num = content['replyCount']\\n ptime = content['ptime']\\n img_list = content['img']\\n if img_list:\\n list_pic = img_list[0]['src']\\n else:\\n list_pic = ''\\n body = content['body']\\n abstract = self._retrieve_abstract(body)\\n body = convert_163(body, img_list)\\n return abstract, body, c_num, ptime, list_pic\\n return '', '', '', '', list_pic\",\n \"def get_sources():\\n url = base_url + \\\"sources\\\"\\n params = {\\\"language\\\": \\\"en\\\"}\\n resp = requests.get(url, params=params)\\n data = resp.json()\\n sources = [src['id'].strip() for src in data['sources']]\\n print(\\\"all the sources\\\")\\n print(sources)\\n return sources\",\n \"def get_news(url, n_pages=1):\\n news = []\\n while n_pages:\\n print(\\\"Collecting data from page: {}\\\".format(url))\\n response = requests.get(url)\\n soup = BeautifulSoup(response.text, \\\"html.parser\\\")\\n news_list = extract_news(soup)\\n next_page = extract_next_page(soup)\\n url = \\\"https://news.ycombinator.com/\\\" + next_page\\n news.extend(news_list)\\n n_pages -= 1\\n return news\",\n \"def get_news(self):\\n if self.api_key_entry.get() == \\\"\\\":\\n return None\\n api = nac(api_key=self.api_key_entry.get())\\n now = datetime.datetime.utcnow()\\n two_weeks = (now-datetime.timedelta(days=14))\\n #today = now.strftime()\\n query = \\\"\\\"\\n for cat in self.sorted_categories():\\n query += f\\\"{cat},\\\"\\n search = api.get_top_headlines(q=query,\\n sources=\\\"bbc-news,the-verge\\\",\\n language=\\\"en\\\")\\n news = \\\"\\\"\\n for article in search[\\\"articles\\\"]:\\n news += f\\\"{search['articles'][article]['title']}\\\\n\\\"\\n self.news_box.delete('1.0', tk.END)\\n self.news_box.insert('1.0', news)\",\n \"def search(self, json_query):\\n res = self.es.search(index=self.index, body=json_query)\\n #print(json.dumps(res, indent=2))\\n # Process the results and return the article objects only\\n articel_list = [src['_source'] for src in res['hits']['hits']]\\n return articel_list\",\n \"def get_news(url, n_pages=1):\\n news = []\\n while n_pages:\\n print(\\\"Collecting data from page: {}\\\".format(url))\\n response = requests.get(url)\\n soup = BeautifulSoup(response.text, \\\"html.parser\\\")\\n news_list = extract_news(soup)\\n print(news_list)\\n next_page = extract_next_page(soup)\\n url = 'https://news.ycombinator.com/' + next_page\\n news.extend(news_list)\\n n_pages -= 1\\n return news\",\n \"def articles(self):\\n return articles.Articles(self)\",\n \"def pull_articles(ls):\\n # pull articles\\n doi = self.search_articles(file)\\n els_key = self.els_key\\n\\n for i in doi:\\n els_url = 'https://api.elsevier.com/content/article/doi/' + doi + '?APIKey=' + els_key\\n r = requests.get(els_url)\\n for num in range(len(ls)):\\n with open(folder + f'/write_test_els_paper{num}.xml', 'wb') as file:\\n file.write(r.content)\",\n \"def article_list(request):\\n try:\\n logger.info('Calling the api' + APIURL + '/articles/?format=json&limit=' + str(COUNT))\\n response = requests.get(APIURL + '/articles/?format=json&limit=' + str(COUNT))\\n parser = json.loads(response.content)\\n preview_article = random_article(parser)\\n next_read = read_next()\\n return render(request, 'article/article_list.html', {'articlelist':parser, 'preview_article': preview_article, 'next_read': next_read})\\n except:\\n logger.error('Calling the api error in article_list')\\n raise Http404(\\\"Article does not exist\\\")\",\n \"def get_article1(url):\\n # set agent\\n agent = 'codeup ds germain' \\n # query\\n response = requests.get(url, headers={'User-Agent': agent}) \\n # soup\\n soup = BeautifulSoup(response.text) \\n # get cat\\n category = soup.find_all('li', {'class':'active-category selected'})[0].text \\n # get raw cards\\n cards = soup.select('.news-card') \\n # create list of dicts for dataframe\\n card_dict_list = [] \\n # iterate each card\\n for card in cards: \\n # headline\\n headline = card.find_all('span', {'itemprop':'headline'})[0].text \\n # publish time\\n publish_time = card.find_all('span', {'class':'time'})[0].text \\n # content\\n content = card.find_all('div', {'itemprop':'articleBody'})[0].text.strip() \\n # create dict\\n card_dict = {'headline':headline, 'publish_time':publish_time,\\n 'category':category, 'content':content} \\n # push dict to list\\n card_dict_list.append(card_dict) \\n # return dataframe\\n return pd.DataFrame(card_dict_list)\",\n \"def get_sources():\\n url = base_url + \\\"sources\\\"\\n params = {\\\"language\\\": \\\"en\\\"}\\n resp = requests.get(url, params=params)\\n data = resp.json()\\n sources = [src['id'].strip() for src in data['sources']]\\n print(\\\"all the sources:\\\")\\n print(sources)\\n return sources\",\n \"def execute_news(date, connection, url, logger):\\n cursor = connection.cursor()\\n if url:\\n logger.info(f\\\"Retrieves news for the selected url ({url}) from database...\\\")\\n cursor.execute('SELECT title, link, full_date, source, description, image, url FROM news WHERE date=:date '\\n 'and url=:url', {'date': date, 'url': url})\\n else:\\n cursor.execute('SELECT title, link, full_date, source, description, image, url FROM news WHERE date=:date',\\n {'date': date})\\n records = cursor.fetchall()\\n articles = []\\n for title, link, full_date, source, description, image, url in records:\\n articles.append(Article(title, link, full_date, source, description, image))\\n return articles\",\n \"def select_news_article(request):\\n global results\\n if request.method == \\\"POST\\\":\\n article_url = request.POST.get('article_url')\\n with open('temp/url.txt', 'w') as f:\\n f.write(article_url)\\n\\n return _start_analysis(request)\\n else:\\n return HttpResponse(\\n json.dumps({\\\"error\\\": \\\"error, GET request not supported\\\"}),\\n content_type=\\\"application/json\\\"\\n )\",\n \"def get_article_data(self, url):\\n article_data = {}\\n \\n response = requests.get(url, headers=HEADERS)\\n \\n soup = BeautifulSoup(response.text, 'html.parser')\\n script_tags = soup.find_all('script', type=\\\"application/ld+json\\\")\\n for script in script_tags:\\n if '\\\"@type\\\":\\\"NewsArticle\\\"' in script.string:\\n article_object = json.loads(script.string)\\n article_data['url'] = article_object.get('url')\\n article_data['publish_date'] = article_object.get('datePublished')\\n article_data['title'] = article_object.get('headline')\\n article_data['text'] = article_object.get('articleBody')\\n article_data['video'] = self._download_file(article_object['video'])\\n break\\n elif '\\\"@type\\\":[\\\"ReportageNewsArticle\\\"]' in script.string:\\n article_object = json.loads(script.string)\\n article_data['url'] = article_object.get('mainEntityOfPage')\\n article_data['publish_date'] = article_object.get('datePublished')\\n article_data['title'] = article_object.get('headline')\\n article_data['text'] = article_object.get('articleBody')\\n article_data['video'] = \\\"media/\\\"\\n break\\n\\n return article_data\",\n \"def select_news(self):\\n data = self.soup.findAll('item')\\n for item in data:\\n news_data = dict()\\n for tag in ['title', 'link']:\\n news_data[tag] = item.find(tag).get_text()\\n\\n news_data['pubDate'] = parse(item.find('pubDate').get_text())\\n media = item.find('media:content')\\n\\n if media:\\n news_data['media'] = media.get('url')\\n else:\\n news_data['media'] = None\\n\\n yield news_data\",\n \"def FindArticles(sesh):\\n\\n rssfeeds = FindRSSFeeds()\\n\\n found = ScraperUtils.FindArticlesFromRSS( rssfeeds, u'dailymail', ScrubFunc )\\n return found\",\n \"def list_articles():\\n\\n return template(\\\"index\\\", articles=get_articles())\",\n \"def get(self, request):\\n query = Article.objects.exclude(in_menu=True)\\n\\n article_filter = Q()\\n searchable_paths = {}\\n\\n search_terms = [term for term in request.GET.get(\\\"query\\\").split(\\\" \\\")]\\n for term in search_terms:\\n searchable_paths[term] = Q(headline__icontains=term) | Q(\\n teaser__icontains=term\\n )\\n\\n for key in searchable_paths.keys():\\n article_filter &= searchable_paths[key]\\n\\n query = query.filter(article_filter).distinct()\\n articles = [\\n {\\n \\\"updated\\\": article.updated,\\n \\\"headline\\\": article.headline,\\n \\\"teaser\\\": article.teaser,\\n \\\"tags\\\": [tag.name for tag in article.tags.all()],\\n \\\"url\\\": reverse(\\n \\\"article.detail\\\",\\n kwargs={\\n \\\"year\\\": article.created.year,\\n \\\"month\\\": article.created.month,\\n \\\"slug\\\": article.slug,\\n },\\n ),\\n }\\n for article in query\\n ]\\n\\n return JsonResponse({\\\"articles\\\": articles})\",\n \"def get_news(news_url):\\n news_final = []\\n try:\\n news_handler = urllib.urlopen(news_url)\\n news = news_handler.read()\\n news = nl2br(news)\\n news = string.split(news, '<br/>')\\n\\n news_array = {}\\n value = {}\\n for newsweb in news:\\n value = string.split(newsweb, '|')\\n if len(value[0]) > 1:\\n news_array[value[0]] = value[1]\\n\\n info = {}\\n for k in news_array:\\n info = k[0:int(k.find(\\\"http://\\\") - 1)]\\n info = string.split(k, ' - ')\\n news_final.append((info[0], info[1], news_array[k]))\\n\\n news_handler.close()\\n except IndexError:\\n pass\\n except IOError:\\n pass\\n\\n return news_final\",\n \"def parse_news(news):\\n default_value = '---'\\n\\n news_list = []\\n for entry in news:\\n title = entry.get('title', default_value)\\n link = entry.get('link', default_value)\\n published = entry.get('published', default_value)\\n source = entry.get('source', default_value)\\n description = entry.get('description', default_value)\\n media_content = entry.get('media_content', default_value)\\n\\n source_title = default_value\\n if source != default_value:\\n source_title = source['title']\\n\\n image = default_value\\n if media_content != image:\\n image = media_content[0]['url']\\n\\n article = Article(title, link, published, source_title, description, image)\\n news_list.append(article)\\n\\n return news_list\",\n \"def articles(self):\\r\\n return Articles(self)\",\n \"def get_news():\\n # empty dataframe\\n df = pd.DataFrame() \\n # read each url in list\\n for url in inshorts_urls(): \\n # add each dataframe of cards to df\\n df = pd.concat([df, get_article(url)])\\n # return all urls' cards\\n return df\",\n \"def get_articles(\\n self,\\n size=None,\\n page=None,\\n order_type='asc',\\n order_by=None\\n ):\\n params = {}\\n\\n if order_type and order_by:\\n params['order_type'] = order_type\\n params['order_by'] = order_by\\n if size:\\n params['size'] = size\\n if page:\\n params['page'] = page\\n\\n params_str = \\\"&\\\".join(f'{k}={v}' for k, v in params.items())\\n\\n r = requests.get(\\n f'{self.api_host}/v1/articles',\\n params=params_str,\\n headers=self.get_request_headers()\\n )\\n\\n response = None\\n if r.status_code == 200:\\n response = r.json()\\n\\n return response\",\n \"def getsources(self,\\n category='general',\\n language=None,\\n country='us',\\n apiKey=None,\\n version=None):\\n\\n if self.version != 2:\\n\\n request_params = {\\n \\\"category\\\":category,\\n \\\"language\\\": language,\\n \\\"country\\\":country,\\n \\\"apiKey\\\": self._api_key,\\n }\\n\\n # retrive the api key if set; otherwise, error\\n if not self._api_key:\\n raise ValueError(\\n 'You must use use an API key; to get a key visit https://news'\\n 'api.org/. If you have an API key, set it using the '\\n 'Api.SetCredentials method.')\\n\\n # if api key is there, set the params\\n else:\\n request_params = {\\n \\\"category\\\": category,\\n 'language': language,\\n \\\"country\\\": country,\\n \\\"apiKey\\\": self._api_key,\\n }\\n\\n\\n # build the url\\n url = self.base_url + self.__endpoints['source']\\n\\n # make the request\\n r = requests.get(url,params=request_params,timeout=self._timeout)\\n\\n\\n # return the json\\n return r.json()\",\n \"def get_from_url(source):\\n try:\\n rss_news = feedparser.parse(source)\\n result = parse_news(rss_news['entries'])\\n except urllib.error.URLError:\\n raise SystemExit(\\\"Source isn't available\\\")\\n else:\\n if len(result) == 0:\\n raise SystemExit('Please, check if the entered link is correct!')\\n else:\\n return result\",\n \"def biological_science_news():\\n\\n return general_scraper(['http://mesva.univaq.it/?q=avvisi/cl-clm/52672'])\",\n \"def get_articles(category, base =\\\"https://inshorts.com/en/read/\\\"):\\n \\n # We concatenate our base_url with the category\\n url = base + category\\n \\n # Set the headers\\n headers = {\\\"User-Agent\\\": \\\"Mozilla/4.5 (compatible; HTTrack 3.0x; Windows 98)\\\"}\\n\\n # Get the http response object from the server\\n response = get(url, headers=headers)\\n\\n # Make soup out of the raw html\\n soup = BeautifulSoup(response.text)\\n \\n # Ignore everything, focusing only on the news cards\\n articles = soup.select(\\\".news-card\\\")\\n \\n output = []\\n \\n # Iterate through every article tag/soup \\n for article in articles:\\n \\n # Returns a dictionary of the article's title, body, and category\\n article_data = get_article(article, category) \\n \\n # Append the dictionary to the list\\n output.append(article_data)\\n \\n # Return the list of dictionaries\\n return output\\n\\n#def get_blog_articles(url):\\n '''\\n This function takes in a url and pull the necessary elements off the website\\n then creates a dictionary with those elements\\n '''\\n\\n # create an empty dictionary to append to\\n blog_dict = {}\\n \\n # fetch the data\\n headers = {'User-Agent': 'Codeup Data Science'}\\n response = get(url, headers=headers)\\n soup = BeautifulSoup(response.text)\\n \\n # pull the elements from the soup\\n article = soup.find('div', class_='jupiterx-post-content') # pulling body of text\\n title = soup.find('h1', class_='jupiterx-post-title') # pulling title as text\\n \\n # append the elements to the dictionary\\n blog_dict = {'title': title.text,\\n 'content': article.text}\\n \\n # return dictionary\\n return blog_dict\",\n \"def news_handle(news_json):\\n #Sets all the variables to global space to be used later.\\n global title_1, title_2, author_1, author_2, source_1, source_2, url_1_final, url_2_final\\n #Handles data from API and assigns data to variables.\\n news_data = news_json['articles']\\n article_1 = news_data[0]\\n article_2 = news_data[1]\\n title_1 = article_1['title']\\n author_1 = article_1['author']\\n source_1 = article_1['source']['name']\\n url_1 = article_1['url']\\n url_1_final = '<a href=\\\"{}\\\">Read More</a>'.format(url_1)\\n title_2 = article_2['title']\\n author_2 = article_2['author']\\n source_2 = article_2['source']['name']\\n url_2 = article_2['url']\\n url_2_final = '<a href=\\\"{}\\\">Read More</a>'.format(url_2)\\n return\",\n \"def show_news(code, BASE_URL):\\n url = \\\"?source={news_id}&apiKey=\\\"\\n response = requests.get((BASE_URL+url+api_key).format(news_id=code))\\n json = response.json()\\n\\n article_list = []\\n for article in json['articles']:\\n print('[{0}] {1}: {2}'.format(len(article_list), \\\"Title\\\", article['title']))\\n print('{0}: {1}'.format(\\\"Author\\\", article['author']))\\n print('{0}: {1}'.format(\\\"Summary\\\", article['description']))\\n print('--------------------------------------------')\\n article_list.append(article['url'])\\n\\n print(\\\"Would you like to open an article in a web browser? Enter (y/n): \\\")\\n\\n choice = (input()).lower()\\n if choice not in VALID:\\n print(\\\"Invalid entry.\\\")\\n sys.exit()\\n\\n if choice == 'y':\\n while choice == 'y':\\n article_number = input(\\\"Enter an article number: \\\")\\n while not article_number.isdigit() or not 0 <= int(article_number) <= len(article_list):\\n print(\\\"Invalid article number. Try again.\\\")\\n article_number = input(\\\"Enter an article number: \\\")\\n\\n webbrowser.open(article_list[int(article_number)])\\n\\n print(\\\"Would you like to view another article? Enter (y/n): \\\")\\n choice = (input()).lower()\\n if choice not in VALID:\\n sys.exit()\\n if choice == 'y':\\n continue\",\n \"def read_next():\\n try:\\n response = requests.get(APIURL + '/articles/?format=json&random=yes')\\n parser = json.loads(response.content)\\n return parser\\n except:\\n raise Http404(\\\"Article does not exist\\\")\",\n \"def get_articles(self, feed_id: int) -> List[Article]:\\n articles = []\\n for row in self._sqlite_connection.execute('SELECT identifier, uri, title, updated, author, content, unread, flag FROM articles WHERE feed_id = ? ORDER BY updated DESC LIMIT 200', [feed_id]):\\n data = ArticleData()\\n data.feed_id = feed_id\\n data.identifier = row['identifier']\\n data.uri = row['uri']\\n data.title = row['title']\\n data.updated = datetime.fromtimestamp(row['updated'], timezone.utc)\\n data.author = row['author']\\n data.content = row['content']\\n data.unread = bool(row['unread'])\\n data.flag = bool(row['flag'])\\n articles.append(Article(data))\\n return articles\",\n \"def make_new_request():\\n urls = [\\n \\\"https://inshorts.com/en/read/business\\\",\\n \\\"https://inshorts.com/en/read/sports\\\",\\n \\\"https://inshorts.com/en/read/technology\\\",\\n \\\"https://inshorts.com/en/read/entertainment\\\"\\n ]\\n\\n output = []\\n \\n for url in urls:\\n # We use .extend in order to make a flat output list.\\n output.extend(get_articles_from_topic(url))\\n \\n df = pd.DataFrame(output)\\n df.to_csv('inshorts_news_articles.csv')\\n return df\",\n \"def nfldotcomnews(self, irc, msg, args):\\n \\n url = self._b64decode('aHR0cDovL3MzLmFtYXpvbmF3cy5jb20vbmZsZ2MvYWxsX25ld3NMaXN0Lmpz')\\n \\n try:\\n req = urllib2.Request(url)\\n html = (urllib2.urlopen(req)).read()\\n except:\\n irc.reply(\\\"Failed to fetch: %s\\\" % url)\\n return\\n \\n try:\\n jsondata = json.loads(html)['content']\\n except:\\n irc.reply(\\\"Failed to parse article json from: %s\\\" % url)\\n return\\n \\n for article in jsondata[0:6]:\\n title = article.get('title', None)\\n desc = article.get('description', None)\\n link = article.get('linkURL', None)\\n date = article.get('date_ago', None)\\n \\n output = \\\"{0} - {1}\\\".format(ircutils.bold(title), self._shortenUrl(link))\\n irc.reply(output)\",\n \"def _extract_articles(self, target):\\n feed_response, modified, etag = self._get_recent_feed(target)\\n\\n # Bozo is a tag which tells that the RSS hasn't been parsed correctly.\\n if feed_response.bozo:\\n exc = feed_response.bozo_exception\\n if not isinstance(exc, self.ALLOWED_EXCEPTIONS):\\n raise exc\\n\\n articles = []\\n count = 0\\n if self._manage_status(feed_response, target):\\n for feed_entry in feed_response.entries:\\n if self._limit and count >= self._limit:\\n logging.info(\\n \\\"Crawling limit of %d article(s) was reached for this target.\\\",\\n count\\n )\\n break\\n try:\\n article = self.extract_article(feed_entry, target)\\n except Exception as exc:\\n # NOTE(cmiN): On Stackdriver Error Reporting we don't want to catch\\n # (with `logging.exception`) \\\"Not Found\\\" errors, because they are\\n # pretty frequent and usual, therefore ignore-able.\\n log_function = (\\n logging.error if \\\"404\\\" in str(exc) else logging.exception\\n )\\n log_function(\\\"Got %s while parsing %r.\\\", exc, feed_entry.id)\\n else:\\n articles.append(article)\\n count += 1\\n target.checkpoint(modified, etag)\\n\\n return articles\",\n \"def get_news(self, news_api_key, company):\\r\\n news_params = {\\r\\n \\\"q\\\": company,\\r\\n \\\"apiKey\\\": news_api_key\\r\\n }\\r\\n\\r\\n news_resp = requests.get(NEWS_API_ENDPOINT, params=news_params)\\r\\n news_resp.raise_for_status()\\r\\n news_data = news_resp.json()\\r\\n return news_data\",\n \"def get(self):\\n return GlobalNews.retrieve()\",\n \"def process(url):\\n feed = feedparser.parse(url)\\n entries = feed.entries\\n ret = []\\n for entry in entries:\\n print entry\\n guid = entry.guid\\n title = translate_html(entry.title)\\n link = entry.link\\n summary = translate_html(entry.summary)\\n try:\\n subject = translate_html(entry.tags[0]['term'])\\n except AttributeError:\\n subject = \\\"\\\"\\n newsStory = NewsStory(guid, title, subject, summary, link)\\n ret.append(newsStory)\\n return ret\",\n \"def topicnews(topic):\\n urlnews=urltop\\n url=urlnews+topic\\n urlapi=url+'&'+'apiKey='\\n urlcoun=urlapi+apikey\\n response=requests.get(urlcoun)\\n data=response.json()\\n return data\",\n \"def getStories(self, source):\\n self.numberOfStoriesOnFrontPage = source.count(\\\"span id=score\\\")\\n # Create the empty stories.\\n newsStories = []\\n for i in range(0, self.numberOfStoriesOnFrontPage):\\n story = HackerNewsStory()\\n newsStories.append(story)\\n \\n soup = BeautifulSoup(source)\\n # Gives URLs, Domains and titles.\\n story_details = soup.findAll(\\\"td\\\", {\\\"class\\\" : \\\"title\\\"}) \\n # Gives score, submitter, comment count and comment URL.\\n story_other_details = soup.findAll(\\\"td\\\", {\\\"class\\\" : \\\"subtext\\\"})\\n\\n # Get story numbers.\\n storyNumbers = []\\n for i in range(0,len(story_details) - 1, 2):\\n story = str(story_details[i]) # otherwise, story_details[i] is a BeautifulSoup-defined object.\\n storyNumber = self.getStoryNumber(story)\\n storyNumbers.append(storyNumber)\\n \\n storyURLs = []\\n storyDomains = []\\n storyTitles = []\\n storyScores = []\\n storySubmitters = []\\n storyCommentCounts = []\\n storyCommentURLs = []\\n storyIDs = []\\n\\n for i in range(1, len(story_details), 2): # Every second cell contains a story.\\n story = str(story_details[i])\\n storyURLs.append(self.getStoryURL(story))\\n storyDomains.append(self.getStoryDomain(story))\\n storyTitles.append(self.getStoryTitle(story))\\n \\n for s in story_other_details:\\n story = str(s)\\n storyScores.append(self.getStoryScore(story))\\n storySubmitters.append(self.getSubmitter(story))\\n storyCommentCounts.append(self.getCommentCount(story))\\n storyCommentURLs.append(self.getCommentsURL(story))\\n storyIDs.append(self.getHNID(story))\\n \\n \\n # Associate the values with our newsStories. \\n for i in range(0, self.numberOfStoriesOnFrontPage):\\n newsStories[i].number = storyNumbers[i]\\n newsStories[i].URL = storyURLs[i]\\n newsStories[i].domain = storyDomains[i]\\n newsStories[i].title = storyTitles[i]\\n newsStories[i].score = storyScores[i]\\n newsStories[i].submitter = storySubmitters[i]\\n newsStories[i].commentCount = storyCommentCounts[i]\\n newsStories[i].commentsURL = storyCommentURLs[i]\\n newsStories[i].id = storyIDs[i]\\n \\n return newsStories\",\n \"def fetch_article_list(self, url):\\n print(url)\\n\\n r = requests.get(url, headers=headers, timeout=10)\\n html = r.text\\n time.sleep(1)\\n\\n if r.status_code is not 200:\\n print('Server dinied. Status:[%s].'%r.status_code)\\n return\\n\\n # local data test\\n #with open('./dataset/sina-blog-list.html', 'r') as f:\\n # html = f.read()\\n\\n #print(html)\\n\\n soup = BeautifulSoup(html, 'html5lib')\\n tags = soup.select('div[class=articleList] > div[class~=articleCell] > p > span[class=atc_title] > a')\\n\\n for t in tags:\\n print('Appened: '+t['href'])\\n self.article_urls.append(t['href'])\\n\\n # Get the url of next blog-list page\\n nxpage = soup.select('div[class=SG_page] > ul > li[class=SG_pgnext] > a')\\n if len(nxpage) > 0:\\n #print ('Next list page: '+nxpage[0]['href'])\\n self.fetch_article_list(nxpage[0]['href'])\\n else:\\n print('Have reached to the botom of blog lists.')\\n\\n\\n # backup lists to local file\\n with open(self.path+'/blog-lists.txt', 'w') as f:\\n f.write('\\\\n'.join(self.article_urls))\",\n \"def get_news(self, until_date=None):\\r\\n if not self.news:\\r\\n more_news = True\\r\\n headers = {\\\"Content-type\\\": \\\"application/x-www-form-urlencoded\\\", \\\"Accept\\\": \\\"text/plain\\\",\\r\\n 'Referer': 'http://' + self.domain + '/login.phtml', \\\"User-Agent\\\": user_agent}\\r\\n req = self.session.get('http://' + self.domain + '/team_news.phtml', headers=headers).content\\r\\n soup = BeautifulSoup(req, \\\"html.parser\\\")\\r\\n newsheader = soup.find_all('div', {'class', 'newsheader'})[1:]\\r\\n for index, i in enumerate(soup.find_all('div', {'class', 'article_content_text'})[1:]):\\r\\n news_date = datetime.strptime(newsheader[index].span['title'][0:8], \\\"%d.%m.%y\\\").date()\\r\\n news_title = newsheader[index].text.split(\\\">\\\")[1].strip()\\r\\n if news_date < until_date:\\r\\n more_news = None\\r\\n break\\r\\n self.news.append([news_date, news_title, i.text])\\r\\n first_news = 10\\r\\n while more_news and first_news < 200:\\r\\n other_news = BeautifulSoup(\\r\\n self.session.post('http://' + self.domain + '/team_news.phtml', headers=headers,\\r\\n data={'newsAction': 'reload', 'first_news': first_news}).content, \\\"html.parser\\\")\\r\\n newsheader = other_news.find_all('div', {'class', 'newsheader'})\\r\\n for index, i in enumerate(other_news.find_all('div', {'class', 'article_content_text'})):\\r\\n news_date = datetime.strptime(newsheader[index].span['title'][0:8], \\\"%d.%m.%y\\\").date()\\r\\n news_title = newsheader[index].text.split(\\\">\\\")[1].strip()\\r\\n if news_date < until_date:\\r\\n more_news = None\\r\\n break\\r\\n self.news.append([news_date, news_title, i.text])\\r\\n first_news += 10\\r\\n time.sleep(1)\\r\\n return self.news\",\n \"def get_news(url, n_pages=1):\\r\\n news = []\\r\\n while n_pages:\\r\\n print(\\\"Collecting data from page: {}\\\".format(url))\\r\\n\\r\\n delay = 2\\r\\n max_retries = 5\\r\\n backoff_factor = 0.3\\r\\n for tryes in range(max_retries):\\r\\n try:\\r\\n response = requests.get(url)\\r\\n except requests.exceptions.RequestException:\\r\\n if tryes == max_retries - 1:\\r\\n raise\\r\\n else:\\r\\n break\\r\\n time.sleep(delay)\\r\\n delay = backoff_factor * (2 ** tryes)\\r\\n\\r\\n soup = BeautifulSoup(response.text, \\\"html.parser\\\")\\r\\n news_list = extract_news(soup)\\r\\n next_page = extract_next_page(soup)\\r\\n url = \\\"https://news.ycombinator.com/\\\" + next_page\\r\\n news.extend(news_list)\\r\\n n_pages -= 1\\r\\n return news\",\n \"def extract_articles(self, parsed_xml):\\n\\n # Iterates over every item (article) in xml\\n for item in parsed_xml.xpath(\\\"//item\\\"):\\n\\n article = {}\\n\\n\\n article['title'] = self.get_text_or_attr(item, 'title')\\n\\n\\n # The article's categories must be always a list, even if it has\\n # only one element.\\n categories = self.get_text_or_attr(item, 'category')\\n\\n if isinstance(categories, str):\\n categories = [categories]\\n\\n article['categories'] = categories\\n\\n\\n url = self.get_text_or_attr(item, 'feedburner:origLink')\\n article['url'] = self.remove_query(url)\\n\\n self.article_url = article['url']\\n\\n\\n # If article's URL is already stored, don't parse it again\\n if Article.objects.filter(url=article['url']).count() > 0:\\n continue\\n\\n\\n # It is interesting to have the publication date as a `dateutil`\\n # object, so we can do whatever manipulation we want.\\n pub_date = self.get_text_or_attr(item, 'pubDate')\\n article['date'] = self.parse_datetime_passing_errors(pub_date)\\n\\n\\n # Get the author attribute and tries to fetch informations about\\n # him/her. An article can have more than one author; on techcrunch's\\n # feed, they are separated by a comma.\\n author_names = self.get_text_or_attr(item, 'dc:creator').split(',')\\n article['authors'] = []\\n\\n for i, name in enumerate(author_names):\\n article['authors'] += [self.get_author(name, i)]\\n\\n\\n # Tries to find the article's thumbnail url\\n thumb = self.get_text_or_attr(item, 'media:thumbnail', 'url')\\n if thumb and thumb[0]:\\n article['thumb'] = self.remove_query(thumb[0])\\n\\n\\n # Gets the article's description and strip all html tags from it\\n content = self.clear_text(item.xpath('description'))\\n content = content.strip(' Read More').strip(' ').strip()\\n\\n\\n article['content'] = content\\n\\n\\n yield article\",\n \"def fetchFullText(self, arnumbers):\\n if type(arnumbers) != list:\\n arnumbers = [arnumbers]\\n browser = webdriver.Chrome()\\n articles = []\\n for num in arnumbers:\\n browser.get(\\\"http://ieeexplore.ieee.org/xpls/icp.jsp?arnumber=\\\" + str(num))\\n # TODO: Ensure the page load is ready\\n page = pq(browser.page_source)\\n articles.append(page('div#article'))\\n browser.quit()\\n return articles\",\n \"def get_articles(request, search_type, search_for):\\n\\n template_name = \\\"home/caos_home_page_search.html\\\"\\n unslug_search = search_for.replace('-', ' ')\\n\\n if 'category' in search_type:\\n get_results = (CaosBlogPage.objects.live()\\n .search(unslug_search, fields=['category']))\\n query = Query.get(unslug_search)\\n query.add_hit()\\n\\n elif 'author' in search_type:\\n get_results = (CaosBlogPage.objects.live()\\n .search(unslug_search, fields=['author']))\\n query = Query.get(unslug_search)\\n query.add_hit()\\n\\n else:\\n get_results = CaosBlogPage.objects.none()\\n\\n context = {\\n 'search_results': get_results,\\n }\\n\\n return render(request, template_name, context)\",\n \"def fetch_article(self):\\n dl = download(self.url, max_content_length=settings.PYPO_MAX_CONTENT_LENGTH)\\n self.title, self.readable_article = parse(self, content_type=dl.content_type,\\n text=dl.text, content=dl.content)\",\n \"def _mw_fetch_article(self, baseurl, title):\\n params = urllib.parse.urlencode({\\n 'action': 'parse',\\n 'page': title,\\n 'prop': 'wikitext|headhtml',\\n 'formatversion': 2,\\n 'format': 'json',\\n 'redirects': True\\n })\\n api_data = self._mw_api_call(baseurl, params)\\n\\n page_title = api_data['parse']['title']\\n content = api_data['parse']['wikitext']\\n html_head = api_data['parse']['headhtml']\\n text = formatter.fmt(content, summary=True)\\n\\n soup = BeautifulSoup(html_head, features=\\\"lxml\\\")\\n if canonical_link := soup.find('link', rel='canonical'):\\n # Wikipedia\\n url = canonical_link.attrs['href']\\n elif og_url := soup.find('meta', property='og:url'):\\n # Fandom\\n url = og_url.attrs['content']\\n else:\\n # Use generic MediaWiki link as fallback (this doesn't look as nice)\\n url = baseurl.replace('api.php', 'index.php?' + urllib.parse.urlencode({\\n 'title': page_title\\n }))\\n\\n return (text, url)\",\n \"def get_news(rss_feed):\\r\\n\\r\\n class _CurrentData(object):\\r\\n \\\"\\\"\\\"Class holding a set of current attributes.\\\"\\\"\\\"\\r\\n item = None\\r\\n text = None\\r\\n\\r\\n def _start_element_handler(name, attrs):\\r\\n \\\"\\\"\\\"Handle XML start-elements.\\\"\\\"\\\"\\r\\n if name == 'item':\\r\\n # Allocate a new item.\\r\\n current.item = NewsItem()\\r\\n\\r\\n def _end_element_handler(name):\\r\\n \\\"\\\"\\\"Handle XML end-elements.\\\"\\\"\\\"\\r\\n if name == 'item':\\r\\n news_items.append(current.item)\\r\\n elif name in ('title', 'description', 'link', 'category'):\\r\\n try:\\r\\n setattr(current.item, name, current.text)\\r\\n except AttributeError:\\r\\n # The parser has run into a non-news item.\\r\\n pass\\r\\n\\r\\n def _char_data_handler(data):\\r\\n \\\"\\\"\\\"Handle XML element character data.\\\"\\\"\\\"\\r\\n current.text = data\\r\\n\\r\\n news_items = list()\\r\\n current = _CurrentData()\\r\\n\\r\\n parser = expat.ParserCreate()\\r\\n parser.StartElementHandler = _start_element_handler\\r\\n parser.EndElementHandler = _end_element_handler\\r\\n parser.CharacterDataHandler = _char_data_handler\\r\\n\\r\\n news_handle = urllib2.urlopen(rss_feed)\\r\\n xml_data = news_handle.read()\\r\\n \\r\\n parser.Parse(xml_data)\\r\\n\\r\\n return news_items\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7267898","0.69831485","0.6910983","0.6892911","0.68473136","0.67912644","0.6780562","0.6722617","0.6709383","0.6705438","0.66801316","0.663876","0.6628921","0.6584156","0.6563533","0.65245885","0.6516218","0.65005183","0.6477813","0.64497817","0.6429842","0.6426314","0.64171225","0.6414864","0.64013606","0.6385523","0.63827646","0.63666064","0.6359781","0.6352771","0.633714","0.6300737","0.6286763","0.6276435","0.62563485","0.62433887","0.62228763","0.621685","0.61916614","0.61895794","0.6183783","0.6181918","0.617361","0.6161764","0.6156393","0.61478657","0.61352974","0.61285126","0.6115038","0.61149275","0.6095193","0.6092527","0.6086235","0.60812","0.6077759","0.6075687","0.60665447","0.60588187","0.6057851","0.604699","0.6036459","0.6036077","0.6028261","0.6012536","0.60060906","0.6005345","0.59942603","0.59912074","0.5981118","0.597425","0.5970329","0.59516865","0.5929778","0.5927937","0.5923663","0.59109646","0.5910471","0.5897099","0.589496","0.5888245","0.588039","0.58797073","0.5864166","0.586371","0.5854915","0.5852799","0.5843531","0.58395183","0.5835782","0.5823942","0.58180076","0.5810515","0.581047","0.58084124","0.5789431","0.5782911","0.5770632","0.57674813","0.57650036","0.5758948"],"string":"[\n \"0.7267898\",\n \"0.69831485\",\n \"0.6910983\",\n \"0.6892911\",\n \"0.68473136\",\n \"0.67912644\",\n \"0.6780562\",\n \"0.6722617\",\n \"0.6709383\",\n \"0.6705438\",\n \"0.66801316\",\n \"0.663876\",\n \"0.6628921\",\n \"0.6584156\",\n \"0.6563533\",\n \"0.65245885\",\n \"0.6516218\",\n \"0.65005183\",\n \"0.6477813\",\n \"0.64497817\",\n \"0.6429842\",\n \"0.6426314\",\n \"0.64171225\",\n \"0.6414864\",\n \"0.64013606\",\n \"0.6385523\",\n \"0.63827646\",\n \"0.63666064\",\n \"0.6359781\",\n \"0.6352771\",\n \"0.633714\",\n \"0.6300737\",\n \"0.6286763\",\n \"0.6276435\",\n \"0.62563485\",\n \"0.62433887\",\n \"0.62228763\",\n \"0.621685\",\n \"0.61916614\",\n \"0.61895794\",\n \"0.6183783\",\n \"0.6181918\",\n \"0.617361\",\n \"0.6161764\",\n \"0.6156393\",\n \"0.61478657\",\n \"0.61352974\",\n \"0.61285126\",\n \"0.6115038\",\n \"0.61149275\",\n \"0.6095193\",\n \"0.6092527\",\n \"0.6086235\",\n \"0.60812\",\n \"0.6077759\",\n \"0.6075687\",\n \"0.60665447\",\n \"0.60588187\",\n \"0.6057851\",\n \"0.604699\",\n \"0.6036459\",\n \"0.6036077\",\n \"0.6028261\",\n \"0.6012536\",\n \"0.60060906\",\n \"0.6005345\",\n \"0.59942603\",\n \"0.59912074\",\n \"0.5981118\",\n \"0.597425\",\n \"0.5970329\",\n \"0.59516865\",\n \"0.5929778\",\n \"0.5927937\",\n \"0.5923663\",\n \"0.59109646\",\n \"0.5910471\",\n \"0.5897099\",\n \"0.589496\",\n \"0.5888245\",\n \"0.588039\",\n \"0.58797073\",\n \"0.5864166\",\n \"0.586371\",\n \"0.5854915\",\n \"0.5852799\",\n \"0.5843531\",\n \"0.58395183\",\n \"0.5835782\",\n \"0.5823942\",\n \"0.58180076\",\n \"0.5810515\",\n \"0.581047\",\n \"0.58084124\",\n \"0.5789431\",\n \"0.5782911\",\n \"0.5770632\",\n \"0.57674813\",\n \"0.57650036\",\n \"0.5758948\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.710921"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":26097,"cells":{"query":{"kind":"string","value":"Huber function. An analytic function that is quadratic around its minimum n and linear in its tails. Its minimum is at offset. Quadratic between offsetdelta and offset + delta and linear outside."},"document":{"kind":"string","value":"def huber(x, offset, delta):\n i = np.abs(x - offset) < delta\n return (x-offset)**2/2 * i + (1 - i)*delta*(np.abs(x-offset) - delta/2)"},"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 beeston_barlow_root1(a, p, U, d):\n return ((-U*p - U + a*p + d*p -\n np.sqrt(U**2*p**2 + 2*U**2*p + U**2 + 2*U*a*p**2 + 2*U*a*p -\n 2*U*d*p**2 - 2*U*d*p + a**2*p**2 + 2*a*d*p**2 + d**2*p**2))/(2*p*(p + 1)))","def trapezium_rule(f, m, x, a, b, n):\n h = (b-a)/float(n)\n s = 0.5*(f(m, x, a) + f(m, x, b))\n for i in range(n):\n s = s + f(m, x, a + i*h)\n return h*s","def Bernstein(i, n, t):\n return special.binom(n, i) * t ** i * (1 - t) ** (n - i)","def f4():\n n = 4\n v = np.arange(n)**0.75 * 0.2\n e = (np.arange(n)+1)**0.7 * 1e-1\n\n n = 12\n v = np.arange(n)\n e = np.array([0.1]*n) * 10e-0\n\n print(Sumb(v,e))\n\n f = plt.figure()\n a = f.add_subplot(111)\n\n dx = 0.0001\n x = np.arange(-1,v[-1]+1,dx)\n y = x.copy()\n y[:] = 0.\n for i in range(n):\n yx = lg(x,v[i],e[i])\n a.plot(x,np.exp(yx),label='{:d}'.format(i))\n y += yx\n y = np.exp((y - np.max(y))/n**2)\n y /= np.sum(y) * dx \n a.plot(x,y,label='sum')\n s = np.argsort(y)[::-1]\n ys = np.cumsum(y[s]) * dx\n yi = np.argwhere(ys > 0.682689492137)[0][0]\n print('mean = {:2f}'.format(x[s[0]]))\n print('sigma = {:2f}'.format(yi*dx/2))\n xy = np.ndarray((yi+2,2))\n i0,i1 = min(s[:yi]), max(s[:yi])\n xy[:yi,0] = x[i0:i1+1]\n xy[:yi,1] = y[i0:i1+1]\n xy[yi:,1] = 0\n xy[yi:,0] = x[[i1,i0]]\n a.add_patch(Polygon(xy,fill=True,color='green',ec='none',alpha=0.25))\n \n leg = plt.legend()\n plt.draw()","def huber(t=1.345):\n return sm.robust.norms.HuberT(t)","def beeston_barlow_root2(a, p, U, d):\n return ((-U*p - U + a*p + d*p +\n np.sqrt(U**2*p**2 + 2*U**2*p + U**2 + 2*U*a*p**2 + 2*U*a*p -\n 2*U*d*p**2 - 2*U*d*p + a**2*p**2 + 2*a*d*p**2 + d**2*p**2))/(2*p*(p + 1)))","def heptagonal(n: int) -> int:\n return int(n * (5 * n - 3) / 2)","def g_sebal_func(ts, albedo_sur, ndvi):\n g = np.copy(ndvi).astype(np.float64)\n np.power(g, 4, out=g)\n g *= -0.98\n g += 1\n g *= ts\n g *= (albedo_sur * 0.0074 + 0.0038)\n return g","def basis_fns(n=0):\n return lambda x: np.sum(x ** (n+1), axis=1)","def b_n(n):\n if n <= 0.36: # MCH03\n ei = np.array([0, 1, 2, 3, 4])\n ai = np.array([0.01945, -0.8902, 10.95, -19.67, 13.43])\n else: # CB99\n ei = np.array([1, 0, -1, -2])\n ai = np.array([2, -1./3, 4./405, 46./25515])\n return np.sum(ai * np.power(float(n), ei))","def nac_w_optimal_r(fan_in, fan_out):\n fan = max(fan_in + fan_out, 5)\n r = scipy.optimize.bisect(lambda r: fan * nac_w_variance(r) - 2, 0, 10)\n return r","def gauss_sum_to(n):\n the_sum = n * (n + 1) / 2\n return the_sum","def gauss_quad_1d(q, n=150):\n z, w = leggauss(n)\n theta = (THETA_HIGH-THETA_LOW)*(z + 1)/2 + THETA_LOW\n sin_theta = abs(sin(theta))\n Zq = kernel_1d(q=q, theta=theta)\n return np.sum(Zq*w*sin_theta)*(THETA_HIGH-THETA_LOW)/2","def skew_js_fgan_lower_bound(f):\n n = tf.cast(f.shape[0], tf.float32)\n alpha = 1/n\n f_diag = tf.linalg.tensor_diag_part(f)\n first_term = tf.reduce_mean(-tf.nn.softplus(-f_diag))\n second_term = (tf.reduce_sum(tf.nn.softplus(f)) - tf.reduce_sum(tf.nn.softplus(f_diag))) / (n * (n - 1.))\n return alpha*first_term - (1-alpha)*second_term","def nu0(self, Td):\n f = lambda nu: self.bCIB + self.cCIB + self.dLnBlackdLnNu(nu, Td)\n nuMin = 1.e1 * 1.e9 # in Hz\n nuMax = 1.e5 * 1.e9 # in Hz\n '''\n Nu = np.linspace(nuMin, nuMax, 201)\n F = np.array(map(f, Nu))\n plt.semilogx(Nu, F)\n plt.semilogx(Nu, 0.*Nu)\n plt.show()\n '''\n result = optimize.brentq(f , nuMin, nuMax)\n return result","def greedy_binning(t, C, n_bins, maxit= 1000):\n b= n_bins\n n_u= generate_n_u(t)\n d= len(n_u)\n cum_n_u= np.hstack([[0], np.cumsum(n_u)])\n tau= np.unique(t)\n tau= np.hstack([tau, [np.max(tau) + 0.1]])\n \n splits= sorted(np.random.randint(1, d, b-1))\n while len(np.unique(splits)) < b-1:\n splits= sorted(np.random.randint(1, d, b-1)) \n bins= np.array([0] + splits + [d])\n \n sums= np.repeat(0.0, n_bins)\n\n for i in range(n_bins):\n sums[i]= block_sum(i, bins, C, n_u)\n \n ns= np.repeat(0.0, n_bins)\n for i in range(n_bins):\n ns[i]= cum_n_u[bins[i+1]] - cum_n_u[bins[i]]\n \n objective= 0.0\n \n for i in range(n_bins):\n objective+= sums[i]/ns[i]\n\n cum_n_u= np.hstack([[0], np.cumsum(n_u)])\n \n it= 0\n while True and it < maxit:\n it+= 1\n \n change_obj, change_idx, step_, new_sum_i, new_sum_im1, new_ns_i, new_ns_im1= 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0\n \n for i in range(1, n_bins):\n for step in [-1, 0]:\n if ns[i + step] > n_u[bins[i] + step]:\n change, sum_i, sum_im1, ns_i, ns_im1 = changes(i, step*2 + 1, bins, C, n_u, ns, sums)\n if change > change_obj:\n change_obj, change_idx, step_, new_sum_i, new_sum_im1, new_ns_i, new_ns_im1= change, i, step*2 + 1, sum_i, sum_im1, ns_i, ns_im1\n \n if change_obj > 0.0:\n objective= objective + change_obj\n bins[change_idx]+= step_\n sums[change_idx]= new_sum_i\n sums[change_idx-1]= new_sum_im1\n ns[change_idx]= new_ns_i\n ns[change_idx-1]= new_ns_im1\n else:\n break\n \n t_binning= []\n for i in range(len(t)):\n for j in range(len(bins)):\n if t[i] >= tau[bins[j]] and t[i] < tau[bins[j+1]]:\n t_binning.append(j)\n \n return np.array(t_binning)","def make_roundflat(\n lower_bound=0.1,\n upper_bound=0.6,\n lower_steepness=2.0,\n upper_steepness=8.0,\n integration_bounds=(0.0, 10.0),\n):\n\n def roundflat(x):\n return -2 * (\n (x / lower_bound) ** (-2 * lower_steepness)\n + (x / upper_bound) ** (2 * upper_steepness)\n )\n\n value = quad(\n lambda x: np.exp(roundflat(x)), integration_bounds[0], integration_bounds[1]\n )[0]\n\n def prior(x):\n return roundflat(x) - np.log(value)\n\n return prior","def thinning_sampler(rng, lamb, xmin=0, lamb_min=1e-10):\n while lamb(xmin) > lamb_min:\n dx = -np.log(rng.rand()) / lamb(xmin)\n x = xmin + dx\n accept_rate = lamb(x) / lamb(xmin)\n\n if rng.rand() < accept_rate:\n return x\n xmin = x\n raise ValueError(\n f\"require lamb({xmin})>{lamb_min} to guarantee cdf(infty)=1\"\n )","def hurst(data):\n\tn = 6\n\tdata = pd.Series(data).pct_change()[1:]\n\tars = list()\n\tlag = list()\n\tfor i in range(n):\n\t\tm = 2 ** i\n\t\tsize = np.size(data) // m\n\t\tlag.append(size)\n\t\tpanel = {}\n\t\tfor j in range(m):\n\t\t\tpanel[str(j)] = data[j * size:(j + 1) * size].values\n\n\t\tpanel = pd.DataFrame(panel)\n\t\tmean = panel.mean()\n\t\tdeviation = (panel - mean).cumsum()\n\t\tmaxi = deviation.max()\n\t\tmini = deviation.min()\n\t\tsigma = panel.std()\n\t\trs = maxi - mini\n\t\trs = rs / sigma\n\t\tars.append(rs.mean())\n\n\tlag = np.log10(lag)\n\tars = np.log10(ars)\n\thurst_exponent = np.polyfit(lag, ars, 1)\n\tresult = hurst_exponent[0]\n\treturn result","def hurst(p,n):\n # Create the range of lag values\n lags = range(2, n)\n tau = []; lagvec = []\n for lag in lags: \n # produce price difference with lag \n pp = subtract(p[lag:],p[:-lag]) \n # Write the different lags into a vector \n lagvec.append(lag) \n # Calculate the variance of the differnce vector \n tau.append(sqrt(std(pp))) \n\n # Use a linear fit to estimate the Hurst Exponent\n poly = polyfit(log(lags), log(tau), 1)\n\n # Return the Hurst exponent from the polyfit output\n return poly[0]*2.0","def js_fgan_lower_bound(f):\n f_diag = tf.linalg.tensor_diag_part(f)\n first_term = tf.reduce_mean(-tf.nn.softplus(-f_diag))\n n = tf.cast(f.shape[0], tf.float32)\n second_term = (tf.reduce_sum(tf.nn.softplus(f)) - tf.reduce_sum(tf.nn.softplus(f_diag))) / (n * (n - 1.))\n return first_term - second_term","def __guassian_kernel(x, sigma=200):\n return (1 / (sqrt(2.*pi) * sigma)) * exp(-x ** 2 / (2.*sigma**2))","def stirling(n):\n return n**n*isqrt(2*math.pi*n)/math.e**n","def uniform(a: float, b: float) -> float:\n ...","def _random_shoot_bernstein_ ( fun ) :\n xmn = fun.xmin ()\n xmx = fun.xmax ()\n ymx = max ( fun.bernstein().pars() )\n i = 0 \n while True : \n x = _uniform_ ( xmn , xmx ) \n y = _uniform_ ( 0 , ymx )\n v = fun ( x )\n if v >= y : return x","def gini(values):\n v = values.copy()\n v.sort()\n\n sum_iy = 0\n for i, y in enumerate(v):\n i += 1\n sum_iy += i*y\n\n sum_y = sum(v)\n n = len(v)\n\n return 1 - (((2*sum_iy)/(n*sum_y)) - ((n+1)/n))","def infs(n: int) -> np.ndarray:\n return np.ones(n) * np.inf","def half_huber_relu(x, d=1):\n x = tf.where(x < 0, 0, x) # Apply flat part of ReLU\n x = tf.where(x > (1/(2*d)), x - 1/(4*d), x) # Apply linear part of ReLU\n x = tf.where((0 <= x) & (x <= (1/(2*d))), d*x**2, x) # Apply huber part of the transformation\n return x","def zzX_mignotte_bound(f):\n a = zzX_max_norm(f)\n b = abs(zzX_zz_LC(f))\n n = sum(zzX_degree_all(f))\n\n return INT_TYPE(isqrt(n+1))*2**n*a*b","def mc_integrate1d(f, a, b, N=10000):\n #use the approximation formula with a translate and scale of unit interval\n points = (b-a)*np.random.rand(N)+a\n y = f(points)\n return (b-a)*sum(y)/N","def fn(u):\n if u == n-1: return 1 # boundary condition \n ans = 0\n for v, _ in graph[u]: \n if dist[u] > dist[v]: ans += fn(v)\n return ans","def mu_HF_approx(t, n):\n eta = eta_HF(n)\n return 1 + t**(3. / 2) - 2 * np.sqrt(pi) / z32 * t * np.sqrt(eta * (1 - t**(3. / 2)))","def gauss_kernel(n_fwhm,sigma):\n\n x_length = int(n_fwhm * sigma + 0.5) #Add 0.5 to approximate to nearest integer\n y_length = x_length\n \n \n x, y = mgrid[-x_length:x_length+1, -y_length:y_length+1]\n g = numpy.exp(-(x**2/(2*(float(sigma)**2))+y**2/(2*(float(sigma)**2))))\n return g / g.sum()","def band(n, steps):\n return math.floor(n * steps) / steps","def bisection(self,\n low_bound: float,\n up_bound: float,\n epsilon: float,\n n: int,\n Rmn: dict,\n ytm: dict) -> float:\n for i in range(100):\n y = (low_bound + up_bound) / 2\n if abs(Bootstrapping.f(self,y, n, Rmn, ytm)) < epsilon:\n ym = ytm[list(ytm.keys())[-1]]\n m = list(ytm.keys())[-1]\n for j in range(list(ytm.keys())[-1] + 1, n + 1):\n ytm[j] = ym + (y - ym) * (j - m) / (n - m)\n return ytm\n else:\n if Bootstrapping.f(self,y, n, Rmn, ytm) * Bootstrapping.f(self,up_bound, n, Rmn, ytm) < 0:\n low_bound = y\n elif Bootstrapping.f(self,y, n, Rmn, ytm) * Bootstrapping.f(self,low_bound, n, Rmn, ytm) < 0:\n up_bound = y","def solve_n_bins(x):\n from scipy.stats import iqr\n\n x = np.asarray(x)\n hat = 2 * iqr(x) / (len(x) ** (1 / 3))\n\n if hat == 0:\n return int(np.sqrt(len(x)))\n else:\n return int(np.ceil((x.max() - x.min()) / hat))","def solve_n_bins(x):\n from scipy.stats import iqr\n\n x = np.asarray(x)\n hat = 2 * iqr(x) / (len(x) ** (1 / 3))\n\n if hat == 0:\n return int(np.sqrt(len(x)))\n else:\n return int(np.ceil((x.max() - x.min()) / hat))","def silverman(n: int, ess: float) -> float:\n\n return (ess * (n + 2) / 4) ** (-1 / (n + 4))","def fn(val):\n if val < x: return min(2*val-1, 2*(x-val))\n k = int(log(val)//log(x))\n ans = k + fn(val - x**k)\n if x**(k+1) < 2*val: \n ans = min(ans, k + 1 + fn(x**(k+1) - val))\n return ans","def heterodyner_signal(self, n=1):\n omega = self.fvec*2*np.pi*n\n phvec = np.cumsum(omega)\n return np.exp(1j*phvec)","def func_hollomon(eps,k,n):\n return k * eps**n","def f(x):\n\treturn np.sin(x / 5.0) * np.exp(x / 10.0) + 5 * np.exp(-x / 2.0)","def psi(n, x):\n H = h(n, x, orthonormal=True)\n weight = np.exp(-(x ** 2) / 2)\n psi = H * weight\n return psi","def minimize_cost_golden(f, vmin, offset=0, step=1, maxiter=1000):\n # type: (Callable[[int], float], float, int, int, Optional[int]) -> MinCostResult\n fib2 = fib1 = fib0 = 0\n cur_idx = 0\n nfev = 0\n xmax = vmax = v_prev = None\n while maxiter is None or nfev < maxiter:\n v_cur = f(step * fib0 + offset)\n nfev += 1\n\n if v_cur >= vmin:\n # found upper bound, use binary search to find answer\n stop = step * fib0 + offset\n return minimize_cost_binary(f, vmin, start=step * (fib1 + 1) + offset,\n stop=stop, save=stop, step=step, nfev=nfev)\n else:\n if vmax is not None and v_cur <= vmax:\n if cur_idx <= 3:\n # special case: 0 <= xmax < 3, and we already checked all possibilities, so\n # we know vmax < vmin. There is no solution and just return.\n return MinCostResult(x=None, xmax=step * xmax + offset, vmax=vmax, nfev=nfev)\n else:\n # we found the bracket that encloses maximum, perform golden section search\n a, x, b = fib2, fib1, fib0\n fx = v_prev\n while x > a + 1 or b > x + 1:\n u = a + b - x\n fu = f(step * u + offset)\n nfev += 1\n\n if fu >= fx:\n if u > x:\n a, x = x, u\n fx = fu\n else:\n x, b = u, x\n fx = fu\n\n if fx >= vmin:\n # found upper bound, use binary search to find answer\n stop = step * x + offset\n return minimize_cost_binary(f, vmin, start=step * (a + 1) + offset,\n stop=stop, save=stop, step=step,\n nfev=nfev)\n else:\n if u > x:\n b = u\n else:\n a = u\n\n # golden section search terminated, the maximum is less than vmin\n return MinCostResult(x=None, xmax=step * x + offset, vmax=fx, nfev=nfev)\n else:\n # still not close to maximum, continue searching\n vmax = v_prev = v_cur\n xmax = fib0\n cur_idx += 1\n if cur_idx <= 3:\n fib2, fib1, fib0 = fib1, fib0, cur_idx\n else:\n fib2, fib1, fib0 = fib1, fib0, fib1 + fib0\n\n raise ValueError('Maximum number of iteration achieved')","def weibull(self, x):\n return (self.k_alp[0]/self.k_alp[1])*(math.pow(x/self.k_alp[1],(self.k_alp[0]-1))) * \\\n np.exp(-1*math.pow(x/self.k_alp[1],self.k_alp[0]))","def romberg(f, a, b, n=20):\n R = [[0.5 * (b - a) * (f(a) + f(b))]] # R[0][0]\n for n in range (1,n+1):\n h = float(b-a)/2**n\n R.append((n+1)*[None]) # Add an empty row.\n R[n][0] = 0.5*R[n-1][0] + h*sum(f(a+(2*k-1)*h) for k in range(1, 2**(n-1)+1)) # for proper limits\n for m in range(1, n+1):\n R[n][m] = R[n][m-1] + (R[n][m-1] - R[n-1][m-1]) / (4**m - 1)\n return R[n][n]","def griewank(x):\n nopt = np.size(x)\n # if (nopt == 2) | (nopt == 10):\n xx = x\n if nopt == 2:\n d = 200.0\n else:\n d = 4000.0\n\n u1 = 0.0\n u2 = 1.0\n for j in range(nopt):\n u1 = u1 + xx[j]**2 / d\n u2 = u2 * np.cos(xx[j] / np.sqrt(float(j + 1)))\n\n f = u1 - u2 + 1\n return f","def f(i):\n return e(2**N-1-i) ^ 2**(N-1)","def lowest_rank_approx(A,e):","def bpmfln(k, n, p):\n return np.exp(combinln(n, k) + k * np.log(p) + (n - k) * np.log(1 - p))","def sig_func(x, w, b):\n z = np.dot(x, np.transpose(w)) + b\n try:\n s = 1 / (1 + exp(-z))\n except OverflowError:\n s = float('inf')\n return s","def f(p, phi, phib, df):\n\treturn -log(p) - df + (p-1)*phi + \\\n\t ( phi*(1-p) + phib + \\\n\t 5./4*alpha*(phi*p)**(9./4)-(9./4)*alpha*(p*phi)**(5./4) - \\\n\t (1./2)(1-p*phi)**2 - (phib/Nb)-5./4*alpha*(phi+phib)**(9./4) + \\\n\t (9./4)*alpha*(phi+phib)**(5.4) + \\\n\t 1./2*(1-phi-phib)**2 ) * Ns","def solve_huang_eq_13(v):\n\n n = len(v)\n u = v - np.ones((n, n)) @ v / (n) + np.ones(n) / (n)\n lambda_bar_star = solve_huang_eq_24(u)\n lambda_star = (lambda_bar_star - u) + np.clip(u - lambda_bar_star, 0, None)\n return u + lambda_star - lambda_bar_star * np.ones(n)","def _bernoulli_lower(self, p, n, delta):\n if p < 1e-6:\n return 0.\n else:\n lower = scipy.stats.beta.ppf(delta / 2, p * n, n - p * n + 1)\n return lower","def halving_sum(n):\n if n == 1:\n return 1\n else:\n return n + halving_sum(n // 2)","def normal_approximation_to_binomial(n, p):\r\n mu = p*n\r\n sigma = math.sqrt(p * (1 - p) * n)\r\n\r\n return mu, sigma","def lehmer(n):\n a = np.outer(np.ones(n), np.arange(1, n + 1))\n a = a / a.T\n a = np.tril(a) + np.tril(a, -1).T\n\n return a","def bn(x, e=1e-7):\n mu = np.mean(x)\n sigma2 = np.var(x)\n x_ = (x - mu) / np.sqrt(sigma2 + e)\n return x_","def Gaussiankernel(size, sigma=1): \n size = int(size) // 2\n # create x grid and y grid\n x, y = np.mgrid[-size:size+1, -size:size+1] \n # gaussian distribution formula\n normal = 1 / np.sqrt(2.0 * np.pi * sigma**2)\n g = np.exp(-((x**2 + y**2) / (2.0*sigma**2))) * normal\n \n return g/g.sum()","def kernel_sigmas(n_kernels):\n bin_size = 2.0 / (n_kernels - 1)\n l_sigma = [0.001] # for exact match. small variance -> exact match\n if n_kernels == 1:\n return l_sigma\n\n l_sigma += [0.1] * (n_kernels - 1)\n print(l_sigma)\n return l_sigma","def B(n, k):\n assert 0 < k <= n\n global lookup\n for index_y in range(len(lookup), n + 1):\n lookup.append([1])\n min_value = min(index_y, k)\n for index_x in range(min_value):\n if index_x < len(lookup[index_y - 1]) - 1:\n lookup[index_y].append(lookup[index_y - 1][index_x] + lookup[index_y - 1][index_x + 1])\n else:\n lookup[index_y].append(lookup[index_y - 1][index_x])\n return lookup[n][k]","def N_gfun(self,y):\n return 0.0","def _lowess_bisquare(t):\n #t = (1-t**2)**2\n t *= t\n t[:] = np.negative(t) #, out=t)\n t += 1\n t *= t","def approx_sigmoid(x, n=3):\n if n == 5:\n cuts = [-5, -2.5, 2.5, 5]\n le = [0] + [x <= cut for cut in cuts] + [1]\n select = [le[i + 1] - le[i] for i in range(5)]\n outputs = [cfix(10 ** -4),\n 0.02776 * x + 0.145,\n 0.17 * x + 0.5,\n 0.02776 * x + 0.85498,\n cfix(1 - 10 ** -4)]\n return sum(a * b for a, b in zip(select, outputs))\n else:\n a = x < -0.5\n b = x > 0.5\n return a.if_else(0, b.if_else(1, 0.5 + x))","def approx_sigmoid(x, n=3):\n if n == 5:\n cuts = [-5, -2.5, 2.5, 5]\n le = [0] + [x <= cut for cut in cuts] + [1]\n select = [le[i + 1] - le[i] for i in range(5)]\n outputs = [cfix(10 ** -4),\n 0.02776 * x + 0.145,\n 0.17 * x + 0.5,\n 0.02776 * x + 0.85498,\n cfix(1 - 10 ** -4)]\n return sum(a * b for a, b in zip(select, outputs))\n else:\n a = x < -0.5\n b = x > 0.5\n return a.if_else(0, b.if_else(1, 0.5 + x))","def g1(s,height=height,k1=k1,k2=k2,peak_0=peak_0):\r\n if (isinstance(s,float)): # Datatype test \r\n if(s<peak_0): # Piecewise function\r\n return height*np.exp(k1*(s-peak_0)) # Left side of the peak\r\n else:\r\n return height*np.exp(k2*(peak_0-s)) # Right side of the peak\r\n else:\r\n ret = np.zeros(len(s),dtype=float)\r\n ret[s<peak_0] = height*np.exp(k1*(s[s<peak_0]-peak_0))\r\n ret[s>=peak_0] = height*np.exp(k2*(peak_0-s[s>=peak_0]))\r\n return ret","def exterior(n):\n\n def inner(x):\n return n * x\n\n return inner","def bdq1(f, x, h=1e-5):\n return (f(x)-f(x-h))/h\n raise NotImplementedError(\"Problem 2 Incomplete\")","def gridded_1d(q, n=300):\n theta = np.linspace(THETA_LOW, THETA_HIGH, n)\n Zq = kernel_1d(q=q, theta=theta)\n Zq *= abs(sin(theta))\n dx = theta[1]-theta[0]\n print(\"rect-%d\"%n, np.sum(Zq)*dx*SCALE)\n print(\"trapz-%d\"%n, np.trapz(Zq, dx=dx)*SCALE)\n print(\"simpson-%d\"%n, simps(Zq, dx=dx)*SCALE)\n print(\"romb-%d\"%n, romb(Zq, dx=dx)*SCALE)","def minimum_spanning_arborescence(sol):","def shannons(data):\n \n from math import log as ln\n\n # from IPython.core.debugger import Tracer\n # Tracer()()\n \n def p(n, N):\n \"\"\" Relative abundance \"\"\"\n if n is 0:\n return 0\n else:\n return (float(n)/N) * ln(float(n)/N)\n \n N = sum(data)\n \n return -sum(p(n, N) for n in data if not n == 0)","def value(a, y, weights, lambda_):\n\t\treturn np.sum(np.nan_to_num(-y*np.log(a + 1e-15)-(1-y)*np.log(1-a + 1e-15))) / (a.shape[0] * a.shape[1])\n\t\t# return unregularize + (0.5*lambda_*np.sum(np.square(weights[-1])) / (a.shape[0] * a.shape[1])) ","def L(n):\n\tif (n==0):\n\t\treturn lambda x: 1.0\n\n\telif (n==1):\n\t\treturn lambda x: x\n\n\telse:\n\t\treturn lambda x: ( (2.0*n-1.0) * x * L(n-1)(x)-(n-1) * L(n-2)(x) ) / n","def _bernoulli_upper(self, p, n, delta):\n if p > 1 - 1e-6:\n return 1.\n else:\n upper = scipy.stats.beta.ppf(1. - delta / 2, p * n + 1, n - p * n)\n return upper","def slerp_gaussian(val, low, high):\n offset = norm.cdf(np.zeros_like(low)) # offset is just [0.5, 0.5, ...]\n low_gau_shifted = norm.cdf(low) - offset\n high_gau_shifted = norm.cdf(high) - offset\n circle_lerped_gau = slerp(val, low_gau_shifted, high_gau_shifted)\n epsilon = 0.001\n clipped_sum = np.clip(circle_lerped_gau + offset, epsilon, 1.0 - epsilon)\n result = norm.ppf(clipped_sum)\n return result","def Z(n):\n count5 = 0\n i = 1\n while 1:\n a = pow(5, i)\n if a > n:\n return count5\n else:\n count5 += n/a\n i += 1","def __R1(x: float) -> np.float64:\n if np.abs(x) < end:\n return np.float64((1 / np.pi) * np.sqrt(2 * N - x * x))\n return np.float64(0.0)","def normal_approximation_to_binomial(n, p):\n mu = p * n\n sigma = math.sqrt(p * (1 - p) * n)\n return mu, sigma","def normal_approximation_to_binomial(n, p):\n mu = p * n\n sigma = math.sqrt(p * (1 - p) * n)\n return mu, sigma","def normal_approximation_to_binomial(n, p):\n mu = p * n\n sigma = math.sqrt(p * (1 - p) * n)\n return mu, sigma","def normal_approximation_to_binomial(n, p):\n mu = p * n\n sigma = math.sqrt(p * (1 - p) * n)\n return mu, sigma","def g(f, x: float):\n return lambda x: f(x + f(x)) / f(x) - 1","def normal_approximation_to_binomial(n, p):\n mu = p * n\n sigma = math.sqrt(p * (1 - p ) * n)\n return mu, sigma","def stick_breaking(n:int,alpha:float) -> np.ndarray:\n betas = G.beta(a=alpha, b=1.0, size=n)\n betas[1:] *= np.cumprod(1-betas[:-1])\n weights = np.sort(betas)[::-1]\n return weights","def sigma_fatigue(N):\n return 855 * N **(-0.109)","def inner_func_1(z,n):\r\n return ((psi(n,(z/(1-z**2))))**2)*((z/(1-z**2))**2)*((1+z**2)/((1-z**2)**2))","def Gaussian_conv(n):\n\n\t# Choose sigma and mu\n\tsigma = 1\n\tmu = 0\n\n\t# Create table\n\tb = sigma * np.random.randn(n) + mu\n\tsum = b.sum()\n\t#b = b/sum\n\tprint(b)\n\treturn b","def zzx_mignotte_bound(f):\n a = zzx_max_norm(f)\n b = abs(poly_LC(f))\n n = zzx_degree(f)\n\n return INT_TYPE(isqrt(n+1))*2**n*a*b","def transsinh_(b=None):\n rlim=1.e-6\n nmax=300\n delta=1.0\n delta_old=delta\n for n in range(nmax):\n f=delta / (np.sinh(delta) + 1.0 * 10 ** (- 60)) - 1.0 / b\n df=(np.sinh(delta) - delta * np.cosh(delta)) / np.sinh(delta) ** 2\n delta=delta - 0.5 * f / df\n if ((n == nmax) and (np.abs((delta - delta_old) / delta_old) > rlim)):\n disp_([char(' Convergence problem in transsinh dist. function !!! ')])\n disp_([char(' residual '),num2str_((delta - delta_old) / delta_old)])\n delta_old=delta\n return delta","def simp(fun, a, b):\n h = 0.5*(b-a)\n c = 0.5*(a+b)\n return (h/3)*(fun(a) + 4*fun(c) + fun(b))","def get_blur_kernel(n):\n return [1/n**2] * n**2","def analyticSol (x):\n\treturn x*(1-x);","def algorithm_2_21(p, t, x):\n\n t = np.array(t, dtype=np.float64)\n b = 1\n mu = index(x, t)\n\n for k in range(1, p + 1):\n t1 = t[mu - k + 1:mu + 1]\n t2 = t[mu + 1:mu + k + 1]\n omega = np.divide(\n (x - t1), (t2 - t1), out=np.zeros_like(t1), where=((t2 - t1) != 0))\n b = np.append((1 - omega) * b, 0) + np.insert((omega * b), 0, 0)\n\n return b","def fakultet (n = 1):\n sum = 1\n for i in range(n, 1, -1):\n sum *= i\n return sum","def powAlpha( n ):\n return (1-betaval)*Fib(n) + Fib(n-1)\n #return Fib(n+1) - Fib(n) * betaval","def APH2ndMomentLowerBound (m1, n):\n\n return float(m1)*m1*(n+1) / n","def function_to_minimize(x):\n return math.sin(x[0]) * math.cos(x[1]) + math.cos(x[0] + x[1]) + random.uniform(-0.02, 0.02)","def bezier(i, n, t):\n return binomial(n, i) * pow(t, i) * pow(1 - t, n - i)","def tanh(a):","def smooth_slowsignal(a, n=10):\n ret = np.cumsum(a, axis=0, dtype=float)\n ret[n:] = ret[n:] - ret[:-n]\n return ret[n - 1 :] / n"],"string":"[\n \"def beeston_barlow_root1(a, p, U, d):\\n return ((-U*p - U + a*p + d*p -\\n np.sqrt(U**2*p**2 + 2*U**2*p + U**2 + 2*U*a*p**2 + 2*U*a*p -\\n 2*U*d*p**2 - 2*U*d*p + a**2*p**2 + 2*a*d*p**2 + d**2*p**2))/(2*p*(p + 1)))\",\n \"def trapezium_rule(f, m, x, a, b, n):\\n h = (b-a)/float(n)\\n s = 0.5*(f(m, x, a) + f(m, x, b))\\n for i in range(n):\\n s = s + f(m, x, a + i*h)\\n return h*s\",\n \"def Bernstein(i, n, t):\\n return special.binom(n, i) * t ** i * (1 - t) ** (n - i)\",\n \"def f4():\\n n = 4\\n v = np.arange(n)**0.75 * 0.2\\n e = (np.arange(n)+1)**0.7 * 1e-1\\n\\n n = 12\\n v = np.arange(n)\\n e = np.array([0.1]*n) * 10e-0\\n\\n print(Sumb(v,e))\\n\\n f = plt.figure()\\n a = f.add_subplot(111)\\n\\n dx = 0.0001\\n x = np.arange(-1,v[-1]+1,dx)\\n y = x.copy()\\n y[:] = 0.\\n for i in range(n):\\n yx = lg(x,v[i],e[i])\\n a.plot(x,np.exp(yx),label='{:d}'.format(i))\\n y += yx\\n y = np.exp((y - np.max(y))/n**2)\\n y /= np.sum(y) * dx \\n a.plot(x,y,label='sum')\\n s = np.argsort(y)[::-1]\\n ys = np.cumsum(y[s]) * dx\\n yi = np.argwhere(ys > 0.682689492137)[0][0]\\n print('mean = {:2f}'.format(x[s[0]]))\\n print('sigma = {:2f}'.format(yi*dx/2))\\n xy = np.ndarray((yi+2,2))\\n i0,i1 = min(s[:yi]), max(s[:yi])\\n xy[:yi,0] = x[i0:i1+1]\\n xy[:yi,1] = y[i0:i1+1]\\n xy[yi:,1] = 0\\n xy[yi:,0] = x[[i1,i0]]\\n a.add_patch(Polygon(xy,fill=True,color='green',ec='none',alpha=0.25))\\n \\n leg = plt.legend()\\n plt.draw()\",\n \"def huber(t=1.345):\\n return sm.robust.norms.HuberT(t)\",\n \"def beeston_barlow_root2(a, p, U, d):\\n return ((-U*p - U + a*p + d*p +\\n np.sqrt(U**2*p**2 + 2*U**2*p + U**2 + 2*U*a*p**2 + 2*U*a*p -\\n 2*U*d*p**2 - 2*U*d*p + a**2*p**2 + 2*a*d*p**2 + d**2*p**2))/(2*p*(p + 1)))\",\n \"def heptagonal(n: int) -> int:\\n return int(n * (5 * n - 3) / 2)\",\n \"def g_sebal_func(ts, albedo_sur, ndvi):\\n g = np.copy(ndvi).astype(np.float64)\\n np.power(g, 4, out=g)\\n g *= -0.98\\n g += 1\\n g *= ts\\n g *= (albedo_sur * 0.0074 + 0.0038)\\n return g\",\n \"def basis_fns(n=0):\\n return lambda x: np.sum(x ** (n+1), axis=1)\",\n \"def b_n(n):\\n if n <= 0.36: # MCH03\\n ei = np.array([0, 1, 2, 3, 4])\\n ai = np.array([0.01945, -0.8902, 10.95, -19.67, 13.43])\\n else: # CB99\\n ei = np.array([1, 0, -1, -2])\\n ai = np.array([2, -1./3, 4./405, 46./25515])\\n return np.sum(ai * np.power(float(n), ei))\",\n \"def nac_w_optimal_r(fan_in, fan_out):\\n fan = max(fan_in + fan_out, 5)\\n r = scipy.optimize.bisect(lambda r: fan * nac_w_variance(r) - 2, 0, 10)\\n return r\",\n \"def gauss_sum_to(n):\\n the_sum = n * (n + 1) / 2\\n return the_sum\",\n \"def gauss_quad_1d(q, n=150):\\n z, w = leggauss(n)\\n theta = (THETA_HIGH-THETA_LOW)*(z + 1)/2 + THETA_LOW\\n sin_theta = abs(sin(theta))\\n Zq = kernel_1d(q=q, theta=theta)\\n return np.sum(Zq*w*sin_theta)*(THETA_HIGH-THETA_LOW)/2\",\n \"def skew_js_fgan_lower_bound(f):\\n n = tf.cast(f.shape[0], tf.float32)\\n alpha = 1/n\\n f_diag = tf.linalg.tensor_diag_part(f)\\n first_term = tf.reduce_mean(-tf.nn.softplus(-f_diag))\\n second_term = (tf.reduce_sum(tf.nn.softplus(f)) - tf.reduce_sum(tf.nn.softplus(f_diag))) / (n * (n - 1.))\\n return alpha*first_term - (1-alpha)*second_term\",\n \"def nu0(self, Td):\\n f = lambda nu: self.bCIB + self.cCIB + self.dLnBlackdLnNu(nu, Td)\\n nuMin = 1.e1 * 1.e9 # in Hz\\n nuMax = 1.e5 * 1.e9 # in Hz\\n '''\\n Nu = np.linspace(nuMin, nuMax, 201)\\n F = np.array(map(f, Nu))\\n plt.semilogx(Nu, F)\\n plt.semilogx(Nu, 0.*Nu)\\n plt.show()\\n '''\\n result = optimize.brentq(f , nuMin, nuMax)\\n return result\",\n \"def greedy_binning(t, C, n_bins, maxit= 1000):\\n b= n_bins\\n n_u= generate_n_u(t)\\n d= len(n_u)\\n cum_n_u= np.hstack([[0], np.cumsum(n_u)])\\n tau= np.unique(t)\\n tau= np.hstack([tau, [np.max(tau) + 0.1]])\\n \\n splits= sorted(np.random.randint(1, d, b-1))\\n while len(np.unique(splits)) < b-1:\\n splits= sorted(np.random.randint(1, d, b-1)) \\n bins= np.array([0] + splits + [d])\\n \\n sums= np.repeat(0.0, n_bins)\\n\\n for i in range(n_bins):\\n sums[i]= block_sum(i, bins, C, n_u)\\n \\n ns= np.repeat(0.0, n_bins)\\n for i in range(n_bins):\\n ns[i]= cum_n_u[bins[i+1]] - cum_n_u[bins[i]]\\n \\n objective= 0.0\\n \\n for i in range(n_bins):\\n objective+= sums[i]/ns[i]\\n\\n cum_n_u= np.hstack([[0], np.cumsum(n_u)])\\n \\n it= 0\\n while True and it < maxit:\\n it+= 1\\n \\n change_obj, change_idx, step_, new_sum_i, new_sum_im1, new_ns_i, new_ns_im1= 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0\\n \\n for i in range(1, n_bins):\\n for step in [-1, 0]:\\n if ns[i + step] > n_u[bins[i] + step]:\\n change, sum_i, sum_im1, ns_i, ns_im1 = changes(i, step*2 + 1, bins, C, n_u, ns, sums)\\n if change > change_obj:\\n change_obj, change_idx, step_, new_sum_i, new_sum_im1, new_ns_i, new_ns_im1= change, i, step*2 + 1, sum_i, sum_im1, ns_i, ns_im1\\n \\n if change_obj > 0.0:\\n objective= objective + change_obj\\n bins[change_idx]+= step_\\n sums[change_idx]= new_sum_i\\n sums[change_idx-1]= new_sum_im1\\n ns[change_idx]= new_ns_i\\n ns[change_idx-1]= new_ns_im1\\n else:\\n break\\n \\n t_binning= []\\n for i in range(len(t)):\\n for j in range(len(bins)):\\n if t[i] >= tau[bins[j]] and t[i] < tau[bins[j+1]]:\\n t_binning.append(j)\\n \\n return np.array(t_binning)\",\n \"def make_roundflat(\\n lower_bound=0.1,\\n upper_bound=0.6,\\n lower_steepness=2.0,\\n upper_steepness=8.0,\\n integration_bounds=(0.0, 10.0),\\n):\\n\\n def roundflat(x):\\n return -2 * (\\n (x / lower_bound) ** (-2 * lower_steepness)\\n + (x / upper_bound) ** (2 * upper_steepness)\\n )\\n\\n value = quad(\\n lambda x: np.exp(roundflat(x)), integration_bounds[0], integration_bounds[1]\\n )[0]\\n\\n def prior(x):\\n return roundflat(x) - np.log(value)\\n\\n return prior\",\n \"def thinning_sampler(rng, lamb, xmin=0, lamb_min=1e-10):\\n while lamb(xmin) > lamb_min:\\n dx = -np.log(rng.rand()) / lamb(xmin)\\n x = xmin + dx\\n accept_rate = lamb(x) / lamb(xmin)\\n\\n if rng.rand() < accept_rate:\\n return x\\n xmin = x\\n raise ValueError(\\n f\\\"require lamb({xmin})>{lamb_min} to guarantee cdf(infty)=1\\\"\\n )\",\n \"def hurst(data):\\n\\tn = 6\\n\\tdata = pd.Series(data).pct_change()[1:]\\n\\tars = list()\\n\\tlag = list()\\n\\tfor i in range(n):\\n\\t\\tm = 2 ** i\\n\\t\\tsize = np.size(data) // m\\n\\t\\tlag.append(size)\\n\\t\\tpanel = {}\\n\\t\\tfor j in range(m):\\n\\t\\t\\tpanel[str(j)] = data[j * size:(j + 1) * size].values\\n\\n\\t\\tpanel = pd.DataFrame(panel)\\n\\t\\tmean = panel.mean()\\n\\t\\tdeviation = (panel - mean).cumsum()\\n\\t\\tmaxi = deviation.max()\\n\\t\\tmini = deviation.min()\\n\\t\\tsigma = panel.std()\\n\\t\\trs = maxi - mini\\n\\t\\trs = rs / sigma\\n\\t\\tars.append(rs.mean())\\n\\n\\tlag = np.log10(lag)\\n\\tars = np.log10(ars)\\n\\thurst_exponent = np.polyfit(lag, ars, 1)\\n\\tresult = hurst_exponent[0]\\n\\treturn result\",\n \"def hurst(p,n):\\n # Create the range of lag values\\n lags = range(2, n)\\n tau = []; lagvec = []\\n for lag in lags: \\n # produce price difference with lag \\n pp = subtract(p[lag:],p[:-lag]) \\n # Write the different lags into a vector \\n lagvec.append(lag) \\n # Calculate the variance of the differnce vector \\n tau.append(sqrt(std(pp))) \\n\\n # Use a linear fit to estimate the Hurst Exponent\\n poly = polyfit(log(lags), log(tau), 1)\\n\\n # Return the Hurst exponent from the polyfit output\\n return poly[0]*2.0\",\n \"def js_fgan_lower_bound(f):\\n f_diag = tf.linalg.tensor_diag_part(f)\\n first_term = tf.reduce_mean(-tf.nn.softplus(-f_diag))\\n n = tf.cast(f.shape[0], tf.float32)\\n second_term = (tf.reduce_sum(tf.nn.softplus(f)) - tf.reduce_sum(tf.nn.softplus(f_diag))) / (n * (n - 1.))\\n return first_term - second_term\",\n \"def __guassian_kernel(x, sigma=200):\\n return (1 / (sqrt(2.*pi) * sigma)) * exp(-x ** 2 / (2.*sigma**2))\",\n \"def stirling(n):\\n return n**n*isqrt(2*math.pi*n)/math.e**n\",\n \"def uniform(a: float, b: float) -> float:\\n ...\",\n \"def _random_shoot_bernstein_ ( fun ) :\\n xmn = fun.xmin ()\\n xmx = fun.xmax ()\\n ymx = max ( fun.bernstein().pars() )\\n i = 0 \\n while True : \\n x = _uniform_ ( xmn , xmx ) \\n y = _uniform_ ( 0 , ymx )\\n v = fun ( x )\\n if v >= y : return x\",\n \"def gini(values):\\n v = values.copy()\\n v.sort()\\n\\n sum_iy = 0\\n for i, y in enumerate(v):\\n i += 1\\n sum_iy += i*y\\n\\n sum_y = sum(v)\\n n = len(v)\\n\\n return 1 - (((2*sum_iy)/(n*sum_y)) - ((n+1)/n))\",\n \"def infs(n: int) -> np.ndarray:\\n return np.ones(n) * np.inf\",\n \"def half_huber_relu(x, d=1):\\n x = tf.where(x < 0, 0, x) # Apply flat part of ReLU\\n x = tf.where(x > (1/(2*d)), x - 1/(4*d), x) # Apply linear part of ReLU\\n x = tf.where((0 <= x) & (x <= (1/(2*d))), d*x**2, x) # Apply huber part of the transformation\\n return x\",\n \"def zzX_mignotte_bound(f):\\n a = zzX_max_norm(f)\\n b = abs(zzX_zz_LC(f))\\n n = sum(zzX_degree_all(f))\\n\\n return INT_TYPE(isqrt(n+1))*2**n*a*b\",\n \"def mc_integrate1d(f, a, b, N=10000):\\n #use the approximation formula with a translate and scale of unit interval\\n points = (b-a)*np.random.rand(N)+a\\n y = f(points)\\n return (b-a)*sum(y)/N\",\n \"def fn(u):\\n if u == n-1: return 1 # boundary condition \\n ans = 0\\n for v, _ in graph[u]: \\n if dist[u] > dist[v]: ans += fn(v)\\n return ans\",\n \"def mu_HF_approx(t, n):\\n eta = eta_HF(n)\\n return 1 + t**(3. / 2) - 2 * np.sqrt(pi) / z32 * t * np.sqrt(eta * (1 - t**(3. / 2)))\",\n \"def gauss_kernel(n_fwhm,sigma):\\n\\n x_length = int(n_fwhm * sigma + 0.5) #Add 0.5 to approximate to nearest integer\\n y_length = x_length\\n \\n \\n x, y = mgrid[-x_length:x_length+1, -y_length:y_length+1]\\n g = numpy.exp(-(x**2/(2*(float(sigma)**2))+y**2/(2*(float(sigma)**2))))\\n return g / g.sum()\",\n \"def band(n, steps):\\n return math.floor(n * steps) / steps\",\n \"def bisection(self,\\n low_bound: float,\\n up_bound: float,\\n epsilon: float,\\n n: int,\\n Rmn: dict,\\n ytm: dict) -> float:\\n for i in range(100):\\n y = (low_bound + up_bound) / 2\\n if abs(Bootstrapping.f(self,y, n, Rmn, ytm)) < epsilon:\\n ym = ytm[list(ytm.keys())[-1]]\\n m = list(ytm.keys())[-1]\\n for j in range(list(ytm.keys())[-1] + 1, n + 1):\\n ytm[j] = ym + (y - ym) * (j - m) / (n - m)\\n return ytm\\n else:\\n if Bootstrapping.f(self,y, n, Rmn, ytm) * Bootstrapping.f(self,up_bound, n, Rmn, ytm) < 0:\\n low_bound = y\\n elif Bootstrapping.f(self,y, n, Rmn, ytm) * Bootstrapping.f(self,low_bound, n, Rmn, ytm) < 0:\\n up_bound = y\",\n \"def solve_n_bins(x):\\n from scipy.stats import iqr\\n\\n x = np.asarray(x)\\n hat = 2 * iqr(x) / (len(x) ** (1 / 3))\\n\\n if hat == 0:\\n return int(np.sqrt(len(x)))\\n else:\\n return int(np.ceil((x.max() - x.min()) / hat))\",\n \"def solve_n_bins(x):\\n from scipy.stats import iqr\\n\\n x = np.asarray(x)\\n hat = 2 * iqr(x) / (len(x) ** (1 / 3))\\n\\n if hat == 0:\\n return int(np.sqrt(len(x)))\\n else:\\n return int(np.ceil((x.max() - x.min()) / hat))\",\n \"def silverman(n: int, ess: float) -> float:\\n\\n return (ess * (n + 2) / 4) ** (-1 / (n + 4))\",\n \"def fn(val):\\n if val < x: return min(2*val-1, 2*(x-val))\\n k = int(log(val)//log(x))\\n ans = k + fn(val - x**k)\\n if x**(k+1) < 2*val: \\n ans = min(ans, k + 1 + fn(x**(k+1) - val))\\n return ans\",\n \"def heterodyner_signal(self, n=1):\\n omega = self.fvec*2*np.pi*n\\n phvec = np.cumsum(omega)\\n return np.exp(1j*phvec)\",\n \"def func_hollomon(eps,k,n):\\n return k * eps**n\",\n \"def f(x):\\n\\treturn np.sin(x / 5.0) * np.exp(x / 10.0) + 5 * np.exp(-x / 2.0)\",\n \"def psi(n, x):\\n H = h(n, x, orthonormal=True)\\n weight = np.exp(-(x ** 2) / 2)\\n psi = H * weight\\n return psi\",\n \"def minimize_cost_golden(f, vmin, offset=0, step=1, maxiter=1000):\\n # type: (Callable[[int], float], float, int, int, Optional[int]) -> MinCostResult\\n fib2 = fib1 = fib0 = 0\\n cur_idx = 0\\n nfev = 0\\n xmax = vmax = v_prev = None\\n while maxiter is None or nfev < maxiter:\\n v_cur = f(step * fib0 + offset)\\n nfev += 1\\n\\n if v_cur >= vmin:\\n # found upper bound, use binary search to find answer\\n stop = step * fib0 + offset\\n return minimize_cost_binary(f, vmin, start=step * (fib1 + 1) + offset,\\n stop=stop, save=stop, step=step, nfev=nfev)\\n else:\\n if vmax is not None and v_cur <= vmax:\\n if cur_idx <= 3:\\n # special case: 0 <= xmax < 3, and we already checked all possibilities, so\\n # we know vmax < vmin. There is no solution and just return.\\n return MinCostResult(x=None, xmax=step * xmax + offset, vmax=vmax, nfev=nfev)\\n else:\\n # we found the bracket that encloses maximum, perform golden section search\\n a, x, b = fib2, fib1, fib0\\n fx = v_prev\\n while x > a + 1 or b > x + 1:\\n u = a + b - x\\n fu = f(step * u + offset)\\n nfev += 1\\n\\n if fu >= fx:\\n if u > x:\\n a, x = x, u\\n fx = fu\\n else:\\n x, b = u, x\\n fx = fu\\n\\n if fx >= vmin:\\n # found upper bound, use binary search to find answer\\n stop = step * x + offset\\n return minimize_cost_binary(f, vmin, start=step * (a + 1) + offset,\\n stop=stop, save=stop, step=step,\\n nfev=nfev)\\n else:\\n if u > x:\\n b = u\\n else:\\n a = u\\n\\n # golden section search terminated, the maximum is less than vmin\\n return MinCostResult(x=None, xmax=step * x + offset, vmax=fx, nfev=nfev)\\n else:\\n # still not close to maximum, continue searching\\n vmax = v_prev = v_cur\\n xmax = fib0\\n cur_idx += 1\\n if cur_idx <= 3:\\n fib2, fib1, fib0 = fib1, fib0, cur_idx\\n else:\\n fib2, fib1, fib0 = fib1, fib0, fib1 + fib0\\n\\n raise ValueError('Maximum number of iteration achieved')\",\n \"def weibull(self, x):\\n return (self.k_alp[0]/self.k_alp[1])*(math.pow(x/self.k_alp[1],(self.k_alp[0]-1))) * \\\\\\n np.exp(-1*math.pow(x/self.k_alp[1],self.k_alp[0]))\",\n \"def romberg(f, a, b, n=20):\\n R = [[0.5 * (b - a) * (f(a) + f(b))]] # R[0][0]\\n for n in range (1,n+1):\\n h = float(b-a)/2**n\\n R.append((n+1)*[None]) # Add an empty row.\\n R[n][0] = 0.5*R[n-1][0] + h*sum(f(a+(2*k-1)*h) for k in range(1, 2**(n-1)+1)) # for proper limits\\n for m in range(1, n+1):\\n R[n][m] = R[n][m-1] + (R[n][m-1] - R[n-1][m-1]) / (4**m - 1)\\n return R[n][n]\",\n \"def griewank(x):\\n nopt = np.size(x)\\n # if (nopt == 2) | (nopt == 10):\\n xx = x\\n if nopt == 2:\\n d = 200.0\\n else:\\n d = 4000.0\\n\\n u1 = 0.0\\n u2 = 1.0\\n for j in range(nopt):\\n u1 = u1 + xx[j]**2 / d\\n u2 = u2 * np.cos(xx[j] / np.sqrt(float(j + 1)))\\n\\n f = u1 - u2 + 1\\n return f\",\n \"def f(i):\\n return e(2**N-1-i) ^ 2**(N-1)\",\n \"def lowest_rank_approx(A,e):\",\n \"def bpmfln(k, n, p):\\n return np.exp(combinln(n, k) + k * np.log(p) + (n - k) * np.log(1 - p))\",\n \"def sig_func(x, w, b):\\n z = np.dot(x, np.transpose(w)) + b\\n try:\\n s = 1 / (1 + exp(-z))\\n except OverflowError:\\n s = float('inf')\\n return s\",\n \"def f(p, phi, phib, df):\\n\\treturn -log(p) - df + (p-1)*phi + \\\\\\n\\t ( phi*(1-p) + phib + \\\\\\n\\t 5./4*alpha*(phi*p)**(9./4)-(9./4)*alpha*(p*phi)**(5./4) - \\\\\\n\\t (1./2)(1-p*phi)**2 - (phib/Nb)-5./4*alpha*(phi+phib)**(9./4) + \\\\\\n\\t (9./4)*alpha*(phi+phib)**(5.4) + \\\\\\n\\t 1./2*(1-phi-phib)**2 ) * Ns\",\n \"def solve_huang_eq_13(v):\\n\\n n = len(v)\\n u = v - np.ones((n, n)) @ v / (n) + np.ones(n) / (n)\\n lambda_bar_star = solve_huang_eq_24(u)\\n lambda_star = (lambda_bar_star - u) + np.clip(u - lambda_bar_star, 0, None)\\n return u + lambda_star - lambda_bar_star * np.ones(n)\",\n \"def _bernoulli_lower(self, p, n, delta):\\n if p < 1e-6:\\n return 0.\\n else:\\n lower = scipy.stats.beta.ppf(delta / 2, p * n, n - p * n + 1)\\n return lower\",\n \"def halving_sum(n):\\n if n == 1:\\n return 1\\n else:\\n return n + halving_sum(n // 2)\",\n \"def normal_approximation_to_binomial(n, p):\\r\\n mu = p*n\\r\\n sigma = math.sqrt(p * (1 - p) * n)\\r\\n\\r\\n return mu, sigma\",\n \"def lehmer(n):\\n a = np.outer(np.ones(n), np.arange(1, n + 1))\\n a = a / a.T\\n a = np.tril(a) + np.tril(a, -1).T\\n\\n return a\",\n \"def bn(x, e=1e-7):\\n mu = np.mean(x)\\n sigma2 = np.var(x)\\n x_ = (x - mu) / np.sqrt(sigma2 + e)\\n return x_\",\n \"def Gaussiankernel(size, sigma=1): \\n size = int(size) // 2\\n # create x grid and y grid\\n x, y = np.mgrid[-size:size+1, -size:size+1] \\n # gaussian distribution formula\\n normal = 1 / np.sqrt(2.0 * np.pi * sigma**2)\\n g = np.exp(-((x**2 + y**2) / (2.0*sigma**2))) * normal\\n \\n return g/g.sum()\",\n \"def kernel_sigmas(n_kernels):\\n bin_size = 2.0 / (n_kernels - 1)\\n l_sigma = [0.001] # for exact match. small variance -> exact match\\n if n_kernels == 1:\\n return l_sigma\\n\\n l_sigma += [0.1] * (n_kernels - 1)\\n print(l_sigma)\\n return l_sigma\",\n \"def B(n, k):\\n assert 0 < k <= n\\n global lookup\\n for index_y in range(len(lookup), n + 1):\\n lookup.append([1])\\n min_value = min(index_y, k)\\n for index_x in range(min_value):\\n if index_x < len(lookup[index_y - 1]) - 1:\\n lookup[index_y].append(lookup[index_y - 1][index_x] + lookup[index_y - 1][index_x + 1])\\n else:\\n lookup[index_y].append(lookup[index_y - 1][index_x])\\n return lookup[n][k]\",\n \"def N_gfun(self,y):\\n return 0.0\",\n \"def _lowess_bisquare(t):\\n #t = (1-t**2)**2\\n t *= t\\n t[:] = np.negative(t) #, out=t)\\n t += 1\\n t *= t\",\n \"def approx_sigmoid(x, n=3):\\n if n == 5:\\n cuts = [-5, -2.5, 2.5, 5]\\n le = [0] + [x <= cut for cut in cuts] + [1]\\n select = [le[i + 1] - le[i] for i in range(5)]\\n outputs = [cfix(10 ** -4),\\n 0.02776 * x + 0.145,\\n 0.17 * x + 0.5,\\n 0.02776 * x + 0.85498,\\n cfix(1 - 10 ** -4)]\\n return sum(a * b for a, b in zip(select, outputs))\\n else:\\n a = x < -0.5\\n b = x > 0.5\\n return a.if_else(0, b.if_else(1, 0.5 + x))\",\n \"def approx_sigmoid(x, n=3):\\n if n == 5:\\n cuts = [-5, -2.5, 2.5, 5]\\n le = [0] + [x <= cut for cut in cuts] + [1]\\n select = [le[i + 1] - le[i] for i in range(5)]\\n outputs = [cfix(10 ** -4),\\n 0.02776 * x + 0.145,\\n 0.17 * x + 0.5,\\n 0.02776 * x + 0.85498,\\n cfix(1 - 10 ** -4)]\\n return sum(a * b for a, b in zip(select, outputs))\\n else:\\n a = x < -0.5\\n b = x > 0.5\\n return a.if_else(0, b.if_else(1, 0.5 + x))\",\n \"def g1(s,height=height,k1=k1,k2=k2,peak_0=peak_0):\\r\\n if (isinstance(s,float)): # Datatype test \\r\\n if(s<peak_0): # Piecewise function\\r\\n return height*np.exp(k1*(s-peak_0)) # Left side of the peak\\r\\n else:\\r\\n return height*np.exp(k2*(peak_0-s)) # Right side of the peak\\r\\n else:\\r\\n ret = np.zeros(len(s),dtype=float)\\r\\n ret[s<peak_0] = height*np.exp(k1*(s[s<peak_0]-peak_0))\\r\\n ret[s>=peak_0] = height*np.exp(k2*(peak_0-s[s>=peak_0]))\\r\\n return ret\",\n \"def exterior(n):\\n\\n def inner(x):\\n return n * x\\n\\n return inner\",\n \"def bdq1(f, x, h=1e-5):\\n return (f(x)-f(x-h))/h\\n raise NotImplementedError(\\\"Problem 2 Incomplete\\\")\",\n \"def gridded_1d(q, n=300):\\n theta = np.linspace(THETA_LOW, THETA_HIGH, n)\\n Zq = kernel_1d(q=q, theta=theta)\\n Zq *= abs(sin(theta))\\n dx = theta[1]-theta[0]\\n print(\\\"rect-%d\\\"%n, np.sum(Zq)*dx*SCALE)\\n print(\\\"trapz-%d\\\"%n, np.trapz(Zq, dx=dx)*SCALE)\\n print(\\\"simpson-%d\\\"%n, simps(Zq, dx=dx)*SCALE)\\n print(\\\"romb-%d\\\"%n, romb(Zq, dx=dx)*SCALE)\",\n \"def minimum_spanning_arborescence(sol):\",\n \"def shannons(data):\\n \\n from math import log as ln\\n\\n # from IPython.core.debugger import Tracer\\n # Tracer()()\\n \\n def p(n, N):\\n \\\"\\\"\\\" Relative abundance \\\"\\\"\\\"\\n if n is 0:\\n return 0\\n else:\\n return (float(n)/N) * ln(float(n)/N)\\n \\n N = sum(data)\\n \\n return -sum(p(n, N) for n in data if not n == 0)\",\n \"def value(a, y, weights, lambda_):\\n\\t\\treturn np.sum(np.nan_to_num(-y*np.log(a + 1e-15)-(1-y)*np.log(1-a + 1e-15))) / (a.shape[0] * a.shape[1])\\n\\t\\t# return unregularize + (0.5*lambda_*np.sum(np.square(weights[-1])) / (a.shape[0] * a.shape[1])) \",\n \"def L(n):\\n\\tif (n==0):\\n\\t\\treturn lambda x: 1.0\\n\\n\\telif (n==1):\\n\\t\\treturn lambda x: x\\n\\n\\telse:\\n\\t\\treturn lambda x: ( (2.0*n-1.0) * x * L(n-1)(x)-(n-1) * L(n-2)(x) ) / n\",\n \"def _bernoulli_upper(self, p, n, delta):\\n if p > 1 - 1e-6:\\n return 1.\\n else:\\n upper = scipy.stats.beta.ppf(1. - delta / 2, p * n + 1, n - p * n)\\n return upper\",\n \"def slerp_gaussian(val, low, high):\\n offset = norm.cdf(np.zeros_like(low)) # offset is just [0.5, 0.5, ...]\\n low_gau_shifted = norm.cdf(low) - offset\\n high_gau_shifted = norm.cdf(high) - offset\\n circle_lerped_gau = slerp(val, low_gau_shifted, high_gau_shifted)\\n epsilon = 0.001\\n clipped_sum = np.clip(circle_lerped_gau + offset, epsilon, 1.0 - epsilon)\\n result = norm.ppf(clipped_sum)\\n return result\",\n \"def Z(n):\\n count5 = 0\\n i = 1\\n while 1:\\n a = pow(5, i)\\n if a > n:\\n return count5\\n else:\\n count5 += n/a\\n i += 1\",\n \"def __R1(x: float) -> np.float64:\\n if np.abs(x) < end:\\n return np.float64((1 / np.pi) * np.sqrt(2 * N - x * x))\\n return np.float64(0.0)\",\n \"def normal_approximation_to_binomial(n, p):\\n mu = p * n\\n sigma = math.sqrt(p * (1 - p) * n)\\n return mu, sigma\",\n \"def normal_approximation_to_binomial(n, p):\\n mu = p * n\\n sigma = math.sqrt(p * (1 - p) * n)\\n return mu, sigma\",\n \"def normal_approximation_to_binomial(n, p):\\n mu = p * n\\n sigma = math.sqrt(p * (1 - p) * n)\\n return mu, sigma\",\n \"def normal_approximation_to_binomial(n, p):\\n mu = p * n\\n sigma = math.sqrt(p * (1 - p) * n)\\n return mu, sigma\",\n \"def g(f, x: float):\\n return lambda x: f(x + f(x)) / f(x) - 1\",\n \"def normal_approximation_to_binomial(n, p):\\n mu = p * n\\n sigma = math.sqrt(p * (1 - p ) * n)\\n return mu, sigma\",\n \"def stick_breaking(n:int,alpha:float) -> np.ndarray:\\n betas = G.beta(a=alpha, b=1.0, size=n)\\n betas[1:] *= np.cumprod(1-betas[:-1])\\n weights = np.sort(betas)[::-1]\\n return weights\",\n \"def sigma_fatigue(N):\\n return 855 * N **(-0.109)\",\n \"def inner_func_1(z,n):\\r\\n return ((psi(n,(z/(1-z**2))))**2)*((z/(1-z**2))**2)*((1+z**2)/((1-z**2)**2))\",\n \"def Gaussian_conv(n):\\n\\n\\t# Choose sigma and mu\\n\\tsigma = 1\\n\\tmu = 0\\n\\n\\t# Create table\\n\\tb = sigma * np.random.randn(n) + mu\\n\\tsum = b.sum()\\n\\t#b = b/sum\\n\\tprint(b)\\n\\treturn b\",\n \"def zzx_mignotte_bound(f):\\n a = zzx_max_norm(f)\\n b = abs(poly_LC(f))\\n n = zzx_degree(f)\\n\\n return INT_TYPE(isqrt(n+1))*2**n*a*b\",\n \"def transsinh_(b=None):\\n rlim=1.e-6\\n nmax=300\\n delta=1.0\\n delta_old=delta\\n for n in range(nmax):\\n f=delta / (np.sinh(delta) + 1.0 * 10 ** (- 60)) - 1.0 / b\\n df=(np.sinh(delta) - delta * np.cosh(delta)) / np.sinh(delta) ** 2\\n delta=delta - 0.5 * f / df\\n if ((n == nmax) and (np.abs((delta - delta_old) / delta_old) > rlim)):\\n disp_([char(' Convergence problem in transsinh dist. function !!! ')])\\n disp_([char(' residual '),num2str_((delta - delta_old) / delta_old)])\\n delta_old=delta\\n return delta\",\n \"def simp(fun, a, b):\\n h = 0.5*(b-a)\\n c = 0.5*(a+b)\\n return (h/3)*(fun(a) + 4*fun(c) + fun(b))\",\n \"def get_blur_kernel(n):\\n return [1/n**2] * n**2\",\n \"def analyticSol (x):\\n\\treturn x*(1-x);\",\n \"def algorithm_2_21(p, t, x):\\n\\n t = np.array(t, dtype=np.float64)\\n b = 1\\n mu = index(x, t)\\n\\n for k in range(1, p + 1):\\n t1 = t[mu - k + 1:mu + 1]\\n t2 = t[mu + 1:mu + k + 1]\\n omega = np.divide(\\n (x - t1), (t2 - t1), out=np.zeros_like(t1), where=((t2 - t1) != 0))\\n b = np.append((1 - omega) * b, 0) + np.insert((omega * b), 0, 0)\\n\\n return b\",\n \"def fakultet (n = 1):\\n sum = 1\\n for i in range(n, 1, -1):\\n sum *= i\\n return sum\",\n \"def powAlpha( n ):\\n return (1-betaval)*Fib(n) + Fib(n-1)\\n #return Fib(n+1) - Fib(n) * betaval\",\n \"def APH2ndMomentLowerBound (m1, n):\\n\\n return float(m1)*m1*(n+1) / n\",\n \"def function_to_minimize(x):\\n return math.sin(x[0]) * math.cos(x[1]) + math.cos(x[0] + x[1]) + random.uniform(-0.02, 0.02)\",\n \"def bezier(i, n, t):\\n return binomial(n, i) * pow(t, i) * pow(1 - t, n - i)\",\n \"def tanh(a):\",\n \"def smooth_slowsignal(a, n=10):\\n ret = np.cumsum(a, axis=0, dtype=float)\\n ret[n:] = ret[n:] - ret[:-n]\\n return ret[n - 1 :] / n\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.5800495","0.57387626","0.56889313","0.56861275","0.56719846","0.5661993","0.5647246","0.56123304","0.56039697","0.55897033","0.55779684","0.556688","0.5564874","0.5559421","0.55498475","0.5544323","0.55239946","0.55232066","0.5518701","0.5500929","0.5491718","0.5489114","0.5477032","0.54716367","0.5461163","0.5457648","0.5451791","0.5440173","0.5438299","0.54196936","0.539126","0.5388475","0.53796","0.5374103","0.5374062","0.5370422","0.5370422","0.53660583","0.53483593","0.53471214","0.53365767","0.53364635","0.53347254","0.5325665","0.53218716","0.5321869","0.5321317","0.53123486","0.5296293","0.52886283","0.52871895","0.52830213","0.52769125","0.52740204","0.52700144","0.526712","0.52667385","0.52653974","0.5263186","0.52623826","0.52608943","0.52567035","0.5253494","0.52528995","0.52528995","0.5252898","0.52519673","0.5247765","0.5244865","0.523987","0.52396667","0.52288914","0.5227789","0.52220494","0.5221353","0.5220771","0.52190757","0.52105147","0.52105147","0.52105147","0.52105147","0.5205661","0.5201102","0.5198663","0.51959306","0.5194009","0.51928455","0.51900065","0.5186247","0.5185582","0.5184882","0.518291","0.5182425","0.5179423","0.51771635","0.51766133","0.51700777","0.5169443","0.5165085","0.5164079"],"string":"[\n \"0.5800495\",\n \"0.57387626\",\n \"0.56889313\",\n \"0.56861275\",\n \"0.56719846\",\n \"0.5661993\",\n \"0.5647246\",\n \"0.56123304\",\n \"0.56039697\",\n \"0.55897033\",\n \"0.55779684\",\n \"0.556688\",\n \"0.5564874\",\n \"0.5559421\",\n \"0.55498475\",\n \"0.5544323\",\n \"0.55239946\",\n \"0.55232066\",\n \"0.5518701\",\n \"0.5500929\",\n \"0.5491718\",\n \"0.5489114\",\n \"0.5477032\",\n \"0.54716367\",\n \"0.5461163\",\n \"0.5457648\",\n \"0.5451791\",\n \"0.5440173\",\n \"0.5438299\",\n \"0.54196936\",\n \"0.539126\",\n \"0.5388475\",\n \"0.53796\",\n \"0.5374103\",\n \"0.5374062\",\n \"0.5370422\",\n \"0.5370422\",\n \"0.53660583\",\n \"0.53483593\",\n \"0.53471214\",\n \"0.53365767\",\n \"0.53364635\",\n \"0.53347254\",\n \"0.5325665\",\n \"0.53218716\",\n \"0.5321869\",\n \"0.5321317\",\n \"0.53123486\",\n \"0.5296293\",\n \"0.52886283\",\n \"0.52871895\",\n \"0.52830213\",\n \"0.52769125\",\n \"0.52740204\",\n \"0.52700144\",\n \"0.526712\",\n \"0.52667385\",\n \"0.52653974\",\n \"0.5263186\",\n \"0.52623826\",\n \"0.52608943\",\n \"0.52567035\",\n \"0.5253494\",\n \"0.52528995\",\n \"0.52528995\",\n \"0.5252898\",\n \"0.52519673\",\n \"0.5247765\",\n \"0.5244865\",\n \"0.523987\",\n \"0.52396667\",\n \"0.52288914\",\n \"0.5227789\",\n \"0.52220494\",\n \"0.5221353\",\n \"0.5220771\",\n \"0.52190757\",\n \"0.52105147\",\n \"0.52105147\",\n \"0.52105147\",\n \"0.52105147\",\n \"0.5205661\",\n \"0.5201102\",\n \"0.5198663\",\n \"0.51959306\",\n \"0.5194009\",\n \"0.51928455\",\n \"0.51900065\",\n \"0.5186247\",\n \"0.5185582\",\n \"0.5184882\",\n \"0.518291\",\n \"0.5182425\",\n \"0.5179423\",\n \"0.51771635\",\n \"0.51766133\",\n \"0.51700777\",\n \"0.5169443\",\n \"0.5165085\",\n \"0.5164079\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.71997106"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":26098,"cells":{"query":{"kind":"string","value":"Numerically stable computation of Shannon's entropy for probability distributions with zerovalued elements."},"document":{"kind":"string","value":"def entropy(p: torch.Tensor):\n nz = (p > 0).to(p.device)\n\n eps = torch.finfo(p.dtype).eps\n p_stable = p.clone().clamp(min=eps, max=1 - eps)\n\n out = torch.where(\n nz,\n p_stable * torch.log(p_stable),\n torch.tensor(0.0, device=p.device, dtype=torch.float),\n )\n\n return -(out).sum(-1)"},"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 entropy(x):\n nz = np.nonzero(x)[0]\n return -np.sum(x[nz]*np.log2(x[nz]))","def shannon_entropy(probs):\n return -(\n math.sum([px * math.log2(px) if px != 0 and not (np.isclose(px, 0)) else 0 for px in probs])\n )","def entropy(P):\n P_nan = P.copy()\n P_nan[P_nan == 0] = np.nan\n return np.nansum(np.multiply(P_nan, np.log2(1 / P_nan)))","def entropy(x):\n x_max, x_min = x.max(), x.min()\n assert (x_min >= 0) and (x_max <= 1)\n if x_min == x_max == 0:\n return np.float32(0.)\n # Take only non-zero values as log(0) = 0 :\n nnz_x = x[np.nonzero(x)]\n entr = -np.sum(nnz_x * np.log2(nnz_x))\n\n return entr","def shannon_entropy(counts):\n freq = np.array(counts) * 1.0 / np.sum(counts)\n return -np.sum([f * np.log2(f) for f in freq if f != 0])","def entropy(data):\n n, m = np.shape(data)\n data = np.tanh(data)\n data = data / np.sum(data, axis=0)\n a = data * 1.0\n a[np.where(data == 0)] = 0.000001\n\n e = (-1.0 / np.log(n)) * np.sum(data * np.log(a), axis=0)\n w = (1 - e) / np.sum(1 - e)\n return w","def entropy(self, priors=None):\n def entropy_f(x):\n x[x != 0] *= np.log(x[x != 0])\n return -x.sum(axis=0)\n return self.utility(entropy_f, priors)","def entropy(self):\n Z = self.sum()\n assert (Z > 0), 'Non-normalizable factor (perhaps log factor?)' # also check for positivity?\n H = 0.0\n for x in np.nditer(self.t, op_flags=['readonly']):\n p = x/Z\n H += 0.0 if p==0 else -p*np.log(p)\n return H","def entropyDistributed(distribution):\n return -sum(map(lambda p : p * log(p, 2), distribution))","def entropy(distribution, unit=2):\n frequencies = distribution.frequencies(normalised=True)\n # check to see if it is a deterministic case (all but one are zero)\n zeros_size = frequencies[frequencies == 0].size\n if zeros_size + 1 == frequencies.size:\n return 0\n else:\n return np.sum(-frequencies * np.log2(frequencies) / np.log2(unit))","def entropy(y):\n EPS = 0.0005\n\n # YOUR CODE HERE\n if len(y) == 0:\n return 0.\n \n pk = np.mean(y, axis=0)\n \n return - np.sum(pk * np.log(pk + EPS))","def entropy_(P):\n res = 0.0\n\n mask = P != 0.0 # avoid 0 in log\n f = lambda x: x*np.log2(x)\n # map-reduce strategy (likely to be more optimized than loops)\n temp = list(map(f, P[mask]))\n res = -np.sum(temp, dtype=float)\n return res","def shannon_entropy(c):\n\n c_normalized = c / float(np.sum(c))\n c_normalized_nonzero = c_normalized[np.nonzero(c_normalized)] # gives 1D array\n entropy = -sum(c_normalized_nonzero * np.log2(c_normalized_nonzero)) # unit in bits\n return entropy","def entropy(counts):\n assert (counts >= 0).all()\n probs = counts / counts.sum()\n probs = probs[probs > 0] # Avoid log(0)\n return - np.sum(probs * np.log2(probs))","def _entropy(data):\n hist = np.array(PIL.Image.fromarray(data).histogram())\n hist = hist / hist.sum()\n hist = hist[hist != 0]\n return -np.sum(hist * np.log2(hist))","def entropy(probabilities):\n return -(sum([p * log(p, 2) if p > 0 else 0 for p in probabilities]))","def ShannonEntropy(self,s):\n e = s[np.nonzero(s)]**2 * np.log(s[np.nonzero(s)]**2)\n return np.sum(e)","def entropy(p):\n assert (p >= 0).all()\n assert abs(np.sum(p)-1) < 1e-6\n return -np.sum(p*np.log(p+1e-12))","def entropy(dist):\n #dist = array([max(d,1e-100) for d in dist])\n dist = dist + 1e-20\n return dot(dist,(log(1.0/dist) * (1.0/log(2.0))).T)","def shannon_entropy(ps: np.ndarray, base: int = 2) -> float:\n\n return -np.sum(ps * np.log(ps) / np.log(base))","def get_entropy(distribution, samples):\n entropy = -tf.reduce_sum(distribution.log_prob(samples), axis=1)\n return entropy","def entropycell(self):\n cells = [0] * self.k\n for i in range(self.width):\n cells[int(self.config[self.t, i])] += 1\n\n \"\"\"Calculates the Shannon entropy and the the average entropy so far.\"\"\"\n shannon = 0\n for i in range(self.k):\n if(cells[i] != 0):\n probability = cells[i] / self.width\n shannon -= probability * np.log2(probability)\n self.average_cell = (self.average_cell * self.t + shannon) / (self.t + 1)","def entropy(temp,pres):\n g_t = liq_g(1,0,temp,pres)\n s = -g_t\n return s","def entropy(data):\n\n freqs = {}\n suma = len(data)\n\n for i in range(0, len(data)):\n freqs[data[i]] = 1.0 + freqs.get(data[i], 0)\n\n res = 0.0\n for i in freqs:\n res += (freqs[i] / suma) * log((freqs[i] / suma), 2)\n return -res","def entropy(img):\n # by calculating\n histogram = img.histogram()\n histogram_size = sum(histogram)\n histogram = [float(h) / histogram_size for h in histogram]\n\n return -sum([p * math.log(p, 2) for p in histogram if p != 0])","def entropyRandom(stream):\n prob = 1.0 / len(stream)\n return -(prob * log(prob, 2)) * len(stream)","def entropy(data):\n e = 0\n\n counter = collections.Counter(data)\n l = len(data)\n for count in counter.values():\n p_x = count / l\n e += - p_x * math.log2(p_x)\n\n return e","def entropy(l):\n\n probabilities = np.bincount(l) / len(l)\n with np.errstate(divide='ignore'): # ignore log(0) errors, we'll handle\n log_probabilities = np.log2(probabilities)\n log_probabilities[~np.isfinite(log_probabilities)] = 0\n return -np.sum(probabilities * log_probabilities)","def __compute_entropy_probability(probability:np.ndarray) -> float:\n entropy = -np.sum(probability * np.log2(probability))\n return entropy","def entropy(y):\n p = _proba(y)\n return (-p * np.log2(p)).sum()","def entropy(self):\n ent = 0.0\n for f in self.byte_freq:\n if f > 0:\n freq = float(f) / self.byte_total\n ent = ent + freq * math.log(freq, 2)\n return -ent","def entropy(y):\r\n\r\n # INSERT YOUR CODE HERE\r\n value, count = np.unique(y,return_counts = True)\r\n Hy = 0.0\r\n prob = count.astype(float)/len(y)\r\n for p in prob:\r\n Hy += -(p)*(np.log2(p))\r\n return Hy\r\n raise Exception('Function not yet implemented!')","def entropy(y):\n return -1 * sum(\n [\n pipe(np.sum(y == value) / len(y), lambda ratio: ratio * np.log(ratio))\n for value in set(y)\n ]\n )","def entropy(s):\n p, lns = Counter(s), float(len(s))\n return -sum( count/lns * math.log(count/lns, 2) for count in p.values())","def compute_entropy(bincounts, include_zeros):\n num_total = tf.cast(tf.reduce_sum(bincounts), tf.float64)\n if not include_zeros:\n bincounts = bincounts[1:]\n mask = tf.greater(bincounts, 0)\n nonzero_bincounts = tf.cast(\n tf.boolean_mask(bincounts, mask), tf.float64)\n num_nonzero = tf.cast(tf.reduce_sum(nonzero_bincounts), tf.float64)\n log_nonzero_bincounts = tf.math.log(nonzero_bincounts)\n log_prob = log_nonzero_bincounts - tf.reduce_logsumexp(\n log_nonzero_bincounts)\n entropy = tf.math.reduce_sum(\n log_prob * tf.exp(log_prob)) / -tf.math.log(tf.cast(2, tf.float64))\n return entropy * num_nonzero / num_total","def entropy(self):\n return -np.sum(self.log_likelihoods * np.exp(self.log_likelihoods))","def entropy(p):\n ent = tf.where(p > np.finfo(np.float32).eps, -p * tf.log(p), tf.zeros_like(p))\n ent = tf.reduce_sum(ent, axis=1)\n return ent","def entropy(self):\n raise NotImplementedError","def entropy_py(p):\n return 2 ** np.sum(-p*np.log2(p+1e-10))","def entropy(self, params):\n log_std = params[:, :, 1]\n return (log_std + 0.5 * (self.LOG2PI + 1)).sum(dim=-1)","def entropy_numba(p):\n return 2 ** np.sum(-p*np.log2(p+1e-10))","def entropy(Y):\n\n temp = np.unique(Y, return_counts=True)\n uniq_Y = list(temp[0])\n Y_count = list(temp[1])\n \n total = sum(Y_count)\n\n ent = 0\n for elem in uniq_Y:\n prob = Y_count[uniq_Y.index(elem)] / total\n # print(\"prob:\", prob)\n ent -= (prob * (math.log2(prob)))\n # print(\"ent:\",ent)\n\n return ent","def entropy(y):\n total = y.size\n value_counts = np.bincount(y).astype(\"float\")\n proportions = value_counts / y.size\n\n return sum(-i * np.log(i) for i in proportions if i)","def entropy(p_list):\n assert len(p_list) > 0\n E = 0.0\n for p in p_list:\n if p == 0.0:\n continue\n E += p*math.log(p)\n return E","def entropy_root(self):\n\t\tif self.size() == 0:\n\t\t\treturn 0\n\n\t\tcounter = collections.Counter(self.y)\n\t\treturn entropy([x[1] for x in counter.items()], self.size())","def entropy(self):\n\n \"\"\"Gets the first neighbours, which are the first 2*r+1 cells.\"\"\"\n current_neighbours = []\n amount = [0] * self.k ** (2 * self.r + 1)\n for i in range(2 * self.r + 1):\n current_neighbours.append(self.config[self.t, i % self.width])\n\n \"\"\"Calculates the rule and adds one to it's amount. It then removes the\n leftmost cell and adds a cell to the right.\"\"\"\n for i in range(len(self.config[self.t]) - 1):\n rule = 0\n for j in range(len(current_neighbours)):\n rule += int(current_neighbours[j] *\n self.k ** ((2 * self.r + 1) - j - 1))\n amount[len(amount) - 1 - rule] += 1\n current_neighbours.pop(0)\n current_neighbours.append(\n self.config[self.t, (2 * self.r + 1 + i) % self.width])\n\n \"\"\"Calculates the rule for the last neighbourhood.\"\"\"\n rule = 0\n for j in range(len(current_neighbours)):\n rule += int(current_neighbours[j] *\n self.k ** ((2 * self.r + 1) - j - 1))\n amount[len(amount)-1 - rule] += 1\n\n \"\"\"Calculates the Shannon entropy and the the average entropy so far.\"\"\"\n shannon = 0\n for i in range(len(amount)):\n if(amount[i] != 0):\n probability = amount[i] / self.width\n shannon -= probability * np.log2(probability)\n self.average_entropy = (self.average_entropy *\n self.t + shannon) / (self.t + 1)","def calculate_entropy(prob):\n return -(prob * math.log(prob,2))","def entropy(*args):\n\n\n values = []\n leaf = -1\n\n for i, val in enumerate(args):\n if(val != 0):\n values.append(val * math.log(val, len(args)))\n if(val == 1):\n leaf = i\n \n return -sum(values), leaf","def entropy(self):\n n = len(self.y)\n sum_ = 0\n for i in np.unique(self.y):\n v = len(self.y[self.y == i])\n sum_ += -((v/n) * log2(v/n))\n return sum_","def entropy(out, dim=1, reduce='mean'):\n log_prob = F.log_softmax(out, dim=dim)\n h = -torch.sum(log_prob.exp() * log_prob, dim=dim)\n if reduce == 'none':\n return h\n if reduce == 'mean':\n return h.mean()\n if reduce == 'sum':\n return h.sum()","def image_entropy(img):\r\n hist = img.histogram()\r\n hist_size = sum(hist)\r\n hist = [float(h) / hist_size for h in hist]\r\n\r\n return -sum([p * math.log(p, 2) for p in hist if p != 0])","def calc_entropy_one(states, T):\n\n # S = - kB \\sum_i p_i \\log p_i = - k_B <\\log p_i>\n P = calc_probabilities_one(states, T)\n return -kB * np.sum(P * np.log(P))","def joint_entropy(P):\n P_nan = P.copy()\n P_nan[P_nan == 0] = np.nan\n return np.nansum(np.multiply(P_nan, np.log2(1 / P_nan)))","def entropy ( target_array ):\n return -1 * sum (\n [\n pipe ( np.sum ( target_array == value ) / len ( target_array ), lambda ratio: ratio * np.log ( ratio ) )\n for value in set ( target_array )\n ]\n ) # End entropy()","def entropy(self, y):\n n = y.size\n if n <= 1:\n return 0\n\n labels, counts = unique(y, return_counts=True)\n\n if counts.size <= 1:\n return 0\n\n probs = counts / n\n entropy = -sum([p * log(p, 2) for p in probs])\n return entropy","def EntropyD(dist): \n if np.any(dist>0):\n dist2 = dist/np.sum(dist)\n return -np.sum(dist2[dist2>0]*np.log2(dist2[dist2>0]))\n else:\n return 0","def compute_entropy(m, nan_as_zero=True):\n # normalize rows (the sum of each row must be 1)\n p = m / m.sum(axis=1, keepdims=True)\n # compute per row entropy (in base 2)\n e = entr(p).sum(axis=1) / np.log(2)\n if nan_as_zero:\n e = np.nan_to_num(e)\n return e","def conditional_entropy(self) -> float:\n pass","def entropy(class_probabilities):\n return sum(-p * math.log(p, 2)\n for p in class_probabilities\n if p) #ignore 0's","def entropy(self, **kwargs) -> TensorType:","def entropy(self, **kwargs) -> TensorType:","def entropy(data):\n strings, lens = Counter(data), np.float(len(data))\n return -sum(count / lens * np.log2(count / lens) for count in strings.values())","def entropy_function(c, n):\n return -(c*1.0/n)*math.log(c*1.0/n,2)","def entropy_fn(args: StepFunctionArgs) -> SingleScorePerStepTensor:\n logits = args.attribution_model.output2logits(args.forward_output)\n out = torch.distributions.Categorical(logits=logits).entropy()\n if out.ndim > 1:\n out = out.squeeze(-1)\n return out","def entropy(self):\r\n return 1/2 * (self.dim * (_LOG_2PI + 1) + self._log_det_cov)","def entropy(n_bits):\n return n_bits and random.getrandbits(n_bits)","def conditional_entropy_hyper(self) -> float:\n pass","def test_dc():\n signal = np.zeros(100)\n pe = permutation_entropy(signal, 2)\n\n assert pe == 0","def getEntropy(self, pVal, nVal):\n totVal = pVal + nVal\n if pVal == 0 or nVal == 0:\n return 0\n\n pProb = pVal/totVal\n nProb = 1 - pProb\n entropy = - (pProb * math.log(pProb, 2) + nProb * math.log(nProb, 2))\n return entropy","def entropy(self):\n return self._normal.entropy()","def entropy(Y):\n unique, count = np.unique(Y, return_counts=True, axis=0)\n prob = count/len(Y)\n en = np.sum((-1)*prob*np.log2(prob))\n return en","def entropy( freq ):\n N = 0.0\n entropy = 0.0\n for x, v in freq.items( ):\n N += v\n entropy -= v * math.log( v, 2 )\n return (N * math.log( N, 2 ) + entropy) / N","def entropy(x, bins, normalize=False, xy_probabilities=False):\n # calculate probabilities if xy_probabilities == False\n if xy_probabilities:\n # if x does not sum up to 1, raise an error\n if not np.isclose(sum(x),1,atol=0.0001):\n raise ValueError('Probabilities in vector x do not sum up to 1.')\n \n # add a small number to all probabilities if zero occurs\n if x.any(0):\n p = x + 1e-15\n else:\n p = x\n else:\n # get the bins\n bins = np.histogram_bin_edges(x, bins)\n\n # calculate the empirical probabilities\n count = np.histogram(x, bins=bins)[0]\n\n # if counts should be None, raise an error\n if np.sum(count) == 0:\n raise ValueError('The histogram cannot be empty. Adjust the bins to ' +\n 'fit the data')\n # calculate the probabilities\n p = (count / np.sum(count)) + 1e-15\n\n\n # calculate the Shannon Entropy\n if normalize:\n # get number of bins\n nbins = len(p)\n # maximal entropy: uniform distribution\n normalizer = np.log2(nbins) \n\n return - p.dot(np.log2(p)) / normalizer\n else:\n return - p.dot(np.log2(p))","def entropy(data, idxList):\n df = data.loc[idxList]\n counts = df.value_counts().to_numpy()\n counts = counts.reshape(1, -1).astype(np.float32)\n counts /= np.sum(counts)\n log_sum = counts @ np.log2(counts.T)\n return -log_sum[0, 0]","def entropy(message):\n n = len(message)\n message = letter_freq(message)\n h = 0\n for n_i in message.values():\n p_i = n_i/n\n h += -p_i*(log2(p_i))\n return h","def _graph_fn_entropy(distribution):\n return distribution.entropy()","def _entropy(P):\n\n #TODO remove the \"+ 1e-20\" inside the log2 computation\n # it's just a hack to avoid to compute log2(0)\n ent = -1.0 * np.sum(P * np.log2(P+1e-20), axis=0)\n return ent","def entropy(self, X):\n if isinstance(X, np.ndarray):\n X = pd.DataFrame(X, index=[str(i) for i in range(len(X))])\n K = self._posterior_covariance(X)\n L = np.linalg.cholesky(K)\n D = len(X)\n return np.sum(np.log(np.diag(L))) + 0.5 * D * np.log(2*np.pi*np.exp(1))","def item_entropy(item_count, total_count):\n # Two cases where the entropy is 0\n if item_count == total_count or item_count == 0:\n return 0\n \n item_prob = 1.0 * item_count / total_count\n return -item_prob * math.log(item_prob)","def H(self, data):\n entropy = 0\n\n if not data:\n return entropy\n\n for x in range(256):\n p_x = float(data.count(chr(x))) / len(data)\n if p_x > 0:\n entropy -= p_x * math.log(p_x, 2)\n\n return entropy","def _entropy(self):\n return self.rv.entropy(*self._pymc_dists_to_value(self.args), **self.kwds)","def _theils_u_compute(confmat: Tensor) ->Tensor:\n confmat = _drop_empty_rows_and_cols(confmat)\n s_xy = _conditional_entropy_compute(confmat)\n total_occurrences = confmat.sum()\n p_x = confmat.sum(0) / total_occurrences\n s_x = -torch.sum(p_x * torch.log(p_x))\n if s_x == 0:\n return torch.tensor(0, device=confmat.device)\n return (s_x - s_xy) / s_x","def entropy(self, base: int = None):\n\n # shannon entropy in nats\n fdist_ = self.fdist\n fdist_[\"prob\"] = fdist_[\"freq\"] / fdist_[\"freq\"].sum()\n fdist_[\"logp\"] = np.log(fdist_[\"prob\"])\n fdist_[\"nats\"] = -fdist_[\"prob\"] * fdist_[\"logp\"]\n entropy_ = fdist_[\"nats\"].sum()\n\n # convert base\n if base:\n entropy_ = entropy_ / np.log(base)\n\n # return\n return entropy_","def entropy(self) -> float:\n probabilities = np.array([len(players) for players in self.answers.values()])\n probabilities = probabilities / sum(probabilities)\n return sc.stats.entropy(probabilities)","def entropy(message):\n message = letter_freq(message)\n n = sum(message.values())\n h = 0\n for n_i in message.values():\n p_i = n_i / n\n h += -p_i * log2(p_i)\n return h","def entropy(self, policy_params):\n return self.head.entropy(policy_params)","def _entropy(self, y):\n # Get size\n n = y.shape[0]\n summation = 0\n\n # Summatory\n for c_i in np.unique(y):\n prob = sum(y == c_i) / float(n)\n summation += prob * np.log2(prob)\n\n return -summation","def entropy(self, logits):\n probs = torch.exp(logits)\n entropy = - torch.sum(probs * logits, dim=-1)\n return entropy","def entropy(class_probabilities):\n return sum(-p * math.log(p,2)\n for p in class_probabilities\n if p)","def spatial_entropy(map_):\n map_ = map_ / np.sum(map_)\n return -1 * np.sum(map_ * np.log(map_))","def entropy(self, args):\n mean, stddev = args\n dist = tfp.distributions.Normal(loc=mean, scale=stddev)\n entropy = dist.entropy()\n return entropy","def entropy(a):\n a = a.upper()\n\n freq = collections.defaultdict(int) # int() is the default constructor for non existent item, and returns 0\n for c in a:\n freq[c] = freq[c] + 1\n\n e = 0.0\n for f in freq.values():\n if f:\n p = f / len(a)\n e += p * math.log(p)\n\n return -e","def _conditional_entropy_compute(confmat: Tensor) ->Tensor:\n confmat = _drop_empty_rows_and_cols(confmat)\n total_occurrences = confmat.sum()\n p_xy_m = confmat / total_occurrences\n p_y = confmat.sum(1) / total_occurrences\n p_y_m = p_y.unsqueeze(1).repeat(1, p_xy_m.shape[1])\n return torch.nansum(p_xy_m * torch.log(p_y_m / p_xy_m))","def calc_entropy(data_set): #calculates total entropy of the dataset\r\n republicans = 0\r\n democrats = 0\r\n total = 0\r\n for data_point in data_set:\r\n party = data_point.dat_party\r\n if party == \"R\":\r\n republicans+=1\r\n elif party == \"D\":\r\n democrats+=1\r\n total+=1\r\n\r\n if total == 0: return 0\r\n prob_dem = democrats/total\r\n prob_rep = republicans/total\r\n if prob_dem == 0: return -(prob_rep * math.log(prob_rep, 2))\r\n if prob_rep == 0: return -(prob_dem * math.log(prob_dem, 2))\r\n\r\n entropy = (-prob_dem * math.log(prob_dem, 2)) -(prob_rep * math.log(prob_rep, 2))\r\n return entropy","def __compute_entropy(row:pd.Series) -> float:\n probability = row.value_counts(normalize=True).values\n entropy = EntropicSolver.__compute_entropy_probability(probability)\n return entropy","def div(self):\n freqList = [i / sum(self.has.values()) for i in self.has.values()]\n entropies = [i * math.log(i, 2) for i in freqList]\n entropy = -sum(entropies)\n return entropy","def entropy(image, selem, out=None, mask=None, shift_x=False, shift_y=False):\n\n return _apply(_crank8.entropy, _crank16.entropy, image, selem, out=out,\n mask=mask, shift_x=shift_x, shift_y=shift_y)","def calc_entropy(column):\n # Compute the counts of each unique value in the column\n counts = numpy.bincount(column)\n # Divide by the total column length to get a probability\n probabilities = counts / len(column)\n \n # Initialize the entropy to 0\n entropy = 0\n # Loop through the probabilities, and add each one to the total entropy\n for prob in probabilities:\n if prob > 0:\n entropy += prob * math.log(prob, 2)\n \n return -entropy","def calculate_entropy(reduced_density_matrix_evals):\n vec_xlogx = vectorize(calculate_xlogx)\n result = -sum(vec_xlogx(reduced_density_matrix_evals,\n\t float_info.epsilon))\n return result","def calc_entropy(column):\r\n # Compute the counts of each unique value in the column\r\n counts = numpy.bincount(column)\r\n # Divide by the total column length to get a probability\r\n probabilities = counts / len(column)\r\n \r\n # Initialize the entropy to 0\r\n entropy = 0\r\n # Loop through the probabilities, and add each one to the total entropy\r\n for prob in probabilities:\r\n if prob > 0:\r\n entropy += prob * math.log(prob, 2)\r\n \r\n return -entropy"],"string":"[\n \"def entropy(x):\\n nz = np.nonzero(x)[0]\\n return -np.sum(x[nz]*np.log2(x[nz]))\",\n \"def shannon_entropy(probs):\\n return -(\\n math.sum([px * math.log2(px) if px != 0 and not (np.isclose(px, 0)) else 0 for px in probs])\\n )\",\n \"def entropy(P):\\n P_nan = P.copy()\\n P_nan[P_nan == 0] = np.nan\\n return np.nansum(np.multiply(P_nan, np.log2(1 / P_nan)))\",\n \"def entropy(x):\\n x_max, x_min = x.max(), x.min()\\n assert (x_min >= 0) and (x_max <= 1)\\n if x_min == x_max == 0:\\n return np.float32(0.)\\n # Take only non-zero values as log(0) = 0 :\\n nnz_x = x[np.nonzero(x)]\\n entr = -np.sum(nnz_x * np.log2(nnz_x))\\n\\n return entr\",\n \"def shannon_entropy(counts):\\n freq = np.array(counts) * 1.0 / np.sum(counts)\\n return -np.sum([f * np.log2(f) for f in freq if f != 0])\",\n \"def entropy(data):\\n n, m = np.shape(data)\\n data = np.tanh(data)\\n data = data / np.sum(data, axis=0)\\n a = data * 1.0\\n a[np.where(data == 0)] = 0.000001\\n\\n e = (-1.0 / np.log(n)) * np.sum(data * np.log(a), axis=0)\\n w = (1 - e) / np.sum(1 - e)\\n return w\",\n \"def entropy(self, priors=None):\\n def entropy_f(x):\\n x[x != 0] *= np.log(x[x != 0])\\n return -x.sum(axis=0)\\n return self.utility(entropy_f, priors)\",\n \"def entropy(self):\\n Z = self.sum()\\n assert (Z > 0), 'Non-normalizable factor (perhaps log factor?)' # also check for positivity?\\n H = 0.0\\n for x in np.nditer(self.t, op_flags=['readonly']):\\n p = x/Z\\n H += 0.0 if p==0 else -p*np.log(p)\\n return H\",\n \"def entropyDistributed(distribution):\\n return -sum(map(lambda p : p * log(p, 2), distribution))\",\n \"def entropy(distribution, unit=2):\\n frequencies = distribution.frequencies(normalised=True)\\n # check to see if it is a deterministic case (all but one are zero)\\n zeros_size = frequencies[frequencies == 0].size\\n if zeros_size + 1 == frequencies.size:\\n return 0\\n else:\\n return np.sum(-frequencies * np.log2(frequencies) / np.log2(unit))\",\n \"def entropy(y):\\n EPS = 0.0005\\n\\n # YOUR CODE HERE\\n if len(y) == 0:\\n return 0.\\n \\n pk = np.mean(y, axis=0)\\n \\n return - np.sum(pk * np.log(pk + EPS))\",\n \"def entropy_(P):\\n res = 0.0\\n\\n mask = P != 0.0 # avoid 0 in log\\n f = lambda x: x*np.log2(x)\\n # map-reduce strategy (likely to be more optimized than loops)\\n temp = list(map(f, P[mask]))\\n res = -np.sum(temp, dtype=float)\\n return res\",\n \"def shannon_entropy(c):\\n\\n c_normalized = c / float(np.sum(c))\\n c_normalized_nonzero = c_normalized[np.nonzero(c_normalized)] # gives 1D array\\n entropy = -sum(c_normalized_nonzero * np.log2(c_normalized_nonzero)) # unit in bits\\n return entropy\",\n \"def entropy(counts):\\n assert (counts >= 0).all()\\n probs = counts / counts.sum()\\n probs = probs[probs > 0] # Avoid log(0)\\n return - np.sum(probs * np.log2(probs))\",\n \"def _entropy(data):\\n hist = np.array(PIL.Image.fromarray(data).histogram())\\n hist = hist / hist.sum()\\n hist = hist[hist != 0]\\n return -np.sum(hist * np.log2(hist))\",\n \"def entropy(probabilities):\\n return -(sum([p * log(p, 2) if p > 0 else 0 for p in probabilities]))\",\n \"def ShannonEntropy(self,s):\\n e = s[np.nonzero(s)]**2 * np.log(s[np.nonzero(s)]**2)\\n return np.sum(e)\",\n \"def entropy(p):\\n assert (p >= 0).all()\\n assert abs(np.sum(p)-1) < 1e-6\\n return -np.sum(p*np.log(p+1e-12))\",\n \"def entropy(dist):\\n #dist = array([max(d,1e-100) for d in dist])\\n dist = dist + 1e-20\\n return dot(dist,(log(1.0/dist) * (1.0/log(2.0))).T)\",\n \"def shannon_entropy(ps: np.ndarray, base: int = 2) -> float:\\n\\n return -np.sum(ps * np.log(ps) / np.log(base))\",\n \"def get_entropy(distribution, samples):\\n entropy = -tf.reduce_sum(distribution.log_prob(samples), axis=1)\\n return entropy\",\n \"def entropycell(self):\\n cells = [0] * self.k\\n for i in range(self.width):\\n cells[int(self.config[self.t, i])] += 1\\n\\n \\\"\\\"\\\"Calculates the Shannon entropy and the the average entropy so far.\\\"\\\"\\\"\\n shannon = 0\\n for i in range(self.k):\\n if(cells[i] != 0):\\n probability = cells[i] / self.width\\n shannon -= probability * np.log2(probability)\\n self.average_cell = (self.average_cell * self.t + shannon) / (self.t + 1)\",\n \"def entropy(temp,pres):\\n g_t = liq_g(1,0,temp,pres)\\n s = -g_t\\n return s\",\n \"def entropy(data):\\n\\n freqs = {}\\n suma = len(data)\\n\\n for i in range(0, len(data)):\\n freqs[data[i]] = 1.0 + freqs.get(data[i], 0)\\n\\n res = 0.0\\n for i in freqs:\\n res += (freqs[i] / suma) * log((freqs[i] / suma), 2)\\n return -res\",\n \"def entropy(img):\\n # by calculating\\n histogram = img.histogram()\\n histogram_size = sum(histogram)\\n histogram = [float(h) / histogram_size for h in histogram]\\n\\n return -sum([p * math.log(p, 2) for p in histogram if p != 0])\",\n \"def entropyRandom(stream):\\n prob = 1.0 / len(stream)\\n return -(prob * log(prob, 2)) * len(stream)\",\n \"def entropy(data):\\n e = 0\\n\\n counter = collections.Counter(data)\\n l = len(data)\\n for count in counter.values():\\n p_x = count / l\\n e += - p_x * math.log2(p_x)\\n\\n return e\",\n \"def entropy(l):\\n\\n probabilities = np.bincount(l) / len(l)\\n with np.errstate(divide='ignore'): # ignore log(0) errors, we'll handle\\n log_probabilities = np.log2(probabilities)\\n log_probabilities[~np.isfinite(log_probabilities)] = 0\\n return -np.sum(probabilities * log_probabilities)\",\n \"def __compute_entropy_probability(probability:np.ndarray) -> float:\\n entropy = -np.sum(probability * np.log2(probability))\\n return entropy\",\n \"def entropy(y):\\n p = _proba(y)\\n return (-p * np.log2(p)).sum()\",\n \"def entropy(self):\\n ent = 0.0\\n for f in self.byte_freq:\\n if f > 0:\\n freq = float(f) / self.byte_total\\n ent = ent + freq * math.log(freq, 2)\\n return -ent\",\n \"def entropy(y):\\r\\n\\r\\n # INSERT YOUR CODE HERE\\r\\n value, count = np.unique(y,return_counts = True)\\r\\n Hy = 0.0\\r\\n prob = count.astype(float)/len(y)\\r\\n for p in prob:\\r\\n Hy += -(p)*(np.log2(p))\\r\\n return Hy\\r\\n raise Exception('Function not yet implemented!')\",\n \"def entropy(y):\\n return -1 * sum(\\n [\\n pipe(np.sum(y == value) / len(y), lambda ratio: ratio * np.log(ratio))\\n for value in set(y)\\n ]\\n )\",\n \"def entropy(s):\\n p, lns = Counter(s), float(len(s))\\n return -sum( count/lns * math.log(count/lns, 2) for count in p.values())\",\n \"def compute_entropy(bincounts, include_zeros):\\n num_total = tf.cast(tf.reduce_sum(bincounts), tf.float64)\\n if not include_zeros:\\n bincounts = bincounts[1:]\\n mask = tf.greater(bincounts, 0)\\n nonzero_bincounts = tf.cast(\\n tf.boolean_mask(bincounts, mask), tf.float64)\\n num_nonzero = tf.cast(tf.reduce_sum(nonzero_bincounts), tf.float64)\\n log_nonzero_bincounts = tf.math.log(nonzero_bincounts)\\n log_prob = log_nonzero_bincounts - tf.reduce_logsumexp(\\n log_nonzero_bincounts)\\n entropy = tf.math.reduce_sum(\\n log_prob * tf.exp(log_prob)) / -tf.math.log(tf.cast(2, tf.float64))\\n return entropy * num_nonzero / num_total\",\n \"def entropy(self):\\n return -np.sum(self.log_likelihoods * np.exp(self.log_likelihoods))\",\n \"def entropy(p):\\n ent = tf.where(p > np.finfo(np.float32).eps, -p * tf.log(p), tf.zeros_like(p))\\n ent = tf.reduce_sum(ent, axis=1)\\n return ent\",\n \"def entropy(self):\\n raise NotImplementedError\",\n \"def entropy_py(p):\\n return 2 ** np.sum(-p*np.log2(p+1e-10))\",\n \"def entropy(self, params):\\n log_std = params[:, :, 1]\\n return (log_std + 0.5 * (self.LOG2PI + 1)).sum(dim=-1)\",\n \"def entropy_numba(p):\\n return 2 ** np.sum(-p*np.log2(p+1e-10))\",\n \"def entropy(Y):\\n\\n temp = np.unique(Y, return_counts=True)\\n uniq_Y = list(temp[0])\\n Y_count = list(temp[1])\\n \\n total = sum(Y_count)\\n\\n ent = 0\\n for elem in uniq_Y:\\n prob = Y_count[uniq_Y.index(elem)] / total\\n # print(\\\"prob:\\\", prob)\\n ent -= (prob * (math.log2(prob)))\\n # print(\\\"ent:\\\",ent)\\n\\n return ent\",\n \"def entropy(y):\\n total = y.size\\n value_counts = np.bincount(y).astype(\\\"float\\\")\\n proportions = value_counts / y.size\\n\\n return sum(-i * np.log(i) for i in proportions if i)\",\n \"def entropy(p_list):\\n assert len(p_list) > 0\\n E = 0.0\\n for p in p_list:\\n if p == 0.0:\\n continue\\n E += p*math.log(p)\\n return E\",\n \"def entropy_root(self):\\n\\t\\tif self.size() == 0:\\n\\t\\t\\treturn 0\\n\\n\\t\\tcounter = collections.Counter(self.y)\\n\\t\\treturn entropy([x[1] for x in counter.items()], self.size())\",\n \"def entropy(self):\\n\\n \\\"\\\"\\\"Gets the first neighbours, which are the first 2*r+1 cells.\\\"\\\"\\\"\\n current_neighbours = []\\n amount = [0] * self.k ** (2 * self.r + 1)\\n for i in range(2 * self.r + 1):\\n current_neighbours.append(self.config[self.t, i % self.width])\\n\\n \\\"\\\"\\\"Calculates the rule and adds one to it's amount. It then removes the\\n leftmost cell and adds a cell to the right.\\\"\\\"\\\"\\n for i in range(len(self.config[self.t]) - 1):\\n rule = 0\\n for j in range(len(current_neighbours)):\\n rule += int(current_neighbours[j] *\\n self.k ** ((2 * self.r + 1) - j - 1))\\n amount[len(amount) - 1 - rule] += 1\\n current_neighbours.pop(0)\\n current_neighbours.append(\\n self.config[self.t, (2 * self.r + 1 + i) % self.width])\\n\\n \\\"\\\"\\\"Calculates the rule for the last neighbourhood.\\\"\\\"\\\"\\n rule = 0\\n for j in range(len(current_neighbours)):\\n rule += int(current_neighbours[j] *\\n self.k ** ((2 * self.r + 1) - j - 1))\\n amount[len(amount)-1 - rule] += 1\\n\\n \\\"\\\"\\\"Calculates the Shannon entropy and the the average entropy so far.\\\"\\\"\\\"\\n shannon = 0\\n for i in range(len(amount)):\\n if(amount[i] != 0):\\n probability = amount[i] / self.width\\n shannon -= probability * np.log2(probability)\\n self.average_entropy = (self.average_entropy *\\n self.t + shannon) / (self.t + 1)\",\n \"def calculate_entropy(prob):\\n return -(prob * math.log(prob,2))\",\n \"def entropy(*args):\\n\\n\\n values = []\\n leaf = -1\\n\\n for i, val in enumerate(args):\\n if(val != 0):\\n values.append(val * math.log(val, len(args)))\\n if(val == 1):\\n leaf = i\\n \\n return -sum(values), leaf\",\n \"def entropy(self):\\n n = len(self.y)\\n sum_ = 0\\n for i in np.unique(self.y):\\n v = len(self.y[self.y == i])\\n sum_ += -((v/n) * log2(v/n))\\n return sum_\",\n \"def entropy(out, dim=1, reduce='mean'):\\n log_prob = F.log_softmax(out, dim=dim)\\n h = -torch.sum(log_prob.exp() * log_prob, dim=dim)\\n if reduce == 'none':\\n return h\\n if reduce == 'mean':\\n return h.mean()\\n if reduce == 'sum':\\n return h.sum()\",\n \"def image_entropy(img):\\r\\n hist = img.histogram()\\r\\n hist_size = sum(hist)\\r\\n hist = [float(h) / hist_size for h in hist]\\r\\n\\r\\n return -sum([p * math.log(p, 2) for p in hist if p != 0])\",\n \"def calc_entropy_one(states, T):\\n\\n # S = - kB \\\\sum_i p_i \\\\log p_i = - k_B <\\\\log p_i>\\n P = calc_probabilities_one(states, T)\\n return -kB * np.sum(P * np.log(P))\",\n \"def joint_entropy(P):\\n P_nan = P.copy()\\n P_nan[P_nan == 0] = np.nan\\n return np.nansum(np.multiply(P_nan, np.log2(1 / P_nan)))\",\n \"def entropy ( target_array ):\\n return -1 * sum (\\n [\\n pipe ( np.sum ( target_array == value ) / len ( target_array ), lambda ratio: ratio * np.log ( ratio ) )\\n for value in set ( target_array )\\n ]\\n ) # End entropy()\",\n \"def entropy(self, y):\\n n = y.size\\n if n <= 1:\\n return 0\\n\\n labels, counts = unique(y, return_counts=True)\\n\\n if counts.size <= 1:\\n return 0\\n\\n probs = counts / n\\n entropy = -sum([p * log(p, 2) for p in probs])\\n return entropy\",\n \"def EntropyD(dist): \\n if np.any(dist>0):\\n dist2 = dist/np.sum(dist)\\n return -np.sum(dist2[dist2>0]*np.log2(dist2[dist2>0]))\\n else:\\n return 0\",\n \"def compute_entropy(m, nan_as_zero=True):\\n # normalize rows (the sum of each row must be 1)\\n p = m / m.sum(axis=1, keepdims=True)\\n # compute per row entropy (in base 2)\\n e = entr(p).sum(axis=1) / np.log(2)\\n if nan_as_zero:\\n e = np.nan_to_num(e)\\n return e\",\n \"def conditional_entropy(self) -> float:\\n pass\",\n \"def entropy(class_probabilities):\\n return sum(-p * math.log(p, 2)\\n for p in class_probabilities\\n if p) #ignore 0's\",\n \"def entropy(self, **kwargs) -> TensorType:\",\n \"def entropy(self, **kwargs) -> TensorType:\",\n \"def entropy(data):\\n strings, lens = Counter(data), np.float(len(data))\\n return -sum(count / lens * np.log2(count / lens) for count in strings.values())\",\n \"def entropy_function(c, n):\\n return -(c*1.0/n)*math.log(c*1.0/n,2)\",\n \"def entropy_fn(args: StepFunctionArgs) -> SingleScorePerStepTensor:\\n logits = args.attribution_model.output2logits(args.forward_output)\\n out = torch.distributions.Categorical(logits=logits).entropy()\\n if out.ndim > 1:\\n out = out.squeeze(-1)\\n return out\",\n \"def entropy(self):\\r\\n return 1/2 * (self.dim * (_LOG_2PI + 1) + self._log_det_cov)\",\n \"def entropy(n_bits):\\n return n_bits and random.getrandbits(n_bits)\",\n \"def conditional_entropy_hyper(self) -> float:\\n pass\",\n \"def test_dc():\\n signal = np.zeros(100)\\n pe = permutation_entropy(signal, 2)\\n\\n assert pe == 0\",\n \"def getEntropy(self, pVal, nVal):\\n totVal = pVal + nVal\\n if pVal == 0 or nVal == 0:\\n return 0\\n\\n pProb = pVal/totVal\\n nProb = 1 - pProb\\n entropy = - (pProb * math.log(pProb, 2) + nProb * math.log(nProb, 2))\\n return entropy\",\n \"def entropy(self):\\n return self._normal.entropy()\",\n \"def entropy(Y):\\n unique, count = np.unique(Y, return_counts=True, axis=0)\\n prob = count/len(Y)\\n en = np.sum((-1)*prob*np.log2(prob))\\n return en\",\n \"def entropy( freq ):\\n N = 0.0\\n entropy = 0.0\\n for x, v in freq.items( ):\\n N += v\\n entropy -= v * math.log( v, 2 )\\n return (N * math.log( N, 2 ) + entropy) / N\",\n \"def entropy(x, bins, normalize=False, xy_probabilities=False):\\n # calculate probabilities if xy_probabilities == False\\n if xy_probabilities:\\n # if x does not sum up to 1, raise an error\\n if not np.isclose(sum(x),1,atol=0.0001):\\n raise ValueError('Probabilities in vector x do not sum up to 1.')\\n \\n # add a small number to all probabilities if zero occurs\\n if x.any(0):\\n p = x + 1e-15\\n else:\\n p = x\\n else:\\n # get the bins\\n bins = np.histogram_bin_edges(x, bins)\\n\\n # calculate the empirical probabilities\\n count = np.histogram(x, bins=bins)[0]\\n\\n # if counts should be None, raise an error\\n if np.sum(count) == 0:\\n raise ValueError('The histogram cannot be empty. Adjust the bins to ' +\\n 'fit the data')\\n # calculate the probabilities\\n p = (count / np.sum(count)) + 1e-15\\n\\n\\n # calculate the Shannon Entropy\\n if normalize:\\n # get number of bins\\n nbins = len(p)\\n # maximal entropy: uniform distribution\\n normalizer = np.log2(nbins) \\n\\n return - p.dot(np.log2(p)) / normalizer\\n else:\\n return - p.dot(np.log2(p))\",\n \"def entropy(data, idxList):\\n df = data.loc[idxList]\\n counts = df.value_counts().to_numpy()\\n counts = counts.reshape(1, -1).astype(np.float32)\\n counts /= np.sum(counts)\\n log_sum = counts @ np.log2(counts.T)\\n return -log_sum[0, 0]\",\n \"def entropy(message):\\n n = len(message)\\n message = letter_freq(message)\\n h = 0\\n for n_i in message.values():\\n p_i = n_i/n\\n h += -p_i*(log2(p_i))\\n return h\",\n \"def _graph_fn_entropy(distribution):\\n return distribution.entropy()\",\n \"def _entropy(P):\\n\\n #TODO remove the \\\"+ 1e-20\\\" inside the log2 computation\\n # it's just a hack to avoid to compute log2(0)\\n ent = -1.0 * np.sum(P * np.log2(P+1e-20), axis=0)\\n return ent\",\n \"def entropy(self, X):\\n if isinstance(X, np.ndarray):\\n X = pd.DataFrame(X, index=[str(i) for i in range(len(X))])\\n K = self._posterior_covariance(X)\\n L = np.linalg.cholesky(K)\\n D = len(X)\\n return np.sum(np.log(np.diag(L))) + 0.5 * D * np.log(2*np.pi*np.exp(1))\",\n \"def item_entropy(item_count, total_count):\\n # Two cases where the entropy is 0\\n if item_count == total_count or item_count == 0:\\n return 0\\n \\n item_prob = 1.0 * item_count / total_count\\n return -item_prob * math.log(item_prob)\",\n \"def H(self, data):\\n entropy = 0\\n\\n if not data:\\n return entropy\\n\\n for x in range(256):\\n p_x = float(data.count(chr(x))) / len(data)\\n if p_x > 0:\\n entropy -= p_x * math.log(p_x, 2)\\n\\n return entropy\",\n \"def _entropy(self):\\n return self.rv.entropy(*self._pymc_dists_to_value(self.args), **self.kwds)\",\n \"def _theils_u_compute(confmat: Tensor) ->Tensor:\\n confmat = _drop_empty_rows_and_cols(confmat)\\n s_xy = _conditional_entropy_compute(confmat)\\n total_occurrences = confmat.sum()\\n p_x = confmat.sum(0) / total_occurrences\\n s_x = -torch.sum(p_x * torch.log(p_x))\\n if s_x == 0:\\n return torch.tensor(0, device=confmat.device)\\n return (s_x - s_xy) / s_x\",\n \"def entropy(self, base: int = None):\\n\\n # shannon entropy in nats\\n fdist_ = self.fdist\\n fdist_[\\\"prob\\\"] = fdist_[\\\"freq\\\"] / fdist_[\\\"freq\\\"].sum()\\n fdist_[\\\"logp\\\"] = np.log(fdist_[\\\"prob\\\"])\\n fdist_[\\\"nats\\\"] = -fdist_[\\\"prob\\\"] * fdist_[\\\"logp\\\"]\\n entropy_ = fdist_[\\\"nats\\\"].sum()\\n\\n # convert base\\n if base:\\n entropy_ = entropy_ / np.log(base)\\n\\n # return\\n return entropy_\",\n \"def entropy(self) -> float:\\n probabilities = np.array([len(players) for players in self.answers.values()])\\n probabilities = probabilities / sum(probabilities)\\n return sc.stats.entropy(probabilities)\",\n \"def entropy(message):\\n message = letter_freq(message)\\n n = sum(message.values())\\n h = 0\\n for n_i in message.values():\\n p_i = n_i / n\\n h += -p_i * log2(p_i)\\n return h\",\n \"def entropy(self, policy_params):\\n return self.head.entropy(policy_params)\",\n \"def _entropy(self, y):\\n # Get size\\n n = y.shape[0]\\n summation = 0\\n\\n # Summatory\\n for c_i in np.unique(y):\\n prob = sum(y == c_i) / float(n)\\n summation += prob * np.log2(prob)\\n\\n return -summation\",\n \"def entropy(self, logits):\\n probs = torch.exp(logits)\\n entropy = - torch.sum(probs * logits, dim=-1)\\n return entropy\",\n \"def entropy(class_probabilities):\\n return sum(-p * math.log(p,2)\\n for p in class_probabilities\\n if p)\",\n \"def spatial_entropy(map_):\\n map_ = map_ / np.sum(map_)\\n return -1 * np.sum(map_ * np.log(map_))\",\n \"def entropy(self, args):\\n mean, stddev = args\\n dist = tfp.distributions.Normal(loc=mean, scale=stddev)\\n entropy = dist.entropy()\\n return entropy\",\n \"def entropy(a):\\n a = a.upper()\\n\\n freq = collections.defaultdict(int) # int() is the default constructor for non existent item, and returns 0\\n for c in a:\\n freq[c] = freq[c] + 1\\n\\n e = 0.0\\n for f in freq.values():\\n if f:\\n p = f / len(a)\\n e += p * math.log(p)\\n\\n return -e\",\n \"def _conditional_entropy_compute(confmat: Tensor) ->Tensor:\\n confmat = _drop_empty_rows_and_cols(confmat)\\n total_occurrences = confmat.sum()\\n p_xy_m = confmat / total_occurrences\\n p_y = confmat.sum(1) / total_occurrences\\n p_y_m = p_y.unsqueeze(1).repeat(1, p_xy_m.shape[1])\\n return torch.nansum(p_xy_m * torch.log(p_y_m / p_xy_m))\",\n \"def calc_entropy(data_set): #calculates total entropy of the dataset\\r\\n republicans = 0\\r\\n democrats = 0\\r\\n total = 0\\r\\n for data_point in data_set:\\r\\n party = data_point.dat_party\\r\\n if party == \\\"R\\\":\\r\\n republicans+=1\\r\\n elif party == \\\"D\\\":\\r\\n democrats+=1\\r\\n total+=1\\r\\n\\r\\n if total == 0: return 0\\r\\n prob_dem = democrats/total\\r\\n prob_rep = republicans/total\\r\\n if prob_dem == 0: return -(prob_rep * math.log(prob_rep, 2))\\r\\n if prob_rep == 0: return -(prob_dem * math.log(prob_dem, 2))\\r\\n\\r\\n entropy = (-prob_dem * math.log(prob_dem, 2)) -(prob_rep * math.log(prob_rep, 2))\\r\\n return entropy\",\n \"def __compute_entropy(row:pd.Series) -> float:\\n probability = row.value_counts(normalize=True).values\\n entropy = EntropicSolver.__compute_entropy_probability(probability)\\n return entropy\",\n \"def div(self):\\n freqList = [i / sum(self.has.values()) for i in self.has.values()]\\n entropies = [i * math.log(i, 2) for i in freqList]\\n entropy = -sum(entropies)\\n return entropy\",\n \"def entropy(image, selem, out=None, mask=None, shift_x=False, shift_y=False):\\n\\n return _apply(_crank8.entropy, _crank16.entropy, image, selem, out=out,\\n mask=mask, shift_x=shift_x, shift_y=shift_y)\",\n \"def calc_entropy(column):\\n # Compute the counts of each unique value in the column\\n counts = numpy.bincount(column)\\n # Divide by the total column length to get a probability\\n probabilities = counts / len(column)\\n \\n # Initialize the entropy to 0\\n entropy = 0\\n # Loop through the probabilities, and add each one to the total entropy\\n for prob in probabilities:\\n if prob > 0:\\n entropy += prob * math.log(prob, 2)\\n \\n return -entropy\",\n \"def calculate_entropy(reduced_density_matrix_evals):\\n vec_xlogx = vectorize(calculate_xlogx)\\n result = -sum(vec_xlogx(reduced_density_matrix_evals,\\n\\t float_info.epsilon))\\n return result\",\n \"def calc_entropy(column):\\r\\n # Compute the counts of each unique value in the column\\r\\n counts = numpy.bincount(column)\\r\\n # Divide by the total column length to get a probability\\r\\n probabilities = counts / len(column)\\r\\n \\r\\n # Initialize the entropy to 0\\r\\n entropy = 0\\r\\n # Loop through the probabilities, and add each one to the total entropy\\r\\n for prob in probabilities:\\r\\n if prob > 0:\\r\\n entropy += prob * math.log(prob, 2)\\r\\n \\r\\n return -entropy\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.78719157","0.77639353","0.7700689","0.75903934","0.7586108","0.7534621","0.7498757","0.74172634","0.7378696","0.7348129","0.7309863","0.7278837","0.7269413","0.72656107","0.72442186","0.72422546","0.72418517","0.72395533","0.71693814","0.71460384","0.7088129","0.7048991","0.70348173","0.7030038","0.7029451","0.69896835","0.6983255","0.69786537","0.6977474","0.69610417","0.6956797","0.6951443","0.69358945","0.6912451","0.69068015","0.6895516","0.68675977","0.6852042","0.6850445","0.6824068","0.68045455","0.6790993","0.67814326","0.67640305","0.67492706","0.67449284","0.6744044","0.67378336","0.6730631","0.6723741","0.6719673","0.67135787","0.6706571","0.66998965","0.66901237","0.6685126","0.6671779","0.66681254","0.6662365","0.6655019","0.6655019","0.66470903","0.6624059","0.66229856","0.66228664","0.662193","0.6603887","0.660082","0.65891033","0.6578206","0.6539567","0.65383214","0.6535508","0.64945763","0.6481724","0.64814836","0.6477211","0.6469955","0.64697945","0.64579445","0.64564365","0.64477867","0.6442593","0.6439547","0.6431112","0.64177996","0.6408287","0.6397887","0.6388194","0.63866925","0.63833684","0.63597155","0.6355711","0.63491523","0.63483727","0.63449347","0.6329007","0.6320574","0.6313058","0.6311226"],"string":"[\n \"0.78719157\",\n \"0.77639353\",\n \"0.7700689\",\n \"0.75903934\",\n \"0.7586108\",\n \"0.7534621\",\n \"0.7498757\",\n \"0.74172634\",\n \"0.7378696\",\n \"0.7348129\",\n \"0.7309863\",\n \"0.7278837\",\n \"0.7269413\",\n \"0.72656107\",\n \"0.72442186\",\n \"0.72422546\",\n \"0.72418517\",\n \"0.72395533\",\n \"0.71693814\",\n \"0.71460384\",\n \"0.7088129\",\n \"0.7048991\",\n \"0.70348173\",\n \"0.7030038\",\n \"0.7029451\",\n \"0.69896835\",\n \"0.6983255\",\n \"0.69786537\",\n \"0.6977474\",\n \"0.69610417\",\n \"0.6956797\",\n \"0.6951443\",\n \"0.69358945\",\n \"0.6912451\",\n \"0.69068015\",\n \"0.6895516\",\n \"0.68675977\",\n \"0.6852042\",\n \"0.6850445\",\n \"0.6824068\",\n \"0.68045455\",\n \"0.6790993\",\n \"0.67814326\",\n \"0.67640305\",\n \"0.67492706\",\n \"0.67449284\",\n \"0.6744044\",\n \"0.67378336\",\n \"0.6730631\",\n \"0.6723741\",\n \"0.6719673\",\n \"0.67135787\",\n \"0.6706571\",\n \"0.66998965\",\n \"0.66901237\",\n \"0.6685126\",\n \"0.6671779\",\n \"0.66681254\",\n \"0.6662365\",\n \"0.6655019\",\n \"0.6655019\",\n \"0.66470903\",\n \"0.6624059\",\n \"0.66229856\",\n \"0.66228664\",\n \"0.662193\",\n \"0.6603887\",\n \"0.660082\",\n \"0.65891033\",\n \"0.6578206\",\n \"0.6539567\",\n \"0.65383214\",\n \"0.6535508\",\n \"0.64945763\",\n \"0.6481724\",\n \"0.64814836\",\n \"0.6477211\",\n \"0.6469955\",\n \"0.64697945\",\n \"0.64579445\",\n \"0.64564365\",\n \"0.64477867\",\n \"0.6442593\",\n \"0.6439547\",\n \"0.6431112\",\n \"0.64177996\",\n \"0.6408287\",\n \"0.6397887\",\n \"0.6388194\",\n \"0.63866925\",\n \"0.63833684\",\n \"0.63597155\",\n \"0.6355711\",\n \"0.63491523\",\n \"0.63483727\",\n \"0.63449347\",\n \"0.6329007\",\n \"0.6320574\",\n \"0.6313058\",\n \"0.6311226\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.73763674"},"document_rank":{"kind":"string","value":"9"}}},{"rowIdx":26099,"cells":{"query":{"kind":"string","value":"Puts User Matches in view, in order by rank ( of matching skills)"},"document":{"kind":"string","value":"def get_context_data(self, **kwargs):\n context = super(ProjectView, self).get_context_data(**kwargs)\n # only load if self.request.user == founder\n if self.request.user.id is self.get_object().founder.id:\n context['match_list'] = Match.objects.filter(project=self.object.id).order_by('-rank')\n return context"},"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 enter_matches_score(match_list):\n for match in match_list:\n view.show(\"{} vs {}\".format(match.player_blanc.player_first_name,\n match.player_black.player_first_name))\n score_blanc = enter_player_score(match.player_blanc)\n match.score_blanc = score_blanc\n score_black = enter_player_score(match.player_black)\n match.score_black = score_black","def matchmaking():\n from app import db, models\n # Split by character as they are stored as a list in the database.\n # Could improve by normalising skills & model already produced -- models::skills\n # Query would change below, but matchmaking algorithm would remain the same.\n uskills = [{'name': str(i.uid), 'skills':\n [str(i) for i in i.skills.split(\",\")]}\n for i in db.session.query(models.UserSkills).all()]\n # Used to select matches that have not been previously paired.\n pairs = [(str(i.uid), str(i.mid))\n for i in db.session.query(models.Pair).all()]\n\n # Find all the users who are not already matched, but should be!\n # IFF at least once skill overlaps then there is a match. We are too kind.\n matches = []\n for user in uskills:\n for other_user in uskills:\n # Do not add already existing pairs and matches are transitive;\n # Jay matched with Aare is the same as Aare matching with Jay.\n cu, ou = (user['name'], other_user['name'])\n if ((cu, ou) in pairs or (ou, cu) in pairs or\n (cu, ou) in matches or (ou, cu) in matches):\n break\n # TODO: skill comparison based on NLTK use (synonyms, etc...)\n # TODO: improve the algorithm to rank and sort pairs by skills.\n if (set(user['skills']).intersection(other_user['skills']) and\n user['name'] != other_user['name']):\n matches.append((user['name'], other_user['name']))\n return matches","def calculate_match_at_backend():\n\n # Calculate lovermatch list for each user\n user_set = UserInfo.objects\n for u1 in user_set:\n matchlist = {}\n for u2 in user_set:\n if u1 != u2:\n features_to_match = u1.features\n weights = u1.percentage\n sim = get_similarity(u1, u2, features_to_match, weights)\n matchlist[u2.name] = sim\n u1.loverMatch = matchlist\n u1.save()\n\n # Calculate lovermatched list for each user\n user_set = UserInfo.objects\n for u1 in user_set:\n matchedlist = {}\n for u2 in user_set:\n if u1 != u2:\n if u1.name in u2.loverMatch.keys():\n matchedlist[u2.name] = u2.loverMatch[u1.name]\n u1.loverMatched = matchedlist\n u1.save()","def leaderboard():\n \n global score_dictinary\n data = []\n fields = []\n scores = []\n names = []\n users = []\n i=0\n \n #Reads the winners from a mongo database \n read_mongo(scores, names)\n \n #Sorts the list in descending order\n quicksort(scores, names, 0, len(scores) - 1)\n \n #Joins the names and scores arrays together\n while i < len(scores):\n users.append(names[i] + \" \" + scores[i])\n i += 1\n \n users = (reversed(users))\n \n return render_template(\"leaderboard.html\", users=users)","def rerank_results(self, results, user_vector, user_gender, user_location, user_sentiment):\n\t\treranked = []\n\t\tuser_vec = ProfileOneHotEncoder.add_info_to_vec(user_vector, user_gender, user_location,\n\t\t user_sentiment).reshape(1, -1)\n\t\tfor i in range(len(results)):\n\t\t\tdoc_infos = Tweet.load(int(results[i]['TweetID']))\n\t\t\tif doc_infos is None:\n\t\t\t\treranked.append({'doc': results[i], 'sim': 0.})\n\t\t\telse:\n\t\t\t\tdoc_vector = ProfileOneHotEncoder.add_info_to_vec(doc_infos.vector, doc_infos.gender, doc_infos.country,\n\t\t\t\t doc_infos.sentiment).reshape(1, -1)\n\t\t\t\tsim = cosine_similarity(user_vec, doc_vector)\n\t\t\t\treranked.append({'doc': doc_infos, 'sim': sim[0][0]})\n\t\treranked = sorted(reranked, key=lambda k: k['sim'], reverse=True)\n\t\treturn [x['doc'] for x in reranked]","def get_user_rankings(self, request):\n users = User.query(User.total_games > 0).fetch()\n users = sorted(users, key=lambda x: x.win_percentage, reverse=True)\n return UserForms(items=[user.to_form() for user in users])","def recommend(self, user):\n K = self.n_sim_user\n N = self.n_rec_movie\n rank = dict()\n watched_movies = self.trainset[user]\n\n # v=similar user, wuv=similarity factor\n for v, wuv in sorted(self.user_sim_mat[user].items(),\n key=itemgetter(1), reverse=True)[0:K]:\n for movie in self.trainset[v]:\n if movie in watched_movies:\n continue\n # predict the user's \"interest\" for each movie\n rank.setdefault(movie, 0)\n rank[movie] += wuv\n # return the N best movies\n return sorted(rank.items(), key=itemgetter(1), reverse=True)[0:N]","def get_user_rankings(self, request):\n difficulty = validateGameDifficultyValue(request, True)\n users = User.query().fetch()\n items = []\n total_games = 0\n wins = 0\n for user in users:\n scores = Score.query()\n # Filter by game difficulty and only wons that resulted in a win\n scores = scores.filter(Score.user == user.key, \\\n Score.difficulty == difficulty)\n scores.fetch()\n\n for score in scores:\n total_games += 1\n if score.won:\n wins += 1\n if total_games > 0:\n items.append(\n RankingForm(user_name=user.name,\n difficulty=getattr(GameDifficulty, difficulty),\n win_percentage=round(float(wins)/total_games*100, 2),\n wins=wins)\n )\n total_games = 0\n wins = 0\n return RankingForms(items=items)","def recommend(self, u):\n # Implement a recommendation function that takes a user vector u\n # and outputs a list of movies recommended by the chatbot\n rec_list = []\n for i, movie in enumerate(self.ratings):\n rxi = 0.0\n for tup in u:\n j = tup[0]\n rxj = tup[1]\n if i == j: # Skip movies in user_vec\n continue\n sij = self.distance(self.ratings[i], self.ratings[j])\n rxi += (rxj * sij)\n movie_rank = [i, rxi] # Store movie index and rating\n rec_list.append(movie_rank)\n rec_list = sorted(rec_list, key=lambda x:x[1], reverse = True) \n return rec_list","def rank_clubs(user):\n if not user.is_authenticated():\n return ((k, None) for k in Klub.objects.all().annotate(c=Count('posel')).order_by('-c'))\n\n clubs = defaultdict(list)\n for deputy, rating in rank(user):\n if deputy.klub is not None:\n clubs[deputy.klub.pk].append(rating)\n ratings = []\n for club in Klub.objects.all():\n if clubs.get(club.pk):\n rating = sum(clubs[club.pk]) / len(clubs[club.pk])\n else:\n rating = 0\n ratings.append((club, rating))\n return sorted(ratings, reverse=True, key=lambda x: x[1])","def rank(args: argparse.Namespace, cookie: str) -> None:\n cookies = {\n \"spip_session\": cookie,\n }\n\n max_len_user = max((len(x) for x in args.user))\n\n for username in args.user:\n\n users: Dict[str, Searchresult] = search_user(username, cookies)\n\n userids = []\n # Parse API search results in order to find the correct users\n for user in users:\n if users[user][\"nom\"] == username:\n userids.append(users[user][\"id_auteur\"])\n\n if not userids:\n raise ValueError(\n (\n \"User %s not found in 50 first search terms.\"\n \" Did you mispell it? \"\n ),\n username,\n )\n\n for userid in userids:\n userinfo = get_user_info(userid, cookies)\n if userinfo is None:\n continue\n\n print(\n f\"[{Fore.GREEN}+{Style.RESET_ALL}] \"\n f\"{userinfo['nom']:<{max_len_user + 2}} \"\n f\"Score: {userinfo['score']:<6} \"\n f\"Position: {userinfo['position']}\"\n )","def match(request, user_id):\n current_user = CustomUser.objects.get(id=request.user.id)\n liked_user = CustomUser.objects.get(id=user_id)\n\n if not Matches.objects.filter(first_user=current_user, second_user=liked_user):\n Matches.objects.create(first_user=current_user, second_user=liked_user)\n\n if Matches.objects.filter(first_user=liked_user, second_user=current_user):\n send_emails_to_users(current_user, liked_user)\n send_emails_to_users(liked_user, current_user)\n\n return redirect('index')","def get_UserMatches(similarity,table_CF, user,n=5):\n similarity_table = similarity(table_CF)\n similar_users = similarity_table[user]\n # Sort the list so the highest scores appear at the top\n similar_users = similar_users.order(ascending = False)\n return similar_users[0:n]","def _match_users_with_karma(rankings, user_key, karma_key):\n if not rankings:\n return []\n\n giver_ids = [r[user_key] for r in rankings]\n ids_to_users = User.objects.select_related(\n 'userprofile').in_bulk(giver_ids)\n return [(ids_to_users[r[user_key]], r[karma_key]) \\\n for r in rankings]","def leaderboard(request):\r\n\tMEDIA_URL = '/media/'\r\n\tgames = Game.objects.all()\r\n\tuser_high_scores = []\r\n\tgame_high_scores = []\r\n\tnew = {}\r\n\t# Get global scores\r\n\tfor game in games:\r\n\t\tgame_intermediate_high = Score.objects.filter(game = game.id).order_by('-current_score').values('game__name', 'player__user__username', 'current_score')[:1]\r\n\t\tif (game_intermediate_high.count() > 0):\r\n\t\t\tgame_high_scores.append(game_intermediate_high)\r\n\t# Check if user is authenticated and get user's scores\r\n\tif (request.user.is_authenticated):\r\n\t\tfor game in games:\r\n\t\t\t\tuser_intermediate_high = Score.objects.filter(game=game.id, player = request.user.profile).order_by('-current_score').values('player__user__username','game__name', 'current_score').distinct()[:1]\r\n\t\t\t\tif (user_intermediate_high.count() > 0):\r\n\t\t\t\t\tuser_high_scores.append(user_intermediate_high)\r\n\r\n\treturn render(request, 'leaderboard.html',{'MEDIA_URL' : MEDIA_URL,'games': games, 'user_high_scores': user_high_scores, 'game_high_scores': game_high_scores})","def rank(userid, args):\r\n testUserid = userid\r\n if len(args):\r\n testUserid = es.getuserid(str(args))\r\n if not es.exists('userid', testUserid):\r\n testUserid = userid\r\n player = players[testUserid]\r\n tokens = {}\r\n tokens['name'] = player['name']\r\n tokens['level'] = player['level']\r\n tokens['xp'] = player['xp']\r\n tokens['nextxp'] = (player['level'] - 1) * int(xpIncrement) + int(startXp)\r\n tokens['credits'] = player['credits']\r\n tokens['rank'] = ranks.getRank(player['steamid'])\r\n tokens['total'] = len( ranks )\r\n for tellUserid in es.getUseridList():\r\n tell(tellUserid, 'rank', tokens)","def update_score_other_rounds(self, match):\n self.match_views.update_score(match)","def get_leaderboard(request):\n\n includedUsers = User.objects.filter(hide_leaderboard=False, is_staff=False)\n\n # ordered list of points, index denoting leaderboard position (rank)\n # distinct values means that everyone with the same points has the same rank\n rankings = []\n for item in includedUsers.values(\"points\").distinct().order_by(\"-points\"):\n rankings.append(item[\"points\"])\n\n includedUsers = includedUsers.order_by(\"-points\")\n\n paginationData = []\n for user in includedUsers:\n # rank is the index of the users points +1 (converting from 0-indexing)\n data = {\"user\": user, \"rank\": rankings.index(user.points) + 1}\n paginationData.append(data)\n\n return JsonResponse(\n json_paginator(request, paginationData, lb_serializer),\n status=200,\n )","def get_user_rankings(self, request):\n users = User.query().order(-User.average_score)\n return RankForms(items=[user.to_rank_form() for user in users])","def rank_by_obscurity():\n obscurities = dict()\n reader = open(\"similarities.txt\", \"r\")\n lines = reader.readlines()\n for line in lines:\n a, b, sim = line.split(\"\\t\")\n if a not in obscurities:\n obscurities[a] = [float(sim), 1.0]\n else:\n obscurities[a] = [obscurities[a][0] + float(sim),\n obscurities[a][1] + 1.0]\n \n ranked = sorted(obscurities.keys(),\n key=lambda x: obscurities[x][0] / obscurities[x][1])\n writer = open(\"obscurities.txt\", \"w\")\n for user in ranked:\n writer.write(user + \"\\n\")","def testHighscore(self):\n \n game = Game.objects.get(title='game0')\n gameplayeds = game.gameplayed_set\n ply_group = Group.objects.get(name='Player')\n for i in range(4):\n user = ply_group.user_set.get(username='ply{}'.format(i))\n gameplayed = gameplayeds.get(user=user)\n gameplayed.gameScore = i\n gameplayed.save()\n \n response = self.client.get(\n reverse('api:game-buyers', args=['v1', 'game0']),\n {'order_by': 'gameScore'},\n format='json'\n )\n self.assertEquals(response.status_code, 200)\n content = self.parser.parse(BytesIO(response.content))\n for i in range(4):\n self.assertEquals(content['results'][i]['user'], 'ply{}'.format(i))\n \n response = self.client.get(\n reverse('api:game-buyers', args=['v1', 'game0']),\n {'order_by': '-gameScore'},\n format='json'\n )\n self.assertEquals(response.status_code, 200)\n content = self.parser.parse(BytesIO(response.content))\n for i in range(4):\n self.assertEquals(content['results'][i]['user'], 'ply{}'.format(3 - i))","def get_players_by_rank(self):\n return sorted(self.participants, key=lambda p: p.tournament_score, reverse=True)","def get_context_data(self,**kwargs):\n\n context = super().get_context_data(**kwargs)\n user = self.get_object()\n query= Result.objects.filter(user=user).order_by('-score')\n context['results']= query[:4]\n\n return context","def assign_rank(request):\n try:\n user = UserProfile.objects.order_by('overall_score')\n count = UserProfile.objects.all().count()\n rockstar = 0.01 * count\n if int(rockstar) < rockstar:\n rockstar = int(rockstar) + 1\n \n master = 0.04 * count\n if int(master) < master:\n master = int(master) + 1\n \n senior = 0.15 * count\n if int(senior) < senior:\n senior = int(senior) + 1\n \n active = 0.3 * count\n if int(active) < active:\n active = int(active) + 1\n \n amateur = 0.5 *count\n if int(amateur) < amateur:\n amateur = int(amateur) + 1\n \n j = 0\n while j < amateur and j < count:\n u = user[j]\n u.rank = Master_Rank.objects.get(position=\"Amatuer\")\n u.save()\n j += 1\n \n k = 0\n while k < active and j < count:\n u = user[j]\n u.rank = Master_Rank.objects.get(position=\"Active translator\")\n u.save()\n j += 1\n k += 1\n \n k = 0\n while k < senior and j < count:\n u = user[j]\n u.rank = Master_Rank.objects.get(position=\"Senior translator\")\n u.save()\n j += 1\n k += 1\n \n k = 0\n while k < master and j < count:\n u = user[j]\n u.rank = Master_Rank.objects.get(position=\"Master translator\")\n u.save()\n j += 1\n k += 1\n \n while j < count:\n u = user[j]\n u.rank = Master_Rank.objects.get(position=\"Rockstar translator\")\n u.save()\n j += 1\n \n \n data = {'msg':''}\n messages.success(request, \"Rank assigned to user successfully.\")\n return render_to_response('my_admin_tools/menu/background_task.html',data,context_instance=RequestContext(request))\n except:\n msg = traceback.format_exc()\n data = {'msg':msg}\n messages.error(request, \"Update user's reputation score failed.\")\n return render_to_response('my_admin_tools/menu/background_task.html',data,context_instance=RequestContext(request))","def rank_skill_support():\n import collections\n score_dict = collections.defaultdict(int)\n for item in classes.Crusader.items:\n for name in classes.Crusader.skill_names:\n if name in inspect.getsource(item):\n score_dict[name] += 1\n\n for name, freq in sorted(score_dict.items(), key=lambda x: -x[1]):\n print(name, freq)","def recache_matches(self):\n matches = (Match.objects\n .filter(Q(winner = self) | Q(loser = self))\n .order_by('-played_time'))[:CACHED_RATING_LIMIT]\n matches = list(matches)\n matches.reverse()\n\n for match in matches:\n self.add_match(match, include_rank=True)\n\n self.save()","def matchscore(self):\n print(self.team1.name + \" \" + str(self.team1score) + \" - \" + str(self.team2score) + \" \" + self.team2.name)","def leaderboard(self):\n return User.objects.filter(documentsetformentry__form__document_set=self).annotate(num_entries=Count('documentsetformentry')).order_by('-num_entries')","def score():\n if flask.request.method == 'GET':\n return \"Give me a POST request to get recommendations\"\n\n # Get input data:\n input_dict = flask.request.json\n # Json Validation:\n # (didn't have time for this, intended to do json validation and return 406 if schema doesn't match)\n # Top n:\n top_n:int = input_dict['top_n']\n # Encode to user embedding\n query_user_embedding_vector:np.ndarray = encode(input_dict)\n # Calculate Cosine Similarity:\n query_user_embedding_vector = query_user_embedding_vector.reshape(1,-1)\n cosine_sims = cosine_similarity(query_user_embedding_vector,user_embedding_array).ravel()\n # Recommend the top N most similar user\n idx:list = (-cosine_sims).argsort()[:top_n].tolist()\n chosen_cosine_sims:list = cosine_sims[idx].tolist()\n chosen_user_handle:list = user_embedding_df.user_handle.iloc[idx].tolist()\n \n # Get User info\n connection = engine.connect()\n cmd = f'select * from display_table where user_handle in {tuple(chosen_user_handle)};'\n result = connection.execute(text(cmd))\n user_info = result.fetchall()\n connection.close()\n\n # Construct output:\n output_dict = {\n chosen_user_handle[idx]:{\n \"similarity\":chosen_cosine_sims[idx],\n \"assessment_tag\":user_info[idx][1],\n \"course_id\":user_info[idx][2],\n \"interest_tag\":user_info[idx][3],\n } for idx in range(len(chosen_user_handle))}\n \n return json.dumps(output_dict)","def recommend(self, u):\n\n sims = {} #similarities\n recommendation = \"\"\n topScore = None\n start = time.time()\n for movie_id, rating in enumerate(u):\n if rating != 0:\n sims[movie_id] = {}\n for r_id, movie in enumerate(self.ratings):\n sims[movie_id][r_id] = self.distance(movie,self.ratings[movie_id])\n # print time.time() - start, \"distance time\"\n\n start = time.time()\n for i, movieRating in enumerate(self.ratings):\n iPrediction = 0\n for movieName in self.ratedMovieList:\n j = self.titlesOnly.index(movieName)\n iPrediction += sims[j][i]*1.0 * self.userRatingVector[j]\n if topScore is None or iPrediction > topScore:\n movie = self.titlesOnly[i]\n if movie not in self.ratedMovieList and movie not in self.recommendedMovies:\n # print(\"prediction score for %s is %.5f\" % (movie, iPrediction))\n topScore = iPrediction\n recommendation = movie\n # print time.time() - start, \"recommendation time\"\n self.recommendedMovies.append(recommendation)\n\n articlePattern = re.match('(.*), (the|a|an|el|la)', recommendation)\n if articlePattern is not None:\n recommendation = articlePattern.group(2) + \" \" + articlePattern.group(1)\n\n return recommendation","def get_high_scores(self, request):\n users = User.query().fetch(limit=request.number_of_results)\n users = sorted(users, key=lambda x: x.wins, reverse=True)\n return UserForms(items=[user.to_form() for user in users])","def test_ranking_achievements(self):\n client = Client()\n user = create_user('passwordmichu', 'michu')\n client.login(username='michu', password='passwordmichu')\n coll = create_collection('Coleccion de cartas')\n problem = create_select_problem(coll, 'Problema')\n create_an_achievement_of_each(coll)\n submit_select_url = reverse('judge:submit', args=[problem.pk])\n client.post(submit_select_url, {'code': problem.solution}, follow=True)\n ranking_url = reverse('judge:result', args=[coll.pk])\n group_a = create_group('1A')\n group_a.user_set.add(user)\n response = client.get(ranking_url, follow=True)\n self.assertIn('x5', response.content.decode('utf-8'))","async def leaderboard(self, ctx):\r\n rank = 1\r\n strlist = []\r\n for k, v in sorted(player.items(), key=lambda x: expose(x[1]), reverse=True): # operator.itemgetter(1)\r\n position = str(rank) + '. ' + k\r\n while len(position) < 25:\r\n position += '\\u00A0'\r\n position += ' | ' + str(round(expose(v), 2)) + u\"\\u000A\"\r\n strlist.append(position)\r\n rank += 1\r\n indx = 10\r\n table = '\\u200b'.join(strlist[indx-10:indx])\r\n header = ('\\u00A0' * 3) + 'User' + ('\\u00A0' * 20) + 'Rating' + u\"\\u000A\"\r\n divider = '_' * 33 + u\"\\u000A\"\r\n\r\n msg = await ctx.send('```' + u\"\\u000A\" + header + divider + table + divider + u\"\\u000A\" '```')\r\n await msg.add_reaction('⬅')\r\n await msg.add_reaction('➡')\r\n\r\n def check(reaction, user):\r\n return user != msg.author and str(reaction.emoji == '➡' or '⬅')\r\n while True:\r\n try:\r\n reaction, user = await bot.wait_for('reaction_add', timeout=60.0, check=check)\r\n if reaction.emoji == '➡':\r\n await msg.remove_reaction(reaction.emoji, user)\r\n if indx < rank-1:\r\n indx += 10\r\n table = '\\u200b'.join(strlist[indx - 10:indx])\r\n await msg.edit(content='```' + u\"\\u000A\" + header + divider + table + divider + u\"\\u000A\" '```')\r\n if reaction.emoji == '⬅':\r\n await msg.remove_reaction(reaction.emoji, user)\r\n if indx > 10:\r\n indx -= 10\r\n table = '\\u200b'.join(strlist[indx - 10:indx])\r\n await msg.edit(content='```' + u\"\\u000A\" + header + divider + table + divider + u\"\\u000A\" '```')\r\n except:\r\n await msg.remove_reaction('⬅', msg.author)\r\n await msg.remove_reaction('➡', msg.author)\r\n break","def recommend(r ,username, users):\r\n # first find nearest neighbor\r\n nearest = computeNearestNeighbor(r, username, users)[0][1]\r\n recommendations = []\r\n # now find bands neighbor rated that user didn't\r\n neighborRatings = users[nearest]\r\n userRatings = users[username]\r\n for artist in neighborRatings:\r\n if not artist in userRatings:\r\n recommendations.append((artist, neighborRatings[artist]))\r\n # using the fn sorted for variety - sort is more efficient\r\n return sorted(recommendations, key=lambda artistTuple: artistTuple[1], reverse = True)","def match_user(dictionary: 'Climatematch', \n match: 'NGOMatch') -> Dict[str, tuple(int,int)]:\n\n total = {}\n \n for user in dictionary:\n total[user] = {}\n skillsCount = 0\n interestCount = 0\n for sk in dictionary[user]['skills']: \n if sk in match['skills']['technical']:\n skillsCount = skillsCount + 1\n elif sk in match['skills']['interpersonal']:\n skillsCount = skillsCount + 1\n \n for inte in dictionary[user]['interest']: \n if inte in match['interest']:\n interestCount = interestCount + 1\n \n total[user][(skillsCount, interestCount)]\n return total","def get_user_rankings(self, request):\n users = User.query().order(-User.win_ratio)\n return RankForms(items=[user.rank_form() for user in users])","def get_best_matching(self):\r\n from django.db.models import Q, Avg\r\n import copy\r\n import operator\r\n if self.user is None:\r\n raise User.DoesNotExist\r\n users = User.objects.all()\r\n if self.type == FRIENDS_ONLY:\r\n friends = Friends.objects.filter(Q(user_one_id=self.user) | Q(user_two_id=self.user))\r\n if len(friends) > 2:\r\n users = users.filter(Q(id=friends.values('user_one_id')) | Q(id=friends.values('user_two_id')))\r\n\r\n user_sims = {}\r\n prefs = {}\r\n for user in users:\r\n tab = {}\r\n scores = Score.objects.values('game_id', 'score').filter(user_id=user)\r\n for score in scores:\r\n tab.update({score['game_id']: score['score']})\r\n prefs.update({copy.deepcopy(user.id): copy.deepcopy(tab)})\r\n\r\n for user in users:\r\n sim = self.pearson(prefs, self.user, user.id)\r\n user_sims.update({user.id: sim})\r\n\r\n print(user_sims)\r\n del user_sims[self.user] # deletion of user for whom the analysis is beeing performed\r\n user_sims = sorted(user_sims.items(), key=operator.itemgetter(1), reverse=True) # dictionary containing user_ids and users' similarities\r\n if len(user_sims) < 3:\r\n return self.get_most_popular()\r\n\r\n games_f = Score.objects.values('game_id', 'score').filter(user_id=user_sims[0][0]).order_by('-score')[:3]\r\n games_s = Score.objects.values('game_id', 'score').filter(user_id=user_sims[1][0]).order_by('-score')[:3]\r\n\r\n recommended_games = {}\r\n grd = {}\r\n games_f_dict = dict([(g['game_id'], g['score']) for g in games_f])\r\n recommended_games.update(dict(sorted(games_f_dict.items(), key=operator.itemgetter(1), reverse=True)))\r\n\r\n games_s_dict = dict([(g['game_id'], g['score']) for g in games_s])\r\n recommended_games.update(dict(sorted(games_s_dict.items(), key=operator.itemgetter(1), reverse=True)))\r\n\r\n for game in recommended_games:\r\n scores = list(Score.objects.values('game_id').filter(game_id=game).annotate(Avg('score')))\r\n idn = scores[0]['game_id']\r\n avg = scores[0]['score__avg']\r\n grd.update({idn: avg})\r\n\r\n return grd","def assignRanks(self):\r\n\t\trank = 0\r\n\t\tscores = list(self._playerScores)\r\n\t\tscores.reverse()\r\n\t\tfor playerScore in scores:\r\n\t\t\tif not playerScore.has(NOT_MET) or not playerScore.value(NOT_MET):\r\n\t\t\t\trank += 1\r\n\t\t\t\tplayerScore.set(RANK, smallText(BugUtil.colorText(u\"%d\" % rank, ScoreOpt.getRankColor())))\r\n\t\tif rank > 0:\r\n\t\t\tself._anyHas[RANK] = True","def calculate_item_relevance_scores(self, user_similarity_profile):\r\n scores = user_similarity_profile.dot(self.ratings_matrix.matrix)\r\n return self.ratings_matrix.normalize_score_vector(scores)","def rank_results(result_list, search_title, search_artist, uploader_list):\n #scores = []\n #search_artist = search_artist.replace(\"+\", \" \").lower()\n search_title = search_title.replace(\"+\", \" \")\n #search_terms = search_title.split() + search_artist.split()\n\n ## Give score to each result\n #for index, title in enumerate(result_list):\n # title = title.lower()\n # score = 0\n\n # # One point for each word in result title\n # for term in search_terms:\n # if term in title:\n # score += 1\n\n # # 2 points if whole title in result, 2 points for whole artist, 4 points for both\n # if search_title in title:\n # score += 2\n # if search_artist in title:\n # score += 2\n # if search_title in title and search_artist in title:\n # score += 4\n # if search_title == title and (uploader_list[index] == search_artist+\" - topic\" or uploader_list[index] == 'various artists - topic' or uploader_list[index] == search_artist or uploader_list[index] == search_artist+'\\\\xa0'):\n # score += 100\n # if 'karaoke' in title:\n # score-=1000\n\n # scores.append(score)\n\n # return scores.index(max(scores))\n for index, title in enumerate(result_list):\n title = title\n if search_title == title:\n return index\n\n return 0","def test_get_user_rankings(self):\n user = User(name=u'no win', email=u'generic@thingy.com')\n user.put()\n \n userone = User(name=u'one win', email=u'generic@thingy.com', total_played=1, wins=1)\n userone.put()\n \n usertwo = User(name=u'two wins', email=u'generic@thingy.com', total_played=2, wins=1)\n usertwo.put()\n \n user_rankings = users.get_user_rankings()\n self.assertEquals(len(user_rankings), 2)\n self.assertEquals(user_rankings.pop().key, usertwo.key)\n self.assertEquals(user_rankings.pop().key, userone.key)","def tournament_matches_report(self):\n tournament_id = self.select_tournament()\n self._model = Tournament({'id': tournament_id})\n self._view.report(self._model.get_matches())\n\n self.tournament_menu()","def which_ranking(self, request):\n options = [\"user ranking\", \"team ranking\"]\n anwser = main_menu(\"Ranking\", options,\n \"Which ranking you want to see\")\n if anwser == options[0]:\n self.generate_rank(request, True)\n else:\n self.generate_rank(request, False)","def rankResults(self, results:set, queryVec: dict):\n rankedResults = {}\n for result in results:\n cosineSim = 0.0\n for word in queryVec.keys():\n if result in self.invertedIndex[word]:\n cosineSim += queryVec[word]*self.invertedIndex[word][result]\n cosineSim += self.boostScore(result, queryVec.keys())\n rankedResults[result] = cosineSim\n\n return rankedResults","def recommend_for_user(self, R, user, n=10, filter_previously_seen=False,\n return_scores=True, **kwargs):","def update_score(self, match, i):\n self.match_views.update_score(match)\n score_p1 = input(f\"Veuillez rentrer le score de \"\n f\"{match[0][0]['first_name']} \"\n f\"{match[0][0]['last_name']} (1/0.5/0) \")\n score_p2 = input(f\"Veuillez rentrer le score de \"\n f\"{match[1][0]['first_name']} \"\n f\"{match[1][0]['last_name']} (1/0.5/0) \")\n self.validate_score(score_p1, score_p2, match, i)\n new_score = float(score_p1)\n new_score2 = float(score_p2)\n return new_score, new_score2","def update(self, rank):\n # calculate MR and MRR\n self.mr += rank\n self.mrr += 1 / rank\n # calculate Hits@k\n if rank <= 1:\n self.hits1 += 1\n self.hits3 += 1\n self.hits5 += 1\n self.hits10 += 1\n elif rank <= 3:\n self.hits3 += 1\n self.hits5 += 1\n self.hits10 += 1\n elif rank <= 5:\n self.hits5 += 1\n self.hits10 += 1\n elif rank <= 10:\n self.hits10 += 1","def rank_partial(self, instance):\n instance = self.rank_generate_all_views(instance)\n instance.getM()\n instance.getW()\n instance.getScore()\n self.instance = instance","def perform_set_score(responder, options):\n match = options['<match-id>']\n tla = options['<tla>']\n score = options['<score>']\n scores.set_match_score(match, tla, score)\n responder('Scored {0} points for {1} in match {2}'.format(score, tla, match))","async def rank(self, channel_id, user_infos, user_id, team_id):\n\n with codecs.open(\"{}/ranking.json\".format(self.dirname), \"r\", \"utf-8\") as json_data:\n data = json.load(json_data)\n\n user = \"{}-{}\".format(user_infos[\"user\"][\"name\"], str(team_id)) # User string to write\n\n if data.get(user):\n # (Epic line to) get the position from ranking array revert sorted depending on the user\n rank = sorted(data.items(), key=operator.itemgetter(1))[::-1].index((user, data.get(user))) + 1\n # Score info message\n message = \"Votre score est de : {} \\n Place au classement : {}\".format(str(data[user]), str(rank))\n else:\n message = \"Vous n'avez pas encore de score. Veuillez jouer !\" # Never played yet\n return await self.sendText(message, channel_id,user_infos, team_id)","def search(query):\n has_some = []\n # list of all users\n users = database_controller.get_all_users()\n # has_all starts off with every user, removes those who don't have one of the required skills\n has_all = users.copy()\n # iterate through every user and look up their skills\n for user in users:\n for skillpath, min_level in query.items():\n # get the skill_id from the database\n skill_id = database_controller.get_skill(skillpath).id\n # get the association of the user with current skill and according minimum level\n skill_assoc = database_controller.get_assocs(users_id=user.id,\n skill_id=skill_id,\n level=min_level,\n type=\"first\")\n # skill_assoc will be None if the user does not have the skill on the desired level or higher\n if skill_assoc is not None:\n # adds user to has_some, in case he does not have other skills\n if user not in has_some:\n has_some.append(user)\n # if the user does not have the current skill at the required level, he gets removed from has_all\n else:\n if user in has_all:\n has_all.remove(user)\n # remove intersection of has_all and has_some\n for user in has_all:\n if user in has_some:\n has_some.remove(user)\n # extract ProfileModels from the results and return them in a dictionary\n has_all = database_controller.get_profile_models(has_all)\n has_some = database_controller.get_profile_models(has_some)\n # sort the results by descending order of sum of queried skills\n has_all.sort(key=lambda p: database_controller.sum_relevant_skills(p, list(query.keys())), reverse=True)\n has_some.sort(key=lambda p: database_controller.sum_relevant_skills(p, list(query.keys())), reverse=True)\n return dict(has_all=has_all, has_some=has_some)","def view_scores(jenni, input):\n scores.view_scores(jenni, input)","def set_rank_order(self):\n for k in self._run:\n self._run[k].sort(key=lambda x:x.get_rank(),reverse=False)\n tot_res = len(self._run[k])\n for r in self._run[k]:\n r.set_score(tot_res - int(r.get_rank()) + 1)\n print r.get_str()","async def team_search(self, ctx: commands.Context, username: str):\n all_usernames = {team_id: team.username for team_id, team in self.teams.items()\n if team is not None}\n suggestions = []\n log.info(repr(fuzzywuzzy.process.extract(\n username, all_usernames, limit=5)))\n for fuzz_username, rating, fuzz_id in fuzzywuzzy.process.extract(\n username, all_usernames, limit=5):\n if rating < 50:\n break\n fuzz_team = self.teams[fuzz_id]\n suggestions.append(\n f'(ID: **{fuzz_team.team_id}**) **{fuzz_team.display_name[:40]}**'\n f' -- {len(fuzz_team.users)} registered members')\n if suggestions:\n await ctx.send('\\n'.join(suggestions))\n else:\n await ctx.send(f\"Couldn't find any teams whose usernames resembled `{username}`\")","def display_current_match(i, player_ratings):\r\n print('MATCH', int(i/2+1))\r\n print(players_table.get(doc_id=player_ratings[i][0])['Nom'], \"(BLANCS)\",\r\n \"contre\",\r\n players_table.get(doc_id=player_ratings[i+1][0])['Nom'], \"(NOIRS)\")","def bestscorers():\n players = Player.query.filter_by(is_admin=False).all()\n players = list(reversed(sorted(players, key=lambda player: player.goals)))\n for player in players:\n player.image = url_for('static', filename='images/players/{}'.format(player.image))\n player.team_name = player.team.name\n player.team_logo = url_for('static', filename='images/teams/{}'.format(player.team.logo_image))\n\n return render_template('standings/best-scorers.html', players=players, title='Best Scorers')","def rank(self, urlids, wordids):\r\n\t\tself.score()\r\n\t\treturn sorted(self.scores.items(), key=lambda v:v[1], reverse=self.rev)","def update_player_rank(self, player, rank, match):\n\n # if player isn't moving, do nothing\n if player.rank == rank:\n return\n\n # if player doesn't have a rank yet, make them last\n if player.rank is None:\n player.rank = self.player_set.count()\n\n # update all the players in the company at or below the target rank\n # but greater than the player's former rank\n ##(self.player_set\n ## .filter(rank__isnull=False, rank__gte=rank, rank__lt=player.rank)\n ## .update(rank=F('rank') + 1)\n ## )\n passed_players = (self.player_set\n .filter(rank__isnull=False, rank__gte=rank, rank__lt=player.rank))\n for p in passed_players:\n p.update_rank(p.rank + 1, match)\n\n # update target player\n player.update_rank(rank, match)","def display_round_matches(player_ratings):\r\n print('\\nMATCHES DE LA RONDE: ')\r\n for i in range(0, len(player_ratings), 2):\r\n print(players_table.get(doc_id=player_ratings[i][0])['Nom'], \"(BLANCS)\",\r\n \"contre\",\r\n players_table.get(doc_id=player_ratings[i+1][0])['Nom'], \"(NOIRS)\")","def modify_ranking(tournament):\n database = TinyDB('db.json')\n # recuperation de tous les joueurs du tournoi\n players_table = database.table('players')\n list_players = get_player_list(tournament)\n # Modification du rang joueur par joueur\n for player in list_players:\n new_ranking = view.modify_ranking_view(player)\n players_table.update({'Classement': new_ranking}, doc_ids=[player.doc_id])","def fullleaderboard (request):\n # Define views here\n score_submit = EventEntryModel.objects.exclude(winner__isnull=True).count()\n active_players = PlayerModel.objects.all()\n\n loaded_points = list(EventEntryModel.objects.aggregate(Sum('points')).values())[0]\n awarded_points = list(EventEntryModel.objects.exclude(winner__isnull=True).aggregate(Sum('points')).values())[0]\n\n context = {\n 'score_submit': score_submit,\n 'active_players': active_players,\n 'loaded_points': loaded_points,\n 'awarded_points': awarded_points,\n }\n\n return render(request, 'fullLeaderboard.html', context=context)","async def ranks(self, ctx):\n database = db[str(ctx.guild.id)]\n all_users = database.find({}).sort('xp', -1)\n\n rank = ''\n index = 1\n for doc in all_users:\n try:\n user = self.client.get_user(doc['user_id'])\n if user is not None and not user.bot:\n s = f'**{index})** {user.display_name}: {doc[\"xp\"]} XP | Level: {get_level_from_xp(doc[\"xp\"])}\\n'\n rank += s\n else:\n continue\n except KeyError:\n continue\n index += 1\n\n rank_embed = discord.Embed(title='Rank', description=rank, color=discord.Color(random.randint(1, 16777215)))\n await ctx.send(embed=rank_embed)","def ranked(pmi_ranking, tscore_ranking, logdice_ranking, tscore, PMI, logdice):\r\n tsc_rank = tscore_ranking[(float(tscore))]\r\n pmi_rank = pmi_ranking[float(PMI)]\r\n logdice_rank = logdice_ranking[float(logdice)]\r\n summary_tscpmi = tsc_rank + pmi_rank\r\n summary_logdpmi = logdice_rank + pmi_rank\r\n return tsc_rank, pmi_rank, logdice_rank, summary_tscpmi, summary_logdpmi","def results():\n df = mdr.elo_ratings.sort_values([\"elo\", \"matches\"], ascending=False)\n df[\"score\"] = (1 / (1 + 10**((1500 - df[\"elo\"]) / 400))) * 100\n df = df[[\"items\", \"score\", \"matches\"]]\n\n html_formatted = df.to_html(classes=[\"table\", \"table-dark\", \"table-hover\"], index=False, float_format=\"%.1f\")\n\n return render_template('results.html', table=html_formatted)","def userSuggestions(database):\n firstname=str(input(\"who do you want to have follow suggestions for :\"))\n usr,find=getByName(database,firstname)\n if not find:\n print(\"the User could not be found\")\n return\n else:\n following=[]\n followers=[]\n for folower in usr.folowed:\n followers.append(folower)\n for folowed in usr.folow:\n following.append(folowed)\n results=[]\n print(\"On what do you want your suggestions to be based on?\\n1. Mutual Interests\\n2. Mutual Connections\\n3. Both\")\n choice=int(input(\"Your choice :\"))\n for key ,usrs in database.items():\n if key not in following: \n correspondant=0\n if choice == 1 or choice == 3:\n for interest in usr.interest:\n if interest in usrs.interest:\n correspondant+=1\n if choice == 2 or choice == 3:\n for folower in followers:\n for folows in usrs.folowed:\n if key == folows:\n correspondant+=1\n results.append([key,correspondant])\n for i in range(len(results)):\n for j in range(0, len(results)-i-1):\n if results[j][1] > results[j+1][1] :\n results[j], results[j+1] = results[j+1], results[j]\n for k in range(5):\n print(results[k][0])","def update_ranking(self, session: Session, sensitivity: int, verbose: bool = False) -> None:\n\n if verbose is True:\n print(f\"Updating {self.uri}...\", end=\" \")\n\n best_vector = _Analysis.make_best_vector(self.levenshtein_vector, sensitivity)\n\n self.ranking = _Ranking()\n self.ranking.score_sum = _Analysis.make_score_sum(best_vector)\n self.ranking.score_average = _Analysis.make_score_average(best_vector)\n self.ranking.score_coverage = _Analysis.make_score_coverage(best_vector)\n self.ranking.recall = _Analysis.make_recall(len(session._scheme.concepts), self.ranking.score_coverage)\n\n if verbose is True:\n print(\"updated.\")","def calculate_scores():\n all_people = models.Leaderboard.query.order_by(\n models.Leaderboard.score.desc()).all()\n print(all_people)\n users = []\n scores = []\n for person in all_people:\n users.append(person.username)\n scores.append(person.score)\n return users, scores","def update_ranking(player):\n database = TinyDB('db.json')\n # recuperation de tous les joueurs du tournoi\n players_table = database.table('players')\n new_ranking = view.modify_ranking_view(player)\n players_table.update({'Classement': new_ranking}, doc_ids=[player.doc_id])","def resultat_match(self, binomes):\n for binome in binomes:\n while True:\n score_un = self.vue.entree_resultats(binome[0])\n score_deux = self.vue.entree_resultats(binome[1])\n if score_un + score_deux != 1:\n self.vue.erreur_score()\n continue\n else:\n binome[0].ajout_score(score_un)\n binome[1].ajout_score(score_deux)\n table_players.update({\"score\": binome[0].points},\n doc_ids=[binome[0].id])\n table_players.update({\"score\": binome[1].points},\n doc_ids=[binome[1].id])\n break\n self.vue.afficher_resultats(binomes)","def _process_match(self, mysql_user, friven_user):\n match_record = self._create_match_record(mysql_user=mysql_user,\n friven_user=friven_user)\n self._metrics_collector.add_sample_row(row_dict=match_record)\n self._metrics_collector.increment_matches()\n self._mysql_writer.add_record(match_record=match_record)","def top_matches(prefs, person, n=5, similarity=sim_pearson):\n scores = [(similarity(prefs, person, other), other)\n for other in prefs if other != person]\n\n scores.sort()\n scores.reverse()\n return scores[0:n]","def recommendUB(user, neighbors, usermovHistDict, topK = 200, nRec = 30):\n simSumDict = defaultdict(float)# similarity sum\n weightedSumDict = defaultdict(float)\n # weighted rating sum\n movIDUserRated = usermovHistDict.get(user, [])\n for (neighbor, simScore, numCommonRating) in neighbors[:topK]:\n mrlistpair = usermovHistDict.get(neighbor)\n if mrlistpair:\n for index in range(0, len(mrlistpair[0])):\n movID = mrlistpair[0][index]\n simSumDict[movID] += simScore\n weightedSumDict[movID] += simScore * mrlistpair[1][index]# sim * rating\n candidates = [(mID, 1.0 * weightedSumDict[mID] / simSumDict[mID]) for (mID) in weightedSumDict]\n candidates.sort(key=lambda x: x[1], reverse=True)\n return (user, candidates[:nRec])","def score(self, user_idx, item_idx=None):\n if item_idx is None:\n if self.train_set.is_unk_user(user_idx):\n raise ScoreException(\n \"Can't make score prediction for (user_id=%d)\" % user_idx\n )\n\n known_item_scores = self.V.dot(self.U[user_idx, :])\n return known_item_scores\n else:\n if self.train_set.is_unk_user(user_idx) or self.train_set.is_unk_item(\n item_idx\n ):\n raise ScoreException(\n \"Can't make score prediction for (user_id=%d, item_id=%d)\"\n % (user_idx, item_idx)\n )\n\n user_pred = self.V[item_idx, :].dot(self.U[user_idx, :])\n\n user_pred = sigmoid(user_pred)\n if self.train_set.min_rating == self.train_set.max_rating:\n user_pred = scale(user_pred, 0.0, self.train_set.max_rating, 0.0, 1.0)\n else:\n user_pred = scale(\n user_pred,\n self.train_set.min_rating,\n self.train_set.max_rating,\n 0.0,\n 1.0,\n )\n\n return user_pred","def liste_triee():\n\n global liste_username_score\n\n liste_username_score = sorted(liste_username_score, reverse = True, key=score_trie)","def update_rankings(conn, users):\n users.sort(key=lambda user: user[\"rank\"])\n # Set tau and draw_probability to more reasonable values than the defaults\n if config.COMPETITION_FINALS_PAIRING:\n trueskill.setup(tau=0.0, draw_probability=0.001)\n else:\n trueskill.setup(tau=0.008, draw_probability=0.001)\n teams = [[trueskill.Rating(mu=user[\"mu\"], sigma=user[\"sigma\"])]\n for user in users]\n new_ratings = trueskill.rate(teams)\n for user, rating in zip(users, new_ratings):\n new_score = rating[0].mu - 3*rating[0].sigma\n conn.execute(model.bots.update().where(\n (model.bots.c.user_id == user[\"user_id\"]) &\n (model.bots.c.id == user[\"bot_id\"]) &\n # Filter on version so we don't update the score for an old\n # version of the bot\n (model.bots.c.version_number == user[\"version_number\"])\n ).values(\n mu=rating[0].mu,\n sigma=rating[0].sigma,\n score=new_score,\n ))\n\n # Update the hackathon scoring tables\n hackathons = conn.execute(sqlalchemy.sql.select([\n model.hackathons.c.id.label(\"hackathon_id\"),\n ]).select_from(\n model.hackathon_participants.join(\n model.hackathons,\n (model.hackathon_participants.c.hackathon_id == model.hackathons.c.id) &\n (model.hackathon_participants.c.user_id == user[\"user_id\"])\n )\n ).where(\n (model.hackathons.c.start_date <= sqlalchemy.sql.func.now()) &\n (model.hackathons.c.end_date > sqlalchemy.sql.func.now())\n ))\n\n for hackathon in hackathons.fetchall():\n hackathon_id = hackathon[\"hackathon_id\"]\n try:\n # Try and insert\n insert_values = {\n \"hackathon_id\": hackathon_id,\n \"user_id\": user[\"user_id\"],\n \"bot_id\": user[\"bot_id\"],\n \"score\": new_score,\n \"mu\": rating[0].mu,\n \"sigma\": rating[0].sigma,\n \"version_number\": user[\"version_number\"],\n \"language\": user[\"language\"],\n \"games_played\": 1,\n }\n\n conn.execute(\n model.hackathon_snapshot.insert().values(\n **insert_values))\n except sqlalchemy.exc.IntegrityError:\n # Row exists, update\n condition = ((model.hackathon_snapshot.c.hackathon_id ==\n hackathon_id) &\n (model.hackathon_snapshot.c.user_id ==\n user[\"user_id\"]) &\n (model.hackathon_snapshot.c.bot_id ==\n user[\"bot_id\"]))\n conn.execute(\n model.hackathon_snapshot.update().values(\n score=new_score,\n mu=rating[0].mu,\n sigma=rating[0].sigma,\n version_number=user[\"version_number\"],\n language=user[\"language\"],\n games_played=model.hackathon_snapshot.c.games_played + 1,\n ).where(condition))","def compare_users(userA, userB):\n reader = open(\"similarities.txt\", \"r\")\n lines = reader.readlines()\n similarity = None\n for line in lines:\n a, b, sim = line.split(\"\\t\")\n if userA == a and userB == b:\n print(sim)\n similarity = float(sim)\n\n reader2 = open(\"sorted_similarities.txt\", \"r\")\n lines = reader2.readlines()\n index = 0.0\n for line in lines:\n sim = float(line)\n if similarity == sim:\n return 1.0 - (float(index) / len(lines))\n index += 1\n\n return None\n \n \n \"\"\"\n if len(sim_dict.keys()) == 0:\n return None\n\n ranked = sorted(sim_dict.keys(), key=lambda x: sim_dict[x],\n reverse=True)\n try:\n index = ranked.index(userB)\n percentile = 1.0 - (float(index) / len(sim_dict.keys()))\n return int(100 * percentile)\n except:\n return None\n \"\"\"","def rd1leaderboard(request):\n\n #Add views\n playing_players = Rd1SlotModel.objects.filter(player_name__isnull=False)\n\n endurance_leader = Rd1SlotModel.objects.aggregate(Max('endurance_score'))\n\n #Add context\n context = {\n 'playing_players': playing_players,\n 'endurance_leader': endurance_leader,\n }\n\n return render(request, 'rd1Leaderboard.html', context=context)","def _rank(self):\r\n return sorted(self.player_points.items(),key=lambda x:x[1],reverse=True)","def scoring(self):\n pass","def score(self, model, context):\n pass","def add_match(self, match):\n\n # update cache results with game result\n if self.cached_results:\n results = json.loads(self.cached_results)\n else:\n results = []\n\n winner = match.winner == self\n opponent = match.loser if winner else match.winner\n new_rating = match.winner_rating_after if winner else \\\n match.loser_rating_after\n\n results.append({\n 'winner': winner,\n 'opponent_name': opponent.name,\n 'played_time': str(match.played_time),\n 'played_timestamp': get_timestamp(match.played_time)\n })\n self.cached_results = json.dumps(results[-CACHED_RATING_LIMIT:])\n\n # update player with new rating\n self.update_rating(new_rating, match)\n\n # save the player in the database\n self.save()","async def ranking(self, channel_id,user_infos, user_id, team_id):\n message = \"Top 10 du Quizz Movie : \\n\" # Begin the ranking message\n\n with codecs.open(\"{}/ranking.json\".format(self.dirname), \"r\", \"utf-8\") as json_data: # Open the ranking file read mode\n data = json.load(json_data)\n\n # Get the top ten players reverse score ordered\n l = sorted(data.items(), key=operator.itemgetter(1), reverse=True)[0:10]\n\n for i in range(len(l)): # For each top ten players\n message += \"Numéro {} : {}, Score : {}\\n\".format(str(i+1), l[i][0], str(l[i][1]))\n return await self.sendText(message, channel_id,user_infos, team_id)","def get(self, request, search_string=None):\n query = SearchQuery(search_string)\n\n username_vector = SearchVector('username', weight='A')\n first_name_vector = SearchVector('first_name', weight='B')\n last_name_vector = SearchVector('last_name', weight='B')\n email_vector = SearchVector('email', weight='B')\n vectors = username_vector + first_name_vector + last_name_vector + email_vector\n qs = User.objects\n qs = qs.annotate(search=vectors).filter(search=query)\n qs = qs.annotate(rank=SearchRank(vectors, query)).order_by('-rank')\n print(qs)\n return Response(UserSerializer(qs, many=True).data)","def playerdetail (request,name):\n\n #Basic player details\n player_image = PlayerModel.objects.get(name=name).image\n player_HC = PlayerModel.objects.get(name=name).HC\n player_highfinish = PlayerModel.objects.get(name=name).highfinish\n player_tournum = PlayerModel.objects.get(name=name).tournum\n player_totalpoints = LeaderBoardModel.objects.get(player=name).overall_total\n player_totalrank = LeaderBoardModel.objects.filter(overall_total__gt=player_totalpoints).count() + 1\n\n\n target_holes = 10 #Change to 10 in production\n\n ##START ROUND 1 CALCULATIONS -->\n #Trigger to show score only when round finished\n try:\n rd1holes_played = Rd1SlotModel.objects.get(player_name__name=name).player_holesplayed\n if rd1holes_played is None:\n rd1holes_played = 0\n else:\n rd1holes_played = Rd1SlotModel.objects.get(player_name__name=name).player_holesplayed\n except:\n rd1holes_played = 0\n\n\n\n #Rd1 Player golf score & rank\n if rd1holes_played >= target_holes:\n rd1golf_score = Rd1SlotModel.objects.get(player_name__name=name).player_score\n rd1golf_scoreRank = Rd1SlotModel.objects.filter(player_score__lt=rd1golf_score).count() + 1\n rd1golf_stbl = Rd1SlotModel.objects.get(player_name__name=name).player_stbl\n rd1golf_stblRank = Rd1SlotModel.objects.filter(player_stbl__gt=rd1golf_stbl).count() + 1\n else:\n rd1golf_score = \"-\"\n rd1golf_scoreRank= \"n/a\"\n rd1golf_stbl = \"-\"\n rd1golf_stblRank= \"n/a\"\n\n #Rd1PlayerPoints\n try:\n rd1golf_points = LeaderBoardModel.objects.get(player=name).rd1_golf\n except:\n rd1golf_points = \"-\"\n try:\n rd1golf_rank = LeaderBoardModel.objects.filter(rd1_golf__gt=rd1golf_points).count() + 1\n except:\n rd1golf_rank = \"-\"\n try:\n rd1ctpld_points = LeaderBoardModel.objects.get(player=name).rd1_ctpld\n except:\n rd1ctpld_points = \"-\"\n try:\n rd1ctpld_rank = LeaderBoardModel.objects.filter(rd1_ctpld__gt=rd1ctpld_points).count() + 1\n except:\n rd1ctpld_rank = \"-\"\n try:\n rd1bonus_points = LeaderBoardModel.objects.get(player=name).rd1_bonus\n except:\n rd1bonus_points = \"-\"\n try:\n rd1bonus_rank = LeaderBoardModel.objects.filter(rd1_bonus__gt=rd1bonus_points).count() + 1\n except:\n rd1bonus_rank = \"-\"\n try:\n rd1total_points = rd1golf_points + rd1ctpld_points + rd1bonus_points\n except:\n rd1total_points = \"-\"\n try:\n rd1total_rank = LeaderBoardModel.objects.filter(rd1_total__gt=rd1total_points).count() + 1\n except:\n rd1total_rank = \"-\"\n\n try:\n round1overall_points = list(LeaderBoardModel.objects.aggregate(Sum('rd1_total')).values())[0]\n except:\n round1overall_points = 0\n\n\n ##START ROUND 2 CALCULATIONS -->\n #Trigger to show score only when round finished\n try:\n rd2holes_played = Rd2SlotModel.objects.get(player_name__name=name).player_holesplayed\n if rd2holes_played is None:\n rd2holes_played = 0\n else:\n rd2holes_played = Rd2SlotModel.objects.get(player_name__name=name).player_holesplayed\n except:\n rd2holes_played = 0\n\n #Rd2 Player golf score & rank\n if rd2holes_played >= target_holes:\n rd2golf_score = Rd2SlotModel.objects.get(player_name__name=name).player_score\n rd2golf_scoreRank = Rd2SlotModel.objects.filter(player_score__lt=rd2golf_score).count() + 1\n rd2golf_stbl = Rd2SlotModel.objects.get(player_name__name=name).player_stbl\n rd2golf_stblRank = Rd2SlotModel.objects.filter(player_stbl__gt=rd2golf_stbl).count() + 1\n else:\n rd2golf_score = \"-\"\n rd2golf_scoreRank= \"n/a\"\n rd2golf_stbl = \"-\"\n rd2golf_stblRank= \"n/a\"\n\n #Rd2PlayerPoints\n try:\n rd2golf_points = LeaderBoardModel.objects.get(player=name).rd2_golf\n except:\n rd2golf_points = \"-\"\n try:\n rd2golf_rank = LeaderBoardModel.objects.filter(rd2_golf__gt=rd2golf_points).count() + 1\n except:\n rd2golf_rank = \"-\"\n try:\n rd2ctpld_points = LeaderBoardModel.objects.get(player=name).rd2_ctpld\n except:\n rd2ctpld_points = \"-\"\n try:\n rd2ctpld_rank = LeaderBoardModel.objects.filter(rd2_ctpld__gt=rd2ctpld_points).count() + 1\n except:\n rd2ctpld_rank = \"-\"\n try:\n rd2bonus_points = LeaderBoardModel.objects.get(player=name).rd2_bonus\n except:\n rd2bonus_points = \"-\"\n try:\n rd2bonus_rank = LeaderBoardModel.objects.filter(rd2_bonus__gt=rd2bonus_points).count() + 1\n except:\n rd2bonus_rank = \"-\"\n try:\n rd2total_points = rd2golf_points + rd2ctpld_points + rd2bonus_points\n except:\n rd2total_points = \"-\"\n try:\n rd2total_rank = LeaderBoardModel.objects.filter(rd2_total__gt=rd2total_points).count() + 1\n except:\n rd2total_rank = \"-\"\n\n try:\n round2overall_points = list(LeaderBoardModel.objects.aggregate(Sum('rd2_total')).values())[0]\n except:\n round2overall_points = 0\n\n ##START ROUND 3 CALCULATIONS -->\n #Trigger to show score only when round finished\n try:\n rd3holes_played = Rd3SlotModel.objects.get(player_name__name=name).player_holesplayed\n if rd3holes_played is None:\n rd3holes_played = 0\n else:\n rd3holes_played = Rd3SlotModel.objects.get(player_name__name=name).player_holesplayed\n except:\n rd3holes_played = 0\n\n #Rd3 Player golf score & rank\n if rd3holes_played >= target_holes:\n rd3golf_score = Rd3SlotModel.objects.get(player_name__name=name).player_score\n rd3golf_scoreRank = Rd3SlotModel.objects.filter(player_score__lt=rd2golf_score).count() + 1\n rd3golf_stbl = Rd3SlotModel.objects.get(player_name__name=name).player_stbl\n rd3golf_stblRank = Rd3SlotModel.objects.filter(player_stbl__gt=rd2golf_stbl).count() + 1\n else:\n rd3golf_score = \"-\"\n rd3golf_scoreRank= \"n/a\"\n rd3golf_stbl = \"-\"\n rd3golf_stblRank= \"n/a\"\n\n #Rd2PlayerPoints\n try:\n rd3golf_points = LeaderBoardModel.objects.get(player=name).rd3_golf\n except:\n rd3golf_points = \"-\"\n try:\n rd3golf_rank = LeaderBoardModel.objects.filter(rd3_golf__gt=rd3golf_points).count() + 1\n except:\n rd3golf_rank = \"-\"\n try:\n rd3ctpld_points = LeaderBoardModel.objects.get(player=name).rd3_ctpld\n except:\n rd3ctpld_points = \"-\"\n try:\n rd3ctpld_rank = LeaderBoardModel.objects.filter(rd3_ctpld__gt=rd3ctpld_points).count() + 1\n except:\n rd3ctpld_rank = \"-\"\n try:\n rd3bonus_points = LeaderBoardModel.objects.get(player=name).rd3_bonus\n except:\n rd3bonus_points = \"-\"\n try:\n rd3bonus_rank = LeaderBoardModel.objects.filter(rd3_bonus__gt=rd3bonus_points).count() + 1\n except:\n rd3bonus_rank = \"-\"\n try:\n rd3total_points = rd3golf_points + rd3ctpld_points + rd3bonus_points\n except:\n rd3total_points = \"-\"\n try:\n rd3total_rank = LeaderBoardModel.objects.filter(rd3_total__gt=rd3total_points).count() + 1\n except:\n rd3total_rank = \"-\"\n\n try:\n round3overall_points = list(LeaderBoardModel.objects.aggregate(Sum('rd3_total')).values())[0]\n except:\n round3overall_points = 0\n\n ##START OTHER_SCORES CALCULATIONS -->\n\n #Other Player Points\n try:\n social_points = LeaderBoardModel.objects.get(player=name).social\n except:\n social_points = \"-\"\n try:\n social_rank = LeaderBoardModel.objects.filter(social__gt=social_points).count() + 1\n except:\n social_rank = \"-\"\n try:\n bestdressed_points = LeaderBoardModel.objects.get(player=name).best_dressed\n except:\n bestdressed_points = \"-\"\n try:\n bestdressed_rank = LeaderBoardModel.objects.filter(best_dressed__gt=bestdressed_points).count() + 1\n except:\n bestdressed_rank = \"-\"\n try:\n tipping_points = LeaderBoardModel.objects.get(player=name).tipping\n except:\n tipping_points = \"-\"\n try:\n tipping_rank = LeaderBoardModel.objects.filter(tipping__gt=tipping_points).count() + 1\n except:\n tipping_rank = \"-\"\n try:\n othertotal_points = social_points + bestdressed_points + tipping_points\n except:\n othertotal_points = \"-\"\n try:\n othertotal_rank = LeaderBoardModel.objects.filter(other_total__gt=othertotal_points).count() + 1\n except:\n othertotal_rank = \"-\"\n\n try:\n otheroverall_points = list(LeaderBoardModel.objects.aggregate(Sum('other_total')).values())[0]\n except:\n otheroverall_points = 0\n\n## == END SCORING CALCS ==\n\n context ={\n 'name': name,\n 'player_image': player_image,\n 'player_HC': player_HC,\n 'player_highfinish': player_highfinish,\n 'player_tournum': player_tournum,\n 'player_totalpoints': player_totalpoints,\n 'player_totalrank': player_totalrank,\n 'rd1golf_score': rd1golf_score,\n 'rd1golf_stbl': rd1golf_stbl,\n 'rd1golf_scoreRank': rd1golf_scoreRank,\n 'rd1golf_stblRank': rd1golf_stblRank,\n 'rd1golf_points': rd1golf_points,\n 'rd1golf_rank': rd1golf_rank,\n 'rd1ctpld_points': rd1ctpld_points,\n 'rd1ctpld_rank': rd1ctpld_rank,\n 'rd1bonus_points': rd1bonus_points,\n 'rd1bonus_rank': rd1bonus_rank,\n 'rd1total_points': rd1total_points,\n 'rd1total_rank': rd1total_rank,\n 'round1overall_points': round1overall_points,\n 'rd2golf_score': rd2golf_score,\n 'rd2golf_stbl': rd2golf_stbl,\n 'rd2golf_scoreRank': rd2golf_scoreRank,\n 'rd2golf_stblRank': rd2golf_stblRank,\n 'rd2golf_points': rd2golf_points,\n 'rd2golf_rank': rd2golf_rank,\n 'rd2ctpld_points': rd2ctpld_points,\n 'rd2ctpld_rank': rd2ctpld_rank,\n 'rd2bonus_points': rd2bonus_points,\n 'rd2bonus_rank': rd2bonus_rank,\n 'rd2total_points': rd2total_points,\n 'rd2total_rank': rd2total_rank,\n 'round2overall_points': round2overall_points,\n 'rd3golf_score': rd3golf_score,\n 'rd3golf_stbl': rd3golf_stbl,\n 'rd3golf_scoreRank': rd3golf_scoreRank,\n 'rd3golf_stblRank': rd3golf_stblRank,\n 'rd3golf_points': rd3golf_points,\n 'rd3golf_rank': rd3golf_rank,\n 'rd3ctpld_points': rd3ctpld_points,\n 'rd3ctpld_rank': rd3ctpld_rank,\n 'rd3bonus_points': rd3bonus_points,\n 'rd3bonus_rank': rd3bonus_rank,\n 'rd3total_points': rd3total_points,\n 'rd3total_rank': rd3total_rank,\n 'round3overall_points': round3overall_points,\n 'social_points': social_points,\n 'social_rank': social_rank,\n 'bestdressed_points': bestdressed_points,\n 'bestdressed_rank': bestdressed_rank,\n 'tipping_points': tipping_points,\n 'tipping_rank': tipping_rank,\n 'othertotal_points': othertotal_points,\n 'othertotal_rank': othertotal_rank,\n 'otheroverall_points': otheroverall_points,\n\n }\n\n return render(request, 'playerDetail.html', context=context)","def order_players_by_initial_rank(self):\n pass","def personalization(prediction, n):\n # prediction\n # n top n recommendation\n\n top_n = get_top_n(prediction, n)\n\n rec_dict = {}\n for uid, user_ratings in top_n.items():\n rec_dict[uid] = [iid for (iid, _) in user_ratings]\n\n rec_user_ls = [pred[0] for pred in prediction]\n rec_item_ls = [pred[1] for pred in prediction]\n\n unique_rec_user_ls = np.unique(rec_user_ls)\n unique_rec_item_ls = np.unique(rec_item_ls)\n\n # assign each item with index number\n unique_rec_item_dict = {item: ind for ind,\n item in enumerate(unique_rec_item_ls)}\n\n n_unique_rec_user = len(unique_rec_user_ls)\n n_unique_rec_item = len(unique_rec_item_ls)\n\n # recommended user item matrix\n rec_matrix = np.zeros(shape=(n_unique_rec_user, n_unique_rec_item))\n\n # represent recommended item for each user as binary 0/1\n for user in range(n_unique_rec_user):\n # get userid\n user_id = unique_rec_user_ls[user]\n # get rec item list\n item_ls = rec_dict[user_id]\n\n for item_id in item_ls:\n # get item index\n item = unique_rec_item_dict[item_id]\n rec_matrix[user, item] = 1\n\n # calculate cosine similarity matrix across all user recommendations\n similarity = cosine_similarity(X=rec_matrix, dense_output=False)\n # calculate average of upper triangle of cosine matrix\n upper_right = np.triu_indices(similarity.shape[0], k=1)\n # personalization is 1-average cosine similarity\n score = 1 - np.mean(similarity[upper_right])\n return score","def bestplayers():\n players = Player.query.filter_by(is_admin=False).all()\n players = list(reversed(sorted(players, key=lambda player: player.points)))\n for player in players:\n player.image = url_for('static', filename='images/players/{}'.format(player.image))\n player.team_name = player.team.name\n player.team_logo = url_for('static', filename='images/teams/{}'.format(player.team.logo_image))\n\n return render_template('standings/best-players.html', players=players, title='Best Players')","def suggestions(request):\n # Get login user profile\n u_profile = UserProfile.objects.get(user=request.user)\n # Search by user profile\n sqs = SearchQuerySet().filter(business_area=u_profile.business_area, city=u_profile.city,\n handicap_36__lte=int(u_profile.handicap_36) + 1,\n handicap_36__gte=int(u_profile.handicap_36) - 1,\n handicap_us__lte=int(u_profile.handicap_us) + 1,\n handicap_us__gte=int(u_profile.handicap_us) - 1)\n # Get user list\n queryset = User.objects.all()\n # Create result list\n results_list = []\n # Get User to list by id\n max_loop = sqs.count()\n for x in range(0, max_loop):\n user = get_object_or_404(queryset, pk=sqs[x].object.id)\n results_list.append(user)\n # Convert to serializer\n serializer = UserSerializer(results_list, many=True)\n if serializer.is_valid:\n return Response({'status': '200', 'code': 'OK_SUGGESTION',\n 'detail': serializer.data}, status=200)\n else:\n return Response({'status': '400', 'code': 'E_INVALID_PARAMETER_VALUES',\n 'detail': serializer.errors}, status=400)","def recommend_me_team(self, user_id: int, user_ids_per_group: list, n_recommendations: int):\n res_arr = np.array([self.user_similarity.loc[user_id, group_ids].mean() for group_ids in user_ids_per_group])\n res_inds = np.argsort(res_arr)[::-1][:n_recommendations]\n return res_inds, res_arr[res_inds]","def display_ranking(args: argparse.Namespace, cookie: str) -> None:\n number: int = args.number\n if not number:\n return\n\n if number >= 50:\n print(\"Number of users to display cannot exceed 49 for now. Exiting.\")\n return\n\n cookies = {\n \"spip_session\": cookie,\n }\n\n ranking = get_global_ranking(args.offset, cookies)\n\n for counter, entry in enumerate(ranking):\n\n if counter >= number:\n break\n\n userid = ranking[entry][\"id_auteur\"]\n userinfo = get_user_info(userid, cookies)\n if userinfo is None:\n continue\n\n print(\n f\"{Fore.YELLOW}{ranking[entry]['place']:>4}{Style.RESET_ALL}. \"\n f\"{userinfo['nom']:<25} \"\n f\"{userinfo['score']} \"\n )\n\n return","def test_search_your_skills(self):\n self.client.login(username='user2@user2.com', password='testpass')\n\n resp = self.client.get(reverse('profiles:search_your_skills'))\n\n # self.user2 has a profile with the Django developer skill\n # self.project has one open position for Django developer, so\n # we should find one result\n self.assertContains(\n resp,\n '1 results were found with: Your Skills'\n )\n self.assertContains(resp, str(self.project))\n\n # various page information\n self.assertContains(resp, 'Test Project')\n self.assertContains(resp, 'All Needs')\n self.assertContains(resp, 'Projects')\n\n self.assertTemplateUsed('homepage.html')","def get_high_scores(self,request):\n if request.number_of_results:\n scores = Score.query(Score.won == True).order(Score.attempts_allowed,Score.guesses).fetch(request.number_of_results)\n else:\n scores = Score.query(Score.won == True).order(Score.attempts_allowed,Score.guesses).fetch()\n return ScoreForms(items=[score.to_form() for score in scores])","def recache_matches(self):\n self.recalculating = True\n self.save()\n\n # give all the players a starting rank\n i = 1\n for player in self.player_set.order_by('id'):\n player.rank = i\n player.rating = DEFAULT_RATING\n player.cached_results = ''\n player.cached_rating_changes = ''\n player.cached_rank_changes = ''\n player.save()\n i += 1\n\n # get all the matches and replay them in order\n matches = self.match_set.order_by('played_time')\n for match in matches:\n match.update_company_ladder()\n\n self.recalculating = False\n self.save()","def tourplayers(request):\n active_players = PlayerModel.objects.order_by('number')\n\n context = {\n 'active_players': active_players,\n }\n\n return render(request, 'tourPlayers.html', context=context)","def score_list_student(request):\n\n takes = Take.objects.filter(student__username=request.data[\"sid\"])\n serializer = TakeSerializer(takes, many=True)\n return Response(serializer.data)","def display_all_round_matches(self):\r\n tournament_name = self.input_name(\"nom du tournoi\")\r\n tournament = tournaments_table.get(Query().Nom == tournament_name)\r\n print(\"Matches du tournoi de\", tournament_name, \"ayant eu lieu: \")\r\n for match in tournament['Matches joues']:\r\n print(players_table.get(doc_id=match[0])['Nom'], \"(BLANCS) contre\",\r\n players_table.get(doc_id=match[1])['Nom'], \"(NOIRS)\")","def set_ranking_users():\n data = select_data_source()\n order_id = data['id']\n from_user = data['from']\n to_user = data['to']\n ranking = float(data['ranking']) # TODO.\n \n if session._id != from_user and session._id != to_user : return permission_denied_return\n \n db = database.getdb()\n \n if not float.is_integer(ranking) or ranking <= 0 or 10 < ranking :\n return ranking_invalid_value_return\n \n ### Check if order exists.\n \n cmd = 'select * from orders where id==\"{0}\"'.format(order_id)\n order_info = db.execute(cmd).fetchall()\n if len(order_info) == 0 :\n return ranking_not_exist_return\n \n ### Check if user is valid.\n cmd = 'select owner, customer from orders where id==\"{0}\"'.format(order_id)\n owner, customer = db.execute(cmd).fetchall()[0]\n \n ### Check and setup ranked info in orders.\n \n if from_user == owner and to_user == customer :\n cmd = 'select owner_ranked from orders where id==\"{0}\"'.format(order_id)\n is_ranked = db.execute(cmd).fetchall()[0][0]\n if is_ranked != 0 :\n return ranking_already_ranked_return\n cmd = 'update orders set owner_ranked=1'\n db.execute(cmd)\n db.commit()\n elif from_user == customer and to_user == owner :\n cmd = 'select customer_ranked from orders where id==\"{0}\"'.format(order_id)\n is_ranked = db.execute(cmd).fetchall()[0][0]\n if is_ranked != 0 :\n return ranking_already_ranked_return\n cmd = 'update orders set customer_ranked=1'\n db.execute(cmd)\n db.commit()\n else :\n return ranking_not_relative_return\n \n ### Update rank to to_user.\n \n cmd = 'select rank, rank_time from users where mail==\"{0}\"'.format(to_user)\n rank, rank_time = db.execute(cmd).fetchall()[0]\n \n rank = (rank * rank_time + ranking) / (rank_time + 1)\n rank_time += 1\n \n cmd = 'update users set rank={0}, rank_time={1} where mail=\"{2}\"'.format(rank, rank_time, to_user)\n db.execute(cmd)\n db.commit()","def rerank(test_predicted_paraphrases, test_features, ranker, minimum_score):\n new_test_predicted_paraphrases = { (w1, w2) : [] for (w1, w2) in test_predicted_paraphrases.keys() }\n\n for ((w1, w2), curr_paraphrases), curr_paraphrase_features in tqdm.tqdm(zip(\n test_predicted_paraphrases.items(), test_features)):\n pars_and_vectors = zip(curr_paraphrases.items(), curr_paraphrase_features)\n\n # Sort the paraphrases according to the ranking\n def compare_paraphrases(p1, p2):\n return ranker.predict((p2[1] - p1[1]).reshape(1, -1))\n\n # Consider both the original score (for the specific noun-compound)\n # and the new rank (which paraphrases are more commonly ranked higher)\n sorted_paraphrases = [(paraphrase, (len(curr_paraphrases) - rank) * float(score))\n for rank, ((paraphrase, score), feature) in\n enumerate(sorted(pars_and_vectors,\n key=functools.cmp_to_key(compare_paraphrases)))]\n\n sorted_paraphrases = sorted(sorted_paraphrases, key=lambda x: x[1], reverse=True)\n\n # Keep only paraphrases with score above threshold. Best score = k * 1 = k,\n new_test_predicted_paraphrases[(w1, w2)] = \\\n [(paraphrase, score) for (paraphrase, score) in sorted_paraphrases if score >= minimum_score]\n\n return new_test_predicted_paraphrases","def compute_matches(self):\n\t\tself.local_database[\"figure_number\"] = [0] * len(self.local_database[\"feature_vectors\"])\n\t\tForensics = wbForensicsHOG(Database=self.local_database)\n\t\tForensics.KDTree_pairs(leaf_size = len(self.local_database)+1)\n\t\tForensics.d_rank(pairs=Forensics.pairs, distances=Forensics.dists, ratios=Forensics.ratios)\n\n\t\tself.local_matches = Forensics.Dist_Rank","def make_player_stats(tournament, lineups):\n positions = find_player_positions(lineups)\n positions = positions.set_index('player_id')[['player_position']]\n player_stats = load_player_stats(tournament)\n player_stats.set_index('player_id', inplace=True)\n mask = player_stats['goals'] > player_stats['shots']\n player_stats.loc[mask, 'shots'] = player_stats[mask]['goals']\n res = player_stats.join(positions)\n res = res[pd.notna(res['player_position'])]\n return res"],"string":"[\n \"def enter_matches_score(match_list):\\n for match in match_list:\\n view.show(\\\"{} vs {}\\\".format(match.player_blanc.player_first_name,\\n match.player_black.player_first_name))\\n score_blanc = enter_player_score(match.player_blanc)\\n match.score_blanc = score_blanc\\n score_black = enter_player_score(match.player_black)\\n match.score_black = score_black\",\n \"def matchmaking():\\n from app import db, models\\n # Split by character as they are stored as a list in the database.\\n # Could improve by normalising skills & model already produced -- models::skills\\n # Query would change below, but matchmaking algorithm would remain the same.\\n uskills = [{'name': str(i.uid), 'skills':\\n [str(i) for i in i.skills.split(\\\",\\\")]}\\n for i in db.session.query(models.UserSkills).all()]\\n # Used to select matches that have not been previously paired.\\n pairs = [(str(i.uid), str(i.mid))\\n for i in db.session.query(models.Pair).all()]\\n\\n # Find all the users who are not already matched, but should be!\\n # IFF at least once skill overlaps then there is a match. We are too kind.\\n matches = []\\n for user in uskills:\\n for other_user in uskills:\\n # Do not add already existing pairs and matches are transitive;\\n # Jay matched with Aare is the same as Aare matching with Jay.\\n cu, ou = (user['name'], other_user['name'])\\n if ((cu, ou) in pairs or (ou, cu) in pairs or\\n (cu, ou) in matches or (ou, cu) in matches):\\n break\\n # TODO: skill comparison based on NLTK use (synonyms, etc...)\\n # TODO: improve the algorithm to rank and sort pairs by skills.\\n if (set(user['skills']).intersection(other_user['skills']) and\\n user['name'] != other_user['name']):\\n matches.append((user['name'], other_user['name']))\\n return matches\",\n \"def calculate_match_at_backend():\\n\\n # Calculate lovermatch list for each user\\n user_set = UserInfo.objects\\n for u1 in user_set:\\n matchlist = {}\\n for u2 in user_set:\\n if u1 != u2:\\n features_to_match = u1.features\\n weights = u1.percentage\\n sim = get_similarity(u1, u2, features_to_match, weights)\\n matchlist[u2.name] = sim\\n u1.loverMatch = matchlist\\n u1.save()\\n\\n # Calculate lovermatched list for each user\\n user_set = UserInfo.objects\\n for u1 in user_set:\\n matchedlist = {}\\n for u2 in user_set:\\n if u1 != u2:\\n if u1.name in u2.loverMatch.keys():\\n matchedlist[u2.name] = u2.loverMatch[u1.name]\\n u1.loverMatched = matchedlist\\n u1.save()\",\n \"def leaderboard():\\n \\n global score_dictinary\\n data = []\\n fields = []\\n scores = []\\n names = []\\n users = []\\n i=0\\n \\n #Reads the winners from a mongo database \\n read_mongo(scores, names)\\n \\n #Sorts the list in descending order\\n quicksort(scores, names, 0, len(scores) - 1)\\n \\n #Joins the names and scores arrays together\\n while i < len(scores):\\n users.append(names[i] + \\\" \\\" + scores[i])\\n i += 1\\n \\n users = (reversed(users))\\n \\n return render_template(\\\"leaderboard.html\\\", users=users)\",\n \"def rerank_results(self, results, user_vector, user_gender, user_location, user_sentiment):\\n\\t\\treranked = []\\n\\t\\tuser_vec = ProfileOneHotEncoder.add_info_to_vec(user_vector, user_gender, user_location,\\n\\t\\t user_sentiment).reshape(1, -1)\\n\\t\\tfor i in range(len(results)):\\n\\t\\t\\tdoc_infos = Tweet.load(int(results[i]['TweetID']))\\n\\t\\t\\tif doc_infos is None:\\n\\t\\t\\t\\treranked.append({'doc': results[i], 'sim': 0.})\\n\\t\\t\\telse:\\n\\t\\t\\t\\tdoc_vector = ProfileOneHotEncoder.add_info_to_vec(doc_infos.vector, doc_infos.gender, doc_infos.country,\\n\\t\\t\\t\\t doc_infos.sentiment).reshape(1, -1)\\n\\t\\t\\t\\tsim = cosine_similarity(user_vec, doc_vector)\\n\\t\\t\\t\\treranked.append({'doc': doc_infos, 'sim': sim[0][0]})\\n\\t\\treranked = sorted(reranked, key=lambda k: k['sim'], reverse=True)\\n\\t\\treturn [x['doc'] for x in reranked]\",\n \"def get_user_rankings(self, request):\\n users = User.query(User.total_games > 0).fetch()\\n users = sorted(users, key=lambda x: x.win_percentage, reverse=True)\\n return UserForms(items=[user.to_form() for user in users])\",\n \"def recommend(self, user):\\n K = self.n_sim_user\\n N = self.n_rec_movie\\n rank = dict()\\n watched_movies = self.trainset[user]\\n\\n # v=similar user, wuv=similarity factor\\n for v, wuv in sorted(self.user_sim_mat[user].items(),\\n key=itemgetter(1), reverse=True)[0:K]:\\n for movie in self.trainset[v]:\\n if movie in watched_movies:\\n continue\\n # predict the user's \\\"interest\\\" for each movie\\n rank.setdefault(movie, 0)\\n rank[movie] += wuv\\n # return the N best movies\\n return sorted(rank.items(), key=itemgetter(1), reverse=True)[0:N]\",\n \"def get_user_rankings(self, request):\\n difficulty = validateGameDifficultyValue(request, True)\\n users = User.query().fetch()\\n items = []\\n total_games = 0\\n wins = 0\\n for user in users:\\n scores = Score.query()\\n # Filter by game difficulty and only wons that resulted in a win\\n scores = scores.filter(Score.user == user.key, \\\\\\n Score.difficulty == difficulty)\\n scores.fetch()\\n\\n for score in scores:\\n total_games += 1\\n if score.won:\\n wins += 1\\n if total_games > 0:\\n items.append(\\n RankingForm(user_name=user.name,\\n difficulty=getattr(GameDifficulty, difficulty),\\n win_percentage=round(float(wins)/total_games*100, 2),\\n wins=wins)\\n )\\n total_games = 0\\n wins = 0\\n return RankingForms(items=items)\",\n \"def recommend(self, u):\\n # Implement a recommendation function that takes a user vector u\\n # and outputs a list of movies recommended by the chatbot\\n rec_list = []\\n for i, movie in enumerate(self.ratings):\\n rxi = 0.0\\n for tup in u:\\n j = tup[0]\\n rxj = tup[1]\\n if i == j: # Skip movies in user_vec\\n continue\\n sij = self.distance(self.ratings[i], self.ratings[j])\\n rxi += (rxj * sij)\\n movie_rank = [i, rxi] # Store movie index and rating\\n rec_list.append(movie_rank)\\n rec_list = sorted(rec_list, key=lambda x:x[1], reverse = True) \\n return rec_list\",\n \"def rank_clubs(user):\\n if not user.is_authenticated():\\n return ((k, None) for k in Klub.objects.all().annotate(c=Count('posel')).order_by('-c'))\\n\\n clubs = defaultdict(list)\\n for deputy, rating in rank(user):\\n if deputy.klub is not None:\\n clubs[deputy.klub.pk].append(rating)\\n ratings = []\\n for club in Klub.objects.all():\\n if clubs.get(club.pk):\\n rating = sum(clubs[club.pk]) / len(clubs[club.pk])\\n else:\\n rating = 0\\n ratings.append((club, rating))\\n return sorted(ratings, reverse=True, key=lambda x: x[1])\",\n \"def rank(args: argparse.Namespace, cookie: str) -> None:\\n cookies = {\\n \\\"spip_session\\\": cookie,\\n }\\n\\n max_len_user = max((len(x) for x in args.user))\\n\\n for username in args.user:\\n\\n users: Dict[str, Searchresult] = search_user(username, cookies)\\n\\n userids = []\\n # Parse API search results in order to find the correct users\\n for user in users:\\n if users[user][\\\"nom\\\"] == username:\\n userids.append(users[user][\\\"id_auteur\\\"])\\n\\n if not userids:\\n raise ValueError(\\n (\\n \\\"User %s not found in 50 first search terms.\\\"\\n \\\" Did you mispell it? \\\"\\n ),\\n username,\\n )\\n\\n for userid in userids:\\n userinfo = get_user_info(userid, cookies)\\n if userinfo is None:\\n continue\\n\\n print(\\n f\\\"[{Fore.GREEN}+{Style.RESET_ALL}] \\\"\\n f\\\"{userinfo['nom']:<{max_len_user + 2}} \\\"\\n f\\\"Score: {userinfo['score']:<6} \\\"\\n f\\\"Position: {userinfo['position']}\\\"\\n )\",\n \"def match(request, user_id):\\n current_user = CustomUser.objects.get(id=request.user.id)\\n liked_user = CustomUser.objects.get(id=user_id)\\n\\n if not Matches.objects.filter(first_user=current_user, second_user=liked_user):\\n Matches.objects.create(first_user=current_user, second_user=liked_user)\\n\\n if Matches.objects.filter(first_user=liked_user, second_user=current_user):\\n send_emails_to_users(current_user, liked_user)\\n send_emails_to_users(liked_user, current_user)\\n\\n return redirect('index')\",\n \"def get_UserMatches(similarity,table_CF, user,n=5):\\n similarity_table = similarity(table_CF)\\n similar_users = similarity_table[user]\\n # Sort the list so the highest scores appear at the top\\n similar_users = similar_users.order(ascending = False)\\n return similar_users[0:n]\",\n \"def _match_users_with_karma(rankings, user_key, karma_key):\\n if not rankings:\\n return []\\n\\n giver_ids = [r[user_key] for r in rankings]\\n ids_to_users = User.objects.select_related(\\n 'userprofile').in_bulk(giver_ids)\\n return [(ids_to_users[r[user_key]], r[karma_key]) \\\\\\n for r in rankings]\",\n \"def leaderboard(request):\\r\\n\\tMEDIA_URL = '/media/'\\r\\n\\tgames = Game.objects.all()\\r\\n\\tuser_high_scores = []\\r\\n\\tgame_high_scores = []\\r\\n\\tnew = {}\\r\\n\\t# Get global scores\\r\\n\\tfor game in games:\\r\\n\\t\\tgame_intermediate_high = Score.objects.filter(game = game.id).order_by('-current_score').values('game__name', 'player__user__username', 'current_score')[:1]\\r\\n\\t\\tif (game_intermediate_high.count() > 0):\\r\\n\\t\\t\\tgame_high_scores.append(game_intermediate_high)\\r\\n\\t# Check if user is authenticated and get user's scores\\r\\n\\tif (request.user.is_authenticated):\\r\\n\\t\\tfor game in games:\\r\\n\\t\\t\\t\\tuser_intermediate_high = Score.objects.filter(game=game.id, player = request.user.profile).order_by('-current_score').values('player__user__username','game__name', 'current_score').distinct()[:1]\\r\\n\\t\\t\\t\\tif (user_intermediate_high.count() > 0):\\r\\n\\t\\t\\t\\t\\tuser_high_scores.append(user_intermediate_high)\\r\\n\\r\\n\\treturn render(request, 'leaderboard.html',{'MEDIA_URL' : MEDIA_URL,'games': games, 'user_high_scores': user_high_scores, 'game_high_scores': game_high_scores})\",\n \"def rank(userid, args):\\r\\n testUserid = userid\\r\\n if len(args):\\r\\n testUserid = es.getuserid(str(args))\\r\\n if not es.exists('userid', testUserid):\\r\\n testUserid = userid\\r\\n player = players[testUserid]\\r\\n tokens = {}\\r\\n tokens['name'] = player['name']\\r\\n tokens['level'] = player['level']\\r\\n tokens['xp'] = player['xp']\\r\\n tokens['nextxp'] = (player['level'] - 1) * int(xpIncrement) + int(startXp)\\r\\n tokens['credits'] = player['credits']\\r\\n tokens['rank'] = ranks.getRank(player['steamid'])\\r\\n tokens['total'] = len( ranks )\\r\\n for tellUserid in es.getUseridList():\\r\\n tell(tellUserid, 'rank', tokens)\",\n \"def update_score_other_rounds(self, match):\\n self.match_views.update_score(match)\",\n \"def get_leaderboard(request):\\n\\n includedUsers = User.objects.filter(hide_leaderboard=False, is_staff=False)\\n\\n # ordered list of points, index denoting leaderboard position (rank)\\n # distinct values means that everyone with the same points has the same rank\\n rankings = []\\n for item in includedUsers.values(\\\"points\\\").distinct().order_by(\\\"-points\\\"):\\n rankings.append(item[\\\"points\\\"])\\n\\n includedUsers = includedUsers.order_by(\\\"-points\\\")\\n\\n paginationData = []\\n for user in includedUsers:\\n # rank is the index of the users points +1 (converting from 0-indexing)\\n data = {\\\"user\\\": user, \\\"rank\\\": rankings.index(user.points) + 1}\\n paginationData.append(data)\\n\\n return JsonResponse(\\n json_paginator(request, paginationData, lb_serializer),\\n status=200,\\n )\",\n \"def get_user_rankings(self, request):\\n users = User.query().order(-User.average_score)\\n return RankForms(items=[user.to_rank_form() for user in users])\",\n \"def rank_by_obscurity():\\n obscurities = dict()\\n reader = open(\\\"similarities.txt\\\", \\\"r\\\")\\n lines = reader.readlines()\\n for line in lines:\\n a, b, sim = line.split(\\\"\\\\t\\\")\\n if a not in obscurities:\\n obscurities[a] = [float(sim), 1.0]\\n else:\\n obscurities[a] = [obscurities[a][0] + float(sim),\\n obscurities[a][1] + 1.0]\\n \\n ranked = sorted(obscurities.keys(),\\n key=lambda x: obscurities[x][0] / obscurities[x][1])\\n writer = open(\\\"obscurities.txt\\\", \\\"w\\\")\\n for user in ranked:\\n writer.write(user + \\\"\\\\n\\\")\",\n \"def testHighscore(self):\\n \\n game = Game.objects.get(title='game0')\\n gameplayeds = game.gameplayed_set\\n ply_group = Group.objects.get(name='Player')\\n for i in range(4):\\n user = ply_group.user_set.get(username='ply{}'.format(i))\\n gameplayed = gameplayeds.get(user=user)\\n gameplayed.gameScore = i\\n gameplayed.save()\\n \\n response = self.client.get(\\n reverse('api:game-buyers', args=['v1', 'game0']),\\n {'order_by': 'gameScore'},\\n format='json'\\n )\\n self.assertEquals(response.status_code, 200)\\n content = self.parser.parse(BytesIO(response.content))\\n for i in range(4):\\n self.assertEquals(content['results'][i]['user'], 'ply{}'.format(i))\\n \\n response = self.client.get(\\n reverse('api:game-buyers', args=['v1', 'game0']),\\n {'order_by': '-gameScore'},\\n format='json'\\n )\\n self.assertEquals(response.status_code, 200)\\n content = self.parser.parse(BytesIO(response.content))\\n for i in range(4):\\n self.assertEquals(content['results'][i]['user'], 'ply{}'.format(3 - i))\",\n \"def get_players_by_rank(self):\\n return sorted(self.participants, key=lambda p: p.tournament_score, reverse=True)\",\n \"def get_context_data(self,**kwargs):\\n\\n context = super().get_context_data(**kwargs)\\n user = self.get_object()\\n query= Result.objects.filter(user=user).order_by('-score')\\n context['results']= query[:4]\\n\\n return context\",\n \"def assign_rank(request):\\n try:\\n user = UserProfile.objects.order_by('overall_score')\\n count = UserProfile.objects.all().count()\\n rockstar = 0.01 * count\\n if int(rockstar) < rockstar:\\n rockstar = int(rockstar) + 1\\n \\n master = 0.04 * count\\n if int(master) < master:\\n master = int(master) + 1\\n \\n senior = 0.15 * count\\n if int(senior) < senior:\\n senior = int(senior) + 1\\n \\n active = 0.3 * count\\n if int(active) < active:\\n active = int(active) + 1\\n \\n amateur = 0.5 *count\\n if int(amateur) < amateur:\\n amateur = int(amateur) + 1\\n \\n j = 0\\n while j < amateur and j < count:\\n u = user[j]\\n u.rank = Master_Rank.objects.get(position=\\\"Amatuer\\\")\\n u.save()\\n j += 1\\n \\n k = 0\\n while k < active and j < count:\\n u = user[j]\\n u.rank = Master_Rank.objects.get(position=\\\"Active translator\\\")\\n u.save()\\n j += 1\\n k += 1\\n \\n k = 0\\n while k < senior and j < count:\\n u = user[j]\\n u.rank = Master_Rank.objects.get(position=\\\"Senior translator\\\")\\n u.save()\\n j += 1\\n k += 1\\n \\n k = 0\\n while k < master and j < count:\\n u = user[j]\\n u.rank = Master_Rank.objects.get(position=\\\"Master translator\\\")\\n u.save()\\n j += 1\\n k += 1\\n \\n while j < count:\\n u = user[j]\\n u.rank = Master_Rank.objects.get(position=\\\"Rockstar translator\\\")\\n u.save()\\n j += 1\\n \\n \\n data = {'msg':''}\\n messages.success(request, \\\"Rank assigned to user successfully.\\\")\\n return render_to_response('my_admin_tools/menu/background_task.html',data,context_instance=RequestContext(request))\\n except:\\n msg = traceback.format_exc()\\n data = {'msg':msg}\\n messages.error(request, \\\"Update user's reputation score failed.\\\")\\n return render_to_response('my_admin_tools/menu/background_task.html',data,context_instance=RequestContext(request))\",\n \"def rank_skill_support():\\n import collections\\n score_dict = collections.defaultdict(int)\\n for item in classes.Crusader.items:\\n for name in classes.Crusader.skill_names:\\n if name in inspect.getsource(item):\\n score_dict[name] += 1\\n\\n for name, freq in sorted(score_dict.items(), key=lambda x: -x[1]):\\n print(name, freq)\",\n \"def recache_matches(self):\\n matches = (Match.objects\\n .filter(Q(winner = self) | Q(loser = self))\\n .order_by('-played_time'))[:CACHED_RATING_LIMIT]\\n matches = list(matches)\\n matches.reverse()\\n\\n for match in matches:\\n self.add_match(match, include_rank=True)\\n\\n self.save()\",\n \"def matchscore(self):\\n print(self.team1.name + \\\" \\\" + str(self.team1score) + \\\" - \\\" + str(self.team2score) + \\\" \\\" + self.team2.name)\",\n \"def leaderboard(self):\\n return User.objects.filter(documentsetformentry__form__document_set=self).annotate(num_entries=Count('documentsetformentry')).order_by('-num_entries')\",\n \"def score():\\n if flask.request.method == 'GET':\\n return \\\"Give me a POST request to get recommendations\\\"\\n\\n # Get input data:\\n input_dict = flask.request.json\\n # Json Validation:\\n # (didn't have time for this, intended to do json validation and return 406 if schema doesn't match)\\n # Top n:\\n top_n:int = input_dict['top_n']\\n # Encode to user embedding\\n query_user_embedding_vector:np.ndarray = encode(input_dict)\\n # Calculate Cosine Similarity:\\n query_user_embedding_vector = query_user_embedding_vector.reshape(1,-1)\\n cosine_sims = cosine_similarity(query_user_embedding_vector,user_embedding_array).ravel()\\n # Recommend the top N most similar user\\n idx:list = (-cosine_sims).argsort()[:top_n].tolist()\\n chosen_cosine_sims:list = cosine_sims[idx].tolist()\\n chosen_user_handle:list = user_embedding_df.user_handle.iloc[idx].tolist()\\n \\n # Get User info\\n connection = engine.connect()\\n cmd = f'select * from display_table where user_handle in {tuple(chosen_user_handle)};'\\n result = connection.execute(text(cmd))\\n user_info = result.fetchall()\\n connection.close()\\n\\n # Construct output:\\n output_dict = {\\n chosen_user_handle[idx]:{\\n \\\"similarity\\\":chosen_cosine_sims[idx],\\n \\\"assessment_tag\\\":user_info[idx][1],\\n \\\"course_id\\\":user_info[idx][2],\\n \\\"interest_tag\\\":user_info[idx][3],\\n } for idx in range(len(chosen_user_handle))}\\n \\n return json.dumps(output_dict)\",\n \"def recommend(self, u):\\n\\n sims = {} #similarities\\n recommendation = \\\"\\\"\\n topScore = None\\n start = time.time()\\n for movie_id, rating in enumerate(u):\\n if rating != 0:\\n sims[movie_id] = {}\\n for r_id, movie in enumerate(self.ratings):\\n sims[movie_id][r_id] = self.distance(movie,self.ratings[movie_id])\\n # print time.time() - start, \\\"distance time\\\"\\n\\n start = time.time()\\n for i, movieRating in enumerate(self.ratings):\\n iPrediction = 0\\n for movieName in self.ratedMovieList:\\n j = self.titlesOnly.index(movieName)\\n iPrediction += sims[j][i]*1.0 * self.userRatingVector[j]\\n if topScore is None or iPrediction > topScore:\\n movie = self.titlesOnly[i]\\n if movie not in self.ratedMovieList and movie not in self.recommendedMovies:\\n # print(\\\"prediction score for %s is %.5f\\\" % (movie, iPrediction))\\n topScore = iPrediction\\n recommendation = movie\\n # print time.time() - start, \\\"recommendation time\\\"\\n self.recommendedMovies.append(recommendation)\\n\\n articlePattern = re.match('(.*), (the|a|an|el|la)', recommendation)\\n if articlePattern is not None:\\n recommendation = articlePattern.group(2) + \\\" \\\" + articlePattern.group(1)\\n\\n return recommendation\",\n \"def get_high_scores(self, request):\\n users = User.query().fetch(limit=request.number_of_results)\\n users = sorted(users, key=lambda x: x.wins, reverse=True)\\n return UserForms(items=[user.to_form() for user in users])\",\n \"def test_ranking_achievements(self):\\n client = Client()\\n user = create_user('passwordmichu', 'michu')\\n client.login(username='michu', password='passwordmichu')\\n coll = create_collection('Coleccion de cartas')\\n problem = create_select_problem(coll, 'Problema')\\n create_an_achievement_of_each(coll)\\n submit_select_url = reverse('judge:submit', args=[problem.pk])\\n client.post(submit_select_url, {'code': problem.solution}, follow=True)\\n ranking_url = reverse('judge:result', args=[coll.pk])\\n group_a = create_group('1A')\\n group_a.user_set.add(user)\\n response = client.get(ranking_url, follow=True)\\n self.assertIn('x5', response.content.decode('utf-8'))\",\n \"async def leaderboard(self, ctx):\\r\\n rank = 1\\r\\n strlist = []\\r\\n for k, v in sorted(player.items(), key=lambda x: expose(x[1]), reverse=True): # operator.itemgetter(1)\\r\\n position = str(rank) + '. ' + k\\r\\n while len(position) < 25:\\r\\n position += '\\\\u00A0'\\r\\n position += ' | ' + str(round(expose(v), 2)) + u\\\"\\\\u000A\\\"\\r\\n strlist.append(position)\\r\\n rank += 1\\r\\n indx = 10\\r\\n table = '\\\\u200b'.join(strlist[indx-10:indx])\\r\\n header = ('\\\\u00A0' * 3) + 'User' + ('\\\\u00A0' * 20) + 'Rating' + u\\\"\\\\u000A\\\"\\r\\n divider = '_' * 33 + u\\\"\\\\u000A\\\"\\r\\n\\r\\n msg = await ctx.send('```' + u\\\"\\\\u000A\\\" + header + divider + table + divider + u\\\"\\\\u000A\\\" '```')\\r\\n await msg.add_reaction('⬅')\\r\\n await msg.add_reaction('➡')\\r\\n\\r\\n def check(reaction, user):\\r\\n return user != msg.author and str(reaction.emoji == '➡' or '⬅')\\r\\n while True:\\r\\n try:\\r\\n reaction, user = await bot.wait_for('reaction_add', timeout=60.0, check=check)\\r\\n if reaction.emoji == '➡':\\r\\n await msg.remove_reaction(reaction.emoji, user)\\r\\n if indx < rank-1:\\r\\n indx += 10\\r\\n table = '\\\\u200b'.join(strlist[indx - 10:indx])\\r\\n await msg.edit(content='```' + u\\\"\\\\u000A\\\" + header + divider + table + divider + u\\\"\\\\u000A\\\" '```')\\r\\n if reaction.emoji == '⬅':\\r\\n await msg.remove_reaction(reaction.emoji, user)\\r\\n if indx > 10:\\r\\n indx -= 10\\r\\n table = '\\\\u200b'.join(strlist[indx - 10:indx])\\r\\n await msg.edit(content='```' + u\\\"\\\\u000A\\\" + header + divider + table + divider + u\\\"\\\\u000A\\\" '```')\\r\\n except:\\r\\n await msg.remove_reaction('⬅', msg.author)\\r\\n await msg.remove_reaction('➡', msg.author)\\r\\n break\",\n \"def recommend(r ,username, users):\\r\\n # first find nearest neighbor\\r\\n nearest = computeNearestNeighbor(r, username, users)[0][1]\\r\\n recommendations = []\\r\\n # now find bands neighbor rated that user didn't\\r\\n neighborRatings = users[nearest]\\r\\n userRatings = users[username]\\r\\n for artist in neighborRatings:\\r\\n if not artist in userRatings:\\r\\n recommendations.append((artist, neighborRatings[artist]))\\r\\n # using the fn sorted for variety - sort is more efficient\\r\\n return sorted(recommendations, key=lambda artistTuple: artistTuple[1], reverse = True)\",\n \"def match_user(dictionary: 'Climatematch', \\n match: 'NGOMatch') -> Dict[str, tuple(int,int)]:\\n\\n total = {}\\n \\n for user in dictionary:\\n total[user] = {}\\n skillsCount = 0\\n interestCount = 0\\n for sk in dictionary[user]['skills']: \\n if sk in match['skills']['technical']:\\n skillsCount = skillsCount + 1\\n elif sk in match['skills']['interpersonal']:\\n skillsCount = skillsCount + 1\\n \\n for inte in dictionary[user]['interest']: \\n if inte in match['interest']:\\n interestCount = interestCount + 1\\n \\n total[user][(skillsCount, interestCount)]\\n return total\",\n \"def get_user_rankings(self, request):\\n users = User.query().order(-User.win_ratio)\\n return RankForms(items=[user.rank_form() for user in users])\",\n \"def get_best_matching(self):\\r\\n from django.db.models import Q, Avg\\r\\n import copy\\r\\n import operator\\r\\n if self.user is None:\\r\\n raise User.DoesNotExist\\r\\n users = User.objects.all()\\r\\n if self.type == FRIENDS_ONLY:\\r\\n friends = Friends.objects.filter(Q(user_one_id=self.user) | Q(user_two_id=self.user))\\r\\n if len(friends) > 2:\\r\\n users = users.filter(Q(id=friends.values('user_one_id')) | Q(id=friends.values('user_two_id')))\\r\\n\\r\\n user_sims = {}\\r\\n prefs = {}\\r\\n for user in users:\\r\\n tab = {}\\r\\n scores = Score.objects.values('game_id', 'score').filter(user_id=user)\\r\\n for score in scores:\\r\\n tab.update({score['game_id']: score['score']})\\r\\n prefs.update({copy.deepcopy(user.id): copy.deepcopy(tab)})\\r\\n\\r\\n for user in users:\\r\\n sim = self.pearson(prefs, self.user, user.id)\\r\\n user_sims.update({user.id: sim})\\r\\n\\r\\n print(user_sims)\\r\\n del user_sims[self.user] # deletion of user for whom the analysis is beeing performed\\r\\n user_sims = sorted(user_sims.items(), key=operator.itemgetter(1), reverse=True) # dictionary containing user_ids and users' similarities\\r\\n if len(user_sims) < 3:\\r\\n return self.get_most_popular()\\r\\n\\r\\n games_f = Score.objects.values('game_id', 'score').filter(user_id=user_sims[0][0]).order_by('-score')[:3]\\r\\n games_s = Score.objects.values('game_id', 'score').filter(user_id=user_sims[1][0]).order_by('-score')[:3]\\r\\n\\r\\n recommended_games = {}\\r\\n grd = {}\\r\\n games_f_dict = dict([(g['game_id'], g['score']) for g in games_f])\\r\\n recommended_games.update(dict(sorted(games_f_dict.items(), key=operator.itemgetter(1), reverse=True)))\\r\\n\\r\\n games_s_dict = dict([(g['game_id'], g['score']) for g in games_s])\\r\\n recommended_games.update(dict(sorted(games_s_dict.items(), key=operator.itemgetter(1), reverse=True)))\\r\\n\\r\\n for game in recommended_games:\\r\\n scores = list(Score.objects.values('game_id').filter(game_id=game).annotate(Avg('score')))\\r\\n idn = scores[0]['game_id']\\r\\n avg = scores[0]['score__avg']\\r\\n grd.update({idn: avg})\\r\\n\\r\\n return grd\",\n \"def assignRanks(self):\\r\\n\\t\\trank = 0\\r\\n\\t\\tscores = list(self._playerScores)\\r\\n\\t\\tscores.reverse()\\r\\n\\t\\tfor playerScore in scores:\\r\\n\\t\\t\\tif not playerScore.has(NOT_MET) or not playerScore.value(NOT_MET):\\r\\n\\t\\t\\t\\trank += 1\\r\\n\\t\\t\\t\\tplayerScore.set(RANK, smallText(BugUtil.colorText(u\\\"%d\\\" % rank, ScoreOpt.getRankColor())))\\r\\n\\t\\tif rank > 0:\\r\\n\\t\\t\\tself._anyHas[RANK] = True\",\n \"def calculate_item_relevance_scores(self, user_similarity_profile):\\r\\n scores = user_similarity_profile.dot(self.ratings_matrix.matrix)\\r\\n return self.ratings_matrix.normalize_score_vector(scores)\",\n \"def rank_results(result_list, search_title, search_artist, uploader_list):\\n #scores = []\\n #search_artist = search_artist.replace(\\\"+\\\", \\\" \\\").lower()\\n search_title = search_title.replace(\\\"+\\\", \\\" \\\")\\n #search_terms = search_title.split() + search_artist.split()\\n\\n ## Give score to each result\\n #for index, title in enumerate(result_list):\\n # title = title.lower()\\n # score = 0\\n\\n # # One point for each word in result title\\n # for term in search_terms:\\n # if term in title:\\n # score += 1\\n\\n # # 2 points if whole title in result, 2 points for whole artist, 4 points for both\\n # if search_title in title:\\n # score += 2\\n # if search_artist in title:\\n # score += 2\\n # if search_title in title and search_artist in title:\\n # score += 4\\n # if search_title == title and (uploader_list[index] == search_artist+\\\" - topic\\\" or uploader_list[index] == 'various artists - topic' or uploader_list[index] == search_artist or uploader_list[index] == search_artist+'\\\\\\\\xa0'):\\n # score += 100\\n # if 'karaoke' in title:\\n # score-=1000\\n\\n # scores.append(score)\\n\\n # return scores.index(max(scores))\\n for index, title in enumerate(result_list):\\n title = title\\n if search_title == title:\\n return index\\n\\n return 0\",\n \"def test_get_user_rankings(self):\\n user = User(name=u'no win', email=u'generic@thingy.com')\\n user.put()\\n \\n userone = User(name=u'one win', email=u'generic@thingy.com', total_played=1, wins=1)\\n userone.put()\\n \\n usertwo = User(name=u'two wins', email=u'generic@thingy.com', total_played=2, wins=1)\\n usertwo.put()\\n \\n user_rankings = users.get_user_rankings()\\n self.assertEquals(len(user_rankings), 2)\\n self.assertEquals(user_rankings.pop().key, usertwo.key)\\n self.assertEquals(user_rankings.pop().key, userone.key)\",\n \"def tournament_matches_report(self):\\n tournament_id = self.select_tournament()\\n self._model = Tournament({'id': tournament_id})\\n self._view.report(self._model.get_matches())\\n\\n self.tournament_menu()\",\n \"def which_ranking(self, request):\\n options = [\\\"user ranking\\\", \\\"team ranking\\\"]\\n anwser = main_menu(\\\"Ranking\\\", options,\\n \\\"Which ranking you want to see\\\")\\n if anwser == options[0]:\\n self.generate_rank(request, True)\\n else:\\n self.generate_rank(request, False)\",\n \"def rankResults(self, results:set, queryVec: dict):\\n rankedResults = {}\\n for result in results:\\n cosineSim = 0.0\\n for word in queryVec.keys():\\n if result in self.invertedIndex[word]:\\n cosineSim += queryVec[word]*self.invertedIndex[word][result]\\n cosineSim += self.boostScore(result, queryVec.keys())\\n rankedResults[result] = cosineSim\\n\\n return rankedResults\",\n \"def recommend_for_user(self, R, user, n=10, filter_previously_seen=False,\\n return_scores=True, **kwargs):\",\n \"def update_score(self, match, i):\\n self.match_views.update_score(match)\\n score_p1 = input(f\\\"Veuillez rentrer le score de \\\"\\n f\\\"{match[0][0]['first_name']} \\\"\\n f\\\"{match[0][0]['last_name']} (1/0.5/0) \\\")\\n score_p2 = input(f\\\"Veuillez rentrer le score de \\\"\\n f\\\"{match[1][0]['first_name']} \\\"\\n f\\\"{match[1][0]['last_name']} (1/0.5/0) \\\")\\n self.validate_score(score_p1, score_p2, match, i)\\n new_score = float(score_p1)\\n new_score2 = float(score_p2)\\n return new_score, new_score2\",\n \"def update(self, rank):\\n # calculate MR and MRR\\n self.mr += rank\\n self.mrr += 1 / rank\\n # calculate Hits@k\\n if rank <= 1:\\n self.hits1 += 1\\n self.hits3 += 1\\n self.hits5 += 1\\n self.hits10 += 1\\n elif rank <= 3:\\n self.hits3 += 1\\n self.hits5 += 1\\n self.hits10 += 1\\n elif rank <= 5:\\n self.hits5 += 1\\n self.hits10 += 1\\n elif rank <= 10:\\n self.hits10 += 1\",\n \"def rank_partial(self, instance):\\n instance = self.rank_generate_all_views(instance)\\n instance.getM()\\n instance.getW()\\n instance.getScore()\\n self.instance = instance\",\n \"def perform_set_score(responder, options):\\n match = options['<match-id>']\\n tla = options['<tla>']\\n score = options['<score>']\\n scores.set_match_score(match, tla, score)\\n responder('Scored {0} points for {1} in match {2}'.format(score, tla, match))\",\n \"async def rank(self, channel_id, user_infos, user_id, team_id):\\n\\n with codecs.open(\\\"{}/ranking.json\\\".format(self.dirname), \\\"r\\\", \\\"utf-8\\\") as json_data:\\n data = json.load(json_data)\\n\\n user = \\\"{}-{}\\\".format(user_infos[\\\"user\\\"][\\\"name\\\"], str(team_id)) # User string to write\\n\\n if data.get(user):\\n # (Epic line to) get the position from ranking array revert sorted depending on the user\\n rank = sorted(data.items(), key=operator.itemgetter(1))[::-1].index((user, data.get(user))) + 1\\n # Score info message\\n message = \\\"Votre score est de : {} \\\\n Place au classement : {}\\\".format(str(data[user]), str(rank))\\n else:\\n message = \\\"Vous n'avez pas encore de score. Veuillez jouer !\\\" # Never played yet\\n return await self.sendText(message, channel_id,user_infos, team_id)\",\n \"def search(query):\\n has_some = []\\n # list of all users\\n users = database_controller.get_all_users()\\n # has_all starts off with every user, removes those who don't have one of the required skills\\n has_all = users.copy()\\n # iterate through every user and look up their skills\\n for user in users:\\n for skillpath, min_level in query.items():\\n # get the skill_id from the database\\n skill_id = database_controller.get_skill(skillpath).id\\n # get the association of the user with current skill and according minimum level\\n skill_assoc = database_controller.get_assocs(users_id=user.id,\\n skill_id=skill_id,\\n level=min_level,\\n type=\\\"first\\\")\\n # skill_assoc will be None if the user does not have the skill on the desired level or higher\\n if skill_assoc is not None:\\n # adds user to has_some, in case he does not have other skills\\n if user not in has_some:\\n has_some.append(user)\\n # if the user does not have the current skill at the required level, he gets removed from has_all\\n else:\\n if user in has_all:\\n has_all.remove(user)\\n # remove intersection of has_all and has_some\\n for user in has_all:\\n if user in has_some:\\n has_some.remove(user)\\n # extract ProfileModels from the results and return them in a dictionary\\n has_all = database_controller.get_profile_models(has_all)\\n has_some = database_controller.get_profile_models(has_some)\\n # sort the results by descending order of sum of queried skills\\n has_all.sort(key=lambda p: database_controller.sum_relevant_skills(p, list(query.keys())), reverse=True)\\n has_some.sort(key=lambda p: database_controller.sum_relevant_skills(p, list(query.keys())), reverse=True)\\n return dict(has_all=has_all, has_some=has_some)\",\n \"def view_scores(jenni, input):\\n scores.view_scores(jenni, input)\",\n \"def set_rank_order(self):\\n for k in self._run:\\n self._run[k].sort(key=lambda x:x.get_rank(),reverse=False)\\n tot_res = len(self._run[k])\\n for r in self._run[k]:\\n r.set_score(tot_res - int(r.get_rank()) + 1)\\n print r.get_str()\",\n \"async def team_search(self, ctx: commands.Context, username: str):\\n all_usernames = {team_id: team.username for team_id, team in self.teams.items()\\n if team is not None}\\n suggestions = []\\n log.info(repr(fuzzywuzzy.process.extract(\\n username, all_usernames, limit=5)))\\n for fuzz_username, rating, fuzz_id in fuzzywuzzy.process.extract(\\n username, all_usernames, limit=5):\\n if rating < 50:\\n break\\n fuzz_team = self.teams[fuzz_id]\\n suggestions.append(\\n f'(ID: **{fuzz_team.team_id}**) **{fuzz_team.display_name[:40]}**'\\n f' -- {len(fuzz_team.users)} registered members')\\n if suggestions:\\n await ctx.send('\\\\n'.join(suggestions))\\n else:\\n await ctx.send(f\\\"Couldn't find any teams whose usernames resembled `{username}`\\\")\",\n \"def display_current_match(i, player_ratings):\\r\\n print('MATCH', int(i/2+1))\\r\\n print(players_table.get(doc_id=player_ratings[i][0])['Nom'], \\\"(BLANCS)\\\",\\r\\n \\\"contre\\\",\\r\\n players_table.get(doc_id=player_ratings[i+1][0])['Nom'], \\\"(NOIRS)\\\")\",\n \"def bestscorers():\\n players = Player.query.filter_by(is_admin=False).all()\\n players = list(reversed(sorted(players, key=lambda player: player.goals)))\\n for player in players:\\n player.image = url_for('static', filename='images/players/{}'.format(player.image))\\n player.team_name = player.team.name\\n player.team_logo = url_for('static', filename='images/teams/{}'.format(player.team.logo_image))\\n\\n return render_template('standings/best-scorers.html', players=players, title='Best Scorers')\",\n \"def rank(self, urlids, wordids):\\r\\n\\t\\tself.score()\\r\\n\\t\\treturn sorted(self.scores.items(), key=lambda v:v[1], reverse=self.rev)\",\n \"def update_player_rank(self, player, rank, match):\\n\\n # if player isn't moving, do nothing\\n if player.rank == rank:\\n return\\n\\n # if player doesn't have a rank yet, make them last\\n if player.rank is None:\\n player.rank = self.player_set.count()\\n\\n # update all the players in the company at or below the target rank\\n # but greater than the player's former rank\\n ##(self.player_set\\n ## .filter(rank__isnull=False, rank__gte=rank, rank__lt=player.rank)\\n ## .update(rank=F('rank') + 1)\\n ## )\\n passed_players = (self.player_set\\n .filter(rank__isnull=False, rank__gte=rank, rank__lt=player.rank))\\n for p in passed_players:\\n p.update_rank(p.rank + 1, match)\\n\\n # update target player\\n player.update_rank(rank, match)\",\n \"def display_round_matches(player_ratings):\\r\\n print('\\\\nMATCHES DE LA RONDE: ')\\r\\n for i in range(0, len(player_ratings), 2):\\r\\n print(players_table.get(doc_id=player_ratings[i][0])['Nom'], \\\"(BLANCS)\\\",\\r\\n \\\"contre\\\",\\r\\n players_table.get(doc_id=player_ratings[i+1][0])['Nom'], \\\"(NOIRS)\\\")\",\n \"def modify_ranking(tournament):\\n database = TinyDB('db.json')\\n # recuperation de tous les joueurs du tournoi\\n players_table = database.table('players')\\n list_players = get_player_list(tournament)\\n # Modification du rang joueur par joueur\\n for player in list_players:\\n new_ranking = view.modify_ranking_view(player)\\n players_table.update({'Classement': new_ranking}, doc_ids=[player.doc_id])\",\n \"def fullleaderboard (request):\\n # Define views here\\n score_submit = EventEntryModel.objects.exclude(winner__isnull=True).count()\\n active_players = PlayerModel.objects.all()\\n\\n loaded_points = list(EventEntryModel.objects.aggregate(Sum('points')).values())[0]\\n awarded_points = list(EventEntryModel.objects.exclude(winner__isnull=True).aggregate(Sum('points')).values())[0]\\n\\n context = {\\n 'score_submit': score_submit,\\n 'active_players': active_players,\\n 'loaded_points': loaded_points,\\n 'awarded_points': awarded_points,\\n }\\n\\n return render(request, 'fullLeaderboard.html', context=context)\",\n \"async def ranks(self, ctx):\\n database = db[str(ctx.guild.id)]\\n all_users = database.find({}).sort('xp', -1)\\n\\n rank = ''\\n index = 1\\n for doc in all_users:\\n try:\\n user = self.client.get_user(doc['user_id'])\\n if user is not None and not user.bot:\\n s = f'**{index})** {user.display_name}: {doc[\\\"xp\\\"]} XP | Level: {get_level_from_xp(doc[\\\"xp\\\"])}\\\\n'\\n rank += s\\n else:\\n continue\\n except KeyError:\\n continue\\n index += 1\\n\\n rank_embed = discord.Embed(title='Rank', description=rank, color=discord.Color(random.randint(1, 16777215)))\\n await ctx.send(embed=rank_embed)\",\n \"def ranked(pmi_ranking, tscore_ranking, logdice_ranking, tscore, PMI, logdice):\\r\\n tsc_rank = tscore_ranking[(float(tscore))]\\r\\n pmi_rank = pmi_ranking[float(PMI)]\\r\\n logdice_rank = logdice_ranking[float(logdice)]\\r\\n summary_tscpmi = tsc_rank + pmi_rank\\r\\n summary_logdpmi = logdice_rank + pmi_rank\\r\\n return tsc_rank, pmi_rank, logdice_rank, summary_tscpmi, summary_logdpmi\",\n \"def results():\\n df = mdr.elo_ratings.sort_values([\\\"elo\\\", \\\"matches\\\"], ascending=False)\\n df[\\\"score\\\"] = (1 / (1 + 10**((1500 - df[\\\"elo\\\"]) / 400))) * 100\\n df = df[[\\\"items\\\", \\\"score\\\", \\\"matches\\\"]]\\n\\n html_formatted = df.to_html(classes=[\\\"table\\\", \\\"table-dark\\\", \\\"table-hover\\\"], index=False, float_format=\\\"%.1f\\\")\\n\\n return render_template('results.html', table=html_formatted)\",\n \"def userSuggestions(database):\\n firstname=str(input(\\\"who do you want to have follow suggestions for :\\\"))\\n usr,find=getByName(database,firstname)\\n if not find:\\n print(\\\"the User could not be found\\\")\\n return\\n else:\\n following=[]\\n followers=[]\\n for folower in usr.folowed:\\n followers.append(folower)\\n for folowed in usr.folow:\\n following.append(folowed)\\n results=[]\\n print(\\\"On what do you want your suggestions to be based on?\\\\n1. Mutual Interests\\\\n2. Mutual Connections\\\\n3. Both\\\")\\n choice=int(input(\\\"Your choice :\\\"))\\n for key ,usrs in database.items():\\n if key not in following: \\n correspondant=0\\n if choice == 1 or choice == 3:\\n for interest in usr.interest:\\n if interest in usrs.interest:\\n correspondant+=1\\n if choice == 2 or choice == 3:\\n for folower in followers:\\n for folows in usrs.folowed:\\n if key == folows:\\n correspondant+=1\\n results.append([key,correspondant])\\n for i in range(len(results)):\\n for j in range(0, len(results)-i-1):\\n if results[j][1] > results[j+1][1] :\\n results[j], results[j+1] = results[j+1], results[j]\\n for k in range(5):\\n print(results[k][0])\",\n \"def update_ranking(self, session: Session, sensitivity: int, verbose: bool = False) -> None:\\n\\n if verbose is True:\\n print(f\\\"Updating {self.uri}...\\\", end=\\\" \\\")\\n\\n best_vector = _Analysis.make_best_vector(self.levenshtein_vector, sensitivity)\\n\\n self.ranking = _Ranking()\\n self.ranking.score_sum = _Analysis.make_score_sum(best_vector)\\n self.ranking.score_average = _Analysis.make_score_average(best_vector)\\n self.ranking.score_coverage = _Analysis.make_score_coverage(best_vector)\\n self.ranking.recall = _Analysis.make_recall(len(session._scheme.concepts), self.ranking.score_coverage)\\n\\n if verbose is True:\\n print(\\\"updated.\\\")\",\n \"def calculate_scores():\\n all_people = models.Leaderboard.query.order_by(\\n models.Leaderboard.score.desc()).all()\\n print(all_people)\\n users = []\\n scores = []\\n for person in all_people:\\n users.append(person.username)\\n scores.append(person.score)\\n return users, scores\",\n \"def update_ranking(player):\\n database = TinyDB('db.json')\\n # recuperation de tous les joueurs du tournoi\\n players_table = database.table('players')\\n new_ranking = view.modify_ranking_view(player)\\n players_table.update({'Classement': new_ranking}, doc_ids=[player.doc_id])\",\n \"def resultat_match(self, binomes):\\n for binome in binomes:\\n while True:\\n score_un = self.vue.entree_resultats(binome[0])\\n score_deux = self.vue.entree_resultats(binome[1])\\n if score_un + score_deux != 1:\\n self.vue.erreur_score()\\n continue\\n else:\\n binome[0].ajout_score(score_un)\\n binome[1].ajout_score(score_deux)\\n table_players.update({\\\"score\\\": binome[0].points},\\n doc_ids=[binome[0].id])\\n table_players.update({\\\"score\\\": binome[1].points},\\n doc_ids=[binome[1].id])\\n break\\n self.vue.afficher_resultats(binomes)\",\n \"def _process_match(self, mysql_user, friven_user):\\n match_record = self._create_match_record(mysql_user=mysql_user,\\n friven_user=friven_user)\\n self._metrics_collector.add_sample_row(row_dict=match_record)\\n self._metrics_collector.increment_matches()\\n self._mysql_writer.add_record(match_record=match_record)\",\n \"def top_matches(prefs, person, n=5, similarity=sim_pearson):\\n scores = [(similarity(prefs, person, other), other)\\n for other in prefs if other != person]\\n\\n scores.sort()\\n scores.reverse()\\n return scores[0:n]\",\n \"def recommendUB(user, neighbors, usermovHistDict, topK = 200, nRec = 30):\\n simSumDict = defaultdict(float)# similarity sum\\n weightedSumDict = defaultdict(float)\\n # weighted rating sum\\n movIDUserRated = usermovHistDict.get(user, [])\\n for (neighbor, simScore, numCommonRating) in neighbors[:topK]:\\n mrlistpair = usermovHistDict.get(neighbor)\\n if mrlistpair:\\n for index in range(0, len(mrlistpair[0])):\\n movID = mrlistpair[0][index]\\n simSumDict[movID] += simScore\\n weightedSumDict[movID] += simScore * mrlistpair[1][index]# sim * rating\\n candidates = [(mID, 1.0 * weightedSumDict[mID] / simSumDict[mID]) for (mID) in weightedSumDict]\\n candidates.sort(key=lambda x: x[1], reverse=True)\\n return (user, candidates[:nRec])\",\n \"def score(self, user_idx, item_idx=None):\\n if item_idx is None:\\n if self.train_set.is_unk_user(user_idx):\\n raise ScoreException(\\n \\\"Can't make score prediction for (user_id=%d)\\\" % user_idx\\n )\\n\\n known_item_scores = self.V.dot(self.U[user_idx, :])\\n return known_item_scores\\n else:\\n if self.train_set.is_unk_user(user_idx) or self.train_set.is_unk_item(\\n item_idx\\n ):\\n raise ScoreException(\\n \\\"Can't make score prediction for (user_id=%d, item_id=%d)\\\"\\n % (user_idx, item_idx)\\n )\\n\\n user_pred = self.V[item_idx, :].dot(self.U[user_idx, :])\\n\\n user_pred = sigmoid(user_pred)\\n if self.train_set.min_rating == self.train_set.max_rating:\\n user_pred = scale(user_pred, 0.0, self.train_set.max_rating, 0.0, 1.0)\\n else:\\n user_pred = scale(\\n user_pred,\\n self.train_set.min_rating,\\n self.train_set.max_rating,\\n 0.0,\\n 1.0,\\n )\\n\\n return user_pred\",\n \"def liste_triee():\\n\\n global liste_username_score\\n\\n liste_username_score = sorted(liste_username_score, reverse = True, key=score_trie)\",\n \"def update_rankings(conn, users):\\n users.sort(key=lambda user: user[\\\"rank\\\"])\\n # Set tau and draw_probability to more reasonable values than the defaults\\n if config.COMPETITION_FINALS_PAIRING:\\n trueskill.setup(tau=0.0, draw_probability=0.001)\\n else:\\n trueskill.setup(tau=0.008, draw_probability=0.001)\\n teams = [[trueskill.Rating(mu=user[\\\"mu\\\"], sigma=user[\\\"sigma\\\"])]\\n for user in users]\\n new_ratings = trueskill.rate(teams)\\n for user, rating in zip(users, new_ratings):\\n new_score = rating[0].mu - 3*rating[0].sigma\\n conn.execute(model.bots.update().where(\\n (model.bots.c.user_id == user[\\\"user_id\\\"]) &\\n (model.bots.c.id == user[\\\"bot_id\\\"]) &\\n # Filter on version so we don't update the score for an old\\n # version of the bot\\n (model.bots.c.version_number == user[\\\"version_number\\\"])\\n ).values(\\n mu=rating[0].mu,\\n sigma=rating[0].sigma,\\n score=new_score,\\n ))\\n\\n # Update the hackathon scoring tables\\n hackathons = conn.execute(sqlalchemy.sql.select([\\n model.hackathons.c.id.label(\\\"hackathon_id\\\"),\\n ]).select_from(\\n model.hackathon_participants.join(\\n model.hackathons,\\n (model.hackathon_participants.c.hackathon_id == model.hackathons.c.id) &\\n (model.hackathon_participants.c.user_id == user[\\\"user_id\\\"])\\n )\\n ).where(\\n (model.hackathons.c.start_date <= sqlalchemy.sql.func.now()) &\\n (model.hackathons.c.end_date > sqlalchemy.sql.func.now())\\n ))\\n\\n for hackathon in hackathons.fetchall():\\n hackathon_id = hackathon[\\\"hackathon_id\\\"]\\n try:\\n # Try and insert\\n insert_values = {\\n \\\"hackathon_id\\\": hackathon_id,\\n \\\"user_id\\\": user[\\\"user_id\\\"],\\n \\\"bot_id\\\": user[\\\"bot_id\\\"],\\n \\\"score\\\": new_score,\\n \\\"mu\\\": rating[0].mu,\\n \\\"sigma\\\": rating[0].sigma,\\n \\\"version_number\\\": user[\\\"version_number\\\"],\\n \\\"language\\\": user[\\\"language\\\"],\\n \\\"games_played\\\": 1,\\n }\\n\\n conn.execute(\\n model.hackathon_snapshot.insert().values(\\n **insert_values))\\n except sqlalchemy.exc.IntegrityError:\\n # Row exists, update\\n condition = ((model.hackathon_snapshot.c.hackathon_id ==\\n hackathon_id) &\\n (model.hackathon_snapshot.c.user_id ==\\n user[\\\"user_id\\\"]) &\\n (model.hackathon_snapshot.c.bot_id ==\\n user[\\\"bot_id\\\"]))\\n conn.execute(\\n model.hackathon_snapshot.update().values(\\n score=new_score,\\n mu=rating[0].mu,\\n sigma=rating[0].sigma,\\n version_number=user[\\\"version_number\\\"],\\n language=user[\\\"language\\\"],\\n games_played=model.hackathon_snapshot.c.games_played + 1,\\n ).where(condition))\",\n \"def compare_users(userA, userB):\\n reader = open(\\\"similarities.txt\\\", \\\"r\\\")\\n lines = reader.readlines()\\n similarity = None\\n for line in lines:\\n a, b, sim = line.split(\\\"\\\\t\\\")\\n if userA == a and userB == b:\\n print(sim)\\n similarity = float(sim)\\n\\n reader2 = open(\\\"sorted_similarities.txt\\\", \\\"r\\\")\\n lines = reader2.readlines()\\n index = 0.0\\n for line in lines:\\n sim = float(line)\\n if similarity == sim:\\n return 1.0 - (float(index) / len(lines))\\n index += 1\\n\\n return None\\n \\n \\n \\\"\\\"\\\"\\n if len(sim_dict.keys()) == 0:\\n return None\\n\\n ranked = sorted(sim_dict.keys(), key=lambda x: sim_dict[x],\\n reverse=True)\\n try:\\n index = ranked.index(userB)\\n percentile = 1.0 - (float(index) / len(sim_dict.keys()))\\n return int(100 * percentile)\\n except:\\n return None\\n \\\"\\\"\\\"\",\n \"def rd1leaderboard(request):\\n\\n #Add views\\n playing_players = Rd1SlotModel.objects.filter(player_name__isnull=False)\\n\\n endurance_leader = Rd1SlotModel.objects.aggregate(Max('endurance_score'))\\n\\n #Add context\\n context = {\\n 'playing_players': playing_players,\\n 'endurance_leader': endurance_leader,\\n }\\n\\n return render(request, 'rd1Leaderboard.html', context=context)\",\n \"def _rank(self):\\r\\n return sorted(self.player_points.items(),key=lambda x:x[1],reverse=True)\",\n \"def scoring(self):\\n pass\",\n \"def score(self, model, context):\\n pass\",\n \"def add_match(self, match):\\n\\n # update cache results with game result\\n if self.cached_results:\\n results = json.loads(self.cached_results)\\n else:\\n results = []\\n\\n winner = match.winner == self\\n opponent = match.loser if winner else match.winner\\n new_rating = match.winner_rating_after if winner else \\\\\\n match.loser_rating_after\\n\\n results.append({\\n 'winner': winner,\\n 'opponent_name': opponent.name,\\n 'played_time': str(match.played_time),\\n 'played_timestamp': get_timestamp(match.played_time)\\n })\\n self.cached_results = json.dumps(results[-CACHED_RATING_LIMIT:])\\n\\n # update player with new rating\\n self.update_rating(new_rating, match)\\n\\n # save the player in the database\\n self.save()\",\n \"async def ranking(self, channel_id,user_infos, user_id, team_id):\\n message = \\\"Top 10 du Quizz Movie : \\\\n\\\" # Begin the ranking message\\n\\n with codecs.open(\\\"{}/ranking.json\\\".format(self.dirname), \\\"r\\\", \\\"utf-8\\\") as json_data: # Open the ranking file read mode\\n data = json.load(json_data)\\n\\n # Get the top ten players reverse score ordered\\n l = sorted(data.items(), key=operator.itemgetter(1), reverse=True)[0:10]\\n\\n for i in range(len(l)): # For each top ten players\\n message += \\\"Numéro {} : {}, Score : {}\\\\n\\\".format(str(i+1), l[i][0], str(l[i][1]))\\n return await self.sendText(message, channel_id,user_infos, team_id)\",\n \"def get(self, request, search_string=None):\\n query = SearchQuery(search_string)\\n\\n username_vector = SearchVector('username', weight='A')\\n first_name_vector = SearchVector('first_name', weight='B')\\n last_name_vector = SearchVector('last_name', weight='B')\\n email_vector = SearchVector('email', weight='B')\\n vectors = username_vector + first_name_vector + last_name_vector + email_vector\\n qs = User.objects\\n qs = qs.annotate(search=vectors).filter(search=query)\\n qs = qs.annotate(rank=SearchRank(vectors, query)).order_by('-rank')\\n print(qs)\\n return Response(UserSerializer(qs, many=True).data)\",\n \"def playerdetail (request,name):\\n\\n #Basic player details\\n player_image = PlayerModel.objects.get(name=name).image\\n player_HC = PlayerModel.objects.get(name=name).HC\\n player_highfinish = PlayerModel.objects.get(name=name).highfinish\\n player_tournum = PlayerModel.objects.get(name=name).tournum\\n player_totalpoints = LeaderBoardModel.objects.get(player=name).overall_total\\n player_totalrank = LeaderBoardModel.objects.filter(overall_total__gt=player_totalpoints).count() + 1\\n\\n\\n target_holes = 10 #Change to 10 in production\\n\\n ##START ROUND 1 CALCULATIONS -->\\n #Trigger to show score only when round finished\\n try:\\n rd1holes_played = Rd1SlotModel.objects.get(player_name__name=name).player_holesplayed\\n if rd1holes_played is None:\\n rd1holes_played = 0\\n else:\\n rd1holes_played = Rd1SlotModel.objects.get(player_name__name=name).player_holesplayed\\n except:\\n rd1holes_played = 0\\n\\n\\n\\n #Rd1 Player golf score & rank\\n if rd1holes_played >= target_holes:\\n rd1golf_score = Rd1SlotModel.objects.get(player_name__name=name).player_score\\n rd1golf_scoreRank = Rd1SlotModel.objects.filter(player_score__lt=rd1golf_score).count() + 1\\n rd1golf_stbl = Rd1SlotModel.objects.get(player_name__name=name).player_stbl\\n rd1golf_stblRank = Rd1SlotModel.objects.filter(player_stbl__gt=rd1golf_stbl).count() + 1\\n else:\\n rd1golf_score = \\\"-\\\"\\n rd1golf_scoreRank= \\\"n/a\\\"\\n rd1golf_stbl = \\\"-\\\"\\n rd1golf_stblRank= \\\"n/a\\\"\\n\\n #Rd1PlayerPoints\\n try:\\n rd1golf_points = LeaderBoardModel.objects.get(player=name).rd1_golf\\n except:\\n rd1golf_points = \\\"-\\\"\\n try:\\n rd1golf_rank = LeaderBoardModel.objects.filter(rd1_golf__gt=rd1golf_points).count() + 1\\n except:\\n rd1golf_rank = \\\"-\\\"\\n try:\\n rd1ctpld_points = LeaderBoardModel.objects.get(player=name).rd1_ctpld\\n except:\\n rd1ctpld_points = \\\"-\\\"\\n try:\\n rd1ctpld_rank = LeaderBoardModel.objects.filter(rd1_ctpld__gt=rd1ctpld_points).count() + 1\\n except:\\n rd1ctpld_rank = \\\"-\\\"\\n try:\\n rd1bonus_points = LeaderBoardModel.objects.get(player=name).rd1_bonus\\n except:\\n rd1bonus_points = \\\"-\\\"\\n try:\\n rd1bonus_rank = LeaderBoardModel.objects.filter(rd1_bonus__gt=rd1bonus_points).count() + 1\\n except:\\n rd1bonus_rank = \\\"-\\\"\\n try:\\n rd1total_points = rd1golf_points + rd1ctpld_points + rd1bonus_points\\n except:\\n rd1total_points = \\\"-\\\"\\n try:\\n rd1total_rank = LeaderBoardModel.objects.filter(rd1_total__gt=rd1total_points).count() + 1\\n except:\\n rd1total_rank = \\\"-\\\"\\n\\n try:\\n round1overall_points = list(LeaderBoardModel.objects.aggregate(Sum('rd1_total')).values())[0]\\n except:\\n round1overall_points = 0\\n\\n\\n ##START ROUND 2 CALCULATIONS -->\\n #Trigger to show score only when round finished\\n try:\\n rd2holes_played = Rd2SlotModel.objects.get(player_name__name=name).player_holesplayed\\n if rd2holes_played is None:\\n rd2holes_played = 0\\n else:\\n rd2holes_played = Rd2SlotModel.objects.get(player_name__name=name).player_holesplayed\\n except:\\n rd2holes_played = 0\\n\\n #Rd2 Player golf score & rank\\n if rd2holes_played >= target_holes:\\n rd2golf_score = Rd2SlotModel.objects.get(player_name__name=name).player_score\\n rd2golf_scoreRank = Rd2SlotModel.objects.filter(player_score__lt=rd2golf_score).count() + 1\\n rd2golf_stbl = Rd2SlotModel.objects.get(player_name__name=name).player_stbl\\n rd2golf_stblRank = Rd2SlotModel.objects.filter(player_stbl__gt=rd2golf_stbl).count() + 1\\n else:\\n rd2golf_score = \\\"-\\\"\\n rd2golf_scoreRank= \\\"n/a\\\"\\n rd2golf_stbl = \\\"-\\\"\\n rd2golf_stblRank= \\\"n/a\\\"\\n\\n #Rd2PlayerPoints\\n try:\\n rd2golf_points = LeaderBoardModel.objects.get(player=name).rd2_golf\\n except:\\n rd2golf_points = \\\"-\\\"\\n try:\\n rd2golf_rank = LeaderBoardModel.objects.filter(rd2_golf__gt=rd2golf_points).count() + 1\\n except:\\n rd2golf_rank = \\\"-\\\"\\n try:\\n rd2ctpld_points = LeaderBoardModel.objects.get(player=name).rd2_ctpld\\n except:\\n rd2ctpld_points = \\\"-\\\"\\n try:\\n rd2ctpld_rank = LeaderBoardModel.objects.filter(rd2_ctpld__gt=rd2ctpld_points).count() + 1\\n except:\\n rd2ctpld_rank = \\\"-\\\"\\n try:\\n rd2bonus_points = LeaderBoardModel.objects.get(player=name).rd2_bonus\\n except:\\n rd2bonus_points = \\\"-\\\"\\n try:\\n rd2bonus_rank = LeaderBoardModel.objects.filter(rd2_bonus__gt=rd2bonus_points).count() + 1\\n except:\\n rd2bonus_rank = \\\"-\\\"\\n try:\\n rd2total_points = rd2golf_points + rd2ctpld_points + rd2bonus_points\\n except:\\n rd2total_points = \\\"-\\\"\\n try:\\n rd2total_rank = LeaderBoardModel.objects.filter(rd2_total__gt=rd2total_points).count() + 1\\n except:\\n rd2total_rank = \\\"-\\\"\\n\\n try:\\n round2overall_points = list(LeaderBoardModel.objects.aggregate(Sum('rd2_total')).values())[0]\\n except:\\n round2overall_points = 0\\n\\n ##START ROUND 3 CALCULATIONS -->\\n #Trigger to show score only when round finished\\n try:\\n rd3holes_played = Rd3SlotModel.objects.get(player_name__name=name).player_holesplayed\\n if rd3holes_played is None:\\n rd3holes_played = 0\\n else:\\n rd3holes_played = Rd3SlotModel.objects.get(player_name__name=name).player_holesplayed\\n except:\\n rd3holes_played = 0\\n\\n #Rd3 Player golf score & rank\\n if rd3holes_played >= target_holes:\\n rd3golf_score = Rd3SlotModel.objects.get(player_name__name=name).player_score\\n rd3golf_scoreRank = Rd3SlotModel.objects.filter(player_score__lt=rd2golf_score).count() + 1\\n rd3golf_stbl = Rd3SlotModel.objects.get(player_name__name=name).player_stbl\\n rd3golf_stblRank = Rd3SlotModel.objects.filter(player_stbl__gt=rd2golf_stbl).count() + 1\\n else:\\n rd3golf_score = \\\"-\\\"\\n rd3golf_scoreRank= \\\"n/a\\\"\\n rd3golf_stbl = \\\"-\\\"\\n rd3golf_stblRank= \\\"n/a\\\"\\n\\n #Rd2PlayerPoints\\n try:\\n rd3golf_points = LeaderBoardModel.objects.get(player=name).rd3_golf\\n except:\\n rd3golf_points = \\\"-\\\"\\n try:\\n rd3golf_rank = LeaderBoardModel.objects.filter(rd3_golf__gt=rd3golf_points).count() + 1\\n except:\\n rd3golf_rank = \\\"-\\\"\\n try:\\n rd3ctpld_points = LeaderBoardModel.objects.get(player=name).rd3_ctpld\\n except:\\n rd3ctpld_points = \\\"-\\\"\\n try:\\n rd3ctpld_rank = LeaderBoardModel.objects.filter(rd3_ctpld__gt=rd3ctpld_points).count() + 1\\n except:\\n rd3ctpld_rank = \\\"-\\\"\\n try:\\n rd3bonus_points = LeaderBoardModel.objects.get(player=name).rd3_bonus\\n except:\\n rd3bonus_points = \\\"-\\\"\\n try:\\n rd3bonus_rank = LeaderBoardModel.objects.filter(rd3_bonus__gt=rd3bonus_points).count() + 1\\n except:\\n rd3bonus_rank = \\\"-\\\"\\n try:\\n rd3total_points = rd3golf_points + rd3ctpld_points + rd3bonus_points\\n except:\\n rd3total_points = \\\"-\\\"\\n try:\\n rd3total_rank = LeaderBoardModel.objects.filter(rd3_total__gt=rd3total_points).count() + 1\\n except:\\n rd3total_rank = \\\"-\\\"\\n\\n try:\\n round3overall_points = list(LeaderBoardModel.objects.aggregate(Sum('rd3_total')).values())[0]\\n except:\\n round3overall_points = 0\\n\\n ##START OTHER_SCORES CALCULATIONS -->\\n\\n #Other Player Points\\n try:\\n social_points = LeaderBoardModel.objects.get(player=name).social\\n except:\\n social_points = \\\"-\\\"\\n try:\\n social_rank = LeaderBoardModel.objects.filter(social__gt=social_points).count() + 1\\n except:\\n social_rank = \\\"-\\\"\\n try:\\n bestdressed_points = LeaderBoardModel.objects.get(player=name).best_dressed\\n except:\\n bestdressed_points = \\\"-\\\"\\n try:\\n bestdressed_rank = LeaderBoardModel.objects.filter(best_dressed__gt=bestdressed_points).count() + 1\\n except:\\n bestdressed_rank = \\\"-\\\"\\n try:\\n tipping_points = LeaderBoardModel.objects.get(player=name).tipping\\n except:\\n tipping_points = \\\"-\\\"\\n try:\\n tipping_rank = LeaderBoardModel.objects.filter(tipping__gt=tipping_points).count() + 1\\n except:\\n tipping_rank = \\\"-\\\"\\n try:\\n othertotal_points = social_points + bestdressed_points + tipping_points\\n except:\\n othertotal_points = \\\"-\\\"\\n try:\\n othertotal_rank = LeaderBoardModel.objects.filter(other_total__gt=othertotal_points).count() + 1\\n except:\\n othertotal_rank = \\\"-\\\"\\n\\n try:\\n otheroverall_points = list(LeaderBoardModel.objects.aggregate(Sum('other_total')).values())[0]\\n except:\\n otheroverall_points = 0\\n\\n## == END SCORING CALCS ==\\n\\n context ={\\n 'name': name,\\n 'player_image': player_image,\\n 'player_HC': player_HC,\\n 'player_highfinish': player_highfinish,\\n 'player_tournum': player_tournum,\\n 'player_totalpoints': player_totalpoints,\\n 'player_totalrank': player_totalrank,\\n 'rd1golf_score': rd1golf_score,\\n 'rd1golf_stbl': rd1golf_stbl,\\n 'rd1golf_scoreRank': rd1golf_scoreRank,\\n 'rd1golf_stblRank': rd1golf_stblRank,\\n 'rd1golf_points': rd1golf_points,\\n 'rd1golf_rank': rd1golf_rank,\\n 'rd1ctpld_points': rd1ctpld_points,\\n 'rd1ctpld_rank': rd1ctpld_rank,\\n 'rd1bonus_points': rd1bonus_points,\\n 'rd1bonus_rank': rd1bonus_rank,\\n 'rd1total_points': rd1total_points,\\n 'rd1total_rank': rd1total_rank,\\n 'round1overall_points': round1overall_points,\\n 'rd2golf_score': rd2golf_score,\\n 'rd2golf_stbl': rd2golf_stbl,\\n 'rd2golf_scoreRank': rd2golf_scoreRank,\\n 'rd2golf_stblRank': rd2golf_stblRank,\\n 'rd2golf_points': rd2golf_points,\\n 'rd2golf_rank': rd2golf_rank,\\n 'rd2ctpld_points': rd2ctpld_points,\\n 'rd2ctpld_rank': rd2ctpld_rank,\\n 'rd2bonus_points': rd2bonus_points,\\n 'rd2bonus_rank': rd2bonus_rank,\\n 'rd2total_points': rd2total_points,\\n 'rd2total_rank': rd2total_rank,\\n 'round2overall_points': round2overall_points,\\n 'rd3golf_score': rd3golf_score,\\n 'rd3golf_stbl': rd3golf_stbl,\\n 'rd3golf_scoreRank': rd3golf_scoreRank,\\n 'rd3golf_stblRank': rd3golf_stblRank,\\n 'rd3golf_points': rd3golf_points,\\n 'rd3golf_rank': rd3golf_rank,\\n 'rd3ctpld_points': rd3ctpld_points,\\n 'rd3ctpld_rank': rd3ctpld_rank,\\n 'rd3bonus_points': rd3bonus_points,\\n 'rd3bonus_rank': rd3bonus_rank,\\n 'rd3total_points': rd3total_points,\\n 'rd3total_rank': rd3total_rank,\\n 'round3overall_points': round3overall_points,\\n 'social_points': social_points,\\n 'social_rank': social_rank,\\n 'bestdressed_points': bestdressed_points,\\n 'bestdressed_rank': bestdressed_rank,\\n 'tipping_points': tipping_points,\\n 'tipping_rank': tipping_rank,\\n 'othertotal_points': othertotal_points,\\n 'othertotal_rank': othertotal_rank,\\n 'otheroverall_points': otheroverall_points,\\n\\n }\\n\\n return render(request, 'playerDetail.html', context=context)\",\n \"def order_players_by_initial_rank(self):\\n pass\",\n \"def personalization(prediction, n):\\n # prediction\\n # n top n recommendation\\n\\n top_n = get_top_n(prediction, n)\\n\\n rec_dict = {}\\n for uid, user_ratings in top_n.items():\\n rec_dict[uid] = [iid for (iid, _) in user_ratings]\\n\\n rec_user_ls = [pred[0] for pred in prediction]\\n rec_item_ls = [pred[1] for pred in prediction]\\n\\n unique_rec_user_ls = np.unique(rec_user_ls)\\n unique_rec_item_ls = np.unique(rec_item_ls)\\n\\n # assign each item with index number\\n unique_rec_item_dict = {item: ind for ind,\\n item in enumerate(unique_rec_item_ls)}\\n\\n n_unique_rec_user = len(unique_rec_user_ls)\\n n_unique_rec_item = len(unique_rec_item_ls)\\n\\n # recommended user item matrix\\n rec_matrix = np.zeros(shape=(n_unique_rec_user, n_unique_rec_item))\\n\\n # represent recommended item for each user as binary 0/1\\n for user in range(n_unique_rec_user):\\n # get userid\\n user_id = unique_rec_user_ls[user]\\n # get rec item list\\n item_ls = rec_dict[user_id]\\n\\n for item_id in item_ls:\\n # get item index\\n item = unique_rec_item_dict[item_id]\\n rec_matrix[user, item] = 1\\n\\n # calculate cosine similarity matrix across all user recommendations\\n similarity = cosine_similarity(X=rec_matrix, dense_output=False)\\n # calculate average of upper triangle of cosine matrix\\n upper_right = np.triu_indices(similarity.shape[0], k=1)\\n # personalization is 1-average cosine similarity\\n score = 1 - np.mean(similarity[upper_right])\\n return score\",\n \"def bestplayers():\\n players = Player.query.filter_by(is_admin=False).all()\\n players = list(reversed(sorted(players, key=lambda player: player.points)))\\n for player in players:\\n player.image = url_for('static', filename='images/players/{}'.format(player.image))\\n player.team_name = player.team.name\\n player.team_logo = url_for('static', filename='images/teams/{}'.format(player.team.logo_image))\\n\\n return render_template('standings/best-players.html', players=players, title='Best Players')\",\n \"def suggestions(request):\\n # Get login user profile\\n u_profile = UserProfile.objects.get(user=request.user)\\n # Search by user profile\\n sqs = SearchQuerySet().filter(business_area=u_profile.business_area, city=u_profile.city,\\n handicap_36__lte=int(u_profile.handicap_36) + 1,\\n handicap_36__gte=int(u_profile.handicap_36) - 1,\\n handicap_us__lte=int(u_profile.handicap_us) + 1,\\n handicap_us__gte=int(u_profile.handicap_us) - 1)\\n # Get user list\\n queryset = User.objects.all()\\n # Create result list\\n results_list = []\\n # Get User to list by id\\n max_loop = sqs.count()\\n for x in range(0, max_loop):\\n user = get_object_or_404(queryset, pk=sqs[x].object.id)\\n results_list.append(user)\\n # Convert to serializer\\n serializer = UserSerializer(results_list, many=True)\\n if serializer.is_valid:\\n return Response({'status': '200', 'code': 'OK_SUGGESTION',\\n 'detail': serializer.data}, status=200)\\n else:\\n return Response({'status': '400', 'code': 'E_INVALID_PARAMETER_VALUES',\\n 'detail': serializer.errors}, status=400)\",\n \"def recommend_me_team(self, user_id: int, user_ids_per_group: list, n_recommendations: int):\\n res_arr = np.array([self.user_similarity.loc[user_id, group_ids].mean() for group_ids in user_ids_per_group])\\n res_inds = np.argsort(res_arr)[::-1][:n_recommendations]\\n return res_inds, res_arr[res_inds]\",\n \"def display_ranking(args: argparse.Namespace, cookie: str) -> None:\\n number: int = args.number\\n if not number:\\n return\\n\\n if number >= 50:\\n print(\\\"Number of users to display cannot exceed 49 for now. Exiting.\\\")\\n return\\n\\n cookies = {\\n \\\"spip_session\\\": cookie,\\n }\\n\\n ranking = get_global_ranking(args.offset, cookies)\\n\\n for counter, entry in enumerate(ranking):\\n\\n if counter >= number:\\n break\\n\\n userid = ranking[entry][\\\"id_auteur\\\"]\\n userinfo = get_user_info(userid, cookies)\\n if userinfo is None:\\n continue\\n\\n print(\\n f\\\"{Fore.YELLOW}{ranking[entry]['place']:>4}{Style.RESET_ALL}. \\\"\\n f\\\"{userinfo['nom']:<25} \\\"\\n f\\\"{userinfo['score']} \\\"\\n )\\n\\n return\",\n \"def test_search_your_skills(self):\\n self.client.login(username='user2@user2.com', password='testpass')\\n\\n resp = self.client.get(reverse('profiles:search_your_skills'))\\n\\n # self.user2 has a profile with the Django developer skill\\n # self.project has one open position for Django developer, so\\n # we should find one result\\n self.assertContains(\\n resp,\\n '1 results were found with: Your Skills'\\n )\\n self.assertContains(resp, str(self.project))\\n\\n # various page information\\n self.assertContains(resp, 'Test Project')\\n self.assertContains(resp, 'All Needs')\\n self.assertContains(resp, 'Projects')\\n\\n self.assertTemplateUsed('homepage.html')\",\n \"def get_high_scores(self,request):\\n if request.number_of_results:\\n scores = Score.query(Score.won == True).order(Score.attempts_allowed,Score.guesses).fetch(request.number_of_results)\\n else:\\n scores = Score.query(Score.won == True).order(Score.attempts_allowed,Score.guesses).fetch()\\n return ScoreForms(items=[score.to_form() for score in scores])\",\n \"def recache_matches(self):\\n self.recalculating = True\\n self.save()\\n\\n # give all the players a starting rank\\n i = 1\\n for player in self.player_set.order_by('id'):\\n player.rank = i\\n player.rating = DEFAULT_RATING\\n player.cached_results = ''\\n player.cached_rating_changes = ''\\n player.cached_rank_changes = ''\\n player.save()\\n i += 1\\n\\n # get all the matches and replay them in order\\n matches = self.match_set.order_by('played_time')\\n for match in matches:\\n match.update_company_ladder()\\n\\n self.recalculating = False\\n self.save()\",\n \"def tourplayers(request):\\n active_players = PlayerModel.objects.order_by('number')\\n\\n context = {\\n 'active_players': active_players,\\n }\\n\\n return render(request, 'tourPlayers.html', context=context)\",\n \"def score_list_student(request):\\n\\n takes = Take.objects.filter(student__username=request.data[\\\"sid\\\"])\\n serializer = TakeSerializer(takes, many=True)\\n return Response(serializer.data)\",\n \"def display_all_round_matches(self):\\r\\n tournament_name = self.input_name(\\\"nom du tournoi\\\")\\r\\n tournament = tournaments_table.get(Query().Nom == tournament_name)\\r\\n print(\\\"Matches du tournoi de\\\", tournament_name, \\\"ayant eu lieu: \\\")\\r\\n for match in tournament['Matches joues']:\\r\\n print(players_table.get(doc_id=match[0])['Nom'], \\\"(BLANCS) contre\\\",\\r\\n players_table.get(doc_id=match[1])['Nom'], \\\"(NOIRS)\\\")\",\n \"def set_ranking_users():\\n data = select_data_source()\\n order_id = data['id']\\n from_user = data['from']\\n to_user = data['to']\\n ranking = float(data['ranking']) # TODO.\\n \\n if session._id != from_user and session._id != to_user : return permission_denied_return\\n \\n db = database.getdb()\\n \\n if not float.is_integer(ranking) or ranking <= 0 or 10 < ranking :\\n return ranking_invalid_value_return\\n \\n ### Check if order exists.\\n \\n cmd = 'select * from orders where id==\\\"{0}\\\"'.format(order_id)\\n order_info = db.execute(cmd).fetchall()\\n if len(order_info) == 0 :\\n return ranking_not_exist_return\\n \\n ### Check if user is valid.\\n cmd = 'select owner, customer from orders where id==\\\"{0}\\\"'.format(order_id)\\n owner, customer = db.execute(cmd).fetchall()[0]\\n \\n ### Check and setup ranked info in orders.\\n \\n if from_user == owner and to_user == customer :\\n cmd = 'select owner_ranked from orders where id==\\\"{0}\\\"'.format(order_id)\\n is_ranked = db.execute(cmd).fetchall()[0][0]\\n if is_ranked != 0 :\\n return ranking_already_ranked_return\\n cmd = 'update orders set owner_ranked=1'\\n db.execute(cmd)\\n db.commit()\\n elif from_user == customer and to_user == owner :\\n cmd = 'select customer_ranked from orders where id==\\\"{0}\\\"'.format(order_id)\\n is_ranked = db.execute(cmd).fetchall()[0][0]\\n if is_ranked != 0 :\\n return ranking_already_ranked_return\\n cmd = 'update orders set customer_ranked=1'\\n db.execute(cmd)\\n db.commit()\\n else :\\n return ranking_not_relative_return\\n \\n ### Update rank to to_user.\\n \\n cmd = 'select rank, rank_time from users where mail==\\\"{0}\\\"'.format(to_user)\\n rank, rank_time = db.execute(cmd).fetchall()[0]\\n \\n rank = (rank * rank_time + ranking) / (rank_time + 1)\\n rank_time += 1\\n \\n cmd = 'update users set rank={0}, rank_time={1} where mail=\\\"{2}\\\"'.format(rank, rank_time, to_user)\\n db.execute(cmd)\\n db.commit()\",\n \"def rerank(test_predicted_paraphrases, test_features, ranker, minimum_score):\\n new_test_predicted_paraphrases = { (w1, w2) : [] for (w1, w2) in test_predicted_paraphrases.keys() }\\n\\n for ((w1, w2), curr_paraphrases), curr_paraphrase_features in tqdm.tqdm(zip(\\n test_predicted_paraphrases.items(), test_features)):\\n pars_and_vectors = zip(curr_paraphrases.items(), curr_paraphrase_features)\\n\\n # Sort the paraphrases according to the ranking\\n def compare_paraphrases(p1, p2):\\n return ranker.predict((p2[1] - p1[1]).reshape(1, -1))\\n\\n # Consider both the original score (for the specific noun-compound)\\n # and the new rank (which paraphrases are more commonly ranked higher)\\n sorted_paraphrases = [(paraphrase, (len(curr_paraphrases) - rank) * float(score))\\n for rank, ((paraphrase, score), feature) in\\n enumerate(sorted(pars_and_vectors,\\n key=functools.cmp_to_key(compare_paraphrases)))]\\n\\n sorted_paraphrases = sorted(sorted_paraphrases, key=lambda x: x[1], reverse=True)\\n\\n # Keep only paraphrases with score above threshold. Best score = k * 1 = k,\\n new_test_predicted_paraphrases[(w1, w2)] = \\\\\\n [(paraphrase, score) for (paraphrase, score) in sorted_paraphrases if score >= minimum_score]\\n\\n return new_test_predicted_paraphrases\",\n \"def compute_matches(self):\\n\\t\\tself.local_database[\\\"figure_number\\\"] = [0] * len(self.local_database[\\\"feature_vectors\\\"])\\n\\t\\tForensics = wbForensicsHOG(Database=self.local_database)\\n\\t\\tForensics.KDTree_pairs(leaf_size = len(self.local_database)+1)\\n\\t\\tForensics.d_rank(pairs=Forensics.pairs, distances=Forensics.dists, ratios=Forensics.ratios)\\n\\n\\t\\tself.local_matches = Forensics.Dist_Rank\",\n \"def make_player_stats(tournament, lineups):\\n positions = find_player_positions(lineups)\\n positions = positions.set_index('player_id')[['player_position']]\\n player_stats = load_player_stats(tournament)\\n player_stats.set_index('player_id', inplace=True)\\n mask = player_stats['goals'] > player_stats['shots']\\n player_stats.loc[mask, 'shots'] = player_stats[mask]['goals']\\n res = player_stats.join(positions)\\n res = res[pd.notna(res['player_position'])]\\n return res\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6061785","0.59745973","0.5851773","0.5811785","0.5756262","0.5733067","0.57015556","0.5699974","0.5658713","0.5638683","0.5615457","0.56053066","0.55809283","0.5551806","0.549049","0.54823655","0.5481128","0.5471227","0.54675686","0.546725","0.5450787","0.5443734","0.5420615","0.54161924","0.54152155","0.54122037","0.536658","0.53660375","0.53646934","0.5347769","0.53444564","0.53293824","0.5322064","0.53196836","0.53121644","0.53024054","0.5302067","0.5293786","0.5285009","0.52834594","0.5282385","0.5275472","0.5262631","0.5262563","0.5257008","0.5249137","0.5246018","0.5232312","0.5226291","0.52112585","0.5209919","0.5207121","0.52022654","0.5187266","0.51865906","0.5180835","0.51778936","0.5177053","0.5173952","0.51713747","0.51647276","0.51634413","0.5154584","0.51506764","0.5148511","0.5140256","0.51373863","0.5135575","0.5130546","0.51305","0.5129829","0.51227945","0.5118109","0.51178813","0.51177776","0.5100862","0.50995374","0.5084864","0.5068692","0.5062203","0.5054236","0.503879","0.50369483","0.5028695","0.5027055","0.5021854","0.50179404","0.5016565","0.5015393","0.5008401","0.50074816","0.49968866","0.4996401","0.49905264","0.49904755","0.498836","0.49845725","0.49759552","0.49735644","0.49680817"],"string":"[\n \"0.6061785\",\n \"0.59745973\",\n \"0.5851773\",\n \"0.5811785\",\n \"0.5756262\",\n \"0.5733067\",\n \"0.57015556\",\n \"0.5699974\",\n \"0.5658713\",\n \"0.5638683\",\n \"0.5615457\",\n \"0.56053066\",\n \"0.55809283\",\n \"0.5551806\",\n \"0.549049\",\n \"0.54823655\",\n \"0.5481128\",\n \"0.5471227\",\n \"0.54675686\",\n \"0.546725\",\n \"0.5450787\",\n \"0.5443734\",\n \"0.5420615\",\n \"0.54161924\",\n \"0.54152155\",\n \"0.54122037\",\n \"0.536658\",\n \"0.53660375\",\n \"0.53646934\",\n \"0.5347769\",\n \"0.53444564\",\n \"0.53293824\",\n \"0.5322064\",\n \"0.53196836\",\n \"0.53121644\",\n \"0.53024054\",\n \"0.5302067\",\n \"0.5293786\",\n \"0.5285009\",\n \"0.52834594\",\n \"0.5282385\",\n \"0.5275472\",\n \"0.5262631\",\n \"0.5262563\",\n \"0.5257008\",\n \"0.5249137\",\n \"0.5246018\",\n \"0.5232312\",\n \"0.5226291\",\n \"0.52112585\",\n \"0.5209919\",\n \"0.5207121\",\n \"0.52022654\",\n \"0.5187266\",\n \"0.51865906\",\n \"0.5180835\",\n \"0.51778936\",\n \"0.5177053\",\n \"0.5173952\",\n \"0.51713747\",\n \"0.51647276\",\n \"0.51634413\",\n \"0.5154584\",\n \"0.51506764\",\n \"0.5148511\",\n \"0.5140256\",\n \"0.51373863\",\n \"0.5135575\",\n \"0.5130546\",\n \"0.51305\",\n \"0.5129829\",\n \"0.51227945\",\n \"0.5118109\",\n \"0.51178813\",\n \"0.51177776\",\n \"0.5100862\",\n \"0.50995374\",\n \"0.5084864\",\n \"0.5068692\",\n \"0.5062203\",\n \"0.5054236\",\n \"0.503879\",\n \"0.50369483\",\n \"0.5028695\",\n \"0.5027055\",\n \"0.5021854\",\n \"0.50179404\",\n \"0.5016565\",\n \"0.5015393\",\n \"0.5008401\",\n \"0.50074816\",\n \"0.49968866\",\n \"0.4996401\",\n \"0.49905264\",\n \"0.49904755\",\n \"0.498836\",\n \"0.49845725\",\n \"0.49759552\",\n \"0.49735644\",\n \"0.49680817\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.496989"},"document_rank":{"kind":"string","value":"99"}}}],"truncated":false,"partial":true},"paginationData":{"pageIndex":260,"numItemsPerPage":100,"numTotalItems":95772,"offset":26000,"length":100}},"jwt":"eyJhbGciOiJFZERTQSIsImtpZCI6IjVHZDBvd0g5MTM2eDZjc1FvbE1zcktNWUZoRVFoUm5PVVVybEpjOUhaUEEifQ.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sInJlcG9JZHMiOlsiNjc1Mzc2ODJkMmE2Mjg0NzVhMWJmZmNkIl0sImlhdCI6MTc5MTE5NDUxOCwianRpIjoiNTVmODQ1NTktYjhhNy00ODNlLWJkNjMtYmI3NzZmMDkxMGFhIiwic3ViIjoiL2RhdGFzZXRzL25vbWljLWFpL2Nvcm5zdGFjay1weXRob24tdjEiLCJleHAiOjE3OTExOTgxMTgsImlzcyI6Imh0dHBzOi8vaHVnZ2luZ2ZhY2UuY28ifQ.4YqnlfpdHGmtgKf6aw7GuzUmV6zGedmnTq4JMdQaRD5aZ0-l2K_7HGUxpfiWo8U7agT_moxih3tkEYytXTkQAA","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 |
|---|---|---|---|---|---|---|
Get all components that are an instance of ``component_type``. | def get_components_by_type(
self, component_type: Union[type, Tuple[type, ...]]
) -> List[Any]:
return [c for c in self._components if isinstance(c, component_type)] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_components_by_type(\n self, component_type: Union[type, Tuple[type, ...]]\n ) -> List[Any]:\n return self._manager.get_components_by_type(component_type)",
"def get_components(self, filter_type=None):\n\n if filter_type is None:\n out = self.components\n elif isi... | [
"0.8547184",
"0.6889929",
"0.61937904",
"0.6006221",
"0.59004545",
"0.5866749",
"0.5784385",
"0.57724464",
"0.57717794",
"0.5745258",
"0.57174027",
"0.5656232",
"0.5640331",
"0.5635853",
"0.5634207",
"0.5617102",
"0.5617102",
"0.5615041",
"0.5579383",
"0.5579383",
"0.5561703"... | 0.8519839 | 1 |
Get the component with name ``name``. Names are guaranteed to be unique. | def get_component(self, name: str) -> Any:
for c in self._components:
if c.name == name:
return c
raise ValueError(f"No component found with name {name}") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_component(self, name):\n for cmpt in self.components:\n if cmpt['name'] == name:\n return cmpt",
"def get_component(self, name: str) -> Any:\n return self._manager.get_component(name)",
"def get(name):\r\n return componentManager.components[name]",
"def comp... | [
"0.8581954",
"0.8132362",
"0.8047409",
"0.75000876",
"0.6909813",
"0.67308456",
"0.65872496",
"0.6536908",
"0.6505384",
"0.6497451",
"0.64877367",
"0.64358634",
"0.64024574",
"0.63259006",
"0.62867415",
"0.62573177",
"0.6255165",
"0.62408423",
"0.62342554",
"0.6208173",
"0.62... | 0.8775755 | 0 |
Get a mapping of component names to components held by the manager. Returns Dict[str, Any] A mapping of component names to components. | def list_components(self) -> Dict[str, Any]:
return {c.name: c for c in self._components} | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def list_components(self) -> Dict[str, Any]:\n return self._manager.list_components()",
"def _getComponentsInfo(self):\n result = {}\n et = ElementTree()\n components = self.agentCompleteConfig.listComponents_() + \\\n self.agentCompleteConfig.listWebapps_()\n ... | [
"0.75031936",
"0.7189879",
"0.67731893",
"0.6669654",
"0.6528711",
"0.6451407",
"0.6451407",
"0.6361447",
"0.6353952",
"0.6353952",
"0.6349827",
"0.63444376",
"0.62123674",
"0.6171588",
"0.6085922",
"0.60758775",
"0.6042098",
"0.60234123",
"0.60161865",
"0.59968275",
"0.59941... | 0.7947298 | 0 |
Separately configure and set up the managers and components held by the component manager, in that order. The setup process involves applying default configurations and then calling the manager or component's setup method. This can result in new components as a side effect of setup because components themselves have ac... | def setup_components(self, builder: "Builder"):
self._setup_components(builder, self._managers + self._components) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def setup_component(self):\n self.conf, self.context = self._init_component()\n self.initialize()",
"def setup(self, manager):\n self._manager = manager\n self._configured = True",
"def _configure(self):\n pass",
"def _configure(self):\n Component._configure(self)\n s... | [
"0.7187612",
"0.6177336",
"0.6071428",
"0.60488313",
"0.60485494",
"0.60239357",
"0.59740335",
"0.5972695",
"0.59304833",
"0.5877998",
"0.5877998",
"0.5877998",
"0.5877998",
"0.5877996",
"0.5876117",
"0.5864202",
"0.582577",
"0.582577",
"0.57516897",
"0.57322896",
"0.5726736"... | 0.7832467 | 0 |
Get the component that has ``name`` if presently held by the component manager. Names are guaranteed to be unique. | def get_component(self, name: str) -> Any:
return self._manager.get_component(name) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_component(self, name: str) -> Any:\n for c in self._components:\n if c.name == name:\n return c\n raise ValueError(f\"No component found with name {name}\")",
"def get_component(self, name):\n for cmpt in self.components:\n if cmpt['name'] == name... | [
"0.8732072",
"0.86204886",
"0.81911695",
"0.7890518",
"0.7057693",
"0.64624846",
"0.63916045",
"0.62333816",
"0.6191701",
"0.61659276",
"0.6148998",
"0.6137814",
"0.6137657",
"0.61212516",
"0.61112803",
"0.6078761",
"0.6061714",
"0.60613793",
"0.6044957",
"0.6036776",
"0.6018... | 0.8089962 | 3 |
Get all components that are an instance of ``component_type``. | def get_components_by_type(
self, component_type: Union[type, Tuple[type, ...]]
) -> List[Any]:
return self._manager.get_components_by_type(component_type) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_components_by_type(\n self, component_type: Union[type, Tuple[type, ...]]\n ) -> List[Any]:\n return [c for c in self._components if isinstance(c, component_type)]",
"def get_components(self, filter_type=None):\n\n if filter_type is None:\n out = self.components\n ... | [
"0.8520174",
"0.6889397",
"0.618709",
"0.59998345",
"0.5893793",
"0.58591384",
"0.57775843",
"0.5769797",
"0.5767334",
"0.5746458",
"0.57118094",
"0.56562984",
"0.56372815",
"0.5633266",
"0.5631094",
"0.56097656",
"0.56097656",
"0.5608077",
"0.5572066",
"0.5572066",
"0.556140... | 0.8547882 | 0 |
Get a mapping of component names to components held by the manager. Returns Dict[str, Any] A dictionary mapping component names to components. | def list_components(self) -> Dict[str, Any]:
return self._manager.list_components() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def list_components(self) -> Dict[str, Any]:\n return {c.name: c for c in self._components}",
"def _getComponentsInfo(self):\n result = {}\n et = ElementTree()\n components = self.agentCompleteConfig.listComponents_() + \\\n self.agentCompleteConfig.listWebapps_()\... | [
"0.79906577",
"0.7245051",
"0.6733757",
"0.66009647",
"0.6538484",
"0.63692576",
"0.63692576",
"0.6338896",
"0.6323031",
"0.63061506",
"0.6275394",
"0.626935",
"0.626935",
"0.6257535",
"0.60413235",
"0.60004747",
"0.5986872",
"0.5979982",
"0.5975458",
"0.59395707",
"0.5929724... | 0.747328 | 1 |
\ creates gaussian kernel with side length l and a sigma of sig | def gkern(l=5, sig=1.):
ax = np.linspace(-(l - 1) / 2., (l - 1) / 2., l)
xx, yy = np.meshgrid(ax, ax)
kernel = np.exp(-0.5 * (np.square(xx) + np.square(yy)) / np.square(sig))
return kernel | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def gkern(l=5, sig=1.):\n\n ax = np.arange(-l // 2 + 1., l // 2 + 1.)\n xx, yy = np.meshgrid(ax, ax)\n\n kernel = np.exp(-(xx**2 + yy**2) / (2. * sig**2))\n\n return kernel / np.sum(kernel)",
"def gkern(l, sig=1.):\n\n ax = np.linspace(-(l - 1) / 2., (l - 1) / 2., l)\n xx, yy = np.meshgrid(ax, ... | [
"0.7510109",
"0.750365",
"0.74863845",
"0.7277165",
"0.7234427",
"0.7157976",
"0.7145621",
"0.7110056",
"0.7061145",
"0.70183206",
"0.69730043",
"0.69548696",
"0.6933574",
"0.6907124",
"0.6884259",
"0.6877862",
"0.6862947",
"0.68579936",
"0.6832131",
"0.6814453",
"0.67858434"... | 0.76391137 | 0 |
Computes the histogram of the input image | def compute_histogram(self, image):
hist = [0] * 256
x, y = image.shape[:2]
#print(image.shape)
for i in range(x):
for j in range(y):
hist[image[i, j]] += 1
return hist | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def hist(img):\n bottom_half = img[img.shape[0]//2:,:] # 0:img.shape[0]//2 is the top half\n histogram = bottom_half.sum(axis=0) \n \n return histogram",
"def compute_histogram(self, image):\n hist = [0] * 256\n [h, w] = image.shape\n print(h,w)\n i = 0\n while i < ... | [
"0.8064423",
"0.8042533",
"0.8037536",
"0.7930632",
"0.76470864",
"0.7604351",
"0.75007343",
"0.73157203",
"0.7244618",
"0.71898323",
"0.7063043",
"0.6988698",
"0.69718844",
"0.69492954",
"0.6931104",
"0.6850683",
"0.6834464",
"0.6798194",
"0.6795921",
"0.6792963",
"0.6772005... | 0.82736444 | 0 |
analyses a histogram it to find the optimal threshold value assuming a bimodal histogram takes as input | def find_optimal_threshold(self, hist):
k = 256
threshold = int(k / 2)
lastexpected1 = lastexpected2 = 0
while True:
expected1 = expected2 = 0
t_exp1 = sum(hist[:threshold])
t_exp2 = sum(hist[threshold:])
for i in range(threshold):
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def find_optimal_threshold(self, hist):\n\n # print(\"number of pixels using sum: \", sum(hist))\n probability = np.array((1/sum(hist))*hist)\n expected_value = probability*np.array(range(256))\n # print(\"probability: \\n\", probability)\n # print(\"expected_value: \\n\", expect... | [
"0.76285255",
"0.7466329",
"0.70402104",
"0.69199985",
"0.68791974",
"0.6473372",
"0.6381232",
"0.63574517",
"0.6347338",
"0.6333977",
"0.6308756",
"0.63060373",
"0.62337613",
"0.6220502",
"0.6215672",
"0.6160417",
"0.6128299",
"0.611688",
"0.60851824",
"0.60622996",
"0.60540... | 0.6990649 | 3 |
Comptues the binary image of the the input image based on histogram analysis and thresholding take as input | def binarize(self, image, threshold):
bin_img = image.copy()
for i in range(image.shape[0]):
for j in range(image.shape[1]):
if image[i, j] >= threshold:
bin_img[i, j] = 0
else:
bin_img[i, j] = 255
return bin_img | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def binarize(self, image, threshold):\n\n bin_img = image.copy()\n [h, w] = bin_img.shape\n opt_threshold = threshold\n print(opt_threshold)\n for row in range(h):\n for col in range(w):\n if bin_img[row, col] > opt_threshold: #greater than threshld whit... | [
"0.7681446",
"0.727308",
"0.7155279",
"0.70369035",
"0.6960089",
"0.6878597",
"0.6873943",
"0.6869752",
"0.6862886",
"0.68430334",
"0.68059856",
"0.67681766",
"0.67679167",
"0.6710474",
"0.66879267",
"0.6674513",
"0.66720676",
"0.666158",
"0.6618548",
"0.6603418",
"0.6565325"... | 0.73140323 | 1 |
Retrieves next url from request | def get_next_url(request, redirect_field_name):
next_url = request.GET.get(redirect_field_name)
if next_url:
kwargs = {'url': next_url, 'require_https': request.is_secure()}
hosts = [request.get_host()]
kwargs['allowed_hosts'] = hosts
if url_has_allowed_host_and_scheme(**kwargs)... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def next_url(request):\n next = request.REQUEST.get(\"next\", \"\")\n host = request.get_host()\n return next if next and is_safe_url(next, host=host) else None",
"def _get_next(request):\r\n next = request.POST.get('next', request.GET.get('next', request.META.get('HTTP_REFERER', None)))\r\n if no... | [
"0.83763486",
"0.8341553",
"0.82828504",
"0.801289",
"0.750873",
"0.73998064",
"0.7358548",
"0.71123296",
"0.70690924",
"0.70580965",
"0.69295985",
"0.6869535",
"0.68539375",
"0.67664903",
"0.67437416",
"0.6702806",
"0.66799146",
"0.666955",
"0.6664403",
"0.657624",
"0.655812... | 0.7040338 | 10 |
Cosine decay schedule with warm up period. | def cosine_decay_with_warmup(global_step,
learning_rate_base,
total_steps,
warmup_learning_rate=0.0,
warmup_steps=0,
hold_base_rate_steps=0):
if total_steps < warmup_steps... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def schedule(epoch):\n return alpha / (1 + (decay_rate * epoch))",
"def cosine_decay(base_lr, max_iteration, cur_step):\n return base_lr * (math.cos( (7*math.pi*cur_step) / (16*max_iteration) ))",
"def get_cosine_schedule_with_warmup(optimizer, num_warmup_steps, num_training_steps, num_cycles=.5, las... | [
"0.6320161",
"0.62945265",
"0.6014468",
"0.6014468",
"0.5992535",
"0.5947948",
"0.594646",
"0.59023297",
"0.5854784",
"0.58509827",
"0.5842494",
"0.582645",
"0.5814702",
"0.57450205",
"0.5581755",
"0.550415",
"0.5425403",
"0.5404061",
"0.5401594",
"0.5377699",
"0.53299034",
... | 0.5282483 | 22 |
Callable to compute the learning rate. | def eager_decay_rate():
learning_rate = 0.5 * learning_rate_base * (1 + tf.cos(
np.pi *
(tf.cast(global_step, tf.float32) - warmup_steps - hold_base_rate_steps
) / float(total_steps - warmup_steps - hold_base_rate_steps)))
if hold_base_rate_steps > 0:
lea... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def learning_rate(epoch):\n self.lr = self.lr / 1.00000001\n return self.lr",
"def __call__(self, epoch):\n # Compute the new dynamic learning rate, log in onto TensorBoard and\n # return the result for the training process\n learning_rate = self.schedule(epoch)\n ... | [
"0.8016885",
"0.8015723",
"0.7786947",
"0.77867407",
"0.7728448",
"0.7575189",
"0.7575189",
"0.7390784",
"0.7385804",
"0.7383413",
"0.7356881",
"0.7334096",
"0.73053694",
"0.72900087",
"0.71948165",
"0.70886856",
"0.7037042",
"0.69462264",
"0.69425225",
"0.69425225",
"0.68872... | 0.6100761 | 65 |
Append new animation. If \p _widget exists in animations, then it target will be changed | def _addLinearAnimation(self, _widget, _target):
self._linear_animations[_widget] = _target | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _addPulseAnimation(self, _widget, _target):\n self._pulse_animations[_widget] = _target",
"def add_animation(self, animation, key):\n\t\tif animation.from_value == animation.to_value:\n\t\t\treturn\n\t\tanimation.attribute = key\n\t\tanimation.layer = self\n\t\tself.animations[key] = animation",
"de... | [
"0.7235232",
"0.640118",
"0.60980463",
"0.5986769",
"0.5909382",
"0.57442415",
"0.5643073",
"0.5543253",
"0.5395823",
"0.5395823",
"0.5395823",
"0.5395823",
"0.5395823",
"0.5395823",
"0.5395823",
"0.5395823",
"0.5395823",
"0.5395823",
"0.5395823",
"0.5262023",
"0.52438366",
... | 0.7617919 | 0 |
Append new animation. If \p _widget exists in animations, then it target will be changed | def _addPulseAnimation(self, _widget, _target):
self._pulse_animations[_widget] = _target | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _addLinearAnimation(self, _widget, _target):\n self._linear_animations[_widget] = _target",
"def add_animation(self, animation, key):\n\t\tif animation.from_value == animation.to_value:\n\t\t\treturn\n\t\tanimation.attribute = key\n\t\tanimation.layer = self\n\t\tself.animations[key] = animation",
"... | [
"0.7618043",
"0.640304",
"0.60984266",
"0.5988012",
"0.591077",
"0.5744295",
"0.5644169",
"0.5541437",
"0.53966963",
"0.53966963",
"0.53966963",
"0.53966963",
"0.53966963",
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"0.53966963",
"0.53966963",
"0.53966963",
"0.53966963",
"0.526167",
"0.5243... | 0.7234984 | 1 |
Mouse button pressed notification | def mousePressed(self, _evt, _id):
_widget = None
if _id == ois.MB_Left:
_widget = self._mouseLeft
elif _id == ois.MB_Right:
_widget = self._mouseRight
elif _id == ois.MB_Middle:
_widget = self._mouseMiddle
if _widget is not N... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def on_mouse_press(self, x, y, button):\n\n pass",
"def ev_mousebuttondown(self, event: MouseButtonDown) -> None:",
"def handle_mouse_press(self, event):",
"def mouse_press_event(self, x: int, y: int, button: int):\n pass",
"def on_mouse_press(self, x, y, button, key_modifiers):\r\n pa... | [
"0.8257931",
"0.79961336",
"0.7979811",
"0.79471374",
"0.7869575",
"0.77890575",
"0.7698362",
"0.7680871",
"0.7555324",
"0.7444486",
"0.7414548",
"0.73654586",
"0.73654586",
"0.7336567",
"0.7282599",
"0.7262663",
"0.7219916",
"0.72072995",
"0.7194889",
"0.7184248",
"0.7181684... | 0.6422405 | 85 |
Mouse button released notification | def mouseReleased(self, _evt, _id):
_widget = None
if _id == ois.MB_Left:
_widget = self._mouseLeft
elif _id == ois.MB_Right:
_widget = self._mouseRight
elif _id == ois.MB_Middle:
_widget = self._mouseMiddle
if _widget is not ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def on_mouse_release(self, x, y, button):\n pass",
"def mouse_release_event(self, x: int, y: int, button: int):\n pass",
"def on_mouse_release(self, x, y, button, key_modifiers):\r\n pass",
"def button_release_event(self, widget, event):\n x, y = event.x, event.y\n\n # x, y... | [
"0.84220487",
"0.83658963",
"0.81159884",
"0.775565",
"0.76365525",
"0.7635038",
"0.74916583",
"0.74270487",
"0.738527",
"0.737185",
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"0.7359813",
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"0.7338642",
"0.7246347",
"0.72372645",
"0.72236484",
"0.71101356",
"0.7078123",
"0.707812... | 0.6166877 | 89 |
Switch between enabled and disabled states | def toggle(self):
if self.is_enabled:
self.disable()
else:
self.enable() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def set_disabled_switch(self, disabled):\n self.disabled = disabled",
"def setEnabled(*args):",
"def setEnabled(*args):",
"def setEnabled(*args):",
"def setEnabled(*args):",
"def setEnabled(*args):",
"def setEnabled(*args):",
"def setEnabled(*args):",
"def setEnabled(*args):",
"def setEnab... | [
"0.7548925",
"0.7019905",
"0.7019905",
"0.7019905",
"0.7019905",
"0.7019905",
"0.7019905",
"0.7019905",
"0.7019905",
"0.7019905",
"0.7019905",
"0.7019905",
"0.6845152",
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"0.6768934",
"0.6768934",
"0.6743892",
"0.6730558",
"0.6703138",
"0.67011505",
"0.6673747",
... | 0.69646096 | 12 |
Updates input icons relative to mouse state | def _updateOnMouseState(self, state):
x = state.X.abs
y = state.Y.abs
mscale = self.mouse_icon.getScale()
if (x + mscale[0] + self.mouse_offset) > render_engine.Window.width:
x = x - mscale[0] - 10
else:
x += self.mouse_offset
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def update(self):\n self.mousePos = pygame.mouse.get_pos()\n self.update_button_hover_status()",
"def update_button_hover_status(self):\n for button in self.playing_buttons:\n button.update(self.mousePos)",
"def update_reset_button(self):\r\n if self.board.hovered_tiles a... | [
"0.63612306",
"0.6020158",
"0.5917613",
"0.5873303",
"0.5816151",
"0.5782121",
"0.57058483",
"0.5668941",
"0.56663585",
"0.56530684",
"0.5630077",
"0.55906516",
"0.5553169",
"0.5495981",
"0.54906726",
"0.5464941",
"0.5448922",
"0.54335225",
"0.5425156",
"0.54172546",
"0.53634... | 0.6588073 | 0 |
Mouse button pressed notification | def mousePressed(self, _evt, _id):
if not self.is_enabled: return False
self.mouse_icon.mousePressed(_evt, _id)
return False | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def on_mouse_press(self, x, y, button):\n\n pass",
"def ev_mousebuttondown(self, event: MouseButtonDown) -> None:",
"def handle_mouse_press(self, event):",
"def mouse_press_event(self, x: int, y: int, button: int):\n pass",
"def on_mouse_press(self, x, y, button, key_modifiers):\r\n pa... | [
"0.8257931",
"0.79961336",
"0.7979811",
"0.79471374",
"0.7869575",
"0.77890575",
"0.7698362",
"0.7680871",
"0.7555324",
"0.7444486",
"0.7414548",
"0.73654586",
"0.73654586",
"0.7336567",
"0.7282599",
"0.7262663",
"0.7219916",
"0.72072995",
"0.7194889",
"0.7184248",
"0.7181684... | 0.70297575 | 31 |
Mouse button released notification | def mouseReleased(self, _evt, _id):
if not self.is_enabled: return False
self.mouse_icon.mouseReleased(_evt, _id)
return False | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def on_mouse_release(self, x, y, button):\n pass",
"def mouse_release_event(self, x: int, y: int, button: int):\n pass",
"def on_mouse_release(self, x, y, button, key_modifiers):\r\n pass",
"def button_release_event(self, widget, event):\n x, y = event.x, event.y\n\n # x, y... | [
"0.84220487",
"0.83658963",
"0.81159884",
"0.775565",
"0.76365525",
"0.7635038",
"0.74916583",
"0.74270487",
"0.738527",
"0.737185",
"0.7367928",
"0.7359813",
"0.73497945",
"0.7341008",
"0.7338642",
"0.7246347",
"0.72372645",
"0.72236484",
"0.71101356",
"0.7078123",
"0.707812... | 0.6976709 | 26 |
Initializes a MOSAIC segmentation head. | def __init__(
self,
num_classes: int,
decoder_input_levels: Optional[List[str]] = None,
decoder_stage_merge_styles: Optional[List[str]] = None,
decoder_filters: Optional[List[int]] = None,
decoder_projected_filters: Optional[List[int]] = None,
encoder_end_level: Optional[int] =... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __init__(self):\n\n self._mh = MasterHead.get_head()",
"def __init__(self, backboneNet, projection_head) -> None:\n super(SimCLR, self).__init__()\n self.Net = backboneNet\n self.projection_head = projection_head",
"def initialise(self):\n # Can take quite a lot of time d... | [
"0.63423765",
"0.6104683",
"0.60890955",
"0.5920527",
"0.5805246",
"0.57884365",
"0.5780198",
"0.5758982",
"0.57516754",
"0.5741528",
"0.56969655",
"0.5690647",
"0.5684974",
"0.56785005",
"0.5676265",
"0.56380767",
"0.56315213",
"0.5619772",
"0.56066364",
"0.5605493",
"0.5593... | 0.0 | -1 |
Transform inputs to outputs. | def transform(self):
self.validate_ports()
# TODO: Find a more reusable way of enforcing this behavior.
if self.distance.variable.ndim != 1:
raise ValueError("`distance` must be a vector.")
if self.equivalence.variable.ndim != 1:
raise ValueError("`equivalence` ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def outputs(self, inputs):\n return inputs",
"def out(self, inputs):",
"def _transform_inputs(self, inputs):\n\n if self.input_transform == 'resize_concat':\n inputs = [inputs[i] for i in self.in_index]\n upsampled_inputs = [\n resize(\n inp... | [
"0.7758695",
"0.7370227",
"0.6850674",
"0.6759104",
"0.6725636",
"0.6614872",
"0.6614872",
"0.6614872",
"0.6522579",
"0.6516416",
"0.6495349",
"0.6469215",
"0.642536",
"0.641889",
"0.63701034",
"0.63644946",
"0.6326372",
"0.6318115",
"0.6252453",
"0.62274414",
"0.61965454",
... | 0.0 | -1 |
Transform inputs to outputs. | def transform(self):
self.validate_ports()
# TODO: Find a more reusable way of enforcing this behavior.
if self.cost_sim.variable.ndim != 1:
raise ValueError("`cost_sim` must be a vector.")
if self.cost_diff.variable.ndim != 1:
raise ValueError("`cost_diff` must... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def outputs(self, inputs):\n return inputs",
"def out(self, inputs):",
"def _transform_inputs(self, inputs):\n\n if self.input_transform == 'resize_concat':\n inputs = [inputs[i] for i in self.in_index]\n upsampled_inputs = [\n resize(\n inp... | [
"0.7758695",
"0.7370227",
"0.6850674",
"0.6759104",
"0.6725636",
"0.6614872",
"0.6614872",
"0.6614872",
"0.6522579",
"0.6516416",
"0.6495349",
"0.6469215",
"0.642536",
"0.641889",
"0.63701034",
"0.63644946",
"0.6326372",
"0.6318115",
"0.6252453",
"0.62274414",
"0.61965454",
... | 0.0 | -1 |
Transform inputs to outputs. | def transform(self):
self.validate_ports()
# TODO: Find a more reusable way of enforcing this behavior.
if self.cost_sim.variable.ndim != 1:
raise ValueError("`cost_sim` must be a vector.")
if self.cost_diff.variable.ndim != 1:
raise ValueError("`cost_diff` must... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def outputs(self, inputs):\n return inputs",
"def out(self, inputs):",
"def _transform_inputs(self, inputs):\n\n if self.input_transform == 'resize_concat':\n inputs = [inputs[i] for i in self.in_index]\n upsampled_inputs = [\n resize(\n inp... | [
"0.77584934",
"0.73698086",
"0.68523467",
"0.6758871",
"0.67265445",
"0.6616303",
"0.6616303",
"0.6616303",
"0.65238136",
"0.65147865",
"0.64958227",
"0.64674926",
"0.64270216",
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"0.63700414",
"0.6367071",
"0.63268214",
"0.63184077",
"0.6252848",
"0.6228795",
"0.6... | 0.0 | -1 |
This funtion should perform the job of projecting the input pointcloud onto the frame of an image captured by a camera with camera matrix as given, of dimensions as given, in pixels. points is an 3 x N array where the ith entry is an (x, y, z) point in 3D space, in the reference frame of the depth camera. This correspo... | def project_points(points, cam_matrix, trans, rot):
# STEP 1: Transform pointcloud into new reference frame.
points = np.dot(rot, points) + trans[:, None]
# STEP 2: Project new pointcloud onto image frame using K matrix.
# gives a 3 x N array of image plane coordinates in homogenous coordinates.
h... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def project(points, camera_params, theta):\n \"\"\"\n Function takes input of 3d_points, transformations and Convert 3-D points to 2-D by projecting onto images. \n Input:\n points: 3D points in world frame\n camera_params: parameters of camera corrosponding to the point\n theta: Need... | [
"0.75861025",
"0.73979294",
"0.73724365",
"0.70940655",
"0.7059192",
"0.7011868",
"0.69861317",
"0.6886492",
"0.685968",
"0.6813133",
"0.6795302",
"0.6780843",
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"0.6705942",
"0.66217476",
"0.65333843",
"0.6509686",
"0.64969933",
"0.6453741",
"0.6448303... | 0.7957179 | 0 |
Attempts to purchase all goods listed in the dict order, depositing them in the Player's cargo holds. Does not care about max cargo. dryRun simply checks if the purchase is possible. remaining controls if the order should be 100% purchased (True), or only purchase goods the player lacks Returns False if some goods are ... | def buyCargo(self, order, dryRun=False, remaining=True):
ply = self.window.playerShip
shop = self.planet.goods
toBuy = order.copy()
for mat in toBuy:
if remaining and mat in ply.cargo:
toBuy[mat] -= ply.cargo[mat].quantity
if toBuy[mat] > 0:
if mat not in shop: return False
if shop[mat]*toBuy[... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def complete_purchase(self, customer_credit=0):\r\n \r\n #take the products first, then tell customer how many tickets to take\r\n #requires IChat interface to be passed to tell customers how many tickets to take\r\n \r\n #switch to list view in the collection window\r\n print... | [
"0.5479375",
"0.53681403",
"0.5336852",
"0.5133763",
"0.51209956",
"0.50936574",
"0.5068974",
"0.5063557",
"0.4996775",
"0.4974593",
"0.49507624",
"0.494863",
"0.4935163",
"0.49151012",
"0.49076858",
"0.4898226",
"0.4866557",
"0.48556313",
"0.48369843",
"0.48305112",
"0.48296... | 0.70759 | 0 |
Initialize with a QPrinter object and a list of pages. pageList may be a list of twotuples (num, page). Otherwise, the pages are numbered from 1 in the progress message. The pages are copied. | def __init__(self, printer, pageList, parent=None):
super().__init__(parent)
self.printer = printer
self.setPageList(pageList) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def setPageList(self, pageList):\n self.pageList = []\n for n, page in enumerate(pageList, 1):\n if isinstance(page, tuple):\n pageNum, page = page\n else:\n pageNum = n\n page = page.copy()\n # set zoom to 1.0 so computations ... | [
"0.6433763",
"0.6421175",
"0.5657116",
"0.5523885",
"0.5351193",
"0.5295668",
"0.52508706",
"0.52336335",
"0.5163632",
"0.5067127",
"0.5064911",
"0.50349927",
"0.500849",
"0.49356413",
"0.4856026",
"0.4848449",
"0.48073077",
"0.48051938",
"0.48050612",
"0.48048058",
"0.479167... | 0.7309939 | 0 |
Set the pagelist to print. pageList may be a list of twotuples (num, page). Otherwise, the pages are numbered from 1 in the progress message. The pages are copied. | def setPageList(self, pageList):
self.pageList = []
for n, page in enumerate(pageList, 1):
if isinstance(page, tuple):
pageNum, page = page
else:
pageNum = n
page = page.copy()
# set zoom to 1.0 so computations based on geom... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def setPageSequence(self, pageSequenceList):\r\n\r\n for index in range(self.pageCount() - 1, -1, -1):\r\n page = self.page(index)\r\n if page:\r\n self.removePage(page)\r\n\r\n count = 0\r\n for pageTitle in pageSequenceList:\r\n self.insertPage... | [
"0.661028",
"0.64478004",
"0.5916455",
"0.56009513",
"0.5516022",
"0.5461271",
"0.54369324",
"0.52646035",
"0.5221855",
"0.51948494",
"0.51564723",
"0.5154594",
"0.503851",
"0.5032567",
"0.49833974",
"0.49527058",
"0.4946429",
"0.49033383",
"0.49002624",
"0.49002624",
"0.4896... | 0.7608234 | 0 |
Paint the pages to the printer in the background. | def work(self):
p = self.printer
p.setFullPage(True)
painter = QPainter(p)
for n, (num, page) in enumerate(self.pageList):
if self.isInterruptionRequested():
self.aborted = True
return p.abort()
self.progress.emit(num, n+1, len(self... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def draw_page(page, stream):\n bleed = {\n side: page.style[f'bleed_{side}'].value\n for side in ('top', 'right', 'bottom', 'left')}\n marks = page.style['marks']\n stacking_context = StackingContext.from_page(page)\n draw_background(\n stream, stacking_context.box.background, clip... | [
"0.6472516",
"0.6322351",
"0.6206114",
"0.61876047",
"0.61129534",
"0.6107652",
"0.60719824",
"0.60593873",
"0.59743327",
"0.59252787",
"0.5870063",
"0.58362466",
"0.5776248",
"0.57747906",
"0.575061",
"0.5728778",
"0.5693118",
"0.5666412",
"0.5624035",
"0.56164974",
"0.55977... | 0.718377 | 0 |
Initializes ourselves with the print job and optional parent widget. | def __init__(self, job, parent=None):
super().__init__(parent)
self._job = job
job.progress.connect(self.showProgress)
job.finished.connect(self.jobFinished)
self.canceled.connect(job.requestInterruption)
self.setMinimumDuration(0)
self.setRange(0, len(job.pageLis... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __init__(self, printer, parent=None):\n QtGui.QWidget.__init__(self, printer, parent)",
"def __init__(self, printer, pageList, parent=None):\n super().__init__(parent)\n self.printer = printer\n self.setPageList(pageList)",
"def __init__(self, job):\n self.job = job\n\n ... | [
"0.7487978",
"0.67818165",
"0.67569965",
"0.64124125",
"0.6201404",
"0.6139698",
"0.5940359",
"0.5934324",
"0.59159744",
"0.5873766",
"0.5867913",
"0.58311474",
"0.58015513",
"0.5784232",
"0.5742336",
"0.57028717",
"0.57011944",
"0.5694068",
"0.5670847",
"0.56562907",
"0.5598... | 0.7860893 | 0 |
Called by the job when printing a page. | def showProgress(self, page, num, total):
self.setValue(num)
self.setLabelText("Printing page {page} ({num} of {total})...".format(
page=page, num=num, total=total)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def work(self):\n p = self.printer\n p.setFullPage(True)\n painter = QPainter(p)\n for n, (num, page) in enumerate(self.pageList):\n if self.isInterruptionRequested():\n self.aborted = True\n return p.abort()\n self.progress.emit(num, ... | [
"0.66130775",
"0.6179133",
"0.60483813",
"0.59712577",
"0.5961468",
"0.58975935",
"0.58974516",
"0.58792794",
"0.58685595",
"0.5856823",
"0.585318",
"0.58435875",
"0.5823067",
"0.5815858",
"0.57982695",
"0.5784894",
"0.5781572",
"0.57630765",
"0.5756924",
"0.57515657",
"0.574... | 0.5346919 | 62 |
Called when the print job has finished. | def jobFinished(self):
if not self._job.result and not self._job.aborted:
self.showErrorMessage()
del self._job
self.deleteLater() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def finished(self):\n\t\telog(\"finished\")",
"def finished(self):\n pass",
"def end(self):\n self.my_print(\"\\t[DONE]\", msg_types.INFO)\n self.in_progress = False",
"def finalize(self):\n sys.stderr.write(f\"{self._message} finished after {(time.time()-self._startTime):.1f}s \"... | [
"0.68025583",
"0.6737423",
"0.668845",
"0.6573926",
"0.6573926",
"0.6492718",
"0.64637226",
"0.64432174",
"0.640725",
"0.638964",
"0.638964",
"0.63772255",
"0.6374147",
"0.6329077",
"0.6324893",
"0.62843454",
"0.6235471",
"0.6216052",
"0.61912054",
"0.61813414",
"0.61813414",... | 0.6165697 | 26 |
Reimplement to show a different or translated error message. | def showErrorMessage(self):
QMessageBox.warning(self.parent(), "Printing Error",
"Could not send the document to the printer.") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def show_error(self, error):\n if (error == \"\"):\n self.ui.errorLabel.setText(\"\")\n else:\n self.ui.errorLabel.setText(\"<span style=\\\"font-weight:600; color:#ff0000;\\\">{0}</span>\".format(error))",
"def error(self, message=None, show_help=True):",
"def __str__(self)... | [
"0.7121053",
"0.7120864",
"0.70434797",
"0.70387185",
"0.7025241",
"0.69732416",
"0.6881497",
"0.6874057",
"0.67731375",
"0.67476714",
"0.67366815",
"0.67256993",
"0.6724372",
"0.67027223",
"0.66665125",
"0.66665125",
"0.66665125",
"0.6626099",
"0.6606675",
"0.65979713",
"0.6... | 0.0 | -1 |
This method will enable delivery confirmations and schedule the first message to be sent to RabbitMQ | def start_publishing(self):
print(f"{self._connection_param}: Issuing consumer related RPC commands")
# self._channel.confirm_delivery(self.on_delivery_confirmation)
self.schedule_next_message(self.SLOW_SEND) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def prepare_for_delivery(self, by=None):",
"def prepare_for_delivery(self, by=None):",
"def prepare_for_delivery(self, by=None):",
"def keystone_amq(self):\n\n connection = pika.BlockingConnection(pika.ConnectionParameters(\n host=self.rabbit_host,\n ... | [
"0.6132878",
"0.6132878",
"0.6132878",
"0.6066696",
"0.6043291",
"0.59991413",
"0.59169674",
"0.58299065",
"0.5776203",
"0.5776203",
"0.5776203",
"0.5642102",
"0.56042206",
"0.55661607",
"0.55595404",
"0.5532968",
"0.5508831",
"0.5474107",
"0.5426646",
"0.54043806",
"0.540246... | 0.631214 | 0 |
Run the example code by connecting and then starting the IOLoop. | def run(self):
while not self._stopping:
try:
self._connection = self.connect()
self._connection.ioloop.start()
except KeyboardInterrupt:
self.stop()
if (self._connection is not None and not self._connection.is_closed):
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def run(self):\n\t\t\n\t\tself.connect(self.config[\"server\"])",
"def run(self):\n ioloop.IOLoop.current().start()",
"def run(self):\n self._connection = self.connect()\n self._connection.ioloop.start()",
"def run(self):\n self._connection = self.connect()\n self._connecti... | [
"0.71295154",
"0.7125487",
"0.67547613",
"0.67547613",
"0.64287305",
"0.6411501",
"0.6342234",
"0.62999034",
"0.6210394",
"0.6209313",
"0.6195795",
"0.6136957",
"0.6037674",
"0.60373646",
"0.5997868",
"0.59889257",
"0.59690315",
"0.59501374",
"0.5940903",
"0.59373194",
"0.590... | 0.0 | -1 |
r""" Computes the chisquare value of the sample data Notes | def _chisquare_value(self):
x2 = np.sum((np.absolute(self.observed - self.expected) - (0.5 * self.continuity_correction)) ** 2 /
self.expected)
return x2 | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def compute(real_data, synthetic_data):\n f_obs, f_exp = get_frequencies(real_data, synthetic_data)\n if len(f_obs) == len(f_exp) == 1:\n pvalue = 1.0\n else:\n _, pvalue = chisquare(f_obs, f_exp)\n\n return pvalue",
"def calculate_chi_squared(self):\n chi... | [
"0.6880414",
"0.6825153",
"0.65589476",
"0.63346523",
"0.61934537",
"0.6188989",
"0.61722326",
"0.61393964",
"0.6095299",
"0.6072217",
"0.6061504",
"0.60480016",
"0.6028417",
"0.600268",
"0.59922135",
"0.5937898",
"0.59304774",
"0.59027916",
"0.588849",
"0.5888303",
"0.588665... | 0.760204 | 0 |
r""" Finds the pvalue of the chisquare statistic. Notes | def _p_value(self):
pval = chi2.sf(self.chi_square, self.degrees_of_freedom)
return pval | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _chisquare_value(self):\n x2 = np.sum((np.absolute(self.observed - self.expected) - (0.5 * self.continuity_correction)) ** 2 /\n self.expected)\n\n return x2",
"def _p_value(self):\n p_value = chi2.sf(self.test_statistic, 2)\n\n return p_value",
"def compute(r... | [
"0.7833191",
"0.7402887",
"0.71125257",
"0.6845936",
"0.6587544",
"0.64726514",
"0.64485335",
"0.6297365",
"0.6246761",
"0.61838526",
"0.61333144",
"0.60871804",
"0.60848254",
"0.60680485",
"0.6063416",
"0.60562605",
"0.59842485",
"0.59655535",
"0.59315586",
"0.59314376",
"0.... | 0.752004 | 1 |
r""" Calculates the associated pvalue of the JarqueBera test statistic. Returns | def _p_value(self):
p_value = chi2.sf(self.test_statistic, 2)
return p_value | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def compute_pvalue(self):\n # Run permutation test\n self.PermutationTest()\n # TS obtained from the original B,T samples\n self.compute_obs_TS()\n \n # Mean and std of the TS distribution\n self.mu = np.mean(self.TS_tilde)\n self.sigma = np.std(s... | [
"0.70886576",
"0.694674",
"0.6699759",
"0.644185",
"0.6402931",
"0.63444334",
"0.63201535",
"0.62870866",
"0.6217986",
"0.6188519",
"0.6146519",
"0.60959625",
"0.60770345",
"0.596563",
"0.5965262",
"0.5932051",
"0.5872446",
"0.58646697",
"0.5859925",
"0.5857311",
"0.5773147",... | 0.6830935 | 2 |
Checks if id resembles a valid Mathetical Reviews identifier. | def is_valid(key):
return key[0:2] == "MR" and key[2:].isdigit() and len(key) in [9, 10] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def check_id(self, id):",
"def validate_tileset_id(tileset_id):\n pattern = r\"^[a-z0-9-_]{1,32}\\.[a-z0-9-_]{1,32}$\"\n\n return re.match(pattern, tileset_id, flags=re.IGNORECASE)",
"def isValid(t_id):\n\tstr_id=str(t_id).strip()\n\treturn str_id.isdigit()",
"def is_id_valid(id_code: str) -> bool:\n ... | [
"0.6459849",
"0.6432315",
"0.64162076",
"0.63578707",
"0.63142574",
"0.6200492",
"0.6198464",
"0.61865556",
"0.61574614",
"0.60886556",
"0.6026036",
"0.5946656",
"0.5944451",
"0.5895949",
"0.58748007",
"0.58158374",
"0.58156914",
"0.57306147",
"0.5724628",
"0.56132466",
"0.56... | 0.0 | -1 |
BibTeX comment explaining error | def bibtex(self):
return "@comment{%(id)s: %(message)s}" % \
{'id': self.id, 'message': self.message} | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def comment():",
"def test_doc_with_comments():\n doc = CoNLL.conll2doc(input_str=RUSSIAN_SAMPLE)\n check_russian_doc(doc)",
"def comment(self, content):\n pass",
"def should_add_pr_comment(self):\n pass",
"def test_issue_edit_comment_deprecated(self):\n pass",
"def docstri... | [
"0.74034363",
"0.6712097",
"0.66532767",
"0.63367194",
"0.619787",
"0.61909366",
"0.60946673",
"0.60149664",
"0.59592676",
"0.58991164",
"0.5883395",
"0.58689094",
"0.5825342",
"0.58130735",
"0.5804278",
"0.57896906",
"0.57857484",
"0.5733003",
"0.57277036",
"0.57164663",
"0.... | 0.6803446 | 1 |
Returns a list of references, corresponding to elts of id_list | def mr2bib(id_list):
d = mr2bib_dict(id_list)
l = []
for id in id_list:
try:
l.append(d[id])
except:
l.append(ReferenceErrorInfo("Not found", id))
return l | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def references_list( self, theWeaver ):\n return [ (c.name, c.seq) \n for c in theWeaver.reference_style.chunkReferencedBy( self ) ]",
"def rel_id_list(rel_list):\n\n rel_ids = []\n for i in range(0, len(rel_list)):\n rel_id = rel_list[i][\"id\"]\n rel_ids.append(rel_id)\n ... | [
"0.7039898",
"0.7037073",
"0.6797277",
"0.6761492",
"0.6631267",
"0.66233087",
"0.6622398",
"0.6528245",
"0.65259904",
"0.6512491",
"0.6483046",
"0.64315706",
"0.63510597",
"0.62785524",
"0.6199104",
"0.619206",
"0.6191878",
"0.6140649",
"0.6140649",
"0.61350185",
"0.61265624... | 0.5944738 | 29 |
Corrects the BibTeX key because the MR API cannot get its act together | def correct_key(goodkey,code):
db = pybtex.database.parse_string(code,"bibtex")
keys = [key for key in db.entries.keys()]
badkey = keys[0]
return code.replace(badkey,goodkey) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def mr_request(key):\n\n # reconstructing the BibTeX code block\n inCodeBlock = False\n code = \"\"\n\n # make the request\n payload = {\"fn\": 130, \"fmt\": \"bibtex\", \"pg1\": \"MR\", \"s1\": key}\n r = requests.get(path, params=payload)\n\n # 401 means not authenticated\n if r.status_code == 401:\n ... | [
"0.61262125",
"0.60784906",
"0.58044267",
"0.5669483",
"0.5512459",
"0.549996",
"0.546403",
"0.5428998",
"0.54042065",
"0.5403589",
"0.53428215",
"0.52821094",
"0.52698106",
"0.52677447",
"0.52602893",
"0.52487",
"0.5242598",
"0.52408123",
"0.5232014",
"0.51742405",
"0.515405... | 0.71422005 | 0 |
Sends a request to the Mathematical Reviews API | def mr_request(key):
# reconstructing the BibTeX code block
inCodeBlock = False
code = ""
# make the request
payload = {"fn": 130, "fmt": "bibtex", "pg1": "MR", "s1": key}
r = requests.get(path, params=payload)
# 401 means not authenticated
if r.status_code == 401:
raise AuthenticationException()... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def call_license_recommender(url, payload):\n response = requests.post(url, data=payload)\n print(response.status_code)\n print(response.text)",
"async def req(self, *, m = \"GET\", u = \"\", t = \"j\", r = None, f = None,\n d = {}, **data):\n m = m.upper()\n if d == {} an... | [
"0.61218435",
"0.5576535",
"0.55509704",
"0.55009675",
"0.5494504",
"0.5473101",
"0.54638946",
"0.5391383",
"0.53691244",
"0.5267628",
"0.5234801",
"0.52088183",
"0.5193367",
"0.5190489",
"0.51851815",
"0.51851815",
"0.5173972",
"0.5172459",
"0.51695144",
"0.5167495",
"0.5166... | 0.4989358 | 46 |
Fetches citations for keys in key_list into a dictionary indexed by key | def mr2bib_dict(key_list):
keys = []
d = {}
# validate keys
for key in key_list:
if is_valid(key):
keys.append(key)
else:
d[key] = ReferenceErrorInfo("Invalid Mathematical Reviews identifier", key)
if len(keys) == 0:
return d
# make the api call
entries = {}
for key in keys:
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_citations_ids_map(id_list):\n create_unverified_context()\n logging.debug('============== IN get_citations_ids_map: ================')\n logging.debug('============== ID LIST: ================')\n logging.debug(id_list)\n linked = {}\n for i in range(0, len(id_list)):\n handle = En... | [
"0.63553035",
"0.6239225",
"0.6104354",
"0.6002555",
"0.58264047",
"0.5761537",
"0.5651292",
"0.5648888",
"0.5609229",
"0.5563336",
"0.5560674",
"0.5547645",
"0.55174834",
"0.5494095",
"0.5494074",
"0.5486462",
"0.5401576",
"0.53930855",
"0.5387903",
"0.53725374",
"0.53647405... | 0.62833595 | 1 |
Produce output and error messages | def run(self):
try:
bib = mr2bib(self.args.id)
except HTTPError as error:
raise FatalError("HTTP Connection Error: {0}".format(error.getcode()))
self.create_output(bib)
self.code = self.tally_errors(bib) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def output(self, msg):",
"def create_output(self, messages):",
"def output_error(text):\n if conf.eval_output:\n info_dict = {'type':'error', 'text' : text}\n output_result_eval(info_dict)\n else:\n output_result('[ERROR] ' + text)",
"def write_output(self):",
"def _output(self, ... | [
"0.74492913",
"0.7304722",
"0.6676063",
"0.6611423",
"0.65172976",
"0.6450008",
"0.6367455",
"0.6312982",
"0.6241445",
"0.62138563",
"0.62100923",
"0.62058467",
"0.6178186",
"0.6165659",
"0.6138401",
"0.6110624",
"0.6110281",
"0.6101742",
"0.60865426",
"0.6075113",
"0.6035412... | 0.0 | -1 |
Format the output and error messages | def create_output(self, bib):
for b in bib:
if isinstance(b, ReferenceErrorInfo):
self.error_count += 1
if self.args.comments:
self.output.append(b.bibtex())
if not self.args.quiet:
self.messages.append(str(b))
else:
self.output.append(b.bibtex()) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def output(self, msg):",
"def create_output(self, messages):",
"def __output(self,msg,status):\n status = int(status)\n if status:\n print \"%s-----------\\033[1;37;42m%s\\033[0m\" % (format(msg,\"<15\"),\"OK\")\n else:\n print \"%s***********\\033[1;37;41m%s\\033[0m\... | [
"0.6797996",
"0.67937696",
"0.6665162",
"0.6554844",
"0.6467793",
"0.6451013",
"0.641069",
"0.6391255",
"0.63800806",
"0.637965",
"0.63150275",
"0.62728184",
"0.62487245",
"0.62406886",
"0.62218165",
"0.61880296",
"0.6166915",
"0.61528933",
"0.61156625",
"0.6094442",
"0.60939... | 0.0 | -1 |
print messages to stderr | def print_messages(self):
if self.messages:
self.messages.append("")
sys.stderr.write(os.linesep.join(self.messages)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def printerr(msg):\n print(msg, file=sys.stderr)",
"def eprint(errmsg):\n print(errmsg, file=STDERR)",
"def print_err(msg):\n print(msg, file=sys.stderr)",
"def printerr(message):\n sys.stderr.write('{}\\n'.format(message))\n sys.stderr.flush()",
"def print_to_stderr(msg):\n sys.stderr.wr... | [
"0.81713575",
"0.81406593",
"0.8059296",
"0.7857656",
"0.77511865",
"0.7741248",
"0.77343106",
"0.7730823",
"0.7613591",
"0.75765413",
"0.75504214",
"0.75362325",
"0.75361323",
"0.743849",
"0.74344194",
"0.7411151",
"0.7405998",
"0.73897713",
"0.73611903",
"0.73316664",
"0.73... | 0.76047915 | 9 |
Run the command line interface | def main(args=None):
cli = Cli(args)
try:
cli.run()
except FatalError as err:
sys.stderr.write(err.args[0] + os.linesep)
return 2
cli.print_output()
cli.print_messages()
return cli.code | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def cli():\n config, auth, execute_now = read_command_line_arguments()\n main(config, auth, execute_now)",
"def cli():\n pass",
"def cli():\r\n pass",
"def cli():",
"def cli():",
"def cli():",
"def cli():",
"def cli():",
"def cli():",
"def cli():",
"def cli():",
"def cli():",
... | [
"0.82951665",
"0.7872211",
"0.7813792",
"0.77932256",
"0.77932256",
"0.77932256",
"0.77932256",
"0.77932256",
"0.77932256",
"0.77932256",
"0.77932256",
"0.77932256",
"0.77932256",
"0.77932256",
"0.77932256",
"0.77932256",
"0.77932256",
"0.77932256",
"0.77932256",
"0.77932256",
... | 0.0 | -1 |
Given the rates, add noise based on numreg | def add_white_noise(rates, numreg):
rtemp = rates.copy().getA()
sdrates = np.sqrt(rtemp * (1 - rtemp) / numreg) + 1e-10
noise = np.random.normal(0, sdrates)
rtemp += noise
return np.matrix(rtemp) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def noise(self, freq: int, /) -> None:",
"def add_uniform_noise(rates, percent):\n raise 0 < percent < 1 or AssertionError\n rtemp = rates.copy().getA()\n noise = np.random.uniform(1 - percent, 1 + percent, np.shape(rtemp))\n rtemp = rtemp * noise\n return np.matrix(rtemp)",
"def add_noise(self,... | [
"0.68984324",
"0.6839585",
"0.67851245",
"0.6750113",
"0.66716886",
"0.66040987",
"0.64807314",
"0.63987464",
"0.6348617",
"0.6240534",
"0.62197",
"0.6102634",
"0.60901725",
"0.60885996",
"0.60824186",
"0.6081778",
"0.6057069",
"0.6057069",
"0.6010699",
"0.6005882",
"0.600388... | 0.8146284 | 0 |
Given the rates, sample new rate uniformly between ((1percent)rates, (1+percent)rates) | def add_uniform_noise(rates, percent):
raise 0 < percent < 1 or AssertionError
rtemp = rates.copy().getA()
noise = np.random.uniform(1 - percent, 1 + percent, np.shape(rtemp))
rtemp = rtemp * noise
return np.matrix(rtemp) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _set_rate(self):\r\n interval = self.data.iloc[2, 0] - self.data.iloc[1, 0]\r\n self.rate = int(1 / interval)",
"def mutate(chrom, rate=100):\n for i in range(len(chrom)):\n chance = randint(0, 100)\n if chance <= rate:\n chrom[i] = randint(0, 13)\n return chrom",... | [
"0.6379363",
"0.6271895",
"0.6191277",
"0.61757326",
"0.615608",
"0.6149897",
"0.6067622",
"0.6067622",
"0.59839284",
"0.5963225",
"0.59294873",
"0.59209937",
"0.5910566",
"0.5888665",
"0.58422065",
"0.58108896",
"0.5792376",
"0.573272",
"0.57085705",
"0.5676844",
"0.5660222"... | 0.687847 | 0 |
This function runs the estimation procedure for the first time slice for given number of demes and repeats the process reps number of times. The values of mean pop size and mig rates is preset but will be changed in future versions. The third parameter here controls the noise amount in the estimates of coalescent inten... | def run_Over_Grid(numdemes = 2, reps = 10, numreg = 100, t = 1000):
Nmean = 2000
Nsd = 100
migMean = 0.0001
migsd = 1e-06
ndc2 = numdemes * (numdemes - 1) / 2
rows = ndc2 + numdemes + 1
I = np.matrix(np.eye(rows))
Ck = I[0:rows - 1, :]
Dk = I[rows - 1, :]
output = []
for r in... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def sample(\n self,\n repetitions,\n nChains=3,\n burnIn=100,\n thin=1,\n convergenceCriteria=0.8,\n variables_of_interest=None,\n DEpairs=2,\n adaptationRate=\"auto\",\n eps=5e-2,\n mConvergence=True,\n mAccept=True,\n ):\n\n ... | [
"0.5957784",
"0.59119064",
"0.5818777",
"0.57929796",
"0.5779033",
"0.57222825",
"0.5718269",
"0.566151",
"0.5655278",
"0.5591914",
"0.5582998",
"0.5532946",
"0.5525634",
"0.55158126",
"0.5478543",
"0.5461477",
"0.54515594",
"0.544741",
"0.5431594",
"0.54281795",
"0.5378584",... | 0.6416229 | 0 |
This function runs the estimation procedure given the population sizes, mig rates, times, pop history. numreg controls the noise in the estimate of coal rates, and reps repeats the procedure multiple times. | def run_for_parms(Ns, ms, ts, popmaps, numreg, reps, compError = False, coal_error_threshold = 0.0001):
true_parms = []
for i in xrange(len(Ns)):
if len(Ns[i]) > 1:
true_parms.append(np.array(Ns[i] + ms[i]))
else:
true_parms.append(np.array(Ns[i]))
true_rates = mig.c... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def run_Over_Grid(numdemes = 2, reps = 10, numreg = 100, t = 1000):\n Nmean = 2000\n Nsd = 100\n migMean = 0.0001\n migsd = 1e-06\n ndc2 = numdemes * (numdemes - 1) / 2\n rows = ndc2 + numdemes + 1\n I = np.matrix(np.eye(rows))\n Ck = I[0:rows - 1, :]\n Dk = I[rows - 1, :]\n output = ... | [
"0.60618323",
"0.5813044",
"0.5810141",
"0.57176125",
"0.5648661",
"0.5644182",
"0.56422013",
"0.56303495",
"0.55385774",
"0.55132926",
"0.5510663",
"0.5444651",
"0.5410555",
"0.5394634",
"0.53916115",
"0.52761513",
"0.5269184",
"0.5255094",
"0.5233879",
"0.52309513",
"0.5229... | 0.5761889 | 3 |
Given the true and the estimated parameter values this function computes the error in the parameter estimates. The order controls the norm used, by default its the maximum so sup norm | def compute_error(true, estimate, order = np.inf):
print true
print estimate
errs = []
for i in xrange(len(true)):
estError = abs(true[i] - estimate[i])
for j in xrange(len(true[i])):
if true[i][j] != 0:
estError[j] = estError[j] / true[i][j]
errs.app... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_error(self, params):\n return self.endog - self.predict(params)",
"def OF1_CalcErrorEstimation(param_list, args):\n #return (sum( \\\n #( OF1_SumOfGauss(param_list, classNum, g_lvls) - histogram ) ** 2) / g_lvls.size) + \\\n #(abs(sum(param_list[:classNum]) - 1) * o)\n return (... | [
"0.5976348",
"0.5961685",
"0.59206486",
"0.59203446",
"0.58393806",
"0.58081555",
"0.5629805",
"0.561695",
"0.5568433",
"0.55431527",
"0.5517937",
"0.5446575",
"0.54422283",
"0.5426394",
"0.54178244",
"0.54091245",
"0.54078066",
"0.53778136",
"0.5358041",
"0.53563577",
"0.531... | 0.6888212 | 0 |
This function processes the timestring from PSMC and converts this to list of time slice lengths | def process_time_string(timestr):
timestr = timestr.strip()
toks = timestr.split('+')
timeslices = []
for t in toks:
tm = t.strip()
mobj = re.search('\\*', tm)
if mobj == None:
timeslices += [int(tm)]
else:
tms = tm.split('*')
timeslice... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def parse_times(time_str):\n warnings = []\n days, interval = time_str.split(',')\n assert int(days) == float(days)\n days = int(days)\n assert int(interval) == float(interval)\n interval = int(interval)\n if interval < 3:\n warnings.append('Minimum interval is 3 hours')\n if days > ... | [
"0.60004324",
"0.5779885",
"0.56531847",
"0.5637275",
"0.5626897",
"0.5624664",
"0.55409265",
"0.5522083",
"0.54359984",
"0.5405842",
"0.53607774",
"0.5295241",
"0.5267315",
"0.526679",
"0.524625",
"0.52031076",
"0.5193402",
"0.5178458",
"0.5174391",
"0.5154917",
"0.51378095"... | 0.7197089 | 0 |
The coalescence matrix C as a vectorization of the upper triangular matrix and npop, the number of demes. | def mkCoalMatrix(C, npop):
C = np.array(C).flatten()
M = np.zeros((npop, npop))
cnt = 0
for i in range(npop):
for j in range(i, npop):
M[i, j] = C[cnt]
if i != j:
M[j, i] = M[i, j]
cnt += 1
return M | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_C(n_c,CV_matrix):\n C = np.zeros((n_c, n_c), dtype=np.float32)\n for i in range(3):\n C += np.asfortranarray(CV_matrix[:, :, i]) @ np.asfortranarray(CV_matrix[:, :, np.mod(i + 2, 3)].T)\n C = (C != 0).astype(np.int32)\n return C",
"def Cijkl(C):\n c = np.zeros(shape=(3, 3, 3, 3))\n ... | [
"0.69439816",
"0.65459836",
"0.6285086",
"0.6249481",
"0.6161368",
"0.6148797",
"0.6126897",
"0.61106193",
"0.60805976",
"0.60785055",
"0.60363877",
"0.5989471",
"0.59715444",
"0.59453815",
"0.5887798",
"0.58766955",
"0.5835838",
"0.57483953",
"0.57132053",
"0.56960946",
"0.5... | 0.73259985 | 0 |
Initialization function of the class. | def __init__(self, popScaling, ratefile, timeStr, ignoreLast = False, logVal = True, verbose = False, varfile=''):
self.verbose = verbose
self.estimatedParms = None
self.modified = False
self.obsRates = []
self.logVal = logVal
self.varGiven = False
self.withinvar ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def init(self):\n pass",
"def init(self):\n pass",
"def init(self):\n pass",
"def init(self):\n pass",
"def init(self):\n pass",
"def init(self):\n pass",
"def init(self):\n pass",
"def init(self):\n pass",
"def initialize(self):\r\n pa... | [
"0.8625162",
"0.8625162",
"0.8625162",
"0.8625162",
"0.8625162",
"0.8625162",
"0.8625162",
"0.8625162",
"0.8624172",
"0.8624172",
"0.861785",
"0.858561",
"0.858561",
"0.858561",
"0.858561",
"0.858561",
"0.85630506",
"0.85348904",
"0.850922",
"0.85069793",
"0.8446883",
"0.83... | 0.0 | -1 |
The rates obtained from PSMC are the prob of coal in that timeslice, not the prob of coal in that timeslice AND not coalescing in any other timeslice. We need the conditional probability of coal in that timeslice given lines have not coalesced in any of the previous timeslices. This function converts the PSMC values in... | def modify_rates(self):
if self.modified:
print 'Already Modified Probabilities'
elif self.varGiven:
print 'You must enter the conditional coalescent probabilties if you want to supply variance of'
print 'the coalescent probabilities. Required since we cannot compute ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def proba(c_pred,m_pred,f_pred, dataset):\n p = np.zeros(10)\n if dataset == 'cifar10':\n for i in range(10):\n if i <4:\n if i <2:\n p[i] = c_pred[0]*(m_pred[0]/(m_pred[0]+m_pred[1]))*(f_pred[i]/np.sum(f_pred[0:2]))\n elif i <4:\n ... | [
"0.5494266",
"0.543836",
"0.539172",
"0.5361028",
"0.5348872",
"0.53372526",
"0.53356594",
"0.53264946",
"0.5319149",
"0.5312941",
"0.52899146",
"0.5248596",
"0.5166682",
"0.51662475",
"0.5162503",
"0.5162355",
"0.5160059",
"0.51591235",
"0.51554865",
"0.51400554",
"0.5133518... | 0.5660929 | 0 |
This function collapses the time slices and the coalescent prbabilities using the time string | def collapse_using_timeStr(self):
if self.modified == True:
raise Exception('Probabilities already modified.\nCollapsing after modification will lead to incorrect results.')
timeUnits = np.array(process_time_string(self.timeStr))
if len(self.timeslices) + 1 == np.sum(timeUnits):
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def filter_time_slices(time_slices, apt_no, exp_no):\n # Removing the extraneous time slices\n if apt_no == '102A' and exp_no == '3':\n discard_ts = time_slices[\n (time_slices.phase == 'Not Found') & (time_slices.magnitude < 100)]\n time_slices = time_slices.ix[time_slices.index - d... | [
"0.58654743",
"0.557243",
"0.54334253",
"0.5312071",
"0.53107566",
"0.5259074",
"0.5191302",
"0.5137161",
"0.5035805",
"0.49849787",
"0.49590302",
"0.49435392",
"0.4941344",
"0.48714745",
"0.48218355",
"0.48059455",
"0.47934845",
"0.47744334",
"0.47464475",
"0.47226936",
"0.4... | 0.5932365 | 0 |
This function estimates the pop and mig in each timeslice and returns it. If useMigration, the threshold is the migration threshold, if not the threshold is the coal rate threshold | def estimate_sim_run(self, merge_threshold = 0.01, useMigration = False, DFO = False, window = 0, hack = False):
if DFO:
self.estimatedParms = mig.comp_N_m(self.obsRates, self.timeslices, merge_threshold, useMigration, self.logVal, self.verbose)
else:
self.estimatedParms = mig.co... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def calibrate_threshold(test_graphs):\r\n best_threshold = None\r\n best_result = None\r\n for threhold in range(1, 50):\r\n cur_res = evaluate_argument_mention(test_graphs, threhold)\r\n if (best_result is None) or (cur_res > best_result):\r\n best_result = cur_res\r\n ... | [
"0.5396551",
"0.53875744",
"0.5384712",
"0.5273905",
"0.5241481",
"0.5240214",
"0.5207714",
"0.5183504",
"0.51663357",
"0.5130545",
"0.5121495",
"0.51151496",
"0.5078505",
"0.50698006",
"0.50516546",
"0.49774256",
"0.49666265",
"0.49366865",
"0.49147338",
"0.48900405",
"0.486... | 0.49437875 | 17 |
Authenticate a request. Returns a `User` if a valid token has been supplied using HTTP Basic authentication. Otherwise returns `None`. | def authenticate(self, request):
auth = get_authorization_header(request).split()
if not auth or auth[0].lower() != b"basic":
return None
if len(auth) == 1:
raise AuthenticationFailed(
"Invalid Basic authorization header. No credentials provided."
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"async def authenticate(self, request):\n\n if \"Authorization\" not in request.headers:\n return\n\n auth = request.headers[\"Authorization\"]\n\n scheme, token = auth.split()\n if scheme.lower() != 'bearer':\n raise AuthenticationError(\n \"Please u... | [
"0.7748496",
"0.7482971",
"0.7319348",
"0.7214823",
"0.69572836",
"0.69473755",
"0.6900045",
"0.68219066",
"0.6816961",
"0.68142855",
"0.6797234",
"0.6780275",
"0.6769279",
"0.67666936",
"0.6727957",
"0.6711529",
"0.66963404",
"0.66865516",
"0.6681249",
"0.6672814",
"0.667254... | 0.791987 | 0 |
Do not enforce CSRF. | def enforce_csrf(self, request):
return # To not perform the csrf check previously happening | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def add_csrf_validation(event):\n if event.request.method == 'POST':\n token = event.request.POST.get('_csrf')\n if token is None or token != event.request.session.get_csrf_token():\n headers = forget(event.request) # force a log out\n raise HTTPForbidden('CSRF token is missi... | [
"0.77730787",
"0.71921813",
"0.6985222",
"0.68456197",
"0.6827762",
"0.68134195",
"0.6702927",
"0.66973877",
"0.66799164",
"0.66241884",
"0.6571412",
"0.6558275",
"0.65252817",
"0.646873",
"0.645416",
"0.63729364",
"0.63329005",
"0.6296769",
"0.6245508",
"0.6245508",
"0.62453... | 0.85253215 | 0 |
Register callback for the end of process. Please note that this method is called by other threads. | def register_callback(self, pid, callback):
self.__cond.acquire()
unhandled_rc = None
if pid in self.__unhandled:
unhandled_rc = self.__unhandled[pid]
del self.__unhandled[pid]
else:
assert not pid in self.__callbacks
self.__callbacks[pid] = callback
if len(self.__callbacks... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def processEnded(self, reason):\n self._processEnded = True\n if self.onProcessEnd:\n d, self.onProcessEnd = self.onProcessEnd, None\n d.callback(None)",
"def set_finish_callback( callback ):",
"def set_finish_callback( callback ):",
"def on_close(self, callback):\n ... | [
"0.6814906",
"0.6735387",
"0.6735387",
"0.64096254",
"0.6239561",
"0.62294984",
"0.618629",
"0.61696166",
"0.61466694",
"0.6146446",
"0.6128145",
"0.6107708",
"0.60805905",
"0.60070384",
"0.5991",
"0.5989766",
"0.5917135",
"0.589538",
"0.5890868",
"0.5878869",
"0.5878317",
... | 0.5892839 | 18 |
Lists all files below the given folder that match the pattern. | def _list_files(folder, pattern):
for root, folders, files in os.walk(folder):
for filename in files:
if fnmatch.fnmatch(filename, pattern):
yield os.path.join(root, filename) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def list_and_filter(self, pattern, root_path):\n for path, dirs, files in os.walk(os.path.abspath(root_path)):\n for filename in fnmatch.filter(files, pattern):\n yield os.path.join(path, filename)",
"def find(pattern):\n files = config.index.files(path_glob=\"*%s*\" % pattern)\n print_files... | [
"0.77849257",
"0.7500726",
"0.74775934",
"0.7411119",
"0.74076384",
"0.7355792",
"0.7233464",
"0.71732324",
"0.71499306",
"0.71378464",
"0.71049553",
"0.70946896",
"0.6977931",
"0.69686073",
"0.6965376",
"0.6960672",
"0.69308126",
"0.69025075",
"0.68708056",
"0.6868468",
"0.6... | 0.81099135 | 1 |
Recursively collects a list of dirs that contain a file matching the given suffix. This works by listing the contents of directories and finding directories that have `_test.py` files. | def _collect_dirs(
start_dir,
blacklist=set(['conftest.py', 'noxfile.py', 'lib', 'third_party']),
suffix='_test.py',
recurse_further=False):
# Collect all the directories that have tests in them.
for parent, subdirs, files in os.walk(start_dir):
if './.' in parent:
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def find_files(suffix, path):\n\n \n result = []\n try:\n \n for file in os.listdir(path): \n if os.path.isfile(os.path.join(path,file)) and file.endswith(suffix): \n result.append(os.path.join(path,file))\n if os.path.isdir(os.path.join... | [
"0.7738707",
"0.7725894",
"0.75248116",
"0.7505463",
"0.74473727",
"0.74190205",
"0.7297259",
"0.72701323",
"0.72310036",
"0.719575",
"0.7136064",
"0.7136064",
"0.7117636",
"0.69994664",
"0.69969666",
"0.6824205",
"0.680646",
"0.6803683",
"0.6758053",
"0.67387193",
"0.6708413... | 0.7264011 | 8 |
Returns a list of files changed for this pull request / push. If running on a public CI like Travis or Circle this is used to only run tests/lint for changed files. | def _get_changed_files():
if not ci_diff_helper:
return None
try:
config = ci_diff_helper.get_config()
except OSError: # Not on CI.
return None
changed_files = ci_diff_helper.get_changed_files('HEAD', config.base)
changed_files = set([
'./{}'.format(filename) for ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_changed_files():\n upstream = \"origin/master\"\n local_commit = subprocess.check_output(\n \"git rev-list HEAD ^{} -- 2>/dev/null | tail -1\".format(upstream),\n shell=True).strip().decode()\n diff_base = subprocess.check_output(\n ['git', 'rev-parse', local_commit +\n ... | [
"0.76511127",
"0.7632489",
"0.73521656",
"0.71647716",
"0.70889413",
"0.70419586",
"0.7040667",
"0.6961542",
"0.69610536",
"0.67889297",
"0.66707456",
"0.66461307",
"0.6640469",
"0.6635299",
"0.651519",
"0.6512538",
"0.65028757",
"0.64318836",
"0.6417526",
"0.6387403",
"0.638... | 0.78147644 | 0 |
Filers the list of sample directories to only include directories that contain files in the list of changed files. | def _filter_samples(sample_dirs, changed_files):
result = []
for sample_dir in sample_dirs:
for changed_file in changed_files:
if changed_file.startswith(sample_dir):
result.append(sample_dir)
return list(set(result)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_filter_files(self):\n expected = [\n (\"/subdir1/fichier1\", False),\n (\"/subdir1/fichier4\", False),\n (\"/subdir1/subsubdir1\", False),\n ]\n files = [\n (\"/subdir1/fichier1\", False),\n (\"/subdir2/fichier2\", False),\n ... | [
"0.638365",
"0.6379942",
"0.6214398",
"0.61015475",
"0.6089934",
"0.6049182",
"0.5995398",
"0.5965479",
"0.5943096",
"0.5874663",
"0.58680815",
"0.5832992",
"0.5808962",
"0.5801182",
"0.57878786",
"0.57864714",
"0.5781348",
"0.5780594",
"0.57367",
"0.57249534",
"0.5714945",
... | 0.80027604 | 0 |
Determines all import names that should be considered "local". This is used when running the linter to insure that import order is properly checked. | def _determine_local_import_names(start_dir):
file_ext_pairs = [os.path.splitext(path) for path in os.listdir(start_dir)]
return [
basename
for basename, extension
in file_ext_pairs
if extension == '.py' or os.path.isdir(
os.path.join(start_dir, basename))
and... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_import_local_methods(self):\n package_foo = determine_package(LocalClass().foo_method)\n package_bar = determine_package(LocalClass().bar_method)\n assert package_foo == package_bar",
"def is_local(self) -> bool:\n if not self.source:\n return False\n\n if s... | [
"0.5883926",
"0.58062863",
"0.57493323",
"0.57031",
"0.56781274",
"0.56347144",
"0.5598635",
"0.5551292",
"0.5530518",
"0.5508783",
"0.55060405",
"0.5462658",
"0.5400261",
"0.5390425",
"0.53739977",
"0.5363772",
"0.53556126",
"0.53410673",
"0.5327128",
"0.5322422",
"0.5312975... | 0.7432707 | 0 |
Installs the App Engine SDK, if needed. | def _setup_appengine_sdk(session):
session.env['GAE_SDK_PATH'] = os.path.join(_GAE_ROOT, 'google_appengine')
session.run('gcp-devrel-py-tools', 'download-appengine-sdk', _GAE_ROOT) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def setup_env():\r\n\r\n # Try to import the appengine code from the system path.\r\n try:\r\n from google.appengine.api import apiproxy_stub_map\r\n except ImportError:\r\n for k in [k for k in sys.modules if k.startswith('google')]:\r\n del sys.modules[k]\r\n\r\n # Not on... | [
"0.7231505",
"0.68327713",
"0.6396035",
"0.61375856",
"0.5877419",
"0.58420604",
"0.5788967",
"0.57099634",
"0.5672816",
"0.56718606",
"0.55840725",
"0.55813754",
"0.5555057",
"0.55090976",
"0.5480833",
"0.54060125",
"0.53963965",
"0.53802866",
"0.5351276",
"0.531847",
"0.529... | 0.7749325 | 0 |
Runs py.test for a particular sample. | def _session_tests(session, sample, post_install=None):
session.install('-r', 'testing/requirements.txt')
session.chdir(sample)
if os.path.exists('requirements.txt'):
session.install('-r', 'requirements.txt')
if post_install:
post_install(session)
session.run(
'pytest',
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def run_test(test_name):\n\n print 'Running %s_test...' % test_name\n os.system('./test_%s.py' % test_name)\n print",
"def run_sample(smp: sample.Sample,\n run_dir: Text,\n summary_file: Optional[Text] = None,\n generate_sample_ns: Optional[int] = None):\n start = ti... | [
"0.65195566",
"0.65083474",
"0.6489226",
"0.64708185",
"0.64350784",
"0.6390061",
"0.6293161",
"0.622205",
"0.6087137",
"0.60797745",
"0.60169667",
"0.5991668",
"0.5964952",
"0.5960034",
"0.59463376",
"0.59389555",
"0.59370387",
"0.59229195",
"0.59209305",
"0.5916534",
"0.590... | 0.0 | -1 |
Runs py.test for an App Engine standard sample. | def gae(session, sample):
# Create a lib directory if needed, otherwise the App Engine vendor library
# will complain.
if not os.path.isdir(os.path.join(sample, 'lib')):
os.mkdir(os.path.join(sample, 'lib'))
_session_tests(session, sample, _setup_appengine_sdk) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_example_runs(self):\n run_example(\n verbose=False,\n testapp=self.testapp,\n )",
"def test_app():\n pass",
"def test_script(self) -> None:\n main()",
"def testapp():\n from space_rocks import main\n app = main({})\n from webtest... | [
"0.7205402",
"0.68938684",
"0.67903644",
"0.6781952",
"0.67707103",
"0.6763475",
"0.67430496",
"0.655691",
"0.65498453",
"0.6525648",
"0.65029895",
"0.64756703",
"0.64599717",
"0.6407252",
"0.6398116",
"0.63732797",
"0.6361551",
"0.6361551",
"0.63599926",
"0.63555795",
"0.635... | 0.6342977 | 35 |
Runs py.test for a sample using Python 2.7 | def py27(session, sample):
_session_tests(session, sample) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test(self):\n for arch, python in self.python:\n self.run(f\"{python} -m pytest\")",
"def __test__():\n#-------------------------------------------------------------------------------\n import pylib.tester as tester\n return 0",
"def task_test(argv):\n run_tests(\"python2\", argv... | [
"0.7058874",
"0.6976574",
"0.6928872",
"0.6905923",
"0.6731107",
"0.6696117",
"0.66346335",
"0.6633901",
"0.66220474",
"0.66138864",
"0.6605148",
"0.66025674",
"0.65968716",
"0.65968716",
"0.65968716",
"0.65968716",
"0.65968716",
"0.65968716",
"0.65968716",
"0.65968716",
"0.6... | 0.6479492 | 43 |
Runs py.test for a sample using Python 3.6 | def py36(session, sample):
_session_tests(session, sample) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test(self):\n for arch, python in self.python:\n self.run(f\"{python} -m pytest\")",
"def task_test(argv):\n run_tests(\"python2\", argv)\n run_tests(\"python3\", argv)",
"def test(session) -> None:\n session.install(\".[test]\")\n session.run(\"pytest\", \"-n\", \"auto\", *se... | [
"0.7342706",
"0.6932851",
"0.6911395",
"0.68314606",
"0.68234855",
"0.68161607",
"0.6808515",
"0.6803258",
"0.68006325",
"0.677856",
"0.6720968",
"0.6645903",
"0.66170406",
"0.66076094",
"0.65739256",
"0.6571476",
"0.6559418",
"0.65576935",
"0.6549422",
"0.65449774",
"0.65419... | 0.0 | -1 |
Runs flake8 on the sample. | def lint(session, sample):
session.install('flake8', 'flake8-import-order')
local_names = _determine_local_import_names(sample)
args = FLAKE8_COMMON_ARGS + [
'--application-import-names', ','.join(local_names),
'.']
session.chdir(sample)
session.run('flake8', *args) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def flake8():\n call([\"flake8\"])",
"def flake8(context):\n exec_cmd = \"flake8 .\"\n run_cmd(context, exec_cmd)",
"def flake8(ctx):\n ctx.run(f\"{VENV_PREFIX} flake8 --config=setup.cfg\")",
"def test_flake8(self):\n result = subprocess.run(['flake8', self.module.__file__])\n self.... | [
"0.8096764",
"0.8072783",
"0.7843559",
"0.73988837",
"0.7372284",
"0.734738",
"0.73003185",
"0.7064382",
"0.7043746",
"0.6899092",
"0.6883284",
"0.6800674",
"0.6674673",
"0.64521545",
"0.6429033",
"0.6402412",
"0.63903666",
"0.63484365",
"0.6312585",
"0.63042206",
"0.62754524... | 0.70643854 | 7 |
Lists all sample directories that do not have tests. | def missing_tests(session):
print('The following samples do not have tests:')
for sample in set(ALL_SAMPLE_DIRECTORIES) - set(ALL_TESTED_SAMPLES):
print('* {}'.format(sample)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def file_list_emptydirs(load):\n # TODO - implement this\n _init()\n\n return []",
"def test_matlab_install_dir_absent(self):\n directories = (\"/\", \"/tmp\")\n for dirname in directories:\n with self.subTest(dirname=dirname):\n self.assertNotIn(\"matlab-install\... | [
"0.66200477",
"0.65644187",
"0.6472452",
"0.64620155",
"0.63552594",
"0.6348389",
"0.6269922",
"0.6265887",
"0.6258353",
"0.6112326",
"0.6103435",
"0.61029524",
"0.610135",
"0.60616046",
"0.6050831",
"0.595446",
"0.5950891",
"0.5949775",
"0.594931",
"0.5892177",
"0.5882108",
... | 0.79405457 | 0 |
(Re)generates the readme for a sample. | def readmegen(session, sample):
session.install('jinja2', 'pyyaml')
if os.path.exists(os.path.join(sample, 'requirements.txt')):
session.install('-r', os.path.join(sample, 'requirements.txt'))
in_file = os.path.join(sample, 'README.rst.in')
session.run('python', 'scripts/readme-gen/readme_gen.... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def readme_md(cls):\n\n template = Helpers.File(Settings.readme_me_template).read()\n\n template = Helpers.Regex(\n template, r\"%%version%%\", replace_with=Settings.version\n ).replace()\n template = Helpers.Regex(\n template, r\"%%lenHosts%%\", replace_with=forma... | [
"0.7287029",
"0.7018073",
"0.6953501",
"0.6933401",
"0.6853328",
"0.6802791",
"0.6781446",
"0.6738891",
"0.6733868",
"0.6717046",
"0.66753453",
"0.66342485",
"0.6618257",
"0.64765066",
"0.644934",
"0.6441836",
"0.6415977",
"0.63786113",
"0.63530713",
"0.63517934",
"0.62726355... | 0.80653775 | 0 |
Checks for out of date requirements and optionally updates them. This is intentionally not parametric, as it's desired to never have two samples with differing versions of dependencies. | def check_requirements(session):
session.install('-r', 'testing/requirements.txt')
if 'update' in session.posargs:
command = 'update-requirements'
else:
command = 'check-requirements'
reqfiles = list(_list_files('.', 'requirements*.txt'))
for reqfile in reqfiles:
session.r... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_upgrade_and_dependency_not_removed_caused_required_by_another_item(self):\n assert self.DEPENDENCY_PUBLIC_ID in self.load_config().protocols\n # do not add dependencies for the package\n\n with self.with_oef_installed(), self.with_config_update(), patch(\n \"aea.cli.add._ad... | [
"0.68966734",
"0.66854733",
"0.6329505",
"0.6213713",
"0.6208097",
"0.6180045",
"0.6092911",
"0.60761565",
"0.6075853",
"0.6070879",
"0.60444874",
"0.6042945",
"0.6040614",
"0.6025732",
"0.5970172",
"0.5963672",
"0.5960131",
"0.5874549",
"0.58727145",
"0.58643824",
"0.5860373... | 0.62337804 | 3 |
Returns a paranoid_pb2.TestResultsEntry protobuf ready for the checks. The created paranoid_pb2.TestResultsEntry is appropriate to be used on tests and have the paranoid_pb2.TestResultsEntry.result filled by the Check function (i.e., set as weak or not). | def _CreateTestResult(self) -> paranoid_pb2.TestResultsEntry:
if self.severity is None:
raise KeyError("Please specify self.severity for %s." % self.check_name)
return paranoid_pb2.TestResultsEntry(
severity=self.severity, test_name=self.check_name, result=False) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def parse_verifier_result(self):\n stat = self.get_verifier_result(self.verification_id)\n try:\n num_executed = stat['num_tests'] - stat['num_skipped']\n try:\n self.result = 100 * stat['num_success'] / num_executed\n except ZeroDivisionError:\n ... | [
"0.5186231",
"0.5027672",
"0.48830792",
"0.4770407",
"0.46957067",
"0.46849295",
"0.46664384",
"0.46516448",
"0.46486202",
"0.4532336",
"0.4530844",
"0.4512337",
"0.44765168",
"0.43914053",
"0.43779433",
"0.43736914",
"0.43657622",
"0.4348417",
"0.43343168",
"0.43205202",
"0.... | 0.7242101 | 0 |
Runs the check among the artifacts (keys/signatures). | def Check(self, artifacts: list[T]) -> bool:
raise NotImplementedError("Subclass didn't implement Check method.") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def run_and_check(self, *args, **kwargs) -> None:\n raise NotImplementedError",
"def check(self):\n json = JsonBackend(\"../src/builder/projects.json\")\n json.load()\n\n TM_ITSELF = 1\n expected_files = TM_ITSELF + sum(p.downloadable is True\n ... | [
"0.62986624",
"0.6155824",
"0.61118424",
"0.60989845",
"0.6006719",
"0.58963877",
"0.5836908",
"0.5789669",
"0.57740337",
"0.5756412",
"0.57358384",
"0.57111585",
"0.5706886",
"0.5692724",
"0.5689134",
"0.56786627",
"0.5625611",
"0.56218255",
"0.5619318",
"0.5589858",
"0.5575... | 0.599099 | 5 |
Create a new 'SubstitutionField'. By default, the pattern and replacement string are empty. | def __init__(self, name, **properties):
# Initialize the base class.
apply(qm.fields.TextField.__init__, (self, name, ";"), properties) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __init__(self, field, derived_field = None):\r\n super(TextSubstituteNode, self).__init__()\r\n\r\n self.field = field\r\n self.derived_field = derived_field\r\n self.substitutions = []",
"def __init__(self, name, **properties):\n # Initialize the base class.\n fields = [Uni... | [
"0.6972568",
"0.62130094",
"0.58522135",
"0.5740111",
"0.5510787",
"0.5459242",
"0.5442237",
"0.54280025",
"0.5401875",
"0.5387175",
"0.5350919",
"0.5333425",
"0.5201174",
"0.51596874",
"0.51431245",
"0.5138764",
"0.51243883",
"0.5123259",
"0.50846165",
"0.5082066",
"0.507800... | 0.4489429 | 67 |
Split a value of this field into the pattern and replacement string. 'value' A value for this field. returns A pair '(pattern, replacement_string)'. | def SplitValue(self, value):
# Be lenient about an empty string.
if value == "":
return ("", "")
# Break it in half.
elements = string.split(value, ";", 1)
# Unescape semicolons in both halves.
elements = map(lambda e: string.replace(e, r"\;", ";"), elements)... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _splitValue(self, value):\n m = ConfigParser.varRe.match(value)\n if m == None:\n raise Exception(self.fh.name + \":\" + str(self.lineNum) + \": bug, can't split line \")\n (beforeRef, junk, varName, afterRef) = m.groups()\n return (beforeRef, varName, afterRef)",
"def ... | [
"0.662261",
"0.5876804",
"0.5876804",
"0.58283037",
"0.5570239",
"0.5308981",
"0.5277081",
"0.5173776",
"0.50996935",
"0.50890404",
"0.50848806",
"0.50799507",
"0.50650334",
"0.49815592",
"0.49710035",
"0.49443537",
"0.49404633",
"0.49323875",
"0.49291474",
"0.49177104",
"0.4... | 0.5772989 | 5 |
Construct the environment for executing the target program. | def MakeEnvironment(self, context):
# Start with any environment variables that are already present
# in the environment.
environment = os.environ.copy()
# Copy context variables into the environment.
for key, value in context.items():
name = "QMV_" + key
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def build_python_environment(self):\n # build command for creating the python environment\n cmd_args = {\n 'exe': self.env_executable,\n 'cmds': \" \".join(self.env_commands),\n 'flags': \" \".join(self.env_flags),\n 'args': \" \".join(self.env_arguments),\... | [
"0.6865318",
"0.67876095",
"0.66827077",
"0.6655347",
"0.65665376",
"0.64506286",
"0.64132625",
"0.6385014",
"0.62479043",
"0.62209445",
"0.6211236",
"0.62018836",
"0.61966336",
"0.6103375",
"0.6073248",
"0.60442287",
"0.60428226",
"0.6014583",
"0.60005015",
"0.59970814",
"0.... | 0.6132058 | 13 |
Run the 'program'. 'program' The path to the program to run. 'arguments' A list of the arguments to the program. This list must contain a first argument corresponding to 'argv[0]'. 'stdin' Content of standard input for the program. 'context' A 'Context' giving runtime parameters to the test. 'result' A 'Result' object.... | def RunProgram(self, program, arguments, stdin, context, result):
# Construct the environment.
environment = self.MakeEnvironment(context)
e_stdin = stdin
c = {}
for pair in context.items():
c[pair[0]] = pair[1]
for substitution in c.keys():
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def RunProgram(self, program, arguments, context, result):\n\n # Construct the environment.\n environment = self.MakeEnvironment(context)\n e_stdin = self.stdin\n c = {}\n for pair in context.items():\n c[pair[0]] = pair[1]\n for substitution in c.keys():\n ... | [
"0.69363123",
"0.63061756",
"0.6056023",
"0.5911862",
"0.5623276",
"0.55638814",
"0.5554909",
"0.5461146",
"0.5455719",
"0.545486",
"0.5423107",
"0.54222107",
"0.5385501",
"0.53396916",
"0.5325689",
"0.5288438",
"0.52590066",
"0.52466375",
"0.5239515",
"0.52368325",
"0.523618... | 0.7962142 | 0 |
Initialization parameters 'context' A 'Context' giving runtime parameters to the test. | def __init__(self, context):
# RunServiceBase.__init__()
self.__context = context | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def init_with_context(self, context):\n pass",
"def initialize(self, context):\n raise NotImplementedError",
"def initialize(self, context):\n pass",
"def initialize(self, context):\n pass",
"def initialize(self, context):\n pass",
"def initialize(self, context):\r\n ... | [
"0.76711184",
"0.73903596",
"0.7277065",
"0.7277065",
"0.7277065",
"0.721566",
"0.70873374",
"0.70873374",
"0.70873374",
"0.70873374",
"0.70873374",
"0.70873374",
"0.70873374",
"0.70873374",
"0.70873374",
"0.70873374",
"0.70873374",
"0.70873374",
"0.70873374",
"0.70873374",
"... | 0.6184149 | 45 |
Restore a database from a backup file. 'database' A database specification. 'backupfile' A backup file name. 'arguments' A list of the arguments to the GBAK without backup file name and database location. 'result' A 'Result' object. The outcome will be 'Result.PASS' when this method is called. The 'result' may be modif... | def RestoreDatabase(self, database, backupfile, arguments, result):
self.RunProgram("\""+self.__context["gbak_path"]+"\"",
[ self.__context["gbak_path"] ] + [ "-C ", backupfile ]
+ arguments + [ database ],
"", self.__context, result) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def restore_backup(self):\n print \"Restoring backup for database: %s\" % self.database['NAME']\n # Fetch the latest backup if filepath not specified\n if not self.filepath:\n print \" Finding latest backup\"\n filepaths = self.storage.list_directory()\n filep... | [
"0.64738756",
"0.6441385",
"0.6043816",
"0.59941",
"0.59136015",
"0.5861948",
"0.5844177",
"0.5674559",
"0.5635281",
"0.5470779",
"0.54675585",
"0.52948487",
"0.5271796",
"0.52364796",
"0.51926756",
"0.5166014",
"0.5152477",
"0.5145427",
"0.508932",
"0.5002277",
"0.49899507",... | 0.8807153 | 0 |
Run an ISQL script. 'database' A database specification. 'script' An ISQL script. 'arguments' A list of the arguments to the ISQL without database location. 'result' A 'Result' object. The outcome will be 'Result.PASS' when this method is called. The 'result' may be modified by this method to indicate outcomes other th... | def RunScript(self, database, script, arguments, result):
self.RunProgram("\""+self.__context["isql_path"]+"\"",
[ self.__context["isql_path"] ] + [ database ] + arguments,
script, self.__context, result) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def Run(self, context, result):\n\n # Was the program not specified?\n\n self.program = context[\"isql_path\"]\n\n if context.has_key(\"database_path\"):\n database = context[\"database_path\"]\n else:\n database = \"\"\n self.RunProgram(self.program,\n\t\t\... | [
"0.63101566",
"0.6261756",
"0.59878993",
"0.57192934",
"0.56583416",
"0.5588258",
"0.5548203",
"0.554179",
"0.54586357",
"0.5448663",
"0.5403661",
"0.5383299",
"0.534008",
"0.53358716",
"0.52607375",
"0.52274305",
"0.5176673",
"0.51720655",
"0.5166656",
"0.51541513",
"0.51159... | 0.8646154 | 0 |
Run an ISQL script. 'script' An (optional) GSEC script. 'arguments' A list of the arguments to the GSEC without ISC4 database location and sysdba username and password. 'result' A 'Result' object. The outcome will be 'Result.PASS' when this method is called. The 'result' may be modified by this method to indicate outco... | def RunGsec(self, script, arguments, result):
try:
self.RunProgram("\""+self.__context["gsec_path"]+"\"",
[ self.__context["gsec_path"], "-database", self.__context["server_location"]+ self.__context["isc4_path"], "-user", "SYSDBA", "-password", "masterkey" ]+arguments,
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def RunScript(self, database, script, arguments, result):\n\n self.RunProgram(\"\\\"\"+self.__context[\"isql_path\"]+\"\\\"\",\n [ self.__context[\"isql_path\"] ] + [ database ] + arguments,\n script, self.__context, result)",
"def execute_script(self, script,... | [
"0.7854742",
"0.61550087",
"0.59776443",
"0.5792648",
"0.5619949",
"0.55894285",
"0.55582255",
"0.5536295",
"0.54101485",
"0.536589",
"0.5349416",
"0.53441995",
"0.5326733",
"0.53031874",
"0.5293181",
"0.529026",
"0.52394783",
"0.5232753",
"0.5228306",
"0.521149",
"0.52065",
... | 0.68645024 | 1 |
Construct the environment for executing the target program. | def MakeEnvironment(self, context):
# Start with any environment variables that are already present
# in the environment.
environment = os.environ.copy()
# Copy context variables into the environment.
for key, value in context.items():
name = "QMV_" + key
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def build_python_environment(self):\n # build command for creating the python environment\n cmd_args = {\n 'exe': self.env_executable,\n 'cmds': \" \".join(self.env_commands),\n 'flags': \" \".join(self.env_flags),\n 'args': \" \".join(self.env_arguments),\... | [
"0.6866149",
"0.67887974",
"0.6682207",
"0.665503",
"0.6565945",
"0.6451024",
"0.64138275",
"0.6385889",
"0.62477255",
"0.6221224",
"0.62101907",
"0.62013006",
"0.6196314",
"0.6132033",
"0.610246",
"0.60736305",
"0.6043668",
"0.60429454",
"0.6000888",
"0.5997547",
"0.5982757"... | 0.60146767 | 18 |
Run the 'program'. 'program' The path to the program to run. 'arguments' A list of the arguments to the program. This list must contain a first argument corresponding to 'argv[0]'. 'context' A 'Context' giving runtime parameters to the test. 'result' A 'Result' object. The outcome will be 'Result.PASS' when this method... | def RunProgram(self, program, arguments, context, result):
# Construct the environment.
environment = self.MakeEnvironment(context)
e_stdin = self.stdin
c = {}
for pair in context.items():
c[pair[0]] = pair[1]
for substitution in c.keys():
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def RunProgram(self, program, arguments, stdin, context, result):\n\n # Construct the environment.\n environment = self.MakeEnvironment(context)\n e_stdin = stdin\n c = {}\n for pair in context.items():\n c[pair[0]] = pair[1]\n for substitution in c.keys():\... | [
"0.77603376",
"0.6088054",
"0.60775167",
"0.58894455",
"0.5795883",
"0.57625437",
"0.5644391",
"0.5612917",
"0.5577483",
"0.54712975",
"0.54391956",
"0.5397551",
"0.5342152",
"0.53416663",
"0.53210723",
"0.5283056",
"0.5247545",
"0.5224499",
"0.51938117",
"0.51841336",
"0.516... | 0.69340014 | 1 |
Split a value of this field into the pattern and replacement string. 'value' A value for this field. returns A pair '(pattern, replacement_string)'. | def SplitValue(self, value):
# Be lenient about an empty string.
if value == "":
return ("", "")
# Break it in half.
elements = string.split(value, ";", 1)
# Unescape semicolons in both halves.
elements = map(lambda e: string.replace(e, r"\;", ";"), elements)... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _splitValue(self, value):\n m = ConfigParser.varRe.match(value)\n if m == None:\n raise Exception(self.fh.name + \":\" + str(self.lineNum) + \": bug, can't split line \")\n (beforeRef, junk, varName, afterRef) = m.groups()\n return (beforeRef, varName, afterRef)",
"def ... | [
"0.6626543",
"0.5880005",
"0.5880005",
"0.5834636",
"0.5569926",
"0.530701",
"0.5272915",
"0.51757324",
"0.5105574",
"0.5092262",
"0.50849164",
"0.50815874",
"0.5064315",
"0.49831337",
"0.49724522",
"0.49412143",
"0.49400538",
"0.4932219",
"0.49313912",
"0.4915455",
"0.490361... | 0.5778173 | 4 |
Perform substitutions on a body of text. returns The string 'text', processed with the substitutions configured for this test instance. | def __PerformSubstitutions(self, text):
for substitution in self.substitutions:
pattern, replacement = self.SplitValue(substitution)
text = re.compile(pattern,re.M).sub(replacement, text)
return text | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def postprocess(self, text):\r\n return text",
"def preprocess(self, text):\r\n return text",
"def post_process_text(self, text):\n\t\treturn text",
"def substitution(plainText, key):\n return plainText",
"def apply(self, text):",
"def applyRegularExpressions(strText, substitutionPatternLi... | [
"0.65611976",
"0.6435312",
"0.6424799",
"0.6276254",
"0.6202044",
"0.61083364",
"0.6037628",
"0.6034791",
"0.59750575",
"0.57668275",
"0.5727748",
"0.57236975",
"0.5721389",
"0.5685642",
"0.56766623",
"0.5662096",
"0.5638508",
"0.56205267",
"0.5587311",
"0.55449075",
"0.55417... | 0.7525169 | 0 |
Run the test. 'context' A 'Context' giving runtime parameters to the test. 'result' A 'Result' object. The outcome will be 'Result.PASS' when this method is called. The 'result' may be modified by this method to indicate outcomes other than 'Result.PASS' or to add annotations. | def Run(self, context, result):
# Was the program not specified?
self.program = context["isql_path"]
if context.has_key("database_path"):
database = context["database_path"]
else:
database = ""
self.RunProgram(self.program,
[ self.program , database ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def run(self, context):\n TestStepBase.run(self, context)\n\n if self._device is None:\n self._raise_config_exception(\"%s is not specified in the bench configuration\" % self._pars.device,\n AcsConfigException.INVALID_BENCH_CONFIG)\n\n # Inse... | [
"0.6657738",
"0.65140593",
"0.6481782",
"0.6363065",
"0.62938815",
"0.6101981",
"0.60296535",
"0.6014694",
"0.5991694",
"0.5981408",
"0.59577173",
"0.59577173",
"0.59303653",
"0.5891765",
"0.58808815",
"0.5873598",
"0.5869656",
"0.5845078",
"0.57987034",
"0.57243335",
"0.5679... | 0.5427483 | 37 |
Returns the sum of each individual digit in a given integer n | def sum_of_digits(n):
return sum(int(c) for c in str(n)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def digitSum ( n ) :\n return sum ( map ( int , str ( n ) ) )",
"def digit_sum(n):\n\treturn sum(int(c) for c in str(n))",
"def digit_sum(n):\n sum_of_digits = 0\n for c in str(n):\n sum_of_digits += int(c)\n return sum_of_digits",
"def digit_sum(n):\n s = 0\n while n:\n s += n ... | [
"0.8983617",
"0.896262",
"0.88677377",
"0.8782588",
"0.87734056",
"0.8756498",
"0.85132176",
"0.84619224",
"0.83623177",
"0.83091414",
"0.82763064",
"0.8267195",
"0.8232159",
"0.80869514",
"0.80285734",
"0.7945917",
"0.7804985",
"0.77949435",
"0.77158666",
"0.7711733",
"0.762... | 0.8821321 | 3 |
Compute softmax values for each sets of scores in x. | def softmax(x):
# Compute and return softmax(x)
denom = sum(np.exp(x))
return [ np.exp(xi)/denom for xi in x ] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def softmax(self, scores):\n\n\n # for each sample, for each class ,caclulate\n # np.exp(scores) : still (n_samples, n_classes)\n\n # axis = 1\n # a00, a01, a02 as a sinlge one to perfrom np_sum\n # which is the same sample \n # sum_exp : still (n_samples, 1)\n\n # ... | [
"0.7800231",
"0.776332",
"0.7762483",
"0.77600384",
"0.76607925",
"0.76607925",
"0.76607925",
"0.76607925",
"0.7585753",
"0.7432371",
"0.7409777",
"0.73932534",
"0.7360278",
"0.72708684",
"0.7263948",
"0.7262435",
"0.7257905",
"0.7231808",
"0.72159433",
"0.7203519",
"0.720315... | 0.731833 | 13 |
Return the nr_terms most frequent terms in article | def common_terms(self, nr_terms=20):
tokens = analysis.extra_tokenize(self.fulltext)
all_to_remove = stopwords + punct + WORDS_FOR_REMOVAL
coretokens = [t.lower() for t in tokens if t.lower() not in all_to_remove]
fd = FreqDist(coretokens)
return fd.most_common(nr_terms) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getNumberTerms(content): \n return Counter(getTerms(content))",
"def most_frequent(corpus):\n fd = nltk.FreqDist(corpus)\n return fd.most_common(10)",
"def get_num_terms(self, documents=None):\n terms = []\n if documents == None:\n docs = self.vocab\n else:\n ... | [
"0.7059205",
"0.7044106",
"0.6974766",
"0.69031334",
"0.6797572",
"0.6758672",
"0.674704",
"0.6637728",
"0.656225",
"0.6542295",
"0.6528525",
"0.6483165",
"0.6466837",
"0.64654",
"0.644185",
"0.641473",
"0.6407708",
"0.6399931",
"0.6389089",
"0.6380951",
"0.6369813",
"0.635... | 0.6191852 | 37 |
Get articles for a gives news source | def for_source(source, articles=None):
if not articles:
articles = load_articles(nl.read_data())
source_arts = [a for a in articles if a.source == source]
for art in source_arts:
yield art | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def listsources():\n\tmain_url = \" https://newsapi.org/v2/sources?apiKey=5f81b593f35d42a8980313250c03d7e7\"\n\n\t# fetching data in json format \n\topen_source = requests.get(main_url).json() \n\n\t# getting all articles in a string sources\n\tsource = open_source[\"sources\"] \n\n\t# empty list which will \n\t# ... | [
"0.7267898",
"0.69831485",
"0.6910983",
"0.6892911",
"0.68473136",
"0.67912644",
"0.6780562",
"0.6722617",
"0.6709383",
"0.6705438",
"0.66801316",
"0.663876",
"0.6628921",
"0.6584156",
"0.6563533",
"0.65245885",
"0.6516218",
"0.65005183",
"0.6477813",
"0.64497817",
"0.6429842... | 0.710921 | 1 |
Huber function. An analytic function that is quadratic around its minimum n and linear in its tails. Its minimum is at offset. Quadratic between offsetdelta and offset + delta and linear outside. | def huber(x, offset, delta):
i = np.abs(x - offset) < delta
return (x-offset)**2/2 * i + (1 - i)*delta*(np.abs(x-offset) - delta/2) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def beeston_barlow_root1(a, p, U, d):\n return ((-U*p - U + a*p + d*p -\n np.sqrt(U**2*p**2 + 2*U**2*p + U**2 + 2*U*a*p**2 + 2*U*a*p -\n 2*U*d*p**2 - 2*U*d*p + a**2*p**2 + 2*a*d*p**2 + d**2*p**2))/(2*p*(p + 1)))",
"def trapezium_rule(f, m, x, a, b, n):\n h = (b-a)/float(n)\n... | [
"0.5800495",
"0.57387626",
"0.56889313",
"0.56861275",
"0.56719846",
"0.5661993",
"0.5647246",
"0.56123304",
"0.56039697",
"0.55897033",
"0.55779684",
"0.556688",
"0.5564874",
"0.5559421",
"0.55498475",
"0.5544323",
"0.55239946",
"0.55232066",
"0.5518701",
"0.5500929",
"0.549... | 0.71997106 | 0 |
Numerically stable computation of Shannon's entropy for probability distributions with zerovalued elements. | def entropy(p: torch.Tensor):
nz = (p > 0).to(p.device)
eps = torch.finfo(p.dtype).eps
p_stable = p.clone().clamp(min=eps, max=1 - eps)
out = torch.where(
nz,
p_stable * torch.log(p_stable),
torch.tensor(0.0, device=p.device, dtype=torch.float),
)
return -(out).sum(-1) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def entropy(x):\n nz = np.nonzero(x)[0]\n return -np.sum(x[nz]*np.log2(x[nz]))",
"def shannon_entropy(probs):\n return -(\n math.sum([px * math.log2(px) if px != 0 and not (np.isclose(px, 0)) else 0 for px in probs])\n )",
"def entropy(P):\n P_nan = P.copy()\n P_nan[P_nan == 0] = np.na... | [
"0.78719157",
"0.77639353",
"0.7700689",
"0.75903934",
"0.7586108",
"0.7534621",
"0.7498757",
"0.74172634",
"0.7378696",
"0.7348129",
"0.7309863",
"0.7278837",
"0.7269413",
"0.72656107",
"0.72442186",
"0.72422546",
"0.72418517",
"0.72395533",
"0.71693814",
"0.71460384",
"0.70... | 0.73763674 | 9 |
Puts User Matches in view, in order by rank ( of matching skills) | def get_context_data(self, **kwargs):
context = super(ProjectView, self).get_context_data(**kwargs)
# only load if self.request.user == founder
if self.request.user.id is self.get_object().founder.id:
context['match_list'] = Match.objects.filter(project=self.object.id).order_by('-ran... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def enter_matches_score(match_list):\n for match in match_list:\n view.show(\"{} vs {}\".format(match.player_blanc.player_first_name,\n match.player_black.player_first_name))\n score_blanc = enter_player_score(match.player_blanc)\n match.score_blanc = scor... | [
"0.6061785",
"0.59745973",
"0.5851773",
"0.5811785",
"0.5756262",
"0.5733067",
"0.57015556",
"0.5699974",
"0.5658713",
"0.5638683",
"0.5615457",
"0.56053066",
"0.55809283",
"0.5551806",
"0.549049",
"0.54823655",
"0.5481128",
"0.5471227",
"0.54675686",
"0.546725",
"0.5450787",... | 0.496989 | 99 |
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