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How to use LoadTarget method in wpt
Best JavaScript code snippet using wpt
basic-popup-and-iframe-tests.https.js
Source:basic-popup-and-iframe-tests.https.js 
...90 eSecureIfCreatorSecure,91 eResultFromExaminationSync),92];93const loadTargets = [94 new LoadTarget("an iframe", eLoadInUnsandboxedIframe),95 new LoadTarget("a sandboxed iframe", eLoadInSandboxedIframe),96 new LoadTarget("a popup", eLoadInPopup),97];98function LoadType(description, loadInFlags, uri, expectedSecureFlag, resultFrom) {99 this.desc = description;100 this.loadInFlags = loadInFlags;101 this.uri = uri;102 this.expectedSecureFlag = expectedSecureFlag;103 this.resultFrom = resultFrom;104}105function LoadTarget(description, loadInFlag) {106 this.desc = description;107 this.loadInFlag = loadInFlag;108}109LoadTarget.prototype.open = function(loadType) {110 let loadTarget = this;111 this.currentTest.step(function() {112 assert_true((loadTarget.loadInFlag & loadType.loadInFlags) != 0,113 loadType.desc + " cannot be tested in " + loadTarget.desc);114 });115 if (this.loadInFlag == eLoadInUnsandboxedIframe) {116 let iframe = document.createElement("iframe");117 document.body.appendChild(iframe);118 iframe[loadType.desc == "a srcdoc" ? "srcdoc" : "src"] = loadType.uri;119 return iframe;...
agent_models.py
Source:agent_models.py
1# !/usr/bin/env python2# -*- coding:utf-8 _*-3# @Author: swang4# @Contact: wang00sheng@gmail.com5# @Project Name: D3QN6# @File: A2C.py7# @Time: 2021/10/29/22:548# @Software: PyCharm9import os10import numpy as np11import tensorflow as tf12import tensorflow.keras as keras13from tensorflow.keras.models import Model, Sequential14from tensorflow.keras.layers import Dense, Activation, Input, Conv2D, Flatten, Lambda, Add, BatchNormalization, Reshape15import tensorflow_probability as tfp16def FeatureExtractor(model_dir='../model/base_model/'):17 '''18 initialize a feature extractor for a d3qn RL agent.19 '''20 ckpt_dir = os.path.join(model_dir, 'feature_extractor', 'ckpt')21 fe = tf.keras.models.load_model(ckpt_dir)22 fe.trainable = False23 fe.compile()24 25 print('-' * 20, 'ResCNN_feature_extractor', '-' * 20, )26 fe.summary()27 28 return fe29def D3QN_Classifier(dueling=True, base_model_dir='../model/base_model/', loadTarget=False,30 load_d3qn_model=False, d3qn_model_dir='../model/d3qn_model/', based_on_base_model=True):31 def __init_d3qn_model__():32 print('-' * 20, 'Initializing a new D3QN model...', '-' * 20, )33 if based_on_base_model:34 print('-' * 20, 'Based on base_model\'s classifier...', '-' * 20, )35 base_classifier = tf.keras.models.load_model(base_ckpt_dir)36 conv, reshape = base_classifier.layers37 # input = base_classifier.input38 # base_classifier = Model(inputs=input, outputs=reshape(conv(input)))39 40 else:41 print('-' * 20, 'Even the classifier is initialized randomly...', '-' * 20, )42 base_classifier = Sequential([43 Input(shape=(30, 8, 82), name='feature_input'),44 Conv2D(1, kernel_size=(30, 1), strides=(1, 1), padding='valid', use_bias=True),45 Reshape((-1,)), ],46 name='output_conv')47 48 if dueling:49 # ------------------------------------- value network -------------------------------------------------#50 state_value = Conv2D(1, kernel_size=(30, 1), strides=(1, 1), padding='valid', use_bias=True,51 name='value_conv')(base_classifier.input)52 # state_value = BatchNormalization(axis=-1)(state_value)53 state_value = Reshape((-1,), name='value_reshape')(state_value)54 state_value = Dense(1, name='value_dense')(state_value)55 56 # ------------------------------------- advantage network -------------------------------------------------#57 action_advantage = base_classifier.output58 # kernel_constraint=max_norm(norm_rate))(feature_extraction.output)59 60 # ------------------------------------- add -------------------------------------------------#61 output = Add()([state_value, action_advantage])62 else:63 output = base_classifier.output64 # outputs = tf.keras.layers.Multiply()([output, [1, 1. / 2460]])65 output = Lambda(lambda x: x / 2460.)(output)66 return Model(inputs=base_classifier.input, outputs=output)67 68 def __load_d3qn_model__():69 try:70 print('-' * 20, 'Loading pre-trained D3QN classifier...', '-' * 20, )71 return tf.keras.models.load_model(d3qn_ckpt_dir)72 except Exception as e:73 print('Warning:', e)74 print('-' * 20, 'Fail to load the pre-trained D3QN model! An initialized model will be used!',75 '-' * 20, )76 return __init_d3qn_model__()77 78 # ------------------------------------- main procedure -------------------------------------------------#79 base_ckpt_dir = os.path.join(base_model_dir, 'classifier', 'ckpt')80 if loadTarget:81 d3qn_ckpt_dir = os.path.join(d3qn_model_dir, 'classifier', 'target_ckpt')82 else:83 d3qn_ckpt_dir = os.path.join(d3qn_model_dir, 'classifier', 'ckpt')84 85 if load_d3qn_model:86 d3qn_model = __load_d3qn_model__()87 else:88 d3qn_model = __init_d3qn_model__()89 90 if loadTarget:91 print('-' * 20, 'd3qn_target_classifier', '-' * 20, )92 else:93 print('-' * 20, 'd3qn_classifier', '-' * 20, )94 d3qn_model.summary()95 96 return d3qn_model97def SAC_actor(base_model_dir='../model/base_model/',98 load_sac_model=False, sac_model_dir='../model/sac_model/', based_on_base_model=True):99 def __init_sac_actor__():100 print('-' * 20, 'Initializing a new SAC_actor...', '-' * 20, )101 if based_on_base_model:102 print('-' * 20, 'Based on base_model\'s classifier...', '-' * 20, )103 base_classifier = tf.keras.models.load_model(base_ckpt_dir)104 else:105 print('-' * 20, 'Even the classifier is initialized randomly...', '-' * 20, )106 base_classifier = Sequential([107 Input(shape=(30, 8, 82), name='feature_input'),108 Conv2D(1, kernel_size=(30, 1), strides=(1, 1), padding='valid', use_bias=True),109 BatchNormalization(axis=-1), Reshape((-1,)), ],110 name='output_conv')111 112 output = Activation('softmax', name='softmax')(base_classifier.output)113 114 return Model(inputs=base_classifier.input, outputs=output)115 116 def __load_sac_actor__():117 try:118 print('-' * 20, 'Loading pre-trained sac_actor...', '-' * 20, )119 return tf.keras.models.load_model(sac_ckpt_dir)120 except Exception as e:121 print('Warning:', e)122 print('-' * 20, 'Fail to load the pre-trained sac_actor! An initialized model will be used!',123 '-' * 20, )124 return __init_sac_actor__()125 126 # ------------------------------------- main procedure -------------------------------------------------#127 base_ckpt_dir = os.path.join(base_model_dir, 'classifier', 'ckpt')128 sac_ckpt_dir = os.path.join(sac_model_dir, 'actor', 'ckpt')129 130 if load_sac_model:131 sac_actor = __load_sac_actor__()132 else:133 sac_actor = __init_sac_actor__()134 135 print('-' * 20, 'sac_actor', '-' * 20, )136 sac_actor.summary()137 138 return sac_actor139def SAC_critic(base_model_dir='../model/base_model/', load_sac_model=False, sac_model_dir='../model/sac_model/',140 based_on_base_model=True, index=None, loadTarget=False):141 def __init_sac_critic__():142 print('-' * 20, 'Initializing a new SAC_critic...', '-' * 20, )143 if based_on_base_model:144 print('-' * 20, 'Based on base_model\'s classifier...', '-' * 20, )145 base_classifier = tf.keras.models.load_model(base_ckpt_dir)146 else:147 print('-' * 20, 'Even the classifier is initialized randomly...', '-' * 20, )148 base_classifier = Sequential([149 Input(shape=(30, 8, 82), name='feature_input'),150 Conv2D(1, kernel_size=(30, 1), strides=(1, 1), padding='valid', use_bias=True),151 Reshape((-1,)), ],152 name='output_conv')153 154 return Model(inputs=base_classifier.input, outputs=base_classifier.output)155 156 def __load_sac_critic__():157 try:158 if loadTarget:159 print('-' * 20, 'Loading pre-trained target sac_critic...', '-' * 20, )160 else:161 print('-' * 20, 'Loading pre-trained sac_critic...', '-' * 20, )162 return tf.keras.models.load_model(sac_ckpt_dir)163 except Exception as e:164 print('Warning:', e)165 print('-' * 20, 'Fail to load the pre-trained sac_critic! An initialized model will be used!',166 '-' * 20, )167 return __init_sac_critic__()168 169 # ------------------------------------- main procedure -------------------------------------------------#170 base_ckpt_dir = os.path.join(base_model_dir, 'classifier', 'ckpt')171 if loadTarget:172 if index is None:173 sac_ckpt_dir = os.path.join(sac_model_dir, 'critic', 'target_ckpt')174 else:175 sac_ckpt_dir = os.path.join(sac_model_dir, 'critic', 'target_ckpt_' + str(index))176 else:177 if index is None:178 sac_ckpt_dir = os.path.join(sac_model_dir, 'critic', 'ckpt')179 else:180 sac_ckpt_dir = os.path.join(sac_model_dir, 'critic', 'ckpt_' + str(index))181 182 if load_sac_model:183 sac_critic = __load_sac_critic__()184 else:185 sac_critic = __init_sac_critic__()186 187 print('-' * 20, 'sac_critic', '-' * 20, )188 sac_critic.summary()189 190 return sac_critic191if __name__ == '__main__':192 print('Hello World!')193 os.environ['CUDA_VISIBLE_DEVICES'] = '-1'194 195 fe = FeatureExtractor()196 cls = D3QN_Classifier(based_on_base_model=True)197 cls = SAC_actor(based_on_base_model=True)198 cls = SAC_critic(based_on_base_model=True)199 ...
Add_Data.py
Source:Add_Data.py
1'''2Created on Oct 12, 20153@author: kyleg4'''5if __name__ == '__main__':6 pass7def main():8 pass9 #LoadARegion()10 LoadAliasTables()11def LoadARegion():12 from arcpy import Append_management, ListFeatureClasses, ListDatasets, env, ListTables13 #importGDB = r"//gisdata/planning/Cart/projects/Conflation/GIS_DATA/GEO_COMM/REGION3_20151002/REGION3_20151002.gdb"14 #importGDB = r"\\gisdata\planning\Cart\projects\Conflation\GIS_DATA\GEO_COMM\REGION4_20151021\REGION4_20151021\REGION4_20151021.gdb"15 importGDB = r"\\gisdata\planning\Cart\projects\Conflation\GIS_DATA\GEO_COMM\REGION5_20151211\REGION5_20151211.gdb"16 LoadTarget = r"\\gisdata\planning\Cart\projects\Conflation\Workflow\conflation_sqlgis_geo.sde\Conflation.GEO."17 env.workspace = importGDB18 ### There are no tables in the conflated dataset products - handle similarly and separately19 skiplist = ['Stitch_Lines', 'RoadCenterlines', 'Overlaps_Gaps_MunicipalBoundary', 'Overlaps_Gaps_FIRE', 'Overlaps_Gaps_LAW', 'Overlaps_Gaps_PSAP', 'Overlaps_Gaps_ESZ', 'Overlaps_Gaps_EMS', 'Overlaps_Gaps_CountyBoundary', 'Overlaps_Gaps_AuthoritativeBoundary']20 tables = ListTables()21 for table in tables:22 print table23 target = LoadTarget+table24 print target25 26 datasets = ListDatasets("*")27 for fd in datasets:28 print fd29 featureClasses = ListFeatureClasses("*", "All", fd)30 for fc in featureClasses:31 print fc32 if fc in skiplist:33 print 'skipping'34 else:35 target = LoadTarget+fd+"/"+fc36 print "loading to "+target37 Append_management(fc, target, schema_type="NO_TEST")38 39def LoadAliasTables():40 from arcpy import Append_management, env, ListTables, ListWorkspaces, CalculateField_management41 importFolder = r"\\gisdata\planning\Cart\projects\Conflation\GIS_DATA\R1\Alias3"42 LoadTarget = r"Database Connections\Conflation2012_sde.sde\Conflation.SDE.RoadAlias"43 env.workspace = importFolder44 GDBList = []45 for gdb in ListWorkspaces("*", "FileGDB"):46 GDBList.append(gdb)47 48 for geodatabase in GDBList:49 env.workspace = geodatabase50 tables = ListTables("RoadAlias")51 for table in tables:52 print table53 CalculateField_management(table, "SEGID", expression="""[STEWARD]&" "& [SEGID]""", expression_type="VB", code_block="")54 Append_management(table, LoadTarget, schema_type="NO_TEST")55 print geodatabase56 57 58 59''' 60 61 ### There are no tables in the conflated dataset products62 tables = ListTables()63 for table in tables:64 print table65 target = LoadTarget+table66 print target67 68 datasets = ListDatasets("*")69 for fd in datasets:70 print fd71 featureClasses = ListFeatureClasses("*", "All", fd)72 for fc in featureClasses:73 print fc74 if fc in skiplist:75 print 'skipping'76 else:77 target = LoadTarget+fd+"/"+fc78 print "loading to "+target79 Append_management(fc, target, schema_type="NO_TEST")80'''...
script_DashPart_Detail.js
Source:script_DashPart_Detail.js
1let loadTarget = document.querySelector(".loading")23function show_loading(){4 loadTarget.style.display = "block";5 loadTarget.style.opacity = 1;6}7function hide_loading(){8 //loadTarget.style.display = "none";9 let loadEffect = setInterval(() => {10 if(!loadTarget.style.opacity) {11 loadTarget.style.opacity = 1;12 }13 if (loadTarget.style.opacity > 0){14 loadTarget.style.opacity -= 0.1;15 } else {16 clearInterval(loadEffect)17 loadTarget.style.display = "none"18 }19 },100)20}21function tampil_data(){22 show_loading()23 fetch("http://192.168.18.68:8055/items/order?fields=customer_id.customer_id,customer_id.customer_name,ticket_id.ticket_id,ticket_id.ticket_type&filter[customer_id]=2")24 .then((response) => response.json())25 .then((response) => {26 response.map(data => {27 //var textNode = document.createTextNode(response.)28 })29 })
...
Using AI Code Generation
1var wpt = require('webpagetest');2var test = wpt('WPT_API_KEY');3 if (err) {4 console.log(err);5 } else {6 console.log(data);7 test.loadTarget(data.data.testId, {location: 'ec2-us-west-1:Firefox'}, function(err, data) {8 if (err) {9 console.log(err);10 } else {11 console.log(data);12 }13 });14 }15});16var wpt = require('webpagetest');17var test = wpt('WPT_API_KEY');18 if (err) {19 console.log(err);20 } else {21 console.log(data);22 }23});24var wpt = require('webpagetest');25var test = wpt('WPT_API_KEY');26test.getTestStatus('131122_1K_1', function(err, data) {27 if (err) {28 console.log(err);29 } else {30 console.log(data);31 }32});33var wpt = require('webpagetest');34var test = wpt('WPT_API_KEY');35test.getTestResults('131122_1K_1', function(err, data) {36 if (err) {37 console.log(err);38 } else {39 console.log(data);40 }41});42var wpt = require('webpagetest');43var test = wpt('WPT_API_KEY');44test.getTestResults('131122_1K_1', {pagespeed: 1, requests: 1}, function(err, data) {45 if (err) {
Using AI Code Generation
1var wpt = require('webpagetest');2var util = require('util');3var fs = require('fs');4var wpt = new WebPageTest('www.webpagetest.org', 'A.0c4a7d9e8f9a7a2d1a2e2f4f8f2a2a4');5 if (err) {6 console.log('Error: ' + err);7 } else {8 console.log('Load Target Data: ' + util.inspect(data));9 }10});11wpt.runTest(data.target, {runs: 1}, function(err, data) {12 if (err) {13 console.log('Error: ' + err);14 } else {15 console.log('Test Data: ' + util.inspect(data));16 }17});18wpt.getTestResults(data.data.testId, function(err, data) {19 if (err) {20 console.log('Error: ' + err);21 } else {22 console.log('Results: ' + util.inspect(data));23 }24});25wpt.getTestResults(data.data.testId, function(err, data) {26 if (err) {27 console.log('Error: ' + err);28 } else {29 fs.writeFile("results.txt", data, function(err) {30 if (err) {31 console.log(err);32 } else {33 console.log("The file was saved!");34 }35 });36 }37});
Using AI Code Generation
1var wpt = require('wpt.js');2 if (err) {3 console.log('Error: ' + err);4 }5 else {6 console.log('Page loaded successfully');7 }8});9 if (err) {10 console.log('Error: ' + err);11 }12 else {13 console.log('Page loaded successfully');14 }15});16var wpt = require('wpt.js');17 if (err) {18 console.log('Error: ' + err);19 }20 else {21 console.log('Test started successfully');22 }23});24 if (err) {25 console.log('Error: ' + err);26 }27 else {28 console.log('Test started successfully');29 }30});
Using AI Code Generation
1var wpt = require('webpagetest');2var wpt = new WebPageTest('www.webpagetest.org');3}, function(err, data) {4 if (err) return console.error(err);5 console.log('Test status:', data.statusText);6 console.log('Test ID:', data.data.testId);7 console.log('Test URL:', data.data.summary);8 console.log('Test runs:', data.data.average.firstView.loadTime);9});10### new WebPageTest([host], [options])11### wpt.getLocations([callback])12### wpt.getTests([callback])13### wpt.getTestStatus(testId, [callback])14Get the status of a test. `callback` is called with `(err, status)`, where `status` is an object with the following properties:15### wpt.getTestResults(testId, [callback])16Get the results of a test. `callback` is called with `(err,
Using AI Code Generation
1var wpt = require('wpt');2var wpt = new WebPageTest('www.webpagetest.org', 'A.d3e3c60c8b8f3d3d0b3c3d3f3d3e3d3d');3 if (err) return console.error(err);4 console.log('Test started:', data.data.testId);5 wpt.getTestResults(data.data.testId, function(err, data) {6 if (err) return console.error(err);7 console.log('Test completed:', data.data.summary);8 });9});10var wpt = require('wpt');11var wpt = new WebPageTest('www.webpagetest.org', 'A.d3e3c60c8b8f3d3d0b3c3d3f3d3e3d3d');12 if (err) return console.error(err);13 console.log('Test started:', data.data.testId);14 wpt.getTestResults(data.data.testId, function(err, data) {15 if (err) return console.error(err);16 console.log('Test completed:', data.data.summary);17 });18});19var wpt = require('wpt');20var wpt = new WebPageTest('www.webpagetest.org', 'A.d3e3c60c8b8f3d3d0b3c3d3f3d3e3d3d');21 if (err) return console.error(err);22 console.log('Test started:', data.data.testId);23 wpt.getTestResults(data.data.testId, function(err, data) {24 if (err) return console.error(err);25 console.log('Test completed:', data.data.summary);26 });27});
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