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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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