How to use render_links method in Playwright Python

Best Python code snippet using playwright-python

drawing_object.py

Source:drawing_object.py Github

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...249 self._relative_vertical_position = relative_vertical_position250 else:251 self._relative_vertical_position = allowed_values[int(relative_vertical_position) if six.PY3 else long(relative_vertical_position)]252 @property253 def render_links(self):254 """Gets the render_links of this DrawingObject. # noqa: E501255 Gets or sets the list of links that originate from this DrawingObjectDto. # noqa: E501256 :return: The render_links of this DrawingObject. # noqa: E501257 :rtype: list[WordsApiLink]258 """259 return self._render_links260 @render_links.setter261 def render_links(self, render_links):262 """Sets the render_links of this DrawingObject.263 Gets or sets the list of links that originate from this DrawingObjectDto. # noqa: E501264 :param render_links: The render_links of this DrawingObject. # noqa: E501265 :type: list[WordsApiLink]266 """267 self._render_links = render_links268 @property269 def top(self):270 """Gets the top of this DrawingObject. # noqa: E501271 Gets or sets the distance in points from the origin to the top side of the image. # noqa: E501272 :return: The top of this DrawingObject. # noqa: E501273 :rtype: float274 """275 return self._top...

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Model.py

Source:Model.py Github

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1# train test split2import sys3import pandas as pd4from sklearn import svm5from plotly import graph_objs as go6from sklearn.ensemble import RandomForestClassifier7from sklearn.metrics import accuracy_score,precision_recall_fscore_support,multilabel_confusion_matrix,roc_curve, roc_auc_score8from sklearn.ensemble import AdaBoostClassifier9from sklearn.linear_model import LogisticRegression10from sklearn.model_selection import train_test_split11from sklearn.pipeline import Pipeline12from sklearn.preprocessing import Normalizer13from FinalBDA696.scripts import Correlation_Brute_Force_Plots as Corr_Brut_Plots, Resp_Pred_Plots as PredRespPlots14def main(input_df_filename,response):15 input_df = pd.read_csv(input_df_filename)16 input_df = input_df.dropna(axis=0, how="any")17 print(input_df.columns)18 #Upon Iteration, we found that HT_Streak and AT_Streak were nostradamus variables and were causing the model19 #performance to skew into unrealistic numbers. So we will drop them from the dataset.20 input_df = input_df.drop(['HT_Streak','AT_Streak'],axis=1)21 X = input_df.drop(['Home_team_wins'], axis=1)22 y = input_df["Home_team_wins"]23 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.15, random_state=1)24 name = []25 Accuracy_fin = []26 Precision_fin = []27 Recall_fin = []28 F_Score = []29 AUC = []30 AUC_Plot = []31 # Making a Pipeline for Normalizing the data and fitting RandomForestClassifier32 pipeline = Pipeline(33 [("Normalize", Normalizer()), ("rf", RandomForestClassifier(random_state=1))]34 )35 pipeline.fit(X_train, y_train)36 predict = pipeline.predict(X_test)37 print(predict)38 # Accuracy of the RandomForestClassifier39 accuracy = accuracy_score(y_test, predict)40 name.append("RF")41 Accuracy_fin.append(accuracy)42 prec,rec,fsc,_ = precision_recall_fscore_support(y_test,predict,average="macro")43 Precision_fin.append(prec)44 Recall_fin.append(rec)45 F_Score.append(fsc)46 print("Accuracy of RF:", accuracy)47 print("Precision Recall Fscore support of RF:", precision_recall_fscore_support(y_test,predict,average="macro"))48 confusion_matrix = multilabel_confusion_matrix(y_test,predict)49 print("RF CM",confusion_matrix)50 roc_auc_rf = roc_auc_score(y_test,predict)51 AUC.append(roc_auc_rf)52 print("RF ROC AUC Score",roc_auc_rf)53 fpr, tpr, _ = roc_curve(y_test, predict, drop_intermediate=False)54 fig = go.Figure(data=go.Scatter(x=fpr, y=tpr, name="Model"))55 fig = fig.add_trace(56 go.Scatter(57 x=[0.0, 1.0],58 y=[0.0, 1.0],59 line=dict(dash="dash"),60 mode="lines",61 showlegend=False,62 )63 )64 # Label the figure65 fig.update_layout(66 title=f"Receiver Operator Characteristic (AUC={round(roc_auc_rf, 6)})",67 xaxis_title="False Positive Rate (FPR)",68 yaxis_title="True Positive Rate (TPR)",69 )70 fig.write_html(71 file=f"../plots/ROC_FOR_RF_MODEL.html",72 include_plotlyjs="cdn",73 )74 AUC_Plot.append(75 "<a href ="76 + "./plots/ROC_FOR_RF_MODEL"77 + ".html"78 + ">"79 + "ROC Curve RF"80 + "</a>"81 )82 # Making a pipeline for Normalizing the data and fitting SVM Classifier83 pipeline = Pipeline(84 [("Normalize", Normalizer()), ("SVM", svm.SVC(kernel="rbf"))]85 )86 pipeline.fit(X_train, y_train)87 predict = pipeline.predict(X_test)88 # Accuracy score for the SVMClassifier89 accuracy = accuracy_score(y_test, predict)90 name.append("SVM")91 Accuracy_fin.append(accuracy)92 prec,rec,fsc,_ = precision_recall_fscore_support(y_test,predict,average="macro")93 Precision_fin.append(prec)94 Recall_fin.append(rec)95 F_Score.append(fsc)96 print("Accuracy of SVM with linear kernel:", accuracy)97 print("Accuracy of SVM:", accuracy)98 print("Precision Recall Fscore support of SVM:", precision_recall_fscore_support(y_test,predict,average="macro"))99 confusion_matrix = multilabel_confusion_matrix(y_test,predict)100 print("SVM CM",confusion_matrix)101 roc_auc_svm = roc_auc_score(y_test,predict)102 AUC.append(roc_auc_svm)103 print("SVM ROC AUC Score",roc_auc_svm)104 fpr1, tpr1, _ = roc_curve(y_test, predict, drop_intermediate=False)105 fig = go.Figure(data=go.Scatter(x=fpr1, y=tpr1, name="Model"))106 fig = fig.add_trace(107 go.Scatter(108 x=[0.0, 1.0],109 y=[0.0, 1.0],110 line=dict(dash="dash"),111 mode="lines",112 showlegend=False,113 )114 )115 # Label the figure116 fig.update_layout(117 title=f"Receiver Operator Characteristic (AUC={round(roc_auc_svm, 6)})",118 xaxis_title="False Positive Rate (FPR)",119 yaxis_title="True Positive Rate (TPR)",120 )121 fig.write_html(122 file=f"../plots/ROC_FOR_SVM_MODEL.html",123 include_plotlyjs="cdn",124 )125 AUC_Plot.append(126 "<a href ="127 + "./plots/ROC_FOR_SVM_MODEL"128 + ".html"129 + ">"130 + "ROC Curve SVM"131 + "</a>"132 )133 # Making a Pipeline for Normalizing the data and fitting AdaBoostClassifier134 pipeline = Pipeline(135 [("Normalize", Normalizer()), ("AdaBoost", AdaBoostClassifier(random_state=1,n_estimators=100))]136 )137 pipeline.fit(X_train, y_train)138 predict = pipeline.predict(X_test)139 print(predict)140 # Accuracy of the AdaBoostClassifier141 accuracy = accuracy_score(y_test, predict)142 name.append("ADABoost")143 Accuracy_fin.append(accuracy)144 prec,rec,fsc,_ = precision_recall_fscore_support(y_test,predict,average="macro")145 Precision_fin.append(prec)146 Recall_fin.append(rec)147 F_Score.append(fsc)148 print("Accuracy of adaboost:", accuracy)149 print("Precision Recall Fscore support of AdaBoost:", precision_recall_fscore_support(y_test,predict,average="macro"))150 confusion_matrix = multilabel_confusion_matrix(y_test,predict)151 print("AdaBoost CM",confusion_matrix)152 roc_auc_AB = roc_auc_score(y_test,predict)153 AUC.append(roc_auc_AB)154 print("AdaBoost ROC AUC Score",roc_auc_AB)155 fpr, tpr, _ = roc_curve(y_test, predict, drop_intermediate=False)156 fig = go.Figure(data=go.Scatter(x=fpr, y=tpr, name="Model"))157 fig = fig.add_trace(158 go.Scatter(159 x=[0.0, 1.0],160 y=[0.0, 1.0],161 line=dict(dash="dash"),162 mode="lines",163 showlegend=False,164 )165 )166 # Label the figure167 fig.update_layout(168 title=f"Receiver Operator Characteristic (AUC={round(roc_auc_AB, 6)})",169 xaxis_title="False Positive Rate (FPR)",170 yaxis_title="True Positive Rate (TPR)",171 )172 fig.write_html(173 file=f"../plots/ROC_FOR_AB_MODEL.html",174 include_plotlyjs="cdn",175 )176 AUC_Plot.append(177 "<a href ="178 + "./plots/ROC_FOR_AB_MODEL"179 + ".html"180 + ">"181 + "ROC Curve AB"182 + "</a>"183 )184 # Making a Pipeline for Normalizing the data and fitting logistic regression model185 pipeline = Pipeline(186 [("Normalize", Normalizer()), ("logit", LogisticRegression(random_state=1))]187 )188 pipeline.fit(X_train, y_train)189 predict = pipeline.predict(X_test)190 print(predict)191 # Accuracy of the log192 accuracy = accuracy_score(y_test, predict)193 name.append("LogisticRegression")194 Accuracy_fin.append(accuracy)195 prec,rec,fsc,_ = precision_recall_fscore_support(y_test,predict,average="macro")196 Precision_fin.append(prec)197 Recall_fin.append(rec)198 F_Score.append(fsc)199 print("Accuracy of log:", accuracy)200 print("Precision Recall Fscore support of log:", precision_recall_fscore_support(y_test,predict,average="macro"))201 confusion_matrix = multilabel_confusion_matrix(y_test,predict)202 print("log CM",confusion_matrix)203 roc_auc_log = roc_auc_score(y_test,predict)204 AUC.append(roc_auc_log)205 print("log ROC AUC Score",roc_auc_log)206 fpr, tpr, _ = roc_curve(y_test, predict, drop_intermediate=False)207 fig = go.Figure(data=go.Scatter(x=fpr, y=tpr, name="Model"))208 fig = fig.add_trace(209 go.Scatter(210 x=[0.0, 1.0],211 y=[0.0, 1.0],212 line=dict(dash="dash"),213 mode="lines",214 showlegend=False,215 )216 )217 # Label the figure218 fig.update_layout(219 title=f"Receiver Operator Characteristic (AUC={round(roc_auc_log, 6)})",220 xaxis_title="False Positive Rate (FPR)",221 yaxis_title="True Positive Rate (TPR)",222 )223 fig.write_html(224 file=f"../plots/ROC_FOR_log_MODEL.html",225 include_plotlyjs="cdn",226 )227 AUC_Plot.append(228 "<a href ="229 + "./plots/ROC_FOR_log_MODEL"230 + ".html"231 + ">"232 + "ROC Curve LOG"233 + "</a>"234 )235 #Putting it All Together236 PRP = PredRespPlots.main(input_df_filename, response)237 Corr1,Corr2,Corr3,Brut1,Brut2,Brut3 = Corr_Brut_Plots.main(input_df_filename,response)238 PRP.to_html("Predictor_vs_Response_Plots.html", render_links=True, escape=False)239 path = open("./Correlation_tables.html", "w")240 path.write(241 Corr1.to_html(render_links=True, escape=False)242 + "<br>"243 + Corr2.to_html(render_links=True, escape=False)244 + "<br>"245 + Corr3.to_html(render_links=True, escape=False)246 )247 # finally we will make the brute force html248 path = open("./Brute_Force_tables.html", "w")249 path.write(250 Brut1.to_html(render_links=True, escape=False)251 + "<br>"252 + Brut2.to_html(render_links=True, escape=False)253 + "<br>"254 + Brut3.to_html(render_links=True, escape=False)255 )256 ModelResult_df = pd.DataFrame(257 columns=[258 "Name",259 "Accuracy",260 "Precision",261 "Recall",262 "F_score",263 "AUC",264 "AUC_Curve"265 ]266 )267 ModelResult_df["Name"] = name268 ModelResult_df["Accuracy"] = Accuracy_fin269 ModelResult_df["Precision"] = Precision_fin270 ModelResult_df["Recall"] = Recall_fin271 ModelResult_df["F_score"] = F_Score272 ModelResult_df["AUC"] = AUC273 ModelResult_df["AUC_Curve"] = AUC_Plot274 print(ModelResult_df.head())275 ModelResult_df.to_html("Model_Performance.html",render_links=True,escape=False)276if __name__ == "__main__":277 input_df_filename = "OutputTable.csv" # sys.argv[1]278 response = "Home_team_wins" # sys.argv[2]...

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vis_planner_3d.py

Source:vis_planner_3d.py Github

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1import tinyik2import numpy as np3import open3d as o3d4import sys5sys.path.insert(0, "/Users/himty/Desktop/Serious_Stuff/UC_Berkeley/3_Junior/EECS 106A/eecs106a-final-project/src/path_planning/moveit_planner")6from next_pt_planner import NextPointPlanner7# Open3d likes to be unblocked :/8# This script puts keyboard input on another thread9import threading10import argparse11# From https://stackoverflow.com/a/57387909/590134612class KeyboardThread(threading.Thread):13 def __init__(self, input_cbk, name='keyboard-input-thread'):14 self.input_cbk = input_cbk15 super(KeyboardThread, self).__init__(name=name)16 self.start()17 def run(self):18 while True:19 self.input_cbk(input()) #waits to get input + Return20class VisPlanner3D():21 def __init__(self, render_links):22 self.render_links = render_links23 # stages are get_ee_pos, get_obj_pos24 self.stage = 'get_ee_pos'25 self.stage2query = {26 'get_ee_pos': 'Enter end effector position as "x y z": ',27 'get_obj_pos': 'Enter object position as "x y z": ',28 }29 self.vis = o3d.visualization.Visualizer()30 self.vis.create_window(window_name='tinyik vizualizer', width=640, height=480)31 self.obj_pos = []32 self.sphere_r = .12 # radius of circles33 self.ee_color = {'name': 'white', 'code': [.2, .2, .2]}34 self.obj_color = {'name': 'red', 'code': [.8, .2, .2]}35 self.near_color = {'name': 'green', 'code': [.1, .8, .1]}36 self.far_color = {'name': 'blue', 'code': [.1, .1, .8]}37 self.planner = NextPointPlanner(2, [])38 self.base_height = 7.539 self.l1 = 1040 self.l2 = 1241 # 'z' and 'x' are rotation axes.42 # Each array is the length of the joint between rotation axes43 # I don't think this library supports prismatic joints? 44 self.arm = tinyik.Actuator([45 'z',46 [0, 0, self.base_height],47 'x',48 [0, self.l1, 0],49 'x',50 [0, self.l2, 0],51 ])52 # Draw some initial things53 axes = o3d.geometry.TriangleMesh.create_coordinate_frame(54 size=0.6, origin=[0, 0, 0])55 self.vis.add_geometry(axes)56 self.render_arm()57 print('Legend:')58 print('- white ball is original end effector position')59 print('- {} ball is object'.format(self.obj_color['name']))60 print('- {} ball is closer to object (next step)'.format(self.near_color['name']))61 print('- {} ball is farther from object (next step)'.format(self.far_color['name']))62 print('Some balls may overlap')63 print()64 print(self.stage2query[self.stage])65 def input_cbk(self, inp):66 if self.stage == 'get_ee_pos':67 if len(inp.strip().split(' ')) != 3:68 # Tell user to try again69 return70 else:71 ee_pos = [float(coord) for coord in inp.split(' ')]72 self.arm.ee = ee_pos73 self.vis.clear_geometries()74 axes = o3d.geometry.TriangleMesh.create_coordinate_frame(75 size=0.6, origin=[0, 0, 0])76 self.vis.add_geometry(axes)77 self.render_arm()78 self.stage = 'get_obj_pos'79 elif self.stage == 'get_obj_pos':80 if len(inp.strip().split(' ')) != 3:81 # Tell user to try again82 return83 else:84 self.obj_pos = [float(coord) for coord in inp.split(' ')]85 obj = tinyik.visualizer.create_sphere(self.obj_pos, r=self.sphere_r, color=self.obj_color['code'])86 self.vis.add_geometry(obj)87 near_pos = self.planner.get_near_point(self.arm.ee, self.obj_pos)88 self.arm.ee = near_pos89 self.render_arm()90 obj = tinyik.visualizer.create_sphere(near_pos, r=self.sphere_r, color=self.near_color['code'])91 self.vis.add_geometry(obj)92 far_pos = self.planner.get_far_point(self.arm.ee, self.obj_pos)93 self.arm.ee = far_pos94 self.render_arm()95 obj = tinyik.visualizer.create_sphere(far_pos, r=self.sphere_r, color=self.far_color['code'])96 self.vis.add_geometry(obj)97 print('near pos', near_pos, 'far pos', far_pos)98 print('Displayed.')99 print()100 self.stage = 'get_ee_pos'101 else:102 raise ValueError('Unknown stage {}'.format(self.stage))103 print(self.stage2query[self.stage])104 def render_arm(self):105 if self.render_links:106 geos = self.get_joint_geometries()107 for geo in geos:108 self.vis.add_geometry(geo)109 else:110 ee_pos = self.arm.ee111 ee = tinyik.visualizer.create_sphere(ee_pos, r=self.sphere_r, color=self.ee_color['code'])112 self.vis.add_geometry(ee)113 def get_joint_geometries(self):114 """115 Copy pasted tinyik.visualize() function but removed116 the blocking function at the end. It also now returns the geometries generated117 """118 119 # Setting some values so the code runs in this class120 actuator = self.arm121 target = None122 root = None123 p = None124 joints = []125 for c in actuator.components:126 if hasattr(c, 'axis'):127 gc = tinyik.visualizer.Joint(c)128 joints.append(gc)129 else:130 gc = tinyik.visualizer.Link(c)131 if root is None:132 root = gc133 p = gc134 else:135 p.child = gc136 p = gc137 for j, a in zip(joints, actuator.angles):138 j.angle = a139 if target:140 geos = root.geo(link_color=[.5, .5, .5])141 actuator.ee = target142 for j, a in zip(joints, actuator.angles):143 j.angle = a144 geos += root.geo()145 geos += [tinyik.visualizer.create_sphere(target, r=self.sphere_r, color=[.8, .2, .2])]146 else:147 geos = root.geo()148 return geos149def visualize_3d(render_links):150 # Rendering updates are asynchronous in this thread151 vis_3d = VisPlanner3D(render_links)152 kthread = KeyboardThread(vis_3d.input_cbk)153 while True:154 vis_3d.vis.poll_events()155 vis_3d.vis.update_renderer()156if __name__ == "__main__":157 parser = argparse.ArgumentParser()158 parser.add_argument('--no_links', action='store_true') # default is false159 args = parser.parse_args()...

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report.py

Source:report.py Github

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1import pandas as pd2from jinja2 import Environment, FileSystemLoader3import seaborn as sns4import matplotlib.pyplot as plt5from matplotlib import rcParams6from matplotlib import colors7from matplotlib import cm8import numpy as np9import os10def generate_report(target):11 rcParams.update({'figure.autolayout': True})12 # loading template13 env = Environment(loader=FileSystemLoader('.'))14 template = env.get_template("templates/template.html")15 cur_path = os.path.abspath(os.getcwd())16 # reading data17 domains_df = pd.read_csv('reports/{}/data/{}_domains.csv'.format(target,target), index_col = 0)18 domains_df = domains_df.fillna('')19 domains_df.style.set_table_attributes('class="table-hover"')20 domains_df['domain'] = domains_df['domain'].apply(lambda x: '<a href="{}">{}</a>'.format(x,x))21 vulns_df = pd.read_csv('reports/{}/data/{}_vulns.csv'.format(target, target), index_col = 0)22 vulns_df = vulns_df.fillna('')23 vulns_df = vulns_df.sort_values(by = ['severity'], ascending=False)24 cwe_df = pd.read_csv('reports/{}/data/{}_cwes.csv'.format(target,target), index_col = 0)25 cwe_df = cwe_df.fillna('')26 impact_df = pd.read_csv('reports/{}/data/{}_impact.csv'.format(target,target), index_col = 0)27 impact_df = impact_df.fillna('')28 tech_df = pd.read_csv('reports/{}/data/{}_technologies.csv'.format(target,target), index_col = 0)29 tech_df = tech_df.fillna('').drop_duplicates().sort_values(by = ['type'])30 techs = []31 nb_techs = []32 for t in domains_df['technologies']:33 techs = t.split(',')34 for tech in techs:35 if tech not in techs:36 techs.append(tech)37 if tech not in nb_techs:38 nb_techs.append(tech)39 # generate plots40 vuln_chart = vulns_df.groupby('domain')['vulnerability'].nunique()41 # print(vuln_chart)42 if len(vuln_chart) > 0:43 chart = vuln_chart.plot.barh(title = 'Vulnerabilities found per domain')44 chart.set_xlabel('Vulnerabilities found')45 chart.set_ylabel('')46 vuln_chart_path = '{}/reports/{}/plots/{}vulnerability_domains.png'.format(cur_path,target, target)47 plt.savefig(vuln_chart_path)48 else:49 vuln_chart_path = 'Not found'50 bins = pd.cut(vulns_df['severity'], list(range(0,11)))51 severity_df = vulns_df.groupby(bins)['severity'].agg(['count'])52 if len(severity_df)>0:53 sev_chart = severity_df.plot.bar(title = 'Vulnerabilities by severity')54 # sev_chart = sns.barplot(x = severity_df.index, y = severity_df.values, orient = "h")55 sev_chart.set_ylabel('Vulnerabilities found')56 sev_chart.set_xlabel('Common Vulnerability Severity Score (CVSS)')57 labels = []58 for l in range(0,11):59 labels.append('{}-{}'.format(l, l+1))60 sev_chart.set_xticklabels(labels, rotation=0)61 # chart = sns.barplot( x = vulns_df.domain.unique(), y = vuln_chart.values, orient = "h")62 sev_chart_path = '{}/reports/{}/plots/{}_vulnerability_severity.png'.format(cur_path,target, target)63 plt.savefig(sev_chart_path)64 else:65 sev_chart_path = 'Not found'66 #generating live links67 vulns_df['domain'] = vulns_df['domain'].apply(lambda x: '<a href="{}">{}</a>'.format(x,x))68 vulns_df['vulnerability'] = vulns_df['vulnerability'].apply(lambda x: '<a href="https://www.cvedetails.com/cve/{}">{}</a>'.format(x,x))69 cwe_df['cve'] = cwe_df['cve'].apply(lambda x: '<a href="https://www.cvedetails.com/cve/{}">{}</a>'.format(x,x))70 cwe_df['cwe_id'] = cwe_df['cwe_id'].apply(lambda x: '<a href="https://cwe.mitre.org/data/definitions/{}.html">{}</a>'.format(x.split('-')[1],x))71 cwe_df['domain'] = cwe_df['domain'].apply(lambda x: '<a href="{}">{}</a>'.format(x,x))72 cwe_df = cwe_df.drop_duplicates()73 mit_df = pd.read_csv('reports/{}/data/{}_mitigation.csv'.format(target,target), index_col = 0)74 mitigations = list()75 for index, row in mit_df.iterrows():76 mitigations.append(row)77 certs_df = pd.read_csv('reports/{}/data/{}_certs.csv'.format(target,target), index_col = 0)78 certs_df = certs_df.fillna('')79 exp_df = pd.read_csv('reports/{}/data/{}_expired_certs.csv'.format(target,target), index_col = 0)80 exp_df = exp_df.fillna('')81 # print(cwe_df)82 try:83 most_common_vulnerability = vulns_df.vulnerability_types.mode().values[0]84 most_common_cwe = cwe_df.cwe_name.mode().values[0]85 most_common_type = tech_df.type.mode().values[0],86 most_common_tech = tech_df.technology.mode().values[0]87 except:88 most_common_vulnerability = ''89 most_common_cwe = ''90 most_common_type = ''91 most_common_tech = ''92 template_vars = {93 "target" : target,94 "nb_domains" : len(domains_df),95 "nb_tech" : len(nb_techs),96 "domains_df": domains_df.to_html(index=False, classes='table-hover', render_links=True, escape=False),97 "unique_cves" : len(vulns_df['vulnerability'].unique()),98 "unique_domains" : len(vulns_df['domain'].unique()),99 "mean_severity" : round(vulns_df['severity'].mean(),1),100 "nb_critical" : len(vulns_df.loc[vulns_df['severity'] >= 8]),101 "most_common_vulnerability": most_common_vulnerability ,102 "vulns_df" : vulns_df.to_html(index=False, classes='table-hover', render_links=True, escape=False),103 "vuln_chart_path" : vuln_chart_path,104 "sev_chart_path" : sev_chart_path,105 "cwe_df" : cwe_df.to_html(index=False, render_links=True, escape=False),106 "unique_cwes" : len(cwe_df['cwe_id'].unique()),107 "most_common_weakness" : most_common_cwe,108 "impact-df" : impact_df.to_html(index=False, render_links=True, escape=False),109 'nb_mit' : len(mitigations),110 'mitigations' : mitigations,111 'unique_tech' : len(tech_df['technology'].unique()),112 'most_common_type' : most_common_type,113 'most_common_tech': most_common_tech,114 'tech_df' : tech_df.to_html(index=False, render_links=True, escape=False),115 'cert_nb' : len(certs_df),116 'exp_nb' : len(exp_df),117 'exp_df' : exp_df.to_html(index=False, render_links=True, escape=False)118 }119 html_out = template.render(template_vars)120 Html_file= open("reports/{}/{}.html".format(target, target),"w")121 Html_file.write(html_out)122 Html_file.close()...

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Playwright tutorial

LambdaTest’s Playwright tutorial will give you a broader idea about the Playwright automation framework, its unique features, and use cases with examples to exceed your understanding of Playwright testing. This tutorial will give A to Z guidance, from installing the Playwright framework to some best practices and advanced concepts.

Chapters:

  1. What is Playwright : Playwright is comparatively new but has gained good popularity. Get to know some history of the Playwright with some interesting facts connected with it.
  2. How To Install Playwright : Learn in detail about what basic configuration and dependencies are required for installing Playwright and run a test. Get a step-by-step direction for installing the Playwright automation framework.
  3. Playwright Futuristic Features: Launched in 2020, Playwright gained huge popularity quickly because of some obliging features such as Playwright Test Generator and Inspector, Playwright Reporter, Playwright auto-waiting mechanism and etc. Read up on those features to master Playwright testing.
  4. What is Component Testing: Component testing in Playwright is a unique feature that allows a tester to test a single component of a web application without integrating them with other elements. Learn how to perform Component testing on the Playwright automation framework.
  5. Inputs And Buttons In Playwright: Every website has Input boxes and buttons; learn about testing inputs and buttons with different scenarios and examples.
  6. Functions and Selectors in Playwright: Learn how to launch the Chromium browser with Playwright. Also, gain a better understanding of some important functions like “BrowserContext,” which allows you to run multiple browser sessions, and “newPage” which interacts with a page.
  7. Handling Alerts and Dropdowns in Playwright : Playwright interact with different types of alerts and pop-ups, such as simple, confirmation, and prompt, and different types of dropdowns, such as single selector and multi-selector get your hands-on with handling alerts and dropdown in Playright testing.
  8. Playwright vs Puppeteer: Get to know about the difference between two testing frameworks and how they are different than one another, which browsers they support, and what features they provide.
  9. Run Playwright Tests on LambdaTest: Playwright testing with LambdaTest leverages test performance to the utmost. You can run multiple Playwright tests in Parallel with the LammbdaTest test cloud. Get a step-by-step guide to run your Playwright test on the LambdaTest platform.
  10. Playwright Python Tutorial: Playwright automation framework support all major languages such as Python, JavaScript, TypeScript, .NET and etc. However, there are various advantages to Python end-to-end testing with Playwright because of its versatile utility. Get the hang of Playwright python testing with this chapter.
  11. Playwright End To End Testing Tutorial: Get your hands on with Playwright end-to-end testing and learn to use some exciting features such as TraceViewer, Debugging, Networking, Component testing, Visual testing, and many more.
  12. Playwright Video Tutorial: Watch the video tutorials on Playwright testing from experts and get a consecutive in-depth explanation of Playwright automation testing.

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