How to use render_links method in Playwright Python

Best Python code snippet using playwright-python

drawing_object.py

Source:drawing_object.py

...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...

Model.py

Source:Model.py

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]...

vis_planner_3d.py

Source:vis_planner_3d.py

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()...

report.py

Source:report.py

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()...

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:

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.
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.
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.
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.
Inputs And Buttons In Playwright: Every website has Input boxes and buttons; learn about testing inputs and buttons with different scenarios and examples.
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.
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.
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.
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.
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.
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.
Playwright Video Tutorial: Watch the video tutorials on Playwright testing from experts and get a consecutive in-depth explanation of Playwright automation testing.