How to use iam method in localstack

Best Python code snippet using localstack_python

test_iam.py

Source:test_iam.py

...611        json.loads(self.object_iam_string), IAM_OBJECT_READ_ROLE,612        self.public_object_read_binding)613    self.new_object_iam_path = self.CreateTempFile(614        contents=json.dumps(self.new_object_iam_policy))615  def test_seek_ahead_iam(self):616    """Ensures that the seek-ahead iterator is being used with iam commands."""617    gsutil_object = self.CreateObject(618        bucket_uri=self.bucket, contents='foobar')619    # This forces the seek-ahead iterator to be utilized.620    with SetBotoConfigForTest([('GSUtil', 'task_estimation_threshold', '1'),621                               ('GSUtil', 'task_estimation_force', 'True')]):622      stderr = self.RunGsUtil(623          ['-m', 'iam', 'set', self.new_object_iam_path, gsutil_object.uri],624          return_stderr=True)625      self.assertIn('Estimated work for this command: objects: 1\n', stderr)626  def test_set_invalid_iam_bucket(self):627    """Ensures invalid content returns error on input check."""628    inpath = self.CreateTempFile(contents='badIam')629    stderr = self.RunGsUtil(['iam', 'set', inpath, self.bucket.uri],...

writerid_estimator_training.py

Source:writerid_estimator_training.py

1# -*- coding: utf-8 -*-2"""3Writer identification based of shape context codebook features and 4a shared kernel GP classifier. This file covers the training and evaluation5of the estimators.6@author: Fredrik Wahlberg <fredrik.wahlberg@it.uu.se>7"""8from __future__ import print_function, division9import numpy as np10import os.path11from model import SharedKernelClassifier12import time13cache_path = os.path.expanduser("~/tmp/gp_multiclass")14def cache_filename_features(N, p, q):15    return os.path.join(cache_path, 16                        "features_N%i_p%i_q%i.npz" % (N, p, q))17def cache_filename_estimator(N, p, q):18    return os.path.join(cache_path, 19                        "estimator_N%i_p%i_q%i.npz" % (N, p, q))20def cache_filename_result_npz(N, p, q):21    return os.path.join(cache_path, 22                        "result_N%i_p%i_q%i.npz" % (N, p, q))23def cache_filename_result_csv(N, p, q):24    return os.path.join(cache_path, 25                        "result_N%i_p%i_q%i.csv" % (N, p, q))26parameter_distribution = {27        'p': list(range(2, 6+1)),28        'q': list(range(4, 9+1)),29        'N': list(range(7, 21+1, 2))}30from sklearn.model_selection import ParameterGrid31valid_parameters = [p for p in ParameterGrid(parameter_distribution) if p['N']>p['p']*2]32print("%i parameter configurations" % len(valid_parameters))33feature_files = [cache_filename_features(N=pa['N'], p=pa['p'], q=pa['q']) 34                 for pa in valid_parameters 35                 if os.path.exists(cache_filename_features(N=pa['N'], p=pa['p'], q=pa['q']))]36import random37random.shuffle(valid_parameters)38#%%39csv_categories = ['Dataset', 'Training authors', 'Training documents', 40                  'Testing authors', 'Testing documents', 'Kernel', 'ARD', 41                  'Codebook size', 'N', 'p', 'q', 'Hard 5-top', 'Hard 3-top', 42                  '1-top', 'Soft 3-top', 'Soft 5-top', 'Likelihood']43with open(os.path.join(cache_path, "00categories.csv"), 'w') as f:44    for i, cat in enumerate(csv_categories):45        f.write('"' + cat + '"')46        if i==len(csv_categories)-1:47            f.write("\n")48        else:49            f.write(";")50for feature_file in feature_files:51    # Read feature data file52    cache_file_data = np.load(feature_file)53    print(cache_file_data.keys())54    data = dict()55    for k in cache_file_data.keys():56        if k in ['cluster_centers', 'codebooks', 'N', 'p', 'q', 'rotationalinvariant', 'authors']:57            data[k] = cache_file_data[k].tolist()58        else:59            data[k] = cache_file_data[k]60    feature_keys = [k for k in data.keys() if k[:8]=='features']61    # Set up output and check for old results62    cvs_filename = cache_filename_result_csv(N=data['N'], p=data['p'], q=data['q'])63    if not os.path.exists(cvs_filename):64        csv_file = open(cvs_filename, 'w')65        # CVL authors are id>100066        authors = data['authors']67        cvl_mask = [a>1000 for a in authors]68        cvl_train_mask = [a>1000 and a<=1050 for a in authors]69        cvl_test_mask = [a>1000 and a>1050 for a in authors]70        cvl_y_train = np.vstack([a for a,b in zip(authors, cvl_train_mask) if b])71        assert np.sum(cvl_train_mask)==len(cvl_y_train)72        cvl_y_test = np.vstack([a for a,b in zip(authors, cvl_test_mask) if b])73        assert np.sum(cvl_test_mask)==len(cvl_y_test)74        75        iam_mask = [a<1000 for a in authors]76        iam_train_mask = [a<1000 and a<=26 for a in authors]77        iam_test_mask = [a<1000 and a>26 for a in authors]78        iam_y_train = np.vstack([a for a,b in zip(authors, iam_train_mask) if b])79        assert np.sum(iam_train_mask)==len(iam_y_train)80        iam_y_test = np.vstack([a for a,b in zip(authors, iam_test_mask) if b])81        assert np.sum(iam_test_mask)==len(iam_y_test)82        cvl_iam_train_mask = [a or b for a, b in zip(cvl_train_mask, iam_train_mask)]83        cvl_iam_test_mask = [a or b for a, b in zip(cvl_test_mask, iam_test_mask)]84        assert np.sum(cvl_iam_train_mask) == np.sum(cvl_train_mask) + np.sum(iam_train_mask)85        assert np.sum(cvl_iam_test_mask) == np.sum(cvl_test_mask) + np.sum(iam_test_mask)86        cvl_iam_y_train = np.vstack([a for a,b in zip(authors, cvl_iam_train_mask) if b])87        assert np.sum(cvl_iam_train_mask)==len(cvl_iam_y_train)88        cvl_iam_y_test = np.vstack([a for a,b in zip(authors, cvl_iam_test_mask) if b])89        assert np.sum(cvl_iam_test_mask)==len(cvl_iam_y_test)90                91        # Model standard parameters92#        kernel='rbf'93        ard=False94        n_iter = 1595        n_restarts = 096        97#        for feature_key in feature_keys[:1]:98        for feature_key in feature_keys:99    100            # Separate feature data sets101            cvl_X_train = data[feature_key][cvl_train_mask, :]102            cvl_X_test = data[feature_key][cvl_test_mask, :]103            assert cvl_X_train.shape[0] == np.sum(cvl_train_mask)104            assert cvl_X_test.shape[0] == np.sum(cvl_test_mask)105            iam_X_train = data[feature_key][iam_train_mask, :]106            iam_X_test = data[feature_key][iam_test_mask, :]107            assert iam_X_train.shape[0] == np.sum(iam_train_mask)108            assert iam_X_test.shape[0] == np.sum(iam_test_mask)109            cvl_iam_X_train = data[feature_key][cvl_iam_train_mask, :]110            cvl_iam_X_test = data[feature_key][cvl_iam_test_mask, :]111            assert cvl_iam_X_train.shape[0] == np.sum(cvl_iam_train_mask)112            assert cvl_iam_X_test.shape[0] == np.sum(cvl_iam_test_mask)113            #%% CVL Estimator114            for kernel in ['matern52', 'rbf']:115#                for ard in [True, False]:116                print("Kernel %s " % kernel, end="")117                if ard:118                    print("with ard")119                else:120                    print("without ard")121                cvl_estimator = SharedKernelClassifier(kernel=kernel, ard=ard, n_iter=n_iter, 122                                                   n_restarts=n_restarts, verbose=True)123                print("Training CVL estimator...", end="")124                t0 = time.time()125                cvl_estimator.fit(cvl_X_train, cvl_y_train)126                print("done (%.1fs)" % (time.time()-t0))127                128                print("CVL 1-top training set: %.1f%%" % (cvl_estimator.score_covar_ntop(cvl_X_train, cvl_y_train)[4]*100))129                ntop = cvl_estimator.score_covar_ntop(cvl_X_test, cvl_y_test)130                print("CVL 1-top test set: %.1f%%" % (ntop[4]*100))131                print("Likelihood %.2f" % cvl_estimator.log_likelihood_)132                ntop2 = cvl_estimator.score_covar_ntop(iam_X_test, iam_y_test)133                print("CVL on IAM 1-top test set: %.1f%%" % (ntop2[4]*100))134        135                dataset = 'CVL'136                d = (dataset, len(np.unique(cvl_y_train)), len(cvl_y_train), 137                     len(np.unique(cvl_y_test)), len(cvl_y_test), kernel, ard, 138                     cvl_X_train.shape[1], data['N'], data['p'], data['q'], ntop[0]*100, 139                     ntop[2]*100, ntop[4]*100, ntop[6]*100, ntop[8]*100, cvl_estimator.log_likelihood_)140                csv_file.write("\"%s\";%i;%i;%i;%i;\"%s\";\"%s\";%i;%i;%i;%i;%f;%f;%f;%f;%f;%f\n" % d)141    142                dataset = 'CVL on IAM'143                d = (dataset, len(np.unique(cvl_y_train)), len(cvl_y_train), 144                     len(np.unique(iam_y_test)), len(iam_y_test), kernel, ard, 145                     cvl_X_train.shape[1], data['N'], data['p'], data['q'], ntop2[0]*100, 146                     ntop2[2]*100, ntop2[4]*100, ntop2[6]*100, ntop2[8]*100, cvl_estimator.log_likelihood_)147                csv_file.write("\"%s\";%i;%i;%i;%i;\"%s\";\"%s\";%i;%i;%i;%i;%f;%f;%f;%f;%f;%f\n" % d)148            #%% IAM Estimator149            iam_estimator = SharedKernelClassifier(kernel=kernel, ard=ard, n_iter=n_iter, 150                                               n_restarts=n_restarts, verbose=True)151            print("Training IAM estimator...", end="")152            t0 = time.time()153            iam_estimator.fit(iam_X_train, iam_y_train)154            print("done (%.1fs)" % (time.time()-t0))155            156            print("IAM 1-top training set: %.1f%%" % (iam_estimator.score_covar_ntop(iam_X_train, iam_y_train)[4]*100))157            ntop = iam_estimator.score_covar_ntop(iam_X_test, iam_y_test)158            print("IAM 1-top test set: %.1f%%" % (ntop[4]*100))159            print("Likelihood %.2f" % iam_estimator.log_likelihood_)160            ntop2 = iam_estimator.score_covar_ntop(cvl_X_test, cvl_y_test)161            print("IAM on CVL 1-top test set: %.1f%%" % (ntop2[4]*100))162    163            dataset = 'IAM'164            d = (dataset, len(np.unique(iam_y_train)), len(iam_y_train), 165                 len(np.unique(iam_y_test)), len(iam_y_test), kernel, ard, 166                 iam_X_train.shape[1], data['N'], data['p'], data['q'], ntop[0]*100, 167                 ntop[2]*100, ntop[4]*100, ntop[6]*100, ntop[8]*100, iam_estimator.log_likelihood_)168            csv_file.write("\"%s\";%i;%i;%i;%i;\"%s\";\"%s\";%i;%i;%i;%i;%f;%f;%f;%f;%f;%f\n" % d)169            dataset = 'IAM on CVL'170            d = (dataset, len(np.unique(iam_y_train)), len(iam_y_train), 171                 len(np.unique(cvl_y_test)), len(cvl_y_test), kernel, ard, 172                 iam_X_train.shape[1], data['N'], data['p'], data['q'], ntop2[0]*100, 173                 ntop2[2]*100, ntop2[4]*100, ntop2[6]*100, ntop2[8]*100, iam_estimator.log_likelihood_)174            csv_file.write("\"%s\";%i;%i;%i;%i;\"%s\";\"%s\";%i;%i;%i;%i;%f;%f;%f;%f;%f;%f\n" % d)175    #        b = estimator.score_unseen_proba_metric(cvl_X_test, cvl_y_test)176            #%% CVL+IAM Estimator177            cvl_iam_estimator = SharedKernelClassifier(kernel=kernel, ard=ard, n_iter=n_iter, 178                                               n_restarts=n_restarts, verbose=True)179            print("Training CVL+IAM estimator...", end="")180            t0 = time.time()181            cvl_iam_estimator.fit(cvl_iam_X_train, cvl_iam_y_train)182            print("done (%.1fs)" % (time.time()-t0))183            184            print("CVL+IAM 1-top training set: %.1f%%" % (cvl_iam_estimator.score_covar_ntop(cvl_iam_X_train, cvl_iam_y_train)[4]*100))185            ntop = cvl_iam_estimator.score_covar_ntop(cvl_iam_X_test, cvl_iam_y_test)186            ntop2 = cvl_iam_estimator.score_covar_ntop(cvl_X_test, cvl_y_test)187            ntop3 = cvl_iam_estimator.score_covar_ntop(iam_X_test, iam_y_test)188            print("CVL+IAM 1-top test set: %.1f%%" % (ntop[4]*100))189            print("CVL+IAM on CVL 1-top test set: %.1f%%" % (ntop2[4]*100))190            print("CVL+IAM on IAM 1-top test set: %.1f%%" % (ntop3[4]*100))191            print("Likelihood %.2f" % cvl_iam_estimator.log_likelihood_)192            dataset = 'CVL+IAM'193            d = (dataset, len(np.unique(cvl_iam_y_train)), len(cvl_iam_y_train), 194                 len(np.unique(cvl_iam_y_test)), len(cvl_iam_y_test), kernel, ard, 195                 cvl_iam_X_train.shape[1], data['N'], data['p'], data['q'], ntop[0]*100, 196                 ntop[2]*100, ntop[4]*100, ntop[6]*100, ntop[8]*100, cvl_iam_estimator.log_likelihood_)197            csv_file.write(("\"%s\";%i;%i;%i;%i;\"%s\";\"%s\";%i;%i;%i;%i;%f;%f;%f;%f;%f;%f\n" % d).replace('.', ','))198            dataset = 'CVL+IAM on CVL'199            d = (dataset, len(np.unique(cvl_iam_y_train)), len(cvl_iam_y_train), 200                 len(np.unique(cvl_y_test)), len(cvl_y_test), kernel, ard, 201                 cvl_iam_X_train.shape[1], data['N'], data['p'], data['q'], ntop2[0]*100, 202                 ntop2[2]*100, ntop2[4]*100, ntop2[6]*100, ntop2[8]*100, cvl_iam_estimator.log_likelihood_)203            csv_file.write(("\"%s\";%i;%i;%i;%i;\"%s\";\"%s\";%i;%i;%i;%i;%f;%f;%f;%f;%f;%f\n" % d).replace('.', ','))204            dataset = 'CVL+IAM on IAM'205            d = (dataset, len(np.unique(cvl_iam_y_train)), len(cvl_iam_y_train), 206                 len(np.unique(iam_y_test)), len(iam_y_test), kernel, ard, 207                 cvl_iam_X_train.shape[1], data['N'], data['p'], data['q'], ntop3[0]*100, 208                 ntop3[2]*100, ntop3[4]*100, ntop3[6]*100, ntop3[8]*100, cvl_iam_estimator.log_likelihood_)209            csv_file.write(("\"%s\";%i;%i;%i;%i;\"%s\";\"%s\";%i;%i;%i;%i;%f;%f;%f;%f;%f;%f\n" % d).replace('.', ','))210#            csv_file.write("\"%s\";%i;%i;%i;%i;\"%s\";\"%s\";%i;%i;%i;%i;%f;%f;%f;%f;%f;%f\n" % d)211            if feature_key==feature_keys[-1]:212                np.savez_compressed(cache_filename_result_npz(N=data['N'], p=data['p'], q=data['q']), 213                                covariance_matrix = np.asarray(cvl_iam_estimator.get_kernel()(cvl_iam_X_test), dtype=np.float16),214                                labels = cvl_iam_y_test.ravel(),215                                likelihood = cvl_iam_estimator.log_likelihood_)216            217        csv_file.close()218    else:...

iam-groups-cf-template.py

Source:iam-groups-cf-template.py

1"""Generating CloudFormation template."""2from troposphere import (3    Template,4    Join,5    Ref,6)7from troposphere.iam import (8    Group,9    ManagedPolicy,10)11from awacs.aws import (12    Action,13    Allow,14    Condition,15    NumericGreaterThan,16    Deny,17    Null,18    Policy,19    Statement,20)21t = Template()22t.add_description("Effective DevOps in AWS: User Groups")23t.add_resource(Group(24    "Admins",25    GroupName="Admins",26    ManagedPolicyArns=[27        "arn:aws:iam::aws:policy/AdministratorAccess"28    ],29))30t.add_resource(ManagedPolicy(31    "CommonIamPolicy",32    Description="Common policy to manage IAM resources",33    PolicyDocument=Policy(34        Version="2012-10-17",35        Statement=[36            Statement(37                Effect=Allow,38                Action=[39                    Action("iam", "GetAccountPasswordPolicy"),40                    Action("iam", "ListUsers"),41                    Action("iam", "ListMFADevices"),42                    Action("iam", "ListVirtualMFADEvices")43                ],44                Resource=["*"]45            ),46            Statement(47                Effect=Allow,48                Action=[49                    Action("iam", "CreateVirtualMFADevice")50                ],51                Resource=[52                    Join(53                        "",54                        [55                            "arn:aws:iam::",56                            Ref("AWS::AccountId"),57                            ":mfa/${aws:username}",58                        ]59                    )60                ]61            ),62            Statement(63                Effect=Allow,64                Action=[65                    Action("iam", "ChangePassword"),66                    Action("iam", "CreateAccessKey"),67                    Action("iam", "CreateLoginProfile"),68                    Action("iam", "DeleteAccessKey"),69                    Action("iam", "DeleteLoginProfile"),70                    Action("iam", "EnableMFADevice"),71                    Action("iam", "GetAccessKeyLastUsed"),72                    Action("iam", "GetLoginProfile"),73                    Action("iam", "GetUser"),74                    Action("iam", "ListAccessKeys"),75                    Action("iam", "UpdateAccessKey"),76                    Action("iam", "UpdateLoginProfile")77                ],78                Resource=[79                    Join(80                        "",81                        [82                            "arn:aws:iam::",83                            Ref("AWS::AccountId"),84                            ":user/${aws:username}",85                        ]86                    )87                ]88            ),89            Statement(90                Effect=Deny,91                NotAction=[92                    Action("iam", "ChangePassword"),93                    Action("iam", "CreateVirtualMFADevice"),94                    Action("iam", "EnableMFADevice"),95                    Action("iam", "GetUser"),96                    Action("iam", "ListMFADevices"),97                    Action("iam", "ListUsers"),98                    Action("iam", "ListVirtualMFADEvices")99                ],100                Resource=["*"],101                Condition=Condition(102                    Null("aws:MultiFactorAuthAge", "true"),103                ),104            ),105            Statement(106                Effect=Deny,107                NotAction=[108                    Action("iam", "ChangePassword"),109                    Action("iam", "CreateVirtualMFADevice"),110                    Action("iam", "EnableMFADevice"),111                    Action("iam", "GetUser"),112                    Action("iam", "ListMFADevices"),113                    Action("iam", "ListUsers"),114                    Action("iam", "ListVirtualMFADEvices")115                ],116                Resource=["*"],117                Condition=Condition(118                    NumericGreaterThan("aws:MultiFactorAuthAge", "43200")119                ),120            ),121        ]122    )123))124t.add_resource(Group(125    "AllUsers",126    GroupName="AllUsers",127    ManagedPolicyArns=[128        Ref("CommonIamPolicy")129    ]130))...

Automation Testing Tutorials

Learn to execute automation testing from scratch with LambdaTest Learning Hub. Right from setting up the prerequisites to run your first automation test, to following best practices and diving deeper into advanced test scenarios. LambdaTest Learning Hubs compile a list of step-by-step guides to help you be proficient with different test automation frameworks i.e. Selenium, Cypress, TestNG etc.