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How to use print_config_table method in localstack

Best Python code snippet using localstack_python

localstack.py

Source:localstack.py

...182    from localstack import config183    assert config184    assert ext_config185    if format == "table":186        print_config_table()187    elif format == "plain":188        print_config_pairs()189    elif format == "dict":190        print_config_dict()191    elif format == "json":192        print_config_json()193    else:194        print_config_pairs()  # fall back to plain195def print_config_json():196    import json197    from localstack import config198    console.print(json.dumps(dict(config.collect_config_items())))199def print_config_pairs():200    from localstack import config201    for key, value in config.collect_config_items():202        console.print(f"{key}={value}")203def print_config_dict():204    from localstack import config205    console.print(dict(config.collect_config_items()))206def print_config_table():207    from rich.table import Table208    from localstack import config209    grid = Table(show_header=True)210    grid.add_column("Key")211    grid.add_column("Value")212    for key, value in config.collect_config_items():213        grid.add_row(key, str(value))214    console.print(grid)215@localstack.command(name="ssh", help="Obtain a shell in the running LocalStack container")216def cmd_ssh():217    from localstack import config218    from localstack.utils.docker_utils import DOCKER_CLIENT219    from localstack.utils.run import run220    if not DOCKER_CLIENT.is_container_running(config.MAIN_CONTAINER_NAME):...

eval_hyperparameter_tuning.py

Source:eval_hyperparameter_tuning.py

...44        print('%argmax: ' + str(np.argmax(results, axis=0)))45        print('%argmin: ' + str(np.argmin(results, axis=0)))46    sys.stdout = orig_stdout        47    return results[:,:n_val-1]48def print_config_table(path_trainings):49    '''50    Print a latex table of configurations to a .txt file51    '''52    header = ['Training', '$N_{Patches pp.}$', 'Patch Size', 'Overlap', 'Loss Weights(C:R)']53    54    # Create list of lists55    configs_used = []56    for i in range(len(path_trainings)):57        configs_used.append([])58        for j in range(len(header)):59            configs_used[i].append([])60    61    # Load data62    for idx, path in enumerate(path_trainings,0):63        with open(path + '/params.txt', 'r') as f:64            params = json.load(f)65        with open(path + '/config.txt', 'r') as f:66            config = json.load(f)67        configs_used[idx][0] = params['n_patches']68        configs_used[idx][1] = params['patch_size']69        configs_used[idx][2] = params['overlap']70        configs_used[idx][3] = config['loss_weights']71    72    # Print data 73    orig_stdout = sys.stdout74    with open('hyperparameter_table.txt', 'w') as f:75        sys.stdout = f76        for h in header:77            print(h, end='')78            if h != header[-1]:79                print(' & ', end='')80            else:81                print(' \\\\ \n\\hline')82#        for idx in range(len(configs_used)):83        for training_idx, line in enumerate(configs_used,0):84            print('H'+f'{training_idx:02}'+ ' & ', end='')85            for idx in range(len(header)):86                print(line[idx], end='')87                if idx != len(header) -1:88                    print(' & ', end='')89            if training_idx != len(configs_used)-1:90                print(' \\\\ \n', end='')             91    sys.stdout = orig_stdout            92    93if __name__ == '__main__':94    src_dir = '/home/s1283/no_backup/s1283/hyperparameter_tuning_01/'95    path_trainings = glob(os.path.join(src_dir, '*/'))96    path_trainings.sort()97    results = print_results_table(path_trainings)98    configs = print_config_table(path_trainings)99#%%100    # Create boxplots for hyperparameter training101    loss_class = results[:,0]102    mse = results[:,-1]103    metrics_class = results[:,1:-1]104    fig, ax1 = plt.subplots(figsize=(5,5))105    bp1 = ax1.boxplot(100*metrics_class,showmeans=True)106    ax1.set_ylim([0,100])107    ax1.set_ylabel('Percentage')108    109    ax1.set_xticklabels(['Accuracy', 'Precision', 'Sensitivity', 'Specificity'])110    ax1.set_title('Classification Metrics')111    plt.savefig('classification_boxplots.png', bbox_inches ='tight')112    fig, ax2 = plt.subplots(figsize=(2,5))...

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