Add plotting of telemetry

    parser.add_argument('-f', '--replay', nargs='+', type=str, 

                        help='Either: path/to/joblive path/to/jobprofile' + \

                             ' -or- filename.npz (overrides --workload option)')

    parser.add_argument('-p', '--plot', nargs='+', choices=['power', 'loss', 'pue', 'temp'],

    parser.add_argument('-p', '--plot', action='store_true', help='Output plots')

                        help='Specify one or more types of plots to generate: power, loss, pue, temp')

    parser.add_argument('--system', type=str, default='frontier', help='System config to use')

    parser.add_argument('-v', '--verbose', action='store_true', help='Enable verbose output')

    print(f'Nodes required (avg): {np.mean(nr_list):.2f}')

    print(f'Nodes required (max): {np.max(nr_list)}')

    print(f'Nodes required (std): {np.std(nr_list):.2f}')

    if args.plot:

        # Define the number of bins you want

        num_bins = 25

        data = nr_list

        # Create logarithmically spaced bins

        #bins = np.logspace(np.log10(min(data)), np.log10(max(data)), num_bins)

        bins = np.logspace(np.log2(min(data)), np.log2(max(data)), num=num_bins, base=2)

        # Set up the figure with the desired dimensions

        plt.figure(figsize=(10, 3))

        # Create the histogram

        plt.hist(nr_list, bins=bins, edgecolor='black')

        # Add a title and labels

        plt.xlabel('Number of Nodes')

        plt.ylabel('Frequency')

        # Set the axes to logarithmic scale

        #plt.xscale('log', base=10)

        plt.xscale('log', base=2)

        #plt.yscale('log')

        # Customize the x-ticks: Choose the positions (1, 8, 64, etc.)

        ticks = [2**i for i in range(0, 14)]

        plt.xticks(ticks, labels=[str(tick) for tick in ticks])

        # Set min-max axis bounds

        plt.xlim(1, max(data))

        # Save the histogram to a file

        plt.savefig('histogram.png', dpi=300, bbox_inches='tight')

        # Plot number of nodes over time

        plt.clf()

        plt.figure(figsize=(10, 2))

        # Create a bar chart

        plt.bar(submit_times, nr_list, width=10.0, color='blue', edgecolor='black', alpha=0.7)

        # Add labels and title

        plt.xlabel('Allocation Time (s)')

        plt.ylabel('Number of Nodes')

        # Set min-max axix bounds

        plt.xlim(1, max(submit_times))

        # Set the y-axis to logarithmic scale with base 2

        #plt.yscale('log', base=2)

        #y_ticks = [2**i for i in range(0, 14)]

        #plt.yticks(y_ticks, labels=[str(tick) for tick in y_ticks])

        # Save the plot to a file

        plt.savefig('nodes_time.png', dpi=300, bbox_inches='tight')