feat: Add plot script

plot.py

@@ -0,0 +1,72 @@
+import json
+
+import matplotlib
+import matplotlib.pyplot as plt
+import numpy as np
+import scienceplots
+
+plt.style.use('science')
+
+
+def plot():
+    # Load the results
+    with open('results/results.json', 'r') as f:
+        data = json.load(f)
+
+    results_filtered = [result for result in data['results'] if
+                        result['id'] != 'warmup' and result['id'] != 'throughput']
+    constant_rate = [result for result in results_filtered if result['executor_type'] == 'ConstantArrivalRate']
+    constant_rate_x = [result['config']['rate'] for result in constant_rate]
+    constant_vus = [result for result in results_filtered if result['executor_type'] == 'ConstantVUs']
+    constant_vus_x = [result['config']['vus'] for result in constant_vus]
+    if len(constant_rate) > 0:
+        plot_inner('Requests/s', constant_rate_x, constant_rate, 'Constant Rate')
+    if len(constant_vus) > 0:
+        plot_inner('VUs', constant_vus_x, constant_vus, 'Constant VUs')
+
+
+def plot_inner(x_name, x_values, results, title):
+    fig, axs = plt.subplots(3, 2, figsize=(15, 20))
+    fig.tight_layout(pad=6.0)
+    fig.subplots_adjust(hspace=0.4, wspace=0.2, bottom=0.15)
+    # compute error rate
+    for result in results:
+        result['error_rate'] = result['failed_requests'] / (
+                result['failed_requests'] + result['successful_requests']) * 100.0
+
+    metrics = ['inter_token_latency_ms_p90', 'time_to_first_token_ms_p90', 'token_throughput_secs',
+               'successful_requests', 'error_rate']
+
+    titles = ['Inter Token Latency P90 (lower is better)', 'TTFT P90 (lower is better)',
+              'Token Throughput (higher is better)', 'Successful requests', 'Error Rate % (lower is better)']
+
+    labels = ['Time (ms)', 'Time (ms)', 'Tokens/s', 'Count', '%']
+
+    x = [result['config']['rate'] for result in results]
+    colors = ['#FF9D00', '#2F5BA1']
+
+    # Plot each metric in its respective subplot
+    for ax, metric, title, label in zip(axs.flatten(), metrics, titles, labels):
+        data = list(map(lambda result: result[metric], results))
+        ax.plot(x, data, marker='o', color=colors[0])
+        ax.set_title(title)
+        ax.tick_params(axis='x', rotation=0)
+        ax.set_ylabel(label)
+        ax.set_xlabel(x_name)
+        # rotate x-axis labels for better readability
+        plt.setp(ax.xaxis.get_majorticklabels(), rotation=-90)
+
+        # Enhance y-axis: more ticks for better granularity
+        y_vals = ax.get_yticks()
+        ax.set_yticks(
+            np.linspace(y_vals[0], y_vals[-1], 10))  # Set 10 ticks spread between the min and max of current y-ticks
+
+        # Add grid lines for better readability
+        ax.grid(True, which='both', axis='y', linestyle='--', linewidth=0.5)
+        ax.set_axisbelow(True)  # Ensure grid lines are below the bars
+
+    plt.show()
+
+
+if __name__ == '__main__':
+    plot()