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Signed-off-by: Bensuperpc <bensuperpc@gmail.com>

tools/graphic.py

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+# Based on work: https://int-i.github.io/python/2021-11-07/matplotlib-google-benchmark-visualization/
+# Modified by: Bensuperpc
+
+from argparse import ArgumentParser
+from itertools import groupby
+import json
+import operator
+import matplotlib.pyplot as plt
+import numpy as np
+import seaborn as sns
+import pandas as pd
+
+# Extract the benchmark name from the benchmark name string
+def extract_label_from_benchmark(benchmark):
+    bench_full_name = benchmark['name']
+    bench_name = bench_full_name.split('/')[0]  # Remove all after /
+    if (bench_name.startswith('BM_')):  # Remove if string start with BM_
+        return bench_name[3:]  # Remove BM_
+    else:
+        return bench_name
+
+# Extract the benchmark size from the benchmark
+def extract_size_from_benchmark(benchmark):
+    bench_name = benchmark['name']
+    return bench_name.split('/')[1]  # Remove all before /
+
+if __name__ == "__main__":
+    # ./prog_name --benchmark_format=json --benchmark_out=result.json
+    parser = ArgumentParser()
+    parser.add_argument('path', help='benchmark result json file')
+    args = parser.parse_args()
+
+    with open(args.path) as file:
+        benchmark_result = json.load(file)
+        benchmarks = benchmark_result['benchmarks']
+        elapsed_times = groupby(benchmarks, extract_label_from_benchmark)
+
+        data1 = None
+        data2 = None
+
+        for key, group in elapsed_times:
+            benchmark = list(group)
+            x = list(map(extract_size_from_benchmark, benchmark))
+            y1 = list(map(operator.itemgetter('bytes_per_second'), benchmark))
+            y2 = list(map(operator.itemgetter('items_per_second'), benchmark))
+
+            if data1 is None:
+                data1 = pd.DataFrame({'size': x, key: y1})
+            else:
+                data1[key] = y1
+            
+            if data2 is None:
+                data2 = pd.DataFrame({'size': x, key: y2})
+            else:
+                data2[key] = y2
+
+        df1 = pd.melt(data1, id_vars=['size'], var_name='Benchmark', value_name='bytes_per_second')
+        df1_max_indices = df1.groupby(['size', 'Benchmark'])['bytes_per_second'].transform(max) == df1['bytes_per_second']
+        df1 = df1.loc[df1_max_indices]
+        df1.reset_index(drop=True, inplace=True)
+
+
+        df2 = pd.melt(data2, id_vars=['size'], var_name='Benchmark', value_name='items_per_second')
+        df2_max_indices = df2.groupby(['size', 'Benchmark'])['items_per_second'].transform(max) == df2['items_per_second']
+        df2 = df2.loc[df2_max_indices]
+        df2.reset_index(drop=True, inplace=True)
+        
+        sns.set_theme()
+
+        fig, ax = plt.subplots(2, 1)
+
+        fig.set_size_inches(16, 9)
+        fig.set_dpi(96)
+
+        sns.lineplot(data=df1, x='size', y='bytes_per_second', hue='Benchmark', ax=ax[0])
+        sns.lineplot(data=df2, x='size', y='items_per_second', hue='Benchmark', ax=ax[1])
+
+        ax[0].set_title('Bytes per second')
+        ax[1].set_title('Items per second')
+
+        ax[0].set_xlabel('Array size')
+        ax[1].set_xlabel('Array size')
+
+        ax[0].set_ylabel('byte per second')
+        ax[1].set_ylabel('items per second')
+
+        fig.tight_layout()
+
+        plt.savefig('benchmark.png', bbox_inches='tight', dpi=300)
+
+        plt.show()