Quicksort and file rearrange

Lab1/quicksort_bench.py

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+import subprocess
+from time import time, sleep
+import matplotlib.pyplot as plt
+
+pivot_strategies = {
+    "rightmost": 0,
+    "middle": 1,
+    "random": 2
+}
+
+def run_benchmark(array, pivot_strategy):
+    input_data = f"{len(array)}\n{pivot_strategies[pivot_strategy]}\n" + "\n".join(map(str, array)) + "\n"
+
+    start = time()
+    process = subprocess.Popen("./dist/quicksort", stdin=subprocess.PIPE)
+    process.communicate(input_data.encode())
+    if process.returncode != 0:
+        raise RuntimeError("Error while benchmarking")
+    end = time()
+
+    return end - start
+
+def generate_v_shaped(array_size):
+    return [i for i in range(array_size // 2, 0, -1)] + [i for i in range(array_size // 2)]
+
+def plot(sequences, array_size):
+    pivot_names = {
+        "rightmost": "Pivot prawy",
+        "middle": "Pivot środkowy",
+        "random": "Pivot losowy"
+    }
+    plt.figure(figsize=(10, 6))
+    for pivot_type, timings in sequences.items():
+        plt.plot(array_size, timings, label=pivot_names[pivot_type])
+    plt.xlabel("Rozmiar tablicy")
+    plt.ylabel("Czas (ms)")
+    plt.title("Quicksort")
+    plt.legend()
+    plt.savefig("./benchmarks/quicksort.png")
+
+if __name__ == "__main__":
+    array_sizes = [i for i in range(1000, 15001, 1000)]
+
+    with open("results.txt", "w") as f:
+        sequenceTiming = {
+                "rightmost": [],
+                "middle": [],
+                "random": [],
+            }
+        for pivot_strategy in pivot_strategies:
+            print(f"Running with pivot strategy {pivot_strategy}")
+            for array_size in array_sizes:
+                sequence = generate_v_shaped(array_size)
+                durations = []
+                for i in range(10):
+                    print(f"     - with size of {array_size} {i+1}/10", end="\r")
+                    duration = run_benchmark(sequence, pivot_strategy)
+                    duration *= 1000
+                    durations.append(duration)
+                    sleep(0.1)
+                print()
+                duration = sum(durations) / len(durations)
+                sequenceTiming[pivot_strategy].append(duration)
+                sleep(0.1)
+        plot(sequenceTiming, array_sizes)
+    print("Benchmark finished")