wip: add mixnet-analysis

mixnet-analysis/.gitignore

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+.venv/
+*.csv

mixnet-analysis/aggregate.py

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+import argparse
+import os
+
+import pandas as pd
+
+
+def aggregate(path: str):
+    dataframes = []
+    for root, dirs, files in os.walk(path):
+        # print(f"root: {root}, dirs: {dirs}, files: {files}")
+        if "paramset.csv" in files:
+            df = pd.read_csv(os.path.join(root, "paramset.csv"))
+
+            assert "data_msg_count_means.csv" in files
+            mean_df = pd.read_csv(os.path.join(root, "data_msg_count_means.csv"))
+            df["mean_data_msg_count"] = mean_df["mean"].values[0]
+
+            assert "latency_stats.csv" in files
+            latency_df = pd.read_csv(os.path.join(root, "latency_stats.csv"))
+            df["latency_min"] = latency_df["min"].values[0]
+            df["latency_median"] = latency_df["median"].values[0]
+            df["latency_mean"] = latency_df["mean"].values[0]
+            df["latency_std"] = latency_df["std"].values[0]
+            df["latency_max"] = latency_df["max"].values[0]
+
+            dataframes.append(df)
+            print(f"Processed {root}")
+
+    if dataframes:
+        df = pd.concat(dataframes).sort_values(by=["paramset", "queue_type"])
+        outpath = os.path.join(path, "aggregated.csv")
+        df.to_csv(outpath, index=False)
+        print(f"Saved {outpath}")
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(
+        description="Aggregate the results of all paramsets of an experiment"
+    )
+    parser.add_argument("path", type=str, help="dir path")
+    args = parser.parse_args()
+    aggregate(args.path)

mixnet-analysis/datamsgcount.py

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+import argparse
+import os
+
+import pandas as pd
+
+
+def analyze(path: str) -> pd.DataFrame:
+    medians = pd.read_csv(path).iloc[:, 1:].median(axis=1)
+    return pd.DataFrame(
+        {
+            "min": medians.min(),
+            "median": medians.median(),
+            "mean": medians.mean(),
+            "std": medians.std(),
+            "max": medians.max(),
+        },
+        index=pd.Series([0]),
+    )
+
+
+def save_means_stats(means: list, outpath: str):
+    series = pd.Series(means)
+    means_stat = pd.DataFrame(
+        {"mean": series.mean(), "std": series.std()}, index=pd.Series([0])
+    )
+    means_stat.to_csv(outpath, index=False)
+    print(f"Saved {outpath}")
+
+
+def analyze_all(path: str):
+    means = []
+    means_outpath = ""
+    for root, dirs, files in os.walk(path):
+        if os.path.basename(root).startswith("paramset_"):
+            if len(means) > 0:
+                assert means_outpath != ""
+                save_means_stats(means, means_outpath)
+            means = []
+            means_outpath = os.path.join(root, "data_msg_count_means.csv")
+        elif "data_msg_counts_stats.csv" in files:
+            stats = pd.read_csv(os.path.join(root, "data_msg_counts_stats.csv"))
+            means.append(stats["mean"].values[0])
+        elif "data_msg_counts.csv" in files:
+            stats = analyze(os.path.join(root, "data_msg_counts.csv"))
+            means.append(stats["mean"].values[0])
+            outpath = os.path.join(root, "data_msg_counts_stats.csv")
+            stats.to_csv(outpath, index=False)
+            print(f"Saved {outpath}")
+
+    if len(means) > 0:
+        assert means_outpath != ""
+        save_means_stats(means, means_outpath)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Analyze message count data")
+    parser.add_argument("path", type=str, help="dir path")
+    args = parser.parse_args()
+    analyze_all(args.path)

mixnet-analysis/latency.py

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+import argparse
+import os
+
+import pandas as pd
+
+
+def save_latency_stats(latencies: list, outpath: str):
+    series = pd.Series(latencies)
+    means_stat = pd.DataFrame(
+        {
+            "min": series.min(),
+            "median": series.median(),
+            "mean": series.mean(),
+            "std": series.std(),
+            "max": series.max(),
+        },
+        index=pd.Series([0]),
+    )
+    means_stat.to_csv(outpath, index=False)
+    print(f"Saved {outpath}")
+
+
+def aggregate(path: str):
+    latencies = []
+    latencies_outpath = ""
+    for root, dirs, files in os.walk(path):
+        if os.path.basename(root).startswith("paramset_"):
+            if len(latencies) > 0:
+                assert latencies_outpath != ""
+                save_latency_stats(latencies, latencies_outpath)
+            latencies = []
+            latencies_outpath = os.path.join(root, "latency_stats.csv")
+        elif "latency.csv" in files:
+            df = pd.read_csv(os.path.join(root, "latency.csv"))
+            latencies.extend(df["latency"].to_list())
+
+    if len(latencies) > 0:
+        assert latencies_outpath != ""
+        save_latency_stats(latencies, latencies_outpath)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Aggregate latencies")
+    parser.add_argument("path", type=str, help="dir path")
+    args = parser.parse_args()
+    aggregate(args.path)

mixnet-analysis/pyrightconfig.json

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+{
+  "venvPath": ".",
+  "venv": ".venv",
+  "typeCheckingMode": "basic",
+  "reportMissingTypeStubs": "none",
+  "ignore": ["reportMissingTypeStubs"]
+}

mixnet-analysis/requirements.txt

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+pandas==2.2.2