remove mixnet-analysis

mixnet-analysis/.gitignore

@@ -1,2 +0,0 @@
-.venv/
-*.csv

mixnet-analysis/aggregate.py

@@ -1,42 +0,0 @@
-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

@@ -1,59 +0,0 @@
-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

@@ -1,46 +0,0 @@
-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

@@ -1,7 +0,0 @@
-{
-  "venvPath": ".",
-  "venv": ".venv",
-  "typeCheckingMode": "basic",
-  "reportMissingTypeStubs": "none",
-  "ignore": ["reportMissingTypeStubs"]
-}

mixnet-analysis/requirements.txt

@@ -1 +0,0 @@
-pandas==2.2.2