Added loss calcs

pq-tls-benchmark-framework/emulation-exp/code/kex/scripts/queries.py

@@ -12,7 +12,7 @@ def main():
     data = pd.read_feather(f"{FEATHERS_DIR}/data.feather")
     # data = pd.read_feather(f"{FEATHERS_DIR}/data_run_20241028.feather")
 
-    p_no_one_sec_delay()
+    loss_calculations()
     # static_scenario_statistical_analysis(data)
     # median_of_all_static_runs_per_algorithm(data)
     # stats_of_qtl95_of_packetloss(data)
@@ -23,11 +23,11 @@ def main():
     # print_kem_ids()
 
 
-def p_no_one_sec_delay():
+def loss_calculations():
     udp_packets_df = pd.read_feather("feathers/udp_packets.feather")
     df = ap.get_packets_sent_by_node(udp_packets_df)
 
-    print("\n\n No one second delay")
+    print("\n\n Loss calculations")
     df = df.drop(columns=["length_public_key", "length_ciphertext"])
     # print(df)
     df["cic"] = df["client_sent_packets_with_crypto_count"] - 1
@@ -39,9 +39,25 @@ def p_no_one_sec_delay():
         ]
     )
 
+    # p_noOneSec does not make sense if cic or sic is bigger than 10 -> look thesis
     df = df.query("cic <= 10 and sic <= 10")
 
-    def calc_p(cic, sic, l):
+    def calc_p_no_loss(cic, sic, l):
+        """
+        Calculates the probability p_noLoss.
+
+        Args:
+            cic: client initial count.
+            sic: server initial count.
+            l: loss probability.
+
+        Returns:
+            p_noLoss as defined in the thesis.
+        """
+
+        return (1 - l) ** (cic + sic)
+
+    def calc_p_no_one_sec_delay(cic, sic, l):
         """
         Calculates the probability p_noOneSec.
 
@@ -65,15 +81,40 @@ def p_no_one_sec_delay():
 
         return term1 + term2
 
-    # print(df)
+    def calc_l_for_no_loss_p(cic, sic, p):
+        """
+        Calculates the loss probability l for a p_noLoss of 0.95.
+
+        Args:
+            cic: client initial count.
+            sic: server initial count.
+            p: probability.
+
+        Returns:
+            l as defined in the thesis.
+        """
+
+        return 1 - (p ** (1 / (cic + sic)))
 
     for l in [0.01, 0.05, 0.10, 0.20]:
+        df[f"p_noLoss_{l}"] = df.apply(
+            lambda row: calc_p_no_loss(row["cic"], row["sic"], l), axis=1
+        )
         df[f"p_noOneSec_{l}"] = df.apply(
-            lambda row: calc_p(row["cic"], row["sic"], l), axis=1
+            lambda row: calc_p_no_one_sec_delay(row["cic"], row["sic"], l), axis=1
         )
 
+    df["l_for_noLoss_p50"] = df.apply(
+        lambda row: calc_l_for_no_loss_p(row["cic"], row["sic"], 0.50), axis=1
+    )
+    df["l_for_noLoss_p95"] = df.apply(
+        lambda row: calc_l_for_no_loss_p(row["cic"], row["sic"], 0.95), axis=1
+    )
+
     print(df)
 
+    return df
+
 
 def static_scenario_statistical_analysis(data):
     ldata = data