- cov["Throughput_Unit"] = df["result_pdr_lower_rate_unit"]
- cov["Throughput_NDR"] = df.apply(
- lambda row: row["result_ndr_lower_rate_value"] / 1e6, axis=1
- )
- cov["Throughput_NDR_Mbps"] = df.apply(
- lambda row: row["result_ndr_lower_bandwidth_value"] /1e9, axis=1
- )
- cov["Throughput_PDR"] = \
- df.apply(lambda row: row["result_pdr_lower_rate_value"] / 1e6, axis=1)
- cov["Throughput_PDR_Mbps"] = df.apply(
- lambda row: row["result_pdr_lower_bandwidth_value"] /1e9, axis=1
- )
- cov["Latency Forward [us]_10% PDR_P50"] = df.apply(
- lambda row: _latency(row["result_latency_forward_pdr_10_hdrh"], 50.0),
- axis=1
- )
- cov["Latency Forward [us]_10% PDR_P90"] = df.apply(
- lambda row: _latency(row["result_latency_forward_pdr_10_hdrh"], 90.0),
- axis=1
- )
- cov["Latency Forward [us]_10% PDR_P99"] = df.apply(
- lambda row: _latency(row["result_latency_forward_pdr_10_hdrh"], 99.0),
- axis=1
- )
- cov["Latency Forward [us]_50% PDR_P50"] = df.apply(
- lambda row: _latency(row["result_latency_forward_pdr_50_hdrh"], 50.0),
- axis=1
- )
- cov["Latency Forward [us]_50% PDR_P90"] = df.apply(
- lambda row: _latency(row["result_latency_forward_pdr_50_hdrh"], 90.0),
- axis=1
- )
- cov["Latency Forward [us]_50% PDR_P99"] = df.apply(
- lambda row: _latency(row["result_latency_forward_pdr_50_hdrh"], 99.0),
- axis=1
- )
- cov["Latency Forward [us]_90% PDR_P50"] = df.apply(
- lambda row: _latency(row["result_latency_forward_pdr_90_hdrh"], 50.0),
- axis=1
- )
- cov["Latency Forward [us]_90% PDR_P90"] = df.apply(
- lambda row: _latency(row["result_latency_forward_pdr_90_hdrh"], 90.0),
- axis=1
- )
- cov["Latency Forward [us]_90% PDR_P99"] = df.apply(
- lambda row: _latency(row["result_latency_forward_pdr_90_hdrh"], 99.0),
- axis=1
- )
- cov["Latency Reverse [us]_10% PDR_P50"] = df.apply(
- lambda row: _latency(row["result_latency_reverse_pdr_10_hdrh"], 50.0),
- axis=1
- )
- cov["Latency Reverse [us]_10% PDR_P90"] = df.apply(
- lambda row: _latency(row["result_latency_reverse_pdr_10_hdrh"], 90.0),
- axis=1
- )
- cov["Latency Reverse [us]_10% PDR_P99"] = df.apply(
- lambda row: _latency(row["result_latency_reverse_pdr_10_hdrh"], 99.0),
- axis=1
- )
- cov["Latency Reverse [us]_50% PDR_P50"] = df.apply(
- lambda row: _latency(row["result_latency_reverse_pdr_50_hdrh"], 50.0),
- axis=1
- )
- cov["Latency Reverse [us]_50% PDR_P90"] = df.apply(
- lambda row: _latency(row["result_latency_reverse_pdr_50_hdrh"], 90.0),
- axis=1
- )
- cov["Latency Reverse [us]_50% PDR_P99"] = df.apply(
- lambda row: _latency(row["result_latency_reverse_pdr_50_hdrh"], 99.0),
- axis=1
- )
- cov["Latency Reverse [us]_90% PDR_P50"] = df.apply(
- lambda row: _latency(row["result_latency_reverse_pdr_90_hdrh"], 50.0),
- axis=1
- )
- cov["Latency Reverse [us]_90% PDR_P90"] = df.apply(
- lambda row: _latency(row["result_latency_reverse_pdr_90_hdrh"], 90.0),
- axis=1
- )
- cov["Latency Reverse [us]_90% PDR_P99"] = df.apply(
- lambda row: _latency(row["result_latency_reverse_pdr_90_hdrh"], 99.0),
- axis=1
- )
+
+ if ttype == "device":
+ cov = cov.assign(Result="PASS")
+ elif ttype == "mrr":
+ cov["Throughput_Unit"] = df["result_receive_rate_rate_unit"]
+ cov["Throughput_AVG"] = df.apply(
+ lambda row: row["result_receive_rate_rate_avg"] / 1e9, axis=1
+ )
+ cov["Throughput_STDEV"] = df.apply(
+ lambda row: row["result_receive_rate_rate_stdev"] / 1e9, axis=1
+ )
+ else: # NDRPDR
+ cov["Throughput_Unit"] = df["result_pdr_lower_rate_unit"]
+ cov["Throughput_NDR"] = df.apply(
+ lambda row: row["result_ndr_lower_rate_value"] / 1e6, axis=1
+ )
+ cov["Throughput_NDR_Gbps"] = df.apply(
+ lambda row: row["result_ndr_lower_bandwidth_value"] / 1e9, axis=1
+ )
+ cov["Throughput_PDR"] = df.apply(
+ lambda row: row["result_pdr_lower_rate_value"] / 1e6, axis=1
+ )
+ cov["Throughput_PDR_Gbps"] = df.apply(
+ lambda row: row["result_pdr_lower_bandwidth_value"] / 1e9, axis=1
+ )
+ if show_latency:
+ for way in ("Forward", "Reverse"):
+ for pdr in (10, 50, 90):
+ for perc in (50, 90, 99):
+ latency = f"result_latency_{way.lower()}_pdr_{pdr}_hdrh"
+ cov[f"Latency {way} [us]_{pdr}% PDR_P{perc}"] = \
+ df.apply(
+ lambda row: _latency(row[latency], perc),
+ axis=1
+ )