"""
"""
+import re
import plotly.graph_objects as go
import pandas as pd
+from copy import deepcopy
+
import hdrh.histogram
import hdrh.codec
-_COLORS = (
- u"#1A1110", u"#DA2647", u"#214FC6", u"#01786F", u"#BD8260", u"#FFD12A",
- u"#A6E7FF", u"#738276", u"#C95A49", u"#FC5A8D", u"#CEC8EF", u"#391285",
- u"#6F2DA8", u"#FF878D", u"#45A27D", u"#FFD0B9", u"#FD5240", u"#DB91EF",
- u"#44D7A8", u"#4F86F7", u"#84DE02", u"#FFCFF1", u"#614051"
-)
_VALUE = {
- "mrr": "result_receive_rate_rate_avg",
+ "mrr": "result_receive_rate_rate_values",
"ndr": "result_ndr_lower_rate_value",
"pdr": "result_pdr_lower_rate_value",
"pdr-lat": "result_latency_forward_pdr_50_avg"
u"result_latency_forward_pdr_90_hdrh": u"High-load, 90% PDR.",
u"result_latency_reverse_pdr_90_hdrh": u"High-load, 90% PDR."
}
+REG_EX_VPP_VERSION = re.compile(r"^(\d{2}).(\d{2})-(rc0|rc1|rc2|release$)")
+
+
+def _get_color(idx: int) -> str:
+ """
+ """
+ _COLORS = (
+ "#1A1110", "#DA2647", "#214FC6", "#01786F", "#BD8260", "#FFD12A",
+ "#A6E7FF", "#738276", "#C95A49", "#FC5A8D", "#CEC8EF", "#391285",
+ "#6F2DA8", "#FF878D", "#45A27D", "#FFD0B9", "#FD5240", "#DB91EF",
+ "#44D7A8", "#4F86F7", "#84DE02", "#FFCFF1", "#614051"
+ )
+ return _COLORS[idx % len(_COLORS)]
+
+
+def get_short_version(version: str, dut_type: str="vpp") -> str:
+ """
+ """
+
+ if dut_type in ("trex", "dpdk"):
+ return version
+
+ s_version = str()
+ groups = re.search(pattern=REG_EX_VPP_VERSION, string=version)
+ if groups:
+ try:
+ s_version = f"{groups.group(1)}.{groups.group(2)}_{groups.group(3)}"
+ except IndexError:
+ pass
+
+ return s_version
def select_iterative_data(data: pd.DataFrame, itm:dict) -> pd.DataFrame:
core = str() if itm["dut"] == "trex" else f"{itm['core']}"
ttype = "ndrpdr" if itm["testtype"] in ("ndr", "pdr") else itm["testtype"]
- dut = "none" if itm["dut"] == "trex" else itm["dut"].upper()
+ dut_v100 = "none" if itm["dut"] == "trex" else itm["dut"]
+ dut_v101 = itm["dut"]
df = data.loc[(
- (data["dut_type"] == dut) &
+ (data["release"] == itm["rls"]) &
+ (
+ (
+ (data["version"] == "1.0.0") &
+ (data["dut_type"].str.lower() == dut_v100)
+ ) |
+ (
+ (data["version"] == "1.0.1") &
+ (data["dut_type"].str.lower() == dut_v101)
+ )
+ ) &
(data["test_type"] == ttype) &
(data["passed"] == True)
)]
- df = df[df.job.str.endswith(f"{topo}-{arch}")]
- df = df[df.test_id.str.contains(
- f"^.*[.|-]{nic}.*{itm['framesize']}-{core}-{drv}{itm['test']}-{ttype}$",
- regex=True
- )].sort_values(by="start_time", ignore_index=True)
+ regex_test = \
+ f"^.*[.|-]{nic}.*{itm['framesize']}-{core}-{drv}{itm['test']}-{ttype}$"
+ df = df[
+ (df.job.str.endswith(f"{topo}-{arch}")) &
+ (df.dut_version.str.contains(itm["dutver"].replace("_", "-"))) &
+ (df.test_id.str.contains(regex_test, regex=True))
+ ]
return df
"""
"""
- fig_tput = go.Figure()
- fig_tsa = go.Figure()
+ fig_tput = None
+ fig_lat = None
+
+ tput_traces = list()
+ y_tput_max = 0
+ lat_traces = list()
+ y_lat_max = 0
+ x_lat = list()
+ for idx, itm in enumerate(sel):
+ itm_data = select_iterative_data(data, itm)
+ if itm["testtype"] == "mrr":
+ y_data = itm_data[_VALUE[itm["testtype"]]].to_list()[0]
+ if y_data.size > 0:
+ y_tput_max = \
+ max(y_data) if max(y_data) > y_tput_max else y_tput_max
+ else:
+ y_data = itm_data[_VALUE[itm["testtype"]]].to_list()
+ if y_data:
+ y_tput_max = \
+ max(y_data) if max(y_data) > y_tput_max else y_tput_max
+ nr_of_samples = len(y_data)
+ tput_kwargs = dict(
+ y=y_data,
+ name=(
+ f"{idx + 1}. "
+ f"({nr_of_samples:02d} "
+ f"run{u's' if nr_of_samples > 1 else u''}) "
+ f"{itm['id']}"
+ ),
+ hoverinfo=u"y+name",
+ boxpoints="all",
+ jitter=0.3,
+ marker=dict(color=_get_color(idx))
+ )
+ tput_traces.append(go.Box(**tput_kwargs))
+
+ show_latency = False
+ if itm["testtype"] == "pdr":
+ y_lat = itm_data[_VALUE["pdr-lat"]].to_list()
+ if y_lat:
+ y_lat_max = max(y_lat) if max(y_lat) > y_lat_max else y_lat_max
+ nr_of_samples = len(y_lat)
+ lat_kwargs = dict(
+ y=y_lat,
+ name=(
+ f"{idx + 1}. "
+ f"({nr_of_samples:02d} "
+ f"run{u's' if nr_of_samples > 1 else u''}) "
+ f"{itm['id']}"
+ ),
+ hoverinfo=u"y+name",
+ boxpoints="all",
+ jitter=0.3,
+ marker=dict(color=_get_color(idx))
+ )
+ x_lat.append(idx + 1)
+ lat_traces.append(go.Box(**lat_kwargs))
+ show_latency = True
+ else:
+ lat_traces.append(go.Box())
- return fig_tput, fig_tsa
+ pl_tput = deepcopy(layout["plot-throughput"])
+ pl_tput[u"xaxis"][u"tickvals"] = [i for i in range(len(sel))]
+ pl_tput[u"xaxis"][u"ticktext"] = [str(i + 1) for i in range(len(sel))]
+ if y_tput_max:
+ pl_tput[u"yaxis"][u"range"] = [0, (int(y_tput_max / 1e6) + 1) * 1e6]
+ fig_tput = go.Figure(data=tput_traces, layout=pl_tput)
+
+ if show_latency:
+ pl_lat = deepcopy(layout["plot-latency"])
+ pl_lat[u"xaxis"][u"tickvals"] = [i for i in range(len(x_lat))]
+ pl_lat[u"xaxis"][u"ticktext"] = x_lat
+ if y_lat_max:
+ pl_lat[u"yaxis"][u"range"] = [0, (int(y_lat_max / 10) + 1) * 10]
+ fig_lat = go.Figure(data=lat_traces, layout=pl_lat)
+
+ return fig_tput, fig_lat
def table_comparison(data: pd.DataFrame, sel:dict) -> pd.DataFrame:
"""
"""
table = pd.DataFrame(
- {
- "Test Case": [
- "64b-2t1c-avf-eth-l2xcbase-eth-2memif-1dcr",
- "64b-2t1c-avf-eth-l2xcbase-eth-2vhostvr1024-1vm-vppl2xc",
- "64b-2t1c-avf-ethip4udp-ip4base-iacl50sl-10kflows",
- "78b-2t1c-avf-ethip6-ip6scale2m-rnd "],
- "2106.0-8": [
- "14.45 +- 0.08",
- "9.63 +- 0.05",
- "9.7 +- 0.02",
- "8.95 +- 0.06"],
- "2110.0-8": [
- "14.45 +- 0.08",
- "9.63 +- 0.05",
- "9.7 +- 0.02",
- "8.95 +- 0.06"],
- "2110.0-9": [
- "14.45 +- 0.08",
- "9.63 +- 0.05",
- "9.7 +- 0.02",
- "8.95 +- 0.06"],
- "2202.0-9": [
- "14.45 +- 0.08",
- "9.63 +- 0.05",
- "9.7 +- 0.02",
- "8.95 +- 0.06"],
- "2110.0-9 vs 2110.0-8": [
- "-0.23 +- 0.62",
- "-1.37 +- 1.3",
- "+0.08 +- 0.2",
- "-2.16 +- 0.83"],
- "2202.0-9 vs 2110.0-9": [
- "+6.95 +- 0.72",
- "+5.35 +- 1.26",
- "+4.48 +- 1.48",
- "+4.09 +- 0.95"]
- }
-)
+ {
+ "Test Case": [
+ "64b-2t1c-avf-eth-l2xcbase-eth-2memif-1dcr",
+ "64b-2t1c-avf-eth-l2xcbase-eth-2vhostvr1024-1vm-vppl2xc",
+ "64b-2t1c-avf-ethip4udp-ip4base-iacl50sl-10kflows",
+ "78b-2t1c-avf-ethip6-ip6scale2m-rnd "],
+ "2106.0-8": [
+ "14.45 +- 0.08",
+ "9.63 +- 0.05",
+ "9.7 +- 0.02",
+ "8.95 +- 0.06"],
+ "2110.0-8": [
+ "14.45 +- 0.08",
+ "9.63 +- 0.05",
+ "9.7 +- 0.02",
+ "8.95 +- 0.06"],
+ "2110.0-9": [
+ "14.45 +- 0.08",
+ "9.63 +- 0.05",
+ "9.7 +- 0.02",
+ "8.95 +- 0.06"],
+ "2202.0-9": [
+ "14.45 +- 0.08",
+ "9.63 +- 0.05",
+ "9.7 +- 0.02",
+ "8.95 +- 0.06"],
+ "2110.0-9 vs 2110.0-8": [
+ "-0.23 +- 0.62",
+ "-1.37 +- 1.3",
+ "+0.08 +- 0.2",
+ "-2.16 +- 0.83"],
+ "2202.0-9 vs 2110.0-9": [
+ "+6.95 +- 0.72",
+ "+5.35 +- 1.26",
+ "+4.48 +- 1.48",
+ "+4.09 +- 0.95"]
+ }
+ )
- return table
+ return pd.DataFrame() #table
def graph_hdrh_latency(data: dict, layout: dict) -> go.Figure:
legendgroup=_GRAPH_LAT_HDRH_DESC[lat_name],
showlegend=bool(idx % 2),
line=dict(
- color=_COLORS[int(idx/2)],
+ color=_get_color(int(idx/2)),
dash=u"solid",
width=1 if idx % 2 else 2
),
Code Review / csit.git / blobdiff