from copy import deepcopy
-import hdrh.histogram
-import hdrh.codec
-
-
-_VALUE = {
- "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"
-}
-_UNIT = {
- "mrr": "result_receive_rate_rate_unit",
- "ndr": "result_ndr_lower_rate_unit",
- "pdr": "result_pdr_lower_rate_unit",
- "pdr-lat": "result_latency_forward_pdr_50_unit"
-}
-_LAT_HDRH = ( # Do not change the order
- "result_latency_forward_pdr_0_hdrh",
- "result_latency_reverse_pdr_0_hdrh",
- "result_latency_forward_pdr_10_hdrh",
- "result_latency_reverse_pdr_10_hdrh",
- "result_latency_forward_pdr_50_hdrh",
- "result_latency_reverse_pdr_50_hdrh",
- "result_latency_forward_pdr_90_hdrh",
- "result_latency_reverse_pdr_90_hdrh",
-)
-# This value depends on latency stream rate (9001 pps) and duration (5s).
-# Keep it slightly higher to ensure rounding errors to not remove tick mark.
-PERCENTILE_MAX = 99.999501
-
-_GRAPH_LAT_HDRH_DESC = {
- u"result_latency_forward_pdr_0_hdrh": u"No-load.",
- u"result_latency_reverse_pdr_0_hdrh": u"No-load.",
- u"result_latency_forward_pdr_10_hdrh": u"Low-load, 10% PDR.",
- u"result_latency_reverse_pdr_10_hdrh": u"Low-load, 10% PDR.",
- u"result_latency_forward_pdr_50_hdrh": u"Mid-load, 50% PDR.",
- u"result_latency_reverse_pdr_50_hdrh": u"Mid-load, 50% PDR.",
- 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$)")
-
-
-def _get_color(idx: int) -> str:
- """
- """
- _COLORS = (
- "#1A1110", "#DA2647", "#214FC6", "#01786F", "#BD8260", "#FFD12A",
- "#A6E7FF", "#738276", "#C95A49", "#FC5A8D", "#CEC8EF", "#391285",
- "#6F2DA8", "#FF878D", "#45A27D", "#FFD0B9", "#FD5240", "#DB91EF",
- "#44D7A8", "#4F86F7", "#84DE02", "#FFCFF1", "#614051"
- )
- return _COLORS[idx % len(_COLORS)]
+from ..utils.constants import Constants as C
+from ..utils.utils import get_color
def get_short_version(version: str, dut_type: str="vpp") -> str:
return version
s_version = str()
- groups = re.search(pattern=REG_EX_VPP_VERSION, string=version)
+ groups = re.search(
+ pattern=re.compile(r"^(\d{2}).(\d{2})-(rc0|rc1|rc2|release$)"),
+ string=version
+ )
if groups:
try:
- s_version = f"{groups.group(1)}.{groups.group(2)}_{groups.group(3)}"
+ s_version = \
+ f"{groups.group(1)}.{groups.group(2)}.{groups.group(3)}".\
+ replace("release", "rls")
except IndexError:
pass
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.dut_version.str.contains(itm["dutver"].replace(".r", "-r").\
+ replace("rls", "release"))) &
(df.test_id.str.contains(regex_test, regex=True))
]
return df
-def graph_iterative(data: pd.DataFrame, sel:dict, layout: dict) -> tuple:
+def graph_iterative(data: pd.DataFrame, sel:dict, layout: dict,
+ normalize: bool) -> tuple:
"""
"""
lat_traces = list()
y_lat_max = 0
x_lat = list()
+ show_latency = False
+ show_tput = False
for idx, itm in enumerate(sel):
itm_data = select_iterative_data(data, itm)
+ if itm_data.empty:
+ continue
+ phy = itm["phy"].split("-")
+ topo_arch = f"{phy[0]}-{phy[1]}" if len(phy) == 4 else str()
+ norm_factor = (C.NORM_FREQUENCY / C.FREQUENCY[topo_arch]) \
+ if normalize else 1.0
if itm["testtype"] == "mrr":
- y_data = itm_data[_VALUE[itm["testtype"]]].to_list()[0]
- if y_data.size > 0:
+ y_data_raw = itm_data[C.VALUE_ITER[itm["testtype"]]].to_list()[0]
+ y_data = [(y * norm_factor) for y in y_data_raw]
+ if len(y_data) > 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()
+ y_data_raw = itm_data[C.VALUE_ITER[itm["testtype"]]].to_list()
+ y_data = [(y * norm_factor) for y in y_data_raw]
if y_data:
y_tput_max = \
max(y_data) if max(y_data) > y_tput_max else y_tput_max
name=(
f"{idx + 1}. "
f"({nr_of_samples:02d} "
- f"run{u's' if nr_of_samples > 1 else u''}) "
+ f"run{'s' if nr_of_samples > 1 else ''}) "
f"{itm['id']}"
),
hoverinfo=u"y+name",
boxpoints="all",
jitter=0.3,
- marker=dict(color=_get_color(idx))
+ marker=dict(color=get_color(idx))
)
tput_traces.append(go.Box(**tput_kwargs))
+ show_tput = True
- show_latency = False
if itm["testtype"] == "pdr":
- y_lat = itm_data[_VALUE["pdr-lat"]].to_list()
+ y_lat_row = itm_data[C.VALUE_ITER["pdr-lat"]].to_list()
+ y_lat = [(y / norm_factor) for y in y_lat_row]
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)
f"run{u's' if nr_of_samples > 1 else u''}) "
f"{itm['id']}"
),
- hoverinfo=u"y+name",
+ hoverinfo="all",
boxpoints="all",
jitter=0.3,
- marker=dict(color=_get_color(idx))
+ marker=dict(color=get_color(idx))
)
x_lat.append(idx + 1)
lat_traces.append(go.Box(**lat_kwargs))
else:
lat_traces.append(go.Box())
- 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_tput:
+ pl_tput = deepcopy(layout["plot-throughput"])
+ pl_tput["xaxis"]["tickvals"] = [i for i in range(len(sel))]
+ pl_tput["xaxis"]["ticktext"] = [str(i + 1) for i in range(len(sel))]
+ if y_tput_max:
+ pl_tput["yaxis"]["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
+ pl_lat["xaxis"]["tickvals"] = [i for i in range(len(x_lat))]
+ pl_lat["xaxis"]["ticktext"] = x_lat
if y_lat_max:
- pl_lat[u"yaxis"][u"range"] = [0, (int(y_lat_max / 10) + 1) * 10]
+ pl_lat["yaxis"]["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:
+def table_comparison(data: pd.DataFrame, sel:dict,
+ normalize: bool) -> pd.DataFrame:
"""
"""
table = pd.DataFrame(
)
return pd.DataFrame() #table
-
-
-def graph_hdrh_latency(data: dict, layout: dict) -> go.Figure:
- """
- """
-
- fig = None
-
- traces = list()
- for idx, (lat_name, lat_hdrh) in enumerate(data.items()):
- try:
- decoded = hdrh.histogram.HdrHistogram.decode(lat_hdrh)
- except (hdrh.codec.HdrLengthException, TypeError) as err:
- continue
- previous_x = 0.0
- prev_perc = 0.0
- xaxis = list()
- yaxis = list()
- hovertext = list()
- for item in decoded.get_recorded_iterator():
- # The real value is "percentile".
- # For 100%, we cut that down to "x_perc" to avoid
- # infinity.
- percentile = item.percentile_level_iterated_to
- x_perc = min(percentile, PERCENTILE_MAX)
- xaxis.append(previous_x)
- yaxis.append(item.value_iterated_to)
- hovertext.append(
- f"<b>{_GRAPH_LAT_HDRH_DESC[lat_name]}</b><br>"
- f"Direction: {(u'W-E', u'E-W')[idx % 2]}<br>"
- f"Percentile: {prev_perc:.5f}-{percentile:.5f}%<br>"
- f"Latency: {item.value_iterated_to}uSec"
- )
- next_x = 100.0 / (100.0 - x_perc)
- xaxis.append(next_x)
- yaxis.append(item.value_iterated_to)
- hovertext.append(
- f"<b>{_GRAPH_LAT_HDRH_DESC[lat_name]}</b><br>"
- f"Direction: {(u'W-E', u'E-W')[idx % 2]}<br>"
- f"Percentile: {prev_perc:.5f}-{percentile:.5f}%<br>"
- f"Latency: {item.value_iterated_to}uSec"
- )
- previous_x = next_x
- prev_perc = percentile
-
- traces.append(
- go.Scatter(
- x=xaxis,
- y=yaxis,
- name=_GRAPH_LAT_HDRH_DESC[lat_name],
- mode=u"lines",
- legendgroup=_GRAPH_LAT_HDRH_DESC[lat_name],
- showlegend=bool(idx % 2),
- line=dict(
- color=_get_color(int(idx/2)),
- dash=u"solid",
- width=1 if idx % 2 else 2
- ),
- hovertext=hovertext,
- hoverinfo=u"text"
- )
- )
- if traces:
- fig = go.Figure()
- fig.add_traces(traces)
- layout_hdrh = layout.get("plot-hdrh-latency", None)
- if lat_hdrh:
- fig.update_layout(layout_hdrh)
-
- return fig
Code Review / csit.git / blobdiff