from copy import deepcopy
-import hdrh.histogram
-import hdrh.codec
-
-
-_NORM_FREQUENCY = 2.0 # [GHz]
-_FREQURENCY = { # [GHz]
- "2n-aws": 1.000,
- "2n-dnv": 2.000,
- "2n-clx": 2.300,
- "2n-icx": 2.600,
- "2n-skx": 2.500,
- "2n-tx2": 2.500,
- "2n-zn2": 2.900,
- "3n-alt": 3.000,
- "3n-aws": 1.000,
- "3n-dnv": 2.000,
- "3n-icx": 2.600,
- "3n-skx": 2.500,
- "3n-tsh": 2.200
-}
-
-_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 = {
- "result_latency_forward_pdr_0_hdrh": "No-load.",
- "result_latency_reverse_pdr_0_hdrh": "No-load.",
- "result_latency_forward_pdr_10_hdrh": "Low-load, 10% PDR.",
- "result_latency_reverse_pdr_10_hdrh": "Low-load, 10% PDR.",
- "result_latency_forward_pdr_50_hdrh": "Mid-load, 50% PDR.",
- "result_latency_reverse_pdr_50_hdrh": "Mid-load, 50% PDR.",
- "result_latency_forward_pdr_90_hdrh": "High-load, 90% PDR.",
- "result_latency_reverse_pdr_90_hdrh": "High-load, 90% PDR."
-}
-
-
-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:
- """
+ """Returns the short version of DUT without build number.
+
+ :param version: Original version string.
+ :param dut_type: DUT type.
+ :type version: str
+ :type dut_type: str
+ :returns: Short verion string.
+ :rtype: str
"""
if dut_type in ("trex", "dpdk"):
def select_iterative_data(data: pd.DataFrame, itm:dict) -> pd.DataFrame:
- """
+ """Select the data for graphs and tables from the provided data frame.
+
+ :param data: Data frame with data for graphs and tables.
+ :param itm: Item (in this case job name) which data will be selected from
+ the input data frame.
+ :type data: pandas.DataFrame
+ :type itm: str
+ :returns: A data frame with selected data.
+ :rtype: pandas.DataFrame
"""
phy = itm["phy"].split("-")
def graph_iterative(data: pd.DataFrame, sel:dict, layout: dict,
normalize: bool) -> tuple:
- """
+ """Generate the statistical box graph with iterative data (MRR, NDR and PDR,
+ for PDR also Latencies).
+
+ :param data: Data frame with iterative data.
+ :param sel: Selected tests.
+ :param layout: Layout of plot.ly graph.
+ :param normalize: If True, the data is normalized to CPU frquency
+ Constants.NORM_FREQUENCY.
+ :param data: pandas.DataFrame
+ :param sel: dict
+ :param layout: dict
+ :param normalize: bool
+ :returns: Tuple of graphs - throughput and latency.
+ :rtype: tuple(plotly.graph_objects.Figure, plotly.graph_objects.Figure)
"""
fig_tput = None
continue
phy = itm["phy"].split("-")
topo_arch = f"{phy[0]}-{phy[1]}" if len(phy) == 4 else str()
- norm_factor = (_NORM_FREQUENCY / _FREQURENCY[topo_arch]) \
+ norm_factor = (C.NORM_FREQUENCY / C.FREQUENCY[topo_arch]) \
if normalize else 1.0
if itm["testtype"] == "mrr":
- y_data_raw = itm_data[_VALUE[itm["testtype"]]].to_list()[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_raw = 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 = \
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
if itm["testtype"] == "pdr":
- y_lat_row = 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
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))
def table_comparison(data: pd.DataFrame, sel:dict,
normalize: bool) -> pd.DataFrame:
- """
+ """Generate the comparison table with selected tests.
+
+ :param data: Data frame with iterative data.
+ :param sel: Selected tests.
+ :param normalize: If True, the data is normalized to CPU frquency
+ Constants.NORM_FREQUENCY.
+ :param data: pandas.DataFrame
+ :param sel: dict
+ :param normalize: bool
+ :returns: Comparison table.
+ :rtype: pandas.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 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: {('W-E', '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: {('W-E', '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="lines",
- legendgroup=_GRAPH_LAT_HDRH_DESC[lat_name],
- showlegend=bool(idx % 2),
- line=dict(
- color=_get_color(int(idx/2)),
- dash="solid",
- width=1 if idx % 2 else 2
- ),
- hovertext=hovertext,
- hoverinfo="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
+ return table
Code Review / csit.git / blobdiff