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Diffstat (limited to 'csit.infra.dash/app/cdash/report/graphs.py')
| -rw-r--r-- | csit.infra.dash/app/cdash/report/graphs.py | 275 |
1 files changed, 275 insertions, 0 deletions
diff --git a/csit.infra.dash/app/cdash/report/graphs.py b/csit.infra.dash/app/cdash/report/graphs.py new file mode 100644 index 0000000000..36f28d09e8 --- /dev/null +++ b/csit.infra.dash/app/cdash/report/graphs.py @@ -0,0 +1,275 @@ +# Copyright (c) 2022 Cisco and/or its affiliates. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at: +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +""" + +import re +import plotly.graph_objects as go +import pandas as pd + +from copy import deepcopy + +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"): + return version + + s_version = str() + groups = re.search( + pattern=re.compile(r"^(\d{2}).(\d{2})-(rc0|rc1|rc2|release$)"), + string=version + ) + if groups: + try: + s_version = \ + f"{groups.group(1)}.{groups.group(2)}.{groups.group(3)}".\ + replace("release", "rls") + except IndexError: + pass + + return s_version + + +def select_iterative_data(data: pd.DataFrame, itm:dict) -> pd.DataFrame: + """Select the data for graphs and tables from the provided data frame. + + :param data: Data frame with data for graphs and tables. + :param itm: Item (in this case job name) which data will be selected from + the input data frame. + :type data: pandas.DataFrame + :type itm: str + :returns: A data frame with selected data. + :rtype: pandas.DataFrame + """ + + phy = itm["phy"].split("-") + if len(phy) == 4: + topo, arch, nic, drv = phy + if drv == "dpdk": + drv = "" + else: + drv += "-" + drv = drv.replace("_", "-") + else: + return None + + core = str() if itm["dut"] == "trex" else f"{itm['core']}" + ttype = "ndrpdr" if itm["testtype"] in ("ndr", "pdr") else itm["testtype"] + dut_v100 = "none" if itm["dut"] == "trex" else itm["dut"] + dut_v101 = itm["dut"] + + df = data.loc[( + (data["release"] == itm["rls"]) & + ( + ( + (data["version"] == "1.0.0") & + (data["dut_type"].str.lower() == dut_v100) + ) | + ( + (data["version"] == "1.0.1") & + (data["dut_type"].str.lower() == dut_v101) + ) + ) & + (data["test_type"] == ttype) & + (data["passed"] == True) + )] + regex_test = \ + f"^.*[.|-]{nic}.*{itm['framesize']}-{core}-{drv}{itm['test']}-{ttype}$" + df = df[ + (df.job.str.endswith(f"{topo}-{arch}")) & + (df.dut_version.str.contains(itm["dutver"].replace(".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, + 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 + fig_lat = None + + tput_traces = list() + y_tput_max = 0 + 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_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[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 + nr_of_samples = len(y_data) + tput_kwargs = dict( + y=y_data, + name=( + f"{idx + 1}. " + f"({nr_of_samples:02d} " + 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)) + ) + tput_traces.append(go.Box(**tput_kwargs)) + show_tput = True + + if itm["testtype"] == "pdr": + 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) + 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="all", + 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()) + + 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["xaxis"]["tickvals"] = [i for i in range(len(x_lat))] + pl_lat["xaxis"]["ticktext"] = x_lat + if y_lat_max: + 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, + 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"] + # } + ) + + return table |