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
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
+ return table
from dash import html
from dash import callback_context, no_update, ALL
from dash import Input, Output, State
+from dash.exceptions import PreventUpdate
from yaml import load, FullLoader, YAMLError
from copy import deepcopy
from ast import literal_eval
gen_new_url
from ..utils.url_processing import url_decode
from ..data.data import Data
-from .graphs import graph_iterative, table_comparison, get_short_version
+from .graphs import graph_iterative, table_comparison, get_short_version, \
+ select_iterative_data
class Layout:
]
ret_val.extend(ctrl_panel.values())
return ret_val
+
+ @app.callback(
+ Output("download-data", "data"),
+ State("selected-tests", "data"),
+ Input("btn-download-data", "n_clicks"),
+ prevent_initial_call=True
+ )
+ def _download_data(store_sel, n_clicks):
+ """Download the data
+
+ :param store_sel: List of tests selected by user stored in the
+ browser.
+ :param n_clicks: Number of clicks on the button "Download".
+ :type store_sel: list
+ :type n_clicks: int
+ :returns: dict of data frame content (base64 encoded) and meta data
+ used by the Download component.
+ :rtype: dict
+ """
+
+ if not n_clicks:
+ raise PreventUpdate
+
+ if not store_sel:
+ raise PreventUpdate
+
+ df = pd.DataFrame()
+ for itm in store_sel:
+ sel_data = select_iterative_data(self.data, itm)
+ if sel_data is None:
+ continue
+ df = pd.concat([df, sel_data], ignore_index=True)
+
+ return dcc.send_data_frame(df.to_csv, C.REPORT_DOWNLOAD_FILE_NAME)
def select_data(data: pd.DataFrame, itm:str, start: datetime,
end: datetime) -> pd.DataFrame:
- """
+ """Select the data for graphs from the provided data frame.
+
+ :param data: Data frame with data for graphs.
+ :param itm: Item (in this case job name) which data will be selected from
+ the input data frame.
+ :param start: The date (and time) when the selected data starts.
+ :param end: The date (and time) when the selected data ends.
+ :type data: pandas.DataFrame
+ :type itm: str
+ :type start: datetime.datetime
+ :type end: datetime.datetime
+ :returns: A data frame with selected data.
+ :rtype: pandas.DataFrame
"""
df = data.loc[
def graph_statistics(df: pd.DataFrame, job:str, layout: dict,
start: datetime=datetime.utcnow()-timedelta(days=180),
end: datetime=datetime.utcnow()) -> tuple:
- """
+ """Generate graphs:
+ 1. Passed / failed tests,
+ 2. Job durations
+ with additional information shown in hover.
+
+ :param df: Data frame with input data.
+ :param job: The name of job which data will be presented in the graphs.
+ :param layout: Layout of plot.ly graph.
+ :param start: The date (and time) when the selected data starts.
+ :param end: The date (and time) when the selected data ends.
+ :type df: pandas.DataFrame
+ :type job: str
+ :type layout: dict
+ :type start: datetime.datetime
+ :type end: datetime.datetime
+ :returns: Tuple with two generated graphs (pased/failed tests and job
+ duration).
+ :rtype: tuple(plotly.graph_objects.Figure, plotly.graph_objects.Figure)
"""
data = select_data(df, job, start, end)
data = select_data(self.data, job, get_date(start), get_date(end))
data = data.drop(columns=["job", ])
- return dcc.send_data_frame(data.T.to_csv, f"{job}-stats.csv")
+ return dcc.send_data_frame(
+ data.T.to_csv, f"{job}-{C.STATS_DOWNLOAD_FILE_NAME}")
@app.callback(
Output("row-metadata", "children"),
def _get_hdrh_latencies(row: pd.Series, name: str) -> dict:
- """
+ """Get the HDRH latencies from the test data.
+
+ :param row: A row fron the data frame with test data.
+ :param name: The test name to be displayed as the graph title.
+ :type row: pandas.Series
+ :type name: str
+ :returns: Dictionary with HDRH latencies.
+ :rtype: dict
"""
latencies = {"name": name}
def select_trending_data(data: pd.DataFrame, itm:dict) -> pd.DataFrame:
- """
+ """Select the data for graphs from the provided data frame.
+
+ :param data: Data frame with data for graphs.
+ :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 _generate_trending_traces(ttype: str, name: str, df: pd.DataFrame,
start: datetime, end: datetime, color: str, norm_factor: float) -> list:
- """
+ """Generate the trending traces for the trending graph.
+
+ :param ttype: Test type (MRR, NDR, PDR).
+ :param name: The test name to be displayed as the graph title.
+ :param df: Data frame with test data.
+ :param start: The date (and time) when the selected data starts.
+ :param end: The date (and time) when the selected data ends.
+ :param color: The color of the trace (samples and trend line).
+ :param norm_factor: The factor used for normalization of the results to CPU
+ frequency set to Constants.NORM_FREQUENCY.
+ :type ttype: str
+ :type name: str
+ :type df: pandas.DataFrame
+ :type start: datetime.datetime
+ :type end: datetime.datetime
+ :type color: str
+ :type norm_factor: float
+ :returns: Traces (samples, trending line, anomalies)
+ :rtype: list
"""
df = df.dropna(subset=[C.VALUE[ttype], ])
def graph_trending(data: pd.DataFrame, sel:dict, layout: dict,
start: datetime, end: datetime, normalize: bool) -> tuple:
- """
+ """Generate the trending graph(s) - MRR, NDR, PDR and for PDR also Latences
+ (result_latency_forward_pdr_50_avg).
+
+ :param data: Data frame with test results.
+ :param sel: Selected tests.
+ :param layout: Layout of plot.ly graph.
+ :param start: The date (and time) when the selected data starts.
+ :param end: The date (and time) when the selected data ends.
+ :param normalize: If True, the data is normalized to CPU frquency
+ Constants.NORM_FREQUENCY.
+ :type data: pandas.DataFrame
+ :type sel: dict
+ :type layout: dict
+ :type start: datetime.datetime
+ :type end: datetype.datetype
+ :type normalize: bool
+ :returns: Trending graph(s)
+ :rtype: tuple(plotly.graph_objects.Figure, plotly.graph_objects.Figure)
"""
if not sel:
def graph_hdrh_latency(data: dict, layout: dict) -> go.Figure:
- """
+ """Generate HDR Latency histogram graphs.
+
+ :param data: HDRH data.
+ :param layout: Layout of plot.ly graph.
+ :type data: dict
+ :type layout: dict
+ :returns: HDR latency Histogram.
+ :rtype: plotly.graph_objects.Figure
"""
fig = None
continue
df = pd.concat([df, sel_data], ignore_index=True)
- return dcc.send_data_frame(df.to_csv, "trending_data.csv")
+ return dcc.send_data_frame(df.to_csv, C.TREND_DOWNLOAD_FILE_NAME)
# Layout of plot.ly graphs.
REPORT_GRAPH_LAYOUT_FILE = "pal/report/layout.yaml"
+ # Default name of downloaded file with selected data.
+ REPORT_DOWNLOAD_FILE_NAME = "iterative_data.csv"
+
############################################################################
# Statistics.
# The default job displayed when the page is loaded first time.
STATS_DEFAULT_JOB = "csit-vpp-perf-mrr-daily-master-2n-icx"
+ # Default name of downloaded file with selected data.
+ STATS_DOWNLOAD_FILE_NAME = "stats.csv"
+
############################################################################
# Trending.
# Layout of plot.ly graphs.
TREND_GRAPH_LAYOUT_FILE = "pal/trending/layout.yaml"
+
+ # Default name of downloaded file with selected data.
+ TREND_DOWNLOAD_FILE_NAME = "trending_data.csv"
Code Review / csit.git / commitdiff