from datetime import datetime
from ..utils.constants import Constants as C
-from ..utils.utils import classify_anomalies
-
-
-def _get_color(idx: int) -> str:
- """
- """
- return C.PLOT_COLORS[idx % len(C.PLOT_COLORS)]
+from ..utils.utils import classify_anomalies, get_color
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:
else:
norm_factor = 1.0
traces = _generate_trending_traces(
- itm["testtype"], name, df, start, end, _get_color(idx), norm_factor
+ itm["testtype"], name, df, start, end, get_color(idx), norm_factor
)
if traces:
if not fig_tput:
if itm["testtype"] == "pdr":
traces = _generate_trending_traces(
- "pdr-lat", name, df, start, end, _get_color(idx), norm_factor
+ "pdr-lat", name, df, start, end, get_color(idx), norm_factor
)
if traces:
if not fig_lat:
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
legendgroup=C.GRAPH_LAT_HDRH_DESC[lat_name],
showlegend=bool(idx % 2),
line=dict(
- color=_get_color(int(idx/2)),
+ color=get_color(int(idx/2)),
dash="solid",
width=1 if idx % 2 else 2
),
Code Review / csit.git / blobdiff