diff options
Diffstat (limited to 'resources/tools/dash/app/pal/trending/graphs.py')
| -rw-r--r-- | resources/tools/dash/app/pal/trending/graphs.py | 17 |
1 files changed, 4 insertions, 13 deletions
diff --git a/resources/tools/dash/app/pal/trending/graphs.py b/resources/tools/dash/app/pal/trending/graphs.py index 06bea25466..1eff4aa889 100644 --- a/resources/tools/dash/app/pal/trending/graphs.py +++ b/resources/tools/dash/app/pal/trending/graphs.py @@ -99,22 +99,18 @@ def select_trending_data(data: pd.DataFrame, itm:dict) -> pd.DataFrame: def _generate_trending_traces(ttype: str, name: str, df: pd.DataFrame, - start: datetime, end: datetime, color: str, norm_factor: float) -> list: + 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) @@ -124,7 +120,6 @@ def _generate_trending_traces(ttype: str, name: str, df: pd.DataFrame, df = df.dropna(subset=[C.VALUE[ttype], ]) if df.empty: return list() - df = df.loc[((df["start_time"] >= start) & (df["start_time"] <= end))] if df.empty: return list() @@ -274,22 +269,18 @@ def _generate_trending_traces(ttype: str, name: str, df: pd.DataFrame, def graph_trending(data: pd.DataFrame, sel:dict, layout: dict, - start: datetime, end: datetime, normalize: bool) -> tuple: + 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) @@ -316,7 +307,7 @@ def graph_trending(data: pd.DataFrame, sel:dict, layout: dict, else: norm_factor = 1.0 traces = _generate_trending_traces( - itm["testtype"], name, df, start, end, get_color(idx), norm_factor + itm["testtype"], name, df, get_color(idx), norm_factor ) if traces: if not fig_tput: @@ -325,7 +316,7 @@ def graph_trending(data: pd.DataFrame, sel:dict, layout: dict, if itm["testtype"] == "pdr": traces = _generate_trending_traces( - "pdr-lat", name, df, start, end, get_color(idx), norm_factor + "pdr-lat", name, df, get_color(idx), norm_factor ) if traces: if not fig_lat: |