X-Git-Url: https://gerrit.fd.io/r/gitweb?p=csit.git;a=blobdiff_plain;f=resources%2Ftools%2Fdash%2Fapp%2Fpal%2Fstats%2Fgraphs.py;h=42f23da5aa1054e04681f37476790b327573e462;hp=37fc1b2e731d31ffb8bd0b34c8d9ed015b137529;hb=808797d2d913eac7581a4e4cba3fb826ddbff775;hpb=cd417be7f836eb9346fad4f87bd4f75dc1d9a429

diff --git a/resources/tools/dash/app/pal/stats/graphs.py b/resources/tools/dash/app/pal/stats/graphs.py
index 37fc1b2e73..42f23da5aa 100644
--- a/resources/tools/dash/app/pal/stats/graphs.py
+++ b/resources/tools/dash/app/pal/stats/graphs.py
@@ -19,12 +19,28 @@ import pandas as pd
 
 from datetime import datetime, timedelta
 
-def select_data(data: pd.DataFrame, itm:str) -> pd.DataFrame:
-    """
+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[(data["job"] == itm)].sort_values(
-        by="start_time", ignore_index=True)
+    df = data.loc[
+        (data["job"] == itm) &
+        (data["start_time"] >= start) & (data["start_time"] <= end)
+    ].sort_values(by="start_time", ignore_index=True)
+    df = df.dropna(subset=["duration", ])
 
     return df
 
@@ -32,30 +48,41 @@ def select_data(data: pd.DataFrame, itm:str) -> pd.DataFrame:
 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.
 
-    data = select_data(df, job)
-    data = data.dropna(subset=["duration", ])
-    if data.empty:
-        return None, None
+    :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 = data.loc[(
-        (data["start_time"] >= start) & (data["start_time"] <= end)
-    )]
+    data = select_data(df, job, start, end)
     if data.empty:
         return None, None
 
     hover = list()
     for _, row in data.iterrows():
+        d_type = "trex" if row["dut_type"] == "none" else row["dut_type"]
         hover_itm = (
-            f"date: {row['start_time'].strftime('%d-%m-%Y %H:%M:%S')}<br>"
+            f"date: {row['start_time'].strftime('%Y-%m-%d %H:%M:%S')}<br>"
             f"duration: "
             f"{(int(row['duration']) // 3600):02d}:"
             f"{((int(row['duration']) % 3600) // 60):02d}<br>"
             f"passed: {row['passed']}<br>"
             f"failed: {row['failed']}<br>"
-            f"{row['dut_type']}-ref: {row['dut_version']}<br>"
+            f"{d_type}-ref: {row['dut_version']}<br>"
             f"csit-ref: {row['job']}/{row['build']}<br>"
             f"hosts: {', '.join(row['hosts'])}"
         )