CDash: Add comparison tables

[csit.git] / csit.infra.dash / app / cdash / report / graphs.py

diff --git a/csit.infra.dash/app/cdash/report/graphs.py b/csit.infra.dash/app/cdash/report/graphs.py
index f10729a..9d10efc 100644 (file)
--- a/csit.infra.dash/app/cdash/report/graphs.py
+++ b/csit.infra.dash/app/cdash/report/graphs.py
@@ -11,10 +11,9 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-"""
+"""Implementation of graphs for iterative data.
 """
 
-import re
 import plotly.graph_objects as go
 import pandas as pd
 
@@ -47,26 +46,20 @@ def select_iterative_data(data: pd.DataFrame, itm:dict) -> pd.DataFrame:
     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"]
-
+    if itm["testtype"] in ("ndr", "pdr"):
+        test_type = "ndrpdr"
+    elif itm["testtype"] == "mrr":
+        test_type = "mrr"
+    elif itm["area"] == "hoststack":
+        test_type = "hoststack"
     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["test_type"] == test_type) &
         (data["passed"] == True)
     )]
+
+    core = str() if itm["dut"] == "trex" else f"{itm['core']}"
+    ttype = "ndrpdr" if itm["testtype"] in ("ndr", "pdr") else itm["testtype"]
     regex_test = \
         f"^.*[.|-]{nic}.*{itm['framesize']}-{core}-{drv}{itm['test']}-{ttype}$"
     df = df[
@@ -87,7 +80,7 @@ def graph_iterative(data: pd.DataFrame, sel:dict, layout: dict,
     :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
+    :param normalize: If True, the data is normalized to CPU frequency
         Constants.NORM_FREQUENCY.
     :param data: pandas.DataFrame
     :param sel: dict
@@ -105,28 +98,35 @@ def graph_iterative(data: pd.DataFrame, sel:dict, layout: dict,
     lat_traces = list()
     y_lat_max = 0
     x_lat = list()
+    y_units = set()
     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["area"] == "hoststack":
+            ttype = f"hoststack-{itm['testtype']}"
+        else:
+            ttype = itm["testtype"]
+
+        y_units.update(itm_data[C.UNIT[ttype]].unique().tolist())
+
         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
+            y_data_raw = itm_data[C.VALUE_ITER[ttype]].to_list()[0]
         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
+            y_data_raw = itm_data[C.VALUE_ITER[ttype]].to_list()
+        y_data = [(y * norm_factor) for y in y_data_raw]
+        if y_data:
+            y_tput_max = max(max(y_data), y_tput_max)
+
         nr_of_samples = len(y_data)
         tput_kwargs = dict(
             y=y_data,
@@ -144,11 +144,14 @@ def graph_iterative(data: pd.DataFrame, sel:dict, layout: dict,
         tput_traces.append(go.Box(**tput_kwargs))
         show_tput = True
 
-        if itm["testtype"] == "pdr":
+        if ttype == "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
+                try:
+                    y_lat_max = max(max(y_lat), y_lat_max)
+                except TypeError:
+                    continue
             nr_of_samples = len(y_lat)
             lat_kwargs = dict(
                 y=y_lat,
@@ -173,8 +176,9 @@ def graph_iterative(data: pd.DataFrame, sel:dict, layout: dict,
         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))]
+        pl_tput["yaxis"]["title"] = f"Throughput [{'|'.join(sorted(y_units))}]"
         if y_tput_max:
-            pl_tput["yaxis"]["range"] = [0, (int(y_tput_max / 1e6) + 1) * 1e6]
+            pl_tput["yaxis"]["range"] = [0, (int(y_tput_max / 1e6) + 2) * 1e6]
         fig_tput = go.Figure(data=tput_traces, layout=pl_tput)
 
     if show_latency: