Report: Add CentOS func tests

[csit.git] / resources / tools / presentation / generator_tables.py

diff --git a/resources/tools/presentation/generator_tables.py b/resources/tools/presentation/generator_tables.py
index cb89a8b..0b92db6 100644 (file)
--- a/resources/tools/presentation/generator_tables.py
+++ b/resources/tools/presentation/generator_tables.py
@@ -22,6 +22,7 @@ import pandas as pd
 
 from string import replace
 from math import isnan
+from collections import OrderedDict
 from numpy import nan
 from xml.etree import ElementTree as ET
 
@@ -360,12 +361,20 @@ def table_performance_comparison(table, input_data):
 
     # Prepare the header of the tables
     try:
-        header = ["Test case",
-                  "{0} Throughput [Mpps]".format(table["reference"]["title"]),
-                  "{0} stdev [Mpps]".format(table["reference"]["title"]),
-                  "{0} Throughput [Mpps]".format(table["compare"]["title"]),
-                  "{0} stdev [Mpps]".format(table["compare"]["title"]),
-                  "Change [%]"]
+        header = ["Test case", ]
+
+        history = table.get("history", None)
+        if history:
+            for item in history:
+                header.extend(
+                    ["{0} Throughput [Mpps]".format(item["title"]),
+                     "{0} Stdev [Mpps]".format(item["title"])])
+        header.extend(
+            ["{0} Throughput [Mpps]".format(table["reference"]["title"]),
+             "{0} Stdev [Mpps]".format(table["reference"]["title"]),
+             "{0} Throughput [Mpps]".format(table["compare"]["title"]),
+             "{0} Stdev [Mpps]".format(table["compare"]["title"]),
+             "Change [%]"])
         header_str = ",".join(header) + "\n"
     except (AttributeError, KeyError) as err:
         logging.error("The model is invalid, missing parameter: {0}".
@@ -400,10 +409,43 @@ def table_performance_comparison(table, input_data):
                     pass
                 except TypeError:
                     tbl_dict.pop(tst_name, None)
+    if history:
+        for item in history:
+            for job, builds in item["data"].items():
+                for build in builds:
+                    for tst_name, tst_data in data[job][str(build)].iteritems():
+                        if tbl_dict.get(tst_name, None) is None:
+                            continue
+                        if tbl_dict[tst_name].get("history", None) is None:
+                            tbl_dict[tst_name]["history"] = OrderedDict()
+                        if tbl_dict[tst_name]["history"].get(item["title"],
+                                                             None) is None:
+                            tbl_dict[tst_name]["history"][item["title"]] = \
+                                list()
+                        try:
+                            tbl_dict[tst_name]["history"][item["title"]].\
+                                append(tst_data["throughput"]["value"])
+                        except (TypeError, KeyError):
+                            pass
 
     tbl_lst = list()
     for tst_name in tbl_dict.keys():
         item = [tbl_dict[tst_name]["name"], ]
+        if history:
+            if tbl_dict[tst_name].get("history", None) is not None:
+                for hist_data in tbl_dict[tst_name]["history"].values():
+                    if hist_data:
+                        data_t = remove_outliers(
+                            hist_data, outlier_const=table["outlier-const"])
+                        if data_t:
+                            item.append(round(mean(data_t) / 1000000, 2))
+                            item.append(round(stdev(data_t) / 1000000, 2))
+                        else:
+                            item.extend([None, None])
+                    else:
+                        item.extend([None, None])
+            else:
+                item.extend([None, None])
         if tbl_dict[tst_name]["ref-data"]:
             data_t = remove_outliers(tbl_dict[tst_name]["ref-data"],
                                      outlier_const=table["outlier-const"])
@@ -426,9 +468,9 @@ def table_performance_comparison(table, input_data):
                 item.extend([None, None])
         else:
             item.extend([None, None])
-        if item[1] is not None and item[3] is not None:
-            item.append(int(relative_change(float(item[1]), float(item[3]))))
-        if len(item) == 6:
+        if item[-4] is not None and item[-2] is not None and item[-4] != 0:
+            item.append(int(relative_change(float(item[-4]), float(item[-2]))))
+        if len(item) == len(header):
             tbl_lst.append(item)
 
     # Sort the table according to the relative change
@@ -749,14 +791,6 @@ def table_performance_trending_dashboard(table, input_data):
             # Test name:
             name = tbl_dict[tst_name]["name"]
 
-            logging.info("{}".format(name))
-            logging.info("pd_data : {}".format(pd_data))
-            logging.info("data_t : {}".format(data_t))
-            logging.info("median_t : {}".format(median_t))
-            logging.info("last_median_t : {}".format(last_median_t))
-            logging.info("median_t_14 : {}".format(median_t_14))
-            logging.info("max_median : {}".format(max_median))
-
             # Classification list:
             classification_lst = list()
             for build_nr, value in pd_data.iteritems():
@@ -806,7 +840,8 @@ def table_performance_trending_dashboard(table, input_data):
         for nrp in range(table["window"], -1, -1):
             tbl_pro = [item for item in tbl_reg if item[5] == nrp]
             for nro in range(table["window"], -1, -1):
-                tbl_out = [item for item in tbl_pro if item[5] == nro]
+                tbl_out = [item for item in tbl_pro if item[6] == nro]
+                tbl_out.sort(key=lambda rel: rel[2])
                 tbl_sorted.extend(tbl_out)
 
     file_name = "{0}{1}".format(table["output-file"], table["output-file-ext"])
@@ -869,8 +904,20 @@ def table_performance_trending_dashboard_html(table, input_data):
         th.text = item
 
     # Rows:
+    colors = {"regression": ("#ffcccc", "#ff9999"),
+              "progression": ("#c6ecc6", "#9fdf9f"),
+              "outlier": ("#e6e6e6", "#cccccc"),
+              "normal": ("#e9f1fb", "#d4e4f7")}
     for r_idx, row in enumerate(csv_lst[1:]):
-        background = "#D4E4F7" if r_idx % 2 else "white"
+        if int(row[4]):
+            color = "regression"
+        elif int(row[5]):
+            color = "progression"
+        elif int(row[6]):
+            color = "outlier"
+        else:
+            color = "normal"
+        background = colors[color][r_idx % 2]
         tr = ET.SubElement(dashboard, "tr", attrib=dict(bgcolor=background))
 
         # Columns: