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-rw-r--r--resources/tools/presentation/generator_tables.py73
-rw-r--r--resources/tools/presentation/specification_CPTA.yaml6
2 files changed, 35 insertions, 44 deletions
diff --git a/resources/tools/presentation/generator_tables.py b/resources/tools/presentation/generator_tables.py
index 50a6623356..59162444dc 100644
--- a/resources/tools/presentation/generator_tables.py
+++ b/resources/tools/presentation/generator_tables.py
@@ -677,6 +677,7 @@ def table_performance_trending_dashboard(table, input_data):
# Prepare the header of the tables
header = ["Test Case",
"Throughput Trend [Mpps]",
+ "Long Trend Compliance",
"Trend Compliance",
"Top Anomaly [Mpps]",
"Change [%]",
@@ -706,12 +707,14 @@ def table_performance_trending_dashboard(table, input_data):
if len(tbl_dict[tst_name]["data"]) > 2:
pd_data = pd.Series(tbl_dict[tst_name]["data"])
- win_size = pd_data.size \
- if pd_data.size < table["window"] else table["window"]
+ win_size = min(pd_data.size, table["window"])
# Test name:
name = tbl_dict[tst_name]["name"]
median = pd_data.rolling(window=win_size, min_periods=2).median()
+ median_idx = pd_data.size - table["long-trend-window"]
+ median_idx = 0 if median_idx < 0 else median_idx
+ max_median = max(median.values[median_idx:])
trimmed_data, _ = split_outliers(pd_data, outlier_const=1.5,
window=win_size)
stdev_t = pd_data.rolling(window=win_size, min_periods=2).std()
@@ -790,48 +793,27 @@ def table_performance_trending_dashboard(table, input_data):
if rel_change_lst[idx] > rel_change_lst[index]:
index = idx
- # if "regression" in classification_lst[first_idx:]:
- # classification = "regression"
- # elif "outlier" in classification_lst[first_idx:]:
- # classification = "outlier"
- # elif "progression" in classification_lst[first_idx:]:
- # classification = "progression"
- # elif "normal" in classification_lst[first_idx:]:
- # classification = "normal"
- # else:
- # classification = None
- #
- # nr_outliers = 0
- # consecutive_outliers = 0
- # failure = False
- # for item in classification_lst[first_idx:]:
- # if item == "outlier":
- # nr_outliers += 1
- # consecutive_outliers += 1
- # if consecutive_outliers == 3:
- # failure = True
- # else:
- # consecutive_outliers = 0
- #
- # idx = len(classification_lst) - 1
- # while idx:
- # if classification_lst[idx] == classification:
- # break
- # idx -= 1
- #
- # if failure:
- # classification = "failure"
- # elif classification == "outlier":
- # classification = "normal"
-
trend = round(float(median_lst[-1]) / 1000000, 2) \
- if not isnan(median_lst[-1]) else ''
+ if not isnan(median_lst[-1]) else '-'
sample = round(float(sample_lst[index]) / 1000000, 2) \
- if not isnan(sample_lst[index]) else ''
+ if not isnan(sample_lst[index]) else '-'
rel_change = rel_change_lst[index] \
- if rel_change_lst[index] is not None else ''
+ if rel_change_lst[index] is not None else '-'
+ if not isnan(max_median):
+ if not isnan(sample_lst[index]):
+ long_trend_threshold = max_median * \
+ (table["long-trend-threshold"] / 100)
+ if sample_lst[index] < long_trend_threshold:
+ long_trend_classification = "failure"
+ else:
+ long_trend_classification = '-'
+ else:
+ long_trend_classification = "failure"
+ else:
+ long_trend_classification = '-'
tbl_lst.append([name,
trend,
+ long_trend_classification,
classification,
'-' if classification == "normal" else sample,
'-' if classification == "normal" else rel_change,
@@ -839,10 +821,13 @@ def table_performance_trending_dashboard(table, input_data):
# Sort the table according to the classification
tbl_sorted = list()
- for classification in ("failure", "regression", "progression", "normal"):
- tbl_tmp = [item for item in tbl_lst if item[2] == classification]
- tbl_tmp.sort(key=lambda rel: rel[0])
- tbl_sorted.extend(tbl_tmp)
+ for long_trend_class in ("failure", '-'):
+ tbl_long = [item for item in tbl_lst if item[2] == long_trend_class]
+ for classification in \
+ ("failure", "regression", "progression", "normal"):
+ tbl_tmp = [item for item in tbl_long if item[3] == classification]
+ tbl_tmp.sort(key=lambda rel: rel[0])
+ tbl_sorted.extend(tbl_tmp)
file_name = "{0}{1}".format(table["output-file"], table["output-file-ext"])
@@ -978,7 +963,7 @@ def table_performance_trending_dashboard_html(table, input_data):
ref = ET.SubElement(td, "a", attrib=dict(href=url))
ref.text = item
- if c_idx == 2:
+ if c_idx == 3:
if item == "regression":
td.set("bgcolor", "#eca1a6")
elif item == "failure":
diff --git a/resources/tools/presentation/specification_CPTA.yaml b/resources/tools/presentation/specification_CPTA.yaml
index 4e2aad6d40..77fee3506a 100644
--- a/resources/tools/presentation/specification_CPTA.yaml
+++ b/resources/tools/presentation/specification_CPTA.yaml
@@ -228,6 +228,8 @@
outlier-const: 1.5
window: 14
evaluated-window: 14
+ long-trend-window: 180
+ long-trend-threshold: 80 # Percent of the highest moving median value
-
type: "table"
@@ -247,6 +249,8 @@
outlier-const: 1.5
window: 14
evaluated-window: 14
+ long-trend-window: 180
+ long-trend-threshold: 80 # Percent of the highest moving median value
-
type: "table"
@@ -266,6 +270,8 @@
outlier-const: 1.5
window: 14
evaluated-window: 14
+ long-trend-window: 180
+ long-trend-threshold: 80 # Percent of the highest moving median value
-
type: "table"