"color": anomalies_res,
"colorscale": color_scale,
"showscale": True,
-
+ "line": {
+ "width": 2
+ },
"colorbar": {
"y": 0.5,
"len": 0.8,
- "title": "Results Clasification",
+ "title": "Circles Marking Data Classification",
"titleside": 'right',
"titlefont": {
"size": 14
},
"tickmode": 'array',
"tickvals": [0.125, 0.375, 0.625, 0.875],
- "ticktext": ["Outlier", "Regress", "Normal", "Progress"],
- "ticks": 'outside',
+ "ticktext": ["Outlier", "Regression", "Normal", "Progression"],
+ "ticks": "",
"ticklen": 0,
"tickangle": -90,
"thickness": 10
if show_moving_median:
data_mean_y = pd.Series(data_y).rolling(
- window=moving_win_size).median()
+ window=moving_win_size, min_periods=2).median()
trace_median = plgo.Scatter(
x=data_x,
y=data_mean_y,
"width": 1,
"color": color,
},
- name='{name}-trend'.format(name=name, size=moving_win_size)
+ name='{name}-trend'.format(name=name)
)
traces.append(trace_median)
chart_data[test_name][int(idx)] = \
test["result"]["throughput"]
except (KeyError, TypeError):
- chart_data[test_name][int(idx)] = None
+ pass
# Add items to the csv table:
for tst_name, tst_data in chart_data.items():
result = "PASS"
elif item == 0.33 or item == 0.0:
result = "FAIL"
- print(results)
- print(result)
- if result == "FAIL":
- return 1
- else:
- return 0
+
+ logging.info("Partial results: {0}".format(results))
+ logging.info("Result: {0}".format(result))
+
+ return result