"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
traces.append(trace_anomalies)
if show_moving_median:
- min_periods = moving_win_size / 2 + 1
data_mean_y = pd.Series(data_y).rolling(
- window=moving_win_size, min_periods=min_periods).median()
+ window=moving_win_size, min_periods=2).median()
trace_median = plgo.Scatter(
x=data_x,
y=data_mean_y,
txt_table = None
with open("{0}.csv".format(file_name), 'rb') as csv_file:
csv_content = csv.reader(csv_file, delimiter=',', quotechar='"')
+ header = True
for row in csv_content:
if txt_table is None:
txt_table = prettytable.PrettyTable(row)
+ header = False
else:
+ if not header:
+ for idx, item in enumerate(row):
+ try:
+ row[idx] = str(round(float(item) / 1000000, 2))
+ except ValueError:
+ pass
txt_table.add_row(row)
txt_table.align["Build Number:"] = "l"
with open("{0}.txt".format(file_name), "w") as txt_file: