f'src="../..{out_file_name.replace(u"_build", u"")}_in.html">'
f'</iframe>\n\n'
)
+
+ # TODO: Use html (rst) list for legend and footnote
if legend:
rst_file.write(legend[1:].replace(u"\n", u" |br| "))
if footnote:
f"Percentage change calculated for mean values.\n"
u"Stdev(Diff): "
u"Standard deviation of percentage change calculated for mean "
- u"values.\n"
- u":END"
+ u"values."
)
except (AttributeError, KeyError) as err:
logging.error(f"The model is invalid, missing parameter: {repr(err)}")
if len(data_t) < 2:
continue
- classification_lst, avgs = classify_anomalies(data_t)
+ classification_lst, avgs, _ = classify_anomalies(data_t)
win_size = min(len(data_t), table[u"window"])
long_win_size = min(len(data_t), table[u"long-trend-window"])
round(last_avg / 1e6, 2),
rel_change_last,
rel_change_long,
- classification_lst[-win_size:].count(u"regression"),
- classification_lst[-win_size:].count(u"progression")])
+ classification_lst[-win_size+1:].count(u"regression"),
+ classification_lst[-win_size+1:].count(u"progression")])
tbl_lst.sort(key=lambda rel: rel[0])
attrib=dict(align=u"left" if c_idx == 0 else u"center")
)
# Name:
- if c_idx == 0:
+ if c_idx == 0 and table.get(u"add-links", True):
ref = ET.SubElement(
tdata,
u"a",
with open(txt_file_name, u'a', encoding='utf-8') as txt_file:
txt_file.write(legend)
txt_file.write(footnote)
- if legend or footnote:
- txt_file.write(u"\n:END")
# Generate html table:
_tpc_generate_html_table(