from copy import deepcopy
from ..data.data import Data
-from ..data.utils import classify_anomalies
+from ..utils.constants import Constants as C
+from ..utils.utils import classify_anomalies
+from ..data.data import Data
from .tables import table_news
"""The layout of the dash app and the callbacks.
"""
- # The default job displayed when the page is loaded first time.
- DEFAULT_JOB = "csit-vpp-perf-mrr-daily-master-2n-icx"
-
- # Time period for regressions and progressions.
- TIME_PERIOD = 21 # [days]
-
def __init__(self, app: Flask, html_layout_file: str, data_spec_file: str,
tooltip_file: str) -> None:
"""Initialization:
data_stats, data_mrr, data_ndrpdr = Data(
data_spec_file=self._data_spec_file,
debug=True
- ).read_stats(days=self.TIME_PERIOD)
+ ).read_stats(days=C.NEWS_TIME_PERIOD)
df_tst_info = pd.concat([data_mrr, data_ndrpdr], ignore_index=True)
job_info["tbed"].append("-".join(lst_job[-2:]))
self.df_job_info = pd.DataFrame.from_dict(job_info)
- self._default = self._set_job_params(self.DEFAULT_JOB)
+ self._default = self._set_job_params(C.NEWS_DEFAULT_JOB)
# Pre-process the data: