from bokeh.models.widgets import Div, \
DatePicker, Select
from datetime import datetime, timedelta, date
import holoviews as hv
from holoviews import opts
from tornado.gen import coroutine
import numpy as np
import pandas as pd
from static.css.KPI_interface import KPI_card_css
lock = Lock()
executor = ThreadPoolExecutor()
logger = mylogger(__file__)
hv.extension('bokeh', logo=False)
renderer = hv.renderer('bokeh')
@coroutine
def KPI_projects_tab(panel_title, DAYS_TO_LOAD=90):
timeline_source = ColumnDataSource(data=dict(
Item=[], Start=[], End=[], Color=[], start=[], end=[], ID=[], ID1=[]))
class Thistab(KPI):
def __init__(self, table, cols=[]):
KPI.__init__(self, table, name='project', cols=cols)
self.table = table
self.df = None
class PythonMongo:
logger = mylogger(__file__)
# port = '9000'
# ch = sa.create_engine('clickhouse://default:@127.0.0.1:8123/aion')
def __init__(self, db):
self.client = MongoClient('localhost', 27017)
self.db = self.client['aion']
collection = 'external'
def load_data(self, table):
try:
df = pd.DataFrame(list(self.db[table].find()))
logger.warning('external:%s', df.head(10))
return df
except Exception:
logger.error('load data', exc_info=True)
def date_to_datetime(self, timestamp):
if isinstance(timestamp, date):
return datetime(timestamp.year, timestamp.month, timestamp.day, 0,
0, 0)
return timestamp
def load_df(self,
start_date,
end_date,
cols=[],
table='',
timestamp_col='startdate_actual',
supplemental_where=None):
try:
start_date = self.date_to_datetime(start_date)
end_date = self.date_to_datetime(end_date)
logger.warning('start date:enddate=%s:%s', start_date, end_date)
if len(cols) > 0:
cols_to_load = {}
cols_to_load['_id'] = 0
for col in cols:
cols_to_load[col] = 1
#logger.warning('LINE 47:%s',col)
logger.warning('table:%s', table)
logger.warning('timestamp col:%s', timestamp_col)
df = json_normalize(
list(self.db[table].find(
{
timestamp_col: {
"$gte": start_date,
"$lte": end_date
}
}, cols_to_load)))
logger.warning('LIne 56: %s', df.head(20))
else:
logger.warning('LINE 61:table:%s', table)
df = json_normalize(
list(self.db[table].find(
{
timestamp_col: {
"$gte": start_date,
"$lte": end_date
}
}, {'_id': False})))
#logger.warning('LINE 68:%s',df.head())
return df
except Exception:
logger.error('load df', exc_info=True)
