def histogram(self):
engine = ds.bi_engine
conn = engine.connect()
s = bizdw_v6.lifecycle_events_subscribes.alias()
df = group_by_minute_and_count(s,conn)
sub = df['sub_count']
print(sub)
tmp_list_1 = []
tmp_list_2 = []
cum_1 = 0
cum_2 = 0
for row_num, i in enumerate(sub):
if row_num < 1440:
cum_1 = cum_1 + i
tmp_list_1.append(cum_1)
else:
cum_2 = cum_2 + i
tmp_list_2.append(cum_2)
cumulative_1 = np.array(tmp_list_1)
cumulative_2 = np.array(tmp_list_2)
diff = []
for row_num, i in enumerate(cumulative_1):
try:
diff.append(cumulative_1[row_num] - cumulative_2[row_num] + random.random())
except:
pass
l = []
for index, i in enumerate(diff):
b = []
b.append(index)
b.append(i)
l.append(b)
self.diff = l
time = []
for index, i in enumerate(df['time']):
if index % 60 == 0:
b = []
b.append(index)
p = str(i).split(" ")[1].split(":")
b.append(p[0]+":"+p[1])
time.append(b)
conn.close()
print(l)
hist_var = json.dumps(dict(title = self.diff, xaxis = time))
cherrypy.response.headers['Content-Type'] = 'application/json'
return hist_var.encode('utf8')
import numpy as np
import pandas as pd
import random
from dteam import datastores
ds = datastores.bi()
from sqlalchemy_test import group_by_minute_and_count
import statsmodels.api as sm
engine = ds.bi_engine
conn = engine.connect()
s = bizdw_v6.lifecycle_events_subscribes.alias()
df = group_by_minute_and_count(s,conn)
tmp_list_1 = []
tmp_list_2 = []
cum_1 = 0
cum_2 = 0
for row_num, i in enumerate(df):
if row_num < 1440:
cum_1 = cum_1 + i
tmp_list_1.append(cum_1)
else:
cum_2 = cum_2 + i
tmp_list_2.append(cum_2)
