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)