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clock.py
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import os
os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'digital_payment_index.settings')
import django
django.setup()
from collections.abc import MutableMapping
import pandas as pd
import requests
import datetime
import calendar
import time
from digital_payment_index.models import daily_index_data
import numpy as np
from apscheduler.schedulers.blocking import BlockingScheduler
sched = BlockingScheduler(timezone='Asia/Kolkata')
@sched.scheduled_job('cron', day_of_week='tue-fri',hour=14, timezone='Asia/Kolkata')
def my_scheduled_job():
theurl ='https://rbidocs.rbi.org.in/rdocs/content/docs/PSDDP04062020.xlsx'
n1=12
mydate = datetime.datetime.now()
count = calendar.monthrange(mydate.year, mydate.month)[1]
month = mydate.strftime("%B")
year = mydate.strftime("%Y")
month_year=str(month)+' '+ str(year)
#print(month_year)
r=requests.get(theurl,verify=False)
output = open('test.xls', 'wb')
output.write(r.content)
output.close()
xls = pd.ExcelFile('test.xls')
df1 = pd.read_excel(xls, month_year)
df1.drop(df1.tail(n1).index,inplace=True)
df1 = df1.reset_index()
col_list= list(df1.columns)
index = 1
len1=df1.shape[0]
old_date = df1.iloc[len1-index]['Data for the day']
date = old_date.strftime("%d")
tmp = daily_index_data.objects.values().last()
if int(date) == 1 :
UPI_Vol = df1.iloc[len1-index]['Unnamed: 7']
UPI_Val = df1.iloc[len1-index]['Unnamed: 8']
IMPS_Vol = df1.iloc[len1-index]['Unnamed: 9']
IMPS_Val = df1.iloc[len1-index]['Unnamed: 10']
NACH_Vol = df1.iloc[len1-index]['Unnamed: 11'] + df1.iloc[len1-index]['Unnamed: 13']
NACH_Val = df1.iloc[len1-index]['Unnamed: 12'] + df1.iloc[len1-index]['Unnamed: 14']
NETC_Vol = df1.iloc[len1-index]['Unnamed: 15']
NETC_Val = df1.iloc[len1-index]['Unnamed: 16']
NEFT_Vol = df1.iloc[len1-index]['Unnamed: 3']
NEFT_Val = df1.iloc[len1-index]['Unnamed: 4']
RTGS_Vol = df1.iloc[len1-index][col_list[2]]
RTGS_Val = df1.iloc[len1-index]['Unnamed: 2']
else:
UPI_Vol = df1.iloc[len1-index]['Unnamed: 7'] + tmp['UPI_Vol']
UPI_Val = df1.iloc[len1-index]['Unnamed: 8'] + tmp['UPI_Val']
IMPS_Vol = df1.iloc[len1-index]['Unnamed: 9'] + tmp['IMPS_Vol']
IMPS_Val = df1.iloc[len1-index]['Unnamed: 10'] + tmp['IMPS_Val']
NACH_Vol = df1.iloc[len1-index]['Unnamed: 11'] + df1.iloc[len1-index]['Unnamed: 13'] + tmp['NACH_Vol']
NACH_Val = df1.iloc[len1-index]['Unnamed: 12'] + df1.iloc[len1-index]['Unnamed: 14'] + tmp['NACH_Val']
NETC_Vol = df1.iloc[len1-index]['Unnamed: 15'] + tmp['NETC_Vol']
NETC_Val = df1.iloc[len1-index]['Unnamed: 16'] + tmp['NETC_Val']
NEFT_Vol = df1.iloc[len1-index]['Unnamed: 3'] + tmp['NEFT_Vol']
NEFT_Val = df1.iloc[len1-index]['Unnamed: 4'] + tmp['NEFT_Val']
RTGS_Vol = df1.iloc[len1-index][col_list[2]] + tmp['RTGS_Vol']
RTGS_Val = df1.iloc[len1-index]['Unnamed: 2'] + tmp['RTGS_Val']
UPI_Vol_0 = 20.30
UPI_Val_0 = 709.78
IMPS_Vol_0 = 528.60
IMPS_Val_0 = 43200.70
NETC_Vol_0 = 31.90
NETC_Val_0 = 88.12
NACH_Vol_0 = 2080.54
NACH_Val_0 = 69924.23
NEFT_Vol_0 = 1663.07
NEFT_Val_0 = 1153763.31
RTGS_Vol_0 = 88.40
RTGS_Val_0 = 8409647.78
p0q0 = 2830565816
p1q1= UPI_Vol*UPI_Val +IMPS_Vol*IMPS_Val + NETC_Vol*NETC_Val + NACH_Vol*NACH_Val + NEFT_Vol*NEFT_Val + RTGS_Vol*RTGS_Val
p0q1 = UPI_Val_0 * UPI_Vol + IMPS_Val_0 * IMPS_Vol + NETC_Val_0 * NETC_Vol + NACH_Val_0 * NACH_Vol + NEFT_Val_0 * NEFT_Vol + RTGS_Val_0 * RTGS_Vol
p1q0 = UPI_Val * UPI_Vol_0 + IMPS_Val * IMPS_Vol_0 + NETC_Val * NETC_Vol_0 + NACH_Val * NACH_Vol_0 + NEFT_Val * NEFT_Vol_0 + RTGS_Val * RTGS_Vol_0
fisher_coeff = (np.sqrt((p1q0/p0q0) * (p1q1/p0q1))) * 100
#print(old_date)
if int(date) > 3 :
op = (fisher_coeff * count) / (int(date))
else:
op = fisher_coeff * ((-10.719 * np.log(int(date)))+ 21.8)
print("fisher_coeffient:",op)
daily_index_data.objects.create(date=old_date,UPI_Vol=UPI_Vol,UPI_Val=UPI_Val,IMPS_Vol=IMPS_Vol,IMPS_Val=IMPS_Val,NACH_Vol=NACH_Vol,NACH_Val=NACH_Val,NETC_Vol=NETC_Vol,NETC_Val=NETC_Val,NEFT_Vol=NEFT_Vol,NEFT_Val=NEFT_Val,RTGS_Vol=RTGS_Vol,RTGS_Val=RTGS_Val,index_value=op)
@sched.scheduled_job('cron', day_of_week='mon', hour=21)
def my_scheduled_job():
theurl ='https://rbidocs.rbi.org.in/rdocs/content/docs/PSDDP04062020.xlsx'
n1=12
mydate = datetime.datetime.now()
count = calendar.monthrange(mydate.year, mydate.month)[1]
month = mydate.strftime("%B")
year = mydate.strftime("%Y")
month_year=str(month)+' '+ str(year)
#print(month_year)
r=requests.get(theurl,verify=False)
output = open('test.xls', 'wb')
output.write(r.content)
output.close()
xls = pd.ExcelFile('test.xls')
df1 = pd.read_excel(xls, month_year)
df1.drop(df1.tail(n1).index,inplace=True)
df1 = df1.reset_index()
col_list= list(df1.columns)
for index in [3,2,1]:
len1=df1.shape[0]
old_date = df1.iloc[len1-index]['Data for the day']
date = old_date.strftime("%d")
tmp = daily_index_data.objects.values().last()
UPI_Vol = df1.iloc[len1-index]['Unnamed: 7'] + tmp['UPI_Vol']
UPI_Val = df1.iloc[len1-index]['Unnamed: 8'] + tmp['UPI_Val']
IMPS_Vol = df1.iloc[len1-index]['Unnamed: 9'] + tmp['IMPS_Vol']
IMPS_Val = df1.iloc[len1-index]['Unnamed: 10'] + tmp['IMPS_Val']
NACH_Vol = df1.iloc[len1-index]['Unnamed: 11'] + df1.iloc[len1-index]['Unnamed: 13'] + tmp['NACH_Vol']
NACH_Val = df1.iloc[len1-index]['Unnamed: 12'] + df1.iloc[len1-index]['Unnamed: 14'] + tmp['NACH_Val']
NETC_Vol = df1.iloc[len1-index]['Unnamed: 15'] + tmp['NETC_Vol']
NETC_Val = df1.iloc[len1-index]['Unnamed: 16'] + tmp['NETC_Val']
NEFT_Vol = df1.iloc[len1-index]['Unnamed: 3'] + tmp['NEFT_Vol']
NEFT_Val = df1.iloc[len1-index]['Unnamed: 4'] + tmp['NEFT_Val']
RTGS_Vol = df1.iloc[len1-index][col_list[2]] + tmp['RTGS_Vol']
RTGS_Val = df1.iloc[len1-index]['Unnamed: 2'] + tmp['RTGS_Val']
UPI_Vol_0 = 20.30
UPI_Val_0 = 709.78
IMPS_Vol_0 = 528.60
IMPS_Val_0 = 43200.70
NETC_Vol_0 = 31.90
NETC_Val_0 = 88.12
NACH_Vol_0 = 2080.54
NACH_Val_0 = 69924.23
NEFT_Vol_0 = 1663.07
NEFT_Val_0 = 1153763.31
RTGS_Vol_0 = 88.40
RTGS_Val_0 = 8409647.78
p0q0 = 2830565816
p1q1= UPI_Vol*UPI_Val +IMPS_Vol*IMPS_Val + NETC_Vol*NETC_Val + NACH_Vol*NACH_Val + NEFT_Vol*NEFT_Val + RTGS_Vol*RTGS_Val
p0q1 = UPI_Val_0 * UPI_Vol + IMPS_Val_0 * IMPS_Vol + NETC_Val_0 * NETC_Vol + NACH_Val_0 * NACH_Vol + NEFT_Val_0 * NEFT_Vol + RTGS_Val_0 * RTGS_Vol
p1q0 = UPI_Val * UPI_Vol_0 + IMPS_Val * IMPS_Vol_0 + NETC_Val * NETC_Vol_0 + NACH_Val * NACH_Vol_0 + NEFT_Val * NEFT_Vol_0 + RTGS_Val * RTGS_Vol_0
fisher_coeff = (np.sqrt((p1q0/p0q0) * (p1q1/p0q1))) * 100
#print(old_date)
if int(date) > 3 :
op = (fisher_coeff * count) / (int(date))
else:
op = fisher_coeff * ((-10.719 * np.log(int(date)))+ 21.8)
print("fisher_coeffient:",op)
daily_index_data.objects.create(date=old_date,UPI_Vol=UPI_Vol,UPI_Val=UPI_Val,IMPS_Vol=IMPS_Vol,IMPS_Val=IMPS_Val,NACH_Vol=NACH_Vol,NACH_Val=NACH_Val,NETC_Vol=NETC_Vol,NETC_Val=NETC_Val,NEFT_Vol=NEFT_Vol,NEFT_Val=NEFT_Val,RTGS_Vol=RTGS_Vol,RTGS_Val=RTGS_Val,index_value=op)
sched.start()