import numpy as np

#other
from_self = ['write down a phrase here that is unique to transfers from your own account']   #transfers from my own account
to_self = ['write down a phrase here that is unique to transfers to your own account']     #transfers to my own account
monthname = ['january', 'february', 'march', 'april', 'may', 'june', 'july',
              'august', 'september', 'october', 'november', 'december'] 

#expected expenditure categories (most entries are removed for privacy)
grocery_stores      = ['kiwi',  'bunnpris', 'coop extra', 'rema', 'rimi',
                        'coop mega', 'esso', 'meny', 'duty-free', 'joker']
item_stores         = ['elkjop','lefdal', 'g sport', 'hm.com',  
                       'clas ohlson', 'netonnet', 'komplett' ]
restaurants         = ['name of restaurant']
bars                = [ 'name of bar', 'vinmonopolet']
medical             = [ 'apotek','helfo','vitusapotek', 'legevakt' ]
gym                 = ['name of gym']
subscriptions       = ['audible', 'spotify', 'netflix' ]
rent                = ['description of rent-transactions']
vipps               = ['vipps by dnb'] #vipps won't tell you where you bought something, so their transactions are harder to sort
services            = ['hair dresser', 'cinema','name of regular service provider']
study_expenses      = ['description of uiversity transaction','name of place i buy books for academic purposes']
holiday             = ['transaction description from place i visited']
transportation      = ['name of buss service', 'name of train service', 'nok 33.00 vipps', 'nok 93.00 vipps'] # vipps entries are prices for buss/train
known_expenditures  = grocery_stores + item_stores + restaurants + bars + medical + gym + subscriptions + rent + transportation + services + study_expenses + holiday + vipps
known_exp_array     = np.array([grocery_stores, item_stores, restaurants, bars, medical, gym, subscriptions, rent, transportation, services, study_expenses, holiday, vipps])
known_exp_array_identifiers = ['grocery stores' , 'item stores' , 'restaurants' , 'bars' , 'medical' , 'gym' , 'subscriptions' ,
                                'rent' , 'transportation' , 'services' , 'study expenses' , 'holiday'  ,'vipps' ]

#expected income categories
student_loan    = ['student loan description']
other           = ['other regular source of money']
work_income     = ['description that is recognized as work income']
known_income    = student_loan + other + work_income
known_inc_array = np.array([student_loan , other , work_income])
known_inc_array_identifiers = ['student loan', 'family', 'work income']

known_source    = known_expenditures + known_income + to_self + from_self #income + expenditure

cb3e04a380ad585adaed942dfe2e8161b0160f5b
<mask token>

<mask token>
from_self = [
    'write down a phrase here that is unique to transfers from your own account'
    ]
to_self = [
    'write down a phrase here that is unique to transfers to your own account']
monthname = ['january', 'february', 'march', 'april', 'may', 'june', 'july',
    'august', 'september', 'october', 'november', 'december']
grocery_stores = ['kiwi', 'bunnpris', 'coop extra', 'rema', 'rimi',
    'coop mega', 'esso', 'meny', 'duty-free', 'joker']
item_stores = ['elkjop', 'lefdal', 'g sport', 'hm.com', 'clas ohlson',
    'netonnet', 'komplett']
restaurants = ['name of restaurant']
bars = ['name of bar', 'vinmonopolet']
medical = ['apotek', 'helfo', 'vitusapotek', 'legevakt']
gym = ['name of gym']
subscriptions = ['audible', 'spotify', 'netflix']
rent = ['description of rent-transactions']
vipps = ['vipps by dnb']
services = ['hair dresser', 'cinema', 'name of regular service provider']
study_expenses = ['description of uiversity transaction',
    'name of place i buy books for academic purposes']
holiday = ['transaction description from place i visited']
transportation = ['name of buss service', 'name of train service',
    'nok 33.00 vipps', 'nok 93.00 vipps']
known_expenditures = (grocery_stores + item_stores + restaurants + bars +
    medical + gym + subscriptions + rent + transportation + services +
    study_expenses + holiday + vipps)
known_exp_array = np.array([grocery_stores, item_stores, restaurants, bars,
    medical, gym, subscriptions, rent, transportation, services,
    study_expenses, holiday, vipps])
known_exp_array_identifiers = ['grocery stores', 'item stores',
    'restaurants', 'bars', 'medical', 'gym', 'subscriptions', 'rent',
    'transportation', 'services', 'study expenses', 'holiday', 'vipps']
student_loan = ['student loan description']
other = ['other regular source of money']
work_income = ['description that is recognized as work income']
known_income = student_loan + other + work_income
known_inc_array = np.array([student_loan, other, work_income])
known_inc_array_identifiers = ['student loan', 'family', 'work income']
known_source = known_expenditures + known_income + to_self + from_self

import numpy as np
from_self = [
    'write down a phrase here that is unique to transfers from your own account'
    ]
to_self = [
    'write down a phrase here that is unique to transfers to your own account']
monthname = ['january', 'february', 'march', 'april', 'may', 'june', 'july',
    'august', 'september', 'october', 'november', 'december']
grocery_stores = ['kiwi', 'bunnpris', 'coop extra', 'rema', 'rimi',
    'coop mega', 'esso', 'meny', 'duty-free', 'joker']
item_stores = ['elkjop', 'lefdal', 'g sport', 'hm.com', 'clas ohlson',
    'netonnet', 'komplett']
restaurants = ['name of restaurant']
bars = ['name of bar', 'vinmonopolet']
medical = ['apotek', 'helfo', 'vitusapotek', 'legevakt']
gym = ['name of gym']
subscriptions = ['audible', 'spotify', 'netflix']
rent = ['description of rent-transactions']
vipps = ['vipps by dnb']
services = ['hair dresser', 'cinema', 'name of regular service provider']
study_expenses = ['description of uiversity transaction',
    'name of place i buy books for academic purposes']
holiday = ['transaction description from place i visited']
transportation = ['name of buss service', 'name of train service',
    'nok 33.00 vipps', 'nok 93.00 vipps']
known_expenditures = (grocery_stores + item_stores + restaurants + bars +
    medical + gym + subscriptions + rent + transportation + services +
    study_expenses + holiday + vipps)
known_exp_array = np.array([grocery_stores, item_stores, restaurants, bars,
    medical, gym, subscriptions, rent, transportation, services,
    study_expenses, holiday, vipps])
known_exp_array_identifiers = ['grocery stores', 'item stores',
    'restaurants', 'bars', 'medical', 'gym', 'subscriptions', 'rent',
    'transportation', 'services', 'study expenses', 'holiday', 'vipps']
student_loan = ['student loan description']
other = ['other regular source of money']
work_income = ['description that is recognized as work income']
known_income = student_loan + other + work_income
known_inc_array = np.array([student_loan, other, work_income])
known_inc_array_identifiers = ['student loan', 'family', 'work income']
known_source = known_expenditures + known_income + to_self + from_self

import numpy as np

#other
from_self = ['write down a phrase here that is unique to transfers from your own account']   #transfers from my own account
to_self = ['write down a phrase here that is unique to transfers to your own account']     #transfers to my own account
monthname = ['january', 'february', 'march', 'april', 'may', 'june', 'july',
              'august', 'september', 'october', 'november', 'december'] 

#expected expenditure categories (most entries are removed for privacy)
grocery_stores      = ['kiwi',  'bunnpris', 'coop extra', 'rema', 'rimi',
                        'coop mega', 'esso', 'meny', 'duty-free', 'joker']
item_stores         = ['elkjop','lefdal', 'g sport', 'hm.com',  
                       'clas ohlson', 'netonnet', 'komplett' ]
restaurants         = ['name of restaurant']
bars                = [ 'name of bar', 'vinmonopolet']
medical             = [ 'apotek','helfo','vitusapotek', 'legevakt' ]
gym                 = ['name of gym']
subscriptions       = ['audible', 'spotify', 'netflix' ]
rent                = ['description of rent-transactions']
vipps               = ['vipps by dnb'] #vipps won't tell you where you bought something, so their transactions are harder to sort
services            = ['hair dresser', 'cinema','name of regular service provider']
study_expenses      = ['description of uiversity transaction','name of place i buy books for academic purposes']
holiday             = ['transaction description from place i visited']
transportation      = ['name of buss service', 'name of train service', 'nok 33.00 vipps', 'nok 93.00 vipps'] # vipps entries are prices for buss/train
known_expenditures  = grocery_stores + item_stores + restaurants + bars + medical + gym + subscriptions + rent + transportation + services + study_expenses + holiday + vipps
known_exp_array     = np.array([grocery_stores, item_stores, restaurants, bars, medical, gym, subscriptions, rent, transportation, services, study_expenses, holiday, vipps])
known_exp_array_identifiers = ['grocery stores' , 'item stores' , 'restaurants' , 'bars' , 'medical' , 'gym' , 'subscriptions' ,
                                'rent' , 'transportation' , 'services' , 'study expenses' , 'holiday'  ,'vipps' ]

#expected income categories
student_loan    = ['student loan description']
other           = ['other regular source of money']
work_income     = ['description that is recognized as work income']
known_income    = student_loan + other + work_income
known_inc_array = np.array([student_loan , other , work_income])
known_inc_array_identifiers = ['student loan', 'family', 'work income']

known_source    = known_expenditures + known_income + to_self + from_self #income + expenditure
