def __init__(self):
self.query = queries(
) #query object has query texts for all database interactions as different functions
self.parse = parse_inputs(
) #parses the LAR row into selected components and stores them in a dictionary
self.connect = connect_DB(
) #sets a database connection and passes a cursor back
self.orig_by_tract_minority_category = [
] #sorted alphabetically: high, low, middle, upper
self.app_by_tract_minority_category = [
] #sorted alphabetically: high, low, middle, upper
self.tract_orig_rates = [
] #holds an alphabetically sorted list of origination rates for tract categorized by minority population
#order of tract_orig_rates list: high, low, middle, upper
self.nonminority_approval_rate = 0 #establishes a variable to hold rates for graphing
self.HMDA_cols = [
'statecode', 'countycode', 'censustractnumber', 'applicantrace1',
'applicantrace2', 'applicantrace3', 'applicantrace4',
'applicantrace5', 'coapplicantrace1', 'coapplicantrace2',
'coapplicantrace3', 'coapplicantrace4', 'coapplicantrace5',
'applicantethnicity', 'coapplicantethnicity', 'applicantincome',
'ratespread', 'lienstatus', 'hoepastatus', 'purchasertype',
'loanamount', 'asofdate', 'hud_median_family_income',
'minority_population_pct', 'tract_to_msa_md_income'
]
from pylab import *
from sklearn.cross_validation import cross_val_score
#from years_of_milwaukee import call_2009
#from years_of_milwaukee import call_2010
#from years_of_milwaukee import call_2011
#from years_of_milwaukee import call_2012
#from years_of_milwaukee import call_2013
#from years_of_milwaukee import call_tracts_2013 as tracts_2013
#from years_of_milwaukee import call_tracts_2012 as tracts_2012
#from years_of_milwaukee import call_tracts_2011 as tracts_2011
#from years_of_milwaukee import call_tracts_2010 as tracts_2010
#from years_of_milwaukee import call_tracts_2009 as tracts_2009
#instantiate classes
#geo = geo_aggregator() #disabled geo_aggregator as it has been used and is not needed
query = queries()
connect = connect_DB()
parse = parse_inputs()
HMDA_cols = ['statecode', 'countycode', 'censustractnumber', 'applicantrace1', 'applicantrace2', 'applicantrace3', 'applicantrace4', 'applicantrace5',
'coapplicantrace1', 'coapplicantrace2', 'coapplicantrace3', 'coapplicantrace4', 'coapplicantrace5', 'applicantethnicity', 'coapplicantethnicity',
'applicantincome', 'ratespread', 'lienstatus', 'hoepastatus', 'purchasertype', 'loanamount', 'year', 'median_family_income',
'minority_population_pct', 'tract_to_msa_md_income', 'actiontype', 'gender']
'''data have been filtered as follows in SQL queries
loan type: conventional only, code 1
property type: 1-4 family only, code 1
loan purpose: purchase only, code 1
occupancy status: owner occupied only, code 1
lien status: first liens only, code 1
income != 0 #removes most non-natural person loans
action type: originations includes only originated loans, applications includes codes 1-4
#from geo_aggregator_2013 import geo_aggregator
from DAT4_library import connect_DB
from DAT4_library import parse_inputs
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from pylab import *
from years_of_milwaukee import call_2009
from years_of_milwaukee import call_2010
from years_of_milwaukee import call_2011
from years_of_milwaukee import call_2012
from years_of_milwaukee import call_2013
#instantiate classes
#geo = geo_aggregator() #disabled geo_aggregator as it has been used and is not needed
query = queries()
connect = connect_DB()
parse = parse_inputs()
#mil_2009 = call_2009()
#mil_2010 = call_2010()
#mil_2011 = call_2011()
#mil_2012 = call_2012()
mil_2013 = call_2013()
class year_calls(object):
pass
class call_tracts_2013(year_calls):
def __init__(self):