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Region.py
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Region.py
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import pdb
import os
import pandas as pd
import sqlite3
from AllPrograms_util import get_region_rowid
def get_inflation(db, base_year):
# base_year is the year for which a dollar is a dollar
base_year = int(base_year)
conn = sqlite3.connect(db)
cursor = conn.cursor()
sql = 'select year, rate from inflation order by year desc'
df_fac = pd.read_sql_query(sql, conn)
inflation_by_year = {}
calculated_inflation = 1.0
for idx, row in df_fac.iterrows():
year = int(row['year'])
if year > base_year:
continue
inflation_by_year[year] = calculated_inflation
if year <= base_year:
calculated_inflation *= 1.0 + .01 * row['rate']
df = pd.DataFrame.from_dict(inflation_by_year, orient='index')
df = df.sort_index()
df.reset_index(inplace=True)
df.columns = ['Year', 'rate']
return df
class Region:
'''
This class represents the data associated with a particular
region--a state, congressional district, watershed, etc.
Attributes
----------
type : str
One of the supported region types--'Nation', 'State',
'Congressional District', 'Watershed', 'Zip Code'
value : str
The actual identifier of the region--e.g. the number of
the congressional district, the watershed name
state : str
The two letter state abbreviation
programs : str
The EPA programs
db_conn : SQLite connection
The connection to the local SQLite database holding
the region's data
data_sets : list
The DataSet objects for the region
'''
def __init__(self, db='region.db', type=None, value=None, state=None,
base_year=2023, programs=None):
self.db = db # the Sqlite3 database
self.type = type # Region type
self.value = value # Region instance
self.state = state # State
self.programs = programs # The EPA programs to include
self.base_year = base_year
if type != None:
conn = sqlite3.connect(self.db)
cursor = conn.cursor()
if self.value == '00' and self.type == 'Congressional District':
#Single-CD state - use statewide data
self.region_id = get_region_rowid(cursor, 'State', self.state, self.value)
else:
self.region_id = get_region_rowid(cursor, self.type, self.state, self.value)
conn.close()
def get_inflation(self):
return get_inflation(self.db, self.base_year)
def get_counties_by_state(self, state):
conn = sqlite3.connect(self.db)
sql = 'select region as county from regions where state=\'{}\''\
' and region_type=\'County\''.format(state)
df = pd.read_sql_query(sql, conn)
return df
def get_cds(self):
conn = sqlite3.connect(self.db)
sql = 'select state, cd from real_cds order by state, cd'
df = pd.read_sql_query(sql, conn)
return df
def get_per_1000(self, type, region):
# type is 'inspections' or 'violations'
# region is 'USA', 'State', 'CD'
# programs is a list of the programs to be included--CAA, CWA, etc.
if (region == 'USA' or region == 'State'):
return self._get_region_per_1000(type, region)
# For CDs we can just use the per_fac table and
# active_facilities for the region
conn = sqlite3.connect(self.db)
sql = 'select program as Program, 1000. * count as Per1000 from per_fac'
sql += ' where region_id={} and type=\'{}\' and year={}'
if (self.programs is not None):
sql += ' and program in (\'{}\')'
sql = sql.format(self.region_id, type, self.base_year, '\',\''.join(self.programs))
else:
sql = sql.format(self.region_id, type, self.base_year)
df = pd.read_sql_query(sql, conn)
return df
def _get_region_per_1000(self, type, region):
# type is 'inspections' or 'violations'
# region is 'USA', 'State', 'CD'
conn = sqlite3.connect(self.db)
sql = 'select program, sum(count) from active_facilities '
if (self.programs is not None):
sql += ' where program in (\'{}\') '
sql = sql.format('\',\''.join(self.programs))
if (region == 'State'):
sql += ' and region_id in ( select rowid from regions '
sql += ' where state=\'{}\' )'
sql = sql.format(self.state)
sql += ' group by program'
df_fac = pd.read_sql_query(sql, conn)
sql = 'select program, sum(count) from {} where year={}'
if (region == 'State'):
sql += ' and region_id in ( select rowid from regions '
sql += ' where state=\'{}\' )'
sql = sql.format(type, self.base_year, self.state)
else:
sql = sql.format(type, self.base_year)
if (self.programs is not None):
sql += ' and program in (\'{}\')'
sql = sql.format('\',\''.join(self.programs))
sql += ' group by program'
df_insp = pd.read_sql_query(sql, conn)
df_joined = df_fac.join(
df_insp.set_index(['program']),
lsuffix='x',
rsuffix='y',
on=['program'])
type_cap = type.capitalize()
df_joined.columns = ['Program', 'Facilities', type_cap]
df_joined['Per1000'] = 1000. * df_joined[type_cap] / df_joined['Facilities']
df = df_joined.drop(['Facilities', type_cap], axis='columns')
return df
def get_recurring_violations(self, program):
conn = sqlite3.connect(self.db)
cursor = conn.cursor()
sql = 'select sum(count) from active_facilities where '
if self.state is None:
sql += ' program=\'{}\''
sql = sql.format(program)
else:
sql += ' program=\'{}\' and region_id in ( select rowid from regions'
sql += ' where state=\'{}\' )'
sql = sql.format(program, self.state)
cursor.execute(sql)
fetched = cursor.fetchone()
state_facilities = fetched[0]
sql = 'select sum(violations) from recurring_violations where '
if self.state is None:
sql += ' program=\'{}\''
sql = sql.format(program)
else:
sql += ' program=\'{}\' and region_id in ( select rowid from regions'
sql += ' where state=\'{}\' )'
sql = sql.format(program, self.state)
cursor.execute(sql)
fetched = cursor.fetchone()
state_violators = fetched[0]
data = [{'State': self.state, 'Facilities': state_violators,
'Percent': 100. * state_violators / state_facilities
if state_facilities > 0 else -1},
]
if self.value is not None:
sql = 'select violations, facilities from recurring_violations '
sql += ' where program=\'{}\' and region_id={}'
sql = sql.format(program, self.region_id)
cursor.execute(sql)
cd_fac_viol = cursor.fetchone()
if cd_fac_viol is not None:
data.append(
{'CD': '{}{}'.format(self.state, self.value),
'Facilities': cd_fac_viol[0],
' Percent': 100. * cd_fac_viol[0] / cd_fac_viol[1]
if cd_fac_viol[1] > 0 else -1
}
)
df = pd.DataFrame(data)
return df
def get_events(self, event_type, program):
conn = sqlite3.connect(self.db)
if event_type == 'inspections':
sql = 'select year Year, sum(count) Count from inspections'
elif event_type == 'enforcements':
sql = 'select year Year, sum(amount) Amount, sum(count) Count '
sql += ' from enforcements'
elif event_type == 'violations':
sql = 'select year Year, sum(count) as Count from violations'
else:
return None
if (self.value is None):
# If state is None, get the count
# for all regions in the USA
if (self.state is None):
sql += ' where '
else:
sql += ' where region_id in ( select rowid from regions where '
sql += ' state=\'{}\') and '
sql = sql.format(self.state)
else:
sql += ' where region_id={} and '
sql = sql.format(self.region_id)
sql += ' year <= {} '
if (program != 'All'):
sql += ' and program=\'{}\''
sql += ' group by year'
if (program == 'All'):
sql = sql.format(self.base_year)
else:
sql = sql.format(self.base_year, program)
df = pd.read_sql_query(sql, conn)
if event_type == 'enforcements':
self._apply_inflation(df)
return df
def _apply_inflation(self, df):
inflation_df = self.get_inflation()
for idx, row in df.iterrows():
df.at[idx, 'Amount'] = row['Amount'] * \
inflation_df[inflation_df['Year'] == row['Year']]['rate'].iloc[0]
def get_non_compliants(self, program):
conn = sqlite3.connect(self.db)
sql = 'select fac_name, noncomp_count, formal_action_count, dfr_url,'
sql += ' fac_lat, fac_long from non_compliants where program=\'{}\''
sql += ' and noncomp_count > 0'
sql = sql.format(program)
if (self.value is None):
if (self.state is not None):
# Look at the entire state
sql += ' and region_id in (select rowid from regions where state=\'{}\')'
sql = sql.format(self.state)
else:
sql += ' and region_id={}'
sql = sql.format(self.region_id)
return pd.read_sql_query(sql, conn).sort_values(by='noncomp_count',
ascending=False)
def get_active_facilities(self, program, table='active_facilities'):
conn = sqlite3.connect(self.db)
cursor = conn.cursor()
if (self.value is None):
if (self.state is None):
# Sum active facilities over all regions in the country
sql = 'select sum(count) from {} where program=\'{}\''
sql = sql.format(table, program)
else:
# Sum active facilities over all regions in the state
sql = 'select sum(count) from {} where region_id in ('
sql += ' select rowid from regions where state=\'{}\') and program=\'{}\''
sql = sql.format(table, self.state, program)
else:
sql = 'select count from {} where region_id={}'
sql += ' and program=\'{}\''
sql = sql.format(table, self.region_id, program)
cursor.execute(sql)
fetch = cursor.fetchone()
return fetch[0] if fetch else 0
def get_ranked(self):
conn = sqlite3.connect(self.db)
state_columns = 'CAA_Insp_Rank, CAA_Viol_Rank, CAA_Enf_Rank, '
state_columns += 'CWA_Insp_Rank, CWA_Viol_Rank, CWA_Enf_Rank, '
state_columns += 'RCRA_Insp_Rank, RCRA_Viol_Rank, RCRA_Enf_Rank'
cd_columns = 'CAA_Insp_Pct, CAA_Viol_Pct, CAA_Enf_Pct, '
cd_columns += 'CWA_Insp_Pct, CWA_Viol_Pct, CWA_Enf_Pct, '
cd_columns += 'RCRA_Insp_Pct, RCRA_Viol_Pct, RCRA_Enf_Pct'
sql = ''
if self.type == 'State':
sql = 'select {} from state_per_1000 where "CD.State" = \'{}\''
sql = sql.format(state_columns, self.state)
elif self.type == 'Congressional District':
sql = 'select {} from cd_per_1000 where "CD.State" = \'{}-{}\''
sql = sql.format(cd_columns, self.state, self.value)
elif self.type == 'County':
sql = 'select {} from county_per_1000 where "CD.State" = \'{}{}\''
sql = sql.format(cd_columns, self.state, self.value)
df = pd.read_sql_query(sql, conn)
df = df.fillna(value=0)
return df