def __init__(self, key, controls_csv, tazset=None, puma_data_dir=None, fips_file=None,
write_households_csv=None, write_persons_csv=None, write_append=False,
start_hhid=1, start_persid=1):
SFCTAStarter.__init__(self, key, controls_csv, tazset, puma_data_dir, fips_file,
write_households_csv, write_persons_csv, write_append,
start_hhid, start_persid)
# Remove 0-group quarters controls
self.controls = self.controls[self.controls['GQPOP']>0]
self.hh_controls = cat.categorize(self.controls,
{("hhsize_cat","1"):"GQPOP"}, index_cols=['SFTAZ'])
# cat_name hhsize_cat
# cat_value 1
# SFTAZ
# 1 5
# 2 5
# 3 4
# 4 7
# 5 6
# 6 12
# 7 3
# 8 2
# 9 6
# 10 29
self.person_controls = cat.categorize(self.controls,
{("gqworker_cat","1" ):"GQWKRS",
("gqworker_cat","0" ):"GQNONWKRS",
("gqage_cat", "0-64"):"GQAGE064",
("gqage_cat", "65+" ):"GQAGE65P" }, index_cols=['SFTAZ'])
def __init__(self, key, controls_csv, tazset=None, puma_data_dir=None, fips_file=None,
write_households_csv=None, write_persons_csv=None, write_append=False,
start_hhid=1, start_persid=1):
SFCTAStarter.__init__(self, key, controls_csv, tazset, puma_data_dir, fips_file,
write_households_csv, write_persons_csv, write_append,
start_hhid, start_persid)
# Remove 0-household controls
self.controls = self.controls[self.controls['HHLDS']>0]
# self.controls = self.controls.iloc[:2,]
print "Household controls has length %d" % len(self.controls)
self.hh_controls = cat.categorize(self.controls,
{("income_cat", "0-25k" ): "HHINCQ1",
("income_cat", "25-45k" ): "HHINCQ2",
("income_cat", "45-75k" ): "HHINCQ3",
("income_cat", "75k+" ): "HHINCQ4",
("hhsize_cat", "1" ): "SZ1_HHLDS",
("hhsize_cat", "2" ): "SZ2_HHLDS",
("hhsize_cat", "3" ): "SZ3_HHLDS",
("hhsize_cat", "4" ): "SZ4_HHLDS",
("hhsize_cat", "5+" ): "SZ5_HHLDS",
("workers_cat", "0" ): "WKR0_HHLDS",
("workers_cat", "1" ): "WKR1_HHLDS",
("workers_cat", "2" ): "WKR2_HHLDS",
("workers_cat", "3+" ): "WKR3_HHLDS",
("htype_cat", "HAGE1K0" ): "HAGE1KIDS0",
("htype_cat", "HAGE1K1" ): "HAGE1KIDS1",
("htype_cat", "HAGE65KALL" ): "HAGE65KIDSWHATEV" },
index_cols=['SFTAZ'])
# print self.hh_controls.loc[1:10,:]
# cat_name hhsize_cat income_cat workers_cat
# cat_value 1 2 3 4 5+ 0-30k 100k+ 30-60k 60-100k 0 1 2 3+
# SFTAZ
# 1 28.365 46.970 40.260 49.410 139.995 51.540 61.848 130.700 59.912 52.155 93.025 96.990 62.830
# 2 36.663 54.540 45.147 51.207 115.443 54.976 73.970 83.514 90.540 48.783 81.507 93.627 79.083
# 3 73.610 104.353 77.074 71.012 106.951 261.136 28.188 106.849 37.827 158.911 196.149 67.115 10.825
# 4 45.133 67.140 55.577 63.037 142.113 67.002 90.663 102.678 111.657 60.053 100.337 115.257 97.353
# 5 34.038 56.364 48.312 59.292 167.994 62.707 75.211 157.363 71.719 62.586 111.630 116.388 75.396
# 6 65.403 94.302 78.078 85.176 184.041 144.312 50.742 138.288 173.658 84.669 148.551 159.705 114.075
# 7 46.920 66.516 49.128 45.264 68.172 166.646 17.759 68.419 24.176 101.292 125.028 42.780 6.900
# 8 54.282 74.883 56.898 53.301 87.636 67.002 74.937 95.038 90.023 61.476 126.222 104.967 34.335
# 9 42.471 63.180 52.299 59.319 133.731 63.566 85.465 97.339 105.630 56.511 94.419 108.459 91.611
# 10 55.480 77.140 60.800 62.700 123.880 90.195 64.092 101.710 124.003 68.780 134.520 122.740 53.960
# todo: add HAGE1KIDS0, HAGE1KIDS1, HAGE1KIDSWHATEV
self.person_controls = cat.categorize(self.controls,
{("age_cat", "0-4" ): "AGE0004",
("age_cat", "5-19" ): "AGE0519",
("age_cat", "20-44"): "AGE2044",
("age_cat", "45-64"): "AGE4564",
("age_cat", "65+" ): "AGE65P"},
index_cols=['SFTAZ'])
def __init__(self,
key,
controls_csv,
tazset=None,
puma_data_dir=None,
fips_file=None,
write_households_csv=None,
write_persons_csv=None,
write_append=False,
start_hhid=1,
start_persid=1):
SFCTAStarter.__init__(self, key, controls_csv, tazset, puma_data_dir,
fips_file, write_households_csv,
write_persons_csv, write_append, start_hhid,
start_persid)
# Remove 0-group quarters controls
self.controls = self.controls[self.controls['GQPOP'] > 0]
self.hh_controls = cat.categorize(self.controls,
{("hhsize_cat", "1"): "GQPOP"},
index_cols=['SFTAZ'])
# cat_name hhsize_cat
# cat_value 1
# SFTAZ
# 1 5
# 2 5
# 3 4
# 4 7
# 5 6
# 6 12
# 7 3
# 8 2
# 9 6
# 10 29
self.person_controls = cat.categorize(self.controls, {
("gqworker_cat", "1"): "GQWKRS",
("gqworker_cat", "0"): "GQNONWKRS",
("gqage_cat", "0-64"): "GQAGE064",
("gqage_cat", "65+"): "GQAGE65P"
},
index_cols=['SFTAZ'])
def get_household_joint_dist_for_geography(self, ind):
# check the cache to see if we've done it already
puma = self.tazToPUMA2010.loc[ind.SFTAZ,'PUMA2010']
if puma in self.h_pums.keys():
return self.h_pums[puma], self.jd_households[puma]
# if not, get the superclass to do a bunch of variable setting
h_pums, p_pums = SFCTAStarter.get_pums(self, puma)
orig_len = len(h_pums)
# filter to housing unit only with number of persons > 0
h_pums = h_pums[h_pums['NP']>0]
# Only Housing units
h_pums = h_pums[h_pums['TYPE']==1]
print "Filtered to %d households from %d originally" % (len(h_pums), orig_len)
# Household income
h_pums['hhinc_2012dollars'] = h_pums['HINCP']*(0.000001*h_pums['ADJINC']) # ADJINC has 6 implied decimal places
h_pums['hhinc_1989dollars'] = 0.54*h_pums['hhinc_2012dollars']
h_pums['hhinc'] = h_pums['hhinc_1989dollars']/1000.0 # in thousands of dollars
# print sum(h_pums.loc[:,'hhinc']<0)
h_pums.loc[h_pums.loc[:,'hhinc']<0, 'hhinc'] = 0.0 # no negatives
# print sum(h_pums.loc[:,'hhinc']>255)
h_pums.loc[h_pums.loc[:,'hhinc']>255,'hhinc'] = 255.0 # max = 255
# For the following, r is a pandas.Series
# It's basically a row from h_pums, so any variables defined above will be available
def hhsize_cat(r):
# NP = number of persons
if r.NP >=5:
return "5+"
elif r.NP == 4:
return "4"
elif r.NP == 3:
return "3"
elif r.NP == 2:
return "2"
elif r.NP == 1:
return "1"
return "1"
def income_cat(r):
if r.hhinc < 25.0:
return "0-25k"
elif r.hhinc < 45.0:
return "25-45k"
elif r.hhinc < 75.0:
return "45-75k"
else:
return "75k+"
def workers_cat(r):
# hmm... WIF = Workers in Family. What about non-family households?
if r.workers >= 3:
return "3+"
elif r.workers == 2:
return "2"
elif r.workers == 1:
return "1"
return "0"
def htype_cat(r):
if r.hhage < 65 and r.NOC==0:
return "HAGE1K0"
elif r.hhage < 65 and r.NOC>0:
return "HAGE1K1"
else:
return "HAGE65KALL"
h_pums, jd_households = cat.joint_distribution(
h_pums,
cat.category_combinations(self.hh_controls.columns),
{"hhsize_cat": hhsize_cat,
"income_cat": income_cat,
"workers_cat": workers_cat,
"htype_cat": htype_cat}
)
# cache them
self.h_pums[puma] = h_pums
self.jd_households[puma] = jd_households
return h_pums, jd_households
def __init__(self,
key,
controls_csv,
tazset=None,
puma_data_dir=None,
fips_file=None,
write_households_csv=None,
write_persons_csv=None,
write_append=False,
start_hhid=1,
start_persid=1):
SFCTAStarter.__init__(self, key, controls_csv, tazset, puma_data_dir,
fips_file, write_households_csv,
write_persons_csv, write_append, start_hhid,
start_persid)
# Remove 0-household controls
self.controls = self.controls[self.controls['HHLDS'] > 0]
# self.controls = self.controls.iloc[:2,]
print "Household controls has length %d" % len(self.controls)
self.hh_controls = cat.categorize(
self.controls, {
("income_cat", "0-25k"): "HHINCQ1",
("income_cat", "25-45k"): "HHINCQ2",
("income_cat", "45-75k"): "HHINCQ3",
("income_cat", "75k+"): "HHINCQ4",
("hhsize_cat", "1"): "SZ1_HHLDS",
("hhsize_cat", "2"): "SZ2_HHLDS",
("hhsize_cat", "3"): "SZ3_HHLDS",
("hhsize_cat", "4"): "SZ4_HHLDS",
("hhsize_cat", "5+"): "SZ5_HHLDS",
("workers_cat", "0"): "WKR0_HHLDS",
("workers_cat", "1"): "WKR1_HHLDS",
("workers_cat", "2"): "WKR2_HHLDS",
("workers_cat", "3+"): "WKR3_HHLDS",
("htype_cat", "HAGE1K0"): "HAGE1KIDS0",
("htype_cat", "HAGE1K1"): "HAGE1KIDS1",
("htype_cat", "HAGE65KALL"): "HAGE65KIDSWHATEV"
},
index_cols=['SFTAZ'])
# print self.hh_controls.loc[1:10,:]
# cat_name hhsize_cat income_cat workers_cat
# cat_value 1 2 3 4 5+ 0-30k 100k+ 30-60k 60-100k 0 1 2 3+
# SFTAZ
# 1 28.365 46.970 40.260 49.410 139.995 51.540 61.848 130.700 59.912 52.155 93.025 96.990 62.830
# 2 36.663 54.540 45.147 51.207 115.443 54.976 73.970 83.514 90.540 48.783 81.507 93.627 79.083
# 3 73.610 104.353 77.074 71.012 106.951 261.136 28.188 106.849 37.827 158.911 196.149 67.115 10.825
# 4 45.133 67.140 55.577 63.037 142.113 67.002 90.663 102.678 111.657 60.053 100.337 115.257 97.353
# 5 34.038 56.364 48.312 59.292 167.994 62.707 75.211 157.363 71.719 62.586 111.630 116.388 75.396
# 6 65.403 94.302 78.078 85.176 184.041 144.312 50.742 138.288 173.658 84.669 148.551 159.705 114.075
# 7 46.920 66.516 49.128 45.264 68.172 166.646 17.759 68.419 24.176 101.292 125.028 42.780 6.900
# 8 54.282 74.883 56.898 53.301 87.636 67.002 74.937 95.038 90.023 61.476 126.222 104.967 34.335
# 9 42.471 63.180 52.299 59.319 133.731 63.566 85.465 97.339 105.630 56.511 94.419 108.459 91.611
# 10 55.480 77.140 60.800 62.700 123.880 90.195 64.092 101.710 124.003 68.780 134.520 122.740 53.960
# todo: add HAGE1KIDS0, HAGE1KIDS1, HAGE1KIDSWHATEV
self.person_controls = cat.categorize(self.controls, {
("age_cat", "0-4"): "AGE0004",
("age_cat", "5-19"): "AGE0519",
("age_cat", "20-44"): "AGE2044",
("age_cat", "45-64"): "AGE4564",
("age_cat", "65+"): "AGE65P"
},
index_cols=['SFTAZ'])
def get_household_joint_dist_for_geography(self, ind):
# check the cache to see if we've done it already
puma = self.tazToPUMA2010.loc[ind.SFTAZ, 'PUMA2010']
if puma in self.h_pums.keys():
return self.h_pums[puma], self.jd_households[puma]
# if not, get the superclass to do a bunch of variable setting
h_pums, p_pums = SFCTAStarter.get_pums(self, puma)
orig_len = len(h_pums)
# filter to housing unit only with number of persons > 0
h_pums = h_pums[h_pums['NP'] > 0]
# Only Housing units
h_pums = h_pums[h_pums['TYPE'] == 1]
print "Filtered to %d households from %d originally" % (len(h_pums),
orig_len)
# Household income
h_pums['hhinc_2012dollars'] = h_pums['HINCP'] * (
0.000001 * h_pums['ADJINC']) # ADJINC has 6 implied decimal places
h_pums['hhinc_1989dollars'] = 0.54 * h_pums['hhinc_2012dollars']
h_pums['hhinc'] = h_pums[
'hhinc_1989dollars'] / 1000.0 # in thousands of dollars
# print sum(h_pums.loc[:,'hhinc']<0)
h_pums.loc[h_pums.loc[:, 'hhinc'] < 0, 'hhinc'] = 0.0 # no negatives
# print sum(h_pums.loc[:,'hhinc']>255)
h_pums.loc[h_pums.loc[:, 'hhinc'] > 255, 'hhinc'] = 255.0 # max = 255
# For the following, r is a pandas.Series
# It's basically a row from h_pums, so any variables defined above will be available
def hhsize_cat(r):
# NP = number of persons
if r.NP >= 5:
return "5+"
elif r.NP == 4:
return "4"
elif r.NP == 3:
return "3"
elif r.NP == 2:
return "2"
elif r.NP == 1:
return "1"
return "1"
def income_cat(r):
if r.hhinc < 25.0:
return "0-25k"
elif r.hhinc < 45.0:
return "25-45k"
elif r.hhinc < 75.0:
return "45-75k"
else:
return "75k+"
def workers_cat(r):
# hmm... WIF = Workers in Family. What about non-family households?
if r.workers >= 3:
return "3+"
elif r.workers == 2:
return "2"
elif r.workers == 1:
return "1"
return "0"
def htype_cat(r):
if r.hhage < 65 and r.NOC == 0:
return "HAGE1K0"
elif r.hhage < 65 and r.NOC > 0:
return "HAGE1K1"
else:
return "HAGE65KALL"
h_pums, jd_households = cat.joint_distribution(
h_pums, cat.category_combinations(self.hh_controls.columns), {
"hhsize_cat": hhsize_cat,
"income_cat": income_cat,
"workers_cat": workers_cat,
"htype_cat": htype_cat
})
# cache them
self.h_pums[puma] = h_pums
self.jd_households[puma] = jd_households
return h_pums, jd_households
def get_household_joint_dist_for_geography(self, ind):
# check the cache to see if we've done it already
puma = self.tazToPUMA2010.loc[ind.SFTAZ,'PUMA2010']
if puma in self.h_pums.keys():
return self.h_pums[puma], self.jd_households[puma]
# if not, get the superclass to do a bunch of variable setting
h_pums, p_pums = SFCTAStarter.get_pums(self, puma)
orig_len = len(h_pums)
# Don't bother filter number of persons -- this should happen with TYPE filter
# h_pums = h_pums[h_pums['NP']==1]
# Only Non-Institutional Group Quarters
h_pums = h_pums[h_pums['TYPE']>2]
print "Filtered to %d GQ 'households' from %d originally" % (len(h_pums), orig_len)
np_bad = (h_pums.NP != 1)
assert(np_bad.sum() == 0)
# Group quarters income -- use PINCP
h_pums.loc[pd.isnull(h_pums.loc[:,'PINCP']),'PINCP'] = 0.0 # no null
h_pums['hhinc_2012dollars'] = h_pums['PINCP']*(0.000001*h_pums['ADJINC']) # ADJINC has 6 implied decimal places
h_pums['hhinc_1989dollars'] = 0.54*h_pums['hhinc_2012dollars']
h_pums['hhinc'] = h_pums['hhinc_1989dollars']/1000.0 # in thousands of dollars
# print sum(h_pums.loc[:,'hhinc']<0)
h_pums.loc[h_pums.loc[:,'hhinc']<0, 'hhinc'] = 0.0 # no negatives
# print sum(h_pums.loc[:,'hhinc']>255)
h_pums.loc[h_pums.loc[:,'hhinc']>255,'hhinc'] = 255.0 # max = 255
# For the following, r is a pandas.Series
# It's basically a row from h_pums, so any variables defined above will be available
def hhsize_cat(r):
# NP = number of persons
if r.NP >=5:
return "5+"
elif r.NP == 4:
return "4"
elif r.NP == 3:
return "3"
elif r.NP == 2:
return "2"
elif r.NP == 1:
return "1"
return "1"
def income_cat(r):
if r.hhinc < 25.0:
return "0-25k"
elif r.hhinc < 45.0:
return "25-45k"
elif r.hhinc < 75.0:
return "45-75k"
else:
return "75k+"
def workers_cat(r):
# hmm... WIF = Workers in Family. What about non-family households?
if r.workers >= 3:
return "3+"
elif r.workers == 2:
return "2"
elif r.workers == 1:
return "1"
return "0"
def htype_cat(r):
if r.hhage < 65 and r.gqchild==0:
return "HAGE1K0"
elif r.hhage < 65 and r.gqchild > 0:
return "HAGE1K1"
else:
return "HAGE65KALL"
category_df = pd.DataFrame({'cat_id':[0], 'hhsize_cat':["1"]})
category_df.set_index(['hhsize_cat'], inplace=True)
h_pums, jd_households = cat.joint_distribution(
h_pums,
category_df,
{"hhsize_cat": hhsize_cat,
"income_cat": income_cat,
"workers_cat": workers_cat,
"htype_cat": htype_cat}
)
# cache them
self.h_pums[puma] = h_pums
self.jd_households[puma] = jd_households
return h_pums, jd_households
def get_household_joint_dist_for_geography(self, ind):
# check the cache to see if we've done it already
puma = self.tazToPUMA2010.loc[ind.SFTAZ, 'PUMA2010']
if puma in self.h_pums.keys():
return self.h_pums[puma], self.jd_households[puma]
# if not, get the superclass to do a bunch of variable setting
h_pums, p_pums = SFCTAStarter.get_pums(self, puma)
orig_len = len(h_pums)
# Don't bother filter number of persons -- this should happen with TYPE filter
# h_pums = h_pums[h_pums['NP']==1]
# Only Non-Institutional Group Quarters
h_pums = h_pums[h_pums['TYPE'] > 2]
print "Filtered to %d GQ 'households' from %d originally" % (
len(h_pums), orig_len)
np_bad = (h_pums.NP != 1)
assert (np_bad.sum() == 0)
# Group quarters income -- use PINCP
h_pums.loc[pd.isnull(h_pums.loc[:, 'PINCP']), 'PINCP'] = 0.0 # no null
h_pums['hhinc_2012dollars'] = h_pums['PINCP'] * (
0.000001 * h_pums['ADJINC']) # ADJINC has 6 implied decimal places
h_pums['hhinc_1989dollars'] = 0.54 * h_pums['hhinc_2012dollars']
h_pums['hhinc'] = h_pums[
'hhinc_1989dollars'] / 1000.0 # in thousands of dollars
# print sum(h_pums.loc[:,'hhinc']<0)
h_pums.loc[h_pums.loc[:, 'hhinc'] < 0, 'hhinc'] = 0.0 # no negatives
# print sum(h_pums.loc[:,'hhinc']>255)
h_pums.loc[h_pums.loc[:, 'hhinc'] > 255, 'hhinc'] = 255.0 # max = 255
# For the following, r is a pandas.Series
# It's basically a row from h_pums, so any variables defined above will be available
def hhsize_cat(r):
# NP = number of persons
if r.NP >= 5:
return "5+"
elif r.NP == 4:
return "4"
elif r.NP == 3:
return "3"
elif r.NP == 2:
return "2"
elif r.NP == 1:
return "1"
return "1"
def income_cat(r):
if r.hhinc < 25.0:
return "0-25k"
elif r.hhinc < 45.0:
return "25-45k"
elif r.hhinc < 75.0:
return "45-75k"
else:
return "75k+"
def workers_cat(r):
# hmm... WIF = Workers in Family. What about non-family households?
if r.workers >= 3:
return "3+"
elif r.workers == 2:
return "2"
elif r.workers == 1:
return "1"
return "0"
def htype_cat(r):
if r.hhage < 65 and r.gqchild == 0:
return "HAGE1K0"
elif r.hhage < 65 and r.gqchild > 0:
return "HAGE1K1"
else:
return "HAGE65KALL"
category_df = pd.DataFrame({'cat_id': [0], 'hhsize_cat': ["1"]})
category_df.set_index(['hhsize_cat'], inplace=True)
h_pums, jd_households = cat.joint_distribution(
h_pums, category_df, {
"hhsize_cat": hhsize_cat,
"income_cat": income_cat,
"workers_cat": workers_cat,
"htype_cat": htype_cat
})
# cache them
self.h_pums[puma] = h_pums
self.jd_households[puma] = jd_households
return h_pums, jd_households
