def makeNode(self, hholds, units, geocode='0'):
hhold_hist, unit_hist = table2hists(
np.array(hholds), self.schema), table2hists(np.array(units),
self.unit_schema,
CC.ATTR_HHGQ,
units=True)
invar = InvariantsMaker.make(schema=CC.SCHEMA_HOUSEHOLD2010,
raw=hhold_hist,
raw_housing=unit_hist,
invariant_names=('tot', 'gqhh_vect'))
cons = HHConstraintsCreator(
hist_shape=(hhold_hist.shape, unit_hist.shape),
invariants=invar,
constraint_names=('no_vacant', 'living_alone',
'size2')).calculateConstraints().constraints_dict
node = GeounitNode(raw=hhold_hist,
raw_housing=unit_hist,
invar=invar,
cons=cons,
geocode_dict={1: 'Stub'},
geocode=geocode)
node.syn = node.raw
return node
def makeNode(self, persons, geocode='0123456789abcdef'):
person_hist, unit_hist = table2hists(np.array(persons),
self.schema,
housing_varname=CC.ATTR_HHGQ)
invar = InvariantsMaker.make(schema=CC.DAS_DHCP_HHGQ,
raw=person_hist,
raw_housing=unit_hist,
invariant_names=('tot', 'gqhh_tot',
'gqhh_vect'))
cons = PConstraintsCreator(
hist_shape=(person_hist.shape, unit_hist.shape),
invariants=invar,
constraint_names=(
'hhgq_total_lb', 'hhgq_total_ub',
'nurse_nva_0')).calculateConstraints().constraints_dict
node = GeounitNode(raw=person_hist,
raw_housing=unit_hist,
invar=invar,
cons=cons,
geocode_dict={
16: 'Block',
12: 'Block_Group',
11: 'Tract',
5: 'County',
2: 'State',
1: 'US'
},
geocode=geocode)
node.syn = node.raw
return node
def test_wconstraints():
geocode_dict = {
16: 'Block',
12: 'Block_Group',
11: 'Tract',
5: 'County'
}
histogram, housing_hist = table2hists(
np.array(
[ # columns: 'hhgq', 'votingage', 'hispanic', 'cenrace', 'unique unitid' (shape 8,2,2,63 + unitUID)
# each row is a person
[0, 1, 1, 20, 0],
[1, 0, 0, 1, 1],
[3, 1, 0, 10, 2],
[3, 0, 0, 15, 2],
[1, 1, 0, 15, 3]
]),
SchemaMaker.fromName(CC.SCHEMA_PL94),
CC.ATTR_HHGQ)
inv_dict = InvariantsMaker.make(schema=CC.SCHEMA_PL94,
raw=histogram,
raw_housing=housing_hist,
invariant_names=("tot", "gqhh_vect",
"gqhh_tot", "va"))
con_dict = cPL94.ConstraintsCreator(hist_shape=(histogram.shape, housing_hist.shape), invariants=inv_dict, constraint_names=("total",))\
.calculateConstraints().constraints_dict
n1 = nodes.GeounitNode(geocode='123456789abcdefg',
geocode_dict=geocode_dict,
raw=histogram,
raw_housing=housing_hist,
cons=con_dict,
invar=inv_dict)
n2 = nodes.GeounitNode(geocode='123456789abcdefg',
geocode_dict=geocode_dict,
raw=histogram,
raw_housing=housing_hist,
cons=con_dict,
invar=inv_dict)
assert n1 == n2
# for adults, have to fill them with 1 minors and 3 adults, leaving 1 adult
# hhgq=1: 3 facilities of other types,
# for minors, can fill them with the 3 adults, leaving 1 minor,
# for adults, have to fill them with 1 minor amd 2 adults, leaving 2 adults
# hhgq=3: 3 facilities of other types,
# for minors, can fill them with the 3 adults, leaving 1 minor,
# for adults, have to fill them with 1 minor amd 2 adults, leaving 2 adults
# other hhgq are 0
'hhgq_va_ub': np.array([[1, 1, 0, 1, 0, 0, 0, 0], # minors / nva
[1, 2, 0, 2, 0, 0, 0, 0]] # adults / va
).transpose().ravel(),
# We have 1 minors total and 4 adults total
# hhgq=0: 1 facility
# for minors, can fill it with 1 adult, 0 minors needed.
# for adults, can fill it with 1 minor, 0 adults needed
# hhgq=1: 2 facilities
# for minors, can fill them with 2 adults, 0 minors needed.
# for adults, can fill them with 1 minor, 1 adult needed
# hhgq=3: 2 facilities
# for minors, can fill them with 2 adults, 0 minors needed.
# for adults, can fill them with 1 minor, 1 adult needed
# other hhgq are 0
'hhgq_va_lb': np.array([[0, 0, 0, 0, 0, 0, 0, 0], # minors
[0, 1, 0, 1, 0, 0, 0, 0] # adults
]).transpose().ravel(),
}
for data in [data1, data2, data_1gqtype]:
data['hist'] = table2hists(np.array(data['data']), schema, CC.ATTR_HHGQ)
3,
'householder_ub_lhs':
0,
'spousesUnmarriedPartners_ub':
3,
'spousesUnmarriedPartners_ub_lhs':
0,
'people100Plus_ub':
6,
'people100Plus_ub_lhs':
1,
'parents_ub':
6,
'parents_ub_lhs':
0,
'parentInLaws_ub':
6,
'parentInLaws_ub_lhs':
0,
}
for data in [data1]:
data['hist'] = table2hists(np.array(data['data']),
schema), table2hists(np.array(data['units']),
unit_schema,
CC.ATTR_RELGQ,
units=True)
data_1gqtype = data1
data2 = data1
def convertPL94(d):
return table2hists(d, SchemaMaker.fromName(CC.SCHEMA_PL94), CC.ATTR_HHGQ)
'size3_eld3':
0,
'size3_le25_eld3':
1, # Line 1: size=3, couple with child, hhage < 24, presence of 75+ elderly
'size4_le25_eld3':
0,
'age_child_1':
1, # line 3: hhsex male, children < 6, hhage 75+
'age_child_2':
0,
'age_child_3':
0,
'hh_elderly_eld3':
1, # line 3: hhage 75+, no one over 60
'hh_elderly_2':
0,
'hh_elderly_3':
0,
}
for data in [data1, data_1gqtype]:
# data['hist'] = table2hists(np.array(data['data']), schema, 'hhsex') # NOTE THAT THE 'hhsex' axis is a stub.
data['hist'] = table2hists(np.array(data['data']),
schema), table2hists(np.array(data['units']),
unit_schema,
CC.ATTR_HHTYPE_DHCH,
units=True)
data2 = data_1gqtype
def p_h_data1(self):
return table2hists(self.d, SchemaMaker.fromName(self.schema_name)), table2hists(self.units,
SchemaMaker.fromName(_unit_schema_dict[self.schema_name]),
self.hhgq_attr, units=True)
def p_h_data1(self):
return table2hists(self.d, SchemaMaker.fromName(self.schema_name), self.hhgq_attr)
