def ipf_config_run(db, synthesis_type, control_variables, dimensions, pumano,
tract, bg):
dbc = db.cursor()
# Creating objective joint distributions to match the resulting sunthetic populations against
adjusting_pums_joint_distribution.create_joint_dist(
db, synthesis_type, control_variables, dimensions, pumano, tract, bg)
adjusting_pums_joint_distribution.adjust_weights(db, synthesis_type,
control_variables, pumano,
tract, bg)
order_dummy = adjusting_pums_joint_distribution.create_aggregation_string(
control_variables)
dbc.execute(
'select frequency from %s_%s_joint_dist where tract = %s and bg = %s order by %s'
% (synthesis_type, pumano, tract, bg, order_dummy))
objective_frequency = numpy.asarray(dbc.fetchall())
# Creating the joint distributions corrected for Zero-cell and Zero-marginal problems
# Example puma x composite_type adjustment for the synthesis type obtained as a parameter
adjusting_pums_joint_distribution.create_joint_dist(
db, synthesis_type, control_variables, dimensions, pumano, tract, bg)
adjusting_pums_joint_distribution.create_adjusted_frequencies(
db, synthesis_type, control_variables, pumano, tract, bg)
adjusting_pums_joint_distribution.adjust_weights(db, synthesis_type,
control_variables, pumano,
tract, bg)
dbc.execute(
'select frequency from %s_%s_joint_dist where tract = %s and bg = %s order by %s'
% (synthesis_type, pumano, tract, bg, order_dummy))
estimated_constraint = numpy.asarray(dbc.fetchall())
return objective_frequency, estimated_constraint
def ipf_config_run (db, synthesis_type, control_variables, dimensions, pumano, tract, bg):
dbc = db.cursor()
# Creating objective joint distributions to match the resulting sunthetic populations against
adjusting_pums_joint_distribution.create_joint_dist(db, synthesis_type, control_variables, dimensions, pumano, tract, bg)
adjusting_pums_joint_distribution.adjust_weights(db, synthesis_type, control_variables, pumano, tract, bg)
order_dummy = adjusting_pums_joint_distribution.create_aggregation_string(control_variables)
dbc.execute('select frequency from %s_%s_joint_dist where tract = %s and bg = %s order by %s'%(synthesis_type, pumano, tract, bg, order_dummy))
objective_frequency = numpy.asarray(dbc.fetchall())
# Creating the joint distributions corrected for Zero-cell and Zero-marginal problems
# Example puma x composite_type adjustment for the synthesis type obtained as a parameter
adjusting_pums_joint_distribution.create_joint_dist(db, synthesis_type, control_variables, dimensions, pumano, tract, bg)
adjusting_pums_joint_distribution.create_adjusted_frequencies(db, synthesis_type, control_variables, pumano, tract, bg)
adjusting_pums_joint_distribution.adjust_weights(db, synthesis_type, control_variables, pumano, tract, bg)
dbc.execute('select frequency from %s_%s_joint_dist where tract = %s and bg = %s order by %s'%(synthesis_type, pumano, tract, bg, order_dummy))
estimated_constraint = numpy.asarray(dbc.fetchall())
return objective_frequency, estimated_constraint
def prepare_data(db):
# Processes/ methods to be called at the beginning of the pop_synthesis process
dbc = db.cursor()
# Identifying the number of housing units to build the Master Matrix
dbc.execute('select * from housing_pums')
housing_units = dbc.rowcount
ti = time.clock()
# Identifying the control variables for the households, gq's, and persons
hhld_control_variables = adjusting_pums_joint_distribution.choose_control_variables(db, 'hhld')
gq_control_variables = adjusting_pums_joint_distribution.choose_control_variables(db, 'gq')
person_control_variables = adjusting_pums_joint_distribution.choose_control_variables(db, 'person')
# Identifying the number of categories within each control variable for the households, gq's, and persons
hhld_dimensions = numpy.asarray(adjusting_pums_joint_distribution.create_dimensions(db, 'hhld', hhld_control_variables))
gq_dimensions = numpy.asarray(adjusting_pums_joint_distribution.create_dimensions(db, 'gq', gq_control_variables))
person_dimensions = numpy.asarray(adjusting_pums_joint_distribution.create_dimensions(db, 'person', person_control_variables))
print 'Dimensions and Control Variables created in %.4f' %(time.clock()-ti)
ti = time.clock()
update_string = adjusting_pums_joint_distribution.create_update_string(db, hhld_control_variables, hhld_dimensions)
adjusting_pums_joint_distribution.add_unique_id(db, 'hhld', update_string)
update_string = adjusting_pums_joint_distribution.create_update_string(db, gq_control_variables, gq_dimensions)
adjusting_pums_joint_distribution.add_unique_id(db, 'gq', update_string)
update_string = adjusting_pums_joint_distribution.create_update_string(db, person_control_variables, person_dimensions)
adjusting_pums_joint_distribution.add_unique_id(db, 'person', update_string)
print 'Uniqueid\'s created in %.4f' %(time.clock()-ti)
ti = time.clock()
# Populating the Master Matrix
populated_matrix = psuedo_sparse_matrix.populate_master_matrix(db, 0, housing_units, hhld_dimensions,
gq_dimensions, person_dimensions)
print 'Frequency Matrix Populated in %.4f' %(time.clock()-ti)
ti = time.clock()
# Sparse representation of the Master Matrix
ps_sp_matrix = psuedo_sparse_matrix.psuedo_sparse_matrix(db, populated_matrix, 0)
print 'Psuedo Sparse Representation of the Frequency Matrix created in %.4f' %(time.clock()-ti)
ti = time.clock()
#______________________________________________________________________
#Creating Index Matrix
index_matrix = psuedo_sparse_matrix.generate_index_matrix(db, 0)
print 'Index matrix created in %.4f' %(time.clock()-ti)
ti = time.clock()
dbc.close()
#______________________________________________________________________
# creating synthetic_population tables in MySQL
drawing_households.create_synthetic_attribute_tables(db)
# Total PUMS Sample x composite_type adjustment for hhld
adjusting_pums_joint_distribution.create_joint_dist(db, 'hhld', hhld_control_variables, hhld_dimensions, 0, 0, 0)
# Total PUMS Sample x composite_type adjustment for gq
adjusting_pums_joint_distribution.create_joint_dist(db, 'gq', gq_control_variables, gq_dimensions, 0, 0, 0)
# Total PUMS Sample x composite_type adjustment for person
adjusting_pums_joint_distribution.create_joint_dist(db, 'person', person_control_variables, person_dimensions, 0, 0, 0)
def prepare_data(db):
# Processes/ methods to be called at the beginning of the pop_synthesis process
dbc = db.cursor()
# Identifying the number of housing units to build the Master Matrix
dbc.execute('select * from housing_pums')
housing_units = dbc.rowcount
ti = time.clock()
# Identifying the control variables for the households, gq's, and persons
hhld_control_variables = adjusting_pums_joint_distribution.choose_control_variables(
db, 'hhld')
gq_control_variables = adjusting_pums_joint_distribution.choose_control_variables(
db, 'gq')
person_control_variables = adjusting_pums_joint_distribution.choose_control_variables(
db, 'person')
# Identifying the number of categories within each control variable for the households, gq's, and persons
hhld_dimensions = numpy.asarray(
adjusting_pums_joint_distribution.create_dimensions(
db, 'hhld', hhld_control_variables))
gq_dimensions = numpy.asarray(
adjusting_pums_joint_distribution.create_dimensions(
db, 'gq', gq_control_variables))
person_dimensions = numpy.asarray(
adjusting_pums_joint_distribution.create_dimensions(
db, 'person', person_control_variables))
print 'Dimensions and Control Variables created in %.4f' % (time.clock() -
ti)
ti = time.clock()
update_string = adjusting_pums_joint_distribution.create_update_string(
db, hhld_control_variables, hhld_dimensions)
adjusting_pums_joint_distribution.add_unique_id(db, 'hhld', update_string)
update_string = adjusting_pums_joint_distribution.create_update_string(
db, gq_control_variables, gq_dimensions)
adjusting_pums_joint_distribution.add_unique_id(db, 'gq', update_string)
update_string = adjusting_pums_joint_distribution.create_update_string(
db, person_control_variables, person_dimensions)
adjusting_pums_joint_distribution.add_unique_id(db, 'person',
update_string)
print 'Uniqueid\'s created in %.4f' % (time.clock() - ti)
ti = time.clock()
# Populating the Master Matrix
populated_matrix = psuedo_sparse_matrix.populate_master_matrix(
db, 0, housing_units, hhld_dimensions, gq_dimensions,
person_dimensions)
print 'Frequency Matrix Populated in %.4f' % (time.clock() - ti)
ti = time.clock()
# Sparse representation of the Master Matrix
ps_sp_matrix = psuedo_sparse_matrix.psuedo_sparse_matrix(
db, populated_matrix, 0)
print 'Psuedo Sparse Representation of the Frequency Matrix created in %.4f' % (
time.clock() - ti)
ti = time.clock()
#______________________________________________________________________
#Creating Index Matrix
index_matrix = psuedo_sparse_matrix.generate_index_matrix(db, 0)
print 'Index matrix created in %.4f' % (time.clock() - ti)
ti = time.clock()
dbc.close()
#______________________________________________________________________
# creating synthetic_population tables in MySQL
drawing_households.create_synthetic_attribute_tables(db)
# Total PUMS Sample x composite_type adjustment for hhld
adjusting_pums_joint_distribution.create_joint_dist(
db, 'hhld', hhld_control_variables, hhld_dimensions, 0, 0, 0)
# Total PUMS Sample x composite_type adjustment for gq
adjusting_pums_joint_distribution.create_joint_dist(
db, 'gq', gq_control_variables, gq_dimensions, 0, 0, 0)
# Total PUMS Sample x composite_type adjustment for person
adjusting_pums_joint_distribution.create_joint_dist(
db, 'person', person_control_variables, person_dimensions, 0, 0, 0)
