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)
if __name__ == '__main__': sample_size = 156601 pumano = 0 db = MySQLdb.connect(user = '******', passwd = '1234', db = 'ncpopsyn') hhld_dimensions = arr([5,7,8]) person_dimensions = arr([2, 10, 7]) hhld_control_variables = adjusting_pums_joint_distribution.choose_control_variables(db, 'hhld') person_control_variables = adjusting_pums_joint_distribution.choose_control_variables(db, 'person') 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, person_control_variables, person_dimensions) adjusting_pums_joint_distribution.add_unique_id(db, 'person', update_string) ti = time.clock() print 'start - %s'%ti populated_matrix = populate_master_matrix(db, pumano, sample_size, hhld_dimensions, person_dimensions) print 'End Populated matrix - %s'%(time.clock()-ti) ti = time.clock() ps_sp_matrix = psuedo_sparse_matrix(db, populated_matrix, pumano) print 'Psuedo Sparse Matrix- %s'%(time.clock()-ti)
if __name__ == '__main__': sample_size = 156601 pumano = 0 db = MySQLdb.connect(user='******', passwd='1234', db='ncpopsyn') hhld_dimensions = arr([5, 7, 8]) person_dimensions = arr([2, 10, 7]) hhld_control_variables = adjusting_pums_joint_distribution.choose_control_variables( db, 'hhld') person_control_variables = adjusting_pums_joint_distribution.choose_control_variables( db, 'person') 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, person_control_variables, person_dimensions) adjusting_pums_joint_distribution.add_unique_id(db, 'person', update_string) ti = time.clock() print 'start - %s' % ti populated_matrix = populate_master_matrix(db, pumano, sample_size, hhld_dimensions, person_dimensions) print 'End Populated matrix - %s' % (time.clock() - ti) ti = time.clock()
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)