census_2000_energy[row[2]] = \ [float(row[3]), float(row[6]), float(row[4])] # test to see how much overlap in census blocks and print out a # message about matching between spreadsheet block IDs and census # block IDs spreadsheet_blocks = set(census_2000_energy.keys()) chicago_blocks = set(CENSUS_BLOCK_2000.keys()) print >> sys.stderr, "Unknown blocks in spreadsheet: %s" % \ spreadsheet_blocks.difference(chicago_blocks) print >> sys.stderr, "# of Missing blocks in spreadsheet: %s" % \ len(chicago_blocks.difference(spreadsheet_blocks)) # --------------------------------------------- maybe read in census information # --------------------------------------- aggregate metrics and write JSON files for zoom_level in [ WARD, COMMUNITY_AREA, NEIGHBORHOOD, SCHOOL, CENSUS_TRACT_2010, ]: aggregate_dict = aggregate_metrics( WARD, CENSUS_BLOCK_2000, CENSUS_BLOCK_2000_INDEX, census_2000_energy, ) write_json(aggregate_dict, None)
# census_2000_energy = {} # for row in reader: # census_2000_energy[row[2]] = \ # [float(row[3]), float(row[6]), float(row[4])] # # test to see how much overlap in census blocks and print out a # # message about matching between spreadsheet block IDs and census # # block IDs # spreadsheet_blocks = set(census_2000_energy.keys()) # chicago_blocks = set(CENSUS_BLOCK_2000.keys()) # print >> sys.stderr, "Unknown blocks in spreadsheet: %s" % \ # spreadsheet_blocks.difference(chicago_blocks) # print >> sys.stderr, "# of Missing blocks in spreadsheet: %s" % \ # len(chicago_blocks.difference(spreadsheet_blocks)) # --------------------------------------------- maybe read in census information # --------------------------------------- aggregate metrics and write JSON files for zoom_level in [ NEIGHBORHOOD, ]: aggregate_dict = aggregate_metrics( zoom_level, CENSUS_BLOCK_2010, CENSUS_BLOCK_2010_INDEX, # census_2000_energy, ) write_json(aggregate_dict, None)
# test to see how much overlap in census blocks and print out a # message about matching between spreadsheet block IDs and census # block IDs spreadsheet_blocks = set(census_2000_energy.keys()) chicago_blocks = set(CENSUS_BLOCK_2000.keys()) print >> sys.stderr, "Unknown blocks in spreadsheet: %s" % \ spreadsheet_blocks.difference(chicago_blocks) print >> sys.stderr, "# of Missing blocks in spreadsheet: %s" % \ len(chicago_blocks.difference(spreadsheet_blocks)) # --------------------------------------------- maybe read in census information # --------------------------------------- aggregate metrics and write JSON files for zoom_level in [ WARD, COMMUNITY_AREA, NEIGHBORHOOD, SCHOOL, CENSUS_TRACT_2010, ]: aggregate_dict = aggregate_metrics( WARD, CENSUS_BLOCK_2000, CENSUS_BLOCK_2000_INDEX, census_2000_energy, ) write_json(aggregate_dict, None)