92 lines
3.9 KiB
Python
92 lines
3.9 KiB
Python
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from sqldumpr import Dumpr
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def get_tablename_query(column_id, boundary_id, timespan):
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"""
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given a column_id, boundary-id (us.census.tiger.block_group), and
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timespan, give back the current table hash from the data observatory
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"""
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q = """
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SELECT t.tablename, geoid_ct.colname colname
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FROM obs_table t,
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obs_column_table geoid_ct,
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obs_column_table data_ct
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WHERE
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t.id = geoid_ct.table_id AND
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t.id = data_ct.table_id AND
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geoid_ct.column_id =
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(SELECT source_id
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FROM obs_column_to_column
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WHERE target_id = '{boundary_id}'
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AND reltype = 'geom_ref'
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) AND
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data_ct.column_id = '{column_id}' AND
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timespan = '{timespan}'
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""".replace('\n','')
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return q.format(column_id=column_id,
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boundary_id=boundary_id,
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timespan=timespan)
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def select_star(tablename):
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return "SELECT * FROM {}".format(tablename)
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cdb = Dumpr('observatory.cartodb.com','')
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metadata = ['obs_table', 'obs_column_table', 'obs_column', 'obs_column_tag', 'obs_tag', 'obs_column_to_column']
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fixtures = [
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('us.census.tiger.census_tract', 'us.census.tiger.census_tract', '2014'),
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('us.census.tiger.block_group', 'us.census.tiger.block_group', '2014'),
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('us.census.tiger.zcta5', 'us.census.tiger.zcta5', '2014'),
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('us.census.tiger.county', 'us.census.tiger.county', '2014'),
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('us.census.acs.B01003001', 'us.census.tiger.census_tract', '2010 - 2014'),
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('us.census.acs.B01003001_quantile', 'us.census.tiger.census_tract', '2010 - 2014'),
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('us.census.acs.B01003001', 'us.census.tiger.block_group', '2010 - 2014'),
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('us.census.spielman_singleton_segments.X10', 'us.census.tiger.census_tract', '2010 - 2014'),
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('us.zillow.AllHomes_Zhvi', 'us.census.tiger.zcta5', '2014-01'),
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('us.zillow.AllHomes_Zhvi', 'us.census.tiger.zcta5', '2016-03'),
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('whosonfirst.wof_country_geom', 'whosonfirst.wof_country_geom', '2016'),
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('us.census.tiger.zcta5_clipped', 'us.census.tiger.zcta5_clipped', '2014'),
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('us.census.tiger.block_group_clipped', 'us.census.tiger.block_group_clipped', '2014'),
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]
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unique_tables = set()
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for f in fixtures:
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column_id, boundary_id, timespan = f
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tablename_query = get_tablename_query(*f)
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resp = cdb.query(tablename_query).json()['rows'][0]
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tablename = resp['tablename']
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colname = resp['colname']
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table_colname = (tablename, colname, boundary_id, )
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if table_colname not in unique_tables:
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print table_colname
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unique_tables.add(table_colname)
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print unique_tables
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with open('src/pg/test/fixtures/load_fixtures.sql', 'w') as outfile:
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with open('src/pg/test/fixtures/drop_fixtures.sql', 'w') as dropfiles:
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outfile.write('SET client_min_messages TO WARNING;\n\set ECHO none\n')
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dropfiles.write('SET client_min_messages TO WARNING;\n\set ECHO none\n')
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for tablename in metadata:
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cdb.dump(select_star(tablename), tablename, outfile, schema='observatory')
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dropfiles.write('DROP TABLE IF EXISTS observatory.{};\n'.format(tablename))
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print tablename
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for tablename, colname, boundary_id in unique_tables:
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if 'zcta5' in boundary_id:
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where = '\'11%\''
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compare = 'LIKE'
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elif 'whosonfirst' in boundary_id:
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where = '(\'85632785\',\'85633051\',\'85633111\',\'85633147\',\'85633253\',\'85633267\')'
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compare = 'IN'
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else:
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where = '\'36047%\''
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compare = 'LIKE'
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print ' '.join([select_star(tablename), "WHERE {}::text {} {}".format(colname, compare, where)])
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cdb.dump(' '.join([select_star(tablename), "WHERE {}::text {} {}".format(colname, compare, where)]),
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tablename, outfile, schema='observatory')
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dropfiles.write('DROP TABLE IF EXISTS observatory.{};\n'.format(tablename))
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