Table name as parameter. Scenarios from cityIO rather than text files…

…. New corktown_dev table.

corktown_offline_indicators.py

@@ -6,7 +6,6 @@
 @author: doorleyr
 """
 
-from toolbox import Indicator, CompositeIndicator
 from proximity_indicator import ProxIndicator
 from innovation_indicator import InnoIndicator
 from economic_indicator import EconomicIndicator
@@ -15,10 +14,12 @@
 
 import json
 import pandas as pd
+import urllib
 
 
-
-folder='../Scenarios/24_Jun_20/'
+table_name='corktown'
+host='https://cityio.media.mit.edu/'
+cityIO_get_url=host+'api/table/'+table_name
 
 # =============================================================================
 # Initialise Indicators
@@ -34,15 +35,16 @@
 # Load contextual data
 # =============================================================================
 
-geogrid=json.load(open('{}corktown_geogrid.geojson'.format(folder)))
+with urllib.request.urlopen(cityIO_get_url+'/GEOGRID') as url:
+    geogrid=json.loads(url.read().decode())
 cell_area=geogrid['properties']['header']['cellSize']**2
 
 updatable=[((feat['properties']['interactive'])or (feat['properties']['static_new'])
            ) for feat in geogrid['features']]
 
-types=json.load(open('{}corktown_types.json'.format(folder)))
+types=geogrid['properties']['types']
 
-static_types=json.load(open('{}corktown_static_types.json'.format(folder)))
+static_types=geogrid['properties']['static_types']
 
 types.update(static_types)
 
@@ -55,12 +57,21 @@
     ind.geogrid_header=geogrid['properties']['header']
 
 # =============================================================================
-# Load the saved land use scenarios
+# Load the saved land use scenarios from city_IO
 # =============================================================================
-geogrid_data_base=json.load(open('{}ford_base.json'.format(folder)))
-geogrid_data_campus=json.load(open('{}ford_campus.json'.format(folder)))
-geogrid_data_housing=json.load(open('{}ford_housing.json'.format(folder)))
-geogrid_data_inno_com=json.load(open('{}ford_inno_com.json'.format(folder)))
+# Scenarios are saved as scenrio0, scenario1 etc. on cityIO
+# scenario name is contained in the 'info' field
+# try 0 to N and save all the results
+# Additional scenarios can be created and saved to cityIO using the CityScope interactive front-end
+all_scenarios={}
+for i in range(10):
+    try:
+        with urllib.request.urlopen(cityIO_get_url+'/scenarios'+str(i)) as url:
+            geogriddata=json.loads(url.read().decode())
+        all_scenarios[geogriddata['info']['name']]=geogriddata['GEOGRIDDATA']
+    except:
+        pass
+print('Downloaded {} land use scenarios'.format(len(all_scenarios)))
 
 # =============================================================================
 # Define some functions to calculate indicators and land use stats for each scenario
@@ -118,29 +129,26 @@ def create_scenario_row(all_ind, stats, scenario_name):
         if not ind['units']==None:
             raw_name+= ' ['+ind['units']+']'
         all_cols[raw_name]=ind['raw_value']
-    for type_name in stats:
-        all_cols[type_name + ' sqm']=stats[type_name]['sqm']
-        all_cols[type_name + ' capacity']=stats[type_name]['capacity']
     return all_cols
 
 # =============================================================================
 # Calculate indicators and land use stats for each scenario
 # =============================================================================
 
-base_indicators=get_all_indicators(geogrid_data_base)
-base_stats=get_type_stats(geogrid_data_base, reporting_types, updatable,cell_area)
+base_indicators=get_all_indicators(all_scenarios['Baseline'])
+base_stats=get_type_stats(all_scenarios['Baseline'], reporting_types, updatable,cell_area)
 
-campus_indicators=get_all_indicators(geogrid_data_campus)
-campus_stats=get_type_stats(geogrid_data_campus, reporting_types, updatable,cell_area)
+campus_indicators=get_all_indicators(all_scenarios['Campus_Only'])
+campus_stats=get_type_stats(all_scenarios['Campus_Only'], reporting_types, updatable,cell_area)
 
-campus_mobility_indicators=get_all_indicators(geogrid_data_campus)
+campus_mobility_indicators=get_all_indicators(all_scenarios['Campus_Only'])
 campus_mobility_stats=campus_stats
 
-housing_indicators=get_all_indicators(geogrid_data_housing)
-housing_stats=get_type_stats(geogrid_data_housing, reporting_types, updatable,cell_area)
+housing_indicators=get_all_indicators(all_scenarios['Campus_Housing'])
+housing_stats=get_type_stats(all_scenarios['Campus_Housing'], reporting_types, updatable,cell_area)
 
-inno_com_indicators=get_all_indicators(geogrid_data_inno_com)
-inno_com_stats=get_type_stats(geogrid_data_inno_com, reporting_types, updatable,cell_area)
+inno_com_indicators=get_all_indicators(all_scenarios['Innovation_Community'])
+inno_com_stats=get_type_stats(all_scenarios['Innovation_Community'], reporting_types, updatable,cell_area)
 
 all_scenarios=[]
 all_scenarios.append(create_scenario_row(base_indicators, base_stats, scenario_name='BAU'))
@@ -175,10 +183,6 @@ def create_scenario_row(all_ind, stats, scenario_name):
         col for col in all_scenarios[0] if 'raw' in col]+ [
         comp_ind for comp_ind in aggregation]
 
-for type_name in reporting_types:
-    col_order.append(type_name + ' sqm')
-    col_order.append(type_name + ' capacity')  
-
 output=output[col_order]
 
-output.to_csv('{}scenario_outputs.csv'.format(folder))
+output.to_csv('scenario_outputs.csv')

listen.py

@@ -2,7 +2,6 @@
 from proximity_indicator import ProxIndicator
 from innovation_indicator import InnoIndicator
 from mobility_indicator import MobilityIndicator
-from aggregate_indicator import AggregateIndicator
 from economic_indicator import EconomicIndicator
 from buildings_indicator import BuildingsIndicator
 from diversity_indicator import DiversityIndicator
@@ -12,23 +11,23 @@
 
 from statistics import mean
 
-def main(host_mode='remote'):
-    reference=json.load(open('./tables/corktown/reference.json'))
+def main(host_mode='remote', table_name='corktown_dev'):
+    reference=json.load(open('./tables/{}/reference.json'.format(table_name)))
     if host_mode=='local':
         host = 'http://127.0.0.1:5000/'
     else:
         host = 'https://cityio.media.mit.edu/'
     # Individual Indicators
     I = InnoIndicator()    
-    P = ProxIndicator(name='proximity',   host=host, indicator_type_in='numeric', table_name='corktown')
-    P_hm = ProxIndicator(name='proximity_heatmap',  host=host, indicator_type_in='heatmap', table_name='corktown')
-    M = MobilityIndicator(name='mobility', table_name='corktown')
-    B= BuildingsIndicator(name='buildings',  host=host,table_name='corktown')
-    D= DiversityIndicator(name='diversity',  table_name='corktown')
+    P = ProxIndicator(name='proximity',   host=host, indicator_type_in='numeric', table_name=table_name)
+    P_hm = ProxIndicator(name='proximity_heatmap',  host=host, indicator_type_in='heatmap', table_name=table_name)
+    M = MobilityIndicator(name='mobility', table_name=table_name)
+    B= BuildingsIndicator(name='buildings',  host=host,table_name=table_name)
+    D= DiversityIndicator(name='diversity',  table_name=table_name)
 
     # 2nd order  individual indicators 
     E = EconomicIndicator(name='Economic',
-                          table_name='corktown')
+                          table_name=table_name)
 
     for indicator in [
             I,
@@ -37,7 +36,7 @@ def main(host_mode='remote'):
             D]:
         indicator.viz_type='bar'
 
-    H = Handler('corktown', quietly=False, host_mode=host_mode, reference=reference)
+    H = Handler(table_name, quietly=False, host_mode=host_mode, reference=reference)
 
     H.add_indicators([
             I,
@@ -93,7 +92,8 @@ def main(host_mode='remote'):
 
 if __name__ == '__main__':
     if len(sys.argv)>1:
-        host_mode=sys.argv[1]
-        main(host_mode=host_mode)
+        table_name=sys.argv[1]
     else:
-        main()
+        table_name='corktown_dev'
+    print('Running for table named {} on city_IO'.format(table_name))
+    main(table_name=table_name)