#
#error http://cityio.media.mit.edu/api/table/lomas/GEOGRID/properties/types are not updated!
#Input : table_name (make sure is an existing table)
#
#Output : new table https://cityscope.media.mit.edu/CS_cityscopeJS/?cityscope=table_name
#
import json
import requests
import shapely
from shapely.geometry.polygon import Polygon
from shapely.geometry import shape
from brix import Handler
table_name='lomas'
H = Handler(table_name,quietly=False)
#Polygon importred from PoW web
lat_point_list = [20.764781341419358, 20.76303528674933, 20.763215914030038, 20.762031797925214, 20.76134942171894,20.76124907201699,20.761430386498358,20.75675402210621, 20.756071622073794, 20.75657338710328, 20.756891146354917, 20.75557076509179, 20.757716955908744, 20.758852159932292, 20.7593438806329, 20.761691654649997, 20.76670887497622, 20.769215940307845, 20.769356147330143, 20.770700755657085, 20.771041923030506, 20.76811216984444, 20.765182062242182]
lon_point_list = [-103.36270688388338,-103.36274981993364,-103.3615690785509,-103.36227752338053,-103.36176229077716,-103.36077476162069,-103.35710590345617,-103.35873747336686,-103.35841545298975, -103.36019729907642,-103.36146340784991,-103.36176355384579,-103.36681530234735,-103.36631873727687,-103.36723112834531,-103.36669703771352,-103.37101723758293,-103.36673933187646,-103.36523770894898,-103.36399256349084,-103.36373494718914,-103.3603015332957,-103.36216925148294]
polygon_geom = Polygon(zip(lon_point_list, lat_point_list))
default_type = 'No_Technology'
timing_now_type = '2021_Year'
timing_future_type = '2025_Year'
new_types=json.load(open('type_definitions/lomas_types.json'))
## CHANGE OPACITY OF TYPES IN THE RGB ##
for i in new_types:
default_color_hex = new_types[i]['color'].lstrip('#')
default_color_rgb = list(int(default_color_hex[i:i+2], 16) for i in (0, 2, 4))
# This script integrates the indicators and any modification of the grid
#
from brix import Handler
from my_indicators import Energy_related_Indicators #Access_to_Sanitation, etc
from my_gridmodification import collapse_technology
H = Handler('lomas', quietly=False)
# Another way to make: '**{k:H.get_table_properties()[k] for k in ['cellSize','latitude','longitude']}'
# props = H.get_table_properties()
# Energy = Energy_related_Indicators(
# longitude=props['longitude'],
# cellSize =props['cellSize'],
# latitude =props['latitude']
# )
Energy = Energy_related_Indicators(**{
k: H.get_table_properties()[k]
for k in ['cellSize', 'latitude', 'longitude']
})
#Sanitation = Access_to_Sanitation()
H.add_geogrid_data_update_function(collapse_technology)
H.add_indicators([
Energy #,
#Sanitation, ect
])
H.listen()
# print(H.update_package())
from brix import Handler
from indicators import Corona
if __name__ == '__main__':
# Create a handler for our table
H = Handler('yourtest', quietly=False)
# geogrid_data = H.get_geogrid_data()
# Create a new instance of the Corona indicator
heat = Corona()
# Add the desired indicators to the table
H.add_indicators([heat])
print(H.list_indicators())
print(H.return_indicator("corona"))
# Listen for updates to the table to recompute indicator data
H.listen()
default_type = 'No_Technology'
default_color_hex = new_types[default_type]['color'].lstrip('#')
for cell in grid_geo['features']:
cell_shape = shape(cell['geometry'])
if not cell_shape.centroid.within(settlement_polygon):
cell['properties']['name']='Street_NoInteractive'
cell['properties']['color'] = [0,0,0,0]
cell['properties'].pop('interactive',None)
else:
cell['properties']['name'] = default_type
#cell['properties']['color'] = default_color_hex
cell['properties']['interactive'] = 'Web'
## POST GRID TO CITY IO ##
new_grid.commit_grid()
print('POST GRID works')
## CHANGE OPACITY ## #not working
H = Handler(table_name)
H.set_opacity({'Street_NoInteractive':0}, default_alpha = 0.7)
H.reset_geogrid_data()
## SAVE THE NEW GRID##
geogrid_out_fname = 'grid_'+ table_name +'.json'
save_json = input(f'Do you want to save your new table {geogrid_out_fname}? (y/n)')
if save_json=='y':
with open(geogrid_out_fname, 'w') as f:
json.dump(grid_geo, f)
from brix import Handler from indicators import Giants table_name = 'minerva' H = Handler(table_name, quietly=False) giants = Giants(quietly=False, color_method='quantile') H.add_indicator(giants) H.listen()
'ref_value': 0.1,
'viz_type': 'radar'
}
def post_trips(self, deckgl_trips):
post_url = 'https://cityio.media.mit.edu/api/table/' + self.table_name
r = requests.post(post_url + '/ABM2',
data=json.dumps(deckgl_trips),
headers={'Content-Type': 'application/json'})
print('Post ABM: {}'.format(r))
if __name__ == "__main__":
table_name = sys.argv[1]
# Load the saved data
geogrid = gpd.read_file('tables/{}/geogrid.geojson'.format(table_name))
zones = gpd.read_file('tables/{}/zones.geojson'.format(table_name))
zones['GEOID'] = zones['GEOID'].astype(int)
zones = zones.set_index('GEOID')
simpop_df = pd.read_csv('tables/{}/simpop_df.csv'.format(table_name))
H = Handler(table_name=table_name)
H.reset_geogrid_data()
d = Density_Indicator(zones=zones)
p = Proximity_Indicator(zones=zones, geogrid=geogrid)
m = Mobility_indicator(zones, geogrid, table_name, simpop_df)
H.add_indicators([d, p, m])
H.listen()
from brix import Handler
import time
import json
import os
import hashlib
# Predefined mapping of scanned Lego types to building types
types = {
-1: "Invalid",
0: "Institutional",
1: "Office",
2: "Park",
}
H = Handler('yourtest', quietly=False)
geogrid_data = H.get_geogrid_data()
def perform_update():
H.update_geogrid_data(update_types)
def update_types(geogrid_data):
scanned = [-1, -1]
with open('CS_CityScoPy/scanner_data.txt') as json_file:
scanned = json.load(json_file)
print(f"Updating grid to \n{scanned}")
# Make sure the data is in the right format
if not type(scanned[0]) == list:
shapefile.loc[
shapefile[colname].isna(),
colname] = shapefile[shapefile[colname].isna()]['AREA'] * density
#New variables density_POB_BY_VIV, VIV_W_ELEC, POB_W_ELEC
density_POB_BY_VIV = (shapefile['POBTOT'] / shapefile['VIVTOT'])
shapefile['VIV_WO_ELEC'] = (shapefile['VIVTOT'] - shapefile['VPH_C_ELEC'])
shapefile['POB_WO_ELEC'] = (shapefile['VIV_WO_ELEC'] * density_POB_BY_VIV)
shapefile[[
'CVEGEO', "VIVTOT", 'VIV_WO_ELEC', 'POBTOT', 'POB_WO_ELEC', 'geometry'
]] #columns of the shapefile that I am interested on
######################## This part takes care of loading the geogrid from the table: lomas ########################
H = Handler('lomas', quietly=False)
#make sure table is connected
geogrid_data = H.get_geogrid_data()
geogrid_data = geogrid_data.remove_noninteractive().as_df(
include_geometries=True)
######################## join the two GeoDataFrames ########################
data_join = gpd.sjoin(
geogrid_data[['id', 'geometry']],
shapefile[[
'CVEGEO', "VIVTOT", 'VIV_WO_ELEC', 'POBTOT', 'POB_WO_ELEC', 'geometry'
]],
op='intersects'
) #geopandas.sjoin(left_df, right_df, how='inner', op='intersects', lsuffix='left', rsuffix='right')
data_join = pd.merge(