config.requests = [
# absolute value
#
# {'dataset':'gridcell',
# 'image_type':'openev_map',
# 'attribute':'urbansim.gridcell.population',
# 'arguments':{
# 'legend_scheme':{'range':[0, 10, 50, 100],
# 'color':['#b2b2aea3', '#ccff00ff','#ffcc00ff','#ff6500ff','#ff0000ff']}
#
## 'scale':[-5000, 250000]
# }
# },
#
# {'dataset':'gridcell',
# 'image_type':'openev_map',
# 'attribute':'urbansim.gridcell.number_of_jobs as employment',
# 'arguments':{
# 'legend_scheme':{'range':[0, 500, 5000, 25000],
# 'color':['#b2b2aea3', '#ccff00ff','#ffcc00ff','#ff6500ff','#ff0000ff']}
#
## 'scale':[1000, 200000]
# }
# },
{'dataset':'faz',
'image_type':'map',
'attribute':'urbansim.faz.population',
'arguments':{
# 'scale':[1, 60000]
}
},
{'dataset':'faz',
'image_type':'map',
'attribute':'psrc.faz.population_per_acre',
'arguments':{
# 'scale':[1, 60000]
}
},
{'dataset':'faz',
'image_type':'map',
'attribute':'psrc.faz.number_of_jobs_without_resource_construction_sectors as employment',
'arguments':{
# 'scale':[1, 60000]
}
},
{'dataset':'faz',
'image_type':'map',
'attribute':'psrc.faz.number_of_jobs_per_acre',
'arguments':{
# 'scale':[1, 60000]
}
},
# ##change value
# {'dataset':'gridcell',
# 'image_type':'openev_map',
# 'attribute':'population_change(DDDD-00)',
# 'arguments':{
# 'operation':'change',
# 'arguments':['urbansim.gridcell.population'],
## 'scale':[-5000, 250000]
# }
# },
#
# {'dataset':'gridcell',
# 'image_type':'openev_map',
# 'attribute':'psrc.gridcell.absolute_number_of_jobs_change as employment_change(DDDD-00)',
# 'arguments':{
# 'scale':[1000, 200000]
# }
# },
#
# {'dataset':'gridcell',
# 'image_type':'openev_map',
# 'attribute':'psrc.gridcell.percent_population_change as percent_population_change(DDDD-00)',
# 'arguments':{
## 'scale':[1000, 200000]
# }
# },
#
# {'dataset':'gridcell',
# 'image_type':'openev_map',
# 'attribute':'psrc.gridcell.percent_number_of_jobs_change as percent_employment_change(DDDD-00)',
# 'arguments':{
## 'scale':[-30000, 30000]
# }
# },
#
{'dataset':'faz',
'image_type':'map',
'attribute':'population_change(DDDD-00)',
'arguments':{
'operation':'change',
'arguments':['urbansim.faz.population'],
# 'scale':[-2000, 40000]
}
},
{'dataset':'faz',
'image_type':'map',
'attribute':'employment_change(DDDD-00)',
'arguments':{
'operation':'change',
'arguments':['urbansim.faz.number_of_jobs'],
# 'scale':[-2000, 40000]
}
},
{'dataset':'faz',
'image_type':'map',
'attribute':'psrc.faz.percent_population_change as percent_population_change(DDDD-00)',
'arguments':{
# 'scale':[1000, 200000]
}
},
{'dataset':'faz',
'image_type':'map',
'attribute':'psrc.faz.percent_number_of_jobs_change as percent_employment_change(DDDD-00)',
'arguments':{
# 'scale':[-30000, 30000]
}
},
###difference (this scenario - baseline)
# {'dataset':'gridcell',
# 'image_type':'openev_map',
# 'attribute':'psrc.gridcell.absolute_population_difference_from_baseline as population_difference_from_baseline',
# 'arguments':{
# 'scale':[-30000, 30000]
# }
# },
#
# {'dataset':'gridcell',
# 'image_type':'openev_map',
# 'attribute':'psrc.gridcell.absolute_number_of_jobs_difference_from_baseline as employment_difference_from_baseline',
# 'arguments':{
## 'scale':[-30000, 30000]
# }
# },
# {'dataset':'gridcell',
# 'image_type':'openev_map',
# 'attribute':'psrc.gridcell.percent_population_difference_from_baseline as percent_population_difference_from_baseline',
# 'arguments':{
# 'scale':[-30000, 30000]
# }
# },
#
# {'dataset':'gridcell',
# 'image_type':'openev_map',
# 'attribute':'psrc.gridcell.percent_number_of_jobs_difference_from_baseline as percent_employment_difference_from_baseline',
# 'arguments':{
## 'scale':[-30000, 30000]
# }
# },
{'dataset':'faz',
'image_type':'map',
'attribute':'psrc.faz.absolute_population_difference_from_baseline',
'arguments':{
# 'scale':[-30000, 30000]
}
},
{'dataset':'faz',
'image_type':'map',
'attribute':'psrc.faz.percent_population_difference_from_baseline',
'arguments':{
# 'scale':[-30000, 30000]
}
},
{'dataset':'faz',
'image_type':'map',
'attribute':'psrc.faz.absolute_number_of_jobs_difference_from_baseline as absolute_employment_difference_from_baseline',
'arguments':{
# 'scale':[-30000, 30000]
}
},
{'dataset':'faz',
'image_type':'map',
'attribute':'psrc.faz.percent_number_of_jobs_difference_from_baseline as percent_employment_difference_from_baseline',
'arguments':{
# 'scale':[-30000, 30000]
}
},
{'dataset':'zone',
'image_type':'map',
'attribute':'psrc.zone.travel_time_hbw_am_drive_alone_to_cbd',
'arguments':{
'scale':[-10, 110]
},
'years':[2000,2006,2011,2016,2021]
},
{'dataset':'zone',
'image_type':'map',
'attribute':'psrc.zone.employment_within_20_minutes_travel_time_hbw_am_drive_alone',
'arguments':{
# 'scale':[-1000, 7000]
},
'years':[2000,2006,2011,2016,2021]
},
{'dataset':'large_area',
'image_type':'table',
'attribute':'psrc.large_area.population',
'arguments':{},
'years':[2000,2010,2020,2025]
},
{'dataset':'large_area',
'image_type':'table',
'attribute':'psrc.large_area.number_of_jobs_without_resource_construction_sectors',
'arguments':{},
'years':[2000,2010,2020,2025]
},
]
# DatasetDescription(dataset_name='gridcell', package_name='urbansim', nchunks=2),
# DatasetDescription(dataset_name='household', package_name='urbansim'),
# DatasetDescription(dataset_name='job', package_name='urbansim'),
# DatasetDescription(dataset_name='zone', package_name='urbansim'),
# DatasetDescription(dataset_name='travel_data', package_name='urbansim')
# ]
# single_year_requests are indicators that are computed for a particular year.
# We give a list of years in single_year_years (is there a better name??)
# The idea is that we'll get a map of psrc.large_area.population, for example,
# for 2000 and another map for 2010.
#
# Note the syntax for specifying the indicators when the indicator name includes the year
# (the substring DDDD will be replaced by the year)
config.request_years = [2025]
config.requests = []
#for m in range(10, 130, 10):
# i = [{'dataset':'zone',
# 'image_type':'map',
# 'attribute':'LNE%sMTW = ln_bounded(psrc.zone.employment_within_%s_minutes_travel_time_hbw_am_transit_walk)' % (m, m),
# 'arguments':{
# # 'scale':[-1000, 7000]
# },
# 'years':[2000,2006,2011,2016,2021]
# },
# {'dataset':'zone',
# 'image_type':'map',
# 'attribute':'LNE%sMDA = ln_bounded(psrc.zone.employment_within_%s_minutes_travel_time_hbw_am_drive_alone)' % (m, m),
# 'arguments':{
# # 'scale':[-1000, 7000]
# },
config.requests = [
# absolute value
#
# {'dataset':'gridcell',
# 'image_type':'openev_map',
# 'attribute':'urbansim.gridcell.population',
# 'arguments':{
# 'legend_scheme':{'range':[0, 10, 50, 100],
# 'color':['#b2b2aea3', '#ccff00ff','#ffcc00ff','#ff6500ff','#ff0000ff']}
#
## 'scale':[-5000, 250000]
# }
# },
#
# {'dataset':'gridcell',
# 'image_type':'openev_map',
# 'attribute':'urbansim.gridcell.number_of_jobs as employment',
# 'arguments':{
# 'legend_scheme':{'range':[0, 500, 5000, 25000],
# 'color':['#b2b2aea3', '#ccff00ff','#ffcc00ff','#ff6500ff','#ff0000ff']}
#
## 'scale':[1000, 200000]
# }
# },
{
'dataset': 'faz',
'image_type': 'map',
'attribute': 'urbansim.faz.population',
'arguments': {
# 'scale':[1, 60000]
}
},
{
'dataset': 'faz',
'image_type': 'map',
'attribute': 'psrc.faz.population_per_acre',
'arguments': {
# 'scale':[1, 60000]
}
},
{
'dataset': 'faz',
'image_type': 'map',
'attribute':
'psrc.faz.number_of_jobs_without_resource_construction_sectors as employment',
'arguments': {
# 'scale':[1, 60000]
}
},
{
'dataset': 'faz',
'image_type': 'map',
'attribute': 'psrc.faz.number_of_jobs_per_acre',
'arguments': {
# 'scale':[1, 60000]
}
},
# ##change value
# {'dataset':'gridcell',
# 'image_type':'openev_map',
# 'attribute':'population_change(DDDD-00)',
# 'arguments':{
# 'operation':'change',
# 'arguments':['urbansim.gridcell.population'],
## 'scale':[-5000, 250000]
# }
# },
#
# {'dataset':'gridcell',
# 'image_type':'openev_map',
# 'attribute':'psrc.gridcell.absolute_number_of_jobs_change as employment_change(DDDD-00)',
# 'arguments':{
# 'scale':[1000, 200000]
# }
# },
#
# {'dataset':'gridcell',
# 'image_type':'openev_map',
# 'attribute':'psrc.gridcell.percent_population_change as percent_population_change(DDDD-00)',
# 'arguments':{
## 'scale':[1000, 200000]
# }
# },
#
# {'dataset':'gridcell',
# 'image_type':'openev_map',
# 'attribute':'psrc.gridcell.percent_number_of_jobs_change as percent_employment_change(DDDD-00)',
# 'arguments':{
## 'scale':[-30000, 30000]
# }
# },
#
{
'dataset': 'faz',
'image_type': 'map',
'attribute': 'population_change(DDDD-00)',
'arguments': {
'operation': 'change',
'arguments': ['urbansim.faz.population'],
# 'scale':[-2000, 40000]
}
},
{
'dataset': 'faz',
'image_type': 'map',
'attribute': 'employment_change(DDDD-00)',
'arguments': {
'operation': 'change',
'arguments': ['urbansim.faz.number_of_jobs'],
# 'scale':[-2000, 40000]
}
},
{
'dataset': 'faz',
'image_type': 'map',
'attribute':
'psrc.faz.percent_population_change as percent_population_change(DDDD-00)',
'arguments': {
# 'scale':[1000, 200000]
}
},
{
'dataset': 'faz',
'image_type': 'map',
'attribute':
'psrc.faz.percent_number_of_jobs_change as percent_employment_change(DDDD-00)',
'arguments': {
# 'scale':[-30000, 30000]
}
},
###difference (this scenario - baseline)
# {'dataset':'gridcell',
# 'image_type':'openev_map',
# 'attribute':'psrc.gridcell.absolute_population_difference_from_baseline as population_difference_from_baseline',
# 'arguments':{
# 'scale':[-30000, 30000]
# }
# },
#
# {'dataset':'gridcell',
# 'image_type':'openev_map',
# 'attribute':'psrc.gridcell.absolute_number_of_jobs_difference_from_baseline as employment_difference_from_baseline',
# 'arguments':{
## 'scale':[-30000, 30000]
# }
# },
# {'dataset':'gridcell',
# 'image_type':'openev_map',
# 'attribute':'psrc.gridcell.percent_population_difference_from_baseline as percent_population_difference_from_baseline',
# 'arguments':{
# 'scale':[-30000, 30000]
# }
# },
#
# {'dataset':'gridcell',
# 'image_type':'openev_map',
# 'attribute':'psrc.gridcell.percent_number_of_jobs_difference_from_baseline as percent_employment_difference_from_baseline',
# 'arguments':{
## 'scale':[-30000, 30000]
# }
# },
{
'dataset': 'faz',
'image_type': 'map',
'attribute': 'psrc.faz.absolute_population_difference_from_baseline',
'arguments': {
# 'scale':[-30000, 30000]
}
},
{
'dataset': 'faz',
'image_type': 'map',
'attribute': 'psrc.faz.percent_population_difference_from_baseline',
'arguments': {
# 'scale':[-30000, 30000]
}
},
{
'dataset': 'faz',
'image_type': 'map',
'attribute':
'psrc.faz.absolute_number_of_jobs_difference_from_baseline as absolute_employment_difference_from_baseline',
'arguments': {
# 'scale':[-30000, 30000]
}
},
{
'dataset': 'faz',
'image_type': 'map',
'attribute':
'psrc.faz.percent_number_of_jobs_difference_from_baseline as percent_employment_difference_from_baseline',
'arguments': {
# 'scale':[-30000, 30000]
}
},
{
'dataset': 'zone',
'image_type': 'map',
'attribute': 'psrc.zone.travel_time_hbw_am_drive_alone_to_cbd',
'arguments': {
'scale': [-10, 110]
},
'years': [2000, 2006, 2011, 2016, 2021]
},
{
'dataset': 'zone',
'image_type': 'map',
'attribute':
'psrc.zone.employment_within_20_minutes_travel_time_hbw_am_drive_alone',
'arguments': {
# 'scale':[-1000, 7000]
},
'years': [2000, 2006, 2011, 2016, 2021]
},
{
'dataset': 'large_area',
'image_type': 'table',
'attribute': 'psrc.large_area.population',
'arguments': {},
'years': [2000, 2010, 2020, 2025]
},
{
'dataset': 'large_area',
'image_type': 'table',
'attribute':
'psrc.large_area.number_of_jobs_without_resource_construction_sectors',
'arguments': {},
'years': [2000, 2010, 2020, 2025]
},
]
# DatasetDescription(dataset_name='gridcell', package_name='urbansim', nchunks=2),
# DatasetDescription(dataset_name='household', package_name='urbansim'),
# DatasetDescription(dataset_name='job', package_name='urbansim'),
# DatasetDescription(dataset_name='zone', package_name='urbansim'),
# DatasetDescription(dataset_name='travel_data', package_name='urbansim')
# ]
# single_year_requests are indicators that are computed for a particular year.
# We give a list of years in single_year_years (is there a better name??)
# The idea is that we'll get a map of psrc.large_area.population, for example,
# for 2000 and another map for 2010.
#
# Note the syntax for specifying the indicators when the indicator name includes the year
# (the substring DDDD will be replaced by the year)
config.request_years = [2025]
config.requests = []
#for m in range(10, 130, 10):
# i = [{'dataset':'zone',
# 'image_type':'map',
# 'attribute':'LNE%sMTW = ln_bounded(psrc.zone.employment_within_%s_minutes_travel_time_hbw_am_transit_walk)' % (m, m),
# 'arguments':{
# # 'scale':[-1000, 7000]
# },
# 'years':[2000,2006,2011,2016,2021]
# },
# {'dataset':'zone',
# 'image_type':'map',
# 'attribute':'LNE%sMDA = ln_bounded(psrc.zone.employment_within_%s_minutes_travel_time_hbw_am_drive_alone)' % (m, m),
# 'arguments':{
# # 'scale':[-1000, 7000]
# },
