visualization_type = 'table',
output_type = output_type,
name = 'tables',
)
# Write a set of indicators sharing a dataset as a Dataset Table
indicators_in_dataset_table = ['zone_population',
'zone_industrial_sqft']
visualizations += visualizer.visualize(
indicators_to_visualize = indicators_in_dataset_table,
computed_indicators = computed_indicators,
visualization_type = 'dataset_table',
output_type = 'csv',
exclude_condition = 'urbansim.zone.population<100', #this accepts any opus expression
name = 'dataset_table',
)
################################################################
#Generate a REPORT with the visualizations
################################################################
from opus_gui.results_manager.run.indicator_framework.reporter.report_factory import ReportFactory
reporter = ReportFactory()
reporter.generate_report(
visualized_indicators = visualized_indicators,
report_type = 'basic',
open_immediately = True,
storage_location = 'c:/my_reports'
)
def main():
from opus_gui.results_manager.run.indicator_framework.representations.indicator import Indicator
indicators = {
'zone_population':
Indicator(dataset_name='zone', attribute='urbansim.zone.population'),
'gridcell_population':
Indicator(dataset_name='gridcell',
attribute='urbansim.gridcell.population'),
'zone_industrial_sqft':
Indicator(dataset_name='zone',
attribute='urbansim.zone.industrial_sqft'),
'gridcell_number_of_jobs':
Indicator(dataset_name='gridcell',
attribute='urbansim.gridcell.number_of_jobs',
name='jobs'),
'zone_number_of_jobs':
Indicator(dataset_name='zone',
attribute='urbansim.gridcell.number_of_jobs',
name='zone_jobs'),
#Expression example (comparison to baseyear)
'large_area_population_change':
Indicator(
dataset_name='large_area',
name='de_population_change',
attribute=
'psrc.large_area.de_population_DDDD - psrc.large_area.de_population_2000',
),
#example using regional-level aggregators
'alldata_home_based_jobs':
Indicator(
attribute=
'alldata.aggregate_all(urbansim.zone.number_of_home_based_jobs)',
dataset_name='alldata',
name='number_of_home_based_jobs'),
}
#################################################################
#DEFINE data source
#################################################################
# define any number of cache directories and/or years
# over which the indicators are computed
from opus_core.configurations.dataset_pool_configuration import DatasetPoolConfiguration
from opus_gui.results_manager.run.indicator_framework.maker.source_data import SourceData
result_template = SourceData(
cache_directory=r'D:\urbansim_cache\run_1090.2006_11_14_12_12',
comparison_cache_directory=
r'D:\urbansim_cache\run_1091.2006_11_14_12_12',
years=[2000, 2010],
dataset_pool_configuration=DatasetPoolConfiguration(
package_order=['urbansim', 'opus_core'], ),
name='run_1090')
################################################################
#COMPUTE indicators
################################################################
# setup an indicator Maker that will compute a set of indicators
# for a given result template
from opus_gui.results_manager.run.indicator_framework.maker.maker import Maker
maker = Maker()
computed_indicators = maker.create_batch(indicators=indicators,
result_template=result_template)
############################################
#VISUALIZE the resulting computed indicators
############################################
from opus_gui.results_manager.run.indicator_framework.visualizer.visualization_factory import VisualizationFactory
from opus_core.database_management.configurations.database_configuration import DatabaseConfiguration
visualizer = VisualizationFactory()
visualizations = []
# View an indicator as a Map
maps = ['zone_population', 'gridcell_population']
visualizations += visualizer.visualize(
indicators_to_visualize=
maps, #override default indicators to visualize (all)
computed_indicators=computed_indicators,
visualization_type='mapnik_map',
name='my_maps')
# View an indicator as a matplotlib Chart
charts = ['gridcell_population']
visualizations += visualizer.visualize(
indicators_to_visualize=charts,
computed_indicators=computed_indicators,
visualization_type='matplotlib_chart',
years=[2010], #override default years to visualize (all)
name='charts')
# Write an indicator as a Table
tables = [
'zone_industrial_sqft', 'large_area_population_change',
'alldata_home_based_jobs'
]
for output_type in ['tab', 'cvs', 'dbf']:
visualizations += visualizer.visualize(
indicators_to_visualize=tables,
computed_indicators=computed_indicators,
visualization_type='table',
output_type=output_type,
name='tables',
)
# Write a set of indicators sharing a dataset as a Dataset Table
indicators_in_dataset_table = ['zone_population', 'zone_industrial_sqft']
visualizations += visualizer.visualize(
indicators_to_visualize=indicators_in_dataset_table,
computed_indicators=computed_indicators,
visualization_type='dataset_table',
output_type='csv',
exclude_condition=
'urbansim.zone.population<100', #this accepts any opus expression
name='dataset_table',
)
################################################################
#Generate a REPORT with the visualizations
################################################################
from opus_gui.results_manager.run.indicator_framework.reporter.report_factory import ReportFactory
reporter = ReportFactory()
reporter.generate_report(visualized_indicators=visualized_indicators,
report_type='basic',
open_immediately=True,
storage_location='c:/my_reports')
def main():
from opus_gui.results_manager.run.indicator_framework.representations.indicator import Indicator
indicators = {
'zone_population':Indicator(
dataset_name = 'zone',
attribute = 'urbansim.zone.population'),
'gridcell_population':Indicator(
dataset_name = 'gridcell',
attribute = 'urbansim.gridcell.population'),
'zone_industrial_sqft':Indicator(
dataset_name = 'zone',
attribute = 'urbansim.zone.industrial_sqft'),
'gridcell_number_of_jobs':Indicator(
dataset_name = 'gridcell',
attribute = 'urbansim.gridcell.number_of_jobs',
name = 'jobs'),
'zone_number_of_jobs':Indicator(
dataset_name = 'zone',
attribute = 'urbansim.gridcell.number_of_jobs',
name = 'zone_jobs'),
#Expression example (comparison to baseyear)
'large_area_population_change':Indicator(
dataset_name = 'large_area',
name = 'de_population_change',
attribute = 'psrc.large_area.de_population_DDDD - psrc.large_area.de_population_2000',
),
#example using regional-level aggregators
'alldata_home_based_jobs':Indicator(
attribute = 'alldata.aggregate_all(urbansim.zone.number_of_home_based_jobs)',
dataset_name = 'alldata',
name = 'number_of_home_based_jobs'),
}
#################################################################
#DEFINE data source
#################################################################
# define any number of cache directories and/or years
# over which the indicators are computed
from opus_core.configurations.dataset_pool_configuration import DatasetPoolConfiguration
from opus_gui.results_manager.run.indicator_framework.maker.source_data import SourceData
result_template = SourceData(
cache_directory = r'D:\urbansim_cache\run_1090.2006_11_14_12_12',
comparison_cache_directory = r'D:\urbansim_cache\run_1091.2006_11_14_12_12',
years = [2000, 2010],
dataset_pool_configuration = DatasetPoolConfiguration(
package_order=['urbansim','opus_core'],
),
name = 'run_1090'
)
################################################################
#COMPUTE indicators
################################################################
# setup an indicator Maker that will compute a set of indicators
# for a given result template
from opus_gui.results_manager.run.indicator_framework.maker.maker import Maker
maker = Maker()
computed_indicators = maker.create_batch(
indicators = indicators,
result_template = result_template)
############################################
#VISUALIZE the resulting computed indicators
############################################
from opus_gui.results_manager.run.indicator_framework.visualizer.visualization_factory import VisualizationFactory
from opus_core.database_management.configurations.database_configuration import DatabaseConfiguration
visualizer = VisualizationFactory()
visualizations = []
# View an indicator as a Map
maps = ['zone_population',
'gridcell_population']
visualizations += visualizer.visualize(
indicators_to_visualize = maps, #override default indicators to visualize (all)
computed_indicators = computed_indicators,
visualization_type = 'mapnik_map',
name = 'my_maps'
)
# View an indicator as a matplotlib Chart
charts = ['gridcell_population']
visualizations += visualizer.visualize(
indicators_to_visualize = charts,
computed_indicators = computed_indicators,
visualization_type = 'matplotlib_chart',
years = [2010], #override default years to visualize (all)
name = 'charts'
)
# Write an indicator as a Table
tables = ['zone_industrial_sqft',
'large_area_population_change',
'alldata_home_based_jobs']
for output_type in ['tab','cvs','dbf']:
visualizations += visualizer.visualize(
indicators_to_visualize = tables,
computed_indicators = computed_indicators,
visualization_type = 'table',
output_type = output_type,
name = 'tables',
)
# Write a set of indicators sharing a dataset as a Dataset Table
indicators_in_dataset_table = ['zone_population',
'zone_industrial_sqft']
visualizations += visualizer.visualize(
indicators_to_visualize = indicators_in_dataset_table,
computed_indicators = computed_indicators,
visualization_type = 'dataset_table',
output_type = 'csv',
exclude_condition = 'urbansim.zone.population<100', #this accepts any opus expression
name = 'dataset_table',
)
################################################################
#Generate a REPORT with the visualizations
################################################################
from opus_gui.results_manager.run.indicator_framework.reporter.report_factory import ReportFactory
reporter = ReportFactory()
reporter.generate_report(
visualized_indicators = visualized_indicators,
report_type = 'basic',
open_immediately = True,
storage_location = 'c:/my_reports'
)
