def go():
from opus_gui.results_manager.run.indicator_framework.representations.indicator import Indicator
import os
indicators = {
'zone_jobs':
Indicator(dataset_name='zone',
attribute='urbansim.zone.number_of_jobs')
}
from opus_core.configurations.dataset_pool_configuration import DatasetPoolConfiguration
from opus_gui.results_manager.run.indicator_framework.maker.source_data import SourceData
project_name = 'eugene_gridcell'
run_name1 = 'base_year_data'
years = [1980, 1980]
source_data = SourceData(
cache_directory=paths.get_opus_data_path_path(project_name, run_name1),
#comparison_cache_directory = '',
years=years,
dataset_pool_configuration=DatasetPoolConfiguration(
package_order=['urbansim', 'opus_core'], ),
project_name=project_name)
################################################################
#COMPUTE indicators
################################################################
from opus_gui.results_manager.run.indicator_framework.maker.maker import Maker
maker = Maker(project_name)
computed_indicators = maker.create_batch(indicators=indicators,
source_data=source_data)
############################################
#VISUALIZE the resulting computed indicators
############################################
from opus_gui.results_manager.run.indicator_framework.visualizer.visualization_factory import VisualizationFactory
visualizer = VisualizationFactory()
visualizations = []
maps = [('zone_jobs', 'matplotlib_chart')]
for item in maps:
vistype = 'mapnik_map' # default to map
if type(item) == type(()): item, vistype = item
print "Generating indicator %s" % item
visualizations += visualizer.visualize(
indicators_to_visualize=[
item
], # override default indicators to visualize (all)
computed_indicators=computed_indicators,
visualization_type=vistype,
name=item)
print "Done generating indicators."
return dict((v.name, v.get_file_path()) for v in visualizations)
print "creating maker (to compute indicators) ..."
maker = Maker( project_name )
computed_indicators = maker.create_batch(
indicators = indicators,
source_data = result_template)
print "... done."
############################################
#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']
# for a given result template
from opus_gui.results_manager.run.indicator_framework.maker.maker import Maker
print "creating maker (to compute indicators) ..."
maker = Maker( project_name )
computed_indicators = maker.create_batch(
indicators = indicators,
source_data = result_template)
print "... done."
############################################
#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 = ['population']
visualizations += visualizer.visualize(
indicators_to_visualize = maps, #override default indicators to visualize (all)
computed_indicators = computed_indicators,
visualization_type = 'mapnik_map',
name = 'population'
)
print 'Finished with creating indicators'
print "creating maker (to compute indicators) ..."
maker = Maker( project_name )
computed_indicators = maker.create_batch(
indicators = indicators,
source_data = source_data)
print "... done."
############################################
#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
visualizations += visualizer.visualize(
indicators_to_visualize = ['population'],
computed_indicators = computed_indicators,
visualization_type = 'mapnik_map',
#name = 'population'
)
visualizations += visualizer.visualize(
indicators_to_visualize = ['income_per_person'],
computed_indicators = computed_indicators,
visualization_type = 'mapnik_map',
#name = 'population'
def _visualize(self, args, cache_directory = None):
self.visualizations = []
indicators_to_visualize = {}
interface = IndicatorFrameworkInterface(self.project)
not_visualized = []
#get common years
years = set([])
for indicator in self.indicators:
years |= set(indicator['years'])
source_data_objs = {}
for indicator in self.indicators:
indicator_name = indicator['indicator_name']
source_data_name = indicator['source_data_name']
dataset_name = indicator['dataset_name']
if (self.indicator_type == 'mapnik_map' or self.indicator_type == 'mapnik_animated_map') and dataset_name not in self.spatial_datasets:
not_visualized.append(indicator)
continue
if source_data_name not in source_data_objs:
source_data = interface.get_source_data(
source_data_name = source_data_name,
years = list(years),
cache_directory = cache_directory
)
source_data_objs[source_data_name] = source_data
else:
source_data = source_data_objs[source_data_name]
indicator = interface.get_indicator(
indicator_name = indicator_name,
dataset_name = dataset_name)
computed_indicator = interface.get_computed_indicator(indicator = indicator,
source_data = source_data,
dataset_name = dataset_name)
computed_indicator.gui_indicator_name = indicator_name
#####################
#hack to get plausible primary keys...
cache_directory = source_data.cache_directory
_storage_location = os.path.join(cache_directory,
'indicators',
'_stored_data',
repr(source_data.years[0]))
storage = StorageFactory().get_storage(
type = 'flt_storage',
storage_location = _storage_location)
cols = storage.get_column_names(
table_name = dataset_name)
primary_keys = [col for col in cols if col.find('_id') != -1]
computed_indicator.primary_keys = primary_keys
##################
name = computed_indicator.get_file_name(
suppress_extension_addition = True)
indicators_to_visualize[name] = computed_indicator
viz_args = {}
if self.indicator_type == 'mapnik_map':
viz_type = self.indicator_type
elif self.indicator_type == 'mapnik_animated_map':
viz_type = self.indicator_type
elif self.indicator_type == 'matplotlib_chart':
viz_type = self.indicator_type
elif self.indicator_type == 'tab':
viz_type = 'table'
if 'output_style' not in viz_args:
viz_args['output_style'] = Table.ALL
viz_args['output_type'] = 'tab'
elif self.indicator_type == 'table_esri':
viz_type = 'table'
if 'output_style' not in viz_args:
viz_args['output_style'] = Table.ALL
viz_args['output_type'] = 'esri'
elif self.indicator_type == 'table_per_year':
viz_type = 'table'
if 'output_style' not in viz_args:
viz_args['output_style'] = Table.PER_YEAR
viz_args['output_type'] = 'tab'
elif self.indicator_type == 'table_per_attribute':
viz_type = 'table'
if 'output_style' not in viz_args:
viz_args['output_style'] = Table.PER_ATTRIBUTE
viz_args['output_type'] = 'tab'
viz_args.update(self.kwargs)
viz_args.update(args)
# try:
# import pydevd;pydevd.settrace()
# except:
# pass
viz_factory = VisualizationFactory()
self.visualizations = viz_factory.visualize(
indicators_to_visualize = indicators_to_visualize.keys(),
computed_indicators = indicators_to_visualize,
visualization_type = viz_type, **viz_args)
if self.visualizations is None:
self.visualizations = []
return not_visualized
def go():
from opus_gui.results_manager.run.indicator_framework.representations.indicator import Indicator
import os
indicators = {
'zone_jobs':Indicator(
dataset_name = 'zone',
attribute = 'urbansim.zone.number_of_jobs')
}
from opus_core.configurations.dataset_pool_configuration import DatasetPoolConfiguration
from opus_gui.results_manager.run.indicator_framework.maker.source_data import SourceData
project_name = 'eugene_gridcell'
run_name1 = 'base_year_data'
years = [1980, 1980]
source_data = SourceData(
cache_directory = paths.get_opus_data_path_path(project_name,run_name1),
#comparison_cache_directory = '',
years = years,
dataset_pool_configuration = DatasetPoolConfiguration(
package_order=['urbansim','opus_core'],
),
project_name = project_name
)
################################################################
#COMPUTE indicators
################################################################
from opus_gui.results_manager.run.indicator_framework.maker.maker import Maker
maker = Maker(project_name)
computed_indicators = maker.create_batch(
indicators = indicators,
source_data = source_data)
############################################
#VISUALIZE the resulting computed indicators
############################################
from opus_gui.results_manager.run.indicator_framework.visualizer.visualization_factory import VisualizationFactory
visualizer = VisualizationFactory()
visualizations = []
maps = [('zone_jobs','matplotlib_chart')]
for item in maps:
vistype = 'mapnik_map' # default to map
if type(item) == type(()): item, vistype = item
print "Generating indicator %s" % item
visualizations += visualizer.visualize(
indicators_to_visualize = [item], # override default indicators to visualize (all)
computed_indicators = computed_indicators,
visualization_type = vistype,
name = item)
print "Done generating indicators."
return dict((v.name,v.get_file_path()) for v in visualizations)
print "creating maker (to compute indicators) ..."
maker = Maker( project_name )
computed_indicators = maker.create_batch(
indicators = indicators,
source_data = result_template)
print "... done."
############################################
#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 = []
for output_type in types:
visualizations += visualizer.visualize(
indicators_to_visualize = ['faz__autogenvar0'],
computed_indicators = computed_indicators,
visualization_type = 'table',
output_type = output_type,
name = 'faz__autogenvar0',
)
for output_type in types:
visualizations += visualizer.visualize(
indicators_to_visualize = ['faz__autogenvar2'],
computed_indicators = computed_indicators,
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')
print "creating maker (to compute indicators) ..."
maker = Maker( project_name )
computed_indicators = maker.create_batch(
indicators = indicators,
source_data = result_template)
print "... done."
############################################
#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 = []
#try: # tnicolai :for debugging
# import pydevd
# pydevd.settrace()
#except: pass
for output_type in types:
visualizations += visualizer.visualize(
indicators_to_visualize = ['alldata__autogenvar2'],
computed_indicators = computed_indicators,
visualization_type = 'table',
output_type = output_type,
name = 'alldata__autogenvar2',
)
# 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']
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'
)
