<jupyter_start><jupyter_text>Extract InputLocator calls  Author : <NAME> Intent : To use this information to figure out what input/output files are used by each module Date : January, 2017  Operation emissions<jupyter_code>run bin/create_function_graph.py -f emissions -s docs/scenario_folder/emissions.trace.pickle
import pickle
import cea.inputlocator
import pandas as pd

trace_data = pickle.load(file=open('docs/scenario_folder/emissions.trace.pickle', 'r'))
all_nodes = set(td[1].fqname for td in trace_data if not td[0].fqname.startswith('cea.inputlocator.InputLocator'))
locator_funcs = [n for n in all_nodes if n.startswith('cea.inputlocator.InputLocator')]
file_names = [eval(f).__doc__ for f in locator_funcs]
file_names
locator_funcs<jupyter_output><empty_output><jupyter_text>Embodied energy<jupyter_code>run bin/create_function_graph.py -f embodied_energy -s docs/scenario_folder/embodied_energy.trace.pickle
trace_data = pickle.load(file=open('docs/scenario_folder/embodied_energy.trace.pickle', 'r'))
all_nodes = set(td[1].fqname for td in trace_data if not td[0].fqname.startswith('cea.inputlocator.InputLocator'))
locator_funcs = [n for n in all_nodes if n.startswith('cea.inputlocator.InputLocator')]
file_names = [eval(f).__doc__ for f in locator_funcs]
file_names
print '\n'.join('* `%s` -> `%s`' % (f, l)  for f, l in zip(file_names, locator_funcs))<jupyter_output>* `scenario/inputs/building-properties/building_occupancy.shp` -> `cea.inputlocator.InputLocator.get_building_occupancy`
* `scenario/inputs/building-properties/architecture.shp
        This file is generated by the properties script.
        This file is used in the embodied energy script (cea/embodied.py)
        and the demand script (cea/demand_main.py)` -> `cea.inputlocator.InputLocator.get_building_architecture`
* `scenario/inputs/building-properties/age.shp` -> `cea.inputlocator.InputLocator.get_building_age`
* `scenario/inputs/building-geometry/zone.shp` -> `cea.inputlocator.InputLocator.get_building_geometry`
* `db/Archetypes/Archetypes_properties.xlsx
        path to database of archetypes file Archetypes_properties.xlsx` -> `cea.inputlocator.InputLocator.get_archetypes_properties`
* `scenario/outputs/data/emissions/Total_LCA_embodied.csv` -> `cea.inputlocator.InputLocator.get_lca_embodied`<jupyter_text>Benchmark graphs<jupyter_code>run bin/create_function_graph.py -f benchmark_graphs -s docs/scenario_folder/benchmark_graphs.trace.pickle
trace_data = pickle.load(file=open('docs/scenario_folder/benchmark_graphs.trace.pickle', 'r'))
all_nodes = set(td[1].fqname for td in trace_data if not td[0].fqname.startswith('cea.inputlocator.InputLocator'))
locator_funcs = [n for n in all_nodes if n.startswith('cea.inputlocator.InputLocator')]
file_names = [eval(f).__doc__ for f in locator_funcs]
print '\n'.join('* `%s` -> `%s`' % (f, l) for f, l in zip(file_names, locator_funcs))<jupyter_output>* `scenario/outputs/data/emissions/Total_LCA_mobility.csv` -> `cea.inputlocator.InputLocator.get_lca_mobility`
* `db/Benchmarks/benchmark_targets.xls` -> `cea.inputlocator.InputLocator.get_data_benchmark`
* `scenario/outputs/data/demand/Total_demand.csv` -> `cea.inputlocator.InputLocator.get_total_demand`
* `scenario/inputs/building-properties/building_occupancy.shp` -> `cea.inputlocator.InputLocator.get_building_occupancy`
* `db/Benchmarks/benchmark_today.xls` -> `cea.inputlocator.InputLocator.get_data_benchmark_today`
* `scenario/outputs/data/emissions/Total_LCA_operation.csv` -> `cea.inputlocator.InputLocator.get_lca_operation`
* `scenario/outputs/data/emissions/Total_LCA_embodied.csv` -> `cea.inputlocator.InputLocator.get_lca_embodied`<jupyter_text>Data helper<jupyter_code>run bin/create_function_graph.py -f data_helper -s docs/scenario_folder/data_helper.trace.pickle
trace_data = pickle.load(file=open('docs/scenario_folder/data_helper.trace.pickle', 'r'))
all_nodes = set(td[1].fqname for td in trace_data if not td[0].fqname.startswith('cea.inputlocator.InputLocator'))
locator_funcs = [n for n in all_nodes if n.startswith('cea.inputlocator.InputLocator')]
file_names = [eval(f).__doc__ for f in locator_funcs]
print '\n'.join('* `%s` -> `%s`' % (f, l) for f, l in zip(file_names, locator_funcs))<jupyter_output>* `scenario/inputs/building-properties/internal_loads.shp` -> `cea.inputlocator.InputLocator.get_building_internal`
* `scenario/inputs/building-properties/architecture.shp
        This file is generated by the properties script.
        This file is used in the embodied energy script (cea/embodied.py)
        and the demand script (cea/demand_main.py)` -> `cea.inputlocator.InputLocator.get_building_architecture`
* `scenario/inputs/building-properties/building_occupancy.shp` -> `cea.inputlocator.InputLocator.get_building_occupancy`
* `scenario/inputs/building-properties/age.shp` -> `cea.inputlocator.InputLocator.get_building_age`
* `db/Archetypes/Archetypes_properties.xlsx
        path to database of archetypes file Archetypes_properties.xlsx` -> `cea.inputlocator.InputLocator.get_archetypes_properties`
* `scenario/inputs/building-properties/thermal_properties.shp` -> `cea.inputlocator.InputLocator.get_building_thermal`
* `scenario/inputs/building-properties/technical_systems.shp` -> `c[...]<jupyter_text>Demand graphs<jupyter_code>run bin/create_function_graph.py -f demand_graphs -s docs/scenario_folder/demand_graphs.trace.pickle
trace_data = pickle.load(file=open('docs/scenario_folder/demand_graphs.trace.pickle', 'r'))
all_nodes = set(td[1].fqname for td in trace_data if not td[0].fqname.startswith('cea.inputlocator.InputLocator'))
locator_funcs = [n for n in all_nodes if n.startswith('cea.inputlocator.InputLocator')]
file_names = [eval(f).__doc__ for f in locator_funcs]
print '\n'.join('* `%s` -> `%s`' % (f, l) for f, l in zip(file_names, locator_funcs))<jupyter_output>* `scenario/outputs/data/demand/{building_name}.csv` -> `cea.inputlocator.InputLocator.get_demand_results_file`
* `scenario/outputs/plots/timeseries/{building_name}.pdf` -> `cea.inputlocator.InputLocator.get_demand_plots_file`
* `scenario/outputs/data/demand/Total_demand.csv` -> `cea.inputlocator.InputLocator.get_total_demand`<jupyter_text>Demand<jupyter_code>run bin/create_function_graph.py -f demand -s docs/scenario_folder/demand.trace.pickle
trace_data = pickle.load(file=open('docs/scenario_folder/demand.trace.pickle', 'r'))
all_nodes = set(td[1].fqname for td in trace_data if not td[0].fqname.startswith('cea.inputlocator.InputLocator'))
locator_funcs = [n for n in all_nodes if n.startswith('cea.inputlocator.InputLocator')]
file_names = [eval(f).__doc__ for f in locator_funcs]
print '\n'.join('* `%s` -> `%s`' % (f, l) for f, l in zip(file_names, locator_funcs))<jupyter_output>* `scenario/outputs/data/solar-radiation/properties_surfaces.csv` -> `cea.inputlocator.InputLocator.get_surface_properties`
* `scenario/inputs/building-properties/overrides.csv
        This file contains overrides to the building properties input files. They are applied after reading
        those files and are matched by column name.
        ` -> `cea.inputlocator.InputLocator.get_building_overrides`
* `scenario/inputs/building-properties/thermal_properties.shp` -> `cea.inputlocator.InputLocator.get_building_thermal`
* `scenario/outputs/data/demand/Total_demand.csv` -> `cea.inputlocator.InputLocator.get_total_demand`
* `scenario/inputs/building-geometry/zone.shp` -> `cea.inputlocator.InputLocator.get_building_geometry`
* `scenario/inputs/building-properties/building_occupancy.shp` -> `cea.inputlocator.InputLocator.get_building_occupancy`
* `scenario/outputs/data/demand` -> `cea.inputlocator.InputLocator.get_demand_results_folder`
* `scenario/outputs/data/solar-radiation/radiation.csv`[...]<jupyter_text>Mobility<jupyter_code>run bin/create_function_graph.py -f mobility -s docs/scenario_folder/mobility.trace.pickle
trace_data = pickle.load(file=open('docs/scenario_folder/mobility.trace.pickle', 'r'))
all_nodes = set(td[1].fqname for td in trace_data if not td[0].fqname.startswith('cea.inputlocator.InputLocator'))
locator_funcs = [n for n in all_nodes if n.startswith('cea.inputlocator.InputLocator')]
file_names = [eval(f).__doc__ for f in locator_funcs]
print '\n'.join('* `%s` -> `%s`' % (f, l) for f, l in zip(file_names, locator_funcs))<jupyter_output>* `scenario/inputs/building-properties/building_occupancy.shp` -> `cea.inputlocator.InputLocator.get_building_occupancy`
* `scenario/outputs/data/emissions` -> `cea.inputlocator.InputLocator.get_lca_emissions_results_folder`
* `scenario/outputs/data/emissions/Total_LCA_mobility.csv` -> `cea.inputlocator.InputLocator.get_lca_mobility`
* `scenario/outputs/data/demand/Total_demand.csv` -> `cea.inputlocator.InputLocator.get_total_demand`
* `db/Benchmarks/mobility.xls` -> `cea.inputlocator.InputLocator.get_data_mobility`<jupyter_text>Heatmaps<jupyter_code>run bin/create_function_graph.py -f heatmaps -s docs/scenario_folder/heatmaps.trace.pickle
trace_data = pickle.load(file=open('docs/scenario_folder/heatmaps.trace.pickle', 'r'))
all_nodes = set(td[1].fqname for td in trace_data if not td[0].fqname.startswith('cea.inputlocator.InputLocator'))
locator_funcs = [n for n in all_nodes if n.startswith('cea.inputlocator.InputLocator')]
file_names = [eval(f).__doc__ for f in locator_funcs]
print '\n'.join('* `%s` -> `%s`' % (f, l) for f, l in zip(file_names, locator_funcs))<jupyter_output>* `Returns the path to a file in the temporary folder with the name `filename`` -> `cea.inputlocator.InputLocator.get_temporary_file`
* `scenario/inputs/building-geometry/zone.shp` -> `cea.inputlocator.InputLocator.get_building_geometry`
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