<jupyter_start><jupyter_text>LauncherThis notebook automatically launches all operations for a complete model run. One can decide to skip network preparation steps, as they take multiple hours for the region of Germany and the networks are readily included in the repository.Detailed explainations of certain steps can be found in the corresponding notebook. All ASSUMPTIONS are gathered in the `parameters.xls` file. This file also includes scenarios.<jupyter_code>kwargs = {'workers':2, 'errout_suffix':True, 'sleep':1,'stdout_path':r'log/out.txt', 'stderr_path':r'log/err.txt'}
scenarios = ['base']
import time
start = time.time()<jupyter_output><empty_output><jupyter_text>Network preparationExecute all `prep1` and `prep2` steps.<jupyter_code># Network generation
parallel_call_notebook('prep10_zones.ipynb', arg_list=scenarios, **kwargs)
parallel_call_notebooks(
    ('prep11_road.ipynb', scenarios),
    ('prep12_rail_coach.ipynb', scenarios),
    ('prep14_bus.ipynb', scenarios),
    **kwargs)
parallel_call_notebook('prep13_air.ipynb', arg_list=scenarios, **kwargs)
# Access/egress distances to PT services based on networks and census data
parallel_call_notebook('prep15_census_distances.ipynb', arg_list=scenarios, **kwargs)
# Network aggregation
# Either cluster short-distance PT
#parallel_call_notebook('prep20_cluster.ipynb', arg_list=scenarios, **kwargs)
#parallel_call_notebook('prep21_access_egress.ipynb', arg_list=scenarios, **kwargs)
# Or aggregate it
parallel_call_notebook('prep20_aggregate_pt.ipynb', arg_list=scenarios, **kwargs)
parallel_call_notebook('prep21_access_egress_road.ipynb', arg_list=scenarios, **kwargs)<jupyter_output><empty_output><jupyter_text>Pathfinders and level of service<jupyter_code># Pathfinders
parallel_call_notebooks(
    ('prep30_pathfinder_pt.ipynb', scenarios),
    ('prep30_pathfinder_road.ipynb', scenarios),
    **kwargs)
# PT LoS attributes
parallel_call_notebook('prep30_pathfinder_pt.ipynb', arg_list=scenarios, **kwargs)
parallel_call_notebook('prep31_prices_pt.ipynb', arg_list=scenarios, **kwargs)
parallel_call_notebook('prep33_non-motor.ipynb', arg_list=scenarios, **kwargs)<jupyter_output><empty_output><jupyter_text>Estimation<jupyter_code># Calibration data preparation
# NOTE: only applicapbe if you have access to the calibration dataset
parallel_call_notebook('cal10_input_data.ipynb', arg_list=scenarios, **kwargs)
parallel_call_notebook('cal11_los_columns.ipynb', arg_list=scenarios, **kwargs)
#parallel_call_notebook('cal1x_validation_data.ipynb', arg_list=scenarios, **kwargs)
#parallel_call_notebook('cal19_vot.ipynb', arg_list=scenarios, **kwargs)
# Estimation
parallel_call_notebook('cal20_estimation.ipynb', arg_list=scenarios, **kwargs)<jupyter_output><empty_output><jupyter_text>ModellingExcept for the logit model step, all modelling steps require access to the OD volumes dataset.<jupyter_code># Generation of the OD matrix
parallel_call_notebook('model_volumes.ipynb', arg_list=scenarios, **kwargs)
# Mode choice probabilities
parallel_call_notebook('model_logit.ipynb', arg_list=scenarios, **kwargs)
# Assignment
parallel_call_notebooks(
    ('model_assignment.ipynb', scenarios),
    ('model_assignment_inner-zonal.ipynb', scenarios),
    **kwargs)
# Post-processing
parallel_call_notebook('model_emissions.ipynb', arg_list=scenarios, **kwargs)
# Total execution time
end = time.time()
int(end - start)<jupyter_output><empty_output>
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