Python wrapper for SLEUTH urban growth model.
Project description
This library is an object-oriented wrapper for the SLEUTH urban growth model.
It will automatically create scenario files from directories containing data layers and it can run simulations through MPI and HT-Condor.
Installation
You may install this library and helper scripts using pip.
$ pip install sleuth_automation
Application Programming Interface
import sleuth_automation as sa # the library must be configured at least with the path to SLEUTH sa.configure(sleuth_path='/opt/sleuth', use_mpi=True, mpi_cores=32) # a directory containing input layers is given to a location instance l = sa.Location('my_location', '/path/to/my_location') l.calibrate_coarse() l.calibrate_fine() l.calibrate_final() l.sleuth_predict(end=2050)
Command Line Interface
A single run may be achieved using the included sleuth_run.py script.
$ sleuth_run.py --sleuth_path /opt/sleuth/ \ --location_dir /path/to/location/ \ --location_name my_location \ --mpi_cores 40 \ --montecarlo_iterations 50 \ --predict_end 2060
This will create scenario files for coarse, fine and final stages of calibration, extracting parameters from the control_stats.log files, and run predict.
If one wants to predict for several locations, one may group them in a directory and run them as a batch. Using the create_sleuth_condor_batch.py one may create a batch run for the HT-Condor queue management system.
$ create_sleuth_condor_batch.py --sleuth_path /opt/sleuth/ \ --region_dir /path/to/locations_group/ \ --mpi_cores 32 \ --predict_end 2060
This will create a submit.condor file in the locations directory, setup with the appropiate sleuth_run.py commands.
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