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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.


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
             use_mpi=True, mpi_cores=32)

# a directory containing input layers is given to a location instance
l = sa.Location('my_location',



Command Line Interface

A single run may be achieved using the included script.

$ --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 one may create a batch run for the HT-Condor queue management system.

$ --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 commands.


Full documentation at

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