{ "info": { "author": "Fidel Serrano, Rodrigo Garcia", "author_email": "rgarcia@iecologia.unam.mx", "bugtrack_url": null, "classifiers": [ "Intended Audience :: Science/Research", "License :: OSI Approved :: GNU General Public License v3 (GPLv3)", "Operating System :: Unix", "Programming Language :: Python :: 2.7", "Topic :: Scientific/Engineering", "Topic :: Scientific/Engineering :: Atmospheric Science", "Topic :: Scientific/Engineering :: Information Analysis" ], "description": "Sleuth Automation\n=================\n\nThis library is an object-oriented wrapper for the\n`SLEUTH urban growth model `_.\n\nIt will automatically create scenario files from directories\ncontaining data layers and it can run simulations through\n`MPI `_ and\n`HT-Condor `_.\n\nInstallation\n------------\n\nYou may install this library and helper scripts using pip.\n\n.. code-block:: bash\n\n $ pip install sleuth_automation\n\n\nApplication Programming Interface\n---------------------------------\n\n.. code-block:: python\n\n import sleuth_automation as sa\n\n # the library must be configured at least with the path to SLEUTH\n sa.configure(sleuth_path='/opt/sleuth',\n\t\t use_mpi=True, mpi_cores=32)\n\n\n # a directory containing input layers is given to a location instance\n l = sa.Location('my_location',\n\t\t '/path/to/my_location')\n\n l.calibrate_coarse()\n l.calibrate_fine()\n l.calibrate_final()\n\n l.sleuth_predict(end=2050)\n\n\nCommand Line Interface\n----------------------\n\nA single run may be achieved using the included **sleuth_run.py** script.\n\n.. code-block:: shell\n\n $ sleuth_run.py --sleuth_path /opt/sleuth/ \\\n\t\t --location_dir /path/to/location/ \\\n\t\t --location_name my_location \\\n\t\t --mpi_cores 40 \\\n\t\t --montecarlo_iterations 50 \\\n\t\t --predict_end 2060\n\n\nThis will create scenario files for coarse, fine and final stages of\ncalibration, extracting parameters from the control_stats.log files,\nand run predict.\n\n\nIf one wants to predict for several locations, one may group them in a\ndirectory and run them as a batch. Using the\n**create_sleuth_condor_batch.py** one may create a batch run for the\nHT-Condor queue management system.\n\n.. code-block:: shell\n\n $ create_sleuth_condor_batch.py --sleuth_path /opt/sleuth/ \\\n\t\t\t\t --region_dir /path/to/locations_group/ \\\n\t\t\t\t --mpi_cores 32 \\\n\t\t\t\t --predict_end 2060\n\nThis will create a **submit.condor** file in the locations directory,\nsetup with the appropiate **sleuth_run.py** commands.\n\n\nDocumentation\n-------------\n.. image:: https://readthedocs.org/projects/sleuth-automation/badge/?version=latest\n\nFull documentation at http://sleuth-automation.readthedocs.io\n\n\n", "description_content_type": "", "docs_url": null, "download_url": "", "downloads": { "last_day": -1, "last_month": -1, "last_week": -1 }, "home_page": "http://github.com/sostenibilidad-unam/sleuth_automation", "keywords": "", "license": "GPLv3", "maintainer": "", "maintainer_email": "", "name": "sleuth-automation", "package_url": 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