# Copyright 2009-2013 Alex Zvoleff # # This file is part of the pyabm agent-based modeling toolkit. # # rcparams.default provides the default values that are used for the pyabm if # no local rc file is provided when the model is run, or that are used if any # parameters are missing from it. # # Parameters are specified in the following format: # # 'parameter_name' : [default_value | validation_function] # Comment # # where 'parameter_name' is the name of the parameter, 'default_value' is the # default value, and validation_function one of the set of validation functions # listed in rcsetup.py. The validation function is used to ensure that only # valid values are provided to the model. # # Any comments or newline characters after the 'START OF RC DEFINITION' line # below will be included in any rc files built using the 'write_RC_file' # function in rcsetup.py. The order in which the parameters are specified in # this file will be retained when rc files are generated by the 'write_RC_file' # function. # # See the README.rst file for author contact information. ################################## ###***START OF RC DEFINITION***### ############################################################################### # pyabm Parameters ############################################################################### # Git binary. If git is used for version control of the model sourcecode, PyABM # will track the version of the code that used used to run the model in the # model results. Or, use 'None' to disable this feature. 'path.git_binary' : [None | validate_git_binary] # Rscript binary path. Will be used by PyABM to call R scripts to plot model # results after running a model. Or, use 'None' to disable this feature. 'path.Rscript_binary' : [None | validate_Rscript_binary] # Default program to use for tailing model logfiles. Use 'None' to disable this # feature. 'path.tail_binary' : [None | validate_tail_binary] # The following parameters are for the threaded_batch_run script in PyABM. The # script will run a total of batchrun.num_runs model runs, by spawning new # processes to run a total of batchrun.num_cores simultaneous model runs. # PyABM will use the Python binary located at batchrun.python_path. 'batchrun.num_runs' : [20 | validate_int] 'batchrun.num_cores' : [1 | validate_int] 'batchrun.python_path' : [None | validate_batchrun_python_binary] # The following parameters are for the 'email_log' function in PyABM. If you # wish to use the email_log function, these parameters must be set to valid # values. Given that the smtp password is stored as plain text, using a # temporary or PyABM specific email account is recommended. 'email_log' : [False | validate_boolean] 'email_log.smtp_server' : ['smtp.example.com' | validate_string] 'email_log.smtp_ssl' : [True | validate_boolean] 'email_log.smtp_username' : ['someone' | validate_string] 'email_log.smtp_password' : ['password' | validate_string] 'email_log.from' : ['someone@example.com' | validate_string] 'email_log.to' : ['someone@example.com' | validate_string] # random_seed seeds the random number generator (useful for regenerating # results later). If it is set to 'None' no seed is given, and a seed will be # chosen by numpy using its default settings. In either case, the seed is that # is used will be saved in the model output for later reuse. 'random_seed' : [None | validate_random_seed]