11/5/2022 0 Comments Netlogo random![]() ![]() DataFrame ( param_values, columns = problem ) with Pool ( 4, initializer = initializer, initargs = ( modelfile ,)) as executor : results = for entry in executor. sample ( problem, n, calc_second_order = True ) # cast the param_values to a dataframe to # include the column labels experiments = pd. command ( 'random-seed n = 1000 param_values = saltelli. ![]() items (): if key = 'random-seed' : #The NetLogo random seed requires a different syntax netlogo. load_model ( modelfile ) def run_simulation ( experiment ): '''run a netlogo model Parameters - experiments : dict ''' #Set the input parameters for key, value in experiment. This example is ran on a mac, linux is expected to be similar but Windows is likely to be slightly differentįrom multiprocessing import Pool import os import pandas as pd import pyNetLogo from SALib.sample import saltelli def initializer ( modelfile ): '''initialize a subprocess Parameters - modelfile : str ''' # we need to set the instantiated netlogo # link as a global so run_simulation can # use it global netlogo netlogo = pyNetLogo. It is recommended to carefully read the documentation provided by both concurrent.futures and mulitprocessing. Parallelization is an advanced topic and the exact way in which it is to be done depends at least in part on the operating system one is using. Here we are going to use the ProcessPoolExecutor, which uses the multiprocessing library. #Netlogo random codeBelow we use multiprocessing, anyone on python3.7 can use theĮither code below or use the ProcessPoolExecuturor from concurrent.futures (recommended). One of the libraries wrapped by concurrent.futures is multiprocessing. This is in fact a high level interface around several other libraries. One of the default libraries that ships with Python is concurrent.futures. There are multiple libraries available in the python ecosystem for performing tasks in parallel. to go ask turtles forward 1 all turtles move forward one step right random 360 and turn a random amount end. Running the experiments in parallel using a Process Pool ¶ ![]()
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