NetLogo-使用BehaviorSpace获取每次重复的所有海龟位置 [英] NetLogo - using BehaviorSpace get all turtles locations as the result of each repetition

问题描述

我正在使用BehaviorSpace使用不同的参数运行该模型数百次.但是我需要知道所有海龟的位置，而不是仅仅知道海龟的数量.如何使用BehaviorSpace实现它?

I am using BehaviorSpace to run the model hundreds of times with different parameters. But I need to know the locations of all turtles as a result instead of only the number of turtles. How can I achieve it with BehaviorSpace?

当前，我通过以下代码将结果输出到csv文件中:

Currently, I output the results in a csv file by this code:

to-report get-locations
  report (list xcor ycor)
end

to generate-output
  file-open "model_r_1.0_locations.csv"
  file-print csv:to-row get-locations
  file-close
end

但是所有结果都被弹出到同一个csv文件中，因此我无法确定每次运行的情况.

but all results are popped into same csv file, so I can't tell the condition of each running.

推荐答案

Seth建议合并csv输出的文件名中的rel ="nofollow noreferrer"> behaviorspace-run-number .它将使您可以将该文件与主BehaviorSpace输出文件中的摘要数据相关联.

Seth's suggestion of incorporating behaviorspace-run-number in the filename of your csv output is one alternative. It would allow you to associate that file with the summary data in your main BehaviorSpace output file.

另一种选择是在行为空间实验定义中将列表报告者作为度量".例如，在您的情况下:

Another option is to include list reporters as "measures" in your behavior space experiment definition. For example, in your case:

map [ t -> [ xcor ] of t ] sort turtles
map [ t -> [ ycor ] of t ] sort turtles

然后，您可以使用自己喜欢的数据分析语言手动"解析结果列表.我以前在Julia中使用了以下功能:

You can then parse the resulting list "manually" in your favourite data analysis language. I've used the following function for this before, in Julia:

parselist(strlist, T = Float64) = parse.(T, split(strlist[2:end-1]))

我确信您可以轻松地用Python或R或您使用的任何语言编写一些等效的代码.

I'm sure you can easily write some equivalent code in Python or R or whatever language you're using.

在上面的示例中，我为xcor和ycor的乌龟输出了单独的列表.您也可以输出一个列表列表"，但解析起来会比较棘手.

In the example above, I've outputted separate lists for the xcor and the ycor of turtles. You could also output a single "list of lists", but the parsing would be trickier.

巧合的是，今天我必须为另一个项目做类似的事情，并且我意识到 csv扩展名和R可以使此操作非常容易.

Coincidentally, I had to do something similar today for a different project, and I realized that a combination of the csv extension and R can make this very easy.

总体思路如下:

• 在NetLogo中，使用 csv:to-string 将列表数据编码为字符串，然后将该字符串直接写入BehaviorSpace输出中.

• In NetLogo, use csv:to-string to encode list data into a string and then write that string directly in the BehaviorSpace output.

在R中，使用 purrr::map 和 readr::read_csv ，后跟

In R, use purrr::map and readr::read_csv, followed by tidyr::unnest, to unpack everything in a neat "one observation per row" dataframe.

换句话说:我们喜欢CSV，因此我们将CSV放入CSV中，以便我们可以在解析的同时进行解析.

In other words: we like CSV, so we put CSV in our CSV so we can parse while we parse.

这是一个完整的例子.假设我们有以下NetLogo模型:

Here is a full-fledged example. Let's say we have the following NetLogo model:

extensions [ csv ]

to setup
  clear-all
  create-turtles 2 [ move-to one-of patches ]
  reset-ticks
end

to go
  ask turtles [ forward 1 ]
  tick
end

to-report positions
  let coords [ (list who xcor ycor) ] of turtles
  report csv:to-string fput ["who" "x" "y"] coords
end

然后，使用我们的positions报告器作为输出，定义以下微小的BehaviorSpace实验，该实验只有两个重复，并且时间限制为两个:

We then define the following tiny BehaviorSpace experiment, with only two repetitions and a time limit of two, using our positions reporter as an output:

用于处理此问题的R代码非常简单明了:

The R code to process this is pleasantly straightforward:

library(tidyverse)

df <- read_csv("experiment-table.csv", skip = 6) %>%
  mutate(positions = map(positions, read_csv)) %>%
  unnest()

这将导致以下数据帧的整洁:

Which results in the following dataframe, all neat and tidy:

> df
# A tibble: 12 x 5
   `[run number]` `[step]`   who      x        y
            <int>    <int> <int>  <dbl>    <dbl>
 1              1        0     0  16     10     
 2              1        0     1  10     -2     
 3              1        1     1   9.03  -2.24  
 4              1        1     0 -16.0   10.1   
 5              1        2     1   8.06  -2.48  
 6              1        2     0 -15.0   10.3   
 7              2        0     1 -14      1     
 8              2        0     0  13     15     
 9              2        1     0  14.0   15.1   
10              2        1     1 -13.7    0.0489
11              2        2     0  15.0   15.1   
12              2        2     1 -13.4   -0.902 

朱莉娅的同一件事:

using CSV, DataFrames
df = CSV.read("experiment-table.csv", header = 7)
cols = filter(col -> col != :positions, names(df))
df = by(df -> CSV.read(IOBuffer(df[:positions][1])), df, cols)

这篇关于NetLogo-使用BehaviorSpace获取每次重复的所有海龟位置的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！