python调用netlogo：pynetlogo【最后有链接】-优快云博客

我们首先实例化指向 NetLogo 的链接，加载模型，然后在 NetLogo 中执行命令。setup

%matplotlib inline

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style('white')
sns.set_context('talk')

import pyNetLogo

netlogo = pyNetLogo.NetLogoLink(gui=True,netlogo_home='D:/NetLogo',netlogo_version='6.0',jvm_home='D:/NetLogo/runtime/bin/server/jvm.dll')

netlogo.load_model('examples/models/Wolf Sheep Predation_v6.nlogo')
netlogo.command('setup')

我们可以使用该方法将属性从 Pandas 数据帧传递给代理，例如，给定属性的初始值。这通过在单个函数调用中同时为多个代理设置多个属性来提高性能。write_NetLogo_attriblist

例如，我们首先将数据从 Excel 文件加载到数据帧中。每行对应于一个代理，每个属性都有列（包括所需的 NetLogo 标识符）。在本例中，我们使用和属性为代理设置坐标。whoxcorycor

agent_xy = pd.read_excel('examples/data/xy_DataFrame.xlsx')
agent_xy[['who','xcor','ycor','value']].head(5)

	who	xcor	ycor	value
0	0	-24.000000	-24.000000	aaa
1	1	-23.666667	-23.666667	bbb
2	2	-23.333333	-23.333333	ccc
3	3	-23.000000	-23.000000	aaa
4	4	-22.666667	-22.666667	bbb

然后，我们可以将数据帧传递给 NetLogo，指定要更新的属性和代理类型：

netlogo.write_NetLogo_attriblist(agent_xy[['who','xcor','ycor']], 'a-sheep')

我们可以通过使用报告方法将数据从 NetLogo 返回到 Python 工作区来检查数据交换。在下面的示例中，这将返回代理的数组和坐标，并按其编号排序。然后将其绘制在常规散点图上。xcorycorsheepwho

该方法直接将字符串传递给 NetLogo 实例，以便可能需要根据 NetLogo 版本调整命令语法。链接对象的属性可用于检查当前版本。默认情况下，链接对象将使用找到的最新 NetLogo 版本。reportnetlogo_version

if netlogo.netlogo_version == '6.0':
    x = netlogo.report('map [s -> [xcor] of s] sort sheep')
    y = netlogo.report('map [s -> [ycor] of s] sort sheep')
elif netlogo.netlogo_version == '5':
    x = netlogo.report('map [[xcor] of ?1] sort sheep')
    y = netlogo.report('map [[ycor] of ?1] sort sheep')

fig, ax = plt.subplots(1)

ax.scatter(x, y, s=4)
ax.set_xlabel('xcor')
ax.set_ylabel('ycor')
ax.set_aspect('equal')
fig.set_size_inches(5,5)

plt.show()

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然后，我们可以运行 100 个基点的模型，并更新绵羊代理的 Python 坐标数组，并为每个代理的能量值返回一个额外的数组。后者绘制在每种代理类型的直方图上。

#We can use either of the following commands to run for 100 ticks:

netlogo.command('repeat 100 [go]')
#netlogo.repeat_command('go', 100)

if netlogo.netlogo_version == '6.0':
    #Return sorted arrays so that the x, y and energy properties of each agent are in the same order
    x = netlogo.report('map [s -> [xcor] of s] sort sheep')
    y = netlogo.report('map [s -> [ycor] of s] sort sheep')
    energy_sheep = netlogo.report('map [s -> [energy] of s] sort sheep')
elif netlogo.netlogo_version == '5':
    x = netlogo.report('map [[xcor] of ?1] sort sheep')
    y = netlogo.report('map [[ycor] of ?1] sort sheep')
    energy_sheep = netlogo.report('map [[energy] of ?1] sort sheep')

energy_wolves = netlogo.report('[energy] of wolves') #NetLogo returns these in random order

from mpl_toolkits.axes_grid1 import make_axes_locatable

fig, ax = plt.subplots(1, 2)

sc = ax[0].scatter(x, y, s=50, c=energy_sheep,
                   cmap=plt.cm.coolwarm)
ax[0].set_xlabel('xcor')
ax[0].set_ylabel('ycor')
ax[0].set_aspect('equal')
divider = make_axes_locatable(ax[0])
cax = divider.append_axes('right', size='5%', pad=0.1)
cbar = plt.colorbar(sc, cax=cax, orientation='vertical')
cbar.set_label('Energy of sheep')

sns.distplot(energy_sheep, kde=False, bins=10,
             ax=ax[1], label='Sheep')
sns.distplot(energy_wolves, kde=False, bins=10,
             ax=ax[1], label='Wolves')
ax[1].set_xlabel('Energy')
ax[1].set_ylabel('Counts')
ax[1].legend()
fig.set_size_inches(14,5)

plt.show()

d:\anaconda3\envs\py9\lib\site-packages\seaborn\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)

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该方法返回一个 Pandas 数据帧，其中包含一个或多个报告器在给定数量的价格变动中报告的值。默认情况下，这假设模型使用“go”NetLogo 命令运行;这可以通过传递可选参数来设置。repeat_reportgo

数据帧按价格变动编制索引，并为每个报告器标记列。在这种情况下，我们跟踪超过200个蜱虫的狼和羊代理的数量;结果首先绘制为时间的函数。然后将狼代理人的数量绘制为绵羊代理人数量的函数，以近似相空间图。

counts = netlogo.repeat_report(['count wolves','count sheep'], 200, go='go')

fig, (ax1, ax2) = plt.subplots(1, 2)

counts.plot(ax=ax1, use_index=True, legend=True)
ax1.set_xlabel('Ticks')
ax1.set_ylabel('Counts')

ax2.plot(counts['count wolves'], counts['count sheep'])
ax2.set_xlabel('Wolves')
ax2.set_ylabel('Sheep')
fig.set_size_inches(12,5)
plt.tight_layout()
plt.show()

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该方法还可以与返回 NetLogo 列表的报告器一起使用。在这种情况下，列表将转换为 numpy 数组。例如，我们跟踪狼和羊代理在5个蜱虫上的能量，并绘制数据帧中记录的最终刻度时狼的能量分布。repeat_report

为了说明不同的数据类型，我们还跟踪报告器（它跨绵羊代理返回字符串属性，转换为numpy对象数组），（返回单个数字变量）和（返回单个字符串变量）。[sheep_str] of sheepcount sheepglob_str

energy_df = netlogo.repeat_report(['[energy] of wolves',
                                   '[energy] of sheep',
                                   '[sheep_str] of sheep',
                                   'count sheep',
                                   'glob_str'], 5)

fig, ax = plt.subplots(1)

sns.distplot(energy_df['[energy] of wolves'].iloc[-1], kde=False, bins=20, ax=ax)
ax.set_xlabel('Energy')
ax.set_ylabel('Counts')
fig.set_size_inches(4,4)

plt.show()

d:\anaconda3\envs\py9\lib\site-packages\pyNetLogo\core.py:602: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.
  result = np.array([np.array(e.split(),
d:\anaconda3\envs\py9\lib\site-packages\pyNetLogo\core.py:606: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.
  result = np.array([np.array([b.strip('"')
d:\anaconda3\envs\py9\lib\site-packages\seaborn\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)

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energy_df.head()

	[energy] of wolves	[energy] of sheep	[sheep_str] of sheep	count sheep	glob_str
305.0	[22.009727835655212, 23.2806453704834, 5.12650...	[35.634368896484375, 12.642822265625, 13.94987...	[sheep, sheep, sheep, sheep, sheep, sheep, she...	142.0	global
306.0	[34.966749370098114, 22.52888834476471, 4.1265...	[12.724678993225098, 0.8940131664276123, 39.65...	[sheep, sheep, sheep, sheep, sheep, sheep, she...	139.0	global
307.0	[26.74847984313965, 3.999619960784912, 45.6639...	[30.60993194580078, 5.73723030090332, 5.079662...	[sheep, sheep, sheep, sheep, sheep, sheep, she...	138.0	global
308.0	[81.13100171089172, 0.08269596099853516, 8.874...	[21.017112731933594, 29.8840389251709, 14.3289...	[sheep, sheep, sheep, sheep, sheep, sheep, she...	138.0	global
309.0	[9.504863917827606, 15.259727835655212, 22.175...	[11.578887939453125, 3.228648841381073, 2.4744...	[sheep, sheep, sheep, sheep, sheep, sheep, she...	125.0	global

该方法可用于返回一个数据帧，该数据帧（对于此示例）包含每个 NetLogo 修补程序的属性。此数据帧实质上复制了 NetLogo 环境，列标签对应于 xcor 补丁坐标，索引位于 pycor 坐标之后。patch_reportcountdown
[11.578887939453125, 3.228648841381073, 2.4744…
[sheep, sheep, sheep, sheep, sheep, sheep, she…
125.0
global

该方法可用于返回一个数据帧，该数据帧（对于此示例）包含每个 NetLogo 修补程序的属性。此数据帧实质上复制了 NetLogo 环境，列标签对应于 xcor 补丁坐标，索引位于 pycor 坐标之后。patch_reportcountdown