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임이 코드를 실행하려고 : 경우형식 오류 : grsim하는 잘못된 인수 - 파이썬
Traceback (most recent call last):
File "/Applications/Python 2.7/tulip-0.3c/examples/robot_discrete_simple.py", line 151, in <module>
vis_depth=vis_depth)
File "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/tulip/grsim.py", line 109, in grsim
raise TypeError("Invalid arguments to grsim")
TypeError: Invalid arguments to grsim
되지 않음이 확인 : 그러나
#!/usr/bin/env python
"""
The example presented at the MURI review, illustrating the use of
jtlvint and automaton modules
Nok Wongpiromsarn ([email protected])
August 3, 2010
minor refactoring by SCL <[email protected]>
1 May 2011.
Small modifications by Yuchen Lin.
12 Aug 2011
"""
#@[email protected]
import sys, os
from subprocess import call
from tulip import *
import tulip.polytope as pc
#@[email protected]
# Specify where the smv file, spc file and aut file will go
#@[email protected]
testfile = 'robot_discrete_simple'
path = os.path.abspath(os.path.dirname(sys.argv[0]))
smvfile = os.path.join(path, 'specs', testfile+'.smv')
spcfile = os.path.join(path, 'specs', testfile+'.spc')
autfile = os.path.join(path, 'specs', testfile+'.aut')
#@[email protected]
# Specify the environment variables
#@[email protected]
env_vars = {'park' : 'boolean'}
#@[email protected]
# Specify the discrete system variable
# Introduce a boolean variable X0reach to handle the spec [](park -> <>X0)
# X0reach starts with TRUE.
# [](next(X0reach) = X0 | (X0reach & !park))
#@[email protected]
sys_disc_vars = {'X0reach' : 'boolean'}
#@[email protected]
# Specify the transition system representing the continuous dynamics
#@[email protected]
disc_dynamics = prop2part.PropPreservingPartition(list_region=[], list_prop_symbol=[])
# These following propositions specify in which cell the robot is,
# i.e., Xi means that the robot is in cell Ci
disc_dynamics.list_prop_symbol = ['X0', 'X1', 'X2', 'X3', 'X4', 'X5']
disc_dynamics.num_prop = len(disc_dynamics.list_prop_symbol)
# Regions. Note that the first argument of Region(poly, prop) should
# be a list of polytopes. But since we are not dealing with the actual
# controller, we will just fill it with a string (think of it as a
# name of the region). The second argument of Region(poly, prop) is a
# list that specifies which propositions in cont_props above is
# satisfied. As specified below, regioni satisfies proposition Xi.
region0 = pc.Region('C0', [1, 0, 0, 0, 0, 0])
region1 = pc.Region('C1', [0, 1, 0, 0, 0, 0])
region2 = pc.Region('C2', [0, 0, 1, 0, 0, 0])
region3 = pc.Region('C3', [0, 0, 0, 1, 0, 0])
region4 = pc.Region('C4', [0, 0, 0, 0, 1, 0])
region5 = pc.Region('C5', [0, 0, 0, 0, 0, 1])
disc_dynamics.list_region = [region0, region1, region2, region3, region4, region5]
disc_dynamics.num_regions = len(disc_dynamics.list_region)
# The transition relation between regions. disc_dynamics.trans[i][j] =
# 1 if starting from region j, the robot can move to region i while
# only staying in the union of region i and region j.
disc_dynamics.trans = [[1, 1, 0, 1, 0, 0], \
[1, 1, 1, 0, 1, 0], \
[0, 1, 1, 0, 0, 1], \
[1, 0, 0, 1, 1, 0], \
[0, 1, 0, 1, 1, 1], \
[0, 0, 1, 0, 1, 1]]
#@[email protected]
#@[email protected]
assumption = 'X0reach & []<>(!park)'
guarantee = '[]<>X5 & []<>(X0reach)'
guarantee += ' & [](next(X0reach) = (X0 | (X0reach & !park)))'
#@[email protected]
# Generate input to JTLV
#@[email protected]
prob = jtlvint.generateJTLVInput(env_vars, sys_disc_vars, [assumption, guarantee],
{}, disc_dynamics, smvfile, spcfile, verbose=2)
#@[email protected]
# Check realizability
#@[email protected]
realizability = jtlvint.checkRealizability(smv_file=smvfile, spc_file=spcfile,
aut_file=autfile, verbose=3)
#@[email protected]
# Compute an automaton
#@[email protected]
jtlvint.computeStrategy(smv_file=smvfile, spc_file=spcfile, aut_file=autfile,
priority_kind=3, verbose=3)
aut = automaton.Automaton(autfile, [], 3)
# Remove dead-end states from automaton.
aut.trimDeadStates()
#@[email protected]
# Visualize automaton with DOT file
# This example uses environment vs. system turn distinction. To
# disable it, just use (the default),
if not aut.writeDotFile(fname="rdsimple_example.dot", hideZeros=True):
# if not aut.writeDotFile("rdsimple_example.dot",
# distinguishTurns={"env": prob.getEnvVars().keys(),
# "sys": prob.getSysVars().keys()},
# turnOrder=("env", "sys")):
# if not aut.writeDotFileEdged(fname="rdsimple_example.dot",
# env_vars = prob.getEnvVars().keys(),
# sys_vars = prob.getSysVars().keys(),
# hideZeros=True):
print "Error occurred while generating DOT file."
else:
try:
call("dot rdsimple_example.dot -Tpng -o rdsimple_example.png".split())
except:
print "Failed to create image from DOT file. To do so, try\n\ndot rdsimple_example.dot -Tpng -o rdsimple_example.png\n"
# Simulate.
#@[email protected]
num_it = 30
env_states = [{'X0reach': True}]
for i in range(1, num_it):
if (i%3 == 0):
env_states.append({'park':True})
else:
env_states.append({'park':False})
graph_vis = raw_input
destfile = 'rdsimple_example.gexf'
label_vars = ['park', 'cellID', 'X0reach']
delay = 2
vis_depth = 3
aut_states = grsim.grsim([aut], aut_trans_dict={}, env_states=env_states,
num_it=num_it, deterministic_env=False,
graph_vis=graph_vis, destfile=destfile,
label_vars=label_vars, delay=delay,
vis_depth=vis_depth)
#@[email protected]
그것을 종료 직전, 그것은 말한다 #simulate에 실패 이 정보는 도움이되지만 DOT 파일도 만들지 못했습니다.
Failed to create image from DOT file. To do so, try
dot rdsimple_example.dot -Tpng -o rdsimple_example.png
왜 내가 TypeError 문제가됩니까? 이 문제를 어떻게 해결할 수 있습니까?
if not (isinstance(aut_list, list) and len(aut_list) > 0 and
isinstance(aut_trans_dict, dict) and
isinstance(env_states, list) and len(env_states) > 0 and
isinstance(num_it, int) and isinstance(deterministic_env, bool) and
isinstance(graph_vis, bool) and isinstance(destfile, str) and
(label_vars == None or isinstance(label_vars, list)) and
isinstance(delay, int) and isinstance(vis_depth, int)):
raise TypeError("Invalid arguments to grsim")
귀하의 graph_vis 변수가 부울이 아니라, raw_input function : grsim에서
덕분에 내가 그것을 가지고 :) – NLed