def readNeos(filename, json_folder):
f = open(filename, 'r')
for i in range(3):
line = f.readline()
obj_value = float(line[17:])
actual = math.exp(obj_value)
p, f = os.path.split(filename)
fname = f[:-4] + '.json'
json_file = os.path.join(json_folder, fname)
STN = loadSTNfromJSONfile(json_file)
result, conflicts, bounds, weight = DC_Checker(STN.copy(), report=False)
contingent = bounds['contingent']
total = 1
for (i, j) in list(STN.contingentEdges.keys()):
edge = STN.contingentEdges[(i, j)]
length = edge.Cij + edge.Cji
total *= length
if (i, j) not in contingent:
actual *= length
return actual, total, float(actual / total)
def processOptimal():
json_folder = input("Please input folder with json file:\n")
json_list = glob.glob(os.path.join(json_folder, '*.json'))
result = {}
for fname in json_list:
p, f = os.path.split(fname)
print("Processing: ", f)
STN = loadSTNfromJSONfile(fname)
new_STN, count = relaxSearch(STN.copy())
new, orig, degree = dynamicMetric(STN.copy(), new_STN.copy())
result[f] = {}
result[f]['shrinked'] = new
result[f]['original'] = orig
result[f]['degree'] = degree
output_folder = input("Please input output folder:\n")
filename = os.path.join(output_folder, 'result_optimal.json')
with open(filename, 'w') as f:
json.dump(result, f)
return result
def sampleAll(listOfFile, success='default', LP='original'):
result = {}
for fname in listOfFile:
p, f = os.path.split(fname)
print("Processing file: ", f)
STN = loadSTNfromJSONfile(fname)
degree, success = sample(STN, success=success, LP=LP)
result[f] = (degree, success)
return result
def sample_from_folder(folder_name,
gauss=False,
success='default',
LP='original'):
results = {}
for filename in os.listdir(folder_name):
print(filename)
STN = loadSTNfromJSONfile(folder_name + filename)
degree, success_rate = sample(STN, success, LP, gauss)
results[filename] = (degree, success_rate)
return results
def generate_result_relax(data_path, out_name):
data_list = glob.glob(os.path.join(data_path, '*.json'))
result = {}
for data in data_list:
STN = loadSTNfromJSONfile(data)
_, count, _, _ = relaxSearch(STN)
path, name = os.path.split(data)
result[name] = count
# return result
# Save the results
with open(out_name, 'w') as f:
json.dump(result, f)
def computeDynamic(nlp=False):
uncertain_folder = input("Please input uncertain STNUs folder:\n")
chain_folder = input("Please input chain STNUs folde:\n")
listOfFile = []
listOfFile += glob.glob(os.path.join(uncertain_folder, '*.json'))
listOfFile += glob.glob(os.path.join(chain_folder, '*.json'))
degree = {}
for fname in listOfFile:
p, f = os.path.split(fname)
print("Processing: ", f)
STN = loadSTNfromJSONfile(fname)
new_STN, count = relaxSearch(STN.copy(), nlp=nlp)
if not new_STN:
degree[f] = 0
else:
degree[f] = dynamicMetric(STN.copy(), new_STN.copy())[2]
return degree
def compare(actual_Dict):
dynamic_folder = input("Please input directory with DC STNUs:\n")
uncertain_folder = input("Please input directory with uncertain STNUs:\n")
compare_Dict = {}
for x in list(actual_Dict.keys()):
actual_volume = actual_Dict[x]
if x[:7] == 'dynamic':
fname = os.path.join(dynamic_folder, x + '.json')
else:
fname = os.path.join(uncertain_folder, x + '.json')
STN = loadSTNfromJSONfile(fname)
_, _, epsilons = originalLP(STN.copy())
original, shrinked = newInterval(STN, epsilons)
old, new, degree = calculateMetric(original, shrinked)
actual = float(actual_volume / old)
compare_Dict[x] = (degree, actual)
return compare_Dict
directory = "small_examples"
data_list = glob.glob(os.path.join(directory, '*.json'))
# testing dream data ##
# directory = 'dataset/dreamdata/'
# folders = os.listdir(directory)
# data_list = []
# result = []
# for folder in folders:
# data = glob.glob(os.path.join(directory, folder, '*.json'))
# data_list += data
for data in data_list:
print("simulating", data)
stn = loadSTNfromJSONfile(data)
stnc = stn.copy()
stnc.floyd_warshall()
a, b, c, d = DC_Checker(stnc)
print(a)
alpha, outputstn = srea(stnc)
e, f, g, t = DC_Checker(outputstn)
if not e:
print(data)
print(outputstn)
print(t)
break
# if outputstn != None:
# a,b,c,d = DC_Checker(outputstn)
def simulate_file(file_name, size, verbose=False, gauss=False, relaxed=False) -> float:
network = loadSTNfromJSONfile(file_name)
result = simulation(network, size, verbose, gauss, relaxed)
if verbose:
print(f"{file_name} worked {100*result}% of the time.")
return result
def prob_of_DC_file(file_name: str) -> float:
network = loadSTNfromJSONfile(file_name)
return prob_of_DC(network)
def dc_checking_file(filename):
STN = loadSTNfromJSONfile(filename)
return dc_checking(STN)
def modelDynamicFile(path):
STN = loadSTNfromJSONfile(path)
p, f = os.path.split(path)
fname = f[:-5] + '.mod'
fname = os.path.join('../../../model/dynamic/model', fname)
modelObjDynamic(STN, fname)