def bubble(array):
list1 = array.tolist()
n = len(list1)
for f in range(n):
for k in range(n - f - 1):
if array[k] > array[k + 1]:
array[k], array[k + 1] = array[k + 1], array[k]
return array
def merge_sort(array):
list1 = array.tolist()
if len(list1) < 2:
return list1
midpoint = len(list1) // 2
return merge(left=merge_sort(array[:midpoint]),
right=merge_sort(array[midpoint:]))
def insert(array):
list1 = array.tolist()
for f in range(1, len(list1)):
item = list1[f]
k = f - 1
while k >= 0 and list1[k] > item:
list1[k + 1] = list1[k]
k -= 1
list1[k + 1] = item
return list1
def trim(self, snd_data): #removes empty audio at the end of the sample
soundsList = array.tolist(
snd_data) #creates a list from the recorded audio array
trimmedSound = array('h')
for i in range(
len(soundsList) - 70000
): #for the length of the recorded audio minus the last second
trimmedSound.append(
soundsList[i]) #add from the list to the new trimmed array
return trimmedSound # return the trimmed audio
def getSelectedScanIssues(self):
issues = self.ctxMenuInvocation.getSelectedIssues()
# parses currently selected finding to a string
if len(issues) >= 1 : # one or more issues can be sent (cmd select for example within target...)
for self.m in issues:
#print self.m
# burp.sfg@3b784b06 # type <type 'burp.sfg'>
# add requestResponseWithMarkers to be global so can be included in scanIssue
requestResponse = self.m.getHttpMessages()
#print "RequestResponse: ", requestResponse
# returns
l = array.tolist(requestResponse)
#print l
#print l[0]
# if there is more than one request response to a finding...
if len(l) > 1:
k = len(l)
q = 1
for r in l:
#call functionality to handle issues
self.processRequest(r, q, k)
q = q + 1
elif len(l) == 1:
k = ""
q = ""
#call functionality to handle issues
self.processRequest(l[0], q, k)
else: # bug: some issues do not have request responses.
k = ""
q = ""
#call functionality to handle issues
self.processRequestWithoutRR(q, k)
def get_torch_image(img, rows, cols, size, idx):
img = np.array(array.tolist(img), dtype=np.uint8)
img = img.reshape(10000, rows, cols)
timg = img[idx,:,:]
return timg
def trim(self,snd_data): #removes empty audio at the end of the sample
soundsList = array.tolist(snd_data) #creates a list from the recorded audio array
trimmedSound = array('h')
for i in range (len(soundsList)-70000): #for the length of the recorded audio minus the last second
trimmedSound.append(soundsList[i]) #add from the list to the new trimmed array
return trimmedSound # return the trimmed audio