def walk(tree, node, array, current_sum, best, best_sum):
if node: print 'walking into ' + str(node.value)
if node:
array.append(node)
current_sum += int(node.value)
# Is the current sum plus a lot of 99 less than out current best?
# if so, give this path up and try another.
envisioned_sum = ((tree[0].depth - len(array)) * 99) + current_sum
if envisioned_sum < best_sum:
# print 'visioned sum ' + str(envisioned_sum) + ' < best ' + str(int(best_sum)) + ' array size ' + str(len(array))
array.pop()
current_sum -= int(node.value)
return best, best_sum
if current_sum > best_sum:
# print 'increasing best sum from ' + str(best_sum) + ' to ' + str(current_sum)
best = list(array)
best_sum = int(current_sum)
if node.left:
best, best_sum = walk(tree, node.left, array, current_sum, best,
best_sum)
if node.right:
best, best_sum = walk(tree, node.right, array, current_sum, best,
best_sum)
array.pop()
return best, best_sum
def walk(tree, node, array, current_sum, best, best_sum): if node: print 'walking into ' + str(node.value) if node: array.append(node) current_sum += int(node.value) # Is the current sum plus a lot of 99 less than out current best? # if so, give this path up and try another. envisioned_sum = ( (tree[0].depth - len(array)) * 99 ) + current_sum if envisioned_sum < best_sum: # print 'visioned sum ' + str(envisioned_sum) + ' < best ' + str(int(best_sum)) + ' array size ' + str(len(array)) array.pop() current_sum -= int(node.value) return best, best_sum if current_sum > best_sum: # print 'increasing best sum from ' + str(best_sum) + ' to ' + str(current_sum) best = list(array) best_sum = int(current_sum) if node.left: best, best_sum = walk(tree, node.left, array, current_sum, best, best_sum) if node.right: best, best_sum = walk(tree, node.right, array, current_sum, best, best_sum) array.pop() return best, best_sum
def kernelMultiplyOne(self, vector):
"""
Multiply the specified kernel by the supplied input heat vector.
Input:
vector: A hash mapping gene labels to floating point values
kernel: a single index for a specific kernel
Returns:
A hash of diffused heats, indexed by the same names as the
input vector
"""
# Have to convert to ordered array format for the input vector
array = []
for label in self.labels:
# Input heats may not actually be in the network.
# Check and initialize to zero if not
if label in vector:
array.append(vector[label])
else:
array.append(0)
# take the dot product
value = self.kernel * array
# Convert back to a hash and return diffused heats
return_vec = {}
idx = 0
for label in self.labels:
return_vec[label] = float(value[idx])
idx += 1
return return_vec
def kernelMultiplyOne(self, vector):
"""
Multiply the specified kernel by the supplied input heat vector.
Input:
vector: A hash mapping gene labels to floating point values
kernel: a single index for a specific kernel
Returns:
A hash of diffused heats, indexed by the same names as the
input vector
"""
# Have to convert to ordered array format for the input vector
array = []
for label in self.labels:
# Input heats may not actually be in the network.
# Check and initialize to zero if not
if label in vector:
array.append(vector[label])
else:
array.append(0)
# take the dot product
value = self.kernel*array
# Convert back to a hash and return diffused heats
return_vec = {}
idx = 0
for label in self.labels:
return_vec[label] = float(value[idx])
idx += 1
return return_vec
def tread_spider():
with open('names2016.csv', 'w', newline='') as f:
id = 1223086
thewriter = csv.writer(f)
while id <= 1225364:
array = []
url = "https://old.mciindia.org/ViewDetails.aspx?ID=" + str(id)
# s = Session()
# s.get('http://sportsbeta.ladbrokes.com/football')
# # now visit disired url with headers/data
# r = s.post(url, data=payload, headers=headers)
source_code = requests.get(url)
plain_text = source_code.text
# plain_text = r.text
# print(plain_text)
print(
"-------------------------------------------------------------------------------"
)
id += 1
soup = BeautifulSoup(plain_text)
for link in soup.find_all('span', {'class': 'label'}):
href = link.string
print(href)
array.append(href)
thewriter.writerow([
array[0], array[1], array[2], array[3], array[4], array[5],
array[6], array[7], array[8], array[9], array[10]
])
def logical_vector(values, labels, label_target):
array = []
if not(len(values) == len(labels)):
raise RuntimeError("length of target array must be the same as label array")
for i in range(len(values)):
if labels[i] == label_target:
array.append(values[i])
return array
def __init__(self, dtype, default_value=None): self.dtype = dtype default_value_str = None if default_value is None: default_value_str = b'\x00' * self.itemsize; array.fromstring(self, default_value_str) else: array.append(self, default_value); default_value_str = array.tostring(self); array.pop(self) self.default_value_str = default_value_str self.min_size_on_reallocate = 0 self.refresh_ndarray()
def IsoGenerator(size, N=1., S=1.):
arr = []
for i in range(0,size):
ra = np.random.uniform(0, 360, size=1)
dec = np.rad2deg(np.arcsin(np.random.uniform(0, 1, size=1))*np.random.choice([1.,-1.], size=1, p=[(float(N)/float(N+S)), (float(S)/float(N+S))]))
en = np.random.uniform(2, 4, size=1)
arr.append([dec, ra, en, np.nan])
return arr
def is_papili_missing_word(word1, word2):
array = []
for i in word2:
array.append(i)
for i in word1:
if i in array:
array.remove(i)
if len(array) > 0:
if array[0] == 'ු' or array[0] == 'ූ' or array[0] == '්' or array[0] == 'ා' or array[0] == 'ි' or array[0] == 'ී' or array[0] == 'ී' or array[0] == 'ෑ' or array[0] == 'ැ':
return True
else:
return False
else:
return False
def is_joiner_missing_word(word1, word2):
array = []
for i in word2:
array.append(i)
for i in word1:
if i in array:
array.remove(i)
if len(array) > 0:
if array[0] == '\u200d':
return True
else:
return False
else:
return False
def get_data(file_name, x, y, z):
array = []
with open(file_name) as f:
for line in f:
array.append(line)
x = np.zeros((len(array), 1))
y = np.zeros((len(array), 1))
z = np.zeros((len(array), 1))
for i in range(0, len(array), 1):
temp = array[i].split()
x[i] = temp[0]
y[i] = temp[1]
z[i] = temp[2]
def decode_line(encoded):
encoded_len = len(encoded)
index = 0
array = []
lat = 0
lng = 0
while index < encoded_len:
b = 0
shift = 0
result = 0
while True:
b = ord(encoded[index]) - 63
index = index + 1
result |= (b & 0x1f) << shift
shift += 5
if b < 0x20:
break
dlat = ~(result >> 1) if result & 1 else result >> 1
lat += dlat
shift = 0
result = 0
while True:
b = ord(encoded[index]) - 63
index = index + 1
result |= (b & 0x1f) << shift
shift += 5
if b < 0x20:
break
dlng = ~(result >> 1) if result & 1 else result >> 1
lng += dlng
array.append((lat * 1e-5, lng * 1e-5))
return array
def add_data(file_name, id):
array = []
with open(file_name) as f:
for line in f:
array.append(line)
number = np.zeros((len(array), 3))
temp = [0.0, 0.0, 0.0]
for i in range(0, len(array), 1):
x = array[i].split()
number[i][0] = x[0]
number[i][1] = x[1]
number[i][2] = x[2]
temp[0] += number[i][0]
temp[1] += number[i][1]
temp[2] += number[i][2]
temp[0] /= len(array)
temp[1] /= len(array)
temp[2] /= len(array)
global X
if len(X) == 0:
X = number
else:
X = np.vstack((X, number))
global Y
part_range[id][0] = len(Y)
if len(Y) == 0:
Y = [id] * len(array)
else:
Y = Y + [id] * len(array)
part_range[id][1] = len(Y)
global S
if len(S) == 0:
S = temp
else:
S = S + temp
def kernelMultiplyOne(self, vector):
"""
Input:
vector: A hash mapping gene labels to floating point values
"""
array = []
# loop over gene names in the network kernel: add the starting value if
# it's present in the supplied input vector
for label in self.labels:
if label in vector:
array.append(vector[label])
else:
array.append(0)
# take the dot product
value = self.kernel*array
return_vec = {}
idx = 0
for label in self.labels:
return_vec[label] = float(value[idx])
idx += 1
return return_vec
def kernelMultiplyOne(self, vector):
"""
Input:
vector: A hash mapping gene labels to floating point values
"""
array = []
# loop over gene names in the network kernel: add the starting value if
# it's present in the supplied input vector
for label in self.labels:
if label in vector:
array.append(vector[label])
else:
array.append(0)
# take the dot product
value = self.kernel * array
return_vec = {}
idx = 0
for label in self.labels:
return_vec[label] = float(value[idx])
idx += 1
return return_vec
class average:
d = int(input("Enter the number of numbers :"))
a = d
c = 0
var = []
sum = 0
for i in range(c, d):
while c < a:
num = int(input("Enter the number :"))
sum = sum + num
var.append(num)
c = c + 1
print("The total +", sum)
print(len(var))
c = len(var)
ave = sum / c
print("Average =", ave)
def recStep(self, x_recmovestep, y_recmovestep, go):
array = []
array.append((x_recmovestep,y_recmovestep))
array.append(self.checkMove(x_recmovestep,y_recmovestep))
array.append(go)
allfork = array[1]
havemoved = array[2]
#print("array",array)
#print("remove",havemoved)
if len(array) == 2:
path.put(array)
else:
allfork.remove(havemoved)
path.put(array)
print ("3.rec",array)
class Student:
d=int(input("Enter The Numbers:"))
a=d
c=0
var=[]
sum =0
# for i in range(c , d):
i=1
while i<=d:
num=str(input("Enter The Student Name : "))
print(i,".",num)
i=i+1
# while c<a:
# num=str(input("Enter The Student Name : "))
num=int(input("Enter The Student Marks in Maths : "))
num=int(input("Enter The Student Marks in English : "))
num=int(input("Enter The Student Marks in Physics : "))
sum=sum+num
var.append(num)
c=c+1
print("The total +",sum)
print(len(var))
c=len(var)
ave=sum/c
print("Average =",ave)
def performAction(self, row):
model = self._extender.tree.getModel()
root = model.getRoot()
root.removeAllChildren()
model.reload()
self.xssroot = DefaultMutableTreeNode('Cross-Site-Scripting')
root.add(self.xssroot)
self.sqliroot = DefaultMutableTreeNode('SQL Injection')
root.add(self.sqliroot)
self.sstiroot = DefaultMutableTreeNode('Server Side Template Injection')
root.add(self.sstiroot)
resultxss = []
resultsqli = []
resultssti = []
logEntry = self._extender._log.get(self._extender.logTable.convertRowIndexToModel(row))
resultxss = logEntry._resultxss
resultsqli = logEntry._resultsqli
resultssti = logEntry._resultssti
parameter = logEntry._parameter
for i in range(len(parameter)):
if resultxss[i] == self._extender.CHECK or resultxss[i] == self._extender.FOUND:
array = []
array.append(parameter[i].getName())
self._extender.addIssues(self.xssroot, array)
if resultsqli[i] == self._extender.CHECK or resultsqli[i] == self._extender.FOUND:
array = []
array.append(parameter[i].getName())
self._extender.addIssues(self.sqliroot, array)
if resultssti[i] == self._extender.CHECK or resultssti[i] == self._extender.FOUND:
array = []
array.append(parameter[i].getName())
self._extender.addIssues(self.sstiroot, array)
self._extender.rowSelected = row
return
for line in obj:
if ('vn') in line:
if ('###') in line:
continue
if 0x11 not in sections:
sections.append(0x11)
line = line[2:]
norm.append([float(x) for x in line.split()])
normNum += 1
if ('v') in line:
if ('###') in line:
continue
if 0x10 not in sections:
sections.append(0x10)
line = line[1:]
array.append([float(x) for x in line.split()])
#print(array)
vertexNum += 1
if ('##') in line:
if ('###') in line:
continue
if 0x20 not in sections:
sections.append(0x20)
texNum = int(line[2:])
print(texNum, "textures found")
if ('#') in line:
if ('###') in line:
continue
facedump.write(line)
line = line[1:]
if ('f') in line:
def renderPage():
array = []
find = mongo.db.cards.find({})
for x in find:
array.append(x)
return render_template('public/doctor.html', data=array)
elif j % 7 == 2:
ret_int = r.kRed + 2
elif j % 7 == 3:
ret_int = r.kRed + 3
elif j % 7 == 4:
ret_int = r.kBlue
elif j % 7 == 5:
ret_int = r.kBlue + 1
elif j % 7 == 6:
ret_int = r.kBlue + 2
return ret_int
for i in xrange(0, 34):
array.append(1000)
pass
array = np.array(array)
mg = TMultiGraph("mg", "")
#for f in args.filename:
# a=str(str(f))
# a=a[:3]+'/'+a[3:]
# Detector.append(a[:19])
#
# pass
leg = r.TLegend()
i = 0
from array import array
from random import randint
x = 20
array = []
for i in range(x):
array.append(randint(1, 1000))
print('These are the 20 random digits with a highest number of', max(array))
for i in array:
print(i, end=' ')
print()
array = []
# pulling data out of the individual challenger files
for item in file_list:
with open(item, 'r') as txt_file:
for x in range(0,25):
if x ==0 :
ccs = []
pc_int = []
for line in txt_file:
data_list = (line.strip().split('\t'))
ccs.append(data_list[0])
pc_int.append(data_list[1])
array.append(ccs)
array.append(percentage(pc_int))
pc_int = []
for line in txt_file:
data_list = (line.strip().split('\t'))
print(data_list)
pc_int.append(data_list[1])
array.append(pc_int)
print(pc_int)
length = len(array)
# writing out the files as tab separated array
def add_items(array, chr_id, p1, p2):
array.append(chr_id)
array.append(p1)
array.append(p2)
def arrayReader(self):
arraylenght = self.intReader()
array = []
for i in range(0, arraylenght):
array.append(self.parseValue())
return array
def absMin(array, pulse, numberOfChannels):
for i in range(0, conf.nChannels):
array.append(np.min(pulse[i]))
