content
stringlengths 7
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# Enter your code for "camelCase" here.
def to_camel(ident):
def a(x):
return x
def b(x):
return x[0].upper() + x[1::]
def c():
yield a
while True:
yield b
d = c()
return "".join(d.__next__()(x) for x in ident.split("_"))
|
def to_camel(ident):
def a(x):
return x
def b(x):
return x[0].upper() + x[1:]
def c():
yield a
while True:
yield b
d = c()
return ''.join((d.__next__()(x) for x in ident.split('_')))
|
# Write a program that asks the user to input any positive integer
# and outputs the successive values of the following calculation.
# At each step calculate the next value by taking the current value and,
# if it is even, divide it by two, but if it is odd, multiply it by three and add one.
# Have the program end if the current value is one.
# There is an ambiguity in the question, however:
# What if the user enters one?
# I chose not to end the program immediately in this case.
# In order not to end the program immediately if the user
# enters one, a 'while (x != 1)': loop will not work.
# Instead, a 'while true:' loop can be used, which breaks
# once 'x = 1'. I appreciate that 'while true:' is not
# generally considered best practice, but the absence of
# of a 'do-while' loop in python makes this unavoidable.
# Store the user's input as an integer.
x = int(input("Enter a positive integer and see what happens: "))
print(x)
# As there is no do-while loop built into Python,
# Use a 'while-true' loop that ends (with 'break') once the relevant variable equals one.
while True:
# To calculate the variable's next value, use an if-else block:
# to determine if the variable is even, use the modulus operator (x % 2 = 0);
# in all other cases the variable is odd.
if x % 2 == 0:
# use floor division '//' so that an integer rather than a float results.
x = x // 2
print(x)
else:
x = (x * 3) + 1
print(x)
if x == 1:
break
|
x = int(input('Enter a positive integer and see what happens: '))
print(x)
while True:
if x % 2 == 0:
x = x // 2
print(x)
else:
x = x * 3 + 1
print(x)
if x == 1:
break
|
class ModelPermissionsMixin(object):
"""
Defines the permissions methods that most models need,
:raises NotImplementedError: if they have not been overridden.
"""
@classmethod
def can_create(cls, user_obj):
"""
CreateView needs permissions at class (table) level.
We'll try it at instance level for a while and see how it goes.
"""
raise NotImplementedError
@classmethod
def can_view_list(cls, user_obj):
"""
ListView needs permissions at class (table) level.
We'll try it at instance level for a while and see how it goes.
"""
raise NotImplementedError
def can_update(self, user_obj):
"""
UpdateView needs permissions at instance (row) level.
"""
raise NotImplementedError
def can_view(self, user_obj):
"""
DetailView needs permissions at instance (row) level.
"""
raise NotImplementedError
def can_delete(self, user_obj):
"""
DeleteView needs permissions at instance (row) level.
"""
raise NotImplementedError
|
class Modelpermissionsmixin(object):
"""
Defines the permissions methods that most models need,
:raises NotImplementedError: if they have not been overridden.
"""
@classmethod
def can_create(cls, user_obj):
"""
CreateView needs permissions at class (table) level.
We'll try it at instance level for a while and see how it goes.
"""
raise NotImplementedError
@classmethod
def can_view_list(cls, user_obj):
"""
ListView needs permissions at class (table) level.
We'll try it at instance level for a while and see how it goes.
"""
raise NotImplementedError
def can_update(self, user_obj):
"""
UpdateView needs permissions at instance (row) level.
"""
raise NotImplementedError
def can_view(self, user_obj):
"""
DetailView needs permissions at instance (row) level.
"""
raise NotImplementedError
def can_delete(self, user_obj):
"""
DeleteView needs permissions at instance (row) level.
"""
raise NotImplementedError
|
class BatmanQuotes(object):
@staticmethod
def get_quote(quotes, hero):
index = int(sorted(hero)[0])
return {'B':'Batman: ','R':'Robin: ','J':'Joker: '}[hero[0]] + quotes[index]
|
class Batmanquotes(object):
@staticmethod
def get_quote(quotes, hero):
index = int(sorted(hero)[0])
return {'B': 'Batman: ', 'R': 'Robin: ', 'J': 'Joker: '}[hero[0]] + quotes[index]
|
class Node:
starting = True
ending = False
name = ""
weight = [1, 20]
active = True
edge_length = 0
nodes = []
def __init__(self, name, grid, posX, posY, active):
self.name = name # Name of the node
self.grid = grid # Which grid the node will be placed
self.posX = posX # Position X on the grid
self.posY = posY # Position Y on the grid
self.active = active # Is active
pass
def add(self, newNode):
#To DO
self.nodes.append(newNode)
print("Added new node")
pass
def remove(self, node):
#To DO
self.nodes = []
print("The node has been removed")
pass
def update(self, index, node):
self.nodes.pop(index)
self.nodes.insert(index, node)
print("The node has been updated")
pass
|
class Node:
starting = True
ending = False
name = ''
weight = [1, 20]
active = True
edge_length = 0
nodes = []
def __init__(self, name, grid, posX, posY, active):
self.name = name
self.grid = grid
self.posX = posX
self.posY = posY
self.active = active
pass
def add(self, newNode):
self.nodes.append(newNode)
print('Added new node')
pass
def remove(self, node):
self.nodes = []
print('The node has been removed')
pass
def update(self, index, node):
self.nodes.pop(index)
self.nodes.insert(index, node)
print('The node has been updated')
pass
|
#!/usr/bin/env python3
dna = "ATGCAGGGGAAACATGATTCAGGAC"
#Make all lowercase
lowerDNA = dna.lower()
#Replace the complementary ones
complA = lowerDNA.replace('a','T')
complAT = complA.replace('t','A')
complATG = complAT.replace('g','C')
complATGC = complATG.replace('c','G')
#reverse the complement
revDNA = complATGC[::-1]
#Print out the answers
print("Original Sequence\t5'",dna,"5'")
print("Complement\t\t3'",complATGC,"5'")
print("Reverse Complement\t5'",revDNA,"3'")
|
dna = 'ATGCAGGGGAAACATGATTCAGGAC'
lower_dna = dna.lower()
compl_a = lowerDNA.replace('a', 'T')
compl_at = complA.replace('t', 'A')
compl_atg = complAT.replace('g', 'C')
compl_atgc = complATG.replace('c', 'G')
rev_dna = complATGC[::-1]
print("Original Sequence\t5'", dna, "5'")
print("Complement\t\t3'", complATGC, "5'")
print("Reverse Complement\t5'", revDNA, "3'")
|
# with open("ReadMe.txt",'r') as file:
# print(file.read()) # Hello from Python 201
# # Running after adding the second line to the .txt file
# with open("ReadMe.txt",'r') as file:
# print(file.read()) # Hello from Python 201
# # This is a new line
# # Running after adding the second line to the .txt file
# with open("ReadMe.txt",'r') as file:
# pass
# print(file.read()) # Traceback (most recent call last):
# # File "ReadingFiles.py", line 15, in <module>
# # print(file.read())
# # ValueError: I/O operation on closed file.
# Assigning the content to a variable and then reading the file
with open("ReadMe.txt",'r') as file:
content = file.read()
# python has closed the file
print("The content is", content) # The content is Hello from Python 201
# This is a new line
|
with open('ReadMe.txt', 'r') as file:
content = file.read()
print('The content is', content)
|
OCTICON_LAW = """
<svg class="octicon octicon-law" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 16 16" width="16" height="16"><path fill-rule="evenodd" d="M8.75.75a.75.75 0 00-1.5 0V2h-.984c-.305 0-.604.08-.869.23l-1.288.737A.25.25 0 013.984 3H1.75a.75.75 0 000 1.5h.428L.066 9.192a.75.75 0 00.154.838l.53-.53-.53.53v.001l.002.002.002.002.006.006.016.015.045.04a3.514 3.514 0 00.686.45A4.492 4.492 0 003 11c.88 0 1.556-.22 2.023-.454a3.515 3.515 0 00.686-.45l.045-.04.016-.015.006-.006.002-.002.001-.002L5.25 9.5l.53.53a.75.75 0 00.154-.838L3.822 4.5h.162c.305 0 .604-.08.869-.23l1.289-.737a.25.25 0 01.124-.033h.984V13h-2.5a.75.75 0 000 1.5h6.5a.75.75 0 000-1.5h-2.5V3.5h.984a.25.25 0 01.124.033l1.29.736c.264.152.563.231.868.231h.162l-2.112 4.692a.75.75 0 00.154.838l.53-.53-.53.53v.001l.002.002.002.002.006.006.016.015.045.04a3.517 3.517 0 00.686.45A4.492 4.492 0 0013 11c.88 0 1.556-.22 2.023-.454a3.512 3.512 0 00.686-.45l.045-.04.01-.01.006-.005.006-.006.002-.002.001-.002-.529-.531.53.53a.75.75 0 00.154-.838L13.823 4.5h.427a.75.75 0 000-1.5h-2.234a.25.25 0 01-.124-.033l-1.29-.736A1.75 1.75 0 009.735 2H8.75V.75zM1.695 9.227c.285.135.718.273 1.305.273s1.02-.138 1.305-.273L3 6.327l-1.305 2.9zm10 0c.285.135.718.273 1.305.273s1.02-.138 1.305-.273L13 6.327l-1.305 2.9z"></path></svg>
"""
|
octicon_law = '\n<svg class="octicon octicon-law" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 16 16" width="16" height="16"><path fill-rule="evenodd" d="M8.75.75a.75.75 0 00-1.5 0V2h-.984c-.305 0-.604.08-.869.23l-1.288.737A.25.25 0 013.984 3H1.75a.75.75 0 000 1.5h.428L.066 9.192a.75.75 0 00.154.838l.53-.53-.53.53v.001l.002.002.002.002.006.006.016.015.045.04a3.514 3.514 0 00.686.45A4.492 4.492 0 003 11c.88 0 1.556-.22 2.023-.454a3.515 3.515 0 00.686-.45l.045-.04.016-.015.006-.006.002-.002.001-.002L5.25 9.5l.53.53a.75.75 0 00.154-.838L3.822 4.5h.162c.305 0 .604-.08.869-.23l1.289-.737a.25.25 0 01.124-.033h.984V13h-2.5a.75.75 0 000 1.5h6.5a.75.75 0 000-1.5h-2.5V3.5h.984a.25.25 0 01.124.033l1.29.736c.264.152.563.231.868.231h.162l-2.112 4.692a.75.75 0 00.154.838l.53-.53-.53.53v.001l.002.002.002.002.006.006.016.015.045.04a3.517 3.517 0 00.686.45A4.492 4.492 0 0013 11c.88 0 1.556-.22 2.023-.454a3.512 3.512 0 00.686-.45l.045-.04.01-.01.006-.005.006-.006.002-.002.001-.002-.529-.531.53.53a.75.75 0 00.154-.838L13.823 4.5h.427a.75.75 0 000-1.5h-2.234a.25.25 0 01-.124-.033l-1.29-.736A1.75 1.75 0 009.735 2H8.75V.75zM1.695 9.227c.285.135.718.273 1.305.273s1.02-.138 1.305-.273L3 6.327l-1.305 2.9zm10 0c.285.135.718.273 1.305.273s1.02-.138 1.305-.273L13 6.327l-1.305 2.9z"></path></svg>\n'
|
__all__ = ['__author__', '__license__', '__version__', '__credits__', '__maintainer__']
__author__ = 'Henrik Nyman'
__license__ = 'MIT'
__version__ = '0.1'
__credits__ = ['Henrik Nyman']
__maintainer__ = 'Henrik Nyman'
|
__all__ = ['__author__', '__license__', '__version__', '__credits__', '__maintainer__']
__author__ = 'Henrik Nyman'
__license__ = 'MIT'
__version__ = '0.1'
__credits__ = ['Henrik Nyman']
__maintainer__ = 'Henrik Nyman'
|
def projectData(X, U, K):
"""computes the projection of
the normalized inputs X into the reduced dimensional space spanned by
the first K columns of U. It returns the projected examples in Z.
"""
# ====================== YOUR CODE HERE ======================
# Instructions: Compute the projection of the data using only the top K
# eigenvectors in U (first K columns).
# For the i-th example X(i,:), the projection on to the k-th
# eigenvector is given as follows:
# x = X(i, :)'
# projection_k = x' * U(:, k)
#
# =============================================================
return Z
|
def project_data(X, U, K):
"""computes the projection of
the normalized inputs X into the reduced dimensional space spanned by
the first K columns of U. It returns the projected examples in Z.
"""
return Z
|
def ssort(array):
for i in range(len(array)):
indxMin = i
for j in range(i+1, len(array)):
if array[j] < array[indxMin]:
indxMin = j
tmp = array[indxMin]
array[indxMin] = array[i]
array[i] = tmp
return array
|
def ssort(array):
for i in range(len(array)):
indx_min = i
for j in range(i + 1, len(array)):
if array[j] < array[indxMin]:
indx_min = j
tmp = array[indxMin]
array[indxMin] = array[i]
array[i] = tmp
return array
|
depl_user = 'darulez'
depl_group = 'docker'
# defaults file for ansible-docker-apps-generator
assembly_path = 'apps-assembly'
apps_path = './apps-universe'
stacks_path = './stacks-universe'
default_docker_network = 'hoa_network'
docker_main_sections = ['docker_from', 'docker_init', 'docker_reqs', 'docker_core', 'docker_post']
|
depl_user = 'darulez'
depl_group = 'docker'
assembly_path = 'apps-assembly'
apps_path = './apps-universe'
stacks_path = './stacks-universe'
default_docker_network = 'hoa_network'
docker_main_sections = ['docker_from', 'docker_init', 'docker_reqs', 'docker_core', 'docker_post']
|
class Solution:
def widthOfBinaryTree(self, root):
"""
:type root: TreeNode
:rtype: int
"""
# bfs
# corner case
if root is None:
return 0
queue = list()
queue.append((root, 0))
level = 1
while len(queue) > 0:
length = len(queue)
tmp = list()
for _ in range(length):
curr, x = queue.pop(0)
tmp.append(x)
if curr.left:
queue.append((curr.left, 2*x))
if curr.right:
queue.append((curr.right, 2*x+1))
level = max(level, max(tmp)-min(tmp)+1)
return level
|
class Solution:
def width_of_binary_tree(self, root):
"""
:type root: TreeNode
:rtype: int
"""
if root is None:
return 0
queue = list()
queue.append((root, 0))
level = 1
while len(queue) > 0:
length = len(queue)
tmp = list()
for _ in range(length):
(curr, x) = queue.pop(0)
tmp.append(x)
if curr.left:
queue.append((curr.left, 2 * x))
if curr.right:
queue.append((curr.right, 2 * x + 1))
level = max(level, max(tmp) - min(tmp) + 1)
return level
|
class TrieNode(object):
__slots__ = ('children', 'isWord')
def __init__(self):
self.children = {}
self.isWord = False
class Trie(object):
def __init__(self):
"""
Initialize your data structure here.
"""
self.root = TrieNode()
def insert(self, word):
"""
Inserts a word into the trie.
:type word: str
:rtype: void
"""
root = self.root
for c in word:
if c not in root.children:
root.children[c] = TrieNode()
root = root.children[c]
root.isWord = True
def search(self, word):
"""
Returns if the word is in the trie.
:type word: str
:rtype: bool
"""
root = self.root
for c in word:
root = root.children.get(c)
if root is None:
return False
return root.isWord
def startsWith(self, prefix):
"""
Returns if there is any word in the trie that starts with the given prefix.
:type prefix: str
:rtype: bool
"""
root = self.root
for c in prefix:
root = root.children.get(c)
if root is None:
return False
return True
|
class Trienode(object):
__slots__ = ('children', 'isWord')
def __init__(self):
self.children = {}
self.isWord = False
class Trie(object):
def __init__(self):
"""
Initialize your data structure here.
"""
self.root = trie_node()
def insert(self, word):
"""
Inserts a word into the trie.
:type word: str
:rtype: void
"""
root = self.root
for c in word:
if c not in root.children:
root.children[c] = trie_node()
root = root.children[c]
root.isWord = True
def search(self, word):
"""
Returns if the word is in the trie.
:type word: str
:rtype: bool
"""
root = self.root
for c in word:
root = root.children.get(c)
if root is None:
return False
return root.isWord
def starts_with(self, prefix):
"""
Returns if there is any word in the trie that starts with the given prefix.
:type prefix: str
:rtype: bool
"""
root = self.root
for c in prefix:
root = root.children.get(c)
if root is None:
return False
return True
|
class FastLabelException(Exception):
def __init__(self, message, errcode):
super(FastLabelException, self).__init__(
"<Response [{}]> {}".format(errcode, message)
)
self.code = errcode
class FastLabelInvalidException(FastLabelException, ValueError):
pass
|
class Fastlabelexception(Exception):
def __init__(self, message, errcode):
super(FastLabelException, self).__init__('<Response [{}]> {}'.format(errcode, message))
self.code = errcode
class Fastlabelinvalidexception(FastLabelException, ValueError):
pass
|
'''ALUMNA: HUAMANI TACOMA ANDY
EJERCICIO : PAINT DASH GRIDS-DFS-RECURSIVE'''
# DESCRIPCION: IMPLEMENTACION DE DFS-RECURSIVE PARA PINTAR LOS DASH GRIDS DE UNA MATRIZ
def dfs_recursive(matrix, startX, startY, simbVisited, simbPared):
# TABLERO, POSICION INICIALX, POSICION INICIALY, SIMBOLO VISITADO, SIMBOLO PARED
try:
if(matrix[startX][startY] != simbPared): # SI EL ELEMENTO NO ES PARED
matrix[startX][startY] = simbVisited # SE CAMBIA POR EL SIMBOLO VISITADO
else: # SI ES PARED NO SE VISITA
print("es pared, no se pude pintar") # SE IMPRIME
return matrix # SE RETORNA LA MATRIZ
except: # SI LA POSICION ESTA FUERA DE LOS LIMITES
print("Fuera del os limites") # SE IMPRIME
return matrix # SE RETORNA LA MATRIZ
print("Punto Central: (" + str(startX)+","+str(startY)+")") # SE IMPRIME LA POSICION DEL PUNTO CENTRAL
dx = [-1,0,1,0] # VECTOR DE MOVIMIENTO EN X
dy = [0,1,0,-1] # VECTOR DE MOVIMIENTO EN Y
for i in range(4): # SE REALIZA EL RECORRIDO EN TODAS LAS DIRECCIONES
nx = dx[i] + startX # SE OBTIENE LA POSICION EN X
ny = dy[i] + startY # SE OBTIENE LA POSICION EN Y
if((nx >= 0 and nx < len(matrix)) and (ny >= 0 and ny < len(matrix[startX]))): # SI LA POSICION ESTA DENTRO DEL TABLERO
if(matrix[nx][ny] != simbVisited and matrix[nx][ny] != simbPared): # SI EL ELEMENTO NO ESTA VISITADO Y NO ES PARED
dfs_recursive(matrix, nx, ny, simbVisited, simbPared) # SE LLAMA AL METODO RECURSIVO
return matrix # SE RETORNA LA MATRIZ
# IMPRIMIR LA MATRIZ
def printMatrix(matrix):
for i in matrix:
for j in i:
print(" " , j , end=" ")
print()
# CASO DE PRUEBA
# MATRIZ
matrix = [
["#","#","#","#","#","#"],
["#","-","-","-","-","#"],
["#","-","-","-","-","#"],
["#","-","-","-","-","#"],
["#","-","-","-","#","-"],
["#","#","#","#","-","-"],
]
# PRUEBA 01
# MATRIZ INICIAL
printMatrix(matrix)
# MATRIZ DE VISITADOS
printMatrix(dfs_recursive(matrix, 1, 2, "o", "#"))
print("\n---------------------------------------------------------")
# PRUEBA 02
# MATRIZ INICIAL
printMatrix(matrix)
# MATRIZ DE VISITADOS
printMatrix(dfs_recursive(matrix, 5, 5, "o", "#"))
|
"""ALUMNA: HUAMANI TACOMA ANDY
EJERCICIO : PAINT DASH GRIDS-DFS-RECURSIVE"""
def dfs_recursive(matrix, startX, startY, simbVisited, simbPared):
try:
if matrix[startX][startY] != simbPared:
matrix[startX][startY] = simbVisited
else:
print('es pared, no se pude pintar')
return matrix
except:
print('Fuera del os limites')
return matrix
print('Punto Central: (' + str(startX) + ',' + str(startY) + ')')
dx = [-1, 0, 1, 0]
dy = [0, 1, 0, -1]
for i in range(4):
nx = dx[i] + startX
ny = dy[i] + startY
if (nx >= 0 and nx < len(matrix)) and (ny >= 0 and ny < len(matrix[startX])):
if matrix[nx][ny] != simbVisited and matrix[nx][ny] != simbPared:
dfs_recursive(matrix, nx, ny, simbVisited, simbPared)
return matrix
def print_matrix(matrix):
for i in matrix:
for j in i:
print(' ', j, end=' ')
print()
matrix = [['#', '#', '#', '#', '#', '#'], ['#', '-', '-', '-', '-', '#'], ['#', '-', '-', '-', '-', '#'], ['#', '-', '-', '-', '-', '#'], ['#', '-', '-', '-', '#', '-'], ['#', '#', '#', '#', '-', '-']]
print_matrix(matrix)
print_matrix(dfs_recursive(matrix, 1, 2, 'o', '#'))
print('\n---------------------------------------------------------')
print_matrix(matrix)
print_matrix(dfs_recursive(matrix, 5, 5, 'o', '#'))
|
__all__ = ["PACK_HEADER_MAX"]
# max scanned length of packet header
PACK_HEADER_MAX = 60
|
__all__ = ['PACK_HEADER_MAX']
pack_header_max = 60
|
'''We sort a large sublist of a given list and go on reducing the size of the list until all elements are sorted.'''
def shellSort(input_list):
mid = len(input_list) // 2
while mid > 0:
for i in range(mid, len(input_list)):
temp = input_list[i]
j = i
# Sort the sub list for this gap
while j >= mid and input_list[j - mid] > temp:
input_list[j] = input_list[j - mid]
j = j - mid
input_list[j] = temp
# Reduce the gap for the next element
mid = mid // 2
list = [25, 2, 30, 1, 45, 39, 11, 110, 29]
shellSort(list)
print(list)
|
"""We sort a large sublist of a given list and go on reducing the size of the list until all elements are sorted."""
def shell_sort(input_list):
mid = len(input_list) // 2
while mid > 0:
for i in range(mid, len(input_list)):
temp = input_list[i]
j = i
while j >= mid and input_list[j - mid] > temp:
input_list[j] = input_list[j - mid]
j = j - mid
input_list[j] = temp
mid = mid // 2
list = [25, 2, 30, 1, 45, 39, 11, 110, 29]
shell_sort(list)
print(list)
|
"""
PASSENGERS
"""
numPassengers = 2341
passenger_arriving = (
(3, 12, 4, 1, 1, 0, 2, 7, 5, 7, 0, 0), # 0
(4, 12, 2, 2, 4, 0, 2, 5, 3, 3, 2, 0), # 1
(3, 2, 3, 2, 2, 0, 6, 7, 5, 5, 2, 0), # 2
(1, 10, 4, 2, 1, 0, 6, 5, 1, 4, 1, 0), # 3
(3, 3, 2, 5, 4, 0, 9, 6, 7, 3, 5, 0), # 4
(5, 7, 4, 2, 3, 0, 5, 7, 4, 5, 0, 0), # 5
(2, 9, 4, 1, 2, 0, 7, 5, 5, 4, 0, 0), # 6
(4, 6, 13, 3, 2, 0, 5, 5, 3, 4, 2, 0), # 7
(1, 2, 7, 4, 3, 0, 2, 6, 4, 6, 3, 0), # 8
(4, 7, 5, 2, 3, 0, 4, 2, 4, 6, 0, 0), # 9
(3, 3, 3, 3, 2, 0, 2, 5, 4, 5, 0, 0), # 10
(2, 2, 4, 3, 2, 0, 4, 2, 6, 0, 4, 0), # 11
(3, 10, 7, 0, 2, 0, 2, 5, 3, 1, 4, 0), # 12
(3, 9, 6, 2, 0, 0, 4, 10, 3, 4, 1, 0), # 13
(5, 6, 6, 3, 1, 0, 5, 6, 3, 4, 0, 0), # 14
(1, 7, 8, 1, 3, 0, 3, 6, 4, 4, 1, 0), # 15
(7, 5, 6, 0, 1, 0, 3, 8, 4, 6, 2, 0), # 16
(0, 4, 5, 3, 4, 0, 4, 6, 6, 3, 3, 0), # 17
(6, 8, 6, 3, 1, 0, 5, 2, 6, 1, 1, 0), # 18
(3, 8, 8, 2, 2, 0, 5, 7, 9, 2, 0, 0), # 19
(3, 5, 6, 4, 2, 0, 8, 8, 4, 3, 2, 0), # 20
(6, 9, 4, 1, 3, 0, 7, 8, 4, 2, 3, 0), # 21
(3, 4, 4, 3, 3, 0, 3, 11, 3, 3, 1, 0), # 22
(3, 8, 3, 2, 0, 0, 4, 4, 4, 4, 2, 0), # 23
(2, 5, 7, 5, 2, 0, 3, 9, 6, 4, 2, 0), # 24
(3, 7, 3, 2, 1, 0, 4, 11, 3, 5, 2, 0), # 25
(0, 12, 5, 0, 2, 0, 3, 6, 4, 2, 2, 0), # 26
(4, 7, 5, 1, 2, 0, 4, 5, 6, 1, 5, 0), # 27
(4, 3, 6, 1, 1, 0, 4, 4, 4, 5, 3, 0), # 28
(1, 8, 6, 3, 3, 0, 4, 5, 5, 6, 1, 0), # 29
(5, 4, 4, 0, 1, 0, 3, 5, 5, 8, 2, 0), # 30
(4, 6, 5, 4, 0, 0, 5, 4, 3, 1, 1, 0), # 31
(4, 10, 4, 2, 0, 0, 3, 2, 7, 6, 1, 0), # 32
(3, 4, 4, 2, 4, 0, 6, 5, 3, 3, 1, 0), # 33
(7, 3, 2, 2, 2, 0, 4, 7, 3, 5, 0, 0), # 34
(5, 8, 6, 3, 3, 0, 7, 7, 3, 4, 0, 0), # 35
(2, 8, 3, 1, 0, 0, 6, 4, 3, 3, 3, 0), # 36
(6, 8, 6, 2, 1, 0, 8, 7, 8, 0, 3, 0), # 37
(0, 6, 4, 5, 0, 0, 2, 7, 3, 2, 2, 0), # 38
(4, 2, 3, 3, 1, 0, 6, 8, 7, 3, 0, 0), # 39
(3, 5, 2, 3, 1, 0, 5, 3, 10, 4, 3, 0), # 40
(6, 5, 7, 2, 0, 0, 6, 4, 1, 4, 4, 0), # 41
(7, 8, 5, 5, 0, 0, 4, 13, 1, 3, 1, 0), # 42
(3, 10, 8, 2, 2, 0, 8, 4, 9, 4, 4, 0), # 43
(4, 6, 7, 4, 0, 0, 8, 7, 4, 0, 0, 0), # 44
(3, 7, 7, 2, 2, 0, 9, 6, 4, 2, 1, 0), # 45
(2, 14, 5, 2, 2, 0, 3, 8, 1, 1, 2, 0), # 46
(8, 4, 2, 1, 2, 0, 8, 6, 2, 3, 0, 0), # 47
(4, 5, 6, 1, 0, 0, 5, 7, 3, 2, 1, 0), # 48
(4, 4, 3, 1, 1, 0, 3, 5, 2, 6, 2, 0), # 49
(1, 9, 5, 6, 0, 0, 7, 5, 5, 1, 2, 0), # 50
(5, 3, 6, 2, 1, 0, 8, 3, 6, 3, 1, 0), # 51
(2, 8, 6, 9, 1, 0, 12, 1, 8, 3, 2, 0), # 52
(4, 8, 7, 3, 0, 0, 8, 8, 5, 4, 0, 0), # 53
(5, 7, 2, 1, 0, 0, 4, 8, 0, 5, 2, 0), # 54
(1, 9, 9, 3, 0, 0, 2, 9, 4, 7, 1, 0), # 55
(2, 8, 6, 3, 4, 0, 3, 11, 5, 4, 3, 0), # 56
(6, 5, 3, 2, 1, 0, 5, 6, 4, 1, 2, 0), # 57
(3, 6, 6, 3, 2, 0, 3, 3, 4, 4, 2, 0), # 58
(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), # 59
)
station_arriving_intensity = (
(2.649651558384548, 6.796460700757575, 7.9942360218509, 6.336277173913043, 7.143028846153846, 4.75679347826087), # 0
(2.6745220100478, 6.872041598712823, 8.037415537524994, 6.371564387077295, 7.196566506410256, 4.7551721391908215), # 1
(2.699108477221734, 6.946501402918069, 8.07957012282205, 6.406074879227053, 7.248974358974359, 4.753501207729468), # 2
(2.72339008999122, 7.019759765625, 8.120668982969152, 6.4397792119565205, 7.300204326923078, 4.7517809103260875), # 3
(2.747345978441128, 7.091736339085298, 8.160681323193373, 6.472647946859904, 7.350208333333334, 4.750011473429951), # 4
(2.7709552726563262, 7.162350775550646, 8.199576348721793, 6.504651645531401, 7.39893830128205, 4.748193123490338), # 5
(2.794197102721686, 7.231522727272727, 8.237323264781493, 6.535760869565218, 7.446346153846154, 4.746326086956522), # 6
(2.817050598722076, 7.299171846503226, 8.273891276599542, 6.565946180555556, 7.492383814102565, 4.744410590277778), # 7
(2.8394948907423667, 7.365217785493826, 8.309249589403029, 6.595178140096618, 7.537003205128205, 4.7424468599033816), # 8
(2.8615091088674274, 7.429580196496212, 8.343367408419024, 6.623427309782609, 7.580156249999999, 4.740435122282609), # 9
(2.8830723831821286, 7.492178731762065, 8.376213938874606, 6.65066425120773, 7.621794871794872, 4.738375603864734), # 10
(2.9041638437713395, 7.55293304354307, 8.407758385996857, 6.676859525966184, 7.661870993589743, 4.736268531099034), # 11
(2.92476262071993, 7.611762784090908, 8.437969955012854, 6.7019836956521734, 7.700336538461538, 4.734114130434782), # 12
(2.944847844112769, 7.668587605657268, 8.46681785114967, 6.726007321859903, 7.737143429487181, 4.731912628321256), # 13
(2.9643986440347283, 7.723327160493828, 8.494271279634388, 6.748900966183574, 7.772243589743589, 4.729664251207729), # 14
(2.9833941505706756, 7.775901100852272, 8.520299445694086, 6.770635190217391, 7.8055889423076925, 4.7273692255434785), # 15
(3.001813493805482, 7.826229078984287, 8.544871554555842, 6.791180555555555, 7.8371314102564105, 4.725027777777778), # 16
(3.019635803824017, 7.874230747141554, 8.567956811446729, 6.810507623792271, 7.866822916666667, 4.722640134359904), # 17
(3.03684021071115, 7.919825757575757, 8.589524421593831, 6.82858695652174, 7.894615384615387, 4.72020652173913), # 18
(3.053405844551751, 7.962933762538579, 8.609543590224222, 6.845389115338164, 7.9204607371794875, 4.717727166364734), # 19
(3.0693118354306894, 8.003474414281705, 8.62798352256498, 6.860884661835749, 7.944310897435898, 4.71520229468599), # 20
(3.084537313432836, 8.041367365056816, 8.644813423843189, 6.875044157608696, 7.9661177884615375, 4.712632133152174), # 21
(3.099061408643059, 8.076532267115601, 8.660002499285918, 6.887838164251208, 7.985833333333332, 4.710016908212561), # 22
(3.1128632511462295, 8.108888772709737, 8.673519954120252, 6.899237243357488, 8.003409455128205, 4.707356846316426), # 23
(3.125921971027217, 8.138356534090908, 8.685334993573264, 6.909211956521739, 8.018798076923076, 4.704652173913043), # 24
(3.1382166983708903, 8.164855203510802, 8.695416822872037, 6.917732865338165, 8.03195112179487, 4.701903117451691), # 25
(3.1497265632621207, 8.188304433221099, 8.703734647243644, 6.9247705314009655, 8.042820512820512, 4.699109903381642), # 26
(3.160430695785777, 8.208623875473483, 8.710257671915166, 6.930295516304349, 8.051358173076924, 4.696272758152174), # 27
(3.1703082260267292, 8.22573318251964, 8.714955102113683, 6.934278381642512, 8.057516025641025, 4.69339190821256), # 28
(3.1793382840698468, 8.239552006611252, 8.717796143066266, 6.936689689009662, 8.061245993589743, 4.690467580012077), # 29
(3.1875, 8.25, 8.71875, 6.9375, 8.0625, 4.6875), # 30
(3.1951370284526854, 8.258678799715907, 8.718034948671496, 6.937353656045752, 8.062043661347518, 4.683376259786773), # 31
(3.202609175191816, 8.267242897727273, 8.715910024154589, 6.93691748366013, 8.06068439716312, 4.677024758454107), # 32
(3.2099197969948845, 8.275691228693182, 8.712405570652175, 6.936195772058824, 8.058436835106383, 4.66850768365817), # 33
(3.217072250639386, 8.284022727272728, 8.70755193236715, 6.935192810457517, 8.05531560283688, 4.657887223055139), # 34
(3.224069892902813, 8.292236328124998, 8.701379453502415, 6.933912888071895, 8.051335328014185, 4.645225564301183), # 35
(3.23091608056266, 8.300330965909092, 8.69391847826087, 6.932360294117648, 8.046510638297873, 4.630584895052474), # 36
(3.2376141703964194, 8.308305575284091, 8.68519935084541, 6.9305393178104575, 8.040856161347516, 4.614027402965184), # 37
(3.2441675191815853, 8.31615909090909, 8.675252415458937, 6.9284542483660125, 8.034386524822695, 4.595615275695485), # 38
(3.250579483695652, 8.323890447443182, 8.664108016304347, 6.926109375, 8.027116356382978, 4.57541070089955), # 39
(3.2568534207161126, 8.331498579545455, 8.651796497584542, 6.923508986928105, 8.019060283687942, 4.5534758662335495), # 40
(3.26299268702046, 8.338982421874999, 8.638348203502416, 6.920657373366013, 8.010232934397163, 4.529872959353657), # 41
(3.269000639386189, 8.34634090909091, 8.62379347826087, 6.917558823529411, 8.000648936170213, 4.504664167916042), # 42
(3.2748806345907933, 8.353572975852272, 8.608162666062801, 6.914217626633987, 7.990322916666666, 4.477911679576878), # 43
(3.2806360294117645, 8.360677556818182, 8.591486111111111, 6.910638071895424, 7.979269503546099, 4.449677681992337), # 44
(3.286270180626598, 8.367653586647727, 8.573794157608697, 6.906824448529411, 7.967503324468085, 4.420024362818591), # 45
(3.291786445012788, 8.374500000000001, 8.555117149758455, 6.902781045751634, 7.955039007092199, 4.389013909711811), # 46
(3.297188179347826, 8.381215731534091, 8.535485431763284, 6.898512152777777, 7.941891179078015, 4.356708510328169), # 47
(3.3024787404092075, 8.387799715909091, 8.514929347826087, 6.894022058823529, 7.928074468085106, 4.323170352323839), # 48
(3.307661484974424, 8.39425088778409, 8.493479242149759, 6.889315053104576, 7.91360350177305, 4.288461623354989), # 49
(3.312739769820972, 8.40056818181818, 8.471165458937199, 6.884395424836602, 7.898492907801418, 4.252644511077794), # 50
(3.317716951726343, 8.406750532670454, 8.448018342391304, 6.879267463235294, 7.882757313829787, 4.215781203148426), # 51
(3.322596387468031, 8.412796875, 8.424068236714975, 6.87393545751634, 7.86641134751773, 4.177933887223055), # 52
(3.3273814338235295, 8.41870614346591, 8.39934548611111, 6.868403696895425, 7.849469636524823, 4.139164750957854), # 53
(3.332075447570333, 8.424477272727271, 8.373880434782608, 6.8626764705882355, 7.831946808510638, 4.099535982008995), # 54
(3.336681785485933, 8.430109197443182, 8.347703426932366, 6.856758067810458, 7.813857491134752, 4.05910976803265), # 55
(3.341203804347826, 8.435600852272726, 8.320844806763285, 6.8506527777777775, 7.795216312056738, 4.017948296684991), # 56
(3.345644860933504, 8.440951171875001, 8.29333491847826, 6.844364889705882, 7.77603789893617, 3.9761137556221886), # 57
(3.3500083120204605, 8.44615909090909, 8.265204106280192, 6.837898692810458, 7.756336879432624, 3.9336683325004165), # 58
(0.0, 0.0, 0.0, 0.0, 0.0, 0.0), # 59
)
passenger_arriving_acc = (
(3, 12, 4, 1, 1, 0, 2, 7, 5, 7, 0, 0), # 0
(7, 24, 6, 3, 5, 0, 4, 12, 8, 10, 2, 0), # 1
(10, 26, 9, 5, 7, 0, 10, 19, 13, 15, 4, 0), # 2
(11, 36, 13, 7, 8, 0, 16, 24, 14, 19, 5, 0), # 3
(14, 39, 15, 12, 12, 0, 25, 30, 21, 22, 10, 0), # 4
(19, 46, 19, 14, 15, 0, 30, 37, 25, 27, 10, 0), # 5
(21, 55, 23, 15, 17, 0, 37, 42, 30, 31, 10, 0), # 6
(25, 61, 36, 18, 19, 0, 42, 47, 33, 35, 12, 0), # 7
(26, 63, 43, 22, 22, 0, 44, 53, 37, 41, 15, 0), # 8
(30, 70, 48, 24, 25, 0, 48, 55, 41, 47, 15, 0), # 9
(33, 73, 51, 27, 27, 0, 50, 60, 45, 52, 15, 0), # 10
(35, 75, 55, 30, 29, 0, 54, 62, 51, 52, 19, 0), # 11
(38, 85, 62, 30, 31, 0, 56, 67, 54, 53, 23, 0), # 12
(41, 94, 68, 32, 31, 0, 60, 77, 57, 57, 24, 0), # 13
(46, 100, 74, 35, 32, 0, 65, 83, 60, 61, 24, 0), # 14
(47, 107, 82, 36, 35, 0, 68, 89, 64, 65, 25, 0), # 15
(54, 112, 88, 36, 36, 0, 71, 97, 68, 71, 27, 0), # 16
(54, 116, 93, 39, 40, 0, 75, 103, 74, 74, 30, 0), # 17
(60, 124, 99, 42, 41, 0, 80, 105, 80, 75, 31, 0), # 18
(63, 132, 107, 44, 43, 0, 85, 112, 89, 77, 31, 0), # 19
(66, 137, 113, 48, 45, 0, 93, 120, 93, 80, 33, 0), # 20
(72, 146, 117, 49, 48, 0, 100, 128, 97, 82, 36, 0), # 21
(75, 150, 121, 52, 51, 0, 103, 139, 100, 85, 37, 0), # 22
(78, 158, 124, 54, 51, 0, 107, 143, 104, 89, 39, 0), # 23
(80, 163, 131, 59, 53, 0, 110, 152, 110, 93, 41, 0), # 24
(83, 170, 134, 61, 54, 0, 114, 163, 113, 98, 43, 0), # 25
(83, 182, 139, 61, 56, 0, 117, 169, 117, 100, 45, 0), # 26
(87, 189, 144, 62, 58, 0, 121, 174, 123, 101, 50, 0), # 27
(91, 192, 150, 63, 59, 0, 125, 178, 127, 106, 53, 0), # 28
(92, 200, 156, 66, 62, 0, 129, 183, 132, 112, 54, 0), # 29
(97, 204, 160, 66, 63, 0, 132, 188, 137, 120, 56, 0), # 30
(101, 210, 165, 70, 63, 0, 137, 192, 140, 121, 57, 0), # 31
(105, 220, 169, 72, 63, 0, 140, 194, 147, 127, 58, 0), # 32
(108, 224, 173, 74, 67, 0, 146, 199, 150, 130, 59, 0), # 33
(115, 227, 175, 76, 69, 0, 150, 206, 153, 135, 59, 0), # 34
(120, 235, 181, 79, 72, 0, 157, 213, 156, 139, 59, 0), # 35
(122, 243, 184, 80, 72, 0, 163, 217, 159, 142, 62, 0), # 36
(128, 251, 190, 82, 73, 0, 171, 224, 167, 142, 65, 0), # 37
(128, 257, 194, 87, 73, 0, 173, 231, 170, 144, 67, 0), # 38
(132, 259, 197, 90, 74, 0, 179, 239, 177, 147, 67, 0), # 39
(135, 264, 199, 93, 75, 0, 184, 242, 187, 151, 70, 0), # 40
(141, 269, 206, 95, 75, 0, 190, 246, 188, 155, 74, 0), # 41
(148, 277, 211, 100, 75, 0, 194, 259, 189, 158, 75, 0), # 42
(151, 287, 219, 102, 77, 0, 202, 263, 198, 162, 79, 0), # 43
(155, 293, 226, 106, 77, 0, 210, 270, 202, 162, 79, 0), # 44
(158, 300, 233, 108, 79, 0, 219, 276, 206, 164, 80, 0), # 45
(160, 314, 238, 110, 81, 0, 222, 284, 207, 165, 82, 0), # 46
(168, 318, 240, 111, 83, 0, 230, 290, 209, 168, 82, 0), # 47
(172, 323, 246, 112, 83, 0, 235, 297, 212, 170, 83, 0), # 48
(176, 327, 249, 113, 84, 0, 238, 302, 214, 176, 85, 0), # 49
(177, 336, 254, 119, 84, 0, 245, 307, 219, 177, 87, 0), # 50
(182, 339, 260, 121, 85, 0, 253, 310, 225, 180, 88, 0), # 51
(184, 347, 266, 130, 86, 0, 265, 311, 233, 183, 90, 0), # 52
(188, 355, 273, 133, 86, 0, 273, 319, 238, 187, 90, 0), # 53
(193, 362, 275, 134, 86, 0, 277, 327, 238, 192, 92, 0), # 54
(194, 371, 284, 137, 86, 0, 279, 336, 242, 199, 93, 0), # 55
(196, 379, 290, 140, 90, 0, 282, 347, 247, 203, 96, 0), # 56
(202, 384, 293, 142, 91, 0, 287, 353, 251, 204, 98, 0), # 57
(205, 390, 299, 145, 93, 0, 290, 356, 255, 208, 100, 0), # 58
(205, 390, 299, 145, 93, 0, 290, 356, 255, 208, 100, 0), # 59
)
passenger_arriving_rate = (
(2.649651558384548, 5.43716856060606, 4.79654161311054, 2.534510869565217, 1.428605769230769, 0.0, 4.75679347826087, 5.714423076923076, 3.801766304347826, 3.1976944087403596, 1.359292140151515, 0.0), # 0
(2.6745220100478, 5.497633278970258, 4.822449322514997, 2.5486257548309177, 1.439313301282051, 0.0, 4.7551721391908215, 5.757253205128204, 3.8229386322463768, 3.2149662150099974, 1.3744083197425645, 0.0), # 1
(2.699108477221734, 5.557201122334455, 4.8477420736932295, 2.562429951690821, 1.4497948717948717, 0.0, 4.753501207729468, 5.799179487179487, 3.8436449275362317, 3.23182804912882, 1.3893002805836137, 0.0), # 2
(2.72339008999122, 5.6158078125, 4.872401389781491, 2.575911684782608, 1.4600408653846155, 0.0, 4.7517809103260875, 5.840163461538462, 3.863867527173912, 3.2482675931876606, 1.403951953125, 0.0), # 3
(2.747345978441128, 5.673389071268238, 4.896408793916024, 2.589059178743961, 1.4700416666666667, 0.0, 4.750011473429951, 5.880166666666667, 3.883588768115942, 3.2642725292773487, 1.4183472678170594, 0.0), # 4
(2.7709552726563262, 5.729880620440516, 4.919745809233076, 2.6018606582125603, 1.47978766025641, 0.0, 4.748193123490338, 5.91915064102564, 3.9027909873188404, 3.279830539488717, 1.432470155110129, 0.0), # 5
(2.794197102721686, 5.785218181818181, 4.942393958868895, 2.614304347826087, 1.4892692307692306, 0.0, 4.746326086956522, 5.957076923076922, 3.9214565217391306, 3.294929305912597, 1.4463045454545453, 0.0), # 6
(2.817050598722076, 5.83933747720258, 4.964334765959725, 2.626378472222222, 1.498476762820513, 0.0, 4.744410590277778, 5.993907051282052, 3.939567708333333, 3.309556510639817, 1.459834369300645, 0.0), # 7
(2.8394948907423667, 5.89217422839506, 4.985549753641817, 2.638071256038647, 1.5074006410256409, 0.0, 4.7424468599033816, 6.0296025641025635, 3.9571068840579704, 3.3236998357612113, 1.473043557098765, 0.0), # 8
(2.8615091088674274, 5.943664157196969, 5.006020445051414, 2.649370923913043, 1.5160312499999997, 0.0, 4.740435122282609, 6.064124999999999, 3.9740563858695652, 3.3373469633676094, 1.4859160392992423, 0.0), # 9
(2.8830723831821286, 5.993742985409652, 5.025728363324764, 2.660265700483092, 1.5243589743589743, 0.0, 4.738375603864734, 6.097435897435897, 3.990398550724638, 3.3504855755498424, 1.498435746352413, 0.0), # 10
(2.9041638437713395, 6.042346434834456, 5.044655031598114, 2.6707438103864733, 1.5323741987179484, 0.0, 4.736268531099034, 6.129496794871794, 4.0061157155797105, 3.3631033543987425, 1.510586608708614, 0.0), # 11
(2.92476262071993, 6.089410227272726, 5.062781973007712, 2.680793478260869, 1.5400673076923075, 0.0, 4.734114130434782, 6.16026923076923, 4.021190217391304, 3.375187982005141, 1.5223525568181815, 0.0), # 12
(2.944847844112769, 6.134870084525814, 5.080090710689802, 2.690402928743961, 1.547428685897436, 0.0, 4.731912628321256, 6.189714743589744, 4.035604393115942, 3.386727140459868, 1.5337175211314535, 0.0), # 13
(2.9643986440347283, 6.1786617283950624, 5.096562767780632, 2.699560386473429, 1.5544487179487176, 0.0, 4.729664251207729, 6.217794871794871, 4.049340579710144, 3.397708511853755, 1.5446654320987656, 0.0), # 14
(2.9833941505706756, 6.220720880681816, 5.112179667416451, 2.708254076086956, 1.5611177884615384, 0.0, 4.7273692255434785, 6.2444711538461535, 4.062381114130434, 3.408119778277634, 1.555180220170454, 0.0), # 15
(3.001813493805482, 6.26098326318743, 5.126922932733505, 2.716472222222222, 1.5674262820512819, 0.0, 4.725027777777778, 6.2697051282051275, 4.074708333333333, 3.4179486218223363, 1.5652458157968574, 0.0), # 16
(3.019635803824017, 6.299384597713242, 5.140774086868038, 2.724203049516908, 1.5733645833333332, 0.0, 4.722640134359904, 6.293458333333333, 4.0863045742753625, 3.4271827245786914, 1.5748461494283106, 0.0), # 17
(3.03684021071115, 6.3358606060606055, 5.153714652956299, 2.7314347826086958, 1.578923076923077, 0.0, 4.72020652173913, 6.315692307692308, 4.097152173913043, 3.435809768637532, 1.5839651515151514, 0.0), # 18
(3.053405844551751, 6.370347010030863, 5.165726154134533, 2.738155646135265, 1.5840921474358973, 0.0, 4.717727166364734, 6.336368589743589, 4.107233469202898, 3.4438174360896885, 1.5925867525077158, 0.0), # 19
(3.0693118354306894, 6.402779531425363, 5.1767901135389875, 2.7443538647342995, 1.5888621794871793, 0.0, 4.71520229468599, 6.355448717948717, 4.11653079710145, 3.4511934090259917, 1.6006948828563408, 0.0), # 20
(3.084537313432836, 6.433093892045452, 5.186888054305913, 2.750017663043478, 1.5932235576923073, 0.0, 4.712632133152174, 6.372894230769229, 4.125026494565217, 3.4579253695372754, 1.608273473011363, 0.0), # 21
(3.099061408643059, 6.46122581369248, 5.19600149957155, 2.7551352657004826, 1.5971666666666662, 0.0, 4.710016908212561, 6.388666666666665, 4.132702898550725, 3.464000999714367, 1.61530645342312, 0.0), # 22
(3.1128632511462295, 6.487111018167789, 5.204111972472151, 2.759694897342995, 1.6006818910256408, 0.0, 4.707356846316426, 6.402727564102563, 4.139542346014493, 3.4694079816481005, 1.6217777545419472, 0.0), # 23
(3.125921971027217, 6.5106852272727265, 5.211200996143958, 2.763684782608695, 1.6037596153846152, 0.0, 4.704652173913043, 6.415038461538461, 4.1455271739130435, 3.474133997429305, 1.6276713068181816, 0.0), # 24
(3.1382166983708903, 6.531884162808641, 5.217250093723222, 2.7670931461352657, 1.606390224358974, 0.0, 4.701903117451691, 6.425560897435896, 4.150639719202899, 3.4781667291488145, 1.6329710407021603, 0.0), # 25
(3.1497265632621207, 6.550643546576878, 5.222240788346187, 2.7699082125603858, 1.6085641025641022, 0.0, 4.699109903381642, 6.434256410256409, 4.154862318840579, 3.4814938588974575, 1.6376608866442195, 0.0), # 26
(3.160430695785777, 6.566899100378786, 5.226154603149099, 2.772118206521739, 1.6102716346153847, 0.0, 4.696272758152174, 6.441086538461539, 4.158177309782609, 3.484103068766066, 1.6417247750946966, 0.0), # 27
(3.1703082260267292, 6.580586546015712, 5.228973061268209, 2.7737113526570045, 1.6115032051282048, 0.0, 4.69339190821256, 6.446012820512819, 4.160567028985507, 3.4859820408454727, 1.645146636503928, 0.0), # 28
(3.1793382840698468, 6.591641605289001, 5.230677685839759, 2.7746758756038647, 1.6122491987179486, 0.0, 4.690467580012077, 6.448996794871794, 4.162013813405797, 3.487118457226506, 1.6479104013222503, 0.0), # 29
(3.1875, 6.6, 5.23125, 2.775, 1.6124999999999998, 0.0, 4.6875, 6.449999999999999, 4.1625, 3.4875, 1.65, 0.0), # 30
(3.1951370284526854, 6.606943039772726, 5.230820969202898, 2.7749414624183006, 1.6124087322695035, 0.0, 4.683376259786773, 6.449634929078014, 4.162412193627451, 3.4872139794685983, 1.6517357599431814, 0.0), # 31
(3.202609175191816, 6.613794318181818, 5.229546014492753, 2.7747669934640515, 1.6121368794326238, 0.0, 4.677024758454107, 6.448547517730495, 4.162150490196078, 3.4863640096618354, 1.6534485795454545, 0.0), # 32
(3.2099197969948845, 6.620552982954545, 5.227443342391305, 2.774478308823529, 1.6116873670212764, 0.0, 4.66850768365817, 6.446749468085105, 4.161717463235294, 3.4849622282608697, 1.6551382457386363, 0.0), # 33
(3.217072250639386, 6.627218181818182, 5.224531159420289, 2.7740771241830067, 1.6110631205673758, 0.0, 4.657887223055139, 6.444252482269503, 4.16111568627451, 3.4830207729468596, 1.6568045454545455, 0.0), # 34
(3.224069892902813, 6.633789062499998, 5.220827672101449, 2.773565155228758, 1.6102670656028368, 0.0, 4.645225564301183, 6.441068262411347, 4.160347732843137, 3.480551781400966, 1.6584472656249996, 0.0), # 35
(3.23091608056266, 6.6402647727272734, 5.2163510869565215, 2.7729441176470586, 1.6093021276595745, 0.0, 4.630584895052474, 6.437208510638298, 4.159416176470589, 3.477567391304347, 1.6600661931818184, 0.0), # 36
(3.2376141703964194, 6.6466444602272725, 5.211119610507246, 2.7722157271241827, 1.6081712322695032, 0.0, 4.614027402965184, 6.432684929078013, 4.158323590686274, 3.474079740338164, 1.6616611150568181, 0.0), # 37
(3.2441675191815853, 6.652927272727272, 5.205151449275362, 2.7713816993464047, 1.6068773049645388, 0.0, 4.595615275695485, 6.427509219858155, 4.157072549019607, 3.4701009661835744, 1.663231818181818, 0.0), # 38
(3.250579483695652, 6.659112357954545, 5.198464809782608, 2.7704437499999996, 1.6054232712765955, 0.0, 4.57541070089955, 6.421693085106382, 4.155665625, 3.4656432065217384, 1.6647780894886361, 0.0), # 39
(3.2568534207161126, 6.6651988636363635, 5.191077898550724, 2.7694035947712417, 1.6038120567375882, 0.0, 4.5534758662335495, 6.415248226950353, 4.154105392156863, 3.4607185990338163, 1.6662997159090909, 0.0), # 40
(3.26299268702046, 6.671185937499998, 5.1830089221014495, 2.768262949346405, 1.6020465868794325, 0.0, 4.529872959353657, 6.40818634751773, 4.152394424019608, 3.455339281400966, 1.6677964843749995, 0.0), # 41
(3.269000639386189, 6.677072727272728, 5.174276086956522, 2.767023529411764, 1.6001297872340425, 0.0, 4.504664167916042, 6.40051914893617, 4.150535294117646, 3.4495173913043478, 1.669268181818182, 0.0), # 42
(3.2748806345907933, 6.682858380681817, 5.164897599637681, 2.7656870506535944, 1.5980645833333331, 0.0, 4.477911679576878, 6.3922583333333325, 4.148530575980392, 3.4432650664251203, 1.6707145951704543, 0.0), # 43
(3.2806360294117645, 6.688542045454545, 5.154891666666667, 2.7642552287581696, 1.5958539007092198, 0.0, 4.449677681992337, 6.383415602836879, 4.146382843137254, 3.4365944444444443, 1.6721355113636363, 0.0), # 44
(3.286270180626598, 6.694122869318181, 5.144276494565218, 2.7627297794117642, 1.593500664893617, 0.0, 4.420024362818591, 6.374002659574468, 4.144094669117647, 3.4295176630434785, 1.6735307173295453, 0.0), # 45
(3.291786445012788, 6.6996, 5.133070289855073, 2.761112418300653, 1.5910078014184397, 0.0, 4.389013909711811, 6.364031205673759, 4.14166862745098, 3.4220468599033818, 1.6749, 0.0), # 46
(3.297188179347826, 6.704972585227273, 5.12129125905797, 2.759404861111111, 1.588378235815603, 0.0, 4.356708510328169, 6.353512943262412, 4.139107291666666, 3.4141941727053133, 1.6762431463068181, 0.0), # 47
(3.3024787404092075, 6.710239772727273, 5.108957608695651, 2.757608823529411, 1.5856148936170211, 0.0, 4.323170352323839, 6.3424595744680845, 4.136413235294117, 3.4059717391304343, 1.6775599431818182, 0.0), # 48
(3.307661484974424, 6.715400710227271, 5.096087545289855, 2.75572602124183, 1.5827207003546098, 0.0, 4.288461623354989, 6.330882801418439, 4.133589031862745, 3.3973916968599034, 1.6788501775568176, 0.0), # 49
(3.312739769820972, 6.720454545454543, 5.082699275362319, 2.7537581699346405, 1.5796985815602835, 0.0, 4.252644511077794, 6.318794326241134, 4.130637254901961, 3.388466183574879, 1.6801136363636358, 0.0), # 50
(3.317716951726343, 6.725400426136363, 5.068811005434783, 2.7517069852941174, 1.5765514627659571, 0.0, 4.215781203148426, 6.306205851063829, 4.127560477941176, 3.3792073369565214, 1.6813501065340908, 0.0), # 51
(3.322596387468031, 6.730237499999999, 5.054440942028985, 2.7495741830065357, 1.573282269503546, 0.0, 4.177933887223055, 6.293129078014184, 4.124361274509804, 3.3696272946859898, 1.6825593749999999, 0.0), # 52
(3.3273814338235295, 6.7349649147727275, 5.039607291666666, 2.7473614787581697, 1.5698939273049646, 0.0, 4.139164750957854, 6.279575709219858, 4.121042218137255, 3.359738194444444, 1.6837412286931819, 0.0), # 53
(3.332075447570333, 6.739581818181817, 5.024328260869565, 2.745070588235294, 1.5663893617021276, 0.0, 4.099535982008995, 6.2655574468085105, 4.117605882352941, 3.3495521739130427, 1.6848954545454542, 0.0), # 54
(3.336681785485933, 6.744087357954545, 5.008622056159419, 2.7427032271241827, 1.5627714982269503, 0.0, 4.05910976803265, 6.251085992907801, 4.114054840686275, 3.3390813707729463, 1.6860218394886362, 0.0), # 55
(3.341203804347826, 6.74848068181818, 4.9925068840579705, 2.740261111111111, 1.5590432624113475, 0.0, 4.017948296684991, 6.23617304964539, 4.110391666666667, 3.328337922705314, 1.687120170454545, 0.0), # 56
(3.345644860933504, 6.752760937500001, 4.976000951086956, 2.7377459558823527, 1.5552075797872338, 0.0, 3.9761137556221886, 6.220830319148935, 4.106618933823529, 3.317333967391304, 1.6881902343750002, 0.0), # 57
(3.3500083120204605, 6.756927272727271, 4.959122463768115, 2.7351594771241827, 1.5512673758865245, 0.0, 3.9336683325004165, 6.205069503546098, 4.102739215686275, 3.3060816425120767, 1.6892318181818178, 0.0), # 58
(0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0), # 59
)
passenger_allighting_rate = (
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 0
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 1
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 2
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 3
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 4
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 5
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 6
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 7
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 8
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 9
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 10
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 11
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 12
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 13
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 14
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 15
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 16
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 17
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 18
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 19
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 20
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 21
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 22
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 23
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 24
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 25
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 26
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 27
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 28
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 29
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 30
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 31
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 32
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 33
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 34
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 35
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 36
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 37
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 38
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 39
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 40
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 41
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 42
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 43
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 44
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 45
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 46
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 47
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 48
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 49
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 50
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 51
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 52
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 53
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 54
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 55
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 56
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 57
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 58
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 59
)
"""
parameters for reproducibiliy. More information: https://numpy.org/doc/stable/reference/random/parallel.html
"""
#initial entropy
entropy = 258194110137029475889902652135037600173
#index for seed sequence child
child_seed_index = (
1, # 0
38, # 1
)
|
"""
PASSENGERS
"""
num_passengers = 2341
passenger_arriving = ((3, 12, 4, 1, 1, 0, 2, 7, 5, 7, 0, 0), (4, 12, 2, 2, 4, 0, 2, 5, 3, 3, 2, 0), (3, 2, 3, 2, 2, 0, 6, 7, 5, 5, 2, 0), (1, 10, 4, 2, 1, 0, 6, 5, 1, 4, 1, 0), (3, 3, 2, 5, 4, 0, 9, 6, 7, 3, 5, 0), (5, 7, 4, 2, 3, 0, 5, 7, 4, 5, 0, 0), (2, 9, 4, 1, 2, 0, 7, 5, 5, 4, 0, 0), (4, 6, 13, 3, 2, 0, 5, 5, 3, 4, 2, 0), (1, 2, 7, 4, 3, 0, 2, 6, 4, 6, 3, 0), (4, 7, 5, 2, 3, 0, 4, 2, 4, 6, 0, 0), (3, 3, 3, 3, 2, 0, 2, 5, 4, 5, 0, 0), (2, 2, 4, 3, 2, 0, 4, 2, 6, 0, 4, 0), (3, 10, 7, 0, 2, 0, 2, 5, 3, 1, 4, 0), (3, 9, 6, 2, 0, 0, 4, 10, 3, 4, 1, 0), (5, 6, 6, 3, 1, 0, 5, 6, 3, 4, 0, 0), (1, 7, 8, 1, 3, 0, 3, 6, 4, 4, 1, 0), (7, 5, 6, 0, 1, 0, 3, 8, 4, 6, 2, 0), (0, 4, 5, 3, 4, 0, 4, 6, 6, 3, 3, 0), (6, 8, 6, 3, 1, 0, 5, 2, 6, 1, 1, 0), (3, 8, 8, 2, 2, 0, 5, 7, 9, 2, 0, 0), (3, 5, 6, 4, 2, 0, 8, 8, 4, 3, 2, 0), (6, 9, 4, 1, 3, 0, 7, 8, 4, 2, 3, 0), (3, 4, 4, 3, 3, 0, 3, 11, 3, 3, 1, 0), (3, 8, 3, 2, 0, 0, 4, 4, 4, 4, 2, 0), (2, 5, 7, 5, 2, 0, 3, 9, 6, 4, 2, 0), (3, 7, 3, 2, 1, 0, 4, 11, 3, 5, 2, 0), (0, 12, 5, 0, 2, 0, 3, 6, 4, 2, 2, 0), (4, 7, 5, 1, 2, 0, 4, 5, 6, 1, 5, 0), (4, 3, 6, 1, 1, 0, 4, 4, 4, 5, 3, 0), (1, 8, 6, 3, 3, 0, 4, 5, 5, 6, 1, 0), (5, 4, 4, 0, 1, 0, 3, 5, 5, 8, 2, 0), (4, 6, 5, 4, 0, 0, 5, 4, 3, 1, 1, 0), (4, 10, 4, 2, 0, 0, 3, 2, 7, 6, 1, 0), (3, 4, 4, 2, 4, 0, 6, 5, 3, 3, 1, 0), (7, 3, 2, 2, 2, 0, 4, 7, 3, 5, 0, 0), (5, 8, 6, 3, 3, 0, 7, 7, 3, 4, 0, 0), (2, 8, 3, 1, 0, 0, 6, 4, 3, 3, 3, 0), (6, 8, 6, 2, 1, 0, 8, 7, 8, 0, 3, 0), (0, 6, 4, 5, 0, 0, 2, 7, 3, 2, 2, 0), (4, 2, 3, 3, 1, 0, 6, 8, 7, 3, 0, 0), (3, 5, 2, 3, 1, 0, 5, 3, 10, 4, 3, 0), (6, 5, 7, 2, 0, 0, 6, 4, 1, 4, 4, 0), (7, 8, 5, 5, 0, 0, 4, 13, 1, 3, 1, 0), (3, 10, 8, 2, 2, 0, 8, 4, 9, 4, 4, 0), (4, 6, 7, 4, 0, 0, 8, 7, 4, 0, 0, 0), (3, 7, 7, 2, 2, 0, 9, 6, 4, 2, 1, 0), (2, 14, 5, 2, 2, 0, 3, 8, 1, 1, 2, 0), (8, 4, 2, 1, 2, 0, 8, 6, 2, 3, 0, 0), (4, 5, 6, 1, 0, 0, 5, 7, 3, 2, 1, 0), (4, 4, 3, 1, 1, 0, 3, 5, 2, 6, 2, 0), (1, 9, 5, 6, 0, 0, 7, 5, 5, 1, 2, 0), (5, 3, 6, 2, 1, 0, 8, 3, 6, 3, 1, 0), (2, 8, 6, 9, 1, 0, 12, 1, 8, 3, 2, 0), (4, 8, 7, 3, 0, 0, 8, 8, 5, 4, 0, 0), (5, 7, 2, 1, 0, 0, 4, 8, 0, 5, 2, 0), (1, 9, 9, 3, 0, 0, 2, 9, 4, 7, 1, 0), (2, 8, 6, 3, 4, 0, 3, 11, 5, 4, 3, 0), (6, 5, 3, 2, 1, 0, 5, 6, 4, 1, 2, 0), (3, 6, 6, 3, 2, 0, 3, 3, 4, 4, 2, 0), (0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0))
station_arriving_intensity = ((2.649651558384548, 6.796460700757575, 7.9942360218509, 6.336277173913043, 7.143028846153846, 4.75679347826087), (2.6745220100478, 6.872041598712823, 8.037415537524994, 6.371564387077295, 7.196566506410256, 4.7551721391908215), (2.699108477221734, 6.946501402918069, 8.07957012282205, 6.406074879227053, 7.248974358974359, 4.753501207729468), (2.72339008999122, 7.019759765625, 8.120668982969152, 6.4397792119565205, 7.300204326923078, 4.7517809103260875), (2.747345978441128, 7.091736339085298, 8.160681323193373, 6.472647946859904, 7.350208333333334, 4.750011473429951), (2.7709552726563262, 7.162350775550646, 8.199576348721793, 6.504651645531401, 7.39893830128205, 4.748193123490338), (2.794197102721686, 7.231522727272727, 8.237323264781493, 6.535760869565218, 7.446346153846154, 4.746326086956522), (2.817050598722076, 7.299171846503226, 8.273891276599542, 6.565946180555556, 7.492383814102565, 4.744410590277778), (2.8394948907423667, 7.365217785493826, 8.309249589403029, 6.595178140096618, 7.537003205128205, 4.7424468599033816), (2.8615091088674274, 7.429580196496212, 8.343367408419024, 6.623427309782609, 7.580156249999999, 4.740435122282609), (2.8830723831821286, 7.492178731762065, 8.376213938874606, 6.65066425120773, 7.621794871794872, 4.738375603864734), (2.9041638437713395, 7.55293304354307, 8.407758385996857, 6.676859525966184, 7.661870993589743, 4.736268531099034), (2.92476262071993, 7.611762784090908, 8.437969955012854, 6.7019836956521734, 7.700336538461538, 4.734114130434782), (2.944847844112769, 7.668587605657268, 8.46681785114967, 6.726007321859903, 7.737143429487181, 4.731912628321256), (2.9643986440347283, 7.723327160493828, 8.494271279634388, 6.748900966183574, 7.772243589743589, 4.729664251207729), (2.9833941505706756, 7.775901100852272, 8.520299445694086, 6.770635190217391, 7.8055889423076925, 4.7273692255434785), (3.001813493805482, 7.826229078984287, 8.544871554555842, 6.791180555555555, 7.8371314102564105, 4.725027777777778), (3.019635803824017, 7.874230747141554, 8.567956811446729, 6.810507623792271, 7.866822916666667, 4.722640134359904), (3.03684021071115, 7.919825757575757, 8.589524421593831, 6.82858695652174, 7.894615384615387, 4.72020652173913), (3.053405844551751, 7.962933762538579, 8.609543590224222, 6.845389115338164, 7.9204607371794875, 4.717727166364734), (3.0693118354306894, 8.003474414281705, 8.62798352256498, 6.860884661835749, 7.944310897435898, 4.71520229468599), (3.084537313432836, 8.041367365056816, 8.644813423843189, 6.875044157608696, 7.9661177884615375, 4.712632133152174), (3.099061408643059, 8.076532267115601, 8.660002499285918, 6.887838164251208, 7.985833333333332, 4.710016908212561), (3.1128632511462295, 8.108888772709737, 8.673519954120252, 6.899237243357488, 8.003409455128205, 4.707356846316426), (3.125921971027217, 8.138356534090908, 8.685334993573264, 6.909211956521739, 8.018798076923076, 4.704652173913043), (3.1382166983708903, 8.164855203510802, 8.695416822872037, 6.917732865338165, 8.03195112179487, 4.701903117451691), (3.1497265632621207, 8.188304433221099, 8.703734647243644, 6.9247705314009655, 8.042820512820512, 4.699109903381642), (3.160430695785777, 8.208623875473483, 8.710257671915166, 6.930295516304349, 8.051358173076924, 4.696272758152174), (3.1703082260267292, 8.22573318251964, 8.714955102113683, 6.934278381642512, 8.057516025641025, 4.69339190821256), (3.1793382840698468, 8.239552006611252, 8.717796143066266, 6.936689689009662, 8.061245993589743, 4.690467580012077), (3.1875, 8.25, 8.71875, 6.9375, 8.0625, 4.6875), (3.1951370284526854, 8.258678799715907, 8.718034948671496, 6.937353656045752, 8.062043661347518, 4.683376259786773), (3.202609175191816, 8.267242897727273, 8.715910024154589, 6.93691748366013, 8.06068439716312, 4.677024758454107), (3.2099197969948845, 8.275691228693182, 8.712405570652175, 6.936195772058824, 8.058436835106383, 4.66850768365817), (3.217072250639386, 8.284022727272728, 8.70755193236715, 6.935192810457517, 8.05531560283688, 4.657887223055139), (3.224069892902813, 8.292236328124998, 8.701379453502415, 6.933912888071895, 8.051335328014185, 4.645225564301183), (3.23091608056266, 8.300330965909092, 8.69391847826087, 6.932360294117648, 8.046510638297873, 4.630584895052474), (3.2376141703964194, 8.308305575284091, 8.68519935084541, 6.9305393178104575, 8.040856161347516, 4.614027402965184), (3.2441675191815853, 8.31615909090909, 8.675252415458937, 6.9284542483660125, 8.034386524822695, 4.595615275695485), (3.250579483695652, 8.323890447443182, 8.664108016304347, 6.926109375, 8.027116356382978, 4.57541070089955), (3.2568534207161126, 8.331498579545455, 8.651796497584542, 6.923508986928105, 8.019060283687942, 4.5534758662335495), (3.26299268702046, 8.338982421874999, 8.638348203502416, 6.920657373366013, 8.010232934397163, 4.529872959353657), (3.269000639386189, 8.34634090909091, 8.62379347826087, 6.917558823529411, 8.000648936170213, 4.504664167916042), (3.2748806345907933, 8.353572975852272, 8.608162666062801, 6.914217626633987, 7.990322916666666, 4.477911679576878), (3.2806360294117645, 8.360677556818182, 8.591486111111111, 6.910638071895424, 7.979269503546099, 4.449677681992337), (3.286270180626598, 8.367653586647727, 8.573794157608697, 6.906824448529411, 7.967503324468085, 4.420024362818591), (3.291786445012788, 8.374500000000001, 8.555117149758455, 6.902781045751634, 7.955039007092199, 4.389013909711811), (3.297188179347826, 8.381215731534091, 8.535485431763284, 6.898512152777777, 7.941891179078015, 4.356708510328169), (3.3024787404092075, 8.387799715909091, 8.514929347826087, 6.894022058823529, 7.928074468085106, 4.323170352323839), (3.307661484974424, 8.39425088778409, 8.493479242149759, 6.889315053104576, 7.91360350177305, 4.288461623354989), (3.312739769820972, 8.40056818181818, 8.471165458937199, 6.884395424836602, 7.898492907801418, 4.252644511077794), (3.317716951726343, 8.406750532670454, 8.448018342391304, 6.879267463235294, 7.882757313829787, 4.215781203148426), (3.322596387468031, 8.412796875, 8.424068236714975, 6.87393545751634, 7.86641134751773, 4.177933887223055), (3.3273814338235295, 8.41870614346591, 8.39934548611111, 6.868403696895425, 7.849469636524823, 4.139164750957854), (3.332075447570333, 8.424477272727271, 8.373880434782608, 6.8626764705882355, 7.831946808510638, 4.099535982008995), (3.336681785485933, 8.430109197443182, 8.347703426932366, 6.856758067810458, 7.813857491134752, 4.05910976803265), (3.341203804347826, 8.435600852272726, 8.320844806763285, 6.8506527777777775, 7.795216312056738, 4.017948296684991), (3.345644860933504, 8.440951171875001, 8.29333491847826, 6.844364889705882, 7.77603789893617, 3.9761137556221886), (3.3500083120204605, 8.44615909090909, 8.265204106280192, 6.837898692810458, 7.756336879432624, 3.9336683325004165), (0.0, 0.0, 0.0, 0.0, 0.0, 0.0))
passenger_arriving_acc = ((3, 12, 4, 1, 1, 0, 2, 7, 5, 7, 0, 0), (7, 24, 6, 3, 5, 0, 4, 12, 8, 10, 2, 0), (10, 26, 9, 5, 7, 0, 10, 19, 13, 15, 4, 0), (11, 36, 13, 7, 8, 0, 16, 24, 14, 19, 5, 0), (14, 39, 15, 12, 12, 0, 25, 30, 21, 22, 10, 0), (19, 46, 19, 14, 15, 0, 30, 37, 25, 27, 10, 0), (21, 55, 23, 15, 17, 0, 37, 42, 30, 31, 10, 0), (25, 61, 36, 18, 19, 0, 42, 47, 33, 35, 12, 0), (26, 63, 43, 22, 22, 0, 44, 53, 37, 41, 15, 0), (30, 70, 48, 24, 25, 0, 48, 55, 41, 47, 15, 0), (33, 73, 51, 27, 27, 0, 50, 60, 45, 52, 15, 0), (35, 75, 55, 30, 29, 0, 54, 62, 51, 52, 19, 0), (38, 85, 62, 30, 31, 0, 56, 67, 54, 53, 23, 0), (41, 94, 68, 32, 31, 0, 60, 77, 57, 57, 24, 0), (46, 100, 74, 35, 32, 0, 65, 83, 60, 61, 24, 0), (47, 107, 82, 36, 35, 0, 68, 89, 64, 65, 25, 0), (54, 112, 88, 36, 36, 0, 71, 97, 68, 71, 27, 0), (54, 116, 93, 39, 40, 0, 75, 103, 74, 74, 30, 0), (60, 124, 99, 42, 41, 0, 80, 105, 80, 75, 31, 0), (63, 132, 107, 44, 43, 0, 85, 112, 89, 77, 31, 0), (66, 137, 113, 48, 45, 0, 93, 120, 93, 80, 33, 0), (72, 146, 117, 49, 48, 0, 100, 128, 97, 82, 36, 0), (75, 150, 121, 52, 51, 0, 103, 139, 100, 85, 37, 0), (78, 158, 124, 54, 51, 0, 107, 143, 104, 89, 39, 0), (80, 163, 131, 59, 53, 0, 110, 152, 110, 93, 41, 0), (83, 170, 134, 61, 54, 0, 114, 163, 113, 98, 43, 0), (83, 182, 139, 61, 56, 0, 117, 169, 117, 100, 45, 0), (87, 189, 144, 62, 58, 0, 121, 174, 123, 101, 50, 0), (91, 192, 150, 63, 59, 0, 125, 178, 127, 106, 53, 0), (92, 200, 156, 66, 62, 0, 129, 183, 132, 112, 54, 0), (97, 204, 160, 66, 63, 0, 132, 188, 137, 120, 56, 0), (101, 210, 165, 70, 63, 0, 137, 192, 140, 121, 57, 0), (105, 220, 169, 72, 63, 0, 140, 194, 147, 127, 58, 0), (108, 224, 173, 74, 67, 0, 146, 199, 150, 130, 59, 0), (115, 227, 175, 76, 69, 0, 150, 206, 153, 135, 59, 0), (120, 235, 181, 79, 72, 0, 157, 213, 156, 139, 59, 0), (122, 243, 184, 80, 72, 0, 163, 217, 159, 142, 62, 0), (128, 251, 190, 82, 73, 0, 171, 224, 167, 142, 65, 0), (128, 257, 194, 87, 73, 0, 173, 231, 170, 144, 67, 0), (132, 259, 197, 90, 74, 0, 179, 239, 177, 147, 67, 0), (135, 264, 199, 93, 75, 0, 184, 242, 187, 151, 70, 0), (141, 269, 206, 95, 75, 0, 190, 246, 188, 155, 74, 0), (148, 277, 211, 100, 75, 0, 194, 259, 189, 158, 75, 0), (151, 287, 219, 102, 77, 0, 202, 263, 198, 162, 79, 0), (155, 293, 226, 106, 77, 0, 210, 270, 202, 162, 79, 0), (158, 300, 233, 108, 79, 0, 219, 276, 206, 164, 80, 0), (160, 314, 238, 110, 81, 0, 222, 284, 207, 165, 82, 0), (168, 318, 240, 111, 83, 0, 230, 290, 209, 168, 82, 0), (172, 323, 246, 112, 83, 0, 235, 297, 212, 170, 83, 0), (176, 327, 249, 113, 84, 0, 238, 302, 214, 176, 85, 0), (177, 336, 254, 119, 84, 0, 245, 307, 219, 177, 87, 0), (182, 339, 260, 121, 85, 0, 253, 310, 225, 180, 88, 0), (184, 347, 266, 130, 86, 0, 265, 311, 233, 183, 90, 0), (188, 355, 273, 133, 86, 0, 273, 319, 238, 187, 90, 0), (193, 362, 275, 134, 86, 0, 277, 327, 238, 192, 92, 0), (194, 371, 284, 137, 86, 0, 279, 336, 242, 199, 93, 0), (196, 379, 290, 140, 90, 0, 282, 347, 247, 203, 96, 0), (202, 384, 293, 142, 91, 0, 287, 353, 251, 204, 98, 0), (205, 390, 299, 145, 93, 0, 290, 356, 255, 208, 100, 0), (205, 390, 299, 145, 93, 0, 290, 356, 255, 208, 100, 0))
passenger_arriving_rate = ((2.649651558384548, 5.43716856060606, 4.79654161311054, 2.534510869565217, 1.428605769230769, 0.0, 4.75679347826087, 5.714423076923076, 3.801766304347826, 3.1976944087403596, 1.359292140151515, 0.0), (2.6745220100478, 5.497633278970258, 4.822449322514997, 2.5486257548309177, 1.439313301282051, 0.0, 4.7551721391908215, 5.757253205128204, 3.8229386322463768, 3.2149662150099974, 1.3744083197425645, 0.0), (2.699108477221734, 5.557201122334455, 4.8477420736932295, 2.562429951690821, 1.4497948717948717, 0.0, 4.753501207729468, 5.799179487179487, 3.8436449275362317, 3.23182804912882, 1.3893002805836137, 0.0), (2.72339008999122, 5.6158078125, 4.872401389781491, 2.575911684782608, 1.4600408653846155, 0.0, 4.7517809103260875, 5.840163461538462, 3.863867527173912, 3.2482675931876606, 1.403951953125, 0.0), (2.747345978441128, 5.673389071268238, 4.896408793916024, 2.589059178743961, 1.4700416666666667, 0.0, 4.750011473429951, 5.880166666666667, 3.883588768115942, 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6.279575709219858, 4.121042218137255, 3.359738194444444, 1.6837412286931819, 0.0), (3.332075447570333, 6.739581818181817, 5.024328260869565, 2.745070588235294, 1.5663893617021276, 0.0, 4.099535982008995, 6.2655574468085105, 4.117605882352941, 3.3495521739130427, 1.6848954545454542, 0.0), (3.336681785485933, 6.744087357954545, 5.008622056159419, 2.7427032271241827, 1.5627714982269503, 0.0, 4.05910976803265, 6.251085992907801, 4.114054840686275, 3.3390813707729463, 1.6860218394886362, 0.0), (3.341203804347826, 6.74848068181818, 4.9925068840579705, 2.740261111111111, 1.5590432624113475, 0.0, 4.017948296684991, 6.23617304964539, 4.110391666666667, 3.328337922705314, 1.687120170454545, 0.0), (3.345644860933504, 6.752760937500001, 4.976000951086956, 2.7377459558823527, 1.5552075797872338, 0.0, 3.9761137556221886, 6.220830319148935, 4.106618933823529, 3.317333967391304, 1.6881902343750002, 0.0), (3.3500083120204605, 6.756927272727271, 4.959122463768115, 2.7351594771241827, 1.5512673758865245, 0.0, 3.9336683325004165, 6.205069503546098, 4.102739215686275, 3.3060816425120767, 1.6892318181818178, 0.0), (0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0))
passenger_allighting_rate = ((0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), (0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1))
'\nparameters for reproducibiliy. More information: https://numpy.org/doc/stable/reference/random/parallel.html\n'
entropy = 258194110137029475889902652135037600173
child_seed_index = (1, 38)
|
"""
Project Euler Problem 12: Highly divisible triangular number
"""
# What is the value of the first triangle number to have over five hundred divisors?
# A simple solution is to not worry about generating primes, and just test odd numbers for divisors
# Since we have to test a lot of numbers, it probably is worth generating a list of primes
START = 1
number_divisors = 0
i = 0
while number_divisors < 500:
i += 1
powers = []
tri_number = (START+i)*(START+i+1)/2
power = 0
while tri_number % 2 == 0:
power += 1
tri_number /= 2
if power > 0:
powers.append(power)
factor = 3
while tri_number > 1:
power = 0
while tri_number % factor == 0:
power += 1
tri_number /= factor
factor += 2
if power > 0:
powers.append(power)
number_divisors = 1
for p in powers:
number_divisors *= p + 1
print((START+i)*(START+i+1)//2)
|
"""
Project Euler Problem 12: Highly divisible triangular number
"""
start = 1
number_divisors = 0
i = 0
while number_divisors < 500:
i += 1
powers = []
tri_number = (START + i) * (START + i + 1) / 2
power = 0
while tri_number % 2 == 0:
power += 1
tri_number /= 2
if power > 0:
powers.append(power)
factor = 3
while tri_number > 1:
power = 0
while tri_number % factor == 0:
power += 1
tri_number /= factor
factor += 2
if power > 0:
powers.append(power)
number_divisors = 1
for p in powers:
number_divisors *= p + 1
print((START + i) * (START + i + 1) // 2)
|
#
"""Make a function map that works the same way as the built-in map function:"""
def square(n):
return n * n
def map(square, list1):
return [ square(num) for num in list1 ]
|
"""Make a function map that works the same way as the built-in map function:"""
def square(n):
return n * n
def map(square, list1):
return [square(num) for num in list1]
|
class Solution:
def isIsomorphic(self, s, t):
"""
:type s: str
:type t: str
:rtype: bool
"""
def check(s, t):
word_map = {}
for i, v in enumerate(s):
tmp = word_map.get(t[i])
if tmp is None:
word_map[t[i]] = s[i]
tmp = s[i]
t = t[:i] + tmp + t[i + 1:]
return s == t
return check(s, t) and check(t, s)
s = 'title'
t = 'paper'
# t = 'title'
# s = 'paler'
print(Solution().isIsomorphic(s, t))
|
class Solution:
def is_isomorphic(self, s, t):
"""
:type s: str
:type t: str
:rtype: bool
"""
def check(s, t):
word_map = {}
for (i, v) in enumerate(s):
tmp = word_map.get(t[i])
if tmp is None:
word_map[t[i]] = s[i]
tmp = s[i]
t = t[:i] + tmp + t[i + 1:]
return s == t
return check(s, t) and check(t, s)
s = 'title'
t = 'paper'
print(solution().isIsomorphic(s, t))
|
# -*- coding: utf-8 -*-
# see LICENSE.rst
# ----------------------------------------------------------------------------
#
# TITLE : data
# PROJECT : astronat
#
# ----------------------------------------------------------------------------
"""Data Management.
Often data is packaged poorly and it can be difficult to understand how
the data should be read.
DON`T PANIC.
This module provides functions to read the contained data.
Routine Listings
----------------
read_constants
__all_constants__
"""
__author__ = "Nathaniel Starkman"
__all__ = [
"read_constants",
"__all_constants__",
]
###############################################################################
# IMPORTS
###############################################################################
# CODE
###############################################################################
def read_constants():
"""Read SI Constants."""
data = {
"G": {
"name": "Gravitational constant",
"value": 6.6743e-11,
"uncertainty": 1.5e-15,
"unit": "m3 / (kg s2)",
"reference": "CODATA 2018",
"source": "CODATA2018",
},
"N_A": {
"name": "Avogadro's number",
"value": 6.02214076e23,
"uncertainty": 0.0,
"unit": "1 / mol",
"reference": "CODATA 2018",
"source": "CODATA2018",
},
"R": {
"name": "Gas constant",
"value": 8.31446261815324,
"uncertainty": 0.0,
"unit": "J / (K mol)",
"reference": "CODATA 2018",
"source": "CODATA2018",
},
"Ryd": {
"name": "Rydberg constant",
"value": 10973731.56816,
"uncertainty": 2.1e-05,
"unit": "1 / m",
"reference": "CODATA 2018",
"source": "CODATA2018",
},
"a0": {
"name": "Bohr radius",
"value": 5.29177210903e-11,
"uncertainty": 8e-21,
"unit": "m",
"reference": "CODATA 2018",
"source": "CODATA2018",
},
"alpha": {
"name": "Fine-structure constant",
"value": 0.0072973525693,
"uncertainty": 1.1e-12,
"unit": "",
"reference": "CODATA 2018",
"source": "CODATA2018",
},
"atm": {
"name": "Standard atmosphere",
"value": 101325,
"uncertainty": 0.0,
"unit": "Pa",
"reference": "CODATA 2018",
"source": "CODATA2018",
},
"b_wien": {
"name": "Wien wavelength displacement law constant",
"value": 0.0028977719551851727,
"uncertainty": 0.0,
"unit": "K m",
"reference": "CODATA 2018",
"source": "CODATA2018",
},
"c": {
"name": "Speed of light in vacuum",
"value": 299792458.0,
"uncertainty": 0.0,
"unit": "m / s",
"reference": "CODATA 2018",
"source": "CODATA2018",
},
"e": {
"name": "Electron charge",
"value": 1.602176634e-19,
"uncertainty": 0.0,
"unit": "C",
"source": "EMCODATA2018",
},
"eps0": {
"name": "Vacuum electric permittivity",
"value": 8.8541878128e-12,
"uncertainty": 1.3e-21,
"unit": "F / m",
"reference": "CODATA 2018",
"source": "EMCODATA2018",
},
"g0": {
"name": "Standard acceleration of gravity",
"value": 9.80665,
"uncertainty": 0.0,
"unit": "m / s2",
"source": "CODATA2018",
},
"h": {
"name": "Planck constant",
"value": 6.62607015e-34,
"uncertainty": 0.0,
"unit": "J s",
"reference": "CODATA 2018",
"source": "CODATA2018",
},
"hbar": {
"name": "Reduced Planck constant",
"value": 1.0545718176461565e-34,
"uncertainty": 0.0,
"unit": "J s",
"reference": "CODATA 2018",
"source": "CODATA2018",
},
"k_B": {
"name": "Boltzmann constant",
"value": 1.380649e-23,
"uncertainty": 0.0,
"unit": "J / K",
"reference": "CODATA 2018",
"source": "CODATA2018",
},
"m_e": {
"name": "Electron mass",
"value": 9.1093837015e-31,
"uncertainty": 2.8e-40,
"unit": "kg",
"reference": "CODATA 2018",
"source": "CODATA2018",
},
"m_n": {
"name": "Neutron mass",
"value": 1.67492749804e-27,
"uncertainty": 9.5e-37,
"unit": "kg",
"reference": "CODATA 2018",
"source": "CODATA2018",
},
"m_p": {
"name": "Proton mass",
"value": 1.67262192369e-27,
"uncertainty": 5.1e-37,
"unit": "kg",
"reference": "CODATA 2018",
"source": "CODATA2018",
},
"mu0": {
"name": "Vacuum magnetic permeability",
"value": 1.25663706212e-06,
"uncertainty": 1.9e-16,
"unit": "N / A2",
"reference": "CODATA 2018",
"source": "CODATA2018",
},
"muB": {
"name": "Bohr magneton",
"value": 9.2740100783e-24,
"uncertainty": 2.8e-33,
"unit": "J / T",
"reference": "CODATA 2018",
"source": "CODATA2018",
},
"sigma_T": {
"name": "Thomson scattering cross-section",
"value": 6.6524587321e-29,
"uncertainty": 6e-38,
"unit": "m2",
"reference": "CODATA 2018",
"source": "CODATA2018",
},
"sigma_sb": {
"name": "Stefan-Boltzmann constant",
"value": 5.6703744191844314e-08,
"uncertainty": 0.0,
"unit": "W / (K4 m2)",
"reference": "CODATA 2018",
"source": "CODATA2018",
},
"u": {
"name": "Atomic mass",
"value": 1.6605390666e-27,
"uncertainty": 5e-37,
"unit": "kg",
"reference": "CODATA 2018",
"source": "CODATA2018",
},
"GM_earth": {
"name": "Nominal Earth mass parameter",
"value": 398600400000000.0,
"uncertainty": 0.0,
"unit": "m3 / s2",
"source": "IAU2015",
},
"GM_jup": {
"name": "Nominal Jupiter mass parameter",
"value": 1.2668653e17,
"uncertainty": 0.0,
"unit": "m3 / s2",
"source": "IAU2015",
},
"GM_sun": {
"name": "Nominal solar mass parameter",
"value": 1.3271244e20,
"uncertainty": 0.0,
"unit": "m3 / s2",
"source": "IAU2015",
},
"L_bol0": {
"name": "Luminosity for absolute bolometric magnitude 0",
"value": 3.0128e28,
"uncertainty": 0.0,
"unit": "W",
"source": "IAU2015",
},
"L_sun": {
"name": "Nominal solar luminosity",
"value": 3.828e26,
"uncertainty": 0.0,
"unit": "W",
"reference": "IAU 2015 Resolution B 3",
"source": "IAU2015",
},
"M_earth": {
"name": "Earth mass",
"value": 5.972167867791379e24,
"uncertainty": 1.3422009501651213e20,
"unit": "kg",
"reference": "IAU 2015 Resolution B 3 + CODATA 2018",
"source": "IAU2015",
},
"M_jup": {
"name": "Jupiter mass",
"value": 1.8981245973360505e27,
"uncertainty": 4.26589589320839e22,
"unit": "kg",
"reference": "IAU 2015 Resolution B 3 + CODATA 2018",
"source": "IAU2015",
},
"M_sun": {
"name": "Solar mass",
"value": 1.988409870698051e30,
"uncertainty": 4.468805426856864e25,
"unit": "kg",
"reference": "IAU 2015 Resolution B 3 + CODATA 2018",
"source": "IAU2015",
},
"R_earth": {
"name": "Nominal Earth equatorial radius",
"value": 6378100.0,
"uncertainty": 0.0,
"unit": "m",
"reference": "IAU 2015 Resolution B 3",
"source": "IAU2015",
},
"R_jup": {
"name": "Nominal Jupiter equatorial radius",
"value": 71492000.0,
"uncertainty": 0.0,
"unit": "m",
"reference": "IAU 2015 Resolution B 3",
"source": "IAU2015",
},
"R_sun": {
"name": "Nominal solar radius",
"value": 695700000.0,
"uncertainty": 0.0,
"unit": "m",
"reference": "IAU 2015 Resolution B 3",
"source": "IAU2015",
},
"au": {
"name": "Astronomical Unit",
"value": 149597870700.0,
"uncertainty": 0.0,
"unit": "m",
"reference": "IAU 2012 Resolution B2",
"source": "IAU2015",
},
"kpc": {
"name": "Kiloparsec",
"value": 3.0856775814671917e19,
"uncertainty": 0.0,
"unit": "m",
"reference": "Derived from au",
"source": "IAU2015",
},
"pc": {
"name": "Parsec",
"value": 3.0856775814671916e16,
"uncertainty": 0.0,
"unit": "m",
"reference": "Derived from au",
"source": "IAU2015",
},
}
return data
# /def
# ------------------------------------------------------------------------
__all_constants__ = frozenset(read_constants().keys())
###############################################################################
# END
|
"""Data Management.
Often data is packaged poorly and it can be difficult to understand how
the data should be read.
DON`T PANIC.
This module provides functions to read the contained data.
Routine Listings
----------------
read_constants
__all_constants__
"""
__author__ = 'Nathaniel Starkman'
__all__ = ['read_constants', '__all_constants__']
def read_constants():
"""Read SI Constants."""
data = {'G': {'name': 'Gravitational constant', 'value': 6.6743e-11, 'uncertainty': 1.5e-15, 'unit': 'm3 / (kg s2)', 'reference': 'CODATA 2018', 'source': 'CODATA2018'}, 'N_A': {'name': "Avogadro's number", 'value': 6.02214076e+23, 'uncertainty': 0.0, 'unit': '1 / mol', 'reference': 'CODATA 2018', 'source': 'CODATA2018'}, 'R': {'name': 'Gas constant', 'value': 8.31446261815324, 'uncertainty': 0.0, 'unit': 'J / (K mol)', 'reference': 'CODATA 2018', 'source': 'CODATA2018'}, 'Ryd': {'name': 'Rydberg constant', 'value': 10973731.56816, 'uncertainty': 2.1e-05, 'unit': '1 / m', 'reference': 'CODATA 2018', 'source': 'CODATA2018'}, 'a0': {'name': 'Bohr radius', 'value': 5.29177210903e-11, 'uncertainty': 8e-21, 'unit': 'm', 'reference': 'CODATA 2018', 'source': 'CODATA2018'}, 'alpha': {'name': 'Fine-structure constant', 'value': 0.0072973525693, 'uncertainty': 1.1e-12, 'unit': '', 'reference': 'CODATA 2018', 'source': 'CODATA2018'}, 'atm': {'name': 'Standard atmosphere', 'value': 101325, 'uncertainty': 0.0, 'unit': 'Pa', 'reference': 'CODATA 2018', 'source': 'CODATA2018'}, 'b_wien': {'name': 'Wien wavelength displacement law constant', 'value': 0.0028977719551851727, 'uncertainty': 0.0, 'unit': 'K m', 'reference': 'CODATA 2018', 'source': 'CODATA2018'}, 'c': {'name': 'Speed of light in vacuum', 'value': 299792458.0, 'uncertainty': 0.0, 'unit': 'm / s', 'reference': 'CODATA 2018', 'source': 'CODATA2018'}, 'e': {'name': 'Electron charge', 'value': 1.602176634e-19, 'uncertainty': 0.0, 'unit': 'C', 'source': 'EMCODATA2018'}, 'eps0': {'name': 'Vacuum electric permittivity', 'value': 8.8541878128e-12, 'uncertainty': 1.3e-21, 'unit': 'F / m', 'reference': 'CODATA 2018', 'source': 'EMCODATA2018'}, 'g0': {'name': 'Standard acceleration of gravity', 'value': 9.80665, 'uncertainty': 0.0, 'unit': 'm / s2', 'source': 'CODATA2018'}, 'h': {'name': 'Planck constant', 'value': 6.62607015e-34, 'uncertainty': 0.0, 'unit': 'J s', 'reference': 'CODATA 2018', 'source': 'CODATA2018'}, 'hbar': {'name': 'Reduced Planck constant', 'value': 1.0545718176461565e-34, 'uncertainty': 0.0, 'unit': 'J s', 'reference': 'CODATA 2018', 'source': 'CODATA2018'}, 'k_B': {'name': 'Boltzmann constant', 'value': 1.380649e-23, 'uncertainty': 0.0, 'unit': 'J / K', 'reference': 'CODATA 2018', 'source': 'CODATA2018'}, 'm_e': {'name': 'Electron mass', 'value': 9.1093837015e-31, 'uncertainty': 2.8e-40, 'unit': 'kg', 'reference': 'CODATA 2018', 'source': 'CODATA2018'}, 'm_n': {'name': 'Neutron mass', 'value': 1.67492749804e-27, 'uncertainty': 9.5e-37, 'unit': 'kg', 'reference': 'CODATA 2018', 'source': 'CODATA2018'}, 'm_p': {'name': 'Proton mass', 'value': 1.67262192369e-27, 'uncertainty': 5.1e-37, 'unit': 'kg', 'reference': 'CODATA 2018', 'source': 'CODATA2018'}, 'mu0': {'name': 'Vacuum magnetic permeability', 'value': 1.25663706212e-06, 'uncertainty': 1.9e-16, 'unit': 'N / A2', 'reference': 'CODATA 2018', 'source': 'CODATA2018'}, 'muB': {'name': 'Bohr magneton', 'value': 9.2740100783e-24, 'uncertainty': 2.8e-33, 'unit': 'J / T', 'reference': 'CODATA 2018', 'source': 'CODATA2018'}, 'sigma_T': {'name': 'Thomson scattering cross-section', 'value': 6.6524587321e-29, 'uncertainty': 6e-38, 'unit': 'm2', 'reference': 'CODATA 2018', 'source': 'CODATA2018'}, 'sigma_sb': {'name': 'Stefan-Boltzmann constant', 'value': 5.6703744191844314e-08, 'uncertainty': 0.0, 'unit': 'W / (K4 m2)', 'reference': 'CODATA 2018', 'source': 'CODATA2018'}, 'u': {'name': 'Atomic mass', 'value': 1.6605390666e-27, 'uncertainty': 5e-37, 'unit': 'kg', 'reference': 'CODATA 2018', 'source': 'CODATA2018'}, 'GM_earth': {'name': 'Nominal Earth mass parameter', 'value': 398600400000000.0, 'uncertainty': 0.0, 'unit': 'm3 / s2', 'source': 'IAU2015'}, 'GM_jup': {'name': 'Nominal Jupiter mass parameter', 'value': 1.2668653e+17, 'uncertainty': 0.0, 'unit': 'm3 / s2', 'source': 'IAU2015'}, 'GM_sun': {'name': 'Nominal solar mass parameter', 'value': 1.3271244e+20, 'uncertainty': 0.0, 'unit': 'm3 / s2', 'source': 'IAU2015'}, 'L_bol0': {'name': 'Luminosity for absolute bolometric magnitude 0', 'value': 3.0128e+28, 'uncertainty': 0.0, 'unit': 'W', 'source': 'IAU2015'}, 'L_sun': {'name': 'Nominal solar luminosity', 'value': 3.828e+26, 'uncertainty': 0.0, 'unit': 'W', 'reference': 'IAU 2015 Resolution B 3', 'source': 'IAU2015'}, 'M_earth': {'name': 'Earth mass', 'value': 5.972167867791379e+24, 'uncertainty': 1.3422009501651213e+20, 'unit': 'kg', 'reference': 'IAU 2015 Resolution B 3 + CODATA 2018', 'source': 'IAU2015'}, 'M_jup': {'name': 'Jupiter mass', 'value': 1.8981245973360505e+27, 'uncertainty': 4.26589589320839e+22, 'unit': 'kg', 'reference': 'IAU 2015 Resolution B 3 + CODATA 2018', 'source': 'IAU2015'}, 'M_sun': {'name': 'Solar mass', 'value': 1.988409870698051e+30, 'uncertainty': 4.468805426856864e+25, 'unit': 'kg', 'reference': 'IAU 2015 Resolution B 3 + CODATA 2018', 'source': 'IAU2015'}, 'R_earth': {'name': 'Nominal Earth equatorial radius', 'value': 6378100.0, 'uncertainty': 0.0, 'unit': 'm', 'reference': 'IAU 2015 Resolution B 3', 'source': 'IAU2015'}, 'R_jup': {'name': 'Nominal Jupiter equatorial radius', 'value': 71492000.0, 'uncertainty': 0.0, 'unit': 'm', 'reference': 'IAU 2015 Resolution B 3', 'source': 'IAU2015'}, 'R_sun': {'name': 'Nominal solar radius', 'value': 695700000.0, 'uncertainty': 0.0, 'unit': 'm', 'reference': 'IAU 2015 Resolution B 3', 'source': 'IAU2015'}, 'au': {'name': 'Astronomical Unit', 'value': 149597870700.0, 'uncertainty': 0.0, 'unit': 'm', 'reference': 'IAU 2012 Resolution B2', 'source': 'IAU2015'}, 'kpc': {'name': 'Kiloparsec', 'value': 3.0856775814671917e+19, 'uncertainty': 0.0, 'unit': 'm', 'reference': 'Derived from au', 'source': 'IAU2015'}, 'pc': {'name': 'Parsec', 'value': 3.0856775814671916e+16, 'uncertainty': 0.0, 'unit': 'm', 'reference': 'Derived from au', 'source': 'IAU2015'}}
return data
__all_constants__ = frozenset(read_constants().keys())
|
#Esta es la respuesta del control
def sumarLista(lista, largo):
if (largo == 0):
return 0
else:
return lista[largo - 1] + sumarLista(lista, largo - 1)
def Desglosar(numero:int,lista=[]):
def rev(l):
if len(l) == 0: return []
return [l[-1]] + rev(l[:-1])
if numero==0:
reversa=rev(lista)
return reversa
else:
lista.append(numero)
return Desglosar(numero-1,lista)
def combinar(lista, contador = 0, listaaux = []):
if contador == len(lista):
return [listaaux]
x = combinar(lista, contador + 1, listaaux)
y = combinar(lista, contador + 1, listaaux + [lista[contador]])
return x + y
n=int(input("Ingrese N:"))
k=int(input("Ingrese K:"))
if n<1 or k<1:
print("Debe ser un valor entero positivo")
else:
desglosar=Desglosar(n)
combinar=combinar(desglosar)
contador=0
while contador < len(combinar):
if sumarLista(combinar[contador], len(combinar[contador])) == k:
print(*combinar[contador])
contador = contador + 1
|
def sumar_lista(lista, largo):
if largo == 0:
return 0
else:
return lista[largo - 1] + sumar_lista(lista, largo - 1)
def desglosar(numero: int, lista=[]):
def rev(l):
if len(l) == 0:
return []
return [l[-1]] + rev(l[:-1])
if numero == 0:
reversa = rev(lista)
return reversa
else:
lista.append(numero)
return desglosar(numero - 1, lista)
def combinar(lista, contador=0, listaaux=[]):
if contador == len(lista):
return [listaaux]
x = combinar(lista, contador + 1, listaaux)
y = combinar(lista, contador + 1, listaaux + [lista[contador]])
return x + y
n = int(input('Ingrese N:'))
k = int(input('Ingrese K:'))
if n < 1 or k < 1:
print('Debe ser un valor entero positivo')
else:
desglosar = desglosar(n)
combinar = combinar(desglosar)
contador = 0
while contador < len(combinar):
if sumar_lista(combinar[contador], len(combinar[contador])) == k:
print(*combinar[contador])
contador = contador + 1
|
# Utility functions for incrementing counts in dictionaries or appending to a list of values
def add_to_dict_num(D, k, v=1):
if k in D:
D[k] += v
else:
D[k] = v
def add_to_dict_list(D, k, v):
if k in D:
D[k].append(v)
else:
D[k] = [v]
def report(min_verbosity, *args):
if report.verbosity >= min_verbosity:
print(f'[{report.context}]', *args)
|
def add_to_dict_num(D, k, v=1):
if k in D:
D[k] += v
else:
D[k] = v
def add_to_dict_list(D, k, v):
if k in D:
D[k].append(v)
else:
D[k] = [v]
def report(min_verbosity, *args):
if report.verbosity >= min_verbosity:
print(f'[{report.context}]', *args)
|
'''
Created on Mar 19, 2022
@author: mballance
'''
class ActivityBlockMetaT(type):
def __init__(self, name, bases, dct):
pass
def __enter__(self):
print("ActivityBlockMetaT.__enter__")
def __exit__(self, t, v, tb):
pass
|
"""
Created on Mar 19, 2022
@author: mballance
"""
class Activityblockmetat(type):
def __init__(self, name, bases, dct):
pass
def __enter__(self):
print('ActivityBlockMetaT.__enter__')
def __exit__(self, t, v, tb):
pass
|
# -*- coding:utf-8 -*-
class Solution:
def LeftRotateString(self, s, n):
# write code here
if len(s)==0:
return s
s = list(s)
def flip(s,start,end):
for i in range(start,(start+end)//2 + 1):
s[i],s[end-i+start] = s[end - i+start],s[i]
return s
n %= len(s)
s = flip(s,0,n-1)
s = flip(s,n,len(s)-1)
s = flip(s,0,len(s)-1)
return "".join(s)
|
class Solution:
def left_rotate_string(self, s, n):
if len(s) == 0:
return s
s = list(s)
def flip(s, start, end):
for i in range(start, (start + end) // 2 + 1):
(s[i], s[end - i + start]) = (s[end - i + start], s[i])
return s
n %= len(s)
s = flip(s, 0, n - 1)
s = flip(s, n, len(s) - 1)
s = flip(s, 0, len(s) - 1)
return ''.join(s)
|
'''
question link- https://leetcode.com/problems/sum-of-all-subset-xor-totals/
Sum of All Subset XOR Totals
Question statement:
The XOR total of an array is defined as the bitwise XOR of all its elements, or 0 if the array is empty.
For example, the XOR total of the array [2,5,6] is 2 XOR 5 XOR 6 = 1.
'''
def subsetXORSum(self, nums):
l = len(nums)
res = 0
stack = [(0, 0)]
while stack:
pos, xor = stack.pop()
res+=xor
for i in range(pos, l):
stack.append((i+1, xor^nums[i]))
return res
|
"""
question link- https://leetcode.com/problems/sum-of-all-subset-xor-totals/
Sum of All Subset XOR Totals
Question statement:
The XOR total of an array is defined as the bitwise XOR of all its elements, or 0 if the array is empty.
For example, the XOR total of the array [2,5,6] is 2 XOR 5 XOR 6 = 1.
"""
def subset_xor_sum(self, nums):
l = len(nums)
res = 0
stack = [(0, 0)]
while stack:
(pos, xor) = stack.pop()
res += xor
for i in range(pos, l):
stack.append((i + 1, xor ^ nums[i]))
return res
|
class Solution:
def uniquePaths(self, m: int, n: int) -> int:
ans = [1]*n
for i in range(1,m):
for j in range(1,n):
ans[j] = ans[j-1] + ans[j]
return ans[-1] if m and n else 0
|
class Solution:
def unique_paths(self, m: int, n: int) -> int:
ans = [1] * n
for i in range(1, m):
for j in range(1, n):
ans[j] = ans[j - 1] + ans[j]
return ans[-1] if m and n else 0
|
'''
Write a Python program to compute the sum of all items of a
given array of integers where each integer is multiplied by its
index. Return 0 if there is no number.
Sample Input:
[1,2,3,4]
[-1,-2,-3,-4]
[]
Sample Output:
20
-20
0
'''
def sum_index_multiplier(nums):
# use enumerate to return count and value
#single line of return statement with for
return sum(j*i for i, j in enumerate(nums))
print(sum_index_multiplier([1,2,3,4]))
print(sum_index_multiplier([-1,-2,-3,-4]))
print(sum_index_multiplier([]))
|
"""
Write a Python program to compute the sum of all items of a
given array of integers where each integer is multiplied by its
index. Return 0 if there is no number.
Sample Input:
[1,2,3,4]
[-1,-2,-3,-4]
[]
Sample Output:
20
-20
0
"""
def sum_index_multiplier(nums):
return sum((j * i for (i, j) in enumerate(nums)))
print(sum_index_multiplier([1, 2, 3, 4]))
print(sum_index_multiplier([-1, -2, -3, -4]))
print(sum_index_multiplier([]))
|
class Node:
def __init__(self, data=None):
self.data = data
self.left = None
self.right = None
class BinaryTree:
def __init__(self, root):
self.root = Node(root)
def display(self, start, traversal=""):
if start != None:
traversal += (str(start.data) + " ")
traversal = self.display(start.left, traversal)
traversal = self.display(start.right, traversal)
return traversal
def is_bst(self):
start = self.root
traversal = ""
if start.data != None:
if start.left.data > start.data:
return False
if start.right.data < start.data:
return False
self.display(start.left, traversal)
self.display(start.right, traversal)
return True
def highest_value(self, start, traversal):
if start != None:
if start.data > traversal:
traversal = start.data
traversal = self.highest_value(start.left, traversal)
traversal = self.highest_value(start.right, traversal)
return traversal
def lowest_value(self, start, traversal):
if start != None:
if start.data < traversal:
traversal = start.data
traversal = self.lowest_value(start.left, traversal)
traversal = self.lowest_value(start.right, traversal)
return traversal
|
class Node:
def __init__(self, data=None):
self.data = data
self.left = None
self.right = None
class Binarytree:
def __init__(self, root):
self.root = node(root)
def display(self, start, traversal=''):
if start != None:
traversal += str(start.data) + ' '
traversal = self.display(start.left, traversal)
traversal = self.display(start.right, traversal)
return traversal
def is_bst(self):
start = self.root
traversal = ''
if start.data != None:
if start.left.data > start.data:
return False
if start.right.data < start.data:
return False
self.display(start.left, traversal)
self.display(start.right, traversal)
return True
def highest_value(self, start, traversal):
if start != None:
if start.data > traversal:
traversal = start.data
traversal = self.highest_value(start.left, traversal)
traversal = self.highest_value(start.right, traversal)
return traversal
def lowest_value(self, start, traversal):
if start != None:
if start.data < traversal:
traversal = start.data
traversal = self.lowest_value(start.left, traversal)
traversal = self.lowest_value(start.right, traversal)
return traversal
|
class Solution:
def expandFromMiddle(self, s:str, l:int, r:int):
while (l >= 0 and r < len(s) and s[l] == s[r]):
l -= 1
r += 1
return (r - l - 1)
def longestPalindrome(self, s: str) -> str:
if len(s) < 1: return 0
start = 0
end = 0
for i in range(len(s)):
l1 = self.expandFromMiddle(s, i, i)
l2 = self.expandFromMiddle(s, i, i+1)
ls = max(l1,l2)
if ls > end - start:
start = i - ((ls - 1)//2)
end = i + (ls//2)
return s[start: end+1]
if __name__ == "__main__":
sol = Solution()
s = "babab"
s = "cbbd"
# s = 'bb'
# s = "babc"
# s = "aca"
# s = "defggbac"
# s = 'a'
s = "babad"
# s = "ccc"
s = "abb"
# s = "reifadyqgztixemwswtccodfnchcovrmiooffbbijkecuvlvukecutasfxqcqygltrogrdxlrslbnzktlanycgtniprjlospzhhgdrqcwlukbpsrumxguskubokxcmswjnssbkutdhppsdckuckcbwbxpmcmdicfjxaanoxndlfpqwneytatcbyjmimyawevmgirunvmdvxwdjbiqszwhfhjmrpexfwrbzkipxfowcbqjckaotmmgkrbjvhihgwuszdrdiijkgjoljjdubcbowvxslctleblfmdzmvdkqdxtiylabrwaccikkpnpsgcotxoggdydqnuogmxttcycjorzrtwtcchxrbbknfmxnonbhgbjjypqhbftceduxgrnaswtbytrhuiqnxkivevhprcvhggugrmmxolvfzwadlnzdwbtqbaveoongezoymdrhywxcxvggsewsxckucmncbrljskgsgtehortuvbtrsfisyewchxlmxqccoplhlzwutoqoctgfnrzhqctxaqacmirrqdwsbdpqttmyrmxxawgtjzqjgffqwlxqxwxrkgtzqkgdulbxmfcvxcwoswystiyittdjaqvaijwscqobqlhskhvoktksvmguzfankdigqlegrxxqpoitdtykfltohnzrcgmlnhddcfmawiriiiblwrttveedkxzzagdzpwvriuctvtrvdpqzcdnrkgcnpwjlraaaaskgguxzljktqvzzmruqqslutiipladbcxdwxhmvevsjrdkhdpxcyjkidkoznuagshnvccnkyeflpyjzlcbmhbytxnfzcrnmkyknbmtzwtaceajmnuyjblmdlbjdjxctvqcoqkbaszvrqvjgzdqpvmucerumskjrwhywjkwgligkectzboqbanrsvynxscpxqxtqhthdytfvhzjdcxgckvgfbldsfzxqdozxicrwqyprgnadfxsionkzzegmeynye"
print(sol.longestPalindrome(s))
|
class Solution:
def expand_from_middle(self, s: str, l: int, r: int):
while l >= 0 and r < len(s) and (s[l] == s[r]):
l -= 1
r += 1
return r - l - 1
def longest_palindrome(self, s: str) -> str:
if len(s) < 1:
return 0
start = 0
end = 0
for i in range(len(s)):
l1 = self.expandFromMiddle(s, i, i)
l2 = self.expandFromMiddle(s, i, i + 1)
ls = max(l1, l2)
if ls > end - start:
start = i - (ls - 1) // 2
end = i + ls // 2
return s[start:end + 1]
if __name__ == '__main__':
sol = solution()
s = 'babab'
s = 'cbbd'
s = 'babad'
s = 'abb'
print(sol.longestPalindrome(s))
|
class Paddle():
def __init__(self, x1, x2 , y1, y2):
self.x1 = x1
self.y1 = y1
self.x2 = x2
self.y2 = y2
#private method
def __setPaddleRight(self):
self.x1 = 4
self.x2 = 4
self.y1 = 0
self.y2 = 1
display.set_pixel(self.x1, self.y1, 9)
display.set_pixel(self.x2, self.y2, 9)
#private method
def __setPaddleLeft(self):
self.x1 = 0
self.x2 = 0
self.y1 = 4
self.y2 = 3
display.set_pixel(self.x1, self.y1, 9)
display.set_pixel(self.x2, self.y2, 9)
def startGame(self):
self.__setPaddleLeft()
self.__setPaddleRight()
def moveUp(self):
self.y1 -= 1
self.y2 -= 1
if self.y1 or self.y2 == 0:
return self.y1, self.y2
def moveDown(self):
self.y += 1
self.y += 1
if self.y1 or self.y2 == 0:
return self.y1, self.y2
def getCurrentPosition(self):
return self.x1, self.y1, self.x2, self.y2
def update(self):
display.set_pixel(self.x1, self.y1, 9)
display.set_pixel(self.x2, self.y2, 9)
|
class Paddle:
def __init__(self, x1, x2, y1, y2):
self.x1 = x1
self.y1 = y1
self.x2 = x2
self.y2 = y2
def __set_paddle_right(self):
self.x1 = 4
self.x2 = 4
self.y1 = 0
self.y2 = 1
display.set_pixel(self.x1, self.y1, 9)
display.set_pixel(self.x2, self.y2, 9)
def __set_paddle_left(self):
self.x1 = 0
self.x2 = 0
self.y1 = 4
self.y2 = 3
display.set_pixel(self.x1, self.y1, 9)
display.set_pixel(self.x2, self.y2, 9)
def start_game(self):
self.__setPaddleLeft()
self.__setPaddleRight()
def move_up(self):
self.y1 -= 1
self.y2 -= 1
if self.y1 or self.y2 == 0:
return (self.y1, self.y2)
def move_down(self):
self.y += 1
self.y += 1
if self.y1 or self.y2 == 0:
return (self.y1, self.y2)
def get_current_position(self):
return (self.x1, self.y1, self.x2, self.y2)
def update(self):
display.set_pixel(self.x1, self.y1, 9)
display.set_pixel(self.x2, self.y2, 9)
|
class Solution(object):
def maxProfit(self, prices):
"""
:type prices: List[int]
:rtype: int
"""
minBuy = 999999 # probably should use sys.maxint
maxProfits = 0
for i in xrange(len(prices)):
minBuy = min(minBuy, prices[i])
maxProfits = max(maxProfits, prices[i] - minBuy)
return maxProfits
|
class Solution(object):
def max_profit(self, prices):
"""
:type prices: List[int]
:rtype: int
"""
min_buy = 999999
max_profits = 0
for i in xrange(len(prices)):
min_buy = min(minBuy, prices[i])
max_profits = max(maxProfits, prices[i] - minBuy)
return maxProfits
|
class Enum(set):
def __getattr__(self, name):
if name in self:
return name
raise AttributeError
def log(message, log_class, log_level):
"""Log information to the console.
If you are angry about having to use Enums instead of typing the classes and levels in, look up how any logging facility works.
Arguments:
message -- the relevant information to be logged
log_class -- the category of information to be logged. Must be within util.log.LogClass
log_level -- the severity of the information being logged. Must be within util.log.LogLevel
"""
assert log_class in LogClass
assert log_level in LogLevel
print("%s/%s: %s" % (log_level, log_class, message))
LogClass = Enum(["CV", "GRAPHICS", "GENERAL"])
LogLevel = Enum(["VERBOSE", "INFO", "WARNING", "ERROR"])
|
class Enum(set):
def __getattr__(self, name):
if name in self:
return name
raise AttributeError
def log(message, log_class, log_level):
"""Log information to the console.
If you are angry about having to use Enums instead of typing the classes and levels in, look up how any logging facility works.
Arguments:
message -- the relevant information to be logged
log_class -- the category of information to be logged. Must be within util.log.LogClass
log_level -- the severity of the information being logged. Must be within util.log.LogLevel
"""
assert log_class in LogClass
assert log_level in LogLevel
print('%s/%s: %s' % (log_level, log_class, message))
log_class = enum(['CV', 'GRAPHICS', 'GENERAL'])
log_level = enum(['VERBOSE', 'INFO', 'WARNING', 'ERROR'])
|
class Solution:
def findContestMatch(self, n):
"""
:type n: int
:rtype: str
"""
ans = list(range(1, n + 1))
while len(ans) > 1:
ans = ["({},{})".format(ans[i], ans[~i]) for i in range(len(ans) // 2)]
return ans[0]
|
class Solution:
def find_contest_match(self, n):
"""
:type n: int
:rtype: str
"""
ans = list(range(1, n + 1))
while len(ans) > 1:
ans = ['({},{})'.format(ans[i], ans[~i]) for i in range(len(ans) // 2)]
return ans[0]
|
N, C, S, *l = map(int, open(0).read().split())
c = 0
S -= 1
ans = 0
for i in l:
ans += c == S
c = (c+i+N)%N
ans += c == S
print(ans)
|
(n, c, s, *l) = map(int, open(0).read().split())
c = 0
s -= 1
ans = 0
for i in l:
ans += c == S
c = (c + i + N) % N
ans += c == S
print(ans)
|
"""Question: https://leetcode.com/problems/palindrome-number/
"""
class Solution:
def isPalindrome(self, x: int) -> bool:
if x < 0:
return False
copy, reverse = x, 0
while copy:
reverse *= 10
reverse += copy % 10
copy = copy // 10
return x == reverse
def isPalindrome_using_str(self, x: int) -> bool:
return str(x) == str(x)[::-1]
if __name__ == '__main__':
x = 121
output = Solution().isPalindrome(x)
print(f'x: {x}\toutput: {output}')
x = -121
output = Solution().isPalindrome(x)
print(f'x: {x}\toutput: {output}')
|
"""Question: https://leetcode.com/problems/palindrome-number/
"""
class Solution:
def is_palindrome(self, x: int) -> bool:
if x < 0:
return False
(copy, reverse) = (x, 0)
while copy:
reverse *= 10
reverse += copy % 10
copy = copy // 10
return x == reverse
def is_palindrome_using_str(self, x: int) -> bool:
return str(x) == str(x)[::-1]
if __name__ == '__main__':
x = 121
output = solution().isPalindrome(x)
print(f'x: {x}\toutput: {output}')
x = -121
output = solution().isPalindrome(x)
print(f'x: {x}\toutput: {output}')
|
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
@author: Sandip Dutta
"""
# To implement simple tree algorithms
# DFS on Binary Tree,
# Problem Stateent : sum of all leaf nodes
#number of nodes of tree
n = 10
'''we take starting node to be 0th node'''
''' tree is a directed graph here'''
tree = [[1, 2],#0
[3, 5],#1
[7, 8],#2
[4],#3
[],#4
[6],#5
[],#6
[],#7
[9],#8
[]]#9
#Records if visited or not
visited = [False] * n
def isLeafNode(neighbour):
'''checks if leaf node or not'''
#[] evaluates to False
# if tree[neighbour] is [], return True
return not tree[neighbour]
def sum_of_leaf_nodes():
'''sum of leaf nodes calculated'''
# Empty tree
if tree == None:
return 0
# Set 0 as visited
visited[0] = True
# total of all leaf nodes
total = 0
# Traverses the tree
for node in range(n):
# Get neighbours
for neighbour in tree[node]:
# If not visited neighbours, then go inside
if not visited[neighbour]:
# Mark as visited
visited[neighbour] = True
# if leaf node, add that value to total
if isLeafNode(neighbour):
total += neighbour
# Return the total
return total
# Print the sum of the root nodesz
print("sum is {}".format(sum_of_leaf_nodes()))
# =============================================================================
# Make sure to turn visited to all False values as we will be reusing that
# array again
# =============================================================================
# Recursive version of the above function
def sum_of_leaf_nodes_R(node):
# Empty tree
if tree == None:
return 0
total = 0
# Set 0 as visited
visited[node] = True
# If leaf node, return the value of the node
if isLeafNode(node):
return node
# checks for all neighbours
for neighbour in tree[node]:
# If unvisited neighbours, visit
if not visited[neighbour]:
# Add leaf node sum to total
total += sum_of_leaf_nodes_R(neighbour)
# Return the total
return total
# Print the sum of the root nodesz
print("sum is {}".format(sum_of_leaf_nodes_R(0)))
|
"""
@author: Sandip Dutta
"""
n = 10
'we take starting node to be 0th node'
' tree is a directed graph here'
tree = [[1, 2], [3, 5], [7, 8], [4], [], [6], [], [], [9], []]
visited = [False] * n
def is_leaf_node(neighbour):
"""checks if leaf node or not"""
return not tree[neighbour]
def sum_of_leaf_nodes():
"""sum of leaf nodes calculated"""
if tree == None:
return 0
visited[0] = True
total = 0
for node in range(n):
for neighbour in tree[node]:
if not visited[neighbour]:
visited[neighbour] = True
if is_leaf_node(neighbour):
total += neighbour
return total
print('sum is {}'.format(sum_of_leaf_nodes()))
def sum_of_leaf_nodes_r(node):
if tree == None:
return 0
total = 0
visited[node] = True
if is_leaf_node(node):
return node
for neighbour in tree[node]:
if not visited[neighbour]:
total += sum_of_leaf_nodes_r(neighbour)
return total
print('sum is {}'.format(sum_of_leaf_nodes_r(0)))
|
def mensagem(cor='', msg='', firula='', tamanho=0):
if '\n' in msg:
linha = msg.find('\n')
else:
linha = len(msg)
limpa = '\033[m'
if tamanho == 0:
tamanho = firula * (linha + 4)
if firula == '':
print(f'{cor} {msg} {limpa}')
else:
print(f'{cor}{tamanho}{limpa}')
print(f'{cor} {msg} \033[m')
print(f'{cor}{tamanho}{limpa}')
|
def mensagem(cor='', msg='', firula='', tamanho=0):
if '\n' in msg:
linha = msg.find('\n')
else:
linha = len(msg)
limpa = '\x1b[m'
if tamanho == 0:
tamanho = firula * (linha + 4)
if firula == '':
print(f'{cor} {msg} {limpa}')
else:
print(f'{cor}{tamanho}{limpa}')
print(f'{cor} {msg} \x1b[m')
print(f'{cor}{tamanho}{limpa}')
|
'''
Leetcode Problem number 687.
Given a binary tree, find the length of the longest path where each node in the path has the same value. This path may or may not pass through the root.
The length of path between two nodes is represented by the number of edges between them.
'''
class Solution:
def longestUnivaluePath(self, root: TreeNode) -> int:
self.max_path = 0;
if(root is None or (root.left is None and root.right is None )):
pass
else:
self.find_max_path(root)
return self.max_path
def set_max_value(self,new_val)-> None:
self.max_path = new_val
def find_max_path(self, root: TreeNode) -> int:
'''
finds longest path of 1 value and updates the overall maximum value.
return : current node's maximum matching value
'''
if(root is None or (root.left is None and root.right is None )):
return 0
cur_length=0
left_is_same = False
left_max_path = 0
right_max_path = 0
if(root.left):
left_max_path = self.find_max_path(root.left)
if(root.left.val == root.val ):
cur_length += 1 + left_max_path;
left_is_same = True
else:
cur_length = 0;
if(root.right):
right_max_path = self.find_max_path(root.right)
if(root.right.val == root.val and left_is_same):
self.set_max_value(max(self.max_path, cur_length + 1 + right_max_path))
if(root.right.val == root.val):
cur_length = max(1 + right_max_path, cur_length);
self.set_max_value(max(self.max_path,cur_length))
return cur_length
|
"""
Leetcode Problem number 687.
Given a binary tree, find the length of the longest path where each node in the path has the same value. This path may or may not pass through the root.
The length of path between two nodes is represented by the number of edges between them.
"""
class Solution:
def longest_univalue_path(self, root: TreeNode) -> int:
self.max_path = 0
if root is None or (root.left is None and root.right is None):
pass
else:
self.find_max_path(root)
return self.max_path
def set_max_value(self, new_val) -> None:
self.max_path = new_val
def find_max_path(self, root: TreeNode) -> int:
"""
finds longest path of 1 value and updates the overall maximum value.
return : current node's maximum matching value
"""
if root is None or (root.left is None and root.right is None):
return 0
cur_length = 0
left_is_same = False
left_max_path = 0
right_max_path = 0
if root.left:
left_max_path = self.find_max_path(root.left)
if root.left.val == root.val:
cur_length += 1 + left_max_path
left_is_same = True
else:
cur_length = 0
if root.right:
right_max_path = self.find_max_path(root.right)
if root.right.val == root.val and left_is_same:
self.set_max_value(max(self.max_path, cur_length + 1 + right_max_path))
if root.right.val == root.val:
cur_length = max(1 + right_max_path, cur_length)
self.set_max_value(max(self.max_path, cur_length))
return cur_length
|
class Except(Exception):
def __init__(*args, **kwargs):
Exception.__init__(*args, **kwargs)
def CheckExceptions(data):
raise Except(data)
|
class Except(Exception):
def __init__(*args, **kwargs):
Exception.__init__(*args, **kwargs)
def check_exceptions(data):
raise except(data)
|
# -*- coding: utf-8 -*-
"""
Created on Sat Aug 22 19:49:17 2020
@author: matth
"""
def _linprog_highs_doc(c, A_ub=None, b_ub=None, A_eq=None, b_eq=None,
bounds=None, method='highs', callback=None,
maxiter=None, disp=False, presolve=True,
time_limit=None,
dual_feasibility_tolerance=None,
primal_feasibility_tolerance=None,
ipm_optimality_tolerance=None,
simplex_dual_edge_weight_strategy=None,
**unknown_options):
r"""
Linear programming: minimize a linear objective function subject to linear
equality and inequality constraints using one of the HiGHS solvers.
Linear programming solves problems of the following form:
.. math::
\min_x \ & c^T x \\
\mbox{such that} \ & A_{ub} x \leq b_{ub},\\
& A_{eq} x = b_{eq},\\
& l \leq x \leq u ,
where :math:`x` is a vector of decision variables; :math:`c`,
:math:`b_{ub}`, :math:`b_{eq}`, :math:`l`, and :math:`u` are vectors; and
:math:`A_{ub}` and :math:`A_{eq}` are matrices.
Alternatively, that's:
minimize::
c @ x
such that::
A_ub @ x <= b_ub
A_eq @ x == b_eq
lb <= x <= ub
Note that by default ``lb = 0`` and ``ub = None`` unless specified with
``bounds``.
Parameters
----------
c : 1-D array
The coefficients of the linear objective function to be minimized.
A_ub : 2-D array, optional
The inequality constraint matrix. Each row of ``A_ub`` specifies the
coefficients of a linear inequality constraint on ``x``.
b_ub : 1-D array, optional
The inequality constraint vector. Each element represents an
upper bound on the corresponding value of ``A_ub @ x``.
A_eq : 2-D array, optional
The equality constraint matrix. Each row of ``A_eq`` specifies the
coefficients of a linear equality constraint on ``x``.
b_eq : 1-D array, optional
The equality constraint vector. Each element of ``A_eq @ x`` must equal
the corresponding element of ``b_eq``.
bounds : sequence, optional
A sequence of ``(min, max)`` pairs for each element in ``x``, defining
the minimum and maximum values of that decision variable. Use ``None``
to indicate that there is no bound. By default, bounds are
``(0, None)`` (all decision variables are non-negative).
If a single tuple ``(min, max)`` is provided, then ``min`` and
``max`` will serve as bounds for all decision variables.
method : str
This is the method-specific documentation for 'highs', which chooses
automatically between
:ref:`'highs-ds' <optimize.linprog-highs-ds>` and
:ref:`'highs-ipm' <optimize.linprog-highs-ipm>`.
:ref:`'interior-point' <optimize.linprog-interior-point>` (default),
:ref:`'revised simplex' <optimize.linprog-revised_simplex>`, and
:ref:`'simplex' <optimize.linprog-simplex>` (legacy)
are also available.
integrality : 1-D array, optional
Indicates the type of integrality constraint on each decision variable.
``0`` : Continuous variable; no integrality constraint.
``1`` : Integer variable; decision variable must be an integer
within `bounds`.
``2`` : Semi-continuous variable; decision variable must be within
`bounds` or take value ``0``.
``3`` : Semi-integer variable; decision variable must be an integer
within `bounds` or take value ``0``.
By default, all variables are continuous.
Options
-------
maxiter : int
The maximum number of iterations to perform in either phase.
For :ref:`'highs-ipm' <optimize.linprog-highs-ipm>`, this does not
include the number of crossover iterations. Default is the largest
possible value for an ``int`` on the platform.
disp : bool (default: ``False``)
Set to ``True`` if indicators of optimization status are to be
printed to the console during optimization.
presolve : bool (default: ``True``)
Presolve attempts to identify trivial infeasibilities,
identify trivial unboundedness, and simplify the problem before
sending it to the main solver. It is generally recommended
to keep the default setting ``True``; set to ``False`` if
presolve is to be disabled.
time_limit : float
The maximum time in seconds allotted to solve the problem;
default is the largest possible value for a ``double`` on the
platform.
dual_feasibility_tolerance : double (default: 1e-07)
Dual feasibility tolerance for
:ref:`'highs-ds' <optimize.linprog-highs-ds>`.
The minimum of this and ``primal_feasibility_tolerance``
is used for the feasibility tolerance of
:ref:`'highs-ipm' <optimize.linprog-highs-ipm>`.
primal_feasibility_tolerance : double (default: 1e-07)
Primal feasibility tolerance for
:ref:`'highs-ds' <optimize.linprog-highs-ds>`.
The minimum of this and ``dual_feasibility_tolerance``
is used for the feasibility tolerance of
:ref:`'highs-ipm' <optimize.linprog-highs-ipm>`.
ipm_optimality_tolerance : double (default: ``1e-08``)
Optimality tolerance for
:ref:`'highs-ipm' <optimize.linprog-highs-ipm>`.
Minimum allowable value is 1e-12.
simplex_dual_edge_weight_strategy : str (default: None)
Strategy for simplex dual edge weights. The default, ``None``,
automatically selects one of the following.
``'dantzig'`` uses Dantzig's original strategy of choosing the most
negative reduced cost.
``'devex'`` uses the strategy described in [15]_.
``steepest`` uses the exact steepest edge strategy as described in
[16]_.
``'steepest-devex'`` begins with the exact steepest edge strategy
until the computation is too costly or inexact and then switches to
the devex method.
Curently, ``None`` always selects ``'steepest-devex'``, but this
may change as new options become available.
unknown_options : dict
Optional arguments not used by this particular solver. If
``unknown_options`` is non-empty, a warning is issued listing
all unused options.
Returns
-------
res : OptimizeResult
A :class:`scipy.optimize.OptimizeResult` consisting of the fields:
x : 1D array
The values of the decision variables that minimizes the
objective function while satisfying the constraints.
fun : float
The optimal value of the objective function ``c @ x``.
slack : 1D array
The (nominally positive) values of the slack,
``b_ub - A_ub @ x``.
con : 1D array
The (nominally zero) residuals of the equality constraints,
``b_eq - A_eq @ x``.
success : bool
``True`` when the algorithm succeeds in finding an optimal
solution.
status : int
An integer representing the exit status of the algorithm.
``0`` : Optimization terminated successfully.
``1`` : Iteration or time limit reached.
``2`` : Problem appears to be infeasible.
``3`` : Problem appears to be unbounded.
``4`` : The HiGHS solver ran into a problem.
message : str
A string descriptor of the exit status of the algorithm.
nit : int
The total number of iterations performed.
For the HiGHS simplex method, this includes iterations in all
phases. For the HiGHS interior-point method, this does not include
crossover iterations.
crossover_nit : int
The number of primal/dual pushes performed during the
crossover routine for the HiGHS interior-point method.
This is ``0`` for the HiGHS simplex method.
ineqlin : OptimizeResult
Solution and sensitivity information corresponding to the
inequality constraints, `b_ub`. A dictionary consisting of the
fields:
residual : np.ndnarray
The (nominally positive) values of the slack variables,
``b_ub - A_ub @ x``. This quantity is also commonly
referred to as "slack".
marginals : np.ndarray
The sensitivity (partial derivative) of the objective
function with respect to the right-hand side of the
inequality constraints, `b_ub`.
eqlin : OptimizeResult
Solution and sensitivity information corresponding to the
equality constraints, `b_eq`. A dictionary consisting of the
fields:
residual : np.ndarray
The (nominally zero) residuals of the equality constraints,
``b_eq - A_eq @ x``.
marginals : np.ndarray
The sensitivity (partial derivative) of the objective
function with respect to the right-hand side of the
equality constraints, `b_eq`.
lower, upper : OptimizeResult
Solution and sensitivity information corresponding to the
lower and upper bounds on decision variables, `bounds`.
residual : np.ndarray
The (nominally positive) values of the quantity
``x - lb`` (lower) or ``ub - x`` (upper).
marginals : np.ndarray
The sensitivity (partial derivative) of the objective
function with respect to the lower and upper
`bounds`.
Notes
-----
Method :ref:`'highs-ds' <optimize.linprog-highs-ds>` is a wrapper
of the C++ high performance dual revised simplex implementation (HSOL)
[13]_, [14]_. Method :ref:`'highs-ipm' <optimize.linprog-highs-ipm>`
is a wrapper of a C++ implementation of an **i**\ nterior-\ **p**\ oint
**m**\ ethod [13]_; it features a crossover routine, so it is as accurate
as a simplex solver. Method :ref:`'highs' <optimize.linprog-highs>` chooses
between the two automatically. For new code involving `linprog`, we
recommend explicitly choosing one of these three method values instead of
:ref:`'interior-point' <optimize.linprog-interior-point>` (default),
:ref:`'revised simplex' <optimize.linprog-revised_simplex>`, and
:ref:`'simplex' <optimize.linprog-simplex>` (legacy).
The result fields `ineqlin`, `eqlin`, `lower`, and `upper` all contain
`marginals`, or partial derivatives of the objective function with respect
to the right-hand side of each constraint. These partial derivatives are
also referred to as "Lagrange multipliers", "dual values", and
"shadow prices". The sign convention of `marginals` is opposite that
of Lagrange multipliers produced by many nonlinear solvers.
References
----------
.. [13] Huangfu, Q., Galabova, I., Feldmeier, M., and Hall, J. A. J.
"HiGHS - high performance software for linear optimization."
Accessed 4/16/2020 at https://www.maths.ed.ac.uk/hall/HiGHS/#guide
.. [14] Huangfu, Q. and Hall, J. A. J. "Parallelizing the dual revised
simplex method." Mathematical Programming Computation, 10 (1),
119-142, 2018. DOI: 10.1007/s12532-017-0130-5
.. [15] Harris, Paula MJ. "Pivot selection methods of the Devex LP code."
Mathematical programming 5.1 (1973): 1-28.
.. [16] Goldfarb, Donald, and John Ker Reid. "A practicable steepest-edge
simplex algorithm." Mathematical Programming 12.1 (1977): 361-371.
"""
pass
def _linprog_highs_ds_doc(c, A_ub=None, b_ub=None, A_eq=None, b_eq=None,
bounds=None, method='highs-ds', callback=None,
maxiter=None, disp=False, presolve=True,
time_limit=None,
dual_feasibility_tolerance=None,
primal_feasibility_tolerance=None,
simplex_dual_edge_weight_strategy=None,
**unknown_options):
r"""
Linear programming: minimize a linear objective function subject to linear
equality and inequality constraints using the HiGHS dual simplex solver.
Linear programming solves problems of the following form:
.. math::
\min_x \ & c^T x \\
\mbox{such that} \ & A_{ub} x \leq b_{ub},\\
& A_{eq} x = b_{eq},\\
& l \leq x \leq u ,
where :math:`x` is a vector of decision variables; :math:`c`,
:math:`b_{ub}`, :math:`b_{eq}`, :math:`l`, and :math:`u` are vectors; and
:math:`A_{ub}` and :math:`A_{eq}` are matrices.
Alternatively, that's:
minimize::
c @ x
such that::
A_ub @ x <= b_ub
A_eq @ x == b_eq
lb <= x <= ub
Note that by default ``lb = 0`` and ``ub = None`` unless specified with
``bounds``.
Parameters
----------
c : 1-D array
The coefficients of the linear objective function to be minimized.
A_ub : 2-D array, optional
The inequality constraint matrix. Each row of ``A_ub`` specifies the
coefficients of a linear inequality constraint on ``x``.
b_ub : 1-D array, optional
The inequality constraint vector. Each element represents an
upper bound on the corresponding value of ``A_ub @ x``.
A_eq : 2-D array, optional
The equality constraint matrix. Each row of ``A_eq`` specifies the
coefficients of a linear equality constraint on ``x``.
b_eq : 1-D array, optional
The equality constraint vector. Each element of ``A_eq @ x`` must equal
the corresponding element of ``b_eq``.
bounds : sequence, optional
A sequence of ``(min, max)`` pairs for each element in ``x``, defining
the minimum and maximum values of that decision variable. Use ``None``
to indicate that there is no bound. By default, bounds are
``(0, None)`` (all decision variables are non-negative).
If a single tuple ``(min, max)`` is provided, then ``min`` and
``max`` will serve as bounds for all decision variables.
method : str
This is the method-specific documentation for 'highs-ds'.
:ref:`'highs' <optimize.linprog-highs>`,
:ref:`'highs-ipm' <optimize.linprog-highs-ipm>`,
:ref:`'interior-point' <optimize.linprog-interior-point>` (default),
:ref:`'revised simplex' <optimize.linprog-revised_simplex>`, and
:ref:`'simplex' <optimize.linprog-simplex>` (legacy)
are also available.
Options
-------
maxiter : int
The maximum number of iterations to perform in either phase.
Default is the largest possible value for an ``int`` on the platform.
disp : bool (default: ``False``)
Set to ``True`` if indicators of optimization status are to be
printed to the console during optimization.
presolve : bool (default: ``True``)
Presolve attempts to identify trivial infeasibilities,
identify trivial unboundedness, and simplify the problem before
sending it to the main solver. It is generally recommended
to keep the default setting ``True``; set to ``False`` if
presolve is to be disabled.
time_limit : float
The maximum time in seconds allotted to solve the problem;
default is the largest possible value for a ``double`` on the
platform.
dual_feasibility_tolerance : double (default: 1e-07)
Dual feasibility tolerance for
:ref:`'highs-ds' <optimize.linprog-highs-ds>`.
primal_feasibility_tolerance : double (default: 1e-07)
Primal feasibility tolerance for
:ref:`'highs-ds' <optimize.linprog-highs-ds>`.
simplex_dual_edge_weight_strategy : str (default: None)
Strategy for simplex dual edge weights. The default, ``None``,
automatically selects one of the following.
``'dantzig'`` uses Dantzig's original strategy of choosing the most
negative reduced cost.
``'devex'`` uses the strategy described in [15]_.
``steepest`` uses the exact steepest edge strategy as described in
[16]_.
``'steepest-devex'`` begins with the exact steepest edge strategy
until the computation is too costly or inexact and then switches to
the devex method.
Curently, ``None`` always selects ``'steepest-devex'``, but this
may change as new options become available.
unknown_options : dict
Optional arguments not used by this particular solver. If
``unknown_options`` is non-empty, a warning is issued listing
all unused options.
Returns
-------
res : OptimizeResult
A :class:`scipy.optimize.OptimizeResult` consisting of the fields:
x : 1D array
The values of the decision variables that minimizes the
objective function while satisfying the constraints.
fun : float
The optimal value of the objective function ``c @ x``.
slack : 1D array
The (nominally positive) values of the slack,
``b_ub - A_ub @ x``.
con : 1D array
The (nominally zero) residuals of the equality constraints,
``b_eq - A_eq @ x``.
success : bool
``True`` when the algorithm succeeds in finding an optimal
solution.
status : int
An integer representing the exit status of the algorithm.
``0`` : Optimization terminated successfully.
``1`` : Iteration or time limit reached.
``2`` : Problem appears to be infeasible.
``3`` : Problem appears to be unbounded.
``4`` : The HiGHS solver ran into a problem.
message : str
A string descriptor of the exit status of the algorithm.
nit : int
The total number of iterations performed. This includes iterations
in all phases.
crossover_nit : int
This is always ``0`` for the HiGHS simplex method.
For the HiGHS interior-point method, this is the number of
primal/dual pushes performed during the crossover routine.
ineqlin : OptimizeResult
Solution and sensitivity information corresponding to the
inequality constraints, `b_ub`. A dictionary consisting of the
fields:
residual : np.ndnarray
The (nominally positive) values of the slack variables,
``b_ub - A_ub @ x``. This quantity is also commonly
referred to as "slack".
marginals : np.ndarray
The sensitivity (partial derivative) of the objective
function with respect to the right-hand side of the
inequality constraints, `b_ub`.
eqlin : OptimizeResult
Solution and sensitivity information corresponding to the
equality constraints, `b_eq`. A dictionary consisting of the
fields:
residual : np.ndarray
The (nominally zero) residuals of the equality constraints,
``b_eq - A_eq @ x``.
marginals : np.ndarray
The sensitivity (partial derivative) of the objective
function with respect to the right-hand side of the
equality constraints, `b_eq`.
lower, upper : OptimizeResult
Solution and sensitivity information corresponding to the
lower and upper bounds on decision variables, `bounds`.
residual : np.ndarray
The (nominally positive) values of the quantity
``x - lb`` (lower) or ``ub - x`` (upper).
marginals : np.ndarray
The sensitivity (partial derivative) of the objective
function with respect to the lower and upper
`bounds`.
Notes
-----
Method :ref:`'highs-ds' <optimize.linprog-highs-ds>` is a wrapper
of the C++ high performance dual revised simplex implementation (HSOL)
[13]_, [14]_. Method :ref:`'highs-ipm' <optimize.linprog-highs-ipm>`
is a wrapper of a C++ implementation of an **i**\ nterior-\ **p**\ oint
**m**\ ethod [13]_; it features a crossover routine, so it is as accurate
as a simplex solver. Method :ref:`'highs' <optimize.linprog-highs>` chooses
between the two automatically. For new code involving `linprog`, we
recommend explicitly choosing one of these three method values instead of
:ref:`'interior-point' <optimize.linprog-interior-point>` (default),
:ref:`'revised simplex' <optimize.linprog-revised_simplex>`, and
:ref:`'simplex' <optimize.linprog-simplex>` (legacy).
The result fields `ineqlin`, `eqlin`, `lower`, and `upper` all contain
`marginals`, or partial derivatives of the objective function with respect
to the right-hand side of each constraint. These partial derivatives are
also referred to as "Lagrange multipliers", "dual values", and
"shadow prices". The sign convention of `marginals` is opposite that
of Lagrange multipliers produced by many nonlinear solvers.
References
----------
.. [13] Huangfu, Q., Galabova, I., Feldmeier, M., and Hall, J. A. J.
"HiGHS - high performance software for linear optimization."
Accessed 4/16/2020 at https://www.maths.ed.ac.uk/hall/HiGHS/#guide
.. [14] Huangfu, Q. and Hall, J. A. J. "Parallelizing the dual revised
simplex method." Mathematical Programming Computation, 10 (1),
119-142, 2018. DOI: 10.1007/s12532-017-0130-5
.. [15] Harris, Paula MJ. "Pivot selection methods of the Devex LP code."
Mathematical programming 5.1 (1973): 1-28.
.. [16] Goldfarb, Donald, and John Ker Reid. "A practicable steepest-edge
simplex algorithm." Mathematical Programming 12.1 (1977): 361-371.
"""
pass
def _linprog_highs_ipm_doc(c, A_ub=None, b_ub=None, A_eq=None, b_eq=None,
bounds=None, method='highs-ipm', callback=None,
maxiter=None, disp=False, presolve=True,
time_limit=None,
dual_feasibility_tolerance=None,
primal_feasibility_tolerance=None,
ipm_optimality_tolerance=None,
**unknown_options):
r"""
Linear programming: minimize a linear objective function subject to linear
equality and inequality constraints using the HiGHS interior point solver.
Linear programming solves problems of the following form:
.. math::
\min_x \ & c^T x \\
\mbox{such that} \ & A_{ub} x \leq b_{ub},\\
& A_{eq} x = b_{eq},\\
& l \leq x \leq u ,
where :math:`x` is a vector of decision variables; :math:`c`,
:math:`b_{ub}`, :math:`b_{eq}`, :math:`l`, and :math:`u` are vectors; and
:math:`A_{ub}` and :math:`A_{eq}` are matrices.
Alternatively, that's:
minimize::
c @ x
such that::
A_ub @ x <= b_ub
A_eq @ x == b_eq
lb <= x <= ub
Note that by default ``lb = 0`` and ``ub = None`` unless specified with
``bounds``.
Parameters
----------
c : 1-D array
The coefficients of the linear objective function to be minimized.
A_ub : 2-D array, optional
The inequality constraint matrix. Each row of ``A_ub`` specifies the
coefficients of a linear inequality constraint on ``x``.
b_ub : 1-D array, optional
The inequality constraint vector. Each element represents an
upper bound on the corresponding value of ``A_ub @ x``.
A_eq : 2-D array, optional
The equality constraint matrix. Each row of ``A_eq`` specifies the
coefficients of a linear equality constraint on ``x``.
b_eq : 1-D array, optional
The equality constraint vector. Each element of ``A_eq @ x`` must equal
the corresponding element of ``b_eq``.
bounds : sequence, optional
A sequence of ``(min, max)`` pairs for each element in ``x``, defining
the minimum and maximum values of that decision variable. Use ``None``
to indicate that there is no bound. By default, bounds are
``(0, None)`` (all decision variables are non-negative).
If a single tuple ``(min, max)`` is provided, then ``min`` and
``max`` will serve as bounds for all decision variables.
method : str
This is the method-specific documentation for 'highs-ipm'.
:ref:`'highs-ipm' <optimize.linprog-highs>`,
:ref:`'highs-ds' <optimize.linprog-highs-ds>`,
:ref:`'interior-point' <optimize.linprog-interior-point>` (default),
:ref:`'revised simplex' <optimize.linprog-revised_simplex>`, and
:ref:`'simplex' <optimize.linprog-simplex>` (legacy)
are also available.
Options
-------
maxiter : int
The maximum number of iterations to perform in either phase.
For :ref:`'highs-ipm' <optimize.linprog-highs-ipm>`, this does not
include the number of crossover iterations. Default is the largest
possible value for an ``int`` on the platform.
disp : bool (default: ``False``)
Set to ``True`` if indicators of optimization status are to be
printed to the console during optimization.
presolve : bool (default: ``True``)
Presolve attempts to identify trivial infeasibilities,
identify trivial unboundedness, and simplify the problem before
sending it to the main solver. It is generally recommended
to keep the default setting ``True``; set to ``False`` if
presolve is to be disabled.
time_limit : float
The maximum time in seconds allotted to solve the problem;
default is the largest possible value for a ``double`` on the
platform.
dual_feasibility_tolerance : double (default: 1e-07)
The minimum of this and ``primal_feasibility_tolerance``
is used for the feasibility tolerance of
:ref:`'highs-ipm' <optimize.linprog-highs-ipm>`.
primal_feasibility_tolerance : double (default: 1e-07)
The minimum of this and ``dual_feasibility_tolerance``
is used for the feasibility tolerance of
:ref:`'highs-ipm' <optimize.linprog-highs-ipm>`.
ipm_optimality_tolerance : double (default: ``1e-08``)
Optimality tolerance for
:ref:`'highs-ipm' <optimize.linprog-highs-ipm>`.
Minimum allowable value is 1e-12.
unknown_options : dict
Optional arguments not used by this particular solver. If
``unknown_options`` is non-empty, a warning is issued listing
all unused options.
Returns
-------
res : OptimizeResult
A :class:`scipy.optimize.OptimizeResult` consisting of the fields:
x : 1D array
The values of the decision variables that minimizes the
objective function while satisfying the constraints.
fun : float
The optimal value of the objective function ``c @ x``.
slack : 1D array
The (nominally positive) values of the slack,
``b_ub - A_ub @ x``.
con : 1D array
The (nominally zero) residuals of the equality constraints,
``b_eq - A_eq @ x``.
success : bool
``True`` when the algorithm succeeds in finding an optimal
solution.
status : int
An integer representing the exit status of the algorithm.
``0`` : Optimization terminated successfully.
``1`` : Iteration or time limit reached.
``2`` : Problem appears to be infeasible.
``3`` : Problem appears to be unbounded.
``4`` : The HiGHS solver ran into a problem.
message : str
A string descriptor of the exit status of the algorithm.
nit : int
The total number of iterations performed.
For the HiGHS interior-point method, this does not include
crossover iterations.
crossover_nit : int
The number of primal/dual pushes performed during the
crossover routine for the HiGHS interior-point method.
ineqlin : OptimizeResult
Solution and sensitivity information corresponding to the
inequality constraints, `b_ub`. A dictionary consisting of the
fields:
residual : np.ndnarray
The (nominally positive) values of the slack variables,
``b_ub - A_ub @ x``. This quantity is also commonly
referred to as "slack".
marginals : np.ndarray
The sensitivity (partial derivative) of the objective
function with respect to the right-hand side of the
inequality constraints, `b_ub`.
eqlin : OptimizeResult
Solution and sensitivity information corresponding to the
equality constraints, `b_eq`. A dictionary consisting of the
fields:
residual : np.ndarray
The (nominally zero) residuals of the equality constraints,
``b_eq - A_eq @ x``.
marginals : np.ndarray
The sensitivity (partial derivative) of the objective
function with respect to the right-hand side of the
equality constraints, `b_eq`.
lower, upper : OptimizeResult
Solution and sensitivity information corresponding to the
lower and upper bounds on decision variables, `bounds`.
residual : np.ndarray
The (nominally positive) values of the quantity
``x - lb`` (lower) or ``ub - x`` (upper).
marginals : np.ndarray
The sensitivity (partial derivative) of the objective
function with respect to the lower and upper
`bounds`.
Notes
-----
Method :ref:`'highs-ipm' <optimize.linprog-highs-ipm>`
is a wrapper of a C++ implementation of an **i**\ nterior-\ **p**\ oint
**m**\ ethod [13]_; it features a crossover routine, so it is as accurate
as a simplex solver.
Method :ref:`'highs-ds' <optimize.linprog-highs-ds>` is a wrapper
of the C++ high performance dual revised simplex implementation (HSOL)
[13]_, [14]_. Method :ref:`'highs' <optimize.linprog-highs>` chooses
between the two automatically. For new code involving `linprog`, we
recommend explicitly choosing one of these three method values instead of
:ref:`'interior-point' <optimize.linprog-interior-point>` (default),
:ref:`'revised simplex' <optimize.linprog-revised_simplex>`, and
:ref:`'simplex' <optimize.linprog-simplex>` (legacy).
The result fields `ineqlin`, `eqlin`, `lower`, and `upper` all contain
`marginals`, or partial derivatives of the objective function with respect
to the right-hand side of each constraint. These partial derivatives are
also referred to as "Lagrange multipliers", "dual values", and
"shadow prices". The sign convention of `marginals` is opposite that
of Lagrange multipliers produced by many nonlinear solvers.
References
----------
.. [13] Huangfu, Q., Galabova, I., Feldmeier, M., and Hall, J. A. J.
"HiGHS - high performance software for linear optimization."
Accessed 4/16/2020 at https://www.maths.ed.ac.uk/hall/HiGHS/#guide
.. [14] Huangfu, Q. and Hall, J. A. J. "Parallelizing the dual revised
simplex method." Mathematical Programming Computation, 10 (1),
119-142, 2018. DOI: 10.1007/s12532-017-0130-5
"""
pass
def _linprog_ip_doc(c, A_ub=None, b_ub=None, A_eq=None, b_eq=None,
bounds=None, method='interior-point', callback=None,
maxiter=1000, disp=False, presolve=True,
tol=1e-8, autoscale=False, rr=True,
alpha0=.99995, beta=0.1, sparse=False,
lstsq=False, sym_pos=True, cholesky=True, pc=True,
ip=False, permc_spec='MMD_AT_PLUS_A', **unknown_options):
r"""
Linear programming: minimize a linear objective function subject to linear
equality and inequality constraints using the interior-point method of
[4]_.
.. deprecated:: 1.9.0
`method='interior-point'` will be removed in SciPy 1.11.0.
It is replaced by `method='highs'` because the latter is
faster and more robust.
Linear programming solves problems of the following form:
.. math::
\min_x \ & c^T x \\
\mbox{such that} \ & A_{ub} x \leq b_{ub},\\
& A_{eq} x = b_{eq},\\
& l \leq x \leq u ,
where :math:`x` is a vector of decision variables; :math:`c`,
:math:`b_{ub}`, :math:`b_{eq}`, :math:`l`, and :math:`u` are vectors; and
:math:`A_{ub}` and :math:`A_{eq}` are matrices.
Alternatively, that's:
minimize::
c @ x
such that::
A_ub @ x <= b_ub
A_eq @ x == b_eq
lb <= x <= ub
Note that by default ``lb = 0`` and ``ub = None`` unless specified with
``bounds``.
Parameters
----------
c : 1-D array
The coefficients of the linear objective function to be minimized.
A_ub : 2-D array, optional
The inequality constraint matrix. Each row of ``A_ub`` specifies the
coefficients of a linear inequality constraint on ``x``.
b_ub : 1-D array, optional
The inequality constraint vector. Each element represents an
upper bound on the corresponding value of ``A_ub @ x``.
A_eq : 2-D array, optional
The equality constraint matrix. Each row of ``A_eq`` specifies the
coefficients of a linear equality constraint on ``x``.
b_eq : 1-D array, optional
The equality constraint vector. Each element of ``A_eq @ x`` must equal
the corresponding element of ``b_eq``.
bounds : sequence, optional
A sequence of ``(min, max)`` pairs for each element in ``x``, defining
the minimum and maximum values of that decision variable. Use ``None``
to indicate that there is no bound. By default, bounds are
``(0, None)`` (all decision variables are non-negative).
If a single tuple ``(min, max)`` is provided, then ``min`` and
``max`` will serve as bounds for all decision variables.
method : str
This is the method-specific documentation for 'interior-point'.
:ref:`'highs' <optimize.linprog-highs>`,
:ref:`'highs-ds' <optimize.linprog-highs-ds>`,
:ref:`'highs-ipm' <optimize.linprog-highs-ipm>`,
:ref:`'revised simplex' <optimize.linprog-revised_simplex>`, and
:ref:`'simplex' <optimize.linprog-simplex>` (legacy)
are also available.
callback : callable, optional
Callback function to be executed once per iteration.
Options
-------
maxiter : int (default: 1000)
The maximum number of iterations of the algorithm.
disp : bool (default: False)
Set to ``True`` if indicators of optimization status are to be printed
to the console each iteration.
presolve : bool (default: True)
Presolve attempts to identify trivial infeasibilities,
identify trivial unboundedness, and simplify the problem before
sending it to the main solver. It is generally recommended
to keep the default setting ``True``; set to ``False`` if
presolve is to be disabled.
tol : float (default: 1e-8)
Termination tolerance to be used for all termination criteria;
see [4]_ Section 4.5.
autoscale : bool (default: False)
Set to ``True`` to automatically perform equilibration.
Consider using this option if the numerical values in the
constraints are separated by several orders of magnitude.
rr : bool (default: True)
Set to ``False`` to disable automatic redundancy removal.
alpha0 : float (default: 0.99995)
The maximal step size for Mehrota's predictor-corrector search
direction; see :math:`\beta_{3}` of [4]_ Table 8.1.
beta : float (default: 0.1)
The desired reduction of the path parameter :math:`\mu` (see [6]_)
when Mehrota's predictor-corrector is not in use (uncommon).
sparse : bool (default: False)
Set to ``True`` if the problem is to be treated as sparse after
presolve. If either ``A_eq`` or ``A_ub`` is a sparse matrix,
this option will automatically be set ``True``, and the problem
will be treated as sparse even during presolve. If your constraint
matrices contain mostly zeros and the problem is not very small (less
than about 100 constraints or variables), consider setting ``True``
or providing ``A_eq`` and ``A_ub`` as sparse matrices.
lstsq : bool (default: ``False``)
Set to ``True`` if the problem is expected to be very poorly
conditioned. This should always be left ``False`` unless severe
numerical difficulties are encountered. Leave this at the default
unless you receive a warning message suggesting otherwise.
sym_pos : bool (default: True)
Leave ``True`` if the problem is expected to yield a well conditioned
symmetric positive definite normal equation matrix
(almost always). Leave this at the default unless you receive
a warning message suggesting otherwise.
cholesky : bool (default: True)
Set to ``True`` if the normal equations are to be solved by explicit
Cholesky decomposition followed by explicit forward/backward
substitution. This is typically faster for problems
that are numerically well-behaved.
pc : bool (default: True)
Leave ``True`` if the predictor-corrector method of Mehrota is to be
used. This is almost always (if not always) beneficial.
ip : bool (default: False)
Set to ``True`` if the improved initial point suggestion due to [4]_
Section 4.3 is desired. Whether this is beneficial or not
depends on the problem.
permc_spec : str (default: 'MMD_AT_PLUS_A')
(Has effect only with ``sparse = True``, ``lstsq = False``, ``sym_pos =
True``, and no SuiteSparse.)
A matrix is factorized in each iteration of the algorithm.
This option specifies how to permute the columns of the matrix for
sparsity preservation. Acceptable values are:
- ``NATURAL``: natural ordering.
- ``MMD_ATA``: minimum degree ordering on the structure of A^T A.
- ``MMD_AT_PLUS_A``: minimum degree ordering on the structure of A^T+A.
- ``COLAMD``: approximate minimum degree column ordering.
This option can impact the convergence of the
interior point algorithm; test different values to determine which
performs best for your problem. For more information, refer to
``scipy.sparse.linalg.splu``.
unknown_options : dict
Optional arguments not used by this particular solver. If
`unknown_options` is non-empty a warning is issued listing all
unused options.
Returns
-------
res : OptimizeResult
A :class:`scipy.optimize.OptimizeResult` consisting of the fields:
x : 1-D array
The values of the decision variables that minimizes the
objective function while satisfying the constraints.
fun : float
The optimal value of the objective function ``c @ x``.
slack : 1-D array
The (nominally positive) values of the slack variables,
``b_ub - A_ub @ x``.
con : 1-D array
The (nominally zero) residuals of the equality constraints,
``b_eq - A_eq @ x``.
success : bool
``True`` when the algorithm succeeds in finding an optimal
solution.
status : int
An integer representing the exit status of the algorithm.
``0`` : Optimization terminated successfully.
``1`` : Iteration limit reached.
``2`` : Problem appears to be infeasible.
``3`` : Problem appears to be unbounded.
``4`` : Numerical difficulties encountered.
message : str
A string descriptor of the exit status of the algorithm.
nit : int
The total number of iterations performed in all phases.
Notes
-----
This method implements the algorithm outlined in [4]_ with ideas from [8]_
and a structure inspired by the simpler methods of [6]_.
The primal-dual path following method begins with initial 'guesses' of
the primal and dual variables of the standard form problem and iteratively
attempts to solve the (nonlinear) Karush-Kuhn-Tucker conditions for the
problem with a gradually reduced logarithmic barrier term added to the
objective. This particular implementation uses a homogeneous self-dual
formulation, which provides certificates of infeasibility or unboundedness
where applicable.
The default initial point for the primal and dual variables is that
defined in [4]_ Section 4.4 Equation 8.22. Optionally (by setting initial
point option ``ip=True``), an alternate (potentially improved) starting
point can be calculated according to the additional recommendations of
[4]_ Section 4.4.
A search direction is calculated using the predictor-corrector method
(single correction) proposed by Mehrota and detailed in [4]_ Section 4.1.
(A potential improvement would be to implement the method of multiple
corrections described in [4]_ Section 4.2.) In practice, this is
accomplished by solving the normal equations, [4]_ Section 5.1 Equations
8.31 and 8.32, derived from the Newton equations [4]_ Section 5 Equations
8.25 (compare to [4]_ Section 4 Equations 8.6-8.8). The advantage of
solving the normal equations rather than 8.25 directly is that the
matrices involved are symmetric positive definite, so Cholesky
decomposition can be used rather than the more expensive LU factorization.
With default options, the solver used to perform the factorization depends
on third-party software availability and the conditioning of the problem.
For dense problems, solvers are tried in the following order:
1. ``scipy.linalg.cho_factor``
2. ``scipy.linalg.solve`` with option ``sym_pos=True``
3. ``scipy.linalg.solve`` with option ``sym_pos=False``
4. ``scipy.linalg.lstsq``
For sparse problems:
1. ``sksparse.cholmod.cholesky`` (if scikit-sparse and SuiteSparse are
installed)
2. ``scipy.sparse.linalg.factorized`` (if scikit-umfpack and SuiteSparse
are installed)
3. ``scipy.sparse.linalg.splu`` (which uses SuperLU distributed with SciPy)
4. ``scipy.sparse.linalg.lsqr``
If the solver fails for any reason, successively more robust (but slower)
solvers are attempted in the order indicated. Attempting, failing, and
re-starting factorization can be time consuming, so if the problem is
numerically challenging, options can be set to bypass solvers that are
failing. Setting ``cholesky=False`` skips to solver 2,
``sym_pos=False`` skips to solver 3, and ``lstsq=True`` skips
to solver 4 for both sparse and dense problems.
Potential improvements for combatting issues associated with dense
columns in otherwise sparse problems are outlined in [4]_ Section 5.3 and
[10]_ Section 4.1-4.2; the latter also discusses the alleviation of
accuracy issues associated with the substitution approach to free
variables.
After calculating the search direction, the maximum possible step size
that does not activate the non-negativity constraints is calculated, and
the smaller of this step size and unity is applied (as in [4]_ Section
4.1.) [4]_ Section 4.3 suggests improvements for choosing the step size.
The new point is tested according to the termination conditions of [4]_
Section 4.5. The same tolerance, which can be set using the ``tol`` option,
is used for all checks. (A potential improvement would be to expose
the different tolerances to be set independently.) If optimality,
unboundedness, or infeasibility is detected, the solve procedure
terminates; otherwise it repeats.
Whereas the top level ``linprog`` module expects a problem of form:
Minimize::
c @ x
Subject to::
A_ub @ x <= b_ub
A_eq @ x == b_eq
lb <= x <= ub
where ``lb = 0`` and ``ub = None`` unless set in ``bounds``. The problem
is automatically converted to the form:
Minimize::
c @ x
Subject to::
A @ x == b
x >= 0
for solution. That is, the original problem contains equality, upper-bound
and variable constraints whereas the method specific solver requires
equality constraints and variable non-negativity. ``linprog`` converts the
original problem to standard form by converting the simple bounds to upper
bound constraints, introducing non-negative slack variables for inequality
constraints, and expressing unbounded variables as the difference between
two non-negative variables. The problem is converted back to the original
form before results are reported.
References
----------
.. [4] Andersen, Erling D., and Knud D. Andersen. "The MOSEK interior point
optimizer for linear programming: an implementation of the
homogeneous algorithm." High performance optimization. Springer US,
2000. 197-232.
.. [6] Freund, Robert M. "Primal-Dual Interior-Point Methods for Linear
Programming based on Newton's Method." Unpublished Course Notes,
March 2004. Available 2/25/2017 at
https://ocw.mit.edu/courses/sloan-school-of-management/15-084j-nonlinear-programming-spring-2004/lecture-notes/lec14_int_pt_mthd.pdf
.. [8] Andersen, Erling D., and Knud D. Andersen. "Presolving in linear
programming." Mathematical Programming 71.2 (1995): 221-245.
.. [9] Bertsimas, Dimitris, and J. Tsitsiklis. "Introduction to linear
programming." Athena Scientific 1 (1997): 997.
.. [10] Andersen, Erling D., et al. Implementation of interior point
methods for large scale linear programming. HEC/Universite de
Geneve, 1996.
"""
pass
def _linprog_rs_doc(c, A_ub=None, b_ub=None, A_eq=None, b_eq=None,
bounds=None, method='interior-point', callback=None,
x0=None, maxiter=5000, disp=False, presolve=True,
tol=1e-12, autoscale=False, rr=True, maxupdate=10,
mast=False, pivot="mrc", **unknown_options):
r"""
Linear programming: minimize a linear objective function subject to linear
equality and inequality constraints using the revised simplex method.
.. deprecated:: 1.9.0
`method='revised simplex'` will be removed in SciPy 1.11.0.
It is replaced by `method='highs'` because the latter is
faster and more robust.
Linear programming solves problems of the following form:
.. math::
\min_x \ & c^T x \\
\mbox{such that} \ & A_{ub} x \leq b_{ub},\\
& A_{eq} x = b_{eq},\\
& l \leq x \leq u ,
where :math:`x` is a vector of decision variables; :math:`c`,
:math:`b_{ub}`, :math:`b_{eq}`, :math:`l`, and :math:`u` are vectors; and
:math:`A_{ub}` and :math:`A_{eq}` are matrices.
Alternatively, that's:
minimize::
c @ x
such that::
A_ub @ x <= b_ub
A_eq @ x == b_eq
lb <= x <= ub
Note that by default ``lb = 0`` and ``ub = None`` unless specified with
``bounds``.
Parameters
----------
c : 1-D array
The coefficients of the linear objective function to be minimized.
A_ub : 2-D array, optional
The inequality constraint matrix. Each row of ``A_ub`` specifies the
coefficients of a linear inequality constraint on ``x``.
b_ub : 1-D array, optional
The inequality constraint vector. Each element represents an
upper bound on the corresponding value of ``A_ub @ x``.
A_eq : 2-D array, optional
The equality constraint matrix. Each row of ``A_eq`` specifies the
coefficients of a linear equality constraint on ``x``.
b_eq : 1-D array, optional
The equality constraint vector. Each element of ``A_eq @ x`` must equal
the corresponding element of ``b_eq``.
bounds : sequence, optional
A sequence of ``(min, max)`` pairs for each element in ``x``, defining
the minimum and maximum values of that decision variable. Use ``None``
to indicate that there is no bound. By default, bounds are
``(0, None)`` (all decision variables are non-negative).
If a single tuple ``(min, max)`` is provided, then ``min`` and
``max`` will serve as bounds for all decision variables.
method : str
This is the method-specific documentation for 'revised simplex'.
:ref:`'highs' <optimize.linprog-highs>`,
:ref:`'highs-ds' <optimize.linprog-highs-ds>`,
:ref:`'highs-ipm' <optimize.linprog-highs-ipm>`,
:ref:`'interior-point' <optimize.linprog-interior-point>` (default),
and :ref:`'simplex' <optimize.linprog-simplex>` (legacy)
are also available.
callback : callable, optional
Callback function to be executed once per iteration.
x0 : 1-D array, optional
Guess values of the decision variables, which will be refined by
the optimization algorithm. This argument is currently used only by the
'revised simplex' method, and can only be used if `x0` represents a
basic feasible solution.
Options
-------
maxiter : int (default: 5000)
The maximum number of iterations to perform in either phase.
disp : bool (default: False)
Set to ``True`` if indicators of optimization status are to be printed
to the console each iteration.
presolve : bool (default: True)
Presolve attempts to identify trivial infeasibilities,
identify trivial unboundedness, and simplify the problem before
sending it to the main solver. It is generally recommended
to keep the default setting ``True``; set to ``False`` if
presolve is to be disabled.
tol : float (default: 1e-12)
The tolerance which determines when a solution is "close enough" to
zero in Phase 1 to be considered a basic feasible solution or close
enough to positive to serve as an optimal solution.
autoscale : bool (default: False)
Set to ``True`` to automatically perform equilibration.
Consider using this option if the numerical values in the
constraints are separated by several orders of magnitude.
rr : bool (default: True)
Set to ``False`` to disable automatic redundancy removal.
maxupdate : int (default: 10)
The maximum number of updates performed on the LU factorization.
After this many updates is reached, the basis matrix is factorized
from scratch.
mast : bool (default: False)
Minimize Amortized Solve Time. If enabled, the average time to solve
a linear system using the basis factorization is measured. Typically,
the average solve time will decrease with each successive solve after
initial factorization, as factorization takes much more time than the
solve operation (and updates). Eventually, however, the updated
factorization becomes sufficiently complex that the average solve time
begins to increase. When this is detected, the basis is refactorized
from scratch. Enable this option to maximize speed at the risk of
nondeterministic behavior. Ignored if ``maxupdate`` is 0.
pivot : "mrc" or "bland" (default: "mrc")
Pivot rule: Minimum Reduced Cost ("mrc") or Bland's rule ("bland").
Choose Bland's rule if iteration limit is reached and cycling is
suspected.
unknown_options : dict
Optional arguments not used by this particular solver. If
`unknown_options` is non-empty a warning is issued listing all
unused options.
Returns
-------
res : OptimizeResult
A :class:`scipy.optimize.OptimizeResult` consisting of the fields:
x : 1-D array
The values of the decision variables that minimizes the
objective function while satisfying the constraints.
fun : float
The optimal value of the objective function ``c @ x``.
slack : 1-D array
The (nominally positive) values of the slack variables,
``b_ub - A_ub @ x``.
con : 1-D array
The (nominally zero) residuals of the equality constraints,
``b_eq - A_eq @ x``.
success : bool
``True`` when the algorithm succeeds in finding an optimal
solution.
status : int
An integer representing the exit status of the algorithm.
``0`` : Optimization terminated successfully.
``1`` : Iteration limit reached.
``2`` : Problem appears to be infeasible.
``3`` : Problem appears to be unbounded.
``4`` : Numerical difficulties encountered.
``5`` : Problem has no constraints; turn presolve on.
``6`` : Invalid guess provided.
message : str
A string descriptor of the exit status of the algorithm.
nit : int
The total number of iterations performed in all phases.
Notes
-----
Method *revised simplex* uses the revised simplex method as described in
[9]_, except that a factorization [11]_ of the basis matrix, rather than
its inverse, is efficiently maintained and used to solve the linear systems
at each iteration of the algorithm.
References
----------
.. [9] Bertsimas, Dimitris, and J. Tsitsiklis. "Introduction to linear
programming." Athena Scientific 1 (1997): 997.
.. [11] Bartels, Richard H. "A stabilization of the simplex method."
Journal in Numerische Mathematik 16.5 (1971): 414-434.
"""
pass
def _linprog_simplex_doc(c, A_ub=None, b_ub=None, A_eq=None, b_eq=None,
bounds=None, method='interior-point', callback=None,
maxiter=5000, disp=False, presolve=True,
tol=1e-12, autoscale=False, rr=True, bland=False,
**unknown_options):
r"""
Linear programming: minimize a linear objective function subject to linear
equality and inequality constraints using the tableau-based simplex method.
.. deprecated:: 1.9.0
`method='simplex'` will be removed in SciPy 1.11.0.
It is replaced by `method='highs'` because the latter is
faster and more robust.
Linear programming solves problems of the following form:
.. math::
\min_x \ & c^T x \\
\mbox{such that} \ & A_{ub} x \leq b_{ub},\\
& A_{eq} x = b_{eq},\\
& l \leq x \leq u ,
where :math:`x` is a vector of decision variables; :math:`c`,
:math:`b_{ub}`, :math:`b_{eq}`, :math:`l`, and :math:`u` are vectors; and
:math:`A_{ub}` and :math:`A_{eq}` are matrices.
Alternatively, that's:
minimize::
c @ x
such that::
A_ub @ x <= b_ub
A_eq @ x == b_eq
lb <= x <= ub
Note that by default ``lb = 0`` and ``ub = None`` unless specified with
``bounds``.
Parameters
----------
c : 1-D array
The coefficients of the linear objective function to be minimized.
A_ub : 2-D array, optional
The inequality constraint matrix. Each row of ``A_ub`` specifies the
coefficients of a linear inequality constraint on ``x``.
b_ub : 1-D array, optional
The inequality constraint vector. Each element represents an
upper bound on the corresponding value of ``A_ub @ x``.
A_eq : 2-D array, optional
The equality constraint matrix. Each row of ``A_eq`` specifies the
coefficients of a linear equality constraint on ``x``.
b_eq : 1-D array, optional
The equality constraint vector. Each element of ``A_eq @ x`` must equal
the corresponding element of ``b_eq``.
bounds : sequence, optional
A sequence of ``(min, max)`` pairs for each element in ``x``, defining
the minimum and maximum values of that decision variable. Use ``None``
to indicate that there is no bound. By default, bounds are
``(0, None)`` (all decision variables are non-negative).
If a single tuple ``(min, max)`` is provided, then ``min`` and
``max`` will serve as bounds for all decision variables.
method : str
This is the method-specific documentation for 'simplex'.
:ref:`'highs' <optimize.linprog-highs>`,
:ref:`'highs-ds' <optimize.linprog-highs-ds>`,
:ref:`'highs-ipm' <optimize.linprog-highs-ipm>`,
:ref:`'interior-point' <optimize.linprog-interior-point>` (default),
and :ref:`'revised simplex' <optimize.linprog-revised_simplex>`
are also available.
callback : callable, optional
Callback function to be executed once per iteration.
Options
-------
maxiter : int (default: 5000)
The maximum number of iterations to perform in either phase.
disp : bool (default: False)
Set to ``True`` if indicators of optimization status are to be printed
to the console each iteration.
presolve : bool (default: True)
Presolve attempts to identify trivial infeasibilities,
identify trivial unboundedness, and simplify the problem before
sending it to the main solver. It is generally recommended
to keep the default setting ``True``; set to ``False`` if
presolve is to be disabled.
tol : float (default: 1e-12)
The tolerance which determines when a solution is "close enough" to
zero in Phase 1 to be considered a basic feasible solution or close
enough to positive to serve as an optimal solution.
autoscale : bool (default: False)
Set to ``True`` to automatically perform equilibration.
Consider using this option if the numerical values in the
constraints are separated by several orders of magnitude.
rr : bool (default: True)
Set to ``False`` to disable automatic redundancy removal.
bland : bool
If True, use Bland's anti-cycling rule [3]_ to choose pivots to
prevent cycling. If False, choose pivots which should lead to a
converged solution more quickly. The latter method is subject to
cycling (non-convergence) in rare instances.
unknown_options : dict
Optional arguments not used by this particular solver. If
`unknown_options` is non-empty a warning is issued listing all
unused options.
Returns
-------
res : OptimizeResult
A :class:`scipy.optimize.OptimizeResult` consisting of the fields:
x : 1-D array
The values of the decision variables that minimizes the
objective function while satisfying the constraints.
fun : float
The optimal value of the objective function ``c @ x``.
slack : 1-D array
The (nominally positive) values of the slack variables,
``b_ub - A_ub @ x``.
con : 1-D array
The (nominally zero) residuals of the equality constraints,
``b_eq - A_eq @ x``.
success : bool
``True`` when the algorithm succeeds in finding an optimal
solution.
status : int
An integer representing the exit status of the algorithm.
``0`` : Optimization terminated successfully.
``1`` : Iteration limit reached.
``2`` : Problem appears to be infeasible.
``3`` : Problem appears to be unbounded.
``4`` : Numerical difficulties encountered.
message : str
A string descriptor of the exit status of the algorithm.
nit : int
The total number of iterations performed in all phases.
References
----------
.. [1] Dantzig, George B., Linear programming and extensions. Rand
Corporation Research Study Princeton Univ. Press, Princeton, NJ,
1963
.. [2] Hillier, S.H. and Lieberman, G.J. (1995), "Introduction to
Mathematical Programming", McGraw-Hill, Chapter 4.
.. [3] Bland, Robert G. New finite pivoting rules for the simplex method.
Mathematics of Operations Research (2), 1977: pp. 103-107.
"""
pass
|
"""
Created on Sat Aug 22 19:49:17 2020
@author: matth
"""
def _linprog_highs_doc(c, A_ub=None, b_ub=None, A_eq=None, b_eq=None, bounds=None, method='highs', callback=None, maxiter=None, disp=False, presolve=True, time_limit=None, dual_feasibility_tolerance=None, primal_feasibility_tolerance=None, ipm_optimality_tolerance=None, simplex_dual_edge_weight_strategy=None, **unknown_options):
"""
Linear programming: minimize a linear objective function subject to linear
equality and inequality constraints using one of the HiGHS solvers.
Linear programming solves problems of the following form:
.. math::
\\min_x \\ & c^T x \\\\
\\mbox{such that} \\ & A_{ub} x \\leq b_{ub},\\\\
& A_{eq} x = b_{eq},\\\\
& l \\leq x \\leq u ,
where :math:`x` is a vector of decision variables; :math:`c`,
:math:`b_{ub}`, :math:`b_{eq}`, :math:`l`, and :math:`u` are vectors; and
:math:`A_{ub}` and :math:`A_{eq}` are matrices.
Alternatively, that's:
minimize::
c @ x
such that::
A_ub @ x <= b_ub
A_eq @ x == b_eq
lb <= x <= ub
Note that by default ``lb = 0`` and ``ub = None`` unless specified with
``bounds``.
Parameters
----------
c : 1-D array
The coefficients of the linear objective function to be minimized.
A_ub : 2-D array, optional
The inequality constraint matrix. Each row of ``A_ub`` specifies the
coefficients of a linear inequality constraint on ``x``.
b_ub : 1-D array, optional
The inequality constraint vector. Each element represents an
upper bound on the corresponding value of ``A_ub @ x``.
A_eq : 2-D array, optional
The equality constraint matrix. Each row of ``A_eq`` specifies the
coefficients of a linear equality constraint on ``x``.
b_eq : 1-D array, optional
The equality constraint vector. Each element of ``A_eq @ x`` must equal
the corresponding element of ``b_eq``.
bounds : sequence, optional
A sequence of ``(min, max)`` pairs for each element in ``x``, defining
the minimum and maximum values of that decision variable. Use ``None``
to indicate that there is no bound. By default, bounds are
``(0, None)`` (all decision variables are non-negative).
If a single tuple ``(min, max)`` is provided, then ``min`` and
``max`` will serve as bounds for all decision variables.
method : str
This is the method-specific documentation for 'highs', which chooses
automatically between
:ref:`'highs-ds' <optimize.linprog-highs-ds>` and
:ref:`'highs-ipm' <optimize.linprog-highs-ipm>`.
:ref:`'interior-point' <optimize.linprog-interior-point>` (default),
:ref:`'revised simplex' <optimize.linprog-revised_simplex>`, and
:ref:`'simplex' <optimize.linprog-simplex>` (legacy)
are also available.
integrality : 1-D array, optional
Indicates the type of integrality constraint on each decision variable.
``0`` : Continuous variable; no integrality constraint.
``1`` : Integer variable; decision variable must be an integer
within `bounds`.
``2`` : Semi-continuous variable; decision variable must be within
`bounds` or take value ``0``.
``3`` : Semi-integer variable; decision variable must be an integer
within `bounds` or take value ``0``.
By default, all variables are continuous.
Options
-------
maxiter : int
The maximum number of iterations to perform in either phase.
For :ref:`'highs-ipm' <optimize.linprog-highs-ipm>`, this does not
include the number of crossover iterations. Default is the largest
possible value for an ``int`` on the platform.
disp : bool (default: ``False``)
Set to ``True`` if indicators of optimization status are to be
printed to the console during optimization.
presolve : bool (default: ``True``)
Presolve attempts to identify trivial infeasibilities,
identify trivial unboundedness, and simplify the problem before
sending it to the main solver. It is generally recommended
to keep the default setting ``True``; set to ``False`` if
presolve is to be disabled.
time_limit : float
The maximum time in seconds allotted to solve the problem;
default is the largest possible value for a ``double`` on the
platform.
dual_feasibility_tolerance : double (default: 1e-07)
Dual feasibility tolerance for
:ref:`'highs-ds' <optimize.linprog-highs-ds>`.
The minimum of this and ``primal_feasibility_tolerance``
is used for the feasibility tolerance of
:ref:`'highs-ipm' <optimize.linprog-highs-ipm>`.
primal_feasibility_tolerance : double (default: 1e-07)
Primal feasibility tolerance for
:ref:`'highs-ds' <optimize.linprog-highs-ds>`.
The minimum of this and ``dual_feasibility_tolerance``
is used for the feasibility tolerance of
:ref:`'highs-ipm' <optimize.linprog-highs-ipm>`.
ipm_optimality_tolerance : double (default: ``1e-08``)
Optimality tolerance for
:ref:`'highs-ipm' <optimize.linprog-highs-ipm>`.
Minimum allowable value is 1e-12.
simplex_dual_edge_weight_strategy : str (default: None)
Strategy for simplex dual edge weights. The default, ``None``,
automatically selects one of the following.
``'dantzig'`` uses Dantzig's original strategy of choosing the most
negative reduced cost.
``'devex'`` uses the strategy described in [15]_.
``steepest`` uses the exact steepest edge strategy as described in
[16]_.
``'steepest-devex'`` begins with the exact steepest edge strategy
until the computation is too costly or inexact and then switches to
the devex method.
Curently, ``None`` always selects ``'steepest-devex'``, but this
may change as new options become available.
unknown_options : dict
Optional arguments not used by this particular solver. If
``unknown_options`` is non-empty, a warning is issued listing
all unused options.
Returns
-------
res : OptimizeResult
A :class:`scipy.optimize.OptimizeResult` consisting of the fields:
x : 1D array
The values of the decision variables that minimizes the
objective function while satisfying the constraints.
fun : float
The optimal value of the objective function ``c @ x``.
slack : 1D array
The (nominally positive) values of the slack,
``b_ub - A_ub @ x``.
con : 1D array
The (nominally zero) residuals of the equality constraints,
``b_eq - A_eq @ x``.
success : bool
``True`` when the algorithm succeeds in finding an optimal
solution.
status : int
An integer representing the exit status of the algorithm.
``0`` : Optimization terminated successfully.
``1`` : Iteration or time limit reached.
``2`` : Problem appears to be infeasible.
``3`` : Problem appears to be unbounded.
``4`` : The HiGHS solver ran into a problem.
message : str
A string descriptor of the exit status of the algorithm.
nit : int
The total number of iterations performed.
For the HiGHS simplex method, this includes iterations in all
phases. For the HiGHS interior-point method, this does not include
crossover iterations.
crossover_nit : int
The number of primal/dual pushes performed during the
crossover routine for the HiGHS interior-point method.
This is ``0`` for the HiGHS simplex method.
ineqlin : OptimizeResult
Solution and sensitivity information corresponding to the
inequality constraints, `b_ub`. A dictionary consisting of the
fields:
residual : np.ndnarray
The (nominally positive) values of the slack variables,
``b_ub - A_ub @ x``. This quantity is also commonly
referred to as "slack".
marginals : np.ndarray
The sensitivity (partial derivative) of the objective
function with respect to the right-hand side of the
inequality constraints, `b_ub`.
eqlin : OptimizeResult
Solution and sensitivity information corresponding to the
equality constraints, `b_eq`. A dictionary consisting of the
fields:
residual : np.ndarray
The (nominally zero) residuals of the equality constraints,
``b_eq - A_eq @ x``.
marginals : np.ndarray
The sensitivity (partial derivative) of the objective
function with respect to the right-hand side of the
equality constraints, `b_eq`.
lower, upper : OptimizeResult
Solution and sensitivity information corresponding to the
lower and upper bounds on decision variables, `bounds`.
residual : np.ndarray
The (nominally positive) values of the quantity
``x - lb`` (lower) or ``ub - x`` (upper).
marginals : np.ndarray
The sensitivity (partial derivative) of the objective
function with respect to the lower and upper
`bounds`.
Notes
-----
Method :ref:`'highs-ds' <optimize.linprog-highs-ds>` is a wrapper
of the C++ high performance dual revised simplex implementation (HSOL)
[13]_, [14]_. Method :ref:`'highs-ipm' <optimize.linprog-highs-ipm>`
is a wrapper of a C++ implementation of an **i**\\ nterior-\\ **p**\\ oint
**m**\\ ethod [13]_; it features a crossover routine, so it is as accurate
as a simplex solver. Method :ref:`'highs' <optimize.linprog-highs>` chooses
between the two automatically. For new code involving `linprog`, we
recommend explicitly choosing one of these three method values instead of
:ref:`'interior-point' <optimize.linprog-interior-point>` (default),
:ref:`'revised simplex' <optimize.linprog-revised_simplex>`, and
:ref:`'simplex' <optimize.linprog-simplex>` (legacy).
The result fields `ineqlin`, `eqlin`, `lower`, and `upper` all contain
`marginals`, or partial derivatives of the objective function with respect
to the right-hand side of each constraint. These partial derivatives are
also referred to as "Lagrange multipliers", "dual values", and
"shadow prices". The sign convention of `marginals` is opposite that
of Lagrange multipliers produced by many nonlinear solvers.
References
----------
.. [13] Huangfu, Q., Galabova, I., Feldmeier, M., and Hall, J. A. J.
"HiGHS - high performance software for linear optimization."
Accessed 4/16/2020 at https://www.maths.ed.ac.uk/hall/HiGHS/#guide
.. [14] Huangfu, Q. and Hall, J. A. J. "Parallelizing the dual revised
simplex method." Mathematical Programming Computation, 10 (1),
119-142, 2018. DOI: 10.1007/s12532-017-0130-5
.. [15] Harris, Paula MJ. "Pivot selection methods of the Devex LP code."
Mathematical programming 5.1 (1973): 1-28.
.. [16] Goldfarb, Donald, and John Ker Reid. "A practicable steepest-edge
simplex algorithm." Mathematical Programming 12.1 (1977): 361-371.
"""
pass
def _linprog_highs_ds_doc(c, A_ub=None, b_ub=None, A_eq=None, b_eq=None, bounds=None, method='highs-ds', callback=None, maxiter=None, disp=False, presolve=True, time_limit=None, dual_feasibility_tolerance=None, primal_feasibility_tolerance=None, simplex_dual_edge_weight_strategy=None, **unknown_options):
"""
Linear programming: minimize a linear objective function subject to linear
equality and inequality constraints using the HiGHS dual simplex solver.
Linear programming solves problems of the following form:
.. math::
\\min_x \\ & c^T x \\\\
\\mbox{such that} \\ & A_{ub} x \\leq b_{ub},\\\\
& A_{eq} x = b_{eq},\\\\
& l \\leq x \\leq u ,
where :math:`x` is a vector of decision variables; :math:`c`,
:math:`b_{ub}`, :math:`b_{eq}`, :math:`l`, and :math:`u` are vectors; and
:math:`A_{ub}` and :math:`A_{eq}` are matrices.
Alternatively, that's:
minimize::
c @ x
such that::
A_ub @ x <= b_ub
A_eq @ x == b_eq
lb <= x <= ub
Note that by default ``lb = 0`` and ``ub = None`` unless specified with
``bounds``.
Parameters
----------
c : 1-D array
The coefficients of the linear objective function to be minimized.
A_ub : 2-D array, optional
The inequality constraint matrix. Each row of ``A_ub`` specifies the
coefficients of a linear inequality constraint on ``x``.
b_ub : 1-D array, optional
The inequality constraint vector. Each element represents an
upper bound on the corresponding value of ``A_ub @ x``.
A_eq : 2-D array, optional
The equality constraint matrix. Each row of ``A_eq`` specifies the
coefficients of a linear equality constraint on ``x``.
b_eq : 1-D array, optional
The equality constraint vector. Each element of ``A_eq @ x`` must equal
the corresponding element of ``b_eq``.
bounds : sequence, optional
A sequence of ``(min, max)`` pairs for each element in ``x``, defining
the minimum and maximum values of that decision variable. Use ``None``
to indicate that there is no bound. By default, bounds are
``(0, None)`` (all decision variables are non-negative).
If a single tuple ``(min, max)`` is provided, then ``min`` and
``max`` will serve as bounds for all decision variables.
method : str
This is the method-specific documentation for 'highs-ds'.
:ref:`'highs' <optimize.linprog-highs>`,
:ref:`'highs-ipm' <optimize.linprog-highs-ipm>`,
:ref:`'interior-point' <optimize.linprog-interior-point>` (default),
:ref:`'revised simplex' <optimize.linprog-revised_simplex>`, and
:ref:`'simplex' <optimize.linprog-simplex>` (legacy)
are also available.
Options
-------
maxiter : int
The maximum number of iterations to perform in either phase.
Default is the largest possible value for an ``int`` on the platform.
disp : bool (default: ``False``)
Set to ``True`` if indicators of optimization status are to be
printed to the console during optimization.
presolve : bool (default: ``True``)
Presolve attempts to identify trivial infeasibilities,
identify trivial unboundedness, and simplify the problem before
sending it to the main solver. It is generally recommended
to keep the default setting ``True``; set to ``False`` if
presolve is to be disabled.
time_limit : float
The maximum time in seconds allotted to solve the problem;
default is the largest possible value for a ``double`` on the
platform.
dual_feasibility_tolerance : double (default: 1e-07)
Dual feasibility tolerance for
:ref:`'highs-ds' <optimize.linprog-highs-ds>`.
primal_feasibility_tolerance : double (default: 1e-07)
Primal feasibility tolerance for
:ref:`'highs-ds' <optimize.linprog-highs-ds>`.
simplex_dual_edge_weight_strategy : str (default: None)
Strategy for simplex dual edge weights. The default, ``None``,
automatically selects one of the following.
``'dantzig'`` uses Dantzig's original strategy of choosing the most
negative reduced cost.
``'devex'`` uses the strategy described in [15]_.
``steepest`` uses the exact steepest edge strategy as described in
[16]_.
``'steepest-devex'`` begins with the exact steepest edge strategy
until the computation is too costly or inexact and then switches to
the devex method.
Curently, ``None`` always selects ``'steepest-devex'``, but this
may change as new options become available.
unknown_options : dict
Optional arguments not used by this particular solver. If
``unknown_options`` is non-empty, a warning is issued listing
all unused options.
Returns
-------
res : OptimizeResult
A :class:`scipy.optimize.OptimizeResult` consisting of the fields:
x : 1D array
The values of the decision variables that minimizes the
objective function while satisfying the constraints.
fun : float
The optimal value of the objective function ``c @ x``.
slack : 1D array
The (nominally positive) values of the slack,
``b_ub - A_ub @ x``.
con : 1D array
The (nominally zero) residuals of the equality constraints,
``b_eq - A_eq @ x``.
success : bool
``True`` when the algorithm succeeds in finding an optimal
solution.
status : int
An integer representing the exit status of the algorithm.
``0`` : Optimization terminated successfully.
``1`` : Iteration or time limit reached.
``2`` : Problem appears to be infeasible.
``3`` : Problem appears to be unbounded.
``4`` : The HiGHS solver ran into a problem.
message : str
A string descriptor of the exit status of the algorithm.
nit : int
The total number of iterations performed. This includes iterations
in all phases.
crossover_nit : int
This is always ``0`` for the HiGHS simplex method.
For the HiGHS interior-point method, this is the number of
primal/dual pushes performed during the crossover routine.
ineqlin : OptimizeResult
Solution and sensitivity information corresponding to the
inequality constraints, `b_ub`. A dictionary consisting of the
fields:
residual : np.ndnarray
The (nominally positive) values of the slack variables,
``b_ub - A_ub @ x``. This quantity is also commonly
referred to as "slack".
marginals : np.ndarray
The sensitivity (partial derivative) of the objective
function with respect to the right-hand side of the
inequality constraints, `b_ub`.
eqlin : OptimizeResult
Solution and sensitivity information corresponding to the
equality constraints, `b_eq`. A dictionary consisting of the
fields:
residual : np.ndarray
The (nominally zero) residuals of the equality constraints,
``b_eq - A_eq @ x``.
marginals : np.ndarray
The sensitivity (partial derivative) of the objective
function with respect to the right-hand side of the
equality constraints, `b_eq`.
lower, upper : OptimizeResult
Solution and sensitivity information corresponding to the
lower and upper bounds on decision variables, `bounds`.
residual : np.ndarray
The (nominally positive) values of the quantity
``x - lb`` (lower) or ``ub - x`` (upper).
marginals : np.ndarray
The sensitivity (partial derivative) of the objective
function with respect to the lower and upper
`bounds`.
Notes
-----
Method :ref:`'highs-ds' <optimize.linprog-highs-ds>` is a wrapper
of the C++ high performance dual revised simplex implementation (HSOL)
[13]_, [14]_. Method :ref:`'highs-ipm' <optimize.linprog-highs-ipm>`
is a wrapper of a C++ implementation of an **i**\\ nterior-\\ **p**\\ oint
**m**\\ ethod [13]_; it features a crossover routine, so it is as accurate
as a simplex solver. Method :ref:`'highs' <optimize.linprog-highs>` chooses
between the two automatically. For new code involving `linprog`, we
recommend explicitly choosing one of these three method values instead of
:ref:`'interior-point' <optimize.linprog-interior-point>` (default),
:ref:`'revised simplex' <optimize.linprog-revised_simplex>`, and
:ref:`'simplex' <optimize.linprog-simplex>` (legacy).
The result fields `ineqlin`, `eqlin`, `lower`, and `upper` all contain
`marginals`, or partial derivatives of the objective function with respect
to the right-hand side of each constraint. These partial derivatives are
also referred to as "Lagrange multipliers", "dual values", and
"shadow prices". The sign convention of `marginals` is opposite that
of Lagrange multipliers produced by many nonlinear solvers.
References
----------
.. [13] Huangfu, Q., Galabova, I., Feldmeier, M., and Hall, J. A. J.
"HiGHS - high performance software for linear optimization."
Accessed 4/16/2020 at https://www.maths.ed.ac.uk/hall/HiGHS/#guide
.. [14] Huangfu, Q. and Hall, J. A. J. "Parallelizing the dual revised
simplex method." Mathematical Programming Computation, 10 (1),
119-142, 2018. DOI: 10.1007/s12532-017-0130-5
.. [15] Harris, Paula MJ. "Pivot selection methods of the Devex LP code."
Mathematical programming 5.1 (1973): 1-28.
.. [16] Goldfarb, Donald, and John Ker Reid. "A practicable steepest-edge
simplex algorithm." Mathematical Programming 12.1 (1977): 361-371.
"""
pass
def _linprog_highs_ipm_doc(c, A_ub=None, b_ub=None, A_eq=None, b_eq=None, bounds=None, method='highs-ipm', callback=None, maxiter=None, disp=False, presolve=True, time_limit=None, dual_feasibility_tolerance=None, primal_feasibility_tolerance=None, ipm_optimality_tolerance=None, **unknown_options):
"""
Linear programming: minimize a linear objective function subject to linear
equality and inequality constraints using the HiGHS interior point solver.
Linear programming solves problems of the following form:
.. math::
\\min_x \\ & c^T x \\\\
\\mbox{such that} \\ & A_{ub} x \\leq b_{ub},\\\\
& A_{eq} x = b_{eq},\\\\
& l \\leq x \\leq u ,
where :math:`x` is a vector of decision variables; :math:`c`,
:math:`b_{ub}`, :math:`b_{eq}`, :math:`l`, and :math:`u` are vectors; and
:math:`A_{ub}` and :math:`A_{eq}` are matrices.
Alternatively, that's:
minimize::
c @ x
such that::
A_ub @ x <= b_ub
A_eq @ x == b_eq
lb <= x <= ub
Note that by default ``lb = 0`` and ``ub = None`` unless specified with
``bounds``.
Parameters
----------
c : 1-D array
The coefficients of the linear objective function to be minimized.
A_ub : 2-D array, optional
The inequality constraint matrix. Each row of ``A_ub`` specifies the
coefficients of a linear inequality constraint on ``x``.
b_ub : 1-D array, optional
The inequality constraint vector. Each element represents an
upper bound on the corresponding value of ``A_ub @ x``.
A_eq : 2-D array, optional
The equality constraint matrix. Each row of ``A_eq`` specifies the
coefficients of a linear equality constraint on ``x``.
b_eq : 1-D array, optional
The equality constraint vector. Each element of ``A_eq @ x`` must equal
the corresponding element of ``b_eq``.
bounds : sequence, optional
A sequence of ``(min, max)`` pairs for each element in ``x``, defining
the minimum and maximum values of that decision variable. Use ``None``
to indicate that there is no bound. By default, bounds are
``(0, None)`` (all decision variables are non-negative).
If a single tuple ``(min, max)`` is provided, then ``min`` and
``max`` will serve as bounds for all decision variables.
method : str
This is the method-specific documentation for 'highs-ipm'.
:ref:`'highs-ipm' <optimize.linprog-highs>`,
:ref:`'highs-ds' <optimize.linprog-highs-ds>`,
:ref:`'interior-point' <optimize.linprog-interior-point>` (default),
:ref:`'revised simplex' <optimize.linprog-revised_simplex>`, and
:ref:`'simplex' <optimize.linprog-simplex>` (legacy)
are also available.
Options
-------
maxiter : int
The maximum number of iterations to perform in either phase.
For :ref:`'highs-ipm' <optimize.linprog-highs-ipm>`, this does not
include the number of crossover iterations. Default is the largest
possible value for an ``int`` on the platform.
disp : bool (default: ``False``)
Set to ``True`` if indicators of optimization status are to be
printed to the console during optimization.
presolve : bool (default: ``True``)
Presolve attempts to identify trivial infeasibilities,
identify trivial unboundedness, and simplify the problem before
sending it to the main solver. It is generally recommended
to keep the default setting ``True``; set to ``False`` if
presolve is to be disabled.
time_limit : float
The maximum time in seconds allotted to solve the problem;
default is the largest possible value for a ``double`` on the
platform.
dual_feasibility_tolerance : double (default: 1e-07)
The minimum of this and ``primal_feasibility_tolerance``
is used for the feasibility tolerance of
:ref:`'highs-ipm' <optimize.linprog-highs-ipm>`.
primal_feasibility_tolerance : double (default: 1e-07)
The minimum of this and ``dual_feasibility_tolerance``
is used for the feasibility tolerance of
:ref:`'highs-ipm' <optimize.linprog-highs-ipm>`.
ipm_optimality_tolerance : double (default: ``1e-08``)
Optimality tolerance for
:ref:`'highs-ipm' <optimize.linprog-highs-ipm>`.
Minimum allowable value is 1e-12.
unknown_options : dict
Optional arguments not used by this particular solver. If
``unknown_options`` is non-empty, a warning is issued listing
all unused options.
Returns
-------
res : OptimizeResult
A :class:`scipy.optimize.OptimizeResult` consisting of the fields:
x : 1D array
The values of the decision variables that minimizes the
objective function while satisfying the constraints.
fun : float
The optimal value of the objective function ``c @ x``.
slack : 1D array
The (nominally positive) values of the slack,
``b_ub - A_ub @ x``.
con : 1D array
The (nominally zero) residuals of the equality constraints,
``b_eq - A_eq @ x``.
success : bool
``True`` when the algorithm succeeds in finding an optimal
solution.
status : int
An integer representing the exit status of the algorithm.
``0`` : Optimization terminated successfully.
``1`` : Iteration or time limit reached.
``2`` : Problem appears to be infeasible.
``3`` : Problem appears to be unbounded.
``4`` : The HiGHS solver ran into a problem.
message : str
A string descriptor of the exit status of the algorithm.
nit : int
The total number of iterations performed.
For the HiGHS interior-point method, this does not include
crossover iterations.
crossover_nit : int
The number of primal/dual pushes performed during the
crossover routine for the HiGHS interior-point method.
ineqlin : OptimizeResult
Solution and sensitivity information corresponding to the
inequality constraints, `b_ub`. A dictionary consisting of the
fields:
residual : np.ndnarray
The (nominally positive) values of the slack variables,
``b_ub - A_ub @ x``. This quantity is also commonly
referred to as "slack".
marginals : np.ndarray
The sensitivity (partial derivative) of the objective
function with respect to the right-hand side of the
inequality constraints, `b_ub`.
eqlin : OptimizeResult
Solution and sensitivity information corresponding to the
equality constraints, `b_eq`. A dictionary consisting of the
fields:
residual : np.ndarray
The (nominally zero) residuals of the equality constraints,
``b_eq - A_eq @ x``.
marginals : np.ndarray
The sensitivity (partial derivative) of the objective
function with respect to the right-hand side of the
equality constraints, `b_eq`.
lower, upper : OptimizeResult
Solution and sensitivity information corresponding to the
lower and upper bounds on decision variables, `bounds`.
residual : np.ndarray
The (nominally positive) values of the quantity
``x - lb`` (lower) or ``ub - x`` (upper).
marginals : np.ndarray
The sensitivity (partial derivative) of the objective
function with respect to the lower and upper
`bounds`.
Notes
-----
Method :ref:`'highs-ipm' <optimize.linprog-highs-ipm>`
is a wrapper of a C++ implementation of an **i**\\ nterior-\\ **p**\\ oint
**m**\\ ethod [13]_; it features a crossover routine, so it is as accurate
as a simplex solver.
Method :ref:`'highs-ds' <optimize.linprog-highs-ds>` is a wrapper
of the C++ high performance dual revised simplex implementation (HSOL)
[13]_, [14]_. Method :ref:`'highs' <optimize.linprog-highs>` chooses
between the two automatically. For new code involving `linprog`, we
recommend explicitly choosing one of these three method values instead of
:ref:`'interior-point' <optimize.linprog-interior-point>` (default),
:ref:`'revised simplex' <optimize.linprog-revised_simplex>`, and
:ref:`'simplex' <optimize.linprog-simplex>` (legacy).
The result fields `ineqlin`, `eqlin`, `lower`, and `upper` all contain
`marginals`, or partial derivatives of the objective function with respect
to the right-hand side of each constraint. These partial derivatives are
also referred to as "Lagrange multipliers", "dual values", and
"shadow prices". The sign convention of `marginals` is opposite that
of Lagrange multipliers produced by many nonlinear solvers.
References
----------
.. [13] Huangfu, Q., Galabova, I., Feldmeier, M., and Hall, J. A. J.
"HiGHS - high performance software for linear optimization."
Accessed 4/16/2020 at https://www.maths.ed.ac.uk/hall/HiGHS/#guide
.. [14] Huangfu, Q. and Hall, J. A. J. "Parallelizing the dual revised
simplex method." Mathematical Programming Computation, 10 (1),
119-142, 2018. DOI: 10.1007/s12532-017-0130-5
"""
pass
def _linprog_ip_doc(c, A_ub=None, b_ub=None, A_eq=None, b_eq=None, bounds=None, method='interior-point', callback=None, maxiter=1000, disp=False, presolve=True, tol=1e-08, autoscale=False, rr=True, alpha0=0.99995, beta=0.1, sparse=False, lstsq=False, sym_pos=True, cholesky=True, pc=True, ip=False, permc_spec='MMD_AT_PLUS_A', **unknown_options):
"""
Linear programming: minimize a linear objective function subject to linear
equality and inequality constraints using the interior-point method of
[4]_.
.. deprecated:: 1.9.0
`method='interior-point'` will be removed in SciPy 1.11.0.
It is replaced by `method='highs'` because the latter is
faster and more robust.
Linear programming solves problems of the following form:
.. math::
\\min_x \\ & c^T x \\\\
\\mbox{such that} \\ & A_{ub} x \\leq b_{ub},\\\\
& A_{eq} x = b_{eq},\\\\
& l \\leq x \\leq u ,
where :math:`x` is a vector of decision variables; :math:`c`,
:math:`b_{ub}`, :math:`b_{eq}`, :math:`l`, and :math:`u` are vectors; and
:math:`A_{ub}` and :math:`A_{eq}` are matrices.
Alternatively, that's:
minimize::
c @ x
such that::
A_ub @ x <= b_ub
A_eq @ x == b_eq
lb <= x <= ub
Note that by default ``lb = 0`` and ``ub = None`` unless specified with
``bounds``.
Parameters
----------
c : 1-D array
The coefficients of the linear objective function to be minimized.
A_ub : 2-D array, optional
The inequality constraint matrix. Each row of ``A_ub`` specifies the
coefficients of a linear inequality constraint on ``x``.
b_ub : 1-D array, optional
The inequality constraint vector. Each element represents an
upper bound on the corresponding value of ``A_ub @ x``.
A_eq : 2-D array, optional
The equality constraint matrix. Each row of ``A_eq`` specifies the
coefficients of a linear equality constraint on ``x``.
b_eq : 1-D array, optional
The equality constraint vector. Each element of ``A_eq @ x`` must equal
the corresponding element of ``b_eq``.
bounds : sequence, optional
A sequence of ``(min, max)`` pairs for each element in ``x``, defining
the minimum and maximum values of that decision variable. Use ``None``
to indicate that there is no bound. By default, bounds are
``(0, None)`` (all decision variables are non-negative).
If a single tuple ``(min, max)`` is provided, then ``min`` and
``max`` will serve as bounds for all decision variables.
method : str
This is the method-specific documentation for 'interior-point'.
:ref:`'highs' <optimize.linprog-highs>`,
:ref:`'highs-ds' <optimize.linprog-highs-ds>`,
:ref:`'highs-ipm' <optimize.linprog-highs-ipm>`,
:ref:`'revised simplex' <optimize.linprog-revised_simplex>`, and
:ref:`'simplex' <optimize.linprog-simplex>` (legacy)
are also available.
callback : callable, optional
Callback function to be executed once per iteration.
Options
-------
maxiter : int (default: 1000)
The maximum number of iterations of the algorithm.
disp : bool (default: False)
Set to ``True`` if indicators of optimization status are to be printed
to the console each iteration.
presolve : bool (default: True)
Presolve attempts to identify trivial infeasibilities,
identify trivial unboundedness, and simplify the problem before
sending it to the main solver. It is generally recommended
to keep the default setting ``True``; set to ``False`` if
presolve is to be disabled.
tol : float (default: 1e-8)
Termination tolerance to be used for all termination criteria;
see [4]_ Section 4.5.
autoscale : bool (default: False)
Set to ``True`` to automatically perform equilibration.
Consider using this option if the numerical values in the
constraints are separated by several orders of magnitude.
rr : bool (default: True)
Set to ``False`` to disable automatic redundancy removal.
alpha0 : float (default: 0.99995)
The maximal step size for Mehrota's predictor-corrector search
direction; see :math:`\\beta_{3}` of [4]_ Table 8.1.
beta : float (default: 0.1)
The desired reduction of the path parameter :math:`\\mu` (see [6]_)
when Mehrota's predictor-corrector is not in use (uncommon).
sparse : bool (default: False)
Set to ``True`` if the problem is to be treated as sparse after
presolve. If either ``A_eq`` or ``A_ub`` is a sparse matrix,
this option will automatically be set ``True``, and the problem
will be treated as sparse even during presolve. If your constraint
matrices contain mostly zeros and the problem is not very small (less
than about 100 constraints or variables), consider setting ``True``
or providing ``A_eq`` and ``A_ub`` as sparse matrices.
lstsq : bool (default: ``False``)
Set to ``True`` if the problem is expected to be very poorly
conditioned. This should always be left ``False`` unless severe
numerical difficulties are encountered. Leave this at the default
unless you receive a warning message suggesting otherwise.
sym_pos : bool (default: True)
Leave ``True`` if the problem is expected to yield a well conditioned
symmetric positive definite normal equation matrix
(almost always). Leave this at the default unless you receive
a warning message suggesting otherwise.
cholesky : bool (default: True)
Set to ``True`` if the normal equations are to be solved by explicit
Cholesky decomposition followed by explicit forward/backward
substitution. This is typically faster for problems
that are numerically well-behaved.
pc : bool (default: True)
Leave ``True`` if the predictor-corrector method of Mehrota is to be
used. This is almost always (if not always) beneficial.
ip : bool (default: False)
Set to ``True`` if the improved initial point suggestion due to [4]_
Section 4.3 is desired. Whether this is beneficial or not
depends on the problem.
permc_spec : str (default: 'MMD_AT_PLUS_A')
(Has effect only with ``sparse = True``, ``lstsq = False``, ``sym_pos =
True``, and no SuiteSparse.)
A matrix is factorized in each iteration of the algorithm.
This option specifies how to permute the columns of the matrix for
sparsity preservation. Acceptable values are:
- ``NATURAL``: natural ordering.
- ``MMD_ATA``: minimum degree ordering on the structure of A^T A.
- ``MMD_AT_PLUS_A``: minimum degree ordering on the structure of A^T+A.
- ``COLAMD``: approximate minimum degree column ordering.
This option can impact the convergence of the
interior point algorithm; test different values to determine which
performs best for your problem. For more information, refer to
``scipy.sparse.linalg.splu``.
unknown_options : dict
Optional arguments not used by this particular solver. If
`unknown_options` is non-empty a warning is issued listing all
unused options.
Returns
-------
res : OptimizeResult
A :class:`scipy.optimize.OptimizeResult` consisting of the fields:
x : 1-D array
The values of the decision variables that minimizes the
objective function while satisfying the constraints.
fun : float
The optimal value of the objective function ``c @ x``.
slack : 1-D array
The (nominally positive) values of the slack variables,
``b_ub - A_ub @ x``.
con : 1-D array
The (nominally zero) residuals of the equality constraints,
``b_eq - A_eq @ x``.
success : bool
``True`` when the algorithm succeeds in finding an optimal
solution.
status : int
An integer representing the exit status of the algorithm.
``0`` : Optimization terminated successfully.
``1`` : Iteration limit reached.
``2`` : Problem appears to be infeasible.
``3`` : Problem appears to be unbounded.
``4`` : Numerical difficulties encountered.
message : str
A string descriptor of the exit status of the algorithm.
nit : int
The total number of iterations performed in all phases.
Notes
-----
This method implements the algorithm outlined in [4]_ with ideas from [8]_
and a structure inspired by the simpler methods of [6]_.
The primal-dual path following method begins with initial 'guesses' of
the primal and dual variables of the standard form problem and iteratively
attempts to solve the (nonlinear) Karush-Kuhn-Tucker conditions for the
problem with a gradually reduced logarithmic barrier term added to the
objective. This particular implementation uses a homogeneous self-dual
formulation, which provides certificates of infeasibility or unboundedness
where applicable.
The default initial point for the primal and dual variables is that
defined in [4]_ Section 4.4 Equation 8.22. Optionally (by setting initial
point option ``ip=True``), an alternate (potentially improved) starting
point can be calculated according to the additional recommendations of
[4]_ Section 4.4.
A search direction is calculated using the predictor-corrector method
(single correction) proposed by Mehrota and detailed in [4]_ Section 4.1.
(A potential improvement would be to implement the method of multiple
corrections described in [4]_ Section 4.2.) In practice, this is
accomplished by solving the normal equations, [4]_ Section 5.1 Equations
8.31 and 8.32, derived from the Newton equations [4]_ Section 5 Equations
8.25 (compare to [4]_ Section 4 Equations 8.6-8.8). The advantage of
solving the normal equations rather than 8.25 directly is that the
matrices involved are symmetric positive definite, so Cholesky
decomposition can be used rather than the more expensive LU factorization.
With default options, the solver used to perform the factorization depends
on third-party software availability and the conditioning of the problem.
For dense problems, solvers are tried in the following order:
1. ``scipy.linalg.cho_factor``
2. ``scipy.linalg.solve`` with option ``sym_pos=True``
3. ``scipy.linalg.solve`` with option ``sym_pos=False``
4. ``scipy.linalg.lstsq``
For sparse problems:
1. ``sksparse.cholmod.cholesky`` (if scikit-sparse and SuiteSparse are
installed)
2. ``scipy.sparse.linalg.factorized`` (if scikit-umfpack and SuiteSparse
are installed)
3. ``scipy.sparse.linalg.splu`` (which uses SuperLU distributed with SciPy)
4. ``scipy.sparse.linalg.lsqr``
If the solver fails for any reason, successively more robust (but slower)
solvers are attempted in the order indicated. Attempting, failing, and
re-starting factorization can be time consuming, so if the problem is
numerically challenging, options can be set to bypass solvers that are
failing. Setting ``cholesky=False`` skips to solver 2,
``sym_pos=False`` skips to solver 3, and ``lstsq=True`` skips
to solver 4 for both sparse and dense problems.
Potential improvements for combatting issues associated with dense
columns in otherwise sparse problems are outlined in [4]_ Section 5.3 and
[10]_ Section 4.1-4.2; the latter also discusses the alleviation of
accuracy issues associated with the substitution approach to free
variables.
After calculating the search direction, the maximum possible step size
that does not activate the non-negativity constraints is calculated, and
the smaller of this step size and unity is applied (as in [4]_ Section
4.1.) [4]_ Section 4.3 suggests improvements for choosing the step size.
The new point is tested according to the termination conditions of [4]_
Section 4.5. The same tolerance, which can be set using the ``tol`` option,
is used for all checks. (A potential improvement would be to expose
the different tolerances to be set independently.) If optimality,
unboundedness, or infeasibility is detected, the solve procedure
terminates; otherwise it repeats.
Whereas the top level ``linprog`` module expects a problem of form:
Minimize::
c @ x
Subject to::
A_ub @ x <= b_ub
A_eq @ x == b_eq
lb <= x <= ub
where ``lb = 0`` and ``ub = None`` unless set in ``bounds``. The problem
is automatically converted to the form:
Minimize::
c @ x
Subject to::
A @ x == b
x >= 0
for solution. That is, the original problem contains equality, upper-bound
and variable constraints whereas the method specific solver requires
equality constraints and variable non-negativity. ``linprog`` converts the
original problem to standard form by converting the simple bounds to upper
bound constraints, introducing non-negative slack variables for inequality
constraints, and expressing unbounded variables as the difference between
two non-negative variables. The problem is converted back to the original
form before results are reported.
References
----------
.. [4] Andersen, Erling D., and Knud D. Andersen. "The MOSEK interior point
optimizer for linear programming: an implementation of the
homogeneous algorithm." High performance optimization. Springer US,
2000. 197-232.
.. [6] Freund, Robert M. "Primal-Dual Interior-Point Methods for Linear
Programming based on Newton's Method." Unpublished Course Notes,
March 2004. Available 2/25/2017 at
https://ocw.mit.edu/courses/sloan-school-of-management/15-084j-nonlinear-programming-spring-2004/lecture-notes/lec14_int_pt_mthd.pdf
.. [8] Andersen, Erling D., and Knud D. Andersen. "Presolving in linear
programming." Mathematical Programming 71.2 (1995): 221-245.
.. [9] Bertsimas, Dimitris, and J. Tsitsiklis. "Introduction to linear
programming." Athena Scientific 1 (1997): 997.
.. [10] Andersen, Erling D., et al. Implementation of interior point
methods for large scale linear programming. HEC/Universite de
Geneve, 1996.
"""
pass
def _linprog_rs_doc(c, A_ub=None, b_ub=None, A_eq=None, b_eq=None, bounds=None, method='interior-point', callback=None, x0=None, maxiter=5000, disp=False, presolve=True, tol=1e-12, autoscale=False, rr=True, maxupdate=10, mast=False, pivot='mrc', **unknown_options):
"""
Linear programming: minimize a linear objective function subject to linear
equality and inequality constraints using the revised simplex method.
.. deprecated:: 1.9.0
`method='revised simplex'` will be removed in SciPy 1.11.0.
It is replaced by `method='highs'` because the latter is
faster and more robust.
Linear programming solves problems of the following form:
.. math::
\\min_x \\ & c^T x \\\\
\\mbox{such that} \\ & A_{ub} x \\leq b_{ub},\\\\
& A_{eq} x = b_{eq},\\\\
& l \\leq x \\leq u ,
where :math:`x` is a vector of decision variables; :math:`c`,
:math:`b_{ub}`, :math:`b_{eq}`, :math:`l`, and :math:`u` are vectors; and
:math:`A_{ub}` and :math:`A_{eq}` are matrices.
Alternatively, that's:
minimize::
c @ x
such that::
A_ub @ x <= b_ub
A_eq @ x == b_eq
lb <= x <= ub
Note that by default ``lb = 0`` and ``ub = None`` unless specified with
``bounds``.
Parameters
----------
c : 1-D array
The coefficients of the linear objective function to be minimized.
A_ub : 2-D array, optional
The inequality constraint matrix. Each row of ``A_ub`` specifies the
coefficients of a linear inequality constraint on ``x``.
b_ub : 1-D array, optional
The inequality constraint vector. Each element represents an
upper bound on the corresponding value of ``A_ub @ x``.
A_eq : 2-D array, optional
The equality constraint matrix. Each row of ``A_eq`` specifies the
coefficients of a linear equality constraint on ``x``.
b_eq : 1-D array, optional
The equality constraint vector. Each element of ``A_eq @ x`` must equal
the corresponding element of ``b_eq``.
bounds : sequence, optional
A sequence of ``(min, max)`` pairs for each element in ``x``, defining
the minimum and maximum values of that decision variable. Use ``None``
to indicate that there is no bound. By default, bounds are
``(0, None)`` (all decision variables are non-negative).
If a single tuple ``(min, max)`` is provided, then ``min`` and
``max`` will serve as bounds for all decision variables.
method : str
This is the method-specific documentation for 'revised simplex'.
:ref:`'highs' <optimize.linprog-highs>`,
:ref:`'highs-ds' <optimize.linprog-highs-ds>`,
:ref:`'highs-ipm' <optimize.linprog-highs-ipm>`,
:ref:`'interior-point' <optimize.linprog-interior-point>` (default),
and :ref:`'simplex' <optimize.linprog-simplex>` (legacy)
are also available.
callback : callable, optional
Callback function to be executed once per iteration.
x0 : 1-D array, optional
Guess values of the decision variables, which will be refined by
the optimization algorithm. This argument is currently used only by the
'revised simplex' method, and can only be used if `x0` represents a
basic feasible solution.
Options
-------
maxiter : int (default: 5000)
The maximum number of iterations to perform in either phase.
disp : bool (default: False)
Set to ``True`` if indicators of optimization status are to be printed
to the console each iteration.
presolve : bool (default: True)
Presolve attempts to identify trivial infeasibilities,
identify trivial unboundedness, and simplify the problem before
sending it to the main solver. It is generally recommended
to keep the default setting ``True``; set to ``False`` if
presolve is to be disabled.
tol : float (default: 1e-12)
The tolerance which determines when a solution is "close enough" to
zero in Phase 1 to be considered a basic feasible solution or close
enough to positive to serve as an optimal solution.
autoscale : bool (default: False)
Set to ``True`` to automatically perform equilibration.
Consider using this option if the numerical values in the
constraints are separated by several orders of magnitude.
rr : bool (default: True)
Set to ``False`` to disable automatic redundancy removal.
maxupdate : int (default: 10)
The maximum number of updates performed on the LU factorization.
After this many updates is reached, the basis matrix is factorized
from scratch.
mast : bool (default: False)
Minimize Amortized Solve Time. If enabled, the average time to solve
a linear system using the basis factorization is measured. Typically,
the average solve time will decrease with each successive solve after
initial factorization, as factorization takes much more time than the
solve operation (and updates). Eventually, however, the updated
factorization becomes sufficiently complex that the average solve time
begins to increase. When this is detected, the basis is refactorized
from scratch. Enable this option to maximize speed at the risk of
nondeterministic behavior. Ignored if ``maxupdate`` is 0.
pivot : "mrc" or "bland" (default: "mrc")
Pivot rule: Minimum Reduced Cost ("mrc") or Bland's rule ("bland").
Choose Bland's rule if iteration limit is reached and cycling is
suspected.
unknown_options : dict
Optional arguments not used by this particular solver. If
`unknown_options` is non-empty a warning is issued listing all
unused options.
Returns
-------
res : OptimizeResult
A :class:`scipy.optimize.OptimizeResult` consisting of the fields:
x : 1-D array
The values of the decision variables that minimizes the
objective function while satisfying the constraints.
fun : float
The optimal value of the objective function ``c @ x``.
slack : 1-D array
The (nominally positive) values of the slack variables,
``b_ub - A_ub @ x``.
con : 1-D array
The (nominally zero) residuals of the equality constraints,
``b_eq - A_eq @ x``.
success : bool
``True`` when the algorithm succeeds in finding an optimal
solution.
status : int
An integer representing the exit status of the algorithm.
``0`` : Optimization terminated successfully.
``1`` : Iteration limit reached.
``2`` : Problem appears to be infeasible.
``3`` : Problem appears to be unbounded.
``4`` : Numerical difficulties encountered.
``5`` : Problem has no constraints; turn presolve on.
``6`` : Invalid guess provided.
message : str
A string descriptor of the exit status of the algorithm.
nit : int
The total number of iterations performed in all phases.
Notes
-----
Method *revised simplex* uses the revised simplex method as described in
[9]_, except that a factorization [11]_ of the basis matrix, rather than
its inverse, is efficiently maintained and used to solve the linear systems
at each iteration of the algorithm.
References
----------
.. [9] Bertsimas, Dimitris, and J. Tsitsiklis. "Introduction to linear
programming." Athena Scientific 1 (1997): 997.
.. [11] Bartels, Richard H. "A stabilization of the simplex method."
Journal in Numerische Mathematik 16.5 (1971): 414-434.
"""
pass
def _linprog_simplex_doc(c, A_ub=None, b_ub=None, A_eq=None, b_eq=None, bounds=None, method='interior-point', callback=None, maxiter=5000, disp=False, presolve=True, tol=1e-12, autoscale=False, rr=True, bland=False, **unknown_options):
"""
Linear programming: minimize a linear objective function subject to linear
equality and inequality constraints using the tableau-based simplex method.
.. deprecated:: 1.9.0
`method='simplex'` will be removed in SciPy 1.11.0.
It is replaced by `method='highs'` because the latter is
faster and more robust.
Linear programming solves problems of the following form:
.. math::
\\min_x \\ & c^T x \\\\
\\mbox{such that} \\ & A_{ub} x \\leq b_{ub},\\\\
& A_{eq} x = b_{eq},\\\\
& l \\leq x \\leq u ,
where :math:`x` is a vector of decision variables; :math:`c`,
:math:`b_{ub}`, :math:`b_{eq}`, :math:`l`, and :math:`u` are vectors; and
:math:`A_{ub}` and :math:`A_{eq}` are matrices.
Alternatively, that's:
minimize::
c @ x
such that::
A_ub @ x <= b_ub
A_eq @ x == b_eq
lb <= x <= ub
Note that by default ``lb = 0`` and ``ub = None`` unless specified with
``bounds``.
Parameters
----------
c : 1-D array
The coefficients of the linear objective function to be minimized.
A_ub : 2-D array, optional
The inequality constraint matrix. Each row of ``A_ub`` specifies the
coefficients of a linear inequality constraint on ``x``.
b_ub : 1-D array, optional
The inequality constraint vector. Each element represents an
upper bound on the corresponding value of ``A_ub @ x``.
A_eq : 2-D array, optional
The equality constraint matrix. Each row of ``A_eq`` specifies the
coefficients of a linear equality constraint on ``x``.
b_eq : 1-D array, optional
The equality constraint vector. Each element of ``A_eq @ x`` must equal
the corresponding element of ``b_eq``.
bounds : sequence, optional
A sequence of ``(min, max)`` pairs for each element in ``x``, defining
the minimum and maximum values of that decision variable. Use ``None``
to indicate that there is no bound. By default, bounds are
``(0, None)`` (all decision variables are non-negative).
If a single tuple ``(min, max)`` is provided, then ``min`` and
``max`` will serve as bounds for all decision variables.
method : str
This is the method-specific documentation for 'simplex'.
:ref:`'highs' <optimize.linprog-highs>`,
:ref:`'highs-ds' <optimize.linprog-highs-ds>`,
:ref:`'highs-ipm' <optimize.linprog-highs-ipm>`,
:ref:`'interior-point' <optimize.linprog-interior-point>` (default),
and :ref:`'revised simplex' <optimize.linprog-revised_simplex>`
are also available.
callback : callable, optional
Callback function to be executed once per iteration.
Options
-------
maxiter : int (default: 5000)
The maximum number of iterations to perform in either phase.
disp : bool (default: False)
Set to ``True`` if indicators of optimization status are to be printed
to the console each iteration.
presolve : bool (default: True)
Presolve attempts to identify trivial infeasibilities,
identify trivial unboundedness, and simplify the problem before
sending it to the main solver. It is generally recommended
to keep the default setting ``True``; set to ``False`` if
presolve is to be disabled.
tol : float (default: 1e-12)
The tolerance which determines when a solution is "close enough" to
zero in Phase 1 to be considered a basic feasible solution or close
enough to positive to serve as an optimal solution.
autoscale : bool (default: False)
Set to ``True`` to automatically perform equilibration.
Consider using this option if the numerical values in the
constraints are separated by several orders of magnitude.
rr : bool (default: True)
Set to ``False`` to disable automatic redundancy removal.
bland : bool
If True, use Bland's anti-cycling rule [3]_ to choose pivots to
prevent cycling. If False, choose pivots which should lead to a
converged solution more quickly. The latter method is subject to
cycling (non-convergence) in rare instances.
unknown_options : dict
Optional arguments not used by this particular solver. If
`unknown_options` is non-empty a warning is issued listing all
unused options.
Returns
-------
res : OptimizeResult
A :class:`scipy.optimize.OptimizeResult` consisting of the fields:
x : 1-D array
The values of the decision variables that minimizes the
objective function while satisfying the constraints.
fun : float
The optimal value of the objective function ``c @ x``.
slack : 1-D array
The (nominally positive) values of the slack variables,
``b_ub - A_ub @ x``.
con : 1-D array
The (nominally zero) residuals of the equality constraints,
``b_eq - A_eq @ x``.
success : bool
``True`` when the algorithm succeeds in finding an optimal
solution.
status : int
An integer representing the exit status of the algorithm.
``0`` : Optimization terminated successfully.
``1`` : Iteration limit reached.
``2`` : Problem appears to be infeasible.
``3`` : Problem appears to be unbounded.
``4`` : Numerical difficulties encountered.
message : str
A string descriptor of the exit status of the algorithm.
nit : int
The total number of iterations performed in all phases.
References
----------
.. [1] Dantzig, George B., Linear programming and extensions. Rand
Corporation Research Study Princeton Univ. Press, Princeton, NJ,
1963
.. [2] Hillier, S.H. and Lieberman, G.J. (1995), "Introduction to
Mathematical Programming", McGraw-Hill, Chapter 4.
.. [3] Bland, Robert G. New finite pivoting rules for the simplex method.
Mathematics of Operations Research (2), 1977: pp. 103-107.
"""
pass
|
"""Devstack environment variables unique to the instructor plugin."""
def plugin_settings(settings):
"""Settings for the instructor plugin."""
# Set this to the dashboard URL in order to display the link from the
# dashboard to the Analytics Dashboard.
settings.ANALYTICS_DASHBOARD_URL = None
|
"""Devstack environment variables unique to the instructor plugin."""
def plugin_settings(settings):
"""Settings for the instructor plugin."""
settings.ANALYTICS_DASHBOARD_URL = None
|
__version__ = '0.54'
class LandDegradationError(Exception):
"""Base class for exceptions in this module."""
def __init__(self, msg=None):
if msg is None:
msg = "An error occurred in the landdegradation module"
super(LandDegradationError, self).__init__(msg)
class GEEError(LandDegradationError):
"""Error related to GEE"""
def __init__(self, msg="Error with GEE JSON IO"):
super(LandDegradationError, self).__init__(msg)
class GEEIOError(GEEError):
"""Error related to GEE"""
def __init__(self, msg="Error with GEE JSON IO"):
super(GEEError, self).__init__(msg)
class GEEImageError(GEEError):
"""Error related to GEE"""
def __init__(self, msg="Error with GEE image handling"):
super(GEEError, self).__init__(msg)
class GEETaskFailure(GEEError):
"""Error running task on GEE"""
def __init__(self, task):
# super(GEEError, self).__init__("Task {} failed".format(task.status().get('id')))
super(GEEError, self).__init__("Task {} failed".format(task))
print(task)
self.task = task
|
__version__ = '0.54'
class Landdegradationerror(Exception):
"""Base class for exceptions in this module."""
def __init__(self, msg=None):
if msg is None:
msg = 'An error occurred in the landdegradation module'
super(LandDegradationError, self).__init__(msg)
class Geeerror(LandDegradationError):
"""Error related to GEE"""
def __init__(self, msg='Error with GEE JSON IO'):
super(LandDegradationError, self).__init__(msg)
class Geeioerror(GEEError):
"""Error related to GEE"""
def __init__(self, msg='Error with GEE JSON IO'):
super(GEEError, self).__init__(msg)
class Geeimageerror(GEEError):
"""Error related to GEE"""
def __init__(self, msg='Error with GEE image handling'):
super(GEEError, self).__init__(msg)
class Geetaskfailure(GEEError):
"""Error running task on GEE"""
def __init__(self, task):
super(GEEError, self).__init__('Task {} failed'.format(task))
print(task)
self.task = task
|
del_items(0x80145350)
SetType(0x80145350, "void _cd_seek(int sec)")
del_items(0x801453BC)
SetType(0x801453BC, "void init_cdstream(int chunksize, unsigned char *buf, int bufsize)")
del_items(0x801453E4)
SetType(0x801453E4, "void flush_cdstream()")
del_items(0x80145438)
SetType(0x80145438, "void reset_cdstream()")
del_items(0x80145468)
SetType(0x80145468, "void kill_stream_handlers()")
del_items(0x80145498)
SetType(0x80145498, "void stream_cdready_handler(unsigned char status, unsigned char *result, int idx, int i, int sec, struct CdlLOC subcode[3])")
del_items(0x80145684)
SetType(0x80145684, "void install_stream_handlers()")
del_items(0x801456C0)
SetType(0x801456C0, "void cdstream_service()")
del_items(0x801457B0)
SetType(0x801457B0, "int cdstream_get_chunk(unsigned char **data, struct strheader **h)")
del_items(0x801458C8)
SetType(0x801458C8, "int cdstream_is_last_chunk()")
del_items(0x801458E0)
SetType(0x801458E0, "void cdstream_discard_chunk()")
del_items(0x80145A00)
SetType(0x80145A00, "void close_cdstream()")
del_items(0x80145A40)
SetType(0x80145A40, "void wait_cdstream()")
del_items(0x80145AF8)
SetType(0x80145AF8, "int open_cdstream(char *fname, int secoffs, int seclen)")
del_items(0x80145BFC)
SetType(0x80145BFC, "int set_mdec_img_buffer(unsigned char *p)")
del_items(0x80145C30)
SetType(0x80145C30, "void start_mdec_decode(unsigned char *data, int x, int y, int w, int h)")
del_items(0x80145DB4)
SetType(0x80145DB4, "void DCT_out_handler()")
del_items(0x80145E50)
SetType(0x80145E50, "void init_mdec(unsigned char *vlc_buffer, unsigned char *vlc_table)")
del_items(0x80145EC0)
SetType(0x80145EC0, "void init_mdec_buffer(char *buf, int size)")
del_items(0x80145EDC)
SetType(0x80145EDC, "int split_poly_area(struct POLY_FT4 *p, struct POLY_FT4 *bp, int offs, struct RECT *r, int sx, int sy, int correct)")
del_items(0x801462CC)
SetType(0x801462CC, "void rebuild_mdec_polys(int x, int y)")
del_items(0x801464A0)
SetType(0x801464A0, "void clear_mdec_frame()")
del_items(0x801464AC)
SetType(0x801464AC, "void draw_mdec_polys()")
del_items(0x801467F8)
SetType(0x801467F8, "void invalidate_mdec_frame()")
del_items(0x8014680C)
SetType(0x8014680C, "int is_frame_decoded()")
del_items(0x80146818)
SetType(0x80146818, "void init_mdec_polys(int x, int y, int w, int h, int bx1, int by1, int bx2, int by2, int correct)")
del_items(0x80146BA8)
SetType(0x80146BA8, "void set_mdec_poly_bright(int br)")
del_items(0x80146C10)
SetType(0x80146C10, "int init_mdec_stream(unsigned char *buftop, int sectors_per_frame, int mdec_frames_per_buffer)")
del_items(0x80146C60)
SetType(0x80146C60, "void init_mdec_audio(int rate)")
del_items(0x80146D68)
SetType(0x80146D68, "void kill_mdec_audio()")
del_items(0x80146D98)
SetType(0x80146D98, "void stop_mdec_audio()")
del_items(0x80146DBC)
SetType(0x80146DBC, "void play_mdec_audio(unsigned char *data, struct asec *h)")
del_items(0x80147054)
SetType(0x80147054, "void set_mdec_audio_volume(short vol, struct SpuVoiceAttr voice_attr)")
del_items(0x80147120)
SetType(0x80147120, "void resync_audio()")
del_items(0x80147150)
SetType(0x80147150, "void stop_mdec_stream()")
del_items(0x801471A4)
SetType(0x801471A4, "void dequeue_stream()")
del_items(0x80147290)
SetType(0x80147290, "void dequeue_animation()")
del_items(0x80147440)
SetType(0x80147440, "void decode_mdec_stream(int frames_elapsed)")
del_items(0x80147620)
SetType(0x80147620, "void play_mdec_stream(char *filename, int speed, int start, int end)")
del_items(0x801476D4)
SetType(0x801476D4, "void clear_mdec_queue()")
del_items(0x80147700)
SetType(0x80147700, "void StrClearVRAM()")
del_items(0x801477C0)
SetType(0x801477C0, "short PlayFMVOverLay(char *filename, int w, int h)")
del_items(0x80147C84)
SetType(0x80147C84, "unsigned short GetDown__C4CPad(struct CPad *this)")
|
del_items(2148815696)
set_type(2148815696, 'void _cd_seek(int sec)')
del_items(2148815804)
set_type(2148815804, 'void init_cdstream(int chunksize, unsigned char *buf, int bufsize)')
del_items(2148815844)
set_type(2148815844, 'void flush_cdstream()')
del_items(2148815928)
set_type(2148815928, 'void reset_cdstream()')
del_items(2148815976)
set_type(2148815976, 'void kill_stream_handlers()')
del_items(2148816024)
set_type(2148816024, 'void stream_cdready_handler(unsigned char status, unsigned char *result, int idx, int i, int sec, struct CdlLOC subcode[3])')
del_items(2148816516)
set_type(2148816516, 'void install_stream_handlers()')
del_items(2148816576)
set_type(2148816576, 'void cdstream_service()')
del_items(2148816816)
set_type(2148816816, 'int cdstream_get_chunk(unsigned char **data, struct strheader **h)')
del_items(2148817096)
set_type(2148817096, 'int cdstream_is_last_chunk()')
del_items(2148817120)
set_type(2148817120, 'void cdstream_discard_chunk()')
del_items(2148817408)
set_type(2148817408, 'void close_cdstream()')
del_items(2148817472)
set_type(2148817472, 'void wait_cdstream()')
del_items(2148817656)
set_type(2148817656, 'int open_cdstream(char *fname, int secoffs, int seclen)')
del_items(2148817916)
set_type(2148817916, 'int set_mdec_img_buffer(unsigned char *p)')
del_items(2148817968)
set_type(2148817968, 'void start_mdec_decode(unsigned char *data, int x, int y, int w, int h)')
del_items(2148818356)
set_type(2148818356, 'void DCT_out_handler()')
del_items(2148818512)
set_type(2148818512, 'void init_mdec(unsigned char *vlc_buffer, unsigned char *vlc_table)')
del_items(2148818624)
set_type(2148818624, 'void init_mdec_buffer(char *buf, int size)')
del_items(2148818652)
set_type(2148818652, 'int split_poly_area(struct POLY_FT4 *p, struct POLY_FT4 *bp, int offs, struct RECT *r, int sx, int sy, int correct)')
del_items(2148819660)
set_type(2148819660, 'void rebuild_mdec_polys(int x, int y)')
del_items(2148820128)
set_type(2148820128, 'void clear_mdec_frame()')
del_items(2148820140)
set_type(2148820140, 'void draw_mdec_polys()')
del_items(2148820984)
set_type(2148820984, 'void invalidate_mdec_frame()')
del_items(2148821004)
set_type(2148821004, 'int is_frame_decoded()')
del_items(2148821016)
set_type(2148821016, 'void init_mdec_polys(int x, int y, int w, int h, int bx1, int by1, int bx2, int by2, int correct)')
del_items(2148821928)
set_type(2148821928, 'void set_mdec_poly_bright(int br)')
del_items(2148822032)
set_type(2148822032, 'int init_mdec_stream(unsigned char *buftop, int sectors_per_frame, int mdec_frames_per_buffer)')
del_items(2148822112)
set_type(2148822112, 'void init_mdec_audio(int rate)')
del_items(2148822376)
set_type(2148822376, 'void kill_mdec_audio()')
del_items(2148822424)
set_type(2148822424, 'void stop_mdec_audio()')
del_items(2148822460)
set_type(2148822460, 'void play_mdec_audio(unsigned char *data, struct asec *h)')
del_items(2148823124)
set_type(2148823124, 'void set_mdec_audio_volume(short vol, struct SpuVoiceAttr voice_attr)')
del_items(2148823328)
set_type(2148823328, 'void resync_audio()')
del_items(2148823376)
set_type(2148823376, 'void stop_mdec_stream()')
del_items(2148823460)
set_type(2148823460, 'void dequeue_stream()')
del_items(2148823696)
set_type(2148823696, 'void dequeue_animation()')
del_items(2148824128)
set_type(2148824128, 'void decode_mdec_stream(int frames_elapsed)')
del_items(2148824608)
set_type(2148824608, 'void play_mdec_stream(char *filename, int speed, int start, int end)')
del_items(2148824788)
set_type(2148824788, 'void clear_mdec_queue()')
del_items(2148824832)
set_type(2148824832, 'void StrClearVRAM()')
del_items(2148825024)
set_type(2148825024, 'short PlayFMVOverLay(char *filename, int w, int h)')
del_items(2148826244)
set_type(2148826244, 'unsigned short GetDown__C4CPad(struct CPad *this)')
|
# Sets an error rate of %0.025 (1 in 4,000) with a capacity of 5MM items.
# See https://hur.st/bloomfilter/?n=5000000&p=0.00025&m=& for more information
# 5MM was chosen for being a whole number roughly 2x the size of our most dense sparse plugin output in late July 2020.
DEFAULT_ERROR_RATE = 0.00025
DEFAULT_CAPACITY = 5000000
KEY_PREFIX = "fwan_dataset_bf:"
# The bloom filters are only useful for per-file-hash Firmware Analysis plugins with sparse output, as those are the
# scenarios where we are likely to avoid unnecessary object storage fetches.
exclusions = [
"augeas", # firmware-file-level
"file_hashes", # too dense
"file_tree", # firmware-level
"file_type", # too dense
"unpack_report", # irrelevant
"unpack_failed", # irrelevant
"printable_strings", # too dense
]
exclusion_prefixes = [
"firmware_cpes", # firmware-level
"sbom", # sbom/unified is firmware-level, sbom/apt_file is irrelevant
"similarity_hash", # irrelevant
]
|
default_error_rate = 0.00025
default_capacity = 5000000
key_prefix = 'fwan_dataset_bf:'
exclusions = ['augeas', 'file_hashes', 'file_tree', 'file_type', 'unpack_report', 'unpack_failed', 'printable_strings']
exclusion_prefixes = ['firmware_cpes', 'sbom', 'similarity_hash']
|
fp = open("./packet.csv", "r")
vals = fp.readlines()
count = 1
pre_val = 0
current = 0
sampling_rate = 31
val_bins = []
for i in range(len(vals)):
pre_val = current
current = int(vals[i])
if current == pre_val:
count = count + 1
else:
count = 1
if count == sampling_rate:
val_bins.append(pre_val)
count = 1
pre_val = 0
current = 0
next_bins = []
for i in range(len(val_bins) // 2):
b0 = val_bins[i * 2]
b1 = val_bins[i * 2 + 1]
if b0 == 1 and b1 == 0:
next_bins.append(1)
elif b0 == 0 and b1 == 1:
next_bins.append(0)
val_bins = next_bins[3:]
c = 0
for i in range(len(val_bins)):
val = int(val_bins[i])
c = (c << 1) | val
if i % 8 == 7:
print(chr(c), end="")
c = 0
print("")
|
fp = open('./packet.csv', 'r')
vals = fp.readlines()
count = 1
pre_val = 0
current = 0
sampling_rate = 31
val_bins = []
for i in range(len(vals)):
pre_val = current
current = int(vals[i])
if current == pre_val:
count = count + 1
else:
count = 1
if count == sampling_rate:
val_bins.append(pre_val)
count = 1
pre_val = 0
current = 0
next_bins = []
for i in range(len(val_bins) // 2):
b0 = val_bins[i * 2]
b1 = val_bins[i * 2 + 1]
if b0 == 1 and b1 == 0:
next_bins.append(1)
elif b0 == 0 and b1 == 1:
next_bins.append(0)
val_bins = next_bins[3:]
c = 0
for i in range(len(val_bins)):
val = int(val_bins[i])
c = c << 1 | val
if i % 8 == 7:
print(chr(c), end='')
c = 0
print('')
|
'''
Specifies the username and password required to log into the iNaturalist website.
These variables are imported into the 'inaturalist_scraper.py' script
'''
username = 'your_iNaturalist_username_here'
password = 'your_iNaturalist_password_here'
|
"""
Specifies the username and password required to log into the iNaturalist website.
These variables are imported into the 'inaturalist_scraper.py' script
"""
username = 'your_iNaturalist_username_here'
password = 'your_iNaturalist_password_here'
|
FRONT_LEFT_WHEEL_TOPIC = "/capo_front_left_wheel_controller/command"
FRONT_RIGHT_WHEEL_TOPIC = "/capo_front_right_wheel_controller/command"
# BACK_RIGHT_WHEEL_TOPIC = "/capo_rear_left_wheel_controller/command",
# BACK_LEFT_WHEEL_TOPIC = "/capo_rear_right_wheel_controller/command"
HEAD_JOINT_TOPIC = "/capo_head_rotation_controller/command"
CAPO_JOINT_STATES = '/joint_states'
TOPNAV_FEEDBACK_TOPIC = '/topnav/feedback'
TOPNAV_GUIDELINES_TOPIC = 'topnav/guidelines'
GAZEBO_MODEL_STATES_TOPIC = "/gazebo/model_states"
|
front_left_wheel_topic = '/capo_front_left_wheel_controller/command'
front_right_wheel_topic = '/capo_front_right_wheel_controller/command'
head_joint_topic = '/capo_head_rotation_controller/command'
capo_joint_states = '/joint_states'
topnav_feedback_topic = '/topnav/feedback'
topnav_guidelines_topic = 'topnav/guidelines'
gazebo_model_states_topic = '/gazebo/model_states'
|
class BasicEnvironment:
"""Object to pass containing data used for solve. Individuals are mapped
to this data upon evaluation. Environments also hold additional evaluation
data."""
def __init__(self, df, _dict=None):
self.df = df
self._dict = _dict
|
class Basicenvironment:
"""Object to pass containing data used for solve. Individuals are mapped
to this data upon evaluation. Environments also hold additional evaluation
data."""
def __init__(self, df, _dict=None):
self.df = df
self._dict = _dict
|
# -*- coding:utf-8 -*-
pizzas = ['fruit', 'buff', 'milk', 'zhishi', 'little']
# for pizza in pizzas:
# print(pizza)
# print(pizza.title() + ", I like eat !")
# print("I like eat pizza!")
pizzas_bak = pizzas[:]
pizzas.append('chicken')
pizzas_bak.append('dog')
print(pizzas_bak)
print(pizzas)
|
pizzas = ['fruit', 'buff', 'milk', 'zhishi', 'little']
pizzas_bak = pizzas[:]
pizzas.append('chicken')
pizzas_bak.append('dog')
print(pizzas_bak)
print(pizzas)
|
class Solution:
def reformatDate(self, date: str) -> str:
pattern = re.compile(r'[0-9]+')
dateList=date.split(' ')
day=pattern.findall(dateList[0])[0]
monthList=["Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec"]
month=monthList.index(dateList[1])+1
year=dateList[2]
return year+'-'+str(month).zfill(2)+'-'+str(day).zfill(2)
|
class Solution:
def reformat_date(self, date: str) -> str:
pattern = re.compile('[0-9]+')
date_list = date.split(' ')
day = pattern.findall(dateList[0])[0]
month_list = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']
month = monthList.index(dateList[1]) + 1
year = dateList[2]
return year + '-' + str(month).zfill(2) + '-' + str(day).zfill(2)
|
inp = open('input_d24.txt').read().strip().split('\n')
graph = dict()
n = 0
yn = len(inp)
xn = len(inp[0])
poss = '01234567'
def find_n(ton):
for y in range(len(inp)):
for x in range(len(inp[y])):
if inp[y][x] == str(ton):
return (x,y)
def exists(n, found):
for ele in list(graph[n]):
if ele[0] == int(found):
return True
return False
def find_adj(x,y):
res = set()
for (a,b) in [(x-1,y), (x+1,y), (x,y-1), (x,y+1)]:
if a >= 0 and a < xn and b >= 0 and b < yn:
if inp[b][a] != '#':
res.add((a,b))
return res
for n in range(8):
x,y = find_n(n)
if n not in graph:
graph[n] = set()
visited = set()
queue = [((x,y), set([(x,y)]))]
while queue:
(vertex, path) = queue.pop(0)
for nex in find_adj(*vertex) - path:
if nex in visited:
continue
visited.add(nex)
found = inp[nex[1]][nex[0]]
if found in poss:
if not exists(n,found):
graph[n].add((int(found), len(path)))
if int(found) not in graph:
graph[int(found)] = set()
graph[int(found)].add((n, len(path)))
else:
p = path.copy()
p.add(nex)
queue.append((nex, p))
print(graph)
print('bruteforcing TSP (fun times)')
smallest = 10000
queue = [(0, set([0]), 0)]
while queue:
(current, visited, count) = queue.pop(0)
if count >= smallest:
continue
for pos in graph[current]:
nv = visited.copy()
nv.add(pos[0])
nc = count + pos[1]
if all([int(p) in nv for p in poss]) and pos[0] == 0 and nc < smallest:
smallest = nc
print(smallest)
elif pos[0] not in visited and nc < smallest:
queue.append((pos[0], nv, nc))
print(smallest)
|
inp = open('input_d24.txt').read().strip().split('\n')
graph = dict()
n = 0
yn = len(inp)
xn = len(inp[0])
poss = '01234567'
def find_n(ton):
for y in range(len(inp)):
for x in range(len(inp[y])):
if inp[y][x] == str(ton):
return (x, y)
def exists(n, found):
for ele in list(graph[n]):
if ele[0] == int(found):
return True
return False
def find_adj(x, y):
res = set()
for (a, b) in [(x - 1, y), (x + 1, y), (x, y - 1), (x, y + 1)]:
if a >= 0 and a < xn and (b >= 0) and (b < yn):
if inp[b][a] != '#':
res.add((a, b))
return res
for n in range(8):
(x, y) = find_n(n)
if n not in graph:
graph[n] = set()
visited = set()
queue = [((x, y), set([(x, y)]))]
while queue:
(vertex, path) = queue.pop(0)
for nex in find_adj(*vertex) - path:
if nex in visited:
continue
visited.add(nex)
found = inp[nex[1]][nex[0]]
if found in poss:
if not exists(n, found):
graph[n].add((int(found), len(path)))
if int(found) not in graph:
graph[int(found)] = set()
graph[int(found)].add((n, len(path)))
else:
p = path.copy()
p.add(nex)
queue.append((nex, p))
print(graph)
print('bruteforcing TSP (fun times)')
smallest = 10000
queue = [(0, set([0]), 0)]
while queue:
(current, visited, count) = queue.pop(0)
if count >= smallest:
continue
for pos in graph[current]:
nv = visited.copy()
nv.add(pos[0])
nc = count + pos[1]
if all([int(p) in nv for p in poss]) and pos[0] == 0 and (nc < smallest):
smallest = nc
print(smallest)
elif pos[0] not in visited and nc < smallest:
queue.append((pos[0], nv, nc))
print(smallest)
|
array = []
for i in range (16):
# array.append([i,0])
array.append([i,5])
print(array)
|
array = []
for i in range(16):
array.append([i, 5])
print(array)
|
#
# PySNMP MIB module Juniper-PPPOE-PROFILE-MIB (http://snmplabs.com/pysmi)
# ASN.1 source file:///Users/davwang4/Dev/mibs.snmplabs.com/asn1/Juniper-PPPOE-PROFILE-MIB
# Produced by pysmi-0.3.4 at Wed May 1 14:03:50 2019
# On host DAVWANG4-M-1475 platform Darwin version 18.5.0 by user davwang4
# Using Python version 3.7.3 (default, Mar 27 2019, 09:23:15)
#
Integer, OctetString, ObjectIdentifier = mibBuilder.importSymbols("ASN1", "Integer", "OctetString", "ObjectIdentifier")
NamedValues, = mibBuilder.importSymbols("ASN1-ENUMERATION", "NamedValues")
ConstraintsUnion, SingleValueConstraint, ValueSizeConstraint, ValueRangeConstraint, ConstraintsIntersection = mibBuilder.importSymbols("ASN1-REFINEMENT", "ConstraintsUnion", "SingleValueConstraint", "ValueSizeConstraint", "ValueRangeConstraint", "ConstraintsIntersection")
juniMibs, = mibBuilder.importSymbols("Juniper-MIBs", "juniMibs")
JuniEnable, JuniSetMap = mibBuilder.importSymbols("Juniper-TC", "JuniEnable", "JuniSetMap")
ObjectGroup, ModuleCompliance, NotificationGroup = mibBuilder.importSymbols("SNMPv2-CONF", "ObjectGroup", "ModuleCompliance", "NotificationGroup")
IpAddress, ModuleIdentity, MibIdentifier, Integer32, Gauge32, iso, Counter32, TimeTicks, Unsigned32, Counter64, NotificationType, ObjectIdentity, Bits, MibScalar, MibTable, MibTableRow, MibTableColumn = mibBuilder.importSymbols("SNMPv2-SMI", "IpAddress", "ModuleIdentity", "MibIdentifier", "Integer32", "Gauge32", "iso", "Counter32", "TimeTicks", "Unsigned32", "Counter64", "NotificationType", "ObjectIdentity", "Bits", "MibScalar", "MibTable", "MibTableRow", "MibTableColumn")
TextualConvention, DisplayString = mibBuilder.importSymbols("SNMPv2-TC", "TextualConvention", "DisplayString")
juniPppoeProfileMIB = ModuleIdentity((1, 3, 6, 1, 4, 1, 4874, 2, 2, 46))
juniPppoeProfileMIB.setRevisions(('2008-06-18 10:29', '2005-07-13 11:40', '2004-06-10 19:25', '2003-03-11 21:58', '2002-09-16 21:44', '2002-08-15 20:34', '2002-08-15 19:07', '2001-03-21 18:32',))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
if mibBuilder.loadTexts: juniPppoeProfileMIB.setRevisionsDescriptions(('Added juniPppoeProfileMaxSessionOverride object.', 'Added MTU control object.', 'Added Remote Circuit Id Capture object.', 'Added Service Name Table object.', 'Replaced Unisphere names with Juniper names.', 'Added PADI flag and packet trace support.', 'Added duplicate MAC address indicator and AC-NAME tag objects.', 'Initial version of this MIB module.',))
if mibBuilder.loadTexts: juniPppoeProfileMIB.setLastUpdated('200806181029Z')
if mibBuilder.loadTexts: juniPppoeProfileMIB.setOrganization('Juniper Networks, Inc.')
if mibBuilder.loadTexts: juniPppoeProfileMIB.setContactInfo(' Juniper Networks, Inc. Postal: 10 Technology Park Drive Westford, MA 01886-3146 USA Tel: +1 978 589 5800 Email: mib@Juniper.net')
if mibBuilder.loadTexts: juniPppoeProfileMIB.setDescription('The point-to-point protocol over Ethernet (PPPoE) profile MIB for the Juniper enterprise.')
juniPppoeProfileObjects = MibIdentifier((1, 3, 6, 1, 4, 1, 4874, 2, 2, 46, 1))
juniPppoeProfile = MibIdentifier((1, 3, 6, 1, 4, 1, 4874, 2, 2, 46, 1, 1))
juniPppoeProfileTable = MibTable((1, 3, 6, 1, 4, 1, 4874, 2, 2, 46, 1, 1, 1), )
if mibBuilder.loadTexts: juniPppoeProfileTable.setStatus('current')
if mibBuilder.loadTexts: juniPppoeProfileTable.setDescription('This table contains profiles for configuring PPPoE interfaces/sessions. Entries in this table are created/deleted as a side-effect of corresponding operations to the juniProfileNameTable in the Juniper-PROFILE-MIB.')
juniPppoeProfileEntry = MibTableRow((1, 3, 6, 1, 4, 1, 4874, 2, 2, 46, 1, 1, 1, 1), ).setIndexNames((0, "Juniper-PPPOE-PROFILE-MIB", "juniPppoeProfileId"))
if mibBuilder.loadTexts: juniPppoeProfileEntry.setStatus('current')
if mibBuilder.loadTexts: juniPppoeProfileEntry.setDescription('A profile describing configuration of a PPPoE interface and its subinterfaces (sessions).')
juniPppoeProfileId = MibTableColumn((1, 3, 6, 1, 4, 1, 4874, 2, 2, 46, 1, 1, 1, 1, 1), Unsigned32())
if mibBuilder.loadTexts: juniPppoeProfileId.setStatus('current')
if mibBuilder.loadTexts: juniPppoeProfileId.setDescription('The integer identifier associated with this profile. A value for this identifier is determined by locating or creating a profile name in the juniProfileNameTable.')
juniPppoeProfileSetMap = MibTableColumn((1, 3, 6, 1, 4, 1, 4874, 2, 2, 46, 1, 1, 1, 1, 2), JuniSetMap()).setMaxAccess("readwrite")
if mibBuilder.loadTexts: juniPppoeProfileSetMap.setStatus('current')
if mibBuilder.loadTexts: juniPppoeProfileSetMap.setDescription("A bitmap representing which objects in this entry have been explicitly configured. See the definition of the JuniSetMap TEXTUAL-CONVENTION for details of use. The INDEX object(s) and this object are excluded from representation (i.e. their bits are never set). When a SET request does not explicitly configure JuniSetMap, bits in JuniSetMap are set as a side-effect of configuring other profile attributes in the same entry. If, however, a SET request explicitly configures JuniSetMap, the explicitly configured value overrides 1) any previous bit settings, and 2) any simultaneous 'side-effect' settings that would otherwise occur. Once set, bits can only be cleared by explicitly configuring JuniSetMap.")
juniPppoeProfileMaxNumSessions = MibTableColumn((1, 3, 6, 1, 4, 1, 4874, 2, 2, 46, 1, 1, 1, 1, 3), Integer32().subtype(subtypeSpec=ValueRangeConstraint(0, 65535))).setMaxAccess("readwrite")
if mibBuilder.loadTexts: juniPppoeProfileMaxNumSessions.setStatus('current')
if mibBuilder.loadTexts: juniPppoeProfileMaxNumSessions.setDescription('The maximum number of PPPoE sessions (subinterfaces) that can be configured on the main PPPoE interface created using this profile. A value of zero indicates no bound is configured.')
juniPppoeProfileSubMotm = MibTableColumn((1, 3, 6, 1, 4, 1, 4874, 2, 2, 46, 1, 1, 1, 1, 4), DisplayString().subtype(subtypeSpec=ValueSizeConstraint(0, 127))).setMaxAccess("readwrite")
if mibBuilder.loadTexts: juniPppoeProfileSubMotm.setStatus('current')
if mibBuilder.loadTexts: juniPppoeProfileSubMotm.setDescription('A message to send via a PADM on the sub-interface when this profile is applied to the IP interface above this PPPoE sub- interface. A client may choose to display this message to the user.')
juniPppoeProfileSubUrl = MibTableColumn((1, 3, 6, 1, 4, 1, 4874, 2, 2, 46, 1, 1, 1, 1, 5), DisplayString().subtype(subtypeSpec=ValueSizeConstraint(0, 127))).setMaxAccess("readwrite")
if mibBuilder.loadTexts: juniPppoeProfileSubUrl.setStatus('current')
if mibBuilder.loadTexts: juniPppoeProfileSubUrl.setDescription('A URL to be sent via a PADM on the sub-interface when this profile is applied to the IP interface above this PPPoE sub-interface. The string entered here can have several substitutions applied: %D is replaced with the profile name %d is replaced with the domain name %u is replaced with the user name %U is replaced with the user/domain name together %% is replaced with the % character The resulting string must not be greater than 127 octets long. The client may use this URL as the initial web-page for the user.')
juniPppoeProfileDupProtect = MibTableColumn((1, 3, 6, 1, 4, 1, 4874, 2, 2, 46, 1, 1, 1, 1, 6), JuniEnable().clone('disable')).setMaxAccess("readwrite")
if mibBuilder.loadTexts: juniPppoeProfileDupProtect.setStatus('current')
if mibBuilder.loadTexts: juniPppoeProfileDupProtect.setDescription('Flag to control whether duplicate MAC addresses are allowed')
juniPppoeProfileAcName = MibTableColumn((1, 3, 6, 1, 4, 1, 4874, 2, 2, 46, 1, 1, 1, 1, 7), DisplayString().subtype(subtypeSpec=ValueSizeConstraint(0, 64))).setMaxAccess("readwrite")
if mibBuilder.loadTexts: juniPppoeProfileAcName.setStatus('current')
if mibBuilder.loadTexts: juniPppoeProfileAcName.setDescription('The name to use for the AC-NAME tag that is sent in any PADO that is sent on this interface.')
juniPppoeProfilePadiFlag = MibTableColumn((1, 3, 6, 1, 4, 1, 4874, 2, 2, 46, 1, 1, 1, 1, 8), JuniEnable().clone('disable')).setMaxAccess("readwrite")
if mibBuilder.loadTexts: juniPppoeProfilePadiFlag.setStatus('current')
if mibBuilder.loadTexts: juniPppoeProfilePadiFlag.setDescription('The PPPoE major interface parameter PADI flag controls whether to always repsond to a PADI with a PADO regardless of the ability to create the session and allow the session establish phase to resolve it.')
juniPppoeProfilePacketTrace = MibTableColumn((1, 3, 6, 1, 4, 1, 4874, 2, 2, 46, 1, 1, 1, 1, 9), JuniEnable().clone('disable')).setMaxAccess("readwrite")
if mibBuilder.loadTexts: juniPppoeProfilePacketTrace.setStatus('current')
if mibBuilder.loadTexts: juniPppoeProfilePacketTrace.setDescription('The PPPoE major interface parameter packet tracing flag controls whether packet tracing is enable or not.')
juniPppoeProfileServiceNameTableName = MibTableColumn((1, 3, 6, 1, 4, 1, 4874, 2, 2, 46, 1, 1, 1, 1, 10), DisplayString().subtype(subtypeSpec=ValueSizeConstraint(0, 31))).setMaxAccess("readwrite")
if mibBuilder.loadTexts: juniPppoeProfileServiceNameTableName.setStatus('current')
if mibBuilder.loadTexts: juniPppoeProfileServiceNameTableName.setDescription('The PPPoE Service name table controls behavior of PADO control packets.')
juniPppoeProfilePadrRemoteCircuitIdCapture = MibTableColumn((1, 3, 6, 1, 4, 1, 4874, 2, 2, 46, 1, 1, 1, 1, 11), JuniEnable().clone('disable')).setMaxAccess("readwrite")
if mibBuilder.loadTexts: juniPppoeProfilePadrRemoteCircuitIdCapture.setStatus('current')
if mibBuilder.loadTexts: juniPppoeProfilePadrRemoteCircuitIdCapture.setDescription('The PPPoE major interface parameter PADR remote circuit id capture flag controls whether the remote circuit id string possibly contained in the PADR packet will be saved and used by AAA to replace the NAS-PORT-ID field.')
juniPppoeProfileMtu = MibTableColumn((1, 3, 6, 1, 4, 1, 4874, 2, 2, 46, 1, 1, 1, 1, 12), Integer32().subtype(subtypeSpec=ConstraintsUnion(ValueRangeConstraint(1, 1), ValueRangeConstraint(2, 2), ValueRangeConstraint(66, 65535), )).clone(1494)).setMaxAccess("readwrite")
if mibBuilder.loadTexts: juniPppoeProfileMtu.setStatus('current')
if mibBuilder.loadTexts: juniPppoeProfileMtu.setDescription('The initial Maximum Transmit Unit (MTU) that the PPPoE major interface entity will advertise to the remote entity. If the value of this variable is 1 then the local PPPoE entity will use an MTU value determined by its underlying media interface. If the value of this variable is 2 then the local PPPoE entity will use a value determined by the PPPoE Max-Mtu-Tag transmitted from the client in the PADR packet. If no Max-Mtu-Tag is received, the value defaults to a maximum of 1494. The operational MTU is limited by the MTU of the underlying media interface minus the PPPoE frame overhead.')
juniPppoeProfileMaxSessionOverride = MibTableColumn((1, 3, 6, 1, 4, 1, 4874, 2, 2, 46, 1, 1, 1, 1, 13), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(1, 2))).clone(namedValues=NamedValues(("override", 1), ("ignore", 2))).clone('ignore')).setMaxAccess("readwrite")
if mibBuilder.loadTexts: juniPppoeProfileMaxSessionOverride.setStatus('current')
if mibBuilder.loadTexts: juniPppoeProfileMaxSessionOverride.setDescription('Configure the action to be taken by PPPoE when RADIUS server returns the PPPoE max-session value: override Override the current PPPoE max-session value with the value returned by RADIUS server Ignore Ignore the max-session value returned by RADIUS server')
juniPppoeProfileConformance = MibIdentifier((1, 3, 6, 1, 4, 1, 4874, 2, 2, 46, 4))
juniPppoeProfileCompliances = MibIdentifier((1, 3, 6, 1, 4, 1, 4874, 2, 2, 46, 4, 1))
juniPppoeProfileGroups = MibIdentifier((1, 3, 6, 1, 4, 1, 4874, 2, 2, 46, 4, 2))
juniPppoeProfileCompliance = ModuleCompliance((1, 3, 6, 1, 4, 1, 4874, 2, 2, 46, 4, 1, 1)).setObjects(("Juniper-PPPOE-PROFILE-MIB", "juniPppoeProfileGroup"))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
juniPppoeProfileCompliance = juniPppoeProfileCompliance.setStatus('obsolete')
if mibBuilder.loadTexts: juniPppoeProfileCompliance.setDescription('Obsolete compliance statement for entities which implement the Juniper PPPoE Profile MIB. This statement became obsolete when the duplicate MAC address indicator and AC-NAME tag were added.')
juniPppoeCompliance2 = ModuleCompliance((1, 3, 6, 1, 4, 1, 4874, 2, 2, 46, 4, 1, 2)).setObjects(("Juniper-PPPOE-PROFILE-MIB", "juniPppoeProfileGroup2"))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
juniPppoeCompliance2 = juniPppoeCompliance2.setStatus('obsolete')
if mibBuilder.loadTexts: juniPppoeCompliance2.setDescription('Obsolete compliance statement for entities which implement the Juniper PPPoE profile MIB. This statement became obsolete when PADI flag, AC-name and packet trace objects were added.')
juniPppoeCompliance3 = ModuleCompliance((1, 3, 6, 1, 4, 1, 4874, 2, 2, 46, 4, 1, 3)).setObjects(("Juniper-PPPOE-PROFILE-MIB", "juniPppoeProfileGroup3"))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
juniPppoeCompliance3 = juniPppoeCompliance3.setStatus('obsolete')
if mibBuilder.loadTexts: juniPppoeCompliance3.setDescription('Obsolete compliance statement for entities which implement the Juniper PPPoE profile MIB. This statement became obsolete when the service name table was added.')
juniPppoeCompliance4 = ModuleCompliance((1, 3, 6, 1, 4, 1, 4874, 2, 2, 46, 4, 1, 4)).setObjects(("Juniper-PPPOE-PROFILE-MIB", "juniPppoeProfileGroup4"))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
juniPppoeCompliance4 = juniPppoeCompliance4.setStatus('obsolete')
if mibBuilder.loadTexts: juniPppoeCompliance4.setDescription('Obsolete compliance statement for entities which implement the Juniper PPPoE profile MIB. This statement became obsolete when the remote circuit id capture was added.')
juniPppoeCompliance5 = ModuleCompliance((1, 3, 6, 1, 4, 1, 4874, 2, 2, 46, 4, 1, 5)).setObjects(("Juniper-PPPOE-PROFILE-MIB", "juniPppoeProfileGroup5"))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
juniPppoeCompliance5 = juniPppoeCompliance5.setStatus('obsolete')
if mibBuilder.loadTexts: juniPppoeCompliance5.setDescription('Obsolete compliance statement for entities which implement the Juniper PPPoE MIB. This statement became obsolete when support was added for MTU configuration.')
juniPppoeCompliance6 = ModuleCompliance((1, 3, 6, 1, 4, 1, 4874, 2, 2, 46, 4, 1, 6)).setObjects(("Juniper-PPPOE-PROFILE-MIB", "juniPppoeProfileGroup6"))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
juniPppoeCompliance6 = juniPppoeCompliance6.setStatus('obsolete')
if mibBuilder.loadTexts: juniPppoeCompliance6.setDescription('Obsolete compliance statement for entities which implement the Juniper PPPoE MIB. This statement became obsolete when support was added for juniPppoeProfileMaxSessionOverride.')
juniPppoeCompliance7 = ModuleCompliance((1, 3, 6, 1, 4, 1, 4874, 2, 2, 46, 4, 1, 7)).setObjects(("Juniper-PPPOE-PROFILE-MIB", "juniPppoeProfileGroup7"))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
juniPppoeCompliance7 = juniPppoeCompliance7.setStatus('current')
if mibBuilder.loadTexts: juniPppoeCompliance7.setDescription('The compliance statement for entities which implement the Juniper PPPoE Profile MIB.')
juniPppoeProfileGroup = ObjectGroup((1, 3, 6, 1, 4, 1, 4874, 2, 2, 46, 4, 2, 1)).setObjects(("Juniper-PPPOE-PROFILE-MIB", "juniPppoeProfileSetMap"), ("Juniper-PPPOE-PROFILE-MIB", "juniPppoeProfileMaxNumSessions"), ("Juniper-PPPOE-PROFILE-MIB", "juniPppoeProfileSubMotm"), ("Juniper-PPPOE-PROFILE-MIB", "juniPppoeProfileSubUrl"))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
juniPppoeProfileGroup = juniPppoeProfileGroup.setStatus('obsolete')
if mibBuilder.loadTexts: juniPppoeProfileGroup.setDescription('Obsolete collection of objects providing management of profile functionality for PPPoE interfaces in a Juniper product. This group became obsolete when the duplicate MAC address indicator and AC-NAME tag objects were added.')
juniPppoeProfileGroup2 = ObjectGroup((1, 3, 6, 1, 4, 1, 4874, 2, 2, 46, 4, 2, 2)).setObjects(("Juniper-PPPOE-PROFILE-MIB", "juniPppoeProfileSetMap"), ("Juniper-PPPOE-PROFILE-MIB", "juniPppoeProfileMaxNumSessions"), ("Juniper-PPPOE-PROFILE-MIB", "juniPppoeProfileSubMotm"), ("Juniper-PPPOE-PROFILE-MIB", "juniPppoeProfileSubUrl"), ("Juniper-PPPOE-PROFILE-MIB", "juniPppoeProfileDupProtect"), ("Juniper-PPPOE-PROFILE-MIB", "juniPppoeProfileAcName"))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
juniPppoeProfileGroup2 = juniPppoeProfileGroup2.setStatus('obsolete')
if mibBuilder.loadTexts: juniPppoeProfileGroup2.setDescription('Obsolete collection of objects providing management of profile functionality for PPPOE interfaces in a Juniper product. This group became obsolete when PADI flag, AC-name and packet trace objects were added.')
juniPppoeProfileGroup3 = ObjectGroup((1, 3, 6, 1, 4, 1, 4874, 2, 2, 46, 4, 2, 3)).setObjects(("Juniper-PPPOE-PROFILE-MIB", "juniPppoeProfileSetMap"), ("Juniper-PPPOE-PROFILE-MIB", "juniPppoeProfileMaxNumSessions"), ("Juniper-PPPOE-PROFILE-MIB", "juniPppoeProfileSubMotm"), ("Juniper-PPPOE-PROFILE-MIB", "juniPppoeProfileSubUrl"), ("Juniper-PPPOE-PROFILE-MIB", "juniPppoeProfileDupProtect"), ("Juniper-PPPOE-PROFILE-MIB", "juniPppoeProfileAcName"), ("Juniper-PPPOE-PROFILE-MIB", "juniPppoeProfilePadiFlag"), ("Juniper-PPPOE-PROFILE-MIB", "juniPppoeProfilePacketTrace"))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
juniPppoeProfileGroup3 = juniPppoeProfileGroup3.setStatus('obsolete')
if mibBuilder.loadTexts: juniPppoeProfileGroup3.setDescription('A collection of objects providing management of profile functionality for PPPOE interfaces in a Juniper product.')
juniPppoeProfileGroup4 = ObjectGroup((1, 3, 6, 1, 4, 1, 4874, 2, 2, 46, 4, 2, 4)).setObjects(("Juniper-PPPOE-PROFILE-MIB", "juniPppoeProfileSetMap"), ("Juniper-PPPOE-PROFILE-MIB", "juniPppoeProfileMaxNumSessions"), ("Juniper-PPPOE-PROFILE-MIB", "juniPppoeProfileSubMotm"), ("Juniper-PPPOE-PROFILE-MIB", "juniPppoeProfileSubUrl"), ("Juniper-PPPOE-PROFILE-MIB", "juniPppoeProfileDupProtect"), ("Juniper-PPPOE-PROFILE-MIB", "juniPppoeProfileAcName"), ("Juniper-PPPOE-PROFILE-MIB", "juniPppoeProfilePadiFlag"), ("Juniper-PPPOE-PROFILE-MIB", "juniPppoeProfilePacketTrace"), ("Juniper-PPPOE-PROFILE-MIB", "juniPppoeProfileServiceNameTableName"))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
juniPppoeProfileGroup4 = juniPppoeProfileGroup4.setStatus('obsolete')
if mibBuilder.loadTexts: juniPppoeProfileGroup4.setDescription('A collection of objects providing management of profile functionality for PPPOE interfaces in a Juniper product.')
juniPppoeProfileGroup5 = ObjectGroup((1, 3, 6, 1, 4, 1, 4874, 2, 2, 46, 4, 2, 5)).setObjects(("Juniper-PPPOE-PROFILE-MIB", "juniPppoeProfileSetMap"), ("Juniper-PPPOE-PROFILE-MIB", "juniPppoeProfileMaxNumSessions"), ("Juniper-PPPOE-PROFILE-MIB", "juniPppoeProfileSubMotm"), ("Juniper-PPPOE-PROFILE-MIB", "juniPppoeProfileSubUrl"), ("Juniper-PPPOE-PROFILE-MIB", "juniPppoeProfileDupProtect"), ("Juniper-PPPOE-PROFILE-MIB", "juniPppoeProfileAcName"), ("Juniper-PPPOE-PROFILE-MIB", "juniPppoeProfilePadiFlag"), ("Juniper-PPPOE-PROFILE-MIB", "juniPppoeProfilePacketTrace"), ("Juniper-PPPOE-PROFILE-MIB", "juniPppoeProfileServiceNameTableName"), ("Juniper-PPPOE-PROFILE-MIB", "juniPppoeProfilePadrRemoteCircuitIdCapture"))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
juniPppoeProfileGroup5 = juniPppoeProfileGroup5.setStatus('obsolete')
if mibBuilder.loadTexts: juniPppoeProfileGroup5.setDescription('A collection of objects providing management of profile functionality for PPPOE interfaces in a Juniper product.')
juniPppoeProfileGroup6 = ObjectGroup((1, 3, 6, 1, 4, 1, 4874, 2, 2, 46, 4, 2, 6)).setObjects(("Juniper-PPPOE-PROFILE-MIB", "juniPppoeProfileSetMap"), ("Juniper-PPPOE-PROFILE-MIB", "juniPppoeProfileMaxNumSessions"), ("Juniper-PPPOE-PROFILE-MIB", "juniPppoeProfileSubMotm"), ("Juniper-PPPOE-PROFILE-MIB", "juniPppoeProfileSubUrl"), ("Juniper-PPPOE-PROFILE-MIB", "juniPppoeProfileDupProtect"), ("Juniper-PPPOE-PROFILE-MIB", "juniPppoeProfileAcName"), ("Juniper-PPPOE-PROFILE-MIB", "juniPppoeProfilePadiFlag"), ("Juniper-PPPOE-PROFILE-MIB", "juniPppoeProfilePacketTrace"), ("Juniper-PPPOE-PROFILE-MIB", "juniPppoeProfileServiceNameTableName"), ("Juniper-PPPOE-PROFILE-MIB", "juniPppoeProfilePadrRemoteCircuitIdCapture"), ("Juniper-PPPOE-PROFILE-MIB", "juniPppoeProfileMtu"))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
juniPppoeProfileGroup6 = juniPppoeProfileGroup6.setStatus('obsolete')
if mibBuilder.loadTexts: juniPppoeProfileGroup6.setDescription('A collection of objects providing management of profile functionality for PPPOE interfaces in a Juniper product.')
juniPppoeProfileGroup7 = ObjectGroup((1, 3, 6, 1, 4, 1, 4874, 2, 2, 46, 4, 2, 7)).setObjects(("Juniper-PPPOE-PROFILE-MIB", "juniPppoeProfileSetMap"), ("Juniper-PPPOE-PROFILE-MIB", "juniPppoeProfileMaxNumSessions"), ("Juniper-PPPOE-PROFILE-MIB", "juniPppoeProfileSubMotm"), ("Juniper-PPPOE-PROFILE-MIB", "juniPppoeProfileSubUrl"), ("Juniper-PPPOE-PROFILE-MIB", "juniPppoeProfileDupProtect"), ("Juniper-PPPOE-PROFILE-MIB", "juniPppoeProfileAcName"), ("Juniper-PPPOE-PROFILE-MIB", "juniPppoeProfilePadiFlag"), ("Juniper-PPPOE-PROFILE-MIB", "juniPppoeProfilePacketTrace"), ("Juniper-PPPOE-PROFILE-MIB", "juniPppoeProfileServiceNameTableName"), ("Juniper-PPPOE-PROFILE-MIB", "juniPppoeProfilePadrRemoteCircuitIdCapture"), ("Juniper-PPPOE-PROFILE-MIB", "juniPppoeProfileMtu"), ("Juniper-PPPOE-PROFILE-MIB", "juniPppoeProfileMaxSessionOverride"))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
juniPppoeProfileGroup7 = juniPppoeProfileGroup7.setStatus('current')
if mibBuilder.loadTexts: juniPppoeProfileGroup7.setDescription('A collection of objects providing management of profile functionality for PPPOE interfaces in a Juniper product.')
mibBuilder.exportSymbols("Juniper-PPPOE-PROFILE-MIB", juniPppoeCompliance5=juniPppoeCompliance5, juniPppoeProfileGroup2=juniPppoeProfileGroup2, juniPppoeProfileMtu=juniPppoeProfileMtu, juniPppoeProfile=juniPppoeProfile, juniPppoeProfileSetMap=juniPppoeProfileSetMap, juniPppoeProfilePadiFlag=juniPppoeProfilePadiFlag, juniPppoeProfileTable=juniPppoeProfileTable, juniPppoeProfileDupProtect=juniPppoeProfileDupProtect, juniPppoeCompliance4=juniPppoeCompliance4, juniPppoeProfileGroup=juniPppoeProfileGroup, juniPppoeProfileMIB=juniPppoeProfileMIB, juniPppoeProfileGroup4=juniPppoeProfileGroup4, juniPppoeProfileCompliances=juniPppoeProfileCompliances, juniPppoeProfileAcName=juniPppoeProfileAcName, juniPppoeProfileGroup3=juniPppoeProfileGroup3, juniPppoeProfilePadrRemoteCircuitIdCapture=juniPppoeProfilePadrRemoteCircuitIdCapture, juniPppoeProfileSubMotm=juniPppoeProfileSubMotm, juniPppoeProfilePacketTrace=juniPppoeProfilePacketTrace, juniPppoeProfileEntry=juniPppoeProfileEntry, juniPppoeProfileGroup5=juniPppoeProfileGroup5, juniPppoeProfileMaxSessionOverride=juniPppoeProfileMaxSessionOverride, juniPppoeCompliance2=juniPppoeCompliance2, juniPppoeProfileGroups=juniPppoeProfileGroups, juniPppoeCompliance6=juniPppoeCompliance6, juniPppoeProfileCompliance=juniPppoeProfileCompliance, juniPppoeCompliance3=juniPppoeCompliance3, juniPppoeCompliance7=juniPppoeCompliance7, juniPppoeProfileGroup6=juniPppoeProfileGroup6, juniPppoeProfileMaxNumSessions=juniPppoeProfileMaxNumSessions, juniPppoeProfileId=juniPppoeProfileId, juniPppoeProfileConformance=juniPppoeProfileConformance, PYSNMP_MODULE_ID=juniPppoeProfileMIB, juniPppoeProfileObjects=juniPppoeProfileObjects, juniPppoeProfileServiceNameTableName=juniPppoeProfileServiceNameTableName, juniPppoeProfileSubUrl=juniPppoeProfileSubUrl, juniPppoeProfileGroup7=juniPppoeProfileGroup7)
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(integer, octet_string, object_identifier) = mibBuilder.importSymbols('ASN1', 'Integer', 'OctetString', 'ObjectIdentifier')
(named_values,) = mibBuilder.importSymbols('ASN1-ENUMERATION', 'NamedValues')
(constraints_union, single_value_constraint, value_size_constraint, value_range_constraint, constraints_intersection) = mibBuilder.importSymbols('ASN1-REFINEMENT', 'ConstraintsUnion', 'SingleValueConstraint', 'ValueSizeConstraint', 'ValueRangeConstraint', 'ConstraintsIntersection')
(juni_mibs,) = mibBuilder.importSymbols('Juniper-MIBs', 'juniMibs')
(juni_enable, juni_set_map) = mibBuilder.importSymbols('Juniper-TC', 'JuniEnable', 'JuniSetMap')
(object_group, module_compliance, notification_group) = mibBuilder.importSymbols('SNMPv2-CONF', 'ObjectGroup', 'ModuleCompliance', 'NotificationGroup')
(ip_address, module_identity, mib_identifier, integer32, gauge32, iso, counter32, time_ticks, unsigned32, counter64, notification_type, object_identity, bits, mib_scalar, mib_table, mib_table_row, mib_table_column) = mibBuilder.importSymbols('SNMPv2-SMI', 'IpAddress', 'ModuleIdentity', 'MibIdentifier', 'Integer32', 'Gauge32', 'iso', 'Counter32', 'TimeTicks', 'Unsigned32', 'Counter64', 'NotificationType', 'ObjectIdentity', 'Bits', 'MibScalar', 'MibTable', 'MibTableRow', 'MibTableColumn')
(textual_convention, display_string) = mibBuilder.importSymbols('SNMPv2-TC', 'TextualConvention', 'DisplayString')
juni_pppoe_profile_mib = module_identity((1, 3, 6, 1, 4, 1, 4874, 2, 2, 46))
juniPppoeProfileMIB.setRevisions(('2008-06-18 10:29', '2005-07-13 11:40', '2004-06-10 19:25', '2003-03-11 21:58', '2002-09-16 21:44', '2002-08-15 20:34', '2002-08-15 19:07', '2001-03-21 18:32'))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
if mibBuilder.loadTexts:
juniPppoeProfileMIB.setRevisionsDescriptions(('Added juniPppoeProfileMaxSessionOverride object.', 'Added MTU control object.', 'Added Remote Circuit Id Capture object.', 'Added Service Name Table object.', 'Replaced Unisphere names with Juniper names.', 'Added PADI flag and packet trace support.', 'Added duplicate MAC address indicator and AC-NAME tag objects.', 'Initial version of this MIB module.'))
if mibBuilder.loadTexts:
juniPppoeProfileMIB.setLastUpdated('200806181029Z')
if mibBuilder.loadTexts:
juniPppoeProfileMIB.setOrganization('Juniper Networks, Inc.')
if mibBuilder.loadTexts:
juniPppoeProfileMIB.setContactInfo(' Juniper Networks, Inc. Postal: 10 Technology Park Drive Westford, MA 01886-3146 USA Tel: +1 978 589 5800 Email: mib@Juniper.net')
if mibBuilder.loadTexts:
juniPppoeProfileMIB.setDescription('The point-to-point protocol over Ethernet (PPPoE) profile MIB for the Juniper enterprise.')
juni_pppoe_profile_objects = mib_identifier((1, 3, 6, 1, 4, 1, 4874, 2, 2, 46, 1))
juni_pppoe_profile = mib_identifier((1, 3, 6, 1, 4, 1, 4874, 2, 2, 46, 1, 1))
juni_pppoe_profile_table = mib_table((1, 3, 6, 1, 4, 1, 4874, 2, 2, 46, 1, 1, 1))
if mibBuilder.loadTexts:
juniPppoeProfileTable.setStatus('current')
if mibBuilder.loadTexts:
juniPppoeProfileTable.setDescription('This table contains profiles for configuring PPPoE interfaces/sessions. Entries in this table are created/deleted as a side-effect of corresponding operations to the juniProfileNameTable in the Juniper-PROFILE-MIB.')
juni_pppoe_profile_entry = mib_table_row((1, 3, 6, 1, 4, 1, 4874, 2, 2, 46, 1, 1, 1, 1)).setIndexNames((0, 'Juniper-PPPOE-PROFILE-MIB', 'juniPppoeProfileId'))
if mibBuilder.loadTexts:
juniPppoeProfileEntry.setStatus('current')
if mibBuilder.loadTexts:
juniPppoeProfileEntry.setDescription('A profile describing configuration of a PPPoE interface and its subinterfaces (sessions).')
juni_pppoe_profile_id = mib_table_column((1, 3, 6, 1, 4, 1, 4874, 2, 2, 46, 1, 1, 1, 1, 1), unsigned32())
if mibBuilder.loadTexts:
juniPppoeProfileId.setStatus('current')
if mibBuilder.loadTexts:
juniPppoeProfileId.setDescription('The integer identifier associated with this profile. A value for this identifier is determined by locating or creating a profile name in the juniProfileNameTable.')
juni_pppoe_profile_set_map = mib_table_column((1, 3, 6, 1, 4, 1, 4874, 2, 2, 46, 1, 1, 1, 1, 2), juni_set_map()).setMaxAccess('readwrite')
if mibBuilder.loadTexts:
juniPppoeProfileSetMap.setStatus('current')
if mibBuilder.loadTexts:
juniPppoeProfileSetMap.setDescription("A bitmap representing which objects in this entry have been explicitly configured. See the definition of the JuniSetMap TEXTUAL-CONVENTION for details of use. The INDEX object(s) and this object are excluded from representation (i.e. their bits are never set). When a SET request does not explicitly configure JuniSetMap, bits in JuniSetMap are set as a side-effect of configuring other profile attributes in the same entry. If, however, a SET request explicitly configures JuniSetMap, the explicitly configured value overrides 1) any previous bit settings, and 2) any simultaneous 'side-effect' settings that would otherwise occur. Once set, bits can only be cleared by explicitly configuring JuniSetMap.")
juni_pppoe_profile_max_num_sessions = mib_table_column((1, 3, 6, 1, 4, 1, 4874, 2, 2, 46, 1, 1, 1, 1, 3), integer32().subtype(subtypeSpec=value_range_constraint(0, 65535))).setMaxAccess('readwrite')
if mibBuilder.loadTexts:
juniPppoeProfileMaxNumSessions.setStatus('current')
if mibBuilder.loadTexts:
juniPppoeProfileMaxNumSessions.setDescription('The maximum number of PPPoE sessions (subinterfaces) that can be configured on the main PPPoE interface created using this profile. A value of zero indicates no bound is configured.')
juni_pppoe_profile_sub_motm = mib_table_column((1, 3, 6, 1, 4, 1, 4874, 2, 2, 46, 1, 1, 1, 1, 4), display_string().subtype(subtypeSpec=value_size_constraint(0, 127))).setMaxAccess('readwrite')
if mibBuilder.loadTexts:
juniPppoeProfileSubMotm.setStatus('current')
if mibBuilder.loadTexts:
juniPppoeProfileSubMotm.setDescription('A message to send via a PADM on the sub-interface when this profile is applied to the IP interface above this PPPoE sub- interface. A client may choose to display this message to the user.')
juni_pppoe_profile_sub_url = mib_table_column((1, 3, 6, 1, 4, 1, 4874, 2, 2, 46, 1, 1, 1, 1, 5), display_string().subtype(subtypeSpec=value_size_constraint(0, 127))).setMaxAccess('readwrite')
if mibBuilder.loadTexts:
juniPppoeProfileSubUrl.setStatus('current')
if mibBuilder.loadTexts:
juniPppoeProfileSubUrl.setDescription('A URL to be sent via a PADM on the sub-interface when this profile is applied to the IP interface above this PPPoE sub-interface. The string entered here can have several substitutions applied: %D is replaced with the profile name %d is replaced with the domain name %u is replaced with the user name %U is replaced with the user/domain name together %% is replaced with the % character The resulting string must not be greater than 127 octets long. The client may use this URL as the initial web-page for the user.')
juni_pppoe_profile_dup_protect = mib_table_column((1, 3, 6, 1, 4, 1, 4874, 2, 2, 46, 1, 1, 1, 1, 6), juni_enable().clone('disable')).setMaxAccess('readwrite')
if mibBuilder.loadTexts:
juniPppoeProfileDupProtect.setStatus('current')
if mibBuilder.loadTexts:
juniPppoeProfileDupProtect.setDescription('Flag to control whether duplicate MAC addresses are allowed')
juni_pppoe_profile_ac_name = mib_table_column((1, 3, 6, 1, 4, 1, 4874, 2, 2, 46, 1, 1, 1, 1, 7), display_string().subtype(subtypeSpec=value_size_constraint(0, 64))).setMaxAccess('readwrite')
if mibBuilder.loadTexts:
juniPppoeProfileAcName.setStatus('current')
if mibBuilder.loadTexts:
juniPppoeProfileAcName.setDescription('The name to use for the AC-NAME tag that is sent in any PADO that is sent on this interface.')
juni_pppoe_profile_padi_flag = mib_table_column((1, 3, 6, 1, 4, 1, 4874, 2, 2, 46, 1, 1, 1, 1, 8), juni_enable().clone('disable')).setMaxAccess('readwrite')
if mibBuilder.loadTexts:
juniPppoeProfilePadiFlag.setStatus('current')
if mibBuilder.loadTexts:
juniPppoeProfilePadiFlag.setDescription('The PPPoE major interface parameter PADI flag controls whether to always repsond to a PADI with a PADO regardless of the ability to create the session and allow the session establish phase to resolve it.')
juni_pppoe_profile_packet_trace = mib_table_column((1, 3, 6, 1, 4, 1, 4874, 2, 2, 46, 1, 1, 1, 1, 9), juni_enable().clone('disable')).setMaxAccess('readwrite')
if mibBuilder.loadTexts:
juniPppoeProfilePacketTrace.setStatus('current')
if mibBuilder.loadTexts:
juniPppoeProfilePacketTrace.setDescription('The PPPoE major interface parameter packet tracing flag controls whether packet tracing is enable or not.')
juni_pppoe_profile_service_name_table_name = mib_table_column((1, 3, 6, 1, 4, 1, 4874, 2, 2, 46, 1, 1, 1, 1, 10), display_string().subtype(subtypeSpec=value_size_constraint(0, 31))).setMaxAccess('readwrite')
if mibBuilder.loadTexts:
juniPppoeProfileServiceNameTableName.setStatus('current')
if mibBuilder.loadTexts:
juniPppoeProfileServiceNameTableName.setDescription('The PPPoE Service name table controls behavior of PADO control packets.')
juni_pppoe_profile_padr_remote_circuit_id_capture = mib_table_column((1, 3, 6, 1, 4, 1, 4874, 2, 2, 46, 1, 1, 1, 1, 11), juni_enable().clone('disable')).setMaxAccess('readwrite')
if mibBuilder.loadTexts:
juniPppoeProfilePadrRemoteCircuitIdCapture.setStatus('current')
if mibBuilder.loadTexts:
juniPppoeProfilePadrRemoteCircuitIdCapture.setDescription('The PPPoE major interface parameter PADR remote circuit id capture flag controls whether the remote circuit id string possibly contained in the PADR packet will be saved and used by AAA to replace the NAS-PORT-ID field.')
juni_pppoe_profile_mtu = mib_table_column((1, 3, 6, 1, 4, 1, 4874, 2, 2, 46, 1, 1, 1, 1, 12), integer32().subtype(subtypeSpec=constraints_union(value_range_constraint(1, 1), value_range_constraint(2, 2), value_range_constraint(66, 65535))).clone(1494)).setMaxAccess('readwrite')
if mibBuilder.loadTexts:
juniPppoeProfileMtu.setStatus('current')
if mibBuilder.loadTexts:
juniPppoeProfileMtu.setDescription('The initial Maximum Transmit Unit (MTU) that the PPPoE major interface entity will advertise to the remote entity. If the value of this variable is 1 then the local PPPoE entity will use an MTU value determined by its underlying media interface. If the value of this variable is 2 then the local PPPoE entity will use a value determined by the PPPoE Max-Mtu-Tag transmitted from the client in the PADR packet. If no Max-Mtu-Tag is received, the value defaults to a maximum of 1494. The operational MTU is limited by the MTU of the underlying media interface minus the PPPoE frame overhead.')
juni_pppoe_profile_max_session_override = mib_table_column((1, 3, 6, 1, 4, 1, 4874, 2, 2, 46, 1, 1, 1, 1, 13), integer32().subtype(subtypeSpec=constraints_union(single_value_constraint(1, 2))).clone(namedValues=named_values(('override', 1), ('ignore', 2))).clone('ignore')).setMaxAccess('readwrite')
if mibBuilder.loadTexts:
juniPppoeProfileMaxSessionOverride.setStatus('current')
if mibBuilder.loadTexts:
juniPppoeProfileMaxSessionOverride.setDescription('Configure the action to be taken by PPPoE when RADIUS server returns the PPPoE max-session value: override Override the current PPPoE max-session value with the value returned by RADIUS server Ignore Ignore the max-session value returned by RADIUS server')
juni_pppoe_profile_conformance = mib_identifier((1, 3, 6, 1, 4, 1, 4874, 2, 2, 46, 4))
juni_pppoe_profile_compliances = mib_identifier((1, 3, 6, 1, 4, 1, 4874, 2, 2, 46, 4, 1))
juni_pppoe_profile_groups = mib_identifier((1, 3, 6, 1, 4, 1, 4874, 2, 2, 46, 4, 2))
juni_pppoe_profile_compliance = module_compliance((1, 3, 6, 1, 4, 1, 4874, 2, 2, 46, 4, 1, 1)).setObjects(('Juniper-PPPOE-PROFILE-MIB', 'juniPppoeProfileGroup'))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
juni_pppoe_profile_compliance = juniPppoeProfileCompliance.setStatus('obsolete')
if mibBuilder.loadTexts:
juniPppoeProfileCompliance.setDescription('Obsolete compliance statement for entities which implement the Juniper PPPoE Profile MIB. This statement became obsolete when the duplicate MAC address indicator and AC-NAME tag were added.')
juni_pppoe_compliance2 = module_compliance((1, 3, 6, 1, 4, 1, 4874, 2, 2, 46, 4, 1, 2)).setObjects(('Juniper-PPPOE-PROFILE-MIB', 'juniPppoeProfileGroup2'))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
juni_pppoe_compliance2 = juniPppoeCompliance2.setStatus('obsolete')
if mibBuilder.loadTexts:
juniPppoeCompliance2.setDescription('Obsolete compliance statement for entities which implement the Juniper PPPoE profile MIB. This statement became obsolete when PADI flag, AC-name and packet trace objects were added.')
juni_pppoe_compliance3 = module_compliance((1, 3, 6, 1, 4, 1, 4874, 2, 2, 46, 4, 1, 3)).setObjects(('Juniper-PPPOE-PROFILE-MIB', 'juniPppoeProfileGroup3'))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
juni_pppoe_compliance3 = juniPppoeCompliance3.setStatus('obsolete')
if mibBuilder.loadTexts:
juniPppoeCompliance3.setDescription('Obsolete compliance statement for entities which implement the Juniper PPPoE profile MIB. This statement became obsolete when the service name table was added.')
juni_pppoe_compliance4 = module_compliance((1, 3, 6, 1, 4, 1, 4874, 2, 2, 46, 4, 1, 4)).setObjects(('Juniper-PPPOE-PROFILE-MIB', 'juniPppoeProfileGroup4'))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
juni_pppoe_compliance4 = juniPppoeCompliance4.setStatus('obsolete')
if mibBuilder.loadTexts:
juniPppoeCompliance4.setDescription('Obsolete compliance statement for entities which implement the Juniper PPPoE profile MIB. This statement became obsolete when the remote circuit id capture was added.')
juni_pppoe_compliance5 = module_compliance((1, 3, 6, 1, 4, 1, 4874, 2, 2, 46, 4, 1, 5)).setObjects(('Juniper-PPPOE-PROFILE-MIB', 'juniPppoeProfileGroup5'))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
juni_pppoe_compliance5 = juniPppoeCompliance5.setStatus('obsolete')
if mibBuilder.loadTexts:
juniPppoeCompliance5.setDescription('Obsolete compliance statement for entities which implement the Juniper PPPoE MIB. This statement became obsolete when support was added for MTU configuration.')
juni_pppoe_compliance6 = module_compliance((1, 3, 6, 1, 4, 1, 4874, 2, 2, 46, 4, 1, 6)).setObjects(('Juniper-PPPOE-PROFILE-MIB', 'juniPppoeProfileGroup6'))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
juni_pppoe_compliance6 = juniPppoeCompliance6.setStatus('obsolete')
if mibBuilder.loadTexts:
juniPppoeCompliance6.setDescription('Obsolete compliance statement for entities which implement the Juniper PPPoE MIB. This statement became obsolete when support was added for juniPppoeProfileMaxSessionOverride.')
juni_pppoe_compliance7 = module_compliance((1, 3, 6, 1, 4, 1, 4874, 2, 2, 46, 4, 1, 7)).setObjects(('Juniper-PPPOE-PROFILE-MIB', 'juniPppoeProfileGroup7'))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
juni_pppoe_compliance7 = juniPppoeCompliance7.setStatus('current')
if mibBuilder.loadTexts:
juniPppoeCompliance7.setDescription('The compliance statement for entities which implement the Juniper PPPoE Profile MIB.')
juni_pppoe_profile_group = object_group((1, 3, 6, 1, 4, 1, 4874, 2, 2, 46, 4, 2, 1)).setObjects(('Juniper-PPPOE-PROFILE-MIB', 'juniPppoeProfileSetMap'), ('Juniper-PPPOE-PROFILE-MIB', 'juniPppoeProfileMaxNumSessions'), ('Juniper-PPPOE-PROFILE-MIB', 'juniPppoeProfileSubMotm'), ('Juniper-PPPOE-PROFILE-MIB', 'juniPppoeProfileSubUrl'))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
juni_pppoe_profile_group = juniPppoeProfileGroup.setStatus('obsolete')
if mibBuilder.loadTexts:
juniPppoeProfileGroup.setDescription('Obsolete collection of objects providing management of profile functionality for PPPoE interfaces in a Juniper product. This group became obsolete when the duplicate MAC address indicator and AC-NAME tag objects were added.')
juni_pppoe_profile_group2 = object_group((1, 3, 6, 1, 4, 1, 4874, 2, 2, 46, 4, 2, 2)).setObjects(('Juniper-PPPOE-PROFILE-MIB', 'juniPppoeProfileSetMap'), ('Juniper-PPPOE-PROFILE-MIB', 'juniPppoeProfileMaxNumSessions'), ('Juniper-PPPOE-PROFILE-MIB', 'juniPppoeProfileSubMotm'), ('Juniper-PPPOE-PROFILE-MIB', 'juniPppoeProfileSubUrl'), ('Juniper-PPPOE-PROFILE-MIB', 'juniPppoeProfileDupProtect'), ('Juniper-PPPOE-PROFILE-MIB', 'juniPppoeProfileAcName'))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
juni_pppoe_profile_group2 = juniPppoeProfileGroup2.setStatus('obsolete')
if mibBuilder.loadTexts:
juniPppoeProfileGroup2.setDescription('Obsolete collection of objects providing management of profile functionality for PPPOE interfaces in a Juniper product. This group became obsolete when PADI flag, AC-name and packet trace objects were added.')
juni_pppoe_profile_group3 = object_group((1, 3, 6, 1, 4, 1, 4874, 2, 2, 46, 4, 2, 3)).setObjects(('Juniper-PPPOE-PROFILE-MIB', 'juniPppoeProfileSetMap'), ('Juniper-PPPOE-PROFILE-MIB', 'juniPppoeProfileMaxNumSessions'), ('Juniper-PPPOE-PROFILE-MIB', 'juniPppoeProfileSubMotm'), ('Juniper-PPPOE-PROFILE-MIB', 'juniPppoeProfileSubUrl'), ('Juniper-PPPOE-PROFILE-MIB', 'juniPppoeProfileDupProtect'), ('Juniper-PPPOE-PROFILE-MIB', 'juniPppoeProfileAcName'), ('Juniper-PPPOE-PROFILE-MIB', 'juniPppoeProfilePadiFlag'), ('Juniper-PPPOE-PROFILE-MIB', 'juniPppoeProfilePacketTrace'))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
juni_pppoe_profile_group3 = juniPppoeProfileGroup3.setStatus('obsolete')
if mibBuilder.loadTexts:
juniPppoeProfileGroup3.setDescription('A collection of objects providing management of profile functionality for PPPOE interfaces in a Juniper product.')
juni_pppoe_profile_group4 = object_group((1, 3, 6, 1, 4, 1, 4874, 2, 2, 46, 4, 2, 4)).setObjects(('Juniper-PPPOE-PROFILE-MIB', 'juniPppoeProfileSetMap'), ('Juniper-PPPOE-PROFILE-MIB', 'juniPppoeProfileMaxNumSessions'), ('Juniper-PPPOE-PROFILE-MIB', 'juniPppoeProfileSubMotm'), ('Juniper-PPPOE-PROFILE-MIB', 'juniPppoeProfileSubUrl'), ('Juniper-PPPOE-PROFILE-MIB', 'juniPppoeProfileDupProtect'), ('Juniper-PPPOE-PROFILE-MIB', 'juniPppoeProfileAcName'), ('Juniper-PPPOE-PROFILE-MIB', 'juniPppoeProfilePadiFlag'), ('Juniper-PPPOE-PROFILE-MIB', 'juniPppoeProfilePacketTrace'), ('Juniper-PPPOE-PROFILE-MIB', 'juniPppoeProfileServiceNameTableName'))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
juni_pppoe_profile_group4 = juniPppoeProfileGroup4.setStatus('obsolete')
if mibBuilder.loadTexts:
juniPppoeProfileGroup4.setDescription('A collection of objects providing management of profile functionality for PPPOE interfaces in a Juniper product.')
juni_pppoe_profile_group5 = object_group((1, 3, 6, 1, 4, 1, 4874, 2, 2, 46, 4, 2, 5)).setObjects(('Juniper-PPPOE-PROFILE-MIB', 'juniPppoeProfileSetMap'), ('Juniper-PPPOE-PROFILE-MIB', 'juniPppoeProfileMaxNumSessions'), ('Juniper-PPPOE-PROFILE-MIB', 'juniPppoeProfileSubMotm'), ('Juniper-PPPOE-PROFILE-MIB', 'juniPppoeProfileSubUrl'), ('Juniper-PPPOE-PROFILE-MIB', 'juniPppoeProfileDupProtect'), ('Juniper-PPPOE-PROFILE-MIB', 'juniPppoeProfileAcName'), ('Juniper-PPPOE-PROFILE-MIB', 'juniPppoeProfilePadiFlag'), ('Juniper-PPPOE-PROFILE-MIB', 'juniPppoeProfilePacketTrace'), ('Juniper-PPPOE-PROFILE-MIB', 'juniPppoeProfileServiceNameTableName'), ('Juniper-PPPOE-PROFILE-MIB', 'juniPppoeProfilePadrRemoteCircuitIdCapture'))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
juni_pppoe_profile_group5 = juniPppoeProfileGroup5.setStatus('obsolete')
if mibBuilder.loadTexts:
juniPppoeProfileGroup5.setDescription('A collection of objects providing management of profile functionality for PPPOE interfaces in a Juniper product.')
juni_pppoe_profile_group6 = object_group((1, 3, 6, 1, 4, 1, 4874, 2, 2, 46, 4, 2, 6)).setObjects(('Juniper-PPPOE-PROFILE-MIB', 'juniPppoeProfileSetMap'), ('Juniper-PPPOE-PROFILE-MIB', 'juniPppoeProfileMaxNumSessions'), ('Juniper-PPPOE-PROFILE-MIB', 'juniPppoeProfileSubMotm'), ('Juniper-PPPOE-PROFILE-MIB', 'juniPppoeProfileSubUrl'), ('Juniper-PPPOE-PROFILE-MIB', 'juniPppoeProfileDupProtect'), ('Juniper-PPPOE-PROFILE-MIB', 'juniPppoeProfileAcName'), ('Juniper-PPPOE-PROFILE-MIB', 'juniPppoeProfilePadiFlag'), ('Juniper-PPPOE-PROFILE-MIB', 'juniPppoeProfilePacketTrace'), ('Juniper-PPPOE-PROFILE-MIB', 'juniPppoeProfileServiceNameTableName'), ('Juniper-PPPOE-PROFILE-MIB', 'juniPppoeProfilePadrRemoteCircuitIdCapture'), ('Juniper-PPPOE-PROFILE-MIB', 'juniPppoeProfileMtu'))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
juni_pppoe_profile_group6 = juniPppoeProfileGroup6.setStatus('obsolete')
if mibBuilder.loadTexts:
juniPppoeProfileGroup6.setDescription('A collection of objects providing management of profile functionality for PPPOE interfaces in a Juniper product.')
juni_pppoe_profile_group7 = object_group((1, 3, 6, 1, 4, 1, 4874, 2, 2, 46, 4, 2, 7)).setObjects(('Juniper-PPPOE-PROFILE-MIB', 'juniPppoeProfileSetMap'), ('Juniper-PPPOE-PROFILE-MIB', 'juniPppoeProfileMaxNumSessions'), ('Juniper-PPPOE-PROFILE-MIB', 'juniPppoeProfileSubMotm'), ('Juniper-PPPOE-PROFILE-MIB', 'juniPppoeProfileSubUrl'), ('Juniper-PPPOE-PROFILE-MIB', 'juniPppoeProfileDupProtect'), ('Juniper-PPPOE-PROFILE-MIB', 'juniPppoeProfileAcName'), ('Juniper-PPPOE-PROFILE-MIB', 'juniPppoeProfilePadiFlag'), ('Juniper-PPPOE-PROFILE-MIB', 'juniPppoeProfilePacketTrace'), ('Juniper-PPPOE-PROFILE-MIB', 'juniPppoeProfileServiceNameTableName'), ('Juniper-PPPOE-PROFILE-MIB', 'juniPppoeProfilePadrRemoteCircuitIdCapture'), ('Juniper-PPPOE-PROFILE-MIB', 'juniPppoeProfileMtu'), ('Juniper-PPPOE-PROFILE-MIB', 'juniPppoeProfileMaxSessionOverride'))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
juni_pppoe_profile_group7 = juniPppoeProfileGroup7.setStatus('current')
if mibBuilder.loadTexts:
juniPppoeProfileGroup7.setDescription('A collection of objects providing management of profile functionality for PPPOE interfaces in a Juniper product.')
mibBuilder.exportSymbols('Juniper-PPPOE-PROFILE-MIB', juniPppoeCompliance5=juniPppoeCompliance5, juniPppoeProfileGroup2=juniPppoeProfileGroup2, juniPppoeProfileMtu=juniPppoeProfileMtu, juniPppoeProfile=juniPppoeProfile, juniPppoeProfileSetMap=juniPppoeProfileSetMap, juniPppoeProfilePadiFlag=juniPppoeProfilePadiFlag, juniPppoeProfileTable=juniPppoeProfileTable, juniPppoeProfileDupProtect=juniPppoeProfileDupProtect, juniPppoeCompliance4=juniPppoeCompliance4, juniPppoeProfileGroup=juniPppoeProfileGroup, juniPppoeProfileMIB=juniPppoeProfileMIB, juniPppoeProfileGroup4=juniPppoeProfileGroup4, juniPppoeProfileCompliances=juniPppoeProfileCompliances, juniPppoeProfileAcName=juniPppoeProfileAcName, juniPppoeProfileGroup3=juniPppoeProfileGroup3, juniPppoeProfilePadrRemoteCircuitIdCapture=juniPppoeProfilePadrRemoteCircuitIdCapture, juniPppoeProfileSubMotm=juniPppoeProfileSubMotm, juniPppoeProfilePacketTrace=juniPppoeProfilePacketTrace, juniPppoeProfileEntry=juniPppoeProfileEntry, juniPppoeProfileGroup5=juniPppoeProfileGroup5, juniPppoeProfileMaxSessionOverride=juniPppoeProfileMaxSessionOverride, juniPppoeCompliance2=juniPppoeCompliance2, juniPppoeProfileGroups=juniPppoeProfileGroups, juniPppoeCompliance6=juniPppoeCompliance6, juniPppoeProfileCompliance=juniPppoeProfileCompliance, juniPppoeCompliance3=juniPppoeCompliance3, juniPppoeCompliance7=juniPppoeCompliance7, juniPppoeProfileGroup6=juniPppoeProfileGroup6, juniPppoeProfileMaxNumSessions=juniPppoeProfileMaxNumSessions, juniPppoeProfileId=juniPppoeProfileId, juniPppoeProfileConformance=juniPppoeProfileConformance, PYSNMP_MODULE_ID=juniPppoeProfileMIB, juniPppoeProfileObjects=juniPppoeProfileObjects, juniPppoeProfileServiceNameTableName=juniPppoeProfileServiceNameTableName, juniPppoeProfileSubUrl=juniPppoeProfileSubUrl, juniPppoeProfileGroup7=juniPppoeProfileGroup7)
|
class Solution(object):
def isValidSudoku(self, board):
return (self.is_row_valid(board) and
self.is_col_valid(board) and
self.is_square_valid(board))
def is_row_valid(self, board):
for row in board:
if not self.is_unit_valid(row):
return False
return True
def is_col_valid(self, board):
# zip turns column into tuple
for col in zip(*board):
if not self.is_unit_valid(col):
return False
return True
def is_square_valid(self, board):
for i in (0, 3, 6):
for j in (0, 3, 6):
square = [board[x][y] for x in range(i, i + 3) for y in range(j, j + 3)]
if not self.is_unit_valid(square):
return False
return True
def is_unit_valid(self, unit):
unit = [i for i in unit if i != '.']
return len(set(unit)) == len(unit)
test1 = [
["5","3",".",".","7",".",".",".","."],
["6",".",".","1","9","5",".",".","."],
[".","9","8",".",".",".",".","6","."],
["8",".",".",".","6",".",".",".","3"],
["4",".",".","8",".","3",".",".","1"],
["7",".",".",".","2",".",".",".","6"],
[".","6",".",".",".",".","2","8","."],
[".",".",".","4","1","9",".",".","5"],
[".",".",".",".","8",".",".","7","9"]
]
test2 = [
["8","3",".",".","7",".",".",".","."],
["6",".",".","1","9","5",".",".","."],
[".","9","8",".",".",".",".","6","."],
["8",".",".",".","6",".",".",".","3"],
["4",".",".","8",".","3",".",".","1"],
["7",".",".",".","2",".",".",".","6"],
[".","6",".",".",".",".","2","8","."],
[".",".",".","4","1","9",".",".","5"],
[".",".",".",".","8",".",".","7","9"]
]
solver = Solution()
print(solver.isValidSudoku(test1))
|
class Solution(object):
def is_valid_sudoku(self, board):
return self.is_row_valid(board) and self.is_col_valid(board) and self.is_square_valid(board)
def is_row_valid(self, board):
for row in board:
if not self.is_unit_valid(row):
return False
return True
def is_col_valid(self, board):
for col in zip(*board):
if not self.is_unit_valid(col):
return False
return True
def is_square_valid(self, board):
for i in (0, 3, 6):
for j in (0, 3, 6):
square = [board[x][y] for x in range(i, i + 3) for y in range(j, j + 3)]
if not self.is_unit_valid(square):
return False
return True
def is_unit_valid(self, unit):
unit = [i for i in unit if i != '.']
return len(set(unit)) == len(unit)
test1 = [['5', '3', '.', '.', '7', '.', '.', '.', '.'], ['6', '.', '.', '1', '9', '5', '.', '.', '.'], ['.', '9', '8', '.', '.', '.', '.', '6', '.'], ['8', '.', '.', '.', '6', '.', '.', '.', '3'], ['4', '.', '.', '8', '.', '3', '.', '.', '1'], ['7', '.', '.', '.', '2', '.', '.', '.', '6'], ['.', '6', '.', '.', '.', '.', '2', '8', '.'], ['.', '.', '.', '4', '1', '9', '.', '.', '5'], ['.', '.', '.', '.', '8', '.', '.', '7', '9']]
test2 = [['8', '3', '.', '.', '7', '.', '.', '.', '.'], ['6', '.', '.', '1', '9', '5', '.', '.', '.'], ['.', '9', '8', '.', '.', '.', '.', '6', '.'], ['8', '.', '.', '.', '6', '.', '.', '.', '3'], ['4', '.', '.', '8', '.', '3', '.', '.', '1'], ['7', '.', '.', '.', '2', '.', '.', '.', '6'], ['.', '6', '.', '.', '.', '.', '2', '8', '.'], ['.', '.', '.', '4', '1', '9', '.', '.', '5'], ['.', '.', '.', '.', '8', '.', '.', '7', '9']]
solver = solution()
print(solver.isValidSudoku(test1))
|
n = int(input())
D = [list(map(int,input().split())) for i in range(n)]
D.sort(key = lambda t: t[0])
S = 0
for i in D:
S += i[1]
S = (S+1)//2
S2 = 0
for i in D:
S2 += i[1]
if S2 >= S:
print(i[0])
break
|
n = int(input())
d = [list(map(int, input().split())) for i in range(n)]
D.sort(key=lambda t: t[0])
s = 0
for i in D:
s += i[1]
s = (S + 1) // 2
s2 = 0
for i in D:
s2 += i[1]
if S2 >= S:
print(i[0])
break
|
#!/usr/local/bin/python3
# Copyright 2019 NineFx Inc.
# Justin Baum
# 20 May 2019
# Precis Code-Generator ReasonML
# https://github.com/NineFX/smeagol/blob/master/spec/code_gen/precis_cp.txt
fp = open('unicodedata.txt', 'r')
ranges = []
line = fp.readline()
prev = ""
start = 0
while line:
if len(line) < 2: break
linesplit = line.split(";")
if ", First" in line:
nextline = fp.readline().split(";")
start = int(linesplit[0], 16)
finish = int(nextline[0], 16)
code = linesplit[4]
ranges.append((start, finish + 1, code))
else:
code = linesplit[4]
if code != prev:
value = int(linesplit[0], 16)
ranges.append((start, value, prev if len(prev) != 0 else "Illegal"))
start = value
prev = code
line = fp.readline()
def splitHalf(listy):
if len(listy) <= 2:
print("switch (point) {")
for item in listy:
if item[0] == item[1] - 1:
print(" | point when (point == " + str(item[0]) + ") => " + str(item[2]))
else:
print(" | point when (point >= " + str(item[0]) + " && point < " + str(item[1]) +") => " + str(item[2]))
print("| _point => raise(PrecisUtils.PrecisError(BidiError))")
print("}")
return
splitValue = listy[len(listy)//2]
firstHalf = listy[:len(listy)//2]
secondHalf = listy[len(listy)//2:]
print("if (point < " +str(splitValue[0]) + ") {")
splitHalf(firstHalf)
print("} else {")
splitHalf(secondHalf)
print("}")
splitHalf(ranges)
|
fp = open('unicodedata.txt', 'r')
ranges = []
line = fp.readline()
prev = ''
start = 0
while line:
if len(line) < 2:
break
linesplit = line.split(';')
if ', First' in line:
nextline = fp.readline().split(';')
start = int(linesplit[0], 16)
finish = int(nextline[0], 16)
code = linesplit[4]
ranges.append((start, finish + 1, code))
else:
code = linesplit[4]
if code != prev:
value = int(linesplit[0], 16)
ranges.append((start, value, prev if len(prev) != 0 else 'Illegal'))
start = value
prev = code
line = fp.readline()
def split_half(listy):
if len(listy) <= 2:
print('switch (point) {')
for item in listy:
if item[0] == item[1] - 1:
print(' | point when (point == ' + str(item[0]) + ') => ' + str(item[2]))
else:
print(' | point when (point >= ' + str(item[0]) + ' && point < ' + str(item[1]) + ') => ' + str(item[2]))
print('| _point => raise(PrecisUtils.PrecisError(BidiError))')
print('}')
return
split_value = listy[len(listy) // 2]
first_half = listy[:len(listy) // 2]
second_half = listy[len(listy) // 2:]
print('if (point < ' + str(splitValue[0]) + ') {')
split_half(firstHalf)
print('} else {')
split_half(secondHalf)
print('}')
split_half(ranges)
|
# Public Attributes
class Employee:
def __init__(self, ID, salary):
# all properties are public
self.ID = ID
self.salary = salary
def displayID(self):
print("ID:", self.ID)
Steve = Employee(3789, 2500)
Steve.displayID()
print(Steve.salary)
|
class Employee:
def __init__(self, ID, salary):
self.ID = ID
self.salary = salary
def display_id(self):
print('ID:', self.ID)
steve = employee(3789, 2500)
Steve.displayID()
print(Steve.salary)
|
m: int; n: int; j: int; i: int
m = int(input("Quantas linhas vai ter cada matriz? "))
n = int(input("Quantas colunas vai ter cada matriz? "))
A: [[int]] = [[0 for x in range(n)] for x in range(m)]
B: [[int]] = [[0 for x in range(n)] for x in range(m)]
C: [[int]] = [[0 for x in range(n)] for x in range(m)]
print("Digite os valores da matriz A:")
for i in range(0, m):
for j in range(0, n):
A[i][j] = int(input(f"Elemento [{i},{j}]: "))
print("Digite os valores da matriz B:")
for i in range(0, m):
for j in range(0, n):
B[i][j] = int(input(f"Elemento [{i},{j}]: "))
for i in range(0, m):
for j in range(0, n):
C[i][j] = A[i][j] + B[i][j]
print("MATRIZ SOMA:")
for i in range(0, m):
for j in range(0, n):
print(f"{C[i][j]} ", end="")
print()
|
m: int
n: int
j: int
i: int
m = int(input('Quantas linhas vai ter cada matriz? '))
n = int(input('Quantas colunas vai ter cada matriz? '))
a: [[int]] = [[0 for x in range(n)] for x in range(m)]
b: [[int]] = [[0 for x in range(n)] for x in range(m)]
c: [[int]] = [[0 for x in range(n)] for x in range(m)]
print('Digite os valores da matriz A:')
for i in range(0, m):
for j in range(0, n):
A[i][j] = int(input(f'Elemento [{i},{j}]: '))
print('Digite os valores da matriz B:')
for i in range(0, m):
for j in range(0, n):
B[i][j] = int(input(f'Elemento [{i},{j}]: '))
for i in range(0, m):
for j in range(0, n):
C[i][j] = A[i][j] + B[i][j]
print('MATRIZ SOMA:')
for i in range(0, m):
for j in range(0, n):
print(f'{C[i][j]} ', end='')
print()
|
# class Tree:
# def __init__(self, val, left=None, right=None):
# self.val = val
# self.left = left
# self.right = right
class Solution:
def solve(self, root):
stack = []
stack.append(root)
level = -1
min_sum = float('inf')
l = 0
while stack:
new = []
s = 0
for node in stack:
if node.left:
new.append(node.left)
if node.right:
new.append(node.right)
s += node.val
if s < min_sum:
level = l
min_sum = s
l += 1
stack = new
return level
|
class Solution:
def solve(self, root):
stack = []
stack.append(root)
level = -1
min_sum = float('inf')
l = 0
while stack:
new = []
s = 0
for node in stack:
if node.left:
new.append(node.left)
if node.right:
new.append(node.right)
s += node.val
if s < min_sum:
level = l
min_sum = s
l += 1
stack = new
return level
|
"""
1. Clarification
2. Possible solutions
- Backtracking
3. Coding
4. Tests
"""
# T=O(sum(feasible solutions' len)), S=O(target)
class Solution:
def combinationSum(self, candidates: List[int], target: int) -> List[List[int]]:
if not candidates or target < 1: return []
ans, tmp = [], []
def backtrack(idx, Sum):
if idx >= len(candidates) or Sum >= target:
if Sum == target:
ans.append(tmp[:])
return
tmp.append(candidates[idx])
backtrack(idx, Sum + candidates[idx])
tmp.pop()
backtrack(idx + 1, Sum)
backtrack(0, 0)
return ans
|
"""
1. Clarification
2. Possible solutions
- Backtracking
3. Coding
4. Tests
"""
class Solution:
def combination_sum(self, candidates: List[int], target: int) -> List[List[int]]:
if not candidates or target < 1:
return []
(ans, tmp) = ([], [])
def backtrack(idx, Sum):
if idx >= len(candidates) or Sum >= target:
if Sum == target:
ans.append(tmp[:])
return
tmp.append(candidates[idx])
backtrack(idx, Sum + candidates[idx])
tmp.pop()
backtrack(idx + 1, Sum)
backtrack(0, 0)
return ans
|
def ack ( m, n ):
'''
ack: Evaluates Ackermann function with the given arguments
m: Positive integer value
n: Positive integer value
'''
# Guard against error from incorrect input
if n < 0 or (not isinstance(n, int)):
return "Error: n is either negative or not an integer"
if m < 0 or (not isinstance(m, int)):
return "Error: m is either negative or not an integer"
if m == 0:
return n + 1
elif m > 0 and n == 0:
return ack(m-1, 1)
else:
return ack(m-1, ack(m, n-1))
print(ack(3, 4))
|
def ack(m, n):
"""
ack: Evaluates Ackermann function with the given arguments
m: Positive integer value
n: Positive integer value
"""
if n < 0 or not isinstance(n, int):
return 'Error: n is either negative or not an integer'
if m < 0 or not isinstance(m, int):
return 'Error: m is either negative or not an integer'
if m == 0:
return n + 1
elif m > 0 and n == 0:
return ack(m - 1, 1)
else:
return ack(m - 1, ack(m, n - 1))
print(ack(3, 4))
|
# Copyright (c) 2021 Ben Maddison. All rights reserved.
#
"""aspa.as_path Module."""
AS_SEQUENCE = 0x0
AS_SET = 0x1
class AsPathSegment(object):
def __init__(self, segment_type, values):
if segment_type not in (AS_SEQUENCE, AS_SET):
raise ValueError(int)
self.type = segment_type
self.values = values
def __repr__(self):
values = map(str, reversed(self.values))
if self.type == AS_SEQUENCE:
return f"{'_'.join(values)}"
else:
return f"[ {' '.join(values)} ]"
class AsPath(object):
def __init__(self, *segments):
for s in segments:
if not isinstance(s, AsPathSegment):
raise TypeError(f"expected AsPathSegment, got {s}")
self.segments = segments
def __repr__(self):
return f"{'_'.join(map(repr, reversed(self.segments)))}"
def flatten(self):
return [AsPathElement(orig_segment_type=s.type, value=v)
for s in self.segments
for v in s.values]
class AsPathElement(object):
def __init__(self, orig_segment_type, value):
self.type = orig_segment_type
self.value = value
|
"""aspa.as_path Module."""
as_sequence = 0
as_set = 1
class Aspathsegment(object):
def __init__(self, segment_type, values):
if segment_type not in (AS_SEQUENCE, AS_SET):
raise value_error(int)
self.type = segment_type
self.values = values
def __repr__(self):
values = map(str, reversed(self.values))
if self.type == AS_SEQUENCE:
return f"{'_'.join(values)}"
else:
return f"[ {' '.join(values)} ]"
class Aspath(object):
def __init__(self, *segments):
for s in segments:
if not isinstance(s, AsPathSegment):
raise type_error(f'expected AsPathSegment, got {s}')
self.segments = segments
def __repr__(self):
return f"{'_'.join(map(repr, reversed(self.segments)))}"
def flatten(self):
return [as_path_element(orig_segment_type=s.type, value=v) for s in self.segments for v in s.values]
class Aspathelement(object):
def __init__(self, orig_segment_type, value):
self.type = orig_segment_type
self.value = value
|
def love():
lover = 'sure'
lover
|
def love():
lover = 'sure'
lover
|
""" Parses a Markdown file in the Task.md format.
:params doc:
:returns: a dictionary containing the main fields of the task.
"""
def remove_obsidian_syntax(string):
"Removes Obsidian syntax from a string, namely []'s, [[]]'s and #'s"
pass
def parse_md(filename):
"Parses a markdown file in the Task.md format"
# initialize the task object
task = {"description": ''}
# start reading the input file
with open(filename, "r") as read_obj:
count = 0
while True:
line = read_obj.readline()
if not line:
break
if count == 2:
task['title'] = remove_obsidian_syntax(' '.join(line.split()[1:]))
if count == 3:
task['status'] = remove_obsidian_syntax(line.split()[1])
if count == 4:
task['priority'] = remove_obsidian_syntax(line.split()[1])
if count == 5:
task['due_date'] = line.split()[2]
if count == 6:
task['start_date'] = line.split()[2]
if count == 7:
task['deliverable'] = remove_obsidian_syntax(' '.join(line.split()[1:]))
if count >= 9:
task['description'] += remove_obsidian_syntax(line)
count += 1
print(task)
return task
if __name__ == "__main__":
parse_md("tests/tasks/sampleTask.md")
|
""" Parses a Markdown file in the Task.md format.
:params doc:
:returns: a dictionary containing the main fields of the task.
"""
def remove_obsidian_syntax(string):
"""Removes Obsidian syntax from a string, namely []'s, [[]]'s and #'s"""
pass
def parse_md(filename):
"""Parses a markdown file in the Task.md format"""
task = {'description': ''}
with open(filename, 'r') as read_obj:
count = 0
while True:
line = read_obj.readline()
if not line:
break
if count == 2:
task['title'] = remove_obsidian_syntax(' '.join(line.split()[1:]))
if count == 3:
task['status'] = remove_obsidian_syntax(line.split()[1])
if count == 4:
task['priority'] = remove_obsidian_syntax(line.split()[1])
if count == 5:
task['due_date'] = line.split()[2]
if count == 6:
task['start_date'] = line.split()[2]
if count == 7:
task['deliverable'] = remove_obsidian_syntax(' '.join(line.split()[1:]))
if count >= 9:
task['description'] += remove_obsidian_syntax(line)
count += 1
print(task)
return task
if __name__ == '__main__':
parse_md('tests/tasks/sampleTask.md')
|
#
class OtsUtil(object):
STEP_THRESHOLD = 408
step = 0
@staticmethod
def log(msg):
if OtsUtil.step >= OtsUtil.STEP_THRESHOLD:
print(msg)
|
class Otsutil(object):
step_threshold = 408
step = 0
@staticmethod
def log(msg):
if OtsUtil.step >= OtsUtil.STEP_THRESHOLD:
print(msg)
|
#Program to Remove Punctuations From a String
string="Wow! What a beautiful nature!"
new_string=string.replace("!","")
print(new_string)
|
string = 'Wow! What a beautiful nature!'
new_string = string.replace('!', '')
print(new_string)
|
def condensate_to_gas_equivalence(api, stb):
"Derivation from real gas equation"
Tsc = 519.57 # standard temp in Rankine
psc = 14.7 # standard pressure in psi
R = 10.732
rho_w = 350.16 # water density in lbm/STB
so = 141.5 / (api + 131.5) # so: specific gravity of oil (dimensionless)
Mo = 5854 / (api - 8.811) # molecular weight of oil
n = (rho_w * so) / Mo
V1stb = ((n * R * Tsc) / psc)
V = V1stb * stb
return(V)
def general_equivalence(gamma, M):
"Calculate equivalence of 1 STB of water/condensate to scf of gas"
# gamma: specific gravity of condensate/water. oil specific gravity use formula: so=141.5/(api+131.5). water specific gravity = 1
# M: molecular weight of condensate/water. oil: Mo = 5854 / (api - 8.811). water: Mw = 18
V1stb = 132849 * (gamma / M)
return(V1stb)
|
def condensate_to_gas_equivalence(api, stb):
"""Derivation from real gas equation"""
tsc = 519.57
psc = 14.7
r = 10.732
rho_w = 350.16
so = 141.5 / (api + 131.5)
mo = 5854 / (api - 8.811)
n = rho_w * so / Mo
v1stb = n * R * Tsc / psc
v = V1stb * stb
return V
def general_equivalence(gamma, M):
"""Calculate equivalence of 1 STB of water/condensate to scf of gas"""
v1stb = 132849 * (gamma / M)
return V1stb
|
def fibonacci():
number = 0
previous_number = 1
while True:
if number == 0:
yield number
number += previous_number
if number == 1:
yield number
number += previous_number
if number == 2:
yield previous_number
if number > 1:
yield number
cutternt_number = previous_number
previous_number = number
number = cutternt_number + previous_number
generator = fibonacci()
for i in range(5):
print(next(generator))
|
def fibonacci():
number = 0
previous_number = 1
while True:
if number == 0:
yield number
number += previous_number
if number == 1:
yield number
number += previous_number
if number == 2:
yield previous_number
if number > 1:
yield number
cutternt_number = previous_number
previous_number = number
number = cutternt_number + previous_number
generator = fibonacci()
for i in range(5):
print(next(generator))
|
class Node:
def __init__(self, data):
self.data = data
self.left = None
self.right = None
def __repr__(self):
return str(self.data)
def count_unival_trees(root):
if not root:
return 0
elif not root.left and not root.right:
return 1
elif not root.left and root.data == root.right.data:
return 1 + count_unival_trees(root.right)
elif not root.right and root.data == root.left.data:
return 1 + count_unival_trees(root.left)
child_counts = count_unival_trees(root.left) + count_unival_trees(root.right)
current_node_count = 0
if root.data == root.left.data and root.data == root.left.data:
current_node_count = 1
return current_node_count + child_counts
node_a = Node('0')
node_b = Node('1')
node_c = Node('0')
node_d = Node('1')
node_e = Node('0')
node_f = Node('1')
node_g = Node('1')
node_a.left = node_b
node_a.right = node_c
node_c.left = node_d
node_c.right = node_e
node_d.left = node_f
node_d.right = node_g
assert count_unival_trees(None) == 0
assert count_unival_trees(node_a) == 5
assert count_unival_trees(node_c) == 4
assert count_unival_trees(node_g) == 1
assert count_unival_trees(node_d) == 3
|
class Node:
def __init__(self, data):
self.data = data
self.left = None
self.right = None
def __repr__(self):
return str(self.data)
def count_unival_trees(root):
if not root:
return 0
elif not root.left and (not root.right):
return 1
elif not root.left and root.data == root.right.data:
return 1 + count_unival_trees(root.right)
elif not root.right and root.data == root.left.data:
return 1 + count_unival_trees(root.left)
child_counts = count_unival_trees(root.left) + count_unival_trees(root.right)
current_node_count = 0
if root.data == root.left.data and root.data == root.left.data:
current_node_count = 1
return current_node_count + child_counts
node_a = node('0')
node_b = node('1')
node_c = node('0')
node_d = node('1')
node_e = node('0')
node_f = node('1')
node_g = node('1')
node_a.left = node_b
node_a.right = node_c
node_c.left = node_d
node_c.right = node_e
node_d.left = node_f
node_d.right = node_g
assert count_unival_trees(None) == 0
assert count_unival_trees(node_a) == 5
assert count_unival_trees(node_c) == 4
assert count_unival_trees(node_g) == 1
assert count_unival_trees(node_d) == 3
|
"""
A valid parentheses string is either empty (""), "(" + A + ")", or A + B, where A and B are valid parentheses
strings, and + represents string concatenation. For example, "", "()", "(())()", and "(()(()))" are all valid
parentheses strings.
A valid parentheses string S is primitive if it is nonempty, and there does not exist a way to split it into
S = A+B, with A and B nonempty valid parentheses strings.
Given a valid parentheses string S, consider its primitive decomposition: S = P_1 + P_2 + ... + P_k, where P_i
are primitive valid parentheses strings.
Return S after removing the outermost parentheses of every primitive string in the primitive decomposition of S.
Example 1:
Input: "(()())(())"
Output: "()()()"
Explanation:
The input string is "(()())(())", with primitive decomposition "(()())" + "(())".
After removing outer parentheses of each part, this is "()()" + "()" = "()()()".
Example 2:
Input: "(()())(())(()(()))"
Output: "()()()()(())"
Explanation:
The input string is "(()())(())(()(()))", with primitive decomposition "(()())" + "(())" + "(()(()))".
After removing outer parentheses of each part, this is "()()" + "()" + "()(())" = "()()()()(())".
Example 3:
Input: "()()"
Output: ""
Explanation:
The input string is "()()", with primitive decomposition "()" + "()".
After removing outer parentheses of each part, this is "" + "" = "".
Note:
1. S.length <= 10000
2. S[i] is "(" or ")"
3. S is a valid parentheses string
"""
class Solution:
def removeOuterParentheses(self, S: str) -> str:
res, stack = [], 0
for s in S:
if s == '(':
if stack > 0:
res.append(s)
stack += 1
else:
stack -= 1
if stack > 0:
res.append(s)
return ''.join(res)
|
"""
A valid parentheses string is either empty (""), "(" + A + ")", or A + B, where A and B are valid parentheses
strings, and + represents string concatenation. For example, "", "()", "(())()", and "(()(()))" are all valid
parentheses strings.
A valid parentheses string S is primitive if it is nonempty, and there does not exist a way to split it into
S = A+B, with A and B nonempty valid parentheses strings.
Given a valid parentheses string S, consider its primitive decomposition: S = P_1 + P_2 + ... + P_k, where P_i
are primitive valid parentheses strings.
Return S after removing the outermost parentheses of every primitive string in the primitive decomposition of S.
Example 1:
Input: "(()())(())"
Output: "()()()"
Explanation:
The input string is "(()())(())", with primitive decomposition "(()())" + "(())".
After removing outer parentheses of each part, this is "()()" + "()" = "()()()".
Example 2:
Input: "(()())(())(()(()))"
Output: "()()()()(())"
Explanation:
The input string is "(()())(())(()(()))", with primitive decomposition "(()())" + "(())" + "(()(()))".
After removing outer parentheses of each part, this is "()()" + "()" + "()(())" = "()()()()(())".
Example 3:
Input: "()()"
Output: ""
Explanation:
The input string is "()()", with primitive decomposition "()" + "()".
After removing outer parentheses of each part, this is "" + "" = "".
Note:
1. S.length <= 10000
2. S[i] is "(" or ")"
3. S is a valid parentheses string
"""
class Solution:
def remove_outer_parentheses(self, S: str) -> str:
(res, stack) = ([], 0)
for s in S:
if s == '(':
if stack > 0:
res.append(s)
stack += 1
else:
stack -= 1
if stack > 0:
res.append(s)
return ''.join(res)
|
# List Operations and Functions
# '+' Operator
print('-----------+ Operator------------')
a = [1,2,3]
b = [4,5,6]
c = a + b
print(c)
print('----------* Operator--------------')
# '*' Operator
a1 = a * 2
print(a1)
print('--------len function----------------')
print(len(a))
print('--------max function----------------')
print(max(a))
print('--------min function----------------')
print(min(a))
print('--------sum function----------------')
print(sum(a))
print("----------Average--------------")
mylist = []
while(True):
value = (input("Enter the number: "))
if value == 'done': break
value = float(value)
mylist.append(value)
average = sum(mylist)/len(mylist)
print("Average : ", average)
# Strings and Lists
print("----------Strings to Lists split function--------------")
mystring = 'Sagar Sanjeev Potnis'
print(mystring)
newlist = mystring.split()
print(newlist)
mystring1 = 'Sagar-Sanjeev-Potnis'
print(mystring1)
newlist1 = mystring1.split('-')
print(newlist1)
print("---------- List to String join function--------------")
joinstring = " ".join(newlist)
print(joinstring)
print("----------map function--------------")
print("Please Enter list :")
maplist = list(map(int, input().split()))
print(maplist)
print("----------Pitfalls and how to avoid them--------------")
pitylist = [5,4,3,2,1]
pitylist = sorted(pitylist)
print(pitylist)
# Sorted function does not modify original list unline sort fucntion!!! so it is better
|
print('-----------+ Operator------------')
a = [1, 2, 3]
b = [4, 5, 6]
c = a + b
print(c)
print('----------* Operator--------------')
a1 = a * 2
print(a1)
print('--------len function----------------')
print(len(a))
print('--------max function----------------')
print(max(a))
print('--------min function----------------')
print(min(a))
print('--------sum function----------------')
print(sum(a))
print('----------Average--------------')
mylist = []
while True:
value = input('Enter the number: ')
if value == 'done':
break
value = float(value)
mylist.append(value)
average = sum(mylist) / len(mylist)
print('Average : ', average)
print('----------Strings to Lists split function--------------')
mystring = 'Sagar Sanjeev Potnis'
print(mystring)
newlist = mystring.split()
print(newlist)
mystring1 = 'Sagar-Sanjeev-Potnis'
print(mystring1)
newlist1 = mystring1.split('-')
print(newlist1)
print('---------- List to String join function--------------')
joinstring = ' '.join(newlist)
print(joinstring)
print('----------map function--------------')
print('Please Enter list :')
maplist = list(map(int, input().split()))
print(maplist)
print('----------Pitfalls and how to avoid them--------------')
pitylist = [5, 4, 3, 2, 1]
pitylist = sorted(pitylist)
print(pitylist)
|
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "9de928d7",
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import csv\n",
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "8f1478f8",
"metadata": {},
"outputs": [],
"source": [
"#Set Path\n",
"pollCSV = os.path.join('Resources', 'election_data.CSV')\n",
"pollCSV = r'C:\\Users\\rzh00\\Documents\\gt-virt-atl-data-pt-09-2021-u-c-master\\02-Homework\\03-Python\\Instructions\\PyPoll\\Resources\\election_data.csv'"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "0f207e3c",
"metadata": {},
"outputs": [],
"source": [
"#Varibales\n",
"candi = []\n",
"vote_count = []\n",
"vote_percent = []\n",
"\n",
"num_vote = 0"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "08423af7",
"metadata": {},
"outputs": [],
"source": [
"#Open CSV\n",
"with open(pollCSV) as csvfile:\n",
" csvreader = csv.reader(csvfile, delimiter = ',')\n",
" csvheader = next(csvreader)\n",
" for row in csvreader: \n",
" num_vote = num_vote + 1 # total votes\n",
" candi_name = row[2] # adding candidate name to array\n",
" \n",
" if candi_name not in candi: #conditional to append any new candidates \n",
" candi.append(candi_name)\n",
" index = candi.index(row[2])\n",
" vote_count.append(1)\n",
" else:\n",
" index = candi.index(row[2])\n",
" vote_count[index] += 1\n",
" \n",
" for i in vote_count: # find the percentage of the votes recieved\n",
" percent = round((i/num_vote) * 100)\n",
" percent = '%.3f%%' % percent\n",
" vote_percent.append(percent)\n",
" \n",
" winner = max(vote_count) # determine winner and update the value\n",
" index = vote_count.index(winner)\n",
" candi_winner = candi[index]"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "400521a2",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Election Results\n",
"--------------------\n",
"Total Votes[4436463, 1408401, 985880, 211260]\n",
"--------------------\n",
"Khan: 63.000% (4436463)\n",
"Correy: 20.000% (1408401)\n",
"Li: 14.000% (985880)\n",
"O'Tooley: 3.000% (211260)\n",
"--------------------\n",
"Winning Candidate: 4436463\n"
]
}
],
"source": [
"#Print Results\n",
"print('Election Results')\n",
"print('-' * 20)\n",
"print(f'Total Votes' + str(vote_count))\n",
"print('-' * 20)\n",
"for i in range(len(candi)):\n",
" print(f'{candi[i]}: {str(vote_percent[i])} ({str(vote_count[i])})')\n",
"print('-' * 20)\n",
"print(f'Winning Candidate: {winner}')"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "18e81981",
"metadata": {},
"outputs": [],
"source": [
"result = os.path.join('output', 'result.txt')\n",
"result = r'C:\\Users\\rzh00\\Documents\\gt-virt-atl-data-pt-09-2021-u-c-master\\02-Homework\\03-Python\\Instructions\\PyPoll\\result.txt'\n",
"\n",
"with open(result, 'w') as txt:\n",
" txt.write('Election Results')\n",
" txt.write('-' * 20)\n",
" txt.write(f'Total Votes' + str(vote_count))\n",
" txt.write('-' * 20)\n",
" for i in range(len(candi)):\n",
" txt.write(f'{candi[i]}: {str(vote_percent[i])} ({str(vote_count[i])})')\n",
" txt.write('-' * 20)\n",
" txt.write(f'Winning Candidate: {winner}')\n",
" txt.close()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c5893e30",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.8"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
|
{'cells': [{'cell_type': 'code', 'execution_count': 1, 'id': '9de928d7', 'metadata': {}, 'outputs': [], 'source': ['import os\n', 'import csv\n', 'import pandas as pd']}, {'cell_type': 'code', 'execution_count': 2, 'id': '8f1478f8', 'metadata': {}, 'outputs': [], 'source': ['#Set Path\n', "pollCSV = os.path.join('Resources', 'election_data.CSV')\n", "pollCSV = r'C:\\Users\\rzh00\\Documents\\gt-virt-atl-data-pt-09-2021-u-c-master\\02-Homework\\03-Python\\Instructions\\PyPoll\\Resources\\election_data.csv'"]}, {'cell_type': 'code', 'execution_count': 3, 'id': '0f207e3c', 'metadata': {}, 'outputs': [], 'source': ['#Varibales\n', 'candi = []\n', 'vote_count = []\n', 'vote_percent = []\n', '\n', 'num_vote = 0']}, {'cell_type': 'code', 'execution_count': 7, 'id': '08423af7', 'metadata': {}, 'outputs': [], 'source': ['#Open CSV\n', 'with open(pollCSV) as csvfile:\n', " csvreader = csv.reader(csvfile, delimiter = ',')\n", ' csvheader = next(csvreader)\n', ' for row in csvreader: \n', ' num_vote = num_vote + 1 # total votes\n', ' candi_name = row[2] # adding candidate name to array\n', ' \n', ' if candi_name not in candi: #conditional to append any new candidates \n', ' candi.append(candi_name)\n', ' index = candi.index(row[2])\n', ' vote_count.append(1)\n', ' else:\n', ' index = candi.index(row[2])\n', ' vote_count[index] += 1\n', ' \n', ' for i in vote_count: # find the percentage of the votes recieved\n', ' percent = round((i/num_vote) * 100)\n', " percent = '%.3f%%' % percent\n", ' vote_percent.append(percent)\n', ' \n', ' winner = max(vote_count) # determine winner and update the value\n', ' index = vote_count.index(winner)\n', ' candi_winner = candi[index]']}, {'cell_type': 'code', 'execution_count': 16, 'id': '400521a2', 'metadata': {}, 'outputs': [{'name': 'stdout', 'output_type': 'stream', 'text': ['Election Results\n', '--------------------\n', 'Total Votes[4436463, 1408401, 985880, 211260]\n', '--------------------\n', 'Khan: 63.000% (4436463)\n', 'Correy: 20.000% (1408401)\n', 'Li: 14.000% (985880)\n', "O'Tooley: 3.000% (211260)\n", '--------------------\n', 'Winning Candidate: 4436463\n']}], 'source': ['#Print Results\n', "print('Election Results')\n", "print('-' * 20)\n", "print(f'Total Votes' + str(vote_count))\n", "print('-' * 20)\n", 'for i in range(len(candi)):\n', " print(f'{candi[i]}: {str(vote_percent[i])} ({str(vote_count[i])})')\n", "print('-' * 20)\n", "print(f'Winning Candidate: {winner}')"]}, {'cell_type': 'code', 'execution_count': 14, 'id': '18e81981', 'metadata': {}, 'outputs': [], 'source': ["result = os.path.join('output', 'result.txt')\n", "result = r'C:\\Users\\rzh00\\Documents\\gt-virt-atl-data-pt-09-2021-u-c-master\\02-Homework\\03-Python\\Instructions\\PyPoll\\result.txt'\n", '\n', "with open(result, 'w') as txt:\n", " txt.write('Election Results')\n", " txt.write('-' * 20)\n", " txt.write(f'Total Votes' + str(vote_count))\n", " txt.write('-' * 20)\n", ' for i in range(len(candi)):\n', " txt.write(f'{candi[i]}: {str(vote_percent[i])} ({str(vote_count[i])})')\n", " txt.write('-' * 20)\n", " txt.write(f'Winning Candidate: {winner}')\n", ' txt.close()']}, {'cell_type': 'code', 'execution_count': null, 'id': 'c5893e30', 'metadata': {}, 'outputs': [], 'source': []}], 'metadata': {'kernelspec': {'display_name': 'Python 3', 'language': 'python', 'name': 'python3'}, 'language_info': {'codemirror_mode': {'name': 'ipython', 'version': 3}, 'file_extension': '.py', 'mimetype': 'text/x-python', 'name': 'python', 'nbconvert_exporter': 'python', 'pygments_lexer': 'ipython3', 'version': '3.8.8'}}, 'nbformat': 4, 'nbformat_minor': 5}
|
"""
A robot is located at the top-left corner of a m x n grid (marked 'Start' in the
diagram below).
The robot can only move either down or right at any point in time. The robot is
trying to reach the bottom-right corner of the grid (marked 'Finish' in the
diagram below).
How many possible unique paths are there?
Above is a 3 x 7 grid. How many possible unique paths are there?
Note: m and n will be at most 100.
"""
class Solution(object):
def uniquePaths(self, m, n):
"""
:type m: int
:type n: int
:rtype: int
"""
if m == 0 or n == 0:
return 0
grid = [[0 for j in range(m)] for i in range(n)]
for i in range(n):
for j in range(m):
if i == 0 or j == 0:
grid[i][j] = 1
else:
grid[i][j] = grid[i - 1][j] + grid[i][j - 1]
return grid[-1][-1]
|
"""
A robot is located at the top-left corner of a m x n grid (marked 'Start' in the
diagram below).
The robot can only move either down or right at any point in time. The robot is
trying to reach the bottom-right corner of the grid (marked 'Finish' in the
diagram below).
How many possible unique paths are there?
Above is a 3 x 7 grid. How many possible unique paths are there?
Note: m and n will be at most 100.
"""
class Solution(object):
def unique_paths(self, m, n):
"""
:type m: int
:type n: int
:rtype: int
"""
if m == 0 or n == 0:
return 0
grid = [[0 for j in range(m)] for i in range(n)]
for i in range(n):
for j in range(m):
if i == 0 or j == 0:
grid[i][j] = 1
else:
grid[i][j] = grid[i - 1][j] + grid[i][j - 1]
return grid[-1][-1]
|
text = input().split(' ')
new_text = ''
for word in text:
if len(word) > 4:
if word[:2] in word[2:]:
word = word[2:]
new_text += ' ' + word
print(new_text[1:])
|
text = input().split(' ')
new_text = ''
for word in text:
if len(word) > 4:
if word[:2] in word[2:]:
word = word[2:]
new_text += ' ' + word
print(new_text[1:])
|
class Solution:
def longestCommonPrefix(self, strs: List[str]) -> str:
if len(strs) == 0:
return ""
resLen = 0
while True:
if len(strs[0]) == resLen:
return strs[0]
curChar = strs[0][resLen]
for i in range(1, len(strs)):
if len(strs[i]) == resLen or strs[i][resLen] != curChar:
return strs[0][: resLen]
resLen += 1
return strs[0][: resLen]
|
class Solution:
def longest_common_prefix(self, strs: List[str]) -> str:
if len(strs) == 0:
return ''
res_len = 0
while True:
if len(strs[0]) == resLen:
return strs[0]
cur_char = strs[0][resLen]
for i in range(1, len(strs)):
if len(strs[i]) == resLen or strs[i][resLen] != curChar:
return strs[0][:resLen]
res_len += 1
return strs[0][:resLen]
|
#!python3
# -*- coding:utf-8 -*-
'''
this code is a sample of code for learn how to use python test modules.
'''
def aaa():
'''
printing tree ! mark.
'''
print("!!!")
def bbb():
'''
printing tree chars.
'''
print("BBB")
def ccc():
'''
printing number with loop.
'''
for i in range(5):
print(i)
|
"""
this code is a sample of code for learn how to use python test modules.
"""
def aaa():
"""
printing tree ! mark.
"""
print('!!!')
def bbb():
"""
printing tree chars.
"""
print('BBB')
def ccc():
"""
printing number with loop.
"""
for i in range(5):
print(i)
|
# Project Framework default is 1 (Model/View/Provider)
# src is source where is the lib folder located
def startmain(src="flutterproject/myapp/lib", pkg="Provider", file="app_widget", home="",
project_framework=1, autoconfigfile=True):
maindart = open(src + "/main.dart", "w")
if project_framework == 1:
maindart.write(
"import 'package:flutter/material.dart';\n"
"import '" + pkg + "/" + file + ".dart';\n\n"
"void main() => runApp(AppWidget());")
else:
print("This project framework is not avaliable yet. :(")
maindart.close()
if autoconfigfile:
configfile(home, src, pkg, file)
# Its not recomended use this manually
def configfile(home, src, pkg, file):
app_widgetdart = open(src+"/"+pkg+"/"+file+".dart", "w")
if home == "":
app_widgetdart.write("import 'package:flutter/material.dart';\n\n"
"class AppWidget extends StatelessWidget {\n"
" @override\n"
" Widget build(BuildContext context) {\n"
" return MaterialApp();\n"
" }\n"
"}")
else:
app_widgetdart.write("import 'package:flutter/material.dart';\n"
"import '../View/Screens/homepage.dart';\n\n"
"class AppWidget extends StatelessWidget {\n"
" @override\n"
" Widget build(BuildContext context) {\n"
" return MaterialApp(\n"
" home:" + home + "(),\n"
" );\n"
" }\n"
"}")
app_widgetdart.close()
|
def startmain(src='flutterproject/myapp/lib', pkg='Provider', file='app_widget', home='', project_framework=1, autoconfigfile=True):
maindart = open(src + '/main.dart', 'w')
if project_framework == 1:
maindart.write("import 'package:flutter/material.dart';\nimport '" + pkg + '/' + file + ".dart';\n\nvoid main() => runApp(AppWidget());")
else:
print('This project framework is not avaliable yet. :(')
maindart.close()
if autoconfigfile:
configfile(home, src, pkg, file)
def configfile(home, src, pkg, file):
app_widgetdart = open(src + '/' + pkg + '/' + file + '.dart', 'w')
if home == '':
app_widgetdart.write("import 'package:flutter/material.dart';\n\nclass AppWidget extends StatelessWidget {\n @override\n Widget build(BuildContext context) {\n return MaterialApp();\n }\n}")
else:
app_widgetdart.write("import 'package:flutter/material.dart';\nimport '../View/Screens/homepage.dart';\n\nclass AppWidget extends StatelessWidget {\n @override\n Widget build(BuildContext context) {\n return MaterialApp(\n home:" + home + '(),\n );\n }\n}')
app_widgetdart.close()
|
def max_consecutive_ones(x):
# e.g. x= 95 (1101111)
"""
Steps
1. x & x<<1 --> 1101111 & 1011110 == 1001110
2. x & x<<1 --> 1001110 & 0011100 == 0001100
3. x & x<<1 --> 0001100 & 0011000 == 0001000
4. x & x<<1 --> 0001000 & 0010000 == 0000000
:param x:
:return:
"""
count = 0
while x > 0:
x = x & (x << 1)
count += 1
return count
if __name__ == '__main__':
print(max_consecutive_ones(7))
|
def max_consecutive_ones(x):
"""
Steps
1. x & x<<1 --> 1101111 & 1011110 == 1001110
2. x & x<<1 --> 1001110 & 0011100 == 0001100
3. x & x<<1 --> 0001100 & 0011000 == 0001000
4. x & x<<1 --> 0001000 & 0010000 == 0000000
:param x:
:return:
"""
count = 0
while x > 0:
x = x & x << 1
count += 1
return count
if __name__ == '__main__':
print(max_consecutive_ones(7))
|
# Avg week temperature
print("Enter temperatures of 7 days:")
a = float(input())
b = float(input())
c = float(input())
d = float(input())
e = float(input())
f = float(input())
g = float(input())
print("Average temperature:", (a+b+c+d+e+f+g)/7)
|
print('Enter temperatures of 7 days:')
a = float(input())
b = float(input())
c = float(input())
d = float(input())
e = float(input())
f = float(input())
g = float(input())
print('Average temperature:', (a + b + c + d + e + f + g) / 7)
|
def digitSum(n,step):
res=0
while(n>0):
res+=n%10
n=n//10
step+=1
if(res<=9):
return (res,step)
else:
return digitSum(res,step)
t=int(input())
for _ in range(t):
minstep=[99999999 for i in range(10)] #cache
hitratio=[0 for i in range(10)]
maxhit=0
n,d=[int(x) for x in input().strip().split()]
if(n==1):
print("{0} {1}".format(n,0))
continue
if(d>9):
d=digitSum(d,0)[0] #minimize it to single digit
steps=0
if(n>9):
n,steps=digitSum(n,steps)
minstep[n]=min(minstep[n],steps)
minstep[n]=min(minstep[n],steps)
hitratio[n]+=1
maxhit=max(maxhit,hitratio[n])
if(n==1):
print("{0} {1}".format(n,steps))
continue
iteration=1
while(n!=1 and iteration<(10**8)):
iteration+=1
#print(minstep)
n=n+d
steps+=1
if(n<10):
minstep[n] = min(minstep[n],steps)
hitratio[n]+=1
maxhit=max(maxhit,hitratio[n])
if(n>9):
n,steps=digitSum(n,steps)
minstep[n] = min(minstep[n],steps)
hitratio[n]+=1
maxhit=max(maxhit,hitratio[n])
if(maxhit>100):
break
tempmin=10
for i in range(2,10):
if(minstep[i]!=99999999 and i<tempmin):
tempmin=i
print("{0} {1}".format(tempmin,minstep[tempmin]))
|
def digit_sum(n, step):
res = 0
while n > 0:
res += n % 10
n = n // 10
step += 1
if res <= 9:
return (res, step)
else:
return digit_sum(res, step)
t = int(input())
for _ in range(t):
minstep = [99999999 for i in range(10)]
hitratio = [0 for i in range(10)]
maxhit = 0
(n, d) = [int(x) for x in input().strip().split()]
if n == 1:
print('{0} {1}'.format(n, 0))
continue
if d > 9:
d = digit_sum(d, 0)[0]
steps = 0
if n > 9:
(n, steps) = digit_sum(n, steps)
minstep[n] = min(minstep[n], steps)
minstep[n] = min(minstep[n], steps)
hitratio[n] += 1
maxhit = max(maxhit, hitratio[n])
if n == 1:
print('{0} {1}'.format(n, steps))
continue
iteration = 1
while n != 1 and iteration < 10 ** 8:
iteration += 1
n = n + d
steps += 1
if n < 10:
minstep[n] = min(minstep[n], steps)
hitratio[n] += 1
maxhit = max(maxhit, hitratio[n])
if n > 9:
(n, steps) = digit_sum(n, steps)
minstep[n] = min(minstep[n], steps)
hitratio[n] += 1
maxhit = max(maxhit, hitratio[n])
if maxhit > 100:
break
tempmin = 10
for i in range(2, 10):
if minstep[i] != 99999999 and i < tempmin:
tempmin = i
print('{0} {1}'.format(tempmin, minstep[tempmin]))
|
######################################################
# #
# author #
# Parth Lathiya #
# https://www.cse.iitb.ac.in/~parthiitb/ #
# #
######################################################
at = int(input().strip())
for att in range(at):
u = list(map(int, input().strip().split()))
u.remove(len(u)-1)
print(max(u))
|
at = int(input().strip())
for att in range(at):
u = list(map(int, input().strip().split()))
u.remove(len(u) - 1)
print(max(u))
|
# 15/15
num_of_flicks = int(input())
art = []
for _ in range(num_of_flicks):
coords = input().split(",")
art.append((int(coords[0]), int(coords[1])))
lowest_x = min(art, key=lambda x: x[0])[0] - 1
lowest_y = min(art, key=lambda x: x[1])[1] - 1
highest_x = max(art, key=lambda x: x[0])[0] + 1
highest_y = max(art, key=lambda x: x[1])[1] + 1
print(f"{lowest_x},{lowest_y}")
print(f"{highest_x},{highest_y}")
|
num_of_flicks = int(input())
art = []
for _ in range(num_of_flicks):
coords = input().split(',')
art.append((int(coords[0]), int(coords[1])))
lowest_x = min(art, key=lambda x: x[0])[0] - 1
lowest_y = min(art, key=lambda x: x[1])[1] - 1
highest_x = max(art, key=lambda x: x[0])[0] + 1
highest_y = max(art, key=lambda x: x[1])[1] + 1
print(f'{lowest_x},{lowest_y}')
print(f'{highest_x},{highest_y}')
|
print("Leap Year Range Calculator: ")
year1=int(input("Enter First Year: "))
year2 = int(input("Enter Last Year: "))
while year1<=year2:
if year1 % 4 == 0 :
print(year1,"is a leap year")
year1= year1 + 1
|
print('Leap Year Range Calculator: ')
year1 = int(input('Enter First Year: '))
year2 = int(input('Enter Last Year: '))
while year1 <= year2:
if year1 % 4 == 0:
print(year1, 'is a leap year')
year1 = year1 + 1
|
I = lambda : int(input())
LI = lambda : [int(x) for x in input().split()]
MI = lambda : map(int, input().split())
SI = lambda : input()
"""
#Leer de archivo
for line in sys.stdin:
...
"""
"""
def fastio():
import sys
from io import StringIO
from atexit import register
global input
sys.stdin = StringIO(sys.stdin.read())
input = lambda : sys.stdin.readline().rstrip()
sys.stdout = StringIO()
register(lambda : sys.__stdout__.write(sys.stdout.getvalue()))
fastio()
"""
class PointSystem:
def oddsOfWinning(self, pointsToWin, pointsToWinBy, skill):
n = 1000
p = [[0 for i in range(n)] for j in range(n)]
p[0][0] = 1
for i in range(n-1):
for j in range(n-1):
if(max(i, j) >= pointsToWin and max(i, j) - min(i, j) >= pointsToWinBy):
continue
p[i+1][j] += p[i][j] * skill/100.0
p[i][j+1] += p[i][j] * (100-skill)/100.0
ans = 0.0
for i in range(n):
for j in range(n):
if(i > j and i >= pointsToWin and i - j >= pointsToWinBy):
ans += p[i][j]
return ans
# x = PointSystem()
# print(x.oddsOfWinning(2, 1, 40))
# print(x.oddsOfWinning(4, 5, 50))
# print(x.oddsOfWinning(3, 3, 25))
|
i = lambda : int(input())
li = lambda : [int(x) for x in input().split()]
mi = lambda : map(int, input().split())
si = lambda : input()
'\n#Leer de archivo\nfor line in sys.stdin:\n ...\n'
'\ndef fastio():\n import sys\n from io import StringIO\n from atexit import register\n global input\n sys.stdin = StringIO(sys.stdin.read())\n input = lambda : sys.stdin.readline().rstrip()\n sys.stdout = StringIO()\n register(lambda : sys.__stdout__.write(sys.stdout.getvalue()))\n\nfastio()\n'
class Pointsystem:
def odds_of_winning(self, pointsToWin, pointsToWinBy, skill):
n = 1000
p = [[0 for i in range(n)] for j in range(n)]
p[0][0] = 1
for i in range(n - 1):
for j in range(n - 1):
if max(i, j) >= pointsToWin and max(i, j) - min(i, j) >= pointsToWinBy:
continue
p[i + 1][j] += p[i][j] * skill / 100.0
p[i][j + 1] += p[i][j] * (100 - skill) / 100.0
ans = 0.0
for i in range(n):
for j in range(n):
if i > j and i >= pointsToWin and (i - j >= pointsToWinBy):
ans += p[i][j]
return ans
|
"""
Generator with a finite loop
can be used with for
"""
def count():
n = 1
while n < 1000:
yield n
n *= 2
gen = count()
for number in gen:
print(number)
|
"""
Generator with a finite loop
can be used with for
"""
def count():
n = 1
while n < 1000:
yield n
n *= 2
gen = count()
for number in gen:
print(number)
|
"""
Constants to be used in the module
"""
__author__ = 'Santiago Flores Kanter (sfloresk@cisco.com)'
QUERY_TARGET_CHILDREN = 'children'
QUERY_TARGET_SELF = 'self'
QUERY_TARGET_SUBTREE = 'subtree'
API_URL = 'api/'
MQ_API2_URL = 'mqapi2/'
|
"""
Constants to be used in the module
"""
__author__ = 'Santiago Flores Kanter (sfloresk@cisco.com)'
query_target_children = 'children'
query_target_self = 'self'
query_target_subtree = 'subtree'
api_url = 'api/'
mq_api2_url = 'mqapi2/'
|
#!/usr/bin/env python
"""
File: pentagon_p_solution-garid.py
Find the perimeter of pentagon.
"""
__author__ = "Ochirgarid Chinzorig (Ochirgarid)"
__version__ = "1.0"
# Open file on read mode
inp = open("../test/test1.txt", "r")
# read input lines one by one
# and convert them to integer
a = int(inp.readline().strip())
b = int(inp.readline().strip())
c = int(inp.readline().strip())
d = int(inp.readline().strip())
e = int(inp.readline().strip())
# must close opened file
inp.close()
# calculate perimeter
p = a + b + c + d + e
# print for std out
print("Perimeter : {}".format(p))
|
"""
File: pentagon_p_solution-garid.py
Find the perimeter of pentagon.
"""
__author__ = 'Ochirgarid Chinzorig (Ochirgarid)'
__version__ = '1.0'
inp = open('../test/test1.txt', 'r')
a = int(inp.readline().strip())
b = int(inp.readline().strip())
c = int(inp.readline().strip())
d = int(inp.readline().strip())
e = int(inp.readline().strip())
inp.close()
p = a + b + c + d + e
print('Perimeter : {}'.format(p))
|
#
# PySNMP MIB module MITEL-IPVIRTUAL-MIB (http://snmplabs.com/pysmi)
# ASN.1 source file:///Users/davwang4/Dev/mibs.snmplabs.com/asn1/MITEL-IPVIRTUAL-MIB
# Produced by pysmi-0.3.4 at Mon Apr 29 20:03:05 2019
# On host DAVWANG4-M-1475 platform Darwin version 18.5.0 by user davwang4
# Using Python version 3.7.3 (default, Mar 27 2019, 09:23:15)
#
OctetString, Integer, ObjectIdentifier = mibBuilder.importSymbols("ASN1", "OctetString", "Integer", "ObjectIdentifier")
NamedValues, = mibBuilder.importSymbols("ASN1-ENUMERATION", "NamedValues")
ConstraintsUnion, SingleValueConstraint, ConstraintsIntersection, ValueRangeConstraint, ValueSizeConstraint = mibBuilder.importSymbols("ASN1-REFINEMENT", "ConstraintsUnion", "SingleValueConstraint", "ConstraintsIntersection", "ValueRangeConstraint", "ValueSizeConstraint")
ModuleCompliance, NotificationGroup = mibBuilder.importSymbols("SNMPv2-CONF", "ModuleCompliance", "NotificationGroup")
IpAddress, iso, Unsigned32, Counter64, TimeTicks, Bits, enterprises, ModuleIdentity, Gauge32, ObjectIdentity, Integer32, NotificationType, MibIdentifier, MibScalar, MibTable, MibTableRow, MibTableColumn, Counter32 = mibBuilder.importSymbols("SNMPv2-SMI", "IpAddress", "iso", "Unsigned32", "Counter64", "TimeTicks", "Bits", "enterprises", "ModuleIdentity", "Gauge32", "ObjectIdentity", "Integer32", "NotificationType", "MibIdentifier", "MibScalar", "MibTable", "MibTableRow", "MibTableColumn", "Counter32")
RowStatus, DisplayString, TextualConvention = mibBuilder.importSymbols("SNMPv2-TC", "RowStatus", "DisplayString", "TextualConvention")
mitelIpGrpIpVirtualGroup = ModuleIdentity((1, 3, 6, 1, 4, 1, 1027, 4, 8, 1, 1, 4))
mitelIpGrpIpVirtualGroup.setRevisions(('2003-03-24 09:31', '1999-03-01 00:00',))
if mibBuilder.loadTexts: mitelIpGrpIpVirtualGroup.setLastUpdated('200303240931Z')
if mibBuilder.loadTexts: mitelIpGrpIpVirtualGroup.setOrganization('MITEL Corporation')
mitel = MibIdentifier((1, 3, 6, 1, 4, 1, 1027))
mitelProprietary = MibIdentifier((1, 3, 6, 1, 4, 1, 1027, 4))
mitelPropIpNetworking = MibIdentifier((1, 3, 6, 1, 4, 1, 1027, 4, 8))
mitelIpNetRouter = MibIdentifier((1, 3, 6, 1, 4, 1, 1027, 4, 8, 1))
mitelRouterIpGroup = MibIdentifier((1, 3, 6, 1, 4, 1, 1027, 4, 8, 1, 1))
mitelIpVGrpPortTable = MibTable((1, 3, 6, 1, 4, 1, 1027, 4, 8, 1, 1, 4, 1), )
if mibBuilder.loadTexts: mitelIpVGrpPortTable.setStatus('current')
mitelIpVGrpPortEntry = MibTableRow((1, 3, 6, 1, 4, 1, 1027, 4, 8, 1, 1, 4, 1, 1), ).setIndexNames((0, "MITEL-IPVIRTUAL-MIB", "mitelIpVGrpPortTableNetAddr"), (0, "MITEL-IPVIRTUAL-MIB", "mitelIpVGrpPortTableIfIndex"))
if mibBuilder.loadTexts: mitelIpVGrpPortEntry.setStatus('current')
mitelIpVGrpPortTableNetAddr = MibTableColumn((1, 3, 6, 1, 4, 1, 1027, 4, 8, 1, 1, 4, 1, 1, 1), IpAddress()).setMaxAccess("readonly")
if mibBuilder.loadTexts: mitelIpVGrpPortTableNetAddr.setStatus('current')
mitelIpVGrpPortTableNetMask = MibTableColumn((1, 3, 6, 1, 4, 1, 1027, 4, 8, 1, 1, 4, 1, 1, 2), IpAddress()).setMaxAccess("readwrite")
if mibBuilder.loadTexts: mitelIpVGrpPortTableNetMask.setStatus('current')
mitelIpVGrpPortTableIfIndex = MibTableColumn((1, 3, 6, 1, 4, 1, 1027, 4, 8, 1, 1, 4, 1, 1, 11), Integer32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: mitelIpVGrpPortTableIfIndex.setStatus('current')
mitelIpVGrpPortTableStatus = MibTableColumn((1, 3, 6, 1, 4, 1, 1027, 4, 8, 1, 1, 4, 1, 1, 12), RowStatus()).setMaxAccess("readcreate")
if mibBuilder.loadTexts: mitelIpVGrpPortTableStatus.setStatus('current')
mitelIpVGrpPortTableCfgMethod = MibTableColumn((1, 3, 6, 1, 4, 1, 1027, 4, 8, 1, 1, 4, 1, 1, 15), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(1, 2, 3, 4))).clone(namedValues=NamedValues(("static", 1), ("addressless", 2), ("dhcp", 3), ("ipcp", 4)))).setMaxAccess("readwrite")
if mibBuilder.loadTexts: mitelIpVGrpPortTableCfgMethod.setStatus('current')
mitelIpVGrpRipTable = MibTable((1, 3, 6, 1, 4, 1, 1027, 4, 8, 1, 1, 4, 2), )
if mibBuilder.loadTexts: mitelIpVGrpRipTable.setStatus('current')
mitelIpVGrpRipEntry = MibTableRow((1, 3, 6, 1, 4, 1, 1027, 4, 8, 1, 1, 4, 2, 1), ).setIndexNames((0, "MITEL-IPVIRTUAL-MIB", "mitelIpVGrpTableRipIpAddr"))
if mibBuilder.loadTexts: mitelIpVGrpRipEntry.setStatus('current')
mitelIpVGrpTableRipIpAddr = MibTableColumn((1, 3, 6, 1, 4, 1, 1027, 4, 8, 1, 1, 4, 2, 1, 1), IpAddress()).setMaxAccess("readonly")
if mibBuilder.loadTexts: mitelIpVGrpTableRipIpAddr.setStatus('current')
mitelIpVGrpTableRipRxPkts = MibTableColumn((1, 3, 6, 1, 4, 1, 1027, 4, 8, 1, 1, 4, 2, 1, 2), Counter32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: mitelIpVGrpTableRipRxPkts.setStatus('current')
mitelIpVGrpTableRipRxBadPkts = MibTableColumn((1, 3, 6, 1, 4, 1, 1027, 4, 8, 1, 1, 4, 2, 1, 3), Counter32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: mitelIpVGrpTableRipRxBadPkts.setStatus('current')
mitelIpVGrpTableRipRxBadRtes = MibTableColumn((1, 3, 6, 1, 4, 1, 1027, 4, 8, 1, 1, 4, 2, 1, 4), Counter32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: mitelIpVGrpTableRipRxBadRtes.setStatus('current')
mitelIpVGrpTableRipTxUpdates = MibTableColumn((1, 3, 6, 1, 4, 1, 1027, 4, 8, 1, 1, 4, 2, 1, 5), Counter32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: mitelIpVGrpTableRipTxUpdates.setStatus('current')
mibBuilder.exportSymbols("MITEL-IPVIRTUAL-MIB", mitelIpVGrpTableRipRxBadRtes=mitelIpVGrpTableRipRxBadRtes, mitel=mitel, mitelIpVGrpRipTable=mitelIpVGrpRipTable, mitelIpVGrpPortTableNetMask=mitelIpVGrpPortTableNetMask, mitelIpVGrpPortTableStatus=mitelIpVGrpPortTableStatus, mitelIpGrpIpVirtualGroup=mitelIpGrpIpVirtualGroup, mitelIpVGrpPortTable=mitelIpVGrpPortTable, mitelIpVGrpTableRipIpAddr=mitelIpVGrpTableRipIpAddr, mitelIpNetRouter=mitelIpNetRouter, PYSNMP_MODULE_ID=mitelIpGrpIpVirtualGroup, mitelIpVGrpPortEntry=mitelIpVGrpPortEntry, mitelIpVGrpPortTableIfIndex=mitelIpVGrpPortTableIfIndex, mitelProprietary=mitelProprietary, mitelPropIpNetworking=mitelPropIpNetworking, mitelIpVGrpTableRipRxPkts=mitelIpVGrpTableRipRxPkts, mitelIpVGrpRipEntry=mitelIpVGrpRipEntry, mitelIpVGrpTableRipRxBadPkts=mitelIpVGrpTableRipRxBadPkts, mitelRouterIpGroup=mitelRouterIpGroup, mitelIpVGrpPortTableCfgMethod=mitelIpVGrpPortTableCfgMethod, mitelIpVGrpTableRipTxUpdates=mitelIpVGrpTableRipTxUpdates, mitelIpVGrpPortTableNetAddr=mitelIpVGrpPortTableNetAddr)
|
(octet_string, integer, object_identifier) = mibBuilder.importSymbols('ASN1', 'OctetString', 'Integer', 'ObjectIdentifier')
(named_values,) = mibBuilder.importSymbols('ASN1-ENUMERATION', 'NamedValues')
(constraints_union, single_value_constraint, constraints_intersection, value_range_constraint, value_size_constraint) = mibBuilder.importSymbols('ASN1-REFINEMENT', 'ConstraintsUnion', 'SingleValueConstraint', 'ConstraintsIntersection', 'ValueRangeConstraint', 'ValueSizeConstraint')
(module_compliance, notification_group) = mibBuilder.importSymbols('SNMPv2-CONF', 'ModuleCompliance', 'NotificationGroup')
(ip_address, iso, unsigned32, counter64, time_ticks, bits, enterprises, module_identity, gauge32, object_identity, integer32, notification_type, mib_identifier, mib_scalar, mib_table, mib_table_row, mib_table_column, counter32) = mibBuilder.importSymbols('SNMPv2-SMI', 'IpAddress', 'iso', 'Unsigned32', 'Counter64', 'TimeTicks', 'Bits', 'enterprises', 'ModuleIdentity', 'Gauge32', 'ObjectIdentity', 'Integer32', 'NotificationType', 'MibIdentifier', 'MibScalar', 'MibTable', 'MibTableRow', 'MibTableColumn', 'Counter32')
(row_status, display_string, textual_convention) = mibBuilder.importSymbols('SNMPv2-TC', 'RowStatus', 'DisplayString', 'TextualConvention')
mitel_ip_grp_ip_virtual_group = module_identity((1, 3, 6, 1, 4, 1, 1027, 4, 8, 1, 1, 4))
mitelIpGrpIpVirtualGroup.setRevisions(('2003-03-24 09:31', '1999-03-01 00:00'))
if mibBuilder.loadTexts:
mitelIpGrpIpVirtualGroup.setLastUpdated('200303240931Z')
if mibBuilder.loadTexts:
mitelIpGrpIpVirtualGroup.setOrganization('MITEL Corporation')
mitel = mib_identifier((1, 3, 6, 1, 4, 1, 1027))
mitel_proprietary = mib_identifier((1, 3, 6, 1, 4, 1, 1027, 4))
mitel_prop_ip_networking = mib_identifier((1, 3, 6, 1, 4, 1, 1027, 4, 8))
mitel_ip_net_router = mib_identifier((1, 3, 6, 1, 4, 1, 1027, 4, 8, 1))
mitel_router_ip_group = mib_identifier((1, 3, 6, 1, 4, 1, 1027, 4, 8, 1, 1))
mitel_ip_v_grp_port_table = mib_table((1, 3, 6, 1, 4, 1, 1027, 4, 8, 1, 1, 4, 1))
if mibBuilder.loadTexts:
mitelIpVGrpPortTable.setStatus('current')
mitel_ip_v_grp_port_entry = mib_table_row((1, 3, 6, 1, 4, 1, 1027, 4, 8, 1, 1, 4, 1, 1)).setIndexNames((0, 'MITEL-IPVIRTUAL-MIB', 'mitelIpVGrpPortTableNetAddr'), (0, 'MITEL-IPVIRTUAL-MIB', 'mitelIpVGrpPortTableIfIndex'))
if mibBuilder.loadTexts:
mitelIpVGrpPortEntry.setStatus('current')
mitel_ip_v_grp_port_table_net_addr = mib_table_column((1, 3, 6, 1, 4, 1, 1027, 4, 8, 1, 1, 4, 1, 1, 1), ip_address()).setMaxAccess('readonly')
if mibBuilder.loadTexts:
mitelIpVGrpPortTableNetAddr.setStatus('current')
mitel_ip_v_grp_port_table_net_mask = mib_table_column((1, 3, 6, 1, 4, 1, 1027, 4, 8, 1, 1, 4, 1, 1, 2), ip_address()).setMaxAccess('readwrite')
if mibBuilder.loadTexts:
mitelIpVGrpPortTableNetMask.setStatus('current')
mitel_ip_v_grp_port_table_if_index = mib_table_column((1, 3, 6, 1, 4, 1, 1027, 4, 8, 1, 1, 4, 1, 1, 11), integer32()).setMaxAccess('readonly')
if mibBuilder.loadTexts:
mitelIpVGrpPortTableIfIndex.setStatus('current')
mitel_ip_v_grp_port_table_status = mib_table_column((1, 3, 6, 1, 4, 1, 1027, 4, 8, 1, 1, 4, 1, 1, 12), row_status()).setMaxAccess('readcreate')
if mibBuilder.loadTexts:
mitelIpVGrpPortTableStatus.setStatus('current')
mitel_ip_v_grp_port_table_cfg_method = mib_table_column((1, 3, 6, 1, 4, 1, 1027, 4, 8, 1, 1, 4, 1, 1, 15), integer32().subtype(subtypeSpec=constraints_union(single_value_constraint(1, 2, 3, 4))).clone(namedValues=named_values(('static', 1), ('addressless', 2), ('dhcp', 3), ('ipcp', 4)))).setMaxAccess('readwrite')
if mibBuilder.loadTexts:
mitelIpVGrpPortTableCfgMethod.setStatus('current')
mitel_ip_v_grp_rip_table = mib_table((1, 3, 6, 1, 4, 1, 1027, 4, 8, 1, 1, 4, 2))
if mibBuilder.loadTexts:
mitelIpVGrpRipTable.setStatus('current')
mitel_ip_v_grp_rip_entry = mib_table_row((1, 3, 6, 1, 4, 1, 1027, 4, 8, 1, 1, 4, 2, 1)).setIndexNames((0, 'MITEL-IPVIRTUAL-MIB', 'mitelIpVGrpTableRipIpAddr'))
if mibBuilder.loadTexts:
mitelIpVGrpRipEntry.setStatus('current')
mitel_ip_v_grp_table_rip_ip_addr = mib_table_column((1, 3, 6, 1, 4, 1, 1027, 4, 8, 1, 1, 4, 2, 1, 1), ip_address()).setMaxAccess('readonly')
if mibBuilder.loadTexts:
mitelIpVGrpTableRipIpAddr.setStatus('current')
mitel_ip_v_grp_table_rip_rx_pkts = mib_table_column((1, 3, 6, 1, 4, 1, 1027, 4, 8, 1, 1, 4, 2, 1, 2), counter32()).setMaxAccess('readonly')
if mibBuilder.loadTexts:
mitelIpVGrpTableRipRxPkts.setStatus('current')
mitel_ip_v_grp_table_rip_rx_bad_pkts = mib_table_column((1, 3, 6, 1, 4, 1, 1027, 4, 8, 1, 1, 4, 2, 1, 3), counter32()).setMaxAccess('readonly')
if mibBuilder.loadTexts:
mitelIpVGrpTableRipRxBadPkts.setStatus('current')
mitel_ip_v_grp_table_rip_rx_bad_rtes = mib_table_column((1, 3, 6, 1, 4, 1, 1027, 4, 8, 1, 1, 4, 2, 1, 4), counter32()).setMaxAccess('readonly')
if mibBuilder.loadTexts:
mitelIpVGrpTableRipRxBadRtes.setStatus('current')
mitel_ip_v_grp_table_rip_tx_updates = mib_table_column((1, 3, 6, 1, 4, 1, 1027, 4, 8, 1, 1, 4, 2, 1, 5), counter32()).setMaxAccess('readonly')
if mibBuilder.loadTexts:
mitelIpVGrpTableRipTxUpdates.setStatus('current')
mibBuilder.exportSymbols('MITEL-IPVIRTUAL-MIB', mitelIpVGrpTableRipRxBadRtes=mitelIpVGrpTableRipRxBadRtes, mitel=mitel, mitelIpVGrpRipTable=mitelIpVGrpRipTable, mitelIpVGrpPortTableNetMask=mitelIpVGrpPortTableNetMask, mitelIpVGrpPortTableStatus=mitelIpVGrpPortTableStatus, mitelIpGrpIpVirtualGroup=mitelIpGrpIpVirtualGroup, mitelIpVGrpPortTable=mitelIpVGrpPortTable, mitelIpVGrpTableRipIpAddr=mitelIpVGrpTableRipIpAddr, mitelIpNetRouter=mitelIpNetRouter, PYSNMP_MODULE_ID=mitelIpGrpIpVirtualGroup, mitelIpVGrpPortEntry=mitelIpVGrpPortEntry, mitelIpVGrpPortTableIfIndex=mitelIpVGrpPortTableIfIndex, mitelProprietary=mitelProprietary, mitelPropIpNetworking=mitelPropIpNetworking, mitelIpVGrpTableRipRxPkts=mitelIpVGrpTableRipRxPkts, mitelIpVGrpRipEntry=mitelIpVGrpRipEntry, mitelIpVGrpTableRipRxBadPkts=mitelIpVGrpTableRipRxBadPkts, mitelRouterIpGroup=mitelRouterIpGroup, mitelIpVGrpPortTableCfgMethod=mitelIpVGrpPortTableCfgMethod, mitelIpVGrpTableRipTxUpdates=mitelIpVGrpTableRipTxUpdates, mitelIpVGrpPortTableNetAddr=mitelIpVGrpPortTableNetAddr)
|
def Reverse(Dna):
tt = {
'A' : 'T',
'T' : 'A',
'G' : 'C',
'C' : 'G',
}
ans = ''
for a in Dna:
ans += tt[a]
return ans[::-1]
def main(infile, outfile):
# Read the input, but do something non-trivial instead of count the lines in the file
inp = lines = [line.rstrip('\n') for line in infile]
print(inp)
output = str(Reverse(inp[0]))
# For debugging, print something to console
print(output)
# Write the output.
outfile.write(output)
|
def reverse(Dna):
tt = {'A': 'T', 'T': 'A', 'G': 'C', 'C': 'G'}
ans = ''
for a in Dna:
ans += tt[a]
return ans[::-1]
def main(infile, outfile):
inp = lines = [line.rstrip('\n') for line in infile]
print(inp)
output = str(reverse(inp[0]))
print(output)
outfile.write(output)
|
# configuracoes pessoais
PERSONAL_NAME = 'YOU'
# configuracoes de email
EMAIL = 'your gmail account'
PASSWORD = 'your gmail PASSWORD'
RECEIVER_EMAIL = 'email to forward the contact messages'
# configuracoes do Google ReCaptha
SECRET_KEY = "Google ReCaptha's Secret key"
SITE_KEY = "Google ReCaptha's Site key"
APP_SECRET_KEY = '65#9DMN_T'
SKILLS = [
{
'name': 'Quick learner',
'strength': '90%'
},
{
'name': 'Flask',
'strength': '70%'
},
{
'name': 'Javascript',
'strength': '50%'
},
{
'name': 'Unit Test',
'strength': '30%'
}
]
EXPERINCES = [
{
'name': 'Intership',
'company': 'Stark Industries',
'location': 'New York City',
'working_period': 'May 2014 - June 2016',
'job_description': 'Developed tests for Mark IV, also designed some helmets for Mr. Stark.'
},
{
'name': 'Developer',
'company': 'Matrix',
'location': 'New York City',
'working_period': 'June 2016 - Currently',
'job_description': 'Created the main training prograns for martial arts.'
}
]
EDUCATION = [
{
'name': 'Bachelor of Computer Science',
'institution': 'University of Brasil',
'location': 'Brasil',
'graduation_year': '2016'
# 'extra_information': None
}
]
OBJECTIVE = 'Python developer'
|
personal_name = 'YOU'
email = 'your gmail account'
password = 'your gmail PASSWORD'
receiver_email = 'email to forward the contact messages'
secret_key = "Google ReCaptha's Secret key"
site_key = "Google ReCaptha's Site key"
app_secret_key = '65#9DMN_T'
skills = [{'name': 'Quick learner', 'strength': '90%'}, {'name': 'Flask', 'strength': '70%'}, {'name': 'Javascript', 'strength': '50%'}, {'name': 'Unit Test', 'strength': '30%'}]
experinces = [{'name': 'Intership', 'company': 'Stark Industries', 'location': 'New York City', 'working_period': 'May 2014 - June 2016', 'job_description': 'Developed tests for Mark IV, also designed some helmets for Mr. Stark.'}, {'name': 'Developer', 'company': 'Matrix', 'location': 'New York City', 'working_period': 'June 2016 - Currently', 'job_description': 'Created the main training prograns for martial arts.'}]
education = [{'name': 'Bachelor of Computer Science', 'institution': 'University of Brasil', 'location': 'Brasil', 'graduation_year': '2016'}]
objective = 'Python developer'
|
"""Data set for text records.
A Dataset maintains the collection of data instances and metadata
associated with the dataset.
"""
class Dataset(object):
"""Data set for text records.
A Dataset maintains the collection of data instances and metadata
associated with the dataset.
Parameters
-------
id : uuid
Unique identifier for a dataset in a TextStudio project.
loader : text_studio.DataLoader
The DataLoader object responsible for loading the data
from an external source and/or writing the data set to an
external location.
file_path : string
The file path points to the location of the data set.
Attributes
-------
instances : list of dicts
Collection of data instances contained in the dataset.
loaded : bool
True if all data instances in the dataset have been loaded
into the dataset. Data instances are not loaded from disk
or an external location until needed.
Methods
-------
load(self, **kwargs):
Load the dataset using the Dataset's loader.
save(self, **kwargs):
Save the dataset using the Dataset's loader.
"""
def __init__(self, id, loader=None, file_path=""):
self.id = id
self.loader = loader
self.file_path = file_path
self.instances = []
self.loaded = False
def load(self, **kwargs):
"""Load the dataset using the Dataset's loader.
Load the data set from its stored location,
populating the data instances collection. Set the
loaded flag to True if the instances were retrieved
successfully.
Parameters
----------
**kwargs : dictionary
Keyword arguments passed to the DataLoader
to configure its settings for loading the dataset.
"""
if self.loader and self.file_path:
with open(self.file_path, "r") as file:
self.instances = self.loader.load(file, **kwargs)
self.loaded = True
def save(self, **kwargs):
"""Save the dataset using the Dataset's loader.
Save the data set in its current state to a storage location.
Parameters
----------
**kwargs : dictionary
Keyword arguments passed to the DataLoader
to configure its settings for writing the dataset.
"""
if self.loader and self.file_path:
with open(self.file_path, "w") as file:
self.loader.save(self.instances, file, **kwargs)
|
"""Data set for text records.
A Dataset maintains the collection of data instances and metadata
associated with the dataset.
"""
class Dataset(object):
"""Data set for text records.
A Dataset maintains the collection of data instances and metadata
associated with the dataset.
Parameters
-------
id : uuid
Unique identifier for a dataset in a TextStudio project.
loader : text_studio.DataLoader
The DataLoader object responsible for loading the data
from an external source and/or writing the data set to an
external location.
file_path : string
The file path points to the location of the data set.
Attributes
-------
instances : list of dicts
Collection of data instances contained in the dataset.
loaded : bool
True if all data instances in the dataset have been loaded
into the dataset. Data instances are not loaded from disk
or an external location until needed.
Methods
-------
load(self, **kwargs):
Load the dataset using the Dataset's loader.
save(self, **kwargs):
Save the dataset using the Dataset's loader.
"""
def __init__(self, id, loader=None, file_path=''):
self.id = id
self.loader = loader
self.file_path = file_path
self.instances = []
self.loaded = False
def load(self, **kwargs):
"""Load the dataset using the Dataset's loader.
Load the data set from its stored location,
populating the data instances collection. Set the
loaded flag to True if the instances were retrieved
successfully.
Parameters
----------
**kwargs : dictionary
Keyword arguments passed to the DataLoader
to configure its settings for loading the dataset.
"""
if self.loader and self.file_path:
with open(self.file_path, 'r') as file:
self.instances = self.loader.load(file, **kwargs)
self.loaded = True
def save(self, **kwargs):
"""Save the dataset using the Dataset's loader.
Save the data set in its current state to a storage location.
Parameters
----------
**kwargs : dictionary
Keyword arguments passed to the DataLoader
to configure its settings for writing the dataset.
"""
if self.loader and self.file_path:
with open(self.file_path, 'w') as file:
self.loader.save(self.instances, file, **kwargs)
|
OCTICON_PAPER_AIRPLANE = """
<svg class="octicon octicon-paper-airplane" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 16 16" width="16" height="16"><path fill-rule="evenodd" d="M1.592 2.712L2.38 7.25h4.87a.75.75 0 110 1.5H2.38l-.788 4.538L13.929 8 1.592 2.712zM.989 8L.064 2.68a1.341 1.341 0 011.85-1.462l13.402 5.744a1.13 1.13 0 010 2.076L1.913 14.782a1.341 1.341 0 01-1.85-1.463L.99 8z"></path></svg>
"""
|
octicon_paper_airplane = '\n<svg class="octicon octicon-paper-airplane" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 16 16" width="16" height="16"><path fill-rule="evenodd" d="M1.592 2.712L2.38 7.25h4.87a.75.75 0 110 1.5H2.38l-.788 4.538L13.929 8 1.592 2.712zM.989 8L.064 2.68a1.341 1.341 0 011.85-1.462l13.402 5.744a1.13 1.13 0 010 2.076L1.913 14.782a1.341 1.341 0 01-1.85-1.463L.99 8z"></path></svg>\n'
|
def test():
a = 10
fun1 = lambda: a
fun1()
print(a)
a += 1
fun1()
print(a)
return fun1
fun = test()
print(f"Fun: {fun()}")
|
def test():
a = 10
fun1 = lambda : a
fun1()
print(a)
a += 1
fun1()
print(a)
return fun1
fun = test()
print(f'Fun: {fun()}')
|
# -*- coding: utf-8 -*-
"""
neutrino_api
This file was automatically generated for NeutrinoAPI by APIMATIC v2.0 ( https://apimatic.io ).
"""
class Blacklist(object):
"""Implementation of the 'Blacklist' model.
TODO: type model description here.
Attributes:
is_listed (bool): true if listed, false if not
list_host (string): the domain/hostname of the DNSBL
list_rating (int): the list rating [1-3] with 1 being the best rating
and 3 the lowest rating
list_name (string): the name of the DNSBL
txt_record (string): the TXT record returned for this listing (if
listed)
return_code (string): the specific return code for this listing (if
listed)
response_time (int): the DNSBL server response time in milliseconds
"""
# Create a mapping from Model property names to API property names
_names = {
"is_listed":'isListed',
"list_host":'listHost',
"list_rating":'listRating',
"list_name":'listName',
"txt_record":'txtRecord',
"return_code":'returnCode',
"response_time":'responseTime'
}
def __init__(self,
is_listed=None,
list_host=None,
list_rating=None,
list_name=None,
txt_record=None,
return_code=None,
response_time=None):
"""Constructor for the Blacklist class"""
# Initialize members of the class
self.is_listed = is_listed
self.list_host = list_host
self.list_rating = list_rating
self.list_name = list_name
self.txt_record = txt_record
self.return_code = return_code
self.response_time = response_time
@classmethod
def from_dictionary(cls,
dictionary):
"""Creates an instance of this model from a dictionary
Args:
dictionary (dictionary): A dictionary representation of the object as
obtained from the deserialization of the server's response. The keys
MUST match property names in the API description.
Returns:
object: An instance of this structure class.
"""
if dictionary is None:
return None
# Extract variables from the dictionary
is_listed = dictionary.get('isListed')
list_host = dictionary.get('listHost')
list_rating = dictionary.get('listRating')
list_name = dictionary.get('listName')
txt_record = dictionary.get('txtRecord')
return_code = dictionary.get('returnCode')
response_time = dictionary.get('responseTime')
# Return an object of this model
return cls(is_listed,
list_host,
list_rating,
list_name,
txt_record,
return_code,
response_time)
|
"""
neutrino_api
This file was automatically generated for NeutrinoAPI by APIMATIC v2.0 ( https://apimatic.io ).
"""
class Blacklist(object):
"""Implementation of the 'Blacklist' model.
TODO: type model description here.
Attributes:
is_listed (bool): true if listed, false if not
list_host (string): the domain/hostname of the DNSBL
list_rating (int): the list rating [1-3] with 1 being the best rating
and 3 the lowest rating
list_name (string): the name of the DNSBL
txt_record (string): the TXT record returned for this listing (if
listed)
return_code (string): the specific return code for this listing (if
listed)
response_time (int): the DNSBL server response time in milliseconds
"""
_names = {'is_listed': 'isListed', 'list_host': 'listHost', 'list_rating': 'listRating', 'list_name': 'listName', 'txt_record': 'txtRecord', 'return_code': 'returnCode', 'response_time': 'responseTime'}
def __init__(self, is_listed=None, list_host=None, list_rating=None, list_name=None, txt_record=None, return_code=None, response_time=None):
"""Constructor for the Blacklist class"""
self.is_listed = is_listed
self.list_host = list_host
self.list_rating = list_rating
self.list_name = list_name
self.txt_record = txt_record
self.return_code = return_code
self.response_time = response_time
@classmethod
def from_dictionary(cls, dictionary):
"""Creates an instance of this model from a dictionary
Args:
dictionary (dictionary): A dictionary representation of the object as
obtained from the deserialization of the server's response. The keys
MUST match property names in the API description.
Returns:
object: An instance of this structure class.
"""
if dictionary is None:
return None
is_listed = dictionary.get('isListed')
list_host = dictionary.get('listHost')
list_rating = dictionary.get('listRating')
list_name = dictionary.get('listName')
txt_record = dictionary.get('txtRecord')
return_code = dictionary.get('returnCode')
response_time = dictionary.get('responseTime')
return cls(is_listed, list_host, list_rating, list_name, txt_record, return_code, response_time)
|
class DmpIo():
def __init__(self):
# inputs
self.event = None
self.username = None
self.password = None
|
class Dmpio:
def __init__(self):
self.event = None
self.username = None
self.password = None
|
#!/usr/bin/env python3
"""
This prints out my node IPs.
nodes-005.py is used to print out.....
al;jsdflkajsdf
l;ajdsl;faj
a;ljsdklfj
--------------------------------------------------
"""
print('10.10.10.5')
print('10.10.10.4')
print('10.10.10.3')
print('10.10.10.2')
print('10.10.10.1')
|
"""
This prints out my node IPs.
nodes-005.py is used to print out.....
al;jsdflkajsdf
l;ajdsl;faj
a;ljsdklfj
--------------------------------------------------
"""
print('10.10.10.5')
print('10.10.10.4')
print('10.10.10.3')
print('10.10.10.2')
print('10.10.10.1')
|
"""
Exercise 7: Rewrite the grade program from the previous chapter using a function called computegrade that takes a score as its parameter and returns a grade as a string.
"""
def computegrade(score):
if score >= 0.9:
return 'A'
elif score >= 0.8:
return 'B'
elif score >= 0.7:
return 'C'
elif score >= 0.6:
return 'D'
else:
return 'F'
try:
score = float(input("Enter score: "))
if score > 1 or score < 0:
raise ValueError('Bad score')
print(computegrade(score))
except:
print('Bad score')
|
"""
Exercise 7: Rewrite the grade program from the previous chapter using a function called computegrade that takes a score as its parameter and returns a grade as a string.
"""
def computegrade(score):
if score >= 0.9:
return 'A'
elif score >= 0.8:
return 'B'
elif score >= 0.7:
return 'C'
elif score >= 0.6:
return 'D'
else:
return 'F'
try:
score = float(input('Enter score: '))
if score > 1 or score < 0:
raise value_error('Bad score')
print(computegrade(score))
except:
print('Bad score')
|
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