content
stringlengths 7
1.05M
|
|---|
BANNERS = '''
___
____ __ _______/ (_)___ __ __
/ __ \/ / / / ___/ / / __ \/ / / /
/ / / / /_/ / /__/ / / /_/ / /_/ /
/_/ /_/\__,_/\___/_/_/ .___/\__, /
/_/ /____/
---split---
__ _
[ | (_)
_ .--. __ _ .---. | | __ _ .--. _ __
[ `.-. |[ | | | / /'`\] | | [ |[ '/'`\ \[ \ [ ]
| | | | | \_/ |,| \__. | | | | | \__/ | \ '/ /
[___||__]'.__.'_/'.___.'[___][___]| ;.__/[\_: /
[__| \__.'
---split---
โโ โ โโ โโ โโโโโโโ โโโ โโโ โโโโโโโ โโ โโ
โ โ โ โ โ โ โ โ โ โ โ โ โ โ โ
โ โโโ โ โ โ โ โ โ โ โ โ โ โโโ โ
โ โ โโโ โ โโโ โ โ โ โโโ โ โ
โ โ โ โ โ โ โโโโโ โ โโโโโ โโ
โ โ โ โ โ โโโโ โ โ โ โ โ
โโโ โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ โโโโโ
---split---
_ _ _ __ _ _
_ _ _ _ __ | |(_)| '_ \| || |
| ' \ | || |/ _|| || || .__/ \_. |
|_||_| \_._|\__||_||_||_| |__/
---split---
โโ
โโ
โโโ โโโโโโโโโโ โโโโโโโโ โโ
โโโโโโโโโโโโโโ โ โฃโโโโโโ โโ
โโโโโโโโโโโโโโโโโโโโโโโโโโ
โโโโโโโโโโโโโโโโโโโโโโโโโโ
โโ โโโโ
โโ โโโโ
---split---
/ \---------------,
\_,| |
| nuclipy |
| ,-------------
\_/____________/
---split---
___ ___
(o o) (o o)
( V ) nuclipy ( V )
--m-m-------------m-m--
---split---
^ ^
(O,O)
( ) nuclipy
-"-"-------------
---split---
\\ =o)
(o> /\\
_(()_nuclipy_\_V_
// \\
\\
---split---
/~_______~\
.---------.
(| nuclipy |)
'---------'
\_~~~~~~~_/
''' |
"""
Largest palindrome product
Problem 4
A palindromic number reads the same both ways.
The largest palindrome made from the product of two 2-digit numbers is 9009 = 91 ร 99.
Find the largest palindrome made from the product of two 3-digit numbers.
"""
# I had a feeling that palindromic numbers needed at least one palindromic factor
# But that isn't true. 26 * 26 gives 676, but 26 is not a palindrome.
# Given that, all I see to do here is search exhaustively.
# Although I can slightly speed this up by avoiding duplicates
palindromes = []
for i in range(100, 1000):
for j in range(i, 1000):
num = i * j
str_num = str(num)
for idx in range(len(str_num) // 2 + 1):
if not str_num[idx] == str_num[-idx - 1]:
break
else:
palindromes.append(num)
print(sorted(palindromes)[-1])
# Although actually, it would probably be quicker if we went the other way and broke once
# we found one. Does this give the same answer?
palindrome = None
for i in range(999, 99, -1):
for j in range(i, 99, -1):
num = i * j
str_num = str(num)
for idx in range(len(str_num) // 2 + 1):
if not str_num[idx] == str_num[-idx - 1]:
break
else:
palindrome = num
break
if palindrome:
break
print(palindrome)
# No, it doesn't because we end up with a pretty high first number, but a fairly low second.
# Maybe instead we try things in 100 number tranches?
def find_palindrome(highest, lowest):
palindrome = None
for i in range(highest, lowest, -1):
for j in range(i, lowest, -1):
num = i * j
str_num = str(num)
for idx in range(len(str_num) // 2 + 1):
if not str_num[idx] == str_num[-idx - 1]:
break
else:
palindrome = num
break
if palindrome:
break
else:
return find_palindrome(highest - 100, lowest - 100)
return palindrome
print(find_palindrome(999, 899))
# This works, but I'm not sure I can prove that it will always work.
# In fact, it isn't exhaustive at all! It will miss much of the solution space.
|
class C:
def m1(self):
pass
# <editor-fold desc="Description">
def m2(self):
pass
def m3(self):
pass
# </editor-fold> |
'''
Created on Aug 14, 2016
@author: rafacarv
'''
|
class CBWGroup(object):
def __init__(self,
id="",
name="",
created_at="",
updated_at=""):
self.group_id = id
self.name = name
self.created_at = created_at
self.updated_at = updated_at
|
def print_board(data, board):
snake_index = 0
for snake in board['snakes']:
if (snake['id'] == data['you']['id']):
break
snake_index += 1
print("My snake: " + str(snake_index))
for i in range(board['height'] - 1, -1, -1):
for j in range(0, board['width'], 1):
found_snake = False
for k in range(len(board['snakes'])):
#head
if ({'x': j, 'y': i} == board['snakes'][k]['body'][0]):
print(str(k), end =">")
found_snake = True
break
#tail
elif ({'x': j, 'y': i} == board['snakes'][k]['body'][len(board['snakes'][k]['body']) - 1]):
print(str(k), end =")")
found_snake = True
break
#body
elif ({'x': j, 'y': i} in board['snakes'][k]['body']):
print(str(k), end =" ")
found_snake = True
break
if (found_snake):
continue
if ({'x': j, 'y': i} in board['food']):
print("$", end =" ")
else:
print("-", end =" ")
print(" ") |
# automatically generated by the FlatBuffers compiler, do not modify
# namespace:
class Value(object):
NONE = 0
boolean = 1
i8 = 2
u8 = 3
i16 = 4
u16 = 5
i32 = 6
u32 = 7
i64 = 8
u64 = 9
f32 = 10
f64 = 11
str = 12
str_list = 13
int32_list = 14
float_list = 15
bin = 16
|
l = [1,2,3]
assert 1 in l
assert 5 not in l
d = {1:2}
assert 1 in d
assert 2 not in d
d[2] = d
assert 2 in d
print("ok")
|
# -*- coding: utf-8 -*-
"""DOCSTRING."""
class Settings(object):
WIDTH = 800
HEIGHT = 600
SCROLL_SPEED = 30
SAVE_FOLDER = 'saves'
|
def encode(message, rails):
period = 2 * rails - 2
rows = [[] for _ in range(rails)]
for i, c in enumerate(message):
rows[min(i % period, period - i % period)].append(c)
return ''.join(''.join(row) for row in rows)
def decode(encoded_message, rails):
period = 2 * rails - 2
rows_size = [0] * rails
rows = []
text = []
for i in range(len(encoded_message)):
rows_size[min(i % period, period - i % period)] += 1
encoded_message = iter(encoded_message)
for size in rows_size:
rows.append([next(encoded_message) for _ in range(size)][::-1])
for i in range(sum(rows_size)):
text.append(rows[min(i % period, period - i % period)].pop())
return ''.join(text)
|
__author__ = 'Kalyan'
notes = '''
1. Read instructions for each function carefully.
2. Feel free to create new functions if needed. Give good names!
3. Use builtins and datatypes that we have seen so far.
4. If something about the function spec is not clear, use the corresponding test
for clarification.
5. Many python builtin functions allow you to pass functions to customize their behavior. This makes it very productive
to get things done in python.
'''
# Given a list of age, height of various people [(name, years, cms), .... ]. Sort them in decreasing by age and increasing by height.
# NOTE: define a function and pass it to the builtin sort function (key) to get this done, don't do your own sort.
# Do the sort in-place (ie) don't create new lists.
def custom_sort(input):
if(input==None):
return None
if(input==[]):
return []
else:
input.sort(key=get_data)
input.sort(key=get_age,reverse=True)
pass
def single_custom_sort_test(input, expected):
custom_sort(input) # sorts in place
assert input == expected
def test_custom_sort():
# boundary cases
single_custom_sort_test(None, None)
single_custom_sort_test([], [])
# no collisions
single_custom_sort_test(
[("Ram", 25, 160), ("Shyam", 30, 162), ("Sita", 15, 130)],
[("Shyam", 30, 162), ("Ram", 25, 160), ("Sita", 15, 130)])
# collisions in age
single_custom_sort_test(
[("Ram", 25, 165), ("Shyam", 30, 162), ("Ravi", 25, 160), ("Gita", 30, 140)],
[("Gita", 30, 140), ("Shyam", 30, 162), ("Ravi", 25, 160), ("Ram", 25, 165)])
# collisions in age and height, then initial order is maintained in stable sorts.
single_custom_sort_test(
[("Ram", 25, 165), ("Shyam", 30, 140), ("Ravi", 25, 165), ("Gita", 30, 140)],
[("Shyam", 30, 140), ("Gita", 30, 140), ("Ram", 25, 165), ("Ravi", 25, 165)])
VOWELS = set("aeiou")
# returns the word with the maximum number of vowels, in case of tie return
# the word which occurs first. Use the builtin max function and pass a key func to get this done.
def max_vowels(words):
max=0
if (words==[]or words== None):
return None
else:
k=words[0]
for i in words:
x=set(i)& VOWELS
if(len(x)>max):
max=len(x)
k=i
return k
pass
def test_max_vowels():
assert None == max_vowels(None)
assert None == max_vowels([])
assert "hello" == max_vowels(["hello", "pot", "gut", "sit"])
assert "engine" == max_vowels(["engine", "hello", "pot", "gut", "sit"])
assert "automobile" == max_vowels(["engine", "hello", "pot", "gut", "sit", "automobile"])
assert "fly" == max_vowels(["fly", "pry", "ply"])
def get_data(o):
return o[2]
def get_age(o):
return o[1] |
a1 = b'\x02'
b1 = 'AB'
c1 = 'EF'
c2 = None
d1 = b'\x03'
bb = a1 + bytes(b1.encode()) + bytes(c1.encode()) + d1
bb2 = a1 + bytes(b1.encode()) + bytes(c2.encode()) + d1
print(type(bb), bb) |
class Solution:
def reverseBits(self, n: int) -> int:
# we can take the last bit of "n" by doing "n % 2"
# and shift the "n" to the right
# then we paste the last bit to the first bit of "res"
# by using `|` operation
# Take 0101 as an example:
# 0010 (1) => 1 000
# 0001 (0) => 10 00
# 0000 (1) => 101 0
# 0000 (0) => 1010
res = 0
for i in range(32):
bit = n % 2
n >>= 1
# res += 2 ** (31-i) if bit else 0
res |= bit << (31-i)
return res |
"Create maps with OpenStreetMap layers in a minute and embed them in your site."
VERSION = (1, 0, 0)
__author__ = 'Yohan Boniface'
__contact__ = "[email protected]"
__homepage__ = "https://github.com/umap-project/umap"
__version__ = ".".join(map(str, VERSION))
|
#!/user/bin/env python
'''structureToPolymerSequences.py:
This mapper maps a structure to it's polypeptides, polynucleotide chain sequences.
For a multi-model structure, only the first model is considered.
'''
__author__ = "Mars (Shih-Cheng) Huang"
__maintainer__ = "Mars (Shih-Cheng) Huang"
__email__ = "[email protected]"
__version__ = "0.2.0"
__status__ = "Done"
class StructureToPolymerSequences(object):
'''This mapper maps a structure to it's polypeptides, polynucleotide chain sequences.
For a multi-model structure, only the first model is considered.
'''
def __init__(self, useChainIdInsteadOfChainName=False, excludeDuplicates=False):
'''Extracts all polymer chains from a structure. If the argument is set to true,
the assigned key is: <PDB ID.Chain ID>, where Chain ID is the unique identifier
assigned to each molecular entity in an mmCIF file. This Chain ID corresponds to
`_atom_site.label_asym_id <http://mmcif.wwpdb.org/dictionaries/mmcif_mdb.dic/Items/_atom_site.label_asym_id.html>`_
field in an mmCIF file.
Parameters
----------
useChainIdInsteadOfChainName : bool
if true, use the Chain Id in the key assignments
excludeDuplicates : bool
if true, return only one chain for each unique sequence= t[1]
'''
self.useChainIdInsteadOfChainName = useChainIdInsteadOfChainName
self.excludeDuplicates = excludeDuplicates
def __call__(self, t):
structure = t[1]
sequences = list()
seqSet = set()
chainToEntityIndex = self._get_chain_to_entity_index(structure)
for i in range(structure.chains_per_model[0]):
polymer = structure.entity_list[chainToEntityIndex[i]]['type'] == 'polymer'
if polymer:
key = t[0]
if '.' in key:
key = key.split('.')[0]
key += '.'
if self.useChainIdInsteadOfChainName:
key += structure.chain_id_list[i]
else:
key += structure.chain_name_list[i]
if self.excludeDuplicates:
if chainToEntityIndex[i] in seqSet:
continue
seqSet.add(chainToEntityIndex[i])
sequences.append(
(key, structure.entity_list[chainToEntityIndex[i]]['sequence']))
return sequences
def _get_chain_to_entity_index(self, structure):
entityChainIndex = [0] * structure.num_chains
for i in range(len(structure.entity_list)):
for j in structure.entity_list[i]['chainIndexList']:
entityChainIndex[j] = i
return entityChainIndex
|
class ChooserStatistician:
def __init__(self, m_iterations):
self.m_iterations = m_iterations
@property
def m_iterations(self):
return self._m_iterations
@m_iterations.setter
def m_iterations(self, m_iterations):
if not m_iterations > 1:
raise ValueError("Number of iterations should be > 1")
self._m_iterations = m_iterations
def get_chooser_p(self, chooser):
return sum(chooser.is_win() for _ in range(self.m_iterations)) / self.m_iterations
|
# a = '42'
# print(type(a))
# a = int(a)
# print(type(a))
# b = 'a2'
# print(type(b))
# b = int(b) ERROR ->>>> this cause error!!! because of 'a' in 'a2'
# c = 3.141592
# print(type(c))
# c = int(c)
# print(c, type(c))
d = '3.141592'
print(type(d))
d = int(float(d))
print(d, type(d))
|
"""
Visualization module.
This module contains visualization functions for functional data.
"""
|
class Config(object):
"""Common configurations"""
MINIFY_PAGE = True
SSL_DISABLE = False
SQLALCHEMY_COMMIT_ON_TEARDOWN = True
SQLALCHEMY_TRACK_MODIFICATIONS = False
SQLALCHEMY_RECORD_QUERIES = True
# MAIL_SERVER = 'smtp.googlemail.com'
# MAIL_PORT = 587
# MAIL_USE_TLS = True
# MAIL_USERNAME = os.environ.get('MAIL_USERNAME')
# MAIL_PASSWORD = os.environ.get('MAIL_PASSWORD')
class DevelopmentConfig(Config):
"""Development configurations"""
DEBUG = True
SQLALCHEMY_ECHO = True
TESTING = False
class TestingConfig(DevelopmentConfig):
TESTING = True
SQLALCHEMY_DATABASE_URI = 'mysql://test_user:test_password@localhost/test_database'
class ProductionConfig(Config):
"""Production configurations"""
DEBUG = False
app_config = {
'development': 'config.DevelopmentConfig',
'production': 'config.ProductionConfig',
'testing': 'config.TestingConfig'
}
|
class MultiStack:
def __init__(self, stacksize):
self.numstacks = 3
self.array = [0] * (stacksize * self.numstacks)
self.sizes = [0] * self.numstacks
self.stacksize = stacksize
def Push(self, item, stacknum):
if self.IsFull(stacknum):
raise Exception("Stack is full")
self.sizes[stacknum] += 1
self.array[self.IndexOfTop(stacknum)] = item
def Pop(self, stacknum):
if self.IsEmpty(stacknum):
raise Exception("Stack is empty")
value = self.array[self.IndexOfTop(stacknum)]
self.array[self.IndexOfTop(stacknum)] = 0
self.sizes[stacknum] -= 1
return value
def Peek(self, stacknum):
if self.IsEmpty(stacknum):
raise Exception("Stack is empty")
return self.array[self.IndexOfTop(stacknum)]
def IsEmpty(self, stacknum):
return self.sizes[stacknum] == 0
def IsFull(self, stacknum):
return self.sizes[stacknum] == self.stacksize
def IndexOfTop(self, stacknum):
offset = stacknum * self.stacksize
return offset + self.sizes[stacknum] - 1
stack = MultiStack(1)
|
# Confidence Interval using Stats Model Summary
thresh = 0.05
intervals = results.conf_int(alpha=thresh)
# Renaming column names
first_col = str(thresh/2*100)+"%"
second_col = str((1-thresh/2)*100)+"%"
intervals = intervals.rename(columns={0:first_col,1:second_col})
display(intervals) |
def loadfile(name):
lines = []
f = open(name, "r")
for x in f:
if x.endswith('\n'):
x = x[:-1]
line = []
for character in x:
line.append(int(character))
lines.append(line)
return lines
def add1ToAll(lines):
for i in range (0, len(lines)):
for j in range(0, len(lines[i])):
lines[i][j] = lines[i][j] + 1
return lines
neighbours = [[-1, -1], [0, -1], [1, -1], [-1, 0], [1, 0], [1, 1], [0, 1], [-1, 1]]
def flash(i, j):
lines[i][j] = 0
count = 1
for coor in neighbours:
if i + coor[0] >= 0 and i + coor[0] < len(lines) and j + coor[1] >= 0 and j + coor[1] < len(lines[0]):
if lines[i + coor[0]][j + coor[1]] != 0:
lines[i + coor[0]][j + coor[1]] += 1
if lines[i + coor[0]][j + coor[1]] > 9:
count += flash(i + coor[0], j + coor[1])
return count
def makeOctopusFlash():
count = 0
for i in range (0, len(lines)):
for j in range(0, len(lines[i])):
if lines[i][j] > 9:
count += flash(i, j)
return count
def goThroughSteps (lines, steps):
countFlashes = 0
for step in range(1, steps + 1):
lines = add1ToAll(lines)
count = makeOctopusFlash()
print("Step: ", step, " Flashes: ", count)
countFlashes += count
return countFlashes
def findAllFlash(lines):
countFlashes = 0
found = False
step = 1
while found == False:
lines = add1ToAll(lines)
count = makeOctopusFlash()
print("Step: ", step, " Flashes: ", count)
if count == 100:
return step
countFlashes += count
step += 1
return 0
lines = loadfile("data.txt")
print(lines)
flashes = goThroughSteps(lines, 100)
lines = loadfile("data.txt")
step = findAllFlash(lines)
print("Opdracht 11a: ", flashes)
print("Opdracht 11b: ", step) |
class BuildingSpecification(object):
def __init__(self,building_type, display_string, other_buildings_available, codes_available):
self.building_type = building_type
self.display_string = display_string
self.other_buildings_available = other_buildings_available # list of building names
self.codes_available = codes_available #list of baseline codes available
|
################################################################################
level_number = 6
dungeon_name = 'Catacombs'
wall_style = 'Catacombs'
monster_difficulty = 3
goes_down = True
entry_position = (0, 0)
phase_door = False
level_teleport = [
(4, True),
(5, True),
(6, False),
]
stairs_previous = [(13, 11)]
stairs_next = []
portal_down = []
portal_up = []
teleports = [
((0, 21), (10, 7)),
((21, 15), (13, 17)),
((20, 19), (8, 11)),
((16, 21), (14, 21)),
]
encounters = [
((1, 0), (60, 7)),
((3, 9), (51, 36)),
((4, 13), (30, 69)),
((7, 17), (10, 99)),
((8, 5), (10, 99)),
((16, 13), (20, 66)),
((18, 14), (20, 53)),
((21, 0), (60, 8)),
]
messages = [
((20, 16), "A message is scrawled on the wall in blood:\nSeek the Mad One's stoney self in Harkyn's domain."),
]
specials = [((19, 20), (22, 255))]
smoke_zones = []
darkness = [(3, 17), (3, 18), (4, 17), (4, 18), (5, 17), (5, 18), (6, 17), (6, 18)]
antimagic_zones = [(13, 18), (13, 19), (13, 20), (14, 17), (14, 18), (14, 19), (14, 20), (18, 20)]
spinners = [(9, 9), (12, 13), (18, 6)]
traps = [(3, 19), (4, 19), (4, 20), (7, 14), (9, 2), (10, 15), (11, 4), (11, 12), (12, 16), (15, 6), (16, 3), (17, 6)]
hitpoint_damage = []
spellpoint_restore = []
stasis_chambers = []
random_encounter = [(0, 4), (0, 8), (1, 16), (2, 5), (2, 7), (3, 0), (3, 13), (4, 5), (4, 11), (5, 1), (6, 8), (6, 11), (7, 1), (7, 2), (7, 3), (7, 4), (9, 3), (9, 4), (9, 8), (10, 1), (11, 3), (11, 4), (11, 7), (12, 21), (13, 0), (13, 3), (13, 14), (14, 0), (14, 12), (14, 21), (16, 0), (16, 17), (16, 19), (17, 21), (19, 2), (19, 9), (19, 13), (19, 17), (19, 19), (19, 21), (21, 5), (21, 9)]
specials_other = [(0, 0), (0, 15), (1, 16), (1, 19), (2, 20), (3, 6), (3, 14), (3, 20), (5, 13), (5, 21), (7, 17), (7, 18), (7, 19), (7, 20), (8, 18), (8, 19), (8, 20), (9, 13), (12, 9), (13, 5), (14, 17), (17, 13), (18, 9), (20, 0), (21, 21)]
map = [
'+-++-------++-++---D---++-++----------++----++-++----++----------+',
'| DD DD || DD || || || || || |',
'+-++-----. |+-++--. .--++-+| .------. || .. || || .. || .------. |',
'+--------. |+----+| |+----+| .-----+| || || .. || .. || |+----+| |',
'| || || || || || || || || || || || |',
'+D---------+| .. || || .. |+-----. || || |+----++D---+| || .. || |',
'+D---++----+| .. || || .. |+----+| || .. |+----++D----. || .. || |',
'| || || DD DD || || || || || || || |',
'| .. || .. |+---D+| |+D---+| .. || |+---D+| .. || .-----++---D+| |',
'| .. || .. .----D-. .-D----. .. || .--++D+| .. || |+----++---D+| |',
'| DD DD || || || || || || |',
'+D---+| .. .----D-. .-D----. .. |+--. |+D++D---+| || .. || .--+| |',
'+D---+| .. |+---D+| |+D---+| .. |+-+| |+D++D----. || || || .--+| |',
'| || || DD DD || || DD || || || || || || |',
'| .. |+----+| .. || || .. |+----++-+| |+D+| .. .. || || |+---D+| |',
'| .. |+----+| .. || || .. |+--------. |+D+| .. .. || || |+---D+| |',
'| DD || || || || || || || || || || |',
'+----+| .. |+----+| |+----+| .----D---++-+| .. .. || || || .. || |',
'+-----. .. |+-++--. .--++-+| |+---D++-----. .. .. || || .. .. || |',
'| DD || DD || || || || || || |',
'| .--------++-++---D---++-+| || .. || .. .. .. .. || |+-------+| |',
'| |+-++-++-++-++-++----++--. || .. || .. .. .. .. || .--------+| |',
'| || || || || || || || || || || || |',
'+D++D++D++D++D++D++--. |+D---++----+| .. .. .. .. |+--------. || |',
'+D++D++D++D++D++D++-+| |+D---++-----. .. .. .. .. |+--------. || |',
'| DD DD || DD || DD || || || || || |',
'+-++D++-++-++D+| |+-+| || .. || .. .. .-D-. .. .. |+----------+| |',
'+-++D++-++-++D+| |+-+| || .. || .. .. |+D+| .. .. .------------. |',
'| DD DD DD || DD DD || DD || DD DD |',
'+D++-++-++D++-++D++-++D++----+| .. .. |+D+| .. .. .. .. .. .-D---+',
'+D++-++-++D++-++D++-++D++-----. .. .. .-D-. .. .. .. .. .. |+D---+',
'| || || DD DD DD DD || || || |',
'+D++D++D++-++-++-++-++D+| .. .. .. .. .. .. .. .. .. .-D---+| .. |',
'+D--D--D++-++-++-++-++D+| .. .. .. .. .. .. .. .. .. |+D---+| .. |',
'| DD || DD DD || || || || |',
'+D----. |+-++-++D++-++-+| .-D-. .. .. .. .. .. .-D---+| .. || .-D+',
'+D++-+| |+-++-++D++-----. |+D+| .. .. .. .. .. |+D---+| .. || |+D+',
'| || || DD || DD DD DD DD || || || DD |',
'+-++D+| |+-++D++-+| .. .. |+D+| .. .. .. .-D---+| .. || .-D+| |+-+',
'+-++D+| |+-++D++-+| .. .. .-D-. .. .. .. |+D---+| .. || |+D+| |+-+',
'| DD || DD DD || DD || || || DD || DD |',
'+-++D++D+| |+-++-+| .. .. .. .. .. .-D---+| .. || .-D+| |+-+| |+-+',
'+-++D--D+| |+-----. .. .. .. .. .. |+D---+| .. || |+D+| |+-+| |+-+',
'| DD DD || || || || DD || DD || DD |',
'+-+| .. |+-+| .. .. .. .. .. .-D---+| .. || .-D+| |+-+| |+-+| |+-+',
'+-+| .. |+-+| .. .. .. .. .. |+D---+| .. || |+D+| |+-+| |+-+| |+-+',
'| DD DD || || || || DD || DD || DD || DD |',
'+-++D---++D+| .. .. .. .-D---+| .. || .-D+| |+-+| |+-+| |+-+| |+-+',
'+-++D++---D-. .. .. .. |+D---+| .. || |+D+| |+-+| |+-+| |+-+| |+-+',
'| DD || || || || DD || DD || DD || DD || DD |',
'+-+| || .. .. .. .-D---+| .. || .-D+| |+-+| |+-+| |+-+| |+-+| |+-+',
'+-+| || .. .. .. |+D---+| .. || |+D+| |+-+| |+-+| |+-+| |+-+| |+-+',
'| DD || || || || DD || DD || DD || DD || DD || DD |',
'+-++D+| .. .-D---+| .. || .-D+| |+-+| |+-+| |+-+| |+-+| |+-+| |+-+',
'+---D-. .. |+D---+| .. || |+D+| |+-+| |+-+| |+-+| |+-+| |+-+| |+-+',
'| || || || DD || DD || DD || DD || DD || DD || DD |',
'| .. .-D---+| .. || .-D+| |+-+| |+-+| |+-+| |+-+| |+-+| |+-+| |+-+',
'| .. |+D---+| .. || |+D+| |+-+| |+-+| |+-+| |+-+| |+-+| |+-+| |+-+',
'| || || || DD || DD || DD || DD || DD || DD || DD || DD |',
'+D---+| .. || .-D+| |+-+| |+-+| |+-+| |+-+| |+-+| |+-+| |+-+| |+-+',
'+D---+| .. || |+D+| |+-+| |+-+| |+-+| |+-+| |+-+| |+-+| |+-+| |+-+',
'| || || DD || DD || DD || DD || DD || DD || DD || DD || DD |',
'| .. || .-D+| |+-+| |+-+| |+-+| |+-+| |+-+| |+-+| |+-+| |+-+| |+-+',
'| .. || |+D+| |+-+| |+-+| |+-+| |+-+| |+-+| |+-+| |+-+| |+-+| |+-+',
'| || DD || DD || DD || DD || DD || DD || DD || DD || DD || DD |',
'+----++-++-++-++-++-++-++-++-++-++-++-++-++-++-++-++-++-++-++-++-+',
]
|
class Solution:
def deleteAndEarn(self, nums: List[int]) -> int:
d = [0] *(max(nums)+1)
for i in nums:
d[i] += i
prev,prever = 0,0
for i in d:
cur = max(prev,prever+i)
prever = prev
prev = cur
return max(cur,prever)
|
"""Helpers for PyTorch-ES examples"""
def weights_init(m):
classname = m.__class__.__name__
if classname.find('Linear') != -1:
m.weight.data.normal_(0.0, 0.02)
|
keywords = ['program', 'label', 'type', 'array', 'of', 'var', 'procedure'
,'function', 'begin', 'end', 'if', 'then', 'else', 'while', 'do'
, 'or', 'and', 'div', 'not']
symbols = ['.', ';', ',', '(', ')', ':', '=',
'<', '>', '+', '-', '*', '[', ':=', '..']
def get_tokens(code):
label = ''
tokens = []
for str in code:
if str in symbols:
if label in keywords:
tokens.append('<' + label + '>')
tokens.append('<' + str + '>')
label = ''
else:
if label is not '': tokens.append('#' + label + '#')
tokens.append('<' + str + '>')
label = ''
elif str is ' ' or str is'\t' or str is '\n':
if label in keywords:
if label is not '' : tokens.append('<' + label + '>')
label = ''
else:
if label is not '':
tokens.append('#' + label + '#')
label = ''
else:
label += str
return tokens
|
"""
PASSENGERS
"""
numPassengers = 19048
passenger_arriving = (
(2, 7, 5, 2, 8, 1, 2, 1, 2, 1, 0, 1, 0, 11, 2, 3, 4, 3, 0, 0, 0, 1, 0, 0, 0, 0), # 0
(10, 5, 4, 4, 7, 4, 2, 0, 1, 3, 0, 0, 0, 6, 8, 4, 3, 2, 3, 5, 3, 1, 1, 0, 0, 0), # 1
(8, 6, 11, 4, 2, 2, 4, 3, 3, 0, 1, 1, 0, 4, 4, 5, 3, 6, 1, 1, 0, 4, 0, 2, 0, 0), # 2
(6, 4, 3, 4, 3, 1, 1, 0, 2, 6, 1, 0, 0, 7, 5, 2, 6, 2, 3, 2, 4, 0, 0, 1, 0, 0), # 3
(5, 10, 2, 8, 0, 2, 6, 1, 1, 2, 0, 0, 0, 3, 5, 4, 2, 6, 2, 0, 0, 4, 4, 0, 1, 0), # 4
(11, 7, 5, 1, 6, 1, 3, 1, 2, 2, 1, 0, 0, 11, 6, 3, 5, 4, 4, 3, 3, 2, 2, 1, 0, 0), # 5
(9, 9, 2, 7, 11, 0, 1, 2, 3, 0, 3, 0, 0, 4, 4, 9, 1, 13, 3, 2, 2, 3, 2, 1, 1, 0), # 6
(7, 5, 5, 6, 8, 2, 4, 3, 2, 0, 0, 0, 0, 7, 5, 7, 3, 6, 5, 1, 2, 4, 3, 1, 0, 0), # 7
(9, 6, 8, 10, 7, 1, 1, 1, 3, 2, 2, 3, 0, 12, 3, 10, 5, 6, 6, 4, 0, 5, 1, 0, 0, 0), # 8
(7, 9, 12, 9, 4, 1, 5, 5, 3, 3, 2, 2, 0, 6, 12, 7, 6, 5, 7, 4, 3, 2, 2, 0, 0, 0), # 9
(9, 9, 10, 14, 6, 5, 6, 2, 2, 2, 0, 2, 0, 8, 6, 10, 5, 4, 2, 4, 1, 1, 4, 1, 1, 0), # 10
(6, 10, 5, 4, 4, 1, 4, 6, 5, 1, 1, 2, 0, 10, 10, 7, 4, 7, 2, 4, 4, 5, 1, 1, 2, 0), # 11
(11, 7, 8, 8, 3, 0, 4, 4, 7, 0, 0, 1, 0, 6, 9, 4, 6, 6, 5, 4, 4, 6, 3, 1, 2, 0), # 12
(7, 9, 5, 13, 7, 7, 2, 4, 1, 1, 2, 1, 0, 11, 6, 8, 5, 8, 3, 3, 6, 3, 1, 3, 1, 0), # 13
(2, 10, 8, 13, 6, 3, 2, 3, 2, 3, 1, 0, 0, 12, 9, 3, 7, 5, 3, 1, 1, 5, 1, 1, 1, 0), # 14
(6, 16, 9, 8, 8, 4, 2, 1, 3, 0, 3, 2, 0, 5, 5, 8, 4, 5, 7, 5, 4, 4, 5, 3, 0, 0), # 15
(10, 12, 6, 9, 8, 4, 6, 4, 2, 1, 1, 2, 0, 8, 13, 6, 6, 5, 5, 4, 4, 4, 3, 0, 0, 0), # 16
(6, 9, 9, 7, 6, 3, 2, 0, 5, 0, 1, 0, 0, 10, 6, 11, 6, 9, 4, 4, 3, 7, 3, 2, 2, 0), # 17
(9, 4, 9, 8, 8, 4, 1, 2, 6, 2, 0, 1, 0, 9, 10, 7, 3, 5, 7, 2, 2, 5, 3, 1, 0, 0), # 18
(8, 13, 15, 10, 8, 3, 3, 2, 0, 1, 1, 2, 0, 7, 13, 9, 3, 8, 4, 5, 0, 3, 1, 4, 2, 0), # 19
(14, 9, 11, 8, 8, 2, 2, 3, 5, 0, 2, 2, 0, 12, 14, 4, 5, 7, 5, 7, 1, 4, 4, 1, 0, 0), # 20
(8, 7, 5, 14, 7, 3, 3, 5, 4, 2, 3, 0, 0, 14, 6, 6, 5, 9, 7, 7, 3, 3, 2, 0, 3, 0), # 21
(16, 11, 12, 15, 4, 6, 2, 3, 5, 0, 3, 1, 0, 11, 14, 5, 7, 6, 9, 6, 1, 2, 4, 0, 0, 0), # 22
(24, 8, 8, 10, 4, 5, 3, 4, 2, 3, 3, 1, 0, 16, 8, 6, 2, 2, 3, 4, 2, 4, 4, 3, 0, 0), # 23
(11, 10, 6, 7, 8, 4, 3, 5, 4, 2, 2, 0, 0, 15, 10, 5, 9, 7, 5, 5, 4, 4, 2, 0, 1, 0), # 24
(9, 8, 8, 5, 8, 3, 3, 5, 3, 1, 2, 1, 0, 7, 9, 12, 5, 2, 1, 3, 4, 3, 2, 1, 0, 0), # 25
(7, 12, 12, 11, 9, 1, 8, 5, 3, 3, 2, 0, 0, 7, 8, 7, 5, 8, 12, 2, 4, 2, 1, 2, 0, 0), # 26
(6, 7, 6, 15, 7, 4, 5, 5, 2, 1, 0, 0, 0, 10, 8, 10, 6, 9, 4, 3, 2, 5, 3, 1, 0, 0), # 27
(11, 11, 11, 7, 7, 4, 2, 9, 5, 1, 1, 0, 0, 10, 11, 4, 5, 15, 8, 2, 1, 1, 1, 1, 1, 0), # 28
(7, 10, 7, 11, 9, 5, 4, 4, 3, 3, 1, 0, 0, 9, 8, 9, 9, 5, 1, 3, 1, 3, 5, 1, 2, 0), # 29
(15, 7, 8, 9, 6, 3, 4, 9, 4, 5, 1, 1, 0, 4, 12, 6, 8, 5, 5, 3, 3, 3, 2, 2, 0, 0), # 30
(9, 12, 12, 7, 9, 5, 2, 4, 3, 3, 3, 0, 0, 10, 5, 8, 5, 6, 7, 7, 2, 4, 4, 1, 0, 0), # 31
(5, 11, 5, 11, 5, 2, 4, 2, 6, 0, 2, 1, 0, 13, 8, 10, 6, 8, 3, 7, 3, 4, 1, 2, 0, 0), # 32
(11, 11, 8, 8, 11, 4, 10, 6, 4, 1, 2, 0, 0, 8, 12, 4, 10, 9, 5, 4, 3, 4, 7, 2, 0, 0), # 33
(6, 10, 9, 10, 5, 2, 4, 3, 6, 3, 0, 2, 0, 10, 10, 6, 6, 11, 5, 6, 6, 6, 3, 0, 2, 0), # 34
(7, 7, 8, 6, 7, 2, 6, 2, 6, 2, 3, 0, 0, 9, 8, 7, 3, 5, 7, 7, 5, 4, 1, 3, 1, 0), # 35
(3, 7, 9, 10, 5, 7, 2, 5, 6, 4, 0, 1, 0, 10, 9, 6, 6, 9, 2, 2, 0, 4, 2, 2, 1, 0), # 36
(10, 7, 8, 20, 3, 4, 2, 7, 3, 1, 2, 1, 0, 10, 6, 5, 4, 9, 5, 2, 6, 5, 3, 2, 1, 0), # 37
(8, 10, 9, 6, 7, 4, 5, 3, 2, 1, 0, 0, 0, 11, 6, 4, 6, 6, 10, 3, 2, 3, 5, 0, 2, 0), # 38
(9, 6, 3, 9, 6, 4, 2, 6, 6, 2, 2, 0, 0, 10, 12, 12, 4, 4, 0, 2, 2, 8, 2, 4, 0, 0), # 39
(11, 8, 7, 8, 5, 1, 3, 5, 7, 1, 0, 0, 0, 9, 6, 11, 8, 11, 2, 4, 4, 1, 2, 0, 1, 0), # 40
(9, 5, 15, 12, 7, 2, 3, 1, 1, 0, 0, 0, 0, 9, 15, 5, 6, 8, 2, 3, 5, 2, 1, 0, 2, 0), # 41
(14, 9, 4, 8, 7, 3, 2, 5, 3, 0, 2, 0, 0, 9, 3, 1, 3, 10, 4, 4, 3, 3, 3, 4, 0, 0), # 42
(10, 3, 6, 14, 10, 3, 2, 5, 4, 1, 2, 1, 0, 14, 7, 9, 8, 7, 8, 3, 5, 5, 2, 2, 0, 0), # 43
(11, 13, 9, 5, 9, 3, 6, 7, 6, 0, 0, 0, 0, 12, 8, 4, 2, 9, 7, 5, 1, 1, 3, 0, 0, 0), # 44
(12, 12, 4, 11, 9, 1, 1, 6, 1, 0, 1, 0, 0, 3, 6, 3, 8, 10, 4, 3, 2, 4, 6, 0, 0, 0), # 45
(6, 12, 5, 6, 5, 6, 3, 4, 3, 2, 1, 1, 0, 8, 5, 4, 3, 8, 4, 3, 1, 3, 7, 1, 1, 0), # 46
(14, 7, 10, 8, 9, 2, 1, 3, 6, 1, 1, 1, 0, 8, 6, 5, 5, 7, 5, 4, 2, 8, 2, 0, 2, 0), # 47
(11, 5, 8, 10, 6, 1, 3, 4, 5, 3, 0, 0, 0, 8, 6, 9, 6, 14, 7, 11, 5, 1, 2, 2, 1, 0), # 48
(9, 3, 9, 9, 6, 2, 5, 7, 5, 2, 1, 0, 0, 7, 8, 5, 6, 10, 4, 5, 4, 5, 2, 3, 6, 0), # 49
(9, 9, 11, 7, 9, 4, 2, 6, 8, 2, 0, 0, 0, 10, 7, 9, 8, 5, 4, 6, 3, 5, 1, 0, 2, 0), # 50
(9, 8, 12, 12, 11, 5, 5, 6, 5, 4, 0, 0, 0, 16, 6, 4, 8, 10, 6, 2, 3, 4, 4, 3, 0, 0), # 51
(4, 6, 9, 9, 7, 2, 3, 4, 3, 4, 3, 1, 0, 9, 10, 6, 2, 4, 8, 2, 0, 2, 4, 1, 0, 0), # 52
(9, 11, 6, 5, 7, 3, 4, 0, 4, 0, 2, 1, 0, 9, 12, 8, 4, 11, 4, 5, 0, 0, 1, 2, 1, 0), # 53
(5, 13, 8, 8, 6, 3, 2, 2, 4, 5, 2, 2, 0, 6, 5, 5, 7, 7, 5, 5, 0, 4, 2, 2, 0, 0), # 54
(16, 7, 8, 10, 7, 5, 1, 6, 6, 4, 2, 2, 0, 7, 11, 4, 5, 7, 4, 2, 1, 5, 4, 2, 0, 0), # 55
(11, 11, 8, 6, 5, 6, 2, 2, 4, 1, 3, 1, 0, 10, 10, 4, 7, 9, 5, 4, 2, 4, 1, 1, 1, 0), # 56
(13, 14, 3, 12, 7, 6, 4, 3, 2, 0, 0, 1, 0, 6, 13, 2, 10, 8, 6, 5, 5, 2, 4, 3, 1, 0), # 57
(3, 6, 6, 8, 12, 5, 3, 3, 5, 1, 1, 1, 0, 10, 2, 9, 5, 11, 6, 1, 1, 3, 2, 1, 1, 0), # 58
(6, 10, 6, 10, 7, 3, 3, 2, 4, 1, 1, 1, 0, 17, 9, 11, 6, 8, 4, 3, 0, 0, 3, 1, 0, 0), # 59
(10, 9, 12, 13, 4, 3, 6, 5, 7, 0, 0, 1, 0, 14, 7, 6, 6, 8, 5, 8, 4, 3, 2, 1, 2, 0), # 60
(13, 10, 5, 6, 6, 2, 1, 4, 4, 4, 2, 2, 0, 8, 4, 11, 5, 11, 3, 2, 1, 4, 2, 4, 0, 0), # 61
(9, 12, 5, 8, 10, 5, 3, 4, 4, 4, 4, 0, 0, 9, 9, 9, 4, 7, 7, 5, 1, 2, 1, 2, 1, 0), # 62
(17, 9, 7, 11, 6, 2, 4, 2, 3, 2, 1, 1, 0, 7, 9, 11, 5, 10, 5, 2, 1, 5, 5, 1, 2, 0), # 63
(8, 7, 7, 9, 9, 7, 6, 8, 3, 3, 3, 1, 0, 8, 6, 4, 4, 13, 9, 3, 1, 3, 5, 1, 0, 0), # 64
(16, 8, 10, 5, 9, 6, 7, 4, 2, 3, 3, 0, 0, 10, 6, 7, 4, 8, 4, 6, 1, 2, 3, 1, 1, 0), # 65
(8, 16, 13, 8, 5, 2, 3, 4, 5, 1, 1, 0, 0, 9, 16, 3, 5, 8, 4, 2, 2, 3, 6, 0, 0, 0), # 66
(7, 9, 9, 6, 7, 3, 2, 3, 4, 1, 2, 2, 0, 13, 9, 7, 6, 7, 0, 3, 2, 3, 4, 2, 1, 0), # 67
(11, 6, 9, 5, 5, 2, 1, 4, 2, 1, 0, 1, 0, 12, 7, 8, 6, 7, 6, 0, 1, 0, 1, 1, 1, 0), # 68
(6, 7, 8, 7, 5, 3, 2, 1, 4, 1, 0, 0, 0, 13, 13, 6, 5, 6, 2, 4, 3, 0, 2, 1, 1, 0), # 69
(9, 9, 8, 3, 9, 5, 0, 0, 2, 2, 1, 1, 0, 10, 7, 5, 4, 5, 3, 4, 2, 3, 1, 1, 0, 0), # 70
(5, 11, 10, 8, 7, 1, 3, 3, 5, 1, 2, 1, 0, 9, 8, 6, 3, 7, 3, 3, 5, 3, 4, 3, 0, 0), # 71
(8, 7, 5, 9, 6, 5, 7, 7, 4, 1, 0, 0, 0, 9, 5, 8, 1, 6, 5, 2, 4, 3, 2, 1, 1, 0), # 72
(11, 3, 8, 6, 13, 7, 7, 3, 2, 1, 2, 1, 0, 17, 12, 9, 1, 11, 7, 4, 2, 3, 1, 2, 0, 0), # 73
(8, 9, 7, 6, 10, 9, 4, 2, 4, 3, 0, 0, 0, 11, 6, 4, 3, 5, 3, 5, 1, 3, 0, 2, 1, 0), # 74
(16, 12, 3, 16, 11, 3, 1, 0, 6, 0, 0, 1, 0, 11, 8, 8, 7, 6, 5, 4, 3, 5, 6, 3, 1, 0), # 75
(14, 4, 9, 4, 5, 2, 7, 4, 4, 1, 1, 0, 0, 8, 9, 2, 3, 10, 6, 5, 0, 4, 2, 2, 1, 0), # 76
(6, 4, 4, 14, 8, 5, 5, 2, 4, 3, 2, 0, 0, 12, 8, 8, 8, 14, 2, 5, 4, 6, 3, 0, 1, 0), # 77
(3, 9, 9, 6, 8, 1, 3, 6, 8, 3, 1, 0, 0, 7, 5, 7, 4, 8, 2, 1, 0, 3, 3, 3, 2, 0), # 78
(5, 6, 11, 13, 4, 4, 1, 4, 5, 4, 3, 0, 0, 12, 6, 5, 8, 9, 10, 5, 5, 6, 2, 2, 3, 0), # 79
(15, 10, 6, 9, 12, 6, 2, 6, 2, 1, 1, 3, 0, 13, 9, 5, 6, 8, 4, 3, 5, 4, 2, 1, 1, 0), # 80
(13, 5, 6, 10, 11, 5, 4, 8, 3, 1, 0, 0, 0, 9, 6, 5, 3, 3, 5, 4, 1, 6, 1, 1, 0, 0), # 81
(14, 6, 6, 6, 9, 4, 5, 4, 3, 1, 0, 1, 0, 11, 9, 5, 6, 7, 4, 4, 2, 2, 2, 1, 0, 0), # 82
(8, 10, 10, 8, 6, 1, 5, 4, 6, 2, 2, 0, 0, 12, 12, 10, 4, 9, 5, 7, 2, 2, 6, 2, 0, 0), # 83
(12, 4, 7, 7, 8, 4, 4, 4, 1, 2, 0, 1, 0, 7, 12, 7, 0, 5, 5, 1, 1, 7, 4, 0, 0, 0), # 84
(10, 10, 2, 8, 6, 1, 2, 3, 2, 1, 4, 2, 0, 13, 8, 7, 4, 11, 8, 4, 3, 3, 1, 0, 0, 0), # 85
(14, 10, 4, 5, 7, 0, 4, 4, 8, 2, 0, 2, 0, 4, 12, 5, 4, 4, 1, 1, 4, 2, 3, 4, 0, 0), # 86
(8, 12, 6, 14, 7, 3, 5, 0, 4, 1, 2, 1, 0, 7, 10, 13, 5, 7, 8, 4, 3, 3, 3, 0, 0, 0), # 87
(13, 12, 10, 10, 6, 5, 3, 0, 5, 4, 0, 2, 0, 10, 6, 9, 2, 6, 4, 3, 4, 6, 2, 3, 1, 0), # 88
(3, 7, 10, 11, 7, 5, 5, 1, 2, 1, 0, 0, 0, 12, 9, 8, 2, 9, 7, 2, 4, 3, 2, 1, 1, 0), # 89
(15, 9, 8, 6, 6, 4, 4, 4, 6, 1, 2, 3, 0, 10, 3, 4, 5, 9, 7, 5, 2, 3, 2, 4, 1, 0), # 90
(9, 11, 8, 15, 9, 2, 3, 3, 4, 7, 1, 2, 0, 9, 16, 7, 9, 7, 3, 3, 6, 3, 2, 2, 0, 0), # 91
(13, 4, 5, 7, 3, 3, 3, 3, 6, 1, 5, 0, 0, 11, 3, 9, 1, 1, 2, 3, 0, 5, 1, 1, 1, 0), # 92
(9, 6, 4, 11, 6, 3, 2, 2, 3, 2, 1, 1, 0, 3, 4, 8, 4, 9, 2, 1, 3, 3, 5, 0, 0, 0), # 93
(15, 6, 8, 10, 7, 4, 2, 3, 5, 1, 0, 1, 0, 5, 5, 4, 5, 11, 5, 3, 2, 2, 1, 1, 0, 0), # 94
(9, 5, 7, 12, 7, 1, 6, 2, 4, 1, 3, 1, 0, 12, 6, 11, 2, 3, 3, 3, 3, 4, 1, 1, 0, 0), # 95
(7, 6, 6, 7, 6, 3, 4, 3, 4, 0, 1, 0, 0, 6, 9, 8, 3, 8, 5, 2, 3, 6, 1, 2, 2, 0), # 96
(9, 7, 10, 8, 13, 4, 3, 3, 5, 0, 4, 1, 0, 8, 10, 6, 5, 5, 8, 4, 3, 6, 4, 0, 1, 0), # 97
(10, 7, 11, 7, 3, 3, 4, 4, 4, 5, 2, 0, 0, 11, 8, 11, 2, 8, 3, 6, 2, 3, 4, 1, 0, 0), # 98
(15, 3, 6, 12, 7, 4, 3, 2, 5, 2, 2, 0, 0, 7, 9, 5, 4, 11, 1, 1, 1, 7, 3, 3, 0, 0), # 99
(8, 6, 7, 7, 9, 5, 4, 3, 2, 3, 1, 0, 0, 12, 7, 6, 6, 6, 3, 1, 2, 2, 1, 0, 0, 0), # 100
(7, 15, 7, 9, 10, 1, 8, 1, 4, 0, 2, 0, 0, 8, 6, 6, 5, 6, 0, 0, 0, 4, 5, 3, 1, 0), # 101
(13, 8, 9, 8, 3, 2, 4, 4, 5, 2, 4, 1, 0, 11, 5, 5, 6, 4, 6, 2, 0, 4, 1, 1, 1, 0), # 102
(11, 5, 9, 7, 4, 9, 6, 2, 1, 3, 1, 0, 0, 10, 5, 7, 3, 7, 6, 3, 1, 3, 4, 2, 0, 0), # 103
(10, 8, 8, 4, 9, 2, 6, 7, 7, 6, 0, 2, 0, 10, 8, 7, 9, 2, 3, 2, 4, 7, 1, 0, 0, 0), # 104
(13, 3, 5, 6, 11, 3, 4, 1, 4, 0, 2, 0, 0, 11, 8, 4, 1, 8, 7, 7, 4, 5, 2, 1, 0, 0), # 105
(9, 7, 9, 11, 5, 1, 5, 4, 3, 0, 0, 1, 0, 10, 10, 6, 5, 11, 1, 4, 3, 5, 0, 0, 2, 0), # 106
(9, 5, 10, 6, 10, 6, 3, 5, 4, 2, 0, 0, 0, 6, 10, 7, 7, 6, 3, 7, 2, 6, 4, 0, 1, 0), # 107
(9, 7, 9, 8, 4, 4, 4, 3, 2, 2, 1, 1, 0, 12, 10, 5, 8, 6, 5, 2, 1, 3, 3, 3, 3, 0), # 108
(7, 10, 7, 5, 13, 2, 7, 1, 4, 2, 2, 1, 0, 6, 9, 7, 2, 8, 2, 5, 2, 4, 5, 2, 1, 0), # 109
(17, 5, 8, 8, 6, 2, 1, 2, 3, 1, 2, 2, 0, 7, 11, 2, 3, 7, 4, 5, 3, 5, 0, 3, 1, 0), # 110
(9, 8, 11, 10, 6, 7, 1, 2, 4, 2, 0, 1, 0, 10, 7, 8, 4, 4, 3, 1, 3, 3, 5, 2, 1, 0), # 111
(10, 3, 9, 8, 7, 3, 0, 2, 4, 2, 0, 3, 0, 8, 8, 7, 5, 7, 6, 3, 0, 3, 3, 2, 0, 0), # 112
(6, 9, 6, 7, 9, 3, 3, 2, 9, 0, 6, 0, 0, 9, 10, 6, 7, 9, 1, 7, 2, 2, 2, 1, 0, 0), # 113
(9, 4, 5, 7, 6, 1, 0, 4, 2, 1, 0, 0, 0, 5, 6, 3, 7, 9, 3, 6, 0, 7, 2, 4, 2, 0), # 114
(10, 8, 6, 4, 2, 3, 5, 2, 3, 2, 2, 1, 0, 12, 10, 5, 2, 6, 1, 5, 3, 0, 2, 1, 1, 0), # 115
(14, 4, 8, 3, 11, 7, 3, 3, 10, 1, 0, 0, 0, 7, 8, 8, 4, 7, 1, 2, 0, 2, 3, 1, 0, 0), # 116
(13, 5, 8, 2, 5, 7, 1, 4, 6, 1, 1, 2, 0, 12, 14, 3, 2, 10, 5, 1, 2, 7, 6, 3, 1, 0), # 117
(7, 6, 4, 14, 4, 1, 6, 3, 2, 1, 0, 2, 0, 11, 6, 6, 5, 9, 4, 3, 3, 4, 1, 0, 0, 0), # 118
(11, 5, 10, 8, 9, 2, 2, 2, 2, 0, 2, 0, 0, 7, 6, 3, 5, 6, 4, 2, 2, 1, 3, 0, 0, 0), # 119
(9, 5, 11, 8, 7, 1, 6, 8, 0, 0, 1, 0, 0, 12, 7, 6, 3, 5, 4, 2, 1, 6, 8, 3, 1, 0), # 120
(5, 10, 8, 7, 8, 6, 5, 3, 4, 0, 0, 0, 0, 18, 7, 5, 3, 4, 4, 5, 4, 1, 3, 1, 0, 0), # 121
(6, 4, 9, 5, 12, 5, 2, 2, 6, 1, 0, 0, 0, 17, 8, 7, 4, 8, 2, 3, 0, 2, 4, 1, 1, 0), # 122
(3, 11, 7, 7, 7, 7, 5, 1, 4, 2, 1, 1, 0, 13, 4, 11, 4, 10, 2, 3, 1, 3, 6, 2, 1, 0), # 123
(17, 6, 8, 13, 6, 5, 3, 3, 1, 0, 1, 0, 0, 5, 12, 7, 3, 12, 3, 4, 6, 5, 5, 1, 2, 0), # 124
(9, 6, 7, 11, 3, 3, 2, 2, 4, 1, 1, 3, 0, 5, 8, 9, 2, 11, 4, 5, 2, 3, 1, 1, 0, 0), # 125
(10, 4, 6, 8, 3, 3, 6, 3, 0, 3, 0, 1, 0, 10, 11, 2, 5, 11, 5, 5, 2, 4, 3, 1, 0, 0), # 126
(9, 9, 13, 7, 3, 1, 1, 3, 3, 1, 1, 3, 0, 10, 5, 9, 5, 12, 2, 0, 1, 3, 0, 1, 0, 0), # 127
(14, 7, 9, 13, 7, 3, 2, 5, 7, 1, 2, 2, 0, 11, 4, 6, 4, 4, 6, 6, 2, 1, 1, 0, 0, 0), # 128
(9, 5, 11, 9, 6, 3, 3, 1, 4, 0, 0, 0, 0, 9, 3, 7, 3, 2, 2, 3, 1, 3, 4, 3, 0, 0), # 129
(8, 2, 6, 8, 6, 1, 5, 1, 4, 0, 1, 1, 0, 7, 7, 3, 6, 10, 3, 4, 5, 2, 1, 1, 1, 0), # 130
(10, 8, 10, 10, 6, 6, 5, 4, 0, 3, 3, 0, 0, 8, 11, 3, 6, 4, 8, 4, 2, 3, 0, 0, 0, 0), # 131
(8, 7, 5, 13, 7, 4, 2, 7, 1, 0, 1, 0, 0, 15, 8, 10, 4, 11, 6, 0, 1, 2, 2, 1, 1, 0), # 132
(16, 7, 4, 13, 4, 2, 0, 1, 2, 1, 1, 0, 0, 10, 4, 4, 6, 6, 5, 4, 2, 4, 2, 2, 1, 0), # 133
(9, 5, 4, 7, 14, 7, 4, 5, 2, 3, 2, 2, 0, 6, 10, 7, 4, 7, 2, 3, 6, 4, 1, 0, 1, 0), # 134
(11, 10, 9, 6, 12, 4, 3, 3, 3, 0, 1, 1, 0, 11, 6, 2, 4, 9, 7, 3, 0, 4, 3, 1, 0, 0), # 135
(6, 4, 9, 9, 7, 4, 4, 3, 3, 0, 2, 1, 0, 14, 10, 5, 3, 10, 3, 4, 2, 4, 1, 3, 1, 0), # 136
(12, 11, 4, 8, 2, 3, 5, 2, 7, 0, 1, 0, 0, 10, 7, 7, 3, 7, 5, 6, 2, 5, 2, 1, 1, 0), # 137
(7, 6, 5, 9, 5, 2, 2, 3, 5, 2, 1, 0, 0, 7, 7, 6, 4, 4, 2, 3, 2, 2, 5, 1, 1, 0), # 138
(7, 4, 11, 10, 9, 3, 1, 4, 1, 2, 0, 1, 0, 7, 5, 9, 4, 5, 4, 7, 6, 2, 4, 2, 2, 0), # 139
(11, 4, 7, 11, 10, 2, 3, 2, 4, 2, 2, 0, 0, 7, 11, 4, 2, 4, 3, 1, 1, 2, 2, 2, 0, 0), # 140
(6, 1, 5, 11, 7, 1, 2, 1, 2, 4, 0, 0, 0, 3, 5, 4, 3, 6, 5, 2, 2, 1, 2, 1, 0, 0), # 141
(4, 3, 4, 3, 8, 2, 2, 4, 3, 0, 1, 1, 0, 10, 11, 4, 9, 7, 1, 1, 3, 2, 2, 2, 2, 0), # 142
(7, 7, 7, 8, 7, 4, 6, 0, 3, 0, 1, 1, 0, 13, 12, 2, 5, 7, 2, 2, 0, 6, 2, 0, 0, 0), # 143
(8, 3, 8, 7, 9, 6, 2, 0, 3, 1, 2, 0, 0, 10, 10, 6, 4, 4, 4, 2, 4, 0, 3, 1, 1, 0), # 144
(15, 6, 3, 8, 7, 4, 2, 4, 6, 3, 2, 0, 0, 8, 5, 7, 1, 11, 2, 3, 1, 3, 3, 2, 0, 0), # 145
(7, 4, 10, 4, 10, 1, 1, 0, 6, 1, 1, 1, 0, 11, 7, 3, 4, 6, 3, 2, 3, 5, 2, 2, 0, 0), # 146
(13, 8, 11, 3, 8, 7, 2, 1, 2, 0, 2, 0, 0, 12, 8, 4, 4, 6, 1, 3, 1, 3, 1, 3, 1, 0), # 147
(7, 5, 10, 3, 7, 2, 3, 6, 2, 1, 1, 1, 0, 9, 8, 5, 4, 7, 3, 1, 0, 5, 2, 2, 0, 0), # 148
(10, 4, 7, 8, 4, 3, 3, 3, 2, 1, 0, 0, 0, 8, 7, 9, 0, 7, 3, 0, 1, 1, 4, 1, 0, 0), # 149
(11, 4, 2, 6, 6, 5, 4, 1, 3, 1, 0, 0, 0, 11, 3, 5, 5, 7, 4, 2, 2, 4, 1, 3, 0, 0), # 150
(11, 12, 7, 9, 5, 4, 1, 6, 4, 0, 1, 1, 0, 6, 7, 6, 4, 8, 4, 3, 4, 3, 4, 1, 0, 0), # 151
(10, 4, 7, 6, 10, 4, 2, 2, 6, 1, 0, 0, 0, 3, 8, 9, 3, 3, 2, 4, 4, 3, 3, 3, 1, 0), # 152
(11, 7, 8, 5, 8, 4, 4, 2, 4, 1, 0, 0, 0, 4, 5, 5, 5, 8, 4, 2, 2, 4, 3, 1, 1, 0), # 153
(12, 5, 7, 7, 5, 5, 0, 2, 2, 2, 0, 0, 0, 7, 8, 4, 4, 6, 2, 1, 5, 5, 2, 1, 3, 0), # 154
(8, 3, 5, 9, 4, 2, 1, 5, 3, 0, 1, 0, 0, 12, 7, 3, 5, 9, 4, 2, 1, 2, 4, 1, 1, 0), # 155
(8, 6, 8, 8, 5, 5, 7, 1, 5, 0, 1, 0, 0, 11, 10, 2, 2, 7, 6, 3, 2, 6, 2, 2, 0, 0), # 156
(6, 7, 5, 6, 6, 2, 2, 1, 3, 1, 0, 0, 0, 7, 7, 2, 4, 8, 2, 3, 3, 4, 3, 3, 0, 0), # 157
(8, 5, 7, 3, 7, 5, 3, 5, 3, 0, 0, 0, 0, 7, 2, 6, 8, 4, 2, 6, 1, 2, 2, 1, 0, 0), # 158
(13, 4, 9, 4, 7, 2, 4, 0, 2, 0, 2, 1, 0, 3, 9, 5, 3, 7, 4, 4, 1, 2, 4, 2, 1, 0), # 159
(8, 4, 2, 6, 5, 6, 1, 5, 2, 1, 2, 0, 0, 6, 7, 4, 3, 8, 2, 4, 1, 3, 3, 2, 0, 0), # 160
(8, 2, 7, 13, 3, 6, 5, 3, 2, 1, 0, 0, 0, 8, 3, 5, 3, 7, 3, 2, 1, 1, 3, 0, 0, 0), # 161
(6, 4, 10, 9, 7, 5, 2, 1, 2, 0, 0, 0, 0, 7, 9, 4, 3, 6, 2, 2, 2, 2, 3, 0, 0, 0), # 162
(2, 8, 2, 3, 12, 3, 5, 5, 0, 1, 1, 0, 0, 14, 3, 4, 2, 9, 4, 6, 2, 1, 1, 2, 0, 0), # 163
(8, 3, 8, 8, 6, 0, 2, 1, 2, 0, 2, 1, 0, 6, 6, 8, 1, 2, 3, 3, 3, 3, 0, 1, 0, 0), # 164
(6, 9, 5, 4, 4, 3, 0, 1, 3, 1, 1, 1, 0, 6, 3, 4, 3, 7, 2, 1, 3, 4, 0, 1, 1, 0), # 165
(6, 2, 5, 7, 8, 2, 0, 0, 3, 1, 3, 2, 0, 10, 8, 7, 2, 2, 8, 2, 1, 3, 3, 0, 0, 0), # 166
(3, 3, 5, 5, 9, 3, 4, 4, 4, 2, 0, 1, 0, 6, 5, 4, 4, 5, 0, 1, 0, 4, 0, 1, 0, 0), # 167
(7, 6, 8, 8, 8, 3, 2, 4, 2, 1, 1, 1, 0, 3, 2, 2, 4, 6, 3, 7, 3, 4, 1, 1, 0, 0), # 168
(12, 3, 7, 7, 3, 2, 0, 4, 2, 2, 2, 0, 0, 5, 5, 2, 5, 6, 0, 3, 2, 3, 1, 1, 0, 0), # 169
(9, 2, 4, 8, 4, 2, 2, 1, 3, 1, 0, 0, 0, 6, 4, 9, 8, 4, 0, 1, 2, 2, 0, 1, 1, 0), # 170
(9, 7, 4, 5, 7, 4, 0, 1, 6, 2, 0, 0, 0, 4, 3, 7, 2, 5, 2, 2, 1, 5, 1, 0, 0, 0), # 171
(3, 1, 7, 9, 4, 1, 0, 1, 4, 1, 1, 1, 0, 8, 5, 2, 0, 4, 1, 5, 2, 3, 3, 1, 0, 0), # 172
(5, 1, 7, 7, 2, 4, 1, 2, 3, 0, 0, 2, 0, 11, 5, 9, 2, 6, 3, 0, 1, 5, 4, 2, 0, 0), # 173
(1, 4, 3, 3, 3, 2, 1, 2, 0, 1, 0, 0, 0, 7, 4, 3, 3, 1, 1, 0, 0, 3, 1, 0, 0, 0), # 174
(6, 2, 4, 6, 4, 1, 0, 2, 2, 0, 1, 0, 0, 4, 6, 5, 0, 4, 2, 2, 0, 1, 0, 0, 0, 0), # 175
(8, 5, 1, 6, 5, 0, 1, 1, 0, 0, 1, 1, 0, 6, 5, 4, 1, 5, 1, 1, 2, 2, 2, 1, 0, 0), # 176
(1, 2, 2, 8, 1, 1, 2, 1, 1, 1, 0, 0, 0, 6, 4, 1, 2, 1, 1, 1, 3, 0, 0, 0, 0, 0), # 177
(6, 7, 5, 3, 1, 1, 0, 2, 2, 1, 0, 0, 0, 6, 4, 5, 3, 6, 4, 1, 0, 1, 2, 0, 0, 0), # 178
(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), # 179
)
station_arriving_intensity = (
(5.020865578371768, 5.525288559693166, 5.211283229612507, 6.214667773863432, 5.554685607609612, 3.1386549320373387, 4.146035615373915, 4.653176172979423, 6.090099062168007, 3.9580150155223697, 4.205265163885603, 4.897915078306173, 5.083880212578363), # 0
(5.354327152019974, 5.890060694144759, 5.555346591330152, 6.625144253276616, 5.922490337474237, 3.3459835840425556, 4.419468941263694, 4.959513722905708, 6.492245326332909, 4.21898069227715, 4.483096135956131, 5.221216660814354, 5.419791647439855), # 1
(5.686723008979731, 6.253385170890979, 5.8980422855474135, 7.033987704664794, 6.288962973749744, 3.5524851145124448, 4.691818507960704, 5.264625247904419, 6.892786806877549, 4.478913775020546, 4.759823148776313, 5.543232652053055, 5.75436482820969), # 2
(6.016757793146562, 6.613820501936447, 6.238010869319854, 7.439576407532074, 6.652661676001902, 3.757340622585113, 4.962003641647955, 5.567301157494507, 7.290135160921093, 4.736782698426181, 5.0343484118273825, 5.862685684930461, 6.086272806254225), # 3
(6.343136148415981, 6.9699251992857745, 6.573892899703036, 7.840288641382569, 7.012144603796492, 3.9597312073986677, 5.2289436685084585, 5.866331861194915, 7.682702045582707, 4.991555897167679, 5.305574134590575, 6.178298392354764, 6.414188632939817), # 4
(6.66456271868351, 7.320257774943588, 6.9043289337525175, 8.234502685720393, 7.36596991669928, 4.158837968091214, 5.491557914725224, 6.160507768524592, 8.068899117981559, 5.242201805918663, 5.572402526547132, 6.488793407234148, 6.736785359632827), # 5
(6.979742147844666, 7.663376740914501, 7.227959528523866, 8.620596820049652, 7.712695774276043, 4.353842003800864, 5.7487657064812625, 6.4486192890024885, 8.447138035236815, 5.487688859352758, 5.833735797178282, 6.792893362476808, 7.052736037699606), # 6
(7.2873790797949685, 7.997840609203132, 7.543425241072635, 8.996949323874462, 8.050880336092554, 4.543924413665721, 5.999486369959585, 6.729456832147552, 8.815830454467644, 5.726985492143586, 6.088476155965268, 7.089320890990929, 7.360713718506519), # 7
(7.586178158429934, 8.322207891814099, 7.849366628454396, 9.361938476698928, 8.379081761714586, 4.7282662968238895, 6.2426392313431975, 7.001810807478725, 9.173388032793206, 5.959060138964774, 6.335525812389321, 7.376798625684702, 7.659391453419917), # 8
(7.874844027645085, 8.635037100752022, 8.144424247724704, 9.713942558027169, 8.69585821070791, 4.906048752413484, 6.47714361681512, 7.264471624514963, 9.518222427332674, 6.182881234489941, 6.573786975931678, 7.654049199466313, 7.947442293806162), # 9
(8.152081331335932, 8.934886748021516, 8.427238655939124, 10.051339847363288, 8.9997678426383, 5.076452879572607, 6.701918852558355, 7.516229692775211, 9.848745295205214, 6.397417213392714, 6.802161856073574, 7.919795245243952, 8.22353929103161), # 10
(8.416594713398005, 9.220315345627206, 8.696450410153215, 10.372508624211397, 9.289368817071534, 5.238659777439368, 6.915884264755916, 7.7558754217784145, 10.163368293529993, 6.601636510346719, 7.019552662296249, 8.17275939592581, 8.486355496462611), # 11
(8.667088817726812, 9.489881405573698, 8.95070006742254, 10.675827168075612, 9.563219293573377, 5.391850545151869, 7.1179591795908115, 7.982199221043521, 10.460503079426179, 6.794507560025572, 7.224861604080934, 8.411664284420068, 8.734563961465534), # 12
(8.902268288217876, 9.74214343986562, 9.188628184802662, 10.959673758460044, 9.819877431709601, 5.5352062818482235, 7.307062923246056, 8.193991500089481, 10.738561310012932, 6.974998797102904, 7.416990890908869, 8.63523254363492, 8.966837737406735), # 13
(9.120837768766716, 9.975659960507588, 9.408875319349146, 11.222426674868792, 10.05790139104599, 5.667908086666534, 7.482114821904661, 8.390042668435246, 10.995954642409421, 7.142078656252334, 7.594842732261284, 8.84218680647856, 9.181849875652563), # 14
(9.321501903268855, 10.188989479504217, 9.610082028117542, 11.462464196805985, 10.275849331148308, 5.789137058744912, 7.642034201749626, 8.569143135599756, 11.23109473373482, 7.29471557214749, 7.757319337619419, 9.031249705859171, 9.37827342756938), # 15
(9.5029653356198, 10.380690508860132, 9.790888868163425, 11.678164603775716, 10.472279411582333, 5.898074297221459, 7.785740388963976, 8.73008331110196, 11.442393241108286, 7.431877979461996, 7.9033229164645125, 9.20114387468494, 9.554781444523545), # 16
(9.663932709715075, 10.549321560579946, 9.949936396542352, 11.867906175282112, 10.645749791913838, 5.993900901234285, 7.9121527097307105, 8.871653604460818, 11.628261821648984, 7.552534312869467, 8.031755678277799, 9.350591945864055, 9.710046977881415), # 17
(9.803108669450204, 10.693441146668274, 10.08586517030988, 12.030067190829278, 10.794818631708589, 6.075797969921503, 8.020190490232851, 8.99264442519526, 11.787112132476096, 7.6556530070435365, 8.141519832540508, 9.478316552304715, 9.842743079009345), # 18
(9.919197858720699, 10.811607779129744, 10.197315746521578, 12.163025929921314, 10.918044090532366, 6.142946602421208, 8.108773056653394, 9.091846182824245, 11.917355830708779, 7.740202496657828, 8.231517588733878, 9.583040326915096, 9.951542799273696), # 19
(10.010904921422082, 10.902379969968962, 10.282928682233003, 12.265160672062354, 11.013984327950944, 6.194527897871518, 8.176819735175362, 9.168049286866717, 12.017404573466198, 7.805151216385958, 8.30065115633915, 9.66348590260339, 10.035119190040824), # 20
(10.076934501449866, 10.964316231190558, 10.341344534499719, 12.334849696756486, 11.081197503530088, 6.229722955410535, 8.223249851981759, 9.220044146841623, 12.085670017867521, 7.849467600901555, 8.34782274483756, 9.718375912277793, 10.092145302677078), # 21
(10.115991242699579, 10.995975074799144, 10.371203860377285, 12.370471283507836, 11.118241776835575, 6.247712874176367, 8.2469827332556, 9.246621172267915, 12.120563821031915, 7.872120084878242, 8.37193456371034, 9.74643298884649, 10.121294188548827), # 22
(10.13039336334264, 10.999723593964335, 10.374923182441702, 12.374930812757203, 11.127732056032597, 6.25, 8.249804002259339, 9.249493827160494, 12.124926234567901, 7.874792272519433, 8.37495803716174, 9.749897576588934, 10.125), # 23
(10.141012413034153, 10.997537037037038, 10.374314814814815, 12.374381944444446, 11.133107613614852, 6.25, 8.248253812636166, 9.2455, 12.124341666666666, 7.87315061728395, 8.37462457912458, 9.749086419753086, 10.125), # 24
(10.15140723021158, 10.993227023319616, 10.373113854595337, 12.373296039094651, 11.138364945594503, 6.25, 8.24519890260631, 9.237654320987655, 12.123186728395062, 7.869918838591678, 8.373963399426362, 9.747485139460448, 10.125), # 25
(10.161577019048034, 10.986859396433472, 10.371336762688616, 12.37168544238683, 11.143503868421105, 6.25, 8.240686718308721, 9.226104938271606, 12.1214762345679, 7.865150708733425, 8.372980483850855, 9.745115683584821, 10.125), # 26
(10.171520983716636, 10.978499999999999, 10.369, 12.369562499999999, 11.148524198544214, 6.25, 8.234764705882354, 9.211, 12.119225, 7.858899999999999, 8.371681818181818, 9.742, 10.125), # 27
(10.181238328390501, 10.968214677640603, 10.366120027434842, 12.366939557613168, 11.153425752413401, 6.25, 8.22748031146615, 9.192487654320988, 12.116447839506172, 7.851220484682213, 8.370073388203018, 9.73816003657979, 10.125), # 28
(10.19072825724275, 10.95606927297668, 10.362713305898492, 12.36382896090535, 11.15820834647822, 6.25, 8.218880981199066, 9.170716049382715, 12.113159567901235, 7.842165935070874, 8.368161179698216, 9.733617741197987, 10.125), # 29
(10.199989974446497, 10.94212962962963, 10.358796296296296, 12.360243055555555, 11.162871797188236, 6.25, 8.209014161220043, 9.145833333333332, 12.109375, 7.83179012345679, 8.365951178451178, 9.728395061728394, 10.125), # 30
(10.209022684174858, 10.926461591220852, 10.354385459533608, 12.356194187242798, 11.167415920993008, 6.25, 8.19792729766804, 9.117987654320988, 12.105108950617284, 7.820146822130773, 8.363449370245666, 9.722513946044812, 10.125), # 31
(10.217825590600954, 10.909131001371742, 10.349497256515773, 12.35169470164609, 11.171840534342095, 6.25, 8.185667836681999, 9.087327160493828, 12.100376234567902, 7.807289803383631, 8.360661740865444, 9.715996342021034, 10.125), # 32
(10.226397897897897, 10.890203703703703, 10.344148148148149, 12.346756944444444, 11.176145453685063, 6.25, 8.172283224400871, 9.054, 12.095191666666667, 7.793272839506173, 8.357594276094275, 9.708864197530863, 10.125), # 33
(10.23473881023881, 10.869745541838133, 10.338354595336076, 12.341393261316872, 11.180330495471466, 6.25, 8.15782090696361, 9.018154320987653, 12.089570061728397, 7.778149702789209, 8.354252961715924, 9.701139460448102, 10.125), # 34
(10.242847531796807, 10.847822359396433, 10.332133058984912, 12.335615997942385, 11.18439547615087, 6.25, 8.142328330509159, 8.979938271604938, 12.083526234567902, 7.761974165523548, 8.350643783514153, 9.692844078646548, 10.125), # 35
(10.250723266745005, 10.824499999999999, 10.3255, 12.3294375, 11.188340212172836, 6.25, 8.12585294117647, 8.9395, 12.077074999999999, 7.7448, 8.346772727272727, 9.684000000000001, 10.125), # 36
(10.258365219256524, 10.799844307270233, 10.318471879286694, 12.322870113168724, 11.192164519986921, 6.25, 8.108442185104494, 8.896987654320988, 12.070231172839506, 7.726680978509374, 8.34264577877541, 9.674629172382259, 10.125), # 37
(10.265772593504476, 10.773921124828533, 10.311065157750342, 12.315926183127573, 11.19586821604269, 6.25, 8.09014350843218, 8.85254938271605, 12.063009567901235, 7.707670873342479, 8.33826892380596, 9.664753543667125, 10.125), # 38
(10.272944593661986, 10.746796296296296, 10.303296296296297, 12.308618055555556, 11.199451116789703, 6.25, 8.071004357298476, 8.806333333333333, 12.055425000000001, 7.687823456790124, 8.333648148148148, 9.654395061728394, 10.125), # 39
(10.279880423902163, 10.718535665294924, 10.295181755829903, 12.300958076131687, 11.202913038677519, 6.25, 8.05107217784233, 8.758487654320989, 12.047492283950618, 7.667192501143119, 8.328789437585733, 9.643575674439873, 10.125), # 40
(10.286579288398128, 10.689205075445816, 10.286737997256516, 12.29295859053498, 11.206253798155702, 6.25, 8.030394416202695, 8.709160493827161, 12.0392262345679, 7.645831778692272, 8.323698777902482, 9.632317329675354, 10.125), # 41
(10.293040391323, 10.658870370370371, 10.277981481481483, 12.284631944444445, 11.209473211673808, 6.25, 8.009018518518518, 8.6585, 12.030641666666668, 7.623795061728395, 8.318382154882155, 9.620641975308642, 10.125), # 42
(10.299262936849892, 10.627597393689987, 10.268928669410151, 12.275990483539095, 11.212571095681403, 6.25, 7.98699193092875, 8.606654320987655, 12.021753395061728, 7.601136122542296, 8.312845554308517, 9.608571559213535, 10.125), # 43
(10.305246129151927, 10.595451989026063, 10.259596021947875, 12.267046553497943, 11.215547266628045, 6.25, 7.964362099572339, 8.553771604938273, 12.0125762345679, 7.577908733424783, 8.307094961965332, 9.596128029263832, 10.125), # 44
(10.310989172402216, 10.5625, 10.25, 12.2578125, 11.218401540963296, 6.25, 7.9411764705882355, 8.5, 12.003124999999999, 7.554166666666667, 8.301136363636363, 9.583333333333332, 10.125), # 45
(10.31649127077388, 10.528807270233196, 10.240157064471878, 12.24830066872428, 11.221133735136716, 6.25, 7.917482490115388, 8.445487654320988, 11.993414506172838, 7.529963694558756, 8.294975745105374, 9.57020941929584, 10.125), # 46
(10.321751628440035, 10.49443964334705, 10.230083676268862, 12.238523405349794, 11.223743665597867, 6.25, 7.893327604292747, 8.390382716049382, 11.983459567901235, 7.505353589391861, 8.288619092156129, 9.55677823502515, 10.125), # 47
(10.326769449573796, 10.459462962962963, 10.219796296296296, 12.228493055555557, 11.22623114879631, 6.25, 7.868759259259259, 8.334833333333334, 11.973275000000001, 7.4803901234567896, 8.28207239057239, 9.543061728395061, 10.125), # 48
(10.331543938348286, 10.42394307270233, 10.209311385459534, 12.218221965020577, 11.228596001181607, 6.25, 7.8438249011538765, 8.278987654320987, 11.96287561728395, 7.455127069044353, 8.275341626137923, 9.529081847279379, 10.125), # 49
(10.336074298936616, 10.387945816186559, 10.198645404663925, 12.207722479423868, 11.230838039203315, 6.25, 7.81857197611555, 8.222993827160494, 11.9522762345679, 7.429618198445358, 8.268432784636488, 9.514860539551899, 10.125), # 50
(10.34035973551191, 10.351537037037037, 10.187814814814814, 12.197006944444444, 11.232957079310998, 6.25, 7.793047930283224, 8.167, 11.941491666666668, 7.403917283950617, 8.261351851851853, 9.50041975308642, 10.125), # 51
(10.344399452247279, 10.314782578875173, 10.176836076817558, 12.186087705761317, 11.234952937954214, 6.25, 7.767300209795852, 8.111154320987653, 11.930536728395062, 7.3780780978509375, 8.254104813567777, 9.485781435756746, 10.125), # 52
(10.348192653315843, 10.27774828532236, 10.165725651577505, 12.174977109053497, 11.23682543158253, 6.25, 7.741376260792383, 8.055604938271605, 11.919426234567903, 7.3521544124371285, 8.246697655568026, 9.470967535436671, 10.125), # 53
(10.351738542890716, 10.2405, 10.154499999999999, 12.1636875, 11.238574376645502, 6.25, 7.715323529411765, 8.000499999999999, 11.908175, 7.3262, 8.239136363636362, 9.456, 10.125), # 54
(10.355036325145022, 10.203103566529492, 10.143175582990398, 12.152231224279834, 11.24019958959269, 6.25, 7.689189461792948, 7.945987654320987, 11.896797839506172, 7.300268632830361, 8.231426923556553, 9.44090077732053, 10.125), # 55
(10.358085204251871, 10.165624828532236, 10.131768861454047, 12.140620627572016, 11.241700886873659, 6.25, 7.663021504074881, 7.892216049382716, 11.885309567901235, 7.274414083219022, 8.223575321112358, 9.425691815272062, 10.125), # 56
(10.360884384384383, 10.12812962962963, 10.120296296296297, 12.128868055555555, 11.243078084937967, 6.25, 7.636867102396514, 7.839333333333334, 11.873725, 7.24869012345679, 8.215587542087542, 9.410395061728394, 10.125), # 57
(10.36343306971568, 10.090683813443073, 10.108774348422497, 12.116985853909464, 11.244331000235174, 6.25, 7.610773702896797, 7.787487654320987, 11.862058950617284, 7.223150525834477, 8.20746957226587, 9.395032464563329, 10.125), # 58
(10.36573046441887, 10.053353223593964, 10.097219478737998, 12.104986368312757, 11.245459449214845, 6.25, 7.584788751714678, 7.736827160493827, 11.850326234567902, 7.197849062642891, 8.1992273974311, 9.379625971650663, 10.125), # 59
(10.367775772667077, 10.016203703703704, 10.085648148148147, 12.092881944444445, 11.246463248326537, 6.25, 7.558959694989106, 7.6875, 11.838541666666668, 7.172839506172839, 8.190867003367003, 9.364197530864198, 10.125), # 60
(10.369568198633415, 9.97930109739369, 10.0740768175583, 12.080684927983539, 11.247342214019811, 6.25, 7.533333978859033, 7.639654320987654, 11.826720061728395, 7.148175628715135, 8.182394375857339, 9.348769090077733, 10.125), # 61
(10.371106946491004, 9.942711248285322, 10.062521947873801, 12.068407664609055, 11.248096162744234, 6.25, 7.507959049463406, 7.5934382716049384, 11.814876234567901, 7.123911202560586, 8.17381550068587, 9.333362597165067, 10.125), # 62
(10.37239122041296, 9.9065, 10.051, 12.056062500000001, 11.248724910949356, 6.25, 7.482882352941176, 7.549, 11.803025, 7.100099999999999, 8.165136363636364, 9.318, 10.125), # 63
(10.373420224572397, 9.870733196159122, 10.039527434842249, 12.043661779835391, 11.249228275084748, 6.25, 7.458151335431292, 7.506487654320988, 11.791181172839506, 7.076795793324188, 8.156362950492579, 9.302703246456334, 10.125), # 64
(10.374193163142438, 9.835476680384087, 10.0281207133059, 12.031217849794238, 11.249606071599967, 6.25, 7.433813443072703, 7.466049382716049, 11.779359567901235, 7.054052354823959, 8.147501247038285, 9.287494284407863, 10.125), # 65
(10.374709240296196, 9.800796296296298, 10.016796296296297, 12.018743055555555, 11.249858116944573, 6.25, 7.409916122004357, 7.427833333333334, 11.767575, 7.031923456790123, 8.138557239057238, 9.272395061728396, 10.125), # 66
(10.374967660206792, 9.766757887517146, 10.005570644718793, 12.006249742798353, 11.24998422756813, 6.25, 7.386506818365206, 7.391987654320989, 11.755842283950617, 7.010462871513489, 8.12953691233321, 9.257427526291723, 10.125), # 67
(10.374791614480825, 9.733248639320323, 9.994405949931412, 11.993641740472357, 11.249877955297345, 6.2498840115836, 7.363515194829646, 7.358343850022862, 11.744087848651121, 6.989620441647166, 8.120285988540376, 9.242530021899743, 10.124875150034294), # 68
(10.373141706924315, 9.699245519713262, 9.982988425925925, 11.980283514492752, 11.248910675381262, 6.248967078189301, 7.340268181346613, 7.325098765432099, 11.731797839506173, 6.968806390704429, 8.10986283891547, 9.227218973359324, 10.12388599537037), # 69
(10.369885787558895, 9.664592459843355, 9.971268432784635, 11.966087124261943, 11.246999314128942, 6.247161255906112, 7.31666013456137, 7.291952446273434, 11.718902892089622, 6.947919524462734, 8.09814888652608, 9.211422761292809, 10.121932334533609), # 70
(10.365069660642929, 9.62931016859153, 9.959250085733881, 11.951073503757382, 11.244168078754136, 6.244495808565767, 7.292701659538988, 7.258915866483768, 11.705422210791038, 6.926960359342639, 8.085187370783862, 9.195152937212715, 10.119039887688615), # 71
(10.358739130434783, 9.593419354838709, 9.946937499999999, 11.935263586956522, 11.240441176470588, 6.2410000000000005, 7.268403361344538, 7.226, 11.691375, 6.905929411764705, 8.07102153110048, 9.17842105263158, 10.115234375), # 72
(10.35094000119282, 9.556940727465816, 9.934334790809327, 11.918678307836823, 11.23584281449205, 6.236703094040542, 7.243775845043092, 7.193215820759031, 11.676780464106082, 6.884827198149493, 8.055694606887588, 9.161238659061919, 10.110541516632374), # 73
(10.341718077175404, 9.519894995353777, 9.921446073388202, 11.901338600375738, 11.230397200032275, 6.231634354519128, 7.218829715699722, 7.160574302697759, 11.661657807498857, 6.863654234917561, 8.039249837556856, 9.143617308016267, 10.104987032750344), # 74
(10.331119162640901, 9.482302867383511, 9.908275462962962, 11.883265398550725, 11.224128540305012, 6.22582304526749, 7.1935755783795, 7.128086419753086, 11.6460262345679, 6.84241103848947, 8.021730462519935, 9.125568551007147, 10.098596643518519), # 75
(10.319189061847677, 9.44418505243595, 9.894827074759945, 11.864479636339238, 11.217061042524005, 6.219298430117361, 7.168024038147495, 7.095763145861912, 11.629904949702789, 6.821098125285779, 8.003179721188491, 9.107103939547082, 10.091396069101508), # 76
(10.305973579054093, 9.40556225939201, 9.881105024005485, 11.845002247718732, 11.209218913903008, 6.212089772900472, 7.142185700068779, 7.063615454961135, 11.613313157293096, 6.7997160117270505, 7.983640852974187, 9.088235025148606, 10.083411029663925), # 77
(10.291518518518519, 9.366455197132618, 9.867113425925925, 11.824854166666666, 11.200626361655774, 6.204226337448559, 7.116071169208425, 7.031654320987655, 11.596270061728394, 6.7782652142338415, 7.9631570972886765, 9.068973359324238, 10.074667245370371), # 78
(10.275869684499314, 9.326884574538697, 9.8528563957476, 11.804056327160493, 11.191307592996047, 6.195737387593354, 7.089691050631501, 6.9998907178783725, 11.578794867398262, 6.756746249226714, 7.941771693543622, 9.049330493586504, 10.065190436385459), # 79
(10.259072881254847, 9.286871100491172, 9.838338048696844, 11.782629663177671, 11.181286815137579, 6.18665218716659, 7.063055949403081, 6.968335619570188, 11.560906778692273, 6.7351596331262265, 7.919527881150688, 9.029317979447935, 10.0550063228738), # 80
(10.241173913043479, 9.246435483870968, 9.8235625, 11.760595108695654, 11.170588235294117, 6.177, 7.036176470588235, 6.937, 11.542625, 6.713505882352941, 7.8964688995215315, 9.008947368421053, 10.044140624999999), # 81
(10.222218584123576, 9.205598433559008, 9.808533864883403, 11.737973597691894, 11.159236060679415, 6.166810089925317, 7.009063219252036, 6.90589483310471, 11.52396873571102, 6.691785513327416, 7.872637988067813, 8.988230212018387, 10.03261906292867), # 82
(10.202252698753504, 9.164380658436214, 9.793256258573388, 11.714786064143853, 11.147254498507221, 6.156111720774272, 6.981726800459553, 6.875031092821216, 11.504957190214906, 6.669999042470211, 7.848078386201194, 8.967178061752461, 10.020467356824417), # 83
(10.181322061191626, 9.122802867383513, 9.777733796296296, 11.691053442028986, 11.134667755991286, 6.144934156378601, 6.954177819275858, 6.844419753086419, 11.485609567901234, 6.648146986201889, 7.822833333333333, 8.945802469135803, 10.007711226851852), # 84
(10.159472475696308, 9.080885769281826, 9.761970593278463, 11.666796665324746, 11.121500040345357, 6.133306660570035, 6.926426880766024, 6.814071787837221, 11.465945073159578, 6.626229860943005, 7.796946068875894, 8.924114985680937, 9.994376393175584), # 85
(10.136749746525913, 9.03865007301208, 9.745970764746229, 11.64203666800859, 11.107775558783183, 6.121258497180309, 6.89848458999512, 6.783998171010516, 11.445982910379517, 6.604248183114124, 7.770459832240534, 8.902127162900394, 9.98048857596022), # 86
(10.113199677938807, 8.996116487455197, 9.729738425925925, 11.61679438405797, 11.09351851851852, 6.108818930041152, 6.870361552028219, 6.75420987654321, 11.425742283950619, 6.582202469135802, 7.743417862838915, 8.879850552306692, 9.96607349537037), # 87
(10.088868074193357, 8.9533057214921, 9.713277692043896, 11.59109074745035, 11.07875312676511, 6.096017222984301, 6.842068371930391, 6.724717878372199, 11.40524239826246, 6.560093235428601, 7.715863400082698, 8.857296705412365, 9.951156871570646), # 88
(10.063800739547922, 8.910238484003717, 9.696592678326475, 11.564946692163177, 11.063503590736707, 6.082882639841488, 6.813615654766708, 6.695533150434385, 11.384502457704619, 6.537920998413083, 7.687839683383544, 8.834477173729935, 9.935764424725651), # 89
(10.03804347826087, 8.866935483870968, 9.6796875, 11.538383152173914, 11.04779411764706, 6.069444444444445, 6.785014005602241, 6.666666666666666, 11.363541666666668, 6.515686274509804, 7.65938995215311, 8.81140350877193, 9.919921875), # 90
(10.011642094590563, 8.823417429974777, 9.662566272290809, 11.511421061460013, 11.031648914709915, 6.055731900624904, 6.756274029502062, 6.638129401005944, 11.342379229538182, 6.4933895801393255, 7.63055744580306, 8.788087262050874, 9.903654942558298), # 91
(9.984642392795372, 8.779705031196071, 9.64523311042524, 11.484081353998926, 11.015092189139029, 6.041774272214601, 6.727406331531242, 6.609932327389118, 11.321034350708734, 6.471031431722209, 7.601385403745053, 8.764539985079297, 9.886989347565157), # 92
(9.957090177133654, 8.735818996415771, 9.62769212962963, 11.456384963768118, 10.998148148148148, 6.027600823045267, 6.69842151675485, 6.582086419753087, 11.299526234567901, 6.448612345679011, 7.57191706539075, 8.74077322936972, 9.869950810185184), # 93
(9.92903125186378, 8.691780034514801, 9.609947445130317, 11.428352824745035, 10.98084099895102, 6.0132408169486355, 6.669330190237961, 6.554602652034752, 11.277874085505259, 6.426132838430297, 7.54219567015181, 8.716798546434674, 9.85256505058299), # 94
(9.90051142124411, 8.647608854374088, 9.592003172153635, 11.400005870907139, 10.963194948761398, 5.9987235177564395, 6.640142957045644, 6.527491998171011, 11.25609710791038, 6.403593426396621, 7.512264457439896, 8.69262748778668, 9.834857788923182), # 95
(9.871576489533012, 8.603326164874554, 9.573863425925927, 11.371365036231884, 10.945234204793028, 5.984078189300411, 6.610870422242971, 6.500765432098766, 11.234214506172838, 6.3809946259985475, 7.482166666666667, 8.668271604938273, 9.816854745370371), # 96
(9.842272260988848, 8.558952674897121, 9.555532321673525, 11.342451254696725, 10.926982974259664, 5.969334095412284, 6.581523190895013, 6.474433927754916, 11.212245484682214, 6.358336953656634, 7.451945537243782, 8.64374244940197, 9.798581640089164), # 97
(9.812644539869984, 8.514509093322713, 9.53701397462277, 11.31328546027912, 10.908465464375052, 5.954520499923793, 6.552111868066842, 6.44850845907636, 11.190209247828074, 6.335620925791441, 7.421644308582906, 8.619051572690298, 9.78006419324417), # 98
(9.782739130434782, 8.470016129032258, 9.5183125, 11.283888586956522, 10.889705882352942, 5.939666666666667, 6.52264705882353, 6.423, 11.168125, 6.312847058823529, 7.391306220095694, 8.59421052631579, 9.761328125), # 99
(9.752601836941611, 8.425494490906676, 9.49943201303155, 11.254281568706388, 10.870728435407084, 5.924801859472641, 6.493139368230145, 6.3979195244627345, 11.146011945587563, 6.290015869173458, 7.36097451119381, 8.569230861790967, 9.742399155521262), # 100
(9.722278463648834, 8.380964887826895, 9.480376628943759, 11.224485339506174, 10.85155733075123, 5.909955342173449, 6.463599401351762, 6.3732780064014625, 11.123889288980338, 6.267127873261788, 7.330692421288912, 8.544124130628353, 9.723303004972564), # 101
(9.691814814814816, 8.336448028673836, 9.461150462962962, 11.194520833333334, 10.832216775599129, 5.895156378600824, 6.43403776325345, 6.349086419753086, 11.1017762345679, 6.244183587509078, 7.300503189792663, 8.518901884340481, 9.704065393518519), # 102
(9.661256694697919, 8.291964622328422, 9.4417576303155, 11.164408984165325, 10.812730977164529, 5.880434232586496, 6.40446505900028, 6.325355738454504, 11.079691986739826, 6.221183528335889, 7.270450056116723, 8.493575674439873, 9.68471204132373), # 103
(9.63064990755651, 8.247535377671579, 9.422202246227709, 11.134170725979603, 10.79312414266118, 5.865818167962201, 6.374891893657326, 6.302096936442616, 11.057655749885688, 6.19812821216278, 7.24057625967275, 8.468157052439054, 9.665268668552812), # 104
(9.600040257648953, 8.203181003584229, 9.402488425925926, 11.103826992753623, 10.773420479302832, 5.851337448559671, 6.345328872289658, 6.279320987654321, 11.035686728395062, 6.175018155410313, 7.210925039872408, 8.442657569850553, 9.64576099537037), # 105
(9.569473549233614, 8.158922208947299, 9.382620284636488, 11.073398718464842, 10.753644194303236, 5.837021338210638, 6.315786599962345, 6.25703886602652, 11.01380412665752, 6.151853874499045, 7.181539636127355, 8.417088778186894, 9.626214741941014), # 106
(9.538995586568856, 8.11477970264171, 9.362601937585735, 11.042906837090714, 10.733819494876139, 5.822899100746838, 6.286275681740461, 6.235261545496114, 10.992027149062643, 6.128635885849539, 7.152463287849252, 8.391462228960604, 9.606655628429355), # 107
(9.508652173913044, 8.070774193548388, 9.3424375, 11.012372282608696, 10.713970588235293, 5.809, 6.256806722689075, 6.214, 10.970375, 6.105364705882353, 7.1237392344497605, 8.365789473684211, 9.587109375), # 108
(9.478489115524543, 8.026926390548255, 9.322131087105625, 10.98181598899624, 10.69412168159445, 5.795353299801859, 6.227390327873262, 6.193265203475081, 10.948866883859168, 6.082040851018047, 7.09541071534054, 8.340082063870238, 9.567601701817559), # 109
(9.448552215661715, 7.983257002522237, 9.301686814128946, 10.951258890230811, 10.674296982167354, 5.7819882639841484, 6.198037102358089, 6.173068129858253, 10.92752200502972, 6.058664837677183, 7.06752096993325, 8.314351551031214, 9.54815832904664), # 110
(9.41888727858293, 7.9397867383512555, 9.281108796296298, 10.920721920289855, 10.654520697167756, 5.768934156378601, 6.168757651208631, 6.153419753086419, 10.906359567901236, 6.035237182280319, 7.040113237639553, 8.288609486679663, 9.528804976851852), # 111
(9.38954010854655, 7.896536306916234, 9.26040114883402, 10.890226013150832, 10.634817033809409, 5.756220240816949, 6.139562579489958, 6.134331047096479, 10.885398776863282, 6.011758401248016, 7.013230757871109, 8.26286742232811, 9.509567365397805), # 112
(9.360504223703044, 7.853598618785952, 9.239617828252069, 10.85983388249204, 10.615175680173705, 5.7438697692145135, 6.1105259636567695, 6.115852568780606, 10.86471281125862, 5.988304736612729, 6.9869239061528665, 8.237192936504428, 9.490443900843221), # 113
(9.331480897900065, 7.811397183525536, 9.219045675021619, 10.829789421277336, 10.595393354566326, 5.731854608529901, 6.082018208410579, 6.09821125950512, 10.84461903571306, 5.965315167912783, 6.961244337113197, 8.211912172112974, 9.471275414160035), # 114
(9.302384903003995, 7.769947198683046, 9.198696932707318, 10.800084505181779, 10.5754076778886, 5.7201435124987645, 6.054059650191562, 6.081402654278709, 10.82512497866879, 5.942825327988077, 6.936154511427094, 8.187037582558851, 9.452006631660376), # 115
(9.273179873237634, 7.729188281291702, 9.178532189983873, 10.770666150266404, 10.555188526383779, 5.708708877287098, 6.026604817527893, 6.065380312898993, 10.80618133922783, 5.920793358449547, 6.911605931271481, 8.162523197487346, 9.43260725975589), # 116
(9.243829442823772, 7.689060048384721, 9.158512035525986, 10.741481372592244, 10.53470577629511, 5.6975230990608905, 5.9996082389477525, 6.050097795163585, 10.787738816492203, 5.899177400908129, 6.887550098823283, 8.13832304654375, 9.413047004858225), # 117
(9.214297245985211, 7.649502116995324, 9.138597058008367, 10.712477188220333, 10.513929303865842, 5.686558573986138, 5.973024442979315, 6.0355086608700965, 10.769748109563935, 5.877935596974759, 6.863938516259424, 8.11439115937335, 9.393295573379024), # 118
(9.184546916944742, 7.610454104156729, 9.118747846105723, 10.683600613211706, 10.492828985339221, 5.675787698228833, 5.946807958150756, 6.021566469816145, 10.752159917545043, 5.857026088260372, 6.840722685756828, 8.090681565621434, 9.373322671729932), # 119
(9.154542089925162, 7.571855626902158, 9.098924988492762, 10.654798663627394, 10.471374696958497, 5.665182867954965, 5.920913312990253, 6.008224781799343, 10.734924939537558, 5.836407016375905, 6.817854109492416, 8.067148294933297, 9.353098006322597), # 120
(9.124246399149268, 7.533646302264829, 9.079089073844187, 10.626018355528434, 10.449536314966918, 5.6547164793305305, 5.89529503602598, 5.995437156617307, 10.717993874643499, 5.816036522932296, 6.795284289643116, 8.043745376954222, 9.33259128356866), # 121
(9.093623478839854, 7.495765747277961, 9.059200690834711, 10.597206704975855, 10.427283715607734, 5.644360928521519, 5.869907655786117, 5.983157154067649, 10.70131742196489, 5.795872749540477, 6.772964728385851, 8.0204268413295, 9.31177220987977), # 122
(9.062636963219719, 7.458153578974774, 9.039220428139036, 10.568310728030694, 10.40458677512419, 5.634088611693925, 5.844705700798839, 5.971338333947983, 10.684846280603754, 5.775873837811387, 6.750846927897544, 7.997146717704421, 9.290610491667572), # 123
(9.031250486511654, 7.420749414388487, 9.01910887443187, 10.539277440753986, 10.381415369759537, 5.623871925013739, 5.819643699592319, 5.959934256055926, 10.668531149662115, 5.755997929355961, 6.728882390355119, 7.973859035724275, 9.269075835343711), # 124
(8.999427682938459, 7.38349287055232, 8.998826618387923, 10.51005385920676, 10.357739375757022, 5.613683264646956, 5.794676180694739, 5.948898480189091, 10.652322728241993, 5.736203165785134, 6.707022617935501, 7.950517825034348, 9.247137947319828), # 125
(8.967132186722928, 7.346323564499494, 8.978334248681898, 10.480586999450054, 10.333528669359893, 5.603495026759568, 5.76975767263427, 5.938184566145092, 10.636171715445418, 5.7164476887098425, 6.685219112815613, 7.927077115279934, 9.224766534007578), # 126
(8.93432763208786, 7.309181113263224, 8.957592353988504, 10.450823877544899, 10.308753126811398, 5.593279607517565, 5.744842703939094, 5.927746073721545, 10.620028810374407, 5.696689639741024, 6.6634233771723785, 7.903490936106316, 9.201931301818599), # 127
(8.900977653256046, 7.272005133876735, 8.93656152298245, 10.420711509552332, 10.28338262435479, 5.583009403086944, 5.719885803137382, 5.917536562716062, 10.603844712130984, 5.6768871604896125, 6.641586913182724, 7.879713317158788, 9.178601957164537), # 128
(8.867045884450281, 7.234735243373241, 8.91520234433844, 10.390196911533382, 10.257387038233311, 5.572656809633695, 5.694841498757313, 5.90750959292626, 10.587570119817174, 5.656998392566545, 6.619661223023571, 7.855698288082636, 9.154748206457038), # 129
(8.832495959893366, 7.197311058785966, 8.893475406731179, 10.359227099549086, 10.230736244690213, 5.562194223323808, 5.669664319327063, 5.89761872414975, 10.571155732535, 5.636981477582757, 6.5975978088718445, 7.831399878523152, 9.130339756107748), # 130
(8.797291513808094, 7.159672197148127, 8.87134129883538, 10.327749089660475, 10.203400119968745, 5.55159404032328, 5.644308793374809, 5.88781751618415, 10.554552249386486, 5.616794557149185, 6.575348172904468, 7.806772118125624, 9.105346312528312), # 131
(8.76139618041726, 7.121758275492944, 8.848760609325746, 10.295709897928587, 10.175348540312154, 5.540828656798102, 5.618729449428725, 5.878059528827073, 10.537710369473654, 5.596395772876765, 6.552863817298364, 7.781769036535342, 9.079737582130376), # 132
(8.724773593943663, 7.083508910853635, 8.825693926876983, 10.263056540414452, 10.146551381963686, 5.529870468914266, 5.592880816016989, 5.868298321876132, 10.520580791898526, 5.575743266376432, 6.53009624423046, 7.756344663397592, 9.053483271325586), # 133
(8.687387388610095, 7.044863720263423, 8.802101840163804, 10.229736033179103, 10.116978521166592, 5.518691872837765, 5.566717421667779, 5.858487455128944, 10.503114215763128, 5.5547951792591235, 6.506996955877678, 7.730453028357666, 9.026553086525583), # 134
(8.649201198639354, 7.005762320755524, 8.777944937860909, 10.195695392283579, 10.08659983416412, 5.507265264734592, 5.540193794909268, 5.84858048838312, 10.48526134016948, 5.533509653135776, 6.483517454416942, 7.704048161060852, 8.99891673414202), # 135
(8.610178658254235, 6.966144329363159, 8.753183808643008, 10.160881633788906, 10.055385197199517, 5.495563040770739, 5.513264464269635, 5.838530981436277, 10.466972864219606, 5.511844829617322, 6.459609242025177, 7.677084091152441, 8.970543920586536), # 136
(8.570283401677534, 6.925949363119547, 8.72777904118481, 10.125241773756125, 10.023304486516034, 5.483557597112198, 5.485883958277055, 5.828292494086029, 10.448199487015533, 5.4897588503147015, 6.435223820879306, 7.649514848277719, 8.941404352270776), # 137
(8.529479063132047, 6.885117039057908, 8.701691224161017, 10.088722828246263, 9.990327578356919, 5.471221329924964, 5.458006805459704, 5.81781858612999, 10.428891907659281, 5.4672098568388465, 6.410312693156252, 7.621294462081978, 8.91146773560639), # 138
(8.487729276840568, 6.843586974211461, 8.67488094624634, 10.051271813320358, 9.956424348965415, 5.458526635375026, 5.429587534345759, 5.807062817365774, 10.409000825252871, 5.444155990800697, 6.38482736103294, 7.592376962210506, 8.880703777005019), # 139
(8.444997677025897, 6.801298785613425, 8.647308796115487, 10.012835745039444, 9.92156467458478, 5.445445909628379, 5.400580673463397, 5.795978747590996, 10.388476938898332, 5.420555393811186, 6.358719326686294, 7.562716378308592, 8.849082182878314), # 140
(8.40124789791083, 6.758192090297021, 8.61893536244316, 9.973361639464553, 9.885718431458253, 5.431951548851015, 5.370940751340795, 5.78451993660327, 10.36727094769768, 5.396366207481251, 6.331940092293238, 7.532266740021525, 8.816572659637913), # 141
(8.356443573718156, 6.714206505295466, 8.58972123390407, 9.93279651265672, 9.848855495829087, 5.418015949208927, 5.340622296506126, 5.772639944200211, 10.345333550752942, 5.371546573421828, 6.304441160030697, 7.500982076994594, 8.783144913695466), # 142
(8.310548338670674, 6.669281647641981, 8.559626999172925, 9.891087380676975, 9.810945743940529, 5.403611506868106, 5.3095798374875685, 5.760292330179432, 10.322615447166147, 5.3460546332438525, 6.276174032075593, 7.4688164188730894, 8.748768651462617), # 143
(8.263525826991184, 6.623357134369786, 8.528613246924428, 9.848181259586356, 9.771959052035829, 5.388710617994547, 5.277767902813299, 5.747430654338549, 10.29906733603931, 5.31984852855826, 6.247090210604851, 7.435723795302299, 8.713413579351014), # 144
(8.215339672902477, 6.576372582512099, 8.496640565833289, 9.804025165445895, 9.731865296358233, 5.3732856787542405, 5.245141021011493, 5.734008476475176, 10.274639916474454, 5.292886400975988, 6.217141197795395, 7.401658235927513, 8.6770494037723), # 145
(8.16595351062735, 6.528267609102142, 8.463669544574216, 9.758566114316626, 9.690634353150992, 5.35730908531318, 5.21165372061033, 5.719979356386927, 10.249283887573606, 5.2651263921079705, 6.186278495824149, 7.3665737703940195, 8.639645831138118), # 146
(8.1153309743886, 6.47898183117313, 8.42966077182191, 9.71175112225958, 9.648236098657351, 5.340753233837358, 5.177260530137981, 5.705296853871415, 10.22294994843879, 5.236526643565146, 6.154453606868036, 7.3304244283471105, 8.601172567860118), # 147
(8.063435698409021, 6.428454865758288, 8.394574836251083, 9.663527205335797, 9.604640409120561, 5.323590520492767, 5.1419159781226265, 5.689914528726257, 10.195588798172029, 5.207045296958447, 6.1216180331039824, 7.29316423943207, 8.561599320349941), # 148
(8.010231316911412, 6.37662632989083, 8.358372326536443, 9.613841379606303, 9.55981716078387, 5.3057933414453995, 5.105574593092441, 5.673785940749067, 10.167151135875338, 5.176640493898813, 6.08772327670891, 7.254747233294191, 8.520895795019237), # 149
(7.955681464118564, 6.323435840603979, 8.321013831352694, 9.562640661132138, 9.513736229890526, 5.287334092861249, 5.0681909035756005, 5.656864649737456, 10.137587660650752, 5.1452703759971765, 6.0527208398597425, 7.215127439578763, 8.479031698279647), # 150
(7.899749774253275, 6.268823014930954, 8.282459939374542, 9.50987206597433, 9.466367492683776, 5.268185170906305, 5.029719438100283, 5.639104215489043, 10.106849071600289, 5.112893084864478, 6.016562224733405, 7.174258887931072, 8.435976736542818), # 151
(7.842399881538343, 6.212727469904973, 8.242671239276701, 9.455482610193918, 9.417680825406869, 5.2483189717465635, 4.9901147251946645, 5.620458197801441, 10.07488606782597, 5.079466762111649, 5.979198933506821, 7.132095607996409, 8.391700616220398), # 152
(7.78359542019656, 6.155088822559256, 8.201608319733868, 9.399419309851933, 9.367646104303056, 5.2277078915480155, 4.949331293386919, 5.600880156472262, 10.041649348429823, 5.044949549349629, 5.940582468356916, 7.088591629420064, 8.346173043724027), # 153
(7.723300024450729, 6.095846689927024, 8.159231769420758, 9.34162918100941, 9.31623320561558, 5.206324326476654, 4.907323671205228, 5.580323651299123, 10.007089612513866, 5.009299588189353, 5.900664331460612, 7.043700981847325, 8.299363725465357), # 154
(7.6614773285236355, 6.034940689041495, 8.115502177012075, 9.282059239727378, 9.263412005587696, 5.184140672698471, 4.864046387177761, 5.558742242079636, 9.971157559180128, 4.972475020241754, 5.859396024994833, 6.997377694923482, 8.251242367856026), # 155
(7.598090966638081, 5.972310436935888, 8.070380131182526, 9.220656502066875, 9.209152380462648, 5.161129326379461, 4.8194539698327, 5.5360894886114185, 9.933803887530626, 4.934433987117773, 5.816729051136504, 6.949575798293822, 8.201778677307685), # 156
(7.533104573016862, 5.907895550643423, 8.023826220606818, 9.157367984088937, 9.153424206483685, 5.137262683685614, 4.773500947698219, 5.512318950692082, 9.894979296667389, 4.895134630428341, 5.772614912062549, 6.900249321603637, 8.150942360231976), # 157
(7.464680946405239, 5.840453120772258, 7.973591953902355, 9.089769581651243, 9.093681105870997, 5.11102447631711, 4.725106720927857, 5.485796952349372, 9.851662091599097, 4.8533659162911436, 5.7255957525389425, 6.847599564194339, 8.096485859415345), # 158
(7.382286766978402, 5.763065319599478, 7.906737818402988, 9.003977158788453, 9.015191309781628, 5.073689648007103, 4.668212763385716, 5.4472135327643825, 9.786427261222144, 4.802280994098745, 5.667416935618994, 6.781362523683108, 8.025427646920194), # 159
(7.284872094904309, 5.675096728540714, 7.821920957955888, 8.89857751040886, 8.916420131346795, 5.024341296047684, 4.602243748383784, 5.3955991895273465, 9.697425227228651, 4.741205651862893, 5.59725950860954, 6.700501948887847, 7.93642060889358), # 160
(7.17322205458596, 5.577120868080469, 7.720046971910309, 8.774572503756728, 8.798393124282113, 4.963577241570314, 4.527681446006876, 5.33160053310978, 9.585829766999018, 4.6706581931709374, 5.515741654599707, 6.605767468907571, 7.830374044819097), # 161
(7.048121770426357, 5.469711258703239, 7.602021459615496, 8.632964006076326, 8.662135842303204, 4.891995305706455, 4.445007626339809, 5.255864173983202, 9.452814657913637, 4.5911569216102315, 5.42348155667862, 6.497908712841293, 7.708197254180333), # 162
(6.9103563668284975, 5.353441420893524, 7.468750020420702, 8.474753884611934, 8.508673839125688, 4.810193309587572, 4.354704059467401, 5.169036722619125, 9.299553677352906, 4.503220140768125, 5.321097397935408, 6.3776753097880325, 7.570799536460879), # 163
(6.760710968195384, 5.228884875135821, 7.321138253675176, 8.300944006607818, 8.339032668465189, 4.718769074345129, 4.257252515474466, 5.071764789489069, 9.127220602697223, 4.407366154231968, 5.209207361459196, 6.245816888846803, 7.419090191144328), # 164
(6.599970698930017, 5.096615141914632, 7.160091758728169, 8.112536239308252, 8.154237884037324, 4.618320421110586, 4.153134764445822, 4.964694985064546, 8.93698921132698, 4.3041132655891134, 5.088429630339111, 6.10308307911662, 7.25397851771427), # 165
(6.428920683435397, 4.957205741714454, 6.9865161349289275, 7.910532449957501, 7.955315039557714, 4.509445171015408, 4.042832576466286, 4.848473919817077, 8.730033280622573, 4.193979778426912, 4.959382387664279, 5.950223509696501, 7.0763738156542955), # 166
(6.248346046114523, 4.811230195019787, 6.801316981626704, 7.695934505799843, 7.74328968874198, 4.392741145191058, 3.9268277216206746, 4.723748204218176, 8.5075265879644, 4.077483996332714, 4.822683816523827, 5.7879878096854585, 6.887185384447996), # 167
(6.059031911370395, 4.659262022315128, 6.605399898170748, 7.469744274079546, 7.519187385305742, 4.268806164768999, 3.805601969993804, 4.5911644487393595, 8.270642910732855, 3.955144222893872, 4.678952100006881, 5.617125608182511, 6.6873225235789615), # 168
(5.861763403606015, 4.501874744084979, 6.399670483910309, 7.232963622040883, 7.28403368296462, 4.138238050880695, 3.6796370916704917, 4.451369263852145, 8.020556026308338, 3.8274787616977366, 4.528805421202568, 5.438386534286672, 6.477694532530785), # 169
(5.657325647224384, 4.339641880813837, 6.185034338194635, 6.98659441692812, 7.038854135434233, 4.001634624657607, 3.549414856735553, 4.305009260028047, 7.7584397120712385, 3.6950059163316578, 4.372861963200016, 5.252520217096959, 6.259210710787055), # 170
(5.4465037666285, 4.173136952986201, 5.962397060372978, 6.731638525985535, 6.784674296430206, 3.8595937072311983, 3.4154170352738054, 4.152731047738583, 7.485467745401956, 3.5582439903829886, 4.211739909088348, 5.060276285712386, 6.032780357831365), # 171
(5.230082886221365, 4.002933481086569, 5.7326642497945866, 6.4690978164573965, 6.5225197196681535, 3.7127131197329337, 3.2781253973700655, 3.9951812374552707, 7.202813903680886, 3.41771128743908, 4.046057441956694, 4.862404369231971, 5.799312773147303), # 172
(5.00884813040598, 3.8296049855994423, 5.4967415058087115, 6.1999741555879755, 6.253415958863702, 3.5615906832942748, 3.1380217131091497, 3.8330064396496235, 6.911651964288422, 3.2739261110872815, 3.8764327448941778, 4.659654096754725, 5.5597172562184625), # 173
(4.783584623585344, 3.653724987009318, 5.2555344277646014, 5.9252694106215404, 5.978388567732466, 3.406824219046685, 2.9955877525758754, 3.6668532647931604, 6.613155704604964, 3.1274067649149466, 3.7034840009899277, 4.452775097379668, 5.314903106528433), # 174
(4.555077490162455, 3.4758670058006946, 5.009948615011508, 5.645985448802367, 5.698463099990069, 3.2490115481216284, 2.851305285855058, 3.497368323357396, 6.308498902010905, 2.9786715525094243, 3.5278293933330693, 4.242517000205814, 5.0657796235608075), # 175
(4.324111854540319, 3.296604562458073, 4.760889666898678, 5.363124137374725, 5.41466510935213, 3.0887504916505666, 2.705656083031515, 3.325198225813849, 5.998855333886642, 2.828238777458067, 3.35008710501273, 4.029629434332179, 4.813256106799174), # 176
(4.0914728411219325, 3.1165111774659513, 4.5092631827753635, 5.077687343582883, 5.128020149534273, 2.9266388707649633, 2.5591219141900625, 3.1509895826340326, 5.68539877761257, 2.6766267433482245, 3.1708753191180357, 3.8148620288577786, 4.5582418557271245), # 177
(3.8579455743102966, 2.9361603713088282, 4.255974761990814, 4.790676934671116, 4.8395537742521135, 2.7632745065962827, 2.4121845494155174, 2.9753890042894655, 5.3693030105690855, 2.52435375376725, 2.9908122187381125, 3.598964412881627, 4.301646169828252), # 178
(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), # 179
)
passenger_arriving_acc = (
(2, 7, 5, 2, 8, 1, 2, 1, 2, 1, 0, 1, 0, 11, 2, 3, 4, 3, 0, 0, 0, 1, 0, 0, 0, 0), # 0
(12, 12, 9, 6, 15, 5, 4, 1, 3, 4, 0, 1, 0, 17, 10, 7, 7, 5, 3, 5, 3, 2, 1, 0, 0, 0), # 1
(20, 18, 20, 10, 17, 7, 8, 4, 6, 4, 1, 2, 0, 21, 14, 12, 10, 11, 4, 6, 3, 6, 1, 2, 0, 0), # 2
(26, 22, 23, 14, 20, 8, 9, 4, 8, 10, 2, 2, 0, 28, 19, 14, 16, 13, 7, 8, 7, 6, 1, 3, 0, 0), # 3
(31, 32, 25, 22, 20, 10, 15, 5, 9, 12, 2, 2, 0, 31, 24, 18, 18, 19, 9, 8, 7, 10, 5, 3, 1, 0), # 4
(42, 39, 30, 23, 26, 11, 18, 6, 11, 14, 3, 2, 0, 42, 30, 21, 23, 23, 13, 11, 10, 12, 7, 4, 1, 0), # 5
(51, 48, 32, 30, 37, 11, 19, 8, 14, 14, 6, 2, 0, 46, 34, 30, 24, 36, 16, 13, 12, 15, 9, 5, 2, 0), # 6
(58, 53, 37, 36, 45, 13, 23, 11, 16, 14, 6, 2, 0, 53, 39, 37, 27, 42, 21, 14, 14, 19, 12, 6, 2, 0), # 7
(67, 59, 45, 46, 52, 14, 24, 12, 19, 16, 8, 5, 0, 65, 42, 47, 32, 48, 27, 18, 14, 24, 13, 6, 2, 0), # 8
(74, 68, 57, 55, 56, 15, 29, 17, 22, 19, 10, 7, 0, 71, 54, 54, 38, 53, 34, 22, 17, 26, 15, 6, 2, 0), # 9
(83, 77, 67, 69, 62, 20, 35, 19, 24, 21, 10, 9, 0, 79, 60, 64, 43, 57, 36, 26, 18, 27, 19, 7, 3, 0), # 10
(89, 87, 72, 73, 66, 21, 39, 25, 29, 22, 11, 11, 0, 89, 70, 71, 47, 64, 38, 30, 22, 32, 20, 8, 5, 0), # 11
(100, 94, 80, 81, 69, 21, 43, 29, 36, 22, 11, 12, 0, 95, 79, 75, 53, 70, 43, 34, 26, 38, 23, 9, 7, 0), # 12
(107, 103, 85, 94, 76, 28, 45, 33, 37, 23, 13, 13, 0, 106, 85, 83, 58, 78, 46, 37, 32, 41, 24, 12, 8, 0), # 13
(109, 113, 93, 107, 82, 31, 47, 36, 39, 26, 14, 13, 0, 118, 94, 86, 65, 83, 49, 38, 33, 46, 25, 13, 9, 0), # 14
(115, 129, 102, 115, 90, 35, 49, 37, 42, 26, 17, 15, 0, 123, 99, 94, 69, 88, 56, 43, 37, 50, 30, 16, 9, 0), # 15
(125, 141, 108, 124, 98, 39, 55, 41, 44, 27, 18, 17, 0, 131, 112, 100, 75, 93, 61, 47, 41, 54, 33, 16, 9, 0), # 16
(131, 150, 117, 131, 104, 42, 57, 41, 49, 27, 19, 17, 0, 141, 118, 111, 81, 102, 65, 51, 44, 61, 36, 18, 11, 0), # 17
(140, 154, 126, 139, 112, 46, 58, 43, 55, 29, 19, 18, 0, 150, 128, 118, 84, 107, 72, 53, 46, 66, 39, 19, 11, 0), # 18
(148, 167, 141, 149, 120, 49, 61, 45, 55, 30, 20, 20, 0, 157, 141, 127, 87, 115, 76, 58, 46, 69, 40, 23, 13, 0), # 19
(162, 176, 152, 157, 128, 51, 63, 48, 60, 30, 22, 22, 0, 169, 155, 131, 92, 122, 81, 65, 47, 73, 44, 24, 13, 0), # 20
(170, 183, 157, 171, 135, 54, 66, 53, 64, 32, 25, 22, 0, 183, 161, 137, 97, 131, 88, 72, 50, 76, 46, 24, 16, 0), # 21
(186, 194, 169, 186, 139, 60, 68, 56, 69, 32, 28, 23, 0, 194, 175, 142, 104, 137, 97, 78, 51, 78, 50, 24, 16, 0), # 22
(210, 202, 177, 196, 143, 65, 71, 60, 71, 35, 31, 24, 0, 210, 183, 148, 106, 139, 100, 82, 53, 82, 54, 27, 16, 0), # 23
(221, 212, 183, 203, 151, 69, 74, 65, 75, 37, 33, 24, 0, 225, 193, 153, 115, 146, 105, 87, 57, 86, 56, 27, 17, 0), # 24
(230, 220, 191, 208, 159, 72, 77, 70, 78, 38, 35, 25, 0, 232, 202, 165, 120, 148, 106, 90, 61, 89, 58, 28, 17, 0), # 25
(237, 232, 203, 219, 168, 73, 85, 75, 81, 41, 37, 25, 0, 239, 210, 172, 125, 156, 118, 92, 65, 91, 59, 30, 17, 0), # 26
(243, 239, 209, 234, 175, 77, 90, 80, 83, 42, 37, 25, 0, 249, 218, 182, 131, 165, 122, 95, 67, 96, 62, 31, 17, 0), # 27
(254, 250, 220, 241, 182, 81, 92, 89, 88, 43, 38, 25, 0, 259, 229, 186, 136, 180, 130, 97, 68, 97, 63, 32, 18, 0), # 28
(261, 260, 227, 252, 191, 86, 96, 93, 91, 46, 39, 25, 0, 268, 237, 195, 145, 185, 131, 100, 69, 100, 68, 33, 20, 0), # 29
(276, 267, 235, 261, 197, 89, 100, 102, 95, 51, 40, 26, 0, 272, 249, 201, 153, 190, 136, 103, 72, 103, 70, 35, 20, 0), # 30
(285, 279, 247, 268, 206, 94, 102, 106, 98, 54, 43, 26, 0, 282, 254, 209, 158, 196, 143, 110, 74, 107, 74, 36, 20, 0), # 31
(290, 290, 252, 279, 211, 96, 106, 108, 104, 54, 45, 27, 0, 295, 262, 219, 164, 204, 146, 117, 77, 111, 75, 38, 20, 0), # 32
(301, 301, 260, 287, 222, 100, 116, 114, 108, 55, 47, 27, 0, 303, 274, 223, 174, 213, 151, 121, 80, 115, 82, 40, 20, 0), # 33
(307, 311, 269, 297, 227, 102, 120, 117, 114, 58, 47, 29, 0, 313, 284, 229, 180, 224, 156, 127, 86, 121, 85, 40, 22, 0), # 34
(314, 318, 277, 303, 234, 104, 126, 119, 120, 60, 50, 29, 0, 322, 292, 236, 183, 229, 163, 134, 91, 125, 86, 43, 23, 0), # 35
(317, 325, 286, 313, 239, 111, 128, 124, 126, 64, 50, 30, 0, 332, 301, 242, 189, 238, 165, 136, 91, 129, 88, 45, 24, 0), # 36
(327, 332, 294, 333, 242, 115, 130, 131, 129, 65, 52, 31, 0, 342, 307, 247, 193, 247, 170, 138, 97, 134, 91, 47, 25, 0), # 37
(335, 342, 303, 339, 249, 119, 135, 134, 131, 66, 52, 31, 0, 353, 313, 251, 199, 253, 180, 141, 99, 137, 96, 47, 27, 0), # 38
(344, 348, 306, 348, 255, 123, 137, 140, 137, 68, 54, 31, 0, 363, 325, 263, 203, 257, 180, 143, 101, 145, 98, 51, 27, 0), # 39
(355, 356, 313, 356, 260, 124, 140, 145, 144, 69, 54, 31, 0, 372, 331, 274, 211, 268, 182, 147, 105, 146, 100, 51, 28, 0), # 40
(364, 361, 328, 368, 267, 126, 143, 146, 145, 69, 54, 31, 0, 381, 346, 279, 217, 276, 184, 150, 110, 148, 101, 51, 30, 0), # 41
(378, 370, 332, 376, 274, 129, 145, 151, 148, 69, 56, 31, 0, 390, 349, 280, 220, 286, 188, 154, 113, 151, 104, 55, 30, 0), # 42
(388, 373, 338, 390, 284, 132, 147, 156, 152, 70, 58, 32, 0, 404, 356, 289, 228, 293, 196, 157, 118, 156, 106, 57, 30, 0), # 43
(399, 386, 347, 395, 293, 135, 153, 163, 158, 70, 58, 32, 0, 416, 364, 293, 230, 302, 203, 162, 119, 157, 109, 57, 30, 0), # 44
(411, 398, 351, 406, 302, 136, 154, 169, 159, 70, 59, 32, 0, 419, 370, 296, 238, 312, 207, 165, 121, 161, 115, 57, 30, 0), # 45
(417, 410, 356, 412, 307, 142, 157, 173, 162, 72, 60, 33, 0, 427, 375, 300, 241, 320, 211, 168, 122, 164, 122, 58, 31, 0), # 46
(431, 417, 366, 420, 316, 144, 158, 176, 168, 73, 61, 34, 0, 435, 381, 305, 246, 327, 216, 172, 124, 172, 124, 58, 33, 0), # 47
(442, 422, 374, 430, 322, 145, 161, 180, 173, 76, 61, 34, 0, 443, 387, 314, 252, 341, 223, 183, 129, 173, 126, 60, 34, 0), # 48
(451, 425, 383, 439, 328, 147, 166, 187, 178, 78, 62, 34, 0, 450, 395, 319, 258, 351, 227, 188, 133, 178, 128, 63, 40, 0), # 49
(460, 434, 394, 446, 337, 151, 168, 193, 186, 80, 62, 34, 0, 460, 402, 328, 266, 356, 231, 194, 136, 183, 129, 63, 42, 0), # 50
(469, 442, 406, 458, 348, 156, 173, 199, 191, 84, 62, 34, 0, 476, 408, 332, 274, 366, 237, 196, 139, 187, 133, 66, 42, 0), # 51
(473, 448, 415, 467, 355, 158, 176, 203, 194, 88, 65, 35, 0, 485, 418, 338, 276, 370, 245, 198, 139, 189, 137, 67, 42, 0), # 52
(482, 459, 421, 472, 362, 161, 180, 203, 198, 88, 67, 36, 0, 494, 430, 346, 280, 381, 249, 203, 139, 189, 138, 69, 43, 0), # 53
(487, 472, 429, 480, 368, 164, 182, 205, 202, 93, 69, 38, 0, 500, 435, 351, 287, 388, 254, 208, 139, 193, 140, 71, 43, 0), # 54
(503, 479, 437, 490, 375, 169, 183, 211, 208, 97, 71, 40, 0, 507, 446, 355, 292, 395, 258, 210, 140, 198, 144, 73, 43, 0), # 55
(514, 490, 445, 496, 380, 175, 185, 213, 212, 98, 74, 41, 0, 517, 456, 359, 299, 404, 263, 214, 142, 202, 145, 74, 44, 0), # 56
(527, 504, 448, 508, 387, 181, 189, 216, 214, 98, 74, 42, 0, 523, 469, 361, 309, 412, 269, 219, 147, 204, 149, 77, 45, 0), # 57
(530, 510, 454, 516, 399, 186, 192, 219, 219, 99, 75, 43, 0, 533, 471, 370, 314, 423, 275, 220, 148, 207, 151, 78, 46, 0), # 58
(536, 520, 460, 526, 406, 189, 195, 221, 223, 100, 76, 44, 0, 550, 480, 381, 320, 431, 279, 223, 148, 207, 154, 79, 46, 0), # 59
(546, 529, 472, 539, 410, 192, 201, 226, 230, 100, 76, 45, 0, 564, 487, 387, 326, 439, 284, 231, 152, 210, 156, 80, 48, 0), # 60
(559, 539, 477, 545, 416, 194, 202, 230, 234, 104, 78, 47, 0, 572, 491, 398, 331, 450, 287, 233, 153, 214, 158, 84, 48, 0), # 61
(568, 551, 482, 553, 426, 199, 205, 234, 238, 108, 82, 47, 0, 581, 500, 407, 335, 457, 294, 238, 154, 216, 159, 86, 49, 0), # 62
(585, 560, 489, 564, 432, 201, 209, 236, 241, 110, 83, 48, 0, 588, 509, 418, 340, 467, 299, 240, 155, 221, 164, 87, 51, 0), # 63
(593, 567, 496, 573, 441, 208, 215, 244, 244, 113, 86, 49, 0, 596, 515, 422, 344, 480, 308, 243, 156, 224, 169, 88, 51, 0), # 64
(609, 575, 506, 578, 450, 214, 222, 248, 246, 116, 89, 49, 0, 606, 521, 429, 348, 488, 312, 249, 157, 226, 172, 89, 52, 0), # 65
(617, 591, 519, 586, 455, 216, 225, 252, 251, 117, 90, 49, 0, 615, 537, 432, 353, 496, 316, 251, 159, 229, 178, 89, 52, 0), # 66
(624, 600, 528, 592, 462, 219, 227, 255, 255, 118, 92, 51, 0, 628, 546, 439, 359, 503, 316, 254, 161, 232, 182, 91, 53, 0), # 67
(635, 606, 537, 597, 467, 221, 228, 259, 257, 119, 92, 52, 0, 640, 553, 447, 365, 510, 322, 254, 162, 232, 183, 92, 54, 0), # 68
(641, 613, 545, 604, 472, 224, 230, 260, 261, 120, 92, 52, 0, 653, 566, 453, 370, 516, 324, 258, 165, 232, 185, 93, 55, 0), # 69
(650, 622, 553, 607, 481, 229, 230, 260, 263, 122, 93, 53, 0, 663, 573, 458, 374, 521, 327, 262, 167, 235, 186, 94, 55, 0), # 70
(655, 633, 563, 615, 488, 230, 233, 263, 268, 123, 95, 54, 0, 672, 581, 464, 377, 528, 330, 265, 172, 238, 190, 97, 55, 0), # 71
(663, 640, 568, 624, 494, 235, 240, 270, 272, 124, 95, 54, 0, 681, 586, 472, 378, 534, 335, 267, 176, 241, 192, 98, 56, 0), # 72
(674, 643, 576, 630, 507, 242, 247, 273, 274, 125, 97, 55, 0, 698, 598, 481, 379, 545, 342, 271, 178, 244, 193, 100, 56, 0), # 73
(682, 652, 583, 636, 517, 251, 251, 275, 278, 128, 97, 55, 0, 709, 604, 485, 382, 550, 345, 276, 179, 247, 193, 102, 57, 0), # 74
(698, 664, 586, 652, 528, 254, 252, 275, 284, 128, 97, 56, 0, 720, 612, 493, 389, 556, 350, 280, 182, 252, 199, 105, 58, 0), # 75
(712, 668, 595, 656, 533, 256, 259, 279, 288, 129, 98, 56, 0, 728, 621, 495, 392, 566, 356, 285, 182, 256, 201, 107, 59, 0), # 76
(718, 672, 599, 670, 541, 261, 264, 281, 292, 132, 100, 56, 0, 740, 629, 503, 400, 580, 358, 290, 186, 262, 204, 107, 60, 0), # 77
(721, 681, 608, 676, 549, 262, 267, 287, 300, 135, 101, 56, 0, 747, 634, 510, 404, 588, 360, 291, 186, 265, 207, 110, 62, 0), # 78
(726, 687, 619, 689, 553, 266, 268, 291, 305, 139, 104, 56, 0, 759, 640, 515, 412, 597, 370, 296, 191, 271, 209, 112, 65, 0), # 79
(741, 697, 625, 698, 565, 272, 270, 297, 307, 140, 105, 59, 0, 772, 649, 520, 418, 605, 374, 299, 196, 275, 211, 113, 66, 0), # 80
(754, 702, 631, 708, 576, 277, 274, 305, 310, 141, 105, 59, 0, 781, 655, 525, 421, 608, 379, 303, 197, 281, 212, 114, 66, 0), # 81
(768, 708, 637, 714, 585, 281, 279, 309, 313, 142, 105, 60, 0, 792, 664, 530, 427, 615, 383, 307, 199, 283, 214, 115, 66, 0), # 82
(776, 718, 647, 722, 591, 282, 284, 313, 319, 144, 107, 60, 0, 804, 676, 540, 431, 624, 388, 314, 201, 285, 220, 117, 66, 0), # 83
(788, 722, 654, 729, 599, 286, 288, 317, 320, 146, 107, 61, 0, 811, 688, 547, 431, 629, 393, 315, 202, 292, 224, 117, 66, 0), # 84
(798, 732, 656, 737, 605, 287, 290, 320, 322, 147, 111, 63, 0, 824, 696, 554, 435, 640, 401, 319, 205, 295, 225, 117, 66, 0), # 85
(812, 742, 660, 742, 612, 287, 294, 324, 330, 149, 111, 65, 0, 828, 708, 559, 439, 644, 402, 320, 209, 297, 228, 121, 66, 0), # 86
(820, 754, 666, 756, 619, 290, 299, 324, 334, 150, 113, 66, 0, 835, 718, 572, 444, 651, 410, 324, 212, 300, 231, 121, 66, 0), # 87
(833, 766, 676, 766, 625, 295, 302, 324, 339, 154, 113, 68, 0, 845, 724, 581, 446, 657, 414, 327, 216, 306, 233, 124, 67, 0), # 88
(836, 773, 686, 777, 632, 300, 307, 325, 341, 155, 113, 68, 0, 857, 733, 589, 448, 666, 421, 329, 220, 309, 235, 125, 68, 0), # 89
(851, 782, 694, 783, 638, 304, 311, 329, 347, 156, 115, 71, 0, 867, 736, 593, 453, 675, 428, 334, 222, 312, 237, 129, 69, 0), # 90
(860, 793, 702, 798, 647, 306, 314, 332, 351, 163, 116, 73, 0, 876, 752, 600, 462, 682, 431, 337, 228, 315, 239, 131, 69, 0), # 91
(873, 797, 707, 805, 650, 309, 317, 335, 357, 164, 121, 73, 0, 887, 755, 609, 463, 683, 433, 340, 228, 320, 240, 132, 70, 0), # 92
(882, 803, 711, 816, 656, 312, 319, 337, 360, 166, 122, 74, 0, 890, 759, 617, 467, 692, 435, 341, 231, 323, 245, 132, 70, 0), # 93
(897, 809, 719, 826, 663, 316, 321, 340, 365, 167, 122, 75, 0, 895, 764, 621, 472, 703, 440, 344, 233, 325, 246, 133, 70, 0), # 94
(906, 814, 726, 838, 670, 317, 327, 342, 369, 168, 125, 76, 0, 907, 770, 632, 474, 706, 443, 347, 236, 329, 247, 134, 70, 0), # 95
(913, 820, 732, 845, 676, 320, 331, 345, 373, 168, 126, 76, 0, 913, 779, 640, 477, 714, 448, 349, 239, 335, 248, 136, 72, 0), # 96
(922, 827, 742, 853, 689, 324, 334, 348, 378, 168, 130, 77, 0, 921, 789, 646, 482, 719, 456, 353, 242, 341, 252, 136, 73, 0), # 97
(932, 834, 753, 860, 692, 327, 338, 352, 382, 173, 132, 77, 0, 932, 797, 657, 484, 727, 459, 359, 244, 344, 256, 137, 73, 0), # 98
(947, 837, 759, 872, 699, 331, 341, 354, 387, 175, 134, 77, 0, 939, 806, 662, 488, 738, 460, 360, 245, 351, 259, 140, 73, 0), # 99
(955, 843, 766, 879, 708, 336, 345, 357, 389, 178, 135, 77, 0, 951, 813, 668, 494, 744, 463, 361, 247, 353, 260, 140, 73, 0), # 100
(962, 858, 773, 888, 718, 337, 353, 358, 393, 178, 137, 77, 0, 959, 819, 674, 499, 750, 463, 361, 247, 357, 265, 143, 74, 0), # 101
(975, 866, 782, 896, 721, 339, 357, 362, 398, 180, 141, 78, 0, 970, 824, 679, 505, 754, 469, 363, 247, 361, 266, 144, 75, 0), # 102
(986, 871, 791, 903, 725, 348, 363, 364, 399, 183, 142, 78, 0, 980, 829, 686, 508, 761, 475, 366, 248, 364, 270, 146, 75, 0), # 103
(996, 879, 799, 907, 734, 350, 369, 371, 406, 189, 142, 80, 0, 990, 837, 693, 517, 763, 478, 368, 252, 371, 271, 146, 75, 0), # 104
(1009, 882, 804, 913, 745, 353, 373, 372, 410, 189, 144, 80, 0, 1001, 845, 697, 518, 771, 485, 375, 256, 376, 273, 147, 75, 0), # 105
(1018, 889, 813, 924, 750, 354, 378, 376, 413, 189, 144, 81, 0, 1011, 855, 703, 523, 782, 486, 379, 259, 381, 273, 147, 77, 0), # 106
(1027, 894, 823, 930, 760, 360, 381, 381, 417, 191, 144, 81, 0, 1017, 865, 710, 530, 788, 489, 386, 261, 387, 277, 147, 78, 0), # 107
(1036, 901, 832, 938, 764, 364, 385, 384, 419, 193, 145, 82, 0, 1029, 875, 715, 538, 794, 494, 388, 262, 390, 280, 150, 81, 0), # 108
(1043, 911, 839, 943, 777, 366, 392, 385, 423, 195, 147, 83, 0, 1035, 884, 722, 540, 802, 496, 393, 264, 394, 285, 152, 82, 0), # 109
(1060, 916, 847, 951, 783, 368, 393, 387, 426, 196, 149, 85, 0, 1042, 895, 724, 543, 809, 500, 398, 267, 399, 285, 155, 83, 0), # 110
(1069, 924, 858, 961, 789, 375, 394, 389, 430, 198, 149, 86, 0, 1052, 902, 732, 547, 813, 503, 399, 270, 402, 290, 157, 84, 0), # 111
(1079, 927, 867, 969, 796, 378, 394, 391, 434, 200, 149, 89, 0, 1060, 910, 739, 552, 820, 509, 402, 270, 405, 293, 159, 84, 0), # 112
(1085, 936, 873, 976, 805, 381, 397, 393, 443, 200, 155, 89, 0, 1069, 920, 745, 559, 829, 510, 409, 272, 407, 295, 160, 84, 0), # 113
(1094, 940, 878, 983, 811, 382, 397, 397, 445, 201, 155, 89, 0, 1074, 926, 748, 566, 838, 513, 415, 272, 414, 297, 164, 86, 0), # 114
(1104, 948, 884, 987, 813, 385, 402, 399, 448, 203, 157, 90, 0, 1086, 936, 753, 568, 844, 514, 420, 275, 414, 299, 165, 87, 0), # 115
(1118, 952, 892, 990, 824, 392, 405, 402, 458, 204, 157, 90, 0, 1093, 944, 761, 572, 851, 515, 422, 275, 416, 302, 166, 87, 0), # 116
(1131, 957, 900, 992, 829, 399, 406, 406, 464, 205, 158, 92, 0, 1105, 958, 764, 574, 861, 520, 423, 277, 423, 308, 169, 88, 0), # 117
(1138, 963, 904, 1006, 833, 400, 412, 409, 466, 206, 158, 94, 0, 1116, 964, 770, 579, 870, 524, 426, 280, 427, 309, 169, 88, 0), # 118
(1149, 968, 914, 1014, 842, 402, 414, 411, 468, 206, 160, 94, 0, 1123, 970, 773, 584, 876, 528, 428, 282, 428, 312, 169, 88, 0), # 119
(1158, 973, 925, 1022, 849, 403, 420, 419, 468, 206, 161, 94, 0, 1135, 977, 779, 587, 881, 532, 430, 283, 434, 320, 172, 89, 0), # 120
(1163, 983, 933, 1029, 857, 409, 425, 422, 472, 206, 161, 94, 0, 1153, 984, 784, 590, 885, 536, 435, 287, 435, 323, 173, 89, 0), # 121
(1169, 987, 942, 1034, 869, 414, 427, 424, 478, 207, 161, 94, 0, 1170, 992, 791, 594, 893, 538, 438, 287, 437, 327, 174, 90, 0), # 122
(1172, 998, 949, 1041, 876, 421, 432, 425, 482, 209, 162, 95, 0, 1183, 996, 802, 598, 903, 540, 441, 288, 440, 333, 176, 91, 0), # 123
(1189, 1004, 957, 1054, 882, 426, 435, 428, 483, 209, 163, 95, 0, 1188, 1008, 809, 601, 915, 543, 445, 294, 445, 338, 177, 93, 0), # 124
(1198, 1010, 964, 1065, 885, 429, 437, 430, 487, 210, 164, 98, 0, 1193, 1016, 818, 603, 926, 547, 450, 296, 448, 339, 178, 93, 0), # 125
(1208, 1014, 970, 1073, 888, 432, 443, 433, 487, 213, 164, 99, 0, 1203, 1027, 820, 608, 937, 552, 455, 298, 452, 342, 179, 93, 0), # 126
(1217, 1023, 983, 1080, 891, 433, 444, 436, 490, 214, 165, 102, 0, 1213, 1032, 829, 613, 949, 554, 455, 299, 455, 342, 180, 93, 0), # 127
(1231, 1030, 992, 1093, 898, 436, 446, 441, 497, 215, 167, 104, 0, 1224, 1036, 835, 617, 953, 560, 461, 301, 456, 343, 180, 93, 0), # 128
(1240, 1035, 1003, 1102, 904, 439, 449, 442, 501, 215, 167, 104, 0, 1233, 1039, 842, 620, 955, 562, 464, 302, 459, 347, 183, 93, 0), # 129
(1248, 1037, 1009, 1110, 910, 440, 454, 443, 505, 215, 168, 105, 0, 1240, 1046, 845, 626, 965, 565, 468, 307, 461, 348, 184, 94, 0), # 130
(1258, 1045, 1019, 1120, 916, 446, 459, 447, 505, 218, 171, 105, 0, 1248, 1057, 848, 632, 969, 573, 472, 309, 464, 348, 184, 94, 0), # 131
(1266, 1052, 1024, 1133, 923, 450, 461, 454, 506, 218, 172, 105, 0, 1263, 1065, 858, 636, 980, 579, 472, 310, 466, 350, 185, 95, 0), # 132
(1282, 1059, 1028, 1146, 927, 452, 461, 455, 508, 219, 173, 105, 0, 1273, 1069, 862, 642, 986, 584, 476, 312, 470, 352, 187, 96, 0), # 133
(1291, 1064, 1032, 1153, 941, 459, 465, 460, 510, 222, 175, 107, 0, 1279, 1079, 869, 646, 993, 586, 479, 318, 474, 353, 187, 97, 0), # 134
(1302, 1074, 1041, 1159, 953, 463, 468, 463, 513, 222, 176, 108, 0, 1290, 1085, 871, 650, 1002, 593, 482, 318, 478, 356, 188, 97, 0), # 135
(1308, 1078, 1050, 1168, 960, 467, 472, 466, 516, 222, 178, 109, 0, 1304, 1095, 876, 653, 1012, 596, 486, 320, 482, 357, 191, 98, 0), # 136
(1320, 1089, 1054, 1176, 962, 470, 477, 468, 523, 222, 179, 109, 0, 1314, 1102, 883, 656, 1019, 601, 492, 322, 487, 359, 192, 99, 0), # 137
(1327, 1095, 1059, 1185, 967, 472, 479, 471, 528, 224, 180, 109, 0, 1321, 1109, 889, 660, 1023, 603, 495, 324, 489, 364, 193, 100, 0), # 138
(1334, 1099, 1070, 1195, 976, 475, 480, 475, 529, 226, 180, 110, 0, 1328, 1114, 898, 664, 1028, 607, 502, 330, 491, 368, 195, 102, 0), # 139
(1345, 1103, 1077, 1206, 986, 477, 483, 477, 533, 228, 182, 110, 0, 1335, 1125, 902, 666, 1032, 610, 503, 331, 493, 370, 197, 102, 0), # 140
(1351, 1104, 1082, 1217, 993, 478, 485, 478, 535, 232, 182, 110, 0, 1338, 1130, 906, 669, 1038, 615, 505, 333, 494, 372, 198, 102, 0), # 141
(1355, 1107, 1086, 1220, 1001, 480, 487, 482, 538, 232, 183, 111, 0, 1348, 1141, 910, 678, 1045, 616, 506, 336, 496, 374, 200, 104, 0), # 142
(1362, 1114, 1093, 1228, 1008, 484, 493, 482, 541, 232, 184, 112, 0, 1361, 1153, 912, 683, 1052, 618, 508, 336, 502, 376, 200, 104, 0), # 143
(1370, 1117, 1101, 1235, 1017, 490, 495, 482, 544, 233, 186, 112, 0, 1371, 1163, 918, 687, 1056, 622, 510, 340, 502, 379, 201, 105, 0), # 144
(1385, 1123, 1104, 1243, 1024, 494, 497, 486, 550, 236, 188, 112, 0, 1379, 1168, 925, 688, 1067, 624, 513, 341, 505, 382, 203, 105, 0), # 145
(1392, 1127, 1114, 1247, 1034, 495, 498, 486, 556, 237, 189, 113, 0, 1390, 1175, 928, 692, 1073, 627, 515, 344, 510, 384, 205, 105, 0), # 146
(1405, 1135, 1125, 1250, 1042, 502, 500, 487, 558, 237, 191, 113, 0, 1402, 1183, 932, 696, 1079, 628, 518, 345, 513, 385, 208, 106, 0), # 147
(1412, 1140, 1135, 1253, 1049, 504, 503, 493, 560, 238, 192, 114, 0, 1411, 1191, 937, 700, 1086, 631, 519, 345, 518, 387, 210, 106, 0), # 148
(1422, 1144, 1142, 1261, 1053, 507, 506, 496, 562, 239, 192, 114, 0, 1419, 1198, 946, 700, 1093, 634, 519, 346, 519, 391, 211, 106, 0), # 149
(1433, 1148, 1144, 1267, 1059, 512, 510, 497, 565, 240, 192, 114, 0, 1430, 1201, 951, 705, 1100, 638, 521, 348, 523, 392, 214, 106, 0), # 150
(1444, 1160, 1151, 1276, 1064, 516, 511, 503, 569, 240, 193, 115, 0, 1436, 1208, 957, 709, 1108, 642, 524, 352, 526, 396, 215, 106, 0), # 151
(1454, 1164, 1158, 1282, 1074, 520, 513, 505, 575, 241, 193, 115, 0, 1439, 1216, 966, 712, 1111, 644, 528, 356, 529, 399, 218, 107, 0), # 152
(1465, 1171, 1166, 1287, 1082, 524, 517, 507, 579, 242, 193, 115, 0, 1443, 1221, 971, 717, 1119, 648, 530, 358, 533, 402, 219, 108, 0), # 153
(1477, 1176, 1173, 1294, 1087, 529, 517, 509, 581, 244, 193, 115, 0, 1450, 1229, 975, 721, 1125, 650, 531, 363, 538, 404, 220, 111, 0), # 154
(1485, 1179, 1178, 1303, 1091, 531, 518, 514, 584, 244, 194, 115, 0, 1462, 1236, 978, 726, 1134, 654, 533, 364, 540, 408, 221, 112, 0), # 155
(1493, 1185, 1186, 1311, 1096, 536, 525, 515, 589, 244, 195, 115, 0, 1473, 1246, 980, 728, 1141, 660, 536, 366, 546, 410, 223, 112, 0), # 156
(1499, 1192, 1191, 1317, 1102, 538, 527, 516, 592, 245, 195, 115, 0, 1480, 1253, 982, 732, 1149, 662, 539, 369, 550, 413, 226, 112, 0), # 157
(1507, 1197, 1198, 1320, 1109, 543, 530, 521, 595, 245, 195, 115, 0, 1487, 1255, 988, 740, 1153, 664, 545, 370, 552, 415, 227, 112, 0), # 158
(1520, 1201, 1207, 1324, 1116, 545, 534, 521, 597, 245, 197, 116, 0, 1490, 1264, 993, 743, 1160, 668, 549, 371, 554, 419, 229, 113, 0), # 159
(1528, 1205, 1209, 1330, 1121, 551, 535, 526, 599, 246, 199, 116, 0, 1496, 1271, 997, 746, 1168, 670, 553, 372, 557, 422, 231, 113, 0), # 160
(1536, 1207, 1216, 1343, 1124, 557, 540, 529, 601, 247, 199, 116, 0, 1504, 1274, 1002, 749, 1175, 673, 555, 373, 558, 425, 231, 113, 0), # 161
(1542, 1211, 1226, 1352, 1131, 562, 542, 530, 603, 247, 199, 116, 0, 1511, 1283, 1006, 752, 1181, 675, 557, 375, 560, 428, 231, 113, 0), # 162
(1544, 1219, 1228, 1355, 1143, 565, 547, 535, 603, 248, 200, 116, 0, 1525, 1286, 1010, 754, 1190, 679, 563, 377, 561, 429, 233, 113, 0), # 163
(1552, 1222, 1236, 1363, 1149, 565, 549, 536, 605, 248, 202, 117, 0, 1531, 1292, 1018, 755, 1192, 682, 566, 380, 564, 429, 234, 113, 0), # 164
(1558, 1231, 1241, 1367, 1153, 568, 549, 537, 608, 249, 203, 118, 0, 1537, 1295, 1022, 758, 1199, 684, 567, 383, 568, 429, 235, 114, 0), # 165
(1564, 1233, 1246, 1374, 1161, 570, 549, 537, 611, 250, 206, 120, 0, 1547, 1303, 1029, 760, 1201, 692, 569, 384, 571, 432, 235, 114, 0), # 166
(1567, 1236, 1251, 1379, 1170, 573, 553, 541, 615, 252, 206, 121, 0, 1553, 1308, 1033, 764, 1206, 692, 570, 384, 575, 432, 236, 114, 0), # 167
(1574, 1242, 1259, 1387, 1178, 576, 555, 545, 617, 253, 207, 122, 0, 1556, 1310, 1035, 768, 1212, 695, 577, 387, 579, 433, 237, 114, 0), # 168
(1586, 1245, 1266, 1394, 1181, 578, 555, 549, 619, 255, 209, 122, 0, 1561, 1315, 1037, 773, 1218, 695, 580, 389, 582, 434, 238, 114, 0), # 169
(1595, 1247, 1270, 1402, 1185, 580, 557, 550, 622, 256, 209, 122, 0, 1567, 1319, 1046, 781, 1222, 695, 581, 391, 584, 434, 239, 115, 0), # 170
(1604, 1254, 1274, 1407, 1192, 584, 557, 551, 628, 258, 209, 122, 0, 1571, 1322, 1053, 783, 1227, 697, 583, 392, 589, 435, 239, 115, 0), # 171
(1607, 1255, 1281, 1416, 1196, 585, 557, 552, 632, 259, 210, 123, 0, 1579, 1327, 1055, 783, 1231, 698, 588, 394, 592, 438, 240, 115, 0), # 172
(1612, 1256, 1288, 1423, 1198, 589, 558, 554, 635, 259, 210, 125, 0, 1590, 1332, 1064, 785, 1237, 701, 588, 395, 597, 442, 242, 115, 0), # 173
(1613, 1260, 1291, 1426, 1201, 591, 559, 556, 635, 260, 210, 125, 0, 1597, 1336, 1067, 788, 1238, 702, 588, 395, 600, 443, 242, 115, 0), # 174
(1619, 1262, 1295, 1432, 1205, 592, 559, 558, 637, 260, 211, 125, 0, 1601, 1342, 1072, 788, 1242, 704, 590, 395, 601, 443, 242, 115, 0), # 175
(1627, 1267, 1296, 1438, 1210, 592, 560, 559, 637, 260, 212, 126, 0, 1607, 1347, 1076, 789, 1247, 705, 591, 397, 603, 445, 243, 115, 0), # 176
(1628, 1269, 1298, 1446, 1211, 593, 562, 560, 638, 261, 212, 126, 0, 1613, 1351, 1077, 791, 1248, 706, 592, 400, 603, 445, 243, 115, 0), # 177
(1634, 1276, 1303, 1449, 1212, 594, 562, 562, 640, 262, 212, 126, 0, 1619, 1355, 1082, 794, 1254, 710, 593, 400, 604, 447, 243, 115, 0), # 178
(1634, 1276, 1303, 1449, 1212, 594, 562, 562, 640, 262, 212, 126, 0, 1619, 1355, 1082, 794, 1254, 710, 593, 400, 604, 447, 243, 115, 0), # 179
)
passenger_arriving_rate = (
(5.020865578371768, 5.064847846385402, 4.342736024677089, 4.661000830397574, 3.7031237384064077, 1.8308820436884476, 2.0730178076869574, 1.938823405408093, 2.030033020722669, 0.9895037538805926, 0.7008775273142672, 0.4081595898588478, 0.0, 5.083880212578363, 4.489755488447325, 3.5043876365713356, 2.968511261641777, 4.060066041445338, 2.7143527675713304, 2.0730178076869574, 1.3077728883488913, 1.8515618692032039, 1.5536669434658585, 0.8685472049354179, 0.4604407133077639, 0.0), # 0
(5.354327152019974, 5.399222302966028, 4.629455492775127, 4.968858189957462, 3.948326891649491, 1.9518237573581576, 2.209734470631847, 2.066464051210712, 2.164081775444303, 1.0547451730692876, 0.7471826893260219, 0.4351013884011963, 0.0, 5.419791647439855, 4.786115272413158, 3.73591344663011, 3.164235519207862, 4.328163550888606, 2.8930496716949965, 2.209734470631847, 1.3941598266843982, 1.9741634458247455, 1.6562860633191545, 0.9258910985550255, 0.49083839117872996, 0.0), # 1
(5.686723008979731, 5.732269739983398, 4.915035237956178, 5.275490778498595, 4.192641982499829, 2.072282983465593, 2.345909253980352, 2.193593853293508, 2.297595602292516, 1.1197284437551367, 0.7933038581293855, 0.46193605433775464, 0.0, 5.75436482820969, 5.0812965977153, 3.9665192906469278, 3.3591853312654094, 4.595191204585032, 3.0710313946109116, 2.345909253980352, 1.480202131046852, 2.0963209912499146, 1.758496926166199, 0.9830070475912357, 0.5211154309075817, 0.0), # 2
(6.016757793146562, 6.062668793441743, 5.198342391099879, 5.579682305649055, 4.435107784001268, 2.191782029841316, 2.4810018208239777, 2.3197088156227115, 2.430045053640364, 1.1841956746065454, 0.8390580686378972, 0.4885571404108718, 0.0, 6.086272806254225, 5.374128544519589, 4.195290343189486, 3.5525870238196355, 4.860090107280728, 3.247592341871796, 2.4810018208239777, 1.5655585927437972, 2.217553892000634, 1.8598941018830188, 1.0396684782199759, 0.551151708494704, 0.0), # 3
(6.343136148415981, 6.389098099345293, 5.478244083085864, 5.880216481036927, 4.674763069197661, 2.3098432043158894, 2.6144718342542292, 2.444304942164548, 2.560900681860902, 1.24788897429192, 0.8842623557650959, 0.514858199362897, 0.0, 6.414188632939817, 5.6634401929918665, 4.42131177882548, 3.743666922875759, 5.121801363721804, 3.422026919030367, 2.6144718342542292, 1.6498880030827783, 2.3373815345988307, 1.9600721603456428, 1.095648816617173, 0.5808270999404813, 0.0), # 4
(6.66456271868351, 6.710236293698289, 5.753607444793765, 6.175877014290295, 4.910646611132853, 2.4259888147198754, 2.745778957362612, 2.566878236885247, 2.689633039327186, 1.310550451479666, 0.9287337544245222, 0.5407327839361791, 0.0, 6.736785359632827, 5.948060623297969, 4.64366877212261, 3.9316513544389973, 5.379266078654372, 3.593629531639346, 2.745778957362612, 1.7328491533713395, 2.4553233055664263, 2.058625671430099, 1.1507214889587531, 0.6100214812452991, 0.0), # 5
(6.979742147844666, 7.024762012504959, 6.023299607103222, 6.465447615037239, 5.141797182850695, 2.5397411688838374, 2.8743828532406313, 2.686924703751037, 2.8157126784122717, 1.3719222148381898, 0.9722892995297139, 0.5660744468730674, 0.0, 7.052736037699606, 6.22681891560374, 4.8614464976485685, 4.115766644514569, 5.631425356824543, 3.761694585251452, 2.8743828532406313, 1.8141008349170267, 2.5708985914253475, 2.1551492050124135, 1.2046599214206444, 0.6386147284095418, 0.0), # 6
(7.2873790797949685, 7.331353891769537, 6.286187700893863, 6.747711992905847, 5.367253557395036, 2.650622574638337, 2.9997431849797924, 2.8039403467281465, 2.9386101514892147, 1.4317463730358968, 1.0147460259942116, 0.5907767409159108, 0.0, 7.360713718506519, 6.498544150075018, 5.073730129971057, 4.2952391191076895, 5.877220302978429, 3.9255164854194056, 2.9997431849797924, 1.8933018390273837, 2.683626778697518, 2.249237330968616, 1.2572375401787725, 0.6664867174335943, 0.0), # 7
(7.586178158429934, 7.628690567496257, 6.54113885704533, 7.021453857524196, 5.586054507809724, 2.7581553398139356, 3.1213196156715988, 2.917421169782802, 3.0577960109310682, 1.4897650347411937, 1.0559209687315536, 0.6147332188070586, 0.0, 7.659391453419917, 6.762065406877643, 5.279604843657768, 4.469295104223581, 6.1155920218621365, 4.084389637695923, 3.1213196156715988, 1.970110957009954, 2.793027253904862, 2.3404846191747324, 1.3082277714090662, 0.6935173243178416, 0.0), # 8
(7.874844027645085, 7.915450675689353, 6.787020206437253, 7.285456918520376, 5.797238807138606, 2.861861772241199, 3.23857180840756, 3.0268631768812346, 3.1727408091108913, 1.5457203086224858, 1.0956311626552797, 0.6378374332888596, 0.0, 7.947442293806162, 7.016211766177453, 5.478155813276398, 4.637160925867456, 6.345481618221783, 4.237608447633728, 3.23857180840756, 2.044186980172285, 2.898619403569303, 2.4284856395067926, 1.3574040412874508, 0.7195864250626686, 0.0), # 9
(8.152081331335932, 8.190312852353056, 7.022698879949271, 7.538504885522466, 5.999845228425533, 2.961264179750688, 3.3509594262791773, 3.1317623719896712, 3.282915098401738, 1.599354303348179, 1.133693642678929, 0.6599829371036627, 0.0, 8.22353929103161, 7.259812308140289, 5.668468213394645, 4.798062910044536, 6.565830196803476, 4.384467320785539, 3.3509594262791773, 2.11518869982192, 2.9999226142127666, 2.5128349618408223, 1.4045397759898541, 0.7445738956684597, 0.0), # 10
(8.416594713398005, 8.451955733491605, 7.247042008461013, 7.779381468158547, 6.192912544714355, 3.055884870172965, 3.457942132377958, 3.2316147590743394, 3.3877894311766643, 1.6504091275866801, 1.1699254437160416, 0.6810632829938176, 0.0, 8.486355496462611, 7.491696112931993, 5.849627218580208, 4.951227382760039, 6.775578862353329, 4.524260662704076, 3.457942132377958, 2.1827749072664036, 3.0964562723571776, 2.5931271560528497, 1.4494084016922026, 0.7683596121356006, 0.0), # 11
(8.667088817726812, 8.699057955109222, 7.458916722852117, 8.006870376056709, 6.375479529048918, 3.1452461513385908, 3.5589795897954057, 3.325916342101467, 3.486834359808726, 1.6986268900063934, 1.2041436006801558, 0.7009720237016724, 0.0, 8.734563961465534, 7.710692260718395, 6.020718003400779, 5.095880670019179, 6.973668719617452, 4.656282878942054, 3.5589795897954057, 2.246604393813279, 3.187739764524459, 2.6689567920189035, 1.4917833445704234, 0.7908234504644749, 0.0), # 12
(8.902268288217876, 8.93029815321015, 7.657190154002218, 8.219755318845033, 6.546584954473067, 3.2288703310781304, 3.653531461623028, 3.414163125037284, 3.579520436670977, 1.7437496992757264, 1.2361651484848115, 0.7196027119695768, 0.0, 8.966837737406735, 7.915629831665344, 6.180825742424058, 5.2312490978271775, 7.159040873341954, 4.7798283750521975, 3.653531461623028, 2.306335950770093, 3.2732924772365335, 2.7399184396150114, 1.5314380308004438, 0.8118452866554684, 0.0), # 13
(9.120837768766716, 9.144354963798623, 7.840729432790956, 8.416820006151594, 6.705267594030659, 3.306279717222145, 3.7410574109523305, 3.4958511118480193, 3.6653182141364735, 1.785519664063084, 1.2658071220435476, 0.7368489005398801, 0.0, 9.181849875652563, 8.10533790593868, 6.329035610217737, 5.3565589921892505, 7.330636428272947, 4.894191556587227, 3.7410574109523305, 2.3616283694443894, 3.3526337970153297, 2.8056066687171985, 1.5681458865581912, 0.8313049967089657, 0.0), # 14
(9.321501903268855, 9.339907022878865, 8.008401690097953, 8.59684814760449, 6.850566220765538, 3.376996617601199, 3.821017100874813, 3.5704763064998986, 3.743698244578273, 1.823678893036873, 1.2928865562699035, 0.752604142154931, 0.0, 9.37827342756938, 8.27864556370424, 6.464432781349516, 5.471036679110618, 7.487396489156546, 4.998666829099858, 3.821017100874813, 2.4121404411437135, 3.425283110382769, 2.865616049201497, 1.6016803380195905, 0.8490824566253515, 0.0), # 15
(9.5029653356198, 9.51563296645512, 8.159074056802854, 8.758623452831788, 6.981519607721555, 3.4405433400458514, 3.892870194481988, 3.6375347129591504, 3.8141310803694286, 1.8579694948654994, 1.3172204860774188, 0.7667619895570784, 0.0, 9.554781444523545, 8.434381885127861, 6.586102430387094, 5.5739084845964975, 7.628262160738857, 5.092548598142811, 3.892870194481988, 2.4575309571756083, 3.4907598038607777, 2.9195411509439295, 1.6318148113605708, 0.8650575424050111, 0.0), # 16
(9.663932709715075, 9.670211430531618, 8.291613663785293, 8.900929631461583, 7.097166527942559, 3.4964421923866666, 3.9560763548653552, 3.6965223351920073, 3.8760872738829946, 1.8881335782173672, 1.3386259463796333, 0.7792159954886714, 0.0, 9.710046977881415, 8.571375950375383, 6.693129731898166, 5.6644007346521, 7.752174547765989, 5.17513126926881, 3.9560763548653552, 2.4974587088476192, 3.5485832639712793, 2.9669765438205284, 1.6583227327570589, 0.8791101300483289, 0.0), # 17
(9.803108669450204, 9.802321051112584, 8.404887641924901, 9.022550393121959, 7.1965457544723925, 3.5442154824542103, 4.010095245116426, 3.746935177164692, 3.929037377492032, 1.9139132517608846, 1.3569199720900849, 0.7898597126920597, 0.0, 9.842743079009345, 8.688456839612655, 6.784599860450424, 5.741739755282652, 7.858074754984064, 5.245709248030569, 4.010095245116426, 2.531582487467293, 3.5982728772361963, 3.0075167977073205, 1.6809775283849802, 0.8911200955556896, 0.0), # 18
(9.919197858720699, 9.910640464202265, 8.497763122101317, 9.122269447440985, 7.2786960603549105, 3.5833855180790386, 4.054386528326697, 3.7882692428434357, 3.9724519435695926, 1.9350506241644574, 1.3719195981223131, 0.7985866939095915, 0.0, 9.951542799273696, 8.784453633005505, 6.859597990611565, 5.80515187249337, 7.944903887139185, 5.30357693998081, 4.054386528326697, 2.55956108434217, 3.6393480301774552, 3.0407564824803295, 1.6995526244202632, 0.9009673149274788, 0.0), # 19
(10.010904921422082, 9.993848305804882, 8.569107235194169, 9.198870504046766, 7.342656218633962, 3.613474607091719, 4.088409867587681, 3.8200205361944657, 4.005801524488732, 1.95128780409649, 1.3834418593898585, 0.805290491883616, 0.0, 10.035119190040824, 8.858195410719775, 6.9172092969492915, 5.853863412289469, 8.011603048977465, 5.348028750672252, 4.088409867587681, 2.5810532907797996, 3.671328109316981, 3.0662901680155894, 1.713821447038834, 0.9085316641640803, 0.0), # 20
(10.076934501449866, 10.050623211924679, 8.6177871120831, 9.251137272567364, 7.387465002353392, 3.6340050573228124, 4.1116249259908795, 3.84168506118401, 4.028556672622507, 1.9623669002253892, 1.39130379080626, 0.8098646593564828, 0.0, 10.092145302677078, 8.90851125292131, 6.9565189540313, 5.887100700676166, 8.057113345245014, 5.378359085657614, 4.1116249259908795, 2.5957178980877234, 3.693732501176696, 3.0837124241891223, 1.72355742241662, 0.91369301926588, 0.0), # 21
(10.115991242699579, 10.079643818565883, 8.642669883647738, 9.277853462630876, 7.41216118455705, 3.644499176602881, 4.1234913666278, 3.852758821778298, 4.040187940343971, 1.968030021219561, 1.3953224272850568, 0.8122027490705409, 0.0, 10.121294188548827, 8.934230239775948, 6.976612136425284, 5.904090063658682, 8.080375880687942, 5.393862350489617, 4.1234913666278, 2.6032136975734863, 3.706080592278525, 3.09261782087696, 1.7285339767295478, 0.9163312562332622, 0.0), # 22
(10.13039336334264, 10.083079961133974, 8.645769318701419, 9.281198109567903, 7.418488037355065, 3.6458333333333335, 4.124902001129669, 3.8539557613168727, 4.0416420781893, 1.9686980681298587, 1.3958263395269568, 0.8124914647157445, 0.0, 10.125, 8.93740611187319, 6.9791316976347835, 5.906094204389575, 8.0832841563786, 5.395538065843622, 4.124902001129669, 2.604166666666667, 3.7092440186775324, 3.0937327031893016, 1.729153863740284, 0.9166436328303613, 0.0), # 23
(10.141012413034153, 10.08107561728395, 8.645262345679013, 9.280786458333335, 7.422071742409901, 3.6458333333333335, 4.124126906318083, 3.852291666666667, 4.041447222222222, 1.968287654320988, 1.39577076318743, 0.8124238683127573, 0.0, 10.125, 8.936662551440328, 6.978853815937151, 5.904862962962962, 8.082894444444443, 5.393208333333334, 4.124126906318083, 2.604166666666667, 3.7110358712049507, 3.0935954861111123, 1.7290524691358027, 0.9164614197530866, 0.0), # 24
(10.15140723021158, 10.077124771376313, 8.644261545496114, 9.279972029320987, 7.4255766303963355, 3.6458333333333335, 4.122599451303155, 3.8490226337448563, 4.041062242798354, 1.96747970964792, 1.3956605665710604, 0.8122904282883707, 0.0, 10.125, 8.935194711172077, 6.978302832855302, 5.902439128943758, 8.082124485596708, 5.388631687242799, 4.122599451303155, 2.604166666666667, 3.7127883151981678, 3.0933240097736636, 1.728852309099223, 0.9161022519433014, 0.0), # 25
(10.161577019048034, 10.071287780064015, 8.642780635573846, 9.278764081790122, 7.429002578947403, 3.6458333333333335, 4.120343359154361, 3.8442103909465026, 4.0404920781893, 1.9662876771833566, 1.3954967473084758, 0.8120929736320684, 0.0, 10.125, 8.933022709952752, 6.977483736542379, 5.898863031550069, 8.0809841563786, 5.381894547325103, 4.120343359154361, 2.604166666666667, 3.7145012894737013, 3.0929213605967085, 1.7285561271147696, 0.915571616369456, 0.0), # 26
(10.171520983716636, 10.063624999999998, 8.640833333333333, 9.277171874999999, 7.432349465696142, 3.6458333333333335, 4.117382352941177, 3.837916666666667, 4.039741666666666, 1.9647250000000003, 1.3952803030303031, 0.8118333333333335, 0.0, 10.125, 8.930166666666667, 6.976401515151515, 5.894175, 8.079483333333332, 5.373083333333334, 4.117382352941177, 2.604166666666667, 3.716174732848071, 3.0923906250000006, 1.7281666666666669, 0.914875, 0.0), # 27
(10.181238328390501, 10.054196787837219, 8.638433356195703, 9.275204668209877, 7.4356171682756, 3.6458333333333335, 4.113740155733075, 3.830203189300412, 4.038815946502057, 1.9628051211705537, 1.3950122313671698, 0.8115133363816492, 0.0, 10.125, 8.926646700198141, 6.9750611568358485, 5.88841536351166, 8.077631893004114, 5.3622844650205765, 4.113740155733075, 2.604166666666667, 3.7178085841378, 3.091734889403293, 1.7276866712391405, 0.9140178898033837, 0.0), # 28
(10.19072825724275, 10.043063500228623, 8.635594421582077, 9.272871720679012, 7.438805564318813, 3.6458333333333335, 4.109440490599533, 3.821131687242798, 4.037719855967078, 1.9605414837677189, 1.3946935299497027, 0.811134811766499, 0.0, 10.125, 8.922482929431489, 6.973467649748514, 5.881624451303155, 8.075439711934155, 5.349584362139917, 4.109440490599533, 2.604166666666667, 3.7194027821594067, 3.0909572402263383, 1.7271188843164156, 0.9130057727480568, 0.0), # 29
(10.199989974446497, 10.03028549382716, 8.63233024691358, 9.270182291666666, 7.441914531458824, 3.6458333333333335, 4.104507080610022, 3.8107638888888884, 4.036458333333333, 1.957947530864198, 1.39432519640853, 0.8106995884773662, 0.0, 10.125, 8.917695473251028, 6.9716259820426485, 5.873842592592593, 8.072916666666666, 5.335069444444444, 4.104507080610022, 2.604166666666667, 3.720957265729412, 3.0900607638888897, 1.7264660493827162, 0.9118441358024693, 0.0), # 30
(10.209022684174858, 10.01592312528578, 8.62865454961134, 9.267145640432098, 7.444943947328672, 3.6458333333333335, 4.09896364883402, 3.799161522633745, 4.035036316872428, 1.9550367055326936, 1.3939082283742779, 0.8102094955037343, 0.0, 10.125, 8.912304450541077, 6.969541141871389, 5.865110116598079, 8.070072633744855, 5.318826131687243, 4.09896364883402, 2.604166666666667, 3.722471973664336, 3.0890485468107003, 1.7257309099222682, 0.910538465935071, 0.0), # 31
(10.217825590600954, 10.00003675125743, 8.624581047096479, 9.263771026234568, 7.447893689561397, 3.6458333333333335, 4.092833918340999, 3.7863863168724285, 4.033458744855967, 1.951822450845908, 1.3934436234775742, 0.8096663618350862, 0.0, 10.125, 8.906329980185948, 6.96721811738787, 5.8554673525377225, 8.066917489711933, 5.3009408436214, 4.092833918340999, 2.604166666666667, 3.7239468447806985, 3.0879236754115236, 1.7249162094192958, 0.909094250114312, 0.0), # 32
(10.226397897897897, 9.98268672839506, 8.620123456790123, 9.260067708333333, 7.450763635790041, 3.6458333333333335, 4.086141612200436, 3.7725000000000004, 4.031730555555555, 1.9483182098765437, 1.392932379349046, 0.8090720164609053, 0.0, 10.125, 8.899792181069957, 6.96466189674523, 5.84495462962963, 8.06346111111111, 5.2815, 4.086141612200436, 2.604166666666667, 3.7253818178950207, 3.086689236111112, 1.724024691358025, 0.9075169753086421, 0.0), # 33
(10.23473881023881, 9.963933413351622, 8.615295496113397, 9.256044945987654, 7.453553663647644, 3.6458333333333335, 4.078910453481805, 3.7575643004115222, 4.029856687242798, 1.9445374256973027, 1.3923754936193207, 0.8084282883706753, 0.0, 10.125, 8.892711172077426, 6.961877468096604, 5.833612277091907, 8.059713374485597, 5.260590020576132, 4.078910453481805, 2.604166666666667, 3.726776831823822, 3.085348315329219, 1.7230590992226795, 0.9058121284865113, 0.0), # 34
(10.242847531796807, 9.943837162780063, 8.610110882487428, 9.25171199845679, 7.456263650767246, 3.6458333333333335, 4.071164165254579, 3.741640946502058, 4.0278420781893, 1.9404935413808875, 1.3917739639190256, 0.807737006553879, 0.0, 10.125, 8.88510707209267, 6.958869819595128, 5.821480624142661, 8.0556841563786, 5.238297325102881, 4.071164165254579, 2.604166666666667, 3.728131825383623, 3.0839039994855972, 1.7220221764974855, 0.9039851966163696, 0.0), # 35
(10.250723266745005, 9.922458333333331, 8.604583333333334, 9.247078125, 7.45889347478189, 3.6458333333333335, 4.062926470588235, 3.724791666666667, 4.025691666666666, 1.9362000000000004, 1.391128787878788, 0.8070000000000002, 0.0, 10.125, 8.877, 6.95564393939394, 5.8086, 8.051383333333332, 5.214708333333334, 4.062926470588235, 2.604166666666667, 3.729446737390945, 3.0823593750000007, 1.7209166666666669, 0.9020416666666666, 0.0), # 36
(10.258365219256524, 9.89985728166438, 8.598726566072246, 9.242152584876543, 7.4614430133246135, 3.6458333333333335, 4.054221092552247, 3.707078189300412, 4.023410390946502, 1.931670244627344, 1.3904409631292352, 0.8062190976985216, 0.0, 10.125, 8.868410074683737, 6.952204815646175, 5.79501073388203, 8.046820781893004, 5.189909465020577, 4.054221092552247, 2.604166666666667, 3.7307215066623067, 3.080717528292182, 1.7197453132144491, 0.8999870256058529, 0.0), # 37
(10.265772593504476, 9.876094364426155, 8.592554298125286, 9.23694463734568, 7.46391214402846, 3.6458333333333335, 4.04507175421609, 3.6885622427983544, 4.021003189300411, 1.92691771833562, 1.3897114873009937, 0.8053961286389272, 0.0, 10.125, 8.859357415028198, 6.948557436504967, 5.780753155006859, 8.042006378600822, 5.163987139917697, 4.04507175421609, 2.604166666666667, 3.73195607201423, 3.078981545781894, 1.7185108596250571, 0.8978267604023779, 0.0), # 38
(10.272944593661986, 9.851229938271604, 8.586080246913582, 9.231463541666667, 7.466300744526468, 3.6458333333333335, 4.035502178649238, 3.6693055555555554, 4.0184750000000005, 1.9219558641975314, 1.3889413580246914, 0.8045329218106996, 0.0, 10.125, 8.849862139917693, 6.944706790123457, 5.765867592592593, 8.036950000000001, 5.137027777777778, 4.035502178649238, 2.604166666666667, 3.733150372263234, 3.07715451388889, 1.7172160493827164, 0.8955663580246914, 0.0), # 39
(10.279880423902163, 9.82532435985368, 8.579318129858253, 9.225718557098766, 7.468608692451679, 3.6458333333333335, 4.025536088921165, 3.649369855967079, 4.015830761316872, 1.9167981252857802, 1.3881315729309558, 0.8036313062033228, 0.0, 10.125, 8.83994436823655, 6.940657864654778, 5.750394375857339, 8.031661522633744, 5.1091177983539104, 4.025536088921165, 2.604166666666667, 3.7343043462258394, 3.0752395190329227, 1.7158636259716507, 0.8932113054412438, 0.0), # 40
(10.286579288398128, 9.79843798582533, 8.57228166438043, 9.219718942901235, 7.4708358654371345, 3.6458333333333335, 4.015197208101347, 3.628816872427984, 4.0130754115226335, 1.9114579446730684, 1.3872831296504138, 0.8026931108062796, 0.0, 10.125, 8.829624218869075, 6.936415648252069, 5.734373834019204, 8.026150823045267, 5.0803436213991775, 4.015197208101347, 2.604166666666667, 3.7354179327185673, 3.073239647633746, 1.7144563328760862, 0.8907670896204848, 0.0), # 41
(10.293040391323, 9.770631172839506, 8.564984567901236, 9.213473958333335, 7.472982141115872, 3.6458333333333335, 4.004509259259259, 3.6077083333333335, 4.010213888888889, 1.9059487654320992, 1.3863970258136926, 0.8017201646090536, 0.0, 10.125, 8.818921810699589, 6.931985129068463, 5.717846296296297, 8.020427777777778, 5.050791666666667, 4.004509259259259, 2.604166666666667, 3.736491070557936, 3.0711579861111122, 1.7129969135802474, 0.8882391975308643, 0.0), # 42
(10.299262936849892, 9.741964277549155, 8.557440557841794, 9.206992862654321, 7.475047397120935, 3.6458333333333335, 3.993495965464375, 3.58610596707819, 4.007251131687243, 1.9002840306355744, 1.3854742590514195, 0.800714296601128, 0.0, 10.125, 8.807857262612407, 6.927371295257098, 5.700852091906722, 8.014502263374485, 5.020548353909466, 3.993495965464375, 2.604166666666667, 3.7375236985604676, 3.0689976208847747, 1.7114881115683587, 0.8856331161408324, 0.0), # 43
(10.305246129151927, 9.712497656607225, 8.549663351623229, 9.200284915123458, 7.477031511085363, 3.6458333333333335, 3.9821810497861696, 3.564071502057614, 4.0041920781893, 1.8944771833561962, 1.3845158269942222, 0.7996773357719861, 0.0, 10.125, 8.796450693491845, 6.92257913497111, 5.683431550068587, 8.0083841563786, 4.98970010288066, 3.9821810497861696, 2.604166666666667, 3.7385157555426813, 3.0667616383744867, 1.709932670324646, 0.8829543324188387, 0.0), # 44
(10.310989172402216, 9.682291666666666, 8.541666666666668, 9.193359375, 7.478934360642197, 3.6458333333333335, 3.9705882352941178, 3.541666666666667, 4.001041666666666, 1.8885416666666672, 1.3835227272727273, 0.798611111111111, 0.0, 10.125, 8.784722222222221, 6.917613636363637, 5.665625, 8.002083333333331, 4.958333333333334, 3.9705882352941178, 2.604166666666667, 3.7394671803210984, 3.064453125000001, 1.7083333333333335, 0.8802083333333335, 0.0), # 45
(10.31649127077388, 9.65140666438043, 8.533464220393233, 9.186225501543209, 7.480755823424477, 3.6458333333333335, 3.958741245057694, 3.518953189300412, 3.997804835390946, 1.8824909236396894, 1.3824959575175624, 0.7975174516079867, 0.0, 10.125, 8.772691967687852, 6.912479787587812, 5.647472770919067, 7.995609670781892, 4.926534465020577, 3.958741245057694, 2.604166666666667, 3.7403779117122387, 3.062075167181071, 1.7066928440786466, 0.8774006058527665, 0.0), # 46
(10.321751628440035, 9.619903006401461, 8.525069730224052, 9.178892554012345, 7.482495777065244, 3.6458333333333335, 3.9466638021463734, 3.4959927983539094, 3.994486522633745, 1.8763383973479657, 1.3814365153593549, 0.7963981862520958, 0.0, 10.125, 8.760380048773053, 6.9071825767967745, 5.629015192043896, 7.98897304526749, 4.894389917695474, 3.9466638021463734, 2.604166666666667, 3.741247888532622, 3.0596308513374493, 1.7050139460448106, 0.8745366369455876, 0.0), # 47
(10.326769449573796, 9.587841049382716, 8.516496913580248, 9.171369791666667, 7.48415409919754, 3.6458333333333335, 3.9343796296296296, 3.4728472222222226, 3.9910916666666667, 1.8700975308641978, 1.3803453984287317, 0.7952551440329219, 0.0, 10.125, 8.74780658436214, 6.901726992143659, 5.610292592592592, 7.982183333333333, 4.861986111111112, 3.9343796296296296, 2.604166666666667, 3.74207704959877, 3.05712326388889, 1.7032993827160496, 0.871621913580247, 0.0), # 48
(10.331543938348286, 9.555281149977136, 8.507759487882945, 9.163666473765433, 7.485730667454405, 3.6458333333333335, 3.9219124505769383, 3.4495781893004116, 3.987625205761317, 1.8637817672610888, 1.3792236043563206, 0.7940901539399483, 0.0, 10.125, 8.73499169333943, 6.896118021781603, 5.5913453017832655, 7.975250411522634, 4.829409465020577, 3.9219124505769383, 2.604166666666667, 3.7428653337272024, 3.054555491255145, 1.7015518975765893, 0.8686619227251944, 0.0), # 49
(10.336074298936616, 9.522283664837678, 8.49887117055327, 9.155791859567902, 7.4872253594688765, 3.6458333333333335, 3.909285988057775, 3.4262474279835393, 3.9840920781893, 1.85740454961134, 1.3780721307727481, 0.7929050449626583, 0.0, 10.125, 8.72195549458924, 6.89036065386374, 5.572213648834019, 7.9681841563786, 4.796746399176955, 3.909285988057775, 2.604166666666667, 3.7436126797344382, 3.051930619855968, 1.6997742341106543, 0.86566215134888, 0.0), # 50
(10.34035973551191, 9.488908950617283, 8.489845679012346, 9.147755208333333, 7.488638052873998, 3.6458333333333335, 3.896523965141612, 3.4029166666666666, 3.9804972222222226, 1.8509793209876546, 1.3768919753086422, 0.7917016460905352, 0.0, 10.125, 8.708718106995885, 6.884459876543211, 5.552937962962963, 7.960994444444445, 4.764083333333334, 3.896523965141612, 2.604166666666667, 3.744319026436999, 3.049251736111112, 1.6979691358024693, 0.8626280864197532, 0.0), # 51
(10.344399452247279, 9.455217363968908, 8.480696730681299, 9.139565779320987, 7.489968625302809, 3.6458333333333335, 3.883650104897926, 3.3796476337448556, 3.976845576131687, 1.8445195244627348, 1.3756841355946297, 0.7904817863130622, 0.0, 10.125, 8.695299649443683, 6.878420677973147, 5.533558573388203, 7.953691152263374, 4.731506687242798, 3.883650104897926, 2.604166666666667, 3.7449843126514044, 3.04652192644033, 1.69613934613626, 0.8595652149062645, 0.0), # 52
(10.348192653315843, 9.421269261545497, 8.471438042981255, 9.131232831790122, 7.491216954388353, 3.6458333333333335, 3.8706881303961915, 3.3565020576131688, 3.9731420781893005, 1.8380386031092826, 1.3744496092613379, 0.7892472946197227, 0.0, 10.125, 8.681720240816947, 6.872248046306688, 5.514115809327846, 7.946284156378601, 4.699102880658437, 3.8706881303961915, 2.604166666666667, 3.7456084771941764, 3.043744277263375, 1.694287608596251, 0.8564790237768635, 0.0), # 53
(10.351738542890716, 9.387125000000001, 8.462083333333332, 9.122765625, 7.492382917763668, 3.6458333333333335, 3.8576617647058824, 3.333541666666666, 3.9693916666666667, 1.8315500000000005, 1.3731893939393938, 0.788, 0.0, 10.125, 8.668, 6.865946969696969, 5.49465, 7.938783333333333, 4.666958333333333, 3.8576617647058824, 2.604166666666667, 3.746191458881834, 3.040921875000001, 1.6924166666666667, 0.8533750000000002, 0.0), # 54
(10.355036325145022, 9.352844935985367, 8.452646319158665, 9.114173418209877, 7.493466393061793, 3.6458333333333335, 3.844594730896474, 3.3108281893004117, 3.9655992798353905, 1.8250671582075908, 1.3719044872594257, 0.7867417314433777, 0.0, 10.125, 8.654159045877153, 6.859522436297127, 5.4752014746227715, 7.931198559670781, 4.6351594650205765, 3.844594730896474, 2.604166666666667, 3.7467331965308963, 3.0380578060699595, 1.6905292638317333, 0.8502586305441244, 0.0), # 55
(10.358085204251871, 9.31848942615455, 8.443140717878373, 9.105465470679011, 7.4944672579157725, 3.6458333333333335, 3.8315107520374405, 3.288423353909465, 3.961769855967078, 1.818603520804756, 1.3705958868520598, 0.7854743179393385, 0.0, 10.125, 8.640217497332722, 6.852979434260299, 5.455810562414267, 7.923539711934156, 4.603792695473251, 3.8315107520374405, 2.604166666666667, 3.7472336289578863, 3.035155156893005, 1.6886281435756747, 0.8471354023776865, 0.0), # 56
(10.360884384384383, 9.284118827160494, 8.433580246913582, 9.096651041666666, 7.495385389958644, 3.6458333333333335, 3.818433551198257, 3.2663888888888892, 3.957908333333333, 1.812172530864198, 1.369264590347924, 0.7841995884773663, 0.0, 10.125, 8.626195473251027, 6.8463229517396185, 5.436517592592593, 7.915816666666666, 4.572944444444445, 3.818433551198257, 2.604166666666667, 3.747692694979322, 3.0322170138888898, 1.6867160493827165, 0.844010802469136, 0.0), # 57
(10.36343306971568, 9.24979349565615, 8.423978623685414, 9.087739390432098, 7.496220666823449, 3.6458333333333335, 3.8053868514483984, 3.2447865226337447, 3.954019650205761, 1.8057876314586196, 1.367911595377645, 0.7829193720469442, 0.0, 10.125, 8.612113092516385, 6.8395579768882255, 5.417362894375858, 7.908039300411522, 4.5427011316872425, 3.8053868514483984, 2.604166666666667, 3.7481103334117245, 3.029246463477367, 1.684795724737083, 0.8408903177869229, 0.0), # 58
(10.36573046441887, 9.215573788294467, 8.414349565614998, 9.078739776234567, 7.49697296614323, 3.6458333333333335, 3.792394375857339, 3.2236779835390945, 3.9501087448559673, 1.799462265660723, 1.3665378995718502, 0.7816354976375554, 0.0, 10.125, 8.597990474013107, 6.83268949785925, 5.398386796982168, 7.900217489711935, 4.513149176954733, 3.792394375857339, 2.604166666666667, 3.748486483071615, 3.02624659207819, 1.6828699131229998, 0.8377794352994972, 0.0), # 59
(10.367775772667077, 9.181520061728396, 8.404706790123456, 9.069661458333334, 7.497642165551024, 3.6458333333333335, 3.779479847494553, 3.203125, 3.946180555555556, 1.7932098765432103, 1.3651445005611673, 0.7803497942386832, 0.0, 10.125, 8.583847736625515, 6.825722502805837, 5.37962962962963, 7.892361111111112, 4.484375, 3.779479847494553, 2.604166666666667, 3.748821082775512, 3.023220486111112, 1.6809413580246915, 0.8346836419753088, 0.0), # 60
(10.369568198633415, 9.147692672610884, 8.395064014631917, 9.060513695987654, 7.498228142679874, 3.6458333333333335, 3.7666669894295164, 3.183189300411523, 3.9422400205761314, 1.7870439071787843, 1.3637323959762233, 0.7790640908398111, 0.0, 10.125, 8.56970499923792, 6.818661979881115, 5.361131721536351, 7.884480041152263, 4.456465020576132, 3.7666669894295164, 2.604166666666667, 3.749114071339937, 3.0201712319958856, 1.6790128029263836, 0.8316084247828076, 0.0), # 61
(10.371106946491004, 9.114151977594878, 8.385434956561502, 9.051305748456791, 7.498730775162823, 3.6458333333333335, 3.753979524731703, 3.1639326131687247, 3.9382920781893, 1.7809778006401469, 1.3623025834476452, 0.7777802164304223, 0.0, 10.125, 8.555582380734645, 6.811512917238226, 5.3429334019204395, 7.8765841563786, 4.429505658436215, 3.753979524731703, 2.604166666666667, 3.7493653875814115, 3.0171019161522645, 1.6770869913123003, 0.8285592706904436, 0.0), # 62
(10.37239122041296, 9.080958333333333, 8.375833333333334, 9.042046875, 7.499149940632904, 3.6458333333333335, 3.741441176470588, 3.1454166666666667, 3.9343416666666666, 1.7750250000000003, 1.360856060606061, 0.7765000000000001, 0.0, 10.125, 8.5415, 6.804280303030303, 5.325075, 7.868683333333333, 4.403583333333334, 3.741441176470588, 2.604166666666667, 3.749574970316452, 3.014015625000001, 1.675166666666667, 0.8255416666666667, 0.0), # 63
(10.373420224572397, 9.048172096479195, 8.366272862368541, 9.032746334876544, 7.4994855167231655, 3.6458333333333335, 3.729075667715646, 3.127703189300412, 3.9303937242798352, 1.7691989483310475, 1.3593938250820965, 0.7752252705380279, 0.0, 10.125, 8.527477975918305, 6.796969125410483, 5.307596844993141, 7.8607874485596705, 4.378784465020577, 3.729075667715646, 2.604166666666667, 3.7497427583615828, 3.0109154449588487, 1.6732545724737085, 0.822561099679927, 0.0), # 64
(10.374193163142438, 9.015853623685413, 8.35676726108825, 9.023413387345679, 7.499737381066645, 3.6458333333333335, 3.7169067215363514, 3.1108539094650207, 3.9264531893004113, 1.7635130887059902, 1.357916874506381, 0.7739578570339887, 0.0, 10.125, 8.513536427373873, 6.7895843725319045, 5.290539266117969, 7.852906378600823, 4.355195473251029, 3.7169067215363514, 2.604166666666667, 3.7498686905333223, 3.0078044624485605, 1.67135345221765, 0.819623056698674, 0.0), # 65
(10.374709240296196, 8.984063271604938, 8.34733024691358, 9.014057291666667, 7.499905411296382, 3.6458333333333335, 3.7049580610021784, 3.094930555555556, 3.9225250000000003, 1.7579808641975312, 1.3564262065095398, 0.7726995884773664, 0.0, 10.125, 8.499695473251029, 6.782131032547699, 5.273942592592592, 7.8450500000000005, 4.332902777777778, 3.7049580610021784, 2.604166666666667, 3.749952705648191, 3.0046857638888897, 1.6694660493827165, 0.8167330246913582, 0.0), # 66
(10.374967660206792, 8.952861396890716, 8.337975537265661, 9.004687307098765, 7.499989485045419, 3.6458333333333335, 3.693253409182603, 3.0799948559670787, 3.9186140946502057, 1.7526157178783728, 1.3549228187222018, 0.7714522938576437, 0.0, 10.125, 8.485975232434079, 6.774614093611008, 5.257847153635117, 7.837228189300411, 4.31199279835391, 3.693253409182603, 2.604166666666667, 3.7499947425227096, 3.001562435699589, 1.6675951074531323, 0.8138964906264289, 0.0), # 67
(10.374791614480825, 8.922144586043629, 8.328671624942844, 8.995231305354269, 7.499918636864896, 3.645765673423767, 3.681757597414823, 3.0659766041761927, 3.9146959495503735, 1.747405110411792, 1.3533809980900628, 0.770210835158312, 0.0, 10.124875150034294, 8.47231918674143, 6.766904990450313, 5.242215331235375, 7.829391899100747, 4.29236724584667, 3.681757597414823, 2.604118338159833, 3.749959318432448, 2.99841043511809, 1.6657343249885688, 0.8111040532766937, 0.0), # 68
(10.373141706924315, 8.890975059737157, 8.319157021604937, 8.985212635869564, 7.499273783587508, 3.6452307956104257, 3.6701340906733066, 3.052124485596708, 3.910599279835391, 1.7422015976761076, 1.3516438064859118, 0.7689349144466104, 0.0, 10.12388599537037, 8.458284058912714, 6.758219032429559, 5.226604793028321, 7.821198559670782, 4.272974279835391, 3.6701340906733066, 2.6037362825788755, 3.749636891793754, 2.9950708786231885, 1.6638314043209876, 0.8082704599761052, 0.0), # 69
(10.369885787558895, 8.859209754856408, 8.309390360653863, 8.974565343196456, 7.497999542752628, 3.6441773992785653, 3.658330067280685, 3.0383135192805977, 3.9063009640298736, 1.736979881115684, 1.3496914810876801, 0.7676185634410675, 0.0, 10.121932334533609, 8.44380419785174, 6.7484574054383994, 5.210939643347051, 7.812601928059747, 4.253638926992837, 3.658330067280685, 2.6029838566275467, 3.748999771376314, 2.991521781065486, 1.6618780721307727, 0.8053827049869463, 0.0), # 70
(10.365069660642929, 8.826867654542236, 8.299375071444901, 8.963305127818035, 7.496112052502757, 3.6426225549966977, 3.646350829769494, 3.0245482777015704, 3.9018074035970125, 1.7317400898356603, 1.347531228463977, 0.7662627447677263, 0.0, 10.119039887688615, 8.428890192444989, 6.737656142319885, 5.195220269506979, 7.803614807194025, 4.234367588782199, 3.646350829769494, 2.6018732535690696, 3.7480560262513785, 2.987768375939346, 1.6598750142889804, 0.8024425140492942, 0.0), # 71
(10.358739130434783, 8.793967741935482, 8.289114583333333, 8.95144769021739, 7.493627450980392, 3.6405833333333337, 3.634201680672269, 3.0108333333333333, 3.897125, 1.7264823529411768, 1.3451702551834133, 0.7648684210526316, 0.0, 10.115234375, 8.413552631578947, 6.7258512759170666, 5.179447058823529, 7.79425, 4.215166666666667, 3.634201680672269, 2.600416666666667, 3.746813725490196, 2.983815896739131, 1.6578229166666667, 0.7994516129032258, 0.0), # 72
(10.35094000119282, 8.760529000176998, 8.27861232567444, 8.939008730877617, 7.490561876328034, 3.638076804856983, 3.621887922521546, 2.9971732586495965, 3.8922601547020275, 1.7212067995373737, 1.3426157678145982, 0.7634365549218266, 0.0, 10.110541516632374, 8.397802104140093, 6.71307883907299, 5.163620398612119, 7.784520309404055, 4.196042562109435, 3.621887922521546, 2.598626289183559, 3.745280938164017, 2.979669576959206, 1.655722465134888, 0.7964117272888181, 0.0), # 73
(10.341718077175404, 8.726570412407629, 8.267871727823502, 8.926003950281803, 7.486931466688183, 3.6351200401361585, 3.609414857849861, 2.9835726261240665, 3.8872192691662857, 1.7159135587293908, 1.3398749729261428, 0.7619681090013557, 0.0, 10.104987032750344, 8.38164919901491, 6.699374864630713, 5.147740676188171, 7.774438538332571, 4.177001676573693, 3.609414857849861, 2.5965143143829703, 3.7434657333440917, 2.975334650093935, 1.6535743455647005, 0.7933245829461482, 0.0), # 74
(10.331119162640901, 8.692110961768218, 8.256896219135802, 8.912449048913043, 7.482752360203341, 3.6317301097393697, 3.59678778918975, 2.9700360082304527, 3.8820087448559666, 1.7106027596223679, 1.336955077086656, 0.7604640459172624, 0.0, 10.098596643518519, 8.365104505089885, 6.684775385433279, 5.131808278867102, 7.764017489711933, 4.158050411522634, 3.59678778918975, 2.594092935528121, 3.7413761801016703, 2.9708163496376816, 1.6513792438271604, 0.7901919056152927, 0.0), # 75
(10.319189061847677, 8.65716963139962, 8.245689228966622, 8.898359727254428, 7.478040695016003, 3.6279240842351275, 3.5840120190737474, 2.956567977442463, 3.876634983234263, 1.7052745313214452, 1.3338632868647486, 0.7589253282955902, 0.0, 10.091396069101508, 8.348178611251491, 6.669316434323743, 5.115823593964334, 7.753269966468526, 4.139195168419449, 3.5840120190737474, 2.5913743458822336, 3.7390203475080015, 2.96611990908481, 1.6491378457933243, 0.7870154210363293, 0.0), # 76
(10.305973579054093, 8.621765404442675, 8.234254186671238, 8.883751685789049, 7.472812609268672, 3.6237190341919425, 3.5710928500343897, 2.9431731062338065, 3.871104385764365, 1.699929002931763, 1.3306068088290313, 0.7573529187623839, 0.0, 10.083411029663925, 8.330882106386222, 6.653034044145156, 5.099787008795288, 7.74220877152873, 4.120442348727329, 3.5710928500343897, 2.58837073870853, 3.736406304634336, 2.9612505619296834, 1.6468508373342476, 0.7837968549493343, 0.0), # 77
(10.291518518518519, 8.585917264038233, 8.222594521604938, 8.868640625, 7.467084241103849, 3.6191320301783265, 3.5580355846042124, 2.9298559670781894, 3.8654233539094642, 1.6945663035584608, 1.327192849548113, 0.7557477799436866, 0.0, 10.074667245370371, 8.313225579380552, 6.635964247740564, 5.083698910675381, 7.7308467078189285, 4.101798353909466, 3.5580355846042124, 2.585094307270233, 3.7335421205519244, 2.956213541666667, 1.6445189043209878, 0.7805379330943849, 0.0), # 78
(10.275869684499314, 8.549644193327138, 8.210713663123, 8.85304224537037, 7.460871728664031, 3.61418014276279, 3.5448455253157505, 2.916621132449322, 3.859598289132754, 1.6891865623066789, 1.3236286155906039, 0.7541108744655421, 0.0, 10.065190436385459, 8.295219619120962, 6.618143077953018, 5.067559686920035, 7.719196578265508, 4.083269585429051, 3.5448455253157505, 2.5815572448305644, 3.7304358643320157, 2.951014081790124, 1.6421427326246, 0.7772403812115581, 0.0), # 79
(10.259072881254847, 8.51296517545024, 8.198615040580703, 8.836972247383253, 7.454191210091719, 3.6088804425138448, 3.5315279747015405, 2.9034731748209115, 3.853635592897424, 1.683789908281557, 1.3199213135251149, 0.7524431649539947, 0.0, 10.0550063228738, 8.27687481449394, 6.599606567625574, 5.05136972484467, 7.707271185794848, 4.064862444749276, 3.5315279747015405, 2.577771744652746, 3.7270956050458595, 2.945657415794418, 1.639723008116141, 0.7739059250409311, 0.0), # 80
(10.241173913043479, 8.475899193548386, 8.186302083333333, 8.82044633152174, 7.447058823529411, 3.60325, 3.5180882352941176, 2.890416666666667, 3.8475416666666664, 1.6783764705882358, 1.3160781499202554, 0.7507456140350878, 0.0, 10.044140624999999, 8.258201754385965, 6.580390749601277, 5.035129411764706, 7.695083333333333, 4.046583333333333, 3.5180882352941176, 2.57375, 3.7235294117647055, 2.940148777173914, 1.6372604166666667, 0.7705362903225808, 0.0), # 81
(10.222218584123576, 8.438465230762423, 8.17377822073617, 8.803480198268922, 7.43949070711961, 3.5973058857897686, 3.504531609626018, 2.8774561804602956, 3.841322911903673, 1.6729463783318543, 1.3121063313446355, 0.7490191843348656, 0.0, 10.03261906292867, 8.23921102768352, 6.560531656723177, 5.018839134995561, 7.682645823807346, 4.0284386526444145, 3.504531609626018, 2.5695042041355487, 3.719745353559805, 2.934493399422974, 1.634755644147234, 0.767133202796584, 0.0), # 82
(10.202252698753504, 8.400682270233196, 8.16104688214449, 8.78608954810789, 7.431502999004814, 3.591065170451659, 3.4908634002297765, 2.8645962886755068, 3.8349857300716352, 1.6674997606175532, 1.3080130643668657, 0.7472648384793719, 0.0, 10.020467356824417, 8.219913223273089, 6.540065321834328, 5.002499281852659, 7.6699714601432705, 4.01043480414571, 3.4908634002297765, 2.5650465503226134, 3.715751499502407, 2.9286965160359637, 1.632209376428898, 0.7636983882030178, 0.0), # 83
(10.181322061191626, 8.362569295101553, 8.14811149691358, 8.768290081521739, 7.423111837327523, 3.584544924554184, 3.477088909637929, 2.851841563786008, 3.8285365226337444, 1.6620367465504726, 1.3038055555555557, 0.7454835390946503, 0.0, 10.007711226851852, 8.200318930041153, 6.519027777777778, 4.986110239651417, 7.657073045267489, 3.9925781893004113, 3.477088909637929, 2.5603892318244172, 3.7115559186637617, 2.922763360507247, 1.629622299382716, 0.7602335722819594, 0.0), # 84
(10.159472475696308, 8.32414528850834, 8.13497549439872, 8.75009749899356, 7.414333360230238, 3.577762218665854, 3.463213440383012, 2.8391965782655086, 3.8219816910531925, 1.6565574652357518, 1.2994910114793157, 0.7436762488067449, 0.0, 9.994376393175584, 8.180438736874192, 6.497455057396579, 4.969672395707254, 7.643963382106385, 3.9748752095717124, 3.463213440383012, 2.5555444419041815, 3.707166680115119, 2.916699166331187, 1.626995098879744, 0.7567404807734855, 0.0), # 85
(10.136749746525913, 8.285429233594407, 8.121642303955191, 8.731527501006443, 7.405183705855455, 3.57073412335518, 3.44924229499756, 2.826665904587715, 3.815327636793172, 1.6510620457785314, 1.2950766387067558, 0.7418439302416996, 0.0, 9.98048857596022, 8.160283232658694, 6.475383193533778, 4.953186137335593, 7.630655273586344, 3.9573322664228017, 3.44924229499756, 2.550524373825129, 3.7025918529277275, 2.910509167002148, 1.6243284607910382, 0.7532208394176735, 0.0), # 86
(10.113199677938807, 8.246440113500597, 8.10811535493827, 8.712595788043478, 7.3956790123456795, 3.563477709190672, 3.4351807760141093, 2.8142541152263374, 3.8085807613168727, 1.645550617283951, 1.290569643806486, 0.7399875460255577, 0.0, 9.96607349537037, 8.139863006281134, 6.452848219032429, 4.936651851851852, 7.6171615226337455, 3.9399557613168725, 3.4351807760141093, 2.54534122085048, 3.6978395061728397, 2.904198596014493, 1.6216230709876542, 0.7496763739545999, 0.0), # 87
(10.088868074193357, 8.207196911367758, 8.094398076703246, 8.693318060587762, 7.385835417843406, 3.5560100467408424, 3.4210341859651954, 2.801965782655083, 3.8017474660874866, 1.6400233088571508, 1.2859772333471164, 0.7381080587843638, 0.0, 9.951156871570646, 8.119188646628, 6.429886166735582, 4.9200699265714505, 7.603494932174973, 3.9227520957171165, 3.4210341859651954, 2.540007176243459, 3.692917708921703, 2.897772686862588, 1.6188796153406495, 0.7461088101243417, 0.0), # 88
(10.063800739547922, 8.16771861033674, 8.080493898605397, 8.673710019122383, 7.375669060491138, 3.5483482065742016, 3.406807827383354, 2.7898054793476605, 3.794834152568206, 1.634480249603271, 1.2813066138972575, 0.7362064311441613, 0.0, 9.935764424725651, 8.098270742585774, 6.4065330694862865, 4.903440748809812, 7.589668305136412, 3.905727671086725, 3.406807827383354, 2.534534433267287, 3.687834530245569, 2.891236673040795, 1.6160987797210793, 0.7425198736669765, 0.0), # 89
(10.03804347826087, 8.128024193548386, 8.06640625, 8.653787364130435, 7.365196078431373, 3.5405092592592595, 3.3925070028011204, 2.7777777777777777, 3.7878472222222226, 1.6289215686274514, 1.2765649920255184, 0.7342836257309943, 0.0, 9.919921875, 8.077119883040936, 6.382824960127592, 4.886764705882353, 7.575694444444445, 3.888888888888889, 3.3925070028011204, 2.5289351851851856, 3.6825980392156863, 2.884595788043479, 1.6132812500000002, 0.7389112903225807, 0.0), # 90
(10.011642094590563, 8.088132644143545, 8.05213856024234, 8.63356579609501, 7.35443260980661, 3.532510275364528, 3.378137014751031, 2.7658872504191434, 3.780793076512727, 1.6233473950348318, 1.2717595743005101, 0.7323406051709063, 0.0, 9.903654942558298, 8.055746656879968, 6.35879787150255, 4.870042185104494, 7.561586153025454, 3.872242150586801, 3.378137014751031, 2.5232216252603767, 3.677216304903305, 2.8778552653650036, 1.6104277120484682, 0.7352847858312315, 0.0), # 91
(9.984642392795372, 8.048062945263066, 8.0376942586877, 8.613061015499195, 7.343394792759352, 3.524368325458518, 3.363703165765621, 2.754138469745466, 3.773678116902911, 1.6177578579305527, 1.2668975672908422, 0.7303783320899415, 0.0, 9.886989347565157, 8.034161652989356, 6.334487836454211, 4.853273573791657, 7.547356233805822, 3.8557938576436523, 3.363703165765621, 2.517405946756084, 3.671697396379676, 2.871020338499732, 1.6075388517375402, 0.7316420859330061, 0.0), # 92
(9.957090177133654, 8.00783408004779, 8.023076774691358, 8.592288722826089, 7.332098765432098, 3.5161004801097393, 3.349210758377425, 2.742536008230453, 3.766508744855967, 1.6121530864197533, 1.261986177565125, 0.7283977691141434, 0.0, 9.869950810185184, 8.012375460255576, 6.309930887825625, 4.836459259259259, 7.533017489711934, 3.839550411522634, 3.349210758377425, 2.5115003429355283, 3.666049382716049, 2.86409624094203, 1.6046153549382718, 0.727984916367981, 0.0), # 93
(9.92903125186378, 7.967465031638567, 8.008289537608597, 8.571264618558777, 7.320560665967347, 3.5077238098867043, 3.3346650951189805, 2.7310844383478132, 3.759291361835086, 1.6065332096075746, 1.2570326116919686, 0.7263998788695563, 0.0, 9.85256505058299, 7.990398667565118, 6.285163058459842, 4.819599628822722, 7.518582723670172, 3.823518213686939, 3.3346650951189805, 2.5055170070619317, 3.6602803329836733, 2.8570882061862592, 1.6016579075217197, 0.7243150028762335, 0.0), # 94
(9.90051142124411, 7.926974783176247, 7.993335976794697, 8.550004403180354, 7.308796632507598, 3.499255385357923, 3.320071478522822, 2.719788332571255, 3.7520323693034596, 1.6008983565991557, 1.2520440762399827, 0.7243856239822234, 0.0, 9.834857788923182, 7.968241863804456, 6.260220381199914, 4.8026950697974655, 7.504064738606919, 3.8077036655997567, 3.320071478522822, 2.4994681323985164, 3.654398316253799, 2.850001467726785, 1.5986671953589393, 0.7206340711978407, 0.0), # 95
(9.871576489533012, 7.886382317801674, 7.978219521604939, 8.528523777173913, 7.296822803195352, 3.4907122770919066, 3.3054352111214853, 2.708652263374486, 3.7447381687242793, 1.5952486564996373, 1.247027777777778, 0.7223559670781895, 0.0, 9.816854745370371, 7.945915637860083, 6.23513888888889, 4.785745969498911, 7.489476337448559, 3.7921131687242804, 3.3054352111214853, 2.4933659122085046, 3.648411401597676, 2.8428412590579715, 1.595643904320988, 0.7169438470728796, 0.0), # 96
(9.842272260988848, 7.845706618655694, 7.962943601394604, 8.506838441022543, 7.284655316173109, 3.482111555657166, 3.2907615954475067, 2.697680803231215, 3.7374151615607376, 1.589584238414159, 1.2419909228739638, 0.7203118707834976, 0.0, 9.798581640089164, 7.923430578618472, 6.209954614369819, 4.768752715242476, 7.474830323121475, 3.7767531245237014, 3.2907615954475067, 2.4872225397551184, 3.6423276580865545, 2.8356128136741816, 1.5925887202789208, 0.7132460562414268, 0.0), # 97
(9.812644539869984, 7.804966668879153, 7.947511645518976, 8.48496409520934, 7.272310309583368, 3.4734702916222124, 3.276055934033421, 2.68687852461515, 3.7300697492760246, 1.5839052314478608, 1.236940718097151, 0.7182542977241916, 0.0, 9.78006419324417, 7.900797274966106, 6.184703590485755, 4.751715694343581, 7.460139498552049, 3.7616299344612103, 3.276055934033421, 2.48105020830158, 3.636155154791684, 2.8283213650697805, 1.589502329103795, 0.7095424244435595, 0.0), # 98
(9.782739130434782, 7.764181451612902, 7.931927083333334, 8.462916440217391, 7.259803921568627, 3.464805555555556, 3.261323529411765, 2.67625, 3.7227083333333333, 1.5782117647058826, 1.2318843700159492, 0.7161842105263159, 0.0, 9.761328125, 7.878026315789473, 6.159421850079745, 4.734635294117647, 7.445416666666667, 3.7467500000000005, 3.261323529411765, 2.474861111111111, 3.6299019607843137, 2.820972146739131, 1.5863854166666669, 0.7058346774193549, 0.0), # 99
(9.752601836941611, 7.723369949997786, 7.916193344192958, 8.44071117652979, 7.247152290271389, 3.4561344180257074, 3.2465696841150726, 2.665799801859473, 3.715337315195854, 1.572503967293365, 1.2268290851989685, 0.714102571815914, 0.0, 9.742399155521262, 7.8551282899750525, 6.134145425994841, 4.717511901880093, 7.430674630391708, 3.732119722603262, 3.2465696841150726, 2.468667441446934, 3.6235761451356945, 2.8135703921765973, 1.5832386688385918, 0.7021245409088898, 0.0), # 100
(9.722278463648834, 7.682551147174654, 7.900313857453133, 8.41836400462963, 7.234371553834153, 3.4474739496011786, 3.231799700675881, 2.6555325026672763, 3.7079630963267793, 1.5667819683154474, 1.2217820702148188, 0.7120103442190294, 0.0, 9.723303004972564, 7.832113786409323, 6.108910351074094, 4.7003459049463405, 7.415926192653559, 3.7177455037341867, 3.231799700675881, 2.4624813925722706, 3.6171857769170765, 2.806121334876544, 1.5800627714906266, 0.6984137406522414, 0.0), # 101
(9.691814814814816, 7.641744026284349, 7.884292052469135, 8.395890625, 7.221477850399419, 3.4388412208504806, 3.217018881626725, 2.645452674897119, 3.7005920781893, 1.56104589687727, 1.2167505316321108, 0.7099084903617069, 0.0, 9.704065393518519, 7.808993393978774, 6.083752658160553, 4.683137690631809, 7.4011841563786, 3.703633744855967, 3.217018881626725, 2.4563151577503435, 3.6107389251997093, 2.798630208333334, 1.5768584104938272, 0.6947040023894864, 0.0), # 102
(9.661256694697919, 7.60096757046772, 7.8681313585962505, 8.373306738123993, 7.208487318109686, 3.430253302342123, 3.20223252950014, 2.63556489102271, 3.6932306622466085, 1.5552958820839726, 1.211741676019454, 0.7077979728699895, 0.0, 9.68471204132373, 7.785777701569883, 6.058708380097269, 4.6658876462519165, 7.386461324493217, 3.689790847431794, 3.20223252950014, 2.4501809302443736, 3.604243659054843, 2.7911022460413317, 1.5736262717192502, 0.6909970518607019, 0.0), # 103
(9.63064990755651, 7.560240762865614, 7.851835205189758, 8.350628044484703, 7.195416095107452, 3.421727264644617, 3.187445946828663, 2.6258737235177567, 3.685885249961896, 1.5495320530406955, 1.2067627099454585, 0.7056797543699213, 0.0, 9.665268668552812, 7.762477298069133, 6.033813549727292, 4.648596159122086, 7.371770499923792, 3.6762232129248593, 3.187445946828663, 2.4440909033175835, 3.597708047553726, 2.783542681494901, 1.5703670410379515, 0.687294614805965, 0.0), # 104
(9.600040257648953, 7.519582586618876, 7.835407021604938, 8.327870244565217, 7.182280319535221, 3.4132801783264752, 3.172664436144829, 2.6163837448559675, 3.6785622427983538, 1.5437545388525786, 1.201820839978735, 0.7035547974875461, 0.0, 9.64576099537037, 7.739102772363006, 6.009104199893674, 4.631263616557734, 7.3571244855967075, 3.662937242798354, 3.172664436144829, 2.4380572702331964, 3.5911401597676105, 2.775956748188406, 1.5670814043209877, 0.6835984169653525, 0.0), # 105
(9.569473549233614, 7.479012024868357, 7.818850237197074, 8.305049038848631, 7.1690961295354905, 3.404929113956206, 3.1578932999811724, 2.6070995275110502, 3.6712680422191735, 1.5379634686247616, 1.1969232726878927, 0.701424064848908, 0.0, 9.626214741941014, 7.715664713337986, 5.9846163634394625, 4.613890405874283, 7.342536084438347, 3.6499393385154706, 3.1578932999811724, 2.4320922242544327, 3.5845480647677452, 2.768349679616211, 1.5637700474394147, 0.6799101840789417, 0.0), # 106
(9.538995586568856, 7.438548060754901, 7.802168281321446, 8.282180127818036, 7.155879663250759, 3.3966911421023225, 3.1431378408702306, 2.5980256439567144, 3.6640090496875475, 1.532158971462385, 1.1920772146415421, 0.6992885190800504, 0.0, 9.606655628429355, 7.692173709880553, 5.96038607320771, 4.596476914387154, 7.328018099375095, 3.6372359015394005, 3.1431378408702306, 2.426207958644516, 3.5779398316253794, 2.760726709272679, 1.5604336562642893, 0.6762316418868093, 0.0), # 107
(9.508652173913044, 7.398209677419356, 7.785364583333334, 8.259279211956523, 7.1426470588235285, 3.3885833333333335, 3.1284033613445374, 2.589166666666667, 3.656791666666667, 1.5263411764705888, 1.1872898724082936, 0.6971491228070177, 0.0, 9.587109375, 7.668640350877193, 5.936449362041468, 4.579023529411765, 7.313583333333334, 3.624833333333334, 3.1284033613445374, 2.4204166666666667, 3.5713235294117642, 2.7530930706521746, 1.557072916666667, 0.6725645161290325, 0.0), # 108
(9.478489115524543, 7.358015858002567, 7.768442572588021, 8.23636199174718, 7.129414454396299, 3.3806227582177515, 3.113695163936631, 2.580527168114617, 3.6496222946197223, 1.5205102127545123, 1.1825684525567568, 0.6950068386558532, 0.0, 9.567601701817559, 7.645075225214384, 5.9128422627837836, 4.561530638263536, 7.299244589239445, 3.612738035360464, 3.113695163936631, 2.4147305415841083, 3.5647072271981495, 2.7454539972490606, 1.5536885145176043, 0.668910532545688, 0.0), # 109
(9.448552215661715, 7.317985585645383, 7.751405678440788, 8.213444167673108, 7.116197988111569, 3.3728264873240867, 3.0990185511790447, 2.5721117207742723, 3.6425073350099066, 1.5146662094192962, 1.177920161655542, 0.6928626292526012, 0.0, 9.54815832904664, 7.621488921778612, 5.8896008082777085, 4.543998628257887, 7.285014670019813, 3.600956409083981, 3.0990185511790447, 2.409161776660062, 3.5580989940557846, 2.737814722557703, 1.5502811356881578, 0.6652714168768531, 0.0), # 110
(9.41888727858293, 7.278137843488651, 7.7342573302469155, 8.190541440217391, 7.103013798111837, 3.365211591220851, 3.0843788256043156, 2.5639248971193416, 3.635453189300412, 1.5088092955700803, 1.173352206273259, 0.6907174572233054, 0.0, 9.528804976851852, 7.597892029456357, 5.866761031366295, 4.526427886710239, 7.270906378600824, 3.5894948559670783, 3.0843788256043156, 2.4037225651577505, 3.5515068990559184, 2.7301804800724643, 1.546851466049383, 0.6616488948626047, 0.0), # 111
(9.38954010854655, 7.238491614673214, 7.717000957361684, 8.167669509863124, 7.089878022539605, 3.357795140476554, 3.069781289744979, 2.5559712696235333, 3.628466258954427, 1.5029396003120044, 1.1688717929785184, 0.6885722851940093, 0.0, 9.509567365397805, 7.574295137134101, 5.844358964892591, 4.5088188009360115, 7.256932517908854, 3.5783597774729463, 3.069781289744979, 2.3984251003403956, 3.5449390112698027, 2.7225565032877084, 1.543400191472337, 0.6580446922430195, 0.0), # 112
(9.360504223703044, 7.1991320672204555, 7.699681523543391, 8.14487541186903, 7.076783786782469, 3.3505906987084666, 3.0552629818283847, 2.548271903658586, 3.6215709370862066, 1.4970761841531826, 1.1644873176921446, 0.6864327447087024, 0.0, 9.490443900843221, 7.550760191795725, 5.8224365884607225, 4.491228552459547, 7.243141874172413, 3.5675806651220205, 3.0552629818283847, 2.3932790705060474, 3.5383918933912346, 2.7149584706230105, 1.5399363047086783, 0.654466551565496, 0.0), # 113
(9.331480897900065, 7.16044741823174, 7.682538062518016, 8.122342065958001, 7.063595569710884, 3.343581854975776, 3.0410091042052896, 2.5409213581271333, 3.6148730119043533, 1.491328791978196, 1.1602073895188663, 0.684326014342748, 0.0, 9.471275414160035, 7.5275861577702265, 5.801036947594331, 4.473986375934587, 7.229746023808707, 3.557289901377987, 3.0410091042052896, 2.3882727535541255, 3.531797784855442, 2.7074473553193346, 1.5365076125036032, 0.6509497652937947, 0.0), # 114
(9.302384903003995, 7.122451598792792, 7.665580777256098, 8.100063378886334, 7.050271785259067, 3.3367503822909463, 3.027029825095781, 2.533917772616129, 3.6083749928895963, 1.4857063319970194, 1.1560257519045158, 0.6822531318799043, 0.0, 9.452006631660376, 7.5047844506789465, 5.7801287595225785, 4.457118995991058, 7.216749985779193, 3.5474848816625806, 3.027029825095781, 2.3833931302078186, 3.5251358926295335, 2.700021126295445, 1.5331161554512198, 0.647495599890254, 0.0), # 115
(9.273179873237634, 7.0850892578507265, 7.648776824986561, 8.077999612699802, 7.036792350922519, 3.330080178417474, 3.0133024087639466, 2.5272417970412473, 3.6020604464092765, 1.480198339612387, 1.1519343218785802, 0.6802102664572789, 0.0, 9.43260725975589, 7.482312931030067, 5.7596716093929015, 4.44059501883716, 7.204120892818553, 3.5381385158577463, 3.0133024087639466, 2.3786286988696244, 3.5183961754612594, 2.6926665375666015, 1.5297553649973124, 0.6440990234409752, 0.0), # 116
(9.243829442823772, 7.04830504435266, 7.632093362938321, 8.056111029444182, 7.02313718419674, 3.323555141118853, 2.9998041194738763, 2.5208740813181603, 3.5959129388307343, 1.4747943502270324, 1.1479250164705472, 0.6781935872119792, 0.0, 9.413047004858225, 7.46012945933177, 5.739625082352736, 4.424383050681096, 7.1918258776614685, 3.5292237138454245, 2.9998041194738763, 2.3739679579420376, 3.51156859209837, 2.6853703431480613, 1.5264186725876645, 0.6407550040320601, 0.0), # 117
(9.214297245985211, 7.0120436072457135, 7.615497548340306, 8.03435789116525, 7.009286202577227, 3.317159168158581, 2.9865122214896576, 2.51479527536254, 3.5899160365213114, 1.46948389924369, 1.143989752709904, 0.6761992632811126, 0.0, 9.393295573379024, 7.438191896092237, 5.71994876354952, 4.40845169773107, 7.179832073042623, 3.5207133855075567, 2.9865122214896576, 2.369399405827558, 3.5046431012886137, 2.678119297055084, 1.5230995096680613, 0.6374585097496104, 0.0), # 118
(9.184546916944742, 6.976249595477001, 7.598956538421437, 8.012700459908778, 6.99521932355948, 3.3108761573001524, 2.973403979075378, 2.5089860290900607, 3.5840533058483475, 1.4642565220650932, 1.1401204476261382, 0.6742234638017862, 0.0, 9.373322671729932, 7.416458101819647, 5.70060223813069, 4.392769566195279, 7.168106611696695, 3.5125804407260848, 2.973403979075378, 2.3649115409286803, 3.49760966177974, 2.670900153302927, 1.5197913076842873, 0.6342045086797276, 0.0), # 119
(9.154542089925162, 6.940867657993644, 7.582437490410635, 7.991098997720545, 6.980916464638998, 3.304690006307063, 2.9604566564951265, 2.5034269924163928, 3.578308313179186, 1.4591017540939766, 1.136309018248736, 0.6722623579111081, 0.0, 9.353098006322597, 7.394885937022188, 5.68154509124368, 4.377305262281929, 7.156616626358372, 3.50479778938295, 2.9604566564951265, 2.360492861647902, 3.490458232319499, 2.663699665906849, 1.516487498082127, 0.6309879689085133, 0.0), # 120
(9.124246399149268, 6.90584244374276, 7.565907561536823, 7.969513766646325, 6.966357543311279, 3.29858461294281, 2.94764751801299, 2.4980988152572112, 3.572664624881166, 1.4540091307330743, 1.1325473816071863, 0.6703121147461852, 0.0, 9.33259128356866, 7.373433262208036, 5.662736908035931, 4.362027392199222, 7.145329249762332, 3.497338341360096, 2.94764751801299, 2.356131866387721, 3.4831787716556395, 2.656504588882109, 1.5131815123073646, 0.6278038585220692, 0.0), # 121
(9.093623478839854, 6.871118601671464, 7.549333909028926, 7.947905028731892, 6.951522477071823, 3.292543874970886, 2.9349538278930587, 2.492982147528187, 3.5671058073216297, 1.4489681873851195, 1.1288274547309753, 0.6683689034441251, 0.0, 9.31177220987977, 7.352057937885375, 5.644137273654876, 4.346904562155357, 7.1342116146432595, 3.490175006539462, 2.9349538278930587, 2.351817053550633, 3.4757612385359113, 2.6493016762439643, 1.5098667818057854, 0.6246471456064968, 0.0), # 122
(9.062636963219719, 6.836640780726876, 7.532683690115864, 7.92623304602302, 6.936391183416127, 3.28655169015479, 2.9223528503994194, 2.4880576391449933, 3.5616154268679177, 1.443968459452847, 1.1251411546495909, 0.6664288931420351, 0.0, 9.290610491667572, 7.330717824562385, 5.625705773247954, 4.33190537835854, 7.123230853735835, 3.4832806948029904, 2.9223528503994194, 2.3475369215391355, 3.4681955917080636, 2.642077682007674, 1.5065367380231727, 0.621512798247898, 0.0), # 123
(9.031250486511654, 6.802353629856113, 7.515924062026559, 7.90445808056549, 6.920943579839691, 3.2805919562580144, 2.9098218497961597, 2.483305940023303, 3.5561770498873715, 1.4389994823389904, 1.1214803983925201, 0.664488252977023, 0.0, 9.269075835343711, 7.309370782747252, 5.6074019919625995, 4.316998447016971, 7.112354099774743, 3.476628316032624, 2.9098218497961597, 2.3432799687557244, 3.4604717899198456, 2.634819360188497, 1.5031848124053118, 0.618395784532374, 0.0), # 124
(8.999427682938459, 6.768201798006293, 7.499022181989936, 7.88254039440507, 6.905159583838015, 3.274648571044058, 2.8973380903473696, 2.478707700078788, 3.5507742427473308, 1.4340507914462837, 1.1178371029892504, 0.6625431520861957, 0.0, 9.247137947319828, 7.2879746729481525, 5.5891855149462515, 4.30215237433885, 7.1015484854946616, 3.470190780110303, 2.8973380903473696, 2.3390346936028985, 3.4525797919190073, 2.6275134648016905, 1.4998044363979874, 0.6152910725460268, 0.0), # 125
(8.967132186722928, 6.734129934124536, 7.481945207234916, 7.8604402495875405, 6.889019112906595, 3.2687054322764144, 2.884878836317135, 2.474243569227122, 3.545390571815139, 1.4291119221774609, 1.1142031854692689, 0.6605897596066612, 0.0, 9.224766534007578, 7.266487355673273, 5.571015927346345, 4.287335766532382, 7.090781143630278, 3.463940996917971, 2.884878836317135, 2.334789594483153, 3.4445095564532977, 2.620146749862514, 1.4963890414469831, 0.6121936303749579, 0.0), # 126
(8.93432763208786, 6.7000826871579555, 7.464660294990421, 7.838117908158674, 6.8725020845409315, 3.26274643771858, 2.872421351969547, 2.469894197383977, 3.5400096034581354, 1.4241724099352562, 1.1105705628620632, 0.6586242446755264, 0.0, 9.201931301818599, 7.244866691430789, 5.552852814310316, 4.272517229805768, 7.080019206916271, 3.457851876337568, 2.872421351969547, 2.3305331697989855, 3.4362510422704657, 2.612705969386225, 1.4929320589980841, 0.6090984261052688, 0.0), # 127
(8.900977653256046, 6.666004706053673, 7.447134602485375, 7.815533632164248, 6.855588416236526, 3.2567554851340508, 2.859942901568691, 2.465640234465026, 3.534614904043661, 1.4192217901224033, 1.1069311521971208, 0.6566427764298991, 0.0, 9.178601957164537, 7.223070540728888, 5.534655760985604, 4.257665370367209, 7.069229808087322, 3.4518963282510366, 2.859942901568691, 2.3262539179528936, 3.427794208118263, 2.6051778773880834, 1.4894269204970751, 0.6060004278230613, 0.0), # 128
(8.867045884450281, 6.631840639758805, 7.4293352869486995, 7.792647683650037, 6.838258025488874, 3.250716472286322, 2.8474207493786565, 2.4614623303859418, 3.529190039939058, 1.4142495981416365, 1.1032768705039286, 0.6546415240068865, 0.0, 9.154748206457038, 7.20105676407575, 5.516384352519642, 4.242748794424909, 7.058380079878116, 3.4460472625403185, 2.8474207493786565, 2.321940337347373, 3.419129012744437, 2.597549227883346, 1.4858670573897401, 0.6028946036144368, 0.0), # 129
(8.832495959893366, 6.5975351372204685, 7.411229505609316, 7.769420324661814, 6.820490829793475, 3.2446132969388883, 2.8348321596635313, 2.457341135062396, 3.5237185775116666, 1.4092453693956895, 1.0995996348119743, 0.6526166565435961, 0.0, 9.130339756107748, 7.178783221979556, 5.4979981740598705, 4.2277361081870675, 7.047437155023333, 3.4402775890873545, 2.8348321596635313, 2.3175809263849203, 3.4102454148967376, 2.589806774887272, 1.4822459011218634, 0.5997759215654973, 0.0), # 130
(8.797291513808094, 6.563032847385783, 7.392784415696151, 7.7458118172453565, 6.802266746645829, 3.238429856855247, 2.8221543966874045, 2.4532572984100627, 3.5181840831288285, 1.4041986392872965, 1.0958913621507447, 0.6505643431771354, 0.0, 9.105346312528312, 7.156207774948489, 5.479456810753724, 4.212595917861889, 7.036368166257657, 3.4345602177740875, 2.8221543966874045, 2.3131641834680337, 3.4011333733229145, 2.5819372724151193, 1.4785568831392302, 0.596639349762344, 0.0), # 131
(8.76139618041726, 6.528278419201865, 7.373967174438122, 7.72178242344644, 6.783565693541435, 3.2321500497988933, 2.8093647247143627, 2.449191470344614, 3.5125701231578845, 1.3990989432191914, 1.0921439695497275, 0.6484807530446118, 0.0, 9.079737582130376, 7.13328828349073, 5.460719847748638, 4.1972968296575734, 7.025140246315769, 3.4288680584824593, 2.8093647247143627, 2.3086786069992096, 3.3917828467707176, 2.573927474482147, 1.4747934348876244, 0.5934798562910787, 0.0), # 132
(8.724773593943663, 6.493216501615832, 7.354744939064153, 7.697292405310838, 6.764367587975791, 3.225757773533322, 2.7964404080084946, 2.445124300781722, 3.5068602639661752, 1.3939358165941083, 1.0883493740384103, 0.6463620552831327, 0.0, 9.053483271325586, 7.10998260811446, 5.44174687019205, 4.181807449782324, 7.0137205279323505, 3.4231740210944106, 2.7964404080084946, 2.3041126953809443, 3.3821837939878954, 2.5657641351036133, 1.4709489878128308, 0.590292409237803, 0.0), # 133
(8.687387388610095, 6.457791743574804, 7.33508486680317, 7.672302024884328, 6.7446523474443945, 3.2192369258220297, 2.7833587108338893, 2.44103643963706, 3.5010380719210428, 1.388698794814781, 1.0844994926462799, 0.6442044190298056, 0.0, 9.026553086525583, 7.0862486093278605, 5.422497463231399, 4.166096384444343, 7.0020761438420855, 3.417451015491884, 2.7833587108338893, 2.2994549470157355, 3.3723261737221972, 2.557434008294776, 1.4670169733606342, 0.5870719766886187, 0.0), # 134
(8.649201198639354, 6.421948794025897, 7.314954114884091, 7.646771544212684, 6.724399889442747, 3.212571404428512, 2.770096897454634, 2.4369085368263, 3.4950871133898262, 1.3833774132839443, 1.0805862424028239, 0.6420040134217377, 0.0, 8.99891673414202, 7.0620441476391145, 5.402931212014119, 4.150132239851832, 6.9901742267796525, 3.41167195155682, 2.770096897454634, 2.2946938603060802, 3.3621999447213735, 2.548923848070895, 1.4629908229768183, 0.583813526729627, 0.0), # 135
(8.610178658254235, 6.385632301916229, 7.294319840535841, 7.62066122534168, 6.703590131466344, 3.205745107116265, 2.7566322321348173, 2.4327212422651154, 3.4889909547398688, 1.3779612074043308, 1.0766015403375297, 0.6397570075960368, 0.0, 8.970543920586536, 7.037327083556404, 5.383007701687648, 4.133883622212991, 6.9779819094797375, 3.4058097391711617, 2.7566322321348173, 2.289817933654475, 3.351795065733172, 2.540220408447227, 1.4588639681071682, 0.58051202744693, 0.0), # 136
(8.570283401677534, 6.348786916192918, 7.273149200987342, 7.593931330317094, 6.682202991010689, 3.1987419316487826, 2.7429419791385277, 2.428455205869179, 3.4827331623385107, 1.3724397125786756, 1.0725373034798844, 0.63745957068981, 0.0, 8.941404352270776, 7.012055277587909, 5.362686517399421, 4.117319137736026, 6.965466324677021, 3.3998372882168506, 2.7429419791385277, 2.284815665463416, 3.3411014955053444, 2.5313104434390317, 1.4546298401974684, 0.577162446926629, 0.0), # 137
(8.529479063132047, 6.311357285803083, 7.251409353467515, 7.566542121184698, 6.660218385571278, 3.1915457757895624, 2.729003402729852, 2.4240910775541624, 3.4762973025530934, 1.3668024642097119, 1.0683854488593754, 0.6351078718401649, 0.0, 8.91146773560639, 6.986186590241813, 5.341927244296877, 4.100407392629135, 6.952594605106187, 3.3937275085758274, 2.729003402729852, 2.2796755541354017, 3.330109192785639, 2.5221807070615663, 1.450281870693503, 0.5737597532548258, 0.0), # 138
(8.487729276840568, 6.273288059693839, 7.229067455205284, 7.538453859990269, 6.63761623264361, 3.184140537302099, 2.7147937671728797, 2.4196095072357395, 3.469666941750957, 1.3610389977001744, 1.0641378935054902, 0.6326980801842089, 0.0, 8.880703777005019, 6.959678882026297, 5.32068946752745, 4.083116993100523, 6.939333883501914, 3.3874533101300353, 2.7147937671728797, 2.274386098072928, 3.318808116321805, 2.51281795333009, 1.4458134910410567, 0.5702989145176218, 0.0), # 139
(8.444997677025897, 6.234523886812306, 7.206090663429573, 7.509626808779583, 6.614376449723186, 3.176510113949888, 2.7002903367316984, 2.4149911448295818, 3.462825646299444, 1.3551388484527966, 1.0597865544477159, 0.6302263648590494, 0.0, 8.849082182878314, 6.932490013449542, 5.298932772238579, 4.0654165453583895, 6.925651292598888, 3.3809876027614147, 2.7002903367316984, 2.2689357956784915, 3.307188224861593, 2.5032089362598615, 1.4412181326859146, 0.5667748988011189, 0.0), # 140
(8.40124789791083, 6.195009416105602, 7.1824461353693, 7.480021229598415, 6.590478954305501, 3.1686384034964257, 2.6854703756703975, 2.4102166402513627, 3.455756982565893, 1.349091551870313, 1.0553233487155398, 0.6276888950017938, 0.0, 8.816572659637913, 6.904577845019731, 5.276616743577699, 4.047274655610939, 6.911513965131786, 3.3743032963519077, 2.6854703756703975, 2.26331314535459, 3.2952394771527507, 2.4933404098661387, 1.4364892270738603, 0.5631826741914184, 0.0), # 141
(8.356443573718156, 6.154689296520844, 7.158101028253392, 7.44959738449254, 6.565903663886058, 3.1605093037052074, 2.670311148253063, 2.4052666434167547, 3.448444516917647, 1.3428866433554572, 1.0507401933384497, 0.6250818397495496, 0.0, 8.783144913695466, 6.875900237245045, 5.253700966692247, 4.028659930066371, 6.896889033835294, 3.3673733007834565, 2.670311148253063, 2.2575066455037196, 3.282951831943029, 2.4831991281641805, 1.4316202056506786, 0.5595172087746222, 0.0), # 142
(8.310548338670674, 6.113508177005149, 7.133022499310772, 7.418315535507731, 6.540630495960352, 3.152106712339729, 2.6547899187437842, 2.4001218042414303, 3.4408718157220486, 1.3365136583109634, 1.0460290053459322, 0.6224013682394242, 0.0, 8.748768651462617, 6.846415050633665, 5.230145026729661, 4.009540974932889, 6.881743631444097, 3.360170525938002, 2.6547899187437842, 2.251504794528378, 3.270315247980176, 2.472771845169244, 1.4266044998621543, 0.5557734706368318, 0.0), # 143
(8.263525826991184, 6.071410706505636, 7.107177705770357, 7.386135944689768, 6.514639368023886, 3.1434145271634857, 2.6388839514066493, 2.3947627726410623, 3.4330224453464364, 1.3299621321395652, 1.0411817017674754, 0.619643649608525, 0.0, 8.713413579351014, 6.816080145693774, 5.205908508837376, 3.9898863964186946, 6.866044890692873, 3.3526678816974873, 2.6388839514066493, 2.245296090831061, 3.257319684011943, 2.4620453148965895, 1.4214355411540713, 0.5519464278641489, 0.0), # 144
(8.215339672902477, 6.0283415339694235, 7.080533804861075, 7.353018874084421, 6.487910197572155, 3.134416645939974, 2.6225705105057466, 2.3891701985313234, 3.424879972158151, 1.3232216002439972, 1.036190199632566, 0.6168048529939595, 0.0, 8.6770494037723, 6.784853382933553, 5.180950998162829, 3.969664800731991, 6.849759944316302, 3.344838277943853, 2.6225705105057466, 2.238869032814267, 3.2439550987860777, 2.451006291361474, 1.4161067609722149, 0.548031048542675, 0.0), # 145
(8.16595351062735, 5.984245308343629, 7.053057953811847, 7.318924585737469, 6.460422902100661, 3.1250969664326886, 2.605826860305165, 2.3833247318278863, 3.4164279625245353, 1.3162815980269928, 1.0310464159706916, 0.6138811475328351, 0.0, 8.639645831138118, 6.7526926228611845, 5.155232079853457, 3.948844794080978, 6.832855925049071, 3.3366546245590407, 2.605826860305165, 2.2322121188804918, 3.2302114510503306, 2.439641528579157, 1.4106115907623695, 0.5440223007585119, 0.0), # 146
(8.1153309743886, 5.93906667857537, 7.024717309851591, 7.283813341694685, 6.4321573991049, 3.1154393864051255, 2.5886302650689905, 2.3772070224464232, 3.40764998281293, 1.3091316608912866, 1.0257422678113395, 0.6108687023622593, 0.0, 8.601172567860118, 6.719555725984851, 5.1287113390566965, 3.9273949826738592, 6.81529996562586, 3.3280898314249923, 2.5886302650689905, 2.2253138474322327, 3.21607869955245, 2.4279377805648954, 1.4049434619703185, 0.5399151525977609, 0.0), # 147
(8.063435698409021, 5.892750293611764, 6.9954790302092364, 7.247645404001847, 6.403093606080374, 3.105427803620781, 2.5709579890613132, 2.3707977203026074, 3.398529599390676, 1.301761324239612, 1.0202696721839972, 0.6077636866193392, 0.0, 8.561599320349941, 6.68540055281273, 5.101348360919985, 3.905283972718835, 6.797059198781352, 3.3191168084236504, 2.5709579890613132, 2.2181627168719866, 3.201546803040187, 2.4158818013339496, 1.3990958060418472, 0.535704572146524, 0.0), # 148
(8.010231316911412, 5.845240802399927, 6.965310272113703, 7.210381034704727, 6.37321144052258, 3.0950461158431497, 2.5527872965462204, 2.3640774753121114, 3.3890503786251127, 1.2941601234747035, 1.0146205461181517, 0.6045622694411826, 0.0, 8.520895795019237, 6.650184963853008, 5.073102730590758, 3.88248037042411, 6.778100757250225, 3.3097084654369557, 2.5527872965462204, 2.21074722560225, 3.18660572026129, 2.403460344901576, 1.3930620544227408, 0.5313855274909026, 0.0), # 149
(7.955681464118564, 5.796482853886981, 6.934178192793912, 7.171980495849104, 6.342490819927017, 3.0842782208357287, 2.5340954517878003, 2.3570269373906068, 3.3791958868835836, 1.2863175939992944, 1.0087868066432906, 0.601260619964897, 0.0, 8.479031698279647, 6.6138668196138655, 5.043934033216452, 3.8589527819978824, 6.758391773767167, 3.2998377123468496, 2.5340954517878003, 2.2030558720255207, 3.1712454099635083, 2.390660165283035, 1.3868356385587826, 0.5269529867169983, 0.0), # 150
(7.899749774253275, 5.746421097020041, 6.902049949478785, 7.132404049480748, 6.310911661789184, 3.0731080163620113, 2.5148597190501416, 2.3496267564537683, 3.3689496905334293, 1.2782232712161197, 1.002760370788901, 0.5978549073275894, 0.0, 8.435976736542818, 6.576403980603482, 5.013801853944504, 3.8346698136483583, 6.737899381066859, 3.2894774590352753, 2.5148597190501416, 2.1950771545442938, 3.155455830894592, 2.377468016493583, 1.3804099898957571, 0.5224019179109128, 0.0), # 151
(7.842399881538343, 5.6950001807462245, 6.868892699397251, 7.091611957645439, 6.278453883604579, 3.0615194001854955, 2.4950573625973322, 2.3418575824172674, 3.3582953559419897, 1.2698666905279126, 0.9965331555844703, 0.5943413006663675, 0.0, 8.391700616220398, 6.537754307330042, 4.982665777922351, 3.809600071583737, 6.716590711883979, 3.2786006153841742, 2.4950573625973322, 2.1867995715610684, 3.1392269418022893, 2.36387065254848, 1.3737785398794504, 0.5177272891587478, 0.0), # 152
(7.78359542019656, 5.642164754012652, 6.834673599778224, 7.049564482388949, 6.245097402868703, 3.049496270069676, 2.4746656466934596, 2.333700065196776, 3.3472164494766075, 1.2612373873374074, 0.9900970780594861, 0.5907159691183387, 0.0, 8.346173043724027, 6.497875660301725, 4.95048539029743, 3.783712162012222, 6.694432898953215, 3.2671800912754865, 2.4746656466934596, 2.17821162147834, 3.1225487014343516, 2.3498548274629836, 1.3669347199556448, 0.5129240685466048, 0.0), # 153
(7.723300024450729, 5.587859465766439, 6.7993598078506325, 7.006221885757057, 6.210822137077053, 3.0370225237780484, 2.453661835602614, 2.325134854707968, 3.3356965375046217, 1.2523248970473384, 0.9834440552434354, 0.5869750818206104, 0.0, 8.299363725465357, 6.456725900026714, 4.917220276217177, 3.7569746911420143, 6.671393075009243, 3.2551887965911552, 2.453661835602614, 2.169301802698606, 3.1054110685385266, 2.335407295252353, 1.3598719615701265, 0.5079872241605854, 0.0), # 154
(7.6614773285236355, 5.532028964954703, 6.762918480843396, 6.961544429795533, 6.175608003725131, 3.0240820590741087, 2.4320231935888805, 2.316142600866515, 3.323719186393376, 1.2431187550604388, 0.9765660041658056, 0.5831148079102902, 0.0, 8.251242367856026, 6.414262887013191, 4.882830020829028, 3.7293562651813157, 6.647438372786752, 3.242599641213121, 2.4320231935888805, 2.160058613624363, 3.0878040018625654, 2.320514809931845, 1.3525836961686795, 0.5029117240867913, 0.0), # 155
(7.598090966638081, 5.474617900524564, 6.725316775985439, 6.915492376550157, 6.139434920308432, 3.0106587737213526, 2.40972698491635, 2.3067039535880913, 3.3112679625102084, 1.2336084967794434, 0.9694548418560842, 0.5791313165244852, 0.0, 8.201778677307685, 6.370444481769337, 4.84727420928042, 3.7008254903383295, 6.622535925020417, 3.2293855350233276, 2.40972698491635, 2.150470552658109, 3.069717460154216, 2.3051641255167192, 1.3450633551970879, 0.49769253641132405, 0.0), # 156
(7.533104573016862, 5.415570921423138, 6.686521850505682, 6.868025988066703, 6.102282804322456, 2.9967365654832747, 2.3867504738491094, 2.2967995627883675, 3.2983264322224626, 1.2237836576070855, 0.9621024853437583, 0.5750207768003032, 0.0, 8.150942360231976, 6.325228544803333, 4.810512426718791, 3.671350972821256, 6.596652864444925, 3.2155193879037145, 2.3867504738491094, 2.140526118202339, 3.051141402161228, 2.2893419960222348, 1.3373043701011365, 0.4923246292202853, 0.0), # 157
(7.464680946405239, 5.353748694041236, 6.644659961585297, 6.817327186238432, 6.062454070580665, 2.9814309445183143, 2.3625533604639286, 2.285748730145572, 3.2838873638663655, 1.213341479072786, 0.9542659587564906, 0.570633297016195, 0.0, 8.096485859415345, 6.276966267178143, 4.771329793782452, 3.640024437218358, 6.567774727732731, 3.200048222203801, 2.3625533604639286, 2.129593531798796, 3.0312270352903323, 2.2724423954128112, 1.3289319923170593, 0.48670442673102154, 0.0), # 158
(7.382286766978402, 5.282809876299521, 6.58894818200249, 6.7529828690913405, 6.010127539854418, 2.95965229467081, 2.334106381692858, 2.2696723053184926, 3.2621424204073812, 1.2005702485246865, 0.9445694892698324, 0.5651135436402591, 0.0, 8.025427646920194, 6.216248980042849, 4.722847446349162, 3.601710745574059, 6.5242848408147625, 3.17754122744589, 2.334106381692858, 2.114037353336293, 3.005063769927209, 2.250994289697114, 1.3177896364004982, 0.4802554432999565, 0.0), # 159
(7.284872094904309, 5.202172001162321, 6.51826746496324, 6.673933132806645, 5.94428008756453, 2.9308657560278157, 2.301121874191892, 2.248166328969728, 3.2324750757428835, 1.1853014129657236, 0.9328765847682567, 0.5583751624073207, 0.0, 7.93642060889358, 6.142126786480525, 4.664382923841283, 3.55590423889717, 6.464950151485767, 3.147432860557619, 2.301121874191892, 2.0934755400198686, 2.972140043782265, 2.2246443776022153, 1.3036534929926482, 0.47292472737839286, 0.0), # 160
(7.17322205458596, 5.11236079574043, 6.4333724765919245, 6.5809293778175455, 5.865595416188075, 2.895420057582683, 2.263840723003438, 2.2215002221290754, 3.1952765889996724, 1.1676645482927346, 0.9192902757666179, 0.5504806224089643, 0.0, 7.830374044819097, 6.055286846498606, 4.596451378833089, 3.5029936448782033, 6.390553177999345, 3.1101003109807053, 2.263840723003438, 2.0681571839876307, 2.9327977080940375, 2.1936431259391824, 1.2866744953183848, 0.46476007234003913, 0.0), # 161
(7.048121770426357, 5.013901987144635, 6.335017883012913, 6.474723004557244, 5.7747572282021356, 2.853663928328766, 2.2225038131699044, 2.1899434058263343, 3.150938219304545, 1.147789230402558, 0.9039135927797701, 0.5414923927367745, 0.0, 7.708197254180333, 5.956416320104519, 4.519567963898851, 3.4433676912076736, 6.30187643860909, 3.065920768156868, 2.2225038131699044, 2.03833137737769, 2.8873786141010678, 2.158241001519082, 1.2670035766025827, 0.4558092715586033, 0.0), # 162
(6.9103563668284975, 4.90732130248573, 6.223958350350585, 6.35606541345895, 5.672449226083792, 2.8059460972594175, 2.1773520297337003, 2.153765301091302, 3.0998512257843016, 1.1258050351920315, 0.8868495663225682, 0.5314729424823361, 0.0, 7.570799536460879, 5.846202367305696, 4.43424783161284, 3.3774151055760937, 6.199702451568603, 3.015271421527823, 2.1773520297337003, 2.0042472123281554, 2.836224613041896, 2.118688471152984, 1.2447916700701172, 0.4461201184077937, 0.0), # 163
(6.760710968195384, 4.793144468874502, 6.100948544729314, 6.225708004955863, 5.559355112310126, 2.752615293367992, 2.128626257737233, 2.113235328953779, 3.0424068675657407, 1.1018415385579923, 0.8682012269098661, 0.5204847407372336, 0.0, 7.419090191144328, 5.725332148109569, 4.34100613454933, 3.305524615673976, 6.0848137351314815, 2.9585294605352903, 2.128626257737233, 1.9661537809771372, 2.779677556155063, 2.075236001651955, 1.2201897089458629, 0.43574040626131844, 0.0), # 164
(6.599970698930017, 4.671897213421746, 5.966743132273474, 6.084402179481189, 5.436158589358215, 2.694020245647842, 2.076567382222911, 2.068622910443561, 2.9789964037756596, 1.0760283163972786, 0.8480716050565187, 0.5085902565930517, 0.0, 7.25397851771427, 5.594492822523568, 4.2403580252825925, 3.2280849491918353, 5.957992807551319, 2.8960720746209856, 2.076567382222911, 1.9243001754627442, 2.7180792946791077, 2.0281340598270634, 1.1933486264546949, 0.42471792849288603, 0.0), # 165
(6.428920683435397, 4.54410526323825, 5.82209677910744, 5.932899337468126, 5.3035433597051425, 2.630509683092322, 2.021416288233143, 2.020197466590449, 2.9100110935408576, 1.0484949446067282, 0.8265637312773799, 0.49585195914137514, 0.0, 7.0763738156542955, 5.454371550555126, 4.1328186563869, 3.145484833820184, 5.820022187081715, 2.8282764532266285, 2.021416288233143, 1.8789354879230868, 2.6517716798525712, 1.9776331124893758, 1.1644193558214881, 0.41310047847620457, 0.0), # 166
(6.248346046114523, 4.410294345434805, 5.667764151355587, 5.771950879349882, 5.1621931258279865, 2.562432334694784, 1.9634138608103373, 1.9682284184242402, 2.835842195988133, 1.0193709990831787, 0.8037806360873045, 0.48233231747378824, 0.0, 6.887185384447996, 5.30565549221167, 4.0189031804365225, 3.058112997249536, 5.671684391976266, 2.755519785793936, 1.9634138608103373, 1.8303088104962744, 2.5810965629139933, 1.9239836264499612, 1.1335528302711175, 0.4009358495849823, 0.0), # 167
(6.059031911370395, 4.270990187122201, 5.50449991514229, 5.60230820555966, 5.012791590203827, 2.490136929448583, 1.902800984996902, 1.9129851869747332, 2.7568809702442847, 0.9887860557234682, 0.7798253500011468, 0.468093800681876, 0.0, 6.6873225235789615, 5.149031807500635, 3.8991267500057343, 2.9663581671704042, 5.513761940488569, 2.6781792617646265, 1.902800984996902, 1.7786692353204163, 2.5063957951019136, 1.867436068519887, 1.100899983028458, 0.3882718351929274, 0.0), # 168
(5.861763403606015, 4.1267185154112305, 5.333058736591924, 5.4247227165306615, 4.856022455309747, 2.413972196347072, 1.8398185458352458, 1.8547371932717271, 2.6735186754361124, 0.9568696904244344, 0.7548009035337614, 0.45319887785722274, 0.0, 6.477694532530785, 4.985187656429449, 3.774004517668807, 2.8706090712733023, 5.347037350872225, 2.596632070580418, 1.8398185458352458, 1.724265854533623, 2.4280112276548733, 1.808240905510221, 1.066611747318385, 0.3751562286737483, 0.0), # 169
(5.657325647224384, 3.978005057412684, 5.154195281828863, 5.23994581269609, 4.692569423622822, 2.334286864383604, 1.7747074283677764, 1.7937538583450197, 2.5861465706904125, 0.9237514790829147, 0.7288103272000027, 0.4377100180914133, 0.0, 6.259210710787055, 4.814810199005545, 3.6440516360000137, 2.7712544372487433, 5.172293141380825, 2.5112554016830275, 1.7747074283677764, 1.6673477602740028, 2.346284711811411, 1.7466486042320304, 1.0308390563657726, 0.36163682340115316, 0.0), # 170
(5.4465037666285, 3.82537554023735, 4.968664216977482, 5.048728894489152, 4.523116197620137, 2.2514296625515327, 1.7077085176369027, 1.7303046032244096, 2.495155915133985, 0.8895609975957474, 0.7019566515147247, 0.4216896904760322, 0.0, 6.032780357831365, 4.638586595236354, 3.509783257573624, 2.6686829927872413, 4.99031183026797, 2.4224264445141737, 1.7077085176369027, 1.6081640446796661, 2.2615580988100685, 1.6829096314963843, 0.9937328433954964, 0.3477614127488501, 0.0), # 171
(5.230082886221365, 3.6693556909960217, 4.777220208162156, 4.851823362343048, 4.348346479778769, 2.1657493198442115, 1.6390626986850327, 1.664658848939696, 2.4009379678936282, 0.8544278218597702, 0.6743429069927823, 0.4052003641026643, 0.0, 5.799312773147303, 4.457204005129307, 3.3717145349639117, 2.56328346557931, 4.8018759357872565, 2.3305223885155746, 1.6390626986850327, 1.5469637998887225, 2.1741732398893845, 1.6172744541143496, 0.9554440416324312, 0.3335777900905475, 0.0), # 172
(5.00884813040598, 3.510471236799489, 4.58061792150726, 4.649980616690982, 4.168943972575801, 2.077594565254994, 1.5690108565545748, 1.5970860165206766, 2.303883988096141, 0.8184815277718206, 0.6460721241490297, 0.3883045080628938, 0.0, 5.5597172562184625, 4.271349588691831, 3.2303606207451483, 2.4554445833154612, 4.607767976192282, 2.235920423128947, 1.5690108565545748, 1.483996118039281, 2.0844719862879004, 1.5499935388969943, 0.916123584301452, 0.31913374879995354, 0.0), # 173
(4.783584623585344, 3.349247904758541, 4.3796120231371685, 4.443952057966156, 3.9855923784883105, 1.987314127777233, 1.4977938762879377, 1.5278555269971503, 2.204385234868321, 0.7818516912287369, 0.6172473334983214, 0.37106459144830567, 0.0, 5.314903106528433, 4.081710505931362, 3.0862366674916064, 2.34555507368621, 4.408770469736642, 2.1389977377960103, 1.4977938762879377, 1.4195100912694523, 1.9927961892441552, 1.4813173526553853, 0.8759224046274336, 0.3044770822507765, 0.0), # 174
(4.555077490162455, 3.18621142198397, 4.174957179176257, 4.2344890866017755, 3.7989753999933793, 1.8952567364042834, 1.425652642927529, 1.457236801398915, 2.102832967336968, 0.7446678881273562, 0.5879715655555117, 0.35354308335048457, 0.0, 5.0657796235608075, 3.8889739168553294, 2.939857827777558, 2.234003664382068, 4.205665934673936, 2.040131521958481, 1.425652642927529, 1.3537548117173452, 1.8994876999966896, 1.411496362200592, 0.8349914358352515, 0.28965558381672457, 0.0), # 175
(4.324111854540319, 3.0218875155865668, 3.9674080557488987, 4.0223431030310435, 3.609776739568087, 1.8017711201294973, 1.3528280415157574, 1.3854992607557703, 1.9996184446288805, 0.7070596943645169, 0.558347850835455, 0.33580245286101496, 0.0, 4.813256106799174, 3.693826981471164, 2.791739254177275, 2.1211790830935504, 3.999236889257761, 1.9396989650580787, 1.3528280415157574, 1.2869793715210696, 1.8048883697840434, 1.3407810343436815, 0.7934816111497798, 0.2747170468715061, 0.0), # 176
(4.0914728411219325, 2.856801912677122, 3.7577193189794698, 3.808265507687162, 3.4186800996895155, 1.7072060079462288, 1.2795609570950313, 1.3129123260975137, 1.8951329258708567, 0.6691566858370562, 0.528479219853006, 0.3179051690714816, 0.0, 4.5582418557271245, 3.496956859786297, 2.6423960992650297, 2.0074700575111684, 3.7902658517417134, 1.838077256536519, 1.2795609570950313, 1.2194328628187348, 1.7093400498447577, 1.269421835895721, 0.751543863795894, 0.25970926478882933, 0.0), # 177
(3.8579455743102966, 2.6914803403664256, 3.5466456349923448, 3.593007701003337, 3.226369182834742, 1.6119101288478317, 1.2060922747077587, 1.239745418453944, 1.7897676701896952, 0.6310884384418126, 0.49846870312301883, 0.299913701073469, 0.0, 4.301646169828252, 3.299050711808158, 2.4923435156150937, 1.8932653153254375, 3.5795353403793904, 1.7356435858355217, 1.2060922747077587, 1.1513643777484512, 1.613184591417371, 1.1976692336677792, 0.7093291269984691, 0.24468003094240237, 0.0), # 178
(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, 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), # 179
)
passenger_allighting_rate = (
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 0
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 1
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 2
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 3
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 4
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 5
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 6
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 7
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 8
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 9
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 10
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 11
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 12
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 13
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 14
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 15
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 16
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 17
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 18
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 19
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 20
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 21
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 22
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 23
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 24
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 25
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 26
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 27
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 28
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 29
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 30
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 31
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 32
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 33
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 34
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 35
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 36
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 37
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 38
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 39
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 40
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 41
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 42
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 43
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 44
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 45
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 46
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 47
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 48
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 49
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 50
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 51
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 52
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 53
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 54
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 55
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 56
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 57
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 58
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 59
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 60
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 61
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 62
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 63
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 64
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 65
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 66
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 67
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 68
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 69
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 70
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 71
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 72
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 73
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 74
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 75
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 76
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 77
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 78
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 79
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 80
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 81
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 82
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 83
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 84
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 85
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 86
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 87
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 88
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 89
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 90
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 91
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 92
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 93
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 94
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 95
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 96
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 97
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 98
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 99
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 100
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 101
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 102
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 103
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 104
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 105
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 106
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 107
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 108
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 109
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 110
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 111
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 112
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 113
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 114
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 115
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 116
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 117
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 118
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 119
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 120
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 121
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 122
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 123
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 124
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 125
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 126
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 127
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 128
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 129
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 130
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 131
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 132
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 133
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 134
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 135
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(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 166
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 167
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 168
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 169
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 170
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 171
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 172
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 173
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 174
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 175
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 176
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 177
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 178
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 179
)
"""
parameters for reproducibiliy. More information: https://numpy.org/doc/stable/reference/random/parallel.html
"""
#initial entropy
entropy = 8991598675325360468762009371570610170
#index for seed sequence child
child_seed_index = (
1, # 0
30, # 1
)
|
# Problem Link : https://codeforces.com/problemset/problem/339/A#
s = input()
nums = list(s[0:len(s):2])
nums.sort()
j = 0
for i in range(len(s)):
if i%2 == 0:
print(nums[j], end="")
j += 1
else:
print("+", end="")
|
#
# PySNMP MIB module CISCO-SNMPv2-CAPABILITY (http://snmplabs.com/pysmi)
# ASN.1 source file:///Users/davwang4/Dev/mibs.snmplabs.com/asn1/CISCO-SNMPv2-CAPABILITY
# Produced by pysmi-0.3.4 at Wed May 1 12:12:36 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")
ValueRangeConstraint, ValueSizeConstraint, ConstraintsIntersection, SingleValueConstraint, ConstraintsUnion = mibBuilder.importSymbols("ASN1-REFINEMENT", "ValueRangeConstraint", "ValueSizeConstraint", "ConstraintsIntersection", "SingleValueConstraint", "ConstraintsUnion")
ciscoAgentCapability, = mibBuilder.importSymbols("CISCO-SMI", "ciscoAgentCapability")
ModuleCompliance, NotificationGroup, AgentCapabilities = mibBuilder.importSymbols("SNMPv2-CONF", "ModuleCompliance", "NotificationGroup", "AgentCapabilities")
TimeTicks, MibScalar, MibTable, MibTableRow, MibTableColumn, IpAddress, MibIdentifier, iso, ObjectIdentity, Integer32, Unsigned32, ModuleIdentity, Counter64, Counter32, Gauge32, NotificationType, Bits = mibBuilder.importSymbols("SNMPv2-SMI", "TimeTicks", "MibScalar", "MibTable", "MibTableRow", "MibTableColumn", "IpAddress", "MibIdentifier", "iso", "ObjectIdentity", "Integer32", "Unsigned32", "ModuleIdentity", "Counter64", "Counter32", "Gauge32", "NotificationType", "Bits")
DisplayString, TextualConvention = mibBuilder.importSymbols("SNMPv2-TC", "DisplayString", "TextualConvention")
ciscoSnmpV2Capability = ModuleIdentity((1, 3, 6, 1, 4, 1, 9, 7, 113))
ciscoSnmpV2Capability.setRevisions(('2007-11-12 00:00', '2006-05-30 00:00', '2006-04-24 00:00', '2004-03-18 00:00', '2002-02-07 00:00', '2002-01-31 00:00', '1994-08-18 00:00',))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
if mibBuilder.loadTexts: ciscoSnmpV2Capability.setRevisionsDescriptions(('Added capability statement ciscoSnmpV2Capc4710aceVA1R700 for ACE 4710 Application Control Engine Appliance.', 'Added capability statement ciscoSnmpV2CapACSWV03R000 for Application Control Engine (ACE). For ciscoSnmpV2CapabilityV10R02 commented out references to object groups snmpStatsGroup, snmpV1Group, snmpORGroup, snmpTrapGroup because they are defined only in the original RFC 1450, not in the latest RFC 3418.', 'Added the VARIATION for the notification authenticationFailure in ciscoSnmpV2CapCatOSV08R0301. Added capability statement ciscoSnmpV2CapCatOSV08R0601.', 'Added ciscoSnmpV2CapCatOSV08R0301.', 'Added following agent capabilities: - ciscoMgxSnmpV2CapabilityV20 for MGX8850 series - ciscoBpxSesSnmpV2CapabilityV10 for BPX SES.', "Added 'ciscoRpmsSnmpV2CapabilityV20' for Cisco Resource Policy Management Server (RPMS) 2.0.", 'Initial version of this MIB module.',))
if mibBuilder.loadTexts: ciscoSnmpV2Capability.setLastUpdated('200711120000Z')
if mibBuilder.loadTexts: ciscoSnmpV2Capability.setOrganization('Cisco Systems, Inc.')
if mibBuilder.loadTexts: ciscoSnmpV2Capability.setContactInfo('Cisco Systems Customer Service Postal: 170 West Tasman Drive San Jose, CA 95134 USA Tel: +1 800 553-NETS E-mail: [email protected]')
if mibBuilder.loadTexts: ciscoSnmpV2Capability.setDescription('Agent capabilities for SNMPv2-MIB')
ciscoSnmpV2CapabilityV10R02 = AgentCapabilities((1, 3, 6, 1, 4, 1, 9, 7, 113, 1))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
ciscoSnmpV2CapabilityV10R02 = ciscoSnmpV2CapabilityV10R02.setProductRelease('Cisco IOS 10.2')
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
ciscoSnmpV2CapabilityV10R02 = ciscoSnmpV2CapabilityV10R02.setStatus('current')
if mibBuilder.loadTexts: ciscoSnmpV2CapabilityV10R02.setDescription('IOS 10.2 SNMPv2 MIB capabilities')
ciscoRpmsSnmpV2CapabilityV20 = AgentCapabilities((1, 3, 6, 1, 4, 1, 9, 7, 113, 2))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
ciscoRpmsSnmpV2CapabilityV20 = ciscoRpmsSnmpV2CapabilityV20.setProductRelease('Cisco Resource Policy Management Server (RPMS) 2.0')
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
ciscoRpmsSnmpV2CapabilityV20 = ciscoRpmsSnmpV2CapabilityV20.setStatus('current')
if mibBuilder.loadTexts: ciscoRpmsSnmpV2CapabilityV20.setDescription('Cisco RPMS 2.0 SNMPv2 MIB capabilities.')
ciscoMgxSnmpV2CapabilityV20 = AgentCapabilities((1, 3, 6, 1, 4, 1, 9, 7, 113, 3))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
ciscoMgxSnmpV2CapabilityV20 = ciscoMgxSnmpV2CapabilityV20.setProductRelease('MGX8850 Release 2.0.00')
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
ciscoMgxSnmpV2CapabilityV20 = ciscoMgxSnmpV2CapabilityV20.setStatus('current')
if mibBuilder.loadTexts: ciscoMgxSnmpV2CapabilityV20.setDescription('SNMPv2-MIB capabilities in MGX Series.')
ciscoBpxSesSnmpV2CapabilityV10 = AgentCapabilities((1, 3, 6, 1, 4, 1, 9, 7, 113, 4))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
ciscoBpxSesSnmpV2CapabilityV10 = ciscoBpxSesSnmpV2CapabilityV10.setProductRelease('Cisco BPX SES Release 1.0.00')
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
ciscoBpxSesSnmpV2CapabilityV10 = ciscoBpxSesSnmpV2CapabilityV10.setStatus('current')
if mibBuilder.loadTexts: ciscoBpxSesSnmpV2CapabilityV10.setDescription('SNMPv2-MIB capabilities.')
ciscoSnmpV2CapCatOSV08R0301 = AgentCapabilities((1, 3, 6, 1, 4, 1, 9, 7, 113, 5))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
ciscoSnmpV2CapCatOSV08R0301 = ciscoSnmpV2CapCatOSV08R0301.setProductRelease('Cisco CatOS 8.3(1) for Catalyst 6000/6500\n and Cisco 7600 series devices.')
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
ciscoSnmpV2CapCatOSV08R0301 = ciscoSnmpV2CapCatOSV08R0301.setStatus('current')
if mibBuilder.loadTexts: ciscoSnmpV2CapCatOSV08R0301.setDescription('SNMPv2-MIB capabilities.')
ciscoSnmpV2CapCatOSV08R0601 = AgentCapabilities((1, 3, 6, 1, 4, 1, 9, 7, 113, 6))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
ciscoSnmpV2CapCatOSV08R0601 = ciscoSnmpV2CapCatOSV08R0601.setProductRelease('Cisco CatOS 8.6(1) for Catalyst 6000/6500\n and Cisco 7600 series devices.')
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
ciscoSnmpV2CapCatOSV08R0601 = ciscoSnmpV2CapCatOSV08R0601.setStatus('current')
if mibBuilder.loadTexts: ciscoSnmpV2CapCatOSV08R0601.setDescription('SNMPv2-MIB capabilities.')
ciscoSnmpV2CapACSWV03R000 = AgentCapabilities((1, 3, 6, 1, 4, 1, 9, 7, 113, 7))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
ciscoSnmpV2CapACSWV03R000 = ciscoSnmpV2CapACSWV03R000.setProductRelease('ACSW (Application Control Software) 3.0\n for Application Control Engine (ACE) \n Service Module.')
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
ciscoSnmpV2CapACSWV03R000 = ciscoSnmpV2CapACSWV03R000.setStatus('current')
if mibBuilder.loadTexts: ciscoSnmpV2CapACSWV03R000.setDescription('SNMPv2-MIB capabilities.')
ciscoSnmpV2Capc4710aceVA1R700 = AgentCapabilities((1, 3, 6, 1, 4, 1, 9, 7, 113, 8))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
ciscoSnmpV2Capc4710aceVA1R700 = ciscoSnmpV2Capc4710aceVA1R700.setProductRelease('ACSW (Application Control Software) A1(7)\n for ACE 4710 Application Control Engine \n Appliance.')
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
ciscoSnmpV2Capc4710aceVA1R700 = ciscoSnmpV2Capc4710aceVA1R700.setStatus('current')
if mibBuilder.loadTexts: ciscoSnmpV2Capc4710aceVA1R700.setDescription('SNMPv2-MIB capabilities.')
mibBuilder.exportSymbols("CISCO-SNMPv2-CAPABILITY", ciscoSnmpV2CapCatOSV08R0601=ciscoSnmpV2CapCatOSV08R0601, ciscoSnmpV2CapACSWV03R000=ciscoSnmpV2CapACSWV03R000, PYSNMP_MODULE_ID=ciscoSnmpV2Capability, ciscoBpxSesSnmpV2CapabilityV10=ciscoBpxSesSnmpV2CapabilityV10, ciscoSnmpV2CapCatOSV08R0301=ciscoSnmpV2CapCatOSV08R0301, ciscoMgxSnmpV2CapabilityV20=ciscoMgxSnmpV2CapabilityV20, ciscoRpmsSnmpV2CapabilityV20=ciscoRpmsSnmpV2CapabilityV20, ciscoSnmpV2Capability=ciscoSnmpV2Capability, ciscoSnmpV2Capc4710aceVA1R700=ciscoSnmpV2Capc4710aceVA1R700, ciscoSnmpV2CapabilityV10R02=ciscoSnmpV2CapabilityV10R02)
|
description = 'Asyn serial controllers in the SINQ AMOR.'
group='lowlevel'
pvprefix = 'SQ:AMOR:serial'
devices = dict(
serial1=device(
'nicos_ess.devices.epics.extensions.EpicsCommandReply',
epicstimeout=3.0,
description='Controller of the devices connected to serial 1',
commandpv=pvprefix + '1.AOUT',
replypv=pvprefix + '1.AINP',
),
serial2=device(
'nicos_ess.devices.epics.extensions.EpicsCommandReply',
epicstimeout=3.0,
description='Controller of the devices connected to serial 2',
commandpv=pvprefix + '2.AOUT',
replypv=pvprefix + '2.AINP',
),
serial3=device(
'nicos_ess.devices.epics.extensions.EpicsCommandReply',
epicstimeout=3.0,
description='Controller of the devices connected to serial 3',
commandpv=pvprefix + '3.AOUT',
replypv=pvprefix + '3.AINP',
),
cter1=device(
'nicos_ess.devices.epics.extensions.EpicsCommandReply',
epicstimeout=3.0,
description='Controller of the counter box',
commandpv='SQ:AMOR:cter1.AOUT',
replypv='SQ:AMOR:cter1.AINP',
),
)
|
# SPDX-FileCopyrightText: 2021 Pierre Constantineau
# SPDX-License-Identifier: MIT
"""
These keycodes are based on Universal Serial Bus HID Usage Tables Document
Version 1.12
Chapter 10: Keyboard/Keypad Page(0x07) - Page 53
https://www.usb.org/sites/default/files/documents/hut1_12v2.pdf
"""
class Keycode:
NO = 0x00
XXXXXXX = 0x00
ROLL_OVER = 0x01
TRANSPARENT = 0x01
TRNS = 0x01
_______ = 0x01
POST_FAIL = 0x02
UNDEFINED = 0x03
A = 0x04
B = 0x05
C = 0x06
D = 0x07
E = 0x08
F = 0x09
G = 0x0A
H = 0x0B
I = 0x0C
J = 0x0D
K = 0x0E
L = 0x0F
M = 0x10
N = 0x11
O = 0x12
P = 0x13
Q = 0x14
R = 0x15
S = 0x16
T = 0x17
U = 0x18
V = 0x19
W = 0x1A
X = 0x1B
Y = 0x1C
Z = 0x1D
ONE = 0x1E
TWO = 0x1F
THREE = 0x20
FOUR = 0x21
FIVE = 0x22
SIX = 0x23
SEVEN = 0x24
EIGHT = 0x25
NINE = 0x26
ZERO = 0x27
ENTER = 0x28
ESCAPE = 0x29
BSPACE = 0x2A
TAB = 0x2B
SPACE = 0x2C
MINUS = 0x2D
EQUAL = 0x2E
LBRACKET = 0x2F
RBRACKET = 0x30
BSLASH = 0x31
NONUS_HASH = 0x32
SCOLON = 0x33
QUOTE = 0x34
GRAVE = 0x35
COMMA = 0x36
DOT = 0x37
SLASH = 0x38
CAPSLOCK = 0x39
F1 = 0x3A
F2 = 0x3B
F3 = 0x3C
F4 = 0x3D
F5 = 0x3E
F6 = 0x3F
F7 = 0x40
F8 = 0x41
F9 = 0x42
F10 = 0x43
F11 = 0x44
F12 = 0x45
PSCREEN = 0x46
SCROLLLOCK = 0x47
PAUSE = 0x48
INSERT = 0x49
HOME = 0x4A
PGUP = 0x4B
DELETE = 0x4C
END = 0x4D
PGDOWN = 0x4E
RIGHT = 0x4F
LEFT = 0x50
DOWN = 0x51
UP = 0x52
NUMLOCK = 0x53
KP_SLASH = 0x54
KP_ASTERISK = 0x55
KP_MINUS = 0x56
KP_PLUS = 0x57
KP_ENTER = 0x58
KP_1 = 0x59
KP_2 = 0x5A
KP_3 = 0x5B
KP_4 = 0x5C
KP_5 = 0x5D
KP_6 = 0x5E
KP_7 = 0x5F
KP_8 = 0x60
KP_9 = 0x61
KP_0 = 0x62
KP_DOT = 0x63
NONUS_BSLASH = 0x64
APPLICATION = 0x65
POWER = 0x66
KP_EQUAL = 0x67
F13 = 0x68
F14 = 0x69
F15 = 0x6A
F16 = 0x6B
F17 = 0x6C
F18 = 0x6D
F19 = 0x6E
F20 = 0x6F
F21 = 0x70
F22 = 0x71
F23 = 0x72
F24 = 0x73
EXECUTE = 0x74
HELP = 0x75
MENU = 0x76
SELECT = 0x77
STOP = 0x78
AGAIN = 0x79
UNDO = 0x7A
CUT = 0x7B
COPY = 0x7C
PASTE = 0x7D
FIND = 0x7E
MUTE = 0x7F
VOLUP = 0x80
VOLDOWN = 0x81
LOCKING_CAPS = 0x82
LOCKING_NUM = 0x83
LOCKING_SCROLL = 0x84
KP_COMMA = 0x85
KP_EQUAL_AS400 = 0x86
INT1 = 0x87
INT2 = 0x88
INT3 = 0x89
INT4 = 0x8A
INT5 = 0x8B
INT6 = 0x8C
INT7 = 0x8D
INT8 = 0x8E
INT9 = 0x8F
LANG1 = 0x90
LANG2 = 0x91
LANG3 = 0x92
LANG4 = 0x93
LANG5 = 0x94
LANG6 = 0x95
LANG7 = 0x96
LANG8 = 0x97
LANG9 = 0x98
ALT_ERASE = 0x99
SYSREQ = 0x9A
CANCEL = 0x9B
CLEAR = 0x9C
PRIOR = 0x9D
RETURN = 0x9E
SEPARATOR = 0x9F
OUT = 0xA0
OPER = 0xA1
CLEAR_AGAIN = 0xA2
CRSEL = 0xA3
EXSEL = 0xA4
# LAST OF THE VALID KEYCODES ANYTHING BELOW SHOULD BE FILTERED OUT
RESERVED_A5 = 0xA5 # Used as macro identifier
RESERVED_A6 = 0xA6
RESERVED_A7 = 0xA7
RESERVED_A8 = 0xA8
RESERVED_A9 = 0xA9
RESERVED_AA = 0xAA
RESERVED_AB = 0xAB
RESERVED_AC = 0xAC
RESERVED_AD = 0xAD
RESERVED_AE = 0xAE
RESERVED_AF = 0xAF
LCTRL = 0xE0
LSHIFT = 0xE1
LALT = 0xE2
LGUI = 0xE3
RCTRL = 0xE4
RSHIFT = 0xE5
RALT = 0xE6
RGUI = 0xE7
LAYER_0 = 0xF0
LAYER_1 = 0xF1
LAYER_2 = 0xF2
LAYER_3 = 0xF3
LAYER_4 = 0xF4
LAYER_5 = 0xF5
LAYER_6 = 0xF6
LAYER_7 = 0xF7
LAYER_8 = 0xF8
LAYER_9 = 0xF9
LAYER_A = 0xFA
LAYER_B = 0xFB
LAYER_C = 0xFC
LAYER_D = 0xFD
LAYER_E = 0xFE
LAYER_F = 0xFF
LCTL = LCTRL
RCTL = RCTRL
LSFT = LSHIFT
RSFT = RSHIFT
ESC = ESCAPE
BSPC = BSPACE
ENT = ENTER
DEL = DELETE
INS = INSERT
CAPS = CAPSLOCK
CLCK = CAPSLOCK
RGHT = RIGHT
PGDN = PGDOWN
PSCR = PSCREEN
SLCK = SCROLLLOCK
PAUS = PAUSE
BRK = PAUSE
NLCK = NUMLOCK
SPC = SPACE
MINS = MINUS
EQL = EQUAL
GRV = GRAVE
RBRC = RBRACKET
LBRC = LBRACKET
COMM = COMMA
BSLS = BSLASH
SLSH = SLASH
SCLN = SCOLON
QUOT = QUOTE
APP = APPLICATION
NUHS = NONUS_HASH
NUBS = NONUS_BSLASH
LCAP = LOCKING_CAPS
LNUM = LOCKING_NUM
LSCR = LOCKING_SCROLL
ERAS = ALT_ERASE
CLR = CLEAR
# Japanese specific
ZKHK = GRAVE
RO = INT1
KANA = INT2
JYEN = INT3
HENK = INT4
MHEN = INT5
# Korean specific
HAEN = LANG1
HANJ = LANG2
# Keypad
P1 = KP_1
P2 = KP_2
P3 = KP_3
P4 = KP_4
P5 = KP_5
P6 = KP_6
P7 = KP_7
P8 = KP_8
P9 = KP_9
P0 = KP_0
PDOT = KP_DOT
PCMM = KP_COMMA
PSLS = KP_SLASH
PAST = KP_ASTERISK
PMNS = KP_MINUS
PPLS = KP_PLUS
PEQL = KP_EQUAL
PENT = KP_ENTER
# Unix function key
EXEC = EXECUTE
SLCT = SELECT
AGIN = AGAIN
PSTE = PASTE
# GUI key aliases
LCMD = LGUI
LWIN = LGUI
RCMD = RGUI
RWIN = RGUI
BIT_LCTRL = (1)
BIT_LSHIFT = (2)
BIT_LALT = (4)
BIT_LGUI = (8)
BIT_RCTRL = (16)
BIT_RSHIFT = (32)
BIT_RALT = (64)
BIT_RGUI = (128)
MOD_LCTRL = (BIT_LCTRL << 8)
MOD_LSHIFT = (BIT_LSHIFT << 8)
MOD_LALT = (BIT_LALT << 8)
MOD_LGUI = (BIT_LGUI << 8)
MOD_RCTRL = (BIT_RCTRL << 8)
MOD_RSHIFT = (BIT_RSHIFT << 8)
MOD_RALT = (BIT_RALT << 8)
MOD_RGUI = (BIT_RGUI << 8)
def MOD(M, KC):
return ( KC | M )
def LALT(KEY):
return ( KEY | ((4) << 8) )
def RALT(KEY):
return ( KEY | ((64)<< 8) )
def LCTL(KEY):
return ( KEY | ((1)<< 8) )
def RCTL(KEY):
return ( KEY | ((16) << 8) )
def RSFT(KEY):
return ( KEY | ((32) << 8) )
def LSFT(KEY):
return ( KEY | ((2) << 8) )
def LGUI(KEY):
return ( KEY | ((8) << 8) )
def RGUI(KEY):
return ( KEY | ((128) << 8) )
def S(KEY):
return ( KEY | ((2) << 8) )
LT = MOD(MOD_LSHIFT, COMMA)
GT = MOD(MOD_LSHIFT, DOT)
TILD = MOD(MOD_LSHIFT, GRV)
EXLM = MOD(MOD_LSHIFT, ONE)
AT = MOD(MOD_LSHIFT, TWO)
HASH = MOD(MOD_LSHIFT, THREE)
DLR = MOD(MOD_LSHIFT, FOUR)
PERC = MOD(MOD_LSHIFT, FIVE)
CIRC = MOD(MOD_LSHIFT, SIX)
AMPR = MOD(MOD_LSHIFT, SEVEN)
ASTR = MOD(MOD_LSHIFT, EIGHT)
LPRN = MOD(MOD_LSHIFT, NINE)
RPRN = MOD(MOD_LSHIFT, ZERO)
UNDS = MOD(MOD_LSHIFT, MINUS)
PLUS = MOD(MOD_LSHIFT, EQUAL)
LCBR = MOD(MOD_LSHIFT, LBRC)
RCBR = MOD(MOD_LSHIFT, RBRC)
PIPE = MOD(MOD_LSHIFT, BSLS)
COLN = MOD(MOD_LSHIFT, SCLN)
DQUO = MOD(MOD_LSHIFT, QUOTE)
DQT = DQUO
LT = MOD(MOD_LSHIFT, COMMA)
GT = MOD(MOD_LSHIFT, DOT)
QUES = MOD(MOD_LSHIFT, SLASH)
NUTL = MOD(MOD_LSHIFT,NUHS)
NUPI = MOD(MOD_LSHIFT,NUBS)
LABK = LT
RABK = GT
def MC(KC):
return (( KC << 8 ) | 0xA5 ) # move KC to upper 8 bits and use RESERVED_A5 keycode for marking this as a macro.
def KB(KC):
return (( KC << 8 ) | 0xA6 ) # move KC to upper 8 bits and use RESERVED_A6 keycode for marking this as a special keyboard function.
def MK(KC):
return (( KC << 8 ) | 0xA7 ) # move KC to upper 8 bits and use RESERVED_A7 keycode for marking this as a media key.
def MS(KC):
return (( KC << 8 ) | 0xA9 ) # move KC to upper 8 bits and use RESERVED_A9 keycode for marking this as a mouse key.
# Mousekey
MS_OFF = MS(A)
MS_UP = MS(B)
MS_DOWN = MS(C)
MS_LEFT = MS(D)
MS_RIGHT = MS(E)
MS_BTN1 = MS(F)
MS_BTN2 = MS(G)
MS_BTN3 = MS(H)
MS_BTN4 = MS(I)
MS_BTN5 = MS(J)
MS_WH_UP = MS(K)
MS_WH_DOWN = MS(L)
MS_WH_DN = MS_WH_DOWN
MS_WH_LEFT = MS(M)
MS_WH_RIGHT = MS(N)
MS_ACCEL0 = MS(O)
MS_ACCEL1 = MS(P)
MS_ACCEL2 = MS(Q)
MS_U = MS_UP
MS_D = MS_DOWN
MS_L = MS_LEFT
MS_R = MS_RIGHT
BTN1 = MS_BTN1
BTN2 = MS_BTN2
BTN3 = MS_BTN3
BTN4 = MS_BTN4
BTN5 = MS_BTN5
WH_U = MS_WH_UP
WH_D = MS_WH_DOWN
WH_L = MS_WH_LEFT
WH_R = MS_WH_RIGHT
ACL0 = MS_ACCEL0
ACL1 = MS_ACCEL1
ACL2 = MS_ACCEL2 |
#!/usr/bin/python3
def NativeZeros(nRows, nCols):
return [range(nRows) for col in range(nCols)]
matrix = NativeZeros(4, 4)
print(matrix)
print(sum([sum(row) for row in matrix]))
|
def prepare_config_line(name, value):
"""
Create the entry for one specific configuration with it's name and value
:param name:
:param value:
:return: string
"""
conf_item = '{}'.format(name)
if value and value.lower() != 'true':
conf_item += ': {}'.format(value.capitalize())
return conf_item
def get_config_string(config):
"""
Use the given config to extract one string for the output.
:param config: a dictionary of configs from a Build object
:return: string representation
"""
configs = {}
for key, entry in config.items():
abbrev = entry.get('abbreviation')
value = entry.get('value')
category = entry.get('category')
string = prepare_config_line(abbrev if abbrev else key, value)
if category:
configs.setdefault(category.capitalize(), []).append(string)
out = ''
for category in configs:
if len(configs[category]) > 0:
out += '**' + category + '**: '
out += ', '.join(configs[category])
out += '\n'
return out if out != '' else None
|
# Copyright 2013 The Chromium Authors. All rights reserved.
# Use of this source code is governed by a BSD-style license that can be
# found in the LICENSE file.
{
'variables': {
'chromium_code': 1,
},
'includes': [
'../build/win_precompile.gypi',
'../chrome/version.gypi',
'blacklist.gypi',
],
'targets': [
{
'target_name': 'chrome_elf',
'type': 'shared_library',
'include_dirs': [
'..',
],
'sources': [
'chrome_elf.def',
'chrome_elf_main.cc',
'chrome_elf_main.h',
],
'dependencies': [
'blacklist',
'chrome_elf_lib',
],
'msvs_settings': {
'VCLinkerTool': {
'BaseAddress': '0x01c20000',
# Set /SUBSYSTEM:WINDOWS.
'SubSystem': '2',
'AdditionalDependencies!': [
'user32.lib',
],
'IgnoreDefaultLibraryNames': [
'user32.lib',
],
},
},
},
{
'target_name': 'chrome_elf_unittests_exe',
'product_name': 'chrome_elf_unittests',
'type': 'executable',
'sources': [
'blacklist/test/blacklist_test.cc',
'create_file/chrome_create_file_unittest.cc',
'elf_imports_unittest.cc',
'ntdll_cache_unittest.cc',
],
'include_dirs': [
'..',
'<(SHARED_INTERMEDIATE_DIR)',
],
'dependencies': [
'chrome_elf_lib',
'../base/base.gyp:base',
'../base/base.gyp:run_all_unittests',
'../base/base.gyp:test_support_base',
'../sandbox/sandbox.gyp:sandbox',
'../testing/gtest.gyp:gtest',
'blacklist',
'blacklist_test_dll_1',
'blacklist_test_dll_2',
'blacklist_test_dll_3',
'blacklist_test_main_dll',
],
'conditions': [
['component=="shared_library"', {
# In component builds, all targets depend on chrome_redirects by
# default. Remove it here so we are able to test it.
'dependencies!': [
'../chrome_elf/chrome_elf.gyp:chrome_redirects',
],
}],
],
},
{
# A dummy target to ensure that chrome_elf.dll and chrome.exe gets built
# when building chrome_elf_unittests.exe without introducing an
# explicit runtime dependency.
'target_name': 'chrome_elf_unittests',
'type': 'none',
'dependencies': [
'../chrome/chrome.gyp:chrome',
'chrome_elf',
'chrome_elf_unittests_exe',
],
},
{
'target_name': 'chrome_elf_lib',
'type': 'static_library',
'include_dirs': [
'..',
],
'sources': [
'chrome_elf_constants.cc',
'chrome_elf_constants.h',
'chrome_elf_types.h',
'create_file/chrome_create_file.cc',
'create_file/chrome_create_file.h',
'ntdll_cache.cc',
'ntdll_cache.h',
],
'conditions': [
['component=="shared_library"', {
# In component builds, all targets depend on chrome_redirects by
# default. Remove it here to avoid a circular dependency.
'dependencies!': [
'../chrome_elf/chrome_elf.gyp:chrome_redirects',
],
}],
],
},
], # targets
'conditions': [
['component=="shared_library"', {
'targets': [
{
'target_name': 'chrome_redirects',
'type': 'shared_library',
'include_dirs': [
'..',
],
'sources': [
'chrome_redirects.def',
],
'dependencies': [
'chrome_elf_lib',
],
'msvs_settings': {
'VCLinkerTool': {
'BaseAddress': '0x01c10000',
# Set /SUBSYSTEM:WINDOWS.
'SubSystem': '2',
},
},
'conditions': [
['component=="shared_library"', {
# In component builds, all targets depend on chrome_redirects by
# default. Remove it here to avoid a circular dependency.
'dependencies!': [
'../chrome_elf/chrome_elf.gyp:chrome_redirects',
],
}],
],
},
],
}],
],
}
|
elem = lambda value, next: {'value': value, 'next': next}
to_str = lambda head: '' if head is None \
else str(head['value']) + ' ' + to_str(head['next'])
values = elem(1, elem(2, elem(3, None)))
# print(to_str(values))
def make_powers(count):
powers = []
for i in range(count):
# powers.append(lambda x: x ** i) # wrong code
powers.append((lambda p: lambda x: x ** p)(i)) # wrong code
return powers
powers = make_powers(5)
# for power in powers:
# print(power(2))
handlers = []
for i in range(1, 4):
def on_click(i=i):
print('Button {} was clicked!'.format(i))
handlers.append(on_click)
for handler in handlers:
handler() |
class Allergies(object):
def __init__(self, score):
self.score = [allergen for num, allergen in list(enumerate([
'eggs',
'peanuts',
'shellfish',
'strawberries',
'tomatoes',
'chocolate',
'pollen',
'cats'
]))
if 0 < (score & (1 << num))]
def is_allergic_to(self, item):
return item in self.score
@property
def lst(self):
return self.score
|
# 1. Sort the 'people' list of dictionaries alphabetically based on the
# 'name' key from each dictionary using the 'sorted' function and store
# the new list as 'sorted_by_name'
people = [
{'name': 'Kevin Bacon', 'age': 61},
{'name': 'Fred Ward', 'age': 77},
{'name': 'finn Carter', 'age': 59},
{'name': 'Ariana Richards', 'age': 40},
{'name': 'Vicotor Wong', 'age': 74},
]
# sorted_by_name = None # AssertionError
sorted_by_name = sorted(people, key=lambda d: d['name'].lower())
assert sorted_by_name == [
{'name': 'Ariana Richards', 'age': 40},
{'name': 'finn Carter', 'age': 59},
{'name': 'Fred Ward', 'age': 77},
{'name': 'Kevin Bacon', 'age': 61},
{'name': 'Vicotor Wong', 'age': 74},
]
# =============================================================================== #
# 2. Use the 'map' function to iterate over 'sorted_by_name' to generate a
# new list called 'name_declarations' where each value is a string with
# '<NAME> is <AGE> years old.' where the '<NAME>' and '<AGE>' values are from
# the dictionary.
# name_declarations = None
# name_declarations = list(map(lambda d: f"{d['name']} is {d['age']} years old", sorted_by_name))
name_declarations = list(
map(lambda d: f"{d['name']} is {d['age']} years old", sorted_by_name)
)
# print(name_declarations)
assert name_declarations == [
"Ariana Richards is 40 years old",
"finn Carter is 59 years old",
"Fred Ward is 77 years old",
"Kevin Bacon is 61 years old",
"Victor Wong is 74 years old",
] |
# Copyright 2017 Pedro M. Baeza <[email protected]>
# License AGPL-3.0 or later (http://www.gnu.org/licenses/agpl).
{
"name": "Weights in the invoices analysis view",
"version": "13.0.1.0.0",
"author": "Tecnativa," "Odoo Community Association (OCA)",
"category": "Inventory, Logistics, Warehousing",
"development_status": "Production/Stable",
"license": "AGPL-3",
"website": "https://www.github.com/account_reporting_weight",
"depends": ["sale"],
"installable": True,
}
|
"""
Projet d'analyse: Sujet 31
Le projet: Il nous est demandรฉ d'approcher la valeur de sin(8/5) tout en certifiant nos resultats.
Cependant, avec la formule de Taylor
sur R combinรฉe a la preuve de Cauchy et a l'etablissement du certificat de convergence,
on s'est vite rendu compte que l'on peut etendre cette definition sur R et ainsi pouvoir
cette de maniere moduler a un ordre k les N premieres decimales du sin de tout reel.
Ci-dessous, le programme informatique traduisant les resultats de nos analyses.
"""
"""
dans ce code python ,on realise le programme en une classe gรฉnรฉrale tout en definissant toutes les fonctions supplemntaires et celles demandรฉes dans l'ennoncรฉ
et dans la deuxieme partie on aura le main pour pouvoir tester le programme
"""
class classe_General:
def __init__(self, val):
"""
Initialisation de la fonction calsse_General pour les calculs mรฉnant a sin(:param val)
:param val: Valeur numerique de type float sur laquelle portera notre analyse
"""
self.val = val
pass
def absolu(self, x):
'''
Fonction retournant la valeur absolue de :param x
:param x: Valeur nรฉgative ou positive
:return: La valeur absolue de :param x'''
if x < 0:
return x*-1
else :
return x
def fonction_factorielle(self, x):
"""
Fonction de calcul du fonction_factorielle de :param x
:param x: Entier naturel dont on veut determiner le factoriel
:return: Le factoriel de :param x
"""
return 1 if x <= 1 else x * self.fonction_factorielle(x - 1)
def fonction_tronque(self, x, pre):
"""
Fonction tronquant :param x a 10 ** -:param precision pres
:param x: Flottant que l'on veut tronquer
:param pre: pre de troncature souhaitee
:return: :param x tronquee a 10 ** -:param precision pres
"""
pre = 10 ** pre
return (int(x * pre)) / pre
def fonction_sinusTaylorien(self, angle, pre):
"""
Fonction calculant la valeur de la definition de sin(:param angle) selont la formule de Taylor a
l'ordre :param pre
:param angle: flottant dont on souhaite obtenir le sin
:param pre: l'ordre n du calcul du developpement limitรฉ menant a sin(:param angle)
:return: sin(:param angle) selon le developpement limitรฉ de Taylor a l'ordre n=:param pre
"""
v = 0.0
for n in range(pre):
v += (((-1) ** n) * ((angle ** ((2 * n) + 1)) / self.fonction_factorielle((2 * n) + 1)))
return v
def r(self, n):
"""
Suite (r(n))_n
:param n: entier naturel representant l'indice n de la suite (r(n))_n
:return: r(:param n)
"""
return self.fonction_sinusTaylorien(self.val, n)
def conv(self, k):
"""
Certificat de convergence de la suite (r(n))_n
:param k: entier naturel representant l'ordre k du calcul du certificat de convergence conv(k)
:return: conv(:param k)
"""
n = 0
ordre = 1 / (10 ** (k))
while True:
n += 1
serie_approchee = (2 ** (n + 1)) / self.fonction_factorielle(n + 1)
if (serie_approchee <= ordre):
break
return n
def fonction_preuveParCauchy(self, epsilon):
"""
Fonction prouvant la stabilite d'un developpement limitรฉ de pre 10 ** -:param epsilon
:param epsilon: Ordre de pre souhaite du developpement limite
:return: L'ordre du developpement limite a partir duquel :param val est stable avec une pre(ordre)
10 ** -:param epsilon
"""
n = 1
while True:
difference_de_cauchy = self.fonction_sinusTaylorien(self.val, n) - self.fonction_sinusTaylorien(self.val, n + 1)
n += 1
if (self.absolu(difference_de_cauchy) < epsilon):
break
return n - 1
# Instance de la classe PRINCIPALE classe_General
valeur = classe_General(8 / 5)
# PROGRAMME PRINCIPAL
if __name__ == '__main__':
# Affichage des resultats attendus
#debut du programme
print('___________________[ DEBUT DU PRROGRAMME ]___________________\n')
resultat = valeur.conv(8)
print(f"resultat = {resultat}")
for i in range(0, resultat+1):
print(f"r({i}) = {valeur.r(i)}")
epsilon = 10 ** -6
print(f"[INFO] Certification d'ordre {epsilon} atteinte ร partir de r({valeur.fonction_preuveParCauchy(epsilon)})")
a = valeur.fonction_tronque(valeur.r(valeur.fonction_preuveParCauchy(epsilon)), 6)
print('a = ', a)
print('\n__________________[ FIN DU PROGRAMME ]__________________\n') |
# Theory: List
# In your programs, you often need to group several elements in
# order to process them as a single object. For this, you will need
# to use different collections. One of the most useful collections
# in Python is a list. It is one of the most important things in
# Python.
# 1. Creating and printing lists
# Look at a simple list that stores several names of dog's breed:
dog_breeds = ['corgi', 'labrador', 'poodle', 'jack russel']
print(dog_breeds)
# In this first line, we use square brackets to create a list that
# contains four elements and then assign it to the dog_breeds
# variable. In the second line, the list is printed through the
# variable's name. All the elements are printed in the same order
# as they were stored in the list because lists are ordered.
# Here is another list that contains five integers:
numbers = [1, 2, 3, 4, 5]
print(numbers) # [1, 2, 3, 4, 5]
# Another way to create a list is to invoke the list function. It is
# used to create a list out of an iterable object: that is, a kind of
# object where you can get its elements one by one. The concept
# of iterability will be explained in detail further on, but let's look
# at the examples below:
list_out_of_string = list('danger!')
print(list_out_of_string) # ['d', 'a', 'n', 'g', 'e', 'r', '!']
# list_out_of_integer = list(235) # TypeError: 'int' object is not iterable
# So, the list function create a list containing each element
# from the given iterable object. For now, remember that a string
# is an example of an iterable object, and an integer is an example
# of non-iterable object. A list itself is also an iterable object.
# Let's also note the difference between the list function and
# creating a list using square brackets:
multi_element_list = list('danger!')
print(multi_element_list) # ['d', 'a', 'n', 'g', 'e', 'r', '!']
singe_element_list = ['danger!']
print(singe_element_list) # ['danger!']
# The square brackets and the list function can also be used to
# create empty lists that do not have elements at all.
empty_list_1 = list()
empty_list_2 = []
# In the following topics, we will consider how to fill empty lists.
# 2. Features of lists
# Lists can store duplicate values as many times as needed.
on_off_list = ['on', 'off', 'on', 'off', 'on']
print(on_off_list) # ['on', 'off', 'on', 'off', 'on']
# Another important thing about lists is that they can contain
# different types of elements. So there are neither restrictions,
# nor fixed list types, and you can add to your list any data you
# want, like in the following example:
different_object = ['a', 1, 'b', 2]
# 3. Length of a list
# Sometimes you need to know how many elements are there in a
# list. There is a built-in function len that can be applied
# to any iterable object, and it returns simply the length of that
# object.
# So, when applied to a list, it returns the number of elements in
# that lists.
numbers = [1, 2, 3, 4, 5]
print(len(numbers)) # 5
empty_list = list()
print(len(empty_list)) # 0
single_element_list = ['danger!']
print(len(single_element_list)) # 1
multi_element_list = list('danger!')
print(len(multi_element_list)) # 7
# In the example above, you can see how the len() function
# works. Again, pay attention to the difference between list()
# and [] as applied to strings: it may not result in what you
# expected:
# 4. Summary
# As a recap, we note that lists are:
# ordered, i.e. each element has a fixed position in a list;
# iterable, i.e. you can get their elements one by one;
# able to store duplicate values;
# able to store different types of elements
|
class State(object):
def __init__(self):
pass
def enter(self):
"""Initialize data that might not be initialized in init"""
pass
def exit(self):
"""State is finished, perform cleanup if necessary"""
pass
def reason(self):
"""Conditional or logic to see if the current state needs to end, and a new one started"""
pass
def act(self):
"""Per-frame behavior"""
pass
class StateMachine(object):
def __init__(self, host, first_state=None):
self.host = host
self.current_state = first_state
def transition(self, new_state):
"""Transition to a new State"""
self.current_state.exit()
self.current_state = new_state
# provide state references to host object and fsm instance
self.current_state.host = self.host
self.current_state.fsm = self
self.current_state.enter()
def update(self):
if self.current_state: # only update if we have a state
new_state = self.current_state.reason()
if new_state: # if reason provides new state
# do transition
self.transition(new_state)
else:
# otherwise act with current state
self.current_state.act() |
# -*- python -*-
# This software was produced by NIST, an agency of the U.S. government,
# and by statute is not subject to copyright in the United States.
# Recipients of this software assume all responsibilities associated
# with its operation, modification and maintenance. However, to
# facilitate maintenance we ask that before distributing modified
# versions of this software, you first contact the authors at
# [email protected].
# Time, in milliseconds, between the time that a progressbar object is
# created and the time that it is installed in the ActivityViewer
# window.
delay = 2000
# Time in milliseconds between progress bar updates.
period = 200
def set_delay(menuitem, milliseconds):
global delay
delay = milliseconds
|
class Solution:
def floodFill(self, image: List[List[int]], sr: int, sc: int, newColor: int) -> List[List[int]]:
m = len(image)
n = len(image[0])
oldColor = image[sr][sc]
if oldColor == newColor:
return image
def dfs(r, c):
nonlocal image, newColor, oldColor
if image[r][c] == oldColor:
image[r][c] = newColor
if r - 1 >= 0:
dfs(r-1, c)
if r + 1 < m:
dfs(r+1, c)
if c - 1 >= 0:
dfs(r, c-1)
if c + 1 < n:
dfs(r, c+1)
dfs(sr, sc)
return image |
#### digonal sum
digonal=[[1,2,3,5],
[4,5,6,4],
[7,8,9,3]
]
def digonaldiffernce(arr):
SUM1=0
SUM2=0
j=0
for i in arr:
SUM1+=i[j]
SUM2+=i[(len(i)-1)-j]
j+=1
return abs(SUM1-SUM2)
print(digonaldiffernce(digonal))
|
"""
Module: 'neopixel' on esp32 1.10.0
"""
# MCU: (sysname='esp32', nodename='esp32', release='1.10.0', version='v1.10 on 2019-01-25', machine='ESP32 module with ESP32')
# Stubber: 1.3.2
class NeoPixel:
''
ORDER = None
def fill():
pass
def write():
pass
def neopixel_write():
pass
|
"""
Movie object
- title
- storyline
- poster url
- trailer url
"""
class Movie:
def __init__(self, title):
self.title = title
self.storyline = ""
self.poster_url = ""
self.trailer_url = ""
|
class Main:
class featured:
it = {'css': '#content > div.row'}
products = {'css': it['css'] + ' .product-layout'}
names = {'css': products['css'] + ' .caption h4 a'}
|
def foo(x):
print(x)
foo([x for x in range(10)])
|
def dogleg(value, units):
return_dict = {}
if units == 'deg/100ft':
return_dict['deg/100ft'] = value
return_dict['deg/30m'] = value * 0.9843004
elif units == 'deg/30m':
return_dict['deg/100ft'] = value * 1.01595
return_dict['deg/30m'] = value
return return_dict
def axial_spring_con(value, units):
return_dict = {}
if units == 'N/m':
return_dict['N/m'] = value
return_dict['lb/in'] = value * 1.016
elif units == 'lb/in':
return_dict['N/m'] = value * 0.984252
return_dict['lb/in'] = value
return return_dict
def axial_dampening_coef(value, units):
return_dict = {}
if units == 'N-s/m':
return_dict['N-s/m'] = value
return_dict['lb-s/in'] = value * 1.016
elif units == 'lb-s/in':
return_dict['N-s/m'] = value * 0.984252
return_dict['lb-s/in'] = value
return return_dict
def torsional_spring_con(value, units):
return_dict = {}
if units == 'N-m/rad':
return_dict['N-m/rad'] = value
return_dict['lb-in/rad'] = value * 1.01595
elif units == 'lb-in/rad':
return_dict['N-m/rad'] = value * 0.9843004
return_dict['lb-in/rad'] = value
return return_dict
def torsional_dampening_coef(value, units):
return_dict = {}
if units == 'N-m-s/rad':
return_dict['N-m-s/rad'] = value
return_dict['lb-in-s/rad'] = value * 1.01595
elif units == 'lb-in-s/rad':
return_dict['N-m-s/rad'] = value * 0.9843004
return_dict['lb-in-s/rad'] = value
return return_dict
def pressure_grad(value, units):
return_dict = {}
if units == 'psi/ft':
return_dict['psi/ft'] = value
return_dict['kPa/m'] = value * 22.5
return_dict['Mpa/m'] = value * 0.0225
return_dict['Pa/m'] = value * 22500
elif units == 'kPa/m':
return_dict['psi/ft'] = value * 0.0444444
return_dict['kPa/m'] = value
return_dict['Mpa/m'] = value * 0.001
return_dict['Pa/m'] = value * 1000
elif units == 'Mpa/m':
return_dict['psi/ft'] = value * 44.4444444
return_dict['kPa/m'] = value * 1000
return_dict['Mpa/m'] = value
return_dict['Pa/m'] = value * 1000000
elif units == 'Pa/m':
return_dict['psi/ft'] = value * 0.0000444
return_dict['kPa/m'] = value * 0.001
return_dict['Mpa/m'] = value * 0.000001
return_dict['Pa/m'] = value
return return_dict
def yield_slurry(value, units):
return_dict = {}
if units == 'ft3/sk':
return_dict['ft3/sk'] = value
return_dict['m3/sk'] = value * 0.028317
return_dict['gal/sk'] = value * 7
elif units == 'm3/sk':
return_dict['ft3/sk'] = value * 35
return_dict['m3/sk'] = value
return_dict['gal/sk'] = value * 264
elif units == 'gal/sk':
return_dict['ft3/sk'] = value * 0.13369
return_dict['m3/sk'] = value * 0.0037857
return_dict['gal/sk'] = value
return return_dict
def footage_cost(value, units):
return_dict = {}
if units == 'cur/ft':
return_dict['cur/ft'] = value
return_dict['cur/m'] = value * 3.2810014
return_dict['cur/1000ft'] = value / 0.001
return_dict['cur/1000m'] = value * 3281.0014
elif units == 'cur/m':
return_dict['cur/ft'] = value * 0.304785
return_dict['cur/m'] = value
return_dict['cur/1000ft'] = value / 0.0003048
return_dict['cur/1000m'] = value * 1000
elif units == 'cur/1000ft':
return_dict['cur/ft'] = value / 1000
return_dict['cur/m'] = value / 3281.00
return_dict['cur/1000ft'] = value
return_dict['cur/1000m'] = value / 3.2810003
elif units == 'cur/1000m':
return_dict['cur/ft'] = value / 305
return_dict['cur/m'] = value / 1000
return_dict['cur/1000ft'] = value / 0.3047851
return_dict['cur/1000m'] = value
return return_dict
def mud_weight(value, units):
return_dict = {}
if units == 'g/cm3':
return_dict['g/cm3'] = value * 1.0
return_dict['g/L'] = value * 1000.0
return_dict['kg/m3'] = value * 1000.0
return_dict['kg/L'] = value * 1.0
return_dict['kPa/m'] = value * 9.8114244
return_dict['lb/ft3'] = value * 62.4336642
return_dict['lb/bbl'] = value * 350.5070669
return_dict['ppg'] = value * 8.3454064
return_dict['psi/ft'] = value * 0.4339843
return_dict['psi/100ft'] = value * 43.3726579
return_dict['SG'] = value * 1.0
elif units == 'g/L':
return_dict['g/cm3'] = value * 0.001
return_dict['g/L'] = value * 1.0
return_dict['kg/m3'] = value * 1.0
return_dict['kg/L'] = value * 0.001
return_dict['kPa/m'] = value * 0.0098114
return_dict['lb/ft3'] = value * 0.0624337
return_dict['lb/bbl'] = value * 0.3505071
return_dict['ppg'] = value * 0.0083454
return_dict['psi/ft'] = value * 0.000434
return_dict['psi/100ft'] = value * 0.0433727
return_dict['SG'] = value * 0.001
elif units == 'kg/m3':
return_dict['g/cm3'] = value * 0.001
return_dict['g/L'] = value * 1.0
return_dict['kg/m3'] = value * 1.0
return_dict['kg/L'] = value * 0.001
return_dict['kPa/m'] = value * 0.0098114
return_dict['lb/ft3'] = value * 0.0624337
return_dict['lb/bbl'] = value * 0.3505071
return_dict['ppg'] = value * 0.0083454
return_dict['psi/ft'] = value * 0.000434
return_dict['psi/100ft'] = value * 0.0433727
return_dict['SG'] = value * 0.001
elif units == 'kg/L':
return_dict['g/cm3'] = value * 1.0
return_dict['g/L'] = value * 1000.0
return_dict['kg/m3'] = value * 1000.0
return_dict['kg/L'] = value * 1.0
return_dict['kPa/m'] = value * 9.8114244
return_dict['lb/ft3'] = value * 62.4336642
return_dict['lb/bbl'] = value * 350.5070669
return_dict['ppg'] = value * 8.3454064
return_dict['psi/ft'] = value * 0.4339843
return_dict['psi/100ft'] = value * 43.3726579
return_dict['SG'] = value * 1.0
elif units == 'kPa/m':
return_dict['g/cm3'] = value * 0.101922
return_dict['g/L'] = value * 101.922
return_dict['kg/m3'] = value * 101.922
return_dict['kg/L'] = value * 0.101922
return_dict['kPa/m'] = value * 1.0
return_dict['lb/ft3'] = value * 6.3633639
return_dict['lb/bbl'] = value * 35.7243813
return_dict['ppg'] = value * 0.8505805
return_dict['psi/ft'] = value * 0.0442325
return_dict['psi/100ft'] = value * 4.420628
return_dict['SG'] = value * 0.101922
elif units == 'lb/ft3':
return_dict['g/cm3'] = value * 0.016017
return_dict['g/L'] = value * 16.017
return_dict['kg/m3'] = value * 16.017
return_dict['kg/L'] = value * 0.016017
return_dict['kPa/m'] = value * 0.1571496
return_dict['lb/ft3'] = value * 1.0
return_dict['lb/bbl'] = value * 5.6140717
return_dict['ppg'] = value * 0.1336684
return_dict['psi/ft'] = value * 0.0069511
return_dict['psi/100ft'] = value * 0.6946999
return_dict['SG'] = value * 0.016017
elif units == 'lb/bbl':
return_dict['g/cm3'] = value * 0.002853
return_dict['g/L'] = value * 2.8530095
return_dict['kg/m3'] = value * 2.8530095
return_dict['kg/L'] = value * 0.002853
return_dict['kPa/m'] = value * 0.0279921
return_dict['lb/ft3'] = value * 0.1781238
return_dict['lb/bbl'] = value * 1.0
return_dict['ppg'] = value * 0.0238095
return_dict['psi/ft'] = value * 0.0012382
return_dict['psi/100ft'] = value * 0.1237426
return_dict['SG'] = value * 0.002853
elif units == 'ppg':
return_dict['g/cm3'] = value * 0.1198264
return_dict['g/L'] = value * 119.8264
return_dict['kg/m3'] = value * 119.8264
return_dict['kg/L'] = value * 0.1198264
return_dict['kPa/m'] = value * 1.1756677
return_dict['lb/ft3'] = value * 7.4812012
return_dict['lb/bbl'] = value * 42.0
return_dict['ppg'] = value * 1.0
return_dict['psi/ft'] = value * 0.0520028
return_dict['psi/100ft'] = value * 5.1971895
return_dict['SG'] = value * 0.1198264
elif units == 'psi/ft':
return_dict['g/cm3'] = value * 2.304231
return_dict['g/L'] = value * 2304.231
return_dict['kg/m3'] = value * 2304.231
return_dict['kg/L'] = value * 2.304231
return_dict['kPa/m'] = value * 22.6077883
return_dict['lb/ft3'] = value * 143.8615846
return_dict['lb/bbl'] = value * 807.6492492
return_dict['ppg'] = value * 19.229744
return_dict['psi/ft'] = value * 1.0
return_dict['psi/100ft'] = value * 99.9406228
return_dict['SG'] = value * 2.304231
elif units == 'psi/100ft':
return_dict['g/cm3'] = value * 0.023056
return_dict['g/L'] = value * 23.056
return_dict['kg/m3'] = value * 23.056
return_dict['kg/L'] = value * 0.023056
return_dict['kPa/m'] = value * 0.2262122
return_dict['lb/ft3'] = value * 1.4394706
return_dict['lb/bbl'] = value * 8.0812909
return_dict['ppg'] = value * 0.1924117
return_dict['psi/ft'] = value * 0.0100059
return_dict['psi/100ft'] = value * 1.0
return_dict['SG'] = value * 0.023056
elif units == 'SG':
return_dict['g/cm3'] = value * 1.0
return_dict['g/L'] = value * 1000.0
return_dict['kg/m3'] = value * 1000.0
return_dict['kg/L'] = value * 1.0
return_dict['kPa/m'] = value * 9.8114244
return_dict['lb/ft3'] = value * 62.4336642
return_dict['lb/bbl'] = value * 350.5070669
return_dict['ppg'] = value * 8.3454064
return_dict['psi/ft'] = value * 0.4339843
return_dict['psi/100ft'] = value * 43.3726579
return_dict['SG'] = value * 1.0
return return_dict
def flow_rate(value, units):
return_dict = {}
if units == 'bbl/hr':
return_dict['bbl/hr'] = value * 1.0
return_dict['bbl/min'] = value * 0.0166667
return_dict['ft3/min'] = value * 0.0935764
return_dict['m3/hr'] = value * 0.1589873
return_dict['m3/min'] = value * 0.0026498
return_dict['gal/hr'] = value * 42.0
return_dict['gpm'] = value * 0.7
return_dict['L/hr'] = value * 158.9872949
return_dict['L/min'] = value * 2.6497882
elif units == 'bbl/min':
return_dict['bbl/hr'] = value * 60.0
return_dict['bbl/min'] = value * 1.0
return_dict['ft3/min'] = value * 5.6145833
return_dict['m3/hr'] = value * 9.5392377
return_dict['m3/min'] = value * 0.1589873
return_dict['gal/hr'] = value * 2520.0
return_dict['gpm'] = value * 42.0
return_dict['L/hr'] = value * 9539.2376957
return_dict['L/min'] = value * 158.9872949
elif units == 'ft3/min':
return_dict['bbl/hr'] = value * 10.6864564
return_dict['bbl/min'] = value * 0.1781076
return_dict['ft3/min'] = value * 1.0
return_dict['m3/hr'] = value * 1.6990108
return_dict['m3/min'] = value * 0.0283168
return_dict['gal/hr'] = value * 448.8311688
return_dict['gpm'] = value * 7.4805195
return_dict['L/hr'] = value * 1699.0107955
return_dict['L/min'] = value * 28.3168466
elif units == 'm3/hr':
return_dict['bbl/hr'] = value * 6.2898108
return_dict['bbl/min'] = value * 0.1048302
return_dict['ft3/min'] = value * 0.5885778
return_dict['m3/hr'] = value * 1.0
return_dict['m3/min'] = value * 0.0166667
return_dict['gal/hr'] = value * 264.1720524
return_dict['gpm'] = value * 4.4028675
return_dict['L/hr'] = value * 1000.0
return_dict['L/min'] = value * 16.6666667
elif units == 'm3/min':
return_dict['bbl/hr'] = value * 377.3886462
return_dict['bbl/min'] = value * 6.2898108
return_dict['ft3/min'] = value * 35.3146667
return_dict['m3/hr'] = value * 60.0
return_dict['m3/min'] = value * 1.0
return_dict['gal/hr'] = value * 15850.3231414
return_dict['gpm'] = value * 264.1720524
return_dict['L/hr'] = value * 60000.0
return_dict['L/min'] = value * 1000.0
elif units == 'gal/hr':
return_dict['bbl/hr'] = value * 0.0238095
return_dict['bbl/min'] = value * 0.0003968
return_dict['ft3/min'] = value * 0.002228
return_dict['m3/hr'] = value * 0.0037854
return_dict['m3/min'] = value * 6.31e-05
return_dict['gal/hr'] = value * 1.0
return_dict['gpm'] = value * 0.0166667
return_dict['L/hr'] = value * 3.7854118
return_dict['L/min'] = value * 0.0630902
elif units == 'gpm':
return_dict['bbl/hr'] = value * 1.4285714
return_dict['bbl/min'] = value * 0.0238095
return_dict['ft3/min'] = value * 0.1336806
return_dict['m3/hr'] = value * 0.2271247
return_dict['m3/min'] = value * 0.0037854
return_dict['gal/hr'] = value * 60.0
return_dict['gpm'] = value * 1.0
return_dict['L/hr'] = value * 227.124707
return_dict['L/min'] = value * 3.7854118
elif units == 'L/hr':
return_dict['bbl/hr'] = value * 0.0062898
return_dict['bbl/min'] = value * 0.0001048
return_dict['ft3/min'] = value * 0.0005886
return_dict['m3/hr'] = value * 0.001
return_dict['m3/min'] = value * 1.67e-05
return_dict['gal/hr'] = value * 0.2641721
return_dict['gpm'] = value * 0.0044029
return_dict['L/hr'] = value * 1.0
return_dict['L/min'] = value * 0.0166667
elif units == 'L/min':
return_dict['bbl/hr'] = value * 0.3773886
return_dict['bbl/min'] = value * 0.0062898
return_dict['ft3/min'] = value * 0.0353147
return_dict['m3/hr'] = value * 0.06
return_dict['m3/min'] = value * 0.001
return_dict['gal/hr'] = value * 15.8503231
return_dict['gpm'] = value * 0.2641721
return_dict['L/hr'] = value * 60.0
return_dict['L/min'] = value * 1.0
return return_dict
def drilling_rate(value, units):
return_dict = {}
if units == 'ft/d':
return_dict['ft/d'] = value * 1.0
return_dict['ft/hr'] = value * 0.0416667
return_dict['ft/min'] = value * 0.0006944
return_dict['ft/s'] = value * 1.16e-05
return_dict['m/d'] = value * 0.3048
return_dict['m/hr'] = value * 0.0127
return_dict['m/min'] = value * 0.0002117
return_dict['m/s'] = value * 3.5e-06
elif units == 'ft/hr':
return_dict['ft/d'] = value * 24.0
return_dict['ft/hr'] = value * 1.0
return_dict['ft/min'] = value * 0.0166667
return_dict['ft/s'] = value * 0.0002778
return_dict['m/d'] = value * 7.3152
return_dict['m/hr'] = value * 0.3048
return_dict['m/min'] = value * 0.00508
return_dict['m/s'] = value * 8.47e-05
elif units == 'ft/min':
return_dict['ft/d'] = value * 1440.0
return_dict['ft/hr'] = value * 60.0
return_dict['ft/min'] = value * 1.0
return_dict['ft/s'] = value * 0.0166667
return_dict['m/d'] = value * 438.9119993
return_dict['m/hr'] = value * 18.288
return_dict['m/min'] = value * 0.3048
return_dict['m/s'] = value * 0.00508
elif units == 'ft/s':
return_dict['ft/d'] = value * 86400.0
return_dict['ft/hr'] = value * 3600.0
return_dict['ft/min'] = value * 60.0
return_dict['ft/s'] = value * 1.0
return_dict['m/d'] = value * 26334.71996
return_dict['m/hr'] = value * 1097.2799983
return_dict['m/min'] = value * 18.288
return_dict['m/s'] = value * 0.3048
elif units == 'm/d':
return_dict['ft/d'] = value * 3.2808399
return_dict['ft/hr'] = value * 0.1367017
return_dict['ft/min'] = value * 0.0022784
return_dict['ft/s'] = value * 3.8e-05
return_dict['m/d'] = value * 1.0
return_dict['m/hr'] = value * 0.0416667
return_dict['m/min'] = value * 0.0006944
return_dict['m/s'] = value * 1.16e-05
elif units == 'm/hr':
return_dict['ft/d'] = value * 78.7401576
return_dict['ft/hr'] = value * 3.2808399
return_dict['ft/min'] = value * 0.0546807
return_dict['ft/s'] = value * 0.0009113
return_dict['m/d'] = value * 24.0
return_dict['m/hr'] = value * 1.0
return_dict['m/min'] = value * 0.0166667
return_dict['m/s'] = value * 0.0002778
elif units == 'm/min':
return_dict['ft/d'] = value * 4724.409456
return_dict['ft/hr'] = value * 196.850394
return_dict['ft/min'] = value * 3.2808399
return_dict['ft/s'] = value * 0.0546807
return_dict['m/d'] = value * 1440.0
return_dict['m/hr'] = value * 60.0
return_dict['m/min'] = value * 1.0
return_dict['m/s'] = value * 0.0166667
elif units == 'm/s':
return_dict['ft/d'] = value * 283464.56736
return_dict['ft/hr'] = value * 11811.02364
return_dict['ft/min'] = value * 196.850394
return_dict['ft/s'] = value * 3.2808399
return_dict['m/d'] = value * 86400.0
return_dict['m/hr'] = value * 3600.0
return_dict['m/min'] = value * 60.0
return_dict['m/s'] = value * 1.0
return return_dict
def weight_length(value, units):
return_dict = {}
if units == 'lb/ft':
return_dict['lb/ft'] = value
return_dict['kg/m'] = value * 1.48816
elif units == 'kg/m':
return_dict['lb/ft'] = value * 0.671969
return_dict['kg/m'] = value
return return_dict
def geothermal_gradient(value, units):
return_dict = {}
if units == 'c/100m':
return_dict['c/100m'] = value
return_dict['f/100ft'] = value * 0.549
elif units == 'f/100ft':
return_dict['c/100m'] = value / 0.549
return_dict['f/100ft'] = value
return return_dict
|
# V0
# V1
# http://bookshadow.com/weblog/2016/10/13/leetcode-battleships-in-a-board/
# IDEA : GREEDY
class Solution(object):
def countBattleships(self, board):
"""
:type board: List[List[str]]
:rtype: int
"""
h = len(board)
w = len(board[0]) if h else 0
ans = 0
for x in range(h):
for y in range(w):
if board[x][y] == 'X':
if x > 0 and board[x - 1][y] == 'X':
continue
if y > 0 and board[x][y - 1] == 'X':
continue
ans += 1
return ans
# V1'
# http://bookshadow.com/weblog/2016/10/13/leetcode-battleships-in-a-board/
# IDEA : DFS
class Solution(object):
def countBattleships(self, board):
"""
:type board: List[List[str]]
:rtype: int
"""
vs = set()
h = len(board)
w = len(board[0]) if h else 0
def dfs(x, y):
for dx, dy in zip((1, 0, -1, 0), (0, 1, 0, -1)):
nx, ny = x + dx, y + dy
if 0 <= nx < h and 0 <= ny < w:
if (nx, ny) not in vs and board[nx][ny] == 'X':
vs.add((nx, ny))
dfs(nx, ny)
ans = 0
for x in range(h):
for y in range(w):
if (x, y) not in vs and board[x][y] == 'X':
ans += 1
vs.add((x, y))
dfs(x, y)
return ans
# V2
# Time: O(m * n)
# Space: O(1)
class Solution(object):
def countBattleships(self, board):
"""
:type board: List[List[str]]
:rtype: int
"""
if not board or not board[0]:
return 0
cnt = 0
for i in range(len(board)):
for j in range(len(board[0])):
cnt += int(board[i][j] == 'X' and
(i == 0 or board[i - 1][j] != 'X') and
(j == 0 or board[i][j - 1] != 'X'))
return cnt |
DYNAMIC_API_URL = 'https://api.vc.bilibili.com/dynamic_svr/v1/dynamic_svr/space_history'
GET_DYNAMIC_DETAIL_API_URL = 'https://api.vc.bilibili.com/dynamic_svr/v1/dynamic_svr/get_dynamic_detail'
USER_INFO_API_URL = 'https://api.bilibili.com/x/space/acc/info'
DYNAMIC_URL = 'https://t.bilibili.com/'
|
def readDiary():
day = input("What day do you want to read? ")
file = open(day, "r")
line = file.read()
print(line)
file.close()
def writeDiary():
day = input("What day is your diary for? ")
file = open(day, "w")
line = input("Enter entry: ")
file.write(line)
file.close()
operation = input("Read entries or write entries (R/W)? ")
if (operation == "R"):
readDiary()
elif (operation == "W"):
writeDiary()
else:
print("Sorry, you can only enter a R (for read) or W (for write). Run the program again.")
print("=== All done ===")
|
# Definition for singly-linked list.
# class ListNode:
# def __init__(self, x):
# self.val = x
# self.next = None
class Solution:
def numComponents(self, head, G):
"""
:type head: ListNode
:type G: List[int]
:rtype: int
"""
bits = []
G = { x:x for x in G }
while head:
if head.val in G:
bits.append(1)
else:
bits.append(0)
head = head.next
counter = 0
flag = True
for bit in bits:
if flag and bit == 1:
counter += 1
if bit == 1:
flag = False
else:
flag = True
return counter
|
T = int(input())
for c in range(T):
N = int(input())
sum = N * (N + 1) // 2
sqs = N * (N + 1) * (2 * N + 1) // 6
d = sum * sum - sqs
print(abs(d))
|
# This file is part of Pynguin.
#
# Pynguin is free software: you can redistribute it and/or modify
# it under the terms of the GNU Lesser General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# Pynguin is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Lesser General Public License for more details.
#
# You should have received a copy of the GNU Lesser General Public License
# along with Pynguin. If not, see <https://www.gnu.org/licenses/>.
"""Provides custom exception types."""
class ConfigurationException(BaseException):
"""An exception type that's raised if the generator has no proper configuration."""
class GenerationException(BaseException):
"""An exception during test generation.
This type shall be used for all exceptions that occur during test generation and
that are caused by the test-generation process.
"""
class ConstructionFailedException(BaseException):
"""An exception used when error occurs during construction of a test case."""
class TimerError(Exception):
"""A custom exception used to report errors in use of Timer class"""
|
# Copyright 2013-2022 Lawrence Livermore National Security, LLC and other
# Spack Project Developers. See the top-level COPYRIGHT file for details.
#
# SPDX-License-Identifier: (Apache-2.0 OR MIT)
class Opencv(CMakePackage, CudaPackage):
"""OpenCV (Open Source Computer Vision Library) is an open source computer
vision and machine learning software library."""
homepage = "https://opencv.org/"
url = "https://github.com/opencv/opencv/archive/4.5.0.tar.gz"
git = "https://github.com/opencv/opencv.git"
maintainers = ["bvanessen", "adamjstewart", "glennpj"]
version("master", branch="master")
version(
"4.5.4",
sha256="c20bb83dd790fc69df9f105477e24267706715a9d3c705ca1e7f613c7b3bad3d",
)
version(
"4.5.2",
sha256="ae258ed50aa039279c3d36afdea5c6ecf762515836b27871a8957c610d0424f8",
)
version(
"4.5.1",
sha256="e27fe5b168918ab60d58d7ace2bd82dd14a4d0bd1d3ae182952c2113f5637513",
)
version(
"4.5.0",
sha256="dde4bf8d6639a5d3fe34d5515eab4a15669ded609a1d622350c7ff20dace1907",
)
version(
"4.2.0",
sha256="9ccb2192d7e8c03c58fee07051364d94ed7599363f3b0dce1c5e6cc11c1bb0ec",
)
version(
"4.1.2",
sha256="385dd0a9c25e67ef0dd60e022d2a2d7b17e2f36819cf3cb46aa8cdff5c5282c9",
)
version(
"4.1.1",
sha256="5de5d96bdfb9dad6e6061d70f47a0a91cee96bb35afb9afb9ecb3d43e243d217",
)
version(
"4.1.0",
sha256="8f6e4ab393d81d72caae6e78bd0fd6956117ec9f006fba55fcdb88caf62989b7",
)
version(
"4.0.1",
sha256="7b86a0ee804244e0c407321f895b15e4a7162e9c5c0d2efc85f1cadec4011af4",
)
version(
"4.0.0",
sha256="3787b3cc7b21bba1441819cb00c636911a846c0392ddf6211d398040a1e4886c",
)
version(
"3.4.12",
sha256="c8919dfb5ead6be67534bf794cb0925534311f1cd5c6680f8164ad1813c88d13",
)
version(
"3.4.6",
sha256="e7d311ff97f376b8ee85112e2b536dbf4bdf1233673500175ed7cf21a0089f6d",
)
version(
"3.4.5",
sha256="0c57d9dd6d30cbffe68a09b03f4bebe773ee44dc8ff5cd6eaeb7f4d5ef3b428e",
)
version(
"3.4.4",
sha256="a35b00a71d77b484f73ec485c65fe56c7a6fa48acd5ce55c197aef2e13c78746",
)
version(
"3.4.3",
sha256="4eef85759d5450b183459ff216b4c0fa43e87a4f6aa92c8af649f89336f002ec",
)
version(
"3.4.1",
sha256="f1b87684d75496a1054405ae3ee0b6573acaf3dad39eaf4f1d66fdd7e03dc852",
)
version(
"3.4.0",
sha256="678cc3d2d1b3464b512b084a8cca1fad7de207c7abdf2caa1fed636c13e916da",
)
version(
"3.3.1",
sha256="5dca3bb0d661af311e25a72b04a7e4c22c47c1aa86eb73e70063cd378a2aa6ee",
)
version(
"3.3.0",
sha256="8bb312b9d9fd17336dc1f8b3ac82f021ca50e2034afc866098866176d985adc6",
)
contrib_vers = [
"3.3.0",
"3.3.1",
"3.4.0",
"3.4.1",
"3.4.3",
"3.4.4",
"3.4.5",
"3.4.6",
"3.4.12",
"4.0.0",
"4.0.1",
"4.1.0",
"4.1.1",
"4.1.2",
"4.2.0",
"4.5.0",
"4.5.1",
"4.5.2",
"4.5.4",
]
for cv in contrib_vers:
resource(
name="contrib",
git="https://github.com/opencv/opencv_contrib.git",
tag="{0}".format(cv),
when="@{0}".format(cv),
)
# Patch to fix conflict between CUDA and OpenCV (reproduced with 3.3.0
# and 3.4.1) header file that have the same name. Problem is fixed in
# the current development branch of OpenCV. See #8461 for more information.
patch("dnn_cuda.patch", when="@3.3.0:3.4.1+cuda+dnn")
patch("opencv3.2_cmake.patch", when="@3.2:3.4.1")
# do not prepend system paths
patch("cmake_no-system-paths.patch")
patch("opencv4.1.1_clp_cmake.patch", when="@4.1.1:")
patch("opencv4.0.0_clp_cmake.patch", when="@4.0.0:4.1.0")
patch("opencv3.4.12_clp_cmake.patch", when="@3.4.12")
patch("opencv3.3_clp_cmake.patch", when="@:3.4.6")
patch("opencv3.4.4_cvv_cmake.patch", when="@3.4.4:")
patch("opencv3.3_cvv_cmake.patch", when="@:3.4.3")
# OpenCV prebuilt apps (variants)
# Defined in `apps/*/CMakeLists.txt` using
# `ocv_add_application(...)`
apps = [
"annotation",
"createsamples",
"interactive-calibration",
"model-diagnostics",
"traincascade",
"version",
"visualisation",
]
# app variants
for app in apps:
variant(app, default=False, description="Install {0} app".format(app))
# app conflicts
with when("+annotation"):
conflicts("~highgui")
conflicts("~imgcodecs")
conflicts("~imgproc")
conflicts("~videoio")
with when("+createsamples"):
conflicts("~calib3d")
conflicts("~features2d")
conflicts("~highgui")
conflicts("~imgcodecs")
conflicts("~imgproc")
conflicts("~objdetect")
conflicts("~videoio")
with when("+interactive-calibration"):
conflicts("~calib3d")
conflicts("~features2d")
conflicts("~highgui")
conflicts("~imgproc")
conflicts("~videoio")
with when("+model-diagnostics"):
conflicts("~dnn")
with when("+traincascade"):
conflicts("~calib3d")
conflicts("~features2d")
conflicts("~highgui")
conflicts("~imgcodecs")
conflicts("~imgproc")
conflicts("~objdetect")
with when("+visualisation"):
conflicts("~highgui")
conflicts("~imgcodecs")
conflicts("~imgproc")
conflicts("~videoio")
# OpenCV modules (variants)
# Defined in `modules/*/CMakeLists.txt` using
# `ocv_add_module(...)` and `ocv_define_module(...)`
modules = [
"calib3d",
"dnn",
"features2d",
"flann",
"gapi",
"highgui",
"imgcodecs",
"imgproc",
"java",
"java_bindings_generator",
"ml",
"objc",
"objc_bindings_generator",
"objdetect",
"photo",
"python2",
"python3",
"python_bindings_generator",
"python_tests",
"stitching",
"ts",
"video",
"videoio",
"world",
]
# These need additional spack packages
# js needs Emscripten
modules_pending = [
"js",
"js_bindings_generator",
]
# module variants
for mod in modules:
# At least one of these modules must be enabled to build OpenCV
variant(mod, default=False, description="Include opencv_{0} module".format(mod))
# module conflicts and dependencies
with when("+calib3d"):
conflicts("~features2d")
conflicts("~flann")
conflicts("~imgproc")
with when("+dnn"):
conflicts("~imgproc")
conflicts("~protobuf")
with when("+features2d"):
conflicts("~imgproc")
with when("+gapi"):
conflicts("~ade")
conflicts("~imgproc")
with when("+highgui"):
conflicts("~imgcodecs")
conflicts("~imgproc")
with when("+imgcodecs"):
conflicts("~imgproc")
with when("+java"):
conflicts("~imgproc")
conflicts("~java_bindings_generator")
conflicts("~python2~python3")
with when("+java_bindings_generator"):
depends_on("java")
depends_on("ant")
with when("+objc"):
conflicts("~imgproc")
conflicts("~objc_bindings_generator")
with when("+objc_bindings_generator"):
conflicts("~imgproc")
with when("+objdetect"):
conflicts("~calib3d")
conflicts("~dnn")
conflicts("~imgproc")
with when("+photo"):
conflicts("~imgproc")
with when("+python2"):
conflicts("+python3")
conflicts("~python_bindings_generator")
depends_on("[email protected]:2.8", type=("build", "link", "run"))
depends_on("py-setuptools", type="build")
depends_on("py-numpy", type=("build", "run"))
extends("python", when="+python2")
with when("+python3"):
conflicts("+python2")
conflicts("~python_bindings_generator")
depends_on("[email protected]:", type=("build", "link", "run"))
depends_on("py-setuptools", type="build")
depends_on("py-numpy", type=("build", "run"))
extends("python", when="+python3")
with when("+stitching"):
conflicts("~calib3d")
conflicts("~features2d")
conflicts("~flann")
conflicts("~imgproc")
with when("+ts"):
conflicts("~highgui")
conflicts("~imgcodecs")
conflicts("~imgproc")
conflicts("~videoio")
with when("+video"):
conflicts("~imgproc")
with when("+videoio"):
conflicts("~ffmpeg")
conflicts("~imgcodecs")
conflicts("~imgproc")
# OpenCV contrib modules (variants)
contrib_modules = [
"alphamat",
"aruco",
"barcode",
"bgsegm",
"bioinspired",
"ccalib",
"cudaarithm",
"cudabgsegm",
"cudacodec",
"cudafeatures2d",
"cudafilters",
"cudaimgproc",
"cudalegacy",
"cudaobjdetect",
"cudaoptflow",
"cudastereo",
"cudawarping",
"cudev",
"cvv",
"datasets",
"dnn_objdetect",
"dnn_superres",
"dpm",
"face",
"freetype",
"fuzzy",
"hdf",
"hfs",
"img_hash",
"intensity_transform",
"line_descriptor",
"matlab",
"mcc",
"optflow",
"phase_unwrapping",
"plot",
"quality",
"rapid",
"reg",
"rgbd",
"saliency",
"sfm",
"shape",
"stereo",
"structured_light",
"superres",
"surface_matching",
"text",
"tracking",
"videostab",
"viz",
"wechat_qrcode",
"xfeatures2d",
"ximgproc",
"xobjdetect",
"xphoto",
]
contrib_modules_pending = [
"julia", # need a way to manage the installation prefix
"ovis", # need ogre
]
for mod in contrib_modules:
variant(
mod,
default=False,
description="Include opencv_{0} contrib module".format(mod),
)
# contrib module conflicts and dependencies
with when("+alphamat"):
conflicts("~eigen")
conflicts("~imgproc")
with when("+aruco"):
conflicts("~calib3d")
conflicts("~imgproc")
with when("+barcode"):
conflicts("~dnn")
conflicts("~imgproc")
with when("+bgsegm"):
conflicts("~calib3d")
conflicts("~imgproc")
conflicts("~video")
with when("+ccalib"):
conflicts("~calib3d")
conflicts("~features2d")
conflicts("~highgui")
conflicts("~imgproc")
with when("+cublas"):
conflicts("~cuda")
conflicts("~cudev")
with when("+cuda"):
conflicts("~cudev")
with when("+cudaarithm"):
conflicts("~cuda")
conflicts("~cublas")
conflicts("~cudev")
conflicts("~cufft")
with when("+cudabgsegm"):
conflicts("~cuda")
conflicts("~cudev")
conflicts("~video")
with when("+cudacodec"):
conflicts("~cudev")
conflicts("~videoio")
with when("+cudafeatures2d"):
conflicts("~cuda")
conflicts("~cudafilters")
conflicts("~cudawarping")
conflicts("~cudev")
conflicts("~features2d")
with when("+cudafilters"):
conflicts("~cuda")
conflicts("~cudaarithm")
conflicts("~cudev")
conflicts("~imgproc")
with when("+cudaimgproc"):
conflicts("~cuda")
conflicts("~cudev")
conflicts("~imgproc")
with when("+cudalegacy"):
conflicts("~cuda")
conflicts("~cudev")
conflicts("~video")
with when("+cudaobjdetect"):
conflicts("~cuda")
conflicts("~cudaarithm")
conflicts("~cudawarping")
conflicts("~cudev")
conflicts("~objdetect")
with when("+cudaoptflow"):
conflicts("~cuda")
conflicts("~cudaarithm")
conflicts("~cudaimgproc")
conflicts("~cudawarping")
conflicts("~cudev")
conflicts("~optflow")
conflicts("~video")
with when("+cudastereo"):
conflicts("~calib3d")
conflicts("~cuda")
conflicts("~cudev")
with when("+cudawarping"):
conflicts("~cuda")
conflicts("~cudev")
conflicts("~imgproc")
with when("+cudev"):
conflicts("~cuda")
with when("+cvv"):
conflicts("~features2d")
conflicts("~imgproc")
conflicts("~qt")
with when("+datasets"):
conflicts("~flann")
conflicts("~imgcodecs")
conflicts("~ml")
with when("+dnn_objdetect"):
conflicts("~dnn")
conflicts("~imgproc")
with when("+dnn_superres"):
conflicts("~dnn")
conflicts("~imgproc")
with when("+dpm"):
conflicts("~imgproc")
conflicts("~objdetect")
with when("+face"):
conflicts("~calib3d")
conflicts("~imgproc")
conflicts("~objdetect")
conflicts("~photo")
with when("+fuzzy"):
conflicts("~imgproc")
with when("+freetype"):
conflicts("~imgproc")
depends_on("freetype")
depends_on("harfbuzz")
with when("+hdf"):
depends_on("hdf5")
with when("+hfs"):
with when("+cuda"):
conflicts("~cudev")
conflicts("~imgproc")
with when("+img_hash"):
conflicts("~imgproc")
with when("+intensity_transform"):
conflicts("~imgproc")
with when("+line_descriptor"):
conflicts("~imgproc")
with when("+matlab"):
conflicts("~python2~python3")
depends_on("matlab")
depends_on("py-jinja2")
with when("+mcc"):
conflicts("~calib3d")
conflicts("~dnn")
conflicts("~imgproc")
with when("+optflow"):
conflicts("~calib3d")
conflicts("~flann")
conflicts("~imgcodecs")
conflicts("~imgproc")
conflicts("~video")
conflicts("~ximgproc")
with when("+phase_unwrapping"):
conflicts("~imgproc")
with when("+plot"):
conflicts("~imgproc")
with when("+quality"):
conflicts("~imgproc")
conflicts("~ml")
with when("+rapid"):
conflicts("~calib3d")
conflicts("~imgproc")
with when("+reg"):
conflicts("~imgproc")
with when("+rgbd"):
conflicts("~calib3d")
conflicts("~eigen")
conflicts("~imgproc")
with when("+saliency"):
conflicts("%intel")
conflicts("~features2d")
conflicts("~imgproc")
with when("+sfm"):
conflicts("~calib3d")
conflicts("~eigen")
conflicts("~features2d")
conflicts("~imgcodecs")
conflicts("~xfeatures2d")
depends_on("ceres-solver")
depends_on("gflags")
depends_on("glog")
with when("+shape"):
conflicts("~calib3d")
conflicts("~imgproc")
with when("+stereo"):
conflicts("~calib3d")
conflicts("~features2d")
conflicts("~imgproc")
conflicts("~tracking")
with when("+structured_light"):
conflicts("~calib3d")
conflicts("~imgproc")
conflicts("~phase_unwrapping")
with when("+superres"):
with when("+cuda"):
conflicts("~cudev")
conflicts("~imgproc")
conflicts("~optflow")
conflicts("~video")
with when("+surface_matching"):
conflicts("~flann")
with when("+text"):
conflicts("~dnn")
conflicts("~features2d")
conflicts("~imgproc")
conflicts("~ml")
with when("+tracking"):
conflicts("~imgproc")
conflicts("~plot")
conflicts("~video")
with when("+videostab"):
with when("+cuda"):
conflicts("~cudev")
conflicts("~calib3d")
conflicts("~features2d")
conflicts("~imgproc")
conflicts("~photo")
conflicts("~video")
with when("+viz"):
conflicts("~vtk")
with when("+wechat_qrcode"):
conflicts("~dnn")
conflicts("~imgproc")
depends_on("libiconv")
with when("+xfeatures2d"):
with when("+cuda"):
conflicts("~cudev")
conflicts("~calib3d")
conflicts("~features2d")
conflicts("~imgproc")
with when("+ximgproc"):
conflicts("~calib3d")
conflicts("~imgcodecs")
conflicts("~imgproc")
conflicts("~video")
with when("+xobjdetect"):
conflicts("~imgcodecs")
conflicts("~imgproc")
conflicts("~objdetect")
with when("+xphoto"):
conflicts("~imgproc")
conflicts("~photo")
# Optional 3rd party components (variants)
# Defined in `CMakeLists.txt` and `modules/gapi/cmake/init.cmake`
# using `OCV_OPTION(WITH_* ...)`
components = [
"1394",
"ade",
"android_mediandk",
"android_native_camera",
"avfoundation",
"cap_ios",
"carotene",
"clp",
"cpufeatures",
"cublas",
"cuda",
"cudnn",
"cufft",
"directx",
"dshow",
"eigen",
"ffmpeg",
"gdal",
"gtk",
"hpx",
"imgcodec_hdr",
"imgcodec_pfm",
"imgcodec_pxm",
"imgcodec_sunraster",
"ipp",
"itt",
"jasper",
"jpeg",
"lapack",
"msmf",
"msmf_dxva",
"onnx",
"opencl",
"opencl_d3d11_nv",
"openexr",
"opengl",
"openjpeg",
"openmp",
"plaidml",
"png",
"protobuf",
"pthreads_pf",
"qt",
"quirc",
"tbb",
"tengine",
"tesseract",
"tiff",
"v4l",
"vtk",
"vulcan",
"webp",
"win32ui",
]
# These likely need additional spack packages
components_pending = [
"aravis",
"gdcm",
"gphoto2",
"gstreamer",
"gtk_2_x", # deprecated in spack
"halide",
"inf_engine",
"librealsense",
"mfx",
"ngraph",
"nvcuvid", # disabled, details: https://github.com/opencv/opencv/issues/14850
"opencl_svm",
"openclamdblas",
"openclamdfft",
"openni",
"openni2",
"openvx",
"pvapi",
"ueye",
"va",
"va_intel",
"ximea",
"xine",
]
# components and modules with the same name
# used in `def cmake_args(self)`
component_and_module = ["freetype", "julia", "matlab"]
for component in components:
variant(
component,
default=False,
description="Include {0} support".format(component),
)
# Other (variants)
variant("shared", default=True, description="Enables the build of shared libraries")
variant("powerpc", default=False, description="Enable PowerPC for GCC")
variant(
"fast-math",
default=False,
description="Enable -ffast-math (not recommended for GCC 4.6.x)",
)
variant("nonfree", default=False, description="Enable non-free algorithms")
# Required (dependencies)
depends_on("[email protected]:", type="build")
depends_on("[email protected]:2.8,3.2:", type="build")
depends_on("java", type="build")
depends_on("[email protected]:")
# Optional 3rd party components (dependencies)
depends_on("clp", when="+clp")
depends_on("[email protected]:", when="+cuda")
depends_on("cuda@:10.2", when="@4.0:4.2+cuda")
depends_on("cuda@:9.0", when="@3.3.1:3.4+cuda")
depends_on("cuda@:8", when="@:3.3.0+cuda")
depends_on("cudnn", when="+cudnn")
depends_on("cudnn@:7.6", when="@4.0:4.2+cudnn")
depends_on("cudnn@:7.3", when="@3.3.1:3.4+cudnn")
depends_on("cudnn@:6", when="@:3.3.0+cudnn")
depends_on("eigen", when="+eigen")
depends_on("ffmpeg+avresample", when="+ffmpeg")
depends_on("gdal", when="+gdal")
depends_on("gtkplus", when="+gtk")
depends_on("hpx", when="+hpx")
depends_on("ipp", when="+ipp")
depends_on("jasper", when="+jasper")
depends_on("jpeg", when="+jpeg")
depends_on("lapack", when="+lapack")
depends_on("onnx", when="+onnx")
depends_on("opencl", when="+opencl")
depends_on("openexr", when="+openexr")
depends_on("gl", when="+opengl")
depends_on("openjpeg@2:", when="+openjpeg")
depends_on("libpng", when="+png")
depends_on("[email protected]:", when="@3.4.1: +protobuf")
depends_on("[email protected]", when="@3.3.0:3.4.0 +protobuf")
depends_on("qt@5:", when="+qt")
depends_on("qt@5:+opengl", when="+qt+opengl")
depends_on("tbb", when="+tbb")
depends_on("libtiff+jpeg+libdeflate+lzma+zlib", when="+tiff")
depends_on("vtk", when="+vtk")
depends_on("libwebp", when="+webp")
depends_on("tesseract", when="+tesseract")
depends_on("leptonica", when="+tesseract")
depends_on("libdc1394", when="+1394")
# Optional 3rd party components (conflicts)
# Defined in `CMakeLists.txt` and `modules/gapi/cmake/init.cmake`
# using `OCV_OPTION(WITH_* ...)`
conflicts("+android_mediandk", when="platform=darwin", msg="Android only")
conflicts("+android_mediandk", when="platform=linux", msg="Android only")
conflicts("+android_mediandk", when="platform=cray", msg="Android only")
conflicts("+android_native_camera", when="platform=darwin", msg="Android only")
conflicts("+android_native_camera", when="platform=linux", msg="Android only")
conflicts("+android_native_camera", when="platform=cray", msg="Android only")
conflicts("+avfoundation", when="platform=linux", msg="iOS/macOS only")
conflicts("+avfoundation", when="platform=cray", msg="iOS/macOS only")
conflicts("+cap_ios", when="platform=darwin", msg="iOS only")
conflicts("+cap_ios", when="platform=linux", msg="iOS only")
conflicts("+cap_ios", when="platform=cray", msg="iOS only")
conflicts("+carotene", when="target=x86:", msg="ARM/AARCH64 only")
conflicts("+carotene", when="target=x86_64:", msg="ARM/AARCH64 only")
conflicts("+cpufeatures", when="platform=darwin", msg="Android only")
conflicts("+cpufeatures", when="platform=linux", msg="Android only")
conflicts("+cpufeatures", when="platform=cray", msg="Android only")
conflicts("+cublas", when="~cuda")
conflicts("+cudnn", when="~cuda")
conflicts("+cufft", when="~cuda")
conflicts("+directx", when="platform=darwin", msg="Windows only")
conflicts("+directx", when="platform=linux", msg="Windows only")
conflicts("+directx", when="platform=cray", msg="Windows only")
conflicts("+dshow", when="platform=darwin", msg="Windows only")
conflicts("+dshow", when="platform=linux", msg="Windows only")
conflicts("+dshow", when="platform=cray", msg="Windows only")
conflicts("+gtk", when="platform=darwin", msg="Linux only")
conflicts("+ipp", when="target=aarch64:", msg="x86 or x86_64 only")
conflicts("+jasper", when="+openjpeg")
conflicts("+msmf", when="platform=darwin", msg="Windows only")
conflicts("+msmf", when="platform=linux", msg="Windows only")
conflicts("+msmf", when="platform=cray", msg="Windows only")
conflicts("+msmf_dxva", when="platform=darwin", msg="Windows only")
conflicts("+msmf_dxva", when="platform=linux", msg="Windows only")
conflicts("+msmf_dxva", when="platform=cray", msg="Windows only")
conflicts("+opencl_d3d11_nv", when="platform=darwin", msg="Windows only")
conflicts("+opencl_d3d11_nv", when="platform=linux", msg="Windows only")
conflicts("+opencl_d3d11_nv", when="platform=cray", msg="Windows only")
conflicts("+opengl", when="~qt")
conflicts("+tengine", when="platform=darwin", msg="Linux only")
conflicts("+tengine", when="target=x86:", msg="ARM/AARCH64 only")
conflicts("+tengine", when="target=x86_64:", msg="ARM/AARCH64 only")
conflicts("+v4l", when="platform=darwin", msg="Linux only")
conflicts("+win32ui", when="platform=darwin", msg="Windows only")
conflicts("+win32ui", when="platform=linux", msg="Windows only")
conflicts("+win32ui", when="platform=cray", msg="Windows only")
def cmake_args(self):
spec = self.spec
args = [
self.define(
"OPENCV_EXTRA_MODULES_PATH",
join_path(self.stage.source_path, "opencv_contrib/modules"),
),
self.define("BUILD_opencv_core", "on"),
]
# OpenCV pre-built apps
apps_list = []
for app in self.apps:
if "+{0}".format(app) in spec:
apps_list.append(app)
if apps_list:
args.append(self.define("BUILD_opencv_apps", "on"))
args.append(self.define("OPENCV_INSTALL_APPS_LIST", ",".join(apps_list)))
else:
args.append(self.define("BUILD_opencv_apps", "off"))
# OpenCV modules
for mod in self.modules:
args.append(self.define_from_variant("BUILD_opencv_" + mod, mod))
if mod in self.component_and_module:
args.append(self.define_from_variant("WITH_" + mod.upper(), mod))
for mod in self.modules_pending:
args.append(self.define("BUILD_opencv_" + mod, "off"))
if mod in self.component_and_module:
args.append(self.define("WITH_" + mod.upper(), "off"))
# OpenCV contrib modules
for mod in self.contrib_modules:
args.append(self.define_from_variant("BUILD_opencv_" + mod, mod))
if mod in self.component_and_module:
args.append(self.define_from_variant("WITH_" + mod.upper(), mod))
for mod in self.contrib_modules_pending:
args.append(self.define("BUILD_opencv_" + mod, "off"))
if mod in self.component_and_module:
args.append(self.define("WITH_" + mod.upper(), "off"))
# Optional 3rd party components
for component in self.components:
args.append(
self.define_from_variant("WITH_" + component.upper(), component)
)
for component in self.components_pending:
args.append(self.define("WITH_" + component.upper(), "off"))
# Other
args.extend(
[
self.define("ENABLE_CONFIG_VERIFICATION", True),
self.define_from_variant("BUILD_SHARED_LIBS", "shared"),
self.define("ENABLE_PRECOMPILED_HEADERS", False),
self.define_from_variant("WITH_LAPACK", "lapack"),
self.define_from_variant("ENABLE_POWERPC", "powerpc"),
self.define_from_variant("ENABLE_FAST_MATH", "fast-math"),
self.define_from_variant("OPENCV_ENABLE_NONFREE", "nonfree"),
]
)
if "+cuda" in spec:
if spec.variants["cuda_arch"].value[0] != "none":
cuda_arch = spec.variants["cuda_arch"].value
args.append(self.define("CUDA_ARCH_BIN", " ".join(cuda_arch)))
# TODO: this CMake flag is deprecated
if spec.target.family == "ppc64le":
args.append(self.define("ENABLE_VSX", True))
# Media I/O
zlib = spec["zlib"]
args.extend(
[
self.define("BUILD_ZLIB", False),
self.define("ZLIB_LIBRARY", zlib.libs[0]),
self.define("ZLIB_INCLUDE_DIR", zlib.headers.directories[0]),
]
)
if "+png" in spec:
libpng = spec["libpng"]
args.extend(
[
self.define("BUILD_PNG", False),
self.define("PNG_LIBRARY", libpng.libs[0]),
self.define("PNG_INCLUDE_DIR", libpng.headers.directories[0]),
]
)
if "+jpeg" in spec:
libjpeg = spec["jpeg"]
args.extend(
[
self.define("BUILD_JPEG", False),
self.define("JPEG_LIBRARY", libjpeg.libs[0]),
self.define("JPEG_INCLUDE_DIR", libjpeg.headers.directories[0]),
]
)
if "+tiff" in spec:
libtiff = spec["libtiff"]
args.extend(
[
self.define("BUILD_TIFF", False),
self.define("TIFF_LIBRARY", libtiff.libs[0]),
self.define("TIFF_INCLUDE_DIR", libtiff.headers.directories[0]),
]
)
if "+jasper" in spec:
jasper = spec["jasper"]
args.extend(
[
self.define("BUILD_JASPER", False),
self.define("JASPER_LIBRARY", jasper.libs[0]),
self.define("JASPER_INCLUDE_DIR", jasper.headers.directories[0]),
]
)
if "+clp" in spec:
clp = spec["clp"]
args.extend(
[
self.define("BUILD_CLP", False),
self.define("CLP_LIBRARIES", clp.prefix.lib),
self.define("CLP_INCLUDE_DIR", clp.headers.directories[0]),
]
)
if "+onnx" in spec:
onnx = spec["onnx"]
args.extend(
[
self.define("BUILD_ONNX", False),
self.define("ORT_LIB", onnx.libs[0]),
self.define("ORT_INCLUDE", onnx.headers.directories[0]),
]
)
if "+tesseract" in spec:
tesseract = spec["tesseract"]
leptonica = spec["leptonica"]
args.extend(
[
self.define("Lept_LIBRARY", leptonica.libs[0]),
self.define("Tesseract_LIBRARY", tesseract.libs[0]),
self.define(
"Tesseract_INCLUDE_DIR", tesseract.headers.directories[0]
),
]
)
# Python
python_exe = spec["python"].command.path
python_lib = spec["python"].libs[0]
python_include_dir = spec["python"].headers.directories[0]
if "+python2" in spec:
args.extend(
[
self.define("PYTHON2_EXECUTABLE", python_exe),
self.define("PYTHON2_LIBRARY", python_lib),
self.define("PYTHON2_INCLUDE_DIR", python_include_dir),
self.define("PYTHON3_EXECUTABLE", ""),
]
)
elif "+python3" in spec:
args.extend(
[
self.define("PYTHON3_EXECUTABLE", python_exe),
self.define("PYTHON3_LIBRARY", python_lib),
self.define("PYTHON3_INCLUDE_DIR", python_include_dir),
self.define("PYTHON2_EXECUTABLE", ""),
]
)
else:
args.extend(
[
self.define("PYTHON2_EXECUTABLE", ""),
self.define("PYTHON3_EXECUTABLE", ""),
]
)
return args
@property
def libs(self):
shared = "+shared" in self.spec
return find_libraries(
"libopencv_*", root=self.prefix, shared=shared, recursive=True
)
|
# @Vipin Chaudhari
host='192.168.1.15'
meetup_rsvp_stream_api_url = "http://stream.meetup.com/2/rsvps"
# Kafka info
kafka_topic = "meetup-rsvp-topic"
kafka_server = host+':9092'
# MySQL info
mysql_user = "python"
mysql_pwd = "python"
mysql_db = "meetup"
mysql_driver = "com.mysql.cj.jdbc.Driver"
mysql_tbl = "MeetupRSVP"
mysql_jdbc_url = "jdbc:mysql://" + host + ":3306/" + mysql_db + "?useJDBCCompliantTimezoneShift=true&useLegacyDatetimeCode=false&serverTimezone=UTC"
# MongoDB info
mongodb_host=host
mongodb_user = "admin"
mongodb_pwd = "admin"
mongodb_db = "meetup"
mongodb_collection = "tbl_meetup_rsvp" |
class Layer:
def __init__(self):
self.input = None
self.output = None
def forward_propagation(self,input):
raise NotImplementedError
def backward_propagation(self, output_error,learning_rate):
raise NotImplementedError |
def to_fp_name(path, prefix='fp'):
if path == 'stdout':
return 'stdout'
if path == 'stderr':
return 'stderr'
if path == 'stdin':
return 'stdin'
# For now, just construct a fd variable name by taking objectionable chars out of the path
cleaned = path.replace('.', '_').replace ('/', '_').replace ('-', '_').replace ('+', 'x')
return "{}_{}".format(prefix, cleaned)
def doinatest():
return "YO"
|
class TreeNode:
def __init__(self, x):
self.val = x
self.left = None
self.right = None
class Solution:
def lowestCommonAncestor(self, root: TreeNode, p: TreeNode, q: TreeNode) -> TreeNode:
if root is None:
return None
left = self.lowestCommonAncestor(root.left, p, q)
right = self.lowestCommonAncestor(root.right, p, q)
if (left and right) or root in [p,q]:
return root
else:
return left or right
|
# instance/config.cfg
SQLALCHEMY_DATABASE_URI= \
"mysql://microaccounts_dev:r783qjkldDsiu@localhost:3306/elixir_beacon_testing"
SECRET_KEY= \
"nsady679d8+eiqรฅowmยดยด`msdjjwi"
|
#!/usr/bin/env python
#pylint: skip-file
# This source code is licensed under the Apache license found in the
# LICENSE file in the root directory of this project.
class AclAce(object):
def __init__(self):
"""
Attributes:
swaggerTypes (dict): The key is attribute name and the value is attribute type.
attributeMap (dict): The key is attribute name and the value is json key in definition.
"""
self.swaggerTypes = {
'result': 'str',
'matchingPorts': 'list[AclMatchingPorts]',
'ace': 'str'
}
self.attributeMap = {
'result': 'result',
'matchingPorts': 'matchingPorts',
'ace': 'ace'
}
self.result = None # str
self.matchingPorts = None # list[AclMatchingPorts]
self.ace = None # str
|
class MarginGetError(Exception):
pass
class PositionGetError(Exception):
pass
class TickGetError(Exception):
pass
class TradeGetError(Exception):
pass
class BarGetError(Exception):
pass
class FillGetError(Exception):
pass
class OrderPostError(Exception):
pass
class OrderGetError(Exception):
pass
class OrderBookGetError(Exception):
pass
class BalanceGetError(Exception):
pass
class AssetTransferError(Exception):
pass
|
class MyStuff(object):
def __init__(self):
self.stark = "I am classy Iron Man."
def groot(self):
print("I am classy groot!")
thing = MyStuff()
thing.groot()
print(thing.stark)
|
expected_output = {
"GigabitEthernet0/1/1": {
"service_policy": {
"output": {
"policy_name": {
"shape-out": {
"class_map": {
"class-default": {
"bytes": 0,
"bytes_output": 0,
"match": ["any"],
"match_evaluation": "match-any",
"no_buffer_drops": 0,
"packets": 0,
"pkts_output": 0,
"queue_depth": 0,
"queue_limit_packets": "64",
"queueing": True,
"rate": {
"drop_rate_bps": 0,
"interval": 300,
"offered_rate_bps": 0,
},
"shape_bc_bps": 2000,
"shape_be_bps": 2000,
"shape_cir_bps": 500000,
"shape_type": "average",
"target_shape_rate": 500000,
"total_drops": 0,
}
}
}
}
}
}
}
}
|
class MXVLANPorts(object):
def __init__(self, session):
super(MXVLANPorts, self).__init__()
self._session = session
def getNetworkAppliancePorts(self, networkId: str):
"""
**List per-port VLAN settings for all ports of a MX.**
https://developer.cisco.com/meraki/api/#!get-network-appliance-ports
- networkId (string)
"""
metadata = {
'tags': ['MX VLAN ports'],
'operation': 'getNetworkAppliancePorts',
}
resource = f'/networks/{networkId}/appliancePorts'
return self._session.get(metadata, resource)
def getNetworkAppliancePort(self, networkId: str, appliancePortId: str):
"""
**Return per-port VLAN settings for a single MX port.**
https://developer.cisco.com/meraki/api/#!get-network-appliance-port
- networkId (string)
- appliancePortId (string)
"""
metadata = {
'tags': ['MX VLAN ports'],
'operation': 'getNetworkAppliancePort',
}
resource = f'/networks/{networkId}/appliancePorts/{appliancePortId}'
return self._session.get(metadata, resource)
def updateNetworkAppliancePort(self, networkId: str, appliancePortId: str, **kwargs):
"""
**Update the per-port VLAN settings for a single MX port.**
https://developer.cisco.com/meraki/api/#!update-network-appliance-port
- networkId (string)
- appliancePortId (string)
- enabled (boolean): The status of the port
- dropUntaggedTraffic (boolean): Trunk port can Drop all Untagged traffic. When true, no VLAN is required. Access ports cannot have dropUntaggedTraffic set to true.
- type (string): The type of the port: 'access' or 'trunk'.
- vlan (integer): Native VLAN when the port is in Trunk mode. Access VLAN when the port is in Access mode.
- allowedVlans (string): Comma-delimited list of the VLAN ID's allowed on the port, or 'all' to permit all VLAN's on the port.
- accessPolicy (string): The name of the policy. Only applicable to Access ports. Valid values are: 'open', '8021x-radius', 'mac-radius', 'hybris-radius' for MX64 or Z3 or any MX supporting the per port authentication feature. Otherwise, 'open' is the only valid value and 'open' is the default value if the field is missing.
"""
kwargs.update(locals())
metadata = {
'tags': ['MX VLAN ports'],
'operation': 'updateNetworkAppliancePort',
}
resource = f'/networks/{networkId}/appliancePorts/{appliancePortId}'
body_params = ['enabled', 'dropUntaggedTraffic', 'type', 'vlan', 'allowedVlans', 'accessPolicy']
payload = {k: v for (k, v) in kwargs.items() if k in body_params}
return self._session.put(metadata, resource, payload)
|
TOKEN = '668308467:AAETX4hdMRnVvYVBP4bK5WDgvL8zIXoHq5g'
main_url = 'https://schedule.vekclub.com/api/v1/'
list_group = 'handbook/groups-by-institution?institutionId=4'
shedule_group ='schedule/group?groupId=' |
# -*- coding: utf-8 -*-
"""This module contains exceptions
"""
class PublishError(RuntimeError):
"""Raised when the published version is not matching the quality
"""
pass
|
# SOME BASIC CONSTANT AND MASS FUNCTION OF POISSION DISTRIBUTION
# e is Euler's number (e = 2.71828...)
# f(k, lambda) = lambda^k * e^-lambda / k!
# Task:
# A random variable, X , follows Poisson distribution with mean of 2.5. Find the probability with which the random variable X is equal to 5.
# Define functions
def factorial(n):
if n == 1 or n == 0:
return 1
if n > 1:
return factorial(n - 1) * n
# Input data
lam = float(input())
k = float(input())
e = 2.71828
# We can show result on the screen
# The round() function returns a floating point number that is a rounded version of the specified number, with the specified number of decimals.
# 3 denotes decimal places (i.e., 1.234 format):
result = ((lam ** k) * (e ** -lam)) / factorial(k)
print(round(result, 3))
|
s1 = {'ab', 3,4, (5,6)}
s2 = {'ab', 7, (7,6)}
print(s1-s2)
# Returns all the items in both s1 and s2
print(s1.intersection(s2))
# Returns all the items in a set
print(s1.union(s2))
print('ab' in s1) # Testing for a member's presence in the set
# Lopping through elements in a set
for element in s1:
print(element)
# Frozen or Immutable sets
s1.add(frozenset(s2))
print(s1)
# Using frozen set as a key to a dictionary
fs1 = frozenset(s1)
fs2 = frozenset(s2)
{fs1: 'fs1', fs2: 'fs2'}
|
# Programa que converte metros em medidas
print('Conversor de metros\n')
medida = float(input('Insira uma medida em metros: '))
print('Essa medida correponde a\nQuilรดmetros: {:.2f}\nHectรดmetros: {:.2f}\nDecรขmetros: {:.2f}\nDecรญmetros: {:.2f}\nCentรญmetros: {:.2f}\nMilรญmetros: {:.2f}'.format(medida/1000, medida/100, medida/10, medida*10, medida*100, medida*1000))
|
AI_FEEDBACK_SCALAS = {
1: "Strongly disagree",
2: "",
3: "",
4: "",
5: "",
6: "",
7: "Strongly agree"
}
AI_FEEDBACK_ACCURACY_SCALAS = {
"no_clue": "I don't know",
"0_percent": "0%",
"20_percent": "20%",
"40_percent": "40%",
"60_percent": "60%",
"80_percent": "80%",
"100_percent": "100%",
}
AI_FEEDBACK_ACCURACY_PROPOSER_SCALAS = {
"ai_much_worse": "Worse than me",
"ai_worse": "",
"ai_sligthly_worse": "",
"ai_equal_to_proposer": "As good as me",
"ai_slighly_better": "",
"ai_better": "",
"ai_much_better": "Better than me",
}
AI_FEEDBACK_ACCURACY_RESPONDER_SCALAS = {
"ai_much_worse": "Worse than the PROPOSER",
"ai_worse": "",
"ai_sligthly_worse": "",
"ai_equal_to_proposer": "As good as the PROPOSER",
"ai_slighly_better": "",
"ai_better": "",
"ai_much_better": "Better than the PROPOSER",
}
AI_FEEDBACK_ACCURACY_RESPONDER_SCALAS_T3X = {
1: "Less",
2: "",
3: "",
4: "Equal",
5: "",
6: "",
7: "More"
}
AI_SYSTEM_DESCRIPTION_BRIEF_STANDALONE_PROPOSER = """Thank you for your offer. You will now make another decision as a PROPOSER. This time you have the option to use an AI Recommendation System (AI System) to help you decide which offer to make. The system was trained using prior interactions of comparable bargaining situations."""
AI_SYSTEM_DESCRIPTION_BRIEF_PROPOSER = """Thank you for your offer. You will now make another decision as a PROPOSER. This time you have the option to use an AI Recommendation System (AI System) to help you decide which offer to make."""
AI_SYSTEM_UNINFORMED_RESPONDER_INFORMATION_PROPOSER = """The RESPONDER does NOT know there is an AI System."""
AI_SYSTEM_INFORMED_RESPONDER_INFORMATION_PROPOSER = """The RESPONDER knows you can use an AI System."""
AI_SYSTEM_DESCRIPTION_EXTENDED_ACC_PROPOSER = """The system was trained using 100 prior interactions of comparable bargaining situations.
- The system learned a fixed optimal offer (AI_OFFER).
- AI_OFFER was found by testing each possible offer on comparable bargaining situations and was selected as the one that provided the highest average gain to PROPOSERs.
- Following the AI System's recommendations, PROPOSERs can gain 80% of the pie left by RESPONDERs.
- Following the AI System's recommendations, PROPOSERs can have 95% of their offers accepted.
- The probability of an offer being accepted is higher than 50% when the offer is greater than or equal to AI_OFFER.
- The probability of an offer being the RESPONDER's minimal offer is higher the closer the offer is to AI_OFFER."""
AI_SYSTEM_DESCRIPTION_EXTENDED_PROPOSER = """The system was trained using 100 prior interactions of comparable bargaining situations.
- The system learned a fixed optimal offer (AI_OFFER).
- AI_OFFER was found by testing each possible offer on these prior bargaining situations and was selected as the one that provided the highest average gain to PROPOSERs.
- Using the same process, the system also constructed an interval that judges offers that deviate from its recommendation."""
AI_SYSTEM_DESCRIPTION_USAGE_PROPOSER = """To use the AI System, simply select a test offer and submit it to the system. The system will tell you its estimates on:
1. The probability that your offer will be accepted by your specific RESPONDER.
2. The probability that your offer is the minimal offer accepted by your specific RESPONDER.
You can use the system as often as you want."""
AI_SYSTEM_DESCRIPTION_BRIEF_STANDALONE_RESPONDER = """Thank you for your minimum offer. You will now make another decision as a RESPONDER. This time your PROPOSER has the option to use an AI Recommendation System (AI System) to help them decide which offer to make.
The system was trained using prior interactions of comparable bargaining situations."""
AI_SYSTEM_DESCRIPTION_BRIEF_RESPONDER = """Thank you for your minimum offer. You will now make another decision as a RESPONDER. This time your PROPOSER has the option to use an AI Recommendation System (AI System) to help them decide which offer to make."""
AI_SYSTEM_DESCRIPTION_EXTENDED_RESPONDER = AI_SYSTEM_DESCRIPTION_EXTENDED_PROPOSER
AI_SYSTEM_AUTO_DESCRIPTION_BRIEF_STANDALONE_RESPONDER = """An AI Machine-Learning System will autonomously make an offer to you on behalf of a human PROPOSER. The system was trained using prior interactions of comparable bargaining situations. The human PROPOSER does not make any decisions, they only receives whatever money the system earns from this task."""
AI_SYSTEM_AUTO_DESCRIPTION_BRIEF_RESPONDER = """An AI Machine-Learning System will autonomously make an offer to you on behalf of a human PROPOSER. The human PROPOSER does not make any decisions, they only receives whatever money the system earns from this task."""
AI_SYSTEM_AUTO_DESCRIPTION_EXTENDED_RESPONDER = """The system was trained using 100 prior interactions of comparable bargaining situations.
- The system learned a fixed optimal offer (AI_OFFER).
- AI_OFFER was found by testing each possible offer on these prior bargaining situations and was selected as the one that provided the highest average gain to PROPOSERs.
- Using the same process, the system also constructed an interval that judges offers that deviate from its recommendation."""
AI_SYSTEM_DESCRIPTION_BRIEF_RESPONDER_T3X="""You have successfully submitted your minimum offer. Now, you have the option to revise your initial minimum offer by making a second decision in your role as RESPONDER.
This second decision will be compared to your PROPOSER's offer and determine your bonus payoff from this task. For this second decision, you receive new information:
Your matched PROPOSER does not actually make an offer themselves. Instead, an AI Machine-Learning System autonomously makes an offer on the human PROPOSER's behalf. The PROPOSER still receives whatever money the system earns from this task.""" |
# -*- coding: utf-8 -*-
class TreeNode:
def __init__(self, x):
self.val = x
self.left = None
self.right = None
class Solution:
def tree2str(self, t):
if t is None:
return ''
result = [str(t.val)]
if t.left is not None or t.right is not None:
result.extend(['(', self.tree2str(t.left), ')'])
if t.right is not None:
result.extend(['(', self.tree2str(t.right), ')'])
return ''.join(result)
if __name__ == '__main__':
solution = Solution()
t0_0 = TreeNode(1)
t0_1 = TreeNode(2)
t0_2 = TreeNode(3)
t0_3 = TreeNode(4)
t0_1.left = t0_3
t0_0.right = t0_2
t0_0.left = t0_1
assert '1(2(4))(3)' == solution.tree2str(t0_0)
t1_0 = TreeNode(1)
t1_1 = TreeNode(2)
t1_2 = TreeNode(3)
t1_3 = TreeNode(4)
t1_1.right = t1_3
t1_0.right = t1_2
t1_0.left = t1_1
assert '1(2()(4))(3)' == solution.tree2str(t1_0)
|
#!/usr/bin/python
# -*- coding: utf-8 -*-
# Simulation : https://framagit.org/kepon/PvMonit/issues/8
# ~ print("PID:0x203");
# ~ print("FW:146");
# ~ print("SER#:HQ18523ZGZI");
# ~ print("V:25260");
# ~ print("I:100");
# ~ print("VPV:28600");
# ~ print("PPV:6");
# ~ print("CS:3");
# ~ print("MPPT:2");
# ~ print("OR:0x00000000");
# ~ print("ERR:0");
# ~ print("LOAD:OFF");
# ~ print("Relay:OFF");
# ~ print("H19:3213");
# ~ print("H20:76");
# ~ print("H21:293");
# ~ print("H22:73");
# ~ print("H23:361");
# Simulation BMV 700
print("AR:0");
print("Alarm:OFF");
print("BMV:700");
print("CE:-36228");
print("FW: 0308");
print("H1:-102738");
print("H10:121");
print("H11:0");
print("H12:0");
print("H17:59983");
print("H18:70519");
print("H2:-36228");
print("H3:-102738");
print("H4:1");
print("H5:0");
print("H6:-24205923");
print("H7:21238");
print("H8:29442");
print("H9:104538");
print("I:-2082");
print("P:-50");
print("PID:0x203");
print("Relay:OFF");
print("SOC:886");
print("TTG:3429");
print("V:24061");
|
# Uso de variรกveis em Strings
first_name = "ada"
last_name = "lovelace"
full_name = f"{first_name} {last_name}"
print(full_name)
# Podemos usar f-strings para escrever mensagens completas usando as informaรงรตes associadas a uma variรกvel
first_name = "ada"
last_name = "lovelace"
full_name = f"{first_name} {last_name}"
print(f"Hello, {full_name.title()}!")
# Podemos usar f-strings para mostrar uma mensagem e, em seguida, atribuir essa mensagem a uma variรกvel
first_name = "ada"
last_name = "lovelace"
full_name = f"{first_name} {last_name}"
message = f"Hello, {full_name.title()}!"
print(message)
|
def check_prime(n):
f = 0
for i in range (2, n//2 +1):
if n%i==0 :
f= 1
break
return f
def min_range(d):
a = (10**(d-1))
return a
def max_range(d):
b = (10**d)
return b
d=int(input("Enter d"))
twin_prime_list = []
for i in range ( min_range(d) , max_range(d) ):
if check_prime(i)== 0 and check_prime(i+2)== 0:
twin_prime_list.append((i, i+2))
with open("twin_prime_list.txt", "w") as f:
for twin_pair in twin_prime_list:
f.write(str(twin_pair))
f.write("\n")
|
print (" ****** Bienvenido a Calculadora Basica 2020 ****** ")
print (" -------------------------------------------------------- ")
num = input("Ingresar el nรบmero entero a calcular: \n")
calculo = input("Ingresar el simbolo de la operaciรณn que desea realizar: (+,-,*,/) \n")
num_dos = input("Ingresar el siguiente nรบmero a calcular: \n")
try:
n = float(num)
nu = float(num_dos)
except:
print(" -------------------------------------------------------- ")
print("El Valor ingresado no es un nรบmero")
quit()
if calculo == "+":
print("El resultado de la suma es: \n", n + nu)
elif calculo == "-":
print("El resultado de la resta es: \n", n - nu)
elif calculo == "*":
print("El resultado de la multiplicaciรณn es: \n", n * nu)
elif calculo == "/":
print("El resultado de la divisiรณn es: \n", n / nu)
else:
print(" ------------------------------------------------------- ")
print ("El valor ingresado no es un simbolo de operaciรณn matematica")
print (" ---------------------------------------------------------- ")
print (" ****** Gracias por usar Calculadora Basica 2020 ******")
|
# -*- coding: utf-8 -*-
"""
Created on Tue Jun 29 10:24:19 2021
@author: USUARIO
"""
|
def valid_parentheses(string):
count = 0
for bracket in string:
if count < 0:
return False
if bracket == "(":
count += 1
if bracket == ")":
count -= 1
else:
continue
return count == 0
def testing():
a = "()"
b = True
print("ะะตัะฝะพ!" if valid_parentheses(a) == b else "ะะต ะฒะตัะฝะพ")
c = ")(()))"
d = False
print("ะะตัะฝะพ!" if valid_parentheses(c) == d else "ะะต ะฒะตัะฝะพ")
e = "(())((()())())"
print("ะะตัะฝะพ!" if valid_parentheses(e) == b else "ะะต ะฒะตัะฝะพ")
if __name__ == "__main__":
testing()
print(valid_parentheses(")"))
|
# Definition for a binary tree node.
class TreeNode:
def __init__(self, val=0, left=None, right=None):
self.val = val
self.left = left
self.right = right
# DFS
class Solution:
def levelOrder(self, root: TreeNode) -> List[List[int]]:
self.ans = []
self.dfs(root, 0)
return self.ans
def dfs(self, node, level):
if not node:
return
if level == len(self.ans):
self.ans.append([node.val])
else:
self.ans[level].append(node.val)
self.dfs(node.left, level + 1)
self.dfs(node.right, level + 1)
# BFS
class Solution:
def levelOrder(self, root: TreeNode) -> List[List[int]]:
ans = []
queue = collections.deque([(root, 0)])
while queue:
node, level = queue.popleft()
if node:
if level == len(ans):
ans.append([node.val])
else:
ans[level].append(node.val)
queue.append((node.left, level + 1))
queue.append((node.right, level + 1))
return ans |
"""
Calculates compound interest over a specified time period.
Since:
1.0.0
Catergory:
Maths
Args:
param1 (int) investment: The amount of original investment.
param2 (int) rate: Interest rate in whole number. i.e. 2% = 2.
param3 (int) time: Length of investment. Used to exponentially raise total.
Assumes interest is applied for every whole integer in param.
Returns:
Integer: Does not round or format the returned value.
Example:
>>> print(compound_interest(10, 3, 5))
11.592740743000002
"""
def compound_interest(investment, rate, time):
counter = 0
total = investment
interest = 1 + (rate * 0.01)
while counter < time:
total = total * interest
counter += 1
return total
|
# Copyright (c) 2016 Alexander Sosedkin <[email protected]>
# Distributed under the terms of the MIT License, see below:
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
"""
This module defines a collection of special methods
and their fallback functions / canonical invocations.
Fallback function invocations will be tried
if the special method is missing.
At least they should fail like the real deal, right?
Taken from https://docs.python.org/3.5/reference/datamodel.html
"""
__all__ = ['SPECIAL_METHODS']
def _raise(exception_cls, *a, **kwa):
raise exception_cls(*a, **kwa)
def _will_give_up(msg):
return lambda *a, **kwa: RuntimeError(msg)
def _setitem(w, k, v):
w[k] = v
def _delitem(w, k):
del w[k]
SPECIAL_METHODS = {
#'__new__': _will_give_up('no __new__'),
#'__init__': _will_give_up('no __init__'),
#'__del__': lambda _: None, # gc will call the right __del__ directly
'__repr__': repr,
'__str__': str,
'__bytes__': bytes,
'__format__': format,
'__lt__': object.__lt__,
'__le__': object.__le__,
'__eq__': object.__eq__,
'__ne__': object.__ne__,
'__gt__': object.__gt__,
'__ge__': object.__ge__,
'__hash__': hash,
'__bool__': bool,
'__getattr__': getattr,
'__getattribute__': object.__getattribute__,
'__setattr__': setattr,
'__delattr__': delattr,
'__dir__': dir,
'__get__': lambda w, i, o: w.__get__(i, o),
'__set__': lambda w, i, o: w.__set__(i, o),
'__delete__': lambda w, i, o: w.__delete__(i, o),
'__prepare__': lambda w, n, b, **kwa: w.__prepare__(n, b, **kwa),
'__instancecheck__': isinstance,
'__subclasscheck__': issubclass,
'__call__': lambda w, *a, **kwa: w(*a, **kwa),
'__len__': len,
'__length_hint__': len, # FIXME
'__getitem__': lambda w, k: w[k],
'__setitem__': _setitem,
#'__missing__',
'__delitem__': _delitem,
'__iter__': iter,
'__reversed__': reversed,
'__contains__': lambda w, i: i in w,
'__neg__': lambda w: -w,
'__pos__': lambda w: +w,
'__abs__': abs,
'__invert__': lambda w: ~w,
'__complex__': complex,
'__int__': int,
'__float__': float,
'__round__': round,
'__index__': lambda w: w.__index__(), # FIXME
'__enter__': lambda w: w.__enter__(), # FIXME
'__exit__': lambda w, et, ev, tb: w.__exit__(et, ev, tb), # FIXME
'__await__': lambda w: w.__await__(), # FIXME
'__aiter__': lambda w: w.__aiter__(), # FIXME
'__anext__': lambda w: w.__anext__(), # FIXME
'__aenter__': lambda w: w.__aenter__(), # FIXME
'__aexit__': lambda w, et, ev, tb: w.__aexit__(et, ev, tb), # FIXME
# # And...
# '__next__',
}
# Also add numerical operators to that dict:
_NUMERIC_SPECIAL_METHODS = set((
'add', 'sub', 'mul', 'matmul', 'truediv', 'floordiv', 'mod', 'divmod',
'pow', 'lshift', 'rshift', 'and', 'xor', 'or',
))
# All of these have three forms (__???__, __r???__ and __i???__)
# and should not require a fallback if missing
def _add_numeric_operators(): # let's not pollute namespace
for numeric_name in _NUMERIC_SPECIAL_METHODS:
for tmpl in ('__%s__', '__r%s__', '__i%s__'):
name = tmpl % numeric_name
try:
SPECIAL_METHODS[name] = getattr(object, name)
except AttributeError:
SPECIAL_METHODS[name] = lambda w, o: NotImplemented
_add_numeric_operators()
del _add_numeric_operators
|
"""
"""
class ConfigurationManager(object):
def __init__(self, full_config):
self._full_config = full_config
def get(self, task_key):
self.result_config = {}
self.current_config = self._full_config.copy()
[self._add_task_options(task_option) for task_option in task_key.split('.')]
return self.result_config
def _add_task_options(self, task_option):
task_config = self.current_config.get(task_option)
if task_config:
self._merge_into_result(task_config)
self._remove_from_result(task_option)
self.current_config = task_config
def _merge_into_result(self, task_config):
self.result_config.update(task_config)
def _remove_from_result(self, task_option):
if task_option in self.result_config:
del self.result_config[task_option]
|
numbers = (input("enter the value of a number"))
def divisors(numbers):
array = []
for item in range(1, numbers):
if(numbers % item == 0):
print("divisors items", item)
array.append(item)
print(array)
# print("all items",item)
divisors(numbers)
|
#!/usr/bin/python
# -*- coding: utf-8 -*-
"""
mw_util.py
Set of helper functions while dealing with MediaWiki.
str2cat
Adds prefix Category if string doesn't have it.
"""
def str2cat(category):
"""Return a category name starting with Category."""
prefix = "Category:"
if not category.startswith(prefix):
category = "%s%s" % (prefix, category)
return category.replace(' ', '_')
|
#!/usr/bin/python3
target = 10
def showVar(a, b):
global target
target = target + b
print('่ฎฟ้ฎtaget:{}'.format(target))
return target
def insert(sql, values):
print("exec sql : {}".format(sql))
print("insert into values:{}".format(values))
return True
showVar(12, 12)
print('ๅ
จๅฑ่ๅดๅ
็target:{}'.format(target))
if __name__ == "__main__":
print("ๆๆฅ่ช่ช่บซไธป็จๅบๆง่ก")
else:
print("ๆๆฅ่ชๅ
ถไป็จๅบๆง่ก:{}".format(__name__))
|
# Space : O(n)
# Time : O(n**2)
class Solution:
def jump(self, nums: List[int]) -> int:
n = len(nums)
dp = [10**5] * n
dp[0] = 0
if n == 1:
return 0
for i in range(n):
for j in range(nums[i]):
dp[j+i+1] = min(dp[j+i+1], dp[i] + 1)
if j+i+1 == n-1:
return dp[-1]
return dp[-1] |
# -*- coding: utf-8 -*-
"""
Created on 2018-03-
@author: Frank Dip
"""
s = "hello boy"
for i,j in enumerate(s):
print(i, j)
|
while True:
a = input()
if int(a) == 42:
break;
print(int(a))
|
# -*- coding: utf-8 -*-
# Contributors : [[email protected],[email protected],
# [email protected],
# [email protected] ]
class CoefficientNotinRangeError(Exception):
"""
Class to throw exception when a coefficient is not in the specified range
"""
def __init__(self, coefficient, coeff_type="Default",
range_min=0, range_max=1):
self.range_max = range_max
self.range_min = range_min
self.coeff_type = coeff_type
self.coefficient = coefficient
super().__init__()
def __str__(self):
return '''\"{0}\" coefficient of value {1} is not in range {2} and {3}'''.format(
self.coeff_type, self.coefficient, self.range_min, self.range_max)
class InvalidImageArrayError(Exception):
"""
Class to throw exception when an image is not valid
"""
def __init__(self, image_type="PIL"):
self.image_type = image_type
super().__init__()
def __str__(self):
return "Image is not a {} Image".format(self.image_type)
class CrucialValueNotFoundError(Exception):
"""
Class to throw exception when an expected value is not found
"""
def __init__(self, operation, value_type="sample"):
self.value_type = value_type
self.operation = operation
super().__init__()
def __str__(self):
return "\"{0}\" value not found for the \"{1}\" mentioned".format(
self.value_type, self.operation)
class OperationNotFoundOrImplemented(Exception):
"""
Class to throw exception when an operation is not found
"""
def __init__(self, module, class_name):
self.module = module
self.class_name = class_name
super().__init__()
def __str__(self):
return "\"{0}\" not found or implemented in the module \"{1}\"".format(
self.class_name, self.module)
class ConfigurationError(Exception):
"""
Class to throw exception when a configuration is not right
"""
def __init__(self, exception_string) -> None:
self.exception_string = exception_string
super().__init__()
def __str__(self) -> str:
return self.exception_string
|
class Solution:
"""
@param matrix: A 2D-array of integers
@return: an integer
"""
def longestContinuousIncreasingSubsequence2(self, matrix):
# write your code here
if not matrix or not matrix[0]:
return 0
m, n = len(matrix), len(matrix[0])
visited = [[0] * n for _ in range(m)]
result = 0
for i in range(m):
for j in range(n):
self.dfs(matrix, i, j, visited)
result = max(result, visited[i][j])
return result
def dfs(self, matrix, i, j, visited):
if visited[i][j] != 0:
return
visited[i][j] = 1
m, n = len(matrix), len(matrix[0])
dx = [0, 0, 1, -1]
dy = [1, -1, 0, 0]
for k in range(4):
nx, ny = i + dx[k], j + dy[k]
if 0 <= nx < len(matrix) and 0 <= ny < len(matrix[0]) and matrix[nx][ny] < matrix[i][j]:
self.dfs(matrix, nx, ny, visited)
visited[i][j] = max(visited[i][j], visited[nx][ny] + 1)
# result = 1
# visited = [[-1] * len(matrix[0]) for _ in range(len(matrix))]
# for i in range(len(matrix)):
# for j in range(len(matrix[0])):
# if visited[i][j] != -1:
# result = max(visited[i][j], result)
# continue
# self.search(visited, matrix, i, j)
# result = max(visited[i][j], result)
# return result
# def search(self, visited, matrix, x, y):
# visited[x][y] = 1
# dx = [0, 0, -1, 1]
# dy = [1, -1, 0, 0]
# for i in range(4):
# nx = dx[i] + x
# ny = dy[i] + y
# if 0 <= nx < len(matrix) and 0 <= ny < len(matrix[0]) and visited[nx][ny] == -1:
# if matrix[x][y] > matrix[nx][ny]:
# self.search(visited, matrix, nx, ny)
# visited[x][y] = max(visited[x][y], visited[nx][ny] + 1)
|
__version__ = '0.0.7'
def get_version():
return __version__
|
# -*- coding: utf-8 -*-
"""
solace.views
~~~~~~~~~~~~
All the view functions are implemented in this package.
:copyright: (c) 2009 by Plurk Inc., see AUTHORS for more details.
:license: BSD, see LICENSE for more details.
"""
|
class BaseASHException(Exception):
"""A base exception handler for the ASH ecosystem."""
def __init__(self, *args):
if args:
self.message = args[0]
else:
self.message = self.__doc__
def __str__(self):
return self.message
class DuplicateObject(BaseASHException):
"""Raised because you attempted to create and add an object, using the exact same id's as a pre-existing one."""
class ObjectMismatch(BaseASHException):
"""Raised because you attempted add a message to a member, but that member didn't create that message."""
class LogicError(BaseASHException):
"""Raised because internal logic has failed. Please create an issue in the github."""
class MissingGuildPermissions(BaseASHException):
"""I need both permissions to kick & ban people from this guild in order to work!""" |
#Question:1
# Initializing matrix
matrix = []
# Taking input from user of rows and column
row = int(input("Enter the number of rows:"))
column = int(input("Enter the number of columns:"))
print("Enter the elements row wise:")
# Getting elements of matrix from user
for i in range(row):
a =[]
for j in range (column):
a.append(int(input()))
matrix.append(a)
print()
# Printing user entered matrix
print("Entered Matrix is: ")
for i in range(row):
for j in range(column):
print(matrix[i][j], end = " ")
print()
print()
#Printing prime numbers of matrix:
print("The prime numbers in the matrix are: ")
for i in range(row):
for j in range(column):
if( matrix[i][j] > 1):
for p in range(2, matrix[i][j]):
if(matrix[i][j] % p) == 0:
break
else:
print(matrix[i][j])
|
devices = \
{
# -------------------------------------------------------------------------
# NXP ARM7TDMI devices Series LPC21xx, LPC22xx, LPC23xx, LPC24xx
"lpc2129":
{
"defines": ["__ARM_LPC2000__"],
"linkerscript": "arm7/lpc/linker/lpc2129.ld",
"size": { "flash": 262144, "ram": 16384 },
},
"lpc2368":
{
"defines": ["__ARM_LPC2000__", "__ARM_LPC23_24__"],
"linkerscript": "arm7/lpc/linker/lpc2368.ld",
"size": { "flash": 524288, "ram": 32768 },
},
"lpc2468":
{
"defines": ["__ARM_LPC2000__", "__ARM_LPC23_24__"],
"linkerscript": "arm7/lpc/linker/lpc2468.ld",
"size": { "flash": 524288, "ram": 65536 },
},
# -------------------------------------------------------------------------
# NXP Cortex-M0 devices Series LPC11xx and LPC11Cxx
"lpc1112_301":
{
"defines": ["__ARM_LPC11XX__",],
"linkerscript": "cortex_m0/lpc/linker/lpc1112_301.ld",
"size": { "flash": 16384, "ram": 8192 },
},
"lpc1114_301":
{
"defines": ["__ARM_LPC11XX__",],
"linkerscript": "cortex_m0/lpc/linker/lpc1114_301.ld",
"size": { "flash": 32768, "ram": 8192 },
},
"lpc1115_303":
{
"defines": ["__ARM_LPC11XX__",],
"linkerscript": "cortex_m0/lpc/linker/lpc1115_303.ld",
"size": { "flash": 65536, "ram": 8192 },
},
# Integrated CAN transceiver
"lpc11c22_301":
{
"defines": ["__ARM_LPC11XX__", "__ARM_LPC11CXX__"],
"linkerscript": "cortex_m0/lpc/linker/lpc11c22.ld",
"size": { "flash": 16384, "ram": 8192 },
},
# Integrated CAN transceiver
"lpc11c24_301":
{
"defines": ["__ARM_LPC11XX__", "__ARM_LPC11CXX__"],
"linkerscript": "cortex_m0/lpc/linker/lpc11c24.ld",
"size": { "flash": 32768, "ram": 8192 },
},
# -------------------------------------------------------------------------
# NXP Cortex-M3 devices Series LPC13xx
# TODO
# -------------------------------------------------------------------------
"lpc1343":
{
"defines": ["__ARM_LPC13XX__",],
"linkerscript": "cortex_m3/lpc/linker/lpc1343.ld",
"size": { "flash": 32768, "ram":8192 },
},
"lpc1769":
{
"defines": ["__ARM_LPC17XX__"],
"linkerscript": "cortex_m3/lpc/linker/lpc1769.ld",
"size": { "flash": 524288, "ram": 65536 }, # 32kB local SRAM + 2x16kB AHB SRAM
},
# -------------------------------------------------------------------------
# AT91SAM7S
"at91sam7s32":
{
"defines": ["__ARM_AT91__", "__ARM_AT91SAM7S__", "__ARM_AT91SAM7S32__"],
"linkerscript": "arm7/at91/linker/at91sam7s32.ld",
"size": { "flash": 32768, "ram": 4096 },
},
"at91sam7s321":
{
"defines": ["__ARM_AT91__", "__ARM_AT91SAM7S__", "__ARM_AT91SAM7S321__"],
"linkerscript": "arm7/at91/linker/at91sam7s32.ld",
"size": { "flash": 32768, "ram": 8192 },
},
"at91sam7s64":
{
"defines": ["__ARM_AT91__", "__ARM_AT91SAM7S__", "__ARM_AT91SAM7S64__"],
"linkerscript": "arm7/at91/linker/at91sam7s64.ld",
"size": { "flash": 65536, "ram": 16384 },
},
"at91sam7s128":
{
"defines": ["__ARM_AT91__", "__ARM_AT91SAM7S__", "__ARM_AT91SAM7S128__"],
"linkerscript": "arm7/at91/linker/at91sam7s128.ld",
"size": { "flash": 131072, "ram": 32768 },
},
"at91sam7s256":
{
"defines": ["__ARM_AT91__", "__ARM_AT91SAM7S__", "__ARM_AT91SAM7S256__"],
"linkerscript": "arm7/at91/linker/at91sam7s256.ld",
"size": { "flash": 262144, "ram": 65536 },
},
"at91sam7s512":
{
"defines": ["__ARM_AT91__", "__ARM_AT91SAM7S__", "__ARM_AT91SAM7S512__"],
"linkerscript": "arm7/at91/linker/at91sam7s512.ld",
"size": { "flash": 524288, "ram": 65536 },
},
# -------------------------------------------------------------------------
# AT91SAM7X
"at91sam7x128":
{
"defines": ["__ARM_AT91__", "__ARM_AT91SAM7X__"],
"linkerscript": "arm7/at91/linker/at91sam7x128.ld",
"size": { "flash": 131072, "ram": 32768 },
},
"at91sam7x256":
{
"defines": ["__ARM_AT91__", "__ARM_AT91SAM7X__"],
"linkerscript": "arm7/at91/linker/at91sam7x256.ld",
"size": { "flash": 262144, "ram": 65536 },
},
"at91sam7x512":
{
"defines": ["__ARM_AT91__", "__ARM_AT91SAM7X__"],
"linkerscript": "arm7/at91/linker/at91sam7x512.ld",
"size": { "flash": 524288, "ram": 131072 },
},
# -------------------------------------------------------------------------
# STM32F103 p s
#
# Pins (p):
# T | 36 pins
# C | 48 pins
# R | 64 pins
# V | 100 pins
# Z | 144 pins
#
# Size (s):
# 4 | 16 kB Flash, 6 kB RAM low density line (T, C, R)
# 6 | 32 kB Flash, 10 kB RAM
# 8 | 64 kB Flash, 20 kB RAM medium density (T, C, R, V)
# B | 128 kB Flash, 20 kB RAM
# C | 256 kB Flash, 48 kB RAM high density (R, V, Z)
# D | 384 kB Flash, 64 kB RAM
# E | 512 kB Flash, 64 kB RAM
# F | 768 kB Flash, 96 kB RAM xl density (R, V, Z)
# G | 1 MB Flash, 96 kB RAM
#
"stm32f103_4":
{
"defines": ["__STM32F103__", "__ARM_STM32__", "STM32F10X", "STM32F10X_LD", "STM32_LOW_DENSITY"],
"linkerscript": "cortex_m3/stm32/linker/stm32f103_4.ld",
"size": { "flash": 16384, "ram": 6144 },
},
"stm32f103_6":
{
"defines": ["__STM32F103__", "__ARM_STM32__", "STM32F10X", "STM32F10X_LD", "STM32_LOW_DENSITY"],
"linkerscript": "cortex_m3/stm32/linker/stm32f103_6.ld",
"size": { "flash": 32768, "ram": 10240 },
},
"stm32f103_8":
{
"defines": ["__STM32F103__", "__ARM_STM32__", "STM32F10X", "STM32F10X_MD", "STM32_MEDIUM_DENSITY"],
"linkerscript": "cortex_m3/stm32/linker/stm32f103_8.ld",
"size": { "flash": 65536, "ram": 20480 },
},
"stm32f103_b":
{
"defines": ["__STM32F103__", "__ARM_STM32__", "STM32F10X", "STM32F10X_MD", "STM32_MEDIUM_DENSITY"],
"linkerscript": "cortex_m3/stm32/linker/stm32f103_b.ld",
"size": { "flash": 131072, "ram": 20480 },
},
"stm32f103_c":
{
"defines": ["__STM32F103__", "__ARM_STM32__", "STM32F10X", "STM32F10X_HD", "STM32_HIGH_DENSITY"],
"linkerscript": "cortex_m3/stm32/linker/stm32f103_c.ld",
"size": { "flash": 262144, "ram": 49152 },
},
"stm32f103_d":
{
"defines": ["__STM32F103__", "__ARM_STM32__", "STM32F10X", "STM32F10X_HD", "STM32_HIGH_DENSITY"],
"linkerscript": "cortex_m3/stm32/linker/stm32f103_d.ld",
"size": { "flash": 393216, "ram": 65536 },
},
"stm32f103_e":
{
"defines": ["__STM32F103__", "__ARM_STM32__", "STM32F10X", "STM32F10X_HD", "STM32_HIGH_DENSITY"],
"linkerscript": "cortex_m3/stm32/linker/stm32f103_e.ld",
"size": { "flash": 524288, "ram": 65536 },
},
"stm32f103_f":
{
"defines": ["__STM32F103__", "__ARM_STM32__", "STM32F10X", "STM32F10X_XL", "STM32_XL_DENSITY"],
"linkerscript": "cortex_m3/stm32/linker/stm32f103_f.ld",
"size": { "flash": 786432, "ram": 98304 },
},
"stm32f103_g":
{
"defines": ["__STM32F103__", "__ARM_STM32__", "STM32F10X", "STM32F10X_XL", "STM32_XL_DENSITY"],
"linkerscript": "cortex_m3/stm32/linker/stm32f103_g.ld",
"size": { "flash": 1048576, "ram": 98304 },
},
# STM32F105 p s (pins: R, V)
#
# Size (s):
# 8 | 64 kB Flash, 20 kB RAM
# B | 128 kB Flash, 32 kB RAM
# C | 256 kB Flash, 64 kB RAM
#
"stm32f105_8":
{
"defines": ["__STM32F105__", "__ARM_STM32__", "STM32F10X", "STM32F10X_CL", "STM32_CONNECTIVITY"],
"linkerscript": "cortex_m3/stm32/linker/stm32f105_8.ld",
"size": { "flash": 65536, "ram": 20480 },
},
"stm32f105_b":
{
"defines": ["__STM32F105__", "__ARM_STM32__", "STM32F10X", "STM32F10X_CL", "STM32_CONNECTIVITY"],
"linkerscript": "cortex_m3/stm32/linker/stm32f105_b.ld",
"size": { "flash": 131072, "ram": 32768 },
},
"stm32f105_c":
{
"defines": ["__STM32F105__", "__ARM_STM32__", "STM32F10X", "STM32F10X_CL", "STM32_CONNECTIVITY"],
"linkerscript": "cortex_m3/stm32/linker/stm32f105_c.ld",
"size": { "flash": 262144, "ram": 65536 },
},
# STM32F107 p s (pins: R, V)
#
# Size (s):
# B | 128 kB Flash, 48 kB RAM
# C | 256 kB Flash, 64 kB RAM
#
"stm32f107_b":
{
"defines": ["__STM32F107__", "__ARM_STM32__", "STM32F10X", "STM32F10X_CL", "STM32_CONNECTIVITY"],
"linkerscript": "cortex_m3/stm32/linker/stm32f107_b.ld",
"size": { "flash": 131072, "ram": 49152 },
},
"stm32f107_c":
{
"defines": ["__STM32F107__", "__ARM_STM32__", "STM32F10X", "STM32F10X_CL", "STM32_CONNECTIVITY"],
"linkerscript": "cortex_m3/stm32/linker/stm32f107_c.ld",
"size": { "flash": 262144, "ram": 65536 },
},
# -------------------------------------------------------------------------
# STM32F205 p s
#
# Pins (p):
# R | 64 pins
# V | 100 pins
# Z | 144 pins
#
# Size (s):
# B | 128 kB Flash, 48+16 kB RAM (R, V)
# C | 256 kB Flash, 80+16 kB RAM (R, V, Z)
# E | 512 kB Flash, 112+16 kB RAM (R, V, Z)
# F | 768 kB Flash, 112+16 kB RAM (R, V, Z)
# G | 1 MB Flash, 112+16 kB RAM (R, V, Z)
#
"stm32f205_b":
{
"defines": ["__STM32F205__", "__ARM_STM32__", "STM32F2XX"],
"linkerscript": "cortex_m3/stm32/linker/stm32f205_b.ld",
"size": { "flash": 131072, "ram": 49152 },
},
"stm32f205_c":
{
"defines": ["__STM32F205__", "__ARM_STM32__", "STM32F2XX"],
"linkerscript": "cortex_m3/stm32/linker/stm32f205_c.ld",
"size": { "flash": 262144, "ram": 81920 },
},
"stm32f205_e":
{
"defines": ["__STM32F205__", "__ARM_STM32__", "STM32F2XX"],
"linkerscript": "cortex_m3/stm32/linker/stm32f205_e.ld",
"size": { "flash": 524288, "ram": 114688 },
},
"stm32f205_f":
{
"defines": ["__STM32F205__", "__ARM_STM32__", "STM32F2XX"],
"linkerscript": "cortex_m3/stm32/linker/stm32f205_f.ld",
"size": { "flash": 786432, "ram": 114688 },
},
"stm32f205_g":
{
"defines": ["__STM32F205__", "__ARM_STM32__", "STM32F2XX"],
"linkerscript": "cortex_m3/stm32/linker/stm32f205_g.ld",
"size": { "flash": 1048576, "ram": 114688 },
},
# STM32F207 p s
#
# Pins (p):
# V | 100 pins
# Z | 144 pins
# I | 176 pins
#
# Size (s):
# C | 256 kB Flash, 112+16 kB RAM
# E | 512 kB Flash, 112+16 kB RAM
# F | 768 kB Flash, 112+16 kB RAM
# G | 1 MB Flash, 112+16 kB RAM
#
"stm32f207_c":
{
"defines": ["__STM32F207__", "__ARM_STM32__", "STM32F2XX"],
"linkerscript": "cortex_m3/stm32/linker/stm32f207_c.ld",
"size": { "flash": 262144, "ram": 114688 },
},
"stm32f207_e":
{
"defines": ["__STM32F207__", "__ARM_STM32__", "STM32F2XX"],
"linkerscript": "cortex_m3/stm32/linker/stm32f207_e.ld",
"size": { "flash": 524288, "ram": 114688 },
},
"stm32f207_f":
{
"defines": ["__STM32F207__", "__ARM_STM32__", "STM32F2XX"],
"linkerscript": "cortex_m3/stm32/linker/stm32f207_f.ld",
"size": { "flash": 786432, "ram": 114688 },
},
"stm32f207_g":
{
"defines": ["__STM32F207__", "__ARM_STM32__", "STM32F2XX"],
"linkerscript": "cortex_m3/stm32/linker/stm32f207_g.ld",
"size": { "flash": 1048576, "ram": 114688 },
},
# -------------------------------------------------------------------------
# STM32F405 p s
#
# Pins (p):
# R | 64 pins
# V | 100 pins
# Z | 144 pins
#
# Size (s):
# G | 1 MB Flash, 112+64+16 kB RAM
#
"stm32f405_g":
{
"defines": ["__STM32F405__", "__ARM_STM32__", "STM32F4XX"],
"linkerscript": "cortex_m3/stm32/linker/stm32f4xx_g.ld",
"size": { "flash": 1048576, "ram": 114688 },
},
# STM32F407 p s
#
# Pins (p):
# V | 100 pins
# Z | 144 pins
# I | 176 pins
#
# Size (s):
# E | 512 kB Flash, 112+64+16 kB RAM
# G | 1 MB Flash, 112+64+16 kB RAM
#
"stm32f407_e":
{
"defines": ["__STM32F407__", "__ARM_STM32__", "STM32F4XX"],
"linkerscript": "cortex_m3/stm32/linker/stm32f4xx_e.ld",
"size": { "flash": 524288, "ram": 114688 },
},
"stm32f407_g":
{
"defines": ["__STM32F407__", "__ARM_STM32__", "STM32F4XX"],
"linkerscript": "cortex_m3/stm32/linker/stm32f4xx_g.ld",
"size": { "flash": 1048576, "ram": 114688 },
},
# -------------------------------------------------------------------------
# STM32 F3 Series
# ARM Cortex-M4F MCU + FPU
# STM32F302 p s
#
# Pins (p):
# C | 48 pins
# R | 64 pins
# V | 100 pins
#
# Size (s):
# B | 128 kB Flash, 8 + 40 kB RAM
# C | 256 kB Flash, 8 + 40 kB RAM
#
"stm32f303_b":
{
"defines": ["__STM32F303__", "__ARM_STM32__", "STM32F3XX", "STM32F30X"],
"linkerscript": "cortex_m3/stm32/linker/stm32f3xx_b.ld",
"size": { "flash": 131072, "ram": 40960 },
},
"stm32f303_c":
{
"defines": ["__STM32F303__", "__ARM_STM32__", "STM32F3XX", "STM32F30X"],
"linkerscript": "cortex_m3/stm32/linker/stm32f3xx_c.ld",
"size": { "flash": 262144, "ram": 40960 },
},
}
|
WALK_UP = 4
WALK_DOWN = 3
WALK_RIGHT = 2
WALK_LEFT = 1
NO_OP = 0
SHOOT = 5
WALK_ACTIONS = [WALK_UP, WALK_DOWN, WALK_RIGHT, WALK_LEFT]
ACTIONS = [NO_OP, WALK_LEFT, WALK_RIGHT, WALK_DOWN, WALK_UP, SHOOT]
|
# Day Six: Lanternfish
file = open("input/06.txt").readlines()
ages = []
for num in file[0].split(","):
ages.append(int(num))
for day in range(80):
for i, fish in enumerate(ages):
if fish == 0:
ages[i] = 6
ages.append(9)
else:
ages[i] = fish-1
print(len(ages))
# ages: 0, 1, 2, 3, 4, 5, 6, 7, 8, 9
ages = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
for num in file[0].split(","):
ages[int(num)] += 1
for day in range(256):
for i, age in enumerate(ages):
if i == 0:
ages[7] += ages[0]
ages[9] += ages[0]
ages[0] -= ages[0]
else:
ages[i-1] += ages[i]
ages[i] -= ages[i]
print(sum(ages))
|
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# https://leetcode.com/problems/subarray-sum-equals-k/description/
# I think it is a sub-problem of: https://leetcode.com/problems/path-sum-iii/description/
class Solution(object):
def subarraySum(self, nums, k):
"""
:type nums: List[int]
:type k: int
:rtype: int
"""
counts = {0:1}
sofar = 0
ret = 0
for num in nums:
sofar += num
complement = sofar - k
ret += counts.get(complement, 0)
counts.setdefault(sofar, 0)
counts[sofar] += 1
return ret
solution = Solution()
assert solution.subarraySum([1,1,1], 2) == 2
|
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