content stringlengths 7 1.05M | fixed_cases stringlengths 1 1.28M |
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#
# PySNMP MIB module FSM7326-POWER-ETHERNET-MIB (http://snmplabs.com/pysmi)
# ASN.1 source file:///Users/davwang4/Dev/mibs.snmplabs.com/asn1/FSM7326-POWER-ETHERNET-MIB
# Produced by pysmi-0.3.4 at Mon Apr 29 19:02:42 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)
#
ObjectIdentifier, OctetString, Integer = mibBuilder.importSymbols("ASN1", "ObjectIdentifier", "OctetString", "Integer")
NamedValues, = mibBuilder.importSymbols("ASN1-ENUMERATION", "NamedValues")
ConstraintsUnion, ValueSizeConstraint, SingleValueConstraint, ValueRangeConstraint, ConstraintsIntersection = mibBuilder.importSymbols("ASN1-REFINEMENT", "ConstraintsUnion", "ValueSizeConstraint", "SingleValueConstraint", "ValueRangeConstraint", "ConstraintsIntersection")
fsm7326, = mibBuilder.importSymbols("FSM7326-REF-MIB", "fsm7326")
pethPsePortEntry, = mibBuilder.importSymbols("POWER-ETHERNET-MIB", "pethPsePortEntry")
NotificationGroup, ModuleCompliance = mibBuilder.importSymbols("SNMPv2-CONF", "NotificationGroup", "ModuleCompliance")
NotificationType, MibIdentifier, Counter64, ObjectIdentity, Unsigned32, ModuleIdentity, iso, Bits, TimeTicks, Integer32, MibScalar, MibTable, MibTableRow, MibTableColumn, Gauge32, IpAddress, Counter32 = mibBuilder.importSymbols("SNMPv2-SMI", "NotificationType", "MibIdentifier", "Counter64", "ObjectIdentity", "Unsigned32", "ModuleIdentity", "iso", "Bits", "TimeTicks", "Integer32", "MibScalar", "MibTable", "MibTableRow", "MibTableColumn", "Gauge32", "IpAddress", "Counter32")
DisplayString, TextualConvention = mibBuilder.importSymbols("SNMPv2-TC", "DisplayString", "TextualConvention")
fsm7326powerEthernetMIB = ModuleIdentity((1, 3, 6, 1, 4, 1, 4526, 1, 9, 15))
fsm7326powerEthernetMIB.setRevisions(('2003-11-10 12:00',))
if mibBuilder.loadTexts: fsm7326powerEthernetMIB.setLastUpdated('200311101200Z')
if mibBuilder.loadTexts: fsm7326powerEthernetMIB.setOrganization('Netgear')
agentPethObjects = MibIdentifier((1, 3, 6, 1, 4, 1, 4526, 1, 9, 15, 1))
agentPethPsePortTable = MibTable((1, 3, 6, 1, 4, 1, 4526, 1, 9, 15, 1, 1), )
if mibBuilder.loadTexts: agentPethPsePortTable.setStatus('current')
agentPethPsePortEntry = MibTableRow((1, 3, 6, 1, 4, 1, 4526, 1, 9, 15, 1, 1, 1), )
pethPsePortEntry.registerAugmentions(("FSM7326-POWER-ETHERNET-MIB", "agentPethPsePortEntry"))
agentPethPsePortEntry.setIndexNames(*pethPsePortEntry.getIndexNames())
if mibBuilder.loadTexts: agentPethPsePortEntry.setStatus('current')
agentPethPowerLimit = MibTableColumn((1, 3, 6, 1, 4, 1, 4526, 1, 9, 15, 1, 1, 1, 1), Gauge32().subtype(subtypeSpec=ValueRangeConstraint(3, 16))).setUnits('Watts').setMaxAccess("readwrite")
if mibBuilder.loadTexts: agentPethPowerLimit.setStatus('current')
agentPethOutputPower = MibTableColumn((1, 3, 6, 1, 4, 1, 4526, 1, 9, 15, 1, 1, 1, 2), Gauge32()).setUnits('Milliwatts').setMaxAccess("readonly")
if mibBuilder.loadTexts: agentPethOutputPower.setStatus('current')
agentPethOutputCurrent = MibTableColumn((1, 3, 6, 1, 4, 1, 4526, 1, 9, 15, 1, 1, 1, 3), Gauge32()).setUnits('Milliamps').setMaxAccess("readonly")
if mibBuilder.loadTexts: agentPethOutputCurrent.setStatus('current')
agentPethOutputVolts = MibTableColumn((1, 3, 6, 1, 4, 1, 4526, 1, 9, 15, 1, 1, 1, 4), Gauge32()).setUnits('Volts').setMaxAccess("readonly")
if mibBuilder.loadTexts: agentPethOutputVolts.setStatus('current')
mibBuilder.exportSymbols("FSM7326-POWER-ETHERNET-MIB", agentPethOutputCurrent=agentPethOutputCurrent, PYSNMP_MODULE_ID=fsm7326powerEthernetMIB, agentPethPowerLimit=agentPethPowerLimit, agentPethOutputVolts=agentPethOutputVolts, fsm7326powerEthernetMIB=fsm7326powerEthernetMIB, agentPethOutputPower=agentPethOutputPower, agentPethPsePortTable=agentPethPsePortTable, agentPethPsePortEntry=agentPethPsePortEntry, agentPethObjects=agentPethObjects)
| (object_identifier, octet_string, integer) = mibBuilder.importSymbols('ASN1', 'ObjectIdentifier', 'OctetString', 'Integer')
(named_values,) = mibBuilder.importSymbols('ASN1-ENUMERATION', 'NamedValues')
(constraints_union, value_size_constraint, single_value_constraint, value_range_constraint, constraints_intersection) = mibBuilder.importSymbols('ASN1-REFINEMENT', 'ConstraintsUnion', 'ValueSizeConstraint', 'SingleValueConstraint', 'ValueRangeConstraint', 'ConstraintsIntersection')
(fsm7326,) = mibBuilder.importSymbols('FSM7326-REF-MIB', 'fsm7326')
(peth_pse_port_entry,) = mibBuilder.importSymbols('POWER-ETHERNET-MIB', 'pethPsePortEntry')
(notification_group, module_compliance) = mibBuilder.importSymbols('SNMPv2-CONF', 'NotificationGroup', 'ModuleCompliance')
(notification_type, mib_identifier, counter64, object_identity, unsigned32, module_identity, iso, bits, time_ticks, integer32, mib_scalar, mib_table, mib_table_row, mib_table_column, gauge32, ip_address, counter32) = mibBuilder.importSymbols('SNMPv2-SMI', 'NotificationType', 'MibIdentifier', 'Counter64', 'ObjectIdentity', 'Unsigned32', 'ModuleIdentity', 'iso', 'Bits', 'TimeTicks', 'Integer32', 'MibScalar', 'MibTable', 'MibTableRow', 'MibTableColumn', 'Gauge32', 'IpAddress', 'Counter32')
(display_string, textual_convention) = mibBuilder.importSymbols('SNMPv2-TC', 'DisplayString', 'TextualConvention')
fsm7326power_ethernet_mib = module_identity((1, 3, 6, 1, 4, 1, 4526, 1, 9, 15))
fsm7326powerEthernetMIB.setRevisions(('2003-11-10 12:00',))
if mibBuilder.loadTexts:
fsm7326powerEthernetMIB.setLastUpdated('200311101200Z')
if mibBuilder.loadTexts:
fsm7326powerEthernetMIB.setOrganization('Netgear')
agent_peth_objects = mib_identifier((1, 3, 6, 1, 4, 1, 4526, 1, 9, 15, 1))
agent_peth_pse_port_table = mib_table((1, 3, 6, 1, 4, 1, 4526, 1, 9, 15, 1, 1))
if mibBuilder.loadTexts:
agentPethPsePortTable.setStatus('current')
agent_peth_pse_port_entry = mib_table_row((1, 3, 6, 1, 4, 1, 4526, 1, 9, 15, 1, 1, 1))
pethPsePortEntry.registerAugmentions(('FSM7326-POWER-ETHERNET-MIB', 'agentPethPsePortEntry'))
agentPethPsePortEntry.setIndexNames(*pethPsePortEntry.getIndexNames())
if mibBuilder.loadTexts:
agentPethPsePortEntry.setStatus('current')
agent_peth_power_limit = mib_table_column((1, 3, 6, 1, 4, 1, 4526, 1, 9, 15, 1, 1, 1, 1), gauge32().subtype(subtypeSpec=value_range_constraint(3, 16))).setUnits('Watts').setMaxAccess('readwrite')
if mibBuilder.loadTexts:
agentPethPowerLimit.setStatus('current')
agent_peth_output_power = mib_table_column((1, 3, 6, 1, 4, 1, 4526, 1, 9, 15, 1, 1, 1, 2), gauge32()).setUnits('Milliwatts').setMaxAccess('readonly')
if mibBuilder.loadTexts:
agentPethOutputPower.setStatus('current')
agent_peth_output_current = mib_table_column((1, 3, 6, 1, 4, 1, 4526, 1, 9, 15, 1, 1, 1, 3), gauge32()).setUnits('Milliamps').setMaxAccess('readonly')
if mibBuilder.loadTexts:
agentPethOutputCurrent.setStatus('current')
agent_peth_output_volts = mib_table_column((1, 3, 6, 1, 4, 1, 4526, 1, 9, 15, 1, 1, 1, 4), gauge32()).setUnits('Volts').setMaxAccess('readonly')
if mibBuilder.loadTexts:
agentPethOutputVolts.setStatus('current')
mibBuilder.exportSymbols('FSM7326-POWER-ETHERNET-MIB', agentPethOutputCurrent=agentPethOutputCurrent, PYSNMP_MODULE_ID=fsm7326powerEthernetMIB, agentPethPowerLimit=agentPethPowerLimit, agentPethOutputVolts=agentPethOutputVolts, fsm7326powerEthernetMIB=fsm7326powerEthernetMIB, agentPethOutputPower=agentPethOutputPower, agentPethPsePortTable=agentPethPsePortTable, agentPethPsePortEntry=agentPethPsePortEntry, agentPethObjects=agentPethObjects) |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""Python Cursor on Target Module Tests."""
__author__ = 'Greg Albrecht W2GMD <oss@undef.net>'
__copyright__ = 'Copyright 2020 Orion Labs, Inc.'
__license__ = 'Apache License, Version 2.0'
| """Python Cursor on Target Module Tests."""
__author__ = 'Greg Albrecht W2GMD <oss@undef.net>'
__copyright__ = 'Copyright 2020 Orion Labs, Inc.'
__license__ = 'Apache License, Version 2.0' |
"""
- start with 1 map to verify
- 100 epochs for training
- make the POD size 80% (tile changes) of map size
- randomize the steps amongst the file so it doesnt overfit
- shuffle the sequences
- env always gvies 2D int array
-
""" | """
- start with 1 map to verify
- 100 epochs for training
- make the POD size 80% (tile changes) of map size
- randomize the steps amongst the file so it doesnt overfit
- shuffle the sequences
- env always gvies 2D int array
-
""" |
"""
On a 2-dimensional grid, there are 4 types of squares:
1 represents the starting square. There is exactly one starting square.
2 represents the ending square. There is exactly one ending square.
0 represents empty squares we can walk over.
-1 represents obstacles that we cannot walk over.
Return the number of 4-directional walks from the starting square to the ending square,
that walk over every non-obstacle square exactly once.
Hint: a standard backtracking problem, similar with 8-queens problem
"""
class Solution:
def uniquePathsIII(self, grid: List[List[int]]) -> int:
rowNum = len(grid)
colNum = len(grid[0])
# Find the starting point, and the number of empty cells.
emptyCount = 0
startPos = (0, 0)
for row in range(0, rowNum):
for col in range(0, colNum):
cell = grid[row][col]
if cell == 0:
emptyCount = emptyCount + 1
elif cell == 1:
startPos = (row, col)
def nextPosition(row, col, dirIndex):
""" calculate the next valid move """
nonlocal rowNum, colNum
# up, right, down, left
DIRECTIONS = [(-1, 0), (0, 1), (1, 0), (0, -1)]
newRow = row + DIRECTIONS[dirIndex][0]
newCol = col + DIRECTIONS[dirIndex][1]
if newRow < 0 or newRow >= rowNum:
return (-1, -1)
elif newCol < 0 or newCol >= colNum:
return (-1, -1)
else:
return (newRow, newCol)
# A standard backtracking algorithm, to explore all paths.
visited = set()
pathCount = 0
def backtracking(pos):
nonlocal pathCount
oldRow, oldCol = pos
for dirIndex in range(0, 4):
nextPos = nextPosition(oldRow, oldCol, dirIndex)
if nextPos[0] == -1 or nextPos in visited:
continue
row, col = nextPos
cell = grid[row][col]
if cell == 2:
# reach the destination
if len(visited) == emptyCount + 1:
pathCount = pathCount + 1
# reach the destination,
# exit to avoid the unnecessary exploration
continue
elif cell == -1:
continue # obstacle
# possible path, explore further, and mark the option
visited.add(nextPos)
backtracking(nextPos)
# unmark the option, try next direction
visited.remove(nextPos)
visited.add(startPos)
backtracking(startPos)
return pathCount
| """
On a 2-dimensional grid, there are 4 types of squares:
1 represents the starting square. There is exactly one starting square.
2 represents the ending square. There is exactly one ending square.
0 represents empty squares we can walk over.
-1 represents obstacles that we cannot walk over.
Return the number of 4-directional walks from the starting square to the ending square,
that walk over every non-obstacle square exactly once.
Hint: a standard backtracking problem, similar with 8-queens problem
"""
class Solution:
def unique_paths_iii(self, grid: List[List[int]]) -> int:
row_num = len(grid)
col_num = len(grid[0])
empty_count = 0
start_pos = (0, 0)
for row in range(0, rowNum):
for col in range(0, colNum):
cell = grid[row][col]
if cell == 0:
empty_count = emptyCount + 1
elif cell == 1:
start_pos = (row, col)
def next_position(row, col, dirIndex):
""" calculate the next valid move """
nonlocal rowNum, colNum
directions = [(-1, 0), (0, 1), (1, 0), (0, -1)]
new_row = row + DIRECTIONS[dirIndex][0]
new_col = col + DIRECTIONS[dirIndex][1]
if newRow < 0 or newRow >= rowNum:
return (-1, -1)
elif newCol < 0 or newCol >= colNum:
return (-1, -1)
else:
return (newRow, newCol)
visited = set()
path_count = 0
def backtracking(pos):
nonlocal pathCount
(old_row, old_col) = pos
for dir_index in range(0, 4):
next_pos = next_position(oldRow, oldCol, dirIndex)
if nextPos[0] == -1 or nextPos in visited:
continue
(row, col) = nextPos
cell = grid[row][col]
if cell == 2:
if len(visited) == emptyCount + 1:
path_count = pathCount + 1
continue
elif cell == -1:
continue
visited.add(nextPos)
backtracking(nextPos)
visited.remove(nextPos)
visited.add(startPos)
backtracking(startPos)
return pathCount |
ATOMICLONG_APPLY = 0x0a01
ATOMICLONG_ALTER = 0x0a02
ATOMICLONG_ALTERANDGET = 0x0a03
ATOMICLONG_GETANDALTER = 0x0a04
ATOMICLONG_ADDANDGET = 0x0a05
ATOMICLONG_COMPAREANDSET = 0x0a06
ATOMICLONG_DECREMENTANDGET = 0x0a07
ATOMICLONG_GET = 0x0a08
ATOMICLONG_GETANDADD = 0x0a09
ATOMICLONG_GETANDSET = 0x0a0a
ATOMICLONG_INCREMENTANDGET = 0x0a0b
ATOMICLONG_GETANDINCREMENT = 0x0a0c
ATOMICLONG_SET = 0x0a0d
| atomiclong_apply = 2561
atomiclong_alter = 2562
atomiclong_alterandget = 2563
atomiclong_getandalter = 2564
atomiclong_addandget = 2565
atomiclong_compareandset = 2566
atomiclong_decrementandget = 2567
atomiclong_get = 2568
atomiclong_getandadd = 2569
atomiclong_getandset = 2570
atomiclong_incrementandget = 2571
atomiclong_getandincrement = 2572
atomiclong_set = 2573 |
# Copyright (c) 2021 Works Applications Co., Ltd.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
class Flags:
def __init__(self, has_ambiguity, is_noun, form_type, acronym_type, variant_type):
"""Constructs flags of a synonym.
Args:
has_ambiguity (bool): ``True`` if a synonym is ambiguous, ``False`` otherwise
is_noun (bool): ``True`` if a synonym is a noun, ``False`` otherwise
form_type (int): a word form type of a synonym
acronym_type (int): an acronym type of a synonym
variant_type (int): a variant type of a synonym
"""
self._has_ambiguity = has_ambiguity
self._is_noun = is_noun
self._form_type = form_type
self._acronym_type = acronym_type
self._variant_type = variant_type
@classmethod
def from_int(cls, flags):
"""Reads and returns flags from the specified int value.
Args:
flags (int): int-type flag
Returns:
Flags: a flags of a synonym
"""
has_ambiguity = ((flags & 0x0001) == 1)
is_noun = ((flags & 0x0002) == 2)
form_type = (flags >> 2) & 0x0007
acronym_type = (flags >> 5) & 0x0003
variant_type = (flags >> 7) & 0x0003
return cls(has_ambiguity, is_noun, form_type, acronym_type, variant_type)
@property
def has_ambiguity(self):
"""bool: ``True`` if a synonym is ambiguous, ``False`` otherwise"""
return self._has_ambiguity
@property
def is_noun(self):
"""bool: ``True`` if a synonym is a noun, ``False`` otherwise"""
return self._is_noun
@property
def form_type(self):
"""int: a word form type of a synonym"""
return self._form_type
@property
def acronym_type(self):
"""int: an acronym type of a synonym"""
return self._acronym_type
@property
def variant_type(self):
"""int: a variant type of a synonym"""
return self._variant_type
def encode(self):
"""Encodes this ``Flags`` object.
Returns:
int: encoded flags
"""
flags = 0
flags |= 1 if self.has_ambiguity else 0
flags |= (1 if self.is_noun else 0) << 1
flags |= self.form_type << 2
flags |= self.acronym_type << 5
flags |= self.variant_type << 7
return flags
| class Flags:
def __init__(self, has_ambiguity, is_noun, form_type, acronym_type, variant_type):
"""Constructs flags of a synonym.
Args:
has_ambiguity (bool): ``True`` if a synonym is ambiguous, ``False`` otherwise
is_noun (bool): ``True`` if a synonym is a noun, ``False`` otherwise
form_type (int): a word form type of a synonym
acronym_type (int): an acronym type of a synonym
variant_type (int): a variant type of a synonym
"""
self._has_ambiguity = has_ambiguity
self._is_noun = is_noun
self._form_type = form_type
self._acronym_type = acronym_type
self._variant_type = variant_type
@classmethod
def from_int(cls, flags):
"""Reads and returns flags from the specified int value.
Args:
flags (int): int-type flag
Returns:
Flags: a flags of a synonym
"""
has_ambiguity = flags & 1 == 1
is_noun = flags & 2 == 2
form_type = flags >> 2 & 7
acronym_type = flags >> 5 & 3
variant_type = flags >> 7 & 3
return cls(has_ambiguity, is_noun, form_type, acronym_type, variant_type)
@property
def has_ambiguity(self):
"""bool: ``True`` if a synonym is ambiguous, ``False`` otherwise"""
return self._has_ambiguity
@property
def is_noun(self):
"""bool: ``True`` if a synonym is a noun, ``False`` otherwise"""
return self._is_noun
@property
def form_type(self):
"""int: a word form type of a synonym"""
return self._form_type
@property
def acronym_type(self):
"""int: an acronym type of a synonym"""
return self._acronym_type
@property
def variant_type(self):
"""int: a variant type of a synonym"""
return self._variant_type
def encode(self):
"""Encodes this ``Flags`` object.
Returns:
int: encoded flags
"""
flags = 0
flags |= 1 if self.has_ambiguity else 0
flags |= (1 if self.is_noun else 0) << 1
flags |= self.form_type << 2
flags |= self.acronym_type << 5
flags |= self.variant_type << 7
return flags |
# -*- coding: utf-8 -*-
qntCasos = int(input())
for caso in range(qntCasos):
strDieta = input()
strCafeDaManha = input()
strAlmoco = input()
EhCheater = False
for indice in range(len(strCafeDaManha)):
if strCafeDaManha[indice] in strDieta: strDieta = strDieta.replace(strCafeDaManha[indice], "")
else: EhCheater = True
for indice in range(len(strAlmoco)):
if strAlmoco[indice] in strDieta: strDieta = strDieta.replace(strAlmoco[indice], "")
else: EhCheater = True
if EhCheater == True: print("CHEATER")
else: print("".join(sorted(strDieta))) | qnt_casos = int(input())
for caso in range(qntCasos):
str_dieta = input()
str_cafe_da_manha = input()
str_almoco = input()
eh_cheater = False
for indice in range(len(strCafeDaManha)):
if strCafeDaManha[indice] in strDieta:
str_dieta = strDieta.replace(strCafeDaManha[indice], '')
else:
eh_cheater = True
for indice in range(len(strAlmoco)):
if strAlmoco[indice] in strDieta:
str_dieta = strDieta.replace(strAlmoco[indice], '')
else:
eh_cheater = True
if EhCheater == True:
print('CHEATER')
else:
print(''.join(sorted(strDieta))) |
def skipbigrams(text):
bigrams = list()
for i in range(0, len(text)-2):
bigrams.append((text[i], text[i+2]))
return bigrams | def skipbigrams(text):
bigrams = list()
for i in range(0, len(text) - 2):
bigrams.append((text[i], text[i + 2]))
return bigrams |
## \file OutputFormat.py
# \author Naveen Ganesh Muralidharan
# \brief Provides the function for writing outputs
## \brief Writes the output values to output.txt
# \param y_t Process Variable: The output value from the power plant
def write_output(y_t):
outputfile = open("output.txt", "w")
print("y_t = ", end="", file=outputfile)
print(y_t, file=outputfile)
outputfile.close()
| def write_output(y_t):
outputfile = open('output.txt', 'w')
print('y_t = ', end='', file=outputfile)
print(y_t, file=outputfile)
outputfile.close() |
class Solution:
def lengthOfLongestSubstring(self, s: str) -> int:
bInx, mLan, sMap = 0, 0, {}
for i, v in enumerate(s):
if v in sMap and sMap[v] >= bInx:
if mLan < i - bInx:
mLan = i - bInx
bInx = sMap[v] + 1
elif i == len(s) - 1 and i - bInx + 1 > mLan:
mLan = i - bInx + 1
sMap[v] = i
return mLan | class Solution:
def length_of_longest_substring(self, s: str) -> int:
(b_inx, m_lan, s_map) = (0, 0, {})
for (i, v) in enumerate(s):
if v in sMap and sMap[v] >= bInx:
if mLan < i - bInx:
m_lan = i - bInx
b_inx = sMap[v] + 1
elif i == len(s) - 1 and i - bInx + 1 > mLan:
m_lan = i - bInx + 1
sMap[v] = i
return mLan |
# https://app.codesignal.com/arcade/code-arcade/lab-of-transformations/ngQTG9kra7GE9pnnK
def newNumeralSystem(az_digit):
# The system effectively is a base-26 numeric system in which the letters
# from A to Z are the az_digits.
#
# Create a mapping from a uppercase letter to its index.
system = { i : chr(i + ord('A')) for i in range(ord('Z') - ord('A') + 1) }
# We want to find which pair of other "az-digits" in the system can be added
# up to the given "digit".
idx_to_find = ord(az_digit) - ord('A')
results = []
# Do it until the middle to only a pair once, with the first lower than the
# second, like "B + F" because "F + B" is a redundancy.
for idx_1 in range(idx_to_find//2 + 1):
idx_2 = idx_to_find - idx_1
if idx_2 in system:
chr_1 = system[idx_1]
chr_2 = system[idx_2]
results.append("{} + {}".format(chr_1, chr_2))
return results
| def new_numeral_system(az_digit):
system = {i: chr(i + ord('A')) for i in range(ord('Z') - ord('A') + 1)}
idx_to_find = ord(az_digit) - ord('A')
results = []
for idx_1 in range(idx_to_find // 2 + 1):
idx_2 = idx_to_find - idx_1
if idx_2 in system:
chr_1 = system[idx_1]
chr_2 = system[idx_2]
results.append('{} + {}'.format(chr_1, chr_2))
return results |
class REPL_STATE:
NONE = 0
CONNECT = 1
CONNECTING = 2
RECEIVE_PING_REPLY = 3
SEND_HANDSHAKE = 4
RECEIVE_AUTH_REPLY = 5
RECEIVE_PORT_REPLY = 6
RECEIVE_IP_REPLY = 7
RECEIVE_CAPA_REPLY = 8
SEND_PSYNC = 9
RECEIVE_PSYNC_REPLY = 10
TRANSFER = 11
CONNECTED = 12
class REPL_SLAVE_STATE:
NONE = 0
CONNECT = 1
CONNECTING = 2
RECEIVE_PONG = 3
SEND_AUTH = 4
RECEIVE_AUTH = 5
SEND_PORT = 6
RECEIVE_PORT = 7
SEND_IP = 8
RECEIVE_IP = 9
SEND_CAPA = 10
SEND_PSYNC = 11
RECEIVE_PSYNC = 12
TRANSFER = 13
CONNECTED = 14
| class Repl_State:
none = 0
connect = 1
connecting = 2
receive_ping_reply = 3
send_handshake = 4
receive_auth_reply = 5
receive_port_reply = 6
receive_ip_reply = 7
receive_capa_reply = 8
send_psync = 9
receive_psync_reply = 10
transfer = 11
connected = 12
class Repl_Slave_State:
none = 0
connect = 1
connecting = 2
receive_pong = 3
send_auth = 4
receive_auth = 5
send_port = 6
receive_port = 7
send_ip = 8
receive_ip = 9
send_capa = 10
send_psync = 11
receive_psync = 12
transfer = 13
connected = 14 |
# The new config inherits a base config to highlight the necessary modification
_base_ = '../retinanet/retinanet_r50_fpn_1x_coco.py'
# We also need to change the num_classes in head to match the dataset's annotation
model = dict(
bbox_head=dict(
num_classes=1))
# dataset settings
dataset_type = 'COCODataset'
classes = ('balloon',)
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True),
dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
dict(type='RandomFlip', flip_ratio=0.5),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(1333, 800),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img']),
])
]
data = dict(
samples_per_gpu=16,
workers_per_gpu=8,
train=dict(
img_prefix='data/balloon/train/',
classes=classes,
ann_file='data/balloon/train/annotation_coco.json'),
val=dict(
img_prefix='data/balloon/val/',
classes=classes,
ann_file='data/balloon/val/annotation_coco.json'),
test=dict(
img_prefix='data/balloon/val/',
classes=classes,
ann_file='data/balloon/val/annotation_coco.json'))
evaluation = dict(interval=1, metric='bbox')
# optimizer
optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=10,
warmup_ratio=0.001,
step=[30, 40])
runner = dict(type='EpochBasedRunner', max_epochs=50)
checkpoint_config = dict(interval=10)
# yapf:disable
log_config = dict(
interval=1,
hooks=[
dict(type='TextLoggerHook'),
dict(type='TensorboardLoggerHook')
])
# yapf:enable
custom_hooks = [dict(type='NumClassCheckHook')]
dist_params = dict(backend='nccl')
log_level = 'INFO'
load_from = 'checkpoints/retinanet_r50_fpn_1x_coco_20200130-c2398f9e.pth'
resume_from = None
workflow = [('train', 1)]
| _base_ = '../retinanet/retinanet_r50_fpn_1x_coco.py'
model = dict(bbox_head=dict(num_classes=1))
dataset_type = 'COCODataset'
classes = ('balloon',)
img_norm_cfg = dict(mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
train_pipeline = [dict(type='LoadImageFromFile'), dict(type='LoadAnnotations', with_bbox=True), dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), dict(type='RandomFlip', flip_ratio=0.5), dict(type='Normalize', **img_norm_cfg), dict(type='Pad', size_divisor=32), dict(type='DefaultFormatBundle'), dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])]
test_pipeline = [dict(type='LoadImageFromFile'), dict(type='MultiScaleFlipAug', img_scale=(1333, 800), flip=False, transforms=[dict(type='Resize', keep_ratio=True), dict(type='RandomFlip'), dict(type='Normalize', **img_norm_cfg), dict(type='Pad', size_divisor=32), dict(type='ImageToTensor', keys=['img']), dict(type='Collect', keys=['img'])])]
data = dict(samples_per_gpu=16, workers_per_gpu=8, train=dict(img_prefix='data/balloon/train/', classes=classes, ann_file='data/balloon/train/annotation_coco.json'), val=dict(img_prefix='data/balloon/val/', classes=classes, ann_file='data/balloon/val/annotation_coco.json'), test=dict(img_prefix='data/balloon/val/', classes=classes, ann_file='data/balloon/val/annotation_coco.json'))
evaluation = dict(interval=1, metric='bbox')
optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
optimizer_config = dict(grad_clip=None)
lr_config = dict(policy='step', warmup='linear', warmup_iters=10, warmup_ratio=0.001, step=[30, 40])
runner = dict(type='EpochBasedRunner', max_epochs=50)
checkpoint_config = dict(interval=10)
log_config = dict(interval=1, hooks=[dict(type='TextLoggerHook'), dict(type='TensorboardLoggerHook')])
custom_hooks = [dict(type='NumClassCheckHook')]
dist_params = dict(backend='nccl')
log_level = 'INFO'
load_from = 'checkpoints/retinanet_r50_fpn_1x_coco_20200130-c2398f9e.pth'
resume_from = None
workflow = [('train', 1)] |
def lucas(n):
if(n==0):
return 2
if(n==1):
return 1
return lucas(n-1)+lucas(n-2)
n=9
print(lucas(n)) | def lucas(n):
if n == 0:
return 2
if n == 1:
return 1
return lucas(n - 1) + lucas(n - 2)
n = 9
print(lucas(n)) |
# -*- coding: utf-8 -*-
"""
Build a program to print a graph like:
*
***
*****
*******
Try to code as easy as you can.
"""
for i in range(1, 5):
print(' ' * (4 - (i - 1)) + '*' * (2 * i - 1))
| """
Build a program to print a graph like:
*
***
*****
*******
Try to code as easy as you can.
"""
for i in range(1, 5):
print(' ' * (4 - (i - 1)) + '*' * (2 * i - 1)) |
def isPandigital(string):
check = "".join(sorted(string))
if check == "123456789":
return True
return False
def check_pandigital_Product(num):
i = 1
while i*i <= num:
if num%i == 0 and isPandigital(str(num) + str(i) + str(num//i)):
return True
i+=1
return False
sum = 0
for i in range(0,10000):
if check_pandigital_Product(i):
sum+=i
print(sum)
| def is_pandigital(string):
check = ''.join(sorted(string))
if check == '123456789':
return True
return False
def check_pandigital__product(num):
i = 1
while i * i <= num:
if num % i == 0 and is_pandigital(str(num) + str(i) + str(num // i)):
return True
i += 1
return False
sum = 0
for i in range(0, 10000):
if check_pandigital__product(i):
sum += i
print(sum) |
# -*- coding: utf-8 -*-
class InstanceLimitReached(Exception):
pass
class InstanceCreationError(Exception):
pass
| class Instancelimitreached(Exception):
pass
class Instancecreationerror(Exception):
pass |
# Copyright 2017 Workiva
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
class FTransportFactory(object):
"""
FTransportFactory is responsible for creating new FTransports.
"""
def get_transport(self, thrift_transport):
"""
Retuns a new FTransport wrapping the given TTransport.
Args:
thrift_transport: TTransport to wrap.
Returns:
new FTranpsort
"""
pass
class FPublisherTransportFactory(object):
"""
FPublisherTransportFactory is responsible for creating new
FPublisherTransports.
"""
def get_transport(self):
"""
Returns a new FPublisherTransport.
"""
pass
class FSubscriberTransportFactory(object):
"""
FSubscriberTransportFactory is responsible for creating new
FSubscriberTransports.
"""
def get_transport(self):
"""
Returns a new FSubscriberTransport.
"""
pass
| class Ftransportfactory(object):
"""
FTransportFactory is responsible for creating new FTransports.
"""
def get_transport(self, thrift_transport):
"""
Retuns a new FTransport wrapping the given TTransport.
Args:
thrift_transport: TTransport to wrap.
Returns:
new FTranpsort
"""
pass
class Fpublishertransportfactory(object):
"""
FPublisherTransportFactory is responsible for creating new
FPublisherTransports.
"""
def get_transport(self):
"""
Returns a new FPublisherTransport.
"""
pass
class Fsubscribertransportfactory(object):
"""
FSubscriberTransportFactory is responsible for creating new
FSubscriberTransports.
"""
def get_transport(self):
"""
Returns a new FSubscriberTransport.
"""
pass |
# generated from genmsg/cmake/pkg-genmsg.context.in
messages_str = "/home/venom/ros/demo2_ws/src/ROS_Gazebo_Tutorial/pal_person_detector_opencv/msg/Detection2d.msg;/home/venom/ros/demo2_ws/src/ROS_Gazebo_Tutorial/pal_person_detector_opencv/msg/Detections2d.msg"
services_str = ""
pkg_name = "pal_person_detector_opencv"
dependencies_str = "geometry_msgs"
langs = "gencpp;geneus;genlisp;gennodejs;genpy"
dep_include_paths_str = "pal_person_detector_opencv;/home/venom/ros/demo2_ws/src/ROS_Gazebo_Tutorial/pal_person_detector_opencv/msg;geometry_msgs;/opt/ros/melodic/share/geometry_msgs/cmake/../msg;std_msgs;/opt/ros/melodic/share/std_msgs/cmake/../msg"
PYTHON_EXECUTABLE = "/usr/bin/python2"
package_has_static_sources = '' == 'TRUE'
genmsg_check_deps_script = "/opt/ros/melodic/share/genmsg/cmake/../../../lib/genmsg/genmsg_check_deps.py"
| messages_str = '/home/venom/ros/demo2_ws/src/ROS_Gazebo_Tutorial/pal_person_detector_opencv/msg/Detection2d.msg;/home/venom/ros/demo2_ws/src/ROS_Gazebo_Tutorial/pal_person_detector_opencv/msg/Detections2d.msg'
services_str = ''
pkg_name = 'pal_person_detector_opencv'
dependencies_str = 'geometry_msgs'
langs = 'gencpp;geneus;genlisp;gennodejs;genpy'
dep_include_paths_str = 'pal_person_detector_opencv;/home/venom/ros/demo2_ws/src/ROS_Gazebo_Tutorial/pal_person_detector_opencv/msg;geometry_msgs;/opt/ros/melodic/share/geometry_msgs/cmake/../msg;std_msgs;/opt/ros/melodic/share/std_msgs/cmake/../msg'
python_executable = '/usr/bin/python2'
package_has_static_sources = '' == 'TRUE'
genmsg_check_deps_script = '/opt/ros/melodic/share/genmsg/cmake/../../../lib/genmsg/genmsg_check_deps.py' |
with open('./results.txt') as f:
lines = f.readlines()
f.close()
goSnakeWins = 0
antiBlobWins = 0
for i in range(0, len(lines)):
#if len(lines[i]) < 100:
#print(lines[i], end = "")
if "DONE" in lines[i]:
#print(lines[i], end = "")
if "20blobby2" in lines[i]:
goSnakeWins += 1
if "anti-blobby" in lines[i]:
antiBlobWins += 1
print("Results:")
print("20blobby2: ", goSnakeWins, " wins")
print("anti-blobby", antiBlobWins, " wins")
| with open('./results.txt') as f:
lines = f.readlines()
f.close()
go_snake_wins = 0
anti_blob_wins = 0
for i in range(0, len(lines)):
if 'DONE' in lines[i]:
if '20blobby2' in lines[i]:
go_snake_wins += 1
if 'anti-blobby' in lines[i]:
anti_blob_wins += 1
print('Results:')
print('20blobby2: ', goSnakeWins, ' wins')
print('anti-blobby', antiBlobWins, ' wins') |
# Problem 136: Single Number
class Solution:
# Approach 1 - Sort
def singleNumber1(self, nums) -> int:
if nums == []:
return None
nums.sort()
index = 0
while index != len(nums)-1 and nums[index] == nums[index+1]:
index += 2
return nums[index]
# Approach 2 - Arithmetic
def singleNumber(self, nums) -> int:
return (2 * sum(set(nums))) - sum(nums)
# Approach 3 - Bit manipulation - XOR
def singleNumber3(self, nums) -> int:
foo = 0
for num in nums:
foo = foo ^ num
return foo
# Test
solution = Solution()
# Expected: 1
nums = [2,3,2,3,6,1,6]
print(solution.singleNumber(nums))
| class Solution:
def single_number1(self, nums) -> int:
if nums == []:
return None
nums.sort()
index = 0
while index != len(nums) - 1 and nums[index] == nums[index + 1]:
index += 2
return nums[index]
def single_number(self, nums) -> int:
return 2 * sum(set(nums)) - sum(nums)
def single_number3(self, nums) -> int:
foo = 0
for num in nums:
foo = foo ^ num
return foo
solution = solution()
nums = [2, 3, 2, 3, 6, 1, 6]
print(solution.singleNumber(nums)) |
def metade(p):
return p / 2
def dobro(p):
return p * 2
def aumentar(p, au):
jur = au / 100
return p + (p * jur)
def diminuir(p, di):
jur = di / 100
return p - (p * jur)
| def metade(p):
return p / 2
def dobro(p):
return p * 2
def aumentar(p, au):
jur = au / 100
return p + p * jur
def diminuir(p, di):
jur = di / 100
return p - p * jur |
# -*- coding: utf-8 -*-
"""
Created on Tue Jun 09 19:02:15 2015
@author: Shamir
"""
for i in range(1, num_rows):
euclidean_distance = []
for j in range(1, num_columns):
prev_column = j - 1
distance = euclidean(file.values[i, prev_column], file.values[i, j])
euclidean_distance.append(distance)
euclidean_distance = np.asarray(euclidean_distance)
threshold = euclidean_distance.max() * 0.2
noisy_data = euclidean_distance[euclidean_distance > threshold]
noisy_index = []
for k in range(len(noisy_data)):
noisy_index.append(np.where(euclidean_distance == noisy_data[k])[0][0] + 1)
if k > 0: # compare with previous index value to...
if noisy_index[k] == noisy_index[k-1]: # check if index values are same because of identical values of adjacent datapoints in the actual dataset
noisy_index[k] = noisy_index[k] + 1 # increment index value by 1 to capture the accurate index
noisy_index = np.asarray(noisy_index)
corrupted_data = 0
for noise in range(len(noisy_index)): # check for every pair because each pair of euclidean peaks corresponds to one noisy/corrupted datapoint
prev_point = noisy_index[noise] - 1
next_point = noisy_index[noise] + 1
secNext_point = noisy_index[noise] + 2
window_bound = noisy_index[noise] + 3
if noise < (len(noisy_index) - 2) and (noisy_index[noise + 1] == noisy_index[noise] + 1): # check if it's a pair of adjacent peaks
if noisy_index[noise + 2] != noisy_index[noise + 1] + 1:
file.values[i, noisy_index[noise]] = linearInterpolation(noisy_index[noise] - 1, noisy_index[noise], noisy_index[noise] + 1)
elif noise < (len(noisy_index) - 3) and (noisy_index[noise + 3] != noisy_index[noise + 2] + 1):
file.values[i, noisy_index[noise]] = linearInterpolation(noisy_index[noise] - 1, noisy_index[noise], noisy_index[noise] + 2)
file.values[i, noisy_index[noise] + 1] = linearInterpolation(noisy_index[noise] - 1, noisy_index[noise] + 1, noisy_index[noise] + 2)
else:
file.values[i, noisy_index[noise]] = linearInterpolation(noisy_index[noise] - 1, noisy_index[noise], noisy_index[noise] + 3)
file.values[i, noisy_index[noise] + 1] = linearInterpolation(noisy_index[noise] - 1, noisy_index[noise] + 1, noisy_index[noise] + 3)
file.values[i, noisy_index[noise] + 2] = linearInterpolation(noisy_index[noise] - 1, noisy_index[noise] + 2, noisy_index[noise] + 3)
| """
Created on Tue Jun 09 19:02:15 2015
@author: Shamir
"""
for i in range(1, num_rows):
euclidean_distance = []
for j in range(1, num_columns):
prev_column = j - 1
distance = euclidean(file.values[i, prev_column], file.values[i, j])
euclidean_distance.append(distance)
euclidean_distance = np.asarray(euclidean_distance)
threshold = euclidean_distance.max() * 0.2
noisy_data = euclidean_distance[euclidean_distance > threshold]
noisy_index = []
for k in range(len(noisy_data)):
noisy_index.append(np.where(euclidean_distance == noisy_data[k])[0][0] + 1)
if k > 0:
if noisy_index[k] == noisy_index[k - 1]:
noisy_index[k] = noisy_index[k] + 1
noisy_index = np.asarray(noisy_index)
corrupted_data = 0
for noise in range(len(noisy_index)):
prev_point = noisy_index[noise] - 1
next_point = noisy_index[noise] + 1
sec_next_point = noisy_index[noise] + 2
window_bound = noisy_index[noise] + 3
if noise < len(noisy_index) - 2 and noisy_index[noise + 1] == noisy_index[noise] + 1:
if noisy_index[noise + 2] != noisy_index[noise + 1] + 1:
file.values[i, noisy_index[noise]] = linear_interpolation(noisy_index[noise] - 1, noisy_index[noise], noisy_index[noise] + 1)
elif noise < len(noisy_index) - 3 and noisy_index[noise + 3] != noisy_index[noise + 2] + 1:
file.values[i, noisy_index[noise]] = linear_interpolation(noisy_index[noise] - 1, noisy_index[noise], noisy_index[noise] + 2)
file.values[i, noisy_index[noise] + 1] = linear_interpolation(noisy_index[noise] - 1, noisy_index[noise] + 1, noisy_index[noise] + 2)
else:
file.values[i, noisy_index[noise]] = linear_interpolation(noisy_index[noise] - 1, noisy_index[noise], noisy_index[noise] + 3)
file.values[i, noisy_index[noise] + 1] = linear_interpolation(noisy_index[noise] - 1, noisy_index[noise] + 1, noisy_index[noise] + 3)
file.values[i, noisy_index[noise] + 2] = linear_interpolation(noisy_index[noise] - 1, noisy_index[noise] + 2, noisy_index[noise] + 3) |
#
# @lc app=leetcode id=146 lang=python3
#
# [146] LRU Cache
#
class ListNode:
def __init__(self, key, val, next=None, prev=None):
self.key = key
self.val = val
self.next = next
self.prev = prev
class LRUCache:
def __init__(self, capacity: int):
self.cache_history = {}
self.head = ListNode(-1, -1)
self.tail = ListNode(-1, -1)
self.head.next = self.tail
self.tail.prev = self.head
self.capacity = capacity
def get(self, key: int) -> int:
if key not in self.cache_history:
return -1
node = self.cache_history[key]
self._remove(node)
self._push_back(node)
return node.val
def put(self, key: int, value: int) -> None:
if self.get(key) != -1:
self.cache_history[key].val = value
return
if len(self.cache_history) >= self.capacity:
self._pop_first()
node = ListNode(key, value)
self._push_back(node)
self.cache_history[key] = node
def _pop_first(self):
del self.cache_history[self.head.next.key]
self._remove(self.head.next)
def _push_back(self, node):
node.next = self.tail
self.tail.prev.next = node
node.prev = self.tail.prev
self.tail.prev = node
def _remove(self, node):
node.prev.next = node.next
node.next.prev = node.prev
# Your LRUCache object will be instantiated and called as such:
# obj = LRUCache(capacity)
# param_1 = obj.get(key)
# obj.put(key,value)
| class Listnode:
def __init__(self, key, val, next=None, prev=None):
self.key = key
self.val = val
self.next = next
self.prev = prev
class Lrucache:
def __init__(self, capacity: int):
self.cache_history = {}
self.head = list_node(-1, -1)
self.tail = list_node(-1, -1)
self.head.next = self.tail
self.tail.prev = self.head
self.capacity = capacity
def get(self, key: int) -> int:
if key not in self.cache_history:
return -1
node = self.cache_history[key]
self._remove(node)
self._push_back(node)
return node.val
def put(self, key: int, value: int) -> None:
if self.get(key) != -1:
self.cache_history[key].val = value
return
if len(self.cache_history) >= self.capacity:
self._pop_first()
node = list_node(key, value)
self._push_back(node)
self.cache_history[key] = node
def _pop_first(self):
del self.cache_history[self.head.next.key]
self._remove(self.head.next)
def _push_back(self, node):
node.next = self.tail
self.tail.prev.next = node
node.prev = self.tail.prev
self.tail.prev = node
def _remove(self, node):
node.prev.next = node.next
node.next.prev = node.prev |
"""Cycle(atoms) -> cycle object for a ring.
cycle.atoms -> atoms around a ring
cycle.bonds -> bonds aroung a ring
cycle.rotate(atom) -> rotate the ring so that atom is in front
"""
class CycleError(Exception):
pass
class Cycle:
def __init__(self, atoms, bonds, aromatic=0):
"""(atoms)->create a cycle object
assumes that the atoms are in traversal order around the ring.
That is [a1, a2, a3] means that there is a bond in the cycle
between a1 and a2 and a2 and a3 and a3 and a1"""
self.atoms = atoms[:]
self.bonds = bonds[:]
self.aromatic = aromatic
for atom in self.atoms:
atom.rings.append(self)
for bond in self.bonds:
bond.rings.append(self)
def __len__(self):
return len(self.atoms)
def rotate(self, atom):
"""(atom)->start the cycle at position atom, assumes
that atom is in the cycle"""
try:
index = self.atoms.index(atom)
except ValueError:
raise CycleError("atom %s not in cycle"%(atom))
self.atoms = self.atoms[index:] + self.atoms[:index]
self.bonds = self.bonds[index:] + self.bonds[:index]
def clone(self):
return Cycle(self.atoms, self.bonds, self.aromatic)
def set_aromatic(self):
"""set the cycle to be an aromatic ring"""
#XXX FIX ME
# this probably shouldn't be here
for atom in self.atoms:
atom.aromatic = 1
for bond in self.bonds:
bond.aromatic = 1
bond.bondorder = 1.5
bond.bondtype = 4
bond.symbol = ":"
bond.fixed = 1
self.aromatic = 1
| """Cycle(atoms) -> cycle object for a ring.
cycle.atoms -> atoms around a ring
cycle.bonds -> bonds aroung a ring
cycle.rotate(atom) -> rotate the ring so that atom is in front
"""
class Cycleerror(Exception):
pass
class Cycle:
def __init__(self, atoms, bonds, aromatic=0):
"""(atoms)->create a cycle object
assumes that the atoms are in traversal order around the ring.
That is [a1, a2, a3] means that there is a bond in the cycle
between a1 and a2 and a2 and a3 and a3 and a1"""
self.atoms = atoms[:]
self.bonds = bonds[:]
self.aromatic = aromatic
for atom in self.atoms:
atom.rings.append(self)
for bond in self.bonds:
bond.rings.append(self)
def __len__(self):
return len(self.atoms)
def rotate(self, atom):
"""(atom)->start the cycle at position atom, assumes
that atom is in the cycle"""
try:
index = self.atoms.index(atom)
except ValueError:
raise cycle_error('atom %s not in cycle' % atom)
self.atoms = self.atoms[index:] + self.atoms[:index]
self.bonds = self.bonds[index:] + self.bonds[:index]
def clone(self):
return cycle(self.atoms, self.bonds, self.aromatic)
def set_aromatic(self):
"""set the cycle to be an aromatic ring"""
for atom in self.atoms:
atom.aromatic = 1
for bond in self.bonds:
bond.aromatic = 1
bond.bondorder = 1.5
bond.bondtype = 4
bond.symbol = ':'
bond.fixed = 1
self.aromatic = 1 |
class Switches:
"""
Abstract class providing common interface to the set of switches
"""
def num_switches(self):
raise NotImplementedError
def switch_state(self, idx):
raise NotImplementedError
| class Switches:
"""
Abstract class providing common interface to the set of switches
"""
def num_switches(self):
raise NotImplementedError
def switch_state(self, idx):
raise NotImplementedError |
# Quiz, Problem 6
def flatten(aList):
'''
aList: a list
Returns a copy of aList, which is a flattened version of aList
'''
aNewList = []
for elt in aList:
if type(elt) == list:
aNewList.extend(flatten(elt))
else:
aNewList.append(elt)
return aNewList | def flatten(aList):
"""
aList: a list
Returns a copy of aList, which is a flattened version of aList
"""
a_new_list = []
for elt in aList:
if type(elt) == list:
aNewList.extend(flatten(elt))
else:
aNewList.append(elt)
return aNewList |
"""
This program uses the reverse method to change a list such that it is in the
reverse order that it was in. Note that it does NOT put it in reverse sorted
order (unless the list was ALREADY in sorted order).
"""
my_list = [1, 4, 2, -4, 10, 0]
print(my_list)
my_list.reverse()
print(my_list) | """
This program uses the reverse method to change a list such that it is in the
reverse order that it was in. Note that it does NOT put it in reverse sorted
order (unless the list was ALREADY in sorted order).
"""
my_list = [1, 4, 2, -4, 10, 0]
print(my_list)
my_list.reverse()
print(my_list) |
# -*- coding: utf-8 -*-
"""
@author: Victor Kohler
@since: date 18/12/2016
@version: 0.1
"""
class MockOpen(object):
"""Simulate the builtin open function"""
def __init__(self):
pass
def close(self):
return True
| """
@author: Victor Kohler
@since: date 18/12/2016
@version: 0.1
"""
class Mockopen(object):
"""Simulate the builtin open function"""
def __init__(self):
pass
def close(self):
return True |
def fractional_knapsack_greedy(value, weight, capacity):
# index = [0, 1, 2, ..., n - 1] for n items
index = list(range(len(value)))
# contains ratios of values to weight
ratio = [v / w for v, w in zip(value, weight)]
# index is sorted according to value-to-weight ratio in decreasing order
index.sort(key=lambda i: ratio[i], reverse=True)
max_value = 0
fractions = [0] * len(value)
for i in index:
if weight[i] <= capacity:
fractions[i] = 1
max_value += value[i]
capacity -= weight[i]
else:
fractions[i] = capacity / weight[i]
max_value += value[i] * capacity / weight[i]
break
return max_value, fractions
n = int(input('Enter number of items: '))
value = input('Enter the values of the {} item(s) in order: '
.format(n)).split()
value = [int(v) for v in value]
weight = input('Enter the positive weights of the {} item(s) in order: '
.format(n)).split()
weight = [int(w) for w in weight]
capacity = int(input('Enter maximum weight: '))
max_value, fractions = fractional_knapsack_greedy(value, weight, capacity)
print('The maximum value of items that can be carried:', max_value)
print('The fractions in which the items should be taken:', fractions)
| def fractional_knapsack_greedy(value, weight, capacity):
index = list(range(len(value)))
ratio = [v / w for (v, w) in zip(value, weight)]
index.sort(key=lambda i: ratio[i], reverse=True)
max_value = 0
fractions = [0] * len(value)
for i in index:
if weight[i] <= capacity:
fractions[i] = 1
max_value += value[i]
capacity -= weight[i]
else:
fractions[i] = capacity / weight[i]
max_value += value[i] * capacity / weight[i]
break
return (max_value, fractions)
n = int(input('Enter number of items: '))
value = input('Enter the values of the {} item(s) in order: '.format(n)).split()
value = [int(v) for v in value]
weight = input('Enter the positive weights of the {} item(s) in order: '.format(n)).split()
weight = [int(w) for w in weight]
capacity = int(input('Enter maximum weight: '))
(max_value, fractions) = fractional_knapsack_greedy(value, weight, capacity)
print('The maximum value of items that can be carried:', max_value)
print('The fractions in which the items should be taken:', fractions) |
#!/usr/bin/env python3
# --------------------------------------------------------------------------- #
# The MIT License (MIT) #
# #
# Copyright (c) 2021 Eliud Cabrera Castillo <e.cabrera-castillo@tum.de> #
# #
# 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. #
# --------------------------------------------------------------------------- #
"""Functions to manipulate the claims found in the network.
These methods are used with lists of claims returned by `claim_search`.
"""
def sort_and_filter(claims, number=0, reverse=False):
"""Sort the input list and remove duplicated items with same claim ID.
Parameters
----------
claims: list of dict
List of claims obtained from `claim_search`.
number: int, optional
It defaults to 0, in which case the returned list will contain
all unique claims.
If this is any other number, it will cut the output list to have
a maximum of `number` claims.
reverse: bool, optional
It defaults to `False`, in which case older items come first
in the output list.
If it is `True` the newest items will come first in the output list.
Returns
-------
list of dict
List of claims obtained from `claim_search`, with the duplicates
removed.
"""
print("Sort claims and remove duplicates")
new_items = []
n_claims = len(claims)
# Make sure the `release_time` exists, and use `timestamp` otherwise
for num, claim in enumerate(claims, start=1):
if "release_time" not in claim["value"]:
name = claim["name"]
print(f'{num:4d}/{n_claims:4d}; "{name}" using "timestamp"')
claim["value"]["release_time"] = claim["timestamp"]
new_items.append(claim)
# Sort by using the original `release_time`.
# New items will come first.
sorted_items = sorted(new_items,
key=lambda v: int(v["value"]["release_time"]),
reverse=True)
unique_ids = []
unique_claims = []
for item in sorted_items:
if item["claim_id"] not in unique_ids:
unique_claims.append(item)
unique_ids.append(item["claim_id"])
if number:
# Cut the older items
unique_claims = unique_claims[0:number]
if not reverse:
# Invert the list so that older items appear first
unique_claims.reverse()
return unique_claims
def downloadable_size(claims, local=False):
"""Calculate the total size of input claims.
Parameters
----------
claims: list of dict
List of claims obtained from `claim_search`,
or if using `local=True`, from `file_list`.
local: bool, optional
It defaults to `False` in which case it assumes the claims
were resolved online from `claim_search`.
If it is `True` it assumes the claims come from `file_list`,
that is, from the claims locally downloaded.
This is necessary because the information is in different fields
depending on where it comes from.
Returns
-------
dict
A dictionary with two keys:
- 'size': total size of the claims in bytes.
It can be divided by 1024 to obtain kibibytes, by another 1024
to obtain mebibytes, and by another 1024 to obtain gibibytes.
- 'duration': total duration of the claims in seconds.
It will count only stream types which have a duration
such as audio and video.
The duration can be divided by 3600 to obtain hours,
then by 24 to obtain days.
"""
if local:
print("Calculate size of fully downloaded claims")
else:
print("Calculate size of downloadable claims")
n_claims = len(claims)
total_size = 0
total_duration = 0
for num, claim in enumerate(claims, start=1):
if local:
vtype = claim["mime_type"]
source_info = claim["metadata"]
alt_name = claim["stream_name"]
else:
vtype = claim["value_type"]
source_info = claim["value"]
alt_name = claim["name"]
if "source" in source_info:
file_name = source_info["source"].get("name", "None")
size = int(source_info["source"].get("size", 0))
else:
file_name = alt_name
size = 0
print(f"{num:4d}/{n_claims:4d}; type: {vtype}; "
f'no source: "{file_name}"')
seconds = 0
if "video" in source_info:
seconds = source_info["video"].get("duration", 0)
elif "audio" in source_info:
seconds = source_info["audio"].get("duration", 0)
total_size += size
total_duration += seconds
return {"size": total_size,
"duration": total_duration}
def sort_filter_size(claims, number=0, reverse=False):
"""Sort, filter the claims, and provide the download size and duration.
Parameters
----------
claims: list of dict
List of claims obtained from `claim_search`.
number: int, optional
It defaults to 0, in which case the returned list will contain
all unique claims.
If this is any other number, it will cut the output list to have
a maximum of `number` claims.
reverse: bool, optional
It defaults to `False`, in which case older items come first
on the output list.
If it is `True` the newest items will come first in the output list.
Returns
-------
dict
A dictionary with three keys:
- 'claims': a list of dictionaries where every dictionary represents
a claim returned by `claim_search`.
The list is ordered in ascending order by default (old claims first),
and in descending order (new claims first) if `reverse=True`.
- 'size': total size of the claims in bytes.
It can be divided by 1024 to obtain kibibytes, by another 1024
to obtain mebibytes, and by another 1024 to obtain gibibytes.
- 'duration': total duration of the claims in seconds.
It will count only stream types which have a duration
such as audio and video.
The duration can be divided by 3600 to obtain hours,
then by 24 to obtain days.
"""
claims = sort_and_filter(claims, number=number, reverse=reverse)
print()
output = downloadable_size(claims)
total_size = output["size"]
total_duration = output["duration"]
n_claims = len(claims)
GB = total_size / (1024**3) # to GiB
hrs = total_duration / 3600
days = hrs / 24
hr = total_duration // 3600
mi = (total_duration % 3600) // 60
sec = (total_duration % 3600) % 60
print(40 * "-")
print(f"Total unique claims: {n_claims}")
print(f"Total download size: {GB:.4f} GiB")
print(f"Total duration: {hr} h {mi} min {sec} s, or {days:.4f} days")
return {"claims": claims,
"size": total_size,
"duration": total_duration}
| """Functions to manipulate the claims found in the network.
These methods are used with lists of claims returned by `claim_search`.
"""
def sort_and_filter(claims, number=0, reverse=False):
"""Sort the input list and remove duplicated items with same claim ID.
Parameters
----------
claims: list of dict
List of claims obtained from `claim_search`.
number: int, optional
It defaults to 0, in which case the returned list will contain
all unique claims.
If this is any other number, it will cut the output list to have
a maximum of `number` claims.
reverse: bool, optional
It defaults to `False`, in which case older items come first
in the output list.
If it is `True` the newest items will come first in the output list.
Returns
-------
list of dict
List of claims obtained from `claim_search`, with the duplicates
removed.
"""
print('Sort claims and remove duplicates')
new_items = []
n_claims = len(claims)
for (num, claim) in enumerate(claims, start=1):
if 'release_time' not in claim['value']:
name = claim['name']
print(f'{num:4d}/{n_claims:4d}; "{name}" using "timestamp"')
claim['value']['release_time'] = claim['timestamp']
new_items.append(claim)
sorted_items = sorted(new_items, key=lambda v: int(v['value']['release_time']), reverse=True)
unique_ids = []
unique_claims = []
for item in sorted_items:
if item['claim_id'] not in unique_ids:
unique_claims.append(item)
unique_ids.append(item['claim_id'])
if number:
unique_claims = unique_claims[0:number]
if not reverse:
unique_claims.reverse()
return unique_claims
def downloadable_size(claims, local=False):
"""Calculate the total size of input claims.
Parameters
----------
claims: list of dict
List of claims obtained from `claim_search`,
or if using `local=True`, from `file_list`.
local: bool, optional
It defaults to `False` in which case it assumes the claims
were resolved online from `claim_search`.
If it is `True` it assumes the claims come from `file_list`,
that is, from the claims locally downloaded.
This is necessary because the information is in different fields
depending on where it comes from.
Returns
-------
dict
A dictionary with two keys:
- 'size': total size of the claims in bytes.
It can be divided by 1024 to obtain kibibytes, by another 1024
to obtain mebibytes, and by another 1024 to obtain gibibytes.
- 'duration': total duration of the claims in seconds.
It will count only stream types which have a duration
such as audio and video.
The duration can be divided by 3600 to obtain hours,
then by 24 to obtain days.
"""
if local:
print('Calculate size of fully downloaded claims')
else:
print('Calculate size of downloadable claims')
n_claims = len(claims)
total_size = 0
total_duration = 0
for (num, claim) in enumerate(claims, start=1):
if local:
vtype = claim['mime_type']
source_info = claim['metadata']
alt_name = claim['stream_name']
else:
vtype = claim['value_type']
source_info = claim['value']
alt_name = claim['name']
if 'source' in source_info:
file_name = source_info['source'].get('name', 'None')
size = int(source_info['source'].get('size', 0))
else:
file_name = alt_name
size = 0
print(f'{num:4d}/{n_claims:4d}; type: {vtype}; no source: "{file_name}"')
seconds = 0
if 'video' in source_info:
seconds = source_info['video'].get('duration', 0)
elif 'audio' in source_info:
seconds = source_info['audio'].get('duration', 0)
total_size += size
total_duration += seconds
return {'size': total_size, 'duration': total_duration}
def sort_filter_size(claims, number=0, reverse=False):
"""Sort, filter the claims, and provide the download size and duration.
Parameters
----------
claims: list of dict
List of claims obtained from `claim_search`.
number: int, optional
It defaults to 0, in which case the returned list will contain
all unique claims.
If this is any other number, it will cut the output list to have
a maximum of `number` claims.
reverse: bool, optional
It defaults to `False`, in which case older items come first
on the output list.
If it is `True` the newest items will come first in the output list.
Returns
-------
dict
A dictionary with three keys:
- 'claims': a list of dictionaries where every dictionary represents
a claim returned by `claim_search`.
The list is ordered in ascending order by default (old claims first),
and in descending order (new claims first) if `reverse=True`.
- 'size': total size of the claims in bytes.
It can be divided by 1024 to obtain kibibytes, by another 1024
to obtain mebibytes, and by another 1024 to obtain gibibytes.
- 'duration': total duration of the claims in seconds.
It will count only stream types which have a duration
such as audio and video.
The duration can be divided by 3600 to obtain hours,
then by 24 to obtain days.
"""
claims = sort_and_filter(claims, number=number, reverse=reverse)
print()
output = downloadable_size(claims)
total_size = output['size']
total_duration = output['duration']
n_claims = len(claims)
gb = total_size / 1024 ** 3
hrs = total_duration / 3600
days = hrs / 24
hr = total_duration // 3600
mi = total_duration % 3600 // 60
sec = total_duration % 3600 % 60
print(40 * '-')
print(f'Total unique claims: {n_claims}')
print(f'Total download size: {GB:.4f} GiB')
print(f'Total duration: {hr} h {mi} min {sec} s, or {days:.4f} days')
return {'claims': claims, 'size': total_size, 'duration': total_duration} |
# Definition for singly-linked list.
# class ListNode(object):
# def __init__(self, x):
# self.val = x
# self.next = None
class Solution(object):
def detectCycle(self, head):
"""
:type head: ListNode
:rtype: ListNode
"""
if head is None:
return None
# slow pointer
p1 = head
# fast pointer
p2 = head
is_cycle = False
# if any poiter is none then there is no cycle
while p2 is not None and p2.next is not None:
p1 = p1.next
p2 = p2.next.next
# if poiters are the same then we found the cycle
if p1 == p2:
# print("p1: {}, p2: {}".format(p1.val, p2.val))
is_cycle = True
break
# check if there is cycle
if is_cycle:
# reset the p1 back to head
p1 = head
while p1 != p2:
p1 = p1.next
p2 = p2.next
# print(p1.val)
# print(p2.val)
return p1
else: # no cycle detected
return None
| class Solution(object):
def detect_cycle(self, head):
"""
:type head: ListNode
:rtype: ListNode
"""
if head is None:
return None
p1 = head
p2 = head
is_cycle = False
while p2 is not None and p2.next is not None:
p1 = p1.next
p2 = p2.next.next
if p1 == p2:
is_cycle = True
break
if is_cycle:
p1 = head
while p1 != p2:
p1 = p1.next
p2 = p2.next
return p1
else:
return None |
class GenResult(object):
"""Wraps the result of a batch generator.
See the comments for BatchExecutor.execute.
"""
# Private attributes:
# mixed _value - The result.
def __init__(self, value):
self._value = value
| class Genresult(object):
"""Wraps the result of a batch generator.
See the comments for BatchExecutor.execute.
"""
def __init__(self, value):
self._value = value |
class FindElements:
def __init__(self, root: TreeNode):
self.values = set()
q = deque([root])
root.val = 0
while q:
n = q.popleft()
self.values.add(n.val)
if n.left:
n.left.val = n.val * 2 + 1
q.append(n.left)
if n.right:
n.right.val = n.val * 2 + 2
q.append(n.right)
def find(self, target: int) -> bool:
return target in self.values
| class Findelements:
def __init__(self, root: TreeNode):
self.values = set()
q = deque([root])
root.val = 0
while q:
n = q.popleft()
self.values.add(n.val)
if n.left:
n.left.val = n.val * 2 + 1
q.append(n.left)
if n.right:
n.right.val = n.val * 2 + 2
q.append(n.right)
def find(self, target: int) -> bool:
return target in self.values |
class Scene:
def __init__(self):
super().__init__()
self.Home_Team_Logo_Image = None
self.Away_Team_Logo_Image = None
self.Home_Team_Score = "0"
self.Away_Team_Score = "0"
self.MainText = None
self.AdditionalText = []
| class Scene:
def __init__(self):
super().__init__()
self.Home_Team_Logo_Image = None
self.Away_Team_Logo_Image = None
self.Home_Team_Score = '0'
self.Away_Team_Score = '0'
self.MainText = None
self.AdditionalText = [] |
##addition_str is a string with a list of numbers separated by the + sign. Write code that uses the accumulation pattern to take the sum of all of the numbers and assigns it to sum_val (an integer). (You should use the .split("+") function to split by "+" and int() to cast to an integer).
addition_str = "2+5+10+20"
addlst = addition_str.split("+")
sum_val = 0
for al in addlst:
aint = int(al)
sum_val = sum_val + aint | addition_str = '2+5+10+20'
addlst = addition_str.split('+')
sum_val = 0
for al in addlst:
aint = int(al)
sum_val = sum_val + aint |
values = [int(e) for e in input("Please insert value:").split()]
print(values)
previousValue = values[0]
for value in values :
if value < previousValue :
previousValue = value
print("False")
exit(0)
print("True")
| values = [int(e) for e in input('Please insert value:').split()]
print(values)
previous_value = values[0]
for value in values:
if value < previousValue:
previous_value = value
print('False')
exit(0)
print('True') |
ls=list(map(int,input('Enter the list of numbers: ').split()))
for i in range(len(ls)-1,0,-1):
for j in range(i):
if ls[j]>ls[j+1]:
ls[j],ls[j+1] = ls[j+1],ls[j]
print('Sorted list:',ls)
| ls = list(map(int, input('Enter the list of numbers: ').split()))
for i in range(len(ls) - 1, 0, -1):
for j in range(i):
if ls[j] > ls[j + 1]:
(ls[j], ls[j + 1]) = (ls[j + 1], ls[j])
print('Sorted list:', ls) |
# MEDIUM
# input => [3,2,4,1]
# Max index, boundary index
# 2 4
# 1st [4, 2, 3, 1]
# 2 [1, 3, 2, 4]
# 1 3
# 1st [3, 1, 2, 4]
# 2 [2, 1, 3, 4]
# 0 2
# 1st [2, 1, 3, 4]
# 2 [1, 2, 3, 4]
class Solution:
def pancakeSort(self, A: List[int]) -> List[int]:
def findMax(index):
large = 0
for i in range(0,index):
if A[i]>A[large]:
large = i
return large
def flip(end):
start = 0
while start < end:
A[start],A[end] = A[end],A[start]
start += 1
end -= 1
n = len(A)
curr = n
result = []
while curr>1:
large = findMax(curr)
if large != curr:
print(large,curr)
flip(large)
result.append(large+1)
print('1st',A)
flip(curr - 1)
result.append(curr)
print("2", A)
curr -= 1
return result
| class Solution:
def pancake_sort(self, A: List[int]) -> List[int]:
def find_max(index):
large = 0
for i in range(0, index):
if A[i] > A[large]:
large = i
return large
def flip(end):
start = 0
while start < end:
(A[start], A[end]) = (A[end], A[start])
start += 1
end -= 1
n = len(A)
curr = n
result = []
while curr > 1:
large = find_max(curr)
if large != curr:
print(large, curr)
flip(large)
result.append(large + 1)
print('1st', A)
flip(curr - 1)
result.append(curr)
print('2', A)
curr -= 1
return result |
class Configurations:
class Settings:
autoclose_squarebrackets= True
autoclose_parentheses= True
autoclose_curlybraces= True
autoclose_doublequotes= True
autoclose_singlequotes= True
current_line_indicator= True
current_line_indicator_symbol= ':'
font_family= "consolas"
font_size= 11
horizontal_scrollbar_show= 'false'
insertion_blink= 300 #or 0
tab_size= 3
text_bottom_lineheight= '6'
text_top_lineheight= '0'
text_wrap= None
border= 0
padding_x= '5'
padding_y= '5'
theme= "data\theme_configs/theme.yaml"
vertical_scrollbar_show= True
web_browser= "chromium"
filename= "quiet_note.txt"
sync_remote= False
#TODO: change key handeling
sync_fb_key={
'apiKey': "",
'authDomain': "",
'databaseURL': "",
'projectId': "",
'storageBucket': "",
'messagingSenderId': "",
'appId': "",
'measurementId': ""
}
class Theme:
comment_color= '#6A737D'
string_color= '#032F62'
number_color= '#005CC5'
type_color= '#005CC5'
keyword_color= '#D73A49'
operator_color= '#D73A49'
bultin_function_color= '#D73A49'
class_self_color= '#24292E'
namespace_color= '#D73A49'
class_name_color= '#E36209'
function_name_color= '#E36209'
font_color= '#24292E'
font_color_linenumbers= '#a4a9aE'
bg_color= '#FFFFFF'
menu_fg_active= '#24292E'
menu_bg_active= '#c1d9e3'
selection_color= '#c1d9e3'
insertion_color = '#eb4034'
s_bg_color = '#eb4034'
s_font_color = '#eb4034'
text_selection_bg_clr = '#eb4034'
troughx_clr = '#a4a9aE'
troughy_clr = '#a4a9aE'
menu_fg = '#000000'
menu_bg = '#E6F0F9'
menubar_bg_active = menu_bg
menubar_fg_active = '#005CC5'
class Patterns:
class _Pattern:
def __init__(self, regex, token, styledict) -> None:
self.regex = regex
self.token = token
self.styledict = styledict
pattern_split = _Pattern(r"(x=.*)",
'Token.Headers',
{'foreground':'#D73A49'})
patternlist = [
_Pattern(r"(\d\d\:\d\d)",
'Token.Time',
{'foreground':'#0055c2'}),
_Pattern(r"(\d\d\.\d\d\.(\d\d(\d\d)?)?)",
'Token.Date',
{'foreground':'#0055c2'}),
_Pattern(r"(==.*)",
'Token.Headers',
{'foreground':'#D73A49'}),
_Pattern(r"(>.*)",
'Token.Fade.Waiting',
{'foreground':'#c0c0c0'}),
_Pattern(r"(<.*)",
'Token.Fade.Next',
{'foreground':'#808080'}),
pattern_split,
_Pattern(r"(\t?x [^\n]*?\n)",
'Token.Done',
{'foreground':'#e0e0e0', 'overstrike':'True'})]
patternmovelist = [
_Pattern(r"(\t?x [^\n]*?\n)",
'Token.Done',
{'foreground':'#e0e0e0', 'overstrike':'True'})]
| class Configurations:
class Settings:
autoclose_squarebrackets = True
autoclose_parentheses = True
autoclose_curlybraces = True
autoclose_doublequotes = True
autoclose_singlequotes = True
current_line_indicator = True
current_line_indicator_symbol = ':'
font_family = 'consolas'
font_size = 11
horizontal_scrollbar_show = 'false'
insertion_blink = 300
tab_size = 3
text_bottom_lineheight = '6'
text_top_lineheight = '0'
text_wrap = None
border = 0
padding_x = '5'
padding_y = '5'
theme = 'data\theme_configs/theme.yaml'
vertical_scrollbar_show = True
web_browser = 'chromium'
filename = 'quiet_note.txt'
sync_remote = False
sync_fb_key = {'apiKey': '', 'authDomain': '', 'databaseURL': '', 'projectId': '', 'storageBucket': '', 'messagingSenderId': '', 'appId': '', 'measurementId': ''}
class Theme:
comment_color = '#6A737D'
string_color = '#032F62'
number_color = '#005CC5'
type_color = '#005CC5'
keyword_color = '#D73A49'
operator_color = '#D73A49'
bultin_function_color = '#D73A49'
class_self_color = '#24292E'
namespace_color = '#D73A49'
class_name_color = '#E36209'
function_name_color = '#E36209'
font_color = '#24292E'
font_color_linenumbers = '#a4a9aE'
bg_color = '#FFFFFF'
menu_fg_active = '#24292E'
menu_bg_active = '#c1d9e3'
selection_color = '#c1d9e3'
insertion_color = '#eb4034'
s_bg_color = '#eb4034'
s_font_color = '#eb4034'
text_selection_bg_clr = '#eb4034'
troughx_clr = '#a4a9aE'
troughy_clr = '#a4a9aE'
menu_fg = '#000000'
menu_bg = '#E6F0F9'
menubar_bg_active = menu_bg
menubar_fg_active = '#005CC5'
class Patterns:
class _Pattern:
def __init__(self, regex, token, styledict) -> None:
self.regex = regex
self.token = token
self.styledict = styledict
pattern_split = __pattern('(x=.*)', 'Token.Headers', {'foreground': '#D73A49'})
patternlist = [__pattern('(\\d\\d\\:\\d\\d)', 'Token.Time', {'foreground': '#0055c2'}), __pattern('(\\d\\d\\.\\d\\d\\.(\\d\\d(\\d\\d)?)?)', 'Token.Date', {'foreground': '#0055c2'}), __pattern('(==.*)', 'Token.Headers', {'foreground': '#D73A49'}), __pattern('(>.*)', 'Token.Fade.Waiting', {'foreground': '#c0c0c0'}), __pattern('(<.*)', 'Token.Fade.Next', {'foreground': '#808080'}), pattern_split, __pattern('(\\t?x [^\\n]*?\\n)', 'Token.Done', {'foreground': '#e0e0e0', 'overstrike': 'True'})]
patternmovelist = [__pattern('(\\t?x [^\\n]*?\\n)', 'Token.Done', {'foreground': '#e0e0e0', 'overstrike': 'True'})] |
'''
Copyright (C) 2020, Sathira Silva.
Approach: Union by path compression algorithm is used. During a single find call or a union call, reattach all the nodes we're following to the root.
Therefore, the height of each subtree will be as minimal as possible.
Before the kruskal algorithm, all the nodes are made their own singleton connected component and the edges are sorted by the weight. The idea is that the
globally minimum weight edge will be the minimum weight edge for all cuts that it crosses. Thus, it is a valid choice of edge for the MST. So, select the
globally minimum weight edge and union the end vertices so that edges that form a cycle won't be selected. Repeat the process until the whole graph becomes
a single connected component.
Time Complexity Analysis: Each Find call will take iterated logarithmic time i.e. O(log*(V)) which is nearly constant time in practice. Since we iterate over the edges
the worst case theoritical time complexity for Union and Find opeerations will be O(Elog(V)). log*(n) is practically bounded by 5. Therefore,
in practice the worst case time complexity is O(E). But still since we have to sort the set of edges O(Elog(V)) will dominate. Thus, the total
time complexity is O(Elog(V)).
'''
def find_set(u, parent):
while u != parent[parent[u]]:
parent[u] = parent[parent[u]]
u = parent[u]
return u
def union(u, v, parent):
parent[find_set(v, parent)] = find_set(u, parent)
def kruskal(n, edges):
parent = [i for i in range(n)]
edges.sort(key = lambda x:x[2])
s = 0
for u, v, w in edges:
if find_set(u, parent) != find_set(v, parent):
union(u, v, parent)
s += w
return str(s)
| """
Copyright (C) 2020, Sathira Silva.
Approach: Union by path compression algorithm is used. During a single find call or a union call, reattach all the nodes we're following to the root.
Therefore, the height of each subtree will be as minimal as possible.
Before the kruskal algorithm, all the nodes are made their own singleton connected component and the edges are sorted by the weight. The idea is that the
globally minimum weight edge will be the minimum weight edge for all cuts that it crosses. Thus, it is a valid choice of edge for the MST. So, select the
globally minimum weight edge and union the end vertices so that edges that form a cycle won't be selected. Repeat the process until the whole graph becomes
a single connected component.
Time Complexity Analysis: Each Find call will take iterated logarithmic time i.e. O(log*(V)) which is nearly constant time in practice. Since we iterate over the edges
the worst case theoritical time complexity for Union and Find opeerations will be O(Elog(V)). log*(n) is practically bounded by 5. Therefore,
in practice the worst case time complexity is O(E). But still since we have to sort the set of edges O(Elog(V)) will dominate. Thus, the total
time complexity is O(Elog(V)).
"""
def find_set(u, parent):
while u != parent[parent[u]]:
parent[u] = parent[parent[u]]
u = parent[u]
return u
def union(u, v, parent):
parent[find_set(v, parent)] = find_set(u, parent)
def kruskal(n, edges):
parent = [i for i in range(n)]
edges.sort(key=lambda x: x[2])
s = 0
for (u, v, w) in edges:
if find_set(u, parent) != find_set(v, parent):
union(u, v, parent)
s += w
return str(s) |
class SensorPack(dict):
''' Fun fact, you can slice using np.s_. E.g.
sensors.at(np.s_[:2])
'''
def at(self, val):
return SensorPack({k: v[val] for k, v in self.items()})
def apply(self, lambda_fn):
return SensorPack({k: lambda_fn(k, v) for k, v in self.items()})
def size(self, idx, key=None):
assert idx == 0, 'can only get batch size for SensorPack'
if key is None:
key = list(self.keys())[0]
return self[key].size(idx)
| class Sensorpack(dict):
""" Fun fact, you can slice using np.s_. E.g.
sensors.at(np.s_[:2])
"""
def at(self, val):
return sensor_pack({k: v[val] for (k, v) in self.items()})
def apply(self, lambda_fn):
return sensor_pack({k: lambda_fn(k, v) for (k, v) in self.items()})
def size(self, idx, key=None):
assert idx == 0, 'can only get batch size for SensorPack'
if key is None:
key = list(self.keys())[0]
return self[key].size(idx) |
s = 1
while s:
a = int(input("Input number a: "))
b = int(input("Input number b: "))
c = input("Choose an operation (+/-/*/:)")
if c == "+":
print(a, c, b, "=", a+b)
elif c == "-":
print(a, c, b, "=", a-b)
elif c == "*":
print(a, c, b, "=", a*b)
elif c == ":":
print(a, c, b, "=", a/b)
else:
print("Undefined operation")
while 1:
r = input("One more loop(y/n)? ")
if r == "y":
break
elif r == "n":
s = 0
break
else:
print("Wrong action")
| s = 1
while s:
a = int(input('Input number a: '))
b = int(input('Input number b: '))
c = input('Choose an operation (+/-/*/:)')
if c == '+':
print(a, c, b, '=', a + b)
elif c == '-':
print(a, c, b, '=', a - b)
elif c == '*':
print(a, c, b, '=', a * b)
elif c == ':':
print(a, c, b, '=', a / b)
else:
print('Undefined operation')
while 1:
r = input('One more loop(y/n)? ')
if r == 'y':
break
elif r == 'n':
s = 0
break
else:
print('Wrong action') |
def menor_a_mayor(lista):
return sorted(lista)
def desordenar(lista):
return menor_a_mayor(lista)
| def menor_a_mayor(lista):
return sorted(lista)
def desordenar(lista):
return menor_a_mayor(lista) |
"""
Description
Insertion sort involves finding the right place for a given element in a sorted list. So in beginning we compare the first
two elements and sort them by comparing them. Then we pick the third element and find its proper position among the previous
two sorted elements. This way we gradually go on adding more elements to the already sorted list by putting them in their
proper position.
"""
def insertion_sort(InputList):
for i in range(1, len(InputList)):
j = i-1
nxt_element = InputList[i]
while (InputList[j] > nxt_element) and (j >= 0):
InputList[j+1] = InputList[j]
j=j-1
InputList[j+1] = nxt_element
list = [3,6,2,6,7,9,10,11,45,67,43]
insertion_sort(list)
print(list) | """
Description
Insertion sort involves finding the right place for a given element in a sorted list. So in beginning we compare the first
two elements and sort them by comparing them. Then we pick the third element and find its proper position among the previous
two sorted elements. This way we gradually go on adding more elements to the already sorted list by putting them in their
proper position.
"""
def insertion_sort(InputList):
for i in range(1, len(InputList)):
j = i - 1
nxt_element = InputList[i]
while InputList[j] > nxt_element and j >= 0:
InputList[j + 1] = InputList[j]
j = j - 1
InputList[j + 1] = nxt_element
list = [3, 6, 2, 6, 7, 9, 10, 11, 45, 67, 43]
insertion_sort(list)
print(list) |
class QuidMustLoginException(Exception):
"""
The refresh token is invalid or expired
"""
class QuidUnauthorizedException(Exception):
"""
The server responded with unauthorized 401 status code
"""
class QuidTooManyRetriesException(Exception):
"""
Request retried too many times
"""
| class Quidmustloginexception(Exception):
"""
The refresh token is invalid or expired
"""
class Quidunauthorizedexception(Exception):
"""
The server responded with unauthorized 401 status code
"""
class Quidtoomanyretriesexception(Exception):
"""
Request retried too many times
""" |
"""
Application constants
"""
class LabelVariants:
"""Valid values for the `variant` field of a label_definition"""
NUMERICAL = "numerical"
BOOLEAN = "boolean"
valid_labels = (NUMERICAL, BOOLEAN)
| """
Application constants
"""
class Labelvariants:
"""Valid values for the `variant` field of a label_definition"""
numerical = 'numerical'
boolean = 'boolean'
valid_labels = (NUMERICAL, BOOLEAN) |
b11, b12, b13, b14, b15, b16, b17, b18, b19, b21, b22, b23, b24, b25, b26, b27, b28, b29, b31, b32, b33, b34, b35, b36, b37, b38, b39, b41, b42, b43, b44, b45, b46, b47, b48, b49, b51, b52, b53, b54, b55, b56, b57, b58, b59, b61, b62, b63, b64, b65, b66, b67, b68, b69, b71, b72, b73, b74, b75, b76, b77, b78, b79, b81, b82, b83, b84, b85, b86, b87, b88, b89, b91, b92, b93, b94, b95, b96, b97, b98, b99=' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' '
def board():
board = """
```
THE BOARD
-------------------------------------
|{0}|{1}|{2}|{9}|{10}|{11}|{18}|{19}|{20}|
|---+---+---|---+---+---|---+---+---|
|{3}|{4}|{5}|{12}|{13}|{14}|{21}|{22}|{23}|
|---+---+---|---+---+---|---+---+---|
|{6}|{7}|{8}|{15}|{16}|{17}|{24}|{25}|{26}|
|-----------+-----------+-----------|
|{27}|{28}|{29}|{36}|{37}|{38}|{45}|{46}|{47}|
|---+---+---|---+---+---|---+---+---|
|{30}|{31}|{32}|{39}|{40}|{41}|{48}|{49}|{50}|
|---+---+---|---+---+---|---+---+---|
|{33}|{34}|{35}|{42}|{43}|{44}|{51}|{52}|{53}|
|-----------+-----------+-----------|
|{54}|{55}|{56}|{63}|{64}|{65}|{72}|{73}|{74}|
|---+---+---|---+---+---|---+---+---|
|{57}|{58}|{59}|{66}|{67}|{68}|{75}|{76}|{77}|
|---+---+---|---+---+---|---+---+---|
|{60}|{61}|{62}|{69}|{70}|{71}|{78}|{79}|{80}|
-------------------------------------
```""".format(b11, b12, b13, b14, b15, b16, b17, b18, b19, b21, b22, b23, b24, b25, b26, b27, b28, b29, b31, b32, b33, b34, b35, b36, b37, b38, b39, b41, b42, b43, b44, b45, b46, b47, b48, b49, b51, b52, b53, b54, b55, b56, b57, b58, b59, b61, b62, b63, b64, b65, b66, b67, b68, b69, b71, b72, b73, b74, b75, b76, b77, b78, b79, b81, b82, b83, b84, b85, b86, b87, b88, b89, b91, b92, b93, b94, b95, b96, b97, b98, b99)
return board
| (b11, b12, b13, b14, b15, b16, b17, b18, b19, b21, b22, b23, b24, b25, b26, b27, b28, b29, b31, b32, b33, b34, b35, b36, b37, b38, b39, b41, b42, b43, b44, b45, b46, b47, b48, b49, b51, b52, b53, b54, b55, b56, b57, b58, b59, b61, b62, b63, b64, b65, b66, b67, b68, b69, b71, b72, b73, b74, b75, b76, b77, b78, b79, b81, b82, b83, b84, b85, b86, b87, b88, b89, b91, b92, b93, b94, b95, b96, b97, b98, b99) = (' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ')
def board():
board = '\n```\n THE BOARD\n-------------------------------------\n|{0}|{1}|{2}|{9}|{10}|{11}|{18}|{19}|{20}|\n|---+---+---|---+---+---|---+---+---|\n|{3}|{4}|{5}|{12}|{13}|{14}|{21}|{22}|{23}|\n|---+---+---|---+---+---|---+---+---|\n|{6}|{7}|{8}|{15}|{16}|{17}|{24}|{25}|{26}|\n|-----------+-----------+-----------|\n|{27}|{28}|{29}|{36}|{37}|{38}|{45}|{46}|{47}|\n|---+---+---|---+---+---|---+---+---|\n|{30}|{31}|{32}|{39}|{40}|{41}|{48}|{49}|{50}|\n|---+---+---|---+---+---|---+---+---|\n|{33}|{34}|{35}|{42}|{43}|{44}|{51}|{52}|{53}|\n|-----------+-----------+-----------|\n|{54}|{55}|{56}|{63}|{64}|{65}|{72}|{73}|{74}|\n|---+---+---|---+---+---|---+---+---|\n|{57}|{58}|{59}|{66}|{67}|{68}|{75}|{76}|{77}|\n|---+---+---|---+---+---|---+---+---|\n|{60}|{61}|{62}|{69}|{70}|{71}|{78}|{79}|{80}|\n-------------------------------------\n```'.format(b11, b12, b13, b14, b15, b16, b17, b18, b19, b21, b22, b23, b24, b25, b26, b27, b28, b29, b31, b32, b33, b34, b35, b36, b37, b38, b39, b41, b42, b43, b44, b45, b46, b47, b48, b49, b51, b52, b53, b54, b55, b56, b57, b58, b59, b61, b62, b63, b64, b65, b66, b67, b68, b69, b71, b72, b73, b74, b75, b76, b77, b78, b79, b81, b82, b83, b84, b85, b86, b87, b88, b89, b91, b92, b93, b94, b95, b96, b97, b98, b99)
return board |
# -*- coding: UTF-8 -*-
class DataTypes:
def __init__(self):
self._dtypes = {
0x00: '<{0}B', # 08-bit unsigned char
0x01: '<{0}b', # 08-bit signed char
0x02: '<{0}H', # 16-bit unsigned short
0x03: '<{0}h', # 16-bit signed short
0x04: '<{0}I', # 32-bit unsigned integer
0x05: '<{0}i', # 32-bit signed integer
0x06: '<{0}f', # 32-bit float
0x07: '<{0}d'} # 64-bit double
def __contains__(self, item):
return item in self._dtypes
def lookup(self, dtype):
return self._dtypes[dtype]
| class Datatypes:
def __init__(self):
self._dtypes = {0: '<{0}B', 1: '<{0}b', 2: '<{0}H', 3: '<{0}h', 4: '<{0}I', 5: '<{0}i', 6: '<{0}f', 7: '<{0}d'}
def __contains__(self, item):
return item in self._dtypes
def lookup(self, dtype):
return self._dtypes[dtype] |
#pirate game
print('Welcome To Sharks And Pirates')
print('This Game Is Licensed Under The MIT Creator License')
print('If You Want The Source Code Please Vist anmoymouse89076 On Github')
#gettings the name
greeting = input('Hello, Possible Pirate! What Is The Password')
if greeting in ('Arrr!'):
print('Go Away Annoying Pirate')
else:
print('Welcome, Pirate Hater!')
name = input('Sorry, My Memory Is Like A Godlfish!, I Cant Remember Anything!, Anyway Whats Your Name!')
print('Cool Name!', name)
print('Alrigth', name, 'Lets Get Going, We Dont Want To Be Late')
piratehead = input('Watch Out, There Is Pirates Ahead, What Do We Do? NO. 1 Do you Try To Avoid Them, NO. 2 Do You Go Up To Them And Tell Them How Much You Hate Them')
if piratehead in ('1'):
print('Lets Go! This Way')
else:
print('WARNING! If We Do This They Could Capture Us')
#if answer = 2
print('I Told You!')
print('Aaaaaa, Their Chasing Us!')
do = input('What Do We Do')
if do in ('go back'):
print('Alight, Thats Probally Safer')
print('This Way!')
if do in('fight them'):
print('hmmm.... Okay, I mean i dont know')
usure = input('Are You Sure You Want To Goahead', name)
if usure in ('yes'):
print('Okay Dont, Say I Didnt Warn You!!')
print('Dont Forget, They Probally Have Weapons')
print('aaaaaaa!')
print('They Cutted My Hand, Im Going!')
print('Bye, Bye!')
if usure in ('no'):
print('Lets Go Back To Option One')
piratehead = input('Watch Out, There Is Pirates Ahead, What Do We Do? NO. 1 Do you Try To Avoid Them, NO. 2 Dont Do Anythin And Go Back?')
if piratehead in ('1'):
print('Alright, GET LOW!!')
print('look There Is A Bush')
print('Lets Hide Behind It')
print('Ohhh, No!')
print('They Found Us!')
print('Lets Run')
ishark = input('Is That A Shark I See?')
if ishark in ('yes'):
print('Lets Try To Avoid It')
if ishark in ('no'):
print('Yeah, It Probally Just My Imagination')
print('Ohhh, No!')
print('It Was A Shark And It Got Me!')
print('Bye, Bye')
print('THANKS FOR PLAYING SHARKS AND PIRATES')
| print('Welcome To Sharks And Pirates')
print('This Game Is Licensed Under The MIT Creator License')
print('If You Want The Source Code Please Vist anmoymouse89076 On Github')
greeting = input('Hello, Possible Pirate! What Is The Password')
if greeting in 'Arrr!':
print('Go Away Annoying Pirate')
else:
print('Welcome, Pirate Hater!')
name = input('Sorry, My Memory Is Like A Godlfish!, I Cant Remember Anything!, Anyway Whats Your Name!')
print('Cool Name!', name)
print('Alrigth', name, 'Lets Get Going, We Dont Want To Be Late')
piratehead = input('Watch Out, There Is Pirates Ahead, What Do We Do? NO. 1 Do you Try To Avoid Them, NO. 2 Do You Go Up To Them And Tell Them How Much You Hate Them')
if piratehead in '1':
print('Lets Go! This Way')
else:
print('WARNING! If We Do This They Could Capture Us')
print('I Told You!')
print('Aaaaaa, Their Chasing Us!')
do = input('What Do We Do')
if do in 'go back':
print('Alight, Thats Probally Safer')
print('This Way!')
if do in 'fight them':
print('hmmm.... Okay, I mean i dont know')
usure = input('Are You Sure You Want To Goahead', name)
if usure in 'yes':
print('Okay Dont, Say I Didnt Warn You!!')
print('Dont Forget, They Probally Have Weapons')
print('aaaaaaa!')
print('They Cutted My Hand, Im Going!')
print('Bye, Bye!')
if usure in 'no':
print('Lets Go Back To Option One')
piratehead = input('Watch Out, There Is Pirates Ahead, What Do We Do? NO. 1 Do you Try To Avoid Them, NO. 2 Dont Do Anythin And Go Back?')
if piratehead in '1':
print('Alright, GET LOW!!')
print('look There Is A Bush')
print('Lets Hide Behind It')
print('Ohhh, No!')
print('They Found Us!')
print('Lets Run')
ishark = input('Is That A Shark I See?')
if ishark in 'yes':
print('Lets Try To Avoid It')
if ishark in 'no':
print('Yeah, It Probally Just My Imagination')
print('Ohhh, No!')
print('It Was A Shark And It Got Me!')
print('Bye, Bye')
print('THANKS FOR PLAYING SHARKS AND PIRATES') |
"""
Given two sorted integer arrays nums1 and nums2, merge nums2 into nums1 as one sorted array.
Note:
The number of elements initialized in nums1 and nums2 are m and n respectively.
You may assume that nums1 has enough space (size that is greater or equal to m + n) to hold additional elements from nums2.
Example:
Input:
nums1 = [1,2,3,0,0,0], m = 3
nums2 = [2,5,6], n = 3
Output: [1,2,2,3,5,6]
"""
class Solution1:
def merge(self, nums1, m, nums2, n):
"""
:type nums1: List[int]
:type m: int
:type nums2: List[int]
:type n: int
:rtype: void Do not return anything, modify nums1 in-place instead.
"""
i, j = m - 1, n - 1
while i >= 0 and j >= 0:
if nums1[i] > nums2[j]:
nums1[i + j + 1] = nums1[i]
i -= 1
else:
nums1[i + j + 1] = nums2[j]
j -= 1
if j >= 0:
nums1[:j + 1] = nums2[:j + 1]
| """
Given two sorted integer arrays nums1 and nums2, merge nums2 into nums1 as one sorted array.
Note:
The number of elements initialized in nums1 and nums2 are m and n respectively.
You may assume that nums1 has enough space (size that is greater or equal to m + n) to hold additional elements from nums2.
Example:
Input:
nums1 = [1,2,3,0,0,0], m = 3
nums2 = [2,5,6], n = 3
Output: [1,2,2,3,5,6]
"""
class Solution1:
def merge(self, nums1, m, nums2, n):
"""
:type nums1: List[int]
:type m: int
:type nums2: List[int]
:type n: int
:rtype: void Do not return anything, modify nums1 in-place instead.
"""
(i, j) = (m - 1, n - 1)
while i >= 0 and j >= 0:
if nums1[i] > nums2[j]:
nums1[i + j + 1] = nums1[i]
i -= 1
else:
nums1[i + j + 1] = nums2[j]
j -= 1
if j >= 0:
nums1[:j + 1] = nums2[:j + 1] |
join_date = '19.5.21'
time_join = '16:00'
name = 'EJ Studios#3379'
exp = 0
next_level_up = 100
level = 0
health = 100
gold = 0
healthpotion = 1
food = 0
wood = 0
stone = 0
iron = 0
active_weapon = 'WOODEN_SWORD'
wood_owned = True
wood_durability = 100
wood_attack = 10
stone_owned = False
stone_durability = 100
stone_attack = 50
iron_owned = False
iron_durability = 100
iron_attack = 100
BAN = False
| join_date = '19.5.21'
time_join = '16:00'
name = 'EJ Studios#3379'
exp = 0
next_level_up = 100
level = 0
health = 100
gold = 0
healthpotion = 1
food = 0
wood = 0
stone = 0
iron = 0
active_weapon = 'WOODEN_SWORD'
wood_owned = True
wood_durability = 100
wood_attack = 10
stone_owned = False
stone_durability = 100
stone_attack = 50
iron_owned = False
iron_durability = 100
iron_attack = 100
ban = False |
class ListUtility:
def list_formating(data):
data_split = data.split()
removers = ["\n", "\r", "\r\n", "\n\r", "\t"]
data_remove = [i for i in data_split if
i not in removers]
data_fixed = ' '.join(data_remove)
return data_fixed.split(".")
def clear_list():
empty_list = []
return empty_list.clear()
| class Listutility:
def list_formating(data):
data_split = data.split()
removers = ['\n', '\r', '\r\n', '\n\r', '\t']
data_remove = [i for i in data_split if i not in removers]
data_fixed = ' '.join(data_remove)
return data_fixed.split('.')
def clear_list():
empty_list = []
return empty_list.clear() |
class Solution:
def correctBinaryTree(self, root: TreeNode) -> TreeNode:
q = deque([root])
seen = set()
while q:
node = q.popleft()
if node.right:
if node.right.right in seen:
node.right = None
return root
q.append(node.right)
seen.add(node.right)
if node.left:
if node.left.right in seen:
node.left = None
return root
q.append(node.left)
seen.add(node.left)
| class Solution:
def correct_binary_tree(self, root: TreeNode) -> TreeNode:
q = deque([root])
seen = set()
while q:
node = q.popleft()
if node.right:
if node.right.right in seen:
node.right = None
return root
q.append(node.right)
seen.add(node.right)
if node.left:
if node.left.right in seen:
node.left = None
return root
q.append(node.left)
seen.add(node.left) |
# Global Variables
P1 = "x" # token for player 1
P2 = "o" # token for player 2
BL = "0" # token for BL/blank space
def isThereAWinInAnyRow(a):
# checking for a wins
if a[0] == P1 and a[1] == P1 and a[2] == P1:
return True
# checking for o wins
if a[0] == P2 and a[1] == P2 and a[2] == P2:
return True
# checking for a wins row 2
if a[3] == P1 and a[4] == P1 and a[5] == P1:
return True
# checking for o wins row 2
if a[3] == P2 and a[4] == P2 and a[5] == P2:
return True
# checking for a wins row 3
if a[6] == P1 and a[7] == P1 and a[8] == P1:
return True
# checking for o wins row 3
if a[6] == P2 and a[7] == P2 and a[8] == P2:
return True
# no winner
return False
def isThereAWinInAnyColumn(a):
# checking for a wins
if a[0] == P1 and a[3] == P1 and a[6] == P1:
return True
# checking for o wins
if a[0] == P2 and a[3] == P2 and a[6] == P2:
return True
# checking for a wins column 2
if a[1] == P1 and a[4] == P1 and a[7] == P1:
return True
# checking for o wins column 2
if a[1] == P2 and a[4] == P2 and a[7] == P2:
return True
# checking for a wins column 3
if a[2] == P1 and a[5] == P1 and a[8] == P1:
return True
# checking for o wins column 3
if a[2] == P2 and a[5] == P2 and a[8] == P2:
return True
# no winner
return False
def isThereAWinInAnyColumn2(a):
# checking for x wins
for i in (0, 3, 6):
if x[i] == BL:
return False
elif x[i] == P1 and x[i - 3] != P1:
return False
# checking for o wins
for i in (0, 3, 6):
if x[i] == BL:
return False
elif x[i] == P2 and x[i - 3] != P2:
return False
def isThereAWinInAnyDiagonal(a):
# checking for a wins
if a[0] == P1 and a[4] == P1 and a[8] == P1:
return True
if a[0] == P2 and a[4] == P2 and a[8] == P2:
return True
# checking for o wins
if a[2] == P1 and a[4] == P1 and a[6] == P1:
return True
if a[2] == P2 and a[4] == P2 and a[6] == P2:
return True
# no winner
return False
def isThereADraw(a):
# checking for BL spaces
if a[0] == BL or a[1] == BL or a[2] == BL or a[3] == BL or a[4] == BL or a[5] == BL or a[6] == BL or a[7] == BL or a[8] == BL:
return False
# BL spaces
return True
def isValidGameState(a):
for i in range (len(a)):
if (len(a)) == 10:
return True
def isThereAWinInAnyRow2(a):
# checking for a wins
for i in range(3, 6):
if a[i] == BL:
return False
elif a[i] == P1 and a[i + 1] != P1:
return False
# checking for o wins
for i in range(3, 6):
if a[i] == BL:
return False
elif a[i] == P2 and a[i + 1] != P2:
return False
# is a winner
return True
def isGameOver(a):
product = 1
for i in range(9):
product *= a[i]
if product == 0:
return False
return True
| p1 = 'x'
p2 = 'o'
bl = '0'
def is_there_a_win_in_any_row(a):
if a[0] == P1 and a[1] == P1 and (a[2] == P1):
return True
if a[0] == P2 and a[1] == P2 and (a[2] == P2):
return True
if a[3] == P1 and a[4] == P1 and (a[5] == P1):
return True
if a[3] == P2 and a[4] == P2 and (a[5] == P2):
return True
if a[6] == P1 and a[7] == P1 and (a[8] == P1):
return True
if a[6] == P2 and a[7] == P2 and (a[8] == P2):
return True
return False
def is_there_a_win_in_any_column(a):
if a[0] == P1 and a[3] == P1 and (a[6] == P1):
return True
if a[0] == P2 and a[3] == P2 and (a[6] == P2):
return True
if a[1] == P1 and a[4] == P1 and (a[7] == P1):
return True
if a[1] == P2 and a[4] == P2 and (a[7] == P2):
return True
if a[2] == P1 and a[5] == P1 and (a[8] == P1):
return True
if a[2] == P2 and a[5] == P2 and (a[8] == P2):
return True
return False
def is_there_a_win_in_any_column2(a):
for i in (0, 3, 6):
if x[i] == BL:
return False
elif x[i] == P1 and x[i - 3] != P1:
return False
for i in (0, 3, 6):
if x[i] == BL:
return False
elif x[i] == P2 and x[i - 3] != P2:
return False
def is_there_a_win_in_any_diagonal(a):
if a[0] == P1 and a[4] == P1 and (a[8] == P1):
return True
if a[0] == P2 and a[4] == P2 and (a[8] == P2):
return True
if a[2] == P1 and a[4] == P1 and (a[6] == P1):
return True
if a[2] == P2 and a[4] == P2 and (a[6] == P2):
return True
return False
def is_there_a_draw(a):
if a[0] == BL or a[1] == BL or a[2] == BL or (a[3] == BL) or (a[4] == BL) or (a[5] == BL) or (a[6] == BL) or (a[7] == BL) or (a[8] == BL):
return False
return True
def is_valid_game_state(a):
for i in range(len(a)):
if len(a) == 10:
return True
def is_there_a_win_in_any_row2(a):
for i in range(3, 6):
if a[i] == BL:
return False
elif a[i] == P1 and a[i + 1] != P1:
return False
for i in range(3, 6):
if a[i] == BL:
return False
elif a[i] == P2 and a[i + 1] != P2:
return False
return True
def is_game_over(a):
product = 1
for i in range(9):
product *= a[i]
if product == 0:
return False
return True |
__version__ = '0.9.1'
__version_name__ = 'Morino Kirin-chan'
def version_string():
return '%(prog)s version %(version)s "{}"'.format(__version_name__)
| __version__ = '0.9.1'
__version_name__ = 'Morino Kirin-chan'
def version_string():
return '%(prog)s version %(version)s "{}"'.format(__version_name__) |
def find_duplicates(arr1, arr2):
"""
differing sizes
>= ints
sorted
unique ints in each arr
arr1 = [1, 2, 3, 5, 6, 7], arr2 = [3, 6, 7, 8, 20]
# Brute force -
- iterative
- quadratic
# Idea - Sets
{1, 2, 3, 5, 6, 7}
[3]
{3, 6, 7} = [3, 6, 7]
S = smaller arraty
L = larger
Space = L
Time = S + L
Idea - Searching
find smaller array - constant
iterate over the smaller one - S iterations
binary search for it in the larger one - log L
if so, add to the intersections
Time: O(S log L)
Space: O(S)
----------------------
constant space
linear time
same length
sesarch
arr1 = [1, 2, 3, 5, 6, 7],
arr2 = [3, 6, 7, 8, 20, 34]
ans = [3]
v
arr1 = [1, 2, 3, 8, 10, 11],
v
arr2 = [3, 6, 7, 8, 20, 34]
<---- 1 -3---------- 7 -->
3-------------------------34
"""
# find smaller array - constant
smaller = None
larger = None
if len(arr1) <= len(arr2):
smaller = arr1
larger = arr2
else:
smaller = arr2
larger = arr1
answer = list()
# init two pointers in both arrays
ps = 0
pl = 0
while ps < len(smaller) and pl < len(larger):
# compare
if smaller[ps] == larger[pl]:
answer.append(smaller[ps])
ps += 1
pl += 1
elif smaller[ps] > larger[pl]:
pl += 1
else:
ps += 1
return answer
"""
def binary_search(target, lo, hi, array):
# calculate the middle index and element
mid = (lo + hi) // 2
middle_elem = array[mid]
# compare it to the target
if target == middle_elem:
# ==, return index
return mid
# !=, last index --> -1
elif lo >= hi:
return -1
# >, search the lower half
elif target > middle_elem:
return binary_search(target, mid + 1, hi, array)
# <, search the greater half
else: # target > middle_elem
return binary_search(target, lo, mid - 1, array)
# find smaller array - constant
smaller = None
larger = None
if len(arr1) <= len(arr2):
smaller = arr1
larger = arr2
else:
smaller = arr2
larger = arr1
# iterate over the smaller one - S iterations
answer = list()
for elem in smaller:
# binary search for it in the larger one - log L
larger_index = binary_search(elem, 0, len(larger) - 1, larger)
# if so, add to the intersections
if larger_index > -1:
answer.append(elem)
return answer
"""
"""
Test Case 1
0. 1. 2. 3. 4. 5
l = arr1 = [1, 2, 3, 5, 6, 7],
l h
m
s = arr2 = [3, 6, 7, 8, 20]
ans = [3, 6, 7]
elem = 3
"""
| def find_duplicates(arr1, arr2):
"""
differing sizes
>= ints
sorted
unique ints in each arr
arr1 = [1, 2, 3, 5, 6, 7], arr2 = [3, 6, 7, 8, 20]
# Brute force -
- iterative
- quadratic
# Idea - Sets
{1, 2, 3, 5, 6, 7}
[3]
{3, 6, 7} = [3, 6, 7]
S = smaller arraty
L = larger
Space = L
Time = S + L
Idea - Searching
find smaller array - constant
iterate over the smaller one - S iterations
binary search for it in the larger one - log L
if so, add to the intersections
Time: O(S log L)
Space: O(S)
----------------------
constant space
linear time
same length
sesarch
arr1 = [1, 2, 3, 5, 6, 7],
arr2 = [3, 6, 7, 8, 20, 34]
ans = [3]
v
arr1 = [1, 2, 3, 8, 10, 11],
v
arr2 = [3, 6, 7, 8, 20, 34]
<---- 1 -3---------- 7 -->
3-------------------------34
"""
smaller = None
larger = None
if len(arr1) <= len(arr2):
smaller = arr1
larger = arr2
else:
smaller = arr2
larger = arr1
answer = list()
ps = 0
pl = 0
while ps < len(smaller) and pl < len(larger):
if smaller[ps] == larger[pl]:
answer.append(smaller[ps])
ps += 1
pl += 1
elif smaller[ps] > larger[pl]:
pl += 1
else:
ps += 1
return answer
'\n def binary_search(target, lo, hi, array):\n # calculate the middle index and element\n mid = (lo + hi) // 2\n middle_elem = array[mid]\n # compare it to the target \n if target == middle_elem:\n # ==, return index\n return mid\n # !=, last index --> -1\n elif lo >= hi:\n return -1\n # >, search the lower half\n elif target > middle_elem:\n return binary_search(target, mid + 1, hi, array)\n # <, search the greater half\n else: # target > middle_elem\n return binary_search(target, lo, mid - 1, array)\n # find smaller array - constant\n smaller = None\n larger = None\n if len(arr1) <= len(arr2):\n smaller = arr1\n larger = arr2\n else:\n smaller = arr2\n larger = arr1\n # iterate over the smaller one - S iterations\n answer = list()\n for elem in smaller:\n # binary search for it in the larger one - log L\n larger_index = binary_search(elem, 0, len(larger) - 1, larger)\n # if so, add to the intersections\n if larger_index > -1:\n answer.append(elem)\n return answer\n '
'\n Test Case 1\n 0. 1. 2. 3. 4. 5 \n l = arr1 = [1, 2, 3, 5, 6, 7], \n l h\n m\n s = arr2 = [3, 6, 7, 8, 20]\n\n ans = [3, 6, 7]\n\n elem = 3\n ' |
# !/usr/bin/env python
# coding: utf-8
'''
Description:
Given a sorted linked list, delete all duplicates such that each element appear only once.
For example,
Given 1->1->2, return 1->2.
Given 1->1->2->3->3, return 1->2->3.
Tags: Linked List
'''
# Definition for singly-linked list.
# class ListNode(object):
# def __init__(self, x):
# self.val = x
# self.next = None
class Solution(object):
def deleteDuplicates(self, head):
"""
:type head: ListNode
:rtype: ListNode
"""
p = head
while p:
while p.next and p.val == p.next.val:
p.next = p.next.next
p = p.next
return head
| """
Description:
Given a sorted linked list, delete all duplicates such that each element appear only once.
For example,
Given 1->1->2, return 1->2.
Given 1->1->2->3->3, return 1->2->3.
Tags: Linked List
"""
class Solution(object):
def delete_duplicates(self, head):
"""
:type head: ListNode
:rtype: ListNode
"""
p = head
while p:
while p.next and p.val == p.next.val:
p.next = p.next.next
p = p.next
return head |
###############################################################################
# Author: Jayden Lee
# Date: 27/06/19
# Purpose: Simple list concatecation and looping example.
###############################################################################
maxPosition = int(input("Please enter the size of the list: "))
theList = []
for loopPostion in range(maxPosition):
name = input("Please enter the " + str(loopPostion)
+ "nth value for the list: ")
theList = theList + [name]
del theList[maxPosition-1]
theList.sort()
theList.append("OwO")
print("The Stored List is: ")
for i in range(maxPosition):
print(" " + theList[i])
| max_position = int(input('Please enter the size of the list: '))
the_list = []
for loop_postion in range(maxPosition):
name = input('Please enter the ' + str(loopPostion) + 'nth value for the list: ')
the_list = theList + [name]
del theList[maxPosition - 1]
theList.sort()
theList.append('OwO')
print('The Stored List is: ')
for i in range(maxPosition):
print(' ' + theList[i]) |
class Solution:
def maxDepth(self, root):
if not root:
return 0
height = 0
for node in root.children:
height = max(self.maxDepth(node), height)
return height + 1
'''
Success
Details
Runtime: 52 ms, faster than 30.42% of Python3
Memory Usage: 15.9 MB, less than 5.09% of Python3
Next challenges:
Maximum Depth of Binary Tree
'''
| class Solution:
def max_depth(self, root):
if not root:
return 0
height = 0
for node in root.children:
height = max(self.maxDepth(node), height)
return height + 1
'\nSuccess\nDetails\nRuntime: 52 ms, faster than 30.42% of Python3\nMemory Usage: 15.9 MB, less than 5.09% of Python3\nNext challenges:\nMaximum Depth of Binary Tree\n' |
#
# @lc app=leetcode.cn id=413 lang=python3
#
# [413] arithmetic-slices
#
None
# @lc code=end | None |
#!/usr/bin/env python3
def mulf(*vars):
"""Returns vars[0] * ...
>>> mulf(1,2,3,4)
24
>>> mulf(1,2,3,4, "Z")
'ZZZZZZZZZZZZZZZZZZZZZZZZ'
"""
v = 1
for i in vars:
v *= i
return v
| def mulf(*vars):
"""Returns vars[0] * ...
>>> mulf(1,2,3,4)
24
>>> mulf(1,2,3,4, "Z")
'ZZZZZZZZZZZZZZZZZZZZZZZZ'
"""
v = 1
for i in vars:
v *= i
return v |
ACTIVE_ALARMS_HEADER = 'ACTIVE ALARMS'
ACTIVE_PROFILES_HEADER = 'ACTIVE PROFILES'
AGENTS_HEADER = 'AGENTS'
DASHBOARD_HEADER = 'DASHBOARD'
LAST_INACTIVE_ALARMS = 'Last 20 Inactive Alarms'
NO_ACTIVE_ALARMS_MSG = 'No active alarms at this time.'
NO_PROFILES_CONFIGURED_MSG = 'No Profiles Configured'
SETUP_PROFILE_HEADER = 'Setup A Profile'
| active_alarms_header = 'ACTIVE ALARMS'
active_profiles_header = 'ACTIVE PROFILES'
agents_header = 'AGENTS'
dashboard_header = 'DASHBOARD'
last_inactive_alarms = 'Last 20 Inactive Alarms'
no_active_alarms_msg = 'No active alarms at this time.'
no_profiles_configured_msg = 'No Profiles Configured'
setup_profile_header = 'Setup A Profile' |
"""
# copy of taiwanReliefData inside armor module
# to create the taiwan coastlines etc for display given a particular grid size
# use:
kwargs = {'files' :['100','1000','2000','3000', 'Coast'],
'width' : 921,
'height' : 881,
'lowerLeft' : (115, 18),
'upperRight' : (126.5, 29),
'folder' : '',
'suffix' : ".DAT",
}
kwargs = {'files' :['100','1000','2000','3000', 'Coast'],
'width' : 921,
'height' : 881,
'lowerLeft' : (115, 18),
'upperRight' : (126.5, 29),
'folder' : '',
'suffix' : ".DAT",
}
from armor.taiwanReliefData import convertToGrid
convertToGrid.main(**kwargs)
"""
| """
# copy of taiwanReliefData inside armor module
# to create the taiwan coastlines etc for display given a particular grid size
# use:
kwargs = {'files' :['100','1000','2000','3000', 'Coast'],
'width' : 921,
'height' : 881,
'lowerLeft' : (115, 18),
'upperRight' : (126.5, 29),
'folder' : '',
'suffix' : ".DAT",
}
kwargs = {'files' :['100','1000','2000','3000', 'Coast'],
'width' : 921,
'height' : 881,
'lowerLeft' : (115, 18),
'upperRight' : (126.5, 29),
'folder' : '',
'suffix' : ".DAT",
}
from armor.taiwanReliefData import convertToGrid
convertToGrid.main(**kwargs)
""" |
print('state dummy{')
parser = open('a_chains.p4').read()
for line in open('a_hdrlist.p4'):
line = line.strip()
if line == '':
continue
handle = line.split()[1].strip(';')
if handle in parser:
continue
print('\tpkt.extract(hdr.{});'.format(handle))
print('\ttransition accept;')
print('}')
| print('state dummy{')
parser = open('a_chains.p4').read()
for line in open('a_hdrlist.p4'):
line = line.strip()
if line == '':
continue
handle = line.split()[1].strip(';')
if handle in parser:
continue
print('\tpkt.extract(hdr.{});'.format(handle))
print('\ttransition accept;')
print('}') |
#defining a dictionary data structure
example_dict = { #key : value of element
"class" : "Astr 119",
"prof" : "Brant",
"awesomeness" : 10
}
print(type(example_dict)) #says "dict"
course = example_dict["class"] #to get a value via the key above
print(course)
example_dict["awesomeness"] += 1 #to change a value in key (increases)
print(example_dict)
for x in example_dict.keys():
print(x,example_dict[x]) #prints dictionary key and element value | example_dict = {'class': 'Astr 119', 'prof': 'Brant', 'awesomeness': 10}
print(type(example_dict))
course = example_dict['class']
print(course)
example_dict['awesomeness'] += 1
print(example_dict)
for x in example_dict.keys():
print(x, example_dict[x]) |
def lucky_sum(a, b, c):
retSum = 0
for i in [a, b, c]:
if i == 13:
break
retSum += i
return retSum
| def lucky_sum(a, b, c):
ret_sum = 0
for i in [a, b, c]:
if i == 13:
break
ret_sum += i
return retSum |
def find_combinations(puzzle_input):
unnecessary_numbers = []
index = 1
while index < len(puzzle_input) - 1:
if puzzle_input[index+1] - puzzle_input[index-1] <= 3:
unnecessary_numbers.append(puzzle_input[index])
del puzzle_input[index]
else:
index += 1
print(unnecessary_numbers)
print(2 ** len(unnecessary_numbers))
with open("input.txt", "r") as puzzle_input:
puzzle_input = [int(line.strip()) for line in puzzle_input]
puzzle_input.sort()
find_combinations(puzzle_input)
"""
1, 4, 5, 6, 7, 10, 11, 12, 15, 16, 19 (full)
1, 4, 5, 6, 7, 10, 12, 15, 16, 19 (remove 11)
1, 4, 5, 7, 10, 12, 15, 16, 19 (remove 6)
1, 4, 6, 7, 10, 12, 15, 16, 19 (remove 5)
1, 4, 7, 10, 12, 15, 16, 19 (remove 6)
1, 4, 5, 7, 10, 11, 12, 15, 16, 19 (remove 6)
1, 4, 7, 10, 11, 12, 15, 16, 19 (remove 5)
1, 4, 6, 7, 10, 11, 12, 15, 16, 19 (remove 5)
"""
"""
1, 4, 5, 6, 7, 10, 11, 12, 15, 16, 19
Unnecessary = 5, 6, 11,
"""
| def find_combinations(puzzle_input):
unnecessary_numbers = []
index = 1
while index < len(puzzle_input) - 1:
if puzzle_input[index + 1] - puzzle_input[index - 1] <= 3:
unnecessary_numbers.append(puzzle_input[index])
del puzzle_input[index]
else:
index += 1
print(unnecessary_numbers)
print(2 ** len(unnecessary_numbers))
with open('input.txt', 'r') as puzzle_input:
puzzle_input = [int(line.strip()) for line in puzzle_input]
puzzle_input.sort()
find_combinations(puzzle_input)
'\n1, 4, 5, 6, 7, 10, 11, 12, 15, 16, 19 (full)\n 1, 4, 5, 6, 7, 10, 12, 15, 16, 19 (remove 11)\n 1, 4, 5, 7, 10, 12, 15, 16, 19 (remove 6)\n 1, 4, 6, 7, 10, 12, 15, 16, 19 (remove 5)\n 1, 4, 7, 10, 12, 15, 16, 19 (remove 6)\n 1, 4, 5, 7, 10, 11, 12, 15, 16, 19 (remove 6)\n 1, 4, 7, 10, 11, 12, 15, 16, 19 (remove 5)\n 1, 4, 6, 7, 10, 11, 12, 15, 16, 19 (remove 5)\n'
'\n1, 4, 5, 6, 7, 10, 11, 12, 15, 16, 19\nUnnecessary = 5, 6, 11,\n' |
class Connect4Board():
def __init__(self):
self.grid = [[' '] * 7 for i in range(6)]
self.turn = True
self.winner = ''
self.legal_moves = [i for i in range(7)]
self.num_total_moves = 7
def is_game_over(self):
for i in range(6):
for j in range(7):
if (self.grid[i][j] != ' ' and (
# Check for a row win
(j < 4 and self.grid[i][j] == self.grid[i][j+1] and
self.grid[i][j] == self.grid[i][j+2] and
self.grid[i][j] == self.grid[i][j+3]) or
# Check for a column win
(i < 3 and self.grid[i][j] == self.grid[i+1][j] and
self.grid[i][j] == self.grid[i+2][j] and
self.grid[i][j] == self.grid[i+3][j]) or
# Check for a diagonal win
(i < 3 and j < 4 and
self.grid[i][j] == self.grid[i+1][j+1] and
self.grid[i][j] == self.grid[i+2][j+2] and
self.grid[i][j] == self.grid[i+3][j+3]))):
self.winner = '0-1' if self.turn else '1-0'
return True
# Check if the board is full
if all([i != ' ' for row in self.grid for i in row]):
self.winner = '1/2-1/2'
return True
def push(self, move):
row_idx = max([i for i in range(6) if self.grid[i][move] == ' '])
self.grid[row_idx][move] = 'X' if self.turn else 'O'
if row_idx == 0:
self.legal_moves.remove(move)
self.turn = False if self.turn else True
def __str__(self):
return '\n' + '\n'.join(
[' '.join(['_' if i == ' ' else i for i in row])
for row in self.grid]) + '\n'
| class Connect4Board:
def __init__(self):
self.grid = [[' '] * 7 for i in range(6)]
self.turn = True
self.winner = ''
self.legal_moves = [i for i in range(7)]
self.num_total_moves = 7
def is_game_over(self):
for i in range(6):
for j in range(7):
if self.grid[i][j] != ' ' and (j < 4 and self.grid[i][j] == self.grid[i][j + 1] and (self.grid[i][j] == self.grid[i][j + 2]) and (self.grid[i][j] == self.grid[i][j + 3]) or (i < 3 and self.grid[i][j] == self.grid[i + 1][j] and (self.grid[i][j] == self.grid[i + 2][j]) and (self.grid[i][j] == self.grid[i + 3][j])) or (i < 3 and j < 4 and (self.grid[i][j] == self.grid[i + 1][j + 1]) and (self.grid[i][j] == self.grid[i + 2][j + 2]) and (self.grid[i][j] == self.grid[i + 3][j + 3]))):
self.winner = '0-1' if self.turn else '1-0'
return True
if all([i != ' ' for row in self.grid for i in row]):
self.winner = '1/2-1/2'
return True
def push(self, move):
row_idx = max([i for i in range(6) if self.grid[i][move] == ' '])
self.grid[row_idx][move] = 'X' if self.turn else 'O'
if row_idx == 0:
self.legal_moves.remove(move)
self.turn = False if self.turn else True
def __str__(self):
return '\n' + '\n'.join([' '.join(['_' if i == ' ' else i for i in row]) for row in self.grid]) + '\n' |
# -*- coding: UTF-8 -*-
#thinkpad-is-saikou
#Copyright (c) 2016 Atnanasi
#Released under the MIT license
#https://github.com/Atnanasi/thinkpad-is-saikou/blob/master/LICENSE
def ThinkpadIs():
return "Saikou"
if __name__=="__main__":
print("Thinkpad is",ThinkpadIs())
| def thinkpad_is():
return 'Saikou'
if __name__ == '__main__':
print('Thinkpad is', thinkpad_is()) |
'''
Created on December 12, 2020
@author: rosed2@mskcc.org
''' | """
Created on December 12, 2020
@author: rosed2@mskcc.org
""" |
"""
https://adventofcode.com/2020/day/1
"""
def two_add_up(inputs, target=2020):
inputs_two = inputs[:]
inputs_two.sort(reverse=True)
for first_input in inputs_two:
temp = first_input
for second_input in inputs_two:
if target - temp - second_input == 0:
result = temp * second_input
return result
def three_add_up(inputs, target=2020):
inputs_three = inputs[:]
inputs_three.sort(reverse=True)
for first_input in inputs_three:
temp1 = first_input
for second_input in inputs_three:
temp2 = second_input
for third_input in inputs_three:
if target - temp1 - temp2 - third_input == 0:
result = temp1 * temp2 * third_input
return result
# Test Case Part One
test_input = [1721, 979, 366, 299, 675, 1456]
assert two_add_up(test_input) == 514579
# Test Case Part Two
test_input = [1721, 979, 366, 299, 675, 1456]
assert three_add_up(test_input) == 241861950
if __name__ == '__main__':
with open('../input_files/day01_input_mb.txt', 'r') as fh:
inputs = [int(line.strip()) for line in fh.readlines()]
# print(inputs)
print(two_add_up(inputs))
print(three_add_up(inputs))
| """
https://adventofcode.com/2020/day/1
"""
def two_add_up(inputs, target=2020):
inputs_two = inputs[:]
inputs_two.sort(reverse=True)
for first_input in inputs_two:
temp = first_input
for second_input in inputs_two:
if target - temp - second_input == 0:
result = temp * second_input
return result
def three_add_up(inputs, target=2020):
inputs_three = inputs[:]
inputs_three.sort(reverse=True)
for first_input in inputs_three:
temp1 = first_input
for second_input in inputs_three:
temp2 = second_input
for third_input in inputs_three:
if target - temp1 - temp2 - third_input == 0:
result = temp1 * temp2 * third_input
return result
test_input = [1721, 979, 366, 299, 675, 1456]
assert two_add_up(test_input) == 514579
test_input = [1721, 979, 366, 299, 675, 1456]
assert three_add_up(test_input) == 241861950
if __name__ == '__main__':
with open('../input_files/day01_input_mb.txt', 'r') as fh:
inputs = [int(line.strip()) for line in fh.readlines()]
print(two_add_up(inputs))
print(three_add_up(inputs)) |
__version__ = "1.0.1"
__changelog__ = {
"1.0.1":"""
- update required minimal pip-tools version to support pip 20.1 see https://github.com/open-zaak/open-zaak/issues/585#issuecomment-626666736
""",
"1.0": """
- Removed dump_changelog
- Added load_version utility
- Added update_changelog_file
"""
}
| __version__ = '1.0.1'
__changelog__ = {'1.0.1': '\n - update required minimal pip-tools version to support pip 20.1 see https://github.com/open-zaak/open-zaak/issues/585#issuecomment-626666736\n ', '1.0': '\n - Removed dump_changelog\n - Added load_version utility\n - Added update_changelog_file\n '} |
# Remove Duplicates from Sorted List II
# https://www.interviewbit.com/problems/remove-duplicates-from-sorted-list-ii/
#
# Given a sorted linked list, delete all nodes that have duplicate numbers, leaving only distinct numbers from the original list.
#
# For example,
# Given 1->2->3->3->4->4->5, return 1->2->5.
# Given 1->1->1->2->3, return 2->3.
#
# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
# Definition for singly-linked list.
class ListNode:
def __init__(self, x):
self.val = x
self.next = None
class Solution:
# @param A : head node of linked list
# @return the head node in the linked list
def deleteDuplicates(self, A):
first = ListNode(0)
first.next = A
tmp = first
while tmp.next and tmp.next.next:
start = end = tmp.next
while end.next and end.next.val == start.val:
end = end.next
if start != end:
tmp.next = end.next
else:
tmp = start
return first.next
# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
if __name__ == "__main__":
head = ListNode(1)
node1 = ListNode(1)
node2 = ListNode(1)
node3 = ListNode(2)
node4 = ListNode(2)
node5 = ListNode(3)
head.next = node1
node1.next = node2
node2.next = node3
node3.next = node4
node4.next = node5
s = Solution()
head = s.deleteDuplicates(head)
| class Listnode:
def __init__(self, x):
self.val = x
self.next = None
class Solution:
def delete_duplicates(self, A):
first = list_node(0)
first.next = A
tmp = first
while tmp.next and tmp.next.next:
start = end = tmp.next
while end.next and end.next.val == start.val:
end = end.next
if start != end:
tmp.next = end.next
else:
tmp = start
return first.next
if __name__ == '__main__':
head = list_node(1)
node1 = list_node(1)
node2 = list_node(1)
node3 = list_node(2)
node4 = list_node(2)
node5 = list_node(3)
head.next = node1
node1.next = node2
node2.next = node3
node3.next = node4
node4.next = node5
s = solution()
head = s.deleteDuplicates(head) |
#!usr/bin/env python3
# _*_ coding:utf-8 _*_
"""
-------------------------------
@Author: yuxy
@Time: 2018/2/2 14:47
@File: Student.py
@Project: PythonSyntax
--------------------------------
"""
class Student(object):
def __init__(self, name, score, gender):
self.__name = name
self.__score = score
self.__gender = gender
def get_name(self):
return self.__name
def set_name(self, name):
self.__name = name
def get_score(self):
return self.__score
def set_score(self, score):
self.__score = score
def get_gender(self):
return self.__gender
def set_gender(self, gender):
self.__gender = gender
def print_score(self):
print('%s: %s' % (self.__name, self.__score))
s = Student('yuxy', 90, 'm')
print(s.get_name())
s.print_score()
print(s._Student__name)
s. set_score(99)
s.print_score() | """
-------------------------------
@Author: yuxy
@Time: 2018/2/2 14:47
@File: Student.py
@Project: PythonSyntax
--------------------------------
"""
class Student(object):
def __init__(self, name, score, gender):
self.__name = name
self.__score = score
self.__gender = gender
def get_name(self):
return self.__name
def set_name(self, name):
self.__name = name
def get_score(self):
return self.__score
def set_score(self, score):
self.__score = score
def get_gender(self):
return self.__gender
def set_gender(self, gender):
self.__gender = gender
def print_score(self):
print('%s: %s' % (self.__name, self.__score))
s = student('yuxy', 90, 'm')
print(s.get_name())
s.print_score()
print(s._Student__name)
s.set_score(99)
s.print_score() |
class Category:
def __init__(self, name):
self.name = name
self.ledger= list()
def deposit(self, amount, description= ""):
to_add= {}
to_add["amount"] = amount
to_add["description"] = description
self.ledger.append(to_add)
def withdraw (self, amount, description= ""):
apporved = self.check_funds(amount)
if apporved == True:
to_add= {}
to_add["amount"] = amount* -1
to_add["description"] = description
self.ledger.append(to_add)
return True
else:
return False
def get_balance(self):
self.current_balance = 0
for each in self.ledger:
dic = each
self.current_balance += dic["amount"]
return self.current_balance
def transfer(self, amount, where_to):
to_account= where_to.name
self.apporved = self.check_funds(amount)
if self.apporved == True:
description_to= "Transfer to {}".format(to_account)
self.withdraw(amount, description_to)
description_from= "Transfer from {}".format(self.name)
where_to.deposit(amount, description_from)
return True
else:
return False
def check_funds(self, amount):
self.funds = self.get_balance()
if self.funds < amount:
return False #Approved
else:
return True #NOT Approved
def __str__(self):
self.title = self.name.center(30, '*')
self.one_lista = list()
self.one_lista.append(self.title)
for each in self.ledger: #Printing:
dic = each
descriptions = "{:<23}".format(dic['description'])
amounts = "{:>7.2f}".format(dic['amount'])
self.one_lista.append("{:<.23}{:>.7}".format(descriptions, amounts))
self.total = "Total: "+ str(self.get_balance())
self.one_lista.append(self.total)
return "\n".join(self.one_lista)
def create_spend_chart(categories):
l_catergoties= categories
grafic = ["100|", " 90|"," 80|"," 70|"," 60|"," 50|"," 40|"," 30|"," 20|"," 10|", " 0|"]
for ctgy in l_catergoties:
total_cash = ctgy.get_balance()
for each in ctgy.ledger:
spent = 0
if each.get("amount") < 0:
if not "Transfer to" in each.get("description"):
spent += each.get("amount")
total_cash += abs(each.get("amount"))
else:
total_cash += abs(each.get("amount"))
else:
spent = spent
porcento= round((abs(spent)*100/total_cash)/10)
for no_o in range(0, 10-porcento):
grafic[no_o] += " "
for o in range(10-porcento, 10+1):
grafic[o] += " o "
width = len(grafic[0])-4
grafic.append(' '+('-'*width))
print('\n'.join(grafic))
| class Category:
def __init__(self, name):
self.name = name
self.ledger = list()
def deposit(self, amount, description=''):
to_add = {}
to_add['amount'] = amount
to_add['description'] = description
self.ledger.append(to_add)
def withdraw(self, amount, description=''):
apporved = self.check_funds(amount)
if apporved == True:
to_add = {}
to_add['amount'] = amount * -1
to_add['description'] = description
self.ledger.append(to_add)
return True
else:
return False
def get_balance(self):
self.current_balance = 0
for each in self.ledger:
dic = each
self.current_balance += dic['amount']
return self.current_balance
def transfer(self, amount, where_to):
to_account = where_to.name
self.apporved = self.check_funds(amount)
if self.apporved == True:
description_to = 'Transfer to {}'.format(to_account)
self.withdraw(amount, description_to)
description_from = 'Transfer from {}'.format(self.name)
where_to.deposit(amount, description_from)
return True
else:
return False
def check_funds(self, amount):
self.funds = self.get_balance()
if self.funds < amount:
return False
else:
return True
def __str__(self):
self.title = self.name.center(30, '*')
self.one_lista = list()
self.one_lista.append(self.title)
for each in self.ledger:
dic = each
descriptions = '{:<23}'.format(dic['description'])
amounts = '{:>7.2f}'.format(dic['amount'])
self.one_lista.append('{:<.23}{:>.7}'.format(descriptions, amounts))
self.total = 'Total: ' + str(self.get_balance())
self.one_lista.append(self.total)
return '\n'.join(self.one_lista)
def create_spend_chart(categories):
l_catergoties = categories
grafic = ['100|', ' 90|', ' 80|', ' 70|', ' 60|', ' 50|', ' 40|', ' 30|', ' 20|', ' 10|', ' 0|']
for ctgy in l_catergoties:
total_cash = ctgy.get_balance()
for each in ctgy.ledger:
spent = 0
if each.get('amount') < 0:
if not 'Transfer to' in each.get('description'):
spent += each.get('amount')
total_cash += abs(each.get('amount'))
else:
total_cash += abs(each.get('amount'))
else:
spent = spent
porcento = round(abs(spent) * 100 / total_cash / 10)
for no_o in range(0, 10 - porcento):
grafic[no_o] += ' '
for o in range(10 - porcento, 10 + 1):
grafic[o] += ' o '
width = len(grafic[0]) - 4
grafic.append(' ' + '-' * width)
print('\n'.join(grafic)) |
#The function mySum is supposed to return the sum of a list of numbers (and 0 if that list is empty), but it has one or more errors in it.
#Use this space to write test cases to determine what errors there are. You will be using this information to answer the next set of multiple choice questions.
assert mySum(0)
mlist=[1]
assert mySum(mlist) == 1
mlist=[1, 2]
assert mySum(mlist) == 3
#answer: A. an empty list, C. a list with more than one item
#test-4-2: Are there any other cases, that we can determine based on the current structure of the function, that also fail for the mySum function?
#B. No
#The class Student is supposed to accept two arguments in its constructor:
# A name string
# An optional integer representing the number of years the student has been at Michigan (default:1)
#Every student has three instance variables:
# self.name (set to the name provided)
# self.years_UM (set to the number of years the student has been at Michigan)
# self.knowledge (initialized to 0)
#There are three methods:
# .study() should increase self.knowledge by 1 and return None
# .getKnowledge() should return the value of self.knowledge
# .year_at_umich() should return the value of self.years_UM
#There are one or more errors in the class. Use this space to write test cases to determine what errors there are. You will be using this information to answer the next set of multiple choice questions.
#a1 = Student("Mauricio")
#print("Name: {}".format(a1.name))
#print("years_UM: {}".format(a1.years_UM))
#print("knowledge: {}".format(a1.knowledge))
#
#print(a1.study())
#print("knowledge: {}".format(a1.knowledge))
#print("getKnowledge: {}".format(a1.getKnowledge()))git
#print("year_at_umich: {}".format(a1.year_at_umich()))
#ANSWERS: C. the attributes/instance variables are not correctly assigned in the constructor D. the method study does not increase self.knowledge
#test-4-4: Are there any other cases, that we can determine based on the current structure of the class, that also fail for the Student class?
#A. Yes
# Correct! There is an issue with the getKnowledge method because it returns None when self.knowledge is 0, even though it returns the correct value when self.knowledge is non-zero. | assert my_sum(0)
mlist = [1]
assert my_sum(mlist) == 1
mlist = [1, 2]
assert my_sum(mlist) == 3 |
def calculate_score(word):
VOWELS = ['A', 'E', 'I', 'O', 'U']
vowels = consonants = 0
for ix, letter in enumerate(reversed(word)):
if letter in VOWELS:
vowels += ix + 1
else:
consonants += ix + 1
return vowels, consonants
def minion_game(word):
kevin, stuart = calculate_score(word)
if kevin < stuart:
print("Stuart %d" % stuart)
elif kevin > stuart:
print("Kevin %d" % kevin)
else:
print("Draw")
if __name__ == '__main__':
s = input()
minion_game(s)
| def calculate_score(word):
vowels = ['A', 'E', 'I', 'O', 'U']
vowels = consonants = 0
for (ix, letter) in enumerate(reversed(word)):
if letter in VOWELS:
vowels += ix + 1
else:
consonants += ix + 1
return (vowels, consonants)
def minion_game(word):
(kevin, stuart) = calculate_score(word)
if kevin < stuart:
print('Stuart %d' % stuart)
elif kevin > stuart:
print('Kevin %d' % kevin)
else:
print('Draw')
if __name__ == '__main__':
s = input()
minion_game(s) |
s = input().split()
d = {}
for i in s:
if i not in d:
d[i] = 0
d[i] += 1
for k, v in d.items():
print(k, v)
| s = input().split()
d = {}
for i in s:
if i not in d:
d[i] = 0
d[i] += 1
for (k, v) in d.items():
print(k, v) |
def urlencode(query, doseq=False, safe='', encoding=None, errors=None):
"""Encode a dict or sequence of two-element tuples into a URL query string.
If any values in the query arg are sequences and doseq is true, each
sequence element is converted to a separate parameter.
If the query arg is a sequence of two-element tuples, the order of the
parameters in the output will match the order of parameters in the
input.
The components of a query arg may each be either a string or a bytes type.
When a component is a string, the safe, encoding and error parameters are
sent to the quote_plus function for encoding.
"""
if hasattr(query, "items"):
query = query.items()
else:
# It's a bother at times that strings and string-like objects are
# sequences.
try:
# non-sequence items should not work with len()
# non-empty strings will fail this
if len(query) and not isinstance(query[0], tuple):
raise TypeError
# Zero-length sequences of all types will get here and succeed,
# but that's a minor nit. Since the original implementation
# allowed empty dicts that type of behavior probably should be
# preserved for consistency
except TypeError:
# ty, va, tb = sys.exc_info()
raise TypeError("not a valid non-string sequence "
"or mapping object")#.with_traceback(tb)
l = []
if not doseq:
for k, v in query:
if isinstance(k, bytes):
k = quote_plus(k, safe)
else:
k = quote_plus(str(k), safe, encoding, errors)
if isinstance(v, bytes):
v = quote_plus(v, safe)
else:
v = quote_plus(str(v), safe, encoding, errors)
l.append(k + '=' + v)
else:
for k, v in query:
if isinstance(k, bytes):
k = quote_plus(k, safe)
else:
k = quote_plus(str(k), safe, encoding, errors)
if isinstance(v, bytes):
v = quote_plus(v, safe)
l.append(k + '=' + v)
elif isinstance(v, str):
v = quote_plus(v, safe, encoding, errors)
l.append(k + '=' + v)
else:
try:
# Is this a sufficient test for sequence-ness?
x = len(v)
except TypeError:
# not a sequence
v = quote_plus(str(v), safe, encoding, errors)
l.append(k + '=' + v)
else:
# loop over the sequence
for elt in v:
if isinstance(elt, bytes):
elt = quote_plus(elt, safe)
else:
elt = quote_plus(str(elt), safe, encoding, errors)
l.append(k + '=' + elt)
return '&'.join(l)
| def urlencode(query, doseq=False, safe='', encoding=None, errors=None):
"""Encode a dict or sequence of two-element tuples into a URL query string.
If any values in the query arg are sequences and doseq is true, each
sequence element is converted to a separate parameter.
If the query arg is a sequence of two-element tuples, the order of the
parameters in the output will match the order of parameters in the
input.
The components of a query arg may each be either a string or a bytes type.
When a component is a string, the safe, encoding and error parameters are
sent to the quote_plus function for encoding.
"""
if hasattr(query, 'items'):
query = query.items()
else:
try:
if len(query) and (not isinstance(query[0], tuple)):
raise TypeError
except TypeError:
raise type_error('not a valid non-string sequence or mapping object')
l = []
if not doseq:
for (k, v) in query:
if isinstance(k, bytes):
k = quote_plus(k, safe)
else:
k = quote_plus(str(k), safe, encoding, errors)
if isinstance(v, bytes):
v = quote_plus(v, safe)
else:
v = quote_plus(str(v), safe, encoding, errors)
l.append(k + '=' + v)
else:
for (k, v) in query:
if isinstance(k, bytes):
k = quote_plus(k, safe)
else:
k = quote_plus(str(k), safe, encoding, errors)
if isinstance(v, bytes):
v = quote_plus(v, safe)
l.append(k + '=' + v)
elif isinstance(v, str):
v = quote_plus(v, safe, encoding, errors)
l.append(k + '=' + v)
else:
try:
x = len(v)
except TypeError:
v = quote_plus(str(v), safe, encoding, errors)
l.append(k + '=' + v)
else:
for elt in v:
if isinstance(elt, bytes):
elt = quote_plus(elt, safe)
else:
elt = quote_plus(str(elt), safe, encoding, errors)
l.append(k + '=' + elt)
return '&'.join(l) |
AES_128_ECB_test_vectors = (
{"key": "911500915E8514174402A13118EA362C",
"plain": "4163F3BEABA14D6C1E406BD5646CAC9A",
"cipher": "39610A1E8F66501D952C27AB52C4DC9A"},
{"key": "BCCF986A4D74B719EEB1D93CDABE96D5",
"plain": "A6325414DDE2E367AABA669766316976",
"cipher": "666C5668ECAAD6E66C7FB569E52AA928"},
{"key": "5AC9583DCAC4CB19A451820A909FAFEC",
"plain": "8C45132DFC87959BF89396844FA1A2F2",
"cipher": "0965016DBE90009C75B4D31C460AC94C"},
{"key": "3880E49151EE2E0BDCBA8C73E0FC84A0",
"plain": "FB7F2920028338CDD37CB0A440E6E337",
"cipher": "B1873D3B12FE1F83F7D7B03104D5F878"},
{"key": "7EC254E4A483777D23A5086858133D15",
"plain": "AD03D4516D30F30C15E5591E0ED6D324",
"cipher": "ED1DCBC75D76E2BD35666BC56939ADDD"}
)
| aes_128_ecb_test_vectors = ({'key': '911500915E8514174402A13118EA362C', 'plain': '4163F3BEABA14D6C1E406BD5646CAC9A', 'cipher': '39610A1E8F66501D952C27AB52C4DC9A'}, {'key': 'BCCF986A4D74B719EEB1D93CDABE96D5', 'plain': 'A6325414DDE2E367AABA669766316976', 'cipher': '666C5668ECAAD6E66C7FB569E52AA928'}, {'key': '5AC9583DCAC4CB19A451820A909FAFEC', 'plain': '8C45132DFC87959BF89396844FA1A2F2', 'cipher': '0965016DBE90009C75B4D31C460AC94C'}, {'key': '3880E49151EE2E0BDCBA8C73E0FC84A0', 'plain': 'FB7F2920028338CDD37CB0A440E6E337', 'cipher': 'B1873D3B12FE1F83F7D7B03104D5F878'}, {'key': '7EC254E4A483777D23A5086858133D15', 'plain': 'AD03D4516D30F30C15E5591E0ED6D324', 'cipher': 'ED1DCBC75D76E2BD35666BC56939ADDD'}) |
# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
# vi: set ft=python sts=4 ts=4 sw=4 et:
''' Very simple spatial image class
The image class maintains the association between a 3D (or greater)
array, and an affine transform that maps voxel coordinates to some real
world space. It also has a ``header`` - some standard set of meta-data
that is specific to the image format - and ``extra`` - a dictionary
container for any other metadata.
It has attributes::
extra
filename (read only)
and methods::
.get_data()
.get_affine()
.get_header()
.to_files() # writes image out to passed or
.get_raw_data()
.write_data(fileobj)
.write_raw_data(fileobj)
There are several ways of writing data.
=======================================
There is the usual way, which is the default::
img.write_data(data, fileobj)
and that is, to take the data array, ``data``, and cast it to the
datatype the header expects, setting any available header scaling
into the header to help the data match.
You can get the data out again with of::
img.get_data(fileobj)
Less commonly, you might want to fetch out the unscaled array via
the header::
unscaled_data = img.get_raw_data(fileobj)
then do something with it. Then put it back again::
img.write_raw_data(modifed_unscaled_data, fileobj)
Sometimes you might to avoid any loss of precision by making the
data type the same as the input::
hdr = img.get_header()
hdr.set_data_dtype(data.dtype)
img.write_data(data, fileobj)
'''
class SpatialImage(object):
_header_maker = dict
''' Template class for lightweight image '''
def __init__(self, data, affine, header=None, extra=None):
if extra is None:
extra = {}
self._data = data
self._affine = affine
self.extra = extra
self._set_header(header)
self._files = {}
def __str__(self):
shape = self.get_shape()
affine = self.get_affine()
return '\n'.join((
str(self.__class__),
'data shape %s' % (shape,),
'affine: ',
'%s' % affine,
'metadata:',
'%s' % self._header))
def get_data(self):
return self._data
def get_shape(self):
if self._data:
return self._data.shape
def get_data_dtype(self):
raise NotImplementedError
def set_data_dtype(self, dtype):
raise NotImplementedError
def get_affine(self):
return self._affine
def get_header(self):
return self._header
def _set_header(self, header=None):
if header is None:
self._header = self._header_maker()
return
# we need to replicate the endianness, for the case where we are
# creating an image from files, and we have not yet loaded the
# data. In that case we need to have the header maintain its
# endianness to get the correct interpretation of the data
self._header = self._header_maker(endianness=header.endianness)
for key, value in header.items():
if key in self._header:
self._header[key] = value
elif key not in self.extra:
self.extra[key] = value
@classmethod
def from_filespec(klass, filespec):
raise NotImplementedError
def from_files(klass, files):
raise NotImplementedError
def from_image(klass, img):
raise NotImplementedError
@staticmethod
def filespec_to_files(filespec):
raise NotImplementedError
def to_filespec(self, filespec):
raise NotImplementedError
def to_files(self, files=None):
raise NotImplementedError
| """ Very simple spatial image class
The image class maintains the association between a 3D (or greater)
array, and an affine transform that maps voxel coordinates to some real
world space. It also has a ``header`` - some standard set of meta-data
that is specific to the image format - and ``extra`` - a dictionary
container for any other metadata.
It has attributes::
extra
filename (read only)
and methods::
.get_data()
.get_affine()
.get_header()
.to_files() # writes image out to passed or
.get_raw_data()
.write_data(fileobj)
.write_raw_data(fileobj)
There are several ways of writing data.
=======================================
There is the usual way, which is the default::
img.write_data(data, fileobj)
and that is, to take the data array, ``data``, and cast it to the
datatype the header expects, setting any available header scaling
into the header to help the data match.
You can get the data out again with of::
img.get_data(fileobj)
Less commonly, you might want to fetch out the unscaled array via
the header::
unscaled_data = img.get_raw_data(fileobj)
then do something with it. Then put it back again::
img.write_raw_data(modifed_unscaled_data, fileobj)
Sometimes you might to avoid any loss of precision by making the
data type the same as the input::
hdr = img.get_header()
hdr.set_data_dtype(data.dtype)
img.write_data(data, fileobj)
"""
class Spatialimage(object):
_header_maker = dict
' Template class for lightweight image '
def __init__(self, data, affine, header=None, extra=None):
if extra is None:
extra = {}
self._data = data
self._affine = affine
self.extra = extra
self._set_header(header)
self._files = {}
def __str__(self):
shape = self.get_shape()
affine = self.get_affine()
return '\n'.join((str(self.__class__), 'data shape %s' % (shape,), 'affine: ', '%s' % affine, 'metadata:', '%s' % self._header))
def get_data(self):
return self._data
def get_shape(self):
if self._data:
return self._data.shape
def get_data_dtype(self):
raise NotImplementedError
def set_data_dtype(self, dtype):
raise NotImplementedError
def get_affine(self):
return self._affine
def get_header(self):
return self._header
def _set_header(self, header=None):
if header is None:
self._header = self._header_maker()
return
self._header = self._header_maker(endianness=header.endianness)
for (key, value) in header.items():
if key in self._header:
self._header[key] = value
elif key not in self.extra:
self.extra[key] = value
@classmethod
def from_filespec(klass, filespec):
raise NotImplementedError
def from_files(klass, files):
raise NotImplementedError
def from_image(klass, img):
raise NotImplementedError
@staticmethod
def filespec_to_files(filespec):
raise NotImplementedError
def to_filespec(self, filespec):
raise NotImplementedError
def to_files(self, files=None):
raise NotImplementedError |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
class Tools:
"""Tools class, tools needed in most of the code all the same"""
#unicode safe code (https://code.djangoproject.com/ticket/170)
def unicode_safe(self,word):
if type(word) == unicode:
word = word.encode('utf-8')
return word
#calculate median for an array of number
def calc_median(self,number_list):
values = sorted(number_list)
if(len(values)%2==1):
#odd number of elements
return values[((len(values)+1)/2)-1]
else:
#even number
lower = values[(len(values)/2)-1]
upper = values[(len(values)/2)]
return (float(lower+upper))/2
#calculate the average
def calc_average(self,sum_of,count):
#no divide / 0
average = 0
if((count)!= 0):
average = float(sum_of)/count
return average
#calculate the some of an array numbers
def calc_sum(self,array_of_numbers):
sum_of = 0
for i in array_of_numbers:
sum_of = sum_of + i
return sum_of
| class Tools:
"""Tools class, tools needed in most of the code all the same"""
def unicode_safe(self, word):
if type(word) == unicode:
word = word.encode('utf-8')
return word
def calc_median(self, number_list):
values = sorted(number_list)
if len(values) % 2 == 1:
return values[(len(values) + 1) / 2 - 1]
else:
lower = values[len(values) / 2 - 1]
upper = values[len(values) / 2]
return float(lower + upper) / 2
def calc_average(self, sum_of, count):
average = 0
if count != 0:
average = float(sum_of) / count
return average
def calc_sum(self, array_of_numbers):
sum_of = 0
for i in array_of_numbers:
sum_of = sum_of + i
return sum_of |
# Update this text to match your story.
start = '''
You wake up one morning and find that you aren't in your bed; you aren't even in your room.
You're in the middle of a giant maze.
A sign is hanging from the ivy: "You have one hour. Don't touch the walls."
There is a hallway to your right and to your left.
'''
print(start)
print("Type 'left' to go left or 'right' to go right.") # Update to match your story.
user_input = input()
if user_input == "left":
print("You decide to go left and...") # Update to match your story.
# Continue code to finish story.
elif user_input == "right":
print("You choose to go right and ...") # Update to match your story.
# Continue code to finish story.
| start = '\nYou wake up one morning and find that you aren\'t in your bed; you aren\'t even in your room.\nYou\'re in the middle of a giant maze.\nA sign is hanging from the ivy: "You have one hour. Don\'t touch the walls."\nThere is a hallway to your right and to your left.\n'
print(start)
print("Type 'left' to go left or 'right' to go right.")
user_input = input()
if user_input == 'left':
print('You decide to go left and...')
elif user_input == 'right':
print('You choose to go right and ...') |
"""
:author: john.sosoka
:date: 5/10/2018
""" | """
:author: john.sosoka
:date: 5/10/2018
""" |
sol_space = {}
def foo(n, d):
'''Recursive function to find number of valid expression with given length and depth'''
global sol_space
if n ==0 and d == 0:
return 1
# invalid cases
if n < 2*d:
return 0
if n % 2 == 1:
return 0
if n == 0 or d == 0:
return 0
# Base cases
if n == 2*d :
return 1
if d == 1:
return 1
# Recursive cases
# Check the solution space first
if (n, d) in sol_space:
return sol_space[(n, d)]
sol = 0
# (d-1) case
sol += foo(n-2, d-1)
# U*V case : d.d, 2 d.d-1, ..., 2 d.1
for i in range(2, n, 2): # i = left block size from [2 to n-2]
for j in range(0, d): #
sol += foo(i, d)*foo(n-i, j)
#sol += foo(i-2, d-1)*foo(n-i-2, d-1)
# if (n, d) == (8, 2):
# print(" - ", (i, d), "*", (n-i, d))
#sol += 2*foo(i-2, d-1)*foo(n-i-2, d-1)
# Update the solution space
sol_space[(n, d)] = sol
return sol
while True:
try:
n, d = list(map(int, input().split()))
print(foo(n, d) if n % 2 == 0 else 0)
except EOFError:
break
| sol_space = {}
def foo(n, d):
"""Recursive function to find number of valid expression with given length and depth"""
global sol_space
if n == 0 and d == 0:
return 1
if n < 2 * d:
return 0
if n % 2 == 1:
return 0
if n == 0 or d == 0:
return 0
if n == 2 * d:
return 1
if d == 1:
return 1
if (n, d) in sol_space:
return sol_space[n, d]
sol = 0
sol += foo(n - 2, d - 1)
for i in range(2, n, 2):
for j in range(0, d):
sol += foo(i, d) * foo(n - i, j)
sol_space[n, d] = sol
return sol
while True:
try:
(n, d) = list(map(int, input().split()))
print(foo(n, d) if n % 2 == 0 else 0)
except EOFError:
break |
# just a bunch of assert methods to use.
class AssertionFailedException(Exception):
"""
Just an exception that we throw when failing an assert
"""
def __init__(self, message):
super(AssertionFailedException, self).__init__(message)
def assert_equals(expected, actual, message = None):
"""
Checks if the given values are equal
:param expected:
:param actual:
:param message:
"""
if expected == actual:
return
if message:
raise AssertionFailedException(message)
message = "Expected: \n%s\n, found instead: \n%s\n." % (expected, actual)
raise AssertionFailedException(message)
def assert_true(expected, message = None):
"""
Checks if the given value is true.
"""
if expected:
return
if message:
raise AssertionFailedException(message)
message = "Expected value to be true. Was instead: %s" % expected
raise AssertionFailedException(message)
def assert_false(expected, message = None):
"""
Checks if the given value is false.
"""
if not expected:
return
if message:
raise AssertionFailedException(message)
message = "Expected value to be false. Was instead: %s" % expected
raise AssertionFailedException(message)
def assert_contains(parent, contained, message = None):
"""
Checkes if the parent contains the contained element.
:param parent:
:param contained:
:param message:
:return:
"""
if contained in parent:
return
if message:
raise AssertionFailedException(message)
message = "Expected '%s' to contain '%s'. It didn't." % (parent, contained)
raise AssertionFailedException(message)
| class Assertionfailedexception(Exception):
"""
Just an exception that we throw when failing an assert
"""
def __init__(self, message):
super(AssertionFailedException, self).__init__(message)
def assert_equals(expected, actual, message=None):
"""
Checks if the given values are equal
:param expected:
:param actual:
:param message:
"""
if expected == actual:
return
if message:
raise assertion_failed_exception(message)
message = 'Expected: \n%s\n, found instead: \n%s\n.' % (expected, actual)
raise assertion_failed_exception(message)
def assert_true(expected, message=None):
"""
Checks if the given value is true.
"""
if expected:
return
if message:
raise assertion_failed_exception(message)
message = 'Expected value to be true. Was instead: %s' % expected
raise assertion_failed_exception(message)
def assert_false(expected, message=None):
"""
Checks if the given value is false.
"""
if not expected:
return
if message:
raise assertion_failed_exception(message)
message = 'Expected value to be false. Was instead: %s' % expected
raise assertion_failed_exception(message)
def assert_contains(parent, contained, message=None):
"""
Checkes if the parent contains the contained element.
:param parent:
:param contained:
:param message:
:return:
"""
if contained in parent:
return
if message:
raise assertion_failed_exception(message)
message = "Expected '%s' to contain '%s'. It didn't." % (parent, contained)
raise assertion_failed_exception(message) |
# See: https://www.codewars.com/kata/56a5d994ac971f1ac500003e
def longest_consec(s, k):
return max([''.join(i) for i in zip(*[s[i:] for i in range(k)])]+[''], key=len)
| def longest_consec(s, k):
return max([''.join(i) for i in zip(*[s[i:] for i in range(k)])] + [''], key=len) |
__version__ = "0.6.0"
__title__ = "pygbif"
__author__ = "Scott Chamberlain"
__license__ = "MIT"
| __version__ = '0.6.0'
__title__ = 'pygbif'
__author__ = 'Scott Chamberlain'
__license__ = 'MIT' |
#!/usr/bin/env python3
#
# # Copyright (c) 2021 Facebook, inc. and its affiliates. All Rights Reserved
#
#
LATEX = r"""
\documentclass{{article}}
\usepackage{{booktabs}}
\title{{Report}}
\author{{DLP and MIB}}
\date{{\relax}}
\begin{{document}}
\section{{In-domain evaluation}}
{indomain_table}
\section{{Out-of-domain evaluation}}
{oodomain_table}
\end{{document}}
"""
if __name__ == '__main__':
pass
| latex = '\n\\documentclass{{article}}\n\\usepackage{{booktabs}}\n\n\\title{{Report}}\n\\author{{DLP and MIB}}\n\\date{{\\relax}}\n\n\\begin{{document}}\n\\section{{In-domain evaluation}}\n{indomain_table}\n\\section{{Out-of-domain evaluation}}\n{oodomain_table}\n\\end{{document}}\n'
if __name__ == '__main__':
pass |
"""
PASSENGERS
"""
numPassengers = 15249
passenger_arriving = (
(3, 1, 2, 3, 3, 1, 1, 1, 1, 1, 0, 1, 0, 4, 1, 4, 3, 4, 2, 3, 1, 1, 1, 2, 0, 0), # 0
(2, 4, 6, 3, 3, 2, 3, 0, 3, 0, 0, 1, 0, 11, 1, 4, 2, 9, 2, 3, 1, 0, 0, 1, 0, 0), # 1
(6, 3, 3, 4, 6, 4, 0, 1, 2, 1, 1, 0, 0, 3, 10, 4, 1, 2, 1, 0, 0, 3, 1, 1, 1, 0), # 2
(7, 2, 5, 1, 3, 1, 4, 3, 3, 0, 1, 0, 0, 5, 5, 8, 5, 2, 2, 4, 1, 2, 3, 0, 1, 0), # 3
(2, 1, 2, 8, 5, 0, 3, 2, 1, 1, 0, 0, 0, 6, 8, 2, 1, 4, 4, 1, 1, 5, 2, 1, 0, 0), # 4
(4, 5, 8, 5, 0, 0, 4, 2, 1, 1, 3, 0, 0, 6, 7, 4, 3, 4, 1, 1, 3, 5, 3, 0, 2, 0), # 5
(4, 6, 6, 4, 2, 3, 3, 4, 3, 1, 0, 0, 0, 6, 7, 4, 3, 6, 6, 2, 3, 3, 2, 1, 0, 0), # 6
(7, 5, 1, 9, 3, 1, 2, 1, 2, 0, 0, 1, 0, 5, 5, 6, 5, 1, 6, 2, 4, 3, 2, 2, 0, 0), # 7
(6, 5, 8, 2, 2, 0, 4, 2, 2, 0, 1, 0, 0, 5, 3, 6, 0, 4, 6, 2, 2, 2, 2, 0, 0, 0), # 8
(9, 8, 4, 3, 3, 0, 4, 3, 1, 1, 1, 0, 0, 5, 4, 2, 5, 6, 4, 2, 0, 2, 2, 0, 0, 0), # 9
(4, 4, 8, 2, 5, 5, 3, 5, 2, 1, 1, 1, 0, 7, 4, 5, 4, 5, 2, 2, 1, 4, 0, 1, 1, 0), # 10
(6, 6, 7, 8, 5, 3, 3, 5, 1, 0, 1, 1, 0, 5, 5, 4, 2, 3, 3, 1, 0, 1, 0, 1, 1, 0), # 11
(1, 8, 7, 4, 3, 2, 5, 2, 1, 2, 1, 0, 0, 8, 0, 7, 4, 8, 7, 3, 6, 3, 0, 2, 1, 0), # 12
(2, 5, 8, 5, 2, 2, 1, 0, 2, 5, 2, 1, 0, 9, 10, 5, 4, 7, 0, 3, 1, 1, 2, 1, 1, 0), # 13
(5, 8, 3, 10, 4, 3, 3, 5, 4, 2, 0, 0, 0, 3, 6, 8, 3, 5, 4, 5, 4, 3, 0, 3, 0, 0), # 14
(6, 7, 9, 5, 12, 3, 1, 6, 1, 0, 0, 1, 0, 4, 6, 7, 4, 6, 4, 2, 0, 3, 0, 1, 0, 0), # 15
(6, 4, 7, 5, 4, 2, 2, 3, 2, 0, 2, 0, 0, 11, 8, 9, 10, 6, 1, 1, 1, 1, 1, 0, 1, 0), # 16
(7, 10, 2, 5, 5, 0, 7, 3, 4, 0, 0, 1, 0, 7, 9, 5, 5, 6, 2, 3, 1, 3, 2, 1, 1, 0), # 17
(9, 10, 1, 7, 5, 3, 8, 2, 2, 3, 0, 0, 0, 9, 3, 5, 3, 10, 2, 5, 1, 4, 2, 0, 2, 0), # 18
(16, 7, 8, 5, 7, 4, 4, 2, 4, 4, 3, 1, 0, 7, 9, 6, 4, 1, 5, 2, 3, 3, 3, 1, 2, 0), # 19
(6, 6, 3, 3, 8, 3, 7, 1, 1, 2, 2, 0, 0, 3, 6, 5, 6, 8, 2, 6, 3, 2, 1, 2, 4, 0), # 20
(9, 9, 8, 7, 6, 4, 2, 1, 4, 2, 1, 0, 0, 12, 4, 7, 4, 2, 5, 5, 2, 3, 3, 0, 0, 0), # 21
(9, 6, 4, 5, 3, 2, 5, 4, 2, 2, 3, 3, 0, 11, 10, 4, 5, 7, 5, 3, 4, 10, 2, 0, 1, 0), # 22
(7, 7, 4, 10, 6, 3, 5, 3, 3, 2, 0, 0, 0, 6, 9, 2, 2, 7, 5, 4, 0, 1, 4, 2, 0, 0), # 23
(7, 15, 2, 8, 9, 2, 5, 5, 1, 1, 0, 1, 0, 8, 11, 4, 7, 8, 3, 0, 0, 1, 3, 1, 1, 0), # 24
(4, 6, 7, 8, 7, 0, 9, 2, 4, 1, 0, 0, 0, 9, 7, 9, 1, 7, 6, 2, 1, 1, 3, 1, 1, 0), # 25
(13, 7, 6, 0, 3, 5, 1, 6, 6, 2, 1, 0, 0, 7, 6, 5, 7, 7, 4, 5, 4, 3, 4, 1, 2, 0), # 26
(11, 11, 5, 10, 3, 1, 1, 1, 4, 0, 0, 1, 0, 8, 4, 7, 7, 6, 3, 5, 4, 4, 2, 5, 0, 0), # 27
(9, 6, 4, 9, 5, 5, 4, 4, 4, 3, 3, 0, 0, 8, 7, 1, 9, 6, 3, 4, 2, 0, 3, 1, 0, 0), # 28
(4, 8, 6, 9, 3, 2, 3, 1, 3, 0, 3, 0, 0, 11, 7, 4, 4, 7, 5, 4, 2, 5, 3, 0, 0, 0), # 29
(7, 6, 11, 4, 7, 3, 5, 3, 5, 2, 0, 2, 0, 9, 5, 7, 5, 6, 1, 6, 0, 2, 0, 0, 0, 0), # 30
(9, 6, 11, 11, 5, 4, 4, 2, 1, 2, 0, 1, 0, 10, 7, 9, 4, 5, 8, 4, 4, 1, 2, 1, 0, 0), # 31
(9, 7, 6, 8, 5, 3, 3, 5, 7, 0, 0, 0, 0, 6, 8, 4, 3, 3, 3, 0, 0, 3, 3, 1, 0, 0), # 32
(5, 4, 8, 7, 4, 2, 4, 1, 7, 2, 1, 1, 0, 4, 8, 6, 4, 7, 2, 4, 2, 4, 2, 0, 2, 0), # 33
(6, 9, 6, 7, 4, 2, 4, 3, 2, 2, 1, 0, 0, 9, 2, 8, 4, 7, 1, 6, 3, 5, 2, 0, 1, 0), # 34
(10, 14, 11, 6, 9, 2, 3, 4, 3, 0, 1, 2, 0, 10, 4, 5, 5, 6, 5, 1, 4, 2, 3, 0, 1, 0), # 35
(7, 8, 4, 8, 6, 6, 4, 1, 5, 1, 4, 0, 0, 12, 7, 9, 2, 11, 1, 5, 1, 1, 1, 1, 0, 0), # 36
(10, 8, 13, 7, 8, 3, 3, 5, 5, 2, 0, 2, 0, 3, 6, 5, 11, 6, 2, 4, 3, 3, 2, 4, 1, 0), # 37
(8, 12, 9, 3, 9, 3, 5, 2, 5, 1, 0, 0, 0, 10, 10, 5, 3, 7, 7, 7, 1, 1, 2, 0, 1, 0), # 38
(4, 11, 10, 4, 9, 2, 2, 2, 1, 1, 0, 0, 0, 13, 5, 6, 3, 0, 2, 2, 1, 6, 2, 4, 0, 0), # 39
(4, 10, 6, 8, 6, 3, 1, 6, 4, 1, 4, 1, 0, 7, 7, 6, 6, 8, 1, 6, 4, 6, 2, 2, 1, 0), # 40
(9, 10, 5, 8, 4, 1, 1, 4, 6, 0, 1, 0, 0, 4, 12, 7, 4, 10, 5, 4, 2, 5, 3, 2, 1, 0), # 41
(6, 11, 6, 4, 4, 3, 4, 6, 2, 1, 4, 0, 0, 10, 9, 6, 2, 4, 3, 2, 2, 3, 2, 3, 5, 0), # 42
(10, 6, 2, 5, 5, 1, 5, 4, 5, 2, 1, 1, 0, 4, 6, 4, 5, 10, 6, 3, 1, 4, 0, 0, 0, 0), # 43
(5, 7, 4, 5, 9, 4, 2, 2, 2, 3, 2, 3, 0, 5, 8, 7, 3, 9, 4, 4, 2, 4, 1, 1, 0, 0), # 44
(11, 4, 8, 11, 6, 4, 6, 1, 3, 1, 0, 0, 0, 9, 10, 3, 5, 8, 1, 4, 4, 3, 5, 2, 0, 0), # 45
(5, 8, 3, 9, 6, 3, 3, 2, 7, 2, 0, 1, 0, 5, 2, 4, 6, 5, 3, 3, 8, 3, 4, 1, 1, 0), # 46
(6, 12, 6, 7, 7, 1, 3, 4, 2, 0, 0, 0, 0, 5, 7, 12, 3, 4, 7, 7, 3, 5, 3, 0, 0, 0), # 47
(7, 8, 6, 8, 3, 3, 4, 4, 3, 2, 2, 0, 0, 4, 5, 2, 6, 8, 2, 3, 4, 3, 2, 1, 1, 0), # 48
(11, 6, 6, 10, 8, 1, 5, 3, 1, 1, 3, 1, 0, 6, 7, 5, 3, 8, 1, 4, 2, 3, 1, 1, 0, 0), # 49
(10, 4, 10, 7, 5, 4, 2, 6, 2, 2, 0, 0, 0, 10, 6, 8, 4, 6, 0, 2, 2, 3, 2, 2, 0, 0), # 50
(9, 7, 6, 7, 12, 1, 3, 5, 2, 1, 1, 0, 0, 8, 8, 7, 3, 3, 4, 1, 2, 2, 2, 1, 2, 0), # 51
(4, 3, 7, 10, 6, 2, 2, 3, 4, 2, 1, 1, 0, 10, 8, 2, 7, 9, 4, 2, 4, 3, 4, 4, 0, 0), # 52
(9, 7, 8, 3, 3, 1, 2, 4, 1, 0, 1, 1, 0, 8, 7, 1, 7, 4, 5, 1, 3, 2, 3, 0, 0, 0), # 53
(6, 9, 5, 7, 5, 2, 4, 2, 2, 2, 4, 1, 0, 6, 7, 4, 4, 7, 2, 4, 1, 4, 4, 0, 1, 0), # 54
(5, 9, 4, 10, 10, 4, 1, 4, 4, 1, 3, 0, 0, 4, 7, 7, 2, 4, 4, 1, 2, 3, 1, 1, 1, 0), # 55
(8, 2, 7, 10, 10, 3, 2, 2, 6, 2, 1, 0, 0, 5, 10, 4, 4, 8, 1, 2, 1, 1, 4, 3, 0, 0), # 56
(8, 10, 11, 5, 8, 6, 2, 2, 5, 1, 0, 0, 0, 4, 6, 11, 0, 3, 2, 5, 4, 1, 4, 2, 0, 0), # 57
(12, 7, 10, 3, 4, 3, 2, 0, 6, 4, 3, 0, 0, 7, 8, 6, 4, 6, 8, 4, 2, 2, 0, 3, 2, 0), # 58
(5, 12, 12, 7, 7, 5, 8, 4, 4, 2, 0, 1, 0, 8, 7, 9, 5, 5, 3, 3, 1, 1, 7, 3, 0, 0), # 59
(8, 15, 6, 13, 3, 2, 3, 3, 4, 1, 0, 0, 0, 6, 7, 6, 3, 7, 2, 6, 2, 3, 2, 3, 1, 0), # 60
(5, 8, 10, 4, 4, 4, 3, 3, 4, 0, 1, 1, 0, 7, 5, 5, 4, 7, 3, 3, 1, 5, 1, 1, 0, 0), # 61
(5, 6, 5, 8, 7, 0, 4, 5, 0, 1, 1, 1, 0, 8, 7, 2, 6, 5, 3, 1, 2, 2, 3, 1, 0, 0), # 62
(5, 5, 6, 10, 6, 2, 2, 4, 4, 4, 0, 0, 0, 6, 6, 10, 5, 5, 4, 4, 3, 3, 2, 0, 0, 0), # 63
(10, 9, 6, 13, 6, 1, 6, 0, 1, 2, 2, 0, 0, 5, 4, 8, 2, 7, 5, 3, 3, 0, 1, 2, 1, 0), # 64
(6, 5, 8, 10, 5, 5, 3, 2, 1, 2, 0, 0, 0, 10, 6, 5, 5, 9, 5, 4, 3, 2, 4, 2, 0, 0), # 65
(10, 7, 4, 7, 6, 4, 4, 2, 1, 0, 1, 1, 0, 9, 7, 6, 7, 8, 3, 3, 2, 3, 4, 2, 2, 0), # 66
(13, 6, 6, 9, 4, 4, 4, 6, 3, 1, 2, 1, 0, 7, 8, 3, 4, 5, 6, 3, 2, 1, 3, 1, 1, 0), # 67
(11, 6, 9, 3, 4, 1, 1, 4, 6, 0, 0, 2, 0, 15, 10, 6, 5, 7, 2, 2, 1, 5, 1, 0, 1, 0), # 68
(14, 9, 8, 3, 4, 2, 0, 1, 5, 1, 3, 2, 0, 6, 7, 10, 3, 4, 3, 5, 2, 1, 2, 1, 1, 0), # 69
(6, 8, 8, 5, 2, 2, 7, 2, 3, 1, 1, 0, 0, 4, 6, 3, 1, 10, 4, 6, 4, 3, 0, 0, 1, 0), # 70
(14, 6, 9, 9, 6, 3, 2, 3, 1, 2, 0, 0, 0, 10, 8, 4, 3, 7, 3, 1, 2, 3, 2, 1, 1, 0), # 71
(11, 9, 7, 7, 8, 3, 0, 5, 6, 0, 0, 1, 0, 11, 12, 4, 1, 7, 4, 3, 2, 1, 0, 0, 1, 0), # 72
(6, 10, 8, 5, 5, 2, 6, 0, 3, 1, 2, 3, 0, 10, 9, 2, 6, 7, 1, 2, 1, 5, 3, 2, 0, 0), # 73
(9, 3, 9, 8, 5, 3, 2, 1, 2, 1, 3, 1, 0, 11, 8, 5, 3, 9, 6, 1, 1, 0, 1, 0, 0, 0), # 74
(7, 7, 5, 7, 3, 1, 1, 4, 3, 0, 2, 0, 0, 4, 4, 3, 2, 4, 1, 0, 3, 6, 1, 0, 2, 0), # 75
(9, 7, 6, 10, 8, 5, 2, 3, 0, 0, 2, 1, 0, 12, 3, 2, 2, 4, 4, 0, 1, 2, 2, 2, 1, 0), # 76
(6, 4, 0, 6, 7, 8, 3, 1, 3, 2, 1, 0, 0, 11, 13, 7, 3, 7, 3, 2, 3, 2, 3, 1, 0, 0), # 77
(7, 7, 9, 8, 8, 3, 0, 4, 0, 1, 2, 0, 0, 8, 9, 6, 6, 3, 3, 3, 1, 6, 0, 4, 0, 0), # 78
(3, 8, 14, 10, 7, 2, 2, 4, 2, 2, 2, 1, 0, 9, 5, 4, 6, 5, 2, 1, 3, 3, 2, 5, 1, 0), # 79
(12, 8, 6, 6, 3, 5, 0, 3, 3, 2, 0, 2, 0, 8, 9, 5, 12, 6, 3, 1, 1, 3, 2, 0, 0, 0), # 80
(7, 5, 7, 12, 8, 2, 3, 3, 6, 1, 2, 0, 0, 7, 7, 9, 2, 4, 3, 3, 1, 3, 2, 1, 1, 0), # 81
(8, 5, 10, 13, 8, 1, 3, 2, 4, 1, 1, 1, 0, 6, 8, 4, 3, 9, 6, 5, 2, 2, 0, 4, 0, 0), # 82
(5, 6, 8, 9, 8, 3, 3, 2, 2, 0, 0, 3, 0, 5, 9, 6, 5, 4, 5, 1, 2, 2, 0, 1, 4, 0), # 83
(11, 9, 5, 10, 7, 1, 2, 1, 3, 0, 0, 0, 0, 7, 5, 3, 2, 6, 3, 5, 2, 7, 6, 1, 1, 0), # 84
(4, 12, 2, 9, 3, 2, 3, 3, 3, 1, 3, 0, 0, 7, 7, 2, 11, 5, 4, 1, 2, 3, 2, 0, 1, 0), # 85
(4, 2, 8, 13, 6, 2, 3, 3, 5, 1, 0, 0, 0, 6, 10, 2, 6, 5, 3, 2, 2, 2, 3, 0, 0, 0), # 86
(10, 13, 7, 9, 8, 4, 3, 2, 3, 0, 0, 0, 0, 4, 7, 3, 4, 5, 3, 4, 6, 6, 6, 0, 0, 0), # 87
(10, 2, 7, 3, 3, 1, 1, 1, 8, 1, 2, 4, 0, 8, 4, 3, 2, 6, 3, 5, 1, 4, 3, 0, 0, 0), # 88
(8, 8, 4, 5, 8, 4, 5, 2, 3, 0, 1, 2, 0, 11, 5, 4, 6, 7, 3, 2, 2, 6, 0, 2, 0, 0), # 89
(8, 5, 8, 7, 3, 4, 3, 1, 4, 3, 1, 0, 0, 6, 3, 9, 2, 11, 0, 2, 3, 4, 1, 0, 0, 0), # 90
(12, 8, 5, 12, 8, 1, 2, 1, 6, 2, 0, 0, 0, 10, 3, 4, 3, 5, 4, 0, 0, 1, 2, 0, 0, 0), # 91
(8, 3, 6, 9, 3, 4, 3, 4, 1, 1, 0, 1, 0, 9, 7, 8, 4, 5, 4, 3, 1, 1, 2, 1, 0, 0), # 92
(6, 5, 4, 6, 8, 4, 2, 5, 4, 3, 2, 0, 0, 11, 6, 2, 4, 4, 1, 6, 2, 3, 0, 0, 0, 0), # 93
(6, 5, 4, 9, 5, 5, 5, 3, 4, 1, 1, 1, 0, 7, 3, 7, 2, 4, 2, 1, 5, 3, 3, 1, 1, 0), # 94
(12, 11, 8, 9, 5, 2, 3, 4, 2, 0, 0, 0, 0, 12, 6, 7, 3, 6, 2, 4, 4, 3, 2, 1, 1, 0), # 95
(7, 5, 8, 10, 5, 1, 3, 0, 5, 0, 0, 0, 0, 8, 1, 0, 8, 2, 1, 4, 2, 6, 3, 1, 0, 0), # 96
(7, 2, 6, 3, 4, 5, 1, 3, 2, 2, 2, 0, 0, 15, 6, 2, 7, 9, 6, 2, 6, 2, 1, 1, 0, 0), # 97
(11, 7, 2, 9, 4, 6, 3, 3, 2, 1, 1, 0, 0, 7, 6, 5, 6, 3, 4, 3, 0, 5, 2, 0, 1, 0), # 98
(10, 8, 6, 8, 9, 3, 1, 1, 5, 1, 0, 2, 0, 7, 8, 4, 2, 6, 0, 4, 2, 6, 4, 1, 0, 0), # 99
(7, 9, 6, 5, 6, 1, 2, 1, 6, 0, 0, 0, 0, 9, 6, 6, 1, 5, 3, 4, 3, 5, 3, 1, 1, 0), # 100
(6, 6, 2, 5, 3, 0, 2, 0, 7, 2, 0, 1, 0, 8, 3, 6, 3, 2, 2, 2, 1, 2, 3, 0, 0, 0), # 101
(6, 9, 4, 1, 2, 2, 5, 2, 4, 2, 0, 0, 0, 6, 11, 4, 7, 13, 8, 1, 2, 0, 2, 0, 0, 0), # 102
(3, 5, 6, 8, 4, 3, 3, 4, 5, 2, 1, 1, 0, 14, 9, 7, 0, 5, 5, 5, 2, 2, 2, 0, 0, 0), # 103
(12, 8, 6, 12, 6, 4, 3, 2, 2, 1, 0, 0, 0, 8, 7, 7, 6, 1, 2, 2, 0, 2, 4, 0, 0, 0), # 104
(4, 4, 7, 8, 4, 5, 2, 1, 0, 0, 0, 1, 0, 10, 8, 2, 4, 9, 4, 4, 2, 1, 5, 0, 1, 0), # 105
(13, 7, 12, 8, 5, 0, 5, 3, 4, 2, 0, 0, 0, 6, 6, 5, 1, 2, 5, 3, 1, 3, 2, 3, 1, 0), # 106
(9, 10, 12, 12, 5, 0, 6, 1, 2, 0, 0, 2, 0, 4, 7, 3, 4, 3, 3, 3, 4, 3, 2, 3, 1, 0), # 107
(4, 4, 1, 10, 6, 2, 0, 5, 1, 2, 4, 0, 0, 12, 6, 4, 4, 6, 1, 1, 3, 1, 1, 0, 0, 0), # 108
(6, 5, 10, 4, 7, 3, 2, 1, 1, 1, 2, 0, 0, 6, 9, 4, 7, 7, 3, 1, 2, 4, 1, 1, 1, 0), # 109
(6, 6, 5, 5, 7, 3, 5, 3, 1, 0, 0, 1, 0, 8, 6, 3, 1, 3, 3, 1, 2, 3, 3, 1, 1, 0), # 110
(6, 7, 2, 8, 3, 3, 2, 1, 4, 2, 0, 0, 0, 5, 5, 3, 2, 7, 3, 0, 3, 4, 0, 1, 0, 0), # 111
(8, 7, 7, 5, 12, 2, 2, 1, 2, 0, 1, 1, 0, 5, 5, 2, 4, 10, 1, 4, 3, 4, 3, 0, 0, 0), # 112
(3, 4, 10, 9, 5, 2, 1, 8, 3, 3, 2, 2, 0, 12, 6, 3, 4, 4, 4, 2, 3, 1, 2, 1, 0, 0), # 113
(5, 3, 10, 5, 9, 2, 1, 3, 1, 1, 0, 2, 0, 8, 3, 4, 3, 9, 3, 3, 3, 3, 0, 2, 2, 0), # 114
(8, 4, 7, 7, 9, 1, 0, 1, 2, 0, 3, 1, 0, 5, 4, 1, 5, 5, 3, 1, 3, 2, 2, 1, 1, 0), # 115
(7, 2, 12, 4, 6, 2, 2, 0, 3, 2, 1, 0, 0, 8, 7, 3, 3, 9, 2, 2, 0, 3, 2, 0, 0, 0), # 116
(6, 4, 1, 10, 5, 2, 0, 2, 3, 2, 0, 0, 0, 10, 8, 8, 6, 10, 3, 5, 0, 5, 0, 1, 0, 0), # 117
(6, 6, 4, 6, 6, 5, 5, 1, 1, 0, 1, 0, 0, 8, 6, 5, 4, 3, 5, 1, 4, 2, 1, 3, 2, 0), # 118
(9, 4, 7, 3, 6, 3, 7, 0, 5, 2, 2, 0, 0, 7, 10, 8, 5, 7, 4, 5, 1, 4, 0, 1, 1, 0), # 119
(7, 4, 8, 8, 10, 5, 1, 1, 2, 1, 1, 2, 0, 6, 9, 6, 5, 6, 2, 2, 2, 1, 0, 1, 0, 0), # 120
(4, 0, 10, 4, 2, 2, 3, 3, 3, 1, 1, 2, 0, 5, 7, 6, 9, 5, 2, 3, 1, 3, 0, 2, 2, 0), # 121
(11, 2, 8, 5, 4, 2, 1, 2, 4, 1, 1, 2, 0, 7, 7, 4, 5, 3, 3, 2, 1, 3, 0, 1, 1, 0), # 122
(10, 9, 2, 5, 9, 2, 2, 0, 2, 0, 2, 1, 0, 7, 9, 3, 2, 8, 2, 0, 0, 1, 2, 1, 0, 0), # 123
(11, 7, 5, 5, 7, 1, 5, 2, 3, 1, 0, 1, 0, 7, 6, 3, 2, 8, 1, 1, 1, 2, 1, 1, 0, 0), # 124
(8, 2, 7, 7, 5, 6, 1, 0, 3, 1, 3, 1, 0, 4, 4, 3, 5, 7, 2, 4, 3, 4, 2, 3, 0, 0), # 125
(7, 3, 5, 7, 6, 2, 2, 1, 5, 0, 0, 1, 0, 6, 5, 7, 4, 4, 3, 3, 4, 7, 1, 2, 0, 0), # 126
(7, 6, 10, 3, 2, 3, 4, 3, 1, 6, 0, 1, 0, 5, 5, 7, 3, 6, 2, 3, 0, 1, 4, 2, 0, 0), # 127
(7, 4, 6, 6, 6, 3, 3, 4, 2, 1, 1, 0, 0, 5, 7, 9, 11, 10, 6, 0, 2, 2, 2, 1, 0, 0), # 128
(10, 5, 10, 10, 7, 3, 7, 2, 4, 3, 1, 1, 0, 2, 11, 5, 6, 2, 5, 2, 5, 2, 2, 2, 0, 0), # 129
(7, 3, 6, 6, 2, 1, 1, 3, 2, 2, 1, 0, 0, 6, 3, 6, 4, 7, 2, 4, 0, 2, 1, 2, 1, 0), # 130
(4, 4, 2, 8, 6, 4, 1, 3, 4, 0, 2, 1, 0, 5, 5, 6, 3, 5, 3, 6, 3, 3, 3, 1, 0, 0), # 131
(4, 5, 3, 7, 3, 5, 3, 0, 1, 3, 1, 0, 0, 13, 9, 5, 4, 7, 0, 2, 1, 5, 3, 2, 1, 0), # 132
(9, 7, 1, 9, 1, 1, 3, 4, 0, 0, 2, 1, 0, 8, 5, 5, 4, 9, 0, 4, 1, 1, 2, 1, 1, 0), # 133
(6, 3, 6, 10, 4, 2, 0, 3, 6, 0, 0, 3, 0, 8, 3, 5, 2, 7, 2, 6, 3, 5, 4, 0, 0, 0), # 134
(6, 9, 7, 6, 6, 3, 3, 2, 1, 1, 0, 0, 0, 11, 5, 0, 4, 3, 4, 4, 3, 5, 1, 0, 2, 0), # 135
(8, 5, 5, 4, 11, 0, 2, 0, 2, 2, 0, 2, 0, 8, 3, 5, 6, 6, 1, 1, 1, 3, 3, 2, 0, 0), # 136
(6, 5, 4, 3, 4, 5, 3, 1, 1, 1, 3, 0, 0, 2, 4, 5, 6, 8, 0, 0, 1, 2, 2, 0, 0, 0), # 137
(2, 7, 6, 6, 2, 4, 2, 4, 1, 0, 1, 0, 0, 10, 3, 2, 5, 7, 4, 0, 3, 3, 1, 2, 0, 0), # 138
(9, 9, 3, 5, 1, 2, 2, 3, 1, 1, 1, 1, 0, 8, 6, 7, 3, 5, 1, 4, 0, 1, 4, 0, 0, 0), # 139
(6, 4, 5, 6, 5, 0, 2, 1, 6, 0, 1, 1, 0, 7, 2, 4, 3, 11, 5, 3, 1, 5, 1, 5, 0, 0), # 140
(6, 1, 8, 4, 3, 6, 1, 1, 0, 1, 0, 0, 0, 6, 4, 6, 2, 6, 3, 1, 0, 2, 3, 2, 1, 0), # 141
(3, 4, 3, 5, 6, 2, 2, 1, 3, 1, 1, 2, 0, 5, 5, 3, 4, 8, 3, 1, 2, 5, 1, 1, 0, 0), # 142
(0, 7, 2, 7, 4, 3, 2, 0, 3, 2, 1, 0, 0, 10, 7, 3, 2, 5, 4, 0, 5, 1, 3, 1, 0, 0), # 143
(5, 9, 6, 11, 7, 2, 4, 1, 4, 1, 0, 0, 0, 4, 3, 4, 7, 5, 2, 1, 2, 7, 1, 0, 1, 0), # 144
(12, 6, 5, 9, 8, 1, 1, 1, 2, 1, 1, 2, 0, 5, 4, 5, 2, 7, 1, 1, 6, 4, 0, 1, 0, 0), # 145
(10, 2, 4, 5, 3, 5, 2, 2, 1, 1, 0, 1, 0, 8, 6, 3, 1, 4, 2, 2, 3, 2, 2, 1, 1, 0), # 146
(9, 2, 4, 3, 8, 1, 5, 2, 1, 0, 0, 1, 0, 8, 4, 5, 7, 4, 1, 1, 2, 4, 1, 1, 0, 0), # 147
(3, 6, 4, 9, 8, 5, 3, 0, 0, 3, 0, 1, 0, 4, 8, 2, 3, 6, 2, 1, 2, 2, 2, 1, 1, 0), # 148
(7, 6, 6, 6, 8, 3, 4, 3, 6, 3, 0, 0, 0, 7, 2, 2, 2, 4, 4, 2, 2, 1, 0, 1, 0, 0), # 149
(7, 3, 4, 10, 5, 1, 0, 2, 3, 2, 1, 0, 0, 10, 7, 3, 3, 3, 7, 1, 1, 2, 0, 3, 1, 0), # 150
(7, 3, 4, 4, 6, 1, 1, 2, 4, 2, 2, 0, 0, 5, 8, 1, 2, 9, 4, 3, 3, 5, 3, 1, 0, 0), # 151
(6, 1, 4, 5, 6, 2, 2, 1, 7, 0, 1, 0, 0, 4, 7, 7, 5, 2, 2, 6, 4, 3, 3, 3, 0, 0), # 152
(4, 5, 3, 5, 3, 1, 1, 1, 1, 3, 1, 1, 0, 6, 3, 3, 7, 2, 2, 5, 0, 1, 1, 0, 0, 0), # 153
(4, 5, 8, 2, 6, 2, 1, 3, 2, 1, 1, 1, 0, 11, 7, 7, 3, 5, 4, 3, 0, 2, 0, 0, 0, 0), # 154
(13, 2, 6, 1, 1, 1, 2, 1, 0, 4, 1, 1, 0, 9, 5, 1, 3, 3, 3, 2, 2, 2, 1, 1, 0, 0), # 155
(7, 3, 8, 6, 5, 2, 2, 3, 3, 0, 0, 1, 0, 6, 4, 6, 2, 4, 1, 0, 1, 1, 2, 0, 2, 0), # 156
(5, 4, 4, 12, 5, 5, 0, 2, 2, 1, 1, 0, 0, 9, 8, 5, 4, 9, 3, 2, 2, 1, 3, 0, 1, 0), # 157
(9, 6, 5, 5, 3, 2, 6, 0, 1, 1, 0, 0, 0, 8, 9, 5, 4, 7, 3, 1, 1, 3, 1, 2, 0, 0), # 158
(6, 5, 4, 7, 6, 2, 2, 1, 0, 1, 2, 0, 0, 5, 10, 6, 2, 4, 1, 1, 0, 0, 3, 1, 0, 0), # 159
(6, 6, 4, 6, 8, 1, 3, 0, 2, 2, 0, 0, 0, 6, 4, 2, 4, 6, 2, 0, 1, 2, 0, 1, 0, 0), # 160
(4, 6, 5, 5, 2, 1, 2, 3, 4, 2, 0, 2, 0, 7, 6, 7, 2, 4, 1, 1, 2, 2, 4, 1, 2, 0), # 161
(1, 2, 3, 1, 5, 3, 2, 0, 2, 1, 0, 0, 0, 11, 7, 3, 5, 8, 0, 1, 1, 3, 1, 1, 0, 0), # 162
(8, 2, 4, 5, 3, 5, 3, 1, 5, 1, 1, 0, 0, 4, 4, 4, 4, 9, 2, 1, 0, 4, 2, 1, 0, 0), # 163
(6, 1, 6, 2, 12, 2, 2, 1, 6, 1, 1, 0, 0, 5, 7, 1, 1, 7, 0, 2, 1, 1, 4, 0, 0, 0), # 164
(4, 6, 8, 6, 8, 1, 1, 1, 1, 2, 1, 0, 0, 6, 2, 2, 2, 4, 1, 1, 2, 1, 0, 3, 0, 0), # 165
(8, 5, 3, 6, 8, 1, 2, 0, 3, 0, 0, 0, 0, 5, 3, 2, 2, 4, 0, 2, 2, 1, 2, 0, 0, 0), # 166
(2, 6, 7, 8, 7, 3, 3, 2, 1, 2, 2, 0, 0, 6, 4, 4, 1, 0, 2, 3, 6, 2, 1, 1, 0, 0), # 167
(6, 1, 1, 1, 4, 5, 2, 2, 1, 0, 0, 0, 0, 6, 5, 0, 1, 7, 0, 1, 3, 0, 1, 1, 1, 0), # 168
(10, 1, 3, 4, 5, 0, 3, 2, 1, 3, 0, 0, 0, 3, 5, 5, 1, 2, 4, 1, 0, 2, 0, 0, 0, 0), # 169
(4, 4, 3, 2, 3, 3, 1, 2, 0, 0, 0, 1, 0, 4, 0, 2, 1, 2, 4, 1, 1, 0, 5, 0, 0, 0), # 170
(2, 3, 12, 8, 8, 2, 1, 2, 1, 1, 1, 0, 0, 2, 3, 1, 3, 5, 5, 1, 2, 1, 2, 2, 0, 0), # 171
(2, 4, 4, 4, 1, 1, 0, 1, 0, 2, 1, 0, 0, 2, 5, 1, 1, 5, 2, 0, 2, 0, 2, 1, 0, 0), # 172
(1, 0, 5, 1, 2, 3, 1, 3, 1, 1, 0, 1, 0, 3, 2, 4, 2, 2, 3, 3, 0, 1, 2, 0, 1, 0), # 173
(3, 6, 2, 3, 2, 1, 1, 2, 0, 1, 0, 0, 0, 3, 2, 4, 2, 4, 0, 0, 0, 3, 0, 1, 0, 0), # 174
(5, 4, 2, 0, 1, 1, 1, 1, 3, 1, 1, 1, 0, 6, 2, 3, 1, 4, 1, 0, 1, 3, 1, 1, 0, 0), # 175
(3, 3, 1, 2, 2, 4, 1, 0, 2, 0, 0, 0, 0, 0, 3, 3, 3, 3, 0, 0, 1, 2, 1, 2, 0, 0), # 176
(6, 2, 4, 3, 0, 1, 1, 1, 1, 1, 0, 0, 0, 8, 2, 1, 3, 2, 1, 0, 1, 0, 1, 0, 1, 0), # 177
(4, 4, 3, 6, 1, 1, 0, 0, 4, 1, 1, 0, 0, 5, 1, 0, 1, 2, 1, 1, 2, 4, 1, 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 = (
(4.0166924626974145, 4.420230847754533, 4.169026583690005, 4.971734219090746, 4.4437484860876895, 2.5109239456298713, 3.3168284922991322, 3.7225409383835384, 4.872079249734406, 3.166412012417896, 3.3642121311084825, 3.918332062644939, 4.067104170062691), # 0
(4.283461721615979, 4.712048555315807, 4.444277273064122, 5.3001154026212935, 4.737992269979389, 2.6767868672340445, 3.535575153010955, 3.9676109783245668, 5.1937962610663275, 3.37518455382172, 3.5864769087649053, 4.176973328651484, 4.3358333179518835), # 1
(4.549378407183785, 5.0027081367127835, 4.718433828437931, 5.627190163731836, 5.0311703789997955, 2.841988091609956, 3.7534548063685635, 4.211700198323536, 5.514229445502039, 3.583131020016437, 3.8078585190210505, 4.434586121642444, 4.603491862567752), # 2
(4.81340623451725, 5.291056401549158, 4.9904086954558835, 5.951661126025659, 5.322129340801522, 3.0058724980680904, 3.9696029133183646, 4.453840925995606, 5.832108128736874, 3.7894261587409446, 4.027478729461906, 4.690148547944369, 4.869018245003381), # 3
(5.074508918732786, 5.57594015942862, 5.259114319762429, 6.272230913106056, 5.609715683037194, 3.1677849659189343, 4.183154934806767, 4.6930654889559325, 6.146161636466166, 3.993244717734143, 4.24445930767246, 4.942638713883811, 5.131350906351854), # 4
(5.331650174946809, 5.856206219954871, 5.523463147002015, 6.587602148576315, 5.892775933359424, 3.3270703744729717, 4.393246331780179, 4.928406214819674, 6.455119294385248, 4.193761444734931, 4.457922021237706, 5.191034725787318, 5.389428287706262), # 5
(5.583793718275733, 6.130701392731601, 5.782367622819093, 6.896477456039722, 6.170156619420835, 3.4830736030406912, 4.59901256518501, 5.158895431201991, 6.757710428189452, 4.390151087482207, 4.666988637742626, 5.434314689981447, 5.642188830159686), # 6
(5.829903263835975, 6.398272487362505, 6.034740192858108, 7.19755945909957, 6.440704268874043, 3.6351395309325767, 4.799589095967668, 5.383565465718042, 7.052664363574116, 4.58158839371487, 4.870780924772215, 5.671456712792743, 5.888570974805216), # 7
(6.068942526743948, 6.65776631345128, 6.279493302763517, 7.489550781359142, 6.703265409371669, 3.782613037459112, 4.994111385074558, 5.60144864598298, 7.338710426234565, 4.76724811117182, 5.068420649911457, 5.901438900547762, 6.127513162735934), # 8
(6.299875222116068, 6.908029680601619, 6.515539398179763, 7.771154046421735, 6.956686568566328, 3.924839001930787, 5.181714893452096, 5.811577299611971, 7.6145779418661395, 4.946304987591954, 5.259029580745342, 6.123239359573051, 6.35795383504493), # 9
(6.5216650650687455, 7.147909398417212, 6.7417909247512995, 8.04107187789063, 7.199814274110641, 4.061162303658086, 5.361535082046684, 6.012983754220169, 7.878996236164172, 5.117933770714171, 5.441729484858859, 6.335836196195162, 6.578831432825289), # 10
(6.7332757707184046, 7.3762522765017655, 6.957160328122573, 8.298006899369119, 7.431495053657227, 4.190927821951495, 5.532707411804733, 6.204700337422732, 8.130694634823994, 5.281309208277375, 5.615642129836999, 6.538207516740648, 6.78908439717009), # 11
(6.93367105418145, 7.591905124458958, 7.160560053938032, 8.54066173446049, 7.650575434858702, 4.313480436121496, 5.694367343672649, 6.385759376834817, 8.368402463540944, 5.435606048020458, 5.7798892832647475, 6.729331427536055, 6.987651169172428), # 12
(7.121814630574301, 7.793714751892496, 7.3509025478421295, 8.767739006768036, 7.855901945367681, 4.428165025478579, 5.845650338596845, 6.555193200071585, 8.590849048010346, 5.579999037682324, 5.933592712727095, 6.908186034907937, 7.173470189925388), # 13
(7.296670215013373, 7.980527968406071, 7.527100255479318, 8.977941339895034, 8.046321112836791, 4.5343264693332275, 5.9856918575237295, 6.7120341347481975, 8.796763713927538, 5.713662925001867, 6.0758741858090275, 7.073749445182848, 7.345479900522051), # 14
(7.457201522615084, 8.151191583603374, 7.688065622494034, 9.169971357444789, 8.220679464918646, 4.63130964699593, 6.1136273613997005, 6.855314508479805, 8.984875786987855, 5.835772457717993, 6.2058554700955355, 7.224999764687337, 7.502618742055505), # 15
(7.602372268495841, 8.304552407088106, 7.83271109453074, 9.342531683020573, 8.377823529265866, 4.718459437777168, 6.228592311171181, 6.984066648881569, 9.153914592886629, 5.945502383569597, 6.32265833317161, 7.360915099747952, 7.643825155618837), # 16
(7.73114616777206, 8.439457248463958, 7.959949117233882, 9.49432494022569, 8.516599833531071, 4.795120720987429, 6.329722167784569, 7.097322883568655, 9.302609457319187, 6.042027450295574, 6.425404542622239, 7.480473556691244, 7.768037582305133), # 17
(7.842486935560164, 8.55475291733462, 8.068692136247904, 9.624053752663423, 8.635854905366871, 4.860638375937203, 6.416152392186281, 7.194115540156209, 9.429689705980877, 6.1245224056348295, 6.513215866032407, 7.582653241843772, 7.874194463207477), # 18
(7.935358286976559, 8.649286223303795, 8.157852597217262, 9.730420743937053, 8.734435272425893, 4.914357281936967, 6.4870184453227155, 7.273476946259397, 9.533884664567024, 6.192161997326263, 6.585214070987103, 7.666432261532077, 7.961234239418957), # 19
(8.008723937137665, 8.72190397597517, 8.226342945786403, 9.812128537649883, 8.811187462360754, 4.955622318297215, 6.54145578814029, 7.334439429493374, 9.61392365877296, 6.2441209731087675, 6.64052092507132, 7.730788722082713, 8.02809535203266), # 20
(8.061547601159893, 8.771452984952447, 8.273075627599775, 9.86787975740519, 8.864958002824071, 4.983778364328429, 6.578599881585408, 7.376035317473299, 9.668536014294018, 6.279574080721244, 6.678258195870048, 7.774700729822235, 8.073716242141662), # 21
(8.092792994159664, 8.796780059839316, 8.296963088301828, 9.89637702680627, 8.89459342146846, 4.998170299341094, 6.59758618660448, 7.397296937814332, 9.696451056825532, 6.297696067902594, 6.697547650968272, 7.797146391077192, 8.097035350839063), # 22
(8.104314690674112, 8.799778875171468, 8.299938545953362, 9.899944650205763, 8.902185644826078, 5.0, 6.599843201807471, 7.399595061728395, 9.699940987654323, 6.299833818015546, 6.699966429729392, 7.799918061271147, 8.1), # 23
(8.112809930427323, 8.79802962962963, 8.299451851851853, 9.899505555555557, 8.906486090891882, 5.0, 6.598603050108934, 7.3964, 9.699473333333334, 6.29852049382716, 6.699699663299665, 7.799269135802469, 8.1), # 24
(8.121125784169264, 8.794581618655693, 8.29849108367627, 9.898636831275722, 8.910691956475603, 5.0, 6.596159122085048, 7.390123456790125, 9.69854938271605, 6.295935070873343, 6.69917071954109, 7.797988111568358, 8.1), # 25
(8.129261615238427, 8.789487517146778, 8.297069410150893, 9.897348353909464, 8.914803094736884, 5.0, 6.592549374646977, 7.380883950617285, 9.69718098765432, 6.29212056698674, 6.698384387080684, 7.7960925468678575, 8.1), # 26
(8.13721678697331, 8.7828, 8.2952, 9.89565, 8.918819358835371, 5.0, 6.587811764705883, 7.3688, 9.69538, 6.28712, 6.697345454545455, 7.793600000000001, 8.1), # 27
(8.1449906627124, 8.774571742112483, 8.292896021947874, 9.893551646090536, 8.922740601930721, 5.0, 6.581984249172921, 7.353990123456791, 9.693158271604938, 6.2809763877457705, 6.696058710562415, 7.790528029263832, 8.1), # 28
(8.1525826057942, 8.764855418381345, 8.290170644718794, 9.89106316872428, 8.926566677182576, 5.0, 6.575104784959253, 7.3365728395061724, 9.690527654320988, 6.273732748056699, 6.6945289437585735, 7.78689419295839, 8.1), # 29
(8.159991979557198, 8.753703703703705, 8.287037037037036, 9.888194444444444, 8.930297437750589, 5.0, 6.567211328976035, 7.316666666666666, 9.6875, 6.265432098765433, 6.692760942760943, 7.782716049382715, 8.1), # 30
(8.167218147339886, 8.741169272976682, 8.283508367626887, 9.88495534979424, 8.933932736794407, 5.0, 6.558341838134432, 7.2943901234567905, 9.684087160493828, 6.256117457704619, 6.6907594961965335, 7.778011156835849, 8.1), # 31
(8.174260472480764, 8.727304801097395, 8.27959780521262, 9.881355761316874, 8.937472427473677, 5.0, 6.548534269345599, 7.269861728395063, 9.680300987654322, 6.245831842706905, 6.688529392692356, 7.772797073616828, 8.1), # 32
(8.181118318318317, 8.712162962962962, 8.27531851851852, 9.877405555555555, 8.94091636294805, 5.0, 6.537826579520697, 7.243200000000001, 9.676153333333334, 6.234618271604939, 6.6860754208754205, 7.7670913580246905, 8.1), # 33
(8.187791048191048, 8.695796433470507, 8.270683676268861, 9.873114609053498, 8.944264396377173, 5.0, 6.526256725570888, 7.214523456790123, 9.671656049382719, 6.222519762231368, 6.68340236937274, 7.760911568358482, 8.1), # 34
(8.194278025437447, 8.678257887517146, 8.26570644718793, 9.868492798353909, 8.947516380920696, 5.0, 6.513862664407327, 7.183950617283951, 9.666820987654322, 6.209579332418839, 6.680515026811323, 7.754275262917239, 8.1), # 35
(8.200578613396004, 8.6596, 8.2604, 9.86355, 8.950672169738269, 5.0, 6.500682352941176, 7.151600000000001, 9.66166, 6.1958400000000005, 6.677418181818182, 7.747200000000001, 8.1), # 36
(8.20669217540522, 8.639875445816186, 8.254777503429356, 9.85829609053498, 8.953731615989538, 5.0, 6.486753748083595, 7.11759012345679, 9.656184938271606, 6.1813447828075, 6.674116623020328, 7.739703337905808, 8.1), # 37
(8.212618074803581, 8.619136899862827, 8.248852126200275, 9.85274094650206, 8.956694572834152, 5.0, 6.4721148067457435, 7.0820395061728405, 9.650407654320988, 6.166136698673983, 6.670615139044769, 7.7318028349337, 8.1), # 38
(8.218355674929589, 8.597437037037038, 8.242637037037039, 9.846894444444445, 8.959560893431762, 5.0, 6.456803485838781, 7.045066666666667, 9.644340000000001, 6.150258765432099, 6.666918518518519, 7.723516049382716, 8.1), # 39
(8.22390433912173, 8.574828532235939, 8.236145404663922, 9.84076646090535, 8.962330430942016, 5.0, 6.440857742273865, 7.006790123456792, 9.637993827160495, 6.133754000914496, 6.663031550068587, 7.714860539551899, 8.1), # 40
(8.229263430718502, 8.551364060356653, 8.229390397805213, 9.834366872427985, 8.965003038524562, 5.0, 6.424315532962156, 6.967328395061729, 9.631380987654321, 6.116665422953818, 6.658959022321986, 7.705853863740284, 8.1), # 41
(8.2344323130584, 8.527096296296298, 8.222385185185187, 9.827705555555557, 8.967578569339047, 5.0, 6.4072148148148145, 6.9268, 9.624513333333335, 6.0990360493827165, 6.654705723905725, 7.696513580246914, 8.1), # 42
(8.239410349479915, 8.50207791495199, 8.215142935528121, 9.820792386831277, 8.970056876545122, 5.0, 6.389593544743001, 6.8853234567901245, 9.617402716049384, 6.080908898033837, 6.650276443446813, 7.6868572473708285, 8.1), # 43
(8.244196903321543, 8.47636159122085, 8.2076768175583, 9.813637242798356, 8.972437813302436, 5.0, 6.371489679657872, 6.843017283950619, 9.610060987654322, 6.062326986739826, 6.645675969572266, 7.676902423411066, 8.1), # 44
(8.248791337921773, 8.450000000000001, 8.200000000000001, 9.80625, 8.974721232770637, 5.0, 6.352941176470589, 6.800000000000001, 9.6025, 6.043333333333334, 6.640909090909091, 7.666666666666666, 8.1), # 45
(8.253193016619106, 8.423045816186557, 8.192125651577504, 9.798640534979425, 8.976906988109373, 5.0, 6.333985992092311, 6.756390123456791, 9.594731604938271, 6.023970955647005, 6.635980596084299, 7.656167535436672, 8.1), # 46
(8.257401302752028, 8.39555171467764, 8.18406694101509, 9.790818724279836, 8.978994932478294, 5.0, 6.3146620834341975, 6.712306172839506, 9.586767654320989, 6.004282871513489, 6.630895273724903, 7.64542258802012, 8.1), # 47
(8.261415559659037, 8.367570370370371, 8.175837037037038, 9.782794444444447, 8.980984919037049, 5.0, 6.295007407407407, 6.667866666666668, 9.57862, 5.984312098765432, 6.625657912457912, 7.634449382716049, 8.1), # 48
(8.26523515067863, 8.339154458161865, 8.167449108367627, 9.774577572016462, 8.982876800945286, 5.0, 6.275059920923102, 6.623190123456791, 9.57030049382716, 5.964101655235483, 6.6202733009103385, 7.623265477823503, 8.1), # 49
(8.268859439149294, 8.310356652949247, 8.15891632373114, 9.766177983539094, 8.984670431362652, 5.0, 6.25485758089244, 6.578395061728395, 9.56182098765432, 5.943694558756287, 6.61474622770919, 7.611888431641519, 8.1), # 50
(8.272287788409528, 8.28122962962963, 8.150251851851852, 9.757605555555557, 8.9863656634488, 5.0, 6.23443834422658, 6.5336, 9.553193333333335, 5.923133827160494, 6.609081481481482, 7.600335802469137, 8.1), # 51
(8.275519561797823, 8.251826063100138, 8.141468861454047, 9.748870164609054, 8.987962350363372, 5.0, 6.213840167836683, 6.488923456790123, 9.54442938271605, 5.90246247828075, 6.603283850854222, 7.588625148605397, 8.1), # 52
(8.278554122652675, 8.222198628257889, 8.132580521262005, 9.739981687242798, 8.989460345266023, 5.0, 6.1931010086339064, 6.444483950617284, 9.535540987654322, 5.881723529949703, 6.597358124454421, 7.576774028349337, 8.1), # 53
(8.281390834312573, 8.192400000000001, 8.1236, 9.73095, 8.990859501316402, 5.0, 6.172258823529412, 6.400399999999999, 9.52654, 5.86096, 6.59130909090909, 7.5648, 8.1), # 54
(8.284029060116017, 8.162482853223594, 8.114540466392318, 9.721784979423868, 8.992159671674152, 5.0, 6.151351569434358, 6.35679012345679, 9.517438271604938, 5.84021490626429, 6.585141538845242, 7.552720621856425, 8.1), # 55
(8.286468163401498, 8.132499862825789, 8.105415089163237, 9.712496502057613, 8.993360709498928, 5.0, 6.130417203259905, 6.313772839506173, 9.508247654320988, 5.819531266575218, 6.578860256889887, 7.54055345221765, 8.1), # 56
(8.288707507507507, 8.102503703703704, 8.096237037037039, 9.703094444444446, 8.994462467950374, 5.0, 6.109493681917211, 6.271466666666668, 9.498980000000001, 5.798952098765433, 6.572470033670034, 7.528316049382716, 8.1), # 57
(8.290746455772544, 8.072547050754459, 8.087019478737998, 9.693588683127572, 8.99546480018814, 5.0, 6.088618962317438, 6.2299901234567905, 9.489647160493828, 5.778520420667582, 6.565975657812697, 7.516025971650663, 8.1), # 58
(8.292584371535098, 8.042682578875171, 8.077775582990398, 9.683989094650206, 8.996367559371876, 5.0, 6.067831001371743, 6.189461728395062, 9.480260987654322, 5.758279250114313, 6.55938191794488, 7.503700777320531, 8.1), # 59
(8.294220618133663, 8.012962962962964, 8.068518518518518, 9.674305555555556, 8.99717059866123, 5.0, 6.0471677559912855, 6.15, 9.470833333333335, 5.738271604938272, 6.552693602693603, 7.491358024691358, 8.1), # 60
(8.295654558906731, 7.983440877914953, 8.05926145404664, 9.664547942386832, 8.997873771215849, 5.0, 6.026667183087227, 6.1117234567901235, 9.461376049382716, 5.718540502972108, 6.545915500685871, 7.4790152720621865, 8.1), # 61
(8.296885557192804, 7.954168998628258, 8.050017558299041, 9.654726131687244, 8.998476930195388, 5.0, 6.006367239570725, 6.074750617283951, 9.451900987654321, 5.699128962048469, 6.539052400548697, 7.4666900777320535, 8.1), # 62
(8.297912976330368, 7.9252, 8.0408, 9.644850000000002, 8.998979928759486, 5.0, 5.986305882352941, 6.039200000000001, 9.44242, 5.68008, 6.532109090909092, 7.4544, 8.1), # 63
(8.298736179657919, 7.896586556927298, 8.0316219478738, 9.634929423868314, 8.999382620067799, 5.0, 5.966521068345034, 6.005190123456791, 9.432944938271605, 5.661436634659351, 6.5250903603940635, 7.442162597165067, 8.1), # 64
(8.29935453051395, 7.86838134430727, 8.02249657064472, 9.624974279835392, 8.999684857279973, 5.0, 5.947050754458163, 5.972839506172839, 9.423487654320988, 5.643241883859168, 6.518000997630629, 7.429995427526291, 8.1), # 65
(8.299767392236957, 7.840637037037038, 8.013437037037038, 9.614994444444445, 8.999886493555659, 5.0, 5.927932897603486, 5.942266666666668, 9.414060000000001, 5.625538765432099, 6.510845791245791, 7.417916049382717, 8.1), # 66
(8.299974128165434, 7.813406310013717, 8.004456515775034, 9.604999794238683, 8.999987382054504, 5.0, 5.909205454692165, 5.913590123456792, 9.404673827160494, 5.608370297210792, 6.5036295298665685, 7.405942021033379, 8.1), # 67
(8.29983329158466, 7.786598911456259, 7.9955247599451305, 9.594913392377887, 8.999902364237876, 4.99990720926688, 5.890812155863717, 5.88667508001829, 9.395270278920897, 5.591696353317733, 6.496228790832301, 7.394024017519794, 8.099900120027435), # 68
(8.298513365539453, 7.75939641577061, 7.98639074074074, 9.584226811594203, 8.99912854030501, 4.999173662551441, 5.872214545077291, 5.860079012345679, 9.385438271604938, 5.575045112563544, 6.487890271132376, 7.38177517868746, 8.099108796296298), # 69
(8.295908630047116, 7.731673967874684, 7.977014746227709, 9.572869699409555, 8.997599451303154, 4.9977290047248895, 5.853328107649096, 5.833561957018748, 9.375122313671698, 5.558335619570188, 6.478519109220864, 7.369138209034247, 8.097545867626888), # 70
(8.292055728514343, 7.703448134873224, 7.967400068587105, 9.560858803005905, 8.995334463003308, 4.995596646852614, 5.8341613276311906, 5.807132693187015, 9.364337768632831, 5.541568287474112, 6.468149896627089, 7.356122349770172, 8.095231910150892), # 71
(8.286991304347827, 7.674735483870967, 7.9575499999999995, 9.548210869565217, 8.99235294117647, 4.992800000000001, 5.81472268907563, 5.7808, 9.353100000000001, 5.524743529411765, 6.456817224880384, 7.342736842105264, 8.0921875), # 72
(8.280752000954257, 7.6455525819726535, 7.947467832647462, 9.534942646269458, 8.988674251593642, 4.989362475232434, 5.795020676034474, 5.754572656607225, 9.341424371284866, 5.507861758519595, 6.444555685510071, 7.328990927249535, 8.0884332133059), # 73
(8.273374461740323, 7.615915996283022, 7.937156858710562, 9.52107088030059, 8.98431776002582, 4.985307483615303, 5.775063772559778, 5.728459442158208, 9.329326245999086, 5.49092338793405, 6.431399870045485, 7.314893846413014, 8.083989626200276), # 74
(8.26489533011272, 7.5858422939068095, 7.92662037037037, 9.50661231884058, 8.97930283224401, 4.980658436213993, 5.754860462703601, 5.7024691358024695, 9.31682098765432, 5.473928830791576, 6.417384370015949, 7.300454840805718, 8.078877314814816), # 75
(8.255351249478142, 7.55534804194876, 7.915861659807956, 9.49158370907139, 8.973648834019205, 4.975438744093889, 5.734419230517997, 5.6766105166895295, 9.303923959762232, 5.4568785002286235, 6.402543776950793, 7.2856831516376666, 8.073116855281206), # 76
(8.244778863243274, 7.524449807513609, 7.904884019204388, 9.476001798174986, 8.967375131122408, 4.9696718183203785, 5.7137485600550235, 5.650892363968908, 9.290650525834478, 5.43977280938164, 6.38691268237935, 7.270588020118885, 8.06672882373114), # 77
(8.233214814814815, 7.493164157706095, 7.893690740740741, 9.459883333333334, 8.96050108932462, 4.963381069958848, 5.69285693536674, 5.625323456790124, 9.277016049382715, 5.422612171387073, 6.370525677830941, 7.255178687459391, 8.059733796296298), # 78
(8.220695747599452, 7.461507659630958, 7.88228511659808, 9.443245061728396, 8.953046074396838, 4.956589910074683, 5.671752840505201, 5.5999125743026985, 9.26303589391861, 5.405396999381371, 6.353417354834898, 7.239464394869204, 8.052152349108367), # 79
(8.207258305003878, 7.429496880392938, 7.870670438957475, 9.426103730542136, 8.945029452110063, 4.949321749733272, 5.650444759522465, 5.574668495656151, 9.248725422953818, 5.388127706500981, 6.335622304920551, 7.223454383558348, 8.04400505829904), # 80
(8.192939130434784, 7.397148387096775, 7.85885, 9.408476086956524, 8.936470588235293, 4.9416, 5.628941176470589, 5.549600000000001, 9.2341, 5.370804705882353, 6.317175119617225, 7.207157894736842, 8.0353125), # 81
(8.177774867298861, 7.364478746847206, 7.8468270919067225, 9.390378878153516, 8.927388848543533, 4.933448071940254, 5.607250575401629, 5.524715866483768, 9.219174988568815, 5.353428410661933, 6.298110390454251, 7.190584169614709, 8.026095250342937), # 82
(8.161802159002804, 7.331504526748971, 7.834605006858711, 9.371828851315083, 8.917803598805778, 4.924889376619419, 5.585381440367643, 5.500024874256973, 9.203965752171925, 5.335999233976169, 6.278462708960955, 7.17374244940197, 8.016373885459535), # 83
(8.145057648953301, 7.29824229390681, 7.822187037037037, 9.35284275362319, 8.907734204793028, 4.915947325102881, 5.563342255420687, 5.475535802469135, 9.188487654320987, 5.3185175889615115, 6.258266666666667, 7.156641975308642, 8.006168981481482), # 84
(8.127577980557048, 7.264708615425461, 7.80957647462277, 9.333437332259797, 8.897200032276286, 4.906645328456029, 5.54114150461282, 5.451257430269777, 9.172756058527662, 5.300983888754405, 6.237556855100715, 7.13929198854475, 7.995501114540467), # 85
(8.10939979722073, 7.230920058409665, 7.796776611796983, 9.313629334406873, 8.886220447026547, 4.897006797744247, 5.518787671996097, 5.4271985368084135, 9.156786328303614, 5.283398546491299, 6.216367865792428, 7.121701730320315, 7.984390860768176), # 86
(8.090559742351045, 7.1968931899641575, 7.7837907407407405, 9.293435507246377, 8.874814814814817, 4.887055144032922, 5.496289241622575, 5.403367901234568, 9.140593827160496, 5.265761975308642, 6.194734290271132, 7.103880441845354, 7.972858796296297), # 87
(8.071094459354686, 7.162644577193681, 7.7706221536351165, 9.27287259796028, 8.863002501412089, 4.876813778387441, 5.473654697544313, 5.37977430269776, 9.124193918609969, 5.248074588342881, 6.172690720066159, 7.085837364329892, 7.960925497256517), # 88
(8.051040591638339, 7.128190787202974, 7.75727414266118, 9.251957353730543, 8.850802872589366, 4.8663061118731905, 5.4508925238133665, 5.356426520347508, 9.107601966163696, 5.230336798730466, 6.150271746706835, 7.067581738983948, 7.948611539780521), # 89
(8.030434782608696, 7.093548387096774, 7.74375, 9.230706521739132, 8.838235294117649, 4.855555555555556, 5.428011204481793, 5.333333333333333, 9.090833333333334, 5.2125490196078434, 6.1275119617224885, 7.049122807017544, 7.9359375000000005), # 90
(8.00931367567245, 7.058733943979822, 7.730053017832647, 9.20913684916801, 8.825319131767932, 4.8445855204999235, 5.405019223601649, 5.3105035208047555, 9.073903383630546, 5.194711664111461, 6.104445956642448, 7.0304698096406995, 7.922923954046638), # 91
(7.9877139142362985, 7.023764024956858, 7.716186488340192, 9.187265083199142, 8.812073751311223, 4.833419417771681, 5.381925065224994, 5.287945861911295, 9.056827480566987, 5.176825145377768, 6.081108322996043, 7.011631988063439, 7.909591478052126), # 92
(7.965672141706924, 6.988655197132617, 7.702153703703704, 9.165107971014494, 8.798518518518518, 4.822080658436214, 5.358737213403881, 5.26566913580247, 9.039620987654322, 5.15888987654321, 6.0575336523126, 6.992618583495776, 7.895960648148147), # 93
(7.943225001491024, 6.953424027611842, 7.6879579561042535, 9.142682259796029, 8.784672799160816, 4.810592653558909, 5.335464152190369, 5.243682121627802, 9.022299268404208, 5.140906270744238, 6.033756536121448, 6.973438837147739, 7.882052040466393), # 94
(7.920409136995288, 6.9180870834992705, 7.673602537722909, 9.120004696725712, 8.770555959009119, 4.798978814205152, 5.312114365636515, 5.221993598536809, 9.004877686328305, 5.122874741117297, 6.009811565951917, 6.954101990229344, 7.867886231138546), # 95
(7.89726119162641, 6.882660931899643, 7.659090740740742, 9.097092028985507, 8.756187363834423, 4.787262551440329, 5.288696337794377, 5.200612345679013, 8.987371604938271, 5.104795700798839, 5.985733333333334, 6.934617283950619, 7.853483796296297), # 96
(7.873817808791078, 6.847162139917697, 7.64442585733882, 9.07396100375738, 8.741586379407732, 4.775467276329827, 5.265218552716011, 5.179547142203933, 8.969796387745772, 5.086669562925308, 5.961556429795026, 6.914993959521576, 7.838865312071332), # 97
(7.850115631895988, 6.811607274658171, 7.629611179698216, 9.050628368223297, 8.726772371500042, 4.763616399939035, 5.241689494453475, 5.158806767261089, 8.952167398262459, 5.068496740633154, 5.937315446866325, 6.895241258152239, 7.824051354595337), # 98
(7.826191304347827, 6.776012903225807, 7.614650000000001, 9.027110869565218, 8.711764705882354, 4.751733333333333, 5.218117647058825, 5.138400000000001, 8.9345, 5.050277647058824, 5.913044976076556, 6.875368421052632, 7.8090625000000005), # 99
(7.80208146955329, 6.740395592725341, 7.59954561042524, 9.00342525496511, 8.696582748325667, 4.739841487578113, 5.194511494584116, 5.118335619570188, 8.916809556470051, 5.032012695338767, 5.888779608955048, 6.855384689432774, 7.79391932441701), # 100
(7.777822770919068, 6.704771910261517, 7.584301303155008, 8.979588271604939, 8.681245864600985, 4.727964273738759, 5.17087952108141, 5.09862240512117, 8.899111431184272, 5.013702298609431, 5.86455393703113, 6.835299304502683, 7.7786424039780515), # 101
(7.753451851851853, 6.669158422939069, 7.56892037037037, 8.955616666666668, 8.665773420479303, 4.7161251028806594, 5.1472302106027605, 5.07926913580247, 8.881420987654321, 4.995346870007263, 5.840402551834131, 6.815121507472385, 7.763252314814816), # 102
(7.729005355758336, 6.633571697862738, 7.5534061042524, 8.93152718733226, 8.650184781731623, 4.704347386069197, 5.123572047200224, 5.060284590763604, 8.86375358939186, 4.976946822668712, 5.816360044893379, 6.794860539551898, 7.747769633058984), # 103
(7.704519926045208, 6.598028302137263, 7.537761796982167, 8.907336580783683, 8.634499314128943, 4.692654534369761, 5.099913514925861, 5.041677549154093, 8.846124599908551, 4.958502569730225, 5.792461007738201, 6.774525641951243, 7.732214934842251), # 104
(7.680032206119162, 6.562544802867383, 7.5219907407407405, 8.883061594202898, 8.618736383442267, 4.681069958847737, 5.076263097831727, 5.023456790123458, 8.82854938271605, 4.940014524328251, 5.768740031897927, 6.754126055880443, 7.716608796296296), # 105
(7.655578839386891, 6.527137767157839, 7.5060962277091905, 8.858718974771874, 8.602915355442589, 4.669617070568511, 5.052629279969876, 5.005631092821217, 8.811043301326016, 4.921483099599236, 5.745231708901884, 6.733671022549515, 7.700971793552812), # 106
(7.631196469255085, 6.491823762113369, 7.490081550068588, 8.83432546967257, 8.587055595900912, 4.65831928059747, 5.0290205453923695, 4.988209236396892, 8.793621719250115, 4.9029087086796315, 5.721970630279402, 6.713169783168484, 7.685324502743484), # 107
(7.606921739130435, 6.456619354838711, 7.473950000000001, 8.809897826086958, 8.571176470588235, 4.647200000000001, 5.0054453781512604, 4.9712000000000005, 8.7763, 4.884291764705883, 5.698991387559809, 6.69263157894737, 7.669687500000001), # 108
(7.582791292419635, 6.421541112438604, 7.4577048696845, 8.785452791196994, 8.55529734527556, 4.636282639841488, 4.98191226229861, 4.954612162780065, 8.759093507087334, 4.865632680814438, 5.676328572272432, 6.67206565109619, 7.654081361454047), # 109
(7.558841772529373, 6.38660560201779, 7.441349451303157, 8.761007112184648, 8.539437585733884, 4.625590611187319, 4.9584296818864715, 4.938454503886603, 8.742017604023777, 4.846931870141747, 5.654016775946601, 6.651481240824971, 7.638526663237312), # 110
(7.535109822866345, 6.351829390681004, 7.424887037037038, 8.736577536231884, 8.523616557734206, 4.615147325102881, 4.935006120966905, 4.922735802469136, 8.725087654320989, 4.828189745824256, 5.632090590111643, 6.630887589343731, 7.623043981481482), # 111
(7.51163208683724, 6.317229045532987, 7.408320919067217, 8.712180810520666, 8.507853627047528, 4.6049761926535595, 4.911650063591967, 4.907464837677184, 8.708319021490626, 4.809406720998413, 5.610584606296888, 6.6102939378624885, 7.607653892318244), # 112
(7.488403378962436, 6.282878895028762, 7.391694262601655, 8.687867105993632, 8.492140544138964, 4.595095815371611, 4.888420770925416, 4.892682055024485, 8.691770249006897, 4.790643789290184, 5.589539124922293, 6.589754349203543, 7.592355120674577), # 113
(7.465184718320052, 6.249117746820429, 7.375236540017295, 8.663831537021869, 8.476314683653062, 4.585483686823921, 4.865614566728464, 4.878569007604096, 8.675695228570449, 4.772252134330226, 5.568995469690558, 6.56952973769038, 7.577020331328028), # 114
(7.441907922403196, 6.215957758946438, 7.358957546165854, 8.640067604145424, 8.460326142310882, 4.576114809999011, 4.84324772015325, 4.865122123422967, 8.660099982935032, 4.754260262390462, 5.548923609141675, 6.549630066047081, 7.561605305328301), # 115
(7.418543898590108, 6.183350625033362, 7.342825751987099, 8.616532920213123, 8.444150821107023, 4.566967101829678, 4.821283854022315, 4.852304250319195, 8.644945071382265, 4.736634686759638, 5.529284745017185, 6.530018557989877, 7.546085807804713), # 116
(7.395063554259018, 6.151248038707777, 7.326809628420789, 8.593185098073794, 8.427764621036088, 4.558018479248712, 4.799686591158202, 4.840078236130868, 8.630191053193762, 4.719341920726503, 5.510040079058626, 6.5106584372350005, 7.53043760388658), # 117
(7.371437796788169, 6.119601693596259, 7.310877646406694, 8.569981750576266, 8.411143443092675, 4.549246859188911, 4.7784195543834524, 4.828406928696078, 8.615798487651148, 4.7023484775798075, 5.49115081300754, 6.49151292749868, 7.51463645870322), # 118
(7.347637533555794, 6.088363283325384, 7.294998276884579, 8.546880490569364, 8.394263188271378, 4.540630158583066, 4.757446366520605, 4.817253175852916, 8.601727934036035, 4.685620870608298, 5.4725781486054625, 6.472545252497148, 7.498658137383946), # 119
(7.323633671940129, 6.057484501521727, 7.27913999079421, 8.523838930901915, 8.377099757566798, 4.532146294363972, 4.736730650392203, 4.806579825439474, 8.587939951630046, 4.669125613100724, 5.454283287593933, 6.453718635946638, 7.482478405058078), # 120
(7.299397119319415, 6.026917041811863, 7.26327125907535, 8.500814684422748, 8.359629051973535, 4.523773183464424, 4.716236028820784, 4.796349725293846, 8.574395099714799, 4.652829218345837, 5.436227431714493, 6.434996301563378, 7.466073026854929), # 121
(7.274898783071883, 5.996612597822369, 7.247360552667769, 8.477765363980685, 8.341826972486187, 4.515488742817215, 4.695926124628894, 4.786525723254119, 8.561053937571911, 4.636698199632382, 5.4183717827086815, 6.416341473063601, 7.4494177679038165), # 122
(7.250109570575775, 5.9665228631798195, 7.231376342511229, 8.454648582424555, 8.323669420099353, 4.50727088935514, 4.675764560639071, 4.7770706671583865, 8.547877024483004, 4.62069907024911, 5.400677542318036, 6.397717374163538, 7.432488393334058), # 123
(7.225000389209324, 5.93659953151079, 7.215287099545496, 8.43142195260319, 8.30513229580763, 4.499097540010991, 4.655714959673856, 4.767947404844741, 8.534824919729692, 4.604798343484769, 5.383105912284096, 6.3790872285794205, 7.4152606682749695), # 124
(7.199542146350767, 5.9067942964418565, 7.199061294710339, 8.408043087365408, 8.286191500605618, 4.490946611717565, 4.635740944555791, 4.759118784151273, 8.521858182593595, 4.588962532628107, 5.3656180943484015, 6.360414260027479, 7.397710357855863), # 125
(7.1737057493783425, 5.877058851599596, 7.182667398945519, 8.384469599560044, 8.266822935487914, 4.482796021407654, 4.615806138107416, 4.750547652916074, 8.508937372356334, 4.573158150967874, 5.348175290252491, 6.341661692223948, 7.379813227206063), # 126
(7.147462105670289, 5.84734489061058, 7.166073883190804, 8.36065910203592, 8.247002501449119, 4.474623686014052, 4.595874163151275, 4.742196858977237, 8.496023048299525, 4.557351711792819, 5.3307387017379035, 6.322792748885053, 7.361545041454879), # 127
(7.120782122604837, 5.817604107101388, 7.14924921838596, 8.336569207641865, 8.226706099483833, 4.466407522469555, 4.575908642509906, 4.73402925017285, 8.483075769704788, 4.5415097283916905, 5.3132695305461795, 6.303770653727031, 7.34288156573163), # 128
(7.093636707560226, 5.787788194698593, 7.132161875470752, 8.312157529226706, 8.20590963058665, 4.458125447706956, 4.555873199005851, 4.726007674341008, 8.47005609585374, 4.5255987140532365, 5.2957289784188575, 6.284558630466109, 7.323798565165631), # 129
(7.065996767914694, 5.757848847028773, 7.1147803253849435, 8.28738167963927, 8.18458899575217, 4.449755378659047, 4.53573145546165, 4.7180949793198, 8.456924586028, 4.509585182066206, 5.278078247097476, 6.2651199028185225, 7.3042718048861985), # 130
(7.037833211046475, 5.727737757718502, 7.097073039068305, 8.262199271728381, 8.162720095974995, 4.441275232258625, 4.515447034699847, 4.71025401294732, 8.443641799509189, 4.493435645719348, 5.260278538323575, 6.2454176945004996, 7.2842770500226495), # 131
(7.009116944333808, 5.697406620394355, 7.079008487460597, 8.23656791834287, 8.140278832249724, 4.432662925438482, 4.49498355954298, 4.7024476230616585, 8.430168295578923, 4.4771166183014115, 5.2422910538386915, 6.225415229228274, 7.263790065704301), # 132
(6.979818875154931, 5.666807128682908, 7.060555141501587, 8.210445232331562, 8.11724110557095, 4.423896375131413, 4.474304652813592, 4.694638657500906, 8.416464633518821, 4.460594613101146, 5.224076995384369, 6.205075730718074, 7.242786617060469), # 133
(6.949909910888076, 5.635890976210739, 7.041681472131043, 8.183788826543283, 8.093582816933274, 4.414953498270212, 4.453373937334223, 4.686789964103155, 8.402491372610504, 4.443836143407299, 5.205597564702143, 6.184362422686133, 7.221242469220467), # 134
(6.919360958911483, 5.604609856604419, 7.022355950288727, 8.156556313826863, 8.069279867331296, 4.405812211787674, 4.432155035927415, 4.678864390706496, 8.388209072135584, 4.426807722508621, 5.186813963533554, 6.163238528848682, 7.199133387313616), # 135
(6.888142926603388, 5.572915463490528, 7.002547046914407, 8.128705307031124, 8.044308157759614, 4.396450432616592, 4.410611571415708, 4.670824785149022, 8.373578291375685, 4.409475863693858, 5.167687393620142, 6.1416672729219535, 7.176435136469229), # 136
(6.856226721342027, 5.540759490495638, 6.982223232947849, 8.100193419004901, 8.018643589212827, 4.386846077689759, 4.388707166621645, 4.662633995268823, 8.358559589612426, 4.391807080251762, 5.1481790567034444, 6.119611878622176, 7.153123481816621), # 137
(6.823583250505639, 5.508093631246327, 6.961352979328814, 8.070978262597011, 7.992262062685535, 4.376977063939971, 4.366405444367763, 4.654254868903992, 8.343113526127425, 4.373767885471078, 5.128250154525002, 6.097035569665582, 7.129174188485113), # 138
(6.790183421472455, 5.4748695793691695, 6.939904756997072, 8.041017450656287, 7.965139479172333, 4.366821308300021, 4.343670027476608, 4.64565025389262, 8.327200660202298, 4.355324792640558, 5.107861888826353, 6.073901569768405, 7.104563021604015), # 139
(6.755998141620719, 5.44103902849074, 6.91784703689239, 8.010268596031556, 7.937251739667824, 4.356356727702703, 4.320464538770717, 4.636782998072797, 8.310781551118666, 4.336444315048949, 5.086975461349035, 6.050173102646873, 7.079265746302652), # 140
(6.720998318328665, 5.406553672237617, 6.895148289954529, 7.978689311571642, 7.908574745166603, 4.345561239080812, 4.296752601072636, 4.6276159492826165, 8.293816758158144, 4.317092965985001, 5.065552073834591, 6.02581339201722, 7.053258127710331), # 141
(6.685154858974525, 5.371365204236373, 6.871776987123257, 7.946237210125377, 7.87908439666327, 4.334412759367142, 4.272497837204901, 4.6181119553601695, 8.276266840602354, 4.2972372587374625, 5.043552928024558, 6.000785661595676, 7.026515930956373), # 142
(6.64843867093654, 5.335425318113585, 6.8477015993383406, 7.91286990454158, 7.848756595152423, 4.322889205494485, 4.247663869990055, 4.608233864143545, 8.258092357732918, 4.276843706595082, 5.020939225660475, 5.975053135098472, 6.999014921170094), # 143
(6.610820661592948, 5.298685707495829, 6.822890597539542, 7.878545007669086, 7.817567241628663, 4.310968494395637, 4.222214322250639, 4.597944523470839, 8.239253868831447, 4.255878822846608, 4.997672168483881, 5.948579036241839, 6.970730863480812), # 144
(6.572271738321982, 5.26109806600968, 6.797312452666631, 7.843220132356716, 7.785492237086586, 4.298628543003392, 4.196112816809195, 4.587206781180141, 8.219711933179564, 4.23430912078079, 4.973712958236316, 5.921326588742011, 6.94163952301784), # 145
(6.5327628085018805, 5.2226140872817135, 6.770935635659374, 7.806852891453301, 7.7525074825207945, 4.285847268250545, 4.169322976488264, 4.575983485109542, 8.199427110058885, 4.212101113686376, 4.949022796659319, 5.893259016315216, 6.911716664910495), # 146
(6.49226477951088, 5.1831854649385045, 6.743728617457528, 7.769400897807664, 7.718588878925882, 4.272602587069886, 4.141808424110385, 4.564237483097132, 8.178359958751033, 4.189221314852117, 4.923562885494429, 5.864339542677689, 6.8809380542880945), # 147
(6.450748558727217, 5.142763892606631, 6.715659869000866, 7.730821764268637, 7.683712327296449, 4.258872416394214, 4.113532782498101, 4.551931622981006, 8.156471038537623, 4.1656362375667575, 4.897294426483186, 5.8345313915456565, 6.8492794562799535), # 148
(6.40818505352913, 5.101301063912665, 6.686697861229155, 7.691073103685042, 7.647853728627097, 4.24463467315632, 4.084459674473953, 4.539028752599253, 8.13372090870027, 4.1413123951190505, 4.870178621367128, 5.803797786635354, 6.81671663601539), # 149
(6.364545171294852, 5.058748672483183, 6.656811065082156, 7.65011252890571, 7.610988983912421, 4.229867274288999, 4.054552722860481, 4.525491719789965, 8.110070128520602, 4.116216300797741, 4.8421766718877945, 5.772101951663011, 6.783225358623717), # 150
(6.31979981940262, 5.015058411944763, 6.625967951499634, 7.607897652779464, 7.573093994147022, 4.214548136725044, 4.023775550480226, 4.511283372391235, 8.085479257280232, 4.090314467891583, 4.813249779786724, 5.739407110344858, 6.748781389234255), # 151
(6.273919905230675, 4.970181975923978, 6.594136991421362, 7.5643860881551355, 7.534144660325495, 4.198655177397251, 3.992091780155732, 4.496366558241153, 8.059908854260776, 4.06357340968932, 4.7833591468054575, 5.705676486397127, 6.713360492976318), # 152
(6.226876336157249, 4.924071058047406, 6.561286655787095, 7.519535447881546, 7.4941168834424445, 4.182166313238413, 3.9594650347095355, 4.48070412517781, 8.03331947874386, 4.035959639479703, 4.752465974685533, 5.670873303536052, 6.676938434979222), # 153
(6.178640019560583, 4.87667735194162, 6.527385415536607, 7.473303344807528, 7.452986564492464, 4.165059461181324, 3.9258589369641825, 4.464258921039298, 8.005671690011093, 4.0074396705514825, 4.72053146516849, 5.63496078547786, 6.639490980372286), # 154
(6.129181862818909, 4.827952551233196, 6.492401741609661, 7.425647391781903, 7.410729604470157, 4.147312538158777, 3.891237109742209, 4.446993793663709, 7.976926047344103, 3.9779800161934036, 4.687516819995866, 5.597902155938786, 6.600993894284821), # 155
(6.078472773310465, 4.7778483495487105, 6.456304104946021, 7.3765252016535, 7.367321904370119, 4.128903461103569, 3.85556317586616, 4.428871590889135, 7.947043110024501, 3.9475471896942183, 4.6533832409092035, 5.559660638635059, 6.561422941846148), # 156
(6.02648365841349, 4.726316440514739, 6.419060976485454, 7.32589438727115, 7.322739365186948, 4.109810146948491, 3.8188007581585754, 4.409855160553666, 7.915983437333911, 3.9161077043426733, 4.618091929650039, 5.52019945728291, 6.520753888185581), # 157
(5.971744757124192, 4.672362496617807, 6.378873563121885, 7.271815665320995, 7.274944884696798, 4.088819581053688, 3.780085376742286, 4.388637561879498, 7.881329673279279, 3.882692733032915, 4.580476602031154, 5.478079651355472, 6.477188687532276), # 158
(5.9058294135827225, 4.610452255679582, 6.32539025472239, 7.203181727030763, 7.212153047825303, 4.058951718405683, 3.734570210708573, 4.357770826211506, 7.829141808977716, 3.8418247952789963, 4.533933548495195, 5.425090018946487, 6.420342117536156), # 159
(5.827897675923448, 4.540077382832571, 6.257536766364711, 7.118862008327088, 7.133136105077437, 4.019473036838147, 3.6817949987070273, 4.316479351621878, 7.757940181782921, 3.792964521490315, 4.477807606887632, 5.360401559110278, 6.349136487114865), # 160
(5.738577643668768, 4.461696694464375, 6.1760375775282474, 7.019658003005382, 7.038714499425691, 3.970861793256251, 3.622145156805501, 4.265280426487824, 7.668663813599214, 3.7365265545367503, 4.412593323679766, 5.284613975126057, 6.264299235855278), # 161
(5.638497416341085, 4.375769006962591, 6.0816171676923965, 6.9063712048610615, 6.929708673842564, 3.9135962445651646, 3.5560061010718473, 4.204691339186562, 7.56225172633091, 3.6729255372881853, 4.338785245342897, 5.198326970273035, 6.166557803344267), # 162
(5.528285093462799, 4.2827531367148195, 5.975000016336562, 6.779803107689547, 6.806939071300551, 3.848154647670058, 3.4837632475739206, 4.1352293780953, 7.439642941882325, 3.6025761126145, 4.2568779183483265, 5.102140247830427, 6.0566396291687035), # 163
(5.408568774556308, 4.183107900108657, 5.856910602940141, 6.640755205286254, 6.6712261347721515, 3.7750152594761035, 3.405802012379573, 4.0574118315912555, 7.301776482157779, 3.525892923385575, 4.167365889167357, 4.996653511077443, 5.935272152915463), # 164
(5.279976559144014, 4.077292113531706, 5.728073406982535, 6.490028991446602, 6.523390307229859, 3.6946563368884693, 3.3225078115566578, 3.971755988051637, 7.149591369061584, 3.4432906124712908, 4.0707437042712895, 4.882466463293296, 5.803182814171416), # 165
(5.143136546748318, 3.9657645933715635, 5.589212907943143, 6.328425959966001, 6.3642520316461715, 3.607556136812327, 3.234266061173029, 3.878779135853662, 6.984026624498059, 3.35518382274153, 3.9675059101314236, 4.760178807757201, 5.661099052523436), # 166
(4.998676836891619, 3.8489841560158298, 5.441053585301364, 6.156747604639875, 6.194631750993584, 3.514192916152847, 3.14146217729654, 3.7789985633745413, 6.80602127037152, 3.2619871970661714, 3.858147053219062, 4.630390247748367, 5.509748307558397), # 167
(4.847225529096317, 3.727409617852103, 5.284319918536599, 5.975795419263637, 6.015349908244594, 3.415044931815199, 3.0444815759950434, 3.672931558991488, 6.616514328586284, 3.1641153783150977, 3.743161680005505, 4.493700486546009, 5.34985801886317), # 168
(4.689410722884812, 3.6014997952679835, 5.119736387128247, 5.786370897632707, 5.827226946371696, 3.310590440704556, 2.9437096733363934, 3.561095411081716, 6.416444821046671, 3.0619830093581895, 3.623044336962055, 4.350709227429338, 5.182155626024628), # 169
(4.525860517779507, 3.47171350465107, 4.948027470555708, 5.589275533542496, 5.631083308347387, 3.2013076997260854, 2.8395318853884426, 3.444007408022438, 6.206751769656991, 2.9560047330653263, 3.498289570560013, 4.202016173677567, 5.007368568629644), # 170
(4.3572030133028, 3.3385095623889605, 4.7699176482983825, 5.385310820788429, 5.427739437144165, 3.087674965784959, 2.7323336282190445, 3.3221848381908665, 5.9883741963215655, 2.846595192306391, 3.3693919272706787, 4.048221028569909, 4.826224286265092), # 171
(4.184066308977092, 3.2023467848692557, 4.586131399835669, 5.175278253165917, 5.218015775734523, 2.970170495786347, 2.6225003178960526, 3.1961449899642167, 5.762251122944709, 2.734169029951264, 3.2368459535653553, 3.889923495385577, 4.639450218517843), # 172
(4.007078504324784, 3.063683988479554, 4.39739320464697, 4.959979324470381, 5.002732767090961, 2.84927254663542, 2.51041737048732, 3.066405151719699, 5.529321571430739, 2.6191408888698255, 3.1011461959153426, 3.72772327740378, 4.44777380497477), # 173
(3.8268676988682753, 2.9229799896074544, 4.204427542211682, 4.740215528497233, 4.782710854185973, 2.725459375237348, 2.3964702020607005, 2.9334826118345285, 5.290524563683971, 2.5019254119319574, 2.9627872007919422, 3.5622200779037345, 4.251922485222747), # 174
(3.6440619921299646, 2.7806936046405557, 4.007958892009206, 4.516788359041894, 4.558770479992055, 2.599209238497303, 2.2810442286840464, 2.797894658685917, 5.046799121608725, 2.3829372420075394, 2.8222635146664556, 3.3940136001646515, 4.052623698848646), # 175
(3.459289483632255, 2.6372836499664585, 3.8087117335189427, 4.29049930989978, 4.331732087481704, 2.4710003933204536, 2.164524866425212, 2.6601585806510792, 4.799084267109314, 2.2625910219664536, 2.680069684010184, 3.2237035474657434, 3.8506048854393393), # 176
(3.273178272897546, 2.493208941972761, 3.607410546220291, 4.062149874866306, 4.102416119627419, 2.3413110966119706, 2.0472975313520503, 2.5207916661072263, 4.548319022090056, 2.1413013946785795, 2.536700255294429, 3.051889623086223, 3.6465934845817), # 177
(3.0863564594482376, 2.348928297047063, 3.404779809592651, 3.832541547736893, 3.871643019401691, 2.210619605277026, 1.929747639532414, 2.3803112034315723, 4.295442408455268, 2.0194830030138, 2.39264977499049, 2.879171530305302, 3.4413169358626017), # 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 = (
(3, 1, 2, 3, 3, 1, 1, 1, 1, 1, 0, 1, 0, 4, 1, 4, 3, 4, 2, 3, 1, 1, 1, 2, 0, 0), # 0
(5, 5, 8, 6, 6, 3, 4, 1, 4, 1, 0, 2, 0, 15, 2, 8, 5, 13, 4, 6, 2, 1, 1, 3, 0, 0), # 1
(11, 8, 11, 10, 12, 7, 4, 2, 6, 2, 1, 2, 0, 18, 12, 12, 6, 15, 5, 6, 2, 4, 2, 4, 1, 0), # 2
(18, 10, 16, 11, 15, 8, 8, 5, 9, 2, 2, 2, 0, 23, 17, 20, 11, 17, 7, 10, 3, 6, 5, 4, 2, 0), # 3
(20, 11, 18, 19, 20, 8, 11, 7, 10, 3, 2, 2, 0, 29, 25, 22, 12, 21, 11, 11, 4, 11, 7, 5, 2, 0), # 4
(24, 16, 26, 24, 20, 8, 15, 9, 11, 4, 5, 2, 0, 35, 32, 26, 15, 25, 12, 12, 7, 16, 10, 5, 4, 0), # 5
(28, 22, 32, 28, 22, 11, 18, 13, 14, 5, 5, 2, 0, 41, 39, 30, 18, 31, 18, 14, 10, 19, 12, 6, 4, 0), # 6
(35, 27, 33, 37, 25, 12, 20, 14, 16, 5, 5, 3, 0, 46, 44, 36, 23, 32, 24, 16, 14, 22, 14, 8, 4, 0), # 7
(41, 32, 41, 39, 27, 12, 24, 16, 18, 5, 6, 3, 0, 51, 47, 42, 23, 36, 30, 18, 16, 24, 16, 8, 4, 0), # 8
(50, 40, 45, 42, 30, 12, 28, 19, 19, 6, 7, 3, 0, 56, 51, 44, 28, 42, 34, 20, 16, 26, 18, 8, 4, 0), # 9
(54, 44, 53, 44, 35, 17, 31, 24, 21, 7, 8, 4, 0, 63, 55, 49, 32, 47, 36, 22, 17, 30, 18, 9, 5, 0), # 10
(60, 50, 60, 52, 40, 20, 34, 29, 22, 7, 9, 5, 0, 68, 60, 53, 34, 50, 39, 23, 17, 31, 18, 10, 6, 0), # 11
(61, 58, 67, 56, 43, 22, 39, 31, 23, 9, 10, 5, 0, 76, 60, 60, 38, 58, 46, 26, 23, 34, 18, 12, 7, 0), # 12
(63, 63, 75, 61, 45, 24, 40, 31, 25, 14, 12, 6, 0, 85, 70, 65, 42, 65, 46, 29, 24, 35, 20, 13, 8, 0), # 13
(68, 71, 78, 71, 49, 27, 43, 36, 29, 16, 12, 6, 0, 88, 76, 73, 45, 70, 50, 34, 28, 38, 20, 16, 8, 0), # 14
(74, 78, 87, 76, 61, 30, 44, 42, 30, 16, 12, 7, 0, 92, 82, 80, 49, 76, 54, 36, 28, 41, 20, 17, 8, 0), # 15
(80, 82, 94, 81, 65, 32, 46, 45, 32, 16, 14, 7, 0, 103, 90, 89, 59, 82, 55, 37, 29, 42, 21, 17, 9, 0), # 16
(87, 92, 96, 86, 70, 32, 53, 48, 36, 16, 14, 8, 0, 110, 99, 94, 64, 88, 57, 40, 30, 45, 23, 18, 10, 0), # 17
(96, 102, 97, 93, 75, 35, 61, 50, 38, 19, 14, 8, 0, 119, 102, 99, 67, 98, 59, 45, 31, 49, 25, 18, 12, 0), # 18
(112, 109, 105, 98, 82, 39, 65, 52, 42, 23, 17, 9, 0, 126, 111, 105, 71, 99, 64, 47, 34, 52, 28, 19, 14, 0), # 19
(118, 115, 108, 101, 90, 42, 72, 53, 43, 25, 19, 9, 0, 129, 117, 110, 77, 107, 66, 53, 37, 54, 29, 21, 18, 0), # 20
(127, 124, 116, 108, 96, 46, 74, 54, 47, 27, 20, 9, 0, 141, 121, 117, 81, 109, 71, 58, 39, 57, 32, 21, 18, 0), # 21
(136, 130, 120, 113, 99, 48, 79, 58, 49, 29, 23, 12, 0, 152, 131, 121, 86, 116, 76, 61, 43, 67, 34, 21, 19, 0), # 22
(143, 137, 124, 123, 105, 51, 84, 61, 52, 31, 23, 12, 0, 158, 140, 123, 88, 123, 81, 65, 43, 68, 38, 23, 19, 0), # 23
(150, 152, 126, 131, 114, 53, 89, 66, 53, 32, 23, 13, 0, 166, 151, 127, 95, 131, 84, 65, 43, 69, 41, 24, 20, 0), # 24
(154, 158, 133, 139, 121, 53, 98, 68, 57, 33, 23, 13, 0, 175, 158, 136, 96, 138, 90, 67, 44, 70, 44, 25, 21, 0), # 25
(167, 165, 139, 139, 124, 58, 99, 74, 63, 35, 24, 13, 0, 182, 164, 141, 103, 145, 94, 72, 48, 73, 48, 26, 23, 0), # 26
(178, 176, 144, 149, 127, 59, 100, 75, 67, 35, 24, 14, 0, 190, 168, 148, 110, 151, 97, 77, 52, 77, 50, 31, 23, 0), # 27
(187, 182, 148, 158, 132, 64, 104, 79, 71, 38, 27, 14, 0, 198, 175, 149, 119, 157, 100, 81, 54, 77, 53, 32, 23, 0), # 28
(191, 190, 154, 167, 135, 66, 107, 80, 74, 38, 30, 14, 0, 209, 182, 153, 123, 164, 105, 85, 56, 82, 56, 32, 23, 0), # 29
(198, 196, 165, 171, 142, 69, 112, 83, 79, 40, 30, 16, 0, 218, 187, 160, 128, 170, 106, 91, 56, 84, 56, 32, 23, 0), # 30
(207, 202, 176, 182, 147, 73, 116, 85, 80, 42, 30, 17, 0, 228, 194, 169, 132, 175, 114, 95, 60, 85, 58, 33, 23, 0), # 31
(216, 209, 182, 190, 152, 76, 119, 90, 87, 42, 30, 17, 0, 234, 202, 173, 135, 178, 117, 95, 60, 88, 61, 34, 23, 0), # 32
(221, 213, 190, 197, 156, 78, 123, 91, 94, 44, 31, 18, 0, 238, 210, 179, 139, 185, 119, 99, 62, 92, 63, 34, 25, 0), # 33
(227, 222, 196, 204, 160, 80, 127, 94, 96, 46, 32, 18, 0, 247, 212, 187, 143, 192, 120, 105, 65, 97, 65, 34, 26, 0), # 34
(237, 236, 207, 210, 169, 82, 130, 98, 99, 46, 33, 20, 0, 257, 216, 192, 148, 198, 125, 106, 69, 99, 68, 34, 27, 0), # 35
(244, 244, 211, 218, 175, 88, 134, 99, 104, 47, 37, 20, 0, 269, 223, 201, 150, 209, 126, 111, 70, 100, 69, 35, 27, 0), # 36
(254, 252, 224, 225, 183, 91, 137, 104, 109, 49, 37, 22, 0, 272, 229, 206, 161, 215, 128, 115, 73, 103, 71, 39, 28, 0), # 37
(262, 264, 233, 228, 192, 94, 142, 106, 114, 50, 37, 22, 0, 282, 239, 211, 164, 222, 135, 122, 74, 104, 73, 39, 29, 0), # 38
(266, 275, 243, 232, 201, 96, 144, 108, 115, 51, 37, 22, 0, 295, 244, 217, 167, 222, 137, 124, 75, 110, 75, 43, 29, 0), # 39
(270, 285, 249, 240, 207, 99, 145, 114, 119, 52, 41, 23, 0, 302, 251, 223, 173, 230, 138, 130, 79, 116, 77, 45, 30, 0), # 40
(279, 295, 254, 248, 211, 100, 146, 118, 125, 52, 42, 23, 0, 306, 263, 230, 177, 240, 143, 134, 81, 121, 80, 47, 31, 0), # 41
(285, 306, 260, 252, 215, 103, 150, 124, 127, 53, 46, 23, 0, 316, 272, 236, 179, 244, 146, 136, 83, 124, 82, 50, 36, 0), # 42
(295, 312, 262, 257, 220, 104, 155, 128, 132, 55, 47, 24, 0, 320, 278, 240, 184, 254, 152, 139, 84, 128, 82, 50, 36, 0), # 43
(300, 319, 266, 262, 229, 108, 157, 130, 134, 58, 49, 27, 0, 325, 286, 247, 187, 263, 156, 143, 86, 132, 83, 51, 36, 0), # 44
(311, 323, 274, 273, 235, 112, 163, 131, 137, 59, 49, 27, 0, 334, 296, 250, 192, 271, 157, 147, 90, 135, 88, 53, 36, 0), # 45
(316, 331, 277, 282, 241, 115, 166, 133, 144, 61, 49, 28, 0, 339, 298, 254, 198, 276, 160, 150, 98, 138, 92, 54, 37, 0), # 46
(322, 343, 283, 289, 248, 116, 169, 137, 146, 61, 49, 28, 0, 344, 305, 266, 201, 280, 167, 157, 101, 143, 95, 54, 37, 0), # 47
(329, 351, 289, 297, 251, 119, 173, 141, 149, 63, 51, 28, 0, 348, 310, 268, 207, 288, 169, 160, 105, 146, 97, 55, 38, 0), # 48
(340, 357, 295, 307, 259, 120, 178, 144, 150, 64, 54, 29, 0, 354, 317, 273, 210, 296, 170, 164, 107, 149, 98, 56, 38, 0), # 49
(350, 361, 305, 314, 264, 124, 180, 150, 152, 66, 54, 29, 0, 364, 323, 281, 214, 302, 170, 166, 109, 152, 100, 58, 38, 0), # 50
(359, 368, 311, 321, 276, 125, 183, 155, 154, 67, 55, 29, 0, 372, 331, 288, 217, 305, 174, 167, 111, 154, 102, 59, 40, 0), # 51
(363, 371, 318, 331, 282, 127, 185, 158, 158, 69, 56, 30, 0, 382, 339, 290, 224, 314, 178, 169, 115, 157, 106, 63, 40, 0), # 52
(372, 378, 326, 334, 285, 128, 187, 162, 159, 69, 57, 31, 0, 390, 346, 291, 231, 318, 183, 170, 118, 159, 109, 63, 40, 0), # 53
(378, 387, 331, 341, 290, 130, 191, 164, 161, 71, 61, 32, 0, 396, 353, 295, 235, 325, 185, 174, 119, 163, 113, 63, 41, 0), # 54
(383, 396, 335, 351, 300, 134, 192, 168, 165, 72, 64, 32, 0, 400, 360, 302, 237, 329, 189, 175, 121, 166, 114, 64, 42, 0), # 55
(391, 398, 342, 361, 310, 137, 194, 170, 171, 74, 65, 32, 0, 405, 370, 306, 241, 337, 190, 177, 122, 167, 118, 67, 42, 0), # 56
(399, 408, 353, 366, 318, 143, 196, 172, 176, 75, 65, 32, 0, 409, 376, 317, 241, 340, 192, 182, 126, 168, 122, 69, 42, 0), # 57
(411, 415, 363, 369, 322, 146, 198, 172, 182, 79, 68, 32, 0, 416, 384, 323, 245, 346, 200, 186, 128, 170, 122, 72, 44, 0), # 58
(416, 427, 375, 376, 329, 151, 206, 176, 186, 81, 68, 33, 0, 424, 391, 332, 250, 351, 203, 189, 129, 171, 129, 75, 44, 0), # 59
(424, 442, 381, 389, 332, 153, 209, 179, 190, 82, 68, 33, 0, 430, 398, 338, 253, 358, 205, 195, 131, 174, 131, 78, 45, 0), # 60
(429, 450, 391, 393, 336, 157, 212, 182, 194, 82, 69, 34, 0, 437, 403, 343, 257, 365, 208, 198, 132, 179, 132, 79, 45, 0), # 61
(434, 456, 396, 401, 343, 157, 216, 187, 194, 83, 70, 35, 0, 445, 410, 345, 263, 370, 211, 199, 134, 181, 135, 80, 45, 0), # 62
(439, 461, 402, 411, 349, 159, 218, 191, 198, 87, 70, 35, 0, 451, 416, 355, 268, 375, 215, 203, 137, 184, 137, 80, 45, 0), # 63
(449, 470, 408, 424, 355, 160, 224, 191, 199, 89, 72, 35, 0, 456, 420, 363, 270, 382, 220, 206, 140, 184, 138, 82, 46, 0), # 64
(455, 475, 416, 434, 360, 165, 227, 193, 200, 91, 72, 35, 0, 466, 426, 368, 275, 391, 225, 210, 143, 186, 142, 84, 46, 0), # 65
(465, 482, 420, 441, 366, 169, 231, 195, 201, 91, 73, 36, 0, 475, 433, 374, 282, 399, 228, 213, 145, 189, 146, 86, 48, 0), # 66
(478, 488, 426, 450, 370, 173, 235, 201, 204, 92, 75, 37, 0, 482, 441, 377, 286, 404, 234, 216, 147, 190, 149, 87, 49, 0), # 67
(489, 494, 435, 453, 374, 174, 236, 205, 210, 92, 75, 39, 0, 497, 451, 383, 291, 411, 236, 218, 148, 195, 150, 87, 50, 0), # 68
(503, 503, 443, 456, 378, 176, 236, 206, 215, 93, 78, 41, 0, 503, 458, 393, 294, 415, 239, 223, 150, 196, 152, 88, 51, 0), # 69
(509, 511, 451, 461, 380, 178, 243, 208, 218, 94, 79, 41, 0, 507, 464, 396, 295, 425, 243, 229, 154, 199, 152, 88, 52, 0), # 70
(523, 517, 460, 470, 386, 181, 245, 211, 219, 96, 79, 41, 0, 517, 472, 400, 298, 432, 246, 230, 156, 202, 154, 89, 53, 0), # 71
(534, 526, 467, 477, 394, 184, 245, 216, 225, 96, 79, 42, 0, 528, 484, 404, 299, 439, 250, 233, 158, 203, 154, 89, 54, 0), # 72
(540, 536, 475, 482, 399, 186, 251, 216, 228, 97, 81, 45, 0, 538, 493, 406, 305, 446, 251, 235, 159, 208, 157, 91, 54, 0), # 73
(549, 539, 484, 490, 404, 189, 253, 217, 230, 98, 84, 46, 0, 549, 501, 411, 308, 455, 257, 236, 160, 208, 158, 91, 54, 0), # 74
(556, 546, 489, 497, 407, 190, 254, 221, 233, 98, 86, 46, 0, 553, 505, 414, 310, 459, 258, 236, 163, 214, 159, 91, 56, 0), # 75
(565, 553, 495, 507, 415, 195, 256, 224, 233, 98, 88, 47, 0, 565, 508, 416, 312, 463, 262, 236, 164, 216, 161, 93, 57, 0), # 76
(571, 557, 495, 513, 422, 203, 259, 225, 236, 100, 89, 47, 0, 576, 521, 423, 315, 470, 265, 238, 167, 218, 164, 94, 57, 0), # 77
(578, 564, 504, 521, 430, 206, 259, 229, 236, 101, 91, 47, 0, 584, 530, 429, 321, 473, 268, 241, 168, 224, 164, 98, 57, 0), # 78
(581, 572, 518, 531, 437, 208, 261, 233, 238, 103, 93, 48, 0, 593, 535, 433, 327, 478, 270, 242, 171, 227, 166, 103, 58, 0), # 79
(593, 580, 524, 537, 440, 213, 261, 236, 241, 105, 93, 50, 0, 601, 544, 438, 339, 484, 273, 243, 172, 230, 168, 103, 58, 0), # 80
(600, 585, 531, 549, 448, 215, 264, 239, 247, 106, 95, 50, 0, 608, 551, 447, 341, 488, 276, 246, 173, 233, 170, 104, 59, 0), # 81
(608, 590, 541, 562, 456, 216, 267, 241, 251, 107, 96, 51, 0, 614, 559, 451, 344, 497, 282, 251, 175, 235, 170, 108, 59, 0), # 82
(613, 596, 549, 571, 464, 219, 270, 243, 253, 107, 96, 54, 0, 619, 568, 457, 349, 501, 287, 252, 177, 237, 170, 109, 63, 0), # 83
(624, 605, 554, 581, 471, 220, 272, 244, 256, 107, 96, 54, 0, 626, 573, 460, 351, 507, 290, 257, 179, 244, 176, 110, 64, 0), # 84
(628, 617, 556, 590, 474, 222, 275, 247, 259, 108, 99, 54, 0, 633, 580, 462, 362, 512, 294, 258, 181, 247, 178, 110, 65, 0), # 85
(632, 619, 564, 603, 480, 224, 278, 250, 264, 109, 99, 54, 0, 639, 590, 464, 368, 517, 297, 260, 183, 249, 181, 110, 65, 0), # 86
(642, 632, 571, 612, 488, 228, 281, 252, 267, 109, 99, 54, 0, 643, 597, 467, 372, 522, 300, 264, 189, 255, 187, 110, 65, 0), # 87
(652, 634, 578, 615, 491, 229, 282, 253, 275, 110, 101, 58, 0, 651, 601, 470, 374, 528, 303, 269, 190, 259, 190, 110, 65, 0), # 88
(660, 642, 582, 620, 499, 233, 287, 255, 278, 110, 102, 60, 0, 662, 606, 474, 380, 535, 306, 271, 192, 265, 190, 112, 65, 0), # 89
(668, 647, 590, 627, 502, 237, 290, 256, 282, 113, 103, 60, 0, 668, 609, 483, 382, 546, 306, 273, 195, 269, 191, 112, 65, 0), # 90
(680, 655, 595, 639, 510, 238, 292, 257, 288, 115, 103, 60, 0, 678, 612, 487, 385, 551, 310, 273, 195, 270, 193, 112, 65, 0), # 91
(688, 658, 601, 648, 513, 242, 295, 261, 289, 116, 103, 61, 0, 687, 619, 495, 389, 556, 314, 276, 196, 271, 195, 113, 65, 0), # 92
(694, 663, 605, 654, 521, 246, 297, 266, 293, 119, 105, 61, 0, 698, 625, 497, 393, 560, 315, 282, 198, 274, 195, 113, 65, 0), # 93
(700, 668, 609, 663, 526, 251, 302, 269, 297, 120, 106, 62, 0, 705, 628, 504, 395, 564, 317, 283, 203, 277, 198, 114, 66, 0), # 94
(712, 679, 617, 672, 531, 253, 305, 273, 299, 120, 106, 62, 0, 717, 634, 511, 398, 570, 319, 287, 207, 280, 200, 115, 67, 0), # 95
(719, 684, 625, 682, 536, 254, 308, 273, 304, 120, 106, 62, 0, 725, 635, 511, 406, 572, 320, 291, 209, 286, 203, 116, 67, 0), # 96
(726, 686, 631, 685, 540, 259, 309, 276, 306, 122, 108, 62, 0, 740, 641, 513, 413, 581, 326, 293, 215, 288, 204, 117, 67, 0), # 97
(737, 693, 633, 694, 544, 265, 312, 279, 308, 123, 109, 62, 0, 747, 647, 518, 419, 584, 330, 296, 215, 293, 206, 117, 68, 0), # 98
(747, 701, 639, 702, 553, 268, 313, 280, 313, 124, 109, 64, 0, 754, 655, 522, 421, 590, 330, 300, 217, 299, 210, 118, 68, 0), # 99
(754, 710, 645, 707, 559, 269, 315, 281, 319, 124, 109, 64, 0, 763, 661, 528, 422, 595, 333, 304, 220, 304, 213, 119, 69, 0), # 100
(760, 716, 647, 712, 562, 269, 317, 281, 326, 126, 109, 65, 0, 771, 664, 534, 425, 597, 335, 306, 221, 306, 216, 119, 69, 0), # 101
(766, 725, 651, 713, 564, 271, 322, 283, 330, 128, 109, 65, 0, 777, 675, 538, 432, 610, 343, 307, 223, 306, 218, 119, 69, 0), # 102
(769, 730, 657, 721, 568, 274, 325, 287, 335, 130, 110, 66, 0, 791, 684, 545, 432, 615, 348, 312, 225, 308, 220, 119, 69, 0), # 103
(781, 738, 663, 733, 574, 278, 328, 289, 337, 131, 110, 66, 0, 799, 691, 552, 438, 616, 350, 314, 225, 310, 224, 119, 69, 0), # 104
(785, 742, 670, 741, 578, 283, 330, 290, 337, 131, 110, 67, 0, 809, 699, 554, 442, 625, 354, 318, 227, 311, 229, 119, 70, 0), # 105
(798, 749, 682, 749, 583, 283, 335, 293, 341, 133, 110, 67, 0, 815, 705, 559, 443, 627, 359, 321, 228, 314, 231, 122, 71, 0), # 106
(807, 759, 694, 761, 588, 283, 341, 294, 343, 133, 110, 69, 0, 819, 712, 562, 447, 630, 362, 324, 232, 317, 233, 125, 72, 0), # 107
(811, 763, 695, 771, 594, 285, 341, 299, 344, 135, 114, 69, 0, 831, 718, 566, 451, 636, 363, 325, 235, 318, 234, 125, 72, 0), # 108
(817, 768, 705, 775, 601, 288, 343, 300, 345, 136, 116, 69, 0, 837, 727, 570, 458, 643, 366, 326, 237, 322, 235, 126, 73, 0), # 109
(823, 774, 710, 780, 608, 291, 348, 303, 346, 136, 116, 70, 0, 845, 733, 573, 459, 646, 369, 327, 239, 325, 238, 127, 74, 0), # 110
(829, 781, 712, 788, 611, 294, 350, 304, 350, 138, 116, 70, 0, 850, 738, 576, 461, 653, 372, 327, 242, 329, 238, 128, 74, 0), # 111
(837, 788, 719, 793, 623, 296, 352, 305, 352, 138, 117, 71, 0, 855, 743, 578, 465, 663, 373, 331, 245, 333, 241, 128, 74, 0), # 112
(840, 792, 729, 802, 628, 298, 353, 313, 355, 141, 119, 73, 0, 867, 749, 581, 469, 667, 377, 333, 248, 334, 243, 129, 74, 0), # 113
(845, 795, 739, 807, 637, 300, 354, 316, 356, 142, 119, 75, 0, 875, 752, 585, 472, 676, 380, 336, 251, 337, 243, 131, 76, 0), # 114
(853, 799, 746, 814, 646, 301, 354, 317, 358, 142, 122, 76, 0, 880, 756, 586, 477, 681, 383, 337, 254, 339, 245, 132, 77, 0), # 115
(860, 801, 758, 818, 652, 303, 356, 317, 361, 144, 123, 76, 0, 888, 763, 589, 480, 690, 385, 339, 254, 342, 247, 132, 77, 0), # 116
(866, 805, 759, 828, 657, 305, 356, 319, 364, 146, 123, 76, 0, 898, 771, 597, 486, 700, 388, 344, 254, 347, 247, 133, 77, 0), # 117
(872, 811, 763, 834, 663, 310, 361, 320, 365, 146, 124, 76, 0, 906, 777, 602, 490, 703, 393, 345, 258, 349, 248, 136, 79, 0), # 118
(881, 815, 770, 837, 669, 313, 368, 320, 370, 148, 126, 76, 0, 913, 787, 610, 495, 710, 397, 350, 259, 353, 248, 137, 80, 0), # 119
(888, 819, 778, 845, 679, 318, 369, 321, 372, 149, 127, 78, 0, 919, 796, 616, 500, 716, 399, 352, 261, 354, 248, 138, 80, 0), # 120
(892, 819, 788, 849, 681, 320, 372, 324, 375, 150, 128, 80, 0, 924, 803, 622, 509, 721, 401, 355, 262, 357, 248, 140, 82, 0), # 121
(903, 821, 796, 854, 685, 322, 373, 326, 379, 151, 129, 82, 0, 931, 810, 626, 514, 724, 404, 357, 263, 360, 248, 141, 83, 0), # 122
(913, 830, 798, 859, 694, 324, 375, 326, 381, 151, 131, 83, 0, 938, 819, 629, 516, 732, 406, 357, 263, 361, 250, 142, 83, 0), # 123
(924, 837, 803, 864, 701, 325, 380, 328, 384, 152, 131, 84, 0, 945, 825, 632, 518, 740, 407, 358, 264, 363, 251, 143, 83, 0), # 124
(932, 839, 810, 871, 706, 331, 381, 328, 387, 153, 134, 85, 0, 949, 829, 635, 523, 747, 409, 362, 267, 367, 253, 146, 83, 0), # 125
(939, 842, 815, 878, 712, 333, 383, 329, 392, 153, 134, 86, 0, 955, 834, 642, 527, 751, 412, 365, 271, 374, 254, 148, 83, 0), # 126
(946, 848, 825, 881, 714, 336, 387, 332, 393, 159, 134, 87, 0, 960, 839, 649, 530, 757, 414, 368, 271, 375, 258, 150, 83, 0), # 127
(953, 852, 831, 887, 720, 339, 390, 336, 395, 160, 135, 87, 0, 965, 846, 658, 541, 767, 420, 368, 273, 377, 260, 151, 83, 0), # 128
(963, 857, 841, 897, 727, 342, 397, 338, 399, 163, 136, 88, 0, 967, 857, 663, 547, 769, 425, 370, 278, 379, 262, 153, 83, 0), # 129
(970, 860, 847, 903, 729, 343, 398, 341, 401, 165, 137, 88, 0, 973, 860, 669, 551, 776, 427, 374, 278, 381, 263, 155, 84, 0), # 130
(974, 864, 849, 911, 735, 347, 399, 344, 405, 165, 139, 89, 0, 978, 865, 675, 554, 781, 430, 380, 281, 384, 266, 156, 84, 0), # 131
(978, 869, 852, 918, 738, 352, 402, 344, 406, 168, 140, 89, 0, 991, 874, 680, 558, 788, 430, 382, 282, 389, 269, 158, 85, 0), # 132
(987, 876, 853, 927, 739, 353, 405, 348, 406, 168, 142, 90, 0, 999, 879, 685, 562, 797, 430, 386, 283, 390, 271, 159, 86, 0), # 133
(993, 879, 859, 937, 743, 355, 405, 351, 412, 168, 142, 93, 0, 1007, 882, 690, 564, 804, 432, 392, 286, 395, 275, 159, 86, 0), # 134
(999, 888, 866, 943, 749, 358, 408, 353, 413, 169, 142, 93, 0, 1018, 887, 690, 568, 807, 436, 396, 289, 400, 276, 159, 88, 0), # 135
(1007, 893, 871, 947, 760, 358, 410, 353, 415, 171, 142, 95, 0, 1026, 890, 695, 574, 813, 437, 397, 290, 403, 279, 161, 88, 0), # 136
(1013, 898, 875, 950, 764, 363, 413, 354, 416, 172, 145, 95, 0, 1028, 894, 700, 580, 821, 437, 397, 291, 405, 281, 161, 88, 0), # 137
(1015, 905, 881, 956, 766, 367, 415, 358, 417, 172, 146, 95, 0, 1038, 897, 702, 585, 828, 441, 397, 294, 408, 282, 163, 88, 0), # 138
(1024, 914, 884, 961, 767, 369, 417, 361, 418, 173, 147, 96, 0, 1046, 903, 709, 588, 833, 442, 401, 294, 409, 286, 163, 88, 0), # 139
(1030, 918, 889, 967, 772, 369, 419, 362, 424, 173, 148, 97, 0, 1053, 905, 713, 591, 844, 447, 404, 295, 414, 287, 168, 88, 0), # 140
(1036, 919, 897, 971, 775, 375, 420, 363, 424, 174, 148, 97, 0, 1059, 909, 719, 593, 850, 450, 405, 295, 416, 290, 170, 89, 0), # 141
(1039, 923, 900, 976, 781, 377, 422, 364, 427, 175, 149, 99, 0, 1064, 914, 722, 597, 858, 453, 406, 297, 421, 291, 171, 89, 0), # 142
(1039, 930, 902, 983, 785, 380, 424, 364, 430, 177, 150, 99, 0, 1074, 921, 725, 599, 863, 457, 406, 302, 422, 294, 172, 89, 0), # 143
(1044, 939, 908, 994, 792, 382, 428, 365, 434, 178, 150, 99, 0, 1078, 924, 729, 606, 868, 459, 407, 304, 429, 295, 172, 90, 0), # 144
(1056, 945, 913, 1003, 800, 383, 429, 366, 436, 179, 151, 101, 0, 1083, 928, 734, 608, 875, 460, 408, 310, 433, 295, 173, 90, 0), # 145
(1066, 947, 917, 1008, 803, 388, 431, 368, 437, 180, 151, 102, 0, 1091, 934, 737, 609, 879, 462, 410, 313, 435, 297, 174, 91, 0), # 146
(1075, 949, 921, 1011, 811, 389, 436, 370, 438, 180, 151, 103, 0, 1099, 938, 742, 616, 883, 463, 411, 315, 439, 298, 175, 91, 0), # 147
(1078, 955, 925, 1020, 819, 394, 439, 370, 438, 183, 151, 104, 0, 1103, 946, 744, 619, 889, 465, 412, 317, 441, 300, 176, 92, 0), # 148
(1085, 961, 931, 1026, 827, 397, 443, 373, 444, 186, 151, 104, 0, 1110, 948, 746, 621, 893, 469, 414, 319, 442, 300, 177, 92, 0), # 149
(1092, 964, 935, 1036, 832, 398, 443, 375, 447, 188, 152, 104, 0, 1120, 955, 749, 624, 896, 476, 415, 320, 444, 300, 180, 93, 0), # 150
(1099, 967, 939, 1040, 838, 399, 444, 377, 451, 190, 154, 104, 0, 1125, 963, 750, 626, 905, 480, 418, 323, 449, 303, 181, 93, 0), # 151
(1105, 968, 943, 1045, 844, 401, 446, 378, 458, 190, 155, 104, 0, 1129, 970, 757, 631, 907, 482, 424, 327, 452, 306, 184, 93, 0), # 152
(1109, 973, 946, 1050, 847, 402, 447, 379, 459, 193, 156, 105, 0, 1135, 973, 760, 638, 909, 484, 429, 327, 453, 307, 184, 93, 0), # 153
(1113, 978, 954, 1052, 853, 404, 448, 382, 461, 194, 157, 106, 0, 1146, 980, 767, 641, 914, 488, 432, 327, 455, 307, 184, 93, 0), # 154
(1126, 980, 960, 1053, 854, 405, 450, 383, 461, 198, 158, 107, 0, 1155, 985, 768, 644, 917, 491, 434, 329, 457, 308, 185, 93, 0), # 155
(1133, 983, 968, 1059, 859, 407, 452, 386, 464, 198, 158, 108, 0, 1161, 989, 774, 646, 921, 492, 434, 330, 458, 310, 185, 95, 0), # 156
(1138, 987, 972, 1071, 864, 412, 452, 388, 466, 199, 159, 108, 0, 1170, 997, 779, 650, 930, 495, 436, 332, 459, 313, 185, 96, 0), # 157
(1147, 993, 977, 1076, 867, 414, 458, 388, 467, 200, 159, 108, 0, 1178, 1006, 784, 654, 937, 498, 437, 333, 462, 314, 187, 96, 0), # 158
(1153, 998, 981, 1083, 873, 416, 460, 389, 467, 201, 161, 108, 0, 1183, 1016, 790, 656, 941, 499, 438, 333, 462, 317, 188, 96, 0), # 159
(1159, 1004, 985, 1089, 881, 417, 463, 389, 469, 203, 161, 108, 0, 1189, 1020, 792, 660, 947, 501, 438, 334, 464, 317, 189, 96, 0), # 160
(1163, 1010, 990, 1094, 883, 418, 465, 392, 473, 205, 161, 110, 0, 1196, 1026, 799, 662, 951, 502, 439, 336, 466, 321, 190, 98, 0), # 161
(1164, 1012, 993, 1095, 888, 421, 467, 392, 475, 206, 161, 110, 0, 1207, 1033, 802, 667, 959, 502, 440, 337, 469, 322, 191, 98, 0), # 162
(1172, 1014, 997, 1100, 891, 426, 470, 393, 480, 207, 162, 110, 0, 1211, 1037, 806, 671, 968, 504, 441, 337, 473, 324, 192, 98, 0), # 163
(1178, 1015, 1003, 1102, 903, 428, 472, 394, 486, 208, 163, 110, 0, 1216, 1044, 807, 672, 975, 504, 443, 338, 474, 328, 192, 98, 0), # 164
(1182, 1021, 1011, 1108, 911, 429, 473, 395, 487, 210, 164, 110, 0, 1222, 1046, 809, 674, 979, 505, 444, 340, 475, 328, 195, 98, 0), # 165
(1190, 1026, 1014, 1114, 919, 430, 475, 395, 490, 210, 164, 110, 0, 1227, 1049, 811, 676, 983, 505, 446, 342, 476, 330, 195, 98, 0), # 166
(1192, 1032, 1021, 1122, 926, 433, 478, 397, 491, 212, 166, 110, 0, 1233, 1053, 815, 677, 983, 507, 449, 348, 478, 331, 196, 98, 0), # 167
(1198, 1033, 1022, 1123, 930, 438, 480, 399, 492, 212, 166, 110, 0, 1239, 1058, 815, 678, 990, 507, 450, 351, 478, 332, 197, 99, 0), # 168
(1208, 1034, 1025, 1127, 935, 438, 483, 401, 493, 215, 166, 110, 0, 1242, 1063, 820, 679, 992, 511, 451, 351, 480, 332, 197, 99, 0), # 169
(1212, 1038, 1028, 1129, 938, 441, 484, 403, 493, 215, 166, 111, 0, 1246, 1063, 822, 680, 994, 515, 452, 352, 480, 337, 197, 99, 0), # 170
(1214, 1041, 1040, 1137, 946, 443, 485, 405, 494, 216, 167, 111, 0, 1248, 1066, 823, 683, 999, 520, 453, 354, 481, 339, 199, 99, 0), # 171
(1216, 1045, 1044, 1141, 947, 444, 485, 406, 494, 218, 168, 111, 0, 1250, 1071, 824, 684, 1004, 522, 453, 356, 481, 341, 200, 99, 0), # 172
(1217, 1045, 1049, 1142, 949, 447, 486, 409, 495, 219, 168, 112, 0, 1253, 1073, 828, 686, 1006, 525, 456, 356, 482, 343, 200, 100, 0), # 173
(1220, 1051, 1051, 1145, 951, 448, 487, 411, 495, 220, 168, 112, 0, 1256, 1075, 832, 688, 1010, 525, 456, 356, 485, 343, 201, 100, 0), # 174
(1225, 1055, 1053, 1145, 952, 449, 488, 412, 498, 221, 169, 113, 0, 1262, 1077, 835, 689, 1014, 526, 456, 357, 488, 344, 202, 100, 0), # 175
(1228, 1058, 1054, 1147, 954, 453, 489, 412, 500, 221, 169, 113, 0, 1262, 1080, 838, 692, 1017, 526, 456, 358, 490, 345, 204, 100, 0), # 176
(1234, 1060, 1058, 1150, 954, 454, 490, 413, 501, 222, 169, 113, 0, 1270, 1082, 839, 695, 1019, 527, 456, 359, 490, 346, 204, 101, 0), # 177
(1238, 1064, 1061, 1156, 955, 455, 490, 413, 505, 223, 170, 113, 0, 1275, 1083, 839, 696, 1021, 528, 457, 361, 494, 347, 204, 101, 0), # 178
(1238, 1064, 1061, 1156, 955, 455, 490, 413, 505, 223, 170, 113, 0, 1275, 1083, 839, 696, 1021, 528, 457, 361, 494, 347, 204, 101, 0), # 179
)
passenger_arriving_rate = (
(4.0166924626974145, 4.051878277108322, 3.4741888197416713, 3.72880066431806, 2.962498990725126, 1.4647056349507583, 1.6584142461495661, 1.5510587243264744, 1.6240264165781353, 0.7916030031044742, 0.5607020218514138, 0.32652767188707826, 0.0, 4.067104170062691, 3.5918043907578605, 2.803510109257069, 2.374809009313422, 3.2480528331562706, 2.171482214057064, 1.6584142461495661, 1.0462183106791132, 1.481249495362563, 1.2429335547726867, 0.6948377639483343, 0.36835257064621113, 0.0), # 0
(4.283461721615979, 4.319377842372822, 3.703564394220102, 3.97508655196597, 3.1586615133195926, 1.561459005886526, 1.7677875765054776, 1.6531712409685695, 1.7312654203554425, 0.8437961384554302, 0.5977461514608177, 0.34808111072095704, 0.0, 4.3358333179518835, 3.8288922179305267, 2.9887307573040878, 2.53138841536629, 3.462530840710885, 2.3144397373559973, 1.7677875765054776, 1.1153278613475186, 1.5793307566597963, 1.3250288506553236, 0.7407128788440204, 0.39267071294298395, 0.0), # 1
(4.549378407183785, 4.585815791986718, 3.9320281903649423, 4.220392622798877, 3.3541135859998636, 1.6578263867724743, 1.8767274031842818, 1.7548750826348067, 1.838076481834013, 0.8957827550041094, 0.6346430865035085, 0.3695488434702037, 0.0, 4.603491862567752, 4.06503727817224, 3.173215432517542, 2.6873482650123277, 3.676152963668026, 2.4568251156887295, 1.8767274031842818, 1.1841617048374817, 1.6770567929999318, 1.4067975409329592, 0.7864056380729886, 0.41689234472606534, 0.0), # 2
(4.81340623451725, 4.850135034753395, 4.1586739128799035, 4.463745844519244, 3.548086227201014, 1.7534256238730528, 1.9848014566591823, 1.8557670524981693, 1.9440360429122914, 0.9473565396852364, 0.6712464549103178, 0.3908457123286974, 0.0, 4.869018245003381, 4.299302835615671, 3.356232274551589, 2.8420696190557084, 3.8880720858245827, 2.598073873497437, 1.9848014566591823, 1.2524468741950376, 1.774043113600507, 1.487915281506415, 0.8317347825759807, 0.4409213667957632, 0.0), # 3
(5.074508918732786, 5.111278479476234, 4.382595266468691, 4.704173184829542, 3.7398104553581293, 1.8478745634527118, 2.0915774674033836, 1.9554439537316386, 2.048720545488722, 0.998311179433536, 0.7074098846120768, 0.41188655949031766, 0.0, 5.131350906351854, 4.530752154393493, 3.5370494230603833, 2.9949335383006073, 4.097441090977444, 2.737621535224294, 2.0915774674033836, 1.3199104024662227, 1.8699052276790646, 1.5680577282765145, 0.8765190532937384, 0.46466167995238505, 0.0), # 4
(5.331650174946809, 5.368189034958631, 4.602885955835013, 4.940701611432236, 3.9285172889062823, 1.9407910517759004, 2.1966231658900894, 2.0535025895081978, 2.151706431461749, 1.048440361183733, 0.7429870035396177, 0.43258622714894324, 0.0, 5.389428287706262, 4.758448498638375, 3.7149350176980884, 3.145321083551198, 4.303412862923498, 2.8749036253114766, 2.1966231658900894, 1.3862793226970715, 1.9642586444531411, 1.6469005371440792, 0.9205771911670025, 0.48801718499623925, 0.0), # 5
(5.583793718275733, 5.619809610003967, 4.8186396856825775, 5.172358092029792, 4.113437746280557, 2.03179293510707, 2.299506282592505, 2.1495397630008295, 2.2525701427298173, 1.097537771870552, 0.777831439623771, 0.45285955749845397, 0.0, 5.642188830159686, 4.981455132482993, 3.889157198118855, 3.2926133156116553, 4.5051402854596345, 3.0093556682011613, 2.299506282592505, 1.4512806679336214, 2.0567188731402783, 1.724119364009931, 0.9637279371365156, 0.5108917827276335, 0.0), # 6
(5.829903263835975, 5.86508311341563, 5.02895016071509, 5.398169594324678, 4.293802845916028, 2.1204980597106697, 2.399794547983834, 2.2431522773825177, 2.350888121191372, 1.1453970984287176, 0.8117968207953693, 0.47262139273272863, 0.0, 5.888570974805216, 5.198835320060014, 4.058984103976846, 3.436191295286152, 4.701776242382744, 3.1404131883355246, 2.399794547983834, 1.514641471221907, 2.146901422958014, 1.799389864774893, 1.0057900321430182, 0.5331893739468755, 0.0), # 7
(6.068942526743948, 6.102952453997006, 5.232911085636264, 5.617163086019357, 4.468843606247779, 2.2065242718511486, 2.497055692537279, 2.333936935826242, 2.446236808744855, 1.1918120277929551, 0.8447367749852429, 0.49178657504564693, 0.0, 6.127513162735934, 5.409652325502115, 4.223683874926214, 3.5754360833788645, 4.89247361748971, 3.2675117101567386, 2.497055692537279, 1.5760887656079634, 2.2344218031238894, 1.872387695339786, 1.046582217127253, 0.5548138594542734, 0.0), # 8
(6.299875222116068, 6.332360540551483, 5.429616165149803, 5.828365534816301, 4.637791045710885, 2.2894894177929594, 2.590857446726048, 2.421490541504988, 2.538192647288713, 1.2365762468979886, 0.8765049301242238, 0.5102699466310877, 0.0, 6.35795383504493, 5.612969412941963, 4.382524650621119, 3.709728740693965, 5.076385294577426, 3.390086758106983, 2.590857446726048, 1.635349584137828, 2.3188955228554424, 1.9427885116054342, 1.0859232330299606, 0.5756691400501349, 0.0), # 9
(6.5216650650687455, 6.552250281882444, 5.6181591039594165, 6.0308039084179725, 4.799876182740427, 2.3690113438005502, 2.680767541023342, 2.505409897591737, 2.6263320787213904, 1.279483442678543, 0.9069549141431433, 0.5279863496829302, 0.0, 6.578831432825289, 5.807849846512232, 4.534774570715716, 3.838450328035629, 5.252664157442781, 3.5075738566284325, 2.680767541023342, 1.6921509598575357, 2.3999380913702133, 2.010267969472658, 1.1236318207918834, 0.5956591165347678, 0.0), # 10
(6.7332757707184046, 6.761564586793285, 5.797633606768811, 6.223505174526839, 4.954330035771484, 2.444707896138372, 2.7663537059023664, 2.585291807259472, 2.7102315449413314, 1.320327302069344, 0.9359403549728333, 0.5448506263950541, 0.0, 6.78908439717009, 5.993356890345594, 4.679701774864166, 3.9609819062080316, 5.420463089882663, 3.619408530163261, 2.7663537059023664, 1.7462199258131228, 2.477165017885742, 2.07450172484228, 1.1595267213537623, 0.6146876897084805, 0.0), # 11
(6.93367105418145, 6.959246364087378, 5.9671333782816935, 6.405496300845368, 5.100383623239134, 2.516196921070873, 2.8471836718363246, 2.6607330736811736, 2.789467487846981, 1.3589015120051147, 0.9633148805441247, 0.5607776189613379, 0.0, 6.987651169172428, 6.168553808574717, 4.816574402720623, 4.0767045360153435, 5.578934975693962, 3.7250263031536432, 2.8471836718363246, 1.7972835150506232, 2.550191811619567, 2.135165433615123, 1.1934266756563388, 0.63265876037158, 0.0), # 12
(7.121814630574301, 7.144238522568122, 6.125752123201774, 6.575804255076027, 5.237267963578454, 2.5830962648625047, 2.9228251692984224, 2.731330500029827, 2.863616349336782, 1.3949997594205812, 0.9889321187878493, 0.5756821695756614, 0.0, 7.173470189925388, 6.332503865332275, 4.944660593939246, 4.184999278261743, 5.727232698673564, 3.8238627000417584, 2.9228251692984224, 1.8450687606160747, 2.618633981789227, 2.1919347516920094, 1.225150424640355, 0.6494762293243748, 0.0), # 13
(7.296670215013373, 7.315483971038899, 6.272583546232765, 6.733456004921276, 5.3642140752245275, 2.6450237737777162, 2.9928459287618647, 2.7966808894784156, 2.932254571309179, 1.428415731250467, 1.0126456976348381, 0.5894791204319041, 0.0, 7.345479900522051, 6.484270324750944, 5.06322848817419, 4.285247193751401, 5.864509142618358, 3.9153532452697823, 2.9928459287618647, 1.8893026955555114, 2.6821070376122638, 2.244485334973759, 1.254516709246553, 0.6650439973671727, 0.0), # 14
(7.457201522615084, 7.471925618303093, 6.406721352078362, 6.877478518083592, 5.480452976612431, 2.701597294080959, 3.0568136806998503, 2.8563810451999188, 2.9949585956626184, 1.4589431144294984, 1.0343092450159228, 0.6020833137239449, 0.0, 7.502618742055505, 6.622916450963392, 5.171546225079613, 4.376829343288494, 5.989917191325237, 3.9989334632798865, 3.0568136806998503, 1.9297123529149707, 2.7402264883062153, 2.2924928393611976, 1.2813442704156726, 0.6792659653002813, 0.0), # 15
(7.602372268495841, 7.612506373164098, 6.527259245442284, 7.006898762265429, 5.585215686177244, 2.7524346720366815, 3.1142961555855906, 2.9100277703673205, 3.0513048642955427, 1.4863755958923994, 1.0537763888619351, 0.6134095916456628, 0.0, 7.643825155618837, 6.747505508102289, 5.268881944309675, 4.459126787677198, 6.102609728591085, 4.074038878514249, 3.1142961555855906, 1.9660247657404866, 2.792607843088622, 2.3356329207551436, 1.3054518490884568, 0.692046033924009, 0.0), # 16
(7.73114616777206, 7.736169144425294, 6.6332909310282355, 7.120743705169268, 5.677733222354047, 2.7971537539093334, 3.1648610838922844, 2.9572178681536063, 3.1008698191063955, 1.510506862573894, 1.0709007571037066, 0.6233727963909371, 0.0, 7.768037582305133, 6.857100760300307, 5.354503785518533, 4.531520587721681, 6.201739638212791, 4.140105015415049, 3.1648610838922844, 1.9979669670780953, 2.8388666111770235, 2.373581235056423, 1.3266581862056472, 0.7032881040386633, 0.0), # 17
(7.842486935560164, 7.841856840890068, 6.723910113539921, 7.218040314497568, 5.757236603577914, 2.8353723859633684, 3.2080761960931405, 2.9975481417317535, 3.1432299019936254, 1.5311306014087078, 1.085535977672068, 0.6318877701536477, 0.0, 7.874194463207477, 6.950765471690124, 5.427679888360339, 4.593391804226123, 6.286459803987251, 4.196567398424455, 3.2080761960931405, 2.0252659899738346, 2.878618301788957, 2.406013438165856, 1.344782022707984, 0.7128960764445517, 0.0), # 18
(7.935358286976559, 7.928512371361812, 6.798210497681052, 7.29781555795279, 5.822956848283928, 2.866708414463231, 3.2435092226613578, 3.030615394274749, 3.1779615548556746, 1.5480404993315662, 1.0975356784978507, 0.6388693551276732, 0.0, 7.961234239418957, 7.027562906404404, 5.4876783924892525, 4.644121497994697, 6.355923109711349, 4.242861551984649, 3.2435092226613578, 2.0476488674737365, 2.911478424141964, 2.4326051859842637, 1.3596420995362106, 0.720773851941983, 0.0), # 19
(8.008723937137665, 7.995078644643906, 6.855285788155336, 7.359096403237412, 5.874124974907169, 2.8907796856733756, 3.270727894070145, 3.0560164289555725, 3.2046412195909864, 1.5610302432771923, 1.106753487511887, 0.6442323935068929, 0.0, 8.02809535203266, 7.08655632857582, 5.533767437559434, 4.683090729831576, 6.409282439181973, 4.278423000537802, 3.270727894070145, 2.0648426326238396, 2.9370624874535847, 2.4530321344124713, 1.3710571576310673, 0.7268253313312643, 0.0), # 20
(8.061547601159893, 8.040498569539743, 6.89422968966648, 7.400909818053892, 5.909972001882714, 2.90720404585825, 3.289299940792704, 3.0733480489472083, 3.222845338098006, 1.5698935201803115, 1.113043032645008, 0.6478917274851863, 0.0, 8.073716242141662, 7.1268090023370485, 5.56521516322504, 4.709680560540933, 6.445690676196012, 4.302687268526092, 3.289299940792704, 2.0765743184701786, 2.954986000941357, 2.466969939351298, 1.378845937933296, 0.730954415412704, 0.0), # 21
(8.092792994159664, 8.063715054852706, 6.91413590691819, 7.422282770104703, 5.92972894764564, 2.915599341282305, 3.29879309330224, 3.0822070574226386, 3.2321503522751773, 1.574424016975649, 1.1162579418280456, 0.6497621992564327, 0.0, 8.097035350839063, 7.147384191820759, 5.581289709140227, 4.723272050926946, 6.464300704550355, 4.315089880391694, 3.29879309330224, 2.0825709580587892, 2.96486447382282, 2.474094256701568, 1.3828271813836381, 0.7330650049866098, 0.0), # 22
(8.104314690674112, 8.066463968907179, 6.916615454961135, 7.424958487654322, 5.9347904298840515, 2.916666666666667, 3.2999216009037355, 3.0831646090534983, 3.2333136625514407, 1.574958454503887, 1.1166610716215655, 0.6499931717725956, 0.0, 8.1, 7.149924889498552, 5.583305358107827, 4.72487536351166, 6.466627325102881, 4.316430452674898, 3.2999216009037355, 2.0833333333333335, 2.9673952149420257, 2.474986162551441, 1.3833230909922272, 0.7333149062642891, 0.0), # 23
(8.112809930427323, 8.06486049382716, 6.916209876543211, 7.4246291666666675, 5.937657393927921, 2.916666666666667, 3.299301525054467, 3.0818333333333334, 3.2331577777777776, 1.5746301234567905, 1.1166166105499442, 0.6499390946502058, 0.0, 8.1, 7.149330041152263, 5.583083052749721, 4.72389037037037, 6.466315555555555, 4.314566666666667, 3.299301525054467, 2.0833333333333335, 2.9688286969639606, 2.4748763888888896, 1.3832419753086422, 0.7331691358024692, 0.0), # 24
(8.121125784169264, 8.06169981710105, 6.915409236396892, 7.423977623456791, 5.940461304317068, 2.916666666666667, 3.298079561042524, 3.0792181069958855, 3.2328497942386836, 1.5739837677183361, 1.1165284532568485, 0.6498323426306966, 0.0, 8.1, 7.148155768937661, 5.5826422662842425, 4.7219513031550076, 6.465699588477367, 4.31090534979424, 3.298079561042524, 2.0833333333333335, 2.970230652158534, 2.474659207818931, 1.3830818472793784, 0.7328818015546411, 0.0), # 25
(8.129261615238427, 8.057030224051212, 6.914224508459078, 7.423011265432098, 5.943202063157923, 2.916666666666667, 3.2962746873234887, 3.0753683127572025, 3.23239366255144, 1.5730301417466854, 1.1163973978467807, 0.6496743789056548, 0.0, 8.1, 7.146418167962202, 5.581986989233903, 4.719090425240055, 6.46478732510288, 4.305515637860084, 3.2962746873234887, 2.0833333333333335, 2.9716010315789614, 2.4743370884773666, 1.3828449016918156, 0.732457293095565, 0.0), # 26
(8.13721678697331, 8.0509, 6.9126666666666665, 7.4217375, 5.945879572556914, 2.916666666666667, 3.2939058823529415, 3.0703333333333336, 3.231793333333333, 1.5717800000000004, 1.1162242424242426, 0.6494666666666669, 0.0, 8.1, 7.144133333333334, 5.581121212121213, 4.715339999999999, 6.463586666666666, 4.298466666666667, 3.2939058823529415, 2.0833333333333335, 2.972939786278457, 2.4739125000000004, 1.3825333333333334, 0.7319000000000001, 0.0), # 27
(8.1449906627124, 8.043357430269776, 6.910746684956561, 7.420163734567902, 5.948493734620481, 2.916666666666667, 3.2909921245864604, 3.06416255144033, 3.231052757201646, 1.570244096936443, 1.116009785093736, 0.6492106691053194, 0.0, 8.1, 7.141317360158513, 5.580048925468679, 4.710732290809328, 6.462105514403292, 4.289827572016462, 3.2909921245864604, 2.0833333333333335, 2.9742468673102405, 2.4733879115226345, 1.3821493369913125, 0.731214311842707, 0.0), # 28
(8.1525826057942, 8.0344508001829, 6.908475537265661, 7.41829737654321, 5.951044451455051, 2.916666666666667, 3.2875523924796264, 3.0569053497942384, 3.2301758847736624, 1.5684331870141752, 1.1157548239597623, 0.6489078494131992, 0.0, 8.1, 7.13798634354519, 5.578774119798812, 4.705299561042525, 6.460351769547325, 4.279667489711934, 3.2875523924796264, 2.0833333333333335, 2.9755222257275253, 2.4727657921810704, 1.3816951074531325, 0.7304046181984455, 0.0), # 29
(8.159991979557198, 8.02422839506173, 6.905864197530864, 7.416145833333333, 5.953531625167059, 2.916666666666667, 3.2836056644880176, 3.048611111111111, 3.2291666666666665, 1.5663580246913587, 1.115460157126824, 0.648559670781893, 0.0, 8.1, 7.134156378600823, 5.57730078563412, 4.699074074074074, 6.458333333333333, 4.268055555555556, 3.2836056644880176, 2.0833333333333335, 2.9767658125835297, 2.4720486111111115, 1.3811728395061729, 0.7294753086419755, 0.0), # 30
(8.167218147339886, 8.012738500228625, 6.902923639689073, 7.41371651234568, 5.955955157862938, 2.916666666666667, 3.279170919067216, 3.039329218106996, 3.2280290534979423, 1.5640293644261551, 1.1151265826994223, 0.6481675964029875, 0.0, 8.1, 7.129843560432862, 5.575632913497111, 4.692088093278464, 6.456058106995885, 4.2550609053497945, 3.279170919067216, 2.0833333333333335, 2.977977578931469, 2.4712388374485603, 1.3805847279378145, 0.7284307727480569, 0.0), # 31
(8.174260472480764, 8.000029401005945, 6.899664837677183, 7.411016820987655, 5.958314951649118, 2.916666666666667, 3.2742671346727996, 3.029109053497943, 3.226766995884774, 1.5614579606767267, 1.1147548987820595, 0.6477330894680691, 0.0, 8.1, 7.125063984148759, 5.573774493910297, 4.684373882030179, 6.453533991769548, 4.24075267489712, 3.2742671346727996, 2.0833333333333335, 2.979157475824559, 2.470338940329219, 1.3799329675354366, 0.7272754000914496, 0.0), # 32
(8.181118318318317, 7.986149382716048, 6.896098765432099, 7.408054166666666, 5.960610908632033, 2.916666666666667, 3.2689132897603486, 3.0180000000000002, 3.2253844444444444, 1.5586545679012351, 1.114345903479237, 0.6472576131687243, 0.0, 8.1, 7.119833744855966, 5.571729517396184, 4.6759637037037045, 6.450768888888889, 4.225200000000001, 3.2689132897603486, 2.0833333333333335, 2.9803054543160163, 2.469351388888889, 1.37921975308642, 0.7260135802469135, 0.0), # 33
(8.187791048191048, 7.971146730681298, 6.892236396890718, 7.404835956790124, 5.962842930918115, 2.916666666666667, 3.263128362785444, 3.006051440329218, 3.2238853497942395, 1.5556299405578424, 1.1139003948954567, 0.6467426306965403, 0.0, 8.1, 7.114168937661942, 5.569501974477284, 4.666889821673526, 6.447770699588479, 4.208472016460905, 3.263128362785444, 2.0833333333333335, 2.9814214654590576, 2.468278652263375, 1.3784472793781437, 0.724649702789209, 0.0), # 34
(8.194278025437447, 7.95506973022405, 6.888088705989941, 7.401369598765432, 5.965010920613797, 2.916666666666667, 3.2569313322036635, 2.9933127572016467, 3.2222736625514408, 1.5523948331047102, 1.1134191711352206, 0.6461896052431033, 0.0, 8.1, 7.108085657674136, 5.5670958556761025, 4.657184499314129, 6.4445473251028815, 4.1906378600823055, 3.2569313322036635, 2.0833333333333335, 2.9825054603068986, 2.4671231995884777, 1.3776177411979884, 0.7231881572930956, 0.0), # 35
(8.200578613396004, 7.937966666666665, 6.8836666666666675, 7.3976625, 5.967114779825512, 2.916666666666667, 3.250341176470588, 2.979833333333334, 3.220553333333333, 1.5489600000000006, 1.1129030303030305, 0.6456000000000002, 0.0, 8.1, 7.101600000000001, 5.564515151515152, 4.64688, 6.441106666666666, 4.1717666666666675, 3.250341176470588, 2.0833333333333335, 2.983557389912756, 2.4658875000000005, 1.3767333333333336, 0.7216333333333333, 0.0), # 36
(8.20669217540522, 7.919885825331503, 6.8789812528577965, 7.393722067901235, 5.969154410659692, 2.916666666666667, 3.2433768740417976, 2.9656625514403294, 3.218728312757202, 1.5453361957018754, 1.1123527705033882, 0.6449752781588174, 0.0, 8.1, 7.09472805974699, 5.561763852516941, 4.636008587105625, 6.437456625514404, 4.1519275720164615, 3.2433768740417976, 2.0833333333333335, 2.984577205329846, 2.4645740226337454, 1.3757962505715595, 0.7199896204846822, 0.0), # 37
(8.212618074803581, 7.9008754915409245, 6.874043438500229, 7.389555709876545, 5.971129715222768, 2.916666666666667, 3.2360574033728717, 2.9508497942386835, 3.2168025514403293, 1.5415341746684963, 1.111769189840795, 0.6443169029111417, 0.0, 8.1, 7.087485932022558, 5.558845949203975, 4.624602524005487, 6.433605102880659, 4.131189711934157, 3.2360574033728717, 2.0833333333333335, 2.985564857611384, 2.4631852366255154, 1.3748086877000458, 0.7182614083219023, 0.0), # 38
(8.218355674929589, 7.880983950617284, 6.868864197530866, 7.3851708333333335, 5.973040595621175, 2.916666666666667, 3.2284017429193903, 2.9354444444444447, 3.21478, 1.5375646913580252, 1.1111530864197532, 0.6436263374485597, 0.0, 8.1, 7.079889711934156, 5.555765432098766, 4.612694074074074, 6.42956, 4.109622222222223, 3.2284017429193903, 2.0833333333333335, 2.9865202978105874, 2.4617236111111116, 1.3737728395061732, 0.7164530864197532, 0.0), # 39
(8.22390433912173, 7.860259487882944, 6.863454503886603, 7.380574845679012, 5.974886953961343, 2.916666666666667, 3.2204288711369324, 2.9194958847736636, 3.212664609053498, 1.5334385002286244, 1.1105052583447648, 0.6429050449626583, 0.0, 8.1, 7.071955494589241, 5.552526291723823, 4.600315500685872, 6.425329218106996, 4.087294238683129, 3.2204288711369324, 2.0833333333333335, 2.9874434769806717, 2.460191615226338, 1.3726909007773205, 0.714569044352995, 0.0), # 40
(8.229263430718502, 7.838750388660264, 6.857825331504345, 7.375775154320989, 5.976668692349708, 2.916666666666667, 3.212157766481078, 2.903053497942387, 3.210460329218107, 1.529166355738455, 1.1098265037203312, 0.6421544886450238, 0.0, 8.1, 7.06369937509526, 5.549132518601655, 4.587499067215363, 6.420920658436214, 4.0642748971193425, 3.212157766481078, 2.0833333333333335, 2.988334346174854, 2.4585917181069967, 1.371565066300869, 0.7126136716963878, 0.0), # 41
(8.2344323130584, 7.816504938271606, 6.85198765432099, 7.370779166666668, 5.978385712892697, 2.916666666666667, 3.2036074074074072, 2.886166666666667, 3.2081711111111115, 1.5247590123456796, 1.1091176206509543, 0.641376131687243, 0.0, 8.1, 7.0551374485596705, 5.5455881032547705, 4.574277037037037, 6.416342222222223, 4.040633333333334, 3.2036074074074072, 2.0833333333333335, 2.9891928564463486, 2.4569263888888897, 1.370397530864198, 0.7105913580246915, 0.0), # 42
(8.239410349479915, 7.7935714220393235, 6.845952446273435, 7.3655942901234575, 5.980037917696748, 2.916666666666667, 3.1947967723715003, 2.868884773662552, 3.2058009053497942, 1.5202272245084596, 1.1083794072411357, 0.6405714372809025, 0.0, 8.1, 7.046285810089926, 5.541897036205678, 4.5606816735253775, 6.4116018106995885, 4.016438683127573, 3.1947967723715003, 2.0833333333333335, 2.990018958848374, 2.4551980967078197, 1.369190489254687, 0.7085064929126659, 0.0), # 43
(8.244196903321543, 7.769998125285779, 6.839730681298583, 7.360227932098766, 5.981625208868291, 2.916666666666667, 3.185744839828936, 2.8512572016460913, 3.2033536625514403, 1.515581746684957, 1.1076126615953779, 0.639741868617589, 0.0, 8.1, 7.037160554793477, 5.538063307976889, 4.54674524005487, 6.4067073251028805, 3.9917600823045283, 3.185744839828936, 2.0833333333333335, 2.9908126044341454, 2.4534093106995893, 1.3679461362597167, 0.7063634659350709, 0.0), # 44
(8.248791337921773, 7.745833333333334, 6.833333333333335, 7.354687500000001, 5.983147488513758, 2.916666666666667, 3.1764705882352944, 2.833333333333334, 3.2008333333333328, 1.510833333333334, 1.106818181818182, 0.638888888888889, 0.0, 8.1, 7.027777777777777, 5.534090909090909, 4.532500000000001, 6.4016666666666655, 3.9666666666666672, 3.1764705882352944, 2.0833333333333335, 2.991573744256879, 2.4515625000000005, 1.366666666666667, 0.7041666666666668, 0.0), # 45
(8.253193016619106, 7.721125331504343, 6.8267713763145865, 7.348980401234568, 5.984604658739582, 2.916666666666667, 3.1669929960461554, 2.81516255144033, 3.198243868312757, 1.5059927389117518, 1.10599676601405, 0.6380139612863894, 0.0, 8.1, 7.018153574150282, 5.5299838300702495, 4.517978216735254, 6.396487736625514, 3.941227572016462, 3.1669929960461554, 2.0833333333333335, 2.992302329369791, 2.4496601337448567, 1.3653542752629175, 0.7019204846822131, 0.0), # 46
(8.257401302752028, 7.695922405121171, 6.8200557841792415, 7.3431140432098765, 5.985996621652196, 2.916666666666667, 3.1573310417170988, 2.7967942386831277, 3.195589218106996, 1.5010707178783727, 1.105149212287484, 0.6371185490016767, 0.0, 8.1, 7.008304039018443, 5.525746061437419, 4.503212153635117, 6.391178436213992, 3.915511934156379, 3.1573310417170988, 2.0833333333333335, 2.992998310826098, 2.4477046810699594, 1.3640111568358484, 0.6996293095564702, 0.0), # 47
(8.261415559659037, 7.670272839506174, 6.8131975308641985, 7.3370958333333345, 5.987323279358032, 2.916666666666667, 3.1475037037037037, 2.7782777777777783, 3.1928733333333335, 1.4960780246913583, 1.1042763187429856, 0.6362041152263375, 0.0, 8.1, 6.998245267489711, 5.521381593714927, 4.488234074074074, 6.385746666666667, 3.88958888888889, 3.1475037037037037, 2.0833333333333335, 2.993661639679016, 2.445698611111112, 1.3626395061728398, 0.6972975308641977, 0.0), # 48
(8.26523515067863, 7.644224919981709, 6.806207590306356, 7.330933179012346, 5.9885845339635235, 2.916666666666667, 3.137529960461551, 2.7596625514403295, 3.190100164609053, 1.491025413808871, 1.1033788834850566, 0.6352721231519587, 0.0, 8.1, 6.987993354671545, 5.5168944174252825, 4.473076241426613, 6.380200329218106, 3.8635275720164617, 3.137529960461551, 2.0833333333333335, 2.9942922669817618, 2.443644393004116, 1.3612415180612714, 0.6949295381801555, 0.0), # 49
(8.268859439149294, 7.617826931870143, 6.799096936442616, 7.324633487654321, 5.989780287575101, 2.916666666666667, 3.12742879044622, 2.7409979423868314, 3.1872736625514397, 1.485923639689072, 1.1024577046181985, 0.6343240359701267, 0.0, 8.1, 6.977564395671393, 5.512288523090993, 4.457770919067215, 6.3745473251028795, 3.8373971193415644, 3.12742879044622, 2.0833333333333335, 2.9948901437875506, 2.441544495884774, 1.3598193872885234, 0.692529721079104, 0.0), # 50
(8.272287788409528, 7.591127160493827, 6.791876543209877, 7.318204166666668, 5.9909104422991994, 2.916666666666667, 3.11721917211329, 2.7223333333333333, 3.184397777777778, 1.4807834567901237, 1.1015135802469138, 0.6333613168724281, 0.0, 8.1, 6.966974485596708, 5.507567901234569, 4.44235037037037, 6.368795555555556, 3.811266666666667, 3.11721917211329, 2.0833333333333335, 2.9954552211495997, 2.4394013888888897, 1.3583753086419754, 0.6901024691358025, 0.0), # 51
(8.275519561797823, 7.564173891175126, 6.78455738454504, 7.311652623456791, 5.991974900242248, 2.916666666666667, 3.1069200839183413, 2.7037181069958844, 3.18147646090535, 1.4756156195701877, 1.1005473084757038, 0.6323854290504498, 0.0, 8.1, 6.956239719554947, 5.502736542378519, 4.4268468587105625, 6.3629529218107, 3.7852053497942384, 3.1069200839183413, 2.0833333333333335, 2.995987450121124, 2.437217541152264, 1.356911476909008, 0.6876521719250116, 0.0), # 52
(8.278554122652675, 7.537015409236398, 6.777150434385004, 7.304986265432099, 5.992973563510682, 2.916666666666667, 3.0965505043169532, 2.6852016460905355, 3.1785136625514405, 1.470430882487426, 1.0995596874090703, 0.6313978356957782, 0.0, 8.1, 6.945376192653559, 5.4977984370453505, 4.411292647462277, 6.357027325102881, 3.7592823045267494, 3.0965505043169532, 2.0833333333333335, 2.996486781755341, 2.4349954218107, 1.355430086877001, 0.6851832190214908, 0.0), # 53
(8.281390834312573, 7.5097000000000005, 6.769666666666667, 7.2982125, 5.993906334210934, 2.916666666666667, 3.086129411764706, 2.6668333333333334, 3.1755133333333334, 1.4652400000000003, 1.098551515151515, 0.6304000000000001, 0.0, 8.1, 6.9344, 5.492757575757575, 4.395720000000001, 6.351026666666667, 3.7335666666666665, 3.086129411764706, 2.0833333333333335, 2.996953167105467, 2.4327375000000004, 1.3539333333333334, 0.6827000000000002, 0.0), # 54
(8.284029060116017, 7.482275948788294, 6.762117055326932, 7.291338734567901, 5.994773114449434, 2.916666666666667, 3.075675784717179, 2.6486625514403292, 3.1724794238683125, 1.4600537265660727, 1.0975235898075406, 0.6293933851547021, 0.0, 8.1, 6.923327236701723, 5.487617949037702, 4.380161179698217, 6.344958847736625, 3.708127572016461, 3.075675784717179, 2.0833333333333335, 2.997386557224717, 2.4304462448559674, 1.3524234110653865, 0.6802069044352995, 0.0), # 55
(8.286468163401498, 7.454791540923639, 6.754512574302698, 7.28437237654321, 5.995573806332619, 2.916666666666667, 3.0652086016299527, 2.6307386831275723, 3.169415884773662, 1.4548828166438048, 1.0964767094816479, 0.6283794543514709, 0.0, 8.1, 6.912173997866179, 5.482383547408239, 4.364648449931414, 6.338831769547324, 3.6830341563786013, 3.0652086016299527, 2.0833333333333335, 2.9977869031663094, 2.4281241255144037, 1.3509025148605398, 0.6777083219021491, 0.0), # 56
(8.288707507507507, 7.427295061728395, 6.746864197530866, 7.277320833333334, 5.996308311966915, 2.916666666666667, 3.0547468409586056, 2.613111111111112, 3.166326666666667, 1.4497380246913585, 1.0954116722783391, 0.627359670781893, 0.0, 8.1, 6.900956378600823, 5.477058361391695, 4.349214074074075, 6.332653333333334, 3.6583555555555565, 3.0547468409586056, 2.0833333333333335, 2.9981541559834577, 2.425773611111112, 1.3493728395061733, 0.6752086419753087, 0.0), # 57
(8.290746455772544, 7.39983479652492, 6.739182898948332, 7.270191512345679, 5.99697653345876, 2.916666666666667, 3.044309481158719, 2.595829218106996, 3.163215720164609, 1.4446301051668957, 1.0943292763021162, 0.6263354976375554, 0.0, 8.1, 6.889690474013108, 5.471646381510581, 4.333890315500686, 6.326431440329218, 3.6341609053497947, 3.044309481158719, 2.0833333333333335, 2.99848826672938, 2.4233971707818935, 1.3478365797896665, 0.6727122542295383, 0.0), # 58
(8.292584371535098, 7.372459030635573, 6.731479652491998, 7.262991820987654, 5.9975783729145835, 2.916666666666667, 3.0339155006858713, 2.578942386831276, 3.160086995884774, 1.4395698125285785, 1.0932303196574802, 0.6253083981100444, 0.0, 8.1, 6.878392379210486, 5.4661515982874, 4.318709437585735, 6.320173991769548, 3.6105193415637866, 3.0339155006858713, 2.0833333333333335, 2.9987891864572918, 2.420997273662552, 1.3462959304984, 0.6702235482395976, 0.0), # 59
(8.294220618133663, 7.345216049382717, 6.723765432098765, 7.255729166666667, 5.998113732440819, 2.916666666666667, 3.0235838779956428, 2.5625000000000004, 3.156944444444445, 1.4345679012345682, 1.092115600448934, 0.6242798353909466, 0.0, 8.1, 6.867078189300411, 5.460578002244669, 4.303703703703704, 6.31388888888889, 3.5875000000000004, 3.0235838779956428, 2.0833333333333335, 2.9990568662204096, 2.4185763888888894, 1.3447530864197532, 0.6677469135802471, 0.0), # 60
(8.295654558906731, 7.3181541380887065, 6.716051211705533, 7.248410956790124, 5.998582514143899, 2.916666666666667, 3.0133335915436135, 2.5465514403292184, 3.1537920164609052, 1.4296351257430273, 1.0909859167809788, 0.623251272671849, 0.0, 8.1, 6.855763999390337, 5.454929583904893, 4.2889053772290815, 6.3075840329218105, 3.5651720164609055, 3.0133335915436135, 2.0833333333333335, 2.9992912570719494, 2.4161369855967085, 1.3432102423411068, 0.6652867398262462, 0.0), # 61
(8.296885557192804, 7.291321582075903, 6.708347965249201, 7.241044598765433, 5.998984620130258, 2.916666666666667, 3.0031836197853625, 2.5311460905349796, 3.1506336625514404, 1.4247822405121175, 1.0898420667581163, 0.6222241731443379, 0.0, 8.1, 6.844465904587715, 5.449210333790581, 4.274346721536352, 6.301267325102881, 3.5436045267489718, 3.0031836197853625, 2.0833333333333335, 2.999492310065129, 2.4136815329218115, 1.3416695930498403, 0.6628474165523549, 0.0), # 62
(8.297912976330368, 7.264766666666667, 6.700666666666668, 7.233637500000001, 5.999319952506323, 2.916666666666667, 2.9931529411764703, 2.5163333333333338, 3.147473333333333, 1.4200200000000003, 1.0886848484848488, 0.6212000000000001, 0.0, 8.1, 6.8332, 5.443424242424244, 4.26006, 6.294946666666666, 3.5228666666666677, 2.9931529411764703, 2.0833333333333335, 2.9996599762531617, 2.411212500000001, 1.3401333333333336, 0.6604333333333334, 0.0), # 63
(8.298736179657919, 7.2385376771833565, 6.693018289894834, 7.226197067901236, 5.999588413378532, 2.916666666666667, 2.983260534172517, 2.5021625514403296, 3.1443149794238683, 1.415359158664838, 1.0875150600656773, 0.6201802164304223, 0.0, 8.1, 6.821982380734645, 5.437575300328387, 4.246077475994513, 6.288629958847737, 3.5030275720164616, 2.983260534172517, 2.0833333333333335, 2.999794206689266, 2.408732355967079, 1.3386036579789669, 0.6580488797439416, 0.0), # 64
(8.29935453051395, 7.212682898948331, 6.685413808870599, 7.218730709876544, 5.999789904853316, 2.916666666666667, 2.9735253772290813, 2.4886831275720165, 3.1411625514403294, 1.4108104709647922, 1.0863334996051048, 0.619166285627191, 0.0, 8.1, 6.8108291418991, 5.431667498025524, 4.232431412894376, 6.282325102880659, 3.484156378600823, 2.9735253772290813, 2.0833333333333335, 2.999894952426658, 2.4062435699588485, 1.33708276177412, 0.6556984453589393, 0.0), # 65
(8.299767392236957, 7.187250617283952, 6.677864197530865, 7.211245833333334, 5.999924329037105, 2.916666666666667, 2.963966448801743, 2.475944444444445, 3.13802, 1.406384691358025, 1.085140965207632, 0.6181596707818932, 0.0, 8.1, 6.799756378600824, 5.425704826038159, 4.2191540740740745, 6.27604, 3.466322222222223, 2.963966448801743, 2.0833333333333335, 2.9999621645185526, 2.4037486111111117, 1.3355728395061732, 0.6533864197530866, 0.0), # 66
(8.299974128165434, 7.162289117512574, 6.670380429812529, 7.203749845679012, 5.999991588036336, 2.916666666666667, 2.9546027273460824, 2.4639958847736634, 3.1348912757201646, 1.4020925743026982, 1.0839382549777616, 0.617161835086115, 0.0, 8.1, 6.788780185947264, 5.419691274888807, 4.206277722908094, 6.269782551440329, 3.4495942386831286, 2.9546027273460824, 2.0833333333333335, 2.999995794018168, 2.401249948559671, 1.3340760859625058, 0.6511171925011432, 0.0), # 67
(8.29983329158466, 7.137715668834903, 6.662937299954276, 7.196185044283415, 5.999934909491917, 2.916612538739013, 2.9454060779318585, 2.452781283340954, 3.131756759640299, 1.3979240883294335, 1.0827047984720504, 0.6161686681266496, 0.0, 8.099900120027435, 6.777855349393144, 5.413523992360251, 4.1937722649883, 6.263513519280598, 3.433893796677336, 2.9454060779318585, 2.0832946705278665, 2.9999674547459585, 2.398728348094472, 1.3325874599908551, 0.648883242621355, 0.0), # 68
(8.298513365539453, 7.112780047789725, 6.655325617283951, 7.188170108695652, 5.999419026870006, 2.916184636488341, 2.9361072725386457, 2.4416995884773662, 3.1284794238683125, 1.3937612781408861, 1.0813150451887295, 0.6151479315572884, 0.0, 8.099108796296298, 6.766627247130171, 5.406575225943647, 4.181283834422658, 6.256958847736625, 3.4183794238683127, 2.9361072725386457, 2.0829890260631005, 2.999709513435003, 2.396056702898551, 1.33106512345679, 0.6466163679808842, 0.0), # 69
(8.295908630047116, 7.087367803885127, 6.647512288523091, 7.179652274557166, 5.998399634202102, 2.9153419194228523, 2.926664053824548, 2.4306508154244786, 3.1250407712238992, 1.3895839048925471, 1.079753184870144, 0.614094850752854, 0.0, 8.097545867626888, 6.755043358281393, 5.3987659243507204, 4.168751714677641, 6.2500815424477985, 3.40291114159427, 2.926664053824548, 2.082387085302037, 2.999199817101051, 2.393217424852389, 1.3295024577046182, 0.6443061639895571, 0.0), # 70
(8.292055728514343, 7.061494123633789, 6.639500057155922, 7.170644102254428, 5.9968896420022055, 2.9140980439973583, 2.9170806638155953, 2.4196386221612562, 3.1214459228776104, 1.3853920718685282, 1.0780249827711816, 0.613010195814181, 0.0, 8.095231910150892, 6.743112153955991, 5.390124913855908, 4.1561762156055835, 6.242891845755221, 3.387494071025759, 2.9170806638155953, 2.081498602855256, 2.9984448210011028, 2.3902147007514767, 1.3279000114311843, 0.6419540112394354, 0.0), # 71
(8.286991304347827, 7.035174193548387, 6.631291666666667, 7.161158152173913, 5.994901960784313, 2.9124666666666674, 2.907361344537815, 2.408666666666667, 3.1177, 1.3811858823529415, 1.0761362041467308, 0.6118947368421054, 0.0, 8.0921875, 6.730842105263158, 5.380681020733653, 4.143557647058824, 6.2354, 3.3721333333333336, 2.907361344537815, 2.080333333333334, 2.9974509803921565, 2.3870527173913048, 1.3262583333333333, 0.6395612903225807, 0.0), # 72
(8.280752000954257, 7.008423200141599, 6.622889860539551, 7.151206984702094, 5.992449501062428, 2.9104614438855867, 2.897510338017237, 2.397738606919677, 3.113808123761622, 1.376965439629899, 1.0740926142516787, 0.6107492439374613, 0.0, 8.0884332133059, 6.7182416833120735, 5.370463071258393, 4.130896318889696, 6.227616247523244, 3.356834049687548, 2.897510338017237, 2.0789010313468475, 2.996224750531214, 2.383735661567365, 1.3245779721079105, 0.6371293818310545, 0.0), # 73
(8.273374461740323, 6.981256329926103, 6.614297382258802, 7.140803160225442, 5.989545173350547, 2.908096032108927, 2.887531886279889, 2.3868581008992535, 3.1097754153330284, 1.3727308469835127, 1.0718999783409144, 0.6095744872010845, 0.0, 8.083989626200276, 6.705319359211929, 5.359499891704571, 4.118192540950537, 6.219550830666057, 3.3416013412589547, 2.887531886279889, 2.0772114515063764, 2.9947725866752735, 2.380267720075148, 1.3228594764517605, 0.6346596663569185, 0.0), # 74
(8.26489533011272, 6.953688769414575, 6.605516975308642, 7.129959239130434, 5.986201888162673, 2.905384087791496, 2.8774302313518003, 2.376028806584362, 3.1056069958847736, 1.3684822076978942, 1.069564061669325, 0.6083712367338099, 0.0, 8.078877314814816, 6.692083604071907, 5.347820308346624, 4.105446623093682, 6.211213991769547, 3.3264403292181073, 2.8774302313518003, 2.0752743484224974, 2.9931009440813363, 2.3766530797101453, 1.3211033950617284, 0.6321535244922342, 0.0), # 75
(8.255351249478142, 6.925735705119696, 6.596551383173297, 7.118687781803542, 5.982432556012803, 2.9023392673881023, 2.8672096152589983, 2.365254381953971, 3.1013079865874102, 1.364219625057156, 1.067090629491799, 0.6071402626364722, 0.0, 8.073116855281206, 6.678542889001194, 5.335453147458995, 4.092658875171468, 6.2026159731748205, 3.311356134735559, 2.8672096152589983, 2.0730994767057873, 2.9912162780064016, 2.372895927267848, 1.3193102766346596, 0.6296123368290635, 0.0), # 76
(8.244778863243274, 6.897412323554141, 6.587403349336991, 7.10700134863124, 5.9782500874149385, 2.8989752273535543, 2.8568742800275118, 2.354538484987045, 3.0968835086114925, 1.3599432023454103, 1.0644854470632252, 0.6058823350099072, 0.0, 8.06672882373114, 6.664705685108978, 5.322427235316125, 4.07982960703623, 6.193767017222985, 3.296353878981863, 2.8568742800275118, 2.0706965909668247, 2.9891250437074692, 2.369000449543747, 1.3174806698673982, 0.6270374839594675, 0.0), # 77
(8.233214814814815, 6.8687338112305865, 6.578075617283951, 7.0949125, 5.97366739288308, 2.895305624142661, 2.84642846768337, 2.343884773662552, 3.092338683127571, 1.3556530428467686, 1.0617542796384905, 0.6045982239549493, 0.0, 8.059733796296298, 6.650580463504441, 5.308771398192452, 4.066959128540305, 6.184677366255142, 3.2814386831275724, 2.84642846768337, 2.0680754458161865, 2.98683369644154, 2.364970833333334, 1.3156151234567903, 0.624430346475508, 0.0), # 78
(8.220695747599452, 6.8397153546617115, 6.5685709304984, 7.082433796296296, 5.968697382931225, 2.891344114210232, 2.8358764202526006, 2.333296905959458, 3.0876786313062032, 1.351349249845343, 1.058902892472483, 0.6032886995724337, 0.0, 8.052152349108367, 6.63617569529677, 5.294514462362415, 4.0540477495360285, 6.1753572626124065, 3.266615668343241, 2.8358764202526006, 2.0652457958644517, 2.9843486914656125, 2.3608112654320994, 1.3137141860996802, 0.6217923049692465, 0.0), # 79
(8.207258305003878, 6.810372140360193, 6.558892032464563, 7.069577797906602, 5.963352968073375, 2.8871043540110755, 2.8252223797612324, 2.3227785398567296, 3.0829084743179394, 1.3470319266252455, 1.055937050820092, 0.6019545319631957, 0.0, 8.04400505829904, 6.621499851595152, 5.2796852541004595, 4.041095779875736, 6.165816948635879, 3.2518899557994216, 2.8252223797612324, 2.0622173957221968, 2.9816764840366874, 2.3565259326355346, 1.3117784064929128, 0.619124740032745, 0.0), # 80
(8.192939130434784, 6.78071935483871, 6.5490416666666675, 7.056357065217393, 5.957647058823529, 2.8826000000000005, 2.8144705882352943, 2.3123333333333336, 3.078033333333333, 1.3427011764705885, 1.0528625199362043, 0.6005964912280702, 0.0, 8.0353125, 6.606561403508772, 5.264312599681022, 4.028103529411765, 6.156066666666666, 3.237266666666667, 2.8144705882352943, 2.059, 2.9788235294117644, 2.3521190217391315, 1.3098083333333335, 0.6164290322580647, 0.0), # 81
(8.177774867298861, 6.750772184609939, 6.539022576588936, 7.042784158615137, 5.951592565695688, 2.877844708631815, 2.8036252877008145, 2.301964944368237, 3.0730583295229383, 1.3383571026654835, 1.0496850650757086, 0.5992153474678925, 0.0, 8.026095250342937, 6.5913688221468165, 5.248425325378542, 4.0150713079964495, 6.146116659045877, 3.2227509221155315, 2.8036252877008145, 2.0556033633084394, 2.975796282847844, 2.3475947195383795, 1.3078045153177873, 0.6137065622372673, 0.0), # 82
(8.161802159002804, 6.720545816186557, 6.528837505715592, 7.028871638486312, 5.945202399203851, 2.8728521363613275, 2.7926907201838214, 2.2916770309404058, 3.067988584057308, 1.3339998084940425, 1.0464104514934927, 0.5978118707834975, 0.0, 8.016373885459535, 6.575930578618472, 5.232052257467463, 4.001999425482127, 6.135977168114616, 3.208347843316568, 2.7926907201838214, 2.052037240258091, 2.9726011996019257, 2.3429572128287712, 1.3057675011431187, 0.6109587105624144, 0.0), # 83
(8.145057648953301, 6.690055436081242, 6.518489197530864, 7.014632065217392, 5.938489469862018, 2.867635939643347, 2.7816711277103434, 2.2814732510288067, 3.0628292181069954, 1.329629397240378, 1.0430444444444447, 0.5963868312757202, 0.0, 8.006168981481482, 6.560255144032922, 5.215222222222223, 3.9888881917211334, 6.125658436213991, 3.194062551440329, 2.7816711277103434, 2.0483113854595336, 2.969244734931009, 2.338210688405798, 1.303697839506173, 0.6081868578255676, 0.0), # 84
(8.127577980557048, 6.659316230806673, 6.507980395518976, 7.000077999194847, 5.931466688184191, 2.862209774932684, 2.77057075230641, 2.2713572626124074, 3.057585352842554, 1.3252459721886014, 1.0395928091834528, 0.5949409990453959, 0.0, 7.995501114540467, 6.544350989499354, 5.197964045917263, 3.9757379165658033, 6.115170705685108, 3.17990016765737, 2.77057075230641, 2.0444355535233454, 2.9657333440920954, 2.3333593330649496, 1.3015960791037953, 0.6053923846187885, 0.0), # 85
(8.10939979722073, 6.6283433868755255, 6.497313843164153, 6.985222000805154, 5.924146964684365, 2.8565872986841443, 2.7593938359980483, 2.2613327236701726, 3.0522621094345377, 1.320849636622825, 1.0360613109654049, 0.5934751441933597, 0.0, 7.984390860768176, 6.528226586126955, 5.180306554827023, 3.9625489098684747, 6.104524218869075, 3.1658658131382413, 2.7593938359980483, 2.040419499060103, 2.9620734823421824, 2.3284073336017186, 1.2994627686328306, 0.6025766715341389, 0.0), # 86
(8.090559742351045, 6.597152090800478, 6.486492283950617, 6.970076630434782, 5.9165432098765445, 2.8507821673525378, 2.7481446208112876, 2.2514032921810703, 3.0468646090534985, 1.3164404938271608, 1.0324557150451887, 0.5919900368204463, 0.0, 7.972858796296297, 6.511890405024908, 5.162278575225944, 3.9493214814814817, 6.093729218106997, 3.1519646090534983, 2.7481446208112876, 2.036272976680384, 2.9582716049382722, 2.3233588768115947, 1.2972984567901236, 0.5997410991636799, 0.0), # 87
(8.071094459354686, 6.565757529094207, 6.475518461362597, 6.95465444847021, 5.908668334274726, 2.8448080373926743, 2.7368273487721564, 2.2415726261240665, 3.0413979728699894, 1.3120186470857205, 1.0287817866776934, 0.5904864470274911, 0.0, 7.960925497256517, 6.495350917302401, 5.143908933388466, 3.9360559412571607, 6.082795945739979, 3.138201676573693, 2.7368273487721564, 2.032005740994767, 2.954334167137363, 2.3182181494900704, 1.2951036922725196, 0.5968870480994735, 0.0), # 88
(8.051040591638339, 6.534174888269392, 6.464395118884317, 6.938968015297907, 5.90053524839291, 2.8386785652593614, 2.7254462619066833, 2.2318443834781285, 3.035867322054565, 1.3075841996826167, 1.025045291117806, 0.5889651449153291, 0.0, 7.948611539780521, 6.478616594068619, 5.125226455589029, 3.9227525990478496, 6.07173464410913, 3.12458213686938, 2.7254462619066833, 2.0276275466138296, 2.950267624196455, 2.312989338432636, 1.2928790237768635, 0.5940158989335812, 0.0), # 89
(8.030434782608696, 6.502419354838709, 6.453125000000001, 6.923029891304349, 5.892156862745098, 2.8324074074074077, 2.7140056022408965, 2.2222222222222223, 3.030277777777778, 1.303137254901961, 1.021251993620415, 0.5874269005847954, 0.0, 7.9359375000000005, 6.461695906432748, 5.106259968102074, 3.9094117647058826, 6.060555555555556, 3.111111111111111, 2.7140056022408965, 2.0231481481481484, 2.946078431372549, 2.3076766304347833, 1.2906250000000001, 0.5911290322580646, 0.0), # 90
(8.00931367567245, 6.470506115314836, 6.441710848193873, 6.906852636876007, 5.883546087845287, 2.826008220291622, 2.7025096118008247, 2.2127098003353147, 3.024634461210182, 1.2986779160278654, 1.0174076594404082, 0.585872484136725, 0.0, 7.922923954046638, 6.444597325503974, 5.0870382972020405, 3.8960337480835956, 6.049268922420364, 3.097793720469441, 2.7025096118008247, 2.0185773002083014, 2.9417730439226437, 2.302284212292003, 1.2883421696387747, 0.5882278286649852, 0.0), # 91
(7.9877139142362985, 6.438450356210453, 6.43015540695016, 6.890448812399356, 5.874715834207482, 2.8194946603668143, 2.690962532612497, 2.203310775796373, 3.018942493522329, 1.2942062863444421, 1.013518053832674, 0.5843026656719533, 0.0, 7.909591478052126, 6.427329322391485, 5.067590269163369, 3.8826188590333257, 6.037884987044658, 3.0846350861149223, 2.690962532612497, 2.0139247574048675, 2.937357917103741, 2.296816270799786, 1.2860310813900322, 0.5853136687464049, 0.0), # 92
(7.965672141706924, 6.406267264038233, 6.418461419753087, 6.873830978260871, 5.865679012345678, 2.8128803840877916, 2.6793686067019404, 2.1940288065843623, 3.013206995884774, 1.2897224691358027, 1.0095889420521, 0.5827182152913147, 0.0, 7.895960648148147, 6.409900368204461, 5.0479447102605, 3.8691674074074074, 6.026413991769548, 3.0716403292181074, 2.6793686067019404, 2.0092002743484225, 2.932839506172839, 2.291276992753624, 1.2836922839506175, 0.5823879330943849, 0.0), # 93
(7.943225001491024, 6.373972025310855, 6.406631630086878, 6.857011694847022, 5.856448532773877, 2.806179047909364, 2.6677320760951844, 2.1848675506782507, 3.007433089468069, 1.2852265676860597, 1.005626089353575, 0.581119903095645, 0.0, 7.882052040466393, 6.392318934052094, 5.028130446767873, 3.855679703058178, 6.014866178936138, 3.058814570949551, 2.6677320760951844, 2.0044136056495456, 2.9282242663869384, 2.2856705649490077, 1.2813263260173757, 0.5794520023009869, 0.0), # 94
(7.920409136995288, 6.341579826540998, 6.394668781435757, 6.840003522544284, 5.847037306006079, 2.799404308286339, 2.6560571828182575, 2.1758306660570037, 3.001625895442768, 1.2807186852793244, 1.0016352609919863, 0.5795084991857787, 0.0, 7.867886231138546, 6.374593491043566, 5.008176304959932, 3.8421560558379726, 6.003251790885536, 3.046162932479805, 2.6560571828182575, 1.9995745059188135, 2.9235186530030397, 2.2800011741814283, 1.2789337562871517, 0.5765072569582727, 0.0), # 95
(7.89726119162641, 6.30910585424134, 6.382575617283951, 6.8228190217391305, 5.8374582425562815, 2.7925698216735255, 2.6443481688971886, 2.1669218106995887, 2.995790534979424, 1.27619892519971, 0.9976222222222224, 0.5778847736625516, 0.0, 7.853483796296297, 6.356732510288067, 4.988111111111112, 3.828596775599129, 5.991581069958848, 3.0336905349794243, 2.6443481688971886, 1.9946927297668038, 2.9187291212781408, 2.2742730072463773, 1.2765151234567904, 0.5735550776583037, 0.0), # 96
(7.873817808791078, 6.276565294924556, 6.370354881115684, 6.805470752818035, 5.827724252938488, 2.7856892445257326, 2.6326092763580053, 2.1581446425849724, 2.9899321292485905, 1.2716673907313272, 0.9935927382991712, 0.576249496626798, 0.0, 7.838865312071332, 6.338744462894778, 4.967963691495855, 3.8150021721939806, 5.979864258497181, 3.0214024996189615, 2.6326092763580053, 1.9897780318040947, 2.913862126469244, 2.2684902509393456, 1.2740709762231368, 0.5705968449931414, 0.0), # 97
(7.850115631895988, 6.243973335103323, 6.35800931641518, 6.787971276167473, 5.817848247666694, 2.7787762332977706, 2.6208447472267373, 2.1495028196921204, 2.9840557994208194, 1.2671241851582886, 0.9895525744777209, 0.5746034381793533, 0.0, 7.824051354595337, 6.320637819972885, 4.947762872388605, 3.801372555474865, 5.968111598841639, 3.0093039475689687, 2.6208447472267373, 1.9848401666412645, 2.908924123833347, 2.2626570920558247, 1.2716018632830361, 0.5676339395548476, 0.0), # 98
(7.826191304347827, 6.211345161290323, 6.3455416666666675, 6.770333152173913, 5.807843137254903, 2.7718444444444446, 2.6090588235294123, 2.1410000000000005, 2.9781666666666666, 1.2625694117647062, 0.9855074960127594, 0.5729473684210528, 0.0, 7.8090625000000005, 6.302421052631579, 4.927537480063797, 3.787708235294118, 5.956333333333333, 2.9974000000000007, 2.6090588235294123, 1.9798888888888888, 2.9039215686274513, 2.256777717391305, 1.2691083333333337, 0.564667741935484, 0.0), # 99
(7.80208146955329, 6.178695959998229, 6.332954675354367, 6.752568941223833, 5.797721832217111, 2.764907534420566, 2.597255747292058, 2.1326398414875785, 2.9722698521566837, 1.258003173834692, 0.9814632681591747, 0.5712820574527312, 0.0, 7.79391932441701, 6.284102631980042, 4.907316340795873, 3.774009521504075, 5.944539704313367, 2.98569577808261, 2.597255747292058, 1.9749339531575472, 2.8988609161085557, 2.250856313741278, 1.2665909350708735, 0.5616996327271119, 0.0), # 100
(7.777822770919068, 6.1460409177397235, 6.320251085962506, 6.734691203703704, 5.787497243067323, 2.757979159680943, 2.585439760540705, 2.124426002133821, 2.9663704770614236, 1.253425574652358, 0.9774256561718551, 0.5696082753752236, 0.0, 7.7786424039780515, 6.265691029127459, 4.887128280859275, 3.760276723957073, 5.932740954122847, 2.9741964029873493, 2.585439760540705, 1.9699851140578162, 2.8937486215336614, 2.244897067901235, 1.2640502171925014, 0.5587309925217931, 0.0), # 101
(7.753451851851853, 6.11339522102748, 6.307433641975309, 6.716712500000001, 5.7771822803195345, 2.7510729766803848, 2.5736151053013803, 2.1163621399176957, 2.9604736625514403, 1.248836717501816, 0.9734004253056887, 0.5679267922893655, 0.0, 7.763252314814816, 6.24719471518302, 4.867002126528443, 3.746510152505447, 5.920947325102881, 2.962906995884774, 2.5736151053013803, 1.965052126200275, 2.8885911401597673, 2.2389041666666674, 1.261486728395062, 0.5557632019115891, 0.0), # 102
(7.729005355758336, 6.080774056374176, 6.294505086877001, 6.698645390499196, 5.766789854487748, 2.7442026418736987, 2.561786023600112, 2.1084519128181682, 2.9545845297972866, 1.2442367056671781, 0.9693933408155633, 0.5662383782959916, 0.0, 7.747769633058984, 6.228622161255906, 4.846966704077817, 3.7327101170015338, 5.909169059594573, 2.951832677945436, 2.561786023600112, 1.960144744195499, 2.883394927243874, 2.2328817968330656, 1.2589010173754003, 0.5527976414885616, 0.0), # 103
(7.704519926045208, 6.048192610292491, 6.281468164151806, 6.680502435587762, 5.756332876085962, 2.7373818117156943, 2.5499567574629305, 2.1006989788142056, 2.948708199969517, 1.2396256424325565, 0.9654101679563669, 0.564543803495937, 0.0, 7.732214934842251, 6.209981838455306, 4.827050839781834, 3.7188769272976687, 5.897416399939034, 2.9409785703398876, 2.5499567574629305, 1.9552727226540672, 2.878166438042981, 2.2268341451959213, 1.2562936328303613, 0.549835691844772, 0.0), # 104
(7.680032206119162, 6.015666069295101, 6.268325617283951, 6.662296195652173, 5.745824255628177, 2.7306241426611804, 2.5381315489158633, 2.0931069958847743, 2.942849794238683, 1.235003631082063, 0.961456671982988, 0.562843837990037, 0.0, 7.716608796296296, 6.1912822178904054, 4.80728335991494, 3.705010893246188, 5.885699588477366, 2.930349794238684, 2.5381315489158633, 1.9504458161865572, 2.8729121278140886, 2.220765398550725, 1.2536651234567902, 0.546878733572282, 0.0), # 105
(7.655578839386891, 5.983209619894685, 6.255080189757659, 6.644039231078905, 5.735276903628392, 2.723943291164965, 2.526314639984938, 2.0856796220088403, 2.9370144337753388, 1.2303707748998092, 0.9575386181503142, 0.5611392518791264, 0.0, 7.700971793552812, 6.172531770670389, 4.787693090751571, 3.691112324699427, 5.8740288675506775, 2.9199514708123764, 2.526314639984938, 1.9456737794035461, 2.867638451814196, 2.214679743692969, 1.2510160379515318, 0.5439281472631533, 0.0), # 106
(7.631196469255085, 5.950838448603921, 6.241734625057157, 6.625744102254428, 5.724703730600607, 2.7173529136818577, 2.5145102726961848, 2.0784205151653716, 2.931207239750038, 1.225727177169908, 0.9536617717132337, 0.5594308152640404, 0.0, 7.685324502743484, 6.153738967904443, 4.768308858566169, 3.6771815315097234, 5.862414479500076, 2.9097887212315205, 2.5145102726961848, 1.9409663669156128, 2.8623518653003037, 2.208581367418143, 1.2483469250114314, 0.5409853135094475, 0.0), # 107
(7.606921739130435, 5.918567741935485, 6.228291666666668, 6.607423369565218, 5.714117647058822, 2.7108666666666674, 2.5027226890756302, 2.0713333333333335, 2.9254333333333333, 1.221072941176471, 0.9498318979266349, 0.5577192982456142, 0.0, 7.669687500000001, 6.134912280701755, 4.749159489633174, 3.6632188235294123, 5.850866666666667, 2.899866666666667, 2.5027226890756302, 1.9363333333333337, 2.857058823529411, 2.20247445652174, 1.2456583333333338, 0.538051612903226, 0.0), # 108
(7.582791292419635, 5.886412686402053, 6.214754058070417, 6.589089593397745, 5.70353156351704, 2.7044982065742014, 2.490956131149305, 2.064421734491694, 2.9196978356957777, 1.2164081702036098, 0.9460547620454054, 0.5560054709246826, 0.0, 7.654081361454047, 6.116060180171507, 4.730273810227027, 3.6492245106108285, 5.839395671391555, 2.8901904282883715, 2.490956131149305, 1.9317844332672867, 2.85176578175852, 2.196363197799249, 1.2429508116140835, 0.5351284260365504, 0.0), # 109
(7.558841772529373, 5.854388468516307, 6.201124542752631, 6.570755334138486, 5.692958390489256, 2.6982611898592697, 2.4792148409432357, 2.0576893766194178, 2.9140058680079255, 1.211732967535437, 0.9423361293244336, 0.554290103402081, 0.0, 7.638526663237312, 6.0971911374228895, 4.711680646622168, 3.63519890260631, 5.828011736015851, 2.880765127267185, 2.4792148409432357, 1.9273294213280499, 2.846479195244628, 2.1902517780461626, 1.2402249085505264, 0.5322171335014826, 0.0), # 110
(7.535109822866345, 5.82251027479092, 6.187405864197532, 6.552433152173913, 5.68241103848947, 2.6921692729766806, 2.4675030604834527, 2.0511399176954734, 2.9083625514403293, 1.2070474364560642, 0.9386817650186072, 0.5525739657786443, 0.0, 7.623043981481482, 6.078313623565086, 4.693408825093036, 3.621142309368192, 5.816725102880659, 2.871595884773663, 2.4675030604834527, 1.9229780521262005, 2.841205519244735, 2.1841443840579715, 1.2374811728395065, 0.5293191158900837, 0.0), # 111
(7.51163208683724, 5.790793291738572, 6.173600765889348, 6.5341356078905, 5.671902418031685, 2.686236112381243, 2.4558250317959835, 2.0447770156988265, 2.9027730071635416, 1.2023516802496035, 0.9350974343828147, 0.5508578281552075, 0.0, 7.607653892318244, 6.059436109707281, 4.675487171914074, 3.6070550407488096, 5.805546014327083, 2.862687821978357, 2.4558250317959835, 1.9187400802723165, 2.8359512090158425, 2.178045202630167, 1.2347201531778695, 0.5264357537944157, 0.0), # 112
(7.488403378962436, 5.759305653776365, 6.159745218834713, 6.515900329495224, 5.661427029425976, 2.6804725589667733, 2.444210385462708, 2.038617522926869, 2.8972567496689656, 1.1976609473225461, 0.9315898541537156, 0.549146195766962, 0.0, 7.592355120674577, 6.0406081534365805, 4.657949270768578, 3.592982841967638, 5.794513499337931, 2.8540645320976163, 2.444210385462708, 1.914623256404838, 2.830713514712988, 2.1719667764984085, 1.2319490437669427, 0.5235732412523969, 0.0), # 113
(7.465184718320052, 5.728357934585393, 6.146030450014413, 6.497873652766401, 5.6508764557687075, 2.674865483980621, 2.432807283364232, 2.0327370865017067, 2.891898409523483, 1.1930630335825567, 0.9281659116150931, 0.5474608114741984, 0.0, 7.577020331328028, 6.022068926216181, 4.640829558075465, 3.5791891007476693, 5.783796819046966, 2.8458319211023895, 2.432807283364232, 1.9106182028433005, 2.8254382278843537, 2.1659578842554676, 1.2292060900028827, 0.5207598122350358, 0.0), # 114
(7.441907922403196, 5.697961279034234, 6.132464621804878, 6.480050703109068, 5.640217428207254, 2.669400305832757, 2.421623860076625, 2.027134218092903, 2.886699994311677, 1.1885650655976157, 0.9248206015236127, 0.5458025055039235, 0.0, 7.561605305328301, 6.003827560543158, 4.6241030076180625, 3.5656951967928463, 5.773399988623354, 2.8379879053300643, 2.421623860076625, 1.9067145041662548, 2.820108714103627, 2.1600169010363564, 1.226492924360976, 0.5179964799122032, 0.0), # 115
(7.418543898590108, 5.668071406280581, 6.119021459989249, 6.462399690159842, 5.629433880738015, 2.664064142733979, 2.4106419270111576, 2.021793437632998, 2.8816483571274216, 1.1841586716899097, 0.9215474575028644, 0.5441682131658231, 0.0, 7.546085807804713, 5.985850344824053, 4.607737287514321, 3.5524760150697285, 5.763296714254843, 2.8305108126861973, 2.4106419270111576, 1.9029029590956992, 2.8147169403690073, 2.154133230053281, 1.22380429199785, 0.5152792187527803, 0.0), # 116
(7.395063554259018, 5.638644035482129, 6.105674690350658, 6.444888823555345, 5.6185097473573915, 2.6588441128950824, 2.399843295579101, 2.0166992650545286, 2.8767303510645874, 1.179835480181626, 0.9183400131764379, 0.5425548697695834, 0.0, 7.53043760388658, 5.968103567465417, 4.591700065882189, 3.5395064405448773, 5.753460702129175, 2.8233789710763397, 2.399843295579101, 1.8991743663536302, 2.8092548736786958, 2.148296274518449, 1.2211349380701317, 0.5126040032256481, 0.0), # 117
(7.371437796788169, 5.60963488579657, 6.092398038672245, 6.427486312932199, 5.607428962061783, 2.6537273345268653, 2.3892097771917262, 2.0118362202900326, 2.871932829217049, 1.175587119394952, 0.9151918021679234, 0.5409594106248901, 0.0, 7.51463645870322, 5.950553516873789, 4.575959010839616, 3.5267613581848556, 5.743865658434098, 2.8165707084060454, 2.3892097771917262, 1.8955195246620464, 2.8037144810308914, 2.142495437644067, 1.218479607734449, 0.5099668077996883, 0.0), # 118
(7.347637533555794, 5.580999676381602, 6.079165230737149, 6.410160367927023, 5.5961754588475845, 2.648700925840122, 2.3787231832603024, 2.0071888232720485, 2.867242644678678, 1.1714052176520746, 0.9120963581009105, 0.5393787710414291, 0.0, 7.498658137383946, 5.933166481455719, 4.560481790504553, 3.5142156529562234, 5.734485289357356, 2.810064352580868, 2.3787231832603024, 1.8919292327429442, 2.7980877294237922, 2.1367201226423416, 1.21583304614743, 0.507363606943782, 0.0), # 119
(7.323633671940129, 5.552694126394916, 6.065949992328509, 6.392879198176436, 5.584733171711198, 2.6437520050456507, 2.3683653251961014, 2.0027415939331146, 2.8626466505433488, 1.1672814032751813, 0.909047214598989, 0.5378098863288866, 0.0, 7.482478405058078, 5.915908749617751, 4.545236072994944, 3.501844209825543, 5.7252933010866975, 2.80383823150636, 2.3683653251961014, 1.8883942893183219, 2.792366585855599, 2.1309597327254792, 1.2131899984657017, 0.5047903751268107, 0.0), # 120
(7.299397119319415, 5.524673954994208, 6.052726049229459, 6.3756110133170605, 5.573086034649023, 2.638867690354248, 2.358118014410392, 1.9984790522057692, 2.858131699904933, 1.1632073045864595, 0.906037905285749, 0.5362496917969483, 0.0, 7.466073026854929, 5.898746609766429, 4.530189526428744, 3.489621913759378, 5.716263399809866, 2.797870673088077, 2.358118014410392, 1.884905493110177, 2.7865430173245116, 2.1252036711056874, 1.2105452098458918, 0.5022430868176554, 0.0), # 121
(7.274898783071883, 5.496894881337171, 6.039467127223141, 6.358324022985514, 5.561217981657458, 2.634035099976709, 2.347963062314447, 1.9943857180225497, 2.8536846458573035, 1.1591745499080957, 0.9030619637847803, 0.5346951227553002, 0.0, 7.4494177679038165, 5.8816463503083005, 4.515309818923901, 3.4775236497242865, 5.707369291714607, 2.7921400052315697, 2.347963062314447, 1.8814536428405064, 2.780608990828729, 2.119441340995172, 1.2078934254446283, 0.49971771648519747, 0.0), # 122
(7.250109570575775, 5.469312624581501, 6.026146952092692, 6.340986436818417, 5.549112946732902, 2.629241352123832, 2.3378822803195356, 1.9904461113159944, 2.8492923414943343, 1.1551747675622777, 0.9001129237196728, 0.5331431145136282, 0.0, 7.432488393334058, 5.864574259649909, 4.500564618598363, 3.4655243026868323, 5.698584682988669, 2.7866245558423923, 2.3378822803195356, 1.8780295372313083, 2.774556473366451, 2.1136621456061393, 1.2052293904185383, 0.49721023859831837, 0.0), # 123
(7.225000389209324, 5.441882903884891, 6.012739249621247, 6.323566464452393, 5.536754863871753, 2.624473565006412, 2.327857479836928, 1.9866447520186423, 2.844941639909897, 1.1511995858711925, 0.897184318714016, 0.5315906023816185, 0.0, 7.4152606682749695, 5.847496626197802, 4.4859215935700805, 3.4535987576135767, 5.689883279819794, 2.781302652826099, 2.327857479836928, 1.87462397500458, 2.7683774319358765, 2.107855488150798, 1.2025478499242495, 0.49471662762589924, 0.0), # 124
(7.199542146350767, 5.414561438405035, 5.99921774559195, 6.306032315524057, 5.524127667070411, 2.619718856835246, 2.3178704722778956, 1.9829661600630304, 2.840619394197865, 1.147240633157027, 0.8942696823914004, 0.5300345216689567, 0.0, 7.397710357855863, 5.8303797383585225, 4.471348411957002, 3.4417218994710805, 5.68123878839573, 2.7761526240882426, 2.3178704722778956, 1.8712277548823186, 2.7620638335352057, 2.1020107718413525, 1.19984354911839, 0.49223285803682143, 0.0), # 125
(7.1737057493783425, 5.387303947299629, 5.985556165787933, 6.288352199670033, 5.511215290325276, 2.614964345821132, 2.307903069053708, 1.9793948553816976, 2.8363124574521112, 1.1432895377419687, 0.8913625483754153, 0.5284718076853291, 0.0, 7.379813227206063, 5.813189884538619, 4.4568127418770755, 3.4298686132259055, 5.6726249149042225, 2.7711527975343766, 2.307903069053708, 1.8678316755865225, 2.755607645162638, 2.0961173998900113, 1.1971112331575866, 0.4897549042999664, 0.0), # 126
(7.147462105670289, 5.360066149726364, 5.9717282359923365, 6.27049432652694, 5.498001667632746, 2.610197150174864, 2.2979370815756375, 1.975915357907182, 2.832007682766508, 1.139337927948205, 0.8884564502896507, 0.5268993957404212, 0.0, 7.361545041454879, 5.795893353144632, 4.442282251448253, 3.4180137838446143, 5.664015365533016, 2.766281501070055, 2.2979370815756375, 1.8644265358391885, 2.749000833816373, 2.0901647755089803, 1.1943456471984675, 0.487278740884215, 0.0), # 127
(7.120782122604837, 5.332803764842939, 5.957707681988301, 6.252426905731399, 5.484470732989221, 2.6054043881072406, 2.287954321254953, 1.9725121875720208, 2.827691923234929, 1.1353774320979229, 0.8855449217576967, 0.5253142211439193, 0.0, 7.34288156573163, 5.778456432583111, 4.427724608788483, 3.4061322962937677, 5.655383846469858, 2.7615170626008294, 2.287954321254953, 1.8610031343623146, 2.7422353664946106, 2.084142301910467, 1.1915415363976603, 0.4848003422584491, 0.0), # 128
(7.093636707560226, 5.305472511807044, 5.9434682295589605, 6.2341181469200295, 5.4706064203911, 2.600573177829058, 2.2779365995029255, 1.9691698643087534, 2.823352031951247, 1.1313996785133094, 0.882621496403143, 0.5237132192055092, 0.0, 7.323798565165631, 5.7608454112606, 4.413107482015715, 3.3941990355399274, 5.646704063902494, 2.756837810032255, 2.2779365995029255, 1.8575522698778983, 2.73530321019555, 2.078039382306677, 1.188693645911792, 0.48231568289154947, 0.0), # 129
(7.065996767914694, 5.2780281097763755, 5.9289836044874535, 6.215536259729452, 5.45639266383478, 2.595690637551111, 2.267865727730825, 1.9658729080499169, 2.818974862009333, 1.1273962955165517, 0.8796797078495794, 0.522093325234877, 0.0, 7.3042718048861985, 5.743026577583645, 4.398398539247896, 3.3821888865496543, 5.637949724018666, 2.7522220712698835, 2.267865727730825, 1.8540647411079363, 2.72819633191739, 2.0718454199098177, 1.1857967208974907, 0.4798207372523978, 0.0), # 130
(7.037833211046475, 5.250426277908626, 5.914227532556921, 6.196649453796286, 5.441813397316663, 2.590743885484198, 2.2577235173499237, 1.9626058387280498, 2.814547266503063, 1.1233589114298372, 0.8767130897205959, 0.5204514745417084, 0.0, 7.2842770500226495, 5.724966219958791, 4.383565448602979, 3.370076734289511, 5.629094533006126, 2.74764817421927, 2.2577235173499237, 1.850531346774427, 2.7209066986583315, 2.0655498179320957, 1.1828455065113843, 0.4773114798098752, 0.0), # 131
(7.009116944333808, 5.222622735361492, 5.8991737395504975, 6.1774259387571515, 5.4268525548331485, 2.5857200398391145, 2.24749177977149, 1.959353176275691, 2.8100560985263074, 1.119279154575353, 0.8737151756397821, 0.5187846024356896, 0.0, 7.263790065704301, 5.706630626792584, 4.36857587819891, 3.3578374637260584, 5.620112197052615, 2.7430944467859675, 2.24749177977149, 1.8469428855993675, 2.7134262774165743, 2.0591419795857178, 1.1798347479100997, 0.474783885032863, 0.0), # 132
(6.979818875154931, 5.194573201292665, 5.883795951251323, 6.1578339242486715, 5.411494070380632, 2.5806062188266576, 2.237152326406796, 1.9560994406253773, 2.80548821117294, 1.1151486532752868, 0.8706794992307283, 0.5170896442265063, 0.0, 7.242786617060469, 5.687986086491568, 4.353397496153641, 3.3454459598258595, 5.61097642234588, 2.7385392168755285, 2.237152326406796, 1.8432901563047555, 2.705747035190316, 2.052611308082891, 1.1767591902502648, 0.4722339273902424, 0.0), # 133
(6.949909910888076, 5.166233394859844, 5.868067893442536, 6.137841619907462, 5.395721877955516, 2.575389540657624, 2.2266869686671114, 1.9528291517096479, 2.8008304575368346, 1.1109590358518249, 0.8675995941170239, 0.5153635352238445, 0.0, 7.221242469220467, 5.668998887462289, 4.3379979705851195, 3.3328771075554737, 5.601660915073669, 2.7339608123935073, 2.2266869686671114, 1.8395639576125886, 2.697860938977758, 2.0459472066358213, 1.1736135786885074, 0.46965758135089497, 0.0), # 134
(6.919360958911483, 5.137559035220717, 5.851963291907273, 6.117417235370148, 5.379519911554198, 2.57005712354281, 2.2160775179637073, 1.9495268294610402, 2.796069690711861, 1.1067019306271555, 0.8644689939222592, 0.5136032107373902, 0.0, 7.199133387313616, 5.649635318111292, 4.322344969611295, 3.320105791881466, 5.592139381423722, 2.7293375612454565, 2.2160775179637073, 1.835755088244864, 2.689759955777099, 2.0391390784567163, 1.1703926583814546, 0.4670508213837017, 0.0), # 135
(6.888142926603388, 5.108505841532984, 5.835455872428673, 6.096528980273343, 5.362872105173076, 2.564596085693012, 2.205305785707854, 1.9461769938120925, 2.7911927637918947, 1.1023689659234648, 0.8612812322700237, 0.5118056060768296, 0.0, 7.176435136469229, 5.629861666845124, 4.306406161350118, 3.3071068977703937, 5.5823855275837895, 2.72464779133693, 2.205305785707854, 1.8318543469235802, 2.681436052586538, 2.0321763267577815, 1.1670911744857346, 0.46440962195754404, 0.0), # 136
(6.856226721342027, 5.079029532954335, 5.818519360789875, 6.075145064253675, 5.345762392808551, 2.558993545319026, 2.1943535833108223, 1.942764164695343, 2.7861865298708084, 1.0979517700629406, 0.8580298427839075, 0.5099676565518481, 0.0, 7.153123481816621, 5.609644222070328, 4.290149213919538, 3.293855310188821, 5.572373059741617, 2.7198698305734803, 2.1943535833108223, 1.8278525323707329, 2.6728811964042754, 2.0250483547512257, 1.1637038721579749, 0.46172995754130325, 0.0), # 137
(6.823583250505639, 5.0490858286424665, 5.801127482774012, 6.053233696947759, 5.3281747084570235, 2.5532366206316497, 2.1832027221838817, 1.9392728620433302, 2.781037842042475, 1.0934419713677697, 0.8547083590875004, 0.508086297472132, 0.0, 7.129174188485113, 5.58894927219345, 4.273541795437502, 3.280325914103308, 5.56207568408495, 2.7149820068606623, 2.1832027221838817, 1.8237404433083213, 2.6640873542285117, 2.017744565649253, 1.1602254965548024, 0.45900780260386065, 0.0), # 138
(6.790183421472455, 5.018630447755072, 5.783253964164227, 6.030763087992216, 5.3100929861148884, 2.547312429841679, 2.171835013738304, 1.9356876057885917, 2.775733553400766, 1.0888311981601397, 0.8513103148043922, 0.5061584641473672, 0.0, 7.104563021604015, 5.567743105621037, 4.256551574021961, 3.2664935944804183, 5.551467106801532, 2.709962648104028, 2.171835013738304, 1.8195088784583422, 2.6550464930574442, 2.0102543626640723, 1.1566507928328456, 0.4562391316140975, 0.0), # 139
(6.755998141620719, 4.987619109449845, 5.764872530743658, 6.007701447023667, 5.291501159778549, 2.5412080911599104, 2.1602322693853586, 1.9319929158636655, 2.770260517039555, 1.0841110787622374, 0.8478292435581727, 0.5041810918872395, 0.0, 7.079265746302652, 5.545992010759633, 4.2391462177908625, 3.2523332362867117, 5.54052103407911, 2.704790082209132, 2.1602322693853586, 1.8151486365427931, 2.6457505798892744, 2.0025671490078896, 1.1529745061487318, 0.45341991904089507, 0.0), # 140
(6.720998318328665, 4.956007532884482, 5.745956908295441, 5.984016983678732, 5.272383163444402, 2.5349107227971404, 2.148376300536318, 1.9281733122010902, 2.7646055860527143, 1.0792732414962505, 0.844258678972432, 0.502151116001435, 0.0, 7.053258127710331, 5.523662276015784, 4.221293394862159, 3.2378197244887508, 5.529211172105429, 2.6994426370815265, 2.148376300536318, 1.8106505162836717, 2.636191581722201, 1.994672327892911, 1.1491913816590882, 0.4505461393531348, 0.0), # 141
(6.685154858974525, 4.923751437216675, 5.726480822602714, 5.959677907594033, 5.252722931108846, 2.5284074429641663, 2.1362489186024507, 1.924213314733404, 2.7587556135341176, 1.0743093146843659, 0.8405921546707598, 0.5000654717996397, 0.0, 7.026515930956373, 5.500720189796036, 4.202960773353798, 3.222927944053097, 5.517511227068235, 2.6938986406267658, 2.1362489186024507, 1.806005316402976, 2.626361465554423, 1.9865593025313446, 1.1452961645205428, 0.4476137670196978, 0.0), # 142
(6.64843867093654, 4.890806541604119, 5.706417999448617, 5.934652428406185, 5.232504396768282, 2.521685369871783, 2.1238319349950276, 1.920097443393144, 2.7526974525776393, 1.0692109266487708, 0.8368232042767458, 0.4979210945915394, 0.0, 6.999014921170094, 5.477132040506932, 4.184116021383729, 3.207632779946312, 5.505394905155279, 2.6881364207504017, 2.1238319349950276, 1.8012038356227023, 2.616252198384141, 1.9782174761353954, 1.1412835998897235, 0.44461877650946546, 0.0), # 143
(6.610820661592948, 4.857128565204509, 5.685742164616285, 5.908908755751814, 5.2117114944191085, 2.5147316217307885, 2.1111071611253194, 1.9158102181128498, 2.746417956277149, 1.0639697057116522, 0.8329453614139802, 0.49571491968682, 0.0, 6.970730863480812, 5.452864116555019, 4.164726807069901, 3.191909117134956, 5.492835912554298, 2.6821343053579896, 2.1111071611253194, 1.796236872664849, 2.6058557472095543, 1.9696362519172719, 1.1371484329232573, 0.44155714229131915, 0.0), # 144
(6.572271738321982, 4.82267322717554, 5.6644270438888595, 5.882415099267537, 5.190328158057724, 2.507533316751979, 2.0980564084045974, 1.9113361588250588, 2.739903977726521, 1.0585772801951978, 0.8289521597060527, 0.4934438823951677, 0.0, 6.94163952301784, 5.4278827063468436, 4.144760798530264, 3.175731840585593, 5.479807955453042, 2.6758706223550823, 2.0980564084045974, 1.7910952262514135, 2.595164079028862, 1.9608050330891795, 1.132885408777772, 0.4384248388341401, 0.0), # 145
(6.5327628085018805, 4.787396246674904, 5.642446363049478, 5.855139668589976, 5.16833832168053, 2.5000775731461515, 2.084661488244132, 1.906659785462309, 2.7331423700196282, 1.0530252784215943, 0.8248371327765532, 0.4911049180262681, 0.0, 6.911716664910495, 5.402154098288948, 4.124185663882766, 3.1590758352647823, 5.4662847400392565, 2.669323699647233, 2.084661488244132, 1.7857696951043938, 2.584169160840265, 1.9517132228633256, 1.1284892726098958, 0.4352178406068095, 0.0), # 146
(6.49226477951088, 4.751253342860296, 5.619773847881273, 5.827050673355748, 5.145725919283921, 2.4923515091241004, 2.0709042120551926, 1.9017656179571385, 2.7261199862503442, 1.0473053287130294, 0.8205938142490716, 0.48869496188980743, 0.0, 6.8809380542880945, 5.375644580787881, 4.102969071245358, 3.1419159861390877, 5.4522399725006885, 2.662471865139994, 2.0709042120551926, 1.7802510779457859, 2.5728629596419603, 1.9423502244519164, 1.1239547695762548, 0.43193212207820875, 0.0), # 147
(6.450748558727217, 4.714200234889411, 5.596383224167389, 5.798116323201478, 5.1224748848643, 2.4843422428966253, 2.0567663912490506, 1.8966381762420859, 2.718823679512541, 1.0414090593916896, 0.8162157377471978, 0.48621094929547143, 0.0, 6.8492794562799535, 5.348320442250185, 4.081078688735989, 3.124227178175068, 5.437647359025082, 2.6552934467389204, 2.0567663912490506, 1.7745301734975893, 2.56123744243215, 1.9327054410671598, 1.1192766448334779, 0.42856365771721927, 0.0), # 148
(6.40818505352913, 4.676192641919942, 5.572248217690963, 5.768304827763782, 5.098569152418064, 2.4760368926745198, 2.0422298372369765, 1.8912619802496888, 2.71124030290009, 1.0353280987797628, 0.8116964368945213, 0.48364981555294617, 0.0, 6.81671663601539, 5.320147971082407, 4.058482184472607, 3.1059842963392876, 5.42248060580018, 2.6477667723495646, 2.0422298372369765, 1.7685977804817998, 2.549284576209032, 1.922768275921261, 1.1144496435381928, 0.42510842199272214, 0.0), # 149
(6.364545171294852, 4.6371862831095845, 5.54734255423513, 5.737584396679283, 5.0739926559416135, 2.467422576668583, 2.0272763614302405, 1.8856215499124855, 2.7033567095068674, 1.0290540751994355, 0.8070294453146325, 0.48100849597191764, 0.0, 6.783225358623717, 5.291093455691093, 4.035147226573162, 3.0871622255983056, 5.406713419013735, 2.63987016987748, 2.0272763614302405, 1.7624446976204164, 2.5369963279708068, 1.912528132226428, 1.1094685108470261, 0.4215623893735987, 0.0), # 150
(6.31979981940262, 4.597136877616033, 5.521639959583029, 5.705923239584598, 5.048729329431348, 2.4584864130896094, 2.011887775240113, 1.8797014051630145, 2.695159752426744, 1.0225786169728959, 0.8022082966311207, 0.4782839258620715, 0.0, 6.748781389234255, 5.261123184482786, 4.011041483155603, 3.067735850918687, 5.390319504853488, 2.6315819672282204, 2.011887775240113, 1.7560617236354352, 2.524364664715674, 1.9019744131948664, 1.1043279919166058, 0.41792153432873036, 0.0), # 151
(6.273919905230675, 4.55600014459698, 5.495114159517802, 5.673289566116352, 5.022763106883663, 2.4492155201483965, 1.996045890077866, 1.8734860659338137, 2.686636284753592, 1.0158933524223301, 0.7972265244675764, 0.475473040533094, 0.0, 6.713360492976318, 5.230203445864033, 3.9861326223378812, 3.04768005726699, 5.373272569507184, 2.622880492307339, 1.996045890077866, 1.7494396572488546, 2.5113815534418316, 1.8910965220387843, 1.0990228319035604, 0.4141818313269982, 0.0), # 152
(6.226876336157249, 4.5137318032101215, 5.467738879822579, 5.63965158591116, 4.996077922294963, 2.4395970160557408, 1.9797325173547677, 1.8669600521574208, 2.677773159581286, 1.008989909869926, 0.7920776624475889, 0.472572775294671, 0.0, 6.676938434979222, 5.19830052824138, 3.9603883122379444, 3.0269697296097773, 5.355546319162572, 2.6137440730203894, 1.9797325173547677, 1.742569297182672, 2.4980389611474814, 1.879883861970387, 1.093547775964516, 0.41033925483728384, 0.0), # 153
(6.178640019560583, 4.4702875726131515, 5.439487846280506, 5.604977508605646, 4.968657709661643, 2.429618019022439, 1.9629294684820913, 1.8601078837663743, 2.6685572300036977, 1.0018599176378709, 0.7867552441947484, 0.4695800654564884, 0.0, 6.639490980372286, 5.165380720021371, 3.9337762209737415, 3.005579752913612, 5.337114460007395, 2.604151037272924, 1.9629294684820913, 1.7354414421588849, 2.4843288548308213, 1.8683258362018824, 1.0878975692561013, 0.40638977932846837, 0.0), # 154
(6.129181862818909, 4.425623171963762, 5.410334784674718, 5.569235543836427, 4.940486402980104, 2.419265647259287, 1.9456185548711045, 1.852914080693212, 2.6589753491147006, 0.9944950040483511, 0.7812528033326445, 0.4664918463282322, 0.0, 6.600993894284821, 5.131410309610554, 3.906264016663222, 2.983485012145053, 5.317950698229401, 2.594079712970497, 1.9456185548711045, 1.7280468908994906, 2.470243201490052, 1.856411847945476, 1.0820669569349437, 0.402329379269433, 0.0), # 155
(6.078472773310465, 4.3796943204196515, 5.3802534207883514, 5.532393901240125, 4.911547936246746, 2.408527018977082, 1.92778158793308, 1.845363162870473, 2.649014370008167, 0.9868867974235548, 0.7755638734848673, 0.46330505321958826, 0.0, 6.561422941846148, 5.09635558541547, 3.8778193674243364, 2.960660392270664, 5.298028740016334, 2.5835084280186624, 1.92778158793308, 1.720376442126487, 2.455773968123373, 1.8441313004133755, 1.0760506841576702, 0.39815402912905923, 0.0), # 156
(6.02648365841349, 4.332456737138511, 5.349217480404546, 5.494420790453363, 4.881826243457965, 2.39738925238662, 1.9094003790792877, 1.8374396502306942, 2.63866114577797, 0.9790269260856685, 0.7696819882750067, 0.4600166214402426, 0.0, 6.520753888185581, 5.060182835842667, 3.848409941375033, 2.937080778257005, 5.27732229155594, 2.5724155103229718, 1.9094003790792877, 1.7124208945618713, 2.4409131217289826, 1.831473596817788, 1.0698434960809091, 0.3938597033762283, 0.0), # 157
(5.971744757124192, 4.28299895523299, 5.315727969268237, 5.453861748990747, 4.849963256464532, 2.3851447556146512, 1.890042688371143, 1.8285989841164574, 2.6271098910930926, 0.9706731832582289, 0.7634127670051923, 0.45650663761295607, 0.0, 6.477188687532276, 5.021573013742516, 3.817063835025962, 2.912019549774686, 5.254219782186185, 2.5600385777630406, 1.890042688371143, 1.7036748254390366, 2.424981628232266, 1.8179539163302492, 1.0631455938536476, 0.38936354138481727, 0.0), # 158
(5.9058294135827225, 4.226247901039617, 5.271158545601992, 5.402386295273073, 4.808102031883535, 2.3677218357366487, 1.8672851053542865, 1.8157378442547942, 2.609713936325905, 0.9604561988197493, 0.7556555914158659, 0.4520908349122073, 0.0, 6.420342117536156, 4.97299918403428, 3.7782779570793297, 2.8813685964592475, 5.21942787265181, 2.542032981956712, 1.8672851053542865, 1.6912298826690346, 2.4040510159417674, 1.8007954317576913, 1.0542317091203985, 0.3842043546399652, 0.0), # 159
(5.827897675923448, 4.161737600929857, 5.214613971970593, 5.339146506245316, 4.755424070051625, 2.344692604822253, 1.8408974993535137, 1.7985330631757823, 2.5859800605943066, 0.948241130372579, 0.7463012678146054, 0.4467001299258565, 0.0, 6.349136487114865, 4.913701429184421, 3.731506339073027, 2.844723391117736, 5.171960121188613, 2.5179462884460952, 1.8408974993535137, 1.6747804320158948, 2.3777120350258123, 1.7797155020817725, 1.0429227943941186, 0.3783397819027143, 0.0), # 160
(5.738577643668768, 4.0898886365923435, 5.146697981273539, 5.264743502254037, 4.69247633295046, 2.3163360460661466, 1.8110725784027506, 1.7772001777032602, 2.556221271199738, 0.9341316386341878, 0.7354322206132944, 0.44038449792717144, 0.0, 6.264299235855278, 4.844229477198885, 3.6771611030664717, 2.8023949159025627, 5.112442542399476, 2.4880802487845646, 1.8110725784027506, 1.6545257471901047, 2.34623816647523, 1.754914500751346, 1.029339596254708, 0.37180805787203125, 0.0), # 161
(5.638497416341085, 4.011121589715708, 5.068014306410331, 5.179778403645797, 4.619805782561709, 2.282931142663013, 1.7780030505359237, 1.7519547246610676, 2.5207505754436363, 0.9182313843220465, 0.7231308742238162, 0.43319391418941966, 0.0, 6.166557803344267, 4.765133056083616, 3.615654371119081, 2.754694152966139, 5.041501150887273, 2.4527366145254947, 1.7780030505359237, 1.630665101902152, 2.3099028912808546, 1.7265928012152658, 1.0136028612820662, 0.36464741724688265, 0.0), # 162
(5.528285093462799, 3.9258570419885843, 4.979166680280469, 5.084852330767161, 4.537959380867034, 2.244756877807534, 1.7418816237869603, 1.7230122408730417, 2.4798809806274416, 0.9006440281536252, 0.7094796530580545, 0.42517835398586895, 0.0, 6.0566396291687035, 4.676961893844558, 3.5473982652902722, 2.701932084460875, 4.959761961254883, 2.4122171372222585, 1.7418816237869603, 1.6033977698625244, 2.268979690433517, 1.6949507769223873, 0.9958333360560938, 0.356896094726235, 0.0), # 163
(5.408568774556308, 3.834515575099602, 4.8807588357834515, 4.980566403964691, 4.447484089848101, 2.2020922346943936, 1.7029010061897865, 1.6905882631630231, 2.433925494052593, 0.881473230846394, 0.6945609815278929, 0.4163877925897869, 0.0, 5.935272152915463, 4.580265718487656, 3.472804907639464, 2.644419692539181, 4.867850988105186, 2.3668235684282326, 1.7029010061897865, 1.5729230247817099, 2.2237420449240504, 1.660188801321564, 0.9761517671566904, 0.34859232500905474, 0.0), # 164
(5.279976559144014, 3.7375177707373965, 4.773394505818779, 4.867521743584952, 4.348926871486572, 2.155216196518274, 1.6612539057783289, 1.6548983283548488, 2.383197123020528, 0.8608226531178229, 0.678457284045215, 0.4068722052744414, 0.0, 5.803182814171416, 4.475594258018854, 3.3922864202260747, 2.582467959353468, 4.766394246041056, 2.3168576596967885, 1.6612539057783289, 1.5394401403701956, 2.174463435743286, 1.622507247861651, 0.954678901163756, 0.33977434279430885, 0.0), # 165
(5.143136546748318, 3.6352842105905996, 4.657677423285953, 4.746319469974501, 4.242834687764114, 2.1044077464738575, 1.6171330305865146, 1.6161579732723592, 2.328008874832686, 0.8387959556853827, 0.661250985021904, 0.39668156731310017, 0.0, 5.661099052523436, 4.363497240444101, 3.3062549251095197, 2.5163878670561473, 4.656017749665372, 2.262621162581303, 1.6171330305865146, 1.5031483903384697, 2.121417343882057, 1.5821064899915007, 0.9315354846571906, 0.33048038278096364, 0.0), # 166
(4.998676836891619, 3.528235476347844, 4.53421132108447, 4.617560703479906, 4.129754500662389, 2.0499458677558273, 1.57073108864827, 1.5745827347393924, 2.2686737567905064, 0.8154967992665431, 0.6430245088698437, 0.3858658539790306, 0.0, 5.509748307558397, 4.244524393769336, 3.215122544349218, 2.4464903977996286, 4.537347513581013, 2.2044158286351494, 1.57073108864827, 1.4642470483970196, 2.0648772503311945, 1.5391869011599693, 0.9068422642168941, 0.32074867966798587, 0.0), # 167
(4.847225529096317, 3.416792149697761, 4.403599932113832, 4.481846564447728, 4.010233272163062, 1.9921095435588663, 1.5222407879975217, 1.5303881495797866, 2.205504776195428, 0.7910288445787746, 0.6238602800009175, 0.3744750405455008, 0.0, 5.34985801886317, 4.119225446000509, 3.1193014000045878, 2.3730865337363234, 4.411009552390856, 2.1425434094117013, 1.5222407879975217, 1.4229353882563331, 2.005116636081531, 1.4939488548159094, 0.8807199864227666, 0.31061746815434194, 0.0), # 168
(4.689410722884812, 3.3013748123289846, 4.26644698927354, 4.33977817322453, 3.884817964247797, 1.9311777570776578, 1.4718548366681967, 1.4837897546173817, 2.1388149403488903, 0.7654957523395476, 0.6038407228270092, 0.3625591022857782, 0.0, 5.182155626024628, 3.9881501251435596, 3.019203614135046, 2.296487257018642, 4.277629880697781, 2.0773056564643344, 1.4718548366681967, 1.3794126836268983, 1.9424089821238986, 1.4465927244081769, 0.853289397854708, 0.30012498293899864, 0.0), # 169
(4.525860517779507, 3.1824040459301473, 4.12335622546309, 4.191956650156872, 3.7540555388982577, 1.8674294915068832, 1.4197659426942213, 1.435003086676016, 2.0689172565523304, 0.7390011832663317, 0.5830482617600022, 0.3501680144731306, 0.0, 5.007368568629644, 3.8518481592044362, 2.9152413088000113, 2.217003549798995, 4.137834513104661, 2.0090043213464224, 1.4197659426942213, 1.3338782082192022, 1.8770277694491289, 1.3973188833856243, 0.824671245092618, 0.28930945872092256, 0.0), # 170
(4.3572030133028, 3.06030043218988, 3.9749313735819856, 4.038983115591321, 3.61849295809611, 1.801143730041226, 1.3661668141095222, 1.3842436825795277, 1.9961247321071884, 0.7116487980765979, 0.5615653212117798, 0.33735175238082576, 0.0, 4.826224286265092, 3.710869276189083, 2.807826606058899, 2.134946394229793, 3.992249464214377, 1.9379411556113388, 1.3661668141095222, 1.2865312357437328, 1.809246479048055, 1.3463277051971074, 0.7949862747163972, 0.27820913019908006, 0.0), # 171
(4.184066308977092, 2.9354845527968174, 3.8217761665297245, 3.881458689874438, 3.4786771838230153, 1.7325994558753692, 1.3112501589480263, 1.331727079151757, 1.9207503743149028, 0.6835422574878162, 0.5394743255942259, 0.3241602912821315, 0.0, 4.639450218517843, 3.5657632041034453, 2.6973716279711297, 2.050626772463448, 3.8415007486298056, 1.8644179108124599, 1.3112501589480263, 1.237571039910978, 1.7393385919115076, 1.2938195632914795, 0.764355233305945, 0.26686223207243803, 0.0), # 172
(4.007078504324784, 2.808376989439591, 3.664494337205808, 3.7199844933527855, 3.3351551780606408, 1.6620756522039952, 1.25520868524366, 1.2776688132165412, 1.8431071904769127, 0.6547852222174565, 0.5168576993192239, 0.310643606450315, 0.0, 4.44777380497477, 3.417079670953465, 2.584288496596119, 1.9643556666523692, 3.6862143809538255, 1.7887363385031578, 1.25520868524366, 1.187196894431425, 1.6675775890303204, 1.2399948311175955, 0.7328988674411617, 0.25530699903996285, 0.0), # 173
(3.8268676988682753, 2.6793983238068333, 3.503689618509735, 3.5551616463729245, 3.1884739027906486, 1.5898513022217866, 1.1982351010303502, 1.2222844215977202, 1.763508187894657, 0.6254813529829895, 0.4937978667986571, 0.2968516731586446, 0.0, 4.251922485222747, 3.26536840474509, 2.468989333993285, 1.8764440589489682, 3.527016375789314, 1.7111981902368083, 1.1982351010303502, 1.1356080730155618, 1.5942369513953243, 1.1850538821243084, 0.700737923701947, 0.24358166580062124, 0.0), # 174
(3.6440619921299646, 2.548969137587176, 3.3399657433410055, 3.3875912692814207, 3.039180319994703, 1.5162053891234268, 1.1405221143420232, 1.165789441119132, 1.682266373869575, 0.595734310501885, 0.4703772524444093, 0.28283446668038764, 0.0, 4.052623698848646, 3.1111791334842636, 2.3518862622220467, 1.7872029315056546, 3.36453274773915, 1.632105217566785, 1.1405221143420232, 1.0830038493738763, 1.5195901599973516, 1.1291970897604737, 0.6679931486682011, 0.23172446705337968, 0.0), # 175
(3.459289483632255, 2.4175100124692537, 3.173926444599119, 3.2178744824248353, 2.8878213916544695, 1.441416896103598, 1.082262433212606, 1.1083994086046165, 1.5996947557031045, 0.5656477554916135, 0.44667828066836407, 0.268641962288812, 0.0, 3.8506048854393393, 2.9550615851769315, 2.23339140334182, 1.69694326647484, 3.199389511406209, 1.551759172046463, 1.082262433212606, 1.0295834972168558, 1.4439106958272347, 1.0726248274749453, 0.6347852889198239, 0.2197736374972049, 0.0), # 176
(3.273178272897546, 2.2854415301416977, 3.006175455183576, 3.0466124061497295, 2.7349440797516125, 1.365764806356983, 1.0236487656760251, 1.050329860878011, 1.5161063406966853, 0.535325348669645, 0.4227833758824049, 0.2543241352571853, 0.0, 3.6465934845817, 2.7975654878290377, 2.113916879412024, 1.6059760460089345, 3.0322126813933705, 1.4704618052292153, 1.0236487656760251, 0.9755462902549877, 1.3674720398758062, 1.0155374687165768, 0.6012350910367152, 0.20776741183106345, 0.0), # 177
(3.0863564594482376, 2.153184272293141, 2.8373165079938762, 2.87440616080267, 2.581095346267794, 1.2895281030782653, 0.964873819766207, 0.9917963347631552, 1.431814136151756, 0.5048707507534501, 0.39877496249841504, 0.2399309608587752, 0.0, 3.4413169358626017, 2.6392405694465264, 1.993874812492075, 1.51461225226035, 2.863628272303512, 1.3885148686684172, 0.964873819766207, 0.9210915021987609, 1.290547673133897, 0.9581353869342235, 0.5674633015987752, 0.1957440247539219, 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
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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), # 150
(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), # 151
(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), # 152
(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), # 153
(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), # 154
(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), # 155
(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), # 156
(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), # 157
(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), # 158
(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), # 159
(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), # 160
(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), # 161
(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), # 162
(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), # 163
(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), # 164
(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), # 165
(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
95, # 1
)
| """
PASSENGERS
"""
num_passengers = 15249
passenger_arriving = ((3, 1, 2, 3, 3, 1, 1, 1, 1, 1, 0, 1, 0, 4, 1, 4, 3, 4, 2, 3, 1, 1, 1, 2, 0, 0), (2, 4, 6, 3, 3, 2, 3, 0, 3, 0, 0, 1, 0, 11, 1, 4, 2, 9, 2, 3, 1, 0, 0, 1, 0, 0), (6, 3, 3, 4, 6, 4, 0, 1, 2, 1, 1, 0, 0, 3, 10, 4, 1, 2, 1, 0, 0, 3, 1, 1, 1, 0), (7, 2, 5, 1, 3, 1, 4, 3, 3, 0, 1, 0, 0, 5, 5, 8, 5, 2, 2, 4, 1, 2, 3, 0, 1, 0), (2, 1, 2, 8, 5, 0, 3, 2, 1, 1, 0, 0, 0, 6, 8, 2, 1, 4, 4, 1, 1, 5, 2, 1, 0, 0), (4, 5, 8, 5, 0, 0, 4, 2, 1, 1, 3, 0, 0, 6, 7, 4, 3, 4, 1, 1, 3, 5, 3, 0, 2, 0), (4, 6, 6, 4, 2, 3, 3, 4, 3, 1, 0, 0, 0, 6, 7, 4, 3, 6, 6, 2, 3, 3, 2, 1, 0, 0), (7, 5, 1, 9, 3, 1, 2, 1, 2, 0, 0, 1, 0, 5, 5, 6, 5, 1, 6, 2, 4, 3, 2, 2, 0, 0), (6, 5, 8, 2, 2, 0, 4, 2, 2, 0, 1, 0, 0, 5, 3, 6, 0, 4, 6, 2, 2, 2, 2, 0, 0, 0), (9, 8, 4, 3, 3, 0, 4, 3, 1, 1, 1, 0, 0, 5, 4, 2, 5, 6, 4, 2, 0, 2, 2, 0, 0, 0), (4, 4, 8, 2, 5, 5, 3, 5, 2, 1, 1, 1, 0, 7, 4, 5, 4, 5, 2, 2, 1, 4, 0, 1, 1, 0), (6, 6, 7, 8, 5, 3, 3, 5, 1, 0, 1, 1, 0, 5, 5, 4, 2, 3, 3, 1, 0, 1, 0, 1, 1, 0), (1, 8, 7, 4, 3, 2, 5, 2, 1, 2, 1, 0, 0, 8, 0, 7, 4, 8, 7, 3, 6, 3, 0, 2, 1, 0), (2, 5, 8, 5, 2, 2, 1, 0, 2, 5, 2, 1, 0, 9, 10, 5, 4, 7, 0, 3, 1, 1, 2, 1, 1, 0), (5, 8, 3, 10, 4, 3, 3, 5, 4, 2, 0, 0, 0, 3, 6, 8, 3, 5, 4, 5, 4, 3, 0, 3, 0, 0), (6, 7, 9, 5, 12, 3, 1, 6, 1, 0, 0, 1, 0, 4, 6, 7, 4, 6, 4, 2, 0, 3, 0, 1, 0, 0), (6, 4, 7, 5, 4, 2, 2, 3, 2, 0, 2, 0, 0, 11, 8, 9, 10, 6, 1, 1, 1, 1, 1, 0, 1, 0), (7, 10, 2, 5, 5, 0, 7, 3, 4, 0, 0, 1, 0, 7, 9, 5, 5, 6, 2, 3, 1, 3, 2, 1, 1, 0), (9, 10, 1, 7, 5, 3, 8, 2, 2, 3, 0, 0, 0, 9, 3, 5, 3, 10, 2, 5, 1, 4, 2, 0, 2, 0), (16, 7, 8, 5, 7, 4, 4, 2, 4, 4, 3, 1, 0, 7, 9, 6, 4, 1, 5, 2, 3, 3, 3, 1, 2, 0), (6, 6, 3, 3, 8, 3, 7, 1, 1, 2, 2, 0, 0, 3, 6, 5, 6, 8, 2, 6, 3, 2, 1, 2, 4, 0), (9, 9, 8, 7, 6, 4, 2, 1, 4, 2, 1, 0, 0, 12, 4, 7, 4, 2, 5, 5, 2, 3, 3, 0, 0, 0), (9, 6, 4, 5, 3, 2, 5, 4, 2, 2, 3, 3, 0, 11, 10, 4, 5, 7, 5, 3, 4, 10, 2, 0, 1, 0), (7, 7, 4, 10, 6, 3, 5, 3, 3, 2, 0, 0, 0, 6, 9, 2, 2, 7, 5, 4, 0, 1, 4, 2, 0, 0), (7, 15, 2, 8, 9, 2, 5, 5, 1, 1, 0, 1, 0, 8, 11, 4, 7, 8, 3, 0, 0, 1, 3, 1, 1, 0), (4, 6, 7, 8, 7, 0, 9, 2, 4, 1, 0, 0, 0, 9, 7, 9, 1, 7, 6, 2, 1, 1, 3, 1, 1, 0), (13, 7, 6, 0, 3, 5, 1, 6, 6, 2, 1, 0, 0, 7, 6, 5, 7, 7, 4, 5, 4, 3, 4, 1, 2, 0), (11, 11, 5, 10, 3, 1, 1, 1, 4, 0, 0, 1, 0, 8, 4, 7, 7, 6, 3, 5, 4, 4, 2, 5, 0, 0), (9, 6, 4, 9, 5, 5, 4, 4, 4, 3, 3, 0, 0, 8, 7, 1, 9, 6, 3, 4, 2, 0, 3, 1, 0, 0), (4, 8, 6, 9, 3, 2, 3, 1, 3, 0, 3, 0, 0, 11, 7, 4, 4, 7, 5, 4, 2, 5, 3, 0, 0, 0), (7, 6, 11, 4, 7, 3, 5, 3, 5, 2, 0, 2, 0, 9, 5, 7, 5, 6, 1, 6, 0, 2, 0, 0, 0, 0), (9, 6, 11, 11, 5, 4, 4, 2, 1, 2, 0, 1, 0, 10, 7, 9, 4, 5, 8, 4, 4, 1, 2, 1, 0, 0), (9, 7, 6, 8, 5, 3, 3, 5, 7, 0, 0, 0, 0, 6, 8, 4, 3, 3, 3, 0, 0, 3, 3, 1, 0, 0), (5, 4, 8, 7, 4, 2, 4, 1, 7, 2, 1, 1, 0, 4, 8, 6, 4, 7, 2, 4, 2, 4, 2, 0, 2, 0), (6, 9, 6, 7, 4, 2, 4, 3, 2, 2, 1, 0, 0, 9, 2, 8, 4, 7, 1, 6, 3, 5, 2, 0, 1, 0), (10, 14, 11, 6, 9, 2, 3, 4, 3, 0, 1, 2, 0, 10, 4, 5, 5, 6, 5, 1, 4, 2, 3, 0, 1, 0), (7, 8, 4, 8, 6, 6, 4, 1, 5, 1, 4, 0, 0, 12, 7, 9, 2, 11, 1, 5, 1, 1, 1, 1, 0, 0), (10, 8, 13, 7, 8, 3, 3, 5, 5, 2, 0, 2, 0, 3, 6, 5, 11, 6, 2, 4, 3, 3, 2, 4, 1, 0), (8, 12, 9, 3, 9, 3, 5, 2, 5, 1, 0, 0, 0, 10, 10, 5, 3, 7, 7, 7, 1, 1, 2, 0, 1, 0), (4, 11, 10, 4, 9, 2, 2, 2, 1, 1, 0, 0, 0, 13, 5, 6, 3, 0, 2, 2, 1, 6, 2, 4, 0, 0), (4, 10, 6, 8, 6, 3, 1, 6, 4, 1, 4, 1, 0, 7, 7, 6, 6, 8, 1, 6, 4, 6, 2, 2, 1, 0), (9, 10, 5, 8, 4, 1, 1, 4, 6, 0, 1, 0, 0, 4, 12, 7, 4, 10, 5, 4, 2, 5, 3, 2, 1, 0), (6, 11, 6, 4, 4, 3, 4, 6, 2, 1, 4, 0, 0, 10, 9, 6, 2, 4, 3, 2, 2, 3, 2, 3, 5, 0), (10, 6, 2, 5, 5, 1, 5, 4, 5, 2, 1, 1, 0, 4, 6, 4, 5, 10, 6, 3, 1, 4, 0, 0, 0, 0), (5, 7, 4, 5, 9, 4, 2, 2, 2, 3, 2, 3, 0, 5, 8, 7, 3, 9, 4, 4, 2, 4, 1, 1, 0, 0), (11, 4, 8, 11, 6, 4, 6, 1, 3, 1, 0, 0, 0, 9, 10, 3, 5, 8, 1, 4, 4, 3, 5, 2, 0, 0), (5, 8, 3, 9, 6, 3, 3, 2, 7, 2, 0, 1, 0, 5, 2, 4, 6, 5, 3, 3, 8, 3, 4, 1, 1, 0), (6, 12, 6, 7, 7, 1, 3, 4, 2, 0, 0, 0, 0, 5, 7, 12, 3, 4, 7, 7, 3, 5, 3, 0, 0, 0), (7, 8, 6, 8, 3, 3, 4, 4, 3, 2, 2, 0, 0, 4, 5, 2, 6, 8, 2, 3, 4, 3, 2, 1, 1, 0), (11, 6, 6, 10, 8, 1, 5, 3, 1, 1, 3, 1, 0, 6, 7, 5, 3, 8, 1, 4, 2, 3, 1, 1, 0, 0), (10, 4, 10, 7, 5, 4, 2, 6, 2, 2, 0, 0, 0, 10, 6, 8, 4, 6, 0, 2, 2, 3, 2, 2, 0, 0), (9, 7, 6, 7, 12, 1, 3, 5, 2, 1, 1, 0, 0, 8, 8, 7, 3, 3, 4, 1, 2, 2, 2, 1, 2, 0), (4, 3, 7, 10, 6, 2, 2, 3, 4, 2, 1, 1, 0, 10, 8, 2, 7, 9, 4, 2, 4, 3, 4, 4, 0, 0), (9, 7, 8, 3, 3, 1, 2, 4, 1, 0, 1, 1, 0, 8, 7, 1, 7, 4, 5, 1, 3, 2, 3, 0, 0, 0), (6, 9, 5, 7, 5, 2, 4, 2, 2, 2, 4, 1, 0, 6, 7, 4, 4, 7, 2, 4, 1, 4, 4, 0, 1, 0), (5, 9, 4, 10, 10, 4, 1, 4, 4, 1, 3, 0, 0, 4, 7, 7, 2, 4, 4, 1, 2, 3, 1, 1, 1, 0), (8, 2, 7, 10, 10, 3, 2, 2, 6, 2, 1, 0, 0, 5, 10, 4, 4, 8, 1, 2, 1, 1, 4, 3, 0, 0), (8, 10, 11, 5, 8, 6, 2, 2, 5, 1, 0, 0, 0, 4, 6, 11, 0, 3, 2, 5, 4, 1, 4, 2, 0, 0), (12, 7, 10, 3, 4, 3, 2, 0, 6, 4, 3, 0, 0, 7, 8, 6, 4, 6, 8, 4, 2, 2, 0, 3, 2, 0), (5, 12, 12, 7, 7, 5, 8, 4, 4, 2, 0, 1, 0, 8, 7, 9, 5, 5, 3, 3, 1, 1, 7, 3, 0, 0), (8, 15, 6, 13, 3, 2, 3, 3, 4, 1, 0, 0, 0, 6, 7, 6, 3, 7, 2, 6, 2, 3, 2, 3, 1, 0), (5, 8, 10, 4, 4, 4, 3, 3, 4, 0, 1, 1, 0, 7, 5, 5, 4, 7, 3, 3, 1, 5, 1, 1, 0, 0), (5, 6, 5, 8, 7, 0, 4, 5, 0, 1, 1, 1, 0, 8, 7, 2, 6, 5, 3, 1, 2, 2, 3, 1, 0, 0), (5, 5, 6, 10, 6, 2, 2, 4, 4, 4, 0, 0, 0, 6, 6, 10, 5, 5, 4, 4, 3, 3, 2, 0, 0, 0), (10, 9, 6, 13, 6, 1, 6, 0, 1, 2, 2, 0, 0, 5, 4, 8, 2, 7, 5, 3, 3, 0, 1, 2, 1, 0), (6, 5, 8, 10, 5, 5, 3, 2, 1, 2, 0, 0, 0, 10, 6, 5, 5, 9, 5, 4, 3, 2, 4, 2, 0, 0), (10, 7, 4, 7, 6, 4, 4, 2, 1, 0, 1, 1, 0, 9, 7, 6, 7, 8, 3, 3, 2, 3, 4, 2, 2, 0), (13, 6, 6, 9, 4, 4, 4, 6, 3, 1, 2, 1, 0, 7, 8, 3, 4, 5, 6, 3, 2, 1, 3, 1, 1, 0), (11, 6, 9, 3, 4, 1, 1, 4, 6, 0, 0, 2, 0, 15, 10, 6, 5, 7, 2, 2, 1, 5, 1, 0, 1, 0), (14, 9, 8, 3, 4, 2, 0, 1, 5, 1, 3, 2, 0, 6, 7, 10, 3, 4, 3, 5, 2, 1, 2, 1, 1, 0), (6, 8, 8, 5, 2, 2, 7, 2, 3, 1, 1, 0, 0, 4, 6, 3, 1, 10, 4, 6, 4, 3, 0, 0, 1, 0), (14, 6, 9, 9, 6, 3, 2, 3, 1, 2, 0, 0, 0, 10, 8, 4, 3, 7, 3, 1, 2, 3, 2, 1, 1, 0), (11, 9, 7, 7, 8, 3, 0, 5, 6, 0, 0, 1, 0, 11, 12, 4, 1, 7, 4, 3, 2, 1, 0, 0, 1, 0), (6, 10, 8, 5, 5, 2, 6, 0, 3, 1, 2, 3, 0, 10, 9, 2, 6, 7, 1, 2, 1, 5, 3, 2, 0, 0), (9, 3, 9, 8, 5, 3, 2, 1, 2, 1, 3, 1, 0, 11, 8, 5, 3, 9, 6, 1, 1, 0, 1, 0, 0, 0), (7, 7, 5, 7, 3, 1, 1, 4, 3, 0, 2, 0, 0, 4, 4, 3, 2, 4, 1, 0, 3, 6, 1, 0, 2, 0), (9, 7, 6, 10, 8, 5, 2, 3, 0, 0, 2, 1, 0, 12, 3, 2, 2, 4, 4, 0, 1, 2, 2, 2, 1, 0), (6, 4, 0, 6, 7, 8, 3, 1, 3, 2, 1, 0, 0, 11, 13, 7, 3, 7, 3, 2, 3, 2, 3, 1, 0, 0), (7, 7, 9, 8, 8, 3, 0, 4, 0, 1, 2, 0, 0, 8, 9, 6, 6, 3, 3, 3, 1, 6, 0, 4, 0, 0), (3, 8, 14, 10, 7, 2, 2, 4, 2, 2, 2, 1, 0, 9, 5, 4, 6, 5, 2, 1, 3, 3, 2, 5, 1, 0), (12, 8, 6, 6, 3, 5, 0, 3, 3, 2, 0, 2, 0, 8, 9, 5, 12, 6, 3, 1, 1, 3, 2, 0, 0, 0), (7, 5, 7, 12, 8, 2, 3, 3, 6, 1, 2, 0, 0, 7, 7, 9, 2, 4, 3, 3, 1, 3, 2, 1, 1, 0), (8, 5, 10, 13, 8, 1, 3, 2, 4, 1, 1, 1, 0, 6, 8, 4, 3, 9, 6, 5, 2, 2, 0, 4, 0, 0), (5, 6, 8, 9, 8, 3, 3, 2, 2, 0, 0, 3, 0, 5, 9, 6, 5, 4, 5, 1, 2, 2, 0, 1, 4, 0), (11, 9, 5, 10, 7, 1, 2, 1, 3, 0, 0, 0, 0, 7, 5, 3, 2, 6, 3, 5, 2, 7, 6, 1, 1, 0), (4, 12, 2, 9, 3, 2, 3, 3, 3, 1, 3, 0, 0, 7, 7, 2, 11, 5, 4, 1, 2, 3, 2, 0, 1, 0), (4, 2, 8, 13, 6, 2, 3, 3, 5, 1, 0, 0, 0, 6, 10, 2, 6, 5, 3, 2, 2, 2, 3, 0, 0, 0), (10, 13, 7, 9, 8, 4, 3, 2, 3, 0, 0, 0, 0, 4, 7, 3, 4, 5, 3, 4, 6, 6, 6, 0, 0, 0), (10, 2, 7, 3, 3, 1, 1, 1, 8, 1, 2, 4, 0, 8, 4, 3, 2, 6, 3, 5, 1, 4, 3, 0, 0, 0), (8, 8, 4, 5, 8, 4, 5, 2, 3, 0, 1, 2, 0, 11, 5, 4, 6, 7, 3, 2, 2, 6, 0, 2, 0, 0), (8, 5, 8, 7, 3, 4, 3, 1, 4, 3, 1, 0, 0, 6, 3, 9, 2, 11, 0, 2, 3, 4, 1, 0, 0, 0), (12, 8, 5, 12, 8, 1, 2, 1, 6, 2, 0, 0, 0, 10, 3, 4, 3, 5, 4, 0, 0, 1, 2, 0, 0, 0), (8, 3, 6, 9, 3, 4, 3, 4, 1, 1, 0, 1, 0, 9, 7, 8, 4, 5, 4, 3, 1, 1, 2, 1, 0, 0), (6, 5, 4, 6, 8, 4, 2, 5, 4, 3, 2, 0, 0, 11, 6, 2, 4, 4, 1, 6, 2, 3, 0, 0, 0, 0), (6, 5, 4, 9, 5, 5, 5, 3, 4, 1, 1, 1, 0, 7, 3, 7, 2, 4, 2, 1, 5, 3, 3, 1, 1, 0), (12, 11, 8, 9, 5, 2, 3, 4, 2, 0, 0, 0, 0, 12, 6, 7, 3, 6, 2, 4, 4, 3, 2, 1, 1, 0), (7, 5, 8, 10, 5, 1, 3, 0, 5, 0, 0, 0, 0, 8, 1, 0, 8, 2, 1, 4, 2, 6, 3, 1, 0, 0), (7, 2, 6, 3, 4, 5, 1, 3, 2, 2, 2, 0, 0, 15, 6, 2, 7, 9, 6, 2, 6, 2, 1, 1, 0, 0), (11, 7, 2, 9, 4, 6, 3, 3, 2, 1, 1, 0, 0, 7, 6, 5, 6, 3, 4, 3, 0, 5, 2, 0, 1, 0), (10, 8, 6, 8, 9, 3, 1, 1, 5, 1, 0, 2, 0, 7, 8, 4, 2, 6, 0, 4, 2, 6, 4, 1, 0, 0), (7, 9, 6, 5, 6, 1, 2, 1, 6, 0, 0, 0, 0, 9, 6, 6, 1, 5, 3, 4, 3, 5, 3, 1, 1, 0), (6, 6, 2, 5, 3, 0, 2, 0, 7, 2, 0, 1, 0, 8, 3, 6, 3, 2, 2, 2, 1, 2, 3, 0, 0, 0), (6, 9, 4, 1, 2, 2, 5, 2, 4, 2, 0, 0, 0, 6, 11, 4, 7, 13, 8, 1, 2, 0, 2, 0, 0, 0), (3, 5, 6, 8, 4, 3, 3, 4, 5, 2, 1, 1, 0, 14, 9, 7, 0, 5, 5, 5, 2, 2, 2, 0, 0, 0), (12, 8, 6, 12, 6, 4, 3, 2, 2, 1, 0, 0, 0, 8, 7, 7, 6, 1, 2, 2, 0, 2, 4, 0, 0, 0), (4, 4, 7, 8, 4, 5, 2, 1, 0, 0, 0, 1, 0, 10, 8, 2, 4, 9, 4, 4, 2, 1, 5, 0, 1, 0), (13, 7, 12, 8, 5, 0, 5, 3, 4, 2, 0, 0, 0, 6, 6, 5, 1, 2, 5, 3, 1, 3, 2, 3, 1, 0), (9, 10, 12, 12, 5, 0, 6, 1, 2, 0, 0, 2, 0, 4, 7, 3, 4, 3, 3, 3, 4, 3, 2, 3, 1, 0), (4, 4, 1, 10, 6, 2, 0, 5, 1, 2, 4, 0, 0, 12, 6, 4, 4, 6, 1, 1, 3, 1, 1, 0, 0, 0), (6, 5, 10, 4, 7, 3, 2, 1, 1, 1, 2, 0, 0, 6, 9, 4, 7, 7, 3, 1, 2, 4, 1, 1, 1, 0), (6, 6, 5, 5, 7, 3, 5, 3, 1, 0, 0, 1, 0, 8, 6, 3, 1, 3, 3, 1, 2, 3, 3, 1, 1, 0), (6, 7, 2, 8, 3, 3, 2, 1, 4, 2, 0, 0, 0, 5, 5, 3, 2, 7, 3, 0, 3, 4, 0, 1, 0, 0), (8, 7, 7, 5, 12, 2, 2, 1, 2, 0, 1, 1, 0, 5, 5, 2, 4, 10, 1, 4, 3, 4, 3, 0, 0, 0), (3, 4, 10, 9, 5, 2, 1, 8, 3, 3, 2, 2, 0, 12, 6, 3, 4, 4, 4, 2, 3, 1, 2, 1, 0, 0), (5, 3, 10, 5, 9, 2, 1, 3, 1, 1, 0, 2, 0, 8, 3, 4, 3, 9, 3, 3, 3, 3, 0, 2, 2, 0), (8, 4, 7, 7, 9, 1, 0, 1, 2, 0, 3, 1, 0, 5, 4, 1, 5, 5, 3, 1, 3, 2, 2, 1, 1, 0), (7, 2, 12, 4, 6, 2, 2, 0, 3, 2, 1, 0, 0, 8, 7, 3, 3, 9, 2, 2, 0, 3, 2, 0, 0, 0), (6, 4, 1, 10, 5, 2, 0, 2, 3, 2, 0, 0, 0, 10, 8, 8, 6, 10, 3, 5, 0, 5, 0, 1, 0, 0), (6, 6, 4, 6, 6, 5, 5, 1, 1, 0, 1, 0, 0, 8, 6, 5, 4, 3, 5, 1, 4, 2, 1, 3, 2, 0), (9, 4, 7, 3, 6, 3, 7, 0, 5, 2, 2, 0, 0, 7, 10, 8, 5, 7, 4, 5, 1, 4, 0, 1, 1, 0), (7, 4, 8, 8, 10, 5, 1, 1, 2, 1, 1, 2, 0, 6, 9, 6, 5, 6, 2, 2, 2, 1, 0, 1, 0, 0), (4, 0, 10, 4, 2, 2, 3, 3, 3, 1, 1, 2, 0, 5, 7, 6, 9, 5, 2, 3, 1, 3, 0, 2, 2, 0), (11, 2, 8, 5, 4, 2, 1, 2, 4, 1, 1, 2, 0, 7, 7, 4, 5, 3, 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passenger_arriving_acc = ((3, 1, 2, 3, 3, 1, 1, 1, 1, 1, 0, 1, 0, 4, 1, 4, 3, 4, 2, 3, 1, 1, 1, 2, 0, 0), (5, 5, 8, 6, 6, 3, 4, 1, 4, 1, 0, 2, 0, 15, 2, 8, 5, 13, 4, 6, 2, 1, 1, 3, 0, 0), (11, 8, 11, 10, 12, 7, 4, 2, 6, 2, 1, 2, 0, 18, 12, 12, 6, 15, 5, 6, 2, 4, 2, 4, 1, 0), (18, 10, 16, 11, 15, 8, 8, 5, 9, 2, 2, 2, 0, 23, 17, 20, 11, 17, 7, 10, 3, 6, 5, 4, 2, 0), (20, 11, 18, 19, 20, 8, 11, 7, 10, 3, 2, 2, 0, 29, 25, 22, 12, 21, 11, 11, 4, 11, 7, 5, 2, 0), (24, 16, 26, 24, 20, 8, 15, 9, 11, 4, 5, 2, 0, 35, 32, 26, 15, 25, 12, 12, 7, 16, 10, 5, 4, 0), (28, 22, 32, 28, 22, 11, 18, 13, 14, 5, 5, 2, 0, 41, 39, 30, 18, 31, 18, 14, 10, 19, 12, 6, 4, 0), (35, 27, 33, 37, 25, 12, 20, 14, 16, 5, 5, 3, 0, 46, 44, 36, 23, 32, 24, 16, 14, 22, 14, 8, 4, 0), (41, 32, 41, 39, 27, 12, 24, 16, 18, 5, 6, 3, 0, 51, 47, 42, 23, 36, 30, 18, 16, 24, 16, 8, 4, 0), (50, 40, 45, 42, 30, 12, 28, 19, 19, 6, 7, 3, 0, 56, 51, 44, 28, 42, 34, 20, 16, 26, 18, 8, 4, 0), (54, 44, 53, 44, 35, 17, 31, 24, 21, 7, 8, 4, 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0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1))
'\nparameters for reproducibiliy. More information: https://numpy.org/doc/stable/reference/random/parallel.html\n'
entropy = 8991598675325360468762009371570610170
child_seed_index = (1, 95) |
class RgbdFrame:
"""
Contains:
rgb : numpy [H, W, C]
depth : numpy [H, W] where unavailable pixels equal 0
timestamp : second
"""
def __init__(self, rgb, depth, timestamp=0):
self.rgb = rgb
self.depth = depth
self.timestamp = timestamp | class Rgbdframe:
"""
Contains:
rgb : numpy [H, W, C]
depth : numpy [H, W] where unavailable pixels equal 0
timestamp : second
"""
def __init__(self, rgb, depth, timestamp=0):
self.rgb = rgb
self.depth = depth
self.timestamp = timestamp |
print('Start')
done_flag = False
total = 0
while not done_flag:
num = int(input('Please input an integer: '))
if num > 0:
total = total + num
else:
done_flag = True
print('Total: ', total)
print('Stop')
| print('Start')
done_flag = False
total = 0
while not done_flag:
num = int(input('Please input an integer: '))
if num > 0:
total = total + num
else:
done_flag = True
print('Total: ', total)
print('Stop') |
'''
Given two strings, print the longest common subsequence
x = "abcdgh"
y = "aebdghr"
LCS = 5 ie. "abdgh"
'''
def print_lcs(x, y, n, m):
dp = [[0 for _ in range(m+1)] for _ in range(n+1)]
for i in range(1, n+1):
for j in range(1, m+1):
if x[i-1] == y[j-1]:
dp[i][j] = 1 + dp[i-1][j-1]
else:
dp[i][j] = max(dp[i][j-1], dp[i-1][j])
lcs = ""
while n != 0 and m != 0:
if x[n-1] == y[m-1]:
lcs = x[n-1] + lcs
n -= 1
m -= 1
else:
if dp[n-1][m] > dp[n][m-1]:
n -= 1
else:
m -= 1
return lcs
if __name__ == "__main__":
x, y = "abcdgh", "aebdghr"
n, m = len(x), len(y)
cache = [[-1 for _ in range(m+1)] for _ in range(n+1)]
print(print_lcs(x, y, n, m)) | """
Given two strings, print the longest common subsequence
x = "abcdgh"
y = "aebdghr"
LCS = 5 ie. "abdgh"
"""
def print_lcs(x, y, n, m):
dp = [[0 for _ in range(m + 1)] for _ in range(n + 1)]
for i in range(1, n + 1):
for j in range(1, m + 1):
if x[i - 1] == y[j - 1]:
dp[i][j] = 1 + dp[i - 1][j - 1]
else:
dp[i][j] = max(dp[i][j - 1], dp[i - 1][j])
lcs = ''
while n != 0 and m != 0:
if x[n - 1] == y[m - 1]:
lcs = x[n - 1] + lcs
n -= 1
m -= 1
elif dp[n - 1][m] > dp[n][m - 1]:
n -= 1
else:
m -= 1
return lcs
if __name__ == '__main__':
(x, y) = ('abcdgh', 'aebdghr')
(n, m) = (len(x), len(y))
cache = [[-1 for _ in range(m + 1)] for _ in range(n + 1)]
print(print_lcs(x, y, n, m)) |
txt_help_text = """
This app will predict the probability of you developing type-2 diabetes.
In order to use it, fill in all the fields and press send. To make sure
everything runs smoothly, there are some restrictions:
1) Negative numbers cannot be given.
2) The correct type of information must be entered e.g no words for age.
3) There are limits to prevent extreme values so the app doesn't break
but feel free to play around.
4) Don't leave any fields empty.
Range
-------
Age: 0 - 130
BMI/Glucose/Blood Pressure: Any positive number
Obtaining the data
--------------------
Age: Current year - your birth year
BMI: weight[kg]/Height^2[m]
Glucose: Use a blood glucose meter
Blood Pressure: Use a blood pressure moniter and take the lower number.
Have fun! And remember, this is just an app. Consult a doctor for serious
medical related issues.
"""
txt_about_text = """
Name: Type-2 diabetes predictor
Version: 2.0
Modules used: Numpy, tkinter, SciKit-learn, Pandas
description: Tests the probability of the user developing type-2 diabetes.
Machine Learning Model: Logistic Regression
Dataset used: rb.gy/mrwkvd
"""
txt_info_text = """
Important notice: This is just an app, consult a doctor for serious medical issues.
---------------------------------------------------------------------------------------------------------
This section contains information on type-2 diabetes:
1) What is it?
Type-2 diabetes occurs when your body cannot produce enough insulin or becomes resistant to it.
2)What causes it?
-Being overweight
-Being too inactive
-A family history of the disease.
3)What are the symptoms?
-Peeing a lot
-tiredness
-constantly thiirsty
-losing weight without trying
-cuts take long to heal
-blurred vision
4)How is it diagnosed?
Go to your GP who will do a urine and blood test. If the test comes back positive, they will
call you back and explain the next step.
5)What is the treatment?
-They will give you medicine to help maintain your blood-sugar levels.
-You will be expected to make lifestyle changes such improving your diet and being more active.
-You will be given regular checkups to make sure everything is ok.
Measurements of a healthy person
----------------------------------
Age: n/a
BMI: 18.5 - 25.0
Glucose: 140[mg/dL]/7.8[mmol/L] 90mins after a meal
Blood Pressure: <80[mm Hg] (This is your lower/diastolic one)
---------------------------------------------------------------------------------------------------------
It is important to remember that diabetes (both type-1 and type-2) are serious illnesses and
if you think you have it then you consult a medical professional. It is a lifetime disease
which means once you get it, there is no permenant cure. So make sure to maintain your health
even if the app says your risk is low. Also remember that this is just an app, it does not
replace the opinion of a medical professional at all.
---------------------------------------------------------------------------------------------------------
Source:
1) https://www.nhs.uk/conditions/diabetes/
2) https://www.nhs.uk/conditions/type-2-diabetes/
3) https://www.diabetes.co.uk/diabetes_care/blood-sugar-level-ranges.html
4) https://www.heart.org/en/health-topics/high-blood-pressure/understanding-blood-pressure-readings
5) https://www.nhs.uk/common-health-questions/lifestyle/what-is-the-body-mass-index-bmi/
"""
lbl_result_text = """
Please fill in all the blanks in the entry fields.
Once done press send to get your result.
It will be displayed on this screen.
This app doesn't replace the opinion of doctors
"""
| txt_help_text = "\nThis app will predict the probability of you developing type-2 diabetes.\nIn order to use it, fill in all the fields and press send. To make sure\neverything runs smoothly, there are some restrictions:\n1) Negative numbers cannot be given.\n2) The correct type of information must be entered e.g no words for age.\n3) There are limits to prevent extreme values so the app doesn't break\nbut feel free to play around.\n4) Don't leave any fields empty.\n\nRange\n-------\nAge: 0 - 130\nBMI/Glucose/Blood Pressure: Any positive number\n\nObtaining the data\n--------------------\nAge: Current year - your birth year\nBMI: weight[kg]/Height^2[m]\nGlucose: Use a blood glucose meter\nBlood Pressure: Use a blood pressure moniter and take the lower number.\n\nHave fun! And remember, this is just an app. Consult a doctor for serious\nmedical related issues.\n"
txt_about_text = '\nName: Type-2 diabetes predictor\nVersion: 2.0\nModules used: Numpy, tkinter, SciKit-learn, Pandas\ndescription: Tests the probability of the user developing type-2 diabetes.\nMachine Learning Model: Logistic Regression\nDataset used: rb.gy/mrwkvd\n'
txt_info_text = '\nImportant notice: This is just an app, consult a doctor for serious medical issues.\n---------------------------------------------------------------------------------------------------------\nThis section contains information on type-2 diabetes:\n1) What is it?\nType-2 diabetes occurs when your body cannot produce enough insulin or becomes resistant to it.\n\n2)What causes it?\n-Being overweight\n-Being too inactive\n-A family history of the disease.\n\n3)What are the symptoms?\n-Peeing a lot\n-tiredness\n-constantly thiirsty\n-losing weight without trying\n-cuts take long to heal\n-blurred vision\n\n4)How is it diagnosed?\nGo to your GP who will do a urine and blood test. If the test comes back positive, they will\ncall you back and explain the next step.\n\n5)What is the treatment?\n-They will give you medicine to help maintain your blood-sugar levels.\n-You will be expected to make lifestyle changes such improving your diet and being more active.\n-You will be given regular checkups to make sure everything is ok.\n\nMeasurements of a healthy person\n----------------------------------\nAge: n/a\nBMI: 18.5 - 25.0\nGlucose: 140[mg/dL]/7.8[mmol/L] 90mins after a meal\nBlood Pressure: <80[mm Hg] (This is your lower/diastolic one)\n---------------------------------------------------------------------------------------------------------\nIt is important to remember that diabetes (both type-1 and type-2) are serious illnesses and\nif you think you have it then you consult a medical professional. It is a lifetime disease\nwhich means once you get it, there is no permenant cure. So make sure to maintain your health\neven if the app says your risk is low. Also remember that this is just an app, it does not\nreplace the opinion of a medical professional at all.\n---------------------------------------------------------------------------------------------------------\nSource:\n1) https://www.nhs.uk/conditions/diabetes/\n2) https://www.nhs.uk/conditions/type-2-diabetes/\n3) https://www.diabetes.co.uk/diabetes_care/blood-sugar-level-ranges.html\n4) https://www.heart.org/en/health-topics/high-blood-pressure/understanding-blood-pressure-readings\n5) https://www.nhs.uk/common-health-questions/lifestyle/what-is-the-body-mass-index-bmi/\n'
lbl_result_text = "\nPlease fill in all the blanks in the entry fields.\nOnce done press send to get your result.\nIt will be displayed on this screen.\n\nThis app doesn't replace the opinion of doctors\n" |
# encoding: utf-8
"""Initialization module for gdcapiwrapper package."""
__version__ = "0.2"
| """Initialization module for gdcapiwrapper package."""
__version__ = '0.2' |
# optimizer
optimizer = dict(type='AdamW', lr=1e-3, betas=(0.9, 0.999), weight_decay=0.05)
# learning policy
lr_config = dict(
policy='CosineAnnealing',
min_lr=0.,
warmup='linear',
warmup_iters=5,
warmup_ratio=1e-4, # cannot be 0
warmup_by_epoch=True)
# runtime settings
runner = dict(type='EpochBasedRunner', max_epochs=100)
| optimizer = dict(type='AdamW', lr=0.001, betas=(0.9, 0.999), weight_decay=0.05)
lr_config = dict(policy='CosineAnnealing', min_lr=0.0, warmup='linear', warmup_iters=5, warmup_ratio=0.0001, warmup_by_epoch=True)
runner = dict(type='EpochBasedRunner', max_epochs=100) |
#
# PySNMP MIB module SA-CM-MTA-MIB (http://snmplabs.com/pysmi)
# ASN.1 source file:///Users/davwang4/Dev/mibs.snmplabs.com/asn1/SA-CM-MTA-MIB
# Produced by pysmi-0.3.4 at Mon Apr 29 20:51:39 2019
# On host DAVWANG4-M-1475 platform Darwin version 18.5.0 by user davwang4
# Using Python version 3.7.3 (default, Mar 27 2019, 09:23:15)
#
OctetString, ObjectIdentifier, Integer = mibBuilder.importSymbols("ASN1", "OctetString", "ObjectIdentifier", "Integer")
NamedValues, = mibBuilder.importSymbols("ASN1-ENUMERATION", "NamedValues")
ConstraintsUnion, SingleValueConstraint, ValueRangeConstraint, ValueSizeConstraint, ConstraintsIntersection = mibBuilder.importSymbols("ASN1-REFINEMENT", "ConstraintsUnion", "SingleValueConstraint", "ValueRangeConstraint", "ValueSizeConstraint", "ConstraintsIntersection")
SnmpAdminString, = mibBuilder.importSymbols("SNMP-FRAMEWORK-MIB", "SnmpAdminString")
ModuleCompliance, NotificationGroup = mibBuilder.importSymbols("SNMPv2-CONF", "ModuleCompliance", "NotificationGroup")
NotificationType, Unsigned32, enterprises, IpAddress, MibIdentifier, iso, ObjectIdentity, TimeTicks, MibScalar, MibTable, MibTableRow, MibTableColumn, Gauge32, ModuleIdentity, Integer32, Counter32, Bits, Counter64 = mibBuilder.importSymbols("SNMPv2-SMI", "NotificationType", "Unsigned32", "enterprises", "IpAddress", "MibIdentifier", "iso", "ObjectIdentity", "TimeTicks", "MibScalar", "MibTable", "MibTableRow", "MibTableColumn", "Gauge32", "ModuleIdentity", "Integer32", "Counter32", "Bits", "Counter64")
TextualConvention, TruthValue, DisplayString = mibBuilder.importSymbols("SNMPv2-TC", "TextualConvention", "TruthValue", "DisplayString")
sa = MibIdentifier((1, 3, 6, 1, 4, 1, 1429))
saVoip = MibIdentifier((1, 3, 6, 1, 4, 1, 1429, 78))
saCmMta = ModuleIdentity((1, 3, 6, 1, 4, 1, 1429, 78, 1))
saCmMta.setRevisions(('2016-12-23 00:00',))
if mibBuilder.loadTexts: saCmMta.setLastUpdated('201612230000Z')
if mibBuilder.loadTexts: saCmMta.setOrganization('Cisco Systems, Inc.')
saCmMtaDevice = MibScalar((1, 3, 6, 1, 4, 1, 1429, 78, 1, 1), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(0, 1))).clone(namedValues=NamedValues(("disable", 0), ("enable", 1)))).setMaxAccess("readonly")
if mibBuilder.loadTexts: saCmMtaDevice.setStatus('current')
saCmMtaIpFilters = MibScalar((1, 3, 6, 1, 4, 1, 1429, 78, 1, 3), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(0, 1))).clone(namedValues=NamedValues(("perSpec", 0), ("openMta", 1)))).setMaxAccess("readwrite")
if mibBuilder.loadTexts: saCmMtaIpFilters.setStatus('current')
saCmMtaSidCount = MibScalar((1, 3, 6, 1, 4, 1, 1429, 78, 1, 5), Integer32().subtype(subtypeSpec=ConstraintsUnion(ValueRangeConstraint(4, 4), ValueRangeConstraint(16, 16), ))).setMaxAccess("readonly")
if mibBuilder.loadTexts: saCmMtaSidCount.setStatus('current')
saCmMtaProvisioningMode = MibScalar((1, 3, 6, 1, 4, 1, 1429, 78, 1, 7), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(0, 1, 2, 3, 4, 5, 6))).clone(namedValues=NamedValues(("packetCable", 0), ("oneConfigFile", 1), ("twoConfigFilesDHCP", 2), ("twoConfigFilesSNMP", 3), ("twoConfigFilesDHCPmacAddress", 4), ("twoConfigFilesMacAddressOnly", 5), ("webPage", 6)))).setMaxAccess("readonly")
if mibBuilder.loadTexts: saCmMtaProvisioningMode.setStatus('current')
saCmMtaDhcpPktcOption = MibScalar((1, 3, 6, 1, 4, 1, 1429, 78, 1, 8), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(0, 1, 2))).clone(namedValues=NamedValues(("require122", 0), ("requireNone", 1), ("require177", 2)))).setMaxAccess("readonly")
if mibBuilder.loadTexts: saCmMtaDhcpPktcOption.setStatus('current')
saCmMtaRequireTod = MibScalar((1, 3, 6, 1, 4, 1, 1429, 78, 1, 10), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(0, 1))).clone(namedValues=NamedValues(("false", 0), ("true", 1))).clone(1)).setMaxAccess("readonly")
if mibBuilder.loadTexts: saCmMtaRequireTod.setStatus('current')
saCmMtaDecryptMtaConfigFile = MibScalar((1, 3, 6, 1, 4, 1, 1429, 78, 1, 13), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(1, 2))).clone(namedValues=NamedValues(("disable", 1), ("RSA-CM-cert", 2))).clone(1)).setMaxAccess("readonly")
if mibBuilder.loadTexts: saCmMtaDecryptMtaConfigFile.setStatus('current')
saCmMtaSwUpgradeControlTimer = MibScalar((1, 3, 6, 1, 4, 1, 1429, 78, 1, 14), Integer32().subtype(subtypeSpec=ValueRangeConstraint(0, 7200))).setUnits('seconds').setMaxAccess("readwrite")
if mibBuilder.loadTexts: saCmMtaSwUpgradeControlTimer.setStatus('current')
saCmMtaDhcpOptionSixty = MibScalar((1, 3, 6, 1, 4, 1, 1429, 78, 1, 20), SnmpAdminString().clone('pktc1.0')).setMaxAccess("readonly")
if mibBuilder.loadTexts: saCmMtaDhcpOptionSixty.setStatus('current')
saCmMtaProvSnmpSetCommunityString = MibScalar((1, 3, 6, 1, 4, 1, 1429, 78, 1, 26), SnmpAdminString().clone('public')).setMaxAccess("readonly")
if mibBuilder.loadTexts: saCmMtaProvSnmpSetCommunityString.setStatus('current')
saCmMtaCliAccess = MibIdentifier((1, 3, 6, 1, 4, 1, 1429, 78, 1, 1001))
saCmMtaCliAccessPasswordType = MibScalar((1, 3, 6, 1, 4, 1, 1429, 78, 1, 1001, 5), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(0, 1, 2))).clone(namedValues=NamedValues(("plain", 0), ("md5", 1), ("pod", 2))))
if mibBuilder.loadTexts: saCmMtaCliAccessPasswordType.setStatus('current')
mibBuilder.exportSymbols("SA-CM-MTA-MIB", PYSNMP_MODULE_ID=saCmMta, saCmMtaDhcpOptionSixty=saCmMtaDhcpOptionSixty, saCmMtaSwUpgradeControlTimer=saCmMtaSwUpgradeControlTimer, saCmMtaCliAccess=saCmMtaCliAccess, saCmMtaIpFilters=saCmMtaIpFilters, sa=sa, saVoip=saVoip, saCmMtaDevice=saCmMtaDevice, saCmMtaProvisioningMode=saCmMtaProvisioningMode, saCmMtaDecryptMtaConfigFile=saCmMtaDecryptMtaConfigFile, saCmMta=saCmMta, saCmMtaCliAccessPasswordType=saCmMtaCliAccessPasswordType, saCmMtaProvSnmpSetCommunityString=saCmMtaProvSnmpSetCommunityString, saCmMtaRequireTod=saCmMtaRequireTod, saCmMtaSidCount=saCmMtaSidCount, saCmMtaDhcpPktcOption=saCmMtaDhcpPktcOption)
| (octet_string, object_identifier, integer) = mibBuilder.importSymbols('ASN1', 'OctetString', 'ObjectIdentifier', 'Integer')
(named_values,) = mibBuilder.importSymbols('ASN1-ENUMERATION', 'NamedValues')
(constraints_union, single_value_constraint, value_range_constraint, value_size_constraint, constraints_intersection) = mibBuilder.importSymbols('ASN1-REFINEMENT', 'ConstraintsUnion', 'SingleValueConstraint', 'ValueRangeConstraint', 'ValueSizeConstraint', 'ConstraintsIntersection')
(snmp_admin_string,) = mibBuilder.importSymbols('SNMP-FRAMEWORK-MIB', 'SnmpAdminString')
(module_compliance, notification_group) = mibBuilder.importSymbols('SNMPv2-CONF', 'ModuleCompliance', 'NotificationGroup')
(notification_type, unsigned32, enterprises, ip_address, mib_identifier, iso, object_identity, time_ticks, mib_scalar, mib_table, mib_table_row, mib_table_column, gauge32, module_identity, integer32, counter32, bits, counter64) = mibBuilder.importSymbols('SNMPv2-SMI', 'NotificationType', 'Unsigned32', 'enterprises', 'IpAddress', 'MibIdentifier', 'iso', 'ObjectIdentity', 'TimeTicks', 'MibScalar', 'MibTable', 'MibTableRow', 'MibTableColumn', 'Gauge32', 'ModuleIdentity', 'Integer32', 'Counter32', 'Bits', 'Counter64')
(textual_convention, truth_value, display_string) = mibBuilder.importSymbols('SNMPv2-TC', 'TextualConvention', 'TruthValue', 'DisplayString')
sa = mib_identifier((1, 3, 6, 1, 4, 1, 1429))
sa_voip = mib_identifier((1, 3, 6, 1, 4, 1, 1429, 78))
sa_cm_mta = module_identity((1, 3, 6, 1, 4, 1, 1429, 78, 1))
saCmMta.setRevisions(('2016-12-23 00:00',))
if mibBuilder.loadTexts:
saCmMta.setLastUpdated('201612230000Z')
if mibBuilder.loadTexts:
saCmMta.setOrganization('Cisco Systems, Inc.')
sa_cm_mta_device = mib_scalar((1, 3, 6, 1, 4, 1, 1429, 78, 1, 1), integer32().subtype(subtypeSpec=constraints_union(single_value_constraint(0, 1))).clone(namedValues=named_values(('disable', 0), ('enable', 1)))).setMaxAccess('readonly')
if mibBuilder.loadTexts:
saCmMtaDevice.setStatus('current')
sa_cm_mta_ip_filters = mib_scalar((1, 3, 6, 1, 4, 1, 1429, 78, 1, 3), integer32().subtype(subtypeSpec=constraints_union(single_value_constraint(0, 1))).clone(namedValues=named_values(('perSpec', 0), ('openMta', 1)))).setMaxAccess('readwrite')
if mibBuilder.loadTexts:
saCmMtaIpFilters.setStatus('current')
sa_cm_mta_sid_count = mib_scalar((1, 3, 6, 1, 4, 1, 1429, 78, 1, 5), integer32().subtype(subtypeSpec=constraints_union(value_range_constraint(4, 4), value_range_constraint(16, 16)))).setMaxAccess('readonly')
if mibBuilder.loadTexts:
saCmMtaSidCount.setStatus('current')
sa_cm_mta_provisioning_mode = mib_scalar((1, 3, 6, 1, 4, 1, 1429, 78, 1, 7), integer32().subtype(subtypeSpec=constraints_union(single_value_constraint(0, 1, 2, 3, 4, 5, 6))).clone(namedValues=named_values(('packetCable', 0), ('oneConfigFile', 1), ('twoConfigFilesDHCP', 2), ('twoConfigFilesSNMP', 3), ('twoConfigFilesDHCPmacAddress', 4), ('twoConfigFilesMacAddressOnly', 5), ('webPage', 6)))).setMaxAccess('readonly')
if mibBuilder.loadTexts:
saCmMtaProvisioningMode.setStatus('current')
sa_cm_mta_dhcp_pktc_option = mib_scalar((1, 3, 6, 1, 4, 1, 1429, 78, 1, 8), integer32().subtype(subtypeSpec=constraints_union(single_value_constraint(0, 1, 2))).clone(namedValues=named_values(('require122', 0), ('requireNone', 1), ('require177', 2)))).setMaxAccess('readonly')
if mibBuilder.loadTexts:
saCmMtaDhcpPktcOption.setStatus('current')
sa_cm_mta_require_tod = mib_scalar((1, 3, 6, 1, 4, 1, 1429, 78, 1, 10), integer32().subtype(subtypeSpec=constraints_union(single_value_constraint(0, 1))).clone(namedValues=named_values(('false', 0), ('true', 1))).clone(1)).setMaxAccess('readonly')
if mibBuilder.loadTexts:
saCmMtaRequireTod.setStatus('current')
sa_cm_mta_decrypt_mta_config_file = mib_scalar((1, 3, 6, 1, 4, 1, 1429, 78, 1, 13), integer32().subtype(subtypeSpec=constraints_union(single_value_constraint(1, 2))).clone(namedValues=named_values(('disable', 1), ('RSA-CM-cert', 2))).clone(1)).setMaxAccess('readonly')
if mibBuilder.loadTexts:
saCmMtaDecryptMtaConfigFile.setStatus('current')
sa_cm_mta_sw_upgrade_control_timer = mib_scalar((1, 3, 6, 1, 4, 1, 1429, 78, 1, 14), integer32().subtype(subtypeSpec=value_range_constraint(0, 7200))).setUnits('seconds').setMaxAccess('readwrite')
if mibBuilder.loadTexts:
saCmMtaSwUpgradeControlTimer.setStatus('current')
sa_cm_mta_dhcp_option_sixty = mib_scalar((1, 3, 6, 1, 4, 1, 1429, 78, 1, 20), snmp_admin_string().clone('pktc1.0')).setMaxAccess('readonly')
if mibBuilder.loadTexts:
saCmMtaDhcpOptionSixty.setStatus('current')
sa_cm_mta_prov_snmp_set_community_string = mib_scalar((1, 3, 6, 1, 4, 1, 1429, 78, 1, 26), snmp_admin_string().clone('public')).setMaxAccess('readonly')
if mibBuilder.loadTexts:
saCmMtaProvSnmpSetCommunityString.setStatus('current')
sa_cm_mta_cli_access = mib_identifier((1, 3, 6, 1, 4, 1, 1429, 78, 1, 1001))
sa_cm_mta_cli_access_password_type = mib_scalar((1, 3, 6, 1, 4, 1, 1429, 78, 1, 1001, 5), integer32().subtype(subtypeSpec=constraints_union(single_value_constraint(0, 1, 2))).clone(namedValues=named_values(('plain', 0), ('md5', 1), ('pod', 2))))
if mibBuilder.loadTexts:
saCmMtaCliAccessPasswordType.setStatus('current')
mibBuilder.exportSymbols('SA-CM-MTA-MIB', PYSNMP_MODULE_ID=saCmMta, saCmMtaDhcpOptionSixty=saCmMtaDhcpOptionSixty, saCmMtaSwUpgradeControlTimer=saCmMtaSwUpgradeControlTimer, saCmMtaCliAccess=saCmMtaCliAccess, saCmMtaIpFilters=saCmMtaIpFilters, sa=sa, saVoip=saVoip, saCmMtaDevice=saCmMtaDevice, saCmMtaProvisioningMode=saCmMtaProvisioningMode, saCmMtaDecryptMtaConfigFile=saCmMtaDecryptMtaConfigFile, saCmMta=saCmMta, saCmMtaCliAccessPasswordType=saCmMtaCliAccessPasswordType, saCmMtaProvSnmpSetCommunityString=saCmMtaProvSnmpSetCommunityString, saCmMtaRequireTod=saCmMtaRequireTod, saCmMtaSidCount=saCmMtaSidCount, saCmMtaDhcpPktcOption=saCmMtaDhcpPktcOption) |
"""
PLUGIN for print function
- action : ACTION_NAME
print
will print the result of action execution to STDOUT
"""
#import logging
#logger = logging.getLogger()
############################################
# Exec if comman execution was NOT successful
def grep( output, lines ):
result = []
for line in output:
if line in lines:
result.append(line)
return result
#############################################
# HELP FUNCTION
############################################
def help( params ):
print( "" )
print( " FUNCTION grep:" )
print( "\t" )
print( "\tIt works with last command output." )
print( "\tFunction keeps all lines contained in the list of grep command")
print( "\tand drops all lines that doesn't")
print( "\t" )
print( "\tHow to define: " )
print( "\t\t- action : SOME_ACTION" )
print( "\t\t grep : " )
print( "\t\t - 'line1' " )
print( "\t\t - 'line2' " )
print( "\t\t - 'line3' " )
print( "\t" )
print( "\tEXAMPLE:" )
print( "\t\t- action : start_VM" )
print( "\t\t grep : " )
print( "\t\t - 'ERROR' " )
print( "\t\t - 'Started' " )
print( "\t\t- " )
print( "\t\t print " )
print( "\t" )
#############################################
# MAIN EXECUTOR FOR PLUGIN
############################################
def run( action, config, data):
exec_function = exec_parameters.get( data["param"], "default" )
output_old = data["lastOutput"]
output_new = grep( output=output_old, lines=data["param"] )
data["lastOutput"] = output_new
| """
PLUGIN for print function
- action : ACTION_NAME
print
will print the result of action execution to STDOUT
"""
def grep(output, lines):
result = []
for line in output:
if line in lines:
result.append(line)
return result
def help(params):
print('')
print(' FUNCTION grep:')
print('\t')
print('\tIt works with last command output.')
print('\tFunction keeps all lines contained in the list of grep command')
print("\tand drops all lines that doesn't")
print('\t')
print('\tHow to define: ')
print('\t\t- action : SOME_ACTION')
print('\t\t grep : ')
print("\t\t - 'line1' ")
print("\t\t - 'line2' ")
print("\t\t - 'line3' ")
print('\t')
print('\tEXAMPLE:')
print('\t\t- action : start_VM')
print('\t\t grep : ')
print("\t\t - 'ERROR' ")
print("\t\t - 'Started' ")
print('\t\t- ')
print('\t\t print ')
print('\t')
def run(action, config, data):
exec_function = exec_parameters.get(data['param'], 'default')
output_old = data['lastOutput']
output_new = grep(output=output_old, lines=data['param'])
data['lastOutput'] = output_new |
# Copyright 2021 The Bazel Authors. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Bazel providers for proto rules."""
ProtoLangToolchainInfo = provider(
doc = "Specifies how to generate language-specific code from .proto files. Used by LANG_proto_library rules.",
fields = dict(
out_replacement_format_flag = "(str) Format string used when passing output to the plugin used by proto compiler.",
plugin_format_flag = "(str) Format string used when passing plugin to proto compiler.",
plugin = "(FilesToRunProvider) Proto compiler plugin.",
runtime = "(Target) Runtime.",
provided_proto_sources = "(list[ProtoSource]) Proto sources provided by the toolchain.",
proto_compiler = "(FilesToRunProvider) Proto compiler.",
protoc_opts = "(list[str]) Options to pass to proto compiler.",
progress_message = "(str) Progress message to set on the proto compiler action.",
mnemonic = "(str) Mnemonic to set on the proto compiler action.",
),
)
| """Bazel providers for proto rules."""
proto_lang_toolchain_info = provider(doc='Specifies how to generate language-specific code from .proto files. Used by LANG_proto_library rules.', fields=dict(out_replacement_format_flag='(str) Format string used when passing output to the plugin used by proto compiler.', plugin_format_flag='(str) Format string used when passing plugin to proto compiler.', plugin='(FilesToRunProvider) Proto compiler plugin.', runtime='(Target) Runtime.', provided_proto_sources='(list[ProtoSource]) Proto sources provided by the toolchain.', proto_compiler='(FilesToRunProvider) Proto compiler.', protoc_opts='(list[str]) Options to pass to proto compiler.', progress_message='(str) Progress message to set on the proto compiler action.', mnemonic='(str) Mnemonic to set on the proto compiler action.')) |
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