content
stringlengths 7
1.05M
|
|---|
Z = [[0,0,0,0,0,0],
[0,0,0,1,0,0],
[0,1,0,1,0,0],
[0,0,1,1,0,0],
[0,0,0,0,0,0],
[0,0,0,0,0,0]]
def compute_neighbours(Z):
rows,cols = len(Z), len(Z[0])
N = [[0,]*(cols) for i in range(rows)]
for x in range(1,cols-1):
for y in range(1,rows-1):
N[y][x] = Z[y-1][x-1]+Z[y][x-1]+Z[y+1][x-1] \
+ Z[y-1][x] +Z[y+1][x] \
+ Z[y-1][x+1]+Z[y][x+1]+Z[y+1][x+1]
return N
def show(Z):
for l in Z[1:-1]: print(l[1:-1])
print()
def iterate(Z):
rows,cols = len(Z), len(Z[0])
N = compute_neighbours(Z)
for x in range(1,cols-1):
for y in range(1,rows-1):
if Z[y][x] == 1 and (N[y][x] < 2 or N[y][x] > 3):
Z[y][x] = 0
elif Z[y][x] == 0 and N[y][x] == 3:
Z[y][x] = 1
return Z
show(Z)
for i in range(4):
iterate(Z)
show(Z)
|
#Exercício025
name = str(input('Qual seu nome completo?: ')).strip().upper()
print('Seu nome tem a palavra SILVA?: {}'.format('SILVA'in name))
print('xD')
|
"""comics_download
By IaninaK susie@example.com
Downloads multiple comics from xkcd.com"""
__version__ = '0.1.0'
|
print('\nVerificando as primeiras letras de um texto\n')
cid = str(input('Que cidade você nasceu ? ')).strip()
print(cid[:5].lower() == 'santo')
fim = input('\nCurso de Python no YouTube, canal CURSO EM VIDEO.')
|
def foo():
pass
def bar(*args, kwonly_arg: str = None):
pass
|
# DATA UNIT CLASS
class _Unit:
def __init__(self, name, con_2_si, con_unit, si_unit):
self._name = name
self._con_2_si = con_2_si
self._con_unit = con_unit
self._si_unit = si_unit
def get_name(self):
return self._name
def get_con_2_si(self):
return self._con_2_si
def get_con_unit(self):
return self._con_unit
def get_si_unit(self):
return self._si_unit
# DATA UNITS
ENERGY = _Unit(name='Energy', con_2_si=1000 * 3600, con_unit='kWh', si_unit='Ws')
DISTANCE = _Unit(name='Distance', con_2_si=1000, con_unit='km', si_unit='m')
|
def missing_number(nums):
arr = [0 for _ in range(len(nums))]
for i in range(len(nums)):
if nums[i] < len(nums):
arr[nums[i]] = -1
for i in range(len(nums)):
if arr[i] != -1:
return i
return len(nums)
|
#!/usr/bin/env python
# -*- encoding: utf-8 -*-
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you 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.
''' system_constants.py: defines constants for generic purposes and system config'''
##### Generic constants #####
KB = 1024
MB = 1024 * KB
GB = 1024 * MB
MS_TO_SEC = 0.001
SEC_TO_NS = 1000000000
NS_TO_MS = 0.000001
RAM_FOR_STMGR = 1 * GB
DEFAULT_RAM_FOR_INSTANCE = 1 * GB
DEFAULT_DISK_PADDING_PER_CONTAINER = 12 * GB
####################################################################################################
############################# Constants for System configuration ##################################
####################################################################################################
# The relative path to the logging directory
HERON_LOGGING_DIRECTORY = 'heron.logging.directory'
# The maximum log file size in MB
HERON_LOGGING_MAXIMUM_SIZE_MB = "heron.logging.maximum.size.mb"
# The maximum number of log files
HERON_LOGGING_MAXIMUM_FILES = "heron.logging.maximum.files"
# The threshold level to log error
HERON_LOGGING_ERR_THRESHOLD = "heron.logging.err.threshold"
# The interval in seconds to get and reset the system metrics.
HERON_METRICS_EXPORT_INTERVAL_SEC = "heron.metrics.export.interval.sec"
####################################################################################################
############################### Sytem config: Instance related #####################################
####################################################################################################
# The queue capacity (num of items) in bolt for buffer packets to read from stream manager
INSTANCE_INTERNAL_BOLT_READ_QUEUE_CAPACITY = "heron.instance.internal.bolt.read.queue.capacity"
# The queue capacity (num of items) in bolt for buffer packets to write to stream manager
INSTANCE_INTERNAL_BOLT_WRITE_QUEUE_CAPACITY = "heron.instance.internal.bolt.write.queue.capacity"
# The queue capacity (num of items) in spout for buffer packets to read from stream manager
INSTANCE_INTERNAL_SPOUT_READ_QUEUE_CAPACITY = "heron.instance.internal.spout.read.queue.capacity"
# The queue capacity (num of items) in spout for buffer packets to write to stream manager
INSTANCE_INTERNAL_SPOUT_WRITE_QUEUE_CAPACITY = "heron.instance.internal.spout.write.queue.capacity"
# The queue capacity (num of items) for metrics packets to write to metrics manager
INSTANCE_INTERNAL_METRICS_WRITE_QUEUE_CAPACITY = \
"heron.instance.internal.metrics.write.queue.capacity"
# Time based, the maximum batch time in ms for instance to read from stream manager per attempt
INSTANCE_NETWORK_READ_BATCH_TIME_MS = "heron.instance.network.read.batch.time.ms"
# Size based, the maximum batch size in bytes to read from stream manager
INSTANCE_NETWORK_READ_BATCH_SIZE_BYTES = "heron.instance.network.read.batch.size.bytes"
# Time based, the maximum batch time in ms for instance to write to stream manager per attempt
INSTANCE_NETWORK_WRITE_BATCH_TIME_MS = "heron.instance.network.write.batch.time.ms"
# Size based, the maximum batch size in bytes to write to stream manager
INSTANCE_NETWORK_WRITE_BATCH_SIZE_BYTES = "heron.instance.network.write.batch.size.bytes"
# The maximum socket's received buffer size in bytes of instance's network options
INSTANCE_NETWORK_OPTIONS_SOCKET_RECEIVED_BUFFER_SIZE_BYTES = \
"heron.instance.network.options.socket.received.buffer.size.bytes"
# The maximum socket's send buffer size in bytes
INSTANCE_NETWORK_OPTIONS_SOCKET_SEND_BUFFER_SIZE_BYTES = \
"heron.instance.network.options.socket.send.buffer.size.bytes"
# The maximum # of data tuple to batch in a HeronDataTupleSet protobuf
INSTANCE_SET_DATA_TUPLE_CAPACITY = "heron.instance.set.data.tuple.capacity"
# The maximum size in bytes of data tuple to batch in a HeronDataTupleSet protobuf
INSTANCE_SET_DATA_TUPLE_SIZE_BYTES = "heron.instance.set.data.tuple.size.bytes"
# The maximum # of control tuple to batch in a HeronControlTupleSet protobuf
INSTANCE_SET_CONTROL_TUPLE_CAPACITY = "heron.instance.set.control.tuple.capacity"
# The maximum time in ms for an spout to do acknowledgement per attempt
INSTANCE_ACK_BATCH_TIME_MS = "heron.instance.ack.batch.time.ms"
# The maximum time in ms for an spout instance to emit tuples per attempt
INSTANCE_EMIT_BATCH_TIME_MS = "heron.instance.emit.batch.time.ms"
# The maximum batch size in bytes for an spout instance to emit tuples per attempt
INSTANCE_EMIT_BATCH_SIZE_BYTES = "heron.instance.emit.batch.size.bytes"
# The maximum time in ms for an bolt instance to execute tuples per attempt
INSTANCE_EXECUTE_BATCH_TIME_MS = "heron.instance.execute.batch.time.ms"
# The maximum batch size in bytes for an bolt instance to execute tuples per attempt
INSTANCE_EXECUTE_BATCH_SIZE_BYTES = "heron.instance.execute.batch.size.bytes"
# The time to wait before the instance exits forcibly when uncaught exception happens
INSTANCE_FORCE_EXIT_TIMEOUT_MS = "heron.instance.force.exit.timeout.ms"
# Interval in seconds to reconnect to the stream manager
INSTANCE_RECONNECT_STREAMMGR_INTERVAL_SEC = "heron.instance.reconnect.streammgr.interval.sec"
# Interval in seconds to reconnect to the metrics manager
INSTANCE_RECONNECT_METRICSMGR_INTERVAL_SEC = "heron.instance.reconnect.metricsmgr.interval.sec"
# Interval in seconds to sample a system metric, for instance, JVM used memory.
INSTANCE_METRICS_SYSTEM_SAMPLE_INTERVAL_SEC = "heron.instance.metrics.system.sample.interval.sec"
# The lookForTimeout Interval in spout instance will be timeoutInSeconds / NBUCKETS
# For instance, if a tuple's timeout is 30s, and NBUCKETS is 10
# The spout instance will check whether there are timeout tuples every 3 seconds
INSTANCE_ACKNOWLEDGEMENT_NBUCKETS = "heron.instance.acknowledgement.nbuckets"
# The expected size on read queue in bolt
INSTANCE_TUNING_EXPECTED_BOLT_READ_QUEUE_SIZE \
= "heron.instance.tuning.expected.bolt.read.queue.size"
# The expected size on write queue in bolt
INSTANCE_TUNING_EXPECTED_BOLT_WRITE_QUEUE_SIZE \
= "heron.instance.tuning.expected.bolt.write.queue.size"
# The expected size on read queue in spout
INSTANCE_TUNING_EXPECTED_SPOUT_READ_QUEUE_SIZE \
= "heron.instance.tuning.expected.spout.read.queue.size"
# The exepected size on write queue in spout
INSTANCE_TUNING_EXPECTED_SPOUT_WRITE_QUEUE_SIZE \
= "heron.instance.tuning.expected.spout.write.queue.size"
# The expected size on metrics write queue
INSTANCE_TUNING_EXPECTED_METRICS_WRITE_QUEUE_SIZE \
= "heron.instance.tuning.expected.metrics.write.queue.size"
# During dynamically tuning, the weight of new sample size to calculate
# the available capacity of queue.
# We use Exponential moving average: En = (1-w) * En-1 + w * An
# http://en.wikipedia.org/wiki/Moving_average#Exponential_moving_average
INSTANCE_TUNING_CURRENT_SAMPLE_WEIGHT = "heron.instance.tuning.current.sample.weight"
# Interval in ms to tune the size of in & out data queue in instance
INSTANCE_TUNING_INTERVAL_MS = "heron.instance.tuning.interval.ms"
####################################################################################################
########################### Sytem config: Metrics Manager related ##################################
####################################################################################################
# Time based, the maximum batch time in ms for instance to read from socket per attempt
METRICSMGR_NETWORK_READ_BATCH_TIME_MS = "heron.metricsmgr.network.read.batch.time.ms"
# Size based,the maximum batch size in bytes to read from socket
METRICSMGR_NETWORK_READ_BATCH_SIZE_BYTES = "heron.metricsmgr.network.read.batch.size.bytes"
# Time based, the maximum batch time in ms to write to socket
METRICSMGR_NETWORK_WRITE_BATCH_TIME_MS = "heron.metricsmgr.network.write.batch.time.ms"
# Size based, the maximum batch size in bytes to write to socket
METRICSMGR_NETWORK_WRITE_BATCH_SIZE_BYTES = "heron.metricsmgr.network.write.batch.size.bytes"
# The maximum socket's received buffer size in bytes
METRICSMGR_NETWORK_OPTIONS_SOCKET_RECEIVED_BUFFER_SIZE_BYTES \
= "heron.metricsmgr.network.options.socket.received.buffer.size.bytes"
# The maximum socket's send buffer size in bytes
METRICSMGR_NETWORK_OPTIONS_SOCKET_SEND_BUFFER_SIZE_BYTES \
= "heron.metricsmgr.network.options.socket.send.buffer.size.bytes"
|
__title__ = "mathy_envs"
__version__ = "0.11.0"
__summary__ = "Learning environments for solving math problems step-by-step"
__uri__ = "https://mathy.ai"
__author__ = "Justin DuJardin"
__email__ = "justin@dujardinconsulting.com"
__license__ = "All rights reserved"
|
class HostInitiators(object):
def read_get(self, name, idx_name, unity_client):
return unity_client.get_host_initiators(idx_name)
class HostInitiatorsColumn(object):
def get_idx(self, name, idx, unity_client):
return unity_client.get_hosts()
|
def check_password_hash(hash,password):
if hash == password:
return True
else:
return False
def get_cards_summary_model():
data = dict()
data["used_on_amazon"] = 0
data['used_on_google'] = 0
data['total_used'] = 0
data['expired'] = 0
data['total'] = 0
data['in_progress'] = 0
return data
def getcards_trim_data(cards):
# result = []
# for card in cards:
# temp = dict()
# temp['Id'] = card.id
# temp['Name'] = card.card_name
# temp['Card Number'] = card.card_number
# temp['Month'] = card.month
# temp['Year'] = card.year
# temp['Address'] = card.address
# result.append(temp)
result = []
for card in cards:
temp = []
temp.append(card.id)
temp.append(card.card_name)
temp.append(card.card_number)
temp.append(card.month)
temp.append(card.year)
temp.append(card.cvv)
temp.append(card.address)
result.append(temp)
return result
|
def extractOneManArmy(item):
"""
"""
vol, chp, frag, postfix = extractVolChapterFragmentPostfix(item['title'])
if not (chp or vol) or 'preview' in item['title'].lower():
return None
if 'DBWG – Chapter' in item['title'] or 'Dragon-Blooded War God' in item['tags']:
return buildReleaseMessageWithType(item, 'Dragon-Blooded War God', vol, chp, frag=frag, postfix=postfix)
if 'Warlock of the Magus World' in item['tags']:
return buildReleaseMessageWithType(item, 'Warlock of the Magus World', vol, chp, frag=frag, postfix=postfix)
return False
|
# decorator.py
def deco(func):
print('ok')
return func
@deco
def foo():
print('foo')
foo()
# ---------等价于-----------
print("-"*10 , '等价于', '-'*10)
def deco1(func):
print('ok')
return func
def foo():
print('foo')
deco1(foo)
foo()
|
KABUM_PAGES = {
"https://www.kabum.com.br/hardware/placas-mae?page_number=1&page_size=100&facet_filters=eyJwcmljZSI6eyJtaW4iOjM2NS45OCwibWF4IjoxMTE3NS4yOX19&sort=price": "placa-mae",
"https://www.kabum.com.br/hardware/processadores?page_number=1&page_size=100&facet_filters=eyJwcmljZSI6eyJtaW4iOjM1MC42OSwibWF4IjoxMTk4OS45OX19&sort=price": "cpu",
"https://www.kabum.com.br/hardware/placa-de-video-vga?page_number=1&page_size=100&facet_filters=eyJwcmljZSI6eyJtaW4iOjI5OC4wOSwibWF4IjoyNzY2NC40NX19&sort=price": "gpu",
"https://www.kabum.com.br/hardware/placa-de-video-vga?page_number=2&page_size=100&facet_filters=eyJwcmljZSI6eyJtaW4iOjI5OC4wOSwibWF4IjoyNzY2NC40NX19&sort=price": "gpu",
"https://www.kabum.com.br/hardware/placa-de-video-vga?page_number=3&page_size=100&facet_filters=eyJwcmljZSI6eyJtaW4iOjI5OC4wOSwibWF4IjoyNzY2NC40NX19&sort=price": "gpu",
}
|
"""Misc utility functions"""
def is_netcdf_asset(asset, strict):
"""
Determine if an asset is netcdf4-python compatible. netcdf4-python currently supports
HDF5, NetCDF3, and NetCDF4. Determination is currently done through MIME if strict
mode is enabled. If strict mode is not enabled, determination is done through 'data'
role.
Parameters
----------
asset : pystac.Asset
an asset to check
Returns
-------
bool
True if netcdf4-python compatible; False otherwise
"""
if strict:
accepted_types = ['application/x-hdf5', 'application/x-netcdf', 'application/x-netcdf4']
return asset.media_type in accepted_types
return 'data' in asset.roles
def get_granule_url(item, granule_urls, strict=True):
"""
Processes an item to find a netcdf4-python compatible asset. If no asset is
found, a RuntimeException is thrown
Parameters
----------
item : pystac.Item
an item to process
granule_urls : list
list to append the asset's url to
"""
for asset in item.assets.values():
if is_netcdf_asset(asset, strict):
granule_urls.append(asset.href)
return
if not strict:
raise RuntimeError(f'A NetCDF4 asset was not found in this item: {item.id}')
get_granule_url(item, granule_urls, False) # Rerun in lax-mode
def get_bbox(item, current_bbox):
"""
Accumulate bboxes from items to generate a bbox which encompasses all items
Parameters
----------
item : pystac.Item
an item to process
current_bbox : list
the bbox to accumulate all items to
"""
if len(current_bbox) == 0:
if item.bbox is not None: # Spec allows for null geometry and bbox
current_bbox[:] = item.bbox
else:
# xmin
if item.bbox[0] < current_bbox[0]:
current_bbox[0] = item.bbox[0]
# ymin
if item.bbox[1] < current_bbox[1]:
current_bbox[1] = item.bbox[1]
# xmax
if item.bbox[2] > current_bbox[2]:
current_bbox[2] = item.bbox[2]
# ymax
if item.bbox[3] > current_bbox[3]:
current_bbox[3] = item.bbox[3]
def get_datetime(item, datetimes):
"""
Accumulate datetimes from items to generate a datetime pair that
encompasses all items
Parameters
----------
item : pystac.Item
an item to process
datetimes : list
datetime pair to accumulate to; first element is start_datetime,
second is end_datetime
"""
if item.datetime is None:
item_start_dt = item.common_metadata.start_datetime
item_end_dt = item.common_metadata.end_datetime
else:
item_start_dt = item.datetime
item_end_dt = item.datetime
if item_start_dt < datetimes[0]:
datetimes[0] = item_start_dt
if item_end_dt > datetimes[1]:
datetimes[1] = item_end_dt
|
#!/usr/bin/env python
'''
you can use this command to run specific Python code:
..code-block:: python
py.test example1.py
'''
def f(x):
return x + 1
def test_f():
assert f(0) == 1
|
# -*- coding: utf-8 -*-
'''
Module takes a series of showimes and first filters them and then prompts the user to select times.
Currently the only filter is automatically applied and filters sceances during work hours.
Currently the program is not intelligent enough to optimize times or ensure that sceances do not overlap.
'''
DATE_INDEX = 1
ERROR_MESSAGE = {
"yes_no": "response must be one of the following: 'yes', 'y', 'no', 'n'",
"showtime_select": "response must be an integer in the range 1-{max_show}"
}
COLOR = {
'purple': '\033[95m',
'cyan': '\033[96m',
'darkcyan': '\033[36m',
'blue': '\033[94m',
'green': '\033[92m',
'yellow': '\033[93m',
'red': '\033[91m',
'bold': '\033[1m',
'underline': '\033[4m',
'end': '\033[0m',
}
def bold(word):
return COLOR['bold']+word+COLOR['end']
def underline(word):
return COLOR['underline']+word+COLOR['end']
def not_during_work(showing, workdays, workhours):
'''takes a datetime and verifies whether it is during work hours or not (default work hours between 8am and 7pm).'''
date = showing[DATE_INDEX]
if date.weekday() not in workdays:
return True
work_start, work_end = workhours
now = (date.hour, date.minute)
return now <= work_start or now >= work_end
def filter_showings(showings, workdays, workhours):
'''for each movie in your watchlist, filters out showtimes during work hours (currently this is the only filter).'''
filtered_showings_dict = {}
for movie, showtimes in showings.items():
filtered_showings = list(filter(lambda x: not_during_work(x, workdays, workhours), showtimes))
if filtered_showings:
filtered_showings_dict[movie] = filtered_showings
return filtered_showings_dict
def get_input(question, response_format, error_message):
''' loops until a response that is in response_format is met'''
while True:
res = input(question)
if res in response_format:
return res
print(error_message)
def select_showings(filtered_showings_dict):
'''for each movie left filtering, asks the user if they want to watch it and provides showtimes to pick from'''
selected_showings = []
for movie, showings in filtered_showings_dict.items():
res = get_input(
f"Watch '{underline(movie.name)}'?\n{bold('Director')}: {movie.director}\n{bold('Description')}: {movie.description}\n[y/n]: ",
{'y', 'yes', 'no', 'n'},
ERROR_MESSAGE['yes_no']
)
if(res in {'n', 'no'}):
print()
continue
for i, showing in enumerate(showings, start=1):
print(f"[{i}]: {showing[1].strftime('%b %d %Y %H:%M')} at {showing[0].name}")
res = get_input(
f"select your showing [1-{len(showings)} or n to cancel]: ",
set(map(str, range(1, len(showings)+1)))|{'n', 'no'},
ERROR_MESSAGE['showtime_select'].format(max_show=str(len(showings)))
)
print()
if res == 'n':
continue
selected_showings.append([movie]+list(showings[int(res)-1]))
return selected_showings
def filter_select_showings(showings, workdays, workhours):
'''main function that calls first filter and then select functions'''
showings = filter_showings(showings, workdays, workhours)
selected_showings_dict = select_showings(showings)
return selected_showings_dict
|
"""Whole-slide image file reader for TensorFlow.
The histomics_stream.ds.init module supports transformations at the beginning of a
tensorflow.data.Dataset workflow. This module defines objects that can be supplied to the
tensorflow.data.Dataset.from_tensor_slices() method.
"""
class Header:
"""A class used to initialize a tensorflow.data.Dataset.
An instance of class histomics_stream.ds.init.Header can be cast to dict and supplied to
tensorflow.data.Dataset.from_tensor_slices to create an instance of a tensorflow dataset object.
The primary functionality of this class over an ordinary dictionary is that (1) it requires all the
named keys, (2) it ensures that each value is a list or tuple, and (3) it expands via repetition any
length-one lists to be the same length as the number of supplied filenames.
Each parameter includes one value per slide to be analyzed. However, if a parameter is a list (or
tuple) of length 1 then that one value is used for every slide.
Parameters
----------
slides : str
A list of names of the slides to be processed
filenames : str
A list of file names that contain the slides data
cases : str
A list of names of cases, where multiple slides could belong to each case
magnifications : str
A list of the desired manification levels that the slides should be analyzed at
read_modes : str
A list of keywords. Currently only "tiled" is supported.
mask_filenames : str
A list of masks for the slides. Each slide's mask will be used to select which tiles of the
slide to process. If the mask does not have one pixel per tile then it will be upsampled or
downsampled as necessary. An empty string indicates that no mask file is being supplied and
that all tiles should be retained.
Notes
-----
Because it has the keys and __getitem__ methods, this class can be cast to a Python dict.
"""
def __init__(self, slides, filenames, cases, magnifications, read_modes, mask_filenames):
self.dictionary = {
"slide": slides,
"filename": filenames,
"case": cases,
"magnification": magnifications,
"read_mode": read_modes,
"mask_filename": mask_filenames,
}
# Convert an entry to a list if it is not already a list or tuple
for key in self.dictionary.keys():
if not isinstance(self.dictionary[key], (list, tuple)):
self.dictionary[key] = [
self.dictionary[key],
]
# Make all singleton values have the same length as `filenames`
if len(filenames) != 1:
for key in self.dictionary.keys():
if key != "filename" and len(self.dictionary[key]) == 1:
self.dictionary[key] = self.dictionary[key] * len(filenames)
def keys(self):
"""The method that returns the keys of the key-value pairs stored by histomics_stream.ds.init.Header."""
return self.dictionary.keys()
def __getitem__(self, key):
"""The method that returns the value corresponding to a key by histomics_stream.ds.init.Header."""
return self.dictionary[key]
|
with open("lfw-gender-0.80.txt") as lfwgender, \
open("manual-gender.txt") as manual, \
open("lfw-gender-final.txt", "w") as final:
nextline = lfwgender.readline()
while nextline:
firstname, gender = nextline.split()
if gender != "?":
final.write(nextline)
else:
manualline = manual.readline()
first, gen = manualline.split()
if firstname != first:
print("Error: %s, %s don't match up" % firstname, first)
else:
final.write(manualline)
nextline = lfwgender.readline()
|
class Person:
def __init__(self, name, age):
self.name = name
self.age = age
def __repr__(self):
return f'Hello, my name is {self.name} and im {self.age}'
class Student(Person):
def __init__(self, name, age, grade):
super().__init__(name, age)
# super(Student, self).__init__(name, age)
# Person.__init__(self, name, age)
self.grade = grade
def __repr__(self):
return f'{super().__repr__()} Im a {self.__class__.__name__} and my grade is {self.grade}'
class Worker(Person):
def __init__(self, name, age, salary):
super().__init__(name, age)
self.salary = salary
def __repr__(self):
return f'{super().__repr__()} Im a {self.__class__.__name__} and my salary is {self.salary}'
person = Person('Bebe Ivan', 12)
student = Student('Bebe Ivan', 12, 6)
worker = Worker('Tati Ivan', 24, 600)
print(person)
print(student)
print(worker)
print(Worker.mro())
# MRO -> в какъв ред се изпълнява кода
|
N, M = [int(n) for n in input().split()]
L, R = (1, N)
for i in range(M):
l, r = [int(n) for n in input().split()]
L, R = (max(l, L), min(r, R))
print(max(0, R-L+1))
|
"""Fake Motor Speed Control for use in CI environments w/o real hardware"""
class MotorSpeedControl(object):
def __init__(self):
self._fail_safe = False
self.foundChip = True
pass
def Init(self):
pass
def SetCommsFailsafe(self, state):
self._fail_safe = state
def MotorsOff(self):
pass
def SetMotor1(self, speed):
pass
def SetMotor2(self, speed):
pass
|
n = int(input())
integer_list = map(int, input().split())
integer_list = tuple(integer_list)
print(hash(integer_list))
|
# This file is used to store password secrets which are not to be committed
# to git. The key has to match one of the sources in the ocd_backend/sources
# directory. If there a secret is required but not supplied in this file, the
# program will fail. A secret like gegevensmagazijn will also match postfixes
# like gegevensmagazijn-moties. The most specific secret is used, so secrets
# like gegevensmagazijn-a will match gegevensmagazijn-a-moties.
#
# Example:
# SECRETS = {
# 'gegevensmagazijn': (
# "some_user",
# "some_password",
# ),
# 'gegevensmagazijn-a': (
# "some_user",
# "some_password",
# ),
# }
SECRETS = {
'<ID>': (
"<USERNAME>",
"<PASSWORD>",
),
}
|
"""
257. Longest String Chain
https://www.lintcode.com/problem/longest-string-chain/description?_from=contest&&fromId=93
dp[i] longest string chian ending in i
dp[i] = max(dp[i], dp[j] + 1) if distance between words[j] and word[i] is 1 for j in range [0, i)
max(dp[i])
dp[i] = 1
longest increasing subsequence变形
"""
class Solution:
"""
@param words: the list of word.
@return: the length of the longest string chain.
"""
def longestStrChain(self, words):
if not words:
return 0
words = sorted(words, key=lambda x: len(x))
n = len(words)
dp = [1] * n
for i in range(n):
for j in range(i - 1, -1 ,-1):
if len(words[j]) == len(words[i]):
continue
if len(words[i]) - len(words[j]) > 1:
break
if self.is_valid(words[j], words[i]):
dp[i] = max(dp[i], dp[j] + 1)
return max(dp)
def is_valid(self, word1, word2):
i = 0
j = 0
diffed_by_one = False
while i < len(word1) and j < len(word2):
if word1[i] == word2[j]:
i += 1
j += 1
else:
if diffed_by_one:
return False
if j + 1 < len(word2) and word1[i] == word2[j + 1]:
j += 1
else:
return False
diffed_by_one = True
return True
s = Solution()
words=["uiykgmcc","jrgbss","mhkqodcpy","lkj","bwqktun","s","nrctyzifwytjblwy","wrp","scqlcwmxw","irqvnxdcxoejuu","gmlckvofwyifmrw","wbzbyrcppaljigvo","lk","kfeouqyyrer","efzzpvi","ubkcitcmwxk","txihn","mdwdmbtx","vuzvcoaif","jwmboqvhpqodsj","wscfvrfl","pzye","waxyoxftvrgqmkg","wwdidopozinxxn","dclpg","xjsvlxktxs","ajj","pvsdastm","tatjxhygidhn","feafycxdxagn","irqvnxxoeuu","kwjo","tztoovsyfwz","prllrw","sclmx","bbmjnwaxcwaml","gl","wiax","uzvcoaif","ztovyfwz","qxy","zuexoxyp","qxyyrl","pvsdasvtm","femafycxdxaagn","rspvccjcm","wvyiax","vst","efzi","fjmdcc","icsinrbpql","ctybiizlcr","ntyzfwytjblw","tatjxhygidhpn","e","kykizdandafusu","pnepuwcsxl","kfeuqyyrer","afplzhbqguu","hvajtj","prll","ildzdimea","zueoxp","ezi","lqr","jkaagljikwamaqvf","mlzwhkxsn","rspvccbcjjtcm","wscfvrl","m","msygukwlkrqboc","pifojogoveub","bkcmwx","jercgybhss","wrpi","aicsinkgrbpqli","aplzbuu","sclcmxw","atpepgsz","govrcuuglaer","bdxjpsvlxkytxs","uikgm","bm","wvyhiqax","znvaasgfvqi","hatpepgsz","hrzebpa","bnfz","lybtqrfzw","taxhygihn","bjnfzk","mhqp","ide","znvcaasgfvqi","ftv","afplzhbqsguuu","thn","pdbccbe","mxevopfoimgjww","fjmdrcce","rspvccjjcm","jv","motnfwohwule","xjsvlxtxs","bqeb","eug","jftavwgl","rzebpa","lybtqrfazw","zuexoxp","jercgybhsys","hajtj","bkcitcmwxk","mbpvxsdastvtm","mowlznwhkxsn","dvenn","rsacxe","tatjxhygihn","cotybiizlcr","bbmnaxaml","pkwrpsi","nqpdbccbkxens","mbpbovxsdastvtm","mj","pxpsvikwekuq","qeug","dmelddga","aicsinkgrbpxqli","bdxjpsvlxktytxs","pkrllrxw","jkgljikwmaqf","iddie","ctybiizcr","nyzfwytjblw","yvuhmiuehspi","keuqre","wzbypaigvo","sck","uzcoaf","dlpg","ubkcpitlscmwxk","molzwhkxsn","pepuwcsxl","laplm","dclpgc","mahkxqodcpy","sclcmx","hvrzebpaz","bgovrcuuglaer","clazpulmw","yvuyhmiuehspiq","wzbycpaljigvo","sceqalciwmxw","hjytflmvsgv","u","hjyvxytfflhmvsgv","jkgjikwmaqf","fefycxdxagn","ftvw","ofncgxrkqvcr","spvcjc","pvsdastvtm","kykzdandaus","wbzbycppaljigvo","haytpepgsz","jmowlznwhkxsn","aplzhbguu","zvyz","nfvqi","jfvtavwsgl","xejnllhfulns","zhhvbiqiw","jkgljikwmaqvf","tyizc","irqvnxcxoejuu","clvazzpulmw","oncgxrqvcr","qlupvpdkhrm","mtnfwohwule","wwdidopzozinxxn","auiykgmcc","wscfvrfyl","pfksmrullrxw","jwmoqvhpqods","ftavwg","iddiea","kcmw","ykkwjwo","pe","aplzbguu","eu","bbmnaxal","ntyswtnlab","zhhhvbhbiqiw","jwmoqvpqods","kykzdndaus","bbmjnaxcwaml","zunvcaasgfvqi","icsingrbpql","sceqalciwmsxyw","yvuhmiuehsp","bxjsvlxktxs","waxoxftvrgqmkg","cogxxpaknks","scllvazzpulmw","tatjxhygeidhpn","ftvwg","tyz","nafvqi","oby","pgzpkhqog","irqvnxxoejuu","oxwpkxlakcp","bnf","oxwnpkxlakcp","bwqktu","ufybbaozoqk","ntydswtnlab","zvyfz","znaafvqi","npdbccbke","mhkqocpy","kuq","bjnfz","taxhyihn","kwrpsi","qifepmcatbdjlf","lzwhks","kfeuqre","mxevopfoimgww","spvcjcm","oncgxrkqvcr","jftavwsgl","soifcbya","jpzyeg","jwmboqvhpqods","lapulm","jrgbhss","xejfnllhfulns","zhhhvbbiqiw","km","kuqre","scxlzlvazzpulmw","ztvyfwz","wbzbycpaljigvo","rzbpa","vsastm","uybaooqk","dn","ykwjwo","ufybmvbaozoqk","nknm","mbpvsdastvtm","dpgzpxykhqog","wzbypajigvo","bnjnfzk","eollbigtftpdrd","zhbiqiw","yvuhiuehp","zhhhvbhbiqiwg","pfksrullrxw","pzyeg","aplzhbqguu","z","hvrzecbpazw","clvazpulmw","tajxhygihn","pgzpxykhqog","fefyxdxagn","wimomuvhn","lqrzw","xejnlhfulns","jhrc","xsxxs","slmx","jrgss","uikgmc","ncgqvcr","womuhn","aryouvtnmme","uzco","zhhhvbiqiw","hjytflhmvsgv","znvaasfvqi","kuqr","ojrpp","ztoovyfwz","zvz","pxpsviweuq","ufybaooqk","xy","jfvvtavwksvgl","raiachv","bmnaxl","rspvccjjtcm","pgzpxkhqog","xhbtfnqebaj","sceqalciwmsxw","jssctk","uzvcoaf","fefydxagn","jhrvc","mbj","raiahv","nrtyzifwytjblwy","mhqcp","jkgjkwmaqf","wscfvrfylhi","lqrz","ahabucermswyrvl","wxoxftvrgqmkg","ku","uyaoq","mhqocp","ykwjo","vstm","ofncgxrkqvcwr","dqvh","taxyihn","idie","bwqtu","tztoovyfwz","rspvcccjjtcm","uojrpp","wmomuhn","cotycbiizlxcr","nrtyzfwytjblw","ocbya","sceqlciwmxw","ajtj","rspvccbcjjthcm","kfeuqyyre","dmelddg","txyihn","ubkcitlscmwxk","ntyswtnla","bdxjpstvlxktytxs","odqdvbh","pxpsvikeewekuq","mdwdmbdtux","vs","bma","wzbypigvo","qxyy","vsstm","hbtnqeba","hrzebpaz","xhbtfnjsqebbaj","ahaucermswyrv","ddmbtx","zhhbiqiw","pxpsvikewekuq","odqdvgbh","bxjpsvlxktxs","jsck","fjmdc","mdwdmbdtx","jqxyyrl","pxpsvikweuq","ctybizcr","dqvbh","lpl","lqrfzw","ufybaozoqk","znvaafvqi","yvuhmiuehp","hvrzebpazw","pfksrllrxw","alzuu","xjsvxtxs","afplzhbqguuu","icsingrbpqli","hjxytflhmvsgv","femafycxdxagn","uyaoqk","gmlckvofwyifrw","cinrbpql","jrcgbhss","oxwpkxlkcp","jkagljikwamaqvf","eollbigtftpdrdy","rspvcjcm","socbya","clapulm","qeb","kwrpi","efzpi","hbtfnqebaj","kykizdnandafusu","sclvazzpulmw","efzzpvvi","jfvvtavwsvgl","mhqocpy","v","mbpbvxsdastvtm","irqvnxouu","hvaajtj","ofnlcgxrkqvcwr","hbtqeba","hbtqeb","jwmqpds","ntrnlhujdslco","zv","npdbccbken","mhp","ddb","prllw","mddmbtx","clazpulm","cogxxpaknkse","bkitcmwxk","oxwpklkcp","tyiz","jwmqvpqods","waxyoxftvrgqmkgb","afplzhbbqsgujuu","bwtu","jercgbhss","rsacx","mahkqodcpy","cotycbiizlcr","ahabucermswyrv","lupvpkhr","dvnn","b","atpepsz","ncgxqvcr","qe","ubkcitlcmwxk","lyqrfzw","wimomuhn","bbmnaxl","motnfwohrwule","yvuyhmiuehspi","jfvvtavwsgl","rac","fefdxagn","bwqkctun","uotjrpp","ddbtx","afplzhbbqsguuu","xss","xsxs","wvyiqax","kykizdandaus","npdbccbkens","r","oxwnpkxjlakcp","tzmteoovsyfwz","kykizdnandafuspu","ahabulcermswyrvl","xjsxxs","qxyyr","ck","xhbtfnqebbaj","nqpdbccbkens","mpvsdastvtm","zuexqoxyp","gmlkvofwyifrw","kmw","txhn","kykizdandausu","molznwhkxsn","lupvpdkhr","jwmqvpds","bktcmwx","wyiax","hzvaajtj","ddbx","pifojogveub","naafvqi","motnfwjohrwule","odqvbh","aicsingrbpqli","jopzyeg","lybtqrfazrw","pijogveub","xzejfnllhfulns","scxllvazzpulmw","irqyvnxdcxfoejuu","cogxpaknks","pdkwrpsi","wzbycpajigvo","xjsxtxs","irqvnxdcxfoejuu","xhbtfnjqebbaj","uybaoqk","oncgxqvcr","aj","pepuwsxl","lytqrfzw","nkm","jrgs","pkrllrw","wscfvrfyli","bbmjnaxcaml","jftavwg","vuzvcozaif","pifjogveub","cmogxxpaknkse","cinrbql","scqlciwmxw","ztvyfz","mxyevopfoimgjpww","soicbya","lupvpdkhrm","ahaucermsyrv","ufybmvbaouzoqk","bdxjpsvlxktxs","hjxytfflhmvsgv","hjvxytfflhmvsgv","nqpdbccbzkxens","wr","kykzdndus","iddimea","fjmdrcc","efzzpi","vsdastm","btqeb","pfkrllrxw","ocby","irqvnxxouu","ildzpdimea","lzwhkxsn","ilddimea","ufybvbaozoqk","mxyevopfoimgjww","jhr","kcmwx","dvn","uzcof","glw","hbtnqebaj","riahv","w","qeugv","kfeuqyre","ilrdzpdimea","lplm","icinrbpql","scqlcmxw","bbmjnaxaml","e","rsac","bf","jwmqvpqds","tzteoovsyfwz","rc","lzwhkxs","jkgljikwamaqvf","tybizc","aplzuu","nrtyzifwytjblw","pze","bktcmwxk","uiykgmc","jsctk","npdbccbe","tybizcr"]
print(s.longestStrChain(words))
|
VERBOSITY = 1
SAMPLE_RATE = 16000
DURATION_MS = 1000
FEATURES_COUNT = 64
DESIRED_SAMPLES = int(SAMPLE_RATE * DURATION_MS / 1000)
DROPOUT_RATE = 0.5
TESING_PERCENTAGE = 15
VALIDATION_PERCENTAGE = 15
VALIDATION_FREQUENCY = 10
SAVE_PERIOD = 10
EPOCHS = 100
BATCH_SIZE = 512
DATASET_URL = 'http://download.tensorflow.org/data/speech_commands_v0.02.tar.gz'
SOUNDS_DIR = './trainset/sounds/'
MFCCS_DIR = './trainset/MFCCS/'
MODELS_DIR = './models/'
LABELS_PATH = MODELS_DIR + 'labels.txt'
WANTED_WORDS = ['left', 'right', 'up', 'down']
|
# https://www.hackerrank.com/challenges/common-child/problem
def longestCommonSubsequence(s1, s2):
remove_element = set(s1) - set(s2)
new_s1 = ""
for element in s1:
if element not in remove_element:
new_s1 += element
s1 = new_s1
memory = [[0] * (len(s2) + 1) for _ in range((len(s1) + 1))]
for index1, value1 in enumerate(s1, 1):
for index2, value2 in enumerate(s2, 1):
if value1 == value2:
memory[index1][index2] = memory[index1 - 1][index2 - 1] + 1
else:
memory[index1][index2] = max(
memory[index1 - 1][index2], memory[index1][index2 - 1]
)
return memory[-1][-1]
print(longestCommonSubsequence("HARRY", "SALLY"))
|
# License: MIT
"""
https://github.com/automl/fanova
"""
__version__ = "2.0.20.dev"
|
class Defines(object):
PROTOCOL_ID = b'BitTorrent protocol'
RM_CLIENT_ID = b'RM'
RM_CLIENT_VERSION = b'0100'
|
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"# lists are used to store a list of things; similar to arrays in java \n",
"# note the use of square brackets and\n",
"\n",
"a = [3, 10, -1]"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[3, 10, -1]\n"
]
}
],
"source": [
"print(a)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"# .append function adds your number to the list\n",
"\n",
"a.append(2)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[3, 10, -1, 2, 2]\n"
]
}
],
"source": [
"print(a)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[3, 10, -1, 2, 2, 'yayay']\n"
]
}
],
"source": [
"# list can contain numbers, text or other lists\n",
"\n",
"a.append(\"yayay\")\n",
"print(a)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[3, 10, -1, 2, 2, 'yayay', [6, 7]]\n"
]
}
],
"source": [
"a.append([6, 7])\n",
"print(a)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[6, 7]"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a.pop()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[3, 10, -1, 2, 2, 'yayay']\n"
]
}
],
"source": [
"print(a)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'yayay'"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a.pop()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[3, 10, -1, 2, 2]\n"
]
}
],
"source": [
"print(a)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"3"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a[0]"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a[3]"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"-1"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a[2]\n"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"-1\n"
]
}
],
"source": [
"print(a[2])"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"a[0] = 4.55"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[4.55, 10, -1, 2, 2]\n"
]
}
],
"source": [
"print(a)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"b = [\"banana\", \"apple\", \"microsoft\"]"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['banana', 'apple', 'microsoft']\n"
]
}
],
"source": [
"print(b)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'banana'"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"b[0]"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'temp' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-22-bb6d55739a6c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mb\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtemp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtemp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'temp' is not defined"
]
}
],
"source": [
"b[0] = temp\n",
"b[0] = b[2]\n",
"b[2] = temp"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'microsoft'"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"b[0]\n",
"b[2]"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'banana'"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"b[0]"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'microsoft'"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"b[0]\n",
"b[2]"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"banana\n",
"microsoft\n"
]
}
],
"source": [
"print(b[0])\n",
"print(b[2])"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'temp' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-27-af3436a9262b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mb\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtemp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mNameError\u001b[0m: name 'temp' is not defined"
]
}
],
"source": [
"b[0] = temp"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [],
"source": [
"temp = b[0]"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [],
"source": [
"b[0] = b[2]\n",
"b[2] = temp"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"microsoft\n",
"banana\n"
]
}
],
"source": [
"print(b[0])\n",
"print(b[2])"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['microsoft', 'apple', 'banana']\n"
]
}
],
"source": [
"print(b)"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['banana', 'apple', 'microsoft']\n"
]
}
],
"source": [
"b[0], b[2] = b[2], b[0]\n",
"print(b)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
|
def find_opposites(seq):
check=set()
res=set()
for i in seq:
if -i in check:
res.add(abs(i))
check.add(i)
return sorted(res)
|
num = int(input('Digite um número:'))
u = num // 1 % 10
d = num // 10 % 10
c = num // 100 % 10
m = num // 1000 % 10
print('Analisando o número {}'.format(num))
print('A quantidade de unidade é {} \n a de dezena é {}'.format(u,d))
print('de centena é {} \n a de milhar é {}'.format(c,m))
|
class Player(object):
"""Klasa obsługująca poszczególnych graczy."""
def __init__(self, name, x, y):
"""Domyślne ustawienia klasy
Args:
name (string): Nazwa gracza
x (int): Pozycja x gracza
y (int): Pozycja y gracza
"""
self.name = name
self.pos_x = x
self.pos_y = y
self.bombs = 1
self.isDead = False
def move(self, x, y):
"""Przesunięcie gracza na daną pozycje
Args:
x (int): Przesuń gracza na pozycje x
y (int): Przesuń gracza na pozycje y
"""
self.pos_x = x
self.pos_y = y
def get_pos_x(self):
"""Zwraca aktualną pozycje gracza
Returns:
x (int): Zwraca x pozycje gracza
"""
return self.pos_x
def get_pos_y(self):
"""Zwraca aktualną pozycje gracza
Returns:
y (int): Zwraca y pozycje gracza
"""
return self.pos_y
def place_bomb(self):
"""Stawianie bomby na pozycji gracza
Returns:
True - zwraca pozycje bomby jeżeli postawiono
False - zwraca pozycje 0,0 czyli błędną
"""
if self.bombs >= 1:
self.bombs -= 1
return self.pos_x, self.pos_y
else:
return 0, 0
def give_bomb(self):
"""Daje graczowi bombę"""
self.bombs += 1
|
"""
(Effective) core (aka one-electron) Hamiltonian
"""
core_wavefunction = {}
# core hamiltonian
core_wavefunction["h_core_a"] = {
"type": "array",
"description": "Alpha-spin core (one-electron) Hamiltonian in the AO basis.",
"items": {"type": "number"},
"shape": {"nao", "nao"}
}
core_wavefunction["h_core_b"] = {
"type": "array",
"description": "Beta-spin core (one-electron) Hamiltonian in the AO basis.",
"items": {"type": "number"},
"shape": {"nao", "nao"}
}
# effective core hamiltonian
core_wavefunction["h_effective_a"] = {
"type": "array",
"description": "Alpha-spin effective core (one-electron) Hamiltonian in the AO basis.",
"items": {"type": "number"},
"shape": {"nao", "nao"}
}
core_wavefunction["h_effective_b"] = {
"type": "array",
"description": "Beta-spin effective core (one-electron) Hamiltonian in the AO basis.",
"items": {"type": "number"},
"shape": {"nao", "nao"}
}
|
# Copyright (c) 2015 The Chromium Authors. All rights reserved.
# Use of this source code is governed by a BSD-style license that can be
# found in the LICENSE file.
"""Generates new baselines for Blink layout tests that need rebaselining.
Intended to be called periodically. Syncs to the Blink repo and runs
'webkit-patch auto-rebaseline', which processes entries in
LayoutTests/TestExpectations that are marked with 'NeedsRebaseline'.
Slaves running this recipe will require SVN access credentials for submitting
patches with the new baselines.
"""
DEPS = [
'depot_tools/bot_update',
'depot_tools/gclient',
'depot_tools/git',
'recipe_engine/path',
'recipe_engine/properties',
'recipe_engine/python',
]
def RunSteps(api):
RIETVELD_REFRESH_TOKEN = '/creds/refresh_tokens/blink_rebaseline_bot_rietveld'
api.gclient.set_config('chromium')
api.bot_update.ensure_checkout(force=True)
cwd = api.path['checkout'].join('third_party', 'WebKit')
api.python('webkit-patch auto-rebaseline',
cwd.join('Tools', 'Scripts', 'webkit-patch'),
['auto-rebaseline', '--verbose', '--auth-refresh-token-json',
RIETVELD_REFRESH_TOKEN],
cwd=cwd)
def GenTests(api):
yield (api.test('rebaseline_o_matic') +
api.properties(mastername='chromium.infra.cron',
buildername='rebaseline-o-matic',
slavename='fake-slave'))
|
_base_ = './kd_resnet18_resnet50_cifar100_equal.py'
# model settings
model = dict(
adaptive=True,
adaptation = dict(out_channels=[256, 512, 1024, 2048], in_channels=[64, 128, 256, 512]),
distill_losses=[
dict(type='NST', mode='feature', loss_weight=1.0),
dict(type='Logits', mode='logits', loss_weight=1.0)
])
|
"""
对文件描述的注释
"""
class Foo(object):
def __init__(self, a1, a2):
self.a1 = a1
self.a2 = a2
# def __call__(self, *args, **kwargs):
# print(11111, args, kwargs)
# return 123
# def __getitem__(self, item):
# print(item)
# return 8
#
# def __setitem__(self, key, value):
# print(key, value, 111111111)
#
# def __delitem__(self, key):
# print(key)
#
# def __add__(self, other):
# return self.a1 + other.a2
#
def __enter__(self):
print('1111')
return 999
#
def __exit__(self, exc_type, exc_val, exc_tb):
print('22222')
# 1. 类名() 自动执行 __init__
# obj = Foo(1, 2)
# 2. 对象() 自动执行 __call__
# obj = Foo()
# ret = obj(6, 4, 2, k1=456)
# print(ret)
# 3. 对象['xx'] 自动执行 __getitem__
# obj = Foo()
# ret = obj['yu']
# print(ret)
# 4. 对象['xx'] = 11 自动执行 __setitem__
# obj = Foo()
# obj['hhh'] = 123
# 5. del 对象[xx] 自动执行 __delitem__
# obj = Foo()
# del obj['uuu']
# 6. 对象+对象 自动执行 __add__
# obj1 = Foo(1, 2)
# obj2 = Foo(88, 99)
# ret = obj2 + obj1
# print(ret)
# 7. with 对象 自动执行 __enter__ / __exit__
# obj = Foo(1, 2)
# with obj as f:
# print(f)
# print('内部代码')
# 8. 真正的构造方法
class Foo(object):
def __init__(self, a1, a2): # 初始化方法
"""
为空对象进行数据初始化
:param a1:
:param a2:
"""
self.a1 = a1
self.a2 = a2
def __new__(cls, *args, **kwargs): # 构造方法
"""
创建一个空对象
:param args:
:param kwargs:
:return:
"""
return object.__new__(cls) # Python内部创建一个当前类的对象(初创时内部是空的.).
obj1 = Foo(1, 2)
print(obj1)
print(obj1.a1)
obj2 = Foo(11, 12)
print(obj2)
|
# configs
COMBINE_FACE_WEIGHT = 10
COMBINE_FEATURE_WEIGHT = 10
FEATURE_DETECT_MAX_CORNERS = 50
FEATURE_DETECT_QUALITY_LEVEL = 0.1
FEATURE_DETECT_MIN_DISTANCE = 10
FACE_DETECT_REJECT_LEVELS = 1.3
FACE_DETECT_LEVEL_WEIGHTS = 5
HAARCASCADE_PROFILEFACE = 'haarcascade_profileface.xml'
HAARCASCADE_FRONTALFACE = 'haarcascade_frontalface_default.xml'
VIS = False
HORIZONTAL_THRESHOLD = 1.1
PORTRAIT_THRESHOLD = 0.9
|
n, m = [int(i) for i in input().split()]
a = [["*" if (i + j) % 2 != 0 else "." for j in range(m)]
for i in range(n)]
for row in a:
print(' '.join(row))
|
# Copyright (c) 2011 Mitch Garnaat http://garnaat.org/
# Copyright (c) 2011 Amazon.com, Inc. or its affiliates. All Rights Reserved
#
# 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, dis-
# tribute, sublicense, and/or sell copies of the Software, and to permit
# persons to whom the Software is furnished to do so, subject to the fol-
# lowing 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 MERCHANTABIL-
# ITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT
# SHALL THE AUTHOR 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.
#
class Schema(object):
"""
Represents a DynamoDB schema.
:ivar hash_key_name: The name of the hash key of the schema.
:ivar hash_key_type: The DynamoDB type specification for the
hash key of the schema.
:ivar range_key_name: The name of the range key of the schema
or None if no range key is defined.
:ivar range_key_type: The DynamoDB type specification for the
range key of the schema or None if no range key is defined.
:ivar dict: The underlying Python dictionary that needs to be
passed to Layer1 methods.
"""
def __init__(self, schema_dict):
self._dict = schema_dict
def __repr__(self):
if self.range_key_name:
s = 'Schema(%s:%s)' % (self.hash_key_name, self.range_key_name)
else:
s = 'Schema(%s)' % self.hash_key_name
return s
@property
def dict(self):
return self._dict
@property
def hash_key_name(self):
return self._dict['HashKeyElement']['AttributeName']
@property
def hash_key_type(self):
return self._dict['HashKeyElement']['AttributeType']
@property
def range_key_name(self):
name = None
if 'RangeKeyElement' in self._dict:
name = self._dict['RangeKeyElement']['AttributeName']
return name
@property
def range_key_type(self):
type = None
if 'RangeKeyElement' in self._dict:
type = self._dict['RangeKeyElement']['AttributeType']
return type
|
###############################################################################
__author__ = "ICHEC"
__copyright__ = 'Copyright 2020, QNLP'
__credits__ = ['{credit_list}']
__license__ = '{license}'
__version__ = '{mayor}.{minor}.{rel}'
__maintainer__ = '{maintainer}'
__email__ = 'lee.oriordan@ichec.ie'
__status__ = 'alpha'
###############################################################################
"""
Base class for pregroup types
"""
class PregroupType():
def __init__(self, label):
self.label = label
def __str__(self):
return self.label
###############################################################################
"""
Simple class for pregroup types with adjoint
"""
class AdjointType(PregroupType):
def __init__(self, label, isAdj, adjOrder=0):
super().__init__(label)
self.isAdj = isAdj
if isAdj:
assert (adjOrder!=0)
self.adjOrder = adjOrder
def __str__(self):
adj_order_str = ""
if self.isAdj:
adj_order_str = "^"
for ii in range(abs(self.adjOrder)):
adj_order_str += 'l' if self.adjOrder < 0 else 'r'
return super().__str__() + ": ADJ=" + str(self.isAdj) + " ORD=" + adj_order_str
###############################################################################
"""
Meaning data type
"""
class Meaning(AdjointType):
def __init__(self, isAdj, adjOrder=0):
super().__init__('s', isAdj, adjOrder)
###############################################################################
"""
Noun data type with matched nltk type defined in matchables()
"""
class Noun(AdjointType):
def __init__(self, isAdj, adjOrder=0):
super().__init__('n', isAdj, adjOrder)
###############################################################################
"""
Verb data type (n^r \otimes s \otimes n^l) with matched nltk type defined in matchables()
"""
class Verb(AdjointType):
def __init__(self, isAdj, adjOrder=0, left_adj : Noun = None,
right_adj : Noun = None,
meaning : Meaning = None):
super().__init__('v', isAdj, adjOrder)
self.left_adj = left_adj
self.right_adj = right_adj
self.meaning = meaning
###############################################################################
"""
Simple sentence data type
"""
class Sentence():
def __init__(self, structure):
self.structure = structure
def __str__(self):
return 's:' + ','.join([s.label for s in self.structure])
###############################################################################
|
#! python3
print("Task 6.1")
persons = {
'first_name': 'john',
'last_name' : 'dog',
'age' : '40',
'city' : 'chicago',
}
print('All information about person:')
print(persons['first_name'])
print(persons['last_name'])
print(persons['age'])
print(persons['city'])
print("Task 6.2")
persons = {
'john': '5',
'caty' : '6',
'bob' : '40',
'lance' : '2',
'wendy' : '99',
}
for k, v in persons.items():
print(f"{k.title()} favorite number is: {v}")
print('Other solutions:')
persons = {
'john': '5',
'caty' : '6',
'bob' : '40',
'lance' : '2',
'wendy' : '99',
}
num = persons['john']
print(f"John's favorite number is {num}.")
num = persons['caty']
print(f"Caty's favorite number is {num}.")
num = persons['bob']
print(f"Bob's favorite number is {num}.")
num = persons['lance']
print(f"Lance's favorite number is {num}.")
num = persons['wendy']
print(f"Wendy's favorite number is {num}.")
print("Task 6.3")
glossary = {
'linux' : 'software',
'variable' : 'value that may vary',
'zen python' : 'rules of a good python programmer',
'function range()' : 'generating numbers',
'lists' : 'a collection of different values',
}
word = 'linux'
print(f"\n{word.title()}: {glossary[word]}")
word = 'variable'
print(f"\n{word.title()}: {glossary[word]}")
word = 'zen python'
print(f"\n{word.title()}: {glossary[word]}")
word = 'function range()'
print(f"\n{word.title()}: {glossary[word]}")
word = 'lists'
print(f"\n{word.title()}: {glossary[word]}")
|
def counting():
y = 1
while y<500:
yield y #This is a great way to iterate through one number at a time. It will go forever if needed.
y += 1
for y in counting():
print(y)
|
"""
pycraigslist.tests.test_unit
~~~~~~~~~~~~~~~~~~~~~~~~~~~~
A suite of modules to unit test the pycraigslist module.
"""
|
def update_weights(weights, error, l_rate, vector):
"""Takes in a given weight, error, learning rate, and vector element
Retuns updated weight
"""
return [weight + (elem * l_rate * error) for elem, weight in zip(vector, weights)]
if __name__ == '__main__':
print(update_weights((.1, .1, .2), -6, .1, (.5, .5, .5)))
|
class StringCalculator:
def __init__(self):
self.string = ''
self.list_of_numbers = []
self.delimiter = ','
def add(self, string_of_numbers):
self.validate_input(string_of_numbers)
self.process_delimiter()
self.generate_list_of_numbers()
return sum(self.list_of_numbers)
def generate_list_of_numbers(self):
negatives = []
for str_number in self.string.split(self.delimiter):
number = int(str_number)
if number < 0: negatives.append(str_number)
if number > 1000: number = 0
self.list_of_numbers.append(number)
if negatives: raise NegativeException("Negatives not allowed: " + ",".join(negatives))
def process_delimiter(self):
if self.string.startswith('//'):
has_bracket_delimiter = self.string.find('[')
if has_bracket_delimiter == -1:
self.delimiter = self.string[2]
else:
delimiters = self.string[3:self.string.find('\n')-1]
delimiters = delimiters.replace('[', self.delimiter)
delimiters = delimiters.replace(']','')
for item in delimiters.split(self.delimiter):
self.string = self.string.replace(item, self.delimiter)
self.string = self.string[self.string.find('\n')+1:]
self.string = self.string.replace('\n', self.delimiter)
def validate_input(self, string_of_numbers):
self.string = string_of_numbers
if self.string == "": self.string = "0"
class NegativeException(Exception):
pass
|
class Airplane:
def __init__(self, x=0, y=0, gravity=2, jump=50):
self.x = x
self.y = y
self.defaultX = x
self.defaultY = y
self.gravity = gravity
self.jump = jump
self.width = 60
self.height = 35
def init(self):
self.x = self.defaultX
self.y = self.defaultY
def down(self):
self.y += self.gravity
self.y = self.y if self.y < 740 else 740
return self.y
def up(self):
self.y -= self.jump
self.y = self.y if self.y > 0 else 0
return self.y
def pos(self):
return self.x, self.y
def isUpCrash(self, x1, x2, y):
if self.x + self.width < x1:
return False
if self.x > x2:
return False
if self.y + 10 > y:
return False
return True
def isDownCrash(self, x1, x2, y):
if self.x + self.width < x1:
return False
if self.x > x2:
return False
if self.y + self.height < y:
return False
return True
|
#!/usr/bin/env python3
def readint():
return int(input())
def readints():
return map(int, input().split())
def readline():
return str(input())
T = readint()
for case in range(T):
N = readint()
if N == 0:
result = "INSOMNIA"
else:
k = 0
s = set()
while len(s) < 10:
s.update(list(str((k+1)*N)))
k += 1
result = str(k*N)
print("Case #%d: %s" % (case+1, result))
|
# Given an array nums of n integers, are there elements a, b, c in nums such that a + b + c = 0? Find all unique triplets in the array which gives the sum of zero.
# Note:
# The solution set must not contain duplicate triplets.
# Example:
# Given array nums = [-1, 0, 1, 2, -1, -4],
# A solution set is:
# [
# [-1, 0, 1],
# [-1, -1, 2]
# ]
class Solution:
def threeSum(self, nums: List[int]) -> List[List[int]]:
nums.sort()
triplets = []
for i in range(len(nums)-2):
if i > 0 and nums[i] == nums[i-1]:
continue
leftIdx = i+1
rightIdx = len(nums)-1
while leftIdx < rightIdx:
currentSum = nums[i] + nums[leftIdx] + nums[rightIdx]
if currentSum < 0:
leftIdx += 1
elif currentSum > 0:
rightIdx -= 1
else:
triplets.append((nums[i], nums[leftIdx], nums[rightIdx]))
while leftIdx < rightIdx and nums[leftIdx] == nums[leftIdx + 1]:
leftIdx += 1
while leftIdx < rightIdx and nums[rightIdx] == nums[rightIdx - 1]:
rightIdx -= 1
leftIdx += 1
rightIdx -= 1
return triplets
|
# Each new term in the Fibonacci sequence is generated by adding the
# previous two terms. By starting with 1 and 2, the first 10 terms will
# be: # 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, ...
# By considering the terms in the Fibonacci sequence whose values do not
# exceed four million, find the sum of the even-valued terms.
# (Link: https://projecteuler.net/problem=2.)
def sum_even_fibonacci_terms(limit):
"""Returns the sum of even fibonacci numbers below a given limit.
"""
# Initializing the Fibonacci sequence:
second_to_last_term = 1
last_term = 1
sum = 0
# Appending numbers below the limit:
while last_term < limit:
new_term = last_term + second_to_last_term
second_to_last_term = last_term
last_term = new_term
# Summing even numbers:
if new_term % 2 == 0:
sum += new_term
return sum
if __name__ == "__main__":
print(sum_even_fibonacci_terms(4000000))
|
"""Module with different tools to facilitate data operations in ESTAMPES
This module provides different simple tools, by submodules to facilitate
common operations like conversions, transformations...
The tools are gathered by submodules based on their intended use:
`atom`
Atom-specific tools.
`comp`
Related to basic software/computing operations (ex: mem. units).
`math`
Simple math functions.
`vib`
Related to vibrations, for instance eigenvectors orientations.
See submodules for details.
"""
|
class Solution:
def fourSumCount(self, nums1: List[int], nums2: List[int], nums3: List[int], nums4: List[int]) -> int:
pass
#######################################################################################################################
"""
num1 => [1,2]
num2 => [-2,-1]
num3 => [-1,2]
num4 => [0,2]
1
"""
#######################################################################################################################
|
dias = int(input('Por quantos dias o carro ficou alugado? '))
km = float(input('Quantos quilometros o carro percorreu? '))
preco = (dias * 60) + (km * 0.15)
print ('O valor a ser pago eh de R${:.2f}!'.format(preco))
|
# fisrt we get the number of people from the user
people = input("enter number of guests \n")
# we use the try methord to prevent the program from crashing if the user puts a string
try:
people = int(people)
if(people > 0 and people <= 50):
price = 4000
elif(people >50 and people <= 100):
price = 10000
elif(people > 100 and people <= 200):
price = 15000
elif(people > 200):
price = 20000
#to out put dollar sign used " '${}'.format() " methord
print('${}'.format(price))
except:
print("Error, not valid")
|
# coding: utf-8
class Person:
number_of_people = 0 # Specific to the class
GRAVITY = 9.8 # we define constants in the generic class
def __init__(self, name):
self.name = name
Person.add_person()
# Decorator
@classmethod
def number_of_people_(cls):
return cls.number_of_people # it acts only in the class itself
@classmethod
def add_person(cls):
cls.number_of_people += 1
p1 = Person("Tim")
p2 = Person("Jill")
print(Person.number_of_people_())
|
seq=input()
def SortList(sequence):
k=0; list1=[]
while k<len(sequence):
num=""
if sequence[k]!=" ":
for j in range(k,len(sequence)):
if sequence[k]!=" ":
num+=sequence[k]
k+=1
else:
k+=1
break
num=int(num)
list1.append(num)
else:
k+=1
print(sorted(list1))
SortList(seq)
|
n = int(input())
for i in range(n):
for j in range(i):
print(' ', end="")
for j in range((n - i) * 2 - 1):
print('*', end="")
print()
for i in range (n - 1):
for j in range(n - i - 2):
print(' ', end="")
for j in range((i + 1) * 2 + 1):
print('*', end="")
print()
|
class Config:
STATE_SHAPE = [84, 84, 3]
SPEED = 20 # cm/step
GOAL_DIRECTION_REWARD = 1
CRASH_REWARD = -10
SIM_DIR = '/home/mate/ucv-pkg-outdoor-8-lite/LinuxNoEditor/outdoor_lite/Binaries/Linux/'
SIM_NAME = 'outdoor_lite'
RANDOM_SPAWN_LOCATIONS = True
MAP_X_MIN = -4000
MAP_X_MAX = 4000
MAP_Y_MIN = -4000
MAP_Y_MAX = 4000
HOST = 'localhost'
SIM_DIR_LIST = ['/home/mate/ucv-pkg-outdoor-8-lite/LinuxNoEditor/outdoor_lite/Binaries/Linux/',
'/home/mate/ucv-pkg-outdoor-8-lite/LinuxNoEditor/outdoor_lite/Binaries/Linux/']
|
"""
PASSENGERS
"""
numPassengers = 22784
passenger_arriving = (
(7, 5, 3, 9, 2, 1, 2, 2, 4, 0, 1, 0, 0, 7, 6, 3, 5, 4, 1, 3, 2, 3, 2, 1, 0, 0), # 0
(6, 11, 5, 8, 5, 3, 0, 2, 0, 0, 2, 0, 0, 6, 9, 8, 4, 3, 4, 2, 0, 2, 3, 0, 3, 0), # 1
(4, 8, 4, 5, 8, 5, 4, 1, 2, 3, 1, 0, 0, 7, 10, 6, 2, 9, 4, 1, 0, 4, 3, 0, 1, 0), # 2
(7, 7, 2, 7, 5, 3, 1, 1, 2, 2, 2, 0, 0, 6, 3, 5, 2, 6, 4, 6, 1, 4, 3, 0, 0, 0), # 3
(6, 7, 8, 13, 8, 2, 5, 2, 5, 2, 0, 0, 0, 6, 9, 4, 7, 6, 3, 3, 2, 2, 4, 0, 3, 0), # 4
(5, 10, 8, 9, 4, 2, 0, 1, 2, 1, 1, 1, 0, 12, 7, 10, 9, 4, 4, 7, 3, 1, 1, 0, 1, 0), # 5
(9, 11, 8, 9, 8, 4, 3, 1, 6, 0, 2, 0, 0, 12, 5, 6, 7, 4, 3, 3, 2, 0, 4, 2, 1, 0), # 6
(7, 6, 11, 8, 8, 3, 3, 3, 2, 2, 5, 0, 0, 2, 9, 8, 2, 9, 3, 4, 3, 4, 1, 1, 2, 0), # 7
(9, 4, 8, 6, 4, 5, 3, 3, 3, 2, 2, 0, 0, 14, 6, 4, 9, 10, 7, 3, 2, 4, 2, 3, 3, 0), # 8
(16, 13, 7, 11, 9, 4, 1, 3, 4, 1, 0, 0, 0, 8, 8, 5, 1, 8, 6, 3, 3, 3, 5, 0, 0, 0), # 9
(8, 5, 8, 6, 7, 3, 5, 2, 2, 2, 0, 0, 0, 6, 9, 6, 4, 8, 4, 3, 4, 6, 2, 0, 2, 0), # 10
(8, 8, 8, 7, 6, 1, 4, 3, 2, 5, 1, 2, 0, 5, 7, 5, 3, 8, 3, 4, 4, 3, 8, 1, 1, 0), # 11
(13, 8, 6, 15, 5, 2, 4, 3, 4, 0, 3, 2, 0, 10, 9, 5, 8, 6, 6, 4, 1, 0, 2, 1, 0, 0), # 12
(10, 11, 10, 13, 3, 5, 4, 4, 4, 2, 1, 2, 0, 18, 11, 10, 7, 9, 6, 6, 1, 2, 6, 4, 1, 0), # 13
(8, 8, 7, 13, 7, 4, 5, 4, 3, 1, 0, 2, 0, 12, 13, 14, 9, 2, 5, 5, 0, 9, 6, 0, 0, 0), # 14
(9, 12, 8, 11, 8, 5, 4, 3, 8, 0, 1, 0, 0, 10, 12, 3, 6, 11, 8, 5, 0, 3, 5, 0, 2, 0), # 15
(13, 12, 6, 13, 9, 3, 6, 8, 3, 2, 4, 1, 0, 8, 12, 6, 3, 9, 6, 5, 5, 6, 6, 3, 0, 0), # 16
(13, 12, 15, 8, 6, 4, 6, 6, 7, 2, 4, 1, 0, 7, 10, 6, 7, 6, 5, 8, 5, 3, 4, 2, 0, 0), # 17
(13, 10, 9, 15, 12, 4, 3, 2, 5, 1, 0, 1, 0, 5, 10, 8, 3, 12, 0, 2, 3, 2, 2, 2, 3, 0), # 18
(12, 18, 6, 12, 4, 6, 5, 6, 4, 3, 0, 3, 0, 14, 13, 8, 12, 10, 2, 8, 2, 10, 3, 3, 0, 0), # 19
(8, 15, 12, 15, 6, 3, 7, 7, 2, 2, 4, 1, 0, 19, 12, 5, 4, 5, 9, 5, 2, 5, 4, 0, 1, 0), # 20
(11, 8, 9, 8, 10, 2, 3, 5, 5, 2, 0, 3, 0, 12, 12, 9, 6, 8, 3, 6, 1, 8, 5, 3, 0, 0), # 21
(15, 10, 15, 12, 9, 4, 6, 2, 8, 3, 2, 1, 0, 15, 8, 6, 3, 10, 9, 7, 5, 6, 2, 3, 1, 0), # 22
(8, 10, 11, 10, 14, 5, 2, 4, 4, 4, 0, 0, 0, 10, 5, 7, 7, 7, 7, 4, 3, 4, 4, 2, 1, 0), # 23
(10, 12, 11, 11, 4, 6, 4, 4, 3, 2, 2, 2, 0, 12, 9, 11, 5, 6, 6, 6, 3, 7, 2, 4, 1, 0), # 24
(12, 10, 5, 7, 14, 1, 9, 7, 5, 0, 1, 0, 0, 11, 11, 11, 9, 8, 4, 4, 5, 4, 1, 1, 0, 0), # 25
(11, 7, 8, 13, 10, 0, 2, 2, 4, 4, 2, 0, 0, 16, 10, 5, 8, 13, 4, 2, 2, 3, 7, 0, 0, 0), # 26
(17, 18, 12, 9, 16, 5, 5, 7, 4, 3, 0, 0, 0, 7, 14, 8, 10, 10, 2, 4, 7, 6, 4, 1, 1, 0), # 27
(7, 10, 12, 10, 12, 5, 4, 4, 5, 6, 2, 1, 0, 15, 12, 5, 7, 6, 3, 5, 6, 3, 3, 4, 2, 0), # 28
(8, 12, 12, 8, 10, 6, 4, 6, 5, 4, 0, 0, 0, 15, 19, 8, 7, 9, 8, 9, 5, 7, 1, 1, 2, 0), # 29
(10, 8, 12, 10, 8, 5, 4, 9, 3, 2, 2, 1, 0, 14, 14, 6, 5, 11, 4, 5, 3, 8, 4, 4, 0, 0), # 30
(15, 7, 5, 11, 8, 2, 7, 4, 8, 0, 1, 1, 0, 6, 10, 4, 10, 17, 6, 5, 3, 6, 3, 2, 0, 0), # 31
(10, 6, 10, 11, 5, 2, 5, 5, 4, 1, 2, 2, 0, 12, 5, 6, 8, 8, 9, 3, 5, 4, 3, 1, 2, 0), # 32
(10, 8, 10, 19, 7, 5, 4, 0, 5, 2, 2, 0, 0, 8, 11, 6, 4, 12, 7, 5, 4, 5, 3, 5, 0, 0), # 33
(8, 14, 10, 11, 6, 4, 2, 6, 6, 1, 0, 0, 0, 7, 13, 6, 9, 4, 4, 4, 2, 2, 7, 1, 0, 0), # 34
(17, 12, 14, 10, 10, 2, 6, 2, 6, 2, 2, 0, 0, 13, 8, 11, 6, 7, 4, 3, 5, 6, 2, 0, 1, 0), # 35
(12, 14, 14, 7, 10, 4, 8, 5, 6, 5, 2, 1, 0, 11, 5, 8, 6, 5, 9, 6, 4, 7, 4, 4, 1, 0), # 36
(14, 14, 13, 11, 13, 2, 6, 3, 0, 0, 1, 1, 0, 13, 12, 7, 6, 14, 5, 2, 6, 7, 1, 3, 0, 0), # 37
(15, 11, 13, 6, 9, 6, 1, 6, 6, 5, 1, 1, 0, 11, 6, 10, 5, 8, 7, 5, 5, 3, 2, 2, 1, 0), # 38
(9, 16, 5, 11, 5, 4, 4, 2, 2, 0, 3, 0, 0, 18, 17, 4, 6, 8, 9, 7, 2, 6, 6, 2, 2, 0), # 39
(18, 12, 7, 10, 8, 2, 4, 3, 3, 1, 0, 1, 0, 10, 9, 9, 5, 10, 3, 9, 3, 2, 3, 2, 2, 0), # 40
(11, 15, 10, 7, 13, 1, 6, 5, 2, 2, 4, 1, 0, 9, 17, 9, 6, 14, 7, 9, 8, 5, 3, 2, 2, 0), # 41
(12, 5, 13, 15, 11, 2, 3, 3, 5, 4, 2, 0, 0, 13, 19, 7, 8, 14, 4, 9, 3, 5, 3, 1, 3, 0), # 42
(15, 11, 12, 8, 6, 4, 2, 5, 5, 0, 3, 0, 0, 10, 13, 12, 11, 16, 2, 3, 0, 4, 2, 2, 1, 0), # 43
(19, 12, 9, 11, 13, 5, 8, 5, 1, 0, 1, 0, 0, 7, 9, 5, 8, 14, 4, 5, 4, 6, 2, 2, 0, 0), # 44
(12, 12, 13, 12, 13, 6, 5, 4, 6, 4, 0, 1, 0, 9, 10, 8, 5, 17, 7, 3, 3, 7, 6, 0, 1, 0), # 45
(9, 12, 6, 12, 9, 5, 2, 4, 6, 0, 4, 4, 0, 12, 11, 7, 6, 9, 10, 4, 3, 3, 3, 1, 1, 0), # 46
(12, 12, 9, 9, 13, 3, 4, 4, 3, 4, 0, 1, 0, 12, 10, 7, 5, 11, 2, 4, 1, 7, 3, 3, 1, 0), # 47
(15, 16, 8, 12, 9, 1, 5, 5, 8, 3, 2, 2, 0, 8, 11, 11, 8, 15, 5, 3, 2, 6, 2, 0, 1, 0), # 48
(18, 16, 4, 12, 13, 7, 2, 3, 8, 0, 1, 0, 0, 9, 9, 10, 5, 11, 4, 2, 4, 10, 5, 1, 1, 0), # 49
(7, 11, 12, 13, 6, 7, 5, 6, 3, 4, 1, 3, 0, 9, 9, 8, 10, 9, 6, 6, 5, 6, 2, 2, 1, 0), # 50
(15, 7, 8, 17, 8, 7, 3, 5, 6, 4, 2, 0, 0, 16, 7, 16, 11, 13, 5, 3, 3, 2, 7, 2, 0, 0), # 51
(12, 11, 12, 17, 13, 2, 3, 2, 4, 0, 2, 2, 0, 8, 7, 8, 8, 10, 6, 1, 3, 3, 3, 2, 1, 0), # 52
(19, 11, 10, 7, 6, 5, 8, 3, 6, 3, 4, 1, 0, 12, 7, 6, 6, 11, 6, 4, 4, 8, 3, 2, 1, 0), # 53
(14, 15, 10, 10, 9, 6, 7, 3, 3, 3, 0, 0, 0, 10, 2, 11, 3, 8, 7, 6, 1, 5, 4, 2, 1, 0), # 54
(7, 11, 11, 13, 6, 3, 6, 7, 3, 2, 1, 0, 0, 13, 11, 12, 7, 9, 7, 7, 5, 2, 3, 1, 1, 0), # 55
(12, 9, 14, 13, 4, 3, 3, 2, 4, 1, 1, 1, 0, 18, 16, 12, 6, 9, 10, 6, 4, 5, 4, 1, 0, 0), # 56
(12, 9, 9, 8, 7, 3, 9, 2, 3, 3, 2, 2, 0, 3, 16, 6, 9, 4, 13, 4, 1, 2, 2, 2, 1, 0), # 57
(12, 10, 12, 8, 5, 3, 3, 8, 5, 4, 0, 0, 0, 9, 16, 2, 5, 7, 8, 4, 2, 2, 4, 2, 1, 0), # 58
(11, 12, 11, 7, 6, 4, 8, 2, 3, 0, 0, 0, 0, 14, 11, 6, 6, 12, 3, 5, 4, 3, 6, 5, 2, 0), # 59
(13, 10, 6, 13, 8, 4, 3, 6, 5, 3, 1, 2, 0, 7, 12, 7, 9, 15, 3, 3, 0, 5, 4, 3, 1, 0), # 60
(17, 11, 12, 11, 9, 4, 3, 4, 4, 3, 5, 0, 0, 10, 8, 10, 7, 15, 8, 1, 4, 6, 3, 1, 0, 0), # 61
(12, 11, 8, 10, 6, 5, 3, 1, 9, 3, 1, 0, 0, 12, 14, 4, 6, 11, 7, 5, 3, 2, 3, 0, 0, 0), # 62
(13, 8, 7, 17, 12, 2, 3, 3, 4, 0, 0, 1, 0, 15, 15, 4, 8, 5, 5, 3, 3, 1, 2, 5, 1, 0), # 63
(13, 12, 8, 16, 7, 1, 5, 2, 2, 3, 2, 0, 0, 9, 12, 8, 8, 5, 6, 2, 4, 2, 1, 4, 1, 0), # 64
(9, 18, 12, 8, 4, 2, 3, 3, 1, 2, 2, 1, 0, 8, 9, 6, 11, 9, 4, 2, 3, 7, 2, 3, 1, 0), # 65
(17, 9, 6, 9, 16, 3, 2, 4, 6, 5, 1, 0, 0, 11, 10, 6, 6, 8, 5, 5, 2, 5, 3, 1, 0, 0), # 66
(13, 8, 11, 8, 7, 7, 5, 0, 4, 3, 1, 0, 0, 14, 18, 5, 5, 13, 4, 3, 3, 9, 5, 2, 0, 0), # 67
(14, 9, 11, 7, 5, 5, 3, 2, 4, 2, 2, 1, 0, 16, 9, 4, 6, 6, 4, 9, 2, 4, 2, 0, 0, 0), # 68
(11, 15, 10, 10, 5, 7, 2, 1, 3, 0, 2, 0, 0, 14, 14, 3, 6, 10, 10, 5, 3, 6, 3, 0, 1, 0), # 69
(12, 9, 10, 8, 11, 5, 4, 2, 5, 1, 2, 1, 0, 6, 5, 8, 7, 15, 9, 3, 2, 9, 12, 1, 0, 0), # 70
(18, 15, 15, 13, 9, 3, 5, 3, 5, 1, 1, 1, 0, 10, 8, 4, 11, 4, 4, 5, 3, 8, 4, 0, 1, 0), # 71
(15, 9, 13, 12, 8, 10, 4, 4, 6, 2, 1, 1, 0, 16, 8, 4, 8, 9, 2, 2, 2, 5, 0, 0, 0, 0), # 72
(13, 15, 15, 6, 9, 6, 3, 5, 2, 0, 3, 2, 0, 12, 5, 8, 4, 18, 4, 9, 4, 5, 5, 2, 0, 0), # 73
(16, 12, 11, 10, 11, 5, 2, 5, 4, 3, 1, 3, 0, 12, 11, 5, 5, 13, 3, 3, 4, 4, 3, 4, 1, 0), # 74
(19, 10, 9, 5, 2, 1, 3, 3, 6, 1, 1, 0, 0, 10, 5, 10, 1, 14, 2, 3, 5, 3, 6, 2, 2, 0), # 75
(19, 7, 7, 11, 10, 6, 3, 8, 8, 1, 3, 1, 0, 15, 12, 2, 3, 8, 5, 3, 3, 2, 6, 2, 1, 0), # 76
(6, 9, 12, 10, 13, 4, 8, 1, 3, 3, 1, 1, 0, 13, 10, 5, 5, 9, 4, 4, 4, 6, 6, 3, 1, 0), # 77
(12, 13, 13, 14, 13, 2, 5, 3, 11, 1, 2, 0, 0, 14, 13, 5, 4, 11, 5, 2, 0, 1, 2, 1, 2, 0), # 78
(7, 8, 5, 10, 11, 1, 4, 1, 3, 1, 1, 2, 0, 16, 13, 8, 4, 7, 4, 3, 1, 4, 1, 2, 2, 0), # 79
(17, 6, 9, 9, 8, 6, 2, 4, 4, 3, 3, 0, 0, 13, 9, 13, 3, 10, 8, 6, 1, 1, 4, 3, 1, 0), # 80
(11, 16, 6, 13, 6, 7, 2, 2, 5, 0, 1, 1, 0, 9, 9, 10, 3, 14, 3, 2, 3, 6, 4, 2, 1, 0), # 81
(16, 12, 15, 14, 8, 3, 6, 0, 4, 2, 2, 0, 0, 15, 13, 5, 5, 10, 2, 7, 1, 5, 2, 2, 0, 0), # 82
(14, 15, 15, 12, 5, 3, 3, 3, 2, 5, 3, 1, 0, 9, 4, 9, 8, 9, 6, 5, 2, 5, 1, 3, 2, 0), # 83
(15, 14, 9, 9, 9, 1, 6, 4, 5, 1, 3, 0, 0, 16, 10, 8, 10, 6, 5, 5, 4, 3, 6, 0, 0, 0), # 84
(3, 12, 11, 9, 15, 2, 7, 7, 4, 1, 2, 0, 0, 11, 7, 6, 15, 7, 7, 2, 3, 2, 3, 1, 0, 0), # 85
(12, 9, 7, 14, 15, 4, 3, 5, 7, 1, 0, 0, 0, 13, 13, 14, 6, 14, 1, 3, 2, 5, 2, 2, 1, 0), # 86
(14, 10, 11, 4, 8, 2, 5, 4, 7, 3, 2, 0, 0, 10, 11, 10, 11, 9, 3, 8, 2, 6, 6, 1, 1, 0), # 87
(9, 6, 11, 13, 9, 10, 4, 3, 4, 3, 3, 0, 0, 12, 8, 9, 3, 11, 8, 7, 0, 5, 6, 4, 2, 0), # 88
(18, 9, 7, 8, 7, 7, 3, 5, 5, 1, 1, 0, 0, 12, 15, 8, 2, 11, 4, 5, 2, 6, 8, 1, 2, 0), # 89
(11, 7, 9, 11, 8, 4, 2, 4, 1, 2, 2, 0, 0, 13, 12, 10, 8, 8, 5, 3, 8, 4, 5, 3, 4, 0), # 90
(18, 10, 10, 8, 8, 3, 3, 1, 4, 3, 0, 0, 0, 10, 10, 4, 6, 7, 5, 0, 2, 8, 3, 1, 0, 0), # 91
(11, 14, 6, 7, 9, 11, 4, 0, 2, 3, 2, 0, 0, 6, 13, 10, 3, 5, 2, 5, 2, 3, 0, 0, 1, 0), # 92
(9, 12, 9, 8, 9, 3, 4, 4, 8, 2, 1, 0, 0, 16, 13, 10, 5, 8, 6, 3, 3, 5, 2, 2, 2, 0), # 93
(12, 11, 13, 13, 11, 1, 3, 2, 7, 5, 1, 2, 0, 13, 5, 9, 7, 3, 2, 8, 3, 4, 2, 0, 2, 0), # 94
(6, 7, 11, 12, 11, 1, 4, 3, 1, 3, 0, 1, 0, 10, 4, 3, 2, 9, 4, 2, 7, 7, 3, 1, 2, 0), # 95
(9, 10, 11, 8, 7, 2, 7, 1, 5, 0, 2, 0, 0, 12, 9, 6, 6, 7, 5, 3, 3, 2, 2, 3, 0, 0), # 96
(10, 11, 10, 10, 7, 5, 6, 5, 5, 2, 2, 0, 0, 11, 6, 14, 2, 9, 3, 7, 8, 5, 6, 0, 0, 0), # 97
(8, 7, 8, 14, 8, 1, 5, 2, 3, 6, 2, 0, 0, 9, 9, 8, 6, 5, 2, 6, 3, 4, 4, 4, 1, 0), # 98
(10, 13, 11, 14, 13, 5, 5, 3, 5, 0, 1, 0, 0, 9, 7, 4, 9, 11, 3, 7, 3, 7, 2, 2, 0, 0), # 99
(12, 12, 12, 16, 8, 2, 0, 0, 2, 1, 1, 1, 0, 11, 8, 11, 3, 9, 3, 3, 2, 4, 2, 3, 1, 0), # 100
(10, 11, 10, 7, 10, 1, 4, 1, 3, 3, 1, 1, 0, 15, 11, 13, 7, 10, 4, 4, 2, 4, 4, 2, 0, 0), # 101
(12, 15, 9, 11, 13, 7, 3, 4, 3, 3, 1, 2, 0, 6, 15, 5, 5, 11, 4, 3, 2, 4, 1, 2, 0, 0), # 102
(12, 11, 4, 4, 6, 5, 4, 4, 5, 1, 2, 0, 0, 11, 6, 14, 4, 5, 5, 3, 3, 2, 0, 1, 1, 0), # 103
(19, 7, 11, 11, 11, 2, 2, 1, 1, 1, 2, 2, 0, 13, 8, 7, 3, 13, 6, 4, 4, 9, 1, 1, 0, 0), # 104
(13, 13, 11, 12, 11, 6, 1, 2, 4, 0, 0, 0, 0, 9, 10, 4, 7, 7, 0, 2, 2, 5, 4, 0, 0, 0), # 105
(12, 14, 10, 11, 7, 5, 3, 6, 4, 3, 4, 0, 0, 10, 8, 6, 4, 4, 0, 5, 1, 7, 0, 5, 1, 0), # 106
(9, 8, 10, 8, 7, 4, 8, 0, 7, 1, 2, 2, 0, 13, 13, 7, 6, 12, 9, 2, 1, 1, 4, 3, 1, 0), # 107
(10, 13, 6, 8, 6, 4, 4, 2, 4, 1, 1, 0, 0, 11, 6, 7, 7, 8, 4, 7, 5, 4, 5, 4, 1, 0), # 108
(18, 9, 12, 11, 6, 2, 6, 5, 7, 1, 3, 0, 0, 11, 4, 6, 4, 13, 6, 1, 1, 4, 4, 1, 0, 0), # 109
(9, 9, 14, 4, 13, 5, 4, 1, 6, 4, 2, 1, 0, 15, 8, 8, 6, 8, 5, 0, 3, 6, 5, 0, 1, 0), # 110
(8, 11, 6, 5, 8, 1, 2, 4, 4, 2, 1, 0, 0, 17, 11, 9, 3, 8, 7, 7, 5, 4, 5, 1, 1, 0), # 111
(11, 3, 8, 11, 11, 5, 2, 4, 6, 0, 1, 0, 0, 5, 7, 8, 7, 11, 2, 3, 3, 3, 5, 1, 1, 0), # 112
(16, 6, 7, 5, 11, 8, 2, 4, 4, 2, 1, 1, 0, 5, 9, 8, 9, 6, 3, 6, 1, 7, 5, 3, 0, 0), # 113
(16, 5, 15, 13, 7, 2, 6, 4, 7, 1, 0, 1, 0, 9, 11, 8, 6, 8, 5, 5, 2, 4, 2, 1, 0, 0), # 114
(9, 7, 8, 8, 10, 7, 2, 2, 6, 2, 2, 1, 0, 11, 8, 2, 7, 8, 3, 0, 3, 6, 4, 0, 0, 0), # 115
(12, 6, 7, 15, 11, 6, 6, 1, 3, 0, 0, 1, 0, 9, 20, 8, 10, 10, 5, 1, 2, 3, 2, 3, 0, 0), # 116
(18, 12, 8, 9, 12, 2, 1, 2, 5, 1, 0, 0, 0, 8, 11, 9, 7, 9, 3, 1, 4, 4, 6, 3, 1, 0), # 117
(12, 7, 9, 11, 5, 2, 6, 3, 2, 3, 2, 1, 0, 14, 9, 6, 3, 9, 6, 2, 3, 5, 4, 1, 1, 0), # 118
(14, 15, 5, 4, 10, 6, 3, 1, 5, 0, 1, 2, 0, 16, 10, 6, 2, 8, 2, 2, 4, 2, 1, 3, 1, 0), # 119
(16, 9, 6, 8, 9, 3, 5, 6, 4, 3, 3, 1, 0, 8, 10, 6, 2, 6, 6, 1, 0, 4, 6, 1, 1, 0), # 120
(14, 6, 14, 12, 6, 4, 5, 2, 4, 2, 3, 3, 0, 13, 6, 3, 3, 8, 2, 1, 1, 5, 2, 1, 1, 0), # 121
(16, 8, 11, 16, 11, 4, 3, 3, 4, 4, 1, 1, 0, 8, 11, 4, 7, 4, 6, 3, 2, 3, 0, 1, 1, 0), # 122
(9, 10, 14, 9, 11, 2, 2, 0, 8, 2, 1, 0, 0, 13, 4, 9, 4, 6, 7, 5, 2, 6, 4, 2, 1, 0), # 123
(10, 11, 12, 11, 5, 7, 2, 1, 5, 2, 3, 0, 0, 17, 5, 5, 4, 9, 1, 0, 5, 10, 2, 2, 1, 0), # 124
(7, 12, 11, 7, 2, 7, 2, 4, 5, 1, 4, 0, 0, 10, 9, 7, 5, 9, 5, 4, 6, 3, 3, 1, 0, 0), # 125
(7, 5, 7, 9, 7, 3, 2, 2, 5, 1, 6, 0, 0, 14, 15, 5, 3, 4, 7, 2, 1, 2, 1, 0, 0, 0), # 126
(15, 10, 12, 11, 8, 6, 2, 3, 7, 0, 0, 0, 0, 9, 2, 6, 4, 11, 9, 4, 2, 2, 6, 3, 1, 0), # 127
(13, 8, 8, 13, 8, 4, 8, 1, 6, 1, 1, 0, 0, 10, 7, 7, 2, 3, 4, 4, 4, 3, 1, 4, 2, 0), # 128
(13, 6, 12, 5, 8, 1, 2, 1, 5, 0, 4, 0, 0, 11, 10, 7, 3, 9, 4, 5, 3, 2, 1, 0, 1, 0), # 129
(17, 5, 6, 6, 8, 3, 5, 3, 4, 1, 1, 0, 0, 11, 11, 4, 5, 10, 5, 6, 2, 3, 7, 2, 0, 0), # 130
(10, 5, 7, 9, 11, 4, 1, 5, 6, 1, 1, 0, 0, 7, 5, 9, 9, 15, 3, 4, 3, 5, 3, 1, 1, 0), # 131
(14, 7, 10, 12, 7, 1, 1, 4, 2, 3, 2, 1, 0, 11, 8, 7, 7, 9, 5, 2, 0, 1, 3, 2, 0, 0), # 132
(10, 7, 12, 12, 9, 8, 4, 3, 2, 0, 0, 2, 0, 10, 5, 9, 7, 14, 11, 6, 7, 6, 2, 3, 2, 0), # 133
(14, 12, 15, 11, 11, 5, 3, 4, 5, 0, 1, 2, 0, 9, 8, 8, 5, 8, 4, 4, 1, 5, 3, 3, 0, 0), # 134
(7, 8, 10, 9, 9, 4, 2, 2, 7, 2, 2, 0, 0, 19, 7, 6, 6, 5, 3, 3, 3, 3, 2, 3, 1, 0), # 135
(15, 10, 11, 10, 10, 6, 3, 4, 4, 1, 2, 1, 0, 12, 6, 10, 3, 6, 1, 3, 4, 3, 1, 0, 1, 0), # 136
(11, 7, 9, 6, 11, 2, 4, 4, 3, 1, 0, 1, 0, 9, 11, 3, 3, 1, 2, 3, 3, 3, 1, 1, 0, 0), # 137
(7, 6, 4, 6, 10, 8, 6, 0, 5, 3, 0, 1, 0, 18, 16, 9, 10, 5, 5, 4, 2, 8, 6, 1, 0, 0), # 138
(12, 8, 10, 10, 7, 2, 6, 4, 4, 1, 1, 1, 0, 13, 11, 12, 8, 14, 6, 3, 5, 5, 1, 1, 1, 0), # 139
(12, 14, 8, 9, 10, 3, 4, 2, 4, 2, 3, 1, 0, 8, 9, 3, 7, 5, 3, 3, 5, 2, 2, 0, 0, 0), # 140
(3, 8, 10, 3, 12, 4, 5, 2, 8, 2, 1, 0, 0, 7, 9, 12, 6, 11, 5, 0, 1, 3, 0, 0, 1, 0), # 141
(8, 10, 12, 12, 6, 1, 4, 4, 2, 3, 1, 1, 0, 12, 7, 5, 4, 8, 3, 6, 0, 2, 3, 1, 3, 0), # 142
(12, 11, 12, 9, 9, 3, 1, 3, 3, 1, 0, 0, 0, 6, 4, 6, 8, 3, 2, 4, 3, 2, 2, 1, 0, 0), # 143
(5, 7, 12, 10, 9, 5, 2, 3, 2, 2, 0, 1, 0, 12, 5, 6, 1, 3, 6, 5, 1, 3, 3, 0, 0, 0), # 144
(7, 13, 9, 12, 11, 5, 7, 6, 6, 0, 3, 2, 0, 7, 7, 11, 5, 8, 2, 7, 1, 4, 3, 3, 0, 0), # 145
(13, 6, 10, 10, 8, 3, 2, 3, 8, 3, 1, 1, 0, 9, 11, 4, 4, 8, 5, 4, 0, 3, 2, 1, 1, 0), # 146
(16, 9, 5, 17, 11, 2, 2, 4, 3, 1, 1, 1, 0, 8, 6, 4, 5, 4, 4, 4, 5, 0, 2, 1, 0, 0), # 147
(10, 6, 10, 5, 13, 4, 0, 5, 7, 0, 2, 0, 0, 11, 6, 7, 3, 7, 1, 6, 2, 7, 4, 0, 0, 0), # 148
(16, 4, 8, 13, 4, 2, 1, 5, 5, 4, 0, 0, 0, 14, 7, 8, 4, 11, 6, 2, 4, 1, 5, 3, 0, 0), # 149
(14, 10, 8, 8, 4, 5, 4, 4, 6, 2, 0, 3, 0, 8, 7, 2, 5, 10, 4, 2, 2, 2, 3, 2, 2, 0), # 150
(10, 7, 8, 8, 5, 3, 3, 2, 4, 2, 1, 0, 0, 13, 7, 7, 3, 8, 0, 2, 2, 4, 3, 0, 1, 0), # 151
(9, 4, 9, 5, 7, 2, 5, 3, 4, 1, 1, 2, 0, 8, 5, 6, 4, 14, 4, 2, 2, 3, 5, 4, 1, 0), # 152
(7, 4, 9, 8, 10, 3, 3, 0, 4, 1, 1, 1, 0, 12, 10, 6, 7, 2, 3, 4, 0, 6, 2, 1, 0, 0), # 153
(14, 8, 9, 3, 7, 5, 0, 1, 2, 0, 1, 0, 0, 15, 6, 4, 6, 11, 2, 5, 2, 1, 2, 3, 0, 0), # 154
(11, 5, 9, 5, 6, 4, 1, 3, 5, 0, 2, 0, 0, 8, 8, 11, 8, 5, 3, 6, 2, 2, 0, 1, 0, 0), # 155
(8, 3, 5, 4, 11, 6, 1, 2, 2, 0, 5, 1, 0, 8, 4, 4, 2, 13, 3, 3, 1, 2, 0, 0, 2, 0), # 156
(13, 10, 9, 9, 6, 2, 2, 2, 4, 1, 1, 0, 0, 14, 10, 3, 4, 11, 1, 6, 3, 5, 2, 1, 1, 0), # 157
(7, 3, 2, 5, 6, 2, 1, 1, 1, 1, 0, 0, 0, 11, 7, 5, 4, 12, 2, 4, 2, 3, 2, 4, 0, 0), # 158
(11, 6, 8, 9, 6, 4, 5, 1, 5, 1, 2, 0, 0, 7, 11, 7, 7, 9, 3, 3, 0, 3, 0, 0, 3, 0), # 159
(11, 3, 4, 6, 6, 2, 1, 2, 4, 4, 2, 3, 0, 6, 8, 5, 3, 5, 4, 1, 0, 3, 1, 2, 1, 0), # 160
(9, 6, 7, 7, 5, 7, 5, 6, 2, 0, 1, 0, 0, 14, 8, 3, 3, 6, 3, 1, 2, 2, 3, 1, 2, 0), # 161
(8, 9, 4, 8, 4, 3, 4, 2, 3, 2, 1, 0, 0, 7, 8, 1, 3, 10, 5, 0, 4, 3, 6, 0, 1, 0), # 162
(7, 5, 8, 8, 6, 1, 2, 6, 3, 2, 0, 1, 0, 7, 9, 4, 4, 15, 5, 3, 5, 3, 3, 4, 0, 0), # 163
(10, 4, 11, 6, 8, 2, 4, 4, 4, 1, 2, 0, 0, 10, 3, 10, 3, 5, 6, 4, 4, 3, 2, 3, 0, 0), # 164
(8, 5, 6, 10, 5, 3, 2, 1, 4, 1, 3, 0, 0, 7, 8, 3, 4, 11, 3, 2, 1, 1, 4, 2, 0, 0), # 165
(8, 9, 9, 7, 2, 1, 0, 1, 4, 1, 0, 1, 0, 9, 8, 8, 3, 6, 3, 3, 2, 3, 1, 3, 0, 0), # 166
(8, 5, 4, 6, 11, 4, 1, 5, 2, 1, 2, 0, 0, 6, 7, 4, 6, 6, 0, 2, 3, 6, 3, 1, 0, 0), # 167
(2, 4, 6, 8, 7, 4, 4, 3, 5, 1, 0, 0, 0, 7, 9, 2, 4, 4, 2, 3, 5, 0, 1, 0, 0, 0), # 168
(5, 3, 9, 7, 6, 3, 4, 4, 1, 2, 0, 2, 0, 3, 7, 3, 7, 8, 6, 1, 3, 2, 2, 3, 1, 0), # 169
(5, 3, 4, 4, 5, 2, 0, 2, 3, 1, 0, 0, 0, 9, 6, 10, 1, 13, 1, 1, 4, 3, 2, 1, 1, 0), # 170
(2, 2, 4, 3, 7, 1, 2, 4, 3, 1, 1, 0, 0, 7, 3, 4, 3, 9, 4, 6, 0, 3, 2, 2, 1, 0), # 171
(4, 6, 8, 6, 6, 5, 2, 2, 1, 2, 0, 1, 0, 5, 4, 8, 1, 4, 3, 5, 4, 2, 2, 1, 0, 0), # 172
(8, 6, 5, 6, 6, 6, 7, 1, 1, 2, 0, 0, 0, 6, 5, 6, 3, 5, 5, 1, 1, 2, 3, 1, 1, 0), # 173
(6, 4, 6, 8, 4, 3, 2, 0, 3, 2, 0, 0, 0, 2, 6, 2, 7, 2, 3, 2, 0, 3, 5, 0, 0, 0), # 174
(2, 3, 3, 5, 7, 2, 0, 1, 5, 2, 1, 0, 0, 5, 5, 7, 2, 3, 1, 1, 1, 4, 2, 1, 0, 0), # 175
(4, 2, 3, 2, 3, 4, 2, 2, 2, 1, 2, 0, 0, 2, 4, 3, 0, 7, 0, 0, 1, 2, 2, 1, 0, 0), # 176
(6, 2, 2, 7, 3, 3, 2, 2, 2, 0, 0, 1, 0, 5, 1, 6, 3, 9, 1, 1, 1, 4, 3, 1, 1, 0), # 177
(5, 2, 3, 7, 8, 3, 1, 2, 2, 1, 1, 0, 0, 3, 4, 3, 1, 4, 2, 0, 1, 2, 0, 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 = (
(6.025038694046121, 6.630346271631799, 6.253539875535008, 7.457601328636119, 6.665622729131534, 3.766385918444806, 4.9752427384486975, 5.583811407575308, 7.308118874601608, 4.749618018626843, 5.046318196662723, 5.877498093967408, 6.100656255094035), # 0
(6.425192582423969, 7.06807283297371, 6.666415909596182, 7.950173103931939, 7.106988404969084, 4.015180300851067, 5.303362729516432, 5.951416467486849, 7.79069439159949, 5.062776830732579, 5.3797153631473575, 6.265459992977225, 6.503749976927826), # 1
(6.8240676107756775, 7.504062205069175, 7.077650742656896, 8.440785245597752, 7.546755568499692, 4.262982137414934, 5.630182209552845, 6.317550297485303, 8.271344168253059, 5.3746965300246545, 5.711787778531575, 6.651879182463666, 6.905237793851628), # 2
(7.220109351775874, 7.936584602323736, 7.485613043183825, 8.927491689038488, 7.983194011202282, 4.508808747102135, 5.954404369977547, 6.680761388993408, 8.74816219310531, 5.684139238111417, 6.041218094192859, 7.035222821916553, 7.30352736750507), # 3
(7.611763378099177, 8.363910239142928, 7.8886714796436435, 9.408346369659084, 8.41457352455579, 4.751677448878401, 6.27473240221015, 7.039598233433898, 9.219242454699248, 5.9898670766012145, 6.36668896150869, 7.413958070825716, 7.69702635952778), # 4
(7.9974752624202115, 8.784309329932306, 8.285194720503021, 9.881403222864472, 8.839163900039136, 4.990605561709457, 6.589869497670269, 7.392609322229511, 9.682678941577871, 6.290642167102395, 6.686883031856559, 7.786552088680978, 8.084142431559393), # 5
(8.375690577413598, 9.196052089097401, 8.673551434228639, 10.344716184059582, 9.255234929131252, 5.224610404561036, 6.898518847777515, 7.738343146802986, 10.136565642284177, 6.58522663122331, 7.000482956613939, 8.15147203497217, 8.463283245239527), # 6
(8.744854895753962, 9.597408731043757, 9.052110289287162, 10.796339188649354, 9.661056403311065, 5.452709296398865, 7.199383643951502, 8.075348198577062, 10.578996545361173, 6.872382590572303, 7.306171387158321, 8.507185069189115, 8.832856462207822), # 7
(9.103413790115921, 9.986649470176918, 9.419239954145274, 11.234326172038713, 10.054898114057503, 5.673919556188667, 7.491167077611837, 8.402172968974469, 11.008065639351846, 7.150872166757728, 7.602630974867185, 8.852158350821643, 9.1912697441039), # 8
(9.449812833174102, 10.362044520902426, 9.773309097269644, 11.656731069632603, 10.43502985284949, 5.88725850289618, 7.772572340178144, 8.717365949417955, 11.421866912799208, 7.419457481387929, 7.888544371118013, 9.184859039359576, 9.536930752567395), # 9
(9.782497597603118, 10.721864097625819, 10.11268638712695, 12.061607816835945, 10.79972141116596, 6.091743455487129, 8.042302623070025, 9.019475631330252, 11.818494354246257, 7.676900656071257, 8.162594227288288, 9.503754294292742, 9.868247149237932), # 10
(10.099913656077605, 11.064378414752648, 10.435740492183857, 12.447010349053675, 11.14724258048584, 6.286391732927242, 8.2990611177071, 9.307050506134097, 12.196041952235992, 7.921963812416062, 8.423463194755499, 9.807311275110973, 10.183626595755133), # 11
(10.400506581272174, 11.387857686688436, 10.740840080907047, 12.810992601690733, 11.475863152288053, 6.470220654182243, 8.541551015508974, 9.578639065252224, 12.552603695311413, 8.153409072030685, 8.669833924897121, 10.093997141304081, 10.48147675375864), # 12
(10.68272194586145, 11.690572127838744, 11.026353821763193, 13.151608510152052, 11.78385291805152, 6.642247538217868, 8.768475507895266, 9.832789800107378, 12.886273572015517, 8.369998556523484, 8.900389069090641, 10.362279052361904, 10.760205284888082), # 13
(10.945005322520059, 11.970791952609106, 11.290650383218976, 13.46691200984255, 12.069481669255188, 6.801489703999841, 8.978537786285592, 10.068051202122295, 13.195145570891304, 8.5704943875028, 9.113811278713541, 10.610624167774272, 11.018219850783076), # 14
(11.185802283922625, 12.22678737540506, 11.53209843374105, 13.754957036167182, 12.33101919737797, 6.946964470493895, 9.17044104209955, 10.282971762719706, 13.477313680481783, 8.753658686576989, 9.308783205143303, 10.837499647031004, 11.253928113083257), # 15
(11.40355840274376, 12.456828610632158, 11.749066641796109, 14.01379752453086, 12.5667352938988, 7.077689156665751, 9.34288846675677, 10.476099973322352, 13.730871889329944, 8.918253575354395, 9.483987499757415, 11.041372649621927, 11.465737733428254), # 16
(11.59671925165809, 12.659185872695934, 11.939923675850823, 14.241487410338534, 12.774899750296605, 7.192681081481142, 9.494583251676852, 10.64598432535298, 13.95391418597878, 9.06304117544336, 9.638106813933359, 11.220710335036866, 11.652056373457699), # 17
(11.763730403340244, 12.832129376001928, 12.103038204371856, 14.436080628995134, 12.953782358050306, 7.290957563905803, 9.62422858827942, 10.791173310234312, 14.144534558971316, 9.186783608452243, 9.76982379904861, 11.373979862765658, 11.811291694811214), # 18
(11.903037430464838, 12.973929334955693, 12.236778895825895, 14.595631115905576, 13.101652908638838, 7.37153592290545, 9.730527667984072, 10.910215419389093, 14.300826996850533, 9.288242995989393, 9.877821106480653, 11.499648392298115, 11.941851359128435), # 19
(12.013085905706498, 13.082855963962754, 12.339514418679602, 14.718192806474825, 13.216781193541133, 7.4334334774458215, 9.812183682210435, 11.00165914424006, 14.420885488159437, 9.36618145966315, 9.96078138760698, 11.59618308312407, 12.042143028048988), # 20
(12.09232140173984, 13.15717947742867, 12.409613441399662, 14.801819636107782, 13.297437004236105, 7.475667546492642, 9.86789982237811, 11.064052976209947, 14.502804021441024, 9.419361121081865, 10.01738729380507, 11.662051094733352, 12.110574363212494), # 21
(12.139189491239494, 13.195170089758973, 12.445444632452743, 14.844565540209402, 13.341890132202689, 7.497255449011639, 9.89637927990672, 11.095945406721498, 14.544676585238298, 9.44654410185389, 10.046321476452407, 11.695719586615787, 12.145553026258591), # 22
(12.156472036011166, 13.199668312757202, 12.449907818930042, 14.849916975308643, 13.353278467239116, 7.5, 9.899764802711205, 11.099392592592592, 14.54991148148148, 9.44975072702332, 10.049949644594088, 11.69987709190672, 12.15), # 23
(12.169214895640982, 13.197044444444446, 12.449177777777777, 14.849258333333335, 13.359729136337823, 7.5, 9.8979045751634, 11.0946, 14.549209999999999, 9.44778074074074, 10.049549494949495, 11.698903703703703, 12.15), # 24
(12.181688676253897, 13.191872427983538, 12.447736625514404, 14.84795524691358, 13.366037934713404, 7.5, 9.894238683127572, 11.085185185185185, 14.547824074074073, 9.443902606310013, 10.048756079311634, 11.696982167352537, 12.15), # 25
(12.19389242285764, 13.184231275720165, 12.445604115226338, 14.846022530864197, 13.372204642105325, 7.5, 9.888824061970466, 11.071325925925926, 14.54577148148148, 9.438180850480109, 10.047576580621024, 11.694138820301784, 12.15), # 26
(12.205825180459962, 13.174199999999997, 12.4428, 14.843474999999998, 13.378229038253057, 7.5, 9.881717647058824, 11.0532, 14.54307, 9.430679999999999, 10.046018181818182, 11.6904, 12.15), # 27
(12.217485994068602, 13.161857613168722, 12.439344032921811, 14.8403274691358, 13.384110902896081, 7.5, 9.87297637375938, 11.030985185185186, 14.539737407407406, 9.421464581618656, 10.04408806584362, 11.685792043895749, 12.15), # 28
(12.2288739086913, 13.147283127572017, 12.43525596707819, 14.83659475308642, 13.389850015773865, 7.5, 9.862657177438878, 11.004859259259257, 14.535791481481482, 9.410599122085047, 10.041793415637859, 11.680341289437584, 12.15), # 29
(12.239987969335797, 13.130555555555555, 12.430555555555555, 14.832291666666666, 13.395446156625884, 7.5, 9.850816993464052, 10.974999999999998, 14.53125, 9.398148148148149, 10.039141414141413, 11.674074074074072, 12.15), # 30
(12.25082722100983, 13.11175390946502, 12.42526255144033, 14.827433024691356, 13.400899105191609, 7.5, 9.837512757201647, 10.941585185185184, 14.52613074074074, 9.384176186556926, 10.0361392442948, 11.667016735253773, 12.15), # 31
(12.261390708721144, 13.09095720164609, 12.419396707818928, 14.822033641975308, 13.406208641210513, 7.5, 9.822801404018398, 10.904792592592594, 14.520451481481482, 9.368747764060357, 10.032794089038532, 11.659195610425241, 12.15), # 32
(12.271677477477477, 13.068244444444444, 12.412977777777778, 14.816108333333332, 13.411374544422076, 7.5, 9.806739869281046, 10.8648, 14.51423, 9.351927407407407, 10.02911313131313, 11.650637037037034, 12.15), # 33
(12.28168657228657, 13.04369465020576, 12.406025514403291, 14.809671913580246, 13.416396594565759, 7.5, 9.789385088356331, 10.821785185185183, 14.507484074074075, 9.33377964334705, 10.025103554059108, 11.641367352537722, 12.15), # 34
(12.291417038156167, 13.01738683127572, 12.398559670781895, 14.802739197530862, 13.421274571381044, 7.5, 9.77079399661099, 10.775925925925925, 14.500231481481482, 9.314368998628257, 10.020772540216983, 11.631412894375858, 12.15), # 35
(12.300867920094007, 12.989399999999998, 12.3906, 14.795324999999998, 13.426008254607403, 7.5, 9.751023529411764, 10.727400000000001, 14.492489999999998, 9.293759999999999, 10.016127272727273, 11.620800000000001, 12.15), # 36
(12.310038263107828, 12.95981316872428, 12.382166255144032, 14.787444135802469, 13.430597423984304, 7.5, 9.730130622125392, 10.676385185185184, 14.484277407407406, 9.272017174211248, 10.01117493453049, 11.609555006858711, 12.15), # 37
(12.31892711220537, 12.928705349794239, 12.37327818930041, 14.779111419753086, 13.435041859251228, 7.5, 9.708172210118615, 10.62305925925926, 14.475611481481481, 9.249205048010975, 10.005922708567153, 11.597704252400549, 12.15), # 38
(12.327533512394384, 12.896155555555554, 12.363955555555556, 14.770341666666667, 13.439341340147644, 7.5, 9.68520522875817, 10.567599999999999, 14.466510000000001, 9.225388148148149, 10.000377777777777, 11.585274074074073, 12.15), # 39
(12.335856508682596, 12.86224279835391, 12.354218106995884, 14.761149691358025, 13.443495646413021, 7.5, 9.661286613410796, 10.510185185185186, 14.456990740740741, 9.200631001371743, 9.99454732510288, 11.572290809327848, 12.15), # 40
(12.343895146077754, 12.82704609053498, 12.344085596707819, 14.751550308641974, 13.447504557786841, 7.5, 9.636473299443233, 10.450992592592593, 14.44707148148148, 9.174998134430727, 9.988438533482979, 11.558780795610424, 12.15), # 41
(12.3516484695876, 12.790644444444444, 12.333577777777778, 14.741558333333334, 13.45136785400857, 7.5, 9.610822222222222, 10.3902, 14.436770000000001, 9.148554074074074, 9.982058585858585, 11.54477037037037, 12.15), # 42
(12.35911552421987, 12.753116872427984, 12.322714403292181, 14.731188580246913, 13.455085314817683, 7.5, 9.584390317114499, 10.327985185185186, 14.426104074074072, 9.121363347050755, 9.97541466517022, 11.530285871056241, 12.15), # 43
(12.366295354982311, 12.714542386831276, 12.31151522633745, 14.72045586419753, 13.458656719953654, 7.5, 9.557234519486807, 10.264525925925927, 14.415091481481479, 9.09349048010974, 9.968513954358398, 11.515353635116599, 12.15), # 44
(12.37318700688266, 12.674999999999999, 12.299999999999999, 14.709375, 13.462081849155954, 7.5, 9.529411764705882, 10.2, 14.403749999999999, 9.065, 9.961363636363636, 11.499999999999998, 12.15), # 45
(12.379789524928656, 12.634568724279836, 12.288188477366253, 14.697960802469135, 13.465360482164058, 7.5, 9.500978988138465, 10.134585185185186, 14.392097407407405, 9.035956433470506, 9.953970894126448, 11.484251303155007, 12.15), # 46
(12.386101954128042, 12.59332757201646, 12.276100411522634, 14.686228086419751, 13.46849239871744, 7.5, 9.471993125151295, 10.068459259259258, 14.380151481481482, 9.006424307270233, 9.946342910587354, 11.468133882030179, 12.15), # 47
(12.392123339488554, 12.551355555555554, 12.263755555555555, 14.674191666666667, 13.471477378555573, 7.5, 9.442511111111111, 10.001800000000001, 14.367930000000001, 8.976468148148147, 9.938486868686867, 11.451674074074074, 12.15), # 48
(12.397852726017943, 12.508731687242797, 12.251173662551441, 14.661866358024692, 13.474315201417928, 7.5, 9.412589881384651, 9.934785185185184, 14.355450740740741, 8.946152482853224, 9.930409951365506, 11.434898216735254, 12.15), # 49
(12.403289158723938, 12.46553497942387, 12.23837448559671, 14.649266975308642, 13.477005647043978, 7.5, 9.38228637133866, 9.867592592592592, 14.342731481481481, 8.91554183813443, 9.922119341563786, 11.417832647462278, 12.15), # 50
(12.408431682614292, 12.421844444444444, 12.225377777777776, 14.636408333333332, 13.479548495173196, 7.5, 9.351657516339868, 9.8004, 14.329790000000001, 8.88470074074074, 9.913622222222223, 11.400503703703704, 12.15), # 51
(12.413279342696734, 12.377739094650208, 12.21220329218107, 14.62330524691358, 13.481943525545056, 7.5, 9.320760251755022, 9.733385185185183, 14.316644074074073, 8.853693717421125, 9.904925776281331, 11.382937722908094, 12.15), # 52
(12.417831183979011, 12.333297942386832, 12.198870781893005, 14.609972530864196, 13.484190517899036, 7.5, 9.28965151295086, 9.666725925925926, 14.303311481481483, 8.822585294924554, 9.89603718668163, 11.365161042524004, 12.15), # 53
(12.42208625146886, 12.2886, 12.185399999999998, 14.596425, 13.486289251974602, 7.5, 9.258388235294117, 9.600599999999998, 14.28981, 8.79144, 9.886963636363634, 11.347199999999999, 12.15), # 54
(12.426043590174027, 12.24372427983539, 12.171810699588478, 14.5826774691358, 13.488239507511228, 7.5, 9.227027354151536, 9.535185185185185, 14.276157407407407, 8.760322359396433, 9.877712308267864, 11.329080932784636, 12.15), # 55
(12.429702245102245, 12.198749794238683, 12.158122633744856, 14.568744753086419, 13.49004106424839, 7.5, 9.195625804889858, 9.470659259259259, 14.262371481481482, 8.729296899862826, 9.868290385334829, 11.310830178326475, 12.15), # 56
(12.433061261261258, 12.153755555555556, 12.144355555555556, 14.554641666666665, 13.49169370192556, 7.5, 9.164240522875817, 9.407200000000001, 14.24847, 8.698428148148148, 9.85870505050505, 11.292474074074073, 12.15), # 57
(12.436119683658815, 12.108820576131688, 12.130529218106995, 14.540383024691355, 13.493197200282209, 7.5, 9.132928443476155, 9.344985185185184, 14.23447074074074, 8.667780631001373, 9.848963486719043, 11.274038957475994, 12.15), # 58
(12.438876557302644, 12.064023868312757, 12.116663374485597, 14.525983641975307, 13.494551339057814, 7.5, 9.101746502057614, 9.284192592592593, 14.220391481481482, 8.637418875171468, 9.839072876917319, 11.255551165980796, 12.15), # 59
(12.441330927200491, 12.019444444444444, 12.102777777777776, 14.511458333333334, 13.495755897991843, 7.5, 9.070751633986927, 9.225, 14.20625, 8.607407407407408, 9.829040404040404, 11.237037037037037, 12.15), # 60
(12.443481838360098, 11.975161316872429, 12.08889218106996, 14.496821913580245, 13.496810656823774, 7.5, 9.040000774630839, 9.167585185185185, 14.192064074074073, 8.577810754458161, 9.818873251028807, 11.218522908093279, 12.15), # 61
(12.445328335789204, 11.931253497942386, 12.075026337448561, 14.482089197530865, 13.497715395293081, 7.5, 9.009550859356088, 9.112125925925925, 14.177851481481481, 8.548693443072702, 9.808578600823045, 11.20003511659808, 12.15), # 62
(12.44686946449555, 11.887799999999999, 12.0612, 14.467275, 13.498469893139227, 7.5, 8.979458823529411, 9.0588, 14.16363, 8.520119999999999, 9.798163636363636, 11.1816, 12.15), # 63
(12.448104269486876, 11.844879835390946, 12.047432921810698, 14.452394135802468, 13.499073930101698, 7.5, 8.94978160251755, 9.007785185185186, 14.149417407407407, 8.492154951989026, 9.787635540591094, 11.1632438957476, 12.15), # 64
(12.449031795770926, 11.802572016460903, 12.033744855967079, 14.437461419753085, 13.49952728591996, 7.5, 8.920576131687243, 8.959259259259259, 14.135231481481481, 8.464862825788751, 9.777001496445942, 11.144993141289435, 12.15), # 65
(12.449651088355436, 11.760955555555556, 12.020155555555556, 14.422491666666666, 13.499829740333487, 7.5, 8.891899346405228, 8.913400000000001, 14.12109, 8.438308148148147, 9.766268686868687, 11.126874074074076, 12.15), # 66
(12.44996119224815, 11.720109465020576, 12.00668477366255, 14.407499691358023, 13.499981073081754, 7.5, 8.863808182038246, 8.870385185185187, 14.10701074074074, 8.412555445816187, 9.755444294799851, 11.108913031550067, 12.15), # 67
(12.44974993737699, 11.679898367184387, 11.993287139917694, 14.392370088566828, 13.499853546356814, 7.49986081390032, 8.836218233795575, 8.830012620027434, 14.092905418381346, 8.3875445299766, 9.74434318624845, 11.09103602627969, 12.149850180041152), # 68
(12.447770048309177, 11.639094623655915, 11.979586111111109, 14.376340217391302, 13.498692810457515, 7.49876049382716, 8.808321817615935, 8.790118518518518, 14.078157407407408, 8.362567668845314, 9.731835406698563, 11.072662768031188, 12.148663194444444), # 69
(12.443862945070673, 11.597510951812026, 11.965522119341562, 14.35930454911433, 13.49639917695473, 7.496593507087334, 8.779992161473643, 8.75034293552812, 14.062683470507546, 8.33750342935528, 9.717778663831295, 11.05370731355137, 12.14631880144033), # 70
(12.438083592771514, 11.555172202309835, 11.951100102880657, 14.341288204508858, 13.493001694504963, 7.49339497027892, 8.751241991446784, 8.710699039780522, 14.046506652949246, 8.312352431211167, 9.702224844940634, 11.034183524655257, 12.142847865226338), # 71
(12.430486956521738, 11.51210322580645, 11.936324999999998, 14.322316304347826, 13.488529411764706, 7.4892, 8.722084033613445, 8.671199999999999, 14.02965, 8.287115294117646, 9.685225837320575, 11.014105263157894, 12.13828125), # 72
(12.421128001431383, 11.46832887295898, 11.921201748971193, 14.302413969404187, 13.48301137739046, 7.48404371284865, 8.69253101405171, 8.631858984910837, 14.012136556927299, 8.261792637779392, 9.666833528265105, 10.993486390874303, 12.132649819958848), # 73
(12.410061692610485, 11.423873994424532, 11.905735288065841, 14.281606320450885, 13.47647664003873, 7.477961225422954, 8.662595658839667, 8.59268916323731, 13.993989368998628, 8.236385081901073, 9.647099805068226, 10.972340769619521, 12.125984439300412), # 74
(12.397342995169081, 11.378763440860213, 11.889930555555553, 14.25991847826087, 13.468954248366014, 7.470987654320988, 8.6322906940554, 8.553703703703704, 13.97523148148148, 8.210893246187362, 9.626076555023921, 10.950682261208575, 12.118315972222222), # 75
(12.383026874217212, 11.33302206292314, 11.873792489711933, 14.237375563607086, 13.460473251028805, 7.463158116140832, 8.601628845776993, 8.514915775034293, 13.955885939643347, 8.185317750342934, 9.60381566542619, 10.928524727456498, 12.10967528292181), # 76
(12.367168294864912, 11.286674711270411, 11.857326028806582, 14.214002697262478, 13.451062696683609, 7.454507727480566, 8.570622840082535, 8.476338545953361, 13.935975788751714, 8.15965921407246, 9.580369023569023, 10.905882030178327, 12.10009323559671), # 77
(12.349822222222222, 11.23974623655914, 11.84053611111111, 14.189824999999999, 13.440751633986928, 7.445071604938271, 8.53928540305011, 8.437985185185186, 13.915524074074073, 8.133918257080609, 9.55578851674641, 10.882768031189086, 12.089600694444444), # 78
(12.331043621399177, 11.192261489446436, 11.823427674897118, 14.164867592592591, 13.429569111595256, 7.434884865112025, 8.5076292607578, 8.399868861454047, 13.894553840877913, 8.108095499072055, 9.530126032252346, 10.859196592303805, 12.07822852366255), # 79
(12.310887457505816, 11.144245320589407, 11.806005658436213, 14.139155595813206, 13.417544178165095, 7.423982624599908, 8.475667139283697, 8.362002743484226, 13.873088134430727, 8.082191559751472, 9.503433457380826, 10.835181575337522, 12.066007587448558), # 80
(12.289408695652174, 11.09572258064516, 11.788274999999999, 14.112714130434783, 13.40470588235294, 7.412399999999999, 8.443411764705882, 8.3244, 13.851149999999999, 8.05620705882353, 9.475762679425838, 10.810736842105262, 12.052968749999998), # 81
(12.26666230094829, 11.046718120270809, 11.770240637860082, 14.085568317230273, 13.391083272815298, 7.40017210791038, 8.410875863102444, 8.28707379972565, 13.828762482853223, 8.030142615992899, 9.447165585681375, 10.785876254422064, 12.039142875514404), # 82
(12.242703238504205, 10.997256790123457, 11.751907510288065, 14.057743276972623, 13.376705398208665, 7.387334064929126, 8.378072160551463, 8.250037311385459, 13.805948628257887, 8.003998850964253, 9.417694063441433, 10.760613674102954, 12.0245608281893), # 83
(12.21758647342995, 10.947363440860215, 11.733280555555554, 14.029264130434782, 13.361601307189543, 7.373920987654321, 8.345013383131029, 8.213303703703703, 13.78273148148148, 7.977776383442266, 9.3874, 10.734962962962962, 12.009253472222222), # 84
(12.191366970835569, 10.897062923138192, 11.714364711934154, 14.000155998389696, 13.345800048414427, 7.359967992684042, 8.311712256919229, 8.176886145404664, 13.759134087791493, 7.951475833131606, 9.356335282651072, 10.708937982817124, 11.9932516718107), # 85
(12.164099695831096, 10.846380087614497, 11.695164917695474, 13.970444001610307, 13.32933067053982, 7.34551019661637, 8.278181507994145, 8.14079780521262, 13.73517949245542, 7.925097819736949, 9.32455179868864, 10.682552595480471, 11.976586291152262), # 86
(12.135839613526569, 10.795339784946236, 11.67568611111111, 13.940153260869563, 13.312222222222223, 7.330582716049382, 8.244433862433862, 8.10505185185185, 13.710890740740743, 7.8986429629629615, 9.292101435406698, 10.655820662768031, 11.959288194444444), # 87
(12.106641689032028, 10.74396686579052, 11.655933230452675, 13.90930889694042, 13.29450375211813, 7.315220667581161, 8.210482046316468, 8.069661454046638, 13.686290877914953, 7.8721118825143215, 9.259036080099238, 10.628756046494837, 11.941388245884776), # 88
(12.076560887457505, 10.69228618080446, 11.63591121399177, 13.877936030595812, 13.276204308884047, 7.299459167809785, 8.176338785720048, 8.034639780521262, 13.661402949245542, 7.845505198095699, 9.225407620060253, 10.601372608475922, 11.922917309670781), # 89
(12.045652173913043, 10.640322580645162, 11.615625, 13.846059782608696, 13.257352941176471, 7.283333333333333, 8.142016806722689, 7.999999999999999, 13.636250000000002, 7.818823529411764, 9.191267942583732, 10.573684210526315, 11.90390625), # 90
(12.013970513508676, 10.588100915969731, 11.59507952674897, 13.813705273752015, 13.237978697651899, 7.266878280749885, 8.107528835402473, 7.965755281207133, 13.610855075445818, 7.79206749616719, 9.15666893496367, 10.54570471446105, 11.884385931069957), # 91
(11.981570871354446, 10.535646037435285, 11.574279732510288, 13.78089762479871, 13.218110626966835, 7.250129126657521, 8.07288759783749, 7.9319187928669415, 13.585241220850481, 7.7652377180666505, 9.121662484494063, 10.517447982095156, 11.864387217078187), # 92
(11.948508212560386, 10.482982795698925, 11.553230555555555, 13.74766195652174, 13.197777777777778, 7.2331209876543205, 8.03810582010582, 7.898503703703704, 13.55943148148148, 7.738334814814813, 9.0863004784689, 10.488927875243665, 11.84394097222222), # 93
(11.914837502236535, 10.43013604141776, 11.531936934156379, 13.714023389694042, 13.177009198741224, 7.215888980338362, 8.003196228285553, 7.865523182441701, 13.53344890260631, 7.7113594061163555, 9.050634804182172, 10.460158255721609, 11.823078060699588), # 94
(11.880613705492932, 10.377130625248904, 11.510403806584362, 13.680007045088566, 13.155833938513677, 7.198468221307727, 7.968171548454772, 7.832990397805213, 13.507316529492455, 7.684312111675945, 9.014717348927874, 10.431152985344015, 11.801829346707818), # 95
(11.845891787439614, 10.323991397849465, 11.488636111111111, 13.645638043478261, 13.134281045751633, 7.180893827160493, 7.933044506691564, 7.800918518518519, 13.481057407407405, 7.657193551198256, 8.9786, 10.401925925925926, 11.780225694444445), # 96
(11.810726713186616, 10.270743209876544, 11.466638786008229, 13.610941505636069, 13.112379569111596, 7.163200914494741, 7.897827829074016, 7.769320713305898, 13.454694581618655, 7.63000434438796, 8.942334644692538, 10.372490939282363, 11.758297968106996), # 97
(11.775173447843981, 10.217410911987256, 11.444416769547324, 13.575942552334944, 13.090158557250062, 7.145424599908551, 7.86253424168021, 7.738210150891632, 13.428251097393689, 7.602745110949729, 8.905973170299486, 10.342861887228358, 11.736077031893004), # 98
(11.739286956521738, 10.16401935483871, 11.421975, 13.540666304347825, 13.06764705882353, 7.1276, 7.827176470588236, 7.707599999999999, 13.40175, 7.575416470588234, 8.869567464114832, 10.313052631578946, 11.71359375), # 99
(11.703122204329933, 10.110593389088011, 11.39931841563786, 13.505137882447665, 13.044874122488501, 7.109762231367169, 7.791767241876174, 7.677503429355281, 13.375214334705076, 7.548019043008149, 8.833169413432572, 10.28307703414916, 11.690878986625515), # 100
(11.6667341563786, 10.057157865392274, 11.376451954732511, 13.469382407407409, 13.021868796901476, 7.091946410608139, 7.756319281622114, 7.647933607681755, 13.348667146776405, 7.5205534479141445, 8.796830905546694, 10.252948956754024, 11.667963605967076), # 101
(11.630177777777778, 10.003737634408603, 11.353380555555555, 13.433425, 12.998660130718955, 7.074187654320988, 7.720845315904139, 7.618903703703703, 13.32213148148148, 7.4930203050108934, 8.760603827751195, 10.222682261208577, 11.644878472222222), # 102
(11.593508033637502, 9.950357546794105, 11.3301091563786, 13.39729078099839, 12.975277172597435, 7.056521079103795, 7.685358070800336, 7.590426886145404, 13.295630384087792, 7.465420234003066, 8.724540067340067, 10.192290809327847, 11.621654449588474), # 103
(11.556779889067812, 9.897042453205893, 11.30664269547325, 13.361004871175522, 12.951748971193416, 7.03898180155464, 7.649870272388791, 7.562516323731138, 13.269186899862826, 7.437753854595336, 8.6886915116073, 10.161788462926864, 11.598322402263374), # 104
(11.520048309178742, 9.843817204301073, 11.28298611111111, 13.324592391304348, 12.928104575163397, 7.021604938271605, 7.614394646747589, 7.535185185185185, 13.242824074074074, 7.410021786492375, 8.653110047846889, 10.131189083820663, 11.574913194444443), # 105
(11.483368259080336, 9.790706650736759, 11.259144341563784, 13.288078462157811, 12.904373033163884, 7.004425605852766, 7.578943919954813, 7.508446639231824, 13.216564951989024, 7.382224649398854, 8.617847563352825, 10.100506533824273, 11.551457690329217), # 106
(11.446794703882626, 9.737735643170053, 11.235122325102882, 13.251488204508856, 12.880583393851365, 6.987478920896206, 7.543530818088553, 7.482313854595337, 13.190432578875171, 7.354363063019446, 8.582955945419101, 10.069754674752724, 11.527986754115226), # 107
(11.410382608695652, 9.684929032258065, 11.210925000000001, 13.214846739130435, 12.856764705882352, 6.9708, 7.508168067226889, 7.4568, 13.16445, 7.326437647058824, 8.548487081339712, 10.038947368421054, 11.504531250000001), # 108
(11.374186938629451, 9.632311668657906, 11.18655730452675, 13.178179186795488, 12.832946017913338, 6.954423959762231, 7.472868393447913, 7.431918244170096, 13.138640260631002, 7.298449021221656, 8.514492858408648, 10.008098476644285, 11.48112204218107), # 109
(11.338262658794058, 9.579908403026684, 11.162024176954734, 13.141510668276974, 12.809156378600823, 6.938385916780978, 7.437644522829707, 7.407681755829903, 13.113026406035663, 7.270397805212619, 8.4810251639199, 9.977221861237457, 11.457789994855966), # 110
(11.302664734299517, 9.527744086021507, 11.137330555555558, 13.104866304347826, 12.785424836601306, 6.922720987654322, 7.402509181450357, 7.384103703703703, 13.087631481481482, 7.242284618736383, 8.448135885167463, 9.946331384015595, 11.434565972222222), # 111
(11.26744813025586, 9.47584356829948, 11.112481378600824, 13.068271215780998, 12.76178044057129, 6.907464288980339, 7.367475095387949, 7.361197256515775, 13.062478532235938, 7.214110081497618, 8.41587690944533, 9.915440906793732, 11.411480838477365), # 112
(11.232605068443652, 9.424318342543142, 11.087541393902482, 13.031800658990448, 12.738210816208445, 6.892643723057416, 7.332631156388123, 7.339023082536727, 13.037655373510344, 7.185965683935275, 8.38430868738344, 9.884631523805313, 11.388532681011865), # 113
(11.197777077480078, 9.373676620230642, 11.062854810025941, 12.995747305532804, 12.71447202547959, 6.8782255302358815, 7.298421850092694, 7.317853511406144, 13.013542842855673, 7.158378201495339, 8.353493204535836, 9.85429460653557, 11.365530496992042), # 114
(11.162861883604794, 9.323936638419655, 11.038436319248781, 12.960101406218135, 12.69048921346632, 6.864172214998518, 7.264871580229873, 7.297683185134451, 12.990149974402547, 7.131390393585692, 8.323385413712511, 9.824445099070621, 11.342407957992451), # 115
(11.127815847885161, 9.275025937550042, 11.014238627980648, 12.924799380319685, 12.666226231660534, 6.8504506527445175, 7.231925781033471, 7.278456375478791, 12.967417607073395, 7.104952030139456, 8.293927117525778, 9.795027836984815, 11.319128711707068), # 116
(11.092595331388527, 9.226872058061664, 10.990214442631183, 12.889777647110693, 12.641646931554133, 6.837027718873069, 7.199529886737303, 7.260117354196302, 12.945286579790643, 7.079012881089755, 8.26506011858794, 9.7659876558525, 11.295656405829869), # 117
(11.057156695182252, 9.179402540394388, 10.96631646961004, 12.8549726258644, 12.61671516463901, 6.8238702887833655, 7.167629331575178, 7.2426103930441155, 12.923697731476722, 7.053522716369711, 8.236726219511308, 9.737269391248018, 11.271954688054828), # 118
(11.02145630033369, 9.132544924988075, 10.942497415326867, 12.820320735854047, 12.591394782407065, 6.810945237874599, 7.136169549780907, 7.225879763779374, 12.902591901054052, 7.028431305912446, 8.208867222908193, 9.708817878745721, 11.247987206075917), # 119
(10.985450507910194, 9.08622675228259, 10.918709986191313, 12.785758396352874, 12.565649636350196, 6.7982194415459585, 7.105095975588303, 7.209869738159211, 12.88190992744507, 7.003688419651087, 8.181424931390898, 9.680577953919956, 11.223717607587115), # 120
(10.949095678979122, 9.040375562717795, 10.894906888613024, 12.75122202663412, 12.539443577960302, 6.7856597751966365, 7.0743540432311764, 7.1945245879407675, 12.861592649572199, 6.979243827518755, 8.154341147571738, 9.652494452345065, 11.199109540282393), # 121
(10.912348174607825, 8.994918896733553, 10.871040829001652, 12.716648045971025, 12.512740458729281, 6.773233114225823, 7.043889186943341, 7.179788584881178, 12.841580906357867, 6.955047299448572, 8.127557674063022, 9.6245122095954, 11.174126651855724), # 122
(10.875164355863662, 8.949784294769728, 10.847064513766842, 12.681972873636832, 12.485504130149028, 6.76090633403271, 7.013646840958606, 7.16560600073758, 12.821815536724504, 6.931048605373665, 8.101016313477052, 9.596576061245305, 11.148732590001085), # 123
(10.837500583813984, 8.904899297266184, 10.822930649318243, 12.647132928904783, 12.457698443711445, 6.748646310016486, 6.983572439510783, 7.151921107267111, 12.802237379594539, 6.9071975152271525, 8.074658868426143, 9.56863084286913, 11.122891002412453), # 124
(10.79931321952615, 8.860191444662783, 10.798591942065508, 12.612064631048112, 12.429287250908427, 6.736419917576347, 6.953611416833687, 7.138678176226909, 12.78278727389039, 6.88344379894216, 8.048427141522602, 9.540621390041217, 11.096565536783794), # 125
(10.760558624067514, 8.815588277399392, 10.774001098418278, 12.576704399340064, 12.400234403231872, 6.724194032111481, 6.923709207161124, 7.12582147937411, 12.763406058534501, 6.859737226451811, 8.022262935378736, 9.51249253833592, 11.069719840809094), # 126
(10.721193158505432, 8.771017335915868, 10.749110824786205, 12.540988653053878, 12.370503752173677, 6.711935529021078, 6.893811244726913, 7.113295288465854, 12.744034572449289, 6.836027567689229, 7.9961080526068535, 9.484189123327578, 11.042317562182317), # 127
(10.681173183907255, 8.72640616065208, 10.72387382757894, 12.504853811462798, 12.340059149225747, 6.699611283704333, 6.863862963764858, 7.101043875259275, 12.72461365455718, 6.8122645925875345, 7.969904295819269, 9.455655980590546, 11.014322348597444), # 128
(10.640455061340337, 8.681682292047888, 10.698242813206127, 12.468236293840057, 12.308864445879973, 6.687188171560433, 6.833809798508775, 7.089011511511512, 12.705084143780608, 6.788398071079854, 7.943593467628284, 9.426837945699162, 10.985697847748446), # 129
(10.598995151872039, 8.63677327054316, 10.672170488077414, 12.431072519458903, 12.276883493628256, 6.6746330679885695, 6.803597183192475, 7.077142468979701, 12.685386879042001, 6.764377773099308, 7.9171173706462135, 9.397679854227782, 10.956407707329298), # 130
(10.556749816569713, 8.591606636577751, 10.645609558602457, 12.39329890759257, 12.244080143962494, 6.661912848387936, 6.773170552049771, 7.06538101942098, 12.665462699263783, 6.740153468579022, 7.890417807485361, 9.36812654175075, 10.926415575033973), # 131
(10.51367541650071, 8.546109930591532, 10.618512731190895, 12.354851877514305, 12.210418248374584, 6.648994388157723, 6.7424753393144705, 7.053671434592488, 12.645252443368385, 6.715674927452118, 7.863436580758037, 9.33812284384241, 10.89568509855645), # 132
(10.469728312732395, 8.500210693024362, 10.59083271225238, 12.315667848497341, 12.175861658356423, 6.63584456269712, 6.711456979220387, 7.041957986251359, 12.624696950278231, 6.690891919651718, 7.8361154930765515, 9.307613596077111, 10.864179925590703), # 133
(10.424864866332113, 8.453836464316106, 10.562522208196564, 12.275683239814922, 12.14037422539991, 6.622430247405318, 6.6800609060013345, 7.0301849461547326, 12.603737058915753, 6.665754215110948, 7.808396347053214, 9.2765436340292, 10.831863703830699), # 134
(10.379041438367224, 8.406914784906629, 10.53353392543309, 12.234834470740294, 12.103919800996945, 6.60871831768151, 6.648232553891121, 7.018296586059743, 12.582313608203375, 6.640211583762931, 7.78022094530033, 9.244857793273022, 10.798700080970423), # 135
(10.332214389905081, 8.35937319523579, 10.50382057037161, 12.193057960546687, 12.066462236639419, 6.594675648924887, 6.615917357123561, 7.0062371777235315, 12.560367437063528, 6.6142137955407865, 7.751531090430213, 9.212500909382928, 10.764652704703844), # 136
(10.28434008201304, 8.311139235743456, 10.473334849421772, 12.150290128507349, 12.027965383819241, 6.580269116534637, 6.583060749932466, 6.993950992903235, 12.537839384418639, 6.587710620377641, 7.722268585055167, 9.179417817933263, 10.729685222724932), # 137
(10.235374875758456, 8.26214044686949, 10.442029468993221, 12.106467393895516, 11.988393094028302, 6.565465595909957, 6.5496081665516455, 6.981382303355987, 12.514670289191137, 6.560651828206615, 7.692375231787501, 9.145553354498373, 10.693761282727667), # 138
(10.185275132208682, 8.212304369053752, 10.409857135495608, 12.06152617598443, 11.947709218758497, 6.550231962450032, 6.515505041214911, 6.968475380838929, 12.490800990303445, 6.532987188960836, 7.661792833239527, 9.110852354652607, 10.656844532406023), # 139
(10.133997212431076, 8.16155854273611, 10.376770555338585, 12.015402894047332, 11.905877609501735, 6.534535091554055, 6.480696808156076, 6.955174497109195, 12.466172326677999, 6.5046664725734225, 7.630463192023552, 9.07525965397031, 10.618898619453978), # 140
(10.081497477492995, 8.109830508356424, 10.342722434931792, 11.968033967357464, 11.862862117749904, 6.518341858621218, 6.445128901608954, 6.9414239239239235, 12.440725137237216, 6.4756394489775015, 7.598328110751885, 9.03872008802583, 10.579887191565495), # 141
(10.027732288461786, 8.057047806354559, 10.307665480684884, 11.919355815188064, 11.818626594994903, 6.501619139050712, 6.408746755807351, 6.927167933040253, 12.41440026090353, 6.445855888106193, 7.565329392036836, 9.001178492393512, 10.539773896434559), # 142
(9.972658006404808, 8.003137977170377, 10.27155239900751, 11.86930485681237, 11.773134892728635, 6.484333808241727, 6.371495804985082, 6.912350796215319, 12.387138536599375, 6.415265559892623, 7.531408838490711, 8.962579702647707, 10.49852238175514), # 143
(9.916230992389421, 7.948028561243743, 10.234335896309313, 11.817817511503627, 11.726350862442994, 6.466452741593456, 6.333321483375959, 6.896916785206259, 12.358880803247171, 6.383818234269912, 7.496508252725821, 8.922868554362758, 10.456096295221217), # 144
(9.858407607482972, 7.891647099014518, 10.195968678999947, 11.764830198535073, 11.67823835562988, 6.4479428145050885, 6.294169225213792, 6.880810171770211, 12.329567899769344, 6.351463681171185, 7.460569437354474, 8.881989883113016, 10.41245928452676), # 145
(9.79914421275282, 7.83392113092257, 10.156403453489059, 11.71027933717995, 11.62876122378119, 6.428770902375816, 6.253984464732396, 6.863975227664311, 12.299140665088327, 6.318151670529565, 7.423534194988978, 8.839888524472823, 10.367574997365741), # 146
(9.73839716926632, 7.774778197407756, 10.115592926186292, 11.654101346711496, 11.577883318388821, 6.4089038806048295, 6.212712636165577, 6.846356224645698, 12.267539938126548, 6.283831972278175, 7.385344328241643, 8.796509314016532, 10.321407081432142), # 147
(9.676122838090825, 7.714145838909944, 10.0734898035013, 11.596232646402955, 11.525568490944673, 6.38830862459132, 6.170299173747152, 6.827897434471509, 12.234706557806435, 6.248454356350137, 7.345941639724779, 8.751797087318483, 10.27391918441993), # 148
(9.612277580293695, 7.651951595868995, 10.030046791843732, 11.536609655527563, 11.471780592940645, 6.366952009734479, 6.126689511710929, 6.80854312889888, 12.200581363050405, 6.211968592678576, 7.3052679320506915, 8.705696679953029, 10.225074954023084), # 149
(9.546817756942277, 7.588123008724775, 9.985216597623232, 11.475168793358565, 11.416483475868631, 6.344800911433499, 6.08182908429072, 6.788237579684948, 12.165105192780901, 6.174324451196612, 7.2632650078316905, 8.658152927494514, 10.174838037935576), # 150
(9.47969972910393, 7.522587617917144, 9.93895192724945, 11.411846479169196, 11.359640991220532, 6.321822205087566, 6.03566332572034, 6.7669250585868514, 12.128218885920345, 6.135471701837373, 7.2198746696800855, 8.609110665517285, 10.123172083851381), # 151
(9.41087985784601, 7.455272963885967, 9.89120548713204, 11.346579132232701, 11.301216990488243, 6.297982766095876, 5.9881376702335976, 6.744549837361729, 12.089863281391164, 6.095360114533979, 7.175038720208185, 8.558514729595691, 10.070040739464476), # 152
(9.340314504235872, 7.386106587071107, 9.841929983680641, 11.279303171822319, 11.241175325163667, 6.273249469857618, 5.939197552064303, 6.721056187766714, 12.049979218115787, 6.053939459219555, 7.128698962028299, 8.506309955304076, 10.015407652468832), # 153
(9.267960029340873, 7.315016027912428, 9.79107812330491, 11.209955017211291, 11.179479846738696, 6.247589191771985, 5.888788405446274, 6.696388381558948, 12.008507535016639, 6.011159505827223, 7.080797197752734, 8.45244117821679, 9.959236470558428), # 154
(9.193772794228362, 7.241928826849794, 9.73860261241449, 11.138471087672853, 11.116094406705235, 6.220968807238165, 5.836855664613313, 6.670490690495563, 11.965389071016153, 5.966970024290105, 7.0312752299938, 8.396853233908178, 9.901490841427231), # 155
(9.117709159965697, 7.166772524323065, 9.684456157419032, 11.06478780248025, 11.050982856555176, 6.193355191655353, 5.7833447637992395, 6.643307386333702, 11.920564665036752, 5.921320784541327, 6.980074861363805, 8.339490957952586, 9.842134412769221), # 156
(9.039725487620235, 7.089474660772107, 9.628591464728181, 10.988841580906724, 10.984109047780422, 6.164715220422736, 5.728201137237862, 6.614782740830498, 11.873975156000865, 5.874161556514009, 6.927137894475059, 8.280299185924363, 9.781130832278372), # 157
(8.957617135686286, 7.008543744926709, 9.568310344682827, 10.907723497981491, 10.912417327045196, 6.133229371580532, 5.6701280651134285, 6.582956342819247, 11.821994509918916, 5.824039099549372, 6.870714903046731, 8.217119477033206, 9.715783031298415), # 158
(8.858744120374082, 6.915678383519373, 9.488085382083584, 10.804772590546143, 10.818229571737954, 6.088427577608523, 5.601855316062859, 6.536656239317259, 11.743712713466573, 5.762737192918494, 6.800900322742793, 8.13763502841973, 9.630513176304232), # 159
(8.741846513885172, 6.810116074248857, 9.386305149547066, 10.67829301249063, 10.699704157616154, 6.0292095552572205, 5.5226924980605405, 6.4747190274328155, 11.636910272674381, 5.689446782235472, 6.716711410331447, 8.040602338665416, 9.523704730672296), # 160
(8.607866465503152, 6.692545041696563, 9.26405636629237, 10.529487004508074, 10.558071749138534, 5.956292689884377, 5.433217735208252, 6.397920639731736, 11.50299572039882, 5.604789831805125, 6.618889985519648, 7.926920962689085, 9.396448853782916), # 161
(8.457746124511628, 6.563653510443886, 9.122425751538595, 10.359556807291591, 10.394563010763845, 5.870394366847746, 5.334009151607771, 6.307037008779842, 11.343377589496363, 5.509388305932277, 6.508177868014344, 7.797490455409552, 9.2498367050164), # 162
(8.292427640194196, 6.424129705072228, 8.962500024504841, 10.16970466153432, 10.210408606950825, 5.772231971505087, 5.22564487136088, 6.20284406714295, 11.159464412823487, 5.40386416892175, 6.38531687752249, 7.653210371745638, 9.084959443753055), # 163
(8.11285316183446, 6.2746618501629845, 8.785365904410211, 9.961132807929381, 10.006839202158226, 5.662522889214155, 5.108703018569359, 6.086117747386882, 10.952664723236667, 5.2888393850783615, 6.251048833751035, 7.494980266616163, 8.902908229373192), # 164
(7.9199648387160195, 6.115938170297558, 8.592110110473802, 9.735043487169902, 9.785085460844789, 5.541984505332703, 4.983761717334986, 5.957633982077455, 10.724387053592375, 5.164935918706936, 6.106115556406933, 7.323699694939943, 8.704774221257123), # 165
(7.714704820122476, 5.948646890057345, 8.383819361914712, 9.492638939949002, 9.546378047469256, 5.41133420521849, 4.851399091759543, 5.818168703780493, 10.476039936747087, 5.0327757341122945, 5.9512588651971345, 7.140268211635801, 8.491648578785155), # 166
(7.498015255337426, 5.773476234023744, 8.161580377952045, 9.235121406959811, 9.291947626490375, 5.27128937422927, 4.712193265944809, 5.668497845061811, 10.209031905557278, 4.892980795599256, 5.787220579828592, 6.94558537162255, 8.264622461337595), # 167
(7.2708382936444735, 5.591114426778154, 7.926479877804897, 8.963693128895455, 9.02302486236689, 5.122567397722799, 4.5667223639925645, 5.509397338487231, 9.924771492879426, 4.746173067472646, 5.614742520008257, 6.740550729819013, 8.024787028294753), # 168
(7.034116084327218, 5.402249692901975, 7.67960458069237, 8.67955634644906, 8.740840419557543, 4.965885661056833, 4.4155645100045895, 5.341643116622574, 9.624667231570005, 4.592974514037284, 5.434566505443081, 6.526063841144007, 7.773233439036942), # 169
(6.78879077666926, 5.207570256976605, 7.422041205833562, 8.383913300313743, 8.44662496252108, 4.8019615495891275, 4.259297828082663, 5.166011112033656, 9.310127654485486, 4.434007099597989, 5.247434355840019, 6.3030242605163505, 7.5110528529444665), # 170
(6.5358045199542, 5.007764343583441, 7.154876472447573, 8.077966231182643, 8.141609155716246, 4.631512448677438, 4.098500442328566, 4.983277257286299, 8.982561294482347, 4.269892788459586, 5.054087890906017, 6.072331542854863, 7.239336429397638), # 171
(6.276099463465638, 4.803520177303883, 6.879197099753504, 7.762917379748876, 7.827023663601784, 4.45525574367952, 3.9337504768440783, 4.794217484946325, 8.643376684417062, 4.101253544926895, 4.855268930348032, 5.834885243078365, 6.959175327776763), # 172
(6.010617756487176, 4.59552598271933, 6.596089806970453, 7.43996898670557, 7.504099150636442, 4.27390881995313, 3.7656260557309795, 4.599607727579548, 8.293982357146106, 3.9287113333047374, 4.651719293873013, 5.59158491610567, 6.671660707462155), # 173
(5.740301548302412, 4.384469984411181, 6.306641313317521, 7.110323292745848, 7.174066281278959, 4.088189062856022, 3.5947053030910503, 4.400223917751792, 7.935786845525956, 3.752888117897936, 4.444180801187913, 5.3433301168556016, 6.37788372783412), # 174
(5.466092988194946, 4.171040406960834, 6.01193833801381, 6.775182538562841, 6.838155719988083, 3.898813857745954, 3.421566343026069, 4.196841988028875, 7.570198682413086, 3.574405863011309, 4.233395271999683, 5.091020400246977, 6.078935548272969), # 175
(5.188934225448382, 3.9559254749496873, 5.713067600278413, 6.43574896484967, 6.497598131222556, 3.7065005899806795, 3.2467872996378175, 3.9902378709766184, 7.1986264006639695, 3.3938865329496806, 4.020104526015276, 4.835555321198615, 5.7759073281590085), # 176
(4.909767409346319, 3.7398134129591414, 5.411115819330436, 6.09322481229946, 6.1536241794411275, 3.511966644917956, 3.0709462970280748, 3.781187499160839, 6.822478533135084, 3.2119520920178695, 3.8050503829416424, 4.5778344346293345, 5.4698902268725496), # 177
(4.629534689172356, 3.5233924455705936, 5.107169714388976, 5.748812321605339, 5.807464529102536, 3.3159294079155393, 2.894621459298621, 3.5704668051473587, 6.443163612682903, 3.0292245045207, 3.588974662485735, 4.318757295457952, 5.161975403793902), # 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 = (
(7, 5, 3, 9, 2, 1, 2, 2, 4, 0, 1, 0, 0, 7, 6, 3, 5, 4, 1, 3, 2, 3, 2, 1, 0, 0), # 0
(13, 16, 8, 17, 7, 4, 2, 4, 4, 0, 3, 0, 0, 13, 15, 11, 9, 7, 5, 5, 2, 5, 5, 1, 3, 0), # 1
(17, 24, 12, 22, 15, 9, 6, 5, 6, 3, 4, 0, 0, 20, 25, 17, 11, 16, 9, 6, 2, 9, 8, 1, 4, 0), # 2
(24, 31, 14, 29, 20, 12, 7, 6, 8, 5, 6, 0, 0, 26, 28, 22, 13, 22, 13, 12, 3, 13, 11, 1, 4, 0), # 3
(30, 38, 22, 42, 28, 14, 12, 8, 13, 7, 6, 0, 0, 32, 37, 26, 20, 28, 16, 15, 5, 15, 15, 1, 7, 0), # 4
(35, 48, 30, 51, 32, 16, 12, 9, 15, 8, 7, 1, 0, 44, 44, 36, 29, 32, 20, 22, 8, 16, 16, 1, 8, 0), # 5
(44, 59, 38, 60, 40, 20, 15, 10, 21, 8, 9, 1, 0, 56, 49, 42, 36, 36, 23, 25, 10, 16, 20, 3, 9, 0), # 6
(51, 65, 49, 68, 48, 23, 18, 13, 23, 10, 14, 1, 0, 58, 58, 50, 38, 45, 26, 29, 13, 20, 21, 4, 11, 0), # 7
(60, 69, 57, 74, 52, 28, 21, 16, 26, 12, 16, 1, 0, 72, 64, 54, 47, 55, 33, 32, 15, 24, 23, 7, 14, 0), # 8
(76, 82, 64, 85, 61, 32, 22, 19, 30, 13, 16, 1, 0, 80, 72, 59, 48, 63, 39, 35, 18, 27, 28, 7, 14, 0), # 9
(84, 87, 72, 91, 68, 35, 27, 21, 32, 15, 16, 1, 0, 86, 81, 65, 52, 71, 43, 38, 22, 33, 30, 7, 16, 0), # 10
(92, 95, 80, 98, 74, 36, 31, 24, 34, 20, 17, 3, 0, 91, 88, 70, 55, 79, 46, 42, 26, 36, 38, 8, 17, 0), # 11
(105, 103, 86, 113, 79, 38, 35, 27, 38, 20, 20, 5, 0, 101, 97, 75, 63, 85, 52, 46, 27, 36, 40, 9, 17, 0), # 12
(115, 114, 96, 126, 82, 43, 39, 31, 42, 22, 21, 7, 0, 119, 108, 85, 70, 94, 58, 52, 28, 38, 46, 13, 18, 0), # 13
(123, 122, 103, 139, 89, 47, 44, 35, 45, 23, 21, 9, 0, 131, 121, 99, 79, 96, 63, 57, 28, 47, 52, 13, 18, 0), # 14
(132, 134, 111, 150, 97, 52, 48, 38, 53, 23, 22, 9, 0, 141, 133, 102, 85, 107, 71, 62, 28, 50, 57, 13, 20, 0), # 15
(145, 146, 117, 163, 106, 55, 54, 46, 56, 25, 26, 10, 0, 149, 145, 108, 88, 116, 77, 67, 33, 56, 63, 16, 20, 0), # 16
(158, 158, 132, 171, 112, 59, 60, 52, 63, 27, 30, 11, 0, 156, 155, 114, 95, 122, 82, 75, 38, 59, 67, 18, 20, 0), # 17
(171, 168, 141, 186, 124, 63, 63, 54, 68, 28, 30, 12, 0, 161, 165, 122, 98, 134, 82, 77, 41, 61, 69, 20, 23, 0), # 18
(183, 186, 147, 198, 128, 69, 68, 60, 72, 31, 30, 15, 0, 175, 178, 130, 110, 144, 84, 85, 43, 71, 72, 23, 23, 0), # 19
(191, 201, 159, 213, 134, 72, 75, 67, 74, 33, 34, 16, 0, 194, 190, 135, 114, 149, 93, 90, 45, 76, 76, 23, 24, 0), # 20
(202, 209, 168, 221, 144, 74, 78, 72, 79, 35, 34, 19, 0, 206, 202, 144, 120, 157, 96, 96, 46, 84, 81, 26, 24, 0), # 21
(217, 219, 183, 233, 153, 78, 84, 74, 87, 38, 36, 20, 0, 221, 210, 150, 123, 167, 105, 103, 51, 90, 83, 29, 25, 0), # 22
(225, 229, 194, 243, 167, 83, 86, 78, 91, 42, 36, 20, 0, 231, 215, 157, 130, 174, 112, 107, 54, 94, 87, 31, 26, 0), # 23
(235, 241, 205, 254, 171, 89, 90, 82, 94, 44, 38, 22, 0, 243, 224, 168, 135, 180, 118, 113, 57, 101, 89, 35, 27, 0), # 24
(247, 251, 210, 261, 185, 90, 99, 89, 99, 44, 39, 22, 0, 254, 235, 179, 144, 188, 122, 117, 62, 105, 90, 36, 27, 0), # 25
(258, 258, 218, 274, 195, 90, 101, 91, 103, 48, 41, 22, 0, 270, 245, 184, 152, 201, 126, 119, 64, 108, 97, 36, 27, 0), # 26
(275, 276, 230, 283, 211, 95, 106, 98, 107, 51, 41, 22, 0, 277, 259, 192, 162, 211, 128, 123, 71, 114, 101, 37, 28, 0), # 27
(282, 286, 242, 293, 223, 100, 110, 102, 112, 57, 43, 23, 0, 292, 271, 197, 169, 217, 131, 128, 77, 117, 104, 41, 30, 0), # 28
(290, 298, 254, 301, 233, 106, 114, 108, 117, 61, 43, 23, 0, 307, 290, 205, 176, 226, 139, 137, 82, 124, 105, 42, 32, 0), # 29
(300, 306, 266, 311, 241, 111, 118, 117, 120, 63, 45, 24, 0, 321, 304, 211, 181, 237, 143, 142, 85, 132, 109, 46, 32, 0), # 30
(315, 313, 271, 322, 249, 113, 125, 121, 128, 63, 46, 25, 0, 327, 314, 215, 191, 254, 149, 147, 88, 138, 112, 48, 32, 0), # 31
(325, 319, 281, 333, 254, 115, 130, 126, 132, 64, 48, 27, 0, 339, 319, 221, 199, 262, 158, 150, 93, 142, 115, 49, 34, 0), # 32
(335, 327, 291, 352, 261, 120, 134, 126, 137, 66, 50, 27, 0, 347, 330, 227, 203, 274, 165, 155, 97, 147, 118, 54, 34, 0), # 33
(343, 341, 301, 363, 267, 124, 136, 132, 143, 67, 50, 27, 0, 354, 343, 233, 212, 278, 169, 159, 99, 149, 125, 55, 34, 0), # 34
(360, 353, 315, 373, 277, 126, 142, 134, 149, 69, 52, 27, 0, 367, 351, 244, 218, 285, 173, 162, 104, 155, 127, 55, 35, 0), # 35
(372, 367, 329, 380, 287, 130, 150, 139, 155, 74, 54, 28, 0, 378, 356, 252, 224, 290, 182, 168, 108, 162, 131, 59, 36, 0), # 36
(386, 381, 342, 391, 300, 132, 156, 142, 155, 74, 55, 29, 0, 391, 368, 259, 230, 304, 187, 170, 114, 169, 132, 62, 36, 0), # 37
(401, 392, 355, 397, 309, 138, 157, 148, 161, 79, 56, 30, 0, 402, 374, 269, 235, 312, 194, 175, 119, 172, 134, 64, 37, 0), # 38
(410, 408, 360, 408, 314, 142, 161, 150, 163, 79, 59, 30, 0, 420, 391, 273, 241, 320, 203, 182, 121, 178, 140, 66, 39, 0), # 39
(428, 420, 367, 418, 322, 144, 165, 153, 166, 80, 59, 31, 0, 430, 400, 282, 246, 330, 206, 191, 124, 180, 143, 68, 41, 0), # 40
(439, 435, 377, 425, 335, 145, 171, 158, 168, 82, 63, 32, 0, 439, 417, 291, 252, 344, 213, 200, 132, 185, 146, 70, 43, 0), # 41
(451, 440, 390, 440, 346, 147, 174, 161, 173, 86, 65, 32, 0, 452, 436, 298, 260, 358, 217, 209, 135, 190, 149, 71, 46, 0), # 42
(466, 451, 402, 448, 352, 151, 176, 166, 178, 86, 68, 32, 0, 462, 449, 310, 271, 374, 219, 212, 135, 194, 151, 73, 47, 0), # 43
(485, 463, 411, 459, 365, 156, 184, 171, 179, 86, 69, 32, 0, 469, 458, 315, 279, 388, 223, 217, 139, 200, 153, 75, 47, 0), # 44
(497, 475, 424, 471, 378, 162, 189, 175, 185, 90, 69, 33, 0, 478, 468, 323, 284, 405, 230, 220, 142, 207, 159, 75, 48, 0), # 45
(506, 487, 430, 483, 387, 167, 191, 179, 191, 90, 73, 37, 0, 490, 479, 330, 290, 414, 240, 224, 145, 210, 162, 76, 49, 0), # 46
(518, 499, 439, 492, 400, 170, 195, 183, 194, 94, 73, 38, 0, 502, 489, 337, 295, 425, 242, 228, 146, 217, 165, 79, 50, 0), # 47
(533, 515, 447, 504, 409, 171, 200, 188, 202, 97, 75, 40, 0, 510, 500, 348, 303, 440, 247, 231, 148, 223, 167, 79, 51, 0), # 48
(551, 531, 451, 516, 422, 178, 202, 191, 210, 97, 76, 40, 0, 519, 509, 358, 308, 451, 251, 233, 152, 233, 172, 80, 52, 0), # 49
(558, 542, 463, 529, 428, 185, 207, 197, 213, 101, 77, 43, 0, 528, 518, 366, 318, 460, 257, 239, 157, 239, 174, 82, 53, 0), # 50
(573, 549, 471, 546, 436, 192, 210, 202, 219, 105, 79, 43, 0, 544, 525, 382, 329, 473, 262, 242, 160, 241, 181, 84, 53, 0), # 51
(585, 560, 483, 563, 449, 194, 213, 204, 223, 105, 81, 45, 0, 552, 532, 390, 337, 483, 268, 243, 163, 244, 184, 86, 54, 0), # 52
(604, 571, 493, 570, 455, 199, 221, 207, 229, 108, 85, 46, 0, 564, 539, 396, 343, 494, 274, 247, 167, 252, 187, 88, 55, 0), # 53
(618, 586, 503, 580, 464, 205, 228, 210, 232, 111, 85, 46, 0, 574, 541, 407, 346, 502, 281, 253, 168, 257, 191, 90, 56, 0), # 54
(625, 597, 514, 593, 470, 208, 234, 217, 235, 113, 86, 46, 0, 587, 552, 419, 353, 511, 288, 260, 173, 259, 194, 91, 57, 0), # 55
(637, 606, 528, 606, 474, 211, 237, 219, 239, 114, 87, 47, 0, 605, 568, 431, 359, 520, 298, 266, 177, 264, 198, 92, 57, 0), # 56
(649, 615, 537, 614, 481, 214, 246, 221, 242, 117, 89, 49, 0, 608, 584, 437, 368, 524, 311, 270, 178, 266, 200, 94, 58, 0), # 57
(661, 625, 549, 622, 486, 217, 249, 229, 247, 121, 89, 49, 0, 617, 600, 439, 373, 531, 319, 274, 180, 268, 204, 96, 59, 0), # 58
(672, 637, 560, 629, 492, 221, 257, 231, 250, 121, 89, 49, 0, 631, 611, 445, 379, 543, 322, 279, 184, 271, 210, 101, 61, 0), # 59
(685, 647, 566, 642, 500, 225, 260, 237, 255, 124, 90, 51, 0, 638, 623, 452, 388, 558, 325, 282, 184, 276, 214, 104, 62, 0), # 60
(702, 658, 578, 653, 509, 229, 263, 241, 259, 127, 95, 51, 0, 648, 631, 462, 395, 573, 333, 283, 188, 282, 217, 105, 62, 0), # 61
(714, 669, 586, 663, 515, 234, 266, 242, 268, 130, 96, 51, 0, 660, 645, 466, 401, 584, 340, 288, 191, 284, 220, 105, 62, 0), # 62
(727, 677, 593, 680, 527, 236, 269, 245, 272, 130, 96, 52, 0, 675, 660, 470, 409, 589, 345, 291, 194, 285, 222, 110, 63, 0), # 63
(740, 689, 601, 696, 534, 237, 274, 247, 274, 133, 98, 52, 0, 684, 672, 478, 417, 594, 351, 293, 198, 287, 223, 114, 64, 0), # 64
(749, 707, 613, 704, 538, 239, 277, 250, 275, 135, 100, 53, 0, 692, 681, 484, 428, 603, 355, 295, 201, 294, 225, 117, 65, 0), # 65
(766, 716, 619, 713, 554, 242, 279, 254, 281, 140, 101, 53, 0, 703, 691, 490, 434, 611, 360, 300, 203, 299, 228, 118, 65, 0), # 66
(779, 724, 630, 721, 561, 249, 284, 254, 285, 143, 102, 53, 0, 717, 709, 495, 439, 624, 364, 303, 206, 308, 233, 120, 65, 0), # 67
(793, 733, 641, 728, 566, 254, 287, 256, 289, 145, 104, 54, 0, 733, 718, 499, 445, 630, 368, 312, 208, 312, 235, 120, 65, 0), # 68
(804, 748, 651, 738, 571, 261, 289, 257, 292, 145, 106, 54, 0, 747, 732, 502, 451, 640, 378, 317, 211, 318, 238, 120, 66, 0), # 69
(816, 757, 661, 746, 582, 266, 293, 259, 297, 146, 108, 55, 0, 753, 737, 510, 458, 655, 387, 320, 213, 327, 250, 121, 66, 0), # 70
(834, 772, 676, 759, 591, 269, 298, 262, 302, 147, 109, 56, 0, 763, 745, 514, 469, 659, 391, 325, 216, 335, 254, 121, 67, 0), # 71
(849, 781, 689, 771, 599, 279, 302, 266, 308, 149, 110, 57, 0, 779, 753, 518, 477, 668, 393, 327, 218, 340, 254, 121, 67, 0), # 72
(862, 796, 704, 777, 608, 285, 305, 271, 310, 149, 113, 59, 0, 791, 758, 526, 481, 686, 397, 336, 222, 345, 259, 123, 67, 0), # 73
(878, 808, 715, 787, 619, 290, 307, 276, 314, 152, 114, 62, 0, 803, 769, 531, 486, 699, 400, 339, 226, 349, 262, 127, 68, 0), # 74
(897, 818, 724, 792, 621, 291, 310, 279, 320, 153, 115, 62, 0, 813, 774, 541, 487, 713, 402, 342, 231, 352, 268, 129, 70, 0), # 75
(916, 825, 731, 803, 631, 297, 313, 287, 328, 154, 118, 63, 0, 828, 786, 543, 490, 721, 407, 345, 234, 354, 274, 131, 71, 0), # 76
(922, 834, 743, 813, 644, 301, 321, 288, 331, 157, 119, 64, 0, 841, 796, 548, 495, 730, 411, 349, 238, 360, 280, 134, 72, 0), # 77
(934, 847, 756, 827, 657, 303, 326, 291, 342, 158, 121, 64, 0, 855, 809, 553, 499, 741, 416, 351, 238, 361, 282, 135, 74, 0), # 78
(941, 855, 761, 837, 668, 304, 330, 292, 345, 159, 122, 66, 0, 871, 822, 561, 503, 748, 420, 354, 239, 365, 283, 137, 76, 0), # 79
(958, 861, 770, 846, 676, 310, 332, 296, 349, 162, 125, 66, 0, 884, 831, 574, 506, 758, 428, 360, 240, 366, 287, 140, 77, 0), # 80
(969, 877, 776, 859, 682, 317, 334, 298, 354, 162, 126, 67, 0, 893, 840, 584, 509, 772, 431, 362, 243, 372, 291, 142, 78, 0), # 81
(985, 889, 791, 873, 690, 320, 340, 298, 358, 164, 128, 67, 0, 908, 853, 589, 514, 782, 433, 369, 244, 377, 293, 144, 78, 0), # 82
(999, 904, 806, 885, 695, 323, 343, 301, 360, 169, 131, 68, 0, 917, 857, 598, 522, 791, 439, 374, 246, 382, 294, 147, 80, 0), # 83
(1014, 918, 815, 894, 704, 324, 349, 305, 365, 170, 134, 68, 0, 933, 867, 606, 532, 797, 444, 379, 250, 385, 300, 147, 80, 0), # 84
(1017, 930, 826, 903, 719, 326, 356, 312, 369, 171, 136, 68, 0, 944, 874, 612, 547, 804, 451, 381, 253, 387, 303, 148, 80, 0), # 85
(1029, 939, 833, 917, 734, 330, 359, 317, 376, 172, 136, 68, 0, 957, 887, 626, 553, 818, 452, 384, 255, 392, 305, 150, 81, 0), # 86
(1043, 949, 844, 921, 742, 332, 364, 321, 383, 175, 138, 68, 0, 967, 898, 636, 564, 827, 455, 392, 257, 398, 311, 151, 82, 0), # 87
(1052, 955, 855, 934, 751, 342, 368, 324, 387, 178, 141, 68, 0, 979, 906, 645, 567, 838, 463, 399, 257, 403, 317, 155, 84, 0), # 88
(1070, 964, 862, 942, 758, 349, 371, 329, 392, 179, 142, 68, 0, 991, 921, 653, 569, 849, 467, 404, 259, 409, 325, 156, 86, 0), # 89
(1081, 971, 871, 953, 766, 353, 373, 333, 393, 181, 144, 68, 0, 1004, 933, 663, 577, 857, 472, 407, 267, 413, 330, 159, 90, 0), # 90
(1099, 981, 881, 961, 774, 356, 376, 334, 397, 184, 144, 68, 0, 1014, 943, 667, 583, 864, 477, 407, 269, 421, 333, 160, 90, 0), # 91
(1110, 995, 887, 968, 783, 367, 380, 334, 399, 187, 146, 68, 0, 1020, 956, 677, 586, 869, 479, 412, 271, 424, 333, 160, 91, 0), # 92
(1119, 1007, 896, 976, 792, 370, 384, 338, 407, 189, 147, 68, 0, 1036, 969, 687, 591, 877, 485, 415, 274, 429, 335, 162, 93, 0), # 93
(1131, 1018, 909, 989, 803, 371, 387, 340, 414, 194, 148, 70, 0, 1049, 974, 696, 598, 880, 487, 423, 277, 433, 337, 162, 95, 0), # 94
(1137, 1025, 920, 1001, 814, 372, 391, 343, 415, 197, 148, 71, 0, 1059, 978, 699, 600, 889, 491, 425, 284, 440, 340, 163, 97, 0), # 95
(1146, 1035, 931, 1009, 821, 374, 398, 344, 420, 197, 150, 71, 0, 1071, 987, 705, 606, 896, 496, 428, 287, 442, 342, 166, 97, 0), # 96
(1156, 1046, 941, 1019, 828, 379, 404, 349, 425, 199, 152, 71, 0, 1082, 993, 719, 608, 905, 499, 435, 295, 447, 348, 166, 97, 0), # 97
(1164, 1053, 949, 1033, 836, 380, 409, 351, 428, 205, 154, 71, 0, 1091, 1002, 727, 614, 910, 501, 441, 298, 451, 352, 170, 98, 0), # 98
(1174, 1066, 960, 1047, 849, 385, 414, 354, 433, 205, 155, 71, 0, 1100, 1009, 731, 623, 921, 504, 448, 301, 458, 354, 172, 98, 0), # 99
(1186, 1078, 972, 1063, 857, 387, 414, 354, 435, 206, 156, 72, 0, 1111, 1017, 742, 626, 930, 507, 451, 303, 462, 356, 175, 99, 0), # 100
(1196, 1089, 982, 1070, 867, 388, 418, 355, 438, 209, 157, 73, 0, 1126, 1028, 755, 633, 940, 511, 455, 305, 466, 360, 177, 99, 0), # 101
(1208, 1104, 991, 1081, 880, 395, 421, 359, 441, 212, 158, 75, 0, 1132, 1043, 760, 638, 951, 515, 458, 307, 470, 361, 179, 99, 0), # 102
(1220, 1115, 995, 1085, 886, 400, 425, 363, 446, 213, 160, 75, 0, 1143, 1049, 774, 642, 956, 520, 461, 310, 472, 361, 180, 100, 0), # 103
(1239, 1122, 1006, 1096, 897, 402, 427, 364, 447, 214, 162, 77, 0, 1156, 1057, 781, 645, 969, 526, 465, 314, 481, 362, 181, 100, 0), # 104
(1252, 1135, 1017, 1108, 908, 408, 428, 366, 451, 214, 162, 77, 0, 1165, 1067, 785, 652, 976, 526, 467, 316, 486, 366, 181, 100, 0), # 105
(1264, 1149, 1027, 1119, 915, 413, 431, 372, 455, 217, 166, 77, 0, 1175, 1075, 791, 656, 980, 526, 472, 317, 493, 366, 186, 101, 0), # 106
(1273, 1157, 1037, 1127, 922, 417, 439, 372, 462, 218, 168, 79, 0, 1188, 1088, 798, 662, 992, 535, 474, 318, 494, 370, 189, 102, 0), # 107
(1283, 1170, 1043, 1135, 928, 421, 443, 374, 466, 219, 169, 79, 0, 1199, 1094, 805, 669, 1000, 539, 481, 323, 498, 375, 193, 103, 0), # 108
(1301, 1179, 1055, 1146, 934, 423, 449, 379, 473, 220, 172, 79, 0, 1210, 1098, 811, 673, 1013, 545, 482, 324, 502, 379, 194, 103, 0), # 109
(1310, 1188, 1069, 1150, 947, 428, 453, 380, 479, 224, 174, 80, 0, 1225, 1106, 819, 679, 1021, 550, 482, 327, 508, 384, 194, 104, 0), # 110
(1318, 1199, 1075, 1155, 955, 429, 455, 384, 483, 226, 175, 80, 0, 1242, 1117, 828, 682, 1029, 557, 489, 332, 512, 389, 195, 105, 0), # 111
(1329, 1202, 1083, 1166, 966, 434, 457, 388, 489, 226, 176, 80, 0, 1247, 1124, 836, 689, 1040, 559, 492, 335, 515, 394, 196, 106, 0), # 112
(1345, 1208, 1090, 1171, 977, 442, 459, 392, 493, 228, 177, 81, 0, 1252, 1133, 844, 698, 1046, 562, 498, 336, 522, 399, 199, 106, 0), # 113
(1361, 1213, 1105, 1184, 984, 444, 465, 396, 500, 229, 177, 82, 0, 1261, 1144, 852, 704, 1054, 567, 503, 338, 526, 401, 200, 106, 0), # 114
(1370, 1220, 1113, 1192, 994, 451, 467, 398, 506, 231, 179, 83, 0, 1272, 1152, 854, 711, 1062, 570, 503, 341, 532, 405, 200, 106, 0), # 115
(1382, 1226, 1120, 1207, 1005, 457, 473, 399, 509, 231, 179, 84, 0, 1281, 1172, 862, 721, 1072, 575, 504, 343, 535, 407, 203, 106, 0), # 116
(1400, 1238, 1128, 1216, 1017, 459, 474, 401, 514, 232, 179, 84, 0, 1289, 1183, 871, 728, 1081, 578, 505, 347, 539, 413, 206, 107, 0), # 117
(1412, 1245, 1137, 1227, 1022, 461, 480, 404, 516, 235, 181, 85, 0, 1303, 1192, 877, 731, 1090, 584, 507, 350, 544, 417, 207, 108, 0), # 118
(1426, 1260, 1142, 1231, 1032, 467, 483, 405, 521, 235, 182, 87, 0, 1319, 1202, 883, 733, 1098, 586, 509, 354, 546, 418, 210, 109, 0), # 119
(1442, 1269, 1148, 1239, 1041, 470, 488, 411, 525, 238, 185, 88, 0, 1327, 1212, 889, 735, 1104, 592, 510, 354, 550, 424, 211, 110, 0), # 120
(1456, 1275, 1162, 1251, 1047, 474, 493, 413, 529, 240, 188, 91, 0, 1340, 1218, 892, 738, 1112, 594, 511, 355, 555, 426, 212, 111, 0), # 121
(1472, 1283, 1173, 1267, 1058, 478, 496, 416, 533, 244, 189, 92, 0, 1348, 1229, 896, 745, 1116, 600, 514, 357, 558, 426, 213, 112, 0), # 122
(1481, 1293, 1187, 1276, 1069, 480, 498, 416, 541, 246, 190, 92, 0, 1361, 1233, 905, 749, 1122, 607, 519, 359, 564, 430, 215, 113, 0), # 123
(1491, 1304, 1199, 1287, 1074, 487, 500, 417, 546, 248, 193, 92, 0, 1378, 1238, 910, 753, 1131, 608, 519, 364, 574, 432, 217, 114, 0), # 124
(1498, 1316, 1210, 1294, 1076, 494, 502, 421, 551, 249, 197, 92, 0, 1388, 1247, 917, 758, 1140, 613, 523, 370, 577, 435, 218, 114, 0), # 125
(1505, 1321, 1217, 1303, 1083, 497, 504, 423, 556, 250, 203, 92, 0, 1402, 1262, 922, 761, 1144, 620, 525, 371, 579, 436, 218, 114, 0), # 126
(1520, 1331, 1229, 1314, 1091, 503, 506, 426, 563, 250, 203, 92, 0, 1411, 1264, 928, 765, 1155, 629, 529, 373, 581, 442, 221, 115, 0), # 127
(1533, 1339, 1237, 1327, 1099, 507, 514, 427, 569, 251, 204, 92, 0, 1421, 1271, 935, 767, 1158, 633, 533, 377, 584, 443, 225, 117, 0), # 128
(1546, 1345, 1249, 1332, 1107, 508, 516, 428, 574, 251, 208, 92, 0, 1432, 1281, 942, 770, 1167, 637, 538, 380, 586, 444, 225, 118, 0), # 129
(1563, 1350, 1255, 1338, 1115, 511, 521, 431, 578, 252, 209, 92, 0, 1443, 1292, 946, 775, 1177, 642, 544, 382, 589, 451, 227, 118, 0), # 130
(1573, 1355, 1262, 1347, 1126, 515, 522, 436, 584, 253, 210, 92, 0, 1450, 1297, 955, 784, 1192, 645, 548, 385, 594, 454, 228, 119, 0), # 131
(1587, 1362, 1272, 1359, 1133, 516, 523, 440, 586, 256, 212, 93, 0, 1461, 1305, 962, 791, 1201, 650, 550, 385, 595, 457, 230, 119, 0), # 132
(1597, 1369, 1284, 1371, 1142, 524, 527, 443, 588, 256, 212, 95, 0, 1471, 1310, 971, 798, 1215, 661, 556, 392, 601, 459, 233, 121, 0), # 133
(1611, 1381, 1299, 1382, 1153, 529, 530, 447, 593, 256, 213, 97, 0, 1480, 1318, 979, 803, 1223, 665, 560, 393, 606, 462, 236, 121, 0), # 134
(1618, 1389, 1309, 1391, 1162, 533, 532, 449, 600, 258, 215, 97, 0, 1499, 1325, 985, 809, 1228, 668, 563, 396, 609, 464, 239, 122, 0), # 135
(1633, 1399, 1320, 1401, 1172, 539, 535, 453, 604, 259, 217, 98, 0, 1511, 1331, 995, 812, 1234, 669, 566, 400, 612, 465, 239, 123, 0), # 136
(1644, 1406, 1329, 1407, 1183, 541, 539, 457, 607, 260, 217, 99, 0, 1520, 1342, 998, 815, 1235, 671, 569, 403, 615, 466, 240, 123, 0), # 137
(1651, 1412, 1333, 1413, 1193, 549, 545, 457, 612, 263, 217, 100, 0, 1538, 1358, 1007, 825, 1240, 676, 573, 405, 623, 472, 241, 123, 0), # 138
(1663, 1420, 1343, 1423, 1200, 551, 551, 461, 616, 264, 218, 101, 0, 1551, 1369, 1019, 833, 1254, 682, 576, 410, 628, 473, 242, 124, 0), # 139
(1675, 1434, 1351, 1432, 1210, 554, 555, 463, 620, 266, 221, 102, 0, 1559, 1378, 1022, 840, 1259, 685, 579, 415, 630, 475, 242, 124, 0), # 140
(1678, 1442, 1361, 1435, 1222, 558, 560, 465, 628, 268, 222, 102, 0, 1566, 1387, 1034, 846, 1270, 690, 579, 416, 633, 475, 242, 125, 0), # 141
(1686, 1452, 1373, 1447, 1228, 559, 564, 469, 630, 271, 223, 103, 0, 1578, 1394, 1039, 850, 1278, 693, 585, 416, 635, 478, 243, 128, 0), # 142
(1698, 1463, 1385, 1456, 1237, 562, 565, 472, 633, 272, 223, 103, 0, 1584, 1398, 1045, 858, 1281, 695, 589, 419, 637, 480, 244, 128, 0), # 143
(1703, 1470, 1397, 1466, 1246, 567, 567, 475, 635, 274, 223, 104, 0, 1596, 1403, 1051, 859, 1284, 701, 594, 420, 640, 483, 244, 128, 0), # 144
(1710, 1483, 1406, 1478, 1257, 572, 574, 481, 641, 274, 226, 106, 0, 1603, 1410, 1062, 864, 1292, 703, 601, 421, 644, 486, 247, 128, 0), # 145
(1723, 1489, 1416, 1488, 1265, 575, 576, 484, 649, 277, 227, 107, 0, 1612, 1421, 1066, 868, 1300, 708, 605, 421, 647, 488, 248, 129, 0), # 146
(1739, 1498, 1421, 1505, 1276, 577, 578, 488, 652, 278, 228, 108, 0, 1620, 1427, 1070, 873, 1304, 712, 609, 426, 647, 490, 249, 129, 0), # 147
(1749, 1504, 1431, 1510, 1289, 581, 578, 493, 659, 278, 230, 108, 0, 1631, 1433, 1077, 876, 1311, 713, 615, 428, 654, 494, 249, 129, 0), # 148
(1765, 1508, 1439, 1523, 1293, 583, 579, 498, 664, 282, 230, 108, 0, 1645, 1440, 1085, 880, 1322, 719, 617, 432, 655, 499, 252, 129, 0), # 149
(1779, 1518, 1447, 1531, 1297, 588, 583, 502, 670, 284, 230, 111, 0, 1653, 1447, 1087, 885, 1332, 723, 619, 434, 657, 502, 254, 131, 0), # 150
(1789, 1525, 1455, 1539, 1302, 591, 586, 504, 674, 286, 231, 111, 0, 1666, 1454, 1094, 888, 1340, 723, 621, 436, 661, 505, 254, 132, 0), # 151
(1798, 1529, 1464, 1544, 1309, 593, 591, 507, 678, 287, 232, 113, 0, 1674, 1459, 1100, 892, 1354, 727, 623, 438, 664, 510, 258, 133, 0), # 152
(1805, 1533, 1473, 1552, 1319, 596, 594, 507, 682, 288, 233, 114, 0, 1686, 1469, 1106, 899, 1356, 730, 627, 438, 670, 512, 259, 133, 0), # 153
(1819, 1541, 1482, 1555, 1326, 601, 594, 508, 684, 288, 234, 114, 0, 1701, 1475, 1110, 905, 1367, 732, 632, 440, 671, 514, 262, 133, 0), # 154
(1830, 1546, 1491, 1560, 1332, 605, 595, 511, 689, 288, 236, 114, 0, 1709, 1483, 1121, 913, 1372, 735, 638, 442, 673, 514, 263, 133, 0), # 155
(1838, 1549, 1496, 1564, 1343, 611, 596, 513, 691, 288, 241, 115, 0, 1717, 1487, 1125, 915, 1385, 738, 641, 443, 675, 514, 263, 135, 0), # 156
(1851, 1559, 1505, 1573, 1349, 613, 598, 515, 695, 289, 242, 115, 0, 1731, 1497, 1128, 919, 1396, 739, 647, 446, 680, 516, 264, 136, 0), # 157
(1858, 1562, 1507, 1578, 1355, 615, 599, 516, 696, 290, 242, 115, 0, 1742, 1504, 1133, 923, 1408, 741, 651, 448, 683, 518, 268, 136, 0), # 158
(1869, 1568, 1515, 1587, 1361, 619, 604, 517, 701, 291, 244, 115, 0, 1749, 1515, 1140, 930, 1417, 744, 654, 448, 686, 518, 268, 139, 0), # 159
(1880, 1571, 1519, 1593, 1367, 621, 605, 519, 705, 295, 246, 118, 0, 1755, 1523, 1145, 933, 1422, 748, 655, 448, 689, 519, 270, 140, 0), # 160
(1889, 1577, 1526, 1600, 1372, 628, 610, 525, 707, 295, 247, 118, 0, 1769, 1531, 1148, 936, 1428, 751, 656, 450, 691, 522, 271, 142, 0), # 161
(1897, 1586, 1530, 1608, 1376, 631, 614, 527, 710, 297, 248, 118, 0, 1776, 1539, 1149, 939, 1438, 756, 656, 454, 694, 528, 271, 143, 0), # 162
(1904, 1591, 1538, 1616, 1382, 632, 616, 533, 713, 299, 248, 119, 0, 1783, 1548, 1153, 943, 1453, 761, 659, 459, 697, 531, 275, 143, 0), # 163
(1914, 1595, 1549, 1622, 1390, 634, 620, 537, 717, 300, 250, 119, 0, 1793, 1551, 1163, 946, 1458, 767, 663, 463, 700, 533, 278, 143, 0), # 164
(1922, 1600, 1555, 1632, 1395, 637, 622, 538, 721, 301, 253, 119, 0, 1800, 1559, 1166, 950, 1469, 770, 665, 464, 701, 537, 280, 143, 0), # 165
(1930, 1609, 1564, 1639, 1397, 638, 622, 539, 725, 302, 253, 120, 0, 1809, 1567, 1174, 953, 1475, 773, 668, 466, 704, 538, 283, 143, 0), # 166
(1938, 1614, 1568, 1645, 1408, 642, 623, 544, 727, 303, 255, 120, 0, 1815, 1574, 1178, 959, 1481, 773, 670, 469, 710, 541, 284, 143, 0), # 167
(1940, 1618, 1574, 1653, 1415, 646, 627, 547, 732, 304, 255, 120, 0, 1822, 1583, 1180, 963, 1485, 775, 673, 474, 710, 542, 284, 143, 0), # 168
(1945, 1621, 1583, 1660, 1421, 649, 631, 551, 733, 306, 255, 122, 0, 1825, 1590, 1183, 970, 1493, 781, 674, 477, 712, 544, 287, 144, 0), # 169
(1950, 1624, 1587, 1664, 1426, 651, 631, 553, 736, 307, 255, 122, 0, 1834, 1596, 1193, 971, 1506, 782, 675, 481, 715, 546, 288, 145, 0), # 170
(1952, 1626, 1591, 1667, 1433, 652, 633, 557, 739, 308, 256, 122, 0, 1841, 1599, 1197, 974, 1515, 786, 681, 481, 718, 548, 290, 146, 0), # 171
(1956, 1632, 1599, 1673, 1439, 657, 635, 559, 740, 310, 256, 123, 0, 1846, 1603, 1205, 975, 1519, 789, 686, 485, 720, 550, 291, 146, 0), # 172
(1964, 1638, 1604, 1679, 1445, 663, 642, 560, 741, 312, 256, 123, 0, 1852, 1608, 1211, 978, 1524, 794, 687, 486, 722, 553, 292, 147, 0), # 173
(1970, 1642, 1610, 1687, 1449, 666, 644, 560, 744, 314, 256, 123, 0, 1854, 1614, 1213, 985, 1526, 797, 689, 486, 725, 558, 292, 147, 0), # 174
(1972, 1645, 1613, 1692, 1456, 668, 644, 561, 749, 316, 257, 123, 0, 1859, 1619, 1220, 987, 1529, 798, 690, 487, 729, 560, 293, 147, 0), # 175
(1976, 1647, 1616, 1694, 1459, 672, 646, 563, 751, 317, 259, 123, 0, 1861, 1623, 1223, 987, 1536, 798, 690, 488, 731, 562, 294, 147, 0), # 176
(1982, 1649, 1618, 1701, 1462, 675, 648, 565, 753, 317, 259, 124, 0, 1866, 1624, 1229, 990, 1545, 799, 691, 489, 735, 565, 295, 148, 0), # 177
(1987, 1651, 1621, 1708, 1470, 678, 649, 567, 755, 318, 260, 124, 0, 1869, 1628, 1232, 991, 1549, 801, 691, 490, 737, 565, 295, 148, 0), # 178
(1987, 1651, 1621, 1708, 1470, 678, 649, 567, 755, 318, 260, 124, 0, 1869, 1628, 1232, 991, 1549, 801, 691, 490, 737, 565, 295, 148, 0), # 179
)
passenger_arriving_rate = (
(6.025038694046121, 6.077817415662483, 5.211283229612507, 5.593200996477089, 4.443748486087689, 2.197058452426137, 2.4876213692243487, 2.3265880864897115, 2.4360396248672025, 1.187404504656711, 0.8410530327771206, 0.4897915078306174, 0.0, 6.100656255094035, 5.38770658613679, 4.205265163885603, 3.562213513970132, 4.872079249734405, 3.257223321085596, 2.4876213692243487, 1.5693274660186693, 2.2218742430438443, 1.8644003321590301, 1.0422566459225016, 0.5525288559693167, 0.0), # 0
(6.425192582423969, 6.479066763559234, 5.555346591330152, 5.9626298279489545, 4.737992269979389, 2.342188508829789, 2.651681364758216, 2.479756861452854, 2.5968981305331633, 1.265694207683145, 0.8966192271912263, 0.5221216660814355, 0.0, 6.503749976927826, 5.743338326895789, 4.483096135956131, 3.7970826230494343, 5.193796261066327, 3.4716596060339957, 2.651681364758216, 1.6729917920212778, 2.3689961349896946, 1.9875432759829852, 1.1110693182660305, 0.589006069414476, 0.0), # 1
(6.8240676107756775, 6.878723687980077, 5.8980422855474135, 6.330588934198314, 5.031170378999795, 2.4867395801587113, 2.8150911047764224, 2.6323126239522097, 2.7571147227510195, 1.3436741325061639, 0.9519646297552626, 0.5543232652053055, 0.0, 6.905237793851628, 6.09755591725836, 4.759823148776313, 4.031022397518491, 5.514229445502039, 3.6852376735330936, 2.8150911047764224, 1.7762425572562224, 2.5155851894998973, 2.1101963113994384, 1.179608457109483, 0.625338517089098, 0.0), # 2
(7.220109351775874, 7.275202552130091, 6.238010869319854, 6.695618766778866, 5.322129340801521, 2.6301384358095787, 2.9772021849887733, 2.7836505787472534, 2.9160540643684367, 1.4210348095278544, 1.0068696823654766, 0.5862685684930461, 0.0, 7.30352736750507, 6.448954253423507, 5.0343484118273825, 4.263104428583563, 5.8321081287368735, 3.8971108102461547, 2.9772021849887733, 1.8786703112925562, 2.6610646704007603, 2.2318729222596225, 1.247602173863971, 0.6613820501936447, 0.0), # 3
(7.611763378099177, 7.666917719214351, 6.573892899703036, 7.056259777244312, 5.609715683037193, 2.7718118451790676, 3.137366201105075, 2.9331659305974576, 3.0730808182330827, 1.4974667691503039, 1.0611148269181152, 0.6178298392354764, 0.0, 7.69702635952778, 6.79612823159024, 5.305574134590575, 4.492400307450911, 6.146161636466165, 4.10643230283644, 3.137366201105075, 1.9798656036993338, 2.8048578415185963, 2.3520865924147714, 1.3147785799406073, 0.6969925199285775, 0.0), # 4
(7.9974752624202115, 8.052283552437947, 6.904328933752518, 7.411052417148355, 5.892775933359424, 2.9111865776638504, 3.2949347488351344, 3.080253884262296, 3.2275596471926233, 1.5726605417755992, 1.1144805053094267, 0.6488793407234149, 0.0, 8.084142431559393, 7.137672747957563, 5.572402526547132, 4.7179816253267965, 6.455119294385247, 4.312355437967215, 3.2949347488351344, 2.079418984045607, 2.946387966679712, 2.4703508057161185, 1.3808657867505036, 0.7320257774943589, 0.0), # 5
(8.375690577413598, 8.42971441500595, 7.227959528523866, 7.758537138044686, 6.170156619420834, 3.047689402660605, 3.4492594238887575, 3.2243096445012442, 3.3788552140947257, 1.6463066578058279, 1.1667471594356567, 0.6792893362476808, 0.0, 8.463283245239527, 7.472182698724488, 5.833735797178282, 4.938919973417482, 6.757710428189451, 4.514033502301742, 3.4492594238887575, 2.176921001900432, 3.085078309710417, 2.586179046014896, 1.4455919057047733, 0.7663376740914501, 0.0), # 6
(8.744854895753962, 8.797624670123444, 7.543425241072636, 8.097254391487015, 6.440704268874043, 3.1807470895660046, 3.599691821975751, 3.3647284160737763, 3.5263321817870574, 1.7180956476430762, 1.2176952311930538, 0.708932089099093, 0.0, 8.832856462207822, 7.798252980090021, 6.088476155965268, 5.154286942929227, 7.052664363574115, 4.7106197825032865, 3.599691821975751, 2.2719622068328604, 3.2203521344370216, 2.699084797162339, 1.508685048214527, 0.7997840609203132, 0.0), # 7
(9.103413790115921, 9.154428680995508, 7.849366628454395, 8.425744629029035, 6.703265409371668, 3.309786407776723, 3.7455835388059184, 3.5009054037393623, 3.669355213117282, 1.7877180416894325, 1.2671051624778642, 0.7376798625684703, 0.0, 9.1912697441039, 8.114478488253173, 6.335525812389321, 5.363154125068296, 7.338710426234564, 4.901267565235107, 3.7455835388059184, 2.3641331484119448, 3.351632704685834, 2.8085815430096788, 1.5698733256908792, 0.8322207891814098, 0.0), # 8
(9.449812833174102, 9.498540810827224, 8.144424247724704, 8.742548302224453, 6.956686568566327, 3.4342341266894385, 3.886286170089072, 3.6322358122574814, 3.8072889709330693, 1.8548643703469827, 1.3147573951863356, 0.7654049199466314, 0.0, 9.536930752567395, 8.419454119412945, 6.573786975931678, 5.564593111040947, 7.614577941866139, 5.0851301371604745, 3.886286170089072, 2.453024376206742, 3.4783432842831634, 2.914182767408151, 1.6288848495449408, 0.8635037100752023, 0.0), # 9
(9.782497597603118, 9.828375422823667, 8.427238655939124, 9.046205862626959, 7.19981427411064, 3.5535170157008253, 4.021151311535013, 3.7581148463876053, 3.9394981180820854, 1.9192251640178146, 1.3604323712147148, 0.7919795245243952, 0.0, 9.868247149237932, 8.711774769768347, 6.802161856073574, 5.757675492053442, 7.878996236164171, 5.261360784942648, 4.021151311535013, 2.5382264397863037, 3.59990713705532, 3.015401954208987, 1.685447731187825, 0.8934886748021517, 0.0), # 10
(10.099913656077605, 10.142346880189926, 8.696450410153215, 9.335257761790256, 7.431495053657226, 3.667061844207558, 4.14953055885355, 3.8779377108892072, 4.065347317411997, 1.980490953104016, 1.40391053245925, 0.8172759395925812, 0.0, 10.183626595755133, 8.99003533551839, 7.019552662296249, 5.9414728593120465, 8.130694634823994, 5.42911279524489, 4.14953055885355, 2.619329888719684, 3.715747526828613, 3.1117525872634197, 1.7392900820306432, 0.9220315345627208, 0.0), # 11
(10.400506581272174, 10.438869546131066, 8.95070006742254, 9.60824445126805, 7.650575434858702, 3.7742953816063087, 4.270775507754487, 3.99109961052176, 4.184201231770471, 2.0383522680076718, 1.444972320816187, 0.8411664284420068, 0.0, 10.48147675375864, 9.252830712862075, 7.224861604080934, 6.115056804023014, 8.368402463540942, 5.587539454730464, 4.270775507754487, 2.6959252725759346, 3.825287717429351, 3.2027481504226842, 1.790140013484508, 0.9489881405573698, 0.0), # 12
(10.68272194586145, 10.716357783852182, 9.188628184802662, 9.863706382614039, 7.85590194536768, 3.8746443972937565, 4.384237753947633, 4.096995750044741, 4.295424524005172, 2.0924996391308714, 1.4833981781817738, 0.8635232543634921, 0.0, 10.760205284888082, 9.498755797998411, 7.416990890908868, 6.277498917392613, 8.590849048010345, 5.735794050062637, 4.384237753947633, 2.7676031409241117, 3.92795097268384, 3.287902127538014, 1.8377256369605324, 0.974214343986562, 0.0), # 13
(10.945005322520059, 10.973225956558347, 9.408875319349146, 10.100184007381912, 8.046321112836791, 3.967535660666574, 4.489268893142796, 4.195021334217623, 4.398381856963768, 2.1426235968757004, 1.518968546452257, 0.8842186806478561, 0.0, 11.018219850783076, 9.726405487126415, 7.594842732261284, 6.4278707906271, 8.796763713927536, 5.873029867904672, 4.489268893142796, 2.833954043333267, 4.023160556418396, 3.3667280024606385, 1.8817750638698296, 0.997565996050759, 0.0), # 14
(11.185802283922625, 11.207888427454638, 9.610082028117542, 10.316217777125386, 8.220679464918646, 4.052395941121439, 4.585220521049775, 4.284571567799878, 4.4924378934939275, 2.1884146716442476, 1.551463867523884, 0.9031249705859171, 0.0, 11.253928113083257, 9.934374676445087, 7.757319337619419, 6.565244014932741, 8.984875786987855, 5.998400194919829, 4.585220521049775, 2.894568529372456, 4.110339732459323, 3.4387392590417964, 1.9220164056235085, 1.0188989479504218, 0.0), # 15
(11.40355840274376, 11.418759559746144, 9.790888868163425, 10.510348143398145, 8.377823529265866, 4.128652008055021, 4.671444233378385, 4.36504165555098, 4.5769572964433145, 2.2295633938385993, 1.5806645832929027, 0.920114387468494, 0.0, 11.465737733428254, 10.121258262153432, 7.9033229164645125, 6.688690181515796, 9.153914592886629, 6.111058317771373, 4.671444233378385, 2.9490371486107296, 4.188911764632933, 3.503449381132716, 1.958177773632685, 1.0380690508860133, 0.0), # 16
(11.59671925165809, 11.604253716637938, 9.949936396542352, 10.6811155577539, 8.51659983353107, 4.1957306308639994, 4.747291625838426, 4.435826802230409, 4.651304728659593, 2.2657602938608403, 1.60635113565556, 0.9350591945864056, 0.0, 11.652056373457699, 10.28565114045046, 8.031755678277799, 6.79728088158252, 9.302609457319186, 6.2101575231225725, 4.747291625838426, 2.9969504506171427, 4.258299916765535, 3.5603718525846344, 1.9899872793084707, 1.0549321560579947, 0.0), # 17
(11.763730403340244, 11.7627852613351, 10.08586517030988, 10.82706047174635, 8.63585490536687, 4.253058578945052, 4.81211429413971, 4.49632221259763, 4.7148448529904385, 2.2966959021130613, 1.6283039665081016, 0.9478316552304716, 0.0, 11.811291694811214, 10.426148207535187, 8.141519832540508, 6.890087706339182, 9.429689705980877, 6.294851097636682, 4.81211429413971, 3.0378989849607514, 4.317927452683435, 3.6090201572487843, 2.0171730340619765, 1.0693441146668274, 0.0), # 18
(11.903037430464838, 11.892768557042718, 10.197315746521578, 10.946723336929182, 8.734435272425891, 4.300062621694845, 4.865263833992036, 4.5459230914121225, 4.766942332283511, 2.3220607489973486, 1.6463035177467755, 0.9583040326915097, 0.0, 11.941851359128435, 10.541344359606605, 8.231517588733878, 6.9661822469920445, 9.533884664567022, 6.364292327976972, 4.865263833992036, 3.071473301210604, 4.367217636212946, 3.648907778976395, 2.039463149304316, 1.0811607779129746, 0.0), # 19
(12.013085905706498, 11.992617966965858, 10.282928682233003, 11.038644604856119, 8.811187462360754, 4.336169528510063, 4.9060918411052175, 4.5840246434333585, 4.806961829386479, 2.341545364915788, 1.66013023126783, 0.9663485902603393, 0.0, 12.042143028048988, 10.62983449286373, 8.30065115633915, 7.024636094747362, 9.613923658772958, 6.417634500806702, 4.9060918411052175, 3.097263948935759, 4.405593731180377, 3.679548201618707, 2.0565857364466007, 1.0902379969968963, 0.0), # 20
(12.09232140173984, 12.060747854309614, 10.341344534499719, 11.101364727080837, 8.86495800282407, 4.360806068787375, 4.933949911189055, 4.6100220734208115, 4.834268007147008, 2.3548402802704667, 1.669564548967512, 0.9718375912277795, 0.0, 12.110574363212494, 10.690213503505571, 8.34782274483756, 7.064520840811399, 9.668536014294016, 6.454030902789136, 4.933949911189055, 3.1148614777052677, 4.432479001412035, 3.7004549090269463, 2.068268906899944, 1.096431623119056, 0.0), # 21
(12.139189491239494, 12.095572582279058, 10.371203860377285, 11.133424155157051, 8.894593421468459, 4.373399011923457, 4.94818963995336, 4.623310586133957, 4.848225528412765, 2.361636025463473, 1.674386912742068, 0.9746432988846491, 0.0, 12.145553026258591, 10.721076287731139, 8.37193456371034, 7.084908076390418, 9.69645105682553, 6.47263482058754, 4.94818963995336, 3.1238564370881834, 4.447296710734229, 3.7111413850523514, 2.0742407720754574, 1.0995975074799145, 0.0), # 22
(12.156472036011166, 12.099695953360769, 10.374923182441702, 11.137437731481482, 8.902185644826076, 4.375, 4.949882401355603, 4.624746913580247, 4.8499704938271595, 2.3624376817558304, 1.6749916074323483, 0.9749897576588934, 0.0, 12.15, 10.724887334247827, 8.37495803716174, 7.087313045267489, 9.699940987654319, 6.474645679012346, 4.949882401355603, 3.125, 4.451092822413038, 3.7124792438271617, 2.0749846364883404, 1.0999723593964337, 0.0), # 23
(12.169214895640982, 12.09729074074074, 10.374314814814815, 11.13694375, 8.906486090891882, 4.375, 4.9489522875817, 4.62275, 4.849736666666666, 2.3619451851851854, 1.6749249158249162, 0.9749086419753087, 0.0, 12.15, 10.723995061728393, 8.37462457912458, 7.085835555555555, 9.699473333333332, 6.47185, 4.9489522875817, 3.125, 4.453243045445941, 3.7123145833333346, 2.074862962962963, 1.099753703703704, 0.0), # 24
(12.181688676253897, 12.092549725651576, 10.373113854595337, 11.135966435185185, 8.910691956475603, 4.375, 4.947119341563786, 4.618827160493828, 4.8492746913580245, 2.3609756515775038, 1.6747926798852726, 0.9747485139460449, 0.0, 12.15, 10.722233653406493, 8.373963399426362, 7.08292695473251, 9.698549382716049, 6.466358024691359, 4.947119341563786, 3.125, 4.455345978237801, 3.711988811728396, 2.0746227709190674, 1.0993227023319616, 0.0), # 25
(12.19389242285764, 12.085545336076818, 10.371336762688616, 11.134516898148147, 8.914803094736882, 4.375, 4.944412030985233, 4.613052469135803, 4.84859049382716, 2.3595452126200276, 1.674596096770171, 0.9745115683584822, 0.0, 12.15, 10.719627251943303, 8.372980483850855, 7.078635637860081, 9.69718098765432, 6.458273456790124, 4.944412030985233, 3.125, 4.457401547368441, 3.71150563271605, 2.0742673525377233, 1.0986859396433473, 0.0), # 26
(12.205825180459962, 12.076349999999996, 10.369, 11.132606249999998, 8.918819358835371, 4.375, 4.940858823529412, 4.6055, 4.84769, 2.35767, 1.674336363636364, 0.9742000000000002, 0.0, 12.15, 10.7162, 8.371681818181818, 7.073009999999999, 9.69538, 6.4477, 4.940858823529412, 3.125, 4.459409679417686, 3.7108687500000004, 2.0738000000000003, 1.09785, 0.0), # 27
(12.217485994068602, 12.065036145404662, 10.366120027434842, 11.13024560185185, 8.92274060193072, 4.375, 4.93648818687969, 4.596243827160494, 4.846579135802468, 2.3553661454046644, 1.6740146776406037, 0.9738160036579792, 0.0, 12.15, 10.711976040237769, 8.370073388203018, 7.066098436213991, 9.693158271604936, 6.434741358024692, 4.93648818687969, 3.125, 4.46137030096536, 3.710081867283951, 2.073224005486969, 1.0968214677640604, 0.0), # 28
(12.2288739086913, 12.051676200274349, 10.362713305898492, 11.127446064814816, 8.926566677182576, 4.375, 4.931328588719439, 4.585358024691358, 4.845263827160494, 2.3526497805212623, 1.6736322359396434, 0.9733617741197987, 0.0, 12.15, 10.706979515317785, 8.368161179698216, 7.057949341563786, 9.690527654320988, 6.419501234567901, 4.931328588719439, 3.125, 4.463283338591288, 3.709148688271606, 2.0725426611796984, 1.0956069272976683, 0.0), # 29
(12.239987969335797, 12.036342592592591, 10.358796296296296, 11.12421875, 8.930297437750589, 4.375, 4.925408496732026, 4.572916666666666, 4.84375, 2.3495370370370376, 1.6731902356902357, 0.9728395061728394, 0.0, 12.15, 10.701234567901233, 8.365951178451178, 7.048611111111112, 9.6875, 6.402083333333333, 4.925408496732026, 3.125, 4.4651487188752945, 3.7080729166666675, 2.0717592592592595, 1.094212962962963, 0.0), # 30
(12.25082722100983, 12.019107750342934, 10.354385459533608, 11.120574768518516, 8.933932736794405, 4.375, 4.918756378600824, 4.558993827160494, 4.842043580246913, 2.346044046639232, 1.6726898740491336, 0.9722513946044812, 0.0, 12.15, 10.694765340649292, 8.363449370245666, 7.038132139917694, 9.684087160493826, 6.382591358024691, 4.918756378600824, 3.125, 4.466966368397203, 3.70685825617284, 2.070877091906722, 1.0926461591220853, 0.0), # 31
(12.261390708721144, 12.000044101508914, 10.349497256515773, 11.11652523148148, 8.937472427473676, 4.375, 4.911400702009199, 4.543663580246914, 4.84015049382716, 2.3421869410150897, 1.672132348173089, 0.9715996342021036, 0.0, 12.15, 10.687595976223138, 8.360661740865444, 7.026560823045267, 9.68030098765432, 6.36112901234568, 4.911400702009199, 3.125, 4.468736213736838, 3.705508410493828, 2.069899451303155, 1.0909131001371744, 0.0), # 32
(12.271677477477477, 11.979224074074073, 10.344148148148149, 11.11208125, 8.94091636294805, 4.375, 4.903369934640523, 4.527, 4.838076666666666, 2.3379818518518523, 1.6715188552188551, 0.9708864197530863, 0.0, 12.15, 10.679750617283949, 8.357594276094275, 7.013945555555555, 9.676153333333332, 6.3378000000000005, 4.903369934640523, 3.125, 4.470458181474025, 3.704027083333334, 2.06882962962963, 1.0890203703703705, 0.0), # 33
(12.28168657228657, 11.956720096021947, 10.338354595336076, 11.107253935185184, 8.944264396377172, 4.375, 4.894692544178166, 4.509077160493827, 4.835828024691358, 2.333444910836763, 1.670850592343185, 0.9701139460448103, 0.0, 12.15, 10.671253406492912, 8.354252961715924, 7.000334732510288, 9.671656049382715, 6.312708024691357, 4.894692544178166, 3.125, 4.472132198188586, 3.7024179783950624, 2.0676709190672153, 1.0869745541838134, 0.0), # 34
(12.291417038156167, 11.932604595336077, 10.332133058984912, 11.102054398148146, 8.947516380920696, 4.375, 4.885396998305495, 4.489969135802469, 4.83341049382716, 2.328592249657065, 1.6701287567028307, 0.969284407864655, 0.0, 12.15, 10.662128486511202, 8.350643783514153, 6.985776748971193, 9.66682098765432, 6.285956790123457, 4.885396998305495, 3.125, 4.473758190460348, 3.7006847993827163, 2.0664266117969827, 1.0847822359396435, 0.0), # 35
(12.300867920094007, 11.906949999999998, 10.3255, 11.096493749999999, 8.950672169738269, 4.375, 4.875511764705882, 4.46975, 4.830829999999999, 2.32344, 1.6693545454545458, 0.9684000000000001, 0.0, 12.15, 10.6524, 8.346772727272727, 6.970319999999999, 9.661659999999998, 6.257650000000001, 4.875511764705882, 3.125, 4.475336084869134, 3.6988312500000005, 2.0651, 1.08245, 0.0), # 36
(12.310038263107828, 11.879828737997256, 10.318471879286694, 11.090583101851852, 8.953731615989536, 4.375, 4.865065311062696, 4.448493827160494, 4.828092469135802, 2.3180042935528125, 1.668529155755082, 0.9674629172382261, 0.0, 12.15, 10.642092089620485, 8.34264577877541, 6.954012880658436, 9.656184938271604, 6.227891358024691, 4.865065311062696, 3.125, 4.476865807994768, 3.696861033950618, 2.063694375857339, 1.0799844307270234, 0.0), # 37
(12.31892711220537, 11.851313237311386, 10.311065157750342, 11.084333564814814, 8.956694572834152, 4.375, 4.854086105059308, 4.426274691358025, 4.825203827160493, 2.312301262002744, 1.6676537847611925, 0.9664753543667125, 0.0, 12.15, 10.631228898033836, 8.33826892380596, 6.936903786008231, 9.650407654320986, 6.196784567901235, 4.854086105059308, 3.125, 4.478347286417076, 3.6947778549382724, 2.0622130315500686, 1.0773921124828534, 0.0), # 38
(12.327533512394384, 11.821475925925924, 10.303296296296297, 11.07775625, 8.959560893431762, 4.375, 4.842602614379085, 4.4031666666666665, 4.82217, 2.3063470370370376, 1.6667296296296297, 0.9654395061728396, 0.0, 12.15, 10.619834567901233, 8.333648148148148, 6.919041111111111, 9.64434, 6.164433333333333, 4.842602614379085, 3.125, 4.479780446715881, 3.6925854166666676, 2.0606592592592596, 1.0746796296296297, 0.0), # 39
(12.335856508682596, 11.790389231824417, 10.295181755829903, 11.070862268518518, 8.962330430942014, 4.375, 4.830643306705398, 4.3792438271604945, 4.818996913580246, 2.3001577503429362, 1.6657578875171468, 0.9643575674439875, 0.0, 12.15, 10.60793324188386, 8.328789437585733, 6.900473251028807, 9.637993827160493, 6.1309413580246925, 4.830643306705398, 3.125, 4.481165215471007, 3.690287422839507, 2.059036351165981, 1.0718535665294926, 0.0), # 40
(12.343895146077754, 11.758125582990397, 10.286737997256516, 11.06366273148148, 8.96500303852456, 4.375, 4.818236649721617, 4.354580246913581, 4.81569049382716, 2.293749533607682, 1.6647397555804966, 0.9632317329675355, 0.0, 12.15, 10.595549062642888, 8.323698777902482, 6.881248600823045, 9.63138098765432, 6.096412345679013, 4.818236649721617, 3.125, 4.48250151926228, 3.6878875771604944, 2.0573475994513033, 1.0689205075445818, 0.0), # 41
(12.3516484695876, 11.724757407407406, 10.277981481481483, 11.056168750000001, 8.967578569339047, 4.375, 4.805411111111111, 4.32925, 4.812256666666666, 2.287138518518519, 1.663676430976431, 0.9620641975308644, 0.0, 12.15, 10.582706172839506, 8.318382154882155, 6.861415555555555, 9.624513333333333, 6.06095, 4.805411111111111, 3.125, 4.483789284669523, 3.6853895833333343, 2.055596296296297, 1.0658870370370372, 0.0), # 42
(12.35911552421987, 11.690357133058985, 10.268928669410151, 11.048391435185184, 8.970056876545122, 4.375, 4.7921951585572495, 4.3033271604938275, 4.80870135802469, 2.280340836762689, 1.6625691108617036, 0.9608571559213536, 0.0, 12.15, 10.569428715134888, 8.312845554308517, 6.841022510288067, 9.61740271604938, 6.024658024691359, 4.7921951585572495, 3.125, 4.485028438272561, 3.682797145061729, 2.0537857338820307, 1.062759739368999, 0.0), # 43
(12.366295354982311, 11.65499718792867, 10.259596021947875, 11.040341898148148, 8.972437813302435, 4.375, 4.778617259743403, 4.2768858024691365, 4.805030493827159, 2.2733726200274353, 1.6614189923930665, 0.9596128029263833, 0.0, 12.15, 10.555740832190216, 8.307094961965332, 6.820117860082305, 9.610060987654318, 5.987640123456791, 4.778617259743403, 3.125, 4.486218906651217, 3.6801139660493836, 2.0519192043895753, 1.0595451989026066, 0.0), # 44
(12.37318700688266, 11.618749999999999, 10.25, 11.03203125, 8.974721232770635, 4.375, 4.764705882352941, 4.25, 4.80125, 2.2662500000000003, 1.6602272727272729, 0.9583333333333333, 0.0, 12.15, 10.541666666666664, 8.301136363636363, 6.79875, 9.6025, 5.95, 4.764705882352941, 3.125, 4.487360616385318, 3.677343750000001, 2.0500000000000003, 1.0562500000000001, 0.0), # 45
(12.379789524928656, 11.581687997256516, 10.240157064471878, 11.023470601851852, 8.976906988109372, 4.375, 4.750489494069233, 4.222743827160494, 4.797365802469135, 2.258989108367627, 1.6589951490210748, 0.9570209419295841, 0.0, 12.15, 10.527230361225422, 8.294975745105374, 6.77696732510288, 9.59473160493827, 5.9118413580246925, 4.750489494069233, 3.125, 4.488453494054686, 3.6744902006172846, 2.048031412894376, 1.0528807270233198, 0.0), # 46
(12.386101954128042, 11.543883607681755, 10.230083676268862, 11.014671064814813, 8.978994932478294, 4.375, 4.7359965625756475, 4.195191358024691, 4.793383827160493, 2.2516060768175588, 1.657723818431226, 0.955677823502515, 0.0, 12.15, 10.512456058527663, 8.288619092156129, 6.754818230452675, 9.586767654320987, 5.873267901234568, 4.7359965625756475, 3.125, 4.489497466239147, 3.6715570216049387, 2.046016735253773, 1.049443964334705, 0.0), # 47
(12.392123339488554, 11.505409259259258, 10.219796296296296, 11.00564375, 8.980984919037049, 4.375, 4.7212555555555555, 4.167416666666667, 4.78931, 2.244117037037037, 1.656414478114478, 0.9543061728395063, 0.0, 12.15, 10.497367901234567, 8.28207239057239, 6.73235111111111, 9.57862, 5.834383333333334, 4.7212555555555555, 3.125, 4.490492459518524, 3.6685479166666677, 2.0439592592592595, 1.0459462962962964, 0.0), # 48
(12.397852726017943, 11.466337379972563, 10.209311385459534, 10.996399768518518, 8.982876800945284, 4.375, 4.706294940692326, 4.139493827160494, 4.78515024691358, 2.2365381207133064, 1.6550683252275846, 0.9529081847279379, 0.0, 12.15, 10.481990032007316, 8.275341626137923, 6.709614362139918, 9.57030049382716, 5.795291358024691, 4.706294940692326, 3.125, 4.491438400472642, 3.665466589506174, 2.0418622770919073, 1.0423943072702333, 0.0), # 49
(12.403289158723938, 11.426740397805213, 10.198645404663925, 10.986950231481481, 8.984670431362652, 4.375, 4.69114318566933, 4.111496913580247, 4.78091049382716, 2.228885459533608, 1.6536865569272978, 0.9514860539551899, 0.0, 12.15, 10.466346593507089, 8.268432784636488, 6.686656378600823, 9.56182098765432, 5.756095679012346, 4.69114318566933, 3.125, 4.492335215681326, 3.6623167438271613, 2.0397290809327853, 1.038794581618656, 0.0), # 50
(12.408431682614292, 11.38669074074074, 10.187814814814814, 10.977306249999998, 8.986365663448797, 4.375, 4.675828758169934, 4.0835, 4.776596666666666, 2.2211751851851855, 1.6522703703703707, 0.9500419753086421, 0.0, 12.15, 10.450461728395062, 8.261351851851853, 6.663525555555555, 9.553193333333333, 5.7169, 4.675828758169934, 3.125, 4.493182831724399, 3.659102083333334, 2.037562962962963, 1.0351537037037037, 0.0), # 51
(12.413279342696734, 11.34626083676269, 10.176836076817558, 10.967478935185184, 8.98796235036337, 4.375, 4.660380125877511, 4.055577160493827, 4.772214691358024, 2.2134234293552817, 1.6508209627135553, 0.9485781435756746, 0.0, 12.15, 10.434359579332419, 8.254104813567777, 6.640270288065844, 9.544429382716048, 5.677808024691357, 4.660380125877511, 3.125, 4.493981175181685, 3.655826311728396, 2.035367215363512, 1.0314782578875175, 0.0), # 52
(12.417831183979011, 11.305523113854596, 10.165725651577505, 10.957479398148147, 8.989460345266023, 4.375, 4.64482575647543, 4.0278024691358025, 4.767770493827161, 2.205646323731139, 1.6493395311136052, 0.9470967535436672, 0.0, 12.15, 10.418064288980338, 8.246697655568026, 6.616938971193416, 9.535540987654322, 5.638923456790124, 4.64482575647543, 3.125, 4.4947301726330116, 3.65249313271605, 2.0331451303155013, 1.0277748285322361, 0.0), # 53
(12.42208625146886, 11.26455, 10.154499999999999, 10.94731875, 8.9908595013164, 4.375, 4.629194117647058, 4.000249999999999, 4.7632699999999994, 2.1978600000000004, 1.6478272727272725, 0.9456, 0.0, 12.15, 10.401599999999998, 8.239136363636362, 6.593579999999999, 9.526539999999999, 5.60035, 4.629194117647058, 3.125, 4.4954297506582, 3.649106250000001, 2.0309, 1.0240500000000001, 0.0), # 54
(12.426043590174027, 11.223413923182441, 10.143175582990398, 10.93700810185185, 8.992159671674152, 4.375, 4.613513677075768, 3.9729938271604937, 4.758719135802469, 2.1900805898491087, 1.6462853847113108, 0.9440900777320531, 0.0, 12.15, 10.384990855052584, 8.231426923556553, 6.570241769547325, 9.517438271604938, 5.562191358024691, 4.613513677075768, 3.125, 4.496079835837076, 3.645669367283951, 2.02863511659808, 1.0203103566529494, 0.0), # 55
(12.429702245102245, 11.182187311385459, 10.131768861454047, 10.926558564814814, 8.993360709498926, 4.375, 4.597812902444929, 3.946108024691358, 4.754123827160494, 2.182324224965707, 1.6447150642224717, 0.9425691815272064, 0.0, 12.15, 10.368260996799268, 8.223575321112358, 6.54697267489712, 9.508247654320988, 5.524551234567902, 4.597812902444929, 3.125, 4.496680354749463, 3.6421861882716056, 2.02635377229081, 1.0165624828532238, 0.0), # 56
(12.433061261261258, 11.140942592592593, 10.120296296296297, 10.915981249999998, 8.994462467950372, 4.375, 4.582120261437908, 3.9196666666666675, 4.74949, 2.1746070370370374, 1.6431175084175085, 0.9410395061728396, 0.0, 12.15, 10.351434567901233, 8.215587542087542, 6.523821111111111, 9.49898, 5.487533333333334, 4.582120261437908, 3.125, 4.497231233975186, 3.638660416666667, 2.0240592592592597, 1.0128129629629632, 0.0), # 57
(12.436119683658815, 11.09975219478738, 10.108774348422497, 10.905287268518517, 8.995464800188138, 4.375, 4.5664642217380775, 3.8937438271604936, 4.744823580246913, 2.1669451577503436, 1.641493914453174, 0.939503246456333, 0.0, 12.15, 10.334535711019662, 8.20746957226587, 6.50083547325103, 9.489647160493826, 5.451241358024691, 4.5664642217380775, 3.125, 4.497732400094069, 3.6350957561728396, 2.0217548696844996, 1.0090683813443075, 0.0), # 58
(12.438876557302644, 11.05868854595336, 10.097219478737998, 10.89448773148148, 8.996367559371876, 4.375, 4.550873251028807, 3.868413580246914, 4.74013049382716, 2.1593547187928674, 1.63984547948622, 0.9379625971650665, 0.0, 12.15, 10.31758856881573, 8.1992273974311, 6.478064156378601, 9.48026098765432, 5.41577901234568, 4.550873251028807, 3.125, 4.498183779685938, 3.6314959104938276, 2.0194438957476, 1.0053353223593966, 0.0), # 59
(12.441330927200491, 11.017824074074072, 10.085648148148147, 10.88359375, 8.997170598661228, 4.375, 4.535375816993463, 3.84375, 4.735416666666667, 2.1518518518518523, 1.6381734006734008, 0.9364197530864199, 0.0, 12.15, 10.300617283950617, 8.190867003367003, 6.455555555555556, 9.470833333333333, 5.3812500000000005, 4.535375816993463, 3.125, 4.498585299330614, 3.6278645833333343, 2.0171296296296295, 1.0016203703703705, 0.0), # 60
(12.443481838360098, 10.977231207133059, 10.0740768175583, 10.872616435185183, 8.997873771215849, 4.375, 4.520000387315419, 3.819827160493827, 4.730688024691357, 2.1444526886145407, 1.6364788751714678, 0.9348769090077733, 0.0, 12.15, 10.283645999085506, 8.182394375857339, 6.4333580658436205, 9.461376049382714, 5.347758024691358, 4.520000387315419, 3.125, 4.498936885607924, 3.624205478395062, 2.0148153635116604, 0.9979301097393691, 0.0), # 61
(12.445328335789204, 10.936982373113853, 10.062521947873801, 10.861566898148148, 8.998476930195388, 4.375, 4.504775429678044, 3.796719135802469, 4.72595049382716, 2.137173360768176, 1.6347631001371743, 0.9333362597165068, 0.0, 12.15, 10.266698856881574, 8.17381550068587, 6.411520082304527, 9.45190098765432, 5.315406790123457, 4.504775429678044, 3.125, 4.499238465097694, 3.620522299382717, 2.0125043895747603, 0.9942711248285323, 0.0), # 62
(12.44686946449555, 10.897149999999998, 10.051, 10.85045625, 8.998979928759484, 4.375, 4.4897294117647055, 3.7745, 4.721209999999999, 2.13003, 1.6330272727272728, 0.9318000000000001, 0.0, 12.15, 10.249799999999999, 8.165136363636364, 6.390089999999999, 9.442419999999998, 5.2843, 4.4897294117647055, 3.125, 4.499489964379742, 3.616818750000001, 2.0102, 0.99065, 0.0), # 63
(12.448104269486876, 10.857806515775033, 10.039527434842249, 10.839295601851852, 8.999382620067799, 4.375, 4.474890801258775, 3.7532438271604947, 4.716472469135802, 2.123038737997257, 1.6312725900985157, 0.9302703246456334, 0.0, 12.15, 10.232973571101967, 8.156362950492579, 6.369116213991769, 9.432944938271604, 5.254541358024692, 4.474890801258775, 3.125, 4.499691310033899, 3.613098533950618, 2.00790548696845, 0.9870733196159123, 0.0), # 64
(12.449031795770926, 10.819024348422495, 10.0281207133059, 10.828096064814813, 8.999684857279973, 4.375, 4.4602880658436215, 3.7330246913580245, 4.711743827160493, 2.1162157064471883, 1.6295002494076571, 0.9287494284407863, 0.0, 12.15, 10.216243712848648, 8.147501247038285, 6.348647119341564, 9.423487654320986, 5.226234567901234, 4.4602880658436215, 3.125, 4.499842428639987, 3.609365354938272, 2.0056241426611803, 0.9835476680384088, 0.0), # 65
(12.449651088355436, 10.780875925925926, 10.016796296296297, 10.81686875, 8.999886493555657, 4.375, 4.445949673202614, 3.7139166666666674, 4.70703, 2.1095770370370373, 1.6277114478114478, 0.9272395061728398, 0.0, 12.15, 10.199634567901235, 8.138557239057238, 6.328731111111111, 9.41406, 5.199483333333334, 4.445949673202614, 3.125, 4.499943246777828, 3.6056229166666673, 2.0033592592592595, 0.9800796296296298, 0.0), # 66
(12.44996119224815, 10.743433676268861, 10.005570644718793, 10.805624768518516, 8.999987382054503, 4.375, 4.431904091019123, 3.695993827160495, 4.702336913580247, 2.103138861454047, 1.625907382466642, 0.9257427526291724, 0.0, 12.15, 10.183170278920894, 8.12953691233321, 6.30941658436214, 9.404673827160494, 5.1743913580246925, 4.431904091019123, 3.125, 4.499993691027251, 3.6018749228395066, 2.0011141289437586, 0.9766757887517148, 0.0), # 67
(12.44974993737699, 10.706573503252354, 9.994405949931412, 10.794277566425121, 8.999902364237876, 4.37491880810852, 4.418109116897788, 3.6791719250114308, 4.6976351394604485, 2.0968861324941503, 1.624057197708075, 0.9242530021899743, 0.0, 12.149850180041152, 10.166783024089716, 8.120285988540376, 6.290658397482449, 9.395270278920897, 5.1508406950160035, 4.418109116897788, 3.1249420057918, 4.499951182118938, 3.598092522141708, 1.9988811899862826, 0.9733248639320324, 0.0), # 68
(12.447770048309177, 10.669170071684588, 9.982988425925925, 10.782255163043477, 8.999128540305009, 4.374276954732511, 4.404160908807968, 3.6625493827160494, 4.692719135802469, 2.090641917211329, 1.621972567783094, 0.9227218973359325, 0.0, 12.148663194444444, 10.149940870695255, 8.10986283891547, 6.271925751633985, 9.385438271604938, 5.127569135802469, 4.404160908807968, 3.1244835390946504, 4.499564270152504, 3.5940850543478264, 1.996597685185185, 0.9699245519713263, 0.0), # 69
(12.443862945070673, 10.63105170582769, 9.971268432784635, 10.769478411835749, 8.997599451303152, 4.373012879134278, 4.389996080736822, 3.645976223136717, 4.687561156835848, 2.0843758573388205, 1.619629777305216, 0.921142276129281, 0.0, 12.14631880144033, 10.13256503742209, 8.09814888652608, 6.25312757201646, 9.375122313671696, 5.104366712391404, 4.389996080736822, 3.123580627953056, 4.498799725651576, 3.5898261372785836, 1.9942536865569274, 0.9664592459843356, 0.0), # 70
(12.438083592771514, 10.592241185450682, 9.959250085733881, 10.755966153381644, 8.995334463003308, 4.371147065996037, 4.375620995723392, 3.629457933241884, 4.682168884316415, 2.078088107802792, 1.6170374741567726, 0.9195152937212715, 0.0, 12.142847865226338, 10.114668230933985, 8.085187370783862, 6.234264323408375, 9.36433776863283, 5.081241106538638, 4.375620995723392, 3.1222479042828835, 4.497667231501654, 3.5853220511272155, 1.9918500171467763, 0.962931016859153, 0.0), # 71
(12.430486956521738, 10.552761290322579, 9.946937499999999, 10.74173722826087, 8.99235294117647, 4.3687000000000005, 4.3610420168067225, 3.6129999999999995, 4.67655, 2.071778823529412, 1.614204306220096, 0.917842105263158, 0.0, 12.13828125, 10.096263157894736, 8.07102153110048, 6.215336470588234, 9.3531, 5.058199999999999, 4.3610420168067225, 3.1205000000000003, 4.496176470588235, 3.5805790760869574, 1.9893874999999999, 0.959341935483871, 0.0), # 72
(12.421128001431383, 10.512634800212398, 9.934334790809327, 10.72681047705314, 8.98867425159364, 4.36569216582838, 4.346265507025855, 3.5966079103795154, 4.670712185642433, 2.0654481594448484, 1.6111389213775176, 0.916123865906192, 0.0, 12.132649819958848, 10.07736252496811, 8.055694606887588, 6.196344478334543, 9.341424371284866, 5.035251074531322, 4.346265507025855, 3.118351547020271, 4.49433712579682, 3.5756034923510476, 1.9868669581618656, 0.9556940727465817, 0.0), # 73
(12.410061692610485, 10.471884494889155, 9.921446073388202, 10.711204740338164, 8.984317760025819, 4.3621440481633895, 4.331297829419833, 3.5802871513488794, 4.664663122999542, 2.0590962704752687, 1.607849967511371, 0.9143617308016269, 0.0, 12.125984439300412, 10.057979038817894, 8.039249837556856, 6.177288811425805, 9.329326245999084, 5.012402011888431, 4.331297829419833, 3.115817177259564, 4.4921588800129095, 3.5704015801127222, 1.9842892146776405, 0.9519894995353778, 0.0), # 74
(12.397342995169081, 10.430533154121862, 9.908275462962962, 10.694938858695652, 8.97930283224401, 4.358076131687243, 4.3161453470277, 3.5640432098765435, 4.6584104938271595, 2.052723311546841, 1.604346092503987, 0.9125568551007147, 0.0, 12.118315972222222, 10.038125406107861, 8.021730462519935, 6.158169934640522, 9.316820987654319, 4.989660493827161, 4.3161453470277, 3.112911522633745, 4.489651416122005, 3.5649796195652184, 1.9816550925925924, 0.9482302867383512, 0.0), # 75
(12.383026874217212, 10.388603557679545, 9.894827074759945, 10.678031672705314, 8.973648834019203, 4.353508901082153, 4.300814422888497, 3.5478815729309554, 4.651961979881115, 2.046329437585734, 1.6006359442376985, 0.9107103939547083, 0.0, 12.10967528292181, 10.01781433350179, 8.003179721188491, 6.138988312757201, 9.30392395976223, 4.967034202103338, 4.300814422888497, 3.1096492150586803, 4.486824417009601, 3.5593438909017725, 1.978965414951989, 0.9444185052435952, 0.0), # 76
(12.367168294864912, 10.34611848533121, 9.881105024005485, 10.660502022946858, 8.967375131122406, 4.34846284103033, 4.285311420041268, 3.531807727480567, 4.645325262917238, 2.0399148035181156, 1.5967281705948373, 0.9088235025148608, 0.0, 12.10009323559671, 9.997058527663466, 7.983640852974187, 6.119744410554345, 9.290650525834476, 4.944530818472794, 4.285311420041268, 3.106044886450236, 4.483687565561203, 3.5535006743156203, 1.9762210048010973, 0.9405562259392011, 0.0), # 77
(12.349822222222222, 10.30310071684588, 9.867113425925925, 10.64236875, 8.960501089324618, 4.3429584362139915, 4.269642701525055, 3.5158271604938274, 4.638508024691357, 2.0334795642701526, 1.5926314194577353, 0.9068973359324239, 0.0, 12.089600694444444, 9.975870695256662, 7.963157097288676, 6.100438692810457, 9.277016049382715, 4.922158024691359, 4.269642701525055, 3.1021131687242796, 4.480250544662309, 3.5474562500000006, 1.9734226851851853, 0.9366455197132618, 0.0), # 78
(12.331043621399177, 10.259573031992566, 9.8528563957476, 10.623650694444443, 8.953046074396838, 4.337016171315348, 4.2538146303789, 3.4999453589391867, 4.631517946959304, 2.0270238747680143, 1.5883543387087244, 0.9049330493586505, 0.0, 12.07822852366255, 9.954263542945155, 7.941771693543622, 6.081071624304041, 9.263035893918609, 4.899923502514861, 4.2538146303789, 3.097868693796677, 4.476523037198419, 3.5412168981481487, 1.97057127914952, 0.9326884574538697, 0.0), # 79
(12.310887457505816, 10.215558210540289, 9.838338048696844, 10.604366696859904, 8.945029452110063, 4.330656531016613, 4.2378335696418485, 3.4841678097850943, 4.624362711476909, 2.0205478899378684, 1.5839055762301377, 0.9029317979447936, 0.0, 12.066007587448558, 9.932249777392729, 7.919527881150689, 6.061643669813604, 9.248725422953818, 4.877834933699132, 4.2378335696418485, 3.093326093583295, 4.4725147260550315, 3.5347888989533023, 1.967667609739369, 0.9286871100491174, 0.0), # 80
(12.289408695652174, 10.171079032258064, 9.8235625, 10.584535597826088, 8.936470588235293, 4.3239, 4.221705882352941, 3.4685000000000006, 4.617049999999999, 2.014051764705883, 1.5792937799043065, 0.9008947368421053, 0.0, 12.052968749999998, 9.909842105263158, 7.8964688995215315, 6.042155294117647, 9.234099999999998, 4.855900000000001, 4.221705882352941, 3.0885, 4.468235294117647, 3.5281785326086967, 1.9647125, 0.9246435483870968, 0.0), # 81
(12.26666230094829, 10.126158276914907, 9.808533864883403, 10.564176237922705, 8.927388848543531, 4.316767062947722, 4.205437931551222, 3.4529474165523544, 4.6095874942844075, 2.007535653998225, 1.5745275976135626, 0.8988230212018388, 0.0, 12.039142875514404, 9.887053233220225, 7.8726379880678135, 6.022606961994674, 9.219174988568815, 4.834126383173296, 4.205437931551222, 3.0834050449626584, 4.4636944242717655, 3.521392079307569, 1.9617067729766806, 0.9205598433559008, 0.0), # 82
(12.242703238504205, 10.080818724279835, 9.793256258573388, 10.543307457729467, 8.917803598805776, 4.30927820454199, 4.189036080275732, 3.4375155464106077, 4.6019828760859625, 2.0009997127410637, 1.569615677240239, 0.8967178061752463, 0.0, 12.0245608281893, 9.863895867927708, 7.848078386201194, 6.00299913822319, 9.203965752171925, 4.812521764974851, 4.189036080275732, 3.078055860387136, 4.458901799402888, 3.5144358192431566, 1.9586512517146777, 0.9164380658436215, 0.0), # 83
(12.21758647342995, 10.035083154121864, 9.777733796296296, 10.521948097826087, 8.907734204793028, 4.301453909465021, 4.1725066915655145, 3.4222098765432096, 4.5942438271604935, 1.994444095860567, 1.5645666666666667, 0.8945802469135803, 0.0, 12.009253472222222, 9.840382716049382, 7.8228333333333335, 5.9833322875817, 9.188487654320987, 4.791093827160494, 4.1725066915655145, 3.0724670781893004, 4.453867102396514, 3.5073160326086965, 1.9555467592592592, 0.9122802867383514, 0.0), # 84
(12.191366970835569, 9.988974346210009, 9.761970593278463, 10.500116998792272, 8.897200032276285, 4.293314662399025, 4.1558561284596145, 3.4070358939186103, 4.58637802926383, 1.9878689582829019, 1.5593892137751788, 0.8924114985680938, 0.0, 11.9932516718107, 9.81652648424903, 7.796946068875894, 5.963606874848704, 9.17275605852766, 4.769850251486054, 4.1558561284596145, 3.0666533302850176, 4.448600016138142, 3.500038999597425, 1.9523941186556926, 0.9080885769281828, 0.0), # 85
(12.164099695831096, 9.942515080313289, 9.745970764746229, 10.477833001207731, 8.886220447026545, 4.284880948026216, 4.139090753997072, 3.391999085505258, 4.578393164151806, 1.9812744549342376, 1.5540919664481068, 0.8902127162900394, 0.0, 11.976586291152262, 9.792339879190433, 7.770459832240534, 5.943823364802712, 9.156786328303612, 4.748798719707362, 4.139090753997072, 3.0606292485901543, 4.443110223513273, 3.4926110004025777, 1.9491941529492458, 0.9038650073012082, 0.0), # 86
(12.135839613526569, 9.895728136200717, 9.729738425925925, 10.455114945652172, 8.874814814814815, 4.276173251028807, 4.122216931216931, 3.3771049382716045, 4.570296913580247, 1.9746607407407408, 1.5486835725677832, 0.8879850552306694, 0.0, 11.959288194444444, 9.76783560753736, 7.743417862838915, 5.923982222222222, 9.140593827160494, 4.727946913580246, 4.122216931216931, 3.054409465020576, 4.437407407407408, 3.4850383152173916, 1.9459476851851853, 0.8996116487455198, 0.0), # 87
(12.106641689032028, 9.84863629364131, 9.713277692043896, 10.431981672705316, 8.863002501412087, 4.2672120560890106, 4.105241023158234, 3.3623589391860995, 4.562096959304984, 1.9680279706285808, 1.5431726800165397, 0.8857296705412365, 0.0, 11.941388245884776, 9.743026375953601, 7.715863400082698, 5.904083911885741, 9.124193918609969, 4.707302514860539, 4.105241023158234, 3.0480086114921505, 4.431501250706043, 3.477327224235106, 1.9426555384087794, 0.8953305721492102, 0.0), # 88
(12.076560887457505, 9.801262332404088, 9.696592678326475, 10.40845202294686, 8.850802872589364, 4.258017847889041, 4.088169392860024, 3.3477665752171926, 4.553800983081847, 1.9613762995239252, 1.537567936676709, 0.8834477173729935, 0.0, 11.922917309670781, 9.717924891102928, 7.687839683383544, 5.884128898571774, 9.107601966163694, 4.68687320530407, 4.088169392860024, 3.041441319920744, 4.425401436294682, 3.469484007648954, 1.9393185356652953, 0.8910238484003719, 0.0), # 89
(12.045652173913043, 9.753629032258065, 9.6796875, 10.384544836956522, 8.838235294117647, 4.248611111111111, 4.071008403361344, 3.333333333333333, 4.545416666666667, 1.9547058823529415, 1.5318779904306221, 0.881140350877193, 0.0, 11.90390625, 9.692543859649122, 7.65938995215311, 5.864117647058823, 9.090833333333334, 4.666666666666666, 4.071008403361344, 3.0347222222222223, 4.419117647058823, 3.461514945652175, 1.9359375, 0.8866935483870969, 0.0), # 90
(12.013970513508676, 9.705759172972254, 9.662566272290809, 10.360278955314012, 8.825319131767932, 4.239012330437433, 4.053764417701236, 3.319064700502972, 4.536951691815272, 1.948016874041798, 1.526111489160612, 0.8788087262050875, 0.0, 11.884385931069957, 9.66689598825596, 7.630557445803059, 5.844050622125392, 9.073903383630544, 4.646690580704161, 4.053764417701236, 3.027865950312452, 4.412659565883966, 3.4534263184380047, 1.9325132544581618, 0.8823417429974777, 0.0), # 91
(11.981570871354446, 9.657675534315677, 9.64523311042524, 10.335673218599032, 8.812073751311223, 4.2292419905502205, 4.036443798918745, 3.304966163694559, 4.528413740283494, 1.941309429516663, 1.5202770807490107, 0.8764539985079298, 0.0, 11.864387217078187, 9.640993983587226, 7.601385403745053, 5.823928288549988, 9.056827480566987, 4.626952629172383, 4.036443798918745, 3.0208871361073006, 4.406036875655611, 3.4452244061996784, 1.9290466220850482, 0.8779705031196072, 0.0), # 92
(11.948508212560386, 9.609400896057348, 9.62769212962963, 10.310746467391306, 8.798518518518518, 4.219320576131687, 4.01905291005291, 3.2910432098765434, 4.51981049382716, 1.9345837037037037, 1.5143834130781502, 0.8740773229369722, 0.0, 11.84394097222222, 9.614850552306692, 7.57191706539075, 5.80375111111111, 9.03962098765432, 4.607460493827161, 4.01905291005291, 3.0138004115226336, 4.399259259259259, 3.436915489130436, 1.925538425925926, 0.8735818996415772, 0.0), # 93
(11.914837502236535, 9.56095803796628, 9.609947445130317, 10.285517542270531, 8.784672799160816, 4.209268571864045, 4.0015981141427766, 3.277301326017376, 4.511149634202103, 1.9278398515290893, 1.5084391340303622, 0.8716798546434675, 0.0, 11.823078060699588, 9.588478401078142, 7.54219567015181, 5.783519554587267, 9.022299268404206, 4.588221856424326, 4.0015981141427766, 3.0066204084743178, 4.392336399580408, 3.4285058474235113, 1.9219894890260634, 0.8691780034514802, 0.0), # 94
(11.880613705492932, 9.512369739811495, 9.592003172153635, 10.260005283816424, 8.770555959009117, 4.199106462429508, 3.984085774227386, 3.2637459990855056, 4.5024388431641515, 1.9210780279189867, 1.5024528914879791, 0.869262748778668, 0.0, 11.801829346707818, 9.561890236565347, 7.512264457439896, 5.763234083756959, 9.004877686328303, 4.569244398719708, 3.984085774227386, 2.9993617588782198, 4.385277979504559, 3.4200017612721423, 1.9184006344307272, 0.8647608854374088, 0.0), # 95
(11.845891787439614, 9.463658781362009, 9.573863425925927, 10.234228532608697, 8.756187363834421, 4.188854732510288, 3.966522253345782, 3.250382716049383, 4.493685802469135, 1.9142983877995645, 1.4964333333333335, 0.8668271604938274, 0.0, 11.780225694444445, 9.5350987654321, 7.482166666666667, 5.742895163398693, 8.98737160493827, 4.5505358024691365, 3.966522253345782, 2.9920390946502056, 4.3780936819172105, 3.411409510869566, 1.9147726851851854, 0.8603326164874555, 0.0), # 96
(11.810726713186616, 9.414847942386832, 9.555532321673525, 10.208206129227051, 8.74158637940773, 4.178533866788599, 3.948913914537008, 3.237216963877458, 4.484898193872885, 1.9075010860969905, 1.4903891074487565, 0.864374244940197, 0.0, 11.758297968106996, 9.508116694342165, 7.451945537243782, 5.7225032582909705, 8.96979638774577, 4.532103749428441, 3.948913914537008, 2.984667047706142, 4.370793189703865, 3.402735376409018, 1.911106464334705, 0.8558952674897121, 0.0), # 97
(11.775173447843981, 9.365960002654985, 9.53701397462277, 10.181956914251208, 8.72677237150004, 4.168164349946655, 3.931267120840105, 3.22425422953818, 4.476083699131229, 1.9006862777374327, 1.484328861716581, 0.8619051572690299, 0.0, 11.736077031893004, 9.480956729959328, 7.421644308582906, 5.702058833212297, 8.952167398262459, 4.513955921353452, 3.931267120840105, 2.9772602499618963, 4.36338618575002, 3.3939856380837368, 1.9074027949245542, 0.8514509093322715, 0.0), # 98
(11.739286956521738, 9.317017741935484, 9.5183125, 10.15549972826087, 8.711764705882352, 4.157766666666667, 3.913588235294118, 3.2115, 4.46725, 1.893854117647059, 1.4782612440191387, 0.859421052631579, 0.0, 11.71359375, 9.453631578947368, 7.391306220095694, 5.681562352941175, 8.9345, 4.4961, 3.913588235294118, 2.9698333333333333, 4.355882352941176, 3.385166576086957, 1.9036625000000003, 0.8470016129032258, 0.0), # 99
(11.703122204329933, 9.268043939997343, 9.49943201303155, 10.128853411835749, 8.696582748325667, 4.147361301630848, 3.895883620938087, 3.1989597622313672, 4.458404778235025, 1.8870047607520377, 1.4721949022387621, 0.8569230861790968, 0.0, 11.690878986625515, 9.426153947970063, 7.36097451119381, 5.661014282256112, 8.91680955647005, 4.4785436671239145, 3.895883620938087, 2.9624009297363205, 4.348291374162834, 3.376284470611917, 1.89988640260631, 0.8425494490906678, 0.0), # 100
(11.6667341563786, 9.219061376609584, 9.480376628943759, 10.102036805555556, 8.681245864600983, 4.136968739521414, 3.878159640811057, 3.1866390032007312, 4.449555715592135, 1.8801383619785366, 1.4661384842577825, 0.8544124130628354, 0.0, 11.667963605967076, 9.398536543691188, 7.330692421288911, 5.640415085935608, 8.89911143118427, 4.461294604481024, 3.878159640811057, 2.9549776710867244, 4.340622932300492, 3.367345601851853, 1.8960753257887522, 0.8380964887826896, 0.0), # 101
(11.630177777777778, 9.170092831541218, 9.461150462962962, 10.07506875, 8.665773420479303, 4.126609465020577, 3.8604226579520695, 3.174543209876543, 4.44071049382716, 1.8732550762527238, 1.4601006379585326, 0.8518901884340482, 0.0, 11.644878472222222, 9.37079207277453, 7.300503189792663, 5.61976522875817, 8.88142098765432, 4.44436049382716, 3.8604226579520695, 2.947578189300412, 4.332886710239651, 3.358356250000001, 1.8922300925925928, 0.8336448028673837, 0.0), # 102
(11.593508033637502, 9.121161084561264, 9.4417576303155, 10.047968085748792, 8.650184781731623, 4.116303962810547, 3.842679035400168, 3.162677869227252, 4.43187679469593, 1.8663550585007669, 1.4540900112233446, 0.8493575674439874, 0.0, 11.621654449588474, 9.342933241883859, 7.270450056116723, 5.599065175502299, 8.86375358939186, 4.427749016918153, 3.842679035400168, 2.940217116293248, 4.325092390865811, 3.3493226952495982, 1.8883515260631, 0.8291964622328422, 0.0), # 103
(11.556779889067812, 9.072288915438735, 9.422202246227709, 10.020753653381641, 8.634499314128943, 4.10607271757354, 3.8249351361943953, 3.151048468221308, 4.423062299954275, 1.8594384636488344, 1.44811525193455, 0.8468157052439055, 0.0, 11.598322402263374, 9.314972757682959, 7.24057625967275, 5.578315390946502, 8.84612459990855, 4.411467855509831, 3.8249351361943953, 2.9329090839811003, 4.317249657064472, 3.3402512177938815, 1.884440449245542, 0.8247535377671579, 0.0), # 104
(11.520048309178742, 9.023499103942651, 9.402488425925926, 9.99344429347826, 8.618736383442265, 4.09593621399177, 3.8071973233737944, 3.1396604938271606, 4.414274691358024, 1.8525054466230941, 1.4421850079744816, 0.8442657569850553, 0.0, 11.574913194444443, 9.286923326835607, 7.210925039872408, 5.557516339869281, 8.828549382716048, 4.395524691358025, 3.8071973233737944, 2.9256687242798356, 4.309368191721132, 3.331148097826088, 1.8804976851851853, 0.8203181003584229, 0.0), # 105
(11.483368259080336, 8.974814429842029, 9.382620284636488, 9.966058846618358, 8.602915355442589, 4.0859149367474465, 3.7894719599774067, 3.12851943301326, 4.405521650663008, 1.8455561623497139, 1.436307927225471, 0.8417088778186895, 0.0, 11.551457690329217, 9.258797656005584, 7.181539636127354, 5.53666848704914, 8.811043301326016, 4.379927206218564, 3.7894719599774067, 2.918510669105319, 4.301457677721294, 3.3220196155394537, 1.8765240569272976, 0.81589222089473, 0.0), # 106
(11.446794703882626, 8.926257672905882, 9.362601937585735, 9.938616153381641, 8.58705559590091, 4.076029370522787, 3.7717654090442765, 3.117630772748057, 4.396810859625057, 1.838590765754862, 1.4304926575698504, 0.8391462228960604, 0.0, 11.527986754115226, 9.230608451856664, 7.152463287849251, 5.515772297264585, 8.793621719250114, 4.36468308184728, 3.7717654090442765, 2.911449550373419, 4.293527797950455, 3.312872051127215, 1.8725203875171472, 0.8114779702641711, 0.0), # 107
(11.410382608695652, 8.877851612903227, 9.3424375, 9.911135054347826, 8.571176470588235, 4.0663, 3.7540840336134447, 3.107, 4.3881499999999996, 1.8316094117647064, 1.4247478468899522, 0.8365789473684213, 0.0, 11.504531250000001, 9.202368421052633, 7.12373923444976, 5.494828235294118, 8.776299999999999, 4.3498, 3.7540840336134447, 2.9045, 4.285588235294117, 3.3037116847826096, 1.8684875000000005, 0.8070774193548388, 0.0), # 108
(11.374186938629451, 8.82961902960308, 9.322131087105625, 9.883634390096615, 8.555297345275559, 4.056747309861302, 3.7364341967239567, 3.0966326017375403, 4.379546753543667, 1.8246122553054145, 1.4190821430681082, 0.8340082063870239, 0.0, 11.48112204218107, 9.174090270257262, 7.09541071534054, 5.473836765916242, 8.759093507087334, 4.335285642432557, 3.7364341967239567, 2.8976766499009297, 4.277648672637779, 3.294544796698873, 1.864426217421125, 0.8026926390548256, 0.0), # 109
(11.338262658794058, 8.78158270277446, 9.301686814128946, 9.85613300120773, 8.539437585733882, 4.047391784788904, 3.7188222614148536, 3.0865340649291264, 4.371008802011888, 1.8175994513031553, 1.4135041939866502, 0.8314351551031215, 0.0, 11.457789994855966, 9.145786706134334, 7.067520969933251, 5.452798353909465, 8.742017604023776, 4.321147690900777, 3.7188222614148536, 2.8909941319920742, 4.269718792866941, 3.2853776670692443, 1.8603373628257893, 0.7983257002522237, 0.0), # 110
(11.302664734299517, 8.733765412186381, 9.281108796296298, 9.82864972826087, 8.523616557734204, 4.038253909465022, 3.7012545907251786, 3.0767098765432097, 4.3625438271604935, 1.8105711546840961, 1.4080226475279107, 0.8288609486679663, 0.0, 11.434565972222222, 9.117470435347629, 7.040113237639553, 5.431713464052287, 8.725087654320987, 4.307393827160493, 3.7012545907251786, 2.8844670781893007, 4.261808278867102, 3.2762165760869575, 1.8562217592592598, 0.7939786738351257, 0.0), # 111
(11.26744813025586, 8.686189937607857, 9.26040114883402, 9.801203411835749, 8.507853627047526, 4.029354168571865, 3.6837375476939744, 3.0671655235482396, 4.354159510745312, 1.803527520374405, 1.402646151574222, 0.8262867422328111, 0.0, 11.411480838477365, 9.08915416456092, 7.013230757871109, 5.4105825611232135, 8.708319021490624, 4.294031732967535, 3.6837375476939744, 2.8781101204084747, 4.253926813523763, 3.2670678039452503, 1.8520802297668042, 0.7896536306916234, 0.0), # 112
(11.232605068443652, 8.638958480664547, 9.239617828252069, 9.773850494242836, 8.492140544138962, 4.02070883845016, 3.6663155781940615, 3.057926284390303, 4.3458851245034475, 1.7964914209838192, 1.3973847812305735, 0.8237192936504428, 0.0, 11.388532681011865, 9.06091223015487, 6.9869239061528665, 5.389474262951456, 8.691770249006895, 4.281096798146424, 3.6663155781940615, 2.8719348846072568, 4.246070272069481, 3.257950164747613, 1.8479235656504138, 0.7853598618785952, 0.0), # 113
(11.197777077480078, 8.592536901878088, 9.219045675021619, 9.746810479149604, 8.47631468365306, 4.012298225970931, 3.649210925046347, 3.04910562975256, 4.337847614285224, 1.7895945503738353, 1.3922488674226394, 0.8211912172112975, 0.0, 11.365530496992042, 9.033103389324271, 6.961244337113197, 5.368783651121505, 8.675695228570447, 4.268747881653584, 3.649210925046347, 2.8659273042649507, 4.23815734182653, 3.248936826383202, 1.8438091350043238, 0.7811397183525536, 0.0), # 114
(11.162861883604794, 8.546941918551349, 9.198696932707318, 9.7200760546636, 8.46032614231088, 4.004100458749136, 3.6324357901149367, 3.0407013271393546, 4.330049991467515, 1.7828475983964234, 1.3872309022854188, 0.8187037582558852, 0.0, 11.342407957992451, 9.005741340814735, 6.936154511427093, 5.348542795189269, 8.66009998293503, 4.256981857995097, 3.6324357901149367, 2.8600717562493823, 4.23016307115544, 3.2400253515545345, 1.8397393865414637, 0.7769947198683046, 0.0), # 115
(11.127815847885161, 8.502107109420871, 9.178532189983873, 9.693599535239764, 8.444150821107023, 3.9960962141009686, 3.6159628905167356, 3.0326901564494966, 4.322472535691132, 1.7762380075348645, 1.3823211862542963, 0.8162523197487347, 0.0, 11.319128711707068, 8.97877551723608, 6.911605931271482, 5.328714022604592, 8.644945071382264, 4.245766219029295, 3.6159628905167356, 2.854354438643549, 4.222075410553511, 3.231199845079922, 1.835706437996775, 0.7729188281291702, 0.0), # 116
(11.092595331388527, 8.457966053223192, 9.158512035525986, 9.66733323533302, 8.427764621036088, 3.988266169342624, 3.5997649433686516, 3.0250488975817924, 4.315095526596881, 1.769753220272439, 1.3775100197646568, 0.8138323046543751, 0.0, 11.295656405829869, 8.952155351198124, 6.887550098823283, 5.309259660817316, 8.630191053193762, 4.23506845661451, 3.5997649433686516, 2.8487615495304452, 4.213882310518044, 3.222444411777674, 1.8317024071051975, 0.768906004838472, 0.0), # 117
(11.057156695182252, 8.414452328694855, 9.138597058008367, 9.6412294693983, 8.411143443092673, 3.980591001790297, 3.583814665787589, 3.0177543304350483, 4.307899243825574, 1.7633806790924282, 1.3727877032518847, 0.811439115937335, 0.0, 11.271954688054828, 8.925830275310684, 6.863938516259424, 5.290142037277283, 8.615798487651148, 4.224856062609067, 3.583814665787589, 2.843279286993069, 4.205571721546336, 3.213743156466101, 1.8277194116016737, 0.7649502116995325, 0.0), # 118
(11.02145630033369, 8.3714995145724, 9.118747846105723, 9.615240551890535, 8.394263188271376, 3.973051388760183, 3.5680847748904534, 3.0107832349080725, 4.300863967018017, 1.757107826478112, 1.3681445371513656, 0.8090681565621435, 0.0, 11.247987206075917, 8.899749722183577, 6.840722685756828, 5.271323479434335, 8.601727934036035, 4.215096528871301, 3.5680847748904534, 2.8378938491144163, 4.197131594135688, 3.2050801839635126, 1.8237495692211447, 0.761045410415673, 0.0), # 119
(10.985450507910194, 8.329041189592374, 9.098924988492762, 9.589318797264655, 8.377099757566796, 3.965628007568476, 3.5525479877941515, 3.0041123908996714, 4.293969975815023, 1.7509221049127721, 1.3635708218984832, 0.8067148294933297, 0.0, 11.223717607587115, 8.873863124426626, 6.817854109492416, 5.252766314738315, 8.587939951630046, 4.20575734725954, 3.5525479877941515, 2.8325914339774827, 4.188549878783398, 3.1964395990882193, 1.8197849976985525, 0.7571855626902159, 0.0), # 120
(10.949095678979122, 8.287010932491311, 9.079089073844187, 9.56341651997559, 8.359629051973535, 3.9583015355313718, 3.5371770216155882, 2.9977185783086533, 4.2871975498573995, 1.7448109568796892, 1.3590568579286233, 0.8043745376954222, 0.0, 11.199109540282393, 8.848119914649644, 6.795284289643115, 5.234432870639067, 8.574395099714799, 4.196806009632114, 3.5371770216155882, 2.8273582396652652, 4.179814525986767, 3.1878055066585307, 1.8158178147688375, 0.753364630226483, 0.0), # 121
(10.912348174607825, 8.245342322005756, 9.059200690834711, 9.537486034478269, 8.341826972486187, 3.951052649965064, 3.5219445934716704, 2.9915785770338243, 4.2805269687859555, 1.7387618248621435, 1.3545929456771704, 0.8020426841329501, 0.0, 11.174126651855724, 8.82246952546245, 6.772964728385852, 5.216285474586429, 8.561053937571911, 4.1882100078473545, 3.5219445934716704, 2.8221804642607595, 4.170913486243093, 3.1791620114927572, 1.8118401381669422, 0.7495765747277962, 0.0), # 122
(10.875164355863662, 8.20396893687225, 9.039220428139036, 9.511479655227625, 8.323669420099352, 3.9438620281857477, 3.506823420479303, 2.9856691669739917, 4.273938512241501, 1.7327621513434166, 1.3501693855795087, 0.7997146717704421, 0.0, 11.148732590001085, 8.796861389474863, 6.750846927897544, 5.1982864540302485, 8.547877024483002, 4.1799368337635885, 3.506823420479303, 2.8170443058469625, 4.161834710049676, 3.170493218409209, 1.8078440856278073, 0.7458153578974774, 0.0), # 123
(10.837500583813984, 8.162824355827334, 9.01910887443187, 9.485349696678588, 8.30513229580763, 3.9367103475096172, 3.4917862197553915, 2.979967128027963, 4.267412459864846, 1.7267993788067886, 1.345776478071024, 0.7973859035724276, 0.0, 11.122891002412453, 8.771244939296702, 6.728882390355119, 5.180398136420364, 8.534824919729692, 4.171953979239149, 3.4917862197553915, 2.8119359625068694, 4.152566147903815, 3.1617832322261967, 1.803821774886374, 0.7420749414388487, 0.0), # 124
(10.79931321952615, 8.121842157607551, 8.998826618387923, 9.459048473286083, 8.286191500605618, 3.9295782852528696, 3.4768057084168436, 2.9744492400945455, 4.260929091296797, 1.7208609497355405, 1.3414045235871004, 0.7950517825034348, 0.0, 11.096565536783794, 8.745569607537782, 6.707022617935502, 5.16258284920662, 8.521858182593594, 4.164228936132364, 3.4768057084168436, 2.806841632323478, 4.143095750302809, 3.153016157762029, 1.799765323677585, 0.7383492870552321, 0.0), # 125
(10.760558624067514, 8.080955920949442, 8.978334248681898, 9.432528299505048, 8.266822935487914, 3.9224465187316975, 3.461854603580562, 2.969092283072546, 4.254468686178167, 1.7149343066129532, 1.3370438225631227, 0.7927077115279934, 0.0, 11.069719840809094, 8.719784826807926, 6.685219112815614, 5.144802919838858, 8.508937372356334, 4.156729196301565, 3.461854603580562, 2.801747513379784, 4.133411467743957, 3.144176099835017, 1.79566684973638, 0.7346323564499494, 0.0), # 126
(10.721193158505432, 8.040099224589545, 8.957592353988504, 9.405741489790408, 8.247002501449117, 3.915295725262296, 3.4469056223634564, 2.9638730368607726, 4.248011524149763, 1.7090068919223076, 1.3326846754344757, 0.7903490936106315, 0.0, 11.042317562182317, 8.693840029716947, 6.6634233771723785, 5.127020675766921, 8.496023048299525, 4.149422251605082, 3.4469056223634564, 2.7966398037587825, 4.1235012507245585, 3.1352471632634704, 1.7915184707977012, 0.7309181113263225, 0.0), # 127
(10.681173183907255, 7.999205647264407, 8.93656152298245, 9.3786403585971, 8.226706099483831, 3.908106582160861, 3.431931481882429, 2.958768281358031, 4.241537884852393, 1.703066148146884, 1.328317382636545, 0.7879713317158789, 0.0, 11.014322348597444, 8.667684648874667, 6.641586913182724, 5.109198444440651, 8.483075769704786, 4.1422755939012434, 3.431931481882429, 2.791504701543472, 4.1133530497419155, 3.1262134528657004, 1.7873123045964903, 0.7272005133876734, 0.0), # 128
(10.640455061340337, 7.958208767710564, 8.91520234433844, 9.351177220380043, 8.205909630586648, 3.9008597667435865, 3.4169048992543876, 2.95375479646313, 4.235028047926869, 1.697099517769964, 1.3239322446047141, 0.7855698288082636, 0.0, 10.985697847748446, 8.641268116890899, 6.619661223023571, 5.0912985533098905, 8.470056095853739, 4.135256715048382, 3.4169048992543876, 2.7863284048168473, 4.102954815293324, 3.117059073460015, 1.783040468867688, 0.7234735243373241, 0.0), # 129
(10.598995151872039, 7.917042164664562, 8.893475406731179, 9.323304389594178, 8.18458899575217, 3.893535956326666, 3.4017985915962377, 2.948809362074875, 4.228462293014, 1.6910944432748274, 1.3195195617743691, 0.7831399878523152, 0.0, 10.956407707329298, 8.614539866375466, 6.5975978088718445, 5.073283329824481, 8.456924586028, 4.128333106904826, 3.4017985915962377, 2.781097111661904, 4.092294497876085, 3.1077681298647266, 1.7786950813462359, 0.7197311058785967, 0.0), # 130
(10.556749816569713, 7.8756394168629384, 8.87134129883538, 9.294974180694428, 8.162720095974995, 3.886115828226296, 3.3865852760248853, 2.943908758092075, 4.221820899754594, 1.685038367144756, 1.3150696345808937, 0.7806772118125626, 0.0, 10.926415575033973, 8.587449329938186, 6.575348172904468, 5.055115101434266, 8.443641799509187, 4.121472261328905, 3.3865852760248853, 2.77579702016164, 4.081360047987498, 3.0983247268981433, 1.7742682597670765, 0.7159672197148127, 0.0), # 131
(10.51367541650071, 7.833934103042237, 8.848760609325746, 9.266138908135728, 8.140278832249722, 3.878580059758672, 3.3712376696572353, 2.9390297644135366, 4.215084147789462, 1.6789187318630299, 1.310572763459673, 0.7781769036535342, 0.0, 10.89568509855645, 8.559945940188875, 6.552863817298364, 5.0367561955890885, 8.430168295578923, 4.114641670178951, 3.3712376696572353, 2.770414328399051, 4.070139416124861, 3.088712969378577, 1.7697521218651495, 0.7121758275492944, 0.0), # 132
(10.469728312732395, 7.791859801938998, 8.825693926876983, 9.236750886373006, 8.117241105570947, 3.870909328239987, 3.3557284896101933, 2.934149160938066, 4.2082323167594105, 1.67272297991293, 1.306019248846092, 0.7756344663397593, 0.0, 10.864179925590703, 8.531979129737351, 6.53009624423046, 5.018168939738788, 8.416464633518821, 4.107808825313293, 3.3557284896101933, 2.764935234457133, 4.058620552785474, 3.078916962124336, 1.765138785375397, 0.7083508910853636, 0.0), # 133
(10.424864866332113, 7.749350092289764, 8.802101840163804, 9.206762429861191, 8.093582816933273, 3.863084310986436, 3.3400304530006673, 2.929243727564472, 4.201245686305251, 1.6664385537777375, 1.3013993911755357, 0.7730453028357667, 0.0, 10.831863703830699, 8.503498331193432, 6.506996955877678, 4.9993156613332115, 8.402491372610502, 4.100941218590261, 3.3400304530006673, 2.7593459364188826, 4.046791408466636, 3.0689208099537315, 1.7604203680327608, 0.7044863720263422, 0.0), # 134
(10.379041438367224, 7.706338552831077, 8.777944937860909, 9.17612585305522, 8.069279867331296, 3.8550856853142146, 3.3241162769455603, 2.92429024419156, 4.194104536067791, 1.6600528959407332, 1.2967034908833885, 0.7704048161060852, 0.0, 10.798700080970423, 8.474452977166937, 6.483517454416942, 4.980158687822199, 8.388209072135583, 4.094006341868184, 3.3241162769455603, 2.753632632367296, 4.034639933665648, 3.0587086176850744, 1.755588987572182, 0.7005762320755525, 0.0), # 135
(10.332214389905081, 7.6627587622994735, 8.753183808643008, 9.144793470410015, 8.044308157759612, 3.8468941285395175, 3.3079586785617807, 2.9192654907181383, 4.186789145687842, 1.653553448885197, 1.2919218484050357, 0.7677084091152441, 0.0, 10.764652704703844, 8.444792500267685, 6.459609242025177, 4.96066034665559, 8.373578291375685, 4.086971687005394, 3.3079586785617807, 2.7477815203853697, 4.022154078879806, 3.0482644901366727, 1.750636761728602, 0.6966144329363159, 0.0), # 136
(10.28434008201304, 7.618544299431501, 8.72777904118481, 9.112717596380511, 8.018643589212827, 3.838490317978539, 3.291530374966233, 2.9141462470430146, 4.179279794806213, 1.6469276550944107, 1.2870447641758613, 0.764951484827772, 0.0, 10.729685222724932, 8.41446633310549, 6.435223820879306, 4.940782965283231, 8.358559589612426, 4.079804745860221, 3.291530374966233, 2.741778798556099, 4.0093217946064135, 3.037572532126838, 1.7455558082369622, 0.6925949363119547, 0.0), # 137
(10.235374875758456, 7.573628742963698, 8.701691224161017, 9.079850545421637, 7.992262062685534, 3.8298549309474748, 3.2748040832758227, 2.908909293064995, 4.1715567630637125, 1.6401629570516543, 1.2820625386312503, 0.7621294462081979, 0.0, 10.693761282727667, 8.383423908290176, 6.410312693156252, 4.920488871154961, 8.343113526127425, 4.0724730102909925, 3.2748040832758227, 2.735610664962482, 3.996131031342767, 3.02661684847388, 1.7403382448322038, 0.6885117039057909, 0.0), # 138
(10.185275132208682, 7.527945671632606, 8.67488094624634, 9.046144631988323, 7.965139479172331, 3.8209686447625186, 3.2577525206074553, 2.903531408682887, 4.163600330101148, 1.6332467972402094, 1.276965472206588, 0.7592376962210506, 0.0, 10.656844532406023, 8.351614658431556, 6.38482736103294, 4.899740391720627, 8.327200660202296, 4.064943972156042, 3.2577525206074553, 2.729263317687513, 3.9825697395861654, 3.0153815439961082, 1.7349761892492683, 0.6843586974211461, 0.0), # 139
(10.133997212431076, 7.481428664174767, 8.647308796115487, 9.011552170535499, 7.937251739667823, 3.811812136739866, 3.240348404078038, 2.897989373795498, 4.155390775559333, 1.626166618143356, 1.2717438653372588, 0.7562716378308593, 0.0, 10.618898619453978, 8.31898801613945, 6.358719326686294, 4.878499854430067, 8.310781551118666, 4.0571851233136975, 3.240348404078038, 2.7227229548141896, 3.9686258698339114, 3.003850723511834, 1.7294617592230976, 0.6801298785613425, 0.0), # 140
(10.081497477492995, 7.4340112993267216, 8.61893536244316, 8.976025475518098, 7.908574745166602, 3.802366084195711, 3.222564450804477, 2.892259968301635, 4.146908379079072, 1.6189098622443758, 1.2663880184586478, 0.7532266740021526, 0.0, 10.579887191565495, 8.285493414023676, 6.331940092293238, 4.856729586733126, 8.293816758158144, 4.049163955622289, 3.222564450804477, 2.7159757744255075, 3.954287372583301, 2.9920084918393663, 1.7237870724886322, 0.675819209029702, 0.0), # 141
(10.027732288461786, 7.385627155825012, 8.58972123390407, 8.939516861391049, 7.879084396663268, 3.792611164446249, 3.2043733779036754, 2.8863199721001056, 4.138133420301177, 1.6114639720265487, 1.2608882320061394, 0.7500982076994595, 0.0, 10.539773896434559, 8.251080284694053, 6.304441160030697, 4.834391916079644, 8.276266840602354, 4.040847960940148, 3.2043733779036754, 2.7090079746044635, 3.939542198331634, 2.9798389537970165, 1.7179442467808141, 0.6714206505295467, 0.0), # 142
(9.972658006404808, 7.336209812406179, 8.559626999172925, 8.901978642609278, 7.848756595152423, 3.7825280548076745, 3.185747902492541, 2.880146165089716, 4.129046178866458, 1.6038163899731561, 1.2552348064151186, 0.746881641887309, 0.0, 10.49852238175514, 8.215698060760397, 6.276174032075593, 4.811449169919467, 8.258092357732917, 4.032204631125603, 3.185747902492541, 2.701805753434053, 3.9243782975762116, 2.967326214203093, 1.7119253998345851, 0.6669281647641981, 0.0), # 143
(9.916230992389421, 7.285692847806764, 8.528613246924428, 8.86336313362772, 7.817567241628662, 3.772097432596183, 3.1666607416879793, 2.8737153271692746, 4.119626934415724, 1.5959545585674784, 1.2494180421209704, 0.7435723795302299, 0.0, 10.456096295221217, 8.179296174832528, 6.247090210604851, 4.787863675702434, 8.239253868831447, 4.023201458036985, 3.1666607416879793, 2.6943553089972734, 3.908783620814331, 2.954454377875907, 1.7057226493848856, 0.6623357134369786, 0.0), # 144
(9.858407607482972, 7.234009840763308, 8.496640565833289, 8.823622648901305, 7.785492237086586, 3.7612999751279688, 3.147084612606896, 2.867004238237588, 4.109855966589781, 1.5878659202927967, 1.2434282395590792, 0.7401658235927514, 0.0, 10.41245928452676, 8.141824059520264, 6.217141197795395, 4.763597760878389, 8.219711933179562, 4.013805933532623, 3.147084612606896, 2.6866428393771202, 3.892746118543293, 2.9412075496337686, 1.699328113166658, 0.6576372582512099, 0.0), # 145
(9.79914421275282, 7.181094370012356, 8.463669544574216, 8.782709502884963, 7.752507482520793, 3.750116359719226, 3.126992232366198, 2.8599896781934633, 4.099713555029442, 1.5795379176323916, 1.2372556991648298, 0.7366573770394019, 0.0, 10.367574997365741, 8.103231147433421, 6.186278495824149, 4.738613752897173, 8.199427110058885, 4.0039855494708485, 3.126992232366198, 2.67865454265659, 3.8762537412603963, 2.927569834294988, 1.6927339089148434, 0.6528267609102142, 0.0), # 146
(9.73839716926632, 7.126880014290443, 8.42966077182191, 8.740576010033621, 7.71858887892588, 3.7385272636861506, 3.1063563180827884, 2.8526484269357075, 4.0891799793755155, 1.570957993069544, 1.2308907213736073, 0.7330424428347111, 0.0, 10.321407081432142, 8.06346687118182, 6.154453606868036, 4.712873979208631, 8.178359958751031, 3.9937077977099906, 3.1063563180827884, 2.670376616918679, 3.85929443946294, 2.9135253366778744, 1.6859321543643822, 0.6478981831173131, 0.0), # 147
(9.676122838090825, 7.071300352334116, 8.394574836251083, 8.697174484802217, 7.6837123272964485, 3.726513364344937, 3.085149586873576, 2.8449572643631287, 4.078235519268811, 1.5621135890875346, 1.2243236066207965, 0.729316423943207, 0.0, 10.27391918441993, 8.022480663375276, 6.1216180331039824, 4.686340767262602, 8.156471038537623, 3.9829401701083804, 3.085149586873576, 2.6617952602463837, 3.8418561636482242, 2.899058161600739, 1.6789149672502168, 0.6428454865758287, 0.0), # 148
(9.612277580293695, 7.014288962879912, 8.358372326536443, 8.652457241645672, 7.647853728627096, 3.71405533901178, 3.0633447558554643, 2.8368929703745334, 4.0668604543501345, 1.5529921481696445, 1.2175446553417821, 0.7254747233294191, 0.0, 10.225074954023084, 7.980221956623609, 6.08772327670891, 4.658976444508932, 8.133720908700269, 3.971650158524347, 3.0633447558554643, 2.6528966707226997, 3.823926864313548, 2.8841524138818913, 1.671674465307289, 0.637662632989083, 0.0), # 149
(9.546817756942277, 6.955779424664377, 8.321013831352694, 8.606376595018924, 7.610988983912421, 3.7011338650028747, 3.04091454214536, 2.828432324868728, 4.0550350642603, 1.5435811127991534, 1.2105441679719486, 0.7215127439578762, 0.0, 10.174838037935576, 7.936640183536638, 6.0527208398597425, 4.630743338397459, 8.1100701285206, 3.95980525481622, 3.04091454214536, 2.6436670464306244, 3.8054944919562104, 2.8687921983396416, 1.6642027662705388, 0.632343584060398, 0.0), # 150
(9.47969972910393, 6.895705316424048, 8.282459939374542, 8.558884859376896, 7.573093994147021, 3.6877296196344136, 3.01783166286017, 2.8195521077445216, 4.042739628640115, 1.5338679254593437, 1.203312444946681, 0.7174258887931072, 0.0, 10.123172083851381, 7.891684776724178, 6.016562224733405, 4.601603776378029, 8.08547925728023, 3.9473729508423303, 3.01783166286017, 2.6340925854531525, 3.7865469970735104, 2.8529616197922993, 1.6564919878749085, 0.6268823014930954, 0.0), # 151
(9.41087985784601, 6.83400021689547, 8.242671239276701, 8.509934349174525, 7.534144660325495, 3.6738232802225945, 2.9940688351167988, 2.8102290989007206, 4.029954427130388, 1.5238400286334952, 1.1958397867013644, 0.713209560799641, 0.0, 10.070040739464476, 7.84530516879605, 5.979198933506821, 4.5715200859004845, 8.059908854260776, 3.9343207384610093, 2.9940688351167988, 2.6241594858732817, 3.7670723301627476, 2.836644783058176, 1.6485342478553402, 0.6212727469904974, 0.0), # 152
(9.340314504235872, 6.770597704815181, 8.201608319733868, 8.459477378866739, 7.4941168834424445, 3.659395524083611, 2.9695987760321514, 2.800440078236131, 4.016659739371929, 1.513484864804889, 1.1881164936713833, 0.7088591629420063, 0.0, 10.015407652468832, 7.797450792362069, 5.940582468356916, 4.5404545944146655, 8.033319478743858, 3.9206161095305836, 2.9695987760321514, 2.6138539457740078, 3.7470584417212223, 2.81982579295558, 1.6403216639467737, 0.6155088822559257, 0.0), # 153
(9.267960029340873, 6.705431358919725, 8.159231769420758, 8.407466262908468, 7.4529865644924636, 3.644427028533658, 2.944394202723137, 2.7901618256495615, 4.002835845005546, 1.5027898764568062, 1.1801328662921224, 0.7043700981847325, 0.0, 9.959236470558428, 7.748071080032056, 5.900664331460612, 4.508369629370417, 8.005671690011091, 3.9062265559093863, 2.944394202723137, 2.603162163238327, 3.7264932822462318, 2.802488754302823, 1.631846353884152, 0.6095846689927024, 0.0), # 154
(9.193772794228362, 6.638434757945644, 8.115502177012075, 8.35385331575464, 7.4107296044701565, 3.62889847088893, 2.9184278323066564, 2.779371121039818, 3.988463023672051, 1.4917425060725265, 1.1718792049989668, 0.6997377694923482, 0.0, 9.901490841427231, 7.6971154644158295, 5.859396024994833, 4.4752275182175785, 7.976926047344102, 3.8911195694557454, 2.9184278323066564, 2.5920703363492357, 3.7053648022350782, 2.7846177719182137, 1.6231004354024152, 0.6034940689041496, 0.0), # 155
(9.117709159965697, 6.569541480629476, 8.070380131182526, 8.298590851860187, 7.367321904370117, 3.612790528465623, 2.8916723818996197, 2.7680447443057092, 3.9735215550122502, 1.480330196135332, 1.163345810227301, 0.6949575798293822, 0.0, 9.842134412769221, 7.644533378123204, 5.816729051136504, 4.440990588405995, 7.9470431100245005, 3.875262642027993, 2.8916723818996197, 2.5805646631897305, 3.6836609521850585, 2.766196950620063, 1.6140760262365055, 0.5972310436935888, 0.0), # 156
(9.039725487620235, 6.498685105707764, 8.023826220606818, 8.241631185680044, 7.322739365186948, 3.59608387857993, 2.864100568618931, 2.756159475346041, 3.957991718666955, 1.4685403891285025, 1.1545229824125098, 0.6900249321603636, 0.0, 9.781130832278372, 7.590274253763999, 5.772614912062549, 4.405621167385506, 7.91598343733391, 3.8586232654844577, 2.864100568618931, 2.568631341842807, 3.661369682593474, 2.7472103952266815, 1.6047652441213638, 0.5907895550643424, 0.0), # 157
(8.957617135686286, 6.424498432849483, 7.973591953902356, 8.180792623486118, 7.274944884696797, 3.5777171334219773, 2.8350640325567142, 2.742898476174686, 3.9406648366396384, 1.4560097748873433, 1.1451191505077887, 0.6847599564194339, 0.0, 9.715783031298415, 7.532359520613772, 5.7255957525389425, 4.368029324662029, 7.881329673279277, 3.840057866644561, 2.8350640325567142, 2.555512238158555, 3.6374724423483986, 2.7269308744953733, 1.5947183907804712, 0.5840453120772259, 0.0), # 158
(8.858744120374082, 6.3393718515594255, 7.906737818402987, 8.103579442909608, 7.212153047825302, 3.551582753604972, 2.8009276580314295, 2.7236067663821912, 3.9145709044888575, 1.4406842982296237, 1.133483387123799, 0.6781362523683109, 0.0, 9.630513176304232, 7.459498776051419, 5.667416935618994, 4.322052894688871, 7.829141808977715, 3.813049472935068, 2.8009276580314295, 2.5368448240035515, 3.606076523912651, 2.7011931476365363, 1.5813475636805976, 0.5763065319599479, 0.0), # 159
(8.741846513885172, 6.242606401394785, 7.821920957955889, 8.008719759367974, 7.133136105077435, 3.517038907233379, 2.7613462490302703, 2.6977995947636733, 3.8789700908914604, 1.4223616955588683, 1.119451901721908, 0.6700501948887847, 0.0, 9.523704730672296, 7.370552143776631, 5.59725950860954, 4.267085086676604, 7.757940181782921, 3.7769194326691427, 2.7613462490302703, 2.512170648023842, 3.5665680525387176, 2.669573253122658, 1.5643841915911778, 0.5675096728540715, 0.0), # 160
(8.607866465503152, 6.134832954888515, 7.7200469719103095, 7.897115253381055, 7.038714499425689, 3.4745040690992197, 2.716608867604126, 2.66580026655489, 3.8343319067996067, 1.4011974579512814, 1.1031483309199415, 0.6605767468907572, 0.0, 9.396448853782916, 7.266344215798328, 5.515741654599707, 4.203592373853843, 7.668663813599213, 3.7321203731768464, 2.716608867604126, 2.481788620785157, 3.5193572497128445, 2.632371751127019, 1.5440093943820619, 0.557712086808047, 0.0), # 161
(8.457746124511628, 6.016682384573562, 7.602021459615496, 7.769667605468694, 6.929708673842563, 3.424396713994519, 2.6670045758038854, 2.627932086991601, 3.781125863165454, 1.3773470764830695, 1.0846963113357242, 0.6497908712841294, 0.0, 9.2498367050164, 7.147699584125422, 5.42348155667862, 4.132041229449208, 7.562251726330908, 3.6791049217882414, 2.6670045758038854, 2.4459976528532277, 3.4648543369212814, 2.5898892018228983, 1.5204042919230993, 0.5469711258703239, 0.0), # 162
(8.292427640194196, 5.888785562982875, 7.468750020420702, 7.6272784961507405, 6.806939071300549, 3.367135316711301, 2.61282243568044, 2.5845183613095624, 3.719821470941162, 1.3509660422304377, 1.0642194795870819, 0.6377675309788032, 0.0, 9.084959443753055, 7.015442840766835, 5.321097397935408, 4.052898126691312, 7.439642941882324, 3.6183257058333878, 2.61282243568044, 2.405096654793786, 3.4034695356502747, 2.5424261653835805, 1.4937500040841403, 0.5353441420893524, 0.0), # 163
(8.11285316183446, 5.751773362649402, 7.321138253675176, 7.470849605947036, 6.67122613477215, 3.3031383520415907, 2.5543515092846794, 2.5358823947445344, 3.650888241078889, 1.3222098462695906, 1.0418414722918394, 0.6245816888846804, 0.0, 8.902908229373192, 6.870398577731482, 5.209207361459196, 3.966629538808771, 7.301776482157778, 3.550235352642348, 2.5543515092846794, 2.3593845371725646, 3.335613067386075, 2.4902832019823458, 1.4642276507350354, 0.5228884875135821, 0.0), # 164
(7.9199648387160195, 5.606276656106095, 7.160091758728169, 7.301282615377426, 6.5233903072298585, 3.2328242947774104, 2.491880858667493, 2.482347492532273, 3.5747956845307916, 1.2912339796767343, 1.0176859260678224, 0.610308307911662, 0.0, 8.704774221257123, 6.713391387028281, 5.088429630339111, 3.873701939030202, 7.149591369061583, 3.4752864895451823, 2.491880858667493, 2.309160210555293, 3.2616951536149292, 2.433760871792476, 1.432018351745634, 0.5096615141914632, 0.0), # 165
(7.714704820122476, 5.452926315885899, 6.9865161349289275, 7.119479204961751, 6.364252031646171, 3.156611619710786, 2.4256995458797714, 2.4242369599085385, 3.492013312249029, 1.2581939335280738, 0.9918764775328559, 0.5950223509696502, 0.0, 8.491648578785155, 6.545245860666151, 4.959382387664279, 3.7745818005842207, 6.984026624498058, 3.393931743871954, 2.4256995458797714, 2.254722585507704, 3.1821260158230853, 2.373159734987251, 1.3973032269857855, 0.4957205741714455, 0.0), # 166
(7.498015255337426, 5.292353214521765, 6.801316981626705, 6.926341055219858, 6.194631750993583, 3.074918801633741, 2.3560966329724047, 2.361874102109088, 3.403010635185759, 1.2232451988998143, 0.9645367633047655, 0.5787987809685459, 0.0, 8.264622461337595, 6.366786590654004, 4.822683816523827, 3.669735596699442, 6.806021270371518, 3.3066237429527234, 2.3560966329724047, 2.196370572595529, 3.0973158754967915, 2.308780351739953, 1.360263396325341, 0.4811230195019787, 0.0), # 167
(7.2708382936444735, 5.125188224546641, 6.605399898170748, 6.722769846671591, 6.015349908244593, 2.9881643153382993, 2.2833611819962822, 2.2955822243696797, 3.308257164293142, 1.1865432668681617, 0.9357904200013762, 0.5617125608182512, 0.0, 8.024787028294753, 6.178838169000762, 4.678952100006881, 3.559629800604484, 6.616514328586284, 3.2138151141175517, 2.2833611819962822, 2.1344030823844995, 3.0076749541222965, 2.2409232822238643, 1.3210799796341497, 0.46592620223151293, 0.0), # 168
(7.034116084327218, 4.952062218493477, 6.399670483910309, 6.509667259836794, 5.827226946371695, 2.8967666356164865, 2.2077822550022947, 2.2256846319260726, 3.2082224105233346, 1.1482436285093212, 0.9057610842405137, 0.5438386534286673, 0.0, 7.773233439036942, 5.982225187715339, 4.528805421202568, 3.444730885527963, 6.416444821046669, 3.1159584846965016, 2.2077822550022947, 2.0691190254403473, 2.9136134731858476, 2.1698890866122653, 1.2799340967820618, 0.450187474408498, 0.0), # 169
(6.78879077666926, 4.773606068895221, 6.185034338194635, 6.2879349752353075, 5.631083308347386, 2.8011442372603246, 2.1296489140413315, 2.1525046300140236, 3.103375884828495, 1.1085017748994974, 0.8745723926400033, 0.525252021709696, 0.0, 7.5110528529444665, 5.777772238806654, 4.372861963200016, 3.325505324698492, 6.20675176965699, 3.013506482019633, 2.1296489140413315, 2.0008173123288033, 2.815541654173693, 2.0959783250784363, 1.2370068676389272, 0.4339641880813838, 0.0), # 170
(6.5358045199542, 4.59045064828482, 5.962397060372978, 6.058474673386982, 5.427739437144163, 2.701715595061839, 2.049250221164283, 2.0763655238692915, 2.994187098160782, 1.0674731971148967, 0.8423479818176697, 0.5060276285712387, 0.0, 7.239336429397638, 5.566303914283624, 4.211739909088348, 3.2024195913446896, 5.988374196321564, 2.906911733417008, 2.049250221164283, 1.9297968536155994, 2.7138697185720817, 2.019491557795661, 1.1924794120745956, 0.4173136952986201, 0.0), # 171
(6.276099463465638, 4.403226829195226, 5.7326642497945866, 5.822188034811656, 5.218015775734522, 2.5988991838130535, 1.9668752384220392, 1.9975906187276353, 2.881125561472354, 1.025313386231724, 0.8092114883913387, 0.4862404369231972, 0.0, 6.959175327776763, 5.348644806155168, 4.046057441956694, 3.075940158695172, 5.762251122944708, 2.7966268662186895, 1.9668752384220392, 1.8563565598664666, 2.609007887867261, 1.9407293449372194, 1.1465328499589174, 0.40029334810865697, 0.0), # 172
(6.010617756487176, 4.212565484159386, 5.4967415058087115, 5.579976740029178, 5.002732767090961, 2.4931134783059927, 1.8828130278654898, 1.916503219824812, 2.7646607857153684, 0.9821778333261846, 0.7752865489788355, 0.4659654096754725, 0.0, 6.671660707462155, 5.125619506430197, 3.8764327448941778, 2.9465334999785533, 5.529321571430737, 2.6831045077547366, 1.8828130278654898, 1.7807953416471376, 2.5013663835454807, 1.859992246676393, 1.0993483011617424, 0.38296049855994424, 0.0), # 173
(5.740301548302412, 4.019097485710249, 5.2555344277646014, 5.332742469559387, 4.782710854185972, 2.3847769533326795, 1.7973526515455251, 1.8334266323965802, 2.645262281841985, 0.9382220294744842, 0.7406968001979856, 0.44527750973796687, 0.0, 6.37788372783412, 4.898052607117634, 3.7034840009899272, 2.814666088423452, 5.29052456368397, 2.5667972853552126, 1.7973526515455251, 1.7034121095233423, 2.391355427092986, 1.7775808231864625, 1.0511068855529204, 0.3653724987009318, 0.0), # 174
(5.466092988194946, 3.823453706380764, 5.009948615011508, 5.08138690392213, 4.558770479992055, 2.2743080836851397, 1.7107831715130346, 1.748684161678698, 2.5233995608043616, 0.8936014657528275, 0.7055658786666139, 0.4242517000205815, 0.0, 6.078935548272969, 4.666768700226395, 3.5278293933330693, 2.680804397258482, 5.046799121608723, 2.4481578263501773, 1.7107831715130346, 1.6245057740608142, 2.2793852399960275, 1.6937956346407106, 1.0019897230023018, 0.3475867005800695, 0.0), # 175
(5.188934225448382, 3.62626501870388, 4.760889666898678, 4.8268117236372525, 4.331732087481704, 2.1621253441553967, 1.6233936498189088, 1.6625991129069244, 2.3995421335546565, 0.8484716332374204, 0.670017421002546, 0.4029629434332179, 0.0, 5.7759073281590085, 4.432592377765396, 3.35008710501273, 2.5454148997122603, 4.799084267109313, 2.327638758069694, 1.6233936498189088, 1.5443752458252833, 2.165866043740852, 1.6089372412124179, 0.9521779333797357, 0.3296604562458073, 0.0), # 176
(4.909767409346319, 3.4281622952125463, 4.5092631827753635, 4.569918609224595, 4.102416119627418, 2.0486472095354746, 1.5354731485140374, 1.5754947913170163, 2.2741595110450277, 0.8029880230044676, 0.6341750638236071, 0.3814862028857779, 0.0, 5.4698902268725496, 4.196348231743556, 3.1708753191180357, 2.408964069013402, 4.548319022090055, 2.2056927078438227, 1.5354731485140374, 1.4633194353824817, 2.051208059813709, 1.5233062030748654, 0.9018526365550728, 0.31165111774659515, 0.0), # 177
(4.629534689172356, 3.2297764084397107, 4.255974761990814, 4.311609241204004, 3.8716430194016906, 1.9342921546173981, 1.4473107296493104, 1.4876945021447328, 2.147721204227634, 0.7573061261301752, 0.5981624437476226, 0.3598964412881627, 0.0, 5.161975403793902, 3.958860854169789, 2.9908122187381125, 2.271918378390525, 4.295442408455268, 2.082772303002626, 1.4473107296493104, 1.3816372532981414, 1.9358215097008453, 1.437203080401335, 0.8511949523981628, 0.29361603713088286, 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
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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), # 118
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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), # 120
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 121
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 122
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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), # 125
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 126
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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), # 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
74, # 1
)
|
class Rectangle:
def __init__(self, width, height):
self.width = width
self.height = height
def __str__(self):
return "Rectangle(width=" + str(self.width) + \
", height=" + str(self.height) + ")"
def set_width(self, width):
self.width = width
def set_height(self, height):
self.height = height
def get_area(self):
return self.width * self.height
def get_perimeter(self):
return 2 * (self.width + self.height)
def get_diagonal(self):
return (self.width ** 2 + self.height ** 2) ** .5
def get_picture(self):
if self.width > 50 or self.height > 50:
return "Too big for picture."
rectangle = ("*" * self.width + "\n") * self.height
return rectangle
def get_amount_inside(self, shape):
max_width = self.width // shape.width
max_height = self.height // shape.height
return max_width * max_height
class Square(Rectangle):
def __init__(self, side):
self.width = side
self.height = side
def set_side(self, side):
self.width = side
self.height = side
def __str__(self):
return "Square(side=" + str(self.width) + ")"
|
frogs = input().split(' ')
while True:
token = input().split(' ')
length = len(frogs) - 1
command = token[0]
if command == 'Join':
name = token[1]
frogs.append(name)
elif command == 'Jump':
name = token[1]
index = int(token[2])
if length >= index:
if index >= 0:
frogs.insert(index, name)
elif command == 'Dive':
index = int(token[1])
if length >= index:
if index >= 0:
frogs.pop(index)
elif command == 'First':
count = int(token[1])
if length <= count:
print(' '.join(frogs))
else:
print(' '.join(frogs[0:count]))
elif command == 'Last':
count = int(token[1])
if length <= count:
print(' '.join(frogs))
else:
print(' '.join(frogs[-count:]))
elif command == 'Print':
operator = token[1]
if operator == 'Normal':
print(f"Frogs: {' '.join(frogs)}")
break
if operator == 'Reversed':
print(f"Frogs: {' '.join(reversed(frogs))}")
break
|
DATABASES = {
'default': {
'ENGINE': 'django.db.backends.', # Add 'postgresql_psycopg2', 'postgresql', 'mysql', 'sqlite3' or 'oracle'.
'NAME': '', # Or path to database file if using sqlite3.
'USER': '', # Not used with sqlite3.
'PASSWORD': '', # Not used with sqlite3.
'HOST': '', # Set to empty string for localhost. Not used with sqlite3.
'PORT': '', # Set to empty string for default. Not used with sqlite3.
}
}
MEDIASYNC = {
'BACKEND': 'mediasync.backends.s3',
'AWS_KEY': '',
'AWS_SECRET': '',
'AWS_BUCKET': '',
'JOINED': {
'style/production.css': (
'yui/reset-fonts-grids.css',
'yui/yahoo-min.js',
'yui/dom-min.js',
'yui/event-min.js',
'style/public_markup.css',
),
},
}
RECAPTCHA_PUBLIC_KEY = ''
RECAPTCHA_PRIVATE_KEY = ''
POSTMARK_API_KEY = 'your-key'
POSTMARK_SENDER = 'sender@signature.com'
|
# -*- coding: utf-8 -*-
model = {
u'en ': 0,
u' de': 1,
u'et ': 2,
u'er ': 3,
u'tt ': 4,
u'om ': 5,
u'för': 6,
u'ar ': 7,
u'de ': 8,
u'att': 9,
u' fö': 10,
u'ing': 11,
u' in': 12,
u' at': 13,
u' i ': 14,
u'det': 15,
u'ch ': 16,
u'an ': 17,
u'gen': 18,
u' an': 19,
u't s': 20,
u'som': 21,
u'te ': 22,
u' oc': 23,
u'ter': 24,
u' ha': 25,
u'lle': 26,
u'och': 27,
u' sk': 28,
u' so': 29,
u'ra ': 30,
u'r a': 31,
u' me': 32,
u'var': 33,
u'nde': 34,
u'är ': 35,
u' ko': 36,
u'on ': 37,
u'ans': 38,
u'int': 39,
u'n s': 40,
u'na ': 41,
u' en': 42,
u' fr': 43,
u' på': 44,
u' st': 45,
u' va': 46,
u'and': 47,
u'nte': 48,
u'på ': 49,
u'ska': 50,
u'ta ': 51,
u' vi': 52,
u'der': 53,
u'äll': 54,
u'örs': 55,
u' om': 56,
u'da ': 57,
u'kri': 58,
u'ka ': 59,
u'nst': 60,
u' ho': 61,
u'as ': 62,
u'stä': 63,
u'r d': 64,
u't f': 65,
u'upp': 66,
u' be': 67,
u'nge': 68,
u'r s': 69,
u'tal': 70,
u'täl': 71,
u'ör ': 72,
u' av': 73,
u'ger': 74,
u'ill': 75,
u'ng ': 76,
u'e s': 77,
u'ekt': 78,
u'ade': 79,
u'era': 80,
u'ers': 81,
u'har': 82,
u'll ': 83,
u'lld': 84,
u'rin': 85,
u'rna': 86,
u'säk': 87,
u'und': 88,
u'inn': 89,
u'lig': 90,
u'ns ': 91,
u' ma': 92,
u' pr': 93,
u' up': 94,
u'age': 95,
u'av ': 96,
u'iva': 97,
u'kti': 98,
u'lda': 99,
u'orn': 100,
u'son': 101,
u'ts ': 102,
u'tta': 103,
u'äkr': 104,
u' sj': 105,
u' ti': 106,
u'avt': 107,
u'ber': 108,
u'els': 109,
u'eta': 110,
u'kol': 111,
u'men': 112,
u'n d': 113,
u't k': 114,
u'vta': 115,
u'år ': 116,
u'juk': 117,
u'man': 118,
u'n f': 119,
u'nin': 120,
u'r i': 121,
u'rsä': 122,
u'sju': 123,
u'sso': 124,
u' är': 125,
u'a s': 126,
u'ach': 127,
u'ag ': 128,
u'bac': 129,
u'den': 130,
u'ett': 131,
u'fte': 132,
u'hor': 133,
u'nba': 134,
u'oll': 135,
u'rnb': 136,
u'ste': 137,
u'til': 138,
u' ef': 139,
u' si': 140,
u'a a': 141,
u'e h': 142,
u'ed ': 143,
u'eft': 144,
u'ga ': 145,
u'ig ': 146,
u'it ': 147,
u'ler': 148,
u'med': 149,
u'n i': 150,
u'nd ': 151,
u'så ': 152,
u'tiv': 153,
u' bl': 154,
u' et': 155,
u' fi': 156,
u' sä': 157,
u'at ': 158,
u'des': 159,
u'e a': 160,
u'gar': 161,
u'get': 162,
u'lan': 163,
u'lss': 164,
u'ost': 165,
u'r b': 166,
u'r e': 167,
u're ': 168,
u'ret': 169,
u'sta': 170,
u't i': 171,
u' ge': 172,
u' he': 173,
u' re': 174,
u'a f': 175,
u'all': 176,
u'bos': 177,
u'ets': 178,
u'lek': 179,
u'let': 180,
u'ner': 181,
u'nna': 182,
u'nne': 183,
u'r f': 184,
u'rit': 185,
u's s': 186,
u'sen': 187,
u'sto': 188,
u'tor': 189,
u'vav': 190,
u'ygg': 191,
u' ka': 192,
u' så': 193,
u' tr': 194,
u' ut': 195,
u'ad ': 196,
u'al ': 197,
u'are': 198,
u'e o': 199,
u'gon': 200,
u'kom': 201,
u'n a': 202,
u'n h': 203,
u'nga': 204,
u'r h': 205,
u'ren': 206,
u't d': 207,
u'tag': 208,
u'tar': 209,
u'tre': 210,
u'ätt': 211,
u' få': 212,
u' hä': 213,
u' se': 214,
u'a d': 215,
u'a i': 216,
u'a p': 217,
u'ale': 218,
u'ann': 219,
u'ara': 220,
u'byg': 221,
u'gt ': 222,
u'han': 223,
u'igt': 224,
u'kan': 225,
u'la ': 226,
u'n o': 227,
u'nom': 228,
u'nsk': 229,
u'omm': 230,
u'r k': 231,
u'r p': 232,
u'r v': 233,
u's f': 234,
u's k': 235,
u't a': 236,
u't p': 237,
u'ver': 238,
u' bo': 239,
u' br': 240,
u' ku': 241,
u' nå': 242,
u'a b': 243,
u'a e': 244,
u'del': 245,
u'ens': 246,
u'es ': 247,
u'fin': 248,
u'ige': 249,
u'm s': 250,
u'n p': 251,
u'någ': 252,
u'or ': 253,
u'r o': 254,
u'rbe': 255,
u'rs ': 256,
u'rt ': 257,
u's a': 258,
u's n': 259,
u'skr': 260,
u't o': 261,
u'ten': 262,
u'tio': 263,
u'ven': 264,
u' al': 265,
u' ja': 266,
u' p ': 267,
u' r ': 268,
u' sa': 269,
u'a h': 270,
u'bet': 271,
u'cke': 272,
u'dra': 273,
u'e f': 274,
u'e i': 275,
u'eda': 276,
u'eno': 277,
u'erä': 278,
u'ess': 279,
u'ion': 280,
u'jag': 281,
u'm f': 282,
u'ne ': 283,
u'nns': 284,
u'pro': 285,
u'r t': 286,
u'rar': 287,
u'riv': 288,
u'rät': 289,
u't e': 290,
u't t': 291,
u'ust': 292,
u'vad': 293,
u'öre': 294,
u' ar': 295,
u' by': 296,
u' kr': 297,
u' mi': 298,
u'arb': 299,
}
|
print('{}\n {:^25}\n {}\n'.format('='*25, '10 TERMOS DE UMA PA', '='*25), end=' ')
primeiro = int(input('Primeiro Termo: '))
razao = int(input(' Razão: '))
acumulador = 0
for c in range(0, 10):
print(primeiro + acumulador, '=> ', end='')
acumulador += razao
print('ACABOU')
|
#
# @lc app=leetcode id=485 lang=python3
#
# [485] Max Consecutive Ones
#
# https://leetcode.com/problems/max-consecutive-ones/description/
#
# algorithms
# Easy (55.77%)
# Likes: 464
# Dislikes: 342
# Total Accepted: 164K
# Total Submissions: 293.3K
# Testcase Example: '[1,0,1,1,0,1]'
#
# Given a binary array, find the maximum number of consecutive 1s in this
# array.
#
# Example 1:
#
# Input: [1,1,0,1,1,1]
# Output: 3
# Explanation: The first two digits or the last three digits are consecutive
# 1s.
# The maximum number of consecutive 1s is 3.
#
#
#
# Note:
#
# The input array will only contain 0 and 1.
# The length of input array is a positive integer and will not exceed 10,000
#
#
#
# @lc code=start
class Solution:
def findMaxConsecutiveOnes(self, nums: List[int]) -> int:
if not nums or max(nums)==0:
return 0
ans,cur=1,1
i = nums.index(1)+1
while i<len(nums):
if nums[i]!=1:
ans = max(ans,cur)
cur = 0
else:
cur+=1
i+=1
return max(ans,cur)
# @lc code=end
|
# 02. Count substring occurrences
text, key = input(), input()
counter = 0
for index in range(0, len(text) - len(key) + 1):
if key.lower() == text[index:index + len(key)].lower():
counter += 1
print(counter)
|
# def func(a):
# return a + 5
func = lambda a: a+5
square = lambda s: s*s
sum = lambda x,y,z: x+y+z
x = 10
print (func(x))
print (square(x))
print (sum(x,4,5))
|
# Numeric Pattern 6
"""
1 3 5 7 9
11 13 15 17 19
21 23 25 27 29
31 33 35 37 39
41 43 45 47 49
"""
i = 0
nums = list(range(1, 50, 2))
for _ in range(5):
j = i + 5
row = " ".join(map(str, nums[i:j]))
print(row)
i = j
|
class ServiceTypes():
MachineLearning = "ml"
Vision = "vision"
ChatBot = "bot"
Speech = "speech"
LangIntent = "intent"
LangEntity = "entity"
|
class Solution(object):
def maxSubArray(self, nums):
idx, sumVal = 0, 0
ans = -float('INF')
while idx < len(nums):
sumVal += nums[idx]
ans = max(ans, sumVal)
if sumVal < 0:
sumVal = 0
idx += 1
return ans
|
# -*- coding: utf-8 -*-
"""
Created on Sat Jul 20 09:42:33 2019
@author: aksha
"""
#printing examples
#print("Hello World")
#
#print("The Lion King")
#print ("was released in 1996")
#print("by Disney.")
#
#print("4*8 is", 4*8)
#print("2**2**2 is", 2**2**2)
#print("1+2+3+4+5 is", 1+2+3+4+5)
#input and variables
#print("Halt!")
#user_input = input("Who goes there? ")
#print("You may pass,", user_input)
#
#a=123.4
#b1 = 'spam'
#first_name = 'bill'
#b = 432
#c = a+b
#print('a+b =', c)
#print('first name is', first_name)
#print('Emails have a lot of', b1)
#assignment
#a = 1
#print(a)
#a = a + 1
#print(a)
#a = a*3
#print(a)
#number = float(input("Type in a number: "))
#integer = int(input("Type in an integer: "))
#text = input("Type in a string: ")
#print("Number is", number)
#print("number is a", type(number))
#print("number*2 =", number*2)
#print('integer is', integer)
#print('it is a', type(integer))
#print('integer*2 =', integer*2)
#print(text)
#print(type(text))
#print(text*5)
#F to C
#fahr_temp = float(input("Fahranheit Temperature: "))
#celc_temp = (fahr_temp - 32)*(5/9)
#print("Celcius Temperature: ", celc_temp)
#math
#weightkg = float(input('Enter weight in kilograms: '))
#stonemass = round(weightkg*2.2/14)
#print('you weigh', stonemass,'stones. ')
#variables
#variablesareboxes = 1
#print(variablesareboxes)
#variablesareboxes = 288
#print(variablesareboxes)
#red = 10
#blue = 8
#print(red, blue)
#red = blue
#print(red, blue)
#yellow = red + blue
#print(yellow)
#red = red+3
#print(yellow)
#print(red)
#stringsandvariables
#question = "What did you have for breakfast?"
#print(question)
#answer = input()
#print("nice. you had " + answer) #concatenation
|
class Document:
def __init__(self, docID, docTitle, docContent, docAll):
self.docID = docID
self.docTitle = docTitle
self.docContent = docContent
self.docAll = docAll
def get_docID(self):
return self.docID
def get_docTitle(self):
return self.docTitle
def get_docContent(self):
return self.docContent
def get_docAll(self):
return self.docAll
def set_docAll(self, newAll):
self.docAll = newAll
|
class Solution:
def findTilt(self, root: TreeNode) -> int:
def traverse(node, res):
if not node:
return 0
left_sum = traverse(node.left, res)
right_sum = traverse(node.right, res)
res[0] += abs(left_sum-right_sum)
return left_sum + right_sum + node.val
res = [0]
traverse(root, res)
return res[0]
|
x = 9
y = 3 #integers
#arithmetic operators
print(x+y) #addition
print(x-y) #subtraction
print(x*y) #multiplication
print(x/y) #division
print(x%y) #modulus
print(x**y) #exponentiation
x = 9.1918123
print(x//y) #floor division
# Assignment operators
x = 9 #sets x to equal 9
x += 3 # x = x +3
print(x)
x = 9
x -= 3
print(x)
x *= 3
print(x)
x /= 3
print(x)
x **= 3
print(x)
#comparison operators
x = 9
y = 3
print(x==y) #True if x=y, false otherwise
print(x!=y) #True is x does not =y. false otherwise
print(x>y) #True if x>y, flase otherwise
print(x<y) #true if x<y, false other
print(x>=y) #true if x>=y, false other
print(x<=y) #true if x<=y, false other
|
class Terminal(object):
check = u'\u2714'
error = u'\u2715'
broken = u'\u2718'
arrow = u'\u21D2'
broken_arrow = u'\u21CF'
under_arrow = u'\u21AA'
class tcolors:
HEADER = '\033[95m'
OKBLUE = '\033[94m'
OKGREEN = '\033[92m'
WARNING = '\033[93m'
FAIL = '\033[91m'
ENDC = '\033[0m'
BOLD = '\033[1m'
UNDERLINE = '\033[4m'
class Outputs(Terminal):
def __init__(self, symlinks):
self.symlinks = symlinks
super().__init__()
def symlink_output(self):
for symlink in self.symlinks:
arrow = self.arrow
if symlink.status == symlink.CREATED:
color = tcolors.OKGREEN
status = self.check
elif symlink.status == symlink.BROKEN:
color = tcolors.WARNING
status = self.broken
arrow = self.broken_arrow
elif symlink.status == symlink.FAILED:
color = tcolors.FAIL
status = self.error
print(
"[" +
color +
f"{status}" +
tcolors.ENDC +
"]",
f"{symlink.src} " +
arrow +
f" {symlink.dest}")
if symlink.status == symlink.BROKEN:
print(f" {self.under_arrow} Warning: Symlink created, but source file does not exist")
elif symlink.status == symlink.FAILED:
print(f" - {symlink.error_message}")
|
expected_output = {
"instance": {
"master": {
"areas": {
"0.0.0.1": {
"interfaces": {
"ge-0/0/2.0": {
"state": "BDR",
"dr_id": "10.16.2.2",
"bdr_id": "10.64.4.4",
"nbrs_count": 5,
}
}
}
}
}
}
}
|
"""
LINK: https://leetcode.com/problems/sqrtx/
Given a non-negative integer x, compute and return the square root of x.
Since the return type is an integer, the decimal digits are truncated, and only the integer part of the result is returned.
Example 1:
Input: x = 4
Output: 2
Example 2:
Input: x = 8
Output: 2
Explanation: The square root of 8 is 2.82842..., and since the decimal part is truncated, 2 is returned.
Constraints:
0 <= x <= 231 - 1
"""
def mySqrt(x):
if x==1:
return 1
left, right = 0, x//2 + 1
while left<right:
mid = (left+right+1)//2
if mid*mid==x:
return mid
elif mid*mid<x:
left = mid
else:
right = mid - 1
return right
|
full_dataset = [
{'name': 'Peach', 'items': ['green shell', 'banana', 'green shell',], 'finish': 3},
{'name': 'Peach', 'items': ['green shell', 'banana', 'green shell',], 'finish': 1},
{'name': 'Bowser', 'items': ['green shell',], 'finish': 1},
{'name': None, 'items': ['green shell',], 'finish': 2},
{'name': 'Bowser', 'items': ['green shell',], 'finish': 1},
{'name': None, 'items': ['red shell',], 'finish': 1},
{'name': 'Yoshi', 'items': ['banana', 'blue shell', 'banana'], 'finish': 7},
{'name': 'DK', 'items': ['blue shell', 'star',], 'finish': 1},
]
|
"""
Using nested loops to iterate over
all the items in a matrix list
"""
#Defining a matrix list
matrix = [
[1,2,3],
[4,5,6],
[7,8,9]
]
#This loop will iterate over all the items inside the matrix list
for row in matrix:
for index in row:
print(index)
|
nome = str(input('Qual é o seu nome? ')).strip().upper()
if nome == 'GABRIEL':
print('Que lindo nome você tem!')
else:
print('Seu nome é tão normal!')
print('Bom dia, {}'.format(nome))
|
def Contract_scalar_1x5(\
t0_6,t1_6,t2_6,\
t0_5,t1_5,t2_5,\
t0_4,t1_4,t2_4,\
t0_3,t1_3,t2_3,\
t0_2,t1_2,t2_2,\
t0_1,t1_1,t2_1,\
t0_0,t1_0,t2_0,\
o1_5,\
o1_4,\
o1_3,\
o1_2,\
o1_1\
):
##############################
# ./input/input_Lx1Ly5.dat
##############################
# (o1_2*(t1_2.conj()*((t0_2*(t0_1*(t1_1.conj()*((o1_1*t1_1)*(t2_1*(t0_0*(t2_0*t1_0)))))))*(t1_2*(t2_2*(t0_3*(t1_3.conj()*((t1_3*o1_3)*(t2_3*(t0_4*(t1_4.conj()*((o1_4*t1_4)*(t2_4*(t0_5*(t1_5.conj()*((o1_5*t1_5)*(t2_5*(t0_6*(t2_6*t1_6)))))))))))))))))))
# cpu_cost= 3.004e+11 memory= 5.0206e+08
# final_bond_order ()
##############################
return np.tensordot(
o1_2, np.tensordot(
t1_2.conj(), np.tensordot(
np.tensordot(
t0_2, np.tensordot(
t0_1, np.tensordot(
t1_1.conj(), np.tensordot(
np.tensordot(
o1_1, t1_1, ([0], [4])
), np.tensordot(
t2_1, np.tensordot(
t0_0, np.tensordot(
t2_0, t1_0, ([1], [0])
), ([0], [1])
), ([1], [1])
), ([3, 4], [1, 4])
), ([2, 3, 4], [4, 6, 0])
), ([0, 2, 3], [5, 2, 0])
), ([0], [0])
), np.tensordot(
t1_2, np.tensordot(
t2_2, np.tensordot(
t0_3, np.tensordot(
t1_3.conj(), np.tensordot(
np.tensordot(
t1_3, o1_3, ([4], [0])
), np.tensordot(
t2_3, np.tensordot(
t0_4, np.tensordot(
t1_4.conj(), np.tensordot(
np.tensordot(
o1_4, t1_4, ([0], [4])
), np.tensordot(
t2_4, np.tensordot(
t0_5, np.tensordot(
t1_5.conj(), np.tensordot(
np.tensordot(
o1_5, t1_5, ([0], [4])
), np.tensordot(
t2_5, np.tensordot(
t0_6, np.tensordot(
t2_6, t1_6, ([0], [1])
), ([1], [1])
), ([0], [1])
), ([2, 3], [4, 1])
), ([1, 2, 4], [6, 4, 0])
), ([1, 2, 3], [5, 2, 0])
), ([0], [3])
), ([2, 3], [5, 1])
), ([1, 2, 4], [6, 4, 0])
), ([1, 2, 3], [5, 2, 0])
), ([0], [3])
), ([1, 2], [5, 1])
), ([1, 2, 4], [6, 4, 2])
), ([1, 2, 3], [5, 2, 0])
), ([0], [3])
), ([1, 2], [5, 1])
), ([0, 1, 4, 5], [5, 0, 1, 3])
), ([0, 1, 2, 3], [0, 4, 3, 1])
), ([0, 1], [1, 0])
)
|
def read_matrix_lines():
return [int(x) for x in input().split(", ")]
def create_matrix(size):
result = [read_matrix_lines() for _ in range(size)]
return result
def get_first_diagonal(matrix, size):
return [matrix[i][i]for i in range(size)]
def get_second_diagonal(matrix, size):
result = []
for r in range(size):
result.append(matrix[r][size - r - 1])
return result
def turn_diagonal_to_str(diagonal):
result = ", ".join(map(str,diagonal))
return result
size = int(input())
matrix = create_matrix(size)
first_diagonal = get_first_diagonal(matrix, size)
second_diagonal = get_second_diagonal(matrix, size)
print(f"First diagonal: {turn_diagonal_to_str(first_diagonal)}. Sum: {sum(first_diagonal)}")
print(f"Second diagonal: {turn_diagonal_to_str(second_diagonal)}. Sum: {sum(second_diagonal)}")
|
"""
Summary
-------
The summary is a brief intro. You can put raw HTML into this field.
"""
summary = '<p> A seasoned Software professional with over 7 years of rich experience in Artificial Intelligence, Machine Learning and Robotics Automation. Responsible for Continous Improvement and Business Digital Trasnformation. </p>'
languages = [
['Hindi', ' (Native)'],
['English', ' (Proffesional)']
]
education = [
['Computer Science', 'Maharshi Arvind Inst. of Sc. & Mgmt.', '2010 - 2013'],
['Udacity', '2020 - 2020']
]
interests = ['Farming', 'Badminton', 'Cooking']
skills = [
['Python & Django', '98%'],
['Azure', '95%'],
['VBA', '99%'],
['Machine Learning', '96%'],
['Computer Vision', '90%'],
['Sketch & Photoshop', '60%']
]
"""
Experience
----------
This should be a list of lists. Each sublist corresponds to a particular job
and is of the form:
['Title', 'Date range', 'Company name and location', 'Description of role']
The 'Description of role' field does not get escaped by the templating engine,
so you can put raw HTML in it if you like.
"""
experience = [
['Robotics Developer',
'2019 - Present',
'Tesco, India',
'<p>Describe your role here lorem ipsum dolor sit amet, consectetuer adipiscing elit. Aenean commodo ligula eget dolor. Aenean massa. Cum sociis natoque penatibus et magnis dis parturient montes, nascetur ridiculus mus. Donec quam felis, ultricies nec, pellentesque eu, pretium quis, sem. Nulla consequat massa quis enim. Donec pede justo.</p> <p>Sed ut perspiciatis unde omnis iste natus error sit voluptatem accusantium doloremque laudantium, totam rem aperiam, eaque ipsa quae ab illo inventore veritatis et quasi architecto beatae vitae dicta sunt explicabo. </p>'
],
['Automation Analyst',
'2017 - 2019',
'Tesco, India',
'<p>Describe your role here lorem ipsum dolor sit amet, consectetuer adipiscing elit. Aenean commodo ligula eget dolor. Aenean massa. Cum sociis natoque penatibus et magnis dis parturient montes, nascetur ridiculus mus. Donec quam felis, ultricies nec, pellentesque eu, pretium quis, sem.</p>'
],
['Analyst',
'2013 - 2017',
'Genpact, India',
'<p>Describe your role here lorem ipsum dolor sit amet, consectetuer adipiscing elit. Aenean commodo ligula eget dolor. Aenean massa. Cum sociis natoque penatibus et magnis dis parturient montes, nascetur ridiculus mus. Donec quam felis, ultricies nec, pellentesque eu, pretium quis, sem.</p>'
]
]
"""
Projects
--------
The project_intro field is for a short introduction to your work.
Project are a list of lists, where each sublist refers to a specific project,
and is of the form:
['Title', 'Description', 'Link to project webpage']
"""
project_intro = '<p>You can list your side projects or open source libraries in this section. Lorem ipsum dolor sit amet, consectetur adipiscing elit. Vestibulum et ligula in nunc bibendum fringilla a eu lectus.</p>'
projects = [
['Velocity',
'A responsive website template designed to help startups promote, market and sell their products.',
'#hook'
],
['DevStudio',
'A responsive website template designed to help startups promote, market and sell their products.',
'http://themes.3rdwavemedia.com/website-templates/responsive-bootstrap-theme-web-development-agencies-devstudio/'
],
['Tempo',
'A responsive website template designed to help startups promote their products or services and to attract users & investors.',
'http://themes.3rdwavemedia.com/website-templates/responsive-bootstrap-theme-for-startups-tempo/'
],
['Atom',
'A comprehensive website template solution for startups/developers to market their mobile apps.',
'http://themes.3rdwavemedia.com/website-templates/responsive-bootstrap-theme-mobile-apps-atom/'
],
['Delta',
'A responsive Bootstrap one page theme designed to help app developers promote their mobile apps.',
'http://themes.3rdwavemedia.com/website-templates/responsive-bootstrap-theme-for-mobile-apps-delta/'
]
]
"""
default_data
------------
This dictionary puts everything together. It will be read by the Flask app when
it is instantiated.
"""
default_data = {
'site_title' : 'Meet your AI Professional',
'name' : 'Sumit K',
'tagline' : 'Robotics Developer',
'email' : 'contact@sumeet.ai',
'phone' : '0123 456 789',
'website' : 'www.sumeet.ai',
'linkedin' : 'https://www.linkedin.com/in/sumit-k-kumawat/',
'github' : 'github.com/sumit-k-kumawat',
'twitter' : '@twittername',
'languages' : languages,
'education' : education,
'interests' : interests,
'skills' : skills,
'summary' : summary,
'experience' : experience,
'project_intro' : project_intro,
'projects' : projects
}
|
# Crie um programa que simule o funcionamento de um caixa eletrônico. No início, pergunte ao usuário qual será o
# valor a ser sacado (número inteiro) e o programa vai informar quantas cédulas de cada valor serão entregues.
# OBS: considere que o caixa possui cédulas de R$50, R$20, R$10 e R$1.
print('*' * 30)
print('{:^30}'.format('BANCO NATH')) # irá centralizar o texto dentro da quantidade de caracteres especificados
print('*'*30)
valor = int(input('Qual valor você quer sacar? R$: '))
print('-'*30)
cedula = 50
count_cedula = 0
while True:
if valor >= cedula:
valor = valor - cedula
count_cedula += 1
else:
if count_cedula > 0:
print('{} cédula(s) de R$ {}.'.format(count_cedula,cedula))
count_cedula = 0
cedula = 20
if valor >= cedula:
valor -= cedula
count_cedula +=1
else:
if count_cedula > 0:
print('{} cédula(s) de R$ {}.'.format(count_cedula,cedula))
count_cedula = 0
cedula = 10
if valor >= cedula:
valor -= cedula
count_cedula += 1
else:
if count_cedula > 0:
print('{} cédula(s) de R$ {}.'.format(count_cedula,cedula))
count_cedula = 0
cedula = 5
if valor >= cedula:
valor -= cedula
count_cedula += 1
else:
if count_cedula > 0:
print('{} cédula(s) de R$ {}.'.format(count_cedula,cedula))
count_cedula = 0
cedula = 1
if valor >= cedula:
valor -= cedula
count_cedula += 1
else:
if count_cedula > 0:
print('{} nota(s) de {}.'.format(count_cedula, cedula))
break
print('-'*30)
print('Operação efetuada com sucesso!')
'''
Resolução do Professor Guanabara:
print('=' * 30)
print('{:^30}'.format('BANCO CEV'))
print('='*30)
valor = int(input('Qual valor você quer sacar? R?$ '))
total = valor
céd = 50
totcéd = 0
while True:
if total >= céd:
total -= céd
totcéd += 1
else:
if totcéd >0:
print(f'Total de {totcéd} cédulas de R$ {céd}')
if céd == 50:
céd = 20
elif céd == 20:
céd = 10
elif céd == 10:
céd = 5
elif céd == 5:
céd = 1
totcéd = 0
if total == 0:
break
print('='*30)
print('Volte sempre ao BANCO CEV! Tenha um bom dia!')
'''
|
#!/usr/bin/python
def param_gui(self):
param_gui = [
self.K1, self.P1, self.e1, self.om1, self.ma1, self.incl1, self.Omega1,
self.K2, self.P2, self.e2, self.om2, self.ma2, self.incl2, self.Omega2,
self.K3, self.P3, self.e3, self.om3, self.ma3, self.incl3, self.Omega3,
self.K4, self.P4, self.e4, self.om4, self.ma4, self.incl4, self.Omega4,
self.K5, self.P5, self.e5, self.om5, self.ma5, self.incl5, self.Omega5,
self.K6, self.P6, self.e6, self.om6, self.ma6, self.incl6, self.Omega6,
self.K7, self.P7, self.e7, self.om7, self.ma7, self.incl7, self.Omega7,
self.K8, self.P8, self.e8, self.om8, self.ma8, self.incl8, self.Omega8,
self.K9, self.P9, self.e9, self.om9, self.ma9, self.incl9, self.Omega9,
]
return param_gui
def param_errors_gui(self):
param_errors_gui = [self.err_K1,self.err_P1,self.err_e1,self.err_om1,self.err_ma1, self.err_i1, self.err_Om1,
self.err_K2,self.err_P2,self.err_e2,self.err_om2,self.err_ma2, self.err_i2, self.err_Om2,
self.err_K3,self.err_P3,self.err_e3,self.err_om3,self.err_ma3, self.err_i3, self.err_Om3,
self.err_K4,self.err_P4,self.err_e4,self.err_om4,self.err_ma4, self.err_i4, self.err_Om4,
self.err_K5,self.err_P5,self.err_e5,self.err_om5,self.err_ma5, self.err_i5, self.err_Om5,
self.err_K6,self.err_P6,self.err_e6,self.err_om6,self.err_ma6, self.err_i6, self.err_Om6,
self.err_K7,self.err_P7,self.err_e7,self.err_om7,self.err_ma7, self.err_i7, self.err_Om7,
self.err_K8,self.err_P8,self.err_e8,self.err_om8,self.err_ma8, self.err_i8, self.err_Om8,
self.err_K9,self.err_P9,self.err_e9,self.err_om9,self.err_ma9, self.err_i9, self.err_Om9,
]
return param_errors_gui
def use_param_gui(self):
use_param_gui = [self.use_K1, self.use_P1, self.use_e1, self.use_om1, self.use_ma1, self.use_incl1, self.use_Omega1,
self.use_K2, self.use_P2, self.use_e2, self.use_om2, self.use_ma2, self.use_incl2, self.use_Omega2,
self.use_K3, self.use_P3, self.use_e3, self.use_om3, self.use_ma3, self.use_incl3, self.use_Omega3,
self.use_K4, self.use_P4, self.use_e4, self.use_om4, self.use_ma4, self.use_incl4, self.use_Omega4,
self.use_K5, self.use_P5, self.use_e5, self.use_om5, self.use_ma5, self.use_incl5, self.use_Omega5,
self.use_K6, self.use_P6, self.use_e6, self.use_om6, self.use_ma6, self.use_incl6, self.use_Omega6,
self.use_K7, self.use_P7, self.use_e7, self.use_om7, self.use_ma7, self.use_incl7, self.use_Omega7,
self.use_K8, self.use_P8, self.use_e8, self.use_om8, self.use_ma8, self.use_incl8, self.use_Omega8,
self.use_K9, self.use_P9, self.use_e9, self.use_om9, self.use_ma9, self.use_incl9, self.use_Omega9,
]
return use_param_gui
###########################################################################
def param_gui_wd(self):
param_gui_wd = [
self.om_dot_1, self.om_dot_2, self.om_dot_3,
self.om_dot_4, self.om_dot_5, self.om_dot_6,
self.om_dot_7, self.om_dot_8, self.om_dot_9
]
return param_gui_wd
def use_param_gui_wd(self):
use_param_gui_wd = [
self.use_om_dot_1, self.use_om_dot_2, self.use_om_dot_3,
self.use_om_dot_4, self.use_om_dot_5, self.use_om_dot_6,
self.use_om_dot_7, self.use_om_dot_8, self.use_om_dot_9
]
return use_param_gui_wd
def param_errors_gui_wd(self):
param_errors_gui_wd = [
self.err_om_dot_1,self.err_om_dot_2,self.err_om_dot_3,
self.err_om_dot_4,self.err_om_dot_5,self.err_om_dot_6,
self.err_om_dot_7,self.err_om_dot_8,self.err_om_dot_9,
]
return param_errors_gui_wd
###########################################################################
def param_gui_tr(self):
param_gui_tr = [
self.t0_1, self.pl_rad_1, self.a_sol_1,
self.t0_2, self.pl_rad_2, self.a_sol_2,
self.t0_3, self.pl_rad_3, self.a_sol_3,
self.t0_4, self.pl_rad_4, self.a_sol_4,
self.t0_5, self.pl_rad_5, self.a_sol_5,
self.t0_6, self.pl_rad_6, self.a_sol_6,
self.t0_7, self.pl_rad_7, self.a_sol_7,
self.t0_8, self.pl_rad_8, self.a_sol_8,
self.t0_9, self.pl_rad_9, self.a_sol_9,
]
return param_gui_tr
def use_param_gui_tr(self):
use_param_gui_tr = [self.use_t0_1, self.use_pl_rad_1, self.use_a_sol_1,
self.use_t0_2, self.use_pl_rad_2, self.use_a_sol_2,
self.use_t0_3, self.use_pl_rad_3, self.use_a_sol_3,
self.use_t0_4, self.use_pl_rad_4, self.use_a_sol_4,
self.use_t0_5, self.use_pl_rad_5, self.use_a_sol_5,
self.use_t0_6, self.use_pl_rad_6, self.use_a_sol_6,
self.use_t0_7, self.use_pl_rad_7, self.use_a_sol_7,
self.use_t0_8, self.use_pl_rad_8, self.use_a_sol_8,
self.use_t0_9, self.use_pl_rad_9, self.use_a_sol_9,
]
return use_param_gui_tr
def err_t0(self):
err_t0 = [self.err_t0_1,self.err_t0_2,self.err_t0_3,
self.err_t0_4,self.err_t0_5,self.err_t0_6,
self.err_t0_7,self.err_t0_8,self.err_t0_9,
]
return err_t0
def err_pl_rad(self):
err_pl_rad = [self.err_pl_rad_1,self.err_pl_rad_2,self.err_pl_rad_3,
self.err_pl_rad_4,self.err_pl_rad_5,self.err_pl_rad_6,
self.err_pl_rad_7,self.err_pl_rad_8,self.err_pl_rad_9,
]
return err_pl_rad
def err_a_sol(self):
err_a_sol = [self.err_a_sol_1,self.err_a_sol_2,self.err_a_sol_3,
self.err_a_sol_4,self.err_a_sol_5,self.err_a_sol_6,
self.err_a_sol_7,self.err_a_sol_8,self.err_a_sol_9,
]
return err_a_sol
###########################################################################
def rvs_data_gui(self):
rvs_data_gui = [
self.Data1,self.Data2,self.Data3,self.Data4,self.Data5,
self.Data6,self.Data7,self.Data8,self.Data9,self.Data10
]
return rvs_data_gui
def rvs_data_jitter_gui(self):
rvs_data_jitter_gui = [
self.jitter_Data1,self.jitter_Data2,self.jitter_Data3,self.jitter_Data4,self.jitter_Data5,
self.jitter_Data6,self.jitter_Data7,self.jitter_Data8,self.jitter_Data9,self.jitter_Data10
]
return rvs_data_jitter_gui
def use_data_offset_gui(self):
use_data_offset_gui = [self.use_offset_Data1,self.use_offset_Data2,self.use_offset_Data3,self.use_offset_Data4,
self.use_offset_Data5,self.use_offset_Data6,self.use_offset_Data7,self.use_offset_Data8,
self.use_offset_Data9,self.use_offset_Data10]
return use_data_offset_gui
def use_data_jitter_gui(self):
use_data_jitter_gui = [self.use_jitter_Data1,self.use_jitter_Data2,self.use_jitter_Data3,self.use_jitter_Data4,self.use_jitter_Data5,
self.use_jitter_Data6,self.use_jitter_Data7,self.use_jitter_Data8,self.use_jitter_Data9,self.use_jitter_Data10]
return use_data_jitter_gui
def data_errors_gui(self):
data_errors_gui = [
self.err_Data1,self.err_Data2,self.err_Data3,self.err_Data4,self.err_Data5,
self.err_Data6,self.err_Data7,self.err_Data8,self.err_Data9,self.err_Data10
]
return data_errors_gui
def data_errors_jitter_gui(self):
data_errors_jitter_gui = [
self.err_jitter_Data1,self.err_jitter_Data2,self.err_jitter_Data3,self.err_jitter_Data4,self.err_jitter_Data5,
self.err_jitter_Data6,self.err_jitter_Data7,self.err_jitter_Data8,self.err_jitter_Data9,self.err_jitter_Data10
]
return data_errors_jitter_gui
def tra_data_gui(self):
tra_data_gui = [
self.trans_Data1,self.trans_Data2,self.trans_Data3,self.trans_Data4,self.trans_Data5,
self.trans_Data6,self.trans_Data7,self.trans_Data8,self.trans_Data9,self.trans_Data10
]
return tra_data_gui
def tra_data_jitter_gui(self):
tra_data_jitter_gui = [
self.jitter_trans_Data1,self.jitter_trans_Data2,self.jitter_trans_Data3,self.jitter_trans_Data4,self.jitter_trans_Data5,
self.jitter_trans_Data6,self.jitter_trans_Data7,self.jitter_trans_Data8,self.jitter_trans_Data9,self.jitter_trans_Data10
]
return tra_data_jitter_gui
def use_tra_data_offset_gui(self):
use_tra_data_offset_gui = [
self.use_offset_trans_Data1,self.use_offset_trans_Data2,self.use_offset_trans_Data3,self.use_offset_trans_Data4,
self.use_offset_trans_Data5,self.use_offset_trans_Data6,self.use_offset_trans_Data7,self.use_offset_trans_Data8,
self.use_offset_trans_Data9,self.use_offset_trans_Data10
]
return use_tra_data_offset_gui
def use_tra_data_jitter_gui(self):
use_tra_data_jitter_gui = [
self.use_jitter_trans_Data1,self.use_jitter_trans_Data2,self.use_jitter_trans_Data3,self.use_jitter_trans_Data4,
self.use_jitter_trans_Data5,self.use_jitter_trans_Data6,self.use_jitter_trans_Data7,self.use_jitter_trans_Data8,
self.use_jitter_trans_Data9,self.use_jitter_trans_Data10
]
return use_tra_data_jitter_gui
def tra_data_errors_gui(self):
tra_data_errors_gui = [
self.err_trans_Data1,self.err_trans_Data2,self.err_trans_Data3,self.err_trans_Data4,self.err_trans_Data5,
self.err_trans_Data6,self.err_trans_Data7,self.err_trans_Data8,self.err_trans_Data9,self.err_trans_Data10
]
return tra_data_errors_gui
def tra_data_errors_jitter_gui(self):
tra_data_errors_jitter_gui = [
self.err_jitter_trans_Data1,self.err_jitter_trans_Data2,self.err_jitter_trans_Data3,self.err_jitter_trans_Data4,
self.err_jitter_trans_Data5,self.err_jitter_trans_Data6,self.err_jitter_trans_Data7,self.err_jitter_trans_Data8,
self.err_jitter_trans_Data9,self.err_jitter_trans_Data10
]
return tra_data_errors_jitter_gui
def param_bounds_gui(self):
param_bounds_gui = [
[self.K_min_1,self.K_max_1],[self.P_min_1,self.P_max_1], [self.e_min_1,self.e_max_1],[self.om_min_1,self.om_max_1], [self.ma_min_1,self.ma_max_1],[self.incl_min_1,self.incl_max_1], [self.Omega_min_1,self.Omega_max_1],[self.t0_min_1,self.t0_max_1],[self.pl_rad_min_1,self.pl_rad_max_1],[self.a_sol_min_1,self.a_sol_max_1],
[self.K_min_2,self.K_max_2],[self.P_min_2,self.P_max_2], [self.e_min_2,self.e_max_2],[self.om_min_2,self.om_max_2], [self.ma_min_2,self.ma_max_2],[self.incl_min_2,self.incl_max_2], [self.Omega_min_2,self.Omega_max_2],[self.t0_min_2,self.t0_max_2],[self.pl_rad_min_2,self.pl_rad_max_2],[self.a_sol_min_2,self.a_sol_max_2],
[self.K_min_3,self.K_max_3],[self.P_min_3,self.P_max_3], [self.e_min_3,self.e_max_3],[self.om_min_3,self.om_max_3], [self.ma_min_3,self.ma_max_3],[self.incl_min_3,self.incl_max_3], [self.Omega_min_3,self.Omega_max_3],[self.t0_min_3,self.t0_max_3],[self.pl_rad_min_3,self.pl_rad_max_3],[self.a_sol_min_3,self.a_sol_max_3],
[self.K_min_4,self.K_max_4],[self.P_min_4,self.P_max_4], [self.e_min_4,self.e_max_4],[self.om_min_4,self.om_max_4], [self.ma_min_4,self.ma_max_4],[self.incl_min_4,self.incl_max_4], [self.Omega_min_4,self.Omega_max_4],[self.t0_min_4,self.t0_max_4],[self.pl_rad_min_4,self.pl_rad_max_4],[self.a_sol_min_4,self.a_sol_max_4],
[self.K_min_5,self.K_max_5],[self.P_min_5,self.P_max_5], [self.e_min_5,self.e_max_5],[self.om_min_5,self.om_max_5], [self.ma_min_5,self.ma_max_5],[self.incl_min_5,self.incl_max_5], [self.Omega_min_5,self.Omega_max_5],[self.t0_min_5,self.t0_max_5],[self.pl_rad_min_5,self.pl_rad_max_5],[self.a_sol_min_5,self.a_sol_max_5],
[self.K_min_6,self.K_max_6],[self.P_min_6,self.P_max_6], [self.e_min_6,self.e_max_6],[self.om_min_6,self.om_max_6], [self.ma_min_6,self.ma_max_6],[self.incl_min_6,self.incl_max_6], [self.Omega_min_6,self.Omega_max_6],[self.t0_min_6,self.t0_max_6],[self.pl_rad_min_6,self.pl_rad_max_6],[self.a_sol_min_6,self.a_sol_max_6],
[self.K_min_7,self.K_max_7],[self.P_min_7,self.P_max_7], [self.e_min_7,self.e_max_7],[self.om_min_7,self.om_max_7], [self.ma_min_7,self.ma_max_7],[self.incl_min_7,self.incl_max_7], [self.Omega_min_7,self.Omega_max_7],[self.t0_min_7,self.t0_max_7],[self.pl_rad_min_7,self.pl_rad_max_7],[self.a_sol_min_7,self.a_sol_max_7],
[self.K_min_8,self.K_max_8],[self.P_min_8,self.P_max_8], [self.e_min_8,self.e_max_8],[self.om_min_8,self.om_max_8], [self.ma_min_8,self.ma_max_8],[self.incl_min_8,self.incl_max_8], [self.Omega_min_8,self.Omega_max_8],[self.t0_min_8,self.t0_max_8],[self.pl_rad_min_8,self.pl_rad_max_8],[self.a_sol_min_8,self.a_sol_max_8],
[self.K_min_9,self.K_max_9],[self.P_min_9,self.P_max_9], [self.e_min_9,self.e_max_9],[self.om_min_9,self.om_max_9], [self.ma_min_9,self.ma_max_9],[self.incl_min_9,self.incl_max_9], [self.Omega_min_9,self.Omega_max_9],[self.t0_min_9,self.t0_max_9],[self.pl_rad_min_9,self.pl_rad_max_9],[self.a_sol_min_9,self.a_sol_max_9]
]
return param_bounds_gui
def offset_bounds_gui(self):
offset_bounds_gui = [
[self.Data1_min,self.Data1_max], [self.Data2_min,self.Data2_max], [self.Data3_min,self.Data3_max], [self.Data4_min,self.Data4_max], [self.Data5_min,self.Data5_max],
[self.Data6_min,self.Data6_max], [self.Data7_min,self.Data7_max], [self.Data8_min,self.Data8_max], [self.Data9_min,self.Data9_max], [self.Data10_min,self.Data10_max]
]
return offset_bounds_gui
def jitter_bounds_gui(self):
jitter_bounds_gui = [
[self.jitter1_min,self.jitter1_max], [self.jitter2_min,self.jitter2_max], [self.jitter3_min,self.jitter3_max], [self.jitter4_min,self.jitter4_max], [self.jitter5_min,self.jitter5_max],
[self.jitter6_min,self.jitter6_max], [self.jitter7_min,self.jitter7_max], [self.jitter8_min,self.jitter8_max], [self.jitter9_min,self.jitter9_max], [self.jitter10_min,self.Data10_max]
]
return jitter_bounds_gui
################### OmDot ########################
def om_dot_bounds_gui(self):
om_dot_bounds_gui = [
[self.omega_dot_min_1,self.omega_dot_max_1], [self.omega_dot_min_2,self.omega_dot_max_2],
[self.omega_dot_min_3,self.omega_dot_max_3], [self.omega_dot_min_4,self.omega_dot_max_4],
[self.omega_dot_min_5,self.omega_dot_max_5], [self.omega_dot_min_6,self.omega_dot_max_6],
[self.omega_dot_min_7,self.omega_dot_max_7], [self.omega_dot_min_8,self.omega_dot_max_8],
[self.omega_dot_min_9,self.omega_dot_max_9]
]
return om_dot_bounds_gui
################### LD ########################
def use_uni_ld_models(self):
use_uni_ld_models = [
self.use_uniform_ld_1,self.use_uniform_ld_2,self.use_uniform_ld_3,self.use_uniform_ld_4,self.use_uniform_ld_5,
self.use_uniform_ld_6,self.use_uniform_ld_7,self.use_uniform_ld_8,self.use_uniform_ld_9,self.use_uniform_ld_10
]
return use_uni_ld_models
def use_lin_ld_models(self):
use_lin_ld_models = [
self.use_linear_ld_1,self.use_linear_ld_2,self.use_linear_ld_3,self.use_linear_ld_4,self.use_linear_ld_5,
self.use_linear_ld_6,self.use_linear_ld_7,self.use_linear_ld_8,self.use_linear_ld_9,self.use_linear_ld_10
]
return use_lin_ld_models
def use_quad_ld_models(self):
use_quad_ld_models =[
self.use_quadratic_ld_1,self.use_quadratic_ld_2,self.use_quadratic_ld_3,self.use_quadratic_ld_4,self.use_quadratic_ld_5,
self.use_quadratic_ld_6,self.use_quadratic_ld_7,self.use_quadratic_ld_8,self.use_quadratic_ld_9,self.use_quadratic_ld_10
]
return use_quad_ld_models
def use_nonlin_ld_models(self):
use_nonlin_ld_models = [
self.use_nonlinear_ld_1,self.use_nonlinear_ld_2,self.use_nonlinear_ld_3,self.use_nonlinear_ld_4,self.use_nonlinear_ld_5,
self.use_nonlinear_ld_6,self.use_nonlinear_ld_7,self.use_nonlinear_ld_8,self.use_nonlinear_ld_9,self.use_nonlinear_ld_10
]
return use_nonlin_ld_models
def lin_u(self):
lin_u = [self.u1_linear_1,self.u1_linear_2,self.u1_linear_3,self.u1_linear_4,self.u1_linear_5,
self.u1_linear_6,self.u1_linear_7,self.u1_linear_8,self.u1_linear_9,self.u1_linear_10
]
return lin_u
def use_lin_u(self):
use_lin_u = [
self.use_u1_linear_1,self.use_u1_linear_2,self.use_u1_linear_3,self.use_u1_linear_4,self.use_u1_linear_5,
self.use_u1_linear_6,self.use_u1_linear_7,self.use_u1_linear_8,self.use_u1_linear_9,self.use_u1_linear_10
]
return use_lin_u
def err_lin_u(self):
err_lin_u = [self.err_u1_linear_1,self.err_u1_linear_2,self.err_u1_linear_3,self.err_u1_linear_4,self.err_u1_linear_5,
self.err_u1_linear_6,self.err_u1_linear_7,self.err_u1_linear_8,self.err_u1_linear_9,self.err_u1_linear_10
]
return err_lin_u
def quad_u1(self):
quad_u1 = [
self.u1_quadratic_1,self.u1_quadratic_2,self.u1_quadratic_3,self.u1_quadratic_4,self.u1_quadratic_5,
self.u1_quadratic_6,self.u1_quadratic_7,self.u1_quadratic_8,self.u1_quadratic_9,self.u1_quadratic_10
]
return quad_u1
def use_quad_u1(self):
use_quad_u1 = [
self.use_u1_quadratic_1,self.use_u1_quadratic_2,self.use_u1_quadratic_3,self.use_u1_quadratic_4,self.use_u1_quadratic_5,
self.use_u1_quadratic_6,self.use_u1_quadratic_7,self.use_u1_quadratic_8,self.use_u1_quadratic_9,self.use_u1_quadratic_10
]
return use_quad_u1
def err_quad_u1(self):
err_quad_u1 = [
self.err_u1_quadratic_1,self.err_u1_quadratic_2,self.err_u1_quadratic_3,self.err_u1_quadratic_4,self.err_u1_quadratic_5,
self.err_u1_quadratic_6,self.err_u1_quadratic_7,self.err_u1_quadratic_8,self.err_u1_quadratic_9,self.err_u1_quadratic_10
]
return err_quad_u1
def quad_u2(self):
quad_u2 = [
self.u2_quadratic_1,self.u2_quadratic_2,self.u2_quadratic_3,self.u2_quadratic_4,self.u2_quadratic_5,
self.u2_quadratic_6,self.u2_quadratic_7,self.u2_quadratic_8,self.u2_quadratic_9,self.u2_quadratic_10
]
return quad_u2
def use_quad_u2(self):
use_quad_u2 = [
self.use_u2_quadratic_1,self.use_u2_quadratic_2,self.use_u2_quadratic_3,self.use_u2_quadratic_4,self.use_u2_quadratic_5,
self.use_u2_quadratic_6,self.use_u2_quadratic_7,self.use_u2_quadratic_8,self.use_u2_quadratic_9,self.use_u2_quadratic_10
]
return use_quad_u2
def err_quad_u2(self):
err_quad_u2 = [
self.err_u2_quadratic_1,self.err_u2_quadratic_2,self.err_u2_quadratic_3,self.err_u2_quadratic_4,self.err_u2_quadratic_5,
self.err_u2_quadratic_6,self.err_u2_quadratic_7,self.err_u2_quadratic_8,self.err_u2_quadratic_9,self.err_u2_quadratic_10
]
return err_quad_u2
def nonlin_u1(self):
nonlin_u1 = [
self.u1_nonlin_1,self.u1_nonlin_2,self.u1_nonlin_3,self.u1_nonlin_4,self.u1_nonlin_5,
self.u1_nonlin_6,self.u1_nonlin_7,self.u1_nonlin_8,self.u1_nonlin_9,self.u1_nonlin_10
]
return nonlin_u1
def use_nonlin_u1(self):
use_nonlin_u1 = [
self.use_u1_nonlin_1,self.use_u1_nonlin_2,self.use_u1_nonlin_3,self.use_u1_nonlin_4,self.use_u1_nonlin_5,
self.use_u1_nonlin_6,self.use_u1_nonlin_7,self.use_u1_nonlin_8,self.use_u1_nonlin_9,self.use_u1_nonlin_10
]
return use_nonlin_u1
def err_nonlin_u1(self):
err_nonlin_u1 = [
self.err_u1_nonlin_1,self.err_u1_nonlin_2,self.err_u1_nonlin_3,self.err_u1_nonlin_4,self.err_u1_nonlin_5,
self.err_u1_nonlin_6,self.err_u1_nonlin_7,self.err_u1_nonlin_8,self.err_u1_nonlin_9,self.err_u1_nonlin_10
]
return err_nonlin_u1
def nonlin_u2(self):
nonlin_u2 = [
self.u2_nonlin_1,self.u2_nonlin_2,self.u2_nonlin_3,self.u2_nonlin_4,self.u2_nonlin_5,
self.u2_nonlin_6,self.u2_nonlin_7,self.u2_nonlin_8,self.u2_nonlin_9,self.u2_nonlin_10
]
return nonlin_u2
def use_nonlin_u2(self):
use_nonlin_u2 = [
self.use_u2_nonlin_1,self.use_u2_nonlin_2,self.use_u2_nonlin_3,self.use_u2_nonlin_4,self.use_u2_nonlin_5,
self.use_u2_nonlin_6,self.use_u2_nonlin_7,self.use_u2_nonlin_8,self.use_u2_nonlin_9,self.use_u2_nonlin_10
]
return use_nonlin_u2
def err_nonlin_u2(self):
err_nonlin_u2 = [
self.err_u2_nonlin_1,self.err_u2_nonlin_2,self.err_u2_nonlin_3,self.err_u2_nonlin_4,self.err_u2_nonlin_5,
self.err_u2_nonlin_6,self.err_u2_nonlin_7,self.err_u2_nonlin_8,self.err_u2_nonlin_9,self.err_u2_nonlin_10
]
return err_nonlin_u2
def nonlin_u3(self):
nonlin_u3 = [
self.u3_nonlin_1,self.u3_nonlin_2,self.u3_nonlin_3,self.u3_nonlin_4,self.u3_nonlin_5,
self.u3_nonlin_6,self.u3_nonlin_7,self.u3_nonlin_8,self.u3_nonlin_9,self.u3_nonlin_10
]
return nonlin_u3
def use_nonlin_u3(self):
use_nonlin_u3 = [
self.use_u3_nonlin_1,self.use_u3_nonlin_2,self.use_u3_nonlin_3,self.use_u3_nonlin_4,self.use_u3_nonlin_5,
self.use_u3_nonlin_6,self.use_u3_nonlin_7,self.use_u3_nonlin_8,self.use_u3_nonlin_9,self.use_u3_nonlin_10
]
return use_nonlin_u3
def err_nonlin_u3(self):
err_nonlin_u3 = [
self.err_u3_nonlin_1,self.err_u3_nonlin_2,self.err_u3_nonlin_3,self.err_u3_nonlin_4,self.err_u3_nonlin_5,
self.err_u3_nonlin_6,self.err_u3_nonlin_7,self.err_u3_nonlin_8,self.err_u3_nonlin_9,self.err_u3_nonlin_10
]
return err_nonlin_u3
def nonlin_u4(self):
nonlin_u4 = [
self.u4_nonlin_1,self.u4_nonlin_2,self.u4_nonlin_3,self.u4_nonlin_4,self.u4_nonlin_5,
self.u4_nonlin_6,self.u4_nonlin_7,self.u4_nonlin_8,self.u4_nonlin_9,self.u4_nonlin_10
]
return nonlin_u4
def use_nonlin_u4(self):
use_nonlin_u4 = [
self.use_u4_nonlin_1,self.use_u4_nonlin_2,self.use_u4_nonlin_3,self.use_u4_nonlin_4,self.use_u4_nonlin_5,
self.use_u4_nonlin_6,self.use_u4_nonlin_7,self.use_u4_nonlin_8,self.use_u4_nonlin_9,self.use_u4_nonlin_10
]
return use_nonlin_u4
def err_nonlin_u4(self):
err_nonlin_u4 = [
self.err_u4_nonlin_1,self.err_u4_nonlin_2,self.err_u4_nonlin_3,self.err_u4_nonlin_4,self.err_u4_nonlin_5,
self.err_u4_nonlin_6,self.err_u4_nonlin_7,self.err_u4_nonlin_8,self.err_u4_nonlin_9,self.err_u4_nonlin_10
]
return err_nonlin_u4
def ld_u1_bounds_gui(self):
ld_u1_bounds_gui = [
[self.u1_min_1,self.u1_max_1],[self.u1_min_2,self.u1_max_2],[self.u1_min_3,self.u1_max_3],
[self.u1_min_4,self.u1_max_4],[self.u1_min_5,self.u1_max_5],[self.u1_min_6,self.u1_max_6],
[self.u1_min_7,self.u1_max_7],[self.u1_min_8,self.u1_max_8],[self.u1_min_9,self.u1_max_9],
[self.u1_min_10,self.u1_max_10]
]
return ld_u1_bounds_gui
def ld_u2_bounds_gui(self):
ld_u2_bounds_gui = [
[self.u2_min_1,self.u2_max_1],[self.u2_min_2,self.u2_max_2],[self.u2_min_3,self.u2_max_3],
[self.u2_min_4,self.u2_max_4],[self.u2_min_5,self.u2_max_5],[self.u2_min_6,self.u2_max_6],
[self.u2_min_7,self.u2_max_7],[self.u2_min_8,self.u2_max_8],[self.u2_min_9,self.u2_max_9],
[self.u2_min_10,self.u2_max_10]
]
return ld_u2_bounds_gui
def ld_u3_bounds_gui(self):
ld_u3_bounds_gui = [
[self.u3_min_1,self.u3_max_1],[self.u3_min_2,self.u3_max_2],[self.u3_min_3,self.u3_max_3],
[self.u3_min_4,self.u3_max_4],[self.u3_min_5,self.u3_max_5],[self.u3_min_6,self.u3_max_6],
[self.u3_min_7,self.u3_max_7],[self.u3_min_8,self.u3_max_8],[self.u3_min_9,self.u3_max_9],
[self.u3_min_10,self.u3_max_10]
]
return ld_u3_bounds_gui
def ld_u4_bounds_gui(self):
ld_u4_bounds_gui = [
[self.u4_min_1,self.u4_max_1],[self.u4_min_2,self.u4_max_2],[self.u4_min_3,self.u4_max_3],
[self.u4_min_4,self.u4_max_4],[self.u4_min_5,self.u4_max_5],[self.u4_min_6,self.u4_max_6],
[self.u4_min_7,self.u4_max_7],[self.u4_min_8,self.u4_max_8],[self.u4_min_9,self.u4_max_9],
[self.u4_min_10,self.u4_max_10]
]
return ld_u4_bounds_gui
################# Normal Prior ################
def param_nr_priors_gui(self):
param_nr_priors_gui = [
[self.K_mean_1,self.K_sigma_1,self.use_K_norm_pr_1],[self.P_mean_1,self.P_sigma_1,self.use_P_norm_pr_1], [self.e_mean_1,self.e_sigma_1,self.use_e_norm_pr_1],[self.om_mean_1,self.om_sigma_1,self.use_om_norm_pr_1], [self.ma_mean_1,self.ma_sigma_1,self.use_ma_norm_pr_1],[self.incl_mean_1,self.incl_sigma_1,self.use_incl_norm_pr_1], [self.Omega_mean_1,self.Omega_sigma_1, self.use_Omega_norm_pr_1],[self.t0_mean_1,self.t0_sigma_1, self.use_t0_norm_pr_1],[self.pl_rad_mean_1,self.pl_rad_sigma_1,self.use_pl_rad_norm_pr_1],[self.a_sol_mean_1,self.a_sol_sigma_1,self.use_a_sol_norm_pr_1],
[self.K_mean_2,self.K_sigma_2,self.use_K_norm_pr_2],[self.P_mean_2,self.P_sigma_2,self.use_P_norm_pr_2], [self.e_mean_2,self.e_sigma_2,self.use_e_norm_pr_2],[self.om_mean_2,self.om_sigma_2,self.use_om_norm_pr_2], [self.ma_mean_2,self.ma_sigma_2,self.use_ma_norm_pr_2],[self.incl_mean_2,self.incl_sigma_2,self.use_incl_norm_pr_2], [self.Omega_mean_2,self.Omega_sigma_2, self.use_Omega_norm_pr_2],[self.t0_mean_2,self.t0_sigma_2, self.use_t0_norm_pr_2],[self.pl_rad_mean_2,self.pl_rad_sigma_2,self.use_pl_rad_norm_pr_2],[self.a_sol_mean_2,self.a_sol_sigma_2,self.use_a_sol_norm_pr_2],
[self.K_mean_3,self.K_sigma_3,self.use_K_norm_pr_3],[self.P_mean_3,self.P_sigma_3,self.use_P_norm_pr_3], [self.e_mean_3,self.e_sigma_3,self.use_e_norm_pr_3],[self.om_mean_3,self.om_sigma_3,self.use_om_norm_pr_3], [self.ma_mean_3,self.ma_sigma_3,self.use_ma_norm_pr_3],[self.incl_mean_3,self.incl_sigma_3,self.use_incl_norm_pr_3], [self.Omega_mean_3,self.Omega_sigma_3, self.use_Omega_norm_pr_3],[self.t0_mean_3,self.t0_sigma_3, self.use_t0_norm_pr_3],[self.pl_rad_mean_3,self.pl_rad_sigma_3,self.use_pl_rad_norm_pr_3],[self.a_sol_mean_3,self.a_sol_sigma_3,self.use_a_sol_norm_pr_3],
[self.K_mean_4,self.K_sigma_4,self.use_K_norm_pr_4],[self.P_mean_4,self.P_sigma_4,self.use_P_norm_pr_4], [self.e_mean_4,self.e_sigma_4,self.use_e_norm_pr_4],[self.om_mean_4,self.om_sigma_4,self.use_om_norm_pr_4], [self.ma_mean_4,self.ma_sigma_4,self.use_ma_norm_pr_4],[self.incl_mean_4,self.incl_sigma_4,self.use_incl_norm_pr_4], [self.Omega_mean_4,self.Omega_sigma_4, self.use_Omega_norm_pr_4],[self.t0_mean_4,self.t0_sigma_4, self.use_t0_norm_pr_4],[self.pl_rad_mean_4,self.pl_rad_sigma_4,self.use_pl_rad_norm_pr_4],[self.a_sol_mean_4,self.a_sol_sigma_4,self.use_a_sol_norm_pr_4],
[self.K_mean_5,self.K_sigma_5,self.use_K_norm_pr_5],[self.P_mean_5,self.P_sigma_5,self.use_P_norm_pr_5], [self.e_mean_5,self.e_sigma_5,self.use_e_norm_pr_5],[self.om_mean_5,self.om_sigma_5,self.use_om_norm_pr_5], [self.ma_mean_5,self.ma_sigma_5,self.use_ma_norm_pr_5],[self.incl_mean_5,self.incl_sigma_5,self.use_incl_norm_pr_5], [self.Omega_mean_5,self.Omega_sigma_5, self.use_Omega_norm_pr_5],[self.t0_mean_5,self.t0_sigma_5, self.use_t0_norm_pr_5],[self.pl_rad_mean_5,self.pl_rad_sigma_5,self.use_pl_rad_norm_pr_5],[self.a_sol_mean_5,self.a_sol_sigma_5,self.use_a_sol_norm_pr_5],
[self.K_mean_6,self.K_sigma_6,self.use_K_norm_pr_6],[self.P_mean_6,self.P_sigma_6,self.use_P_norm_pr_6], [self.e_mean_6,self.e_sigma_6,self.use_e_norm_pr_6],[self.om_mean_6,self.om_sigma_6,self.use_om_norm_pr_6], [self.ma_mean_6,self.ma_sigma_6,self.use_ma_norm_pr_6],[self.incl_mean_6,self.incl_sigma_6,self.use_incl_norm_pr_6], [self.Omega_mean_6,self.Omega_sigma_6, self.use_Omega_norm_pr_6],[self.t0_mean_6,self.t0_sigma_6, self.use_t0_norm_pr_6],[self.pl_rad_mean_6,self.pl_rad_sigma_6,self.use_pl_rad_norm_pr_6],[self.a_sol_mean_6,self.a_sol_sigma_6,self.use_a_sol_norm_pr_6],
[self.K_mean_7,self.K_sigma_7,self.use_K_norm_pr_7],[self.P_mean_7,self.P_sigma_7,self.use_P_norm_pr_7], [self.e_mean_7,self.e_sigma_7,self.use_e_norm_pr_7],[self.om_mean_7,self.om_sigma_7,self.use_om_norm_pr_7], [self.ma_mean_7,self.ma_sigma_7,self.use_ma_norm_pr_7],[self.incl_mean_7,self.incl_sigma_7,self.use_incl_norm_pr_7], [self.Omega_mean_7,self.Omega_sigma_7, self.use_Omega_norm_pr_7],[self.t0_mean_7,self.t0_sigma_7, self.use_t0_norm_pr_7],[self.pl_rad_mean_7,self.pl_rad_sigma_7,self.use_pl_rad_norm_pr_7],[self.a_sol_mean_7,self.a_sol_sigma_7,self.use_a_sol_norm_pr_7],
[self.K_mean_8,self.K_sigma_8,self.use_K_norm_pr_8],[self.P_mean_8,self.P_sigma_8,self.use_P_norm_pr_8], [self.e_mean_8,self.e_sigma_8,self.use_e_norm_pr_8],[self.om_mean_8,self.om_sigma_8,self.use_om_norm_pr_8], [self.ma_mean_8,self.ma_sigma_8,self.use_ma_norm_pr_8],[self.incl_mean_8,self.incl_sigma_8,self.use_incl_norm_pr_8], [self.Omega_mean_8,self.Omega_sigma_8, self.use_Omega_norm_pr_8],[self.t0_mean_8,self.t0_sigma_8, self.use_t0_norm_pr_8],[self.pl_rad_mean_8,self.pl_rad_sigma_8,self.use_pl_rad_norm_pr_8],[self.a_sol_mean_8,self.a_sol_sigma_8,self.use_a_sol_norm_pr_8],
[self.K_mean_9,self.K_sigma_9,self.use_K_norm_pr_9],[self.P_mean_9,self.P_sigma_9,self.use_P_norm_pr_9], [self.e_mean_9,self.e_sigma_9,self.use_e_norm_pr_9],[self.om_mean_9,self.om_sigma_9,self.use_om_norm_pr_9], [self.ma_mean_9,self.ma_sigma_9,self.use_ma_norm_pr_9],[self.incl_mean_9,self.incl_sigma_9,self.use_incl_norm_pr_9], [self.Omega_mean_9,self.Omega_sigma_9, self.use_Omega_norm_pr_9],[self.t0_mean_9,self.t0_sigma_9, self.use_t0_norm_pr_9],[self.pl_rad_mean_9,self.pl_rad_sigma_9,self.use_pl_rad_norm_pr_9],[self.a_sol_mean_9,self.a_sol_sigma_9,self.use_a_sol_norm_pr_9],
]
return param_nr_priors_gui
################# Jeff Prior ################
def param_jeff_priors_gui(self):
param_jeff_priors_gui = [
[self.K_jeff_alpha_1,self.K_jeff_beta_1,self.use_K_jeff_pr_1],[self.P_jeff_alpha_1,self.P_jeff_beta_1,self.use_P_jeff_pr_1], [self.e_jeff_alpha_1,self.e_jeff_beta_1,self.use_e_jeff_pr_1],[self.om_jeff_alpha_1,self.om_jeff_beta_1,self.use_om_jeff_pr_1], [self.ma_jeff_alpha_1,self.ma_jeff_beta_1,self.use_ma_jeff_pr_1],[self.incl_jeff_alpha_1,self.incl_jeff_beta_1,self.use_incl_jeff_pr_1], [self.Omega_jeff_alpha_1,self.Omega_jeff_beta_1, self.use_Omega_jeff_pr_1],[self.t0_jeff_alpha_1,self.t0_jeff_beta_1, self.use_t0_jeff_pr_1],[self.pl_rad_jeff_alpha_1,self.pl_rad_jeff_beta_1,self.use_pl_rad_jeff_pr_1],[self.a_sol_jeff_alpha_1,self.a_sol_jeff_beta_1,self.use_a_sol_jeff_pr_1],
[self.K_jeff_alpha_2,self.K_jeff_beta_2,self.use_K_jeff_pr_2],[self.P_jeff_alpha_2,self.P_jeff_beta_2,self.use_P_jeff_pr_2], [self.e_jeff_alpha_2,self.e_jeff_beta_2,self.use_e_jeff_pr_2],[self.om_jeff_alpha_2,self.om_jeff_beta_2,self.use_om_jeff_pr_2], [self.ma_jeff_alpha_2,self.ma_jeff_beta_2,self.use_ma_jeff_pr_2],[self.incl_jeff_alpha_2,self.incl_jeff_beta_2,self.use_incl_jeff_pr_2], [self.Omega_jeff_alpha_2,self.Omega_jeff_beta_2, self.use_Omega_jeff_pr_2],[self.t0_jeff_alpha_2,self.t0_jeff_beta_2, self.use_t0_jeff_pr_2],[self.pl_rad_jeff_alpha_2,self.pl_rad_jeff_beta_2,self.use_pl_rad_jeff_pr_2],[self.a_sol_jeff_alpha_2,self.a_sol_jeff_beta_2,self.use_a_sol_jeff_pr_2],
[self.K_jeff_alpha_3,self.K_jeff_beta_3,self.use_K_jeff_pr_3],[self.P_jeff_alpha_3,self.P_jeff_beta_3,self.use_P_jeff_pr_3], [self.e_jeff_alpha_3,self.e_jeff_beta_3,self.use_e_jeff_pr_3],[self.om_jeff_alpha_3,self.om_jeff_beta_3,self.use_om_jeff_pr_3], [self.ma_jeff_alpha_3,self.ma_jeff_beta_3,self.use_ma_jeff_pr_3],[self.incl_jeff_alpha_3,self.incl_jeff_beta_3,self.use_incl_jeff_pr_3], [self.Omega_jeff_alpha_3,self.Omega_jeff_beta_3, self.use_Omega_jeff_pr_3],[self.t0_jeff_alpha_3,self.t0_jeff_beta_3, self.use_t0_jeff_pr_3],[self.pl_rad_jeff_alpha_3,self.pl_rad_jeff_beta_3,self.use_pl_rad_jeff_pr_3],[self.a_sol_jeff_alpha_3,self.a_sol_jeff_beta_3,self.use_a_sol_jeff_pr_3],
[self.K_jeff_alpha_4,self.K_jeff_beta_4,self.use_K_jeff_pr_4],[self.P_jeff_alpha_4,self.P_jeff_beta_4,self.use_P_jeff_pr_4], [self.e_jeff_alpha_4,self.e_jeff_beta_4,self.use_e_jeff_pr_4],[self.om_jeff_alpha_4,self.om_jeff_beta_4,self.use_om_jeff_pr_4], [self.ma_jeff_alpha_4,self.ma_jeff_beta_4,self.use_ma_jeff_pr_4],[self.incl_jeff_alpha_4,self.incl_jeff_beta_4,self.use_incl_jeff_pr_4], [self.Omega_jeff_alpha_4,self.Omega_jeff_beta_4, self.use_Omega_jeff_pr_4],[self.t0_jeff_alpha_4,self.t0_jeff_beta_4, self.use_t0_jeff_pr_4],[self.pl_rad_jeff_alpha_4,self.pl_rad_jeff_beta_4,self.use_pl_rad_jeff_pr_4],[self.a_sol_jeff_alpha_4,self.a_sol_jeff_beta_4,self.use_a_sol_jeff_pr_4],
[self.K_jeff_alpha_5,self.K_jeff_beta_5,self.use_K_jeff_pr_5],[self.P_jeff_alpha_5,self.P_jeff_beta_5,self.use_P_jeff_pr_5], [self.e_jeff_alpha_5,self.e_jeff_beta_5,self.use_e_jeff_pr_5],[self.om_jeff_alpha_5,self.om_jeff_beta_5,self.use_om_jeff_pr_5], [self.ma_jeff_alpha_5,self.ma_jeff_beta_5,self.use_ma_jeff_pr_5],[self.incl_jeff_alpha_5,self.incl_jeff_beta_5,self.use_incl_jeff_pr_5], [self.Omega_jeff_alpha_5,self.Omega_jeff_beta_5, self.use_Omega_jeff_pr_5],[self.t0_jeff_alpha_5,self.t0_jeff_beta_5, self.use_t0_jeff_pr_5],[self.pl_rad_jeff_alpha_5,self.pl_rad_jeff_beta_5,self.use_pl_rad_jeff_pr_5],[self.a_sol_jeff_alpha_5,self.a_sol_jeff_beta_5,self.use_a_sol_jeff_pr_5],
[self.K_jeff_alpha_6,self.K_jeff_beta_6,self.use_K_jeff_pr_6],[self.P_jeff_alpha_6,self.P_jeff_beta_6,self.use_P_jeff_pr_6], [self.e_jeff_alpha_6,self.e_jeff_beta_6,self.use_e_jeff_pr_6],[self.om_jeff_alpha_6,self.om_jeff_beta_6,self.use_om_jeff_pr_6], [self.ma_jeff_alpha_6,self.ma_jeff_beta_6,self.use_ma_jeff_pr_6],[self.incl_jeff_alpha_6,self.incl_jeff_beta_6,self.use_incl_jeff_pr_6], [self.Omega_jeff_alpha_6,self.Omega_jeff_beta_6, self.use_Omega_jeff_pr_6],[self.t0_jeff_alpha_6,self.t0_jeff_beta_6, self.use_t0_jeff_pr_6],[self.pl_rad_jeff_alpha_6,self.pl_rad_jeff_beta_6,self.use_pl_rad_jeff_pr_6],[self.a_sol_jeff_alpha_6,self.a_sol_jeff_beta_6,self.use_a_sol_jeff_pr_6],
[self.K_jeff_alpha_7,self.K_jeff_beta_7,self.use_K_jeff_pr_7],[self.P_jeff_alpha_7,self.P_jeff_beta_7,self.use_P_jeff_pr_7], [self.e_jeff_alpha_7,self.e_jeff_beta_7,self.use_e_jeff_pr_7],[self.om_jeff_alpha_7,self.om_jeff_beta_7,self.use_om_jeff_pr_7], [self.ma_jeff_alpha_7,self.ma_jeff_beta_7,self.use_ma_jeff_pr_7],[self.incl_jeff_alpha_7,self.incl_jeff_beta_7,self.use_incl_jeff_pr_7], [self.Omega_jeff_alpha_7,self.Omega_jeff_beta_7, self.use_Omega_jeff_pr_7],[self.t0_jeff_alpha_7,self.t0_jeff_beta_7, self.use_t0_jeff_pr_7],[self.pl_rad_jeff_alpha_7,self.pl_rad_jeff_beta_7,self.use_pl_rad_jeff_pr_7],[self.a_sol_jeff_alpha_7,self.a_sol_jeff_beta_7,self.use_a_sol_jeff_pr_7],
[self.K_jeff_alpha_8,self.K_jeff_beta_8,self.use_K_jeff_pr_8],[self.P_jeff_alpha_8,self.P_jeff_beta_8,self.use_P_jeff_pr_8], [self.e_jeff_alpha_8,self.e_jeff_beta_8,self.use_e_jeff_pr_8],[self.om_jeff_alpha_8,self.om_jeff_beta_8,self.use_om_jeff_pr_8], [self.ma_jeff_alpha_8,self.ma_jeff_beta_8,self.use_ma_jeff_pr_8],[self.incl_jeff_alpha_8,self.incl_jeff_beta_8,self.use_incl_jeff_pr_8], [self.Omega_jeff_alpha_8,self.Omega_jeff_beta_8, self.use_Omega_jeff_pr_8],[self.t0_jeff_alpha_8,self.t0_jeff_beta_8, self.use_t0_jeff_pr_8],[self.pl_rad_jeff_alpha_8,self.pl_rad_jeff_beta_8,self.use_pl_rad_jeff_pr_8],[self.a_sol_jeff_alpha_8,self.a_sol_jeff_beta_8,self.use_a_sol_jeff_pr_8],
[self.K_jeff_alpha_9,self.K_jeff_beta_9,self.use_K_jeff_pr_9],[self.P_jeff_alpha_9,self.P_jeff_beta_9,self.use_P_jeff_pr_9], [self.e_jeff_alpha_9,self.e_jeff_beta_9,self.use_e_jeff_pr_9],[self.om_jeff_alpha_9,self.om_jeff_beta_9,self.use_om_jeff_pr_9], [self.ma_jeff_alpha_9,self.ma_jeff_beta_9,self.use_ma_jeff_pr_9],[self.incl_jeff_alpha_9,self.incl_jeff_beta_9,self.use_incl_jeff_pr_9], [self.Omega_jeff_alpha_9,self.Omega_jeff_beta_9, self.use_Omega_jeff_pr_9],[self.t0_jeff_alpha_9,self.t0_jeff_beta_9, self.use_t0_jeff_pr_9],[self.pl_rad_jeff_alpha_9,self.pl_rad_jeff_beta_9,self.use_pl_rad_jeff_pr_9],[self.a_sol_jeff_alpha_9,self.a_sol_jeff_beta_9,self.use_a_sol_jeff_pr_9],
]
return param_jeff_priors_gui
################### GP ########################
def gp_rot_params(self):
gp_rot_params = [
self.GP_rot_kernel_Amp,
self.GP_rot_kernel_time_sc,
self.GP_rot_kernel_Per,
self.GP_rot_kernel_fact
]
return gp_rot_params
def gp_rot_errors_gui(self):
gp_rot_errors_gui = [
self.err_rot_kernel_Amp,
self.err_rot_kernel_time_sc,
self.err_rot_kernel_Per,
self.err_rot_kernel_fact
]
return gp_rot_errors_gui
def use_gp_rot_params(self):
use_gp_rot_params = [
self.use_GP_rot_kernel_Amp,
self.use_GP_rot_kernel_time_sc,
self.use_GP_rot_kernel_Per,
self.use_GP_rot_kernel_fact
]
return use_gp_rot_params
def gp_sho_params(self):
gp_sho_params = [
self.GP_sho_kernel_S,
self.GP_sho_kernel_Q,
self.GP_sho_kernel_omega
]
return gp_sho_params
def use_gp_sho_params(self):
use_gp_sho_params = [
self.use_GP_sho_kernel_S,
self.use_GP_sho_kernel_Q,
self.use_GP_sho_kernel_omega
]
return use_gp_sho_params
def gp_sho_errors_gui(self):
gp_sho_errors_gui = [
self.err_sho_kernel_S,
self.err_sho_kernel_Q,
self.err_sho_kernel_omega
]
return gp_sho_errors_gui
def tra_gp_rot_params(self):
tra_gp_rot_params = [
self.tra_GP_rot_kernel_Amp,
self.tra_GP_rot_kernel_time_sc,
self.tra_GP_rot_kernel_Per,
self.tra_GP_rot_kernel_fact]
return tra_gp_rot_params
def use_tra_gp_rot_params(self):
use_tra_gp_rot_params = [
self.use_tra_GP_rot_kernel_Amp,
self.use_tra_GP_rot_kernel_time_sc,
self.use_tra_GP_rot_kernel_Per,
self.use_tra_GP_rot_kernel_fact
]
return use_tra_gp_rot_params
def tra_gp_sho_params(self):
tra_gp_sho_params = [
self.tra_GP_sho_kernel_S,
self.tra_GP_sho_kernel_Q,
self.tra_GP_sho_kernel_omega]
return tra_gp_sho_params
def use_tra_gp_sho_params(self):
use_tra_gp_sho_params = [
self.use_tra_GP_sho_kernel_S,
self.use_tra_GP_sho_kernel_Q,
self.use_tra_GP_sho_kernel_omega]
return use_tra_gp_sho_params
################ labels ##########################
def param_a_gui(self):
param_a_gui = [
self.label_a1, self.label_a2, self.label_a3,
self.label_a4, self.label_a5, self.label_a6,
self.label_a7, self.label_a8, self.label_a9
]
return param_a_gui
def param_mass_gui(self):
param_mass_gui = [
self.label_mass1, self.label_mass2, self.label_mass3,
self.label_mass4, self.label_mass5, self.label_mass6,
self.label_mass7, self.label_mass8, self.label_mass9
]
return param_mass_gui
def param_t_peri_gui(self):
param_t_peri_gui = [
self.label_t_peri1, self.label_t_peri2, self.label_t_peri3,
self.label_t_peri4, self.label_t_peri5, self.label_t_peri6,
self.label_t_peri7, self.label_t_peri8, self.label_t_peri9
]
return param_t_peri_gui
def planet_checked_gui(self):
planet_checked_gui = [
self.use_Planet1,self.use_Planet2,self.use_Planet3,
self.use_Planet4,self.use_Planet5,self.use_Planet6,
self.use_Planet7,self.use_Planet8,self.use_Planet9
]
return planet_checked_gui
def bin_rv_data(self):
bin_rv_data = [
[self.RV_bin_data_1,self.use_RV_bin_data_1],[self.RV_bin_data_2,self.use_RV_bin_data_2],
[self.RV_bin_data_3,self.use_RV_bin_data_3],[self.RV_bin_data_4,self.use_RV_bin_data_4],
[self.RV_bin_data_5,self.use_RV_bin_data_5],[self.RV_bin_data_6,self.use_RV_bin_data_6],
[self.RV_bin_data_7,self.use_RV_bin_data_7],[self.RV_bin_data_8,self.use_RV_bin_data_8],
[self.RV_bin_data_9,self.use_RV_bin_data_9],[self.RV_bin_data_10,self.use_RV_bin_data_10]
]
return bin_rv_data
def add_rv_error(self):
add_rv_error = [
[self.inflate_RV_sigma_1,self.use_inflate_RV_sigma_1],[self.inflate_RV_sigma_2,self.use_inflate_RV_sigma_2],
[self.inflate_RV_sigma_3,self.use_inflate_RV_sigma_3],[self.inflate_RV_sigma_4,self.use_inflate_RV_sigma_4],
[self.inflate_RV_sigma_5,self.use_inflate_RV_sigma_5],[self.inflate_RV_sigma_6,self.use_inflate_RV_sigma_6],
[self.inflate_RV_sigma_7,self.use_inflate_RV_sigma_7],[self.inflate_RV_sigma_8,self.use_inflate_RV_sigma_8],
[self.inflate_RV_sigma_9,self.use_inflate_RV_sigma_9],[self.inflate_RV_sigma_10,self.use_inflate_RV_sigma_10]
]
return add_rv_error
def rv_sigma_clip(self):
rv_sigma_clip = [
[self.RV_sigma_clip_1,self.use_RV_sigma_clip_1],[self.RV_sigma_clip_2,self.use_RV_sigma_clip_2],
[self.RV_sigma_clip_3,self.use_RV_sigma_clip_3],[self.RV_sigma_clip_4,self.use_RV_sigma_clip_4],
[self.RV_sigma_clip_5,self.use_RV_sigma_clip_5],[self.RV_sigma_clip_6,self.use_RV_sigma_clip_6],
[self.RV_sigma_clip_7,self.use_RV_sigma_clip_7],[self.RV_sigma_clip_8,self.use_RV_sigma_clip_8],
[self.RV_sigma_clip_9,self.use_RV_sigma_clip_9],[self.RV_sigma_clip_10,self.use_RV_sigma_clip_10]
]
return rv_sigma_clip
def act_sigma_clip(self):
act_sigma_clip = [
[self.act_sigma_clip_1,self.use_act_sigma_clip_1],[self.act_sigma_clip_2,self.use_act_sigma_clip_2],
[self.act_sigma_clip_3,self.use_act_sigma_clip_3],[self.act_sigma_clip_4,self.use_act_sigma_clip_4],
[self.act_sigma_clip_5,self.use_act_sigma_clip_5],[self.act_sigma_clip_6,self.use_act_sigma_clip_6],
[self.act_sigma_clip_7,self.use_act_sigma_clip_7],[self.act_sigma_clip_8,self.use_act_sigma_clip_8],
[self.act_sigma_clip_9,self.use_act_sigma_clip_9],[self.act_sigma_clip_10,self.use_act_sigma_clip_10]
]
return act_sigma_clip
def act_remove_mean(self):
act_remove_mean = [
self.act_remove_mean_1,self.act_remove_mean_2,self.act_remove_mean_3,
self.act_remove_mean_4,self.act_remove_mean_5,self.act_remove_mean_6,
self.act_remove_mean_7,self.act_remove_mean_8,self.act_remove_mean_9,
self.act_remove_mean_10
]
return act_remove_mean
#def tra_sigma_clip(self):
# tra_sigma_clip = [
# [self.tra_sigma_clip_1,self.use_tra_sigma_clip_1],[self.tra_sigma_clip_2,self.use_tra_sigma_clip_2],
# [self.tra_sigma_clip_3,self.use_tra_sigma_clip_3],[self.tra_sigma_clip_4,self.use_tra_sigma_clip_4],
# [self.tra_sigma_clip_5,self.use_tra_sigma_clip_5],[self.tra_sigma_clip_6,self.use_tra_sigma_clip_6],
# [self.tra_sigma_clip_7,self.use_tra_sigma_clip_7],[self.tra_sigma_clip_8,self.use_tra_sigma_clip_8],
# [self.tra_sigma_clip_9,self.use_tra_sigma_clip_9],[self.tra_sigma_clip_10,self.use_tra_sigma_clip_10]
# ]
# return tra_sigma_clip
def tra_norm(self):
tra_norm = [
self.tra_norm_1,self.tra_norm_2,self.tra_norm_3,self.tra_norm_4,self.tra_norm_5,
self.tra_norm_6,self.tra_norm_7,self.tra_norm_8,self.tra_norm_9,self.tra_norm_10
]
return tra_norm
def tra_dilution(self):
tra_dilution = [
[self.tra_dilution_1,self.use_tra_dilution_1], [self.tra_dilution_2,self.use_tra_dilution_2],
[self.tra_dilution_3,self.use_tra_dilution_3], [self.tra_dilution_4,self.use_tra_dilution_4],
[self.tra_dilution_5,self.use_tra_dilution_5], [self.tra_dilution_6,self.use_tra_dilution_6],
[self.tra_dilution_7,self.use_tra_dilution_7], [self.tra_dilution_8,self.use_tra_dilution_8],
[self.tra_dilution_9,self.use_tra_dilution_9], [self.tra_dilution_10,self.use_tra_dilution_10]
]
return tra_dilution
######################### Arb N-body #################################
def arb_param_gui(self):
arb_param_gui = [
self.arb_K_1, self.arb_P_1, self.arb_e_1, self.arb_om_1, self.arb_ma_1, self.arb_incl_1, self.arb_Om_1,
self.arb_K_2, self.arb_P_2, self.arb_e_2, self.arb_om_2, self.arb_ma_2, self.arb_incl_2, self.arb_Om_2,
self.arb_K_3, self.arb_P_3, self.arb_e_3, self.arb_om_3, self.arb_ma_3, self.arb_incl_3, self.arb_Om_3,
self.arb_K_4, self.arb_P_4, self.arb_e_4, self.arb_om_4, self.arb_ma_4, self.arb_incl_4, self.arb_Om_4,
self.arb_K_5, self.arb_P_5, self.arb_e_5, self.arb_om_5, self.arb_ma_5, self.arb_incl_5, self.arb_Om_5,
self.arb_K_6, self.arb_P_6, self.arb_e_6, self.arb_om_6, self.arb_ma_6, self.arb_incl_6, self.arb_Om_6,
self.arb_K_7, self.arb_P_7, self.arb_e_7, self.arb_om_7, self.arb_ma_7, self.arb_incl_7, self.arb_Om_7,
self.arb_K_8, self.arb_P_8, self.arb_e_8, self.arb_om_8, self.arb_ma_8, self.arb_incl_8, self.arb_Om_8,
self.arb_K_9, self.arb_P_9, self.arb_e_9, self.arb_om_9, self.arb_ma_9, self.arb_incl_9, self.arb_Om_9,
]
return arb_param_gui
def arb_param_gui_use(self):
arb_param_gui_use = [
self.use_arb_Planet_1,self.use_arb_Planet_2,self.use_arb_Planet_3,
self.use_arb_Planet_4,self.use_arb_Planet_5,self.use_arb_Planet_6,
self.use_arb_Planet_7,self.use_arb_Planet_8,self.use_arb_Planet_9
]
return arb_param_gui_use
def ttv_data_to_planet(self):
ttv_data_to_planet = [
self.ttv_data_planet_1,self.ttv_data_planet_2,self.ttv_data_planet_3,self.ttv_data_planet_4,self.ttv_data_planet_5,
self.ttv_data_planet_6,self.ttv_data_planet_7,self.ttv_data_planet_8,self.ttv_data_planet_9,self.ttv_data_planet_10,
]
return ttv_data_to_planet
def use_ttv_data_to_planet(self):
use_ttv_data_to_planet = [
self.use_ttv_data_1,self.use_ttv_data_2,self.use_ttv_data_3,self.use_ttv_data_4,self.use_ttv_data_5,
self.use_ttv_data_6,self.use_ttv_data_7,self.use_ttv_data_8,self.use_ttv_data_9,self.use_ttv_data_10,
]
return use_ttv_data_to_planet
|
class HaltListener:
def stream_read_halted(self, chunk: int, _time: int) -> None:
pass
def stream_read_resumed(self, chunk: int, _time: int):
pass
|
class Solution:
def solve(self, words):
bitmasks = []
for j,word in enumerate(words):
bitmask = [0]*26
distincts = 0
for char in word:
i = ord(char) - ord('a')
if bitmask[i] == 0: distincts += 1
bitmask[i] = 1
if distincts == 26: break
bitmasks.append(bitmask)
ans = 0
for i in range(len(words)):
js = set(range(i+1,len(words)))
for letter_i in range(26):
if bitmasks[i][letter_i] == 0: continue
for j in list(js):
if bitmasks[j][letter_i] == 1: js.remove(j)
for j in js:
ans = max(ans, len(words[i]) + len(words[j]))
return ans
|
n = {1:0,2:0,3:0,4:0,'nulo':0,'branco':0}
while True:
op = int(input('''
------faça seu voto------
1 - Jose
2 - Pedro
3 - Antoinho
4 - Adelson
5 - Voto Nulo
6 - Voto em Branco
: '''))
if op == 0:
break
elif op ==1:
n[1] +=1
elif op ==2:
n[2] +=1
elif op ==3:
n[3] +=1
elif op ==4:
n[4] +=1
elif op ==5:
n['nulo'] +=1
elif op ==6:
n['branco'] +=1
print('------------------------')
for x,y in n.items():
print('{} = {}'.format(x,y))
|
# print(range(5))
# a = range(1,10,5)
# for x in a:
# print(x)
# num =1
# rem = num%2
# if rem==0:
# print('The number is even')
# else:
# print('The number is odd')
# print("I am not inside if statement")
# num = -2
# if num>0:
# print("Number is positive")
# if (num % 2) == 0:
# print('Number is even')
# else:
# print('Number is odd')
# elif num<0:
# print("Number is negative")
# else:
# print("Number is zero")
# var = False
# if var:
# print('Hello')
# amount = int(input("Enter amount: "))
# if amount >= 1000:
# discount = amount*0.10
# print ("Discount",discount)
# else:
# discount = amount*0.05
# print ("Discount",discount)
# print ("Net payable:",amount-discount)
var = 100
if(var == 100): print ("This is 100")
|
# 함수로 가장 작은 값 리턴하기
count = int(input())
lst = [0]*count
elements = map(int, input().split())
times = 0
for i in elements:
lst[times] = i
times += 1
def f():
min_num = lst[0]
for i in range(count):
if min_num < lst[i]:
max_num = lst[i]
print(min_num)
f()
|
def entrada_notas():
notas = input().split(' ')
nota1 = float(notas[0])
nota2 = float(notas[1])
nota3 = float(notas[2])
nota4 = float(notas[3])
return nota1, nota2, nota3, nota4
def media(n1, n2, n3, n4):
med = (2 * n1 + 3 * n2 + 4 * n3 + n4)/10
print(f'Media: {med:.1f}')
if med >= 7.0:
print('Aluno aprovado.')
elif med < 5.0:
print('Aluno reprovado.')
elif 5.0 <= med <= 6.9:
print('Aluno em exame.')
exame_final = float(input())
print(f'Nota do exame: {exame_final:.1f}')
nota_final = (exame_final + med)/2
if nota_final >= 5.0:
print('Aluno aprovado.')
else:
print('Aluno reprovado')
print(f'Media final: {nota_final}')
exame1, exame2, exame3, exame4 = entrada_notas()
media(exame1, exame2, exame3, exame4)
|
"""
Design a Data Structure SpecialStack that supports all the stack operations
like push(), pop(), isEmpty(), isFull() and an additional operation getMin()
which should return minimum element from the SpecialStack. All these operations
of SpecialStack must be O(1). To implement SpecialStack, you should only use
standard Stack data structure and no other data structure like arrays, list, .. etc.
"""
class Stack:
"""
We can store data in 2 stacks
1. normal stack
2. min stack with min value of the stack at the top
"""
def __init__(self, max_size: int):
self.stack: list = []
self.min_stack: list = []
self.size = 0
self.max_size = max_size
def push(self, value: int):
if self.is_empty():
self.min_stack.append(value)
elif self.is_full():
raise MemoryError("Stack is full")
else:
self.min_stack.append(min(value, self.min_stack[-1]))
self.stack.append(value)
self.size += 1
def pop(self) -> int:
if self.is_empty():
raise AssertionError("Stack is Empty")
else:
self.min_stack.pop()
self.size -= 1
return self.stack.pop()
def is_empty(self):
return self.size == 0
def is_full(self):
return self.size == self.max_size
def get_min(self):
return self.min_stack[-1]
if __name__ == "__main__":
s = Stack(5)
s.push(30)
print(s.get_min())
s.push(20)
print(s.get_min())
s.push(40)
print(s.get_min())
s.push(10)
print(s.get_min())
s.push(50)
print(s.get_min())
s.pop()
print(s.get_min())
s.pop()
print(s.get_min())
s.pop()
print(s.get_min())
s.pop()
print(s.get_min())
|
"""Lambda Expressions
@see: https://docs.python.org/3/tutorial/controlflow.html#lambda-expressions
Small anonymous functions can be created with the lambda keyword. Lambda functions can be used
wherever function objects are required. They are syntactically restricted to a single expression.
Semantically, they are just syntactic sugar for a normal function definition. Like nested function
definitions, lambda functions can reference variables from the containing scope.
"""
def test_lambda_expressions():
"""Lambda Expressions"""
# This function returns the sum of its two arguments: lambda a, b: a+b
# Like nested function definitions, lambda functions can reference variables from the
# containing scope.
def make_increment_function(delta):
"""This example uses a lambda expression to return a function"""
return lambda number: number + delta
increment_function = make_increment_function(42)
assert increment_function(0) == 42
assert increment_function(1) == 43
assert increment_function(2) == 44
# Another use of lambda is to pass a small function as an argument.
pairs = [(1, 'one'), (2, 'two'), (3, 'three'), (4, 'four')]
# Sort pairs by text key.
pairs.sort(key=lambda pair: pair[1])
assert pairs == [(4, 'four'), (1, 'one'), (3, 'three'), (2, 'two')]
|
#! /usr/bin/env python
'''
(This question is super complicated and artificial.)
(Refer Sumita Arora: Strings Q5)
'''
A = int(input("Enter the integer: "))
S = input("Enter the string: ")
# extract digits
D = int(''.join( e for e in S if e.isdigit() ))
R = A + D
print("{} + {} = {}".format(A, D, R))
|
#!/usr/bin/env python
population = input("Please enter population value: ")
print("You have entered: ", population)
|
class HttpServerHTMLGenerator:
"""
Generates the HTML for the debug_http_server.
"""
def __init__(self):
pass
@staticmethod
def generate_main_table(url_records):
ret_html = '<html><head><meta charset="utf-8"/></head><table border=\'1\'>{0}</table></html>'
main_table = "<tr><td>Application ID</td><td>URL</td><td>View</td></tr>"
for url_record in url_records:
main_table += "<tr>" \
"<td>{0}</td>" \
"<td><a href='{1}'>{1}</a></td>" \
"<td><a href='/view_html?id={2}'>View Clean</a></td></tr>".format(url_record.get('id'),
url_record.get('pp_url'),
url_record.get('id'))
return ret_html, main_table
@staticmethod
def generate_show_paragraphs(paragraphs_records):
ret_html = '<html><head><meta charset="utf-8"/></head><table border=\'1\'>{0}</table></html>'
main_table = "<tr><td>Paragraph #</td><td>Paragraph Text</td></tr>"
for paragraph_record in paragraphs_records:
main_table += "<tr><td>{0}</td><td>{1}</td></tr>".format(
paragraph_record.get('index'),
paragraph_record.get('paragraph'))
return ret_html, main_table
@staticmethod
def generate_main_paragraphs_table(url_records):
ret_html = '<html><head><meta charset="utf-8"/></head><table border=\'1\'>{0}</table></html>'
main_table = "<tr><td>Privacy Policy ID</td><td>URL</td><td>View</td></tr>"
for url_record in url_records:
main_table += "<tr>" \
"<td>{0}</td>" \
"<td><a href='{1}'>{1}</a></td>" \
"<td><a href='/view_pp_and_paragraphs?id={2}'>View Paragraphs</a></td>" \
"</tr>"\
.format(url_record.get('privacy_policy_id'), url_record.get('pp_url'),
url_record.get('privacy_policy_id'))
return ret_html, main_table
|
op_str = 'acc +7'
def parse_operation(operation_str):
op_list = operation_str.strip().split()
operation = op_list[0]
op_sign = op_list[1][0]
steps = op_list[1][1:]
return {'operation': operation, 'sign': op_sign, 'steps': int(steps), 'visited': False}
#print(parse_operation(op_str))
#operations_file = open('data/test_operations.txt')
operations_file = open('data/operations.txt')
operations_dicts = [parse_operation(item) for item in operations_file.readlines()]
operations_file.close()
#print(operations_dicts[0])
accumulator = 0
location = 0
cycle = False
while 1 == 1:
if operations_dicts[location]['visited']:
break
operations_dicts[location]['visited'] = True
if operations_dicts[location]['operation'] == 'acc':
if operations_dicts[location]['sign'] == '+':
accumulator += operations_dicts[location]['steps']
elif operations_dicts[location]['sign'] == '-':
accumulator -= operations_dicts[location]['steps']
location += 1
elif operations_dicts[location]['operation'] == 'nop':
location += 1
elif operations_dicts[location]['operation'] == 'jmp':
if operations_dicts[location]['sign'] == '+':
location += operations_dicts[location]['steps']
elif operations_dicts[location]['sign'] == '-':
location -= operations_dicts[location]['steps']
print(accumulator)
|
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# ============================================================================
# ReMorse - Morse code encode/decode module for Python
# Copyright (C) 2018 by Ralf Kilian
# Distributed under the MIT License (https://opensource.org/licenses/MIT)
#
# GitHub: https://github.com/urbanware-org/remorse
# GitLab: https://gitlab.com/urbanware-org/remorse
# ============================================================================
__version__ = "1.0.2"
MORSE_ALPHABET = {"a": "·−", "b": "−···", "c": "−·−·", "d": "−··",
"e": "·", "f": "··−·", "g": "−−·", "h": "····",
"i": "··", "j": "·−−−", "k": "−·−", "l": "·−··",
"m": "−−", "n": "−·", "o": "−−−", "p": "·−−·",
"q": "−−·−", "r": "·−·", "s": "···", "t": "−",
"u": "··−", "v": "···−", "w": "·−−", "x": "−··−",
"y": "−·−−", "z": "−−··", "1": "·−−−−", "2": "··−−−",
"3": "···−−", "4": "····−", "5": "·····", "6": "−····",
"7": "−−···", "8": "−−−··", "9": "−−−−·", "0": "−−−−−"}
def get_version():
"""
Return the version of this module.
"""
return __version__
def string2morse(string):
"""
Convert (encode) a string into Morse code.
"""
output = ""
while " " * 2 in string:
string = string.replace((" " * 2), " ")
for char in string.lower().strip():
if char == " ":
output += (" " * 3)
continue
else:
output += MORSE_ALPHABET.get(char, "") + " "
return output.strip()
def morse2string(morse_code):
"""
Convert (decode) Morse code into a string.
"""
output = ""
morse_code = \
morse_code.replace(".", "·").replace("-", "−").replace("_", "−")
morse = {v: k for k, v in MORSE_ALPHABET.items()}
for word in morse_code.split(" " * 3):
for letter in word.split(" "):
output += morse.get(letter, "")
output += " "
while " " * 2 in output:
output = output.replace((" " * 2), " ")
return output.strip()
# EOF
|
SOC_IRAM_LOW = 0x40020000
SOC_IRAM_HIGH = 0x40070000
SOC_DRAM_LOW = 0x3ffb0000
SOC_DRAM_HIGH = 0x40000000
SOC_RTC_DRAM_LOW = 0x3ff9e000
SOC_RTC_DRAM_HIGH = 0x3ffa0000
SOC_RTC_DATA_LOW = 0x50000000
SOC_RTC_DATA_HIGH = 0x50002000
|
"""
problem: 1018 - Banknotes
url: https://www.urionlinejudge.com.br/judge/en/problems/view/1018
"""
valor = int(input())
notas = [100,50,20,10,5,2,1]
resto = valor
print(valor)
for n in notas:
qtd = resto // n
resto = resto % n
print('{:d} nota(s) de R$ {:d},00'.format(qtd, n))
|
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