hexsha
string | size
int64 | ext
string | lang
string | max_stars_repo_path
string | max_stars_repo_name
string | max_stars_repo_head_hexsha
string | max_stars_repo_licenses
list | max_stars_count
int64 | max_stars_repo_stars_event_min_datetime
string | max_stars_repo_stars_event_max_datetime
string | max_issues_repo_path
string | max_issues_repo_name
string | max_issues_repo_head_hexsha
string | max_issues_repo_licenses
list | max_issues_count
int64 | max_issues_repo_issues_event_min_datetime
string | max_issues_repo_issues_event_max_datetime
string | max_forks_repo_path
string | max_forks_repo_name
string | max_forks_repo_head_hexsha
string | max_forks_repo_licenses
list | max_forks_count
int64 | max_forks_repo_forks_event_min_datetime
string | max_forks_repo_forks_event_max_datetime
string | content
string | avg_line_length
float64 | max_line_length
int64 | alphanum_fraction
float64 | qsc_code_num_words_quality_signal
int64 | qsc_code_num_chars_quality_signal
float64 | qsc_code_mean_word_length_quality_signal
float64 | qsc_code_frac_words_unique_quality_signal
float64 | qsc_code_frac_chars_top_2grams_quality_signal
float64 | qsc_code_frac_chars_top_3grams_quality_signal
float64 | qsc_code_frac_chars_top_4grams_quality_signal
float64 | qsc_code_frac_chars_dupe_5grams_quality_signal
float64 | qsc_code_frac_chars_dupe_6grams_quality_signal
float64 | qsc_code_frac_chars_dupe_7grams_quality_signal
float64 | qsc_code_frac_chars_dupe_8grams_quality_signal
float64 | qsc_code_frac_chars_dupe_9grams_quality_signal
float64 | qsc_code_frac_chars_dupe_10grams_quality_signal
float64 | qsc_code_frac_chars_replacement_symbols_quality_signal
float64 | qsc_code_frac_chars_digital_quality_signal
float64 | qsc_code_frac_chars_whitespace_quality_signal
float64 | qsc_code_size_file_byte_quality_signal
float64 | qsc_code_num_lines_quality_signal
float64 | qsc_code_num_chars_line_max_quality_signal
float64 | qsc_code_num_chars_line_mean_quality_signal
float64 | qsc_code_frac_chars_alphabet_quality_signal
float64 | qsc_code_frac_chars_comments_quality_signal
float64 | qsc_code_cate_xml_start_quality_signal
float64 | qsc_code_frac_lines_dupe_lines_quality_signal
float64 | qsc_code_cate_autogen_quality_signal
float64 | qsc_code_frac_lines_long_string_quality_signal
float64 | qsc_code_frac_chars_string_length_quality_signal
float64 | qsc_code_frac_chars_long_word_length_quality_signal
float64 | qsc_code_frac_lines_string_concat_quality_signal
float64 | qsc_code_cate_encoded_data_quality_signal
float64 | qsc_code_frac_chars_hex_words_quality_signal
float64 | qsc_code_frac_lines_prompt_comments_quality_signal
float64 | qsc_code_frac_lines_assert_quality_signal
float64 | qsc_codepython_cate_ast_quality_signal
float64 | qsc_codepython_frac_lines_func_ratio_quality_signal
float64 | qsc_codepython_cate_var_zero_quality_signal
bool | qsc_codepython_frac_lines_pass_quality_signal
float64 | qsc_codepython_frac_lines_import_quality_signal
float64 | qsc_codepython_frac_lines_simplefunc_quality_signal
float64 | qsc_codepython_score_lines_no_logic_quality_signal
float64 | qsc_codepython_frac_lines_print_quality_signal
float64 | qsc_code_num_words
int64 | qsc_code_num_chars
int64 | qsc_code_mean_word_length
int64 | qsc_code_frac_words_unique
null | qsc_code_frac_chars_top_2grams
int64 | qsc_code_frac_chars_top_3grams
int64 | qsc_code_frac_chars_top_4grams
int64 | qsc_code_frac_chars_dupe_5grams
int64 | qsc_code_frac_chars_dupe_6grams
int64 | qsc_code_frac_chars_dupe_7grams
int64 | qsc_code_frac_chars_dupe_8grams
int64 | qsc_code_frac_chars_dupe_9grams
int64 | qsc_code_frac_chars_dupe_10grams
int64 | qsc_code_frac_chars_replacement_symbols
int64 | qsc_code_frac_chars_digital
int64 | qsc_code_frac_chars_whitespace
int64 | qsc_code_size_file_byte
int64 | qsc_code_num_lines
int64 | qsc_code_num_chars_line_max
int64 | qsc_code_num_chars_line_mean
int64 | qsc_code_frac_chars_alphabet
int64 | qsc_code_frac_chars_comments
int64 | qsc_code_cate_xml_start
int64 | qsc_code_frac_lines_dupe_lines
int64 | qsc_code_cate_autogen
int64 | qsc_code_frac_lines_long_string
int64 | qsc_code_frac_chars_string_length
int64 | qsc_code_frac_chars_long_word_length
int64 | qsc_code_frac_lines_string_concat
null | qsc_code_cate_encoded_data
int64 | qsc_code_frac_chars_hex_words
int64 | qsc_code_frac_lines_prompt_comments
int64 | qsc_code_frac_lines_assert
int64 | qsc_codepython_cate_ast
int64 | qsc_codepython_frac_lines_func_ratio
int64 | qsc_codepython_cate_var_zero
int64 | qsc_codepython_frac_lines_pass
int64 | qsc_codepython_frac_lines_import
int64 | qsc_codepython_frac_lines_simplefunc
int64 | qsc_codepython_score_lines_no_logic
int64 | qsc_codepython_frac_lines_print
int64 | effective
string | hits
int64 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
ebc490bea67c102bec880a94000f61e89d45c863
| 170
|
py
|
Python
|
aziende/admin.py
|
luca772005/studio
|
8d19d28f13f400aa4dde84c36e44cf5891d18ddd
|
[
"MIT"
] | null | null | null |
aziende/admin.py
|
luca772005/studio
|
8d19d28f13f400aa4dde84c36e44cf5891d18ddd
|
[
"MIT"
] | null | null | null |
aziende/admin.py
|
luca772005/studio
|
8d19d28f13f400aa4dde84c36e44cf5891d18ddd
|
[
"MIT"
] | null | null | null |
from django.contrib import admin
from aziende.models import Azienda
# Register your models here.
@admin.register(Azienda)
class AziendaAdmin(admin.ModelAdmin):
pass
| 21.25
| 37
| 0.8
| 22
| 170
| 6.181818
| 0.681818
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| 0
| 0
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| 170
| 8
| 38
| 21.25
| 0.918919
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| true
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| 1
| 0
|
0
| 6
|
ebe7d7dacffead944fd340e71684583d203cdc23
| 125
|
py
|
Python
|
ctr-in-action/deepctr/__init__.py
|
wdxtub/compute-ad-note
|
0ea1927acece84aae40b71009d5e865ae14ef57a
|
[
"MIT"
] | 21
|
2019-04-30T08:55:46.000Z
|
2022-03-02T07:37:39.000Z
|
ctr-in-action/deepctr/__init__.py
|
wdxtub/compute-ad-note
|
0ea1927acece84aae40b71009d5e865ae14ef57a
|
[
"MIT"
] | null | null | null |
ctr-in-action/deepctr/__init__.py
|
wdxtub/compute-ad-note
|
0ea1927acece84aae40b71009d5e865ae14ef57a
|
[
"MIT"
] | 3
|
2019-08-09T05:15:17.000Z
|
2021-01-20T09:12:12.000Z
|
from . import layers
from . import models
from .utils import check_version
__version__ = '0.4.1'
check_version(__version__)
| 17.857143
| 32
| 0.784
| 18
| 125
| 4.888889
| 0.555556
| 0.227273
| 0.431818
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| 0.136
| 125
| 6
| 33
| 20.833333
| 0.787037
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|
0
| 6
|
cce60edafccb1c58d9e5204c496e3f2f1b6845b4
| 168,346
|
py
|
Python
|
tests/test_scottbrian_paratools/test_smart_event.py
|
ScottBrian/scottbrian_paratools
|
1b370d985f6d3fd28efc85dee6e3404f86bc7d12
|
[
"MIT"
] | null | null | null |
tests/test_scottbrian_paratools/test_smart_event.py
|
ScottBrian/scottbrian_paratools
|
1b370d985f6d3fd28efc85dee6e3404f86bc7d12
|
[
"MIT"
] | null | null | null |
tests/test_scottbrian_paratools/test_smart_event.py
|
ScottBrian/scottbrian_paratools
|
1b370d985f6d3fd28efc85dee6e3404f86bc7d12
|
[
"MIT"
] | null | null | null |
"""test_smart_event.py module."""
###############################################################################
# Standard Library
###############################################################################
from enum import Enum
import logging
import time
from typing import Any, cast, Dict, Final, List, Optional, Union
import threading
###############################################################################
# Third Party
###############################################################################
import pytest
###############################################################################
# Local
###############################################################################
from .conftest import Cmds, ThreadPairDesc, ThreadPairDescs, ExpLogMsgs
from scottbrian_paratools.smart_event import (
SmartEvent,
WUCond,
SmartEventConflictDeadlockDetected,
SmartEventInconsistentFlagSettings,
SmartEventRemoteThreadNotAlive,
SmartEventWaitDeadlockDetected,
SmartEventWaitUntilTimeout)
from scottbrian_paratools.thread_pair import (
ThreadPair,
ThreadPairAlreadyPairedWithRemote,
ThreadPairDetectedOpFromForeignThread,
ThreadPairErrorInRegistry,
ThreadPairIncorrectNameSpecified,
ThreadPairNameAlreadyInUse,
ThreadPairNotPaired,
ThreadPairPairWithSelfNotAllowed,
ThreadPairPairWithTimedOut,
ThreadPairRemotePairedWithOther)
logger = logging.getLogger(__name__)
logger.debug('about to start the tests')
###############################################################################
# SmartEvent test exceptions
###############################################################################
class ErrorTstSmartEvent(Exception):
"""Base class for exception in this module."""
pass
class IncorrectActionSpecified(ErrorTstSmartEvent):
"""IncorrectActionSpecified exception class."""
pass
class UnrecognizedMessageType(ErrorTstSmartEvent):
"""UnrecognizedMessageType exception class."""
pass
class UnrecognizedCmd(ErrorTstSmartEvent):
"""UnrecognizedCmd exception class."""
pass
###############################################################################
# Cmd Constants
###############################################################################
Cmd = Enum('Cmd', 'Wait Wait_TOT Wait_TOF Wait_Clear Resume Sync Exit '
'Next_Action')
###############################################################################
# Action
###############################################################################
Action = Enum('Action',
'MainWait '
'MainSync MainSync_TOT MainSync_TOF '
'MainResume MainResume_TOT MainResume_TOF '
'ThreadWait ThreadWait_TOT ThreadWait_TOF '
'ThreadResume ')
###############################################################################
# action_arg fixtures
###############################################################################
action_arg_list = [Action.MainWait,
Action.MainSync,
Action.MainSync_TOT,
Action.MainSync_TOF,
Action.MainResume,
Action.MainResume_TOT,
Action.MainResume_TOF,
Action.ThreadWait,
Action.ThreadWait_TOT,
Action.ThreadWait_TOF,
Action.ThreadResume]
action_arg_list1 = [Action.MainWait
# Action.MainResume,
# Action.MainResume_TOT,
# Action.MainResume_TOF,
# Action.ThreadWait,
# Action.ThreadWait_TOT,
# Action.ThreadWait_TOF,
# Action.ThreadResume
]
action_arg_list2 = [ # Action.MainWait,
# Action.MainResume,
# Action.MainResume_TOT,
Action.MainResume_TOF
# Action.ThreadWait,
# Action.ThreadWait_TOT,
# Action.ThreadWait_TOF,
# Action.ThreadResume
]
@pytest.fixture(params=action_arg_list) # type: ignore
def action_arg1(request: Any) -> Any:
"""Using different reply messages.
Args:
request: special fixture that returns the fixture params
Returns:
The params values are returned one at a time
"""
return request.param
@pytest.fixture(params=action_arg_list) # type: ignore
def action_arg2(request: Any) -> Any:
"""Using different reply messages.
Args:
request: special fixture that returns the fixture params
Returns:
The params values are returned one at a time
"""
return request.param
###############################################################################
# timeout_arg fixtures
###############################################################################
timeout_arg_list = [None, 'TO_False', 'TO_True']
@pytest.fixture(params=timeout_arg_list) # type: ignore
def timeout_arg1(request: Any) -> Any:
"""Using different requests.
Args:
request: special fixture that returns the fixture params
Returns:
The params values are returned one at a time
"""
return request.param
@pytest.fixture(params=timeout_arg_list) # type: ignore
def timeout_arg2(request: Any) -> Any:
"""Using different requests.
Args:
request: special fixture that returns the fixture params
Returns:
The params values are returned one at a time
"""
return request.param
###############################################################################
# code fixtures
###############################################################################
code_arg_list = [None, 42]
@pytest.fixture(params=code_arg_list) # type: ignore
def code_arg1(request: Any) -> Any:
"""Using different codes.
Args:
request: special fixture that returns the fixture params
Returns:
The params values are returned one at a time
"""
return cast(int, request.param)
@pytest.fixture(params=code_arg_list) # type: ignore
def code_arg2(request: Any) -> Any:
"""Using different codes.
Args:
request: special fixture that returns the fixture params
Returns:
The params values are returned one at a time
"""
return cast(int, request.param)
###############################################################################
# log_msg fixtures
###############################################################################
log_msg_arg_list = [None, 'log msg1']
@pytest.fixture(params=log_msg_arg_list) # type: ignore
def log_msg_arg1(request: Any) -> Any:
"""Using different log messages.
Args:
request: special fixture that returns the fixture params
Returns:
The params values are returned one at a time
"""
return cast(int, request.param)
@pytest.fixture(params=log_msg_arg_list) # type: ignore
def log_msg_arg2(request: Any) -> Any:
"""Using different log messages.
Args:
request: special fixture that returns the fixture params
Returns:
The params values are returned one at a time
"""
return cast(int, request.param)
###############################################################################
# log_enabled fixtures
###############################################################################
log_enabled_list = [True, False]
@pytest.fixture(params=log_enabled_list) # type: ignore
def log_enabled_arg(request: Any) -> bool:
"""Using different log messages.
Args:
request: special fixture that returns the fixture params
Returns:
The params values are returned one at a time
"""
return cast(bool, request.param)
###############################################################################
# TestSmartEventBasic class to test SmartEvent methods
###############################################################################
###############################################################################
# SmartEventDesc class
###############################################################################
class SmartEventDesc(ThreadPairDesc):
"""Describes a SmartEvent with name and thread to verify."""
def __init__(self,
name: Optional[str] = '',
s_event: Optional[SmartEvent] = None,
thread: Optional[threading.Thread] = None, # type: ignore
state: Optional[int] = 0, # 0 is unknown
paired_with: Optional[Any] = None) -> None:
"""Initialize the SmartEventDesc.
Args:
name: name of the SmartEvent
s_event: the SmartEvent being tracked by this desc
thread: the thread associated with this SmartEvent
state: describes whether the SmartEvent is alive and registered
paired_with: names the SmartEvent paired with this one, if one
"""
ThreadPairDesc.__init__(self,
name=name,
thread_pair=s_event,
thread=thread,
state=state,
paired_with=paired_with)
def verify_state(self) -> None:
"""Verify the state of the SmartEvent."""
ThreadPairDesc.verify_state(self)
self.verify_smart_event_desc()
if self.paired_with is not None:
self.paired_with.verify_smart_event_desc()
###########################################################################
# verify_smart_event_init
###########################################################################
def verify_smart_event_desc(self) -> None:
"""Verify the SmartEvent object is initialized correctly."""
assert isinstance(self.thread_pair, SmartEvent)
assert isinstance(self.thread_pair.event, threading.Event)
# assert isinstance(self.thread, threading.Thread)
assert self.thread_pair.name == self.name
assert self.thread_pair.thread is self.thread
assert not self.thread_pair.wait_wait
assert not self.thread_pair.wait_timeout_specified
assert not self.thread_pair.deadlock
assert not self.thread_pair.conflict
assert self.thread_pair.code is None
# class SmartEventDescs:
# """Contains a collection of SmartEventDesc items."""
#
# ###########################################################################
# # __init__
# ###########################################################################
# def __init__(self):
# """Initialize object."""
# self._descs_lock = threading.RLock()
# self.descs: Dict[str, SmartEventDesc] = {}
#
# ###########################################################################
# # add_desc
# ###########################################################################
# def add_desc(self,
# desc: SmartEventDesc,
# verify: bool = True) -> None:
# """Add desc to collection.
#
# Args:
# desc: the desc to add
# verify: specify False when verification should not be done
#
# """
# with self._descs_lock:
# self.cleanup_registry()
# desc.state = SmartEventDesc.STATE_ALIVE_REGISTERED
# self.descs[desc.name] = desc
# if verify:
# self.verify_registry()
#
# ###########################################################################
# # thread_end
# ###########################################################################
# def thread_end(self,
# name: str) -> None:
# """Update SmartEventDescs to show a thread ended.
#
# Args:
# name: name of SmartEvent for desc to be updated
#
# """
# with self._descs_lock:
# # Note that this action does not cause registry cleanup
# # make sure thread is not alive
# assert not self.descs[name].s_event.thread.is_alive()
#
# # make sure we are transitioning correctly
# assert (self.descs[name].state
# == SmartEventDesc.STATE_ALIVE_REGISTERED)
# self.descs[name].state = SmartEventDesc.STATE_NOT_ALIVE_REGISTERED
#
# ###################################################################
# # verify the registry
# ###################################################################
# self.verify_registry()
#
# ###########################################################################
# # cleanup
# ###########################################################################
# def cleanup(self) -> None:
# """Perform cleanup for SmartEventDescs."""
# # Cleanup applies to all of the descs and is done
# # when first thing when a new SmartEvent is instantiated and
# # registered, or when a pair_with is done. This action is called
# # here for the other cases that trigger cleanup, such as
# # getting a SmartEventRemoteThreadNotAlive error.
# with self._descs_lock:
# self.cleanup_registry()
#
# ###################################################################
# # verify the registry
# ###################################################################
# self.verify_registry()
#
# ###########################################################################
# # paired
# ###########################################################################
# def paired(self,
# name1: Optional[str] = '',
# name2: Optional[str] = '',
# verify: bool = True) -> None:
# """Update SmartEventDescs to show paired status.
#
# Args:
# name1: name of SmartEvent for desc that is paired with name2
# name2: name of SmartEvent for desc that is paired with name1, or
# null if name1 became unpaired
# verify: specify False when verification should not be done
#
# """
# with self._descs_lock:
# self.cleanup_registry()
# # make sure we can allow the pair
# assert self.descs[name1].s_event.thread.is_alive()
# assert (self.descs[name1].state
# == SmartEventDesc.STATE_ALIVE_REGISTERED)
# assert name1 in SmartEvent._registry
# assert name1 in self.descs
#
# # note that name2 will normally be the SmartEventDesc
# # that we are pairing with, but it could be None in the case
# # where we are doing a second or subsequent pairing but the
# # remote fails to to do the pair, which means we lose the
# # residual name2 SmartEventDesc
# if name2:
# assert name2 in SmartEvent._registry
# assert self.descs[name2].s_event.thread.is_alive()
# assert (self.descs[name2].state
# == SmartEventDesc.STATE_ALIVE_REGISTERED)
# assert name2 in SmartEvent._registry
# assert name2 in self.descs
# self.descs[name1].paired_with = self.descs[name2]
# self.descs[name2].paired_with = self.descs[name1]
# else:
# self.descs[name1].paired_with = None
#
# ###################################################################
# # verify the registry
# ###################################################################
# if verify:
# self.verify_registry()
#
# ###########################################################################
# # verify_registry
# ###########################################################################
# def verify_registry(self):
# """Verify the registry."""
# with self._descs_lock:
# num_registered = 0
# for key, item in self.descs.items():
# if (item.state == SmartEventDesc.STATE_ALIVE_REGISTERED
# or item.state
# == SmartEventDesc.STATE_NOT_ALIVE_REGISTERED):
# num_registered += 1
# item.verify_state()
#
# assert len(SmartEvent._registry) == num_registered
#
# ###########################################################################
# # cleanup_registry
# ###########################################################################
# def cleanup_registry(self):
# """Cleanup the registry."""
# for key, item in self.descs.items():
# if item.state == SmartEventDesc.STATE_NOT_ALIVE_REGISTERED:
# assert not item.s_event.thread.is_alive()
# item.state = SmartEventDesc.STATE_NOT_ALIVE_UNREGISTERED
###############################################################################
# outer_f1
###############################################################################
def outer_f1(cmds: Cmds,
descs: ThreadPairDescs,
) -> None:
"""Outer function to test SmartEvent.
Args:
cmds: Cmds object to tell alpha when to go
descs: tracks set of SmartEventDesc items
"""
logger.debug('outer_f1 entered')
s_event = SmartEvent(name='beta')
descs.add_desc(SmartEventDesc(name='beta',
s_event=s_event))
# tell alpha OK to verify (i.e., beta_smart_event set with s_event)
cmds.queue_cmd('alpha', 'go')
s_event.pair_with(remote_name='alpha')
assert s_event.sync(log_msg='outer beta sync point 1')
assert s_event.wait(log_msg='outer f1 wait 12')
assert s_event.sync(log_msg='outer beta sync point 2')
assert s_event.resume(log_msg='outer f1 resume 23')
assert s_event.sync(log_msg='outer beta sync point 3')
logger.debug('outer f1 exiting')
###############################################################################
# OuterThreadApp class
###############################################################################
class OuterThreadApp(threading.Thread):
"""Outer thread app for test."""
def __init__(self,
cmds: Cmds,
descs: ThreadPairDescs
) -> None:
"""Initialize the object.
Args:
cmds: used to tell alpha to go
descs: tracks set of ThreadPairDescs items
"""
super().__init__()
self.cmds = cmds
self.descs = descs
self.s_event = SmartEvent(name='beta', thread=self)
def run(self) -> None:
"""Run the test."""
print('beta run started')
# normally, the add_desc is done just after the instantiation, but
# in this case the thread is not made alive until now, and the
# add_desc checks that the thread is alive
self.descs.add_desc(SmartEventDesc(name='beta',
s_event=self.s_event,
thread=self))
self.cmds.queue_cmd('alpha')
self.s_event.pair_with(remote_name='alpha')
self.descs.paired('alpha', 'beta')
assert self.s_event.sync(log_msg='outer beta sync point 1')
assert self.s_event.wait(log_msg='outer f1 wait 12')
assert self.s_event.sync(log_msg='outer beta sync point 2')
assert self.s_event.resume(log_msg='outer f1 resume 23')
assert self.s_event.sync(log_msg='outer beta sync point 3')
logger.debug('beta run exiting')
###############################################################################
# OuterThreadEventApp class
###############################################################################
class OuterThreadEventApp(threading.Thread, SmartEvent):
"""Outer thread event app for test."""
def __init__(self,
cmds: Cmds,
descs: ThreadPairDescs) -> None:
"""Initialize the object.
Args:
cmds: used to send cmds between threads
descs: tracks set of SmartEventDesc items
"""
threading.Thread.__init__(self)
SmartEvent.__init__(self, name='beta', thread=self)
self.cmds = cmds
self.descs = descs
def run(self):
"""Run the test."""
print('beta run started')
# normally, the add_desc is done just after the instantiation, but
# in this case the thread is not made alive until now, and the
# add_desc checks that the thread is alive
self.descs.add_desc(SmartEventDesc(name='beta',
s_event=self,
thread=self))
self.cmds.queue_cmd('alpha')
self.pair_with(remote_name='alpha', timeout=3)
self.descs.paired('alpha', 'beta')
assert self.sync(log_msg='outer beta sync point 1')
assert self.wait(log_msg='outer f1 wait 12')
assert self.sync(log_msg='outer beta sync point 2')
assert self.resume(log_msg='outer f1 resume 23')
assert self.sync(log_msg='outer beta sync point 3')
logger.debug('beta run exiting')
###############################################################################
# TestSmartEventBasic class
###############################################################################
class TestSmartEventBasic:
"""Test class for SmartEvent basic tests."""
###########################################################################
# repr for SmartEvent
###########################################################################
def test_smart_event_repr(self,
thread_exc: Any) -> None:
"""Test event with code repr.
Args:
thread_exc: captures thread exceptions
"""
descs = ThreadPairDescs()
smart_event = SmartEvent(name='alpha')
descs.add_desc(SmartEventDesc(name='alpha',
s_event=smart_event,
thread=threading.current_thread()))
expected_repr_str = 'SmartEvent(name="alpha")'
assert repr(smart_event) == expected_repr_str
smart_event2 = SmartEvent(name="AlphaDog")
descs.add_desc(SmartEventDesc(name='AlphaDog',
s_event=smart_event2,
thread=threading.current_thread()))
expected_repr_str = 'SmartEvent(name="AlphaDog")'
assert repr(smart_event2) == expected_repr_str
def f1():
s_event = SmartEvent(name='beta1')
descs.add_desc(SmartEventDesc(name='beta1',
s_event=s_event,
thread=threading.current_thread()))
f1_expected_repr_str = 'SmartEvent(name="beta1")'
assert repr(s_event) == f1_expected_repr_str
cmds.queue_cmd('alpha', 'go')
cmds.get_cmd('beta1')
def f2():
s_event = SmartEvent(name='beta2')
descs.add_desc(SmartEventDesc(name='beta2',
s_event=s_event,
thread=threading.current_thread()))
f1_expected_repr_str = 'SmartEvent(name="beta2")'
assert repr(s_event) == f1_expected_repr_str
cmds.queue_cmd('alpha', 'go')
cmds.get_cmd('beta2')
cmds = Cmds()
a_thread1 = threading.Thread(target=f1)
a_thread1.start()
cmds.get_cmd('alpha')
a_thread2 = threading.Thread(target=f2)
a_thread2.start()
cmds.get_cmd('alpha')
cmds.queue_cmd('beta1', 'go')
a_thread1.join()
descs.thread_end('beta1')
cmds.queue_cmd('beta2', 'go')
a_thread2.join()
descs.thread_end('beta2')
###########################################################################
# test_smart_event_instantiate_with_errors
###########################################################################
def test_smart_event_instantiate_with_errors(self) -> None:
"""Test register_thread alpha first."""
descs = ThreadPairDescs()
smart_event = SmartEvent(name='alpha')
descs.add_desc(SmartEventDesc(name='alpha',
s_event=smart_event,
thread=threading.current_thread()))
# not OK to instantiate a new smart_event with same name
with pytest.raises(ThreadPairNameAlreadyInUse):
_ = SmartEvent(name='alpha')
with pytest.raises(ThreadPairIncorrectNameSpecified):
_ = SmartEvent(name=42) # type: ignore
# try wait, resume, and pause_until without having been paired
with pytest.raises(ThreadPairNotPaired):
smart_event.wait()
with pytest.raises(ThreadPairNotPaired):
smart_event.resume()
with pytest.raises(ThreadPairNotPaired):
smart_event.pause_until(WUCond.RemoteWaiting)
# try to pair with unknown remote
with pytest.raises(ThreadPairPairWithTimedOut):
smart_event.pair_with(remote_name='beta', timeout=0.1)
# try to pair with bad name
with pytest.raises(ThreadPairIncorrectNameSpecified):
smart_event.pair_with(remote_name=3) # type: ignore
# make sure everything still the same
descs.verify_registry()
###########################################################################
# test_smart_event_pairing_with_errors
###########################################################################
def test_smart_event_pairing_with_errors(self) -> None:
"""Test register_thread during instantiation."""
def f1(name: str) -> None:
"""Func to test instantiate SmartEvent.
Args:
name: name to use for s_event
"""
logger.debug(f'{name} f1 entered')
s_event = SmartEvent(name=name)
descs.add_desc(SmartEventDesc(name=name,
s_event=s_event))
cmds.queue_cmd('alpha', 'go')
# not OK to pair with self
with pytest.raises(ThreadPairPairWithSelfNotAllowed):
s_event.pair_with(remote_name=name)
s_event.pair_with(remote_name='alpha')
# not OK to pair with remote a second time
with pytest.raises(ThreadPairAlreadyPairedWithRemote):
s_event.pair_with(remote_name='alpha')
s_event.sync(timeout=3,
log_msg=f'{name} f1 sync point 1')
logger.debug(f'{name} f1 exiting')
cmds = Cmds()
descs = ThreadPairDescs()
beta_t = threading.Thread(target=f1, args=('beta',))
smart_event = SmartEvent(name='alpha')
descs.add_desc(SmartEventDesc(name='alpha',
s_event=smart_event,
thread=threading.current_thread()))
beta_t.start()
# not OK to pair with self
with pytest.raises(ThreadPairPairWithSelfNotAllowed):
smart_event.pair_with(remote_name='alpha')
cmds.get_cmd('alpha')
smart_event.pair_with(remote_name='beta')
descs.paired('alpha', 'beta')
# not OK to pair with remote a second time
with pytest.raises(ThreadPairAlreadyPairedWithRemote):
smart_event.pair_with(remote_name='beta')
smart_event.sync(log_msg='alpha sync point 1')
beta_t.join()
descs.thread_end(name='beta')
# at this point, f1 has ended. But, the registry will not have changed,
# so everything will still show paired, even both alpha and beta
# SmartEvents. Alpha SmartEvent will detect that beta is no longer
# alive if a function is attempted.
descs.verify_registry()
#######################################################################
# second case - f1 with same name beta
#######################################################################
beta_t2 = threading.Thread(target=f1, args=('beta',))
beta_t2.start()
cmds.get_cmd('alpha')
smart_event.pair_with(remote_name='beta')
descs.paired('alpha', 'beta')
smart_event.sync(log_msg='alpha sync point 1 again')
beta_t2.join()
descs.thread_end(name='beta')
# at this point, f1 has ended. But, the registry will not have changed,
# so everything will still show paired, even both alpha and beta
# SmartEvents. Alpha SmartEvent will detect that beta is no longer
# alive if a function is attempted.
descs.verify_registry()
#######################################################################
# third case, use different name for f1. Should clean up old beta
# from the registry.
#######################################################################
with pytest.raises(ThreadPairNameAlreadyInUse):
smart_event = SmartEvent(name='alpha') # create fresh
beta_t3 = threading.Thread(target=f1, args=('charlie',))
beta_t3.start()
cmds.get_cmd('alpha')
smart_event.pair_with(remote_name='charlie')
descs.paired('alpha', 'charlie')
assert 'beta' not in SmartEvent._registry
smart_event.sync(log_msg='alpha sync point 1 again')
beta_t3.join()
descs.thread_end(name='charlie')
# at this point, f1 has ended. But, the registry will not have changed,
# so everything will still show paired, even both alpha and charlie
# SmartEvents. Alpha SmartEvent will detect that charlie is no longer
# alive if a function is attempted.
# change name in SmartEvent, then register a new entry to force the
# ThreadPairErrorInRegistry error
smart_event.remote.name = 'bad_name'
with pytest.raises(ThreadPairErrorInRegistry):
_ = SmartEvent(name='alpha2')
# restore the good name to allow verify_registry to succeed
smart_event.remote.name = 'charlie'
descs.verify_registry()
###########################################################################
# test_smart_event_pairing_with_multiple_threads
###########################################################################
def test_smart_event_pairing_with_multiple_threads(self) -> None:
"""Test register_thread during instantiation."""
def f1(name: str) -> None:
"""Func to test instantiate SmartEvent.
Args:
name: name to use for s_event
"""
logger.debug(f'{name} f1 entered')
s_event = SmartEvent(name=name)
descs.add_desc(SmartEventDesc(name=name,
s_event=s_event))
# not OK to pair with self
with pytest.raises(ThreadPairPairWithSelfNotAllowed):
s_event.pair_with(remote_name=name)
cmds.queue_cmd('alpha', 'go')
s_event.pair_with(remote_name='alpha')
descs.paired('alpha', 'beta')
# alpha needs to wait until we are officially paired to avoid
# timing issue when pairing with charlie
cmds.queue_cmd('alpha')
# not OK to pair with remote a second time
with pytest.raises(ThreadPairAlreadyPairedWithRemote):
s_event.pair_with(remote_name='alpha')
cmds.queue_cmd('alpha', 'go')
s_event.sync(log_msg=f'{name} f1 sync point 1')
logger.debug(f'{name} f1 exiting')
def f2(name: str) -> None:
"""Func to test instantiate SmartEvent.
Args:
name: name to use for s_event
"""
logger.debug(f'{name} f2 entered')
s_event = SmartEvent(name=name)
descs.add_desc(SmartEventDesc(name=name,
s_event=s_event))
# not OK to pair with self
with pytest.raises(ThreadPairPairWithSelfNotAllowed):
s_event.pair_with(remote_name=name)
with pytest.raises(ThreadPairPairWithTimedOut):
s_event.pair_with(remote_name='alpha', timeout=1)
s_event.pair_with(remote_name='alpha2')
descs.paired('alpha2', 'charlie')
# not OK to pair with remote a second time
with pytest.raises(ThreadPairAlreadyPairedWithRemote):
s_event.pair_with(remote_name='alpha2')
cmds.queue_cmd('alpha', 'go')
s_event.sync(log_msg=f'{name} f1 sync point 1')
logger.debug(f'{name} f2 exiting')
#######################################################################
# mainline
#######################################################################
descs = ThreadPairDescs()
cmds = Cmds()
beta_t = threading.Thread(target=f1, args=('beta',))
charlie_t = threading.Thread(target=f2, args=('charlie',))
smart_event = SmartEvent(name='alpha')
descs.add_desc(SmartEventDesc(name='alpha',
s_event=smart_event))
beta_t.start()
cmds.get_cmd('alpha')
smart_event.pair_with(remote_name='beta')
#######################################################################
# pair with charlie
#######################################################################
cmds.get_cmd('alpha')
smart_event2 = SmartEvent(name='alpha2')
descs.add_desc(SmartEventDesc(name='alpha2',
s_event=smart_event2))
charlie_t.start()
smart_event2.pair_with(remote_name='charlie')
cmds.get_cmd('alpha')
smart_event.sync(log_msg='alpha sync point 1')
beta_t.join()
descs.thread_end(name='beta')
smart_event2.sync(log_msg='alpha sync point 2')
charlie_t.join()
descs.thread_end(name='charlie')
# at this point, f1 and f2 have ended. But, the registry will not have
# changed, so everything will still show paired, even all
# SmartEvents. Any SmartEvents requests will detect that
# their pairs are no longer active and will trigger cleanup to
# remove any not alive entries from the registry. The SmartEvent
# objects for not alive threads remain pointed to by the alive
# entries so that they may still report SmartEventRemoteThreadNotAlive.
descs.verify_registry()
# cause cleanup via a sync request
with pytest.raises(SmartEventRemoteThreadNotAlive):
smart_event.sync(log_msg='mainline sync point 3')
descs.cleanup()
# try to pair with old beta - should timeout
with pytest.raises(ThreadPairPairWithTimedOut):
smart_event.pair_with(remote_name='beta', timeout=1)
# the pair_with sets smart_event.remote to none before trying the
# pair_with, and leaves it None when pair_with fails
descs.paired('alpha')
# try to pair with old charlie - should timeout
with pytest.raises(ThreadPairPairWithTimedOut):
smart_event.pair_with(remote_name='charlie', timeout=1)
# try to pair with nobody - should timeout
with pytest.raises(ThreadPairPairWithTimedOut):
smart_event.pair_with(remote_name='nobody', timeout=1)
# try to pair with old beta - should timeout
with pytest.raises(ThreadPairPairWithTimedOut):
smart_event2.pair_with(remote_name='beta', timeout=1)
# the pair_with sets smart_event.remote to none before trying the
# pair_with, and leaves it None when pair_with fails
descs.paired('alpha2')
# try to pair with old charlie - should timeout
with pytest.raises(ThreadPairPairWithTimedOut):
smart_event2.pair_with(remote_name='charlie', timeout=1)
# try to pair with nobody - should timeout
with pytest.raises(ThreadPairPairWithTimedOut):
smart_event2.pair_with(remote_name='nobody', timeout=1)
descs.verify_registry()
###########################################################################
# test_smart_event_pairing_with_multiple_threads
###########################################################################
def test_smart_event_remote_pair_with_other_error(self) -> None:
"""Test pair_with error case."""
def f1() -> None:
"""Func to test pair_with SmartEvent."""
logger.debug('beta f1 entered')
s_event = SmartEvent(name='beta')
descs.add_desc(SmartEventDesc(name='beta',
s_event=s_event))
cmds.queue_cmd('alpha', 'go')
with pytest.raises(ThreadPairRemotePairedWithOther):
s_event.pair_with(remote_name='alpha')
cmds.get_cmd('beta')
logger.debug(f'beta f1 exiting')
def f2() -> None:
"""Func to test pair_with SmartEvent."""
logger.debug('charlie f2 entered')
s_event = SmartEvent(name='charlie')
descs.add_desc(SmartEventDesc(name='charlie',
s_event=s_event),
verify=False)
cmds.queue_cmd('alpha', 'go')
s_event.pair_with(remote_name='alpha')
descs.paired('alpha', 'charlie', verify=False)
cmds.queue_cmd('alpha', 'go')
s_event.sync(log_msg='charlie f1 sync point 1')
logger.debug(f'charlie f2 exiting')
#######################################################################
# mainline
#######################################################################
descs = ThreadPairDescs()
cmds = Cmds()
beta_t = threading.Thread(target=f1)
charlie_t = threading.Thread(target=f2)
smart_event = SmartEvent(name='alpha')
descs.add_desc(SmartEventDesc(name='alpha',
s_event=smart_event))
beta_t.start()
cmds.get_cmd('alpha')
beta_se = SmartEvent._registry['beta']
# make sure beta has alpha as target of pair_with
while beta_se.remote is None:
time.sleep(1)
#######################################################################
# pair with charlie
#######################################################################
charlie_t.start()
cmds.get_cmd('alpha')
smart_event.pair_with(remote_name='charlie')
cmds.get_cmd('alpha')
cmds.queue_cmd('beta')
# wait for beta to raise ThreadPairRemotePairedWithOther and end
beta_t.join()
descs.thread_end(name='beta')
# sync up with charlie to allow charlie to exit
smart_event.sync(log_msg='alpha sync point 1')
charlie_t.join()
descs.thread_end(name='charlie')
# cause cleanup via a sync request
with pytest.raises(SmartEventRemoteThreadNotAlive):
smart_event.sync(log_msg='mainline sync point 3')
descs.cleanup()
###########################################################################
# test_smart_event_pairing_cleanup
###########################################################################
def test_smart_event_pairing_cleanup(self) -> None:
"""Test register_thread during instantiation."""
def f1(name: str, remote_name: str, idx: int) -> None:
"""Func to test instantiate SmartEvent.
Args:
name: name to use for s_event
remote_name: name to pair with
idx: index into beta_smart_events
"""
logger.debug(f'{name} f1 entered, remote {remote_name}, idx {idx}')
s_event = SmartEvent(name=name)
descs.add_desc(SmartEventDesc(name=name,
s_event=s_event))
cmds.queue_cmd('alpha')
s_event.pair_with(remote_name=remote_name,
log_msg=f'f1 {name} pair with {remote_name} '
f'for idx {idx}')
s_event.sync(log_msg=f'{name} f1 sync point 1')
assert s_event.sync(timeout=3,
log_msg=f'{name} f1 sync point 2')
logger.debug(f'{name} f1 exiting')
#######################################################################
# mainline start
#######################################################################
cmds = Cmds()
descs = ThreadPairDescs()
#######################################################################
# create 4 beta threads
#######################################################################
beta_t0 = threading.Thread(target=f1, args=('beta0', 'alpha0', 0))
beta_t1 = threading.Thread(target=f1, args=('beta1', 'alpha1', 1))
beta_t2 = threading.Thread(target=f1, args=('beta2', 'alpha2', 2))
beta_t3 = threading.Thread(target=f1, args=('beta3', 'alpha3', 3))
#######################################################################
# create alpha0 SmartEvent and desc, and verify
#######################################################################
smart_event0 = SmartEvent(name='alpha0')
descs.add_desc(SmartEventDesc(name='alpha0',
s_event=smart_event0))
#######################################################################
# create alpha1 SmartEvent and desc, and verify
#######################################################################
smart_event1 = SmartEvent(name='alpha1')
descs.add_desc(SmartEventDesc(name='alpha1',
s_event=smart_event1))
#######################################################################
# create alpha2 SmartEvent and desc, and verify
#######################################################################
smart_event2 = SmartEvent(name='alpha2')
descs.add_desc(SmartEventDesc(name='alpha2',
s_event=smart_event2))
#######################################################################
# create alpha3 SmartEvent and desc, and verify
#######################################################################
smart_event3 = SmartEvent(name='alpha3')
descs.add_desc(SmartEventDesc(name='alpha3',
s_event=smart_event3))
#######################################################################
# start beta0 thread, and verify
#######################################################################
beta_t0.start()
cmds.get_cmd('alpha')
smart_event0.pair_with(remote_name='beta0')
smart_event0.sync(log_msg='alpha0 sync point 1')
descs.paired('alpha0', 'beta0')
#######################################################################
# start beta1 thread, and verify
#######################################################################
beta_t1.start()
cmds.get_cmd('alpha')
smart_event1.pair_with(remote_name='beta1')
smart_event1.sync(log_msg='alpha1 sync point 1')
descs.paired('alpha1', 'beta1')
#######################################################################
# start beta2 thread, and verify
#######################################################################
beta_t2.start()
cmds.get_cmd('alpha')
smart_event2.pair_with(remote_name='beta2')
smart_event2.sync(log_msg='alpha2 sync point 1')
descs.paired('alpha2', 'beta2')
#######################################################################
# start beta3 thread, and verify
#######################################################################
beta_t3.start()
cmds.get_cmd('alpha')
smart_event3.pair_with(remote_name='beta3')
smart_event3.sync(log_msg='alpha3 sync point 1')
descs.paired('alpha3', 'beta3')
#######################################################################
# let beta0 finish
#######################################################################
smart_event0.sync(log_msg='alpha0 sync point 1')
beta_t0.join()
descs.thread_end(name='beta0')
#######################################################################
# replace old beta0 w new beta0 - should cleanup registry old beta0
#######################################################################
beta_t0 = threading.Thread(target=f1, args=('beta0', 'alpha0', 0))
beta_t0.start()
cmds.get_cmd('alpha')
smart_event0.pair_with(remote_name='beta0')
smart_event0.sync(log_msg='alpha0 sync point 1')
descs.paired('alpha0', 'beta0')
#######################################################################
# let beta1 and beta3 finish
#######################################################################
smart_event1.sync(log_msg='alpha1 sync point 2')
beta_t1.join()
descs.thread_end(name='beta1')
smart_event3.sync(log_msg='alpha3 sync point 3')
beta_t3.join()
descs.thread_end(name='beta3')
#######################################################################
# replace old beta1 w new beta1 - should cleanup old beta1 and beta3
#######################################################################
beta_t1 = threading.Thread(target=f1, args=('beta1', 'alpha1', 1))
beta_t1.start()
cmds.get_cmd('alpha')
smart_event1.pair_with(remote_name='beta1')
smart_event1.sync(log_msg='alpha1 sync point 1')
descs.paired('alpha1', 'beta1')
# should get not alive for beta3
with pytest.raises(SmartEventRemoteThreadNotAlive):
smart_event3.sync(log_msg='mainline sync point 4')
# should still be the same
descs.verify_registry()
#######################################################################
# get a new beta3 going
#######################################################################
beta_t3 = threading.Thread(target=f1, args=('beta3', 'alpha3', 3))
beta_t3.start()
cmds.get_cmd('alpha')
smart_event3.pair_with(remote_name='beta3')
smart_event3.sync(log_msg='alpha3 sync point 1')
descs.paired('alpha3', 'beta3')
#######################################################################
# let beta1 and beta2 finish
#######################################################################
smart_event1.sync(log_msg='alpha1 sync point 5')
beta_t1.join()
descs.thread_end(name='beta1')
smart_event2.sync(log_msg='alpha2 sync point 6')
beta_t2.join()
descs.thread_end(name='beta2')
#######################################################################
# trigger cleanup for beta1 and beta2
#######################################################################
with pytest.raises(SmartEventRemoteThreadNotAlive):
smart_event2.sync(log_msg='alpha2 sync point 7')
descs.cleanup()
#######################################################################
# should get SmartEventRemoteThreadNotAlive for beta1 and beta2
#######################################################################
with pytest.raises(SmartEventRemoteThreadNotAlive):
smart_event1.sync(log_msg='alpha1 sync point 8')
with pytest.raises(SmartEventRemoteThreadNotAlive):
smart_event2.sync(log_msg='alpha 2 sync point 9')
descs.verify_registry()
#######################################################################
# get a new beta2 going
#######################################################################
beta_t2 = threading.Thread(target=f1, args=('beta2', 'alpha2', 2))
beta_t2.start()
cmds.get_cmd('alpha')
smart_event2.pair_with(remote_name='beta2')
smart_event2.sync(log_msg='alpha2 sync point 1')
descs.paired('alpha2', 'beta2')
smart_event2.sync(log_msg='alpha2 sync point 2')
beta_t2.join()
descs.thread_end(name='beta2')
#######################################################################
# let beta0 complete
#######################################################################
smart_event0.sync(log_msg='alpha0 sync point 2')
beta_t0.join()
descs.thread_end(name='beta0')
#######################################################################
# let beta3 complete
#######################################################################
smart_event3.sync(log_msg='alpha0 sync point 2')
beta_t3.join()
descs.thread_end(name='beta3')
###########################################################################
# test_smart_event_foreign_op_detection
###########################################################################
def test_smart_event_foreign_op_detection(self) -> None:
"""Test register_thread with f1."""
#######################################################################
# mainline and f1 - mainline pairs with beta
#######################################################################
logger.debug('start test 1')
def f1():
print('beta f1 entered')
s_event = SmartEvent(name='beta')
descs.add_desc(SmartEventDesc(name='beta',
s_event=s_event))
my_c_thread = threading.current_thread()
assert s_event.thread is my_c_thread
assert s_event.thread is threading.current_thread()
s_event.pair_with(remote_name='alpha')
s_event.sync(log_msg='f1 beta sync point 1')
logger.debug('f1 beta about to enter cmd loop')
while True:
beta_cmd = cmds.get_cmd('beta')
if beta_cmd == Cmd.Exit:
break
logger.debug(f'thread_func1 received cmd: {beta_cmd}')
if beta_cmd == Cmd.Wait:
assert s_event.wait()
elif beta_cmd == Cmd.Resume:
with pytest.raises(SmartEventWaitUntilTimeout):
s_event.pause_until(WUCond.RemoteWaiting,
timeout=0.002)
with pytest.raises(SmartEventWaitUntilTimeout):
s_event.pause_until(WUCond.RemoteWaiting, timeout=0.01)
with pytest.raises(SmartEventWaitUntilTimeout):
s_event.pause_until(WUCond.RemoteWaiting, timeout=0.02)
s_event.sync(log_msg='f1 beta sync point 2')
s_event.pause_until(WUCond.RemoteWaiting)
s_event.pause_until(WUCond.RemoteWaiting, timeout=0.001)
s_event.pause_until(WUCond.RemoteWaiting, timeout=0.01)
s_event.pause_until(WUCond.RemoteWaiting, timeout=0.02)
s_event.pause_until(WUCond.RemoteWaiting, timeout=-0.02)
s_event.pause_until(WUCond.RemoteWaiting, timeout=-1)
s_event.pause_until(WUCond.RemoteWaiting, timeout=0)
s_event.resume()
def foreign1(s_event):
logger.debug('foreign1 entered')
with pytest.raises(ThreadPairDetectedOpFromForeignThread):
s_event.resume()
with pytest.raises(ThreadPairDetectedOpFromForeignThread):
s_event.pair_with(remote_name='beta')
with pytest.raises(ThreadPairDetectedOpFromForeignThread):
s_event.pair_with(remote_name='beta', timeout=1)
with pytest.raises(ThreadPairDetectedOpFromForeignThread):
s_event.pause_until(WUCond.RemoteWaiting, timeout=0.02)
with pytest.raises(ThreadPairDetectedOpFromForeignThread):
s_event.pause_until(WUCond.RemoteWaiting, timeout=0.02)
with pytest.raises(ThreadPairDetectedOpFromForeignThread):
s_event.pause_until(WUCond.RemoteWaiting)
with pytest.raises(ThreadPairDetectedOpFromForeignThread):
s_event.wait()
with pytest.raises(ThreadPairDetectedOpFromForeignThread):
s_event.sync()
logger.debug('foreign1 exiting')
cmds = Cmds()
descs = ThreadPairDescs()
smart_event1 = SmartEvent(name='alpha')
descs.add_desc(SmartEventDesc(name='alpha',
s_event=smart_event1))
alpha_t = threading.current_thread()
my_f1_thread = threading.Thread(target=f1)
my_foreign1_thread = threading.Thread(target=foreign1,
args=(smart_event1,))
with pytest.raises(ThreadPairNotPaired):
smart_event1.pause_until(WUCond.RemoteWaiting, timeout=-0.002)
with pytest.raises(ThreadPairNotPaired):
smart_event1.pause_until(WUCond.RemoteWaiting, timeout=0)
with pytest.raises(ThreadPairNotPaired):
smart_event1.pause_until(WUCond.RemoteWaiting, timeout=0.002)
with pytest.raises(ThreadPairNotPaired):
smart_event1.pause_until(WUCond.RemoteWaiting, timeout=0.2)
with pytest.raises(ThreadPairNotPaired):
smart_event1.pause_until(WUCond.RemoteWaiting)
logger.debug('mainline about to start beta thread')
my_f1_thread.start()
smart_event1.pair_with(remote_name='beta')
descs.paired('alpha', 'beta')
smart_event1.sync(log_msg='mainline sync point 1')
cmds.queue_cmd('beta', Cmd.Wait)
my_foreign1_thread.start() # attempt to resume beta (should fail)
my_foreign1_thread.join()
logger.debug('about to pause_until RemoteWaiting')
smart_event1.pause_until(WUCond.RemoteWaiting)
smart_event1.pause_until(WUCond.RemoteWaiting, timeout=0.001)
smart_event1.pause_until(WUCond.RemoteWaiting, timeout=0.01)
smart_event1.pause_until(WUCond.RemoteWaiting, timeout=0.02)
smart_event1.pause_until(WUCond.RemoteWaiting, timeout=-0.02)
smart_event1.pause_until(WUCond.RemoteWaiting, timeout=-1)
smart_event1.pause_until(WUCond.RemoteWaiting, timeout=0)
smart_event1.resume()
cmds.queue_cmd('beta', Cmd.Resume)
smart_event1.sync(log_msg='mainline sync point 2')
assert smart_event1.wait()
cmds.queue_cmd('beta', Cmd.Exit)
my_f1_thread.join()
descs.thread_end(name='beta')
with pytest.raises(SmartEventRemoteThreadNotAlive):
smart_event1.resume()
with pytest.raises(SmartEventRemoteThreadNotAlive):
smart_event1.wait()
with pytest.raises(SmartEventRemoteThreadNotAlive):
smart_event1.pause_until(WUCond.RemoteWaiting)
with pytest.raises(SmartEventRemoteThreadNotAlive):
smart_event1.sync(log_msg='mainline sync point 3')
assert smart_event1.thread is alpha_t
###########################################################################
# test_smart_event_outer_thread_f1
###########################################################################
def test_smart_event_outer_thread_f1(self) -> None:
"""Test simple sequence with outer thread f1."""
#######################################################################
# mainline
#######################################################################
logger.debug('mainline starting')
cmds = Cmds()
descs = ThreadPairDescs()
smart_event = SmartEvent(name='alpha')
descs.add_desc(SmartEventDesc(name='alpha',
s_event=smart_event))
f1_thread = threading.Thread(target=outer_f1, args=(cmds, descs))
f1_thread.start()
cmds.get_cmd('alpha')
smart_event.pair_with(remote_name='beta')
descs.paired('alpha', 'beta')
smart_event.sync(log_msg='mainline sync point 1')
smart_event.resume(log_msg='alpha resume 12')
smart_event.sync(log_msg='mainline sync point 2')
smart_event.wait(log_msg='alpha wait 23')
smart_event.sync(log_msg='mainline sync point 3')
f1_thread.join()
descs.thread_end(name='beta')
logger.debug('mainline exiting')
###########################################################################
# test_smart_event_outer_thread_app
###########################################################################
def test_smart_event_outer_thread_app(self) -> None:
"""Test simple sequence with outer thread app."""
#######################################################################
# mainline
#######################################################################
logger.debug('mainline starting')
cmds = Cmds()
descs = ThreadPairDescs()
smart_event = SmartEvent(name='alpha')
descs.add_desc(SmartEventDesc(name='alpha',
s_event=smart_event))
thread_app = OuterThreadApp(cmds=cmds, descs=descs)
thread_app.start()
cmds.get_cmd('alpha')
smart_event.pair_with(remote_name='beta', timeout=3)
smart_event.sync(log_msg='mainline sync point 1')
smart_event.resume(log_msg='alpha resume 12')
smart_event.sync(log_msg='mainline sync point 2')
smart_event.wait(log_msg='alpha wait 23')
smart_event.sync(log_msg='mainline sync point 3')
thread_app.join()
descs.thread_end(name='beta')
logger.debug('mainline exiting')
###########################################################################
# test_smart_event_outer_thread_app
###########################################################################
def test_smart_event_outer_thread_event_app(self) -> None:
"""Test simple sequence with outer thread event app."""
#######################################################################
# mainline
#######################################################################
logger.debug('mainline starting')
cmds = Cmds()
descs = ThreadPairDescs()
smart_event = SmartEvent(name='alpha')
descs.add_desc(SmartEventDesc(name='alpha',
s_event=smart_event))
thread_event_app = OuterThreadEventApp(cmds=cmds, descs=descs)
thread_event_app.start()
cmds.get_cmd('alpha')
smart_event.pair_with(remote_name='beta', timeout=3)
smart_event.sync(log_msg='mainline sync point 1')
smart_event.resume(log_msg='alpha resume 12')
smart_event.sync(log_msg='mainline sync point 2')
smart_event.wait(log_msg='alpha wait 23')
smart_event.sync(log_msg='mainline sync point 3')
thread_event_app.join()
descs.thread_end(name='beta')
logger.debug('mainline exiting')
###########################################################################
# test_smart_event_wait_deadlock_detection
###########################################################################
def test_smart_event_wait_deadlock_detection(self) -> None:
"""Test deadlock detection with f1."""
#######################################################################
# f1
#######################################################################
def f1(ml_thread):
logger.debug('beta f1 beta entered')
s_event = SmartEvent(name='beta')
descs.add_desc(SmartEventDesc(name='beta',
s_event=s_event))
my_c_thread = threading.current_thread()
cmds.get_cmd('beta')
s_event.pair_with(remote_name='alpha')
assert s_event.remote.thread is ml_thread
assert s_event.remote.thread is alpha_t
assert s_event.thread is my_c_thread
assert s_event.thread is threading.current_thread()
s_event.sync(log_msg='beta f1 thread sync point 1')
with pytest.raises(SmartEventWaitDeadlockDetected):
s_event.wait()
s_event.sync(log_msg='beta f1 thread sync point 2')
s_event.wait() # clear the resume that comes after the deadlock
s_event.sync(log_msg='beta f1 thread sync point 3')
s_event.pause_until(WUCond.RemoteWaiting, timeout=2)
with pytest.raises(SmartEventWaitDeadlockDetected):
s_event.wait()
s_event.sync(log_msg='beta f1 thread sync point 4')
s_event.resume()
#######################################################################
# mainline start
#######################################################################
cmds = Cmds()
descs = ThreadPairDescs()
alpha_t = threading.current_thread()
smart_event = SmartEvent(name='alpha')
descs.add_desc(SmartEventDesc(name='alpha',
s_event=smart_event))
my_f1_thread = threading.Thread(target=f1, args=(alpha_t,))
with pytest.raises(ThreadPairNotPaired):
smart_event.pause_until(WUCond.RemoteWaiting, timeout=-0.002)
with pytest.raises(ThreadPairNotPaired):
smart_event.pause_until(WUCond.RemoteWaiting, timeout=0)
with pytest.raises(ThreadPairNotPaired):
smart_event.pause_until(WUCond.RemoteWaiting, timeout=0.002)
with pytest.raises(ThreadPairNotPaired):
smart_event.pause_until(WUCond.RemoteWaiting, timeout=0.2)
with pytest.raises(ThreadPairNotPaired):
smart_event.pause_until(WUCond.RemoteWaiting)
my_f1_thread.start()
with pytest.raises(ThreadPairNotPaired):
smart_event.pause_until(WUCond.RemoteWaiting)
# tell f1 to proceed to pair_with
cmds.queue_cmd('beta', Cmd.Exit)
smart_event.pair_with(remote_name='beta', timeout=3)
descs.paired('alpha', 'beta')
smart_event.sync(log_msg='mainline sync point 1')
with pytest.raises(SmartEventWaitDeadlockDetected):
smart_event.wait()
smart_event.sync(log_msg='mainline sync point 2')
smart_event.resume()
smart_event.sync(log_msg='mainline sync point 3')
with pytest.raises(SmartEventWaitDeadlockDetected):
smart_event.wait()
smart_event.sync(log_msg='mainline sync point 4')
assert smart_event.wait() # clear resume
my_f1_thread.join()
descs.thread_end(name='beta')
with pytest.raises(SmartEventRemoteThreadNotAlive):
smart_event.resume()
descs.cleanup()
with pytest.raises(SmartEventRemoteThreadNotAlive):
smart_event.wait()
with pytest.raises(SmartEventRemoteThreadNotAlive):
smart_event.sync(log_msg='mainline sync point 5')
assert smart_event.thread is alpha_t
assert smart_event.remote.thread is my_f1_thread
###########################################################################
# test_smart_event_inner_thread_app
###########################################################################
def test_smart_event_inner_thread_app(self) -> None:
"""Test SmartEvent with thread_app."""
#######################################################################
# ThreadApp
#######################################################################
class MyThread(threading.Thread):
"""MyThread class to test SmartEvent."""
def __init__(self,
alpha_smart_event: SmartEvent,
alpha_thread: threading.Thread
) -> None:
"""Initialize the object.
Args:
alpha_smart_event: alpha SmartEvent to use for verification
alpha_thread: alpha thread to use for verification
"""
super().__init__()
self.s_event = SmartEvent(name='beta', thread=self)
self.alpha_s_event = alpha_smart_event
self.alpha_thread = alpha_thread
def run(self):
"""Run the tests."""
logger.debug('run started')
# normally, the add_desc is done just after the
# instantiation, but
# in this case the thread is not made alive until now, and the
# add_desc checks that the thread is alive
descs.add_desc(SmartEventDesc(name='beta',
s_event=self.s_event,
thread=self))
cmds.queue_cmd('alpha')
self.s_event.pair_with(remote_name='alpha')
descs.paired('alpha', 'beta')
assert self.s_event.remote is self.alpha_s_event
assert (self.s_event.remote.thread
is self.alpha_thread)
assert self.s_event.remote.thread is alpha_t
assert self.s_event.thread is self
my_run_thread = threading.current_thread()
assert self.s_event.thread is my_run_thread
assert self.s_event.thread is threading.current_thread()
with pytest.raises(SmartEventWaitUntilTimeout):
self.s_event.pause_until(WUCond.RemoteResume,
timeout=0.009)
self.s_event.sync(log_msg='beta run sync point 1')
self.s_event.pause_until(WUCond.RemoteResume, timeout=5)
self.s_event.pause_until(WUCond.RemoteResume)
assert self.s_event.wait(log_msg='beta run wait 12')
self.s_event.sync(log_msg='beta run sync point 2')
self.s_event.sync(log_msg='beta run sync point 3')
self.s_event.resume()
self.s_event.sync(log_msg='beta run sync point 4')
logger.debug('beta run exiting 45')
#######################################################################
# mainline starts
#######################################################################
cmds = Cmds()
descs = ThreadPairDescs()
alpha_t = threading.current_thread()
smart_event1 = SmartEvent(name='alpha')
descs.add_desc(SmartEventDesc(name='alpha',
s_event=smart_event1))
my_taa_thread = MyThread(smart_event1, alpha_t)
my_taa_thread.start()
cmds.get_cmd('alpha')
smart_event1.pair_with(remote_name='beta')
smart_event1.sync(log_msg='mainline sync point 1')
assert smart_event1.resume(log_msg='mainline resume 12')
smart_event1.sync(log_msg='mainline sync point 2')
with pytest.raises(SmartEventWaitUntilTimeout):
smart_event1.pause_until(WUCond.RemoteResume, timeout=0.009)
smart_event1.sync(log_msg='mainline sync point 3')
smart_event1.pause_until(WUCond.RemoteResume, timeout=5)
smart_event1.pause_until(WUCond.RemoteResume)
assert smart_event1.wait(log_msg='mainline wait 34')
smart_event1.sync(log_msg='mainline sync point 4')
my_taa_thread.join()
descs.thread_end('beta')
with pytest.raises(SmartEventRemoteThreadNotAlive):
smart_event1.resume()
descs.cleanup()
with pytest.raises(SmartEventRemoteThreadNotAlive):
smart_event1.wait()
with pytest.raises(SmartEventRemoteThreadNotAlive):
smart_event1.pause_until(WUCond.RemoteWaiting)
with pytest.raises(SmartEventRemoteThreadNotAlive):
smart_event1.pause_until(WUCond.RemoteResume)
with pytest.raises(ThreadPairPairWithTimedOut):
smart_event1.pair_with(remote_name='beta', timeout=1)
descs.paired('alpha')
with pytest.raises(ThreadPairNotPaired):
smart_event1.wait()
with pytest.raises(ThreadPairNotPaired):
smart_event1.pause_until(WUCond.RemoteWaiting)
with pytest.raises(ThreadPairNotPaired):
smart_event1.pause_until(WUCond.RemoteResume)
assert smart_event1.thread is alpha_t
assert smart_event1.remote is None
descs.verify_registry()
###########################################################################
# test_smart_event_inner_thread_app2
###########################################################################
def test_smart_event_inner_thread_app2(self) -> None:
"""Test SmartEvent with thread_app."""
#######################################################################
# mainline and ThreadApp - mainline provide beta SmartEvent
#######################################################################
class MyThread2(threading.Thread):
def __init__(self,
s_event: SmartEvent,
alpha_t1: threading.Thread):
super().__init__()
self.s_event = s_event
# not really a good idea to set the thread - this test case
# may not be realistic - need to consider whether the idea
# of passing in a pre-instantiated SmartEvent (which gets
# its thread set during instantiation) is something we want
# to support given that we have to change the thread
self.s_event.thread = self
self.alpha_t1 = alpha_t1
def run(self):
print('run started')
# normally, the add_desc is done just after the
# instantiation, but
# in this case the thread is not made alive until now, and the
# add_desc checks that the thread is alive
descs.add_desc(SmartEventDesc(name='beta',
s_event=self.s_event,
thread=self))
cmds.queue_cmd('alpha')
self.s_event.pair_with(remote_name='alpha2')
assert self.s_event.remote.thread is self.alpha_t1
assert self.s_event.remote.thread is alpha_t
assert self.s_event.thread is self
my_run_thread = threading.current_thread()
assert self.s_event.thread is my_run_thread
assert self.s_event.thread is threading.current_thread()
with pytest.raises(SmartEventWaitDeadlockDetected):
self.s_event.wait()
assert self.s_event.wait()
self.s_event.pause_until(WUCond.RemoteWaiting)
self.s_event.pause_until(WUCond.RemoteWaiting, timeout=2)
self.s_event.resume()
cmds = Cmds()
descs = ThreadPairDescs()
smart_event2 = SmartEvent(name='alpha2')
descs.add_desc(SmartEventDesc(name='alpha2',
s_event=smart_event2))
smart_event3 = SmartEvent(name='beta')
alpha_t = threading.current_thread()
my_tab_thread = MyThread2(smart_event3, alpha_t)
my_tab_thread.start()
cmds.get_cmd('alpha')
smart_event2.pair_with(remote_name='beta')
descs.paired('alpha2', 'beta')
smart_event2.pause_until(WUCond.RemoteWaiting)
with pytest.raises(SmartEventWaitDeadlockDetected):
smart_event2.wait()
smart_event2.resume()
assert smart_event2.wait()
my_tab_thread.join()
descs.thread_end('beta')
with pytest.raises(SmartEventRemoteThreadNotAlive):
smart_event2.resume()
descs.cleanup()
with pytest.raises(SmartEventRemoteThreadNotAlive):
smart_event2.wait()
with pytest.raises(SmartEventRemoteThreadNotAlive):
smart_event2.pause_until(WUCond.RemoteWaiting)
with pytest.raises(SmartEventRemoteThreadNotAlive):
smart_event2.pause_until(WUCond.RemoteResume)
assert smart_event2.thread is alpha_t
assert smart_event2.remote.thread is my_tab_thread
descs.verify_registry()
###########################################################################
# test_smart_event_inner_thread_event_app
###########################################################################
def test_smart_event_inner_thread_event_app(self) -> None:
"""Test SmartEvent with thread_event_app."""
#######################################################################
# mainline and ThreadEventApp - mainline sets alpha and beta
#######################################################################
class MyThreadEvent1(threading.Thread, SmartEvent):
def __init__(self,
alpha_t1: threading.Thread):
threading.Thread.__init__(self)
SmartEvent.__init__(self, name='beta', thread=self)
self.alpha_t1 = alpha_t1
def run(self):
logger.debug('run started')
# normally, the add_desc is done just after the
# instantiation, but
# in this case the thread is not made alive until now, and the
# add_desc checks that the thread is alive
descs.add_desc(SmartEventDesc(name='beta',
s_event=self,
thread=self))
cmds.queue_cmd('alpha')
self.pair_with(remote_name='alpha')
descs.paired('alpha', 'beta')
assert self.remote.thread is self.alpha_t1
assert self.remote.thread is alpha_t
assert self.thread is self
my_run_thread = threading.current_thread()
assert self.thread is my_run_thread
assert self.thread is threading.current_thread()
assert self.wait()
self.pause_until(WUCond.RemoteWaiting, timeout=2)
with pytest.raises(SmartEventWaitDeadlockDetected):
self.wait()
self.resume()
logger.debug('run exiting')
cmds = Cmds()
descs = ThreadPairDescs()
alpha_t = threading.current_thread()
my_te1_thread = MyThreadEvent1(alpha_t)
with pytest.raises(ThreadPairDetectedOpFromForeignThread):
my_te1_thread.pause_until(WUCond.RemoteWaiting,
timeout=0.005)
with pytest.raises(ThreadPairDetectedOpFromForeignThread):
my_te1_thread.wait(timeout=0.005)
with pytest.raises(ThreadPairDetectedOpFromForeignThread):
my_te1_thread.resume(timeout=0.005)
with pytest.raises(ThreadPairDetectedOpFromForeignThread):
my_te1_thread.sync(timeout=0.005)
with pytest.raises(ThreadPairDetectedOpFromForeignThread):
my_te1_thread.pair_with(remote_name='alpha', timeout=0.5)
assert my_te1_thread.remote is None
assert my_te1_thread.thread is my_te1_thread
my_te1_thread.start()
cmds.get_cmd('alpha')
smart_event = SmartEvent(name='alpha')
descs.add_desc(SmartEventDesc(name='alpha',
s_event=smart_event))
with pytest.raises(ThreadPairNotPaired):
smart_event.sync()
with pytest.raises(ThreadPairNotPaired):
smart_event.wait()
with pytest.raises(ThreadPairNotPaired):
smart_event.resume()
smart_event.pair_with(remote_name='beta')
smart_event.resume()
with pytest.raises(SmartEventWaitDeadlockDetected):
smart_event.wait()
assert smart_event.wait()
my_te1_thread.join()
descs.thread_end('beta')
with pytest.raises(SmartEventRemoteThreadNotAlive):
smart_event.resume()
with pytest.raises(SmartEventRemoteThreadNotAlive):
smart_event.wait()
with pytest.raises(SmartEventRemoteThreadNotAlive):
smart_event.pause_until(WUCond.RemoteWaiting)
with pytest.raises(SmartEventRemoteThreadNotAlive):
smart_event.pause_until(WUCond.RemoteResume)
assert my_te1_thread.remote is not None
assert my_te1_thread.remote.thread is not None
assert my_te1_thread.remote.thread is alpha_t
assert my_te1_thread.thread is my_te1_thread
###########################################################################
# test_smart_event_inner_thread_event_app2
###########################################################################
def test_smart_event_inner_thread_event_app2(self) -> None:
"""Test SmartEvent with thread_event_app."""
#######################################################################
# mainline and ThreadApp - mainline sets alpha thread_app sets beta
#######################################################################
class MyThreadEvent2(threading.Thread, SmartEvent):
def __init__(self,
alpha_t1: threading.Thread):
threading.Thread.__init__(self)
SmartEvent.__init__(self, name='beta', thread=self)
self.alpha_t1 = alpha_t1
def run(self):
logger.debug('run started')
assert self.remote is None
assert self.thread is self
my_run_thread = threading.current_thread()
assert self.thread is my_run_thread
assert self.thread is threading.current_thread()
# normally, the add_desc is done just after the
# instantiation, but
# in this case the thread is not made alive until now, and the
# add_desc checks that the thread is alive
descs.add_desc(SmartEventDesc(name='beta',
s_event=self,
thread=self))
cmds.queue_cmd('alpha')
self.pair_with(remote_name='alpha')
assert self.remote.thread is self.alpha_t1
assert self.remote.thread is alpha_t
descs.paired('alpha', 'beta')
with pytest.raises(SmartEventWaitDeadlockDetected):
self.wait()
assert self.wait()
self.resume()
logger.debug('run exiting')
cmds = Cmds()
descs = ThreadPairDescs()
alpha_t = threading.current_thread()
my_te2_thread = MyThreadEvent2(alpha_t)
my_te2_thread.start()
cmds.get_cmd('alpha')
smart_event = SmartEvent(name='alpha')
descs.add_desc(SmartEventDesc(name='alpha',
s_event=smart_event))
smart_event.pair_with(remote_name='beta')
smart_event.pause_until(WUCond.RemoteWaiting, timeout=2)
with pytest.raises(SmartEventWaitDeadlockDetected):
smart_event.wait()
assert smart_event.resume()
assert smart_event.wait()
my_te2_thread.join()
descs.thread_end('beta')
with pytest.raises(SmartEventRemoteThreadNotAlive):
smart_event.resume()
with pytest.raises(SmartEventRemoteThreadNotAlive):
smart_event.wait()
with pytest.raises(SmartEventRemoteThreadNotAlive):
smart_event.pause_until(WUCond.RemoteWaiting, timeout=2)
with pytest.raises(SmartEventRemoteThreadNotAlive):
smart_event.pause_until(WUCond.RemoteResume, timeout=2)
assert smart_event.thread is alpha_t
assert smart_event.remote.thread is my_te2_thread
###########################################################################
# test_smart_event_two_f_threads
###########################################################################
def test_smart_event_two_f_threads(self) -> None:
"""Test register_thread with thread_event_app."""
#######################################################################
# two threads - mainline sets alpha and beta
#######################################################################
def fa1():
logger.debug('fa1 entered')
my_fa_thread = threading.current_thread()
s_event = SmartEvent(name='fa1')
descs.add_desc(SmartEventDesc(name='fa1',
s_event=s_event,
thread=my_fa_thread))
assert s_event.thread is my_fa_thread
s_event.pair_with(remote_name='fb1')
descs.paired('fa1', 'fb1')
logger.debug('fa1 about to wait')
s_event.wait()
logger.debug('fa1 back from wait')
s_event.pause_until(WUCond.RemoteWaiting, timeout=2)
s_event.resume()
def fb1():
logger.debug('fb1 entered')
my_fb_thread = threading.current_thread()
s_event = SmartEvent(name='fb1')
descs.add_desc(SmartEventDesc(name='fb1',
s_event=s_event,
thread=my_fb_thread))
assert s_event.thread is my_fb_thread
s_event.pair_with(remote_name='fa1')
logger.debug('fb1 about to resume')
s_event.resume()
s_event.wait()
# tell mainline we are out of the wait - OK to do descs fa1 end
cmds.queue_cmd('alpha')
# wait for mainline to give to go ahead after doing descs fa1 end
cmds.get_cmd('beta')
with pytest.raises(SmartEventRemoteThreadNotAlive):
s_event.resume()
descs.cleanup()
with pytest.raises(SmartEventRemoteThreadNotAlive):
s_event.wait()
with pytest.raises(SmartEventRemoteThreadNotAlive):
s_event.pause_until(WUCond.RemoteWaiting)
#######################################################################
# mainline
#######################################################################
cmds = Cmds()
descs = ThreadPairDescs()
fa1_thread = threading.Thread(target=fa1)
fb1_thread = threading.Thread(target=fb1)
logger.debug('starting fa1_thread')
fa1_thread.start()
logger.debug('starting fb1_thread')
fb1_thread.start()
fa1_thread.join()
cmds.get_cmd('alpha')
descs.thread_end('fa1')
cmds.queue_cmd('beta', 'go')
fb1_thread.join()
descs.thread_end('fb1')
###########################################################################
# test_smart_event_two_f_threads2
###########################################################################
def test_smart_event_two_f_threads2(self) -> None:
"""Test register_thread with thread_event_app."""
#######################################################################
# two threads - fa2 and fb2 set their own threads
#######################################################################
def fa2():
logger.debug('fa2 entered')
s_event = SmartEvent(name='fa2')
my_fa_thread = threading.current_thread()
assert s_event.thread is my_fa_thread
descs.add_desc(SmartEventDesc(name='fa2',
s_event=s_event,
thread=my_fa_thread))
s_event.pair_with(remote_name='fb2')
cmds.get_cmd('beta')
logger.debug('fa2 about to deadlock')
with pytest.raises(SmartEventWaitDeadlockDetected):
logger.debug('fa2 about to wait')
s_event.wait()
logger.debug('fa2 back from wait')
logger.debug('fa2 about to pause_until')
s_event.pause_until(WUCond.RemoteWaiting, timeout=2)
logger.debug('fa2 about to resume')
s_event.resume()
s_event.wait()
logger.debug('fa2 exiting')
def fb2():
logger.debug('fb2 entered')
s_event = SmartEvent(name='fb2')
my_fb_thread = threading.current_thread()
descs.add_desc(SmartEventDesc(name='fb2',
s_event=s_event,
thread=my_fb_thread))
assert s_event.thread is my_fb_thread
s_event.pair_with(remote_name='fa2')
descs.paired('fa2', 'fb2')
cmds.queue_cmd('beta')
logger.debug('fb2 about to deadlock')
with pytest.raises(SmartEventWaitDeadlockDetected):
logger.debug('fb2 about to wait')
s_event.wait()
logger.debug('fb2 back from wait')
logger.debug('fb2 about to pause_until')
logger.debug('fb2 about to wait')
s_event.wait()
s_event.resume()
# tell mainline we are out of the wait - OK to do descs fa1 end
cmds.queue_cmd('alpha')
# wait for mainline to give to go ahead after doing descs fa1 end
cmds.get_cmd('beta')
logger.debug('fb2 about to try resume for SmartEventRemoteThreadNotAlive')
with pytest.raises(SmartEventRemoteThreadNotAlive):
s_event.resume()
descs.cleanup()
logger.debug('fb2 about to try wait for SmartEventRemoteThreadNotAlive')
with pytest.raises(SmartEventRemoteThreadNotAlive):
s_event.wait()
logger.debug('fb2 exiting')
cmds = Cmds()
descs = ThreadPairDescs()
fa2_thread = threading.Thread(target=fa2)
fb2_thread = threading.Thread(target=fb2)
fa2_thread.start()
fb2_thread.start()
fa2_thread.join()
cmds.get_cmd('alpha')
descs.thread_end('fa2')
cmds.queue_cmd('beta', 'go')
fb2_thread.join()
descs.thread_end('fb2')
###############################################################################
# TestResumeExc Class
###############################################################################
class TestResumeExc:
"""Test SmartEvent resume() exceptions."""
###########################################################################
# test_smart_event_sync_f1
###########################################################################
def test_smart_event_resume_exc_f1(self) -> None:
"""Test register_thread with f1."""
def f1():
logger.debug('f1 beta entered')
s_event = SmartEvent(name='beta')
descs.add_desc(SmartEventDesc(name='beta',
s_event=s_event,
thread=threading.current_thread()))
cmds.queue_cmd('alpha')
s_event.pair_with(remote_name='alpha')
descs.paired('alpha', 'beta')
s_event.sync(log_msg='f1 beta sync point 1')
cmds.queue_cmd('alpha', 'go')
cmds.get_cmd('beta')
s_event.sync(log_msg='f1 beta sync point 2')
s_event.resume(log_msg='f1 beta resume 3')
s_event.sync(log_msg='f1 beta sync point 4')
logger.debug('f1 beta exiting 5')
logger.debug('mainline entered')
cmds = Cmds()
descs = ThreadPairDescs()
smart_event1 = SmartEvent(name='alpha')
descs.add_desc(SmartEventDesc(name='alpha',
s_event=smart_event1,
thread=threading.current_thread()))
f1_thread = threading.Thread(target=f1)
f1_thread.start()
cmds.get_cmd('alpha')
smart_event1.pair_with(remote_name='beta')
assert smart_event1.sync(log_msg='mainline sync point 1')
cmds.get_cmd('alpha')
smart_event1.remote.deadlock = True
smart_event1.remote.conflict = True
with pytest.raises(SmartEventInconsistentFlagSettings):
smart_event1.resume(log_msg='alpha error resume 1a')
smart_event1.remote.deadlock = False
smart_event1.remote.conflict = False
smart_event1.remote.wait_wait = True
smart_event1.remote.sync_wait = True
with pytest.raises(SmartEventInconsistentFlagSettings):
smart_event1.resume(log_msg='alpha error resume 1b')
smart_event1.remote.wait_wait = False
smart_event1.remote.sync_wait = False
smart_event1.remote.deadlock = True
with pytest.raises(SmartEventInconsistentFlagSettings):
smart_event1.resume(log_msg='alpha error resume 1c')
smart_event1.remote.deadlock = False
smart_event1.remote.conflict = True
with pytest.raises(SmartEventInconsistentFlagSettings):
smart_event1.resume(log_msg='alpha error resume 1d')
smart_event1.remote.conflict = False
cmds.queue_cmd('beta', 'go')
smart_event1.sync(log_msg='mainline sync point 2')
smart_event1.wait(log_msg='mainline wait 3')
smart_event1.sync(log_msg='mainline sync point 4')
f1_thread.join()
descs.thread_end('beta')
with pytest.raises(SmartEventRemoteThreadNotAlive):
smart_event1.resume(log_msg='mainline sync point 5')
descs.cleanup()
logger.debug('mainline exiting')
###############################################################################
# TestSync Class
###############################################################################
class TestSync:
"""Test SmartEvent sync function."""
###########################################################################
# test_smart_event_sync_f1
###########################################################################
def test_smart_event_sync_f1(self) -> None:
"""Test register_thread with f1."""
def f1():
logger.debug('f1 beta entered')
s_event = SmartEvent(name='beta')
descs.add_desc(SmartEventDesc(name='beta',
s_event=s_event,
thread=threading.current_thread()))
cmds.queue_cmd('alpha')
s_event.pair_with(remote_name='alpha')
s_event.sync(log_msg='f1 beta sync point 1')
s_event.wait()
s_event.sync(log_msg='f1 beta sync point 2')
s_event.resume()
s_event.sync(log_msg='f1 beta sync point 3')
s_event.sync(log_msg='f1 beta sync point 4')
s_event.wait()
logger.debug('f1 beta exiting')
logger.debug('mainline entered')
cmds = Cmds()
descs = ThreadPairDescs()
smart_event1 = SmartEvent(name='alpha')
descs.add_desc(SmartEventDesc(name='alpha',
s_event=smart_event1,
thread=threading.current_thread()))
f1_thread = threading.Thread(target=f1)
f1_thread.start()
cmds.get_cmd('alpha')
smart_event1.pair_with(remote_name='beta')
descs.paired('alpha', 'beta')
smart_event1.sync(log_msg='mainline sync point 1')
smart_event1.resume()
smart_event1.sync(log_msg='mainline sync point 2')
smart_event1.wait()
smart_event1.sync(log_msg='mainline sync point 3')
smart_event1.resume()
smart_event1.sync(log_msg='mainline sync point 4')
f1_thread.join()
descs.thread_end('beta')
logger.debug('mainline exiting')
###########################################################################
# test_smart_event_sync_exc
###########################################################################
def test_smart_event_sync_exc(self,
thread_exc: Any) -> None:
"""Test register_thread with f1.
Args:
thread_exc: capture thread exceptions
"""
def f1():
logger.debug('f1 beta entered')
s_event = SmartEvent(name='beta')
descs.add_desc(SmartEventDesc(name='beta',
s_event=s_event,
thread=threading.current_thread()))
cmds.queue_cmd('alpha')
s_event.pair_with(remote_name='alpha')
descs.paired('alpha', 'beta')
assert s_event.sync(log_msg='f1 beta sync point 1')
with pytest.raises(SmartEventConflictDeadlockDetected):
s_event.wait(log_msg='f1 beta wait 2')
assert s_event.sync(log_msg='f1 beta sync point 3')
s_event.resume(log_msg='f1 beta resume 4')
assert s_event.sync(log_msg='f1 beta sync point 5')
assert s_event.wait(log_msg='f1 beta wait 6')
s_event.pause_until(WUCond.RemoteWaiting)
s_event.resume()
assert s_event.sync(log_msg='f1 beta sync point 8')
# When one thread issues a sync request, and the other issues a
# wait request, a conflict deadlock is recognized. The
# process is of conflict detection is that one side recognizes the
# conflict, sets a flag to tell the other side that the conflict
# exists, and then raises the SmartEventConflictDeadlockDetected error.
# The other side, upon seeing the conflict flag set, will also
# raise the SmartEventConflictDeadlockDetected error.
# We want to ensure that sync code that detects the conflict is
# exercised here which requires setting certain flags in a way
# that coaxes each side into behaving such that the sync
# detection code will run. We will do this as follows:
# make sure alpha is in sync code now looping in phase 1
while not s_event.remote.sync_wait:
time.sleep(.1)
# make alpha think it is in sync phase 2 and continue looping
# until beta sets sync_cleanup from True back to False
with s_event.status.status_lock:
s_event.remote.sync_wait = False
s_event.status.sync_cleanup = True
# pre-resume to set beta event and set alpha wait_wait to get beta
# thinking alpha is resumed and waiting and will eventually
# leave (i.e., get beta the think that alpha not in a sync
# deadlock)
s_event.resume()
s_event.remote.wait_wait = True
# Now issue the wait. There is no way to prove that alpha saw
# the deadlock first, but we will see later whether the code
# coverage will show that the sync detection code ran.
with pytest.raises(SmartEventConflictDeadlockDetected):
s_event.wait(log_msg='f1 beta wait 89')
s_event.status.sync_cleanup = False
assert s_event.sync(log_msg='f1 beta sync point 9')
logger.debug('f1 beta exiting 10')
logger.debug('mainline entered')
cmds = Cmds()
descs = ThreadPairDescs()
smart_event1 = SmartEvent(name='alpha')
descs.add_desc(SmartEventDesc(name='alpha',
s_event=smart_event1,
thread=threading.current_thread()))
f1_thread = threading.Thread(target=f1)
f1_thread.start()
cmds.get_cmd('alpha')
smart_event1.pair_with(remote_name='beta')
assert smart_event1.sync(log_msg='mainline sync point 1')
# See the comments in f1 regarding the detection and handling of a
# confict deadlock. We need to force the code in the following
# scenario to behave such that beta will be the side that detects
# the conflict. This will be done as follows:
# make sure beta is looping in wait code
smart_event1.pause_until(WUCond.RemoteWaiting)
# set remote.wait_wait to False to trick alpha in the folloiwng
# sync request to think alpha is NOT in wait request code so
# that alpha does not detect the conflict.
smart_event1.remote.wait_wait = False
# Issue the sync request. If all goes well, beta will see the conflict
# first, set the conflict flag and then raise the
# SmartEventConflictDeadlockDetected error. We can't prove that it worked out
# that way, but the coverage report will tell us whether the
# detection code in wait ran.
with pytest.raises(SmartEventConflictDeadlockDetected):
smart_event1.sync(log_msg='mainline sync point 2')
assert smart_event1.sync(log_msg='mainline sync point 3')
assert smart_event1.wait(log_msg='mainline wait 4')
assert smart_event1.sync(log_msg='mainline sync point 5')
smart_event1.resume(log_msg='mainline resume 6')
assert not smart_event1.sync(log_msg='mainline sync point 7',
timeout=0.5)
assert smart_event1.wait()
assert smart_event1.sync(log_msg='mainline sync point 8')
# thread will ensure we see conflict first
with pytest.raises(SmartEventConflictDeadlockDetected):
smart_event1.sync(log_msg='mainline sync point 10')
logger.debug('mainline about to issue wait to clear trick pre-resume')
smart_event1.wait() # clear the trick pre-resume from beta
assert smart_event1.sync(log_msg='mainline sync point 9')
f1_thread.join()
descs.thread_end('beta')
with pytest.raises(SmartEventRemoteThreadNotAlive):
smart_event1.sync(log_msg='mainline sync point 10')
descs.cleanup()
logger.debug('mainline exiting 9')
###############################################################################
# TestWaitClear Class
###############################################################################
class TestWaitClear:
"""Test SmartEvent clearing of event set flag."""
###########################################################################
# test_smart_event_f1_clear
###########################################################################
def test_smart_event_f1_clear(self) -> None:
"""Test smart event timeout with f1 thread."""
def f1():
logger.debug('f1 entered')
s_event = SmartEvent(name='beta')
descs.add_desc(SmartEventDesc(name='beta',
s_event=s_event,
thread=threading.current_thread()))
cmds.queue_cmd('alpha')
s_event.pair_with(remote_name='alpha')
descs.paired('alpha', 'beta')
cmds.start_clock(iter=1)
assert s_event.wait()
assert 2 <= cmds.duration() <= 3
assert not s_event.remote.event.is_set()
cmds.start_clock(iter=2)
assert s_event.wait()
assert 2 <= cmds.duration() <= 3
assert not s_event.remote.event.is_set()
cmds.pause(2, iter=3)
s_event.resume()
cmds.pause(2, iter=4)
s_event.resume()
cmds = Cmds()
descs = ThreadPairDescs()
smart_event = SmartEvent(name='alpha')
descs.add_desc(SmartEventDesc(name='alpha',
s_event=smart_event,
thread=threading.current_thread()))
beta_thread = threading.Thread(target=f1)
beta_thread.start()
cmds.get_cmd('alpha')
smart_event.pair_with(remote_name='beta')
cmds.pause(2, iter=1)
smart_event.resume()
cmds.pause(2, iter=2)
smart_event.resume()
cmds.start_clock(iter=3)
assert smart_event.wait()
assert 2 <= cmds.duration() <= 3
assert not smart_event.remote.event.is_set()
cmds.start_clock(iter=4)
assert smart_event.wait()
assert 2 <= cmds.duration() <= 3
assert not smart_event.remote.event.is_set()
beta_thread.join()
descs.thread_end('beta')
###########################################################################
# test_smart_event_thread_app_clear
###########################################################################
def test_smart_event_thread_app_clear(self) -> None:
"""Test smart event timeout with thread_app thread."""
class MyThread(threading.Thread):
def __init__(self) -> None:
super().__init__()
self.s_event = SmartEvent(name='beta', thread=self)
def run(self):
logger.debug('ThreadApp run entered')
# s_event = SmartEvent(name='beta')
descs.add_desc(SmartEventDesc(name='beta',
s_event=self.s_event,
thread=self))
cmds.queue_cmd('alpha')
self.s_event.pair_with(remote_name='alpha')
assert not self.s_event.remote.event.is_set()
assert not self.s_event.event.is_set()
self.s_event.sync(log_msg='beta run sync point 1')
cmds.start_clock(iter=1)
assert self.s_event.wait(log_msg='beta run wait 12')
assert 2 <= cmds.duration() <= 3
assert not self.s_event.remote.event.is_set()
assert not self.s_event.event.is_set()
self.s_event.sync(log_msg='beta run sync point 2')
cmds.start_clock(iter=2)
assert self.s_event.wait(log_msg='beta run wait 23')
assert 2 <= cmds.duration() <= 3
assert not self.s_event.remote.event.is_set()
assert not self.s_event.event.is_set()
self.s_event.sync(log_msg='beta run sync point 3')
cmds.pause(2, iter=3)
self.s_event.resume(log_msg='beta run resume 34')
self.s_event.sync(log_msg='beta run sync point 4')
cmds.pause(2, iter=4)
self.s_event.resume(log_msg='beta run resume 45')
self.s_event.sync(log_msg='beta run sync point 5')
logger.debug('beta run exiting 910')
cmds = Cmds()
descs = ThreadPairDescs()
smart_event = SmartEvent(name='alpha')
descs.add_desc(SmartEventDesc(name='alpha',
s_event=smart_event,
thread=threading.current_thread()))
thread_app = MyThread()
thread_app.start()
cmds.get_cmd('alpha')
smart_event.pair_with(remote_name='beta')
descs.paired('alpha', 'beta')
smart_event.sync(log_msg='mainline sync point 1')
cmds.pause(2, iter=1)
smart_event.resume(log_msg='mainline resume 12')
smart_event.sync(log_msg='mainline sync point 2')
cmds.pause(2, iter=2)
smart_event.resume(log_msg='mainline resume 23')
smart_event.sync(log_msg='mainline sync point 3')
cmds.start_clock(iter=3)
assert smart_event.wait(log_msg='mainline wait 34')
assert 2 <= cmds.duration() <= 3
assert not smart_event.event.is_set()
assert not smart_event.remote.event.is_set()
smart_event.sync(log_msg='mainline sync point 4')
cmds.start_clock(iter=4)
assert smart_event.wait(log_msg='mainline sync point 45')
assert 2 <= cmds.duration() <= 3
assert not smart_event.event.is_set()
assert not smart_event.remote.event.is_set()
smart_event.sync(log_msg='mainline sync point 5')
thread_app.join()
descs.thread_end('beta')
###############################################################################
# TestSmartEventTimeout Class
###############################################################################
class TestSmartEventTimeout:
"""Test SmartEvent timeout cases."""
###########################################################################
# test_smart_event_f1_wait_time_out
###########################################################################
def test_smart_event_f1_wait_time_out(self) -> None:
"""Test smart event wait timeout with f1 thread."""
def f1():
logger.debug('f1 entered')
s_event = SmartEvent(name='beta')
descs.add_desc(SmartEventDesc(name='beta',
s_event=s_event,
thread=threading.current_thread()))
cmds.queue_cmd('alpha')
s_event.pair_with(remote_name='alpha')
descs.paired('alpha', 'beta')
s_event.sync(log_msg='f1 beta sync point 1')
assert s_event.wait(timeout=2)
s_event.sync(log_msg='f1 beta sync point 2')
s_time = time.time()
assert not s_event.wait(timeout=0.5)
assert 0.5 <= time.time() - s_time <= 0.75
s_event.sync(log_msg='f1 beta sync point 3')
s_event.pause_until(WUCond.RemoteWaiting)
s_event.resume(log_msg='f1 beta resume 34')
s_event.sync(log_msg='f1 beta sync point 4')
s_event.sync(log_msg='f1 beta sync point 5')
cmds = Cmds()
descs = ThreadPairDescs()
smart_event = SmartEvent(name='alpha')
descs.add_desc(SmartEventDesc(name='alpha',
s_event=smart_event,
thread=threading.current_thread()))
beta_thread = threading.Thread(target=f1)
beta_thread.start()
cmds.get_cmd('alpha')
smart_event.pair_with(remote_name='beta')
smart_event.pause_until(WUCond.ThreadsReady)
smart_event.sync(log_msg='mainline sync point 1')
smart_event.pause_until(WUCond.RemoteWaiting)
smart_event.resume(log_msg='mainline resume 12')
smart_event.sync(log_msg='mainline sync point 2')
smart_event.sync(log_msg='mainline sync point 3')
assert smart_event.wait(timeout=2)
smart_event.sync(log_msg='mainline sync point 4')
start_time = time.time()
assert not smart_event.wait(timeout=0.75)
assert 0.75 <= time.time() - start_time <= 1
smart_event.sync(log_msg='mainline sync point 5')
beta_thread.join()
descs.thread_end('beta')
###########################################################################
# test_smart_event_f1_resume_time_out
###########################################################################
def test_smart_event_f1_resume_time_out(self) -> None:
"""Test smart event wait timeout with f1 thread."""
def f1() -> None:
"""The remote thread for requests."""
logger.debug('f1 entered')
s_event = SmartEvent(name='beta')
descs.add_desc(SmartEventDesc(name='beta',
s_event=s_event,
thread=threading.current_thread()))
cmds.queue_cmd('alpha')
s_event.pair_with(remote_name='alpha')
# s_event.sync(log_msg='f1 beta sync point 1')
# the first resume will set the flag ON and the flag will stay ON
# since there is no matching wait
assert not s_event.event.is_set()
assert s_event.resume(timeout=2)
assert s_event.event.is_set()
# this second resume will timeout waiting for the flag to go OFF
cmds.start_clock(iter=1)
assert not s_event.resume(timeout=0.5)
assert 0.5 <= cmds.duration() <= 0.75
assert s_event.event.is_set()
s_event.sync(log_msg='f1 beta sync point 1')
s_event.sync(log_msg='f1 beta sync point 2')
# this first resume will complete within the timeout
s_event.remote.wait_wait = True # simulate waiting
s_event.remote.deadlock = True # simulate deadlock
cmds.start_clock(iter=2)
assert s_event.resume(timeout=1)
assert 0.5 <= cmds.duration() <= 0.75
# s_event.sync(log_msg='f1 beta sync point 3')
s_event.sync(log_msg='f1 beta sync point 4')
# this resume will timeout
s_event.remote.wait_wait = True # simulate waiting
s_event.remote.deadlock = True # simulate deadlock
cmds.start_clock(iter=3)
assert not s_event.resume(timeout=0.5)
assert 0.5 <= cmds.duration() <= 0.75
s_event.sync(log_msg='f1 beta sync point 5')
s_event.sync(log_msg='f1 beta sync point 6')
# this wait will clear the flag - use timeout to prevent f1 beta
# sync from raising SmartEventConflictDeadlockDetected
assert s_event.wait(log_msg='f1 beta wait 67',
timeout=1)
s_event.sync(log_msg='f1 beta sync point 7')
cmds.pause(0.5, iter=5) # we purposely skipped 4
# clear the deadlock within the resume timeout to allow mainline
# resume to complete
s_event.deadlock = False
s_event.wait_wait = False
s_event.sync(log_msg='f1 beta sync point 8')
cmds.pause(0.75, iter=6)
# clear the deadlock after resume timeout to cause ml to timeout
s_event.deadlock = False
s_event.wait_wait = False
s_event.sync(log_msg='f1 beta sync point 9')
cmds = Cmds()
descs = ThreadPairDescs()
smart_event = SmartEvent(name='alpha')
descs.add_desc(SmartEventDesc(name='alpha',
s_event=smart_event,
thread=threading.current_thread()))
beta_thread = threading.Thread(target=f1)
beta_thread.start()
cmds.get_cmd('alpha')
smart_event.pair_with(remote_name='beta')
descs.paired('alpha', 'beta')
smart_event.pause_until(WUCond.ThreadsReady)
smart_event.sync(log_msg='mainline sync point 1')
# this wait will clear the flag - use timeout to prevent sync
# from raising SmartEventConflictDeadlockDetected
assert smart_event.remote.event.is_set()
assert smart_event.wait(log_msg='mainline wait 12',
timeout=1)
assert not smart_event.wait_timeout_specified
smart_event.sync(log_msg='mainline sync point 2')
cmds.pause(0.5, iter=2) # we purposely skipped iter=1
# clear the deadlock within resume timeout to allow f1 resume to
# complete
smart_event.deadlock = False
smart_event.wait_wait = False
# smart_event.sync(log_msg='mainline sync point 3')
smart_event.sync(log_msg='mainline sync point 4')
cmds.pause(0.75, iter=3)
# clear the deadlock after the resume timeout to cause f1 to timeout
smart_event.deadlock = False
smart_event.wait_wait = False
smart_event.sync(log_msg='mainline sync point 5')
# the first resume will set the flag ON and the flag will stay ON
# since there is no matching wait
assert smart_event.resume(timeout=2)
# this second resume will timeout waiting for the flag to go OFF
cmds.start_clock(iter=4)
assert not smart_event.resume(timeout=0.3)
assert 0.3 <= cmds.duration() <= 0.6
smart_event.sync(log_msg='mainline sync point 6')
smart_event.sync(log_msg='mainline sync point 7')
# this first resume will complete within the timeout
smart_event.remote.wait_wait = True # simulate waiting
smart_event.remote.deadlock = True # simulate deadlock
cmds.start_clock(iter=5)
assert smart_event.resume(timeout=1)
assert 0.5 <= cmds.duration() <= 0.75
smart_event.sync(log_msg='mainline sync point 8')
# this resume will timeout
smart_event.remote.wait_wait = True # simulate waiting
smart_event.remote.deadlock = True # simulate deadlock
cmds.start_clock(iter=6)
assert not smart_event.resume(timeout=0.5)
assert 0.5 <= cmds.duration() <= 0.75
smart_event.sync(log_msg='mainline sync point 9')
beta_thread.join()
descs.thread_end('beta')
###########################################################################
# test_smart_event_thread_app_time_out
###########################################################################
def test_smart_event_thread_app_time_out(self) -> None:
"""Test smart event timeout with thread_app thread."""
class MyThread(threading.Thread):
def __init__(self):
super().__init__()
self.s_event = SmartEvent(name='beta', thread=self)
def run(self):
logger.debug('ThreadApp run entered')
descs.add_desc(SmartEventDesc(name='beta',
s_event=self.s_event,
thread=self))
cmds.queue_cmd('alpha')
self.s_event.pair_with(remote_name='alpha')
descs.paired('alpha', 'beta')
cmds.start_clock(iter=1)
assert not self.s_event.wait(timeout=2)
assert 2 <= cmds.duration() < 3
assert self.s_event.sync(log_msg='beta sync point 1')
assert self.s_event.sync(log_msg='beta sync point 2')
cmds = Cmds()
descs = ThreadPairDescs()
smart_event = SmartEvent(name='alpha')
descs.add_desc(SmartEventDesc(name='alpha',
s_event=smart_event,
thread=threading.current_thread()))
thread_app = MyThread()
thread_app.start()
cmds.get_cmd('alpha')
smart_event.pair_with(remote_name='beta')
assert smart_event.sync(log_msg='alpha sync point 1')
cmds.start_clock(iter=2)
assert not smart_event.wait(timeout=2)
assert 2 <= cmds.duration() < 3
assert smart_event.sync(log_msg='alpha sync point 2')
thread_app.join()
descs.thread_end('beta')
###############################################################################
# TestSmartEventCode Class
###############################################################################
class TestSmartEventCode:
"""Test SmartEvent resume codes."""
###########################################################################
# test_smart_event_f1_event_code
###########################################################################
def test_smart_event_f1_event_code(self) -> None:
"""Test smart event code with f1 thread."""
def f1():
logger.debug('f1 entered')
s_event = SmartEvent(name='beta')
descs.add_desc(SmartEventDesc(name='beta',
s_event=s_event,
thread=threading.current_thread()))
cmds.queue_cmd('alpha')
s_event.pair_with(remote_name='alpha')
assert not s_event.remote.code
assert not s_event.code
assert not s_event.get_code()
s_event.sync(log_msg='beta sync point 1')
assert s_event.wait(timeout=2)
assert not s_event.remote.code
assert s_event.code == 42
assert 42 == s_event.get_code()
s_event.sync(log_msg='beta sync point 2')
s_event.resume(code='forty-two')
assert s_event.remote.code == 'forty-two'
assert s_event.code == 42
assert 42 == s_event.get_code()
s_event.sync(log_msg='beta sync point 3')
assert s_event.remote.code == 'forty-two'
assert s_event.code == 42
assert 42 == s_event.get_code()
assert not s_event.wait(timeout=.5)
assert s_event.remote.code == 'forty-two'
assert s_event.code == 42
assert 42 == s_event.get_code()
s_event.sync(log_msg='beta sync point 4')
s_event.sync(log_msg='beta sync point 5')
assert s_event.remote.code == 'forty-two'
assert s_event.code == 'twenty one'
assert 'twenty one' == s_event.get_code()
assert s_event.remote.event.is_set()
cmds = Cmds()
descs = ThreadPairDescs()
smart_event = SmartEvent(name='alpha')
descs.add_desc(SmartEventDesc(name='alpha',
s_event=smart_event,
thread=threading.current_thread()))
beta_thread = threading.Thread(target=f1)
beta_thread.start()
cmds.get_cmd('alpha')
smart_event.pair_with(remote_name='beta')
descs.paired('alpha', 'beta')
smart_event.sync(log_msg='mainline sync point 1')
assert not smart_event.get_code()
assert not smart_event.code
assert not smart_event.remote.code
smart_event.resume(code=42)
assert not smart_event.get_code()
assert not smart_event.code
assert smart_event.remote.code == 42
smart_event.sync(log_msg='mainline sync point 2')
assert smart_event.wait()
assert smart_event.get_code() == 'forty-two'
assert smart_event.code == 'forty-two'
assert smart_event.remote.code == 42
smart_event.sync(log_msg='mainline sync point 3')
smart_event.sync(log_msg='mainline sync point 4')
smart_event.resume(code='twenty one')
smart_event.sync(log_msg='mainline sync point 5')
beta_thread.join()
smart_event.code = None
smart_event.remote.code = None
descs.thread_end('beta')
###########################################################################
# test_smart_event_thread_app_event_code
###########################################################################
def test_smart_event_thread_app_event_code(self) -> None:
"""Test smart event code with thread_app thread."""
class MyThread(threading.Thread):
def __init__(self):
super().__init__()
self.s_event = SmartEvent(name='beta', thread=self)
def run(self):
logger.debug('ThreadApp run entered')
descs.add_desc(SmartEventDesc(name='beta',
s_event=self.s_event,
thread=self))
cmds.queue_cmd('alpha')
self.s_event.pair_with(remote_name='alpha')
descs.paired('alpha', 'beta')
assert self.s_event.get_code() is None
assert not self.s_event.wait(timeout=2, log_msg='beta wait 1')
self.s_event.sync(log_msg='beta sync point 2')
self.s_event.sync(log_msg='beta sync point 3')
assert self.s_event.remote.event.is_set()
assert self.s_event.code == 42
assert self.s_event.get_code() == 42
self.s_event.resume(log_msg='beta resume 4',
code='forty-two')
cmds = Cmds()
descs = ThreadPairDescs()
smart_event = SmartEvent(name='alpha')
descs.add_desc(SmartEventDesc(name='alpha',
s_event=smart_event,
thread=threading.current_thread()))
thread_app = MyThread()
thread_app.start()
cmds.get_cmd('alpha')
smart_event.pair_with(remote_name='beta')
smart_event.pause_until(WUCond.ThreadsReady)
smart_event.sync(log_msg='mainline sync point 2')
smart_event.resume(code=42)
smart_event.sync(log_msg='mainline sync point 3')
assert smart_event.wait(log_msg='mainline wait 4')
assert smart_event.get_code() == 'forty-two'
thread_app.join()
smart_event.code = None
smart_event.remote.code = None
descs.thread_end('beta')
###########################################################################
# test_smart_event_thread_event_app_event_code
###########################################################################
def test_smart_event_thread_event_app_event_code(self) -> None:
"""Test smart event code with thread_event_app thread."""
class MyThread(threading.Thread, SmartEvent):
def __init__(self) -> None:
threading.Thread.__init__(self)
SmartEvent.__init__(self, name='beta', thread=self)
def run(self):
logger.debug('ThreadApp run entered')
descs.add_desc(SmartEventDesc(name='beta',
s_event=self,
thread=self))
cmds.queue_cmd('alpha')
self.pair_with(remote_name='alpha')
assert not self.remote.code
assert not self.code
assert not self.get_code()
self.sync(log_msg='beta sync point 1')
assert not self.wait(timeout=0.5)
assert not self.remote.code
assert not self.code
assert not self.get_code()
self.sync(log_msg='beta sync point 2')
self.sync(log_msg='beta sync point 3')
assert not self.remote.code
assert self.code == 42
assert self.get_code() == 42
self.resume(code='forty-two')
assert self.remote.code == 'forty-two'
assert self.code == 42
assert self.get_code() == 42
self.sync(log_msg='beta sync point 4')
self.sync(log_msg='beta sync point 5')
assert self.remote.code == 'forty-two'
assert self.code == 42
assert self.get_code() == 42
assert self.wait(timeout=0.5, log_msg='beta wait 56')
assert self.remote.code == 'forty-two'
assert self.code == 42
assert self.get_code() == 42
self.sync(log_msg='beta sync point 6')
cmds = Cmds()
descs = ThreadPairDescs()
thread_event_app = MyThread()
thread_event_app.start()
cmds.get_cmd('alpha')
time.sleep(2) # make beta loop in pair_with
smart_event = SmartEvent(name='alpha')
descs.add_desc(SmartEventDesc(name='alpha',
s_event=smart_event,
thread=threading.current_thread()))
smart_event.pair_with(remote_name='beta')
descs.paired('alpha', 'beta')
assert not smart_event.code
assert not smart_event.remote.code
assert not smart_event.get_code()
smart_event.sync(log_msg='mainline sync point 1')
smart_event.sync(log_msg='mainline sync point 2')
assert not smart_event.code
assert not smart_event.remote.code
assert not smart_event.get_code()
smart_event.resume(code=42, log_msg='mainline resume for beta 56')
assert not smart_event.code
assert smart_event.remote.code == 42
assert not smart_event.get_code()
smart_event.sync(log_msg='mainline sync point 3')
smart_event.sync(log_msg='mainline sync point 4')
assert smart_event.code == 'forty-two'
assert smart_event.remote.code == 42
assert smart_event.get_code() == 'forty-two'
assert smart_event.wait()
assert smart_event.code == 'forty-two'
assert smart_event.remote.code == 42
assert smart_event.get_code() == 'forty-two'
smart_event.sync(log_msg='mainline sync point 5')
assert smart_event.code == 'forty-two'
assert smart_event.remote.code == 42
assert smart_event.get_code() == 'forty-two'
smart_event.sync(log_msg='mainline sync point 6')
thread_event_app.join()
smart_event.code = None
smart_event.remote.code = None
descs.thread_end('beta')
###############################################################################
# TestSmartEventLogger Class
###############################################################################
class TestSmartEventLogger:
"""Test log messages."""
###########################################################################
# test_smart_event_f1_event_logger
###########################################################################
def test_smart_event_f1_event_logger(self,
caplog,
log_enabled_arg) -> None:
"""Test smart event logger with f1 thread.
Args:
caplog: fixture to capture log messages
log_enabled_arg: fixture to indicate whether log is enabled
"""
def f1():
exp_log_msgs.add_msg('f1 entered')
logger.debug('f1 entered')
s_event = SmartEvent(name='beta')
descs.add_desc(SmartEventDesc(name='beta',
s_event=s_event,
thread=threading.current_thread()))
cmds.queue_cmd('alpha')
exp_log_msgs.add_beta_pair_with_msg('beta pair_with alpha 1',
['beta', 'alpha'])
s_event.pair_with(remote_name='alpha',
log_msg='beta pair_with alpha 1')
descs.paired('alpha', 'beta')
exp_log_msgs.add_beta_sync_msg('beta sync point 1')
s_event.sync(log_msg='beta sync point 1')
exp_log_msgs.add_beta_wait_msg('wait for mainline to post 12')
assert s_event.wait(log_msg='wait for mainline to post 12')
exp_log_msgs.add_beta_sync_msg('beta sync point 2')
s_event.sync(log_msg='beta sync point 2')
exp_log_msgs.add_beta_resume_msg('post mainline 23')
s_event.resume(log_msg='post mainline 23')
exp_log_msgs.add_beta_sync_msg('beta sync point 3')
s_event.sync(log_msg='beta sync point 3')
exp_log_msgs.add_beta_sync_msg('beta sync point 4')
s_event.sync(log_msg='beta sync point 4')
cmds = Cmds()
descs = ThreadPairDescs()
if log_enabled_arg:
logging.getLogger().setLevel(logging.DEBUG)
else:
logging.getLogger().setLevel(logging.INFO)
alpha_call_seq = ('test_smart_event.py::TestSmartEventLogger.'
'test_smart_event_f1_event_logger')
beta_call_seq = ('test_smart_event.py::f1')
exp_log_msgs = ExpLogMsgs(alpha_call_seq, beta_call_seq)
l_msg = 'mainline started'
exp_log_msgs.add_msg(l_msg)
logger.debug(l_msg)
smart_event = SmartEvent(name='alpha')
descs.add_desc(SmartEventDesc(name='alpha',
s_event=smart_event,
thread=threading.current_thread()))
beta_thread = threading.Thread(target=f1)
beta_thread.start()
cmds.get_cmd('alpha')
exp_log_msgs.add_alpha_pair_with_msg('alpha pair_with beta 1',
['alpha', 'beta'])
smart_event.pair_with(remote_name='beta',
log_msg='alpha pair_with beta 1')
exp_log_msgs.add_alpha_sync_msg('mainline sync point 1')
smart_event.sync(log_msg='mainline sync point 1')
smart_event.pause_until(WUCond.RemoteWaiting)
exp_log_msgs.add_alpha_resume_msg('post beta 12')
smart_event.resume(log_msg='post beta 12')
exp_log_msgs.add_alpha_sync_msg('mainline sync point 2')
smart_event.sync(log_msg='mainline sync point 2')
exp_log_msgs.add_alpha_sync_msg('mainline sync point 3')
smart_event.sync(log_msg='mainline sync point 3')
exp_log_msgs.add_alpha_wait_msg('wait for pre-post 23')
assert smart_event.wait(log_msg='wait for pre-post 23')
exp_log_msgs.add_alpha_sync_msg('mainline sync point 4')
smart_event.sync(log_msg='mainline sync point 4')
beta_thread.join()
descs.thread_end('beta')
exp_log_msgs.add_msg('mainline all tests complete')
logger.debug('mainline all tests complete')
exp_log_msgs.verify_log_msgs(caplog=caplog,
log_enabled_tf=log_enabled_arg)
# restore root to debug
logging.getLogger().setLevel(logging.DEBUG)
###########################################################################
# test_smart_event_thread_app_event_logger
###########################################################################
def test_smart_event_thread_app_event_logger(self,
caplog,
log_enabled_arg) -> None:
"""Test smart event logger with thread_app thread.
Args:
caplog: fixture to capture log messages
log_enabled_arg: fixture to indicate whether log is enabled
"""
class MyThread(threading.Thread):
def __init__(self,
exp_log_msgs1: ExpLogMsgs):
super().__init__()
self.s_event = SmartEvent(name='beta', thread=self)
self.exp_log_msgs = exp_log_msgs1
def run(self):
l_msg = 'ThreadApp run entered'
self.exp_log_msgs.add_msg(l_msg)
logger.debug(l_msg)
descs.add_desc(SmartEventDesc(name='beta',
s_event=self.s_event,
thread=self))
cmds.queue_cmd('alpha')
self.exp_log_msgs.add_beta_pair_with_msg('beta pair alpha 2',
['beta', 'alpha'])
self.s_event.pair_with(remote_name='alpha',
log_msg='beta pair alpha 2')
self.exp_log_msgs.add_beta_sync_msg('beta sync point 1')
self.s_event.sync(log_msg='beta sync point 1')
self.exp_log_msgs.add_beta_wait_msg('wait 12')
assert self.s_event.wait(log_msg='wait 12')
self.exp_log_msgs.add_beta_sync_msg('beta sync point 2')
self.s_event.sync(log_msg='beta sync point 2')
self.s_event.pause_until(WUCond.RemoteWaiting)
self.exp_log_msgs.add_beta_resume_msg('post mainline 34',
True, 'forty-two')
self.s_event.resume(code='forty-two',
log_msg='post mainline 34')
self.exp_log_msgs.add_beta_sync_msg('beta sync point 3')
self.s_event.sync(log_msg='beta sync point 3')
self.exp_log_msgs.add_beta_sync_msg('beta sync point 4')
self.s_event.sync(log_msg='beta sync point 4')
cmds = Cmds()
descs = ThreadPairDescs()
if log_enabled_arg:
logging.getLogger().setLevel(logging.DEBUG)
else:
logging.getLogger().setLevel(logging.INFO)
alpha_call_seq = ('test_smart_event.py::TestSmartEventLogger.'
'test_smart_event_thread_app_event_logger')
beta_call_seq = 'test_smart_event.py::MyThread.run'
exp_log_msgs = ExpLogMsgs(alpha_call_seq, beta_call_seq)
l_msg = 'mainline starting'
exp_log_msgs.add_msg(l_msg)
logger.debug(l_msg)
smart_event = SmartEvent(name='alpha')
descs.add_desc(SmartEventDesc(name='alpha',
s_event=smart_event,
thread=threading.current_thread()))
thread_app = MyThread(exp_log_msgs)
thread_app.start()
cmds.get_cmd('alpha')
exp_log_msgs.add_alpha_pair_with_msg('alpha pair beta 2',
['alpha', 'beta'])
smart_event.pair_with(remote_name='beta',
log_msg='alpha pair beta 2')
descs.paired('alpha', 'beta')
exp_log_msgs.add_alpha_sync_msg('mainline sync point 1')
smart_event.sync(log_msg='mainline sync point 1')
smart_event.pause_until(WUCond.RemoteWaiting)
exp_log_msgs.add_alpha_resume_msg(
f'post thread {smart_event.remote.name} 23', True, 42)
smart_event.resume(log_msg=f'post thread {smart_event.remote.name} 23',
code=42)
exp_log_msgs.add_alpha_sync_msg('mainline sync point 2')
smart_event.sync(log_msg='mainline sync point 2')
exp_log_msgs.add_alpha_wait_msg('wait for post from thread 34')
assert smart_event.wait(log_msg='wait for post from thread 34')
exp_log_msgs.add_alpha_sync_msg('mainline sync point 3')
smart_event.sync(log_msg='mainline sync point 3')
exp_log_msgs.add_alpha_sync_msg('mainline sync point 4')
smart_event.sync(log_msg='mainline sync point 4')
thread_app.join()
smart_event.code = None
smart_event.remote.code = None
descs.thread_end('beta')
l_msg = 'mainline all tests complete'
exp_log_msgs.add_msg(l_msg)
logger.debug('mainline all tests complete')
exp_log_msgs.verify_log_msgs(caplog=caplog,
log_enabled_tf=log_enabled_arg)
# restore root to debug
logging.getLogger().setLevel(logging.DEBUG)
###########################################################################
# test_smart_event_thread_event_app_event_logger
###########################################################################
def test_smart_event_thread_event_app_event_logger(self,
caplog,
log_enabled_arg
) -> None:
"""Test smart event logger with thread_event_app thread.
Args:
caplog: fixture to capture log messages
log_enabled_arg: fixture to indicate whether log is enabled
"""
class MyThread(threading.Thread, SmartEvent):
def __init__(self,
exp_log_msgs1: ExpLogMsgs):
threading.Thread.__init__(self)
SmartEvent.__init__(self, name='beta', thread=self)
self.exp_log_msgs = exp_log_msgs1
def run(self):
self.exp_log_msgs.add_msg('ThreadApp run entered')
logger.debug('ThreadApp run entered')
descs.add_desc(SmartEventDesc(name='beta',
s_event=self,
thread=self))
cmds.queue_cmd('alpha')
self.exp_log_msgs.add_beta_pair_with_msg('beta to alpha 3',
['beta', 'alpha'])
self.pair_with(remote_name='alpha',
log_msg='beta to alpha 3')
descs.paired('alpha', 'beta')
self.exp_log_msgs.add_beta_sync_msg('beta sync point 1')
self.sync(log_msg='beta sync point 1')
self.exp_log_msgs.add_beta_wait_msg(
'wait for mainline to post 12')
assert self.wait(log_msg='wait for mainline to post 12')
self.exp_log_msgs.add_beta_sync_msg('beta sync point 2')
self.sync(log_msg='beta sync point 2')
self.pause_until(WUCond.RemoteWaiting)
self.exp_log_msgs.add_beta_resume_msg('post mainline 23')
self.resume(log_msg='post mainline 23')
self.exp_log_msgs.add_beta_sync_msg('beta sync point 3')
self.sync(log_msg='beta sync point 3')
cmds = Cmds()
descs = ThreadPairDescs()
if log_enabled_arg:
logging.getLogger().setLevel(logging.DEBUG)
else:
logging.getLogger().setLevel(logging.INFO)
alpha_call_seq = ('test_smart_event.py::TestSmartEventLogger.'
'test_smart_event_thread_event_app_event_logger')
beta_call_seq = 'test_smart_event.py::MyThread.run'
exp_log_msgs = ExpLogMsgs(alpha_call_seq, beta_call_seq)
l_msg = 'mainline starting'
exp_log_msgs.add_msg(l_msg)
logger.debug(l_msg)
smart_event = SmartEvent(name='alpha')
descs.add_desc(SmartEventDesc(name='alpha',
s_event=smart_event,
thread=threading.current_thread()))
thread_event_app = MyThread(exp_log_msgs1=exp_log_msgs)
thread_event_app.start()
cmds.get_cmd('alpha')
exp_log_msgs.add_alpha_pair_with_msg('alpha to beta 3',
['alpha', 'beta'])
smart_event.pair_with(remote_name='beta',
log_msg='alpha to beta 3')
exp_log_msgs.add_alpha_sync_msg('mainline sync point 1')
smart_event.sync(log_msg='mainline sync point 1')
smart_event.pause_until(WUCond.RemoteWaiting)
exp_log_msgs.add_alpha_resume_msg(
f'post thread {thread_event_app.name} 12')
smart_event.resume(log_msg=f'post thread '
f'{thread_event_app.name} 12')
exp_log_msgs.add_alpha_sync_msg('mainline sync point 2')
smart_event.sync(log_msg='mainline sync point 2')
exp_log_msgs.add_alpha_wait_msg('wait for post from thread 23')
assert smart_event.wait(log_msg='wait for post from thread 23')
exp_log_msgs.add_alpha_sync_msg('mainline sync point 3')
smart_event.sync(log_msg='mainline sync point 3')
thread_event_app.join()
descs.thread_end('beta')
exp_log_msgs.add_msg('mainline all tests complete')
logger.debug('mainline all tests complete')
exp_log_msgs.verify_log_msgs(caplog=caplog,
log_enabled_tf=log_enabled_arg)
# restore root to debug
logging.getLogger().setLevel(logging.DEBUG)
###############################################################################
# TestCombos Class
###############################################################################
class TestCombos:
"""Test various combinations of SmartEvent."""
###########################################################################
# test_smart_event_thread_f1_combos
###########################################################################
def test_smart_event_f1_combos(self,
action_arg1: Any,
code_arg1: Any,
log_msg_arg1: Any,
action_arg2: Any,
caplog: Any,
thread_exc: Any) -> None:
"""Test the SmartEvent with f1 combos.
Args:
action_arg1: first action
code_arg1: whether to set and recv a code
log_msg_arg1: whether to specify a log message
action_arg2: second action
caplog: fixture to capture log messages
thread_exc: intercepts thread exceptions
"""
alpha_call_seq = ('test_smart_event.py::TestCombos.action_loop')
beta_call_seq = ('test_smart_event.py::thread_func1')
exp_log_msgs = ExpLogMsgs(alpha_call_seq, beta_call_seq)
l_msg = 'mainline entered'
exp_log_msgs.add_msg(l_msg)
logger.debug(l_msg)
cmds = Cmds()
descs = ThreadPairDescs()
cmds.l_msg = log_msg_arg1
cmds.r_code = code_arg1
f1_thread = threading.Thread(target=thread_func1,
args=(cmds,
descs,
exp_log_msgs))
l_msg = 'mainline about to start thread_func1'
exp_log_msgs.add_msg(l_msg)
logger.debug(l_msg)
f1_thread.start()
self.action_loop(action1=action_arg1,
action2=action_arg2,
cmds=cmds,
descs=descs,
exp_log_msgs=exp_log_msgs,
thread_exc1=thread_exc)
l_msg = 'main completed all actions'
exp_log_msgs.add_msg(l_msg)
logger.debug(l_msg)
cmds.queue_cmd('beta', Cmd.Exit)
f1_thread.join()
descs.thread_end('beta')
if log_msg_arg1:
exp_log_msgs.verify_log_msgs(caplog=caplog, log_enabled_tf=True)
###########################################################################
# test_smart_event_thread_f1_combos
###########################################################################
def test_smart_event_f1_f2_combos(self,
action_arg1: Any,
code_arg1: Any,
log_msg_arg1: Any,
action_arg2: Any,
caplog: Any,
thread_exc: Any) -> None:
"""Test the SmartEvent with f1 anf f2 combos.
Args:
action_arg1: first action
code_arg1: whether to set and recv a code
log_msg_arg1: whether to specify a log message
action_arg2: second action
caplog: fixture to capture log messages
thread_exc: intercepts thread exceptions
"""
alpha_call_seq = ('test_smart_event.py::TestCombos.action_loop')
beta_call_seq = ('test_smart_event.py::thread_func1')
exp_log_msgs = ExpLogMsgs(alpha_call_seq, beta_call_seq)
l_msg = 'mainline entered'
exp_log_msgs.add_msg(l_msg)
logger.debug(l_msg)
cmds = Cmds()
descs = ThreadPairDescs()
cmds.l_msg = log_msg_arg1
cmds.r_code = code_arg1
f1_thread = threading.Thread(target=thread_func1,
args=(cmds,
descs,
exp_log_msgs))
f2_thread = threading.Thread(target=self.action_loop,
args=(action_arg1,
action_arg2,
cmds,
descs,
exp_log_msgs,
thread_exc))
l_msg = 'mainline about to start thread_func1'
exp_log_msgs.add_msg(l_msg)
logger.debug(l_msg)
f1_thread.start()
f2_thread.start()
l_msg = 'main completed all actions'
exp_log_msgs.add_msg(l_msg)
logger.debug(l_msg)
f2_thread.join()
descs.thread_end('alpha')
cmds.queue_cmd('beta', Cmd.Exit)
f1_thread.join()
descs.thread_end('beta')
if log_msg_arg1:
exp_log_msgs.verify_log_msgs(caplog=caplog, log_enabled_tf=True)
###########################################################################
# test_smart_event_thread_thread_app_combos
###########################################################################
def test_smart_event_thread_app_combos(self,
action_arg1: Any,
code_arg1: Any,
log_msg_arg1: Any,
action_arg2: Any,
caplog: Any,
thread_exc: Any) -> None:
"""Test the SmartEvent with ThreadApp combos.
Args:
action_arg1: first action
code_arg1: whether to set and recv a code
log_msg_arg1: whether to specify a log message
action_arg2: second action
caplog: fixture to capture log messages
thread_exc: intercepts thread exceptions
"""
class SmartEventApp(threading.Thread):
"""SmartEventApp class with thread."""
def __init__(self,
cmds: Cmds,
exp_log_msgs: ExpLogMsgs
) -> None:
"""Initialize the object.
Args:
cmds: commands for beta to do
exp_log_msgs: container for expected log messages
"""
super().__init__()
self.smart_event = SmartEvent(name='beta', thread=self)
self.cmds = cmds
self.exp_log_msgs = exp_log_msgs
def run(self):
"""Thread to send and receive messages."""
l_msg = 'SmartEventApp run started'
self.exp_log_msgs.add_msg(l_msg)
logger.debug(l_msg)
thread_func1(
cmds=self.cmds,
descs=descs,
exp_log_msgs=self.exp_log_msgs,
s_event=self.smart_event)
l_msg = 'SmartEventApp run exiting'
self.exp_log_msgs.add_msg(l_msg)
logger.debug(l_msg)
alpha_call_seq = ('test_smart_event.py::TestCombos.action_loop')
beta_call_seq = ('test_smart_event.py::thread_func1')
exp_log_msgs = ExpLogMsgs(alpha_call_seq, beta_call_seq)
l_msg = 'mainline entered'
exp_log_msgs.add_msg(l_msg)
logger.debug(l_msg)
cmds = Cmds()
descs = ThreadPairDescs()
cmds.l_msg = log_msg_arg1
cmds.r_code = code_arg1
f1_thread = SmartEventApp(cmds,
exp_log_msgs)
l_msg = 'mainline about to start SmartEventApp'
exp_log_msgs.add_msg(l_msg)
logger.debug(l_msg)
f1_thread.start()
self.action_loop(action1=action_arg1,
action2=action_arg2,
cmds=cmds,
descs=descs,
exp_log_msgs=exp_log_msgs,
thread_exc1=thread_exc)
l_msg = 'main completed all actions'
exp_log_msgs.add_msg(l_msg)
logger.debug(l_msg)
cmds.queue_cmd('beta', Cmd.Exit)
f1_thread.join()
descs.thread_end('beta')
if log_msg_arg1:
exp_log_msgs.verify_log_msgs(caplog=caplog, log_enabled_tf=True)
###########################################################################
# test_smart_event_thread_thread_app_combos
###########################################################################
def test_smart_event_thread_event_app_combos(self,
action_arg1: Any,
code_arg1: Any,
log_msg_arg1: Any,
action_arg2: Any,
caplog: Any,
thread_exc: Any) -> None:
"""Test the SmartEvent with ThreadApp combos.
Args:
action_arg1: first action
code_arg1: whether to set and recv a code
log_msg_arg1: whether to specify a log message
action_arg2: second action
caplog: fixture to capture log messages
thread_exc: intercepts thread exceptions
"""
class SmartEventApp(threading.Thread, SmartEvent):
"""SmartEventApp class with thread and event."""
def __init__(self,
cmds: Cmds,
exp_log_msgs: ExpLogMsgs
) -> None:
"""Initialize the object.
Args:
cmds: commands for beta to do
exp_log_msgs: container for expected log messages
"""
threading.Thread.__init__(self)
SmartEvent.__init__(self,
name='beta',
thread=self)
self.cmds = cmds
self.exp_log_msgs = exp_log_msgs
def run(self):
"""Thread to send and receive messages."""
l_msg = 'SmartEventApp run started'
self.exp_log_msgs.add_msg(l_msg)
logger.debug(l_msg)
thread_func1(
cmds=self.cmds,
descs=descs,
exp_log_msgs=self.exp_log_msgs,
s_event=self)
l_msg = 'SmartEventApp run exiting'
self.exp_log_msgs.add_msg(l_msg)
logger.debug(l_msg)
alpha_call_seq = ('test_smart_event.py::TestCombos.action_loop')
beta_call_seq = ('test_smart_event.py::thread_func1')
exp_log_msgs = ExpLogMsgs(alpha_call_seq, beta_call_seq)
l_msg = 'mainline entered'
exp_log_msgs.add_msg(l_msg)
logger.debug(l_msg)
cmds = Cmds()
descs = ThreadPairDescs()
cmds.l_msg = log_msg_arg1
cmds.r_code = code_arg1
f1_thread = SmartEventApp(cmds,
exp_log_msgs)
l_msg = 'mainline about to start SmartEventApp'
exp_log_msgs.add_msg(l_msg)
logger.debug(l_msg)
f1_thread.start()
self.action_loop(action1=action_arg1,
action2=action_arg2,
cmds=cmds,
descs=descs,
exp_log_msgs=exp_log_msgs,
thread_exc1=thread_exc)
l_msg = 'main completed all actions'
exp_log_msgs.add_msg(l_msg)
logger.debug(l_msg)
cmds.queue_cmd('beta', Cmd.Exit)
f1_thread.join()
descs.thread_end('beta')
if log_msg_arg1:
exp_log_msgs.verify_log_msgs(caplog=caplog, log_enabled_tf=True)
###########################################################################
# action loop
###########################################################################
def action_loop(self,
action1: Any,
action2: Any,
cmds: Cmds,
descs: ThreadPairDescs,
exp_log_msgs: Any,
thread_exc1: Any
) -> None:
"""Actions to perform with the thread.
Args:
action1: first smart event request to do
action2: second smart event request to do
cmds: contains cmd queues and other test args
descs: tracking and verification for registry
exp_log_msgs: container for expected log messages
thread_exc1: contains any uncaptured errors from thread
Raises:
IncorrectActionSpecified: The Action is not recognized
UnrecognizedCmd: beta send mainline an unrecognized command
"""
cmds.get_cmd('alpha') # go1
smart_event = SmartEvent(name='alpha')
descs.add_desc(SmartEventDesc(name='alpha',
s_event=smart_event,
thread=threading.current_thread()))
cmds.queue_cmd('beta') # go2
smart_event.pair_with(remote_name='beta')
cmds.get_cmd('alpha') # go3
actions = []
actions.append(action1)
actions.append(action2)
for action in actions:
if action == Action.MainWait:
l_msg = 'main starting Action.MainWait'
exp_log_msgs.add_msg(l_msg)
logger.debug(l_msg)
cmds.queue_cmd('beta', Cmd.Resume)
assert smart_event.wait()
if cmds.r_code:
assert smart_event.code == cmds.r_code
assert cmds.r_code == smart_event.get_code()
elif action == Action.MainSync:
l_msg = 'main starting Action.MainSync'
exp_log_msgs.add_msg(l_msg)
logger.debug(l_msg)
cmds.queue_cmd('beta', Cmd.Sync)
if cmds.l_msg:
exp_log_msgs.add_alpha_sync_msg(cmds.l_msg, True)
assert smart_event.sync(log_msg=cmds.l_msg)
else:
assert smart_event.sync()
elif action == Action.MainSync_TOT:
l_msg = 'main starting Action.MainSync_TOT'
exp_log_msgs.add_msg(l_msg)
logger.debug(l_msg)
cmds.queue_cmd('beta', Cmd.Sync)
if cmds.l_msg:
exp_log_msgs.add_alpha_sync_msg(cmds.l_msg, True)
assert smart_event.sync(timeout=5,
log_msg=cmds.l_msg)
else:
assert smart_event.sync(timeout=5)
elif action == Action.MainSync_TOF:
l_msg = 'main starting Action.MainSync_TOF'
exp_log_msgs.add_msg(l_msg)
logger.debug(l_msg)
l_msg = r'alpha timeout of a sync\(\) request.'
exp_log_msgs.add_msg(l_msg)
if cmds.l_msg:
exp_log_msgs.add_alpha_sync_msg(cmds.l_msg, False)
assert not smart_event.sync(timeout=0.3,
log_msg=cmds.l_msg)
else:
assert not smart_event.sync(timeout=0.3)
# for this case, we did not tell beta to do anything, so
# we need to tell ourselves to go to next action.
# Note that we could use a continue, but we also want
# to check for thread exception which is what we do
# at the bottom
cmds.queue_cmd('alpha', Cmd.Next_Action)
elif action == Action.MainResume:
l_msg = 'main starting Action.MainResume'
exp_log_msgs.add_msg(l_msg)
logger.debug(l_msg)
if cmds.r_code:
assert smart_event.resume(code=cmds.r_code)
assert smart_event.remote.code == cmds.r_code
else:
assert smart_event.resume()
assert not smart_event.remote.code
assert smart_event.event.is_set()
cmds.queue_cmd('beta', Cmd.Wait)
elif action == Action.MainResume_TOT:
l_msg = 'main starting Action.MainResume_TOT'
exp_log_msgs.add_msg(l_msg)
logger.debug(l_msg)
if cmds.r_code:
assert smart_event.resume(code=cmds.r_code, timeout=0.5)
assert smart_event.remote.code == cmds.r_code
else:
assert smart_event.resume(timeout=0.5)
assert not smart_event.remote.code
assert smart_event.event.is_set()
cmds.queue_cmd('beta', Cmd.Wait)
elif action == Action.MainResume_TOF:
l_msg = 'main starting Action.MainResume_TOF'
exp_log_msgs.add_msg(l_msg)
logger.debug(l_msg)
l_msg = (f'{smart_event.name} timeout '
r'of a resume\(\) request with '
r'self.event.is_set\(\) = True and '
'self.remote.deadlock = False')
exp_log_msgs.add_msg(l_msg)
assert not smart_event.event.is_set()
# pre-resume to set flag
if cmds.r_code:
assert smart_event.resume(code=cmds.r_code)
assert smart_event.remote.code == cmds.r_code
else:
assert smart_event.resume()
assert not smart_event.remote.code
assert smart_event.event.is_set()
if cmds.r_code:
start_time = time.time()
assert not smart_event.resume(code=cmds.r_code,
timeout=0.3)
assert 0.3 <= (time.time() - start_time) <= 0.5
assert smart_event.remote.code == cmds.r_code
else:
start_time = time.time()
assert not smart_event.resume(timeout=0.5)
assert 0.5 <= (time.time() - start_time) <= 0.75
assert not smart_event.remote.code
assert smart_event.event.is_set()
# tell thread to clear wait
cmds.queue_cmd('beta', Cmd.Wait_Clear)
elif action == Action.ThreadWait:
l_msg = 'main starting Action.ThreadWait'
exp_log_msgs.add_msg(l_msg)
logger.debug(l_msg)
cmds.queue_cmd('beta', Cmd.Wait)
smart_event.pause_until(WUCond.RemoteWaiting)
if cmds.r_code:
smart_event.resume(code=cmds.r_code)
assert smart_event.remote.code == cmds.r_code
else:
smart_event.resume()
elif action == Action.ThreadWait_TOT:
l_msg = 'main starting Action.ThreadWait_TOT'
exp_log_msgs.add_msg(l_msg)
logger.debug(l_msg)
cmds.queue_cmd('beta', Cmd.Wait_TOT)
smart_event.pause_until(WUCond.RemoteWaiting)
# time.sleep(0.3)
if cmds.r_code:
smart_event.resume(code=cmds.r_code)
assert smart_event.remote.code == cmds.r_code
else:
smart_event.resume()
elif action == Action.ThreadWait_TOF:
l_msg = 'main starting Action.ThreadWait_TOF'
exp_log_msgs.add_msg(l_msg)
logger.debug(l_msg)
cmds.queue_cmd('beta', Cmd.Wait_TOF)
smart_event.pause_until(WUCond.RemoteWaiting)
elif action == Action.ThreadResume:
l_msg = 'main starting Action.ThreadResume'
exp_log_msgs.add_msg(l_msg)
logger.debug(l_msg)
cmds.queue_cmd('beta', Cmd.Resume)
smart_event.pause_until(WUCond.RemoteResume)
assert smart_event.wait()
if cmds.r_code:
assert smart_event.code == cmds.r_code
assert cmds.r_code == smart_event.get_code()
else:
raise IncorrectActionSpecified('The Action is not recognized')
while True:
thread_exc1.raise_exc_if_one() # detect thread error
alpha_cmd = cmds.get_cmd('alpha')
if alpha_cmd == Cmd.Next_Action:
break
else:
raise UnrecognizedCmd
# clear the codes to allow verify registry to work
smart_event.code = None
smart_event.remote.code = None
###############################################################################
# thread_func1
###############################################################################
def thread_func1(cmds: Cmds,
descs: ThreadPairDescs,
exp_log_msgs: Any,
s_event: Optional[SmartEvent] = None,
) -> None:
"""Thread to test SmartEvent scenarios.
Args:
cmds: commands to do
descs: used to verify registry and SmartEvent status
exp_log_msgs: expected log messages
s_event: instance of SmartEvent
Raises:
UnrecognizedCmd: Thread received an unrecognized command
"""
l_msg = 'thread_func1 beta started'
exp_log_msgs.add_msg(l_msg)
logger.debug(l_msg)
if s_event is None:
s_event = SmartEvent(name='beta')
descs.add_desc(SmartEventDesc(name='beta',
s_event=s_event,
thread=threading.current_thread()))
cmds.queue_cmd('alpha', 'go1')
cmds.get_cmd('beta') # go2
s_event.pair_with(remote_name='alpha')
descs.paired('alpha', 'beta')
cmds.queue_cmd('alpha', 'go3')
while True:
beta_cmd = cmds.get_cmd('beta')
if beta_cmd == Cmd.Exit:
break
l_msg = f'thread_func1 received cmd: {beta_cmd}'
exp_log_msgs.add_msg(l_msg)
logger.debug(l_msg)
if beta_cmd == Cmd.Wait:
l_msg = 'thread_func1 doing Wait'
exp_log_msgs.add_msg(l_msg)
logger.debug(l_msg)
if cmds.l_msg:
exp_log_msgs.add_beta_wait_msg(cmds.l_msg, True)
assert s_event.wait(log_msg=cmds.l_msg)
else:
assert s_event.wait()
if cmds.r_code:
assert s_event.code == cmds.r_code
assert cmds.r_code == s_event.get_code()
cmds.queue_cmd('alpha', Cmd.Next_Action)
elif beta_cmd == Cmd.Wait_TOT:
l_msg = 'thread_func1 doing Wait_TOT'
exp_log_msgs.add_msg(l_msg)
logger.debug(l_msg)
if cmds.l_msg:
exp_log_msgs.add_beta_wait_msg(cmds.l_msg, True)
assert s_event.wait(log_msg=cmds.l_msg)
else:
assert s_event.wait()
if cmds.r_code:
assert s_event.code == cmds.r_code
assert cmds.r_code == s_event.get_code()
cmds.queue_cmd('alpha', Cmd.Next_Action)
elif beta_cmd == Cmd.Wait_TOF:
l_msg = 'thread_func1 doing Wait_TOF'
exp_log_msgs.add_msg(l_msg)
logger.debug(l_msg)
l_msg = (f'{s_event.name} timeout of a '
r'wait\(\) request with '
'self.wait_wait = True and '
'self.sync_wait = False')
exp_log_msgs.add_msg(l_msg)
start_time = time.time()
if cmds.l_msg:
exp_log_msgs.add_beta_wait_msg(cmds.l_msg, False)
assert not s_event.wait(timeout=0.5,
log_msg=cmds.l_msg)
else:
assert not s_event.wait(timeout=0.5)
assert 0.5 < (time.time() - start_time) < 0.75
cmds.queue_cmd('alpha', Cmd.Next_Action)
elif beta_cmd == Cmd.Wait_Clear:
l_msg = 'thread_func1 doing Wait_Clear'
exp_log_msgs.add_msg(l_msg)
logger.debug(l_msg)
if cmds.l_msg:
exp_log_msgs.add_beta_wait_msg(cmds.l_msg, True)
assert s_event.wait(log_msg=cmds.l_msg)
else:
assert s_event.wait()
if cmds.r_code:
assert s_event.code == cmds.r_code
assert cmds.r_code == s_event.get_code()
cmds.queue_cmd('alpha', Cmd.Next_Action)
elif beta_cmd == Cmd.Sync:
l_msg = 'thread_func1 beta doing Sync'
exp_log_msgs.add_msg(l_msg)
logger.debug(l_msg)
if cmds.l_msg:
exp_log_msgs.add_beta_sync_msg(cmds.l_msg, True)
assert s_event.sync(log_msg=cmds.l_msg)
else:
assert s_event.sync()
cmds.queue_cmd('alpha', Cmd.Next_Action)
elif beta_cmd == Cmd.Resume:
l_msg = 'thread_func1 beta doing Resume'
exp_log_msgs.add_msg(l_msg)
logger.debug(l_msg)
if cmds.r_code:
if cmds.l_msg:
exp_log_msgs.add_beta_resume_msg(cmds.l_msg,
True,
cmds.r_code)
assert s_event.resume(code=cmds.r_code,
log_msg=cmds.l_msg)
else:
assert s_event.resume(code=cmds.r_code)
assert s_event.remote.code == cmds.r_code
else:
if cmds.l_msg:
exp_log_msgs.add_beta_resume_msg(cmds.l_msg, True)
assert s_event.resume(log_msg=cmds.l_msg)
else:
assert s_event.resume()
cmds.queue_cmd('alpha', Cmd.Next_Action)
else:
raise UnrecognizedCmd('Thread received an unrecognized cmd')
l_msg = 'thread_func1 beta exiting'
exp_log_msgs.add_msg(l_msg)
logger.debug(l_msg)
| 37.343833
| 86
| 0.517203
| 17,524
| 168,346
| 4.738359
| 0.034581
| 0.032661
| 0.023243
| 0.024749
| 0.823472
| 0.781586
| 0.73186
| 0.703788
| 0.662636
| 0.615235
| 0
| 0.013152
| 0.305834
| 168,346
| 4,507
| 87
| 37.352119
| 0.697399
| 0.143365
| 0
| 0.717572
| 0
| 0
| 0.102036
| 0.007958
| 0
| 0
| 0
| 0
| 0.12376
| 1
| 0.040063
| false
| 0.001587
| 0.00357
| 0
| 0.057914
| 0.001587
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
692c2967dc8b83a8426ca865233d5ebc02f155aa
| 20
|
py
|
Python
|
oletools/thirdparty/xglob/__init__.py
|
maniVix/oletools
|
63c4d5261521664f4df7c8f055227118d4fc9349
|
[
"BSD-2-Clause"
] | 2,059
|
2016-05-27T10:27:15.000Z
|
2022-03-30T19:54:08.000Z
|
oletools/thirdparty/xglob/__init__.py
|
maniVix/oletools
|
63c4d5261521664f4df7c8f055227118d4fc9349
|
[
"BSD-2-Clause"
] | 626
|
2016-05-28T15:05:12.000Z
|
2022-03-27T06:08:47.000Z
|
oletools/thirdparty/xglob/__init__.py
|
maniVix/oletools
|
63c4d5261521664f4df7c8f055227118d4fc9349
|
[
"BSD-2-Clause"
] | 530
|
2016-05-30T10:18:08.000Z
|
2022-03-30T07:41:27.000Z
|
from .xglob import *
| 20
| 20
| 0.75
| 3
| 20
| 5
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
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| 0.15
| 20
| 1
| 20
| 20
| 0.882353
| 0
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| 0
| 0
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| 0
| 1
| 0
| true
| 0
| 1
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| 1
| 0
| null | 0
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| 0
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| 0
| 0
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| 0
| 0
| 1
| 0
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| 0
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| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
15c428f7603c9d66155cd6b93f1920ec4033bd6c
| 414
|
py
|
Python
|
zenithml/torch/layers/__init__.py
|
zenith-ml/zenithml
|
b2e68d31f9649d5477f8321b4394822f6f3a8e0c
|
[
"MIT"
] | null | null | null |
zenithml/torch/layers/__init__.py
|
zenith-ml/zenithml
|
b2e68d31f9649d5477f8321b4394822f6f3a8e0c
|
[
"MIT"
] | 1
|
2022-03-07T16:29:32.000Z
|
2022-03-07T16:29:32.000Z
|
zenithml/torch/layers/__init__.py
|
zenith-ml/zenithml
|
b2e68d31f9649d5477f8321b4394822f6f3a8e0c
|
[
"MIT"
] | null | null | null |
from zenithml.torch.layers.preprocess.numerical import NumericalLayer
from zenithml.torch.layers.preprocess.normalizers import MinMaxNormalizeLayer, LogNormalizeLayer, BucketizedLayer
from zenithml.torch.layers.preprocess.nhotencoder import NHotEncodingLayer
from zenithml.torch.layers.preprocess.embedding import EmbeddingLayer
# from zenithml.tf.layers.preprocess.cosine_similarity import CosineSimilarityLayer
| 59.142857
| 113
| 0.886473
| 43
| 414
| 8.511628
| 0.465116
| 0.163934
| 0.185792
| 0.251366
| 0.360656
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.057971
| 414
| 6
| 114
| 69
| 0.938462
| 0.195652
| 0
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| true
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| null | 0
| 0
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| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
15fd3f98e57322cd2e9f70dbcf1cb9a28d020819
| 81
|
py
|
Python
|
src/py/route.py
|
AlecioFuranze/zeloo7
|
d3e662679363dac4457b349cd5531ce92b1a0456
|
[
"MIT"
] | null | null | null |
src/py/route.py
|
AlecioFuranze/zeloo7
|
d3e662679363dac4457b349cd5531ce92b1a0456
|
[
"MIT"
] | null | null | null |
src/py/route.py
|
AlecioFuranze/zeloo7
|
d3e662679363dac4457b349cd5531ce92b1a0456
|
[
"MIT"
] | null | null | null |
from ast import arg
class Route:
def RETURN():
return None
| 11.571429
| 19
| 0.555556
| 10
| 81
| 4.5
| 0.9
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.395062
| 81
| 7
| 20
| 11.571429
| 0.918367
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.25
| true
| 0
| 0.25
| 0.25
| 1
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
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| 0
| 0
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| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| 1
| 1
| 0
|
0
| 6
|
c60834a29854695db62c550ebda2c672458088b2
| 117
|
py
|
Python
|
tests/pyflakes_bears/pep8_naming_test_files/E05/invalid_nested_class.py
|
MacBox7/coala-pyflakes
|
637f8a2e77973384be79d30b0dae1f43072e60c8
|
[
"MIT"
] | null | null | null |
tests/pyflakes_bears/pep8_naming_test_files/E05/invalid_nested_class.py
|
MacBox7/coala-pyflakes
|
637f8a2e77973384be79d30b0dae1f43072e60c8
|
[
"MIT"
] | 12
|
2018-05-21T06:12:59.000Z
|
2018-07-30T10:37:16.000Z
|
tests/pyflakes_bears/pep8_naming_test_files/E05/invalid_nested_class.py
|
MacBox7/coala-pyflakes
|
637f8a2e77973384be79d30b0dae1f43072e60c8
|
[
"MIT"
] | 1
|
2018-06-10T16:16:47.000Z
|
2018-06-10T16:16:47.000Z
|
def foo():
'''
>>> class Good():
... class bad():
... pass
'''
pass
| 14.625
| 28
| 0.264957
| 8
| 117
| 3.875
| 0.75
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.529915
| 117
| 7
| 29
| 16.714286
| 0.563636
| 0.478632
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.5
| true
| 0.5
| 0
| 0
| 0.5
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 1
| 1
| 0
| 0
| 0
| 0
|
0
| 6
|
d6b4438311ed0428830b2410cd875a8f77f091a1
| 27
|
py
|
Python
|
erdospy/test/__init__.py
|
NiMlr/erdospy
|
9c060fa9285da7346e0b6d367555b45f4dcc82f6
|
[
"MIT"
] | null | null | null |
erdospy/test/__init__.py
|
NiMlr/erdospy
|
9c060fa9285da7346e0b6d367555b45f4dcc82f6
|
[
"MIT"
] | null | null | null |
erdospy/test/__init__.py
|
NiMlr/erdospy
|
9c060fa9285da7346e0b6d367555b45f4dcc82f6
|
[
"MIT"
] | 1
|
2022-01-19T11:51:51.000Z
|
2022-01-19T11:51:51.000Z
|
from .tests import testall
| 13.5
| 26
| 0.814815
| 4
| 27
| 5.5
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.148148
| 27
| 1
| 27
| 27
| 0.956522
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
d6dfd7325555dd2299c95baebd138e4fb7043ffb
| 157
|
py
|
Python
|
atomate/lammps/firetasks/__init__.py
|
dongsenfo/atomate
|
01558e8c3e38470c02bc8b50c0ee3aa6198e5206
|
[
"BSD-3-Clause-LBNL"
] | 1
|
2019-09-02T00:55:26.000Z
|
2019-09-02T00:55:26.000Z
|
atomate/lammps/firetasks/__init__.py
|
dongsenfo/atomate
|
01558e8c3e38470c02bc8b50c0ee3aa6198e5206
|
[
"BSD-3-Clause-LBNL"
] | 1
|
2019-04-09T20:55:30.000Z
|
2019-04-09T21:30:24.000Z
|
atomate/lammps/firetasks/__init__.py
|
dongsenfo/atomate
|
01558e8c3e38470c02bc8b50c0ee3aa6198e5206
|
[
"BSD-3-Clause-LBNL"
] | 3
|
2017-03-31T07:42:39.000Z
|
2018-10-10T12:52:49.000Z
|
from __future__ import unicode_literals
# from .write_inputs import *
# from .run_calc import *
# from .parse_outputs import *
# from .glue_tasks import *
| 19.625
| 39
| 0.757962
| 21
| 157
| 5.238095
| 0.619048
| 0.272727
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.165605
| 157
| 7
| 40
| 22.428571
| 0.839695
| 0.675159
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
ba567f961a219cedc74092a14f49b5f7723f7c6e
| 188
|
py
|
Python
|
experiments/archived/20210119/bag_model/__init__.py
|
fxnnxc/text_summarization
|
b8c8a5f491bc44622203602941c1514b2e006fe3
|
[
"Apache-2.0"
] | 5
|
2020-10-14T02:30:44.000Z
|
2021-05-06T12:48:28.000Z
|
experiments/archived/20210119/bag_model/__init__.py
|
fxnnxc/text_summarization
|
b8c8a5f491bc44622203602941c1514b2e006fe3
|
[
"Apache-2.0"
] | 2
|
2020-12-19T05:59:31.000Z
|
2020-12-22T11:05:31.000Z
|
experiments/archived/20210119/bag_model/__init__.py
|
fxnnxc/text_summarization
|
b8c8a5f491bc44622203602941c1514b2e006fe3
|
[
"Apache-2.0"
] | null | null | null |
from . import text_summarization_annealing # noqa
from .models import model, hub_interface # noqa
from .criterions import kld_loss
from .modules import pgn
#from .modules import # noqa
| 37.6
| 50
| 0.792553
| 26
| 188
| 5.576923
| 0.576923
| 0.110345
| 0.234483
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.154255
| 188
| 5
| 51
| 37.6
| 0.91195
| 0.196809
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
ba6be2e01f5c2a7f71fb4e9813ef4f46239426f3
| 4,420
|
py
|
Python
|
tests/unit-tests/test_config_titlefix.py
|
hazemelraffiee/confluencebuilder
|
c283e7fb513c156b9b6e0ba3694fc3e0468a74c9
|
[
"BSD-2-Clause"
] | 90
|
2016-07-21T00:39:19.000Z
|
2019-03-08T08:27:17.000Z
|
tests/unit-tests/test_config_titlefix.py
|
hazemelraffiee/confluencebuilder
|
c283e7fb513c156b9b6e0ba3694fc3e0468a74c9
|
[
"BSD-2-Clause"
] | 124
|
2016-10-18T20:06:48.000Z
|
2019-03-08T04:41:53.000Z
|
tests/unit-tests/test_config_titlefix.py
|
hazemelraffiee/confluencebuilder
|
c283e7fb513c156b9b6e0ba3694fc3e0468a74c9
|
[
"BSD-2-Clause"
] | 39
|
2016-07-21T00:39:52.000Z
|
2019-03-06T14:33:31.000Z
|
# -*- coding: utf-8 -*-
"""
:copyright: Copyright 2020-2022 Sphinx Confluence Builder Contributors (AUTHORS)
:license: BSD-2-Clause (LICENSE)
"""
from tests.lib.testcase import ConfluenceTestCase
from tests.lib.testcase import setup_builder
from tests.lib import parse
import os
class TestConfluenceConfigTitlefix(ConfluenceTestCase):
@classmethod
def setUpClass(cls):
super(TestConfluenceConfigTitlefix, cls).setUpClass()
cls.config['root_doc'] = 'titlefix'
cls.dataset = os.path.join(cls.datasets, 'common')
cls.filenames = [
'titlefix',
'titlefix-child',
]
@setup_builder('confluence')
def test_storage_config_titlefix_none(self):
out_dir = self.build(self.dataset, filenames=self.filenames)
with parse('titlefix', out_dir) as data:
page_ref = data.find('ri:page')
self.assertIsNotNone(page_ref)
self.assertEqual(page_ref['ri:content-title'],
'titlefix-child')
with parse('titlefix-child', out_dir) as data:
page_ref = data.find('ri:page')
self.assertIsNotNone(page_ref)
self.assertEqual(page_ref['ri:content-title'], 'titlefix')
@setup_builder('confluence')
def test_storage_config_titlefix_postfix(self):
config = dict(self.config)
config['confluence_publish_postfix'] = '-mypostfix'
out_dir = self.build(self.dataset, config=config,
filenames=self.filenames)
with parse('titlefix', out_dir) as data:
page_ref = data.find('ri:page')
self.assertIsNotNone(page_ref)
self.assertEqual(page_ref['ri:content-title'],
'titlefix-child-mypostfix')
with parse('titlefix-child', out_dir) as data:
page_ref = data.find('ri:page')
self.assertIsNotNone(page_ref)
self.assertEqual(page_ref['ri:content-title'], 'titlefix-mypostfix')
@setup_builder('confluence')
def test_storage_config_titlefix_prefix(self):
config = dict(self.config)
config['confluence_publish_prefix'] = 'myprefix-'
out_dir = self.build(self.dataset, config=config,
filenames=self.filenames)
with parse('titlefix', out_dir) as data:
page_ref = data.find('ri:page')
self.assertIsNotNone(page_ref)
self.assertEqual(page_ref['ri:content-title'],
'myprefix-titlefix-child')
with parse('titlefix-child', out_dir) as data:
page_ref = data.find('ri:page')
self.assertIsNotNone(page_ref)
self.assertEqual(page_ref['ri:content-title'], 'myprefix-titlefix')
@setup_builder('confluence')
def test_storage_config_titlefix_prefix_and_postfix(self):
config = dict(self.config)
config['confluence_publish_prefix'] = 'myprefix-'
config['confluence_publish_postfix'] = '-mypostfix'
out_dir = self.build(self.dataset, config=config,
filenames=self.filenames)
with parse('titlefix', out_dir) as data:
page_ref = data.find('ri:page')
self.assertIsNotNone(page_ref)
self.assertEqual(page_ref['ri:content-title'],
'myprefix-titlefix-child-mypostfix')
with parse('titlefix-child', out_dir) as data:
page_ref = data.find('ri:page')
self.assertIsNotNone(page_ref)
self.assertEqual(page_ref['ri:content-title'],
'myprefix-titlefix-mypostfix')
@setup_builder('confluence')
def test_storage_config_titlefix_ignore_root(self):
config = dict(self.config)
config['confluence_ignore_titlefix_on_index'] = True
config['confluence_publish_postfix'] = '-mypostfix'
config['confluence_publish_prefix'] = 'myprefix-'
out_dir = self.build(self.dataset, config=config,
filenames=self.filenames)
with parse('titlefix', out_dir) as data:
page_ref = data.find('ri:page')
self.assertIsNotNone(page_ref)
self.assertEqual(page_ref['ri:content-title'],
'myprefix-titlefix-child-mypostfix')
with parse('titlefix-child', out_dir) as data:
page_ref = data.find('ri:page')
self.assertIsNotNone(page_ref)
self.assertEqual(page_ref['ri:content-title'], 'titlefix')
| 37.142857
| 80
| 0.63552
| 495
| 4,420
| 5.490909
| 0.143434
| 0.077263
| 0.062546
| 0.04415
| 0.842899
| 0.809419
| 0.799853
| 0.785136
| 0.76674
| 0.711553
| 0
| 0.002995
| 0.24457
| 4,420
| 118
| 81
| 37.457627
| 0.811021
| 0.030769
| 0
| 0.7
| 0
| 0
| 0.206735
| 0.076707
| 0
| 0
| 0
| 0
| 0.222222
| 1
| 0.066667
| false
| 0
| 0.044444
| 0
| 0.122222
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
241715314e6cac2f918c86b960e56f1eed869c44
| 2,686
|
py
|
Python
|
epytope/Data/pssms/smm/mat/A_24_02_11.py
|
christopher-mohr/epytope
|
8ac9fe52c0b263bdb03235a5a6dffcb72012a4fd
|
[
"BSD-3-Clause"
] | 7
|
2021-02-01T18:11:28.000Z
|
2022-01-31T19:14:07.000Z
|
epytope/Data/pssms/smm/mat/A_24_02_11.py
|
christopher-mohr/epytope
|
8ac9fe52c0b263bdb03235a5a6dffcb72012a4fd
|
[
"BSD-3-Clause"
] | 22
|
2021-01-02T15:25:23.000Z
|
2022-03-14T11:32:53.000Z
|
epytope/Data/pssms/smm/mat/A_24_02_11.py
|
christopher-mohr/epytope
|
8ac9fe52c0b263bdb03235a5a6dffcb72012a4fd
|
[
"BSD-3-Clause"
] | 4
|
2021-05-28T08:50:38.000Z
|
2022-03-14T11:45:32.000Z
|
A_24_02_11 = {0: {'A': 0.219, 'C': -0.201, 'E': 0.106, 'D': 0.153, 'G': 0.052, 'F': 0.18, 'I': 0.146, 'H': 0.226, 'K': 0.166, 'M': 0.196, 'L': -0.246, 'N': 0.084, 'Q': 0.266, 'P': 0.052, 'S': -0.448, 'R': -0.13, 'T': -0.152, 'W': 0.0, 'V': -0.32, 'Y': -0.348}, 1: {'A': 0.011, 'C': 0.021, 'E': 0.324, 'D': 0.012, 'G': 0.0, 'F': -0.364, 'I': -0.055, 'H': 0.0, 'K': 0.0, 'M': -0.218, 'L': 0.072, 'N': 0.0, 'Q': 0.318, 'P': -0.114, 'S': 0.248, 'R': -0.152, 'T': 0.241, 'W': -0.298, 'V': 0.287, 'Y': -0.333}, 2: {'A': -0.213, 'C': 0.051, 'E': 0.01, 'D': 0.029, 'G': 0.017, 'F': -0.055, 'I': 0.067, 'H': 0.044, 'K': -0.121, 'M': 0.032, 'L': -0.007, 'N': 0.006, 'Q': 0.129, 'P': 0.017, 'S': -0.028, 'R': 0.171, 'T': 0.047, 'W': -0.07, 'V': -0.155, 'Y': 0.031}, 3: {'A': 0.159, 'C': -0.091, 'E': 0.19, 'D': 0.277, 'G': -0.039, 'F': 0.163, 'I': 0.0, 'H': -0.007, 'K': -0.26, 'M': 0.021, 'L': -0.06, 'N': -0.035, 'Q': 0.126, 'P': -0.359, 'S': 0.187, 'R': -0.162, 'T': -0.062, 'W': -0.415, 'V': 0.303, 'Y': 0.065}, 4: {'A': 0.079, 'C': 0.001, 'E': 0.082, 'D': 0.096, 'G': -0.025, 'F': -0.005, 'I': -0.029, 'H': 0.023, 'K': -0.102, 'M': 0.055, 'L': -0.19, 'N': -0.058, 'Q': -0.005, 'P': 0.038, 'S': -0.022, 'R': -0.047, 'T': 0.03, 'W': 0.0, 'V': 0.077, 'Y': 0.003}, 5: {'A': -0.179, 'C': 0.125, 'E': 0.047, 'D': 0.049, 'G': 0.064, 'F': -0.141, 'I': 0.11, 'H': 0.06, 'K': -0.145, 'M': -0.124, 'L': 0.072, 'N': 0.072, 'Q': 0.107, 'P': 0.045, 'S': 0.08, 'R': 0.006, 'T': -0.153, 'W': -0.009, 'V': -0.1, 'Y': 0.015}, 6: {'A': 0.019, 'C': 0.079, 'E': 0.018, 'D': 0.109, 'G': 0.057, 'F': -0.008, 'I': 0.065, 'H': -0.075, 'K': -0.025, 'M': 0.011, 'L': -0.011, 'N': 0.016, 'Q': -0.042, 'P': 0.094, 'S': -0.132, 'R': -0.088, 'T': 0.075, 'W': -0.101, 'V': -0.025, 'Y': -0.037}, 7: {'A': -0.0, 'C': 0.0, 'E': -0.0, 'D': 0.0, 'G': 0.0, 'F': -0.0, 'I': 0.0, 'H': 0.0, 'K': 0.0, 'M': -0.0, 'L': 0.0, 'N': 0.0, 'Q': 0.0, 'P': 0.0, 'S': 0.0, 'R': -0.0, 'T': -0.0, 'W': 0.0, 'V': 0.0, 'Y': 0.0}, 8: {'A': -0.0, 'C': 0.0, 'E': -0.0, 'D': 0.0, 'G': -0.0, 'F': -0.0, 'I': -0.0, 'H': -0.0, 'K': 0.0, 'M': -0.0, 'L': 0.0, 'N': 0.0, 'Q': 0.0, 'P': 0.0, 'S': 0.0, 'R': 0.0, 'T': -0.0, 'W': 0.0, 'V': 0.0, 'Y': 0.0}, 9: {'A': -0.148, 'C': 0.0, 'E': 0.05, 'D': 0.014, 'G': 0.083, 'F': -0.058, 'I': 0.016, 'H': 0.009, 'K': 0.092, 'M': 0.036, 'L': -0.033, 'N': 0.004, 'Q': 0.053, 'P': 0.048, 'S': -0.052, 'R': -0.095, 'T': -0.172, 'W': 0.0, 'V': 0.095, 'Y': 0.058}, 10: {'A': 0.241, 'C': 0.0, 'E': 0.0, 'D': 0.0, 'G': 0.0, 'F': -0.627, 'I': 0.068, 'H': 0.0, 'K': 0.304, 'M': 0.145, 'L': -0.174, 'N': 0.0, 'Q': -0.002, 'P': -0.285, 'S': 0.053, 'R': 0.125, 'T': 0.128, 'W': -0.105, 'V': 0.089, 'Y': 0.04}, -1: {'con': 4.13708}}
| 2,686
| 2,686
| 0.374162
| 679
| 2,686
| 1.4757
| 0.243004
| 0.10978
| 0.01497
| 0.01996
| 0.198603
| 0.141717
| 0.141717
| 0.141717
| 0.133733
| 0.133733
| 0
| 0.348723
| 0.169397
| 2,686
| 1
| 2,686
| 2,686
| 0.100403
| 0
| 0
| 0
| 0
| 0
| 0.082992
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
242a209c7e330946c06da5e7d3926e203e3673d7
| 143
|
py
|
Python
|
kwat/vcf/__init__.py
|
KwatME/ccal
|
d96dfa811482eee067f346386a2181ec514625f4
|
[
"MIT"
] | 5
|
2017-05-05T17:50:28.000Z
|
2019-01-30T19:23:02.000Z
|
kwat/vcf/__init__.py
|
KwatME/ccal
|
d96dfa811482eee067f346386a2181ec514625f4
|
[
"MIT"
] | 5
|
2017-05-05T01:52:31.000Z
|
2019-04-20T21:06:05.000Z
|
kwat/vcf/__init__.py
|
KwatME/ccal
|
d96dfa811482eee067f346386a2181ec514625f4
|
[
"MIT"
] | 5
|
2017-07-17T18:55:54.000Z
|
2019-02-02T04:46:19.000Z
|
from .ANN import ANN
from .COLUMN import COLUMN
from .count_variant import count_variant
from .read import read
from .read_row import read_row
| 23.833333
| 40
| 0.825175
| 24
| 143
| 4.75
| 0.333333
| 0.210526
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.13986
| 143
| 5
| 41
| 28.6
| 0.926829
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
2444bfda5192c4823c0aa33cb28aafc9bb284d33
| 48
|
py
|
Python
|
urdf2optcontrol/__init__.py
|
abcamiletto/urdf2optcontrol
|
39b3f761a4685cc7d50b48793b6b2906c89b1694
|
[
"MIT"
] | null | null | null |
urdf2optcontrol/__init__.py
|
abcamiletto/urdf2optcontrol
|
39b3f761a4685cc7d50b48793b6b2906c89b1694
|
[
"MIT"
] | null | null | null |
urdf2optcontrol/__init__.py
|
abcamiletto/urdf2optcontrol
|
39b3f761a4685cc7d50b48793b6b2906c89b1694
|
[
"MIT"
] | null | null | null |
from urdf2optcontrol.optimizer import optimizer
| 24
| 47
| 0.895833
| 5
| 48
| 8.6
| 0.8
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.022727
| 0.083333
| 48
| 1
| 48
| 48
| 0.954545
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
79ec537d798dd165bdadeed344519ce946fbb58f
| 17
|
py
|
Python
|
atmPy/data_archives/__init__.py
|
wblumberg/atm-py
|
253fc427fb366667da2d46a9af4a5d6550a13a6d
|
[
"MIT"
] | 5
|
2015-09-09T20:06:59.000Z
|
2021-03-17T17:41:40.000Z
|
atmPy/data_archives/__init__.py
|
wblumberg/atm-py
|
253fc427fb366667da2d46a9af4a5d6550a13a6d
|
[
"MIT"
] | 9
|
2016-02-22T18:15:21.000Z
|
2020-01-09T15:56:30.000Z
|
atmPy/data_archives/__init__.py
|
wblumberg/atm-py
|
253fc427fb366667da2d46a9af4a5d6550a13a6d
|
[
"MIT"
] | 3
|
2016-04-19T16:19:35.000Z
|
2017-08-18T16:01:40.000Z
|
from . import arm
| 17
| 17
| 0.764706
| 3
| 17
| 4.333333
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.176471
| 17
| 1
| 17
| 17
| 0.928571
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
79fd5c1e63ef69fca3001ea30cfe970aedb5057c
| 117
|
py
|
Python
|
mbuild/lib/bulk_materials/__init__.py
|
daico007/mbuild
|
8baa19a46d1ece809f214a2e1a2f8984dd923b56
|
[
"MIT"
] | 101
|
2017-02-14T18:23:52.000Z
|
2022-03-20T03:29:59.000Z
|
mbuild/lib/bulk_materials/__init__.py
|
Leticia-maria/mbuild
|
b6278441ff6d6cf1f954affe3d0fbeec17bbbc6d
|
[
"MIT"
] | 689
|
2017-02-13T04:40:30.000Z
|
2022-03-31T19:57:32.000Z
|
mbuild/lib/bulk_materials/__init__.py
|
Leticia-maria/mbuild
|
b6278441ff6d6cf1f954affe3d0fbeec17bbbc6d
|
[
"MIT"
] | 82
|
2017-02-13T21:08:48.000Z
|
2022-03-21T21:55:43.000Z
|
"""mBuild bulk materials library."""
from mbuild.lib.bulk_materials.amorphous_silica_bulk import AmorphousSilicaBulk
| 39
| 79
| 0.846154
| 14
| 117
| 6.857143
| 0.714286
| 0.270833
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.068376
| 117
| 2
| 80
| 58.5
| 0.880734
| 0.25641
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
30081b99ed015655f62a2925588934f7ea214a6c
| 1,543
|
py
|
Python
|
tests/test_router.py
|
elizabrock/adventofcode
|
542d86df55696e60b9044a468d9231650b93594c
|
[
"MIT"
] | null | null | null |
tests/test_router.py
|
elizabrock/adventofcode
|
542d86df55696e60b9044a468d9231650b93594c
|
[
"MIT"
] | null | null | null |
tests/test_router.py
|
elizabrock/adventofcode
|
542d86df55696e60b9044a468d9231650b93594c
|
[
"MIT"
] | null | null | null |
import unittest
from day3 import Router, Path, Traveler
class TestRouter(unittest.TestCase):
def test_houses_visited_single_visitor_2(self):
input = '>'
expected = 2
path = Path()
Router.route(input, Traveler(path))
self.assertEqual(expected, path.houses_visited())
def test_houses_visited_single_visitor_4(self):
input = '^>v<'
expected = 4
path = Path()
Router.route(input, Traveler(path))
self.assertEqual(expected, path.houses_visited())
def test_houses_visited_long_single_visitor_2(self):
input = '^v^v^v^v^v'
expected = 2
path = Path()
Router.route(input, Traveler(path))
self.assertEqual(expected, path.houses_visited())
def test_houses_visited_multiple_visitors_2(self):
input = '^v'
expected = 3
path = Path()
Router.route(input, Traveler(path), Traveler(path))
self.assertEqual(expected, path.houses_visited())
def test_houses_visited_multiple_visitors_4(self):
input = '^>v<'
expected = 3
path = Path()
Router.route(input, Traveler(path), Traveler(path))
self.assertEqual(expected, path.houses_visited())
def test_houses_visited_long_multiple_visitors_2(self):
input = '^v^v^v^v^v'
expected = 11
path = Path()
Router.route(input, Traveler(path), Traveler(path))
self.assertEqual(expected, path.houses_visited())
if __name__ == '__main__':
unittest.main()
| 30.86
| 59
| 0.635126
| 182
| 1,543
| 5.131868
| 0.175824
| 0.167024
| 0.083512
| 0.12848
| 0.890792
| 0.844754
| 0.777302
| 0.777302
| 0.777302
| 0.736617
| 0
| 0.012079
| 0.248866
| 1,543
| 49
| 60
| 31.489796
| 0.793788
| 0
| 0
| 0.634146
| 0
| 0
| 0.025275
| 0
| 0
| 0
| 0
| 0
| 0.146341
| 1
| 0.146341
| false
| 0
| 0.04878
| 0
| 0.219512
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
302b5c1fae21cc0412ea3e093f2daee245ab78e4
| 277
|
py
|
Python
|
bindings/python/hgdb/__init__.py
|
mithro/hgdb
|
8a028b9ee8751a2cc77cd1c889e96059747b2f18
|
[
"BSD-2-Clause"
] | null | null | null |
bindings/python/hgdb/__init__.py
|
mithro/hgdb
|
8a028b9ee8751a2cc77cd1c889e96059747b2f18
|
[
"BSD-2-Clause"
] | null | null | null |
bindings/python/hgdb/__init__.py
|
mithro/hgdb
|
8a028b9ee8751a2cc77cd1c889e96059747b2f18
|
[
"BSD-2-Clause"
] | null | null | null |
try:
from .client import HGDBClient, HGDBClientException
from .symbol import (SymbolTableProvider, VariableSymbol, GeneratorVariableSymbol, ContextVariableSymbol,
BreakpointSymbol)
except ImportError:
pass
from .db import DebugSymbolTable
| 30.777778
| 109
| 0.743682
| 21
| 277
| 9.809524
| 0.809524
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.212996
| 277
| 8
| 110
| 34.625
| 0.944954
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0.142857
| 0.571429
| 0
| 0.571429
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 1
| 0
| 1
| 0
|
0
| 6
|
063ec248cab953998ddab52490481255e605a0c3
| 24
|
py
|
Python
|
getpw/__init__.py
|
N0x1s/getpw
|
ab1acae4ff80d756663845ba8b56dbeadb6a4d4a
|
[
"MIT"
] | 2
|
2020-07-12T07:00:48.000Z
|
2020-08-20T18:14:35.000Z
|
getpw/__init__.py
|
N0x1s/getpw
|
ab1acae4ff80d756663845ba8b56dbeadb6a4d4a
|
[
"MIT"
] | null | null | null |
getpw/__init__.py
|
N0x1s/getpw
|
ab1acae4ff80d756663845ba8b56dbeadb6a4d4a
|
[
"MIT"
] | 1
|
2021-01-26T15:07:02.000Z
|
2021-01-26T15:07:02.000Z
|
from .main import getpw
| 12
| 23
| 0.791667
| 4
| 24
| 4.75
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.166667
| 24
| 1
| 24
| 24
| 0.95
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
069a99b2d207f477a99a4ee093bdde5d744a1f44
| 4,481
|
py
|
Python
|
tests/regressiontests/forms/localflavor/sk.py
|
huicheese/Django-test3
|
ac11d2dce245b48392e52d1f4acfd5e7433b243e
|
[
"BSD-3-Clause"
] | 23
|
2015-01-26T12:16:59.000Z
|
2022-02-10T10:58:40.000Z
|
tests/regressiontests/forms/localflavor/sk.py
|
joetyson/django
|
c3699190186561d5c216b2a77ecbfc487d42a734
|
[
"BSD-3-Clause"
] | 1
|
2015-11-25T10:57:23.000Z
|
2017-02-25T07:54:03.000Z
|
tests/regressiontests/forms/localflavor/sk.py
|
joetyson/django
|
c3699190186561d5c216b2a77ecbfc487d42a734
|
[
"BSD-3-Clause"
] | 30
|
2015-03-25T19:40:07.000Z
|
2021-05-28T22:59:26.000Z
|
# -*- coding: utf-8 -*-
# Tests for the contrib/localflavor/ SK form fields.
tests = r"""
# SKPostalCodeField #########################################################
>>> from django.contrib.localflavor.sk.forms import SKPostalCodeField
>>> f = SKPostalCodeField()
>>> f.clean('84545x')
Traceback (most recent call last):
...
ValidationError: [u'Enter a postal code in the format XXXXX or XXX XX.']
>>> f.clean('91909')
u'91909'
>>> f.clean('917 01')
u'91701'
# SKRegionSelect ############################################################
>>> from django.contrib.localflavor.sk.forms import SKRegionSelect
>>> w = SKRegionSelect()
>>> w.render('regions', 'TT')
u'<select name="regions">\n<option value="BB">Banska Bystrica region</option>\n<option value="BA">Bratislava region</option>\n<option value="KE">Kosice region</option>\n<option value="NR">Nitra region</option>\n<option value="PO">Presov region</option>\n<option value="TN">Trencin region</option>\n<option value="TT" selected="selected">Trnava region</option>\n<option value="ZA">Zilina region</option>\n</select>'
# SKDistrictSelect ##########################################################
>>> from django.contrib.localflavor.sk.forms import SKDistrictSelect
>>> w = SKDistrictSelect()
>>> w.render('Districts', 'RK')
u'<select name="Districts">\n<option value="BB">Banska Bystrica</option>\n<option value="BS">Banska Stiavnica</option>\n<option value="BJ">Bardejov</option>\n<option value="BN">Banovce nad Bebravou</option>\n<option value="BR">Brezno</option>\n<option value="BA1">Bratislava I</option>\n<option value="BA2">Bratislava II</option>\n<option value="BA3">Bratislava III</option>\n<option value="BA4">Bratislava IV</option>\n<option value="BA5">Bratislava V</option>\n<option value="BY">Bytca</option>\n<option value="CA">Cadca</option>\n<option value="DT">Detva</option>\n<option value="DK">Dolny Kubin</option>\n<option value="DS">Dunajska Streda</option>\n<option value="GA">Galanta</option>\n<option value="GL">Gelnica</option>\n<option value="HC">Hlohovec</option>\n<option value="HE">Humenne</option>\n<option value="IL">Ilava</option>\n<option value="KK">Kezmarok</option>\n<option value="KN">Komarno</option>\n<option value="KE1">Kosice I</option>\n<option value="KE2">Kosice II</option>\n<option value="KE3">Kosice III</option>\n<option value="KE4">Kosice IV</option>\n<option value="KEO">Kosice - okolie</option>\n<option value="KA">Krupina</option>\n<option value="KM">Kysucke Nove Mesto</option>\n<option value="LV">Levice</option>\n<option value="LE">Levoca</option>\n<option value="LM">Liptovsky Mikulas</option>\n<option value="LC">Lucenec</option>\n<option value="MA">Malacky</option>\n<option value="MT">Martin</option>\n<option value="ML">Medzilaborce</option>\n<option value="MI">Michalovce</option>\n<option value="MY">Myjava</option>\n<option value="NO">Namestovo</option>\n<option value="NR">Nitra</option>\n<option value="NM">Nove Mesto nad Vahom</option>\n<option value="NZ">Nove Zamky</option>\n<option value="PE">Partizanske</option>\n<option value="PK">Pezinok</option>\n<option value="PN">Piestany</option>\n<option value="PT">Poltar</option>\n<option value="PP">Poprad</option>\n<option value="PB">Povazska Bystrica</option>\n<option value="PO">Presov</option>\n<option value="PD">Prievidza</option>\n<option value="PU">Puchov</option>\n<option value="RA">Revuca</option>\n<option value="RS">Rimavska Sobota</option>\n<option value="RV">Roznava</option>\n<option value="RK" selected="selected">Ruzomberok</option>\n<option value="SB">Sabinov</option>\n<option value="SC">Senec</option>\n<option value="SE">Senica</option>\n<option value="SI">Skalica</option>\n<option value="SV">Snina</option>\n<option value="SO">Sobrance</option>\n<option value="SN">Spisska Nova Ves</option>\n<option value="SL">Stara Lubovna</option>\n<option value="SP">Stropkov</option>\n<option value="SK">Svidnik</option>\n<option value="SA">Sala</option>\n<option value="TO">Topolcany</option>\n<option value="TV">Trebisov</option>\n<option value="TN">Trencin</option>\n<option value="TT">Trnava</option>\n<option value="TR">Turcianske Teplice</option>\n<option value="TS">Tvrdosin</option>\n<option value="VK">Velky Krtis</option>\n<option value="VT">Vranov nad Toplou</option>\n<option value="ZM">Zlate Moravce</option>\n<option value="ZV">Zvolen</option>\n<option value="ZC">Zarnovica</option>\n<option value="ZH">Ziar nad Hronom</option>\n<option value="ZA">Zilina</option>\n</select>'
"""
| 140.03125
| 3,188
| 0.700736
| 668
| 4,481
| 4.700599
| 0.341317
| 0.193949
| 0.332484
| 0.487261
| 0.220064
| 0.123248
| 0.039172
| 0
| 0
| 0
| 0
| 0.008233
| 0.051328
| 4,481
| 31
| 3,189
| 144.548387
| 0.730416
| 0.016068
| 0
| 0
| 0
| 0.086957
| 0.995688
| 0.697912
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.130435
| 0
| 0.130435
| 0
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| 0
| 0
| null | 0
| 1
| 1
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| 0
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| 0
| 1
| 1
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| 1
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
23159cfa74dfdec1ad7127226ba277cf96d5ec57
| 47
|
py
|
Python
|
vnpy/api/lhang/__init__.py
|
firekay/vnpy
|
22311a882c465312ebad20eaa1cb607fa7b8b7af
|
[
"MIT"
] | 5
|
2018-01-25T01:59:41.000Z
|
2022-03-11T10:08:58.000Z
|
vnpy/api/lhang/__init__.py
|
ianchan1988/vnpy
|
5fd3814ba5ce3559ef1cb1a04c877a06114b4728
|
[
"MIT"
] | null | null | null |
vnpy/api/lhang/__init__.py
|
ianchan1988/vnpy
|
5fd3814ba5ce3559ef1cb1a04c877a06114b4728
|
[
"MIT"
] | 6
|
2018-03-12T06:08:21.000Z
|
2021-11-18T15:14:40.000Z
|
# encoding: UTF-8
from vnlhang import LhangApi
| 15.666667
| 28
| 0.787234
| 7
| 47
| 5.285714
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.025
| 0.148936
| 47
| 3
| 28
| 15.666667
| 0.9
| 0.319149
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
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| 1
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| 1
| 1
| 0
| null | 0
| 0
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| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
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| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
2342e6f5d5a14d9ffb94a6dfe26cfec369450b05
| 7,259
|
py
|
Python
|
tests/test_models.py
|
ivoire/KissCache
|
ccdf6454bccc29dbd0cd30b00c95a37fa3680b2c
|
[
"MIT"
] | null | null | null |
tests/test_models.py
|
ivoire/KissCache
|
ccdf6454bccc29dbd0cd30b00c95a37fa3680b2c
|
[
"MIT"
] | null | null | null |
tests/test_models.py
|
ivoire/KissCache
|
ccdf6454bccc29dbd0cd30b00c95a37fa3680b2c
|
[
"MIT"
] | 1
|
2020-09-29T21:05:43.000Z
|
2020-09-29T21:05:43.000Z
|
# -*- coding: utf-8 -*-
# vim: set ts=4
#
# Copyright 2019 Linaro Limited
#
# Author: Rémi Duraffort <[email protected]>
#
# SPDX-License-Identifier: MIT
import pytest
import time
from kiss_cache.models import Resource
def test_resource_parse_ttl():
assert Resource.parse_ttl("1d") == 60 * 60 * 24
assert Resource.parse_ttl("18d") == 60 * 60 * 24 * 18
assert Resource.parse_ttl("5h") == 60 * 60 * 5
assert Resource.parse_ttl("34m") == 60 * 34
assert Resource.parse_ttl("500s") == 500
assert Resource.parse_ttl("42s") == 42
with pytest.raises(NotImplementedError, match="Unknown TTL value"):
Resource.parse_ttl("42t")
with pytest.raises(Exception, match="The TTL should be positive"):
Resource.parse_ttl("-1s")
def test_resource_path(db):
res = Resource.objects.create(url="https://example.com/kernel")
assert (
res.path == "76/66828e5a43fe3e8c06c2e62ad216cc354c91da92f093d6d8a7c3dc9d1baa82"
)
def test_resource_total_size(db):
assert Resource.total_size() == 0
Resource.objects.create(url="http://example.com", content_length=4212)
Resource.objects.create(url="http://example.org", content_length=5379)
Resource.objects.create(url="http://example.net", content_length=2)
assert Resource.total_size() == 4212 + 5379 + 2
def test_resource_total_size(db, settings):
settings.RESOURCE_QUOTA = 12
assert Resource.is_over_quota() is False
Resource.objects.create(url="http://example.com", content_length=4212)
Resource.objects.create(url="http://example.org", content_length=5379)
Resource.objects.create(url="http://example.net", content_length=2)
assert Resource.is_over_quota() is True
settings.RESOURCE_QUOTA = 4212 + 5379 + 2 - 1
assert Resource.is_over_quota() is True
settings.RESOURCE_QUOTA = 4212 + 5379 + 2 + 1
assert Resource.is_over_quota() is False
settings.RESOURCE_QUOTA = 0
assert Resource.is_over_quota() is False
def test_resource_progress(db, settings, tmpdir):
settings.DOWNLOAD_PATH = str(tmpdir)
res = Resource.objects.create(url="https://example.com/kernel")
assert res.progress() == "??"
res.content_length = 56
assert res.progress() == 0
(tmpdir / "76").mkdir()
(
tmpdir / "76/66828e5a43fe3e8c06c2e62ad216cc354c91da92f093d6d8a7c3dc9d1baa82"
).write_text("hello", encoding="utf-8")
assert res.progress() == 8
def test_resource_stream(db, monkeypatch, settings, tmpdir):
monkeypatch.setattr(time, "sleep", lambda d: d)
settings.DOWNLOAD_PATH = str(tmpdir)
res = Resource.objects.create(url="https://example.com/kernel")
(tmpdir / "76").mkdir()
with (
tmpdir / "76/66828e5a43fe3e8c06c2e62ad216cc354c91da92f093d6d8a7c3dc9d1baa82"
).open("wb") as f_out:
f_out.write(b"hello")
f_out.flush()
it = res.stream()
assert next(it) == b"hello"
f_out.write(b" world")
f_out.flush()
assert next(it) == b" world"
res.status_code = 200
res.state = Resource.STATE_FINISHED
res.save()
f_out.write(b"!")
f_out.flush()
assert next(it) == b"!"
with pytest.raises(StopIteration):
next(it)
def test_resource_stream_errors(db, monkeypatch, settings, tmpdir):
monkeypatch.setattr(time, "sleep", lambda d: d)
settings.DOWNLOAD_PATH = str(tmpdir)
res = Resource.objects.create(url="https://example.com/kernel", content_length=11)
(tmpdir / "76").mkdir()
with (
tmpdir / "76/66828e5a43fe3e8c06c2e62ad216cc354c91da92f093d6d8a7c3dc9d1baa82"
).open("wb") as f_out:
f_out.write(b"hello")
f_out.flush()
it = res.stream()
assert next(it) == b"hello"
f_out.write(b" world")
f_out.flush()
assert next(it) == b" world"
res.status_code = 403
res.state = Resource.STATE_FINISHED
res.save()
# The length is right: no exception will be raised
with pytest.raises(StopIteration):
next(it)
def test_resource_stream_errors_2(db, monkeypatch, settings, tmpdir):
monkeypatch.setattr(time, "sleep", lambda d: d)
settings.DOWNLOAD_PATH = str(tmpdir)
res = Resource.objects.create(url="https://example.com/kernel", content_length=13)
(tmpdir / "76").mkdir()
with (
tmpdir / "76/66828e5a43fe3e8c06c2e62ad216cc354c91da92f093d6d8a7c3dc9d1baa82"
).open("wb") as f_out:
f_out.write(b"hello")
f_out.flush()
it = res.stream()
assert next(it) == b"hello"
f_out.write(b" world")
f_out.flush()
assert next(it) == b" world"
res.status_code = 403
res.state = Resource.STATE_FINISHED
res.save()
# The length is wrong: an exception should be raised
with pytest.raises(Exception, match="Resource length streamed is wrong: 11 vs 13"):
next(it)
def test_resource_stream_errors_3(db, monkeypatch, settings, tmpdir):
monkeypatch.setattr(time, "sleep", lambda d: d)
settings.DOWNLOAD_PATH = str(tmpdir)
res = Resource.objects.create(url="https://example.com/kernel")
(tmpdir / "76").mkdir()
with (
tmpdir / "76/66828e5a43fe3e8c06c2e62ad216cc354c91da92f093d6d8a7c3dc9d1baa82"
).open("wb") as f_out:
f_out.write(b"hello")
f_out.flush()
it = res.stream()
assert next(it) == b"hello"
f_out.write(b" world")
f_out.flush()
assert next(it) == b" world"
res.delete()
# the length is unknown: an exception should be raised
with pytest.raises(Exception, match="Resource was deleted and length is unknown"):
next(it)
def test_resource_stream_errors_4(db, monkeypatch, settings, tmpdir):
monkeypatch.setattr(time, "sleep", lambda d: d)
settings.DOWNLOAD_PATH = str(tmpdir)
res = Resource.objects.create(url="https://example.com/kernel", content_length=13)
(tmpdir / "76").mkdir()
with (
tmpdir / "76/66828e5a43fe3e8c06c2e62ad216cc354c91da92f093d6d8a7c3dc9d1baa82"
).open("wb") as f_out:
f_out.write(b"hello")
f_out.flush()
it = res.stream()
assert next(it) == b"hello"
f_out.write(b" world")
f_out.flush()
assert next(it) == b" world"
res.delete()
# the length is unknown: an exception should be raised
with pytest.raises(
Exception, match="Resource was deleted and streamed length is wrong: 11 vs 13"
):
next(it)
def test_resource_stream_errors_5(db, monkeypatch, settings, tmpdir):
monkeypatch.setattr(time, "sleep", lambda d: d)
settings.DOWNLOAD_PATH = str(tmpdir)
res = Resource.objects.create(url="https://example.com/kernel", content_length=11)
(tmpdir / "76").mkdir()
with (
tmpdir / "76/66828e5a43fe3e8c06c2e62ad216cc354c91da92f093d6d8a7c3dc9d1baa82"
).open("wb") as f_out:
f_out.write(b"hello")
f_out.flush()
it = res.stream()
assert next(it) == b"hello"
f_out.write(b" world")
f_out.flush()
assert next(it) == b" world"
res.delete()
# the length is good: no exception should be raised
with pytest.raises(StopIteration):
next(it)
| 32.262222
| 87
| 0.653396
| 917
| 7,259
| 5.045802
| 0.152672
| 0.027664
| 0.06354
| 0.072617
| 0.787119
| 0.780635
| 0.769397
| 0.731576
| 0.720553
| 0.720553
| 0
| 0.079359
| 0.21711
| 7,259
| 224
| 88
| 32.40625
| 0.734823
| 0.055242
| 0
| 0.720238
| 0
| 0
| 0.18235
| 0.075979
| 0
| 0
| 0
| 0
| 0.178571
| 1
| 0.065476
| false
| 0
| 0.017857
| 0
| 0.083333
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
237cf8d677c370493bef079416fe4e3a6b86b1ed
| 49
|
py
|
Python
|
src/larksuiteoapi/api/response/__init__.py
|
keeperlibofan/oapi-sdk-python
|
5743dd34eb0bfc30693e9b6e6f5cf35ac82edb26
|
[
"Apache-2.0"
] | 50
|
2021-04-11T05:24:10.000Z
|
2022-03-29T10:14:13.000Z
|
src/larksuiteoapi/api/response/__init__.py
|
keeperlibofan/oapi-sdk-python
|
5743dd34eb0bfc30693e9b6e6f5cf35ac82edb26
|
[
"Apache-2.0"
] | 20
|
2021-04-07T15:17:44.000Z
|
2022-03-23T06:27:12.000Z
|
src/larksuiteoapi/api/response/__init__.py
|
keeperlibofan/oapi-sdk-python
|
5743dd34eb0bfc30693e9b6e6f5cf35ac82edb26
|
[
"Apache-2.0"
] | 8
|
2021-04-25T15:02:17.000Z
|
2022-03-13T15:00:59.000Z
|
# -*- coding: UTF-8 -*-
from .response import *
| 12.25
| 23
| 0.571429
| 6
| 49
| 4.666667
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.025641
| 0.204082
| 49
| 3
| 24
| 16.333333
| 0.692308
| 0.428571
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
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| 1
| 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
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| 0
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| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
88bb346440052826b7651c1b2bd9b57c6b6d23ee
| 3,715
|
py
|
Python
|
11/solution.py
|
Hegemege/advent-of-code-2021
|
e3ee5de81a03ef726baf638dd99ba85e85fd537a
|
[
"MIT"
] | null | null | null |
11/solution.py
|
Hegemege/advent-of-code-2021
|
e3ee5de81a03ef726baf638dd99ba85e85fd537a
|
[
"MIT"
] | null | null | null |
11/solution.py
|
Hegemege/advent-of-code-2021
|
e3ee5de81a03ef726baf638dd99ba85e85fd537a
|
[
"MIT"
] | null | null | null |
class Octopus:
def __init__(self, energy):
self.flashed = False
self.energy = energy
self.neighbors = []
self.flashes = 0
def flash(self):
if self.flashed:
return
self.flashed = True
self.flashes += 1
for neighbor in self.neighbors:
neighbor.energy += 1
if neighbor.energy > 9:
neighbor.flash()
def part1(input_data):
height = len(input_data)
width = len(input_data[0])
grid = [[Octopus(int(y)) for y in x] for x in input_data]
for j in range(height):
for i in range(width):
cell = grid[j][i]
if j > 0:
cell.neighbors.append(grid[j - 1][i])
if j < height - 1:
cell.neighbors.append(grid[j + 1][i])
if i > 0:
cell.neighbors.append(grid[j][i - 1])
if i < width - 1:
cell.neighbors.append(grid[j][i + 1])
if j > 0 and i > 0:
cell.neighbors.append(grid[j - 1][i - 1])
if j < height - 1 and i > 0:
cell.neighbors.append(grid[j + 1][i - 1])
if j > 0 and i < width - 1:
cell.neighbors.append(grid[j - 1][i + 1])
if j < height - 1 and i < width - 1:
cell.neighbors.append(grid[j + 1][i + 1])
for i in range(100):
# Reset flashed for all octopuses and add 1 energy
for row in grid:
for octopus in row:
octopus.flashed = False
octopus.energy += 1
for row in grid:
for octopus in row:
if octopus.energy > 9:
octopus.flash()
for row in grid:
for octopus in row:
if octopus.flashed:
octopus.energy = 0
return sum([sum([y.flashes for y in x]) for x in grid])
def part2(input_data):
height = len(input_data)
width = len(input_data[0])
grid = [[Octopus(int(y)) for y in x] for x in input_data]
for j in range(height):
for i in range(width):
cell = grid[j][i]
if j > 0:
cell.neighbors.append(grid[j - 1][i])
if j < height - 1:
cell.neighbors.append(grid[j + 1][i])
if i > 0:
cell.neighbors.append(grid[j][i - 1])
if i < width - 1:
cell.neighbors.append(grid[j][i + 1])
if j > 0 and i > 0:
cell.neighbors.append(grid[j - 1][i - 1])
if j < height - 1 and i > 0:
cell.neighbors.append(grid[j + 1][i - 1])
if j > 0 and i < width - 1:
cell.neighbors.append(grid[j - 1][i + 1])
if j < height - 1 and i < width - 1:
cell.neighbors.append(grid[j + 1][i + 1])
i = 0
while True:
i += 1
# Reset flashed for all octopuses and add 1 energy
for row in grid:
for octopus in row:
octopus.flashed = False
octopus.energy += 1
for row in grid:
for octopus in row:
if octopus.energy > 9:
octopus.flash()
all_flashed = True
for row in grid:
for octopus in row:
if octopus.flashed:
octopus.energy = 0
else:
all_flashed = False
if all_flashed:
return i
if __name__ == "__main__":
with open("input", "r") as input_file:
input_data = list(map(lambda x: x.strip(), input_file.readlines()))
print(part1(input_data))
print(part2(input_data))
| 31.752137
| 75
| 0.474563
| 495
| 3,715
| 3.505051
| 0.119192
| 0.051873
| 0.175216
| 0.212104
| 0.738329
| 0.738329
| 0.738329
| 0.730836
| 0.730836
| 0.730836
| 0
| 0.032825
| 0.417766
| 3,715
| 116
| 76
| 32.025862
| 0.769302
| 0.02611
| 0
| 0.68
| 0
| 0
| 0.003873
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.04
| false
| 0
| 0
| 0
| 0.08
| 0.02
| 0
| 0
| 0
| null | 0
| 0
| 1
| 0
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
00397b0a764373c133aa03603627a896723055b9
| 12,961
|
py
|
Python
|
asn1PERser/test/per/encoder/test_per_encode_enumerated.py
|
erupikus/asn1PERser
|
11dc2985107a9fbba00bea83c1021d2665e8f193
|
[
"MIT"
] | 3
|
2021-06-14T03:29:37.000Z
|
2021-11-15T09:45:11.000Z
|
asn1PERser/test/per/encoder/test_per_encode_enumerated.py
|
erupikus/asn1PERser
|
11dc2985107a9fbba00bea83c1021d2665e8f193
|
[
"MIT"
] | null | null | null |
asn1PERser/test/per/encoder/test_per_encode_enumerated.py
|
erupikus/asn1PERser
|
11dc2985107a9fbba00bea83c1021d2665e8f193
|
[
"MIT"
] | null | null | null |
import pytest
from pyasn1.type.namedval import NamedValues
from asn1PERser.codec.per.encoder import encode as per_encoder
from asn1PERser.classes.data.builtin.EnumeratedType import EnumeratedType
from asn1PERser.classes.types.constraint import ExtensionMarker
def SCHEMA_my_enum(enumerationRoot_list, extensionMarker_value=False):
class MyEnum(EnumeratedType):
'''
MyEnum ::= ENUMERATED {
e0,
e1,
.
.
.
eN-1
eN
}
'''
subtypeSpec = ExtensionMarker(extensionMarker_value)
enumerationRoot = NamedValues(
*[(item, index) for index, item in enumerate(enumerationRoot_list)]
)
extensionAddition = NamedValues(
)
namedValues = enumerationRoot + extensionAddition
return MyEnum
def SCHEMA_my_ext_enum(enumerationRoot_list, extensionAddition_list, extensionMarker_value=False):
class MyEnum(EnumeratedType):
'''
MyEnum::= ENUMERATED
{
e0,
e1,
.
.
.
eN - 1
eN,
...,
eN+1
.
.
.
eM-1,
eM
}
'''
subtypeSpec = ExtensionMarker(extensionMarker_value)
enumerationRoot = NamedValues(
*[(item, index) for index, item in enumerate(enumerationRoot_list)]
)
extensionAddition = NamedValues(
*[(item, index) for index, item in enumerate(extensionAddition_list, start=len(enumerationRoot_list))]
)
namedValues = enumerationRoot + extensionAddition
return MyEnum
def DATA_my_enum(enum, value):
return enum(value)
short_enum = ['a0', 'a1']
enumeration_list = ['e0', 'e1', 'e2', 'e3', 'e4', 'e5', 'e6', 'e7', 'e8', 'e9',
'e10', 'e11', 'e12', 'e13', 'e14', 'e15', 'e16', 'e17', 'e18', 'e19',
'e20', 'e21', 'e22', 'e23', 'e24', 'e25', 'e26', 'e27', 'e28', 'e29',
'e30', 'e31', 'e32', 'e33', 'e34', 'e35', 'e36', 'e37', 'e38', 'e39',
'e40', 'e41', 'e42', 'e43', 'e44', 'e45', 'e46', 'e47', 'e48', 'e49',
'e50', 'e51', 'e52', 'e53', 'e54', 'e55', 'e56', 'e57', 'e58', 'e59',
'e60', 'e61', 'e62', 'e63', 'e64', 'e65', 'e66', 'e67', 'e68', 'e69',
'e70', 'e71', 'e72', 'e73', 'e74', 'e75', 'e76', 'e77', 'e78', 'e79',
'e80', 'e81', 'e82', 'e83', 'e84', 'e85', 'e86', 'e87', 'e88', 'e89',
'e90', 'e91', 'e92', 'e93', 'e94', 'e95', 'e96', 'e97', 'e98', 'e99',
'e100', 'e101', 'e102', 'e103', 'e104', 'e105', 'e106', 'e107', 'e108', 'e109',
'e110', 'e111', 'e112', 'e113', 'e114', 'e115', 'e116', 'e117', 'e118', 'e119',
'e120', 'e121', 'e122', 'e123', 'e124', 'e125', 'e126', 'e127', 'e128', 'e129',
'e130', 'e131', 'e132', 'e133', 'e134', 'e135', 'e136', 'e137', 'e138', 'e139',
'e140', 'e141', 'e142', 'e143', 'e144', 'e145', 'e146', 'e147', 'e148', 'e149',
'e150', 'e151', 'e152', 'e153', 'e154', 'e155', 'e156', 'e157', 'e158', 'e159',
'e160', 'e161', 'e162', 'e163', 'e164', 'e165', 'e166', 'e167', 'e168', 'e169',
'e170', 'e171', 'e172', 'e173', 'e174', 'e175', 'e176', 'e177', 'e178', 'e179',
'e180', 'e181', 'e182', 'e183', 'e184', 'e185', 'e186', 'e187', 'e188', 'e189',
'e190', 'e191', 'e192', 'e193', 'e194', 'e195', 'e196', 'e197', 'e198', 'e199',
'e200', 'e201', 'e202', 'e203', 'e204', 'e205', 'e206', 'e207', 'e208', 'e209',
'e210', 'e211', 'e212', 'e213', 'e214', 'e215', 'e216', 'e217', 'e218', 'e219',
'e220', 'e221', 'e222', 'e223', 'e224', 'e225', 'e226', 'e227', 'e228', 'e229',
'e230', 'e231', 'e232', 'e233', 'e234', 'e235', 'e236', 'e237', 'e238', 'e239',
'e240', 'e241', 'e242', 'e243', 'e244', 'e245', 'e246', 'e247', 'e248', 'e249',
'e250', 'e251', 'e252', 'e253', 'e254', 'e255', 'e256', 'e257', 'e258', 'e259']
@pytest.mark.parametrize("enumerated, encoded", [
(DATA_my_enum(SCHEMA_my_enum(enumerationRoot_list=enumeration_list[0:2]), 'e0'), '00'),
(DATA_my_enum(SCHEMA_my_enum(enumerationRoot_list=enumeration_list[0:2]), 'e1'), '80'),
(DATA_my_enum(SCHEMA_my_enum(enumerationRoot_list=enumeration_list[0:10]), 'e9'), '90'),
(DATA_my_enum(SCHEMA_my_enum(enumerationRoot_list=enumeration_list[0:17]), 'e9'), '48'),
(DATA_my_enum(SCHEMA_my_enum(enumerationRoot_list=enumeration_list[0:33]), 'e9'), '24'),
(DATA_my_enum(SCHEMA_my_enum(enumerationRoot_list=enumeration_list[0:33]), 'e32'), '80'),
(DATA_my_enum(SCHEMA_my_enum(enumerationRoot_list=enumeration_list[0:100]), 'e98'), 'C4'),
(DATA_my_enum(SCHEMA_my_enum(enumerationRoot_list=enumeration_list[0:130]), 'e126'), '7E'),
(DATA_my_enum(SCHEMA_my_enum(enumerationRoot_list=enumeration_list[0:130]), 'e127'), '7F'),
(DATA_my_enum(SCHEMA_my_enum(enumerationRoot_list=enumeration_list[0:130]), 'e128'), '80'),
(DATA_my_enum(SCHEMA_my_enum(enumerationRoot_list=enumeration_list[0:260]), 'e128'), '0080'),
(DATA_my_enum(SCHEMA_my_enum(enumerationRoot_list=enumeration_list[0:260]), 'e254'), '00FE'),
(DATA_my_enum(SCHEMA_my_enum(enumerationRoot_list=enumeration_list[0:260]), 'e255'), '00FF'),
(DATA_my_enum(SCHEMA_my_enum(enumerationRoot_list=enumeration_list[0:260]), 'e256'), '0100'),
])
def test_no_extension_marker_enumerated_can_be_encoded(enumerated, encoded):
assert per_encoder(enumerated) == bytearray.fromhex(encoded)
@pytest.mark.parametrize("enumerated, encoded", [
(DATA_my_enum(SCHEMA_my_enum(enumerationRoot_list=enumeration_list[0:2], extensionMarker_value=True), 'e0'), '00'),
(DATA_my_enum(SCHEMA_my_enum(enumerationRoot_list=enumeration_list[0:2], extensionMarker_value=True), 'e1'), '40'),
(DATA_my_enum(SCHEMA_my_enum(enumerationRoot_list=enumeration_list[0:10], extensionMarker_value=True), 'e9'), '48'),
(DATA_my_enum(SCHEMA_my_enum(enumerationRoot_list=enumeration_list[0:17], extensionMarker_value=True), 'e9'), '24'),
(DATA_my_enum(SCHEMA_my_enum(enumerationRoot_list=enumeration_list[0:33], extensionMarker_value=True), 'e9'), '12'),
(DATA_my_enum(SCHEMA_my_enum(enumerationRoot_list=enumeration_list[0:33], extensionMarker_value=True), 'e32'), '40'),
(DATA_my_enum(SCHEMA_my_enum(enumerationRoot_list=enumeration_list[0:100], extensionMarker_value=True), 'e98'), '62'),
(DATA_my_enum(SCHEMA_my_enum(enumerationRoot_list=enumeration_list[0:130], extensionMarker_value=True), 'e126'), '3F00'),
(DATA_my_enum(SCHEMA_my_enum(enumerationRoot_list=enumeration_list[0:130], extensionMarker_value=True), 'e127'), '3F80'),
(DATA_my_enum(SCHEMA_my_enum(enumerationRoot_list=enumeration_list[0:130], extensionMarker_value=True), 'e128'), '4000'),
(DATA_my_enum(SCHEMA_my_enum(enumerationRoot_list=enumeration_list[0:260], extensionMarker_value=True), 'e128'), '000080'),
(DATA_my_enum(SCHEMA_my_enum(enumerationRoot_list=enumeration_list[0:260], extensionMarker_value=True), 'e254'), '0000FE'),
(DATA_my_enum(SCHEMA_my_enum(enumerationRoot_list=enumeration_list[0:260], extensionMarker_value=True), 'e255'), '0000FF'),
(DATA_my_enum(SCHEMA_my_enum(enumerationRoot_list=enumeration_list[0:260], extensionMarker_value=True), 'e256'), '000100'),
])
def test_extension_marker_is_present_and_extension_addition_is_empty_but_value_is_from_root_can_be_encoded(enumerated, encoded):
assert per_encoder(enumerated) == bytearray.fromhex(encoded)
@pytest.mark.parametrize("enumerated, encoded", [
(DATA_my_enum(SCHEMA_my_ext_enum(enumerationRoot_list=enumeration_list[0:2], extensionAddition_list=short_enum, extensionMarker_value=True), 'e0'), '00'),
(DATA_my_enum(SCHEMA_my_ext_enum(enumerationRoot_list=enumeration_list[0:2], extensionAddition_list=short_enum, extensionMarker_value=True), 'e1'), '40'),
(DATA_my_enum(SCHEMA_my_ext_enum(enumerationRoot_list=enumeration_list[0:10], extensionAddition_list=short_enum, extensionMarker_value=True), 'e9'), '48'),
(DATA_my_enum(SCHEMA_my_ext_enum(enumerationRoot_list=enumeration_list[0:17], extensionAddition_list=short_enum, extensionMarker_value=True), 'e9'), '24'),
(DATA_my_enum(SCHEMA_my_ext_enum(enumerationRoot_list=enumeration_list[0:33], extensionAddition_list=short_enum, extensionMarker_value=True), 'e9'), '12'),
(DATA_my_enum(SCHEMA_my_ext_enum(enumerationRoot_list=enumeration_list[0:33], extensionAddition_list=short_enum, extensionMarker_value=True), 'e32'), '40'),
(DATA_my_enum(SCHEMA_my_ext_enum(enumerationRoot_list=enumeration_list[0:100], extensionAddition_list=short_enum, extensionMarker_value=True), 'e98'), '62'),
(DATA_my_enum(SCHEMA_my_ext_enum(enumerationRoot_list=enumeration_list[0:130], extensionAddition_list=short_enum, extensionMarker_value=True), 'e126'), '3F00'),
(DATA_my_enum(SCHEMA_my_ext_enum(enumerationRoot_list=enumeration_list[0:130], extensionAddition_list=short_enum, extensionMarker_value=True), 'e127'), '3F80'),
(DATA_my_enum(SCHEMA_my_ext_enum(enumerationRoot_list=enumeration_list[0:130], extensionAddition_list=short_enum, extensionMarker_value=True), 'e128'), '4000'),
(DATA_my_enum(SCHEMA_my_ext_enum(enumerationRoot_list=enumeration_list[0:260], extensionAddition_list=short_enum, extensionMarker_value=True), 'e128'), '000080'),
(DATA_my_enum(SCHEMA_my_ext_enum(enumerationRoot_list=enumeration_list[0:260], extensionAddition_list=short_enum, extensionMarker_value=True), 'e254'), '0000FE'),
(DATA_my_enum(SCHEMA_my_ext_enum(enumerationRoot_list=enumeration_list[0:260], extensionAddition_list=short_enum, extensionMarker_value=True), 'e255'), '0000FF'),
(DATA_my_enum(SCHEMA_my_ext_enum(enumerationRoot_list=enumeration_list[0:260], extensionAddition_list=short_enum, extensionMarker_value=True), 'e256'), '000100'),
])
def test_extension_marker_is_present_and_extension_addition_is_not_empty_but_value_is_from_root_can_be_encoded(enumerated, encoded):
assert per_encoder(enumerated) == bytearray.fromhex(encoded)
@pytest.mark.parametrize("enumerated, encoded", [
(DATA_my_enum(SCHEMA_my_ext_enum(enumerationRoot_list=short_enum, extensionAddition_list=enumeration_list[0:2], extensionMarker_value=True), 'e0'), '80'),
(DATA_my_enum(SCHEMA_my_ext_enum(enumerationRoot_list=short_enum, extensionAddition_list=enumeration_list[0:2], extensionMarker_value=True), 'e1'), '81'),
(DATA_my_enum(SCHEMA_my_ext_enum(enumerationRoot_list=short_enum, extensionAddition_list=enumeration_list[0:10], extensionMarker_value=True), 'e9'), '89'),
(DATA_my_enum(SCHEMA_my_ext_enum(enumerationRoot_list=short_enum, extensionAddition_list=enumeration_list[0:17], extensionMarker_value=True), 'e9'), '89'),
(DATA_my_enum(SCHEMA_my_ext_enum(enumerationRoot_list=short_enum, extensionAddition_list=enumeration_list[0:33], extensionMarker_value=True), 'e9'), '89'),
(DATA_my_enum(SCHEMA_my_ext_enum(enumerationRoot_list=short_enum, extensionAddition_list=enumeration_list[0:33], extensionMarker_value=True), 'e32'), 'A0'),
(DATA_my_enum(SCHEMA_my_ext_enum(enumerationRoot_list=short_enum, extensionAddition_list=enumeration_list[0:100], extensionMarker_value=True), 'e98'), 'C00162'),
(DATA_my_enum(SCHEMA_my_ext_enum(enumerationRoot_list=short_enum, extensionAddition_list=enumeration_list[0:130], extensionMarker_value=True), 'e126'), 'C0017E'),
(DATA_my_enum(SCHEMA_my_ext_enum(enumerationRoot_list=short_enum, extensionAddition_list=enumeration_list[0:130], extensionMarker_value=True), 'e127'), 'C0017F'),
(DATA_my_enum(SCHEMA_my_ext_enum(enumerationRoot_list=short_enum, extensionAddition_list=enumeration_list[0:130], extensionMarker_value=True), 'e128'), 'C00180'),
(DATA_my_enum(SCHEMA_my_ext_enum(enumerationRoot_list=short_enum, extensionAddition_list=enumeration_list[0:260], extensionMarker_value=True), 'e128'), 'C00180'),
(DATA_my_enum(SCHEMA_my_ext_enum(enumerationRoot_list=short_enum, extensionAddition_list=enumeration_list[0:260], extensionMarker_value=True), 'e254'), 'C001FE'),
(DATA_my_enum(SCHEMA_my_ext_enum(enumerationRoot_list=short_enum, extensionAddition_list=enumeration_list[0:260], extensionMarker_value=True), 'e255'), 'C001FF'),
(DATA_my_enum(SCHEMA_my_ext_enum(enumerationRoot_list=short_enum, extensionAddition_list=enumeration_list[0:260], extensionMarker_value=True), 'e256'), 'C0020100'),
])
def test_extension_marker_is_present_and_value_is_from_extension_can_be_encoded(enumerated, encoded):
assert per_encoder(enumerated) == bytearray.fromhex(encoded)
| 73.225989
| 168
| 0.682509
| 1,579
| 12,961
| 5.259658
| 0.233692
| 0.062131
| 0.160626
| 0.107887
| 0.824684
| 0.821433
| 0.803131
| 0.794582
| 0.783504
| 0.776159
| 0
| 0.105946
| 0.155235
| 12,961
| 176
| 169
| 73.642045
| 0.652571
| 0.015894
| 0
| 0.204724
| 0
| 0
| 0.110605
| 0
| 0
| 0
| 0
| 0
| 0.031496
| 1
| 0.055118
| false
| 0
| 0.03937
| 0.007874
| 0.19685
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
0041a6d8129ee5f06d99b37cd61cca2482630a2f
| 84
|
py
|
Python
|
Chinese/HPS3D_SDK_ROS_Demo/devel/lib/python2.7/dist-packages/hps_camera/msg/__init__.py
|
hypersen/HPS3D_SDK
|
dcafdb0aac1a38db2c3424c6dbc0864e108cd7fc
|
[
"MIT"
] | 13
|
2019-04-30T02:23:59.000Z
|
2021-11-12T04:05:21.000Z
|
English/HPS3D_SDK_ROS_Demo/devel/lib/python2.7/dist-packages/hps_camera/msg/__init__.py
|
hypersen/HPS3D_SDK
|
dcafdb0aac1a38db2c3424c6dbc0864e108cd7fc
|
[
"MIT"
] | 3
|
2019-10-24T03:49:21.000Z
|
2020-03-09T01:33:45.000Z
|
Chinese/HPS3D_SDK_ROS_Demo/devel/lib/python2.7/dist-packages/hps_camera/msg/__init__.py
|
hypersen/HPS3D_SDK
|
dcafdb0aac1a38db2c3424c6dbc0864e108cd7fc
|
[
"MIT"
] | 4
|
2019-10-23T02:06:02.000Z
|
2020-11-19T08:49:56.000Z
|
from ._PointCloudData import *
from ._distance import *
from ._measureData import *
| 21
| 30
| 0.785714
| 9
| 84
| 7
| 0.555556
| 0.31746
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.142857
| 84
| 3
| 31
| 28
| 0.875
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
cc4c1e54fba50bcdc91d167be088d9b7b84f2552
| 11,209
|
py
|
Python
|
day15_p1.py
|
venomousmoog/adventofcode2021
|
0111db027954ab38d6c17e0c8048df3449cb90d2
|
[
"Apache-2.0"
] | null | null | null |
day15_p1.py
|
venomousmoog/adventofcode2021
|
0111db027954ab38d6c17e0c8048df3449cb90d2
|
[
"Apache-2.0"
] | null | null | null |
day15_p1.py
|
venomousmoog/adventofcode2021
|
0111db027954ab38d6c17e0c8048df3449cb90d2
|
[
"Apache-2.0"
] | null | null | null |
import heapq
import sys
def compute(data):
map = [[int(e) for e in l] for l in data.split('\n')]
w = len(map)
h = len(map[0])
end = (w-1, h-1)
# pq format is [cost, length, counter, position]
q = [[0, 0, 0, (0,0)]]
counter = 1
visited = set([(0,0)])
while len(q) > 0:
cost, length, _, p = heapq.heappop(q)
# print(f'{cost}, {length}, {_}, {p}')
if p == end:
print(cost)
return
# add neighbors if they aren't in the path already:
neighbors = [(p[0], p[1]+1), (p[0], p[1]-1), (p[0]+1, p[1]), (p[0]-1, p[1])]
for n in neighbors:
if not n in visited and not (n[0] < 0 or n[0] >= w or n[1] < 0 or n[1] >= h):
# print(f'n = ({n[0]}, {n[1]})')
heapq.heappush(q, [cost + map[n[0]][n[1]], length+1, counter, n])
counter = counter + 1
visited.add(n)
test_data = """1163751742
1381373672
2136511328
3694931569
7463417111
1319128137
1359912421
3125421639
1293138521
2311944581"""
data="""4395194575929822238989941988598994946581953236424841813955769288219282998336161199274582725132193662
1715719228759138638397684864996679719167258159599658826174926447916886499139963731181569842792979836
5641957947399279962631596818774779918898868615738299911674929785663922472189972649893918935926989965
2585497851318911812329521892518876883669883794214786673934129871993567882759933254875129861732949942
6326121293629918828545199576444799485199997872984968987116189717399321966789925619859799917896971919
1197891193163157699998465137799499428622885997715629793728799611991922169771186415959393796833328797
9971941176236517711968176833694122359799994332972933319744917758263654615939671218999722759897946883
8999294422711677672138234871949951867361994331595339178487499997983949131794946775549222972399289896
3349291493887252933563637858998292429461181979119894978282958316785791165839546981395193797183659157
7189439835616936936452916284895714389978973983979946899115361688238479929831181859649911588342717174
9919687931991844963923118455824186376858799159986659568599889942949751127924998243532126861192227813
8556752119948211196297781958736978151712384292658126551391659754132796214991112228251319499881769877
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8229274989319485131759119628887993878981894123939971871579472951958128273735938172877719459979616966
9499516139937388199431887411757519697113346973918231323926539878654177977781846978129796428916386862
4976998292388572314456265734789687366686748398788899297684913137779698579739992995231592719964862978
2298177775583527949898397959425767832825991552239939999392925878819892226485971811999876289184259875
3598947472448691635139959991352673979496318718231961399989962929352899269143994934946299795998128783
7112174898192377964819299748639161162263488897467958394886818188981289197818113459389985525596199819
9987787819769819287382267885519776534891123996871992292115788186562861498191537481789591841846186882
4932928968769839163293479931958989358947715254682158299764179952675896919281473293998826971956719781
9992216325832365211598714373826949876761198899344963795899592692291996816722739297999357184959969777
8299492186897712559157176181191385891631253997468944991976578985988414979191999779899999718894199893
9599634119932298479281989351167939998231691111219295175188529818284912884666292812879581588751975666
9893411889151989895121929229639869648431989868714855378743883971235628399646641369715828182779286514
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8193892754125669889688695711759861961188261942984936153189888489199611811681269985959959968811384341
8299917437379188117917628771687562449889987984991225519931596159725913681979787939969976651741749698
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4798696188279263112441176132921127173319858683256972916675925279917946959468161918992879929186429996
1875628214911419676889344457796569819463131975985822779517679476958819822619947289779578492991712138
9729987223699868162597477587276969892989969399943929635225993221991199187491492967948629623789668744
9916111911755127191948896779129983784992661981998299971599989198761289123181443629292169995715974949
1291917878889332658369889135368111113759781552671763529821597651871398558593277892399994199844188899
8857659891128611558849143993177991511814858869793389498556946631129988799922937638978992359857839357
9498927473319717382175369484488993971151116118963956979372884748999382475159478948297342397888172486
8838311993693897959989699851182958819758515849648216159982472145775919486496793748386329827875114795
2646323635456985931835993825299725819616464197571881778646919914938572377378887919941193912366189981
4192777568853894179559191115999836896832987112829422358328999616261299876112799681459689496973841216
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4279199328962391513117193251941896142249199541185874189116658753849872967489875833815972485753775799
2692166517168484891439119972374885161996829937192277189246449949998287236914964598317589854294191818
9691899795175315938351268169518149836746199789392955191246577759192964575629739479919199793594669346
4928854341918568913973278223498427458884195453535861967991191927617293996995778496126372492999583688
1869735122593925853281477159999674511464912381748647211595414615939131393112325927225913832758726989
8673156999937894893938221988172251959669284384873549417558434149199712793826799147878341238872169799
3786244397665631994388328169996499172551393184212436839161983999116249888235795513969896377319987648
3299841254618376148274689819516491144697712179196959898422484829331951929556251191292922792943475111
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9447171299311825169951839995999391971743173914736767913282767941796338297879485196369168312316114619
8965937476731877998867589955999781971348978541526728915989299973637949395212351839999632681871119852
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5863889995599382179127198593932798869731787297368813494929776247986339942885147596969931861841289387
8291299218914728523912933158961272669279911995972979992183926289243118311811961558131192613197929186
8961913468988371188879695178682994979962878269287243998918854923819591848296141587688276923892888765
5496139281898589971291971391186884911787687869879279111147189911627194976288937484497924811159764722
9875395289318577859812192389977827241588661341589921792229379945659193616899945199691585158611997752
9897872534836989139768375684846127999667957297219249999952812149314798361868111441889987449192925623
9138263544984999193841859937147586599945426897796171621827994461911885818295944939999799882269659399
6914856489683269528879792799877759191976961973971938559173939387419513299922889959976555221521424697
1715191986151162997669282366919988483318991958172785998996643612786988727959277619145619891677993797
9942283447167676428491385853611565159691399722133269739927118911631657887991431834279716429273499866
4799972923699768188516165542947747617263791192642512198299894199889995551779128118616736724861699788
4958919625759529361425491889628879199991912868912939317496198948944989139197771291619455967185949537
2223977898998997958945226793679996855922957491361985987152262111657894546195945683493497719917994917
2169115388563391991925213886793982979499187969173993849967129291479453796965869487632218484794186428
8181979148789991463199414217818677797879614992639757619958818656897787992873919194129981917198598753
7298898662149861558411999137899133141939277162229327197891618169842134976193137994322715445297289799
3295636966178697931489122998118891198718116639897914658911799996899497168942241323161993721164174647
1198816571471558697411816978791898982893995992519493175459877178916533784561911924112689959897991892
5618962846185938989183714393155171999657459292691693826899689462277599981997875426218165946485993981
9347291669523992969899819288568678793199991148799181869131292289712895996712494789999957779656718174
1211541591292966678912459115993944851245448935997663676619889855226196181522818261837465192373166996
1797339189173753393939899772787971418172195729879466938989599928482964996915184691991154998224136189
3421551897114792849579376681776925441448992599913934985689149424792976946888488111919491919499858968
1971473988843177925911719936118862197889673997928889139314775919195258317837961344298715732432565457"""
compute(data)
| 75.228188
| 108
| 0.940673
| 270
| 11,209
| 39.040741
| 0.592593
| 0.001138
| 0.000854
| 0.000759
| 0.001897
| 0.001897
| 0.001138
| 0
| 0
| 0
| 0
| 0.943059
| 0.041128
| 11,209
| 149
| 109
| 75.228188
| 0.037681
| 0.014631
| 0
| 0
| 0
| 0
| 0.924819
| 0.905797
| 0
| 1
| 0
| 0
| 0
| 1
| 0.007576
| false
| 0
| 0.015152
| 0
| 0.030303
| 0.007576
| 0
| 0
| 1
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
cca6ce054f235573223e0acef42ab3b27c27d276
| 4,094
|
py
|
Python
|
tests/test_hotfix_class.py
|
wo1fsea/PyHotfixer
|
c80088618e0f469743335119c9451b84e42d3312
|
[
"MIT"
] | null | null | null |
tests/test_hotfix_class.py
|
wo1fsea/PyHotfixer
|
c80088618e0f469743335119c9451b84e42d3312
|
[
"MIT"
] | 1
|
2019-08-24T16:08:15.000Z
|
2019-08-24T16:08:15.000Z
|
tests/test_hotfix_class.py
|
wo1fsea/PyHotfixer
|
c80088618e0f469743335119c9451b84e42d3312
|
[
"MIT"
] | null | null | null |
# -*- coding: utf-8 -*-
"""----------------------------------------------------------------------------
Author:
Huang Quanyong
[email protected]
Date:
2019/8/19
Description:
test_hotfix_class.py
----------------------------------------------------------------------------"""
import unittest
import os
import sys
import shutil
from pyhotfixer import hotfix
FILE_NAME = "class_test.py"
FILE_NAME_V1 = "class_test_v1.py"
FILE_NAME_V2 = "class_test_v2.py"
class HotfixClassTestCase(unittest.TestCase):
def setUp(self):
cur_dir = os.path.dirname(os.path.abspath(__file__))
self.module_file = os.path.join(cur_dir, FILE_NAME)
self.module_file_v1 = os.path.join(cur_dir, FILE_NAME_V1)
self.module_file_v2 = os.path.join(cur_dir, FILE_NAME_V2)
if os.path.exists(self.module_file):
os.remove(self.module_file)
def tearDown(self):
if os.path.exists(self.module_file):
os.remove(self.module_file)
def test_hotfix_class(self):
shutil.copy(self.module_file_v1, self.module_file)
sys.modules.pop("class_test", None)
import class_test
hotfix_class_obj = class_test.HotfixClass()
self.assertEqual(hotfix_class_obj.no_hotfix_data, 1)
self.assertEqual(hotfix_class_obj.hotfix_data, 1)
self.assertEqual(hotfix_class_obj.no_hotfix_method(), 1)
self.assertEqual(hotfix_class_obj.hotfix_method(), 1)
self.assertEqual(hotfix_class_obj.no_hotfix_classmethod(), 1)
self.assertEqual(hotfix_class_obj.hotfix_classmethod(), 1)
self.assertEqual(hotfix_class_obj.hotfix_property, 1)
self.assertEqual(hotfix_class_obj.no_hotfix_property, 1)
self.assertEqual(hotfix_class_obj.replace_data_with_func, 1)
self.assertEqual(hotfix_class_obj.replace_data_with_static_method, 1)
self.assertEqual(hotfix_class_obj.replace_data_with_class_method, 1)
self.assertEqual(class_test.HotfixClass.InnerClass1.func(), 1)
self.assertEqual(class_test.HotfixClass.InnerClass2.func(), 1)
self.assertEqual(class_test.HotfixClass.InnerClass, class_test.HotfixClass.InnerClass1)
no_hotfix_class_obj = class_test.NoHotfixClass()
self.assertEqual(no_hotfix_class_obj.no_hotfix_data, 1)
self.assertEqual(no_hotfix_class_obj.no_hotfix_method(), 1)
another_no_hotfix_class_obj = class_test.AnotherNoHotfixClass()
self.assertEqual(another_no_hotfix_class_obj.no_hotfix_data, 1)
self.assertEqual(another_no_hotfix_class_obj.no_hotfix_method(), 1)
shutil.copy(self.module_file_v2, self.module_file)
hotfix(["class_test"])
self.assertEqual(hotfix_class_obj.no_hotfix_data, 1)
self.assertEqual(hotfix_class_obj.hotfix_data, 2)
self.assertEqual(hotfix_class_obj.no_hotfix_method(), 1)
self.assertEqual(hotfix_class_obj.hotfix_method(), 2)
self.assertEqual(hotfix_class_obj.no_hotfix_classmethod(), 1)
self.assertEqual(hotfix_class_obj.hotfix_classmethod(), 2)
self.assertEqual(hotfix_class_obj.no_hotfix_property, 1)
self.assertEqual(hotfix_class_obj.hotfix_property, 2)
self.assertEqual(hotfix_class_obj.replace_data_with_func(), 2)
self.assertEqual(hotfix_class_obj.replace_data_with_static_method(), 2)
self.assertEqual(hotfix_class_obj.replace_data_with_class_method(), 2)
self.assertEqual(class_test.HotfixClass.InnerClass1.func(), 2)
self.assertEqual(class_test.HotfixClass.InnerClass2.func(), 2)
self.assertEqual(class_test.HotfixClass.InnerClass, class_test.HotfixClass.InnerClass2)
no_hotfix_class_obj = class_test.NoHotfixClass()
self.assertEqual(no_hotfix_class_obj.no_hotfix_data, 1)
self.assertEqual(no_hotfix_class_obj.no_hotfix_method(), 1)
another_no_hotfix_class_obj = class_test.AnotherNoHotfixClass()
self.assertEqual(another_no_hotfix_class_obj.no_hotfix_data, 1)
self.assertEqual(another_no_hotfix_class_obj.no_hotfix_method(), 1)
| 40.137255
| 95
| 0.711285
| 534
| 4,094
| 5.06367
| 0.129213
| 0.154586
| 0.181213
| 0.211538
| 0.807322
| 0.781065
| 0.780325
| 0.678254
| 0.667899
| 0.579882
| 0
| 0.01686
| 0.159746
| 4,094
| 102
| 96
| 40.137255
| 0.769186
| 0.072789
| 0
| 0.363636
| 0
| 0
| 0.017146
| 0
| 0
| 0
| 0
| 0
| 0.545455
| 1
| 0.045455
| false
| 0
| 0.090909
| 0
| 0.151515
| 0
| 0
| 0
| 0
| null | 0
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
aea98b7c660918a200179398adca2ee696d030ce
| 69
|
py
|
Python
|
_ext/python/crawlab/client/__init__.py
|
crawlab-team/crawlab-python-sdk
|
35f83f8d76046d3ee2700d63e96624ed534c1ca5
|
[
"BSD-3-Clause"
] | null | null | null |
_ext/python/crawlab/client/__init__.py
|
crawlab-team/crawlab-python-sdk
|
35f83f8d76046d3ee2700d63e96624ed534c1ca5
|
[
"BSD-3-Clause"
] | null | null | null |
_ext/python/crawlab/client/__init__.py
|
crawlab-team/crawlab-python-sdk
|
35f83f8d76046d3ee2700d63e96624ed534c1ca5
|
[
"BSD-3-Clause"
] | null | null | null |
from .request import *
from .response import *
from .client import *
| 17.25
| 23
| 0.73913
| 9
| 69
| 5.666667
| 0.555556
| 0.392157
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.173913
| 69
| 3
| 24
| 23
| 0.894737
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
aeb44b2e87a480204cd75439d61dcdeb9f902a63
| 44
|
py
|
Python
|
turbulenz_local/lib/__init__.py
|
turbulenz/turbulenz_local
|
3c96c1229514dcd4893b7055599f55ed185db95a
|
[
"MIT"
] | 12
|
2015-01-26T16:15:30.000Z
|
2022-03-17T19:29:34.000Z
|
turbulenz_local/lib/__init__.py
|
mcanthony/turbulenz_local
|
3c96c1229514dcd4893b7055599f55ed185db95a
|
[
"MIT"
] | null | null | null |
turbulenz_local/lib/__init__.py
|
mcanthony/turbulenz_local
|
3c96c1229514dcd4893b7055599f55ed185db95a
|
[
"MIT"
] | 5
|
2015-02-23T13:39:14.000Z
|
2021-02-14T15:35:43.000Z
|
# Copyright (c) 2011,2013 Turbulenz Limited
| 22
| 43
| 0.772727
| 6
| 44
| 5.666667
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.210526
| 0.136364
| 44
| 1
| 44
| 44
| 0.684211
| 0.931818
| 0
| null | 0
| null | 0
| 0
| null | 0
| 0
| 0
| null | 1
| null | true
| 0
| 0
| null | null | null | 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
4e1adb9dddcda20dec1e35de3a167c83ab5d7ef4
| 293
|
py
|
Python
|
grr/server/grr_response_server/check_lib/__init__.py
|
khanhgithead/grr
|
8ad8a4d2c5a93c92729206b7771af19d92d4f915
|
[
"Apache-2.0"
] | 4,238
|
2015-01-01T15:34:50.000Z
|
2022-03-31T08:18:05.000Z
|
grr/server/grr_response_server/check_lib/__init__.py
|
khanhgithead/grr
|
8ad8a4d2c5a93c92729206b7771af19d92d4f915
|
[
"Apache-2.0"
] | 787
|
2015-01-02T21:34:24.000Z
|
2022-03-02T13:26:38.000Z
|
grr/server/grr_response_server/check_lib/__init__.py
|
khanhgithead/grr
|
8ad8a4d2c5a93c92729206b7771af19d92d4f915
|
[
"Apache-2.0"
] | 856
|
2015-01-02T02:50:11.000Z
|
2022-03-31T11:11:53.000Z
|
#!/usr/bin/env python
"""This is the check capabilities used to post-process host data."""
# pylint: disable=g-import-not-at-top,unused-import
from grr_response_server.check_lib import checks
from grr_response_server.check_lib import hints
from grr_response_server.check_lib import triggers
| 32.555556
| 68
| 0.8157
| 48
| 293
| 4.791667
| 0.645833
| 0.091304
| 0.195652
| 0.273913
| 0.456522
| 0.456522
| 0.456522
| 0
| 0
| 0
| 0
| 0
| 0.098976
| 293
| 8
| 69
| 36.625
| 0.871212
| 0.453925
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
4e29f4a6ddaa3fbe68306b2d5b1a9c2d2b637e6e
| 52
|
py
|
Python
|
tests/test_foo.py
|
AlexDev-py/vkquick
|
ef21d71e03b92ac4a2da4b63b6cc13d0e12ab674
|
[
"MIT"
] | 1
|
2021-05-27T10:13:50.000Z
|
2021-05-27T10:13:50.000Z
|
tests/test_foo.py
|
AlexDev-py/vkquick
|
ef21d71e03b92ac4a2da4b63b6cc13d0e12ab674
|
[
"MIT"
] | null | null | null |
tests/test_foo.py
|
AlexDev-py/vkquick
|
ef21d71e03b92ac4a2da4b63b6cc13d0e12ab674
|
[
"MIT"
] | null | null | null |
def test_run():
import vkquick
assert True
| 10.4
| 18
| 0.653846
| 7
| 52
| 4.714286
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.288462
| 52
| 4
| 19
| 13
| 0.891892
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.333333
| 1
| 0.333333
| true
| 0
| 0.333333
| 0
| 0.666667
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
9d62960a19296d3b6b3b3dc5c0021f2c5e0a5dc1
| 43
|
py
|
Python
|
joke teller.py
|
naitikvaru/python_joke_teller
|
d46817c3100db1a8bb8c720c37a8e23d2791e792
|
[
"Apache-2.0"
] | 1
|
2021-01-25T09:30:05.000Z
|
2021-01-25T09:30:05.000Z
|
joke teller.py
|
naitikvaru/python_joke_teller
|
d46817c3100db1a8bb8c720c37a8e23d2791e792
|
[
"Apache-2.0"
] | null | null | null |
joke teller.py
|
naitikvaru/python_joke_teller
|
d46817c3100db1a8bb8c720c37a8e23d2791e792
|
[
"Apache-2.0"
] | null | null | null |
import pyjokes
print(pyjokes.get_joke())
| 14.333333
| 26
| 0.767442
| 6
| 43
| 5.333333
| 0.833333
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.116279
| 43
| 2
| 27
| 21.5
| 0.842105
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.5
| 0
| 0.5
| 0.5
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 1
|
0
| 6
|
9dc180c8f96c452d5b34c28555523efd9495758d
| 92
|
py
|
Python
|
protopigeon/__init__.py
|
gregorynicholas/proto-pigeon
|
65a5d961e7a8506f3a968b21aaf68f625fd13190
|
[
"Apache-2.0"
] | null | null | null |
protopigeon/__init__.py
|
gregorynicholas/proto-pigeon
|
65a5d961e7a8506f3a968b21aaf68f625fd13190
|
[
"Apache-2.0"
] | null | null | null |
protopigeon/__init__.py
|
gregorynicholas/proto-pigeon
|
65a5d961e7a8506f3a968b21aaf68f625fd13190
|
[
"Apache-2.0"
] | null | null | null |
from protorpc.messages import *
from protorpc.protojson import *
from .translators import *
| 23
| 32
| 0.804348
| 11
| 92
| 6.727273
| 0.545455
| 0.324324
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.130435
| 92
| 3
| 33
| 30.666667
| 0.925
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
9dc27fe56cbaefc95334e4f2be612f9a88fe50b5
| 722
|
py
|
Python
|
src/tests/test_inference.py
|
Islandora-Image-Segmentation/Newspaper-Navigator-API
|
99a47e8e43c42252d3e56ea2a6e8159ef8ef6ab9
|
[
"Unlicense"
] | null | null | null |
src/tests/test_inference.py
|
Islandora-Image-Segmentation/Newspaper-Navigator-API
|
99a47e8e43c42252d3e56ea2a6e8159ef8ef6ab9
|
[
"Unlicense"
] | 5
|
2021-02-03T22:21:09.000Z
|
2021-04-13T18:23:23.000Z
|
src/tests/test_inference.py
|
Islandora-Image-Segmentation/Newspaper-Navigator-API
|
99a47e8e43c42252d3e56ea2a6e8159ef8ef6ab9
|
[
"Unlicense"
] | 1
|
2021-01-21T20:53:12.000Z
|
2021-01-21T20:53:12.000Z
|
import os
from PIL import Image
from inference import predict
from . import TEST_ASSETS_DIR
def test_inference_one():
""" Test for the segmentation ML model.
This test requires the model weights `model_final.pth` to be present in src/resources.
"""
img = Image.open(os.path.join(TEST_ASSETS_DIR, "test_image_one.png"))
result = predict(img)
assert len(result.bounding_boxes) > 0
def test_inference_two():
""" Test for the segmentation ML model.
This test requires the model weights `model_final.pth` to be present in src/resources.
"""
img = Image.open(os.path.join(TEST_ASSETS_DIR, "test_image_two.png"))
result = predict(img)
assert len(result.bounding_boxes) > 0
| 30.083333
| 91
| 0.716066
| 109
| 722
| 4.577982
| 0.366972
| 0.06012
| 0.078156
| 0.088176
| 0.761523
| 0.761523
| 0.761523
| 0.761523
| 0.761523
| 0.761523
| 0
| 0.003413
| 0.188366
| 722
| 23
| 92
| 31.391304
| 0.848123
| 0.342105
| 0
| 0.333333
| 0
| 0
| 0.081264
| 0
| 0
| 0
| 0
| 0
| 0.166667
| 1
| 0.166667
| false
| 0
| 0.333333
| 0
| 0.5
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
|
0
| 6
|
9dfccceac3794b799a0a49e9d8a55e6c045cd319
| 231
|
py
|
Python
|
template/osi/__base__.py
|
clayne/syringe-1
|
4a431aa65c371a2018fca95145a3952ba802a609
|
[
"BSD-2-Clause"
] | 25
|
2015-04-14T21:53:46.000Z
|
2022-03-30T19:15:24.000Z
|
template/osi/__base__.py
|
clayne/syringe-1
|
4a431aa65c371a2018fca95145a3952ba802a609
|
[
"BSD-2-Clause"
] | 5
|
2020-03-23T20:19:59.000Z
|
2021-05-24T19:38:31.000Z
|
template/osi/__base__.py
|
clayne/syringe-1
|
4a431aa65c371a2018fca95145a3952ba802a609
|
[
"BSD-2-Clause"
] | 7
|
2015-07-31T13:26:37.000Z
|
2021-03-05T19:35:37.000Z
|
from ptypes import ptype
class stackable:
def nextlayer(self):
'''returns a tuple of (type,remaining)'''
raise NotImplementedError
class terminal(stackable):
def nextlayer(self):
return None, None
| 21
| 49
| 0.670996
| 26
| 231
| 5.961538
| 0.769231
| 0.154839
| 0.270968
| 0.322581
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.242424
| 231
| 10
| 50
| 23.1
| 0.885714
| 0.151515
| 0
| 0.285714
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.285714
| false
| 0
| 0.142857
| 0.142857
| 0.857143
| 0
| 1
| 0
| 0
| null | 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 1
| 0
|
0
| 6
|
d1bfed058e2615e29f744e8f5f8be9cb60aa782d
| 28
|
py
|
Python
|
app/viewer/__init__.py
|
Dbrown411/py-oct
|
e5b56344f864a8c00fe9ec1a00adbacfe833d27e
|
[
"MIT"
] | null | null | null |
app/viewer/__init__.py
|
Dbrown411/py-oct
|
e5b56344f864a8c00fe9ec1a00adbacfe833d27e
|
[
"MIT"
] | null | null | null |
app/viewer/__init__.py
|
Dbrown411/py-oct
|
e5b56344f864a8c00fe9ec1a00adbacfe833d27e
|
[
"MIT"
] | null | null | null |
from .viewer_panels import *
| 28
| 28
| 0.821429
| 4
| 28
| 5.5
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.107143
| 28
| 1
| 28
| 28
| 0.88
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
ae3b1822a80991fb2229b2bb27e0703919ff258a
| 171
|
py
|
Python
|
utility/__init__.py
|
Krishna10798/Multi-User-Blog
|
a7b177acc4f900bfa92d00435c94ffbde1570496
|
[
"MIT"
] | 5
|
2016-06-03T17:25:31.000Z
|
2017-10-01T19:06:53.000Z
|
utility/__init__.py
|
Krishna10798/Multi-User-Blog
|
a7b177acc4f900bfa92d00435c94ffbde1570496
|
[
"MIT"
] | 6
|
2017-01-22T20:13:22.000Z
|
2017-02-07T21:32:01.000Z
|
utility/__init__.py
|
Krishna10798/Multi-User-Blog
|
a7b177acc4f900bfa92d00435c94ffbde1570496
|
[
"MIT"
] | 2
|
2017-04-05T10:45:35.000Z
|
2020-10-02T08:17:56.000Z
|
from utility import hash_str, check_secure_val, make_secure_val,\
valid_email, valid_username, valid_password
from filters import filterKey, showCount
| 42.75
| 65
| 0.760234
| 22
| 171
| 5.545455
| 0.727273
| 0.147541
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.19883
| 171
| 3
| 66
| 57
| 0.890511
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0.333333
| 0.666667
| 0
| 0.666667
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 1
| 0
| 1
| 0
|
0
| 6
|
882f2dbb562039181c8b403715d1e5532ef471d5
| 77
|
py
|
Python
|
footer/menus/__init__.py
|
AutomataRaven/azaharTEA
|
2d5a7d96b37bca9b3a914e305e493824a0f60207
|
[
"MIT"
] | 5
|
2019-03-10T16:33:21.000Z
|
2021-04-07T17:24:32.000Z
|
footer/menus/__init__.py
|
Errantgod/azaharTEA
|
2d5a7d96b37bca9b3a914e305e493824a0f60207
|
[
"MIT"
] | 8
|
2017-02-11T06:21:28.000Z
|
2017-02-22T05:50:35.000Z
|
footer/menus/__init__.py
|
Errantgod/azaharTEA
|
2d5a7d96b37bca9b3a914e305e493824a0f60207
|
[
"MIT"
] | 2
|
2019-10-05T20:20:15.000Z
|
2020-06-28T18:46:58.000Z
|
__all__ = ['highlightmenu.HighlightMenu','highlightmenu.HighligthStyleMenu']
| 38.5
| 76
| 0.831169
| 5
| 77
| 12
| 0.6
| 0.866667
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.038961
| 77
| 1
| 77
| 77
| 0.810811
| 0
| 0
| 0
| 0
| 0
| 0.766234
| 0.766234
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
884fbbaaf1972040f6f08afa8a95355013cc1899
| 3,465
|
py
|
Python
|
test/test_apktool/test_repack.py
|
mehrdad-shokri/QT4A
|
d74a8a29e09df82e95158304ecfc05cd51e6ffa7
|
[
"BSD-3-Clause"
] | 343
|
2016-09-27T11:20:53.000Z
|
2022-01-08T15:49:02.000Z
|
test/test_apktool/test_repack.py
|
mehrdad-shokri/QT4A
|
d74a8a29e09df82e95158304ecfc05cd51e6ffa7
|
[
"BSD-3-Clause"
] | 51
|
2018-11-07T03:08:51.000Z
|
2021-12-29T06:37:20.000Z
|
test/test_apktool/test_repack.py
|
mehrdad-shokri/QT4A
|
d74a8a29e09df82e95158304ecfc05cd51e6ffa7
|
[
"BSD-3-Clause"
] | 88
|
2016-09-28T04:32:12.000Z
|
2022-03-10T07:08:23.000Z
|
# -*- coding: UTF-8 -*-
#
# Tencent is pleased to support the open source community by making QTA available.
# Copyright (C) 2016THL A29 Limited, a Tencent company. All rights reserved.
# Licensed under the BSD 3-Clause License (the "License"); you may not use this
# file except in compliance with the License. You may obtain a copy of the License at
#
# https://opensource.org/licenses/BSD-3-Clause
#
# 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.
#
'''repack.py unittest
'''
try:
from unittest import mock
except:
import mock
import os
import tempfile
import unittest
from qt4a.apktool import apkfile, repack
class TestRepack(unittest.TestCase):
def test_get_apk_signature(self):
apk_path = os.path.join(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))), 'qt4a', 'androiddriver', 'tools', 'QT4AHelper.apk')
apk_file = apkfile.APKFile(apk_path)
for it in apk_file.list_dir('META-INF'):
if it.lower().endswith('.rsa'):
tmp_rsa_path = tempfile.mkstemp('.rsa')[1]
apk_file.extract_file('META-INF/%s' % it, tmp_rsa_path)
orig_signature = repack.get_apk_signature(tmp_rsa_path).strip()
self.assertEqual(orig_signature, '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')
return
else:
raise RuntimeError('No signature file found in QT4AHelper.apk')
if __name__ == '__main__':
unittest.main()
| 70.714286
| 1,818
| 0.849639
| 236
| 3,465
| 12.338983
| 0.563559
| 0.020604
| 0.013393
| 0.015453
| 0.015453
| 0.015453
| 0.015453
| 0.015453
| 0
| 0
| 0
| 0.420495
| 0.101587
| 3,465
| 49
| 1,819
| 70.714286
| 0.514937
| 0.206061
| 0
| 0
| 0
| 0
| 0.687408
| 0.646413
| 0
| 1
| 0
| 0
| 0.043478
| 1
| 0.043478
| false
| 0
| 0.26087
| 0
| 0.391304
| 0
| 0
| 0
| 1
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
88620e63502d6b10ac3da1f667be0aa99c61bea9
| 112
|
py
|
Python
|
src/DeepMatter/VAE_PFM/__init__.py
|
m3-learning/DeepMatter
|
54828a7972bf7912b848b0682b4e4452daa078cb
|
[
"BSD-3-Clause"
] | 2
|
2021-06-13T21:47:37.000Z
|
2021-06-15T03:24:24.000Z
|
src/DeepMatter/VAE_PFM/__init__.py
|
m3-learning/DeepMatter
|
54828a7972bf7912b848b0682b4e4452daa078cb
|
[
"BSD-3-Clause"
] | null | null | null |
src/DeepMatter/VAE_PFM/__init__.py
|
m3-learning/DeepMatter
|
54828a7972bf7912b848b0682b4e4452daa078cb
|
[
"BSD-3-Clause"
] | null | null | null |
"""
"""
from . import core
from . import file
from . import machine_learning
from . import dictionary_learning
| 14
| 33
| 0.741071
| 14
| 112
| 5.785714
| 0.5
| 0.493827
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.169643
| 112
| 8
| 33
| 14
| 0.870968
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
88758064ff8c543995f64f961cd108ca766b93aa
| 44
|
py
|
Python
|
scivision_treecrown_plugin/__init__.py
|
ots22/scivision-treecrown-plugin
|
4410b8fe6fc83461ff2fb83b93d89cbcd88176a9
|
[
"BSD-3-Clause"
] | null | null | null |
scivision_treecrown_plugin/__init__.py
|
ots22/scivision-treecrown-plugin
|
4410b8fe6fc83461ff2fb83b93d89cbcd88176a9
|
[
"BSD-3-Clause"
] | null | null | null |
scivision_treecrown_plugin/__init__.py
|
ots22/scivision-treecrown-plugin
|
4410b8fe6fc83461ff2fb83b93d89cbcd88176a9
|
[
"BSD-3-Clause"
] | 1
|
2022-01-12T15:30:01.000Z
|
2022-01-12T15:30:01.000Z
|
from .model import DeepForest, DetectreeRGB
| 22
| 43
| 0.840909
| 5
| 44
| 7.4
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.113636
| 44
| 1
| 44
| 44
| 0.948718
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
889dabd7345e2e9f22c1a18cd7cf243e3da6a912
| 72
|
py
|
Python
|
timeseries/timeseries/models/__init__.py
|
takotab/timeseriesAI2
|
04740e576392adcf03766a158fb644b49458cecb
|
[
"Apache-2.0"
] | null | null | null |
timeseries/timeseries/models/__init__.py
|
takotab/timeseriesAI2
|
04740e576392adcf03766a158fb644b49458cecb
|
[
"Apache-2.0"
] | null | null | null |
timeseries/timeseries/models/__init__.py
|
takotab/timeseriesAI2
|
04740e576392adcf03766a158fb644b49458cecb
|
[
"Apache-2.0"
] | null | null | null |
from .layers import *
from .ResNet import *
from .InceptionTime import *
| 24
| 28
| 0.763889
| 9
| 72
| 6.111111
| 0.555556
| 0.363636
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.152778
| 72
| 3
| 28
| 24
| 0.901639
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
88a87f23e8bf350873820e3c8955dd79655f4cf6
| 44
|
py
|
Python
|
code_icc/utils/__init__.py
|
ThmCuong/IIC-Python3
|
5a02b40ffa07b159fa7e89cf5b4ed781f4798ff1
|
[
"MIT"
] | null | null | null |
code_icc/utils/__init__.py
|
ThmCuong/IIC-Python3
|
5a02b40ffa07b159fa7e89cf5b4ed781f4798ff1
|
[
"MIT"
] | null | null | null |
code_icc/utils/__init__.py
|
ThmCuong/IIC-Python3
|
5a02b40ffa07b159fa7e89cf5b4ed781f4798ff1
|
[
"MIT"
] | null | null | null |
from . import cluster, segmentation, semisup
| 44
| 44
| 0.818182
| 5
| 44
| 7.2
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.113636
| 44
| 1
| 44
| 44
| 0.923077
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
88b034f0dcdc50334e685f61a03bea54e7210f43
| 6,968
|
py
|
Python
|
screenpy/actions/select.py
|
pjbarbatsis/screenpy
|
523e536efc4185ba69d3a0df41d5ab883dd18a60
|
[
"MIT"
] | null | null | null |
screenpy/actions/select.py
|
pjbarbatsis/screenpy
|
523e536efc4185ba69d3a0df41d5ab883dd18a60
|
[
"MIT"
] | null | null | null |
screenpy/actions/select.py
|
pjbarbatsis/screenpy
|
523e536efc4185ba69d3a0df41d5ab883dd18a60
|
[
"MIT"
] | null | null | null |
from typing import Union
from selenium.webdriver.support.ui import Select as SelSelect
from ..actor import Actor
from ..pacing import beat, MINOR
from ..target import Target
class Select:
"""
Selects an option from a dropdown menu. This is a superclass that will
create the correct specific Select action that will need to be used,
depending on how the option needs to be selected. Some examples of
invocations:
Select.the_option_named("January").from_the(MONTH_DROPDOWN)
Select.the_option_at_index(0).from_the(MONTH_DROPDOWN)
Select.the_option_with_value("jan").from_the(MONTH_DROPDOWN)
It can then be passed along to the |Actor| to perform the action.
"""
@staticmethod
def the_option_named(text: str) -> "SelectByText":
"""
Instantiate a |SelectByText| class which will select the option
with the given text.
Args:
text (str): The text of the option to select.
Returns:
|SelectByText|
"""
return SelectByText(text)
@staticmethod
def the_option_at_index(index: Union[int, str]) -> "SelectByIndex":
"""
Instantiate a |SelectByIndex| class which will select the option
at the specified index. This index is 0-based.
Args:
index (Union[int, str]): The index (0-based) of the option to
select.
Returns:
|SelectByIndex|
"""
return SelectByIndex(index)
@staticmethod
def the_option_with_value(value: str) -> "SelectByValue":
"""
Instantiate a |SelectByText| class which will select the option
with the given text.
Args:
value (str): The text of the option to select.
Returns:
|SelectByText|
"""
return SelectByValue(value)
class SelectByText:
"""
A specialized Select action that chooses the option by text. This
class is meant to be accessed via the Select action's static
|Select.the_option_named| method. A typical invocation might look
like:
Select.the_option_named("January").from_the(MONTH_DROPDOWN)
It can then be passed along to the |Actor| to perform the action.
"""
def from_the(self, target: Target) -> "SelectByText":
"""
Provides the |Target| to select the option from.
Args:
target (Target): The |Target| describing the dropdown element
to select from
Returns:
|SelectByText|
"""
self.target = target
return self
def from_(self, target: Target) -> "SelectByText":
"""Syntactic sugar for |SelectByText.from_the|."""
return self.from_the(target)
@beat("{0} selects the option '{text}' from the {target}.", gravitas=MINOR)
def perform_as(self, the_actor: Actor) -> None:
"""
Asks the actor to attempt to find the dropdown element described
by the stored target, then performs the select action.
Args:
the_actor (Actor): The |Actor| who will perform the action.
Raises:
|UnableToPerformException|: if the actor does not have the
ability to |BrowseTheWeb|.
"""
element = self.target.found_by(the_actor)
select = SelSelect(element)
select.select_by_visible_text(self.text)
def __init__(self, text: str, target: Target = None) -> None:
self.target = target
self.text = text
class SelectByIndex:
"""
A specialized |Select| action that chooses the option by its index.
This class is meant to be accessed via the Select action's static
|Select.the_option_at_index| method. A typical invocation might look
like:
Select.the_option_at_index(0).from_the(MONTH_DROPDOWN)
It can then be passed along to the |Actor| to perform the action.
"""
def from_the(self, target: Target) -> "SelectByIndex":
"""
Provides the |Target| to select the option from.
Args:
target (Target): The |Target| describing the dropdown element
to select from
Returns:
|SelectByIndex|
"""
self.target = target
return self
def from_(self, target: Target) -> "SelectByIndex":
"""Syntactic sugar for |SelectByIndex.from_the|."""
return self.from_the(target)
@beat("{0} selects the option at index {index} from the {target}.", gravitas=MINOR)
def perform_as(self, the_actor: Actor) -> None:
"""
Asks the actor to attempt to find the dropdown element described
by the stored target, then performs the select action.
Args:
the_actor (Actor): The |Actor| who will perform the
action.
Raises:
|UnableToPerformException|: if the actor does not have the
ability to |BrowseTheWeb|.
"""
element = self.target.found_by(the_actor)
select = SelSelect(element)
select.select_by_index(self.index)
def __init__(self, index: Union[int, str], target: Target = None) -> None:
self.target = target
self.index = str(index)
class SelectByValue:
"""
A specialized Select action that chooses the option by its value. This
class is meant to be accessed via the Select action's static
|Select.the_option_with_value| method. A typical invocation might look
like:
Select.the_option_with_value("jan").from_the(MONTH_DROPDOWN)
It can then be passed along to the |Actor| to perform the action.
"""
def from_the(self, target: Target) -> "SelectByValue":
"""
Provides the |Target| to select the option from.
Args:
target (Target): The |Target| describing the dropdown element
to select from
Returns:
|SelectByValue|
"""
self.target = target
return self
def from_(self, target: Target) -> "SelectByValue":
"""Syntactic sugar for |SelectByValue.from_the|."""
return self.from_the(target)
@beat(
"{0} selects the option with value '{value}' from the {target}.", gravitas=MINOR
)
def perform_as(self, the_actor: Actor) -> None:
"""
Asks the actor to attempt to find the dropdown element described
by the stored target, then performs the select action.
Args:
the_actor (Actor): The |Actor| who will perform the action.
Raises:
|UnableToPerformException|: if the actor does not have the
ability to |BrowseTheWeb|.
"""
element = self.target.found_by(the_actor)
select = SelSelect(element)
select.select_by_value(self.value)
def __init__(self, value: Union[int, str], target: Target = None) -> None:
self.target = target
self.value = str(value)
| 30.968889
| 88
| 0.623565
| 852
| 6,968
| 4.994131
| 0.139671
| 0.059224
| 0.052879
| 0.028202
| 0.757932
| 0.73772
| 0.724794
| 0.724794
| 0.724794
| 0.690012
| 0
| 0.001418
| 0.291762
| 6,968
| 224
| 89
| 31.107143
| 0.86079
| 0.519661
| 0
| 0.40678
| 0
| 0
| 0.110248
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.254237
| false
| 0
| 0.084746
| 0
| 0.559322
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
88b3a979c462adc0b16ff8e206dd1af0f2239eb5
| 32
|
py
|
Python
|
medial_axis/__init__.py
|
dipaco/cpma
|
76e24a231cbfa91fa145ed10ed24c4ca0110e2fc
|
[
"MIT"
] | 2
|
2022-02-23T22:48:18.000Z
|
2022-02-24T11:37:07.000Z
|
medial_axis/__init__.py
|
dipaco/cpma
|
76e24a231cbfa91fa145ed10ed24c4ca0110e2fc
|
[
"MIT"
] | null | null | null |
medial_axis/__init__.py
|
dipaco/cpma
|
76e24a231cbfa91fa145ed10ed24c4ca0110e2fc
|
[
"MIT"
] | null | null | null |
from .cpma import cpma, cpma_3d
| 16
| 31
| 0.78125
| 6
| 32
| 4
| 0.666667
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.037037
| 0.15625
| 32
| 1
| 32
| 32
| 0.851852
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
ee36d364841eed8d9573c0dc610ef982b324709f
| 67
|
py
|
Python
|
snakewm/apps/system/exit snakewm/__init__.py
|
Admicos/snakeware
|
ee5992120a683d74b09e694265abc27425fee8a4
|
[
"MIT"
] | null | null | null |
snakewm/apps/system/exit snakewm/__init__.py
|
Admicos/snakeware
|
ee5992120a683d74b09e694265abc27425fee8a4
|
[
"MIT"
] | null | null | null |
snakewm/apps/system/exit snakewm/__init__.py
|
Admicos/snakeware
|
ee5992120a683d74b09e694265abc27425fee8a4
|
[
"MIT"
] | null | null | null |
import pygame
def load(manager, params):
return pygame.quit()
| 13.4
| 26
| 0.716418
| 9
| 67
| 5.333333
| 0.888889
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.179104
| 67
| 4
| 27
| 16.75
| 0.872727
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.333333
| false
| 0
| 0.333333
| 0.333333
| 1
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 1
| 1
| 0
|
0
| 6
|
ee51dba66bc87a3cf8ddd5830c99d95fa4f07c5b
| 50,450
|
py
|
Python
|
pmutt/tests/reaction/test_pmutt_reaction.py
|
wittregr/pMuTT
|
1678fd3d3a10d8ef5389c02970a7ebaa92fc7344
|
[
"MIT"
] | 28
|
2018-10-29T17:44:30.000Z
|
2022-03-23T14:20:16.000Z
|
pmutt/tests/reaction/test_pmutt_reaction.py
|
wittregr/pMuTT
|
1678fd3d3a10d8ef5389c02970a7ebaa92fc7344
|
[
"MIT"
] | 101
|
2018-10-18T19:49:30.000Z
|
2022-01-19T10:59:57.000Z
|
pmutt/tests/reaction/test_pmutt_reaction.py
|
wittregr/pMuTT
|
1678fd3d3a10d8ef5389c02970a7ebaa92fc7344
|
[
"MIT"
] | 16
|
2018-12-15T17:01:21.000Z
|
2022-01-03T17:42:23.000Z
|
# -*- coding: utf-8 -*-
"""
pmutt.test_pmutt_model_reaction
Tests for pmutt module
"""
import unittest
import numpy as np
from ase.build import molecule
from pmutt import constants as c
from pmutt import reaction as rxn
from pmutt.empirical.nasa import Nasa
from pmutt.statmech import StatMech, presets
class TestReaction(unittest.TestCase):
def setUp(self):
'''Reactions using Nasa polynomial'''
self.H2O_nasa = Nasa(name='H2O',
T_low=200.,
T_mid=1000.,
T_high=3500.,
elements={
'H': 2,
'O': 1
},
a_low=[
4.19864056E+00, -2.03643410E-03,
6.52040211E-06, -5.48797062E-09,
1.77197817E-12, -3.02937267E+04,
-8.49032208E-01
],
a_high=[
3.03399249E+00, 2.17691804E-03,
-1.64072518E-07, -9.70419870E-11,
1.68200992E-14, -3.00042971E+04,
4.96677010E+00
])
self.H2_nasa = Nasa(name='H2',
T_low=200.,
T_mid=1000.,
T_high=3500.,
elements={'H': 2},
a_low=[
2.34433112E+00, 7.98052075E-03,
-1.94781510E-05, 2.01572094E-08,
-7.37611761E-12, -9.17935173E+02,
6.83010238E-01
],
a_high=[
3.33727920E+00, -4.94024731E-05,
4.99456778E-07, -1.79566394E-10,
2.00255376E-14, -9.50158922E+02,
-3.20502331E+00
])
self.O2_nasa = Nasa(name='O2',
T_low=200.,
T_mid=1000.,
T_high=3500.,
elements={'O': 2},
a_low=[
3.78245636E+00, -2.99673416E-03,
9.84730201E-06, -9.68129509E-09,
3.24372837E-12, -1.06394356E+03, 3.65767573E+00
],
a_high=[
3.28253784E+00, 1.48308754E-03,
-7.57966669E-07, 2.09470555E-10,
-2.16717794E-14, -1.08845772E+03,
5.45323129E+00
])
self.rxn_nasa = rxn.Reaction(reactants=[self.H2_nasa, self.O2_nasa],
reactants_stoich=[1., 0.5],
products=[self.H2O_nasa],
products_stoich=[1.])
self.rxn_nasa_dict = {
'class':
"<class 'pmutt.reaction.Reaction'>",
'products': [{
'T_high':
3500.0,
'T_low':
200.0,
'T_mid':
1000.0,
'a_high': [
3.03399249, 0.00217691804, -1.64072518e-07, -9.7041987e-11,
1.68200992e-14, -30004.2971, 4.9667701
],
'a_low': [
4.19864056, -0.0020364341, 6.52040211e-06, -5.48797062e-09,
1.77197817e-12, -30293.7267, -0.849032208
],
'class':
"<class 'pmutt.empirical.nasa.Nasa'>",
'elements': {
'H': 2,
'O': 1
},
'name':
'H2O',
'notes':
None,
'phase':
None,
'model':
None,
'misc_models':
None,
'cat_site':
None,
'n_sites':
None,
'smiles':
None,
'type':
'nasa'
}],
'products_stoich': [1.0],
'reactants': [{
'T_high':
3500.0,
'T_low':
200.0,
'T_mid':
1000.0,
'a_high': [
3.3372792, -4.94024731e-05, 4.99456778e-07,
-1.79566394e-10, 2.00255376e-14, -950.158922, -3.20502331
],
'a_low': [
2.34433112, 0.00798052075, -1.9478151e-05, 2.01572094e-08,
-7.37611761e-12, -917.935173, 0.683010238
],
'class':
"<class 'pmutt.empirical.nasa.Nasa'>",
'elements': {
'H': 2
},
'name':
'H2',
'notes':
None,
'phase':
None,
'model':
None,
'misc_models':
None,
'cat_site':
None,
'n_sites':
None,
'smiles':
None,
'type':
'nasa'
}, {
'T_high':
3500.0,
'T_low':
200.0,
'T_mid':
1000.0,
'a_high': [
3.28253784, 0.00148308754, -7.57966669e-07, 2.09470555e-10,
-2.16717794e-14, -1088.45772, 5.45323129
],
'a_low': [
3.78245636, -0.00299673416, 9.84730201e-06,
-9.68129509e-09, 3.24372837e-12, -1063.94356, 3.65767573
],
'class':
"<class 'pmutt.empirical.nasa.Nasa'>",
'elements': {
'O': 2
},
'name':
'O2',
'notes':
None,
'phase':
None,
'model':
None,
'misc_models':
None,
'cat_site':
None,
'n_sites':
None,
'smiles':
None,
'type':
'nasa'
}],
'reactants_stoich': [1.0, 0.5],
'transition_state':
None,
'transition_state_stoich':
None,
'reaction_str': 'H2+0.50O2=H2O',
}
'''Reactions using StatMech'''
ideal_gas_param = presets['idealgas']
self.H2O_sm = StatMech(name='H2O',
atoms=molecule('H2O'),
symmetrynumber=2,
vib_wavenumbers=[3825.434, 3710.2642, 1582.432],
potentialenergy=-6.7598,
spin=0.,
**ideal_gas_param)
self.H2_sm = StatMech(name='H2',
atoms=molecule('H2'),
symmetrynumber=2,
vib_wavenumbers=[4306.1793],
potentialenergy=-14.2209,
spin=0.,
**ideal_gas_param)
self.O2_sm = StatMech(name='O2',
atoms=molecule('O2'),
symmetrynumber=2,
vib_wavenumbers=[1556.],
potentialenergy=-9.862407,
spin=1.,
**ideal_gas_param)
# This is an arbitrary transition state for testing
self.H2O_TS_sm = StatMech(name='H2O_TS',
atoms=molecule('H2O'),
symmetrynumber=1.,
vib_wavenumbers=[4000., 3900., 1600.],
potentialenergy=-5.7598,
spin=0.,
**ideal_gas_param)
self.rxn_sm = rxn.Reaction(reactants=[self.H2_sm, self.O2_sm],
reactants_stoich=[1., 0.5],
products=[self.H2O_sm],
products_stoich=[1.],
transition_state=[self.H2O_TS_sm],
transition_state_stoich=[1.])
self.species_dict = {
'H2O': self.H2O_sm,
'H2': self.H2_sm,
'O2': self.O2_sm,
'H2O_TS': self.H2O_TS_sm
}
self.maxDiff = None
def test_compare_element_balance(self):
self.assertIsNone(self.rxn_nasa.check_element_balance())
def test_get_species(self):
self.assertDictEqual(self.rxn_sm.get_species(key='name'),
self.species_dict)
def test_get_q_state(self):
exp_q_react = self.H2_sm.get_q(T=c.T0('K')) \
* self.O2_sm.get_q(T=c.T0('K'))**0.5
exp_q_prod = self.H2O_sm.get_q(T=c.T0('K'))
exp_q_TS = self.H2O_TS_sm.get_q(T=c.T0('K'))
self.assertAlmostEqual(
self.rxn_sm.get_q_state(state='reactants', T=c.T0('K')),
exp_q_react)
self.assertAlmostEqual(
self.rxn_sm.get_q_state(state='products', T=c.T0('K')), exp_q_prod)
self.assertAlmostEqual(
self.rxn_sm.get_q_state(state='transition state', T=c.T0('K')),
exp_q_TS)
def test_get_CvoR_state(self):
exp_CvoR_react = self.H2_sm.get_CvoR(T=c.T0('K')) \
+ self.O2_sm.get_CvoR(T=c.T0('K'))*0.5
exp_CvoR_prod = self.H2O_sm.get_CvoR(T=c.T0('K'))
exp_CvoR_TS = self.H2O_TS_sm.get_CvoR(T=c.T0('K'))
self.assertAlmostEqual(
self.rxn_sm.get_CvoR_state(state='reactants', T=c.T0('K')),
exp_CvoR_react)
self.assertAlmostEqual(
self.rxn_sm.get_CvoR_state(state='products', T=c.T0('K')),
exp_CvoR_prod)
self.assertAlmostEqual(
self.rxn_sm.get_CvoR_state(state='transition state', T=c.T0('K')),
exp_CvoR_TS)
def test_get_Cv_state(self):
units = 'J/mol/K'
exp_Cv_react = self.H2_sm.get_Cv(T=c.T0('K'), units=units) \
+ self.O2_sm.get_Cv(T=c.T0('K'), units=units)*0.5
exp_Cv_prod = self.H2O_sm.get_Cv(T=c.T0('K'), units=units)
exp_Cv_TS = self.H2O_TS_sm.get_Cv(T=c.T0('K'), units=units)
self.assertAlmostEqual(
self.rxn_sm.get_Cv_state(state='reactants',
T=c.T0('K'),
units=units), exp_Cv_react)
self.assertAlmostEqual(
self.rxn_sm.get_Cv_state(state='products',
T=c.T0('K'),
units=units), exp_Cv_prod)
self.assertAlmostEqual(
self.rxn_sm.get_Cv_state(state='transition state',
T=c.T0('K'),
units=units), exp_Cv_TS)
def test_get_CpoR_state(self):
exp_CpoR_react = self.H2_sm.get_CpoR(T=c.T0('K')) \
+ self.O2_sm.get_CpoR(T=c.T0('K'))*0.5
exp_CpoR_prod = self.H2O_sm.get_CpoR(T=c.T0('K'))
exp_CpoR_TS = self.H2O_TS_sm.get_CpoR(T=c.T0('K'))
self.assertAlmostEqual(
self.rxn_sm.get_CpoR_state(state='reactants', T=c.T0('K')),
exp_CpoR_react)
self.assertAlmostEqual(
self.rxn_sm.get_CpoR_state(state='products', T=c.T0('K')),
exp_CpoR_prod)
self.assertAlmostEqual(
self.rxn_sm.get_CpoR_state(state='transition state', T=c.T0('K')),
exp_CpoR_TS)
def test_get_Cp_state(self):
units = 'J/mol/K'
exp_Cp_react = self.H2_sm.get_Cp(T=c.T0('K'), units=units) \
+ self.O2_sm.get_Cp(T=c.T0('K'), units=units)*0.5
exp_Cp_prod = self.H2O_sm.get_Cp(T=c.T0('K'), units=units)
exp_Cp_TS = self.H2O_TS_sm.get_Cp(T=c.T0('K'), units=units)
self.assertAlmostEqual(
self.rxn_sm.get_Cp_state(state='reactants',
T=c.T0('K'),
units=units), exp_Cp_react)
self.assertAlmostEqual(
self.rxn_sm.get_Cp_state(state='products',
T=c.T0('K'),
units=units), exp_Cp_prod)
self.assertAlmostEqual(
self.rxn_sm.get_Cp_state(state='transition state',
T=c.T0('K'),
units=units), exp_Cp_TS)
def test_get_EoRT_state(self):
exp_EoRT_react = self.H2_sm.get_EoRT(T=c.T0('K')) \
+ self.O2_sm.get_EoRT(T=c.T0('K'))*0.5
exp_EoRT_prod = self.H2O_sm.get_EoRT(T=c.T0('K'))
exp_EoRT_TS = self.H2O_TS_sm.get_EoRT(T=c.T0('K'))
self.assertAlmostEqual(
self.rxn_sm.get_EoRT_state(state='reactants', T=c.T0('K')),
exp_EoRT_react)
self.assertAlmostEqual(
self.rxn_sm.get_EoRT_state(state='products', T=c.T0('K')),
exp_EoRT_prod)
self.assertAlmostEqual(
self.rxn_sm.get_EoRT_state(state='transition state', T=c.T0('K')),
exp_EoRT_TS)
def test_get_E_state(self):
units = 'J/mol'
exp_E_react = self.H2_sm.get_E(T=c.T0('K'), units=units) \
+ self.O2_sm.get_E(T=c.T0('K'), units=units)*0.5
exp_E_prod = self.H2O_sm.get_E(T=c.T0('K'), units=units)
exp_E_TS = self.H2O_TS_sm.get_E(T=c.T0('K'), units=units)
self.assertAlmostEqual(
self.rxn_sm.get_E_state(state='reactants',
T=c.T0('K'),
units=units), exp_E_react)
self.assertAlmostEqual(
self.rxn_sm.get_E_state(state='products', T=c.T0('K'),
units=units), exp_E_prod)
self.assertAlmostEqual(
self.rxn_sm.get_E_state(state='transition state',
T=c.T0('K'),
units=units), exp_E_TS)
def test_get_UoRT_state(self):
exp_UoRT_react = self.H2_sm.get_UoRT(T=c.T0('K')) \
+ self.O2_sm.get_UoRT(T=c.T0('K'))*0.5
exp_UoRT_prod = self.H2O_sm.get_UoRT(T=c.T0('K'))
exp_UoRT_TS = self.H2O_TS_sm.get_UoRT(T=c.T0('K'))
self.assertAlmostEqual(
self.rxn_sm.get_UoRT_state(state='reactants', T=c.T0('K')),
exp_UoRT_react)
self.assertAlmostEqual(
self.rxn_sm.get_UoRT_state(state='products', T=c.T0('K')),
exp_UoRT_prod)
self.assertAlmostEqual(
self.rxn_sm.get_UoRT_state(state='transition state', T=c.T0('K')),
exp_UoRT_TS)
def test_get_U_state(self):
units = 'J/mol'
exp_U_react = self.H2_sm.get_U(T=c.T0('K'), units=units) \
+ self.O2_sm.get_U(T=c.T0('K'), units=units)*0.5
exp_U_prod = self.H2O_sm.get_U(T=c.T0('K'), units=units)
exp_U_TS = self.H2O_TS_sm.get_U(T=c.T0('K'), units=units)
self.assertAlmostEqual(
self.rxn_sm.get_U_state(state='reactants',
T=c.T0('K'),
units=units), exp_U_react)
self.assertAlmostEqual(
self.rxn_sm.get_U_state(state='products', T=c.T0('K'),
units=units), exp_U_prod)
self.assertAlmostEqual(
self.rxn_sm.get_U_state(state='transition state',
T=c.T0('K'),
units=units), exp_U_TS)
def test_get_HoRT_state(self):
exp_HoRT_react = self.H2_sm.get_HoRT(T=c.T0('K')) \
+ self.O2_sm.get_HoRT(T=c.T0('K'))*0.5
exp_HoRT_prod = self.H2O_sm.get_HoRT(T=c.T0('K'))
exp_HoRT_TS = self.H2O_TS_sm.get_HoRT(T=c.T0('K'))
self.assertAlmostEqual(
self.rxn_sm.get_HoRT_state(state='reactants', T=c.T0('K')),
exp_HoRT_react)
self.assertAlmostEqual(
self.rxn_sm.get_HoRT_state(state='products', T=c.T0('K')),
exp_HoRT_prod)
self.assertAlmostEqual(
self.rxn_sm.get_HoRT_state(state='transition state', T=c.T0('K')),
exp_HoRT_TS)
def test_get_H_state(self):
units = 'J/mol'
exp_H_react = self.H2_sm.get_H(T=c.T0('K'), units=units) \
+ self.O2_sm.get_H(T=c.T0('K'), units=units)*0.5
exp_H_prod = self.H2O_sm.get_H(T=c.T0('K'), units=units)
exp_H_TS = self.H2O_TS_sm.get_H(T=c.T0('K'), units=units)
self.assertAlmostEqual(
self.rxn_sm.get_H_state(state='reactants',
T=c.T0('K'),
units=units), exp_H_react)
self.assertAlmostEqual(
self.rxn_sm.get_H_state(state='products', T=c.T0('K'),
units=units), exp_H_prod)
self.assertAlmostEqual(
self.rxn_sm.get_H_state(state='transition state',
T=c.T0('K'),
units=units), exp_H_TS)
def test_get_SoR_state(self):
exp_SoR_react = self.H2_sm.get_SoR(T=c.T0('K')) \
+ self.O2_sm.get_SoR(T=c.T0('K'))*0.5
exp_SoR_prod = self.H2O_sm.get_SoR(T=c.T0('K'))
exp_SoR_TS = self.H2O_TS_sm.get_SoR(T=c.T0('K'))
self.assertAlmostEqual(
self.rxn_sm.get_SoR_state(state='reactants', T=c.T0('K')),
exp_SoR_react)
self.assertAlmostEqual(
self.rxn_sm.get_SoR_state(state='products', T=c.T0('K')),
exp_SoR_prod)
self.assertAlmostEqual(
self.rxn_sm.get_SoR_state(state='transition state', T=c.T0('K')),
exp_SoR_TS)
def test_get_S_state(self):
units = 'J/mol/K'
exp_S_react = self.H2_sm.get_S(T=c.T0('K'), units=units) \
+ self.O2_sm.get_S(T=c.T0('K'), units=units)*0.5
exp_S_prod = self.H2O_sm.get_S(T=c.T0('K'), units=units)
exp_S_TS = self.H2O_TS_sm.get_S(T=c.T0('K'), units=units)
self.assertAlmostEqual(
self.rxn_sm.get_S_state(state='reactants',
T=c.T0('K'),
units=units), exp_S_react)
self.assertAlmostEqual(
self.rxn_sm.get_S_state(state='products', T=c.T0('K'),
units=units), exp_S_prod)
self.assertAlmostEqual(
self.rxn_sm.get_S_state(state='transition state',
T=c.T0('K'),
units=units), exp_S_TS)
def test_get_FoRT_state(self):
exp_FoRT_react = self.H2_sm.get_FoRT(T=c.T0('K')) \
+ self.O2_sm.get_FoRT(T=c.T0('K'))*0.5
exp_FoRT_prod = self.H2O_sm.get_FoRT(T=c.T0('K'))
exp_FoRT_TS = self.H2O_TS_sm.get_FoRT(T=c.T0('K'))
self.assertAlmostEqual(
self.rxn_sm.get_FoRT_state(state='reactants', T=c.T0('K')),
exp_FoRT_react)
self.assertAlmostEqual(
self.rxn_sm.get_FoRT_state(state='products', T=c.T0('K')),
exp_FoRT_prod)
self.assertAlmostEqual(
self.rxn_sm.get_FoRT_state(state='transition state', T=c.T0('K')),
exp_FoRT_TS)
def test_get_F_state(self):
units = 'J/mol'
exp_F_react = self.H2_sm.get_F(T=c.T0('K'), units=units) \
+ self.O2_sm.get_F(T=c.T0('K'), units=units)*0.5
exp_F_prod = self.H2O_sm.get_F(T=c.T0('K'), units=units)
exp_F_TS = self.H2O_TS_sm.get_F(T=c.T0('K'), units=units)
self.assertAlmostEqual(
self.rxn_sm.get_F_state(state='reactants',
T=c.T0('K'),
units=units), exp_F_react)
self.assertAlmostEqual(
self.rxn_sm.get_F_state(state='products', T=c.T0('K'),
units=units), exp_F_prod)
self.assertAlmostEqual(
self.rxn_sm.get_F_state(state='transition state',
T=c.T0('K'),
units=units), exp_F_TS)
def test_get_GoRT_state(self):
exp_GoRT_react = self.H2_sm.get_GoRT(T=c.T0('K')) \
+ self.O2_sm.get_GoRT(T=c.T0('K'))*0.5
exp_GoRT_prod = self.H2O_sm.get_GoRT(T=c.T0('K'))
exp_GoRT_TS = self.H2O_TS_sm.get_GoRT(T=c.T0('K'))
self.assertAlmostEqual(
self.rxn_sm.get_GoRT_state(state='reactants', T=c.T0('K')),
exp_GoRT_react)
self.assertAlmostEqual(
self.rxn_sm.get_GoRT_state(state='products', T=c.T0('K')),
exp_GoRT_prod)
self.assertAlmostEqual(
self.rxn_sm.get_GoRT_state(state='transition state', T=c.T0('K')),
exp_GoRT_TS)
def test_get_G_state(self):
units = 'J/mol'
exp_G_react = self.H2_sm.get_G(T=c.T0('K'), units=units) \
+ self.O2_sm.get_G(T=c.T0('K'), units=units)*0.5
exp_G_prod = self.H2O_sm.get_G(T=c.T0('K'), units=units)
exp_G_TS = self.H2O_TS_sm.get_G(T=c.T0('K'), units=units)
self.assertAlmostEqual(
self.rxn_sm.get_G_state(state='reactants',
T=c.T0('K'),
units=units), exp_G_react)
self.assertAlmostEqual(
self.rxn_sm.get_G_state(state='products', T=c.T0('K'),
units=units), exp_G_prod)
self.assertAlmostEqual(
self.rxn_sm.get_G_state(state='transition state',
T=c.T0('K'),
units=units), exp_G_TS)
def test_get_delta_CvoR(self):
exp_sm_CvoR = self.H2O_sm.get_CvoR(T=c.T0('K')) \
- self.H2_sm.get_CvoR(T=c.T0('K')) \
- self.O2_sm.get_CvoR(T=c.T0('K'))*0.5
self.assertAlmostEqual(self.rxn_sm.get_delta_CvoR(T=c.T0('K')),
exp_sm_CvoR)
self.assertAlmostEqual(
self.rxn_sm.get_delta_CvoR(T=c.T0('K'), rev=True), -exp_sm_CvoR)
exp_sm_CvoR_TS = self.H2O_TS_sm.get_CvoR(T=c.T0('K')) \
- self.H2_sm.get_CvoR(T=c.T0('K')) \
- self.O2_sm.get_CvoR(T=c.T0('K'))*0.5
exp_sm_CvoR_rev_TS = self.H2O_TS_sm.get_CvoR(T=c.T0('K')) \
- self.H2O_sm.get_CvoR(T=c.T0('K'))
self.assertAlmostEqual(
self.rxn_sm.get_delta_CvoR(T=c.T0('K'), act=True), exp_sm_CvoR_TS)
self.assertAlmostEqual(
self.rxn_sm.get_delta_CvoR(T=c.T0('K'), rev=True, act=True),
exp_sm_CvoR_rev_TS)
self.assertAlmostEqual(self.rxn_sm.get_CvoR_act(T=c.T0('K'), rev=True),
exp_sm_CvoR_rev_TS)
def test_get_delta_Cv(self):
units = 'J/mol/K'
exp_sm_Cv = self.H2O_sm.get_Cv(T=c.T0('K'), units=units) \
- self.H2_sm.get_Cv(T=c.T0('K'), units=units) \
- self.O2_sm.get_Cv(T=c.T0('K'), units=units)*0.5
self.assertAlmostEqual(
self.rxn_sm.get_delta_Cv(T=c.T0('K'), units=units), exp_sm_Cv)
self.assertAlmostEqual(
self.rxn_sm.get_delta_Cv(T=c.T0('K'), units=units, rev=True),
-exp_sm_Cv)
exp_sm_Cv_TS = self.H2O_TS_sm.get_Cv(T=c.T0('K'), units=units) \
- self.H2_sm.get_Cv(T=c.T0('K'), units=units) \
- self.O2_sm.get_Cv(T=c.T0('K'), units=units)*0.5
exp_sm_Cv_rev_TS = self.H2O_TS_sm.get_Cv(T=c.T0('K'), units=units) \
- self.H2O_sm.get_Cv(T=c.T0('K'), units=units)
self.assertAlmostEqual(
self.rxn_sm.get_delta_Cv(T=c.T0('K'), act=True, units=units),
exp_sm_Cv_TS)
self.assertAlmostEqual(
self.rxn_sm.get_delta_Cv(T=c.T0('K'),
rev=True,
units=units,
act=True), exp_sm_Cv_rev_TS)
self.assertAlmostEqual(
self.rxn_sm.get_Cv_act(T=c.T0('K'), rev=True, units=units),
exp_sm_Cv_rev_TS)
def test_get_delta_CpoR(self):
exp_nasa_CpoR = self.H2O_nasa.get_CpoR(T=c.T0('K')) \
- self.H2_nasa.get_CpoR(T=c.T0('K')) \
- self.O2_nasa.get_CpoR(T=c.T0('K'))*0.5
exp_sm_CpoR = self.H2O_sm.get_CpoR(T=c.T0('K')) \
- self.H2_sm.get_CpoR(T=c.T0('K')) \
- self.O2_sm.get_CpoR(T=c.T0('K'))*0.5
self.assertAlmostEqual(self.rxn_nasa.get_delta_CpoR(T=c.T0('K')),
exp_nasa_CpoR)
self.assertAlmostEqual(
self.rxn_nasa.get_delta_CpoR(T=c.T0('K'), rev=True),
-exp_nasa_CpoR)
self.assertAlmostEqual(self.rxn_sm.get_delta_CpoR(T=c.T0('K')),
exp_sm_CpoR)
self.assertAlmostEqual(
self.rxn_sm.get_delta_CpoR(T=c.T0('K'), rev=True), -exp_sm_CpoR)
exp_sm_CpoR_TS = self.H2O_TS_sm.get_CpoR(T=c.T0('K')) \
- self.H2_sm.get_CpoR(T=c.T0('K')) \
- self.O2_sm.get_CpoR(T=c.T0('K'))*0.5
exp_sm_CpoR_rev_TS = self.H2O_TS_sm.get_CpoR(T=c.T0('K')) \
- self.H2O_sm.get_CpoR(T=c.T0('K'))
self.assertAlmostEqual(
self.rxn_sm.get_delta_CpoR(T=c.T0('K'), act=True), exp_sm_CpoR_TS)
self.assertAlmostEqual(
self.rxn_sm.get_delta_CpoR(T=c.T0('K'), rev=True, act=True),
exp_sm_CpoR_rev_TS)
self.assertAlmostEqual(self.rxn_sm.get_CpoR_act(T=c.T0('K'), rev=True),
exp_sm_CpoR_rev_TS)
def test_get_delta_Cp(self):
units = 'J/mol/K'
exp_nasa_Cp = self.H2O_nasa.get_Cp(T=c.T0('K'), units=units) \
- self.H2_nasa.get_Cp(T=c.T0('K'), units=units) \
- self.O2_nasa.get_Cp(T=c.T0('K'), units=units)*0.5
exp_sm_Cp = self.H2O_sm.get_Cp(T=c.T0('K'), units=units) \
- self.H2_sm.get_Cp(T=c.T0('K'), units=units) \
- self.O2_sm.get_Cp(T=c.T0('K'), units=units)*0.5
self.assertAlmostEqual(
self.rxn_nasa.get_delta_Cp(T=c.T0('K'), units=units), exp_nasa_Cp)
self.assertAlmostEqual(
self.rxn_nasa.get_delta_Cp(T=c.T0('K'), units=units, rev=True),
-exp_nasa_Cp)
self.assertAlmostEqual(
self.rxn_sm.get_delta_Cp(T=c.T0('K'), units=units), exp_sm_Cp)
self.assertAlmostEqual(
self.rxn_sm.get_delta_Cp(T=c.T0('K'), units=units, rev=True),
-exp_sm_Cp)
exp_sm_Cp_TS = self.H2O_TS_sm.get_Cp(T=c.T0('K'), units=units) \
- self.H2_sm.get_Cp(T=c.T0('K'), units=units) \
- self.O2_sm.get_Cp(T=c.T0('K'), units=units)*0.5
exp_sm_Cp_rev_TS = self.H2O_TS_sm.get_Cp(T=c.T0('K'), units=units) \
- self.H2O_sm.get_Cp(T=c.T0('K'), units=units)
self.assertAlmostEqual(
self.rxn_sm.get_delta_Cp(T=c.T0('K'), act=True, units=units),
exp_sm_Cp_TS)
self.assertAlmostEqual(
self.rxn_sm.get_delta_Cp(T=c.T0('K'),
rev=True,
act=True,
units=units), exp_sm_Cp_rev_TS)
self.assertAlmostEqual(
self.rxn_sm.get_Cp_act(T=c.T0('K'), rev=True, units=units),
exp_sm_Cp_rev_TS)
def test_get_delta_EoRT(self):
exp_sm_EoRT = self.H2O_sm.get_EoRT(T=c.T0('K')) \
- self.H2_sm.get_EoRT(T=c.T0('K')) \
- self.O2_sm.get_EoRT(T=c.T0('K'))*0.5
self.assertAlmostEqual(self.rxn_sm.get_delta_EoRT(T=c.T0('K')),
exp_sm_EoRT)
self.assertAlmostEqual(
self.rxn_sm.get_delta_EoRT(T=c.T0('K'), rev=True), -exp_sm_EoRT)
exp_sm_EoRT_TS = self.H2O_TS_sm.get_EoRT(T=c.T0('K')) \
- self.H2_sm.get_EoRT(T=c.T0('K')) \
- self.O2_sm.get_EoRT(T=c.T0('K'))*0.5
exp_sm_EoRT_rev_TS = self.H2O_TS_sm.get_EoRT(T=c.T0('K')) \
- self.H2O_sm.get_EoRT(T=c.T0('K'))
self.assertAlmostEqual(
self.rxn_sm.get_delta_EoRT(T=c.T0('K'), act=True), exp_sm_EoRT_TS)
self.assertAlmostEqual(
self.rxn_sm.get_delta_EoRT(T=c.T0('K'), rev=True, act=True),
exp_sm_EoRT_rev_TS)
def test_get_delta_E(self):
units = 'J/mol'
exp_sm_E = self.H2O_sm.get_E(T=c.T0('K'), units=units) \
- self.H2_sm.get_E(T=c.T0('K'), units=units) \
- self.O2_sm.get_E(T=c.T0('K'), units=units)*0.5
self.assertAlmostEqual(
self.rxn_sm.get_delta_E(T=c.T0('K'), units=units), exp_sm_E)
self.assertAlmostEqual(
self.rxn_sm.get_delta_E(T=c.T0('K'), rev=True, units=units),
-exp_sm_E)
exp_sm_E_TS = self.H2O_TS_sm.get_E(T=c.T0('K'), units=units) \
- self.H2_sm.get_E(T=c.T0('K'), units=units) \
- self.O2_sm.get_E(T=c.T0('K'), units=units)*0.5
exp_sm_E_rev_TS = self.H2O_TS_sm.get_E(T=c.T0('K'), units=units) \
- self.H2O_sm.get_E(T=c.T0('K'), units=units)
self.assertAlmostEqual(
self.rxn_sm.get_delta_E(T=c.T0('K'), act=True, units=units),
exp_sm_E_TS)
self.assertAlmostEqual(
self.rxn_sm.get_delta_E(T=c.T0('K'),
rev=True,
act=True,
units=units), exp_sm_E_rev_TS)
def test_get_delta_UoRT(self):
exp_sm_UoRT = self.H2O_sm.get_UoRT(T=c.T0('K')) \
- self.H2_sm.get_UoRT(T=c.T0('K')) \
- self.O2_sm.get_UoRT(T=c.T0('K'))*0.5
self.assertAlmostEqual(self.rxn_sm.get_delta_UoRT(T=c.T0('K')),
exp_sm_UoRT)
self.assertAlmostEqual(
self.rxn_sm.get_delta_UoRT(T=c.T0('K'), rev=True), -exp_sm_UoRT)
exp_sm_UoRT_TS = self.H2O_TS_sm.get_UoRT(T=c.T0('K')) \
- self.H2_sm.get_UoRT(T=c.T0('K')) \
- self.O2_sm.get_UoRT(T=c.T0('K'))*0.5
exp_sm_UoRT_rev_TS = self.H2O_TS_sm.get_UoRT(T=c.T0('K')) \
- self.H2O_sm.get_UoRT(T=c.T0('K'))
self.assertAlmostEqual(
self.rxn_sm.get_delta_UoRT(T=c.T0('K'), act=True), exp_sm_UoRT_TS)
self.assertAlmostEqual(
self.rxn_sm.get_delta_UoRT(T=c.T0('K'), rev=True, act=True),
exp_sm_UoRT_rev_TS)
self.assertAlmostEqual(self.rxn_sm.get_UoRT_act(T=c.T0('K'), rev=True),
exp_sm_UoRT_rev_TS)
def test_get_delta_U(self):
units = 'J/mol'
exp_sm_U = self.H2O_sm.get_U(T=c.T0('K'), units=units) \
- self.H2_sm.get_U(T=c.T0('K'), units=units) \
- self.O2_sm.get_U(T=c.T0('K'), units=units)*0.5
self.assertAlmostEqual(
self.rxn_sm.get_delta_U(T=c.T0('K'), units=units), exp_sm_U)
self.assertAlmostEqual(
self.rxn_sm.get_delta_U(T=c.T0('K'), rev=True, units=units),
-exp_sm_U)
exp_sm_U_TS = self.H2O_TS_sm.get_U(T=c.T0('K'), units=units) \
- self.H2_sm.get_U(T=c.T0('K'), units=units) \
- self.O2_sm.get_U(T=c.T0('K'), units=units)*0.5
exp_sm_U_rev_TS = self.H2O_TS_sm.get_U(T=c.T0('K'), units=units) \
- self.H2O_sm.get_U(T=c.T0('K'), units=units)
self.assertAlmostEqual(
self.rxn_sm.get_delta_U(T=c.T0('K'), act=True, units=units),
exp_sm_U_TS)
self.assertAlmostEqual(
self.rxn_sm.get_delta_U(T=c.T0('K'),
rev=True,
act=True,
units=units), exp_sm_U_rev_TS)
self.assertAlmostEqual(
self.rxn_sm.get_U_act(T=c.T0('K'), rev=True, units=units),
exp_sm_U_rev_TS)
def test_get_delta_HoRT(self):
exp_nasa_HoRT = self.H2O_nasa.get_HoRT(T=c.T0('K')) \
- self.H2_nasa.get_HoRT(T=c.T0('K')) \
- self.O2_nasa.get_HoRT(T=c.T0('K'))*0.5
exp_sm_HoRT = self.H2O_sm.get_HoRT(T=c.T0('K')) \
- self.H2_sm.get_HoRT(T=c.T0('K')) \
- self.O2_sm.get_HoRT(T=c.T0('K'))*0.5
self.assertAlmostEqual(self.rxn_nasa.get_delta_HoRT(T=c.T0('K')),
exp_nasa_HoRT)
self.assertAlmostEqual(
self.rxn_nasa.get_delta_HoRT(T=c.T0('K'), rev=True),
-exp_nasa_HoRT)
self.assertAlmostEqual(self.rxn_sm.get_delta_HoRT(T=c.T0('K')),
exp_sm_HoRT)
self.assertAlmostEqual(
self.rxn_sm.get_delta_HoRT(T=c.T0('K'), rev=True), -exp_sm_HoRT)
exp_sm_HoRT_TS = self.H2O_TS_sm.get_HoRT(T=c.T0('K')) \
- self.H2_sm.get_HoRT(T=c.T0('K')) \
- self.O2_sm.get_HoRT(T=c.T0('K'))*0.5
exp_sm_HoRT_rev_TS = self.H2O_TS_sm.get_HoRT(T=c.T0('K')) \
- self.H2O_sm.get_HoRT(T=c.T0('K'))
self.assertAlmostEqual(
self.rxn_sm.get_delta_HoRT(T=c.T0('K'), act=True), exp_sm_HoRT_TS)
self.assertAlmostEqual(
self.rxn_sm.get_delta_HoRT(T=c.T0('K'), rev=True, act=True),
exp_sm_HoRT_rev_TS)
self.assertAlmostEqual(self.rxn_sm.get_HoRT_act(T=c.T0('K'), rev=True),
exp_sm_HoRT_rev_TS)
def test_get_delta_H(self):
units = 'J/mol'
exp_nasa_H = self.H2O_nasa.get_H(T=c.T0('K'), units=units) \
- self.H2_nasa.get_H(T=c.T0('K'), units=units) \
- self.O2_nasa.get_H(T=c.T0('K'), units=units)*0.5
exp_sm_H = self.H2O_sm.get_H(T=c.T0('K'), units=units) \
- self.H2_sm.get_H(T=c.T0('K'), units=units) \
- self.O2_sm.get_H(T=c.T0('K'), units=units)*0.5
self.assertAlmostEqual(
self.rxn_nasa.get_delta_H(T=c.T0('K'), units=units), exp_nasa_H)
self.assertAlmostEqual(
self.rxn_nasa.get_delta_H(T=c.T0('K'), units=units, rev=True),
-exp_nasa_H)
self.assertAlmostEqual(
self.rxn_sm.get_delta_H(T=c.T0('K'), units=units), exp_sm_H)
self.assertAlmostEqual(
self.rxn_sm.get_delta_H(T=c.T0('K'), units=units, rev=True),
-exp_sm_H)
exp_sm_H_TS = self.H2O_TS_sm.get_H(T=c.T0('K'), units=units) \
- self.H2_sm.get_H(T=c.T0('K'), units=units) \
- self.O2_sm.get_H(T=c.T0('K'), units=units)*0.5
exp_sm_H_rev_TS = self.H2O_TS_sm.get_H(T=c.T0('K'), units=units) \
- self.H2O_sm.get_H(T=c.T0('K'), units=units)
self.assertAlmostEqual(
self.rxn_sm.get_delta_H(T=c.T0('K'), act=True, units=units),
exp_sm_H_TS)
self.assertAlmostEqual(
self.rxn_sm.get_delta_H(T=c.T0('K'),
rev=True,
act=True,
units=units), exp_sm_H_rev_TS)
self.assertAlmostEqual(
self.rxn_sm.get_H_act(T=c.T0('K'), rev=True, units=units),
exp_sm_H_rev_TS)
def test_get_delta_SoR(self):
exp_nasa_SoR = self.H2O_nasa.get_SoR(T=c.T0('K')) \
- self.H2_nasa.get_SoR(T=c.T0('K')) \
- self.O2_nasa.get_SoR(T=c.T0('K'))*0.5
exp_sm_SoR = self.H2O_sm.get_SoR(T=c.T0('K')) \
- self.H2_sm.get_SoR(T=c.T0('K')) \
- self.O2_sm.get_SoR(T=c.T0('K'))*0.5
self.assertAlmostEqual(self.rxn_nasa.get_delta_SoR(T=c.T0('K')),
exp_nasa_SoR)
self.assertAlmostEqual(
self.rxn_nasa.get_delta_SoR(T=c.T0('K'), rev=True), -exp_nasa_SoR)
self.assertAlmostEqual(self.rxn_sm.get_delta_SoR(T=c.T0('K')),
exp_sm_SoR)
self.assertAlmostEqual(
self.rxn_sm.get_delta_SoR(T=c.T0('K'), rev=True), -exp_sm_SoR)
exp_sm_SoR_TS = self.H2O_TS_sm.get_SoR(T=c.T0('K')) \
- self.H2_sm.get_SoR(T=c.T0('K')) \
- self.O2_sm.get_SoR(T=c.T0('K'))*0.5
exp_sm_SoR_rev_TS = self.H2O_TS_sm.get_SoR(T=c.T0('K')) \
- self.H2O_sm.get_SoR(T=c.T0('K'))
self.assertAlmostEqual(
self.rxn_sm.get_delta_SoR(T=c.T0('K'), act=True), exp_sm_SoR_TS)
self.assertAlmostEqual(
self.rxn_sm.get_delta_SoR(T=c.T0('K'), rev=True, act=True),
exp_sm_SoR_rev_TS)
self.assertAlmostEqual(self.rxn_sm.get_SoR_act(T=c.T0('K'), rev=True),
exp_sm_SoR_rev_TS)
def test_get_delta_S(self):
units = 'J/mol/K'
exp_nasa_S = self.H2O_nasa.get_S(T=c.T0('K'), units=units) \
- self.H2_nasa.get_S(T=c.T0('K'), units=units) \
- self.O2_nasa.get_S(T=c.T0('K'), units=units)*0.5
exp_sm_S = self.H2O_sm.get_S(T=c.T0('K'), units=units) \
- self.H2_sm.get_S(T=c.T0('K'), units=units) \
- self.O2_sm.get_S(T=c.T0('K'), units=units)*0.5
self.assertAlmostEqual(
self.rxn_nasa.get_delta_S(T=c.T0('K'), units=units), exp_nasa_S)
self.assertAlmostEqual(
self.rxn_nasa.get_delta_S(T=c.T0('K'), units=units, rev=True),
-exp_nasa_S)
self.assertAlmostEqual(
self.rxn_sm.get_delta_S(T=c.T0('K'), units=units), exp_sm_S)
self.assertAlmostEqual(
self.rxn_sm.get_delta_S(T=c.T0('K'), rev=True, units=units),
-exp_sm_S)
exp_sm_S_TS = self.H2O_TS_sm.get_S(T=c.T0('K'), units=units) \
- self.H2_sm.get_S(T=c.T0('K'), units=units) \
- self.O2_sm.get_S(T=c.T0('K'), units=units)*0.5
exp_sm_S_rev_TS = self.H2O_TS_sm.get_S(T=c.T0('K'), units=units) \
- self.H2O_sm.get_S(T=c.T0('K'), units=units)
self.assertAlmostEqual(
self.rxn_sm.get_delta_S(T=c.T0('K'), act=True, units=units),
exp_sm_S_TS)
self.assertAlmostEqual(
self.rxn_sm.get_delta_S(T=c.T0('K'),
rev=True,
act=True,
units=units), exp_sm_S_rev_TS)
self.assertAlmostEqual(
self.rxn_sm.get_S_act(T=c.T0('K'), rev=True, units=units),
exp_sm_S_rev_TS)
def test_get_delta_FoRT(self):
exp_sm_FoRT = self.H2O_sm.get_FoRT(T=c.T0('K')) \
- self.H2_sm.get_FoRT(T=c.T0('K')) \
- self.O2_sm.get_FoRT(T=c.T0('K'))*0.5
self.assertAlmostEqual(self.rxn_sm.get_delta_FoRT(T=c.T0('K')),
exp_sm_FoRT)
self.assertAlmostEqual(
self.rxn_sm.get_delta_FoRT(T=c.T0('K'), rev=True), -exp_sm_FoRT)
exp_sm_FoRT_TS = self.H2O_TS_sm.get_FoRT(T=c.T0('K')) \
- self.H2_sm.get_FoRT(T=c.T0('K')) \
- self.O2_sm.get_FoRT(T=c.T0('K'))*0.5
exp_sm_FoRT_rev_TS = self.H2O_TS_sm.get_FoRT(T=c.T0('K')) \
- self.H2O_sm.get_FoRT(T=c.T0('K'))
self.assertAlmostEqual(
self.rxn_sm.get_delta_FoRT(T=c.T0('K'), act=True), exp_sm_FoRT_TS)
self.assertAlmostEqual(
self.rxn_sm.get_delta_FoRT(T=c.T0('K'), rev=True, act=True),
exp_sm_FoRT_rev_TS)
self.assertAlmostEqual(self.rxn_sm.get_FoRT_act(T=c.T0('K'), rev=True),
exp_sm_FoRT_rev_TS)
def test_get_delta_F(self):
units = 'J/mol'
exp_sm_F = self.H2O_sm.get_F(T=c.T0('K'), units=units) \
- self.H2_sm.get_F(T=c.T0('K'), units=units) \
- self.O2_sm.get_F(T=c.T0('K'), units=units)*0.5
self.assertAlmostEqual(
self.rxn_sm.get_delta_F(T=c.T0('K'), units=units), exp_sm_F)
self.assertAlmostEqual(
self.rxn_sm.get_delta_F(T=c.T0('K'), units=units, rev=True),
-exp_sm_F)
exp_sm_F_TS = self.H2O_TS_sm.get_F(T=c.T0('K'), units=units) \
- self.H2_sm.get_F(T=c.T0('K'), units=units) \
- self.O2_sm.get_F(T=c.T0('K'), units=units)*0.5
exp_sm_F_rev_TS = self.H2O_TS_sm.get_F(T=c.T0('K'), units=units) \
- self.H2O_sm.get_F(T=c.T0('K'), units=units)
self.assertAlmostEqual(
self.rxn_sm.get_delta_F(T=c.T0('K'), act=True, units=units),
exp_sm_F_TS)
self.assertAlmostEqual(
self.rxn_sm.get_delta_F(T=c.T0('K'),
rev=True,
act=True,
units=units), exp_sm_F_rev_TS)
self.assertAlmostEqual(
self.rxn_sm.get_F_act(T=c.T0('K'), rev=True, units=units),
exp_sm_F_rev_TS)
def test_get_delta_GoRT(self):
exp_nasa_GoRT = self.H2O_nasa.get_GoRT(T=c.T0('K')) \
- self.H2_nasa.get_GoRT(T=c.T0('K')) \
- self.O2_nasa.get_GoRT(T=c.T0('K'))*0.5
exp_sm_GoRT = self.H2O_sm.get_GoRT(T=c.T0('K')) \
- self.H2_sm.get_GoRT(T=c.T0('K')) \
- self.O2_sm.get_GoRT(T=c.T0('K'))*0.5
self.assertAlmostEqual(self.rxn_nasa.get_delta_GoRT(T=c.T0('K')),
exp_nasa_GoRT)
self.assertAlmostEqual(
self.rxn_nasa.get_delta_GoRT(T=c.T0('K'), rev=True),
-exp_nasa_GoRT)
self.assertAlmostEqual(self.rxn_sm.get_delta_GoRT(T=c.T0('K')),
exp_sm_GoRT)
self.assertAlmostEqual(
self.rxn_sm.get_delta_GoRT(T=c.T0('K'), rev=True), -exp_sm_GoRT)
exp_sm_GoRT_TS = self.H2O_TS_sm.get_GoRT(T=c.T0('K')) \
- self.H2_sm.get_GoRT(T=c.T0('K')) \
- self.O2_sm.get_GoRT(T=c.T0('K'))*0.5
exp_sm_GoRT_rev_TS = self.H2O_TS_sm.get_GoRT(T=c.T0('K')) \
- self.H2O_sm.get_GoRT(T=c.T0('K'))
self.assertAlmostEqual(
self.rxn_sm.get_delta_GoRT(T=c.T0('K'), act=True), exp_sm_GoRT_TS)
self.assertAlmostEqual(
self.rxn_sm.get_delta_GoRT(T=c.T0('K'), rev=True, act=True),
exp_sm_GoRT_rev_TS)
self.assertAlmostEqual(self.rxn_sm.get_GoRT_act(T=c.T0('K'), rev=True),
exp_sm_GoRT_rev_TS)
def test_get_delta_G(self):
units = 'J/mol'
exp_nasa_G = self.H2O_nasa.get_G(T=c.T0('K'), units=units) \
- self.H2_nasa.get_G(T=c.T0('K'), units=units) \
- self.O2_nasa.get_G(T=c.T0('K'), units=units)*0.5
exp_sm_G = self.H2O_sm.get_G(T=c.T0('K'), units=units) \
- self.H2_sm.get_G(T=c.T0('K'), units=units) \
- self.O2_sm.get_G(T=c.T0('K'), units=units)*0.5
self.assertAlmostEqual(
self.rxn_nasa.get_delta_G(T=c.T0('K'), units=units), exp_nasa_G)
self.assertAlmostEqual(
self.rxn_nasa.get_delta_G(T=c.T0('K'), rev=True, units=units),
-exp_nasa_G)
self.assertAlmostEqual(
self.rxn_sm.get_delta_G(T=c.T0('K'), units=units), exp_sm_G)
self.assertAlmostEqual(
self.rxn_sm.get_delta_G(T=c.T0('K'), rev=True, units=units),
-exp_sm_G)
exp_sm_G_TS = self.H2O_TS_sm.get_G(T=c.T0('K'), units=units) \
- self.H2_sm.get_G(T=c.T0('K'), units=units) \
- self.O2_sm.get_G(T=c.T0('K'), units=units)*0.5
exp_sm_G_rev_TS = self.H2O_TS_sm.get_G(T=c.T0('K'), units=units) \
- self.H2O_sm.get_G(T=c.T0('K'), units=units)
self.assertAlmostEqual(
self.rxn_sm.get_delta_G(T=c.T0('K'), act=True, units=units),
exp_sm_G_TS)
self.assertAlmostEqual(
self.rxn_sm.get_delta_G(T=c.T0('K'),
rev=True,
act=True,
units=units), exp_sm_G_rev_TS)
self.assertAlmostEqual(
self.rxn_sm.get_G_act(T=c.T0('K'), rev=True, units=units),
exp_sm_G_rev_TS)
def test_get_EoRT_act(self):
exp_sm_EoRT = self.H2O_TS_sm.get_HoRT(T=c.T0('K')) \
- self.H2_sm.get_HoRT(T=c.T0('K')) \
- self.O2_sm.get_HoRT(T=c.T0('K'))*0.5
exp_sm_EoRT_rev = self.H2O_TS_sm.get_HoRT(T=c.T0('K')) \
- self.H2O_sm.get_HoRT(T=c.T0('K'))
self.assertAlmostEqual(self.rxn_sm.get_EoRT_act(T=c.T0('K')),
exp_sm_EoRT)
self.assertAlmostEqual(self.rxn_sm.get_EoRT_act(T=c.T0('K'), rev=True),
exp_sm_EoRT_rev)
def test_get_E_act(self):
units = 'J/mol'
exp_sm_E = self.H2O_TS_sm.get_H(T=c.T0('K'), units=units) \
- self.H2_sm.get_H(T=c.T0('K'), units=units) \
- self.O2_sm.get_H(T=c.T0('K'), units=units)*0.5
exp_sm_E_rev = self.H2O_TS_sm.get_H(T=c.T0('K'), units=units) \
- self.H2O_sm.get_H(T=c.T0('K'), units=units)
self.assertAlmostEqual(self.rxn_sm.get_E_act(T=c.T0('K'), units=units),
exp_sm_E)
self.assertAlmostEqual(
self.rxn_sm.get_E_act(T=c.T0('K'), rev=True, units=units),
exp_sm_E_rev)
def test_get_A(self):
# Testing partition function method
exp_sm_q = self.H2O_TS_sm.get_q(T=c.T0('K'), include_ZPE=False) \
/ self.H2_sm.get_q(T=c.T0('K'), include_ZPE=False) \
/ self.O2_sm.get_q(T=c.T0('K'), include_ZPE=False)**0.5
exp_sm_A = c.kb('J/K') * c.T0('K') / c.h('J s') * exp_sm_q
exp_sm_q_rev = self.H2O_TS_sm.get_q(T=c.T0('K'), include_ZPE=False) \
/ self.H2O_sm.get_q(T=c.T0('K'), include_ZPE=False)
exp_sm_A_rev = c.kb('J/K') * c.T0('K') / c.h('J s') * exp_sm_q_rev
np.testing.assert_almost_equal(self.rxn_sm.get_A(T=c.T0('K')),
exp_sm_A,
decimal=0)
np.testing.assert_almost_equal(self.rxn_sm.get_A(T=c.T0('K'),
rev=True),
exp_sm_A_rev,
decimal=0)
# Testing entropy method
exp_sm_SoR = self.H2O_TS_sm.get_SoR(T=c.T0('K')) \
- self.H2_sm.get_SoR(T=c.T0('K')) \
- self.O2_sm.get_SoR(T=c.T0('K'))*0.5
exp_sm_A = c.kb('J/K') * c.T0('K') / c.h('J s') * np.exp(exp_sm_SoR)
exp_sm_SoR_rev = self.H2O_TS_sm.get_SoR(T=c.T0('K')) \
- self.H2O_sm.get_SoR(T=c.T0('K'))
exp_sm_A_rev = c.kb('J/K')*c.T0('K')/c.h('J s') \
* np.exp(exp_sm_SoR_rev)
np.testing.assert_almost_equal(self.rxn_sm.get_A(T=c.T0('K'),
use_q=False),
exp_sm_A,
decimal=0)
np.testing.assert_almost_equal(self.rxn_sm.get_A(T=c.T0('K'),
rev=True,
use_q=False),
exp_sm_A_rev,
decimal=0)
def test_from_string(self):
reaction_str = 'H2+0.5O2=H2O_TS=H2O'
self.assertEqual(
rxn.Reaction.from_string(reaction_str=reaction_str,
species=self.species_dict), self.rxn_sm)
def test_to_dict(self):
self.assertEqual(self.rxn_nasa.to_dict(), self.rxn_nasa_dict)
def test_from_dict(self):
self.assertEqual(rxn.Reaction.from_dict(self.rxn_nasa_dict),
self.rxn_nasa)
class TestHelperReaction(unittest.TestCase):
def test__parse_reaction(self):
reaction_str = 'H2+0.5O2=H2O'
expected_output = (['H2', 'O2'], [1., 0.5], ['H2O'], [1.], None, None)
self.assertTupleEqual(rxn._parse_reaction(reaction_str=reaction_str),
expected_output)
reaction_str = ' H2 + 0.5 O2 = H2O '
expected_output = (['H2', 'O2'], [1., 0.5], ['H2O'], [1.], None, None)
self.assertTupleEqual(rxn._parse_reaction(reaction_str=reaction_str),
expected_output)
reaction_str = ' H2 + 0.5 O2 = H2O_TS = H2O '
expected_output = (['H2',
'O2'], [1., 0.5], ['H2O'], [1.], ['H2O_TS'], [1.])
self.assertTupleEqual(rxn._parse_reaction(reaction_str=reaction_str),
expected_output)
def test__parse_reaction_state(self):
reaction_str = 'H2+0.5O2'
expected_output = (['H2', 'O2'], [1., 0.5])
self.assertTupleEqual(
rxn._parse_reaction_state(reaction_str=reaction_str),
expected_output)
if __name__ == '__main__':
unittest.main()
| 45.368705
| 79
| 0.493102
| 7,127
| 50,450
| 3.213133
| 0.036481
| 0.052009
| 0.069345
| 0.085808
| 0.869258
| 0.845459
| 0.811397
| 0.781572
| 0.721004
| 0.608996
| 0
| 0.059797
| 0.361903
| 50,450
| 1,111
| 80
| 45.409541
| 0.651559
| 0.004281
| 0
| 0.422812
| 0
| 0
| 0.036383
| 0.00265
| 0
| 0
| 0
| 0
| 0.159292
| 1
| 0.043265
| false
| 0
| 0.006883
| 0
| 0.052114
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
c9c4340717c089c14afaf955660c897b8a591ed7
| 888
|
py
|
Python
|
leetcode/0525 Duplicate Zeros.py
|
jaredliw/python-question-bank
|
9c8c246623d8d171f875700b57772df0afcbdcdf
|
[
"MIT"
] | 1
|
2021-04-08T07:49:15.000Z
|
2021-04-08T07:49:15.000Z
|
leetcode/0525 Duplicate Zeros.py
|
jaredliw/leetcode-solutions
|
9c8c246623d8d171f875700b57772df0afcbdcdf
|
[
"MIT"
] | null | null | null |
leetcode/0525 Duplicate Zeros.py
|
jaredliw/leetcode-solutions
|
9c8c246623d8d171f875700b57772df0afcbdcdf
|
[
"MIT"
] | 1
|
2022-01-23T02:12:24.000Z
|
2022-01-23T02:12:24.000Z
|
class Solution(object):
def duplicateZeros(self, arr):
"""
:type arr: List[int]
:rtype: None Do not return anything, modify arr in-place instead.
"""
# Runtime: 1116 ms
# Memory: 13.8 MB
idx = 0
while idx < len(arr):
if arr[idx] == 0:
for ptr in range(len(arr) - 1, idx, -1):
arr[ptr] = arr[ptr - 1]
idx += 1
idx += 1
class Solution(object):
def duplicateZeros(self, arr):
"""
:type arr: List[int]
:rtype: None Do not return anything, modify arr in-place instead.
"""
# Runtime: 52 ms
# Memory: 13.6 MB
idx = 0
while idx < len(arr):
if arr[idx] == 0:
arr.pop()
arr.insert(idx + 1, 0)
idx += 1
idx += 1
| 26.909091
| 73
| 0.438063
| 107
| 888
| 3.635514
| 0.373832
| 0.061697
| 0.051414
| 0.113111
| 0.74036
| 0.74036
| 0.74036
| 0.74036
| 0.74036
| 0.74036
| 0
| 0.050607
| 0.443694
| 888
| 32
| 74
| 27.75
| 0.736842
| 0.268018
| 0
| 0.777778
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.111111
| false
| 0
| 0
| 0
| 0.222222
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
c9ebe1a4995c10230f4b07090310d96859c733ea
| 34
|
py
|
Python
|
BOJ/05000~05999/5800~5899/5893.py
|
shinkeonkim/today-ps
|
f3e5e38c5215f19579bb0422f303a9c18c626afa
|
[
"Apache-2.0"
] | 2
|
2020-01-29T06:54:41.000Z
|
2021-11-07T13:23:27.000Z
|
BOJ/05000~05999/5800~5899/5893.py
|
shinkeonkim/Today_PS
|
bb0cda0ee1b9c57e1cfa38355e29d0f1c6167a44
|
[
"Apache-2.0"
] | null | null | null |
BOJ/05000~05999/5800~5899/5893.py
|
shinkeonkim/Today_PS
|
bb0cda0ee1b9c57e1cfa38355e29d0f1c6167a44
|
[
"Apache-2.0"
] | null | null | null |
print(bin(int(input(),2)*17)[2:])
| 17
| 33
| 0.588235
| 7
| 34
| 2.857143
| 0.857143
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.121212
| 0.029412
| 34
| 1
| 34
| 34
| 0.484848
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0
| 0
| 0
| 1
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 1
|
0
| 6
|
c9ed639e46e39e69c8c56c39a9d2538e17b38213
| 137
|
py
|
Python
|
tests/fixtures/submodels.py
|
pashashocky/xsdata
|
1cd681598d2235626d0e21716fc9fb885d26e351
|
[
"MIT"
] | null | null | null |
tests/fixtures/submodels.py
|
pashashocky/xsdata
|
1cd681598d2235626d0e21716fc9fb885d26e351
|
[
"MIT"
] | null | null | null |
tests/fixtures/submodels.py
|
pashashocky/xsdata
|
1cd681598d2235626d0e21716fc9fb885d26e351
|
[
"MIT"
] | null | null | null |
from dataclasses import dataclass
from tests.fixtures.models import ChoiceType
@dataclass
class ChoiceTypeChild(ChoiceType):
pass
| 15.222222
| 44
| 0.817518
| 15
| 137
| 7.466667
| 0.733333
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.138686
| 137
| 8
| 45
| 17.125
| 0.949153
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0.2
| 0.4
| 0
| 0.6
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 1
| 0
| 1
| 0
|
0
| 6
|
c9fd05b42d2b6ad20ef495b96ef65927a85ba566
| 49
|
py
|
Python
|
popgen/utils/__init__.py
|
linzwatt/PopGen
|
f4162ef0284960f6a1d599904171383efd9ee232
|
[
"MIT"
] | 58
|
2020-05-18T02:44:59.000Z
|
2022-02-11T02:44:55.000Z
|
popgen/utils/__init__.py
|
linzwatt/PopGen
|
f4162ef0284960f6a1d599904171383efd9ee232
|
[
"MIT"
] | null | null | null |
popgen/utils/__init__.py
|
linzwatt/PopGen
|
f4162ef0284960f6a1d599904171383efd9ee232
|
[
"MIT"
] | 2
|
2020-05-19T03:11:33.000Z
|
2021-10-07T08:44:47.000Z
|
from .sigmoid_annealing import sigmoid_annealing
| 24.5
| 48
| 0.897959
| 6
| 49
| 7
| 0.666667
| 0.761905
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.081633
| 49
| 1
| 49
| 49
| 0.933333
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 1
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 6
|
a01433f8f21f3eb4499439d7b6f08ce5c77bb94b
| 136
|
py
|
Python
|
util.py
|
sharpvik/fin
|
9afec321dc5f404f296fd7ba55914f298396b68f
|
[
"MIT"
] | null | null | null |
util.py
|
sharpvik/fin
|
9afec321dc5f404f296fd7ba55914f298396b68f
|
[
"MIT"
] | null | null | null |
util.py
|
sharpvik/fin
|
9afec321dc5f404f296fd7ba55914f298396b68f
|
[
"MIT"
] | null | null | null |
import numpy as np
import pandas as pd
def sma(price: np.ndarray, period: int) -> np.ndarray:
return price.rolling(period).mean()
| 19.428571
| 54
| 0.713235
| 22
| 136
| 4.409091
| 0.681818
| 0.185567
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.169118
| 136
| 6
| 55
| 22.666667
| 0.858407
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.25
| false
| 0
| 0.5
| 0.25
| 1
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 1
| 1
| 0
|
0
| 6
|
a01a07ee474a76e542f569c64b64ab6845b8a8a6
| 26,100
|
py
|
Python
|
tests/test_neoschema_data_import.py
|
BrainAnnex/brain-annex
|
07701ba0309c448e9030a19a10dca4d73c155afe
|
[
"MIT"
] | null | null | null |
tests/test_neoschema_data_import.py
|
BrainAnnex/brain-annex
|
07701ba0309c448e9030a19a10dca4d73c155afe
|
[
"MIT"
] | 3
|
2021-12-19T03:58:42.000Z
|
2022-02-11T07:40:46.000Z
|
tests/test_neoschema_data_import.py
|
BrainAnnex/brain-annex
|
07701ba0309c448e9030a19a10dca4d73c155afe
|
[
"MIT"
] | null | null | null |
# Testing of Schema-based Data Import
# *** CAUTION! *** The database gets cleared out during some of the tests!
# NOTES: - some tests require APOC
# - some tests may fail their date check if done close to midnight, server time
import pytest
from BrainAnnex.modules.neo_access import neo_access
from BrainAnnex.modules.utilities.comparisons import compare_recordsets
from BrainAnnex.modules.neo_schema.neo_schema import NeoSchema
from tests.test_neoschema import create_sample_schema_1, create_sample_schema_2
# Provide a database connection that can be used by the various tests that need it
@pytest.fixture(scope="module")
def db():
neo_obj = neo_access.NeoAccess(debug=False)
NeoSchema.set_database(neo_obj)
yield neo_obj
def test_create_data_nodes_from_python_data_1(db):
db.empty_dbase()
# Set up the Schema
sch_1 = NeoSchema.new_class_with_properties(class_name="Import Data",
property_list=["source", "date"])
sch_2 = NeoSchema.new_class_with_properties(class_name="my_class_1",
property_list=["legit", "other"])
NeoSchema.create_class_relationship(from_id=sch_1, to_id=sch_2, rel_name="imported_data")
# 1-layer dictionary, with a key in the Schema and one not
data = {"legit": 123, "unexpected": 456}
# Import step
node_id_list = NeoSchema.create_data_nodes_from_python_data(data, class_name="my_class_1")
assert len(node_id_list) == 1
root_id = node_id_list[0]
q = '''
MATCH (c1:CLASS {name:"Import Data"})<-[:SCHEMA]-
(n1:`Import Data`)-[:imported_data]->(n2:my_class_1)
-[:SCHEMA]->(c2:CLASS {name:"my_class_1"})
WHERE id(n2) = $item_id
RETURN n2
'''
root_node = db.query(q, data_binding={"item_id": root_id}, single_row=True)
root_record = root_node["n2"]
assert root_record["legit"] == 123
assert "item_id" in root_record
assert "unexpected" not in root_record # Only the key in the Schema gets imported
def test_create_data_nodes_from_python_data_2(db):
db.empty_dbase()
# Set up the Schema. Nothing in it yet, other than the "Import Data" node
NeoSchema.new_class_with_properties(class_name="Import Data",
property_list=["source", "date"])
data = {"arbitrary": "Doesn't matter"}
# Import step
with pytest.raises(Exception):
NeoSchema.create_data_nodes_from_python_data(data, class_name="non_existent_class")
# Even though the import got aborted and raised an Exception, the `Import Data` is left behind;
# locate it by its date stamp
q = '''
MATCH (n:`Import Data` {date: date()}) RETURN n
'''
result = db.query(q)
assert len(result) == 1
def test_create_data_nodes_from_python_data_3(db):
db.empty_dbase()
# Set up Schema that only contains parts of the attributes in the data - and lacks the "result" relationship
sch_1 = NeoSchema.new_class_with_properties(class_name="Import Data",
property_list=["source", "date"])
sch_2 = NeoSchema.new_class_with_properties(class_name="patient",
property_list=["age", "balance"])
NeoSchema.create_class_relationship(from_id=sch_1, to_id=sch_2, rel_name="imported_data")
data = { "name": "Stephanie",
"age": 23,
"referred by": None,
"result": {
"biomarker": "insulin",
"value": 123.
},
"balance": 150.25,
"extraneous": "I don't belong",
"insurance": False
}
# Import
node_id_list = NeoSchema.create_data_nodes_from_python_data(data, class_name="patient")
assert len(node_id_list) == 1
root_id = node_id_list[0]
q = '''
MATCH (c1:CLASS {name:"Import Data"})<-[:SCHEMA]-
(n1:`Import Data`)-[:imported_data]->(n2:patient)
-[:SCHEMA]->(c2:CLASS {name:"patient"})
WHERE id(n2) = $item_id
RETURN n2
'''
root_node = db.query(q, data_binding={"item_id": root_id}, single_row=True)
# Only the keys in the Schema gets imported; the relationship "result" is not in the Schema, either
root_record = root_node["n2"]
assert root_record["age"] == 23
assert root_record["balance"] == 150.25
assert "item_id" in root_record
assert len(root_record) == 3 # Only the keys in the Schema gets imported
q = '''MATCH (n:patient)-[:result]-(m) RETURN n, m'''
res = db.query(q)
assert len(res) == 0 # "result" is not in the Schema
def test_create_data_nodes_from_python_data_4(db):
db.empty_dbase()
sch_info = create_sample_schema_1() # Schema with patient/result/doctor
# Add to the Schema the "Import Data" node, and a link to the Class of the import's root
sch_import = NeoSchema.new_class_with_properties(class_name="Import Data",
property_list=["source", "date"])
NeoSchema.create_class_relationship(from_id=sch_import, to_id=sch_info["patient"], rel_name="imported_data")
data = { "name": "Stephanie",
"age": 23,
"referred by": None,
"result": { # Note: this doesn't match the "HAS_RESULT" in the Schema
"biomarker": "insulin",
"value": 123.
},
"balance": 150.25,
"extraneous": "I don't belong",
"insurance": False
}
# Import
node_id_list = NeoSchema.create_data_nodes_from_python_data(data, class_name="patient")
assert len(node_id_list) == 1
root_id = node_id_list[0]
q = '''
MATCH (c1:CLASS {name:"Import Data"})<-[:SCHEMA]-
(n1:`Import Data`)-[:imported_data]->(n2:patient)
-[:SCHEMA]->(c2:CLASS {name:"patient"})
WHERE id(n2) = $root_id
RETURN n2
'''
root_node = db.query(q, data_binding={"root_id": root_id}, single_row=True)
# Only the keys in the Schema gets imported; the relationship "result" is not in the Schema, either
root_record = root_node["n2"]
assert root_record["name"] == "Stephanie"
assert root_record["age"] == 23
assert root_record["balance"] == 150.25
assert "item_id" in root_record
assert len(root_record) == 4 # Only the keys in the Schema gets imported
q = '''MATCH (n:patient)-[:result]-(m) RETURN n, m'''
res = db.query(q)
assert len(res) == 0 # the relationship "result" is not in the Schema
# Count the links from the "patient" data node (the root)
assert db.count_links(match=root_id, rel_name="SCHEMA", rel_dir="OUT", neighbor_labels="CLASS") == 1
assert db.count_links(match=root_id, rel_name="imported_data", rel_dir="IN", neighbor_labels="Import Data") == 1
assert db.count_links(match=root_id, rel_name="result", rel_dir="BOTH") == 0
def test_create_data_nodes_from_python_data_5(db):
db.empty_dbase()
sch_info = create_sample_schema_1() # Schema with patient/result/doctor
# Add to the Schema the "Import Data" node, and a link to the Class of the import's root
sch_import = NeoSchema.new_class_with_properties(class_name="Import Data",
property_list=["source", "date"])
NeoSchema.create_class_relationship(from_id=sch_import, to_id=sch_info["patient"], rel_name="imported_data")
data = { "name": "Stephanie",
"age": 23,
"referred by": None,
"HAS_RESULT": {
"biomarker": "insulin",
"value": 123.,
"intruder": "the schema doesn't know me"
},
"balance": 150.25,
"extraneous": "I don't belong",
"WRONG_LINK_TO_DOCTOR" : {
"name": "Dr. Kane",
"hospital": "Mt. Zion",
"specialty": "OB/GYN"
},
"insurance": False
}
# Import
node_id_list = NeoSchema.create_data_nodes_from_python_data(data, class_name="patient")
assert len(node_id_list) == 1
root_id = node_id_list[0]
# Traverse a loop in the graph, from the patient data node, back to itself,
# and finally to the `Import Data` node - going thru data and schema nodes
q = '''
MATCH (p:patient {name: "Stephanie", age: 23, balance: 150.25})-[:HAS_RESULT]->
(r:result {biomarker:"insulin", value: 123.0})-[:SCHEMA]->(cl_r:CLASS {name:"result"})
<-[:HAS_RESULT]-(cl_p:CLASS {name:"patient"})<-[:SCHEMA]-(p)<-[:imported_data]-(i: `Import Data`)
WHERE i.date = date() AND id(p) = $root_id
RETURN p, r, cl_r, cl_p, i
'''
result = db.query(q, data_binding={"root_id": root_id})
#print(result)
# Only the keys in the Schema gets imported; the relationship "HAS_RESULT" is in the Schema
assert len(result) == 1
# The relationship "WRONG_LINK_TO_DOCTOR" is not in the Schema
q = '''MATCH (n:patient)-[:WRONG_LINK_TO_DOCTOR]-(m) RETURN n, m'''
result = db.query(q)
assert len(result) == 0
# Count the links from the "patient" data node (the root)
assert db.count_links(match=root_id, rel_name="HAS_RESULT", rel_dir="OUT", neighbor_labels="result") == 1
assert db.count_links(match=root_id, rel_name="SCHEMA", rel_dir="OUT", neighbor_labels="CLASS") == 1
assert db.count_links(match=root_id, rel_name="imported_data", rel_dir="IN", neighbor_labels="Import Data") == 1
assert db.count_links(match=root_id, rel_name="WRONG_LINK_TO_DOCTOR", rel_dir="BOTH") == 0
# Locate the "result" data node, and count the links in/out of it
match = db.find(labels="result")
assert db.count_links(match=match, rel_name="HAS_RESULT", rel_dir="IN", neighbor_labels="patient") == 1
assert db.count_links(match=match, rel_name="SCHEMA", rel_dir="OUT", neighbor_labels="CLASS") == 1
def test_create_data_nodes_from_python_data_6(db):
db.empty_dbase()
sch_info = create_sample_schema_1() # Schema with patient/result/doctor
# Add to the Schema the "Import Data" node, and a link to the Class of the import's root
sch_import = NeoSchema.new_class_with_properties(class_name="Import Data",
property_list=["source", "date"])
NeoSchema.create_class_relationship(from_id=sch_import, to_id=sch_info["patient"], rel_name="imported_data")
data = { "name": "Stephanie",
"age": 23,
"referred by": None,
"HAS_RESULT": {
"biomarker": "insulin",
"value": 123.,
"intruder": "the schema doesn't know me"
},
"balance": 150.25,
"extraneous": "I don't belong",
"IS_ATTENDED_BY" : {
"name": "Dr. Kane",
"hospital": "Mt. Zion",
"specialty": "OB/GYN"
},
"insurance": False
}
# Import
node_id_list = NeoSchema.create_data_nodes_from_python_data(data, class_name="patient")
assert len(node_id_list) == 1
root_id = node_id_list[0]
# Traverse a loop in the graph, from the patient data node, back to itself,
# and finally to the `Import Data` node - going thru data and schema nodes
q = '''
MATCH (p:patient {name: "Stephanie", age: 23, balance: 150.25})-[:HAS_RESULT]->
(r:result {biomarker:"insulin", value: 123.0})-[:SCHEMA]->(cl_r:CLASS {name:"result"})
<-[:HAS_RESULT]-(cl_p:CLASS {name:"patient"})<-[:SCHEMA]-(p)<-[:imported_data]-(i: `Import Data`)
WHERE i.date = date() AND id(p) = $root_id
RETURN p, r, cl_r, cl_p, i
'''
result = db.query(q, data_binding={"root_id": root_id})
#print(result)
assert len(result) == 1
# Again, traverse a loop in the graph, from the patient data node, back to itself,
# but this time going thru the `doctor` data and schema nodes
q = '''
MATCH (p:patient {name: "Stephanie", age: 23, balance: 150.25})-[:IS_ATTENDED_BY]->
(d:doctor {name:"Dr. Kane", specialty: "OB/GYN"})-[:SCHEMA]->(cl_d:CLASS {name:"doctor"})
<-[:IS_ATTENDED_BY]-(cl_p:CLASS {name:"patient"})<-[:SCHEMA]-(p)
WHERE id(p) = $root_id
RETURN p, d, cl_d, cl_p
'''
result = db.query(q, data_binding={"root_id": root_id})
#print(result)
assert len(result) == 1
# Count the links from the "patient" data node (the root)
assert db.count_links(match=root_id, rel_name="HAS_RESULT", rel_dir="OUT", neighbor_labels="result") == 1
assert db.count_links(match=root_id, rel_name="IS_ATTENDED_BY", rel_dir="OUT", neighbor_labels="doctor") == 1
assert db.count_links(match=root_id, rel_name="SCHEMA", rel_dir="OUT", neighbor_labels="CLASS") == 1
assert db.count_links(match=root_id, rel_name="imported_data", rel_dir="IN", neighbor_labels="Import Data") == 1
# Locate the "result" data node, and count the links in/out of it
match = db.find(labels="result")
assert db.count_links(match=match, rel_name="HAS_RESULT", rel_dir="IN", neighbor_labels="patient") == 1
assert db.count_links(match=match, rel_name="SCHEMA", rel_dir="OUT", neighbor_labels="CLASS") == 1
# Locate the "doctor" data node, and count the links in/out of it
match = db.find(labels="doctor")
assert db.count_links(match=match, rel_name="IS_ATTENDED_BY", rel_dir="IN", neighbor_labels="patient") == 1
assert db.count_links(match=match, rel_name="SCHEMA", rel_dir="OUT", neighbor_labels="CLASS") == 1
# Locate the "Import Data" data node, and count the links in/out of it
match = db.find(labels="Import Data")
assert db.count_links(match=match, rel_name="imported_data", rel_dir="OUT", neighbor_labels="patient") == 1
assert db.count_links(match=match, rel_name="SCHEMA", rel_dir="OUT", neighbor_labels="CLASS") == 1
def test_create_data_nodes_from_python_data_7(db):
db.empty_dbase()
sch_info = create_sample_schema_2() # Class "quotes" with relationship "in_category" to class "Categories"
# Add to the Schema the "Import Data" node, and a link to the Class of the import's root
sch_import = NeoSchema.new_class_with_properties(class_name="Import Data",
property_list=["source", "date"])
NeoSchema.create_class_relationship(from_id=sch_import, to_id=sch_info["quotes"], rel_name="imported_data")
data = [
{ "quote": "I wasn't kissing her. I was whispering in her mouth",
"attribution": "Chico Marx"
}
]
# Import
node_id_list = NeoSchema.create_data_nodes_from_python_data(data, class_name="quotes")
print("node_id_list: ", node_id_list)
assert len(node_id_list) == 1
root_id = node_id_list[0]
# Traverse a loop in the graph, from the `quotes` data node, back to itself,
# going thru the data and schema nodes
q = '''
MATCH (q :quotes {attribution: "Chico Marx", quote: "I wasn't kissing her. I was whispering in her mouth"})
-[:SCHEMA]->(cl_q :CLASS {name:"quotes"})
<-[:imported_data]-(cl_i :CLASS {name:"Import Data"})<-[:SCHEMA]-(i: `Import Data`)
-[:imported_data]->(q)
WHERE i.date = date() AND id(q) = $quote_id
RETURN q, cl_q, cl_i
'''
result = db.query(q, data_binding={"quote_id": root_id})
#print(result)
assert len(result) == 1
# Locate the "Import Data" data node, and count the links in/out of it
match = db.find(labels="Import Data")
assert db.count_links(match=match, rel_name="imported_data", rel_dir="OUT", neighbor_labels="quotes") == 1
assert db.count_links(match=match, rel_name="SCHEMA", rel_dir="OUT", neighbor_labels="CLASS") == 1
# Locate the "quotes" data node (the root), and count the links in/out of it
assert db.count_links(match=root_id, rel_name="SCHEMA", rel_dir="OUT", neighbor_labels="CLASS") == 1
assert db.count_links(match=root_id, rel_name="imported_data", rel_dir="IN", neighbor_labels="Import Data") == 1
def test_create_data_nodes_from_python_data_8(db):
# Similar to test_create_data_nodes_from_python_data_8, but importing 2 quotes instead of 1,
# and introducing non-Schema data
db.empty_dbase()
sch_info = create_sample_schema_2() # Class "quotes" with relationship "in_category" to class "Categories"
# Add to the Schema the "Import Data" node, and a link to the Class of the import's root
sch_import = NeoSchema.new_class_with_properties(class_name="Import Data",
property_list=["source", "date"])
NeoSchema.create_class_relationship(from_id=sch_import, to_id=sch_info["quotes"], rel_name="imported_data")
data = [
{ "quote": "I wasn't kissing her. I was whispering in her mouth",
"attribution": "Chico Marx"
},
{ "quote": "Inspiration exists, but it has to find us working",
"attribution": "Pablo Picasso",
"extraneous": "I don't belong in the Schema"
},
{ "junk": "This whole record has no place in the Schema"
}
]
# Import
node_id_list = NeoSchema.create_data_nodes_from_python_data(data, class_name="quotes")
print("node_id_list: ", node_id_list)
assert len(node_id_list) == 2
# Locate the "Import Data" data node
match = db.find(labels="Import Data")
assert db.count_links(match=match, rel_name="imported_data", rel_dir="OUT", neighbor_labels="quotes") == 2
assert db.count_links(match=match, rel_name="SCHEMA", rel_dir="OUT", neighbor_labels="CLASS") == 1
for root_id in node_id_list:
# Traverse a loop in the graph, from the `quotes` data node, back to itself,
# going thru the data and schema nodes
q = '''
MATCH (q :quotes)
-[:SCHEMA]->(cl_q :CLASS {name:"quotes"})
<-[:imported_data]-(cl_i :CLASS {name:"Import Data"})<-[:SCHEMA]-(i: `Import Data`)
-[:imported_data]->(q)
WHERE i.date = date() AND id(q) = $quote_id
RETURN q, cl_q, cl_i
'''
result = db.query(q, data_binding={"quote_id": root_id})
print(result)
assert len(result) == 1
# Locate the "quotes" data node, and count the links in/out of it
assert db.count_links(match=root_id, rel_name="SCHEMA", rel_dir="OUT", neighbor_labels="CLASS") == 1
assert db.count_links(match=root_id, rel_name="imported_data", rel_dir="IN", neighbor_labels="Import Data") == 1
def test_create_data_nodes_from_python_data_9(db):
# Similar to test_create_data_nodes_from_python_data_8, but also using the class "Categories"
db.empty_dbase()
sch_info = create_sample_schema_2() # Class "quotes" with relationship "in_category" to class "Categories"
# Add to the Schema the "Import Data" node, and a link to the Class of the import's root
sch_import = NeoSchema.new_class_with_properties(class_name="Import Data",
property_list=["source", "date"])
NeoSchema.create_class_relationship(from_id=sch_import, to_id=sch_info["quotes"], rel_name="imported_data")
data = [
{ "quote": "I wasn't kissing her. I was whispering in her mouth",
"attribution": "Chico Marx",
"in_category": { "name": "Literature",
"remarks": "English only",
"junk": "trying to sneak in"
}
},
{ "quote": "Inspiration exists, but it has to find us working",
"attribution": "Pablo Picasso",
"in_category": { "name": "Famous Quotes"
}
}
]
# Import
node_id_list = NeoSchema.create_data_nodes_from_python_data(data, class_name="quotes")
print("node_id_list: ", node_id_list)
assert len(node_id_list) == 2
# Locate the "Import Data" data node
match = db.find(labels="Import Data")
assert db.count_links(match=match, rel_name="imported_data", rel_dir="OUT", neighbor_labels="quotes") == 2
assert db.count_links(match=match, rel_name="SCHEMA", rel_dir="OUT", neighbor_labels="CLASS") == 1
for root_id in node_id_list:
# Locate the "quotes" data node, and count the links in/out of it
assert db.count_links(match=root_id, rel_name="SCHEMA", rel_dir="OUT", neighbor_labels="CLASS") == 1
assert db.count_links(match=root_id, rel_name="imported_data", rel_dir="IN", neighbor_labels="Import Data") == 1
assert db.count_links(match=root_id, rel_name="in_category", rel_dir="OUT", neighbor_labels="Categories") == 1
# Traverse a loop in the graph, from the `quotes` data node, back to itself,
# going thru the data and schema nodes
q = '''
MATCH (q :quotes)
-[:SCHEMA]->(cl_q :CLASS {name:"quotes"})
<-[:imported_data]-(cl_i :CLASS {name:"Import Data"})<-[:SCHEMA]-(i: `Import Data`)
-[:imported_data]->(q)
WHERE i.date = date() AND id(q) = $quote_id
RETURN q, cl_q, cl_i
'''
result = db.query(q, data_binding={"quote_id": root_id})
#print(result)
assert len(result) == 1
# Traverse a longer loop in the graph, again from the `quotes` data node to itself,
# but this time also passing thru the category data and schema nodes
q = '''
MATCH (q :quotes)-[:in_category]->(cat :Categories)
-[:SCHEMA]->(cl_c :CLASS {name:"Categories"})<-[:in_category]-
(cl_q :CLASS {name:"quotes"})
<-[:imported_data]-(cl_i :CLASS {name:"Import Data"})<-[:SCHEMA]-(i: `Import Data`)
-[:imported_data]->(q)
WHERE i.date = date() AND id(q) = $quote_id
RETURN q, cat, cl_q, cl_i
'''
result = db.query(q, data_binding={"quote_id": root_id})
#print(result)
assert len(result) == 1
record = result[0]
author = record["q"]["attribution"]
assert author == 'Chico Marx' or author == 'Pablo Picasso'
if author == 'Chico Marx':
assert record["cat"]["name"] == "Literature"
assert record["q"]["quote"] == "I wasn't kissing her. I was whispering in her mouth"
else:
assert record["cat"]["name"] == "Famous Quotes"
assert record["q"]["quote"] == "Inspiration exists, but it has to find us working"
# END of for loop
# Add an extra quote, connected to 2 categories
data = { "quote": "My destination is no longer a place, rather a new way of seeing",
"attribution": "Proust",
"in_category": [
{
"name": "French Literature"
}
,
{
"name": "Philosophy"
}
],
"verified": False
}
# Import
new_node_id_list = NeoSchema.create_data_nodes_from_python_data(data, class_name="quotes")
#print("new_node_id_list: ", new_node_id_list)
assert len(new_node_id_list) == 1
new_root_id = new_node_id_list[0]
# Locate the latest "quotes" data node, and count the links in/out of it
assert db.count_links(match=new_root_id, rel_name="imported_data", rel_dir="IN", neighbor_labels="Import Data") == 1
assert db.count_links(match=new_root_id, rel_name="in_category", rel_dir="OUT", neighbor_labels="Categories") == 2
assert db.count_links(match=new_root_id, rel_name="SCHEMA", rel_dir="OUT", neighbor_labels="CLASS") == 1
# Traverse a loop in the graph, from the `quotes` data node back to itself,
# going thru the 2 category data nodes and their shared Schema node
q = '''
MATCH (q :quotes)-[:in_category]->(cat1 :Categories {name: "French Literature"})
-[:SCHEMA]->(cl_c :CLASS {name:"Categories"})
<-[:SCHEMA]-(cat2 :Categories {name: "Philosophy"})
<-[:in_category]-(q)
WHERE id(q) = $quote_id
RETURN q, cat1, cl_c, cat2
'''
result = db.query(q, data_binding={"quote_id": new_root_id})
#print(result)
assert len(result) == 1
record = result[0]
assert record["q"]["attribution"] == "Proust"
assert record["q"]["quote"] == "My destination is no longer a place, rather a new way of seeing"
assert record["q"]["verified"] == False
# Locate the data node for the Class "Import Data", and count the links in/out of it
match = db.find(labels="CLASS", key_name="name", key_value="Import Data")
assert db.count_links(match=match, rel_name="SCHEMA", rel_dir="IN", neighbor_labels="Import Data") == 2
assert db.count_links(match=match, rel_name="imported_data", rel_dir="OUT", neighbor_labels="CLASS") == 1
# Verify the data types
q = '''
MATCH (n)
WHERE id(n) = $quote_id
RETURN apoc.meta.cypher.types(n) AS data_types
'''
data_types = db.query(q, data_binding={"quote_id": new_root_id}, single_cell="data_types")
#print(data_types)
assert data_types == {'verified': 'BOOLEAN', 'attribution': 'STRING', 'quote': 'STRING', 'item_id': 'INTEGER'}
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4e51921e5b118fffffcbc443b9ecf21dc41de5d2
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py
|
Python
|
ontquery/__init__.py
|
tmsincomb/ontquery
|
aff90a66437559ddc7a0dd8ed8d42c13d535361f
|
[
"MIT"
] | 1
|
2021-11-28T23:51:22.000Z
|
2021-11-28T23:51:22.000Z
|
ontquery/__init__.py
|
tmsincomb/ontquery
|
aff90a66437559ddc7a0dd8ed8d42c13d535361f
|
[
"MIT"
] | 25
|
2018-03-22T00:39:06.000Z
|
2021-08-05T20:35:32.000Z
|
ontquery/__init__.py
|
tmsincomb/ontquery
|
aff90a66437559ddc7a0dd8ed8d42c13d535361f
|
[
"MIT"
] | 1
|
2018-11-08T23:16:55.000Z
|
2018-11-08T23:16:55.000Z
|
from ontquery.query import OntQuery, OntQueryCli
from ontquery.terms import OntCuries, OntId, OntTerm
from ontquery import plugin
__all__ = ['OntCuries', 'OntId', 'OntTerm', 'OntQuery', 'OntQueryCli']
__version__ = '0.2.8'
| 28.125
| 70
| 0.76
| 27
| 225
| 6.037037
| 0.555556
| 0.220859
| 0.257669
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.015152
| 0.12
| 225
| 7
| 71
| 32.142857
| 0.808081
| 0
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| 0
| 0.2
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| false
| 0
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| 0
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| 0
| 0
| 0
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| 1
| 0
| 0
| 0
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| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
4e68b61fd0f5bf90a0d85e3610556a48c748c1dd
| 29
|
py
|
Python
|
code/dpp/centers/splines/__init__.py
|
bsouhaib/qf-tpp
|
a5adf3f7203b920528c1c397329c4afd9039c3b4
|
[
"MIT"
] | null | null | null |
code/dpp/centers/splines/__init__.py
|
bsouhaib/qf-tpp
|
a5adf3f7203b920528c1c397329c4afd9039c3b4
|
[
"MIT"
] | null | null | null |
code/dpp/centers/splines/__init__.py
|
bsouhaib/qf-tpp
|
a5adf3f7203b920528c1c397329c4afd9039c3b4
|
[
"MIT"
] | null | null | null |
from .splines_utils import *
| 14.5
| 28
| 0.793103
| 4
| 29
| 5.5
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.137931
| 29
| 1
| 29
| 29
| 0.88
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
4e8b22e8f1306c05ef156e89e39585a504ea8c29
| 31
|
py
|
Python
|
farm_management/users/views/__init__.py
|
alexanders0/farm-management
|
53ed821bbbed312848cf331f8f961ef16c59fb99
|
[
"MIT"
] | 1
|
2021-07-11T23:26:10.000Z
|
2021-07-11T23:26:10.000Z
|
farm_management/users/views/__init__.py
|
alexanders0/farm-management
|
53ed821bbbed312848cf331f8f961ef16c59fb99
|
[
"MIT"
] | 1
|
2021-07-13T02:09:37.000Z
|
2021-07-17T09:30:15.000Z
|
farm_management/users/views/__init__.py
|
alexanders0/farm-management
|
53ed821bbbed312848cf331f8f961ef16c59fb99
|
[
"MIT"
] | null | null | null |
from .users import UserViewSet
| 15.5
| 30
| 0.83871
| 4
| 31
| 6.5
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.129032
| 31
| 1
| 31
| 31
| 0.962963
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
4ead5749dcb225ee8bbc27e19af1a7455dcd288e
| 360
|
py
|
Python
|
utils.py
|
jroivas/klapi
|
786f614d1bf0c40b6186501ef7fd8e1855d2a811
|
[
"MIT"
] | 3
|
2018-02-21T17:54:14.000Z
|
2019-08-31T21:34:14.000Z
|
utils.py
|
jroivas/klapi
|
786f614d1bf0c40b6186501ef7fd8e1855d2a811
|
[
"MIT"
] | null | null | null |
utils.py
|
jroivas/klapi
|
786f614d1bf0c40b6186501ef7fd8e1855d2a811
|
[
"MIT"
] | 4
|
2018-02-21T21:50:23.000Z
|
2021-11-25T06:56:33.000Z
|
import random
import string
import uuid
def generatePassword(characters=8):
# Disabling 0 and O to prevent misreadings
return ''.join(random.SystemRandom().choice(string.letters.replace('O','') + string.digits.replace('0','')) for _ in range(characters))
def generateID():
return str(uuid.uuid4())
def generateApiKey():
return generateID()
| 24
| 139
| 0.716667
| 44
| 360
| 5.840909
| 0.659091
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.012987
| 0.144444
| 360
| 14
| 140
| 25.714286
| 0.821429
| 0.111111
| 0
| 0
| 1
| 0
| 0.006309
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.333333
| false
| 0.111111
| 0.333333
| 0.333333
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 1
| 1
| 1
| 0
|
0
| 6
|
4ed09b86ee8fee898c014e6a5e644dac64447c46
| 96
|
py
|
Python
|
venv/lib/python3.8/site-packages/cffi/cparser.py
|
Retraces/UkraineBot
|
3d5d7f8aaa58fa0cb8b98733b8808e5dfbdb8b71
|
[
"MIT"
] | 2
|
2022-03-13T01:58:52.000Z
|
2022-03-31T06:07:54.000Z
|
venv/lib/python3.8/site-packages/cffi/cparser.py
|
DesmoSearch/Desmobot
|
b70b45df3485351f471080deb5c785c4bc5c4beb
|
[
"MIT"
] | 19
|
2021-11-20T04:09:18.000Z
|
2022-03-23T15:05:55.000Z
|
venv/lib/python3.8/site-packages/cffi/cparser.py
|
DesmoSearch/Desmobot
|
b70b45df3485351f471080deb5c785c4bc5c4beb
|
[
"MIT"
] | null | null | null |
/home/runner/.cache/pip/pool/ac/ef/f5/a442d1c35808d431359b8822da293924c7e9c68b800022d7a513e55440
| 96
| 96
| 0.895833
| 9
| 96
| 9.555556
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.46875
| 0
| 96
| 1
| 96
| 96
| 0.427083
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | null | 0
| 0
| null | null | 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 1
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
14f8f3b64dae2bee5ae5872ffbfcb6c6724b53f4
| 22
|
py
|
Python
|
signalr/hubs/__init__.py
|
talboren/signalr-client-py
|
bc41ab6602348258140372a5d78dc0e4f8f6205d
|
[
"Apache-2.0"
] | 58
|
2015-08-28T18:45:54.000Z
|
2022-01-21T17:53:43.000Z
|
signalr/hubs/__init__.py
|
talboren/signalr-client-py
|
bc41ab6602348258140372a5d78dc0e4f8f6205d
|
[
"Apache-2.0"
] | 48
|
2015-08-29T18:19:59.000Z
|
2021-07-13T07:32:40.000Z
|
signalr/hubs/__init__.py
|
talboren/signalr-client-py
|
bc41ab6602348258140372a5d78dc0e4f8f6205d
|
[
"Apache-2.0"
] | 67
|
2015-08-28T22:44:47.000Z
|
2022-03-03T12:37:14.000Z
|
from ._hub import Hub
| 11
| 21
| 0.772727
| 4
| 22
| 4
| 0.75
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.181818
| 22
| 1
| 22
| 22
| 0.888889
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
09181bda01b879cac99a828ce93251a3f19decd3
| 2,001
|
py
|
Python
|
test/test_user_reset_password_requests_api.py
|
Apteco/apteco-api
|
7440c98ab10ea6d8a5997187f6fc739ce1c75d2b
|
[
"Apache-2.0"
] | 2
|
2020-05-21T14:24:16.000Z
|
2020-12-03T19:56:34.000Z
|
test/test_user_reset_password_requests_api.py
|
Apteco/apteco-api
|
7440c98ab10ea6d8a5997187f6fc739ce1c75d2b
|
[
"Apache-2.0"
] | null | null | null |
test/test_user_reset_password_requests_api.py
|
Apteco/apteco-api
|
7440c98ab10ea6d8a5997187f6fc739ce1c75d2b
|
[
"Apache-2.0"
] | null | null | null |
# coding: utf-8
"""
Apteco API
An API to allow access to Apteco Marketing Suite resources # noqa: E501
The version of the OpenAPI document: v2
Contact: [email protected]
Generated by: https://openapi-generator.tech
"""
from __future__ import absolute_import
import unittest
import apteco_api
from apteco_api.api.user_reset_password_requests_api import UserResetPasswordRequestsApi # noqa: E501
from apteco_api.rest import ApiException
class TestUserResetPasswordRequestsApi(unittest.TestCase):
"""UserResetPasswordRequestsApi unit test stubs"""
def setUp(self):
self.api = apteco_api.api.user_reset_password_requests_api.UserResetPasswordRequestsApi() # noqa: E501
def tearDown(self):
pass
def test_user_reset_password_requests_confirm_reset_password_request(self):
"""Test case for user_reset_password_requests_confirm_reset_password_request
Confirms a given reset password request and changes the password # noqa: E501
"""
pass
def test_user_reset_password_requests_create_reset_password_request(self):
"""Test case for user_reset_password_requests_create_reset_password_request
Creates a new reset password requests, which will check that the provided email address exists and then issue a confirmation notification # noqa: E501
"""
pass
def test_user_reset_password_requests_get_reset_password_request(self):
"""Test case for user_reset_password_requests_get_reset_password_request
Requires OrbitAdmin: Returns details for a given reset password request # noqa: E501
"""
pass
def test_user_reset_password_requests_get_reset_password_requests(self):
"""Test case for user_reset_password_requests_get_reset_password_requests
Requires OrbitAdmin: Returns all the current reset password requests in the system. # noqa: E501
"""
pass
if __name__ == '__main__':
unittest.main()
| 31.761905
| 159
| 0.749625
| 249
| 2,001
| 5.682731
| 0.349398
| 0.20212
| 0.207774
| 0.176678
| 0.466431
| 0.429682
| 0.429682
| 0.421908
| 0.284806
| 0.25371
| 0
| 0.014312
| 0.196902
| 2,001
| 62
| 160
| 32.274194
| 0.86621
| 0.493253
| 0
| 0.25
| 0
| 0
| 0.008979
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.3
| false
| 0.6
| 0.25
| 0
| 0.6
| 0
| 0
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 1
| 0
|
0
| 6
|
119da60e693c246bbdc5e969411cea8dcd3424df
| 219
|
py
|
Python
|
manim_rubikscube/__init__.py
|
WampyCakes/manim-rubikscube
|
890cc853e3492e6afa338cfadb72e4df1e4aa632
|
[
"MIT"
] | 20
|
2020-12-15T01:38:46.000Z
|
2022-02-25T02:56:50.000Z
|
manim_rubikscube/__init__.py
|
WampyCakes/manim-rubikscube
|
890cc853e3492e6afa338cfadb72e4df1e4aa632
|
[
"MIT"
] | 6
|
2021-08-07T00:22:04.000Z
|
2021-11-08T10:49:02.000Z
|
manim_rubikscube/__init__.py
|
WampyCakes/manim-rubikscube
|
890cc853e3492e6afa338cfadb72e4df1e4aa632
|
[
"MIT"
] | 4
|
2021-05-03T16:11:31.000Z
|
2021-10-05T12:55:37.000Z
|
from .cube import *
from .cube_animations import *
try:
import importlib.metadata as importlib_metadata
except ModuleNotFoundError:
import importlib_metadata
__version__ = importlib_metadata.version(__name__)
| 21.9
| 51
| 0.812785
| 24
| 219
| 6.916667
| 0.5
| 0.409639
| 0.277108
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.136986
| 219
| 9
| 52
| 24.333333
| 0.878307
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.714286
| 0
| 0.714286
| 0
| 1
| 0
| 0
| null | 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
e120e8b38359a048db23721f886eb24137bb5457
| 16,775
|
py
|
Python
|
tests/test_key.py
|
dajiaji/pyseto
|
6e3f1259bd1a1671cccd75cb557bb63182f9e01a
|
[
"MIT"
] | 25
|
2021-09-06T08:53:45.000Z
|
2022-02-19T20:17:23.000Z
|
tests/test_key.py
|
dajiaji/pyseto
|
6e3f1259bd1a1671cccd75cb557bb63182f9e01a
|
[
"MIT"
] | 124
|
2021-09-05T05:44:05.000Z
|
2022-03-27T05:57:25.000Z
|
tests/test_key.py
|
dajiaji/pyseto
|
6e3f1259bd1a1671cccd75cb557bb63182f9e01a
|
[
"MIT"
] | 3
|
2021-09-11T02:37:09.000Z
|
2022-01-06T10:49:14.000Z
|
from secrets import token_bytes
import pytest
from pyseto import DecryptError, Key, NotSupportedError
from pyseto.key_interface import KeyInterface
from pyseto.utils import base64url_decode
from .utils import load_jwk, load_key
class TestKey:
"""
Tests for Key.
"""
@pytest.mark.parametrize(
"version, purpose, key",
[
(1, "local", token_bytes(32)),
(2, "local", token_bytes(32)),
(3, "local", token_bytes(32)),
(4, "local", token_bytes(32)),
],
)
def test_key_new_local(self, version, purpose, key):
k = Key.new(version, purpose, key)
assert isinstance(k, KeyInterface)
assert k.version == version
assert k.purpose == purpose
with pytest.raises(NotSupportedError) as err:
k.sign(b"Hello world!")
pytest.fail("Key.sign() should fail.")
assert "A key for local does not have sign()." in str(err.value)
with pytest.raises(NotSupportedError) as err:
k.verify(b"xxxxxx")
pytest.fail("Key.verify() should fail.")
assert "A key for local does not have verify()." in str(err.value)
@pytest.mark.parametrize(
"version, purpose, key",
[
(1, "public", load_key("keys/private_key_rsa.pem")),
(1, "public", load_key("keys/public_key_rsa.pem")),
(2, "public", load_key("keys/private_key_ed25519.pem")),
(2, "public", load_key("keys/public_key_ed25519.pem")),
(3, "public", load_key("keys/private_key_ecdsa_p384.pem")),
(3, "public", load_key("keys/public_key_ecdsa_p384.pem")),
(4, "public", load_key("keys/private_key_ed25519.pem")),
(4, "public", load_key("keys/public_key_ed25519.pem")),
],
)
def test_key_new_public(self, version, purpose, key):
k = Key.new(version, purpose, key)
assert isinstance(k, KeyInterface)
assert k.version == version
assert k.purpose == purpose
with pytest.raises(NotSupportedError) as err:
k.encrypt(b"Hello world!")
pytest.fail("Key.sign() should fail.")
assert "A key for public does not have encrypt()." in str(err.value)
with pytest.raises(NotSupportedError) as err:
k.decrypt(b"xxxxxx")
pytest.fail("Key.verify() should fail.")
assert "A key for public does not have decrypt()." in str(err.value)
@pytest.mark.parametrize(
"version, key, msg",
[
(1, load_key("keys/private_key_ed25519.pem"), "The key is not RSA key."),
(1, load_key("keys/public_key_ed25519.pem"), "The key is not RSA key."),
(
1,
load_key("keys/private_key_ecdsa_p384.pem"),
"The key is not RSA key.",
),
(
1,
load_key("keys/public_key_ecdsa_p384.pem"),
"The key is not RSA key.",
),
(2, load_key("keys/private_key_rsa.pem"), "The key is not Ed25519 key."),
(2, load_key("keys/public_key_rsa.pem"), "The key is not Ed25519 key."),
(
2,
load_key("keys/private_key_ecdsa_p384.pem"),
"The key is not Ed25519 key.",
),
(
2,
load_key("keys/public_key_ecdsa_p384.pem"),
"The key is not Ed25519 key.",
),
(
3,
load_key("keys/private_key_ed25519.pem"),
"The key is not ECDSA key.",
),
(
3,
load_key("keys/public_key_ed25519.pem"),
"The key is not ECDSA key.",
),
(3, load_key("keys/private_key_rsa.pem"), "The key is not ECDSA key."),
(3, load_key("keys/public_key_rsa.pem"), "The key is not ECDSA key."),
(4, load_key("keys/private_key_rsa.pem"), "The key is not Ed25519 key."),
(4, load_key("keys/public_key_rsa.pem"), "The key is not Ed25519 key."),
(
4,
load_key("keys/private_key_ecdsa_p384.pem"),
"The key is not Ed25519 key.",
),
(
4,
load_key("keys/public_key_ecdsa_p384.pem"),
"The key is not Ed25519 key.",
),
],
)
def test_key_new_public_with_wrong_key(self, version, key, msg):
with pytest.raises(ValueError) as err:
Key.new(version, "public", key)
pytest.fail("Key.new should fail.")
assert msg in str(err.value)
@pytest.mark.parametrize(
"version, purpose, key, msg",
[
("v*", "local", token_bytes(32), "Invalid version: v*."),
("v0", "local", token_bytes(32), "Invalid version: v0."),
(0, "local", token_bytes(32), "Invalid version: 0."),
(
"v*",
"public",
load_key("keys/private_key_rsa.pem"),
"Invalid version: v*.",
),
(
"v0",
"public",
load_key("keys/private_key_rsa.pem"),
"Invalid version: v0.",
),
(
0,
"public",
load_key("keys/private_key_rsa.pem"),
"Invalid version: 0.",
),
(1, "xxx", token_bytes(32), "Invalid purpose: xxx."),
(1, "public", "-----BEGIN BAD", "Invalid or unsupported PEM format."),
],
)
def test_key_new_with_invalid_arg(self, version, purpose, key, msg):
with pytest.raises(ValueError) as err:
Key.new(version, purpose, key)
pytest.fail("Key.new should fail.")
assert msg in str(err.value)
@pytest.mark.parametrize(
"version, key",
[
# (1, load_jwk("keys/private_key_rsa.json")),
# (1, load_jwk("keys/public_key_rsa.json")),
(2, load_jwk("keys/private_key_ed25519.json")),
(2, load_jwk("keys/public_key_ed25519.json")),
(3, load_jwk("keys/private_key_ecdsa_p384.json")),
(3, load_jwk("keys/public_key_ecdsa_p384.json")),
(4, load_jwk("keys/private_key_ed25519.json")),
(4, load_jwk("keys/public_key_ed25519.json")),
],
)
def test_key_from_asymmetric_params(self, version, key):
k = Key.from_asymmetric_key_params(version, x=key["x"], y=key["y"], d=key["d"])
assert isinstance(k, KeyInterface)
assert k.version == version
assert k.purpose == "public"
@pytest.mark.parametrize(
"paserk",
[
"k1.local.AAAAAAAAAAAAAAAA",
"k1.public.AAAAAAAAAAAAAAAA",
"k2.local.AAAAAAAAAAAAAAAA",
"k2.public.AAAAAAAAAAAAAAAA",
"k3.local.AAAAAAAAAAAAAAAA",
"k3.public.AAAAAAAAAAAAAAAA",
"k4.local.AAAAAAAAAAAAAAAA",
"k4.public.AAAAAAAAAAAAAAAA",
],
)
def test_key_from_paserk_with_wrapping_key_and_password(self, paserk):
with pytest.raises(ValueError) as err:
Key.from_paserk(paserk, wrapping_key="xxx", password="yyy")
pytest.fail("Key.from_paserk should fail.")
assert "Only one of wrapping_key or password should be specified." in str(err.value)
@pytest.mark.parametrize(
"paserk, msg",
[
("k1.local.AAAAAAAAAAAAAAAA", "Invalid PASERK type: local."),
("k1.public.AAAAAAAAAAAAAAAA", "Invalid PASERK type: public."),
("k2.local.AAAAAAAAAAAAAAAA", "Invalid PASERK type: local."),
("k2.public.AAAAAAAAAAAAAAAA", "Invalid PASERK type: public."),
("k3.local.AAAAAAAAAAAAAAAA", "Invalid PASERK type: local."),
("k3.public.AAAAAAAAAAAAAAAA", "Invalid PASERK type: public."),
("k4.local.AAAAAAAAAAAAAAAA", "Invalid PASERK type: local."),
("k4.public.AAAAAAAAAAAAAAAA", "Invalid PASERK type: public."),
],
)
def test_key_from_paserk_with_password_for_wrong_paserk(self, paserk, msg):
with pytest.raises(ValueError) as err:
Key.from_paserk(paserk, password="yyy")
pytest.fail("Key.from_paserk should fail.")
assert msg in str(err.value)
@pytest.mark.parametrize(
"paserk, msg",
[
("v1.local.AAAAAAAAAAAAAAAA", "Invalid PASERK version: v1."),
("*.local.AAAAAAAAAAAAAAAA", "Invalid PASERK version: *."),
("k1.xxx.AAAAAAAAAAAAAAAA", "Invalid PASERK key type: xxx."),
],
)
def test_key_from_paserk_with_invalid_args(self, paserk, msg):
with pytest.raises(ValueError) as err:
Key.from_paserk(paserk)
pytest.fail("Key.from_paserk should fail.")
assert msg in str(err.value)
@pytest.mark.parametrize(
"version",
[
1,
2,
3,
4,
],
)
def test_key_from_paserk_for_local_with_wrong_wrapping_key(self, version):
k = Key.new(version, "local", token_bytes(32))
wk1 = token_bytes(32)
wk2 = token_bytes(32)
wpk = k.to_paserk(wrapping_key=wk1)
with pytest.raises(DecryptError) as err:
Key.from_paserk(wpk, wrapping_key=wk2)
pytest.fail("Key.from_paserk() should fail.")
assert "Failed to unwrap a key." in str(err.value)
@pytest.mark.parametrize(
"version",
[
1,
2,
3,
4,
],
)
def test_key_from_paserk_for_local_with_wrong_password(self, version):
k = Key.new(version, "local", token_bytes(32))
wk1 = token_bytes(32)
wk2 = token_bytes(32)
wpk = k.to_paserk(password=wk1)
with pytest.raises(DecryptError) as err:
Key.from_paserk(wpk, password=wk2)
pytest.fail("Key.from_paserk() should fail.")
assert "Failed to unwrap a key." in str(err.value)
@pytest.mark.parametrize(
"version, key",
[
(1, load_key("keys/private_key_rsa.pem")),
(2, load_key("keys/private_key_ed25519.pem")),
(3, load_key("keys/private_key_ecdsa_p384.pem")),
(4, load_key("keys/private_key_ed25519.pem")),
],
)
def test_key_from_paserk_for_private_key_with_wrong_wrapping_key(self, version, key):
k = Key.new(version, "public", key)
wk1 = token_bytes(32)
wk2 = token_bytes(32)
wpk = k.to_paserk(wrapping_key=wk1)
with pytest.raises(DecryptError) as err:
Key.from_paserk(wpk, wrapping_key=wk2)
pytest.fail("Key.from_paserk() should fail.")
assert "Failed to unwrap a key." in str(err.value)
@pytest.mark.parametrize(
"version, key",
[
(1, load_key("keys/public_key_rsa.pem")),
(2, load_key("keys/public_key_ed25519.pem")),
(3, load_key("keys/public_key_ecdsa_p384.pem")),
(4, load_key("keys/public_key_ed25519.pem")),
],
)
def test_key_from_paserk_for_public_key_with_wrapping_key(self, version, key):
k = Key.new(version, "public", key)
wk = token_bytes(32)
with pytest.raises(ValueError) as err:
k.to_paserk(wrapping_key=wk)
pytest.fail("to_paserk() should fail.")
assert "Public key cannot be wrapped." in str(err.value)
@pytest.mark.parametrize(
"version, key",
[
(1, load_key("keys/public_key_rsa.pem")),
(2, load_key("keys/public_key_ed25519.pem")),
(3, load_key("keys/public_key_ecdsa_p384.pem")),
(4, load_key("keys/public_key_ed25519.pem")),
],
)
def test_key_from_paserk_for_public_key_with_password(self, version, key):
k = Key.new(version, "public", key)
wk = token_bytes(32)
with pytest.raises(ValueError) as err:
k.to_paserk(password=wk)
pytest.fail("to_paserk() should fail.")
assert "Public key cannot be wrapped." in str(err.value)
@pytest.mark.parametrize(
"version, key, msg",
[
(
1,
load_jwk("keys/private_key_rsa.json"),
"v1.public is not supported on from_key_parameters.",
),
(999, load_jwk("keys/private_key_ed25519.json"), "Invalid version: 999."),
(0, load_jwk("keys/private_key_ed25519.json"), "Invalid version: 0."),
(
2,
{"x": b"xxx", "y": b"", "d": b"ddd"},
"Only one of x or d should be set for v2.public.",
),
(2, {"x": b"xxx", "y": b"", "d": b""}, "Failed to load key."),
(2, {"x": b"", "y": b"", "d": b"ddd"}, "Failed to load key."),
(
2,
{"x": b"", "y": b"", "d": b""},
"x or d should be set for v2.public.",
),
(
3,
{"x": b"xxx", "y": b"", "d": b"ddd"},
"x and y (and d) should be set for v3.public.",
),
(
3,
{"x": b"", "y": b"yyy", "d": b"ddd"},
"x and y (and d) should be set for v3.public.",
),
(3, {"x": b"xxx", "y": b"yyy", "d": b""}, "Failed to load key."),
(
3,
{
"x": base64url_decode("_XyN9woHaS0mPimSW-etwJMEDSzxIMjp4PjezavU8SHJoClz1bQrcmPb1ZJxHxhI"),
"y": base64url_decode("GCNfc32p9sRotx7u2oDGJ3Eqz6q5zPHLdizNn83oRsUTN31eCWfGLHWRury3xF50"),
"d": b"ddd",
},
"Failed to load key.",
),
(
4,
{"x": b"xxx", "y": b"", "d": b"ddd"},
"Only one of x or d should be set for v4.public.",
),
(4, {"x": b"xxx", "y": b"", "d": b""}, "Failed to load key."),
(4, {"x": b"", "y": b"", "d": b"ddd"}, "Failed to load key."),
(
4,
{"x": b"", "y": b"", "d": b""},
"x or d should be set for v4.public.",
),
],
)
def test_key_from_asymmetric_params_with_invalid_arg(self, version, key, msg):
with pytest.raises(ValueError) as err:
Key.from_asymmetric_key_params(version, x=key["x"], y=key["y"], d=key["d"])
pytest.fail("Key.from_asymmetric_key_params() should fail.")
assert msg in str(err.value)
@pytest.mark.parametrize(
"version, purpose, key",
[
(1, "public", load_key("keys/public_key_rsa.pem")),
(2, "public", load_key("keys/public_key_ed25519.pem")),
(3, "public", load_key("keys/public_key_ecdsa_p384.pem")),
(4, "public", load_key("keys/public_key_ed25519.pem")),
],
)
def test_key_to_paserk_public(self, version, purpose, key):
k = Key.new(version, purpose, key)
assert k.to_paserk().startswith(f"k{k.version}.public.")
@pytest.mark.parametrize(
"version, purpose, key",
[
(1, "public", load_key("keys/private_key_rsa.pem")),
(2, "public", load_key("keys/private_key_ed25519.pem")),
(3, "public", load_key("keys/private_key_ecdsa_p384.pem")),
(4, "public", load_key("keys/private_key_ed25519.pem")),
],
)
def test_key_to_paserk_secret(self, version, purpose, key):
k = Key.new(version, purpose, key)
assert k.to_paserk().startswith(f"k{k.version}.secret.")
@pytest.mark.parametrize(
"version, purpose, key",
[
(1, "local", token_bytes(32)),
(2, "local", token_bytes(32)),
(3, "local", token_bytes(32)),
(4, "local", token_bytes(32)),
(1, "public", load_key("keys/private_key_rsa.pem")),
(2, "public", load_key("keys/private_key_ed25519.pem")),
(3, "public", load_key("keys/private_key_ecdsa_p384.pem")),
(4, "public", load_key("keys/private_key_ed25519.pem")),
],
)
def test_key_to_paserk_secret_with_wrapping_key_and_password(self, version, purpose, key):
k = Key.new(version, purpose, key)
with pytest.raises(ValueError) as err:
k.to_paserk(wrapping_key="xxx", password="yyy")
pytest.fail("to_paserk() should fail.")
assert "Only one of wrapping_key or password should be specified." in str(err.value)
| 38.652074
| 110
| 0.536095
| 2,018
| 16,775
| 4.265114
| 0.064916
| 0.047171
| 0.06518
| 0.056466
| 0.882305
| 0.856745
| 0.769374
| 0.753456
| 0.73568
| 0.710352
| 0
| 0.037802
| 0.321908
| 16,775
| 433
| 111
| 38.741339
| 0.718857
| 0.00608
| 0
| 0.568579
| 0
| 0
| 0.306676
| 0.136768
| 0
| 0
| 0
| 0
| 0.067332
| 1
| 0.042394
| false
| 0.032419
| 0.014963
| 0
| 0.05985
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
012e1de5dd6d5c0f6de8094bdd072d509d7efa1d
| 257,269
|
py
|
Python
|
instances/passenger_demand/pas-20210422-1717-int8e-1/29.py
|
LHcau/scheduling-shared-passenger-and-freight-transport-on-a-fixed-infrastructure
|
bba1e6af5bc8d9deaa2dc3b83f6fe9ddf15d2a11
|
[
"BSD-3-Clause"
] | null | null | null |
instances/passenger_demand/pas-20210422-1717-int8e-1/29.py
|
LHcau/scheduling-shared-passenger-and-freight-transport-on-a-fixed-infrastructure
|
bba1e6af5bc8d9deaa2dc3b83f6fe9ddf15d2a11
|
[
"BSD-3-Clause"
] | null | null | null |
instances/passenger_demand/pas-20210422-1717-int8e-1/29.py
|
LHcau/scheduling-shared-passenger-and-freight-transport-on-a-fixed-infrastructure
|
bba1e6af5bc8d9deaa2dc3b83f6fe9ddf15d2a11
|
[
"BSD-3-Clause"
] | null | null | null |
"""
PASSENGERS
"""
numPassengers = 15114
passenger_arriving = (
(7, 3, 2, 4, 5, 2, 1, 3, 1, 0, 0, 0, 0, 7, 5, 1, 2, 3, 1, 2, 0, 3, 2, 3, 1, 0), # 0
(3, 3, 3, 6, 3, 2, 1, 5, 3, 2, 0, 0, 0, 4, 4, 3, 2, 7, 3, 4, 0, 0, 2, 1, 1, 0), # 1
(7, 4, 1, 1, 4, 1, 0, 4, 1, 1, 0, 0, 0, 4, 4, 4, 3, 4, 1, 1, 2, 2, 5, 0, 0, 0), # 2
(4, 4, 3, 3, 0, 0, 2, 1, 1, 4, 0, 1, 0, 6, 4, 6, 3, 4, 3, 3, 2, 3, 0, 1, 0, 0), # 3
(8, 6, 7, 3, 6, 2, 0, 3, 1, 1, 0, 1, 0, 4, 0, 6, 0, 2, 2, 1, 1, 5, 4, 2, 1, 0), # 4
(2, 3, 8, 3, 3, 2, 3, 2, 0, 1, 2, 0, 0, 3, 4, 5, 1, 8, 3, 2, 1, 4, 1, 0, 0, 0), # 5
(6, 12, 3, 5, 1, 1, 1, 1, 2, 2, 1, 0, 0, 7, 4, 1, 6, 1, 3, 3, 2, 2, 1, 1, 0, 0), # 6
(6, 7, 4, 2, 3, 4, 3, 2, 2, 0, 2, 1, 0, 5, 3, 3, 4, 5, 4, 0, 4, 2, 1, 0, 1, 0), # 7
(6, 4, 8, 4, 8, 1, 1, 2, 2, 0, 1, 0, 0, 7, 6, 5, 6, 5, 2, 4, 4, 1, 1, 0, 3, 0), # 8
(7, 10, 8, 3, 4, 3, 5, 5, 2, 1, 0, 0, 0, 9, 9, 4, 2, 3, 2, 2, 1, 1, 3, 3, 0, 0), # 9
(2, 8, 6, 5, 5, 4, 4, 2, 2, 0, 2, 0, 0, 8, 6, 4, 4, 10, 4, 6, 0, 2, 1, 3, 0, 0), # 10
(7, 3, 7, 8, 4, 3, 2, 2, 0, 1, 2, 0, 0, 7, 6, 3, 1, 1, 4, 3, 0, 4, 0, 3, 1, 0), # 11
(6, 4, 8, 7, 3, 4, 3, 2, 5, 0, 2, 1, 0, 4, 8, 6, 6, 2, 5, 4, 0, 4, 1, 1, 1, 0), # 12
(5, 11, 5, 7, 0, 3, 0, 2, 5, 3, 1, 0, 0, 6, 7, 3, 4, 8, 6, 5, 3, 3, 1, 1, 1, 0), # 13
(11, 6, 3, 9, 6, 0, 6, 9, 6, 0, 0, 0, 0, 9, 4, 3, 4, 8, 0, 1, 0, 2, 5, 0, 1, 0), # 14
(7, 5, 3, 13, 4, 1, 2, 5, 0, 2, 1, 0, 0, 5, 6, 3, 2, 4, 5, 5, 1, 1, 2, 1, 0, 0), # 15
(4, 6, 9, 3, 5, 4, 3, 1, 1, 3, 0, 0, 0, 4, 5, 4, 5, 9, 3, 2, 2, 6, 2, 1, 0, 0), # 16
(9, 7, 5, 5, 5, 0, 1, 0, 2, 2, 1, 1, 0, 7, 11, 7, 2, 2, 3, 5, 4, 2, 3, 1, 1, 0), # 17
(4, 12, 2, 6, 4, 2, 3, 4, 6, 1, 2, 0, 0, 6, 11, 5, 8, 6, 3, 2, 2, 3, 3, 2, 0, 0), # 18
(10, 8, 8, 6, 5, 0, 1, 1, 3, 1, 1, 0, 0, 3, 3, 1, 7, 7, 9, 1, 0, 1, 5, 1, 1, 0), # 19
(8, 7, 4, 4, 9, 3, 3, 6, 3, 0, 1, 0, 0, 8, 7, 9, 5, 12, 5, 1, 4, 4, 1, 3, 1, 0), # 20
(10, 13, 5, 11, 6, 4, 4, 1, 2, 0, 3, 1, 0, 6, 7, 6, 6, 7, 3, 4, 3, 2, 0, 1, 0, 0), # 21
(12, 6, 4, 8, 7, 4, 2, 7, 0, 2, 3, 0, 0, 10, 3, 6, 1, 9, 9, 0, 2, 8, 1, 1, 0, 0), # 22
(8, 9, 6, 7, 7, 4, 2, 5, 2, 2, 1, 2, 0, 12, 6, 1, 7, 6, 5, 2, 1, 3, 1, 2, 0, 0), # 23
(11, 6, 7, 7, 8, 3, 3, 2, 2, 3, 0, 0, 0, 9, 10, 5, 5, 6, 5, 5, 2, 3, 1, 0, 1, 0), # 24
(10, 2, 9, 3, 4, 2, 5, 4, 6, 2, 0, 1, 0, 10, 4, 5, 1, 6, 2, 4, 5, 5, 6, 0, 0, 0), # 25
(13, 8, 10, 10, 5, 3, 4, 2, 1, 1, 1, 0, 0, 11, 4, 5, 6, 5, 4, 5, 1, 2, 4, 0, 0, 0), # 26
(7, 10, 10, 3, 3, 3, 3, 1, 1, 1, 1, 0, 0, 11, 4, 4, 8, 9, 3, 6, 0, 3, 3, 0, 2, 0), # 27
(10, 10, 7, 8, 8, 4, 1, 3, 4, 0, 3, 0, 0, 11, 4, 9, 3, 10, 5, 4, 4, 3, 4, 1, 1, 0), # 28
(8, 5, 9, 6, 6, 5, 3, 0, 8, 1, 1, 0, 0, 9, 6, 4, 5, 5, 3, 3, 1, 2, 3, 1, 1, 0), # 29
(8, 6, 6, 8, 2, 1, 8, 1, 5, 0, 2, 0, 0, 11, 7, 1, 4, 5, 2, 2, 1, 3, 0, 1, 1, 0), # 30
(8, 9, 9, 8, 6, 4, 2, 5, 4, 3, 1, 1, 0, 6, 4, 4, 7, 8, 4, 0, 2, 1, 4, 3, 2, 0), # 31
(6, 5, 12, 7, 8, 3, 3, 2, 1, 2, 1, 0, 0, 8, 2, 2, 6, 6, 6, 3, 2, 6, 2, 3, 0, 0), # 32
(5, 6, 8, 6, 8, 2, 0, 1, 1, 2, 0, 0, 0, 9, 11, 9, 0, 8, 6, 2, 5, 5, 1, 0, 1, 0), # 33
(4, 6, 5, 9, 7, 4, 3, 0, 3, 1, 1, 1, 0, 12, 4, 5, 4, 6, 9, 5, 2, 2, 4, 2, 0, 0), # 34
(11, 4, 8, 7, 6, 4, 2, 1, 1, 1, 1, 0, 0, 7, 9, 4, 7, 3, 1, 4, 4, 3, 4, 2, 1, 0), # 35
(5, 10, 8, 7, 14, 2, 3, 1, 1, 0, 2, 0, 0, 11, 9, 6, 4, 7, 2, 1, 5, 1, 4, 1, 1, 0), # 36
(12, 7, 9, 9, 6, 1, 2, 2, 5, 2, 2, 0, 0, 9, 8, 6, 4, 4, 3, 3, 1, 3, 1, 3, 0, 0), # 37
(11, 6, 5, 11, 2, 2, 2, 2, 4, 1, 1, 0, 0, 11, 5, 4, 5, 5, 2, 2, 2, 1, 2, 2, 2, 0), # 38
(11, 5, 7, 6, 7, 1, 3, 5, 3, 2, 1, 0, 0, 11, 11, 6, 3, 7, 3, 0, 5, 5, 2, 0, 0, 0), # 39
(8, 5, 6, 5, 8, 6, 3, 4, 5, 2, 2, 1, 0, 7, 13, 7, 2, 5, 9, 3, 2, 2, 1, 1, 0, 0), # 40
(4, 10, 8, 12, 6, 1, 1, 2, 1, 1, 0, 0, 0, 10, 3, 1, 6, 14, 3, 6, 2, 3, 2, 0, 2, 0), # 41
(3, 12, 6, 8, 7, 2, 3, 4, 1, 2, 1, 2, 0, 6, 7, 6, 1, 6, 2, 2, 0, 2, 1, 3, 0, 0), # 42
(6, 6, 7, 1, 7, 2, 3, 1, 4, 1, 0, 0, 0, 10, 8, 2, 2, 5, 5, 4, 1, 5, 1, 1, 1, 0), # 43
(5, 10, 4, 4, 1, 4, 2, 3, 2, 2, 3, 1, 0, 15, 5, 1, 7, 0, 4, 2, 1, 2, 1, 1, 0, 0), # 44
(10, 7, 4, 9, 9, 1, 2, 5, 3, 2, 1, 0, 0, 10, 8, 6, 3, 2, 4, 4, 0, 5, 4, 2, 1, 0), # 45
(12, 5, 9, 8, 5, 2, 2, 2, 3, 3, 2, 0, 0, 10, 5, 7, 8, 7, 6, 3, 0, 5, 5, 0, 0, 0), # 46
(11, 6, 11, 12, 7, 1, 4, 3, 4, 0, 2, 2, 0, 14, 5, 4, 4, 9, 3, 4, 6, 2, 3, 2, 0, 0), # 47
(10, 11, 6, 7, 6, 2, 1, 2, 1, 3, 1, 0, 0, 13, 11, 7, 4, 4, 4, 5, 1, 4, 3, 1, 0, 0), # 48
(5, 9, 3, 8, 0, 3, 6, 6, 1, 0, 1, 0, 0, 6, 4, 2, 7, 8, 6, 2, 2, 1, 10, 3, 1, 0), # 49
(8, 10, 7, 8, 10, 3, 3, 4, 2, 1, 1, 2, 0, 7, 11, 6, 1, 13, 3, 5, 0, 0, 0, 0, 1, 0), # 50
(4, 7, 5, 8, 12, 3, 3, 3, 4, 1, 1, 0, 0, 6, 7, 4, 5, 6, 4, 3, 2, 1, 1, 1, 1, 0), # 51
(9, 6, 6, 3, 6, 1, 5, 1, 4, 2, 1, 0, 0, 4, 4, 6, 4, 6, 3, 3, 2, 4, 0, 2, 2, 0), # 52
(13, 7, 2, 3, 7, 2, 6, 5, 4, 0, 0, 2, 0, 12, 6, 6, 4, 7, 2, 2, 2, 4, 5, 1, 0, 0), # 53
(5, 12, 8, 5, 7, 7, 2, 3, 3, 1, 2, 0, 0, 6, 7, 9, 2, 6, 3, 1, 2, 7, 4, 1, 0, 0), # 54
(5, 8, 9, 5, 3, 1, 1, 2, 4, 1, 1, 0, 0, 10, 6, 6, 5, 10, 3, 5, 2, 2, 3, 0, 2, 0), # 55
(10, 8, 5, 10, 5, 4, 4, 1, 2, 1, 2, 0, 0, 3, 3, 5, 5, 5, 4, 3, 2, 2, 2, 1, 1, 0), # 56
(4, 6, 8, 5, 4, 2, 2, 3, 3, 3, 2, 0, 0, 9, 6, 5, 3, 9, 5, 4, 1, 5, 2, 0, 0, 0), # 57
(13, 13, 8, 10, 4, 4, 3, 0, 4, 2, 1, 1, 0, 9, 4, 4, 2, 6, 2, 5, 4, 0, 5, 0, 0, 0), # 58
(9, 5, 7, 12, 6, 3, 2, 4, 5, 2, 3, 2, 0, 9, 9, 2, 4, 8, 5, 2, 2, 2, 4, 0, 0, 0), # 59
(10, 6, 4, 4, 4, 3, 4, 11, 6, 2, 0, 0, 0, 5, 7, 4, 6, 5, 6, 8, 1, 2, 2, 1, 0, 0), # 60
(7, 7, 6, 10, 5, 3, 3, 3, 4, 1, 1, 1, 0, 4, 10, 5, 4, 6, 7, 4, 2, 2, 1, 4, 1, 0), # 61
(10, 3, 6, 5, 3, 2, 2, 3, 1, 2, 2, 0, 0, 6, 6, 1, 2, 5, 2, 1, 1, 1, 2, 2, 1, 0), # 62
(9, 12, 5, 7, 4, 2, 2, 1, 2, 3, 1, 0, 0, 8, 5, 2, 8, 5, 4, 2, 1, 3, 5, 0, 2, 0), # 63
(8, 13, 3, 8, 6, 2, 3, 1, 2, 1, 0, 1, 0, 10, 3, 3, 3, 8, 5, 1, 2, 0, 5, 2, 1, 0), # 64
(9, 8, 4, 5, 8, 7, 7, 3, 1, 1, 2, 2, 0, 15, 7, 4, 8, 3, 4, 2, 0, 3, 3, 3, 0, 0), # 65
(13, 5, 2, 7, 9, 0, 1, 4, 2, 3, 1, 0, 0, 8, 6, 6, 3, 4, 2, 4, 6, 6, 6, 1, 2, 0), # 66
(11, 3, 8, 11, 7, 5, 4, 2, 4, 2, 1, 4, 0, 1, 7, 3, 6, 4, 0, 2, 2, 4, 5, 1, 1, 0), # 67
(5, 7, 6, 3, 6, 3, 2, 3, 4, 3, 1, 0, 0, 6, 7, 5, 4, 5, 4, 4, 3, 3, 1, 3, 1, 0), # 68
(8, 11, 8, 8, 3, 2, 1, 1, 5, 0, 3, 1, 0, 12, 10, 6, 5, 7, 7, 1, 1, 4, 0, 2, 1, 0), # 69
(2, 8, 4, 4, 5, 2, 3, 4, 5, 2, 2, 1, 0, 6, 4, 5, 2, 6, 3, 4, 2, 2, 0, 1, 0, 0), # 70
(6, 11, 4, 12, 8, 3, 7, 0, 1, 0, 0, 0, 0, 8, 9, 5, 0, 9, 3, 2, 3, 3, 3, 0, 1, 0), # 71
(10, 10, 7, 5, 9, 0, 2, 3, 4, 2, 1, 0, 0, 5, 8, 3, 2, 13, 1, 2, 1, 5, 2, 0, 0, 0), # 72
(9, 9, 7, 4, 4, 10, 2, 2, 7, 2, 0, 1, 0, 6, 3, 5, 1, 8, 4, 2, 0, 0, 3, 6, 0, 0), # 73
(7, 8, 1, 1, 12, 2, 1, 1, 5, 2, 1, 1, 0, 6, 4, 6, 2, 3, 2, 2, 4, 3, 0, 2, 0, 0), # 74
(10, 9, 7, 7, 7, 4, 2, 4, 4, 2, 2, 1, 0, 6, 4, 6, 3, 8, 2, 4, 4, 5, 4, 3, 0, 0), # 75
(11, 9, 8, 4, 5, 4, 3, 4, 1, 2, 3, 0, 0, 6, 2, 7, 2, 7, 3, 3, 2, 1, 2, 1, 2, 0), # 76
(6, 6, 5, 6, 6, 1, 2, 3, 1, 0, 0, 1, 0, 7, 9, 4, 4, 2, 3, 4, 0, 2, 5, 1, 1, 0), # 77
(11, 8, 12, 6, 14, 1, 0, 1, 2, 0, 0, 1, 0, 16, 9, 4, 2, 8, 2, 3, 6, 0, 4, 2, 0, 0), # 78
(4, 6, 3, 8, 6, 6, 4, 3, 2, 3, 0, 0, 0, 8, 5, 2, 5, 7, 4, 7, 2, 4, 2, 0, 1, 0), # 79
(12, 7, 5, 7, 6, 2, 0, 4, 2, 1, 0, 2, 0, 4, 5, 7, 5, 7, 2, 5, 2, 4, 2, 1, 0, 0), # 80
(9, 8, 6, 6, 7, 4, 0, 1, 4, 3, 0, 0, 0, 4, 4, 7, 7, 5, 5, 4, 0, 4, 1, 2, 1, 0), # 81
(8, 10, 3, 9, 3, 3, 3, 2, 3, 1, 0, 1, 0, 10, 5, 5, 3, 5, 0, 5, 0, 4, 2, 2, 1, 0), # 82
(5, 3, 7, 7, 3, 1, 1, 5, 0, 3, 0, 0, 0, 8, 10, 5, 3, 9, 3, 3, 2, 4, 3, 1, 0, 0), # 83
(12, 10, 4, 4, 11, 2, 6, 3, 6, 2, 0, 0, 0, 3, 5, 9, 1, 6, 5, 5, 2, 2, 2, 0, 0, 0), # 84
(13, 9, 10, 6, 5, 3, 2, 3, 4, 3, 0, 1, 0, 7, 8, 9, 3, 4, 4, 2, 2, 6, 2, 0, 1, 0), # 85
(6, 5, 10, 5, 2, 6, 1, 3, 5, 1, 0, 1, 0, 13, 6, 3, 1, 3, 5, 1, 2, 0, 2, 0, 3, 0), # 86
(5, 5, 5, 7, 8, 4, 5, 1, 5, 0, 1, 0, 0, 13, 3, 5, 3, 9, 1, 5, 1, 1, 3, 0, 0, 0), # 87
(10, 11, 4, 9, 4, 5, 4, 3, 2, 1, 1, 1, 0, 4, 9, 4, 4, 3, 4, 1, 6, 3, 0, 1, 1, 0), # 88
(9, 7, 3, 3, 7, 1, 2, 1, 6, 2, 2, 0, 0, 11, 9, 4, 9, 8, 8, 0, 2, 1, 2, 2, 0, 0), # 89
(5, 11, 8, 6, 5, 0, 6, 4, 2, 1, 2, 2, 0, 13, 9, 4, 4, 3, 3, 4, 4, 3, 1, 0, 0, 0), # 90
(7, 11, 6, 7, 5, 1, 6, 1, 1, 1, 0, 0, 0, 5, 10, 5, 4, 6, 1, 4, 2, 2, 1, 0, 0, 0), # 91
(6, 4, 3, 10, 5, 3, 2, 1, 3, 1, 1, 0, 0, 12, 8, 1, 4, 6, 0, 3, 0, 4, 4, 3, 1, 0), # 92
(9, 9, 7, 9, 7, 3, 1, 2, 1, 0, 1, 0, 0, 9, 10, 2, 4, 5, 3, 3, 0, 2, 1, 1, 2, 0), # 93
(8, 10, 6, 6, 7, 4, 3, 2, 5, 3, 2, 0, 0, 1, 7, 8, 6, 3, 1, 5, 2, 1, 6, 0, 2, 0), # 94
(8, 12, 9, 5, 12, 3, 4, 2, 3, 1, 2, 0, 0, 4, 5, 7, 7, 6, 3, 4, 3, 2, 2, 1, 0, 0), # 95
(5, 8, 3, 4, 10, 6, 2, 0, 8, 2, 2, 0, 0, 8, 8, 4, 4, 3, 4, 2, 2, 2, 2, 4, 2, 0), # 96
(8, 6, 10, 6, 5, 1, 1, 2, 4, 0, 1, 0, 0, 5, 9, 3, 4, 4, 1, 3, 4, 0, 4, 1, 1, 0), # 97
(5, 8, 7, 6, 8, 4, 2, 1, 5, 0, 3, 1, 0, 15, 4, 6, 4, 12, 6, 2, 2, 1, 2, 2, 0, 0), # 98
(7, 10, 6, 10, 10, 0, 0, 1, 2, 0, 1, 1, 0, 6, 9, 7, 5, 5, 2, 1, 1, 2, 4, 1, 0, 0), # 99
(6, 1, 3, 2, 4, 4, 3, 0, 0, 1, 1, 2, 0, 10, 10, 5, 3, 4, 4, 2, 0, 0, 1, 1, 0, 0), # 100
(7, 9, 9, 5, 5, 2, 4, 4, 3, 1, 0, 0, 0, 7, 5, 3, 2, 13, 3, 3, 2, 2, 1, 2, 1, 0), # 101
(5, 4, 5, 5, 9, 5, 1, 0, 4, 0, 2, 1, 0, 7, 2, 5, 2, 3, 4, 2, 0, 1, 0, 1, 1, 0), # 102
(3, 7, 4, 9, 4, 6, 1, 2, 4, 4, 0, 0, 0, 11, 11, 4, 1, 7, 4, 5, 1, 3, 2, 0, 0, 0), # 103
(4, 9, 2, 11, 4, 3, 1, 1, 1, 0, 1, 0, 0, 18, 6, 9, 3, 10, 4, 0, 1, 1, 3, 1, 0, 0), # 104
(7, 3, 8, 7, 7, 6, 4, 3, 4, 1, 1, 1, 0, 7, 6, 3, 2, 9, 3, 1, 1, 5, 3, 1, 3, 0), # 105
(7, 9, 5, 10, 4, 2, 2, 2, 4, 2, 1, 1, 0, 7, 6, 7, 1, 5, 4, 1, 4, 9, 0, 0, 0, 0), # 106
(2, 7, 7, 10, 4, 2, 4, 0, 4, 0, 2, 1, 0, 7, 4, 4, 6, 5, 2, 4, 2, 0, 2, 2, 0, 0), # 107
(8, 7, 6, 8, 6, 1, 6, 4, 7, 2, 2, 1, 0, 6, 4, 1, 3, 10, 3, 2, 4, 4, 2, 0, 0, 0), # 108
(6, 10, 6, 7, 5, 6, 2, 4, 2, 0, 1, 0, 0, 6, 9, 3, 1, 4, 4, 3, 1, 0, 3, 0, 2, 0), # 109
(3, 6, 6, 7, 5, 5, 0, 3, 2, 0, 0, 1, 0, 9, 3, 7, 2, 7, 1, 3, 4, 2, 0, 1, 1, 0), # 110
(7, 6, 11, 11, 2, 1, 0, 3, 1, 0, 0, 1, 0, 9, 1, 5, 3, 7, 2, 3, 1, 2, 2, 1, 0, 0), # 111
(11, 6, 2, 7, 5, 3, 3, 1, 5, 0, 2, 2, 0, 4, 11, 3, 5, 3, 3, 4, 0, 2, 4, 1, 2, 0), # 112
(7, 12, 4, 6, 5, 0, 3, 0, 4, 1, 2, 1, 0, 8, 7, 5, 2, 4, 2, 3, 3, 1, 5, 1, 1, 0), # 113
(7, 7, 6, 6, 6, 5, 2, 0, 3, 1, 1, 0, 0, 9, 4, 6, 0, 6, 4, 4, 4, 6, 0, 0, 1, 0), # 114
(4, 7, 3, 4, 5, 2, 5, 0, 1, 0, 1, 0, 0, 3, 9, 5, 6, 7, 3, 1, 3, 2, 2, 1, 0, 0), # 115
(9, 4, 5, 12, 4, 7, 2, 1, 3, 3, 0, 0, 0, 6, 4, 3, 6, 7, 4, 2, 4, 3, 2, 0, 0, 0), # 116
(9, 7, 9, 4, 8, 3, 6, 2, 3, 0, 0, 0, 0, 10, 5, 2, 2, 7, 7, 4, 4, 1, 1, 3, 1, 0), # 117
(6, 12, 11, 3, 5, 4, 5, 4, 4, 0, 1, 2, 0, 5, 10, 3, 5, 3, 2, 0, 6, 3, 1, 3, 0, 0), # 118
(7, 6, 9, 8, 8, 4, 2, 1, 5, 0, 1, 1, 0, 2, 8, 6, 6, 4, 3, 2, 1, 3, 1, 1, 0, 0), # 119
(6, 7, 8, 5, 7, 2, 1, 4, 2, 1, 1, 0, 0, 7, 4, 6, 2, 4, 4, 3, 0, 9, 0, 2, 0, 0), # 120
(8, 6, 5, 10, 6, 5, 4, 7, 2, 0, 2, 1, 0, 11, 11, 6, 3, 1, 2, 2, 5, 3, 4, 2, 1, 0), # 121
(5, 3, 5, 7, 5, 4, 1, 1, 2, 1, 0, 1, 0, 5, 9, 6, 2, 4, 1, 4, 0, 2, 1, 1, 3, 0), # 122
(7, 2, 5, 5, 3, 2, 0, 2, 2, 2, 1, 1, 0, 6, 4, 2, 2, 6, 2, 3, 2, 1, 0, 2, 0, 0), # 123
(10, 3, 6, 6, 9, 4, 0, 1, 3, 2, 1, 0, 0, 8, 4, 4, 2, 4, 1, 1, 3, 1, 0, 0, 1, 0), # 124
(5, 3, 3, 8, 2, 2, 0, 3, 0, 1, 3, 0, 0, 11, 6, 4, 2, 6, 3, 4, 0, 2, 3, 3, 1, 0), # 125
(7, 5, 6, 8, 4, 2, 2, 3, 3, 2, 1, 0, 0, 12, 6, 3, 4, 6, 3, 4, 4, 2, 2, 2, 2, 0), # 126
(7, 6, 8, 12, 5, 2, 5, 2, 0, 0, 1, 0, 0, 5, 7, 7, 2, 2, 2, 1, 4, 0, 3, 0, 0, 0), # 127
(8, 7, 3, 5, 3, 0, 6, 2, 4, 3, 0, 1, 0, 9, 7, 3, 2, 7, 3, 1, 4, 0, 1, 1, 0, 0), # 128
(9, 6, 7, 10, 3, 2, 3, 0, 3, 1, 1, 0, 0, 8, 6, 8, 1, 2, 3, 3, 2, 1, 1, 0, 0, 0), # 129
(4, 5, 11, 8, 5, 1, 2, 1, 1, 1, 2, 1, 0, 7, 6, 5, 3, 4, 2, 4, 5, 3, 2, 3, 0, 0), # 130
(11, 4, 7, 6, 6, 3, 1, 4, 1, 3, 2, 0, 0, 5, 4, 3, 5, 7, 3, 1, 2, 2, 1, 2, 1, 0), # 131
(7, 6, 2, 9, 7, 2, 2, 2, 4, 0, 1, 0, 0, 13, 5, 3, 6, 3, 3, 1, 1, 1, 3, 1, 0, 0), # 132
(9, 7, 8, 4, 4, 1, 1, 3, 2, 1, 3, 0, 0, 9, 5, 6, 5, 4, 3, 2, 4, 3, 3, 0, 1, 0), # 133
(4, 5, 3, 5, 8, 2, 3, 4, 3, 0, 0, 0, 0, 12, 7, 3, 4, 4, 4, 3, 3, 3, 5, 1, 0, 0), # 134
(11, 7, 2, 4, 5, 3, 3, 2, 2, 0, 1, 0, 0, 9, 4, 0, 5, 3, 3, 3, 0, 2, 2, 1, 0, 0), # 135
(9, 5, 5, 10, 6, 1, 0, 1, 4, 1, 0, 0, 0, 6, 4, 6, 8, 5, 2, 2, 1, 1, 2, 0, 0, 0), # 136
(6, 7, 0, 3, 7, 3, 4, 2, 3, 0, 0, 1, 0, 5, 6, 9, 6, 9, 4, 3, 1, 4, 0, 1, 1, 0), # 137
(7, 3, 4, 8, 6, 1, 2, 5, 8, 1, 2, 1, 0, 8, 5, 2, 1, 7, 2, 4, 0, 6, 2, 0, 0, 0), # 138
(4, 4, 4, 9, 3, 4, 3, 4, 1, 0, 0, 0, 0, 7, 5, 4, 3, 9, 3, 3, 1, 3, 1, 0, 0, 0), # 139
(9, 4, 2, 6, 8, 2, 1, 2, 2, 2, 1, 2, 0, 4, 8, 0, 6, 6, 3, 2, 1, 7, 1, 2, 0, 0), # 140
(3, 5, 5, 7, 4, 1, 0, 3, 2, 1, 2, 0, 0, 6, 2, 7, 2, 7, 4, 3, 3, 2, 1, 0, 1, 0), # 141
(6, 5, 4, 5, 2, 3, 2, 1, 2, 0, 2, 0, 0, 9, 7, 1, 0, 4, 1, 2, 1, 1, 5, 1, 1, 0), # 142
(2, 8, 3, 5, 6, 2, 3, 1, 4, 0, 0, 1, 0, 12, 4, 1, 3, 6, 3, 1, 3, 8, 2, 2, 0, 0), # 143
(7, 4, 6, 9, 5, 4, 1, 6, 6, 0, 2, 1, 0, 6, 6, 1, 3, 3, 6, 3, 2, 2, 3, 0, 0, 0), # 144
(8, 5, 7, 11, 5, 3, 2, 1, 2, 1, 1, 0, 0, 7, 3, 6, 2, 3, 1, 2, 3, 3, 0, 0, 0, 0), # 145
(7, 3, 7, 3, 5, 3, 1, 1, 1, 2, 1, 0, 0, 7, 4, 1, 3, 5, 2, 3, 2, 1, 4, 2, 0, 0), # 146
(4, 11, 6, 6, 8, 3, 2, 1, 5, 0, 0, 1, 0, 5, 9, 2, 4, 3, 0, 2, 0, 4, 2, 0, 0, 0), # 147
(3, 5, 0, 2, 4, 0, 0, 2, 2, 0, 1, 1, 0, 8, 4, 5, 2, 6, 3, 4, 1, 3, 4, 2, 1, 0), # 148
(9, 2, 10, 4, 6, 1, 3, 0, 5, 2, 2, 0, 0, 4, 4, 2, 2, 5, 1, 1, 2, 2, 1, 0, 1, 0), # 149
(4, 3, 7, 6, 4, 0, 1, 1, 2, 0, 1, 0, 0, 6, 3, 6, 1, 6, 1, 3, 2, 2, 2, 2, 0, 0), # 150
(4, 6, 1, 1, 3, 3, 2, 1, 2, 1, 2, 1, 0, 7, 7, 5, 6, 4, 1, 2, 2, 1, 0, 0, 0, 0), # 151
(8, 5, 4, 2, 4, 2, 2, 2, 0, 1, 0, 0, 0, 6, 4, 5, 1, 7, 3, 2, 0, 3, 5, 0, 0, 0), # 152
(5, 3, 6, 7, 2, 3, 2, 3, 2, 0, 1, 1, 0, 6, 7, 5, 2, 5, 4, 3, 2, 2, 1, 2, 0, 0), # 153
(5, 2, 3, 3, 2, 2, 1, 2, 2, 2, 1, 1, 0, 8, 3, 5, 5, 1, 1, 1, 1, 3, 1, 0, 0, 0), # 154
(6, 6, 7, 3, 3, 1, 2, 3, 7, 0, 2, 0, 0, 4, 5, 2, 1, 9, 4, 2, 0, 1, 4, 2, 2, 0), # 155
(5, 3, 3, 5, 5, 1, 2, 2, 0, 1, 1, 0, 0, 6, 5, 4, 3, 6, 5, 2, 1, 1, 1, 0, 0, 0), # 156
(7, 3, 5, 5, 3, 2, 2, 1, 3, 0, 1, 1, 0, 9, 4, 5, 5, 7, 1, 2, 5, 2, 5, 0, 0, 0), # 157
(3, 2, 4, 5, 9, 1, 0, 1, 4, 0, 0, 0, 0, 7, 8, 4, 4, 5, 3, 5, 1, 4, 1, 3, 0, 0), # 158
(1, 5, 7, 8, 3, 4, 1, 1, 3, 3, 1, 0, 0, 4, 9, 4, 5, 4, 1, 3, 3, 7, 2, 1, 0, 0), # 159
(6, 8, 6, 3, 7, 2, 0, 3, 4, 0, 0, 0, 0, 14, 5, 5, 2, 4, 2, 3, 1, 3, 1, 1, 0, 0), # 160
(4, 8, 2, 6, 5, 2, 1, 0, 2, 1, 0, 0, 0, 0, 1, 5, 3, 3, 2, 1, 1, 2, 2, 2, 0, 0), # 161
(2, 7, 11, 8, 3, 1, 2, 1, 1, 0, 1, 0, 0, 5, 9, 7, 4, 7, 2, 1, 0, 4, 1, 1, 1, 0), # 162
(7, 3, 1, 4, 4, 3, 1, 2, 0, 2, 0, 0, 0, 10, 4, 2, 3, 5, 5, 1, 2, 0, 2, 2, 1, 0), # 163
(0, 3, 7, 3, 4, 2, 1, 0, 3, 0, 0, 0, 0, 7, 6, 1, 6, 4, 2, 3, 1, 2, 2, 1, 0, 0), # 164
(4, 3, 2, 5, 2, 4, 1, 1, 2, 1, 0, 1, 0, 7, 8, 1, 3, 3, 5, 4, 1, 4, 1, 2, 1, 0), # 165
(5, 6, 3, 3, 7, 4, 3, 2, 2, 0, 0, 0, 0, 4, 7, 1, 5, 2, 1, 4, 1, 4, 0, 1, 0, 0), # 166
(3, 3, 5, 7, 3, 1, 1, 1, 0, 3, 1, 2, 0, 2, 3, 4, 0, 3, 0, 0, 0, 3, 2, 3, 0, 0), # 167
(6, 5, 3, 5, 6, 0, 1, 2, 2, 0, 1, 0, 0, 4, 5, 3, 2, 9, 2, 1, 4, 0, 1, 1, 0, 0), # 168
(4, 4, 3, 3, 6, 0, 1, 1, 2, 0, 0, 0, 0, 6, 4, 1, 6, 6, 1, 3, 1, 3, 2, 2, 1, 0), # 169
(5, 5, 8, 3, 0, 3, 3, 1, 3, 0, 1, 1, 0, 8, 4, 0, 3, 3, 1, 1, 0, 3, 1, 0, 0, 0), # 170
(6, 2, 4, 11, 5, 0, 1, 1, 3, 1, 0, 0, 0, 3, 7, 2, 2, 5, 1, 3, 2, 6, 0, 2, 0, 0), # 171
(2, 3, 1, 3, 3, 2, 1, 2, 0, 0, 1, 0, 0, 6, 5, 3, 3, 3, 5, 1, 1, 3, 2, 0, 0, 0), # 172
(5, 6, 4, 2, 3, 4, 1, 1, 3, 0, 0, 0, 0, 4, 6, 0, 0, 2, 1, 0, 0, 0, 0, 1, 0, 0), # 173
(8, 2, 3, 1, 2, 2, 0, 0, 2, 1, 1, 0, 0, 4, 1, 4, 2, 2, 1, 1, 0, 3, 2, 1, 1, 0), # 174
(1, 3, 4, 3, 1, 1, 1, 0, 1, 1, 0, 0, 0, 4, 6, 2, 2, 5, 3, 1, 2, 3, 2, 1, 1, 0), # 175
(3, 5, 1, 5, 2, 0, 0, 1, 2, 1, 0, 0, 0, 7, 3, 2, 3, 4, 1, 1, 1, 2, 0, 0, 0, 0), # 176
(4, 3, 3, 2, 2, 2, 2, 0, 1, 0, 0, 0, 0, 3, 1, 5, 0, 1, 3, 1, 1, 0, 2, 1, 1, 0), # 177
(6, 2, 4, 2, 4, 3, 1, 2, 2, 0, 0, 0, 0, 5, 5, 2, 1, 3, 2, 0, 1, 2, 1, 0, 1, 0), # 178
(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), # 179
)
station_arriving_intensity = (
(4.0166924626974145, 4.420230847754533, 4.169026583690005, 4.971734219090746, 4.4437484860876895, 2.5109239456298713, 3.3168284922991322, 3.7225409383835384, 4.872079249734406, 3.166412012417896, 3.3642121311084825, 3.918332062644939, 4.067104170062691), # 0
(4.283461721615979, 4.712048555315807, 4.444277273064122, 5.3001154026212935, 4.737992269979389, 2.6767868672340445, 3.535575153010955, 3.9676109783245668, 5.1937962610663275, 3.37518455382172, 3.5864769087649053, 4.176973328651484, 4.3358333179518835), # 1
(4.549378407183785, 5.0027081367127835, 4.718433828437931, 5.627190163731836, 5.0311703789997955, 2.841988091609956, 3.7534548063685635, 4.211700198323536, 5.514229445502039, 3.583131020016437, 3.8078585190210505, 4.434586121642444, 4.603491862567752), # 2
(4.81340623451725, 5.291056401549158, 4.9904086954558835, 5.951661126025659, 5.322129340801522, 3.0058724980680904, 3.9696029133183646, 4.453840925995606, 5.832108128736874, 3.7894261587409446, 4.027478729461906, 4.690148547944369, 4.869018245003381), # 3
(5.074508918732786, 5.57594015942862, 5.259114319762429, 6.272230913106056, 5.609715683037194, 3.1677849659189343, 4.183154934806767, 4.6930654889559325, 6.146161636466166, 3.993244717734143, 4.24445930767246, 4.942638713883811, 5.131350906351854), # 4
(5.331650174946809, 5.856206219954871, 5.523463147002015, 6.587602148576315, 5.892775933359424, 3.3270703744729717, 4.393246331780179, 4.928406214819674, 6.455119294385248, 4.193761444734931, 4.457922021237706, 5.191034725787318, 5.389428287706262), # 5
(5.583793718275733, 6.130701392731601, 5.782367622819093, 6.896477456039722, 6.170156619420835, 3.4830736030406912, 4.59901256518501, 5.158895431201991, 6.757710428189452, 4.390151087482207, 4.666988637742626, 5.434314689981447, 5.642188830159686), # 6
(5.829903263835975, 6.398272487362505, 6.034740192858108, 7.19755945909957, 6.440704268874043, 3.6351395309325767, 4.799589095967668, 5.383565465718042, 7.052664363574116, 4.58158839371487, 4.870780924772215, 5.671456712792743, 5.888570974805216), # 7
(6.068942526743948, 6.65776631345128, 6.279493302763517, 7.489550781359142, 6.703265409371669, 3.782613037459112, 4.994111385074558, 5.60144864598298, 7.338710426234565, 4.76724811117182, 5.068420649911457, 5.901438900547762, 6.127513162735934), # 8
(6.299875222116068, 6.908029680601619, 6.515539398179763, 7.771154046421735, 6.956686568566328, 3.924839001930787, 5.181714893452096, 5.811577299611971, 7.6145779418661395, 4.946304987591954, 5.259029580745342, 6.123239359573051, 6.35795383504493), # 9
(6.5216650650687455, 7.147909398417212, 6.7417909247512995, 8.04107187789063, 7.199814274110641, 4.061162303658086, 5.361535082046684, 6.012983754220169, 7.878996236164172, 5.117933770714171, 5.441729484858859, 6.335836196195162, 6.578831432825289), # 10
(6.7332757707184046, 7.3762522765017655, 6.957160328122573, 8.298006899369119, 7.431495053657227, 4.190927821951495, 5.532707411804733, 6.204700337422732, 8.130694634823994, 5.281309208277375, 5.615642129836999, 6.538207516740648, 6.78908439717009), # 11
(6.93367105418145, 7.591905124458958, 7.160560053938032, 8.54066173446049, 7.650575434858702, 4.313480436121496, 5.694367343672649, 6.385759376834817, 8.368402463540944, 5.435606048020458, 5.7798892832647475, 6.729331427536055, 6.987651169172428), # 12
(7.121814630574301, 7.793714751892496, 7.3509025478421295, 8.767739006768036, 7.855901945367681, 4.428165025478579, 5.845650338596845, 6.555193200071585, 8.590849048010346, 5.579999037682324, 5.933592712727095, 6.908186034907937, 7.173470189925388), # 13
(7.296670215013373, 7.980527968406071, 7.527100255479318, 8.977941339895034, 8.046321112836791, 4.5343264693332275, 5.9856918575237295, 6.7120341347481975, 8.796763713927538, 5.713662925001867, 6.0758741858090275, 7.073749445182848, 7.345479900522051), # 14
(7.457201522615084, 8.151191583603374, 7.688065622494034, 9.169971357444789, 8.220679464918646, 4.63130964699593, 6.1136273613997005, 6.855314508479805, 8.984875786987855, 5.835772457717993, 6.2058554700955355, 7.224999764687337, 7.502618742055505), # 15
(7.602372268495841, 8.304552407088106, 7.83271109453074, 9.342531683020573, 8.377823529265866, 4.718459437777168, 6.228592311171181, 6.984066648881569, 9.153914592886629, 5.945502383569597, 6.32265833317161, 7.360915099747952, 7.643825155618837), # 16
(7.73114616777206, 8.439457248463958, 7.959949117233882, 9.49432494022569, 8.516599833531071, 4.795120720987429, 6.329722167784569, 7.097322883568655, 9.302609457319187, 6.042027450295574, 6.425404542622239, 7.480473556691244, 7.768037582305133), # 17
(7.842486935560164, 8.55475291733462, 8.068692136247904, 9.624053752663423, 8.635854905366871, 4.860638375937203, 6.416152392186281, 7.194115540156209, 9.429689705980877, 6.1245224056348295, 6.513215866032407, 7.582653241843772, 7.874194463207477), # 18
(7.935358286976559, 8.649286223303795, 8.157852597217262, 9.730420743937053, 8.734435272425893, 4.914357281936967, 6.4870184453227155, 7.273476946259397, 9.533884664567024, 6.192161997326263, 6.585214070987103, 7.666432261532077, 7.961234239418957), # 19
(8.008723937137665, 8.72190397597517, 8.226342945786403, 9.812128537649883, 8.811187462360754, 4.955622318297215, 6.54145578814029, 7.334439429493374, 9.61392365877296, 6.2441209731087675, 6.64052092507132, 7.730788722082713, 8.02809535203266), # 20
(8.061547601159893, 8.771452984952447, 8.273075627599775, 9.86787975740519, 8.864958002824071, 4.983778364328429, 6.578599881585408, 7.376035317473299, 9.668536014294018, 6.279574080721244, 6.678258195870048, 7.774700729822235, 8.073716242141662), # 21
(8.092792994159664, 8.796780059839316, 8.296963088301828, 9.89637702680627, 8.89459342146846, 4.998170299341094, 6.59758618660448, 7.397296937814332, 9.696451056825532, 6.297696067902594, 6.697547650968272, 7.797146391077192, 8.097035350839063), # 22
(8.104314690674112, 8.799778875171468, 8.299938545953362, 9.899944650205763, 8.902185644826078, 5.0, 6.599843201807471, 7.399595061728395, 9.699940987654323, 6.299833818015546, 6.699966429729392, 7.799918061271147, 8.1), # 23
(8.112809930427323, 8.79802962962963, 8.299451851851853, 9.899505555555557, 8.906486090891882, 5.0, 6.598603050108934, 7.3964, 9.699473333333334, 6.29852049382716, 6.699699663299665, 7.799269135802469, 8.1), # 24
(8.121125784169264, 8.794581618655693, 8.29849108367627, 9.898636831275722, 8.910691956475603, 5.0, 6.596159122085048, 7.390123456790125, 9.69854938271605, 6.295935070873343, 6.69917071954109, 7.797988111568358, 8.1), # 25
(8.129261615238427, 8.789487517146778, 8.297069410150893, 9.897348353909464, 8.914803094736884, 5.0, 6.592549374646977, 7.380883950617285, 9.69718098765432, 6.29212056698674, 6.698384387080684, 7.7960925468678575, 8.1), # 26
(8.13721678697331, 8.7828, 8.2952, 9.89565, 8.918819358835371, 5.0, 6.587811764705883, 7.3688, 9.69538, 6.28712, 6.697345454545455, 7.793600000000001, 8.1), # 27
(8.1449906627124, 8.774571742112483, 8.292896021947874, 9.893551646090536, 8.922740601930721, 5.0, 6.581984249172921, 7.353990123456791, 9.693158271604938, 6.2809763877457705, 6.696058710562415, 7.790528029263832, 8.1), # 28
(8.1525826057942, 8.764855418381345, 8.290170644718794, 9.89106316872428, 8.926566677182576, 5.0, 6.575104784959253, 7.3365728395061724, 9.690527654320988, 6.273732748056699, 6.6945289437585735, 7.78689419295839, 8.1), # 29
(8.159991979557198, 8.753703703703705, 8.287037037037036, 9.888194444444444, 8.930297437750589, 5.0, 6.567211328976035, 7.316666666666666, 9.6875, 6.265432098765433, 6.692760942760943, 7.782716049382715, 8.1), # 30
(8.167218147339886, 8.741169272976682, 8.283508367626887, 9.88495534979424, 8.933932736794407, 5.0, 6.558341838134432, 7.2943901234567905, 9.684087160493828, 6.256117457704619, 6.6907594961965335, 7.778011156835849, 8.1), # 31
(8.174260472480764, 8.727304801097395, 8.27959780521262, 9.881355761316874, 8.937472427473677, 5.0, 6.548534269345599, 7.269861728395063, 9.680300987654322, 6.245831842706905, 6.688529392692356, 7.772797073616828, 8.1), # 32
(8.181118318318317, 8.712162962962962, 8.27531851851852, 9.877405555555555, 8.94091636294805, 5.0, 6.537826579520697, 7.243200000000001, 9.676153333333334, 6.234618271604939, 6.6860754208754205, 7.7670913580246905, 8.1), # 33
(8.187791048191048, 8.695796433470507, 8.270683676268861, 9.873114609053498, 8.944264396377173, 5.0, 6.526256725570888, 7.214523456790123, 9.671656049382719, 6.222519762231368, 6.68340236937274, 7.760911568358482, 8.1), # 34
(8.194278025437447, 8.678257887517146, 8.26570644718793, 9.868492798353909, 8.947516380920696, 5.0, 6.513862664407327, 7.183950617283951, 9.666820987654322, 6.209579332418839, 6.680515026811323, 7.754275262917239, 8.1), # 35
(8.200578613396004, 8.6596, 8.2604, 9.86355, 8.950672169738269, 5.0, 6.500682352941176, 7.151600000000001, 9.66166, 6.1958400000000005, 6.677418181818182, 7.747200000000001, 8.1), # 36
(8.20669217540522, 8.639875445816186, 8.254777503429356, 9.85829609053498, 8.953731615989538, 5.0, 6.486753748083595, 7.11759012345679, 9.656184938271606, 6.1813447828075, 6.674116623020328, 7.739703337905808, 8.1), # 37
(8.212618074803581, 8.619136899862827, 8.248852126200275, 9.85274094650206, 8.956694572834152, 5.0, 6.4721148067457435, 7.0820395061728405, 9.650407654320988, 6.166136698673983, 6.670615139044769, 7.7318028349337, 8.1), # 38
(8.218355674929589, 8.597437037037038, 8.242637037037039, 9.846894444444445, 8.959560893431762, 5.0, 6.456803485838781, 7.045066666666667, 9.644340000000001, 6.150258765432099, 6.666918518518519, 7.723516049382716, 8.1), # 39
(8.22390433912173, 8.574828532235939, 8.236145404663922, 9.84076646090535, 8.962330430942016, 5.0, 6.440857742273865, 7.006790123456792, 9.637993827160495, 6.133754000914496, 6.663031550068587, 7.714860539551899, 8.1), # 40
(8.229263430718502, 8.551364060356653, 8.229390397805213, 9.834366872427985, 8.965003038524562, 5.0, 6.424315532962156, 6.967328395061729, 9.631380987654321, 6.116665422953818, 6.658959022321986, 7.705853863740284, 8.1), # 41
(8.2344323130584, 8.527096296296298, 8.222385185185187, 9.827705555555557, 8.967578569339047, 5.0, 6.4072148148148145, 6.9268, 9.624513333333335, 6.0990360493827165, 6.654705723905725, 7.696513580246914, 8.1), # 42
(8.239410349479915, 8.50207791495199, 8.215142935528121, 9.820792386831277, 8.970056876545122, 5.0, 6.389593544743001, 6.8853234567901245, 9.617402716049384, 6.080908898033837, 6.650276443446813, 7.6868572473708285, 8.1), # 43
(8.244196903321543, 8.47636159122085, 8.2076768175583, 9.813637242798356, 8.972437813302436, 5.0, 6.371489679657872, 6.843017283950619, 9.610060987654322, 6.062326986739826, 6.645675969572266, 7.676902423411066, 8.1), # 44
(8.248791337921773, 8.450000000000001, 8.200000000000001, 9.80625, 8.974721232770637, 5.0, 6.352941176470589, 6.800000000000001, 9.6025, 6.043333333333334, 6.640909090909091, 7.666666666666666, 8.1), # 45
(8.253193016619106, 8.423045816186557, 8.192125651577504, 9.798640534979425, 8.976906988109373, 5.0, 6.333985992092311, 6.756390123456791, 9.594731604938271, 6.023970955647005, 6.635980596084299, 7.656167535436672, 8.1), # 46
(8.257401302752028, 8.39555171467764, 8.18406694101509, 9.790818724279836, 8.978994932478294, 5.0, 6.3146620834341975, 6.712306172839506, 9.586767654320989, 6.004282871513489, 6.630895273724903, 7.64542258802012, 8.1), # 47
(8.261415559659037, 8.367570370370371, 8.175837037037038, 9.782794444444447, 8.980984919037049, 5.0, 6.295007407407407, 6.667866666666668, 9.57862, 5.984312098765432, 6.625657912457912, 7.634449382716049, 8.1), # 48
(8.26523515067863, 8.339154458161865, 8.167449108367627, 9.774577572016462, 8.982876800945286, 5.0, 6.275059920923102, 6.623190123456791, 9.57030049382716, 5.964101655235483, 6.6202733009103385, 7.623265477823503, 8.1), # 49
(8.268859439149294, 8.310356652949247, 8.15891632373114, 9.766177983539094, 8.984670431362652, 5.0, 6.25485758089244, 6.578395061728395, 9.56182098765432, 5.943694558756287, 6.61474622770919, 7.611888431641519, 8.1), # 50
(8.272287788409528, 8.28122962962963, 8.150251851851852, 9.757605555555557, 8.9863656634488, 5.0, 6.23443834422658, 6.5336, 9.553193333333335, 5.923133827160494, 6.609081481481482, 7.600335802469137, 8.1), # 51
(8.275519561797823, 8.251826063100138, 8.141468861454047, 9.748870164609054, 8.987962350363372, 5.0, 6.213840167836683, 6.488923456790123, 9.54442938271605, 5.90246247828075, 6.603283850854222, 7.588625148605397, 8.1), # 52
(8.278554122652675, 8.222198628257889, 8.132580521262005, 9.739981687242798, 8.989460345266023, 5.0, 6.1931010086339064, 6.444483950617284, 9.535540987654322, 5.881723529949703, 6.597358124454421, 7.576774028349337, 8.1), # 53
(8.281390834312573, 8.192400000000001, 8.1236, 9.73095, 8.990859501316402, 5.0, 6.172258823529412, 6.400399999999999, 9.52654, 5.86096, 6.59130909090909, 7.5648, 8.1), # 54
(8.284029060116017, 8.162482853223594, 8.114540466392318, 9.721784979423868, 8.992159671674152, 5.0, 6.151351569434358, 6.35679012345679, 9.517438271604938, 5.84021490626429, 6.585141538845242, 7.552720621856425, 8.1), # 55
(8.286468163401498, 8.132499862825789, 8.105415089163237, 9.712496502057613, 8.993360709498928, 5.0, 6.130417203259905, 6.313772839506173, 9.508247654320988, 5.819531266575218, 6.578860256889887, 7.54055345221765, 8.1), # 56
(8.288707507507507, 8.102503703703704, 8.096237037037039, 9.703094444444446, 8.994462467950374, 5.0, 6.109493681917211, 6.271466666666668, 9.498980000000001, 5.798952098765433, 6.572470033670034, 7.528316049382716, 8.1), # 57
(8.290746455772544, 8.072547050754459, 8.087019478737998, 9.693588683127572, 8.99546480018814, 5.0, 6.088618962317438, 6.2299901234567905, 9.489647160493828, 5.778520420667582, 6.565975657812697, 7.516025971650663, 8.1), # 58
(8.292584371535098, 8.042682578875171, 8.077775582990398, 9.683989094650206, 8.996367559371876, 5.0, 6.067831001371743, 6.189461728395062, 9.480260987654322, 5.758279250114313, 6.55938191794488, 7.503700777320531, 8.1), # 59
(8.294220618133663, 8.012962962962964, 8.068518518518518, 9.674305555555556, 8.99717059866123, 5.0, 6.0471677559912855, 6.15, 9.470833333333335, 5.738271604938272, 6.552693602693603, 7.491358024691358, 8.1), # 60
(8.295654558906731, 7.983440877914953, 8.05926145404664, 9.664547942386832, 8.997873771215849, 5.0, 6.026667183087227, 6.1117234567901235, 9.461376049382716, 5.718540502972108, 6.545915500685871, 7.4790152720621865, 8.1), # 61
(8.296885557192804, 7.954168998628258, 8.050017558299041, 9.654726131687244, 8.998476930195388, 5.0, 6.006367239570725, 6.074750617283951, 9.451900987654321, 5.699128962048469, 6.539052400548697, 7.4666900777320535, 8.1), # 62
(8.297912976330368, 7.9252, 8.0408, 9.644850000000002, 8.998979928759486, 5.0, 5.986305882352941, 6.039200000000001, 9.44242, 5.68008, 6.532109090909092, 7.4544, 8.1), # 63
(8.298736179657919, 7.896586556927298, 8.0316219478738, 9.634929423868314, 8.999382620067799, 5.0, 5.966521068345034, 6.005190123456791, 9.432944938271605, 5.661436634659351, 6.5250903603940635, 7.442162597165067, 8.1), # 64
(8.29935453051395, 7.86838134430727, 8.02249657064472, 9.624974279835392, 8.999684857279973, 5.0, 5.947050754458163, 5.972839506172839, 9.423487654320988, 5.643241883859168, 6.518000997630629, 7.429995427526291, 8.1), # 65
(8.299767392236957, 7.840637037037038, 8.013437037037038, 9.614994444444445, 8.999886493555659, 5.0, 5.927932897603486, 5.942266666666668, 9.414060000000001, 5.625538765432099, 6.510845791245791, 7.417916049382717, 8.1), # 66
(8.299974128165434, 7.813406310013717, 8.004456515775034, 9.604999794238683, 8.999987382054504, 5.0, 5.909205454692165, 5.913590123456792, 9.404673827160494, 5.608370297210792, 6.5036295298665685, 7.405942021033379, 8.1), # 67
(8.29983329158466, 7.786598911456259, 7.9955247599451305, 9.594913392377887, 8.999902364237876, 4.99990720926688, 5.890812155863717, 5.88667508001829, 9.395270278920897, 5.591696353317733, 6.496228790832301, 7.394024017519794, 8.099900120027435), # 68
(8.298513365539453, 7.75939641577061, 7.98639074074074, 9.584226811594203, 8.99912854030501, 4.999173662551441, 5.872214545077291, 5.860079012345679, 9.385438271604938, 5.575045112563544, 6.487890271132376, 7.38177517868746, 8.099108796296298), # 69
(8.295908630047116, 7.731673967874684, 7.977014746227709, 9.572869699409555, 8.997599451303154, 4.9977290047248895, 5.853328107649096, 5.833561957018748, 9.375122313671698, 5.558335619570188, 6.478519109220864, 7.369138209034247, 8.097545867626888), # 70
(8.292055728514343, 7.703448134873224, 7.967400068587105, 9.560858803005905, 8.995334463003308, 4.995596646852614, 5.8341613276311906, 5.807132693187015, 9.364337768632831, 5.541568287474112, 6.468149896627089, 7.356122349770172, 8.095231910150892), # 71
(8.286991304347827, 7.674735483870967, 7.9575499999999995, 9.548210869565217, 8.99235294117647, 4.992800000000001, 5.81472268907563, 5.7808, 9.353100000000001, 5.524743529411765, 6.456817224880384, 7.342736842105264, 8.0921875), # 72
(8.280752000954257, 7.6455525819726535, 7.947467832647462, 9.534942646269458, 8.988674251593642, 4.989362475232434, 5.795020676034474, 5.754572656607225, 9.341424371284866, 5.507861758519595, 6.444555685510071, 7.328990927249535, 8.0884332133059), # 73
(8.273374461740323, 7.615915996283022, 7.937156858710562, 9.52107088030059, 8.98431776002582, 4.985307483615303, 5.775063772559778, 5.728459442158208, 9.329326245999086, 5.49092338793405, 6.431399870045485, 7.314893846413014, 8.083989626200276), # 74
(8.26489533011272, 7.5858422939068095, 7.92662037037037, 9.50661231884058, 8.97930283224401, 4.980658436213993, 5.754860462703601, 5.7024691358024695, 9.31682098765432, 5.473928830791576, 6.417384370015949, 7.300454840805718, 8.078877314814816), # 75
(8.255351249478142, 7.55534804194876, 7.915861659807956, 9.49158370907139, 8.973648834019205, 4.975438744093889, 5.734419230517997, 5.6766105166895295, 9.303923959762232, 5.4568785002286235, 6.402543776950793, 7.2856831516376666, 8.073116855281206), # 76
(8.244778863243274, 7.524449807513609, 7.904884019204388, 9.476001798174986, 8.967375131122408, 4.9696718183203785, 5.7137485600550235, 5.650892363968908, 9.290650525834478, 5.43977280938164, 6.38691268237935, 7.270588020118885, 8.06672882373114), # 77
(8.233214814814815, 7.493164157706095, 7.893690740740741, 9.459883333333334, 8.96050108932462, 4.963381069958848, 5.69285693536674, 5.625323456790124, 9.277016049382715, 5.422612171387073, 6.370525677830941, 7.255178687459391, 8.059733796296298), # 78
(8.220695747599452, 7.461507659630958, 7.88228511659808, 9.443245061728396, 8.953046074396838, 4.956589910074683, 5.671752840505201, 5.5999125743026985, 9.26303589391861, 5.405396999381371, 6.353417354834898, 7.239464394869204, 8.052152349108367), # 79
(8.207258305003878, 7.429496880392938, 7.870670438957475, 9.426103730542136, 8.945029452110063, 4.949321749733272, 5.650444759522465, 5.574668495656151, 9.248725422953818, 5.388127706500981, 6.335622304920551, 7.223454383558348, 8.04400505829904), # 80
(8.192939130434784, 7.397148387096775, 7.85885, 9.408476086956524, 8.936470588235293, 4.9416, 5.628941176470589, 5.549600000000001, 9.2341, 5.370804705882353, 6.317175119617225, 7.207157894736842, 8.0353125), # 81
(8.177774867298861, 7.364478746847206, 7.8468270919067225, 9.390378878153516, 8.927388848543533, 4.933448071940254, 5.607250575401629, 5.524715866483768, 9.219174988568815, 5.353428410661933, 6.298110390454251, 7.190584169614709, 8.026095250342937), # 82
(8.161802159002804, 7.331504526748971, 7.834605006858711, 9.371828851315083, 8.917803598805778, 4.924889376619419, 5.585381440367643, 5.500024874256973, 9.203965752171925, 5.335999233976169, 6.278462708960955, 7.17374244940197, 8.016373885459535), # 83
(8.145057648953301, 7.29824229390681, 7.822187037037037, 9.35284275362319, 8.907734204793028, 4.915947325102881, 5.563342255420687, 5.475535802469135, 9.188487654320987, 5.3185175889615115, 6.258266666666667, 7.156641975308642, 8.006168981481482), # 84
(8.127577980557048, 7.264708615425461, 7.80957647462277, 9.333437332259797, 8.897200032276286, 4.906645328456029, 5.54114150461282, 5.451257430269777, 9.172756058527662, 5.300983888754405, 6.237556855100715, 7.13929198854475, 7.995501114540467), # 85
(8.10939979722073, 7.230920058409665, 7.796776611796983, 9.313629334406873, 8.886220447026547, 4.897006797744247, 5.518787671996097, 5.4271985368084135, 9.156786328303614, 5.283398546491299, 6.216367865792428, 7.121701730320315, 7.984390860768176), # 86
(8.090559742351045, 7.1968931899641575, 7.7837907407407405, 9.293435507246377, 8.874814814814817, 4.887055144032922, 5.496289241622575, 5.403367901234568, 9.140593827160496, 5.265761975308642, 6.194734290271132, 7.103880441845354, 7.972858796296297), # 87
(8.071094459354686, 7.162644577193681, 7.7706221536351165, 9.27287259796028, 8.863002501412089, 4.876813778387441, 5.473654697544313, 5.37977430269776, 9.124193918609969, 5.248074588342881, 6.172690720066159, 7.085837364329892, 7.960925497256517), # 88
(8.051040591638339, 7.128190787202974, 7.75727414266118, 9.251957353730543, 8.850802872589366, 4.8663061118731905, 5.4508925238133665, 5.356426520347508, 9.107601966163696, 5.230336798730466, 6.150271746706835, 7.067581738983948, 7.948611539780521), # 89
(8.030434782608696, 7.093548387096774, 7.74375, 9.230706521739132, 8.838235294117649, 4.855555555555556, 5.428011204481793, 5.333333333333333, 9.090833333333334, 5.2125490196078434, 6.1275119617224885, 7.049122807017544, 7.9359375000000005), # 90
(8.00931367567245, 7.058733943979822, 7.730053017832647, 9.20913684916801, 8.825319131767932, 4.8445855204999235, 5.405019223601649, 5.3105035208047555, 9.073903383630546, 5.194711664111461, 6.104445956642448, 7.0304698096406995, 7.922923954046638), # 91
(7.9877139142362985, 7.023764024956858, 7.716186488340192, 9.187265083199142, 8.812073751311223, 4.833419417771681, 5.381925065224994, 5.287945861911295, 9.056827480566987, 5.176825145377768, 6.081108322996043, 7.011631988063439, 7.909591478052126), # 92
(7.965672141706924, 6.988655197132617, 7.702153703703704, 9.165107971014494, 8.798518518518518, 4.822080658436214, 5.358737213403881, 5.26566913580247, 9.039620987654322, 5.15888987654321, 6.0575336523126, 6.992618583495776, 7.895960648148147), # 93
(7.943225001491024, 6.953424027611842, 7.6879579561042535, 9.142682259796029, 8.784672799160816, 4.810592653558909, 5.335464152190369, 5.243682121627802, 9.022299268404208, 5.140906270744238, 6.033756536121448, 6.973438837147739, 7.882052040466393), # 94
(7.920409136995288, 6.9180870834992705, 7.673602537722909, 9.120004696725712, 8.770555959009119, 4.798978814205152, 5.312114365636515, 5.221993598536809, 9.004877686328305, 5.122874741117297, 6.009811565951917, 6.954101990229344, 7.867886231138546), # 95
(7.89726119162641, 6.882660931899643, 7.659090740740742, 9.097092028985507, 8.756187363834423, 4.787262551440329, 5.288696337794377, 5.200612345679013, 8.987371604938271, 5.104795700798839, 5.985733333333334, 6.934617283950619, 7.853483796296297), # 96
(7.873817808791078, 6.847162139917697, 7.64442585733882, 9.07396100375738, 8.741586379407732, 4.775467276329827, 5.265218552716011, 5.179547142203933, 8.969796387745772, 5.086669562925308, 5.961556429795026, 6.914993959521576, 7.838865312071332), # 97
(7.850115631895988, 6.811607274658171, 7.629611179698216, 9.050628368223297, 8.726772371500042, 4.763616399939035, 5.241689494453475, 5.158806767261089, 8.952167398262459, 5.068496740633154, 5.937315446866325, 6.895241258152239, 7.824051354595337), # 98
(7.826191304347827, 6.776012903225807, 7.614650000000001, 9.027110869565218, 8.711764705882354, 4.751733333333333, 5.218117647058825, 5.138400000000001, 8.9345, 5.050277647058824, 5.913044976076556, 6.875368421052632, 7.8090625000000005), # 99
(7.80208146955329, 6.740395592725341, 7.59954561042524, 9.00342525496511, 8.696582748325667, 4.739841487578113, 5.194511494584116, 5.118335619570188, 8.916809556470051, 5.032012695338767, 5.888779608955048, 6.855384689432774, 7.79391932441701), # 100
(7.777822770919068, 6.704771910261517, 7.584301303155008, 8.979588271604939, 8.681245864600985, 4.727964273738759, 5.17087952108141, 5.09862240512117, 8.899111431184272, 5.013702298609431, 5.86455393703113, 6.835299304502683, 7.7786424039780515), # 101
(7.753451851851853, 6.669158422939069, 7.56892037037037, 8.955616666666668, 8.665773420479303, 4.7161251028806594, 5.1472302106027605, 5.07926913580247, 8.881420987654321, 4.995346870007263, 5.840402551834131, 6.815121507472385, 7.763252314814816), # 102
(7.729005355758336, 6.633571697862738, 7.5534061042524, 8.93152718733226, 8.650184781731623, 4.704347386069197, 5.123572047200224, 5.060284590763604, 8.86375358939186, 4.976946822668712, 5.816360044893379, 6.794860539551898, 7.747769633058984), # 103
(7.704519926045208, 6.598028302137263, 7.537761796982167, 8.907336580783683, 8.634499314128943, 4.692654534369761, 5.099913514925861, 5.041677549154093, 8.846124599908551, 4.958502569730225, 5.792461007738201, 6.774525641951243, 7.732214934842251), # 104
(7.680032206119162, 6.562544802867383, 7.5219907407407405, 8.883061594202898, 8.618736383442267, 4.681069958847737, 5.076263097831727, 5.023456790123458, 8.82854938271605, 4.940014524328251, 5.768740031897927, 6.754126055880443, 7.716608796296296), # 105
(7.655578839386891, 6.527137767157839, 7.5060962277091905, 8.858718974771874, 8.602915355442589, 4.669617070568511, 5.052629279969876, 5.005631092821217, 8.811043301326016, 4.921483099599236, 5.745231708901884, 6.733671022549515, 7.700971793552812), # 106
(7.631196469255085, 6.491823762113369, 7.490081550068588, 8.83432546967257, 8.587055595900912, 4.65831928059747, 5.0290205453923695, 4.988209236396892, 8.793621719250115, 4.9029087086796315, 5.721970630279402, 6.713169783168484, 7.685324502743484), # 107
(7.606921739130435, 6.456619354838711, 7.473950000000001, 8.809897826086958, 8.571176470588235, 4.647200000000001, 5.0054453781512604, 4.9712000000000005, 8.7763, 4.884291764705883, 5.698991387559809, 6.69263157894737, 7.669687500000001), # 108
(7.582791292419635, 6.421541112438604, 7.4577048696845, 8.785452791196994, 8.55529734527556, 4.636282639841488, 4.98191226229861, 4.954612162780065, 8.759093507087334, 4.865632680814438, 5.676328572272432, 6.67206565109619, 7.654081361454047), # 109
(7.558841772529373, 6.38660560201779, 7.441349451303157, 8.761007112184648, 8.539437585733884, 4.625590611187319, 4.9584296818864715, 4.938454503886603, 8.742017604023777, 4.846931870141747, 5.654016775946601, 6.651481240824971, 7.638526663237312), # 110
(7.535109822866345, 6.351829390681004, 7.424887037037038, 8.736577536231884, 8.523616557734206, 4.615147325102881, 4.935006120966905, 4.922735802469136, 8.725087654320989, 4.828189745824256, 5.632090590111643, 6.630887589343731, 7.623043981481482), # 111
(7.51163208683724, 6.317229045532987, 7.408320919067217, 8.712180810520666, 8.507853627047528, 4.6049761926535595, 4.911650063591967, 4.907464837677184, 8.708319021490626, 4.809406720998413, 5.610584606296888, 6.6102939378624885, 7.607653892318244), # 112
(7.488403378962436, 6.282878895028762, 7.391694262601655, 8.687867105993632, 8.492140544138964, 4.595095815371611, 4.888420770925416, 4.892682055024485, 8.691770249006897, 4.790643789290184, 5.589539124922293, 6.589754349203543, 7.592355120674577), # 113
(7.465184718320052, 6.249117746820429, 7.375236540017295, 8.663831537021869, 8.476314683653062, 4.585483686823921, 4.865614566728464, 4.878569007604096, 8.675695228570449, 4.772252134330226, 5.568995469690558, 6.56952973769038, 7.577020331328028), # 114
(7.441907922403196, 6.215957758946438, 7.358957546165854, 8.640067604145424, 8.460326142310882, 4.576114809999011, 4.84324772015325, 4.865122123422967, 8.660099982935032, 4.754260262390462, 5.548923609141675, 6.549630066047081, 7.561605305328301), # 115
(7.418543898590108, 6.183350625033362, 7.342825751987099, 8.616532920213123, 8.444150821107023, 4.566967101829678, 4.821283854022315, 4.852304250319195, 8.644945071382265, 4.736634686759638, 5.529284745017185, 6.530018557989877, 7.546085807804713), # 116
(7.395063554259018, 6.151248038707777, 7.326809628420789, 8.593185098073794, 8.427764621036088, 4.558018479248712, 4.799686591158202, 4.840078236130868, 8.630191053193762, 4.719341920726503, 5.510040079058626, 6.5106584372350005, 7.53043760388658), # 117
(7.371437796788169, 6.119601693596259, 7.310877646406694, 8.569981750576266, 8.411143443092675, 4.549246859188911, 4.7784195543834524, 4.828406928696078, 8.615798487651148, 4.7023484775798075, 5.49115081300754, 6.49151292749868, 7.51463645870322), # 118
(7.347637533555794, 6.088363283325384, 7.294998276884579, 8.546880490569364, 8.394263188271378, 4.540630158583066, 4.757446366520605, 4.817253175852916, 8.601727934036035, 4.685620870608298, 5.4725781486054625, 6.472545252497148, 7.498658137383946), # 119
(7.323633671940129, 6.057484501521727, 7.27913999079421, 8.523838930901915, 8.377099757566798, 4.532146294363972, 4.736730650392203, 4.806579825439474, 8.587939951630046, 4.669125613100724, 5.454283287593933, 6.453718635946638, 7.482478405058078), # 120
(7.299397119319415, 6.026917041811863, 7.26327125907535, 8.500814684422748, 8.359629051973535, 4.523773183464424, 4.716236028820784, 4.796349725293846, 8.574395099714799, 4.652829218345837, 5.436227431714493, 6.434996301563378, 7.466073026854929), # 121
(7.274898783071883, 5.996612597822369, 7.247360552667769, 8.477765363980685, 8.341826972486187, 4.515488742817215, 4.695926124628894, 4.786525723254119, 8.561053937571911, 4.636698199632382, 5.4183717827086815, 6.416341473063601, 7.4494177679038165), # 122
(7.250109570575775, 5.9665228631798195, 7.231376342511229, 8.454648582424555, 8.323669420099353, 4.50727088935514, 4.675764560639071, 4.7770706671583865, 8.547877024483004, 4.62069907024911, 5.400677542318036, 6.397717374163538, 7.432488393334058), # 123
(7.225000389209324, 5.93659953151079, 7.215287099545496, 8.43142195260319, 8.30513229580763, 4.499097540010991, 4.655714959673856, 4.767947404844741, 8.534824919729692, 4.604798343484769, 5.383105912284096, 6.3790872285794205, 7.4152606682749695), # 124
(7.199542146350767, 5.9067942964418565, 7.199061294710339, 8.408043087365408, 8.286191500605618, 4.490946611717565, 4.635740944555791, 4.759118784151273, 8.521858182593595, 4.588962532628107, 5.3656180943484015, 6.360414260027479, 7.397710357855863), # 125
(7.1737057493783425, 5.877058851599596, 7.182667398945519, 8.384469599560044, 8.266822935487914, 4.482796021407654, 4.615806138107416, 4.750547652916074, 8.508937372356334, 4.573158150967874, 5.348175290252491, 6.341661692223948, 7.379813227206063), # 126
(7.147462105670289, 5.84734489061058, 7.166073883190804, 8.36065910203592, 8.247002501449119, 4.474623686014052, 4.595874163151275, 4.742196858977237, 8.496023048299525, 4.557351711792819, 5.3307387017379035, 6.322792748885053, 7.361545041454879), # 127
(7.120782122604837, 5.817604107101388, 7.14924921838596, 8.336569207641865, 8.226706099483833, 4.466407522469555, 4.575908642509906, 4.73402925017285, 8.483075769704788, 4.5415097283916905, 5.3132695305461795, 6.303770653727031, 7.34288156573163), # 128
(7.093636707560226, 5.787788194698593, 7.132161875470752, 8.312157529226706, 8.20590963058665, 4.458125447706956, 4.555873199005851, 4.726007674341008, 8.47005609585374, 4.5255987140532365, 5.2957289784188575, 6.284558630466109, 7.323798565165631), # 129
(7.065996767914694, 5.757848847028773, 7.1147803253849435, 8.28738167963927, 8.18458899575217, 4.449755378659047, 4.53573145546165, 4.7180949793198, 8.456924586028, 4.509585182066206, 5.278078247097476, 6.2651199028185225, 7.3042718048861985), # 130
(7.037833211046475, 5.727737757718502, 7.097073039068305, 8.262199271728381, 8.162720095974995, 4.441275232258625, 4.515447034699847, 4.71025401294732, 8.443641799509189, 4.493435645719348, 5.260278538323575, 6.2454176945004996, 7.2842770500226495), # 131
(7.009116944333808, 5.697406620394355, 7.079008487460597, 8.23656791834287, 8.140278832249724, 4.432662925438482, 4.49498355954298, 4.7024476230616585, 8.430168295578923, 4.4771166183014115, 5.2422910538386915, 6.225415229228274, 7.263790065704301), # 132
(6.979818875154931, 5.666807128682908, 7.060555141501587, 8.210445232331562, 8.11724110557095, 4.423896375131413, 4.474304652813592, 4.694638657500906, 8.416464633518821, 4.460594613101146, 5.224076995384369, 6.205075730718074, 7.242786617060469), # 133
(6.949909910888076, 5.635890976210739, 7.041681472131043, 8.183788826543283, 8.093582816933274, 4.414953498270212, 4.453373937334223, 4.686789964103155, 8.402491372610504, 4.443836143407299, 5.205597564702143, 6.184362422686133, 7.221242469220467), # 134
(6.919360958911483, 5.604609856604419, 7.022355950288727, 8.156556313826863, 8.069279867331296, 4.405812211787674, 4.432155035927415, 4.678864390706496, 8.388209072135584, 4.426807722508621, 5.186813963533554, 6.163238528848682, 7.199133387313616), # 135
(6.888142926603388, 5.572915463490528, 7.002547046914407, 8.128705307031124, 8.044308157759614, 4.396450432616592, 4.410611571415708, 4.670824785149022, 8.373578291375685, 4.409475863693858, 5.167687393620142, 6.1416672729219535, 7.176435136469229), # 136
(6.856226721342027, 5.540759490495638, 6.982223232947849, 8.100193419004901, 8.018643589212827, 4.386846077689759, 4.388707166621645, 4.662633995268823, 8.358559589612426, 4.391807080251762, 5.1481790567034444, 6.119611878622176, 7.153123481816621), # 137
(6.823583250505639, 5.508093631246327, 6.961352979328814, 8.070978262597011, 7.992262062685535, 4.376977063939971, 4.366405444367763, 4.654254868903992, 8.343113526127425, 4.373767885471078, 5.128250154525002, 6.097035569665582, 7.129174188485113), # 138
(6.790183421472455, 5.4748695793691695, 6.939904756997072, 8.041017450656287, 7.965139479172333, 4.366821308300021, 4.343670027476608, 4.64565025389262, 8.327200660202298, 4.355324792640558, 5.107861888826353, 6.073901569768405, 7.104563021604015), # 139
(6.755998141620719, 5.44103902849074, 6.91784703689239, 8.010268596031556, 7.937251739667824, 4.356356727702703, 4.320464538770717, 4.636782998072797, 8.310781551118666, 4.336444315048949, 5.086975461349035, 6.050173102646873, 7.079265746302652), # 140
(6.720998318328665, 5.406553672237617, 6.895148289954529, 7.978689311571642, 7.908574745166603, 4.345561239080812, 4.296752601072636, 4.6276159492826165, 8.293816758158144, 4.317092965985001, 5.065552073834591, 6.02581339201722, 7.053258127710331), # 141
(6.685154858974525, 5.371365204236373, 6.871776987123257, 7.946237210125377, 7.87908439666327, 4.334412759367142, 4.272497837204901, 4.6181119553601695, 8.276266840602354, 4.2972372587374625, 5.043552928024558, 6.000785661595676, 7.026515930956373), # 142
(6.64843867093654, 5.335425318113585, 6.8477015993383406, 7.91286990454158, 7.848756595152423, 4.322889205494485, 4.247663869990055, 4.608233864143545, 8.258092357732918, 4.276843706595082, 5.020939225660475, 5.975053135098472, 6.999014921170094), # 143
(6.610820661592948, 5.298685707495829, 6.822890597539542, 7.878545007669086, 7.817567241628663, 4.310968494395637, 4.222214322250639, 4.597944523470839, 8.239253868831447, 4.255878822846608, 4.997672168483881, 5.948579036241839, 6.970730863480812), # 144
(6.572271738321982, 5.26109806600968, 6.797312452666631, 7.843220132356716, 7.785492237086586, 4.298628543003392, 4.196112816809195, 4.587206781180141, 8.219711933179564, 4.23430912078079, 4.973712958236316, 5.921326588742011, 6.94163952301784), # 145
(6.5327628085018805, 5.2226140872817135, 6.770935635659374, 7.806852891453301, 7.7525074825207945, 4.285847268250545, 4.169322976488264, 4.575983485109542, 8.199427110058885, 4.212101113686376, 4.949022796659319, 5.893259016315216, 6.911716664910495), # 146
(6.49226477951088, 5.1831854649385045, 6.743728617457528, 7.769400897807664, 7.718588878925882, 4.272602587069886, 4.141808424110385, 4.564237483097132, 8.178359958751033, 4.189221314852117, 4.923562885494429, 5.864339542677689, 6.8809380542880945), # 147
(6.450748558727217, 5.142763892606631, 6.715659869000866, 7.730821764268637, 7.683712327296449, 4.258872416394214, 4.113532782498101, 4.551931622981006, 8.156471038537623, 4.1656362375667575, 4.897294426483186, 5.8345313915456565, 6.8492794562799535), # 148
(6.40818505352913, 5.101301063912665, 6.686697861229155, 7.691073103685042, 7.647853728627097, 4.24463467315632, 4.084459674473953, 4.539028752599253, 8.13372090870027, 4.1413123951190505, 4.870178621367128, 5.803797786635354, 6.81671663601539), # 149
(6.364545171294852, 5.058748672483183, 6.656811065082156, 7.65011252890571, 7.610988983912421, 4.229867274288999, 4.054552722860481, 4.525491719789965, 8.110070128520602, 4.116216300797741, 4.8421766718877945, 5.772101951663011, 6.783225358623717), # 150
(6.31979981940262, 5.015058411944763, 6.625967951499634, 7.607897652779464, 7.573093994147022, 4.214548136725044, 4.023775550480226, 4.511283372391235, 8.085479257280232, 4.090314467891583, 4.813249779786724, 5.739407110344858, 6.748781389234255), # 151
(6.273919905230675, 4.970181975923978, 6.594136991421362, 7.5643860881551355, 7.534144660325495, 4.198655177397251, 3.992091780155732, 4.496366558241153, 8.059908854260776, 4.06357340968932, 4.7833591468054575, 5.705676486397127, 6.713360492976318), # 152
(6.226876336157249, 4.924071058047406, 6.561286655787095, 7.519535447881546, 7.4941168834424445, 4.182166313238413, 3.9594650347095355, 4.48070412517781, 8.03331947874386, 4.035959639479703, 4.752465974685533, 5.670873303536052, 6.676938434979222), # 153
(6.178640019560583, 4.87667735194162, 6.527385415536607, 7.473303344807528, 7.452986564492464, 4.165059461181324, 3.9258589369641825, 4.464258921039298, 8.005671690011093, 4.0074396705514825, 4.72053146516849, 5.63496078547786, 6.639490980372286), # 154
(6.129181862818909, 4.827952551233196, 6.492401741609661, 7.425647391781903, 7.410729604470157, 4.147312538158777, 3.891237109742209, 4.446993793663709, 7.976926047344103, 3.9779800161934036, 4.687516819995866, 5.597902155938786, 6.600993894284821), # 155
(6.078472773310465, 4.7778483495487105, 6.456304104946021, 7.3765252016535, 7.367321904370119, 4.128903461103569, 3.85556317586616, 4.428871590889135, 7.947043110024501, 3.9475471896942183, 4.6533832409092035, 5.559660638635059, 6.561422941846148), # 156
(6.02648365841349, 4.726316440514739, 6.419060976485454, 7.32589438727115, 7.322739365186948, 4.109810146948491, 3.8188007581585754, 4.409855160553666, 7.915983437333911, 3.9161077043426733, 4.618091929650039, 5.52019945728291, 6.520753888185581), # 157
(5.971744757124192, 4.672362496617807, 6.378873563121885, 7.271815665320995, 7.274944884696798, 4.088819581053688, 3.780085376742286, 4.388637561879498, 7.881329673279279, 3.882692733032915, 4.580476602031154, 5.478079651355472, 6.477188687532276), # 158
(5.9058294135827225, 4.610452255679582, 6.32539025472239, 7.203181727030763, 7.212153047825303, 4.058951718405683, 3.734570210708573, 4.357770826211506, 7.829141808977716, 3.8418247952789963, 4.533933548495195, 5.425090018946487, 6.420342117536156), # 159
(5.827897675923448, 4.540077382832571, 6.257536766364711, 7.118862008327088, 7.133136105077437, 4.019473036838147, 3.6817949987070273, 4.316479351621878, 7.757940181782921, 3.792964521490315, 4.477807606887632, 5.360401559110278, 6.349136487114865), # 160
(5.738577643668768, 4.461696694464375, 6.1760375775282474, 7.019658003005382, 7.038714499425691, 3.970861793256251, 3.622145156805501, 4.265280426487824, 7.668663813599214, 3.7365265545367503, 4.412593323679766, 5.284613975126057, 6.264299235855278), # 161
(5.638497416341085, 4.375769006962591, 6.0816171676923965, 6.9063712048610615, 6.929708673842564, 3.9135962445651646, 3.5560061010718473, 4.204691339186562, 7.56225172633091, 3.6729255372881853, 4.338785245342897, 5.198326970273035, 6.166557803344267), # 162
(5.528285093462799, 4.2827531367148195, 5.975000016336562, 6.779803107689547, 6.806939071300551, 3.848154647670058, 3.4837632475739206, 4.1352293780953, 7.439642941882325, 3.6025761126145, 4.2568779183483265, 5.102140247830427, 6.0566396291687035), # 163
(5.408568774556308, 4.183107900108657, 5.856910602940141, 6.640755205286254, 6.6712261347721515, 3.7750152594761035, 3.405802012379573, 4.0574118315912555, 7.301776482157779, 3.525892923385575, 4.167365889167357, 4.996653511077443, 5.935272152915463), # 164
(5.279976559144014, 4.077292113531706, 5.728073406982535, 6.490028991446602, 6.523390307229859, 3.6946563368884693, 3.3225078115566578, 3.971755988051637, 7.149591369061584, 3.4432906124712908, 4.0707437042712895, 4.882466463293296, 5.803182814171416), # 165
(5.143136546748318, 3.9657645933715635, 5.589212907943143, 6.328425959966001, 6.3642520316461715, 3.607556136812327, 3.234266061173029, 3.878779135853662, 6.984026624498059, 3.35518382274153, 3.9675059101314236, 4.760178807757201, 5.661099052523436), # 166
(4.998676836891619, 3.8489841560158298, 5.441053585301364, 6.156747604639875, 6.194631750993584, 3.514192916152847, 3.14146217729654, 3.7789985633745413, 6.80602127037152, 3.2619871970661714, 3.858147053219062, 4.630390247748367, 5.509748307558397), # 167
(4.847225529096317, 3.727409617852103, 5.284319918536599, 5.975795419263637, 6.015349908244594, 3.415044931815199, 3.0444815759950434, 3.672931558991488, 6.616514328586284, 3.1641153783150977, 3.743161680005505, 4.493700486546009, 5.34985801886317), # 168
(4.689410722884812, 3.6014997952679835, 5.119736387128247, 5.786370897632707, 5.827226946371696, 3.310590440704556, 2.9437096733363934, 3.561095411081716, 6.416444821046671, 3.0619830093581895, 3.623044336962055, 4.350709227429338, 5.182155626024628), # 169
(4.525860517779507, 3.47171350465107, 4.948027470555708, 5.589275533542496, 5.631083308347387, 3.2013076997260854, 2.8395318853884426, 3.444007408022438, 6.206751769656991, 2.9560047330653263, 3.498289570560013, 4.202016173677567, 5.007368568629644), # 170
(4.3572030133028, 3.3385095623889605, 4.7699176482983825, 5.385310820788429, 5.427739437144165, 3.087674965784959, 2.7323336282190445, 3.3221848381908665, 5.9883741963215655, 2.846595192306391, 3.3693919272706787, 4.048221028569909, 4.826224286265092), # 171
(4.184066308977092, 3.2023467848692557, 4.586131399835669, 5.175278253165917, 5.218015775734523, 2.970170495786347, 2.6225003178960526, 3.1961449899642167, 5.762251122944709, 2.734169029951264, 3.2368459535653553, 3.889923495385577, 4.639450218517843), # 172
(4.007078504324784, 3.063683988479554, 4.39739320464697, 4.959979324470381, 5.002732767090961, 2.84927254663542, 2.51041737048732, 3.066405151719699, 5.529321571430739, 2.6191408888698255, 3.1011461959153426, 3.72772327740378, 4.44777380497477), # 173
(3.8268676988682753, 2.9229799896074544, 4.204427542211682, 4.740215528497233, 4.782710854185973, 2.725459375237348, 2.3964702020607005, 2.9334826118345285, 5.290524563683971, 2.5019254119319574, 2.9627872007919422, 3.5622200779037345, 4.251922485222747), # 174
(3.6440619921299646, 2.7806936046405557, 4.007958892009206, 4.516788359041894, 4.558770479992055, 2.599209238497303, 2.2810442286840464, 2.797894658685917, 5.046799121608725, 2.3829372420075394, 2.8222635146664556, 3.3940136001646515, 4.052623698848646), # 175
(3.459289483632255, 2.6372836499664585, 3.8087117335189427, 4.29049930989978, 4.331732087481704, 2.4710003933204536, 2.164524866425212, 2.6601585806510792, 4.799084267109314, 2.2625910219664536, 2.680069684010184, 3.2237035474657434, 3.8506048854393393), # 176
(3.273178272897546, 2.493208941972761, 3.607410546220291, 4.062149874866306, 4.102416119627419, 2.3413110966119706, 2.0472975313520503, 2.5207916661072263, 4.548319022090056, 2.1413013946785795, 2.536700255294429, 3.051889623086223, 3.6465934845817), # 177
(3.0863564594482376, 2.348928297047063, 3.404779809592651, 3.832541547736893, 3.871643019401691, 2.210619605277026, 1.929747639532414, 2.3803112034315723, 4.295442408455268, 2.0194830030138, 2.39264977499049, 2.879171530305302, 3.4413169358626017), # 178
(0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0), # 179
)
passenger_arriving_acc = (
(7, 3, 2, 4, 5, 2, 1, 3, 1, 0, 0, 0, 0, 7, 5, 1, 2, 3, 1, 2, 0, 3, 2, 3, 1, 0), # 0
(10, 6, 5, 10, 8, 4, 2, 8, 4, 2, 0, 0, 0, 11, 9, 4, 4, 10, 4, 6, 0, 3, 4, 4, 2, 0), # 1
(17, 10, 6, 11, 12, 5, 2, 12, 5, 3, 0, 0, 0, 15, 13, 8, 7, 14, 5, 7, 2, 5, 9, 4, 2, 0), # 2
(21, 14, 9, 14, 12, 5, 4, 13, 6, 7, 0, 1, 0, 21, 17, 14, 10, 18, 8, 10, 4, 8, 9, 5, 2, 0), # 3
(29, 20, 16, 17, 18, 7, 4, 16, 7, 8, 0, 2, 0, 25, 17, 20, 10, 20, 10, 11, 5, 13, 13, 7, 3, 0), # 4
(31, 23, 24, 20, 21, 9, 7, 18, 7, 9, 2, 2, 0, 28, 21, 25, 11, 28, 13, 13, 6, 17, 14, 7, 3, 0), # 5
(37, 35, 27, 25, 22, 10, 8, 19, 9, 11, 3, 2, 0, 35, 25, 26, 17, 29, 16, 16, 8, 19, 15, 8, 3, 0), # 6
(43, 42, 31, 27, 25, 14, 11, 21, 11, 11, 5, 3, 0, 40, 28, 29, 21, 34, 20, 16, 12, 21, 16, 8, 4, 0), # 7
(49, 46, 39, 31, 33, 15, 12, 23, 13, 11, 6, 3, 0, 47, 34, 34, 27, 39, 22, 20, 16, 22, 17, 8, 7, 0), # 8
(56, 56, 47, 34, 37, 18, 17, 28, 15, 12, 6, 3, 0, 56, 43, 38, 29, 42, 24, 22, 17, 23, 20, 11, 7, 0), # 9
(58, 64, 53, 39, 42, 22, 21, 30, 17, 12, 8, 3, 0, 64, 49, 42, 33, 52, 28, 28, 17, 25, 21, 14, 7, 0), # 10
(65, 67, 60, 47, 46, 25, 23, 32, 17, 13, 10, 3, 0, 71, 55, 45, 34, 53, 32, 31, 17, 29, 21, 17, 8, 0), # 11
(71, 71, 68, 54, 49, 29, 26, 34, 22, 13, 12, 4, 0, 75, 63, 51, 40, 55, 37, 35, 17, 33, 22, 18, 9, 0), # 12
(76, 82, 73, 61, 49, 32, 26, 36, 27, 16, 13, 4, 0, 81, 70, 54, 44, 63, 43, 40, 20, 36, 23, 19, 10, 0), # 13
(87, 88, 76, 70, 55, 32, 32, 45, 33, 16, 13, 4, 0, 90, 74, 57, 48, 71, 43, 41, 20, 38, 28, 19, 11, 0), # 14
(94, 93, 79, 83, 59, 33, 34, 50, 33, 18, 14, 4, 0, 95, 80, 60, 50, 75, 48, 46, 21, 39, 30, 20, 11, 0), # 15
(98, 99, 88, 86, 64, 37, 37, 51, 34, 21, 14, 4, 0, 99, 85, 64, 55, 84, 51, 48, 23, 45, 32, 21, 11, 0), # 16
(107, 106, 93, 91, 69, 37, 38, 51, 36, 23, 15, 5, 0, 106, 96, 71, 57, 86, 54, 53, 27, 47, 35, 22, 12, 0), # 17
(111, 118, 95, 97, 73, 39, 41, 55, 42, 24, 17, 5, 0, 112, 107, 76, 65, 92, 57, 55, 29, 50, 38, 24, 12, 0), # 18
(121, 126, 103, 103, 78, 39, 42, 56, 45, 25, 18, 5, 0, 115, 110, 77, 72, 99, 66, 56, 29, 51, 43, 25, 13, 0), # 19
(129, 133, 107, 107, 87, 42, 45, 62, 48, 25, 19, 5, 0, 123, 117, 86, 77, 111, 71, 57, 33, 55, 44, 28, 14, 0), # 20
(139, 146, 112, 118, 93, 46, 49, 63, 50, 25, 22, 6, 0, 129, 124, 92, 83, 118, 74, 61, 36, 57, 44, 29, 14, 0), # 21
(151, 152, 116, 126, 100, 50, 51, 70, 50, 27, 25, 6, 0, 139, 127, 98, 84, 127, 83, 61, 38, 65, 45, 30, 14, 0), # 22
(159, 161, 122, 133, 107, 54, 53, 75, 52, 29, 26, 8, 0, 151, 133, 99, 91, 133, 88, 63, 39, 68, 46, 32, 14, 0), # 23
(170, 167, 129, 140, 115, 57, 56, 77, 54, 32, 26, 8, 0, 160, 143, 104, 96, 139, 93, 68, 41, 71, 47, 32, 15, 0), # 24
(180, 169, 138, 143, 119, 59, 61, 81, 60, 34, 26, 9, 0, 170, 147, 109, 97, 145, 95, 72, 46, 76, 53, 32, 15, 0), # 25
(193, 177, 148, 153, 124, 62, 65, 83, 61, 35, 27, 9, 0, 181, 151, 114, 103, 150, 99, 77, 47, 78, 57, 32, 15, 0), # 26
(200, 187, 158, 156, 127, 65, 68, 84, 62, 36, 28, 9, 0, 192, 155, 118, 111, 159, 102, 83, 47, 81, 60, 32, 17, 0), # 27
(210, 197, 165, 164, 135, 69, 69, 87, 66, 36, 31, 9, 0, 203, 159, 127, 114, 169, 107, 87, 51, 84, 64, 33, 18, 0), # 28
(218, 202, 174, 170, 141, 74, 72, 87, 74, 37, 32, 9, 0, 212, 165, 131, 119, 174, 110, 90, 52, 86, 67, 34, 19, 0), # 29
(226, 208, 180, 178, 143, 75, 80, 88, 79, 37, 34, 9, 0, 223, 172, 132, 123, 179, 112, 92, 53, 89, 67, 35, 20, 0), # 30
(234, 217, 189, 186, 149, 79, 82, 93, 83, 40, 35, 10, 0, 229, 176, 136, 130, 187, 116, 92, 55, 90, 71, 38, 22, 0), # 31
(240, 222, 201, 193, 157, 82, 85, 95, 84, 42, 36, 10, 0, 237, 178, 138, 136, 193, 122, 95, 57, 96, 73, 41, 22, 0), # 32
(245, 228, 209, 199, 165, 84, 85, 96, 85, 44, 36, 10, 0, 246, 189, 147, 136, 201, 128, 97, 62, 101, 74, 41, 23, 0), # 33
(249, 234, 214, 208, 172, 88, 88, 96, 88, 45, 37, 11, 0, 258, 193, 152, 140, 207, 137, 102, 64, 103, 78, 43, 23, 0), # 34
(260, 238, 222, 215, 178, 92, 90, 97, 89, 46, 38, 11, 0, 265, 202, 156, 147, 210, 138, 106, 68, 106, 82, 45, 24, 0), # 35
(265, 248, 230, 222, 192, 94, 93, 98, 90, 46, 40, 11, 0, 276, 211, 162, 151, 217, 140, 107, 73, 107, 86, 46, 25, 0), # 36
(277, 255, 239, 231, 198, 95, 95, 100, 95, 48, 42, 11, 0, 285, 219, 168, 155, 221, 143, 110, 74, 110, 87, 49, 25, 0), # 37
(288, 261, 244, 242, 200, 97, 97, 102, 99, 49, 43, 11, 0, 296, 224, 172, 160, 226, 145, 112, 76, 111, 89, 51, 27, 0), # 38
(299, 266, 251, 248, 207, 98, 100, 107, 102, 51, 44, 11, 0, 307, 235, 178, 163, 233, 148, 112, 81, 116, 91, 51, 27, 0), # 39
(307, 271, 257, 253, 215, 104, 103, 111, 107, 53, 46, 12, 0, 314, 248, 185, 165, 238, 157, 115, 83, 118, 92, 52, 27, 0), # 40
(311, 281, 265, 265, 221, 105, 104, 113, 108, 54, 46, 12, 0, 324, 251, 186, 171, 252, 160, 121, 85, 121, 94, 52, 29, 0), # 41
(314, 293, 271, 273, 228, 107, 107, 117, 109, 56, 47, 14, 0, 330, 258, 192, 172, 258, 162, 123, 85, 123, 95, 55, 29, 0), # 42
(320, 299, 278, 274, 235, 109, 110, 118, 113, 57, 47, 14, 0, 340, 266, 194, 174, 263, 167, 127, 86, 128, 96, 56, 30, 0), # 43
(325, 309, 282, 278, 236, 113, 112, 121, 115, 59, 50, 15, 0, 355, 271, 195, 181, 263, 171, 129, 87, 130, 97, 57, 30, 0), # 44
(335, 316, 286, 287, 245, 114, 114, 126, 118, 61, 51, 15, 0, 365, 279, 201, 184, 265, 175, 133, 87, 135, 101, 59, 31, 0), # 45
(347, 321, 295, 295, 250, 116, 116, 128, 121, 64, 53, 15, 0, 375, 284, 208, 192, 272, 181, 136, 87, 140, 106, 59, 31, 0), # 46
(358, 327, 306, 307, 257, 117, 120, 131, 125, 64, 55, 17, 0, 389, 289, 212, 196, 281, 184, 140, 93, 142, 109, 61, 31, 0), # 47
(368, 338, 312, 314, 263, 119, 121, 133, 126, 67, 56, 17, 0, 402, 300, 219, 200, 285, 188, 145, 94, 146, 112, 62, 31, 0), # 48
(373, 347, 315, 322, 263, 122, 127, 139, 127, 67, 57, 17, 0, 408, 304, 221, 207, 293, 194, 147, 96, 147, 122, 65, 32, 0), # 49
(381, 357, 322, 330, 273, 125, 130, 143, 129, 68, 58, 19, 0, 415, 315, 227, 208, 306, 197, 152, 96, 147, 122, 65, 33, 0), # 50
(385, 364, 327, 338, 285, 128, 133, 146, 133, 69, 59, 19, 0, 421, 322, 231, 213, 312, 201, 155, 98, 148, 123, 66, 34, 0), # 51
(394, 370, 333, 341, 291, 129, 138, 147, 137, 71, 60, 19, 0, 425, 326, 237, 217, 318, 204, 158, 100, 152, 123, 68, 36, 0), # 52
(407, 377, 335, 344, 298, 131, 144, 152, 141, 71, 60, 21, 0, 437, 332, 243, 221, 325, 206, 160, 102, 156, 128, 69, 36, 0), # 53
(412, 389, 343, 349, 305, 138, 146, 155, 144, 72, 62, 21, 0, 443, 339, 252, 223, 331, 209, 161, 104, 163, 132, 70, 36, 0), # 54
(417, 397, 352, 354, 308, 139, 147, 157, 148, 73, 63, 21, 0, 453, 345, 258, 228, 341, 212, 166, 106, 165, 135, 70, 38, 0), # 55
(427, 405, 357, 364, 313, 143, 151, 158, 150, 74, 65, 21, 0, 456, 348, 263, 233, 346, 216, 169, 108, 167, 137, 71, 39, 0), # 56
(431, 411, 365, 369, 317, 145, 153, 161, 153, 77, 67, 21, 0, 465, 354, 268, 236, 355, 221, 173, 109, 172, 139, 71, 39, 0), # 57
(444, 424, 373, 379, 321, 149, 156, 161, 157, 79, 68, 22, 0, 474, 358, 272, 238, 361, 223, 178, 113, 172, 144, 71, 39, 0), # 58
(453, 429, 380, 391, 327, 152, 158, 165, 162, 81, 71, 24, 0, 483, 367, 274, 242, 369, 228, 180, 115, 174, 148, 71, 39, 0), # 59
(463, 435, 384, 395, 331, 155, 162, 176, 168, 83, 71, 24, 0, 488, 374, 278, 248, 374, 234, 188, 116, 176, 150, 72, 39, 0), # 60
(470, 442, 390, 405, 336, 158, 165, 179, 172, 84, 72, 25, 0, 492, 384, 283, 252, 380, 241, 192, 118, 178, 151, 76, 40, 0), # 61
(480, 445, 396, 410, 339, 160, 167, 182, 173, 86, 74, 25, 0, 498, 390, 284, 254, 385, 243, 193, 119, 179, 153, 78, 41, 0), # 62
(489, 457, 401, 417, 343, 162, 169, 183, 175, 89, 75, 25, 0, 506, 395, 286, 262, 390, 247, 195, 120, 182, 158, 78, 43, 0), # 63
(497, 470, 404, 425, 349, 164, 172, 184, 177, 90, 75, 26, 0, 516, 398, 289, 265, 398, 252, 196, 122, 182, 163, 80, 44, 0), # 64
(506, 478, 408, 430, 357, 171, 179, 187, 178, 91, 77, 28, 0, 531, 405, 293, 273, 401, 256, 198, 122, 185, 166, 83, 44, 0), # 65
(519, 483, 410, 437, 366, 171, 180, 191, 180, 94, 78, 28, 0, 539, 411, 299, 276, 405, 258, 202, 128, 191, 172, 84, 46, 0), # 66
(530, 486, 418, 448, 373, 176, 184, 193, 184, 96, 79, 32, 0, 540, 418, 302, 282, 409, 258, 204, 130, 195, 177, 85, 47, 0), # 67
(535, 493, 424, 451, 379, 179, 186, 196, 188, 99, 80, 32, 0, 546, 425, 307, 286, 414, 262, 208, 133, 198, 178, 88, 48, 0), # 68
(543, 504, 432, 459, 382, 181, 187, 197, 193, 99, 83, 33, 0, 558, 435, 313, 291, 421, 269, 209, 134, 202, 178, 90, 49, 0), # 69
(545, 512, 436, 463, 387, 183, 190, 201, 198, 101, 85, 34, 0, 564, 439, 318, 293, 427, 272, 213, 136, 204, 178, 91, 49, 0), # 70
(551, 523, 440, 475, 395, 186, 197, 201, 199, 101, 85, 34, 0, 572, 448, 323, 293, 436, 275, 215, 139, 207, 181, 91, 50, 0), # 71
(561, 533, 447, 480, 404, 186, 199, 204, 203, 103, 86, 34, 0, 577, 456, 326, 295, 449, 276, 217, 140, 212, 183, 91, 50, 0), # 72
(570, 542, 454, 484, 408, 196, 201, 206, 210, 105, 86, 35, 0, 583, 459, 331, 296, 457, 280, 219, 140, 212, 186, 97, 50, 0), # 73
(577, 550, 455, 485, 420, 198, 202, 207, 215, 107, 87, 36, 0, 589, 463, 337, 298, 460, 282, 221, 144, 215, 186, 99, 50, 0), # 74
(587, 559, 462, 492, 427, 202, 204, 211, 219, 109, 89, 37, 0, 595, 467, 343, 301, 468, 284, 225, 148, 220, 190, 102, 50, 0), # 75
(598, 568, 470, 496, 432, 206, 207, 215, 220, 111, 92, 37, 0, 601, 469, 350, 303, 475, 287, 228, 150, 221, 192, 103, 52, 0), # 76
(604, 574, 475, 502, 438, 207, 209, 218, 221, 111, 92, 38, 0, 608, 478, 354, 307, 477, 290, 232, 150, 223, 197, 104, 53, 0), # 77
(615, 582, 487, 508, 452, 208, 209, 219, 223, 111, 92, 39, 0, 624, 487, 358, 309, 485, 292, 235, 156, 223, 201, 106, 53, 0), # 78
(619, 588, 490, 516, 458, 214, 213, 222, 225, 114, 92, 39, 0, 632, 492, 360, 314, 492, 296, 242, 158, 227, 203, 106, 54, 0), # 79
(631, 595, 495, 523, 464, 216, 213, 226, 227, 115, 92, 41, 0, 636, 497, 367, 319, 499, 298, 247, 160, 231, 205, 107, 54, 0), # 80
(640, 603, 501, 529, 471, 220, 213, 227, 231, 118, 92, 41, 0, 640, 501, 374, 326, 504, 303, 251, 160, 235, 206, 109, 55, 0), # 81
(648, 613, 504, 538, 474, 223, 216, 229, 234, 119, 92, 42, 0, 650, 506, 379, 329, 509, 303, 256, 160, 239, 208, 111, 56, 0), # 82
(653, 616, 511, 545, 477, 224, 217, 234, 234, 122, 92, 42, 0, 658, 516, 384, 332, 518, 306, 259, 162, 243, 211, 112, 56, 0), # 83
(665, 626, 515, 549, 488, 226, 223, 237, 240, 124, 92, 42, 0, 661, 521, 393, 333, 524, 311, 264, 164, 245, 213, 112, 56, 0), # 84
(678, 635, 525, 555, 493, 229, 225, 240, 244, 127, 92, 43, 0, 668, 529, 402, 336, 528, 315, 266, 166, 251, 215, 112, 57, 0), # 85
(684, 640, 535, 560, 495, 235, 226, 243, 249, 128, 92, 44, 0, 681, 535, 405, 337, 531, 320, 267, 168, 251, 217, 112, 60, 0), # 86
(689, 645, 540, 567, 503, 239, 231, 244, 254, 128, 93, 44, 0, 694, 538, 410, 340, 540, 321, 272, 169, 252, 220, 112, 60, 0), # 87
(699, 656, 544, 576, 507, 244, 235, 247, 256, 129, 94, 45, 0, 698, 547, 414, 344, 543, 325, 273, 175, 255, 220, 113, 61, 0), # 88
(708, 663, 547, 579, 514, 245, 237, 248, 262, 131, 96, 45, 0, 709, 556, 418, 353, 551, 333, 273, 177, 256, 222, 115, 61, 0), # 89
(713, 674, 555, 585, 519, 245, 243, 252, 264, 132, 98, 47, 0, 722, 565, 422, 357, 554, 336, 277, 181, 259, 223, 115, 61, 0), # 90
(720, 685, 561, 592, 524, 246, 249, 253, 265, 133, 98, 47, 0, 727, 575, 427, 361, 560, 337, 281, 183, 261, 224, 115, 61, 0), # 91
(726, 689, 564, 602, 529, 249, 251, 254, 268, 134, 99, 47, 0, 739, 583, 428, 365, 566, 337, 284, 183, 265, 228, 118, 62, 0), # 92
(735, 698, 571, 611, 536, 252, 252, 256, 269, 134, 100, 47, 0, 748, 593, 430, 369, 571, 340, 287, 183, 267, 229, 119, 64, 0), # 93
(743, 708, 577, 617, 543, 256, 255, 258, 274, 137, 102, 47, 0, 749, 600, 438, 375, 574, 341, 292, 185, 268, 235, 119, 66, 0), # 94
(751, 720, 586, 622, 555, 259, 259, 260, 277, 138, 104, 47, 0, 753, 605, 445, 382, 580, 344, 296, 188, 270, 237, 120, 66, 0), # 95
(756, 728, 589, 626, 565, 265, 261, 260, 285, 140, 106, 47, 0, 761, 613, 449, 386, 583, 348, 298, 190, 272, 239, 124, 68, 0), # 96
(764, 734, 599, 632, 570, 266, 262, 262, 289, 140, 107, 47, 0, 766, 622, 452, 390, 587, 349, 301, 194, 272, 243, 125, 69, 0), # 97
(769, 742, 606, 638, 578, 270, 264, 263, 294, 140, 110, 48, 0, 781, 626, 458, 394, 599, 355, 303, 196, 273, 245, 127, 69, 0), # 98
(776, 752, 612, 648, 588, 270, 264, 264, 296, 140, 111, 49, 0, 787, 635, 465, 399, 604, 357, 304, 197, 275, 249, 128, 69, 0), # 99
(782, 753, 615, 650, 592, 274, 267, 264, 296, 141, 112, 51, 0, 797, 645, 470, 402, 608, 361, 306, 197, 275, 250, 129, 69, 0), # 100
(789, 762, 624, 655, 597, 276, 271, 268, 299, 142, 112, 51, 0, 804, 650, 473, 404, 621, 364, 309, 199, 277, 251, 131, 70, 0), # 101
(794, 766, 629, 660, 606, 281, 272, 268, 303, 142, 114, 52, 0, 811, 652, 478, 406, 624, 368, 311, 199, 278, 251, 132, 71, 0), # 102
(797, 773, 633, 669, 610, 287, 273, 270, 307, 146, 114, 52, 0, 822, 663, 482, 407, 631, 372, 316, 200, 281, 253, 132, 71, 0), # 103
(801, 782, 635, 680, 614, 290, 274, 271, 308, 146, 115, 52, 0, 840, 669, 491, 410, 641, 376, 316, 201, 282, 256, 133, 71, 0), # 104
(808, 785, 643, 687, 621, 296, 278, 274, 312, 147, 116, 53, 0, 847, 675, 494, 412, 650, 379, 317, 202, 287, 259, 134, 74, 0), # 105
(815, 794, 648, 697, 625, 298, 280, 276, 316, 149, 117, 54, 0, 854, 681, 501, 413, 655, 383, 318, 206, 296, 259, 134, 74, 0), # 106
(817, 801, 655, 707, 629, 300, 284, 276, 320, 149, 119, 55, 0, 861, 685, 505, 419, 660, 385, 322, 208, 296, 261, 136, 74, 0), # 107
(825, 808, 661, 715, 635, 301, 290, 280, 327, 151, 121, 56, 0, 867, 689, 506, 422, 670, 388, 324, 212, 300, 263, 136, 74, 0), # 108
(831, 818, 667, 722, 640, 307, 292, 284, 329, 151, 122, 56, 0, 873, 698, 509, 423, 674, 392, 327, 213, 300, 266, 136, 76, 0), # 109
(834, 824, 673, 729, 645, 312, 292, 287, 331, 151, 122, 57, 0, 882, 701, 516, 425, 681, 393, 330, 217, 302, 266, 137, 77, 0), # 110
(841, 830, 684, 740, 647, 313, 292, 290, 332, 151, 122, 58, 0, 891, 702, 521, 428, 688, 395, 333, 218, 304, 268, 138, 77, 0), # 111
(852, 836, 686, 747, 652, 316, 295, 291, 337, 151, 124, 60, 0, 895, 713, 524, 433, 691, 398, 337, 218, 306, 272, 139, 79, 0), # 112
(859, 848, 690, 753, 657, 316, 298, 291, 341, 152, 126, 61, 0, 903, 720, 529, 435, 695, 400, 340, 221, 307, 277, 140, 80, 0), # 113
(866, 855, 696, 759, 663, 321, 300, 291, 344, 153, 127, 61, 0, 912, 724, 535, 435, 701, 404, 344, 225, 313, 277, 140, 81, 0), # 114
(870, 862, 699, 763, 668, 323, 305, 291, 345, 153, 128, 61, 0, 915, 733, 540, 441, 708, 407, 345, 228, 315, 279, 141, 81, 0), # 115
(879, 866, 704, 775, 672, 330, 307, 292, 348, 156, 128, 61, 0, 921, 737, 543, 447, 715, 411, 347, 232, 318, 281, 141, 81, 0), # 116
(888, 873, 713, 779, 680, 333, 313, 294, 351, 156, 128, 61, 0, 931, 742, 545, 449, 722, 418, 351, 236, 319, 282, 144, 82, 0), # 117
(894, 885, 724, 782, 685, 337, 318, 298, 355, 156, 129, 63, 0, 936, 752, 548, 454, 725, 420, 351, 242, 322, 283, 147, 82, 0), # 118
(901, 891, 733, 790, 693, 341, 320, 299, 360, 156, 130, 64, 0, 938, 760, 554, 460, 729, 423, 353, 243, 325, 284, 148, 82, 0), # 119
(907, 898, 741, 795, 700, 343, 321, 303, 362, 157, 131, 64, 0, 945, 764, 560, 462, 733, 427, 356, 243, 334, 284, 150, 82, 0), # 120
(915, 904, 746, 805, 706, 348, 325, 310, 364, 157, 133, 65, 0, 956, 775, 566, 465, 734, 429, 358, 248, 337, 288, 152, 83, 0), # 121
(920, 907, 751, 812, 711, 352, 326, 311, 366, 158, 133, 66, 0, 961, 784, 572, 467, 738, 430, 362, 248, 339, 289, 153, 86, 0), # 122
(927, 909, 756, 817, 714, 354, 326, 313, 368, 160, 134, 67, 0, 967, 788, 574, 469, 744, 432, 365, 250, 340, 289, 155, 86, 0), # 123
(937, 912, 762, 823, 723, 358, 326, 314, 371, 162, 135, 67, 0, 975, 792, 578, 471, 748, 433, 366, 253, 341, 289, 155, 87, 0), # 124
(942, 915, 765, 831, 725, 360, 326, 317, 371, 163, 138, 67, 0, 986, 798, 582, 473, 754, 436, 370, 253, 343, 292, 158, 88, 0), # 125
(949, 920, 771, 839, 729, 362, 328, 320, 374, 165, 139, 67, 0, 998, 804, 585, 477, 760, 439, 374, 257, 345, 294, 160, 90, 0), # 126
(956, 926, 779, 851, 734, 364, 333, 322, 374, 165, 140, 67, 0, 1003, 811, 592, 479, 762, 441, 375, 261, 345, 297, 160, 90, 0), # 127
(964, 933, 782, 856, 737, 364, 339, 324, 378, 168, 140, 68, 0, 1012, 818, 595, 481, 769, 444, 376, 265, 345, 298, 161, 90, 0), # 128
(973, 939, 789, 866, 740, 366, 342, 324, 381, 169, 141, 68, 0, 1020, 824, 603, 482, 771, 447, 379, 267, 346, 299, 161, 90, 0), # 129
(977, 944, 800, 874, 745, 367, 344, 325, 382, 170, 143, 69, 0, 1027, 830, 608, 485, 775, 449, 383, 272, 349, 301, 164, 90, 0), # 130
(988, 948, 807, 880, 751, 370, 345, 329, 383, 173, 145, 69, 0, 1032, 834, 611, 490, 782, 452, 384, 274, 351, 302, 166, 91, 0), # 131
(995, 954, 809, 889, 758, 372, 347, 331, 387, 173, 146, 69, 0, 1045, 839, 614, 496, 785, 455, 385, 275, 352, 305, 167, 91, 0), # 132
(1004, 961, 817, 893, 762, 373, 348, 334, 389, 174, 149, 69, 0, 1054, 844, 620, 501, 789, 458, 387, 279, 355, 308, 167, 92, 0), # 133
(1008, 966, 820, 898, 770, 375, 351, 338, 392, 174, 149, 69, 0, 1066, 851, 623, 505, 793, 462, 390, 282, 358, 313, 168, 92, 0), # 134
(1019, 973, 822, 902, 775, 378, 354, 340, 394, 174, 150, 69, 0, 1075, 855, 623, 510, 796, 465, 393, 282, 360, 315, 169, 92, 0), # 135
(1028, 978, 827, 912, 781, 379, 354, 341, 398, 175, 150, 69, 0, 1081, 859, 629, 518, 801, 467, 395, 283, 361, 317, 169, 92, 0), # 136
(1034, 985, 827, 915, 788, 382, 358, 343, 401, 175, 150, 70, 0, 1086, 865, 638, 524, 810, 471, 398, 284, 365, 317, 170, 93, 0), # 137
(1041, 988, 831, 923, 794, 383, 360, 348, 409, 176, 152, 71, 0, 1094, 870, 640, 525, 817, 473, 402, 284, 371, 319, 170, 93, 0), # 138
(1045, 992, 835, 932, 797, 387, 363, 352, 410, 176, 152, 71, 0, 1101, 875, 644, 528, 826, 476, 405, 285, 374, 320, 170, 93, 0), # 139
(1054, 996, 837, 938, 805, 389, 364, 354, 412, 178, 153, 73, 0, 1105, 883, 644, 534, 832, 479, 407, 286, 381, 321, 172, 93, 0), # 140
(1057, 1001, 842, 945, 809, 390, 364, 357, 414, 179, 155, 73, 0, 1111, 885, 651, 536, 839, 483, 410, 289, 383, 322, 172, 94, 0), # 141
(1063, 1006, 846, 950, 811, 393, 366, 358, 416, 179, 157, 73, 0, 1120, 892, 652, 536, 843, 484, 412, 290, 384, 327, 173, 95, 0), # 142
(1065, 1014, 849, 955, 817, 395, 369, 359, 420, 179, 157, 74, 0, 1132, 896, 653, 539, 849, 487, 413, 293, 392, 329, 175, 95, 0), # 143
(1072, 1018, 855, 964, 822, 399, 370, 365, 426, 179, 159, 75, 0, 1138, 902, 654, 542, 852, 493, 416, 295, 394, 332, 175, 95, 0), # 144
(1080, 1023, 862, 975, 827, 402, 372, 366, 428, 180, 160, 75, 0, 1145, 905, 660, 544, 855, 494, 418, 298, 397, 332, 175, 95, 0), # 145
(1087, 1026, 869, 978, 832, 405, 373, 367, 429, 182, 161, 75, 0, 1152, 909, 661, 547, 860, 496, 421, 300, 398, 336, 177, 95, 0), # 146
(1091, 1037, 875, 984, 840, 408, 375, 368, 434, 182, 161, 76, 0, 1157, 918, 663, 551, 863, 496, 423, 300, 402, 338, 177, 95, 0), # 147
(1094, 1042, 875, 986, 844, 408, 375, 370, 436, 182, 162, 77, 0, 1165, 922, 668, 553, 869, 499, 427, 301, 405, 342, 179, 96, 0), # 148
(1103, 1044, 885, 990, 850, 409, 378, 370, 441, 184, 164, 77, 0, 1169, 926, 670, 555, 874, 500, 428, 303, 407, 343, 179, 97, 0), # 149
(1107, 1047, 892, 996, 854, 409, 379, 371, 443, 184, 165, 77, 0, 1175, 929, 676, 556, 880, 501, 431, 305, 409, 345, 181, 97, 0), # 150
(1111, 1053, 893, 997, 857, 412, 381, 372, 445, 185, 167, 78, 0, 1182, 936, 681, 562, 884, 502, 433, 307, 410, 345, 181, 97, 0), # 151
(1119, 1058, 897, 999, 861, 414, 383, 374, 445, 186, 167, 78, 0, 1188, 940, 686, 563, 891, 505, 435, 307, 413, 350, 181, 97, 0), # 152
(1124, 1061, 903, 1006, 863, 417, 385, 377, 447, 186, 168, 79, 0, 1194, 947, 691, 565, 896, 509, 438, 309, 415, 351, 183, 97, 0), # 153
(1129, 1063, 906, 1009, 865, 419, 386, 379, 449, 188, 169, 80, 0, 1202, 950, 696, 570, 897, 510, 439, 310, 418, 352, 183, 97, 0), # 154
(1135, 1069, 913, 1012, 868, 420, 388, 382, 456, 188, 171, 80, 0, 1206, 955, 698, 571, 906, 514, 441, 310, 419, 356, 185, 99, 0), # 155
(1140, 1072, 916, 1017, 873, 421, 390, 384, 456, 189, 172, 80, 0, 1212, 960, 702, 574, 912, 519, 443, 311, 420, 357, 185, 99, 0), # 156
(1147, 1075, 921, 1022, 876, 423, 392, 385, 459, 189, 173, 81, 0, 1221, 964, 707, 579, 919, 520, 445, 316, 422, 362, 185, 99, 0), # 157
(1150, 1077, 925, 1027, 885, 424, 392, 386, 463, 189, 173, 81, 0, 1228, 972, 711, 583, 924, 523, 450, 317, 426, 363, 188, 99, 0), # 158
(1151, 1082, 932, 1035, 888, 428, 393, 387, 466, 192, 174, 81, 0, 1232, 981, 715, 588, 928, 524, 453, 320, 433, 365, 189, 99, 0), # 159
(1157, 1090, 938, 1038, 895, 430, 393, 390, 470, 192, 174, 81, 0, 1246, 986, 720, 590, 932, 526, 456, 321, 436, 366, 190, 99, 0), # 160
(1161, 1098, 940, 1044, 900, 432, 394, 390, 472, 193, 174, 81, 0, 1246, 987, 725, 593, 935, 528, 457, 322, 438, 368, 192, 99, 0), # 161
(1163, 1105, 951, 1052, 903, 433, 396, 391, 473, 193, 175, 81, 0, 1251, 996, 732, 597, 942, 530, 458, 322, 442, 369, 193, 100, 0), # 162
(1170, 1108, 952, 1056, 907, 436, 397, 393, 473, 195, 175, 81, 0, 1261, 1000, 734, 600, 947, 535, 459, 324, 442, 371, 195, 101, 0), # 163
(1170, 1111, 959, 1059, 911, 438, 398, 393, 476, 195, 175, 81, 0, 1268, 1006, 735, 606, 951, 537, 462, 325, 444, 373, 196, 101, 0), # 164
(1174, 1114, 961, 1064, 913, 442, 399, 394, 478, 196, 175, 82, 0, 1275, 1014, 736, 609, 954, 542, 466, 326, 448, 374, 198, 102, 0), # 165
(1179, 1120, 964, 1067, 920, 446, 402, 396, 480, 196, 175, 82, 0, 1279, 1021, 737, 614, 956, 543, 470, 327, 452, 374, 199, 102, 0), # 166
(1182, 1123, 969, 1074, 923, 447, 403, 397, 480, 199, 176, 84, 0, 1281, 1024, 741, 614, 959, 543, 470, 327, 455, 376, 202, 102, 0), # 167
(1188, 1128, 972, 1079, 929, 447, 404, 399, 482, 199, 177, 84, 0, 1285, 1029, 744, 616, 968, 545, 471, 331, 455, 377, 203, 102, 0), # 168
(1192, 1132, 975, 1082, 935, 447, 405, 400, 484, 199, 177, 84, 0, 1291, 1033, 745, 622, 974, 546, 474, 332, 458, 379, 205, 103, 0), # 169
(1197, 1137, 983, 1085, 935, 450, 408, 401, 487, 199, 178, 85, 0, 1299, 1037, 745, 625, 977, 547, 475, 332, 461, 380, 205, 103, 0), # 170
(1203, 1139, 987, 1096, 940, 450, 409, 402, 490, 200, 178, 85, 0, 1302, 1044, 747, 627, 982, 548, 478, 334, 467, 380, 207, 103, 0), # 171
(1205, 1142, 988, 1099, 943, 452, 410, 404, 490, 200, 179, 85, 0, 1308, 1049, 750, 630, 985, 553, 479, 335, 470, 382, 207, 103, 0), # 172
(1210, 1148, 992, 1101, 946, 456, 411, 405, 493, 200, 179, 85, 0, 1312, 1055, 750, 630, 987, 554, 479, 335, 470, 382, 208, 103, 0), # 173
(1218, 1150, 995, 1102, 948, 458, 411, 405, 495, 201, 180, 85, 0, 1316, 1056, 754, 632, 989, 555, 480, 335, 473, 384, 209, 104, 0), # 174
(1219, 1153, 999, 1105, 949, 459, 412, 405, 496, 202, 180, 85, 0, 1320, 1062, 756, 634, 994, 558, 481, 337, 476, 386, 210, 105, 0), # 175
(1222, 1158, 1000, 1110, 951, 459, 412, 406, 498, 203, 180, 85, 0, 1327, 1065, 758, 637, 998, 559, 482, 338, 478, 386, 210, 105, 0), # 176
(1226, 1161, 1003, 1112, 953, 461, 414, 406, 499, 203, 180, 85, 0, 1330, 1066, 763, 637, 999, 562, 483, 339, 478, 388, 211, 106, 0), # 177
(1232, 1163, 1007, 1114, 957, 464, 415, 408, 501, 203, 180, 85, 0, 1335, 1071, 765, 638, 1002, 564, 483, 340, 480, 389, 211, 107, 0), # 178
(1232, 1163, 1007, 1114, 957, 464, 415, 408, 501, 203, 180, 85, 0, 1335, 1071, 765, 638, 1002, 564, 483, 340, 480, 389, 211, 107, 0), # 179
)
passenger_arriving_rate = (
(4.0166924626974145, 4.051878277108322, 3.4741888197416713, 3.72880066431806, 2.962498990725126, 1.4647056349507583, 1.6584142461495661, 1.5510587243264744, 1.6240264165781353, 0.7916030031044742, 0.5607020218514138, 0.32652767188707826, 0.0, 4.067104170062691, 3.5918043907578605, 2.803510109257069, 2.374809009313422, 3.2480528331562706, 2.171482214057064, 1.6584142461495661, 1.0462183106791132, 1.481249495362563, 1.2429335547726867, 0.6948377639483343, 0.36835257064621113, 0.0), # 0
(4.283461721615979, 4.319377842372822, 3.703564394220102, 3.97508655196597, 3.1586615133195926, 1.561459005886526, 1.7677875765054776, 1.6531712409685695, 1.7312654203554425, 0.8437961384554302, 0.5977461514608177, 0.34808111072095704, 0.0, 4.3358333179518835, 3.8288922179305267, 2.9887307573040878, 2.53138841536629, 3.462530840710885, 2.3144397373559973, 1.7677875765054776, 1.1153278613475186, 1.5793307566597963, 1.3250288506553236, 0.7407128788440204, 0.39267071294298395, 0.0), # 1
(4.549378407183785, 4.585815791986718, 3.9320281903649423, 4.220392622798877, 3.3541135859998636, 1.6578263867724743, 1.8767274031842818, 1.7548750826348067, 1.838076481834013, 0.8957827550041094, 0.6346430865035085, 0.3695488434702037, 0.0, 4.603491862567752, 4.06503727817224, 3.173215432517542, 2.6873482650123277, 3.676152963668026, 2.4568251156887295, 1.8767274031842818, 1.1841617048374817, 1.6770567929999318, 1.4067975409329592, 0.7864056380729886, 0.41689234472606534, 0.0), # 2
(4.81340623451725, 4.850135034753395, 4.1586739128799035, 4.463745844519244, 3.548086227201014, 1.7534256238730528, 1.9848014566591823, 1.8557670524981693, 1.9440360429122914, 0.9473565396852364, 0.6712464549103178, 0.3908457123286974, 0.0, 4.869018245003381, 4.299302835615671, 3.356232274551589, 2.8420696190557084, 3.8880720858245827, 2.598073873497437, 1.9848014566591823, 1.2524468741950376, 1.774043113600507, 1.487915281506415, 0.8317347825759807, 0.4409213667957632, 0.0), # 3
(5.074508918732786, 5.111278479476234, 4.382595266468691, 4.704173184829542, 3.7398104553581293, 1.8478745634527118, 2.0915774674033836, 1.9554439537316386, 2.048720545488722, 0.998311179433536, 0.7074098846120768, 0.41188655949031766, 0.0, 5.131350906351854, 4.530752154393493, 3.5370494230603833, 2.9949335383006073, 4.097441090977444, 2.737621535224294, 2.0915774674033836, 1.3199104024662227, 1.8699052276790646, 1.5680577282765145, 0.8765190532937384, 0.46466167995238505, 0.0), # 4
(5.331650174946809, 5.368189034958631, 4.602885955835013, 4.940701611432236, 3.9285172889062823, 1.9407910517759004, 2.1966231658900894, 2.0535025895081978, 2.151706431461749, 1.048440361183733, 0.7429870035396177, 0.43258622714894324, 0.0, 5.389428287706262, 4.758448498638375, 3.7149350176980884, 3.145321083551198, 4.303412862923498, 2.8749036253114766, 2.1966231658900894, 1.3862793226970715, 1.9642586444531411, 1.6469005371440792, 0.9205771911670025, 0.48801718499623925, 0.0), # 5
(5.583793718275733, 5.619809610003967, 4.8186396856825775, 5.172358092029792, 4.113437746280557, 2.03179293510707, 2.299506282592505, 2.1495397630008295, 2.2525701427298173, 1.097537771870552, 0.777831439623771, 0.45285955749845397, 0.0, 5.642188830159686, 4.981455132482993, 3.889157198118855, 3.2926133156116553, 4.5051402854596345, 3.0093556682011613, 2.299506282592505, 1.4512806679336214, 2.0567188731402783, 1.724119364009931, 0.9637279371365156, 0.5108917827276335, 0.0), # 6
(5.829903263835975, 5.86508311341563, 5.02895016071509, 5.398169594324678, 4.293802845916028, 2.1204980597106697, 2.399794547983834, 2.2431522773825177, 2.350888121191372, 1.1453970984287176, 0.8117968207953693, 0.47262139273272863, 0.0, 5.888570974805216, 5.198835320060014, 4.058984103976846, 3.436191295286152, 4.701776242382744, 3.1404131883355246, 2.399794547983834, 1.514641471221907, 2.146901422958014, 1.799389864774893, 1.0057900321430182, 0.5331893739468755, 0.0), # 7
(6.068942526743948, 6.102952453997006, 5.232911085636264, 5.617163086019357, 4.468843606247779, 2.2065242718511486, 2.497055692537279, 2.333936935826242, 2.446236808744855, 1.1918120277929551, 0.8447367749852429, 0.49178657504564693, 0.0, 6.127513162735934, 5.409652325502115, 4.223683874926214, 3.5754360833788645, 4.89247361748971, 3.2675117101567386, 2.497055692537279, 1.5760887656079634, 2.2344218031238894, 1.872387695339786, 1.046582217127253, 0.5548138594542734, 0.0), # 8
(6.299875222116068, 6.332360540551483, 5.429616165149803, 5.828365534816301, 4.637791045710885, 2.2894894177929594, 2.590857446726048, 2.421490541504988, 2.538192647288713, 1.2365762468979886, 0.8765049301242238, 0.5102699466310877, 0.0, 6.35795383504493, 5.612969412941963, 4.382524650621119, 3.709728740693965, 5.076385294577426, 3.390086758106983, 2.590857446726048, 1.635349584137828, 2.3188955228554424, 1.9427885116054342, 1.0859232330299606, 0.5756691400501349, 0.0), # 9
(6.5216650650687455, 6.552250281882444, 5.6181591039594165, 6.0308039084179725, 4.799876182740427, 2.3690113438005502, 2.680767541023342, 2.505409897591737, 2.6263320787213904, 1.279483442678543, 0.9069549141431433, 0.5279863496829302, 0.0, 6.578831432825289, 5.807849846512232, 4.534774570715716, 3.838450328035629, 5.252664157442781, 3.5075738566284325, 2.680767541023342, 1.6921509598575357, 2.3999380913702133, 2.010267969472658, 1.1236318207918834, 0.5956591165347678, 0.0), # 10
(6.7332757707184046, 6.761564586793285, 5.797633606768811, 6.223505174526839, 4.954330035771484, 2.444707896138372, 2.7663537059023664, 2.585291807259472, 2.7102315449413314, 1.320327302069344, 0.9359403549728333, 0.5448506263950541, 0.0, 6.78908439717009, 5.993356890345594, 4.679701774864166, 3.9609819062080316, 5.420463089882663, 3.619408530163261, 2.7663537059023664, 1.7462199258131228, 2.477165017885742, 2.07450172484228, 1.1595267213537623, 0.6146876897084805, 0.0), # 11
(6.93367105418145, 6.959246364087378, 5.9671333782816935, 6.405496300845368, 5.100383623239134, 2.516196921070873, 2.8471836718363246, 2.6607330736811736, 2.789467487846981, 1.3589015120051147, 0.9633148805441247, 0.5607776189613379, 0.0, 6.987651169172428, 6.168553808574717, 4.816574402720623, 4.0767045360153435, 5.578934975693962, 3.7250263031536432, 2.8471836718363246, 1.7972835150506232, 2.550191811619567, 2.135165433615123, 1.1934266756563388, 0.63265876037158, 0.0), # 12
(7.121814630574301, 7.144238522568122, 6.125752123201774, 6.575804255076027, 5.237267963578454, 2.5830962648625047, 2.9228251692984224, 2.731330500029827, 2.863616349336782, 1.3949997594205812, 0.9889321187878493, 0.5756821695756614, 0.0, 7.173470189925388, 6.332503865332275, 4.944660593939246, 4.184999278261743, 5.727232698673564, 3.8238627000417584, 2.9228251692984224, 1.8450687606160747, 2.618633981789227, 2.1919347516920094, 1.225150424640355, 0.6494762293243748, 0.0), # 13
(7.296670215013373, 7.315483971038899, 6.272583546232765, 6.733456004921276, 5.3642140752245275, 2.6450237737777162, 2.9928459287618647, 2.7966808894784156, 2.932254571309179, 1.428415731250467, 1.0126456976348381, 0.5894791204319041, 0.0, 7.345479900522051, 6.484270324750944, 5.06322848817419, 4.285247193751401, 5.864509142618358, 3.9153532452697823, 2.9928459287618647, 1.8893026955555114, 2.6821070376122638, 2.244485334973759, 1.254516709246553, 0.6650439973671727, 0.0), # 14
(7.457201522615084, 7.471925618303093, 6.406721352078362, 6.877478518083592, 5.480452976612431, 2.701597294080959, 3.0568136806998503, 2.8563810451999188, 2.9949585956626184, 1.4589431144294984, 1.0343092450159228, 0.6020833137239449, 0.0, 7.502618742055505, 6.622916450963392, 5.171546225079613, 4.376829343288494, 5.989917191325237, 3.9989334632798865, 3.0568136806998503, 1.9297123529149707, 2.7402264883062153, 2.2924928393611976, 1.2813442704156726, 0.6792659653002813, 0.0), # 15
(7.602372268495841, 7.612506373164098, 6.527259245442284, 7.006898762265429, 5.585215686177244, 2.7524346720366815, 3.1142961555855906, 2.9100277703673205, 3.0513048642955427, 1.4863755958923994, 1.0537763888619351, 0.6134095916456628, 0.0, 7.643825155618837, 6.747505508102289, 5.268881944309675, 4.459126787677198, 6.102609728591085, 4.074038878514249, 3.1142961555855906, 1.9660247657404866, 2.792607843088622, 2.3356329207551436, 1.3054518490884568, 0.692046033924009, 0.0), # 16
(7.73114616777206, 7.736169144425294, 6.6332909310282355, 7.120743705169268, 5.677733222354047, 2.7971537539093334, 3.1648610838922844, 2.9572178681536063, 3.1008698191063955, 1.510506862573894, 1.0709007571037066, 0.6233727963909371, 0.0, 7.768037582305133, 6.857100760300307, 5.354503785518533, 4.531520587721681, 6.201739638212791, 4.140105015415049, 3.1648610838922844, 1.9979669670780953, 2.8388666111770235, 2.373581235056423, 1.3266581862056472, 0.7032881040386633, 0.0), # 17
(7.842486935560164, 7.841856840890068, 6.723910113539921, 7.218040314497568, 5.757236603577914, 2.8353723859633684, 3.2080761960931405, 2.9975481417317535, 3.1432299019936254, 1.5311306014087078, 1.085535977672068, 0.6318877701536477, 0.0, 7.874194463207477, 6.950765471690124, 5.427679888360339, 4.593391804226123, 6.286459803987251, 4.196567398424455, 3.2080761960931405, 2.0252659899738346, 2.878618301788957, 2.406013438165856, 1.344782022707984, 0.7128960764445517, 0.0), # 18
(7.935358286976559, 7.928512371361812, 6.798210497681052, 7.29781555795279, 5.822956848283928, 2.866708414463231, 3.2435092226613578, 3.030615394274749, 3.1779615548556746, 1.5480404993315662, 1.0975356784978507, 0.6388693551276732, 0.0, 7.961234239418957, 7.027562906404404, 5.4876783924892525, 4.644121497994697, 6.355923109711349, 4.242861551984649, 3.2435092226613578, 2.0476488674737365, 2.911478424141964, 2.4326051859842637, 1.3596420995362106, 0.720773851941983, 0.0), # 19
(8.008723937137665, 7.995078644643906, 6.855285788155336, 7.359096403237412, 5.874124974907169, 2.8907796856733756, 3.270727894070145, 3.0560164289555725, 3.2046412195909864, 1.5610302432771923, 1.106753487511887, 0.6442323935068929, 0.0, 8.02809535203266, 7.08655632857582, 5.533767437559434, 4.683090729831576, 6.409282439181973, 4.278423000537802, 3.270727894070145, 2.0648426326238396, 2.9370624874535847, 2.4530321344124713, 1.3710571576310673, 0.7268253313312643, 0.0), # 20
(8.061547601159893, 8.040498569539743, 6.89422968966648, 7.400909818053892, 5.909972001882714, 2.90720404585825, 3.289299940792704, 3.0733480489472083, 3.222845338098006, 1.5698935201803115, 1.113043032645008, 0.6478917274851863, 0.0, 8.073716242141662, 7.1268090023370485, 5.56521516322504, 4.709680560540933, 6.445690676196012, 4.302687268526092, 3.289299940792704, 2.0765743184701786, 2.954986000941357, 2.466969939351298, 1.378845937933296, 0.730954415412704, 0.0), # 21
(8.092792994159664, 8.063715054852706, 6.91413590691819, 7.422282770104703, 5.92972894764564, 2.915599341282305, 3.29879309330224, 3.0822070574226386, 3.2321503522751773, 1.574424016975649, 1.1162579418280456, 0.6497621992564327, 0.0, 8.097035350839063, 7.147384191820759, 5.581289709140227, 4.723272050926946, 6.464300704550355, 4.315089880391694, 3.29879309330224, 2.0825709580587892, 2.96486447382282, 2.474094256701568, 1.3828271813836381, 0.7330650049866098, 0.0), # 22
(8.104314690674112, 8.066463968907179, 6.916615454961135, 7.424958487654322, 5.9347904298840515, 2.916666666666667, 3.2999216009037355, 3.0831646090534983, 3.2333136625514407, 1.574958454503887, 1.1166610716215655, 0.6499931717725956, 0.0, 8.1, 7.149924889498552, 5.583305358107827, 4.72487536351166, 6.466627325102881, 4.316430452674898, 3.2999216009037355, 2.0833333333333335, 2.9673952149420257, 2.474986162551441, 1.3833230909922272, 0.7333149062642891, 0.0), # 23
(8.112809930427323, 8.06486049382716, 6.916209876543211, 7.4246291666666675, 5.937657393927921, 2.916666666666667, 3.299301525054467, 3.0818333333333334, 3.2331577777777776, 1.5746301234567905, 1.1166166105499442, 0.6499390946502058, 0.0, 8.1, 7.149330041152263, 5.583083052749721, 4.72389037037037, 6.466315555555555, 4.314566666666667, 3.299301525054467, 2.0833333333333335, 2.9688286969639606, 2.4748763888888896, 1.3832419753086422, 0.7331691358024692, 0.0), # 24
(8.121125784169264, 8.06169981710105, 6.915409236396892, 7.423977623456791, 5.940461304317068, 2.916666666666667, 3.298079561042524, 3.0792181069958855, 3.2328497942386836, 1.5739837677183361, 1.1165284532568485, 0.6498323426306966, 0.0, 8.1, 7.148155768937661, 5.5826422662842425, 4.7219513031550076, 6.465699588477367, 4.31090534979424, 3.298079561042524, 2.0833333333333335, 2.970230652158534, 2.474659207818931, 1.3830818472793784, 0.7328818015546411, 0.0), # 25
(8.129261615238427, 8.057030224051212, 6.914224508459078, 7.423011265432098, 5.943202063157923, 2.916666666666667, 3.2962746873234887, 3.0753683127572025, 3.23239366255144, 1.5730301417466854, 1.1163973978467807, 0.6496743789056548, 0.0, 8.1, 7.146418167962202, 5.581986989233903, 4.719090425240055, 6.46478732510288, 4.305515637860084, 3.2962746873234887, 2.0833333333333335, 2.9716010315789614, 2.4743370884773666, 1.3828449016918156, 0.732457293095565, 0.0), # 26
(8.13721678697331, 8.0509, 6.9126666666666665, 7.4217375, 5.945879572556914, 2.916666666666667, 3.2939058823529415, 3.0703333333333336, 3.231793333333333, 1.5717800000000004, 1.1162242424242426, 0.6494666666666669, 0.0, 8.1, 7.144133333333334, 5.581121212121213, 4.715339999999999, 6.463586666666666, 4.298466666666667, 3.2939058823529415, 2.0833333333333335, 2.972939786278457, 2.4739125000000004, 1.3825333333333334, 0.7319000000000001, 0.0), # 27
(8.1449906627124, 8.043357430269776, 6.910746684956561, 7.420163734567902, 5.948493734620481, 2.916666666666667, 3.2909921245864604, 3.06416255144033, 3.231052757201646, 1.570244096936443, 1.116009785093736, 0.6492106691053194, 0.0, 8.1, 7.141317360158513, 5.580048925468679, 4.710732290809328, 6.462105514403292, 4.289827572016462, 3.2909921245864604, 2.0833333333333335, 2.9742468673102405, 2.4733879115226345, 1.3821493369913125, 0.731214311842707, 0.0), # 28
(8.1525826057942, 8.0344508001829, 6.908475537265661, 7.41829737654321, 5.951044451455051, 2.916666666666667, 3.2875523924796264, 3.0569053497942384, 3.2301758847736624, 1.5684331870141752, 1.1157548239597623, 0.6489078494131992, 0.0, 8.1, 7.13798634354519, 5.578774119798812, 4.705299561042525, 6.460351769547325, 4.279667489711934, 3.2875523924796264, 2.0833333333333335, 2.9755222257275253, 2.4727657921810704, 1.3816951074531325, 0.7304046181984455, 0.0), # 29
(8.159991979557198, 8.02422839506173, 6.905864197530864, 7.416145833333333, 5.953531625167059, 2.916666666666667, 3.2836056644880176, 3.048611111111111, 3.2291666666666665, 1.5663580246913587, 1.115460157126824, 0.648559670781893, 0.0, 8.1, 7.134156378600823, 5.57730078563412, 4.699074074074074, 6.458333333333333, 4.268055555555556, 3.2836056644880176, 2.0833333333333335, 2.9767658125835297, 2.4720486111111115, 1.3811728395061729, 0.7294753086419755, 0.0), # 30
(8.167218147339886, 8.012738500228625, 6.902923639689073, 7.41371651234568, 5.955955157862938, 2.916666666666667, 3.279170919067216, 3.039329218106996, 3.2280290534979423, 1.5640293644261551, 1.1151265826994223, 0.6481675964029875, 0.0, 8.1, 7.129843560432862, 5.575632913497111, 4.692088093278464, 6.456058106995885, 4.2550609053497945, 3.279170919067216, 2.0833333333333335, 2.977977578931469, 2.4712388374485603, 1.3805847279378145, 0.7284307727480569, 0.0), # 31
(8.174260472480764, 8.000029401005945, 6.899664837677183, 7.411016820987655, 5.958314951649118, 2.916666666666667, 3.2742671346727996, 3.029109053497943, 3.226766995884774, 1.5614579606767267, 1.1147548987820595, 0.6477330894680691, 0.0, 8.1, 7.125063984148759, 5.573774493910297, 4.684373882030179, 6.453533991769548, 4.24075267489712, 3.2742671346727996, 2.0833333333333335, 2.979157475824559, 2.470338940329219, 1.3799329675354366, 0.7272754000914496, 0.0), # 32
(8.181118318318317, 7.986149382716048, 6.896098765432099, 7.408054166666666, 5.960610908632033, 2.916666666666667, 3.2689132897603486, 3.0180000000000002, 3.2253844444444444, 1.5586545679012351, 1.114345903479237, 0.6472576131687243, 0.0, 8.1, 7.119833744855966, 5.571729517396184, 4.6759637037037045, 6.450768888888889, 4.225200000000001, 3.2689132897603486, 2.0833333333333335, 2.9803054543160163, 2.469351388888889, 1.37921975308642, 0.7260135802469135, 0.0), # 33
(8.187791048191048, 7.971146730681298, 6.892236396890718, 7.404835956790124, 5.962842930918115, 2.916666666666667, 3.263128362785444, 3.006051440329218, 3.2238853497942395, 1.5556299405578424, 1.1139003948954567, 0.6467426306965403, 0.0, 8.1, 7.114168937661942, 5.569501974477284, 4.666889821673526, 6.447770699588479, 4.208472016460905, 3.263128362785444, 2.0833333333333335, 2.9814214654590576, 2.468278652263375, 1.3784472793781437, 0.724649702789209, 0.0), # 34
(8.194278025437447, 7.95506973022405, 6.888088705989941, 7.401369598765432, 5.965010920613797, 2.916666666666667, 3.2569313322036635, 2.9933127572016467, 3.2222736625514408, 1.5523948331047102, 1.1134191711352206, 0.6461896052431033, 0.0, 8.1, 7.108085657674136, 5.5670958556761025, 4.657184499314129, 6.4445473251028815, 4.1906378600823055, 3.2569313322036635, 2.0833333333333335, 2.9825054603068986, 2.4671231995884777, 1.3776177411979884, 0.7231881572930956, 0.0), # 35
(8.200578613396004, 7.937966666666665, 6.8836666666666675, 7.3976625, 5.967114779825512, 2.916666666666667, 3.250341176470588, 2.979833333333334, 3.220553333333333, 1.5489600000000006, 1.1129030303030305, 0.6456000000000002, 0.0, 8.1, 7.101600000000001, 5.564515151515152, 4.64688, 6.441106666666666, 4.1717666666666675, 3.250341176470588, 2.0833333333333335, 2.983557389912756, 2.4658875000000005, 1.3767333333333336, 0.7216333333333333, 0.0), # 36
(8.20669217540522, 7.919885825331503, 6.8789812528577965, 7.393722067901235, 5.969154410659692, 2.916666666666667, 3.2433768740417976, 2.9656625514403294, 3.218728312757202, 1.5453361957018754, 1.1123527705033882, 0.6449752781588174, 0.0, 8.1, 7.09472805974699, 5.561763852516941, 4.636008587105625, 6.437456625514404, 4.1519275720164615, 3.2433768740417976, 2.0833333333333335, 2.984577205329846, 2.4645740226337454, 1.3757962505715595, 0.7199896204846822, 0.0), # 37
(8.212618074803581, 7.9008754915409245, 6.874043438500229, 7.389555709876545, 5.971129715222768, 2.916666666666667, 3.2360574033728717, 2.9508497942386835, 3.2168025514403293, 1.5415341746684963, 1.111769189840795, 0.6443169029111417, 0.0, 8.1, 7.087485932022558, 5.558845949203975, 4.624602524005487, 6.433605102880659, 4.131189711934157, 3.2360574033728717, 2.0833333333333335, 2.985564857611384, 2.4631852366255154, 1.3748086877000458, 0.7182614083219023, 0.0), # 38
(8.218355674929589, 7.880983950617284, 6.868864197530866, 7.3851708333333335, 5.973040595621175, 2.916666666666667, 3.2284017429193903, 2.9354444444444447, 3.21478, 1.5375646913580252, 1.1111530864197532, 0.6436263374485597, 0.0, 8.1, 7.079889711934156, 5.555765432098766, 4.612694074074074, 6.42956, 4.109622222222223, 3.2284017429193903, 2.0833333333333335, 2.9865202978105874, 2.4617236111111116, 1.3737728395061732, 0.7164530864197532, 0.0), # 39
(8.22390433912173, 7.860259487882944, 6.863454503886603, 7.380574845679012, 5.974886953961343, 2.916666666666667, 3.2204288711369324, 2.9194958847736636, 3.212664609053498, 1.5334385002286244, 1.1105052583447648, 0.6429050449626583, 0.0, 8.1, 7.071955494589241, 5.552526291723823, 4.600315500685872, 6.425329218106996, 4.087294238683129, 3.2204288711369324, 2.0833333333333335, 2.9874434769806717, 2.460191615226338, 1.3726909007773205, 0.714569044352995, 0.0), # 40
(8.229263430718502, 7.838750388660264, 6.857825331504345, 7.375775154320989, 5.976668692349708, 2.916666666666667, 3.212157766481078, 2.903053497942387, 3.210460329218107, 1.529166355738455, 1.1098265037203312, 0.6421544886450238, 0.0, 8.1, 7.06369937509526, 5.549132518601655, 4.587499067215363, 6.420920658436214, 4.0642748971193425, 3.212157766481078, 2.0833333333333335, 2.988334346174854, 2.4585917181069967, 1.371565066300869, 0.7126136716963878, 0.0), # 41
(8.2344323130584, 7.816504938271606, 6.85198765432099, 7.370779166666668, 5.978385712892697, 2.916666666666667, 3.2036074074074072, 2.886166666666667, 3.2081711111111115, 1.5247590123456796, 1.1091176206509543, 0.641376131687243, 0.0, 8.1, 7.0551374485596705, 5.5455881032547705, 4.574277037037037, 6.416342222222223, 4.040633333333334, 3.2036074074074072, 2.0833333333333335, 2.9891928564463486, 2.4569263888888897, 1.370397530864198, 0.7105913580246915, 0.0), # 42
(8.239410349479915, 7.7935714220393235, 6.845952446273435, 7.3655942901234575, 5.980037917696748, 2.916666666666667, 3.1947967723715003, 2.868884773662552, 3.2058009053497942, 1.5202272245084596, 1.1083794072411357, 0.6405714372809025, 0.0, 8.1, 7.046285810089926, 5.541897036205678, 4.5606816735253775, 6.4116018106995885, 4.016438683127573, 3.1947967723715003, 2.0833333333333335, 2.990018958848374, 2.4551980967078197, 1.369190489254687, 0.7085064929126659, 0.0), # 43
(8.244196903321543, 7.769998125285779, 6.839730681298583, 7.360227932098766, 5.981625208868291, 2.916666666666667, 3.185744839828936, 2.8512572016460913, 3.2033536625514403, 1.515581746684957, 1.1076126615953779, 0.639741868617589, 0.0, 8.1, 7.037160554793477, 5.538063307976889, 4.54674524005487, 6.4067073251028805, 3.9917600823045283, 3.185744839828936, 2.0833333333333335, 2.9908126044341454, 2.4534093106995893, 1.3679461362597167, 0.7063634659350709, 0.0), # 44
(8.248791337921773, 7.745833333333334, 6.833333333333335, 7.354687500000001, 5.983147488513758, 2.916666666666667, 3.1764705882352944, 2.833333333333334, 3.2008333333333328, 1.510833333333334, 1.106818181818182, 0.638888888888889, 0.0, 8.1, 7.027777777777777, 5.534090909090909, 4.532500000000001, 6.4016666666666655, 3.9666666666666672, 3.1764705882352944, 2.0833333333333335, 2.991573744256879, 2.4515625000000005, 1.366666666666667, 0.7041666666666668, 0.0), # 45
(8.253193016619106, 7.721125331504343, 6.8267713763145865, 7.348980401234568, 5.984604658739582, 2.916666666666667, 3.1669929960461554, 2.81516255144033, 3.198243868312757, 1.5059927389117518, 1.10599676601405, 0.6380139612863894, 0.0, 8.1, 7.018153574150282, 5.5299838300702495, 4.517978216735254, 6.396487736625514, 3.941227572016462, 3.1669929960461554, 2.0833333333333335, 2.992302329369791, 2.4496601337448567, 1.3653542752629175, 0.7019204846822131, 0.0), # 46
(8.257401302752028, 7.695922405121171, 6.8200557841792415, 7.3431140432098765, 5.985996621652196, 2.916666666666667, 3.1573310417170988, 2.7967942386831277, 3.195589218106996, 1.5010707178783727, 1.105149212287484, 0.6371185490016767, 0.0, 8.1, 7.008304039018443, 5.525746061437419, 4.503212153635117, 6.391178436213992, 3.915511934156379, 3.1573310417170988, 2.0833333333333335, 2.992998310826098, 2.4477046810699594, 1.3640111568358484, 0.6996293095564702, 0.0), # 47
(8.261415559659037, 7.670272839506174, 6.8131975308641985, 7.3370958333333345, 5.987323279358032, 2.916666666666667, 3.1475037037037037, 2.7782777777777783, 3.1928733333333335, 1.4960780246913583, 1.1042763187429856, 0.6362041152263375, 0.0, 8.1, 6.998245267489711, 5.521381593714927, 4.488234074074074, 6.385746666666667, 3.88958888888889, 3.1475037037037037, 2.0833333333333335, 2.993661639679016, 2.445698611111112, 1.3626395061728398, 0.6972975308641977, 0.0), # 48
(8.26523515067863, 7.644224919981709, 6.806207590306356, 7.330933179012346, 5.9885845339635235, 2.916666666666667, 3.137529960461551, 2.7596625514403295, 3.190100164609053, 1.491025413808871, 1.1033788834850566, 0.6352721231519587, 0.0, 8.1, 6.987993354671545, 5.5168944174252825, 4.473076241426613, 6.380200329218106, 3.8635275720164617, 3.137529960461551, 2.0833333333333335, 2.9942922669817618, 2.443644393004116, 1.3612415180612714, 0.6949295381801555, 0.0), # 49
(8.268859439149294, 7.617826931870143, 6.799096936442616, 7.324633487654321, 5.989780287575101, 2.916666666666667, 3.12742879044622, 2.7409979423868314, 3.1872736625514397, 1.485923639689072, 1.1024577046181985, 0.6343240359701267, 0.0, 8.1, 6.977564395671393, 5.512288523090993, 4.457770919067215, 6.3745473251028795, 3.8373971193415644, 3.12742879044622, 2.0833333333333335, 2.9948901437875506, 2.441544495884774, 1.3598193872885234, 0.692529721079104, 0.0), # 50
(8.272287788409528, 7.591127160493827, 6.791876543209877, 7.318204166666668, 5.9909104422991994, 2.916666666666667, 3.11721917211329, 2.7223333333333333, 3.184397777777778, 1.4807834567901237, 1.1015135802469138, 0.6333613168724281, 0.0, 8.1, 6.966974485596708, 5.507567901234569, 4.44235037037037, 6.368795555555556, 3.811266666666667, 3.11721917211329, 2.0833333333333335, 2.9954552211495997, 2.4394013888888897, 1.3583753086419754, 0.6901024691358025, 0.0), # 51
(8.275519561797823, 7.564173891175126, 6.78455738454504, 7.311652623456791, 5.991974900242248, 2.916666666666667, 3.1069200839183413, 2.7037181069958844, 3.18147646090535, 1.4756156195701877, 1.1005473084757038, 0.6323854290504498, 0.0, 8.1, 6.956239719554947, 5.502736542378519, 4.4268468587105625, 6.3629529218107, 3.7852053497942384, 3.1069200839183413, 2.0833333333333335, 2.995987450121124, 2.437217541152264, 1.356911476909008, 0.6876521719250116, 0.0), # 52
(8.278554122652675, 7.537015409236398, 6.777150434385004, 7.304986265432099, 5.992973563510682, 2.916666666666667, 3.0965505043169532, 2.6852016460905355, 3.1785136625514405, 1.470430882487426, 1.0995596874090703, 0.6313978356957782, 0.0, 8.1, 6.945376192653559, 5.4977984370453505, 4.411292647462277, 6.357027325102881, 3.7592823045267494, 3.0965505043169532, 2.0833333333333335, 2.996486781755341, 2.4349954218107, 1.355430086877001, 0.6851832190214908, 0.0), # 53
(8.281390834312573, 7.5097000000000005, 6.769666666666667, 7.2982125, 5.993906334210934, 2.916666666666667, 3.086129411764706, 2.6668333333333334, 3.1755133333333334, 1.4652400000000003, 1.098551515151515, 0.6304000000000001, 0.0, 8.1, 6.9344, 5.492757575757575, 4.395720000000001, 6.351026666666667, 3.7335666666666665, 3.086129411764706, 2.0833333333333335, 2.996953167105467, 2.4327375000000004, 1.3539333333333334, 0.6827000000000002, 0.0), # 54
(8.284029060116017, 7.482275948788294, 6.762117055326932, 7.291338734567901, 5.994773114449434, 2.916666666666667, 3.075675784717179, 2.6486625514403292, 3.1724794238683125, 1.4600537265660727, 1.0975235898075406, 0.6293933851547021, 0.0, 8.1, 6.923327236701723, 5.487617949037702, 4.380161179698217, 6.344958847736625, 3.708127572016461, 3.075675784717179, 2.0833333333333335, 2.997386557224717, 2.4304462448559674, 1.3524234110653865, 0.6802069044352995, 0.0), # 55
(8.286468163401498, 7.454791540923639, 6.754512574302698, 7.28437237654321, 5.995573806332619, 2.916666666666667, 3.0652086016299527, 2.6307386831275723, 3.169415884773662, 1.4548828166438048, 1.0964767094816479, 0.6283794543514709, 0.0, 8.1, 6.912173997866179, 5.482383547408239, 4.364648449931414, 6.338831769547324, 3.6830341563786013, 3.0652086016299527, 2.0833333333333335, 2.9977869031663094, 2.4281241255144037, 1.3509025148605398, 0.6777083219021491, 0.0), # 56
(8.288707507507507, 7.427295061728395, 6.746864197530866, 7.277320833333334, 5.996308311966915, 2.916666666666667, 3.0547468409586056, 2.613111111111112, 3.166326666666667, 1.4497380246913585, 1.0954116722783391, 0.627359670781893, 0.0, 8.1, 6.900956378600823, 5.477058361391695, 4.349214074074075, 6.332653333333334, 3.6583555555555565, 3.0547468409586056, 2.0833333333333335, 2.9981541559834577, 2.425773611111112, 1.3493728395061733, 0.6752086419753087, 0.0), # 57
(8.290746455772544, 7.39983479652492, 6.739182898948332, 7.270191512345679, 5.99697653345876, 2.916666666666667, 3.044309481158719, 2.595829218106996, 3.163215720164609, 1.4446301051668957, 1.0943292763021162, 0.6263354976375554, 0.0, 8.1, 6.889690474013108, 5.471646381510581, 4.333890315500686, 6.326431440329218, 3.6341609053497947, 3.044309481158719, 2.0833333333333335, 2.99848826672938, 2.4233971707818935, 1.3478365797896665, 0.6727122542295383, 0.0), # 58
(8.292584371535098, 7.372459030635573, 6.731479652491998, 7.262991820987654, 5.9975783729145835, 2.916666666666667, 3.0339155006858713, 2.578942386831276, 3.160086995884774, 1.4395698125285785, 1.0932303196574802, 0.6253083981100444, 0.0, 8.1, 6.878392379210486, 5.4661515982874, 4.318709437585735, 6.320173991769548, 3.6105193415637866, 3.0339155006858713, 2.0833333333333335, 2.9987891864572918, 2.420997273662552, 1.3462959304984, 0.6702235482395976, 0.0), # 59
(8.294220618133663, 7.345216049382717, 6.723765432098765, 7.255729166666667, 5.998113732440819, 2.916666666666667, 3.0235838779956428, 2.5625000000000004, 3.156944444444445, 1.4345679012345682, 1.092115600448934, 0.6242798353909466, 0.0, 8.1, 6.867078189300411, 5.460578002244669, 4.303703703703704, 6.31388888888889, 3.5875000000000004, 3.0235838779956428, 2.0833333333333335, 2.9990568662204096, 2.4185763888888894, 1.3447530864197532, 0.6677469135802471, 0.0), # 60
(8.295654558906731, 7.3181541380887065, 6.716051211705533, 7.248410956790124, 5.998582514143899, 2.916666666666667, 3.0133335915436135, 2.5465514403292184, 3.1537920164609052, 1.4296351257430273, 1.0909859167809788, 0.623251272671849, 0.0, 8.1, 6.855763999390337, 5.454929583904893, 4.2889053772290815, 6.3075840329218105, 3.5651720164609055, 3.0133335915436135, 2.0833333333333335, 2.9992912570719494, 2.4161369855967085, 1.3432102423411068, 0.6652867398262462, 0.0), # 61
(8.296885557192804, 7.291321582075903, 6.708347965249201, 7.241044598765433, 5.998984620130258, 2.916666666666667, 3.0031836197853625, 2.5311460905349796, 3.1506336625514404, 1.4247822405121175, 1.0898420667581163, 0.6222241731443379, 0.0, 8.1, 6.844465904587715, 5.449210333790581, 4.274346721536352, 6.301267325102881, 3.5436045267489718, 3.0031836197853625, 2.0833333333333335, 2.999492310065129, 2.4136815329218115, 1.3416695930498403, 0.6628474165523549, 0.0), # 62
(8.297912976330368, 7.264766666666667, 6.700666666666668, 7.233637500000001, 5.999319952506323, 2.916666666666667, 2.9931529411764703, 2.5163333333333338, 3.147473333333333, 1.4200200000000003, 1.0886848484848488, 0.6212000000000001, 0.0, 8.1, 6.8332, 5.443424242424244, 4.26006, 6.294946666666666, 3.5228666666666677, 2.9931529411764703, 2.0833333333333335, 2.9996599762531617, 2.411212500000001, 1.3401333333333336, 0.6604333333333334, 0.0), # 63
(8.298736179657919, 7.2385376771833565, 6.693018289894834, 7.226197067901236, 5.999588413378532, 2.916666666666667, 2.983260534172517, 2.5021625514403296, 3.1443149794238683, 1.415359158664838, 1.0875150600656773, 0.6201802164304223, 0.0, 8.1, 6.821982380734645, 5.437575300328387, 4.246077475994513, 6.288629958847737, 3.5030275720164616, 2.983260534172517, 2.0833333333333335, 2.999794206689266, 2.408732355967079, 1.3386036579789669, 0.6580488797439416, 0.0), # 64
(8.29935453051395, 7.212682898948331, 6.685413808870599, 7.218730709876544, 5.999789904853316, 2.916666666666667, 2.9735253772290813, 2.4886831275720165, 3.1411625514403294, 1.4108104709647922, 1.0863334996051048, 0.619166285627191, 0.0, 8.1, 6.8108291418991, 5.431667498025524, 4.232431412894376, 6.282325102880659, 3.484156378600823, 2.9735253772290813, 2.0833333333333335, 2.999894952426658, 2.4062435699588485, 1.33708276177412, 0.6556984453589393, 0.0), # 65
(8.299767392236957, 7.187250617283952, 6.677864197530865, 7.211245833333334, 5.999924329037105, 2.916666666666667, 2.963966448801743, 2.475944444444445, 3.13802, 1.406384691358025, 1.085140965207632, 0.6181596707818932, 0.0, 8.1, 6.799756378600824, 5.425704826038159, 4.2191540740740745, 6.27604, 3.466322222222223, 2.963966448801743, 2.0833333333333335, 2.9999621645185526, 2.4037486111111117, 1.3355728395061732, 0.6533864197530866, 0.0), # 66
(8.299974128165434, 7.162289117512574, 6.670380429812529, 7.203749845679012, 5.999991588036336, 2.916666666666667, 2.9546027273460824, 2.4639958847736634, 3.1348912757201646, 1.4020925743026982, 1.0839382549777616, 0.617161835086115, 0.0, 8.1, 6.788780185947264, 5.419691274888807, 4.206277722908094, 6.269782551440329, 3.4495942386831286, 2.9546027273460824, 2.0833333333333335, 2.999995794018168, 2.401249948559671, 1.3340760859625058, 0.6511171925011432, 0.0), # 67
(8.29983329158466, 7.137715668834903, 6.662937299954276, 7.196185044283415, 5.999934909491917, 2.916612538739013, 2.9454060779318585, 2.452781283340954, 3.131756759640299, 1.3979240883294335, 1.0827047984720504, 0.6161686681266496, 0.0, 8.099900120027435, 6.777855349393144, 5.413523992360251, 4.1937722649883, 6.263513519280598, 3.433893796677336, 2.9454060779318585, 2.0832946705278665, 2.9999674547459585, 2.398728348094472, 1.3325874599908551, 0.648883242621355, 0.0), # 68
(8.298513365539453, 7.112780047789725, 6.655325617283951, 7.188170108695652, 5.999419026870006, 2.916184636488341, 2.9361072725386457, 2.4416995884773662, 3.1284794238683125, 1.3937612781408861, 1.0813150451887295, 0.6151479315572884, 0.0, 8.099108796296298, 6.766627247130171, 5.406575225943647, 4.181283834422658, 6.256958847736625, 3.4183794238683127, 2.9361072725386457, 2.0829890260631005, 2.999709513435003, 2.396056702898551, 1.33106512345679, 0.6466163679808842, 0.0), # 69
(8.295908630047116, 7.087367803885127, 6.647512288523091, 7.179652274557166, 5.998399634202102, 2.9153419194228523, 2.926664053824548, 2.4306508154244786, 3.1250407712238992, 1.3895839048925471, 1.079753184870144, 0.614094850752854, 0.0, 8.097545867626888, 6.755043358281393, 5.3987659243507204, 4.168751714677641, 6.2500815424477985, 3.40291114159427, 2.926664053824548, 2.082387085302037, 2.999199817101051, 2.393217424852389, 1.3295024577046182, 0.6443061639895571, 0.0), # 70
(8.292055728514343, 7.061494123633789, 6.639500057155922, 7.170644102254428, 5.9968896420022055, 2.9140980439973583, 2.9170806638155953, 2.4196386221612562, 3.1214459228776104, 1.3853920718685282, 1.0780249827711816, 0.613010195814181, 0.0, 8.095231910150892, 6.743112153955991, 5.390124913855908, 4.1561762156055835, 6.242891845755221, 3.387494071025759, 2.9170806638155953, 2.081498602855256, 2.9984448210011028, 2.3902147007514767, 1.3279000114311843, 0.6419540112394354, 0.0), # 71
(8.286991304347827, 7.035174193548387, 6.631291666666667, 7.161158152173913, 5.994901960784313, 2.9124666666666674, 2.907361344537815, 2.408666666666667, 3.1177, 1.3811858823529415, 1.0761362041467308, 0.6118947368421054, 0.0, 8.0921875, 6.730842105263158, 5.380681020733653, 4.143557647058824, 6.2354, 3.3721333333333336, 2.907361344537815, 2.080333333333334, 2.9974509803921565, 2.3870527173913048, 1.3262583333333333, 0.6395612903225807, 0.0), # 72
(8.280752000954257, 7.008423200141599, 6.622889860539551, 7.151206984702094, 5.992449501062428, 2.9104614438855867, 2.897510338017237, 2.397738606919677, 3.113808123761622, 1.376965439629899, 1.0740926142516787, 0.6107492439374613, 0.0, 8.0884332133059, 6.7182416833120735, 5.370463071258393, 4.130896318889696, 6.227616247523244, 3.356834049687548, 2.897510338017237, 2.0789010313468475, 2.996224750531214, 2.383735661567365, 1.3245779721079105, 0.6371293818310545, 0.0), # 73
(8.273374461740323, 6.981256329926103, 6.614297382258802, 7.140803160225442, 5.989545173350547, 2.908096032108927, 2.887531886279889, 2.3868581008992535, 3.1097754153330284, 1.3727308469835127, 1.0718999783409144, 0.6095744872010845, 0.0, 8.083989626200276, 6.705319359211929, 5.359499891704571, 4.118192540950537, 6.219550830666057, 3.3416013412589547, 2.887531886279889, 2.0772114515063764, 2.9947725866752735, 2.380267720075148, 1.3228594764517605, 0.6346596663569185, 0.0), # 74
(8.26489533011272, 6.953688769414575, 6.605516975308642, 7.129959239130434, 5.986201888162673, 2.905384087791496, 2.8774302313518003, 2.376028806584362, 3.1056069958847736, 1.3684822076978942, 1.069564061669325, 0.6083712367338099, 0.0, 8.078877314814816, 6.692083604071907, 5.347820308346624, 4.105446623093682, 6.211213991769547, 3.3264403292181073, 2.8774302313518003, 2.0752743484224974, 2.9931009440813363, 2.3766530797101453, 1.3211033950617284, 0.6321535244922342, 0.0), # 75
(8.255351249478142, 6.925735705119696, 6.596551383173297, 7.118687781803542, 5.982432556012803, 2.9023392673881023, 2.8672096152589983, 2.365254381953971, 3.1013079865874102, 1.364219625057156, 1.067090629491799, 0.6071402626364722, 0.0, 8.073116855281206, 6.678542889001194, 5.335453147458995, 4.092658875171468, 6.2026159731748205, 3.311356134735559, 2.8672096152589983, 2.0730994767057873, 2.9912162780064016, 2.372895927267848, 1.3193102766346596, 0.6296123368290635, 0.0), # 76
(8.244778863243274, 6.897412323554141, 6.587403349336991, 7.10700134863124, 5.9782500874149385, 2.8989752273535543, 2.8568742800275118, 2.354538484987045, 3.0968835086114925, 1.3599432023454103, 1.0644854470632252, 0.6058823350099072, 0.0, 8.06672882373114, 6.664705685108978, 5.322427235316125, 4.07982960703623, 6.193767017222985, 3.296353878981863, 2.8568742800275118, 2.0706965909668247, 2.9891250437074692, 2.369000449543747, 1.3174806698673982, 0.6270374839594675, 0.0), # 77
(8.233214814814815, 6.8687338112305865, 6.578075617283951, 7.0949125, 5.97366739288308, 2.895305624142661, 2.84642846768337, 2.343884773662552, 3.092338683127571, 1.3556530428467686, 1.0617542796384905, 0.6045982239549493, 0.0, 8.059733796296298, 6.650580463504441, 5.308771398192452, 4.066959128540305, 6.184677366255142, 3.2814386831275724, 2.84642846768337, 2.0680754458161865, 2.98683369644154, 2.364970833333334, 1.3156151234567903, 0.624430346475508, 0.0), # 78
(8.220695747599452, 6.8397153546617115, 6.5685709304984, 7.082433796296296, 5.968697382931225, 2.891344114210232, 2.8358764202526006, 2.333296905959458, 3.0876786313062032, 1.351349249845343, 1.058902892472483, 0.6032886995724337, 0.0, 8.052152349108367, 6.63617569529677, 5.294514462362415, 4.0540477495360285, 6.1753572626124065, 3.266615668343241, 2.8358764202526006, 2.0652457958644517, 2.9843486914656125, 2.3608112654320994, 1.3137141860996802, 0.6217923049692465, 0.0), # 79
(8.207258305003878, 6.810372140360193, 6.558892032464563, 7.069577797906602, 5.963352968073375, 2.8871043540110755, 2.8252223797612324, 2.3227785398567296, 3.0829084743179394, 1.3470319266252455, 1.055937050820092, 0.6019545319631957, 0.0, 8.04400505829904, 6.621499851595152, 5.2796852541004595, 4.041095779875736, 6.165816948635879, 3.2518899557994216, 2.8252223797612324, 2.0622173957221968, 2.9816764840366874, 2.3565259326355346, 1.3117784064929128, 0.619124740032745, 0.0), # 80
(8.192939130434784, 6.78071935483871, 6.5490416666666675, 7.056357065217393, 5.957647058823529, 2.8826000000000005, 2.8144705882352943, 2.3123333333333336, 3.078033333333333, 1.3427011764705885, 1.0528625199362043, 0.6005964912280702, 0.0, 8.0353125, 6.606561403508772, 5.264312599681022, 4.028103529411765, 6.156066666666666, 3.237266666666667, 2.8144705882352943, 2.059, 2.9788235294117644, 2.3521190217391315, 1.3098083333333335, 0.6164290322580647, 0.0), # 81
(8.177774867298861, 6.750772184609939, 6.539022576588936, 7.042784158615137, 5.951592565695688, 2.877844708631815, 2.8036252877008145, 2.301964944368237, 3.0730583295229383, 1.3383571026654835, 1.0496850650757086, 0.5992153474678925, 0.0, 8.026095250342937, 6.5913688221468165, 5.248425325378542, 4.0150713079964495, 6.146116659045877, 3.2227509221155315, 2.8036252877008145, 2.0556033633084394, 2.975796282847844, 2.3475947195383795, 1.3078045153177873, 0.6137065622372673, 0.0), # 82
(8.161802159002804, 6.720545816186557, 6.528837505715592, 7.028871638486312, 5.945202399203851, 2.8728521363613275, 2.7926907201838214, 2.2916770309404058, 3.067988584057308, 1.3339998084940425, 1.0464104514934927, 0.5978118707834975, 0.0, 8.016373885459535, 6.575930578618472, 5.232052257467463, 4.001999425482127, 6.135977168114616, 3.208347843316568, 2.7926907201838214, 2.052037240258091, 2.9726011996019257, 2.3429572128287712, 1.3057675011431187, 0.6109587105624144, 0.0), # 83
(8.145057648953301, 6.690055436081242, 6.518489197530864, 7.014632065217392, 5.938489469862018, 2.867635939643347, 2.7816711277103434, 2.2814732510288067, 3.0628292181069954, 1.329629397240378, 1.0430444444444447, 0.5963868312757202, 0.0, 8.006168981481482, 6.560255144032922, 5.215222222222223, 3.9888881917211334, 6.125658436213991, 3.194062551440329, 2.7816711277103434, 2.0483113854595336, 2.969244734931009, 2.338210688405798, 1.303697839506173, 0.6081868578255676, 0.0), # 84
(8.127577980557048, 6.659316230806673, 6.507980395518976, 7.000077999194847, 5.931466688184191, 2.862209774932684, 2.77057075230641, 2.2713572626124074, 3.057585352842554, 1.3252459721886014, 1.0395928091834528, 0.5949409990453959, 0.0, 7.995501114540467, 6.544350989499354, 5.197964045917263, 3.9757379165658033, 6.115170705685108, 3.17990016765737, 2.77057075230641, 2.0444355535233454, 2.9657333440920954, 2.3333593330649496, 1.3015960791037953, 0.6053923846187885, 0.0), # 85
(8.10939979722073, 6.6283433868755255, 6.497313843164153, 6.985222000805154, 5.924146964684365, 2.8565872986841443, 2.7593938359980483, 2.2613327236701726, 3.0522621094345377, 1.320849636622825, 1.0360613109654049, 0.5934751441933597, 0.0, 7.984390860768176, 6.528226586126955, 5.180306554827023, 3.9625489098684747, 6.104524218869075, 3.1658658131382413, 2.7593938359980483, 2.040419499060103, 2.9620734823421824, 2.3284073336017186, 1.2994627686328306, 0.6025766715341389, 0.0), # 86
(8.090559742351045, 6.597152090800478, 6.486492283950617, 6.970076630434782, 5.9165432098765445, 2.8507821673525378, 2.7481446208112876, 2.2514032921810703, 3.0468646090534985, 1.3164404938271608, 1.0324557150451887, 0.5919900368204463, 0.0, 7.972858796296297, 6.511890405024908, 5.162278575225944, 3.9493214814814817, 6.093729218106997, 3.1519646090534983, 2.7481446208112876, 2.036272976680384, 2.9582716049382722, 2.3233588768115947, 1.2972984567901236, 0.5997410991636799, 0.0), # 87
(8.071094459354686, 6.565757529094207, 6.475518461362597, 6.95465444847021, 5.908668334274726, 2.8448080373926743, 2.7368273487721564, 2.2415726261240665, 3.0413979728699894, 1.3120186470857205, 1.0287817866776934, 0.5904864470274911, 0.0, 7.960925497256517, 6.495350917302401, 5.143908933388466, 3.9360559412571607, 6.082795945739979, 3.138201676573693, 2.7368273487721564, 2.032005740994767, 2.954334167137363, 2.3182181494900704, 1.2951036922725196, 0.5968870480994735, 0.0), # 88
(8.051040591638339, 6.534174888269392, 6.464395118884317, 6.938968015297907, 5.90053524839291, 2.8386785652593614, 2.7254462619066833, 2.2318443834781285, 3.035867322054565, 1.3075841996826167, 1.025045291117806, 0.5889651449153291, 0.0, 7.948611539780521, 6.478616594068619, 5.125226455589029, 3.9227525990478496, 6.07173464410913, 3.12458213686938, 2.7254462619066833, 2.0276275466138296, 2.950267624196455, 2.312989338432636, 1.2928790237768635, 0.5940158989335812, 0.0), # 89
(8.030434782608696, 6.502419354838709, 6.453125000000001, 6.923029891304349, 5.892156862745098, 2.8324074074074077, 2.7140056022408965, 2.2222222222222223, 3.030277777777778, 1.303137254901961, 1.021251993620415, 0.5874269005847954, 0.0, 7.9359375000000005, 6.461695906432748, 5.106259968102074, 3.9094117647058826, 6.060555555555556, 3.111111111111111, 2.7140056022408965, 2.0231481481481484, 2.946078431372549, 2.3076766304347833, 1.2906250000000001, 0.5911290322580646, 0.0), # 90
(8.00931367567245, 6.470506115314836, 6.441710848193873, 6.906852636876007, 5.883546087845287, 2.826008220291622, 2.7025096118008247, 2.2127098003353147, 3.024634461210182, 1.2986779160278654, 1.0174076594404082, 0.585872484136725, 0.0, 7.922923954046638, 6.444597325503974, 5.0870382972020405, 3.8960337480835956, 6.049268922420364, 3.097793720469441, 2.7025096118008247, 2.0185773002083014, 2.9417730439226437, 2.302284212292003, 1.2883421696387747, 0.5882278286649852, 0.0), # 91
(7.9877139142362985, 6.438450356210453, 6.43015540695016, 6.890448812399356, 5.874715834207482, 2.8194946603668143, 2.690962532612497, 2.203310775796373, 3.018942493522329, 1.2942062863444421, 1.013518053832674, 0.5843026656719533, 0.0, 7.909591478052126, 6.427329322391485, 5.067590269163369, 3.8826188590333257, 6.037884987044658, 3.0846350861149223, 2.690962532612497, 2.0139247574048675, 2.937357917103741, 2.296816270799786, 1.2860310813900322, 0.5853136687464049, 0.0), # 92
(7.965672141706924, 6.406267264038233, 6.418461419753087, 6.873830978260871, 5.865679012345678, 2.8128803840877916, 2.6793686067019404, 2.1940288065843623, 3.013206995884774, 1.2897224691358027, 1.0095889420521, 0.5827182152913147, 0.0, 7.895960648148147, 6.409900368204461, 5.0479447102605, 3.8691674074074074, 6.026413991769548, 3.0716403292181074, 2.6793686067019404, 2.0092002743484225, 2.932839506172839, 2.291276992753624, 1.2836922839506175, 0.5823879330943849, 0.0), # 93
(7.943225001491024, 6.373972025310855, 6.406631630086878, 6.857011694847022, 5.856448532773877, 2.806179047909364, 2.6677320760951844, 2.1848675506782507, 3.007433089468069, 1.2852265676860597, 1.005626089353575, 0.581119903095645, 0.0, 7.882052040466393, 6.392318934052094, 5.028130446767873, 3.855679703058178, 6.014866178936138, 3.058814570949551, 2.6677320760951844, 2.0044136056495456, 2.9282242663869384, 2.2856705649490077, 1.2813263260173757, 0.5794520023009869, 0.0), # 94
(7.920409136995288, 6.341579826540998, 6.394668781435757, 6.840003522544284, 5.847037306006079, 2.799404308286339, 2.6560571828182575, 2.1758306660570037, 3.001625895442768, 1.2807186852793244, 1.0016352609919863, 0.5795084991857787, 0.0, 7.867886231138546, 6.374593491043566, 5.008176304959932, 3.8421560558379726, 6.003251790885536, 3.046162932479805, 2.6560571828182575, 1.9995745059188135, 2.9235186530030397, 2.2800011741814283, 1.2789337562871517, 0.5765072569582727, 0.0), # 95
(7.89726119162641, 6.30910585424134, 6.382575617283951, 6.8228190217391305, 5.8374582425562815, 2.7925698216735255, 2.6443481688971886, 2.1669218106995887, 2.995790534979424, 1.27619892519971, 0.9976222222222224, 0.5778847736625516, 0.0, 7.853483796296297, 6.356732510288067, 4.988111111111112, 3.828596775599129, 5.991581069958848, 3.0336905349794243, 2.6443481688971886, 1.9946927297668038, 2.9187291212781408, 2.2742730072463773, 1.2765151234567904, 0.5735550776583037, 0.0), # 96
(7.873817808791078, 6.276565294924556, 6.370354881115684, 6.805470752818035, 5.827724252938488, 2.7856892445257326, 2.6326092763580053, 2.1581446425849724, 2.9899321292485905, 1.2716673907313272, 0.9935927382991712, 0.576249496626798, 0.0, 7.838865312071332, 6.338744462894778, 4.967963691495855, 3.8150021721939806, 5.979864258497181, 3.0214024996189615, 2.6326092763580053, 1.9897780318040947, 2.913862126469244, 2.2684902509393456, 1.2740709762231368, 0.5705968449931414, 0.0), # 97
(7.850115631895988, 6.243973335103323, 6.35800931641518, 6.787971276167473, 5.817848247666694, 2.7787762332977706, 2.6208447472267373, 2.1495028196921204, 2.9840557994208194, 1.2671241851582886, 0.9895525744777209, 0.5746034381793533, 0.0, 7.824051354595337, 6.320637819972885, 4.947762872388605, 3.801372555474865, 5.968111598841639, 3.0093039475689687, 2.6208447472267373, 1.9848401666412645, 2.908924123833347, 2.2626570920558247, 1.2716018632830361, 0.5676339395548476, 0.0), # 98
(7.826191304347827, 6.211345161290323, 6.3455416666666675, 6.770333152173913, 5.807843137254903, 2.7718444444444446, 2.6090588235294123, 2.1410000000000005, 2.9781666666666666, 1.2625694117647062, 0.9855074960127594, 0.5729473684210528, 0.0, 7.8090625000000005, 6.302421052631579, 4.927537480063797, 3.787708235294118, 5.956333333333333, 2.9974000000000007, 2.6090588235294123, 1.9798888888888888, 2.9039215686274513, 2.256777717391305, 1.2691083333333337, 0.564667741935484, 0.0), # 99
(7.80208146955329, 6.178695959998229, 6.332954675354367, 6.752568941223833, 5.797721832217111, 2.764907534420566, 2.597255747292058, 2.1326398414875785, 2.9722698521566837, 1.258003173834692, 0.9814632681591747, 0.5712820574527312, 0.0, 7.79391932441701, 6.284102631980042, 4.907316340795873, 3.774009521504075, 5.944539704313367, 2.98569577808261, 2.597255747292058, 1.9749339531575472, 2.8988609161085557, 2.250856313741278, 1.2665909350708735, 0.5616996327271119, 0.0), # 100
(7.777822770919068, 6.1460409177397235, 6.320251085962506, 6.734691203703704, 5.787497243067323, 2.757979159680943, 2.585439760540705, 2.124426002133821, 2.9663704770614236, 1.253425574652358, 0.9774256561718551, 0.5696082753752236, 0.0, 7.7786424039780515, 6.265691029127459, 4.887128280859275, 3.760276723957073, 5.932740954122847, 2.9741964029873493, 2.585439760540705, 1.9699851140578162, 2.8937486215336614, 2.244897067901235, 1.2640502171925014, 0.5587309925217931, 0.0), # 101
(7.753451851851853, 6.11339522102748, 6.307433641975309, 6.716712500000001, 5.7771822803195345, 2.7510729766803848, 2.5736151053013803, 2.1163621399176957, 2.9604736625514403, 1.248836717501816, 0.9734004253056887, 0.5679267922893655, 0.0, 7.763252314814816, 6.24719471518302, 4.867002126528443, 3.746510152505447, 5.920947325102881, 2.962906995884774, 2.5736151053013803, 1.965052126200275, 2.8885911401597673, 2.2389041666666674, 1.261486728395062, 0.5557632019115891, 0.0), # 102
(7.729005355758336, 6.080774056374176, 6.294505086877001, 6.698645390499196, 5.766789854487748, 2.7442026418736987, 2.561786023600112, 2.1084519128181682, 2.9545845297972866, 1.2442367056671781, 0.9693933408155633, 0.5662383782959916, 0.0, 7.747769633058984, 6.228622161255906, 4.846966704077817, 3.7327101170015338, 5.909169059594573, 2.951832677945436, 2.561786023600112, 1.960144744195499, 2.883394927243874, 2.2328817968330656, 1.2589010173754003, 0.5527976414885616, 0.0), # 103
(7.704519926045208, 6.048192610292491, 6.281468164151806, 6.680502435587762, 5.756332876085962, 2.7373818117156943, 2.5499567574629305, 2.1006989788142056, 2.948708199969517, 1.2396256424325565, 0.9654101679563669, 0.564543803495937, 0.0, 7.732214934842251, 6.209981838455306, 4.827050839781834, 3.7188769272976687, 5.897416399939034, 2.9409785703398876, 2.5499567574629305, 1.9552727226540672, 2.878166438042981, 2.2268341451959213, 1.2562936328303613, 0.549835691844772, 0.0), # 104
(7.680032206119162, 6.015666069295101, 6.268325617283951, 6.662296195652173, 5.745824255628177, 2.7306241426611804, 2.5381315489158633, 2.0931069958847743, 2.942849794238683, 1.235003631082063, 0.961456671982988, 0.562843837990037, 0.0, 7.716608796296296, 6.1912822178904054, 4.80728335991494, 3.705010893246188, 5.885699588477366, 2.930349794238684, 2.5381315489158633, 1.9504458161865572, 2.8729121278140886, 2.220765398550725, 1.2536651234567902, 0.546878733572282, 0.0), # 105
(7.655578839386891, 5.983209619894685, 6.255080189757659, 6.644039231078905, 5.735276903628392, 2.723943291164965, 2.526314639984938, 2.0856796220088403, 2.9370144337753388, 1.2303707748998092, 0.9575386181503142, 0.5611392518791264, 0.0, 7.700971793552812, 6.172531770670389, 4.787693090751571, 3.691112324699427, 5.8740288675506775, 2.9199514708123764, 2.526314639984938, 1.9456737794035461, 2.867638451814196, 2.214679743692969, 1.2510160379515318, 0.5439281472631533, 0.0), # 106
(7.631196469255085, 5.950838448603921, 6.241734625057157, 6.625744102254428, 5.724703730600607, 2.7173529136818577, 2.5145102726961848, 2.0784205151653716, 2.931207239750038, 1.225727177169908, 0.9536617717132337, 0.5594308152640404, 0.0, 7.685324502743484, 6.153738967904443, 4.768308858566169, 3.6771815315097234, 5.862414479500076, 2.9097887212315205, 2.5145102726961848, 1.9409663669156128, 2.8623518653003037, 2.208581367418143, 1.2483469250114314, 0.5409853135094475, 0.0), # 107
(7.606921739130435, 5.918567741935485, 6.228291666666668, 6.607423369565218, 5.714117647058822, 2.7108666666666674, 2.5027226890756302, 2.0713333333333335, 2.9254333333333333, 1.221072941176471, 0.9498318979266349, 0.5577192982456142, 0.0, 7.669687500000001, 6.134912280701755, 4.749159489633174, 3.6632188235294123, 5.850866666666667, 2.899866666666667, 2.5027226890756302, 1.9363333333333337, 2.857058823529411, 2.20247445652174, 1.2456583333333338, 0.538051612903226, 0.0), # 108
(7.582791292419635, 5.886412686402053, 6.214754058070417, 6.589089593397745, 5.70353156351704, 2.7044982065742014, 2.490956131149305, 2.064421734491694, 2.9196978356957777, 1.2164081702036098, 0.9460547620454054, 0.5560054709246826, 0.0, 7.654081361454047, 6.116060180171507, 4.730273810227027, 3.6492245106108285, 5.839395671391555, 2.8901904282883715, 2.490956131149305, 1.9317844332672867, 2.85176578175852, 2.196363197799249, 1.2429508116140835, 0.5351284260365504, 0.0), # 109
(7.558841772529373, 5.854388468516307, 6.201124542752631, 6.570755334138486, 5.692958390489256, 2.6982611898592697, 2.4792148409432357, 2.0576893766194178, 2.9140058680079255, 1.211732967535437, 0.9423361293244336, 0.554290103402081, 0.0, 7.638526663237312, 6.0971911374228895, 4.711680646622168, 3.63519890260631, 5.828011736015851, 2.880765127267185, 2.4792148409432357, 1.9273294213280499, 2.846479195244628, 2.1902517780461626, 1.2402249085505264, 0.5322171335014826, 0.0), # 110
(7.535109822866345, 5.82251027479092, 6.187405864197532, 6.552433152173913, 5.68241103848947, 2.6921692729766806, 2.4675030604834527, 2.0511399176954734, 2.9083625514403293, 1.2070474364560642, 0.9386817650186072, 0.5525739657786443, 0.0, 7.623043981481482, 6.078313623565086, 4.693408825093036, 3.621142309368192, 5.816725102880659, 2.871595884773663, 2.4675030604834527, 1.9229780521262005, 2.841205519244735, 2.1841443840579715, 1.2374811728395065, 0.5293191158900837, 0.0), # 111
(7.51163208683724, 5.790793291738572, 6.173600765889348, 6.5341356078905, 5.671902418031685, 2.686236112381243, 2.4558250317959835, 2.0447770156988265, 2.9027730071635416, 1.2023516802496035, 0.9350974343828147, 0.5508578281552075, 0.0, 7.607653892318244, 6.059436109707281, 4.675487171914074, 3.6070550407488096, 5.805546014327083, 2.862687821978357, 2.4558250317959835, 1.9187400802723165, 2.8359512090158425, 2.178045202630167, 1.2347201531778695, 0.5264357537944157, 0.0), # 112
(7.488403378962436, 5.759305653776365, 6.159745218834713, 6.515900329495224, 5.661427029425976, 2.6804725589667733, 2.444210385462708, 2.038617522926869, 2.8972567496689656, 1.1976609473225461, 0.9315898541537156, 0.549146195766962, 0.0, 7.592355120674577, 6.0406081534365805, 4.657949270768578, 3.592982841967638, 5.794513499337931, 2.8540645320976163, 2.444210385462708, 1.914623256404838, 2.830713514712988, 2.1719667764984085, 1.2319490437669427, 0.5235732412523969, 0.0), # 113
(7.465184718320052, 5.728357934585393, 6.146030450014413, 6.497873652766401, 5.6508764557687075, 2.674865483980621, 2.432807283364232, 2.0327370865017067, 2.891898409523483, 1.1930630335825567, 0.9281659116150931, 0.5474608114741984, 0.0, 7.577020331328028, 6.022068926216181, 4.640829558075465, 3.5791891007476693, 5.783796819046966, 2.8458319211023895, 2.432807283364232, 1.9106182028433005, 2.8254382278843537, 2.1659578842554676, 1.2292060900028827, 0.5207598122350358, 0.0), # 114
(7.441907922403196, 5.697961279034234, 6.132464621804878, 6.480050703109068, 5.640217428207254, 2.669400305832757, 2.421623860076625, 2.027134218092903, 2.886699994311677, 1.1885650655976157, 0.9248206015236127, 0.5458025055039235, 0.0, 7.561605305328301, 6.003827560543158, 4.6241030076180625, 3.5656951967928463, 5.773399988623354, 2.8379879053300643, 2.421623860076625, 1.9067145041662548, 2.820108714103627, 2.1600169010363564, 1.226492924360976, 0.5179964799122032, 0.0), # 115
(7.418543898590108, 5.668071406280581, 6.119021459989249, 6.462399690159842, 5.629433880738015, 2.664064142733979, 2.4106419270111576, 2.021793437632998, 2.8816483571274216, 1.1841586716899097, 0.9215474575028644, 0.5441682131658231, 0.0, 7.546085807804713, 5.985850344824053, 4.607737287514321, 3.5524760150697285, 5.763296714254843, 2.8305108126861973, 2.4106419270111576, 1.9029029590956992, 2.8147169403690073, 2.154133230053281, 1.22380429199785, 0.5152792187527803, 0.0), # 116
(7.395063554259018, 5.638644035482129, 6.105674690350658, 6.444888823555345, 5.6185097473573915, 2.6588441128950824, 2.399843295579101, 2.0166992650545286, 2.8767303510645874, 1.179835480181626, 0.9183400131764379, 0.5425548697695834, 0.0, 7.53043760388658, 5.968103567465417, 4.591700065882189, 3.5395064405448773, 5.753460702129175, 2.8233789710763397, 2.399843295579101, 1.8991743663536302, 2.8092548736786958, 2.148296274518449, 1.2211349380701317, 0.5126040032256481, 0.0), # 117
(7.371437796788169, 5.60963488579657, 6.092398038672245, 6.427486312932199, 5.607428962061783, 2.6537273345268653, 2.3892097771917262, 2.0118362202900326, 2.871932829217049, 1.175587119394952, 0.9151918021679234, 0.5409594106248901, 0.0, 7.51463645870322, 5.950553516873789, 4.575959010839616, 3.5267613581848556, 5.743865658434098, 2.8165707084060454, 2.3892097771917262, 1.8955195246620464, 2.8037144810308914, 2.142495437644067, 1.218479607734449, 0.5099668077996883, 0.0), # 118
(7.347637533555794, 5.580999676381602, 6.079165230737149, 6.410160367927023, 5.5961754588475845, 2.648700925840122, 2.3787231832603024, 2.0071888232720485, 2.867242644678678, 1.1714052176520746, 0.9120963581009105, 0.5393787710414291, 0.0, 7.498658137383946, 5.933166481455719, 4.560481790504553, 3.5142156529562234, 5.734485289357356, 2.810064352580868, 2.3787231832603024, 1.8919292327429442, 2.7980877294237922, 2.1367201226423416, 1.21583304614743, 0.507363606943782, 0.0), # 119
(7.323633671940129, 5.552694126394916, 6.065949992328509, 6.392879198176436, 5.584733171711198, 2.6437520050456507, 2.3683653251961014, 2.0027415939331146, 2.8626466505433488, 1.1672814032751813, 0.909047214598989, 0.5378098863288866, 0.0, 7.482478405058078, 5.915908749617751, 4.545236072994944, 3.501844209825543, 5.7252933010866975, 2.80383823150636, 2.3683653251961014, 1.8883942893183219, 2.792366585855599, 2.1309597327254792, 1.2131899984657017, 0.5047903751268107, 0.0), # 120
(7.299397119319415, 5.524673954994208, 6.052726049229459, 6.3756110133170605, 5.573086034649023, 2.638867690354248, 2.358118014410392, 1.9984790522057692, 2.858131699904933, 1.1632073045864595, 0.906037905285749, 0.5362496917969483, 0.0, 7.466073026854929, 5.898746609766429, 4.530189526428744, 3.489621913759378, 5.716263399809866, 2.797870673088077, 2.358118014410392, 1.884905493110177, 2.7865430173245116, 2.1252036711056874, 1.2105452098458918, 0.5022430868176554, 0.0), # 121
(7.274898783071883, 5.496894881337171, 6.039467127223141, 6.358324022985514, 5.561217981657458, 2.634035099976709, 2.347963062314447, 1.9943857180225497, 2.8536846458573035, 1.1591745499080957, 0.9030619637847803, 0.5346951227553002, 0.0, 7.4494177679038165, 5.8816463503083005, 4.515309818923901, 3.4775236497242865, 5.707369291714607, 2.7921400052315697, 2.347963062314447, 1.8814536428405064, 2.780608990828729, 2.119441340995172, 1.2078934254446283, 0.49971771648519747, 0.0), # 122
(7.250109570575775, 5.469312624581501, 6.026146952092692, 6.340986436818417, 5.549112946732902, 2.629241352123832, 2.3378822803195356, 1.9904461113159944, 2.8492923414943343, 1.1551747675622777, 0.9001129237196728, 0.5331431145136282, 0.0, 7.432488393334058, 5.864574259649909, 4.500564618598363, 3.4655243026868323, 5.698584682988669, 2.7866245558423923, 2.3378822803195356, 1.8780295372313083, 2.774556473366451, 2.1136621456061393, 1.2052293904185383, 0.49721023859831837, 0.0), # 123
(7.225000389209324, 5.441882903884891, 6.012739249621247, 6.323566464452393, 5.536754863871753, 2.624473565006412, 2.327857479836928, 1.9866447520186423, 2.844941639909897, 1.1511995858711925, 0.897184318714016, 0.5315906023816185, 0.0, 7.4152606682749695, 5.847496626197802, 4.4859215935700805, 3.4535987576135767, 5.689883279819794, 2.781302652826099, 2.327857479836928, 1.87462397500458, 2.7683774319358765, 2.107855488150798, 1.2025478499242495, 0.49471662762589924, 0.0), # 124
(7.199542146350767, 5.414561438405035, 5.99921774559195, 6.306032315524057, 5.524127667070411, 2.619718856835246, 2.3178704722778956, 1.9829661600630304, 2.840619394197865, 1.147240633157027, 0.8942696823914004, 0.5300345216689567, 0.0, 7.397710357855863, 5.8303797383585225, 4.471348411957002, 3.4417218994710805, 5.68123878839573, 2.7761526240882426, 2.3178704722778956, 1.8712277548823186, 2.7620638335352057, 2.1020107718413525, 1.19984354911839, 0.49223285803682143, 0.0), # 125
(7.1737057493783425, 5.387303947299629, 5.985556165787933, 6.288352199670033, 5.511215290325276, 2.614964345821132, 2.307903069053708, 1.9793948553816976, 2.8363124574521112, 1.1432895377419687, 0.8913625483754153, 0.5284718076853291, 0.0, 7.379813227206063, 5.813189884538619, 4.4568127418770755, 3.4298686132259055, 5.6726249149042225, 2.7711527975343766, 2.307903069053708, 1.8678316755865225, 2.755607645162638, 2.0961173998900113, 1.1971112331575866, 0.4897549042999664, 0.0), # 126
(7.147462105670289, 5.360066149726364, 5.9717282359923365, 6.27049432652694, 5.498001667632746, 2.610197150174864, 2.2979370815756375, 1.975915357907182, 2.832007682766508, 1.139337927948205, 0.8884564502896507, 0.5268993957404212, 0.0, 7.361545041454879, 5.795893353144632, 4.442282251448253, 3.4180137838446143, 5.664015365533016, 2.766281501070055, 2.2979370815756375, 1.8644265358391885, 2.749000833816373, 2.0901647755089803, 1.1943456471984675, 0.487278740884215, 0.0), # 127
(7.120782122604837, 5.332803764842939, 5.957707681988301, 6.252426905731399, 5.484470732989221, 2.6054043881072406, 2.287954321254953, 1.9725121875720208, 2.827691923234929, 1.1353774320979229, 0.8855449217576967, 0.5253142211439193, 0.0, 7.34288156573163, 5.778456432583111, 4.427724608788483, 3.4061322962937677, 5.655383846469858, 2.7615170626008294, 2.287954321254953, 1.8610031343623146, 2.7422353664946106, 2.084142301910467, 1.1915415363976603, 0.4848003422584491, 0.0), # 128
(7.093636707560226, 5.305472511807044, 5.9434682295589605, 6.2341181469200295, 5.4706064203911, 2.600573177829058, 2.2779365995029255, 1.9691698643087534, 2.823352031951247, 1.1313996785133094, 0.882621496403143, 0.5237132192055092, 0.0, 7.323798565165631, 5.7608454112606, 4.413107482015715, 3.3941990355399274, 5.646704063902494, 2.756837810032255, 2.2779365995029255, 1.8575522698778983, 2.73530321019555, 2.078039382306677, 1.188693645911792, 0.48231568289154947, 0.0), # 129
(7.065996767914694, 5.2780281097763755, 5.9289836044874535, 6.215536259729452, 5.45639266383478, 2.595690637551111, 2.267865727730825, 1.9658729080499169, 2.818974862009333, 1.1273962955165517, 0.8796797078495794, 0.522093325234877, 0.0, 7.3042718048861985, 5.743026577583645, 4.398398539247896, 3.3821888865496543, 5.637949724018666, 2.7522220712698835, 2.267865727730825, 1.8540647411079363, 2.72819633191739, 2.0718454199098177, 1.1857967208974907, 0.4798207372523978, 0.0), # 130
(7.037833211046475, 5.250426277908626, 5.914227532556921, 6.196649453796286, 5.441813397316663, 2.590743885484198, 2.2577235173499237, 1.9626058387280498, 2.814547266503063, 1.1233589114298372, 0.8767130897205959, 0.5204514745417084, 0.0, 7.2842770500226495, 5.724966219958791, 4.383565448602979, 3.370076734289511, 5.629094533006126, 2.74764817421927, 2.2577235173499237, 1.850531346774427, 2.7209066986583315, 2.0655498179320957, 1.1828455065113843, 0.4773114798098752, 0.0), # 131
(7.009116944333808, 5.222622735361492, 5.8991737395504975, 6.1774259387571515, 5.4268525548331485, 2.5857200398391145, 2.24749177977149, 1.959353176275691, 2.8100560985263074, 1.119279154575353, 0.8737151756397821, 0.5187846024356896, 0.0, 7.263790065704301, 5.706630626792584, 4.36857587819891, 3.3578374637260584, 5.620112197052615, 2.7430944467859675, 2.24749177977149, 1.8469428855993675, 2.7134262774165743, 2.0591419795857178, 1.1798347479100997, 0.474783885032863, 0.0), # 132
(6.979818875154931, 5.194573201292665, 5.883795951251323, 6.1578339242486715, 5.411494070380632, 2.5806062188266576, 2.237152326406796, 1.9560994406253773, 2.80548821117294, 1.1151486532752868, 0.8706794992307283, 0.5170896442265063, 0.0, 7.242786617060469, 5.687986086491568, 4.353397496153641, 3.3454459598258595, 5.61097642234588, 2.7385392168755285, 2.237152326406796, 1.8432901563047555, 2.705747035190316, 2.052611308082891, 1.1767591902502648, 0.4722339273902424, 0.0), # 133
(6.949909910888076, 5.166233394859844, 5.868067893442536, 6.137841619907462, 5.395721877955516, 2.575389540657624, 2.2266869686671114, 1.9528291517096479, 2.8008304575368346, 1.1109590358518249, 0.8675995941170239, 0.5153635352238445, 0.0, 7.221242469220467, 5.668998887462289, 4.3379979705851195, 3.3328771075554737, 5.601660915073669, 2.7339608123935073, 2.2266869686671114, 1.8395639576125886, 2.697860938977758, 2.0459472066358213, 1.1736135786885074, 0.46965758135089497, 0.0), # 134
(6.919360958911483, 5.137559035220717, 5.851963291907273, 6.117417235370148, 5.379519911554198, 2.57005712354281, 2.2160775179637073, 1.9495268294610402, 2.796069690711861, 1.1067019306271555, 0.8644689939222592, 0.5136032107373902, 0.0, 7.199133387313616, 5.649635318111292, 4.322344969611295, 3.320105791881466, 5.592139381423722, 2.7293375612454565, 2.2160775179637073, 1.835755088244864, 2.689759955777099, 2.0391390784567163, 1.1703926583814546, 0.4670508213837017, 0.0), # 135
(6.888142926603388, 5.108505841532984, 5.835455872428673, 6.096528980273343, 5.362872105173076, 2.564596085693012, 2.205305785707854, 1.9461769938120925, 2.7911927637918947, 1.1023689659234648, 0.8612812322700237, 0.5118056060768296, 0.0, 7.176435136469229, 5.629861666845124, 4.306406161350118, 3.3071068977703937, 5.5823855275837895, 2.72464779133693, 2.205305785707854, 1.8318543469235802, 2.681436052586538, 2.0321763267577815, 1.1670911744857346, 0.46440962195754404, 0.0), # 136
(6.856226721342027, 5.079029532954335, 5.818519360789875, 6.075145064253675, 5.345762392808551, 2.558993545319026, 2.1943535833108223, 1.942764164695343, 2.7861865298708084, 1.0979517700629406, 0.8580298427839075, 0.5099676565518481, 0.0, 7.153123481816621, 5.609644222070328, 4.290149213919538, 3.293855310188821, 5.572373059741617, 2.7198698305734803, 2.1943535833108223, 1.8278525323707329, 2.6728811964042754, 2.0250483547512257, 1.1637038721579749, 0.46172995754130325, 0.0), # 137
(6.823583250505639, 5.0490858286424665, 5.801127482774012, 6.053233696947759, 5.3281747084570235, 2.5532366206316497, 2.1832027221838817, 1.9392728620433302, 2.781037842042475, 1.0934419713677697, 0.8547083590875004, 0.508086297472132, 0.0, 7.129174188485113, 5.58894927219345, 4.273541795437502, 3.280325914103308, 5.56207568408495, 2.7149820068606623, 2.1832027221838817, 1.8237404433083213, 2.6640873542285117, 2.017744565649253, 1.1602254965548024, 0.45900780260386065, 0.0), # 138
(6.790183421472455, 5.018630447755072, 5.783253964164227, 6.030763087992216, 5.3100929861148884, 2.547312429841679, 2.171835013738304, 1.9356876057885917, 2.775733553400766, 1.0888311981601397, 0.8513103148043922, 0.5061584641473672, 0.0, 7.104563021604015, 5.567743105621037, 4.256551574021961, 3.2664935944804183, 5.551467106801532, 2.709962648104028, 2.171835013738304, 1.8195088784583422, 2.6550464930574442, 2.0102543626640723, 1.1566507928328456, 0.4562391316140975, 0.0), # 139
(6.755998141620719, 4.987619109449845, 5.764872530743658, 6.007701447023667, 5.291501159778549, 2.5412080911599104, 2.1602322693853586, 1.9319929158636655, 2.770260517039555, 1.0841110787622374, 0.8478292435581727, 0.5041810918872395, 0.0, 7.079265746302652, 5.545992010759633, 4.2391462177908625, 3.2523332362867117, 5.54052103407911, 2.704790082209132, 2.1602322693853586, 1.8151486365427931, 2.6457505798892744, 2.0025671490078896, 1.1529745061487318, 0.45341991904089507, 0.0), # 140
(6.720998318328665, 4.956007532884482, 5.745956908295441, 5.984016983678732, 5.272383163444402, 2.5349107227971404, 2.148376300536318, 1.9281733122010902, 2.7646055860527143, 1.0792732414962505, 0.844258678972432, 0.502151116001435, 0.0, 7.053258127710331, 5.523662276015784, 4.221293394862159, 3.2378197244887508, 5.529211172105429, 2.6994426370815265, 2.148376300536318, 1.8106505162836717, 2.636191581722201, 1.994672327892911, 1.1491913816590882, 0.4505461393531348, 0.0), # 141
(6.685154858974525, 4.923751437216675, 5.726480822602714, 5.959677907594033, 5.252722931108846, 2.5284074429641663, 2.1362489186024507, 1.924213314733404, 2.7587556135341176, 1.0743093146843659, 0.8405921546707598, 0.5000654717996397, 0.0, 7.026515930956373, 5.500720189796036, 4.202960773353798, 3.222927944053097, 5.517511227068235, 2.6938986406267658, 2.1362489186024507, 1.806005316402976, 2.626361465554423, 1.9865593025313446, 1.1452961645205428, 0.4476137670196978, 0.0), # 142
(6.64843867093654, 4.890806541604119, 5.706417999448617, 5.934652428406185, 5.232504396768282, 2.521685369871783, 2.1238319349950276, 1.920097443393144, 2.7526974525776393, 1.0692109266487708, 0.8368232042767458, 0.4979210945915394, 0.0, 6.999014921170094, 5.477132040506932, 4.184116021383729, 3.207632779946312, 5.505394905155279, 2.6881364207504017, 2.1238319349950276, 1.8012038356227023, 2.616252198384141, 1.9782174761353954, 1.1412835998897235, 0.44461877650946546, 0.0), # 143
(6.610820661592948, 4.857128565204509, 5.685742164616285, 5.908908755751814, 5.2117114944191085, 2.5147316217307885, 2.1111071611253194, 1.9158102181128498, 2.746417956277149, 1.0639697057116522, 0.8329453614139802, 0.49571491968682, 0.0, 6.970730863480812, 5.452864116555019, 4.164726807069901, 3.191909117134956, 5.492835912554298, 2.6821343053579896, 2.1111071611253194, 1.796236872664849, 2.6058557472095543, 1.9696362519172719, 1.1371484329232573, 0.44155714229131915, 0.0), # 144
(6.572271738321982, 4.82267322717554, 5.6644270438888595, 5.882415099267537, 5.190328158057724, 2.507533316751979, 2.0980564084045974, 1.9113361588250588, 2.739903977726521, 1.0585772801951978, 0.8289521597060527, 0.4934438823951677, 0.0, 6.94163952301784, 5.4278827063468436, 4.144760798530264, 3.175731840585593, 5.479807955453042, 2.6758706223550823, 2.0980564084045974, 1.7910952262514135, 2.595164079028862, 1.9608050330891795, 1.132885408777772, 0.4384248388341401, 0.0), # 145
(6.5327628085018805, 4.787396246674904, 5.642446363049478, 5.855139668589976, 5.16833832168053, 2.5000775731461515, 2.084661488244132, 1.906659785462309, 2.7331423700196282, 1.0530252784215943, 0.8248371327765532, 0.4911049180262681, 0.0, 6.911716664910495, 5.402154098288948, 4.124185663882766, 3.1590758352647823, 5.4662847400392565, 2.669323699647233, 2.084661488244132, 1.7857696951043938, 2.584169160840265, 1.9517132228633256, 1.1284892726098958, 0.4352178406068095, 0.0), # 146
(6.49226477951088, 4.751253342860296, 5.619773847881273, 5.827050673355748, 5.145725919283921, 2.4923515091241004, 2.0709042120551926, 1.9017656179571385, 2.7261199862503442, 1.0473053287130294, 0.8205938142490716, 0.48869496188980743, 0.0, 6.8809380542880945, 5.375644580787881, 4.102969071245358, 3.1419159861390877, 5.4522399725006885, 2.662471865139994, 2.0709042120551926, 1.7802510779457859, 2.5728629596419603, 1.9423502244519164, 1.1239547695762548, 0.43193212207820875, 0.0), # 147
(6.450748558727217, 4.714200234889411, 5.596383224167389, 5.798116323201478, 5.1224748848643, 2.4843422428966253, 2.0567663912490506, 1.8966381762420859, 2.718823679512541, 1.0414090593916896, 0.8162157377471978, 0.48621094929547143, 0.0, 6.8492794562799535, 5.348320442250185, 4.081078688735989, 3.124227178175068, 5.437647359025082, 2.6552934467389204, 2.0567663912490506, 1.7745301734975893, 2.56123744243215, 1.9327054410671598, 1.1192766448334779, 0.42856365771721927, 0.0), # 148
(6.40818505352913, 4.676192641919942, 5.572248217690963, 5.768304827763782, 5.098569152418064, 2.4760368926745198, 2.0422298372369765, 1.8912619802496888, 2.71124030290009, 1.0353280987797628, 0.8116964368945213, 0.48364981555294617, 0.0, 6.81671663601539, 5.320147971082407, 4.058482184472607, 3.1059842963392876, 5.42248060580018, 2.6477667723495646, 2.0422298372369765, 1.7685977804817998, 2.549284576209032, 1.922768275921261, 1.1144496435381928, 0.42510842199272214, 0.0), # 149
(6.364545171294852, 4.6371862831095845, 5.54734255423513, 5.737584396679283, 5.0739926559416135, 2.467422576668583, 2.0272763614302405, 1.8856215499124855, 2.7033567095068674, 1.0290540751994355, 0.8070294453146325, 0.48100849597191764, 0.0, 6.783225358623717, 5.291093455691093, 4.035147226573162, 3.0871622255983056, 5.406713419013735, 2.63987016987748, 2.0272763614302405, 1.7624446976204164, 2.5369963279708068, 1.912528132226428, 1.1094685108470261, 0.4215623893735987, 0.0), # 150
(6.31979981940262, 4.597136877616033, 5.521639959583029, 5.705923239584598, 5.048729329431348, 2.4584864130896094, 2.011887775240113, 1.8797014051630145, 2.695159752426744, 1.0225786169728959, 0.8022082966311207, 0.4782839258620715, 0.0, 6.748781389234255, 5.261123184482786, 4.011041483155603, 3.067735850918687, 5.390319504853488, 2.6315819672282204, 2.011887775240113, 1.7560617236354352, 2.524364664715674, 1.9019744131948664, 1.1043279919166058, 0.41792153432873036, 0.0), # 151
(6.273919905230675, 4.55600014459698, 5.495114159517802, 5.673289566116352, 5.022763106883663, 2.4492155201483965, 1.996045890077866, 1.8734860659338137, 2.686636284753592, 1.0158933524223301, 0.7972265244675764, 0.475473040533094, 0.0, 6.713360492976318, 5.230203445864033, 3.9861326223378812, 3.04768005726699, 5.373272569507184, 2.622880492307339, 1.996045890077866, 1.7494396572488546, 2.5113815534418316, 1.8910965220387843, 1.0990228319035604, 0.4141818313269982, 0.0), # 152
(6.226876336157249, 4.5137318032101215, 5.467738879822579, 5.63965158591116, 4.996077922294963, 2.4395970160557408, 1.9797325173547677, 1.8669600521574208, 2.677773159581286, 1.008989909869926, 0.7920776624475889, 0.472572775294671, 0.0, 6.676938434979222, 5.19830052824138, 3.9603883122379444, 3.0269697296097773, 5.355546319162572, 2.6137440730203894, 1.9797325173547677, 1.742569297182672, 2.4980389611474814, 1.879883861970387, 1.093547775964516, 0.41033925483728384, 0.0), # 153
(6.178640019560583, 4.4702875726131515, 5.439487846280506, 5.604977508605646, 4.968657709661643, 2.429618019022439, 1.9629294684820913, 1.8601078837663743, 2.6685572300036977, 1.0018599176378709, 0.7867552441947484, 0.4695800654564884, 0.0, 6.639490980372286, 5.165380720021371, 3.9337762209737415, 3.005579752913612, 5.337114460007395, 2.604151037272924, 1.9629294684820913, 1.7354414421588849, 2.4843288548308213, 1.8683258362018824, 1.0878975692561013, 0.40638977932846837, 0.0), # 154
(6.129181862818909, 4.425623171963762, 5.410334784674718, 5.569235543836427, 4.940486402980104, 2.419265647259287, 1.9456185548711045, 1.852914080693212, 2.6589753491147006, 0.9944950040483511, 0.7812528033326445, 0.4664918463282322, 0.0, 6.600993894284821, 5.131410309610554, 3.906264016663222, 2.983485012145053, 5.317950698229401, 2.594079712970497, 1.9456185548711045, 1.7280468908994906, 2.470243201490052, 1.856411847945476, 1.0820669569349437, 0.402329379269433, 0.0), # 155
(6.078472773310465, 4.3796943204196515, 5.3802534207883514, 5.532393901240125, 4.911547936246746, 2.408527018977082, 1.92778158793308, 1.845363162870473, 2.649014370008167, 0.9868867974235548, 0.7755638734848673, 0.46330505321958826, 0.0, 6.561422941846148, 5.09635558541547, 3.8778193674243364, 2.960660392270664, 5.298028740016334, 2.5835084280186624, 1.92778158793308, 1.720376442126487, 2.455773968123373, 1.8441313004133755, 1.0760506841576702, 0.39815402912905923, 0.0), # 156
(6.02648365841349, 4.332456737138511, 5.349217480404546, 5.494420790453363, 4.881826243457965, 2.39738925238662, 1.9094003790792877, 1.8374396502306942, 2.63866114577797, 0.9790269260856685, 0.7696819882750067, 0.4600166214402426, 0.0, 6.520753888185581, 5.060182835842667, 3.848409941375033, 2.937080778257005, 5.27732229155594, 2.5724155103229718, 1.9094003790792877, 1.7124208945618713, 2.4409131217289826, 1.831473596817788, 1.0698434960809091, 0.3938597033762283, 0.0), # 157
(5.971744757124192, 4.28299895523299, 5.315727969268237, 5.453861748990747, 4.849963256464532, 2.3851447556146512, 1.890042688371143, 1.8285989841164574, 2.6271098910930926, 0.9706731832582289, 0.7634127670051923, 0.45650663761295607, 0.0, 6.477188687532276, 5.021573013742516, 3.817063835025962, 2.912019549774686, 5.254219782186185, 2.5600385777630406, 1.890042688371143, 1.7036748254390366, 2.424981628232266, 1.8179539163302492, 1.0631455938536476, 0.38936354138481727, 0.0), # 158
(5.9058294135827225, 4.226247901039617, 5.271158545601992, 5.402386295273073, 4.808102031883535, 2.3677218357366487, 1.8672851053542865, 1.8157378442547942, 2.609713936325905, 0.9604561988197493, 0.7556555914158659, 0.4520908349122073, 0.0, 6.420342117536156, 4.97299918403428, 3.7782779570793297, 2.8813685964592475, 5.21942787265181, 2.542032981956712, 1.8672851053542865, 1.6912298826690346, 2.4040510159417674, 1.8007954317576913, 1.0542317091203985, 0.3842043546399652, 0.0), # 159
(5.827897675923448, 4.161737600929857, 5.214613971970593, 5.339146506245316, 4.755424070051625, 2.344692604822253, 1.8408974993535137, 1.7985330631757823, 2.5859800605943066, 0.948241130372579, 0.7463012678146054, 0.4467001299258565, 0.0, 6.349136487114865, 4.913701429184421, 3.731506339073027, 2.844723391117736, 5.171960121188613, 2.5179462884460952, 1.8408974993535137, 1.6747804320158948, 2.3777120350258123, 1.7797155020817725, 1.0429227943941186, 0.3783397819027143, 0.0), # 160
(5.738577643668768, 4.0898886365923435, 5.146697981273539, 5.264743502254037, 4.69247633295046, 2.3163360460661466, 1.8110725784027506, 1.7772001777032602, 2.556221271199738, 0.9341316386341878, 0.7354322206132944, 0.44038449792717144, 0.0, 6.264299235855278, 4.844229477198885, 3.6771611030664717, 2.8023949159025627, 5.112442542399476, 2.4880802487845646, 1.8110725784027506, 1.6545257471901047, 2.34623816647523, 1.754914500751346, 1.029339596254708, 0.37180805787203125, 0.0), # 161
(5.638497416341085, 4.011121589715708, 5.068014306410331, 5.179778403645797, 4.619805782561709, 2.282931142663013, 1.7780030505359237, 1.7519547246610676, 2.5207505754436363, 0.9182313843220465, 0.7231308742238162, 0.43319391418941966, 0.0, 6.166557803344267, 4.765133056083616, 3.615654371119081, 2.754694152966139, 5.041501150887273, 2.4527366145254947, 1.7780030505359237, 1.630665101902152, 2.3099028912808546, 1.7265928012152658, 1.0136028612820662, 0.36464741724688265, 0.0), # 162
(5.528285093462799, 3.9258570419885843, 4.979166680280469, 5.084852330767161, 4.537959380867034, 2.244756877807534, 1.7418816237869603, 1.7230122408730417, 2.4798809806274416, 0.9006440281536252, 0.7094796530580545, 0.42517835398586895, 0.0, 6.0566396291687035, 4.676961893844558, 3.5473982652902722, 2.701932084460875, 4.959761961254883, 2.4122171372222585, 1.7418816237869603, 1.6033977698625244, 2.268979690433517, 1.6949507769223873, 0.9958333360560938, 0.356896094726235, 0.0), # 163
(5.408568774556308, 3.834515575099602, 4.8807588357834515, 4.980566403964691, 4.447484089848101, 2.2020922346943936, 1.7029010061897865, 1.6905882631630231, 2.433925494052593, 0.881473230846394, 0.6945609815278929, 0.4163877925897869, 0.0, 5.935272152915463, 4.580265718487656, 3.472804907639464, 2.644419692539181, 4.867850988105186, 2.3668235684282326, 1.7029010061897865, 1.5729230247817099, 2.2237420449240504, 1.660188801321564, 0.9761517671566904, 0.34859232500905474, 0.0), # 164
(5.279976559144014, 3.7375177707373965, 4.773394505818779, 4.867521743584952, 4.348926871486572, 2.155216196518274, 1.6612539057783289, 1.6548983283548488, 2.383197123020528, 0.8608226531178229, 0.678457284045215, 0.4068722052744414, 0.0, 5.803182814171416, 4.475594258018854, 3.3922864202260747, 2.582467959353468, 4.766394246041056, 2.3168576596967885, 1.6612539057783289, 1.5394401403701956, 2.174463435743286, 1.622507247861651, 0.954678901163756, 0.33977434279430885, 0.0), # 165
(5.143136546748318, 3.6352842105905996, 4.657677423285953, 4.746319469974501, 4.242834687764114, 2.1044077464738575, 1.6171330305865146, 1.6161579732723592, 2.328008874832686, 0.8387959556853827, 0.661250985021904, 0.39668156731310017, 0.0, 5.661099052523436, 4.363497240444101, 3.3062549251095197, 2.5163878670561473, 4.656017749665372, 2.262621162581303, 1.6171330305865146, 1.5031483903384697, 2.121417343882057, 1.5821064899915007, 0.9315354846571906, 0.33048038278096364, 0.0), # 166
(4.998676836891619, 3.528235476347844, 4.53421132108447, 4.617560703479906, 4.129754500662389, 2.0499458677558273, 1.57073108864827, 1.5745827347393924, 2.2686737567905064, 0.8154967992665431, 0.6430245088698437, 0.3858658539790306, 0.0, 5.509748307558397, 4.244524393769336, 3.215122544349218, 2.4464903977996286, 4.537347513581013, 2.2044158286351494, 1.57073108864827, 1.4642470483970196, 2.0648772503311945, 1.5391869011599693, 0.9068422642168941, 0.32074867966798587, 0.0), # 167
(4.847225529096317, 3.416792149697761, 4.403599932113832, 4.481846564447728, 4.010233272163062, 1.9921095435588663, 1.5222407879975217, 1.5303881495797866, 2.205504776195428, 0.7910288445787746, 0.6238602800009175, 0.3744750405455008, 0.0, 5.34985801886317, 4.119225446000509, 3.1193014000045878, 2.3730865337363234, 4.411009552390856, 2.1425434094117013, 1.5222407879975217, 1.4229353882563331, 2.005116636081531, 1.4939488548159094, 0.8807199864227666, 0.31061746815434194, 0.0), # 168
(4.689410722884812, 3.3013748123289846, 4.26644698927354, 4.33977817322453, 3.884817964247797, 1.9311777570776578, 1.4718548366681967, 1.4837897546173817, 2.1388149403488903, 0.7654957523395476, 0.6038407228270092, 0.3625591022857782, 0.0, 5.182155626024628, 3.9881501251435596, 3.019203614135046, 2.296487257018642, 4.277629880697781, 2.0773056564643344, 1.4718548366681967, 1.3794126836268983, 1.9424089821238986, 1.4465927244081769, 0.853289397854708, 0.30012498293899864, 0.0), # 169
(4.525860517779507, 3.1824040459301473, 4.12335622546309, 4.191956650156872, 3.7540555388982577, 1.8674294915068832, 1.4197659426942213, 1.435003086676016, 2.0689172565523304, 0.7390011832663317, 0.5830482617600022, 0.3501680144731306, 0.0, 5.007368568629644, 3.8518481592044362, 2.9152413088000113, 2.217003549798995, 4.137834513104661, 2.0090043213464224, 1.4197659426942213, 1.3338782082192022, 1.8770277694491289, 1.3973188833856243, 0.824671245092618, 0.28930945872092256, 0.0), # 170
(4.3572030133028, 3.06030043218988, 3.9749313735819856, 4.038983115591321, 3.61849295809611, 1.801143730041226, 1.3661668141095222, 1.3842436825795277, 1.9961247321071884, 0.7116487980765979, 0.5615653212117798, 0.33735175238082576, 0.0, 4.826224286265092, 3.710869276189083, 2.807826606058899, 2.134946394229793, 3.992249464214377, 1.9379411556113388, 1.3661668141095222, 1.2865312357437328, 1.809246479048055, 1.3463277051971074, 0.7949862747163972, 0.27820913019908006, 0.0), # 171
(4.184066308977092, 2.9354845527968174, 3.8217761665297245, 3.881458689874438, 3.4786771838230153, 1.7325994558753692, 1.3112501589480263, 1.331727079151757, 1.9207503743149028, 0.6835422574878162, 0.5394743255942259, 0.3241602912821315, 0.0, 4.639450218517843, 3.5657632041034453, 2.6973716279711297, 2.050626772463448, 3.8415007486298056, 1.8644179108124599, 1.3112501589480263, 1.237571039910978, 1.7393385919115076, 1.2938195632914795, 0.764355233305945, 0.26686223207243803, 0.0), # 172
(4.007078504324784, 2.808376989439591, 3.664494337205808, 3.7199844933527855, 3.3351551780606408, 1.6620756522039952, 1.25520868524366, 1.2776688132165412, 1.8431071904769127, 0.6547852222174565, 0.5168576993192239, 0.310643606450315, 0.0, 4.44777380497477, 3.417079670953465, 2.584288496596119, 1.9643556666523692, 3.6862143809538255, 1.7887363385031578, 1.25520868524366, 1.187196894431425, 1.6675775890303204, 1.2399948311175955, 0.7328988674411617, 0.25530699903996285, 0.0), # 173
(3.8268676988682753, 2.6793983238068333, 3.503689618509735, 3.5551616463729245, 3.1884739027906486, 1.5898513022217866, 1.1982351010303502, 1.2222844215977202, 1.763508187894657, 0.6254813529829895, 0.4937978667986571, 0.2968516731586446, 0.0, 4.251922485222747, 3.26536840474509, 2.468989333993285, 1.8764440589489682, 3.527016375789314, 1.7111981902368083, 1.1982351010303502, 1.1356080730155618, 1.5942369513953243, 1.1850538821243084, 0.700737923701947, 0.24358166580062124, 0.0), # 174
(3.6440619921299646, 2.548969137587176, 3.3399657433410055, 3.3875912692814207, 3.039180319994703, 1.5162053891234268, 1.1405221143420232, 1.165789441119132, 1.682266373869575, 0.595734310501885, 0.4703772524444093, 0.28283446668038764, 0.0, 4.052623698848646, 3.1111791334842636, 2.3518862622220467, 1.7872029315056546, 3.36453274773915, 1.632105217566785, 1.1405221143420232, 1.0830038493738763, 1.5195901599973516, 1.1291970897604737, 0.6679931486682011, 0.23172446705337968, 0.0), # 175
(3.459289483632255, 2.4175100124692537, 3.173926444599119, 3.2178744824248353, 2.8878213916544695, 1.441416896103598, 1.082262433212606, 1.1083994086046165, 1.5996947557031045, 0.5656477554916135, 0.44667828066836407, 0.268641962288812, 0.0, 3.8506048854393393, 2.9550615851769315, 2.23339140334182, 1.69694326647484, 3.199389511406209, 1.551759172046463, 1.082262433212606, 1.0295834972168558, 1.4439106958272347, 1.0726248274749453, 0.6347852889198239, 0.2197736374972049, 0.0), # 176
(3.273178272897546, 2.2854415301416977, 3.006175455183576, 3.0466124061497295, 2.7349440797516125, 1.365764806356983, 1.0236487656760251, 1.050329860878011, 1.5161063406966853, 0.535325348669645, 0.4227833758824049, 0.2543241352571853, 0.0, 3.6465934845817, 2.7975654878290377, 2.113916879412024, 1.6059760460089345, 3.0322126813933705, 1.4704618052292153, 1.0236487656760251, 0.9755462902549877, 1.3674720398758062, 1.0155374687165768, 0.6012350910367152, 0.20776741183106345, 0.0), # 177
(3.0863564594482376, 2.153184272293141, 2.8373165079938762, 2.87440616080267, 2.581095346267794, 1.2895281030782653, 0.964873819766207, 0.9917963347631552, 1.431814136151756, 0.5048707507534501, 0.39877496249841504, 0.2399309608587752, 0.0, 3.4413169358626017, 2.6392405694465264, 1.993874812492075, 1.51461225226035, 2.863628272303512, 1.3885148686684172, 0.964873819766207, 0.9210915021987609, 1.290547673133897, 0.9581353869342235, 0.5674633015987752, 0.1957440247539219, 0.0), # 178
(0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0), # 179
)
passenger_allighting_rate = (
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 0
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 1
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 2
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 3
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 4
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 5
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 6
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 7
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 8
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 9
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 10
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 11
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 12
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 13
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 14
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 15
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 16
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 17
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 18
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 19
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 20
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 21
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 22
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 23
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 24
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 25
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 26
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 27
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 28
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 29
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 30
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 31
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 32
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 33
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 34
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 35
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 36
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 37
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 38
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 39
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 40
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 41
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 42
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 43
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 44
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 45
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 46
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 47
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 48
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 49
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 50
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 51
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 52
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 53
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 54
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 55
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 56
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 57
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 58
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 59
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 60
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 61
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 62
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 63
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 64
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 65
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 66
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 67
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 68
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 69
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 70
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 71
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 72
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 73
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 74
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 75
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 76
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 77
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 78
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 79
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 80
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 81
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 82
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 83
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 84
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 85
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 86
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 87
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 88
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 89
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 90
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 91
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 92
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 93
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 94
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 95
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 96
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 97
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 98
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 99
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 100
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 101
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 102
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 103
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 104
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 105
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 106
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 107
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 108
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 109
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 110
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 111
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 112
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 113
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 114
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 115
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 116
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 117
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 118
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 119
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 120
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 121
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 122
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 123
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 124
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 125
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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), # 156
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 157
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 158
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 159
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 160
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 161
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 162
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 163
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 164
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 165
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 166
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 167
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 168
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 169
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 170
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 171
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 172
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 173
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 174
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 175
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 176
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 177
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 178
(0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 179
)
"""
parameters for reproducibiliy. More information: https://numpy.org/doc/stable/reference/random/parallel.html
"""
#initial entropy
entropy = 8991598675325360468762009371570610170
#index for seed sequence child
child_seed_index = (
1, # 0
28, # 1
)
| 275.154011
| 493
| 0.768631
| 32,987
| 257,269
| 5.994301
| 0.21175
| 0.360484
| 0.345919
| 0.655426
| 0.38044
| 0.372364
| 0.368101
| 0.366422
| 0.365936
| 0.365936
| 0
| 0.849141
| 0.096141
| 257,269
| 934
| 494
| 275.448608
| 0.0012
| 0.015591
| 0
| 0.200873
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0.005459
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
015025fdf2e87c9c574357746f35501754b41457
| 128
|
py
|
Python
|
humanlikehearing/library/__init__.py
|
neural-reckoning/HumanlikeHearing
|
c73bb8418cb2214fdc74ed88fabba76fae20fbab
|
[
"BSD-3-Clause"
] | 6
|
2021-05-21T14:47:47.000Z
|
2021-11-03T09:07:26.000Z
|
humanlikehearing/library/__init__.py
|
neural-reckoning/HumanlikeHearing
|
c73bb8418cb2214fdc74ed88fabba76fae20fbab
|
[
"BSD-3-Clause"
] | null | null | null |
humanlikehearing/library/__init__.py
|
neural-reckoning/HumanlikeHearing
|
c73bb8418cb2214fdc74ed88fabba76fae20fbab
|
[
"BSD-3-Clause"
] | 1
|
2021-05-20T08:34:25.000Z
|
2021-05-20T08:34:25.000Z
|
from . import a_weighting
from . import audio_format
from . import speech_voltmeter_svp56
from . import voice_activity_detection
| 32
| 38
| 0.851563
| 18
| 128
| 5.722222
| 0.666667
| 0.38835
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.017699
| 0.117188
| 128
| 4
| 38
| 32
| 0.893805
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
0157c0b566db787c1e7a34afa73ebd6dd84b1ea5
| 234
|
py
|
Python
|
module10-packages/deepcloudlabs/hr.py
|
deepcloudlabs/dcl160-2021-jul-05
|
0162afb24a5b89633531dbb1c195898d5066077a
|
[
"MIT"
] | null | null | null |
module10-packages/deepcloudlabs/hr.py
|
deepcloudlabs/dcl160-2021-jul-05
|
0162afb24a5b89633531dbb1c195898d5066077a
|
[
"MIT"
] | null | null | null |
module10-packages/deepcloudlabs/hr.py
|
deepcloudlabs/dcl160-2021-jul-05
|
0162afb24a5b89633531dbb1c195898d5066077a
|
[
"MIT"
] | null | null | null |
class Employee:
def __init__(self, fullname, email, salary):
self.fullname = fullname
self.email = email
self.salary = salary
def __str__(self):
return f"employee (full name: {self.fullname})"
| 26
| 55
| 0.623932
| 27
| 234
| 5.111111
| 0.481481
| 0.26087
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.273504
| 234
| 8
| 56
| 29.25
| 0.811765
| 0
| 0
| 0
| 0
| 0
| 0.15812
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.285714
| false
| 0
| 0
| 0.142857
| 0.571429
| 0
| 1
| 0
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 1
| 0
|
0
| 6
|
6d6c5997630f689944c394d8a9c06c73275708a7
| 57
|
py
|
Python
|
pynq_chainer/overlays/__init__.py
|
tkat0/pynq-chainer
|
30ffed5d08cd37207841623fcdaae4328d6e26eb
|
[
"MIT"
] | 6
|
2017-08-20T10:23:57.000Z
|
2020-02-06T18:35:06.000Z
|
pynq_chainer/overlays/__init__.py
|
tkat0/pynq-chainer
|
30ffed5d08cd37207841623fcdaae4328d6e26eb
|
[
"MIT"
] | null | null | null |
pynq_chainer/overlays/__init__.py
|
tkat0/pynq-chainer
|
30ffed5d08cd37207841623fcdaae4328d6e26eb
|
[
"MIT"
] | null | null | null |
from .mmult import Mmult
from .bin_mmult import BinMmult
| 19
| 31
| 0.824561
| 9
| 57
| 5.111111
| 0.555556
| 0.478261
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.140351
| 57
| 2
| 32
| 28.5
| 0.938776
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
6d95a31307d990df2f369a1de094bbb25dab1383
| 21
|
py
|
Python
|
register/util/signals/__init__.py
|
kws/building-register
|
f9bdd4580481e5453320e721a988b74f0f4e6354
|
[
"MIT"
] | null | null | null |
register/util/signals/__init__.py
|
kws/building-register
|
f9bdd4580481e5453320e721a988b74f0f4e6354
|
[
"MIT"
] | 2
|
2021-11-09T10:27:49.000Z
|
2021-11-17T10:53:01.000Z
|
register/util/signals/__init__.py
|
kws/building-register
|
f9bdd4580481e5453320e721a988b74f0f4e6354
|
[
"MIT"
] | 1
|
2021-11-09T10:28:22.000Z
|
2021-11-09T10:28:22.000Z
|
from .slack import *
| 10.5
| 20
| 0.714286
| 3
| 21
| 5
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.190476
| 21
| 1
| 21
| 21
| 0.882353
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
6db4ec4822c4a826863782f209c4e2bac4be99e3
| 1,670
|
py
|
Python
|
django_migration_utils/rename_table.py
|
mcldev/django-migration-utils
|
deccb0a6e1dfc3b4c5e786cfe5c45445894f8daa
|
[
"MIT"
] | null | null | null |
django_migration_utils/rename_table.py
|
mcldev/django-migration-utils
|
deccb0a6e1dfc3b4c5e786cfe5c45445894f8daa
|
[
"MIT"
] | null | null | null |
django_migration_utils/rename_table.py
|
mcldev/django-migration-utils
|
deccb0a6e1dfc3b4c5e786cfe5c45445894f8daa
|
[
"MIT"
] | null | null | null |
def fwd_rename_app(apps, schema_editor, apps_to_rename):
for old_appname, new_appname in apps_to_rename:
# Renaming model from 'Foo' to 'Bar'
schema_editor.execute("UPDATE django_migrations SET app_name = %s WHERE app_name = %s", [new_appname, old_appname])
schema_editor.execute("UPDATE django_content_type SET app_label = %s WHERE app_label = %s", [new_appname, old_appname])
new_app = apps.get_app_config(new_appname)
app_models = new_app.get_models(include_auto_created=True)
for model in app_models:
if model._meta.proxy == True:
continue
new_table_name = model._meta.db_table
old_table_name = old_appname + new_table_name[len(new_appname):]
schema_editor.alter_db_table(old_table_name, new_table_name)
def back_rename_app(apps, schema_editor, apps_to_rename):
for old_appname, new_appname in apps_to_rename:
# Renaming model back from 'Bar' to 'Foo'
schema_editor.execute("UPDATE django_migrations SET app_name = %s WHERE app_name = %s", [old_appname, new_appname])
schema_editor.execute("UPDATE django_content_type SET app_label = %s WHERE app_label = %s", [old_appname, new_appname])
new_app = apps.get_app_config(new_appname)
app_models = new_app.get_models(include_auto_created=True)
for model in app_models:
if model._meta.proxy == True:
continue
old_table_name = model._meta.db_table
new_table_name = old_appname + old_table_name[len(new_appname):]
schema_editor.alter_db_table(old_table_name, new_table_name)
| 38.837209
| 127
| 0.692814
| 241
| 1,670
| 4.385892
| 0.186722
| 0.094607
| 0.073794
| 0.075686
| 0.919584
| 0.859035
| 0.811731
| 0.811731
| 0.811731
| 0.811731
| 0
| 0
| 0.226946
| 1,670
| 42
| 128
| 39.761905
| 0.818745
| 0.044311
| 0
| 0.583333
| 0
| 0
| 0.161108
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.083333
| false
| 0
| 0
| 0
| 0.083333
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
09851e19dc80a219351bd82c39fe59cda99f0943
| 505
|
py
|
Python
|
common/test_input_validation.py
|
lbozarth/exercise-toes
|
25aed0949ae57a04fb738c2bf6c2a7ade0eece2b
|
[
"MIT"
] | 4
|
2019-09-18T03:32:25.000Z
|
2020-04-27T14:08:16.000Z
|
common/test_input_validation.py
|
lbozarth/exercise-toes
|
25aed0949ae57a04fb738c2bf6c2a7ade0eece2b
|
[
"MIT"
] | 3
|
2019-07-02T02:37:44.000Z
|
2019-08-24T05:12:08.000Z
|
common/test_input_validation.py
|
lbozarth/exercise-toes
|
25aed0949ae57a04fb738c2bf6c2a7ade0eece2b
|
[
"MIT"
] | 13
|
2019-07-02T00:37:47.000Z
|
2020-09-28T20:30:28.000Z
|
from common.input_validation import (
extract_phone_number,
)
def test_extract_phone_number():
assert extract_phone_number('510501622') == None
assert extract_phone_number('5105016227') == '15105016227'
assert extract_phone_number('15105016227') == '15105016227'
assert extract_phone_number('+15105016227') == '15105016227'
assert extract_phone_number('My number is 510 501 6227') == '15105016227'
assert extract_phone_number('My number is (510) 501-6227.') == '15105016227'
| 38.846154
| 80
| 0.744554
| 59
| 505
| 6.067797
| 0.338983
| 0.268156
| 0.402235
| 0.402235
| 0.594972
| 0.594972
| 0.594972
| 0.594972
| 0.594972
| 0.594972
| 0
| 0.267281
| 0.140594
| 505
| 12
| 81
| 42.083333
| 0.557604
| 0
| 0
| 0
| 0
| 0
| 0.29703
| 0
| 0
| 0
| 0
| 0
| 0.6
| 1
| 0.1
| true
| 0
| 0.1
| 0
| 0.2
| 0
| 0
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
09903cb3b4510862bc3ddebafa79c5bd9b3ab96e
| 26
|
py
|
Python
|
PartSongSet/__init__.py
|
jcksnvllxr80/MidiController
|
de6d3c983cd27408e88a744a0a4d3c887efa3d54
|
[
"MIT"
] | 1
|
2021-06-06T15:36:27.000Z
|
2021-06-06T15:36:27.000Z
|
PartSongSet/__init__.py
|
jcksnvllxr80/MidiController
|
de6d3c983cd27408e88a744a0a4d3c887efa3d54
|
[
"MIT"
] | 1
|
2021-06-06T15:37:42.000Z
|
2021-06-06T15:37:42.000Z
|
PartSongSet/__init__.py
|
jcksnvllxr80/MidiController
|
de6d3c983cd27408e88a744a0a4d3c887efa3d54
|
[
"MIT"
] | null | null | null |
from PartSongSet import *
| 13
| 25
| 0.807692
| 3
| 26
| 7
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.153846
| 26
| 1
| 26
| 26
| 0.954545
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
09a5bcdf9e4eed883b56ec4fc1779c06f61b9301
| 3,210
|
py
|
Python
|
tests/unit/test_process.py
|
tholom/pake
|
6777d63255eb3e4e834b77c9a1504b72dd2ed296
|
[
"BSD-3-Clause"
] | 3
|
2019-08-28T21:54:30.000Z
|
2021-10-13T22:00:59.000Z
|
tests/unit/test_process.py
|
tholom/pake
|
6777d63255eb3e4e834b77c9a1504b72dd2ed296
|
[
"BSD-3-Clause"
] | 1
|
2021-01-05T01:37:57.000Z
|
2021-01-05T14:10:17.000Z
|
tests/unit/test_process.py
|
tholom/pake
|
6777d63255eb3e4e834b77c9a1504b72dd2ed296
|
[
"BSD-3-Clause"
] | 1
|
2021-01-16T18:44:36.000Z
|
2021-01-16T18:44:36.000Z
|
import sys
import unittest
import os
script_dir = os.path.dirname(os.path.realpath(__file__))
sys.path.insert(1, os.path.abspath(
os.path.join(script_dir, os.path.join('..', '..'))))
from pake import process
import pake.program
import pake
class ProcessTest(unittest.TestCase):
def test_call(self):
cmd = [sys.executable, os.path.join(script_dir, 'timeout.py')]
with self.assertRaises(process.TimeoutExpired) as exc:
process.call(*cmd, timeout=0.1, stderr=process.DEVNULL, stdout=process.DEVNULL)
self.assertSequenceEqual((cmd, 0.1), exc.exception.cmd)
self.assertNotEqual(process.call(sys.executable, os.path.join(script_dir, 'throw.py'),
stderr=process.DEVNULL, stdout=process.DEVNULL), 0)
self.assertNotEqual(process.call(sys.executable, os.path.join(script_dir, 'killself.py'),
stderr=process.DEVNULL, stdout=process.DEVNULL), 0)
def test_check_call(self):
cmd = [sys.executable, os.path.join(script_dir, 'timeout.py')]
with self.assertRaises(process.TimeoutExpired) as exc:
process.check_call(cmd, timeout=0.1,
stderr=process.DEVNULL, stdout=process.DEVNULL)
self.assertSequenceEqual((cmd, 0.1), exc.exception.cmd)
_ = str(exc.exception) # just test for serialization exceptions
cmd = [sys.executable, os.path.join(script_dir, 'throw.py')]
with self.assertRaises(process.CalledProcessException) as exc:
process.check_call(cmd, stderr=process.DEVNULL, stdout=process.DEVNULL)
self.assertListEqual(cmd, exc.exception.cmd)
_ = str(exc.exception) # just test for serialization exceptions
# Check pake propagates the exception correctly
pake.de_init(clear_conf=False)
pk = pake.init()
@pk.task
def dummy(ctx):
process.check_call(cmd, stderr=process.DEVNULL, stdout=process.DEVNULL)
with self.assertRaises(pake.TaskException) as exc:
pk.run(tasks=dummy)
self.assertEqual(type(exc.exception.exception), process.CalledProcessException)
def test_check_output(self):
cmd = [sys.executable, os.path.join(script_dir, 'timeout.py')]
with self.assertRaises(process.TimeoutExpired) as exc:
process.check_output(*cmd, timeout=0.1, stderr=process.DEVNULL)
_ = str(exc.exception) # just test for serialization exceptions
cmd = [sys.executable, os.path.join(script_dir, 'throw.py')]
with self.assertRaises(process.CalledProcessException) as exc:
process.check_output(cmd, stderr=process.DEVNULL)
_ = str(exc.exception) # just test for serialization exceptions
# Check pake propagates the exception correctly
pake.de_init(clear_conf=False)
pk = pake.init()
@pk.task
def dummy(ctx):
process.check_output(cmd, stderr=process.DEVNULL)
with self.assertRaises(pake.TaskException) as exc:
pk.run(tasks=dummy)
self.assertEqual(type(exc.exception.exception), process.CalledProcessException)
| 33.4375
| 97
| 0.658255
| 383
| 3,210
| 5.43342
| 0.180157
| 0.100913
| 0.043248
| 0.061509
| 0.879385
| 0.870255
| 0.867371
| 0.84815
| 0.807785
| 0.804421
| 0
| 0.005255
| 0.229283
| 3,210
| 95
| 98
| 33.789474
| 0.835893
| 0.076947
| 0
| 0.642857
| 0
| 0
| 0.023342
| 0
| 0
| 0
| 0
| 0
| 0.25
| 1
| 0.089286
| false
| 0
| 0.107143
| 0
| 0.214286
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
09fe26b416f8f39b1bc3594cc62188984305284f
| 8,296
|
py
|
Python
|
test/test_npu/test_network_ops/test_not_equal.py
|
Ascend/pytorch
|
39849cf72dafe8d2fb68bd1679d8fd54ad60fcfc
|
[
"BSD-3-Clause"
] | 1
|
2021-12-02T03:07:35.000Z
|
2021-12-02T03:07:35.000Z
|
test/test_npu/test_network_ops/test_not_equal.py
|
Ascend/pytorch
|
39849cf72dafe8d2fb68bd1679d8fd54ad60fcfc
|
[
"BSD-3-Clause"
] | 1
|
2021-11-12T07:23:03.000Z
|
2021-11-12T08:28:13.000Z
|
test/test_npu/test_network_ops/test_not_equal.py
|
Ascend/pytorch
|
39849cf72dafe8d2fb68bd1679d8fd54ad60fcfc
|
[
"BSD-3-Clause"
] | null | null | null |
# Copyright (c) 2020, Huawei Technologies.All rights reserved.
#
# Licensed under the BSD 3-Clause License (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://opensource.org/licenses/BSD-3-Clause
#
# 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.
import torch
import numpy as np
import copy
from common_utils import TestCase, run_tests
from common_device_type import dtypes, instantiate_device_type_tests
from util_test import create_common_tensor
class TestNotEqual(TestCase):
def cpu_op_exec(self, input1, input2):
output = torch.ne(input1, input2)
output = output.numpy().astype(np.int32)
return output
def npu_op_exec(self, input1, input2):
output = torch.ne(input1, input2)
output = output.to("cpu")
output = output.numpy().astype(np.int32)
return output
def cpu_op_inplace_exec(self, input1, input2):
input1.ne_(input2)
output = input1.numpy().astype(np.int32)
return output
def npu_op_inplace_exec(self, input1, input2):
input1.ne_(input2)
output = input1.to("cpu")
output = output.numpy().astype(np.int32)
return output
def npu_op_exec_out(self, input1, input2, out):
torch.ne(input1, input2, out=out)
output = out.to("cpu")
output = output.numpy().astype(np.int32)
return output
def not_equal_scalar_result(self, shape_format):
for item in shape_format:
scalar = np.random.uniform(0, 100)
cpu_input1, npu_input1 = create_common_tensor(item[0], 0, 100)
npu_input3 = copy.deepcopy(cpu_input1).to("npu").to(torch.bool)
if cpu_input1.dtype == torch.float16:
cpu_input1 = cpu_input1.to(torch.float32)
cpu_output = self.cpu_op_exec(cpu_input1, scalar)
npu_output = self.npu_op_exec(npu_input1, scalar)
npu_output_out = self.npu_op_exec_out(npu_input1, scalar, npu_input3)
cpu_output_inp = self.cpu_op_inplace_exec(cpu_input1, scalar)
npu_output_inp = self.npu_op_inplace_exec(npu_input1, scalar)
self.assertRtolEqual(cpu_output, npu_output)
self.assertRtolEqual(cpu_output, npu_output_out)
self.assertRtolEqual(cpu_output_inp, npu_output_inp)
def not_equal_result(self, shape_format):
for item in shape_format:
cpu_input1, npu_input1 = create_common_tensor(item[0], 0, 100)
cpu_input2, npu_input2 = create_common_tensor(item[1], 0, 100)
npu_input3 = copy.deepcopy(cpu_input1).to("npu").to(torch.bool)
if cpu_input1.dtype == torch.float16:
cpu_input1 = cpu_input1.to(torch.float32)
cpu_input2 = cpu_input2.to(torch.float32)
cpu_output = self.cpu_op_exec(cpu_input1, cpu_input2)
npu_output = self.npu_op_exec(npu_input1, npu_input2)
npu_output_out = self.npu_op_exec_out(npu_input1, npu_input2, npu_input3)
cpu_output_inp = self.cpu_op_inplace_exec(cpu_input1, cpu_input2)
npu_output_inp = self.npu_op_inplace_exec(npu_input1, npu_input2)
self.assertRtolEqual(cpu_output, npu_output)
self.assertRtolEqual(cpu_output, npu_output_out)
self.assertRtolEqual(cpu_output_inp, npu_output_inp)
def test_not_equal_shape_format_fp16_1d(self, device):
format_list = [-1, 0]
shape_format = [[[np.float16, i, [16]], [np.float16, i, [16]]] for i in format_list]
self.not_equal_result(shape_format)
def test_not_equal_shape_format_fp32_1d(self, device):
format_list = [-1, 0]
shape_format = [[[np.float32, i, [16]], [np.float32, i, [16]]] for i in format_list]
self.not_equal_result(shape_format)
def test_not_equal_shape_format_fp16_2d(self, device):
format_list = [-1, 0]
shape_format = [[[np.float16, i, [448, 1]], [np.float16, i, [448, 1]]] for i in format_list]
self.not_equal_result(shape_format)
def test_not_equal_shape_format_fp32_2d(self, device):
format_list = [-1, 0]
shape_format = [[[np.float32, i, [448, 1]], [np.float32, i, [448, 1]]] for i in format_list]
self.not_equal_result(shape_format)
def test_not_equal_shape_format_fp16_3d(self, device):
format_list = [-1, 0]
shape_format = [[[np.float16, i, [16, 640, 640]], [np.float16, i, [16, 640, 640]]] for i in format_list]
self.not_equal_result(shape_format)
def test_not_equal_shape_format_fp32_3d(self, device):
format_list = [-1, 0, 3]
shape_format = [[[np.float32, i, [16, 640, 640]], [np.float32, i, [16, 640, 640]]] for i in format_list]
self.not_equal_result(shape_format)
def test_not_equal_shape_format_fp16_4d(self, device):
format_list = [-1, 0, 3]
shape_format = [[[np.float16, i, [32, 3, 3, 3]], [np.float16, i, [32, 3, 3, 3]]] for i in format_list]
self.not_equal_result(shape_format)
def test_not_equal_shape_format_fp32_4d(self, device):
format_list = [-1, 0, 3]
shape_format = [[[np.float32, i, [32, 3, 3, 3]], [np.float32, i, [32, 3, 3, 3]]] for i in format_list]
self.not_equal_result(shape_format)
# scala-----------------------------------------------------------------
def test_not_equal_scalar_shape_format_fp16_1d(self, device):
format_list = [-1, 0, 3]
shape_format = [[[np.float16, i, 18]] for i in format_list]
self.not_equal_scalar_result(shape_format)
def test_not_equal_scalar_shape_format_fp32_1d(self, device):
format_list = [-1, 0, 3]
shape_format = [[[np.float32, i, [18]]] for i in format_list]
self.not_equal_scalar_result(shape_format)
def test_not_equal_scalar_shape_format_fp16_2d(self, device):
format_list = [-1, 0]
shape_format = [[[np.float16, i, [64, 7]]] for i in format_list]
self.not_equal_scalar_result(shape_format)
def test_not_equal_scalar_shape_format_fp32_2d(self, device):
format_list = [-1, 0]
shape_format = [[[np.float32, i, [64, 7]]] for i in format_list]
self.not_equal_scalar_result(shape_format)
def test_not_equal_scalar_shape_format_fp32_3d(self, device):
format_list = [-1, 0]
shape_format = [[[np.float32, i, [64, 24, 38]]] for i in format_list]
self.not_equal_scalar_result(shape_format)
def test_not_equal_scalar_shape_format_fp16_4d(self, device):
format_list = [-1, 0]
shape_format = [[[np.float16, i, [32, 3, 3, 3]]] for i in format_list]
self.not_equal_scalar_result(shape_format)
def test_not_equal_scalar_shape_format_fp32_4d(self, device):
format_list = [-1, 0]
shape_format = [[[np.float32, i, [32, 3, 3, 3]]] for i in format_list]
self.not_equal_scalar_result(shape_format)
def test_not_equal_shape_format_int32_1d(self, device):
format_list = [-1, 0]
shape_format = [[[np.int32, i, [16]], [np.int32, i, [16]]] for i in format_list]
self.not_equal_result(shape_format)
def test_not_equal_shape_format_int32_2d(self, device):
format_list = [-1, 0]
shape_format = [[[np.int32, i, [448, 1]], [np.int32, i, [448, 1]]] for i in format_list]
self.not_equal_result(shape_format)
def test_not_equal_shape_format_int32_3d(self, device):
format_list = [-1, 0]
shape_format = [[[np.int32, i, [16, 640, 640]], [np.int32, i, [16, 640, 640]]] for i in format_list]
self.not_equal_result(shape_format)
def test_not_equal_shape_format_int32_4d(self, device):
format_list = [-1, 0]
shape_format = [[[np.int32, i, [32, 3, 3, 3]], [np.int32, i, [32, 3, 3, 3]]] for i in format_list]
self.not_equal_result(shape_format)
instantiate_device_type_tests(TestNotEqual, globals(), except_for="cpu")
if __name__ == "__main__":
run_tests()
| 43.434555
| 112
| 0.659595
| 1,229
| 8,296
| 4.135069
| 0.115541
| 0.132035
| 0.037387
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| 0.813066
| 0.800472
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| 0.739079
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| 0.062413
| 0.219744
| 8,296
| 190
| 113
| 43.663158
| 0.722694
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| 0.003405
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| 0.042857
| 1
| 0.185714
| false
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| 0.042857
| 0
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| 1
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| null | 0
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| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
61d42e66ae0c1adfd19e78af91bc948fbb48ddbb
| 124
|
py
|
Python
|
tests/missing_data/test_missing_data_air_passengers_None_None.py
|
shaido987/pyaf
|
b9afd089557bed6b90b246d3712c481ae26a1957
|
[
"BSD-3-Clause"
] | 377
|
2016-10-13T20:52:44.000Z
|
2022-03-29T18:04:14.000Z
|
tests/missing_data/test_missing_data_air_passengers_None_None.py
|
ysdede/pyaf
|
b5541b8249d5a1cfdc01f27fdfd99b6580ed680b
|
[
"BSD-3-Clause"
] | 160
|
2016-10-13T16:11:53.000Z
|
2022-03-28T04:21:34.000Z
|
tests/missing_data/test_missing_data_air_passengers_None_None.py
|
ysdede/pyaf
|
b5541b8249d5a1cfdc01f27fdfd99b6580ed680b
|
[
"BSD-3-Clause"
] | 63
|
2017-03-09T14:51:18.000Z
|
2022-03-27T20:52:57.000Z
|
import tests.missing_data.test_missing_data_air_passengers_generic as gen
gen.test_air_passengers_missing_data(None, None)
| 31
| 73
| 0.895161
| 20
| 124
| 5.05
| 0.55
| 0.326733
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.056452
| 124
| 3
| 74
| 41.333333
| 0.863248
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| null | 0
| 0
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| 0
| 1
| 1
| 1
| 0
| 0
| 0
|
0
| 6
|
febb379b1fadcfc13939427c562a9743628bb217
| 290
|
py
|
Python
|
packages/watchmen-auth/src/watchmen_auth/__init__.py
|
Indexical-Metrics-Measure-Advisory/watchmen
|
c54ec54d9f91034a38e51fd339ba66453d2c7a6d
|
[
"MIT"
] | null | null | null |
packages/watchmen-auth/src/watchmen_auth/__init__.py
|
Indexical-Metrics-Measure-Advisory/watchmen
|
c54ec54d9f91034a38e51fd339ba66453d2c7a6d
|
[
"MIT"
] | null | null | null |
packages/watchmen-auth/src/watchmen_auth/__init__.py
|
Indexical-Metrics-Measure-Advisory/watchmen
|
c54ec54d9f91034a38e51fd339ba66453d2c7a6d
|
[
"MIT"
] | null | null | null |
from .auth_helper import authorize, authorize_token
from .authentication import AuthenticationDetails, AuthenticationManager, AuthenticationProvider, AuthenticationScheme
from .authorization import AuthFailOn401, AuthFailOn403, Authorization
from .principal_service import PrincipalService
| 58
| 118
| 0.889655
| 25
| 290
| 10.2
| 0.68
| 0
| 0
| 0
| 0
| 0
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| 0.022388
| 0.075862
| 290
| 4
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| 72.5
| 0.929104
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|
0
| 6
|
3a0932e5c800ae98fd8480d1b623bc123ef3f697
| 14,281
|
py
|
Python
|
Gui/Images/title_icons.py
|
marioharper182/OptionsPricing
|
0212a1e421380e7a903439aaef93d99373e71fc6
|
[
"Apache-2.0"
] | null | null | null |
Gui/Images/title_icons.py
|
marioharper182/OptionsPricing
|
0212a1e421380e7a903439aaef93d99373e71fc6
|
[
"Apache-2.0"
] | null | null | null |
Gui/Images/title_icons.py
|
marioharper182/OptionsPricing
|
0212a1e421380e7a903439aaef93d99373e71fc6
|
[
"Apache-2.0"
] | null | null | null |
#----------------------------------------------------------------------
# This file was generated by img2py.py
#
from wx.lib.embeddedimage import PyEmbeddedImage
AmericanOption = PyEmbeddedImage(
"iVBORw0KGgoAAAANSUhEUgAAAEAAAABACAYAAACqaXHeAAAABHNCSVQICAgIfAhkiAAAAAlw"
"SFlzAAAHIQAAByEBauL93wAAABl0RVh0U29mdHdhcmUAd3d3Lmlua3NjYXBlLm9yZ5vuPBoA"
"AAcESURBVHic7ZpbaB1VFIa/ld7S1t7UYmq8JDWpWOut0lqLiCCCPgjewBel9EEpiAiC2Bcf"
"fFFBvIEoivgkIihFQQShUhBbsGhbtWrTS1Jr29TWS7SX2Jhk+bD2zpmzz5w5M3MmmVbzw2LP"
"mbNn7bXX7HXbe1BVVBVgBvAI8AOgwCDwNrDM9/kvkrjJIyIPAW9Si16gW1VHY/4769ESuX64"
"Tp/FwO0TIEspmBq5PpnQ7+/xFiQtRGQBcKGjRY5aMBkHI+0g8CuwTVXryh9VwEvAamBa0OcL"
"RxMGEZkFXAEsA6507RKgHWjNyG5IRL4GNntS1WNjY3kf4AZeDzwbMDgP6ATWAFuALap6IKMQ"
"sYhMdGmErnTjtSQ82iy2q+pyqF4BAAMxnU8BC4FHHSEih3DKAL7BzCe6/Hx7DpXlGqWLsYlf"
"CkgDYUeAXcAeYB+wN9L2A6OOFFBVHRWReUAX0O3IX18PTMf8miEaEoB1nlGEWjEnGN4fDxoF"
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"AmTzmOd4RmUCmlPAaaxW2AC8gHn1MIf4DfgM+BRYC9zUgOddde4PAfuxtLwXiwK9WBQYcTQa"
"uZ6L5Q1R6gZmh4ybUcAMYKGqbhSRjcDVMX0UU85+4NWUfEeA74GtEdqpqiMZ5fsy+kNEBAvZ"
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"mzBBQ4ST/xtLX7dhub+PAr1p/ISItFMdCdYCC6J9ilDAINWCrwfmYEWUxxBWhIAVQFBt+x69"
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"aSXeCZ5Q1VNJz3o0mwfsUNWXRUQxxxZ62R7gXsxW02yr1aS5bgfoOuKjwPw6fFREDmMl9B7M"
"fPZQcCIElge8gdl+TYjBwtfdbuC2FPxmiMgqqqNAdw65BNs9agduSerYrAJuc23SjvHTpN/d"
"eS/j+McwRzmEVZK+nYb5mnl1niukFogiaYLNbm0dp5LI9ERod6NIICLzsQqww9GTWFFWWCpc"
"FEaweqFqgkCPqh7Oy1RVB4DtjhCRNQRVaR4F/OzadrK/3V8JJuhon6oOZWEkInOwt9qKhdhp"
"kfYf4Cfgp6QtccingNdU9TkRmY7Z2WJsmbVjthXa41herqq/ZxnI1QGdmDNd4lpPi1KwUBE5"
"gq2u/U7GKuQ2AffGfJhpCiKykNoJLsHC3fSERxuypnJ4cmNchzwKWCoiK7B09LfUkljS0kX1"
"BP31goRHQwxjGeNuoI/kKNCBraBLsbylBnkU8KAjROS4E6YPSzmFWnv0uzOXkM1nHCXGKWKK"
"z5Qeu82QNiwlrirdm40Cc4BrHOXBIGZCoVPsUdU/m5RtDGqHAv0iUuMQ8yjgY+BHKs4vKSUF"
"c4wHiInjwAGNns01gIhMc+N5E+qkfhToc+QryP1xPPMoYLOqPhcINp/6UeBEo1AUQkTaiHeK"
"i3PKDJXTpyoUkgi5hCPuXLEu3MFoN/FOcW4RcoVDElNs5VHAWhHppuL8eoE+Vf0FxmJ3nBMM"
"3+hFcQIloJ9aE0qKAp1UTNSb680Emy55FOAnUwURGca8fDO5/0kqviLqM3ar6vGMvP7AdpKi"
"Mu4gcNhF1gJpeY1iDimuuDlYoDypkEcBT2Gbl9EokCoVxia6V1VPZxlQRFqwZCZ0ilmiQB8F"
"bYgMq6pnWihE5FzinWIXdTK5OphK5YuThh0nFK6I6iI+zJ2fg+UAlQ85Wsl4epxHAc+IyKME"
"UYDGqbCfcAfxO8JxGMZMZ6dr+yN0BDgS5hgiMgPz9DMxhd6AfWmympgTrLQK6Mc86tXuGf+t"
"T6PT3rQYwRS5EzsY9W1P1rzf+ZfT2MroB74D3gIQkfOwqvBa3z/8Smwd8HrAc6bXsojMxjYq"
"vUZXkf4cfwQrcPqBg9gnuX6yu7Jmi0Uhkwm4PbhNVA4aBPs4Yja25FqD9iSVJXvsTPzctikn"
"6AqZQr4ZLAvj+THiWYFJBZQtQNmYVEDZApSNSQWULUDZmFRA2QKUjUkFlC1A2ZhUQNkClI1J"
"BZQtQNmYVEDZApSN/70C/gVDTFLx+fxz1wAAAABJRU5ErkJggg==")
#----------------------------------------------------------------------
AsianOption = PyEmbeddedImage(
"iVBORw0KGgoAAAANSUhEUgAAAEAAAABACAMAAACdt4HsAAAAA3NCSVQICAjb4U/gAAAACXBI"
"WXMAABIFAAASBQEoPImrAAAAGXRFWHRTb2Z0d2FyZQB3d3cuaW5rc2NhcGUub3Jnm+48GgAA"
"ArJQTFRF////AAAAAAAAAAAAAAAAMzMzKysrJCQkICAgHBwcGhoaFxcXFRUVFBQUJCQSIiIi"
"ICAgHh4eHBwcGxsbGBgYISEWICAVHR0dHBwcGxsbGhoaGhoaIRkZICAYHx8XHh4XHR0dHBwc"
"HBwcGxsbGhoaIBoaHx8ZHh4YHh4YHR0XHBwcGxsbGxsbHxoaHh4ZHR0ZHR0YHBwcHBwcGxsb"
"GxsbHxoaHhoaHh4aHR0ZHR0ZHBwcGxsbHxsbHhsbHhoaHh4aHR0aHR0ZHBwYHBwcHxsbHhsb"
"HhoaHR0aHR0aHBwZHBwZHBwZHhsbHhsbHhsbHR0aHR0aHR0aHBwaHBwZHBwZHhsZHhsbHR0b"
"HR0aHR0aHBwaHBwaHBwZHhwZHhsbHRsbHR0bHR0aHR0aHBwaHhwZHhsZHR0bHR0bHR0aHBwa"
"HBwaHhwaHhwaHhsZHRsZHR0bHR0bHBwaHBwaHhwaHhwaHRsaHRsZHRsZHR0bHBwaHBwaHhwa"
"HhwaHRwaHRsaHRsZHR0ZHR0bHBwbHBwaHRwaHRsaHRsaHR0ZHR0ZHBwbHhwaHhwaHRwaHRwa"
"HRsaHRsaHR0aHR0ZHBwbHhwbHhwaHRwaHRsaHRsaHR0aHR0aHBwZHhwbHRwbHRwaHRwaHRwa"
"HRsaHR0aHBwaHBwaHhwZHRwbHRwaHRwaHRwaHRsaHR0aHBwaHBwaHhwaHRwbHRwaHRwaHRsa"
"HR0aHhwaHRwaHRwaHRwZHRwaHR0aHBwaHhwaHRwaHRwaHRwbHRwaHhwaHRwaHRwaHRwaHRwa"
"HRwbHRwaHRsaHBwaHRwaHRwaHRwaHRwaHRwaHRwaHRsaHBwaHRwaHRwaHRwaHRwaHRwaHRwa"
"HRwaHRwaHhwaHRwaHRwaHRwaHRwaHRwaHRwaHRwaHRwaHRwaHRwaHRwaHRwaHRwaDWpeAgAA"
"AOV0Uk5TAAECAwQFBgcICQoLDA0ODxAREhMVFxgaGxwdHh8gISIjJCUmJygpKissLS8wMTM0"
"NTY3ODk6Ozw9PkBBQkNERUZHSUpLTE1PUFFSU1RVVldYWVpbXF1fYGFiY2RlZmdoaWprbW9w"
"cnN0dXZ3eHl6e3x+f4CBgoOEhYeIiYqLjI2Oj5CRk5WWl5iZmpucnZ6foKGio6Smp6ipqqus"
"ra6vsLGys7S1tre4ubq7vL2+wMHCw8TGx8jJzM3Oz9HS09XY2drb3N3e3+Di4+Tl5ufo6err"
"7O3u7/Dx8vP09fb3+Pn6+/z9/uypTYUAAAP/SURBVBgZ7cH7f811AMfx99lmRtkQR3UYG5my"
"Qke5pCJtmS0lCdMypkVpK0lRGUbYEllaipKIXLcIa7kucskUErlszvb6P/p8D99l53xnzE8e"
"D8+ndNutL0Q3pcWEPN2ErvPOka4GikrOPwx80kwN4Hoou/gSfkm6QS37jJ275RQ1Fuh6udze"
"1FnrjxFgt6Rps8b1jw6VM5e7W1LGh0uLD1biqFTSCoyKPasWzZmWPX7UkJSXxk6cMmPekuVr"
"i0v2HqngWkqzPJKSaZhdkzvL4j5IQ1QN1xWbaZBtsjXuN3ziN9ywSbpa5MubuC7VpT988fH7"
"E0enPN5MtUVVU6eS6a+PTh62H2OQ6rQfS2UllvIpowb3S9mH32b5dffBKtUt5onuHVs3Udgi"
"jARZEvEbrsu+g9mqX/RFWCG/u/DrIL8JGG+7JYXH9n9lXKTqEHkAdkXJ7wRGdVMZrT7lsoPF"
"h6swMuUsfD3GpvZSWPME/IrmF67dXkGAqQpwX95n+XNn5h7gslMXuLYhCjCL6+I7UbZj/Vf5"
"H70QqgBPEej88bJfilYX5uW881ra0IQ+aT44Eq+6dXnu2ZTkpJTvgR0DunhahCnAT5Cr+rUB"
"BspJMcxQ/VoCXjkpgTmqnwfwysleWKH6xQFe1dLymTcmTeitI7BTdfKkpKU+30FSD8Crq6VX"
"Yll4Gs6GKiZjweKC9EYKMLgayxKX+gHe6Jw1q5aNiZJfaC5GWcdLQHy381jeVKCn9wBFbikR"
"8M7HUhwhv5AiqIoLx0jXYyeBneEK4vZRfoekoYA3ZCmwuImumAaVaodRIKUAAxUsCXweSXnA"
"q3oUKhrJVgB88C3GmbbhOXAuVMEKAd+Ggm0YVesOAbGybaBGxUngpBz8RaDBsm2ltj/lYCOB"
"hsm2DLhQhd8F4JgcZMDZ5NjOn2OsHlgAxMo2A+iZimVtxEdwVA56wNeSojFa6U4475ItE2ga"
"jSVDT8IhOXgAlktyY0jycU41xgEtPFgy1Qt+l4NYWCfJjSHpIpdUIw1ofQ+WbHWD3+TgXtgq"
"yY0h6V9wyTYS8NyNZaruh31y0Ap2SXJjhEhnIEK2F4GYNlhmKAbK5CAaTkt6BKOtGgMxso0E"
"uruxzFUPqAxTsCFAV0XMxFjwcBYwQLb3gBEPYikJyQQ6Klguxu7T/C9LtkLAdxy/coxEBSsl"
"UJ5s2wiQqmD5XGX/UWC8bD8SoKeCJQJvteu0EuNURAwQL9t04PC7ZzGq4nKAWAWLg79DpD4Y"
"P0sXwSNbNpClhRjligc6KVgX+ENSe4yV0uR/vlSNEUBvJVQDG+Xawq9hChayvXq2jEkb1izq"
"pNrCBo3pLal93769wiVFuuSouW67Zf0HmwyT/NCKhBoAAAAASUVORK5CYII=")
#----------------------------------------------------------------------
EuropeanOption = PyEmbeddedImage(
"iVBORw0KGgoAAAANSUhEUgAAAEAAAABACAYAAACqaXHeAAAABHNCSVQICAgIfAhkiAAAAAlw"
"SFlzAAAwfQAAMH0BS0BPwgAAABl0RVh0U29mdHdhcmUAd3d3Lmlua3NjYXBlLm9yZ5vuPBoA"
"AALNSURBVHic7Zs9axRRFIafExfcBGFVNCiBoIKsCGpEiHaKlZUfoJjSwkp/gZ1dLFMGK0GE"
"BAQFFQKCHxBBKz8KXUWyq5WiYgISVyQei5mNN9fBmN2ZOca5DwwsZ+fcee+7c+7cuTMrqkqR"
"6bIWYE2p9UFENgMnDbXkybiq1iE2QER2A/uBYUtVOdIUkbuq+pR4DGgAWrCtpqrzY8DHjvxc"
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"3r5V4BXRjUSLDUATmHZi3XHcLQuIzqyaF9sIzBKVkZvfC7xpM78HWMfCsgDYDjz3Yg2I6qCJ"
"/d1Z3lsT2CmtJTERcX/ZItCtqs3CjwHBAGsB1gQDjI//FfhidGwFZ1k8J74BF4EHwDOiecUP"
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"1gQDrAVYEwywFmBNMMBagDXBAGsB1gQDrAVYEwywFmBNMMBagDXBAGsB1hTegFL8j7Fqyu32"
"i8gQcE9V33XSkIj0AgeB/lSU/eKEiLwAGCG7J7CHUngociBDfaOFL4HCG1ACbgAd1ekfeJlC"
"G1PAuRTaSeLx/PsBRaXwJfAT3BK6v9OwyUMAAAAASUVORK5CYII=")
#----------------------------------------------------------------------
ImpliedVol = PyEmbeddedImage(
"iVBORw0KGgoAAAANSUhEUgAAAEAAAABACAYAAACqaXHeAAAABHNCSVQICAgIfAhkiAAAAAlw"
"SFlzAAAdmwAAHZsBHYZu2AAAABl0RVh0U29mdHdhcmUAd3d3Lmlua3NjYXBlLm9yZ5vuPBoA"
"AAWcSURBVHic7ZpdiFVVFMd/a2Y0dWZ0orRGRtRJy1FzRkELKjA1oSkjCKqHoogICguKHgr1"
"IaSHPh8Ci3roGwPLosKi0oIoAoV0FGf8oqYya2pmSBud0VFXD3tfOXM9n/fsc08D9w+Lc8+9"
"+6z9P/+79t7rrH1EVSkFInIp8B2wTlXfK8nJ/wBVKa5tAC4DNorIFhGZKSLjCuaIX+ZII4AX"
"7cBPwGDBROQ+R74zhSsBinEE2JyRb6dII8DtIb89rKpHU/guG6SUSVBEWoBdwFifnz9X1fa0"
"xMqFxBEgIgK8hv/NA+xPxajMqCnxml+AXnteBdwAjHdFqpxILICqDgN3eb8TkVnAq8AyQNxQ"
"Kw9Ch4CIzBKRt0SkLqydqh5S1eXAPcAWlwQzh6r6GvAQcBxQYHZQu7gGNKT1kYXViMgkoBEz"
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"A4x3MSaB5y2PQ8BjQJc9/xu40vUc4O14OvAxCbM+YCZwEljvSIAP7A2vt+eTgd1ZiZCW7AXA"
"h5bcANDiQIC11l8PMCdrEdIQvQXoZuRSOQxsAMam8FuLmWsU+CNEhAV5C/AMcJbz84X9wJSU"
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"pDWJ71EhAGQnwqgRABKL0BbHp7M5QESqgXofG4d5vvi3yAa0xM5jzgn9mDlhV6ivJBzstvc8"
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"JhImYoSaSPSGzBBGlE+A123o+3GLK8IKVd3p+2vA2luPqQPuIHjtPQ18DbyA2RBZBEyIubY3"
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"MPl1kJNBoC2LjM/D4d6IG/kiRcZ4ngjFTi7EjOEgB91AfcYCzANOhXDYHXJtYhH8nDyB/2Nu"
"wT4D5mVw44JZ47tC+h4A2iP8xBGhH1jkK4B1shL4EjMBBTn5AXgEuI4SNz4xe5IrgXWY5Cao"
"r+PARmBuTL9xI6EpdBm0Fdo7Me8DLApsaPAbppLTT/QyOBmYj8nagnAG2Aa8C3ykCbfSYi6R"
"b8ROhETkCuBqzBida4/TcfNCxDDmH+u0thf4VlX/TOM0hgh7UqXCIlILtGC2u/3S4LBUuGA/"
"AwfVvHniHBEibE4SAc3ArZgw3wd0q+pZh0THWpJzrD2rqlG71nF9N2GSttmer08CS5IIsBSj"
"ZAFDmLDdB/yKfcDxWOH8JObFizpr9Z7PE4FmzA03MzLNHq+qQ/FvM5J/PfAksBj4HXhOVfem"
"ESBrOBUgCKOqHpAFKgLkTSBvVATIm0DeqAiQN4G8UREgbwJ5oyJA3gTyRkWAvAnkjYoACdo6"
"KU7ExFlrmSOJAN8Dm7IiUoRHVfVUWXpKWMauAW4C3sFUfcPKzkntMPAisCTLjZdiK7koamt4"
"hfpdiz1OY2TJqw7zNukJRpbLjmGKoV2YkloXcEjTVGhLxH+OUEeDxifcOAAAAABJRU5ErkJg"
"gg==")
#----------------------------------------------------------------------
LookBackOption = PyEmbeddedImage(
"iVBORw0KGgoAAAANSUhEUgAAAEAAAABACAMAAACdt4HsAAAAA3NCSVQICAjb4U/gAAAACXBI"
"WXMAAAGcAAABnAH6gGQJAAAAGXRFWHRTb2Z0d2FyZQB3d3cuaW5rc2NhcGUub3Jnm+48GgAA"
"AaFQTFRF////AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA"
"AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA"
"AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA"
"AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA"
"AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA"
"AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA"
"AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA"
"AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAxZP4DgAAAIp0"
"Uk5TAAECAwQFBwgJDQ4PEBIUFhcZGhscHh8iIyYnKCorLTM0Njc6PEZJTE1OT1BRU1VWXF5f"
"ZGdpamxtbnBxdHV3eH2AgYKGiImKjI+QkpSWnJ2en6GjpaanqaqrrK2xuLm6vr/Aw8XGx8jJ"
"y8zQ0dTX2Nna3N3e4OLj6Onq6+zu7/L09fb3+fr7/P3+brfoEQAAAnpJREFUWMPtV2lD00AQ"
"HVraKlYqHlW8UBAVUPFWVFRUsIp4n2BVPFFQEW21gAeUo+RX285u0t1kj0m/6vuUvJn3kmxm"
"d2cB/oOCRDpVV6N0defQ6KdfjuMs5t+OXGmLhFOvO/9y0ZFQuNW5iixvujbvKDDTEyPJU4NF"
"R4OpboK+/YdjwNgOizzWvyKkL+THnmYnCiL1e7958N5Vxc9OpvkfrN91edzjSxcM+saPbtr3"
"Yw1yKN3vDez9uE6/1n3Oz95EMLrh5jIP39Xo17znCcNJdcLOSZ7Qq44/YdGVi9riTWb5OOxT"
"Rbv5MHd4TH0fw2GPiWZ40nZF/cywWJcwk/gbZ4W0QUaNBg2GWeQGmA2izxm3169vZfx43GIA"
"STaSE/7p+RDpP1vBZgAtrDCPyPqNS8geBbsB/1vfZHIAyQ9AMdjGCqpF5CKzyJ0jGcBtZC9J"
"NcZKaBPNYA8bb5E6gdRroBlEppFOC9QDZHqIBnAH6eMCk8cKb6IaHEB6AK7mXCCx7N3mHkVF"
"gyJnz7gGzUjfg8Qrzcr3NSW9gYs+16ABb1+UV5BJpX62GcwGMFe5/Vy+2FxQ6IttYDOYwjld"
"udo9F9CXDkIIA+go+Q3Ogt3A+4QyTvmyroPdwBtEREZKGhHmeTwnI/AbGeoeC/o3pC3YLST3"
"Tavl8KWRtP/6S9krh+ktJH1wMvFymG+l9Q+K6YzlUOqi6VULCpbDaaJetaRVyiFD1KsXVYBD"
"1HZOvazTodtYqNBubVToN1fa8w3bO+n7jQ2GHbYWxwJ7k2UEqc3Tgd5oKrA+XKurmDkhm22z"
"Aand1xtQDxxqg1BHHtmgpkNXFbGaj33/Bv4C7Cs45u1y0kgAAAAASUVORK5CYII=")
#----------------------------------------------------------------------
VanillaOption = PyEmbeddedImage(
"iVBORw0KGgoAAAANSUhEUgAAAEAAAABACAMAAACdt4HsAAAAA3NCSVQICAjb4U/gAAAACXBI"
"WXMAAAJbAAACWwFBeK9IAAAAGXRFWHRTb2Z0d2FyZQB3d3cuaW5rc2NhcGUub3Jnm+48GgAA"
"AddQTFRF////AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA"
"AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA"
"AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA"
"AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA"
"AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA"
"AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA"
"AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA"
"AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA"
"AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAJ/rhuQAAAJx0"
"Uk5TAAECAwUGBwgJCgsNDhAREhMXGBkbHh8gISIjJCYoKisuMDEzNTY3ODs+P0FCQ0RISU1O"
"UVNUVVZXWV1eX2BhYmNkZWZoam9ydnh6fH6BgoOEhYaHiImLlZeYmZudoqOlpqipqq2ur7Cx"
"tbe4ubq7vL/Cw8XHyMzP0NHS09fY2dvc4OHj5OXm5+jp6+zt7u/w8vP09vf4+fr7/P3+JuId"
"/QAAAqhJREFUGBnlwftbk2UAx+Hvkm1SjlQ0wMwsRS2ygwyNskCRxHPTICrJNA3MbNraMipC"
"MFFpE1cy4P38sT7oLq6xPe/eQz9235KrdZ1fZCaLxcnMl/tfVHDbLiywYuny6wpm/TcOq13Z"
"oAA67lD2sEDZvXfk2wdLGE763P4tEbV1nb3uYDiH5VNyEePPN7Viz+8YzofyZUcJWBxcqwqx"
"T0vA4h750PAr8M9uVdlRBP6Iy9txjCOq0YNxRp7is8BlLdvZoUqXgEKjvPQCU02SNl5w5l9Q"
"hcRt4LC8ZIFeSYnfgC5V6gUy8hArAW2SrmOcU6U2YD6m+nYBU5LexrjYrFWmgN2q7zQwIikN"
"fKUqI0BK9UTOYvRIDfMwt1ZVPsZIReQqOsayl6XXgG9V7SWWjUblZgij0CXpEHBKNd4vYAzJ"
"xV4HSG+S9MoUMKBam9KAs1d240A2JumNPMZHsohlgXFZtQOFZkmNf2PcaJJNcwFol80wMCij"
"H1g6s0Z2g8CwbHLAZhkpKHTIzWYgJ5v7kJcPebgvi6gDOfmQAycqi1mYkA8TMCuba1CKylO0"
"BNdkkwL65KkPSMmmE7i7XR623wU6ZRO/BRSTqitZBG7FZbV1DnCu9rfKRWv/VQeY2yoX3TxT"
"unfbYqbEM91ydXAaT9MHVUd8IE9d+YG46kskj52/OfPYYubm+WPJhP4fvvthbNRiLP29/PkZ"
"Fz/Jn/dw8a78iYxj9Yv86sbqgPxaM4nFxHPyrQ+LHvkX/Ysa0w0K4Cg1PlEQz89S5UGjAjlJ"
"lRMKpukRq8wlFNAQq3ymoJofU+HfDQpsmAqfK7jWBVYstCiEr1kxojBedShztimUUcquKJx2"
"ynYqpDRP/aiw3uKpDoWWxcgqvH0Y+xReJAOZiP6DllyuRXU9AXB3E/FsJnLpAAAAAElFTkSu"
"QmCC")
| 72.492386
| 78
| 0.861494
| 442
| 14,281
| 27.834842
| 0.932127
| 0.128749
| 0.15801
| 0.163862
| 0.085833
| 0.085833
| 0.07998
| 0
| 0
| 0
| 0
| 0.108767
| 0.063931
| 14,281
| 196
| 79
| 72.862245
| 0.811565
| 0.032001
| 0
| 0.104972
| 1
| 0
| 0.892653
| 0.892074
| 0
| 1
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.005525
| 0
| 0.005525
| 0
| 0
| 0
| 1
| null | 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 1
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
3a14903ea7bd0b540425e0813e50ee827bf95f35
| 4,431
|
py
|
Python
|
tests/core/validation/test_transaction_validation.py
|
dbfreem/py-evm
|
02a1f6f38884b1f7a89640c2095ea5b0f20687c3
|
[
"MIT"
] | 1,641
|
2017-11-24T04:24:22.000Z
|
2022-03-31T14:59:30.000Z
|
tests/core/validation/test_transaction_validation.py
|
UniqueMR/py-evm
|
026ee20f8d9b70d7c1b6a4fb9484d5489d425e54
|
[
"MIT"
] | 1,347
|
2017-11-23T10:37:36.000Z
|
2022-03-20T16:31:44.000Z
|
tests/core/validation/test_transaction_validation.py
|
UniqueMR/py-evm
|
026ee20f8d9b70d7c1b6a4fb9484d5489d425e54
|
[
"MIT"
] | 567
|
2017-11-22T18:03:27.000Z
|
2022-03-28T17:49:08.000Z
|
import pytest
from eth.vm.forks.london.transactions import UnsignedDynamicFeeTransaction
from eth.vm.forks.berlin.transactions import UnsignedAccessListTransaction
from eth_utils import ValidationError
@pytest.mark.parametrize(
"unsigned_access_list_transaction,is_valid",
(
# While ethereum tests do not yet have Berlin or London transaction tests,
# this adds a few tests to test some obvious cases, especially positive test cases.
(UnsignedAccessListTransaction(
chain_id=123456789,
nonce=0,
gas_price=1000000000,
gas=40000,
to=b'\xf0' * 20,
value=0,
data=b'',
access_list=((b'\xf0' * 20, (1, 2)),),
), True),
(UnsignedAccessListTransaction(
chain_id=0,
nonce=0,
gas_price=0,
gas=0,
to=b'\xf0' * 20,
value=0,
data=b'',
access_list=(),
), True),
(UnsignedAccessListTransaction(
chain_id=123456789,
nonce=0,
gas_price=1000000000,
gas=40000,
to=b'\xf0' * 20,
value=0,
data=b'',
access_list=((b'\xf0' * 20, ()),),
), True),
(UnsignedAccessListTransaction(
chain_id=123456789,
nonce=0,
gas_price=1000000000,
gas=40000,
to=b'\xf0' * 20,
value=0,
data=b'',
access_list=((b'\xf0' * 19, (1,)),), # access_list address fails validation
), False),
(UnsignedAccessListTransaction(
chain_id='1', # chain_id fails validation
nonce=0,
gas_price=0,
gas=0,
to=b'\xf0' * 20,
value=0,
data=b'',
access_list=(),
), False),
)
)
def test_validate_unsigned_access_list_transaction(unsigned_access_list_transaction, is_valid):
if is_valid:
unsigned_access_list_transaction.validate()
else:
with pytest.raises(ValidationError):
unsigned_access_list_transaction.validate()
@pytest.mark.parametrize(
"unsigned_dynamic_fee_transaction,is_valid",
(
# While ethereum tests do not yet have Berlin or London transaction tests,
# this adds a few tests to test some obvious cases, especially positive test cases.
(UnsignedDynamicFeeTransaction(
chain_id=123456789,
nonce=0,
max_fee_per_gas=1000000000,
max_priority_fee_per_gas=1000000000,
gas=40000,
to=b'\xf0' * 20,
value=0,
data=b'',
access_list=((b'\xf0' * 20, (1, 2)),),
), True),
(UnsignedDynamicFeeTransaction(
chain_id=0,
nonce=0,
max_fee_per_gas=0,
max_priority_fee_per_gas=0,
gas=0,
to=b'\xf0' * 20,
value=0,
data=b'',
access_list=(),
), True),
(UnsignedDynamicFeeTransaction(
chain_id=123456789,
nonce=0,
max_fee_per_gas=1000000000,
max_priority_fee_per_gas=1000000000,
gas=40000,
to=b'\xf0' * 20,
value=0,
data=b'',
access_list=((b'\xf0' * 20, ()),),
), True),
(UnsignedDynamicFeeTransaction(
chain_id=123456789,
nonce=0,
max_fee_per_gas=1000000000,
max_priority_fee_per_gas=1000000000,
gas=40000,
to=b'\xf0' * 20,
value=0,
data=b'',
access_list=((b'\xf0' * 19, (1,)),), # access_list address fails validation
), False),
(UnsignedDynamicFeeTransaction(
chain_id='1', # chain_id fails validation
nonce=0,
max_fee_per_gas=1000000000,
max_priority_fee_per_gas=1000000000,
gas=0,
to=b'\xf0' * 20,
value=0,
data=b'',
access_list=(),
), False),
)
)
def test_validate_unsigned_dynamic_fee_transaction(unsigned_dynamic_fee_transaction, is_valid):
if is_valid:
unsigned_dynamic_fee_transaction.validate()
else:
with pytest.raises(ValidationError):
unsigned_dynamic_fee_transaction.validate()
| 31.204225
| 95
| 0.538479
| 457
| 4,431
| 4.991247
| 0.159737
| 0.074529
| 0.036826
| 0.035072
| 0.833406
| 0.783428
| 0.743972
| 0.713284
| 0.658922
| 0.632617
| 0
| 0.098592
| 0.359061
| 4,431
| 141
| 96
| 31.425532
| 0.704577
| 0.098172
| 0
| 0.877863
| 0
| 0
| 0.037121
| 0.020567
| 0
| 0
| 0
| 0
| 0
| 1
| 0.015267
| false
| 0
| 0.030534
| 0
| 0.045802
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
3a37fecb08f7c959192857aa8caa64a101fca251
| 88
|
py
|
Python
|
src/apps/trainings/managers/network_pandas_queryset.py
|
sanderland/katago-server
|
6414fab080d007c05068a06ff4f25907b92848bd
|
[
"MIT"
] | 27
|
2020-05-03T11:01:27.000Z
|
2022-03-17T05:33:10.000Z
|
src/apps/trainings/managers/network_pandas_queryset.py
|
sanderland/katago-server
|
6414fab080d007c05068a06ff4f25907b92848bd
|
[
"MIT"
] | 54
|
2020-05-09T01:18:41.000Z
|
2022-01-22T10:31:15.000Z
|
src/apps/trainings/managers/network_pandas_queryset.py
|
sanderland/katago-server
|
6414fab080d007c05068a06ff4f25907b92848bd
|
[
"MIT"
] | 9
|
2020-09-29T11:31:32.000Z
|
2022-03-09T01:37:50.000Z
|
from django.db.models import QuerySet
class NetworkPandasQuerySet(QuerySet):
pass
| 14.666667
| 38
| 0.795455
| 10
| 88
| 7
| 0.9
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.147727
| 88
| 5
| 39
| 17.6
| 0.933333
| 0
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| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0.333333
| 0.333333
| 0
| 0.666667
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 1
| 0
| 1
| 0
|
0
| 6
|
28b93ab00455aaf88ae61353248e105753ebd46c
| 39
|
py
|
Python
|
pyhumio/__init__.py
|
dsb-automation/pyhumio
|
25ba7955659c4d12219f0d107858609c572fcfc9
|
[
"MIT"
] | null | null | null |
pyhumio/__init__.py
|
dsb-automation/pyhumio
|
25ba7955659c4d12219f0d107858609c572fcfc9
|
[
"MIT"
] | 4
|
2020-01-03T13:57:22.000Z
|
2020-01-09T06:56:12.000Z
|
pyhumio/__init__.py
|
dsb-automation/pyhumio
|
25ba7955659c4d12219f0d107858609c572fcfc9
|
[
"MIT"
] | null | null | null |
from .humio_handler import HumioHandler
| 39
| 39
| 0.897436
| 5
| 39
| 6.8
| 1
| 0
| 0
| 0
| 0
| 0
| 0
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| 0
| 0.076923
| 39
| 1
| 39
| 39
| 0.944444
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| 1
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| 1
| 0
|
0
| 6
|
28d071e92ce6f962f5bd2401eae1b192a2314d6c
| 53
|
py
|
Python
|
sdk/exception/validation_failed_exception.py
|
CLG0125/elemesdk
|
344466398bad7cf026e082e47c77d3ca98621ef3
|
[
"MIT"
] | 1
|
2021-04-03T05:11:29.000Z
|
2021-04-03T05:11:29.000Z
|
sdk/exception/validation_failed_exception.py
|
CLG0125/elemesdk
|
344466398bad7cf026e082e47c77d3ca98621ef3
|
[
"MIT"
] | null | null | null |
sdk/exception/validation_failed_exception.py
|
CLG0125/elemesdk
|
344466398bad7cf026e082e47c77d3ca98621ef3
|
[
"MIT"
] | null | null | null |
class ValidationFailedException(Exception):pass
| 17.666667
| 47
| 0.811321
| 4
| 53
| 10.75
| 1
| 0
| 0
| 0
| 0
| 0
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| 0
| 0
| 0
| 0
| 0
| 0.132075
| 53
| 3
| 48
| 17.666667
| 0.934783
| 0
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| 0
| 0
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| 0
| 0
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| 0
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| true
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| null | 0
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| 0
| 0
| 0
| 1
| 1
| 0
| 0
| 1
| 0
|
0
| 6
|
e921e1cf8ba174583d008a8bc6e545b14fa1daff
| 21
|
py
|
Python
|
calabro/widgets/__init__.py
|
CarlosGabaldon/calabro
|
11aec7fc30517ccc8a0b9f7468a803508ec56cf7
|
[
"MIT"
] | 2
|
2015-12-03T16:38:26.000Z
|
2016-05-08T08:34:36.000Z
|
calabro/widgets/__init__.py
|
CarlosGabaldon/calabro
|
11aec7fc30517ccc8a0b9f7468a803508ec56cf7
|
[
"MIT"
] | null | null | null |
calabro/widgets/__init__.py
|
CarlosGabaldon/calabro
|
11aec7fc30517ccc8a0b9f7468a803508ec56cf7
|
[
"MIT"
] | 2
|
2020-10-27T05:26:11.000Z
|
2021-03-24T18:10:20.000Z
|
from widgets import *
| 21
| 21
| 0.809524
| 3
| 21
| 5.666667
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.142857
| 21
| 1
| 21
| 21
| 0.944444
| 0
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| 0
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| 0
| true
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| 1
| 1
| 0
| null | 0
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| 0
| 0
| 0
| 0
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| 0
| 0
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| 1
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| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
3a7570710b5a36e6b5ff9f11a6ff0fe0b03b1d9b
| 146
|
py
|
Python
|
racing_rl/training/utils.py
|
luigiberducci/racing-rl
|
37b265d16eda4313a0b30550222ed731131829ab
|
[
"MIT"
] | 5
|
2021-12-08T17:25:38.000Z
|
2022-02-08T09:36:14.000Z
|
racing_rl/training/utils.py
|
luigiberducci/racing-rl
|
37b265d16eda4313a0b30550222ed731131829ab
|
[
"MIT"
] | null | null | null |
racing_rl/training/utils.py
|
luigiberducci/racing-rl
|
37b265d16eda4313a0b30550222ed731131829ab
|
[
"MIT"
] | null | null | null |
import numpy as np
import random
import torch
def seeding(seed: int):
np.random.seed(seed)
random.seed(seed)
torch.manual_seed(seed)
| 16.222222
| 27
| 0.719178
| 23
| 146
| 4.521739
| 0.478261
| 0.230769
| 0.269231
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.184932
| 146
| 9
| 27
| 16.222222
| 0.87395
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.142857
| false
| 0
| 0.428571
| 0
| 0.571429
| 0
| 1
| 0
| 0
| null | 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
3aa32962d64e1eacf24c70a96b03b8b8ae9ab2a7
| 83
|
py
|
Python
|
applitools/target.py
|
applitools/eyes.selenium.python
|
3a09a3372a3a8915b3c97ee54fc223580c45c0a3
|
[
"Apache-2.0"
] | 11
|
2016-04-20T21:21:37.000Z
|
2020-04-27T19:46:56.000Z
|
applitools/target.py
|
applitools/eyes.selenium.python
|
3a09a3372a3a8915b3c97ee54fc223580c45c0a3
|
[
"Apache-2.0"
] | 15
|
2017-01-11T04:58:31.000Z
|
2019-09-13T18:00:35.000Z
|
applitools/target.py
|
applitools/eyes.selenium.python
|
3a09a3372a3a8915b3c97ee54fc223580c45c0a3
|
[
"Apache-2.0"
] | 15
|
2016-03-23T22:06:39.000Z
|
2020-06-14T09:11:58.000Z
|
from applitools.selenium.target import * # noqa
from applitools.core import logger
| 27.666667
| 47
| 0.819277
| 11
| 83
| 6.181818
| 0.727273
| 0.411765
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.120482
| 83
| 2
| 48
| 41.5
| 0.931507
| 0.048193
| 0
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| true
| 0
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| null | 1
| 0
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| null | 0
| 0
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| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
3af1d8e881bccb47a8d735f192d06cfc668b7e78
| 103
|
py
|
Python
|
toolbox/metrics/metrics_base.py
|
ML-Dashboard/ML-ToolBox
|
c724a5f2705262737c4d0456fb6087851d3aa656
|
[
"MIT"
] | null | null | null |
toolbox/metrics/metrics_base.py
|
ML-Dashboard/ML-ToolBox
|
c724a5f2705262737c4d0456fb6087851d3aa656
|
[
"MIT"
] | null | null | null |
toolbox/metrics/metrics_base.py
|
ML-Dashboard/ML-ToolBox
|
c724a5f2705262737c4d0456fb6087851d3aa656
|
[
"MIT"
] | null | null | null |
from abc import ABC
from toolbox.trackable import Trackable
class Metrics(Trackable, ABC):
pass
| 12.875
| 39
| 0.76699
| 14
| 103
| 5.642857
| 0.571429
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.184466
| 103
| 7
| 40
| 14.714286
| 0.940476
| 0
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| 0
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| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0.25
| 0.5
| 0
| 0.75
| 0
| 1
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| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
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| 1
| 0
| 0
| 0
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| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 1
| 0
| 1
| 0
|
0
| 6
|
c91659e395352cdd02a8e67c10713c2c817ca195
| 120
|
py
|
Python
|
barcoderegression/__init__.py
|
jacksonloper/barcoderegression
|
6c819c7dfe5996e73bc61590af5a744f913ddd02
|
[
"MIT"
] | 1
|
2020-03-19T17:34:43.000Z
|
2020-03-19T17:34:43.000Z
|
barcoderegression/__init__.py
|
jacksonloper/barcoderegression
|
6c819c7dfe5996e73bc61590af5a744f913ddd02
|
[
"MIT"
] | null | null | null |
barcoderegression/__init__.py
|
jacksonloper/barcoderegression
|
6c819c7dfe5996e73bc61590af5a744f913ddd02
|
[
"MIT"
] | null | null | null |
from . import parameters
from . import helpers
from . import updates
from . import simulations
from . import training
| 15
| 25
| 0.775
| 15
| 120
| 6.2
| 0.466667
| 0.537634
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.183333
| 120
| 7
| 26
| 17.142857
| 0.94898
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
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| 1
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| 1
| 0
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
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| 0
| 0
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| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
a3936acfb6f62187fd8f9bdfd876ce1491d2035a
| 3,532
|
py
|
Python
|
src/preprocessing/minmax.py
|
kjhall01/xcast
|
17edfd8c79c5163b004800170bbcf21302e8c792
|
[
"MIT"
] | 11
|
2021-11-01T21:38:17.000Z
|
2022-03-30T11:46:32.000Z
|
src/preprocessing/minmax.py
|
kjhall01/xcast
|
17edfd8c79c5163b004800170bbcf21302e8c792
|
[
"MIT"
] | 7
|
2021-10-30T16:55:47.000Z
|
2021-12-04T18:51:50.000Z
|
src/preprocessing/minmax.py
|
kjhall01/xcast
|
17edfd8c79c5163b004800170bbcf21302e8c792
|
[
"MIT"
] | 1
|
2021-11-18T10:35:29.000Z
|
2021-11-18T10:35:29.000Z
|
from ..core.utilities import *
class MinMax:
def __init__(self, min=-1, max=1):
self.range_min, self.range_max = min, max
self.range = max - min
self.min, self.max, self.x_range = None, None, None
def fit(self, X, x_lat_dim=None, x_lon_dim=None, x_sample_dim=None, x_feature_dim=None):
x_lat_dim, x_lon_dim, x_sample_dim, x_feature_dim = guess_coords(X, x_lat_dim, x_lon_dim, x_sample_dim, x_feature_dim)
check_all(X, x_lat_dim, x_lon_dim, x_sample_dim, x_feature_dim)
self.sample_dim, self.lat_dim, self.lon_dim, self.feature_dim = x_sample_dim, x_lat_dim, x_lon_dim, x_feature_dim
X1 = X.isel()
self.min = X1.min(x_sample_dim)
self.max = X1.max(x_sample_dim)
self.x_range = self.max - self.min
self.x_range = self.x_range.where(self.x_range != 0, other=1)
def transform(self, X, x_lat_dim=None, x_lon_dim=None, x_sample_dim=None, x_feature_dim=None):
x_lat_dim, x_lon_dim, x_sample_dim, x_feature_dim = guess_coords(X, x_lat_dim, x_lon_dim, x_sample_dim, x_feature_dim)
check_all(X, x_lat_dim, x_lon_dim, x_sample_dim, x_feature_dim)
#X1 = X.rename({x_lat_dim: self.lat_dim, x_lon_dim: self.lon_dim, x_sample_dim: self.sample_dim})
self.min = self.min.rename({ self.lat_dim:x_lat_dim, self.lon_dim:x_lon_dim, self.feature_dim:x_feature_dim})
self.max = self.max.rename({ self.lat_dim:x_lat_dim, self.lon_dim:x_lon_dim, self.feature_dim:x_feature_dim})
self.x_range = self.max.rename({ self.lat_dim:x_lat_dim, self.lon_dim:x_lon_dim, self.feature_dim:x_feature_dim})
self.sample_dim, self.lat_dim, self.lon_dim, self.feature_dim = x_sample_dim, x_lat_dim, x_lon_dim, x_feature_dim
assert self.min is not None and self.max is not None, '{} Must Fit MinMaxScaler before transform'.format(dt.datetime.now())
r = ((X - self.min) / self.x_range) * self.range + self.range_min
r.attrs['generated_by'] = r.attrs['generated_by'] + '\n XCAST MinMax Transform' if 'generated_by' in r.attrs.keys() else '\n XCAST MinMax Transform'
return r
def inverse_transform(self, X, x_lat_dim=None, x_lon_dim=None, x_sample_dim=None, x_feature_dim=None):
x_lat_dim, x_lon_dim, x_sample_dim, x_feature_dim = guess_coords(X, x_lat_dim, x_lon_dim, x_sample_dim, x_feature_dim)
check_all(X, x_lat_dim, x_lon_dim, x_sample_dim, x_feature_dim)
assert self.min is not None and self.max is not None, '{} Must Fit MinMaxScaler before inverse transform'.format(dt.datetime.now())
self.min = self.min.rename({ self.lat_dim:x_lat_dim, self.lon_dim:x_lon_dim, self.feature_dim:x_feature_dim})
self.max = self.max.rename({ self.lat_dim:x_lat_dim, self.lon_dim:x_lon_dim, self.feature_dim:x_feature_dim})
self.x_range = self.max.rename({ self.lat_dim:x_lat_dim, self.lon_dim:x_lon_dim, self.feature_dim:x_feature_dim})
self.sample_dim, self.lat_dim, self.lon_dim, self.feature_dim = x_sample_dim, x_lat_dim, x_lon_dim, x_feature_dim
ret = []
for i in range(X.shape[list(X.dims).index(self.feature_dim)]):
sd = {x_feature_dim: i}
self.max.coords[self.feature_dim] = [X.coords[self.feature_dim].values[i]]
self.min.coords[self.feature_dim] = [X.coords[self.feature_dim].values[i]]
self.x_range.coords[self.feature_dim] = [X.coords[self.feature_dim].values[i]]
ret.append(((X.isel(**sd) - self.range_min) / self.range) * self.x_range + self.min)
r = xr.concat(ret, self.feature_dim)
r.attrs['generated_by'] = r.attrs['generated_by'] + '\n XCAST MinMax Inverse Transform' if 'generated_by' in r.attrs.keys() else '\n XCAST MinMax Inverse Transform'
return r
| 66.641509
| 169
| 0.75
| 682
| 3,532
| 3.532258
| 0.09824
| 0.102947
| 0.063927
| 0.078871
| 0.830635
| 0.761312
| 0.733084
| 0.733084
| 0.733084
| 0.733084
| 0
| 0.002567
| 0.11778
| 3,532
| 52
| 170
| 67.923077
| 0.770539
| 0.02718
| 0
| 0.386364
| 0
| 0
| 0.082702
| 0
| 0
| 0
| 0
| 0
| 0.045455
| 1
| 0.090909
| false
| 0
| 0.022727
| 0
| 0.181818
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
6e6e39e9a16da5cc73e65182a05f39d6f1b647b4
| 3,078
|
py
|
Python
|
tests/test_PerCarRaceStatusData.py
|
jdamiani27/PyRaceView
|
9c62861e2b659407948c77a780f47c95482cdfbf
|
[
"MIT"
] | 4
|
2019-09-10T18:14:53.000Z
|
2022-01-08T02:01:01.000Z
|
tests/test_PerCarRaceStatusData.py
|
jdamiani27/pyraceview
|
9c62861e2b659407948c77a780f47c95482cdfbf
|
[
"MIT"
] | null | null | null |
tests/test_PerCarRaceStatusData.py
|
jdamiani27/pyraceview
|
9c62861e2b659407948c77a780f47c95482cdfbf
|
[
"MIT"
] | null | null | null |
import pytest
from pyraceview.messages import MsgRaceStatus
raw = (
b"\xab\xcd*T\x02Cs\x04\x90\x90[3\x10\xcc\x89\xb8V\x00\x00"
b"\x00\x05q\xe0\x03 \x00y\x86\xb9\x00\x00$\x00\x10\x10\x06"
b"\x0e\xe8\x00\x06\x0f\x91k\xe5\x00\x00&\x00\x15\xce\x05\xb9x"
b"\x00\x07\t\x8d^\x8d\x00\x00\x16\x00\x1c\xc2\x05\xcb\xa1\x90"
b"\x05\xdd\x91L\xb3\x00\x00\x04\x00!\xece\xb1\xf9\x90\x07\x9f"
b"\x91@\xe5\x00\x00(\x00%R\x05P\xc8\x00\x05G\x8d2a\x00\x00\x0c"
b"\x00'\xe8\x05d\x01\x90\x07;\x911i\x00\x00\x08\x00+z\x05'!"
b"\x90\x07m\x91&S\x00\x00\x12\x005\x08\x05\x1ba\x90\x07m\x91"
b"\x00\xb5\x00\x00T\x006\xaa\x05\x00\xa8\x00\x07\xd1\x90\xfaC"
b"\x00\x00`\x00<\x1c\x05\xa9q\x90\t/\x8c\xe8g\x00\x00\x1c\x00=>"
b'\x053\xf1\x90\x08\x99\x90\xe2\xd3\x00\x00"\x00?(\x05jh\x00\ta'
b"\x8c\xdb\xcf\x00\x00\x18\x00@\xa6\xe0\x00\x01\x90\x08g\x90\xd6"
b"\xe7\x00\x00\x02\x00A\xec\xe0\x00\x00\x00\x08\x03\x8c\xd3\xc9"
b"\x00\x00R\x00E\xde\x00\x00\x01\x90\ta\x90\xc3\xa3\x00\x00\xbe"
b"\x00If\x00\x00\x00\x00\x08\x99\x8c\xb9\x9f\x00\x00\x14\x00p$"
b"\x00\x00\x01\x90\x08\x99\x8c\x16\xdb\x00\x00D\x00\xbeD\x05\x95!"
b"\x90\x07\x9e~\x04Q\x00\x00,\x00\xc1\xde\x05\xaf\xd9\x90\x084}"
b"\xf7\xd9\x00\x00L\x00\xdc\x88\x04\x8e`\x00\x06@i\x97\x9b\x00"
b"\x000\x10\x00\x02\x05\x8d\xa8\x00\x08\x98~\x15\x0b\x00\x00\xb0"
b"\x10\x00\x02\x05\xda\x91\x90\x08\x98}\xf1\xc7\x00\x00\x06\x10"
b"\x00\x02\x05\xc3\x10\x00\x05\xaa}\xeb\x9b\x00\x00\x1a\x10\x00"
b"\x02\x85\xb4!\x90\t\xc4}\xe0\xa1\x00\x00H\x10\x00\x02\x05\xb9y"
b"\x90\n(}\xd9\xa3\x00\x00@\x10\x00\x02\x05\xb0\xe9\x90\x08\x02m"
b"\xca\xe7\x00\x00*\x10\x00\x02\x06\x1b\xb9\x90\t\x92}\xc0\xc7\x00"
b"\x00\x10\x10\x00\x02\x04\x8b1\x90\x07:}\xaaE\x00\x00\x01\x10\x00"
b"\x02\x04\xa5\xe0\x00\x06@m\x9es\x00\x00^\x10\x00\x04\x06\x02\x18"
b"\x00\t`n\x0e{\x00\x00J\x10\x00\x04\x05\xb8a\x90\t.q\xc6\x9f\x00"
b"\x00f\x10\x00\x06\x04\xfbY\x90\x07:}\xb2\xc3\x00\x00h\x10\x00\x08"
b"\x04\xaeq\x93&\xa4q\xa9\xb9\x00\x00j\x10\x00\n\x05\xb9x\x00\x07"
b"\xd0u\xfcm\x00\x006\x10\x00\x0c\x05\x7f\xc8\x00\t\xc4u\xd0\xb7"
b"\x00\x00\x9a\x10\x00\x0c\x04\x98\x00\x00\x06@u\x8b\xdd\x00\x00"
b"\x1e\x10\x00J\x04\xa5\xe0\x00\x04~\xc5\x92\x1d\x00\x00"
)
@pytest.fixture
def status():
return MsgRaceStatus(raw).car_data[-1]
def test_car_id(status):
assert status.car_id == 30
def test_status(status):
assert status.status == 0
def test_tol_type(status):
assert status.tol_type == 1
def test_time_off_leader(status):
assert status.time_off_leader == 37.0
def test_event(status):
assert status.event == 0
def test_speed(status):
assert status.speed == 38.076
def test_throttle(status):
assert status.throttle == 0
def test_brake(status):
assert status.brake == 0
def test_rpm(status):
assert status.rpm == 2300
def test_fuel(status):
assert status.fuel == 49
def test_steer_angle(status):
assert status.steer_angle == 0
def test_lap_fraction(status):
assert status.lap_fraction == 0.5147
| 33.456522
| 72
| 0.677063
| 630
| 3,078
| 3.268254
| 0.320635
| 0.119475
| 0.104905
| 0.023312
| 0.01457
| 0
| 0
| 0
| 0
| 0
| 0
| 0.28
| 0.106563
| 3,078
| 91
| 73
| 33.824176
| 0.468727
| 0
| 0
| 0
| 0
| 0.507937
| 0.630604
| 0.624756
| 0
| 0
| 0
| 0
| 0.190476
| 1
| 0.206349
| false
| 0
| 0.031746
| 0.015873
| 0.253968
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 1
| 1
| 1
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
42d7eadf9caf3c559e38e0388f54bfdb1cc28385
| 359
|
py
|
Python
|
9_Lesson9/exceptions/example4-else.py
|
turovod/Otus
|
57433c6944bca155177b07ff361139ff30f7f692
|
[
"MIT"
] | null | null | null |
9_Lesson9/exceptions/example4-else.py
|
turovod/Otus
|
57433c6944bca155177b07ff361139ff30f7f692
|
[
"MIT"
] | null | null | null |
9_Lesson9/exceptions/example4-else.py
|
turovod/Otus
|
57433c6944bca155177b07ff361139ff30f7f692
|
[
"MIT"
] | null | null | null |
my_dict = {"a": 1, "b": 2, "c": 3}
# Without finally
try:
value = my_dict["a"]
except KeyError:
print("A KeyError occurred!")
else:
print("No error occurred!")
# With finally
try:
value = my_dict["a"]
except KeyError:
print("A KeyError occurred!")
else:
print("No error occurred!")
finally:
print("The finally statement ran!")
| 16.318182
| 39
| 0.623955
| 50
| 359
| 4.42
| 0.46
| 0.081448
| 0.095023
| 0.153846
| 0.742081
| 0.742081
| 0.742081
| 0.742081
| 0.742081
| 0.742081
| 0
| 0.010638
| 0.214485
| 359
| 21
| 40
| 17.095238
| 0.77305
| 0.077994
| 0
| 0.8
| 0
| 0
| 0.32622
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0.333333
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
6e20205fbd1c98e8f2cf3708b6365082d71f74ab
| 7,100
|
py
|
Python
|
chess/ChessConstants.py
|
vinoo999/alpha-zero-general
|
01bd6ac40d7b1fed97a84e37f7a549be8d50f668
|
[
"MIT"
] | 2
|
2018-04-01T05:08:44.000Z
|
2018-04-20T01:58:46.000Z
|
chess/ChessConstants.py
|
vinoo999/alpha-zero-general
|
01bd6ac40d7b1fed97a84e37f7a549be8d50f668
|
[
"MIT"
] | null | null | null |
chess/ChessConstants.py
|
vinoo999/alpha-zero-general
|
01bd6ac40d7b1fed97a84e37f7a549be8d50f668
|
[
"MIT"
] | null | null | null |
#######################################################
# BOARD CONSTANTS
#######################################################
BLACK = 'b'
WHITE = 'w'
EMPTY = -1
PAWN = 'p'
KNIGHT = 'n'
BISHOP = 'b'
ROOK = 'r'
QUEEN = 'q'
KING = 'k'
SYMBOLS = 'pnbrqkPNBRQK'
DEFAULT_POSITION = 'rnbqkbnr/pppppppp/8/8/8/8/PPPPPPPP/RNBQKBNR w KQkq - 0 1'
POSSIBLE_RESULTS = ['1-0', '0-1', '1/2-1/2', '*']
# Directions pawns can move: forward 1, forward 2, right (capture), left (capture)
PAWN_OFFSETS = {
'b': [16, 32, 17, 15],
'w': [-16, -32, -17, -15]
}
# Directions different pieces can move
PIECE_OFFSETS = {
'n': [-18, -33, -31, -14, 18, 33, 31, 14],
'b': [-17, -15, 17, 15],
'r': [-16, 1, 16, -1],
'q': [-17, -16, -15, 1, 17, 16, 15, -1],
'k': [-17, -16, -15, 1, 17, 16, 15, -1]
}
MCTS_MAPPING = {
'p' : 1,
'n' : 2,
'b' : 3,
'r' : 4,
'q' : 5,
'k' : 6,
}
MCTS_DECODER = {
1 : 'p',
2 : 'n',
3 : 'b',
4 : 'r',
5 : 'q',
6 : 'k'
}
MCTS_COLOR_MAP = { 'w' : 1, 'b' : -1 }
ATTACKS = [
20,0, 0, 0, 0, 0, 0, 24, 0, 0, 0, 0, 0, 0,20, 0,
0, 20,0, 0, 0, 0, 0, 24, 0, 0, 0, 0, 0,20, 0, 0,
0, 0, 20, 0, 0, 0, 0, 24, 0, 0, 0, 0,20, 0, 0, 0,
0, 0, 0,20, 0, 0, 0, 24, 0, 0, 0,20, 0, 0, 0, 0,
0, 0, 0, 0,20, 0, 0, 24, 0, 0,20, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0,20, 2, 24, 2,20, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 2,53, 56, 53, 2, 0, 0, 0, 0, 0, 0,
24,24,24,24,24,24,56, 0, 56,24,24,24,24,24,24, 0,
0, 0, 0, 0, 0, 2,53, 56, 53, 2, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0,20, 2, 24, 2,20, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0,20, 0, 0, 24, 0, 0,20, 0, 0, 0, 0, 0,
0, 0, 0,20, 0, 0, 0, 24, 0, 0, 0,20, 0, 0, 0, 0,
0, 0, 20, 0, 0, 0, 0, 24, 0, 0, 0, 0,20, 0, 0, 0,
0, 20,0, 0, 0, 0, 0, 24, 0, 0, 0, 0, 0,20, 0, 0,
20,0, 0, 0, 0, 0, 0, 24, 0, 0, 0, 0, 0, 0,20
]
RAYS = [
17, 0, 0, 0, 0, 0, 0, 16, 0, 0, 0, 0, 0, 0, 15, 0,
0, 17, 0, 0, 0, 0, 0, 16, 0, 0, 0, 0, 0, 15, 0, 0,
0, 0, 17, 0, 0, 0, 0, 16, 0, 0, 0, 0, 15, 0, 0, 0,
0, 0, 0, 17, 0, 0, 0, 16, 0, 0, 0, 15, 0, 0, 0, 0,
0, 0, 0, 0, 17, 0, 0, 16, 0, 0, 15, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 17, 0, 16, 0, 15, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 17, 16, 15, 0, 0, 0, 0, 0, 0, 0,
1, 1, 1, 1, 1, 1, 1, 0, -1, -1, -1,-1, -1, -1, -1, 0,
0, 0, 0, 0, 0, 0,-15,-16,-17, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0,-15, 0,-16, 0,-17, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0,-15, 0, 0,-16, 0, 0,-17, 0, 0, 0, 0, 0,
0, 0, 0,-15, 0, 0, 0,-16, 0, 0, 0,-17, 0, 0, 0, 0,
0, 0,-15, 0, 0, 0, 0,-16, 0, 0, 0, 0,-17, 0, 0, 0,
0,-15, 0, 0, 0, 0, 0,-16, 0, 0, 0, 0, 0,-17, 0, 0,
-15, 0, 0, 0, 0, 0, 0,-16, 0, 0, 0, 0, 0, 0,-17
]
SHIFTS = { 'p': 0, 'n': 1, 'b': 2, 'r': 3, 'q': 4, 'k': 5 }
FLAGS = {
'NORMAL': 'n',
'CAPTURE': 'c',
'BIG_PAWN': 'b',
'EP_CAPTURE': 'e',
'PROMOTION': 'p',
'KSIDE_CASTLE': 'k',
'QSIDE_CASTLE': 'q'
}
BITS = {
'NORMAL': 1,
'CAPTURE': 2,
'BIG_PAWN': 4,
'EP_CAPTURE': 8,
'PROMOTION': 16,
'KSIDE_CASTLE': 32,
'QSIDE_CASTLE': 64
}
RANK_1 = 7
RANK_2 = 6
RANK_3 = 5
RANK_4 = 4
RANK_5 = 3
RANK_6 = 2
RANK_7 = 1
RANK_8 = 0
SQUARES = {
'a8': 0, 'b8': 1, 'c8': 2, 'd8': 3, 'e8': 4, 'f8': 5, 'g8': 6, 'h8': 7,
'a7': 16, 'b7': 17, 'c7': 18, 'd7': 19, 'e7': 20, 'f7': 21, 'g7': 22, 'h7': 23,
'a6': 32, 'b6': 33, 'c6': 34, 'd6': 35, 'e6': 36, 'f6': 37, 'g6': 38, 'h6': 39,
'a5': 48, 'b5': 49, 'c5': 50, 'd5': 51, 'e5': 52, 'f5': 53, 'g5': 54, 'h5': 55,
'a4': 64, 'b4': 65, 'c4': 66, 'd4': 67, 'e4': 68, 'f4': 69, 'g4': 70, 'h4': 71,
'a3': 80, 'b3': 81, 'c3': 82, 'd3': 83, 'e3': 84, 'f3': 85, 'g3': 86, 'h3': 87,
'a2': 96, 'b2': 97, 'c2': 98, 'd2': 99, 'e2': 100, 'f2': 101, 'g2': 102, 'h2': 103,
'a1': 112, 'b1': 113, 'c1': 114, 'd1': 115, 'e1': 116, 'f1': 117, 'g1': 118, 'h1': 119
}
ROOKS = {
'w': [{'square': SQUARES['a1'], 'flag': BITS['QSIDE_CASTLE']},
{'square': SQUARES['h1'], 'flag': BITS['KSIDE_CASTLE']}],
'b': [{'square': SQUARES['a8'], 'flag': BITS['QSIDE_CASTLE']},
{'square': SQUARES['h8'], 'flag': BITS['KSIDE_CASTLE']}]
}
KING_EVAL = [
[ -3.0, -4.0, -4.0, -5.0, -5.0, -4.0, -4.0, -3.0],
[ -3.0, -4.0, -4.0, -5.0, -5.0, -4.0, -4.0, -3.0],
[ -3.0, -4.0, -4.0, -5.0, -5.0, -4.0, -4.0, -3.0],
[ -3.0, -4.0, -4.0, -5.0, -5.0, -4.0, -4.0, -3.0],
[ -2.0, -3.0, -3.0, -4.0, -4.0, -3.0, -3.0, -2.0],
[ -1.0, -2.0, -2.0, -2.0, -2.0, -2.0, -2.0, -1.0],
[ 2.0, 2.0, 0.0, 0.0, 0.0, 0.0, 2.0, 2.0 ],
[ 2.0, 3.0, 1.0, 0.0, 0.0, 1.0, 3.0, 2.0 ]
]
QUEEN_EVAL = [
[ -2.0, -1.0, -1.0, -0.5, -0.5, -1.0, -1.0, -2.0],
[ -1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -1.0],
[ -1.0, 0.0, 0.5, 0.5, 0.5, 0.5, 0.0, -1.0],
[ -0.5, 0.0, 0.5, 0.5, 0.5, 0.5, 0.0, -0.5],
[ 0.0, 0.0, 0.5, 0.5, 0.5, 0.5, 0.0, -0.5],
[ -1.0, 0.5, 0.5, 0.5, 0.5, 0.5, 0.0, -1.0],
[ -1.0, 0.0, 0.5, 0.0, 0.0, 0.0, 0.0, -1.0],
[ -2.0, -1.0, -1.0, -0.5, -0.5, -1.0, -1.0, -2.0]
]
ROOK_EVAL = [
[ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[ 0.5, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.5],
[ -0.5, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -0.5],
[ -0.5, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -0.5],
[ -0.5, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -0.5],
[ -0.5, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -0.5],
[ -0.5, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -0.5],
[ 0.0, 0.0, 0.0, 0.5, 0.5, 0.0, 0.0, 0.0]
]
BISHOP_EVAL = [
[ -2.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -2.0],
[ -1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -1.0],
[ -1.0, 0.0, 0.5, 1.0, 1.0, 0.5, 0.0, -1.0],
[ -1.0, 0.5, 0.5, 1.0, 1.0, 0.5, 0.5, -1.0],
[ -1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, -1.0],
[ -1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, -1.0],
[ -1.0, 0.5, 0.0, 0.0, 0.0, 0.0, 0.5, -1.0],
[ -2.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -2.0]
]
KNIGHT_EVAL = [
[-5.0, -4.0, -3.0, -3.0, -3.0, -3.0, -4.0, -5.0],
[-4.0, -2.0, 0.0, 0.0, 0.0, 0.0, -2.0, -4.0],
[-3.0, 0.0, 1.0, 1.5, 1.5, 1.0, 0.0, -3.0],
[-3.0, 0.5, 1.5, 2.0, 2.0, 1.5, 0.5, -3.0],
[-3.0, 0.0, 1.5, 2.0, 2.0, 1.5, 0.0, -3.0],
[-3.0, 0.5, 1.0, 1.5, 1.5, 1.0, 0.5, -3.0],
[-4.0, -2.0, 0.0, 0.5, 0.5, 0.0, -2.0, -4.0],
[-5.0, -4.0, -3.0, -3.0, -3.0, -3.0, -4.0, -5.0]
]
PAWN_EVAL = [
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0],
[1.0, 1.0, 2.0, 3.0, 3.0, 2.0, 1.0, 1.0],
[0.5, 0.5, 1.0, 2.5, 2.5, 1.0, 0.5, 0.5],
[0.0, 0.0, 0.0, 2.0, 2.0, 0.0, 0.0, 0.0],
[0.5, -0.5, -1.0, 0.0, 0.0, -1.0, -0.5, 0.5],
[0.5, 1.0, 1.0, -2.0, -2.0, 1.0, 1.0, 0.5],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
]
| 34.299517
| 91
| 0.350704
| 1,644
| 7,100
| 1.493917
| 0.120438
| 0.431596
| 0.49715
| 0.517915
| 0.584283
| 0.579397
| 0.540309
| 0.52728
| 0.495928
| 0.439739
| 0
| 0.348683
| 0.320986
| 7,100
| 206
| 92
| 34.466019
| 0.160755
| 0.018732
| 0
| 0.141243
| 0
| 0
| 0.06975
| 0.006275
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
6e3c5a9ddf06276d103c81ac8ad94fb0fb6514a5
| 118
|
py
|
Python
|
HW9/YuliiaKutsyk/task_9_3_adam_and_eve.py
|
kolyasalubov/Lv-677.PythonCore
|
c9f9107c734a61e398154a90b8a3e249276c2704
|
[
"MIT"
] | null | null | null |
HW9/YuliiaKutsyk/task_9_3_adam_and_eve.py
|
kolyasalubov/Lv-677.PythonCore
|
c9f9107c734a61e398154a90b8a3e249276c2704
|
[
"MIT"
] | null | null | null |
HW9/YuliiaKutsyk/task_9_3_adam_and_eve.py
|
kolyasalubov/Lv-677.PythonCore
|
c9f9107c734a61e398154a90b8a3e249276c2704
|
[
"MIT"
] | 6
|
2022-02-22T22:30:49.000Z
|
2022-03-28T12:51:19.000Z
|
def God():
return[Man(), Woman()]
class Human:
pass
class Man(Human):
pass
class Woman(Human):
pass
| 10.727273
| 26
| 0.59322
| 16
| 118
| 4.375
| 0.5
| 0.385714
| 0.4
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.262712
| 118
| 11
| 27
| 10.727273
| 0.804598
| 0
| 0
| 0.375
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.125
| true
| 0.375
| 0
| 0.125
| 0.5
| 0
| 1
| 0
| 0
| null | 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 1
| 0
| 0
|
0
| 6
|
281cb4af9b2a8025dc33a240eb0ab8b2185baf9e
| 79
|
py
|
Python
|
dev/frontend/controllers/__init__.py
|
frederikgram/describe
|
5c21edcf9b35811d34a9446eda34d5a92974c8e9
|
[
"MIT"
] | 2
|
2021-03-05T20:49:08.000Z
|
2021-03-10T01:32:19.000Z
|
prod/frontend/controllers/__init__.py
|
frederikgram/describe
|
5c21edcf9b35811d34a9446eda34d5a92974c8e9
|
[
"MIT"
] | 1
|
2020-03-24T19:54:42.000Z
|
2020-03-24T19:54:42.000Z
|
prod/frontend/controllers/__init__.py
|
frederikgram/describe
|
5c21edcf9b35811d34a9446eda34d5a92974c8e9
|
[
"MIT"
] | null | null | null |
from .template_builders import *
from .actions import *
from .startup import *
| 19.75
| 32
| 0.772152
| 10
| 79
| 6
| 0.6
| 0.333333
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.151899
| 79
| 3
| 33
| 26.333333
| 0.895522
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
28399693cca3a1d8ecb762bf723d2265891d81b3
| 531
|
py
|
Python
|
tests/conftest.py
|
zillow/aiographite
|
9597d55a0610df6e27579f2aa19d96089e68e1dc
|
[
"MIT"
] | 14
|
2016-08-20T08:36:50.000Z
|
2018-01-05T21:11:40.000Z
|
tests/conftest.py
|
zillow/aiographite
|
9597d55a0610df6e27579f2aa19d96089e68e1dc
|
[
"MIT"
] | 28
|
2016-08-23T20:23:29.000Z
|
2017-09-11T19:22:43.000Z
|
tests/conftest.py
|
zillow/aiographite
|
9597d55a0610df6e27579f2aa19d96089e68e1dc
|
[
"MIT"
] | 5
|
2016-11-14T22:30:16.000Z
|
2021-01-14T08:37:24.000Z
|
import pytest
@pytest.fixture
def metric_parts():
return ['sproc performance', '[email protected]', '::EH12']
@pytest.fixture
def timestamp():
return 1471640923
@pytest.fixture
def metric_value_tuple_list():
return [
('zillow', 124), ('trulia', 223),
('hotpad', 53534), ('streeteasy', 13424)]
@pytest.fixture
def metric_value_timestamp_list():
return [
('zillow', 124, 1471640958), ('trulia', 223, 1471640923),
('hotpad', 53534, 1471640943), ('streeteasy', 13424, 1471640989)]
| 20.423077
| 73
| 0.644068
| 56
| 531
| 5.982143
| 0.5
| 0.155224
| 0.191045
| 0.197015
| 0.161194
| 0
| 0
| 0
| 0
| 0
| 0
| 0.195349
| 0.190207
| 531
| 25
| 74
| 21.24
| 0.583721
| 0
| 0
| 0.352941
| 0
| 0
| 0.177024
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.235294
| true
| 0
| 0.058824
| 0.235294
| 0.529412
| 0
| 0
| 0
| 0
| null | 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| 1
| 1
| 0
|
0
| 6
|
28705f35215e5e57d817eb86b61bc419951baa0d
| 26
|
py
|
Python
|
routely/__init__.py
|
jhags/routely
|
975c1fc4acdc916014b6cf9bd6ff64ad00fdec6b
|
[
"MIT"
] | 1
|
2020-11-24T17:59:49.000Z
|
2020-11-24T17:59:49.000Z
|
routely/__init__.py
|
jhags/routely
|
975c1fc4acdc916014b6cf9bd6ff64ad00fdec6b
|
[
"MIT"
] | null | null | null |
routely/__init__.py
|
jhags/routely
|
975c1fc4acdc916014b6cf9bd6ff64ad00fdec6b
|
[
"MIT"
] | null | null | null |
from .routely import Route
| 26
| 26
| 0.846154
| 4
| 26
| 5.5
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.115385
| 26
| 1
| 26
| 26
| 0.956522
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
287bfc19d47e9525ab44fd369cb133542a7224d5
| 23,359
|
py
|
Python
|
MCMC_plotting.py
|
jlindsey1/MappingExoplanets
|
008028562805ea81b91b044f0ef5bdfed55b858c
|
[
"MIT"
] | null | null | null |
MCMC_plotting.py
|
jlindsey1/MappingExoplanets
|
008028562805ea81b91b044f0ef5bdfed55b858c
|
[
"MIT"
] | null | null | null |
MCMC_plotting.py
|
jlindsey1/MappingExoplanets
|
008028562805ea81b91b044f0ef5bdfed55b858c
|
[
"MIT"
] | null | null | null |
#!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
Created on Wed Nov 15 21:12:41 2017
@author: Jordan
"""
# The following plots several figures from the MCMC. Not all are relevant, but the code has been kept in this single file
# for simplicity.
print "Start..."
# Import modules
import numpy as np
import matplotlib.pyplot as plt
from lmfit.models import SkewedGaussianModel
import matplotlib.ticker as mticker
from mpl_toolkits.axes_grid1.inset_locator import inset_axes
# Import MCMC results
with open('hist_values1.txt') as f: hist_values1 = f.read().splitlines()
with open('hist_values2.txt') as f: hist_values2 = f.read().splitlines()
with open('hist_values3.txt') as f: hist_values3 = f.read().splitlines()
with open('hist_values4.txt') as f: hist_values4 = f.read().splitlines()
with open('hist_values5.txt') as f: hist_values5 = f.read().splitlines()
hist_values1=[float(i) for i in hist_values1]
hist_values2=[float(i) for i in hist_values2]
hist_values3=[float(i) for i in hist_values3]
hist_values4=[float(i) for i in hist_values4]
hist_values5=[float(i) for i in hist_values5]
# Double Ttot and Tfull as only half values were used in the MCMC (to simplify maths)
hist_values2=np.array(hist_values2)*2
hist_values5=np.array(hist_values5)*2
include_middle=True
if include_middle==True: inputfile='generated_data1'
if include_middle==False: inputfile='generated_data_nomid'
chi2file=np.genfromtxt(str(inputfile)+'.txt', names=True, delimiter=';',dtype=None)
modeldata1=np.genfromtxt('uniformingress1.txt', names=True, delimiter=';',dtype=None) #Uniform model
modeldata2=np.genfromtxt('uniformegress1.txt', names=True, delimiter=';',dtype=None)
#modeldata1=np.genfromtxt('nolimbingress1.txt', names=True, delimiter=';',dtype=None) #No-limb model
#modeldata2=np.genfromtxt('nolimbgress1.txt', names=True, delimiter=';',dtype=None)
# Import graph specifications
graphspecs=np.genfromtxt('graph_specs.txt', names=True, delimiter=';',dtype=None)
P_total,P_full,P,flux_star,t_occultation,Initial,Length,Nslices=graphspecs['P_total'],graphspecs['P_full'],graphspecs['P'],graphspecs['flux_star'],graphspecs['t_occultation'],graphspecs['Initial'],graphspecs['Length'],graphspecs['Nslices']
print P_total,P_full,P,flux_star,t_occultation,Initial,Length,Nslices
P_total_initial=P_total*2
P_full_initial=P_full*2
Initial_initial=Initial
savefigures=False
sigma_value=35*1e-6 #SD per point
mean=np.mean(hist_values1)
median=np.median(hist_values1)
standard_dev=np.std(hist_values1)
mean2=np.mean(hist_values2)
median2=np.median(hist_values2)
standard_dev2=np.std(hist_values2)
mean3=np.mean(hist_values5)
median3=np.median(hist_values5)
standard_dev3=np.std(hist_values5)
print "mean: ", mean, "SD: ", standard_dev, "Median: ", median
print "mean2: ", mean2, "SD2: ", standard_dev2, "Median2: ", median2
print "mean3: ", mean3, "SD3: ", standard_dev3, "Median3: ", median3
# Defines the model generation function
def generate_model(full,tot,mid,verbose):
Initial=mid
P_full=full
P_total=tot
if verbose==True: print "Details: ", Initial, P_full, P_total, Length
plotrange=np.linspace(-P_total+Initial,-P_full+Initial, num=Nslices)
plotrange2=np.linspace(P_full+Initial,P_total+Initial, num=Nslices)
stepdifference=np.abs(plotrange[0]-plotrange[1])
rangedifference=np.abs(plotrange2[0]-plotrange[-1])
Nsteps_needed=int(round(rangedifference/stepdifference))
plotrange3=np.linspace(plotrange[-1]+stepdifference,plotrange2[0]-stepdifference,num=Nsteps_needed)
uniform_curve_x,uniform_curve_y=[],[]
total_amount = np.sum(modeldata1['bin_values'])
for i in range(Nslices):
total_amount = total_amount - modeldata1['bin_values'][i]
fractional_flux = (total_amount+flux_star)/(flux_star)
uniform_curve_x.append(plotrange[i])
uniform_curve_y.append(fractional_flux)
if include_middle==True:
for i in range(len(plotrange3)):
uniform_curve_x.append(plotrange3[i])
uniform_curve_y.append(1.)
total_amount = 0
for i in range(Nslices):
total_amount = total_amount + modeldata2['bin_values'][Nslices-i-1]
fractional_flux = (total_amount+flux_star)/(flux_star)
uniform_curve_x.append(plotrange2[i])
uniform_curve_y.append(fractional_flux)
maxvalue=np.max(uniform_curve_y)
uniform_curve_x.append(1)
uniform_curve_y.append(maxvalue)
uniform_curve_x.insert(0,0)
uniform_curve_y.insert(0,maxvalue)
return uniform_curve_x,uniform_curve_y
interpolation_datax,interpolation_dataf=generate_model(0.00730,0.0080,0.50035,verbose=False)
plt.plot(interpolation_datax,interpolation_dataf)
plt.scatter(chi2file['x_values'],chi2file['flux_values'],c='b',s=8,lw=0)#,zorder=2)
if sigma_value!=0: plt.errorbar(chi2file['x_values'],chi2file['flux_values'],yerr=sigma_value,c='#696969',lw=1,ls='none')
plt.xlim(0.47,0.53)
plt.ylim(np.min(chi2file['flux_values']),np.max(chi2file['flux_values']))
plt.xlabel('Phase')
plt.ylabel('$F(t)/F$')
if savefigures==True: plt.savefig('final-mcmc-lightcurve1.pdf')
plt.show()
heatmap, xedges, yedges = np.histogram2d(hist_values1, hist_values3, bins=(100,100),normed=True)
extent = [xedges[0], xedges[-1], yedges[0], yedges[-1]]
fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, sharex='col', sharey='row')
contourplot=ax3.imshow(heatmap.T, extent=extent, origin='lower', cmap='Greys')
ax2.axis('off')
ax1.hist(hist_values1,bins=100,normed=1,edgecolor="black",facecolor="black",histtype="step")
ax4.hist(hist_values3,bins=100,normed=1,edgecolor="black",facecolor="black",histtype="step", orientation="horizontal")
ax3.axis('tight')
ax3.ticklabel_format(useOffset=False)
#myLocator = mticker.MultipleLocator(0.00)
#ax3.xaxis.set_major_locator(myLocator)
ax3.set_xlabel('Midpoint Phase Position')
ax3.set_ylabel('Chi-Squared Value')
ax1.set_ylabel('PDF')
ax4.set_xlabel('PDF')
ax3.set_xlim(np.min(hist_values1),np.max(hist_values1))
ax3.set_ylim(np.min(hist_values3)*0.95,np.max(hist_values3))
if savefigures==True: plt.savefig('chisquared-corner1.pdf')
plt.show()
plt.hist2d(hist_values1,hist_values3, bins=100)
plt.xlabel('Midpoint Phase Position')
plt.ylabel('Chi-Squared')
if savefigures==True: plt.savefig('chisquared-hist1.pdf')
plt.show()
plt.hist2d(hist_values2,hist_values3, bins=100)
plt.xlabel('Total Duration Phase')
plt.ylabel('Chi-Squared')
if savefigures==True: plt.savefig('chisquared-hist2.pdf')
plt.show()
plt.hist2d(hist_values1,hist_values3, bins=200)
plt.xlabel('Midpoint Phase Position')
plt.ylabel('Chi-Squared')
if savefigures==True: plt.savefig('chisquared-hist3.pdf')
plt.show()
plt.hist2d(hist_values2,hist_values3, bins=200)
plt.xlabel('Total Duration Phase')
plt.ylabel('Chi-Squared')
if savefigures==True: plt.savefig('chisquared-hist4.pdf')
plt.show()
plt.hist2d(hist_values5,hist_values3, bins=200)
plt.xlabel('Full Duration Phase')
plt.ylabel('Chi-Squared')
if savefigures==True: plt.savefig('chisquared-hist5.pdf')
plt.show()
heatmap, xedges, yedges = np.histogram2d(hist_values2, hist_values3, bins=(100,100),normed=True)
extent = [xedges[0], xedges[-1], yedges[0], yedges[-1]]
fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, sharex='col', sharey='row')
contourplot=ax3.imshow(heatmap.T, extent=extent, origin='lower', cmap='Greys')
ax2.axis('off')
ax1.hist(hist_values2,bins=100,normed=1,edgecolor="black",facecolor="black",histtype="step")
ax4.hist(hist_values3,bins=100,normed=1,edgecolor="black",facecolor="black",histtype="step", orientation="horizontal")
ax3.axis('tight')
ax3.ticklabel_format(useOffset=False)
#myLocator = mticker.MultipleLocator(0.00)
#ax3.xaxis.set_major_locator(myLocator)
ax3.set_xlabel('Total Duration Phase')
ax3.set_ylabel('Chi-Squared Value')
ax1.set_ylabel('Marginalised Chi-Squared PDF')
ax4.set_xlabel('Marginalised Chi-Squared PDF')
ax3.set_xlim(np.min(hist_values2),np.max(hist_values2))
ax3.set_ylim(np.min(hist_values3)*0.95,np.max(hist_values3))
if savefigures==True: plt.savefig('chisquared-corner2.pdf')
plt.show()
y,x,_=plt.hist(hist_values1,bins=100,normed=1,edgecolor="black",facecolor="black",histtype="step",label="PDF")
plt.axvline(x=Initial_initial,c='k',lw=2,label='Origin')
plt.xlabel('Midpoint Phase Position')
plt.ylabel('Marginalised Chi-Squared PDF')
plt.ylim(0,y.max()*(1.05))
plt.vlines(x=(mean), ymin=0, ymax=y.max()*(1.05), color='g', label='Mean')
plt.vlines(x=(mean-standard_dev), ymin=0, ymax=y.max()*(1.05), color='r', label='$\sigma_-$')
plt.vlines(x=(mean-standard_dev*2), ymin=0, ymax=y.max()*(1.05), color='m', label='$2\sigma_-$')
plt.vlines(x=(mean+standard_dev), ymin=0, ymax=y.max()*(1.05), color='b', label='$\sigma_+$')
plt.vlines(x=(mean+standard_dev*2), ymin=0, ymax=y.max()*(1.05), color='c', label='$2\sigma_+$')
plt.legend()
if savefigures==True: plt.savefig('PDF1-modified.pdf')
plt.show()
n_hist, b_hist, patches_hist = plt.hist(hist_values1,bins=200,normed=1,edgecolor="black",facecolor="black",histtype="step",label="PDF")
plt.hist(hist_values1,bins=200,normed=1,facecolor="black",edgecolor='None',alpha=0.1,label="PDF")
plt.xlabel('Midpoint Phase Position')
plt.ylabel('Normalised PDF')
if savefigures == True: plt.savefig('plottemp.pdf')
bin_max = np.where(n_hist == n_hist.max())
print "Mode:", b_hist[bin_max][0]
### CONFIDENCE INTERVAL SELECTOR: ########################################
bin_heights, bin_borders, _ = n_hist, b_hist, patches_hist
bin_center = bin_borders[:-1] + np.diff(bin_borders) / 2
xvals, yvals = bin_center, bin_heights
model = SkewedGaussianModel()
params = model.guess(yvals, x=xvals)
result = model.fit(yvals, params, x=xvals)
print result.fit_report()
plt.plot(xvals, result.best_fit,c='c',lw=2)
#Mode Finder:
maxval=0
maxvalx=0
for i in range(len(xvals)):
if result.best_fit[i]>maxval:
maxval=result.best_fit[i]
maxvalx=xvals[i]
print "Curve Mode:", maxvalx
area = np.trapz(result.best_fit, x=xvals)#, dx=5)
print "area =", area
summation1=0
summation2=0
prev_highest=[0]
prev_highest_position=[1e9]
i=0
newx1=[]
newy1=[]
newx2=[]
newy2=[]
while i < len(xvals):
position1=result.best_fit[i]
newx1.append(xvals[i])
newy1.append(position1)
summation1=np.trapz(newy1,x=newx1)
found = False
for j in range(len(xvals)):
loc=len(xvals)-1-j
if loc==-1: raise Exception("Array error.")
position2=result.best_fit[loc]
if (position2>=position1) and (found==False) and (xvals[loc]<=prev_highest_position[-1]) and (position2 >= prev_highest[-1]):
if (position2>1e3*position1) and (position1!=0): raise Exception("Corresponding position for probability=({}) not correctly found. E1".format(position1))
found = True
prev_highest.append(position2)
prev_highest_position.append(xvals[loc])
#plt.axvline(xvals[loc],c='m')
if j>=len(n_hist) and found==False:
raise Exception("Corresponding position for probability=({}) not found. E2".format(position1))
if found == True:
newx2.append(xvals[loc])
newy2.append(position2)
break
summation2=np.abs(np.trapz(newy2,x=newx2))
testcondition=1-(summation1+summation2)
if testcondition<0.69:
plt.axvline(Initial_initial,c='r')
plt.axvline(maxvalx,c='k')
plt.axvline(newx1[-1],c='#505050')
plt.axvline(newx2[-1],c='#505050')
print "Lower: ", np.abs(maxvalx-newx1[-1])
print "Upper: ", np.abs(maxvalx-newx2[-1])
break
else: i+=1
#plt.axvline(xvals[i],c='b')
print testcondition
if savefigures == True: plt.savefig('asymmetric1.pdf')
plt.show()
###
y,x,_=plt.hist(hist_values2,bins=100,normed=1,edgecolor="black",facecolor="black",histtype="step",label="PDF")
plt.axvline(x=P_total_initial,c='k',lw=2,label='Origin')
plt.xlabel('Total Duration Phase')
plt.ylabel('Marginalised Chi-Squared PDF')
plt.ylim(0,y.max()*(1.05))
plt.vlines(x=(mean2), ymin=0, ymax=y.max()*(1.05), color='g', label='Mean')
plt.vlines(x=(mean2-standard_dev2), ymin=0, ymax=y.max()*(1.05), color='r', label='$\sigma_-$')
plt.vlines(x=(mean2-standard_dev2*2), ymin=0, ymax=y.max()*(1.05), color='m', label='$2\sigma_-$')
plt.vlines(x=(mean2+standard_dev2), ymin=0, ymax=y.max()*(1.05), color='b', label='$\sigma_+$')
plt.vlines(x=(mean2+standard_dev2*2), ymin=0, ymax=y.max()*(1.05), color='c', label='$2\sigma_+$')
plt.legend()
if savefigures==True: plt.savefig('PDF2-modified.pdf')
plt.show()
n_hist, b_hist, patches_hist = plt.hist(hist_values2,bins=200,normed=1,edgecolor="black",facecolor="black",histtype="step",label="PDF")
plt.hist(hist_values2,bins=200,normed=1,facecolor="black",edgecolor='None',alpha=0.1,label="PDF")
plt.xlabel('Total Occultation Duration')
plt.ylabel('Normalised PDF')
if savefigures == True: plt.savefig('plottemp2.pdf')
bin_max = np.where(n_hist == n_hist.max())
print "Mode:", b_hist[bin_max][0]
### CONFIDENCE INTERVAL SELECTOR: ########################################
bin_heights, bin_borders, _ = n_hist, b_hist, patches_hist
bin_center = bin_borders[:-1] + np.diff(bin_borders) / 2
xvals, yvals = bin_center, bin_heights
model = SkewedGaussianModel()
params = model.guess(yvals, x=xvals)
result = model.fit(yvals, params, x=xvals)
print result.fit_report()
plt.plot(xvals, result.best_fit,c='c',lw=2)
#Mode Finder:
maxval=0
maxvalx=0
for i in range(len(xvals)):
if result.best_fit[i]>maxval:
maxval=result.best_fit[i]
maxvalx=xvals[i]
print "Curve Mode:", maxvalx
area = np.trapz(result.best_fit, x=xvals)#, dx=5)
print "area =", area
summation1=0
summation2=0
prev_highest=[0]
prev_highest_position=[1e9]
i=0
newx1=[]
newy1=[]
newx2=[]
newy2=[]
while i < len(xvals):
position1=result.best_fit[i]
newx1.append(xvals[i])
newy1.append(position1)
summation1=np.trapz(newy1,x=newx1)
found = False
for j in range(len(xvals)):
loc=len(xvals)-1-j
if loc==-1: raise Exception("Array error.")
position2=result.best_fit[loc]
if (position2>=position1) and (found==False) and (xvals[loc]<=prev_highest_position[-1]) and (position2 >= prev_highest[-1]):
if (position2>1e3*position1) and (position1!=0): raise Exception("Corresponding position for probability=({}) not correctly found. E1".format(position1))
found = True
prev_highest.append(position2)
prev_highest_position.append(xvals[loc])
#plt.axvline(xvals[loc],c='m')
if j>=len(n_hist) and found==False:
raise Exception("Corresponding position for probability=({}) not found. E2".format(position1))
if found == True:
newx2.append(xvals[loc])
newy2.append(position2)
break
summation2=np.abs(np.trapz(newy2,x=newx2))
testcondition=1-(summation1+summation2)
if testcondition<0.69:
plt.axvline(maxvalx,c='k')
plt.axvline(P_total_initial,c='r')
plt.axvline(newx1[-1],c='#505050')
plt.axvline(newx2[-1],c='#505050')
print "Lower: ", np.abs(maxvalx-newx1[-1])
print "Upper: ", np.abs(maxvalx-newx2[-1])
break
else: i+=1
print testcondition
if savefigures == True: plt.savefig('asymmetric2.pdf')
plt.show()
###
y,x,_=plt.hist(hist_values5,bins=100,normed=1,edgecolor="black",facecolor="black",histtype="step",label="PDF")
plt.axvline(x=P_full_initial,c='k',lw=2,label='Origin')
plt.xlabel('Full Duration Phase')
plt.ylabel('Marginalised Chi-Squared PDF')
plt.ylim(0,y.max()*(1.05))
plt.vlines(x=(mean3), ymin=0, ymax=y.max()*(1.05), color='g', label='Mean')
plt.vlines(x=(mean3-standard_dev3), ymin=0, ymax=y.max()*(1.05), color='r', label='$\sigma_-$')
plt.vlines(x=(mean3-standard_dev3*2), ymin=0, ymax=y.max()*(1.05), color='m', label='$2\sigma_-$')
plt.vlines(x=(mean3+standard_dev3), ymin=0, ymax=y.max()*(1.05), color='b', label='$\sigma_+$')
plt.vlines(x=(mean3+standard_dev3*2), ymin=0, ymax=y.max()*(1.05), color='c', label='$2\sigma_+$')
plt.legend()
if savefigures==True: plt.savefig('PDF3-modified.pdf')
plt.show()
n_hist, b_hist, patches_hist = plt.hist(hist_values5,bins=200,normed=1,edgecolor="black",facecolor="black",histtype="step",label="PDF")
plt.hist(hist_values5,bins=200,normed=1,facecolor="black",edgecolor='None',alpha=0.1,label="PDF")
plt.xlabel('Full Occultation Duration')
plt.ylabel('Normalised PDF')
if savefigures == True: plt.savefig('plottemp3.pdf')
bin_max = np.where(n_hist == n_hist.max())
print "Mode:", b_hist[bin_max][0]
### CONFIDENCE INTERVAL SELECTOR: ########################################
bin_heights, bin_borders, _ = n_hist, b_hist, patches_hist
bin_center = bin_borders[:-1] + np.diff(bin_borders) / 2
xvals, yvals = bin_center, bin_heights
model = SkewedGaussianModel()
params = model.guess(yvals, x=xvals)
result = model.fit(yvals, params, x=xvals)
print result.fit_report()
plt.plot(xvals, result.best_fit,c='c',lw=2)
#Mode Finder:
maxval=0
maxvalx=0
for i in range(len(xvals)):
if result.best_fit[i]>maxval:
maxval=result.best_fit[i]
maxvalx=xvals[i]
print "Curve Mode:", maxvalx
area = np.trapz(result.best_fit, x=xvals)#, dx=5)
print "area =", area
summation1=0
summation2=0
prev_highest=[0]
prev_highest_position=[1e9]
i=0
newx1=[]
newy1=[]
newx2=[]
newy2=[]
while i < len(xvals):
position1=result.best_fit[i]
newx1.append(xvals[i])
newy1.append(position1)
summation1=np.trapz(newy1,x=newx1)
found = False
for j in range(len(xvals)):
loc=len(xvals)-1-j
if loc==-1: raise Exception("Array error.")
position2=result.best_fit[loc]
if (position2>=position1) and (found==False) and (xvals[loc]<=prev_highest_position[-1]) and (position2 >= prev_highest[-1]):
if (position2>1e3*position1) and (position1!=0): raise Exception("Corresponding position for probability=({}) not correctly found. E1".format(position1))
found = True
prev_highest.append(position2)
prev_highest_position.append(xvals[loc])
#plt.axvline(xvals[loc],c='m')
if j>=len(n_hist) and found==False:
raise Exception("Corresponding position for probability=({}) not found. E2".format(position1))
if found == True:
newx2.append(xvals[loc])
newy2.append(position2)
break
summation2=np.abs(np.trapz(newy2,x=newx2))
testcondition=1-(summation1+summation2)
if testcondition<0.69:
plt.axvline(maxvalx,c='k')
plt.axvline(P_full_initial,c='r')
plt.axvline(newx1[-1],c='#505050')
plt.axvline(newx2[-1],c='#505050')
print "Lower: ", np.abs(maxvalx-newx1[-1])
print "Upper: ", np.abs(maxvalx-newx2[-1])
break
else: i+=1
print testcondition
if savefigures == True: plt.savefig('asymmetric3.pdf')
plt.show()
###
xpoints1=np.linspace(0,len(hist_values1),num=len(hist_values1))
xpoints2=np.linspace(0,len(hist_values2),num=len(hist_values2))
plt.scatter(xpoints1,hist_values1,c='r',s=3)
plt.xlabel('Number of Samples')
plt.ylabel('Midpoint Phase Position')
if savefigures==True: plt.savefig('parameter-variation1.pdf')
plt.show()
plt.scatter(xpoints2,hist_values2,c='b',s=3)
plt.xlabel('Number of Samples')
plt.ylabel('Total Duration Phase')
if savefigures==True: plt.savefig('parameter-variation2.pdf')
plt.show()
plt.scatter(xpoints2,hist_values5,c='b',s=3)
plt.xlabel('Number of Samples')
plt.ylabel('Full Duration Phase')
if savefigures==True: plt.savefig('parameter-variation3.pdf')
plt.show()
plt.scatter(xpoints2,hist_values4,c='m',s=3)
plt.xlabel('Number of Samples')
plt.ylabel('Reduced Chi Squared')
if savefigures==True: plt.savefig('parameter-variation3.pdf')
plt.show()
heatmap, xedges, yedges = np.histogram2d(hist_values1, hist_values2, bins=(100,100),normed=True)
extent = [xedges[0], xedges[-1], yedges[0], yedges[-1]]
fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, sharex='col', sharey='row')
contourplot=ax3.imshow(heatmap.T, extent=extent, origin='lower', cmap='Greys')
axins1 = inset_axes(ax3,
width="5%",
height="92.5%",
loc=1)
plt.colorbar(contourplot, cax=axins1, orientation="vertical")
ax2.axis('off')
ax1.hist(hist_values1,bins=100,normed=1,edgecolor="black",facecolor="black",histtype="step")
ax4.hist(hist_values2,bins=100,normed=1,edgecolor="black",facecolor="black",histtype="step", orientation="horizontal")
ax3.axis('tight')
ax3.ticklabel_format(useOffset=False)
myLocator = mticker.MultipleLocator(0.0003)
ax3.xaxis.set_major_locator(myLocator)
ax3.set_xlabel('Midpoint Position')
ax3.set_ylabel('Total Duration')
ax1.set_ylabel('Marginalised PDF')
ax4.set_xlabel('Marginalised PDF')
ax3.set_xlim(np.min(hist_values1),np.max(hist_values1))
ax3.set_ylim(np.min(hist_values2),np.max(hist_values2))
if savefigures==True: plt.savefig('corner-modified.pdf')
plt.show()
heatmap, xedges, yedges = np.histogram2d(hist_values1, hist_values5, bins=(100,100),normed=True)
extent = [xedges[0], xedges[-1], yedges[0], yedges[-1]]
fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, sharex='col', sharey='row')
contourplot=ax3.imshow(heatmap.T, extent=extent, origin='lower', cmap='Greys')
axins1 = inset_axes(ax3,
width="5%",
height="92.5%",
loc=1)
plt.colorbar(contourplot, cax=axins1, orientation="vertical")#, ticks=[1, 2, 3])
#plt.colorbar(contourplot,ax=ax3)
ax2.axis('off')
ax1.hist(hist_values1,bins=100,normed=1,edgecolor="black",facecolor="black",histtype="step")
ax4.hist(hist_values5,bins=100,normed=1,edgecolor="black",facecolor="black",histtype="step", orientation="horizontal")
ax3.axis('tight')
ax3.ticklabel_format(useOffset=False)
myLocator = mticker.MultipleLocator(0.0003)
ax3.xaxis.set_major_locator(myLocator)
ax3.set_xlabel('Midpoint Position')
ax3.set_ylabel('Full Duration')
ax1.set_ylabel('Marginalised PDF')
ax4.set_xlabel('Marginalised PDF')
ax3.set_xlim(np.min(hist_values1),np.max(hist_values1))
ax3.set_ylim(np.min(hist_values5),np.max(hist_values5))
if savefigures==True: plt.savefig('corner-modified2.pdf')
plt.show()
heatmap, xedges, yedges = np.histogram2d(hist_values2, hist_values5, bins=(100,100),normed=True)
extent = [xedges[0], xedges[-1], yedges[0], yedges[-1]]
fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, sharex='col', sharey='row')
contourplot=ax3.imshow(heatmap.T, extent=extent, origin='lower', cmap='Greys')
axins1 = inset_axes(ax3,
width="5%",
height="92.5%",
loc=1)
plt.colorbar(contourplot, cax=axins1, orientation="vertical")
ax2.axis('off')
ax1.hist(hist_values2,bins=100,normed=1,edgecolor="black",facecolor="black",histtype="step")
ax4.hist(hist_values5,bins=100,normed=1,edgecolor="black",facecolor="black",histtype="step", orientation="horizontal")
ax3.axis('tight')
ax3.ticklabel_format(useOffset=False)
#myLocator = mticker.MultipleLocator(0.00)
#ax3.xaxis.set_major_locator(myLocator)
ax3.set_xlabel('Total Duration')
ax3.set_ylabel('Full Duration')
ax1.set_ylabel('Marginalised PDF')
ax4.set_xlabel('Marginalised PDF')
ax3.set_xlim(np.min(hist_values2),np.max(hist_values2))
ax3.set_ylim(np.min(hist_values5),np.max(hist_values5))
if savefigures==True: plt.savefig('corner-modified3.pdf')
plt.show()
########################################
print "Done."
| 39.861775
| 239
| 0.70414
| 3,480
| 23,359
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| 0.02543
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0
| 6
|
289f797580cc0c2b3ee620c50ae7da1a504e304d
| 17,485
|
py
|
Python
|
Phenotyping/Phenotyping.py
|
lsymuyu/Digital-Plant-Phenotyping-Platform
|
ca94a918061103d3032ca2e891c2c9a7cffe33e0
|
[
"MIT"
] | 10
|
2019-01-26T04:47:49.000Z
|
2020-10-01T04:14:49.000Z
|
Phenotyping/Phenotyping.py
|
lsymuyu/Digital-Plant-Phenotyping-Platform
|
ca94a918061103d3032ca2e891c2c9a7cffe33e0
|
[
"MIT"
] | null | null | null |
Phenotyping/Phenotyping.py
|
lsymuyu/Digital-Plant-Phenotyping-Platform
|
ca94a918061103d3032ca2e891c2c9a7cffe33e0
|
[
"MIT"
] | 3
|
2019-03-06T00:04:04.000Z
|
2022-01-07T08:18:01.000Z
|
'''
The main function to conduct phenotyping experiments
11/09/2017
Shouyang Liu
'''
import os
from adel import AdelR
from adel.geometric_elements import Leaves
from adel.AdelR import R_xydb, R_srdb, genGeoLeaf
import pandas as pd
from adel.plantgen import plantgen_interface
import numpy as np
from adel.astk_interface import AdelWheat
from adel.stand.Generate_canopy import get_exposed_areas
from adel.postprocessing import axis_statistics_simple, plot_statistics_simple, plot_statistics_simple_filter, axis_statistics_simple_filter
from adel.povray.povray_ind import povray_Green
from pyDOE import *
from scipy.stats import uniform
from openalea.core.path import path
from adel.ADEL_OPT.Adel_OPT_Ind import Adel_Leaves, Adel_development
import prosail
from adel.ADEL_OPT.Adel_OPT_Ind import plot_LAI
from adel.macro.povray_pixels_several_colors import set_color_metamers_organs
from adel.povray.FAPAR import Sampling_diagnal, Hemispherical_IM, Sampling_GF, Hemispherical_IM_Sun
from adel.povray.GF_RGB import Green_Fract, Pov_Scene
from adel.povray.Canray import duplicate_scene, Optical_canopy, Optical_soil, povray_RF
def Phenotyping_Wheat(Param, Ind, thermals, Canopy, Adel_output,
LAI = False,save_scene = False,
GF = False, GF_camera = [],
FAPAR = False, Sunlit_TT = [], Sunlit_Ang = [],
Multi_spectral = False, Ray_camera = [], Ray_light = []):
try:
# Adel parameters
development_parameters = Adel_development(N_phytomer_potential = float(Param['N_leaf']), a_cohort = float(Param['a_cohort']),
TT_hs_0 = float(Param['T_cohort']), TT_flag_ligulation = float(Param['TT_flag_ligulation']),
n0 = float(Param['n0']), n1 = float(Param['n1']), n2 = float(Param['n2']),
number_tillers = float(Param['number_tillers']),
Lamina_L1 = float(Param['Lamina_L1']), N2 = float(Param['N2']), incl1 = float(Param['incl1']),
incl2 = float(Param['incl2']), N_elg = float(Param['N_elg']), density = float(Param['Density']))
wheat_leaves = Adel_Leaves(incline = float(Param['incl_leaf']), dev_Az_Leaf = float(Param['dev_Az_Leaf']))
# canopy configuration
sim_width = float(Canopy['width']) # m, generate three rows
dup_length = float(Canopy['length'])
Row_spacing = float(Param['Row_spacing'])
run_adel_pars = {'senescence_leaf_shrink': 0.01, 'leafDuration': 2, 'fracLeaf': 0.2, 'stemDuration': 2. / 1.2,
'dHS_col': 0.2, 'dHS_en': 0, 'epsillon': 1e-6, 'HSstart_inclination_tiller': 1,
'rate_inclination_tiller': float(Param['rate_Tiller']), 'drop_empty': True}
# build the distribution pattern table to interpolate the density
Wheat_Adel = AdelWheat(density = float(Param['Density']), duplicate = 40, devT = development_parameters,
leaves = wheat_leaves, pattern='regular', run_adel_pars = run_adel_pars,
incT = float(Param['Deta_Incl_Tiller']), ibmM = float(Param['incl_main']),
depMin = float(Param['min_Tiller']), dep = float(Param['max_Tiller']),
inter_row = Row_spacing, width = sim_width, length = dup_length)
del development_parameters, wheat_leaves
domain = Wheat_Adel.domain
domain_area = Wheat_Adel.domain_area
nplants = Wheat_Adel.nplants
for TT in thermals:
Canopy_Adel = Wheat_Adel.setup_canopy(age=TT)
plantgl_scene = set_color_metamers_organs(Canopy_Adel)[0]
# Summary LAI
if LAI:
new_plot_df = plot_LAI(Canopy_Adel, TT, domain_area, nplants, Adel_output, Ind)
if 'plot_df' in locals():
plot_df = pd.concat([plot_df,new_plot_df])
else:
plot_df = new_plot_df
del Canopy_Adel
# Save geometry file
name_canopy = '%s%s%s%s.bgeom'%('Ind_',Ind,'_TT_',TT)
if save_scene:
plantgl_scene.save(Adel_output + '/' + name_canopy, 'BGEOM')
# Green fraction
if GF:
sampling_times = GF_camera['Times_sampling']
cameras = Sampling_GF(domain, sampling_times,
Azimuth = GF_camera['azimuth'], Zenith = GF_camera['zenith'],
Row_spacing = Row_spacing, fov = GF_camera['fov'])[0]
povfile_mesh, povfile_box, z_top = Pov_Scene(plantgl_scene, domain,
output_directory = Adel_output,
thermal = TT, Ind = Ind)
povfile_scene, new_df = Green_Fract(povfile_mesh, povfile_box,
thermal = TT, Ind = Ind, cameras = cameras,
image_height = GF_camera['image_height'], image_width = GF_camera['image_width'],
relative_height = GF_camera['distance'], z_top = 0,
output_directory = Adel_output)
if 'result_df' in locals():
result_df = pd.concat([result_df,new_df])
else:
result_df = new_df
# Fisheye for FAPAR
if FAPAR:
Azimuth_fisheye = [0]
Zenith_fisheye = [0]
sampling_times = 7
dup_width = 8.0
New_canopy, New_nplants, New_domain, New_area = duplicate_scene(plantgl_scene, nplants, canopy_width = dup_width,
canopy_length = dup_length, sim_width = sim_width,
Row_spacing = Row_spacing)
domain = New_domain
del plantgl_scene
cameras_fisheye = Sampling_diagnal(New_domain, sampling_times,
Azimuth_fisheye, Zenith_fisheye,
Row_spacing, fov_fisheye)[0]
povfile_mesh_new, povfile_box_new, z_top_new = Pov_Scene(New_canopy, New_domain,
output_directory = Adel_output,
thermal = TT, Ind = Ind)
del New_canopy
povray_image_fisheye = Hemispherical_IM(povfile_mesh = povfile_mesh_new, z_top = z_top_new,
cameras = cameras_fisheye,
image_height = 2000, image_width = 2000,
relative_height = relative_height,
output_directory = Adel_output)
if TT in Sunlit_TT:
for A_sun in Sunlit_Ang:
povray_image_fisheye = Hemispherical_IM_Sun(povfile_mesh = povfile_mesh_new, z_top = z_top_new,
cameras = cameras_fisheye, A_sun = A_sun,
image_height = 2000, image_width = 2000,
relative_height = relative_height,
output_directory = Adel_output)
# Simulate BRDF (need large scene)
if Multi_spectral:
# Setting of prosail
RT = prosail.prospect_5b(n = Param['N'], cab = Param['Cab'], car = Param['Car'],
cbrown = Param['Cbrown'], cw = Param['Cw'], cm = Param['Cm'])
Full_wave = range(400, 2501)
R = RT[:,0]
T = RT[:,1]
for wave in Ray_camera['Waves']:
Plant_optical = Optical_canopy(wave=wave, Full_wave=Full_wave, R=R, T=T)
soil_ref = Optical_soil(wave, brightness=Param['brightness'])
Output_file = povray_RF(Ray_light=Ray_light, Ray_camera=Ray_camera, Plant_optical=Plant_optical,
soil_ref=soil_ref, domain=domain, povfile_scene=povfile_mesh,
wave=wave, soil_type = Param['soil_type'],
dict=Adel_output)
if not os.path.exists(Output_file):
Output_file = povray_RF(Ray_light=Ray_light, Ray_camera=Ray_camera, Plant_optical=Plant_optical,
soil_ref=soil_ref, domain=domain, povfile_scene=povfile_mesh,
wave=wave,soil_type = Param['soil_type'], dict=Adel_output)
if 'plot_df' in locals():
result_plot_path = path(os.path.join(Adel_output, '%s%s%s'%('plot_LAI_',Ind,'.csv')))
plot_df.to_csv(result_plot_path, index=False)
if 'result_df' in locals():
result_df_path = path(os.path.join(Adel_output, '%s%s%s'%('Fraction_',Ind,'.csv')))
result_df.to_csv(result_df_path, index=False)
except TypeError:
print 'Pass it and move forward!!!***'
result_df_path = []
pass
return Adel_output
def Phenotyping_Wheat_TT(Param, Ind, TT, Adel_output,
Ray_light = [], Ray_camera = [], Zenith_GF = [],
FAPAR = True, GF = True, Multi_spectral = False,
save_scene = False):
try:
# Adel parameters
Row_spacing = float(Param['Row_spacing'])
sim_width = 1.0
dup_length = 12.0
development_parameters = Adel_development(N_phytomer_potential = float(Param['N_leaf']), a_cohort = float(Param['a_cohort']),
TT_hs_0 = float(Param['T_cohort']), TT_flag_ligulation = float(Param['TT_flag_ligulation']),
n0 = float(Param['n0']), n1 = float(Param['n1']), n2 = float(Param['n2']), number_tillers = float(Param['number_tillers']),
Lamina_L1 = float(Param['Lamina_L1']), N2 = float(Param['N2']), incl1 = float(Param['incl1']),
incl2 = float(Param['incl2']), N_elg = float(Param['N_elg']), density = float(Param['Density']))
wheat_leaves = Adel_Leaves(incline = float(Param['incl_leaf']), dev_Az_Leaf = float(Param['dev_Az_Leaf']))
run_adel_pars = {'senescence_leaf_shrink': 0.01, 'leafDuration': 2, 'fracLeaf': 0.2, 'stemDuration': 2. / 1.2,
'dHS_col': 0.2, 'dHS_en': 0, 'epsillon': 1e-6, 'HSstart_inclination_tiller': 1,
'rate_inclination_tiller': float(Param['rate_Tiller']), 'drop_empty': True}
# build the distribution pattern table to interpolate the density
Wheat_Adel = AdelWheat(density = float(Param['Density']), duplicate = 20, devT = development_parameters,
leaves = wheat_leaves, pattern='regular', run_adel_pars = run_adel_pars,
incT = float(Param['Deta_Incl_Tiller']), ibmM = float(Param['incl_main']),
depMin = float(Param['min_Tiller']), dep = float(Param['max_Tiller']),
inter_row = Row_spacing, width = sim_width, length = dup_length)
del Param, development_parameters, wheat_leaves
domain = Wheat_Adel.domain
domain_area = Wheat_Adel.domain_area
nplants = Wheat_Adel.nplants
Canopy_Adel = Wheat_Adel.setup_canopy(age=TT)
del Wheat_Adel
plantgl_scene = set_color_metamers_organs(Canopy_Adel)[0]
# Summary LAI
plot_df = plot_LAI(Canopy_Adel, TT, domain_area, nplants, Adel_output, Ind)
result_plot_path = path(os.path.join(Adel_output, '%s%s%s%s%s'%('plot_LAI_',Ind,'_TT_',TT,'.csv')))
plot_df.to_csv(result_plot_path, index=False)
del plot_df, Canopy_Adel
# Save geometry file
name_canopy = '%s%s%s%s.bgeom'%('Ind_',Ind,'_TT_',TT)
if save_scene:
plantgl_scene.save(Adel_output + '/' + name_canopy, 'BGEOM')
# Common setting
relative_height = 200 # camera above the canopy
# Green fraction
if GF:
Azimuth = [0]
fov = [10]
sampling_times = 4
cameras = Sampling_GF(domain, sampling_times,
Azimuth, Zenith_GF,
Row_spacing, fov)[0]
povfile_mesh, povfile_box, z_top = Pov_Scene(plantgl_scene, domain,
output_directory = Adel_output,
thermal = TT, Ind = Ind)
povfile_scene, result_df = Green_Fract(povfile_mesh, povfile_box,
thermal = TT, Ind = Ind, cameras = cameras,
image_height = 1000, image_width = 1000,
relative_height = relative_height, z_top = z_top,
output_directory = Adel_output)
result_df_path = path(os.path.join(Adel_output, '%s%s%s%s%s'%('Fraction_',Ind,'_TT_',TT,'.csv')))
result_df.to_csv(result_df_path, index=False)
# Fisheye for FAPAR
if FAPAR:
Azimuth_fisheye = [0]
Zenith_fisheye = [0]
fov_fisheye = [120]
dup_width = 12.0
sampling_times = 7
New_canopy, New_nplants, New_domain, New_area = duplicate_scene(plantgl_scene, nplants, canopy_width = dup_width,
canopy_length = dup_length, sim_width = sim_width,
Row_spacing = Row_spacing)
del plantgl_scene
cameras_fisheye = Sampling_diagnal(New_domain, sampling_times,
Azimuth_fisheye, Zenith_fisheye,
Row_spacing, fov_fisheye)[0]
povfile_mesh_new, povfile_box_new, z_top_new = Pov_Scene(New_canopy, New_domain,
output_directory = Adel_output,
thermal = TT, Ind = Ind)
del New_canopy
povray_image_fisheye = Hemispherical_IM(povfile_mesh = povfile_mesh_new, z_top = z_top_new,
cameras = cameras_fisheye,
image_height = 2000, image_width = 2000,
relative_height = relative_height,
output_directory = Adel_output)
# Simulate BRDF (need large scene)
if Multi_spectral:
# Setting of prosail
RT = prosail.prospect_5b(n = Param['N'], cab = Param['Cab'], car = Param['Car'],
cbrown = Param['Cbrown'], cw = Param['Cw'], cm = Param['Cm'])
Full_wave = range(400, 2501)
R = RT[:,0]
T = RT[:,1]
for wave in Waves_camera:
Plant_optical = Optical_canopy(wave=wave, Full_wave=Full_wave, R=R, T=T)
soil_ref = Optical_soil(wave, brightness=Param['brightness'])
Output_file = povray_RF(Ray_light=Ray_light, Ray_camera=Ray_camera, Plant_optical=Plant_optical,
soil_ref=soil_ref, domain=New_domain, povfile_scene=povfile_scene,
wave=wave,
dict=Adel_output)
if not os.path.exists(Output_file):
Output_file = povray_RF(Ray_light=Ray_light, Ray_camera=Ray_camera, Plant_optical=Plant_optical,
soil_ref=soil_ref, domain=New_domain, povfile_scene=povfile_scene,
wave=wave,
dict=Adel_output)
except TypeError:
print 'Pass it and move forward!!!***'
result_df_path = []
pass
return Adel_output
| 57.327869
| 173
| 0.509122
| 1,801
| 17,485
| 4.622987
| 0.148251
| 0.055249
| 0.004324
| 0.027024
| 0.774802
| 0.751021
| 0.741412
| 0.725799
| 0.709945
| 0.709945
| 0
| 0.015112
| 0.405834
| 17,485
| 305
| 174
| 57.327869
| 0.786312
| 0.027109
| 0
| 0.646809
| 0
| 0
| 0.061816
| 0.0084
| 0
| 0
| 0
| 0
| 0
| 0
| null | null | 0.017021
| 0.089362
| null | null | 0.008511
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
95594f3fa1667e6fd47d1aa58e6c4955b0c3c4cb
| 42
|
py
|
Python
|
ini2.py
|
lmartinho/rosalind-solutions
|
b29f9aa25095ddc88a0b50505206acab00f490a0
|
[
"MIT"
] | null | null | null |
ini2.py
|
lmartinho/rosalind-solutions
|
b29f9aa25095ddc88a0b50505206acab00f490a0
|
[
"MIT"
] | null | null | null |
ini2.py
|
lmartinho/rosalind-solutions
|
b29f9aa25095ddc88a0b50505206acab00f490a0
|
[
"MIT"
] | null | null | null |
a=807
b=905
#a=3
#b=5
c=a*a+b*b
print(c)
| 5.25
| 9
| 0.547619
| 15
| 42
| 1.533333
| 0.533333
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.228571
| 0.166667
| 42
| 7
| 10
| 6
| 0.428571
| 0.142857
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0.25
| 1
| 1
| 1
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
957ed5e04776cf8ef2caf46b88ed0f837a9bba94
| 85
|
py
|
Python
|
05-Inheritance/problem-5-Restaurant/project/beverage/hot_beverage.py
|
Beshkov/OOP
|
297edadb3e7801dfeee5752a20aae6aead8da610
|
[
"MIT"
] | 1
|
2021-05-24T17:51:53.000Z
|
2021-05-24T17:51:53.000Z
|
05-Inheritance/problem-5-Restaurant/project/beverage/hot_beverage.py
|
Beshkov/Python_OOP
|
297edadb3e7801dfeee5752a20aae6aead8da610
|
[
"MIT"
] | null | null | null |
05-Inheritance/problem-5-Restaurant/project/beverage/hot_beverage.py
|
Beshkov/Python_OOP
|
297edadb3e7801dfeee5752a20aae6aead8da610
|
[
"MIT"
] | null | null | null |
from project.beverage.beverage import Beverage
class HotBeverage(Beverage):
pass
| 21.25
| 46
| 0.811765
| 10
| 85
| 6.9
| 0.7
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.129412
| 85
| 4
| 47
| 21.25
| 0.932432
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0.333333
| 0.333333
| 0
| 0.666667
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 1
| 0
| 1
| 0
|
0
| 6
|
95851e8b11992cb1ca7532138883ea0ffb258740
| 14,797
|
py
|
Python
|
tests/unittests/test_folder.py
|
ZPascal/grafana_api_sdk
|
97c347790200e8e9a2aafd47e322297aa97b964c
|
[
"Apache-2.0"
] | 2
|
2022-02-01T20:18:48.000Z
|
2022-02-02T01:22:14.000Z
|
tests/unittests/test_folder.py
|
ZPascal/grafana_api_sdk
|
97c347790200e8e9a2aafd47e322297aa97b964c
|
[
"Apache-2.0"
] | 5
|
2022-01-12T06:55:54.000Z
|
2022-03-26T13:35:50.000Z
|
tests/unittests/test_folder.py
|
ZPascal/grafana_api_sdk
|
97c347790200e8e9a2aafd47e322297aa97b964c
|
[
"Apache-2.0"
] | null | null | null |
from unittest import TestCase
from unittest.mock import MagicMock, Mock, patch
from src.grafana_api.model import APIModel
from src.grafana_api.folder import Folder
class FolderTestCase(TestCase):
@patch("src.grafana_api.api.Api.call_the_api")
def test_get_folders(self, call_the_api_mock):
model: APIModel = APIModel(host=MagicMock(), token=MagicMock())
folder: Folder = Folder(grafana_api_model=model)
mock: Mock = Mock()
mock.json = Mock(return_value=list([{"title": None, "id": 12}]))
call_the_api_mock.return_value = mock
self.assertEqual(list([{"title": None, "id": 12}]), folder.get_folders())
@patch("src.grafana_api.api.Api.call_the_api")
def test_get_folders_error_response(self, call_the_api_mock):
model: APIModel = APIModel(host=MagicMock(), token=MagicMock())
folder: Folder = Folder(grafana_api_model=model)
mock: Mock = Mock()
mock.json = Mock(return_value=list())
call_the_api_mock.return_value = mock
with self.assertRaises(Exception):
folder.get_folders()
@patch("src.grafana_api.api.Api.call_the_api")
def test_get_folder_by_uid(self, call_the_api_mock):
model: APIModel = APIModel(host=MagicMock(), token=MagicMock())
folder: Folder = Folder(grafana_api_model=model)
mock: Mock = Mock()
mock.json = Mock(return_value=dict({"title": None, "id": 12}))
call_the_api_mock.return_value = mock
self.assertEqual(
dict({"title": None, "id": 12}), folder.get_folder_by_uid("xty13y")
)
@patch("src.grafana_api.api.Api.call_the_api")
def test_get_folder_by_uid_no_uid(self, call_the_api_mock):
model: APIModel = APIModel(host=MagicMock(), token=MagicMock())
folder: Folder = Folder(grafana_api_model=model)
mock: Mock = Mock()
mock.json = Mock(return_value=dict())
call_the_api_mock.return_value = mock
with self.assertRaises(ValueError):
folder.get_folder_by_uid("")
@patch("src.grafana_api.api.Api.call_the_api")
def test_get_folder_by_uid_error_response(self, call_the_api_mock):
model: APIModel = APIModel(host=MagicMock(), token=MagicMock())
folder: Folder = Folder(grafana_api_model=model)
mock: Mock = Mock()
mock.json = Mock(return_value=dict())
call_the_api_mock.return_value = mock
with self.assertRaises(Exception):
folder.get_folder_by_uid("xty13y")
@patch("src.grafana_api.api.Api.call_the_api")
def test_get_folder_by_id(self, call_the_api_mock):
model: APIModel = APIModel(host=MagicMock(), token=MagicMock())
folder: Folder = Folder(grafana_api_model=model)
mock: Mock = Mock()
mock.json = Mock(return_value=dict({"title": None, "id": 12}))
call_the_api_mock.return_value = mock
self.assertEqual(dict({"title": None, "id": 12}), folder.get_folder_by_id(12))
@patch("src.grafana_api.api.Api.call_the_api")
def test_get_folder_by_id_no_id(self, call_the_api_mock):
model: APIModel = APIModel(host=MagicMock(), token=MagicMock())
folder: Folder = Folder(grafana_api_model=model)
mock: Mock = Mock()
mock.json = Mock(return_value=dict())
call_the_api_mock.return_value = mock
with self.assertRaises(ValueError):
folder.get_folder_by_id(0)
@patch("src.grafana_api.api.Api.call_the_api")
def test_get_folder_by_id_error_response(self, call_the_api_mock):
model: APIModel = APIModel(host=MagicMock(), token=MagicMock())
folder: Folder = Folder(grafana_api_model=model)
mock: Mock = Mock()
mock.json = Mock(return_value=dict())
call_the_api_mock.return_value = mock
with self.assertRaises(Exception):
folder.get_folder_by_id(10)
@patch("src.grafana_api.api.Api.call_the_api")
def test_create_folder(self, call_the_api_mock):
model: APIModel = APIModel(host=MagicMock(), token=MagicMock())
folder: Folder = Folder(grafana_api_model=model)
mock: Mock = Mock()
mock.json = Mock(return_value=dict({"title": None, "id": 12}))
call_the_api_mock.return_value = mock
self.assertEqual(dict({"title": None, "id": 12}), folder.create_folder("test"))
@patch("src.grafana_api.api.Api.call_the_api")
def test_create_folder_specified_uid(self, call_the_api_mock):
model: APIModel = APIModel(host=MagicMock(), token=MagicMock())
folder: Folder = Folder(grafana_api_model=model)
mock: Mock = Mock()
mock.json = Mock(return_value=dict({"title": None, "id": 12, "uid": "test"}))
call_the_api_mock.return_value = mock
self.assertEqual(
dict({"title": None, "id": 12, "uid": "test"}),
folder.create_folder("test", "test"),
)
@patch("src.grafana_api.api.Api.call_the_api")
def test_create_folder_no_title(self, call_the_api_mock):
model: APIModel = APIModel(host=MagicMock(), token=MagicMock())
folder: Folder = Folder(grafana_api_model=model)
mock: Mock = Mock()
mock.json = Mock(return_value=dict())
call_the_api_mock.return_value = mock
with self.assertRaises(ValueError):
folder.create_folder(MagicMock())
@patch("src.grafana_api.api.Api.call_the_api")
def test_create_folder_error_response(self, call_the_api_mock):
model: APIModel = APIModel(host=MagicMock(), token=MagicMock())
folder: Folder = Folder(grafana_api_model=model)
mock: Mock = Mock()
mock.json = Mock(return_value=dict())
call_the_api_mock.return_value = mock
with self.assertRaises(Exception):
folder.create_folder("test")
@patch("src.grafana_api.api.Api.call_the_api")
def test_update_folder(self, call_the_api_mock):
model: APIModel = APIModel(host=MagicMock(), token=MagicMock())
folder: Folder = Folder(grafana_api_model=model)
mock: Mock = Mock()
mock.json = Mock(return_value=dict({"title": "test1", "id": 12}))
call_the_api_mock.return_value = mock
self.assertEqual(
dict({"title": "test1", "id": 12}),
folder.update_folder("test", "test1", 10),
)
@patch("src.grafana_api.api.Api.call_the_api")
def test_update_folder_no_uid(self, call_the_api_mock):
model: APIModel = APIModel(host=MagicMock(), token=MagicMock())
folder: Folder = Folder(grafana_api_model=model)
mock: Mock = Mock()
mock.json = Mock(return_value=dict({"title": "test", "id": 12}))
call_the_api_mock.return_value = mock
self.assertEqual(
dict({"title": "test", "id": 12}),
folder.update_folder("test", overwrite=True),
)
@patch("src.grafana_api.api.Api.call_the_api")
def test_update_folder_overwrite_true(self, call_the_api_mock):
model: APIModel = APIModel(host=MagicMock(), token=MagicMock())
folder: Folder = Folder(grafana_api_model=model)
mock: Mock = Mock()
mock.json = Mock(return_value=dict({"title": "test", "id": 12}))
call_the_api_mock.return_value = mock
self.assertEqual(
dict({"title": "test", "id": 12}),
folder.update_folder("test", "test", overwrite=True),
)
@patch("src.grafana_api.api.Api.call_the_api")
def test_update_folder_no_title(self, call_the_api_mock):
model: APIModel = APIModel(host=MagicMock(), token=MagicMock())
folder: Folder = Folder(grafana_api_model=model)
mock: Mock = Mock()
mock.json = Mock(return_value=dict())
call_the_api_mock.return_value = mock
with self.assertRaises(ValueError):
folder.update_folder(MagicMock(), MagicMock())
@patch("src.grafana_api.api.Api.call_the_api")
def test_update_folder_error_response(self, call_the_api_mock):
model: APIModel = APIModel(host=MagicMock(), token=MagicMock())
folder: Folder = Folder(grafana_api_model=model)
mock: Mock = Mock()
mock.json = Mock(return_value=dict())
call_the_api_mock.return_value = mock
with self.assertRaises(Exception):
folder.update_folder("test", "test", 10)
@patch("src.grafana_api.api.Api.call_the_api")
def test_delete_folder(self, call_the_api_mock):
model: APIModel = APIModel(host=MagicMock(), token=MagicMock())
folder: Folder = Folder(grafana_api_model=model)
mock: Mock = Mock()
mock.json = Mock(return_value=dict({"message": "Folder deleted"}))
call_the_api_mock.return_value = mock
self.assertEqual(None, folder.delete_folder("test"))
@patch("src.grafana_api.api.Api.call_the_api")
def test_delete_folder_no_uid(self, call_the_api_mock):
model: APIModel = APIModel(host=MagicMock(), token=MagicMock())
folder: Folder = Folder(grafana_api_model=model)
mock: Mock = Mock()
mock.json = Mock(return_value=dict())
call_the_api_mock.return_value = mock
with self.assertRaises(ValueError):
folder.delete_folder("")
@patch("src.grafana_api.api.Api.call_the_api")
def test_delete_folder_error_response(self, call_the_api_mock):
model: APIModel = APIModel(host=MagicMock(), token=MagicMock())
folder: Folder = Folder(grafana_api_model=model)
mock: Mock = Mock()
mock.json = Mock(return_value=dict({"message": "error"}))
call_the_api_mock.return_value = mock
with self.assertRaises(Exception):
folder.delete_folder("test")
@patch("src.grafana_api.api.Api.call_the_api")
def test_get_folder_permissions(self, call_the_api_mock):
model: APIModel = APIModel(host=MagicMock(), token=MagicMock())
folder: Folder = Folder(grafana_api_model=model)
mock: Mock = Mock()
mock.json = Mock(return_value=list([{"id": "test"}]))
call_the_api_mock.return_value = mock
self.assertEqual(list([{"id": "test"}]), folder.get_folder_permissions("test"))
@patch("src.grafana_api.api.Api.call_the_api")
def test_get_folder_permissions_no_uid(self, call_the_api_mock):
model: APIModel = APIModel(host=MagicMock(), token=MagicMock())
folder: Folder = Folder(grafana_api_model=model)
call_the_api_mock.return_value = list()
with self.assertRaises(ValueError):
folder.get_folder_permissions("")
@patch("src.grafana_api.api.Api.call_the_api")
def test_get_folder_permissions_error_response(self, call_the_api_mock):
model: APIModel = APIModel(host=MagicMock(), token=MagicMock())
folder: Folder = Folder(grafana_api_model=model)
mock: Mock = Mock()
mock.json = Mock(return_value=list([{"test": "test"}]))
call_the_api_mock.return_value = mock
with self.assertRaises(Exception):
folder.get_folder_permissions("test")
@patch("src.grafana_api.api.Api.call_the_api")
def test_update_folder_permissions(self, call_the_api_mock):
model: APIModel = APIModel(host=MagicMock(), token=MagicMock())
folder: Folder = Folder(grafana_api_model=model)
mock: Mock = Mock()
mock.json = Mock(return_value=dict({"message": "Folder permissions updated"}))
call_the_api_mock.return_value = mock
self.assertEqual(
None, folder.update_folder_permissions("test", dict({"test": "test"}))
)
@patch("src.grafana_api.api.Api.call_the_api")
def test_update_folder_permissions_no_uid(self, call_the_api_mock):
model: APIModel = APIModel(host=MagicMock(), token=MagicMock())
folder: Folder = Folder(grafana_api_model=model)
call_the_api_mock.return_value = dict()
with self.assertRaises(ValueError):
folder.update_folder_permissions("", dict())
@patch("src.grafana_api.api.Api.call_the_api")
def test_update_folder_permissions_error_response(self, call_the_api_mock):
model: APIModel = APIModel(host=MagicMock(), token=MagicMock())
folder: Folder = Folder(grafana_api_model=model)
mock: Mock = Mock()
mock.json = Mock(return_value=dict({"message": "test"}))
call_the_api_mock.return_value = mock
with self.assertRaises(Exception):
folder.update_folder_permissions("test", dict({"test": "test"}))
@patch("src.grafana_api.folder.Folder.get_all_folder_ids_and_names")
def test_get_folder_id_by_dashboard_path(self, all_folder_ids_and_names_mock):
model: APIModel = APIModel(host=MagicMock(), token=MagicMock())
folder: Folder = Folder(grafana_api_model=model)
all_folder_ids_and_names_mock.return_value = list([{"title": "test", "id": 12}])
self.assertEqual(
12, folder.get_folder_id_by_dashboard_path(dashboard_path="test")
)
def test_get_folder_id_by_dashboard_path_general_path(self):
model: APIModel = APIModel(host=MagicMock(), token=MagicMock())
folder: Folder = Folder(grafana_api_model=model)
self.assertEqual(
0, folder.get_folder_id_by_dashboard_path(dashboard_path="General")
)
def test_get_folder_id_by_dashboard_path_no_dashboard_path_defined(self):
model: APIModel = APIModel(host=MagicMock(), token=MagicMock())
folder: Folder = Folder(grafana_api_model=model)
with self.assertRaises(ValueError):
folder.get_folder_id_by_dashboard_path(dashboard_path="")
@patch("src.grafana_api.folder.Folder.get_all_folder_ids_and_names")
def test_get_folder_id_by_dashboard_path_no_title_match(
self, all_folder_ids_and_names_mock
):
model: APIModel = APIModel(host=MagicMock(), token=MagicMock())
folder: Folder = Folder(grafana_api_model=model)
all_folder_ids_and_names_mock.return_value = list(
[{"title": None, "id": "xty13y"}]
)
with self.assertRaises(Exception):
folder.get_folder_id_by_dashboard_path(dashboard_path="test")
@patch("src.grafana_api.api.Api.call_the_api")
def test_get_all_folder_ids_and_names(self, call_the_api_mock):
model: APIModel = APIModel(host=MagicMock(), token=MagicMock())
folder: Folder = Folder(grafana_api_model=model)
mock: Mock = Mock()
mock.json = Mock(
return_value=list([{"title": "test", "id": 12, "test": "test"}])
)
call_the_api_mock.return_value = mock
self.assertEqual(
list([{"title": "test", "id": 12}]), folder.get_all_folder_ids_and_names()
)
| 37.27204
| 88
| 0.666013
| 1,912
| 14,797
| 4.834728
| 0.034519
| 0.061337
| 0.087624
| 0.081783
| 0.95608
| 0.952293
| 0.946452
| 0.923842
| 0.918542
| 0.903938
| 0
| 0.005039
| 0.208691
| 14,797
| 396
| 89
| 37.366162
| 0.784439
| 0
| 0
| 0.648936
| 0
| 0
| 0.101575
| 0.073528
| 0
| 0
| 0
| 0
| 0.109929
| 1
| 0.109929
| false
| 0
| 0.014184
| 0
| 0.12766
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 6
|
250134cc0bb723243fb93d037d86132674af522e
| 30
|
py
|
Python
|
options/__init__.py
|
kwshh/ImageDeconvlution
|
561468463372a5727b553efa0330fc75901e29fc
|
[
"MIT"
] | 25
|
2019-05-10T13:51:25.000Z
|
2021-10-13T01:35:43.000Z
|
options/__init__.py
|
kwshh/ImageDeconvlution
|
561468463372a5727b553efa0330fc75901e29fc
|
[
"MIT"
] | 8
|
2019-05-10T13:51:07.000Z
|
2021-06-03T07:13:28.000Z
|
options/__init__.py
|
kwshh/ImageDeconvlution
|
561468463372a5727b553efa0330fc75901e29fc
|
[
"MIT"
] | 7
|
2020-08-15T09:16:11.000Z
|
2021-07-06T21:54:20.000Z
|
from .running_options import *
| 30
| 30
| 0.833333
| 4
| 30
| 6
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.1
| 30
| 1
| 30
| 30
| 0.888889
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 6
|
252ba86e61e67a1ee61878dd74bc3c6a8fc40da6
| 38,070
|
py
|
Python
|
instances/passenger_demand/pas-20210421-2109-int16e/42.py
|
LHcau/scheduling-shared-passenger-and-freight-transport-on-a-fixed-infrastructure
|
bba1e6af5bc8d9deaa2dc3b83f6fe9ddf15d2a11
|
[
"BSD-3-Clause"
] | null | null | null |
instances/passenger_demand/pas-20210421-2109-int16e/42.py
|
LHcau/scheduling-shared-passenger-and-freight-transport-on-a-fixed-infrastructure
|
bba1e6af5bc8d9deaa2dc3b83f6fe9ddf15d2a11
|
[
"BSD-3-Clause"
] | null | null | null |
instances/passenger_demand/pas-20210421-2109-int16e/42.py
|
LHcau/scheduling-shared-passenger-and-freight-transport-on-a-fixed-infrastructure
|
bba1e6af5bc8d9deaa2dc3b83f6fe9ddf15d2a11
|
[
"BSD-3-Clause"
] | null | null | null |
"""
PASSENGERS
"""
numPassengers = 3556
passenger_arriving = (
(4, 10, 7, 6, 3, 0, 9, 9, 3, 9, 4, 0), # 0
(4, 11, 9, 0, 1, 0, 9, 10, 7, 7, 1, 0), # 1
(8, 9, 8, 4, 4, 0, 6, 13, 7, 3, 4, 0), # 2
(2, 13, 5, 6, 0, 0, 8, 14, 6, 5, 6, 0), # 3
(5, 5, 8, 4, 6, 0, 8, 3, 7, 7, 4, 0), # 4
(2, 12, 7, 4, 2, 0, 4, 6, 3, 4, 0, 0), # 5
(6, 17, 11, 3, 0, 0, 8, 12, 6, 8, 4, 0), # 6
(1, 6, 9, 1, 6, 0, 7, 11, 8, 9, 0, 0), # 7
(8, 9, 9, 6, 2, 0, 10, 9, 5, 3, 2, 0), # 8
(3, 15, 8, 5, 5, 0, 5, 10, 9, 9, 3, 0), # 9
(4, 11, 2, 5, 4, 0, 9, 8, 4, 10, 2, 0), # 10
(7, 4, 7, 1, 3, 0, 1, 10, 7, 3, 2, 0), # 11
(3, 13, 7, 2, 2, 0, 11, 12, 4, 8, 1, 0), # 12
(0, 9, 9, 4, 1, 0, 5, 9, 5, 4, 2, 0), # 13
(6, 12, 15, 5, 3, 0, 2, 10, 5, 3, 1, 0), # 14
(6, 22, 11, 2, 3, 0, 3, 12, 5, 9, 3, 0), # 15
(2, 7, 10, 5, 3, 0, 5, 11, 6, 1, 3, 0), # 16
(7, 6, 8, 3, 1, 0, 7, 11, 7, 7, 1, 0), # 17
(3, 8, 15, 4, 2, 0, 6, 15, 5, 4, 0, 0), # 18
(6, 10, 4, 4, 2, 0, 9, 9, 7, 3, 2, 0), # 19
(8, 13, 6, 5, 5, 0, 9, 10, 3, 6, 5, 0), # 20
(4, 10, 8, 4, 1, 0, 2, 12, 5, 1, 3, 0), # 21
(6, 11, 5, 0, 4, 0, 8, 7, 5, 1, 1, 0), # 22
(3, 9, 5, 6, 3, 0, 10, 8, 10, 10, 1, 0), # 23
(4, 7, 9, 3, 0, 0, 6, 13, 7, 7, 5, 0), # 24
(4, 8, 5, 4, 2, 0, 7, 4, 7, 4, 5, 0), # 25
(8, 4, 4, 4, 3, 0, 9, 6, 5, 9, 4, 0), # 26
(5, 10, 6, 9, 1, 0, 9, 9, 8, 5, 4, 0), # 27
(2, 13, 5, 3, 3, 0, 12, 10, 5, 7, 5, 0), # 28
(9, 6, 10, 6, 3, 0, 10, 3, 7, 10, 1, 0), # 29
(4, 9, 7, 6, 4, 0, 4, 16, 9, 7, 3, 0), # 30
(3, 6, 9, 6, 4, 0, 10, 7, 6, 1, 2, 0), # 31
(3, 11, 11, 4, 2, 0, 7, 7, 1, 4, 6, 0), # 32
(4, 14, 9, 3, 4, 0, 8, 7, 7, 4, 4, 0), # 33
(5, 8, 9, 5, 2, 0, 6, 11, 6, 12, 3, 0), # 34
(4, 13, 7, 7, 2, 0, 6, 7, 8, 4, 2, 0), # 35
(6, 11, 7, 6, 5, 0, 10, 8, 5, 4, 0, 0), # 36
(9, 6, 5, 4, 2, 0, 9, 9, 6, 5, 1, 0), # 37
(7, 7, 3, 6, 0, 0, 11, 3, 5, 2, 1, 0), # 38
(7, 5, 12, 6, 4, 0, 7, 10, 7, 8, 1, 0), # 39
(12, 10, 9, 3, 2, 0, 6, 14, 4, 4, 3, 0), # 40
(7, 8, 7, 2, 3, 0, 5, 11, 7, 5, 4, 0), # 41
(5, 10, 8, 5, 6, 0, 6, 15, 9, 5, 3, 0), # 42
(8, 8, 5, 1, 0, 0, 5, 9, 6, 6, 2, 0), # 43
(2, 10, 6, 4, 2, 0, 7, 11, 1, 10, 4, 0), # 44
(4, 18, 9, 9, 0, 0, 10, 10, 8, 5, 4, 0), # 45
(8, 11, 4, 4, 6, 0, 4, 8, 4, 6, 1, 0), # 46
(2, 14, 11, 5, 4, 0, 5, 8, 4, 6, 5, 0), # 47
(4, 5, 3, 4, 3, 0, 9, 8, 5, 6, 4, 0), # 48
(5, 14, 8, 3, 1, 0, 5, 9, 6, 4, 3, 0), # 49
(6, 13, 6, 4, 4, 0, 9, 12, 11, 4, 6, 0), # 50
(11, 9, 9, 3, 1, 0, 7, 12, 9, 6, 4, 0), # 51
(4, 9, 5, 9, 1, 0, 7, 12, 4, 2, 0, 0), # 52
(7, 9, 8, 8, 0, 0, 3, 11, 6, 8, 3, 0), # 53
(6, 12, 11, 1, 0, 0, 7, 9, 3, 5, 3, 0), # 54
(3, 10, 7, 3, 4, 0, 7, 6, 5, 4, 3, 0), # 55
(9, 2, 6, 3, 3, 0, 6, 9, 8, 5, 1, 0), # 56
(8, 7, 7, 6, 2, 0, 4, 7, 10, 6, 4, 0), # 57
(4, 16, 8, 1, 1, 0, 4, 14, 3, 9, 0, 0), # 58
(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), # 59
)
station_arriving_intensity = (
(4.239442493415277, 10.874337121212122, 12.79077763496144, 10.138043478260869, 11.428846153846154, 7.610869565217392), # 0
(4.27923521607648, 10.995266557940518, 12.859864860039991, 10.194503019323673, 11.51450641025641, 7.608275422705315), # 1
(4.318573563554774, 11.114402244668911, 12.927312196515281, 10.249719806763286, 11.598358974358975, 7.60560193236715), # 2
(4.357424143985952, 11.231615625000002, 12.993070372750644, 10.303646739130434, 11.680326923076926, 7.60284945652174), # 3
(4.395753565505805, 11.346778142536477, 13.057090117109396, 10.356236714975847, 11.760333333333335, 7.600018357487922), # 4
(4.433528436250122, 11.459761240881035, 13.11932215795487, 10.407442632850241, 11.838301282051281, 7.597108997584541), # 5
(4.470715364354698, 11.570436363636365, 13.179717223650389, 10.457217391304349, 11.914153846153846, 7.594121739130435), # 6
(4.507280957955322, 11.678674954405162, 13.238226042559269, 10.50551388888889, 11.987814102564105, 7.591056944444445), # 7
(4.543191825187787, 11.784348456790122, 13.294799343044847, 10.552285024154589, 12.059205128205129, 7.587914975845411), # 8
(4.578414574187884, 11.88732831439394, 13.34938785347044, 10.597483695652175, 12.12825, 7.584696195652175), # 9
(4.612915813091406, 11.987485970819305, 13.401942302199371, 10.64106280193237, 12.194871794871796, 7.581400966183574), # 10
(4.646662150034143, 12.084692869668913, 13.452413417594972, 10.682975241545895, 12.25899358974359, 7.578029649758455), # 11
(4.679620193151888, 12.178820454545454, 13.500751928020566, 10.723173913043478, 12.320538461538462, 7.574582608695652), # 12
(4.71175655058043, 12.26974016905163, 13.546908561839473, 10.761611714975846, 12.37942948717949, 7.5710602053140095), # 13
(4.743037830455566, 12.357323456790127, 13.590834047415022, 10.798241545893719, 12.435589743589743, 7.567462801932367), # 14
(4.773430640913081, 12.441441761363635, 13.632479113110538, 10.833016304347826, 12.488942307692309, 7.563790760869566), # 15
(4.802901590088772, 12.521966526374861, 13.671794487289347, 10.86588888888889, 12.539410256410257, 7.560044444444445), # 16
(4.831417286118428, 12.598769195426486, 13.708730898314768, 10.896812198067634, 12.586916666666667, 7.556224214975846), # 17
(4.8589443371378405, 12.671721212121213, 13.74323907455013, 10.925739130434785, 12.631384615384619, 7.552330434782609), # 18
(4.8854493512828014, 12.740694020061728, 13.775269744358756, 10.952622584541063, 12.67273717948718, 7.5483634661835755), # 19
(4.910898936689104, 12.805559062850728, 13.804773636103969, 10.9774154589372, 12.710897435897436, 7.544323671497584), # 20
(4.935259701492538, 12.866187784090906, 13.831701478149103, 11.000070652173914, 12.74578846153846, 7.540211413043479), # 21
(4.958498253828894, 12.922451627384962, 13.856003998857469, 11.020541062801932, 12.777333333333331, 7.5360270531400975), # 22
(4.980581201833967, 12.97422203633558, 13.877631926592404, 11.038779589371982, 12.805455128205129, 7.531770954106282), # 23
(5.001475153643547, 13.021370454545455, 13.896535989717222, 11.054739130434783, 12.830076923076923, 7.52744347826087), # 24
(5.0211467173934246, 13.063768325617284, 13.91266691659526, 11.068372584541065, 12.851121794871794, 7.523044987922706), # 25
(5.039562501219393, 13.101287093153758, 13.925975435589832, 11.079632850241545, 12.86851282051282, 7.518575845410628), # 26
(5.056689113257243, 13.133798200757575, 13.936412275064265, 11.088472826086958, 12.88217307692308, 7.514036413043479), # 27
(5.072493161642767, 13.161173092031426, 13.943928163381893, 11.09484541062802, 12.89202564102564, 7.509427053140097), # 28
(5.086941254511755, 13.183283210578004, 13.948473828906026, 11.09870350241546, 12.89799358974359, 7.504748128019324), # 29
(5.1000000000000005, 13.200000000000001, 13.950000000000001, 11.100000000000001, 12.9, 7.5), # 30
(5.112219245524297, 13.213886079545453, 13.948855917874395, 11.099765849673204, 12.89926985815603, 7.4934020156588375), # 31
(5.124174680306906, 13.227588636363638, 13.945456038647343, 11.099067973856208, 12.897095035460993, 7.483239613526571), # 32
(5.135871675191815, 13.241105965909092, 13.93984891304348, 11.097913235294119, 12.893498936170213, 7.469612293853072), # 33
(5.147315601023018, 13.254436363636366, 13.93208309178744, 11.096308496732028, 12.888504964539008, 7.452619556888223), # 34
(5.158511828644501, 13.267578124999998, 13.922207125603865, 11.094260620915033, 12.882136524822696, 7.432360902881893), # 35
(5.169465728900256, 13.280529545454549, 13.91026956521739, 11.091776470588236, 12.874417021276598, 7.408935832083959), # 36
(5.180182672634271, 13.293288920454547, 13.896318961352657, 11.088862908496733, 12.865369858156027, 7.382443844744294), # 37
(5.190668030690537, 13.305854545454546, 13.8804038647343, 11.08552679738562, 12.855018439716313, 7.352984441112776), # 38
(5.200927173913044, 13.318224715909091, 13.862572826086955, 11.081775, 12.843386170212765, 7.32065712143928), # 39
(5.21096547314578, 13.330397727272729, 13.842874396135267, 11.077614379084968, 12.830496453900707, 7.285561385973679), # 40
(5.220788299232737, 13.342371874999998, 13.821357125603866, 11.073051797385622, 12.816372695035462, 7.247796734965852), # 41
(5.230401023017903, 13.354145454545458, 13.798069565217393, 11.068094117647059, 12.801038297872342, 7.207462668665667), # 42
(5.239809015345269, 13.365716761363636, 13.773060265700483, 11.06274820261438, 12.784516666666667, 7.164658687323005), # 43
(5.249017647058824, 13.377084090909092, 13.746377777777779, 11.05702091503268, 12.76683120567376, 7.119484291187739), # 44
(5.258032289002557, 13.388245738636364, 13.718070652173916, 11.050919117647059, 12.748005319148938, 7.072038980509745), # 45
(5.266858312020461, 13.399200000000002, 13.688187439613529, 11.044449673202614, 12.72806241134752, 7.022422255538898), # 46
(5.275501086956522, 13.409945170454547, 13.656776690821255, 11.037619444444445, 12.707025886524825, 6.970733616525071), # 47
(5.283965984654732, 13.420479545454548, 13.623886956521739, 11.030435294117646, 12.68491914893617, 6.9170725637181425), # 48
(5.292258375959079, 13.430801420454543, 13.589566787439615, 11.022904084967323, 12.66176560283688, 6.861538597367982), # 49
(5.300383631713555, 13.440909090909088, 13.553864734299518, 11.015032679738564, 12.63758865248227, 6.804231217724471), # 50
(5.308347122762149, 13.450800852272728, 13.516829347826087, 11.006827941176471, 12.612411702127659, 6.7452499250374816), # 51
(5.316154219948849, 13.460475, 13.47850917874396, 10.998296732026144, 12.58625815602837, 6.684694219556889), # 52
(5.3238102941176475, 13.469929829545457, 13.438952777777779, 10.98944591503268, 12.559151418439718, 6.622663601532567), # 53
(5.331320716112533, 13.479163636363635, 13.398208695652173, 10.980282352941177, 12.531114893617023, 6.559257571214393), # 54
(5.338690856777493, 13.488174715909091, 13.356325483091787, 10.970812908496733, 12.502171985815604, 6.494575628852241), # 55
(5.3459260869565215, 13.496961363636363, 13.313351690821257, 10.961044444444445, 12.472346099290782, 6.428717274695986), # 56
(5.353031777493607, 13.505521875000003, 13.269335869565218, 10.950983823529413, 12.441660638297872, 6.361782008995502), # 57
(5.360013299232737, 13.513854545454544, 13.224326570048309, 10.940637908496733, 12.410139007092198, 6.293869332000667), # 58
(0.0, 0.0, 0.0, 0.0, 0.0, 0.0), # 59
)
passenger_arriving_acc = (
(4, 10, 7, 6, 3, 0, 9, 9, 3, 9, 4, 0), # 0
(8, 21, 16, 6, 4, 0, 18, 19, 10, 16, 5, 0), # 1
(16, 30, 24, 10, 8, 0, 24, 32, 17, 19, 9, 0), # 2
(18, 43, 29, 16, 8, 0, 32, 46, 23, 24, 15, 0), # 3
(23, 48, 37, 20, 14, 0, 40, 49, 30, 31, 19, 0), # 4
(25, 60, 44, 24, 16, 0, 44, 55, 33, 35, 19, 0), # 5
(31, 77, 55, 27, 16, 0, 52, 67, 39, 43, 23, 0), # 6
(32, 83, 64, 28, 22, 0, 59, 78, 47, 52, 23, 0), # 7
(40, 92, 73, 34, 24, 0, 69, 87, 52, 55, 25, 0), # 8
(43, 107, 81, 39, 29, 0, 74, 97, 61, 64, 28, 0), # 9
(47, 118, 83, 44, 33, 0, 83, 105, 65, 74, 30, 0), # 10
(54, 122, 90, 45, 36, 0, 84, 115, 72, 77, 32, 0), # 11
(57, 135, 97, 47, 38, 0, 95, 127, 76, 85, 33, 0), # 12
(57, 144, 106, 51, 39, 0, 100, 136, 81, 89, 35, 0), # 13
(63, 156, 121, 56, 42, 0, 102, 146, 86, 92, 36, 0), # 14
(69, 178, 132, 58, 45, 0, 105, 158, 91, 101, 39, 0), # 15
(71, 185, 142, 63, 48, 0, 110, 169, 97, 102, 42, 0), # 16
(78, 191, 150, 66, 49, 0, 117, 180, 104, 109, 43, 0), # 17
(81, 199, 165, 70, 51, 0, 123, 195, 109, 113, 43, 0), # 18
(87, 209, 169, 74, 53, 0, 132, 204, 116, 116, 45, 0), # 19
(95, 222, 175, 79, 58, 0, 141, 214, 119, 122, 50, 0), # 20
(99, 232, 183, 83, 59, 0, 143, 226, 124, 123, 53, 0), # 21
(105, 243, 188, 83, 63, 0, 151, 233, 129, 124, 54, 0), # 22
(108, 252, 193, 89, 66, 0, 161, 241, 139, 134, 55, 0), # 23
(112, 259, 202, 92, 66, 0, 167, 254, 146, 141, 60, 0), # 24
(116, 267, 207, 96, 68, 0, 174, 258, 153, 145, 65, 0), # 25
(124, 271, 211, 100, 71, 0, 183, 264, 158, 154, 69, 0), # 26
(129, 281, 217, 109, 72, 0, 192, 273, 166, 159, 73, 0), # 27
(131, 294, 222, 112, 75, 0, 204, 283, 171, 166, 78, 0), # 28
(140, 300, 232, 118, 78, 0, 214, 286, 178, 176, 79, 0), # 29
(144, 309, 239, 124, 82, 0, 218, 302, 187, 183, 82, 0), # 30
(147, 315, 248, 130, 86, 0, 228, 309, 193, 184, 84, 0), # 31
(150, 326, 259, 134, 88, 0, 235, 316, 194, 188, 90, 0), # 32
(154, 340, 268, 137, 92, 0, 243, 323, 201, 192, 94, 0), # 33
(159, 348, 277, 142, 94, 0, 249, 334, 207, 204, 97, 0), # 34
(163, 361, 284, 149, 96, 0, 255, 341, 215, 208, 99, 0), # 35
(169, 372, 291, 155, 101, 0, 265, 349, 220, 212, 99, 0), # 36
(178, 378, 296, 159, 103, 0, 274, 358, 226, 217, 100, 0), # 37
(185, 385, 299, 165, 103, 0, 285, 361, 231, 219, 101, 0), # 38
(192, 390, 311, 171, 107, 0, 292, 371, 238, 227, 102, 0), # 39
(204, 400, 320, 174, 109, 0, 298, 385, 242, 231, 105, 0), # 40
(211, 408, 327, 176, 112, 0, 303, 396, 249, 236, 109, 0), # 41
(216, 418, 335, 181, 118, 0, 309, 411, 258, 241, 112, 0), # 42
(224, 426, 340, 182, 118, 0, 314, 420, 264, 247, 114, 0), # 43
(226, 436, 346, 186, 120, 0, 321, 431, 265, 257, 118, 0), # 44
(230, 454, 355, 195, 120, 0, 331, 441, 273, 262, 122, 0), # 45
(238, 465, 359, 199, 126, 0, 335, 449, 277, 268, 123, 0), # 46
(240, 479, 370, 204, 130, 0, 340, 457, 281, 274, 128, 0), # 47
(244, 484, 373, 208, 133, 0, 349, 465, 286, 280, 132, 0), # 48
(249, 498, 381, 211, 134, 0, 354, 474, 292, 284, 135, 0), # 49
(255, 511, 387, 215, 138, 0, 363, 486, 303, 288, 141, 0), # 50
(266, 520, 396, 218, 139, 0, 370, 498, 312, 294, 145, 0), # 51
(270, 529, 401, 227, 140, 0, 377, 510, 316, 296, 145, 0), # 52
(277, 538, 409, 235, 140, 0, 380, 521, 322, 304, 148, 0), # 53
(283, 550, 420, 236, 140, 0, 387, 530, 325, 309, 151, 0), # 54
(286, 560, 427, 239, 144, 0, 394, 536, 330, 313, 154, 0), # 55
(295, 562, 433, 242, 147, 0, 400, 545, 338, 318, 155, 0), # 56
(303, 569, 440, 248, 149, 0, 404, 552, 348, 324, 159, 0), # 57
(307, 585, 448, 249, 150, 0, 408, 566, 351, 333, 159, 0), # 58
(307, 585, 448, 249, 150, 0, 408, 566, 351, 333, 159, 0), # 59
)
passenger_arriving_rate = (
(4.239442493415277, 8.699469696969697, 7.674466580976864, 4.055217391304347, 2.2857692307692306, 0.0, 7.610869565217392, 9.143076923076922, 6.082826086956521, 5.1163110539845755, 2.174867424242424, 0.0), # 0
(4.27923521607648, 8.796213246352414, 7.715918916023995, 4.077801207729468, 2.3029012820512818, 0.0, 7.608275422705315, 9.211605128205127, 6.116701811594203, 5.1439459440159965, 2.1990533115881035, 0.0), # 1
(4.318573563554774, 8.891521795735128, 7.7563873179091685, 4.099887922705314, 2.3196717948717946, 0.0, 7.60560193236715, 9.278687179487179, 6.1498318840579715, 5.170924878606112, 2.222880448933782, 0.0), # 2
(4.357424143985952, 8.9852925, 7.795842223650386, 4.121458695652173, 2.336065384615385, 0.0, 7.60284945652174, 9.34426153846154, 6.18218804347826, 5.197228149100257, 2.246323125, 0.0), # 3
(4.395753565505805, 9.07742251402918, 7.834254070265637, 4.142494685990338, 2.352066666666667, 0.0, 7.600018357487922, 9.408266666666668, 6.213742028985508, 5.222836046843758, 2.269355628507295, 0.0), # 4
(4.433528436250122, 9.167808992704828, 7.8715932947729215, 4.1629770531400965, 2.367660256410256, 0.0, 7.597108997584541, 9.470641025641024, 6.244465579710145, 5.247728863181948, 2.291952248176207, 0.0), # 5
(4.470715364354698, 9.25634909090909, 7.907830334190233, 4.182886956521739, 2.382830769230769, 0.0, 7.594121739130435, 9.531323076923076, 6.274330434782609, 5.271886889460156, 2.3140872727272725, 0.0), # 6
(4.507280957955322, 9.34293996352413, 7.942935625535561, 4.2022055555555555, 2.397562820512821, 0.0, 7.591056944444445, 9.590251282051284, 6.303308333333334, 5.295290417023708, 2.3357349908810323, 0.0), # 7
(4.543191825187787, 9.427478765432097, 7.976879605826908, 4.220914009661835, 2.4118410256410256, 0.0, 7.587914975845411, 9.647364102564103, 6.3313710144927535, 5.317919737217938, 2.3568696913580243, 0.0), # 8
(4.578414574187884, 9.509862651515151, 8.009632712082263, 4.23899347826087, 2.4256499999999996, 0.0, 7.584696195652175, 9.702599999999999, 6.358490217391305, 5.339755141388175, 2.377465662878788, 0.0), # 9
(4.612915813091406, 9.589988776655444, 8.041165381319622, 4.256425120772947, 2.438974358974359, 0.0, 7.581400966183574, 9.755897435897436, 6.384637681159421, 5.360776920879748, 2.397497194163861, 0.0), # 10
(4.646662150034143, 9.66775429573513, 8.071448050556983, 4.273190096618357, 2.4517987179487175, 0.0, 7.578029649758455, 9.80719487179487, 6.409785144927537, 5.380965367037988, 2.4169385739337823, 0.0), # 11
(4.679620193151888, 9.743056363636363, 8.100451156812339, 4.289269565217391, 2.4641076923076923, 0.0, 7.574582608695652, 9.85643076923077, 6.433904347826087, 5.400300771208226, 2.4357640909090907, 0.0), # 12
(4.71175655058043, 9.815792135241303, 8.128145137103683, 4.304644685990338, 2.475885897435898, 0.0, 7.5710602053140095, 9.903543589743592, 6.456967028985507, 5.418763424735789, 2.4539480338103257, 0.0), # 13
(4.743037830455566, 9.8858587654321, 8.154500428449014, 4.3192966183574875, 2.4871179487179482, 0.0, 7.567462801932367, 9.948471794871793, 6.478944927536231, 5.4363336189660085, 2.471464691358025, 0.0), # 14
(4.773430640913081, 9.953153409090907, 8.179487467866322, 4.33320652173913, 2.4977884615384616, 0.0, 7.563790760869566, 9.991153846153846, 6.499809782608695, 5.452991645244214, 2.488288352272727, 0.0), # 15
(4.802901590088772, 10.017573221099887, 8.203076692373608, 4.346355555555555, 2.507882051282051, 0.0, 7.560044444444445, 10.031528205128204, 6.519533333333333, 5.468717794915738, 2.504393305274972, 0.0), # 16
(4.831417286118428, 10.079015356341188, 8.22523853898886, 4.358724879227053, 2.517383333333333, 0.0, 7.556224214975846, 10.069533333333332, 6.538087318840581, 5.483492359325907, 2.519753839085297, 0.0), # 17
(4.8589443371378405, 10.13737696969697, 8.245943444730077, 4.370295652173914, 2.5262769230769235, 0.0, 7.552330434782609, 10.105107692307694, 6.55544347826087, 5.4972956298200515, 2.5343442424242424, 0.0), # 18
(4.8854493512828014, 10.192555216049382, 8.265161846615253, 4.381049033816424, 2.534547435897436, 0.0, 7.5483634661835755, 10.138189743589743, 6.571573550724637, 5.510107897743501, 2.5481388040123454, 0.0), # 19
(4.910898936689104, 10.244447250280581, 8.282864181662381, 4.3909661835748794, 2.542179487179487, 0.0, 7.544323671497584, 10.168717948717948, 6.58644927536232, 5.5219094544415865, 2.5611118125701453, 0.0), # 20
(4.935259701492538, 10.292950227272724, 8.299020886889462, 4.400028260869565, 2.5491576923076917, 0.0, 7.540211413043479, 10.196630769230767, 6.600042391304348, 5.53268059125964, 2.573237556818181, 0.0), # 21
(4.958498253828894, 10.337961301907969, 8.313602399314481, 4.408216425120773, 2.555466666666666, 0.0, 7.5360270531400975, 10.221866666666664, 6.6123246376811595, 5.542401599542987, 2.584490325476992, 0.0), # 22
(4.980581201833967, 10.379377629068463, 8.326579155955441, 4.415511835748792, 2.5610910256410255, 0.0, 7.531770954106282, 10.244364102564102, 6.623267753623189, 5.551052770636961, 2.5948444072671157, 0.0), # 23
(5.001475153643547, 10.417096363636363, 8.337921593830332, 4.421895652173912, 2.5660153846153846, 0.0, 7.52744347826087, 10.264061538461538, 6.632843478260869, 5.558614395886888, 2.6042740909090907, 0.0), # 24
(5.0211467173934246, 10.451014660493826, 8.347600149957156, 4.427349033816426, 2.5702243589743587, 0.0, 7.523044987922706, 10.280897435897435, 6.641023550724639, 5.565066766638103, 2.6127536651234564, 0.0), # 25
(5.039562501219393, 10.481029674523006, 8.355585261353898, 4.431853140096617, 2.5737025641025637, 0.0, 7.518575845410628, 10.294810256410255, 6.647779710144927, 5.570390174235932, 2.6202574186307515, 0.0), # 26
(5.056689113257243, 10.507038560606059, 8.361847365038559, 4.435389130434783, 2.5764346153846156, 0.0, 7.514036413043479, 10.305738461538462, 6.653083695652175, 5.574564910025706, 2.6267596401515148, 0.0), # 27
(5.072493161642767, 10.52893847362514, 8.366356898029135, 4.437938164251207, 2.578405128205128, 0.0, 7.509427053140097, 10.313620512820512, 6.656907246376812, 5.5775712653527565, 2.632234618406285, 0.0), # 28
(5.086941254511755, 10.546626568462402, 8.369084297343615, 4.439481400966184, 2.579598717948718, 0.0, 7.504748128019324, 10.318394871794872, 6.659222101449276, 5.57938953156241, 2.6366566421156006, 0.0), # 29
(5.1000000000000005, 10.56, 8.370000000000001, 4.44, 2.58, 0.0, 7.5, 10.32, 6.660000000000001, 5.58, 2.64, 0.0), # 30
(5.112219245524297, 10.571108863636361, 8.369313550724637, 4.439906339869282, 2.5798539716312057, 0.0, 7.4934020156588375, 10.319415886524823, 6.659859509803923, 5.579542367149758, 2.6427772159090903, 0.0), # 31
(5.124174680306906, 10.582070909090909, 8.367273623188405, 4.439627189542483, 2.5794190070921985, 0.0, 7.483239613526571, 10.317676028368794, 6.659440784313724, 5.578182415458937, 2.6455177272727273, 0.0), # 32
(5.135871675191815, 10.592884772727274, 8.363909347826088, 4.439165294117647, 2.5786997872340423, 0.0, 7.469612293853072, 10.314799148936169, 6.658747941176471, 5.575939565217392, 2.6482211931818185, 0.0), # 33
(5.147315601023018, 10.603549090909091, 8.359249855072465, 4.438523398692811, 2.5777009929078014, 0.0, 7.452619556888223, 10.310803971631206, 6.657785098039217, 5.572833236714976, 2.6508872727272728, 0.0), # 34
(5.158511828644501, 10.614062499999998, 8.353324275362318, 4.437704248366013, 2.576427304964539, 0.0, 7.432360902881893, 10.305709219858157, 6.65655637254902, 5.568882850241546, 2.6535156249999994, 0.0), # 35
(5.169465728900256, 10.624423636363638, 8.346161739130434, 4.436710588235294, 2.5748834042553193, 0.0, 7.408935832083959, 10.299533617021277, 6.655065882352941, 5.564107826086956, 2.6561059090909094, 0.0), # 36
(5.180182672634271, 10.634631136363637, 8.337791376811595, 4.435545163398693, 2.573073971631205, 0.0, 7.382443844744294, 10.29229588652482, 6.65331774509804, 5.558527584541062, 2.6586577840909094, 0.0), # 37
(5.190668030690537, 10.644683636363636, 8.32824231884058, 4.4342107189542475, 2.5710036879432625, 0.0, 7.352984441112776, 10.28401475177305, 6.651316078431372, 5.5521615458937195, 2.661170909090909, 0.0), # 38
(5.200927173913044, 10.654579772727272, 8.317543695652173, 4.43271, 2.568677234042553, 0.0, 7.32065712143928, 10.274708936170212, 6.649065, 5.545029130434782, 2.663644943181818, 0.0), # 39
(5.21096547314578, 10.664318181818182, 8.305724637681159, 4.431045751633987, 2.566099290780141, 0.0, 7.285561385973679, 10.264397163120565, 6.646568627450981, 5.537149758454106, 2.6660795454545454, 0.0), # 40
(5.220788299232737, 10.673897499999997, 8.29281427536232, 4.429220718954248, 2.563274539007092, 0.0, 7.247796734965852, 10.253098156028368, 6.643831078431373, 5.5285428502415455, 2.6684743749999993, 0.0), # 41
(5.230401023017903, 10.683316363636365, 8.278841739130435, 4.427237647058823, 2.560207659574468, 0.0, 7.207462668665667, 10.240830638297872, 6.640856470588235, 5.519227826086957, 2.6708290909090913, 0.0), # 42
(5.239809015345269, 10.692573409090908, 8.26383615942029, 4.4250992810457515, 2.556903333333333, 0.0, 7.164658687323005, 10.227613333333332, 6.637648921568627, 5.509224106280192, 2.673143352272727, 0.0), # 43
(5.249017647058824, 10.701667272727272, 8.247826666666667, 4.422808366013072, 2.5533662411347517, 0.0, 7.119484291187739, 10.213464964539007, 6.634212549019608, 5.498551111111111, 2.675416818181818, 0.0), # 44
(5.258032289002557, 10.71059659090909, 8.23084239130435, 4.420367647058823, 2.5496010638297872, 0.0, 7.072038980509745, 10.198404255319149, 6.630551470588235, 5.487228260869566, 2.6776491477272724, 0.0), # 45
(5.266858312020461, 10.71936, 8.212912463768117, 4.417779869281045, 2.5456124822695037, 0.0, 7.022422255538898, 10.182449929078015, 6.626669803921568, 5.475274975845411, 2.67984, 0.0), # 46
(5.275501086956522, 10.727956136363636, 8.194066014492753, 4.415047777777778, 2.5414051773049646, 0.0, 6.970733616525071, 10.165620709219858, 6.6225716666666665, 5.462710676328501, 2.681989034090909, 0.0), # 47
(5.283965984654732, 10.736383636363637, 8.174332173913044, 4.412174117647059, 2.536983829787234, 0.0, 6.9170725637181425, 10.147935319148935, 6.618261176470588, 5.449554782608695, 2.6840959090909093, 0.0), # 48
(5.292258375959079, 10.744641136363633, 8.15374007246377, 4.409161633986929, 2.5323531205673757, 0.0, 6.861538597367982, 10.129412482269503, 6.613742450980394, 5.435826714975845, 2.6861602840909082, 0.0), # 49
(5.300383631713555, 10.752727272727268, 8.13231884057971, 4.406013071895425, 2.527517730496454, 0.0, 6.804231217724471, 10.110070921985816, 6.6090196078431385, 5.421545893719807, 2.688181818181817, 0.0), # 50
(5.308347122762149, 10.760640681818181, 8.110097608695652, 4.4027311764705885, 2.5224823404255314, 0.0, 6.7452499250374816, 10.089929361702126, 6.604096764705883, 5.406731739130435, 2.6901601704545453, 0.0), # 51
(5.316154219948849, 10.768379999999999, 8.087105507246376, 4.399318692810457, 2.517251631205674, 0.0, 6.684694219556889, 10.069006524822695, 6.5989780392156865, 5.391403671497584, 2.6920949999999997, 0.0), # 52
(5.3238102941176475, 10.775943863636364, 8.063371666666667, 4.395778366013072, 2.5118302836879436, 0.0, 6.622663601532567, 10.047321134751774, 6.593667549019608, 5.375581111111111, 2.693985965909091, 0.0), # 53
(5.331320716112533, 10.783330909090907, 8.038925217391304, 4.392112941176471, 2.5062229787234043, 0.0, 6.559257571214393, 10.024891914893617, 6.5881694117647065, 5.359283478260869, 2.6958327272727267, 0.0), # 54
(5.338690856777493, 10.790539772727271, 8.013795289855072, 4.388325163398693, 2.5004343971631204, 0.0, 6.494575628852241, 10.001737588652482, 6.58248774509804, 5.342530193236715, 2.697634943181818, 0.0), # 55
(5.3459260869565215, 10.79756909090909, 7.988011014492754, 4.384417777777777, 2.494469219858156, 0.0, 6.428717274695986, 9.977876879432625, 6.576626666666667, 5.325340676328502, 2.6993922727272723, 0.0), # 56
(5.353031777493607, 10.804417500000001, 7.96160152173913, 4.380393529411765, 2.4883321276595742, 0.0, 6.361782008995502, 9.953328510638297, 6.570590294117648, 5.307734347826087, 2.7011043750000003, 0.0), # 57
(5.360013299232737, 10.811083636363634, 7.934595942028984, 4.376255163398692, 2.4820278014184396, 0.0, 6.293869332000667, 9.928111205673758, 6.564382745098039, 5.289730628019323, 2.7027709090909084, 0.0), # 58
(0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0), # 59
)
passenger_allighting_rate = (
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 0
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 1
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 2
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 3
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 4
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 5
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 6
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 7
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 8
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 9
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 10
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 11
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 12
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 13
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 14
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 15
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 16
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 17
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 18
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 19
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 20
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 21
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 22
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 23
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 24
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 25
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 26
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 27
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 28
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 29
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 30
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 31
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 32
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 33
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 34
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 35
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 36
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 37
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 38
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 39
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 40
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 41
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 42
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 43
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 44
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 45
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 46
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 47
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 48
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 49
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 50
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 51
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 52
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 53
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 54
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 55
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 56
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 57
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 58
(0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1, 0, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 0.16666666666666666, 1), # 59
)
"""
parameters for reproducibiliy. More information: https://numpy.org/doc/stable/reference/random/parallel.html
"""
#initial entropy
entropy = 258194110137029475889902652135037600173
#index for seed sequence child
child_seed_index = (
1, # 0
41, # 1
)
| 113.641791
| 214
| 0.730391
| 5,147
| 38,070
| 5.400233
| 0.230814
| 0.310847
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| 0.466271
| 0.326606
| 0.325814
| 0.325814
| 0.325814
| 0.325814
| 0.325814
| 0
| 0.820002
| 0.118571
| 38,070
| 334
| 215
| 113.982036
| 0.008314
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| 0.015823
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| null | 0
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| 0
| 0
| 0
|
0
| 6
|
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