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elemenpy/sm/boson.py | roadnarrows-robotics/elemenpy | 0 | 61820 | """
Bosons.
Package:
RoadNarrows elemenpy package.
File:
boson.py
Link:
https://github.com/roadnarrows-robotics/
Copyright:
(c) 2019. RoadNarrows LLC
http://www.roadnarrows.com
All Rights Reserved
License:
MIT
"""
from copy import copy
from enum import Enum
from elemenpy.core.common import (isderivedclass)
from elemenpy.core.format import (Format, default_encoder)
from elemenpy.core.prettyprint import (print2cols)
from elemenpy.sm.standardmodel import (StandardModel as sm, SubatomicParticle)
from elemenpy.sm.spin import (SpinQuantumNumber)
from elemenpy.sm.electriccharge import (ElectricCharge)
from elemenpy.sm.colorcharge import (ColorCharge)
# -----------------------------------------------------------------------------
# Boson Base Class
# -----------------------------------------------------------------------------
class Boson(SubatomicParticle):
""" Boson base class. """
class BosonSubfamily(Enum):
""" Boson subfamily enumeration. """
UNKNOWN = 0
SCALAR = 1 # scalar
VECTOR = 2 # vector
Classification = sm.Classification.BOSON
Family = sm.Family.BOSON
Statistics = sm.Statistics.BOSONIC
Name = 'boson'
Symbol = 'boson'
Subfamily = BosonSubfamily.UNKNOWN
# registered boson subclasses by the @Boson.subclass decorator
Subclasses = {}
# . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
# Class Methods
# . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
@classmethod
def subclass(klass):
"""
Boson subclass decorator to add a subclass to an internal list.
"""
def wrap(D):
"""
Store derived subclass.
Parameters:
D Prospective derived class.
"""
if isderivedclass(D, klass):
klass.Subclasses[D.__name__] = D
return D
return wrap
@classmethod
def finalize_boson_family(klass):
"""
Finalize all registered boson subclass attributes.
Bosons are interdependent.
"""
for qname, qklass in klass.Subclasses.items():
qklass.finalize_boson()
@classmethod
def boson_family(klass):
"""
Get the dictionary of all registered boson subclasses.
Returns:
{qname: qclass, ...}
"""
return klass.Subclasses
@classmethod
def boson_class(klass, qname):
"""
Get the boson subclass.
Parameters:
qname Boson subclass name.
Returns:
qclass
"""
return klass.Subclasses[qname]
@classmethod
def subfamily(klass):
""" Return boson subfamily. """
return klass.Subfamily
@classmethod
def print_boson_properties(klass, indent=0, **print_kwargs):
"""
Print fixed meson particle properties to output stream.
Parameters:
indent Line indentation.
print_kwargs Print control keyword arguments.
"""
klass.print_subatomic_properties(indent=indent, **print_kwargs)
#print(f"{'':<{indent+2}}Boson", **print_kwargs)
print2cols([
('Subfamily', klass.Subfamily.name),],
c1width=16, indent=indent, **print_kwargs)
# . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
# Class Instance Methods
# . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
def __init__(self):
""" Boson initializer. """
SubatomicParticle.__init__(self)
def __repr__(self):
return f"{self.__module__}.{self.__class__.__name__}"\
f"()"
def __str__(self):
return self.name
@property
def subfamily(self):
""" Return boson subfamily. """
return self.Subfamily
def print_state(self, indent=0, **print_kwargs):
"""
Print boson state to output stream using default encoder.
Parameters:
indent Line indentation.
print_kwargs Print control keyword arguments.
"""
SubatomicParticle.print_state(self, indent=indent, **print_kwargs)
# -----------------------------------------------------------------------------
# Photon Class
# -----------------------------------------------------------------------------
@Boson.subclass()
class Photon(Boson):
""" Photon class. """
#
# Class Fixed Properties
#
Pid = sm.ParticleId.PHOTON
Name = "photon"
Symbol = default_encoder('$sm(gamma)')
RestMass = 0.0
ElecCharge = ElectricCharge(0)
QSpin = SpinQuantumNumber(1) # intrinsic spin number
Subfamily = Boson.BosonSubfamily.VECTOR
# . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
# Class Methods
# . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
@classmethod
def finalize_boson(klass):
"""
Finalize boson's class attibutes.
Finalization can only proceed when all boson classes have been
defined due to interdependencies.
"""
klass.AntiParticle = klass.boson_class('Photon')
@classmethod
def print_properties(klass, indent=0, **print_kwargs):
klass.print_boson_properties(indent=indent, **print_kwargs)
# . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
# Class Instance Methods
# . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
def __init__(self):
""" Photon initializer. """
Boson.__init__(self)
def __repr__(self):
return f"{self.__module__}.{self.__class__.__name__}"\
f"()"
def __str__(self):
return self.Name
def print_state(self, indent=0, **print_kwargs):
"""
Print photon state to output stream using default encoder.
Parameters:
indent Line indentation.
print_kwargs Print control keyword arguments.
"""
Boson.print_state(self, indent=indent, **print_kwargs)
# -----------------------------------------------------------------------------
# WBosonN Class
# -----------------------------------------------------------------------------
@Boson.subclass()
class WBosonN(Boson):
""" WBosonN class. """
#
# Class Fixed Properties
#
Pid = sm.ParticleId.W_BOSON_N
Name = "W-boson-"
Symbol = default_encoder('$sm(W-)')
RestMass = 80.385e3
ElecCharge = ElectricCharge(-1)
QSpin = SpinQuantumNumber(1) # intrinsic spin number
Subfamily = Boson.BosonSubfamily.VECTOR
# . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
# Class Methods
# . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
@classmethod
def finalize_boson(klass):
"""
Finalize boson's class attibutes.
Finalization can only proceed when all boson classes have been
defined due to interdependencies.
"""
klass.AntiParticle = klass.boson_class('WBosonP')
@classmethod
def print_properties(klass, indent=0, **print_kwargs):
klass.print_boson_properties(indent=indent, **print_kwargs)
# . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
# Class Instance Methods
# . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
def __init__(self):
""" W- boson initializer. """
Boson.__init__(self)
def __repr__(self):
return f"{self.__module__}.{self.__class__.__name__}"\
f"()"
def __str__(self):
return self.Name
def print_state(self, indent=0, **print_kwargs):
"""
Print W- boson state to output stream using default encoder.
Parameters:
indent Line indentation.
print_kwargs Print control keyword arguments.
"""
Boson.print_state(self, indent=indent, **print_kwargs)
# -----------------------------------------------------------------------------
# WBosonP Class
# -----------------------------------------------------------------------------
@Boson.subclass()
class WBosonP(Boson):
""" WBosonP class. """
#
# Class Fixed Properties
#
Pid = sm.ParticleId.W_BOSON_P
Name = "W-boson+"
Symbol = default_encoder('$sm(W+)')
RestMass = 80.385e3
ElecCharge = ElectricCharge(1)
QSpin = SpinQuantumNumber(1) # intrinsic spin number
Subfamily = Boson.BosonSubfamily.VECTOR
# . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
# Class Methods
# . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
@classmethod
def finalize_boson(klass):
"""
Finalize boson's class attibutes.
Finalization can only proceed when all boson classes have been
defined due to interdependencies.
"""
klass.AntiParticle = klass.boson_class('WBosonN')
@classmethod
def print_properties(klass, indent=0, **print_kwargs):
klass.print_boson_properties(indent=indent, **print_kwargs)
# . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
# Class Instance Methods
# . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
def __init__(self):
""" W+ boson initializer. """
Boson.__init__(self)
def __repr__(self):
return f"{self.__module__}.{self.__class__.__name__}"\
f"()"
def __str__(self):
return self.Name
def print_state(self, indent=0, **print_kwargs):
"""
Print W+ boson state to output stream using default encoder.
Parameters:
indent Line indentation.
print_kwargs Print control keyword arguments.
"""
Boson.print_state(self, indent=indent, **print_kwargs)
# -----------------------------------------------------------------------------
# ZBoson Class
# -----------------------------------------------------------------------------
@Boson.subclass()
class ZBoson(Boson):
""" ZBoson class. """
#
# Class Fixed Properties
#
Pid = sm.ParticleId.Z_BOSON
Name = "Z-boson"
Symbol = default_encoder('$sm(Z)')
RestMass = 91.1875e3
ElecCharge = ElectricCharge(0)
QSpin = SpinQuantumNumber(1) # intrinsic spin number
Subfamily = Boson.BosonSubfamily.VECTOR
# . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
# Class Methods
# . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
@classmethod
def finalize_boson(klass):
"""
Finalize boson's class attibutes.
Finalization can only proceed when all boson classes have been
defined due to interdependencies.
"""
klass.AntiParticle = klass.boson_class('ZBoson')
@classmethod
def print_properties(klass, indent=0, **print_kwargs):
klass.print_boson_properties(indent=indent, **print_kwargs)
# . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
# Class Instance Methods
# . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
def __init__(self):
""" Z boson initializer. """
Boson.__init__(self)
def __repr__(self):
return f"{self.__module__}.{self.__class__.__name__}"\
f"()"
def __str__(self):
return self.Name
def print_state(self, indent=0, **print_kwargs):
"""
Print Z boson state to output stream using default encoder.
Parameters:
indent Line indentation.
print_kwargs Print control keyword arguments.
"""
Boson.print_state(self, indent=indent, **print_kwargs)
# -----------------------------------------------------------------------------
# Gluon Class
# -----------------------------------------------------------------------------
@Boson.subclass()
class Gluon(Boson):
""" Gluon class. """
#
# Class Fixed Properties
#
Pid = sm.ParticleId.GLUON
Name = "gluon"
Symbol = default_encoder('$sm(g)')
RestMass = 0.0
ElecCharge = ElectricCharge(0)
QSpin = SpinQuantumNumber(1) # intrinsic spin number
Subfamily = Boson.BosonSubfamily.VECTOR
# . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
# Class Methods
# . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
@classmethod
def finalize_boson(klass):
"""
Finalize boson's class attibutes.
Finalization can only proceed when all boson classes have been
defined due to interdependencies.
"""
klass.AntiParticle = klass.boson_class('Gluon')
@classmethod
def print_properties(klass, indent=0, **print_kwargs):
klass.print_boson_properties(indent=indent, **print_kwargs)
# . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
# Class Instance Methods
# . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
def __init__(self, color, anticolor):
"""
Gluon initializer.
Parameters:
color Primary color charge.
anticolor Anticolor charge.
"""
Boson.__init__(self)
self._color_charge = ColorCharge(color)
self._anticolor_charge = ColorCharge(anticolor)
if not self.color_charge.is_primary_color():
raise ValueError(
f"{self.name} '{self.color_charge.name}' is not a primary color")
if not self.anticolor_charge.is_anticolor():
raise ValueError(
f"{self.name} '{self.anticolor_charge.name}' is not an anticolor")
if self.color_charge == self.anticolor_charge.complement:
raise ValueError(f"{self.name} " +
f"'{self.color_charge.name}-{self.anticolor_charge.name}' " +
"defines a meson")
def __repr__(self):
return f"{self.__module__}.{self.__class__.__name__}"\
f"{self.color_charge!r}, {self.anticolor_charge!r})"
def __str__(self):
return self.fqname
def __eq__(self, gluon):
"""
Equal to. self == gluon.
Two gluons are considered equal if they are of the same kind.
That is, gluons with the same color charges.
"""
return self.color_charge == gluon.color_charge and \
self.anticolor_charge == gluon.anticolor_charge
def __ne__(self, gluon):
"""
Not equal to. self != gluon.
Two gluons are considered not equal if they are not of the same kind.
That is, gluons that do not have the same color charges.
"""
return self.color_charge != gluon.color_charge or \
self.anticolor_charge != gluon.anticolor_charge
@property
def fqname(self):
return f"{self.color_charge.name}-{self.anticolor_charge.name} {self.name}"
@property
def color_charge(self):
""" Return primary color charge. """
return self._color_charge
@property
def anticolor_charge(self):
""" Return anticolor charge. """
return self._anticolor_charge
def print_state(self, indent=0, **print_kwargs):
"""
Print gluon state to output stream using default encoder.
Parameters:
indent Line indentation.
print_kwargs Print control keyword arguments.
"""
Boson.print_state(self, indent=indent, **print_kwargs)
print2cols([
('FQ Name', self.fqname),
('Color Charge',
f"{self.color_charge.symbol} {self.color_charge.name}"),
('Anticolor Charge',
f"{self.anticolor_charge.symbol} {self.anticolor_charge.name}"),],
indent=indent, **print_kwargs)
# -----------------------------------------------------------------------------
# HiggsBoson Class
# -----------------------------------------------------------------------------
@Boson.subclass()
class HiggsBoson(Boson):
""" HiggsBoson class. """
#
# Class Fixed Properties
#
Pid = sm.ParticleId.HIGGS_BOSON
Name = "higgs-boson"
Symbol = default_encoder('$sm(H0)')
RestMass = 125.09e3
ElecCharge = ElectricCharge(0)
QSpin = SpinQuantumNumber(0)
Subfamily = Boson.BosonSubfamily.SCALAR
# . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
# Class Methods
# . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
@classmethod
def finalize_boson(klass):
"""
Finalize boson's class attibutes.
Finalization can only proceed when all boson classes have been
defined due to interdependencies.
"""
klass.AntiParticle = klass.boson_class('HiggsBoson')
@classmethod
def print_properties(klass, indent=0, **print_kwargs):
klass.print_boson_properties(indent=indent, **print_kwargs)
# . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
# Class Instance Methods
# . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
def __init__(self):
""" Higgs boson initializer. """
Boson.__init__(self)
def __repr__(self):
return f"{self.__module__}.{self.__class__.__name__}"\
f"()"
def __str__(self):
return self.Name
def print_state(self, indent=0, **print_kwargs):
"""
Print Higgs boson state to output stream using default encoder.
Parameters:
indent Line indentation.
print_kwargs Print control keyword arguments.
"""
Boson.print_state(self, indent=indent, **print_kwargs)
# -----------------------------------------------------------------------------
# On module load execution
# -----------------------------------------------------------------------------
Boson.finalize_boson_family()
# -----------------------------------------------------------------------------
# Unit tests
# -----------------------------------------------------------------------------
if __name__ == "__main__":
import sys
import tests.utboson as ut
sys.exit(ut.utmain())
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] |
setup.py | jlapolla/scriptutils | 0 | 130152 | <filename>setup.py<gh_stars>0
# For general information, refer to:
# https://docs.python.org/3.6/distutils/index.html
# https://docs.python.org/3.6/distributing/index.html#distributing-index
# https://setuptools.readthedocs.io/en/latest/index.html
#
# For arguments to setup(), refer to:
# https://docs.python.org/3.6/distutils/apiref.html#distutils.core.setup
# https://setuptools.readthedocs.io/en/latest/setuptools.html#new-and-changed-setup-keywords
from setuptools import setup, find_packages
setup(
name='jlapolla-scriptutils',
version='0.1.0.dev1',
packages=find_packages('src'),
package_dir={'': 'src'},
)
| [
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13,
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3577,
29918,
3972,
3790,
29915,
2396,
525,
4351,
16675,
13,
29897,
13,
2
] |
web/utils.py | otoyo/satisfactory-mobile | 0 | 168160 | class CSVDumper:
@classmethod
def dump(self, matrix, encoding='sjis'):
rows = []
for row in matrix:
fields = []
for field in row:
if field is None:
fields.append('')
elif str(field).isdigit():
fields.append(str(field))
else:
fields.append('"{0}"'.format(str(field).replace('"', '""')))
rows.append(','.join(fields))
return '\n'.join(rows).encode(encoding, 'ignore')
| [
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525,
17281,
1495,
13,
2
] |
hargreaves/session/__init__.py | dastra/hargreaves-sdk-python | 0 | 40654 | <reponame>dastra/hargreaves-sdk-python
import logging
from requests_tracker.session import WebSessionFactory
from requests_tracker.storage import ICookieStorage
from ..config.models import ApiConfiguration
from ..utils.cookies import HLCookieHelper
from ..session.shared import LoggedInSession
logging.getLogger(__name__).addHandler(logging.NullHandler())
def create_session(
cookies_storage: ICookieStorage,
config: ApiConfiguration,
retry_count: int = 1,
timeout: float = 15.00):
"""
Creates a WebSession that will automatically handle login redirects
:param timeout:
:param retry_count:
:param cookies_storage:
:param config:
:return:
"""
web_session = WebSessionFactory.create(
cookies_storage,
default_referer='https://online.hl.co.uk/',
sensitive_values=[config.username, config.password,
config.secure_number, config.date_of_birth],
sensitive_params=['secure-number['],
retry_count=retry_count,
timeout=timeout
)
HLCookieHelper.set_default_cookies(web_session.cookies)
return LoggedInSession(
web_session=web_session,
config=config)
| [
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583,
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13,
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736,
4522,
3192,
797,
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13,
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1856,
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29922,
2676,
29918,
7924,
29892,
13,
4706,
2295,
29922,
2917,
29897,
13,
2
] |
lists_mutation/remove_keyword.py | magicalcarpet/the_complete_python_course | 0 | 124285 | nintendo_games = ['Zelda', 'Mario', 'Donkey Kong', 'Zelda']
nintendo_games.remove('Zelda')
print(nintendo_games)
if 'Wario' in nintendo_games:
nintendo_games.remove('Wario')
| [
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rplugin/python3/denite/ui/default.py | timgates42/denite.nvim | 0 | 4 | <gh_stars>0
# ============================================================================
# FILE: default.py
# AUTHOR: <NAME> <<EMAIL> at g<EMAIL>>
# License: MIT license
# ============================================================================
import re
import typing
from denite.util import echo, error, clearmatch, regex_convert_py_vim
from denite.util import Nvim, UserContext, Candidates, Candidate
from denite.parent import SyncParent
class Default(object):
@property
def is_async(self) -> bool:
return self._is_async
def __init__(self, vim: Nvim) -> None:
self._vim = vim
self._denite: typing.Optional[SyncParent] = None
self._selected_candidates: typing.List[int] = []
self._candidates: Candidates = []
self._cursor = 0
self._entire_len = 0
self._result: typing.List[typing.Any] = []
self._context: UserContext = {}
self._bufnr = -1
self._winid = -1
self._winrestcmd = ''
self._initialized = False
self._winheight = 0
self._winwidth = 0
self._winminheight = -1
self._is_multi = False
self._is_async = False
self._matched_pattern = ''
self._displayed_texts: typing.List[str] = []
self._statusline_sources = ''
self._titlestring = ''
self._ruler = False
self._prev_action = ''
self._prev_status: typing.Dict[str, typing.Any] = {}
self._prev_curpos: typing.List[typing.Any] = []
self._save_window_options: typing.Dict[str, typing.Any] = {}
self._sources_history: typing.List[typing.Any] = []
self._previous_text = ''
self._floating = False
self._filter_floating = False
self._updated = False
self._timers: typing.Dict[str, int] = {}
self._matched_range_id = -1
self._matched_char_id = -1
self._check_matchdelete = bool(self._vim.call(
'denite#util#check_matchdelete'))
def start(self, sources: typing.List[typing.Any],
context: UserContext) -> typing.List[typing.Any]:
if not self._denite:
# if hasattr(self._vim, 'run_coroutine'):
# self._denite = ASyncParent(self._vim)
# else:
self._denite = SyncParent(self._vim)
self._result = []
context['sources_queue'] = [sources]
self._start_sources_queue(context)
return self._result
def do_action(self, action_name: str,
command: str = '', is_manual: bool = False) -> None:
if is_manual:
candidates = self._get_selected_candidates()
elif self._get_cursor_candidate():
candidates = [self._get_cursor_candidate()]
else:
candidates = []
if not self._denite or not candidates or not action_name:
return
self._prev_action = action_name
action = self._denite.get_action(
self._context, action_name, candidates)
if not action:
return
post_action = self._context['post_action']
is_quit = action['is_quit'] or post_action == 'quit'
if is_quit:
self.quit()
self._denite.do_action(self._context, action_name, candidates)
self._result = candidates
if command != '':
self._vim.command(command)
if is_quit and post_action == 'open':
# Re-open denite buffer
prev_cursor = self._cursor
cursor_candidate = self._get_cursor_candidate()
self._init_buffer()
self.redraw(False)
if cursor_candidate == self._get_candidate(prev_cursor):
# Restore the cursor
self._move_to_pos(prev_cursor)
# Disable quit flag
is_quit = False
if not is_quit and is_manual:
self._selected_candidates = []
self.redraw(action['is_redraw'])
if is_manual and self._context['sources_queue']:
self._context['input'] = ''
self._context['quick_move'] = ''
self._start_sources_queue(self._context)
return
def redraw(self, is_force: bool = True) -> None:
self._context['is_redraw'] = is_force
if is_force:
self._gather_candidates()
if self._update_candidates():
self._update_buffer()
else:
self._update_status()
self._context['is_redraw'] = False
def quit(self) -> None:
if self._denite:
self._denite.on_close(self._context)
self._quit_buffer()
self._result = []
return
def _restart(self) -> None:
self._context['input'] = ''
self._quit_buffer()
self._init_denite()
self._gather_candidates()
self._init_buffer()
self._update_candidates()
self._update_buffer()
def _start_sources_queue(self, context: UserContext) -> None:
if not context['sources_queue']:
return
self._sources_history.append({
'sources': context['sources_queue'][0],
'path': context['path'],
})
self._start(context['sources_queue'][0], context)
if context['sources_queue']:
context['sources_queue'].pop(0)
context['path'] = self._context['path']
def _start(self, sources: typing.List[typing.Any],
context: UserContext) -> None:
from denite.ui.map import do_map
self._vim.command('silent! autocmd! denite')
if re.search(r'\[Command Line\]$', self._vim.current.buffer.name):
# Ignore command line window.
return
resume = self._initialized and context['resume']
if resume:
# Skip the initialization
update = ('immediately', 'immediately_1',
'cursor_pos', 'prev_winid',
'start_filter', 'quick_move')
for key in update:
self._context[key] = context[key]
self._check_move_option()
if self._check_do_option():
return
self._init_buffer()
if context['refresh']:
self.redraw()
self._move_to_pos(self._cursor)
else:
if self._context != context:
self._context.clear()
self._context.update(context)
self._context['sources'] = sources
self._context['is_redraw'] = False
self._is_multi = len(sources) > 1
if not sources:
# Ignore empty sources.
error(self._vim, 'Empty sources')
return
self._init_denite()
self._gather_candidates()
self._update_candidates()
self._init_cursor()
self._check_move_option()
if self._check_do_option():
return
self._init_buffer()
self._update_displayed_texts()
self._update_buffer()
self._move_to_pos(self._cursor)
if self._context['quick_move'] and do_map(self, 'quick_move', []):
return
if self._context['start_filter']:
do_map(self, 'open_filter_buffer', [])
def _init_buffer(self) -> None:
self._prev_status = dict()
self._displayed_texts = []
self._prev_bufnr = self._vim.current.buffer.number
self._prev_curpos = self._vim.call('getcurpos')
self._prev_wininfo = self._get_wininfo()
self._prev_winid = self._context['prev_winid']
self._winrestcmd = self._vim.call('winrestcmd')
self._ruler = self._vim.options['ruler']
self._switch_buffer()
self._bufnr = self._vim.current.buffer.number
self._winid = self._vim.call('win_getid')
self._resize_buffer(True)
self._winheight = self._vim.current.window.height
self._winwidth = self._vim.current.window.width
self._bufvars = self._vim.current.buffer.vars
self._bufvars['denite'] = {
'buffer_name': self._context['buffer_name'],
}
self._bufvars['denite_statusline'] = {}
self._vim.vars['denite#_previewed_buffers'] = {}
self._save_window_options = {}
window_options = {
'colorcolumn',
'concealcursor',
'conceallevel',
'cursorcolumn',
'cursorline',
'foldcolumn',
'foldenable',
'list',
'number',
'relativenumber',
'signcolumn',
'spell',
'winfixheight',
'wrap',
}
for k in window_options:
self._save_window_options[k] = self._vim.current.window.options[k]
# Note: Have to use setlocal instead of "current.window.options"
# "current.window.options" changes global value instead of local in
# neovim.
self._vim.command('setlocal colorcolumn=')
self._vim.command('setlocal conceallevel=3')
self._vim.command('setlocal concealcursor=inv')
self._vim.command('setlocal nocursorcolumn')
self._vim.command('setlocal nofoldenable')
self._vim.command('setlocal foldcolumn=0')
self._vim.command('setlocal nolist')
self._vim.command('setlocal nonumber')
self._vim.command('setlocal norelativenumber')
self._vim.command('setlocal nospell')
self._vim.command('setlocal winfixheight')
self._vim.command('setlocal nowrap')
if self._context['prompt']:
self._vim.command('setlocal signcolumn=yes')
else:
self._vim.command('setlocal signcolumn=auto')
if self._context['cursorline']:
self._vim.command('setlocal cursorline')
options = self._vim.current.buffer.options
if self._floating:
# Disable ruler
self._vim.options['ruler'] = False
options['buftype'] = 'nofile'
options['bufhidden'] = 'delete'
options['swapfile'] = False
options['buflisted'] = False
options['modeline'] = False
options['modifiable'] = False
options['filetype'] = 'denite'
if self._vim.call('exists', '#WinEnter'):
self._vim.command('doautocmd WinEnter')
if self._vim.call('exists', '#BufWinEnter'):
self._vim.command('doautocmd BufWinEnter')
if not self._vim.call('has', 'nvim'):
# In Vim8, FileType autocmd is not fired after set filetype option.
self._vim.command('silent doautocmd FileType denite')
if self._context['auto_action']:
self._vim.command('autocmd denite '
'CursorMoved <buffer> '
'call denite#call_map("auto_action")')
self._init_syntax()
def _switch_buffer(self) -> None:
split = self._context['split']
if (split != 'no' and self._winid > 0 and
self._vim.call('win_gotoid', self._winid)):
if split != 'vertical' and not self._floating:
# Move the window to bottom
self._vim.command('wincmd J')
self._winrestcmd = ''
return
self._floating = split in [
'floating',
'floating_relative_cursor',
'floating_relative_window',
]
self._filter_floating = False
if self._vim.current.buffer.options['filetype'] != 'denite':
self._titlestring = self._vim.options['titlestring']
command = 'edit'
if split == 'tab':
self._vim.command('tabnew')
elif self._floating:
self._split_floating(split)
elif self._context['filter_split_direction'] == 'floating':
self._filter_floating = True
elif split != 'no':
command = self._get_direction()
command += ' vsplit' if split == 'vertical' else ' split'
bufname = '[denite]-' + self._context['buffer_name']
if self._vim.call('exists', '*bufadd'):
bufnr = self._vim.call('bufadd', bufname)
vertical = 'vertical' if split == 'vertical' else ''
command = (
'buffer' if split
in ['no', 'tab', 'floating',
'floating_relative_window',
'floating_relative_cursor'] else 'sbuffer')
self._vim.command(
'silent keepalt %s %s %s %s' % (
self._get_direction(),
vertical,
command,
bufnr,
)
)
else:
self._vim.call(
'denite#util#execute_path',
f'silent keepalt {command}', bufname)
def _get_direction(self) -> str:
direction = str(self._context['direction'])
if direction == 'dynamictop' or direction == 'dynamicbottom':
self._update_displayed_texts()
winwidth = self._vim.call('winwidth', 0)
is_fit = not [x for x in self._displayed_texts
if self._vim.call('strwidth', x) > winwidth]
if direction == 'dynamictop':
direction = 'aboveleft' if is_fit else 'topleft'
else:
direction = 'belowright' if is_fit else 'botright'
return direction
def _get_wininfo(self) -> typing.List[typing.Any]:
return [
self._vim.options['columns'], self._vim.options['lines'],
self._vim.call('win_getid'), self._vim.call('tabpagebuflist')
]
def _switch_prev_buffer(self) -> None:
if (self._prev_bufnr == self._bufnr or
self._vim.buffers[self._prev_bufnr].name == ''):
self._vim.command('enew')
else:
self._vim.command('buffer ' + str(self._prev_bufnr))
def _init_syntax(self) -> None:
self._vim.command('syntax case ignore')
self._vim.command('highlight default link deniteInput ModeMsg')
self._vim.command('highlight link deniteMatchedRange ' +
self._context['highlight_matched_range'])
self._vim.command('highlight link deniteMatchedChar ' +
self._context['highlight_matched_char'])
self._vim.command('highlight default link ' +
'deniteStatusLinePath Comment')
self._vim.command('highlight default link ' +
'deniteStatusLineNumber LineNR')
self._vim.command('highlight default link ' +
'deniteSelectedLine Statement')
if self._floating:
self._vim.current.window.options['winhighlight'] = (
'Normal:' + self._context['highlight_window_background']
)
self._vim.command(('syntax match deniteSelectedLine /^[%s].*/' +
' contains=deniteConcealedMark') % (
self._context['selected_icon']))
self._vim.command(('syntax match deniteConcealedMark /^[ %s]/' +
' conceal contained') % (
self._context['selected_icon']))
if self._denite:
self._denite.init_syntax(self._context, self._is_multi)
def _update_candidates(self) -> bool:
if not self._denite:
return False
[self._is_async, pattern, statuses, self._entire_len,
self._candidates] = self._denite.filter_candidates(self._context)
prev_displayed_texts = self._displayed_texts
self._update_displayed_texts()
prev_matched_pattern = self._matched_pattern
self._matched_pattern = pattern
prev_statusline_sources = self._statusline_sources
self._statusline_sources = ' '.join(statuses)
if self._is_async:
self._start_timer('update_candidates')
else:
self._stop_timer('update_candidates')
updated = (self._displayed_texts != prev_displayed_texts or
self._matched_pattern != prev_matched_pattern or
self._statusline_sources != prev_statusline_sources)
if updated:
self._updated = True
self._start_timer('update_buffer')
if self._context['search'] and self._context['input']:
self._vim.call('setreg', '/', self._context['input'])
return self._updated
def _update_displayed_texts(self) -> None:
candidates_len = len(self._candidates)
if not self._is_async and self._context['auto_resize']:
winminheight = self._context['winminheight']
max_height = min(self._context['winheight'],
self._get_max_height())
if (winminheight != -1 and candidates_len < winminheight):
self._winheight = winminheight
elif candidates_len > max_height:
self._winheight = max_height
elif candidates_len != self._winheight:
self._winheight = candidates_len
max_source_name_len = 0
if self._candidates:
max_source_name_len = max([
len(self._get_display_source_name(x['source_name']))
for x in self._candidates])
self._context['max_source_name_len'] = max_source_name_len
self._context['max_source_name_format'] = (
'{:<' + str(self._context['max_source_name_len']) + '}')
self._displayed_texts = [
self._get_candidate_display_text(i)
for i in range(0, candidates_len)
]
def _update_buffer(self) -> None:
is_current_buffer = self._bufnr == self._vim.current.buffer.number
self._update_status()
if self._check_matchdelete and self._context['match_highlight']:
matches = [x['id'] for x in
self._vim.call('getmatches', self._winid)]
if self._matched_range_id in matches:
self._vim.call('matchdelete',
self._matched_range_id, self._winid)
self._matched_range_id = -1
if self._matched_char_id in matches:
self._vim.call('matchdelete',
self._matched_char_id, self._winid)
self._matched_char_id = -1
if self._matched_pattern != '':
self._matched_range_id = self._vim.call(
'matchadd', 'deniteMatchedRange',
r'\c' + regex_convert_py_vim(self._matched_pattern),
10, -1, {'window': self._winid})
matched_char_pattern = '[{}]'.format(re.sub(
r'([\[\]\\^-])',
r'\\\1',
self._context['input'].replace(' ', '')
))
self._matched_char_id = self._vim.call(
'matchadd', 'deniteMatchedChar',
matched_char_pattern,
10, -1, {'window': self._winid})
prev_linenr = self._vim.call('line', '.')
prev_candidate = self._get_cursor_candidate()
buffer = self._vim.buffers[self._bufnr]
buffer.options['modifiable'] = True
self._vim.vars['denite#_candidates'] = [
x['word'] for x in self._candidates]
buffer[:] = self._displayed_texts
buffer.options['modifiable'] = False
self._previous_text = self._context['input']
self._resize_buffer(is_current_buffer)
is_changed = (self._context['reversed'] or
(is_current_buffer and
self._previous_text != self._context['input']))
if self._updated and is_changed:
if not is_current_buffer:
save_winid = self._vim.call('win_getid')
self._vim.call('win_gotoid', self._winid)
self._init_cursor()
self._move_to_pos(self._cursor)
if not is_current_buffer:
self._vim.call('win_gotoid', save_winid)
elif is_current_buffer:
self._vim.call('cursor', [prev_linenr, 0])
if is_current_buffer:
if (self._context['auto_action'] and
prev_candidate != self._get_cursor_candidate()):
self.do_action(self._context['auto_action'])
self._updated = False
self._stop_timer('update_buffer')
def _update_status(self) -> None:
inpt = ''
if self._context['input']:
inpt = self._context['input'] + ' '
if self._context['error_messages']:
inpt = '[ERROR] ' + inpt
path = '[' + self._context['path'] + ']'
status = {
'input': inpt,
'sources': self._statusline_sources,
'path': path,
# Extra
'buffer_name': self._context['buffer_name'],
'line_total': len(self._candidates),
}
if status == self._prev_status:
return
self._bufvars['denite_statusline'] = status
self._prev_status = status
linenr = "printf('%'.(len(line('$'))+2).'d/%d',line('.'),line('$'))"
if self._context['statusline']:
if self._floating or self._filter_floating:
self._vim.options['titlestring'] = (
"%{denite#get_status('input')}%* " +
"%{denite#get_status('sources')} " +
" %{denite#get_status('path')}%*" +
"%{" + linenr + "}%*")
else:
winnr = self._vim.call('win_id2win', self._winid)
self._vim.call('setwinvar', winnr, '&statusline', (
"%#deniteInput#%{denite#get_status('input')}%* " +
"%{denite#get_status('sources')} %=" +
"%#deniteStatusLinePath# %{denite#get_status('path')}%*" +
"%#deniteStatusLineNumber#%{" + linenr + "}%*"))
def _get_display_source_name(self, name: str) -> str:
source_names = self._context['source_names']
if not self._is_multi or source_names == 'hide':
source_name = ''
else:
short_name = (re.sub(r'([a-zA-Z])[a-zA-Z]+', r'\1', name)
if re.search(r'[^a-zA-Z]', name) else name[:2])
source_name = short_name if source_names == 'short' else name
return source_name
def _get_candidate_display_text(self, index: int) -> str:
source_names = self._context['source_names']
candidate = self._candidates[index]
terms = []
if self._is_multi and source_names != 'hide':
terms.append(self._context['max_source_name_format'].format(
self._get_display_source_name(candidate['source_name'])))
encoding = self._context['encoding']
abbr = candidate.get('abbr', candidate['word']).encode(
encoding, errors='replace').decode(encoding, errors='replace')
terms.append(abbr[:int(self._context['max_candidate_width'])])
return (str(self._context['selected_icon'])
if index in self._selected_candidates
else ' ') + ' '.join(terms).replace('\n', '')
def _get_max_height(self) -> int:
return int(self._vim.options['lines']) if not self._floating else (
int(self._vim.options['lines']) -
int(self._context['winrow']) -
int(self._vim.options['cmdheight']))
def _resize_buffer(self, is_current_buffer: bool) -> None:
split = self._context['split']
if (split == 'no' or split == 'tab' or
self._vim.call('winnr', '$') == 1):
return
winheight = max(self._winheight, 1)
winwidth = max(self._winwidth, 1)
is_vertical = split == 'vertical'
if not is_current_buffer:
restore = self._vim.call('win_getid')
self._vim.call('win_gotoid', self._winid)
if not is_vertical and self._vim.current.window.height != winheight:
if self._floating:
wincol = self._context['winrow']
row = wincol
if split == 'floating':
if self._context['auto_resize'] and row > 1:
row += self._context['winheight']
row -= self._winheight
self._vim.call('nvim_win_set_config', self._winid, {
'relative': 'editor',
'row': row,
'col': self._context['wincol'],
'width': winwidth,
'height': winheight,
})
filter_row = 0 if wincol == 1 else row + winheight
filter_col = self._context['wincol']
else:
init_pos = self._vim.call('nvim_win_get_config',
self._winid)
self._vim.call('nvim_win_set_config', self._winid, {
'relative': 'win',
'win': init_pos['win'],
'row': init_pos['row'],
'col': init_pos['col'],
'width': winwidth,
'height': winheight,
})
filter_col = init_pos['col']
if init_pos['anchor'] == 'NW':
winpos = self._vim.call('nvim_win_get_position',
self._winid)
filter_row = winpos[0] + winheight
filter_winid = self._vim.vars['denite#_filter_winid']
self._context['filter_winrow'] = row
if self._vim.call('win_id2win', filter_winid) > 0:
self._vim.call('nvim_win_set_config', filter_winid, {
'relative': 'editor',
'row': filter_row,
'col': filter_col,
})
self._vim.command('resize ' + str(winheight))
if self._context['reversed']:
self._vim.command('normal! zb')
elif is_vertical and self._vim.current.window.width != winwidth:
self._vim.command('vertical resize ' + str(winwidth))
if not is_current_buffer:
self._vim.call('win_gotoid', restore)
def _check_do_option(self) -> bool:
if self._context['do'] != '':
self._do_command(self._context['do'])
return True
elif (self._candidates and self._context['immediately'] or
len(self._candidates) == 1 and self._context['immediately_1']):
self._do_immediately()
return True
return not (self._context['empty'] or
self._is_async or self._candidates)
def _check_move_option(self) -> None:
if self._context['cursor_pos'].isnumeric():
self._cursor = int(self._context['cursor_pos']) + 1
elif re.match(r'\+\d+', self._context['cursor_pos']):
for _ in range(int(self._context['cursor_pos'][1:])):
self._move_to_next_line()
elif re.match(r'-\d+', self._context['cursor_pos']):
for _ in range(int(self._context['cursor_pos'][1:])):
self._move_to_prev_line()
elif self._context['cursor_pos'] == '$':
self._move_to_last_line()
def _do_immediately(self) -> None:
goto = self._winid > 0 and self._vim.call(
'win_gotoid', self._winid)
if goto:
# Jump to denite window
self._init_buffer()
self.do_action('default')
candidate = self._get_cursor_candidate()
if not candidate:
return
echo(self._vim, 'Normal', '[{}/{}] {}'.format(
self._cursor, len(self._candidates),
candidate.get('abbr', candidate['word'])))
if goto:
# Move to the previous window
self._vim.command('wincmd p')
def _do_command(self, command: str) -> None:
self._init_cursor()
cursor = 1
while cursor < len(self._candidates):
self.do_action('default', command)
self._move_to_next_line()
self._quit_buffer()
def _cleanup(self) -> None:
self._stop_timer('update_candidates')
self._stop_timer('update_buffer')
if self._vim.current.buffer.number == self._bufnr:
self._cursor = self._vim.call('line', '.')
# Note: Close filter window before preview window
self._vim.call('denite#filter#_close_filter_window')
if not self._context['has_preview_window']:
self._vim.command('pclose!')
# Clear previewed buffers
for bufnr in self._vim.vars['denite#_previewed_buffers'].keys():
if not self._vim.call('win_findbuf', bufnr):
self._vim.command('silent bdelete ' + str(bufnr))
self._vim.vars['denite#_previewed_buffers'] = {}
self._vim.command('highlight! link CursorLine CursorLine')
if self._floating or self._filter_floating:
self._vim.options['titlestring'] = self._titlestring
self._vim.options['ruler'] = self._ruler
def _close_current_window(self) -> None:
if self._vim.call('winnr', '$') == 1:
self._vim.command('buffer #')
else:
self._vim.command('close!')
def _quit_buffer(self) -> None:
self._cleanup()
if self._vim.call('bufwinnr', self._bufnr) < 0:
# Denite buffer is already closed
return
winids = self._vim.call('win_findbuf',
self._vim.vars['denite#_filter_bufnr'])
if winids:
# Quit filter buffer
self._vim.call('win_gotoid', winids[0])
self._close_current_window()
# Move to denite window
self._vim.call('win_gotoid', self._winid)
# Restore the window
if self._context['split'] == 'no':
self._switch_prev_buffer()
for k, v in self._save_window_options.items():
self._vim.current.window.options[k] = v
else:
if self._context['split'] == 'tab':
self._vim.command('tabclose!')
if self._context['split'] != 'tab':
self._close_current_window()
self._vim.call('win_gotoid', self._prev_winid)
# Restore the position
self._vim.call('setpos', '.', self._prev_curpos)
if self._get_wininfo() and self._get_wininfo() == self._prev_wininfo:
# Note: execute restcmd twice to restore layout properly
self._vim.command(self._winrestcmd)
self._vim.command(self._winrestcmd)
clearmatch(self._vim)
def _get_cursor_candidate(self) -> Candidate:
return self._get_candidate(self._cursor)
def _get_candidate(self, pos: int) -> Candidate:
if not self._candidates or pos > len(self._candidates):
return {}
return self._candidates[pos - 1]
def _get_selected_candidates(self) -> Candidates:
if not self._selected_candidates:
return [self._get_cursor_candidate()
] if self._get_cursor_candidate() else []
return [self._candidates[x] for x in self._selected_candidates]
def _init_denite(self) -> None:
if self._denite:
self._denite.start(self._context)
self._denite.on_init(self._context)
self._initialized = True
self._winheight = self._context['winheight']
self._winwidth = self._context['winwidth']
def _gather_candidates(self) -> None:
self._selected_candidates = []
if self._denite:
self._denite.gather_candidates(self._context)
def _init_cursor(self) -> None:
if self._context['reversed']:
self._move_to_last_line()
else:
self._move_to_first_line()
def _move_to_pos(self, pos: int) -> None:
self._vim.call('cursor', pos, 0)
self._cursor = pos
if self._context['reversed']:
self._vim.command('normal! zb')
def _move_to_next_line(self) -> None:
if self._cursor < len(self._candidates):
self._cursor += 1
def _move_to_prev_line(self) -> None:
if self._cursor >= 1:
self._cursor -= 1
def _move_to_first_line(self) -> None:
self._cursor = 1
def _move_to_last_line(self) -> None:
self._cursor = len(self._candidates)
def _start_timer(self, key: str) -> None:
if key in self._timers:
return
if key == 'update_candidates':
self._timers[key] = self._vim.call(
'denite#helper#_start_update_candidates_timer', self._bufnr)
elif key == 'update_buffer':
self._timers[key] = self._vim.call(
'denite#helper#_start_update_buffer_timer', self._bufnr)
def _stop_timer(self, key: str) -> None:
if key not in self._timers:
return
self._vim.call('timer_stop', self._timers[key])
# Note: After timer_stop is called, self._timers may be removed
if key in self._timers:
self._timers.pop(key)
def _split_floating(self, split: str) -> None:
# Use floating window
if split == 'floating':
self._vim.call(
'nvim_open_win',
self._vim.call('bufnr', '%'), True, {
'relative': 'editor',
'row': self._context['winrow'],
'col': self._context['wincol'],
'width': self._context['winwidth'],
'height': self._context['winheight'],
})
elif split == 'floating_relative_cursor':
opened_pos = (self._vim.call('nvim_win_get_position', 0)[0] +
self._vim.call('winline') - 1)
if self._context['auto_resize']:
height = max(self._winheight, 1)
width = max(self._winwidth, 1)
else:
width = self._context['winwidth']
height = self._context['winheight']
if opened_pos + height + 3 > self._vim.options['lines']:
anchor = 'SW'
row = 0
self._context['filter_winrow'] = row + opened_pos
else:
anchor = 'NW'
row = 1
self._context['filter_winrow'] = row + height + opened_pos
self._vim.call(
'nvim_open_win',
self._vim.call('bufnr', '%'), True, {
'relative': 'cursor',
'row': row,
'col': 0,
'width': width,
'height': height,
'anchor': anchor,
})
elif split == 'floating_relative_window':
self._vim.call(
'nvim_open_win',
self._vim.call('bufnr', '%'), True, {
'relative': 'win',
'row': self._context['winrow'],
'col': self._context['wincol'],
'width': self._context['winwidth'],
'height': self._context['winheight'],
})
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] |
racecar_gym/envs/scenarios.py | luigiberducci/racecar_gym | 16 | 22518 | from dataclasses import dataclass
from typing import Dict
from racecar_gym.bullet import load_world, load_vehicle
from racecar_gym.tasks import Task, get_task
from racecar_gym.core import World, Agent
from .specs import ScenarioSpec, TaskSpec
def task_from_spec(spec: TaskSpec) -> Task:
task = get_task(spec.task_name)
return task(**spec.params)
@dataclass
class MultiAgentScenario:
world: World
agents: Dict[str, Agent]
@staticmethod
def from_spec(path: str, rendering: bool = None) -> 'MultiAgentScenario':
spec = ScenarioSpec()
spec.load(path)
if rendering:
spec.world.rendering = rendering
agents = dict([
(s.id, Agent(id=s.id, vehicle=load_vehicle(s.vehicle), task=task_from_spec(s.task)))
for s in spec.agents
])
return MultiAgentScenario(world=load_world(spec.world, agents=list(agents.values())), agents=agents)
@dataclass
class SingleAgentScenario:
world: World
agent: Agent
@staticmethod
def from_spec(path: str, rendering: bool = None) -> 'SingleAgentScenario':
spec = ScenarioSpec()
spec.load(path)
if rendering:
spec.world.rendering = rendering
agent_spec = spec.agents[0]
agent = Agent(id=agent_spec.id, vehicle=load_vehicle(agent_spec.vehicle), task=task_from_spec(agent_spec.task))
return SingleAgentScenario(world=load_world(spec.world, agents=[agent]), agent=agent)
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src/pyrobot/habitat/base.py | cihuang123/pyrobot | 2,150 | 15131 | import numpy as np
import math
import pyrobot.utils.util as prutil
import rospy
import habitat_sim.agent as habAgent
import habitat_sim.utils as habUtils
from habitat_sim.agent.controls import ActuationSpec
import habitat_sim.errors
import quaternion
from tf.transformations import euler_from_quaternion, euler_from_matrix
class LoCoBotBase(object):
"""docstring for SimpleBase"""
def __init__(self, configs, simulator):
self.configs = configs
self.sim = simulator.sim
self.agent = self.sim.get_agent(self.configs.COMMON.SIMULATOR.DEFAULT_AGENT_ID)
self.transform = None
self.init_state = self.get_full_state()
def execute_action(self, action_name, actuation):
# actions = "turn_right" or "turn_left" or "move_forward"
# returns a bool showing if collided or not
return self._act(action_name, actuation)
def get_full_state(self):
# Returns habitat_sim.agent.AgentState
return self.agent.get_state()
def _rot_matrix(self, habitat_quat):
quat_list = [habitat_quat.x, habitat_quat.y, habitat_quat.z, habitat_quat.w]
return prutil.quat_to_rot_mat(quat_list)
def get_state(self, state_type="odom"):
# Returns (x, y, yaw)
assert state_type == "odom", "Error: Only Odom state is available"
cur_state = self.get_full_state()
init_rotation = self._rot_matrix(self.init_state.rotation)
# true position here refers to the relative position from
# where `self.init_state` is treated as origin
true_position = cur_state.position - self.init_state.position
true_position = np.matmul(init_rotation.transpose(), true_position, dtype=np.float64)
cur_rotation = self._rot_matrix(cur_state.rotation)
cur_rotation = np.matmul(init_rotation.transpose(), cur_rotation, dtype=np.float64)
(r, pitch, yaw) = euler_from_matrix(cur_rotation, axes="sxzy")
# Habitat has y perpendicular to map where as ROS has z perpendicular
# to the map. Where as x is same.
# Here ROS_X = -1 * habitat_z and ROS_Y = -1*habitat_x
return (-1 * true_position[2], -1 * true_position[0], yaw)
def stop(self):
raise NotImplementedError("Veclocity control is not supported in Habitat-Sim!!")
def set_vel(self, fwd_speed, turn_speed, exe_time=1):
raise NotImplementedError("Veclocity control is not supported in Habitat-Sim!!")
def go_to_relative(
self, xyt_position, use_map=False, close_loop=False, smooth=False
):
"""
Moves the robot to the robot to given
goal state relative to its initial pose.
:param xyt_position: The relative goal state of the form (x,y,t)
:param use_map: When set to "True", ensures that controler is
using only free space on the map to move the robot.
:param close_loop: When set to "True", ensures that controler is
operating in open loop by
taking account of odometry.
:param smooth: When set to "True", ensures that the motion
leading to the goal is a smooth one.
:type xyt_position: list
:type use_map: bool
:type close_loop: bool
:type smooth: bool
:return: True if successful; False otherwise (timeout, etc.)
:rtype: bool
"""
try:
if use_map:
raise NotImplementedError(
"Using map feature is not yet supported for Habitat-Sim"
)
if close_loop:
raise NotImplementedError(
"Closed-loop postion control is not supported in Habitat-Sim!"
)
if smooth:
raise NotImplementedError(
"Smooth position control feature is not yet for Habitat-Sim"
)
except Exception as error:
print(error)
return False
(cur_x, cur_y, cur_yaw) = self.get_state()
abs_yaw = cur_yaw + xyt_position[2]
return self._go_to_relative_pose(xyt_position[0], xyt_position[1], abs_yaw)
def go_to_absolute(
self, xyt_position, use_map=False, close_loop=False, smooth=False
):
"""
Moves the robot to the robot to given goal state in the world frame.
:param xyt_position: The goal state of the form (x,y,t)
in the world (map) frame.
:param use_map: When set to "True", ensures that controler is using
only free space on the map to move the robot.
:param close_loop: When set to "True", ensures that controler is
operating in open loop by
taking account of odometry.
:param smooth: When set to "True", ensures that the motion
leading to the goal is a smooth one.
:type xyt_position: list
:type use_map: bool
:type close_loop: bool
:type smooth: bool
:return: True if successful; False otherwise (timeout, etc.)
:rtype: bool
"""
try:
if use_map:
raise NotImplementedError(
"Using map feature is not yet supported for Habitat-Sim"
)
if close_loop:
raise NotImplementedError(
"Closed-loop postion control is not supported in Habitat-Sim!"
)
if smooth:
raise NotImplementedError(
"Smooth position control feature is not yet for Habitat-Sim"
)
except Exception as error:
print(error)
return False
(cur_x, cur_y, cur_yaw) = self.get_state()
rel_X = xyt_position[0] - cur_x
rel_Y = xyt_position[1] - cur_y
abs_yaw = xyt_position[2]
# convert rel_X & rel_Y from global frame to current frame
R = np.array([[np.cos(cur_yaw), np.sin(cur_yaw)],
[-np.sin(cur_yaw), np.cos(cur_yaw)]])
rel_x, rel_y = np.matmul(R, np.array([rel_X, rel_Y]).reshape(-1,1))
return self._go_to_relative_pose(rel_x[0], rel_y[0], abs_yaw)
def _act(self, action_name, actuation):
"""Take the action specified by action_id
:param action_id: ID of the action. Retreives the action from
`agent_config.action_space <AgentConfiguration.action_space>`
:return: Whether or not the action taken resulted in a collision
"""
did_collide = False
act_spec = ActuationSpec(actuation)
did_collide = self.agent.controls.action(
self.agent.scene_node, action_name, act_spec, apply_filter=True
)
return did_collide
def _go_to_relative_pose(self, rel_x, rel_y, abs_yaw):
# clip relative movements beyond 10 micrometer precision
# this is done to improve determinism, as habitat-sim doesn't
# seem to precisely move the robot beyond sub milimeter precision anyways
if abs(rel_x) < 1e-5:
rel_x = 0
if abs(rel_y) < 1e-5:
rel_y = 0
if math.sqrt(rel_x ** 2 + rel_y ** 2) > 0.0:
# rotate to point to (x, y) point
action_name = "turn_left"
if rel_y < 0.0:
action_name = "turn_right"
v1 = np.asarray([1, 0], dtype=np.float64)
v2 = np.asarray([rel_x, rel_y], dtype=np.float64)
cosine_angle = np.dot(v1, v2) / (np.linalg.norm(v1) * np.linalg.norm(v2))
angle = np.arccos(cosine_angle)
did_collide = self._act(action_name, math.degrees(angle))
if did_collide:
print("Error: Collision accured while 1st rotating!")
return False
# move to (x,y) point
did_collide = self._act("move_forward", math.sqrt(rel_x ** 2 + rel_y ** 2))
if did_collide:
print("Error: Collision accured while moving straight!")
return False
# rotate to match the final yaw!
(cur_x, cur_y, cur_yaw) = self.get_state()
rel_yaw = abs_yaw - cur_yaw
# clip to micro-degree precision to preserve determinism
if abs(rel_yaw) < 1e-4:
rel_yaw = 0
action_name = "turn_left"
if rel_yaw < 0.0:
action_name = "turn_right"
rel_yaw *= -1
did_collide = self._act(action_name, math.degrees(rel_yaw))
if did_collide:
print("Error: Collision accured while rotating!")
return False
return True
def track_trajectory(self, states, controls, close_loop):
"""
State trajectory that the robot should track.
:param states: sequence of (x,y,t) states that the robot should track.
:param controls: optionally specify control sequence as well.
:param close_loop: whether to close loop on the
computed control sequence or not.
:type states: list
:type controls: list
:type close_loop: bool
:return: True if successful; False otherwise (timeout, etc.)
:rtype: bool
"""
raise NotImplementedError
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chowkidar/graphql/view.py | aswinshenoy/chowkidar-ariadne | 1 | 87042 | <gh_stars>1-10
from http.cookies import SimpleCookie
from typing import Optional, Callable, Any
from channels.db import database_sync_to_async
from django.contrib.auth.models import AnonymousUser
from django.http import HttpRequest, HttpResponseBadRequest
from django.utils.decorators import method_decorator
from django.views.decorators.csrf import csrf_exempt
from django.contrib.auth import get_user_model
from ariadne.asgi import GraphQL
from ariadne.contrib.django.views import GraphQLView as AriadneView
from ariadne.exceptions import HttpBadRequestError
from ariadne.types import ContextValue
from ..auth import respond_handling_authentication
from ..auth.verify import resolve_user_from_request, resolve_user_from_tokens
User = get_user_model()
class GraphQLResolverContext:
def __init__(self, request, user=None):
self.request = request
self.user = user if user else AnonymousUser
@method_decorator(csrf_exempt, name="dispatch")
class GraphQLView(AriadneView):
def post(
self, request: HttpRequest, *args, **kwargs
):
if not self.schema:
raise ValueError("GraphQLView was initialized without schema.")
try:
data = self.extract_data_from_request(request)
except HttpBadRequestError as error:
return HttpResponseBadRequest(error.message)
success, result = self.execute_query(request, data)
status_code = 200 if success else 400
return respond_handling_authentication(request=request, result=result, status_code=status_code)
@staticmethod
def get_context_for_request(request: HttpRequest) -> Optional[ContextValue]:
user = resolve_user_from_request(request)
return GraphQLResolverContext(request=request, user=user)
# This is an ASGI2 compatibility class so that we can use ariadne subscriptions with channels.
# Once channels supports ASGI3 this class can go away:
# https://github.com/mirumee/ariadne/issues/210
class AuthenticatedChannel(GraphQL):
def __call__(self, scope) -> Callable:
self.scope = scope
async def handle(receive, send):
await super(AuthenticatedChannel, self).__call__(scope, receive, send)
return handle
@database_sync_to_async
def resolve_user(self, cookie):
return resolve_user_from_tokens(
token=cookie['JWT_TOKEN'].value if 'JWT_TOKEN' in cookie else None,
refreshToken=cookie['JWT_REFRESH_TOKEN'].value if 'JWT_REFRESH_TOKEN' in cookie else None
)
async def get_context_for_request(self, request: Any) -> Any:
headers = dict(self.scope['headers'])
user = AnonymousUser()
if b'cookie' in headers:
cookie = SimpleCookie()
cookie.load(str(headers[b'cookie']))
user = await self.resolve_user(cookie)
if user is None:
user = AnonymousUser()
return {"request": request, "user": user}
__all__ = [
'GraphQLView',
'AuthenticatedChannel'
]
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] |
bin/base64_.py | pelavarre/pybashish | 4 | 123466 | #!/usr/bin/env python3
# quirks:
# doesn't redefine the 'import base64' of https://docs.python.org/3/library/base64.html
import sys
sys.stderr.write("base64.py: error: not implemented\n")
sys.exit(2) # exit 2 from rejecting usage
# copied from: git clone https://github.com/pelavarre/pybashish.git
| [
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] |
dist-packages/dtk/ui/paned.py | Jianwei-Wang/python2.7_lib | 0 | 31538 | <gh_stars>0
#! /usr/bin/env python
# -*- coding: utf-8 -*-
# Copyright (C) 2011 ~ 2012 Deepin, Inc.
# 2011 ~ 2012 <NAME>
# 2011 ~ 2012 <NAME>
#
# Author: <NAME> <<EMAIL>>
# Maintainer: <NAME> <<EMAIL>>
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
from draw import draw_pixbuf
from utils import is_in_rect, color_hex_to_cairo
from constant import PANED_HANDLE_SIZE
import gobject
import gtk
import math
from theme import ui_theme
# Load customize rc style before any other.
gtk.rc_parse_string("style 'my_style' {\n GtkPaned::handle-size = %s\n }\nwidget '*' style 'my_style'" % (PANED_HANDLE_SIZE))
class Paned(gtk.Paned):
'''
Paned.
@undocumented: do_enter_notify_event
@undocumented: do_button_press_event
@undocumented: do_size_allocate
@undocumented: do_enter_notify_event
@undocumented: is_in_button
@undocumented: draw_handle
@undocumented: do_expose_event
gtk.Paned with custom better apperance.
'''
def __init__(self,
shrink_first,
enable_animation=False,
always_show_button=False,
enable_drag=False,
handle_color=ui_theme.get_color("paned_line")
):
'''
Initialize Paned class.
'''
gtk.Paned.__init__(self)
self.shrink_first = shrink_first
self.enable_animation = enable_animation
self.always_show_button = always_show_button
self.enable_drag = enable_drag
self.handle_color = handle_color
self.bheight = ui_theme.get_pixbuf("paned/paned_up_normal.png").get_pixbuf().get_width()
self.saved_position = -1
self.handle_size = PANED_HANDLE_SIZE - 1
self.show_button = False
self.init_button("normal")
self.animation_delay = 20 # milliseconds
self.animation_times = 10
self.animation_position_frames = []
self.press_coordinate = None
def init_button(self, status):
if self.get_orientation() == gtk.ORIENTATION_HORIZONTAL:
if self.shrink_first:
self.button_pixbuf = ui_theme.get_pixbuf("paned/paned_left_%s.png" % status).get_pixbuf()
else:
self.button_pixbuf = ui_theme.get_pixbuf("paned/paned_right_%s.png" % status).get_pixbuf()
else:
if self.shrink_first:
self.button_pixbuf = ui_theme.get_pixbuf("paned/paned_up_%s.png" % status).get_pixbuf()
else:
self.button_pixbuf = ui_theme.get_pixbuf("paned/paned_down_%s.png" % status).get_pixbuf()
def do_expose_event(self, e):
'''
To intercept the default expose event and draw custom handle
after the **gtk.Container** expose evetn.
So the gtk.Paned's expose event callback is ignore.
'''
gtk.Container.do_expose_event(self, e)
self.draw_handle(e)
return False
def draw_handle(self, e):
'''
Draw the cusom handle apperance.
'''
handle = self.get_handle_window()
line_width = 1
cr = handle.cairo_create()
cr.set_source_rgb(*color_hex_to_cairo(self.handle_color.get_color()))
(width, height) = handle.get_size()
if self.get_orientation() == gtk.ORIENTATION_HORIZONTAL:
if self.shrink_first:
if self.get_position() != 0:
cr.rectangle(0, 0, line_width, height)
cr.fill()
if self.always_show_button or self.show_button:
if self.get_position() == 0:
pixbuf = ui_theme.get_pixbuf("paned/paned_right_normal.png").get_pixbuf()
else:
pixbuf = ui_theme.get_pixbuf("paned/paned_left_normal.png").get_pixbuf()
draw_pixbuf(cr,
pixbuf,
0,
(height - self.bheight) / 2)
else:
cr.rectangle(width - line_width, 0, line_width, height)
cr.fill()
if self.always_show_button or self.show_button:
if self.get_position() == 0:
pixbuf = ui_theme.get_pixbuf("paned/paned_left_normal.png").get_pixbuf()
else:
pixbuf = ui_theme.get_pixbuf("paned/paned_right_normal.png").get_pixbuf()
draw_pixbuf(cr,
pixbuf,
0,
(height - self.bheight) / 2)
else:
if self.shrink_first:
cr.rectangle(0, 0, width, line_width)
cr.fill()
if self.always_show_button or self.show_button:
if self.get_position() == 0:
pixbuf = ui_theme.get_pixbuf("paned/paned_down_normal.png").get_pixbuf()
else:
pixbuf = ui_theme.get_pixbuf("paned/paned_up_normal.png").get_pixbuf()
draw_pixbuf(cr,
pixbuf,
(width - self.bheight) / 2,
0)
else:
cr.rectangle(0, height - line_width, width, line_width)
cr.fill()
if self.always_show_button or self.show_button:
if self.get_position() == 0:
pixbuf = ui_theme.get_pixbuf("paned/paned_up_normal.png").get_pixbuf()
else:
pixbuf = ui_theme.get_pixbuf("paned/paned_down_normal.png").get_pixbuf()
draw_pixbuf(cr,
pixbuf,
(width - self.bheight) / 2,
0)
def is_in_button(self, x, y):
'''
Detection of wheter the mouse pointer is in the handler's button.
'''
handle = self.get_handle_window()
(width, height) = handle.get_size()
if self.get_orientation() == gtk.ORIENTATION_HORIZONTAL:
rect = (0, (height - self.bheight) / 2, width, self.bheight)
else:
rect = ((width - self.bheight) / 2, 0, self.bheight, height)
return is_in_rect((x, y), rect)
def do_enter_notify_event(self, e):
self.show_button = True
self.queue_draw()
def do_leave_notify_event(self, e):
self.show_button = False
self.init_button("normal")
self.queue_draw()
def do_motion_notify_event(self, e):
'''
change the cursor style when move in handler
'''
# Reset press coordinate if motion mouse after press event.
self.press_coordinate = None
handle = self.get_handle_window()
(width, height) = handle.get_size()
if self.is_in_button(e.x, e.y):
handle.set_cursor(gtk.gdk.Cursor(gtk.gdk.HAND1))
self.init_button("hover")
else:
if self.enable_drag:
handle.set_cursor(self.cursor_type)
gtk.Paned.do_motion_notify_event(self, e)
else:
handle.set_cursor(None)
self.init_button("normal")
def do_button_press_event(self, e):
'''
when press the handler's button change the position.
'''
handle = self.get_handle_window()
if e.window == handle:
if self.is_in_button(e.x, e.y):
self.init_button("press")
self.do_press_actoin()
else:
(width, height) = handle.get_size()
if is_in_rect((e.x, e.y), (0, 0, width, height)):
self.press_coordinate = (e.x, e.y)
gtk.Paned.do_button_press_event(self, e)
else:
gtk.Paned.do_button_press_event(self, e)
return True
def do_button_release_event(self, e):
'''
docs
'''
gtk.Paned.do_button_release_event(self, e)
# Do press event if not in button and finish `click` event.
if (not self.is_in_button(e.x, e.y)) and self.press_coordinate == (e.x, e.y):
self.do_press_actoin()
return True
def do_press_actoin(self):
'''
docs
'''
if self.saved_position == -1:
self.saved_position = self.get_position()
if self.shrink_first:
self.change_position(0)
else:
if self.get_orientation() == gtk.ORIENTATION_HORIZONTAL:
self.change_position(self.allocation.width)
else:
self.change_position(self.allocation.height)
else:
self.change_position(self.saved_position)
self.saved_position = -1
def change_position(self, new_position):
current_position = self.get_position()
if self.enable_animation:
if new_position != current_position:
for i in range(0, self.animation_times + 1):
step = int(math.sin(math.pi * i / 2 / self.animation_times) * (new_position - current_position))
self.animation_position_frames.append(current_position + step)
if self.animation_position_frames[-1] != new_position:
self.animation_position_frames.append(new_position)
gtk.timeout_add(self.animation_delay, self.update_position)
else:
self.set_position(new_position)
def update_position(self):
self.set_position(self.animation_position_frames.pop(0))
if self.animation_position_frames == []:
return False
else:
return True
def do_size_allocate(self, e):
gtk.Paned.do_size_allocate(self, e)
if self.shrink_first:
child = self.get_child2()
else:
child = self.get_child1()
if child == None: return
rect = child.allocation
offset = self.handle_size
if self.get_orientation() == gtk.ORIENTATION_HORIZONTAL:
if self.shrink_first:
rect.x -= offset
rect.width += offset
else:
rect.width += offset
else:
if self.shrink_first:
rect.y -= offset
rect.height += offset
else:
rect.height += offset
child.size_allocate(rect)
class HPaned(Paned):
def __init__(self,
shrink_first=True,
enable_animation=False,
always_show_button=False,
enable_drag=False,
handle_color=ui_theme.get_color("paned_line")
):
Paned.__init__(self, shrink_first, enable_animation, always_show_button, enable_drag, handle_color)
self.set_orientation(gtk.ORIENTATION_HORIZONTAL)
self.cursor_type = gtk.gdk.Cursor(gtk.gdk.SB_H_DOUBLE_ARROW)
class VPaned(Paned):
def __init__(self,
shrink_first=True,
enable_animation=False,
always_show_button=False,
enable_drag=False,
handle_color=ui_theme.get_color("paned_line")
):
Paned.__init__(self, shrink_first, enable_animation, always_show_button, enable_drag, handle_color)
self.set_orientation(gtk.ORIENTATION_VERTICAL)
self.cursor_type = gtk.gdk.Cursor(gtk.gdk.SB_V_DOUBLE_ARROW)
gobject.type_register(Paned)
gobject.type_register(HPaned)
gobject.type_register(VPaned)
if __name__ == '__main__':
w = gtk.Window()
w.set_size_request(700, 400)
#w.modify_bg(gtk.STATE_NORMAL, gtk.gdk.Color('yellow'))
box = gtk.VBox()
p = VPaned()
c1 = gtk.Button("11111111111111111111111")
c1.modify_bg(gtk.STATE_NORMAL, gtk.gdk.Color('blue'))
c2 = gtk.Button("122222222222222222222222")
c1.modify_bg(gtk.STATE_NORMAL, gtk.gdk.Color('red'))
p.add1(c1)
p.add2(c2)
box.pack_start(p)
p = HPaned()
c1 = gtk.Button("11111111111111111111111")
c1.modify_bg(gtk.STATE_NORMAL, gtk.gdk.Color('blue'))
c2 = gtk.Button("122222222222222222222222")
c1.modify_bg(gtk.STATE_NORMAL, gtk.gdk.Color('red'))
p.add1(c1)
p.add2(c2)
box.pack_start(p)
w.add(box)
w.connect('destroy', gtk.main_quit)
w.show_all()
gtk.main()
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] |
main.py | Forcide/ApacheParser | 0 | 35204 | <reponame>Forcide/ApacheParser
from modules import menu, hosts, logMail, status, webpagina, zoekInLog
def main():
""""
Dit is de start file/functie van het programma, hierin worden alle modules geladen en zo nodig uitgevoerd.
Het menu wordt gestart en de keuze wordt verwezen naar een van de modules.
Geimporteerde modules:
- menu
- hosts
- logMail
- status
- webpagina
- zoekInLog
"""
keuze = menu.menu()
if keuze == 1:
logMail.logMail()
elif keuze == 2:
webpagina.bezochteWebpagina()
elif keuze == 3:
hosts.uniekeHosts()
elif keuze == 4:
status.aantalStatus()
elif keuze == 5:
zoekInLog.zoekInLog()
elif keuze == 6:
exit()
hoofdmenu = menu.menuAfsluiten()
if hoofdmenu == 'J':
main()
elif hoofdmenu == 'N':
exit()
main()
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hello-world1.py | lpereira1/pynet-study | 0 | 1604874 | print("hello world!")
print("The world doesnt actually care.")
| [
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Programs/Python/pysample_games.py | ShotaroBaba/Coding_practice | 0 | 164547 | <gh_stars>0
# Import Modules
import os, pygame
from pygame.locals import *
from pygame.compat import geterror
import time
from random import randint
if not pygame.font: print('Warning, fonts disabled')
if not pygame.mixer: print('Warning, sound disabled')
# Load data for generating image on the screen.
data_dir = "../data/test_game_data"
bullet_pos_pad_height = -20
enemy_shoot_interval = 500
enemy_generate_interval = 800
out_of_screen_margin = 80
# Method to load image [1]
def load_image(name, colorkey=None):
fullname = os.path.join(data_dir, name)
try:
image = pygame.image.load(fullname)
except pygame.error:
print('Cannot load image:', fullname)
raise SystemExit(str(geterror()))
image = image.convert()
if colorkey is not None:
if colorkey is -1:
colorkey = image.get_at((0, 0))
image.set_colorkey(colorkey, RLEACCEL)
return image, image.get_rect()
# Method to load sound [1]
# Will be added later.
def load_sound(name):
class NoneSound:
def play(self): pass
if not pygame.mixer or not pygame.mixer.get_init():
return NoneSound()
fullname = os.path.join(data_dir, name)
try:
sound = pygame.mixer.Sound(fullname)
except pygame.error:
print('Cannot load sound: %s' % fullname)
raise SystemExit(str(geterror()))
return sound
# Bullet class
class Bullet(pygame.sprite.Sprite):
# Later the move value might be added for
# achieving flexibility.
def __init__(self, player_rect, pos_pad = (-20, -20), bullet_image = "bullet.png"):
pygame.sprite.Sprite.__init__(self)
self.image, self.rect = load_image(bullet_image, -1)
screen = pygame.display.get_surface()
self.mask = pygame.mask.from_surface(self.image)
self.area = screen.get_rect()
new_pos = self.rect.move((player_rect.right + pos_pad[0], player_rect.top - pos_pad[1]))
self.rect = new_pos
# Move left every time
# TODO: Bullet can track the movement of the player.
def update(self, left_or_right):
if left_or_right == "right":
newpos = self.rect.move((1, 0))
self.rect = newpos
if self.rect.right > self.area.right or \
self.rect.left < self.area.left or \
self.rect.bottom > self.area.bottom or \
self.rect.top < self.area.top:
self.kill()
elif left_or_right == "left":
newpos = self.rect.move((-3, 0))
self.rect = newpos
if self.rect.right > self.area.right or \
self.rect.left < self.area.left or \
self.rect.bottom > self.area.bottom or \
self.rect.top < self.area.top:
self.kill()
def hit(self):
self.count += 1
# enemy defeated if a certain number of bullet are hit.
if(self.count > self.num_count):
self.kill()
del self
class Player(pygame.sprite.Sprite):
def __init__(self):
pygame.sprite.Sprite.__init__(self)
self.image, self.rect = load_image('player.png', -1)
screen = pygame.display.get_surface()
self.area = screen.get_rect()
self.mask = pygame.mask.from_surface(self.image)
self.num_count = 10
self.count = 0
# TODO: Implement level up later
self.power_up = 0
def move_down(self):
if self.rect.bottom < self.area.bottom:
newpos = self.rect.move((0, 1))
self.rect = newpos
def move_up(self):
if self.rect.top > self.area.top:
newpos = self.rect.move((0, -1))
self.rect = newpos
def move_left(self):
if self.rect.left > self.area.left:
newpos = self.rect.move((-1, 0))
self.rect = newpos
def move_right(self):
if self.rect.right < self.area.right:
newpos = self.rect.move((1, 0))
self.rect = newpos
def hit(self):
self.count += 1
# Player is defeated if a certain number of bullet are hit.
if(self.count > self.num_count):
self.kill()
del self
# If enemy is normal, then it is counted as 10.
class Enemy(pygame.sprite.Sprite):
def __init__(self, num_count = 10, rect_random = False):
pygame.sprite.Sprite.__init__(self)
self.image, self.rect = load_image('enemy.png', -1)
screen = pygame.display.get_surface()
self.area = screen.get_rect()
self.mask = pygame.mask.from_surface(self.image)
newpos = self.rect.move((self.area.right - self.rect.right + out_of_screen_margin,0))
self.rect = newpos
if rect_random:
self.rect.y = randint(0, self.area.bottom - self.area.top - (self.rect.bottom - self.rect.top))
self.count = 0
self.num_count = num_count
self.out_of_screen_left = self.area.left - out_of_screen_margin
def update(self):
newpos = self.rect.move((-1, 0))
self.rect = newpos
if self.rect.left < self.out_of_screen_left:
newpos = self.rect.move((self.area.right - self.rect.right + out_of_screen_margin,0))
self.rect = newpos
self.rect.y = randint(0, self.area.bottom - self.area.top - (self.rect.bottom - self.rect.top))
def hit(self):
self.count += 1
# enemy defeated if a certain number of bullet are hit.
if(self.count > self.num_count):
self.kill()
del self
def main():
# Background is black.
black = (0, 0, 0)
# Conduct Initialization
pygame.init()
screen = pygame.display.set_mode((600, 480))
# Create background
background = pygame.Surface(screen.get_size())
background = background.convert()
background.fill(black)
# Show background
screen.blit(background, (0, 0))
pygame.display.flip()
# Initialise everything here...
clock = pygame.time.Clock()
player = Player()
enemy = Enemy()
screen.blit(player.image, player.rect)
player_sprites = pygame.sprite.RenderPlain((player))
enemy_sprites = pygame.sprite.RenderPlain((enemy))
player_bullet_sprites = pygame.sprite.RenderPlain(())
enemy_bullet_sprites = pygame.sprite.RenderPlain(())
pygame.display.flip()
# Set user events and timer
enemy_shoot_event = USEREVENT + 1
difficulty_up_event = USEREVENT + 2
enemy_added_event = USEREVENT + 3
# Set the timers for these events
pygame.time.set_timer(enemy_shoot_event, enemy_shoot_interval)
pygame.time.set_timer(enemy_added_event, enemy_generate_interval)
going = True
count = 0
while going:
clock.tick(70)
key_state = pygame.key.get_pressed()
if key_state[K_ESCAPE]:
going = False
if key_state[K_UP]:
player.move_up()
# player.move_down()
if key_state[K_DOWN]:
player.move_down()
# player.move_up()
if key_state[K_LEFT]:
player.move_left()
# player.move_left()
if key_state[K_RIGHT]:
player.move_right()
# player.move_right()
if key_state[K_SPACE]:
player_bullet_sprites.add(Bullet(player.rect))
# Handling the input events
for event in pygame.event.get():
if event.type == QUIT:
going = False
# Shoot every 0.5 seconds.
if event.type == enemy_shoot_event:
for ene in enemy_sprites.sprites():
enemy_bullet_sprites.add(Bullet(ene.rect, (-30, -15), "enemy_bullet.png"))
if event.type == enemy_added_event:
enemy_sprites.add(Enemy(rect_random = True))
# Enemy move
for ene in enemy_sprites.sprites():
ene.update()
# Check whether the player bullet is hit by the enemy.
for i in pygame.sprite.groupcollide(enemy_sprites, player_bullet_sprites, dokilla = False, dokillb = True, collided = pygame.sprite.collide_mask).keys():
i.hit()
# If the player is hit by the enemy, then it will disappear
pygame.sprite.groupcollide(enemy_sprites, player_sprites, dokilla = True, dokillb = True, collided = pygame.sprite.collide_mask)
# Check whether the enemy bullet is hit by the player.
if pygame.sprite.spritecollide(player, enemy_bullet_sprites, True, collided = pygame.sprite.collide_mask) != []:
player.hit()
if len(enemy_sprites.sprites()) == 0:
enemy_sprites.add(Enemy(rect_random = True))
# Update locations
player_sprites.update()
enemy_sprites.update()
player_bullet_sprites.update("right")
enemy_bullet_sprites.update("left")
# Collided with enemy
screen.blit(background, (0, 0))
player_sprites.draw(screen)
enemy_sprites.draw(screen)
player_bullet_sprites.draw(screen)
enemy_bullet_sprites.draw(screen)
pygame.display.flip()
pygame.quit()
if __name__ == '__main__':
main()
# Reference:
# Magimel, F., & <NAME>. (2019). chimp.py (Version 1.9.6) [Computer Software].
# Retreived from https://github.com/pygame/pygame/blob/master/examples/chimp.py | [
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] |
collect_face_data.py | consolas-K/Dlib_faceRecognition | 1 | 144149 | import cv2 as cv
import time
import os
import config
class face_detect():
def __init__(self):
self.start_time = 0 # 用于计算帧率
self.fps = 0 # 帧率
self.image = None
self.face_img = None
self.face_num = 0 # 这一帧的人脸个数
self.last_face_num = 0 # 上一帧的人脸个数
self.face_num_change_flag = False # 当前帧人脸数量变化的标志位，用于后续人脸识别提高帧率
self.quit_flag = False # 退出程序标志位
self.buildNewFolder = False # 按下"n"新建文件夹标志位
self.save_flag = False # 按下“s”保存人脸数据标志位
self.face_flag = False # 人脸检测标志位
self.img_num = 0 # 人脸数据文件夹内的图像个数
self.collect_face_data = True # 是否进行人脸数据的采集，只有为真时才会进行采集
def get_fps(self):
now = time.time()
time_period = now - self.start_time
self.fps = 1.0 / time_period
self.start_time = now
color = (0,255,0)
if self.fps < 15:
color = (0,0,255)
cv.putText(self.image, str(self.fps.__round__(2)), (20, 50), cv.FONT_HERSHEY_DUPLEX, 1, color)
def key_scan(self, key):
if self.collect_face_data == True:
if self.save_flag == True and self.buildNewFolder == True:
if self.face_img.size > 0:
cv.imwrite(
config.faceData_path + 'person_{}/{}.png'.format(config.num_of_person_in_lib - 1, self.img_num),
self.face_img)
self.img_num += 1
if key == ord('s'):
self.save_flag = not self.save_flag
if key == ord('n'):
os.makedirs(config.faceData_path + 'person_{}'.format(config.num_of_person_in_lib))
config.num_of_person_in_lib += 1
print("新文件夹建立成功!!")
self.buildNewFolder = True
if key == ord('q'): self.quit_flag = True
def face_detecting(self):
face_location = []
all_face_location = []
faces = config.detector(self.image, 0)
self.face_num = len(faces)
if self.face_num != self.last_face_num:
self.face_num_change_flag = True
print("脸数改变，由{}张变为{}张".format(self.last_face_num, self.face_num))
self.check_times = 0
self.last_face_num = self.face_num
else:
self.face_num_change_flag = False
if len(faces) != 0:
self.face_flag = True
for i, face in enumerate(faces):
face_location.append(face)
w, h = (face.right() - face.left()), (face.bottom() - face.top())
left, right, top, bottom = face.left() - w//4, face.right() + w//4, face.top() - h//2, face.bottom() + h//4
all_face_location.append([left, right, top, bottom])
return face_location, all_face_location
else:
self.face_flag = False
return None
def show(self, camera):
while camera.isOpened() and not self.quit_flag:
val, self.image = camera.read()
if val == False: continue
key = cv.waitKey(1)
res = self.face_detecting()
if res is not None:
_, all_face_location = res
for i in range(self.face_num):
[left, right, top, bottom] = all_face_location[i]
self.face_img = self.image[top:bottom, left:right]
cv.rectangle(self.image, (left, top), (right, bottom), (0, 0, 255))
if self.collect_face_data == True:
cv.putText(self.image, "Face", (int((left + right) / 2) - 50, bottom + 20), cv.FONT_HERSHEY_COMPLEX, 1,
(255, 255, 255))
self.key_scan(key)
self.get_fps()
cv.namedWindow('camera', 0)
cv.imshow('camera', self.image)
camera.release()
cv.destroyAllWindows()
def main():
try:
cam = cv.VideoCapture(0)
face_detect().show(cam)
finally:
cam.release()
cv.destroyAllWindows()
print("程序退出!!")
if __name__ == '__main__':
main() | [
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CloudJudge/info.py | zhuxiyulu/CloudJudge | 1 | 106793 | <reponame>zhuxiyulu/CloudJudge
# 前台信息编码表
FrontMessageCode = {}
# 后台信息编码表
BackMessageCode = {}
# 编码、语言
FrontMessageCode['1'] = {"Help": "Username_Empty", "Zh": "用户不能为空"}
FrontMessageCode['2'] = {"Help": "Password_Empty", "Zh": "密码不能为空"}
FrontMessageCode['3'] = {"Help": "Username_NotEqual_Password", "Zh": "用户名和密码不匹配"}
FrontMessageCode['4'] = {"Help": "LogoutFailed", "Zh": "登出失败"}
FrontMessageCode['5'] = {"Help": "Captcha_Empty", "Zh": "验证码不能为空"}
FrontMessageCode['6'] = {"Help": "Captcha_Wrong", "Zh": "验证码错误"}
FrontMessageCode['7'] = {"Help": "User_Exists", "Zh": "该用户名已存在"}
for key in FrontMessageCode.keys():
BackMessageCode[FrontMessageCode[key]['Help']] = key
def getFrontMessage():
return FrontMessageCode
def getBackMessage():
return BackMessageCode | [
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pyfu/cli/dashboard.py | danbarrese/pyfu | 0 | 102777 | <filename>pyfu/cli/dashboard.py
import sys
import os
from sys import exit
import yaml
from pyfu.ui import ui
yaml_path = os.path.expanduser("~") + '/.dashboard/dashboard.yaml'
if not os.path.isfile(yaml_path):
print("No yaml file: " + yaml_path)
exit(1)
yaml_contents = '\n'.join([line.rstrip('\n') for line in open(yaml_path)])
if not yaml_contents:
print("Your yaml file is empty: " + yaml_path)
exit(1)
properties = yaml.load(yaml_contents)
if len(sys.argv) == 1:
dashboards = []
if properties:
dashboards = [d['name'] for d in properties['dashboards']]
dashboards = sorted(dashboards)
print("Which dashboard? " + str(dashboards))
exit(1)
name = sys.argv[1]
d = [d for d in properties['dashboards'] if d['name'] == name][0]
dashboard = ui.Dashboard(d, name)
dashboard.run()
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triplicate/main.py | markjoshua12/game-jam-2020 | 0 | 1607851 | """
Starting Template
Once you have learned how to use classes, you can begin your program with this
template.
If Python and Arcade are installed, this example can be run from the command line with:
python -m arcade.examples.starting_template
"""
import arcade
import random
import math
from Criteria import *
SCREEN_WIDTH = 1280
SCREEN_HEIGHT = 720
SCREEN_TITLE = "Triplicate"
DEFAULT_GRAVITY = -3
RESOURCE_PATH = "resources"
levels = []
# ON_KEY_RELEASE apply negative vector on key release.
ON_KEY_RELEASE = False
class ObjectLoader:
def __init__(self):
pass
def load(self, tick):
pass
class Factory:
def create(self):
pass
class FormFactory(Factory):
def __init__(self, color, val):
self.color = color
self.val = val
def create(self):
return Form(self.color, self.val)
class CarFactory(Factory):
def __init__(self, color, val):
self.color = color
self.val = val
def create(self):
return SwervingCar1Up(self.color, self.val)
class ShipFactory(Factory):
def __init__(self, color, val, level):
self.color = color
self.val = val
self.level = level
def create(self):
return Ship(self.color, self.val, self.level)
class PoopFactory(Factory):
def __init__(self, color, val):
self.color = color
self.val = val
def create(self):
return Poop(self.color, self.val)
class FactoryWeight:
def __init__(self, factory, r):
self.factory = factory
self.range = r
def GetLaneCenter(j):
return (SCREEN_WIDTH / 2) - ((160 * 5)/2) + (j * 160) + 80
class WeightedObjectLaneLoader(ObjectLoader):
def __init__(self, tr, factories):
super().__init__()
self.factories = factories
self.tr = tr
self.t = 0
def load(self, tick):
if tick % self.tr == 0:
self.t += 1
x = random.randrange(0, 100)
for factory in self.factories:
if x in factory.range:
o = factory.factory.create()
o.center_x = GetLaneCenter(random.randrange(0, 5))
return o
return None
class WeightedObjectLoader(ObjectLoader):
def __init__(self, tr, factories):
super().__init__()
self.factories = factories
self.tr = tr
self.t = 0
def load(self, tick):
if tick % self.tr == 0:
self.t += 1
x = random.randrange(0, 100)
for factory in self.factories:
if x in factory.range:
return factory.factory.create()
return None
class SequenceFactoryObjectLoader(ObjectLoader):
def __init__(self, tr, objects):
self.tr = tr
self.objects = objects
def load(self, tick):
if tick % self.tr == 0:
self.t += 1
if len(self.objects) > 0:
return self.objects.pop().load()
return None
class SequenceObjectLoader(ObjectLoader):
def __init__(self, tr, objects):
self.tr = tr
self.objects = objects
def load(self, tick):
if tick % self.tr == 0:
self.t += 1
return self.objects.pop()
class RBObjectLoader(ObjectLoader):
def __init__(self):
self.Toggle = True
def load(self, tick):
if tick % 180 == 0:
self.Toggle = not self.Toggle
if self.Toggle:
return Form((255, 0, 0, 255), "R")
else:
return Form((0, 0, 255, 255), "B")
class RGBObjectLoader(ObjectLoader):
def __init__(self):
self.t = 0
def load(self, tick):
if tick % 180 == 0:
self.t += 1
x = self.t % 3
if x == 0:
return Form((255, 0, 0, 255), "R")
elif x == 1:
return Form((0, 255, 0, 255), "G")
elif x == 2:
return Form((0, 0, 255, 255), "B")
class RBPObjectLoader(ObjectLoader):
def __init__(self):
self.t = 0
def load(self, tick):
if tick % 180 == 0:
self.t += 1
x = random.randrange(0, 3)
if x == 0:
return Form((255, 0, 0, 255), "R")
elif x == 1:
return Poop((0, 255, 0, 255), "G")
elif x == 2:
return Form((0, 0, 255, 255), "B")
class RGBPObjectLoader(ObjectLoader):
def __init__(self, tr):
self.t = 0
self.tr = tr
def load(self, tick):
if tick % self.tr == 0:
self.t += 1
x = random.randrange(0, 4)
if x == 0:
return Form((255, 0, 0, 255), "R")
elif x == 1:
return Form((0, 255, 0, 255), "G")
elif x == 2:
return Form((0, 0, 255, 255), "B")
elif x == 3:
return Poop((255, 255, 255, 255), "S")
class Level:
def __init__(self):
# You may want many lists. Lists for coins, monsters, etc.
self.bucket_list = None
self.object_list = None
self.object_loader = None
# This holds the background images. If you don't want changing
# background images, you can delete this part.
self.background = None
self.music = None
self.tick = 0
self.selected_bucket = None
self.score = 0
self.run = True
def update(self):
if self.run:
self.tick += 1
if self.object_loader is not None:
ret = self.object_loader.load(self.tick)
if ret is not None:
self.object_list.append(ret)
if self.bucket_list is not None:
for bucket in self.bucket_list:
bucket.update()
if self.object_list is not None:
hit_list = bucket.collides_with_list(self.object_list)
for obj in hit_list:
if bucket.score(obj):
self.score += obj.pass_val
else:
self.score += obj.fail_val
self.object_list.remove(obj)
if self.object_list is not None:
for obj in self.object_list:
obj.update()
if obj.center_y < 5:
self.score += obj.miss_val
self.object_list.remove(obj)
def draw(self):
if self.background is not None:
arcade.draw_lrwh_rectangle_textured(0, 0,
SCREEN_WIDTH, SCREEN_HEIGHT,
self.background)
if self.bucket_list is not None:
self.bucket_list.draw()
if self.object_list is not None:
self.object_list.draw()
if self.selected_bucket is not None:
arcade.draw_rectangle_outline(self.bucket_list[self.selected_bucket].center_x,
self.bucket_list[self.selected_bucket].center_y,
self.bucket_list[self.selected_bucket].height * 1.2,
self.bucket_list[self.selected_bucket].width * 1.2,
self.bucket_list[self.selected_bucket].color, (self.tick % 12) + 2, 0)
arcade.draw_text("Time Left {:d}".format(int((self.length - self.tick) / 60)), (SCREEN_WIDTH / 6) * 4,
SCREEN_HEIGHT - (SCREEN_HEIGHT / 10), arcade.color.BLACK, 60)
arcade.draw_text("Score: {}".format(self.score), 0, (SCREEN_HEIGHT / 10) * 9,
arcade.color.RED, 64)
if self.tick > self.length:
arcade.draw_text("GAME OVER", (SCREEN_WIDTH / 5) * 1, (SCREEN_HEIGHT / 6) * 3, arcade.color.BLACK, 128)
def next_bucket(self):
# print(self.selected_bucket)
if self.selected_bucket is None and len(self.bucket_list) > 0:
self.selected_bucket = 0
else:
self.selected_bucket += 1
if self.selected_bucket >= len(self.bucket_list):
self.selected_bucket = 0
# print(self.selected_bucket)
# Maybe play a sound here?
def prev_bucket(self):
# print(self.selected_bucket)
if self.selected_bucket is None and len(self.bucket_list) > 0:
self.selected_bucket = len(self.bucket_list) - 1
else:
self.selected_bucket -= 1
if self.selected_bucket < 0:
self.selected_bucket = len(self.bucket_list) - 1
def move_bucket(self, x):
if self.selected_bucket is not None:
self.bucket_list[self.selected_bucket].v_x += x
def stop_bucket(self):
if self.selected_bucket is not None:
self.bucket_list[self.selected_bucket].v_x = 0
def stop_all_buckets(self):
for x in self.bucket_list:
x.v_x = 0
def on_key_press(self, key, key_modifiers):
"""
Called whenever a key on the keyboard is pressed.
For a full list of keys, see:
http://arcade.academy/arcade.key.html
"""
if key == arcade.key.TAB:
if key_modifiers & arcade.key.MOD_SHIFT:
self.prev_bucket()
else:
self.next_bucket()
elif key == arcade.key.LEFT or key == arcade.key.A:
self.move_bucket(-1)
elif key == arcade.key.RIGHT or key == arcade.key.D:
self.move_bucket(1)
elif key == arcade.key.S:
self.stop_all_buckets()
elif key == arcade.key.SPACE:
self.stop_bucket()
elif key == arcade.key.Q:
exit(0)
def on_key_release(self, key, key_modifiers):
"""
Called whenever the user lets off a previously pressed key.
"""
if ON_KEY_RELEASE:
if key == arcade.key.LEFT:
self.move_bucket(1)
elif key == arcade.key.RIGHT:
self.move_bucket(-1)
class SpaceLevel(Level):
def __init__(self):
super().__init__()
self.bucket_list = arcade.SpriteList()
self.bucket_list.append(Bucket((255, 0, 0, 255), [IsFormCriteria(), IsColorCriteria((255, 0, 0))]))
self.bucket_list[0].center_x = SCREEN_WIDTH / 4
self.selected_bucket = 0
self.object_list = arcade.SpriteList()
self.length = 60 * 120
self.tree_tick = False
self.background = arcade.load_texture("resources/Background-4.png")
self.object_loader = WeightedObjectLaneLoader(60, [FactoryWeight(ShipFactory((255, 0, 0, 255), "R", self), range(0, 10)),
FactoryWeight(ShipFactory((0, 255, 0, 255), "G", self), range(40, 50)),
FactoryWeight(ShipFactory((0, 0, 255, 255), "B", self), range(50, 90)),
FactoryWeight(PoopFactory((255, 255, 255, 255), "R"), range(95, 100))])
def update(self):
super().update()
if not self.run:
return
if (self.tick % 60) == 0:
self.tree_tick = not self.tree_tick
tree1 = Tree((255,255,255,255),"Tree")
tree1.center_x = GetLaneCenter(-1) + (-80 if self.tree_tick else 0)
tree1.center_y = SCREEN_HEIGHT + 20
tree2 = Tree((255,255,255,255),"Tree")
tree2.center_x = GetLaneCenter(5) + (80 if self.tree_tick else 0)
tree2.center_y = SCREEN_HEIGHT + 20
self.object_list.append(tree1)
self.object_list.append(tree2)
if self.tick > self.length:
self.run = False
def draw_road(self, cx, cy, sx, sy, offset):
# Clear screen and start render process
zx = (cx - (sx / 2))
zy = (cy - (sy / 2))
line_height = 64
margin_x = 32
lane_width = (128 + margin_x)
arcade.draw_rectangle_filled(cx, cy, sx, sy, arcade.color.BATTLESHIP_GREY)
num_lines = (sy / line_height) / 4
num_lanes = (sx / lane_width) - 1
j = 0
while j < num_lanes:
j += 1
i = 0
y_pos = offset
while i < num_lines:
arcade.draw_rectangle_filled(zx + (j * lane_width), zy + offset + (i * line_height * 4), (margin_x / 2),
line_height, arcade.color.WHITE_SMOKE)
i += 1
def draw(self):
self.draw_road((SCREEN_WIDTH / 2), SCREEN_HEIGHT / 2, (160 * 5), SCREEN_HEIGHT+512, -((self.tick*8) % 256))
super().draw()
class CarLevel(Level):
def __init__(self):
super().__init__()
self.bucket_list = arcade.SpriteList()
self.bucket_list.append(Bucket((255, 0, 0, 255), [IsFormCriteria(), IsColorCriteria((255, 0, 0))]))
self.bucket_list[0].center_x = SCREEN_WIDTH / 4
self.selected_bucket = 0
self.object_list = arcade.SpriteList()
self.length = 60 * 120
self.tree_tick = False
self.object_loader = WeightedObjectLaneLoader(60, [FactoryWeight(CarFactory((255, 0, 0, 255), "R"), range(0, 10)),
FactoryWeight(CarFactory((0, 255, 0, 255), "G"), range(40, 50)),
FactoryWeight(CarFactory((0, 0, 255, 255), "B"), range(50, 90)),
FactoryWeight(PoopFactory((255, 255, 255, 255), "R"), range(95, 100))])
def update(self):
super().update()
if not self.run:
return
if (self.tick % 60) == 0:
self.tree_tick = not self.tree_tick
tree1 = Tree((255,255,255,255),"Tree")
tree1.center_x = GetLaneCenter(-1) + (-80 if self.tree_tick else 0)
tree1.center_y = SCREEN_HEIGHT + 20
tree2 = Tree((255,255,255,255),"Tree")
tree2.center_x = GetLaneCenter(5) + (80 if self.tree_tick else 0)
tree2.center_y = SCREEN_HEIGHT + 20
self.object_list.append(tree1)
self.object_list.append(tree2)
if self.tick > self.length:
self.run = False
def draw_road(self, cx, cy, sx, sy, offset):
# Clear screen and start render process
zx = (cx - (sx / 2))
zy = (cy - (sy / 2))
line_height = 64
margin_x = 32
lane_width = (128 + margin_x)
arcade.draw_rectangle_filled(cx, cy, sx, sy, arcade.color.BATTLESHIP_GREY)
num_lines = (sy / line_height) / 4
num_lanes = (sx / lane_width) - 1
j = 0
while j < num_lanes:
j += 1
i = 0
y_pos = offset
while i < num_lines:
arcade.draw_rectangle_filled(zx + (j * lane_width), zy + offset + (i * line_height * 4), (margin_x / 2),
line_height, arcade.color.WHITE_SMOKE)
i += 1
def draw(self):
self.draw_road((SCREEN_WIDTH / 2), SCREEN_HEIGHT / 2, (160 * 5), SCREEN_HEIGHT+512, -((self.tick*8) % 256))
super().draw()
class Level1(Level):
def __init__(self):
super().__init__()
self.bucket_list = arcade.SpriteList()
self.bucket_list.append(Bucket((255, 0, 0, 255), [IsFormCriteria(), IsColorCriteria((255, 0, 0))]))
self.bucket_list.append(Bucket((0, 255, 0, 255), [IsFormCriteria(), IsColorCriteria((0, 255, 0))]))
self.bucket_list.append(Bucket((0, 0, 255, 255), [IsFormCriteria(), IsColorCriteria((0, 0, 255))]))
self.bucket_list[0].center_x = SCREEN_WIDTH / 4
self.bucket_list[1].center_x = (SCREEN_WIDTH / 4) * 2
self.bucket_list[2].center_x = (SCREEN_WIDTH / 4) * 3
self.selected_bucket = 1
self.object_loader = RGBObjectLoader()
self.object_list = arcade.SpriteList()
self.length = 60 * 120
def update(self):
super().update()
if self.tick > self.length:
self.run = False
def draw(self):
x = 300
y = 300
radius = 200
arcade.draw_circle_filled(x, y, radius, arcade.color.YELLOW)
# Draw the right eye
x = 370
y = 350
radius = 20
arcade.draw_circle_filled(x, y, radius, arcade.color.BLACK)
# Draw the left eye
x = 230
y = 350
radius = 20
arcade.draw_circle_filled(x, y, radius, arcade.color.BLACK)
# Draw the smile
x = 300
y = 280
width = 120
height = 100
start_angle = 190
end_angle = 350
arcade.draw_arc_outline(x, y, width, height, arcade.color.BLACK,
start_angle, end_angle, 10)
super().draw()
class Level2(Level):
def __init__(self):
super().__init__()
self.bucket_list = arcade.SpriteList()
self.bucket_list.append(Bucket((255, 0, 255, 255), [IsFormCriteria(), OrCriteria(IsColorCriteria((255, 0, 0)),
IsColorCriteria(
(0, 0, 255)))]))
self.bucket_list[0].center_x = (SCREEN_WIDTH / 4) * 2
self.selected_bucket = 0
self.object_loader = RBPObjectLoader()
self.object_list = arcade.SpriteList()
self.length = 60 * 120
self.background = arcade.load_texture("resources/remodeling_an_office_bathroom.jpg")
def update(self):
super().update()
if self.tick > self.length:
self.run = False
def draw(self):
super().draw()
class Level3(Level):
def __init__(self):
super().__init__()
self.bucket_list = arcade.SpriteList()
self.bucket_list.append(Bucket((255, 0, 0, 255), [IsFormCriteria(), IsColorCriteria((255, 0, 0))]))
self.bucket_list.append(Bucket((0, 255, 0, 255), [IsFormCriteria(), IsColorCriteria((0, 255, 0))]))
self.bucket_list.append(Bucket((0, 0, 255, 255), [IsFormCriteria(), IsColorCriteria((0, 0, 255))]))
self.bucket_list[0].center_x = SCREEN_WIDTH / 5
self.bucket_list[1].center_x = (SCREEN_WIDTH / 5) * 4
self.bucket_list[2].center_x = (SCREEN_WIDTH / 5) * 3
self.bucket_list.append(Bucket((255, 0, 255, 255), [IsFormCriteria(), OrCriteria(IsColorCriteria((255, 0, 0)),
IsColorCriteria(
(0, 0, 255)))]))
self.bucket_list[3].center_x = (SCREEN_WIDTH / 5) * 2
self.selected_bucket = 3
self.object_loader = RGBPObjectLoader(60)
self.object_list = arcade.SpriteList()
self.length = 60 * 90
self.background = arcade.load_texture("resources/OfficeSpacePrinterScene.jpg")
def update(self):
super().update()
if self.tick > self.length:
self.run = False
def draw(self):
super().draw()
class Level4(Level):
def __init__(self):
super().__init__()
self.bucket_list = arcade.SpriteList()
self.bucket_list.append(Bucket((255, 0, 0, 255), [IsFormCriteria(), IsColorCriteria((255, 0, 0))]))
self.bucket_list.append(Bucket((0, 255, 0, 255), [IsFormCriteria(), IsColorCriteria((0, 255, 0))]))
self.bucket_list.append(Bucket((0, 0, 255, 255), [IsFormCriteria(), IsColorCriteria((0, 0, 255))]))
self.bucket_list.append(Bucket((255, 0, 255, 255), [IsFormCriteria(), OrCriteria(IsColorCriteria((255, 0, 0)),
IsColorCriteria(
(0, 0, 255)))]))
self.bucket_list[0].center_x = SCREEN_WIDTH / 5
self.bucket_list[1].center_x = (SCREEN_WIDTH / 5) * 4
self.bucket_list[2].center_x = (SCREEN_WIDTH / 5) * 3
self.bucket_list[3].center_x = (SCREEN_WIDTH / 5) * 2
self.selected_bucket = 3
self.object_loader = WeightedObjectLoader(60, [FactoryWeight(FormFactory((255, 0, 0, 255), "R"), range(0, 40)),
FactoryWeight(FormFactory((0, 255, 0, 255), "G"), range(40, 50)),
FactoryWeight(FormFactory((0, 0, 255, 255), "B"), range(50, 90)),
FactoryWeight(PoopFactory((255, 255, 255, 255), "R"), range(95, 100))])
self.object_list = arcade.SpriteList()
self.length = 60 * 90
self.background = arcade.load_texture("resources/ModernOffice.jpg")
def update(self):
super().update()
if self.tick > self.length:
self.run = False
def draw(self):
super().draw()
class Level5(Level):
def __init__(self):
super().__init__()
self.bucket_list = arcade.SpriteList()
self.bucket_list.append(Bucket((0, 255, 0, 255), [IsFormCriteria(), IsColorCriteria((0, 255, 0))]))
self.bucket_list.append(Bucket((255, 0, 255, 255), [IsFormCriteria(), OrCriteria(IsColorCriteria((255, 0, 0)),
IsColorCriteria(
(0, 0, 255)))]))
self.bucket_list[0].center_x = SCREEN_WIDTH / 5
self.bucket_list[1].center_x = (SCREEN_WIDTH / 5) * 4
self.selected_bucket = 0
self.object_loader = WeightedObjectLoader(180, [FactoryWeight(FormFactory((255, 0, 0, 255), "R"), range(0, 40)),
FactoryWeight(FormFactory((0, 255, 0, 255), "G"),
range(40, 50)),
FactoryWeight(FormFactory((0, 0, 255, 255), "B"),
range(50, 90)),
FactoryWeight(PoopFactory((200, 50, 100, 255), "S"),
range(95, 100))])
self.object_list = arcade.SpriteList()
self.length = 60 * 120
self.background = arcade.load_texture("resources/OfficeScene1.jpg")
def update(self):
super().update()
if self.tick > self.length:
self.run = False
def draw(self):
super().draw()
class Bucket(arcade.Sprite):
"""
This class represents the coins on our screen. It is a child class of
the arcade library's "Sprite" class.
"""
# need a 'criteria'
def __init__(self, color, criteria):
super().__init__("resources/trash.png", 1)
self.criteria = criteria
self.color = color
self.center_y = (self.height / 4) * 2
self.v_x = 0
def score(self, other):
for x in self.criteria:
if not x.check(other):
return False
return True
def update(self):
self.center_x += self.v_x
if self.center_x >= SCREEN_WIDTH:
self.center_x = SCREEN_WIDTH - 1
self.v_x = 0
if self.center_x <= 0:
self.center_x = 1
self.v_x = 0
class FallingObject(arcade.Sprite):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.v_y = DEFAULT_GRAVITY
self.pass_val = 100
self.fail_val = -100
self.miss_val = -50
self.type = "FallingObject"
self.v_x = 0
self.center_y = random.randrange(SCREEN_HEIGHT + 20,
SCREEN_HEIGHT + 100)
self.center_x = random.randrange(SCREEN_WIDTH)
self.tick = 0
def reset_pos(self):
# Reset the coin to a random spot above the screen
self.center_y = random.randrange(SCREEN_HEIGHT + 20,
SCREEN_HEIGHT + 100)
self.center_x = random.randrange(SCREEN_WIDTH)
def process_miss(self):
self.reset_pos()
def update(self):
# Move the coin
self.tick += 1
self.center_y += self.v_y
self.center_x += self.v_x
if self.center_x >= SCREEN_WIDTH:
self.center_x = SCREEN_WIDTH - 1
self.v_x = 0
if self.center_x <= 0:
self.center_x = 1
self.v_x = 0
if self.top < 0:
self.process_miss()
class Bullet(FallingObject):
def __init__(self, color, val):
super().__init__("resources/pencil2.png", 1)
self.val = val
self.color = color
self.type = "Bullet"
self.v_y = DEFAULT_GRAVITY * 0.5
self.pass_val = 100
self.fail_val = -200
self.miss_val = 10
def shootat(self,target):
BULLET_SPEED = random.randrange(2,8)
start_x = self.center_x
start_y = self.center_y
# Get the destination location for the bullet
dest_x = target.center_x + random.randrange(-200,200)
dest_y = target.center_y + random.randrange(-200,200)
# Do math to calculate how to get the bullet to the destination.
# Calculation the angle in radians between the start points
# and end points. This is the angle the bullet will travel.
x_diff = dest_x - start_x
y_diff = dest_y - start_y
angle = math.atan2(y_diff, x_diff)
# Taking into account the angle, calculate our change_x
# and change_y. Velocity is how fast the bullet travels.
self.v_x = math.cos(angle) * BULLET_SPEED
self.v_y = math.sin(angle) * BULLET_SPEED
class Ship(FallingObject):
def __init__(self, color, val, level):
super().__init__("resources/ships/ship_{}.png".format(random.randrange(0,31)), 0.5)
self.level = level
self.val = val
self.color = color
self.type = "Ship"
self.v_y = DEFAULT_GRAVITY * 0.5
self.pass_val = 100
self.fail_val = -200
self.miss_val = 100
def update(self):
super().update()
if (self.tick % 60) == 0:
if random.randrange(0,2) == 0:
b = Bullet((255,255,255,255),self.val)
b.shootat(self.level.bucket_list[self.level.selected_bucket])
self.level.object_list.append(b)
b.center_x = self.center_x
b.center_y = self.center_y
class Car1Down(FallingObject):
def __init__(self, color, val):
super().__init__("resources/car-enemy2.png", 8)
self.val = val
self.color = color
self.type = "Car"
self.v_y = DEFAULT_GRAVITY
self.pass_val = 100
self.fail_val = -200
self.miss_val = 50
class SwervingCar1Down(FallingObject):
def __init__(self, color, val):
super().__init__("resources/car-enemy2.png", 8)
self.val = val
self.color = color
self.type = "Car"
self.v_y = DEFAULT_GRAVITY * 0.5
self.pass_val = 100
self.fail_val = -200
self.miss_val = 100
def update(self):
chance = random.randrange(0,100)
if chance < 5:
# Start Swerving
self.v_x = 1
super().update()
class Car1Up(FallingObject):
def __init__(self, color, val):
super().__init__("resources/car-enemy.png", 8)
self.val = val
self.color = color
self.type = "Car"
self.v_y = DEFAULT_GRAVITY
self.pass_val = 100
self.fail_val = -200
self.miss_val = 50
class SwervingCar1Up(FallingObject):
def __init__(self, color, val):
super().__init__("resources/car-enemy.png", 7)
self.val = val
self.color = color
self.type = "Car"
self.v_y = DEFAULT_GRAVITY * 2
self.pass_val = 100
self.fail_val = -200
self.miss_val = 100
def update(self):
chance = random.randrange(0,100 * 60)
if chance == 1:
# Start Swerving
self.v_x = random.randrange(0,6)
super().update()
class Car2Up(FallingObject):
def __init__(self, color, val):
super().__init__("resources/car-player.png", 8)
self.val = val
self.color = color
self.type = "Car"
self.v_y = DEFAULT_GRAVITY * 0.25
self.pass_val = 100
self.fail_val = -200
self.miss_val = 50
class SwervingCar2Up(FallingObject):
def __init__(self, color, val):
super().__init__("resources/car-player.png", 8)
self.val = val
self.color = color
self.type = "Car"
self.v_y = DEFAULT_GRAVITY * 0.25
self.pass_val = 100
self.fail_val = -200
self.miss_val = 100
def update(self):
chance = random.randrange(0,100 * 60 * 5)
if chance == 1:
# Start Swerving
self.v_x = random.randrange(0,6)
super().update()
class Tree(FallingObject):
def __init__(self, color, val):
super().__init__("resources/tree.png", 4)
self.val = val
self.color = color
self.type = "Poop"
self.v_y = -8
self.pass_val = 100
self.fail_val = -1500
self.miss_val = 0
class Poop(FallingObject):
def __init__(self, color, val):
super().__init__("resources/poo.png", 0.05)
self.val = val
self.color = color
self.type = "Poop"
self.v_y = DEFAULT_GRAVITY * 0.5
self.pass_val = 100
self.fail_val = -500
self.miss_val = 50
class Form(FallingObject):
"""
This class represents the coins on our screen. It is a child class of
the arcade library's "Sprite" class.
"""
def __init__(self, color, val):
super().__init__("resources/form-icon.png", 0.5)
self.val = val
self.color = color
self.type = "Form"
self.v_y = DEFAULT_GRAVITY
class MyGame(arcade.Window):
"""
Main application class.
NOTE: Go ahead and delete the methods you don't need.
If you do need a method, delete the 'pass' and replace it
with your own code. Don't leave 'pass' in this program.
"""
def __init__(self, width, height, title):
super().__init__(width, height, title)
arcade.set_background_color(arcade.color.AMAZON)
self.level = None
# If you have sprite lists, you should create them here,
# and set them to None
def setup(self):
# Create your sprites and sprite lists here
# self.level = Level1()
pass
def on_draw(self):
"""
Render the screen.
"""
# This command should happen before we start drawing. It will clear
# the screen to the background color, and erase what we drew last frame.
arcade.start_render()
if self.level is not None:
self.level.draw()
else:
arcade.draw_text(
"Triplicate - A Falling Objects Game\nControls:\nA, Left = Move Bucket Left\nD, Right Arrow = Move Bucket Right\nTab = Next Bucket(hold shift for Prev)\nS = Stop all buckets\nSpace - Stop current bucket\n0 - Instructions\n1-7 - Select Level\n1-5 are Normal, 6 is Car, 7 is Space",
0, 0, arcade.color.BLACK, 60)
# Finish drawing and display the result
# arcade.finish_render()
# Call draw() on all your sprite lists below
def on_update(self, delta_time):
"""
All the logic to move, and the game logic goes here.
Normally, you'll call update() on the sprite lists that
need it.
"""
if self.level is not None:
self.level.update()
def on_key_press(self, key, key_modifiers):
"""
Called whenever a key on the keyboard is pressed.
For a full list of keys, see:
http://arcade.academy/arcade.key.html
"""
if key == arcade.key.Q: # and key_modifiers & arcade.key.MOD_CTRL:
exit(0)
elif key == arcade.key.KEY_0:
self.level = None
elif key == arcade.key.KEY_1:
self.level = Level1()
elif key == arcade.key.KEY_2:
self.level = Level2()
elif key == arcade.key.KEY_3:
self.level = Level3()
elif key == arcade.key.KEY_4:
self.level = Level4()
elif key == arcade.key.KEY_5:
self.level = Level5()
elif key == arcade.key.KEY_6:
self.level = CarLevel()
elif key == arcade.key.KEY_7:
self.level = SpaceLevel()
elif self.level is not None:
self.level.on_key_press(key, key_modifiers)
def on_key_release(self, key, key_modifiers):
"""
Called whenever the user lets off a previously pressed key.
"""
if self.level is not None:
self.level.on_key_release(key, key_modifiers)
def on_mouse_motion(self, x, y, delta_x, delta_y):
"""
Called whenever the mouse moves.
"""
pass
def on_mouse_press(self, x, y, button, key_modifiers):
"""
Called when the user presses a mouse button.
"""
pass
def on_mouse_release(self, x, y, button, key_modifiers):
"""
Called when a user releases a mouse button.
"""
pass
def main():
""" Main method """
game = MyGame(SCREEN_WIDTH, SCREEN_HEIGHT, SCREEN_TITLE)
game.setup()
arcade.run()
if __name__ == "__main__":
main()
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29911,
6472,
5926,
19451,
13,
23397,
29918,
29954,
4717,
29963,
11937,
353,
448,
29941,
30004,
13,
1525,
27839,
4741,
29918,
10145,
353,
376,
13237,
19451,
13,
5563,
29879,
353,
5159,
30004,
13,
29937,
6732,
29918,
10818,
29918,
14829,
3394,
8178,
4608,
373,
1820,
6507,
22993,
13,
1164,
29918,
10818,
29918,
14829,
353,
7700,
30004,
13,
30004,
13,
30004,
13,
1990,
4669,
10036,
29901,
30004,
13,
1678,
822,
4770,
2344,
12035,
1311,
1125,
30004,
13,
4706,
1209,
30004,
13,
30004,
13,
1678,
822,
2254,
29898,
1311,
29892,
16892,
1125,
30004,
13,
4706,
1209,
30004,
13,
30004,
13,
30004,
13,
1990,
27561,
29901,
30004,
13,
1678,
822,
1653,
29898,
1311,
1125,
30004,
13,
4706,
1209,
30004,
13,
30004,
13,
30004,
13,
1990,
3812,
5126,
29898,
5126,
1125,
30004,
13,
1678,
822,
4770,
2344,
12035,
1311,
29892,
2927,
29892,
659,
1125,
30004,
13,
4706,
1583,
29889,
2780,
353,
2927,
30004,
13,
4706,
1583,
29889,
791,
353,
659,
30004,
13,
30004,
13,
1678,
822,
1653,
29898,
1311,
1125,
30004,
13,
4706,
736,
3812,
29898,
1311,
29889,
2780,
29892,
1583,
29889,
791,
8443,
13,
30004,
13,
30004,
13,
1990,
1704,
5126,
29898,
5126,
1125,
30004,
13,
1678,
822,
4770,
2344,
12035,
1311,
29892,
2927,
29892,
659,
1125,
30004,
13,
4706,
1583,
29889,
2780,
353,
2927,
30004,
13,
4706,
1583,
29889,
791,
353,
659,
30004,
13,
30004,
13,
1678,
822,
1653,
29898,
1311,
1125,
30004,
13,
4706,
736,
317,
556,
1747,
8179,
29896,
3373,
29898,
1311,
29889,
2780,
29892,
1583,
29889,
791,
8443,
13,
30004,
13,
30004,
13,
1990,
1383,
666,
5126,
29898,
5126,
1125,
30004,
13,
1678,
822,
4770,
2344,
12035,
1311,
29892,
2927,
29892,
659,
29892,
3233,
1125,
30004,
13,
4706,
1583,
29889,
2780,
353,
2927,
30004,
13,
4706,
1583,
29889,
791,
353,
659,
30004,
13,
4706,
1583,
29889,
5563,
353,
3233,
30004,
13,
30004,
13,
1678,
822,
1653,
29898,
1311,
1125,
30004,
13,
4706,
736,
1383,
666,
29898,
1311,
29889,
2780,
29892,
1583,
29889,
791,
29892,
1583,
29889,
5563,
8443,
13,
30004,
13,
30004,
13,
1990,
3929,
459,
5126,
29898,
5126,
1125,
30004,
13,
1678,
822,
4770,
2344,
12035,
1311,
29892,
2927,
29892,
659,
1125,
30004,
13,
4706,
1583,
29889,
2780,
353,
2927,
30004,
13,
4706,
1583,
29889,
791,
353,
659,
30004,
13,
30004,
13,
1678,
822,
1653,
29898,
1311,
1125,
30004,
13,
4706,
736,
3929,
459,
29898,
1311,
29889,
2780,
29892,
1583,
29889,
791,
8443,
13,
30004,
13,
30004,
13,
1990,
27561,
22676,
29901,
30004,
13,
1678,
822,
4770,
2344,
12035,
1311,
29892,
12529,
29892,
364,
1125,
30004,
13,
4706,
1583,
29889,
14399,
353,
12529,
30004,
13,
4706,
1583,
29889,
3881,
353,
364,
30004,
13,
30004,
13,
1753,
3617,
29931,
1662,
13409,
29898,
29926,
1125,
30004,
13,
1678,
736,
313,
7187,
1525,
1430,
29918,
22574,
847,
29871,
29906,
29897,
448,
5135,
29896,
29953,
29900,
334,
29871,
29945,
6802,
29906,
29897,
718,
313,
29926,
334,
29871,
29896,
29953,
29900,
29897,
718,
29871,
29947,
29900,
30004,
13,
30004,
13,
30004,
13,
1990,
1334,
523,
287,
2061,
29931,
1662,
10036,
29898,
2061,
10036,
1125,
30004,
13,
4706,
822,
4770,
2344,
12035,
1311,
29892,
534,
29892,
2114,
3842,
1125,
30004,
13,
9651,
2428,
2141,
1649,
2344,
1649,
26471,
13,
9651,
1583,
29889,
17028,
3842,
353,
2114,
3842,
30004,
13,
9651,
1583,
29889,
509,
353,
534,
30004,
13,
9651,
1583,
29889,
29873,
353,
29871,
29900,
30004,
13,
30004,
13,
4706,
822,
2254,
29898,
1311,
29892,
16892,
1125,
30004,
13,
9651,
565,
16892,
1273,
1583,
29889,
509,
1275,
29871,
29900,
29901,
30004,
13,
18884,
1583,
29889,
29873,
4619,
29871,
29896,
30004,
13,
18884,
921,
353,
4036,
29889,
9502,
3881,
29898,
29900,
29892,
29871,
29896,
29900,
29900,
8443,
13,
18884,
363,
12529,
297,
1583,
29889,
17028,
3842,
29901,
30004,
13,
462,
1678,
565,
921,
297,
12529,
29889,
3881,
29901,
30004,
13,
462,
4706,
288,
353,
12529,
29889,
14399,
29889,
3258,
26471,
13,
462,
4706,
288,
29889,
5064,
29918,
29916,
353,
3617,
29931,
1662,
13409,
29898,
8172,
29889,
9502,
3881,
29898,
29900,
29892,
29871,
29945,
876,
30004,
13,
462,
4706,
736,
288,
30004,
13,
9651,
736,
6213,
30004,
13,
30004,
13,
30004,
13,
1990,
1334,
523,
287,
2061,
10036,
29898,
2061,
10036,
1125,
30004,
13,
1678,
822,
4770,
2344,
12035,
1311,
29892,
534,
29892,
2114,
3842,
1125,
30004,
13,
4706,
2428,
2141,
1649,
2344,
1649,
26471,
13,
4706,
1583,
29889,
17028,
3842,
353,
2114,
3842,
30004,
13,
4706,
1583,
29889,
509,
353,
534,
30004,
13,
4706,
1583,
29889,
29873,
353,
29871,
29900,
30004,
13,
30004,
13,
1678,
822,
2254,
29898,
1311,
29892,
16892,
1125,
30004,
13,
4706,
565,
16892,
1273,
1583,
29889,
509,
1275,
29871,
29900,
29901,
30004,
13,
9651,
1583,
29889,
29873,
4619,
29871,
29896,
30004,
13,
9651,
921,
353,
4036,
29889,
9502,
3881,
29898,
29900,
29892,
29871,
29896,
29900,
29900,
8443,
13,
9651,
363,
12529,
297,
1583,
29889,
17028,
3842,
29901,
30004,
13,
18884,
565,
921,
297,
12529,
29889,
3881,
29901,
30004,
13,
462,
1678,
736,
12529,
29889,
14399,
29889,
3258,
26471,
13,
4706,
736,
6213,
30004,
13,
30004,
13,
30004,
13,
1990,
922,
3910,
5126,
2061,
10036,
29898,
2061,
10036,
1125,
30004,
13,
1678,
822,
4770,
2344,
12035,
1311,
29892,
534,
29892,
3618,
1125,
30004,
13,
4706,
1583,
29889,
509,
353,
534,
30004,
13,
4706,
1583,
29889,
12650,
353,
3618,
30004,
13,
30004,
13,
1678,
822,
2254,
29898,
1311,
29892,
16892,
1125,
30004,
13,
4706,
565,
16892,
1273,
1583,
29889,
509,
1275,
29871,
29900,
29901,
30004,
13,
9651,
1583,
29889,
29873,
4619,
29871,
29896,
30004,
13,
9651,
565,
7431,
29898,
1311,
29889,
12650,
29897,
1405,
29871,
29900,
29901,
30004,
13,
18884,
736,
1583,
29889,
12650,
29889,
7323,
2141,
1359,
26471,
13,
9651,
736,
6213,
30004,
13,
30004,
13,
30004,
13,
1990,
922,
3910,
2061,
10036,
29898,
2061,
10036,
1125,
30004,
13,
1678,
822,
4770,
2344,
12035,
1311,
29892,
534,
29892,
3618,
1125,
30004,
13,
4706,
1583,
29889,
509,
353,
534,
30004,
13,
4706,
1583,
29889,
12650,
353,
3618,
30004,
13,
30004,
13,
1678,
822,
2254,
29898,
1311,
29892,
16892,
1125,
30004,
13,
4706,
565,
16892,
1273,
1583,
29889,
509,
1275,
29871,
29900,
29901,
30004,
13,
9651,
1583,
29889,
29873,
4619,
29871,
29896,
30004,
13,
9651,
736,
1583,
29889,
12650,
29889,
7323,
26471,
13,
30004,
13,
30004,
13,
1990,
390,
29933,
2061,
10036,
29898,
2061,
10036,
1125,
30004,
13,
1678,
822,
4770,
2344,
12035,
1311,
1125,
30004,
13,
4706,
1583,
29889,
27199,
353,
5852,
30004,
13,
30004,
13,
1678,
822,
2254,
29898,
1311,
29892,
16892,
1125,
30004,
13,
4706,
565,
16892,
1273,
29871,
29896,
29947,
29900,
1275,
29871,
29900,
29901,
30004,
13,
9651,
1583,
29889,
27199,
353,
451,
1583,
29889,
27199,
30004,
13,
9651,
565,
1583,
29889,
27199,
29901,
30004,
13,
18884,
736,
3812,
3552,
29906,
29945,
29945,
29892,
29871,
29900,
29892,
29871,
29900,
29892,
29871,
29906,
29945,
29945,
511,
376,
29934,
1159,
30004,
13,
9651,
1683,
29901,
30004,
13,
18884,
736,
3812,
3552,
29900,
29892,
29871,
29900,
29892,
29871,
29906,
29945,
29945,
29892,
29871,
29906,
29945,
29945,
511,
376,
29933,
1159,
30004,
13,
30004,
13,
30004,
13,
1990,
390,
7210,
2061,
10036,
29898,
2061,
10036,
1125,
30004,
13,
1678,
822,
4770,
2344,
12035,
1311,
1125,
30004,
13,
4706,
1583,
29889,
29873,
353,
29871,
29900,
30004,
13,
30004,
13,
1678,
822,
2254,
29898,
1311,
29892,
16892,
1125,
30004,
13,
4706,
565,
16892,
1273,
29871,
29896,
29947,
29900,
1275,
29871,
29900,
29901,
30004,
13,
9651,
1583,
29889,
29873,
4619,
29871,
29896,
30004,
13,
9651,
921,
353,
1583,
29889,
29873,
1273,
29871,
29941,
30004,
13,
9651,
565,
921,
1275,
29871,
29900,
29901,
30004,
13,
18884,
736,
3812,
3552,
29906,
29945,
29945,
29892,
29871,
29900,
29892,
29871,
29900,
29892,
29871,
29906,
29945,
29945,
511,
376,
29934,
1159,
30004,
13,
9651,
25342,
921,
1275,
29871,
29896,
29901,
30004,
13,
18884,
736,
3812,
3552,
29900,
29892,
29871,
29906,
29945,
29945,
29892,
29871,
29900,
29892,
29871,
29906,
29945,
29945,
511,
376,
29954,
1159,
30004,
13,
9651,
25342,
921,
1275,
29871,
29906,
29901,
30004,
13,
18884,
736,
3812,
3552,
29900,
29892,
29871,
29900,
29892,
29871,
29906,
29945,
29945,
29892,
29871,
29906,
29945,
29945,
511,
376,
29933,
1159,
30004,
13,
30004,
13,
30004,
13,
1990,
390,
29933,
29925,
2061,
10036,
29898,
2061,
10036,
1125,
30004,
13,
1678,
822,
4770,
2344,
12035,
1311,
1125,
30004,
13,
4706,
1583,
29889,
29873,
353,
29871,
29900,
30004,
13,
30004,
13,
1678,
822,
2254,
29898,
1311,
29892,
16892,
1125,
30004,
13,
4706,
565,
16892,
1273,
29871,
29896,
29947,
29900,
1275,
29871,
29900,
29901,
30004,
13,
9651,
1583,
29889,
29873,
4619,
29871,
29896,
30004,
13,
9651,
921,
353,
4036,
29889,
9502,
3881,
29898,
29900,
29892,
29871,
29941,
8443,
13,
9651,
565,
921,
1275,
29871,
29900,
29901,
30004,
13,
18884,
736,
3812,
3552,
29906,
29945,
29945,
29892,
29871,
29900,
29892,
29871,
29900,
29892,
29871,
29906,
29945,
29945,
511,
376,
29934,
1159,
30004,
13,
9651,
25342,
921,
1275,
29871,
29896,
29901,
30004,
13,
18884,
736,
3929,
459,
3552,
29900,
29892,
29871,
29906,
29945,
29945,
29892,
29871,
29900,
29892,
29871,
29906,
29945,
29945,
511,
376,
29954,
1159,
30004,
13,
9651,
25342,
921,
1275,
29871,
29906,
29901,
30004,
13,
18884,
736,
3812,
3552,
29900,
29892,
29871,
29900,
29892,
29871,
29906,
29945,
29945,
29892,
29871,
29906,
29945,
29945,
511,
376,
29933,
1159,
30004,
13,
30004,
13,
30004,
13,
1990,
390,
7210,
29925,
2061,
10036,
29898,
2061,
10036,
1125,
30004,
13,
1678,
822,
4770,
2344,
12035,
1311,
29892,
534,
1125,
30004,
13,
4706,
1583,
29889,
29873,
353,
29871,
29900,
30004,
13,
4706,
1583,
29889,
509,
353,
534,
30004,
13,
30004,
13,
1678,
822,
2254,
29898,
1311,
29892,
16892,
1125,
30004,
13,
4706,
565,
16892,
1273,
1583,
29889,
509,
1275,
29871,
29900,
29901,
30004,
13,
9651,
1583,
29889,
29873,
4619,
29871,
29896,
30004,
13,
9651,
921,
353,
4036,
29889,
9502,
3881,
29898,
29900,
29892,
29871,
29946,
8443,
13,
9651,
565,
921,
1275,
29871,
29900,
29901,
30004,
13,
18884,
736,
3812,
3552,
29906,
29945,
29945,
29892,
29871,
29900,
29892,
29871,
29900,
29892,
29871,
29906,
29945,
29945,
511,
376,
29934,
1159,
30004,
13,
9651,
25342,
921,
1275,
29871,
29896,
29901,
30004,
13,
18884,
736,
3812,
3552,
29900,
29892,
29871,
29906,
29945,
29945,
29892,
29871,
29900,
29892,
29871,
29906,
29945,
29945,
511,
376,
29954,
1159,
30004,
13,
9651,
25342,
921,
1275,
29871,
29906,
29901,
30004,
13,
18884,
736,
3812,
3552,
29900,
29892,
29871,
29900,
29892,
29871,
29906,
29945,
29945,
29892,
29871,
29906,
29945,
29945,
511,
376,
29933,
1159,
30004,
13,
9651,
25342,
921,
1275,
29871,
29941,
29901,
30004,
13,
18884,
736,
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13,
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2541,
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13,
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13,
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297,
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29889,
3318,
29918,
1761,
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13,
462,
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13,
462,
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565,
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29889,
5064,
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29891,
529,
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29945,
29901,
30004,
13,
462,
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1583,
29889,
13628,
4619,
5446,
29889,
9894,
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791,
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13,
462,
4706,
1583,
29889,
3318,
29918,
1761,
29889,
5992,
29898,
5415,
8443,
13,
30004,
13,
1678,
822,
4216,
29898,
1311,
1125,
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13,
4706,
565,
1583,
29889,
7042,
338,
451,
6213,
29901,
30004,
13,
9651,
564,
6332,
29889,
4012,
29918,
29212,
1332,
29918,
1621,
2521,
29918,
726,
2955,
29898,
29900,
29892,
29871,
29900,
11167,
13,
462,
462,
18884,
12314,
1525,
1430,
29918,
22574,
29892,
12314,
1525,
1430,
29918,
9606,
22530,
11167,
13,
462,
462,
18884,
1583,
29889,
7042,
8443,
13,
4706,
565,
1583,
29889,
21454,
29918,
1761,
338,
451,
6213,
29901,
30004,
13,
9651,
1583,
29889,
21454,
29918,
1761,
29889,
4012,
26471,
13,
4706,
565,
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29889,
3318,
29918,
1761,
338,
451,
6213,
29901,
30004,
13,
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1583,
29889,
3318,
29918,
1761,
29889,
4012,
26471,
13,
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565,
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29918,
21454,
338,
451,
6213,
29901,
30004,
13,
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564,
6332,
29889,
4012,
29918,
1621,
2521,
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449,
1220,
29898,
1311,
29889,
21454,
29918,
1761,
29961,
1311,
29889,
8391,
29918,
21454,
1822,
5064,
29918,
29916,
11167,
13,
462,
462,
3986,
1583,
29889,
21454,
29918,
1761,
29961,
1311,
29889,
8391,
29918,
21454,
1822,
5064,
29918,
29891,
11167,
13,
462,
462,
3986,
1583,
29889,
21454,
29918,
1761,
29961,
1311,
29889,
8391,
29918,
21454,
1822,
3545,
334,
29871,
29896,
29889,
29906,
11167,
13,
462,
462,
3986,
1583,
29889,
21454,
29918,
1761,
29961,
1311,
29889,
8391,
29918,
21454,
1822,
2103,
334,
29871,
29896,
29889,
29906,
11167,
13,
462,
462,
3986,
1583,
29889,
21454,
29918,
1761,
29961,
1311,
29889,
8391,
29918,
21454,
1822,
2780,
29892,
313,
1311,
29889,
24667,
1273,
29871,
29896,
29906,
29897,
718,
29871,
29906,
29892,
29871,
29900,
8443,
13,
4706,
564,
6332,
29889,
4012,
29918,
726,
703,
2481,
19941,
12365,
29881,
29913,
1642,
4830,
29898,
524,
3552,
1311,
29889,
2848,
448,
1583,
29889,
24667,
29897,
847,
29871,
29953,
29900,
8243,
313,
7187,
1525,
1430,
29918,
22574,
847,
29871,
29953,
29897,
334,
29871,
29946,
11167,
13,
462,
308,
12314,
1525,
1430,
29918,
9606,
22530,
448,
313,
7187,
1525,
1430,
29918,
9606,
22530,
847,
29871,
29896,
29900,
511,
564,
6332,
29889,
2780,
29889,
13367,
11375,
29892,
29871,
29953,
29900,
8443,
13,
4706,
564,
6332,
29889,
4012,
29918,
726,
703,
20097,
29901,
6571,
1642,
4830,
29898,
1311,
29889,
13628,
511,
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29900,
29892,
313,
7187,
1525,
1430,
29918,
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22530,
847,
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29900,
29897,
334,
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29929,
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13,
462,
308,
564,
6332,
29889,
2780,
29889,
19386,
29892,
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29953,
29946,
8443,
13,
4706,
565,
1583,
29889,
24667,
1405,
1583,
29889,
2848,
29901,
30004,
13,
9651,
564,
6332,
29889,
4012,
29918,
726,
703,
12739,
2303,
438,
5348,
613,
313,
7187,
1525,
1430,
29918,
22574,
847,
29871,
29945,
29897,
334,
29871,
29896,
29892,
313,
7187,
1525,
1430,
29918,
9606,
22530,
847,
29871,
29953,
29897,
334,
29871,
29941,
29892,
564,
6332,
29889,
2780,
29889,
13367,
11375,
29892,
29871,
29896,
29906,
29947,
8443,
13,
30004,
13,
1678,
822,
2446,
29918,
21454,
29898,
1311,
1125,
30004,
13,
4706,
396,
1596,
29898,
1311,
29889,
8391,
29918,
21454,
8443,
13,
4706,
565,
1583,
29889,
8391,
29918,
21454,
338,
6213,
322,
7431,
29898,
1311,
29889,
21454,
29918,
1761,
29897,
1405,
29871,
29900,
29901,
30004,
13,
9651,
1583,
29889,
8391,
29918,
21454,
353,
29871,
29900,
30004,
13,
4706,
1683,
29901,
30004,
13,
9651,
1583,
29889,
8391,
29918,
21454,
4619,
29871,
29896,
30004,
13,
9651,
565,
1583,
29889,
8391,
29918,
21454,
6736,
7431,
29898,
1311,
29889,
21454,
29918,
1761,
1125,
30004,
13,
18884,
1583,
29889,
8391,
29918,
21454,
353,
29871,
29900,
30004,
13,
4706,
396,
1596,
29898,
1311,
29889,
8391,
29918,
21454,
8443,
13,
4706,
396,
7198,
1708,
263,
6047,
1244,
29973,
30004,
13,
30004,
13,
1678,
822,
12379,
29918,
21454,
29898,
1311,
1125,
30004,
13,
4706,
396,
1596,
29898,
1311,
29889,
8391,
29918,
21454,
8443,
13,
4706,
565,
1583,
29889,
8391,
29918,
21454,
338,
6213,
322,
7431,
29898,
1311,
29889,
21454,
29918,
1761,
29897,
1405,
29871,
29900,
29901,
30004,
13,
9651,
1583,
29889,
8391,
29918,
21454,
353,
7431,
29898,
1311,
29889,
21454,
29918,
1761,
29897,
448,
29871,
29896,
30004,
13,
4706,
1683,
29901,
30004,
13,
9651,
1583,
29889,
8391,
29918,
21454,
22361,
29871,
29896,
30004,
13,
9651,
565,
1583,
29889,
8391,
29918,
21454,
529,
29871,
29900,
29901,
30004,
13,
18884,
1583,
29889,
8391,
29918,
21454,
353,
7431,
29898,
1311,
29889,
21454,
29918,
1761,
29897,
448,
29871,
29896,
30004,
13,
30004,
13,
1678,
822,
4337,
29918,
21454,
29898,
1311,
29892,
921,
1125,
30004,
13,
4706,
565,
1583,
29889,
8391,
29918,
21454,
338,
451,
6213,
29901,
30004,
13,
9651,
1583,
29889,
21454,
29918,
1761,
29961,
1311,
29889,
8391,
29918,
21454,
1822,
29894,
29918,
29916,
4619,
921,
30004,
13,
30004,
13,
1678,
822,
5040,
29918,
21454,
29898,
1311,
1125,
30004,
13,
4706,
565,
1583,
29889,
8391,
29918,
21454,
338,
451,
6213,
29901,
30004,
13,
9651,
1583,
29889,
21454,
29918,
1761,
29961,
1311,
29889,
8391,
29918,
21454,
1822,
29894,
29918,
29916,
353,
29871,
29900,
30004,
13,
30004,
13,
1678,
822,
5040,
29918,
497,
29918,
2423,
9737,
29898,
1311,
1125,
30004,
13,
4706,
363,
921,
297,
1583,
29889,
21454,
29918,
1761,
29901,
30004,
13,
9651,
921,
29889,
29894,
29918,
29916,
353,
29871,
29900,
30004,
13,
30004,
13,
1678,
822,
373,
29918,
1989,
29918,
2139,
29898,
1311,
29892,
1820,
29892,
1820,
29918,
1545,
14903,
1125,
30004,
13,
4706,
9995,
30004,
13,
4706,
3037,
839,
10940,
263,
1820,
373,
278,
12247,
338,
15385,
22993,
13,
30004,
13,
4706,
1152,
263,
2989,
1051,
310,
6611,
29892,
1074,
29901,
30004,
13,
4706,
1732,
597,
279,
6332,
29889,
562,
1943,
1357,
29914,
279,
6332,
29889,
1989,
29889,
1420,
30004,
13,
4706,
9995,
30004,
13,
4706,
565,
1820,
1275,
564,
6332,
29889,
1989,
29889,
29911,
2882,
29901,
30004,
13,
9651,
565,
1820,
29918,
1545,
14903,
669,
564,
6332,
29889,
1989,
29889,
6720,
29928,
29918,
7068,
6545,
29911,
29901,
30004,
13,
18884,
1583,
29889,
16304,
29918,
21454,
26471,
13,
9651,
1683,
29901,
30004,
13,
18884,
1583,
29889,
4622,
29918,
21454,
26471,
13,
4706,
25342,
1820,
1275,
564,
6332,
29889,
1989,
29889,
28024,
470,
1820,
1275,
564,
6332,
29889,
1989,
29889,
29909,
29901,
30004,
13,
9651,
1583,
29889,
11631,
29918,
21454,
6278,
29896,
8443,
13,
4706,
25342,
1820,
1275,
564,
6332,
29889,
1989,
29889,
22789,
3912,
470,
1820,
1275,
564,
6332,
29889,
1989,
29889,
29928,
29901,
30004,
13,
9651,
1583,
29889,
11631,
29918,
21454,
29898,
29896,
8443,
13,
4706,
25342,
1820,
1275,
564,
6332,
29889,
1989,
29889,
29903,
29901,
30004,
13,
9651,
1583,
29889,
9847,
29918,
497,
29918,
2423,
9737,
26471,
13,
4706,
25342,
1820,
1275,
564,
6332,
29889,
1989,
29889,
5550,
11538,
29901,
30004,
13,
9651,
1583,
29889,
9847,
29918,
21454,
26471,
13,
4706,
25342,
1820,
1275,
564,
6332,
29889,
1989,
29889,
29984,
29901,
30004,
13,
9651,
6876,
29898,
29900,
8443,
13,
30004,
13,
1678,
822,
373,
29918,
1989,
29918,
14096,
29898,
1311,
29892,
1820,
29892,
1820,
29918,
1545,
14903,
1125,
30004,
13,
4706,
9995,
30004,
13,
4706,
3037,
839,
10940,
278,
1404,
16869,
1283,
263,
9251,
15385,
1820,
22993,
13,
4706,
9995,
30004,
13,
4706,
565,
6732,
29918,
10818,
29918,
14829,
29901,
30004,
13,
9651,
565,
1820,
1275,
564,
6332,
29889,
1989,
29889,
28024,
29901,
30004,
13,
18884,
1583,
29889,
11631,
29918,
21454,
29898,
29896,
8443,
13,
9651,
25342,
1820,
1275,
564,
6332,
29889,
1989,
29889,
22789,
3912,
29901,
30004,
13,
18884,
1583,
29889,
11631,
29918,
21454,
6278,
29896,
8443,
13,
30004,
13,
30004,
13,
1990,
14121,
10108,
29898,
10108,
1125,
30004,
13,
1678,
822,
4770,
2344,
12035,
1311,
1125,
30004,
13,
4706,
2428,
2141,
1649,
2344,
1649,
26471,
13,
4706,
1583,
29889,
21454,
29918,
1761,
353,
564,
6332,
29889,
29903,
558,
568,
1293,
26471,
13,
4706,
1583,
29889,
21454,
29918,
1761,
29889,
4397,
29898,
29933,
2707,
300,
3552,
29906,
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2141,
5504,
26471,
13,
30004,
13,
30004,
13,
1990,
1704,
29906,
3373,
29898,
29943,
27855,
2061,
1125,
30004,
13,
1678,
822,
4770,
2344,
12035,
1311,
29892,
2927,
29892,
659,
1125,
30004,
13,
4706,
2428,
2141,
1649,
2344,
1649,
703,
13237,
29914,
4287,
29899,
9106,
29889,
2732,
613,
29871,
29947,
8443,
13,
4706,
1583,
29889,
791,
353,
659,
30004,
13,
4706,
1583,
29889,
2780,
353,
2927,
30004,
13,
4706,
1583,
29889,
1853,
353,
376,
8179,
19451,
13,
4706,
1583,
29889,
29894,
29918,
29891,
353,
22236,
29918,
29954,
4717,
29963,
11937,
334,
29871,
29900,
29889,
29906,
29945,
30004,
13,
4706,
1583,
29889,
3364,
29918,
791,
353,
29871,
29896,
29900,
29900,
30004,
13,
4706,
1583,
29889,
14057,
29918,
791,
353,
448,
29906,
29900,
29900,
30004,
13,
4706,
1583,
29889,
9894,
29918,
791,
353,
29871,
29945,
29900,
30004,
13,
30004,
13,
30004,
13,
1990,
317,
556,
1747,
8179,
29906,
3373,
29898,
29943,
27855,
2061,
1125,
30004,
13,
1678,
822,
4770,
2344,
12035,
1311,
29892,
2927,
29892,
659,
1125,
30004,
13,
4706,
2428,
2141,
1649,
2344,
1649,
703,
13237,
29914,
4287,
29899,
9106,
29889,
2732,
613,
29871,
29947,
8443,
13,
4706,
1583,
29889,
791,
353,
659,
30004,
13,
4706,
1583,
29889,
2780,
353,
2927,
30004,
13,
4706,
1583,
29889,
1853,
353,
376,
8179,
19451,
13,
4706,
1583,
29889,
29894,
29918,
29891,
353,
22236,
29918,
29954,
4717,
29963,
11937,
334,
29871,
29900,
29889,
29906,
29945,
30004,
13,
4706,
1583,
29889,
3364,
29918,
791,
353,
29871,
29896,
29900,
29900,
30004,
13,
4706,
1583,
29889,
14057,
29918,
791,
353,
448,
29906,
29900,
29900,
30004,
13,
4706,
1583,
29889,
9894,
29918,
791,
353,
29871,
29896,
29900,
29900,
30004,
13,
30004,
13,
1678,
822,
2767,
29898,
1311,
1125,
30004,
13,
4706,
8825,
353,
4036,
29889,
9502,
3881,
29898,
29900,
29892,
29896,
29900,
29900,
334,
29871,
29953,
29900,
334,
29871,
29945,
8443,
13,
4706,
565,
8825,
1275,
29871,
29896,
29901,
30004,
13,
9651,
396,
7370,
317,
556,
1747,
30004,
13,
9651,
1583,
29889,
29894,
29918,
29916,
353,
4036,
29889,
9502,
3881,
29898,
29900,
29892,
29953,
8443,
13,
4706,
2428,
2141,
5504,
26471,
13,
30004,
13,
30004,
13,
1990,
15472,
29898,
29943,
27855,
2061,
1125,
30004,
13,
1678,
822,
4770,
2344,
12035,
1311,
29892,
2927,
29892,
659,
1125,
30004,
13,
4706,
2428,
2141,
1649,
2344,
1649,
703,
13237,
29914,
8336,
29889,
2732,
613,
29871,
29946,
8443,
13,
4706,
1583,
29889,
791,
353,
659,
30004,
13,
4706,
1583,
29889,
2780,
353,
2927,
30004,
13,
4706,
1583,
29889,
1853,
353,
376,
9837,
459,
19451,
13,
4706,
1583,
29889,
29894,
29918,
29891,
353,
448,
29947,
30004,
13,
4706,
1583,
29889,
3364,
29918,
791,
353,
29871,
29896,
29900,
29900,
30004,
13,
4706,
1583,
29889,
14057,
29918,
791,
353,
448,
29896,
29945,
29900,
29900,
30004,
13,
4706,
1583,
29889,
9894,
29918,
791,
353,
29871,
29900,
30004,
13,
30004,
13,
30004,
13,
1990,
3929,
459,
29898,
29943,
27855,
2061,
1125,
30004,
13,
1678,
822,
4770,
2344,
12035,
1311,
29892,
2927,
29892,
659,
1125,
30004,
13,
4706,
2428,
2141,
1649,
2344,
1649,
703,
13237,
29914,
1129,
29877,
29889,
2732,
613,
29871,
29900,
29889,
29900,
29945,
8443,
13,
4706,
1583,
29889,
791,
353,
659,
30004,
13,
4706,
1583,
29889,
2780,
353,
2927,
30004,
13,
4706,
1583,
29889,
1853,
353,
376,
9837,
459,
19451,
13,
4706,
1583,
29889,
29894,
29918,
29891,
353,
22236,
29918,
29954,
4717,
29963,
11937,
334,
29871,
29900,
29889,
29945,
30004,
13,
4706,
1583,
29889,
3364,
29918,
791,
353,
29871,
29896,
29900,
29900,
30004,
13,
4706,
1583,
29889,
14057,
29918,
791,
353,
448,
29945,
29900,
29900,
30004,
13,
4706,
1583,
29889,
9894,
29918,
791,
353,
29871,
29945,
29900,
30004,
13,
30004,
13,
30004,
13,
1990,
3812,
29898,
29943,
27855,
2061,
1125,
30004,
13,
1678,
9995,
30004,
13,
1678,
910,
770,
11524,
278,
1302,
1144,
373,
1749,
4315,
29889,
739,
338,
263,
2278,
770,
310,
30004,
13,
1678,
278,
564,
6332,
3489,
29915,
29879,
376,
29903,
558,
568,
29908,
770,
22993,
13,
1678,
9995,
30004,
13,
30004,
13,
1678,
822,
4770,
2344,
12035,
1311,
29892,
2927,
29892,
659,
1125,
30004,
13,
4706,
2428,
2141,
1649,
2344,
1649,
703,
13237,
29914,
689,
29899,
4144,
29889,
2732,
613,
29871,
29900,
29889,
29945,
8443,
13,
4706,
1583,
29889,
791,
353,
659,
30004,
13,
4706,
1583,
29889,
2780,
353,
2927,
30004,
13,
4706,
1583,
29889,
1853,
353,
376,
2500,
19451,
13,
4706,
1583,
29889,
29894,
29918,
29891,
353,
22236,
29918,
29954,
4717,
29963,
11937,
30004,
13,
30004,
13,
30004,
13,
1990,
1619,
14199,
29898,
279,
6332,
29889,
5907,
1125,
30004,
13,
1678,
9995,
30004,
13,
1678,
4241,
2280,
770,
22993,
13,
30004,
13,
1678,
6058,
29923,
29901,
2921,
14432,
322,
5217,
278,
3519,
366,
1016,
29915,
29873,
817,
22993,
13,
1678,
960,
366,
437,
817,
263,
1158,
29892,
5217,
278,
525,
3364,
29915,
322,
5191,
372,
30004,
13,
1678,
411,
596,
1914,
775,
29889,
3872,
29915,
29873,
5967,
525,
3364,
29915,
297,
445,
1824,
22993,
13,
1678,
9995,
30004,
13,
30004,
13,
1678,
822,
4770,
2344,
12035,
1311,
29892,
2920,
29892,
3171,
29892,
3611,
1125,
30004,
13,
4706,
2428,
2141,
1649,
2344,
12035,
2103,
29892,
3171,
29892,
3611,
8443,
13,
30004,
13,
4706,
564,
6332,
29889,
842,
29918,
7042,
29918,
2780,
29898,
279,
6332,
29889,
2780,
29889,
29909,
1529,
29999,
1164,
8443,
13,
4706,
1583,
29889,
5563,
353,
6213,
30004,
13,
4706,
396,
960,
366,
505,
29227,
8857,
29892,
366,
881,
1653,
963,
1244,
11167,
13,
4706,
396,
322,
731,
963,
304,
6213,
30004,
13,
30004,
13,
1678,
822,
6230,
29898,
1311,
1125,
30004,
13,
4706,
396,
6204,
596,
7689,
3246,
322,
29227,
8857,
1244,
30004,
13,
4706,
396,
1583,
29889,
5563,
353,
21597,
29896,
26471,
13,
4706,
1209,
30004,
13,
30004,
13,
1678,
822,
373,
29918,
4012,
29898,
1311,
1125,
30004,
13,
4706,
9995,
30004,
13,
4706,
26000,
278,
4315,
22993,
13,
4706,
9995,
30004,
13,
30004,
13,
4706,
396,
910,
1899,
881,
3799,
1434,
591,
1369,
11580,
29889,
739,
674,
2821,
30004,
13,
4706,
396,
278,
4315,
304,
278,
3239,
2927,
29892,
322,
604,
559,
825,
591,
15010,
1833,
3515,
22993,
13,
4706,
564,
6332,
29889,
2962,
29918,
9482,
26471,
13,
30004,
13,
4706,
565,
1583,
29889,
5563,
338,
451,
6213,
29901,
30004,
13,
9651,
1583,
29889,
5563,
29889,
4012,
26471,
13,
4706,
1683,
29901,
30004,
13,
9651,
564,
6332,
29889,
4012,
29918,
726,
29898,
30004,
13,
18884,
376,
29911,
6472,
5926,
448,
319,
14053,
292,
4669,
29879,
8448,
29905,
29876,
17825,
3583,
29876,
29909,
29892,
19941,
353,
25249,
16281,
300,
19941,
29905,
29876,
29928,
29892,
10428,
826,
798,
353,
25249,
16281,
300,
10428,
29905,
29876,
8863,
353,
8084,
16281,
300,
29898,
8948,
9500,
363,
4721,
29894,
2144,
29876,
29903,
353,
22303,
599,
1321,
9737,
29905,
29876,
14936,
448,
22303,
1857,
20968,
29905,
29876,
29900,
448,
2799,
582,
1953,
29905,
29876,
29896,
29899,
29955,
448,
7605,
21597,
29905,
29876,
29896,
29899,
29945,
526,
21981,
29892,
29871,
29953,
338,
1704,
29892,
29871,
29955,
338,
14121,
15231,
13,
462,
29900,
29892,
29871,
29900,
29892,
564,
6332,
29889,
2780,
29889,
13367,
11375,
29892,
29871,
29953,
29900,
8443,
13,
4706,
396,
4231,
728,
11580,
322,
2479,
278,
1121,
30004,
13,
4706,
396,
564,
6332,
29889,
4951,
728,
29918,
9482,
26471,
13,
4706,
396,
8251,
4216,
580,
373,
599,
596,
29227,
8857,
2400,
30004,
13,
30004,
13,
1678,
822,
373,
29918,
5504,
29898,
1311,
29892,
19471,
29918,
2230,
1125,
30004,
13,
4706,
9995,
30004,
13,
4706,
2178,
278,
5900,
304,
4337,
29892,
322,
278,
3748,
5900,
5771,
1244,
22993,
13,
4706,
5655,
635,
29892,
366,
29915,
645,
1246,
2767,
580,
373,
278,
29227,
8857,
393,
30004,
13,
4706,
817,
372,
22993,
13,
4706,
9995,
30004,
13,
4706,
565,
1583,
29889,
5563,
338,
451,
6213,
29901,
30004,
13,
9651,
1583,
29889,
5563,
29889,
5504,
26471,
13,
30004,
13,
1678,
822,
373,
29918,
1989,
29918,
2139,
29898,
1311,
29892,
1820,
29892,
1820,
29918,
1545,
14903,
1125,
30004,
13,
4706,
9995,
30004,
13,
4706,
3037,
839,
10940,
263,
1820,
373,
278,
12247,
338,
15385,
22993,
13,
30004,
13,
4706,
1152,
263,
2989,
1051,
310,
6611,
29892,
1074,
29901,
30004,
13,
4706,
1732,
597,
279,
6332,
29889,
562,
1943,
1357,
29914,
279,
6332,
29889,
1989,
29889,
1420,
30004,
13,
4706,
9995,
30004,
13,
4706,
565,
1820,
1275,
564,
6332,
29889,
1989,
29889,
29984,
29901,
396,
322,
1820,
29918,
1545,
14903,
669,
564,
6332,
29889,
1989,
29889,
6720,
29928,
29918,
1783,
2241,
29901,
30004,
13,
9651,
6876,
29898,
29900,
8443,
13,
4706,
25342,
1820,
1275,
564,
6332,
29889,
1989,
29889,
10818,
29918,
29900,
29901,
30004,
13,
9651,
1583,
29889,
5563,
353,
6213,
30004,
13,
4706,
25342,
1820,
1275,
564,
6332,
29889,
1989,
29889,
10818,
29918,
29896,
29901,
30004,
13,
9651,
1583,
29889,
5563,
353,
21597,
29896,
26471,
13,
4706,
25342,
1820,
1275,
564,
6332,
29889,
1989,
29889,
10818,
29918,
29906,
29901,
30004,
13,
9651,
1583,
29889,
5563,
353,
21597,
29906,
26471,
13,
4706,
25342,
1820,
1275,
564,
6332,
29889,
1989,
29889,
10818,
29918,
29941,
29901,
30004,
13,
9651,
1583,
29889,
5563,
353,
21597,
29941,
26471,
13,
4706,
25342,
1820,
1275,
564,
6332,
29889,
1989,
29889,
10818,
29918,
29946,
29901,
30004,
13,
9651,
1583,
29889,
5563,
353,
21597,
29946,
26471,
13,
4706,
25342,
1820,
1275,
564,
6332,
29889,
1989,
29889,
10818,
29918,
29945,
29901,
30004,
13,
9651,
1583,
29889,
5563,
353,
21597,
29945,
26471,
13,
4706,
25342,
1820,
1275,
564,
6332,
29889,
1989,
29889,
10818,
29918,
29953,
29901,
30004,
13,
9651,
1583,
29889,
5563,
353,
1704,
10108,
26471,
13,
4706,
25342,
1820,
1275,
564,
6332,
29889,
1989,
29889,
10818,
29918,
29955,
29901,
30004,
13,
9651,
1583,
29889,
5563,
353,
14121,
10108,
26471,
13,
4706,
25342,
1583,
29889,
5563,
338,
451,
6213,
29901,
30004,
13,
9651,
1583,
29889,
5563,
29889,
265,
29918,
1989,
29918,
2139,
29898,
1989,
29892,
1820,
29918,
1545,
14903,
8443,
13,
30004,
13,
1678,
822,
373,
29918,
1989,
29918,
14096,
29898,
1311,
29892,
1820,
29892,
1820,
29918,
1545,
14903,
1125,
30004,
13,
4706,
9995,
30004,
13,
4706,
3037,
839,
10940,
278,
1404,
16869,
1283,
263,
9251,
15385,
1820,
22993,
13,
4706,
9995,
30004,
13,
4706,
565,
1583,
29889,
5563,
338,
451,
6213,
29901,
30004,
13,
9651,
1583,
29889,
5563,
29889,
265,
29918,
1989,
29918,
14096,
29898,
1989,
29892,
1820,
29918,
1545,
14903,
8443,
13,
30004,
13,
1678,
822,
373,
29918,
15769,
29918,
29885,
8194,
29898,
1311,
29892,
921,
29892,
343,
29892,
19471,
29918,
29916,
29892,
19471,
29918,
29891,
1125,
30004,
13,
4706,
9995,
30004,
13,
4706,
3037,
839,
10940,
278,
9495,
16229,
22993,
13,
4706,
9995,
30004,
13,
4706,
1209,
30004,
13,
30004,
13,
1678,
822,
373,
29918,
15769,
29918,
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models/cnn.py | AnanyaAppan/DeblurGANv2 | 0 | 104047 | from torch.nn import Sequential, Module, ConvTranspose2d
class CNN(Module):
def __init__(self):
super(CNN, self).__init__()
self.layers = Sequential(
# Conv2d(6, 32, kernel_size=5, stride=1, padding=2),
ConvTranspose2d(3, 3, kernel_size=5, stride=1, padding=2),
)
# Defining the forward pass
# def forward(self, img, prev_img):
def forward(self,img, attention_map, downsampled_attention_map):
x = self.layers(img)
return x
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BeamlineStatusLogger/logger.py | DESY-P02-1/BeamlineStatusLogger | 0 | 40333 | <reponame>DESY-P02-1/BeamlineStatusLogger
from collections import Iterable
def as_iterable(object):
if isinstance(object, Iterable):
return object
else:
return [object]
class Logger:
def __init__(self, source, processors, sink, timer):
self.source = source
self.processors = as_iterable(processors)
self.sink = sink
self.timer = timer
def run(self):
self.timer.reset()
success = True
while self.timer(success):
data = self.source.read()
for proc in self.processors:
data = proc(data)
success = self.sink.write(data)
def abort(self):
self.timer.abort()
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legacy_code/legacy_2c_main_mortality_rnn.py | mtkier94/Neural-calibration-of-hidden-inhomogeneous-Marko-chains | 0 | 115939 | <reponame>mtkier94/Neural-calibration-of-hidden-inhomogeneous-Marko-chains
import numpy as np
import pandas as pd
import os
import matplotlib.pyplot as plt
from sklearn.utils import shuffle
from itertools import product as iter_prod
import tensorflow as tf
from tensorflow.keras.models import Model, load_model
from tensorflow.keras.layers import Dense, Input, GRU, SimpleRNN
from global_vars import T_MAX
from functions.sub_data_prep import create_trainingdata_baseline
from functions.tf_model_base import create_baseline_model_rnn, transfer_weights_dense2simpleRNN
from functions.sub_backtesting import check_if_rnn_version
#from sub_tf_functions import create_baseline_model_rnn, transfer_weights_dense2simpleRNN
from global_vars import T_MAX
from global_vars import path_data, path_models_baseline_transfer
if __name__ == '__main__':
raise ValueError('Content of this script has been moved/ appended to main_baseline.py')
baseline_sex = 'female'
p_survive = pd.read_csv(os.path.join(path_data,r'DAV2008T{}.csv'.format(baseline_sex)), delimiter = ';', header=None ).loc[:,0].values.reshape((-1,1))
if baseline_sex == 'female':
p_other_sex = pd.read_csv(os.path.join(path_data,r'DAV2008T{}.csv'.format('male')), delimiter = ';', header=None ).loc[:,0].values.reshape((-1,1))
tag_other_sex = 'DAVT2008male'
elif baseline_sex == 'male':
p_other_sex = pd.read_csv(os.path.join(path_data,r'DAV2008T{}.csv'.format('female')), delimiter = ';', header=None ).loc[:,0].values.reshape((-1,1))
tag_other_sex = 'DAVT2008female'
else:
raise ValueError('Unknown baseline_sex')
assert(T_MAX == len(p_survive)-1)
freqs = [1,1/2, 1/3, 1/6, 1/12]
x, y = create_trainingdata_baseline(frequencies = freqs, surv_probs=p_survive, age_scale=T_MAX)
x, y = shuffle(x, y)
n_in = x.shape[1]
n_out = 2
# RNN inputs: A 3D tensor, with shape [batch, timesteps, feature]
model_rnn = create_baseline_model_rnn(input_shape=(None, n_in), n_out= n_out, hidden_layers=[40,40,20])
#model_rnn.summary()
if os.path.exists(os.path.join(path_models_baseline_transfer, r'ffn_davT{}.h5'.format(baseline_sex))):
model_pretrained = load_model(os.path.join(path_models_baseline_transfer, r'ffn_davT{}.h5'.format(baseline_sex)))
model_pretrained.evaluate(x,y, batch_size=64)
print('loss-type: ', model_pretrained.loss)
transfer_weights_dense2simpleRNN(dense_model= model_pretrained, rnn_model = model_rnn)
model_rnn.save(os.path.join(path_models_baseline_transfer, r'rnn_davT{}.h5'.format(baseline_sex)))
print('Weights transferred from ffn to rnn!')
assert (check_if_rnn_version(model_ffn=model_pretrained, model_rnn=model_rnn)==True).all()
else:
print('Model cannot be loaded or trained!')
exit()
pred = model_rnn.predict(x.reshape(1,-1,n_in))
print('shape of predictions: ', pred.shape)
plt.plot(x[:,0]*T_MAX, model_pretrained.predict(x)[:,0], 'xg', alpha = .5, label='ffn')
plt.plot(x[:,0]*T_MAX, model_rnn.predict(x.reshape(1,-1,n_in))[0,:,0].flatten(), 'ob', alpha = .2, label='rnn')
plt.plot(x[:,0]*T_MAX, y[:,0], linestyle = 'None', marker = '_', color = 'red', label='DAV')
plt.plot(x[:,0]*T_MAX, model_pretrained.predict(x)[:,1], 'xg', alpha = .5)
plt.plot(x[:,0]*T_MAX, model_rnn.predict(x.reshape(1,-1,n_in))[0,:,1].flatten(), 'ob', alpha = .2)
plt.plot(x[:,0]*T_MAX, y[:,1], linestyle = 'None', marker = '_', color = 'red',)
plt.yscale('log')
plt.title('Fit - FFN vs. RNN vs DAVT2008{}'.format(baseline_sex))
plt.legend()
#plt.legend(bbox_to_anchor=(1.05, 1))
plt.show() | [
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petisco/extra/rabbitmq/application/message/formatter/rabbitmq_message_queue_name_formatter.py | alice-biometrics/petisco | 19 | 186219 | from petisco.base.domain.message.message import Message
class RabbitMqMessageQueueNameFormatter:
@staticmethod
def format(message: Message, exchange_name: str = None) -> str:
message_name = message.name.replace(".", "_")
message_type = message.type if message.type != "domain_event" else "event"
message_format = f"{message.version}.{message_type}.{message_name}"
return f"{exchange_name}.{message_format}" if exchange_name else message_name
@staticmethod
def format_retry(message: Message, exchange_name: str = None) -> str:
queue_name = RabbitMqMessageQueueNameFormatter.format(message, exchange_name)
return f"retry.{queue_name}"
@staticmethod
def format_dead_letter(message: Message, exchange_name: str = None) -> str:
queue_name = RabbitMqMessageQueueNameFormatter.format(message, exchange_name)
return f"dead_letter.{queue_name}"
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dataset.py | IRMVLab/HALFlow | 9 | 64241 | '''
Provider for dataset
'''
import os
import os.path
import numpy as np
import time
class SceneflowDataset():
def __init__(self, root='/tmp/FlyingThings3D_subset_processed_35m', npoints=8192, mode = 'train_ft3d'):
self.npoints = npoints
self.mode = mode
self.root = root
if self.mode == 'eval_kitti':
self.samples = self.make_dataset()
self.datapath = root
self.file_list = os.listdir(self.datapath)
self.npoints = 16384
elif self.mode == 'train_ft3d':
self.datapath = os.path.join(self.root, 'train')
self.file_list = os.listdir(self.datapath)
elif self.mode == 'eval_ft3d':
self.datapath = os.path.join(self.root, 'val')
self.file_list = os.listdir(self.datapath)
def __getitem__(self, index):
np.random.seed(0)
if self.mode == 'eval_kitti':
fn = self.samples[index]
else:
fn = self.file_list[index]
fn = os.path.join(self.datapath, fn)
pc1 = os.path.join(fn,'pc1.npy')
pc2 = os.path.join(fn,'pc2.npy')
with open(pc1, 'rb') as fp:
pos1 = np.load(fp)
with open(pc2, 'rb') as fp2:
pos2 = np.load(fp2)
flow = pos2[:, :3] - pos1[:, :3]
if self.mode == 'eval_kitti':
is_ground = np.logical_or(pos1[:,1] < -1.35, pos2[:,1] < -1.35)
not_ground = np.logical_not(is_ground)
near_mask = np.logical_and(pos1[:, 2] < 35, pos2[:, 2] < 35)
near_mask = np.logical_and(not_ground, near_mask)
indices = np.where(near_mask)[0]
else:
near_mask = np.logical_and(pos1[:, 2] < 35, pos2[:, 2] < 35)
indices = np.where(near_mask)[0]
if len(indices) >= self.npoints:
sample_idx1 = np.random.choice(indices, self.npoints, replace=False)
else:
sample_idx1 = np.concatenate((indices, np.random.choice(indices, self.npoints - len(indices), replace=True)), axis=-1)
if len(indices) >= self.npoints:
sample_idx2 = np.random.choice(indices, self.npoints, replace=False)
else:
sample_idx2 = np.concatenate((indices, np.random.choice(indices, self.npoints - len(indices), replace=True)), axis=-1)
pos1 = pos1[sample_idx1, :]
pos2 = pos2[sample_idx2, :]
flow = flow[sample_idx1, :]
if self.mode == 'eval_kitti':
return pos1, pos2, flow, fn
else:
return pos1, pos2, flow
def __len__(self):
return len(self.file_list)
def make_dataset(self):
do_mapping = True
root = os.path.realpath(os.path.expanduser(self.root))
all_paths = sorted(os.walk(root))
useful_paths = [item[0] for item in all_paths if len(item[1]) == 0]
try:
assert (len(useful_paths) == 200)
except AssertionError:
print('assert (len(useful_paths) == 200) failed!', len(useful_paths))
if do_mapping:
mapping_path = os.path.join(os.path.dirname(__file__), 'KITTI_mapping.txt')
print('mapping_path', mapping_path)
with open(mapping_path) as fd:
lines = fd.readlines()
lines = [line.strip() for line in lines]
useful_paths = [path for path in useful_paths if lines[int(os.path.split(path)[-1])] != '']
res_paths = useful_paths
return res_paths
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lib_dsp/iir/iir/design/iir.py | PyGears/lib-dsp | 3 | 23343 | <gh_stars>1-10
from pygears import gear, Intf
from pygears.lib import dreg, decouple, saturate, qround
@gear
def iir_1dsos(din, *, a, b, gain):
# add input gain and init delayed inputs
zu0 = din * gain
zu1 = zu0 | dreg(init=0)
zu2 = zu1 | dreg(init=0)
# perform b coefficient sum
a1 = (zu1 * b[1]) + (zu2 * b[2])
a2 = a1 + (zu0 * b[0])
# declare output interface and its type
y = Intf(a2.dtype)
# init delayed outputs
zy1 = y | decouple(init=0)
zy2 = zy1 | dreg(init=0)
# perform a coefficient sum
b1 = (zy2 * a[2]) + (zy1 * a[1])
# add both sums and set output
y |= (a2 - b1) | qround(fract=a2.dtype.fract) | saturate(t=a2.dtype)
return y
@gear
def iir_2tsos(din, *, a, b, gain):
# add input gain
x = din * gain
# declare output interface and its type
y = Intf(din.dtype)
# perform first tap multiplication and sum
z0 = ((x * b[2]) - (y * a[2]))
# delay first sum output
z0_delayed = z0 | dreg(init=0)
# perform second tap multiplication and sum
z1 = ((x * b[1]) + z0_delayed - (y * a[1]))
# delay second sum output
z1_delayed = z1 | decouple(init=0)
# perform final sum and set output
y |= ((x * b[0]) + z1_delayed) | qround(fract=din.dtype.fract) | saturate(t=din.dtype)
return y
@gear
def iir_df1dsos(din, *, a, b, gain, ogain):
# init temp
temp = din
# add cascades for all b coefficients
for i in range(len(b)):
# format every cascaded output as input
temp = temp | iir_1dsos(a=a[i], b=b[i], gain=gain[i]) | qround(fract=din.dtype.fract) | saturate(t=din.dtype)
# add output gain and format as input
dout = (temp * ogain) | qround(fract=din.dtype.fract) | saturate(t=din.dtype)
return dout
@gear
def iir_df2tsos(din, *, a, b, gain, ogain):
# init temp
temp = din
# add cascades for all b coefficients
for i in range(len(b)):
# format every cascaded output as input
temp = temp | iir_2tsos(a=a[i], b=b[i], gain=gain[i])
# add output gain and format as input
dout = (temp * ogain) | qround(fract=din.dtype.fract) | saturate(t=din.dtype)
return dout
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extract_results.py | goodman1204/birank | 0 | 112614 | <gh_stars>0
dataset=["gossipcop_fake" "gossipcop_real" "politifact_fake" "politifact_real" "Aminer"]
methods=["proposed" "HITS" "CoHITS" "BGRM" "BiRank"]
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mkvendor.py | AnClark/makevendor.py | 0 | 166017 | #!/usr/bin/env python3
import sys, os
import re, struct
from argparse import ArgumentParser, FileType, Namespace
"""
======================== CONSTANTS ========================
"""
SUPPORTED_ROM = {
"cm": "cm.mk",
"lineageos": "lineage.mk",
"mokee": "mk___DEVICE__.mk",
"omnirom": "omni___DEVICE__.mk"
}
"""
======================== GLOBAL PARAMETERS ========================
"""
ARGS = "" # Parsed argument namespace
"""
======================== UTILITIES ========================
"""
def LOG(msg):
'''
Print debug messages when --verbose is assigned.
'''
if ARGS.verbose:
sys.stderr.write(msg)
def parse_bootimg_for_mkvendor(bootimg):
'''
parse C8600-compatible bootimg for mkvendor.
Comparing with its original version, this one kept all bootimg data
in objects.
Modified from bootimg.py written by <NAME> <<EMAIL>>.
write kernel to kernel[.gz]
write ramdisk to ramdisk[.gz]
write second to second[.gz]
write dtimg to dt.img
write extra to unknown
Argument:
bootimg: File object of opened bootimg
'''
latin = lambda x: x.encode('latin')
# Use Namespace to store parsed data
result = Namespace (
metadata = "",
kernel = b"",
ramdisk = b"",
second = b"",
dtimg = b"",
kerneldt = b"",
unknown = b"",
image_format = {}
)
bootimg.seek(0)
( magic,
kernel_size, kernel_addr,
ramdisk_size, ramdisk_addr,
second_size, second_addr,
tags_addr, page_size, dt_size, os_version,
name, cmdline, id4x8
) = struct.unpack('<8s10I16s512s32s', bootimg.read(608))
bootimg.seek(page_size - 608, 1)
base = kernel_addr - 0x00008000
assert magic.decode('latin') == 'ANDROID!', 'Invalid bootimg'
# assert base == ramdisk_addr - 0x01000000, 'invalid bootimg'
# assert base == second_addr - 0x00f00000, 'invalid bootimg'
# assert base == tags_addr - 0x00000100, 'invalid bootimg'
def say(v):
b7 = 127
b4 = 15
a = (v >> 25) & b7
b = (v >> 18) & b7
c = (v >> 11) & b7
y = ((v >> 4) & b7) + 2000
m = v & b4
return '%d.%d.%d %s-%s' % (a, b, c, y, m)
LOG("================= Bootimg Information =================\n")
LOG('* kernel_addr=0x%x\n' % kernel_addr)
LOG('* ramdisk_addr=0x%x\n' % ramdisk_addr)
LOG('* second_addr=0x%x\n' % second_addr)
LOG('* tags_addr=0x%x\n' % tags_addr)
# LOG('base=0x%x\n' % base)
LOG('* page_size=%d\n' % page_size)
LOG('* os_version=0x%08x(%s)\n' % (os_version, say(os_version)))
LOG('* name="%s"\n' % name.decode('latin').strip('\x00'))
LOG('* cmdline="%s"\n' % cmdline.decode('latin').strip('\x00'))
while True:
if bootimg.read(page_size) == struct.pack('%ds' % page_size, latin('')):
continue
bootimg.seek(-page_size, 1)
size = bootimg.tell()
break
padding = lambda x: (~x + 1) & (size - 1)
LOG('* padding_size=%d\n' % size)
LOG("======================================================\n")
# OUT1: Write metadata
metadata = {
'kernel_addr': kernel_addr,
'ramdisk_addr': ramdisk_addr,
'second_addr': second_addr,
'tags_addr': tags_addr,
'page_size': page_size,
'name': name.decode('latin').strip('\x00'),
'cmdline': cmdline.decode('latin').strip('\x00'),
'padding_size': size,
'os_version': os_version,
}
result.metadata = metadata
gzname = lambda x: x == struct.pack('3B', 0x1f, 0x8b, 0x08) and '.gz' or ''
# OUT2: Kernel image & DT image
kernel = bootimg.read(kernel_size)
magic = struct.pack('>I', 0xd00dfeed)
pos = kernel.find(magic)
if pos > 0:
result.kernel = kernel[:pos]
result.kerneldt = kernel[pos:]
else:
result.kernel = kernel
result.image_format["kernel"] = gzname(kernel[:3])
bootimg.seek(padding(kernel_size), 1)
# OUT3: Ramdisk
ramdisk = bootimg.read(ramdisk_size)
result.ramdisk = ramdisk
result.image_format["ramdisk"] = gzname(ramdisk[:3])
bootimg.seek(padding(ramdisk_size), 1)
# OUT4: Second image
if second_size:
second = bootimg.read(second_size)
result.second = second
result.image_format["second"] = gzname(second[:3])
bootimg.seek(padding(second_size), 1)
# OUT5: DTB
if dt_size:
dtimg = bootimg.read(dt_size)
result.dtimg = dtimg
bootimg.seek(padding(dt_size), 1)
# OUT6: Unknown
unknown = bootimg.read()
if unknown:
result.unknown = unknown
bootimg.close()
return result
def parse_bootimg():
return parse_bootimg_for_mkvendor(ARGS.boot_img)
def parse_cmdline():
parser = ArgumentParser(
description="Generate device configurations from prebuilt boot/recovery image.",
epilog="""
The boot.img argument is the extracted recovery or boot image.
It should not be provided for devices
that have non standard boot images (ie, Samsung).
"""
)
parser.add_argument(
"--boot-img",
"-b",
help="Path to boot image",
type=FileType("rb"),
required=True
)
parser.add_argument(
"--manufacturer",
"-m",
help="Device manufacturer name",
type=str,
required=True
)
parser.add_argument(
"--device",
"-d",
help="Device name",
type=str,
required=True
)
parser.add_argument(
"--rom",
"-r",
help="Your Android ROM type. Currently supports: %s" % SUPPORTED_ROM.keys(),
choices=SUPPORTED_ROM.keys(),
required=True
)
parser.add_argument(
"--output",
"-o",
type=str,
help="Target directory to put generated device configuration. Default is ./out",
default="./out"
)
parser.add_argument(
"--verbose",
"-v",
help="Print debug messages",
action='count' # Treat as a switch
)
return parser.parse_args()
"""
======================== WORKFLOWS ========================
"""
def analyze_kernel_image():
# TODO: 分析内核映像的处理器类型，来决定BoardConfig.mk中的架构部分该填啥。
pass
def make_use_of_ramdisk():
# TODO: 解包Ramdisk，并从中提取有用的文件。
# 包括：init.{ro.hardware}.rc、ueventd.{ro.hardware}.rc、fstab.{ro.hardware}、recovery.fstab。
pass
def render_templates():
# TODO: 创建若干个闭包函数，处理具体的模板文件
# TODO: 用assert，在文件不存在时退出
pass
def generate_vendorsetup_sh():
pass
"""
======================== MAIN ENTRANCE ========================
"""
def main():
# Parse command line.
# This process can block some illegal inputs.
global ARGS
ARGS = parse_cmdline()
# Parse boot image.
LOG("Parsing boot image...\n")
BOOTIMG_DATA = parse_bootimg_for_mkvendor(ARGS.boot_img)
if __name__ == "__main__":
main()
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580,
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] |
randomtesting.py | ManderaGeneral/generalbrowser | 0 | 1606421 |
from generalbrowser.assets.account.client import AccountClient
from generalbrowser.assets.account.clientpage import GeneralSigninPage
client = AccountClient(domain="http://127.0.0.1:8000")
client.create_page()
| [
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BlurDetection.py | samaritan-security/samaritan-backend | 0 | 13497 | <filename>BlurDetection.py
import cv2
def variance_of_laplacian(image):
return cv2.Laplacian(image, cv2.CV_64F).var()
"""
checks if an image is blurry
returns True if blurry, False otherwise
"""
def detect_blurry_image(image, threshold):
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
blur = variance_of_laplacian(image)
if(blur < threshold):
return True
return False | [
1,
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] |
stock/quant/best_perform.py | shenzhongqiang/cnstock_py | 2 | 120699 | import numpy as np
import pandas as pd
from stock.utils.symbol_util import get_stock_symbols, get_archived_trading_dates
from stock.marketdata.storefactory import get_store
from config import store_type
pd.set_option('display.max_rows', None)
store = get_store(store_type)
def get_equity_value(exsymbols, date):
closes = []
for exsymbol in exsymbols:
df = store.get(exsymbol)
if date in df.index:
closes.append(df.loc[date].close)
else:
close = df.loc[:date].iloc[-1].close
closes.append(close)
return np.mean(closes)
exsymbols = store.get_stock_exsymbols()
columns = ["exsymbol", "profit"]
df_date = pd.DataFrame(columns=columns)
for exsymbol in exsymbols:
df = store.get(exsymbol)
if len(df) < 400:
continue
close_min = df.close.iloc[-30:].min()
profit = df.iloc[-1].close / close_min - 1
df_date.loc[len(df_date)] = [exsymbol, profit]
df_date.dropna(how="any", inplace=True)
df_top = df_date.sort_values(["profit"]).tail(10)
df_bottom = df_date.sort_values(["profit"]).head(100)
print(df_top)
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] |
Asap-3.8.4/Projects/NanoparticleMC/misc/plot_coveragevsT.py | auag92/n2dm | 1 | 135827 | import os
if not os.path.exists('AdsorptionParameters.py'):
os.symlink('../AdsorptionParameters.py', 'AdsorptionParameters.py')
import matplotlib.pyplot as plt
import asap3.nanoparticle_mc.langmuirExpression as le
import numpy as np
#Test the CO Plot now
#Get one coverage for each CN for each temperature at 1mbar
x = np.linspace(0.1,800,100)
covs = np.zeros((len(x),16))
for i in range(len(x)):
covs[i] = le.getCoverages(T=x[i],P=1E2,species="AuCO")
CN_4 = [c[4] for c in covs]
CN_6 = [c[6] for c in covs]
CN_8 = [c[8] for c in covs]
CN_9 = [c[9] for c in covs]
CN_12 = [c[12] for c in covs]
plt.plot(x,CN_4,label="CN 4")
plt.plot(x,CN_6,label="CN 6")
plt.plot(x,CN_8,label="CN 8")
plt.plot(x,CN_9,label="CN 9")
plt.plot(x,CN_12,label="CN 12")
plt.legend(loc=3)
plt.ylim([0,1.1])
plt.xlabel("Temperature[K]",fontsize=20)
plt.ylabel("Coverage",fontsize=20)
plt.show()
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testg.py | dcn01/AndroidDropFrameAnalysis | 2 | 14091 | # fpsAllFrameRead = open("profileAllFrame.txt", "r")
# profileDataReadList =[]
# t = []
# for line in fpsAllFrameRead.readlines():
# profileDataReadList.append(line)
#
# for line in profileDataReadList:
# splitByComma = line.split(",")
# l = len(splitByComma)
# print str(l)
a = 34.4/(1000/60)
print str(a)
# fin = ""
# c = 0
# e = len(willBeInsertIntoSqlList)
# for tmplist in willBeInsertIntoSqlList:
# splitByT = tmplist.split("\t")
# if c==0:
# fin = fin +"{"
#
# if c==e -1:
# fin = fin+str(c)+":{\"Draw\":"+splitByT[1]+",\"Prepare\":"+splitByT[2]+",\"Process\":"+splitByT[3]+",\"Execute\":"+splitByT[4].strip()+"}}"
# else:
# fin = fin+str(c)+":{\"Draw\":"+splitByT[1]+",\"Prepare\":"+splitByT[2]+",\"Process\":"+splitByT[3]+",\"Execute\":"+splitByT[4].strip()+"},"
#
# c = c+1
# fin = "var person_data = "+fin+";\nvar svg_width = 88350;"
# dataWrite = open("./output/js/data.js", "w")
# dataWrite.write(fin)
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] |
curso em video/python/mundo 1/ex033.py | KenzoDezotti/cursoemvideo | 0 | 30385 | <filename>curso em video/python/mundo 1/ex033.py
c = int(input('digite o primeiro numero: '))
b = int(input('digite o segundo numero: '))
a = int(input('digite o terceiro numero: '))
cores= {'vermelho': '\033[0;31m',
'azul' : '\033[1;34m',
'zero': '\033[m' }
# qual o maior
maior = a
if b > c and b > a:
maior = b
if c > b and c > a:
maior = c
print('O maior valor foi {}{}{}'.format(cores['azul'],maior,cores['zero']))
# qual o menor
menor = c
if a < b and a < c:
menor = a
if b < a and b < c:
menor = b
print('O menor valor foi {}{}{}'.format(cores['vermelho'],menor,cores['zero']))
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cartography/selection/train_dy_filtering.py | dyahadila/ood_cartography | 0 | 196635 | """
Filtering and dataset mapping methods based on training dynamics.
By default, this module reads training dynamics from a given trained model and
computes the metrics---confidence, variability, correctness,
as well as baseline metrics of forgetfulness and threshold closeness
for each instance in the training data.
If specified, data maps can be plotted with respect to confidence and variability.
Moreover, datasets can be filtered with respect any of the other metrics.
"""
import argparse
import json
import logging
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import seaborn as sns
import torch
import tqdm
import imageio
from collections import defaultdict
from typing import List
from cartography.data_utils import read_data, read_jsonl, copy_dev_test
from cartography.selection.selection_utils import read_dynamics
# TODO(SS): Named tuple for tasks and filtering methods.
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", level=logging.INFO
)
logger = logging.getLogger(__name__)
def compute_forgetfulness(correctness_trend: List[float]) -> int:
"""
Given a epoch-wise trend of train predictions, compute frequency with which
an example is forgotten, i.e. predicted incorrectly _after_ being predicted correctly.
Based on: https://arxiv.org/abs/1812.05159
"""
if not any(correctness_trend): # Example is never predicted correctly, or learnt!
return 1000
learnt = False # Predicted correctly in the current epoch.
times_forgotten = 0
for is_correct in correctness_trend:
if (not learnt and not is_correct) or (learnt and is_correct):
# nothing changed.
continue
elif learnt and not is_correct:
# Forgot after learning at some point!
learnt = False
times_forgotten += 1
elif not learnt and is_correct:
# Learnt!
learnt = True
return times_forgotten
def compute_correctness(trend: List[float]) -> float:
"""
Aggregate #times an example is predicted correctly during all training epochs.
"""
return sum(trend)
def compute_train_dy_metrics_per_epoch(training_dynamics, heuristics, original_id, mode="train"):
"""
Given the training dynamics (logits for each training instance across epochs), compute metrics
based on it, for data map coorodinates.
Computed metrics are: confidence, variability, correctness, forgetfulness, threshold_closeness---
the last two being baselines from prior work
(Example Forgetting: https://arxiv.org/abs/1812.05159 and
Active Bias: https://arxiv.org/abs/1704.07433 respectively).
Returns:
- DataFrame with these metrics.
- DataFrame with more typical training evaluation metrics, such as accuracy / loss.
"""
confidence_ = {}
variability_ = {}
threshold_closeness_ = {}
correctness_ = {}
forgetfulness_ = {}
lexical = {}
constituent = {}
subsequence= {}
original_ids = {}
ood={}
predicted_labels = {}
golds_labels = {}
# Functions to be applied to the data.
variability_func = lambda conf: np.std(conf)
# if include_ci: # Based on prior work on active bias (https://arxiv.org/abs/1704.07433)
# variability_func = lambda conf: np.sqrt(np.var(conf) + np.var(conf) * np.var(conf) / (len(conf)-1))
threshold_closeness_func = lambda conf: conf * (1 - conf)
loss = torch.nn.CrossEntropyLoss()
num_tot_epochs = len(list(training_dynamics.values())[0]["logits"])
# if burn_out < num_tot_epochs:
# logger.info(f"Computing training dynamics. Burning out at {burn_out} of {num_tot_epochs}. ")
# else:
logger.info(f"Computing training dynamics across {num_tot_epochs} epochs")
logger.info("Metrics computed: confidence, variability, correctness, forgetfulness, threshold_closeness")
logits = {i: [] for i in range(num_tot_epochs)}
targets = {i: [] for i in range(num_tot_epochs)}
training_accuracy = defaultdict(float)
for guid in tqdm.tqdm(training_dynamics):
correctness_trend = []
true_probs_trend = []
correctness_ep = []
confidence_ep = []
variability_ep = []
prediction_ep = []
record = training_dynamics[guid]
for i, epoch_logits in enumerate(record["logits"]):
if i >= len(logits.keys()):
break
probs = torch.nn.functional.softmax(torch.Tensor(epoch_logits), dim=-1)
true_class_prob = float(probs[record["gold"]])
true_probs_trend.append(true_class_prob)
prediction = np.argmax(epoch_logits)
is_correct = (prediction == record["gold"]).item()
correctness_trend.append(is_correct)
training_accuracy[i] += is_correct
logits[i].append(epoch_logits)
targets[i].append(record["gold"])
correctness_ep.append(compute_correctness(correctness_trend))
confidence_ep.append(np.mean(true_probs_trend))
variability_ep.append(variability_func(true_probs_trend))
prediction_ep.append(prediction.item())
correctness_[guid] = correctness_ep
confidence_[guid] = confidence_ep
variability_[guid] = variability_ep
# if burn_out < num_tot_epochs:
# correctness_trend = correctness_trend[:burn_out]
# true_probs_trend = true_probs_trend[:burn_out]
# correctness_[guid] = compute_correctness(correctness_trend)
# confidence_[guid] = np.mean(true_probs_trend)
# variability_[guid] = variability_func(true_probs_trend)
# forgetfulness_[guid] = compute_forgetfulness(correctness_trend)
# threshold_closeness_[guid] = threshold_closeness_func(confidence_[guid])
lexical[guid] = heuristics[guid]["lexical"]
constituent[guid] = heuristics[guid]["constituent"]
subsequence[guid] = heuristics[guid]["subsequence"]
ood[guid] = heuristics[guid]["ood"]
original_ids[guid] = original_id[guid]
predicted_labels[guid] = prediction_ep
# Should not affect ranking, so ignoring.
epsilon_var = np.mean(list(variability_.values()))
column_names = ['guid',
'index',
# 'threshold_closeness',
'confidence',
'variability',
'correctness',
# 'forgetfulness',
'pred_label',
'lexical', 'constituent', 'subsequence', 'original_id']
if mode != "train":
column_names.insert(-1, "ood")
df = pd.DataFrame([[guid,
i,
# threshold_closeness_[guid],
confidence_[guid],
variability_[guid],
correctness_[guid],
predicted_labels[guid],
# forgetfulness_[guid],
lexical[guid],
constituent[guid],
subsequence[guid],
ood[guid],
original_ids[guid]
] for i, guid in enumerate(correctness_)], columns=column_names)
df_train = pd.DataFrame([[i,
loss(torch.Tensor(logits[i]), torch.LongTensor(targets[i])).item() / len(
training_dynamics),
training_accuracy[i] / len(training_dynamics)
] for i in range(num_tot_epochs)],
columns=['epoch', 'loss', 'train_acc'])
else:
df = pd.DataFrame([[guid,
i,
# threshold_closeness_[guid],
confidence_[guid],
variability_[guid],
correctness_[guid],
predicted_labels[guid],
# forgetfulness_[guid],
lexical[guid],
constituent[guid],
subsequence[guid],
original_ids[guid]
] for i, guid in enumerate(correctness_)], columns=column_names)
df_train = pd.DataFrame([[i,loss(torch.Tensor(logits[i]), torch.LongTensor(targets[i])).item() / len(training_dynamics),training_accuracy[i] / len(training_dynamics)] for i in range(num_tot_epochs)], columns=['epoch', 'loss', 'train_acc'])
df.to_csv(f"ALL_SAMPLES_{mode}.csv")
return df, df_train
def consider_ascending_order(filtering_metric: str) -> bool:
"""
Determine if the metric values' sorting order to get the most `valuable` examples for training.
"""
if filtering_metric == "variability":
return False
elif filtering_metric == "confidence":
return True
elif filtering_metric == "threshold_closeness":
return False
elif filtering_metric == "forgetfulness":
return False
elif filtering_metric == "correctness":
return True
else:
raise NotImplementedError(f"Filtering based on {filtering_metric} not implemented!")
def write_filtered_data(args, train_dy_metrics):
"""
Filter data based on the given metric, and write it in TSV format to train GLUE-style classifier.
"""
# First save the args for filtering, to keep track of which model was used for filtering.
argparse_dict = vars(args)
with open(os.path.join(args.filtering_output_dir, f"filtering_configs.json"), "w") as outfile:
outfile.write(json.dumps(argparse_dict, indent=4, sort_keys=True) + "\n")
# Determine whether to sort data in ascending order or not, based on the metric.
is_ascending = consider_ascending_order(args.metric)
if args.worst:
is_ascending = not is_ascending
# Sort by selection.
sorted_scores = train_dy_metrics.sort_values(by=[args.metric],
ascending=is_ascending)
original_train_file = os.path.join(os.path.join(args.data_dir, args.task_name), f"train.tsv")
train_numeric, header = read_data(original_train_file, task_name=args.task_name, guid_as_int=True)
for fraction in [0.01, 0.05, 0.10, 0.1667, 0.25, 0.3319, 0.50, 0.75]:
outdir = os.path.join(args.filtering_output_dir,
f"cartography_{args.metric}_{fraction:.2f}/{args.task_name}")
if not os.path.exists(outdir):
os.makedirs(outdir)
# Dev and test need not be subsampled.
copy_dev_test(args.task_name,
from_dir=os.path.join(args.data_dir, args.task_name),
to_dir=outdir)
num_samples = int(fraction * len(train_numeric))
with open(os.path.join(outdir, f"train.tsv"), "w") as outfile:
outfile.write(header + "\n")
selected = sorted_scores.head(n=num_samples+1)
if args.both_ends:
hardest = sorted_scores.head(n=int(num_samples * 0.7))
easiest = sorted_scores.tail(n=num_samples - hardest.shape[0])
selected = pd.concat([hardest, easiest])
fm = args.metric
logger.info(f"Selecting both ends: {fm} = "
f"({hardest.head(1)[fm].values[0]:3f}: {hardest.tail(1)[fm].values[0]:3f}) "
f"& ({easiest.head(1)[fm].values[0]:3f}: {easiest.tail(1)[fm].values[0]:3f})")
selection_iterator = tqdm.tqdm(range(len(selected)))
for idx in selection_iterator:
selection_iterator.set_description(
f"{args.metric} = {selected.iloc[idx][args.metric]:.4f}")
selected_id = selected.iloc[idx]["guid"]
if args.task_name in ["SNLI", "MNLI"]:
selected_id = int(selected_id)
elif args.task_name == "WINOGRANDE":
selected_id = str(int(selected_id))
record = train_numeric[selected_id]
outfile.write(record + "\n")
logger.info(f"Wrote {num_samples} samples to {outdir}.")
def mix_heuristics_label_eval(df):
df_lex_supp = df.loc[(df["lexical"] == 1)& (df["constituent"] == 0) &(df["subsequence"] == 0)]
df_lex_supp['mix_heurstic_label'] = f"lexical support (ood: " \
f"{df_lex_supp.loc[df_lex_supp['ood']==1].shape[0]} id: {df_lex_supp.loc[df_lex_supp['ood']==0].shape[0]})"
df_lex_cont = df.loc[(df["lexical"] == -1) & (df["constituent"] == 0) & (df["subsequence"] == 0)]
df_lex_cont['mix_heurstic_label'] = f"lexical contradict (ood: " \
f"{df_lex_cont.loc[df_lex_cont['ood']==1].shape[0]} id: {df_lex_cont.loc[df_lex_cont['ood']==0].shape[0]})"
df_const_supp = df.loc[(df["lexical"] == 0) & (df["constituent"] == 1) & (df["subsequence"] == 0)]
df_const_supp['mix_heurstic_label'] = f"constituent support (ood: " \
f"{df_const_supp.loc[df_const_supp['ood']==1].shape[0]} id: {df_const_supp.loc[df_const_supp['ood']==0].shape[0]})"
df_const_cont = df.loc[(df["lexical"] == 0) & (df["constituent"] == -1) & (df["subsequence"] == 0)]
df_const_cont['mix_heurstic_label'] = f"constituent contradict (ood: " \
f"{df_const_cont.loc[df_const_cont['ood']==1].shape[0]} id: {df_const_cont.loc[df_const_cont['ood']==0].shape[0]})"
df_sub_supp = df.loc[(df["lexical"] == 0) & (df["constituent"] == 0) & (df["subsequence"] == 1)]
df_sub_supp['mix_heurstic_label'] = f"subsequence support (ood: " \
f"{df_sub_supp.loc[df_sub_supp['ood']==1].shape[0]} id: {df_sub_supp.loc[df_sub_supp['ood']==0].shape[0]})"
df_sub_cont = df.loc[(df["lexical"] == 0) & (df["constituent"] == 0) & (df["subsequence"] == -1)]
df_sub_cont['mix_heurstic_label'] = f"subsequence contradict (ood: " \
f"{df_sub_cont.loc[df_sub_cont['ood']==1].shape[0]} id: {df_sub_cont.loc[df_sub_cont['ood']==0].shape[0]})"
df_mix = pd.concat([df_lex_supp, df_lex_cont, df_const_supp, df_const_cont,
df_sub_supp, df_sub_cont])
return df_mix
def mix_heuristics_label_train(df):
df_lex_supp = df.loc[(df["lexical"] == 1)& (df["constituent"] == 0) &(df["subsequence"] == 0)]
df_lex_supp['mix_heurstic_label'] = f"lexical support ({df_lex_supp.shape[0]}"
df_lex_cont = df.loc[(df["lexical"] == -1) & (df["constituent"] == 0) & (df["subsequence"] == 0)]
df_lex_cont['mix_heurstic_label'] = f"lexical contradict ({df_lex_cont.shape[0]}"
df_const_supp = df.loc[(df["lexical"] == 0) & (df["constituent"] == 1) & (df["subsequence"] == 0)]
df_const_supp['mix_heurstic_label'] = f"constituent support ({df_const_supp.shape[0]}"
df_const_cont = df.loc[(df["lexical"] == 0) & (df["constituent"] == -1) & (df["subsequence"] == 0)]
df_const_cont['mix_heurstic_label'] = f"constituent contradict ({df_const_cont.shape[0]}"
df_sub_supp = df.loc[(df["lexical"] == 0) & (df["constituent"] == 0) & (df["subsequence"] == 1)]
df_sub_supp['mix_heurstic_label'] = f"subsequence support ({df_sub_supp.shape[0]}"
df_sub_cont = df.loc[(df["lexical"] == 0) & (df["constituent"] == 0) & (df["subsequence"] == -1)]
df_sub_cont['mix_heurstic_label'] = f"subsequence contradict ({df_sub_cont.shape[0]}"
df_mix = pd.concat([df_lex_supp, df_lex_cont, df_const_supp, df_const_cont,
df_sub_supp, df_sub_cont])
return df_mix
def get_ambiguous_heuristics_samples(df, model_dir, df_orig=pd.read_csv("/home/jusun/adila001/MNLI/train_heuristic.tsv", sep='\t|\n'),
heu='lexical'):
df = df.loc[(df[heu] != 0)]
df = df.loc[df["variability"] >= 0.3]
df_heuristics_ORIG = df_orig.loc[df['original_id'].tolist()]
df_heuristics_ORIG= df_heuristics_ORIG.drop(['index', 'promptID', 'pairID'], axis=1)
df_heuristics_ORIG['confidence'] = df['confidence'].tolist()
df_heuristics_ORIG['variability'] = df['variability'].tolist()
csv_dir = os.path.join(model_dir, 'unique_samples_csv')
if not os.path.exists(csv_dir):
os.makedirs(csv_dir)
df_heuristics_ORIG.to_csv(os.path.join(csv_dir, 'ambiguous_samples.csv'))
def get_sorted_samples(df, model_dir, df_orig=pd.read_csv('/home/jusun/adila001/MNLI/train_heuristic.tsv', sep='\t|\n'),
n_sample=30,
decoded_label=["contradiction", "entailment", "neutral"],
columns_order = ['index', 'genre', 'sentence1', 'sentence2', 'variability',
'confidence', 'var_ep', 'conf_ep', 'gold_label', 'pred_label'], mode="train"):
csv_dir = os.path.join(model_dir, 'ANALYSIS_CLEAN', 'SORTED')
if not os.path.exists(csv_dir):
os.makedirs(csv_dir)
# heuristics = top_heuristic_obj.keys()
ep_number = len(df['variability'].tolist()[0])
for ep in range(ep_number):
# for heu in heuristics:
# df_heuristic = df.loc[df[heu] != 0]
df_copy = df.copy()
df_copy['var_ep'] = np.asarray(df_copy['variability'].tolist())[:, ep]
df_copy['conf_ep'] = np.asarray(df_copy['confidence'].tolist())[:, ep]
df_copy['pred_label'] = np.asarray(df_copy['pred_label'].tolist())[:, ep]
df_copy['pred_label'] = [decoded_label[pred] for pred in df_copy['pred_label']]
# random_sample = df_heuristic.sample(n= top_heuristic_obj[heu])
# top_n_var = df_copy.nlargest(n_sample, 'var_ep')
# top_n_conf = df_copy.nsmallest(df_copy.shape[0], 'conf_ep')
top_n_conf = df_copy.sort_values(by=['conf_ep'])
# top_n_var_ORIG = df_orig.loc[top_n_var['original_id'].tolist()]
# # top_n_var_ORIG = top_n_var_ORIG.drop(cols_to_drop, axis=1)
# top_n_var_ORIG['variability'] = top_n_var['variability'].tolist()
# top_n_var_ORIG['confidence'] = top_n_var['confidence'].tolist()
# top_n_var_ORIG['var_ep'] = top_n_var['var_ep'].tolist()
# top_n_var_ORIG['conf_ep'] = top_n_var['conf_ep'].tolist()
# top_n_var_ORIG['pred_label'] = top_n_var['pred_label'].tolist()
top_n_conf_ORIG = df_orig.loc[top_n_conf['original_id'].tolist()]
# top_n_conf_ORIG = top_n_conf_ORIG.drop(cols_to_drop, axis=1)
top_n_conf_ORIG['variability'] = top_n_conf['variability'].tolist()
top_n_conf_ORIG['confidence'] = top_n_conf['confidence'].tolist()
top_n_conf_ORIG['var_ep'] = top_n_conf['var_ep'].tolist()
top_n_conf_ORIG['conf_ep'] = top_n_conf['conf_ep'].tolist()
top_n_conf_ORIG['pred_label'] = top_n_conf['pred_label'].tolist()
# top_n_var_ORIG = top_n_var_ORIG[columns_order]
top_n_conf_ORIG = top_n_conf_ORIG[columns_order]
prefix = mode.upper()
# top_n_var_ORIG.to_csv(os.path.join(csv_dir, "{}_SORTED_VAR_ep_{}.csv".format(prefix, ep)))
top_n_conf_ORIG.to_csv(os.path.join(csv_dir, "{}_CONF_ep_{}_SORTED.csv".format(prefix, ep)))
# return top_n_var_ORIG, top_n_conf_ORIG
def get_top_n_heuristics_samples(df, model_dir, df_orig=pd.read_csv('/home/jusun/adila001/MNLI/train_heuristic.tsv', sep='\t|\n'),
top_heuristic_obj = {'lexical': 20, 'constituent': 20, 'subsequence': 20},
decoded_label=["contradiction", "entailment", "neutral"],
columns_order = ['genre', 'sentence1', 'sentence2', 'variability',
'confidence', 'var_ep', 'conf_ep', 'gold_label', 'pred_label']):
csv_dir = os.path.join(model_dir, 'heuristics_only_csv_EVAL')
if not os.path.exists(csv_dir):
os.makedirs(csv_dir)
heuristics = top_heuristic_obj.keys()
ep_number = len(df['variability'].tolist()[0])
for ep in range(ep_number):
for heu in heuristics:
df_heuristic = df.loc[df[heu] != 0]
df_heuristic['var_ep'] = np.asarray(df_heuristic['variability'].tolist())[:,ep]
df_heuristic['conf_ep'] = np.asarray(df_heuristic['confidence'].tolist())[:, ep]
df_heuristic['pred_label'] = np.asarray(df_heuristic['pred_label'].tolist())[:, ep]
df_heuristic['pred_label'] = [decoded_label[pred] for pred in df_heuristic['pred_label']]
# random_sample = df_heuristic.sample(n= top_heuristic_obj[heu])
top_n_var = df_heuristic.nlargest(top_heuristic_obj[heu],'var_ep')
top_n_conf = df_heuristic.nlargest(top_heuristic_obj[heu],'conf_ep')
# random_sample_ORIG = df_orig.loc[random_sample['original_id'].tolist()]
# random_sample_ORIG = random_sample_ORIG.drop(['index', 'promptID', 'pairID'], axis=1)
# random_sample_ORIG['variability'] = random_sample['variability'].tolist()
# random_sample_ORIG['confidence'] = random_sample['confidence'].tolist()
# random_sample_ORIG['var_ep'] = random_sample['var_ep'].tolist()
# random_sample_ORIG['conf_ep'] = random_sample['conf_ep'].tolist()
top_n_var_ORIG = df_orig.loc[top_n_var['original_id'].tolist()]
# top_n_var_ORIG = top_n_var_ORIG.drop(cols_to_drop, axis=1)
top_n_var_ORIG['variability'] = top_n_var['variability'].tolist()
top_n_var_ORIG['confidence'] = top_n_var['confidence'].tolist()
top_n_var_ORIG['var_ep'] = top_n_var['var_ep'].tolist()
top_n_var_ORIG['conf_ep'] = top_n_var['conf_ep'].tolist()
top_n_var_ORIG['pred_label'] = top_n_var['pred_label'].tolist()
top_n_conf_ORIG = df_orig.loc[top_n_conf['original_id'].tolist()]
# top_n_conf_ORIG = top_n_conf_ORIG.drop(cols_to_drop, axis=1)
top_n_conf_ORIG['variability'] = top_n_conf['variability'].tolist()
top_n_conf_ORIG['confidence'] = top_n_conf['confidence'].tolist()
top_n_conf_ORIG['var_ep'] = top_n_conf['var_ep'].tolist()
top_n_conf_ORIG['conf_ep'] = top_n_conf['conf_ep'].tolist()
top_n_conf_ORIG['pred_label'] = top_n_conf['pred_label'].tolist()
top_n_var_ORIG = top_n_var_ORIG[columns_order]
top_n_conf_ORIG = top_n_conf_ORIG[columns_order]
# print(random_sample_ORIG)
# print(f"{heu}_EP_{ep}")
# print(top_n_var_ORIG)
# print(top_n_conf_ORIG)
# top_n_var_ORIG.to_csv(os.path.join(csv_dir, "{}_TOP_VAR_ep_{}.csv".format(heu, ep)))
top_n_conf_ORIG.to_csv(os.path.join(csv_dir, "{}_TOP_CONF_ep_{}_LARGEST.csv".format(heu, ep)))
# return top_n_var_ORIG, top_n_conf_ORIG
# random_sample_ORIG.to_csv(os.path.join(csv_dir,"{}_RANDOM.csv".format(heu)),index=False)
# top_n_var_ORIG.to_csv(os.path.join(csv_dir, "{}_TOP_VAR.csv".format(heu)), index=False)
# top_n_conf_ORIG.to_csv(os.path.join(csv_dir, "{}_TOP_CONF.csv".format(heu)), index=False)
def find_max_var(var_arr):
return np.amax(var_arr)
def plot_train_epochs(args, training_dynamics, heuristics, original_id, gif =True):
total_epochs = len(list(training_dynamics.values())[0]["logits"])
df, _ = compute_train_dy_metrics_per_epoch(training_dynamics, heuristics, original_id)
train_dy_filename = os.path.join(args.model_dir, f"td_metrics.jsonl")
df.to_json(train_dy_filename,
orient='records',
lines=True)
logger.info(f"Metrics based on Training Dynamics written to {train_dy_filename}")
df_heuristics = df.loc[(df["lexical"] != 0) | (df["constituent"] != 0) | (df["subsequence"] != 0)]
df_others = df.loc[(df["lexical"] == 0) & (df["constituent"] == 0) & (df["subsequence"] == 0)]
max_instances_heuristic = {
'lexical': df_heuristics.loc[df_heuristics['lexical'] != 0].shape[0],
'subsequence': df_heuristics.loc[df_heuristics['subsequence'] != 0].shape[0],
'constituent': df_heuristics.loc[df_heuristics['constituent'] != 0].shape[0]
}
heuristics = ['lexical', 'constituent', 'subsequence']
max_var = find_max_var(df_heuristics['variability'].tolist())
for heuristic in heuristics:
figs = []
max_instances_to_plot = max_instances_heuristic[heuristic]
df_current_heuristic = df_heuristics.loc[df_heuristics[heuristic] != 0]
# df_others_sampled = df_others.sample(n=max_instances_to_plot-df_current_heuristic.shape[0])
df_others_sampled = df_others.sample(n=df_current_heuristic.shape[0]*2)
df_current_heuristic = df_current_heuristic.append(df_others_sampled, ignore_index=True)
# ### DEV ###
# for ep in range(total_epochs):
# ### DEV ###
for ep in range(2,total_epochs):
df_current_heuristic_epoch = df_current_heuristic.copy()
# print(df_current_heuristic_epoch['confidence'])
confidence_epoch = np.asarray(df_current_heuristic_epoch['confidence'].tolist())[:,ep].flatten()
var_epoch = np.asarray(df_current_heuristic_epoch['variability'].tolist())[:,ep].flatten()
correctness_epoch = np.asarray(df_current_heuristic_epoch['correctness'].tolist())[:,ep].flatten()
df_current_heuristic_epoch.drop(['confidence', 'variability', 'correctness'], axis=1)
df_current_heuristic_epoch['confidence'] = confidence_epoch
df_current_heuristic_epoch['variability'] = var_epoch
df_current_heuristic_epoch['correctness'] = correctness_epoch
fig = plot_heuristics_mix(df_current_heuristic_epoch, os.path.join(args.plots_dir, 'train_plots'), hue_metric=heuristic,
title='{}_epoch_{}'.format(heuristic, ep), max_var=max_var)
figs.append(convert_fig_to_arr(fig))
if gif:
kwargs_write = {'fps': 1.0, 'quantizer': 'nq'}
gif_path = os.path.join(args.plots_dir, "train_plots", f'TRAIN_{ep}_epochs.gif')
# gif_path = f'{args.plots_dir}/{heuristic}_{ep}_epochs.gif'
imageio.mimsave(gif_path, figs, fps=1)
logger.info(f"Aminated gif saved to {gif_path}")
df_heuristics_mix = mix_heuristics_label_train(df_heuristics)
figs = []
# ### DEV ###
# for ep in range(total_epochs):
# ### DEV ###
df_others_sampled = df_others.sample(n=df_heuristics_mix.shape[0] * 2)
for ep in range(2,total_epochs):
df_heuristic_mix_epoch = df_heuristics_mix.copy()
confidence_epoch = np.asarray(df_heuristic_mix_epoch['confidence'].tolist())[:, ep].flatten()
var_epoch = np.asarray(df_heuristic_mix_epoch['variability'].tolist())[:, ep].flatten()
correctness_epoch = np.asarray(df_heuristic_mix_epoch['correctness'].tolist())[:, ep].flatten()
df_heuristic_mix_epoch.drop(['confidence', 'variability', 'correctness'], axis=1)
df_heuristic_mix_epoch['confidence'] = confidence_epoch
df_heuristic_mix_epoch['variability'] = var_epoch
df_heuristic_mix_epoch['correctness'] = correctness_epoch
# df_heuristic_mix_epoch = pd.concat([df_others_sampled, df_heuristic_mix_epoch])
fig = plot_heuristics_only(df_heuristic_mix_epoch,os.path.join(args.plots_dir, "train_plots"),title=f'HEURISTICS_ONLY_{ep}', max_var=max_var)
figs.append(convert_fig_to_arr(fig))
if gif:
kwargs_write = {'fps': 1.0, 'quantizer': 'nq'}
gif_path = os.path.join(args.plots_dir, "train_plots", f'TRAIN_{ep}_epochs_ALL.gif')
# gif_path = f'{args.plots_dir}/HEURISTICS_ONLY_{ep}_epochs.gif'
imageio.mimsave(gif_path, figs, fps=1)
logger.info(f"Aminated gif saved to {gif_path}")
def plot_eval_epochs(args, id_obj, ood_obj, gif =True):
id_dynamics, id_heuristics, id_original_idx, id_pred = id_obj[0], id_obj[1], id_obj[2], id_obj[3]
ood_dynamics, ood_heuristics, ood_original_idx, ood_pred = ood_obj[0], ood_obj[1], ood_obj[2], ood_obj[3]
total_epochs = len(list(id_dynamics.values())[0]["logits"])
df_id, _ = compute_train_dy_metrics_per_epoch(id_dynamics, id_heuristics, id_original_idx, mode="eval")
df_ood, _ = compute_train_dy_metrics_per_epoch(ood_dynamics, ood_heuristics, ood_original_idx, mode="eval")
df_ood['ood'] = 1
df_id['ood'] = 0
id_dy_filename = os.path.join(args.model_dir, f"iid_metrics.jsonl")
df_id.to_json(id_dy_filename,
orient='records',
lines=True)
ood_dy_filename = os.path.join(args.model_dir, f"ood_metrics.jsonl")
df_ood.to_json(ood_dy_filename,
orient='records',
lines=True)
logger.info(f"Metrics based on Eval Dynamics written to {id_dy_filename} and {ood_dy_filename}")
df = pd.concat([df_id, df_ood])
max_var = find_max_var(df['variability'].tolist())
df_heuristics = df.loc[(df["lexical"] != 0) | (df["constituent"] != 0) | (df["subsequence"] != 0)]
df_ood = df.loc[(df["ood"] != 0)]
df_concern = pd.concat([df_heuristics, df_ood])
df_concern = mix_heuristics_label_eval(df_concern)
df_others = df.loc[(df["lexical"] == 0) & (df["constituent"] == 0) & (df["subsequence"] == 0) & (df["ood"] == 0)]
print(df_others.shape)
print(df_ood.shape)
df_others_sample = df_others.sample(n= int(np.ceil(df_ood.shape[0])) if df_ood.shape[0] < df_others.shape[0] else df_others.shape[0] )
df = pd.concat([df_concern, df_others_sample])
df = df.fillna("no heuristic")
print(df_heuristics.shape[0], df_others_sample.shape[0], df_ood.shape[0])
figs = []
palette=iter(sns.husl_palette(len(np.unique(df["mix_heurstic_label"].tolist()))+1))
# ### DEV ###
# for ep in range(total_epochs):
# ### DEV ###
for ep in range(2, total_epochs):
df_heuristic_mix_epoch = df.copy()
confidence_epoch = np.asarray(df_heuristic_mix_epoch['confidence'].tolist())[:, ep].flatten()
var_epoch = np.asarray(df_heuristic_mix_epoch['variability'].tolist())[:, ep].flatten()
correctness_epoch = np.asarray(df_heuristic_mix_epoch['correctness'].tolist())[:, ep].flatten()
df_heuristic_mix_epoch.drop(['confidence', 'variability', 'correctness'], axis=1)
df_heuristic_mix_epoch['confidence'] = confidence_epoch
df_heuristic_mix_epoch['variability'] = var_epoch
df_heuristic_mix_epoch['correctness'] = correctness_epoch
fig = plot_heuristics_only(df_heuristic_mix_epoch, os.path.join(args.plots_dir, "eval_plots"), title=f'EVAL_{ep}',
max_var=max_var, style="ood")
figs.append(convert_fig_to_arr(fig))
if gif:
kwargs_write = {'fps': 1.0, 'quantizer': 'nq'}
gif_path = os.path.join(args.plots_dir, "eval_plots", f'EVAL_{ep}_epochs.gif')
imageio.mimsave(gif_path, figs, fps=1)
logger.info(f"Aminated gif saved to {gif_path}")
def convert_fig_to_arr(fig):
fig.canvas.draw() # draw the canvas, cache the renderer
image = np.frombuffer(fig.canvas.tostring_rgb(), dtype='uint8')
image = image.reshape(fig.canvas.get_width_height()[::-1] + (3,))
return image
def compute_train_dy_metrics(training_dynamics, heuristics, original_id, burn_out):
confidence_ = {}
variability_ = {}
threshold_closeness_ = {}
correctness_ = {}
forgetfulness_ = {}
lexical = {}
constituent = {}
subsequence = {}
original_ids = {}
# Functions to be applied to the data.
variability_func = lambda conf: np.std(conf)
threshold_closeness_func = lambda conf: conf * (1 - conf)
loss = torch.nn.CrossEntropyLoss()
num_tot_epochs = len(list(training_dynamics.values())[0]["logits"])
logger.info(f"Computing training dynamics across {num_tot_epochs} epochs")
logger.info("Metrics computed: confidence, variability, correctness, forgetfulness, threshold_closeness")
logits = {i: [] for i in range(num_tot_epochs)}
targets = {i: [] for i in range(num_tot_epochs)}
training_accuracy = defaultdict(float)
for guid in tqdm.tqdm(training_dynamics):
correctness_trend = []
true_probs_trend = []
record = training_dynamics[guid]
for i, epoch_logits in enumerate(record["logits"]):
if i >= len(logits.keys()):
break
probs = torch.nn.functional.softmax(torch.Tensor(epoch_logits), dim=-1)
true_class_prob = float(probs[record["gold"]])
true_probs_trend.append(true_class_prob)
prediction = np.argmax(epoch_logits)
is_correct = (prediction == record["gold"]).item()
correctness_trend.append(is_correct)
training_accuracy[i] += is_correct
logits[i].append(epoch_logits)
targets[i].append(record["gold"])
if burn_out < num_tot_epochs:
correctness_trend = correctness_trend[:burn_out]
true_probs_trend = true_probs_trend[:burn_out]
correctness_[guid] = compute_correctness(correctness_trend)
confidence_[guid] = np.mean(true_probs_trend)
variability_[guid] = variability_func(true_probs_trend)
forgetfulness_[guid] = compute_forgetfulness(correctness_trend)
threshold_closeness_[guid] = threshold_closeness_func(confidence_[guid])
lexical[guid] = heuristics[guid]["lexical"]
constituent[guid] = heuristics[guid]["constituent"]
subsequence[guid] = heuristics[guid]["subsequence"]
original_ids[guid] = original_id[guid]
# Should not affect ranking, so ignoring.
epsilon_var = np.mean(list(variability_.values()))
column_names = ['guid',
'index',
'threshold_closeness',
'confidence',
'variability',
'correctness',
'forgetfulness', 'lexical', 'constituent', 'subsequence', 'original_id']
df = pd.DataFrame([[guid,
i,
threshold_closeness_[guid],
confidence_[guid],
variability_[guid],
correctness_[guid],
forgetfulness_[guid],
lexical[guid],
constituent[guid],
subsequence[guid],
original_ids[guid]
] for i, guid in enumerate(correctness_)], columns=column_names)
df_train = pd.DataFrame([[i,
loss(torch.Tensor(logits[i]), torch.LongTensor(targets[i])).item() / len(
training_dynamics),
training_accuracy[i] / len(training_dynamics)
] for i in range(num_tot_epochs)],
columns=['epoch', 'loss', 'train_acc'])
return df, df_train
def plot_heuristics_only(
df: pd.DataFrame,
plot_dir: os.path,
title: str = '', save=True, max_var=0.5, style=None, palette = None):
if not os.path.exists(plot_dir):
os.makedirs(plot_dir)
main_metric = 'variability'
other_metric = 'confidence'
hue = "mix_heurstic_label"
num_hues = len(df[hue].unique().tolist())
fig, ax0 = plt.subplots(1, 1, figsize=(12, 10))
# Choose a palette.
pal = sns.diverging_palette(260, 15, n=num_hues, sep=10, center="dark")
plot = sns.scatterplot(x=main_metric,
y=other_metric,
ax=ax0,
data=df,
hue=hue,
# palette=pal,
# style=hue,
s=30,
style=style,
marker = 'o' if style is not None else None,
# palette="tab10"
# palette=['green', 'orange', 'brown', 'dodgerblue', 'red']
palette = palette if palette else "tab10"
)
# Annotate Regions.
bb = lambda c: dict(boxstyle="round,pad=0.3", ec=c, lw=2, fc="white")
func_annotate = lambda text, xyc, bbc: ax0.annotate(text,
xy=xyc,
xycoords="axes fraction",
fontsize=15,
color='black',
va="center",
ha="center",
rotation=350,
bbox=bb(bbc))
an1 = func_annotate("ambiguous", xyc=(0.9, 0.5), bbc='black')
an2 = func_annotate("easy-to-learn", xyc=(0.27, 0.85), bbc='r')
an3 = func_annotate("hard-to-learn", xyc=(0.35, 0.25), bbc='b')
plot.legend(ncol=1, bbox_to_anchor=[0.175, 0.5], loc='right', fontsize ='small')
plot.set_xlabel('variability')
plot.set_ylabel('confidence')
plot.set_title(title)
ax0.set_xlim(0, max_var)
ax0.set_ylim(0, 1)
fig.tight_layout()
filename = f'{plot_dir}/{title}.png'
if save:
fig.savefig(filename, dpi=300)
logger.info(f"Plot saved to {filename}")
return fig
def plot_heuristics_mix(
df: pd.DataFrame,
plot_dir: os.path,
hue_metric: str = 'lexical',
title: str = '',
save=True,
max_var = 0.5):
if not os.path.exists(plot_dir):
os.makedirs(plot_dir)
# Normalize correctness to a value between 0 and 1.
dataframe = df.assign(corr_frac=lambda d: d.correctness / d.correctness.max())
dataframe['correct.'] = [f"{x:.1f}" for x in dataframe['corr_frac']]
main_metric = 'variability'
other_metric = 'confidence'
hue = hue_metric
num_hues = len(dataframe[hue].unique().tolist())
style = hue_metric if num_hues < 8 else None
fig, ax0 = plt.subplots(1, 1, figsize=(8, 6))
# Make the scatterplot.
# Choose a palette.
pal = sns.diverging_palette(260, 15, n=num_hues, sep=10, center="dark")
plot = sns.scatterplot(x=main_metric,
y=other_metric,
ax=ax0,
data=df,
hue=hue,
palette=pal,
style=style,
s=30)
# Annotate Regions.
bb = lambda c: dict(boxstyle="round,pad=0.3", ec=c, lw=2, fc="white")
func_annotate = lambda text, xyc, bbc: ax0.annotate(text,
xy=xyc,
xycoords="axes fraction",
fontsize=15,
color='black',
va="center",
ha="center",
rotation=350,
bbox=bb(bbc))
an1 = func_annotate("ambiguous", xyc=(0.9, 0.5), bbc='black')
an2 = func_annotate("easy-to-learn", xyc=(0.27, 0.85), bbc='r')
an3 = func_annotate("hard-to-learn", xyc=(0.35, 0.25), bbc='b')
plot.legend(ncol=1, bbox_to_anchor=[0.175, 0.5], loc='right')
plot.set_xlabel('variability')
plot.set_ylabel('confidence')
plot.set_title(title)
ax0.set_xlim(0, max_var)
ax0.set_ylim(0, 1)
fig.tight_layout()
filename = f'{plot_dir}/{title}.png'
# print('PLOT HIST', filename)
if save:
fig.savefig(filename, dpi=300)
logger.info(f"Plot saved to {filename}")
return fig
def plot_data_map(dataframe: pd.DataFrame,
plot_dir: os.path,
hue_metric: str = 'correct.',
title: str = '',
model: str = 'RoBERTa',
show_hist: bool = False,
max_instances_to_plot = 55000):
# Set style.
sns.set(style='whitegrid', font_scale=1.6, font='Georgia', context='paper')
logger.info(f"Plotting figure for {title} using the {model} model ...")
# Subsample data to plot, so the plot is not too busy.
dataframe = dataframe.sample(n=max_instances_to_plot if dataframe.shape[0] > max_instances_to_plot else len(dataframe))
# Normalize correctness to a value between 0 and 1.
dataframe = dataframe.assign(corr_frac = lambda d: d.correctness / d.correctness.max())
dataframe['correct.'] = [f"{x:.1f}" for x in dataframe['corr_frac']]
main_metric = 'variability'
other_metric = 'confidence'
hue = hue_metric
num_hues = len(dataframe[hue].unique().tolist())
style = hue_metric if num_hues < 8 else None
if not show_hist:
fig, ax0 = plt.subplots(1, 1, figsize=(8, 6))
else:
fig = plt.figure(figsize=(14, 10), )
gs = fig.add_gridspec(3, 2, width_ratios=[5, 1])
ax0 = fig.add_subplot(gs[:, 0])
# Make the scatterplot.
# Choose a palette.
pal = sns.diverging_palette(260, 15, n=num_hues, sep=10, center="dark")
plot = sns.scatterplot(x=main_metric,
y=other_metric,
ax=ax0,
data=dataframe,
hue=hue,
palette=pal,
style=style,
s=30)
# Annotate Regions.
bb = lambda c: dict(boxstyle="round,pad=0.3", ec=c, lw=2, fc="white")
func_annotate = lambda text, xyc, bbc : ax0.annotate(text,
xy=xyc,
xycoords="axes fraction",
fontsize=15,
color='black',
va="center",
ha="center",
rotation=350,
bbox=bb(bbc))
an1 = func_annotate("ambiguous", xyc=(0.9, 0.5), bbc='black')
an2 = func_annotate("easy-to-learn", xyc=(0.27, 0.85), bbc='r')
an3 = func_annotate("hard-to-learn", xyc=(0.35, 0.25), bbc='b')
if not show_hist:
plot.legend(ncol=1, bbox_to_anchor=[0.175, 0.5], loc='right')
else:
plot.legend(fancybox=True, shadow=True, ncol=1)
plot.set_xlabel('variability')
plot.set_ylabel('confidence')
if show_hist:
plot.set_title(f"{title}-{model} Data Map", fontsize=17)
# Make the histograms.
ax1 = fig.add_subplot(gs[0, 1])
ax2 = fig.add_subplot(gs[1, 1])
ax3 = fig.add_subplot(gs[2, 1])
plott0 = dataframe.hist(column=['confidence'], ax=ax1, color='#622a87')
plott0[0].set_title('')
plott0[0].set_xlabel('confidence')
plott0[0].set_ylabel('density')
plott1 = dataframe.hist(column=['variability'], ax=ax2, color='teal')
plott1[0].set_title('')
plott1[0].set_xlabel('variability')
plott1[0].set_ylabel('density')
plot2 = sns.countplot(x="correct.", data=dataframe, ax=ax3, color='#86bf91')
ax3.xaxis.grid(True) # Show the vertical gridlines
plot2.set_title('')
plot2.set_xlabel('correctness')
plot2.set_ylabel('density')
fig.tight_layout()
filename = f'{plot_dir}/{title}_{model}.pdf' if show_hist else f'figures/compact_{title}_{model}.png'
print('PLOT ORIGINAL', filename)
fig.savefig(filename, dpi=300)
logger.info(f"Plot saved to {filename}")
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--filter",
action="store_true",
help="Whether to filter data subsets based on specified `metric`.")
parser.add_argument("--plot_train",
action="store_true",
help="Whether to plot train data maps and save as `png`.")
parser.add_argument("--plot_eval",
action="store_true",
help="Whether to plot eval data maps and save as `png`.")
parser.add_argument("--model_dir",
"-o",
required=True,
type=os.path.abspath,
help="Directory where model training dynamics stats reside.")
parser.add_argument("--data_dir",
"-d",
default="/Users/swabhas/data/glue/WINOGRANDE/xl/",
type=os.path.abspath,
help="Directory where data for task resides.")
parser.add_argument("--plots_dir",
default="./cartography/",
type=os.path.abspath,
help="Directory where plots are to be saved.")
parser.add_argument("--task_name",
"-t",
default="WINOGRANDE",
choices=("SNLI", "MNLI", "QNLI", "WINOGRANDE", "RTE", "WNLI"),
help="Which task are we plotting or filtering for.")
parser.add_argument('--metric',
choices=('threshold_closeness',
'confidence',
'variability',
'correctness',
'forgetfulness'),
help="Metric to filter data by.",)
parser.add_argument("--include_ci",
action="store_true",
help="Compute the confidence interval for variability.")
parser.add_argument("--filtering_output_dir",
"-f",
default="./filtered/",
type=os.path.abspath,
help="Output directory where filtered datasets are to be written.")
parser.add_argument("--worst",
action="store_true",
help="Select from the opposite end of the spectrum acc. to metric,"
"for baselines")
parser.add_argument("--both_ends",
action="store_true",
help="Select from both ends of the spectrum acc. to metric,")
parser.add_argument("--burn_out",
type=int,
default=100,
help="# Epochs for which to compute train dynamics.")
parser.add_argument("--model",
default="RoBERTa",
help="Model for which data map is being plotted")
parser.add_argument("--plot_gif",
action="store_true",
help="Whether to plot gif or not")
args = parser.parse_args()
# if args.plot_gif:
# assert len(os.listdir(args.plots_dir)) > 0
# plot_gif(args.plots_dir)
# exit()
# total_epochs = len(list(training_dynamics.values())[0]["logits"])
# if args.burn_out > total_epochs:
# args.burn_out = total_epochs
# logger.info(f"Total epochs found: {args.burn_out}")
# train_dy_metrics, _ = compute_train_dy_metrics(training_dynamics, heuristics, original_id, args.burn_out)
#
# burn_out_str = f"_{args.burn_out}" if args.burn_out > total_epochs else ""
# train_dy_filename = os.path.join(args.model_dir, f"td_metrics{burn_out_str}.jsonl")
# train_dy_metrics.to_json(train_dy_filename,
# orient='records',
# lines=True)
# logger.info(f"Metrics based on Training Dynamics written to {train_dy_filename}")
# if args.filter:
# assert args.filtering_output_dir
# if not os.path.exists(args.filtering_output_dir):
# os.makedirs(args.filtering_output_dir)
# assert args.metric
# write_filtered_data(args, train_dy_metrics)
assert args.plots_dir
args.plots_dir = os.path.join(args.model_dir, "plots")
if not os.path.exists(args.plots_dir):
os.makedirs(args.plots_dir)
if args.plot_train:
# plot_data_map(train_dy_metrics, args.plots_dir, title=args.task_name, show_hist=True, model=args.model)
# plot_heuristics_mix(args, train_dy_metrics, args.plots_dir, title=args.task_name)
# plot_heuristics_only(args, train_dy_metrics, args.plots_dir, title=args.task_name)
# get_ambiguous_heuristics_samples(train_dy_metrics, args.model_dir)
# get_top_n_heuristics_samples(train_dy_metrics, args.model_dir)
training_dynamics, heuristics, original_id, pred_labels = read_dynamics(args.model_dir,
strip_last=True if args.task_name in [
"QNLI"] else False,
burn_out=args.burn_out if args.burn_out < 100 else None)
df_train, _ = compute_train_dy_metrics_per_epoch(training_dynamics, heuristics, original_id, mode="eval")
# plot_train_epochs(args, training_dynamics, heuristics, original_id, gif=True)
get_sorted_samples(df_train, args.model_dir,
pd.read_csv('/home/jusun/adila001/RTE/train_heuristic.tsv',
sep='\t|\n'),
decoded_label=["entailment", "not_entailment"],
columns_order=['index', 'sentence1', 'sentence2', 'variability',
'confidence', 'var_ep', 'conf_ep', 'lexical',
'subsequence',
'gold_label', 'pred_label'])
# get_top_n_heuristics_samples(df_train, args.model_dir,
# pd.read_csv('/home/jusun/adila001/RTE/train_heuristic.tsv',
# sep='\t|\n'),
# columns_order=['sentence1', 'sentence2', 'variability',
# 'confidence', 'var_ep', 'conf_ep', 'lexical',
# 'subsequence',
# 'gold_label', 'pred_label'],
# decoded_label=["entailment", "not_entailment"],
# top_heuristic_obj={'lexical': 10})
if args.plot_eval:
# get_ambiguous_heuristics_samples(train_dy_metrics, args.model_dir)
eval_ID_dynamics, heuristics_ID, original_id_ID, pred_labels_ID = read_dynamics(args.model_dir,
strip_last=True if args.task_name in [
"QNLI"] else False,
burn_out=args.burn_out if args.burn_out < 100 else None, mode="eval_ID")
eval_OOD_dynamics, heuristics_OOD, original_id_OOD, pred_labels_OOD = read_dynamics(args.model_dir,
strip_last=True if args.task_name in [
"QNLI"] else False,
burn_out=args.burn_out if args.burn_out < 100 else None, mode="eval_OOD")
df_id, _ = compute_train_dy_metrics_per_epoch(eval_ID_dynamics, heuristics_ID, original_id_ID, mode="in_dist")
df_ood, _ = compute_train_dy_metrics_per_epoch(eval_OOD_dynamics, heuristics_OOD, original_id_OOD, mode="ood")
# get_top_n_heuristics_samples(df_id, args.model_dir,
# pd.read_csv('/home/jusun/adila001/MNLI/dev_matched_heuristic.tsv', sep='\t|\n'),
# # decoded_label=["entailment", "not_entailment"],
# columns_order=['sentence1', 'sentence2', 'variability',
# 'confidence', 'var_ep', 'conf_ep', 'lexical', 'constituent',
# 'subsequence',
# 'gold_label', 'pred_label'],
# top_heuristic_obj={'lexical': 30})
# # df_ood['ood'] = 1
# get_top_n_heuristics_samples(df_ood, args.model_dir,
# pd.read_csv('/home/jusun/adila001/WNLI/train_heuristic.tsv', sep='\t|\n'),
# decoded_label=["not_entailment", "entailment"],
# columns_order=['sentence1', 'sentence2', 'variability',
# 'confidence', 'var_ep', 'conf_ep', 'lexical', 'subsequence',
# 'gold_label', 'pred_label'],
# top_heuristic_obj={'lexical':30})
get_sorted_samples(df_id, args.model_dir,
pd.read_csv('/home/jusun/adila001/RTE/dev_heuristic.tsv',
sep='\t|\n'),
decoded_label=["entailment", "not_entailment"],
columns_order=['index', 'sentence1', 'sentence2', 'variability',
'confidence', 'var_ep', 'conf_ep', 'lexical',
'subsequence',
'gold_label', 'pred_label'], mode='in_dist')
df_ood['ood'] = 1
get_sorted_samples(df_ood, args.model_dir,
pd.read_csv('/home/jusun/adila001/WNLI/train_heuristic.tsv',
sep='\t|\n'),
decoded_label=["not_entailment", "entailment"],
columns_order=['index', 'sentence1', 'sentence2', 'variability',
'confidence', 'var_ep', 'conf_ep', 'lexical',
'subsequence',
'gold_label', 'pred_label'], mode='ood')
# print(id_conf)
# print(ood_conf)
plot_eval_epochs(args, [eval_ID_dynamics, heuristics_ID, original_id_ID, pred_labels_ID],
[eval_OOD_dynamics, heuristics_OOD, original_id_OOD, pred_labels_OOD], gif=True)
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29918,
21574,
29892,
4839,
353,
1303,
29918,
1272,
29898,
13492,
29918,
14968,
29918,
1445,
29892,
3414,
29918,
978,
29922,
5085,
29889,
7662,
29918,
978,
29892,
16605,
29918,
294,
29918,
524,
29922,
5574,
29897,
13,
13,
29871,
363,
15958,
297,
518,
29900,
29889,
29900,
29896,
29892,
29871,
29900,
29889,
29900,
29945,
29892,
29871,
29900,
29889,
29896,
29900,
29892,
29871,
29900,
29889,
29896,
29953,
29953,
29955,
29892,
29871,
29900,
29889,
29906,
29945,
29892,
29871,
29900,
29889,
29941,
29941,
29896,
29929,
29892,
29871,
29900,
29889,
29945,
29900,
29892,
29871,
29900,
29889,
29955,
29945,
5387,
13,
1678,
714,
3972,
353,
2897,
29889,
2084,
29889,
7122,
29898,
5085,
29889,
4572,
292,
29918,
4905,
29918,
3972,
29892,
13,
462,
3986,
285,
29908,
13823,
5275,
648,
5085,
29889,
16414,
3227,
29888,
13857,
29901,
29889,
29906,
29888,
6822,
29912,
5085,
29889,
7662,
29918,
978,
27195,
13,
1678,
565,
451,
2897,
29889,
2084,
29889,
9933,
29898,
449,
3972,
1125,
13,
418,
2897,
29889,
29885,
12535,
12935,
29898,
449,
3972,
29897,
13,
13,
1678,
396,
9481,
322,
1243,
817,
451,
367,
1014,
11249,
29881,
29889,
13,
1678,
3509,
29918,
3359,
29918,
1688,
29898,
5085,
29889,
7662,
29918,
978,
29892,
13,
462,
29871,
515,
29918,
3972,
29922,
359,
29889,
2084,
29889,
7122,
29898,
5085,
29889,
1272,
29918,
3972,
29892,
6389,
29889,
7662,
29918,
978,
511,
13,
462,
29871,
304,
29918,
3972,
29922,
449,
3972,
29897,
13,
13,
1678,
954,
29918,
27736,
353,
938,
29898,
29888,
13857,
334,
7431,
29898,
14968,
29918,
21574,
876,
13,
1678,
411,
1722,
29898,
359,
29889,
2084,
29889,
7122,
29898,
449,
3972,
29892,
285,
29908,
14968,
29889,
1372,
29894,
4968,
376,
29893,
1159,
408,
714,
1445,
29901,
13,
418,
714,
1445,
29889,
3539,
29898,
6672,
718,
6634,
29876,
1159,
13,
418,
4629,
353,
12705,
29918,
1557,
2361,
29889,
2813,
29898,
29876,
29922,
1949,
29918,
27736,
29974,
29896,
29897,
13,
418,
565,
6389,
29889,
20313,
29918,
1975,
29901,
13,
4706,
2898,
342,
353,
12705,
29918,
1557,
2361,
29889,
2813,
29898,
29876,
29922,
524,
29898,
1949,
29918,
27736,
334,
29871,
29900,
29889,
29955,
876,
13,
4706,
19075,
353,
12705,
29918,
1557,
2361,
29889,
18237,
29898,
29876,
29922,
1949,
29918,
27736,
448,
2898,
342,
29889,
12181,
29961,
29900,
2314,
13,
4706,
4629,
353,
10518,
29889,
17685,
4197,
6800,
342,
29892,
19075,
2314,
13,
4706,
285,
29885,
353,
6389,
29889,
16414,
13,
4706,
17927,
29889,
3888,
29898,
29888,
29908,
3549,
292,
1716,
10614,
29901,
426,
24826,
29913,
353,
376,
13,
462,
1678,
285,
29908,
3319,
6800,
342,
29889,
2813,
29898,
29896,
9601,
24826,
1822,
5975,
29961,
29900,
5387,
29941,
29888,
6177,
426,
6800,
342,
29889,
18237,
29898,
29896,
9601,
24826,
1822,
5975,
29961,
29900,
5387,
29941,
29888,
1800,
376,
13,
462,
1678,
285,
29908,
29987,
21313,
29872,
6840,
342,
29889,
2813,
29898,
29896,
9601,
24826,
1822,
5975,
29961,
29900,
5387,
29941,
29888,
6177,
426,
29872,
6840,
342,
29889,
18237,
29898,
29896,
9601,
24826,
1822,
5975,
29961,
29900,
5387,
29941,
29888,
1800,
1159,
13,
13,
418,
9262,
29918,
17609,
353,
260,
29939,
18933,
29889,
29873,
29939,
18933,
29898,
3881,
29898,
2435,
29898,
8391,
4961,
13,
418,
363,
22645,
297,
9262,
29918,
17609,
29901,
13,
4706,
9262,
29918,
17609,
29889,
842,
29918,
8216,
29898,
13,
3986,
285,
29908,
29912,
5085,
29889,
16414,
29913,
353,
426,
8391,
29889,
309,
542,
29961,
13140,
3816,
5085,
29889,
16414,
5387,
29889,
29946,
29888,
27195,
13,
13,
4706,
4629,
29918,
333,
353,
4629,
29889,
309,
542,
29961,
13140,
29962,
3366,
2543,
333,
3108,
13,
4706,
565,
6389,
29889,
7662,
29918,
978,
297,
6796,
19296,
5265,
613,
376,
29924,
29940,
5265,
3108,
29901,
13,
3986,
4629,
29918,
333,
353,
938,
29898,
8391,
29918,
333,
29897,
13,
4706,
25342,
6389,
29889,
7662,
29918,
978,
1275,
376,
25152,
29949,
14345,
2190,
2287,
1115,
13,
3986,
4629,
29918,
333,
353,
851,
29898,
524,
29898,
8391,
29918,
333,
876,
13,
4706,
2407,
353,
7945,
29918,
21574,
29961,
8391,
29918,
333,
29962,
13,
4706,
714,
1445,
29889,
3539,
29898,
11651,
718,
6634,
29876,
1159,
13,
13,
1678,
17927,
29889,
3888,
29898,
29888,
29908,
29956,
4859,
426,
1949,
29918,
27736,
29913,
11916,
304,
426,
449,
3972,
1836,
1159,
13,
13,
1753,
6837,
29918,
354,
332,
6765,
29918,
1643,
29918,
14513,
29898,
2176,
1125,
13,
1678,
4489,
29918,
2506,
29918,
19303,
353,
4489,
29889,
2029,
15625,
2176,
3366,
2506,
936,
3108,
1275,
29871,
29896,
26927,
313,
2176,
3366,
3075,
1981,
296,
3108,
1275,
29871,
29900,
29897,
669,
29898,
2176,
3366,
1491,
16506,
3108,
1275,
29871,
29900,
4638,
13,
1678,
4489,
29918,
2506,
29918,
19303,
1839,
28084,
29918,
354,
26268,
293,
29918,
1643,
2033,
353,
285,
29908,
2506,
936,
2304,
313,
2092,
29901,
376,
320,
13,
462,
462,
4706,
285,
29908,
29912,
2176,
29918,
2506,
29918,
19303,
29889,
2029,
29961,
2176,
29918,
2506,
29918,
19303,
1839,
2092,
2033,
1360,
29896,
1822,
12181,
29961,
29900,
12258,
1178,
29901,
426,
2176,
29918,
2506,
29918,
19303,
29889,
2029,
29961,
2176,
29918,
2506,
29918,
19303,
1839,
2092,
2033,
1360,
29900,
1822,
12181,
29961,
29900,
29962,
1800,
29908,
13,
1678,
4489,
29918,
2506,
29918,
1285,
353,
4489,
29889,
2029,
15625,
2176,
3366,
2506,
936,
3108,
1275,
448,
29896,
29897,
669,
313,
2176,
3366,
3075,
1981,
296,
3108,
1275,
29871,
29900,
29897,
669,
313,
2176,
3366,
1491,
16506,
3108,
1275,
29871,
29900,
4638,
13,
1678,
4489,
29918,
2506,
29918,
1285,
1839,
28084,
29918,
354,
26268,
293,
29918,
1643,
2033,
353,
285,
29908,
2506,
936,
27877,
313,
2092,
29901,
376,
320,
13,
462,
462,
4706,
285,
29908,
29912,
2176,
29918,
2506,
29918,
1285,
29889,
2029,
29961,
2176,
29918,
2506,
29918,
1285,
1839,
2092,
2033,
1360,
29896,
1822,
12181,
29961,
29900,
12258,
1178,
29901,
426,
2176,
29918,
2506,
29918,
1285,
29889,
2029,
29961,
2176,
29918,
2506,
29918,
1285,
1839,
2092,
2033,
1360,
29900,
1822,
12181,
29961,
29900,
29962,
1800,
29908,
13,
1678,
4489,
29918,
3075,
29918,
19303,
353,
4489,
29889,
2029,
15625,
2176,
3366,
2506,
936,
3108,
1275,
29871,
29900,
29897,
669,
313,
2176,
3366,
3075,
1981,
296,
3108,
1275,
29871,
29896,
29897,
669,
313,
2176,
3366,
1491,
16506,
3108,
1275,
29871,
29900,
4638,
13,
1678,
4489,
29918,
3075,
29918,
19303,
1839,
28084,
29918,
354,
26268,
293,
29918,
1643,
2033,
353,
285,
29908,
3075,
1981,
296,
2304,
313,
2092,
29901,
376,
320,
13,
462,
462,
4706,
285,
29908,
29912,
2176,
29918,
3075,
29918,
19303,
29889,
2029,
29961,
2176,
29918,
3075,
29918,
19303,
1839,
2092,
2033,
1360,
29896,
1822,
12181,
29961,
29900,
12258,
1178,
29901,
426,
2176,
29918,
3075,
29918,
19303,
29889,
2029,
29961,
2176,
29918,
3075,
29918,
19303,
1839,
2092,
2033,
1360,
29900,
1822,
12181,
29961,
29900,
29962,
1800,
29908,
13,
1678,
4489,
29918,
3075,
29918,
1285,
353,
4489,
29889,
2029,
15625,
2176,
3366,
2506,
936,
3108,
1275,
29871,
29900,
29897,
669,
313,
2176,
3366,
3075,
1981,
296,
3108,
1275,
448,
29896,
29897,
669,
313,
2176,
3366,
1491,
16506,
3108,
1275,
29871,
29900,
4638,
13,
1678,
4489,
29918,
3075,
29918,
1285,
1839,
28084,
29918,
354,
26268,
293,
29918,
1643,
2033,
353,
285,
29908,
3075,
1981,
296,
27877,
313,
2092,
29901,
376,
320,
13,
462,
462,
4706,
285,
29908,
29912,
2176,
29918,
3075,
29918,
1285,
29889,
2029,
29961,
2176,
29918,
3075,
29918,
1285,
1839,
2092,
2033,
1360,
29896,
1822,
12181,
29961,
29900,
12258,
1178,
29901,
426,
2176,
29918,
3075,
29918,
1285,
29889,
2029,
29961,
2176,
29918,
3075,
29918,
1285,
1839,
2092,
2033,
1360,
29900,
1822,
12181,
29961,
29900,
29962,
1800,
29908,
13,
1678,
4489,
29918,
1491,
29918,
19303,
353,
4489,
29889,
2029,
15625,
2176,
3366,
2506,
936,
3108,
1275,
29871,
29900,
29897,
669,
313,
2176,
3366,
3075,
1981,
296,
3108,
1275,
29871,
29900,
29897,
669,
313,
2176,
3366,
1491,
16506,
3108,
1275,
29871,
29896,
4638,
13,
1678,
4489,
29918,
1491,
29918,
19303,
1839,
28084,
29918,
354,
26268,
293,
29918,
1643,
2033,
353,
285,
29908,
1491,
16506,
2304,
313,
2092,
29901,
376,
320,
13,
462,
462,
4706,
285,
29908,
29912,
2176,
29918,
1491,
29918,
19303,
29889,
2029,
29961,
2176,
29918,
1491,
29918,
19303,
1839,
2092,
2033,
1360,
29896,
1822,
12181,
29961,
29900,
12258,
1178,
29901,
426,
2176,
29918,
1491,
29918,
19303,
29889,
2029,
29961,
2176,
29918,
1491,
29918,
19303,
1839,
2092,
2033,
1360,
29900,
1822,
12181,
29961,
29900,
29962,
1800,
29908,
13,
1678,
4489,
29918,
1491,
29918,
1285,
353,
4489,
29889,
2029,
15625,
2176,
3366,
2506,
936,
3108,
1275,
29871,
29900,
29897,
669,
313,
2176,
3366,
3075,
1981,
296,
3108,
1275,
29871,
29900,
29897,
669,
313,
2176,
3366,
1491,
16506,
3108,
1275,
448,
29896,
4638,
13,
1678,
4489,
29918,
1491,
29918,
1285,
1839,
28084,
29918,
354,
26268,
293,
29918,
1643,
2033,
353,
285,
29908,
1491,
16506,
27877,
313,
2092,
29901,
376,
320,
13,
462,
462,
4706,
285,
29908,
29912,
2176,
29918,
1491,
29918,
1285,
29889,
2029,
29961,
2176,
29918,
1491,
29918,
1285,
1839,
2092,
2033,
1360,
29896,
1822,
12181,
29961,
29900,
12258,
1178,
29901,
426,
2176,
29918,
1491,
29918,
1285,
29889,
2029,
29961,
2176,
29918,
1491,
29918,
1285,
1839,
2092,
2033,
1360,
29900,
1822,
12181,
29961,
29900,
29962,
1800,
29908,
13,
1678,
4489,
29918,
28084,
353,
10518,
29889,
17685,
4197,
2176,
29918,
2506,
29918,
19303,
29892,
4489,
29918,
2506,
29918,
1285,
29892,
4489,
29918,
3075,
29918,
19303,
29892,
4489,
29918,
3075,
29918,
1285,
29892,
13,
462,
1678,
4489,
29918,
1491,
29918,
19303,
29892,
4489,
29918,
1491,
29918,
1285,
2314,
13,
1678,
736,
4489,
29918,
28084,
13,
13,
1753,
6837,
29918,
354,
332,
6765,
29918,
1643,
29918,
14968,
29898,
2176,
1125,
13,
1678,
4489,
29918,
2506,
29918,
19303,
353,
4489,
29889,
2029,
15625,
2176,
3366,
2506,
936,
3108,
1275,
29871,
29896,
26927,
313,
2176,
3366,
3075,
1981,
296,
3108,
1275,
29871,
29900,
29897,
669,
29898,
2176,
3366,
1491,
16506,
3108,
1275,
29871,
29900,
4638,
13,
1678,
4489,
29918,
2506,
29918,
19303,
1839,
28084,
29918,
354,
26268,
293,
29918,
1643,
2033,
353,
285,
29908,
2506,
936,
2304,
21313,
2176,
29918,
2506,
29918,
19303,
29889,
12181,
29961,
29900,
29962,
5038,
13,
1678,
4489,
29918,
2506,
29918,
1285,
353,
4489,
29889,
2029,
15625,
2176,
3366,
2506,
936,
3108,
1275,
448,
29896,
29897,
669,
313,
2176,
3366,
3075,
1981,
296,
3108,
1275,
29871,
29900,
29897,
669,
313,
2176,
3366,
1491,
16506,
3108,
1275,
29871,
29900,
4638,
13,
1678,
4489,
29918,
2506,
29918,
1285,
1839,
28084,
29918,
354,
26268,
293,
29918,
1643,
2033,
353,
285,
29908,
2506,
936,
27877,
21313,
2176,
29918,
2506,
29918,
1285,
29889,
12181,
29961,
29900,
29962,
5038,
13,
1678,
4489,
29918,
3075,
29918,
19303,
353,
4489,
29889,
2029,
15625,
2176,
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] |
tests/utils.py | reasoned-ai/norm | 8 | 42289 | import unittest
import hashids
import time
import os
hashid = hashids.Hashids()
__all__ = ['user_tester', 'NormTestCase']
os.environ['NORM_DATA_STORAGE_ROOT'] = 'data'
os.environ['NORM_DB_PATH'] = 'norm/db/norm.db'
from norm.config import session
def user_tester():
from norm.models.user import User
tester = session.query(User).filter(User.username == 'tester',
User.email == '<EMAIL>').first()
if tester is None:
tester = User(username='tester', first_name='tester', last_name='norm',
email='<EMAIL>')
session.add(tester)
session.commit()
return tester
class NormTestCase(unittest.TestCase):
def setUp(self):
from norm.engine import NormCompiler
self.session = session
# override norm configuration
self.user = user_tester()
self.context_id = hashid.encode(int(time.time() * 1000))
self.executor = NormCompiler(self.context_id, self.user, self.session)
def tearDown(self):
self.session.rollback()
self.session.close()
def execute(self, script):
return self.executor.execute(script)
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run.py | hkx97/echo | 4 | 119189 | """
1.初始化数据和模型
1.1解析dicom文件
1.2加载model和模型权重
2.预测ED ES
2.1数据输入给Siamese-model，输出n*2的tensor
2.2n*2的tensor输入给后处理算法-->ED ES
3.预测结构参数
3.1ED ES 输入给seg-model
3.2分割后的mask通过后处理方法得到height area
3.3如果单个视角则直接计算volume，如果两个视角则通过simpson方法计算volume
3.4计算EF
4.plot—figure and visualization
"""
from postprocess import cardiac_parameter
from postprocess.postprocessing import *
from model import load_Comparison_model
from model import u_net
from preprocess import interpretDicom
from plot_tool import visualization
import cv2
import os
os.environ["CUDA_VISIBLE_DEVICES"] = "2"
# 初始化模型
EDESPredictionModel = load_Comparison_model.load_model(input_size=128, load_weight=True,\
weight_path="model_weight/a4c.hdf5") # 这里注意
segEDModel = u_net.u_net((128, 128, 1), loadWeight=True,\
weigthPath="./model_weight/seg-a4c-trainbyed.hdf5")
segESModel = u_net.u_net((128, 128, 1), loadWeight=True,\
weigthPath="./model_weight/seg-a4c-trainbyes.hdf5")
# 防止ED、ES为[]
# 初始化数据
while True:
result1, result2 = {}, {}
print("please input a filepath，such as root/a/b/c/../1.dcm：")
while True:
src_path = input()
if src_path == "quit":
exit()
key = 0
try:
assert os.path.exists(src_path) == True, "path error!"
except Exception as ex:
print(ex, "please check it and try again or input quit！")
key = 1
if key == 0:
break
data, originalFrames = interpretDicom.interpretDicom(128,src_path) # 加载Dicom数据视频帧shape = （n，128，128，1）
modelOutTensor = EDESPredictionModel.predict([data[:-1], data[1:]])
EDFrameNumber = sliding_window(modelOutTensor, delete_a4cd_frames(modelOutTensor), 1) # return a List
ESFrameNumber = sliding_window(modelOutTensor, delete_a4cs_frames(modelOutTensor), 0)
maskED = segEDModel.predict(data[EDFrameNumber[0]-1:EDFrameNumber[0]]).reshape(128, 128) # 随机将第一帧作为ED，输出mask-->(1,128,128,1)
maskES = segESModel.predict(data[ESFrameNumber[0]-1:ESFrameNumber[0]]).reshape(128, 128)
scale = interpretDicom.parse_scale(src_path)
EDParameter = cardiac_parameter.cmpt_single_volum_(maskED, scale=scale) # 注意scale
ESParameter = cardiac_parameter.cmpt_single_volum_(maskES, scale=scale)
EF = (EDParameter[-1] - ESParameter[-1])/EDParameter[-1]
parameterAll = EDFrameNumber[0:1]+list(EDParameter)+ESFrameNumber[0:1]+list(ESParameter)
parameterNames1 = ["ED Frame number:", " LV Length:", " LV Area:", " LV volume:"]
parameterNames2 = ["ES Frame number:", " LV Length:", " LV Area:", " LV volume:"]
measurement = ["", "cm", "cm²", "ml"]
for i, j in enumerate(parameterNames1):
result1[j] = "%.2f" % (EDFrameNumber[0:1]+list(EDParameter))[i]+measurement[i]
for i, j in enumerate(parameterNames2):
result2[j] = "%.2f" % (ESFrameNumber[0:1]+list(ESParameter))[i]+measurement[i]
# 可视化
window = visualization.window(1200, 800)
window[:600, :, :] = visualization.plotMask(maskED, originalFrames[EDFrameNumber[0]-1]) # 在原图中画出心内膜
window[600:, :, :] = visualization.plotMask(maskES, originalFrames[ESFrameNumber[0]-1])
cv2.imwrite("./img.png", window*255)
# 添加文字
srcImg = cv2.imread("./img.png")
os.remove("./img.png")
srcImg = visualization.putTextIntoImg(srcImg, result1, loc=100, EF=EF)
srcImg = visualization.putTextIntoImg(srcImg, result2, loc=700, EF=EF, k=1)
# plot the figure
visualization.visualize(srcImg)
print("Press y to continue, else quit！")
ifContinue = input()
if ifContinue != "y":
break
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] |
imagepy/core/mark/mark.py | adines/imagepy | 1 | 150185 | import numpy as np
from math import sin, cos
from ..manager import ConfigManager
point = {'type':'point', 'color':(255,0,0), 'lw':1, 'body':(10,10)}
points = {'type':'points', 'color':(255,0,0), 'lw':1, 'body':[(10,10),(100,200)]}
line = {'type':'line', 'color':(255,0,0), 'lw':1, 'style':'-', 'body':[(10,10),(100,200),(200,200)]}
lines = {'type':'lines', 'color':(255,0,0), 'lw':1, 'style':'-', 'body':[[(10,10),(100,200),(200,200)],[(150,10),(50,250)]]}
polygon = {'type':'polygon', 'color':(255,0,0), 'fcolor':(255,255,0), 'lw':1, 'style':'o', 'body':[(10,10),(100,200),(200,200)]}
polygons = {'type':'polygons', 'color':(255,0,0), 'fcolor':(255,255,0,30), 'fill':False, 'lw':1, 'style':'o', 'body':[[(10,10),(100,200),(200,200)],[(150,10),(50,250),(288,0)]]}
circle = {'type':'circle', 'color':(255,0,0), 'fcolor':(255,255,0), 'fill':False, 'body':(100,100,50)}
circles = {'type':'circles', 'color':(255,0,0), 'fcolor':(255,255,0), 'fill':False, 'body':[(100,100,50),(300,300,100)]}
ellipse = {'type':'ellipse', 'color':(255,0,0), 'fcolor':(255,255,0), 'fill':False, 'body':(100,100,100,50,1)}
ellipses = {'type':'ellipses', 'color':(255,0,0), 'fcolor':(255,255,0), 'fill':False, 'body':[(100,100,100,50,1),(200,250,50,100,3.14)]}
rectangle = {'type':'rectangle', 'color':(255,0,0), 'fcolor':(255,255,0), 'fill':True, 'body':(100,100,80,50)}
rectangles = {'type':'rectangles', 'color':(255,0,0), 'fcolor':(255,255,0), 'fill':False, 'body':[(100,100,80,50),(200,200,80,100)]}
text = {'type':'text', 'color':(255,255,0), 'fcolor':(0,0,0), 'size':8, 'pt':True, 'body':(100,200,'id=0')}
texts = {'type':'texts', 'color':(255,255,0), 'fcolor':(0,0,0), 'size':8, 'pt':True, 'body':[(100,200,'id=0'),(180,250,'id=1')]}
layer = {'type':'layer', 'num':-1, 'clolor':(255,255,0), 'fcolor':(255,255,255), 'fill':False,
'body':[point, points, line, lines, polygon, polygons, circle, circles, ellipse, ellipses, rectangle, rectangles, text, texts]}
layers = {'type':'layers', 'num':-1, 'clolor':(255,255,0), 'fcolor':(255,255,255), 'fill':False,
'body':{1:points, 2:line, 3:layer}}
def plot(pts, dc, f, **key):
pen, brush = dc.GetPen(), dc.GetBrush()
width, color = pen.GetWidth(), pen.GetColour()
fcolor, style = brush.GetColour(), brush.GetStyle()
if 'color' in pts:
pen.SetColour(pts['color'])
if 'fcolor' in pts:
brush.SetColour(pts['fcolor'])
if 'lw' in pts:
pen.SetWidth(pts['lw'])
if 'fill' in pts:
brush.SetStyle((106,100)[pts['fill']])
dc.SetPen(pen)
dc.SetBrush(brush)
if pts['type'] == 'point':
pen.SetWidth(1)
brush.SetStyle(100)
brush.SetColour(pen.GetColour())
dc.SetPen(pen)
dc.SetBrush(brush)
r = pts['r'] if 'r' in pts else 2
x, y = f(*pts['body'])
dc.DrawEllipse (x-r,y-r,r*2,r*2)
pen.SetWidth(pts['lw'] if 'lw' in pts else width)
brush.SetStyle((106,100)[pts['fill']] if 'fill' in pts else style)
brush.SetColour(pts['fc'] if 'fc' in pts else fcolor)
dc.SetPen(pen)
dc.SetBrush(brush)
elif pts['type'] in {'points','line','polygon'}:
lst, plst = [], []
r = pts['r'] if 'r' in pts else 2
for p in pts['body']:
x, y = f(*p)
lst.append((x-r,y-r,r*2,r*2))
plst.append((x,y))
isline = 'style' in pts and '-' in pts['style']
ispoint = 'style' in pts and 'o' in pts['style']
if pts['type'] == 'polygon':
dc.DrawPolygon(plst)
if isline or pts['type'] == 'line':
dc.DrawLines(plst)
if pts['type']=='points' or ispoint:
pen.SetWidth(1)
brush.SetStyle(100)
brush.SetColour(pen.GetColour())
dc.SetPen(pen)
dc.SetBrush(brush)
dc.DrawEllipseList(lst)
pen.SetWidth(pts['lw'] if 'lw' in pts else width)
brush.SetStyle((106,100)[pts['fill']] if 'fill' in pts else style)
brush.SetColour(pts['fc'] if 'fc' in pts else fcolor)
dc.SetPen(pen)
dc.SetBrush(brush)
elif pts['type'] in {'lines','polygons'}:
lst, plst = [], []
r = pts['r'] if 'r' in pts else 2
for i in pts['body']:
line = []
for p in i:
x, y = f(*p)
lst.append((x-r,y-r,r*2,r*2))
line.append((x,y))
plst.append(line)
isline = 'style' in pts and '-' in pts['style']
ispoint = 'style' in pts and 'o' in pts['style']
if pts['type'] == 'polygons':
dc.DrawPolygonList(plst)
if isline or pts['type'] == 'line':
for line in plst:
dc.DrawLines(line)
if pts['type']=='points' or ispoint:
pen.SetWidth(1)
brush.SetStyle(100)
brush.SetColour(pen.GetColour())
dc.SetPen(pen)
dc.SetBrush(brush)
dc.DrawEllipseList(lst)
pen.SetWidth(pts['lw'] if 'lw' in pts else width)
brush.SetStyle((106,100)[pts['fill']] if 'fill' in pts else style)
brush.SetColour(pts['fc'] if 'fc' in pts else fcolor)
dc.SetPen(pen)
dc.SetBrush(brush)
pen.SetWidth(width)
pen.SetColour(color)
brush.SetColour(fcolor)
brush.SetStyle(style)
dc.SetPen(pen)
dc.SetBrush(brush)
def draw_circle(pts, dc, f, **key):
pen, brush = dc.GetPen(), dc.GetBrush()
width, color = pen.GetWidth(), pen.GetColour()
fcolor, style = brush.GetColour(), brush.GetStyle()
if 'color' in pts:
pen.SetColour(pts['color'])
if 'fcolor' in pts:
brush.SetColour(pts['fcolor'])
if 'lw' in pts:
pen.SetWidth(pts['lw'])
if 'fill' in pts:
brush.SetStyle((106,100)[pts['fill']])
dc.SetPen(pen)
dc.SetBrush(brush)
if pts['type'] == 'circle':
x, y ,r = pts['body']
x, y = f(x, y)
dc.DrawCircle(x, y, r*key['k'])
if pts['type'] == 'circles':
lst = []
for x, y ,r in pts['body']:
x, y = f(x, y)
r *= key['k']
lst.append((x-r,y-r,r*2,r*2))
dc.DrawEllipseList(lst)
pen.SetWidth(width)
pen.SetColour(color)
brush.SetColour(fcolor)
brush.SetStyle(style)
dc.SetPen(pen)
dc.SetBrush(brush)
def make_ellipse(l1, l2, ang):
m = np.array([[l1*cos(-ang),-l2*sin(-ang)],
[l1*sin(-ang),l2*cos(-ang)]])
a = np.linspace(0, np.pi*2, 36)
xys = np.array((np.cos(a), np.sin(a)))
return np.dot(m, xys).T
def draw_ellipse(pts, dc, f, **key):
pen, brush = dc.GetPen(), dc.GetBrush()
width, color = pen.GetWidth(), pen.GetColour()
fcolor, style = brush.GetColour(), brush.GetStyle()
if 'color' in pts:
pen.SetColour(pts['color'])
if 'fcolor' in pts:
brush.SetColour(pts['fcolor'])
if 'lw' in pts:
pen.SetWidth(pts['lw'])
if 'fill' in pts:
brush.SetStyle((106,100)[pts['fill']])
dc.SetPen(pen)
dc.SetBrush(brush)
if pts['type'] == 'ellipse':
x, y ,l1, l2, a = pts['body']
elp = make_ellipse(l1,l2,a)
elp = elp*key['k']+f(x,y)
dc.DrawPolygon(elp)
if pts['type'] == 'ellipses':
lst = []
for x, y, l1, l2, a in pts['body']:
elp = make_ellipse(l1,l2,a)
lst.append(elp*key['k']+f(x,y))
dc.DrawPolygonList(lst)
pen.SetWidth(width)
pen.SetColour(color)
brush.SetColour(fcolor)
brush.SetStyle(style)
dc.SetPen(pen)
dc.SetBrush(brush)
def draw_rectangle(pts, dc, f, **key):
pen, brush = dc.GetPen(), dc.GetBrush()
width, color = pen.GetWidth(), pen.GetColour()
fcolor, style = brush.GetColour(), brush.GetStyle()
if 'color' in pts:
pen.SetColour(pts['color'])
if 'fcolor' in pts:
brush.SetColour(pts['fcolor'])
if 'lw' in pts:
pen.SetWidth(pts['lw'])
if 'fill' in pts:
brush.SetStyle((106,100)[pts['fill']])
dc.SetPen(pen)
dc.SetBrush(brush)
if pts['type'] == 'rectangle':
x, y, w, h = pts['body']
x, y = f(x, y)
w, h = w*key['k'], h*key['k']
dc.DrawRectangle(x-w/2, y-h/2, w, h)
if pts['type'] == 'rectangles':
lst = []
for x, y, w, h in pts['body']:
x, y = f(x, y)
w, h = w*key['k'], h*key['k']
lst.append((x-w/2, y-h/2, w, h))
dc.DrawRectangleList(lst)
pen.SetWidth(width)
pen.SetColour(color)
brush.SetColour(fcolor)
brush.SetStyle(style)
dc.SetPen(pen)
dc.SetBrush(brush)
def draw_text(pts, dc, f, **key):
pen, brush, font = dc.GetPen(), dc.GetBrush(), dc.GetFont()
width, color = pen.GetWidth(), pen.GetColour()
fcolor, style = brush.GetColour(), brush.GetStyle()
size = font.GetPointSize()
tcolor = dc.GetTextForeground()
bcolor = dc.GetTextBackground()
if 'color' in pts:
pen.SetColour(pts['color'])
dc.SetTextForeground(pts['color'])
brush.SetColour(pen.GetColour())
brush.SetStyle(100)
if 'fcolor' in pts:
print('hahaha')
dc.SetTextBackground(pts['fcolor'])
if 'size' in pts:
font.SetPointSize(pts['size'])
dc.SetPen(pen)
dc.SetBrush(brush)
dc.SetFont(font)
if pts['type'] == 'text':
x, y, text = pts['body']
x, y = f(x, y)
dc.DrawText(text, x+3, y+3)
if not 'pt' in pts or pts['pt']:
dc.DrawEllipse(x-2,y-2,4,4)
if pts['type'] == 'texts':
tlst, clst, elst = [], [], []
for x, y, text in pts['body']:
x, y = f(x, y)
tlst.append(text)
clst.append((x+3, y+3))
elst.append((x-2, y-2, 4, 4))
dc.DrawTextList(tlst, clst)
if not 'pt' in pts or pts['pt']:
dc.DrawEllipseList(elst)
font.SetPointSize(size)
pen.SetColour(color)
brush.SetColour(fcolor)
brush.SetStyle(style)
dc.SetPen(pen)
dc.SetBrush(brush)
dc.SetFont(font)
dc.SetTextForeground(tcolor)
dc.SetTextBackground(bcolor)
draw_dic = {'points':plot, 'point':plot, 'line':plot, 'polygon':plot, 'lines':plot, 'polygons':plot,
'circle':draw_circle, 'circles':draw_circle, 'ellipse':draw_ellipse, 'ellipses':draw_ellipse,
'rectangle':draw_rectangle, 'rectangles':draw_rectangle, 'text':draw_text, 'texts':draw_text}
def draw(obj, dc, f, **key): draw_dic[obj['type']](obj, dc, f, **key)
def draw_layer(pts, dc, f, **key):
pen, brush = dc.GetPen(), dc.GetBrush()
width, color = pen.GetWidth(), pen.GetColour()
fcolor, style = brush.GetColour(), brush.GetStyle()
if 'color' in pts:
pen.SetColour(pts['color'])
if 'fcolor' in pts:
brush.SetColour(pts['fcolor'])
if 'lw' in pts:
pen.SetWidth(pts['lw'])
if 'fill' in pts:
brush.SetStyle((106,100)[pts['fill']])
dc.SetPen(pen)
dc.SetBrush(brush)
for i in pts['body']:draw(i, dc, f, **key)
pen.SetWidth(width)
pen.SetColour(color)
brush.SetColour(fcolor)
brush.SetStyle(style)
dc.SetPen(pen)
dc.SetBrush(brush)
draw_dic['layer'] = draw_layer
def draw_layers(pts, dc, f, **key):
pen, brush = dc.GetPen(), dc.GetBrush()
width, color = pen.GetWidth(), pen.GetColour()
fcolor, style = brush.GetColour(), brush.GetStyle()
if 'color' in pts:
pen.SetColour(pts['color'])
if 'fcolor' in pts:
brush.SetColour(pts['fcolor'])
if 'lw' in pts:
pen.SetWidth(pts['lw'])
if 'fill' in pts:
brush.SetStyle((106,100)[pts['fill']])
dc.SetPen(pen)
dc.SetBrush(brush)
print(pts['body'].keys())
if key['cur'] in pts['body']:
draw(pts['body'][key['cur']], dc, f, **key)
pen.SetWidth(width)
pen.SetColour(color)
brush.SetColour(fcolor)
brush.SetStyle(style)
dc.SetPen(pen)
dc.SetBrush(brush)
draw_dic['layers'] = draw_layers
class GeometryMark:
def __init__(self, body):
self.body = body
def draw(self, dc, f, **key):
pen, brush, font = dc.GetPen(), dc.GetBrush(), dc.GetFont()
pen.SetColour(ConfigManager.get('mark_color') or (255,255,0))
brush.SetColour(ConfigManager.get('mark_fcolor') or (255,255,255))
brush.SetStyle((106,100)[ConfigManager.get('mark_fill') or False])
pen.SetWidth(ConfigManager.get('mark_lw') or 1)
dc.SetTextForeground(ConfigManager.get('mark_tcolor') or (255,0,0))
font.SetPointSize(ConfigManager.get('mark_tsize') or 8)
dc.SetPen(pen); dc.SetBrush(brush); dc.SetFont(font);
draw(self.body, dc, f, **key)
if __name__ == '__main__':
print(make_ellipse(0,0,2,1,0)) | [
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19536,
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1454,
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answer/14.py | harurunrunrun/edu90_myanswer | 0 | 155731 | n,*a=map(int,open(0).read().split());print(sum(abs(i-j)for i,j in zip(sorted(a[:n]),sorted(a[n:])))) | [
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LoanPrediction/loan_prediction.py | ketanzabak/DataScienceChallenge | 0 | 188945 | # -*- coding: utf-8 -*-
# Importing the libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from scipy.stats import mode
from sklearn.preprocessing import LabelEncoder
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
from sklearn.linear_model import Ridge
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import GridSearchCV
from xgboost import XGBRegressor
"""
Combine train and test data
Add one more column to the combined data to identify train and test data.
"""
def combine_datasets(train, test):
dataset_train['source'] = 'train'
dataset_test['source'] = 'test'
combined_data = pd.concat([dataset_train,dataset_test], ignore_index=True)
return combined_data;
"""
Print the summary of the given dataframe.
This method prints following summary details of the dataframe:
1: shape
2: Null values per column
"""
def print_dataset_summary(dataset):
print("\n\n<------ Summary for data --->")
print("\tShape of train data",dataset.shape)
print("\tPrinting null values per column :")
print(combined_data.apply(lambda x: sum(x.isnull())))
"""
Calculate mean square error.
"""
def calculate_mse(Y_pred, Y_actual):
# calculate MSE
mse = np.mean((Y_pred-Y_actual)**2)
return mse
def plot_residual_graph(Y_pred, Y_actual):
# Plot the graph and check the data pattern.
# residual plot
x_plot = plt.scatter(Y_pred, (Y_pred - Y_actual), c='b')
plt.hlines(y=0, xmin= -1000, xmax=5000)
plt.title('Residual plot')
def unique_val_categorical_col(categorical_columns):
for column in categorical_columns:
print("<--------- Column name: ",column," ----------->")
print(combined_data[column].value_counts())
def plot_categorical_features(df, categorical_columns):
print("Size of list: ",len(categorical_columns))
for column in categorical_columns:
df[column].value_counts().plot(kind="bar",title=column)
plt.show()
# Importing the dataset
dataset_train = pd.read_csv('data/train_av.csv')
dataset_test = pd.read_csv('data/test_av.csv')
combined_data = combine_datasets(dataset_train, dataset_test)
print_dataset_summary(dataset_train)
print_dataset_summary(dataset_test)
print_dataset_summary(combined_data)
#get categorical column
categorical_column = [x for x in combined_data.dtypes.index if combined_data.dtypes[x] == 'object']
print(categorical_column)
head = combined_data.head()
unique_val_categorical_col(categorical_column)
plot_categorical_features(combined_data, categorical_column)
# Missing value imputation.
combined_data["Gender"].fillna("Male",inplace=True)
combined_data["Married"].fillna("Yes",inplace=True)
combined_data["Credit_History"].fillna(1,inplace=True)
combined_data["Credit_History"].fillna(1,inplace=True)
combined_data['Dependents'] = combined_data['Dependents'].map({'3+':'3', '1' : '1', '0' : '0', '2' : '2'})
combined_data['Dependents'].fillna
combined_data["Credit_History"].value_counts()
combined_data["Dependents"].isnull().sum()
# step 10
# separate train and test data
# Remove Item_Outlet_Sales from test data
train = combined_data.loc[combined_data['source']=="train"]
test = combined_data.loc[combined_data['source']=="test"]
train.drop(['source'],axis=1,inplace=True)
# target variable name.
target = ''
IDcol = []
submissionCols = []
predictors = [x for x in train.columns if x not in [target]+IDcol]
X = train[predictors]
Y = train[target]
#split the data into train and test data. Cross validation.
X_train, X_test, Y_train , Y_test = train_test_split(X,Y,test_size = 0.2, random_state = 0)
'''
# Uncomment this code to use LinearRegression.
# Predict Values using LinearRegression Model
linear_regression = LinearRegression()
linear_regression.fit(X_train,Y_train)
Y_predict = linear_regression.predict(X_test)
print(calculate_mse(Y_predict, Y_test))
# calculate R-Squared Adjusted.
score = linear_regression.score(X_test,Y_test)
print(score)
# plot the graph.
plot_residual_graph(Y_predict, Y_test)
test[target] = linear_regression.predict(test[predictors])
# Linear model processing end here...
'''
'''
# Uncomment this code for using Polynomial Regression.
# Predict the values using Polynomial regression.
# Fitting Polynomial Regression to the dataset
from sklearn.preprocessing import PolynomialFeatures
poly_reg = PolynomialFeatures(degree = 2)
X_train_poly = poly_reg.fit_transform(X_train)
poly_reg.fit(X_train_poly, Y_train)
polynomial_regression = LinearRegression()
polynomial_regression.fit(X_train_poly, Y_train)
X_test_poly = poly_reg.fit_transform(X_test)
Y_predict_poly = polynomial_regression.predict(X_test_poly)
# calculate MSE
print(calculate_mse(Y_predict_poly, Y_test))
# calculate R-Squared Adjusted.
score = polynomial_regression.score(X_test_poly,Y_test)
print(score)
plot_residual_graph(Y_predict_poly, Y_test)
# predict for test data.
test[target] = polynomial_regression.predict(poly_reg.fit_transform(test[predictors]))
# checking magnitude of coefficient.
coeff = polynomial_regression.coef_
print(max(coeff))
print(min(coeff))
print(sum(coeff)/len(coeff))
# Evaluating model performance using k-fold cross validation.
poly_regression_accuracies = cross_val_score(estimator = polynomial_regression, X = X_train_poly,
y = Y_train, cv = 10)
print(poly_regression_accuracies.mean())
print(poly_regression_accuracies.std())
# Polynomical regression processing ends here.
'''
'''
# Uncomment this code to use RidgeRegression.
# Training the model using Ridge Regression.
ridge_regression = Ridge(normalize = True)
ridge_regression.fit(X_train, Y_train)
Y_pred_ridge = ridge_regression.predict(X_test)
print(calculate_mse(Y_pred_ridge, Y_test))
ridge_regression.score(X_test, Y_test)
ridge_coeff = ridge_regression.coef_
print(max(ridge_coeff))
print(min(ridge_coeff))
print(sum(ridge_coeff)/len(ridge_coeff))
# Evaluting model performance using k-folde cross validation.
ridge_accuracies = cross_val_score(estimator = ridge_regression, X = X_train,
y = Y_train, cv = 10)
print(ridge_accuracies.mean())
print(ridge_accuracies.std())
# Applying Grid Search to find the best model and the best parameters
alphas = np.array([0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9])
fit_interceptOptions = ([True, False])
solverOptions = (['svd', 'cholesky', 'sparse_cg', 'sag'])
parameters = dict(alpha=alphas, fit_intercept=fit_interceptOptions, solver=solverOptions)
grid_search = GridSearchCV(estimator = ridge_regression,
param_grid = parameters)
grid_search = grid_search.fit(X_train, Y_train)
best_accuracy = grid_search.best_score_
best_parameters = grid_search.best_params_
test[target] = grid_search.predict(test[predictors])
# Ridge regression ends here..
'''
# Method to evaludate model performance
def evaluate_model_performance(model, x_train, y_train, x_test, y_test):
model.fit(x_train, y_train)
y_predict = model.predict(x_test)
print("Mean Square Error: ",calculate_mse(y_predict, y_test))
accurracy = cross_val_score(estimator = model, X = x_train, y = y_train, cv = 10)
print("Accurracy Mean: ", accurracy.mean())
print("Accurracy Std : ", accurracy.std())
# Training model using XGBoost.
xgbRegressor = XGBRegressor()
evaluate_model_performance(xgbRegressor, X_train, Y_train, X_test, Y_test)
'''
Performance of above model :
Mean Square Error: 1186173.7950376957
Accurracy Mean: 0.594592170829
Accurracy Std : 0.019906704365
'''
test[target] = xgbRegressor.predict(test[predictors])
submission = test[IDcol]
submission[target] = test[target]
submission.to_csv(DIR+"/ridge_regression.csv", index=False) | [
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python/DeepLearning/lesson1/week4/mytest/main.py | TimVan1596/ACM-ICPC | 1 | 1601477 | import numpy as np
import numpy.random
import matplotlib.pyplot as plt
import random
# 设置常量
# HIDDEN_LAYER_NUM = 隐层的个数
# LEARNING_RATE = 学习率
# NET_DEEP_ARRAY = 神经网络的深度(输入层X为0)对应的神经元个数
# DEFAULT_TRAIN_TIMES = 默认训练次数
LEARNING_RATE = 1.2
NET_DEEP_ARRAY = []
DEFAULT_TRAIN_TIMES = 5000
# RANDOM_SEED = 随机数的种子
RANDOM_SEED = 2021
# 激活函数
# def sigmoid(x):
# return 1 / (1 + np.exp(-x))
def sigmoid(x):
s = 1 / (1 + np.exp(-x))
return s
# 深层神经网络主驱动
def deep_neural_network(X, Y
, net_deep_array=[0, 6, 1], learning_rate=LEARNING_RATE
, train_times=DEFAULT_TRAIN_TIMES, random_seed=RANDOM_SEED):
# 绘图
plt.title("week4 深层神经网络")
plt.xlabel("x/times")
plt.ylabel("损失值（越小越好）")
plt.rcParams['font.sans-serif'] = ['SimHei'] # 显示中文标签
plt.rcParams['axes.unicode_minus'] = False # 这两行需要手动设置
x = []
y = []
# 初始化基本参数
net_deep = len(net_deep_array)
net_deep_array[0] = X.shape[0]
numpy.random.seed(random_seed)
m = X.shape[1]
W, b = initial_parameters(net_deep_array)
# 暂存每一个深度的参数值
Z = [0] * net_deep
A = [0] * net_deep
# 后向传播用于梯度下降
dZ = [0] * net_deep
dW = [0] * net_deep
db = [0] * net_deep
dA = [0] * net_deep
A[0] = X
# 训练次数
for i in range(0, train_times, 1):
# 每次前向传播中的纵向深度
for L in range(1, net_deep, 1):
activate = 'tanh'
# 最后一次使用sigmoid激活函数
if L == net_deep - 1:
activate = 'sigmoid'
# 进行前向传播
forward_parameter = forward_propagation(A[L - 1], {
'W': W[L],
'b': b[L],
}, activate)
Z[L] = forward_parameter.get('Z')
A[L] = forward_parameter.get('A')
assert (Z[L].shape == (net_deep_array[L], m))
# 计算成本cost
cost_value = cost(A[net_deep - 1], Y)
if i % 20 == 0:
x.append(i)
y.append(cost_value)
if i % 500 == 0:
print("第", i, "次迭代，成本值为：", np.squeeze(cost_value))
# 后向传播用于梯度下降
# 倒序计算出
dAL = 0
for L in range(net_deep - 1, 0, -1):
if L == net_deep - 1:
dAL = -np.divide(Y, A[net_deep - 1]) + np.divide(1 - Y, 1 - A[net_deep - 1])
dZL = dAL * sigmoid(Z[net_deep - 1]) * sigmoid(1 - Z[net_deep - 1])
else:
dZL = dAL * (1 - (np.tanh(Z[L]) * np.tanh(Z[L])))
dWL = (1 / m) * (np.dot(dZL, A[L - 1].T))
dbL = (1 / m) * np.sum(dZL, axis=1, keepdims=True)
# 提供给下一次的循环
dAL = np.dot(W[L].T, dZL)
# 更新参数
W[L] = W[L] - learning_rate * dWL
b[L] = b[L] - learning_rate * dbL
plt.plot(x, y, color='orange')
plt.show()
parameter = {
'W': W,
'b': b,
'x': x,
'y': y,
'net_deep_array': net_deep_array,
}
return parameter
# 前向传播
def forward_propagation(A_Last, parameter, activate='tanh'):
W = parameter.get('W')
b = parameter.get('b')
Z = np.dot(W, A_Last) + b
if activate == 'tanh':
A = np.tanh(Z)
elif activate == 'sigmoid':
A = sigmoid(Z)
return {
'Z': Z,
'A': A,
}
# 后向传播
def backward_propagation(dA, Z, A_Last_T, W):
dZ = dA * sigmoid(Z) * (1 - sigmoid(Z))
# 计算该结果的成本
def cost(A, Y):
A = np.squeeze(A)
Y = np.squeeze(Y)
assert (A.shape[0] == Y.shape[0])
m = A.shape[0]
# 这里使用 np.multiply 是因为只有一维所以对应想乘
# a = np.log(A)
# b = np.multiply(np.log(A), Y)
# c = np.log(1 - A)
# d = np.multiply((1 - Y), np.log(1 - A))
temp = np.multiply(np.log(A), Y) + np.multiply((1 - Y), np.log(1 - A))
# 取了一次绝对值
temp = np.maximum(temp, -temp)
cost_ret = np.sum(temp) * (-1 / m)
cost_ret = np.maximum(cost_ret, -cost_ret)
return float(cost_ret)
# 初始化W和b的参数
def initial_parameters(net_deep_array):
net_deep = len(net_deep_array)
W = []
b = []
for L in range(net_deep):
WL = np.random.randn(net_deep_array[L], net_deep_array[L - 1]) * 0.01
bL = np.zeros(shape=(net_deep_array[L], 1))
W.append(WL)
b.append(bL)
return W, b
# 对深层神经网络得出的结果进行测试
def test_network(X, Y, parameter):
W = parameter.get('W')
b = parameter.get('b')
net_deep_array = parameter.get('net_deep_array')
net_deep = len(net_deep_array)
A = X
# 每次前向传播中的纵向深度
for L in range(1, net_deep, 1):
activate = 'tanh'
# 最后一次使用sigmoid激活函数
if L == net_deep - 1:
activate = 'sigmoid'
# 进行前向传播
forward_parameter = forward_propagation(A, {
'W': W[L],
'b': b[L],
}, activate)
A = forward_parameter.get('A')
# 计算成本cost
cost_value = cost(A, Y)
print(numpy.around(np.squeeze(A), 1))
print(Y)
m = A.shape[1]
for i in range(0, A.shape[1]):
if A[0, i] > 0.5:
A[0, i] = 1
else:
A[0, i] = 0
print("成本cost=" + str(cost_value))
print("准确性: " + str(float(np.sum((A == Y)) * 100 / m)) + "%")
# 获得数据
# num = 样本数量
def get_number(num):
X = [
[],
[],
[]
]
Y = []
i = 0
for i in range(num):
x = random.randint(-5, 15)
y = random.randint(0, 150)
z = random.randint(0, 150)
temp = np.exp(x) + 3 * y + z
result = 1
if temp < 500:
result = 0
X[0].append(x)
X[1].append(y)
X[2].append(z)
Y.append(result)
# print("-- 当 i =" + str(i))
# print("x=" + str(x))
# print("y=" + str(y))
# print("z=" + str(z))
# print("temp=" + str(temp))
# print("result=" + str(result))
# print("-" * 10)
return X, Y
if __name__ == '__main__':
# 测试集进行学习的次数
# 初始化训练的数据
data_X, data_Y = get_number(5000)
data_X = np.array(data_X)
data_Y = np.array(data_Y)
print(data_X.shape)
print(data_Y.shape)
parameter = deep_neural_network(data_X, data_Y, train_times=5000)
# 对测试集数据进行评估准确性
test_X, test_Y = get_number(15)
test_X = np.array(test_X)
test_Y = np.array(test_Y)
test_network(test_X, test_Y, parameter=parameter)
plt.title("week4 深层神经网络")
plt.xlabel("x/times")
plt.ylabel("损失值（越小越好）")
plt.rcParams['font.sans-serif'] = ['SimHei'] # 显示中文标签
plt.rcParams['axes.unicode_minus'] = False # 这两行需要手动设置
x = parameter.get('x')
y = parameter.get('y')
plt.plot(x, y, color='orange')
plt.show()
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] |
libraries/unified-model/unified_model/utils.py | felixridinger/machine-learning-lab | 55 | 70633 | <reponame>felixridinger/machine-learning-lab
import os
import re
import inspect
ITEM_COLUMN = "item"
SCORE_COLUMN = "score"
def resolve_camel_case(name):
s1 = re.sub('(.)([A-Z][a-z]+)', r'\1_\2', name)
return re.sub('([a-z0-9])([A-Z])', r'\1_\2', s1).lower()
def truncate_middle(s, n):
if len(s) <= n:
# string is already short-enough
return s
# half of the size, minus the 3 .'s
n_2 = int(n) / 2 - 3
# whatever's left
n_1 = n - n_2 - 3
return '{0}...{1}'.format(s[:int(n_1)], s[-int(n_2):])
def md5(file_path):
import hashlib
# return hashlib.md5(open(fname, 'rb').read()).hexdigest()
hash_md5 = hashlib.md5()
with open(file_path, "rb") as f:
for chunk in iter(lambda: f.read(4096), b""):
hash_md5.update(chunk)
return hash_md5.hexdigest()
def simplify(name):
return resolve_camel_case(name).strip().replace(" ", "_")
def get_file_name(file_path):
return os.path.splitext(os.path.basename(file_path))[0]
def overrides(method):
# actually can't do this because a method is really just a function while inside a class def'n
#assert(inspect.ismethod(method))
stack = inspect.stack()
base_classes = re.search(r'class.+\((.+)\)\s*\:', stack[2][4][0]).group(1)
# handle multiple inheritance
base_classes = [s.strip() for s in base_classes.split(',')]
if not base_classes:
raise ValueError('overrides decorator: unable to determine base class')
# stack[0]=overrides, stack[1]=inside class def'n, stack[2]=outside class def'n
derived_class_locals = stack[2][0].f_locals
# replace each class name in base_classes with the actual class type
for i, base_class in enumerate(base_classes):
if '.' not in base_class:
base_classes[i] = derived_class_locals[base_class]
else:
components = base_class.split('.')
# obj is either a module or a class
obj = derived_class_locals[components[0]]
for c in components[1:]:
assert(inspect.ismodule(obj) or inspect.isclass(obj))
obj = getattr(obj, c)
base_classes[i] = obj
assert( any( hasattr(cls, method.__name__) for cls in base_classes ) )
return method
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] |
cms_people/urls/people.py | python-spain/djangocms-people | 1 | 50339 | from django.conf.urls import url
from cms_people.views.people import PersonView, PeopleView, PeopleMapResultView
urlpatterns = [
url('^people_map_result$', PeopleMapResultView.as_view(), name='people_map_result'),
url('^(?P<username>.+)$', PersonView.as_view(), name='person'),
url('^$', PeopleView.as_view(), name='people'),
] | [
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services/stats.py | cybersturmer/findragon-core-api | 1 | 114783 | <reponame>cybersturmer/findragon-core-api
from eod import EodHistoricalData
from fastapi import (
Depends
)
from sqlalchemy.orm import Session
from database import get_session
from settings import settings
from typing import TypedDict
class AssetTotalStats(TypedDict):
id: int
code: str
price: float
amount: int
total_purchase_cost: float
total_market_price: float
portfolio_change: float
portfolio_change_p: float
change: float
change_p: float
class Stats:
def __init__(self, orm_session: Session = Depends(get_session)):
self.orm_session = orm_session
def get_assets_stats(self):
# Check if this SQL works for Postgres / Aurora
# @todo only for current portfolio and maybe user
transactions_list = \
self.orm_session \
.execute(
"SELECT portfolio_assets.id, portfolio_assets.ticker || '.' || portfolio_assets.exchange as code, "
"SUM(portfolio_transactions.amount_change) AS amount, "
"SUM(portfolio_transactions.cost_change) AS total_purchase "
"FROM portfolio_transactions, portfolio_assets "
"WHERE portfolio_assets.id == portfolio_transactions.asset_id "
"GROUP BY portfolio_transactions.asset_id"
)
asset_query_list = []
mapping_list = {}
response_data = {}
for row in transactions_list:
_asset_id, _code, _amount, _total_purchase_cost = row
asset = AssetTotalStats(
id=_asset_id,
code=_code,
price=0.0,
amount=_amount,
total_purchase_cost=_total_purchase_cost,
total_market_price=0.0,
portfolio_change=0.0,
portfolio_change_p=0.0,
change=0.0,
change_p=0.0
)
mapping_list[_code] = _asset_id
asset_query_list.append(_code)
response_data[_asset_id] = asset
separator = ','
asset_query = separator.join(asset_query_list)
client = EodHistoricalData(settings.eod.api_token)
eod_response = client.get_prices_live(asset_query)
eod_response_wrapped = eod_response if type(eod_response) == list else [eod_response]
eod_data_for_asset: dict
for eod_data_for_asset in eod_response_wrapped:
_code = eod_data_for_asset['code']
_asset_id = mapping_list[_code]
_price = float(eod_data_for_asset['previousClose'])
_total_market_price = _price * response_data[_asset_id]['amount']
_total_purchase_cost = response_data[_asset_id]['total_purchase_cost']
_total_diff = _total_market_price - _total_purchase_cost
_total_ratio = _total_diff / _total_purchase_cost * 100
response_data[_asset_id]['portfolio_change'] = _total_diff
response_data[_asset_id]['portfolio_change_p'] = _total_ratio
response_data[_asset_id]['price'] = _price
response_data[_asset_id]['total_market_price'] = _total_market_price
response_data[_asset_id]['change'] = eod_data_for_asset['change']
response_data[_asset_id]['change_p'] = eod_data_for_asset['change_p']
return list(response_data.values())
def get_total_income_stats(self):
pass
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] |
nova/virt/hyperv/driver.py | bopopescu/stacklab-nova | 0 | 70292 | # vim: tabstop=4 shiftwidth=4 softtabstop=4
# Copyright (c) 2010 Cloud.com, Inc
# Copyright (c) 2012 Cloudbase Solutions Srl
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations
# under the License.
"""
A connection to Hyper-V .
Uses Windows Management Instrumentation (WMI) calls to interact with Hyper-V
Hyper-V WMI usage:
http://msdn.microsoft.com/en-us/library/cc723875%28v=VS.85%29.aspx
The Hyper-V object model briefly:
The physical computer and its hosted virtual machines are each represented
by the Msvm_ComputerSystem class.
Each virtual machine is associated with a
Msvm_VirtualSystemGlobalSettingData (vs_gs_data) instance and one or more
Msvm_VirtualSystemSettingData (vmsetting) instances. For each vmsetting
there is a series of Msvm_ResourceAllocationSettingData (rasd) objects.
The rasd objects describe the settings for each device in a VM.
Together, the vs_gs_data, vmsettings and rasds describe the configuration
of the virtual machine.
Creating new resources such as disks and nics involves cloning a default
rasd object and appropriately modifying the clone and calling the
AddVirtualSystemResources WMI method
Changing resources such as memory uses the ModifyVirtualSystemResources
WMI method
Using the Python WMI library:
Tutorial:
http://timgolden.me.uk/python/wmi/tutorial.html
Hyper-V WMI objects can be retrieved simply by using the class name
of the WMI object and optionally specifying a column to filter the
result set. More complex filters can be formed using WQL (sql-like)
queries.
The parameters and return tuples of WMI method calls can gleaned by
examining the doc string. For example:
>>> vs_man_svc.ModifyVirtualSystemResources.__doc__
ModifyVirtualSystemResources (ComputerSystem, ResourceSettingData[])
=> (Job, ReturnValue)'
When passing setting data (ResourceSettingData) to the WMI method,
an XML representation of the data is passed in using GetText_(1).
Available methods on a service can be determined using method.keys():
>>> vs_man_svc.methods.keys()
vmsettings and rasds for a vm can be retrieved using the 'associators'
method with the appropriate return class.
Long running WMI commands generally return a Job (an instance of
Msvm_ConcreteJob) whose state can be polled to determine when it finishes
"""
from nova.openstack.common import log as logging
from nova.virt import driver
from nova.virt.hyperv import livemigrationops
from nova.virt.hyperv import snapshotops
from nova.virt.hyperv import vmops
from nova.virt.hyperv import volumeops
LOG = logging.getLogger(__name__)
class HyperVDriver(driver.ComputeDriver):
def __init__(self):
super(HyperVDriver, self).__init__()
self._volumeops = volumeops.VolumeOps()
self._vmops = vmops.VMOps(self._volumeops)
self._snapshotops = snapshotops.SnapshotOps()
self._livemigrationops = livemigrationops.LiveMigrationOps(
self._volumeops)
def init_host(self, host):
self._host = host
def list_instances(self):
return self._vmops.list_instances()
def spawn(self, context, instance, image_meta, injected_files,
admin_password, network_info=None, block_device_info=None):
self._vmops.spawn(context, instance, image_meta, network_info,
block_device_info)
def reboot(self, instance, network_info, reboot_type,
block_device_info=None):
self._vmops.reboot(instance, network_info, reboot_type)
def destroy(self, instance, network_info=None, cleanup=True):
self._vmops.destroy(instance, network_info, cleanup)
def get_info(self, instance):
return self._vmops.get_info(instance)
def attach_volume(self, connection_info, instance_name, mountpoint):
"""Attach volume storage to VM instance"""
return self._volumeops.attach_volume(connection_info,
instance_name,
mountpoint)
def detach_volume(self, connection_info, instance_name, mountpoint):
"""Detach volume storage to VM instance"""
return self._volumeops.detach_volume(connection_info,
instance_name,
mountpoint)
def get_volume_connector(self, instance):
return self._volumeops.get_volume_connector(instance)
def poll_rescued_instances(self, timeout):
pass
def get_available_resource(self):
return self._vmops.get_available_resource()
def update_host_status(self):
"""See xenapi_conn.py implementation."""
pass
def get_host_stats(self, refresh=False):
"""See xenapi_conn.py implementation."""
return {}
def host_power_action(self, host, action):
"""Reboots, shuts down or powers up the host."""
pass
def set_host_enabled(self, host, enabled):
"""Sets the specified host's ability to accept new instances."""
pass
def snapshot(self, context, instance, name):
self._snapshotops.snapshot(context, instance, name)
def pause(self, instance):
self._vmops.pause(instance)
def unpause(self, instance):
self._vmops.unpause(instance)
def suspend(self, instance):
self._vmops.suspend(instance)
def resume(self, instance):
self._vmops.resume(instance)
def power_off(self, instance):
self._vmops.power_off(instance)
def power_on(self, instance):
self._vmops.power_on(instance)
def live_migration(self, context, instance_ref, dest, post_method,
recover_method, block_migration=False):
self._livemigrationops.live_migration(context, instance_ref, dest,
post_method, recover_method, block_migration)
def compare_cpu(self, cpu_info):
return self._livemigrationops.compare_cpu(cpu_info)
def pre_live_migration(self, context, instance, block_device_info,
network_info):
self._livemigrationops.pre_live_migration(context, instance,
block_device_info, network_info)
def post_live_migration_at_destination(self, ctxt, instance_ref,
network_info, block_migration):
self._livemigrationops.post_live_migration_at_destination(ctxt,
instance_ref, network_info, block_migration)
def check_can_live_migrate_destination(self, ctxt, instance,
block_migration, disk_over_commit):
pass
def check_can_live_migrate_destination_cleanup(self, ctxt,
dest_check_data):
pass
def check_can_live_migrate_source(self, ctxt, instance, dest_check_data):
pass
def plug_vifs(self, instance, network_info):
LOG.debug(_("plug_vifs called"), instance=instance)
def unplug_vifs(self, instance, network_info):
LOG.debug(_("plug_vifs called"), instance=instance)
def ensure_filtering_rules_for_instance(self, instance_ref, network_info):
LOG.debug(_("ensure_filtering_rules_for_instance called"),
instance=instance_ref)
def unfilter_instance(self, instance, network_info):
"""Stop filtering instance"""
LOG.debug(_("unfilter_instance called"), instance=instance)
def confirm_migration(self, migration, instance, network_info):
"""Confirms a resize, destroying the source VM"""
LOG.debug(_("confirm_migration called"), instance=instance)
def finish_revert_migration(self, instance, network_info,
block_device_info=None):
"""Finish reverting a resize, powering back on the instance"""
LOG.debug(_("finish_revert_migration called"), instance=instance)
def finish_migration(self, context, migration, instance, disk_info,
network_info, image_meta, resize_instance=False,
block_device_info=None):
"""Completes a resize, turning on the migrated instance"""
LOG.debug(_("finish_migration called"), instance=instance)
def get_console_output(self, instance):
LOG.debug(_("get_console_output called"), instance=instance)
return ''
def legacy_nwinfo(self):
return False
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app/main/services/serial_port_services.py | longlhh90/serial-port-service-flask | 0 | 1606758 | <reponame>longlhh90/serial-port-service-flask<filename>app/main/services/serial_port_services.py
from app.serialcore import get_list_ports, init_serial_port, write_to_serial_port
from flask_restful import Resource, reqparse
from flask_jwt import jwt_required
from app.init.api import api
from app.utils import Collections
import serial
# HELPERS
def positive_integer(value):
if value <= 0:
raise ValueError("Value is not positive")
return value
def message_validator(value):
if not isinstance(value, str):
raise ValueError("Message should be string")
if len(value) > 500:
raise ValueError("Maximum length of message is 500")
return value
@api.resource('/ports/')
class PortListInfo(Resource):
@jwt_required()
def get(self):
data = get_list_ports()
return data, 200
@api.resource('/send-msg/')
class SerialPortMessage(Resource):
parser = reqparse.RequestParser()
parser.add_argument(
'message',
type=message_validator,
required=True,
help="{error_msg}"
)
@jwt_required()
def post(self):
args = SerialPortMessage.parser.parse_args()
message = bytes(args["message"], 'ascii')
response_msg, status_code = write_to_serial_port(message)
return {"message": response_msg}, status_code
@api.resource('/init-port/')
class InitSerialPort(Resource):
parser = reqparse.RequestParser(bundle_errors=True)
parser.add_argument(
'port',
type=str,
location='json',
required=True,
help="This field cannot be blank."
)
parser.add_argument(
'baudrate',
choices=serial.Serial.BAUDRATES,
type=int,
required=False,
help="Bad choice: {error_msg}." +
"The choices are: {}".format(serial.Serial.BAUDRATES)
)
parser.add_argument(
'bytesize',
choices=serial.Serial.BYTESIZES,
type=int,
required=False,
help="Bad choice: {error_msg}." +
"The choices are: {}".format(serial.Serial.BYTESIZES)
)
parser.add_argument(
'stopbits',
choices=serial.Serial.STOPBITS,
type=int,
required=False,
help="Bad choice: {error_msg}." +
"The choices are: {}".format(serial.Serial.STOPBITS)
)
parser.add_argument(
'parity',
choices=serial.Serial.PARITIES,
type=str,
required=False,
help="Bad choice: {error_msg}." +
"The choices are: {}".format(serial.Serial.PARITIES)
)
parser.add_argument(
'flow_control',
choices=('xonxoff', 'rtscts', 'dsrdtr'),
type=str,
required=False,
help="Bad choice: {error_msg}. The choices are: ('xonxoff', 'rtscts', 'dsrdtr')"
)
parser.add_argument(
'write_timeout',
type=positive_integer,
required=False,
help="Error: {error_msg}"
)
parser.add_argument(
'timeout',
type=positive_integer,
required=False,
help="Error: {error_msg}"
)
@jwt_required()
def post(self):
args = Collections.drop_none(InitSerialPort.parser.parse_args())
response_msg, status_code = init_serial_port(**args)
return {"message": response_msg}, status_code
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] |
Estructura de control selectivo_Algoritmo/Ejercicio_10.py | Karollg12/Algoritmos_Programacion | 0 | 83556 | """
Entradas
salario --> int --> s
categoria --> int --> c
Salidas
aumento --> int --> a
salario nuevo --> int --> sn
"""
s = int (input ( "Digotar el salario:" ))
c = int (input ( " Digitar categoria del 1 al 5: "))
if ( c == 1 ):
a = s * .10
if ( c == 2 ):
a = s * .15
if ( c == 3 ):
a == s * .20
if ( c == 4 ):
a = s * .40
if ( c == 5 ):
a = s * .60
sn = s + a
print ( "El aumento es de:" + str (a))
print ( "Valor del nuevo sueldo:" + str (sn))
| [
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utils/model_utils.py | Pandinosaurus/PerceptualImageError | 0 | 27539 | import tensorflow as tf
def extract_image_patches(image_batch, patch_size,patch_stride):
patches = tf.extract_image_patches(images =image_batch,ksizes=[1,patch_size,patch_size,1],rates=[1,1,1,1],strides=[1,patch_stride,patch_stride,1],padding='VALID')
patches_shape = patches.get_shape().as_list()
return tf.reshape(patches,[-1,patch_size,patch_size,3])#, patches_shape[1]*patches_shape[2] # NOTE: assuming 3 channels
def conv_init(name,input_channels, filter_height, filter_width, num_filters, groups=1):
weights = get_scope_variable(name, 'weights', shape=[filter_height, filter_width, input_channels/groups, num_filters], trainable=False)
biases = get_scope_variable(name, 'biases', shape = [num_filters],trainable=False)
def fc_init(name, num_in, num_out):
weights = get_scope_variable(name, 'weights', shape=[num_in, num_out], trainable=False)
biases = get_scope_variable(name, 'biases', shape=[num_out], trainable=False)
def conv(x, filter_height, filter_width, num_filters, stride_y, stride_x, name, padding='SAME', relu=True):
input_channels = int(x.get_shape().as_list()[3])
convolve = lambda i, k: tf.nn.conv2d(i, k, strides = [1, stride_y, stride_x, 1], padding = padding)
weights = get_scope_variable(name, 'weights', shape=[filter_height, filter_width, input_channels, num_filters])
biases = get_scope_variable(name, 'biases', shape = [num_filters])
conv = convolve(x, weights)
bias_val = tf.reshape(tf.nn.bias_add(conv, biases), tf.shape(conv))
if relu == True:
relu = tf.nn.relu(bias_val, name = name)
return relu
else:
return bias_val
def fc(x, num_in, num_out, name, relu = True):
weights = get_scope_variable(name, 'weights', shape=[num_in, num_out])
biases = get_scope_variable(name, 'biases', shape=[num_out])
act = tf.nn.xw_plus_b(x, weights, biases, name=name)
if relu == True:
relu = tf.nn.relu(act)
return relu
else:
return act
def max_pool(x, filter_height, filter_width, stride_y, stride_x, name, padding='SAME'):
return tf.nn.max_pool(x, ksize=[1, filter_height, filter_width, 1], strides = [1, stride_y, stride_x, 1], padding = padding, name = name)
def dropout(x, keep_prob):
return tf.nn.dropout(x, keep_prob)
def get_scope_variable(scope_name, var, shape=None, initialvals=None,trainable=False):
with tf.variable_scope(scope_name,reuse=tf.AUTO_REUSE) as scope:
v = tf.get_variable(var,shape,dtype=tf.float32, initializer=initialvals,trainable=trainable)
return v
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] |
scripts/adder.mem.py | devbored/pineapplecore | 0 | 1600318 | <gh_stars>0
#! /usr/bin/env python3
from common import *
n_vectors = 32
test_vector_a = [randImmI() for x in range(n_vectors)]
test_vector_b = [randImmI() for x in range(n_vectors)]
test_vector_subEn = [random.randint(0,1) for x in range(n_vectors)]
if __name__ == "__main__":
outfile = f"{basenameNoExt('build', __file__)}.mem"
with open(outfile, 'w') as fp:
for i in range(n_vectors):
test_vector = f"{test_vector_a[i] & 0xffffffff:032b}"
test_vector += f"{test_vector_b[i] & 0xffffffff:032b}"
test_vector += f"{test_vector_subEn[i] & 0x1:01b}"
print(test_vector, file=fp)
outfileGold = f"{basenameNoExt('build', __file__)}_gold.mem"
with open(outfileGold, 'w') as fp:
for i in range(n_vectors):
if test_vector_subEn[i] == 0:
test_vector = f"{(test_vector_a[i] + test_vector_b[i]) & 0xffffffff:032b}"
print(test_vector, file=fp)
else:
test_vector = f"{(test_vector_a[i] - test_vector_b[i]) & 0xffffffff:032b}"
print(test_vector, file=fp)
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2
] |
src/python/fsqio/pants/buildgen/python/register.py | stuhood/fsqio | 0 | 103238 | # coding=utf-8
# Copyright 2016 Foursquare Labs Inc. All Rights Reserved.
from __future__ import absolute_import
from pants.goal.task_registrar import TaskRegistrar as task
from fsqio.pants.buildgen.python.buildgen_python import BuildgenPython
from fsqio.pants.buildgen.python.map_python_exported_symbols import MapPythonExportedSymbols
def register_goals():
task(
name='map-python-exported-symbols',
action=MapPythonExportedSymbols,
).install()
task(
name='python',
action=BuildgenPython,
).install('buildgen')
| [
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python/ray/rllib/models/tf/tf_modelv2.py | alex-petrenko/ray | 1 | 21822 | <gh_stars>1-10
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from ray.rllib.models.modelv2 import ModelV2
from ray.rllib.utils import try_import_tf
tf = try_import_tf()
class TFModelV2(ModelV2):
"""TF version of ModelV2."""
def __init__(self, obs_space, action_space, output_spec, model_config,
name):
ModelV2.__init__(
self,
obs_space,
action_space,
output_spec,
model_config,
name,
framework="tf")
| [
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] |
Dataset/Leetcode/valid/66/141.py | kkcookies99/UAST | 0 | 106290 | class Solution:
def XXX(self, digits: List[int]) -> List[int]:
return [int(i) for i in (list(str(int(''.join([str(i) for i in digits]) ) + 1 )))]
| [
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maxSquare.py | passionzhan/LeetCode | 1 | 1610817 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
# @File : maxSquare.py
# @Author: Zhan
# @Date : 7/11/2019
# @Desc : 最大正方形
# 在一个由 0 和 1 组成的二维矩阵内，找到只包含 1 的最大正方形，并返回其面积。
#
# 示例:
#
# 输入:
#
# 1 0 1 0 0
# 1 0 1 1 1
# 1 1 1 1 1
# 1 0 0 1 0
#
# 输出: 4
class Solution(object):
def maximalSquare(self, matrix):
"""
:type matrix: List[List[str]]
:rtype: int
"""
nrow = len(matrix)
if nrow <= 0:
return 0
ncol = len(matrix[0])
if ncol <= 0:
return 0
pre = 0
dp = [0 for item in matrix[0]]
rst = 0
for i in range(0, nrow):
for j in range(0, ncol):
if j == 0:
pre = dp[j]
dp[j] = int(matrix[i][j])
elif matrix[i][j] == "1":
tmp = dp[j]
dp[j] = min(pre, dp[j - 1], dp[j]) + 1
pre = tmp
else:
pre = dp[j]
dp[j] = 0
rst = max(rst, dp[j])
return rst * rst
if __name__ == '__main__':
a = [["1","0","1","0","0"],
["1","0","1","1","1"],
["1","1","1","1","1"],
["1","0","1","1","1"]
]
# a = [7, 6, 4, 3, 1]
# a = [-1]
print(Solution().maximalSquare(a)) | [
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] |
solum/api/controllers/v1/assembly.py | devdattakulkarni/test-solum | 0 | 9570 | <gh_stars>0
# Copyright 2013 - Red Hat, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations
# under the License.
import pecan
from pecan import rest
import wsme
import wsmeext.pecan as wsme_pecan
from solum.api.controllers.v1.datamodel import assembly
import solum.api.controllers.v1.userlog as userlog_controller
from solum.api.handlers import assembly_handler
from solum.common import exception
from solum.common import request
from solum import objects
from solum.openstack.common.gettextutils import _
class AssemblyController(rest.RestController):
"""Manages operations on a single assembly."""
def __init__(self, assembly_id):
super(AssemblyController, self).__init__()
self._id = assembly_id
@pecan.expose()
def _lookup(self, primary_key, *remainder):
if remainder and not remainder[-1]:
remainder = remainder[:-1]
if primary_key == 'logs':
logs = userlog_controller.UserlogsController(self._id)
return logs, remainder
@exception.wrap_wsme_pecan_controller_exception
@wsme_pecan.wsexpose(assembly.Assembly)
def get(self):
"""Return this assembly."""
request.check_request_for_https()
handler = assembly_handler.AssemblyHandler(
pecan.request.security_context)
return assembly.Assembly.from_db_model(handler.get(self._id),
pecan.request.host_url)
@exception.wrap_wsme_pecan_controller_exception
@wsme_pecan.wsexpose(assembly.Assembly, body=assembly.Assembly)
def put(self, data):
"""Modify this assembly."""
handler = assembly_handler.AssemblyHandler(
pecan.request.security_context)
res = handler.update(self._id,
data.as_dict(objects.registry.Assembly))
return assembly.Assembly.from_db_model(res, pecan.request.host_url)
@exception.wrap_wsme_pecan_controller_exception
@wsme_pecan.wsexpose(status_code=204)
def delete(self):
"""Delete this assembly."""
handler = assembly_handler.AssemblyHandler(
pecan.request.security_context)
return handler.delete(self._id)
class AssembliesController(rest.RestController):
"""Manages operations on the assemblies collection."""
@pecan.expose()
def _lookup(self, assembly_id, *remainder):
if remainder and not remainder[-1]:
remainder = remainder[:-1]
return AssemblyController(assembly_id), remainder
@exception.wrap_wsme_pecan_controller_exception
@wsme_pecan.wsexpose(assembly.Assembly, body=assembly.Assembly,
status_code=201)
def post(self, data):
"""Create a new assembly."""
js_data = data.as_dict(objects.registry.Assembly)
if data.plan_uri is not wsme.Unset:
plan_uri = data.plan_uri
if plan_uri.startswith(pecan.request.host_url):
pl_uuid = plan_uri.split('/')[-1]
pl = objects.registry.Plan.get_by_uuid(
pecan.request.security_context, pl_uuid)
js_data['plan_id'] = pl.id
else:
# TODO(asalkeld) we are not hosting the plan so
# download the plan and insert it into our db.
raise exception.BadRequest(reason=_(
'The plan was not hosted in solum'))
if js_data.get('plan_id') is None:
raise exception.BadRequest(reason=_(
'The plan was not given or could not be found'))
handler = assembly_handler.AssemblyHandler(
pecan.request.security_context)
return assembly.Assembly.from_db_model(
handler.create(js_data), pecan.request.host_url)
@exception.wrap_wsme_pecan_controller_exception
@wsme_pecan.wsexpose([assembly.Assembly])
def get_all(self):
"""Return all assemblies, based on the query provided."""
request.check_request_for_https()
handler = assembly_handler.AssemblyHandler(
pecan.request.security_context)
return [assembly.Assembly.from_db_model(assm, pecan.request.host_url)
for assm in handler.get_all()]
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] |
test/app-client/scripts/esvm_client.py | susanwab/elijah-provisioning | 19 | 144543 | #!/usr/bin/env python
#
# Cloudlet Infrastructure for Mobile Computing
#
# Author: <NAME> <<EMAIL>>
#
# Copyright (C) 2011-2013 Carnegie Mellon University
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
import os
import sys
import socket
from optparse import OptionParser
import time
import struct
import math
def get_local_ipaddress():
s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
s.connect(("gmail.com",80))
ipaddress = (s.getsockname()[0])
s.close()
return ipaddress
def process_command_line(argv):
global operation_mode
parser = OptionParser(usage="usage: %prog [option]", version="ESVM Desktop Client")
parser.add_option(
'-i', '--input', action='store', type='string', dest='input_dir',
help='Set Input image directory')
parser.add_option(
'-s', '--server', action='store', type='string', dest='server_address', default="localhost",
help='Set Input image directory')
parser.add_option(
'-p', '--port', action='store', type='int', dest='server_port', default=9095,
help='Set Input image directory')
parser.add_option(
'-r', '--repeat', action='store', type='int', dest='conn_repeat', default=100,
help='Repeat connecting number')
settings, args = parser.parse_args(argv)
if not len(args) == 0:
parser.error('program takes no command-line arguments; "%s" ignored.' % (args,))
if not settings.input_dir:
parser.error("input directory does no exists at :%s" % (settings.input_dir))
if not os.path.isdir(settings.input_dir):
parser.error("input directory does no exists at :%s" % (settings.input_dir))
return settings, args
def send_request(address, port, inputs, conn_repeat):
# connection
conn_count = 0
connect_start_time = time.time()
while conn_count < conn_repeat:
try:
print "Connecting..."
sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
sock.setblocking(True)
sock.connect((address, port))
conn_count += 1
break
except socket.error, msg:
print "Connection failed, retry"
sock.close()
time.sleep(0.1)
if (sock == None) or (sock.fileno() <= 0):
sys.stderr.write("Connection faild to (%s:%d)\n" % (address, port))
sys.exit(1)
connect_end_time = time.time()
print "Connecting to (%s, %d) takes %f seconds" % \
(address, port, (connect_end_time-connect_start_time))
# send requests
current_duration = -1
print "image\tstart\tend\tduration\tjitter"
for each_input in inputs:
start_time_request = time.time() * 1000.0
binary = open(each_input, 'rb').read();
ret_data = esvm_request(sock, binary)
# print result
end_time_request = time.time() * 1000.0
prev_duration = current_duration
current_duration = end_time_request-start_time_request
if prev_duration == -1: # fisrt response
print "%s\t%014.2f\t%014.2f\t%014.2f\t0" % (each_input, start_time_request,\
end_time_request, \
end_time_request-start_time_request)
else:
print "%s\t%014.2f\t%014.2f\t%014.2f\t%014.2f" % (each_input, round(start_time_request, 3), \
end_time_request, \
current_duration, \
math.fabs(current_duration-prev_duration))
def esvm_request(sock, data):
length = len(data)
# send
sock.sendall(struct.pack("!I", length))
sock.sendall(data)
#recv
data = sock.recv(4)
ret_size = struct.unpack("!I", data)[0]
ret_data = ''
if not ret_size == 0:
ret_data = sock.recv(ret_size)
return ret_data
return None
def main(argv=None):
global LOCAL_IPADDRESS
settings, args = process_command_line(sys.argv[1:])
files = [os.path.join(settings.input_dir, file) for file in os.listdir(settings.input_dir) if file[-3:] == "jpg" or file[-3:] == "JPG"]
send_request(settings.server_address, settings.server_port, files, settings.conn_repeat)
return 0
if __name__ == "__main__":
status = main()
sys.exit(status)
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kenlm_training/tests/test_minify.py | ruinunca/data_tooling | 435 | 30404 | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
#
import json
from pathlib import Path
import pytest
import cc_net
import cc_net.minify as minify
from cc_net import jsonql, process_wet_file
from cc_net.minify import (
HASH_SIZE,
decode_hashes,
encode_hashes,
encode_line_ids,
get_hashes,
)
def test_encode_decode():
sentences = ["Hello world !", "Is everyone happy in here ?"]
hashes = get_hashes(sentences)
assert all([len(h) == HASH_SIZE for h in hashes])
hashes_int = [minify._b2i(h) for h in hashes]
encoded = encode_hashes(hashes)
decoded = decode_hashes(encoded)
assert all([len(d) == HASH_SIZE for d in decoded])
decoded_int = [minify._b2i(d) for d in decoded]
assert hashes_int == decoded_int
assert hashes == decoded
def test_minify():
doc = {
"raw_content": "Hello world !\nIs everyone happy in here ?",
"language": "en",
"perplexity": 120.0,
"line_ids": [0, 4],
}
expected = {"line_ids": "AAAEAA==", "language": "en", "perplexity": 120.0}
minifier = minify.Minifier()
assert expected == minifier(doc)
@pytest.fixture
def http_from_disk(monkeypatch):
def read_sample_file(url: str, n_retry: int = 3) -> bytes:
expected_url = process_wet_file.WET_URL_ROOT + "/crawl-data/sample.warc.wet"
assert expected_url == url
file = Path(__file__).parent / "data" / "sample.warc.txt"
return file.read_bytes()
monkeypatch.setattr(cc_net.jsonql, "request_get_content", read_sample_file)
def test_minify_and_fetch(http_from_disk, tmp_path: Path):
full_quotes = """Don't part with your illusions. When they are gone you may still exist, but you have ceased to live.
Education: that which reveals to the wise, and conceals from the stupid, the vast limits of their knowledge.
Facts are stubborn things, but statistics are more pliable.
Fiction is obliged to stick to possibilities. Truth isn't."""
# We don't need no education.
chosen_quotes = "\n".join(
l for l in full_quotes.splitlines() if "Education" not in l
)
cc_doc = {
"url": "http://sample_english.com",
"date_download": "2019-03-18T00:00:00Z",
"digest": "sha1:XQZHW7QWIG54HVAV3KPRW6MK5ILDNCER",
"source_domain": "sample_english.com",
"title": "Famous Mark Twain Quotes",
"raw_content": full_quotes,
"cc_segment": "crawl-data/sample.warc.wet",
"nlines": 4,
"length": 353,
}
ccnet_metadata = {
"language": "en",
"language_score": 0.99,
"perplexity": 151.5,
"bucket": "head",
"raw_content": chosen_quotes,
"nlines": 3,
"length": len(chosen_quotes),
"original_nlines": 4,
"original_length": 353,
"line_ids": [0, 2, 3],
}
ccnet_doc = dict(cc_doc, **ccnet_metadata)
mini = minify.Minifier()(ccnet_doc.copy())
assert mini is not ccnet_doc
important_fields = [
"url",
"digest",
"cc_segment",
"language",
"language_score",
"perplexity",
"bucket",
"line_ids",
]
expected = {k: ccnet_doc[k] for k in important_fields}
expected["line_ids"] = encode_line_ids(expected["line_ids"]) # type: ignore
assert expected == mini
with jsonql.open_write(tmp_path / "sample.json") as o:
print(json.dumps(mini), file=o)
fetcher = minify.MetadataFetcher(tmp_path)
# line_ids is removed when unminifying
ccnet_doc.pop("line_ids")
assert ccnet_doc == fetcher(cc_doc)
def test_fetch(http_from_disk, tmp_path: Path):
mini_docs = [
{
"url": "http://sample_chinese.com",
"digest": "sha1:Y4E6URVYGIAFNVRTPZ5S3J64RTZTP6HJ",
"cc_segment": "crawl-data/sample.warc.wet",
"line_ids": encode_line_ids([2]),
"bucket": "not_that_great",
},
{
"url": "http://sample_english.com",
"digest": "sha1:XQZHW7QWIG54HVAV3KPRW6MK5ILDNCER",
"cc_segment": "crawl-data/sample.warc.wet",
"line_ids": encode_line_ids([3]),
"bucket": "top_notch",
},
]
with jsonql.open_write(tmp_path / "sample.json") as o:
for mini in mini_docs:
print(json.dumps(mini), file=o)
fetcher = minify.MetadataFetcher(tmp_path)
cc = process_wet_file.CCSegmentsReader(["crawl-data/sample.warc.wet"])
docs = [d for d in fetcher.map(cc) if d is not None]
assert cc.retrieved_segments == 1
# Note: documents are retrieved as they are ordered in the .warc.wet file
assert [
"Facts are stubborn things, but statistics are more pliable.",
"事實是固執的東西，但統計數字卻比較柔和。",
] == [d["raw_content"] for d in docs]
assert ["top_notch", "not_that_great"] == [d["bucket"] for d in docs]
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q2_autopepsirf/actions/diffEnrich.py | Annabelle-Brown/q2-autopepsirf | 0 | 150398 | <reponame>Annabelle-Brown/q2-autopepsirf
import pandas as pd
import qiime2
from collections import defaultdict
import csv, os
from q2_pepsirf.format_types import PepsirfInfoSumOfProbesFmt, PepsirfInfoSNPNFormat, PepsirfContingencyTSVFormat, ZscoreNanFormat, EnrichedPeptideDirFmt
# Name: diffenrich
# Process: automatically runs through q2-ps-plot modules and q2-pepsirf modules
# Method Input/Parameters: default ctx, raw_data, bins, negative_controls, negative_ids,
# negative_names, thresh_file, exact_z_thresh, exact_zenrich_thresh, step_z_thresh,
# upper_z_thresh, lower_z_thresh, raw_constraint, pepsirf_binary
# Method output/Returned: col_sum, diff, diff_ratio, zscore_out, nan_out, sample_names,
# read_counts, rc_boxplot_out, enrich_dir, enrichedCountsBoxplot, zscore_scatter, colsum_scatter
# Dependencies: (ps-plot: raedCountsBoxplot, enrichmentRCBoxplot, repScatters, zenrich),
# (pepsirf: norm, zscore, infoSNPN, infoSumOfProbes, enrich)
def diffEnrich(
ctx,
raw_data,
bins,
negative_control=None,
negative_id=None,
negative_names=None,
thresh_file = None,
exact_z_thresh = None,
exact_cs_thresh = None,
exact_zenrich_thresh = None,
pepsirf_tsv_dir = "./",
tsv_base_str = None,
step_z_thresh = 5,
upper_z_thresh = 30,
lower_z_thresh = 5,
raw_constraint = 300000,
pepsirf_binary = "pepsirf"
):
if pepsirf_tsv_dir:
if not os.path.isdir(pepsirf_tsv_dir):
os.mkdir(pepsirf_tsv_dir)
# collect the actions from ps-plot and q2-pepsirf to be executed
norm = ctx.get_action('pepsirf', 'norm')
zscore = ctx.get_action('pepsirf', 'zscore')
infoSNPN = ctx.get_action('pepsirf', 'infoSNPN')
enrich = ctx.get_action('pepsirf', 'enrich')
infoSOP = ctx.get_action('pepsirf', 'infoSumOfProbes')
RCBoxplot = ctx.get_action('ps-plot', 'readCountsBoxplot')
enrichBoxplot = ctx.get_action('ps-plot', 'enrichmentRCBoxplot')
repScatter = ctx.get_action('ps-plot', 'repScatters')
zenrich = ctx.get_action('ps-plot', 'zenrich')
# run norm module to recieved col-sum
col_sum, = norm(peptide_scores = raw_data,
normalize_approach = "col_sum",
negative_control = None,
negative_id = None,
negative_names = None,
precision = 2,
pepsirf_binary = pepsirf_binary)
if pepsirf_tsv_dir and tsv_base_str:
cs_base = "%s_CS.tsv" % (tsv_base_str)
cs_tsv = col_sum.view(PepsirfContingencyTSVFormat)
cs_tsv.save(os.path.join(pepsirf_tsv_dir, cs_base), ext = ".tsv") #requires qiime2-2021.11
# run norm module to recieve diff
diff, = norm(peptide_scores = col_sum,
normalize_approach = "diff",
negative_control = negative_control,
negative_id = negative_id,
negative_names = negative_names,
precision = 2,
pepsirf_binary = pepsirf_binary)
if pepsirf_tsv_dir and tsv_base_str:
diff_base = "%s_SBD.tsv" % (tsv_base_str)
diff_tsv = diff.view(PepsirfContingencyTSVFormat)
diff_tsv.save(os.path.join(pepsirf_tsv_dir, diff_base), ext = ".tsv")
# run norm module to recieve diff-ratio
diff_ratio, = norm(peptide_scores = col_sum,
normalize_approach = "diff_ratio",
negative_control = negative_control,
negative_id = negative_id,
negative_names = negative_names,
precision = 2,
pepsirf_binary = pepsirf_binary)
if pepsirf_tsv_dir and tsv_base_str:
diffR_base = "%s_SBDR.tsv" % (tsv_base_str)
diffR_tsv = diff_ratio.view(PepsirfContingencyTSVFormat)
diffR_tsv.save(os.path.join(pepsirf_tsv_dir, diffR_base), ext = ".tsv")
# run zscore module to recieve zscore and nan files
zscore_out, nan_out = zscore(
scores = diff,
bins = bins,
hdi = 0.95,
pepsirf_binary = pepsirf_binary
)
if pepsirf_tsv_dir and tsv_base_str:
zscore_base = "%s_Z-HDI95.tsv" % (tsv_base_str)
zscore_tsv = zscore_out.view(PepsirfContingencyTSVFormat)
zscore_tsv.save(os.path.join(pepsirf_tsv_dir, zscore_base), ext = ".tsv")
nan_base = "%s_Z-HDI95.nan" % (tsv_base_str)
nan_tsv = nan_out.view(ZscoreNanFormat)
nan_tsv.save(os.path.join(pepsirf_tsv_dir, nan_base), ext = ".nan")
# run info module to collect sample names
sample_names, = infoSNPN(
input = raw_data,
get = "samples",
pepsirf_binary = pepsirf_binary
)
if pepsirf_tsv_dir and tsv_base_str:
sn_base = "%s_SN.tsv" % (tsv_base_str)
sn_tsv = sample_names.view(PepsirfInfoSNPNFormat)
sn_tsv.save(os.path.join(pepsirf_tsv_dir, sn_base), ext = ".tsv")
# run info to collect read counts
read_counts, = infoSOP(
input = raw_data,
pepsirf_binary = pepsirf_binary
)
if pepsirf_tsv_dir and tsv_base_str:
rc_base = "%s_RC.tsv" % (tsv_base_str)
rc_tsv = read_counts.view(PepsirfInfoSumOfProbesFmt)
rc_tsv.save(os.path.join(pepsirf_tsv_dir, rc_base), ext = ".tsv")
# run readCounts boxplot module to recieve visualization
rc_boxplot_out, = RCBoxplot(
read_counts = read_counts,
png_out_dir = pepsirf_tsv_dir
)
# create variables for source file creation
sourceDic = defaultdict(list)
sampleNM = sample_names.view(PepsirfInfoSNPNFormat)
source = os.path.join(pepsirf_tsv_dir, "samples_source.tsv")
# create list for collection of sample names
if not negative_names:
negative_names = []
# open samples file and collect samples into a dictionary
with open( str(sampleNM) ) as SN:
for line in SN:
sample = line.strip()
sourceLS = sample.rsplit('_', 1)
sourced = sourceLS[0]
sourceDic[sourced].append(sample)
if not negative_names:
negative_names.append(sample)
# create a source file written with column 1 as the sample names
# and the column 2 as the source column
# the source file will be put in the tsv directory
with open( source , "w" ) as tsvWriter:
writer = csv.writer(tsvWriter, delimiter='\t')
writer.writerow(['sampleID', 'source'])
for srce, samples in sourceDic.items():
for name in samples:
writer.writerow([name, srce])
# convert source file to metadata column to be used within the modules
source_col = qiime2.Metadata.load(source).get_column("source")
# run enrich module
enrich_dir, = enrich(
source = source_col,
thresh_file = thresh_file,
zscores = zscore_out,
col_sum = col_sum,
exact_z_thresh = exact_z_thresh,
exact_cs_thresh = exact_cs_thresh,
raw_scores = raw_data,
raw_constraint = raw_constraint,
enrichment_failure = True,
pepsirf_binary = pepsirf_binary
)
print("enrich working")
if pepsirf_tsv_dir and tsv_base_str:
enrich_base = "enriched"
enrich_tsv = enrich_dir.view(EnrichedPeptideDirFmt)
enrich_tsv.save(os.path.join(pepsirf_tsv_dir, enrich_base))
# run enrichment boxplot module to recieve visualization
enrichedCountsBoxplot, = enrichBoxplot(
enriched_dir = enrich_dir,
png_out_dir = pepsirf_tsv_dir
)
# run repScatter module to collect visualization
zscore_scatter, = repScatter(
source = source_col,
plot_log = False,
zscore = zscore_out
)
# run repScatter module to collect visualization
colsum_scatter, = repScatter(
source = source_col,
plot_log = True,
col_sum = col_sum
)
# run the zenrich module to collect visualization
zenrich_out, = zenrich(
data = col_sum,
zscores = zscore_out,
negative_controls = negative_names,
source = source_col,
negative_data = negative_control,
step_z_thresh = step_z_thresh,
upper_z_thresh = upper_z_thresh,
lower_z_thresh = lower_z_thresh,
exact_z_thresh = exact_zenrich_thresh,
pepsirf_binary = pepsirf_binary
)
# return all files created
return (
col_sum, diff, diff_ratio, zscore_out, nan_out, sample_names,
read_counts, rc_boxplot_out, enrich_dir, enrichedCountsBoxplot,
zscore_scatter, colsum_scatter, zenrich_out
) | [
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] |
run.py | vprime/web-scraper | 1 | 152172 | from flask import Flask, request, jsonify, send_from_directory, redirect, url_for
app = Flask(__name__, static_url_path='', static_folder='web-frontend/dist/spa-mat')
@app.after_request
def after_request(response):
response.headers.add('Access-Control-Allow-Origin', '*')
response.headers.add('Access-Control-Allow-Headers', 'Content-Type,Authorization')
response.headers.add('Access-Control-Allow-Methods', 'GET,PUT,POST,DELETE,OPTIONS')
return response
@app.route('/scrape', methods=['POST'])
def scrape():
if request.method == 'POST':
from scraper import Scraper
scraper = Scraper(request.json['address'])
return jsonify(
source=request.json['address'],
status=scraper.request.status_code,
detections=scraper.List
)
@app.route('/<path:path>')
def frontend(path):
if (path == ''):
path = 'index.html'
return send_from_directory('web-frontend/dist/spa-mat', path)
@app.route('/')
def home():
return frontend('index.html')
if __name__ == '__main__':
app.run(debug=True, host='0.0.0.0') | [
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openstackclient/network/v2/floating_ip.py | redhat-openstack/python-openstackclient | 0 | 96745 | <gh_stars>0
# Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations
# under the License.
#
"""IP Floating action implementations"""
from openstackclient.common import utils
from openstackclient.i18n import _
from openstackclient.network import common
def _get_columns(item):
columns = list(item.keys())
if 'tenant_id' in columns:
columns.remove('tenant_id')
columns.append('project_id')
return tuple(sorted(columns))
def _get_attrs(client_manager, parsed_args):
attrs = {}
network_client = client_manager.network
if parsed_args.network is not None:
network = network_client.find_network(parsed_args.network,
ignore_missing=False)
attrs['floating_network_id'] = network.id
if parsed_args.subnet is not None:
subnet = network_client.find_subnet(parsed_args.subnet,
ignore_missing=False)
attrs['subnet_id'] = subnet.id
if parsed_args.port is not None:
port = network_client.find_port(parsed_args.port,
ignore_missing=False)
attrs['port_id'] = port.id
if parsed_args.floating_ip_address is not None:
attrs['floating_ip_address'] = parsed_args.floating_ip_address
if parsed_args.fixed_ip_address is not None:
attrs['fixed_ip_address'] = parsed_args.fixed_ip_address
return attrs
class CreateFloatingIP(common.NetworkAndComputeShowOne):
"""Create floating IP"""
def update_parser_common(self, parser):
# In Compute v2 network, floating IPs could be allocated from floating
# IP pools, which are actually external networks. So deprecate the
# parameter "pool", and use "network" instead.
parser.add_argument(
'network',
metavar='<network>',
help=_("Network to allocate floating IP from (name or ID)")
)
return parser
def update_parser_network(self, parser):
parser.add_argument(
'--subnet',
metavar='<subnet>',
help=_("Subnet on which you want to create the floating IP "
"(name or ID)")
)
parser.add_argument(
'--port',
metavar='<port>',
help=_("Port to be associated with the floating IP "
"(name or ID)")
)
parser.add_argument(
'--floating-ip-address',
metavar='<floating-ip-address>',
dest='floating_ip_address',
help=_("Floating IP address")
)
parser.add_argument(
'--fixed-ip-address',
metavar='<fixed-ip-address>',
dest='fixed_ip_address',
help=_("Fixed IP address mapped to the floating IP")
)
return parser
def take_action_network(self, client, parsed_args):
attrs = _get_attrs(self.app.client_manager, parsed_args)
obj = client.create_ip(**attrs)
columns = _get_columns(obj)
data = utils.get_item_properties(obj, columns)
return (columns, data)
def take_action_compute(self, client, parsed_args):
obj = client.floating_ips.create(parsed_args.network)
columns = _get_columns(obj._info)
data = utils.get_dict_properties(obj._info, columns)
return (columns, data)
class DeleteFloatingIP(common.NetworkAndComputeCommand):
"""Delete floating IP"""
def update_parser_common(self, parser):
parser.add_argument(
'floating_ip',
metavar="<floating-ip>",
help=_("Floating IP to delete (IP address or ID)")
)
return parser
def take_action_network(self, client, parsed_args):
obj = client.find_ip(parsed_args.floating_ip)
client.delete_ip(obj)
def take_action_compute(self, client, parsed_args):
obj = utils.find_resource(
client.floating_ips,
parsed_args.floating_ip,
)
client.floating_ips.delete(obj.id)
class ListFloatingIP(common.NetworkAndComputeLister):
"""List floating IP(s)"""
def take_action_network(self, client, parsed_args):
columns = (
'id',
'floating_ip_address',
'fixed_ip_address',
'port_id',
)
headers = (
'ID',
'Floating IP Address',
'Fixed IP Address',
'Port',
)
query = {}
data = client.ips(**query)
return (headers,
(utils.get_item_properties(
s, columns,
formatters={},
) for s in data))
def take_action_compute(self, client, parsed_args):
columns = (
'ID',
'IP',
'Fixed IP',
'Instance ID',
'Pool',
)
headers = (
'ID',
'Floating IP Address',
'Fixed IP Address',
'Server',
'Pool',
)
data = client.floating_ips.list()
return (headers,
(utils.get_item_properties(
s, columns,
formatters={},
) for s in data))
class ShowFloatingIP(common.NetworkAndComputeShowOne):
"""Show floating IP details"""
def update_parser_common(self, parser):
parser.add_argument(
'floating_ip',
metavar="<floating-ip>",
help=_("Floating IP to display (IP address or ID)")
)
return parser
def take_action_network(self, client, parsed_args):
obj = client.find_ip(parsed_args.floating_ip, ignore_missing=False)
columns = _get_columns(obj)
data = utils.get_item_properties(obj, columns)
return (columns, data)
def take_action_compute(self, client, parsed_args):
obj = utils.find_resource(
client.floating_ips,
parsed_args.floating_ip,
)
columns = _get_columns(obj._info)
data = utils.get_dict_properties(obj._info, columns)
return (columns, data)
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analysis.py | cua-cua/coink | 0 | 76348 | import numpy
import pandas
from pathlib import Path
def raw_to_dataframe(raw):
data = numpy.frombuffer(raw, dtype='int16')
data = data.reshape((data.size // 4, 4))
df = pandas.DataFrame(data, columns=['t', 'x', 'y', 'z'])
# Center the curve
shift = len(df) // 2 - df['x'].idxmin()
df = df.reindex(numpy.roll(df.index, shift)).reset_index(drop=True)
df['t'] = df['t'].diff().cumsum() * 1e-6
df.loc[0, 't'] = 0.
df = df.set_index('t')
return df
def read_data(fname):
df = pandas.read_csv(fname, dtype='h')
df['t'] = df['t'].diff().cumsum() * 1e-6
df.loc[0, 't'] = 0.
df = df.set_index('t')
return df
def normalize(df):
df = df.astype(float)
df /= df.iloc[:50].mean()
df -= 1
zone = df[df['x'] < -0.2]
diff = zone.index[-1] - zone.index[0]
t0 = zone.index[0] - diff
t1 = zone.index[-1] + diff
df = df[t0:t1]
df.index -= df.index[0]
return df
def load_data(coin, path: Path = None):
if path is None:
path = Path('data')
if isinstance(path, str):
path = Path(path)
data = []
for fname in path.glob('%s-*.csv' % coin):
df = read_data(fname)
df = normalize(df)
data.append(df)
return data
def axis_features(curve):
diff = 0
low = curve[0]
d0_low = low
for value in curve:
low = min(low, value)
d = value - low
if d > diff:
diff = d
d0_low = low
if value == curve.min():
break
return {'min': curve.min(), 'l0': d0_low, 'd0': diff}
def summary(curve, coin):
x = axis_features(curve['x'])
y = axis_features(curve['y'])
z = axis_features(curve['z'])
summary = {
'coin': coin,
'min_x': x['min'],
'min_y': y['min'],
'min_z': z['min'],
'l0_x': x['l0'],
'l0_y': y['l0'],
'l0_z': z['l0'],
'd0_x': x['d0'],
'd0_y': y['d0'],
'd0_z': z['d0'],
}
return summary
def data_summary(c1, c2):
data = zip(c1 + c2, ['1 €'] * 10 + ['2 €'] * 10)
return pandas.DataFrame([summary(curve, coin) for curve, coin in data])
def compare_min(df):
df.boxplot(['min_x', 'min_y', 'min_z'], by='coin',
layout=(1, 3), figsize=(12, 6))
def compare_d0(df):
df.boxplot(['d0_x', 'd0_y', 'd0_z'], by='coin',
layout=(1, 3), figsize=(12, 6))
def compare_l0(df):
df.boxplot(['l0_x', 'l0_y', 'l0_z'], by='coin',
layout=(1, 3), figsize=(12, 6))
def classify_coin(curve):
features_x = axis_features(curve['x'])
if -0.40 > features_x['min'] > -0.55:
return 1
if -0.55 > features_x['min'] > -0.75:
return 2
return -1
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Script-003-Boxplots.py | paulinelemenkova/Python-script-003-Boxplots | 0 | 147567 | <filename>Script-003-Boxplots.py<gh_stars>0
#!/usr/bin/env python
# coding: utf-8
import os
import pandas as pd
import seaborn as sb
from matplotlib import pyplot as plt
sb.set_style('whitegrid')
sb.set_context('paper')
os.chdir('/Users/pauline/Documents/Python')
dfB = pd.read_csv("Tab-Bathy.csv")
# define variables and plotting
sb.boxplot(data=dfB, orient="v", palette='coolwarm', saturation=1,
width=0.8, dodge=True, fliersize=5, linewidth=0.2,
whis=5, notch=True, ax=None
)
sb.despine(offset=10, trim=True) # offset the spines away from the sample data
plt.xticks(rotation=45)
plt.yticks(rotation=45)
plt.title('Box-and-whisker plot for the Mariana Trench bathymetry',
fontsize=12, fontfamily='sans-serif'
)
# printing and saving
plt.tight_layout()
plt.savefig('plot_Boxplot.png', dpi=300)
plt.show()
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gol-py/src/patterns.py | Tajnymag/vsb-pai-game-of-life | 0 | 114887 | <reponame>Tajnymag/vsb-pai-game-of-life<filename>gol-py/src/patterns.py
import os
from multiprocessing import Pool, Process
from multiprocessing.sharedctypes import Array, RawArray
import numpy as np
from numpy.typing import NDArray
from rle import RuleLengthEncoded
beehive = RuleLengthEncoded("""#N Beehive
#O <NAME>
#C An extremely common 6-cell still life.
#C www.conwaylife.com/wiki/index.php?title=Beehive
x = 4, y = 3, rule = B3/S23
b2ob$o2bo$b2o!""")
glider = RuleLengthEncoded("""#N Glider
#O <NAME>
#C The smallest, most common, and first discovered spaceship. Diagonal, has period 4 and speed c/4.
#C www.conwaylife.com/wiki/index.php?title=Glider
x = 3, y = 3, rule = B3/S23
bob$2bo$3o!""")
block = RuleLengthEncoded("""#N Block
#C An extremely common 4-cell still life.
#C www.conwaylife.com/wiki/index.php?title=Block
x = 2, y = 2, rule = B3/S23
2o$2o!""")
blinker = RuleLengthEncoded("""#N Blinker
#O <NAME>
#C A period 2 oscillator that is the smallest and most common oscillator.
#C www.conwaylife.com/wiki/index.php?title=Blinker
x = 3, y = 1, rule = B3/S23
3o!""")
r_pentomino = RuleLengthEncoded("""#N R-pentomino
#C A methuselah with lifespan 1103.
#C www.conwaylife.com/wiki/index.php?title=R-pentomino
x = 3, y = 3, rule = B3/S23
b2o$2ob$bo!""")
n_glider_loop = RuleLengthEncoded("""#N Glider loop
#C A pattern in which two gliders are bounced back and forth along an
#C ever-lengthening track.
#C www.conwaylife.com/wiki/index.php?title=Glider_loop
x = 73, y = 150, rule = b3/s23
45bo27b$37b3o5bo27b$36bo2b2o3bobo26b$35bo5bo3bo27b$41bo3bo27b$36b2o2bo
32b$25b2o11b2o33b$25b2o46b6$61bo11b$17b2o40b2ob2o9b$17b2o42bo11b3$59b
2o12b$58bo2bo11b$51bo5bo3b2o10b$51bo5bo4bo10b$9b2o40bo6bo3bo10b$9b2o
37bob3obo3bo2bo11b$48b5obo5bo12b$48bobo22b$52b2o17bob$52b2o9b3o5bob$
52bo9bo2b2o3bobo$51b2o8bo5bo3bob$46b2o3bo15bo3bob$17b3o31bo10b2o2bo6b$
20bo29b2obo10b2o7b$15b2o4bo30bo20b$18bo3bo50b$14bo4bo2bo50b$14bo3bo3bo
50b$14b3obob2o51b$18bo54b$14b4o7b3o45b$14b2o8bo3bo44b$24bo4bo28bo14b$
26bo3bo25b2o9b2o4b$23b3obo2bo24b2o8b3obo3b$22bo7bo23bo10bo3bo3b$22bobo
3bo26b5o5bob2o4b$21b2obo3bo27bo10bo5b$21b2ob3o46b5$63b2o8b$63b2o8b4$
43b3o27b$46bo26b$41b2o4bo25b$44bo3bo6b2o16b$40bo4bo2bo6b2o16b$40bo3bo
3bo24b$40b3obob2o25b$44bo28b$40b4o29b$40b2o31b2$47b2o24b$47b2o24b2$33b
o39b$33bobo37b$33b2o38b3$38b2o33b$37bobo33b$39bo33b2$24b2o47b$24b2o47b
2$31b2o40b$29b4o40b$28bo44b$25b2obob3o40b$24bo3bo3bo40b$16b2o6bo2bo4bo
40b$16b2o6bo3bo44b$25bo4b2o41b$26bo46b$27b3o43b4$8b2o63b$8b2o63b5$46b
3ob2o21b$5bo10bo27bo3bob2o21b$4b2obo5b5o26bo3bobo22b$3bo3bo10bo23bo7bo
22b$3bob3o8b2o24bo2bob3o23b$4b2o9b2o25bo3bo26b$14bo28bo4bo24b$44bo3bo
8b2o14b$45b3o7b4o14b$54bo18b$51b2obob3o14b$50bo3bo3bo14b$50bo2bo4bo14b
$50bo3bo18b$20bo30bo4b2o15b$7b2o10bob2o29bo20b$6bo2b2o10bo31b3o17b$bo
3bo15bo3b2o46b$bo3bo5bo8b2o51b$obo3b2o2bo9bo52b$bo5b3o9b2o52b$bo17b2o
52b$22bobo48b$12bo5bob5o48b$11bo2bo3bob3obo37b2o9b$10bo3bo6bo40b2o9b$
10bo4bo5bo51b$10b2o3bo5bo51b$11bo2bo58b$12b2o59b3$11bo42b2o17b$9b2ob2o
40b2o17b$11bo61b6$46b2o25b$33b2o11b2o25b$32bo2b2o36b$27bo3bo41b$27bo3b
o5bo35b$26bobo3b2o2bo36b$27bo5b3o37b$27bo!""")
ALL_PATTERNS = [beehive, glider, block, blinker, r_pentomino, n_glider_loop]
PATTERN_COLORS_RGB = [
(0, 0, 0),
(255, 0, 0),
(0, 255, 0),
(0, 0, 255),
(255, 255, 0),
(255, 0, 255),
(0, 255, 255),
(255, 255, 255),
]
def copy_to_board(x: int, y: int, pattern: RuleLengthEncoded, board: NDArray[bool]):
(board_width, board_height) = board.shape
for pattern_y in range(0, pattern.height):
for pattern_x in range(0, pattern.width):
board_x = x + pattern_x
board_y = y + pattern_y
if not 0 <= board_x < board_width:
continue
if not 0 <= board_y < board_height:
continue
board[board_y][board_x] = pattern.data[pattern_y][pattern_x]
def detect_patterns_thread(from_index: int, to_index: int, board_width: int, board_height: int, board_buffer: RawArray, output_buffer: Array):
board = np.frombuffer(board_buffer, dtype=bool)
for i in range(from_index, to_index):
for pattern_id, pattern in enumerate(ALL_PATTERNS):
pattern_start = i
skip_pattern = False
for pattern_y in range(0, pattern.height):
for pattern_x in range(0, pattern.width):
cell_index = pattern_start + pattern_y * board_width + pattern_x
if cell_index >= board.size:
skip_pattern = True
break
if output_buffer[cell_index] != 0:
skip_pattern = True
break
pattern_value = pattern.data[pattern_y][pattern_x]
cell_value = board[cell_index]
if cell_value != pattern_value:
skip_pattern = True
break
if not skip_pattern:
for pattern_y in range(0, pattern.height):
for pattern_x in range(0, pattern.width):
cell_index = pattern_start + pattern_y * board_width + pattern_x
output_buffer[cell_index] = pattern_id
def detect_patterns(board: NDArray[bool], output_buffer: Array):
(board_width, board_height) = board.shape
board_buffer = RawArray("b", board.ravel())
jobs_count = os.cpu_count()
cells_count = board_width * board_height
chunk_size = cells_count // jobs_count
odd_chunks = cells_count % jobs_count
jobs = []
for job_index in range(0, jobs_count):
from_index = job_index * chunk_size
to_index = from_index + chunk_size + (1 if job_index < odd_chunks else 0)
p = Process(target=detect_patterns_thread, args=(from_index, to_index, board_width, board_height, board_buffer, output_buffer))
p.start()
jobs.append(p)
for p in jobs:
print(f"joined process {p.pid}")
p.join()
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zfs/replicate/filesystem/list.py | MarcoPolo/zfs-replicate | 0 | 1616840 | # -*- coding: utf-8 -*-
"""ZFs FileSystem List."""
import re
from typing import List
from .. import subprocess
from ..error import ZFSReplicateError
from . import type
from .type import FileSystem
RE_WHITESPACE = re.compile(b"[ \t]+")
def list(filesystem: FileSystem, ssh_command: str) -> List[FileSystem]:
"""List ZFS FileSystem."""
command = _list(filesystem)
if ssh_command is not None:
command = ssh_command + " " + command
proc = subprocess.open(command)
output, error = proc.communicate()
if error is not None:
error = error.strip(b"\n").strip(b"\r").replace(b"WARNING: ENABLED NONE CIPHER", b"")
if proc.returncode:
raise ZFSReplicateError(
f"error encountered while listing filesystems of '{filesystem.name}': {error!r}",
filesystem,
error,
)
return _filesystems(output)
def _list(filesystem: FileSystem) -> str:
options = ["-H", "-o name,readonly", "-t filesystem,volume", "-r"]
return f"/usr/bin/env - zfs list {' '.join(options)} '{filesystem.name}'"
def _filesystems(zfs_list_output: bytes) -> List[FileSystem]:
return [_filesystem(x) for x in zfs_list_output.split(b"\n") if x != b""]
def _filesystem(zfs_list_line: bytes) -> FileSystem:
name, readonly = RE_WHITESPACE.sub(b" ", zfs_list_line).split(b" ")
return type.filesystem(name=name.decode("utf-8"), readonly=readonly == b"on")
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refactorings/increase_field_visibility.py | Amin-MAG/CodART | 1 | 108766 | """
"""
__version__ = '0.1.0'
__author__ = 'Morteza'
import os
try:
import understand as und
except ModuleNotFoundError:
# Error handling
pass
from antlr4 import *
from antlr4.TokenStreamRewriter import TokenStreamRewriter
from gen.java.JavaParser import JavaParser
from gen.javaLabeled.JavaLexer import JavaLexer
from gen.javaLabeled.JavaParserLabeled import JavaParserLabeled
from gen.javaLabeled.JavaParserLabeledListener import JavaParserLabeledListener
class IncreaseFieldVisibilityRefactoringListener(JavaParserLabeledListener):
"""
## Introduction
Increase the visibility of a field from private to package, package to protected or protected to public.
## Pre and Post Conditions
### Pre Conditions:
1. User must enter the field's name, and the source class's name for the refactoring
in order to increase the target field's visibility.
### Post Conditions:
No specific Post Condition
"""
def __init__(self, common_token_stream: CommonTokenStream = None, source_class=None, field_name: str = None):
"""To implement Increase Field Visibility refactoring based on its actors.
Detects the required field and increases/changes its visibility status.
Args:
common_token_stream (CommonTokenStream): A stream of tokens generated by parsing the main file using the ANTLR parser generator
source_class (str): Name of the class in which the refactoring has to be done
field_name (str): Name of the field whose visibility status has to be changed
Returns:
No returns
"""
if field_name is None:
self.field_name = ""
else:
self.field_name = field_name
if source_class is None:
self.source_class = ""
else:
self.source_class = source_class
if common_token_stream is None:
raise ValueError('common_token_stream is None')
else:
self.token_stream_rewriter = TokenStreamRewriter(common_token_stream)
self.is_source_class = False
self.detected_field = None
self.detected_method = None
self.TAB = "\t"
self.NEW_LINE = "\n"
self.code = ""
self.tempdeclarationcode = ""
def enterClassDeclaration(self, ctx: JavaParserLabeled.ClassDeclarationContext):
class_identifier = ctx.IDENTIFIER().getText()
if class_identifier == self.source_class:
self.is_source_class = True
else:
self.is_source_class = False
def exitFieldDeclaration(self, ctx: JavaParserLabeled.FieldDeclarationContext):
if not self.is_source_class:
return None
grand_parent_ctx = ctx.parentCtx.parentCtx
# field_identifier = ctx.variableDeclarators().getText().split(",")
field_identifier = ctx.variableDeclarators().variableDeclarator(0).variableDeclaratorId().IDENTIFIER().getText()
if self.field_name in field_identifier:
if grand_parent_ctx.modifier() == []:
self.token_stream_rewriter.replaceRange(
from_idx=ctx.typeType().start.tokenIndex,
to_idx=ctx.typeType().stop.tokenIndex,
text='private ' + ctx.typeType().getText()
)
elif grand_parent_ctx.modifier(0).getText() == 'public':
self.token_stream_rewriter.replaceRange(
from_idx=grand_parent_ctx.modifier(0).start.tokenIndex,
to_idx=grand_parent_ctx.modifier(0).stop.tokenIndex,
text='private')
elif grand_parent_ctx.modifier(0).getText() != 'private':
self.token_stream_rewriter.replaceRange(
from_idx=grand_parent_ctx.modifier(0).start.tokenIndex,
to_idx=grand_parent_ctx.modifier(0).stop.tokenIndex,
text='private ' + grand_parent_ctx.modifier(0).getText())
# generate accessor and mutator methods
# Accessor body
new_code = '\n\t'
new_code += 'public ' + ctx.typeType().getText() + ' get' + str.capitalize(self.field_name)
new_code += '() { \n\t\t return this.' + self.field_name + ';' + '\n\t}'
# Mutator body
new_code += '\n\t'
new_code += 'public void set' + str.capitalize(self.field_name)
new_code += '(' + ctx.typeType().getText() + ' ' + self.field_name + ') { \n\t\t'
new_code += 'this.' + self.field_name + ' = ' + self.field_name + ';' + '\n\t}\n'
self.token_stream_rewriter.insertAfter(ctx.stop.tokenIndex, new_code)
class PropagationIncreaseFieldVisibilityRefactoringListener(JavaParserLabeledListener):
def __init__(self, common_token_stream: CommonTokenStream = None, using_field_name=None, object_name=None,
propagated_class_name=None):
"""
Used for propagation purposes in the other classes of the project: implement the propagation
Args:
common_token_stream (CommonTokenStream): A stream of tokens generated by parsing the main file using the ANTLR parser generator
using_field_name (str): Name of the field which has to be propagated
object_name (str): Name of the objects that need to be changed with the propagation operation
propagated_class_name (str): Name of the class in which the propagation operation needs to be implemented
Returns: No returns
"""
if using_field_name is None:
self.using_field_name = []
else:
self.using_field_name = using_field_name
if object_name is None:
self.object_name = []
else:
self.object_name = object_name
if propagated_class_name is None:
self.propagated_class_name = []
else:
self.propagated_class_name = propagated_class_name
if common_token_stream is None:
raise ValueError('common_token_stream is None')
else:
self.token_stream_rewriter = TokenStreamRewriter(common_token_stream)
self.is_class = False
def enterClassDeclaration(self, ctx: JavaParserLabeled.ClassDeclarationContext):
# print("Propagation started, please wait...")
class_identifier = ctx.IDENTIFIER().getText()
if class_identifier == self.propagated_class_name:
self.is_class = True
print("Propagation started, please wait...")
else:
self.is_class = False
def enterVariableDeclarator(self, ctx: JavaParserLabeled.VariableDeclaratorContext):
if not self.is_class:
return None
usingfieldidentifier = ctx.variableDeclaratorId().IDENTIFIER().getText()
grand_child_ctx = ctx.variableInitializer().expression()
if usingfieldidentifier in self.using_field_name:
objectidentifier = grand_child_ctx.expression(0).primary().IDENTIFIER().getText()
if objectidentifier in self.object_name:
self.token_stream_rewriter.replaceRange(
from_idx=grand_child_ctx.start.tokenIndex,
to_idx=grand_child_ctx.stop.tokenIndex,
text=grand_child_ctx.expression(0).primary().IDENTIFIER().getText() + '.' + 'get' + str.capitalize(
grand_child_ctx.IDENTIFIER().getText()) + '()'
)
def enterExpression(self, ctx: JavaParserLabeled.ExpressionContext):
if not self.is_class:
return
if ctx.expression(0) != None:
if ctx.expression(0).primary() != None:
if ctx.expression(0).primary().IDENTIFIER().getText() in self.object_name:
parent_ctx = ctx.parentCtx
count = parent_ctx.getChildCount()
if count == 3:
expressiontext = parent_ctx.children[2].getText()
self.token_stream_rewriter.replaceRange(
from_idx=parent_ctx.start.tokenIndex,
to_idx=parent_ctx.stop.tokenIndex,
text=ctx.expression(0).primary().IDENTIFIER().getText() + '.' + 'set' + str.capitalize(
ctx.IDENTIFIER().getText()) + '(' + expressiontext + ')'
)
class PropagationIncreaseFieldVisibility_GetObjects_RefactoringListener(JavaParserLabeledListener):
def __init__(self, common_token_stream: CommonTokenStream = None, source_class=None,
propagated_class_name=None):
"""Used for propagation purposes in the other classes of the project:
Detect the objects which have to be propagated
Args:
common_token_stream (CommonTokenStream): A stream of tokens generated by parsing the main file using the ANTLR parser generator
source_class (str): Name of the class in which the propagation has to be implemented
propagated_class_name (str): Name of the class which has to be propagated
Returns: No returns
"""
if source_class is None:
self.source_class = []
else:
self.source_class = source_class
if propagated_class_name is None:
self.propagated_class_name = []
else:
self.propagated_class_name = propagated_class_name
if common_token_stream is None:
raise ValueError('common_token_stream is None')
else:
self.token_stream_rewriter = TokenStreamRewriter(common_token_stream)
self.is_class = False
self.current_class = ''
self.objects = list()
def enterClassDeclaration(self, ctx: JavaParserLabeled.ClassDeclarationContext):
# print("Propagation started, please wait...")
class_identifier = ctx.IDENTIFIER().getText()
if class_identifier in self.propagated_class_name:
self.is_class = True
print("Propagation started, please wait...")
self.current_class = class_identifier
else:
self.is_class = False
def enterVariableDeclarator(self, ctx: JavaParserLabeled.VariableDeclaratorContext):
if not self.is_class:
return None
grand_parent_ctx = ctx.parentCtx.parentCtx
if grand_parent_ctx.typeType().classOrInterfaceType() != None:
className = grand_parent_ctx.typeType().classOrInterfaceType().IDENTIFIER(0).getText()
if className in self.source_class:
objectname = ctx.variableDeclaratorId().IDENTIFIER().getText()
self.objects.append(objectname)
def main():
print("Increase Field Visibility")
udb_path = "/home/ali/Documents/compiler/Research/xerces2-j/xerces2-j3.udb"
class_name = "ListNode"
field_name = "uri"
file_list_to_be_propagate = set()
propagate_classes = set()
file_list_include_file_name_that_edited = ""
mainfile = ""
db = und.open(udb_path)
for field in db.ents("public variable"):
if (str(field) == str(class_name + "." + field_name)):
# get path file include this field.
print(field)
if (field.parent().parent().relname() is not None):
mainfile = field.parent().parent().longname()
print(mainfile)
print(field.parent().parent().longname())
else:
for ref in field.refs("Definein"):
mainfile = (ref.file().longname())
print(mainfile)
print(ref.file().relname())
# get propagate class and their file
for ref in field.refs("Setby , Useby"):
if not (str(ref.ent()) == str(field.parent())
or str(ref.ent().parent()) == str(field.parent())):
propagate_classes.add(str(ref.ent().parent()))
file_list_to_be_propagate.add(ref.file().longname())
file_list_to_be_propagate = list(file_list_to_be_propagate)
propagate_classes = list(propagate_classes)
flag_file_is_refatored = False
corpus = open(
r"../filename_status_database.txt", encoding="utf-8").read()
if corpus.find("name:" + mainfile) == -1:
with open("../filename_status_database.txt", mode='w', encoding="utf-8", newline='') as f:
f.write(corpus + "\nname:" + mainfile)
f.flush()
os.fsync(f.fileno())
file_list_include_file_name_that_edited += mainfile + "\n"
else:
flag_file_is_refatored = True
print("file already edited")
print(mainfile)
stream = FileStream(mainfile, encoding='utf8')
# Step 2: Create an instance of AssignmentStLexer
lexer = JavaLexer(stream)
# Step 3: Convert the input source into a list of tokens
token_stream = CommonTokenStream(lexer)
# Step 4: Create an instance of the AssignmentStParser
parser = JavaParser(token_stream)
parser.getTokenStream()
parse_tree = parser.compilationUnit()
my_listener = IncreaseFieldVisibilityRefactoringListener(common_token_stream=token_stream,
source_class=class_name,
field_name=field_name)
walker = ParseTreeWalker()
walker.walk(t=parse_tree, listener=my_listener)
with open(mainfile, "w") as f:
f.write(my_listener.token_stream_rewriter.getDefaultText())
print(file_list_to_be_propagate)
for file in file_list_to_be_propagate:
flag_file_edited = False
corpus = open(
r"filename_status_database.txt", encoding="utf-8").read()
if (corpus.find("name:" + file) == -1):
with open("filename_status_database.txt", mode='w', encoding="utf-8", newline='') as f:
f.write(corpus + "\nname:" + file)
f.flush()
os.fsync(f.fileno())
file_list_include_file_name_that_edited += file + "\n"
else:
flag_file_edited = True
print(file)
stream = FileStream(file, encoding='utf8')
# input_stream = StdinStream()
# Step 2: Create an instance of AssignmentStLexer
lexer = JavaLexer(stream)
# Step 3: Convert the input source into a list of tokens
token_stream = CommonTokenStream(lexer)
# Step 4: Create an instance of the AssignmentStParser
parser = JavaParser(token_stream)
parser.getTokenStream()
parse_tree = parser.compilationUnit()
# get object
my_listener_get_object = PropagationIncreaseFieldVisibility_GetObjects_RefactoringListener(token_stream,
source_class=class_name,
propagated_class_name=propagate_classes)
walker = ParseTreeWalker()
walker.walk(t=parse_tree, listener=my_listener_get_object)
my_listener = PropagationIncreaseFieldVisibilityRefactoringListener(common_token_stream=token_stream,
using_field_name=field_name,
object_name=my_listener_get_object.objects,
propagated_class_name=propagate_classes)
walker = ParseTreeWalker()
walker.walk(t=parse_tree, listener=my_listener)
with open(file, "w") as f:
f.write(my_listener.token_stream_rewriter.getDefaultText())
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] |
Analysis/Metric_Impact_Hijacking/give_metric_ases_from_clusters.py | cgeorgitsis/ai4netmon | 0 | 25075 | <filename>Analysis/Metric_Impact_Hijacking/give_metric_ases_from_clusters.py
import pandas as pd
import numpy as np
import random, os, json
from collections import defaultdict
from matplotlib import pyplot as plt
from sklearn.cluster import KMeans, SpectralClustering
from scipy.spatial.distance import pdist, squareform
from scipy.sparse import csgraph
from numpy import linalg as LA
from sklearn.metrics import silhouette_score
PROXIMITY_FNAME = 'selected_ripe_ris__monitors_from_pathlens_100k.json'
def get_argmax_total_similarity(similarity_matrix, from_items=None, rank_normalization=False):
'''
Finds the item of a matrix (similarity_matrix) that has the maximum aggregate similarity to all other items.
If the "from_items" is not None, then only the rows/columns of the matrix in the from_items list are taken into account.
:param similarity_matrix: (pandas.DataFrame) an NxN dataframe; should be (a) symmetric and (b) values {i,j} to
represent the similarity between item of row i and column j
:param from_items: (list/set) a subset of the items (rows/columns) from which the item with the max similiarity will be selected
:param rank_normalization: (boolean) whether to modify the similarity matrix giving more emphasis to most similar values per row
by dividing each element with the rank it appears in the sorted list of values of the row
e.g., a_row = [0.5, 0.3, 0.4] --> modified_row = [0.5/1, 0.3/3, 0.4/2] = [0.5, 0.1, 0.2]
e.g., a_row = [0.1, 0.1, 0.4] --> modified_row = [0.1/2, 0.1/3, 0.4/1] = [0.05, 0.033, 0.4]
:return: (scalar, e.g., str or int) the index of the item in the dataframe that has the max total similarity
'''
if from_items is None:
df = similarity_matrix.copy()
else:
df = similarity_matrix.loc[from_items, from_items].copy()
np.fill_diagonal(df.values, np.nan) # set self-similarity to nan so that it is not taken into account
if rank_normalization:
for p1 in df.index:
sorted_indexes = list(df.loc[p1, :].sort_values(ascending=False).index)
df.loc[p1, sorted_indexes] = df.loc[p1, sorted_indexes] * [1.0 / i for i in range(1, 1 + df.shape[0])]
sum_similarities = np.nansum(df, axis=1)
if np.max(sum_similarities) == 0: # all similarities are nan or zero
next_item = random.sample(from_items, 1)[0]
else:
next_item = df.index[np.argmax(sum_similarities)]
return next_item
def greedy_most_similar_elimination(similarity_matrix, rank_normalization=False):
'''
Selects iteratively the item in the given similarity_matrix that has the maximum aggregate similarity to all other items. At each iteration,
only the similarities among the non-selected items are taken into account. At each iteration, the selected item is placed in the beginning of
a list. At the end, this list is returned. Example: returned_list = [item_selected_last, ..., item_selected_first]
:param similarity_matrix: (pandas.DataFrame) an NxN dataframe; should be (a) symmetric and (b) values {i,j} to
represent the similarity between item of row i and column j
:param rank_normalization: (boolean) whether to modify the similarity matrix giving more emphasis to most similar values per row
:return: (list) a list of ordered items (from the input's index); the first item is the least similar
'''
selected_items = []
for i in range(similarity_matrix.shape[0]):
from_items = list(set(similarity_matrix.index) - set(selected_items))
next_item = get_argmax_total_similarity(similarity_matrix, from_items=from_items,
rank_normalization=rank_normalization)
selected_items.insert(0, next_item)
return selected_items
def get_argmin_total_similarity(similarity_matrix, from_items=None):
'''
Finds the item of a matrix (similarity_matrix) that has the minimum aggregate similarity to all other items.
If the "from_items" is not None, then only the (a) rows of the matrix in the from_items list and (b) the columns
of the matrix NOT in the from_items list are taken into account.
:param similarity_matrix: (pandas.DataFrame) an NxN dataframe; should be (a) symmetric and (b) values {i,j} to
represent the similarity between item of row i and column j
:param from_items: (list/set) a subset of the items (rows/columns) from which the item with the min similiarity will be selected
:return: (scalar, e.g., str or int) the index of the item in the dataframe that has the min total similarity
'''
df = similarity_matrix.copy()
np.fill_diagonal(df.values, np.nan) # set self-similarity to nan so that it is not taken into account
if from_items is not None:
other_items = list(set(df.index) - set(from_items))
df = df.loc[from_items, other_items]
sum_similarities = np.nansum(df, axis=1)
if np.max(sum_similarities) == 0: # all similarities are nan or zero
next_item = random.sample(from_items, 1)[0]
else:
next_item = df.index[np.argmin(sum_similarities)]
return next_item
def greedy_least_similar_selection(similarity_matrix, nb_items=None):
'''
Selects iteratively the item in the given similarity_matrix that has the minimum aggregate similarity to all other items. At each iteration,
only the similarities among the non-selected items and the already selected items are taken into account. At each iteration, the selected item is
placed in the end of a list. At the end, this list is returned. Example: returned_list = [item_selected_first, ..., item_selected_last]
:param similarity_matrix: (pandas.DataFrame) an NxN dataframe; should be (a) symmetric and (b) values {i,j} to
represent the similarity between item of row i and column j
:param nb_items: (int) number of items to be selected; if None all items are selected in the returned list
:return: (list) a list of ordered items (from the input's index); the first item is the least similar
'''
selected_items = []
nb_total_items = similarity_matrix.shape[0]
if (nb_items is None) or (nb_items > nb_total_items):
nb_items = nb_total_items
for i in range(nb_items):
if len(selected_items) == 0:
from_items = None
else:
from_items = list(set(similarity_matrix.index) - set(selected_items))
next_item = get_argmin_total_similarity(similarity_matrix, from_items=from_items)
selected_items.append(next_item)
return selected_items
def sample_from_clusters(cluster_members_dict, nb_items=None):
'''
Samples items from the clusters, starting from a random item in the largest cluster, then a random item in the second largest cluster, and so on.
When elements of all clusters are selected, then starts again from the largest cluster, until all items (or up to nb_items) are selected.
:param cluster_members_dict: (dict of lists) dict of the form {cluster label: list of members of the cluster}
:param nb_items: (int) number of items to be selected; if None all items are selected in the returned list
:return: (list) a list of ordered items that are the samples from clusters
'''
nb_clusters = len(cluster_members_dict.keys())
nb_all_items = sum([len(v) for v in cluster_members_dict.values()])
if (nb_items is None) or (nb_items > nb_all_items):
nb_items = nb_all_items
sorted_clusters = sorted(cluster_members_dict, key=lambda k: len(cluster_members_dict.get(k)), reverse=True)
selected_items = []
for i in range(nb_items):
ind = i % nb_clusters # iterate over the sorted_clusters by getting the index of the current cluster
current_cluster = sorted_clusters[ind]
len_current_cluster = len(cluster_members_dict[current_cluster])
if len_current_cluster > 0:
next_item_ind = random.sample(range(len_current_cluster), 1)[0]
next_item = cluster_members_dict[current_cluster].pop(next_item_ind)
selected_items.append(next_item)
i += 1
return selected_items
def random_selection(similarity_matrix, nb_items=None):
"""
Selects randomly an item from the given similarity_matrix
:param similarity_matrix: (pandas.DataFrame) an NxN dataframe; should be (a) symmetric and (b) values {i,j} to
represent the similarity between item of row i and column j
:param nb_items: (int) number of items to be selected; if None all items are selected in the returned list
:return: (list) a list of random items
"""
selected_items = []
nb_total_items = similarity_matrix.shape[0]
if (nb_items is None) or (nb_items > nb_total_items):
nb_items = nb_total_items
for i in range(nb_items):
temp = random.sample(list(similarity_matrix), 1)[0]
selected_items.append(temp)
return selected_items
def getAffinityMatrix(coordinates, k=7):
"""
The Affinity matrix determines how close or similar are 2 points in our space.
Calculate affinity matrix based on input coordinates matrix and the number
of nearest neighbours.
Apply local scaling based on the k nearest neighbour
References:
https://papers.nips.cc/paper/2619-self-tuning-spectral-clustering.pdf
"""
# calculate euclidian distance matrix
dists = squareform(pdist(coordinates))
# for each row, sort the distances ascendingly and take the index of the
# k-th position (nearest neighbour)
knn_distances = np.sort(dists, axis=0)[k]
knn_distances = knn_distances[np.newaxis].T
# calculate sigma_i * sigma_j
local_scale = knn_distances.dot(knn_distances.T)
affinity_matrix = dists * dists
affinity_matrix = -affinity_matrix / local_scale
# divide square distance matrix by local scale
affinity_matrix[np.where(np.isnan(affinity_matrix))] = 0.0
# apply exponential
affinity_matrix = np.exp(affinity_matrix)
np.fill_diagonal(affinity_matrix, 0)
return affinity_matrix
def eigenDecomposition(A, topK=5):
"""
:param A: Affinity matrix
:param topK: Top k
:return A tuple containing:
- the optimal number of clusters by eigengap heuristic
- all eigen values
- all eigen vectors
This method performs the eigen decomposition on a given affinity matrix,
following the steps recommended in the paper:
1. Construct the normalized affinity matrix: L = D−1/2ADˆ −1/2.
2. Find the eigenvalues and their associated eigen vectors
3. Identify the maximum gap which corresponds to the number of clusters
by eigengap heuristic
References:
https://papers.nips.cc/paper/2619-self-tuning-spectral-clustering.pdf
http://www.kyb.mpg.de/fileadmin/user_upload/files/publications/attachments/Luxburg07_tutorial_4488%5b0%5d.pdf
"""
L = csgraph.laplacian(A, normed=True)
n_components = A.shape[0]
# LM parameter : Eigenvalues with largest magnitude (eigs, eigsh), that is, largest eigenvalues in
# the euclidean norm of complex numbers.
# eigenvalues, eigenvectors = eigsh(L, k=n_components, which="LM", sigma=1.0, maxiter=5000)
eigenvalues, eigenvectors = LA.eig(L)
plt.title('Largest eigen values of input matrix')
plt.scatter(np.arange(len(eigenvalues)), eigenvalues)
plt.grid()
plt.show()
# Identify the optimal number of clusters as the index corresponding
# to the larger gap between eigen values
index_largest_gap = np.argsort(np.diff(eigenvalues))[::-1][:topK]
nb_clusters = index_largest_gap + 1
return nb_clusters, eigenvalues, eigenvectors
def get_optimal_number_of_clusters(similarity):
'''
A function that help us identify which is the optimal number of cluster for Kmeans
:param similarity: The similarity matrix from graph embeddings
'''
distortions = []
for i in range(1, 20):
clustering = KMeans(n_clusters=i, init='random', n_init=10, max_iter=300, tol=1e-04, random_state=0).fit(
similarity)
distortions.append(clustering.inertia_)
plt.plot(range(1, 20), distortions, marker='o')
plt.xlabel('Number of clusters (k)')
plt.ylabel('Sum of squared distance')
plt.title("Elbow Method for Optimal k")
plt.show()
def get_plot_for_different_k_values(similarity, model_name):
"""
This function plots points after applying a cluster method for k=3,4,5,6. Furthermore prints silhouette score for each k
:param similarity: Contains our dataset (The similarity of RIPE monitors)
:return: A list containing silhouette score
"""
silhouette_scores = []
f = plt.figure()
f.add_subplot(2, 2, 1)
for i in range(3, 7):
if model_name == 'Spectral':
sc = SpectralClustering(n_clusters=i, affinity='precomputed').fit(similarity)
else:
sc = KMeans(n_clusters=i, init='random', n_init=10, max_iter=300, tol=1e-04, random_state=0).fit(similarity)
silhouette_scores.append(silhouette_score(similarity, sc.labels_))
f.add_subplot(2, 2, i - 2)
plt.scatter(similarity[:, 0], similarity[:, 1], s=5, c=sc.labels_, label="n_cluster-" + str(i))
plt.legend()
plt.show()
return silhouette_scores
def plot_silhouette_score_for_various_k(similarity, model_name):
"""
In this function we plot the silhouette score for various number of K (number of clusters)
:param similarity: Contains our dataset (The similarity of RIPE monitors)
:param model_name: The clustering algorithm we use (K-means or SpectralClustering)
"""
sil = []
for i in range(2, 21):
if model_name == 'Spectral':
sc = SpectralClustering(n_clusters=i, affinity='precomputed').fit(similarity)
else:
sc = KMeans(n_clusters=i, init='random', n_init=10, max_iter=300, tol=1e-04, random_state=0).fit(similarity)
sil.append(silhouette_score(similarity, sc.labels_))
plt.plot(range(2, 21), sil[:], '--bo')
plt.title('Silhouette score for different cluster sizes for ' + str(model_name))
plt.xlabel('Silhouette Score')
plt.ylabel('Number of clusters (K)')
plt.show()
def clustering_based_selection(similarity_matrix, clustering_method, nb_clusters, nb_items=None, **kwargs):
'''
Applies a clustering algorithm to the similarity matrix to cluster items, and then selects samples from the classes.
:param similarity_matrix: (pandas.DataFrame) an NxN dataframe; should be (a) symmetric and (b) values {i,j} to
represent the similarity between item of row i and column j
:param clustering_method: (str) 'SpectralClustering' or 'Kmeans'
:param nb_clusters: (int) number of clusters
:param nb_items: (int) number of items to be selected; if None all items are selected in the returned list
:param **kwargs: (dict) optional kwargs for the clustering algorithms
:return: (list) a list of ordered items that are the samples from clusters
'''
sim = similarity_matrix.to_numpy()
sim = np.nan_to_num(sim, nan=0)
if clustering_method == 'SpectralClustering':
clustering = getAffinityMatrix(sim, k=7)
k, eigenvalues, eigenvectors = eigenDecomposition(sim)
clustering = SpectralClustering(n_clusters=nb_clusters, affinity='precomputed', **kwargs).fit(sim)
labels = clustering.labels_
plt.scatter(sim[:, 0], sim[:, 1], c=labels)
plt.title('Number of Clusters: ' + str(nb_clusters))
plt.show()
model = 'Spectral'
silhouette_scores = get_plot_for_different_k_values(sim, model)
# print(silhouette_scores)
# print(f'Optimal number of clusters {k}')
plot_silhouette_score_for_various_k(sim, model)
elif clustering_method == 'Kmeans':
get_optimal_number_of_clusters(sim)
clustering = KMeans(n_clusters=nb_clusters, **kwargs).fit(sim)
labels = clustering.labels_
plt.scatter(sim[:, 0], sim[:, 1], c=labels)
plt.title('Number of Clusters: ' + str(nb_clusters))
plt.show()
model = 'Kmeans'
silhouette_scores = get_plot_for_different_k_values(sim, model)
# print(silhouette_scores)
plot_silhouette_score_for_various_k(sim, model)
else:
raise ValueError
cluster_members_dict = defaultdict(list)
for i, label in enumerate(clustering.labels_):
cluster_members_dict[label].append(similarity_matrix.index[i])
return sample_from_clusters(cluster_members_dict, nb_items=nb_items)
def select_from_similarity_matrix(similarity_matrix, method, **kwargs):
if method == 'Greedy min':
selected_items = greedy_most_similar_elimination(similarity_matrix, **kwargs)
elif method == 'Greedy max':
selected_items = greedy_least_similar_selection(similarity_matrix, **kwargs)
elif method == 'Clustering':
selected_items = clustering_based_selection(similarity_matrix, **kwargs)
elif method == 'Random':
selected_items = random_selection(similarity_matrix, **kwargs)
else:
raise ValueError
return selected_items
def return_the_selected_monitors_from_methods():
similarity_matrix = pd.read_csv('ALL_RIPE_RIS_withASns_similarity_embeddings_BGP2VEC_20210107.csv',
header=0, index_col=0)
similarity_matrix.columns = similarity_matrix.columns.astype(float)
selected_items_greedy_random = select_from_similarity_matrix(similarity_matrix, 'Random')
selected_items_greedy_min = select_from_similarity_matrix(similarity_matrix, 'Greedy min')
selected_items_greedy_max = select_from_similarity_matrix(similarity_matrix, 'Greedy max')
kwargs = {'clustering_method': 'Kmeans', 'nb_clusters': 10}
selected_items_Kmeans = select_from_similarity_matrix(similarity_matrix, 'Clustering', **kwargs)
kwargs = {'clustering_method': 'SpectralClustering', 'nb_clusters': 10}
selected_items_Spectral = select_from_similarity_matrix(similarity_matrix, 'Clustering', **kwargs)
return selected_items_greedy_random, selected_items_greedy_min, selected_items_greedy_max, selected_items_Kmeans, selected_items_Spectral
# method_param_dict = {
# 'Greedy min': {'method': 'Greedy min', 'sim_matrix': similarity_matrix, 'args': {}},
# 'Greedy max': {'method': 'Greedy max', 'sim_matrix': similarity_matrix, 'args': {}},
# 'Clustering kmeans k10 full': {'method': 'Clustering', 'sim_matrix': similarity_matrix, 'args': {'clustering_method': 'Kmeans', 'nb_clusters': 7}},
# 'Clustering spectral k10': {'method': 'Clustering', 'sim_matrix': similarity_matrix, 'args': {'clustering_method': 'SpectralClustering', 'nb_clusters': 7}}}
#
# for m, params in method_param_dict.items():
# selected_items = select_from_similarity_matrix(params['sim_matrix'], params['method'], **params['args'])
# print('\t{} [DONE]'.format(m))
# with open('dataset_selected_monitors_ripe_ris_pathlens_100k_{}.json'.format('_'.join(m.lower().translate('()').split(' '))), 'w') as f:
# json.dump(selected_items, f)
#
# asns_per_method = dict()
# for m, params in method_param_dict.items():
# with open('dataset_selected_monitors_ripe_ris_pathlens_100k_{}.json'.format('_'.join(m.lower().split(' '))), 'r') as f:
# selected_items = json.load(f)
# asns_per_method[m] = selected_items
# print('\t{} [DONE]'.format(m))
# with open(PROXIMITY_FNAME, 'w') as f:
# json.dump(asns_per_method, f)
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example_runtime_metrics.py | joannak-vmware/wavefront-pyformance | 0 | 88516 | <reponame>joannak-vmware/wavefront-pyformance<gh_stars>0
#! /usr/bin/env python3
"""Python Runtime Metric Collection Example."""
import argparse
import time
from wavefront_pyformance import tagged_registry
from wavefront_pyformance import wavefront_reporter
def report_metrics(host, server='', token=''):
"""Runtime Metric Reporting Function Example."""
reg = tagged_registry.TaggedRegistry()
wf_proxy_reporter = wavefront_reporter.WavefrontProxyReporter(
host=host, port=2878, distribution_port=2878, registry=reg,
source='runtime-metric-test',
tags={'global_tag1': 'val1', 'global_tag2': 'val2'},
prefix='python.proxy.',
enable_runtime_metrics=True).report_minute_distribution()
wf_direct_reporter = wavefront_reporter.WavefrontDirectReporter(
server=server, token=token, registry=reg,
source='runtime-metric-test',
tags={'global_tag1': 'val1', 'global_tag2': 'val2'},
prefix='python.direct.',
enable_runtime_metrics=True).report_minute_distribution()
wf_proxy_reporter.report_now()
wf_proxy_reporter.stop()
wf_direct_reporter.report_now()
wf_direct_reporter.stop()
if __name__ == '__main__':
# python example_runtime_metrics.py proxy_host server_url server_token
arg = argparse.ArgumentParser()
arg.add_argument('host', help='Wavefront proxy host name.')
arg.add_argument('server', help='Wavefront server for direct ingestion.')
arg.add_argument('token', help='Wavefront API token.')
ARGS = arg.parse_args()
while True:
report_metrics(ARGS.host, ARGS.server, ARGS.token)
time.sleep(5)
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] |
AppEquipos/views.py | angel1194/Documentacion | 0 | 108606 | from __future__ import unicode_literals
from django.shortcuts import render, HttpResponse, redirect, get_object_or_404
from .models import Post,PostMarcas,Images
# Create your views here.
from django.forms import modelformset_factory
from django.core.exceptions import ObjectDoesNotExist
from django.urls import reverse
from django.db.models import Q
def generales(request):
query = request.GET.get('q')
if query:
post= Post.objects.filter(modelo__icontains = query)
else:
post= Post.objects.all()
return render(request,"index.html",{'post':post,"query":query})
def acer(request):
post=None
error=None
try:
post = Post.objects.filter(
marca=PostMarcas.objects.get(title="ACER")
)
except ObjectDoesNotExist as e:
error=e
return render(request,"marcas/acer.html",{"post":post,"error":error})
def apple(request):
post=None
error=None
try:
post = Post.objects.filter(
marca=PostMarcas.objects.get(title="APPLE")
)
except ObjectDoesNotExist as e:
error=e
return render(request,"marcas/apple.html",{"post":post,"error":error})
def asus(request):
error=None
post=None
try:
post = Post.objects.filter(
marca=PostMarcas.objects.get(title="ASUS")
)
except ObjectDoesNotExist as e:
error=e
return render(request,"marcas/asus.html",{"post":post,"error":error})
def compaq(request):
error=None
post = Post.objects.filter(
marca=PostMarcas.objects.get(title="COMPAQ")
)
return render(request,"marcas/compaq.html",{"post":post})
def dell(request):
error=None
post=None
try:
post = Post.objects.filter(
marca=PostMarcas.objects.get(title="DELL")
)
except ObjectDoesNotExist as e:
error=e
return render(request,"marcas/dell.html",{"post":post,"error":error})
def gateway(request):
post=None
error=None
try:
post = Post.objects.filter(
marca=PostMarcas.objects.get(title="GATEWAY")
)
except ObjectDoesNotExist as e:
error=e
return render(request,"marcas/gateway.html",{"post":post,"error":error})
def hp(request):
post=None
error=None
try:
post = Post.objects.filter(
marca=PostMarcas.objects.get(title="HP")
)
except ObjectDoesNotExist as e:
error=e
return render(request,"marcas/hp.html",{"post":post,"error":error})
def huawei(request):
error=None
post=None
try:
post = Post.objects.filter(
marca=PostMarcas.objects.get(title="HUAWEI")
)
except ObjectDoesNotExist as e:
error=e
return render(request,"marcas/huawei.html",{"post":post,"error":error})
def lenovo(request):
error=None
post=None
try:
post = Post.objects.filter(
marca=PostMarcas.objects.get(title="LENOVO")
)
except ObjectDoesNotExist as e:
error=e
return render(request,"marcas/lenovo.html",{"post":post,"error":error})
def msi(request):
post=None
error=None
try:
post = Post.objects.filter(
marca=PostMarcas.objects.get(title="MSI")
)
except ObjectDoesNotExist as e:
error=e
return render(request,"marcas/msi.html",{"post":post,"error":error})
def samsung(request):
post=None
error=None
try:
post = Post.objects.filter(
marca=PostMarcas.objects.get(title="SAMSUNG")
)
except ObjectDoesNotExist as e:
error=e
return render(request,"marcas/samsung.html",{"post":post,"error":error})
def sony(request):
post=None
error=None
try:
post = Post.objects.filter(
marca=PostMarcas.objects.get(title="SONY")
)
except ObjectDoesNotExist as e:
error=e
return render(request,"marcas/sony.html",{"post":post,"error":error})
def toshiba(request):
post=None
error=None
try:
post = Post.objects.filter(
marca=PostMarcas.objects.get(title="TOSHIBA")
)
except ObjectDoesNotExist as e:
error=e
return render(request,"marcas/toshiba.html",{"post":post,"error":error})
def detalles(request,id):
detail= Post.objects.get(id=id)
return render(request,"detalles.html",{'detail':detail})
def documentacion(request):
return render(request,"blog/documentacion.html")
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app/ml/artificial_user.py | sp0x/orion | 0 | 114967 |
class World:
def __init__(self):
pass
def get_state(self):
pass
def execute_action(self,a):
pass
class GameTree:
class GameNode:
def __init__(self, state, action, par):
self.state = state
self.action = action
self.visited = 0.0
self.par = par
# key = the goal aka desired result, val = the times that goal was reached through this node
self.goal_outcomes = dict()
# key = state of the system in which this node can be accessed, val = list<GameNode>
self.children = dict()
def probability(self):
"""The likelihood of ending up in this state"""
return self.visited / self.par.visited
def probability_for(self, goal):
"""The likelihood that the given GameNode will lead the 'player' to their final goal"""
return self.goal_outcomes.get(goal,0.0) / self.visited
def get_utility(self, goal, policy_params):
"""Returns the utility that the 'player' will receive if they pick this action"""
pass
def apply_action(self, world):
self.visited += 1.0
world.execute_action(self.action)
def update(self, goal, win=0):
"""
Updates the number of times that this node led to the desired goal
:param goal: the goal that was supposed to be reached
:param win: 1 if goal was reached 0 otherwise
:return:
"""
self.goal_outcomes[goal] = self.goal_outcomes.get(goal) + win
self.par.update(goal, win)
def prune(self):
pass
def get_next_move(self, state, goal, policy_params):
"""
Returns the best action for a certain goal given the current state of the world
:param state: the current state of the world
:param goal: the desired final outcome
:param policy_params: additional data regarding the choice of a next action
:return: GameNode with highest utility
"""
if goal in self.children:
return self.children.get(goal)[0]
best = None
max_u = 0
nodes = self.children.get(state)
for n in nodes:
u = n.get_utility(goal, policy_params)
if u > max_u:
max_u = u
best = n
return best
def __init__(self, policy):
self.root = None
self.cursor = None
self.policy = policy
def get_next_action(self, state, goal):
if not self.cursor:
self.cursor = self.root
new_c = self.cursor.get_next_move(state, goal,self.policy)
self.cursor = new_c
return new_c
class AIUser:
def __init__(self, world):
"""
Creates a new AI user
:param world: World instance wrapper for the environment
"""
self.world = world
self.game_tree = GameTree(None)
def learn(self):
pass
def execute(self, goal):
state = self.world.get_state()
while state != goal:
a = self.game_tree.get_next_action(state, goal)
a.apply_action(self.world)
state = self.world.get_state()
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] |
anomaly_detection_time_series/MatrixProfileAD.py | Vincent-Vercruyssen/absent_pattern_detection | 4 | 166707 | """ Matrix profile anomaly detection.
Reference:
<NAME>., <NAME>., <NAME>., <NAME>., <NAME>., <NAME>., <NAME>. (2016, December).
Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets.
In Data Mining (ICDM), 2016 IEEE 16th International Conference on (pp. 1317-1322). IEEE.
"""
# Authors: <NAME>, 2018.
import math
import numpy as np
import pandas as pd
import scipy.signal as sps
from tqdm import tqdm
from .BaseDetector import BaseDetector
# -------------
# CLASSES
# -------------
class MatrixProfileAD(BaseDetector):
""" Anomaly detection in time series using the matrix profile
Parameters
----------
m : int (default=10)
Window size.
contamination : float (default=0.1)
Estimate of the expected percentage of anomalies in the data.
Comments
--------
- This only works on time series data.
"""
def __init__(self, m=10, contamination=0.1,
tol=1e-8, verbose=False):
super(MatrixProfileAD, self).__init__()
self.m = int(m)
self.contamination = float(contamination)
self.tol = float(tol)
self.verbose = bool(verbose)
def ab_join(self, T, split):
""" Compute the ABjoin and BAjoin side-by-side,
where `split` determines the splitting point.
"""
# algorithm options
excZoneLen = int(np.round(self.m * 0.5))
radius = 1.1
dataLen = len(T)
proLen = dataLen - self.m + 1
# change Nan and Inf to zero
T = np.nan_to_num(T)
# precompute the mean, standard deviation
s = pd.Series(T)
dataMu = s.rolling(self.m).mean().values[self.m-1:dataLen]
dataSig = s.rolling(self.m).std().values[self.m-1:dataLen]
matrixProfile = np.ones(proLen) * np.inf
idxOrder = excZoneLen + np.arange(0, proLen, 1)
idxOrder = idxOrder[np.random.permutation(len(idxOrder))]
# construct the matrixprofile
for i, idx in enumerate(idxOrder):
# query
query = T[idx:idx+self.m-1]
# distance profile
distProfile = self._diagonal_dist(T, idx, dataLen, self.m, proLen, dataMu, dataSig)
distProfile = abs(distProfile)
distProfile = np.sqrt(distProfile)
# position magic
pos1 = np.arange(idx, proLen, 1)
pos2 = np.arange(0, proLen-idx+1, 1)
# split magic
distProfile = distProfile[np.where((pos2 <= split) & (pos1 > split))[0]]
pos1Split = pos1[np.where((pos2 <= split) & (pos1 > split))[0]]
pos2Split = pos2[np.where((pos2 <= split) & (pos1 > split))[0]]
pos1 = pos1Split
pos2 = pos2Split
# update magic
updatePos = np.where(matrixProfile[pos1] > distProfile)[0]
matrixProfile[pos1[updatePos]] = distProfile[updatePos]
updatePos = np.where(matrixProfile[pos2] > distProfile)[0]
matrixProfile[pos2[updatePos]] = distProfile[updatePos]
return matrixProfile
def fit_predict(self, T):
""" Fit the model to the time series T.
:param T : np.array(), shape (n_samples)
The time series data for which to compute the matrix profile.
:returns y_score : np.array(), shape (n_samples)
Anomaly score for the samples in T.
:returns y_pred : np.array(), shape (n_samples)
Returns -1 for inliers and +1 for anomalies/outliers.
"""
return self.fit(np.array([])).predict(T)
def fit(self, T=np.array([])):
""" Fit the model to the time series T.
:param T : np.array(), shape (n_samples)
The time series data for which to compute the matrix profile.
:returns self : object
"""
self.T_train = T
return self
def predict(self, T=np.array([])):
""" Compute the anomaly score + predict the label of each sample in T.
:returns y_score : np.array(), shape (n_samples)
Anomaly score for the samples in T.
:returns y_pred : np.array(), shape (n_samples)
Returns -1 for inliers and +1 for anomalies/outliers.
"""
# fuse T_train and T
nt = len(T)
nT = np.concatenate((self.T_train, T))
n = len(nT)
# compute the matrix profile
matrix_profile = self._compute_matrix_profile_stomp(nT, self.m)
# transform to an anomaly score (1NN distance)
# the largest distance = the largest anomaly
# rescale between 0 and 1, this yields the anomaly score
y_score = (matrix_profile - min(matrix_profile)) / (max(matrix_profile) - min(matrix_profile))
y_score = np.append(y_score, np.zeros(n-len(matrix_profile), dtype=float))
# prediction threshold + absolute predictions
self.threshold = np.sort(y_score)[int(n * (1.0 - self.contamination))]
y_pred = np.ones(n, dtype=float)
y_pred[y_score < self.threshold] = -1
# cut y_pred and y_score to match length of T
return y_score[-nt:], y_pred[-nt:]
def _compute_matrix_profile_stomp(self, T, m):
""" Compute the matrix profile and profile index for time series T using correct STOMP.
:param T : np.array(), shape (n_samples)
The time series data for which to compute the matrix profile.
:param m : int
Length of the query.
:returns matrix_profile : np.array(), shape (n_samples)
The matrix profile (distance) for the time series T.
comments
--------
- Includes a fix for straight line time series segments.
"""
n = len(T)
# precompute the mean, standard deviation
s = pd.Series(T)
data_m = s.rolling(m).mean().values[m-1:n]
data_s = s.rolling(m).std().values[m-1:n]
# where the data is zero
idxz = np.where(data_s < 1e-8)[0]
data_s[idxz] = 0.0
idxn = np.where(data_s > 0.0)[0]
zero_s = False
if len(idxz) > 0:
zero_s = True
# precompute distance to straight line segment of 0s
slD = np.zeros(n-m+1, dtype=float)
if zero_s:
for i in range(n-m+1):
Tsegm = T[i:i+m]
Tm = data_m[i]
Ts = data_s[i]
if Ts == 0.0: # data_s is effectively 0
slD[i] = 0.0
else:
Tn = (Tsegm - Tm) / Ts
slD[i] = np.sqrt(np.sum(Tn ** 2))
# compute the first dot product
q = T[:m]
QT = sps.convolve(T.copy(), q[::-1], 'valid', 'direct')
QT_first = QT.copy()
# compute the distance profile
D = self._compute_fixed_distance_profile(T[:m], QT, n, m, data_m, data_s, data_m[0], data_s[0], slD.copy(), idxz, idxn, zero_s)
# initialize matrix profile
matrix_profile = D
# in-order evaluation of the rest of the profile
for i in tqdm(range(1, n-m+1, 1), disable=not(self.verbose)):
# update the dot product
QT[1:] = QT[:-1] - (T[:n-m] * T[i-1]) + (T[m:n] * T[i+m-1])
QT[0] = QT_first[i]
# compute the distance profile: without function calls!
if data_s[i] == 0.0: # query_s is effectively 0
D = slD.copy()
elif zero_s:
D[idxn] = np.sqrt(2 * (m - (QT[idxn] - m * data_m[idxn] * data_m[i]) / (data_s[idxn] * data_s[i])))
nq = (q - data_m[i]) / data_s[i]
d = np.sqrt(np.sum(nq ** 2))
D[idxz] = d
else:
D = np.sqrt(2 * (m - (QT - m * data_m * data_m[i]) / (data_s * data_s[i])))
# update the matrix profile
exclusion_range = (int(max(0, round(i-m/2))), int(min(round(i+m/2+1), n-m+1)))
D[exclusion_range[0]:exclusion_range[1]] = np.inf
ix = np.where(D < matrix_profile)[0]
matrix_profile[ix] = D[ix]
# matrix_profile = np.minimum(matrix_profile, D)
return matrix_profile
def _compute_fixed_distance_profile(self, q, QT, n, m, data_m, data_s, query_m, query_s, slD, idxz, idxn, zero_s):
""" Compute the fixed distance profile """
D = np.zeros(n-m+1, dtype=float)
if query_s == 0.0: # query_s is effectively 0
return slD
if zero_s:
D[idxn] = np.sqrt(2 * (m - (QT[idxn] - m * data_m[idxn] * query_m) / (data_s[idxn] * query_s)))
nq = (q - query_m) / query_s
d = np.sqrt(np.sum(nq ** 2))
D[idxz] = d
else:
D = np.sqrt(2 * (m - (QT - m * data_m * query_m) / (data_s * query_s)))
return D
def _compute_matrix_profile_stamp(self, T, m):
""" Compute the matrix profile and profile index for time series T using STAMP.
:param T : np.array(), shape (n_samples)
The time series data for which to compute the matrix profile.
:param m : int
Length of the query.
:returns matrix_profile : np.array(), shape (n_samples)
The matrix profile (distance) for the time series T.
:returns profile_index : np.array(), shape (n_samples)
The matrix profile index accompanying the matrix profile.
comments
--------
- Uses the STAMP algorithm to compute the matrix profile.
- Includes a fix for straight line time series segments.
"""
n = len(T)
# initialize the empty profile and index
matrix_profile = np.ones(n-m+1) * np.inf
# precompute the mean, standard deviation
s = pd.Series(T)
data_m = s.rolling(m).mean().values[m-1:n]
data_s = s.rolling(m).std().values[m-1:n]
# where the data is zero
idxz = np.where(data_s < 1e-8)[0]
data_s[idxz] = 0.0
idxn = np.where(data_s > 0.0)[0]
zero_s = False
if len(idxz) > 0:
zero_s = True
# precompute distance to straight line segment of 0s
# brute force distance computation (because the dot_product is zero!)
# --> this is a structural issue with the MASS algorithm for fast distance computation
slD = np.zeros(n-m+1, dtype=float)
if zero_s:
for i in range(n-m+1):
Tsegm = T[i:i+m]
Tm = data_m[i]
Ts = data_s[i]
if Ts == 0.0: # data_s is effectively 0
slD[i] = 0.0
else:
Tn = (Tsegm - Tm) / Ts
slD[i] = np.sqrt(np.sum(Tn ** 2))
# random search order for the outer loop
indices = np.arange(0, n-m+1, 1)
np.random.shuffle(indices)
# compute the matrix profile
if self.verbose: print('Iterations:', len(indices))
for i, idx in tqdm(enumerate(indices), disable=not(self.verbose)):
# query for which to compute the distance profile
query = T[idx:idx+m]
# normalized distance profile (using MASS)
D = self._compute_MASS(query, T, n, m, data_m, data_s, data_m[idx], data_s[idx], slD.copy())
# update the matrix profile (keeping minimum distances)
# self-join is True! (we only look at constructing the matrix profile for a single time series)
exclusion_range = (int(max(0, round(idx-m/2))), int(min(round(idx+m/2+1), n-m+1)))
D[exclusion_range[0]:exclusion_range[1]] = np.inf
ix = np.where(D < matrix_profile)[0]
matrix_profile[ix] = D[ix]
return matrix_profile
def _compute_MASS(self, query, T, n, m, data_m, data_s, query_m, query_s, slD):
""" Compute the distance profile using the MASS algorithm.
:param query : np.array(), shape (self.m)
Query segment for which to compute the distance profile.
:param T : np.array(), shape (n_samples)
The time series data for which to compute the matrix profile.
:param n : int
Length of time series T.
:param m : int
Length of the query.
:param data_f : np.array, shape (n + m)
FFT transform of T.
:param data_m : np.array, shape (n - m + 1)
Mean of every segment of length m of T.
:param data_s : np.array, shape (n - m + 1)
STD of every segment of length m of T.
:param query_m : float
Mean of the query segment.
:param query_s : float
Standard deviation of the query segment.
:returns dist_profile : np.array(), shape (n_samples)
Distance profile of the query to time series T.
"""
# CASE 1: query is a straight line segment of 0s
if query_s < 1e-8:
return slD
# CASE 2: query is every other possible subsequence
# compute the sliding dot product
reverse_query = query[::-1]
dot_product = sps.fftconvolve(T, reverse_query, 'valid')
# compute the distance profile without correcting for standard deviation of the main signal being 0
# since this is numpy, it will result in np.inf if the data_sig is 0
dist_profile = np.sqrt(2 * (m - (dot_product - m * query_m * data_m) / (query_s * data_s)))
# correct for data_s being 0
zero_idxs = np.where(data_s < 1e-8)[0]
if len(zero_idxs) > 0:
n_query = (query - query_m) / query_s
d = np.linalg.norm(n_query - np.zeros(m, dtype=float))
dist_profile[zero_idxs] = d
return dist_profile
def _compute_brute_force_distance_profile(self, query, T, n, m, data_f, data_m, data_s, query_m, query_s):
""" Compute the brute force distance profile. """
dist_profile = np.zeros(n-m+1, dtype=float)
# normalize query
if query_m < 1e-8:
n_query = np.zeros(m, dtype=float)
else:
n_query = (query - query_m) / query_s
# compute the distance profile
for i in range(n-m+1):
T_segm = T[i:i+m]
Tm = data_m[i]
Ts = data_s[i]
# normalize time series segment
if Ts < 1e-8:
T_norm = np.zeros(m, dtype=float)
else:
T_norm = (T_segm - Tm) / Ts
# compute distance
dist_profile[i] = np.linalg.norm(T_norm - n_query)
return dist_profile
def _diagonal_dist(self, data, idx, dataLen, subLen, proLen, dataMu, dataSig):
""" Compute the diagonal distance (as in the original matrix profile code) """
xTerm = np.dot(np.ones(proLen-idx+1), np.dot(data[idx-1:idx+subLen-1], data[:subLen]))
mTerm = data[idx-1:proLen-1] * data[:proLen-idx]
aTerm = data[idx+subLen-1:] * data[subLen:dataLen-idx+1]
if proLen != idx:
xTerm[1:] = xTerm[1:] - np.cumsum(mTerm) + np.cumsum(aTerm)
distProfile = np.divide(xTerm - subLen * dataMu[idx-1:] * dataMu[:proLen-idx+1],
subLen * dataSig[idx-1:] * dataSig[:proLen-idx+1])
distProfile = 2 * subLen * (1 - distProfile)
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experiments/annotations.py | stattikcms/stattik | 1 | 117238 | import inspect
from typing import Union, get_origin, get_args
def f(x: int, y: int | None ) -> int:
return x + y
a = inspect.get_annotations(f)
print(a)
y = a['y']
print(y)
t = type(y)
print(t)
#print(t.__dict__)
print(get_args(y))
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] |
blacklistparser/core/types.py | Armorless-Visage/blacklistparser | 0 | 130823 | <reponame>Armorless-Visage/blacklistparser
#!/usr/bin/env python3
# <NAME> (c) 2019 ISC
# Full licence terms located in LICENCE file
from os import path
'''
some types for argparse type argument
'''
def base_path_type(pathname):
'''
custom type for checking that a file path is valid
returns input if the base dir exists, None otherwise
'''
if path.isdir(path.dirname(pathname)) is True:
return pathname
return None
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carCapture.py | ishmam367/Vehcle-Monitoring-System-BLPR- | 2 | 28267 | <gh_stars>1-10
import cv2
import time
#print(cv2.__version__)
cascade_src = 'cars.xml'
video_src = 'v3.avi'
start_time = time.time()
cap = cv2.VideoCapture(video_src)
#framerate = cap.get(5)
car_cascade = cv2.CascadeClassifier(cascade_src)
i=0
while True:
i=i+1
ret, img = cap.read()
if (type(img) == type(None)):
break
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
cars = car_cascade.detectMultiScale(gray, 1.1, 1)
for (x,y,w,h) in cars:
box=cv2.rectangle(img,(x,y),(x+w,y+h),(0,0,255),2)
cv2.imshow('video', img)
cv2.imwrite("template {0}.jpg".format(i),box)
print("--- %s seconds ---" % (time.time() - start_time))
time.sleep(2)
if cv2.waitKey(33) == 27:
break
cv2.destroyAllWindows()
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ewah/uploaders/base.py | soltanianalytics/ewah | 2 | 185947 | from airflow.utils.log.logging_mixin import LoggingMixin
from ewah.hooks.base import EWAHBaseHook
from ewah.constants import EWAHConstants as EC
import math
import pickle
import os
from copy import deepcopy
from tempfile import TemporaryDirectory
from typing import Optional, Type, Union, Dict, List, Any
class EWAHBaseUploader(LoggingMixin):
"""Base class for all EWAH uploader classes aka Uploaders.
Uploaders are classes that can receive data in a standardized format and upload
it to a target data storage. Each Uploader uploads data to one type of target.
For instance, the EWAHPostgresUploader loads data to PostgreSQL databases.
The base class contains the basic methods shared by all Uploaders for the purpose of
uploading data. Each target shall have a subclass derived from this class.
"""
upload_call_count = 0
def __init__(
self,
dwh_engine: str,
dwh_conn: Type[EWAHBaseHook],
load_strategy: str,
cleaner: "EWAHCleaner",
table_name: str,
schema_name: str,
schema_suffix: str = "_next",
database_name: Optional[str] = None,
primary_key: Optional[List[str]] = None,
use_temp_pickling: bool = False,
pickling_upload_chunk_size: int = 100000,
pickle_compression: Optional[str] = None,
deduplication_before_upload: bool = False,
) -> None:
assert pickle_compression is None or pickle_compression in (
"gzip",
"bz2",
"lzma",
)
if deduplication_before_upload:
assert (
primary_key
), "Must set primary key if using deduplication_before_upload!"
if use_temp_pickling:
# Set a function to use for temporary pickle files
if pickle_compression is None:
self.pickle_file_open = open
else:
self.pickle_file_open = __import__(pickle_compression).open
# Prepare file used for temporary data pickling
self.temp_pickle_folder = TemporaryDirectory()
self.temp_file_name = (
self.temp_pickle_folder.name + os.sep + "temp_pickle_file"
)
self.temp_pickle_file = self.pickle_file_open(self.temp_file_name, "wb")
super().__init__()
self.dwh_engine = dwh_engine
self.dwh_conn = dwh_conn
self.dwh_hook = dwh_conn.get_hook()
self.load_strategy = load_strategy
self.cleaner = cleaner
self.table_name = table_name
self.schema_name = schema_name
self.schema_suffix = schema_suffix
self.database_name = database_name
self.primary_key = primary_key
self.use_temp_pickling = use_temp_pickling
self.pickling_upload_chunk_size = pickling_upload_chunk_size
self.deduplication_before_upload = deduplication_before_upload
@classmethod
def get_cleaner_callables(cls):
# overwrite me for cleaner callables that are always called
return []
@classmethod
def get_schema_tasks(cls, *args, **kwargs):
"""
This classmethod needs to be overwritten by a child class and return
a tuple of two tasks, the kickoff and final tasks for the data loading DAGs.
The kickoff task is supposed to create a new, empty schema to use during data
loading.
The final task is supposed to replace the old with the new schema e.g. by
removing the old schema and renaming the new schema.
"""
raise Exception("Not implemented!")
@property
def columns_definition(self):
return self.cleaner.get_columns_definition(dwh_engine=self.dwh_engine)
def _get_column_type(self, column_definition: dict) -> str:
"""Return the column type of the field. Returns the DWH engine specific default
if there is none.
:param column_definition: Dictionary containing details on this column,
including optionally the column type.
"""
return column_definition.get(
EC.QBC_FIELD_TYPE,
EC.QBC_TYPE_MAPPING[self.dwh_engine].get(str),
)
def upload_data(
self,
data: List[Dict[str, any]],
metadata: Optional[Dict[str, Any]] = None,
) -> None:
data = self.cleaner.clean_rows(rows=data, metadata=metadata)
if self.primary_key:
for pk_name in self.primary_key:
if not pk_name in self.columns_definition.keys():
raise Exception(
("Column {0} does not exist but is " + "expected!").format(
pk_name
)
)
if self.use_temp_pickling:
# earmark for later upload
self._upload_via_pickling(data=data)
else:
# upload straightaway
self._upload_data(data)
def _upload_data(self, data=None):
if not data:
self.log.info("No data to upload!")
return
self.upload_call_count += 1
self.log.info(
"Chunk {1}: Uploading {0} rows of data.".format(
str(len(data)),
str(self.upload_call_count),
)
)
if self.deduplication_before_upload:
# Some endpoints can't help but return the same record multiple times.
# This is needed to deduplicate before uploading data.
# It should be avoided whenever possible, however.
self.log.info("Deduplicating data...")
temp_dict = {}
while data:
datum = data.pop(0)
id_tuple = ()
for key in self.primary_key:
id_tuple += (datum.get(key),)
temp_dict[id_tuple] = datum
while temp_dict:
key, value = temp_dict.popitem()
data.append(value)
if (self.upload_call_count > 1) or (
not (self.load_strategy == EC.LS_INSERT_REPLACE)
):
self.log.info("Checking for, and applying schema changes.")
self.log.info(
"Added fields:\n\t{0}\n".format(
"\n\t".join(
self.detect_and_apply_schema_changes()
or ["No fields were added."]
)
)
)
self.create_or_update_table(
data=data,
upload_call_count=self.upload_call_count,
primary_key=self.primary_key,
commit=False, # See note below for reason
)
""" Note on committing changes:
The hook used for data uploading is created at the beginning of the
execute function and automatically committed and closed at the end.
DO NOT commit in this function, as multiple uploads may be required,
and any intermediate commit may be subsequently followed by an
error, which would then result in incomplete data committed.
"""
def _upload_via_pickling(self, data: Union[dict, List[dict]]):
"""Call this function to earmark a dictionary for later upload."""
assert self.use_temp_pickling, "Can only call function if using temp pickling!"
if isinstance(data, dict):
pickle.dump(data, self.temp_pickle_file)
elif isinstance(data, list):
self.log.info(
"Pickling {0} rows of data for later upload...".format(len(data))
)
for row in data:
assert isinstance(
row, dict
), "Invalid data format for function! Must be dict or list of dicts!"
pickle.dump(row, self.temp_pickle_file)
else:
raise Exception(
"Invalid data format for function! Must be dict or list of dicts!"
)
def _upload_from_pickle(self):
"""Call this function to upload previously pickled data."""
assert self.use_temp_pickling
assert hasattr(self, "temp_pickle_file")
self.temp_pickle_file.close()
self.temp_pickle_file = self.pickle_file_open(self.temp_file_name, "rb")
keep_unpickling = True
while keep_unpickling:
raw_data = []
for _ in range(self.pickling_upload_chunk_size):
try:
raw_data.append(pickle.load(self.temp_pickle_file))
except EOFError:
# all done
keep_unpickling = False
break
self._upload_data(raw_data)
# re-open a new pickle file for new data
self.temp_pickle_file.close()
self.temp_pickle_file = self.pickle_file_open(self.temp_file_name, "wb")
def finalize_upload(self):
if self.use_temp_pickling:
self._upload_from_pickle()
def close(self):
self.dwh_hook.close()
def copy_table(self) -> None:
"""Copy the existing version of the table, including all data, if it exists."""
test_kwargs = {"table_name": self.table_name, "schema_name": self.schema_name}
if self.database_name:
test_kwargs["database_name"] = self.database_name
if self.test_if_table_exists(**test_kwargs):
if self.database_name:
kwargs = {"database_name": self.database_name}
else:
kwargs = {}
self.dwh_hook.execute(
sql=self._COPY_TABLE.format(
old_schema=self.schema_name,
old_table=self.table_name,
new_schema=self.schema_name + self.schema_suffix,
new_table=self.table_name,
**kwargs,
),
commit=False,
)
def detect_and_apply_schema_changes(self):
# Note: Don't commit any changes!
params = {
"schema_name": self.schema_name + self.schema_suffix,
"table_name": self.table_name,
}
if self.dwh_engine == EC.DWH_ENGINE_SNOWFLAKE:
params["database_name"] = self.database_name
elif self.dwh_engine == EC.DWH_ENGINE_BIGQUERY:
params["project_id"] = self.database_name
if not self.test_if_table_exists(**params):
# Table did not previously exist, so there is nothing to do
return
list_of_old_columns = [
col[0].strip()
for col in self.dwh_hook.execute_and_return_result(
sql=self._QUERY_SCHEMA_CHANGES_COLUMNS.format(**params),
params=params,
return_dict=False,
)
]
list_of_columns = [
col_name.strip() for col_name in list(self.columns_definition.keys())
]
new_columns = []
for column in list_of_columns:
if not (column in list_of_old_columns):
new_columns += [column]
add_params = deepcopy(params)
add_params["column_name"] = column
add_params["column_type"] = self._get_column_type(
self.columns_definition[column]
)
self.dwh_hook.execute(
sql=self._QUERY_SCHEMA_CHANGES_ADD_COLUMN.format(
**add_params,
),
commit=False,
)
return new_columns
def create_or_update_table(
self,
data,
upload_call_count,
primary_key=None,
commit=False,
):
# check this again with Snowflake!!
database_name = self.database_name
self.log.info(
"Uploading {0} rows of data...".format(
str(len(data)),
)
)
kwargs = {
"data": data,
"table_name": self.table_name,
"schema_name": self.schema_name,
"schema_suffix": self.schema_suffix,
"columns_definition": self.columns_definition,
"load_strategy": self.load_strategy,
"upload_call_count": upload_call_count,
"primary_key": primary_key,
}
if database_name:
kwargs["database_name"] = database_name
self._create_or_update_table(**kwargs)
if commit:
self.dwh_hook.commit()
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] |
numpysamples.py | rresender/python-samples | 0 | 69312 | import numpy
n = int(input())
matrix = []
for i in range(n):
matrix.append(list(map(float,input().split())))
print(round(numpy.linalg.det(matrix), 2))
| [
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sensors/pointsamplecam.py | BOTSlab/alvin | 2 | 94027 | """ A PointSampleCam emulates a camera which has been calibrated to associate real-world coordinates (xr, yr) with each pixel position (xp, yp). A calibration data file is consulted which provides these associations. """
import pymunk
import numpy as np
from math import atan2, sqrt, fabs
from common import *
from pymunk import ShapeFilter
from configsingleton import ConfigSingleton
# Our mechanism for selectively importing pyglet/GUI-related stuff.
import gui_setting
if gui_setting.use:
import pyglet
class PointSampleImage:
def __init__(self, calib_array, neighbour_array):
self.calib_array = calib_array # How costly is this?
self.neighbour_array = neighbour_array
# A list of masks, where each entry in the list corresponds to a row of
# calib_array.
self.n_rows = self.calib_array.shape[0]
self.masks = [0] * self.n_rows
class PointSampleCam:
def __init__(self, calib_filename, detection_mask, acceptance_mask, frontal_only):
config = ConfigSingleton.get_instance()
self.min_distance = config.getfloat("PointSampleCam", "min_distance")
self.max_distance = config.getfloat("PointSampleCam", "max_distance")
self.max_abs_angle = config.getfloat("PointSampleCam", "max_abs_angle")
# The detection mask is used to indicate all types of objects that
# the sensor should be sensitive to. However, if a detected object
# doesn't also match the acceptance mask then it will be treated as
# a wall.
self.detection_mask = detection_mask
self.acceptance_mask = acceptance_mask
self.calib_array = np.loadtxt(calib_filename, delimiter=',')
self.calib_array[:,2] *= CM_TO_PIXELS
self.calib_array[:,3] *= CM_TO_PIXELS
# We will also store within calib_array the following additional
# quantities derived from (xr, yr) so that we don't need to compute
# these again.
# angle of (xr, yr) w.r.t. to X_R axis --- atan2(yr, xr)
# length of (xr, yr) --- sqrt(xr*xr + yr*yr)
n_rows = self.calib_array.shape[0]
# Add the two extra columns
self.calib_array = np.append(self.calib_array, np.zeros((n_rows, 2)), axis=1)
for i in range(n_rows):
(xr, yr) = self.calib_array[i,2], self.calib_array[i,3]
self.calib_array[i,4] = atan2(yr, xr)
self.calib_array[i,5] = sqrt(xr*xr + yr*yr)
if frontal_only:
# Delete all rows with distance outside of [min_distance,
# max_distance] and angle outside of [-max_abs_angle, max_abs_angle]
delete_indices = []
for i in range(n_rows):
angle = self.calib_array[i,4]
dist = self.calib_array[i,5]
if fabs(dist) < self.min_distance:
delete_indices.append(i)
if fabs(dist) > self.max_distance:
delete_indices.append(i)
if fabs(angle) > self.max_abs_angle:
delete_indices.append(i)
self.calib_array = np.delete(self.calib_array, delete_indices, axis=0)
# We also pre-compute the indices of the neighbours for each pixel.
self.neighbour_array = []
n_rows = self.calib_array.shape[0]
for i in range(n_rows):
#(ixi, iyi) = self.calib_array[i,0], self.calib_array[i,1]
(ixr, iyr) = self.calib_array[i,2], self.calib_array[i,3]
nghbrs = []
for j in range(i+1, n_rows):
#(jxi, jyi) = self.calib_array[j,0], self.calib_array[j,1]
(jxr, jyr) = self.calib_array[j,2], self.calib_array[j,3]
""" Determining neighbourhood based on 8-adjacency
dx = ixi - jxi
dy = iyi - jyi
ij_dist = sqrt(dx*dx + dy*dy)
if ij_dist <= sqrt(2) + 0.01:
nghbrs.append(j)
"""
# Determining neighbourhood based on a threshold distance
dx = ixr - jxr
dy = iyr - jyr
ij_dist = sqrt(dx*dx + dy*dy)
if ij_dist <= 50:
nghbrs.append(j)
self.neighbour_array.append(nghbrs)
self.shape_filter = ShapeFilter(mask=self.detection_mask)
def compute(self, env, robot):
image = PointSampleImage(self.calib_array, self.neighbour_array)
n_rows = self.calib_array.shape[0]
for i in range(n_rows):
# Coordinates of sensed point in robot ref. frame
(xr, yr) = self.calib_array[i,2], self.calib_array[i,3]
# Coordinates in world coordinates
(xw, yw) = robot.body.local_to_world((xr, yr))
query_info = env.point_query_nearest((xw, yw), 0, self.shape_filter)
if query_info != None:
object_mask = query_info.shape.filter.categories
if object_mask & self.acceptance_mask == 0:
# The detected shape is not accepted, we will treat
# it as a wall.
object_mask = WALL_MASK
image.masks[i] = object_mask
return image
def visualize(self, robot, image):
n_rows = self.calib_array.shape[0]
for i in range(n_rows):
# Coordinates of sensed point in robot ref. frame
(xr, yr) = self.calib_array[i,2], self.calib_array[i,3]
# Coordinates in world coordinates
(xw, yw) = robot.body.local_to_world((xr, yr))
pyglet.gl.glPointSize(3);
if image.masks[i] == 0:
color = (255, 255, 255)
elif image.masks[i] == WALL_MASK:
color = (255, 255, 0)
elif image.masks[i] == ROBOT_MASK:
color = (0, 255, 255)
elif image.masks[i] == BLAST_LANDMARK_MASK:
color = (0, 0, 255)
elif image.masks[i] == POLE_LANDMARK_MASK:
color = (255, 0, 0)
elif image.masks[i] == ARC_LANDMARK_MASK:
color = (0, 255, 0)
elif image.masks[i] == RED_PUCK_MASK:
color = (255, 0, 255)
elif image.masks[i] == GREEN_PUCK_MASK:
color = (0, 255, 255)
elif image.masks[i] == BLUE_PUCK_MASK:
color = (255, 255, 0)
else:
print("Unknown mask: {}".format(image.masks[i]))
pyglet.graphics.draw(1, pyglet.gl.GL_POINTS,
('v2f', (xw, yw)), ('c3B', color))
pyglet.gl.glPointSize(1);
"""
# make module runnable from command line
if __name__ == '__main__':
print "RUN"
sampler = PointSampler("../data/phase1_160x120.csv", 0, 0)
sampler.compute(None, None)
"""
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29916,
29893,
29892,
343,
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8243,
6702,
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29933,
742,
2927,
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13,
13,
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5228,
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416,
13,
13,
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13,
29937,
1207,
3883,
1065,
29876,
519,
515,
1899,
1196,
13,
361,
4770,
978,
1649,
1275,
525,
1649,
3396,
1649,
2396,
13,
1678,
1596,
376,
29934,
3904,
29908,
13,
1678,
3514,
20069,
353,
8984,
22966,
20069,
703,
6995,
1272,
29914,
21646,
29896,
29918,
29896,
29953,
29900,
29916,
29896,
29906,
29900,
29889,
7638,
613,
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29900,
29892,
29871,
29900,
29897,
13,
1678,
3514,
20069,
29889,
26017,
29898,
8516,
29892,
6213,
29897,
13,
13,
15945,
29908,
13,
2
] |
tests/inputs/numpy-lib/54-some-numpy-ufuncs.py | helq/pytropos | 4 | 118302 | import numpy as np
lst = [[1, 3, 4], [2, 8, 9]]
a = np.size(lst)
a_ = np.array(lst).size
b = np.shape(lst)
b_ = np.array(lst).shape
c = np.ndim(lst)
c_ = np.array(lst).ndim
d = np.dtype(lst)
d_ = np.array(lst).dtype
# show_store()
| [
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20155,
467,
29881,
1853,
13,
13,
29937,
1510,
29918,
8899,
580,
13,
2
] |
tests/test_exceptions.py | david-house-harvard/canvas_python_sdk | 21 | 94507 | import unittest
from canvas_sdk.exceptions import CanvasAPIError
class TestExceptions(unittest.TestCase):
longMessage = True
def setUp(self):
self.default_api_error = CanvasAPIError()
def test_default_status_for_canvas_api_error(self):
""" Test expected default status for instance of CanvasAPIError """
self.assertEqual(self.default_api_error.status_code, 500)
def test_default_message_for_canvas_api_error(self):
""" Test expected default msg attribute for instance of CanvasAPIError """
self.assertIsNone(self.default_api_error.error_msg)
def test_default_error_json_for_canvas_api_error(self):
""" Test expected default error_json attribute for instance of CanvasAPIError """
self.assertIsNone(self.default_api_error.error_json)
def test_default_str_for_canvas_api_error(self):
""" Test default CanvasAPIError instance represented as a str """
self.assertEqual('500', str(self.default_api_error))
def test_instance_str_for_canvas_api_error(self):
""" Test string representation of CanvasAPIError with custom attributes """
status = 404
error_msg = 'This is a test message'
error_json = {'Some error json'}
api_error = CanvasAPIError(status_code=status, msg=error_msg, error_json=error_json)
self.assertEqual('%d: %s' % (status, error_msg), str(api_error))
| [
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2
] |
anopool/pool.py | willtrnr/anopool | 0 | 31968 | <filename>anopool/pool.py
"""Generic object pool"""
from __future__ import annotations
__all__ = [
"Manager",
"Pool",
]
import contextlib
import dataclasses
import logging
import queue
import threading
from abc import ABCMeta
from typing import Generator, Generic, Optional, TypeVar
from ._common import DEFAULT_SIZE
from .exceptions import PoolClosedError
logger = logging.getLogger(__name__)
_T = TypeVar("_T")
class Manager(Generic[_T], metaclass=ABCMeta):
"""An pool object manager.
Manages the lifecycle of pool objects.
"""
def create(self) -> _T:
"""Create a new pool object."""
def recycle(self, __obj: _T) -> None:
"""Check liveness and reset released objects.
If the object is no longer valid, this method should raise an exception to
signal it and prevent its return to the pool. A slot will be open to allow its
replacement.
Args:
obj: The returned pool object.
Raises:
Exception: When the object is no longer valid.
"""
def discard(self, __obj: _T) -> None:
"""Perform cleanup of discarded objects.
This method is called for discarding both invalid objects that failed the
recycling and live objects on pool closure. Liveness should not be assumed and
this method should ideally not raise any exception unless there's a failure
that will lead to a resource leak.
Args:
obj: The object to be discarded.
"""
# pylint: disable=missing-class-docstring
@dataclasses.dataclass
class PoolState(Generic[_T]):
is_open: threading.Event
count: threading.Semaphore
lock: threading.Condition
idle: queue.SimpleQueue[_T]
class Pool(Generic[_T]):
"""An object pool.
Args:
manager: The object manager to use.
maxsize: Optional; The maximum number of concurrent objects available.
"""
_manager: Manager[_T]
_max_size: int
_state: PoolState[_T]
def __init__(
self,
manager: Manager[_T],
max_size: Optional[int] = None,
) -> None:
if max_size is None:
max_size = DEFAULT_SIZE
elif max_size <= 0:
raise ValueError("max_size must be at least 1")
self._manager = manager
self._max_size = max_size
self._init_state()
def __enter__(self: _T_Pool) -> _T_Pool:
self.open()
return self
def __exit__(self, exc_type, exc_value, exc_tb) -> None:
del exc_type, exc_value, exc_tb
self.close()
def _init_state(self) -> None:
self._state = PoolState(
is_open=threading.Event(),
count=threading.BoundedSemaphore(self._max_size),
lock=threading.Condition(lock=threading.Lock()),
idle=queue.SimpleQueue(),
)
def is_open(self) -> bool:
"""Check if the pool is open.
Returns:
bool: Whether the pool is open.
"""
return self._state.is_open.is_set()
def open(self) -> None:
"""Initialize the pool."""
self._state.is_open.set()
def close(self) -> None:
"""Close the pool and discard its objects."""
state = self._state
if not state.is_open.is_set():
return
self._init_state()
state.is_open.clear()
while True:
try:
self._manager.discard(state.idle.get_nowait())
except queue.Empty:
break
except Exception: # pylint: disable=broad-except
logger.warning("Discard error, possible resource leak", exc_info=True)
with state.lock:
state.lock.notify_all()
@contextlib.contextmanager
def acquire(self) -> Generator[_T, None, None]:
"""Acquire an object from the pool.
Yields:
An object from the pool.
"""
state = self._state
while True:
if not state.is_open.is_set():
raise PoolClosedError()
# Try to get an object from the pool first
try:
obj = state.idle.get_nowait()
logger.debug("Checked out object from pool: %s", obj)
break
except queue.Empty:
pass
# If we can allocate more, create a new one
# pylint: disable=consider-using-with
if state.count.acquire(blocking=False):
try:
obj = self._manager.create()
logger.debug("Created new object: %s", obj)
break
except:
state.count.release()
raise
# Wait until an object is available or we can allocate more
with state.lock:
logger.debug("Waiting for free object or slot")
state.lock.wait()
try:
yield obj
finally:
try:
if not state.is_open.is_set():
raise PoolClosedError()
self._manager.recycle(obj)
logger.debug("Object succeeded recycle: %s", obj)
if not state.is_open.is_set():
raise PoolClosedError()
state.idle.put(obj)
logger.debug("Object returned to pool: %s", obj)
except Exception: # pylint: disable=broad-except
logger.debug("Recycle failed discarding: %s", obj, exc_info=True)
try:
self._manager.discard(obj)
except Exception: # pylint: disable=broad-except
logger.warning(
"Discard error, possible resource leak", exc_info=True
)
state.count.release()
finally:
with state.lock:
state.lock.notify()
_T_Pool = TypeVar("_T_Pool", bound=Pool)
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src/main_scrape.py | shhudspeth/covid_california_scrape | 0 | 64554 | <filename>src/main_scrape.py
# main.py
import csv
import itertools
import requests
import bs4
import re
import pandas as pd
import scrape_utilities
import sys
from datetime import date
def main(file_name):
list_of_counties = scrape_utilities.read_csv(file_name)
print('making a dictionary of data')
dictionary_data = scrape_utilities.make_data_dict(list_of_counties)
print('writing to dictionary')
scrape_utilities.write_to_csv(dictionary_data)
if __name__ == '__main__':
args = sys.argv
if len(args) >= 2:
file = args[1]
main(file)
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docs/examples/callback_example.py | seanrcollings/arc | 2 | 123801 | import arc
@arc.command()
def command():
print("command execution")
@command.callback()
def callback(args: dict, ctx: arc.Context):
print("before execution")
yield
print("after execution")
command() | [
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SolarTracer.py | fatyogi/epever-upower-tracer | 2 | 185712 | import minimalmodbus
# on/off
ON = 1
OFF = 0
# PV array
PVvolt = 0x3100
PVamps = 0x3101
PVwattL = 0x3102
PVwattH = 0x3103
PVstat = 0x3201
# battery
BAvolt = 0x3104
BAamps = 0x3105
BAwattL = 0x3106
BAwattH = 0x3107
BAtemp = 0x3110
BAperc = 0x311A
BAstat = 0x3200
# DC load
DCvolt = 0x310C
DCamps = 0x310D
DCwattL = 0x310E
DCwattH = 0x310F
# Statistics
PVkwhTotal = 0x3312
DCkwhTotal = 0x330A
PVkwhToday = 0x330C
DCkwhToday = 0x3304
# Settings
BatteryType=0x9000
BatteryCapacity=0x9001
TempCompensationCoeff=0x9002
OverVoltageDisconnect=0x9003
ChargingLimitVoltage=0x9004
OverVoltageReconnect=0x9005
EqualizationVoltage=0x9006
BoostVoltage=0x9007
FloatVoltage=0x9008
BoostReconnectVoltage=0x9009
LowVoltageReconnect=0x900A
UnderVoltageRecover=0x900B
UnderVoltageWarning=0x900C
LowVoltageDisconnect=0x900D
DischargingLimitVoltage=0x900E
class SolarTracer:
"""A member of SolarTracer communication class."""
# connect to device
def __init__(self, device = '/dev/ttyXRUSB0', serialid = 1):
self.device = device
self.id = serialid
self.instrument = 0
def connect(self):
try:
self.instrument = minimalmodbus.Instrument(self.device, self.id)
except minimalmodbus.serial.SerialException:
return -1
self.instrument.serial.baudrate = 115200
self.instrument.serial.bytesize = 8
self.instrument.serial.parity = minimalmodbus.serial.PARITY_NONE
self.instrument.serial.stopbits = 1
self.instrument.serial.timeout = 1.2
self.instrument.mode = minimalmodbus.MODE_RTU
return 0
# read informational register
def readReg(self,register):
try:
reading = self.instrument.read_register(register, 2, 4)
return reading
except IOError:
return -2
# read parameter
def readParam(self,register,decimals=2,func=3):
try:
reading = self.instrument.read_register(register, decimals, func)
return reading
except IOError:
return -2
# write parameter
def writeParam(self,register,value,decimals=2,func=16):
try:
reading = self.instrument.write_register(register, value, decimals, func)
return 0
except IOError as err:
return -2
except ValueError:
print "Could not convert data!"
return -3
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main.py | GuilhermeMikin/EnergyEfficiencyproject | 0 | 124070 | <gh_stars>0
from clientModbus import ClienteMODBUS
dbpath = "C:\\Users\\<NAME>\\Documents\\GitHub\\EnergyEfficiencyproject\\DB\\database1.db"
c = ClienteMODBUS('127.0.0.1', 502, dbpath=dbpath)
c.atendimento() | [
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chatapp/backend/endpoints/messages.py | Nipa-Code/ChatApp | 0 | 87305 | <filename>chatapp/backend/endpoints/messages.py
from fastapi import APIRouter, Request
from chatapp.backend.utils.auth import (
tokenList,
bannedList,
auth,
AuthenticationRequired,
)
from chatapp.backend.errors.error import NonExistentValueError
from chatapp.backend.ratelimits import limiter
import logging
from datetime import datetime
log = logging.getLogger(__name__)
router = APIRouter()
@router.get("/messages")
@AuthenticationRequired
async def messages_get(request: Request, token: str, message: str):
log.info(f"GET request to endpoint /messages from client {request.client.host}")
return {"content": message, "time": datetime.now().strftime("%Y-%m-%d %H:%M:%S")}
@router.post("/messages")
@AuthenticationRequired
async def messages_post(token: str, request: Request, message: str = None):
log.info(f"POST request to endpoint /messages from client {request.client.host}")
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leo/modes/velocity.py | ATikhonov2/leo-editor | 1,550 | 44096 | # Leo colorizer control file for velocity mode.
# This file is in the public domain.
# Properties for velocity mode.
properties = {
"commentEnd": "*#",
"commentStart": "#*",
"lineComment": "##",
}
# Attributes dict for velocity_main ruleset.
velocity_main_attributes_dict = {
"default": "null",
"digit_re": "",
"escape": "",
"highlight_digits": "true",
"ignore_case": "true",
"no_word_sep": "",
}
# Attributes dict for velocity_velocity ruleset.
velocity_velocity_attributes_dict = {
"default": "null",
"digit_re": "",
"escape": "",
"highlight_digits": "true",
"ignore_case": "true",
"no_word_sep": "",
}
# Attributes dict for velocity_javascript ruleset.
velocity_javascript_attributes_dict = {
"default": "MARKUP",
"digit_re": "",
"escape": "",
"highlight_digits": "true",
"ignore_case": "true",
"no_word_sep": "",
}
# Attributes dict for velocity_javascript2 ruleset.
velocity_javascript2_attributes_dict = {
"default": "MARKUP",
"digit_re": "(0x[[:xdigit:]]+[lL]?|[[:digit:]]+(e[[:digit:]]*)?[lLdDfF]?)",
"escape": "\\",
"highlight_digits": "true",
"ignore_case": "false",
"no_word_sep": "",
}
# Attributes dict for velocity_back_to_html ruleset.
velocity_back_to_html_attributes_dict = {
"default": "MARKUP",
"digit_re": "(0x[[:xdigit:]]+[lL]?|[[:digit:]]+(e[[:digit:]]*)?[lLdDfF]?)",
"escape": "\\",
"highlight_digits": "true",
"ignore_case": "false",
"no_word_sep": "",
}
# Attributes dict for velocity_css ruleset.
velocity_css_attributes_dict = {
"default": "MARKUP",
"digit_re": "(0x[[:xdigit:]]+[lL]?|[[:digit:]]+(e[[:digit:]]*)?[lLdDfF]?)",
"escape": "\\",
"highlight_digits": "true",
"ignore_case": "false",
"no_word_sep": "",
}
# Attributes dict for velocity_css2 ruleset.
velocity_css2_attributes_dict = {
"default": "MARKUP",
"digit_re": "[[:digit:]]+(pt|pc|in|mm|cm|em|ex|px|ms|s|%)",
"escape": "\\",
"highlight_digits": "true",
"ignore_case": "true",
"no_word_sep": "-_",
}
# Dictionary of attributes dictionaries for velocity mode.
attributesDictDict = {
"velocity_back_to_html": velocity_back_to_html_attributes_dict,
"velocity_css": velocity_css_attributes_dict,
"velocity_css2": velocity_css2_attributes_dict,
"velocity_javascript": velocity_javascript_attributes_dict,
"velocity_javascript2": velocity_javascript2_attributes_dict,
"velocity_main": velocity_main_attributes_dict,
"velocity_velocity": velocity_velocity_attributes_dict,
}
# Keywords dict for velocity_main ruleset.
velocity_main_keywords_dict = {}
# Keywords dict for velocity_velocity ruleset.
velocity_velocity_keywords_dict = {
"#else": "keyword1",
"#elseif": "keyword1",
"#end": "keyword1",
"#foreach": "keyword1",
"#if": "keyword1",
"#include": "keyword1",
"#macro": "keyword1",
"#parse": "keyword1",
"#set": "keyword1",
"#stop": "keyword1",
}
# Keywords dict for velocity_javascript ruleset.
velocity_javascript_keywords_dict = {}
# Keywords dict for velocity_javascript2 ruleset.
velocity_javascript2_keywords_dict = {}
# Keywords dict for velocity_back_to_html ruleset.
velocity_back_to_html_keywords_dict = {}
# Keywords dict for velocity_css ruleset.
velocity_css_keywords_dict = {}
# Keywords dict for velocity_css2 ruleset.
velocity_css2_keywords_dict = {}
# Dictionary of keywords dictionaries for velocity mode.
keywordsDictDict = {
"velocity_back_to_html": velocity_back_to_html_keywords_dict,
"velocity_css": velocity_css_keywords_dict,
"velocity_css2": velocity_css2_keywords_dict,
"velocity_javascript": velocity_javascript_keywords_dict,
"velocity_javascript2": velocity_javascript2_keywords_dict,
"velocity_main": velocity_main_keywords_dict,
"velocity_velocity": velocity_velocity_keywords_dict,
}
# Rules for velocity_main ruleset.
def velocity_rule0(colorer, s, i):
return colorer.match_span(s, i, kind="comment1", begin="<!--", end="-->",
at_line_start=False, at_whitespace_end=False, at_word_start=False,
delegate="",exclude_match=False,
no_escape=False, no_line_break=False, no_word_break=False)
def velocity_rule1(colorer, s, i):
return colorer.match_span(s, i, kind="markup", begin="<SCRIPT", end="</SCRIPT>",
at_line_start=False, at_whitespace_end=False, at_word_start=False,
delegate="velocity::javascript",exclude_match=False,
no_escape=False, no_line_break=False, no_word_break=False)
def velocity_rule2(colorer, s, i):
return colorer.match_span(s, i, kind="markup", begin="<STYLE", end="</STYLE>",
at_line_start=False, at_whitespace_end=False, at_word_start=False,
delegate="velocity::css",exclude_match=False,
no_escape=False, no_line_break=False, no_word_break=False)
def velocity_rule3(colorer, s, i):
return colorer.match_span(s, i, kind="keyword2", begin="<!", end=">",
at_line_start=False, at_whitespace_end=False, at_word_start=False,
delegate="xml::dtd-tags",exclude_match=False,
no_escape=False, no_line_break=False, no_word_break=False)
def velocity_rule4(colorer, s, i):
return colorer.match_span(s, i, kind="markup", begin="<", end=">",
at_line_start=False, at_whitespace_end=False, at_word_start=False,
delegate="html::tags",exclude_match=False,
no_escape=False, no_line_break=False, no_word_break=False)
def velocity_rule5(colorer, s, i):
return colorer.match_span(s, i, kind="literal2", begin="&", end=";",
at_line_start=False, at_whitespace_end=False, at_word_start=False,
delegate="",exclude_match=False,
no_escape=False, no_line_break=False, no_word_break=True)
# Rules dict for velocity_main ruleset.
rulesDict1 = {
"&": [velocity_rule5,],
"<": [velocity_rule0,velocity_rule1,velocity_rule2,velocity_rule3,velocity_rule4,],
}
# Rules for velocity_velocity ruleset.
def velocity_rule6(colorer, s, i):
return colorer.match_span(s, i, kind="comment2", begin="#*", end="*#",
at_line_start=False, at_whitespace_end=False, at_word_start=False,
delegate="",exclude_match=False,
no_escape=False, no_line_break=False, no_word_break=False)
def velocity_rule7(colorer, s, i):
return colorer.match_eol_span(s, i, kind="comment3", seq="##",
at_line_start=False, at_whitespace_end=False, at_word_start=False,
delegate="", exclude_match=False)
def velocity_rule8(colorer, s, i):
return colorer.match_span(s, i, kind="keyword3", begin="${", end="}",
at_line_start=False, at_whitespace_end=False, at_word_start=False,
delegate="",exclude_match=False,
no_escape=False, no_line_break=True, no_word_break=False)
def velocity_rule9(colorer, s, i):
return colorer.match_mark_following(s, i, kind="keyword3", pattern="$!",
at_line_start=False, at_whitespace_end=False, at_word_start=False, exclude_match=False)
def velocity_rule10(colorer, s, i):
return colorer.match_mark_following(s, i, kind="keyword3", pattern="$",
at_line_start=False, at_whitespace_end=False, at_word_start=False, exclude_match=False)
def velocity_rule11(colorer, s, i):
return colorer.match_keywords(s, i)
# Rules dict for velocity_velocity ruleset.
rulesDict2 = {
"#": [velocity_rule6,velocity_rule7,velocity_rule11,],
"$": [velocity_rule8,velocity_rule9,velocity_rule10,],
"0": [velocity_rule11,],
"1": [velocity_rule11,],
"2": [velocity_rule11,],
"3": [velocity_rule11,],
"4": [velocity_rule11,],
"5": [velocity_rule11,],
"6": [velocity_rule11,],
"7": [velocity_rule11,],
"8": [velocity_rule11,],
"9": [velocity_rule11,],
"@": [velocity_rule11,],
"A": [velocity_rule11,],
"B": [velocity_rule11,],
"C": [velocity_rule11,],
"D": [velocity_rule11,],
"E": [velocity_rule11,],
"F": [velocity_rule11,],
"G": [velocity_rule11,],
"H": [velocity_rule11,],
"I": [velocity_rule11,],
"J": [velocity_rule11,],
"K": [velocity_rule11,],
"L": [velocity_rule11,],
"M": [velocity_rule11,],
"N": [velocity_rule11,],
"O": [velocity_rule11,],
"P": [velocity_rule11,],
"Q": [velocity_rule11,],
"R": [velocity_rule11,],
"S": [velocity_rule11,],
"T": [velocity_rule11,],
"U": [velocity_rule11,],
"V": [velocity_rule11,],
"W": [velocity_rule11,],
"X": [velocity_rule11,],
"Y": [velocity_rule11,],
"Z": [velocity_rule11,],
"a": [velocity_rule11,],
"b": [velocity_rule11,],
"c": [velocity_rule11,],
"d": [velocity_rule11,],
"e": [velocity_rule11,],
"f": [velocity_rule11,],
"g": [velocity_rule11,],
"h": [velocity_rule11,],
"i": [velocity_rule11,],
"j": [velocity_rule11,],
"k": [velocity_rule11,],
"l": [velocity_rule11,],
"m": [velocity_rule11,],
"n": [velocity_rule11,],
"o": [velocity_rule11,],
"p": [velocity_rule11,],
"q": [velocity_rule11,],
"r": [velocity_rule11,],
"s": [velocity_rule11,],
"t": [velocity_rule11,],
"u": [velocity_rule11,],
"v": [velocity_rule11,],
"w": [velocity_rule11,],
"x": [velocity_rule11,],
"y": [velocity_rule11,],
"z": [velocity_rule11,],
}
# Rules for velocity_javascript ruleset.
def velocity_rule12(colorer, s, i):
return colorer.match_seq(s, i, kind="markup", seq=">",
at_line_start=False, at_whitespace_end=False, at_word_start=False, delegate="velocity::javascript2")
def velocity_rule13(colorer, s, i):
return colorer.match_seq(s, i, kind="markup", seq="SRC=",
at_line_start=False, at_whitespace_end=False, at_word_start=False, delegate="velocity::back_to_html")
# Rules dict for velocity_javascript ruleset.
rulesDict3 = {
">": [velocity_rule12,],
"S": [velocity_rule13,],
}
# Rules for velocity_javascript2 ruleset.
# Rules dict for velocity_javascript2 ruleset.
rulesDict4 = {}
# Rules for velocity_back_to_html ruleset.
def velocity_rule14(colorer, s, i):
return colorer.match_seq(s, i, kind="markup", seq=">",
at_line_start=False, at_whitespace_end=False, at_word_start=False, delegate="velocity::main")
# Rules dict for velocity_back_to_html ruleset.
rulesDict5 = {
">": [velocity_rule14,],
}
# Rules for velocity_css ruleset.
def velocity_rule15(colorer, s, i):
return colorer.match_seq(s, i, kind="markup", seq=">",
at_line_start=False, at_whitespace_end=False, at_word_start=False, delegate="velocity::css2")
# Rules dict for velocity_css ruleset.
rulesDict6 = {
">": [velocity_rule15,],
}
# Rules for velocity_css2 ruleset.
# Rules dict for velocity_css2 ruleset.
rulesDict7 = {}
# x.rulesDictDict for velocity mode.
rulesDictDict = {
"velocity_back_to_html": rulesDict5,
"velocity_css": rulesDict6,
"velocity_css2": rulesDict7,
"velocity_javascript": rulesDict3,
"velocity_javascript2": rulesDict4,
"velocity_main": rulesDict1,
"velocity_velocity": rulesDict2,
}
# Import dict for velocity mode.
importDict = {
"velocity_css2": ["velocity_css2::velocity","css::main",],
"velocity_javascript2": ["velocity_javascript2::velocity","javascript::main",],
"velocity_main": ["velocity_main::velocity",],
}
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Week-06/file-io.py | ltagliaferri/dh-rutgers | 5 | 89097 | <gh_stars>1-10
# Program to work with file input / output (i/o)
# This is pulling the days.txt file in this directory in this repo
path = 'days.txt'
days_file = open(path,'r')
days = days_file.read()
new_path = 'new_days.txt'
new_days = open(new_path,'w')
title = 'Days of the Week\n'
new_days.write(title)
print(title)
new_days.write(days)
print(days)
days_file.close()
new_days.close() | [
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] |
exps/point_cloud/modelnet40_gbest.py | ranigb/Set-Tree | 17 | 127471 | import os
import random
import numpy as np
import argparse
import logging
import pickle
from pprint import pformat
from exps.data import get_modelnet40_data_fps
from settree.set_data import SetDataset, OPERATIONS, flatten_datasets
import exps.eval_utils as eval
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument("--exp_name", type=str, default='test')
parser.add_argument("--log", action='store_true')
parser.add_argument("--seed", type=int, default=45)
parser.add_argument('--save', action='store_true')
args = parser.parse_args()
log_dir = os.path.join(os.path.abspath('__file__' + '/../'), 'outputs', 'fps')
eval.create_logger(log_dir=log_dir,
log_name=args.exp_name,
dump=args.log)
logging.info('Args:\n' + pformat(vars(args)))
np.random.seed(args.seed)
random.seed(args.seed)
# x_train, y_train, x_test, y_test = get_modelnet40_data(down_sample=10,
# do_standardize=True,
# flip=False,
# seed=args.seed)
x_train, y_train, x_test, y_test = get_modelnet40_data_fps()
ds_train = SetDataset(records=x_train, is_init=True)
ds_test = SetDataset(records=x_test, is_init=True)
logging.info(args)
shared_gbdt_params = {'n_estimators': 150,
'learning_rate': 0.1,
'max_depth': 6,
'max_features': None,
'subsample': 1,
'random_state': args.seed}
set_params = {'n_estimators': shared_gbdt_params['n_estimators'],
'operations': OPERATIONS,
'splitter': 'sklearn',
'use_attention_set': True,
'use_attention_set_comp': False,
'attention_set_limit': 5,
'max_depth': shared_gbdt_params['max_depth'],
'max_features': shared_gbdt_params['max_features'],
'subsample': shared_gbdt_params['subsample'],
'random_state': shared_gbdt_params['random_state'],
'save_path': os.path.join(log_dir, '{}_checkpoint.pkl'.format(args.exp_name)),
'validation_fraction': 0.1,
'tol': 1e-3,
'n_iter_no_change': 5,
'verbose': 3}
sklearn_params = {'n_estimators': shared_gbdt_params['n_estimators'],
'criterion': 'mse',
'learning_rate': shared_gbdt_params['learning_rate'],
'max_depth': shared_gbdt_params['max_depth'],
'max_features': shared_gbdt_params['max_features'],
'subsample': shared_gbdt_params['subsample'],
'validation_fraction': 0.1,
'tol': 1e-3,
'n_iter_no_change': 5,
'verbose': 3,
'random_state': shared_gbdt_params['random_state']}
xgboost_params = {#'objective': 'binary:logistic', # 'multi:softmax', binary:logistic
'max_depth': shared_gbdt_params['max_depth'],
'n_jobs': 10,
'learning_rate': shared_gbdt_params['learning_rate'],
'n_estimators': shared_gbdt_params['n_estimators'],
'colsample_bytree': shared_gbdt_params['max_features'],
'subsample': shared_gbdt_params['subsample'],
'reg_lambda': 0,
'reg_alpha': 0,
'verbosity': 0,
'random_state': shared_gbdt_params['random_state'],
'seed': shared_gbdt_params['random_state']}
x_train, x_test = flatten_datasets(ds_train, ds_test,
operations_list=set_params['operations'],
ds_val=None)
xgboost_gbtd = eval.train_and_predict_xgboost(xgboost_params,
x_train, y_train,
x_test, y_test,
val_x=None, val_y=None,
early_stopping_rounds=None,
mode='multi_cls')
set_gbtd = eval.train_and_predict_set_gbdt(set_params,
ds_train, y_train,
ds_test, y_test,
eval_train=False,
mode='multi_cls')
if args.save:
pkl_filename = os.path.join(log_dir, '{}_model.pkl'.format(args.exp_name))
with open(pkl_filename, 'wb') as file:
pickle.dump(set_gbtd, file)
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django_docutils/rst_post/models/__init__.py | tony/django-docutils | 10 | 146275 | # flake8: NOQA F4
from .checks import _check_postpage_post_back_relation, _check_root_page
from .post import RSTPostBase, get_post_model, get_post_models
from .post_page import RSTPostPageBase, get_postpage_models
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candy.py | KevinLuo41/LeetCodeInPython | 19 | 176226 | #!/usr/bin/env python
# encoding: utf-8
"""
candy.py
Created by Shengwei on 2014-07-22.
"""
# https://oj.leetcode.com/problems/candy/
# tags: hard, array, greedy, logic
"""
There are N children standing in a line. Each child is assigned a rating value.
You are giving candies to these children subjected to the following requirements:
Each child must have at least one candy.
Children with a higher rating get more candies than their neighbors.
What is the minimum candies you must give?
"""
# https://oj.leetcode.com/discuss/76/does-anyone-have-a-better-idea
# TODO: try alternative
class Solution:
# @param ratings, a list of integer
# @return an integer
def candy(self, ratings):
count = [1]
for index in xrange(1, len(ratings)):
# note: child gets the same rating can have fewer
# candies than the neighbors
# e.g., rating [1, 2, 2, 3] -> candies [1, 2, 1, 2]
if ratings[index] > ratings[index-1]:
count.append(count[-1] + 1)
else:
count.append(1)
for index in xrange(-2, -len(ratings)-1, -1):
if ratings[index] > ratings[index+1]:
count[index] = max(count[index], count[index+1] + 1)
return sum(count)
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imagenet/load_pretrained_weights_to_matlab.py | ZhanqiZhang66/dcgan_code | 1 | 173537 | <gh_stars>1-10
import sys
sys.path.append('..')
import tensorflow as tf
import numpy as np
import theano
import theano.tensor as T
#from theano.sandbox.cuda.dnn import dnn_conv
# from lib import costs
# from lib import inits
# from lib import updates
# from lib import activations
# from lib.vis import color_grid_vis
# from lib.rng import py_rng, np_rng
# from lib.ops import batchnorm, conv_cond_concat, deconv, dropout, l2normalize
# from lib.metrics import nnc_score, nnd_score
from lib.theano_utils import floatX, sharedX, intX
#from lib.data_utils import OneHot, shuffle, iter_data, center_crop, patch
from sklearn.externals import joblib
#%%
"""
This example loads the 32x32 imagenet model used in the paper,
generates 400 random samples, and sorts them according to the
discriminator's probability of being real and renders them to
the file samples.png
"""
model_path = 'C:/Users/zhanq/OneDrive - Washington University in St. Louis/GitHub/dcgan_code/models/imagenet_gan_pretrain_128f_relu_lrelu_7l_3x3_256z/'
gen_params = [sharedX(p) for p in joblib.load(model_path+'30_gen_params.jl')]
discrim_params = [sharedX(p) for p in joblib.load(model_path+'30_discrim_params.jl')]
#%%
npGANparam = [param.get_value() for param in gen_params]
for paramname in gen_params:
print(paramname,paramname.container.data.shape)
#%%
# Python3 program to Convert a
# list to dictionary
def Convert(lst):
res_dct = {lst[i]: lst[i + 1] for i in range(0, len(lst), 2)}
return res_dct
#%%
import torch.utils.model_zoo as model_zoo
import torch.onnx
from scipy.io import loadmat
dcGANmatW = loadmat(r"C:\Users\zhanq\OneDrive - Washington University in St. Louis\GitHub\Matlab-GAN\DCGAN\DCGAN_paramsGen.mat", struct_as_record=False, squeeze_me=True)["paramsGen"]
#state_dict = model_zoo.load_url(model_url, progress=False)
state_dict = Convert(gen_params)
#%%
for paramname, matpname in zip(state_dict.keys(), dcGANmatW._fieldnames):
matweight = dcGANmatW.__getattribute__(matpname)
matweightArray = matweight[0][3].tolist()
print(matweightArray)
trcweight = state_dict[paramname]
trcmatW = torch.from_numpy(matweight)
if len(trcweight.shape) == 4: # conv2 layer
trcmatW = trcmatW.permute(3, 2, 0, 1)
# matlab weight shape [FilterSize(1),FilterSize(2),NumChannels,NumFilters]
# torch weight shape `[out_channels(NumFilters), in_channels(NumChannels), kernel_size ]`
elif len(trcweight.shape) == 2: # fc layer matmul weight is 2d
pass
assert trcmatW.shape == trcweight.shape
state_dict[paramname] = trcmatW
print(paramname, matpname, trcweight.shape, matweight.shape, )
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] |
cam/03_face_recognition.py | kimtaehoho/osscap2020 | 0 | 15395 | # -*- coding: utf-8 -*-
#import game
from glob import glob
file1 = glob("01_face_dataset.py")
file2 = glob("02_face_training.py")
import facedataset
import facetrain
import cv2
import numpy as np
import os
from PIL import Image
#facedataset.first()
#facetrain.second()
recognizer = cv2.face.LBPHFaceRecognizer_create()
recognizer.read('trainer/trainer.yml')
cascadePath = "haarcascades/haarcascade_frontalface_default.xml"
faceCascade = cv2.CascadeClassifier(cascadePath);
font = cv2.FONT_HERSHEY_SIMPLEX
#iniciate id counter
id = 0
# names related to ids: example ==> loze: id=1, etc
# 이런식으로 사용자의 이름을 사용자 수만큼 추가해준다.
names = ['None', 'kkh', 'kth', 'ldh']
# Initialize and start realtime video capture
cam = cv2.VideoCapture(0)
cam.set(3, 640) # set video widht
cam.set(4, 480) # set video height
# Define min window size to be recognized as a face
minW = 0.1*cam.get(3)
minH = 0.1*cam.get(4)
while True:
ret, img =cam.read()
#img = cv2.flip(img, -1) # Flip vertically
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
faces = faceCascade.detectMultiScale(
gray,
scaleFactor = 1.2,
minNeighbors = 5,
minSize = (int(minW), int(minH)),
)
for(x,y,w,h) in faces:
cv2.rectangle(img, (x,y), (x+w,y+h), (0,255,0), 2)
id, confidence = recognizer.predict(gray[y:y+h,x:x+w])
# Check if confidence is less them 100 ==> "0" is perfect match
if (confidence < 100):
id = names[id]
confidence = " {0}%".format(round(100 - confidence))
#game.start()
else:
facedataset.first()
facetrain.second()
#exec(open(file1.read())
#exec(open(file2.read())
#game.start()
confidence = " {0}%".format(round(100 - confidence))
cv2.putText(img, str(id), (x+5,y-5), font, 1, (255,255,255), 2)
cv2.putText(img, str(confidence), (x+5,y+h-5), font, 1, (255,255,0), 1)
cv2.imshow('camera',img)
k = cv2.waitKey(10) & 0xff # Press 'ESC' for exiting video
if k == 27:
break
# Do a bit of cleanup
print("\n [INFO] Exiting Program and cleanup stuff")
cam.release()
cv2.destroyAllWindows()
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] |
try-except.py | kmarcini/Learn-Python---Full-Course-for-Beginners-Tutorial- | 0 | 3786 | <reponame>kmarcini/Learn-Python---Full-Course-for-Beginners-Tutorial-<filename>try-except.py
try:
# num = 10 / 0
number = int(input("Enter a number: "))
print(number)
# catch specific errors
except ZeroDivisionError as err:
print(err)
except ValueError:
print("Invalid input")
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sistema_operacional.py | JorgeMGuimaraes/escalonador | 1 | 89853 | <filename>sistema_operacional.py
#!/usr/bin/python3
## imports
import io
from escalonador import Escalonador
from processo import Processo
from recursos import Recursos
from typing import List
## classes
## definicoes
def processa_entrada(quantum: int) -> List[Processo]:
filename ='entrada'
contador_processos = 0
processos = []
with open(file=filename,mode='r') as arquivo:
for linha in arquivo:
if linha.startswith('#') or linha == '\n' or linha == '':
continue
valores = linha.split(',')
chegada = int(valores[0].strip())
prioridade = int(valores[1].strip())
duracao = int(valores[2].strip())
memoria = int(valores[3].strip())
discos = int(valores[4].strip())
entrada = Processo(
id_processo = contador_processos,
chegada = chegada,
prioridade = prioridade,
duracao = duracao,
memoria = memoria,
discos = discos )
entrada.define_quantum(quantum)
processos.append(entrada)
contador_processos += 1
return processos
def imprime_header(quantum: int) -> None:
espacamento = ' '
s = 'Estado Atual:\n'
s += f'Quantum: {quantum}\n'
print(s)
return
def main():
# TODO: pegar valores do usuário
quantum = 2
contador_quanta = 0
cpus = 4
discos = 4
memoria = 16 * 1024
recursos = Recursos(cpus, discos, memoria)
escalonador = Escalonador(recursos, processa_entrada(quantum))
escalonador.imprime_processos_recebidos()
print()
while escalonador.continuar_processando():
imprime_header(contador_quanta)
escalonador.ativa_prontos_suspensos()
escalonador.executa_leitura_gravacao()
escalonador.reserva_discos()
escalonador.instanciar_processos(contador_quanta)
escalonador.admitir_processos()
escalonador.despachar()
escalonador.processar_threads()
escalonador.atualiza_estado_idle(contador_quanta)
recursos.imprime_processadores()
recursos.imprime_memoria()
recursos.imprime_discos()
escalonador.imprime_log_processos()
escalonador.imprime_andamento(contador_quanta)
contador_quanta += 1
_ = input()
return
## Programa principal
if __name__ == "__main__": main()
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tests/resources/test_todo_items.py | andela-ojoloko/Todo-Api | 0 | 158648 | import json
import jwt
from ..base import BaseTestCase
from api.models import Todo, User, db, TodoItem
from api.util import generate_token
from datetime import datetime, timedelta
class TodosTestCase(BaseTestCase):
def setUp(self):
db.drop_all()
db.create_all()
self.user_1 = User(
first_name="existing_first_name",
last_name="existing_last_name",
password= "<PASSWORD>",
email="<EMAIL>"
)
self.user_2 = User(
first_name="existing_first_name",
last_name="existing_last_name",
password= "<PASSWORD>",
email="<EMAIL>"
)
self.todo_1 = Todo(
title="firt todo"
)
self.todo_2 = Todo(
title="second todo"
)
self.user_1.todos.append(self.todo_1)
self.user_2.todos.append(self.todo_2)
self.user_1.save()
self.user_2.save()
self.token_1 = generate_token(self.user_1)
self.token_2 = generate_token(self.user_2)
self.headers_1 = { "Authorization": f"Bearer {self.token_1}"}
self.headers_2 = { "Authorization": f"Bearer {self.token_2}"}
def test_creating_todo_item_succesfully(self):
"""
tests succesfull creation of todo item
"""
todo_item = {"content": "cut the onions"}
response = self.client.post(f'/api/todos/{self.todo_1.id}/todo_items', json=todo_item, headers=self.headers_1)
json_data = response.get_json()
self.assertStatus(response, 201)
self.assertEqual(json_data["todo_item"]["content"], todo_item["content"])
self.assertEqual(json_data["message"], "todo item created")
def test_creating_todo_item_for_non_existent_todo(self):
"""
tests creating todo item for non existent todo
"""
todo_item = {"content": "cut the onions"}
response = self.client.post('/api/todos/190000099/todo_items', json=todo_item, headers=self.headers_1)
json_data = response.get_json()
self.assertStatus(response, 404)
self.assertEqual(json_data["message"], "todo does not exist")
def test_creating_todo_item_with_empty_content(self):
"""
tests creating todo item with empty content
"""
todo_item = {"content": ""}
response = self.client.post(f'/api/todos/{self.todo_1.id}/todo_items', json=todo_item, headers=self.headers_1)
json_data = response.get_json()
self.assertStatus(response, 422)
self.assertEqual(json_data["message"], "validation failed")
self.assertEqual(json_data["errors"]["content"][0], "Data not provided.")
def test_successfull_deletion_of_todo_item(self):
"""
tests deletion of single todo succesfully
"""
todo_item_1 = TodoItem(content="test content 1", todo_id=self.todo_1.id)
todo_item_1.save()
todo_item_2 = TodoItem(content="test content 2", todo_id=self.todo_2.id)
todo_item_2.save()
response = self.client.delete(f'/api/todos/{self.todo_1.id}/todo_items/{todo_item_1.id}', headers=self.headers_1)
json_data = response.get_json()
self.assertStatus(response, 200)
self.assertEqual(json_data["message"], "todo item deleted")
def test_deletion_of_todo_item_with_non_existent_todo_id_in_url(self):
"""
tests deletion of todo todo item with no existent todo_id in url
"""
todo_item_1 = TodoItem(content="test content 1", todo_id=self.todo_1.id)
todo_item_1.save()
todo_item_2 = TodoItem(content="test content 2", todo_id=self.todo_2.id)
todo_item_2.save()
response = self.client.delete(f'/api/todos/999999999/todo_items/{todo_item_1.id}', headers=self.headers_1)
json_data = response.get_json()
self.assertStatus(response, 404)
self.assertEqual(json_data["message"], "todo does not exist")
def test_deletion_of_todo_item_with_non_existent_todo_item_id_in_url(self):
"""
tests deletion of todo todo item with no existent todo_item_id in url
"""
todo_item_1 = TodoItem(content="test content 1", todo_id=self.todo_1.id)
todo_item_1.save()
todo_item_2 = TodoItem(content="test content 2", todo_id=self.todo_2.id)
todo_item_2.save()
response = self.client.delete(f'/api/todos/{self.todo_1.id}/todo_items/99999999', headers=self.headers_1)
json_data = response.get_json()
self.assertStatus(response, 404)
self.assertEqual(json_data["message"], "todo item does not exist")
def test_deletion_of_todo_item_not_created_by_user(self):
"""
tests deletion of todo item not created by user
"""
todo_item_1 = TodoItem(content="test content 1", todo_id=self.todo_1.id)
todo_item_1.save()
todo_item_2 = TodoItem(content="test content 2", todo_id=self.todo_2.id)
todo_item_2.save()
response = self.client.delete(f'/api/todos/{self.todo_2.id}/todo_items/{todo_item_2.id}', headers=self.headers_1)
json_data = response.get_json()
self.assertStatus(response, 401)
self.assertEqual(json_data["message"], "unauthorized")
def test_updating_of_todo_item_not_created_by_user(self):
"""
tests update of todo item not created by user
"""
todo_item_1 = TodoItem(content="test content 1", todo_id=self.todo_1.id)
todo_item_1.save()
todo_item_2 = TodoItem(content="test content 2", todo_id=self.todo_2.id)
todo_item_2.save()
todo_item_update = {
"complete": True
}
response = self.client.put(f'/api/todos/{self.todo_2.id}/todo_items/{todo_item_2.id}', headers=self.headers_1,
json=todo_item_update)
json_data = response.get_json()
self.assertStatus(response, 401)
self.assertEqual(json_data["message"], "unauthorized")
def test_update_of_todo_item_with_non_existent_todo_id_in_url(self):
"""
tests update of todo todo item with no existent todo_id in url
"""
todo_item_1 = TodoItem(content="test content 1", todo_id=self.todo_1.id)
todo_item_1.save()
todo_item_2 = TodoItem(content="test content 2", todo_id=self.todo_2.id)
todo_item_2.save()
todo_item_update = {
"complete": True
}
response = self.client.put(f'/api/todos/999999999/todo_items/{todo_item_1.id}', headers=self.headers_1,
json=todo_item_update)
json_data = response.get_json()
self.assertStatus(response, 404)
self.assertEqual(json_data["message"], "todo does not exist")
def test_update_of_todo_item_with_non_existent_todo_item_id_in_url(self):
"""
tests update of todo todo item with no existent todo_item_id in url
"""
todo_item_1 = TodoItem(content="test content 1", todo_id=self.todo_1.id)
todo_item_1.save()
todo_item_2 = TodoItem(content="test content 2", todo_id=self.todo_2.id)
todo_item_2.save()
todo_item_update = {
"complete": True
}
response = self.client.put(f'/api/todos/{self.todo_1.id}/todo_items/99999999', headers=self.headers_1,
json=todo_item_update)
json_data = response.get_json()
self.assertStatus(response, 404)
self.assertEqual(json_data["message"], "todo item does not exist")
def test_update_of_todo_item_successfully(self):
"""
tests update of todo todo item succesfully
"""
todo_item_1 = TodoItem(content="test content 1", todo_id=self.todo_1.id)
todo_item_1.save()
todo_item_2 = TodoItem(content="test content 2", todo_id=self.todo_2.id)
todo_item_2.save()
todo_item_update = {
"complete": True
}
response = self.client.put(f'/api/todos/{self.todo_1.id}/todo_items/{todo_item_1.id}', headers=self.headers_1,
json=todo_item_update)
json_data = response.get_json()
self.assertStatus(response, 200)
self.assertEqual(json_data["message"], "todo_item updated")
self.assertEqual(json_data["todo_item"]["complete"], todo_item_update["complete"])
def test_update_of_todo_item_with_invalid_data(self):
"""
tests update of todo todo item with invalid data
"""
todo_item_1 = TodoItem(content="test content 1", todo_id=self.todo_1.id)
todo_item_1.save()
todo_item_2 = TodoItem(content="test content 2", todo_id=self.todo_2.id)
todo_item_2.save()
todo_item_update = {
"complete": "how"
}
response = self.client.put(f'/api/todos/{self.todo_1.id}/todo_items/{todo_item_1.id}', headers=self.headers_1,
json=todo_item_update)
json_data = response.get_json()
self.assertStatus(response, 422)
self.assertEqual(json_data["message"], "validation failed")
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] |
tensorflow/contrib/distributions/python/ops/gaussian.py | vmalarcon/tensorflow | 21 | 97402 | <gh_stars>10-100
# Copyright 2016 Google Inc. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""The Normal (Gaussian) distribution class.
@@Gaussian
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import math
from tensorflow.python.framework import ops
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import constant_op
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import random_ops
class Gaussian(object):
"""The Normal (Gaussian) distribution with mean mu and stddev sigma.
The PDF of this distribution is:
f(x) = sqrt(1/(2*pi*sigma^2)) exp(-(x-mu)^2/(2*sigma^2))
"""
def __init__(self, mu, sigma):
"""Construct a new Gaussian distribution with mean mu and stddev sigma.
Args:
mu: Scalar tensor, the mean of the distribution.
sigma: Scalar tensor, the precision of the distribution.
Raises:
TypeError: if mu and sigma are different dtypes.
"""
self._mu = ops.convert_to_tensor(mu)
self._sigma = ops.convert_to_tensor(sigma)
if mu.dtype != sigma.dtype:
raise TypeError("Expected same dtype for mu, sigma but got: %s vs. %s"
% (mu.dtype, sigma.dtype))
@property
def dtype(self):
return self._mu.dtype
@property
def shape(self):
return constant_op.constant([]) # Scalar
@property
def mu(self):
return self._mu
@property
def sigma(self):
return self._sigma
def log_pdf(self, x):
"""Log likelihood of observations in x under Gaussian with mu and sigma.
Args:
x: 1-D, a vector of observations.
Returns:
log_lik: 1-D, a vector of log likelihoods of `x` under the model.
"""
return (-0.5*math.log(2 * math.pi) - math_ops.log(self._sigma)
-0.5*math_ops.square((x - self._mu) / self._sigma))
def cdf(self, x):
"""CDF of observations in x under Gaussian with mu and sigma.
Args:
x: 1-D, a vector of observations.
Returns:
cdf: 1-D, a vector of CDFs of `x` under the model.
"""
return (0.5 + 0.5*math_ops.erf(
1.0/(math.sqrt(2.0) * self._sigma)*(x - self._mu)))
def log_cdf(self, x):
"""Log of the CDF of observations x under Gaussian with mu and sigma."""
return math_ops.log(self.cdf(x))
def pdf(self, x):
"""The PDF for observations x.
Args:
x: 1-D, a vector of observations.
Returns:
pdf: 1-D, a vector of pdf values of `x` under the model.
"""
return math_ops.exp(self.log_pdf(x))
def sample(self, n, seed=None):
"""Sample `n` observations from this Distribution.
Args:
n: Scalar int `Tensor`, the number of observations to sample.
seed: Python integer, the random seed.
Returns:
samples: A vector of samples with shape `[n]`.
"""
return random_ops.random_normal(
shape=array_ops.expand_dims(n, 0), mean=self._mu,
stddev=self._sigma, dtype=self._mu.dtype, seed=seed)
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rllib/algorithms/mpc/mppi_shooting.py | shenao-zhang/DCPU | 0 | 96489 | """MPC Algorithms."""
import torch
from torch.distributions import MultivariateNormal
from rllib.util.parameter_decay import Constant, ParameterDecay
from .abstract_solver import MPCSolver
class MPPIShooting(MPCSolver):
"""Solve MPC using Model Predictive Path Integral control.
References
----------
<NAME>., <NAME>., <NAME>., <NAME>., & <NAME>. (2016).
Aggressive driving with model predictive path integral control. ICRA.
<NAME>., <NAME>., & <NAME>. (2015).
Model predictive path integral control using covariance variable importance
sampling. arXiv.
<NAME>., <NAME>., <NAME>., & <NAME>. (2019).
Deep Dynamics Models for Learning Dexterous Manipulation. arXiv.
"""
def __init__(self, kappa=1.0, filter_coefficients=(0.25, 0.8, 0), *args, **kwargs):
super().__init__(*args, **kwargs)
if not isinstance(kappa, ParameterDecay):
kappa = Constant(kappa)
self.kappa = kappa
self.filter_coefficients = torch.tensor(filter_coefficients)
self.filter_coefficients /= torch.sum(self.filter_coefficients)
def get_candidate_action_sequence(self):
"""Get candidate actions by sampling from a multivariate normal."""
noise_dist = MultivariateNormal(torch.zeros_like(self.mean), self.covariance)
noise = noise_dist.sample((self.num_samples,))
lag = len(self.filter_coefficients)
for i in range(self.horizon):
weights = self.filter_coefficients[: min(i + 1, lag)]
aux = torch.einsum(
"i, ki...j-> k...j",
weights.flip(0),
noise[:, max(0, i - lag + 1) : i + 1, ..., :],
)
noise[:, i, ..., :] = aux / torch.sum(weights)
action_sequence = self.mean.unsqueeze(0).repeat_interleave(self.num_samples, 0)
action_sequence += noise
action_sequence = action_sequence.permute(
tuple(torch.arange(1, action_sequence.dim() - 1)) + (0, -1)
)
if self.clamp:
return action_sequence.clamp(-1.0, 1.0)
return action_sequence
def get_best_action(self, action_sequence, returns):
"""Get best action by a weighted average of e^kappa returns."""
returns = self.kappa() * returns
weights = torch.exp(returns - torch.max(returns))
normalization = weights.sum()
weights = weights.unsqueeze(0).unsqueeze(-1)
weights = weights.repeat_interleave(self.horizon, 0).repeat_interleave(
self.dim_action, -1
)
return (weights * action_sequence).sum(dim=-2) / normalization
def update_sequence_generation(self, elite_actions):
"""Update distribution by the fitting the elite_actions to the mean."""
self.mean = elite_actions
self.kappa.update()
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] |
Young_Massive_Stars/Code_v32_bar/plots/Plot_Surfaces.py | lhquirogan/Galactic_Maser_Simulator | 0 | 87217 | # -*- coding: utf-8 -*-
'''
Arecibo:
Sources that satisfy Arecibo limits= 76 (Observations)
Index factor of the observed flux density function -0.471161157475 (Observation)
################################################################################
MMB:
Sources that satisfy MMB limits= 927 (Observations)
Index factor of the observed flux density function -0.581896115547
(Observation)
'''
#------------------------------------------------------------------------
#Path
%cd "/Users/luishenryquiroganunez/Documents/Leiden_University/3_Semester/MSc_thesis/Codes/Code_v31/surface_bestnalpha/Matrix_ind_num_nbar8_sigma4"
#Importing Libraries
import csv
import numpy
from pylab import *
import matplotlib.pyplot as plt
import matplotlib.patches as patches
import astropy
from astropy.io import ascii
import matplotlib.lines as mlines
from math import *
import scipy
from scipy import interpolate
from scipy import ndimage
import os
#------------------------------------------------------------------------
#Reading the cube
names_txt_files=[]
num_files=(len([name for name in os.listdir('.') if os.path.isfile(name)]))-1
for i in range(num_files):
names_txt_files.append('Calc_Num_Ind_'+str(i)+'.txt')
#Creating cubes
data_dummy=numpy.loadtxt(names_txt_files[0])
size=data_dummy.shape
cube_MMB_num=numpy.zeros([int(numpy.sqrt(size[0])),int(numpy.sqrt(size[0])),len(names_txt_files)])
cube_MMB_ind=numpy.zeros([int(numpy.sqrt(size[0])),int(numpy.sqrt(size[0])),len(names_txt_files)])
cube_MMB_chi2=numpy.zeros([int(numpy.sqrt(size[0])),int(numpy.sqrt(size[0])),len(names_txt_files)])
cube_MMB_KLD=numpy.zeros([int(numpy.sqrt(size[0])),int(numpy.sqrt(size[0])),len(names_txt_files)])
cube_Arecibo_num=numpy.zeros([int(numpy.sqrt(size[0])),int(numpy.sqrt(size[0])),len(names_txt_files)])
cube_Arecibo_ind=numpy.zeros([int(numpy.sqrt(size[0])),int(numpy.sqrt(size[0])),len(names_txt_files)])
cube_Arecibo_chi2=numpy.zeros([int(numpy.sqrt(size[0])),int(numpy.sqrt(size[0])),len(names_txt_files)])
cube_Arecibo_KLD=numpy.zeros([int(numpy.sqrt(size[0])),int(numpy.sqrt(size[0])),len(names_txt_files)])
#Constants of the observations
num_MMB_obs=908#837#927
ind_MMB_obs=-0.599716013349 #-0.657976860525#-0.581896115547
num_Arecibo_obs=76
ind_Arecibo_obs=-0.471161157475
#------------------------------------------------------------------------
#Getting the data
for i in range(len(names_txt_files)):
data=numpy.loadtxt(names_txt_files[i])
#Sorting the data
num_0=data[:,0]
ind_0=data[:,1]
MMB_num=abs(data[:,2]-num_MMB_obs)
MMB_ind=abs(abs(data[:,3])-abs(ind_MMB_obs))
MMB_chi2=data[:,4]
MMB_KLD=data[:,5]
Arecibo_num=abs(data[:,6]-num_Arecibo_obs)
Arecibo_ind=abs(abs(data[:,7])-abs(ind_Arecibo_obs))
Arecibo_chi2=data[:,8]
Arecibo_KLD=data[:,9]
data_dims=(int(numpy.sqrt(size[0])),int(numpy.sqrt(size[0])))
num = np.asarray(num_0).reshape(data_dims)
ind = np.asarray(ind_0).reshape(data_dims)
MMB_n = np.asarray(MMB_num).reshape(data_dims)
MMB_i= np.asarray(MMB_ind).reshape(data_dims)
MMB_chi = np.asarray(MMB_chi2).reshape(data_dims)
MMB_KLD2 = np.asarray(MMB_KLD).reshape(data_dims)
Arecibo_n= np.asarray(Arecibo_num).reshape(data_dims)
Arecibo_i= np.asarray(Arecibo_ind).reshape(data_dims)
Arecibo_chi = np.asarray(Arecibo_chi2).reshape(data_dims)
Arecibo_KLD2 = np.asarray(Arecibo_KLD).reshape(data_dims)
cube_MMB_num[:,:,i]=MMB_n
cube_MMB_ind[:,:,i]=MMB_i
cube_MMB_chi2[:,:,i]=MMB_chi
cube_MMB_KLD[:,:,i]=MMB_KLD2
cube_Arecibo_num[:,:,i]=Arecibo_n
cube_Arecibo_ind[:,:,i]=Arecibo_i
cube_Arecibo_chi2[:,:,i]=Arecibo_chi
cube_Arecibo_KLD[:,:,i]=Arecibo_KLD2
#------------------------------------------------------------------------
#Colapsing the cubes
final_MMB_num=numpy.mean(cube_MMB_num,axis=2)
final_MMB_ind=numpy.mean(cube_MMB_ind,axis=2)
final_MMB_chi2=numpy.mean(cube_MMB_chi2,axis=2)
final_MMB_KLD=numpy.mean(cube_MMB_KLD,axis=2)
final_Arecibo_num=numpy.mean(cube_Arecibo_num,axis=2)
final_Arecibo_ind=numpy.mean(cube_Arecibo_ind,axis=2)
final_Arecibo_chi2=numpy.mean(cube_Arecibo_chi2,axis=2)
final_Arecibo_KLD=numpy.mean(cube_Arecibo_KLD,axis=2)
############################################################################################################
#PLOTSSSS!
############################################################################################################
# One Surface Plot
#h = plt.contourf(num[:,0],ind[0,:],MMB_n)
h=imshow(final_MMB_num, interpolation='none', origin='bottom', extent=[numpy.min(num[:,0]), numpy.max(num[:,0]), numpy.min(ind[0,:]), np.max(ind[0,:])],aspect='auto')
plt.colorbar(h, orientation='vertical')
plt.xlabel("Initial Number of Sources")
plt.ylabel("Initial Index value")
plt.show()
#------------------------------------------------------------------------
# Four Surface Plot
fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, sharex='col', sharey='row')
a=ax1.imshow(final_MMB_num, interpolation='none', origin='bottom', extent=[numpy.min(num[:,0]), numpy.max(num[:,0]), numpy.min(ind[0,:]), np.max(ind[0,:])],aspect='auto')
#ax1.colorbar(a, orientation='vertical')
ax1.set_title('MMB Survey')
b=ax2.imshow(final_Arecibo_num, interpolation='none', origin='bottom', extent=[numpy.min(num[:,0]), numpy.max(num[:,0]), numpy.min(ind[0,:]), np.max(ind[0,:])],aspect='auto')
ax2.set_title('Arecibo Survey')
c=ax3.imshow(final_MMB_ind, interpolation='none', origin='bottom', extent=[numpy.min(num[:,0]), numpy.max(num[:,0]), numpy.min(ind[0,:]), np.max(ind[0,:])],aspect='auto')
d=ax4.imshow(final_Arecibo_ind, interpolation='none', origin='bottom', extent=[numpy.min(num[:,0]), numpy.max(num[:,0]), numpy.min(ind[0,:]), np.max(ind[0,:])],aspect='auto')
fig.subplots_adjust(right=0.8)
cbar_ax_a = fig.add_axes([0.85, 0.53, 0.05, 0.37])#[left, bottom, width, height]
fig.colorbar(a, cax=cbar_ax_a)
cbar_ax_b = fig.add_axes([0.85, 0.1, 0.05, 0.37])#[left, bottom, width, height]
fig.colorbar(b, cax=cbar_ax_b)
plt.show()
#------------------------------------------------------------------------
# Only MMB Surface Plot
#With KLD
matplotlib.rcParams.update({'font.size': 17})
fig, ax1 = plt.subplots(1, 1)
a=ax1.imshow(final_MMB_KLD, interpolation='none', origin='bottom', extent=[numpy.min(num[:,0]), numpy.max(num[:,0]), numpy.min(ind[0,:]), np.max(ind[0,:])],aspect='auto')
#ax1.colorbar(a, orientation='vertical')
ax1.set_title('Initial Conditions $N_i$ and $\\alpha$ compared MMB Survey')
ax1.set_ylabel('Initial Index Value $(\\alpha)$')
ax1.set_xlabel('Initial Number of Sources $(N_i)$')
fig.subplots_adjust(right=0.8)
cbar_ax_a = fig.add_axes([0.85, 0.1, 0.05, 0.8])#[left, bottom, width, height]
fig.colorbar(a, cax=cbar_ax_a)
plt.ylabel("$KL$ Differentation")
fig.set_label('Initial Index Value')
plt.show()
#With chi 2
matplotlib.rcParams.update({'font.size': 17})
fig, ax1 = plt.subplots(1, 1)
a=ax1.imshow(numpy.log(final_MMB_chi2), interpolation='none', origin='bottom', extent=[numpy.min(num[:,0]), numpy.max(num[:,0]), numpy.min(ind[0,:]), np.max(ind[0,:])],aspect='auto')
#ax1.colorbar(a, orientation='vertical')
ax1.set_title('Initial Conditions $N_i$ and $\\alpha$ compared MMB Survey')
ax1.set_ylabel('Initial Index Value $(\\alpha)$')
ax1.set_xlabel('Initial Number of Sources $(N_i)$')
fig.subplots_adjust(right=0.8)
cbar_ax_a = fig.add_axes([0.85, 0.1, 0.05, 0.8])#[left, bottom, width, height]
fig.colorbar(a, cax=cbar_ax_a)
plt.ylabel("log($\\chi^2$)")
fig.set_label('Initial Index Value')
plt.show()
#pyplot.savefig("Surface.png",bbox_inches='tight')
#With Num and Ind
matplotlib.rcParams.update({'font.size': 17})
fig, (ax1,ax2) = plt.subplots(2, 1, sharex='col')
a=ax1.imshow(MMB_n, interpolation='none', origin='bottom', extent=[numpy.min(num[:,0]), numpy.max(num[:,0]), numpy.min(ind[0,:]), np.max(ind[0,:])],aspect='auto')
#ax1.colorbar(a, orientation='vertical')
ax1.set_title('Initial Conditions $N_i$ and $\\alpha$ compared MMB Survey')
ax1.set_ylabel('Initial Index Value $(\\alpha)$')
b=ax2.imshow(MMB_i, interpolation='none', origin='bottom', extent=[numpy.min(num[:,0]), numpy.max(num[:,0]), numpy.min(ind[0,:]), np.max(ind[0,:])],aspect='auto')
ax2.set_xlabel('Initial Number of Sources $(N_i)$')
ax2.set_ylabel('Initial Index Value $(\\alpha)$')
fig.subplots_adjust(right=0.8)
cbar_ax_a = fig.add_axes([0.85, 0.53, 0.05, 0.37])#[left, bottom, width, height]
fig.colorbar(a, cax=cbar_ax_a)
plt.ylabel("|$N-N_{obs}$|")
cbar_ax_b = fig.add_axes([0.85, 0.1, 0.05, 0.37])#[left, bottom, width, height]
fig.colorbar(b, cax=cbar_ax_b)
plt.ylabel("|$ \\beta-\\beta_{obs}$|")
fig.set_label('Initial Index Value')
plt.show()
#------------------------------------------------------------------------
# Only Arecibo Surface Plot
from matplotlib.colors import LogNorm
matplotlib.rcParams.update({'font.size': 17})
fig, (ax1,ax2) = plt.subplots(2, 1, sharex='col')
a=ax1.imshow(Arecibo_n, interpolation='none', origin='bottom', extent=[numpy.min(num[:,0]), numpy.max(num[:,0]), numpy.min(ind[0,:]), np.max(ind[0,:])],aspect='auto',norm=LogNorm(vmin=1, vmax=100))
#ax1.colorbar(a, orientation='vertical')
ax1.set_title('Initial Conditions $N_i$ and $\\alpha$ compared Arecibo Survey')
ax1.set_ylabel('Initial Index Value $(\\alpha)$')
b=ax2.imshow(Arecibo_i, interpolation='none', origin='bottom', extent=[numpy.min(num[:,0]), numpy.max(num[:,0]), numpy.min(ind[0,:]), np.max(ind[0,:])],aspect='auto',norm=LogNorm(vmin=0.1, vmax=1))
ax2.set_xlabel('Initial Number of Sources $(N_i)$')
ax2.set_ylabel('Initial Index Value $(\\alpha)$')
fig.subplots_adjust(right=0.8)
cbar_ax_a = fig.add_axes([0.85, 0.53, 0.05, 0.37])#[left, bottom, width, height]
fig.colorbar(a, cax=cbar_ax_a)
plt.ylabel("|$N-N_{obs}$|")
cbar_ax_b = fig.add_axes([0.85, 0.1, 0.05, 0.37])#[left, bottom, width, height]
fig.colorbar(b, cax=cbar_ax_b)
plt.ylabel("|$ \\beta-\\beta_{obs}$|")
fig.set_label('Initial Index Value')
plt.show()
#pyplot.savefig("Surface_Arecibo.png",bbox_inches='tight')
#------------------------------------------------------------------------
#Calculting the minimum between two plots of the MMB
#Constants of the observations
num_MMB_obs=908
ind_MMB_obs=-0.599716013349
#Getting the data
data=numpy.loadtxt('Calc_Num_Ind_0.txt')
#Sorting the data
num_0=data[:,0]
ind_0=data[:,1]
MMB_num=abs(data[:,2]-num_MMB_obs)/num_MMB_obs
MMB_ind=abs(data[:,3]-ind_MMB_obs)/abs(ind_MMB_obs)
#data_dims=(5,5)
MMB_n = np.asarray(MMB_num).reshape(data_dims)
MMB_i= np.asarray(MMB_ind).reshape(data_dims)
Total=numpy.log10(((MMB_n+MMB_i)/2.0))
Total=Total*(-1)
Total=Total/(numpy.max(Total))
num = np.asarray(num_0).reshape(data_dims)
ind = np.asarray(ind_0).reshape(data_dims)
img_gaus = ndimage.filters.gaussian_filter(Total, 2, mode='nearest')
num_masers=numpy.linspace(numpy.min(num[:,0]),numpy.max(num[:,0]),100)
ind_masers=numpy.linspace(numpy.min(ind[0,:]),numpy.max(ind[0,:]),100)
X,Y=numpy.meshgrid(num_masers,ind_masers)
#Plot
matplotlib.rcParams.update({'font.size': 17})
#fig, ax = plt.subplots()
imshow(Total, interpolation='none', origin='bottom', extent=[numpy.min(num[:,0]), numpy.max(num[:,0]), numpy.min(ind[0,:]), np.max(ind[0,:])],aspect='auto')
#imshow(img_gaus, interpolation='none', origin='bottom', extent=[numpy.min(num[:,0]), numpy.max(num[:,0]), numpy.min(ind[0,:]), np.max(ind[0,:])],aspect='auto')
#CS = plt.contour(x, y, Total,colors='k')
#heatmap = ax.pcolor(Total)
cbar=plt.colorbar()
levels_=[0.2,0.3,0.4,0.5,0.6]
levels_range=[0.4]
levels_center=[0.4]
cs=plt.contour(X, Y, img_gaus,50, colors='k',levels=levels_)
plt.clabel(cs, inline=1, fontsize=10)
plt.xlabel('Initial Number of Sources $(N_i)$')
plt.ylabel('Initial Index Value $(\\alpha)$')
#cbar.ax.setlabel('Arbitrary Units', rotation=270)
cbar.set_label('Arbitrary Units')
#pyplot.savefig("Surface_both_MMB.png",bbox_inches='tight')
#Calcutaing the center and error
cs2=plt.contour(X, Y, img_gaus,50, colors='k',levels=levels_)
p = cs2.collections[0].get_paths()[0]
v = p.vertices
x = v[:,0]
y = v[:,1]
cs3=plt.contour(X, Y, img_gaus,50, colors='k',levels=levels_center)
p = cs2.collections[0].get_paths()[0]
v = p.vertices
x_center = v[:,0]
y_center = v[:,1]
def fitEllipse(x,y):
x = x[:,np.newaxis]
y = y[:,np.newaxis]
D = np.hstack((x*x, x*y, y*y, x, y, np.ones_like(x)))
S = np.dot(D.T,D)
C = np.zeros([6,6])
C[0,2] = C[2,0] = 2; C[1,1] = -1
E, V = eig(np.dot(inv(S), C))
n = np.argmax(np.abs(E))
a = V[:,n]
return a
def ellipse_center(a):
b,c,d,f,g,a = a[1]/2, a[2], a[3]/2, a[4]/2, a[5], a[0]
num = b*b-a*c
x0=(c*d-b*f)/num
y0=(a*f-b*d)/num
return np.array([x0,y0])
aa=fitEllipse(x_center,y_center)
center=ellipse_center(aa)
#array([ 1540.99789764, -1.56126529])
error_Num=numpy.max([abs(numpy.max(x)-center[0]),abs(numpy.min(x)-center[0])])
error_Ind=numpy.max(abs(numpy.max(y)-center[1]),abs(numpy.min(y)-center[1]))
#(172.83114895668996, 0.22829395805862451)
#------------------------------------------------------------------------
#Calculting the minimum of the MMB (ESAT ES!)
img_gaus = ndimage.filters.gaussian_filter(numpy.log(final_MMB_chi2), 2, mode='nearest')
num_masers=numpy.linspace(numpy.min(num[:,0]),numpy.max(num[:,0]),100)
ind_masers=numpy.linspace(numpy.min(ind[0,:]),numpy.max(ind[0,:]),100)
X,Y=numpy.meshgrid(num_masers,ind_masers)
#Plot
matplotlib.rcParams.update({'font.size': 27})
fig,ax1= plt.subplots(1, 1,figsize=(12,12))
a=ax1.imshow(numpy.log(final_MMB_chi2), interpolation='nearest', origin='bottom', extent=[numpy.min(num[:,0]), numpy.max(num[:,0]), numpy.min(ind[0,:]), np.max(ind[0,:])],aspect='auto')
ax1.set_xticks([1000,1200,1400,1600,1800])
ax1.set_yticks([-1.2,-1.4,-1.6,-1.8,-2.0])
#ax1.set_title('Initial Conditions $N_i$ and $\\alpha$ compared MMB Survey')
ax1.set_ylabel('Initial Index Value $(\\alpha)$')
ax1.set_xlabel('Initial Number of Sources $(N)$')
fig.subplots_adjust(right=0.8)
cbar_ax_a = fig.add_axes([0.85, 0.1, 0.05, 0.8])#[left, bottom, width, height]
cbar=fig.colorbar(a, cax=cbar_ax_a)
levels_=numpy.array([4.65,4.7,4.9,5.4,5.7,6.0,7.2,8.4])
#levels_=numpy.array([4.65, 4.7, 4.8])
cs=ax1.contour(X, Y, img_gaus,hold='on', levels=levels_, colors='k',origin='image')
#ax1.clabel(cs, inline=5, fontsize=10)
cbar.set_label('log($\\xi^2$)')
cbar.set_ticks([5.0,6.0,7.0,8.0])
#pyplot.savefig("../../plots/Surface_both_MMB.pdf",bbox_inches='tight')
#levels_center=[4.65]
levels_center=[4.9]
cs3=plt.contour(X, Y, img_gaus, colors='k',levels=levels_center)
p = cs3.collections[0].get_paths()[0]
v = p.vertices
x_center = v[:,0]
y_center = v[:,1]
#3D
def fitEllipse(x,y):
x = x[:,np.newaxis]
y = y[:,np.newaxis]
D = np.hstack((x*x, x*y, y*y, x, y, np.ones_like(x)))
S = np.dot(D.T,D)
C = np.zeros([6,6])
C[0,2] = C[2,0] = 2; C[1,1] = -1
E, V = eig(np.dot(inv(S), C))
n = np.argmax(np.abs(E))
a = V[:,n]
return a
def ellipse_center(a):
b,c,d,f,g,a = a[1]/2, a[2], a[3]/2, a[4]/2, a[5], a[0]
num = b*b-a*c
x0=(c*d-b*f)/num
y0=(a*f-b*d)/num
return np.array([x0,y0])
aa=fitEllipse(x_center,y_center)
center=ellipse_center(aa)
#array([ 1298.10797886, -1.43666962])
levels_=numpy.array([4.7])
#Calcutaing the center and error
cs2=plt.contour(X, Y, img_gaus, colors='k',levels=levels_)
p = cs2.collections[0].get_paths()[0]
v = p.vertices
x = v[:,0]
y = v[:,1]
error_Num=numpy.max([abs(numpy.max(x)-center[0]),abs(numpy.min(x)-center[0])])
error_Ind=numpy.max(abs(numpy.max(y)-center[1]),abs(numpy.min(y)-center[1]))
#140, 0.33
###############################################################################################
#Calculate beta and N for one same galaxy
###############################################################################################
%cd "/Users/luishenryquiroganunez/Documents/Leiden_University/3_Semester/MSc_thesis/Codes/Code_v31/surface_bestnalpha/One_same_galaxy"
names_txt_files=os.listdir('.')
data=numpy.loadtxt(names_txt_files[1])
N_MMB=np.mean(data[0])
err_N_MMB=np.std(data[0])
alpha_MMB=np.mean(data[1])
err_alpha_MMB=np.std(data[1])
N_Arecibo=np.mean(data[2])
err_N_Arecibo=np.std(data[2])
alpha_Arecibo=np.mean(data[3])
err_alpha_Arecibo=np.std(data[3])
print ('Number of sources detected (N) in MMB (%s +/- %s) and Arecibo (%s +/- %s)' %(N_MMB, err_N_MMB, N_Arecibo, err_N_Arecibo))
print ('Slope Flux Desnisty Function (alpha) in MMB (%s +/- %s) and Arecibo (%s +/- %s)' %(alpha_MMB, err_alpha_MMB, alpha_Arecibo, err_alpha_Arecibo))
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Corona Virus game/gameset.py | karansondh/VirusInvasion2020 | 0 | 151199 | class Settings:
""" All Game Settings """
def __init__(self):
self.screen_width = 1000
self.screen_height = 600
self.bg_color = (180, 255, 255)
self.ship_speed = 1.25
self.ship_limit = 3
self.bullet_speed = 1
self.bullet_width = 3
self.bullet_height = 15
self.bullet_color = (60, 60, 60)
self.bullet_allowed = 4
self.virus_speed = 0.50
self.fleet_drop_speed = 5
self.speed_upscale = 1.20
self.score_upscale = 1.25
self.initialize_dynamic_settings()
self.fleet_direction = 1
def initialize_dynamic_settings(self):
self.ship_speed = 1.25
self.bullet_speed = 1
self.virus_speed = 0.50
self.fleet_direction = 1
self.virus_points = 50
def increase_speed(self):
#self.ship_speed *= self.speed_upscale
self.bullet_speed *= self.speed_upscale
self.virus_speed *= self.speed_upscale
self.virus_points = int(self.virus_points * self.score_upscale)
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buoi12/bai5.py | Viet7501/viet1 | 0 | 82128 | file1 = ""
file2 = ""
with open('Hello.txt') as f:
file1 = f.read()
with open('Hi.txt') as f:
file2 = f.read()
file1 += "\n"
file1 += file2
with open('file3.txt', 'w') as f:
f.write(file1) | [
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start_bot_async.py | kaefik/py-tlg-bot-run-command | 0 | 136823 | """
Версия телеграмм бота с использованием библиотеки Telethon
"""
import os
# pip3 install python-dotenv
from dotenv import load_dotenv
# pip3 install Telethon
from telethon import TelegramClient, events, connection, Button
import asyncio
import logging
# import re
from i_utils import run_cmd
from sqlitelib.sqliteutils import User, SettingUser, Role, SettingOne, SettingTwo
# ---- Начальные данные ----
# подготовка для логирования работы программы
logging.basicConfig(format='[%(levelname) 5s/%(asctime)s] %(name)s: %(message)s',
level=logging.WARNING)
logging.getLogger('asyncio').setLevel(logging.ERROR)
# END подготовка для логирования работы программы
# загрузка данных для того чтобы телеграмм бот залогинился в Телеграмм
dotenv_path = os.path.join(os.path.dirname(__file__), ".env")
# print((dotenv_path))
if os.path.exists(dotenv_path):
load_dotenv(dotenv_path)
app_api_id = os.getenv("TLG_APP_API_ID")
app_api_hash = os.getenv("TLG_APP_API_HASH")
app_name = os.getenv("TLG_APP_NAME")
bot_token = os.getenv("I_BOT_TOKEN")
proxy_server = os.getenv("TLG_PROXY_SERVER")
proxy_port = os.getenv("TLG_PROXY_PORT")
proxy_key = os.getenv("TLG_PROXY_KEY")
# клиент с правами администратора
admin_client = int(os.getenv("TLG_ADMIN_ID_CLIENT"))
# END загрузка данных для того чтобы телеграмм бот залогинился в Телеграмм
# настройки пользователей бота в том числе и администратора admin_client
name_file_settings = 'settings.db'
if not os.path.exists(name_file_settings):
print('нет файла настроек')
name_admin = ''
settings = SettingUser(namedb=name_file_settings)
admin_User = User(id=admin_client, role=Role.admin, active=True)
settings.add_user(admin_User)
else:
print('есть файл настроек')
settings = SettingUser(namedb=name_file_settings)
# получение всех пользователей из БД
# clients = settings.get_all_user() # список всех клиентов
admin_client = settings.get_user_type(Role.admin) # список администраторов бота
# END настройки бота
if proxy_server is None or proxy_port is None or proxy_key is None:
print("Нет настроек MTProto прокси сервера телеграмма.\n" \
"Попытка подключения клиента без прокси.")
bot = TelegramClient(app_name, app_api_id, app_api_hash).start(
bot_token=bot_token
)
else:
proxy = (proxy_server, int(proxy_port), proxy_key)
bot = TelegramClient(app_name, app_api_id, app_api_hash,
connection=connection.ConnectionTcpMTProxyRandomizedIntermediate,
proxy=proxy).start(bot_token=bot_token)
# кнопки главного режима для администратора
button_main_admin = [
[Button.text("/help"),
Button.text("/admin"),
Button.text("/settings")]
]
# кнопки главного режима для обычного пользователя
button_main_user = [
[Button.text("/help"),
Button.text("/admin")]
]
# кнопки для режима администратора
button_admin = [
[Button.text("/AddUser"),
Button.text("/DelUser"),
Button.text("/InfoUser"),
Button.text("/ExitAdmin")]
]
# кнопки для режима настроек пользователя
button_settings = [
[Button.text("/SettingOne"),
Button.text("/ExitSettings")]
]
# выбор типа результирующего файла
button_settingone = [
[
Button.text("/SubSettingOne"),
Button.text("/ExitTypeResult"),
]
]
# ---- END Начальные данные ----
# ----- Вспомогательные функции
def get_help(filename='help.txt'):
"""
получаем текст для помощи о командах бота
"""
help_text = ""
with open(filename, "r") as f:
help_text = f.read()
return help_text
# проверка на разрешенного пользователя
def is_allow_user(iduser, allow_users):
for user in allow_users:
if user.id == iduser:
return True
return False
# добавляем пользователя в БД пользователей которые имеют доступ к боту
# возвращает True
def add_new_user(id, settings):
new_user = User(id=id)
settings.add_user(new_user)
return True
# возвращает список пользователей которые имеют доступ к боту
def read_user_db(settings):
result = []
clients = settings.get_all_user()
for cl in clients:
result.append(cl.__str__())
return result
async def get_name_user(client, user_id):
"""
получаем информацию о пользователе телеграмма по его user_id (user_id тип int)
"""
try:
new_name_user = await client.get_entity(user_id)
new_name_user = new_name_user.first_name
except ValueError as err:
print('Ошибка получения информации о пользователе по id: ', err)
new_name_user = ''
return new_name_user
async def check_name_user_empty(client, sender_id, db):
"""
проверим есть ли у этого пользователя имя пользователя в нашей БД настроек
возвращает имя пользователя
"""
user_name = db.get_user(sender_id)
# print(f'Имя пользователя в БД {user_name}')
user_name_tlg = await get_name_user(client, sender_id)
# print(f'Имя пользователя в Телеграмме {user_name_tlg}')
if len(user_name.name) == 0:
user_name.name = user_name_tlg
db.update_user(user_name)
return user_name
def allow_user_id(users):
result = []
for user in users:
result.append(user.id)
return set(result)
# ----- END Вспомогательные функции
# @bot.on(events.CallbackQuery)
# async def handler(event):
# await event.answer('You clicked {}!'.format(event.data))
# выполнение команды /start
@bot.on(events.NewMessage(pattern='/start'))
async def start_cmd(event):
sender = await event.get_sender()
# проверка на право доступа к боту
sender_id = sender.id
if not is_allow_user(sender_id, settings.get_all_user()):
await event.respond(f"Доступ запрещен. Обратитесь к администратору" \
f" чтобы добавил ваш ID в белый список. Ваш ID {sender_id}")
return
# END проверка на право доступа к боту
user_name = await check_name_user_empty(event.client, sender_id, settings)
if user_name.role == Role.admin:
buttons = button_main_admin
else:
buttons = button_main_user
await event.respond(f"Привет, {user_name.name}! Я рад видеть тебя!\n" \
"Этот бот выполняет ваши команды.",
buttons=buttons)
raise events.StopPropagation
# выполнение команды /about
@bot.on(events.NewMessage(chats=allow_user_id(settings.get_all_user()), pattern='/about'))
# параметр chats - множество id пользователей которые имеют доступ к методу
async def about_cmd(event):
print(allow_user_id(settings.get_all_user()))
await event.respond("Я выполняю ваши команды.")
# выполнение команды /help
@bot.on(events.NewMessage(chats=allow_user_id(settings.get_all_user()), pattern='/help'))
async def help_cmd(event):
sender = await event.get_sender()
await event.respond(get_help('help.txt'))
# -------------------- команды администрирования пользователей телеграмм бота ----------------------------------
@bot.on(events.NewMessage(chats=allow_user_id(settings.get_all_user()), pattern='/admin'))
async def admin_cmd(event):
sender = await event.get_sender()
await event.respond("Вы вошли в режим администратора",
buttons=button_admin)
@bot.on(events.NewMessage(chats=allow_user_id(settings.get_all_user()), pattern='/AddUser'))
async def add_user_admin(event):
# sender = await event.get_sender()
await event.respond("Выполняется команда /AddUSer")
# диалог с запросом информации нужной для работы команды /AddUser
chat_id = event.chat_id
async with bot.conversation(chat_id) as conv:
# response = conv.wait_event(events.NewMessage(incoming=True))
await conv.send_message("Привет! Введите номер id пользователя"
"который нужно добавить для доступа к боту:")
id_new_user = await conv.get_response()
id_new_user = id_new_user.message
# print("id_new_user ", id_new_user)
while not all(x.isdigit() for x in id_new_user):
await conv.send_message("ID нового пользователя - это число. Попробуйте еще раз.")
id_new_user = await conv.get_response()
id_new_user = id_new_user.message
# print("id_new_user ", id_new_user)
new_name_user = await get_name_user(event.client, int(id_new_user))
print('Имя нового пользователя', new_name_user)
add_new_user(id_new_user, settings)
await conv.send_message(f"Добавили нового пользователя с ID: {id_new_user} с именем {new_name_user}")
@bot.on(events.NewMessage(chats=allow_user_id(settings.get_all_user()), pattern='/DelUser'))
async def del_user_admin(event):
# sender = await event.get_sender()
await event.respond("Выполняется команда /DelUSer")
# диалог с запросом информации нужной для работы команды /DelUser
chat_id = event.chat_id
async with bot.conversation(chat_id) as conv:
# response = conv.wait_event(events.NewMessage(incoming=True))
await conv.send_message("Привет! Введите номер id пользователя "
"который нужно запретить доступ к боту")
id_del_user = await conv.get_response()
id_del_user = id_del_user.message
while not any(x.isdigit() for x in id_del_user):
await conv.send_message("ID пользователя - это число. "
"Попробуйте еще раз.")
id_del_user = await conv.get_response()
id_del_user = id_del_user.message
# проверяем на то если пользователь админ который захочет удалить себя это не получится
if not is_allow_user(int(id_del_user), admin_client):
settings.del_user(int(id_del_user))
await conv.send_message(f"Пользователю с ID: {id_del_user} "
"доступ к боту запрещен.")
else:
await conv.send_message("Удаление пользователя с правами администратора запрещено.")
@bot.on(events.NewMessage(chats=allow_user_id(settings.get_all_user()), pattern='/InfoUser'))
async def info_user_admin(event):
ids = read_user_db(settings)
ids = [str(x) for x in ids]
strs = '\n'.join(ids)
await event.respond(f"Пользователи которые имеют доступ:\n{strs}")
@bot.on(events.NewMessage(chats=allow_user_id(settings.get_all_user()), pattern='/ExitAdmin'))
async def exit_admin_admin(event):
sender = await event.get_sender()
await event.respond(f"Вы вышли из режима администратора.",
buttons=button_main_admin)
# -------------------- END команды администрирования пользователей телеграмм бота ----------------------------------
# ---------------------- Команды settings
@bot.on(events.NewMessage(chats=allow_user_id(settings.get_all_user()), pattern='/settings'))
async def settings_cmd(event):
await event.respond("Вы вошли в режим настроек пользователя",
buttons=button_settings)
@bot.on(events.NewMessage(chats=allow_user_id(settings.get_all_user()), pattern='/ExitSettings'))
async def exit_settings_cmd(event):
sender = await event.get_sender()
sender_id = sender.id
user_name = await check_name_user_empty(event.client, sender_id, settings)
if user_name.role == Role.admin:
buttons = button_main_admin
else:
buttons = button_main_user
await event.respond("Вы вышли из режима настроек пользователя.", buttons=buttons)
# ---------------------- END Команды settings
# ---------------------- Команды для телеграмм бота которые образуют его основные функции
@bot.on(events.NewMessage(chats=allow_user_id(settings.get_all_user()), pattern='ls|dir'))
async def run_cmd_one(event):
"""
пример реализации команды ls Linux
"""
# выделение параметра команды
message_text = event.raw_text.split()
# print(message_text)
param_cmds = ''
if len(message_text) > 1:
param_cmds = message_text[1]
print(param_cmds)
# проверка существования папки
if not os.path.exists(param_cmds):
await event.respond(f"Папки {param_cmds} не существует.")
return
# команда которая будет выполняться на сервере
cmds = f"ls {param_cmds}"
await event.respond(f"Выполняем команду {cmds}")
done, _ = await asyncio.wait([run_cmd(cmds)])
# result - результат выполнения команды cmds
# error - ошибка, если команда cmds завершилась с ошибкой
# code - код работы команды, если 0 , то команда прошла без ошибок
result, error, code = done.pop().result()
if code != 0:
await event.respond(f"!!!! код: {code} \n" f"Внутренняя ошибка: {error}")
return
result = result.decode("utf-8")
str_result = result # result.split("\n")
await event.respond(f"Результат: \n {str_result}")
# ---------------------- END Команды для телеграмм бота которые образуют его основные функции
def main():
bot.run_until_disconnected()
if __name__ == '__main__':
main()
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overholt/users/__init__.py | prdonahue/overholt | 1,152 | 74826 | # -*- coding: utf-8 -*-
"""
overholt.users
~~~~~~~~~~~~~~
overholt users package
"""
from ..core import Service
from .models import User
class UsersService(Service):
__model__ = User
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anki/importing/apkg.py | bdunnette/omeka2anki | 2 | 102355 | # -*- coding: utf-8 -*-
# Copyright: <NAME> <<EMAIL>>
# License: GNU AGPL, version 3 or later; http://www.gnu.org/licenses/agpl.html
import zipfile, os
import unicodedata
from anki.utils import tmpfile, json
from anki.importing.anki2 import Anki2Importer
class AnkiPackageImporter(Anki2Importer):
def run(self):
# extract the deck from the zip file
self.zip = z = zipfile.ZipFile(self.file)
col = z.read("collection.anki2")
colpath = tmpfile(suffix=".anki2")
open(colpath, "wb").write(col)
self.file = colpath
# we need the media dict in advance, and we'll need a map of fname ->
# number to use during the import
self.nameToNum = {}
for k, v in json.loads(z.read("media")).items():
self.nameToNum[v] = k
# run anki2 importer
Anki2Importer.run(self)
# import static media
for file, c in self.nameToNum.items():
if not file.startswith("_") and not file.startswith("latex-"):
continue
path = os.path.join(self.col.media.dir(),
unicodedata.normalize("NFC", file))
if not os.path.exists(path):
open(path, "wb").write(z.read(c))
def _srcMediaData(self, fname):
if fname in self.nameToNum:
return self.zip.read(self.nameToNum[fname])
return None
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nlp/layers/linears.py | zhihao-chen/NLP-experiments | 4 | 15608 | # -*- coding: utf8 -*-
"""
======================================
Project Name: NLP
File Name: linears
Author: czh
Create Date: 2021/11/15
--------------------------------------
Change Activity:
======================================
"""
import math
import torch
import torch.nn as nn
import torch.nn.functional as func
from torch.nn.parameter import Parameter
class Linears(nn.Module):
def __init__(self, input_dim: int, output_dim: int = 1, bias: bool = True):
super().__init__()
self.fn1 = nn.Linear(input_dim, input_dim)
self.fn2 = nn.Linear(input_dim, input_dim)
self.fn3 = nn.Linear(input_dim, output_dim, bias=bias)
nn.init.orthogonal_(self.fn1.weight, gain=1)
nn.init.orthogonal_(self.fn2.weight, gain=1)
nn.init.orthogonal_(self.fn3.weight, gain=1)
def forward(self, hidden_states: torch.Tensor, encoder_hidden_states: torch.Tensor):
logits = self.fn3(torch.tanh(
self.fn1(hidden_states).unsqueeze(2) + self.fn2(encoder_hidden_states).unsqueeze(1)
)).squeeze()
return logits
class EntityLinears(nn.Module):
def __init__(self, input_dim: int, output_dim: int = 1, bias: bool = True):
super().__init__()
self.head = Linears(input_dim=input_dim, output_dim=output_dim, bias=bias)
self.tail = Linears(input_dim=input_dim, output_dim=output_dim, bias=bias)
def forward(self, hidden_states: torch.Tensor, encoder_hidden_states: torch.Tensor):
# [bsz, num_triples, seq_len, output_dim]
return self.head(hidden_states, encoder_hidden_states), self.tail(hidden_states, encoder_hidden_states)
class FeedForwardNetwork(nn.Module):
def __init__(self, input_size, hidden_size, output_size, dropout_rate=0):
super(FeedForwardNetwork, self).__init__()
self.dropout_rate = dropout_rate
self.linear1 = nn.Linear(input_size, hidden_size)
self.linear2 = nn.Linear(hidden_size, output_size)
def forward(self, x):
x_proj = func.dropout(func.relu(self.linear1(x)), p=self.dropout_rate, training=self.training)
x_proj = self.linear2(x_proj)
return x_proj
class PoolerStartLogits(nn.Module):
"""
bert_ner_span
"""
def __init__(self, hidden_size, num_classes):
super(PoolerStartLogits, self).__init__()
self.dense = nn.Linear(hidden_size, num_classes)
def forward(self, hidden_states):
x = self.dense(hidden_states)
return x
class PoolerEndLogits(nn.Module):
"""
bert_ner_span
"""
def __init__(self, hidden_size, num_classes):
super(PoolerEndLogits, self).__init__()
self.dense_0 = nn.Linear(hidden_size, hidden_size)
self.activation = nn.Tanh()
self.LayerNorm = nn.LayerNorm(hidden_size)
self.dense_1 = nn.Linear(hidden_size, num_classes)
def forward(self, hidden_states, start_positions=None):
x = self.dense_0(torch.cat([hidden_states, start_positions], dim=-1))
x = self.activation(x)
x = self.LayerNorm(x)
x = self.dense_1(x)
return x
class MultiNonLinearClassifier(nn.Module):
def __init__(self, hidden_size, num_label, dropout_rate, act_func="gelu", intermediate_hidden_size=None):
super(MultiNonLinearClassifier, self).__init__()
self.num_label = num_label
self.intermediate_hidden_size = hidden_size if intermediate_hidden_size is None else intermediate_hidden_size
self.classifier1 = nn.Linear(hidden_size, self.intermediate_hidden_size)
self.classifier2 = nn.Linear(self.intermediate_hidden_size, self.num_label)
self.dropout = nn.Dropout(dropout_rate)
self.act_func = act_func
def forward(self, input_features):
features_output1 = self.classifier1(input_features)
if self.act_func == "gelu":
features_output1 = func.gelu(features_output1)
elif self.act_func == "relu":
features_output1 = func.relu(features_output1)
elif self.act_func == "tanh":
features_output1 = func.tanh(features_output1)
else:
raise ValueError
features_output1 = self.dropout(features_output1)
features_output2 = self.classifier2(features_output1)
return features_output2
class SingleLinearClassifier(nn.Module):
def __init__(self, hidden_size, num_label):
super(SingleLinearClassifier, self).__init__()
self.num_label = num_label
self.classifier = nn.Linear(hidden_size, num_label)
def forward(self, input_features):
features_output = self.classifier(input_features)
return features_output
class BERTTaggerClassifier(nn.Module):
def __init__(self, hidden_size, num_label, dropout_rate, act_func="gelu", intermediate_hidden_size=None):
super(BERTTaggerClassifier, self).__init__()
self.num_label = num_label
self.intermediate_hidden_size = hidden_size if intermediate_hidden_size is None else intermediate_hidden_size
self.classifier1 = nn.Linear(hidden_size, self.intermediate_hidden_size)
self.classifier2 = nn.Linear(self.intermediate_hidden_size, self.num_label)
self.dropout = nn.Dropout(dropout_rate)
self.act_func = act_func
def forward(self, input_features):
features_output1 = self.classifier1(input_features)
if self.act_func == "gelu":
features_output1 = func.gelu(features_output1)
elif self.act_func == "relu":
features_output1 = func.relu(features_output1)
elif self.act_func == "tanh":
features_output1 = func.tanh(features_output1)
else:
raise ValueError
features_output1 = self.dropout(features_output1)
features_output2 = self.classifier2(features_output1)
return features_output2
class ClassifierLayer(nn.Module):
# https://github.com/Akeepers/LEAR/blob/master/utils/model_utils.py
def __init__(self, class_num, out_features, bias=True):
super(ClassifierLayer, self).__init__()
self.class_num = class_num
self.out_features = out_features
self.weight = Parameter(torch.Tensor(class_num, out_features))
if bias:
self.bias = Parameter(torch.Tensor(class_num))
else:
self.register_parameter('bias', None)
self.reset_parameters()
def reset_parameters(self) -> None:
nn.init.kaiming_uniform_(self.weight, a=math.sqrt(5))
if self.bias is not None:
fan_in, _ = nn.init._calculate_fan_in_and_fan_out(self.weight)
bound = 1 / math.sqrt(fan_in)
nn.init.uniform_(self.bias, -bound, bound)
def forward(self, inputs):
x = torch.mul(inputs, self.weight)
# (class_num, 1)
x = torch.sum(x, -1) # [-1, class_num]
if self.bias is not None:
x = x + self.bias
return x
def extra_repr(self):
return 'class_num={}, out_features={}, bias={}'.format(
self.class_num, self.out_features, self.bias is not None)
class MultiNonLinearClassifierForMultiLabel(nn.Module):
# https://github.com/Akeepers/LEAR/blob/master/utils/model_utils.py
def __init__(self, hidden_size, num_label, dropout_rate):
super(MultiNonLinearClassifierForMultiLabel, self).__init__()
self.num_label = num_label
self.classifier1 = nn.Linear(hidden_size, hidden_size)
self.classifier2 = ClassifierLayer(num_label, hidden_size)
self.dropout = nn.Dropout(dropout_rate)
def forward(self, input_features):
features_output1 = self.classifier1(input_features)
features_output1 = func.gelu(features_output1)
features_output1 = self.dropout(features_output1)
features_output2 = self.classifier2(features_output1)
return features_output2
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] |
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